diff --git a/CBThallLogs.csv.xlsx b/CBThallLogs.csv.xlsx
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index 0000000..ec0a9f9
Binary files /dev/null and b/CBThallLogs.csv.xlsx differ
diff --git a/Data_preprocessing_Numpy.csv b/Data_preprocessing_Numpy.csv
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--- /dev/null
+++ b/Data_preprocessing_Numpy.csv
@@ -0,0 +1,31 @@
+Column1;Column2;Column3;Column4;Column5;Column6
+;50;365;2801;3626;4676
+;40;365;3121;5381;15281
+;40;365;3121;4501;16381
+;50;365;1700;3200;16550
+;40;365;3401;4421;13101
+;50;365;-1850;-530;1480
+;50;365;3750;6250;20250
+;50;365;3751;5251;20250
+;;;5453;6953;22250
+;;;5100;6400;22250
+;40;365;3400;5400;16600
+;40;365;3360;;
+;40;365;1940;;
+;50;365;5500;7500;22250
+;;;;;
+;50;365;1250;2650;19250
+;50;365;5500;5500;22250
+;40;365;3500;4400;15600
+;40;365;8200;13200;16600
+;40;365;3080;4600;16600
+;50;365;;;20250
+;40;365;3440;3440;15600
+;;365;;;
+;40;365;2120;3280;15600
+;50;;;6900;22250
+;40;365;4900;4900;16600
+;40;365;4000;5000;16600
+;50;365;3400;5000;20250
+;125;;;;54625
+;40;;3441;4541;16600
diff --git a/EDA_CHATGPT.ipynb b/EDA_CHATGPT.ipynb
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index 0000000..6397847
--- /dev/null
+++ b/EDA_CHATGPT.ipynb
@@ -0,0 +1,573 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rawdata = pd.read_csv('properties.csv')\n",
+    "data = rawdata.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PropertyID</th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Size_sqft</th>\n",
+       "      <th>SalePrice</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2001</td>\n",
+       "      <td>Suburb</td>\n",
+       "      <td>2996</td>\n",
+       "      <td>355372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2002</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>2887</td>\n",
+       "      <td>505830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2003</td>\n",
+       "      <td>Suburb</td>\n",
+       "      <td>957</td>\n",
+       "      <td>338789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2004</td>\n",
+       "      <td>Countryside</td>\n",
+       "      <td>1814</td>\n",
+       "      <td>419335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2005</td>\n",
+       "      <td>Suburb</td>\n",
+       "      <td>850</td>\n",
+       "      <td>357301</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PropertyID     Location  Size_sqft  SalePrice\n",
+       "0        2001       Suburb       2996     355372\n",
+       "1        2002     Downtown       2887     505830\n",
+       "2        2003       Suburb        957     338789\n",
+       "3        2004  Countryside       1814     419335\n",
+       "4        2005       Suburb        850     357301"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PropertyID</th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Size_sqft</th>\n",
+       "      <th>SalePrice</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Suburb</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2050.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1832.410000</td>\n",
+       "      <td>401866.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>29.011492</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>695.535926</td>\n",
+       "      <td>116274.958848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2001.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>806.000000</td>\n",
+       "      <td>205943.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2025.750000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1154.000000</td>\n",
+       "      <td>303015.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2050.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1816.000000</td>\n",
+       "      <td>403301.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2075.250000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2438.000000</td>\n",
+       "      <td>506435.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2100.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2996.000000</td>\n",
+       "      <td>598378.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         PropertyID Location    Size_sqft      SalePrice\n",
+       "count    100.000000      100   100.000000     100.000000\n",
+       "unique          NaN        3          NaN            NaN\n",
+       "top             NaN   Suburb          NaN            NaN\n",
+       "freq            NaN       40          NaN            NaN\n",
+       "mean    2050.500000      NaN  1832.410000  401866.300000\n",
+       "std       29.011492      NaN   695.535926  116274.958848\n",
+       "min     2001.000000      NaN   806.000000  205943.000000\n",
+       "25%     2025.750000      NaN  1154.000000  303015.500000\n",
+       "50%     2050.500000      NaN  1816.000000  403301.500000\n",
+       "75%     2075.250000      NaN  2438.000000  506435.250000\n",
+       "max     2100.000000      NaN  2996.000000  598378.000000"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe(include= 'all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "My observations:\n",
+    "The data contains 100 unique input, with a range of 2001 to 2100\n",
+    "Size_sqft: range from 806 to 2996 and an average size of 1832\n",
+    "SalePrice: range from 205943 to 598378 and an average size 401866 \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_corr = data[['Size_sqft', 'SalePrice']].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Size_sqft</th>\n",
+       "      <th>SalePrice</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Size_sqft</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.030733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SalePrice</th>\n",
+       "      <td>-0.030733</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Size_sqft  SalePrice\n",
+       "Size_sqft   1.000000  -0.030733\n",
+       "SalePrice  -0.030733   1.000000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize= (8,6))\n",
+    "sns.heatmap(data_corr, cmap= \"coolwarm\", annot= True)\n",
+    "plt.title(\"Correlation Matrix Heatmap\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_5204\\1751354969.py:18: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.countplot(data= data, x = \"Location\", palette = \"viridis\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "colors = sns.color_palette(\"viridis\", 10)\n",
+    "sns.histplot(data['Size_sqft'], kde=True, color=colors[4], bins=10)\n",
+    "plt.xlabel('Size')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title(\"Distribution of size\")\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "colors = sns.color_palette(\"viridis\", 10)\n",
+    "sns.histplot(data['SalePrice'], kde=True, color=colors[5], bins=10)\n",
+    "plt.xlabel('price')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title(\"Distribution of SalePrice\")\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "sns.countplot(data= data, x = \"Location\", palette = \"viridis\")\n",
+    "plt.title(\"Distribution of property by Location\")\n",
+    "plt.xlabel(\"Location\")\n",
+    "plt.ylabel(\"No of Properties\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_5204\\217171152.py:10: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(data=data, x= 'Location', y = 'SalePrice', palette=\"viridis\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,10))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "sns.scatterplot(data=data, x= 'Size_sqft', y = 'SalePrice', hue = 'Location', palette=\"viridis\", s=80)\n",
+    "plt.xlabel('Size')\n",
+    "plt.ylabel('Price')\n",
+    "plt.title(\"Distribution of size vs Price\")\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "sns.boxplot(data=data, x= 'Location', y = 'SalePrice', palette=\"viridis\")\n",
+    "plt.xlabel('Location')\n",
+    "plt.ylabel('Price')\n",
+    "plt.title(\"Location Distribution vs Price\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check for outliers\n",
+    "def detect_outliers(data):\n",
+    "    Q1 = data.quantile(0.25)\n",
+    "    Q3 = data.quantile(0.75)\n",
+    "    IQR = Q3 - Q1\n",
+    "    Lowerbound = Q1 - 1.5 * IQR\n",
+    "    Upperbound = Q3 + 1.5 * IQR\n",
+    "    non_outliers = ((data >= Q1) & (data <= Q3))\n",
+    "    outliers = data.loc[non_outliers]\n",
+    "    return outliers\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     355372\n",
+       "1     505830\n",
+       "2     338789\n",
+       "3     419335\n",
+       "4     357301\n",
+       "5     303756\n",
+       "7     416148\n",
+       "9     467473\n",
+       "11    450825\n",
+       "12    349978\n",
+       "13    335603\n",
+       "15    464817\n",
+       "16    489508\n",
+       "17    371794\n",
+       "18    503101\n",
+       "19    321393\n",
+       "23    431974\n",
+       "25    426285\n",
+       "26    335385\n",
+       "28    441358\n",
+       "33    453159\n",
+       "34    463415\n",
+       "35    343515\n",
+       "36    473749\n",
+       "40    402900\n",
+       "41    303831\n",
+       "42    332684\n",
+       "45    366166\n",
+       "46    337055\n",
+       "50    382323\n",
+       "53    316581\n",
+       "54    480899\n",
+       "55    456802\n",
+       "56    386984\n",
+       "57    387550\n",
+       "59    339934\n",
+       "60    346495\n",
+       "62    358459\n",
+       "64    481837\n",
+       "68    469341\n",
+       "69    430403\n",
+       "72    485397\n",
+       "73    441171\n",
+       "74    306472\n",
+       "75    403703\n",
+       "80    486149\n",
+       "87    493408\n",
+       "89    357476\n",
+       "90    386302\n",
+       "98    502089\n",
+       "Name: SalePrice, dtype: int64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outliers = detect_outliers(data['SalePrice'])\n",
+    "outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/IPCbt.xlsx b/IPCbt.xlsx
new file mode 100644
index 0000000..9fbfc4a
Binary files /dev/null and b/IPCbt.xlsx differ
diff --git a/Kaggle_advanced_GB_algorithm_4_life_expectancy.ipynb b/Kaggle_advanced_GB_algorithm_4_life_expectancy.ipynb
new file mode 100644
index 0000000..30c60f5
--- /dev/null
+++ b/Kaggle_advanced_GB_algorithm_4_life_expectancy.ipynb
@@ -0,0 +1,43 @@
+#XGBoost
+import xgboost as xgb
+from sklearn import preprocessing
+X = df.drop(columns='Life expectancy')
+y = df['Life expectancy']
+
+lbl = preprocessing.LabelEncoder()
+#Country','Year','Status
+X['Country'] = lbl.fit_transform(X['Country'].astype(str))
+X['Year'] = lbl.fit_transform(X['Year'].astype(str))
+X['Status'] = lbl.fit_transform(X['Status'].astype(str))
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, 
+                                                    random_state=1)
+
+start = time.time()
+#X_train["Species"].astype("category")
+xgbr = xgb.XGBRegressor()
+
+xgbr.fit(X_train, y_train)
+y_pred = xgbr.predict(X_test)
+
+xgb_rmse = np.sqrt(RSquared(y_test, y_pred))
+print("R Squared for XGBoost: ", np.mean(xgb_rmse))
+
+end = time.time()
+diff = end - start
+print('Execution time:', diff)
+
+This script is using the XGBoost library to train a regression model and predict ‘Life expectancy’ from a dataset. Here’s a step-by-step breakdown:
+
+Import necessary libraries: The script starts by importing the necessary libraries - xgboost, sklearn.preprocessing, and sklearn.model_selection.
+Prepare the data: The target variable ‘Life expectancy’ is separated from the rest of the dataset. The remaining columns are stored in X and ‘Life expectancy’ is stored in y.
+Encode categorical features: The LabelEncoder from sklearn.preprocessing is used to convert categorical features ‘Country’, ‘Year’, and ‘Status’ into numerical labels.
+Split the data: The train_test_split function from sklearn.model_selection is used to split the dataset into a training set and a test set. 80% of the data is used for training and 20% is used for testing.
+Train the model: An instance of XGBRegressor (a regression model from the XGBoost library) is created and then fitted on the training data.
+Make predictions: The trained model is used to make predictions on the test data.
+Evaluate the model: The R-squared value is calculated for the predictions. R-squared is a statistical measure that represents the proportion of the variance for a dependent variable that’s explained by an independent variable or variables in a regression model. The RSquared function used here is not defined in the provided code, so you would need to define it or import it from an appropriate library.
+Measure execution time: The script also measures and prints the time it took to train the model and make predictions.
+Please note that this is a simple example. The actual process of training an XGBoost model involves many more steps, including data preprocessing, parameter tuning, and model evaluation.
+
+I hope this helps! If you have any other questions, feel free to ask. 😊
\ No newline at end of file
diff --git a/Kaggle_exp.ipynb b/Kaggle_exp.ipynb
new file mode 100644
index 0000000..5003666
--- /dev/null
+++ b/Kaggle_exp.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Can a 19-year-old run for president? False\n",
+      "Can a 45-year-old run for president? True\n"
+     ]
+    }
+   ],
+   "source": [
+    "def can_run_for_president(age):\n",
+    "    \"\"\"Can someone of the given age run for president in the US?\"\"\"\n",
+    "    # The US Constitution says you must be at least 35 years old\n",
+    "    return age >= 35\n",
+    "\n",
+    "print(\"Can a 19-year-old run for president?\", can_run_for_president(19))\n",
+    "print(\"Can a 45-year-old run for president?\", can_run_for_president(45))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Is 100 odd? False\n",
+      "Is -1 odd? True\n"
+     ]
+    }
+   ],
+   "source": [
+    "def is_odd(n):\n",
+    "    return (n % 2) == 1\n",
+    "\n",
+    "print(\"Is 100 odd?\", is_odd(100))\n",
+    "print(\"Is -1 odd?\", is_odd(-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Value = -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code goes here. Define a function called 'sign'\n",
+    "def sign(n):\n",
+    "    if n < 0:\n",
+    "        print(\"Value = -1\")\n",
+    "    elif n > 0:\n",
+    "        print(\"Value = 1\")\n",
+    "    else:\n",
+    "        print(\"Value = 0\")\n",
+    "# Check your answer\n",
+    "sign(-100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Value = -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code goes here. Define a function called 'sign'\n",
+    "def sign(n):\n",
+    "    if n < 0:\n",
+    "        print(\"Value = -1\")\n",
+    "    elif n > 0:\n",
+    "        print(\"Value = 1\")\n",
+    "    else:\n",
+    "        print(\"Value = 0\")\n",
+    "# Check your answer\n",
+    "sign(-100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code goes here. Define a function called 'sign'\n",
+    "def sign(n):\n",
+    "    if n < 0:\n",
+    "        print(\"Value = -1\")\n",
+    "    elif n > 0:\n",
+    "        print(\"Value = 1\")\n",
+    "    else:\n",
+    "        print(\"Value = 0\")\n",
+    "    return\n",
+    "# Check your answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Value = -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "sign(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_smash(total_candies):\n",
+    "    if total_candies == 1:\n",
+    "        print(\"Splitting 1 candy\")\n",
+    "    else:\n",
+    "        print(\"Splitting\", total_candies, \"candy\" )\n",
+    "        return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Splitting 6 candy\n"
+     ]
+    }
+   ],
+   "source": [
+    "to_smash(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_smash(total_candies):\n",
+    "    if total_candies == 1:\n",
+    "      print(\"Splitting\", total_candies, \"candy\" if total_candies == 1 else \"Candies\")\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Splitting 1 candy\n"
+     ]
+    }
+   ],
+   "source": [
+    " to_smash(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "def prepared_for_weather(have_umbrella, rain_level, have_hood, is_workday):\n",
+    "    # Don't change this code. Our goal is just to find the bug, not fix it!\n",
+    "    return have_umbrella or rain_level < 5 and have_hood or not rain_level > 0 and is_workday\n",
+    "\n",
+    "# Change the values of these inputs so they represent a case where prepared_for_weather\n",
+    "# returns the wrong answer.\n",
+    "have_umbrella = False\n",
+    "rain_level = 0.0\n",
+    "have_hood = False\n",
+    "is_workday = False\n",
+    "\n",
+    "# Check what the function returns given the current values of the variables above\n",
+    "actual = prepared_for_weather(have_umbrella, rain_level, have_hood, is_workday)\n",
+    "print(actual)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def concise_is_negative(number):\n",
+    "    return number < 0\n",
+    "concise_is_negative(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def exactly_one_sauce(ketchup, mustard, onion):\n",
+    "    \"\"\"Return whether the customer wants either ketchup or mustard, but not both.\n",
+    "    (You may be familiar with this operation under the name \"exclusive or\")\n",
+    "    \"\"\"\n",
+    "    return (ketchup and not mustard) or (mustard and not ketchup)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def wants_plain_hotdog(ketchup, mustard, onion):\n",
+    "    \"\"\"Return whether the customer wants a plain hot dog with no toppings.\n",
+    "    \"\"\"\n",
+    "    return not (ketchup or mustard or onion)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def select_second(L):\n",
+    "    \"\"\"Return the second element of the given list. If the list has no second\n",
+    "    element, return None.\n",
+    "    \"\"\"\n",
+    "    if len(L) > 2:\n",
+    "        return L[1]\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'function' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[64], line 6\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Given a list of teams, where each team is a list of names, return the 2nd player (captain)\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03m    from the last listed team\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m teams[ :,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m----> 6\u001b[0m \u001b[43mlosing_team_captain\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'function' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "def losing_team_captain(teams):\n",
+    "    \"\"\"Given a list of teams, where each team is a list of names, return the 2nd player (captain)\n",
+    "    from the last listed team\n",
+    "    \"\"\"\n",
+    "    return teams[ :,-1]\n",
+    "losing_team_captain[teams]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Absceetism_MAchineLearning.ipynb b/Machine_learning_project/Absceetism_MAchineLearning.ipynb
similarity index 100%
rename from Absceetism_MAchineLearning.ipynb
rename to Machine_learning_project/Absceetism_MAchineLearning.ipynb
diff --git a/Abscenteism_Integration.ipynb b/Machine_learning_project/Abscenteism_Integration.ipynb
similarity index 100%
rename from Abscenteism_Integration.ipynb
rename to Machine_learning_project/Abscenteism_Integration.ipynb
diff --git a/Abscenteism_module.py b/Machine_learning_project/Abscenteism_module.py
similarity index 100%
rename from Abscenteism_module.py
rename to Machine_learning_project/Abscenteism_module.py
diff --git a/Abscenteism_project.ipynb b/Machine_learning_project/Abscenteism_project.ipynb
similarity index 100%
rename from Abscenteism_project.ipynb
rename to Machine_learning_project/Abscenteism_project.ipynb
diff --git a/Abscenteism_project_Preprocesing_Steps.ipynb b/Machine_learning_project/Abscenteism_project_Preprocesing_Steps.ipynb
similarity index 100%
rename from Abscenteism_project_Preprocesing_Steps.ipynb
rename to Machine_learning_project/Abscenteism_project_Preprocesing_Steps.ipynb
diff --git a/Abscentism_data.csv b/Machine_learning_project/Abscentism_data.csv
similarity index 97%
rename from Abscentism_data.csv
rename to Machine_learning_project/Abscentism_data.csv
index 1584780..67ff98a 100644
--- a/Abscentism_data.csv
+++ b/Machine_learning_project/Abscentism_data.csv
@@ -1,701 +1,701 @@
-,Reason_1,Reason_2,Reason_3,Reason_4,Transportation Expense,Distance to Work,Age,Daily Work Load Average,Body Mass Index,Education,Children,Pets,Absenteeism Time in Hours,Month Value,Day of the Week
-0,0,0,0,1,289,36,33,239.554,30,0,2,1,4,7,1
-1,0,0,0,0,118,13,50,239.554,31,0,1,0,0,7,1
-2,0,0,0,1,179,51,38,239.554,31,0,0,0,2,7,2
-3,1,0,0,0,279,5,39,239.554,24,0,2,0,4,7,3
-4,0,0,0,1,289,36,33,239.554,30,0,2,1,2,7,3
-5,0,0,0,1,179,51,38,239.554,31,0,0,0,2,7,4
-6,0,0,0,1,361,52,28,239.554,27,0,1,4,8,7,4
-7,0,0,0,1,260,50,36,239.554,23,0,4,0,4,7,4
-8,0,0,1,0,155,12,34,239.554,25,0,2,0,40,7,0
-9,0,0,0,1,235,11,37,239.554,29,1,1,1,8,7,0
-10,1,0,0,0,260,50,36,239.554,23,0,4,0,8,7,0
-11,1,0,0,0,260,50,36,239.554,23,0,4,0,8,7,1
-12,1,0,0,0,260,50,36,239.554,23,0,4,0,8,7,2
-13,1,0,0,0,179,51,38,239.554,31,0,0,0,1,7,2
-14,0,0,0,1,179,51,38,239.554,31,0,0,0,4,7,2
-15,1,0,0,0,246,25,41,239.554,23,0,0,0,8,7,4
-16,0,0,0,1,179,51,38,239.554,31,0,0,0,2,7,4
-17,0,0,1,0,179,51,38,239.554,31,0,0,0,8,7,0
-18,1,0,0,0,189,29,33,239.554,25,0,2,2,8,7,3
-19,0,0,0,1,248,25,47,205.917,32,0,2,1,2,8,2
-20,1,0,0,0,330,16,28,205.917,25,1,0,0,8,8,2
-21,1,0,0,0,179,51,38,205.917,31,0,0,0,1,8,0
-22,1,0,0,0,361,52,28,205.917,27,0,1,4,40,8,0
-23,0,0,0,1,260,50,36,205.917,23,0,4,0,4,8,4
-24,0,0,1,0,289,36,33,205.917,30,0,2,1,8,8,0
-25,0,0,0,1,361,52,28,205.917,27,0,1,4,7,8,0
-26,0,0,0,1,289,36,33,205.917,30,0,2,1,1,8,1
-27,0,0,0,1,157,27,29,205.917,22,0,0,0,4,8,2
-28,0,0,1,0,289,36,33,205.917,30,0,2,1,8,8,2
-29,0,0,0,1,179,51,38,205.917,31,0,0,0,2,8,4
-30,0,0,1,0,179,51,38,205.917,31,0,0,0,8,8,0
-31,0,0,1,0,235,29,48,205.917,33,0,1,5,8,8,3
-32,0,0,0,1,235,11,37,205.917,29,1,1,1,4,8,3
-33,0,0,1,0,235,29,48,205.917,33,0,1,5,8,8,0
-34,0,0,0,1,179,51,38,205.917,31,0,0,0,2,8,0
-35,0,0,0,1,361,52,28,205.917,27,0,1,4,1,8,0
-36,0,0,0,1,289,36,33,205.917,30,0,2,1,8,8,1
-37,1,0,0,0,291,50,32,205.917,23,0,0,0,4,8,3
-38,0,0,0,1,235,29,48,205.917,33,0,1,5,8,8,4
-39,0,0,0,1,260,50,36,205.917,23,0,4,0,4,8,4
-40,0,0,0,1,184,42,27,241.476,21,0,0,0,2,9,1
-41,0,0,0,1,118,10,37,241.476,28,0,0,0,4,9,0
-42,0,0,0,1,179,51,38,241.476,31,0,0,0,4,9,1
-43,0,0,1,0,235,20,43,241.476,38,0,1,0,8,9,1
-44,0,0,0,1,155,12,34,241.476,25,0,2,0,2,9,2
-45,0,0,0,1,118,10,37,241.476,28,0,0,0,3,9,6
-46,0,0,0,1,179,51,38,241.476,31,0,0,0,3,9,0
-47,0,0,0,1,291,31,40,241.476,25,0,1,1,4,9,3
-48,0,0,0,1,260,50,36,241.476,23,0,4,0,8,9,4
-49,1,0,0,0,291,31,40,241.476,25,0,1,1,32,9,0
-50,0,0,0,0,260,50,36,241.476,23,0,4,0,0,9,0
-51,0,0,0,0,225,26,28,241.476,24,0,1,2,0,9,0
-52,0,0,0,1,225,26,28,241.476,24,0,1,2,2,9,1
-53,0,0,0,1,118,10,37,241.476,28,0,0,0,2,9,1
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diff --git a/Absenteeism_data.csv b/Machine_learning_project/Absenteeism_data.csv
similarity index 97%
rename from Absenteeism_data.csv
rename to Machine_learning_project/Absenteeism_data.csv
index ee1b851..0a8e3a9 100644
--- a/Absenteeism_data.csv
+++ b/Machine_learning_project/Absenteeism_data.csv
@@ -1,701 +1,701 @@
-ID,Reason for Absence,Date,Transportation Expense,Distance to Work,Age,Daily Work Load Average,Body Mass Index,Education,Children,Pets,Absenteeism Time in Hours
-11,26,07/07/2015,289,36,33,239.554,30,1,2,1,4
-36,0,14/07/2015,118,13,50,239.554,31,1,1,0,0
-3,23,15/07/2015,179,51,38,239.554,31,1,0,0,2
-7,7,16/07/2015,279,5,39,239.554,24,1,2,0,4
-11,23,23/07/2015,289,36,33,239.554,30,1,2,1,2
-3,23,10/07/2015,179,51,38,239.554,31,1,0,0,2
-10,22,17/07/2015,361,52,28,239.554,27,1,1,4,8
-20,23,24/07/2015,260,50,36,239.554,23,1,4,0,4
-14,19,06/07/2015,155,12,34,239.554,25,1,2,0,40
-1,22,13/07/2015,235,11,37,239.554,29,3,1,1,8
-20,1,20/07/2015,260,50,36,239.554,23,1,4,0,8
-20,1,14/07/2015,260,50,36,239.554,23,1,4,0,8
-20,11,15/07/2015,260,50,36,239.554,23,1,4,0,8
-3,11,15/07/2015,179,51,38,239.554,31,1,0,0,1
-3,23,15/07/2015,179,51,38,239.554,31,1,0,0,4
-24,14,17/07/2015,246,25,41,239.554,23,1,0,0,8
-3,23,17/07/2015,179,51,38,239.554,31,1,0,0,2
-3,21,27/07/2015,179,51,38,239.554,31,1,0,0,8
-6,11,30/07/2015,189,29,33,239.554,25,1,2,2,8
-33,23,05/08/2015,248,25,47,205.917,32,1,2,1,2
-18,10,12/08/2015,330,16,28,205.917,25,2,0,0,8
-3,11,03/08/2015,179,51,38,205.917,31,1,0,0,1
-10,13,10/08/2015,361,52,28,205.917,27,1,1,4,40
-20,28,14/08/2015,260,50,36,205.917,23,1,4,0,4
-11,18,17/08/2015,289,36,33,205.917,30,1,2,1,8
-10,25,24/08/2015,361,52,28,205.917,27,1,1,4,7
-11,23,04/08/2015,289,36,33,205.917,30,1,2,1,1
-30,28,12/08/2015,157,27,29,205.917,22,1,0,0,4
-11,18,19/08/2015,289,36,33,205.917,30,1,2,1,8
-3,23,28/08/2015,179,51,38,205.917,31,1,0,0,2
-3,18,17/08/2015,179,51,38,205.917,31,1,0,0,8
-2,18,27/08/2015,235,29,48,205.917,33,1,1,5,8
-1,23,27/08/2015,235,11,37,205.917,29,3,1,1,4
-2,18,17/08/2015,235,29,48,205.917,33,1,1,5,8
-3,23,17/08/2015,179,51,38,205.917,31,1,0,0,2
-10,23,17/08/2015,361,52,28,205.917,27,1,1,4,1
-11,24,04/08/2015,289,36,33,205.917,30,1,2,1,8
-19,11,20/08/2015,291,50,32,205.917,23,1,0,0,4
-2,28,21/08/2015,235,29,48,205.917,33,1,1,5,8
-20,23,28/08/2015,260,50,36,205.917,23,1,4,0,4
-27,23,01/09/2015,184,42,27,241.476,21,1,0,0,2
-34,23,07/09/2015,118,10,37,241.476,28,1,0,0,4
-3,23,01/09/2015,179,51,38,241.476,31,1,0,0,4
-5,19,08/09/2015,235,20,43,241.476,38,1,1,0,8
-14,23,09/09/2015,155,12,34,241.476,25,1,2,0,2
-34,23,13/09/2015,118,10,37,241.476,28,1,0,0,3
-3,23,14/09/2015,179,51,38,241.476,31,1,0,0,3
-15,23,24/09/2015,291,31,40,241.476,25,1,1,1,4
-20,22,04/09/2015,260,50,36,241.476,23,1,4,0,8
-15,14,14/09/2015,291,31,40,241.476,25,1,1,1,32
-20,0,21/09/2015,260,50,36,241.476,23,1,4,0,0
-29,0,28/09/2015,225,26,28,241.476,24,1,1,2,0
-28,23,08/09/2015,225,26,28,241.476,24,1,1,2,2
-34,23,15/09/2015,118,10,37,241.476,28,1,0,0,2
-11,0,22/09/2015,289,36,33,241.476,30,1,2,1,0
-36,0,29/09/2015,118,13,50,241.476,31,1,1,0,0
-28,18,16/09/2015,225,26,28,241.476,24,1,1,2,3
-3,23,23/09/2015,179,51,38,241.476,31,1,0,0,3
-13,0,30/09/2015,369,17,31,241.476,25,1,3,0,0
-33,23,11/09/2015,248,25,47,241.476,32,1,2,1,1
-3,23,18/09/2015,179,51,38,241.476,31,1,0,0,3
-20,23,25/09/2015,260,50,36,241.476,23,1,4,0,4
-3,23,06/10/2015,179,51,38,253.465,31,1,0,0,3
-34,23,13/10/2015,118,10,37,253.465,28,1,0,0,3
-36,0,14/10/2015,118,13,50,253.465,31,1,1,0,0
-22,23,15/10/2015,179,26,30,253.465,19,3,0,0,1
-3,23,16/10/2015,179,51,38,253.465,31,1,0,0,3
-28,23,16/10/2015,225,26,28,253.465,24,1,1,2,3
-34,23,06/10/2015,118,10,37,253.465,28,1,0,0,3
-28,23,14/10/2015,225,26,28,253.465,24,1,1,2,2
-33,23,21/10/2015,248,25,47,253.465,32,1,2,1,2
-15,23,22/10/2015,291,31,40,253.465,25,1,1,1,5
-3,23,21/10/2015,179,51,38,253.465,31,1,0,0,8
-28,23,21/10/2015,225,26,28,253.465,24,1,1,2,3
-20,19,22/10/2015,260,50,36,253.465,23,1,4,0,16
-15,14,13/10/2015,291,31,40,253.465,25,1,1,1,8
-28,28,20/10/2015,225,26,28,253.465,24,1,1,2,2
-11,26,21/10/2015,289,36,33,253.465,30,1,2,1,8
-10,23,23/10/2015,361,52,28,253.465,27,1,1,4,1
-20,28,30/10/2015,260,50,36,253.465,23,1,4,0,3
-3,23,05/11/2015,179,51,38,306.345,31,1,0,0,1
-28,23,04/11/2015,225,26,28,306.345,24,1,1,2,1
-3,13,05/11/2015,179,51,38,306.345,31,1,0,0,8
-17,21,12/11/2015,179,22,40,306.345,22,2,2,0,8
-15,23,19/11/2015,291,31,40,306.345,25,1,1,1,5
-14,10,02/11/2015,155,12,34,306.345,25,1,2,0,32
-6,22,09/11/2015,189,29,33,306.345,25,1,2,2,8
-15,14,16/11/2015,291,31,40,306.345,25,1,1,1,40
-28,23,18/11/2015,225,26,28,306.345,24,1,1,2,1
-14,6,20/11/2015,155,12,34,306.345,25,1,2,0,8
-28,23,18/11/2015,225,26,28,306.345,24,1,1,2,3
-17,21,25/11/2015,179,22,40,306.345,22,2,2,0,8
-28,13,20/11/2015,225,26,28,306.345,24,1,1,2,3
-20,28,27/11/2015,260,50,36,306.345,23,1,4,0,4
-33,28,02/11/2015,248,25,47,306.345,32,1,2,1,1
-28,28,10/11/2015,225,26,28,306.345,24,1,1,2,3
-11,7,11/11/2015,289,36,33,306.345,30,1,2,1,24
-15,23,26/11/2015,291,31,40,306.345,25,1,1,1,3
-33,23,01/12/2015,248,25,47,261.306,32,1,2,1,1
-34,19,01/12/2015,118,10,37,261.306,28,1,0,0,64
-36,23,02/12/2015,118,13,50,261.306,31,1,1,0,2
-1,26,02/12/2015,235,11,37,261.306,29,3,1,1,8
-28,23,03/12/2015,225,26,28,261.306,24,1,1,2,2
-20,26,04/12/2015,260,50,36,261.306,23,1,4,0,8
-34,19,08/12/2015,118,10,37,261.306,28,1,0,0,56
-10,22,09/12/2015,361,52,28,261.306,27,1,1,4,8
-28,28,10/12/2015,225,26,28,261.306,24,1,1,2,3
-20,28,11/12/2015,260,50,36,261.306,23,1,4,0,3
-28,23,15/12/2015,225,26,28,261.306,24,1,1,2,2
-10,22,16/12/2015,361,52,28,261.306,27,1,1,4,8
-34,27,11/12/2015,118,10,37,261.306,28,1,0,0,2
-24,19,18/12/2015,246,25,41,261.306,23,1,0,0,8
-28,23,18/12/2015,225,26,28,261.306,24,1,1,2,2
-28,23,06/01/2016,225,26,28,308.593,24,1,1,2,1
-34,19,04/01/2016,118,10,37,308.593,28,1,0,0,1
-34,27,05/01/2016,118,10,37,308.593,28,1,0,0,1
-14,18,05/01/2016,155,12,34,308.593,25,1,2,0,8
-28,27,06/01/2016,225,26,28,308.593,24,1,1,2,2
-27,23,07/01/2016,184,42,27,308.593,21,1,0,0,2
-28,28,07/01/2016,225,26,28,308.593,24,1,1,2,2
-28,27,08/01/2016,225,26,28,308.593,24,1,1,2,1
-34,27,11/01/2016,118,10,37,308.593,28,1,0,0,2
-28,27,12/01/2016,225,26,28,308.593,24,1,1,2,2
-34,27,12/01/2016,118,10,37,308.593,28,1,0,0,2
-34,27,13/01/2016,118,10,37,308.593,28,1,0,0,2
-34,27,14/01/2016,118,10,37,308.593,28,1,0,0,2
-34,27,15/01/2016,118,10,37,308.593,28,1,0,0,2
-34,27,11/01/2016,118,10,37,308.593,28,1,0,0,2
-34,27,12/01/2016,118,10,37,308.593,28,1,0,0,2
-22,18,12/01/2016,179,26,30,308.593,19,3,0,0,8
-11,18,12/01/2016,289,36,33,308.593,30,1,2,1,8
-34,27,13/01/2016,118,10,37,308.593,28,1,0,0,2
-27,23,21/01/2016,184,42,27,308.593,21,1,0,0,2
-34,27,28/01/2016,118,10,37,308.593,28,1,0,0,2
-34,27,25/01/2016,118,10,37,308.593,28,1,0,0,0
-28,23,26/01/2016,225,26,28,308.593,24,1,1,2,1
-11,22,28/01/2016,289,36,33,308.593,30,1,2,1,3
-27,23,05/02/2016,184,42,27,302.585,21,1,0,0,1
-24,1,03/02/2016,246,25,41,302.585,23,1,0,0,8
-3,11,10/02/2016,179,51,38,302.585,31,1,0,0,8
-14,28,11/02/2016,155,12,34,302.585,25,1,2,0,2
-6,23,11/02/2016,189,29,33,302.585,25,1,2,2,8
-20,28,12/02/2016,260,50,36,302.585,23,1,4,0,2
-11,22,12/02/2016,289,36,33,302.585,30,1,2,1,8
-31,11,08/02/2016,388,15,50,302.585,24,1,0,0,8
-31,1,09/02/2016,388,15,50,302.585,24,1,0,0,8
-28,28,15/02/2016,225,26,28,302.585,24,1,1,2,2
-28,23,09/02/2016,225,26,28,302.585,24,1,1,2,2
-22,23,16/02/2016,179,26,30,302.585,19,3,0,0,1
-27,23,23/02/2016,184,42,27,302.585,21,1,0,0,8
-28,25,25/02/2016,225,26,28,302.585,24,1,1,2,3
-18,18,15/02/2016,330,16,28,302.585,25,2,0,0,8
-18,23,16/02/2016,330,16,28,302.585,25,2,0,0,1
-28,23,17/02/2016,225,26,28,302.585,24,1,1,2,1
-6,19,25/02/2016,189,29,33,302.585,25,1,2,2,8
-19,28,01/03/2016,291,50,32,343.253,23,1,0,0,2
-20,19,01/03/2016,260,50,36,343.253,23,1,4,0,8
-30,19,08/03/2016,157,27,29,343.253,22,1,0,0,3
-17,17,15/03/2016,179,22,40,343.253,22,2,2,0,8
-15,22,16/03/2016,291,31,40,343.253,25,1,1,1,8
-20,13,02/03/2016,260,50,36,343.253,23,1,4,0,8
-22,13,03/03/2016,179,26,30,343.253,19,3,0,0,8
-33,14,04/03/2016,248,25,47,343.253,32,1,2,1,3
-20,13,04/03/2016,260,50,36,343.253,23,1,4,0,40
-17,11,07/03/2016,179,22,40,343.253,22,2,2,0,40
-14,1,14/03/2016,155,12,34,343.253,25,1,2,0,16
-20,26,21/03/2016,260,50,36,343.253,23,1,4,0,16
-14,13,22/03/2016,155,12,34,343.253,25,1,2,0,8
-11,6,24/03/2016,289,36,33,343.253,30,1,2,1,8
-17,8,24/03/2016,179,22,40,343.253,22,2,2,0,8
-20,28,25/03/2016,260,50,36,343.253,23,1,4,0,4
-28,23,25/03/2016,225,26,28,343.253,24,1,1,2,1
-7,14,28/03/2016,279,5,39,343.253,24,1,2,0,8
-3,13,29/03/2016,179,51,38,343.253,31,1,0,0,24
-28,23,30/03/2016,225,26,28,343.253,24,1,1,2,2
-28,11,28/03/2016,225,26,28,343.253,24,1,1,2,8
-22,13,28/03/2016,179,26,30,343.253,19,3,0,0,1
-28,11,29/03/2016,225,26,28,343.253,24,1,1,2,8
-28,11,30/03/2016,225,26,28,343.253,24,1,1,2,16
-3,13,30/03/2016,179,51,38,343.253,31,1,0,0,3
-7,14,17/03/2016,279,5,39,343.253,24,1,2,0,16
-28,28,18/03/2016,225,26,28,343.253,24,1,1,2,2
-33,14,25/03/2016,248,25,47,343.253,32,1,2,1,3
-28,28,28/03/2016,225,26,28,343.253,24,1,1,2,1
-15,28,06/04/2016,291,31,40,326.452,25,1,1,1,1
-28,23,13/04/2016,225,26,28,326.452,24,1,1,2,1
-14,28,12/04/2016,155,12,34,326.452,25,1,2,0,1
-24,13,13/04/2016,246,25,41,326.452,23,1,0,0,24
-14,23,14/04/2016,155,12,34,326.452,25,1,2,0,1
-28,28,15/04/2016,225,26,28,326.452,24,1,1,2,2
-20,28,15/04/2016,260,50,36,326.452,23,1,4,0,4
-3,13,06/04/2016,179,51,38,326.452,31,1,0,0,24
-36,23,07/04/2016,118,13,50,326.452,31,1,1,0,1
-15,23,08/04/2016,291,31,40,326.452,25,1,1,1,3
-24,14,08/04/2016,246,25,41,326.452,23,1,0,0,8
-15,28,22/04/2016,291,31,40,326.452,25,1,1,1,1
-33,28,29/04/2016,248,25,47,326.452,32,1,2,1,8
-20,19,29/04/2016,260,50,36,326.452,23,1,4,0,56
-11,19,26/04/2016,289,36,33,326.452,30,1,2,1,8
-14,12,27/04/2016,155,12,34,326.452,25,1,2,0,24
-23,19,27/04/2016,378,49,36,326.452,21,1,2,4,8
-11,13,28/04/2016,289,36,33,326.452,30,1,2,1,16
-1,7,08/04/2016,235,11,37,326.452,29,3,1,1,3
-2,0,04/04/2016,235,29,48,326.452,33,1,1,5,0
-11,13,04/05/2016,289,36,33,378.884,30,1,2,1,8
-14,28,05/05/2016,155,12,34,378.884,25,1,2,0,2
-14,28,02/05/2016,155,12,34,378.884,25,1,2,0,1
-3,18,03/05/2016,179,51,38,378.884,31,1,0,0,8
-28,19,03/05/2016,225,26,28,378.884,24,1,1,2,8
-27,7,11/05/2016,184,42,27,378.884,21,1,0,0,4
-14,28,09/05/2016,155,12,34,378.884,25,1,2,0,2
-3,12,10/05/2016,179,51,38,378.884,31,1,0,0,1
-11,13,04/05/2016,289,36,33,378.884,30,1,2,1,24
-7,0,11/05/2016,279,5,39,378.884,24,1,2,0,0
-18,0,18/05/2016,330,16,28,378.884,25,2,0,0,0
-23,0,25/05/2016,378,49,36,378.884,21,1,2,4,0
-31,0,25/05/2016,388,15,50,378.884,24,1,0,0,0
-3,11,24/05/2016,179,51,38,378.884,31,1,0,0,1
-36,13,11/05/2016,118,13,50,378.884,31,1,1,0,24
-10,22,27/05/2016,361,52,28,378.884,27,1,1,4,8
-24,19,06/06/2016,246,25,41,377.55,23,1,0,0,8
-10,22,13/06/2016,361,52,28,377.55,27,1,1,4,8
-24,10,14/06/2016,246,25,41,377.55,23,1,0,0,24
-15,23,16/06/2016,291,31,40,377.55,25,1,1,1,4
-24,10,17/06/2016,246,25,41,377.55,23,1,0,0,8
-3,11,13/06/2016,179,51,38,377.55,31,1,0,0,8
-14,23,20/06/2016,155,12,34,377.55,25,1,2,0,4
-24,10,27/06/2016,246,25,41,377.55,23,1,0,0,8
-36,13,29/06/2016,118,13,50,377.55,31,1,1,0,8
-1,13,03/06/2016,235,11,37,377.55,29,3,1,1,16
-36,23,07/06/2016,118,13,50,377.55,31,1,1,0,1
-36,13,08/06/2016,118,13,50,377.55,31,1,1,0,80
-23,22,09/06/2016,378,49,36,377.55,21,1,2,4,8
-3,11,10/06/2016,179,51,38,377.55,31,1,0,0,2
-32,28,20/06/2016,289,48,49,377.55,36,1,0,2,2
-28,28,30/06/2016,225,26,28,377.55,24,1,1,2,2
-14,19,05/07/2016,155,12,34,275.312,25,1,2,0,16
-36,1,06/07/2016,118,13,50,275.312,31,1,1,0,8
-34,5,08/07/2016,118,10,37,275.312,28,1,0,0,8
-34,26,08/07/2016,118,10,37,275.312,28,1,0,0,4
-18,26,12/07/2016,330,16,28,275.312,25,2,0,0,8
-22,18,14/07/2016,179,26,30,275.312,19,3,0,0,8
-14,25,15/07/2016,155,12,34,275.312,25,1,2,0,2
-18,1,18/07/2016,330,16,28,275.312,25,2,0,0,8
-18,1,19/07/2016,330,16,28,275.312,25,2,0,0,8
-30,25,18/07/2016,157,27,29,275.312,22,1,0,0,3
-10,22,19/07/2016,361,52,28,275.312,27,1,1,4,8
-11,26,20/07/2016,289,36,33,275.312,30,1,2,1,8
-3,26,21/07/2016,179,51,38,275.312,31,1,0,0,8
-11,19,11/07/2016,289,36,33,275.312,30,1,2,1,32
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-3,23,11/09/2017,179,51,38,261.756,31,1,0,0,4
-28,23,13/09/2017,225,26,28,261.756,24,1,1,2,1
-22,23,18/09/2017,179,26,30,261.756,19,3,0,0,2
-13,23,19/09/2017,369,17,31,261.756,25,1,3,0,8
-10,22,26/09/2017,361,52,28,261.756,27,1,1,4,8
-32,4,05/10/2017,289,48,49,284.853,36,1,0,2,1
-25,11,05/10/2017,235,16,32,284.853,25,3,0,0,3
-24,26,06/10/2017,246,25,41,284.853,23,1,0,0,8
-32,14,11/10/2017,289,48,49,284.853,36,1,0,2,3
-15,28,11/10/2017,291,31,40,284.853,25,1,1,1,2
-34,23,17/10/2017,118,10,37,284.853,28,1,0,0,2
-32,23,19/10/2017,289,48,49,284.853,36,1,0,2,2
-15,23,20/10/2017,291,31,40,284.853,25,1,1,1,1
-28,23,24/10/2017,225,26,28,284.853,24,1,1,2,2
-13,23,24/10/2017,369,17,31,284.853,25,1,3,0,8
-13,23,24/10/2017,369,17,31,284.853,25,1,3,0,3
-28,23,24/10/2017,225,26,28,284.853,24,1,1,2,4
-13,26,24/10/2017,369,17,31,284.853,25,1,3,0,8
-3,28,25/10/2017,179,51,38,284.853,31,1,0,0,3
-9,1,25/10/2017,228,14,58,284.853,22,1,2,1,1
-15,23,25/10/2017,291,31,40,284.853,25,1,1,1,1
-13,10,26/10/2017,369,17,31,284.853,25,1,3,0,8
-28,13,26/10/2017,225,26,28,284.853,24,1,1,2,1
-13,10,27/10/2017,369,17,31,284.853,25,1,3,0,8
-28,10,27/10/2017,225,26,28,284.853,24,1,1,2,3
-6,23,29/10/2017,189,29,33,284.853,25,1,2,2,8
-25,6,29/10/2017,235,16,32,284.853,25,3,0,0,8
-33,10,29/10/2017,248,25,47,284.853,32,1,2,1,8
-28,0,29/10/2017,225,26,28,284.853,24,1,1,2,0
-28,13,31/10/2017,225,26,28,284.853,24,1,1,2,3
-3,21,07/11/2017,179,51,38,268.519,31,1,0,0,1
-34,28,08/11/2017,118,10,37,268.519,28,1,0,0,3
-18,2,08/11/2017,330,16,28,268.519,25,2,0,0,24
-3,28,10/11/2017,179,51,38,268.519,31,1,0,0,1
-34,9,14/11/2017,118,10,37,268.519,28,1,0,0,8
-11,24,15/11/2017,289,36,33,268.519,30,1,2,1,8
-25,1,17/11/2017,235,16,32,268.519,25,3,0,0,8
-28,23,17/11/2017,225,26,28,268.519,24,1,1,2,4
-10,22,21/11/2017,361,52,28,268.519,27,1,1,4,8
-15,28,22/11/2017,291,31,40,268.519,25,1,1,1,2
-34,13,23/11/2017,118,10,37,268.519,28,1,0,0,2
-28,14,23/11/2017,225,26,28,268.519,24,1,1,2,3
-3,28,27/11/2017,179,51,38,268.519,31,1,0,0,1
-34,23,27/11/2017,118,10,37,268.519,28,1,0,0,8
-34,8,28/11/2017,118,10,37,268.519,28,1,0,0,8
-28,23,28/11/2017,225,26,28,268.519,24,1,1,2,2
-15,0,28/11/2017,291,31,40,268.519,25,1,1,1,0
-11,0,29/11/2017,289,36,33,268.519,30,1,2,1,0
-33,14,30/11/2017,248,25,47,268.519,32,1,2,1,4
-5,0,30/11/2017,235,20,43,268.519,38,1,1,0,0
-28,23,24/11/2017,225,26,28,268.519,24,1,1,2,2
-13,26,24/11/2017,369,17,31,268.519,25,1,3,0,8
-10,28,27/11/2017,361,52,28,268.519,27,1,1,4,2
-3,13,05/12/2017,179,51,38,280.549,31,1,0,0,32
-15,28,06/12/2017,291,31,40,280.549,25,1,1,1,1
-28,23,06/12/2017,225,26,28,280.549,24,1,1,2,3
-22,13,08/12/2017,179,26,30,280.549,19,3,0,0,1
-28,23,08/12/2017,225,26,28,280.549,24,1,1,2,3
-28,23,13/12/2017,225,26,28,280.549,24,1,1,2,3
-10,14,14/12/2017,361,52,28,280.549,27,1,1,4,4
-17,18,15/12/2017,179,22,40,280.549,22,2,2,0,2
-5,26,15/12/2017,235,20,43,280.549,38,1,1,0,8
-12,18,18/12/2017,233,51,31,280.549,21,2,1,8,8
-22,13,19/12/2017,179,26,30,280.549,19,3,0,0,16
-28,23,19/12/2017,225,26,28,280.549,24,1,1,2,2
-28,23,21/12/2017,225,26,28,280.549,24,1,1,2,3
-28,23,18/12/2017,225,26,28,280.549,24,1,1,2,2
-14,18,19/12/2017,155,12,34,280.549,25,1,2,0,80
-22,12,01/01/2018,179,26,30,313.532,19,3,0,0,24
-22,12,04/01/2018,179,26,30,313.532,19,3,0,0,16
-17,25,04/01/2018,179,22,40,313.532,22,2,2,0,2
-17,25,05/01/2018,179,22,40,313.532,22,2,2,0,2
-22,13,08/01/2018,179,26,30,313.532,19,3,0,0,3
-17,25,10/01/2018,179,22,40,313.532,22,2,2,0,2
-32,10,11/01/2018,289,48,49,313.532,36,1,0,2,8
-17,18,12/01/2018,179,22,40,313.532,22,2,2,0,3
-22,27,15/01/2018,179,26,30,313.532,19,3,0,0,2
-14,18,16/01/2018,155,12,34,313.532,25,1,2,0,8
-22,27,17/01/2018,179,26,30,313.532,19,3,0,0,2
-3,27,17/01/2018,179,51,38,313.532,31,1,0,0,3
-11,13,17/01/2018,289,36,33,313.532,30,1,2,1,8
-3,27,18/01/2018,179,51,38,313.532,31,1,0,0,3
-3,27,26/01/2018,179,51,38,313.532,31,1,0,0,2
-3,13,06/02/2018,179,51,38,264.249,31,1,0,0,8
-28,23,06/02/2018,225,26,28,264.249,24,1,1,2,3
-33,1,07/02/2018,248,25,47,264.249,32,1,2,1,8
-3,27,07/02/2018,179,51,38,264.249,31,1,0,0,2
-28,28,08/02/2018,225,26,28,264.249,24,1,1,2,3
-3,27,08/02/2018,179,51,38,264.249,31,1,0,0,2
-22,27,08/02/2018,179,26,30,264.249,19,3,0,0,2
-29,28,09/02/2018,225,15,41,264.249,28,4,2,2,2
-3,27,09/02/2018,179,51,38,264.249,31,1,0,0,2
-12,19,12/02/2018,233,51,31,264.249,21,2,1,8,2
-3,27,12/02/2018,179,51,38,264.249,31,1,0,0,2
-28,7,13/02/2018,225,26,28,264.249,24,1,1,2,8
-3,27,14/02/2018,179,51,38,264.249,31,1,0,0,3
-3,27,15/02/2018,179,51,38,264.249,31,1,0,0,3
-28,25,15/02/2018,225,26,28,264.249,24,1,1,2,3
-22,13,15/02/2018,179,26,30,264.249,19,3,0,0,2
-17,23,16/02/2018,179,22,40,264.249,22,2,2,0,2
-3,27,16/02/2018,179,51,38,264.249,31,1,0,0,3
-12,12,21/02/2018,233,51,31,264.249,21,2,1,8,3
-22,27,21/02/2018,179,26,30,264.249,19,3,0,0,2
-3,27,21/02/2018,179,51,38,264.249,31,1,0,0,2
-3,13,22/02/2018,179,51,38,264.249,31,1,0,0,8
-3,27,23/02/2018,179,51,38,264.249,31,1,0,0,2
-14,25,26/02/2018,155,12,34,264.249,25,1,2,0,5
-25,25,26/02/2018,235,16,32,264.249,25,3,0,0,3
-3,27,26/02/2018,179,51,38,264.249,31,1,0,0,2
-28,7,26/02/2018,225,26,28,264.249,24,1,1,2,2
-3,27,27/02/2018,179,51,38,264.249,31,1,0,0,2
-33,23,27/02/2018,248,25,47,264.249,32,1,2,1,2
-28,25,27/02/2018,225,26,28,264.249,24,1,1,2,2
-3,27,28/02/2018,179,51,38,264.249,31,1,0,0,2
-3,27,22/02/2018,179,51,38,264.249,31,1,0,0,2
-25,25,23/02/2018,235,16,32,264.249,25,3,0,0,2
-3,27,05/03/2018,179,51,38,222.196,31,1,0,0,2
-33,23,05/03/2018,248,25,47,222.196,32,1,2,1,2
-9,25,06/03/2018,228,14,58,222.196,22,1,2,1,3
-33,25,06/03/2018,248,25,47,222.196,32,1,2,1,3
-9,12,06/03/2018,228,14,58,222.196,22,1,2,1,112
-3,27,07/03/2018,179,51,38,222.196,31,1,0,0,2
-28,27,08/03/2018,225,26,28,222.196,24,1,1,2,2
-3,27,08/03/2018,179,51,38,222.196,31,1,0,0,3
-28,25,08/03/2018,225,26,28,222.196,24,1,1,2,2
-22,27,09/03/2018,179,26,30,222.196,19,3,0,0,3
-25,25,12/03/2018,235,16,32,222.196,25,3,0,0,3
-10,19,12/03/2018,361,52,28,222.196,27,1,1,4,8
-3,13,13/03/2018,179,51,38,222.196,31,1,0,0,8
-3,27,14/03/2018,179,51,38,222.196,31,1,0,0,2
-3,27,15/03/2018,179,51,38,222.196,31,1,0,0,3
-22,27,16/03/2018,179,26,30,222.196,19,3,0,0,2
-3,10,19/03/2018,179,51,38,222.196,31,1,0,0,4
-33,13,19/03/2018,248,25,47,222.196,32,1,2,1,2
-3,27,19/03/2018,179,51,38,222.196,31,1,0,0,3
-28,7,19/03/2018,225,26,28,222.196,24,1,1,2,8
-3,27,20/03/2018,179,51,38,222.196,31,1,0,0,2
-11,23,21/03/2018,289,36,33,222.196,30,1,2,1,8
-9,25,21/03/2018,228,14,58,222.196,22,1,2,1,2
-3,27,21/03/2018,179,51,38,222.196,31,1,0,0,2
-33,23,22/03/2018,248,25,47,222.196,32,1,2,1,3
-3,27,22/03/2018,179,51,38,222.196,31,1,0,0,3
-22,23,23/03/2018,179,26,30,222.196,19,3,0,0,2
-3,27,23/03/2018,179,51,38,222.196,31,1,0,0,3
-3,27,27/03/2018,179,51,38,222.196,31,1,0,0,3
-16,23,28/03/2018,118,15,46,222.196,25,1,2,0,8
-14,13,28/03/2018,155,12,34,222.196,25,1,2,0,24
-3,27,28/03/2018,179,51,38,222.196,31,1,0,0,3
-3,27,29/03/2018,179,51,38,222.196,31,1,0,0,3
-22,13,26/03/2018,179,26,30,222.196,19,3,0,0,2
-11,19,26/03/2018,289,36,33,222.196,30,1,2,1,104
-13,22,28/03/2018,369,17,31,222.196,25,1,3,0,8
-28,13,02/04/2018,225,26,28,246.288,24,1,1,2,8
-34,10,02/04/2018,118,10,37,246.288,28,1,0,0,8
-10,19,03/04/2018,361,52,28,246.288,27,1,1,4,8
-33,19,04/04/2018,248,25,47,246.288,32,1,2,1,8
-6,13,05/04/2018,189,29,33,246.288,25,1,2,2,8
-22,27,06/04/2018,179,26,30,246.288,19,3,0,0,2
-13,7,09/04/2018,369,17,31,246.288,25,1,3,0,24
-17,16,10/04/2018,179,22,40,246.288,22,2,2,0,2
-36,23,10/04/2018,118,13,50,246.288,31,1,1,0,3
-10,23,10/04/2018,361,52,28,246.288,27,1,1,4,2
-34,10,11/04/2018,118,10,37,246.288,28,1,0,0,2
-1,22,13/04/2018,235,11,37,246.288,29,3,1,1,8
-22,27,13/04/2018,179,26,30,246.288,19,3,0,0,2
-28,19,16/04/2018,225,26,28,246.288,24,1,1,2,8
-25,16,17/04/2018,235,16,32,246.288,25,3,0,0,3
-22,27,20/04/2018,179,26,30,246.288,19,3,0,0,2
-14,28,24/04/2018,155,12,34,246.288,25,1,2,0,4
-28,19,26/04/2018,225,26,28,246.288,24,1,1,2,8
-36,14,26/04/2018,118,13,50,246.288,31,1,1,0,2
-22,27,27/04/2018,179,26,30,246.288,19,3,0,0,2
-1,22,07/05/2018,235,11,37,237.656,29,3,1,1,8
-29,19,09/05/2018,225,15,41,237.656,28,4,2,2,3
-25,28,09/05/2018,235,16,32,237.656,25,3,0,0,2
-34,8,09/05/2018,118,10,37,237.656,28,1,0,0,3
-5,26,09/05/2018,235,20,43,237.656,38,1,1,0,8
-22,13,10/05/2018,179,26,30,237.656,19,3,0,0,1
-15,28,10/05/2018,291,31,40,237.656,25,1,1,1,2
-29,14,10/05/2018,225,15,41,237.656,28,4,2,2,8
-26,19,11/05/2018,300,26,43,237.656,25,1,2,1,64
-29,22,11/05/2018,225,15,41,237.656,28,4,2,2,8
-22,27,11/05/2018,179,26,30,237.656,19,3,0,0,2
-36,23,14/05/2018,118,13,50,237.656,31,1,1,0,2
-36,5,15/05/2018,118,13,50,237.656,31,1,1,0,3
-34,28,15/05/2018,118,10,37,237.656,28,1,0,0,1
-36,0,15/05/2018,118,13,50,237.656,31,1,1,0,0
-22,27,16/05/2018,179,26,30,237.656,19,3,0,0,2
-23,0,16/05/2018,378,49,36,237.656,21,1,2,4,0
-17,16,18/05/2018,179,22,40,237.656,22,2,2,0,1
-14,10,21/05/2018,155,12,34,237.656,25,1,2,0,48
-25,10,21/05/2018,235,16,32,237.656,25,3,0,0,8
-15,22,23/05/2018,291,31,40,237.656,25,1,1,1,8
-17,10,23/05/2018,179,22,40,237.656,22,2,2,0,8
-28,6,23/05/2018,225,26,28,237.656,24,1,1,2,3
-18,10,24/05/2018,330,16,28,237.656,25,2,0,0,8
-25,23,24/05/2018,235,16,32,237.656,25,3,0,0,2
-15,28,31/05/2018,291,31,40,237.656,25,1,1,1,2
+ID,Reason for Absence,Date,Transportation Expense,Distance to Work,Age,Daily Work Load Average,Body Mass Index,Education,Children,Pets,Absenteeism Time in Hours
+11,26,07/07/2015,289,36,33,239.554,30,1,2,1,4
+36,0,14/07/2015,118,13,50,239.554,31,1,1,0,0
+3,23,15/07/2015,179,51,38,239.554,31,1,0,0,2
+7,7,16/07/2015,279,5,39,239.554,24,1,2,0,4
+11,23,23/07/2015,289,36,33,239.554,30,1,2,1,2
+3,23,10/07/2015,179,51,38,239.554,31,1,0,0,2
+10,22,17/07/2015,361,52,28,239.554,27,1,1,4,8
+20,23,24/07/2015,260,50,36,239.554,23,1,4,0,4
+14,19,06/07/2015,155,12,34,239.554,25,1,2,0,40
+1,22,13/07/2015,235,11,37,239.554,29,3,1,1,8
+20,1,20/07/2015,260,50,36,239.554,23,1,4,0,8
+20,1,14/07/2015,260,50,36,239.554,23,1,4,0,8
+20,11,15/07/2015,260,50,36,239.554,23,1,4,0,8
+3,11,15/07/2015,179,51,38,239.554,31,1,0,0,1
+3,23,15/07/2015,179,51,38,239.554,31,1,0,0,4
+24,14,17/07/2015,246,25,41,239.554,23,1,0,0,8
+3,23,17/07/2015,179,51,38,239.554,31,1,0,0,2
+3,21,27/07/2015,179,51,38,239.554,31,1,0,0,8
+6,11,30/07/2015,189,29,33,239.554,25,1,2,2,8
+33,23,05/08/2015,248,25,47,205.917,32,1,2,1,2
+18,10,12/08/2015,330,16,28,205.917,25,2,0,0,8
+3,11,03/08/2015,179,51,38,205.917,31,1,0,0,1
+10,13,10/08/2015,361,52,28,205.917,27,1,1,4,40
+20,28,14/08/2015,260,50,36,205.917,23,1,4,0,4
+11,18,17/08/2015,289,36,33,205.917,30,1,2,1,8
+10,25,24/08/2015,361,52,28,205.917,27,1,1,4,7
+11,23,04/08/2015,289,36,33,205.917,30,1,2,1,1
+30,28,12/08/2015,157,27,29,205.917,22,1,0,0,4
+11,18,19/08/2015,289,36,33,205.917,30,1,2,1,8
+3,23,28/08/2015,179,51,38,205.917,31,1,0,0,2
+3,18,17/08/2015,179,51,38,205.917,31,1,0,0,8
+2,18,27/08/2015,235,29,48,205.917,33,1,1,5,8
+1,23,27/08/2015,235,11,37,205.917,29,3,1,1,4
+2,18,17/08/2015,235,29,48,205.917,33,1,1,5,8
+3,23,17/08/2015,179,51,38,205.917,31,1,0,0,2
+10,23,17/08/2015,361,52,28,205.917,27,1,1,4,1
+11,24,04/08/2015,289,36,33,205.917,30,1,2,1,8
+19,11,20/08/2015,291,50,32,205.917,23,1,0,0,4
+2,28,21/08/2015,235,29,48,205.917,33,1,1,5,8
+20,23,28/08/2015,260,50,36,205.917,23,1,4,0,4
+27,23,01/09/2015,184,42,27,241.476,21,1,0,0,2
+34,23,07/09/2015,118,10,37,241.476,28,1,0,0,4
+3,23,01/09/2015,179,51,38,241.476,31,1,0,0,4
+5,19,08/09/2015,235,20,43,241.476,38,1,1,0,8
+14,23,09/09/2015,155,12,34,241.476,25,1,2,0,2
+34,23,13/09/2015,118,10,37,241.476,28,1,0,0,3
+3,23,14/09/2015,179,51,38,241.476,31,1,0,0,3
+15,23,24/09/2015,291,31,40,241.476,25,1,1,1,4
+20,22,04/09/2015,260,50,36,241.476,23,1,4,0,8
+15,14,14/09/2015,291,31,40,241.476,25,1,1,1,32
+20,0,21/09/2015,260,50,36,241.476,23,1,4,0,0
+29,0,28/09/2015,225,26,28,241.476,24,1,1,2,0
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diff --git a/Absenteeism_new_data2.csv b/Machine_learning_project/Absenteeism_new_data2.csv
similarity index 100%
rename from Absenteeism_new_data2.csv
rename to Machine_learning_project/Absenteeism_new_data2.csv
diff --git a/absenteeism_module1.py b/Machine_learning_project/absenteeism_module1.py
similarity index 100%
rename from absenteeism_module1.py
rename to Machine_learning_project/absenteeism_module1.py
diff --git a/My_Ustacky_Project/.ipynb_checkpoints/My Project-checkpoint.ipynb b/My_Ustacky_Project/.ipynb_checkpoints/My Project-checkpoint.ipynb
new file mode 100644
index 0000000..fc865a9
--- /dev/null
+++ b/My_Ustacky_Project/.ipynb_checkpoints/My Project-checkpoint.ipynb	
@@ -0,0 +1,1384 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7c0676cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import pandas as pd\n",
+    "os.chdir(r\"C:\\Users\\Esther\\Desktop\\Ustacky\\My project\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d99ac026",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extension = 'csv'\n",
+    "file_1 =  [i for i in glob.glob('*.{}'.format(extension))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "37838777",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja_Branch.csv', 'Lagos_Branch.csv', 'Port_Harcourt_Branch.csv']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c20066af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read each dataframe\n",
+    "df_A = pd.read_csv('Abuja_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b3211088",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "1  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "2  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "3  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "4  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
+       "1      5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
+       "2      9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
+       "3     33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
+       "4     14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0   59227.2                 4.761905       2961.36     5.9  \n",
+       "1   20851.2                 4.761905       1042.56     4.5  \n",
+       "2   36734.4                 4.761905       1836.72     6.8  \n",
+       "3  202435.2                 4.761905      10121.76     4.5  \n",
+       "4   29016.0                 4.761905       1450.80     4.4  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_A.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a303bab3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_L = pd.read_csv('Lagos_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "42599319",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1     16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2     20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3     31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4     24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0  188218.8                 4.761905       9410.94     9.1  \n",
+       "1  116751.6                 4.761905       5837.58     7.4  \n",
+       "2  167673.6                 4.761905       8383.68     8.4  \n",
+       "3  217501.2                 4.761905      10875.06     5.3  \n",
+       "4  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_L.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "304da609",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_p = pd.read_csv('Port_Harcourt_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c1440da4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>226-31-3081</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>5500.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>28879.20</td>\n",
+       "      <td>3/8/2019</td>\n",
+       "      <td>10:29</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>27504.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699-14-3026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30740.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>225941.94</td>\n",
+       "      <td>3/25/2019</td>\n",
+       "      <td>18:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>215182.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>315-22-5665</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>26481.6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>278056.80</td>\n",
+       "      <td>2/24/2019</td>\n",
+       "      <td>11:38</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>264816.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>300-71-4605</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30974.4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>162615.60</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>11:24</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>154872.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>4.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>183-56-6882</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>35791.2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>150323.04</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>10:42</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>143164.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0  226-31-3081      C  Port Harcourt        Normal  Female   \n",
+       "1  699-14-3026      C  Port Harcourt        Normal    Male   \n",
+       "2  315-22-5665      C  Port Harcourt        Normal  Female   \n",
+       "3  300-71-4605      C  Port Harcourt        Member    Male   \n",
+       "4  183-56-6882      C  Port Harcourt        Member  Female   \n",
+       "\n",
+       "             Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0  Electronic accessories      5500.8         5   1375.20   28879.20   \n",
+       "1  Electronic accessories     30740.4         7  10759.14  225941.94   \n",
+       "2      Home and lifestyle     26481.6        10  13240.80  278056.80   \n",
+       "3  Electronic accessories     30974.4         5   7743.60  162615.60   \n",
+       "4      Food and beverages     35791.2         4   7158.24  150323.04   \n",
+       "\n",
+       "        Date   Time Payment      cogs  gross margin percentage  gross income  \\\n",
+       "0   3/8/2019  10:29    Cash   27504.0                 4.761905       1375.20   \n",
+       "1  3/25/2019  18:30    Epay  215182.8                 4.761905      10759.14   \n",
+       "2  2/24/2019  11:38    Epay  264816.0                 4.761905      13240.80   \n",
+       "3  2/25/2019  11:24    Epay  154872.0                 4.761905       7743.60   \n",
+       "4   2/6/2019  10:42    Epay  143164.8                 4.761905       7158.24   \n",
+       "\n",
+       "   Rating  \n",
+       "0     9.6  \n",
+       "1     4.1  \n",
+       "2     8.0  \n",
+       "3     4.8  \n",
+       "4     7.5  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_p.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9147fea6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a list of the dataframes\n",
+    "pd_list = [df_L, df_A, df_p]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b96a216a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine all the df\n",
+    "df_ABC = pd.concat(pd_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "99822910",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ABC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "c71bf693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Export combined data as csv\n",
+    "df_ABC.to_csv('Market_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "34a6a9dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>323</td>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>324</td>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>325</td>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>326</td>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>327</td>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 18 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0   Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0             0  750-67-8428      A          Lagos        Member  Female   \n",
+       "1             1  631-41-3108      A          Lagos        Normal    Male   \n",
+       "2             2  123-19-1176      A          Lagos        Member    Male   \n",
+       "3             3  373-73-7910      A          Lagos        Normal    Male   \n",
+       "4             4  355-53-5943      A          Lagos        Member  Female   \n",
+       "..          ...          ...    ...            ...           ...     ...   \n",
+       "995         323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996         324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997         325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998         326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999         327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "995      12594.96     6.1  \n",
+       "996      12142.62     6.0  \n",
+       "997      14821.20     4.3  \n",
+       "998       1097.10     5.9  \n",
+       "999        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('Market_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7ade140",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/My_Ustacky_Project/.ipynb_checkpoints/MyProject_step2-checkpoint.ipynb b/My_Ustacky_Project/.ipynb_checkpoints/MyProject_step2-checkpoint.ipynb
new file mode 100644
index 0000000..503b7da
--- /dev/null
+++ b/My_Ustacky_Project/.ipynb_checkpoints/MyProject_step2-checkpoint.ipynb
@@ -0,0 +1,6196 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "01a24aab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import glob\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import datetime as dt\n",
+    "os.chdir(r\"C:\\Users\\Chizy\\Desktop\\MyContent\\Ustacky\\My project\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d5bf8d2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extension = 'csv'\n",
+    "file_1 =  [i for i in glob.glob('*.{}'.format(extension))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "735f6be0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja_Branch.csv',\n",
+       " 'Lagos_Branch.csv',\n",
+       " 'Market_data.csv',\n",
+       " 'Port_Harcourt_Branch.csv']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f6ac03d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read each dataframe\n",
+    "df_A = pd.read_csv('Abuja_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "ab893353",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>552-44-5977</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>22320.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8928.00</td>\n",
+       "      <td>187488.00</td>\n",
+       "      <td>1/3/2019</td>\n",
+       "      <td>19:08</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>178560.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8928.00</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>430-53-4718</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>27133.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10853.28</td>\n",
+       "      <td>227918.88</td>\n",
+       "      <td>1/28/2019</td>\n",
+       "      <td>15:46</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>217065.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10853.28</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>602-16-6955</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>27576.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13788.00</td>\n",
+       "      <td>289548.00</td>\n",
+       "      <td>1/24/2019</td>\n",
+       "      <td>18:10</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>275760.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13788.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>690-01-6631</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>6296.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3148.20</td>\n",
+       "      <td>66112.20</td>\n",
+       "      <td>2/22/2019</td>\n",
+       "      <td>18:35</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>62964.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>3148.20</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>303-96-2227</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>35056.8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>17528.40</td>\n",
+       "      <td>368096.40</td>\n",
+       "      <td>3/2/2019</td>\n",
+       "      <td>17:16</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>350568.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>17528.40</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>332 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0    692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "1    351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "2    529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "3    299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "4    319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "..           ...    ...    ...           ...     ...                     ...   \n",
+       "327  552-44-5977      B  Abuja        Member    Male       Health and beauty   \n",
+       "328  430-53-4718      B  Abuja        Member    Male       Health and beauty   \n",
+       "329  602-16-6955      B  Abuja        Normal  Female       Sports and travel   \n",
+       "330  690-01-6631      B  Abuja        Normal    Male     Fashion accessories   \n",
+       "331  303-96-2227      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0       19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
+       "1        5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
+       "2        9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
+       "3       33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
+       "4       14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
+       "..          ...       ...       ...        ...        ...    ...     ...   \n",
+       "327     22320.0         8   8928.00  187488.00   1/3/2019  19:08    Card   \n",
+       "328     27133.2         8  10853.28  227918.88  1/28/2019  15:46    Card   \n",
+       "329     27576.0        10  13788.00  289548.00  1/24/2019  18:10    Epay   \n",
+       "330      6296.4        10   3148.20   66112.20  2/22/2019  18:35    Epay   \n",
+       "331     35056.8        10  17528.40  368096.40   3/2/2019  17:16    Epay   \n",
+       "\n",
+       "         cogs  gross margin percentage  gross income  Rating  \n",
+       "0     59227.2                 4.761905       2961.36     5.9  \n",
+       "1     20851.2                 4.761905       1042.56     4.5  \n",
+       "2     36734.4                 4.761905       1836.72     6.8  \n",
+       "3    202435.2                 4.761905      10121.76     4.5  \n",
+       "4     29016.0                 4.761905       1450.80     4.4  \n",
+       "..        ...                      ...           ...     ...  \n",
+       "327  178560.0                 4.761905       8928.00     6.2  \n",
+       "328  217065.6                 4.761905      10853.28     8.4  \n",
+       "329  275760.0                 4.761905      13788.00     6.0  \n",
+       "330   62964.0                 4.761905       3148.20     6.6  \n",
+       "331  350568.0                 4.761905      17528.40     4.4  \n",
+       "\n",
+       "[332 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aff26318",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_L = pd.read_csv('Lagos_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "f81b0249",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>886-18-2897</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>20361.6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5090.40</td>\n",
+       "      <td>106898.40</td>\n",
+       "      <td>3/22/2019</td>\n",
+       "      <td>19:06</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>101808.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5090.40</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>745-74-0715</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>20890.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2089.08</td>\n",
+       "      <td>43870.68</td>\n",
+       "      <td>3/10/2019</td>\n",
+       "      <td>20:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>41781.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2089.08</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>727-02-1313</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>11462.4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>573.12</td>\n",
+       "      <td>12035.52</td>\n",
+       "      <td>2/9/2019</td>\n",
+       "      <td>13:22</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>11462.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>573.12</td>\n",
+       "      <td>7.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>347-56-2442</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>23695.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1184.76</td>\n",
+       "      <td>24879.96</td>\n",
+       "      <td>2/22/2019</td>\n",
+       "      <td>15:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>23695.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1184.76</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>849-09-3807</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>31802.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11130.84</td>\n",
+       "      <td>233747.64</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>13:28</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>222616.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>11130.84</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>340 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0    750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1    631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2    123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3    373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4    355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "..           ...    ...    ...           ...     ...                     ...   \n",
+       "335  886-18-2897      A  Lagos        Normal  Female      Food and beverages   \n",
+       "336  745-74-0715      A  Lagos        Normal    Male  Electronic accessories   \n",
+       "337  727-02-1313      A  Lagos        Member    Male      Food and beverages   \n",
+       "338  347-56-2442      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "339  849-09-3807      A  Lagos        Member  Female     Fashion accessories   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0       26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1       16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2       20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3       31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4       24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "..          ...       ...       ...        ...        ...    ...     ...   \n",
+       "335     20361.6         5   5090.40  106898.40  3/22/2019  19:06    Card   \n",
+       "336     20890.8         2   2089.08   43870.68  3/10/2019  20:46    Epay   \n",
+       "337     11462.4         1    573.12   12035.52   2/9/2019  13:22    Cash   \n",
+       "338     23695.2         1   1184.76   24879.96  2/22/2019  15:33    Cash   \n",
+       "339     31802.4         7  11130.84  233747.64  2/18/2019  13:28    Cash   \n",
+       "\n",
+       "         cogs  gross margin percentage  gross income  Rating  \n",
+       "0    188218.8                 4.761905       9410.94     9.1  \n",
+       "1    116751.6                 4.761905       5837.58     7.4  \n",
+       "2    167673.6                 4.761905       8383.68     8.4  \n",
+       "3    217501.2                 4.761905      10875.06     5.3  \n",
+       "4    148694.4                 4.761905       7434.72     5.8  \n",
+       "..        ...                      ...           ...     ...  \n",
+       "335  101808.0                 4.761905       5090.40     4.5  \n",
+       "336   41781.6                 4.761905       2089.08     8.8  \n",
+       "337   11462.4                 4.761905        573.12     7.7  \n",
+       "338   23695.2                 4.761905       1184.76     4.1  \n",
+       "339  222616.8                 4.761905      11130.84     6.6  \n",
+       "\n",
+       "[340 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4e90494b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_p = pd.read_csv('Port_Harcourt_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "7bd0e3d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>226-31-3081</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>5500.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>28879.20</td>\n",
+       "      <td>3/8/2019</td>\n",
+       "      <td>10:29</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>27504.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699-14-3026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30740.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>225941.94</td>\n",
+       "      <td>3/25/2019</td>\n",
+       "      <td>18:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>215182.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>315-22-5665</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>26481.6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>278056.80</td>\n",
+       "      <td>2/24/2019</td>\n",
+       "      <td>11:38</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>264816.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>300-71-4605</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30974.4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>162615.60</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>11:24</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>154872.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>4.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>183-56-6882</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>35791.2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>150323.04</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>10:42</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>143164.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>328 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    226-31-3081      C  Port Harcourt        Normal  Female   \n",
+       "1    699-14-3026      C  Port Harcourt        Normal    Male   \n",
+       "2    315-22-5665      C  Port Harcourt        Normal  Female   \n",
+       "3    300-71-4605      C  Port Harcourt        Member    Male   \n",
+       "4    183-56-6882      C  Port Harcourt        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0    Electronic accessories      5500.8         5   1375.20   28879.20   \n",
+       "1    Electronic accessories     30740.4         7  10759.14  225941.94   \n",
+       "2        Home and lifestyle     26481.6        10  13240.80  278056.80   \n",
+       "3    Electronic accessories     30974.4         5   7743.60  162615.60   \n",
+       "4        Food and beverages     35791.2         4   7158.24  150323.04   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     3/8/2019  10:29    Cash   27504.0                 4.761905   \n",
+       "1    3/25/2019  18:30    Epay  215182.8                 4.761905   \n",
+       "2    2/24/2019  11:38    Epay  264816.0                 4.761905   \n",
+       "3    2/25/2019  11:24    Epay  154872.0                 4.761905   \n",
+       "4     2/6/2019  10:42    Epay  143164.8                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         1375.20     9.6  \n",
+       "1        10759.14     4.1  \n",
+       "2        13240.80     8.0  \n",
+       "3         7743.60     4.8  \n",
+       "4         7158.24     7.5  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[328 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e3802334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a list of the dataframes\n",
+    "pd_list = [df_L, df_A, df_p]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0db90e96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine all the df\n",
+    "df_ABC = pd.concat(pd_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1b43e9eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ABC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "09a3d601",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Export combined data as csv\n",
+    "df_ABC.to_csv('Market_data.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "dc2f293c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "995      12594.96     6.1  \n",
+       "996      12142.62     6.0  \n",
+       "997      14821.20     4.3  \n",
+       "998       1097.10     5.9  \n",
+       "999        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('Market_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "66c0ffff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1     16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2     20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3     31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4     24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0  188218.8                 4.761905       9410.94     9.1  \n",
+       "1  116751.6                 4.761905       5837.58     7.4  \n",
+       "2  167673.6                 4.761905       8383.68     8.4  \n",
+       "3  217501.2                 4.761905      10875.06     5.3  \n",
+       "4  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View the first 5rows of the dataset\n",
+    "df = pd.read_csv('Market_data.csv')\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "022eba6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 17)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To check no of rows and column\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6ff0da19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',\n",
+       "       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',\n",
+       "       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',\n",
+       "       'Rating'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To check various column name in the dataset\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401ddd35",
+   "metadata": {},
+   "source": [
+    "# Statistical summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6185861a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1.000000e+03</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>20041.966800</td>\n",
+       "      <td>5.510000</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>116268.029640</td>\n",
+       "      <td>110731.456800</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>6.97270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9538.066205</td>\n",
+       "      <td>2.923431</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>88518.720636</td>\n",
+       "      <td>84303.543463</td>\n",
+       "      <td>6.131498e-14</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>1.71858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3628.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>3844.260000</td>\n",
+       "      <td>3661.200000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>11835.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>44792.055000</td>\n",
+       "      <td>42659.100000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>5.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>19882.800000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>91385.280000</td>\n",
+       "      <td>87033.600000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>7.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>28056.600000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>169686.090000</td>\n",
+       "      <td>161605.800000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>8.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>35985.600000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>375354.000000</td>\n",
+       "      <td>357480.000000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>10.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unit price     Quantity        Tax 5%          Total           cogs  \\\n",
+       "count   1000.000000  1000.000000   1000.000000    1000.000000    1000.000000   \n",
+       "mean   20041.966800     5.510000   5536.572840  116268.029640  110731.456800   \n",
+       "std     9538.066205     2.923431   4215.177173   88518.720636   84303.543463   \n",
+       "min     3628.800000     1.000000    183.060000    3844.260000    3661.200000   \n",
+       "25%    11835.000000     3.000000   2132.955000   44792.055000   42659.100000   \n",
+       "50%    19882.800000     5.000000   4351.680000   91385.280000   87033.600000   \n",
+       "75%    28056.600000     8.000000   8080.290000  169686.090000  161605.800000   \n",
+       "max    35985.600000    10.000000  17874.000000  375354.000000  357480.000000   \n",
+       "\n",
+       "       gross margin percentage  gross income      Rating  \n",
+       "count             1.000000e+03   1000.000000  1000.00000  \n",
+       "mean              4.761905e+00   5536.572840     6.97270  \n",
+       "std               6.131498e-14   4215.177173     1.71858  \n",
+       "min               4.761905e+00    183.060000     4.00000  \n",
+       "25%               4.761905e+00   2132.955000     5.50000  \n",
+       "50%               4.761905e+00   4351.680000     7.00000  \n",
+       "75%               4.761905e+00   8080.290000     8.50000  \n",
+       "max               4.761905e+00  17874.000000    10.00000  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To get statistical surmary\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a5e9895",
+   "metadata": {},
+   "source": [
+    "# Deductions:\n",
+    "There are 1000 rows.\n",
+    "\n",
+    "The mean, std, min, max and various percentage of the total summation of values for each columns with numeric values were provided with a single command (Describe) which shows the Standard Deviation (STD) of the unit price and gross income to be 9538.066205 and 4215.177173 respectively. Also, the mean value of the unit price and gross income is 20041.966800 and 5536.572840 respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "eaf11085",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
+       "0         False   False  False          False   False         False   \n",
+       "1         False   False  False          False   False         False   \n",
+       "2         False   False  False          False   False         False   \n",
+       "3         False   False  False          False   False         False   \n",
+       "4         False   False  False          False   False         False   \n",
+       "..          ...     ...    ...            ...     ...           ...   \n",
+       "995       False   False  False          False   False         False   \n",
+       "996       False   False  False          False   False         False   \n",
+       "997       False   False  False          False   False         False   \n",
+       "998       False   False  False          False   False         False   \n",
+       "999       False   False  False          False   False         False   \n",
+       "\n",
+       "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
+       "0         False     False   False  False  False  False    False  False   \n",
+       "1         False     False   False  False  False  False    False  False   \n",
+       "2         False     False   False  False  False  False    False  False   \n",
+       "3         False     False   False  False  False  False    False  False   \n",
+       "4         False     False   False  False  False  False    False  False   \n",
+       "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
+       "995       False     False   False  False  False  False    False  False   \n",
+       "996       False     False   False  False  False  False    False  False   \n",
+       "997       False     False   False  False  False  False    False  False   \n",
+       "998       False     False   False  False  False  False    False  False   \n",
+       "999       False     False   False  False  False  False    False  False   \n",
+       "\n",
+       "     gross margin percentage  gross income  Rating  \n",
+       "0                      False         False   False  \n",
+       "1                      False         False   False  \n",
+       "2                      False         False   False  \n",
+       "3                      False         False   False  \n",
+       "4                      False         False   False  \n",
+       "..                       ...           ...     ...  \n",
+       "995                    False         False   False  \n",
+       "996                    False         False   False  \n",
+       "997                    False         False   False  \n",
+       "998                    False         False   False  \n",
+       "999                    False         False   False  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Missing value:\n",
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "30d2d644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 0\n",
+       "Branch                     0\n",
+       "City                       0\n",
+       "Customer type              0\n",
+       "Gender                     0\n",
+       "Product line               0\n",
+       "Unit price                 0\n",
+       "Quantity                   0\n",
+       "Tax 5%                     0\n",
+       "Total                      0\n",
+       "Date                       0\n",
+       "Time                       0\n",
+       "Payment                    0\n",
+       "cogs                       0\n",
+       "gross margin percentage    0\n",
+       "gross income               0\n",
+       "Rating                     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "72bdce31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                     1000\n",
+       "City                       1000\n",
+       "Customer type              1000\n",
+       "Gender                     1000\n",
+       "Product line               1000\n",
+       "Unit price                 1000\n",
+       "Quantity                   1000\n",
+       "Tax 5%                     1000\n",
+       "Total                      1000\n",
+       "Date                       1000\n",
+       "Time                       1000\n",
+       "Payment                    1000\n",
+       "cogs                       1000\n",
+       "gross margin percentage    1000\n",
+       "gross income               1000\n",
+       "Rating                     1000\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.notnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26717e81",
+   "metadata": {},
+   "source": [
+    "# There are no missing values in the set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7810eb93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   Invoice ID               1000 non-null   object \n",
+      " 1   Branch                   1000 non-null   object \n",
+      " 2   City                     1000 non-null   object \n",
+      " 3   Customer type            1000 non-null   object \n",
+      " 4   Gender                   1000 non-null   object \n",
+      " 5   Product line             1000 non-null   object \n",
+      " 6   Unit price               1000 non-null   float64\n",
+      " 7   Quantity                 1000 non-null   int64  \n",
+      " 8   Tax 5%                   1000 non-null   float64\n",
+      " 9   Total                    1000 non-null   float64\n",
+      " 10  Date                     1000 non-null   object \n",
+      " 11  Time                     1000 non-null   object \n",
+      " 12  Payment                  1000 non-null   object \n",
+      " 13  cogs                     1000 non-null   float64\n",
+      " 14  gross margin percentage  1000 non-null   float64\n",
+      " 15  gross income             1000 non-null   float64\n",
+      " 16  Rating                   1000 non-null   float64\n",
+      "dtypes: float64(7), int64(1), object(9)\n",
+      "memory usage: 132.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the information of the dataframe\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f670b3ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Converting to datetime:\n",
+    "df['Date'] = pd.to_datetime(df['Date'])\n",
+    "df['Time'] = pd.to_datetime(df['Time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "15cdbef0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[ns]')"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Date'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "1dbc2015",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[ns]')"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Time'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f94d858f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>2019-01-05</td>\n",
+       "      <td>2023-07-17 13:08:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>2019-03-03</td>\n",
+       "      <td>2023-07-17 13:23:00</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>2019-01-27</td>\n",
+       "      <td>2023-07-17 20:33:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2019-02-08</td>\n",
+       "      <td>2023-07-17 10:37:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2019-02-25</td>\n",
+       "      <td>2023-07-17 14:36:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date                Time  \\\n",
+       "0     26888.4         7   9410.94  197629.74 2019-01-05 2023-07-17 13:08:00   \n",
+       "1     16678.8         7   5837.58  122589.18 2019-03-03 2023-07-17 13:23:00   \n",
+       "2     20959.2         8   8383.68  176057.28 2019-01-27 2023-07-17 20:33:00   \n",
+       "3     31071.6         7  10875.06  228376.26 2019-02-08 2023-07-17 10:37:00   \n",
+       "4     24782.4         6   7434.72  156129.12 2019-02-25 2023-07-17 14:36:00   \n",
+       "\n",
+       "  Payment      cogs  gross margin percentage  gross income  Rating  \n",
+       "0    Epay  188218.8                 4.761905       9410.94     9.1  \n",
+       "1    Card  116751.6                 4.761905       5837.58     7.4  \n",
+       "2    Epay  167673.6                 4.761905       8383.68     8.4  \n",
+       "3    Epay  217501.2                 4.761905      10875.06     5.3  \n",
+       "4    Epay  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1d8ff8ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extract features from date & time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "79154e07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['day'] = df['Date'].dt.day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1280e135",
+   "metadata": {},
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b0c8b6ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['month'] = df['Date'].dt.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "dc123e2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019     0   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019     0   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019     0   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019     0   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019     0   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "f1628da8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['year'] = df['Date'].dt.year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "f4cfc25c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019     0   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019     0   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019     0   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019     0   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019     0   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "458d83be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['hour'] = df['Time'].dt.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "c3db5ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019    13   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019    13   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019    20   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019    10   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019    14   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "23ca4780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Minute'] = df['Time'].dt.minute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "0078b0cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019    13   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019    13   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019    20   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019    10   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019    14   \n",
+       "\n",
+       "   Minute  \n",
+       "0       8  \n",
+       "1      23  \n",
+       "2      33  \n",
+       "3      37  \n",
+       "4      36  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "d5ee1db5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "194e69de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_hour = pd.Series(df.nunique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "3f8951f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "5c9bd0e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique(axis = 'columns' == 22)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "496b249f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      21\n",
+       "1      20\n",
+       "2      21\n",
+       "3      21\n",
+       "4      21\n",
+       "       ..\n",
+       "995    21\n",
+       "996    21\n",
+       "997    21\n",
+       "998    20\n",
+       "999    19\n",
+       "Length: 1000, dtype: int64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique(axis = 'columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "72fca3a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['2023-07-17T13:08:00.000000000', '2023-07-17T13:23:00.000000000',\n",
+       "       '2023-07-17T20:33:00.000000000', '2023-07-17T10:37:00.000000000',\n",
+       "       '2023-07-17T14:36:00.000000000', '2023-07-17T17:15:00.000000000',\n",
+       "       '2023-07-17T10:25:00.000000000', '2023-07-17T16:48:00.000000000',\n",
+       "       '2023-07-17T19:21:00.000000000', '2023-07-17T11:03:00.000000000',\n",
+       "       '2023-07-17T10:39:00.000000000', '2023-07-17T18:00:00.000000000',\n",
+       "       '2023-07-17T11:15:00.000000000', '2023-07-17T17:36:00.000000000',\n",
+       "       '2023-07-17T19:20:00.000000000', '2023-07-17T12:17:00.000000000',\n",
+       "       '2023-07-17T15:36:00.000000000', '2023-07-17T10:12:00.000000000',\n",
+       "       '2023-07-17T19:15:00.000000000', '2023-07-17T17:17:00.000000000',\n",
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+       "       '2023-07-17T11:28:00.000000000', '2023-07-17T15:55:00.000000000',\n",
+       "       '2023-07-17T12:27:00.000000000', '2023-07-17T16:40:00.000000000',\n",
+       "       '2023-07-17T10:04:00.000000000', '2023-07-17T18:50:00.000000000',\n",
+       "       '2023-07-17T10:55:00.000000000', '2023-07-17T15:48:00.000000000',\n",
+       "       '2023-07-17T20:35:00.000000000', '2023-07-17T16:57:00.000000000',\n",
+       "       '2023-07-17T16:42:00.000000000', '2023-07-17T20:38:00.000000000',\n",
+       "       '2023-07-17T10:16:00.000000000', '2023-07-17T16:44:00.000000000',\n",
+       "       '2023-07-17T14:50:00.000000000', '2023-07-17T20:19:00.000000000',\n",
+       "       '2023-07-17T20:07:00.000000000', '2023-07-17T20:48:00.000000000',\n",
+       "       '2023-07-17T17:03:00.000000000', '2023-07-17T18:44:00.000000000',\n",
+       "       '2023-07-17T13:26:00.000000000', '2023-07-17T20:34:00.000000000',\n",
+       "       '2023-07-17T20:59:00.000000000', '2023-07-17T15:39:00.000000000',\n",
+       "       '2023-07-17T19:25:00.000000000', '2023-07-17T16:37:00.000000000',\n",
+       "       '2023-07-17T12:28:00.000000000', '2023-07-17T13:48:00.000000000',\n",
+       "       '2023-07-17T11:56:00.000000000', '2023-07-17T16:21:00.000000000',\n",
+       "       '2023-07-17T19:44:00.000000000', '2023-07-17T16:20:00.000000000',\n",
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+       "       '2023-07-17T15:00:00.000000000', '2023-07-17T20:50:00.000000000',\n",
+       "       '2023-07-17T19:58:00.000000000', '2023-07-17T10:02:00.000000000',\n",
+       "       '2023-07-17T17:07:00.000000000', '2023-07-17T17:22:00.000000000',\n",
+       "       '2023-07-17T13:29:00.000000000', '2023-07-17T17:20:00.000000000',\n",
+       "       '2023-07-17T13:42:00.000000000', '2023-07-17T14:44:00.000000000',\n",
+       "       '2023-07-17T10:21:00.000000000', '2023-07-17T19:05:00.000000000',\n",
+       "       '2023-07-17T11:34:00.000000000', '2023-07-17T18:58:00.000000000',\n",
+       "       '2023-07-17T20:01:00.000000000', '2023-07-17T20:37:00.000000000',\n",
+       "       '2023-07-17T12:12:00.000000000', '2023-07-17T15:32:00.000000000',\n",
+       "       '2023-07-17T12:31:00.000000000', '2023-07-17T20:04:00.000000000',\n",
+       "       '2023-07-17T13:38:00.000000000', '2023-07-17T17:30:00.000000000',\n",
+       "       '2023-07-17T16:53:00.000000000', '2023-07-17T16:51:00.000000000',\n",
+       "       '2023-07-17T20:15:00.000000000', '2023-07-17T14:11:00.000000000',\n",
+       "       '2023-07-17T12:10:00.000000000', '2023-07-17T20:47:00.000000000',\n",
+       "       '2023-07-17T11:17:00.000000000', '2023-07-17T15:52:00.000000000',\n",
+       "       '2023-07-17T11:43:00.000000000', '2023-07-17T10:30:00.000000000',\n",
+       "       '2023-07-17T10:06:00.000000000', '2023-07-17T12:16:00.000000000',\n",
+       "       '2023-07-17T14:58:00.000000000', '2023-07-17T14:45:00.000000000',\n",
+       "       '2023-07-17T17:10:00.000000000', '2023-07-17T14:27:00.000000000',\n",
+       "       '2023-07-17T12:51:00.000000000', '2023-07-17T13:56:00.000000000',\n",
+       "       '2023-07-17T16:18:00.000000000', '2023-07-17T15:14:00.000000000',\n",
+       "       '2023-07-17T20:10:00.000000000', '2023-07-17T14:24:00.000000000',\n",
+       "       '2023-07-17T12:39:00.000000000', '2023-07-17T12:41:00.000000000',\n",
+       "       '2023-07-17T15:20:00.000000000', '2023-07-17T16:33:00.000000000',\n",
+       "       '2023-07-17T20:44:00.000000000', '2023-07-17T20:30:00.000000000',\n",
+       "       '2023-07-17T16:50:00.000000000', '2023-07-17T17:52:00.000000000',\n",
+       "       '2023-07-17T11:33:00.000000000', '2023-07-17T18:38:00.000000000',\n",
+       "       '2023-07-17T15:11:00.000000000', '2023-07-17T15:45:00.000000000',\n",
+       "       '2023-07-17T16:35:00.000000000', '2023-07-17T13:50:00.000000000',\n",
+       "       '2023-07-17T17:56:00.000000000', '2023-07-17T13:49:00.000000000',\n",
+       "       '2023-07-17T14:05:00.000000000', '2023-07-17T19:12:00.000000000'],\n",
+       "      dtype='datetime64[ns]')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(df['Time'], name = 'hour').unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d564b9cc",
+   "metadata": {},
+   "source": [
+    "# Examples on Unique Values in Columns eg Branch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "64651e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Invoice ID',\n",
+       " 'Branch',\n",
+       " 'City',\n",
+       " 'Customer type',\n",
+       " 'Gender',\n",
+       " 'Product line',\n",
+       " 'Payment']"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical_columns = [col for col in df.columns if df[col].dtype == \"object\" ]\n",
+    "categorical_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "df0c3d1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A', 'B', 'C']"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Branch'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "64a7a37a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Branch Column : 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Number of unique values in the Branch Column : {}\". format(len(df['Branch'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c991f6c",
+   "metadata": {},
+   "source": [
+    "# Unique Values in Columns - Payment, Product line, Gender and Customer type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "3c560e79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Epay', 'Card', 'Cash']"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "46c1d063",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Health and beauty',\n",
+       " 'Home and lifestyle',\n",
+       " 'Sports and travel',\n",
+       " 'Electronic accessories',\n",
+       " 'Food and beverages',\n",
+       " 'Fashion accessories']"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "02857072",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Product line Column : 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Number of unique values in the Product line Column : {}\". format(len(df['Product line'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "0fd80ec0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Female', 'Male']"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "0bbf9240",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Member', 'Normal']"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Customer type'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e358b2fb",
+   "metadata": {},
+   "source": [
+    "# Value_Count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "18d19007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fashion accessories       178\n",
+       "Food and beverages        174\n",
+       "Electronic accessories    170\n",
+       "Sports and travel         166\n",
+       "Home and lifestyle        160\n",
+       "Health and beauty         152\n",
+       "Name: Product line, dtype: int64"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "07448b7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "3448491c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Lagos            340\n",
+       "Abuja            332\n",
+       "Port Harcourt    328\n",
+       "Name: City, dtype: int64"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['City'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "3fcb5833",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Epay    345\n",
+       "Cash    344\n",
+       "Card    311\n",
+       "Name: Payment, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8a1b933",
+   "metadata": {},
+   "source": [
+    "# Groupby City column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "185c52ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "City = df.groupby('City')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "59eee3e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.groupby.generic.DataFrameGroupBy"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(City)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "7ee2f6e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Abuja': [340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, ...], 'Lagos': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], 'Port Harcourt': [672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, ...]}"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "5626b15d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            6652396.8\n",
+       "Lagos            6705176.4\n",
+       "Port Harcourt    6684393.6\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['Unit price'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "487b1c9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            20037.339759\n",
+       "Lagos            19721.107059\n",
+       "Port Harcourt    20379.248780\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['Unit price'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "2e398df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>18</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>16</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>15</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "340  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "341  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "342  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "343  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "344  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "340     19742.4         3   2961.36   62188.56  ...    Card   59227.2   \n",
+       "341      5212.8         4   1042.56   21893.76  ...    Epay   20851.2   \n",
+       "342      9183.6         4   1836.72   38571.12  ...    Cash   36734.4   \n",
+       "343     33739.2         6  10121.76  212556.96  ...    Cash  202435.2   \n",
+       "344     14508.0         2   1450.80   30466.80  ...    Epay   29016.0   \n",
+       "\n",
+       "    gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "340                4.761905       2961.36     5.9   20      2  2019    13   \n",
+       "341                4.761905       1042.56     4.5    6      2  2019    18   \n",
+       "342                4.761905       1836.72     6.8    9      3  2019    17   \n",
+       "343                4.761905      10121.76     4.5   15      1  2019    16   \n",
+       "344                4.761905       1450.80     4.4   11      3  2019    15   \n",
+       "\n",
+       "     Minute  \n",
+       "340      27  \n",
+       "341       7  \n",
+       "342       3  \n",
+       "343      19  \n",
+       "344      30  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Abuja').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "c3775a60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1820531.52"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Abuja')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "1caaa895",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            1820531.52\n",
+       "Lagos            1820577.78\n",
+       "Port Harcourt    1895463.54\n",
+       "Name: gross income, dtype: float64"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "05fe8fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1820577.78"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Lagos')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "688e914b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1895463.54"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Port Harcourt')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "cc493a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_Aggregate = City.agg({'gross income': 'sum'})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "5cb92d4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gross income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>City</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Abuja</th>\n",
+       "      <td>1820531.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lagos</th>\n",
+       "      <td>1820577.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Port Harcourt</th>\n",
+       "      <td>1895463.54</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               gross income\n",
+       "City                       \n",
+       "Abuja            1820531.52\n",
+       "Lagos            1820577.78\n",
+       "Port Harcourt    1895463.54"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_Aggregate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "29d2b82a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gross income    1895463.54\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_Aggregate.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "66b1220f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            17528.4\n",
+       "Lagos            17816.4\n",
+       "Port Harcourt    17874.0\n",
+       "Name: gross income, dtype: float64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['gross income'].max('City')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "fc4ee498",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_with_highest_income = city_Aggregate['gross income'].idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "ab710a2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Port Harcourt'"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_with_highest_income"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60754059",
+   "metadata": {},
+   "source": [
+    "# The city with the higehest gross income is Port Harcout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e53b88a7",
+   "metadata": {},
+   "source": [
+    "# Data Visualization using seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "ffc9432a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Data = df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "1bf2829b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>19</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  ...  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74  ...   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18  ...   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28  ...   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26  ...   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12  ...   \n",
+       "..                      ...         ...       ...       ...        ...  ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16  ...   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02  ...   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20  ...   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10  ...   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30  ...   \n",
+       "\n",
+       "    Payment      cogs gross margin percentage  gross income  Rating  day  \\\n",
+       "0      Epay  188218.8                4.761905       9410.94     9.1    5   \n",
+       "1      Card  116751.6                4.761905       5837.58     7.4    3   \n",
+       "2      Epay  167673.6                4.761905       8383.68     8.4   27   \n",
+       "3      Epay  217501.2                4.761905      10875.06     5.3    8   \n",
+       "4      Epay  148694.4                4.761905       7434.72     5.8   25   \n",
+       "..      ...       ...                     ...           ...     ...  ...   \n",
+       "995    Cash  251899.2                4.761905      12594.96     6.1   23   \n",
+       "996    Cash  242852.4                4.761905      12142.62     6.0    9   \n",
+       "997    Epay  296424.0                4.761905      14821.20     4.3   29   \n",
+       "998    Epay   21942.0                4.761905       1097.10     5.9   18   \n",
+       "999    Epay   14526.0                4.761905        726.30     6.2   29   \n",
+       "\n",
+       "     month  year  hour  Minute  \n",
+       "0        1  2019    13       8  \n",
+       "1        3  2019    13      23  \n",
+       "2        1  2019    20      33  \n",
+       "3        2  2019    10      37  \n",
+       "4        2  2019    14      36  \n",
+       "..     ...   ...   ...     ...  \n",
+       "995      1  2019    10      33  \n",
+       "996      1  2019    11      40  \n",
+       "997      3  2019    19      12  \n",
+       "998      2  2019    11      40  \n",
+       "999      1  2019    13      46  \n",
+       "\n",
+       "[1000 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "cd9e9763",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'City with max Gross income'}, xlabel='City', ylabel='gross income'>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "city_Aggregate.plot(kind='bar', ylabel = 'gross income', title = 'City with max Gross income', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "caa28f73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Customer_type_counts = Data['Customer type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "69c66158",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Member    501\n",
+       "Normal    499\n",
+       "Name: Customer type, dtype: int64"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Customer_type_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "68cf8924",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Customer_type_counts')"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(Customer_type_counts.values, labels=Customer_type_counts.index, autopct='%1.1f%%')\n",
+    "plt.title('Customer_type_counts')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "id": "96ab0009",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gender_counts = Data['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "38c9b442",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "dcf83ead",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Gender_counts.values')"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(gender_counts.values, labels=gender_counts.index, autopct='%1.1f%%')\n",
+    "plt.title('Gender_counts.values')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "c6911fd2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Hours spent')"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'City', y = 'hour', hue = 'Gender', aspect = 4)\n",
+    "plt.title('Hours spent')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75c59cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "a30ec3cd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_Sales Record')]"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, x = 'Branch').set(title = 'Highest_Sales Record')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aef1baed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "e817ede3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_Payment')]"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Payment').set(title = 'Highest_Payment')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb2b6f6f",
+   "metadata": {},
+   "source": [
+    "# Highest product line sold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "c19b150c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_productline')]"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Product line', hue = 'City').set(title = 'Highest_productline')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "9150e7bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Most used_Payment_type')]"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Product line', hue = 'Payment').set(title = 'Most used_Payment_type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "7726fd8a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Payment/Branch')]"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Payment', hue = 'Branch').set(title = 'Payment/Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35f6f49e",
+   "metadata": {},
+   "source": [
+    "# Branch with lowest rating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "480fddfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Branch_lowest rating')]"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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aOXKkNm/erJMnT+rixYvmQ6bp53vdbY6drQryEiQFLTuvLav313SYNX1oMx1yNa0rUaKEGjRoYLEuq/l16UPBq6++qoiIiGz/3Gn06NHav3+/3n33XfMZqJIUExOjOXPmKCAgoEDvpXq3/i9JCxcuNP+x5OfnpxkzZmjPnj2KiYnR5cuXzZ8B02HR/O7/ecnInyUULhyKBWxUs2ZNHTlyRDdv3tQff/xx18nhmfnhhx/Mc/UWLVqU4URtSQVyb8sKFSro7NmzOn/+fL7vqyClHzX9+++/79redIgz/aE2E9M8O9P17GrUqJHhIdaAgABFRkYqPDxcgwYNynJ+XfrD1teuXbP5jM5q1appyJAhGjJkiFJSUnTgwAGtWbNG8+bNU0JCgkJCQlS3bl116NDBpv3klQULFki6faLRpk2bMh3xLUr3dzX1uYSEBCUnJ2c5apfbEVsgPUbsABulv2ZZZncQyA7TXJxy5cplGuokFcity0yX5jh8+LASEhLyfX8FxcXFxXzW5d69e7Nse/HiRfMFqDMKWOXLl1ft2rUl3R6Fi4iIUEpKiipXrmwxH8s0ehcZGal//vkny+vXVahQwXwW844dO8yHbPOCk5OTGjRooHfffVfff/+9efkPP/xg0c6eI0umM0rbt2+faahLS0uzuAB0YWe6Nl1KSkqmdyrJznoguwh2gA3S0tLMtwOSbl+OJLdMh+GSkpIy/YV+/fp1LV26NNf7yK6nnnpK0u3bpM2dOzff91eQTNfsi4qK0s6dOzNtZxo9Sv+cO5nCWWRkpHnSuynImTRq1EguLi5KSEjQzJkzM51fZ2I6k/ns2bPmC2Dntccff9w8Z+zOs2ZdXV3N/87s1l35xfSHUVZTGtatW3fXkWTTayjo+jOS/jp7Wf1/bty4sUBPBoFxEewAG3z11VfmUR1/f3/zZRhyw9fXV9LtX2orV660Wp+SkqI33nhD586dy/U+suu5554zh9SQkJAsb4MWFxdXoJPYbdWnTx/zfK9hw4ZleFjvwIEDmjx5sqTblw7573//m+G2TCHOdIKCZB3YXF1dzdebmzVrlqSM59eZvPHGG+bRqpEjRyoyMjLL12M6zJvet99+a3HrqoyeYwpPd94KLv0JM3/99VeW+85rps/Ahg0bMjxh5a+//tJbb7111+2YXkNB15+Rxx9/XI888ogkaf78+Rn+f166dEmjR48u6NJgUMyxA7KQ0Z0nTLc3Wr16tZYvXy7p9sTw9957z6Z9PfPMM/rggw+UlJSkAQMG6MiRI2rRooXKlCmjY8eOafbs2Tpw4IAaN26sXbt22bSvu3FxcdHs2bP13//+V4mJierSpYu6dOmijh07ysvLS7du3VJUVJS2bdum1atXKyIiItN7xRY2tWvX1pAhQ/TZZ5/p+PHjatasmYYMGaL69etb3FLs+vXrcnBw0JQpUyzuOpFe+nl2pkPWd47YmZZFRkaa22R2f1hJ8vLy0tSpU9W3b18lJCSoY8eO6ty5szp06CAfHx+lpqbq/PnzOnDggNauXaujR4/qo48+sgiUr7/+usaOHaugoCA1atRIvr6+cnV11eXLlxUREaE5c+ZIkpydndWrVy+r/VetWlVnz57VF198IU9PTz344INycnKSdPtiuqVLl87hu5493bp109ixY3Xu3Dm1adNGgwcP1sMPP6zExET9/PPPmjlzppKTk1W3bt0sD8c2atRI0dHR+vHHH/X111+rUaNG5lG80qVLZ3lZovzwySefKCgoSDdv3lSnTp0yvKXYhQsX5Ofnp8OHD3OiBWxCsAOyMHfu3LseiixdurQmT56c6eG67Kpatao+/fRTvfHGG0pMTNTkyZPNo0YmnTp10osvvpjpCFJeCggI0PLly/Xyyy/r8uXLWrZsmZYtW5bv+y0IY8eO1fXr1zVr1izFxMTozTfftGrj6uqqKVOmWNwn9k7lypXTww8/rN9++03S7fuoZnTnhMDAQH300UcWj7PStWtXubq6atCgQYqPj9fSpUuzPASfUdD6+++/NX/+fKv7yJqUKFFCU6ZMyXDkcNiwYXrzzTcVHR1tvt+wyfTp0/XCCy9kWX9uvf7669q2bZu2bt2qkydPmu+ykr7mWbNmaePGjVkGu4EDB2rVqlVKSkrS0KFDLdZ169bNfJ/ggtKkSRNNmzZNgwcP1o0bN/TJJ5/ok08+Ma93dnbWZ599pp07d+rw4cMWh8OBnCLYATlUrFgxubm5qWbNmnryySfVo0cPm6/3ZtKjRw89+OCDmjp1qnbv3q0rV66oQoUKqlOnjl544QU988wzVncxyE9PPPGEDhw4oK+//lobNmzQ8ePHlZCQoPLly8vDw0NNmjRR586di8xonYmDg4MmTZqkzp07a+7cuYqIiNDFixfl7OwsLy8vtWzZUv369ZOXl9ddt9WsWTNzsMsssD3++ONycXFRUlJSlu3S69ixo5544gl988032rJli37//XfFxcXJ2dlZFSpUUK1atRQQEKCOHTta3SP1119/1bZt27Rjxw79+eefunjxoq5cuaKSJUvq/vvvV4sWLfTyyy9n+vr69OmjSpUq6euvv9bhw4cVHx9v04lB2VWsWDEtW7ZMc+fO1bfffqvjx48rLS1NHh4eatGihV5//XXVrFlTGzduzHI7/v7+2rRpk/lzdPHiRbvPt+vevbv8/f01depUhYWF6fLly6pYsaIaNWqkAQMGqGHDhuY7vthyUXDAIT4+vuhcCAgAAIOqX7++/vrrLz377LOaPXu2vctBEcXJEwAA2Nm+ffvMJ3s0bNjQztWgKCPYAQCQz0z3AM5IXFyc3njjDUlS8eLF1alTp4IqCwbEHDsAAPJZly5d5OHhoY4dO6pOnToqU6aM4uPjtWfPHn311Vfma/MNGzaMe8bCJsyxA1CoxMbG5uqWUcWLF7c6iQAoLEzz57LSu3dvffLJJ+ZLywC5QbADUKj069dPS5YsyfHzvLy8uCUTCq2IiAj9+OOP2rlzpy5cuKBLly7JyclJ7u7uaty4sV588UU1adLE3mXCADgUCwBAPmvatKmaNm1q7zJwD2DEDgAAwCA4KxYAAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgEwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRTKYLd06VINGTJELVq0kLu7u9zc3BQaGppp+4SEBI0ePVp16tSRu7u76tSpo9GjRyshIaEAqwYAALAvZ3sXkJEPP/xQMTExqlChgipXrqyYmJhM2167dk1BQUE6fPiwWrZsqS5duujIkSOaMWOGwsLCtGHDBt13330FWD0AAIB9FMpg98UXX8jX11fe3t6aPHmy3nvvvUzbTpkyRYcPH9bgwYMt2k2YMEEfffSRpkyZotGjRxdE2YXSiBEj7F1CkRASEmLvEpBL9PHso58XXfTz7KGPF9Jg16JFi2y1S0tL08KFC1WqVCm9/fbbFuuGDRum2bNna9GiRRo1apQcHBzyodLC7/jx4/YuAchX9HHcC+jnyK5CGeyy688//9S5c+fUqlUrq8Otrq6uatq0qdavX6+oqCjdf//9WW4rMTExP0tFIcf/P+4F9HMYnVH7uKura7bbFvlgJ0m+vr4ZrjeFuT///POuwS42NlYpKSl5WyCKjKzmcQJGQT+H0Rmxjzs5OWWaczJSpIOd6azXsmXLZri+dOnSFu2y4unpmXeFocjx8vKydwlAvqOfw+jo40U82OWlnAxzFiUPPfSQvUuQJEVHR1sNkbu6usrHx8dOFVky6v//vaCw9HGJfo78U1j6OX288CvSwa5MmTKSpCtXrmS4/urVqxbt7kWF5QyhESNGWE3+9fHxKTT1oegqTH2Ifo78Ulj6EH288CuUFyjOLtO8uaioqAzXm+bg3W1+HQAAgBEU+WDn4eGh3bt369q1axbrEhMTFRERIQ8PjxxNOgQAACiqinSwc3BwUM+ePfXvv//qo48+slj32WefKT4+Xj179rxnr2EHAADuLYVyjt2CBQsUGRkpSTp69KgkaeHChQoPD5ckBQUFKTg4WJI0ePBg/fjjj5oyZYoOHTqkevXq6ciRI9q8ebP8/Pw0ePBg+7wIAACAAlYog11kZKSWLFlisWzXrl3atWuXJMnb29sc7O677z6tXbtWISEhWr16tcLDw1W5cmX1799fI0aM4D6xAADgnlEog93MmTM1c+bMbLcvW7asJkyYoAkTJuRjVQAAAIVbkZ5jBwAAgP9DsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBGCLYpaamavbs2WrevLk8PDzk5eWlDh06aP369fYuDQAAoMAU+WCXlpaml156SW+//bauXr2qHj16qFOnTjpx4oS6d++u2bNn27tEAACAAuFs7wJstXr1aq1evVqNGzfWypUrVaJECUnSuHHj1KJFC40dO1bt2rWTj4+PnSsFAADIX0V+xG7dunWSpGHDhplDnSRVqFBB/fv3V1JSkkJDQ+1VHgAAQIEp8sHu4sWLkpThiJxpWVhYWIHWBAAAYA9F/lBsxYoVJUnR0dF66KGHLNZFR0dLkk6ePHnX7SQmJuZ9cTBLTU3NcBnvO4yEfg6jo4/bh6ura7bbFvlg17p1a33//feaPHmymjdvbn7xcXFxmjlzpiTpypUrd91ObGysUlJS8rXWe1lSUlKGy2JiYuxQDZA/6OcwOvp4wXNycpKvr2+22xf5YNelSxeFhoYqLCxMTZs2VatWrXTr1i2tW7dOlSpVknT7TbkbT0/P/C71nubi4pLhMi8vLztUA+QP+jmMjj5e+BX5YOfs7Gwesfv+++/1zTffqEyZMgoODtagQYPUoEEDVahQ4a7byckwJ3LO0dF6OqejoyPvOwyFfg6jo48XfkU+2Em3/1oYOXKkRo4cabHcdNJE/fr17VEWAABAgSryZ8Vm5bvvvpMkde7c2c6VAAAA5D9DBLuEhASrZatWrdKiRYv06KOPqmPHjnaoCgAAoGAZ4lBs69atVbVqVdWsWVOurq7au3evwsPDVb16dc2fPz9bJ08AAAAUdYYIds8884zWrFmjX3/9VTdv3pSPj4+GDx+uN954Q2XKlLF3eQAAAAXCEMFu1KhRGjVqlL3LAAAAsCtDzLEDAAAAwQ4AAMAwCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgEwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgEwQ4AAMAgnO1dQFE3ffp0nT592t5lFHrR0dEZLhsxYoQdqilavL29NWDAAHuXARga3+XZw3d57hXUdznBzkanT5/W8ePH7V1GkZSYmMh7B6BQ4Ls89/guL1w4FAsAAGAQBDsAAACDINgBAAAYBHPs8kGqUzEllXK3dxkoglz+vSjHlJv2LgOApOLOkkd5J3uXgSLoXFyKkm/ZZ98Eu3yQVMpdZxq9aO8yUARV2/2NSlw5a+8yAOh2qBvy9H32LgNF0Oc/XFP0xRS77JtDsQAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIbikGIEvTp0/X6dOn7V1GkRAdHZ3hshEjRtihmqLF29tbAwYMsHcZQJFHsAOQpdOnT+v48eP2LqPISkxM5P0DUGA4FAsAAGAQBDsAAACDINgBAAAYBHPsAOSYi4PkVdzB3mWgiIpJTlNSmr2rAIyJYAcgx7yKO2i8p6u9y0AR9V5sok6S7IB8waFYAAAAgyDYAQAAGIQhgl1aWppWr16t4OBgPfTQQ/Lw8FDDhg01ZMgQnTp1yt7lAQAAFAhDBLsxY8aoV69eOnnypIKCgtS3b1/5+Pjom2++UbNmzXT06FF7lwgAAJDvivzJExcuXNDMmTPl7e2t8PBwlSlTxrxuxowZGj16tKZPn67p06fbsUoAAID8V+RH7E6fPq3U1FQ1btzYItRJUrt27SRJly5dskdpAAAABarIB7v7779fxYsX165du3T16lWLdZs2bZIkNWvWzB6lAQAAFKgifyi2fPnyGjt2rMaOHatGjRqpffv2KlWqlI4ePart27frpZde0muvvXbX7SQmJuZq/6mpqbl6HpBdqampue6febV/IL/Rz2F0tvRxV9fsXze0yAc7SRo0aJCqVKmioUOHau7cuebljRo10rPPPqtixYrddRuxsbFKSUnJ8b6TkpJy/BwgJ5KSkhQTE2PX/QP5jX4Oo8ttH3dycpKvr2+22xsi2H388cf66KOPNHLkSD3//PNyc3PT4cOH9c4776hjx476+uuv9Z///CfLbXh6euZq3y4uLrl6HpBdLi4u8vLysuv+gfxGP4fRFVQfL/LBbseOHfrf//6n/v3768033zQvb9y4sZYuXap69epp9OjRdw12ORnmTM/RschPU0Qh5+jomOv+mVf7B/Ib/RxGV1B9vMj35KxOkKhYsaIefvhhnTlzRpcvXy7o0gAAAApUkQ92ycnJkjK/pIlpefHixQusJgAAAHso8sGucePGkm5fjPjKlSsW6xYvXqyoqCjVq1dPpUuXtkd5AAAABabIz7F7+umn9fXXXys8PFwNGjRQ+/bt5ebmpiNHjmjbtm1ycXHRxIkT7V0mAABAvivywc7JyUnLly/XrFmztGLFCi1fvlzJyclyd3dX165dNXToUD388MP2LhMAACDfFflgJ90+hXjw4MEaPHiwvUsBAACwmyI/xw4AAAC3EewAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIZ1ueXK5cOTk4OGTZxsHBQaVLl9aDDz6o4OBg9e3bVyVKlLBltwAAAMiATcGuadOmSkhI0JEjR+Tk5KRq1arJ3d1dFy9e1NmzZ3Xr1i098sgjSklJ0W+//aa9e/dq2bJl+vHHH1WmTJm8eg0AAACQjYdiZ8+erfj4eHXv3l2HDh3SgQMHtGnTJh04cEAHDx5Ut27ddOXKFX3//ff6448/9OKLL+ro0aP67LPP8qp+AAAA/H82BbuxY8fK09NT06dPl6enp8U6T09PzZgxQx4eHho7dqxKlSqlTz/9VLVq1dLatWttKhoAAADWbAp227dvV2BgYJZtAgICtH379ts7c3RUkyZNdObMGVt2CwAAgAzYFOySkpJ04cKFLNtcuHBBiYmJ5selSpWSs7NNU/sAAACQAZuCnb+/v1asWKH9+/dnuH7fvn1asWKF6tata1526tQpVapUyZbdAgAAIAM2DZ298847euaZZ9SmTRsFBQXpscceU8WKFXXp0iXt2bNH69evl6Ojo0aPHi1J+vfff7V161Y9/fTTeVE7AAAA0rEp2AUGBmrp0qUaPHiwVq9erdWrV8vBwUFpaWmSpGrVqmny5MnmeXiOjo768ccfrU60AAAAgO1snuz25JNP6uDBg4qMjNSRI0d09epVlS5dWnXq1FGTJk3k6Ph/R3tLliwpPz8/W3cJAACADOTJWQyOjo4KCAhQQEBAXmwOAAAAucC9YgEAAAzC5hG7S5cuKTQ0VPv27dOVK1eUkpJi1cbBwUGrV6+2dVcAAADIgk3B7siRI/rPf/6j+Ph48wkTGXFwcLBlNwAAAMgGm4LdmDFj9M8//2j48OHq2bOnqlatKicnp7yqDQAAADlgU7D75ZdfFBQUpHfeeSev6gEAAEAu2XTyRLFixVSjRo28qgUAAAA2sGnErnnz5pneTuxe5vLvRVXb/Y29y0AR5PLvRXuXAOD/OxeXos9/uGbvMlAEnYuzPpG0oNgU7N5//321bt1aX3zxhQYNGpRXNRV5jik3VeLKWXuXAQCwQfItKfqi/X5BA7lhU7D7+OOPVatWLY0fP17z5s1TnTp1VKZMGat2Dg4OmjZtmi27AgAAwF3YFOwWL15s/vepU6d06tSpDNvld7ALDQ3VgAEDsmzTvHlzrqUHAAAMzaZgd/DgwbyqwyZ+fn4aMWJEhutWr16tY8eOqVWrVgVcFQAAQMGyKdh5e3vnVR028ff3l7+/v9Xy5ORkzZkzR87OzurWrVuB1ZPqVExJpdwLbH8wDpd/L8ox5aa9ywAgqbiz5FGea7Mi587FpSj5ln32bfMtxQqztWvXKi4uTkFBQXJ3L7iglVTKXWcavVhg+4NxVNv9DSfeAIWER3knDXn6PnuXgSLo8x+u2e3EmxwFu507d0qSGjRoIFdXV/Pj7AgICMhZZXlg4cKFkqRevXoV+L4BAAAKWo6CXXBwsBwcHLRnzx498MAD5sfZERcXl6sCc+v06dPasWOHPD091bp167u2T0xMzNV+UlNTc/U8ILtSU1Nz3T/zav9AfqOfw+hs6eOurq7ZbpujYPf222/LwcFBFSpUsHhcGIWGhio1NVXdu3fP1v1rY2NjlZKS82HTpKSk3JQHZFtSUpJiYmLsun8gv9HPYXS57eNOTk7y9fXNdvscBbtRo0Zl+biwSE1NVWhoqBwcHNSjR49sPcfT0zNX+3JxccnV84DscnFxkZeXl133D+Q3+jmMrqD6uE0nT8TExKhs2bIZXpTY5OrVq4qPjy/QD+y2bdt05swZPfHEE6pevXq2npOTYc70HB1tut0ucFeOjo657p95tX8gv9HPYXQF1cdt6sl169bVrFmzsmwzd+5c1a1b15bd5BgnTQAAgHuRTcEuLS1NaWlpd21TkOLi4rR+/XqVK1dOwcHBBbpvAAAAe8r3sefY2FiVKlUqv3dj9u233yo5OVnPPvsscyYAAMA9Jcdz7EJCQiweh4eHZ9guJSVFsbGxWrFihRo0aJC76nJh0aJFkjgMCwAA7j05DnaTJk0y/9vBwUHh4eGZhjtJ8vDw0HvvvZe76nJo7969Onr0qBo0aKBHHnmkQPYJAABQWOQ42K1Zs0bS7blz//nPf9S9e/cM78Pq5OSkcuXKqWbNmgV2tlGDBg0UHx9fIPsCAAAobHIc7AIDA83/HjFihJo1a2aX24UBAADAkk3XsRs5cmRe1QEAAAAb2RTs0jtz5ozOnz+f6W1ZGNUDAADIXzYHux9//FHjxo3Tn3/+mWW7uLg4W3cFAACALNh0VkNYWJh69Oiha9eu6dVXX1VaWpqaNm2ql156SbVq1VJaWpratm2rt99+O6/qBQAAQCZsCnaff/657rvvPm3fvt18fbtmzZrps88+086dOzV27Fj9/PPP6tChQ54UCwAAgMzZdCh23759CgoKkru7u3lZamqqpNvXuBs2bJg2bdqkCRMm6Ntvv7WtUgCFRkxymt6LTbR3GSiiYpIL9laTwL3EpmB348YNeXp6mh+7uLjo6tWrFm0aNmyo0NBQW3YDoJBJSpNOJvHLGQAKG5sOxbq7u+vSpUvmxx4eHvr9998t2sTFxSklJcWW3QAAACAbbAp2derU0bFjx8yPmzVrprCwMC1fvlzXrl3TTz/9pJUrV3J7LwAAgAJgU7Br3769Dh8+rNOnT0uShg0bpvvuu0+vvvqqvLy81LVrV6WkpGjMmDF5UiwAAAAyZ9Mcu549e6pnz57mx9WrV9e2bds0ffp0nTp1Sl5eXurdu7f8/f1tLhRA4eHiIHkVd7B3GSiiYpLTxBRNIH/k2Z0nTGrUqKFPPvnE/PjUqVPq16+fZs6cmde7AmAnXsUdNN7T1d5loIh6LzaRk2+AfGLTodisxMTE6I033tDjjz+upUuX5tduAAAA8P/lKthFRkYqODhYXl5eql69urp166YTJ05Ikq5fv67Ro0erYcOGWrhwoSpWrGi+eDEAAADyT44PxR44cEBPP/20kpOTzcs2bNig/fv3a/369XrhhRf0+++/y8PDQ4MHD9ZLL70kFxeXPC0aAAAA1nI8YjdlyhQlJydr/PjxOnnypE6ePKnRo0frwoULat++vf744w8NHz5c+/bt02uvvUaoAwAAKCA5HrHbvXu3mjdvriFDhpiXvfXWW9qxY4ciIiL0/vvva+DAgXlZIwAAALIhxyN2f//9t+rVq2e1/NFHH5Ukde/e3eaiAAAAkHM5Dna3bt1SyZIlrZablpUvX972qgAAAJBj+Xa5EwAAABSsXF2geNmyZfr1118tlkVFRUmSunbtatXewcFBy5Yty82uAAAAkE25CnZRUVHmIHenLVu2WC1zcODWQwAAAPktx8Hu4MGD+VEHAAAAbJTjYOft7Z0fdQAAAMBGnDwBAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGIShgt2aNWv09NNPq0aNGqpSpYr8/f3Vp08fnTlzxt6lAQAA5DtnexeQF9LS0jR06FDNnz9fNWrUUOfOnVWqVCmdO3dOO3fuVExMjKpVq2bvMgEAAPKVIYLdl19+qfnz5+vVV1/VpEmT5OTkZLH+1q1bdqoMAACg4BT5Q7E3btxQSEiIqlevrokTJ1qFOklydjZEfgUAAMhSkU8827Zt0z///KPu3bsrJSVF69ev159//qmyZcuqRYsW8vX1tXeJAAAABaLIB7v9+/dLuj0qFxgYqBMnTpjXOTo6qn///vrwww/vup3ExMRc7T81NTVXzwOyKzU1Ndf9M6/2D+Q3+jmMzpY+7urqmu22RT7YXbp0SZI0bdo01a1bV1u3blXNmjV16NAhDRkyRNOmTVONGjXUp0+fLLcTGxurlJSUHO8/KSkpV3UD2ZWUlKSYmBi77h/Ib/RzGF1u+7iTk1OOjj4W+WBn+iurePHiCg0NlYeHhySpadOm+uabbxQQEKBp06bdNdh5enrmav8uLi65eh6QXS4uLvLy8rLr/oH8Rj+H0RVUHy/ywa5MmTKSpHr16plDnUnt2rVVvXp1RUVFKT4+Xm5ubpluJyfDnOk5Ohb5809QyDk6Oua6f+bV/oH8Rj+H0RVUHy/yPfnBBx+UJJUtWzbD9abl9py7AQAAUBCKfLBr1qyZJOmPP/6wWnfz5k1FRUXpvvvuU8WKFQu6NAAAgAJV5INdjRo19OSTTyoqKkoLFiywWDd58mRduXJFQUFBXMsOAAAYniHSzqeffqq2bdvqjTfe0Lp16/Tggw/q0KFD+vnnn+Xl5aUPPvjA3iUCAADkuyI/YifdHrXbtm2bunfvrgMHDujLL79UVFSUXn31VW3dulWVK1e2d4kAAAD5zhAjdpJUrVo1zZgxw95lAAAA2I0hRuwAAABAsAMAADAMgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMwtneBRiRy78XVW33N/YuA0WQy78X7V0CgP/vXFyKPv/hmr3LQBF0Li7Fbvsm2OUDx5SbKnHlrL3LAADYIPmWFH3Rfr+ggdzgUCwAAIBBEOwAAAAMgmAHAABgEMyxs5G3t7e9SygSoqOjlZiYaLHM1dVVPj4+dqqo6KCPAfmPz1n28F2eewXVxwh2NhowYIC9SygSRowYoePHj1ss8/HxUUhIiJ0qAoD/w3d59vBdXvhxKBYAAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIMg2AEAABgEwQ4AAMAgCHYAAAAGwS3FAORYTHKa3otNvHtDIAMxyWn2LgEwLIIdgBxLSpNOJvHLGQAKGw7FAgAAGATBDgAAwCAIdgAAAAbBHDsAWfL29rZ3CUVGdHS0EhMtTypxdXWVj4+PnSoqOuhnQN4g2AHI0oABA+xdQpExYsQIHT9+3GKZj4+PQkJC7FQRgHsNh2IBAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBGCLY+fn5yc3NLcOfoUOH2rs8AACAAmGYO0+UKVNG/fr1s1pev359O1QDAABQ8AwT7MqWLatRo0bZuwwAAAC7McShWAAAABhoxC45OVmLFy/WuXPn5Obmpscff1x+fn72LgsAAKDAGCbYXbhwQf3797dY1rp1a3355ZeqUKHCXZ+fmJiYX6VBUmpqaobLeN9hJPRzGB193D5cXV2z3dYQwa5Hjx4KCAhQ7dq1Vbx4cR0/flwhISHavHmzunXrpo0bN8rBwSHLbcTGxiolJaWAKr73JCUlZbgsJibGDtUA+YN+DqOjjxc8Jycn+fr6Zru9IYLdiBEjLB43bNhQS5cuVVBQkCIjI7Vp0ya1a9cuy214enrmZ4n3PBcXlwyXeXl52aEaIH/Qz2F09PHCzxDBLiOOjo7q3r27IiMjtXv37rsGu5wMcyLnHB2tz9NxdHTkfYeh0M9hdPTxws/QZ8Wa5tZdv37dzpUAAADkP0MHu71790qSvL297VwJAABA/ivywe73339XfHy81fLIyEhNnz5dLi4u6tixY8EXBgAAUMCK/By7lStXaurUqWrevLm8vb3l4uKiY8eOaevWrXJ0dNTkyZOZ1AkAAO4JRT7YNWvWTH/88YcOHjyoiIgIJSYmyt3dXZ06dVL//v3VoEEDe5cIAABQIIp8sAsMDFRgYKC9ywAAALC7Ij/HDgAAALcR7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGwAwAAMAiCHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGwAwAAMAiCHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGwAwAAMAiCHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGwAwAAMAiCHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDINgBAAAYBMEOAADAIAh2AAAABkGwAwAAMAiCHQAAgEEQ7AAAAAyCYAcAAGAQBDsAAACDMGSwmzJlitzc3OTm5qZffvnF3uUAAAAUCMMFu+PHj2vChAm677777F0KAABAgTJUsEtJSVG/fv1Up04dBQUF2bscAACAAuUQHx+fZu8i8sqnn36qkJAQ7dixQ1OnTtWSJUu0efNmPfbYY/YuzW5GjBhh7xIkSdHR0UpMTLRY5urqKh8fHztVZCkkJMTeJSCXCksfl+jnyD+FpZ/Txws/Z3sXkFeOHj2qkJAQDR8+XLVr17Z3OYXG8ePH7V1CphITEwt1fSgaCnsfop8jLxTmPkQfL1wMEexu3bql/v37q2bNmho6dGiutnHnXyC4t/D/j3sB/RxGZ9Q+7urqmu22hgh2n376qY4cOaItW7aoWLFiudpGbGysUlJS8rgyFBUxMTH2LgHId/RzGJ0R+7iTk5N8fX2z3b7IB7vDhw/rk08+0aBBg1SvXr1cb8fT0zPvikKR4+XlZe8SgHxHP4fR0ccNEOz69eunGjVqaOTIkTZtJyfDnEXJQw89ZO8SigSj/v/fC+jj2Uc/L7ro59lDHzfAWbFubm7Zardo0SIFBwfnbzEAAAB2VORH7Hr27Jnh8oiICP35559q3769KlasKG9v7wKuDAAAoGAV+RG7zPTr14/r2AEAgHuKoe48AQAAcC8j2AEAABiEYQ/FAgAA3GsYsQMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgAAwCAIdgAAAAZBsAMAADAIgh0AAIBBEOwAAAAMgmAHAABgEAQ7AAAAgyDYAQAAGATBDgCy4ObmpqCgoHzbflhYmNzc3DRx4sR82weAewfBDpKk6Ohoubm5qXPnzvYuBSgwr732mtzc3FSzZk3dunXL3uWgiDF9b6b/qVSpkh555BG98sorOnLkSL7tO7d/EISGhsrNzU2TJ0/OtM3kyZPl5uam0NBQW8s0hKL2x5ezvQsAAHtISEjQmjVr5ODgoIsXL2rjxo35OjKXmQYNGmjPnj2qUKFCge8beaNGjRp69tlnJUnXrl3Tr7/+qu+//15r1qzR6tWr1ahRIztXiHsJI3YA7knLly/X9evXNXDgQDk4OGjhwoV2qaNkyZKqWbMmwa4I8/X11ahRozRq1Ch9+OGH2rBhg4YPH66kpCR98MEH9i4P9xiCHXLk3LlzmjBhglq3bq0HHnhA7u7u8vPz05tvvqm///47w+dER0erd+/eql69uqpWraoOHTpo586dmjhxotzc3BQWFmb1nMWLF6t169aqWrWqqlatqtatW2vx4sUZbn/VqlXq0KGDHnjgAVWuXFmPPPKIOnfurLVr1+bpa4exLFy4UMWLF9ewYcPUuHFjbd68WefPn8+0/ZkzZ9S7d2/VqFFDnp6eCgoK0u7du63aBQUFyc3NLcNt9OvXT25uboqOjjYvy+wwz88//6wBAwaoYcOG5s9BixYtNH/+/Fy9XhSsvn37SpL2799vXnbr1i1Nnz5dAQEBqlKliry9vRUcHKyNGzdaPd90yDQ0NFQbN25U+/btVa1aNfn5+WnixInq2LGjJCkkJMTiUHD6vpXX1qxZoz59+qh+/fry8PCQt7e32rdvr1WrVlm1NR2m7tevn/744w/16NFDvr6+VjWuX79enTp1Uo0aNVS5cmX5+fmpb9++Onr0qMX24uLiNGrUKPn7+8vd3V0PPPCAevfurd9//91q3zn9DBbG99oWHIpFjkRERGj69Olq3ry5GjRooGLFiunQoUOaO3eufvrpJ+3YsUNly5Y1t4+NjVW7du10/vx5tW3bVnXq1NGJEyfUqVMnNWvWLMN9jBo1SjNnzpSnp6d69OghBwcHrVmzRv3799eRI0c0YcIEc9u5c+fqzTffVJUqVRQcHKzy5cvr/Pnz2rdvn9atW6fg4OB8f09Q9Pz222/at2+fgoODVa5cOT3//POKjIzUkiVLNHToUKv28fHxeuqpp1S5cmW99NJLio2N1cqVK9WxY0ctX748075siylTpigqKkqPPfaYPD09deXKFW3ZskVDhgzRiRMn9L///S/P94m84+DgYPE4LS1NvXv31po1a/TAAw/olVde0fXr17Vy5Uo999xzmjRpkl5//XWr7axatUpbt25Vu3bt1KdPH/37778KDAzU6dOntWTJEgUEBCgwMNDcPv33b157//33VaxYMTVu3FhVqlTRpUuX9OOPP+rFF19USEiIXnvtNavn/PXXX2rdurVq166tbt266Z9//lHx4sUlSePGjdPUqVNVrlw5BQUFqVKlSjp79qx27NihevXq6eGHH5Z0O9S1bt1aUVFRCgwMVOfOnXX69GmtWrVKmzZt0sqVK/X444/b/PoK03ttC4IdcqR58+Y6fvy4SpUqZbF8yZIl6tevn+bMmaPhw4ebl7/77rs6f/68PvjgAw0aNMi8PDQ0VAMGDLDafkREhGbOnKmHHnpImzZtMn9wRo0apTZt2mjGjBnq2LGjmjRpIklasGCBihcvrvDwcFWsWNFiW3FxcXn2umEspsOuzz33nCTp6aef1ogRI7Ro0aIMg91vv/2m5557TrNmzTL/wu7Zs6c6duyowYMH69dff5WjY94eAPn0009VvXp1i2W3bt1S165dNWvWLL3++uvy8vLK030i73z55ZeSpPr160uSli5dqjVr1iggIEArV640h5s333xTLVq00NixY/XUU09Z/Z9v3rxZK1euVIsWLaz2sWTJEgUGBmrUqFE5rm/79u1KTEzMcN2uXbsyXP7dd99Z1ffvv/+qbdu2+t///qeePXuqZMmSVtt666239M4771gs37Rpk6ZOnaqHH35Ya9euVfny5c3rbt26ZfH9PW7cOEVFRWnYsGEaN26ceXn37t3VuXNn9evXT7/88ovNn8H8eq8LGodikSOVKlWyCnWS9Pzzz6tMmTLavn27eVlSUpJWrVold3d3q7/kunfvrpo1a1ptx3S4deTIkRZ/DZUtW1YjRoywaGNSrFgxOTtb/42S/osCMElOTtayZcvk5uamdu3aSbrdvzp06KA///xTO3futHqOk5OTxo4dazEKExgYqLZt2yoqKirDQ7K2uvMXqCQ5Ozurd+/eSklJyXAKA+wjKipKEydO1MSJEzVmzBg99dRT+uSTT+Tq6moOIqbvrffff98c6iSpatWq6t+/v27evKnvvvvOattBQUEZBg1b7dixQyEhIRn+7NixI8PnZNQnS5Uqpe7duyshIUH79u2zWl+5cmW99dZbVsu/+uorSdKkSZOsvqudnZ3l7u4u6fbndfny5SpfvrzFoIEktWrVSq1atdKff/6ZJ5/B/HqvCxrBDjm2evVqderUSffff78qVKggNzc3lStXTgkJCRZzlE6cOKGkpCTVr1/f4otMun2Y4rHHHrPa9qFDhyTJYrjbxLTs8OHD5mXPPPOMrl27pqZNm2rMmDHasGGD4uPj8+JlwqDWrVunuLg4derUyaJfPv/885KkRYsWWT3Hy8tL1apVs1puGjlO3yfzytWrVzVhwgQFBASoatWq5nk9vXr1kqQs5wOiYP3111/mUPTll18qJiZGXbt21U8//WQ+RHjo0CGVKFFCDRo0sHp+Rt9tJhm1zwvjx49XfHx8hj/jx4/P8Dl///23Ro8erccff1weHh7mPjlmzBhJGffJOnXqWH3/S9LevXvl4uKS4Xd9en/88Ydu3LihBg0aWI0GSlm/dzmVX+91QeNQLHLkiy++0NixY1WxYkU9+eST8vT0lKurqyRp5syZSkpKMre9evWqJGV6tp/pL7L0rl69KkdHR6vDqqb2jo6OSkhIMC8bPHiwypcvr3nz5mn69OmaNm2anJ2d1aZNG02cODHDvzBxbzMFN9NhWJNWrVqpcuXKWrVqlUJCQlSmTBnzukqVKmW4LdPy9H0yLyQnJys4OFgHDx6Uv7+/nnvuOZUvX15OTk7m+T7pP2uwr1atWmn58uVZtrl69aqqVq2a4TrTd2FG/SizvlfQ/vnnH7Vs2VJnzpxR48aN9cQTT6hs2bJycnLS4cOHtX79+gz7ZGb1X7lyRR4eHnc9fGr6PZLZdrJ673KqsLzXtiLYIdtu3bqljz/+WB4eHgoLC7MIX2lpaZo6dapF+9KlS0uSLl++nOH2Ll68aLWsdOnSSk1N1aVLl6w+ZH///bdSU1PN25Vuj/z16tVLvXr1UlxcnCIiIrR8+XKtXLlSUVFRioiIkJOTU65fM4zlzJkz2rZtmySZD8NmZMWKFXrppZfMjzM749u0PH0INP2iunXrltUUgez+8lm/fr0OHjyoXr16WX2uli9friVLlmRrOyg8Spcufdd+lP67zeTOkzDsZeHChTpz5ozGjBljdUh08uTJWr9+fYbPy6z+smXL6uLFi0pNTc0y3Jnek5y8d7n9DBaW99pWHIpFtl2+fFkJCQlq2LCh1Yja/v37dePGDYtlDz74oFxcXHTgwAElJydbrEtLS9Ovv/5qtQ9/f39JUnh4uNU609wnPz+/DOsrX768goOD9fXXX5tP8oiKisr+C4ThhYaGKjU1VU2aNFHPnj2tfkyjeHde0y4mJkZnzpyx2l5kZKQkyz5pusxCbGysRdvU1NRs34ngr7/+kiS1b98+032iaPH399eNGze0d+9eq3V3+27LiOkP1pSUlLwp8C7yuk82aNBASUlJGX7Xp1ezZk25urpq3759un79utX6jN67vPgMplfQ77WtCHbItkqVKqlEiRI6ePCgxQcsPj5eb7/9tlV7FxcX/fe//9WFCxfMZ4iZLFmyRMePH7d6Trdu3STdvl5Q+r+sEhISFBISYtFGkn766SerW0HdvHlT//zzjySZDxMDaWlpCg0NlYODg2bOnKkvvvjC6ufLL7+Uv7+/9u7da3EdrZSUFH3wwQdKS0szLwsPD9emTZvk6+trcWcB01mQd57kM3369Gxf98p0tuudZyeGh4frm2++ydkLR6Fg+t567733dPPmTfPy2NhYTZ8+Xc7Ozua7V2RHuXLlzM8vCJn1ye+++06bNm3K8fZeeeUVSbdPlDN9X5vcunXLfESnePHi6ty5sy5fvqzPPvvMot327du1efNm+fr6qnHjxublefEZTK+g32tbcSgWFo4ePap+/fpluK5u3brq06ePpk2bpsDAQD311FO6evWqtmzZIi8vL3l4eFg9Z9y4cdq+fbvGjh2rsLAw+fn56cSJE9q4caNat26tLVu2WAzDBwQEqG/fvpo9e7aaNm2qjh07Ki0tTWvXrtWZM2f02muvKSAgwNy+d+/eKlmypBo3biwvLy/dvHlT27dv1++//65OnTpxOQiY7dixQ6dPn1azZs2ynHv5wgsv6NChQ1q4cKH5osGPPPKIdu7cqdatW6t58+Y6d+6cVqxYoWLFimnKlCkWffiFF17QlClTNGnSJB0+fFg1atTQ/v37dezYMQUEBGR41u2dnnrqKXl7e2vKlCk6duyYateubf7cBAUFafXq1Ta/HyhYzz//vNasWaP169crICBA7dq1M1/HLi4uTh9++GGO5gTXrFlTHh4eWrFihUqWLClPT085ODjo5Zdfzpfrqz333HP6/PPP9fbbbyssLExeXl767bfftH37dnXs2FFr1qzJ0fbatm2rQYMG6YsvvtCjjz6q4OBgVapUSbGxsfr55581cOBA9e/fX9LtMLxz50598skn2rNnjxo2bGi+jl3JkiU1ffr0PP8MplfQ77WtCHawcO7cuUzn71y5ckXffPONypUrp8WLF2vu3LmqVKmSOnXqpFGjRpnPEEyvWrVq2rRpk959911t3bpV4eHhqlu3rlasWKEffvhBkvW8ko8++kj+/v6aN2+eeXSiVq1aGjlypHr06GHRdvz48dqyZYv27t2rDRs2qGTJkvL19dXnn39u1Rb3NtPh1bv1i65du2rs2LFatmyZ3nvvPUm3D+18++23Gjt2rL7++mslJSWpYcOGGjdunMVIgXT78g6rV6/WuHHjtG3bNoWFhSkwMFBbtmzRxx9/nK1fKqVKlTJvIyIiQuHh4apVq5bmzJmjSpUqEeyKIAcHBy1YsEAzZ87UkiVLNHv2bBUvXlz+/v4aMGCAOnTokKPtOTk5aeHChRo/fryWLl1qPsmgU6dO+RI2qlatqnXr1mn8+PHavn27UlJS5O/vr5UrV+rMmTM5DnaS9MEHH+ixxx7TnDlztGrVKiUlJaly5cpq1qyZWrZsaW5XsWJF/fTTT/roo4+0fv16RUZGqkyZMurQoYNGjBhhvpCxSV58BtMr6PfaVg7x8fFpd28G5L2nnnpKe/bs0enTpzO8Nh5wL9iyZYu6dOmicePGadiwYfYuB0ARxxw75LuMrm20bNky7dq1Sy1atCDU4Z5mOsHH09PTzpUAMAJG7JDvatSoIX9/fz300EPmax6Fh4erdOnS2rBhgx555BF7lwgUuL1792rFihVavHixkpKSdPDgQcNcRwuA/TDHDvnu5Zdf1o8//qj9+/fr+vXrqlixorp27aq33norw9uKAfeC3bt3a/78+apdu7Y++OADQh2APMGIHQAAgEEwxw4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwA2mjhxotzc3BQWFmbvUgDc4wh2AJCJAwcOaODAgXr00Ufl6empKlWqqF69eurbt6+2bduW5XOjo6Pl5uamfv36FVC1AMAFigHASmpqqsaMGaMZM2bI2dlZzZs3V/v27VWsWDGdOnVKmzZt0rJlyzR69Gi9/fbb6tu3rzp37qxq1arZu3QA9ziCHQDc4cMPP9SMGTPk5+enBQsWqEaNGhbrb9y4oTlz5iguLk6SVKFCBVWoUMEepQKABQ7FAkA6UVFRmjJlisqXL6/ly5dbhTpJKlGihN544w2NGjVKkvUcu9DQUNWtW1eStGTJErm5uZl/wsLCNGHCBLm5uemHH37IsIavvvpKbm5umj59ev68SACGRbADgHQWL16slJQU9e7dW+7u7lm2dXFxyXC5n5+fXn/9dUlSnTp1NGLECPOPt7e3evXqJScnJ33zzTcZPn/BggUqXry4unXrZtuLAXDP4VAsAKSza9cuSVLz5s1zvQ1/f3+VLVtWs2bNkp+fn3lkL73WrVtr06ZNio6Olo+Pj3n5oUOHdOjQIXXq1Enly5fPdQ0A7k2M2AFAOhcvXpQkeXp65ut+XnrpJaWlpWnRokUWyxcsWCBJevHFF/N1/wCMiWAHAHbQtm1bVa1aVYsXL1ZqaqokKTExUd99952qV69u04ghgHsXwQ4A0jHNq4uNjc3X/Tg5OalHjx46e/astmzZIklatWqVrly5op49e8rBwSFf9w/AmAh2AJBO48aNJUk///xzvu/LdBKF6fDrggUL5OzsrO7du+f7vgEYE8EOANLp3r27nJycNH/+fF26dCnLtklJSZmuc3JykiSlpKRk2qZq1apq3bq1Nm7cqF27dikiIkJt2rSRh4dH7ooHcM8j2AFAOr6+vho8eLAuX76sLl266NSpU1ZtEhMTNW3aNE2aNCnT7bi5ucnBweGuh3R79+6tmzdvqnfv3kpLS+OkCQA24XInAHCHMWPGKDExUTNmzNBjjz2m5s2bq3bt2ipWrJiio6O1fft2xcXFacyYMZluo1SpUnr00UcVERGh/v376/7775ejo6O6dOkiLy8vc7u2bduqWrVqOnPmjDw9PdWmTZuCeIkADIpgBwB3cHR01IQJE9S1a1fNnTtXERERioiIUGpqqipXrqwnn3xSL7zwglq2bJnldr788kuNHj1a69atU0JCgtLS0tSwYUOLYOfo6Khnn31Wn332mfkwMADklkN8fHyavYsAgHtZ165dtWXLFh04cMDiYsUAkFPMsQMAOzp27Ji2bNmi1q1bE+oA2IxDsQBgB999951OnDihb7/9VpL01ltv2bkiAEZAsAMAO5g/f74iIyPl5eWlL774Qo8//ri9SwJgAMyxAwAAMAjm2AEAABgEwQ4AAMAgCHYAAAAGQbADAAAwCIIdAACAQRDsAAAADIJgBwAAYBAEOwAAAIP4f6C+W7iLfAapAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(orient = 'v', x = 'City', y = 'Rating', data = Data).set(title = 'Branch_lowest rating')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "8f40e524",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Gender/Branch')]"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Gender', hue = 'Branch').set(title = 'Gender/Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c69c2742",
+   "metadata": {},
+   "source": [
+    "# Using Catplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "ac69c43b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc8074aca60>"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(data = Data, x = 'Product line', y = 'Quantity', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b909985",
+   "metadata": {},
+   "source": [
+    "# Using Catplot on total column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "2e377480",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc806fdbdc0>"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Total', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "b4a4684f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc8070a6800>"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AAAAAAAAAZAoESQAAAAAAAAAAAAAAgEyBIAkAAAAAAAAAAAAAAJApECQBAAAAAAAAAAAAAAAyBYIkAAAAAAAAAAAAAABApkCQBAAAAAAAAAAAAAAAyBQIkgAAAAAAAAAAAAAAAJkCQRIAAAAAAAAAAAAAACBTIEgCAAAAAAAAAAAAAABkCgRJAAAAAAAAAAAAAACATIEgCQAAAAAAAAAAAAAAkCn8P7KvI8BxUm4vAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Unit price', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "f50c5fe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc807e24e50>"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Quantity', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b24cf5c",
+   "metadata": {},
+   "source": [
+    "# Summary of Insight\n",
+    " It was observed that the city with the highest gross income is 'Port Harcourt'.\n",
+    " \n",
+    " It can be observed that the most frequent customers are of the female gender and the most bought product is the 'Home and     lifestyle'. Also, the use of catplot with kind = 'point' is an easy to read representation.\n",
+    "\n",
+    " The most used payment method with respect to 'Product line' is Cash\n",
+    " \n",
+    " The branch with the lowest rating is \"B\" while 'C' has the highest\n",
+    " \n",
+    " It is observed that the most frequent customers are of the female gender and the most bought product is the 'Home and lifestyle'.\n",
+    " \n",
+    " It is observed that the most expensive 'product line' is the 'food and beverages' and it is mostly bought by the female gender."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dd96b18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8343c810",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/My_Ustacky_Project/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/My_Ustacky_Project/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/My_Ustacky_Project/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/My_Ustacky_Project/Abuja_Branch.csv b/My_Ustacky_Project/Abuja_Branch.csv
new file mode 100644
index 0000000..4bce687
--- /dev/null
+++ b/My_Ustacky_Project/Abuja_Branch.csv
@@ -0,0 +1,333 @@
+Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
+692-92-5582,B,Abuja,Member,Female,Food and beverages,19742.4,3,2961.36,62188.55999999999,2/20/2019,13:27,Card,59227.200000000004,4.7619047619999995,2961.36,5.9
+351-62-0822,B,Abuja,Member,Female,Fashion accessories,5212.8,4,1042.5600000000002,21893.760000000002,2/6/2019,18:07,Epay,20851.2,4.7619047619999995,1042.5600000000002,4.5
+529-56-3974,B,Abuja,Member,Male,Electronic accessories,9183.6,4,1836.72,38571.12,3/9/2019,17:03,Cash,36734.4,4.7619047619999995,1836.72,6.8
+299-46-1805,B,Abuja,Member,Female,Sports and travel,33739.2,6,10121.76,212556.96000000002,1/15/2019,16:19,Cash,202435.2,4.7619047619999995,10121.76,4.5
+319-50-3348,B,Abuja,Normal,Female,Home and lifestyle,14507.999999999998,2,1450.8000000000002,30466.8,3/11/2019,15:30,Epay,29015.999999999996,4.7619047619999995,1450.8000000000002,4.4
+371-85-5789,B,Abuja,Normal,Male,Health and beauty,31672.800000000003,3,4750.92,99769.32,3/5/2019,10:40,Epay,95018.4,4.7619047619999995,4750.92,5.1
+273-16-6619,B,Abuja,Normal,Male,Home and lifestyle,11952.000000000002,2,1195.2,25099.2,3/15/2019,12:20,Card,23904.000000000004,4.7619047619999995,1195.2,4.4
+649-29-6775,B,Abuja,Normal,Male,Fashion accessories,12067.2,1,603.36,12670.56,2/8/2019,15:31,Cash,12067.2,4.7619047619999995,603.36,6.7
+145-94-9061,B,Abuja,Normal,Female,Food and beverages,31809.6,5,7952.4,167000.4,1/25/2019,19:48,Cash,159048.0,4.7619047619999995,7952.4,9.6
+871-79-8483,B,Abuja,Normal,Male,Fashion accessories,33886.799999999996,5,8471.699999999999,177905.7,2/25/2019,19:39,Card,169434.0,4.7619047619999995,8471.699999999999,4.8
+149-71-6266,B,Abuja,Member,Male,Sports and travel,28105.199999999997,9,12647.34,265594.13999999996,1/28/2019,12:43,Cash,252946.8,4.7619047619999995,12647.34,4.5
+640-49-2076,B,Abuja,Normal,Male,Sports and travel,30160.8,8,12064.32,253350.71999999997,1/10/2019,14:49,Cash,241286.4,4.7619047619999995,12064.32,5.1
+777-82-7220,B,Abuja,Member,Male,Home and lifestyle,10843.2,8,4337.28,91082.87999999999,3/3/2019,13:01,Cash,86745.6,4.7619047619999995,4337.28,7.7
+280-35-5823,B,Abuja,Member,Female,Home and lifestyle,31219.2,1,1560.96,32780.159999999996,1/17/2019,18:45,Epay,31219.2,4.7619047619999995,1560.96,7.9
+354-25-5821,B,Abuja,Member,Female,Sports and travel,24883.2,6,7464.96,156764.15999999997,2/8/2019,13:03,Cash,149299.2,4.7619047619999995,7464.96,5.6
+132-32-9879,B,Abuja,Member,Female,Electronic accessories,33825.6,4,6765.120000000001,142067.52,3/9/2019,18:00,Cash,135302.4,4.7619047619999995,6765.120000000001,9.5
+370-41-7321,B,Abuja,Member,Male,Health and beauty,20408.399999999998,9,9183.78,192859.38,2/27/2019,17:24,Card,183675.6,4.7619047619999995,9183.78,8.4
+727-46-3608,B,Abuja,Member,Female,Food and beverages,7203.6,9,3241.62,68074.02,2/6/2019,15:47,Epay,64832.4,4.7619047619999995,3241.62,4.1
+669-54-1719,B,Abuja,Member,Male,Electronic accessories,6814.8,6,2044.4399999999998,42933.24,2/10/2019,12:45,Card,40888.8,4.7619047619999995,2044.4399999999998,8.1
+616-24-2851,B,Abuja,Member,Female,Fashion accessories,6433.200000000001,4,1286.6399999999999,27019.440000000002,3/22/2019,14:42,Epay,25732.800000000003,4.7619047619999995,1286.6399999999999,6.5
+242-55-6721,B,Abuja,Normal,Male,Home and lifestyle,5817.6,2,581.76,12216.96,3/7/2019,11:49,Epay,11635.2,4.7619047619999995,581.76,6.5
+347-34-2234,B,Abuja,Member,Female,Sports and travel,19825.2,9,8921.34,187348.14,2/3/2019,13:40,Epay,178426.8,4.7619047619999995,8921.34,10.0
+853-23-2453,B,Abuja,Member,Male,Health and beauty,27266.399999999998,4,5453.28,114518.88,2/14/2019,14:35,Cash,109065.59999999999,4.7619047619999995,5453.28,7.6
+109-28-2512,B,Abuja,Member,Female,Fashion accessories,35139.6,6,10541.880000000001,221379.47999999998,1/7/2019,15:01,Epay,210837.59999999998,4.7619047619999995,10541.880000000001,9.9
+510-95-6347,B,Abuja,Member,Female,Food and beverages,17467.2,3,2620.08,55021.68,3/5/2019,18:17,Epay,52401.6,4.7619047619999995,2620.08,4.0
+847-38-7188,B,Abuja,Normal,Female,Food and beverages,34804.8,3,5220.72,109635.12000000001,1/26/2019,19:56,Epay,104414.40000000001,4.7619047619999995,5220.72,6.4
+318-68-5053,B,Abuja,Normal,Female,Health and beauty,27716.399999999998,6,8314.92,174613.31999999998,2/27/2019,17:55,Cash,166298.4,4.7619047619999995,8314.92,6.1
+152-08-9985,B,Abuja,Member,Male,Health and beauty,23169.6,9,10426.32,218952.72,3/12/2019,12:09,Card,208526.4,4.7619047619999995,10426.32,8.6
+766-85-7061,B,Abuja,Normal,Male,Health and beauty,31633.2,10,15816.6,332148.6,3/29/2019,10:25,Epay,316332.0,4.7619047619999995,15816.6,5.1
+733-01-9107,B,Abuja,Normal,Male,Home and lifestyle,29772.0,6,8931.6,187563.6,3/5/2019,18:14,Cash,178632.0,4.7619047619999995,8931.6,7.4
+716-39-1409,B,Abuja,Normal,Male,Health and beauty,10926.0,7,3824.1000000000004,80306.09999999999,3/19/2019,18:19,Cash,76482.0,4.7619047619999995,3824.1000000000004,8.0
+479-26-8945,B,Abuja,Member,Female,Sports and travel,5936.4,2,593.64,12466.439999999999,2/5/2019,11:32,Epay,11872.8,4.7619047619999995,593.64,4.6
+227-78-1148,B,Abuja,Normal,Female,Fashion accessories,26222.4,7,9177.84,192734.64,2/15/2019,12:44,Cash,183556.8,4.7619047619999995,9177.84,8.4
+291-32-1427,B,Abuja,Member,Male,Fashion accessories,7898.400000000001,5,1974.6000000000001,41466.6,3/5/2019,12:29,Epay,39492.0,4.7619047619999995,1974.6000000000001,5.3
+659-65-8956,B,Abuja,Member,Male,Fashion accessories,18489.6,1,924.48,19414.08,1/16/2019,15:26,Epay,18489.6,4.7619047619999995,924.48,5.2
+378-24-2715,B,Abuja,Normal,Male,Home and lifestyle,19238.399999999998,2,1923.8399999999997,40400.64,1/20/2019,20:38,Epay,38476.799999999996,4.7619047619999995,1923.8399999999997,4.1
+219-22-9386,B,Abuja,Member,Male,Sports and travel,35985.6,9,16193.52,340063.92,3/9/2019,17:26,Card,323870.4,4.7619047619999995,16193.52,4.2
+268-27-6179,B,Abuja,Member,Female,Fashion accessories,20329.2,8,8131.68,170765.28,3/9/2019,14:57,Epay,162633.6,4.7619047619999995,8131.68,7.3
+549-84-7482,B,Abuja,Normal,Female,Sports and travel,32500.8,9,14625.359999999999,307132.56,2/8/2019,11:15,Epay,292507.2,4.7619047619999995,14625.359999999999,7.2
+191-10-6171,B,Abuja,Normal,Female,Fashion accessories,14263.199999999999,7,4992.12,104834.52,1/25/2019,13:18,Cash,99842.4,4.7619047619999995,4992.12,7.5
+695-51-0018,B,Abuja,Normal,Female,Sports and travel,12542.400000000001,4,2508.48,52678.08,2/10/2019,18:36,Cash,50169.600000000006,4.7619047619999995,2508.48,7.4
+590-83-4591,B,Abuja,Member,Male,Electronic accessories,31482.0,6,9444.6,198336.59999999998,2/17/2019,14:40,Card,188892.00000000003,4.7619047619999995,9444.6,8.8
+241-72-9525,B,Abuja,Normal,Male,Sports and travel,18687.6,10,9343.8,196219.8,2/16/2019,12:21,Cash,186876.0,4.7619047619999995,9343.8,8.2
+262-47-2794,B,Abuja,Member,Male,Home and lifestyle,25869.6,8,10347.84,217304.64,3/6/2019,15:07,Card,206956.8,4.7619047619999995,10347.84,6.2
+608-96-3517,B,Abuja,Member,Female,Fashion accessories,32954.4,4,6590.88,138408.48,3/23/2019,19:20,Card,131817.6,4.7619047619999995,6590.88,4.8
+279-74-2924,B,Abuja,Member,Male,Electronic accessories,25981.2,1,1299.06,27280.26,1/4/2019,19:40,Cash,25981.2,4.7619047619999995,1299.06,6.1
+307-85-2293,B,Abuja,Normal,Male,Home and lifestyle,18100.8,5,4525.2,95029.20000000001,3/7/2019,13:58,Epay,90504.0,4.7619047619999995,4525.2,9.7
+743-04-1105,B,Abuja,Member,Male,Health and beauty,34999.2,9,15749.639999999998,330742.44,3/30/2019,14:43,Epay,314992.8,4.7619047619999995,15749.639999999998,6.0
+423-57-2993,B,Abuja,Normal,Male,Sports and travel,33620.4,6,10086.119999999999,211808.52,3/27/2019,19:18,Epay,201722.40000000002,4.7619047619999995,10086.119999999999,10.0
+548-46-9322,B,Abuja,Normal,Male,Food and beverages,14364.0,10,7182.0,150822.0,2/20/2019,15:24,Card,143640.0,4.7619047619999995,7182.0,5.9
+505-02-0892,B,Abuja,Member,Male,Health and beauty,15325.2,8,6130.08,128731.68000000001,2/25/2019,14:12,Epay,122601.6,4.7619047619999995,6130.08,5.6
+316-55-4634,B,Abuja,Member,Male,Food and beverages,28818.0,5,7204.5,151294.5,1/26/2019,12:45,Card,144090.0,4.7619047619999995,7204.5,9.4
+608-27-6295,B,Abuja,Member,Male,Electronic accessories,19040.4,6,5712.12,119954.52,1/19/2019,17:34,Card,114242.4,4.7619047619999995,5712.12,9.8
+414-12-7047,B,Abuja,Normal,Male,Food and beverages,7124.4,8,2849.76,59844.96,1/18/2019,12:04,Epay,56995.2,4.7619047619999995,2849.76,8.7
+895-66-0685,B,Abuja,Member,Male,Food and beverages,6508.799999999999,3,976.3199999999999,20502.72,3/5/2019,19:46,Epay,19526.4,4.7619047619999995,976.3199999999999,8.0
+305-14-0245,B,Abuja,Member,Female,Home and lifestyle,34016.4,8,13606.56,285737.76,3/3/2019,19:00,Epay,272131.2,4.7619047619999995,13606.56,7.5
+732-04-5373,B,Abuja,Member,Male,Home and lifestyle,16729.2,4,3345.84,70262.64,2/8/2019,10:53,Cash,66916.8,4.7619047619999995,3345.84,7.0
+284-34-9626,B,Abuja,Normal,Female,Home and lifestyle,27734.4,3,4160.16,87363.36,2/11/2019,10:39,Card,83203.2,4.7619047619999995,4160.16,7.2
+437-58-8131,B,Abuja,Normal,Female,Fashion accessories,26467.199999999997,2,2646.7200000000003,55581.119999999995,1/15/2019,13:41,Epay,52934.399999999994,4.7619047619999995,2646.7200000000003,4.6
+641-43-2399,B,Abuja,Normal,Male,Home and lifestyle,9198.0,4,1839.6000000000001,38631.6,1/26/2019,20:23,Epay,36792.0,4.7619047619999995,1839.6000000000001,5.7
+542-41-0513,B,Abuja,Member,Female,Electronic accessories,20696.4,4,4139.28,86924.88,3/15/2019,11:57,Cash,82785.6,4.7619047619999995,4139.28,6.6
+875-46-5808,B,Abuja,Member,Male,Health and beauty,9324.0,10,4662.0,97902.0,2/6/2019,14:51,Epay,93240.0,4.7619047619999995,4662.0,8.7
+394-43-4238,B,Abuja,Member,Male,Home and lifestyle,6397.2,5,1599.3,33585.3,2/15/2019,12:42,Card,31985.999999999996,4.7619047619999995,1599.3,5.4
+573-58-9734,B,Abuja,Normal,Female,Fashion accessories,10933.2,3,1639.98,34439.579999999994,3/28/2019,13:41,Epay,32799.6,4.7619047619999995,1639.98,5.1
+817-69-8206,B,Abuja,Normal,Female,Electronic accessories,35902.8,9,16156.26,339281.45999999996,3/2/2019,19:42,Card,323125.2,4.7619047619999995,16156.26,6.5
+142-63-6033,B,Abuja,Normal,Male,Home and lifestyle,33249.6,5,8312.4,174560.4,3/20/2019,19:17,Epay,166248.0,4.7619047619999995,8312.4,4.9
+656-16-1063,B,Abuja,Normal,Male,Sports and travel,16711.2,3,2506.68,52640.27999999999,1/4/2019,13:24,Card,50133.6,4.7619047619999995,2506.68,4.4
+891-58-8335,B,Abuja,Member,Female,Sports and travel,10659.6,7,3730.86,78348.06,3/11/2019,15:53,Cash,74617.2,4.7619047619999995,3730.86,6.5
+560-30-5617,B,Abuja,Normal,Female,Sports and travel,8917.2,5,2229.3,46815.299999999996,3/24/2019,18:27,Cash,44586.0,4.7619047619999995,2229.3,8.5
+549-03-9315,B,Abuja,Normal,Male,Fashion accessories,34153.200000000004,8,13661.28,286886.88,2/12/2019,12:58,Epay,273225.60000000003,4.7619047619999995,13661.28,8.7
+790-29-1172,B,Abuja,Normal,Female,Food and beverages,20642.4,3,3096.36,65023.55999999999,3/10/2019,18:59,Card,61927.200000000004,4.7619047619999995,3096.36,7.9
+239-36-3640,B,Abuja,Normal,Male,Electronic accessories,16326.0,6,4897.8,102853.79999999999,1/31/2019,13:44,Epay,97956.00000000001,4.7619047619999995,4897.8,6.1
+468-01-2051,B,Abuja,Normal,Male,Food and beverages,22348.8,7,7822.080000000001,164263.68,3/6/2019,13:46,Epay,156441.6,4.7619047619999995,7822.080000000001,5.4
+836-82-5858,B,Abuja,Member,Male,Health and beauty,24973.2,9,11237.94,235996.74000000002,1/26/2019,19:14,Epay,224758.80000000002,4.7619047619999995,11237.94,4.0
+466-61-5506,B,Abuja,Member,Female,Electronic accessories,32652.0,6,9795.6,205707.59999999998,2/26/2019,10:52,Cash,195912.00000000003,4.7619047619999995,9795.6,5.3
+289-65-5721,B,Abuja,Normal,Female,Fashion accessories,29293.2,2,2929.32,61515.72,1/26/2019,19:28,Cash,58586.4,4.7619047619999995,2929.32,6.5
+545-46-3100,B,Abuja,Member,Female,Electronic accessories,3812.4,3,571.86,12009.06,3/12/2019,13:52,Card,11437.2,4.7619047619999995,571.86,8.7
+418-02-5978,B,Abuja,Normal,Female,Health and beauty,30272.4,9,13622.58,286074.18,2/11/2019,10:54,Cash,272451.6,4.7619047619999995,13622.58,8.0
+269-04-5750,B,Abuja,Member,Male,Fashion accessories,26575.199999999997,4,5315.04,111615.84000000001,2/21/2019,18:31,Cash,106300.79999999999,4.7619047619999995,5315.04,6.7
+346-84-3103,B,Abuja,Member,Female,Electronic accessories,4759.2,5,1189.8,24985.8,3/2/2019,19:26,Cash,23795.999999999996,4.7619047619999995,1189.8,4.3
+376-02-8238,B,Abuja,Normal,Male,Home and lifestyle,33793.200000000004,8,13517.28,283862.88,2/2/2019,18:42,Card,270345.60000000003,4.7619047619999995,13517.28,8.3
+866-05-7563,B,Abuja,Member,Female,Electronic accessories,29304.000000000004,3,4395.6,92307.6,2/9/2019,19:43,Cash,87912.0,4.7619047619999995,4395.6,4.8
+785-13-7708,B,Abuja,Normal,Male,Food and beverages,26301.600000000002,7,9205.56,193316.76,1/14/2019,19:06,Card,184111.2,4.7619047619999995,9205.56,4.2
+845-51-0542,B,Abuja,Member,Male,Food and beverages,16758.0,9,7541.1,158363.1,2/2/2019,15:34,Epay,150822.0,4.7619047619999995,7541.1,6.4
+110-48-7033,B,Abuja,Member,Male,Fashion accessories,11743.199999999999,4,2348.64,49321.439999999995,1/29/2019,14:12,Cash,46972.799999999996,4.7619047619999995,2348.64,9.0
+655-85-5130,B,Abuja,Member,Female,Fashion accessories,13787.999999999998,4,2757.6,57909.600000000006,3/13/2019,19:22,Cash,55151.99999999999,4.7619047619999995,2757.6,5.7
+154-74-7179,B,Abuja,Normal,Male,Sports and travel,19602.0,1,980.1,20582.1,2/26/2019,19:24,Epay,19602.0,4.7619047619999995,980.1,7.9
+571-94-0759,B,Abuja,Member,Female,Food and beverages,26855.999999999996,10,13427.999999999998,281988.0,1/8/2019,20:55,Cash,268560.0,4.7619047619999995,13427.999999999998,9.5
+783-09-1637,B,Abuja,Normal,Female,Sports and travel,24274.800000000003,5,6068.700000000001,127442.7,3/6/2019,18:13,Epay,121373.99999999999,4.7619047619999995,6068.700000000001,6.3
+477-24-6490,B,Abuja,Normal,Female,Health and beauty,35895.6,6,10768.68,226142.28,2/26/2019,16:52,Epay,215373.6,4.7619047619999995,10768.68,7.9
+566-19-5475,B,Abuja,Normal,Male,Fashion accessories,17269.2,7,6044.22,126928.62,1/7/2019,20:52,Cash,120884.40000000001,4.7619047619999995,6044.22,6.2
+498-41-1961,B,Abuja,Normal,Male,Health and beauty,24004.800000000003,5,6001.200000000001,126025.2,2/20/2019,18:01,Cash,120023.99999999999,4.7619047619999995,6001.200000000001,7.6
+283-79-9594,B,Abuja,Normal,Female,Food and beverages,17463.6,7,6112.26,128357.45999999999,1/25/2019,13:30,Card,122245.2,4.7619047619999995,6112.26,5.2
+139-20-0155,B,Abuja,Member,Male,Electronic accessories,14507.999999999998,10,7253.999999999999,152334.0,1/24/2019,17:37,Card,145080.0,4.7619047619999995,7253.999999999999,7.0
+585-03-5943,B,Abuja,Normal,Male,Health and beauty,6519.599999999999,10,3259.7999999999997,68455.8,3/13/2019,11:46,Epay,65196.0,4.7619047619999995,3259.7999999999997,5.9
+573-10-3877,B,Abuja,Member,Male,Health and beauty,14043.599999999999,1,702.18,14745.78,3/12/2019,16:46,Card,14043.599999999999,4.7619047619999995,702.18,4.7
+396-90-2219,B,Abuja,Normal,Female,Electronic accessories,5385.6,8,2154.24,45239.04,2/23/2019,12:29,Cash,43084.8,4.7619047619999995,2154.24,8.6
+532-59-7201,B,Abuja,Member,Male,Sports and travel,28774.800000000003,6,8632.44,181281.24000000002,1/31/2019,14:04,Cash,172648.8,4.7619047619999995,8632.44,5.5
+276-54-0879,B,Abuja,Normal,Male,Sports and travel,35186.4,4,7037.28,147782.88,3/12/2019,19:53,Epay,140745.6,4.7619047619999995,7037.28,6.4
+730-61-8757,B,Abuja,Member,Male,Health and beauty,18406.8,4,3681.36,77308.56,1/25/2019,10:11,Card,73627.2,4.7619047619999995,3681.36,4.0
+868-81-1752,B,Abuja,Normal,Male,Home and lifestyle,7927.2,9,3567.24,74912.04000000001,2/7/2019,18:48,Cash,71344.8,4.7619047619999995,3567.24,6.8
+692-27-8933,B,Abuja,Normal,Female,Sports and travel,20862.0,6,6258.6,131430.6,2/24/2019,13:02,Cash,125172.0,4.7619047619999995,6258.6,5.2
+374-17-3652,B,Abuja,Member,Female,Food and beverages,15415.2,9,6936.840000000001,145673.64,2/5/2019,15:26,Card,138736.8,4.7619047619999995,6936.840000000001,8.9
+378-07-7001,B,Abuja,Member,Male,Electronic accessories,17312.4,3,2596.86,54534.06,2/10/2019,18:23,Card,51937.200000000004,4.7619047619999995,2596.86,7.8
+433-75-6987,B,Abuja,Member,Female,Health and beauty,20149.2,7,7052.22,148096.62,3/5/2019,19:06,Epay,141044.4,4.7619047619999995,7052.22,8.9
+873-95-4984,B,Abuja,Member,Female,Health and beauty,27684.000000000004,7,9689.4,203477.40000000002,2/15/2019,20:21,Cash,193787.99999999997,4.7619047619999995,9689.4,7.7
+400-45-1220,B,Abuja,Normal,Female,Health and beauty,4860.0,10,2430.0,51030.0,2/27/2019,11:06,Card,48600.0,4.7619047619999995,2430.0,4.8
+115-99-4379,B,Abuja,Member,Female,Fashion accessories,19702.8,7,6895.98,144815.58,3/14/2019,19:02,Card,137919.6,4.7619047619999995,6895.98,8.5
+565-67-6697,B,Abuja,Member,Male,Home and lifestyle,9720.0,9,4374.0,91854.0,3/2/2019,14:16,Cash,87480.0,4.7619047619999995,4374.0,4.8
+889-04-9723,B,Abuja,Member,Female,Food and beverages,32090.4,4,6418.08,134779.68000000002,1/7/2019,12:20,Card,128361.6,4.7619047619999995,6418.08,7.8
+453-63-6187,B,Abuja,Normal,Male,Electronic accessories,9900.0,3,1485.0,31185.0,3/1/2019,15:40,Epay,29700.0,4.7619047619999995,1485.0,6.5
+578-80-7669,B,Abuja,Normal,Male,Sports and travel,26989.2,1,1349.46,28338.660000000003,3/16/2019,16:58,Cash,26989.2,4.7619047619999995,1349.46,5.6
+201-86-2184,B,Abuja,Member,Female,Electronic accessories,9453.6,7,3308.76,69483.95999999999,2/2/2019,19:40,Cash,66175.2,4.7619047619999995,3308.76,9.9
+261-12-8671,B,Abuja,Normal,Female,Fashion accessories,21945.6,2,2194.56,46085.759999999995,1/25/2019,19:39,Card,43891.2,4.7619047619999995,2194.56,4.9
+843-01-4703,B,Abuja,Member,Female,Home and lifestyle,12736.800000000001,9,5731.5599999999995,120362.76000000001,1/5/2019,19:50,Card,114631.20000000001,4.7619047619999995,5731.5599999999995,9.6
+182-69-8360,B,Abuja,Normal,Female,Electronic accessories,8514.0,4,1702.8000000000002,35758.8,1/30/2019,13:32,Card,34056.0,4.7619047619999995,1702.8000000000002,4.0
+868-52-7573,B,Abuja,Normal,Female,Food and beverages,35888.4,5,8972.1,188414.09999999998,1/14/2019,12:09,Cash,179442.0,4.7619047619999995,8972.1,9.9
+525-88-7307,B,Abuja,Member,Male,Sports and travel,27295.199999999997,1,1364.7600000000002,28659.960000000003,1/31/2019,13:19,Cash,27295.199999999997,4.7619047619999995,1364.7600000000002,5.8
+596-42-3999,B,Abuja,Normal,Male,Food and beverages,6559.2,7,2295.7200000000003,48210.12,3/10/2019,14:04,Card,45914.4,4.7619047619999995,2295.7200000000003,6.6
+173-82-9529,B,Abuja,Normal,Female,Fashion accessories,13662.000000000002,10,6831.000000000001,143451.0,1/26/2019,14:51,Cash,136620.0,4.7619047619999995,6831.000000000001,9.7
+760-54-1821,B,Abuja,Normal,Male,Home and lifestyle,4892.4,9,2201.58,46233.18,3/15/2019,10:26,Cash,44031.6,4.7619047619999995,2201.58,5.8
+793-10-3222,B,Abuja,Member,Female,Health and beauty,14781.6,6,4434.48,93124.08,3/5/2019,13:30,Card,88689.6,4.7619047619999995,4434.48,8.3
+346-12-3257,B,Abuja,Member,Male,Electronic accessories,6926.4,9,3116.8799999999997,65454.479999999996,3/4/2019,16:28,Cash,62337.6,4.7619047619999995,3116.8799999999997,8.0
+831-64-0259,B,Abuja,Normal,Female,Fashion accessories,14310.0,5,3577.5,75127.5,2/22/2019,10:43,Epay,71550.0,4.7619047619999995,3577.5,9.6
+725-32-9708,B,Abuja,Member,Female,Fashion accessories,24735.6,4,4947.12,103889.52,1/4/2019,19:01,Cash,98942.4,4.7619047619999995,4947.12,4.1
+244-08-0162,B,Abuja,Normal,Female,Health and beauty,12315.6,10,6157.8,129313.79999999999,1/2/2019,13:00,Cash,123156.00000000001,4.7619047619999995,6157.8,5.1
+569-71-4390,B,Abuja,Normal,Male,Sports and travel,7873.200000000001,2,787.3199999999999,16533.72,1/25/2019,14:29,Epay,15746.400000000001,4.7619047619999995,787.3199999999999,6.9
+268-03-6164,B,Abuja,Normal,Male,Health and beauty,34599.6,1,1729.98,36329.579999999994,1/25/2019,16:28,Epay,34599.6,4.7619047619999995,1729.98,7.8
+848-07-1692,B,Abuja,Normal,Female,Health and beauty,20599.2,2,2059.92,43258.32,1/12/2019,17:13,Epay,41198.4,4.7619047619999995,2059.92,8.3
+301-81-8610,B,Abuja,Member,Male,Fashion accessories,9151.2,8,3660.4799999999996,76870.08,3/19/2019,19:42,Card,73209.6,4.7619047619999995,3660.4799999999996,6.7
+198-84-7132,B,Abuja,Member,Male,Fashion accessories,14619.6,9,6578.82,138155.22,1/2/2019,13:40,Cash,131576.4,4.7619047619999995,6578.82,7.0
+650-98-6268,B,Abuja,Member,Female,Food and beverages,7513.200000000001,3,1126.98,23666.579999999998,3/20/2019,13:53,Card,22539.6,4.7619047619999995,1126.98,8.0
+741-73-3559,B,Abuja,Normal,Male,Sports and travel,24217.199999999997,5,6054.299999999999,127140.3,2/27/2019,17:27,Cash,121086.00000000001,4.7619047619999995,6054.299999999999,6.9
+286-75-7818,B,Abuja,Normal,Male,Fashion accessories,24868.8,2,2486.8799999999997,52224.479999999996,1/31/2019,19:48,Card,49737.6,4.7619047619999995,2486.8799999999997,6.9
+616-87-0016,B,Abuja,Normal,Male,Fashion accessories,34394.4,7,12038.04,252798.83999999997,3/9/2019,14:36,Card,240760.8,4.7619047619999995,12038.04,9.6
+837-55-7229,B,Abuja,Normal,Female,Fashion accessories,17078.399999999998,1,853.92,17932.32,2/22/2019,18:19,Card,17078.399999999998,4.7619047619999995,853.92,6.8
+394-30-3170,B,Abuja,Member,Female,Sports and travel,31834.800000000003,8,12733.92,267412.32,3/22/2019,19:35,Card,254678.40000000002,4.7619047619999995,12733.92,4.3
+390-80-5128,B,Abuja,Member,Female,Health and beauty,6893.999999999999,1,344.7,7238.700000000001,1/28/2019,17:58,Card,6893.999999999999,4.7619047619999995,344.7,9.5
+296-11-7041,B,Abuja,Member,Female,Health and beauty,9745.2,1,487.26,10232.460000000001,1/12/2019,20:07,Card,9745.2,4.7619047619999995,487.26,5.3
+449-27-2918,B,Abuja,Member,Female,Sports and travel,14083.199999999999,1,704.16,14787.36,3/26/2019,11:02,Card,14083.199999999999,4.7619047619999995,704.16,9.6
+891-01-7034,B,Abuja,Normal,Female,Electronic accessories,26895.6,6,8068.68,169442.28,1/1/2019,19:07,Cash,161373.6,4.7619047619999995,8068.68,6.7
+744-09-5786,B,Abuja,Normal,Male,Electronic accessories,7923.6,6,2377.08,49918.67999999999,1/2/2019,18:50,Cash,47541.6,4.7619047619999995,2377.08,7.6
+767-54-1907,B,Abuja,Member,Female,Fashion accessories,10641.6,5,2660.4,55868.4,2/13/2019,16:59,Cash,53208.00000000001,4.7619047619999995,2660.4,6.9
+710-46-4433,B,Abuja,Member,Female,Food and beverages,27864.000000000004,9,12538.8,263314.8,2/15/2019,14:15,Card,250776.0,4.7619047619999995,12538.8,4.5
+533-33-5337,B,Abuja,Normal,Male,Electronic accessories,28580.4,10,14290.2,300094.2,2/7/2019,20:24,Cash,285804.0,4.7619047619999995,14290.2,6.2
+234-03-4040,B,Abuja,Member,Female,Food and beverages,26298.0,10,13149.0,276129.0,3/3/2019,12:25,Card,262980.0,4.7619047619999995,13149.0,8.7
+554-53-3790,B,Abuja,Normal,Male,Sports and travel,13327.2,6,3998.16,83961.36,3/22/2019,18:33,Cash,79963.2,4.7619047619999995,3998.16,4.5
+731-59-7531,B,Abuja,Member,Male,Health and beauty,26125.199999999997,8,10450.08,219451.68000000002,3/30/2019,17:58,Cash,209001.59999999998,4.7619047619999995,10450.08,4.6
+701-69-8742,B,Abuja,Normal,Male,Sports and travel,12373.199999999999,10,6186.599999999999,129918.59999999999,3/16/2019,10:11,Epay,123732.0,4.7619047619999995,6186.599999999999,6.7
+305-18-3552,B,Abuja,Member,Male,Home and lifestyle,21736.8,10,10868.4,228236.4,2/12/2019,16:19,Cash,217367.99999999997,4.7619047619999995,10868.4,6.0
+438-01-4015,B,Abuja,Member,Female,Sports and travel,17816.4,4,3563.2799999999997,74828.88,3/21/2019,15:25,Epay,71265.6,4.7619047619999995,3563.2799999999997,6.6
+709-58-4068,B,Abuja,Normal,Female,Fashion accessories,14792.400000000001,10,7396.200000000001,155320.2,2/28/2019,14:42,Cash,147924.0,4.7619047619999995,7396.200000000001,7.3
+627-95-3243,B,Abuja,Member,Female,Home and lifestyle,27964.800000000003,9,12584.159999999998,264267.36,2/4/2019,13:21,Epay,251683.2,4.7619047619999995,12584.159999999998,9.8
+686-41-0932,B,Abuja,Normal,Female,Fashion accessories,12492.000000000002,2,1249.2,26233.2,3/13/2019,19:48,Epay,24984.000000000004,4.7619047619999995,1249.2,8.2
+608-04-3797,B,Abuja,Member,Female,Health and beauty,9115.2,8,3646.08,76567.68,3/5/2019,20:24,Epay,72921.6,4.7619047619999995,3646.08,8.7
+437-53-3084,B,Abuja,Normal,Male,Fashion accessories,35960.4,2,3596.0399999999995,75516.84,2/26/2019,11:48,Epay,71920.8,4.7619047619999995,3596.0399999999995,7.1
+632-32-4574,B,Abuja,Normal,Male,Sports and travel,27331.2,8,10932.480000000001,229582.08000000002,3/20/2019,14:14,Cash,218649.6,4.7619047619999995,10932.480000000001,5.5
+401-18-8016,B,Abuja,Member,Female,Sports and travel,35326.799999999996,1,1766.3400000000001,37093.14,1/21/2019,17:36,Cash,35326.799999999996,4.7619047619999995,1766.3400000000001,8.9
+277-63-2961,B,Abuja,Member,Male,Sports and travel,26629.2,1,1331.46,27960.659999999996,2/3/2019,15:53,Card,26629.2,4.7619047619999995,1331.46,5.4
+282-35-2475,B,Abuja,Normal,Female,Sports and travel,33591.6,2,3359.16,70542.36,3/25/2019,17:53,Cash,67183.2,4.7619047619999995,3359.16,6.3
+511-54-3087,B,Abuja,Normal,Male,Sports and travel,31842.0,1,1592.1000000000001,33434.1,2/25/2019,16:36,Card,31842.0,4.7619047619999995,1592.1000000000001,9.5
+387-49-4215,B,Abuja,Member,Female,Sports and travel,17460.0,3,2619.0,54999.0,1/8/2019,12:50,Cash,52380.0,4.7619047619999995,2619.0,6.7
+862-17-9201,B,Abuja,Normal,Female,Food and beverages,30258.0,6,9077.4,190625.4,1/29/2019,10:48,Card,181548.0,4.7619047619999995,9077.4,7.7
+291-21-5991,B,Abuja,Member,Male,Health and beauty,22064.4,5,5516.1,115838.09999999999,3/29/2019,14:28,Cash,110322.0,4.7619047619999995,5516.1,7.0
+347-72-6115,B,Abuja,Member,Female,Sports and travel,32666.399999999998,7,11433.24,240098.03999999998,1/16/2019,18:03,Card,228664.8,4.7619047619999995,11433.24,6.2
+425-85-2085,B,Abuja,Member,Male,Health and beauty,19749.6,5,4937.4,103685.4,3/29/2019,16:48,Epay,98748.0,4.7619047619999995,4937.4,9.8
+734-91-1155,B,Abuja,Normal,Female,Electronic accessories,16455.6,3,2468.3399999999997,51835.14,3/26/2019,10:34,Card,49366.799999999996,4.7619047619999995,2468.3399999999997,7.7
+286-62-6248,B,Abuja,Normal,Male,Fashion accessories,14115.6,4,2823.1200000000003,59285.520000000004,1/16/2019,20:03,Card,56462.4,4.7619047619999995,2823.1200000000003,9.0
+339-38-9982,B,Abuja,Member,Male,Fashion accessories,21549.6,2,2154.96,45254.16,1/13/2019,14:55,Epay,43099.2,4.7619047619999995,2154.96,6.7
+827-44-5872,B,Abuja,Member,Female,Food and beverages,19569.6,10,9784.8,205480.8,2/7/2019,11:28,Card,195696.0,4.7619047619999995,9784.8,6.1
+857-67-9057,B,Abuja,Normal,Male,Electronic accessories,8262.0,10,4131.0,86751.0,2/6/2019,19:20,Epay,82620.0,4.7619047619999995,4131.0,8.2
+339-12-4827,B,Abuja,Member,Female,Fashion accessories,26625.6,1,1331.28,27956.88,1/5/2019,11:32,Card,26625.6,4.7619047619999995,1331.28,5.0
+173-50-1108,B,Abuja,Member,Female,Sports and travel,7264.8,4,1452.9600000000003,30512.16,2/13/2019,12:14,Card,29059.2,4.7619047619999995,1452.9600000000003,5.0
+841-18-8232,B,Abuja,Normal,Female,Food and beverages,25632.0,1,1281.6,26913.600000000002,1/5/2019,20:40,Card,25632.0,4.7619047619999995,1281.6,9.2
+701-23-5550,B,Abuja,Member,Male,Home and lifestyle,13971.6,4,2794.32,58680.72,3/19/2019,13:40,Epay,55886.4,4.7619047619999995,2794.32,4.9
+539-21-7227,B,Abuja,Normal,Female,Sports and travel,18554.4,5,4638.6,97410.59999999999,1/26/2019,17:45,Cash,92772.0,4.7619047619999995,4638.6,4.2
+747-58-7183,B,Abuja,Normal,Male,Fashion accessories,20617.2,3,3092.5800000000004,64944.18,2/9/2019,20:31,Epay,61851.6,4.7619047619999995,3092.5800000000004,6.5
+582-52-8065,B,Abuja,Normal,Female,Fashion accessories,19551.600000000002,9,8798.22,184762.62000000002,2/22/2019,10:49,Cash,175964.4,4.7619047619999995,8798.22,8.9
+210-57-1719,B,Abuja,Normal,Female,Health and beauty,20966.4,9,9434.880000000001,198132.48,2/5/2019,12:34,Cash,188697.59999999998,4.7619047619999995,9434.880000000001,9.7
+356-44-8813,B,Abuja,Normal,Male,Home and lifestyle,13492.8,3,2023.9200000000003,42502.32000000001,1/20/2019,13:45,Card,40478.4,4.7619047619999995,2023.9200000000003,7.7
+198-66-9832,B,Abuja,Member,Female,Fashion accessories,25934.4,2,2593.44,54462.24,2/4/2019,19:38,Cash,51868.8,4.7619047619999995,2593.44,9.5
+174-75-0888,B,Abuja,Normal,Male,Electronic accessories,7768.799999999999,9,3495.96,73415.15999999999,3/14/2019,12:32,Cash,69919.2,4.7619047619999995,3495.96,7.3
+134-54-4720,B,Abuja,Normal,Female,Electronic accessories,15271.2,8,6108.48,128278.08000000002,1/30/2019,13:58,Epay,122169.6,4.7619047619999995,6108.48,5.7
+514-37-2845,B,Abuja,Normal,Male,Fashion accessories,35730.0,2,3573.0000000000005,75033.0,3/20/2019,13:02,Cash,71460.0,4.7619047619999995,3573.0000000000005,9.0
+241-11-2261,B,Abuja,Normal,Female,Fashion accessories,28749.6,7,10062.36,211309.56,1/10/2019,10:33,Card,201247.19999999998,4.7619047619999995,10062.36,5.5
+834-83-1826,B,Abuja,Member,Female,Home and lifestyle,29534.4,5,7383.6,155055.6,2/25/2019,17:16,Card,147672.0,4.7619047619999995,7383.6,7.6
+343-61-3544,B,Abuja,Member,Male,Sports and travel,9601.2,10,4800.6,100812.6,1/29/2019,11:48,Cash,96012.0,4.7619047619999995,4800.6,8.6
+355-34-6244,B,Abuja,Normal,Male,Food and beverages,26060.4,2,2606.04,54726.840000000004,1/13/2019,19:55,Card,52120.8,4.7619047619999995,2606.04,8.1
+339-96-8318,B,Abuja,Member,Male,Fashion accessories,29271.600000000002,7,10245.06,215146.26,3/1/2019,19:49,Epay,204901.19999999998,4.7619047619999995,10245.06,6.3
+458-61-0011,B,Abuja,Normal,Male,Food and beverages,21708.0,4,4341.6,91173.59999999999,2/20/2019,18:43,Cash,86832.0,4.7619047619999995,4341.6,5.8
+207-73-1363,B,Abuja,Normal,Male,Health and beauty,25023.600000000002,2,2502.36,52549.56,3/1/2019,12:15,Epay,50047.200000000004,4.7619047619999995,2502.36,8.1
+359-90-3665,B,Abuja,Member,Female,Fashion accessories,6508.799999999999,4,1301.76,27336.960000000003,1/14/2019,18:03,Card,26035.199999999997,4.7619047619999995,1301.76,9.5
+375-72-3056,B,Abuja,Normal,Male,Sports and travel,22701.600000000002,3,3405.24,71510.04000000001,1/19/2019,15:58,Epay,68104.8,4.7619047619999995,3405.24,7.0
+585-11-6748,B,Abuja,Member,Male,Sports and travel,34848.0,3,5227.2,109771.20000000001,3/15/2019,13:05,Cash,104543.99999999999,4.7619047619999995,5227.2,5.3
+470-31-3286,B,Abuja,Normal,Male,Health and beauty,5335.2,3,800.2800000000001,16805.88,3/1/2019,11:30,Card,16005.6,4.7619047619999995,800.2800000000001,8.7
+642-30-6693,B,Abuja,Normal,Female,Sports and travel,19623.6,6,5887.079999999999,123628.68000000001,3/17/2019,13:54,Epay,117741.6,4.7619047619999995,5887.079999999999,7.8
+830-58-2383,B,Abuja,Normal,Male,Home and lifestyle,11430.0,4,2286.0,48006.0,2/8/2019,15:26,Cash,45720.0,4.7619047619999995,2286.0,8.6
+349-97-8902,B,Abuja,Member,Male,Food and beverages,20840.4,2,2084.04,43764.840000000004,1/17/2019,10:37,Epay,41680.8,4.7619047619999995,2084.04,8.9
+789-23-8625,B,Abuja,Member,Male,Fashion accessories,33559.2,3,5033.879999999999,105711.48,1/24/2019,11:45,Cash,100677.6,4.7619047619999995,5033.879999999999,7.2
+327-40-9673,B,Abuja,Member,Male,Sports and travel,26135.999999999996,6,7840.8,164656.8,1/13/2019,19:51,Cash,156816.0,4.7619047619999995,7840.8,6.9
+670-79-6321,B,Abuja,Member,Female,Home and lifestyle,34052.4,7,11918.339999999998,250285.13999999998,1/17/2019,15:27,Card,238366.8,4.7619047619999995,11918.339999999998,4.9
+852-62-7105,B,Abuja,Normal,Female,Fashion accessories,29970.0,10,14985.0,314685.0,1/12/2019,11:25,Card,299700.0,4.7619047619999995,14985.0,4.4
+598-06-7312,B,Abuja,Member,Male,Fashion accessories,32886.0,1,1644.3,34530.3,2/16/2019,15:42,Cash,32886.0,4.7619047619999995,1644.3,6.8
+135-13-8269,B,Abuja,Member,Female,Food and beverages,28396.8,2,2839.68,59633.28,1/26/2019,16:04,Cash,56793.6,4.7619047619999995,2839.68,9.1
+628-90-8624,B,Abuja,Member,Male,Health and beauty,29728.8,10,14864.4,312152.4,3/14/2019,14:41,Cash,297288.0,4.7619047619999995,14864.4,5.0
+420-04-7590,B,Abuja,Normal,Male,Home and lifestyle,35892.0,3,5383.8,113059.8,3/18/2019,11:29,Epay,107676.00000000001,4.7619047619999995,5383.8,4.7
+182-88-2763,B,Abuja,Member,Male,Food and beverages,28767.6,3,4315.139999999999,90617.94,3/20/2019,19:28,Card,86302.8,4.7619047619999995,4315.139999999999,5.0
+188-55-0967,B,Abuja,Member,Male,Health and beauty,23929.2,10,11964.6,251256.59999999998,1/15/2019,15:01,Card,239292.00000000003,4.7619047619999995,11964.6,5.0
+364-33-8584,B,Abuja,Member,Female,Food and beverages,6346.799999999999,5,1586.6999999999998,33320.700000000004,3/8/2019,15:27,Cash,31734.000000000004,4.7619047619999995,1586.6999999999998,8.5
+665-63-9737,B,Abuja,Normal,Male,Fashion accessories,18871.2,3,2830.68,59444.28,2/27/2019,17:36,Epay,56613.6,4.7619047619999995,2830.68,7.5
+695-09-5146,B,Abuja,Member,Female,Food and beverages,35564.4,3,5334.66,112027.85999999999,2/23/2019,20:00,Epay,106693.2,4.7619047619999995,5334.66,6.4
+794-32-2436,B,Abuja,Member,Male,Electronic accessories,20041.2,2,2004.1200000000001,42086.520000000004,3/27/2019,15:08,Epay,40082.4,4.7619047619999995,2004.1200000000001,6.0
+574-31-8277,B,Abuja,Member,Male,Fashion accessories,12106.800000000001,1,605.34,12712.140000000001,3/20/2019,19:55,Cash,12106.800000000001,4.7619047619999995,605.34,5.6
+369-82-2676,B,Abuja,Normal,Male,Electronic accessories,27237.6,5,6809.4,142997.4,1/15/2019,18:22,Epay,136188.0,4.7619047619999995,6809.4,7.8
+563-47-4072,B,Abuja,Normal,Female,Health and beauty,20091.600000000002,6,6027.48,126577.08,1/22/2019,11:52,Cash,120549.6,4.7619047619999995,6027.48,9.9
+149-15-7606,B,Abuja,Member,Male,Sports and travel,13435.2,9,6045.84,126962.64000000001,3/6/2019,15:31,Epay,120916.8,4.7619047619999995,6045.84,5.1
+133-77-3154,B,Abuja,Member,Male,Fashion accessories,21664.8,4,4332.96,90992.15999999999,2/16/2019,18:04,Card,86659.2,4.7619047619999995,4332.96,9.4
+322-02-2271,B,Abuja,Normal,Female,Sports and travel,15469.199999999999,3,2320.38,48727.98,2/3/2019,11:46,Cash,46407.6,4.7619047619999995,2320.38,9.3
+725-67-2480,B,Abuja,Member,Female,Fashion accessories,21150.0,6,6345.0,133245.0,3/24/2019,18:14,Card,126900.0,4.7619047619999995,6345.0,5.9
+779-42-2410,B,Abuja,Member,Male,Food and beverages,20786.4,3,3117.96,65477.15999999999,2/20/2019,13:06,Epay,62359.2,4.7619047619999995,3117.96,7.7
+190-14-3147,B,Abuja,Normal,Female,Health and beauty,6469.2,4,1293.84,27170.639999999996,2/23/2019,20:43,Epay,25876.8,4.7619047619999995,1293.84,6.4
+679-22-6530,B,Abuja,Normal,Female,Sports and travel,14623.199999999999,2,1462.3200000000002,30708.72,1/17/2019,10:01,Card,29246.399999999998,4.7619047619999995,1462.3200000000002,4.1
+642-61-4706,B,Abuja,Member,Male,Food and beverages,33624.0,2,3362.4,70610.4,3/30/2019,16:34,Cash,67248.0,4.7619047619999995,3362.4,5.5
+576-31-4774,B,Abuja,Normal,Female,Health and beauty,26427.6,3,3964.14,83246.94,3/2/2019,13:10,Epay,79282.8,4.7619047619999995,3964.14,4.0
+242-11-3142,B,Abuja,Member,Male,Fashion accessories,30157.199999999997,2,3015.7200000000003,63330.12,2/24/2019,19:57,Cash,60314.399999999994,4.7619047619999995,3015.7200000000003,4.6
+752-23-3760,B,Abuja,Member,Female,Sports and travel,23068.8,7,8074.08,169555.68000000002,2/19/2019,19:29,Card,161481.6,4.7619047619999995,8074.08,7.3
+528-87-5606,B,Abuja,Member,Female,Electronic accessories,14212.8,1,710.64,14923.439999999997,2/12/2019,19:43,Cash,14212.8,4.7619047619999995,710.64,6.5
+320-85-2052,B,Abuja,Normal,Female,Sports and travel,12531.6,1,626.5799999999999,13158.18,1/14/2019,10:11,Card,12531.6,4.7619047619999995,626.5799999999999,7.0
+510-79-0415,B,Abuja,Member,Female,Sports and travel,8308.8,6,2492.64,52345.44,1/24/2019,19:20,Epay,49852.799999999996,4.7619047619999995,2492.64,4.9
+241-96-5076,B,Abuja,Member,Female,Home and lifestyle,17676.0,2,1767.6000000000001,37119.6,1/8/2019,12:58,Card,35352.0,4.7619047619999995,1767.6000000000001,6.4
+767-97-4650,B,Abuja,Member,Female,Sports and travel,23338.8,2,2333.8799999999997,49011.48,1/8/2019,11:59,Card,46677.6,4.7619047619999995,2333.8799999999997,8.0
+826-58-8051,B,Abuja,Normal,Male,Home and lifestyle,22388.399999999998,4,4477.679999999999,94031.28,1/6/2019,19:46,Epay,89553.59999999999,4.7619047619999995,4477.679999999999,4.3
+176-64-7711,B,Abuja,Normal,Male,Food and beverages,11635.2,3,1745.28,36650.88,3/27/2019,19:11,Card,34905.6,4.7619047619999995,1745.28,4.3
+191-29-0321,B,Abuja,Member,Female,Fashion accessories,7117.2,10,3558.6,74730.6,2/27/2019,18:57,Card,71172.0,4.7619047619999995,3558.6,5.0
+729-06-2010,B,Abuja,Member,Male,Health and beauty,28969.2,9,13036.14,273758.94,1/6/2019,11:18,Cash,260722.80000000002,4.7619047619999995,13036.14,9.2
+640-48-5028,B,Abuja,Member,Female,Home and lifestyle,31820.4,9,14319.18,300702.77999999997,3/2/2019,12:40,Cash,286383.6,4.7619047619999995,14319.18,6.3
+186-79-9562,B,Abuja,Normal,Male,Health and beauty,25837.199999999997,7,9043.019999999999,189903.42,3/29/2019,14:06,Cash,180860.4,4.7619047619999995,9043.019999999999,8.9
+834-45-5519,B,Abuja,Normal,Female,Electronic accessories,15480.0,4,3096.0,65016.0,1/31/2019,20:48,Epay,61920.0,4.7619047619999995,3096.0,7.6
+821-14-9046,B,Abuja,Member,Female,Fashion accessories,6292.8,6,1887.84,39644.64,1/18/2019,15:04,Card,37756.799999999996,4.7619047619999995,1887.84,6.1
+418-05-0656,B,Abuja,Normal,Female,Fashion accessories,9201.6,7,3220.56,67631.76,2/2/2019,20:42,Cash,64411.2,4.7619047619999995,3220.56,7.1
+776-68-1096,B,Abuja,Normal,Male,Home and lifestyle,15883.199999999999,3,2382.48,50032.079999999994,3/18/2019,13:45,Card,47649.600000000006,4.7619047619999995,2382.48,7.9
+434-35-9162,B,Abuja,Member,Male,Food and beverages,8402.4,4,1680.48,35290.079999999994,2/4/2019,18:53,Epay,33609.6,4.7619047619999995,1680.48,7.4
+174-64-0215,B,Abuja,Normal,Male,Sports and travel,25106.399999999998,10,12553.199999999999,263617.2,3/5/2019,17:49,Card,251064.0,4.7619047619999995,12553.199999999999,8.9
+299-29-0180,B,Abuja,Member,Female,Home and lifestyle,18784.8,7,6574.679999999999,138068.28,3/9/2019,10:54,Cash,131493.6,4.7619047619999995,6574.679999999999,9.3
+438-23-1242,B,Abuja,Normal,Male,Electronic accessories,27316.8,7,9560.880000000001,200778.48000000004,1/24/2019,10:38,Epay,191217.59999999998,4.7619047619999995,9560.880000000001,8.9
+238-45-6950,B,Abuja,Member,Male,Food and beverages,19339.2,1,966.9600000000002,20306.160000000003,3/1/2019,20:03,Epay,19339.2,4.7619047619999995,966.9600000000002,6.4
+197-77-7132,B,Abuja,Member,Male,Electronic accessories,32961.6,8,13184.640000000001,276877.44,1/12/2019,18:22,Epay,263692.8,4.7619047619999995,13184.640000000001,6.0
+457-13-1708,B,Abuja,Member,Male,Fashion accessories,23482.800000000003,10,11741.400000000001,246569.4,1/8/2019,19:07,Card,234827.99999999997,4.7619047619999995,11741.400000000001,5.2
+487-79-6868,B,Abuja,Member,Female,Home and lifestyle,4424.4,9,1990.98,41810.58,3/26/2019,19:28,Card,39819.6,4.7619047619999995,1990.98,8.0
+210-30-7976,B,Abuja,Member,Female,Fashion accessories,8035.2,4,1607.0399999999997,33747.84,3/14/2019,11:16,Epay,32140.8,4.7619047619999995,1607.0399999999997,4.1
+775-72-1988,B,Abuja,Normal,Male,Home and lifestyle,26380.8,5,6595.2,138499.2,1/24/2019,15:05,Epay,131904.0,4.7619047619999995,6595.2,8.4
+785-96-0615,B,Abuja,Member,Female,Electronic accessories,12866.400000000001,8,5146.5599999999995,108077.76000000001,2/17/2019,15:28,Epay,102931.20000000001,4.7619047619999995,5146.5599999999995,4.9
+842-40-8179,B,Abuja,Member,Female,Food and beverages,27792.0,10,13896.0,291816.0,2/11/2019,10:38,Card,277920.0,4.7619047619999995,13896.0,5.6
+525-09-8450,B,Abuja,Normal,Male,Electronic accessories,25966.8,10,12983.4,272651.4,1/31/2019,15:12,Card,259667.99999999997,4.7619047619999995,12983.4,4.2
+593-14-4239,B,Abuja,Normal,Female,Home and lifestyle,34365.6,8,13746.24,288671.04,3/5/2019,19:40,Epay,274924.8,4.7619047619999995,13746.24,4.7
+388-76-2555,B,Abuja,Normal,Male,Sports and travel,4928.4,6,1478.52,31048.920000000006,2/13/2019,13:59,Cash,29570.4,4.7619047619999995,1478.52,6.3
+711-31-1234,B,Abuja,Normal,Female,Electronic accessories,34430.4,4,6886.08,144607.68000000002,3/16/2019,18:51,Cash,137721.6,4.7619047619999995,6886.08,7.9
+707-32-7409,B,Abuja,Member,Female,Sports and travel,34394.4,4,6878.88,144456.48,2/26/2019,11:58,Epay,137577.6,4.7619047619999995,6878.88,4.5
+120-54-2248,B,Abuja,Normal,Female,Food and beverages,10389.6,5,2597.4,54545.399999999994,1/22/2019,18:08,Card,51948.00000000001,4.7619047619999995,2597.4,8.0
+875-31-8302,B,Abuja,Normal,Male,Sports and travel,33616.799999999996,1,1680.84,35297.64,1/3/2019,13:07,Cash,33616.799999999996,4.7619047619999995,1680.84,9.6
+457-94-0464,B,Abuja,Member,Male,Electronic accessories,31633.2,9,14234.94,298933.74,1/31/2019,20:32,Epay,284698.8,4.7619047619999995,14234.94,5.6
+744-16-7898,B,Abuja,Normal,Female,Home and lifestyle,35053.200000000004,10,17526.600000000002,368058.6,1/15/2019,13:48,Card,350532.0,4.7619047619999995,17526.600000000002,4.9
+605-83-1050,B,Abuja,Normal,Male,Fashion accessories,9784.8,2,978.48,20548.08,3/15/2019,16:26,Epay,19569.6,4.7619047619999995,978.48,4.3
+359-94-5395,B,Abuja,Normal,Male,Health and beauty,33400.8,1,1670.0399999999997,35070.84,3/15/2019,10:50,Card,33400.8,4.7619047619999995,1670.0399999999997,9.8
+751-15-6198,B,Abuja,Normal,Male,Sports and travel,8283.6,6,2485.08,52186.68,1/12/2019,16:45,Epay,49701.6,4.7619047619999995,2485.08,7.9
+831-81-6575,B,Abuja,Member,Female,Electronic accessories,27212.4,9,12245.580000000002,257157.18000000002,2/23/2019,11:12,Cash,244911.59999999998,4.7619047619999995,12245.580000000002,8.0
+559-61-5987,B,Abuja,Normal,Female,Health and beauty,6390.0,1,319.5,6709.5,1/14/2019,10:38,Cash,6390.0,4.7619047619999995,319.5,8.6
+565-91-4567,B,Abuja,Normal,Male,Health and beauty,3870.0,8,1548.0,32508.0,3/15/2019,14:38,Epay,30960.0,4.7619047619999995,1548.0,6.2
+499-27-7781,B,Abuja,Normal,Female,Food and beverages,19155.6,8,7662.240000000001,160907.04,3/14/2019,16:45,Epay,153244.8,4.7619047619999995,7662.240000000001,5.0
+869-11-3082,B,Abuja,Member,Male,Health and beauty,34617.6,4,6923.5199999999995,145393.91999999998,1/27/2019,20:03,Card,138470.4,4.7619047619999995,6923.5199999999995,8.4
+190-59-3964,B,Abuja,Member,Male,Food and beverages,16977.6,5,4244.4,89132.4,2/3/2019,14:35,Card,84888.0,4.7619047619999995,4244.4,6.0
+366-43-6862,B,Abuja,Normal,Male,Electronic accessories,19040.4,4,3808.08,79969.68000000001,3/25/2019,16:32,Epay,76161.6,4.7619047619999995,3808.08,6.7
+109-86-4363,B,Abuja,Member,Female,Sports and travel,21628.8,7,7570.080000000001,158971.68000000002,2/14/2019,11:36,Card,151401.6,4.7619047619999995,7570.080000000001,4.5
+222-42-0244,B,Abuja,Member,Female,Health and beauty,25959.6,9,11681.82,245318.21999999997,1/28/2019,13:53,Card,233636.4,4.7619047619999995,11681.82,7.7
+636-17-0325,B,Abuja,Normal,Male,Health and beauty,22525.2,4,4505.04,94605.84000000001,2/25/2019,18:37,Cash,90100.8,4.7619047619999995,4505.04,9.5
+343-75-9322,B,Abuja,Member,Female,Sports and travel,4266.0,8,1706.4,35834.4,1/9/2019,16:34,Cash,34128.0,4.7619047619999995,1706.4,4.1
+427-45-9297,B,Abuja,Member,Female,Home and lifestyle,14662.8,7,5131.98,107771.58,3/12/2019,11:01,Epay,102639.6,4.7619047619999995,5131.98,5.4
+670-71-7306,B,Abuja,Normal,Male,Sports and travel,16066.800000000001,6,4820.04,101220.84000000001,1/2/2019,20:08,Card,96400.79999999999,4.7619047619999995,4820.04,5.1
+291-59-1384,B,Abuja,Normal,Male,Electronic accessories,21708.0,1,1085.4,22793.399999999998,2/28/2019,17:38,Cash,21708.0,4.7619047619999995,1085.4,6.0
+866-70-2814,B,Abuja,Normal,Female,Electronic accessories,19004.4,10,9502.2,199546.19999999998,2/25/2019,11:58,Epay,190044.0,4.7619047619999995,9502.2,10.0
+895-03-6665,B,Abuja,Normal,Female,Fashion accessories,13143.599999999999,9,5914.62,124207.02,2/16/2019,10:52,Cash,118292.4,4.7619047619999995,5914.62,4.2
+770-42-8960,B,Abuja,Normal,Male,Food and beverages,7603.200000000001,8,3041.28,63866.88,1/1/2019,19:31,Cash,60825.600000000006,4.7619047619999995,3041.28,6.3
+234-36-2483,B,Abuja,Normal,Male,Health and beauty,20732.4,6,6219.72,130614.11999999998,2/15/2019,13:51,Cash,124394.40000000001,4.7619047619999995,6219.72,5.1
+152-03-4217,B,Abuja,Normal,Female,Home and lifestyle,4060.7999999999997,9,1827.3600000000001,38374.56,3/17/2019,11:55,Card,36547.2,4.7619047619999995,1827.3600000000001,4.3
+533-66-5566,B,Abuja,Normal,Female,Home and lifestyle,18385.2,7,6434.820000000001,135131.22,1/12/2019,11:42,Cash,128696.40000000001,4.7619047619999995,6434.820000000001,7.0
+361-59-0574,B,Abuja,Member,Male,Sports and travel,32590.8,8,13036.319999999998,273762.72,3/15/2019,14:48,Card,260726.4,4.7619047619999995,13036.319999999998,6.5
+544-55-9589,B,Abuja,Member,Female,Electronic accessories,7714.8,10,3857.4,81005.4,1/28/2019,11:51,Cash,77148.0,4.7619047619999995,3857.4,6.2
+608-05-3804,B,Abuja,Member,Male,Electronic accessories,14310.0,1,715.5,15025.499999999998,2/25/2019,20:19,Cash,14310.0,4.7619047619999995,715.5,6.1
+761-49-0439,B,Abuja,Member,Female,Electronic accessories,4356.0,8,1742.3999999999999,36590.4,1/19/2019,10:17,Epay,34848.0,4.7619047619999995,1742.3999999999999,8.6
+490-95-0021,B,Abuja,Member,Female,Food and beverages,11955.6,10,5977.8,125533.79999999999,1/8/2019,14:25,Epay,119556.00000000001,4.7619047619999995,5977.8,6.0
+311-13-6971,B,Abuja,Member,Male,Sports and travel,11516.4,10,5758.2,120922.2,2/20/2019,15:18,Card,115163.99999999999,4.7619047619999995,5758.2,9.9
+114-35-5271,B,Abuja,Normal,Female,Electronic accessories,20847.6,8,8339.04,175119.84,2/7/2019,15:06,Cash,166780.8,4.7619047619999995,8339.04,8.1
+715-20-1673,B,Abuja,Normal,Male,Electronic accessories,10216.8,5,2554.2,53638.200000000004,3/6/2019,20:57,Cash,51084.0,4.7619047619999995,2554.2,9.4
+811-35-1094,B,Abuja,Member,Male,Electronic accessories,18162.0,6,5448.6,114420.59999999999,2/6/2019,15:16,Card,108972.0,4.7619047619999995,5448.6,8.9
+699-88-1972,B,Abuja,Normal,Male,Health and beauty,35697.6,8,14279.04,299859.83999999997,1/28/2019,17:47,Card,285580.8,4.7619047619999995,14279.04,4.2
+509-10-0516,B,Abuja,Normal,Male,Home and lifestyle,16549.2,4,3309.8399999999997,69506.64,2/9/2019,12:02,Epay,66196.8,4.7619047619999995,3309.8399999999997,5.1
+851-98-3555,B,Abuja,Normal,Female,Health and beauty,29836.8,5,7459.2,156643.2,3/24/2019,14:08,Card,149184.0,4.7619047619999995,7459.2,6.6
+624-01-8356,B,Abuja,Normal,Female,Home and lifestyle,17643.6,10,8821.8,185257.80000000002,1/27/2019,10:44,Card,176436.0,4.7619047619999995,8821.8,4.2
+313-66-9943,B,Abuja,Member,Female,Food and beverages,10494.0,3,1574.1,33056.1,3/27/2019,20:29,Card,31482.0,4.7619047619999995,1574.1,7.3
+777-67-2495,B,Abuja,Normal,Male,Home and lifestyle,24829.2,3,3724.3799999999997,78211.98000000001,2/22/2019,11:26,Epay,74487.6,4.7619047619999995,3724.3799999999997,8.7
+636-98-3364,B,Abuja,Member,Female,Electronic accessories,9453.6,3,1418.04,29778.839999999997,3/2/2019,12:36,Epay,28360.8,4.7619047619999995,1418.04,6.3
+181-82-6255,B,Abuja,Normal,Female,Home and lifestyle,5893.200000000001,6,1767.9600000000003,37127.16,2/8/2019,10:58,Cash,35359.2,4.7619047619999995,1767.9600000000003,7.0
+226-34-0034,B,Abuja,Normal,Female,Electronic accessories,4960.8,4,992.1600000000001,20835.36,1/10/2019,11:10,Epay,19843.2,4.7619047619999995,992.1600000000001,9.0
+321-49-7382,B,Abuja,Member,Male,Sports and travel,31791.600000000002,1,1589.58,33381.18,2/15/2019,17:38,Card,31791.600000000002,4.7619047619999995,1589.58,5.2
+431-66-2305,B,Abuja,Normal,Female,Electronic accessories,31770.0,9,14296.5,300226.5,2/15/2019,20:51,Card,285930.0,4.7619047619999995,14296.5,7.6
+825-94-5922,B,Abuja,Normal,Male,Sports and travel,9111.6,2,911.1600000000001,19134.36,3/2/2019,19:26,Epay,18223.2,4.7619047619999995,911.1600000000001,7.2
+641-62-7288,B,Abuja,Normal,Male,Home and lifestyle,35971.2,6,10791.36,226618.56,3/24/2019,13:33,Epay,215827.19999999998,4.7619047619999995,10791.36,7.1
+501-61-1753,B,Abuja,Normal,Female,Home and lifestyle,22734.0,6,6820.2,143224.2,1/3/2019,20:24,Epay,136404.0,4.7619047619999995,6820.2,9.8
+676-10-2200,B,Abuja,Member,Male,Fashion accessories,19360.8,1,968.0399999999998,20328.839999999997,2/3/2019,20:13,Epay,19360.8,4.7619047619999995,968.0399999999998,4.7
+365-16-4334,B,Abuja,Normal,Female,Food and beverages,9514.8,8,3805.9200000000005,79924.32,2/24/2019,14:26,Epay,76118.4,4.7619047619999995,3805.9200000000005,8.9
+503-21-4385,B,Abuja,Member,Male,Health and beauty,14367.599999999999,3,2155.1400000000003,45257.94,2/21/2019,12:40,Epay,43102.8,4.7619047619999995,2155.1400000000003,9.3
+305-89-2768,B,Abuja,Member,Female,Home and lifestyle,7883.999999999999,3,1182.6000000000001,24834.6,1/9/2019,18:43,Epay,23652.0,4.7619047619999995,1182.6000000000001,4.7
+574-80-1489,B,Abuja,Member,Female,Food and beverages,22626.0,4,4525.2,95029.20000000001,2/25/2019,13:22,Epay,90504.0,4.7619047619999995,4525.2,8.7
+200-40-6154,B,Abuja,Member,Male,Home and lifestyle,23727.6,6,7118.28,149483.88,2/9/2019,11:45,Cash,142365.6,4.7619047619999995,7118.28,5.7
+430-02-3888,B,Abuja,Normal,Male,Electronic accessories,16567.2,6,4970.16,104373.36,2/7/2019,15:55,Cash,99403.2,4.7619047619999995,4970.16,7.1
+794-42-3736,B,Abuja,Normal,Male,Food and beverages,11998.8,2,1199.88,25197.48,1/26/2019,14:41,Card,23997.6,4.7619047619999995,1199.88,6.4
+172-42-8274,B,Abuja,Normal,Female,Electronic accessories,13777.2,2,1377.72,28932.120000000003,3/2/2019,18:18,Card,27554.4,4.7619047619999995,1377.72,5.8
+214-30-2776,B,Abuja,Member,Female,Electronic accessories,12416.400000000001,5,3104.1000000000004,65186.1,3/11/2019,19:44,Card,62081.99999999999,4.7619047619999995,3104.1000000000004,9.0
+746-04-1077,B,Abuja,Member,Female,Food and beverages,30466.8,10,15233.4,319901.4,1/1/2019,11:36,Card,304668.0,4.7619047619999995,15233.4,9.0
+448-34-8700,B,Abuja,Member,Male,Home and lifestyle,13287.599999999999,7,4650.66,97663.86,2/10/2019,13:51,Epay,93013.2,4.7619047619999995,4650.66,6.7
+452-04-8808,B,Abuja,Normal,Male,Electronic accessories,31348.8,7,10972.08,230413.68,1/26/2019,15:17,Cash,219441.59999999998,4.7619047619999995,10972.08,5.5
+883-69-1285,B,Abuja,Member,Male,Fashion accessories,17971.2,2,1797.12,37739.520000000004,3/6/2019,11:55,Card,35942.4,4.7619047619999995,1797.12,7.0
+518-71-6847,B,Abuja,Member,Male,Food and beverages,9576.0,6,2872.8,60328.8,2/26/2019,15:10,Epay,57456.0,4.7619047619999995,2872.8,4.9
+156-20-0370,B,Abuja,Normal,Female,Electronic accessories,9162.0,1,458.09999999999997,9620.1,3/10/2019,18:10,Card,9162.0,4.7619047619999995,458.09999999999997,5.1
+151-33-7434,B,Abuja,Normal,Female,Food and beverages,24397.199999999997,1,1219.8600000000001,25617.06,2/4/2019,20:43,Card,24397.199999999997,4.7619047619999995,1219.8600000000001,6.5
+374-38-5555,B,Abuja,Normal,Female,Fashion accessories,22935.6,5,5733.9,120411.90000000001,2/7/2019,19:30,Epay,114678.0,4.7619047619999995,5733.9,8.5
+764-44-8999,B,Abuja,Normal,Female,Health and beauty,5313.6,2,531.36,11158.56,2/18/2019,14:42,Epay,10627.2,4.7619047619999995,531.36,4.3
+552-44-5977,B,Abuja,Member,Male,Health and beauty,22320.0,8,8928.0,187487.99999999997,1/3/2019,19:08,Card,178560.0,4.7619047619999995,8928.0,6.2
+430-53-4718,B,Abuja,Member,Male,Health and beauty,27133.2,8,10853.28,227918.88000000003,1/28/2019,15:46,Card,217065.6,4.7619047619999995,10853.28,8.4
+602-16-6955,B,Abuja,Normal,Female,Sports and travel,27575.999999999996,10,13787.999999999998,289548.0,1/24/2019,18:10,Epay,275760.0,4.7619047619999995,13787.999999999998,6.0
+690-01-6631,B,Abuja,Normal,Male,Fashion accessories,6296.4,10,3148.2,66112.2,2/22/2019,18:35,Epay,62964.0,4.7619047619999995,3148.2,6.6
+303-96-2227,B,Abuja,Normal,Female,Home and lifestyle,35056.799999999996,10,17528.399999999998,368096.4,3/2/2019,17:16,Epay,350568.0,4.7619047619999995,17528.399999999998,4.4
diff --git a/My_Ustacky_Project/An update of COVID-19 outbreak in Nigeria_160223_8.pdf b/My_Ustacky_Project/An update of COVID-19 outbreak in Nigeria_160223_8.pdf
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+    <img class=3D"avatar mr-2 flex-shrink-0 js-jump-to-suggestion-avatar d-=
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+uto overflow-hidden text-left no-wrap css-truncate css-truncate-target">
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+    <div class=3D"border rounded-2 flex-shrink-0 color-bg-subtle px-1 color=
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+      <span class=3D"js-jump-to-badge-search-text-default d-none" aria-labe=
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+        In this repository
+      </span>
+      <span class=3D"js-jump-to-badge-search-text-global d-none" aria-label=
+=3D"in all of GitHub">
+        All GitHub
+      </span>
+      <span aria-hidden=3D"true" class=3D"d-inline-block ml-1 v-align-middl=
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+
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+  <path d=3D"M15 8a7.002 7.002 0 00-7-7" stroke=3D"currentColor" stroke-wid=
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+th>
+</svg>
+    </li>
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+
+          </div>
+      </label>
+</form>  </div>
+</div>
+
+        <nav id=3D"global-nav" class=3D"d-flex flex-column flex-md-row flex=
+-self-stretch flex-md-self-auto" aria-label=3D"Global">
+    <a class=3D"Header-link py-md-3 d-block d-md-none py-2 border-top borde=
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+tps://github.com/dashboard">Dashboard</a>
+
+  <a class=3D"js-selected-navigation-item Header-link mt-md-n3 mb-md-n3 py-=
+2 py-md-3 mr-0 mr-md-3 border-top border-md-top-0 border-white-fade" data-h=
+otkey=3D"g p" data-ga-click=3D"Header, click, Nav menu - item:pulls context=
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+tps://github.com/pulls">
+      Pull<span class=3D"d-inline d-md-none d-lg-inline"> request</span>s
+</a>
+  <a class=3D"js-selected-navigation-item Header-link mt-md-n3 mb-md-n3 py-=
+2 py-md-3 mr-0 mr-md-3 border-top border-md-top-0 border-white-fade" data-h=
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+d-links=3D"/issues /issues/assigned /issues/mentioned /issues" href=3D"http=
+s://github.com/issues">Issues</a>
+
+      <a class=3D"js-selected-navigation-item Header-link mt-md-n3 mb-md-n3=
+ py-2 py-md-3 mr-0 mr-md-3 border-top border-md-top-0 border-white-fade" da=
+ta-ga-click=3D"Header, click, Nav menu - item:workspaces context:user" data=
+-turbo=3D"false" data-selected-links=3D"/codespaces /codespaces" href=3D"ht=
+tps://github.com/codespaces">Codespaces</a>
+
+    <div class=3D"d-flex position-relative">
+      <a class=3D"js-selected-navigation-item Header-link flex-auto mt-md-n=
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+=3D"https://github.com/marketplace">Marketplace</a>
+    </div>
+
+  <a class=3D"js-selected-navigation-item Header-link mt-md-n3 mb-md-n3 py-=
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+https://github.com/explore">Explore</a>
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+    <a class=3D"Header-link d-block d-md-none mr-0 mr-md-3 py-2 py-md-3 bor=
+der-top border-md-top-0 border-white-fade" data-turbo=3D"false" href=3D"htt=
+ps://github.com/settings/profile">Settings</a>
+
+    <a class=3D"Header-link d-block d-md-none mr-0 mr-md-3 py-2 py-md-3 bor=
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+      <img class=3D"avatar avatar-user" loading=3D"lazy" decoding=3D"async"=
+ src=3D"https://avatars.githubusercontent.com/u/111289202?s=3D40&amp;v=3D4"=
+ width=3D"20" height=3D"20" alt=3D"@Est1y">
+      Est1y
+</a>
+    <!-- '"` --><!-- </textarea></xmp> --><form data-turbo=3D"false" action=
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+</svg>
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+</form></nav>
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+    </div>
+
+    <div class=3D"Header-item Header-item--full flex-justify-center d-md-no=
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+        <a class=3D"Header-link" href=3D"https://github.com/" data-hotkey=
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+    </div>
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+go to notifications&quot;,&quot;label&quot;:&quot;icon:read&quot;}">
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+</svg>
+  </a>
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+</notification-indicator>
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+
+
+    <div class=3D"Header-item position-relative d-none d-md-flex">
+        <details class=3D"details-overlay details-reset">
+  <summary class=3D"Header-link" aria-label=3D"Create new=E2=80=A6" data-an=
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+<a role=3D"menuitem" class=3D"dropdown-item" href=3D"https://github.com/new=
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+  <a role=3D"menuitem" class=3D"dropdown-item" href=3D"https://github.com/n=
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+
+  <a role=3D"menuitem" class=3D"dropdown-item" href=3D"https://github.com/c=
+odespaces/new">
+    New codespace
+  </a>
+
+<a role=3D"menuitem" class=3D"dropdown-item" href=3D"https://gist.github.co=
+m/" data-ga-click=3D"Header, create new gist">
+  New gist
+</a>
+
+  <a role=3D"menuitem" class=3D"dropdown-item" href=3D"https://github.com/o=
+rganizations/new" data-ga-click=3D"Header, create new organization">
+    New organization
+  </a>
+
+
+
+  <span class=3D"sentinel" tabindex=3D"0" aria-hidden=3D"true"></span></det=
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+</details>
+
+    </div>
+
+    <div class=3D"Header-item position-relative mr-0 d-none d-md-flex">
+       =20
+  <details class=3D"details-overlay details-reset js-feature-preview-indica=
+tor-container" data-feature-preview-indicator-src=3D"/users/Est1y/feature_p=
+review/indicator_check">
+
+  <summary class=3D"Header-link" aria-label=3D"View profile and more" data-=
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+ot;:&quot;show menu&quot;,&quot;label&quot;:&quot;icon:avatar&quot;}" aria-=
+haspopup=3D"menu" role=3D"button">
+    <img src=3D"https://avatars.githubusercontent.com/u/111289202?s=3D40&am=
+p;v=3D4" alt=3D"@Est1y" size=3D"20" height=3D"20" width=3D"20" data-view-co=
+mponent=3D"true" class=3D"avatar avatar-small circle">
+      <span class=3D"unread-indicator js-feature-preview-indicator" style=
+=3D"top: 1px;"></span>
+    <span class=3D"dropdown-caret"></span>
+  </summary>
+  <details-menu class=3D"dropdown-menu dropdown-menu-sw" style=3D"width: 18=
+0px" preload=3D"" role=3D"menu" data-focus-trap=3D"suspended"><span class=
+=3D"sentinel" tabindex=3D"0" aria-hidden=3D"true"></span>
+      <include-fragment src=3D"/users/111289202/menu" loading=3D"lazy">
+        <p class=3D"text-center mt-3" data-hide-on-error=3D"">
+          <svg style=3D"box-sizing: content-box; color: var(--color-icon-pr=
+imary);" width=3D"32" height=3D"32" viewBox=3D"0 0 16 16" fill=3D"none" dat=
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+  <path d=3D"M15 8a7.002 7.002 0 00-7-7" stroke=3D"currentColor" stroke-wid=
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+th>
+</svg>
+        </p>
+       =20
+      <template shadowmode=3D"open"><slot></slot></template></include-fragm=
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+  <span class=3D"sentinel" tabindex=3D"0" aria-hidden=3D"true"></span></det=
+ails-menu>
+</details>
+
+    </div>
+</header>
+
+           =20
+    </div>
+
+  <div id=3D"start-of-content" class=3D"show-on-focus"></div>
+
+
+
+
+
+
+
+    <div id=3D"js-flash-container" data-turbo-replace=3D"">
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+
+
+
+
+  <template class=3D"js-flash-template">
+   =20
+<div class=3D"flash flash-full   {{ className }}">
+  <div class=3D"px-2">
+    <button autofocus=3D"" class=3D"flash-close js-flash-close" type=3D"but=
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+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
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+h>
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+    </button>
+    <div aria-atomic=3D"true" role=3D"alert" class=3D"js-flash-alert">
+     =20
+      <div>{{ message }}</div>
+
+    </div>
+  </div>
+</div>
+  </template>
+</div>
+
+
+   =20
+    <notification-shelf-watcher data-base-url=3D"https://github.com/notific=
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+
+
+
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+      <details class=3D"details-reset details-overlay details-overlay-dark =
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+  <summary aria-label=3D"command palette trigger" tabindex=3D"-1" role=3D"b=
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+        command-palette-input-ready:command-palette#inputReady
+        command-palette-page-stack-updated:command-palette#updateInputScope
+        itemsUpdated:command-palette#itemsUpdated
+        keydown:command-palette#onKeydown
+        loadingStateChanged:command-palette#loadingStateChanged
+        selectedItemChanged:command-palette#selectedItemChanged
+        pageFetchError:command-palette#pageFetchError
+      " data-catalyst=3D"">
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+        <command-palette-mode data-char=3D"#" data-scope-types=3D"[&quot;&q=
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+        <command-palette-mode data-char=3D"#" data-scope-types=3D"[&quot;ow=
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+        <command-palette-mode data-char=3D"!" data-scope-types=3D"[&quot;ow=
+ner&quot;,&quot;repository&quot;]" data-placeholder=3D"Search projects" dat=
+a-catalyst=3D""></command-palette-mode>
+        <command-palette-mode data-char=3D"@" data-scope-types=3D"[&quot;&q=
+uot;]" data-placeholder=3D"Search or jump to a user, organization, or repos=
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+        <command-palette-mode data-char=3D"@" data-scope-types=3D"[&quot;ow=
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+        <command-palette-mode data-char=3D"/" data-scope-types=3D"[&quot;re=
+pository&quot;]" data-placeholder=3D"Search files" data-catalyst=3D""></com=
+mand-palette-mode>
+        <command-palette-mode data-char=3D"?" data-placeholder=3D"" data-ca=
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+        <command-palette-mode data-char=3D">" data-placeholder=3D"Run a com=
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+        <command-palette-mode data-char=3D"" data-scope-types=3D"[&quot;&qu=
+ot;]" data-placeholder=3D"Search or jump to..." data-catalyst=3D""></comman=
+d-palette-mode>
+        <command-palette-mode data-char=3D"" data-scope-types=3D"[&quot;own=
+er&quot;]" data-placeholder=3D"Search or jump to..." data-catalyst=3D""></c=
+ommand-palette-mode>
+      <command-palette-mode class=3D"js-command-palette-default-mode" data-=
+char=3D"" data-placeholder=3D"Search or jump to..." data-scope-types=3D"" d=
+ata-catalyst=3D""></command-palette-mode>
+
+      <command-palette-input placeholder=3D"Search or jump to..." data-acti=
+on=3D"
+          command-palette-input:command-palette#onInput
+          command-palette-select:command-palette#onSelect
+          command-palette-descope:command-palette#onDescope
+          command-palette-cleared:command-palette#onInputClear
+        " data-catalyst=3D"" class=3D"d-flex flex-items-center flex-nowrap =
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+        <div class=3D"js-search-icon d-flex flex-items-center mr-2" style=
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+          <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" ver=
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+        </div>
+       =20
+        <command-palette-scope data-catalyst=3D"" class=3D"d-inline-flex">
+         =20
+              <command-palette-token data-text=3D"Est1y" data-id=3D"U_kgDOB=
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+              <command-palette-token data-text=3D"Pandas-Analytics-Project"=
+ data-id=3D"R_kgDOJD8LTQ" data-type=3D"repository" data-value=3D"Pandas-Ana=
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+le text-normal">&nbsp;&nbsp;/&nbsp;&nbsp;</span></command-palette-token>
+        </command-palette-scope>
+        <div class=3D"command-palette-input-group flex-1 form-control borde=
+r-0 box-shadow-none" style=3D"z-index: 0">
+          <div class=3D"command-palette-typeahead position-absolute d-flex =
+flex-items-center Truncate">
+            <span class=3D"typeahead-segment input-mirror" data-target=3D"c=
+ommand-palette-input.mirror"></span>
+            <span class=3D"Truncate-text" data-target=3D"command-palette-in=
+put.typeaheadText"></span>
+            <span class=3D"typeahead-segment" data-target=3D"command-palett=
+e-input.typeaheadPlaceholder"></span>
+          </div>
+          <input class=3D"js-overlay-input typeahead-input d-none" disabled=
+=3D"" tabindex=3D"-1" aria-label=3D"Hidden input for typeahead">
+          <input type=3D"text" autocomplete=3D"off" autocorrect=3D"off" aut=
+ocapitalize=3D"off" spellcheck=3D"false" class=3D"js-input typeahead-input =
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+te-page-stack" role=3D"combobox" data-action=3D"
+              input:command-palette-input#onInput
+              keydown:command-palette-input#onKeydown
+            " placeholder=3D"Search or jump to...">
+        </div>
+          <div data-view-component=3D"true" class=3D"position-relative d-in=
+line-block">
+        <tool-tip id=3D"tooltip-1be6e39f-3984-4ef3-877d-b38977da0937" for=
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+n=3D"true" role=3D"tooltip">Clear Command Palette<template shadowmode=3D"op=
+en"><slot></slot></template></tool-tip>
+</div>
+      </command-palette-input>
+
+      <command-palette-page-stack data-default-scope-id=3D"R_kgDOJD8LTQ" da=
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+rent-query-text=3D"">
+         =20
+         =20
+         =20
+         =20
+         =20
+         =20
+         =20
+         =20
+         =20
+         =20
+         =20
+         =20
+         =20
+       =20
+       =20
+
+       =20
+
+       =20
+         =20
+         =20
+      </command-palette-page-stack>
+
+      <server-defined-provider data-type=3D"search-links" data-targets=3D"c=
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+ta-src=3D"" data-supports-commands=3D""></server-defined-provider>
+      <server-defined-provider data-type=3D"help" data-targets=3D"command-p=
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+         =20
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+         =20
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+         =20
+         =20
+         =20
+         =20
+         =20
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+        <server-defined-provider data-type=3D"commands" data-fetch-debounce=
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+defined-provider>
+        <server-defined-provider data-type=3D"prefetched" data-fetch-deboun=
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+verDefinedProviderElements" data-supports-commands=3D"" data-catalyst=3D"">=
+</server-defined-provider>
+        <server-defined-provider data-type=3D"remote" data-fetch-debounce=
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+t;#&quot;,&quot;#&quot;]" data-supported-scope-types=3D"[&quot;owner&quot;,=
+&quot;repository&quot;,&quot;&quot;]" data-targets=3D"command-palette.serve=
+rDefinedProviderElements" data-supports-commands=3D"" data-catalyst=3D""></=
+server-defined-provider>
+        <server-defined-provider data-type=3D"remote" data-fetch-debounce=
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+ot;@&quot;,&quot;@&quot;]" data-supported-scope-types=3D"[&quot;&quot;,&quo=
+t;owner&quot;]" data-targets=3D"command-palette.serverDefinedProviderElemen=
+ts" data-supports-commands=3D"" data-catalyst=3D""></server-defined-provide=
+r>
+        <server-defined-provider data-type=3D"remote" data-fetch-debounce=
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+modes=3D"[&quot;@&quot;,&quot;@&quot;,&quot;&quot;,&quot;&quot;]" data-supp=
+orted-scope-types=3D"[&quot;&quot;,&quot;owner&quot;]" data-targets=3D"comm=
+and-palette.serverDefinedProviderElements" data-supports-commands=3D"" data=
+-catalyst=3D""></server-defined-provider>
+        <server-defined-provider data-type=3D"prefetched" data-fetch-deboun=
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+=3D"command-palette.serverDefinedProviderElements" data-supports-commands=
+=3D"" data-catalyst=3D""></server-defined-provider>
+        <server-defined-provider data-type=3D"remote" data-fetch-debounce=
+=3D"200" data-src=3D"/command_palette/discussions" data-supported-modes=3D"=
+[&quot;#&quot;]" data-supported-scope-types=3D"[&quot;owner&quot;,&quot;rep=
+ository&quot;]" data-targets=3D"command-palette.serverDefinedProviderElemen=
+ts" data-supports-commands=3D"" data-catalyst=3D""></server-defined-provide=
+r>
+        <server-defined-provider data-type=3D"remote" data-fetch-debounce=
+=3D"200" data-src=3D"/command_palette/projects" data-supported-modes=3D"[&q=
+uot;#&quot;,&quot;!&quot;]" data-supported-scope-types=3D"[&quot;owner&quot=
+;,&quot;repository&quot;]" data-targets=3D"command-palette.serverDefinedPro=
+viderElements" data-supports-commands=3D"" data-catalyst=3D""></server-defi=
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+        <server-defined-provider data-type=3D"prefetched" data-fetch-deboun=
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+        <server-defined-provider data-type=3D"remote" data-fetch-debounce=
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+data-targets=3D"command-palette.serverDefinedProviderElements" data-support=
+s-commands=3D"" data-catalyst=3D""></server-defined-provider>
+        <server-defined-provider data-type=3D"remote" data-fetch-debounce=
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+orted-modes=3D"[&quot;@&quot;,&quot;@&quot;,&quot;&quot;,&quot;&quot;]" dat=
+a-supported-scope-types=3D"[&quot;&quot;,&quot;owner&quot;]" data-targets=
+=3D"command-palette.serverDefinedProviderElements" data-supports-commands=
+=3D"" data-catalyst=3D""></server-defined-provider>
+    <client-defined-provider data-catalyst=3D"" data-provider-id=3D"main-wi=
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+  </details-dialog>
+</details>
+
+<div class=3D"position-fixed bottom-0 left-0 ml-5 mb-5 js-command-palette-t=
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+ =20
+
+ =20
+
+ =20
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+
+ =20
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+ =20
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+  <div class=3D"application-main " data-commit-hovercards-enabled=3D"" data=
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+e" class=3D"">
+    <main id=3D"js-repo-pjax-container">
+     =20
+ =20
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+
+   =20
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+
+
+
+
+
+ =20
+  <div id=3D"repository-container-header" class=3D"pt-3 hide-full-screen" s=
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+
+      <div class=3D"d-flex flex-wrap flex-justify-end mb-3  px-3 px-md-4 px=
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+        <div class=3D"flex-auto min-width-0 width-fit mr-3">
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+    <span class=3D"mx-1 flex-self-stretch color-fg-muted">/</span>
+    <strong itemprop=3D"name" class=3D"mr-2 flex-self-stretch">
+      <a data-pjax=3D"#repo-content-pjax-container" data-turbo-frame=3D"rep=
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+  </div>
+    <span class=3D"text-small lh-condensed-ultra no-wrap mt-1" data-reposit=
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+ject</a>
+    </span>
+
+
+        </div>
+
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+
+        <summary data-hydro-click=3D"{&quot;event_type&quot;:&quot;reposito=
+ry.click&quot;,&quot;payload&quot;:{&quot;target&quot;:&quot;WATCH_BUTTON&q=
+uot;,&quot;repository_id&quot;:608111437,&quot;originating_url&quot;:&quot;=
+https://github.com/Est1y/Pandas-Analytics-Project/blob/main/README.md&quot;=
+,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"3271eabb9345fcaa=
+d7bea85a09378b1ba5bcfb0429e8353fdc3f3506fc9e31e7" data-ga-click=3D"Reposito=
+ry, click Watch settings, action:blob#show" aria-label=3D"Notification sett=
+ings" data-view-component=3D"true" class=3D"btn-sm btn" aria-haspopup=3D"me=
+nu" role=3D"button">    <span data-menu-button=3D"">
+           =20
+           =20
+            <span data-target=3D"notifications-list-subscription-form.watch=
+ButtonCopy">
+              <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16"=
+ version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon=
+ octicon-eye">
+    <path fill-rule=3D"evenodd" d=3D"M1.679 7.932c.412-.621 1.242-1.75 2.36=
+6-2.717C5.175 4.242 6.527 3.5 8 3.5c1.473 0 2.824.742 3.955 1.715 1.124.967=
+ 1.954 2.096 2.366 2.717a.119.119 0 010 .136c-.412.621-1.242 1.75-2.366 2.7=
+17C10.825 11.758 9.473 12.5 8 12.5c-1.473 0-2.824-.742-3.955-1.715C2.92 9.8=
+18 2.09 8.69 1.679 8.068a.119.119 0 010-.136zM8 2c-1.981 0-3.67.992-4.933 2=
+.078C1.797 5.169.88 6.423.43 7.1a1.619 1.619 0 000 1.798c.45.678 1.367 1.93=
+2 2.637 3.024C4.329 13.008 6.019 14 8 14c1.981 0 3.67-.992 4.933-2.078 1.27=
+-1.091 2.187-2.345 2.637-3.023a1.619 1.619 0 000-1.798c-.45-.678-1.367-1.93=
+2-2.637-3.023C11.671 2.992 9.981 2 8 2zm0 8a2 2 0 100-4 2 2 0 000 4z"></pat=
+h>
+</svg>
+              Watch
+            </span>
+          </span>
+            <span id=3D"repo-notifications-counter" data-target=3D"notifica=
+tions-list-subscription-form.socialCount" data-pjax-replace=3D"true" data-t=
+urbo-replace=3D"true" title=3D"0" data-view-component=3D"true" class=3D"Cou=
+nter">0</span>
+          <span class=3D"dropdown-caret"></span>
+</summary>
+        <details-menu class=3D"SelectMenu  " role=3D"menu" data-target=3D"n=
+otifications-list-subscription-form.menu" data-focus-trap=3D"suspended"><sp=
+an class=3D"sentinel" tabindex=3D"0" aria-hidden=3D"true"></span>
+          <div class=3D"SelectMenu-modal notifications-component-menu-modal=
+">
+            <header class=3D"SelectMenu-header">
+              <h3 class=3D"SelectMenu-title">Notifications</h3>
+              <button class=3D"SelectMenu-closeButton" type=3D"button" aria=
+-label=3D"Close menu" data-action=3D"click:notifications-list-subscription-=
+form#closeMenu">
+                <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 1=
+6" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octic=
+on octicon-x">
+    <path fill-rule=3D"evenodd" d=3D"M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.=
+22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l=
+-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></pat=
+h>
+</svg>
+              </button>
+            </header>
+
+            <div class=3D"SelectMenu-list">
+              <!-- '"` --><!-- </textarea></xmp> --><form data-target=3D"no=
+tifications-list-subscription-form.form" data-action=3D"submit:notification=
+s-list-subscription-form#submitForm" data-turbo=3D"false" action=3D"https:/=
+/github.com/notifications/subscribe" accept-charset=3D"UTF-8" method=3D"pos=
+t">
+
+               =20
+
+                <button type=3D"submit" name=3D"do" value=3D"included" clas=
+s=3D"SelectMenu-item flex-items-start" role=3D"menuitemradio" aria-checked=
+=3D"true" data-targets=3D"notifications-list-subscription-form.subscription=
+Buttons">
+                  <span class=3D"f5">
+                    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 =
+16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"o=
+cticon octicon-check SelectMenu-icon SelectMenu-icon--check">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+                  </span>
+                  <div>
+                    <div class=3D"f5 text-bold">
+                      Participating and @mentions
+                    </div>
+                    <div class=3D"text-small color-fg-muted text-normal pb-=
+1">
+                      Only receive notifications from this repository when =
+participating or @mentioned.
+                    </div>
+                  </div>
+                </button>
+
+                <button type=3D"submit" name=3D"do" value=3D"subscribed" cl=
+ass=3D"SelectMenu-item flex-items-start" role=3D"menuitemradio" aria-checke=
+d=3D"false" data-targets=3D"notifications-list-subscription-form.subscripti=
+onButtons">
+                  <span class=3D"f5">
+                    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 =
+16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"o=
+cticon octicon-check SelectMenu-icon SelectMenu-icon--check">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+                  </span>
+                  <div>
+                    <div class=3D"f5 text-bold">
+                      All Activity
+                    </div>
+                    <div class=3D"text-small color-fg-muted text-normal pb-=
+1">
+                      Notified of all notifications on this repository.
+                    </div>
+                  </div>
+                </button>
+
+                <button type=3D"submit" name=3D"do" value=3D"ignore" class=
+=3D"SelectMenu-item flex-items-start" role=3D"menuitemradio" aria-checked=
+=3D"false" data-targets=3D"notifications-list-subscription-form.subscriptio=
+nButtons">
+                  <span class=3D"f5">
+                    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 =
+16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"o=
+cticon octicon-check SelectMenu-icon SelectMenu-icon--check">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+                  </span>
+                  <div>
+                    <div class=3D"f5 text-bold">
+                      Ignore
+                    </div>
+                    <div class=3D"text-small color-fg-muted text-normal pb-=
+1">
+                      Never be notified.
+                    </div>
+                  </div>
+                </button>
+</form>
+              <button class=3D"SelectMenu-item flex-items-start pr-3" type=
+=3D"button" role=3D"menuitemradio" data-target=3D"notifications-list-subscr=
+iption-form.customButton" data-action=3D"click:notifications-list-subscript=
+ion-form#openCustomDialog" aria-haspopup=3D"true" aria-checked=3D"false">
+                <span class=3D"f5">
+                  <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16=
+ 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"oct=
+icon octicon-check SelectMenu-icon SelectMenu-icon--check">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+                </span>
+                <div>
+                  <div class=3D"d-flex flex-items-start flex-justify-betwee=
+n">
+                    <div class=3D"f5 text-bold">Custom</div>
+                    <div class=3D"f5 pr-1">
+                      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 =
+0 16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D=
+"octicon octicon-arrow-right">
+    <path fill-rule=3D"evenodd" d=3D"M8.22 2.97a.75.75 0 011.06 0l4.25 4.25=
+a.75.75 0 010 1.06l-4.25 4.25a.75.75 0 01-1.06-1.06l2.97-2.97H3.75a.75.75 0=
+ 010-1.5h7.44L8.22 4.03a.75.75 0 010-1.06z"></path>
+</svg>
+                    </div>
+                  </div>
+                  <div class=3D"text-small color-fg-muted text-normal pb-1"=
+>
+                    Select events you want to be notified of in addition to=
+ participating and @mentions.
+                  </div>
+                </div>
+              </button>
+
+                <div class=3D"px-3 py-2 d-flex color-bg-subtle flex-items-c=
+enter">
+                  <span class=3D"f5">
+                    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 =
+16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"o=
+cticon octicon-device-mobile SelectMenu-icon SelectMenu-icon--device-mobile=
+">
+    <path fill-rule=3D"evenodd" d=3D"M3.75 0A1.75 1.75 0 002 1.75v12.5c0 .9=
+66.784 1.75 1.75 1.75h8.5A1.75 1.75 0 0014 14.25V1.75A1.75 1.75 0 0012.25 0=
+h-8.5zM3.5 1.75a.25.25 0 01.25-.25h8.5a.25.25 0 01.25.25v12.5a.25.25 0 01-.=
+25.25h-8.5a.25.25 0 01-.25-.25V1.75zM8 13a1 1 0 100-2 1 1 0 000 2z"></path>
+</svg>
+                  </span>
+                  <span classname=3D"text-small color-fg-muted text-normal =
+pb-1">
+                    Get push notifications on <a target=3D"_blank" rel=3D"n=
+oopener noreferrer" href=3D"https://apps.apple.com/app/apple-store/id147737=
+6905?ct=3Dwatch-dropdown&amp;mt=3D8&amp;pt=3D524675">iOS</a> or <a target=
+=3D"_blank" rel=3D"noopener noreferrer" href=3D"https://play.google.com/sto=
+re/apps/details?id=3Dcom.github.android&amp;referrer=3Dutm_campaign%3Dwatch=
+-dropdown%26utm_medium%3Dweb%26utm_source%3Dgithub">Android</a>.
+                  </span>
+                </div>
+            </div>
+          </div>
+        <span class=3D"sentinel" tabindex=3D"0" aria-hidden=3D"true"></span=
+></details-menu>
+
+       =20
+
+
+        <div class=3D"notifications-component-dialog-overlay"></div>
+      </details>
+    </notifications-list-subscription-form>
+
+
+
+  </li>
+
+  <li>
+        <div data-view-component=3D"true" class=3D"d-flex">
+        <div data-view-component=3D"true" class=3D"position-relative d-inli=
+ne-block">
+    <button icon=3D"repo-forked" id=3D"fork-button" aria-disabled=3D"true" =
+type=3D"button" data-view-component=3D"true" class=3D"btn-sm btn BtnGroup-i=
+tem border-right-0" aria-describedby=3D"tooltip-d6afc0ab-ebc3-45b8-83c2-5d7=
+0620827ed">      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 =
+16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octi=
+con octicon-repo-forked mr-2">
+    <path fill-rule=3D"evenodd" d=3D"M5 3.25a.75.75 0 11-1.5 0 .75.75 0 011=
+.5 0zm0 2.122a2.25 2.25 0 10-1.5 0v.878A2.25 2.25 0 005.75 8.5h1.5v2.128a2.=
+251 2.251 0 101.5 0V8.5h1.5a2.25 2.25 0 002.25-2.25v-.878a2.25 2.25 0 10-1.=
+5 0v.878a.75.75 0 01-.75.75h-4.5A.75.75 0 015 6.25v-.878zm3.75 7.378a.75.75=
+ 0 11-1.5 0 .75.75 0 011.5 0zm3-8.75a.75.75 0 100-1.5.75.75 0 000 1.5z"></p=
+ath>
+</svg>Fork
+        <span id=3D"repo-network-counter" data-pjax-replace=3D"true" data-t=
+urbo-replace=3D"true" title=3D"44" data-view-component=3D"true" class=3D"Co=
+unter">44</span>
+</button>    <tool-tip id=3D"tooltip-d6afc0ab-ebc3-45b8-83c2-5d70620827ed" =
+for=3D"fork-button" data-direction=3D"s" data-type=3D"description" data-vie=
+w-component=3D"true" class=3D"position-absolute sr-only" role=3D"tooltip" s=
+tyle=3D"left: -71.9688px; top: 38px;">Cannot fork because you own this repo=
+sitory and are not a member of any organizations.<template shadowmode=3D"op=
+en"><slot></slot></template></tool-tip>
+</div>
+      <details group_item=3D"true" id=3D"my-forks-menu-608111437" data-view=
+-component=3D"true" class=3D"details-reset details-overlay BtnGroup-parent =
+d-inline-block position-relative">
+              <summary aria-label=3D"See your forks of this repository" dat=
+a-view-component=3D"true" class=3D"btn-sm btn BtnGroup-item px-2 float-none=
+" aria-haspopup=3D"menu" role=3D"button">    <svg aria-hidden=3D"true" heig=
+ht=3D"16" viewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" data-view-comp=
+onent=3D"true" class=3D"octicon octicon-triangle-down">
+    <path d=3D"M4.427 7.427l3.396 3.396a.25.25 0 00.354 0l3.396-3.396A.25.2=
+5 0 0011.396 7H4.604a.25.25 0 00-.177.427z"></path>
+</svg>
+</summary>
+  <details-menu class=3D"SelectMenu right-0" src=3D"/Est1y/Pandas-Analytics=
+-Project/my_forks_menu_content?can_fork=3Dfalse" role=3D"menu" data-focus-t=
+rap=3D"suspended"><span class=3D"sentinel" tabindex=3D"0" aria-hidden=3D"tr=
+ue"></span>
+    <div class=3D"SelectMenu-modal">
+        <button class=3D"SelectMenu-closeButton position-absolute right-0 m=
+-2" type=3D"button" aria-label=3D"Close menu" data-toggle-for=3D"details-3d=
+ba36">
+          <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" ver=
+sion=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon oct=
+icon-x">
+    <path fill-rule=3D"evenodd" d=3D"M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.=
+22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l=
+-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></pat=
+h>
+</svg>
+        </button>
+      <div id=3D"filter-menu-3dba36" class=3D"d-flex flex-column flex-1 ove=
+rflow-hidden">
+        <div class=3D"SelectMenu-list">
+
+            <include-fragment class=3D"SelectMenu-loading" aria-label=3D"Lo=
+ading">
+              <svg role=3D"menuitem" style=3D"box-sizing: content-box; colo=
+r: var(--color-icon-primary);" width=3D"32" height=3D"32" viewBox=3D"0 0 16=
+ 16" fill=3D"none" data-view-component=3D"true" class=3D"anim-rotate">
+  <circle cx=3D"8" cy=3D"8" r=3D"7" stroke=3D"currentColor" stroke-opacity=
+=3D"0.25" stroke-width=3D"2" vector-effect=3D"non-scaling-stroke"></circle>
+  <path d=3D"M15 8a7.002 7.002 0 00-7-7" stroke=3D"currentColor" stroke-wid=
+th=3D"2" stroke-linecap=3D"round" vector-effect=3D"non-scaling-stroke"></pa=
+th>
+</svg>
+            <template shadowmode=3D"open"><slot></slot></template></include=
+-fragment>
+        </div>
+       =20
+      </div>
+    </div>
+  <span class=3D"sentinel" tabindex=3D"0" aria-hidden=3D"true"></span></det=
+ails-menu>
+</details></div>
+  </li>
+
+  <li>
+        <template class=3D"js-unstar-confirmation-dialog-template">
+  <div class=3D"Box-header">
+    <h2 class=3D"Box-title">Unstar this repository?</h2>
+  </div>
+  <div class=3D"Box-body">
+    <p class=3D"mb-3">
+      This will remove {{ repoNameWithOwner }} from the {{ listsWithCount }=
+} that it's been added to.
+    </p>
+    <div class=3D"form-actions">
+      <!-- '"` --><!-- </textarea></xmp> --><form class=3D"js-social-confir=
+mation-form" data-turbo=3D"false" action=3D"https://github.com/Est1y/Pandas=
+-Analytics-Project/blob/main/%7B%7B%20confirmUrl%20%7D%7D" accept-charset=
+=3D"UTF-8" method=3D"post">
+       =20
+       =20
+          <button data-close-dialog=3D"true" type=3D"submit" data-view-comp=
+onent=3D"true" class=3D"btn-danger btn width-full">    Unstar
+</button>
+</form>    </div>
+  </div>
+</template>
+
+  <div data-view-component=3D"true" class=3D"js-toggler-container js-social=
+-container starring-container d-flex">
+    <div data-view-component=3D"true" class=3D"starred BtnGroup flex-1">
+      <!-- '"` --><!-- </textarea></xmp> --><form class=3D"js-social-form B=
+tnGroup-parent flex-auto js-deferred-toggler-target" data-turbo=3D"false" a=
+ction=3D"https://github.com/Est1y/Pandas-Analytics-Project/unstar" accept-c=
+harset=3D"UTF-8" method=3D"post">
+         =20
+       =20
+          <button data-hydro-click=3D"{&quot;event_type&quot;:&quot;reposit=
+ory.click&quot;,&quot;payload&quot;:{&quot;target&quot;:&quot;UNSTAR_BUTTON=
+&quot;,&quot;repository_id&quot;:608111437,&quot;originating_url&quot;:&quo=
+t;https://github.com/Est1y/Pandas-Analytics-Project/blob/main/README.md&quo=
+t;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"99564a3d549282=
+edc7e90453d6992773ae2efb20fd124f88673890f3917728f4" data-ga-click=3D"Reposi=
+tory, click unstar button, action:blob#show; text:Unstar" aria-label=3D"Uns=
+tar this repository (0)" type=3D"submit" data-view-component=3D"true" class=
+=3D"rounded-left-2 btn-sm btn BtnGroup-item">    <svg aria-hidden=3D"true" =
+height=3D"16" viewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" data-view-=
+component=3D"true" class=3D"octicon octicon-star-fill starred-button-icon d=
+-inline-block mr-2">
+    <path fill-rule=3D"evenodd" d=3D"M8 .25a.75.75 0 01.673.418l1.882 3.815=
+ 4.21.612a.75.75 0 01.416 1.279l-3.046 2.97.719 4.192a.75.75 0 01-1.088.791=
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+1.416-1.28l4.21-.611L7.327.668A.75.75 0 018 .25z"></path>
+</svg><span data-view-component=3D"true" class=3D"d-inline">
+            Starred
+</span>            <span id=3D"repo-stars-counter-unstar" aria-label=3D"0 u=
+sers starred this repository" data-singular-suffix=3D"user starred this rep=
+ository" data-plural-suffix=3D"users starred this repository" data-turbo-re=
+place=3D"true" title=3D"0" data-view-component=3D"true" class=3D"Counter js=
+-social-count">0</span>
+</button></form>        <details id=3D"details-user-list-608111437" data-vi=
+ew-component=3D"true" class=3D"details-reset details-overlay BtnGroup-paren=
+t js-user-list-menu d-inline-block position-relative">
+        <summary aria-label=3D"Add this repository to a list" data-view-com=
+ponent=3D"true" class=3D"btn-sm btn BtnGroup-item px-2 float-none" aria-has=
+popup=3D"menu" role=3D"button">    <svg aria-hidden=3D"true" height=3D"16" =
+viewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" data-view-component=3D"t=
+rue" class=3D"octicon octicon-triangle-down">
+    <path d=3D"M4.427 7.427l3.396 3.396a.25.25 0 00.354 0l3.396-3.396A.25.2=
+5 0 0011.396 7H4.604a.25.25 0 00-.177.427z"></path>
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+7.06l2.47 2.47a.75.75 0 001.06 0l5.25-5.25z"></path>
+</svg>
+        <span data-content=3D"Insights">Insights</span>
+          <span id=3D"insights-repo-tab-count" data-pjax-replace=3D"" data-=
+turbo-replace=3D"" title=3D"Not available" data-view-component=3D"true" cla=
+ss=3D"Counter"></span>
+
+
+   =20
+</a></li>
+      <li data-view-component=3D"true" class=3D"d-inline-flex">
+  <a id=3D"settings-tab" href=3D"https://github.com/Est1y/Pandas-Analytics-=
+Project/settings" data-tab-item=3D"i7settings-tab" data-selected-links=3D"c=
+ode_review_limits codespaces_repository_settings collaborators custom_tabs =
+hooks integration_installations interaction_limits issue_template_editor ke=
+y_links_settings notifications repo_actions_settings repo_announcements rep=
+o_branch_settings repo_keys_settings repo_pages_settings repo_rule_insights=
+ repo_rulesets repo_protected_tags_settings repo_settings repository_runtim=
+e reported_content repo_custom_properties repository_actions_settings_add_n=
+ew_runner repository_actions_settings_general repository_actions_settings_r=
+unners repository_environments role_details secrets secrets_settings_action=
+s secrets_settings_codespaces secrets_settings_dependabot security_analysis=
+ /Est1y/Pandas-Analytics-Project/settings" data-pjax=3D"#repo-content-pjax-=
+container" data-turbo-frame=3D"repo-content-turbo-frame" data-analytics-eve=
+nt=3D"{&quot;category&quot;:&quot;Underline navbar&quot;,&quot;action&quot;=
+:&quot;Click tab&quot;,&quot;label&quot;:&quot;Settings&quot;,&quot;target&=
+quot;:&quot;UNDERLINE_NAV.TAB&quot;}" data-view-component=3D"true" class=3D=
+"UnderlineNav-item no-wrap js-responsive-underlinenav-item js-selected-navi=
+gation-item">
+   =20
+                <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 1=
+6" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octic=
+on octicon-gear UnderlineNav-octicon d-none d-sm-inline">
+    <path fill-rule=3D"evenodd" d=3D"M7.429 1.525a6.593 6.593 0 011.142 0c.=
+036.003.108.036.137.146l.289 1.105c.147.56.55.967.997 1.189.174.086.341.183=
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+ 0c-.236 0-.47.01-.701.03-.743.065-1.29.615-1.458 1.261l-.29 1.106c-.017.06=
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+ 2.776c-.644-.177-1.392.02-1.82.63a7.977 7.977 0 00-.704 1.217c-.315.675-.1=
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+94-.158-.212-.224L10.16 1.29C9.99.645 9.444.095 8.701.031A8.094 8.094 0 008=
+ 0zm1.5 8a1.5 1.5 0 11-3 0 1.5 1.5 0 013 0zM11 8a3 3 0 11-6 0 3 3 0 016 0z"=
+></path>
+</svg>
+        <span data-content=3D"Settings">Settings</span>
+          <span id=3D"settings-repo-tab-count" data-pjax-replace=3D"" data-=
+turbo-replace=3D"" title=3D"Not available" data-view-component=3D"true" cla=
+ss=3D"Counter"></span>
+
+
+   =20
+</a></li>
+</ul>
+    <div style=3D"visibility:hidden;" data-view-component=3D"true" class=3D=
+"UnderlineNav-actions js-responsive-underlinenav-overflow position-absolute=
+ pr-3 pr-md-4 pr-lg-5 right-0">      <details data-view-component=3D"true" =
+class=3D"details-overlay details-reset position-relative">
+  <summary role=3D"button" data-view-component=3D"true" aria-haspopup=3D"me=
+nu">          <div class=3D"UnderlineNav-item mr-0 border-0">
+            <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" v=
+ersion=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon o=
+cticon-kebab-horizontal">
+    <path d=3D"M8 9a1.5 1.5 0 100-3 1.5 1.5 0 000 3zM1.5 9a1.5 1.5 0 100-3 =
+1.5 1.5 0 000 3zm13 0a1.5 1.5 0 100-3 1.5 1.5 0 000 3z"></path>
+</svg>
+            <span class=3D"sr-only">More</span>
+          </div>
+</summary>
+  <details-menu role=3D"menu" data-view-component=3D"true" class=3D"dropdow=
+n-menu dropdown-menu-sw" data-focus-trap=3D"suspended"><span class=3D"senti=
+nel" tabindex=3D"0" aria-hidden=3D"true"></span>          <ul>
+             =20
+             =20
+             =20
+             =20
+             =20
+             =20
+             =20
+             =20
+          </ul>
+<span class=3D"sentinel" tabindex=3D"0" aria-hidden=3D"true"></span></detai=
+ls-menu>
+</details></div>
+</nav>
+
+  </div>
+
+ =20
+
+
+
+  <turbo-frame id=3D"repo-content-turbo-frame" target=3D"_top" data-turbo-a=
+ction=3D"advance" class=3D"" src=3D"https://github.com/Est1y/Pandas-Analyti=
+cs-Project" complete=3D"">
+      <a href=3D"https://github.dev/" class=3D"d-none js-github-dev-shortcu=
+t" data-hotkey=3D".">Open in github.dev</a>
+  <a href=3D"https://github.dev/" class=3D"d-none js-github-dev-new-tab-sho=
+rtcut" data-hotkey=3D"Shift+.,Shift+>,>" target=3D"_blank">Open in a new gi=
+thub.dev tab</a>
+
+   =20
+  <h1 class=3D"sr-only">Est1y/Pandas-Analytics-Project</h1>
+  <div class=3D"clearfix  container-xl mt-4  px-3 px-md-4 px-lg-5">
+   =20
+
+<div>
+ =20
+   =20
+
+  <div class=3D"d-none d-lg-block mt-6 mr-3 Popover top-0 right-0 color-sha=
+dow-medium col-3">
+   =20
+  </div>
+
+ =20
+
+ =20
+
+
+
+  <div data-view-component=3D"true" class=3D"Layout Layout--flowRow-until-m=
+d Layout--sidebarPosition-end Layout--sidebarPosition-flowRow-end">
+  <div data-view-component=3D"true" class=3D"Layout-main">       =20
+       =20
+       =20
+              <div class=3D"js-socket-channel js-updatable-content" data-ch=
+annel=3D"eyJjIjoicmVwbzo2MDgxMTE0Mzc6cG9zdC1yZWNlaXZlOjExMTI4OTIwMiIsInQiOj=
+E2Nzc3NjExNzZ9--705115739ffa5364a6c32207cfd89c788e24cd984328610956281f2aaa3=
+040cb" data-url=3D"/Est1y/Pandas-Analytics-Project/show_partial?partial=3Dt=
+ree%2Frecently_touched_branches_list">
+    </div>
+
+        <div class=3D"file-navigation mb-3 d-flex flex-items-start">
+ =20
+<div class=3D"position-relative">
+  <details class=3D"js-branch-select-menu details-reset details-overlay mr-=
+0 mb-0 " id=3D"branch-select-menu" data-hydro-click-payload=3D"{&quot;event=
+_type&quot;:&quot;repository.click&quot;,&quot;payload&quot;:{&quot;target&=
+quot;:&quot;REFS_SELECTOR_MENU&quot;,&quot;repository_id&quot;:608111437,&q=
+uot;originating_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-P=
+roject&quot;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"7861=
+cc5c7f0ed85ca90fd39d175606cd0369167ada64deb519b3302440fb3816">
+    <summary class=3D"btn css-truncate" data-hotkey=3D"w" title=3D"Switch b=
+ranches or tags">
+      <svg text=3D"gray" aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 =
+16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"o=
+cticon octicon-git-branch">
+    <path fill-rule=3D"evenodd" d=3D"M11.75 2.5a.75.75 0 100 1.5.75.75 0 00=
+0-1.5zm-2.25.75a2.25 2.25 0 113 2.122V6A2.5 2.5 0 0110 8.5H6a1 1 0 00-1 1v1=
+.128a2.251 2.251 0 11-1.5 0V5.372a2.25 2.25 0 111.5 0v1.836A2.492 2.492 0 0=
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+5.75 0 000-1.5zM3.5 3.25a.75.75 0 111.5 0 .75.75 0 01-1.5 0z"></path>
+</svg>
+      <span class=3D"css-truncate-target" data-menu-button=3D"">main</span>
+      <span class=3D"dropdown-caret"></span>
+    </summary>
+
+   =20
+<div class=3D"SelectMenu">
+  <div class=3D"SelectMenu-modal">
+    <header class=3D"SelectMenu-header">
+      <span class=3D"SelectMenu-title">Switch branches/tags</span>
+      <button class=3D"SelectMenu-closeButton" type=3D"button" data-toggle-=
+for=3D"branch-select-menu"><svg aria-label=3D"Close menu" aria-hidden=3D"fa=
+lse" role=3D"img" height=3D"16" viewBox=3D"0 0 16 16" version=3D"1.1" width=
+=3D"16" data-view-component=3D"true" class=3D"octicon octicon-x">
+    <path fill-rule=3D"evenodd" d=3D"M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.=
+22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l=
+-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></pat=
+h>
+</svg></button>
+    </header>
+
+    <input-demux data-action=3D"tab-container-change:input-demux#storeInput=
+ tab-container-changed:input-demux#updateInput" data-catalyst=3D"">
+      <tab-container class=3D"d-flex flex-column js-branches-tags-tabs" sty=
+le=3D"min-height: 0;">
+        <div class=3D"SelectMenu-filter">
+          <input data-target=3D"input-demux.source" id=3D"context-commitish=
+-filter-field" class=3D"SelectMenu-input form-control" aria-owns=3D"ref-lis=
+t-branches" data-controls-ref-menu-id=3D"ref-list-branches" autofocus=3D"" =
+autocomplete=3D"off" aria-label=3D"Find or create a branch=E2=80=A6" placeh=
+older=3D"Find or create a branch=E2=80=A6" type=3D"text">
+        </div>
+
+        <div class=3D"SelectMenu-tabs" role=3D"tablist" data-target=3D"inpu=
+t-demux.control">
+          <button class=3D"SelectMenu-tab" type=3D"button" role=3D"tab" ari=
+a-selected=3D"true" tabindex=3D"0">Branches</button>
+          <button class=3D"SelectMenu-tab" type=3D"button" role=3D"tab" ari=
+a-selected=3D"false" tabindex=3D"-1">Tags</button>
+        </div>
+
+        <div role=3D"tabpanel" id=3D"ref-list-branches" data-filter-placeho=
+lder=3D"Find or create a branch=E2=80=A6" tabindex=3D"" class=3D"d-flex fle=
+x-column flex-auto overflow-auto">
+          <ref-selector type=3D"branch" data-targets=3D"input-demux.sinks" =
+data-action=3D"
+              input-entered:ref-selector#inputEntered
+              tab-selected:ref-selector#tabSelected
+              focus-list:ref-selector#focusFirstListMember
+            " query-endpoint=3D"/Est1y/Pandas-Analytics-Project/refs" can-c=
+reate=3D"" cache-key=3D"v0:1677667650.220712" current-committish=3D"bWFpbg=
+=3D=3D" default-branch=3D"bWFpbg=3D=3D" name-with-owner=3D"RXN0MXkvUGFuZGFz=
+LUFuYWx5dGljcy1Qcm9qZWN0" prefetch-on-mouseover=3D"" data-catalyst=3D"">
+
+            <template data-target=3D"ref-selector.fetchFailedTemplate">
+              <div class=3D"SelectMenu-message" data-index=3D"{{ index }}">=
+Could not load branches</div>
+            </template>
+
+              <template data-target=3D"ref-selector.noMatchTemplate">
+    <create-repo-from-selector check-tag-name-exists-path=3D"/Est1y/Pandas-=
+Analytics-Project/branches/check_tag_name_exists">
+      <!-- '"` --><!-- </textarea></xmp> --><form data-turbo=3D"false" acti=
+on=3D"https://github.com/Est1y/Pandas-Analytics-Project/branches" accept-ch=
+arset=3D"UTF-8" method=3D"post">
+       =20
+       =20
+       =20
+
+        <button class=3D"SelectMenu-item wb-break-word" type=3D"submit" rol=
+e=3D"menuitem" data-index=3D"{{ index }}" data-action=3D"click:create-repo-=
+from-selector#createBranch" data-hydro-click=3D"{&quot;event_type&quot;:&qu=
+ot;repository.click&quot;,&quot;payload&quot;:{&quot;target&quot;:&quot;REF=
+S_SELECTOR_MENU_CREATE_BRANCH&quot;,&quot;repository_id&quot;:608111437,&qu=
+ot;ref_name&quot;:&quot;{{ refName }}&quot;,&quot;originating_url&quot;:&qu=
+ot;https://github.com/Est1y/Pandas-Analytics-Project&quot;,&quot;user_id&qu=
+ot;:111289202}}" data-hydro-click-hmac=3D"f8b211b005305db1ef3afd4c91ae34c80=
+14bee23a230b288119daed2c998e1c7">
+          <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" ver=
+sion=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon oct=
+icon-git-branch SelectMenu-icon flex-self-baseline">
+    <path fill-rule=3D"evenodd" d=3D"M11.75 2.5a.75.75 0 100 1.5.75.75 0 00=
+0-1.5zm-2.25.75a2.25 2.25 0 113 2.122V6A2.5 2.5 0 0110 8.5H6a1 1 0 00-1 1v1=
+.128a2.251 2.251 0 11-1.5 0V5.372a2.25 2.25 0 111.5 0v1.836A2.492 2.492 0 0=
+16 7h4a1 1 0 001-1v-.628A2.25 2.25 0 019.5 3.25zM4.25 12a.75.75 0 100 1.5.7=
+5.75 0 000-1.5zM3.5 3.25a.75.75 0 111.5 0 .75.75 0 01-1.5 0z"></path>
+</svg>
+          <div>
+            <span class=3D"text-bold">Create branch: {{ refName }}</span>
+            <span class=3D"color-fg-muted">from =E2=80=98main=E2=80=99</spa=
+n>
+           =20
+          </div>
+        </button>
+</form>    </create-repo-from-selector>
+</template>
+
+
+            <div data-target=3D"ref-selector.listContainer" role=3D"menu" c=
+lass=3D"SelectMenu-list " data-turbo-frame=3D"repo-content-turbo-frame" sty=
+le=3D"max-height: 330px;"><div style=3D"position: relative; overflow: hidde=
+n; width: 100%; min-height: 100%; will-change: transform; height: 33px;"><d=
+iv style=3D"position:absolute; top:0; left:0; height:100%; width:100%; over=
+flow:visible;">
+  <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/tree/main" c=
+lass=3D"SelectMenu-item" role=3D"menuitemradio" rel=3D"nofollow" aria-check=
+ed=3D"true" data-index=3D"0">
+    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check SelectMenu-icon SelectMenu-icon--check">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    <span class=3D"flex-1 css-truncate css-truncate-overflow ">main</span>
+    <span class=3D"Label Label--secondary flex-self-start">default</span>
+  </a>
+<span></span></div></div></div>
+
+             =20
+
+<template data-target=3D"ref-selector.itemTemplate">
+  <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/tree/%7B%7B%=
+20urlEncodedRefName%20%7D%7D" class=3D"SelectMenu-item" role=3D"menuitemrad=
+io" rel=3D"nofollow" aria-checked=3D"{{ isCurrent }}" data-index=3D"{{ inde=
+x }}">
+    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check SelectMenu-icon SelectMenu-icon--check">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    <span class=3D"flex-1 css-truncate css-truncate-overflow {{ isFiltering=
+Class }}">{{ refName }}</span>
+   =20
+  </a>
+</template>
+
+
+              <footer class=3D"SelectMenu-footer"><a href=3D"https://github=
+.com/Est1y/Pandas-Analytics-Project/branches">View all branches</a></footer=
+>
+          </ref-selector>
+
+        </div>
+
+       =20
+      </tab-container>
+    </input-demux>
+  </div>
+</div>
+
+  </details>
+
+</div>
+
+
+<div class=3D"Overlay--hidden Overlay-backdrop--center" data-modal-dialog-o=
+verlay=3D"">
+  <modal-dialog role=3D"dialog" id=3D"warn-tag-match-create-branch-dialog" =
+aria-modal=3D"true" aria-labelledby=3D"warn-tag-match-create-branch-dialog-=
+header" data-view-component=3D"true" class=3D"Overlay Overlay--width-large =
+Overlay--height-auto Overlay--motion-scaleFade">
+      <header class=3D"Overlay-header Overlay-header--large Overlay-header-=
+-divided">
+        <div class=3D"Overlay-headerContentWrap">
+          <div class=3D"Overlay-titleWrap">
+            <h1 id=3D"warn-tag-match-create-branch-dialog-header" class=3D"=
+Overlay-title">Name already in use</h1>
+          </div>
+          <div class=3D"Overlay-actionWrap">
+            <button data-close-dialog-id=3D"warn-tag-match-create-branch-di=
+alog" aria-label=3D"Close" type=3D"button" data-view-component=3D"true" cla=
+ss=3D"close-button Overlay-closeButton"><svg aria-hidden=3D"true" height=3D=
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+</svg></button>
+          </div>
+        </div>
+      </header>
+    <div class=3D"Overlay-body ">
+     =20
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+ the provided branch name. Many Git commands accept both tag and branch nam=
+es, so creating this branch may cause unexpected behavior. Are you sure you=
+ want to create this branch?
+</div>
+
+    </div>
+      <footer class=3D"Overlay-footer Overlay-footer--alignEnd">
+            <button data-close-dialog-id=3D"warn-tag-match-create-branch-di=
+alog" type=3D"button" data-view-component=3D"true" class=3D"btn">    Cancel
+</button>
+            <button data-submit-dialog-id=3D"warn-tag-match-create-branch-d=
+ialog" type=3D"button" data-view-component=3D"true" class=3D"btn-danger btn=
+">    Create
+</button>
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+
+
+
+  <div class=3D"flex-self-center flex-self-stretch d-none flex-items-center=
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+    <a data-pjax=3D"#repo-content-pjax-container" data-turbo-frame=3D"repo-=
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+          <svg text=3D"gray" aria-hidden=3D"true" height=3D"16" viewBox=3D"=
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+</svg>
+          <strong>1</strong>
+          <span class=3D"color-fg-muted">branch</span>
+    </a>
+    <a data-pjax=3D"#repo-content-pjax-container" data-turbo-frame=3D"repo-=
+content-turbo-frame" href=3D"https://github.com/Est1y/Pandas-Analytics-Proj=
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+      <svg text=3D"gray" aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 =
+16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"o=
+cticon octicon-tag">
+    <path fill-rule=3D"evenodd" d=3D"M2.5 7.775V2.75a.25.25 0 01.25-.25h5.0=
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+</svg>
+        <strong>0</strong>
+        <span class=3D"color-fg-muted">tags</span>
+    </a>
+  </div>
+
+  <div class=3D"flex-auto"></div>
+
+  <a class=3D"btn ml-2 d-none d-md-block" data-hydro-click=3D"{&quot;event_=
+type&quot;:&quot;repository.click&quot;,&quot;payload&quot;:{&quot;target&q=
+uot;:&quot;FIND_FILE_BUTTON&quot;,&quot;repository_id&quot;:608111437,&quot=
+;originating_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Proj=
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+hotkey=3D"t" href=3D"https://github.com/Est1y/Pandas-Analytics-Project/find=
+/main">
+  Go to file
+</a>
+  <details data-view-component=3D"true" class=3D"details-overlay details-re=
+set position-relative d-block">
+    <summary role=3D"button" data-view-component=3D"true" class=3D"btn ml-2=
+">    <span class=3D"d-none d-md-flex flex-items-center">
+        Add file
+        <span class=3D"dropdown-caret ml-1"></span>
+      </span>
+      <span class=3D"d-inline-block d-md-none">
+        <svg aria-label=3D"More options" role=3D"img" height=3D"16" viewBox=
+=3D"0 0 16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" cl=
+ass=3D"octicon octicon-kebab-horizontal">
+    <path d=3D"M8 9a1.5 1.5 0 100-3 1.5 1.5 0 000 3zM1.5 9a1.5 1.5 0 100-3 =
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+</svg>
+      </span>
+</summary>
+  <div data-view-component=3D"true">      <ul class=3D"dropdown-menu dropdo=
+wn-menu-sw">
+        <li class=3D"d-block d-md-none">
+          <a class=3D"dropdown-item" data-hydro-click=3D"{&quot;event_type&=
+quot;:&quot;repository.click&quot;,&quot;payload&quot;:{&quot;target&quot;:=
+&quot;FIND_FILE_BUTTON&quot;,&quot;repository_id&quot;:608111437,&quot;orig=
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+k-hmac=3D"ef13c41cc0b709af399d98636878901415f8ca0bdb8666e78209547d792fc800"=
+ data-ga-click=3D"Repository, find file, location:repo overview" data-hotke=
+y=3D"t" href=3D"https://github.com/Est1y/Pandas-Analytics-Project/find/main=
+">
+            Go to file
+</a>        </li>
+          <li class=3D"d-block d-md-none dropdown-divider" role=3D"none"></=
+li>
+          <li><!-- '"` --><!-- </textarea></xmp> --><form data-turbo=3D"fal=
+se" action=3D"https://github.com/Est1y/Pandas-Analytics-Project/new/main" a=
+ccept-charset=3D"UTF-8" method=3D"post">
+    <button type=3D"submit" data-view-component=3D"true" class=3D"dropdown-=
+item btn-link">    Create new file
+</button></form></li>
+
+          <li><a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/=
+upload/main" class=3D"dropdown-item">
+  Upload files
+</a></li>
+
+      </ul>
+</div>
+</details>
+
+
+    <span class=3D"d-none d-md-flex ml-2">
+       =20
+<get-repo class=3D"" data-catalyst=3D"">
+    <feature-callout class=3D"feature-callout position-relative" data-query=
+-path=3D"/settings/notice-dismissals/codespaces_code_tab_individuals" data-=
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+alyst=3D"" data-dismiss-event=3D"click" data-label-class=3D"new-feature-lab=
+el">
+   =20
+    <details class=3D"position-relative details-overlay details-reset js-co=
+despaces-details-container" data-action=3D"toggle:get-repo#onDetailsToggle"=
+>
+        <summary data-hydro-click=3D"{&quot;event_type&quot;:&quot;reposito=
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+ot;target&quot;:&quot;CLONE_OR_DOWNLOAD_BUTTON&quot;,&quot;originating_url&=
+quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot;,&quot;u=
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+=3D"0 0 16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" cl=
+ass=3D"octicon octicon-code mr-1">
+    <path fill-rule=3D"evenodd" d=3D"M4.72 3.22a.75.75 0 011.06 1.06L2.06 8=
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+</svg>
+
+        Code<span class=3D"dropdown-caret"></span>
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+        <div class=3D"dropdown-menu dropdown-menu-sw p-0" style=3D"top:6px;=
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+          <div data-target=3D"get-repo.modal">
+    <tab-container data-view-component=3D"true">
+  <div with_panel=3D"true" data-view-component=3D"true" class=3D"tabnav hx_=
+tabnav-in-dropdown color-bg-subtle m-0">
+   =20
+    <ul role=3D"tablist" aria-label=3D"Choose where to access your code" da=
+ta-view-component=3D"true" class=3D"tabnav-tabs d-flex">
+        <li role=3D"presentation" data-view-component=3D"true" class=3D"hx_=
+tabnav-in-dropdown-wrapper flex-1 d-inline-flex">
+  <button data-tab=3D"local" data-action=3D"click:get-repo#localTabSelected=
+ focusin:get-repo#localTabSelected" id=3D"local-tab" type=3D"button" role=
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+ponent=3D"true" class=3D"tabnav-tab flex-1" tabindex=3D"0">
+   =20
+      <span data-view-component=3D"true">Local</span>
+   =20
+</button></li>
+        <li role=3D"presentation" data-view-component=3D"true" class=3D"hx_=
+tabnav-in-dropdown-wrapper flex-1 d-inline-flex">
+  <button data-tab=3D"cloud" data-action=3D"click:get-repo#cloudTabSelected=
+ focusin:get-repo#cloudTabSelected" data-target=3D"feature-callout.dismisse=
+r" id=3D"cloud-tab" type=3D"button" role=3D"tab" aria-controls=3D"cloud-pan=
+el" data-view-component=3D"true" class=3D"tabnav-tab flex-1" aria-selected=
+=3D"false" tabindex=3D"-1">
+   =20
+      <span data-view-component=3D"true">          <span>Codespaces</span>
+            <span data-targets=3D"feature-callout.labelees" data-test-selec=
+tor=3D"codespaces-new-label" data-view-component=3D"true" class=3D"Label ne=
+w-label-hidden Label--success ml-1 new-feature-label">New</span>
+</span>
+   =20
+</button></li>
+</ul>   =20
+</div>    <div id=3D"local-panel" role=3D"tabpanel" tabindex=3D"0" aria-lab=
+elledby=3D"local-tab" data-view-component=3D"true">          <ul class=3D"l=
+ist-style-none">
+              <li class=3D"Box-row p-3">
+  <a class=3D"Link--muted float-right tooltipped tooltipped-s" href=3D"http=
+s://docs.github.com/articles/which-remote-url-should-i-use" target=3D"_blan=
+k" aria-label=3D"Which remote URL should I use?">
+  <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=3D"=
+1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon-que=
+stion">
+    <path fill-rule=3D"evenodd" d=3D"M8 1.5a6.5 6.5 0 100 13 6.5 6.5 0 000-=
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+</svg>
+</a>
+
+<div class=3D"text-bold">
+  <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=3D"=
+1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon-ter=
+minal mr-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 2.75C0 1.784.784 1 1.75 1h12.5c.966=
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+</svg>
+  Clone
+</div>
+
+<tab-container>
+    <!-- '"` --><!-- </textarea></xmp> --><form class=3D"js-new-badge-autod=
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+notice/gh_cli_new" accept-charset=3D"UTF-8" method=3D"post">
+</form>
+  <div class=3D"UnderlineNav my-2 box-shadow-none">
+    <div class=3D"UnderlineNav-body" role=3D"tablist">
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+data-turbo=3D"false" action=3D"https://github.com/users/set_protocol?protoc=
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+            <button name=3D"protocol_selector" type=3D"submit" role=3D"tab"=
+ class=3D"UnderlineNav-item" aria-selected=3D"true" value=3D"http" data-hyd=
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+            <button name=3D"protocol_selector" type=3D"submit" role=3D"tab"=
+ class=3D"UnderlineNav-item" value=3D"ssh" data-hydro-click=3D"{&quot;event=
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+            <button name=3D"protocol_selector" type=3D"submit" role=3D"tab"=
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+" tabindex=3D"-1">
+              GitHub CLI
+                <span data-view-component=3D"true" class=3D"Label lh-conden=
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+</button></form>    </div>
+  </div>
+
+  <div role=3D"tabpanel">
+    <div class=3D"input-group">
+  <input type=3D"text" class=3D"form-control input-monospace input-sm color=
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+  <div class=3D"input-group-button">
+    <clipboard-copy value=3D"https://github.com/Est1y/Pandas-Analytics-Proj=
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+;,&quot;label&quot;:&quot;ref_loc:bar;is_fork:true&quot;}" href=3D"https://=
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+ics-Project:main...main">13 commits ahead</a> of Ustacky-dev:main</span>.
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+75 0 01-1.5 0zM3.75 12a.75.75 0 100 1.5.75.75 0 000-1.5z"></path>
+</svg>
+          Contribute
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+    <path d=3D"M4.427 7.427l3.396 3.396a.25.25 0 00.354 0l3.396-3.396A.25.2=
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+        <div class=3D"position-relative">
+          <div class=3D"dropdown-menu dropdown-menu-sw pb-0 branch-contribu=
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+16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"o=
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+    <path fill-rule=3D"evenodd" d=3D"M8 2.5a5.487 5.487 0 00-4.131 1.869l1.=
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+                  Sync fork
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+ data-filterable-type=3D"substring">
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#analyze-supermarket-data-across-the-count=
+ry---company-xyz">Analyze Supermarket Data Across the Country - Company XYZ=
+</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#project-steps">Project Steps</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#1-the-first-major-task-was-data-explorati=
+on">1. The first major task was data exploration</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#2-the-second-dealing-with-datetime-featur=
+es">2. The second: Dealing with datetime features</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#3-check-for-unique-value-in-columns">3. C=
+heck for unique value in columns</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#4--aggregration-with-groupby">4.  Aggregr=
+ation with GroupBy</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#5-data-visualization">5. Data Visualizati=
+on</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px; background-color: var(--color-accent-em=
+phasis);" data-action=3D"click:readme-toc#blur" data-targets=3D"readme-toc.=
+entries" data-hydro-click=3D"{&quot;event_type&quot;:&quot;repository_toc_m=
+enu.click&quot;,&quot;payload&quot;:{&quot;target&quot;:&quot;entry&quot;,&=
+quot;repository_id&quot;:608111437,&quot;originating_url&quot;:&quot;https:=
+//github.com/Est1y/Pandas-Analytics-Project&quot;,&quot;user_id&quot;:11128=
+9202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bdae5297c0a7e1b6927407499c5293=
+d6a735e77acbc615cbb81a" href=3D"https://github.com/Est1y/Pandas-Analytics-P=
+roject#insights" aria-current=3D"page">Insights</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#future-work">Future Work</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#standout-section">Standout Section</a>
+          <a role=3D"menuitem" class=3D"filter-item SelectMenu-item ws-norm=
+al wb-break-word line-clamp-2 py-1 text-emphasized" style=3D"-webkit-box-or=
+ient: vertical; padding-left: 12px;" data-action=3D"click:readme-toc#blur" =
+data-targets=3D"readme-toc.entries" data-hydro-click=3D"{&quot;event_type&q=
+uot;:&quot;repository_toc_menu.click&quot;,&quot;payload&quot;:{&quot;targe=
+t&quot;:&quot;entry&quot;,&quot;repository_id&quot;:608111437,&quot;origina=
+ting_url&quot;:&quot;https://github.com/Est1y/Pandas-Analytics-Project&quot=
+;,&quot;user_id&quot;:111289202}}" data-hydro-click-hmac=3D"f1a5bb04c1f4bda=
+e5297c0a7e1b6927407499c5293d6a735e77acbc615cbb81a" href=3D"https://github.c=
+om/Est1y/Pandas-Analytics-Project#executive-summary">Executive Summary.</a>
+      </div>
+    </div>
+  <span class=3D"sentinel" tabindex=3D"0" aria-hidden=3D"true"></span></det=
+ails-menu>
+</details>
+
+            <h2 class=3D"Box-title">
+              <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project#=
+readme" data-view-component=3D"true" class=3D"Link--primary">README.md</a>
+            </h2>
+          </div>
+            <div>
+              <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/=
+edit/main/README.md" class=3D"btn btn-octicon float-right p-2" aria-label=
+=3D"Edit this file"><svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 =
+16 16" version=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"o=
+cticon octicon-pencil">
+    <path fill-rule=3D"evenodd" d=3D"M11.013 1.427a1.75 1.75 0 012.474 0l1.=
+086 1.086a1.75 1.75 0 010 2.474l-8.61 8.61c-.21.21-.47.364-.756.445l-3.251.=
+93a.75.75 0 01-.927-.928l.929-3.25a1.75 1.75 0 01.445-.758l8.61-8.61zm1.414=
+ 1.06a.25.25 0 00-.354 0L10.811 3.75l1.439 1.44 1.263-1.263a.25.25 0 000-.3=
+54l-1.086-1.086zM11.189 6.25L9.75 4.81l-6.286 6.287a.25.25 0 00-.064.108l-.=
+558 1.953 1.953-.558a.249.249 0 00.108-.064l6.286-6.286z"></path>
+</svg></a>
+            </div>
+        </div>
+
+          <div data-target=3D"readme-toc.content" class=3D"Box-body px-5 pb=
+-5">
+            <article class=3D"markdown-body entry-content container-lg" ite=
+mprop=3D"text"><p dir=3D"auto">Project title:</p>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-analyze-supermarket-=
+data-across-the-country---company-xyz" class=3D"anchor" aria-hidden=3D"true=
+" href=3D"https://github.com/Est1y/Pandas-Analytics-Project#analyze-superma=
+rket-data-across-the-country---company-xyz"><svg class=3D"octicon octicon-l=
+ink" viewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" height=3D"16" aria-=
+hidden=3D"true"><path fill-rule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001.=
+06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.=
+06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69=
+ 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 =
+00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1=
+.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Analyze Supermarket Data Across=
+ the Country - Company XYZ</h1>
+<p dir=3D"auto">Project description:
+Company XYZ owns a supermarket chain across the country. Each major branch =
+located in 3 cities across the country recorded sales information for 3 mon=
+ths, to help the company understand sales trends and determine its growth, =
+as the rise of supermarkets competition is seen.</p>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-project-steps" class=
+=3D"anchor" aria-hidden=3D"true" href=3D"https://github.com/Est1y/Pandas-An=
+alytics-Project#project-steps"><svg class=3D"octicon octicon-link" viewBox=
+=3D"0 0 16 16" version=3D"1.1" width=3D"16" height=3D"16" aria-hidden=3D"tr=
+ue"><path fill-rule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001.06 1.06l1.25=
+-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 =
+3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 =
+010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2=
+.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2=
+ 0 01-2.83 0z"></path></svg></a>Project Steps</h1>
+<ul dir=3D"auto">
+<li>Various liberies were imported such as: os, glob, numpy, pandas, matplo=
+tlib.pyplot etc</li>
+<li>The data containing information of each branch in the 3 major cities we=
+re loaded and combined using pd.concat to form a single dataframe with csv =
+extention and then   save as a new file in the local directory conataining =
+the project.</li>
+</ul>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-1-the-first-major-ta=
+sk-was-data-exploration" class=3D"anchor" aria-hidden=3D"true" href=3D"http=
+s://github.com/Est1y/Pandas-Analytics-Project#1-the-first-major-task-was-da=
+ta-exploration"><svg class=3D"octicon octicon-link" viewBox=3D"0 0 16 16" v=
+ersion=3D"1.1" width=3D"16" height=3D"16" aria-hidden=3D"true"><path fill-r=
+ule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.=
+83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2=
+.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5=
+a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0=
+ 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"><=
+/path></svg></a>1. The first major task was data exploration</h1>
+<div class=3D"snippet-clipboard-content notranslate position-relative overf=
+low-auto"><pre class=3D"notranslate"><code>   * The first 5 rows of the new=
+ly created file 'Market_data.csv' was displayed
+   * no of rows and columns was checked using df.shape
+   * check various column name in the dataset using df.columns=20
+   * statistical surmary was checked using df.describe()
+   * Missing data was checked using the isnull
+   * General Information of the data was checked using .info
+</code></pre><div class=3D"zeroclipboard-container position-absolute right-=
+0 top-0">
+    <clipboard-copy aria-label=3D"Copy" class=3D"ClipboardButton btn js-cli=
+pboard-copy m-2 p-0 tooltipped-no-delay" data-copy-feedback=3D"Copied!" dat=
+a-tooltip-direction=3D"w" value=3D"   * The first 5 rows of the newly creat=
+ed file 'Market_data.csv' was displayed
+   * no of rows and columns was checked using df.shape
+   * check various column name in the dataset using df.columns=20
+   * statistical surmary was checked using df.describe()
+   * Missing data was checked using the isnull
+   * General Information of the data was checked using .info" tabindex=3D"0=
+" role=3D"button">
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-copy js-clipboard-copy-icon m-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.7=
+5 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00=
+.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 01=
+0 14.25v-7.5z"></path><path fill-rule=3D"evenodd" d=3D"M5 1.75C5 .784 5.784=
+ 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75=
+ 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h=
+7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+</svg>
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check js-clipboard-check-icon color-fg-success d-none m-2">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    </clipboard-copy>
+  </div></div>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-2-the-second-dealing=
+-with-datetime-features" class=3D"anchor" aria-hidden=3D"true" href=3D"http=
+s://github.com/Est1y/Pandas-Analytics-Project#2-the-second-dealing-with-dat=
+etime-features"><svg class=3D"octicon octicon-link" viewBox=3D"0 0 16 16" v=
+ersion=3D"1.1" width=3D"16" height=3D"16" aria-hidden=3D"true"><path fill-r=
+ule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.=
+83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2=
+.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5=
+a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0=
+ 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"><=
+/path></svg></a>2. The second: Dealing with datetime features</h1>
+<div class=3D"snippet-clipboard-content notranslate position-relative overf=
+low-auto"><pre class=3D"notranslate"><code>   * Convert the date column to =
+datetime
+   * The above was also done for the time column
+   * Extract year, month and day from the date column and also hour from fr=
+om the time column
+   * Determine the unique ours of sales in the supermarket and return resul=
+t as an array.
+</code></pre><div class=3D"zeroclipboard-container position-absolute right-=
+0 top-0">
+    <clipboard-copy aria-label=3D"Copy" class=3D"ClipboardButton btn js-cli=
+pboard-copy m-2 p-0 tooltipped-no-delay" data-copy-feedback=3D"Copied!" dat=
+a-tooltip-direction=3D"w" value=3D"   * Convert the date column to datetime
+   * The above was also done for the time column
+   * Extract year, month and day from the date column and also hour from fr=
+om the time column
+   * Determine the unique ours of sales in the supermarket and return resul=
+t as an array." tabindex=3D"0" role=3D"button">
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-copy js-clipboard-copy-icon m-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.7=
+5 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00=
+.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 01=
+0 14.25v-7.5z"></path><path fill-rule=3D"evenodd" d=3D"M5 1.75C5 .784 5.784=
+ 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75=
+ 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h=
+7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+</svg>
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check js-clipboard-check-icon color-fg-success d-none m-2">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    </clipboard-copy>
+  </div></div>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-3-check-for-unique-v=
+alue-in-columns" class=3D"anchor" aria-hidden=3D"true" href=3D"https://gith=
+ub.com/Est1y/Pandas-Analytics-Project#3-check-for-unique-value-in-columns">=
+<svg class=3D"octicon octicon-link" viewBox=3D"0 0 16 16" version=3D"1.1" w=
+idth=3D"16" height=3D"16" aria-hidden=3D"true"><path fill-rule=3D"evenodd" =
+d=3D"M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5=
+a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0=
+ 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 =
+.75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.2=
+5-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>=
+3. Check for unique value in columns</h1>
+<div class=3D"snippet-clipboard-content notranslate position-relative overf=
+low-auto"><pre class=3D"notranslate"><code>   * Get a list of the categoric=
+al column in the dataset, check if each element in the column is of type ob=
+ject
+   * generate the unique values in the categorical columns in this case, Pa=
+yment, product line, gender and customer type
+   * Get a Series containing counts of unique values for various categories
+</code></pre><div class=3D"zeroclipboard-container position-absolute right-=
+0 top-0">
+    <clipboard-copy aria-label=3D"Copy" class=3D"ClipboardButton btn js-cli=
+pboard-copy m-2 p-0 tooltipped-no-delay" data-copy-feedback=3D"Copied!" dat=
+a-tooltip-direction=3D"w" value=3D"   * Get a list of the categorical colum=
+n in the dataset, check if each element in the column is of type object
+   * generate the unique values in the categorical columns in this case, Pa=
+yment, product line, gender and customer type
+   * Get a Series containing counts of unique values for various categories=
+" tabindex=3D"0" role=3D"button">
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-copy js-clipboard-copy-icon m-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.7=
+5 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00=
+.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 01=
+0 14.25v-7.5z"></path><path fill-rule=3D"evenodd" d=3D"M5 1.75C5 .784 5.784=
+ 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75=
+ 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h=
+7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+</svg>
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check js-clipboard-check-icon color-fg-success d-none m-2">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    </clipboard-copy>
+  </div></div>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-4--aggregration-with=
+-groupby" class=3D"anchor" aria-hidden=3D"true" href=3D"https://github.com/=
+Est1y/Pandas-Analytics-Project#4--aggregration-with-groupby"><svg class=3D"=
+octicon octicon-link" viewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" he=
+ight=3D"16" aria-hidden=3D"true"><path fill-rule=3D"evenodd" d=3D"M7.775 3.=
+275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83=
+ 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l=
+-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.0=
+6-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 =
+0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>4.  Aggregrati=
+on with GroupBy</h1>
+<div class=3D"snippet-clipboard-content notranslate position-relative overf=
+low-auto"><pre class=3D"notranslate"><code>   * Create a groupby object wit=
+h the "City Column", and aggregation function of sum and mean.
+   * Display a table that shows the gross income of each city, and determin=
+e the city with the highest total gross income.
+</code></pre><div class=3D"zeroclipboard-container position-absolute right-=
+0 top-0">
+    <clipboard-copy aria-label=3D"Copy" class=3D"ClipboardButton btn js-cli=
+pboard-copy m-2 p-0 tooltipped-no-delay" data-copy-feedback=3D"Copied!" dat=
+a-tooltip-direction=3D"w" value=3D"   * Create a groupby object with the &q=
+uot;City Column&quot;, and aggregation function of sum and mean.
+   * Display a table that shows the gross income of each city, and determin=
+e the city with the highest total gross income." tabindex=3D"0" role=3D"but=
+ton">
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-copy js-clipboard-copy-icon m-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.7=
+5 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00=
+.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 01=
+0 14.25v-7.5z"></path><path fill-rule=3D"evenodd" d=3D"M5 1.75C5 .784 5.784=
+ 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75=
+ 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h=
+7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+</svg>
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check js-clipboard-check-icon color-fg-success d-none m-2">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    </clipboard-copy>
+  </div></div>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-5-data-visualization=
+" class=3D"anchor" aria-hidden=3D"true" href=3D"https://github.com/Est1y/Pa=
+ndas-Analytics-Project#5-data-visualization"><svg class=3D"octicon octicon-=
+link" viewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" height=3D"16" aria=
+-hidden=3D"true"><path fill-rule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001=
+.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1=
+.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.6=
+9 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0=
+ 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-=
+1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>5. Data Visualization</h1>
+<div class=3D"snippet-clipboard-content notranslate position-relative overf=
+low-auto"><pre class=3D"notranslate"><code>   * Determine the branch with t=
+he highest sales record using countplot.
+   * Determine the highest &amp; lowest sold product line, using Countplot
+   * Determine the Payment channel used by most customer to pay for each pr=
+oduct line.
+   * Determine the Payment channel for each branch.
+   * Determine the branch with the lowest rating
+   * Generate visualization for the "product line" per gender
+   * Generate visualization for the "product line" per "Total" column
+   * plot Product line per unit price, and Product line per Quantity
+</code></pre><div class=3D"zeroclipboard-container position-absolute right-=
+0 top-0">
+    <clipboard-copy aria-label=3D"Copy" class=3D"ClipboardButton btn js-cli=
+pboard-copy m-2 p-0 tooltipped-no-delay" data-copy-feedback=3D"Copied!" dat=
+a-tooltip-direction=3D"w" value=3D"   * Determine the branch with the highe=
+st sales record using countplot.
+   * Determine the highest &amp; lowest sold product line, using Countplot
+   * Determine the Payment channel used by most customer to pay for each pr=
+oduct line.
+   * Determine the Payment channel for each branch.
+   * Determine the branch with the lowest rating
+   * Generate visualization for the &quot;product line&quot; per gender
+   * Generate visualization for the &quot;product line&quot; per &quot;Tota=
+l&quot; column
+   * plot Product line per unit price, and Product line per Quantity" tabin=
+dex=3D"0" role=3D"button">
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-copy js-clipboard-copy-icon m-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.7=
+5 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00=
+.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 01=
+0 14.25v-7.5z"></path><path fill-rule=3D"evenodd" d=3D"M5 1.75C5 .784 5.784=
+ 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75=
+ 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h=
+7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+</svg>
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check js-clipboard-check-icon color-fg-success d-none m-2">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    </clipboard-copy>
+  </div></div>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-insights" class=3D"a=
+nchor" aria-hidden=3D"true" href=3D"https://github.com/Est1y/Pandas-Analyti=
+cs-Project#insights"><svg class=3D"octicon octicon-link" viewBox=3D"0 0 16 =
+16" version=3D"1.1" width=3D"16" height=3D"16" aria-hidden=3D"true"><path f=
+ill-rule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0=
+ 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.9=
+5 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.=
+5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 =
+3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 =
+0z"></path></svg></a>Insights</h1>
+<div class=3D"snippet-clipboard-content notranslate position-relative overf=
+low-auto"><pre class=3D"notranslate"><code>  Insights gotten from analysing=
+ the dataset consisting of sales record of the 3 branches( Lagos, Abuja and=
+ Port-Harcourt) of the supermarket belonging to Company XYZ are:
+=20
+  * Dataset consist of 1000 rows and 18 columns
+  * Each city is represented as branch; A - Lagos Branch; B - Abuja Branch;=
+ C - Port Harcourt Branch
+  * Dataset contains no null values
+  * The dataset contains records of 501 female customer and 499 male custom=
+ers
+  * Port-Harcourt (C) records the highest revenue(gross income) while Lagos=
+ (A) follows next and then Abuja (B)
+  * 'A' has the highest no of customers while followed by 'B' and the least=
+ is 'C'
+  * The most used payment method in general is EBAY and the least is Card
+  * The most sold product line in all 3 branches is 'food and beverages' in=
+ 'C', next is 'Home and lifestyle' in 'A' and the 3rd most sold productline=
+ is 'fashion and accessories' in 'C'. However, in 'B', the most sold is 'Tr=
+avel and sport'.
+  * The most used payment method with respect to 'Product line' is Cash
+  * The branch with the lowest rating is "B" while 'C' has the highest
+  * It is observed that the most frequent customers are of the female gende=
+r and the most bought product is the 'Home and lifestyle'.
+  * It is observed that the most expensive 'product line' is the 'food and =
+beverages' and it is mostly bought by the female gender.
+</code></pre><div class=3D"zeroclipboard-container position-absolute right-=
+0 top-0">
+    <clipboard-copy aria-label=3D"Copy" class=3D"ClipboardButton btn js-cli=
+pboard-copy m-2 p-0 tooltipped-no-delay" data-copy-feedback=3D"Copied!" dat=
+a-tooltip-direction=3D"w" value=3D"  Insights gotten from analysing the dat=
+aset consisting of sales record of the 3 branches( Lagos, Abuja and Port-Ha=
+rcourt) of the supermarket belonging to Company XYZ are:
+=20
+  * Dataset consist of 1000 rows and 18 columns
+  * Each city is represented as branch; A - Lagos Branch; B - Abuja Branch;=
+ C - Port Harcourt Branch
+  * Dataset contains no null values
+  * The dataset contains records of 501 female customer and 499 male custom=
+ers
+  * Port-Harcourt (C) records the highest revenue(gross income) while Lagos=
+ (A) follows next and then Abuja (B)
+  * 'A' has the highest no of customers while followed by 'B' and the least=
+ is 'C'
+  * The most used payment method in general is EBAY and the least is Card
+  * The most sold product line in all 3 branches is 'food and beverages' in=
+ 'C', next is 'Home and lifestyle' in 'A' and the 3rd most sold productline=
+ is 'fashion and accessories' in 'C'. However, in 'B', the most sold is 'Tr=
+avel and sport'.
+  * The most used payment method with respect to 'Product line' is Cash
+  * The branch with the lowest rating is &quot;B&quot; while 'C' has the hi=
+ghest
+  * It is observed that the most frequent customers are of the female gende=
+r and the most bought product is the 'Home and lifestyle'.
+  * It is observed that the most expensive 'product line' is the 'food and =
+beverages' and it is mostly bought by the female gender." tabindex=3D"0" ro=
+le=3D"button">
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-copy js-clipboard-copy-icon m-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.7=
+5 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00=
+.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 01=
+0 14.25v-7.5z"></path><path fill-rule=3D"evenodd" d=3D"M5 1.75C5 .784 5.784=
+ 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75=
+ 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h=
+7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+</svg>
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check js-clipboard-check-icon color-fg-success d-none m-2">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    </clipboard-copy>
+  </div></div>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-future-work" class=
+=3D"anchor" aria-hidden=3D"true" href=3D"https://github.com/Est1y/Pandas-An=
+alytics-Project#future-work"><svg class=3D"octicon octicon-link" viewBox=3D=
+"0 0 16 16" version=3D"1.1" width=3D"16" height=3D"16" aria-hidden=3D"true"=
+><path fill-rule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.=
+25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5=
+ 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010=
+-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 =
+2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 =
+01-2.83 0z"></path></svg></a>Future Work</h1>
+<p dir=3D"auto">I will explore other data visualization framework to get a =
+more understandable presentation.</p>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-standout-section" cl=
+ass=3D"anchor" aria-hidden=3D"true" href=3D"https://github.com/Est1y/Pandas=
+-Analytics-Project#standout-section"><svg class=3D"octicon octicon-link" vi=
+ewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" height=3D"16" aria-hidden=
+=3D"true"><path fill-rule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001.06 1.0=
+6l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.0=
+6 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a=
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+5 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.=
+25a2 2 0 01-2.83 0z"></path></svg></a>Standout Section</h1>
+<p dir=3D"auto">I inserted all required liberies in the first cell of my wo=
+rk for the purpose of orderliness unlike the import intruction stated in th=
+e notebook
+I extracted the minute feature from the Time column and saved it to a new m=
+inute column</p>
+<h1 tabindex=3D"-1" dir=3D"auto"><a id=3D"user-content-executive-summary" c=
+lass=3D"anchor" aria-hidden=3D"true" href=3D"https://github.com/Est1y/Panda=
+s-Analytics-Project#executive-summary"><svg class=3D"octicon octicon-link" =
+viewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" height=3D"16" aria-hidde=
+n=3D"true"><path fill-rule=3D"evenodd" d=3D"M7.775 3.275a.75.75 0 001.06 1.=
+06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.=
+06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64=
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+95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1=
+.25a2 2 0 01-2.83 0z"></path></svg></a>Executive Summary.</h1>
+</article>
+          </div>
+      </div>
+
+  </readme-toc>
+
+
+</div>
+  <div data-view-component=3D"true" class=3D"Layout-sidebar">     =20
+
+      <div class=3D"BorderGrid BorderGrid--spacious" data-pjax=3D"">
+        <div class=3D"BorderGrid-row hide-sm hide-md">
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+          <svg aria-label=3D"Edit repository metadata" role=3D"img" height=
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+></path>
+</svg>
+        </div>
+      </summary>
+
+  <details-dialog class=3D"Box d-flex flex-column anim-fade-in fast Box-ove=
+rlay--wide overflow-x-hidden" aria-label=3D"Edit repository details" role=
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+h>
+</svg>
+      </button>
+        <h1 class=3D"Box-title">Edit repository details</h1>
+    </div>
+      <div class=3D"Box-body overflow-auto">
+              <div class=3D"js-topic-form-area">
+        <!-- '"` --><!-- </textarea></xmp> --><form id=3D"repo_metadata_for=
+m" data-turbo=3D"false" action=3D"https://github.com/Est1y/Pandas-Analytics=
+-Project/settings/update_meta" accept-charset=3D"UTF-8" method=3D"post">
+          <div class=3D"form-group mt-0 mb-3 js-length-limited-input-contai=
+ner">
+            <div class=3D"mb-2">
+              <label for=3D"repo_description">Description</label>
+            </div>
+            <input type=3D"text" id=3D"repo_description" class=3D"form-cont=
+rol input-contrast width-full js-length-limited-input" name=3D"repo_descrip=
+tion" placeholder=3D"Short description of this repository" autofocus=3D"" v=
+alue=3D"A data analysis project with Python" data-input-max-length=3D"350" =
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+              <label for=3D"repo_homepage">Website</label>
+            </div>
+             <input type=3D"url" id=3D"repo_homepage" class=3D"color-bg-def=
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+bel>
+                    </div>
+                    <div class=3D"tag-input form-control d-inline-block col=
+or-bg-default py-0 position-relative">
+                      <ul class=3D"js-tag-input-selected-tags d-inline">
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+    <path fill-rule=3D"evenodd" d=3D"M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.=
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+h>
+</svg>
+                          </button>
+                         =20
+                        </li>
+
+                      </ul>
+
+                      <auto-complete src=3D"/Est1y/Pandas-Analytics-Project=
+/topic_autocomplete" for=3D"repo-topic-popup">
+                        <input type=3D"text" id=3D"repo_topics" class=3D"ta=
+g-input-inner form-control color-bg-default short d-inline-block p-0 my-1 b=
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+                       =20
+                      </auto-complete>
+                    </div>
+                  </div>
+                </div>
+              </div>
+
+
+          <div class=3D"form-group mt-3 mb-0" role=3D"group" aria-labelledb=
+y=3D"hidden_sidebar_options">
+            <div class=3D"text-bold mb-2" id=3D"hidden_sidebar_options">Inc=
+lude in the home page</div>
+            <label class=3D"d-block mb-2 text-normal">
+              <input class=3D"mr-1" type=3D"checkbox" value=3D"1" checked=
+=3D"checked" name=3D"repo_sections[releases]" id=3D"repo_sections_releases"=
+> Releases
+            </label>
+            <label class=3D"d-block mb-2 text-normal">
+              <input class=3D"mr-1" type=3D"checkbox" value=3D"1" checked=
+=3D"checked" name=3D"repo_sections[packages]" id=3D"repo_sections_packages"=
+> Packages
+            </label>
+
+              <label class=3D"d-block text-normal">
+                <input class=3D"mr-1" type=3D"checkbox" value=3D"1" checked=
+=3D"checked" name=3D"repo_sections[environments]" id=3D"repo_sections_envir=
+onments"> Environments
+              </label>
+          </div>
+
+</form>
+      </div>
+
+      </div>
+        <div class=3D"Box-footer">
+                <div class=3D"form-actions">
+        <button type=3D"submit" class=3D"btn btn-primary" form=3D"repo_meta=
+data_form">Save changes</button>
+        <button type=3D"reset" class=3D"btn" data-close-dialog=3D"" form=3D=
+"repo_metadata_form">Cancel</button>
+      </div>
+
+        </div>
+  </details-dialog>
+</details>
+<h2 class=3D"mb-3 h4">About</h2>
+
+    <p class=3D"f4 my-3">
+      A data analysis project with Python
+    </p>
+
+  <h3 class=3D"sr-only">Topics</h3>
+  <div class=3D"my-3">
+   =20
+  </div>
+
+  <h3 class=3D"sr-only">Resources</h3>
+  <div class=3D"mt-2">
+    <a class=3D"Link--muted" data-analytics-event=3D"{&quot;category&quot;:=
+&quot;Repository Overview&quot;,&quot;action&quot;:&quot;click&quot;,&quot;=
+label&quot;:&quot;location:sidebar;file:readme&quot;}" href=3D"https://gith=
+ub.com/Est1y/Pandas-Analytics-Project#readme">
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-book mr-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 1.75A.75.75 0 01.75 1h4.253c1.227 0=
+ 2.317.59 3 1.501A3.744 3.744 0 0111.006 1h4.245a.75.75 0 01.75.75v10.5a.75=
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+.25 2.25 0 012.25-2.25H14.5v9h-3.757c-.71 0-1.4.201-1.992.572l.004-7.322zm-=
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+3.75 0 011.994.574z"></path>
+</svg>
+      Readme
+</a>  </div>
+
+
+
+
+
+<include-fragment src=3D"/Est1y/Pandas-Analytics-Project/hovercards/citatio=
+n/sidebar_partial?tree_name=3Dmain" class=3D"is-error">
+<template shadowmode=3D"open"><slot></slot></template></include-fragment>
+
+<h3 class=3D"sr-only">Stars</h3>
+<div class=3D"mt-2">
+  <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/stargazers" =
+data-view-component=3D"true" class=3D"Link--muted">
+    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-star mr-2">
+    <path fill-rule=3D"evenodd" d=3D"M8 .25a.75.75 0 01.673.418l1.882 3.815=
+ 4.21.612a.75.75 0 01.416 1.279l-3.046 2.97.719 4.192a.75.75 0 01-1.088.791=
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+096-.45a.75.75 0 01-.564-.41L8 2.694v.001z"></path>
+</svg>
+    <strong>0</strong>
+    stars
+</a></div>
+
+<h3 class=3D"sr-only">Watchers</h3>
+<div class=3D"mt-2">
+  <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/watchers" da=
+ta-view-component=3D"true" class=3D"Link--muted">
+    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
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+    <path fill-rule=3D"evenodd" d=3D"M1.679 7.932c.412-.621 1.242-1.75 2.36=
+6-2.717C5.175 4.242 6.527 3.5 8 3.5c1.473 0 2.824.742 3.955 1.715 1.124.967=
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+h>
+</svg>
+    <strong>0</strong>
+    watching
+</a></div>
+
+<h3 class=3D"sr-only">Forks</h3>
+<div class=3D"mt-2">
+  <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/forks" data-=
+view-component=3D"true" class=3D"Link--muted">
+    <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-repo-forked mr-2">
+    <path fill-rule=3D"evenodd" d=3D"M5 3.25a.75.75 0 11-1.5 0 .75.75 0 011=
+.5 0zm0 2.122a2.25 2.25 0 10-1.5 0v.878A2.25 2.25 0 005.75 8.5h1.5v2.128a2.=
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+ath>
+</svg>
+    <strong>44</strong>
+    forks
+</a></div>
+
+          </div>
+        </div>
+
+       =20
+       =20
+            <div class=3D"BorderGrid-row">
+              <div class=3D"BorderGrid-cell">
+                <h2 class=3D"h4 mb-3" data-pjax=3D"#repo-content-pjax-conta=
+iner" data-turbo-frame=3D"repo-content-turbo-frame">
+  <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/releases" da=
+ta-view-component=3D"true" class=3D"Link--primary no-underline" data-turbo-=
+frame=3D"repo-content-turbo-frame">
+    Releases
+</a></h2>
+
+    <div class=3D"text-small color-fg-muted">No releases published</div>
+    <div class=3D" text-small"><a href=3D"https://github.com/Est1y/Pandas-A=
+nalytics-Project/releases/new">Create a new release</a></div>
+
+              </div>
+            </div>
+
+       =20
+       =20
+            <div class=3D"BorderGrid-row">
+              <div class=3D"BorderGrid-cell">
+                <h2 class=3D"h4 mb-3">
+  <a href=3D"https://github.com/users/Est1y/packages?repo_name=3DPandas-Ana=
+lytics-Project" data-view-component=3D"true" class=3D"Link--primary no-unde=
+rline">
+    Packages=20
+</a></h2>
+
+
+      <div class=3D"text-small color-fg-muted">
+        No packages published <br>
+          <a href=3D"https://github.com/Est1y/Pandas-Analytics-Project/pack=
+ages">Publish your first package</a>
+      </div>
+
+
+
+              </div>
+            </div>
+
+       =20
+       =20
+       =20
+       =20
+            <div class=3D"BorderGrid-row">
+              <div class=3D"BorderGrid-cell">
+                <h2 class=3D"h4 mb-3">Languages</h2>
+<div class=3D"mb-2">
+  <span data-view-component=3D"true" class=3D"Progress">
+    <span style=3D"background-color:#DA5B0B !important;;width: 100.0%;" ite=
+mprop=3D"keywords" aria-label=3D"Jupyter Notebook 100.0" data-view-componen=
+t=3D"true" class=3D"Progress-item color-bg-success-emphasis"></span>
+</span></div>
+<ul class=3D"list-style-none">
+    <li class=3D"d-inline">
+        <span class=3D"d-inline-flex flex-items-center flex-nowrap text-sma=
+ll mr-3">
+          <svg style=3D"color:#DA5B0B;" aria-hidden=3D"true" height=3D"16" =
+viewBox=3D"0 0 16 16" version=3D"1.1" width=3D"16" data-view-component=3D"t=
+rue" class=3D"octicon octicon-dot-fill mr-2">
+    <path fill-rule=3D"evenodd" d=3D"M8 4a4 4 0 100 8 4 4 0 000-8z"></path>
+</svg>
+          <span class=3D"color-fg-default text-bold mr-1">Jupyter Notebook<=
+/span>
+          <span>100.0%</span>
+        </span>
+    </li>
+</ul>
+
+              </div>
+            </div>
+
+              </div>
+</div>
+ =20
+</div></div>
+
+  </div>
+
+  </turbo-frame>
+
+
+    </main>
+  </div>
+
+  </div>
+
+          <footer class=3D"footer width-full container-xl p-responsive">
+  <h2 class=3D"sr-only">Footer</h2>
+
+  <div class=3D"position-relative d-flex flex-items-center pb-2 f6 color-fg=
+-muted border-top color-border-muted flex-column-reverse flex-lg-row flex-w=
+rap flex-lg-nowrap mt-6 pt-6">
+    <div class=3D"list-style-none d-flex flex-wrap col-0 col-lg-2 flex-just=
+ify-start flex-lg-justify-between mb-2 mb-lg-0">
+      <div class=3D"mt-2 mt-lg-0 d-flex flex-items-center">
+        <a aria-label=3D"Homepage" title=3D"GitHub" class=3D"footer-octicon=
+ mr-2" href=3D"https://github.com/">
+          <svg aria-hidden=3D"true" height=3D"24" viewBox=3D"0 0 16 16" ver=
+sion=3D"1.1" width=3D"24" data-view-component=3D"true" class=3D"octicon oct=
+icon-mark-github">
+    <path fill-rule=3D"evenodd" d=3D"M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.5=
+3 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69=
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+2.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 =
+0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.3=
+2-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 =
+2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0=
+ 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8=
+-8-8z"></path>
+</svg>
+</a>        <span>
+        =C2=A9 2023 GitHub, Inc.
+        </span>
+      </div>
+    </div>
+
+    <nav aria-label=3D"footer" class=3D"col-12 col-lg-8">
+      <h3 class=3D"sr-only" id=3D"sr-footer-heading">Footer navigation</h3>
+      <ul class=3D"list-style-none d-flex flex-wrap col-12 flex-justify-cen=
+ter flex-lg-justify-between mb-2 mb-lg-0" aria-labelledby=3D"sr-footer-head=
+ing">
+          <li class=3D"mr-3 mr-lg-0"><a href=3D"https://docs.github.com/sit=
+e-policy/github-terms/github-terms-of-service" data-analytics-event=3D"{&qu=
+ot;category&quot;:&quot;Footer&quot;,&quot;action&quot;:&quot;go to terms&q=
+uot;,&quot;label&quot;:&quot;text:terms&quot;}">Terms</a></li>
+          <li class=3D"mr-3 mr-lg-0"><a href=3D"https://docs.github.com/sit=
+e-policy/privacy-policies/github-privacy-statement" data-analytics-event=3D=
+"{&quot;category&quot;:&quot;Footer&quot;,&quot;action&quot;:&quot;go to pr=
+ivacy&quot;,&quot;label&quot;:&quot;text:privacy&quot;}">Privacy</a></li>
+          <li class=3D"mr-3 mr-lg-0"><a data-analytics-event=3D"{&quot;cate=
+gory&quot;:&quot;Footer&quot;,&quot;action&quot;:&quot;go to security&quot;=
+,&quot;label&quot;:&quot;text:security&quot;}" href=3D"https://github.com/s=
+ecurity">Security</a></li>
+          <li class=3D"mr-3 mr-lg-0"><a href=3D"https://www.githubstatus.co=
+m/" data-analytics-event=3D"{&quot;category&quot;:&quot;Footer&quot;,&quot;=
+action&quot;:&quot;go to status&quot;,&quot;label&quot;:&quot;text:status&q=
+uot;}">Status</a></li>
+          <li class=3D"mr-3 mr-lg-0"><a data-ga-click=3D"Footer, go to help=
+, text:Docs" href=3D"https://docs.github.com/">Docs</a></li>
+          <li class=3D"mr-3 mr-lg-0"><a href=3D"https://support.github.com/=
+?tags=3Ddotcom-footer" data-analytics-event=3D"{&quot;category&quot;:&quot;=
+Footer&quot;,&quot;action&quot;:&quot;go to contact&quot;,&quot;label&quot;=
+:&quot;text:contact&quot;}">Contact GitHub</a></li>
+          <li class=3D"mr-3 mr-lg-0"><a href=3D"https://github.com/pricing"=
+ data-analytics-event=3D"{&quot;category&quot;:&quot;Footer&quot;,&quot;act=
+ion&quot;:&quot;go to Pricing&quot;,&quot;label&quot;:&quot;text:Pricing&qu=
+ot;}">Pricing</a></li>
+        <li class=3D"mr-3 mr-lg-0"><a href=3D"https://docs.github.com/" dat=
+a-analytics-event=3D"{&quot;category&quot;:&quot;Footer&quot;,&quot;action&=
+quot;:&quot;go to api&quot;,&quot;label&quot;:&quot;text:api&quot;}">API</a=
+></li>
+        <li class=3D"mr-3 mr-lg-0"><a href=3D"https://services.github.com/"=
+ data-analytics-event=3D"{&quot;category&quot;:&quot;Footer&quot;,&quot;act=
+ion&quot;:&quot;go to training&quot;,&quot;label&quot;:&quot;text:training&=
+quot;}">Training</a></li>
+          <li class=3D"mr-3 mr-lg-0"><a href=3D"https://github.blog/" data-=
+analytics-event=3D"{&quot;category&quot;:&quot;Footer&quot;,&quot;action&qu=
+ot;:&quot;go to blog&quot;,&quot;label&quot;:&quot;text:blog&quot;}">Blog</=
+a></li>
+          <li><a data-ga-click=3D"Footer, go to about, text:about" href=3D"=
+https://github.com/about">About</a></li>
+      </ul>
+    </nav>
+  </div>
+
+  <div class=3D"d-flex flex-justify-center pb-6">
+    <span class=3D"f6 color-fg-muted"></span>
+  </div>
+</footer>
+
+
+
+
+ =20
+
+ =20
+    <template id=3D"site-details-dialog">
+  <details class=3D"details-reset details-overlay details-overlay-dark lh-d=
+efault color-fg-default hx_rsm" open=3D"">
+    <summary role=3D"button" aria-label=3D"Close dialog"></summary>
+    <details-dialog class=3D"Box Box--overlay d-flex flex-column anim-fade-=
+in fast hx_rsm-dialog hx_rsm-modal">
+      <button class=3D"Box-btn-octicon m-0 btn-octicon position-absolute ri=
+ght-0 top-0" type=3D"button" aria-label=3D"Close dialog" data-close-dialog=
+=3D"">
+        <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" versi=
+on=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octic=
+on-x">
+    <path fill-rule=3D"evenodd" d=3D"M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.=
+22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l=
+-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></pat=
+h>
+</svg>
+      </button>
+      <div class=3D"octocat-spinner my-6 js-details-dialog-spinner"></div>
+    </details-dialog>
+  </details>
+</template>
+
+    <div class=3D"Popover js-hovercard-content position-absolute" style=3D"=
+display: none; outline: none;" tabindex=3D"0">
+  <div class=3D"Popover-message Popover-message--bottom-left Popover-messag=
+e--large Box color-shadow-large" style=3D"width:360px;"></div>
+</div>
+
+    <template id=3D"snippet-clipboard-copy-button">
+  <div class=3D"zeroclipboard-container position-absolute right-0 top-0">
+    <clipboard-copy aria-label=3D"Copy" class=3D"ClipboardButton btn js-cli=
+pboard-copy m-2 p-0 tooltipped-no-delay" data-copy-feedback=3D"Copied!" dat=
+a-tooltip-direction=3D"w">
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-copy js-clipboard-copy-icon m-2">
+    <path fill-rule=3D"evenodd" d=3D"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.7=
+5 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00=
+.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 01=
+0 14.25v-7.5z"></path><path fill-rule=3D"evenodd" d=3D"M5 1.75C5 .784 5.784=
+ 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75=
+ 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h=
+7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+</svg>
+      <svg aria-hidden=3D"true" height=3D"16" viewBox=3D"0 0 16 16" version=
+=3D"1.1" width=3D"16" data-view-component=3D"true" class=3D"octicon octicon=
+-check js-clipboard-check-icon color-fg-success d-none m-2">
+    <path fill-rule=3D"evenodd" d=3D"M13.78 4.22a.75.75 0 010 1.06l-7.25 7.=
+25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.=
+75 0 011.06 0z"></path>
+</svg>
+    </clipboard-copy>
+  </div>
+</template>
+
+
+   =20
+
+
+    </div>
+
+    <div id=3D"js-global-screen-reader-notice" class=3D"sr-only" aria-live=
+=3D"polite">Est1y/Pandas-Analytics-Project: A data analysis project with Py=
+thon</div>
+ =20
+
+
+<div id=3D"torrent-scanner-popup" style=3D"display: none;"><template shadow=
+mode=3D"open"><link rel=3D"stylesheet" href=3D"chrome-extension://aegnopegb=
+bhjeeiganiajffnalhlkkjb/css/custom.css"><div id=3D"yf-bt-wrapper" class=3D"=
+free"><div class=3D"header"><img class=3D"sts-logo" src=3D"chrome-extension=
+://aegnopegbbhjeeiganiajffnalhlkkjb/img/assets/ts-free-logo.png"><div class=
+=3D"search-content"><input id=3D"search-input" class=3D"search-input" type=
+=3D"search" placeholder=3D"Start your search here..."><span id=3D"search-bt=
+n" class=3D"search-btn"></span></div></div><div class=3D"container"><div cl=
+ass=3D"main-container"><div id=3D"torrent-data" class=3D"torrent-content"><=
+div class=3D"t-table"><div class=3D"t-header"><div class=3D"t-name">Torrent=
+ search results</div></div><div id=3D"checked-sites" class=3D"checked-sites=
+-section"><div class=3D"left">Checked Sites</div><div class=3D"right"><span=
+ id=3D"sites-count" class=3D"sites-count">0</span></div></div><div id=3D"ta=
+ble-body" class=3D"t-body"><div id=3D"loading" class=3D"spinner"><div class=
+=3D"bounce1"></div><div class=3D"bounce2"></div><div class=3D"bounce3"></di=
+v></div><div class=3D"table-message-container" id=3D"table-message"><p>No i=
+tems to list <br> Use the search bar above for instant results</p></div></d=
+iv></div></div><div class=3D"tooltip"><p class=3D"tooltip-text">To see sear=
+ch results, type here and hit `Enter`</p></div><div class=3D"footer"><span>=
+<span id=3D"numberScanned" class=3D"numberScanned">No results</span></span>=
+</div><div class=3D"upgradeProPanel"><div class=3D"upgradeProPanelTitle">Tr=
+y our Pro Versions to unlock:</div><div class=3D"upgradeProPanelList"><div>=
+<p>Faster Results</p></div><div><p>Secure Torrenting</p></div><div><p>Unlim=
+ited Search Results with detailed torrent info</p></div><div><p>1-YR Subscr=
+iption to CyberGhost VPN <span style=3D"font-size:8px;">(PRO+VPN only)</spa=
+n></p></div></div><a class=3D"upgrade-to-pro-button-2" id=3D"buy-pro-vpn" h=
+ref=3D"https://www.utorrent.com/webpro-offer/?utm_source=3DLavasoft&amp;utm=
+_medium=3Dversion_1.0&amp;utm_campaign=3DScanner" target=3D"_blank">BUY PRO=
+ + VPN</a><a class=3D"upgrade-to-pro-button-2" id=3D"buy-pro" href=3D"https=
+://www.utorrent.com/webpro-offer/?utm_source=3DLavasoft&amp;utm_medium=3Dve=
+rsion_1.0&amp;utm_campaign=3DScanner" target=3D"_blank">BUY PRO</a></div></=
+div><div class=3D"sync-container nav-se-container"><div class=3D"nav-se-con=
+tent"><img class=3D"sync-icon nav-se-icon" src=3D"chrome-extension://aegnop=
+egbbhjeeiganiajffnalhlkkjb/img/assets/icon-sync.svg"><div class=3D"nav-se-t=
+itle">One more step to go before you start torrenting!</div><p class=3D"nav=
+-se-text">This extension can sync results with BitTorrent and/or uTorrent f=
+or instant downloading.</p><p class=3D"nav-se-text">To activate this featur=
+e, please click on the button below, and then on the Chrome message to acti=
+vate the 'Messaging Permission'.</p><button class=3D"sync-permission-btn na=
+v-se-btn">Activate Messaging Permission</button></div><div class=3D"nav-se-=
+content display-none"><img class=3D"sync-icon nav-se-icon" src=3D"chrome-ex=
+tension://aegnopegbbhjeeiganiajffnalhlkkjb/img/assets/icon-sync.svg"><div c=
+lass=3D"nav-se-title">Syncing...</div><p class=3D"nav-se-text">Please allow=
+ Messaging Permissions in the proceeding Chrome message.</p></div><div clas=
+s=3D"nav-se-content display-none"><img class=3D"sync-icon nav-se-icon" src=
+=3D"chrome-extension://aegnopegbbhjeeiganiajffnalhlkkjb/img/assets/icon-suc=
+cess.svg"><div class=3D"nav-se-title">Sync Complete</div><p class=3D"nav-se=
+-text">You have successfully activated the =E2=80=9CMessaging Permission=E2=
+=80=9D feature. All your search results will sync with BitTorrent and/or uT=
+orrent.</p></div></div><div class=3D"license-container nav-se-container"><d=
+iv class=3D"nav-se-content"><img class=3D"nav-se-icon" src=3D"chrome-extens=
+ion://aegnopegbbhjeeiganiajffnalhlkkjb/img/assets/icon-key2.svg"><div class=
+=3D"nav-se-title">Enter License Key</div><p class=3D"nav-se-text">Enter you=
+r license key and click on the activate button to start using <span>=C2=B5T=
+orrent Web Pro.</span></p><input type=3D"text" id=3D"license-input-key" cla=
+ss=3D"license-input-key" placeholder=3D"Enter Key"><div class=3D"license-sp=
+inner"><div class=3D"bounce1"></div><div class=3D"bounce2"></div><div class=
+=3D"bounce3"></div></div><button id=3D"license-activate-button" class=3D"li=
+cense-activate-button nav-se-btn">Activate</button><p>Don't have a license =
+key? <a class=3D"license-buy-link" target=3D"_blank">Click here</a></p></di=
+v><div class=3D"nav-se-content display-none"><img class=3D"nav-se-icon" src=
+=3D"chrome-extension://aegnopegbbhjeeiganiajffnalhlkkjb/img/assets/icon-suc=
+cess.svg"><div class=3D"nav-se-title">Happy Torrenting!</div><p class=3D"na=
+v-se-text">You are now an active PRO user</p><p class=3D"nav-se-text">Your =
+key is valid until <span class=3D"expiry-date"></span></p></div><div class=
+=3D"nav-se-content display-none"><img class=3D"nav-se-icon" src=3D"chrome-e=
+xtension://aegnopegbbhjeeiganiajffnalhlkkjb/img/assets/icon-alert.svg"><div=
+ class=3D"nav-se-title">Your license key has expired</div><p class=3D"nav-s=
+e-text">Looks like your license key has expired, to renew your PRO license =
+key, please select a license type:</p><a class=3D"upgrade-to-pro-button-2 b=
+uy-license-expiry-button" href=3D"https://www.utorrent.com/webpro-offer/?ut=
+m_source=3DLavasoft&amp;utm_medium=3Dversion_1.0&amp;utm_campaign=3DScanner=
+" target=3D"_blank">Buy Pro + VPN</a><a class=3D"upgrade-to-pro-button-2 bu=
+y-license-expiry-button" href=3D"https://www.utorrent.com/webpro-offer/?utm=
+_source=3DLavasoft&amp;utm_medium=3Dversion_1.0&amp;utm_campaign=3DScanner"=
+ target=3D"_blank">Buy Pro</a><p class=3D"nav-se-text">Already have a licen=
+se key? <a class=3D"link" id=3D"show-license-panel">Click here</a></p></div=
+><div class=3D"nav-se-content display-none"><img class=3D"nav-se-icon" src=
+=3D"chrome-extension://aegnopegbbhjeeiganiajffnalhlkkjb/img/assets/icon-suc=
+cess.svg"><div class=3D"nav-se-title">Pro User</div><p class=3D"nav-se-text=
+">Your key is valid until <span class=3D"expiry-date"></span></p><p class=
+=3D"nav-se-text">Your License Key:</p><p class=3D"nav-se-text"></p><p class=
+=3D"nav-se-text margin-top-50">Switch back to Torrent Scanner Free?</p><but=
+ton class=3D"activate-free-btn nav-se-btn">Revert to Free Version</button><=
+/div></div><div class=3D"feedback-container nav-se-container"><div class=3D=
+"nav-se-content"><img class=3D"nav-se-icon" src=3D"chrome-extension://aegno=
+pegbbhjeeiganiajffnalhlkkjb/img/assets/icon-feedback.svg"><div class=3D"nav=
+-se-title">Feedback</div><p class=3D"nav-se-text">Help us improve Torrent S=
+canner, send us comments, bugs, feedback, and suggestions.</p><button id=3D=
+"feedback-button" class=3D"feedback-button nav-se-btn">Send Feedback</butto=
+n></div></div><div class=3D"settings-container nav-se-container"><div class=
+=3D"nav-se-content"><div class=3D"settings-title">Settings</div></div><div =
+class=3D"s-table"><div class=3D"s-row"><div class=3D"s-title">FAQ<div class=
+=3D"s-arrow"><span class=3D"arrow-down"></span></div></div><div class=3D"s-=
+content faq-content"><p class=3D"faq-text">FAQ: <a href=3D"chrome-extension=
+://aegnopegbbhjeeiganiajffnalhlkkjb/faq.html" target=3D"_blank">Click here<=
+/a></p></div></div><div class=3D"s-row"><div class=3D"s-title">Rate the ext=
+ension<div class=3D"s-arrow"><span class=3D"arrow-down"></span></div></div>=
+<div class=3D"s-content"><p class=3D"rate-text">How did you like the extens=
+ion experience?</p><div class=3D"rating"><span class=3D"rating-star">=E2=98=
+=85</span><span class=3D"rating-star">=E2=98=85</span><span class=3D"rating=
+-star">=E2=98=85</span><span class=3D"rating-star">=E2=98=85</span><span cl=
+ass=3D"rating-star">=E2=98=85</span></div><button class=3D"rating-btn nav-s=
+e-btn" disabled=3D"">Submit</button></div></div><div class=3D"s-row"><div c=
+lass=3D"s-title">About<div class=3D"s-arrow"><span class=3D"arrow-down"></s=
+pan></div></div><div class=3D"s-content about-content"><div class=3D"about-=
+version">Version 1.3.0 <br><br> What's New</div><div class=3D"about-new"><p=
+></p><ul><li>Experience a complete new User Interface of the extension. It =
+is enhanced and user friendly now.</li><li>Squashed some bugs.</li></ul><p>=
+</p></div></div></div><div class=3D"s-row"><div class=3D"s-title">Privacy P=
+olicy<div class=3D"s-arrow"><span class=3D"arrow-down"></span></div></div><=
+div class=3D"s-content"><div class=3D"policy-text">Adaware Software (727035=
+6 Canada Inc.) is the operator of the Adaware products suites and related s=
+ervices (the =E2=80=9C<b>Company</b>=E2=80=9D, =E2=80=9D<b>we</b>=E2=80=9D =
+or =E2=80=9C<b>us</b>=E2=80=9D). We respect your privacy rights and we are =
+committed to protecting them. This privacy policy (=E2=80=9C<b>Privacy Poli=
+cy</b>=E2=80=9D or simply =E2=80=9C<b>policy</b>=E2=80=9D) governs our prod=
+ucts, services and websites that link to this Privacy Policy, and describes=
+ our practices of processing data from you. By =E2=80=9C<b>you</b>=E2=80=9D=
+, we refer to either or all of the following: (i) visitors to our websites =
+that links to this Privacy Policy (=E2=80=9C<b>Visitor</b>=E2=80=9D and =E2=
+=80=9C<b>Website</b>=E2=80=9D, respectively); (ii) our customers using our =
+software products and Services (=E2=80=9C<b>User</b>=E2=80=9D); and (c) a b=
+usiness customer, a business partner that has a contractual relationship wi=
+th us or a prospective customer that is yet to be engaged in a contract wit=
+h us (=E2=80=9CBusiness Customer=E2=80=9D). Unless explicitly mentioned oth=
+erwise, the information in this Privacy Policy refers to any and all data s=
+ubject types (=E2=80=9Cyou=E2=80=9D or =E2=80=9Cyour=E2=80=99). <br><br> Fo=
+r the purpose of this policy, the =E2=80=9C<b>Service(s)</b>=E2=80=9D shall=
+ include any software licensed by the Company, including features offered b=
+y or within the installed software or additional software scripts available=
+ therein (either downloaded from one of our websites, pre-installed on your=
+ device, downloaded through a third party website, obtained on a physical m=
+edium, or otherwise), or services provided through and/or on top such softw=
+are, services offered on our websites, communication forums, support servic=
+es, account operation, updates, enhancements, new features, premium support=
+, extended guarantees, online version and free versions of a software or ad=
+ditional services or features as we ay make available from time to time. <b=
+r><br> If you are a California resident, please also see our <a href=3D"htt=
+ps://www.adaware.com/CCPA/" target=3D"_blank">CCPA Notice</a>. <br><br> <a =
+href=3D"https://www.adaware.com/privacy-policy/" target=3D"_blank">Read mor=
+e</a></div></div></div><div class=3D"s-row" style=3D"display: none;"><div c=
+lass=3D"s-title">Contact Us<div class=3D"s-arrow"><span class=3D"arrow-down=
+"></span></div></div><div class=3D"s-content"><div class=3D"contact-text">F=
+or any payment and order-related support, please contact us at Email: <a hr=
+ef=3D"mailto:support@torrentscanner.zendesk.com">support@torrentscanner.zen=
+desk.com</a> or <a href=3D"mailto:pcsoftwareinfo.com">pcsoftwareinfo.com</a=
+><br><br>Phone: <a href=3D"https://pcsoftwareinfo.com/contact.aspx" target=
+=3D"_blank">Click here</a></div></div></div></div></div></div><div class=3D=
+"nav"><button id=3D"btnSync" class=3D"nav-btn">Sync</button><button id=3D"b=
+tnLicense" class=3D"nav-btn">License</button><button id=3D"btnHome" class=
+=3D"nav-btn">Home</button><button id=3D"btnFeedback" class=3D"nav-btn">Feed=
+back</button><button id=3D"btnSettings" class=3D"nav-btn">Settings</button>=
+</div></div></template></div></body><grammarly-desktop-integration data-gra=
+mmarly-shadow-root=3D"true"><template shadowmode=3D"open"><div aria-label=
+=3D"grammarly-integration" role=3D"group" tabindex=3D"-1" class=3D"grammarl=
+y-desktop-integration" data-content=3D"{&quot;mode&quot;:&quot;full&quot;,&=
+quot;isActive&quot;:true,&quot;isUserDisabled&quot;:false}"></div></templat=
+e></grammarly-desktop-integration></html>
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+
+@charset "utf-8";
+
+.user-mention[href$=3D"/Est1y"] { color: var(--color-user-mention-fg); back=
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+@charset "utf-8";
+
+:root, [data-color-mode=3D"light"][data-light-theme=3D"light"], [data-color=
+-mode=3D"dark"][data-dark-theme=3D"light"] { }
+
+:root, [data-color-mode=3D"light"][data-light-theme=3D"light"], [data-color=
+-mode=3D"dark"][data-dark-theme=3D"light"] { --color-canvas-default-transpa=
+rent: rgba(255,255,255,0); --color-page-header-bg: #f6f8fa; --color-marketi=
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+92f; --color-diff-blob-addition-num-bg: #ccffd8; --color-diff-blob-addition=
+-line-bg: #e6ffec; --color-diff-blob-addition-word-bg: #abf2bc; --color-dif=
+f-blob-deletion-num-text: #24292f; --color-diff-blob-deletion-fg: #24292f; =
+--color-diff-blob-deletion-num-bg: #ffd7d5; --color-diff-blob-deletion-line=
+-bg: #ffebe9; --color-diff-blob-deletion-word-bg: rgba(255,129,130,0.4); --=
+color-diff-blob-hunk-num-bg: rgba(84,174,255,0.4); --color-diff-blob-expand=
+er-icon: #57606a; --color-diff-blob-selected-line-highlight-mix-blend-mode:=
+ multiply; --color-diffstat-deletion-border: rgba(27,31,36,0.15); --color-d=
+iffstat-addition-border: rgba(27,31,36,0.15); --color-diffstat-addition-bg:=
+ #2da44e; --color-search-keyword-hl: #fff8c5; --color-prettylights-syntax-c=
+omment: #6e7781; --color-prettylights-syntax-constant: #0550ae; --color-pre=
+ttylights-syntax-entity: #8250df; --color-prettylights-syntax-storage-modif=
+ier-import: #24292f; --color-prettylights-syntax-entity-tag: #116329; --col=
+or-prettylights-syntax-keyword: #cf222e; --color-prettylights-syntax-string=
+: #0a3069; --color-prettylights-syntax-variable: #953800; --color-prettylig=
+hts-syntax-brackethighlighter-unmatched: #82071e; --color-prettylights-synt=
+ax-invalid-illegal-text: #f6f8fa; --color-prettylights-syntax-invalid-illeg=
+al-bg: #82071e; --color-prettylights-syntax-carriage-return-text: #f6f8fa; =
+--color-prettylights-syntax-carriage-return-bg: #cf222e; --color-prettyligh=
+ts-syntax-string-regexp: #116329; --color-prettylights-syntax-markup-list: =
+#3b2300; --color-prettylights-syntax-markup-heading: #0550ae; --color-prett=
+ylights-syntax-markup-italic: #24292f; --color-prettylights-syntax-markup-b=
+old: #24292f; --color-prettylights-syntax-markup-deleted-text: #82071e; --c=
+olor-prettylights-syntax-markup-deleted-bg: #ffebe9; --color-prettylights-s=
+yntax-markup-inserted-text: #116329; --color-prettylights-syntax-markup-ins=
+erted-bg: #dafbe1; --color-prettylights-syntax-markup-changed-text: #953800=
+; --color-prettylights-syntax-markup-changed-bg: #ffd8b5; --color-prettylig=
+hts-syntax-markup-ignored-text: #eaeef2; --color-prettylights-syntax-markup=
+-ignored-bg: #0550ae; --color-prettylights-syntax-meta-diff-range: #8250df;=
+ --color-prettylights-syntax-brackethighlighter-angle: #57606a; --color-pre=
+ttylights-syntax-sublimelinter-gutter-mark: #8c959f; --color-prettylights-s=
+yntax-constant-other-reference-link: #0a3069; --color-codemirror-text: #242=
+92f; --color-codemirror-bg: #ffffff; --color-codemirror-gutters-bg: #ffffff=
+; --color-codemirror-guttermarker-text: #ffffff; --color-codemirror-gutterm=
+arker-subtle-text: #6e7781; --color-codemirror-linenumber-text: #57606a; --=
+color-codemirror-cursor: #24292f; --color-codemirror-selection-bg: rgba(84,=
+174,255,0.4); --color-codemirror-activeline-bg: rgba(234,238,242,0.5); --co=
+lor-codemirror-matchingbracket-text: #24292f; --color-codemirror-lines-bg: =
+#ffffff; --color-codemirror-syntax-comment: #24292f; --color-codemirror-syn=
+tax-constant: #0550ae; --color-codemirror-syntax-entity: #8250df; --color-c=
+odemirror-syntax-keyword: #cf222e; --color-codemirror-syntax-storage: #cf22=
+2e; --color-codemirror-syntax-string: #0a3069; --color-codemirror-syntax-su=
+pport: #0550ae; --color-codemirror-syntax-variable: #953800; --color-checks=
+-bg: #24292f; --color-checks-run-border-width: 0px; --color-checks-containe=
+r-border-width: 0px; --color-checks-text-primary: #f6f8fa; --color-checks-t=
+ext-secondary: #8c959f; --color-checks-text-link: #54aeff; --color-checks-b=
+tn-icon: #afb8c1; --color-checks-btn-hover-icon: #f6f8fa; --color-checks-bt=
+n-hover-bg: rgba(255,255,255,0.125); --color-checks-input-text: #eaeef2; --=
+color-checks-input-placeholder-text: #8c959f; --color-checks-input-focus-te=
+xt: #8c959f; --color-checks-input-bg: #32383f; --color-checks-input-shadow:=
+ none; --color-checks-donut-error: #fa4549; --color-checks-donut-pending: #=
+bf8700; --color-checks-donut-success: #2da44e; --color-checks-donut-neutral=
+: #afb8c1; --color-checks-dropdown-text: #afb8c1; --color-checks-dropdown-b=
+g: #32383f; --color-checks-dropdown-border: #424a53; --color-checks-dropdow=
+n-shadow: rgba(27,31,36,0.3); --color-checks-dropdown-hover-text: #f6f8fa; =
+--color-checks-dropdown-hover-bg: #424a53; --color-checks-dropdown-btn-hove=
+r-text: #f6f8fa; --color-checks-dropdown-btn-hover-bg: #32383f; --color-che=
+cks-scrollbar-thumb-bg: #57606a; --color-checks-header-label-text: #d0d7de;=
+ --color-checks-header-label-open-text: #f6f8fa; --color-checks-header-bord=
+er: #32383f; --color-checks-header-icon: #8c959f; --color-checks-line-text:=
+ #d0d7de; --color-checks-line-num-text: rgba(140,149,159,0.75); --color-che=
+cks-line-timestamp-text: #8c959f; --color-checks-line-hover-bg: #32383f; --=
+color-checks-line-selected-bg: rgba(33,139,255,0.15); --color-checks-line-s=
+elected-num-text: #54aeff; --color-checks-line-dt-fm-text: #24292f; --color=
+-checks-line-dt-fm-bg: #9a6700; --color-checks-gate-bg: rgba(125,78,0,0.15)=
+; --color-checks-gate-text: #d0d7de; --color-checks-gate-waiting-text: #d4a=
+72c; --color-checks-step-header-open-bg: #32383f; --color-checks-step-error=
+-text: #ff8182; --color-checks-step-warning-text: #d4a72c; --color-checks-l=
+ogline-text: #8c959f; --color-checks-logline-num-text: rgba(140,149,159,0.7=
+5); --color-checks-logline-debug-text: #c297ff; --color-checks-logline-erro=
+r-text: #d0d7de; --color-checks-logline-error-num-text: #ff8182; --color-ch=
+ecks-logline-error-bg: rgba(164,14,38,0.15); --color-checks-logline-warning=
+-text: #d0d7de; --color-checks-logline-warning-num-text: #d4a72c; --color-c=
+hecks-logline-warning-bg: rgba(125,78,0,0.15); --color-checks-logline-comma=
+nd-text: #54aeff; --color-checks-logline-section-text: #4ac26b; --color-che=
+cks-ansi-black: #24292f; --color-checks-ansi-black-bright: #32383f; --color=
+-checks-ansi-white: #d0d7de; --color-checks-ansi-white-bright: #d0d7de; --c=
+olor-checks-ansi-gray: #8c959f; --color-checks-ansi-red: #ff8182; --color-c=
+hecks-ansi-red-bright: #ffaba8; --color-checks-ansi-green: #4ac26b; --color=
+-checks-ansi-green-bright: #6fdd8b; --color-checks-ansi-yellow: #d4a72c; --=
+color-checks-ansi-yellow-bright: #eac54f; --color-checks-ansi-blue: #54aeff=
+; --color-checks-ansi-blue-bright: #80ccff; --color-checks-ansi-magenta: #c=
+297ff; --color-checks-ansi-magenta-bright: #d8b9ff; --color-checks-ansi-cya=
+n: #76e3ea; --color-checks-ansi-cyan-bright: #b3f0ff; --color-project-heade=
+r-bg: #24292f; --color-project-sidebar-bg: #ffffff; --color-project-gradien=
+t-in: #ffffff; --color-project-gradient-out: rgba(255,255,255,0); --color-m=
+ktg-btn-bg: #1b1f23; --color-mktg-btn-shadow-outline: rgb(0 0 0 / 15%) 0 0 =
+0 1px inset; --color-mktg-btn-shadow-focus: rgb(0 0 0 / 15%) 0 0 0 4px; --c=
+olor-mktg-btn-shadow-hover: 0 3px 2px rgba(0, 0, 0, 0.07), 0 7px 5px rgba(0=
+, 0, 0, 0.04), 0 12px 10px rgba(0, 0, 0, 0.03), 0 22px 18px rgba(0, 0, 0, 0=
+.03), 0 42px 33px rgba(0, 0, 0, 0.02), 0 100px 80px rgba(0, 0, 0, 0.02); --=
+color-mktg-btn-shadow-hover-muted: rgb(0 0 0 / 70%) 0 0 0 2px inset; --colo=
+r-avatar-bg: #ffffff; --color-avatar-border: rgba(27,31,36,0.15); --color-a=
+vatar-stack-fade: #afb8c1; --color-avatar-stack-fade-more: #d0d7de; --color=
+-avatar-child-shadow: -2px -2px 0 rgba(255,255,255,0.8); --color-topic-tag-=
+border: rgba(0,0,0,0); --color-counter-border: rgba(0,0,0,0); --color-selec=
+t-menu-backdrop-border: rgba(0,0,0,0); --color-select-menu-tap-highlight: r=
+gba(175,184,193,0.5); --color-select-menu-tap-focus-bg: #b6e3ff; --color-ov=
+erlay-shadow: 0 1px 3px rgba(27,31,36,0.12), 0 8px 24px rgba(66,74,83,0.12)=
+; --color-header-text: rgba(255,255,255,0.7); --color-header-bg: #24292f; -=
+-color-header-divider: #57606a; --color-header-logo: #ffffff; --color-heade=
+r-search-bg: #24292f; --color-header-search-border: #57606a; --color-sidena=
+v-selected-bg: #ffffff; --color-menu-bg-active: rgba(0,0,0,0); --color-inpu=
+t-disabled-bg: rgba(175,184,193,0.2); --color-timeline-badge-bg: #eaeef2; -=
+-color-ansi-black: #24292f; --color-ansi-black-bright: #57606a; --color-ans=
+i-white: #6e7781; --color-ansi-white-bright: #8c959f; --color-ansi-gray: #6=
+e7781; --color-ansi-red: #cf222e; --color-ansi-red-bright: #a40e26; --color=
+-ansi-green: #116329; --color-ansi-green-bright: #1a7f37; --color-ansi-yell=
+ow: #4d2d00; --color-ansi-yellow-bright: #633c01; --color-ansi-blue: #0969d=
+a; --color-ansi-blue-bright: #218bff; --color-ansi-magenta: #8250df; --colo=
+r-ansi-magenta-bright: #a475f9; --color-ansi-cyan: #1b7c83; --color-ansi-cy=
+an-bright: #3192aa; --color-btn-text: #24292f; --color-btn-bg: #f6f8fa; --c=
+olor-btn-border: rgba(27,31,36,0.15); --color-btn-shadow: 0 1px 0 rgba(27,3=
+1,36,0.04); --color-btn-inset-shadow: inset 0 1px 0 rgba(255,255,255,0.25);=
+ --color-btn-hover-bg: #f3f4f6; --color-btn-hover-border: rgba(27,31,36,0.1=
+5); --color-btn-active-bg: hsla(220,14%,93%,1); --color-btn-active-border: =
+rgba(27,31,36,0.15); --color-btn-selected-bg: hsla(220,14%,94%,1); --color-=
+btn-focus-bg: #f6f8fa; --color-btn-focus-border: rgba(27,31,36,0.15); --col=
+or-btn-focus-shadow: 0 0 0 3px rgba(9,105,218,0.3); --color-btn-shadow-acti=
+ve: inset 0 0.15em 0.3em rgba(27,31,36,0.15); --color-btn-shadow-input-focu=
+s: 0 0 0 0.2em rgba(9,105,218,0.3); --color-btn-counter-bg: rgba(27,31,36,0=
+.08); --color-btn-primary-text: #ffffff; --color-btn-primary-bg: #2da44e; -=
+-color-btn-primary-border: rgba(27,31,36,0.15); --color-btn-primary-shadow:=
+ 0 1px 0 rgba(27,31,36,0.1); --color-btn-primary-inset-shadow: inset 0 1px =
+0 rgba(255,255,255,0.03); --color-btn-primary-hover-bg: #2c974b; --color-bt=
+n-primary-hover-border: rgba(27,31,36,0.15); --color-btn-primary-selected-b=
+g: hsla(137,55%,36%,1); --color-btn-primary-selected-shadow: inset 0 1px 0 =
+rgba(0,45,17,0.2); --color-btn-primary-disabled-text: rgba(255,255,255,0.8)=
+; --color-btn-primary-disabled-bg: #94d3a2; --color-btn-primary-disabled-bo=
+rder: rgba(27,31,36,0.15); --color-btn-primary-focus-bg: #2da44e; --color-b=
+tn-primary-focus-border: rgba(27,31,36,0.15); --color-btn-primary-focus-sha=
+dow: 0 0 0 3px rgba(45,164,78,0.4); --color-btn-primary-icon: rgba(255,255,=
+255,0.8); --color-btn-primary-counter-bg: rgba(255,255,255,0.2); --color-bt=
+n-outline-text: #0969da; --color-btn-outline-hover-text: #ffffff; --color-b=
+tn-outline-hover-bg: #0969da; --color-btn-outline-hover-border: rgba(27,31,=
+36,0.15); --color-btn-outline-hover-shadow: 0 1px 0 rgba(27,31,36,0.1); --c=
+olor-btn-outline-hover-inset-shadow: inset 0 1px 0 rgba(255,255,255,0.03); =
+--color-btn-outline-hover-counter-bg: rgba(255,255,255,0.2); --color-btn-ou=
+tline-selected-text: #ffffff; --color-btn-outline-selected-bg: hsla(212,92%=
+,42%,1); --color-btn-outline-selected-border: rgba(27,31,36,0.15); --color-=
+btn-outline-selected-shadow: inset 0 1px 0 rgba(0,33,85,0.2); --color-btn-o=
+utline-disabled-text: rgba(9,105,218,0.5); --color-btn-outline-disabled-bg:=
+ #f6f8fa; --color-btn-outline-disabled-counter-bg: rgba(9,105,218,0.05); --=
+color-btn-outline-focus-border: rgba(27,31,36,0.15); --color-btn-outline-fo=
+cus-shadow: 0 0 0 3px rgba(5,80,174,0.4); --color-btn-outline-counter-bg: r=
+gba(9,105,218,0.1); --color-btn-danger-text: #cf222e; --color-btn-danger-ho=
+ver-text: #ffffff; --color-btn-danger-hover-bg: #a40e26; --color-btn-danger=
+-hover-border: rgba(27,31,36,0.15); --color-btn-danger-hover-shadow: 0 1px =
+0 rgba(27,31,36,0.1); --color-btn-danger-hover-inset-shadow: inset 0 1px 0 =
+rgba(255,255,255,0.03); --color-btn-danger-hover-counter-bg: rgba(255,255,2=
+55,0.2); --color-btn-danger-selected-text: #ffffff; --color-btn-danger-sele=
+cted-bg: hsla(356,72%,44%,1); --color-btn-danger-selected-border: rgba(27,3=
+1,36,0.15); --color-btn-danger-selected-shadow: inset 0 1px 0 rgba(76,0,20,=
+0.2); --color-btn-danger-disabled-text: rgba(207,34,46,0.5); --color-btn-da=
+nger-disabled-bg: #f6f8fa; --color-btn-danger-disabled-counter-bg: rgba(207=
+,34,46,0.05); --color-btn-danger-focus-border: rgba(27,31,36,0.15); --color=
+-btn-danger-focus-shadow: 0 0 0 3px rgba(164,14,38,0.4); --color-btn-danger=
+-counter-bg: rgba(207,34,46,0.1); --color-btn-danger-icon: #cf222e; --color=
+-btn-danger-hover-icon: #ffffff; --color-underlinenav-icon: #6e7781; --colo=
+r-underlinenav-border-hover: rgba(175,184,193,0.2); --color-action-list-ite=
+m-inline-divider: rgba(208,215,222,0.48); --color-action-list-item-default-=
+hover-bg: rgba(208,215,222,0.32); --color-action-list-item-default-hover-bo=
+rder: rgba(0,0,0,0); --color-action-list-item-default-active-bg: rgba(208,2=
+15,222,0.48); --color-action-list-item-default-active-border: rgba(0,0,0,0)=
+; --color-action-list-item-default-selected-bg: rgba(208,215,222,0.24); --c=
+olor-action-list-item-danger-hover-bg: rgba(255,235,233,0.64); --color-acti=
+on-list-item-danger-active-bg: #ffebe9; --color-action-list-item-danger-hov=
+er-text: #cf222e; --color-switch-track-bg: #eaeef2; --color-switch-track-bo=
+rder: #afb8c1; --color-switch-track-checked-bg: #ddf4ff; --color-switch-tra=
+ck-checked-hover-bg: #b6e3ff; --color-switch-track-checked-active-bg: #80cc=
+ff; --color-switch-track-checked-border: #54aeff; --color-switch-knob-check=
+ed-bg: #0969da; --color-switch-knob-checked-disabled-bg: #6e7781; --color-s=
+egmented-control-bg: #eaeef2; --color-segmented-control-button-hover-bg: rg=
+ba(175,184,193,0.2); --color-segmented-control-button-active-bg: rgba(175,1=
+84,193,0.4); --color-segmented-control-button-selected-border: #6e7781; --c=
+olor-tree-view-item-chevron-hover-bg: rgba(208,215,222,0.32); --color-tree-=
+view-item-directory-fill: #54aeff; --color-fg-default: #24292f; --color-fg-=
+muted: #57606a; --color-fg-subtle: #6e7781; --color-fg-on-emphasis: #ffffff=
+; --color-canvas-default: #ffffff; --color-canvas-overlay: #ffffff; --color=
+-canvas-inset: #f6f8fa; --color-canvas-subtle: #f6f8fa; --color-border-defa=
+ult: #d0d7de; --color-border-muted: hsla(210,18%,87%,1); --color-border-sub=
+tle: rgba(27,31,36,0.15); --color-shadow-small: 0 1px 0 rgba(27,31,36,0.04)=
+; --color-shadow-medium: 0 3px 6px rgba(140,149,159,0.15); --color-shadow-l=
+arge: 0 8px 24px rgba(140,149,159,0.2); --color-shadow-extra-large: 0 12px =
+28px rgba(140,149,159,0.3); --color-neutral-emphasis-plus: #24292f; --color=
+-neutral-emphasis: #6e7781; --color-neutral-muted: rgba(175,184,193,0.2); -=
+-color-neutral-subtle: rgba(234,238,242,0.5); --color-accent-fg: #0969da; -=
+-color-accent-emphasis: #0969da; --color-accent-muted: rgba(84,174,255,0.4)=
+; --color-accent-subtle: #ddf4ff; --color-success-fg: #1a7f37; --color-succ=
+ess-emphasis: #2da44e; --color-success-muted: rgba(74,194,107,0.4); --color=
+-success-subtle: #dafbe1; --color-attention-fg: #9a6700; --color-attention-=
+emphasis: #bf8700; --color-attention-muted: rgba(212,167,44,0.4); --color-a=
+ttention-subtle: #fff8c5; --color-severe-fg: #bc4c00; --color-severe-emphas=
+is: #bc4c00; --color-severe-muted: rgba(251,143,68,0.4); --color-severe-sub=
+tle: #fff1e5; --color-danger-fg: #cf222e; --color-danger-emphasis: #cf222e;=
+ --color-danger-muted: rgba(255,129,130,0.4); --color-danger-subtle: #ffebe=
+9; --color-open-fg: #1a7f37; --color-open-emphasis: #2da44e; --color-open-m=
+uted: rgba(74,194,107,0.4); --color-open-subtle: #dafbe1; --color-closed-fg=
+: #cf222e; --color-closed-emphasis: #cf222e; --color-closed-muted: rgba(255=
+,129,130,0.4); --color-closed-subtle: #ffebe9; --color-done-fg: #8250df; --=
+color-done-emphasis: #8250df; --color-done-muted: rgba(194,151,255,0.4); --=
+color-done-subtle: #fbefff; --color-sponsors-fg: #bf3989; --color-sponsors-=
+emphasis: #bf3989; --color-sponsors-muted: rgba(255,128,200,0.4); --color-s=
+ponsors-subtle: #ffeff7; --color-primer-fg-disabled: #8c959f; --color-prime=
+r-canvas-backdrop: rgba(27,31,36,0.5); --color-primer-canvas-sticky: rgba(2=
+55,255,255,0.95); --color-primer-border-active: #fd8c73; --color-primer-bor=
+der-contrast: rgba(27,31,36,0.1); --color-primer-shadow-highlight: inset 0 =
+1px 0 rgba(255,255,255,0.25); --color-primer-shadow-inset: inset 0 1px 0 rg=
+ba(208,215,222,0.2); --color-primer-shadow-focus: 0 0 0 3px rgba(9,105,218,=
+0.3); --color-scale-black: #1b1f24; --color-scale-white: #ffffff; --color-s=
+cale-gray-0: #f6f8fa; --color-scale-gray-1: #eaeef2; --color-scale-gray-2: =
+#d0d7de; --color-scale-gray-3: #afb8c1; --color-scale-gray-4: #8c959f; --co=
+lor-scale-gray-5: #6e7781; --color-scale-gray-6: #57606a; --color-scale-gra=
+y-7: #424a53; --color-scale-gray-8: #32383f; --color-scale-gray-9: #24292f;=
+ --color-scale-blue-0: #ddf4ff; --color-scale-blue-1: #b6e3ff; --color-scal=
+e-blue-2: #80ccff; --color-scale-blue-3: #54aeff; --color-scale-blue-4: #21=
+8bff; --color-scale-blue-5: #0969da; --color-scale-blue-6: #0550ae; --color=
+-scale-blue-7: #033d8b; --color-scale-blue-8: #0a3069; --color-scale-blue-9=
+: #002155; --color-scale-green-0: #dafbe1; --color-scale-green-1: #aceebb; =
+--color-scale-green-2: #6fdd8b; --color-scale-green-3: #4ac26b; --color-sca=
+le-green-4: #2da44e; --color-scale-green-5: #1a7f37; --color-scale-green-6:=
+ #116329; --color-scale-green-7: #044f1e; --color-scale-green-8: #003d16; -=
+-color-scale-green-9: #002d11; --color-scale-yellow-0: #fff8c5; --color-sca=
+le-yellow-1: #fae17d; --color-scale-yellow-2: #eac54f; --color-scale-yellow=
+-3: #d4a72c; --color-scale-yellow-4: #bf8700; --color-scale-yellow-5: #9a67=
+00; --color-scale-yellow-6: #7d4e00; --color-scale-yellow-7: #633c01; --col=
+or-scale-yellow-8: #4d2d00; --color-scale-yellow-9: #3b2300; --color-scale-=
+orange-0: #fff1e5; --color-scale-orange-1: #ffd8b5; --color-scale-orange-2:=
+ #ffb77c; --color-scale-orange-3: #fb8f44; --color-scale-orange-4: #e16f24;=
+ --color-scale-orange-5: #bc4c00; --color-scale-orange-6: #953800; --color-=
+scale-orange-7: #762c00; --color-scale-orange-8: #5c2200; --color-scale-ora=
+nge-9: #471700; --color-scale-red-0: #ffebe9; --color-scale-red-1: #ffcecb;=
+ --color-scale-red-2: #ffaba8; --color-scale-red-3: #ff8182; --color-scale-=
+red-4: #fa4549; --color-scale-red-5: #cf222e; --color-scale-red-6: #a40e26;=
+ --color-scale-red-7: #82071e; --color-scale-red-8: #660018; --color-scale-=
+red-9: #4c0014; --color-scale-purple-0: #fbefff; --color-scale-purple-1: #e=
+cd8ff; --color-scale-purple-2: #d8b9ff; --color-scale-purple-3: #c297ff; --=
+color-scale-purple-4: #a475f9; --color-scale-purple-5: #8250df; --color-sca=
+le-purple-6: #6639ba; --color-scale-purple-7: #512a97; --color-scale-purple=
+-8: #3e1f79; --color-scale-purple-9: #2e1461; --color-scale-pink-0: #ffeff7=
+; --color-scale-pink-1: #ffd3eb; --color-scale-pink-2: #ffadda; --color-sca=
+le-pink-3: #ff80c8; --color-scale-pink-4: #e85aad; --color-scale-pink-5: #b=
+f3989; --color-scale-pink-6: #99286e; --color-scale-pink-7: #772057; --colo=
+r-scale-pink-8: #611347; --color-scale-pink-9: #4d0336; --color-scale-coral=
+-0: #fff0eb; --color-scale-coral-1: #ffd6cc; --color-scale-coral-2: #ffb4a1=
+; --color-scale-coral-3: #fd8c73; --color-scale-coral-4: #ec6547; --color-s=
+cale-coral-5: #c4432b; --color-scale-coral-6: #9e2f1c; --color-scale-coral-=
+7: #801f0f; --color-scale-coral-8: #691105; --color-scale-coral-9: #510901;=
+ }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme=3D"light"] { --color-canvas-d=
+efault-transparent: rgba(255,255,255,0); --color-page-header-bg: #f6f8fa; -=
+-color-marketing-icon-primary: #218bff; --color-marketing-icon-secondary: #=
+54aeff; --color-diff-blob-addition-num-text: #24292f; --color-diff-blob-add=
+ition-fg: #24292f; --color-diff-blob-addition-num-bg: #ccffd8; --color-diff=
+-blob-addition-line-bg: #e6ffec; --color-diff-blob-addition-word-bg: #abf2b=
+c; --color-diff-blob-deletion-num-text: #24292f; --color-diff-blob-deletion=
+-fg: #24292f; --color-diff-blob-deletion-num-bg: #ffd7d5; --color-diff-blob=
+-deletion-line-bg: #ffebe9; --color-diff-blob-deletion-word-bg: rgba(255,12=
+9,130,0.4); --color-diff-blob-hunk-num-bg: rgba(84,174,255,0.4); --color-di=
+ff-blob-expander-icon: #57606a; --color-diff-blob-selected-line-highlight-m=
+ix-blend-mode: multiply; --color-diffstat-deletion-border: rgba(27,31,36,0.=
+15); --color-diffstat-addition-border: rgba(27,31,36,0.15); --color-diffsta=
+t-addition-bg: #2da44e; --color-search-keyword-hl: #fff8c5; --color-prettyl=
+ights-syntax-comment: #6e7781; --color-prettylights-syntax-constant: #0550a=
+e; --color-prettylights-syntax-entity: #8250df; --color-prettylights-syntax=
+-storage-modifier-import: #24292f; --color-prettylights-syntax-entity-tag: =
+#116329; --color-prettylights-syntax-keyword: #cf222e; --color-prettylights=
+-syntax-string: #0a3069; --color-prettylights-syntax-variable: #953800; --c=
+olor-prettylights-syntax-brackethighlighter-unmatched: #82071e; --color-pre=
+ttylights-syntax-invalid-illegal-text: #f6f8fa; --color-prettylights-syntax=
+-invalid-illegal-bg: #82071e; --color-prettylights-syntax-carriage-return-t=
+ext: #f6f8fa; --color-prettylights-syntax-carriage-return-bg: #cf222e; --co=
+lor-prettylights-syntax-string-regexp: #116329; --color-prettylights-syntax=
+-markup-list: #3b2300; --color-prettylights-syntax-markup-heading: #0550ae;=
+ --color-prettylights-syntax-markup-italic: #24292f; --color-prettylights-s=
+yntax-markup-bold: #24292f; --color-prettylights-syntax-markup-deleted-text=
+: #82071e; --color-prettylights-syntax-markup-deleted-bg: #ffebe9; --color-=
+prettylights-syntax-markup-inserted-text: #116329; --color-prettylights-syn=
+tax-markup-inserted-bg: #dafbe1; --color-prettylights-syntax-markup-changed=
+-text: #953800; --color-prettylights-syntax-markup-changed-bg: #ffd8b5; --c=
+olor-prettylights-syntax-markup-ignored-text: #eaeef2; --color-prettylights=
+-syntax-markup-ignored-bg: #0550ae; --color-prettylights-syntax-meta-diff-r=
+ange: #8250df; --color-prettylights-syntax-brackethighlighter-angle: #57606=
+a; --color-prettylights-syntax-sublimelinter-gutter-mark: #8c959f; --color-=
+prettylights-syntax-constant-other-reference-link: #0a3069; --color-codemir=
+ror-text: #24292f; --color-codemirror-bg: #ffffff; --color-codemirror-gutte=
+rs-bg: #ffffff; --color-codemirror-guttermarker-text: #ffffff; --color-code=
+mirror-guttermarker-subtle-text: #6e7781; --color-codemirror-linenumber-tex=
+t: #57606a; --color-codemirror-cursor: #24292f; --color-codemirror-selectio=
+n-bg: rgba(84,174,255,0.4); --color-codemirror-activeline-bg: rgba(234,238,=
+242,0.5); --color-codemirror-matchingbracket-text: #24292f; --color-codemir=
+ror-lines-bg: #ffffff; --color-codemirror-syntax-comment: #24292f; --color-=
+codemirror-syntax-constant: #0550ae; --color-codemirror-syntax-entity: #825=
+0df; --color-codemirror-syntax-keyword: #cf222e; --color-codemirror-syntax-=
+storage: #cf222e; --color-codemirror-syntax-string: #0a3069; --color-codemi=
+rror-syntax-support: #0550ae; --color-codemirror-syntax-variable: #953800; =
+--color-checks-bg: #24292f; --color-checks-run-border-width: 0px; --color-c=
+hecks-container-border-width: 0px; --color-checks-text-primary: #f6f8fa; --=
+color-checks-text-secondary: #8c959f; --color-checks-text-link: #54aeff; --=
+color-checks-btn-icon: #afb8c1; --color-checks-btn-hover-icon: #f6f8fa; --c=
+olor-checks-btn-hover-bg: rgba(255,255,255,0.125); --color-checks-input-tex=
+t: #eaeef2; --color-checks-input-placeholder-text: #8c959f; --color-checks-=
+input-focus-text: #8c959f; --color-checks-input-bg: #32383f; --color-checks=
+-input-shadow: none; --color-checks-donut-error: #fa4549; --color-checks-do=
+nut-pending: #bf8700; --color-checks-donut-success: #2da44e; --color-checks=
+-donut-neutral: #afb8c1; --color-checks-dropdown-text: #afb8c1; --color-che=
+cks-dropdown-bg: #32383f; --color-checks-dropdown-border: #424a53; --color-=
+checks-dropdown-shadow: rgba(27,31,36,0.3); --color-checks-dropdown-hover-t=
+ext: #f6f8fa; --color-checks-dropdown-hover-bg: #424a53; --color-checks-dro=
+pdown-btn-hover-text: #f6f8fa; --color-checks-dropdown-btn-hover-bg: #32383=
+f; --color-checks-scrollbar-thumb-bg: #57606a; --color-checks-header-label-=
+text: #d0d7de; --color-checks-header-label-open-text: #f6f8fa; --color-chec=
+ks-header-border: #32383f; --color-checks-header-icon: #8c959f; --color-che=
+cks-line-text: #d0d7de; --color-checks-line-num-text: rgba(140,149,159,0.75=
+); --color-checks-line-timestamp-text: #8c959f; --color-checks-line-hover-b=
+g: #32383f; --color-checks-line-selected-bg: rgba(33,139,255,0.15); --color=
+-checks-line-selected-num-text: #54aeff; --color-checks-line-dt-fm-text: #2=
+4292f; --color-checks-line-dt-fm-bg: #9a6700; --color-checks-gate-bg: rgba(=
+125,78,0,0.15); --color-checks-gate-text: #d0d7de; --color-checks-gate-wait=
+ing-text: #d4a72c; --color-checks-step-header-open-bg: #32383f; --color-che=
+cks-step-error-text: #ff8182; --color-checks-step-warning-text: #d4a72c; --=
+color-checks-logline-text: #8c959f; --color-checks-logline-num-text: rgba(1=
+40,149,159,0.75); --color-checks-logline-debug-text: #c297ff; --color-check=
+s-logline-error-text: #d0d7de; --color-checks-logline-error-num-text: #ff81=
+82; --color-checks-logline-error-bg: rgba(164,14,38,0.15); --color-checks-l=
+ogline-warning-text: #d0d7de; --color-checks-logline-warning-num-text: #d4a=
+72c; --color-checks-logline-warning-bg: rgba(125,78,0,0.15); --color-checks=
+-logline-command-text: #54aeff; --color-checks-logline-section-text: #4ac26=
+b; --color-checks-ansi-black: #24292f; --color-checks-ansi-black-bright: #3=
+2383f; --color-checks-ansi-white: #d0d7de; --color-checks-ansi-white-bright=
+: #d0d7de; --color-checks-ansi-gray: #8c959f; --color-checks-ansi-red: #ff8=
+182; --color-checks-ansi-red-bright: #ffaba8; --color-checks-ansi-green: #4=
+ac26b; --color-checks-ansi-green-bright: #6fdd8b; --color-checks-ansi-yello=
+w: #d4a72c; --color-checks-ansi-yellow-bright: #eac54f; --color-checks-ansi=
+-blue: #54aeff; --color-checks-ansi-blue-bright: #80ccff; --color-checks-an=
+si-magenta: #c297ff; --color-checks-ansi-magenta-bright: #d8b9ff; --color-c=
+hecks-ansi-cyan: #76e3ea; --color-checks-ansi-cyan-bright: #b3f0ff; --color=
+-project-header-bg: #24292f; --color-project-sidebar-bg: #ffffff; --color-p=
+roject-gradient-in: #ffffff; --color-project-gradient-out: rgba(255,255,255=
+,0); --color-mktg-btn-bg: #1b1f23; --color-mktg-btn-shadow-outline: rgb(0 0=
+ 0 / 15%) 0 0 0 1px inset; --color-mktg-btn-shadow-focus: rgb(0 0 0 / 15%) =
+0 0 0 4px; --color-mktg-btn-shadow-hover: 0 3px 2px rgba(0, 0, 0, 0.07), 0 =
+7px 5px rgba(0, 0, 0, 0.04), 0 12px 10px rgba(0, 0, 0, 0.03), 0 22px 18px r=
+gba(0, 0, 0, 0.03), 0 42px 33px rgba(0, 0, 0, 0.02), 0 100px 80px rgba(0, 0=
+, 0, 0.02); --color-mktg-btn-shadow-hover-muted: rgb(0 0 0 / 70%) 0 0 0 2px=
+ inset; --color-avatar-bg: #ffffff; --color-avatar-border: rgba(27,31,36,0.=
+15); --color-avatar-stack-fade: #afb8c1; --color-avatar-stack-fade-more: #d=
+0d7de; --color-avatar-child-shadow: -2px -2px 0 rgba(255,255,255,0.8); --co=
+lor-topic-tag-border: rgba(0,0,0,0); --color-counter-border: rgba(0,0,0,0);=
+ --color-select-menu-backdrop-border: rgba(0,0,0,0); --color-select-menu-ta=
+p-highlight: rgba(175,184,193,0.5); --color-select-menu-tap-focus-bg: #b6e3=
+ff; --color-overlay-shadow: 0 1px 3px rgba(27,31,36,0.12), 0 8px 24px rgba(=
+66,74,83,0.12); --color-header-text: rgba(255,255,255,0.7); --color-header-=
+bg: #24292f; --color-header-divider: #57606a; --color-header-logo: #ffffff;=
+ --color-header-search-bg: #24292f; --color-header-search-border: #57606a; =
+--color-sidenav-selected-bg: #ffffff; --color-menu-bg-active: rgba(0,0,0,0)=
+; --color-input-disabled-bg: rgba(175,184,193,0.2); --color-timeline-badge-=
+bg: #eaeef2; --color-ansi-black: #24292f; --color-ansi-black-bright: #57606=
+a; --color-ansi-white: #6e7781; --color-ansi-white-bright: #8c959f; --color=
+-ansi-gray: #6e7781; --color-ansi-red: #cf222e; --color-ansi-red-bright: #a=
+40e26; --color-ansi-green: #116329; --color-ansi-green-bright: #1a7f37; --c=
+olor-ansi-yellow: #4d2d00; --color-ansi-yellow-bright: #633c01; --color-ans=
+i-blue: #0969da; --color-ansi-blue-bright: #218bff; --color-ansi-magenta: #=
+8250df; --color-ansi-magenta-bright: #a475f9; --color-ansi-cyan: #1b7c83; -=
+-color-ansi-cyan-bright: #3192aa; --color-btn-text: #24292f; --color-btn-bg=
+: #f6f8fa; --color-btn-border: rgba(27,31,36,0.15); --color-btn-shadow: 0 1=
+px 0 rgba(27,31,36,0.04); --color-btn-inset-shadow: inset 0 1px 0 rgba(255,=
+255,255,0.25); --color-btn-hover-bg: #f3f4f6; --color-btn-hover-border: rgb=
+a(27,31,36,0.15); --color-btn-active-bg: hsla(220,14%,93%,1); --color-btn-a=
+ctive-border: rgba(27,31,36,0.15); --color-btn-selected-bg: hsla(220,14%,94=
+%,1); --color-btn-focus-bg: #f6f8fa; --color-btn-focus-border: rgba(27,31,3=
+6,0.15); --color-btn-focus-shadow: 0 0 0 3px rgba(9,105,218,0.3); --color-b=
+tn-shadow-active: inset 0 0.15em 0.3em rgba(27,31,36,0.15); --color-btn-sha=
+dow-input-focus: 0 0 0 0.2em rgba(9,105,218,0.3); --color-btn-counter-bg: r=
+gba(27,31,36,0.08); --color-btn-primary-text: #ffffff; --color-btn-primary-=
+bg: #2da44e; --color-btn-primary-border: rgba(27,31,36,0.15); --color-btn-p=
+rimary-shadow: 0 1px 0 rgba(27,31,36,0.1); --color-btn-primary-inset-shadow=
+: inset 0 1px 0 rgba(255,255,255,0.03); --color-btn-primary-hover-bg: #2c97=
+4b; --color-btn-primary-hover-border: rgba(27,31,36,0.15); --color-btn-prim=
+ary-selected-bg: hsla(137,55%,36%,1); --color-btn-primary-selected-shadow: =
+inset 0 1px 0 rgba(0,45,17,0.2); --color-btn-primary-disabled-text: rgba(25=
+5,255,255,0.8); --color-btn-primary-disabled-bg: #94d3a2; --color-btn-prima=
+ry-disabled-border: rgba(27,31,36,0.15); --color-btn-primary-focus-bg: #2da=
+44e; --color-btn-primary-focus-border: rgba(27,31,36,0.15); --color-btn-pri=
+mary-focus-shadow: 0 0 0 3px rgba(45,164,78,0.4); --color-btn-primary-icon:=
+ rgba(255,255,255,0.8); --color-btn-primary-counter-bg: rgba(255,255,255,0.=
+2); --color-btn-outline-text: #0969da; --color-btn-outline-hover-text: #fff=
+fff; --color-btn-outline-hover-bg: #0969da; --color-btn-outline-hover-borde=
+r: rgba(27,31,36,0.15); --color-btn-outline-hover-shadow: 0 1px 0 rgba(27,3=
+1,36,0.1); --color-btn-outline-hover-inset-shadow: inset 0 1px 0 rgba(255,2=
+55,255,0.03); --color-btn-outline-hover-counter-bg: rgba(255,255,255,0.2); =
+--color-btn-outline-selected-text: #ffffff; --color-btn-outline-selected-bg=
+: hsla(212,92%,42%,1); --color-btn-outline-selected-border: rgba(27,31,36,0=
+.15); --color-btn-outline-selected-shadow: inset 0 1px 0 rgba(0,33,85,0.2);=
+ --color-btn-outline-disabled-text: rgba(9,105,218,0.5); --color-btn-outlin=
+e-disabled-bg: #f6f8fa; --color-btn-outline-disabled-counter-bg: rgba(9,105=
+,218,0.05); --color-btn-outline-focus-border: rgba(27,31,36,0.15); --color-=
+btn-outline-focus-shadow: 0 0 0 3px rgba(5,80,174,0.4); --color-btn-outline=
+-counter-bg: rgba(9,105,218,0.1); --color-btn-danger-text: #cf222e; --color=
+-btn-danger-hover-text: #ffffff; --color-btn-danger-hover-bg: #a40e26; --co=
+lor-btn-danger-hover-border: rgba(27,31,36,0.15); --color-btn-danger-hover-=
+shadow: 0 1px 0 rgba(27,31,36,0.1); --color-btn-danger-hover-inset-shadow: =
+inset 0 1px 0 rgba(255,255,255,0.03); --color-btn-danger-hover-counter-bg: =
+rgba(255,255,255,0.2); --color-btn-danger-selected-text: #ffffff; --color-b=
+tn-danger-selected-bg: hsla(356,72%,44%,1); --color-btn-danger-selected-bor=
+der: rgba(27,31,36,0.15); --color-btn-danger-selected-shadow: inset 0 1px 0=
+ rgba(76,0,20,0.2); --color-btn-danger-disabled-text: rgba(207,34,46,0.5); =
+--color-btn-danger-disabled-bg: #f6f8fa; --color-btn-danger-disabled-counte=
+r-bg: rgba(207,34,46,0.05); --color-btn-danger-focus-border: rgba(27,31,36,=
+0.15); --color-btn-danger-focus-shadow: 0 0 0 3px rgba(164,14,38,0.4); --co=
+lor-btn-danger-counter-bg: rgba(207,34,46,0.1); --color-btn-danger-icon: #c=
+f222e; --color-btn-danger-hover-icon: #ffffff; --color-underlinenav-icon: #=
+6e7781; --color-underlinenav-border-hover: rgba(175,184,193,0.2); --color-a=
+ction-list-item-inline-divider: rgba(208,215,222,0.48); --color-action-list=
+-item-default-hover-bg: rgba(208,215,222,0.32); --color-action-list-item-de=
+fault-hover-border: rgba(0,0,0,0); --color-action-list-item-default-active-=
+bg: rgba(208,215,222,0.48); --color-action-list-item-default-active-border:=
+ rgba(0,0,0,0); --color-action-list-item-default-selected-bg: rgba(208,215,=
+222,0.24); --color-action-list-item-danger-hover-bg: rgba(255,235,233,0.64)=
+; --color-action-list-item-danger-active-bg: #ffebe9; --color-action-list-i=
+tem-danger-hover-text: #cf222e; --color-switch-track-bg: #eaeef2; --color-s=
+witch-track-border: #afb8c1; --color-switch-track-checked-bg: #ddf4ff; --co=
+lor-switch-track-checked-hover-bg: #b6e3ff; --color-switch-track-checked-ac=
+tive-bg: #80ccff; --color-switch-track-checked-border: #54aeff; --color-swi=
+tch-knob-checked-bg: #0969da; --color-switch-knob-checked-disabled-bg: #6e7=
+781; --color-segmented-control-bg: #eaeef2; --color-segmented-control-butto=
+n-hover-bg: rgba(175,184,193,0.2); --color-segmented-control-button-active-=
+bg: rgba(175,184,193,0.4); --color-segmented-control-button-selected-border=
+: #6e7781; --color-tree-view-item-chevron-hover-bg: rgba(208,215,222,0.32);=
+ --color-tree-view-item-directory-fill: #54aeff; --color-fg-default: #24292=
+f; --color-fg-muted: #57606a; --color-fg-subtle: #6e7781; --color-fg-on-emp=
+hasis: #ffffff; --color-canvas-default: #ffffff; --color-canvas-overlay: #f=
+fffff; --color-canvas-inset: #f6f8fa; --color-canvas-subtle: #f6f8fa; --col=
+or-border-default: #d0d7de; --color-border-muted: hsla(210,18%,87%,1); --co=
+lor-border-subtle: rgba(27,31,36,0.15); --color-shadow-small: 0 1px 0 rgba(=
+27,31,36,0.04); --color-shadow-medium: 0 3px 6px rgba(140,149,159,0.15); --=
+color-shadow-large: 0 8px 24px rgba(140,149,159,0.2); --color-shadow-extra-=
+large: 0 12px 28px rgba(140,149,159,0.3); --color-neutral-emphasis-plus: #2=
+4292f; --color-neutral-emphasis: #6e7781; --color-neutral-muted: rgba(175,1=
+84,193,0.2); --color-neutral-subtle: rgba(234,238,242,0.5); --color-accent-=
+fg: #0969da; --color-accent-emphasis: #0969da; --color-accent-muted: rgba(8=
+4,174,255,0.4); --color-accent-subtle: #ddf4ff; --color-success-fg: #1a7f37=
+; --color-success-emphasis: #2da44e; --color-success-muted: rgba(74,194,107=
+,0.4); --color-success-subtle: #dafbe1; --color-attention-fg: #9a6700; --co=
+lor-attention-emphasis: #bf8700; --color-attention-muted: rgba(212,167,44,0=
+.4); --color-attention-subtle: #fff8c5; --color-severe-fg: #bc4c00; --color=
+-severe-emphasis: #bc4c00; --color-severe-muted: rgba(251,143,68,0.4); --co=
+lor-severe-subtle: #fff1e5; --color-danger-fg: #cf222e; --color-danger-emph=
+asis: #cf222e; --color-danger-muted: rgba(255,129,130,0.4); --color-danger-=
+subtle: #ffebe9; --color-open-fg: #1a7f37; --color-open-emphasis: #2da44e; =
+--color-open-muted: rgba(74,194,107,0.4); --color-open-subtle: #dafbe1; --c=
+olor-closed-fg: #cf222e; --color-closed-emphasis: #cf222e; --color-closed-m=
+uted: rgba(255,129,130,0.4); --color-closed-subtle: #ffebe9; --color-done-f=
+g: #8250df; --color-done-emphasis: #8250df; --color-done-muted: rgba(194,15=
+1,255,0.4); --color-done-subtle: #fbefff; --color-sponsors-fg: #bf3989; --c=
+olor-sponsors-emphasis: #bf3989; --color-sponsors-muted: rgba(255,128,200,0=
+.4); --color-sponsors-subtle: #ffeff7; --color-primer-fg-disabled: #8c959f;=
+ --color-primer-canvas-backdrop: rgba(27,31,36,0.5); --color-primer-canvas-=
+sticky: rgba(255,255,255,0.95); --color-primer-border-active: #fd8c73; --co=
+lor-primer-border-contrast: rgba(27,31,36,0.1); --color-primer-shadow-highl=
+ight: inset 0 1px 0 rgba(255,255,255,0.25); --color-primer-shadow-inset: in=
+set 0 1px 0 rgba(208,215,222,0.2); --color-primer-shadow-focus: 0 0 0 3px r=
+gba(9,105,218,0.3); --color-scale-black: #1b1f24; --color-scale-white: #fff=
+fff; --color-scale-gray-0: #f6f8fa; --color-scale-gray-1: #eaeef2; --color-=
+scale-gray-2: #d0d7de; --color-scale-gray-3: #afb8c1; --color-scale-gray-4:=
+ #8c959f; --color-scale-gray-5: #6e7781; --color-scale-gray-6: #57606a; --c=
+olor-scale-gray-7: #424a53; --color-scale-gray-8: #32383f; --color-scale-gr=
+ay-9: #24292f; --color-scale-blue-0: #ddf4ff; --color-scale-blue-1: #b6e3ff=
+; --color-scale-blue-2: #80ccff; --color-scale-blue-3: #54aeff; --color-sca=
+le-blue-4: #218bff; --color-scale-blue-5: #0969da; --color-scale-blue-6: #0=
+550ae; --color-scale-blue-7: #033d8b; --color-scale-blue-8: #0a3069; --colo=
+r-scale-blue-9: #002155; --color-scale-green-0: #dafbe1; --color-scale-gree=
+n-1: #aceebb; --color-scale-green-2: #6fdd8b; --color-scale-green-3: #4ac26=
+b; --color-scale-green-4: #2da44e; --color-scale-green-5: #1a7f37; --color-=
+scale-green-6: #116329; --color-scale-green-7: #044f1e; --color-scale-green=
+-8: #003d16; --color-scale-green-9: #002d11; --color-scale-yellow-0: #fff8c=
+5; --color-scale-yellow-1: #fae17d; --color-scale-yellow-2: #eac54f; --colo=
+r-scale-yellow-3: #d4a72c; --color-scale-yellow-4: #bf8700; --color-scale-y=
+ellow-5: #9a6700; --color-scale-yellow-6: #7d4e00; --color-scale-yellow-7: =
+#633c01; --color-scale-yellow-8: #4d2d00; --color-scale-yellow-9: #3b2300; =
+--color-scale-orange-0: #fff1e5; --color-scale-orange-1: #ffd8b5; --color-s=
+cale-orange-2: #ffb77c; --color-scale-orange-3: #fb8f44; --color-scale-oran=
+ge-4: #e16f24; --color-scale-orange-5: #bc4c00; --color-scale-orange-6: #95=
+3800; --color-scale-orange-7: #762c00; --color-scale-orange-8: #5c2200; --c=
+olor-scale-orange-9: #471700; --color-scale-red-0: #ffebe9; --color-scale-r=
+ed-1: #ffcecb; --color-scale-red-2: #ffaba8; --color-scale-red-3: #ff8182; =
+--color-scale-red-4: #fa4549; --color-scale-red-5: #cf222e; --color-scale-r=
+ed-6: #a40e26; --color-scale-red-7: #82071e; --color-scale-red-8: #660018; =
+--color-scale-red-9: #4c0014; --color-scale-purple-0: #fbefff; --color-scal=
+e-purple-1: #ecd8ff; --color-scale-purple-2: #d8b9ff; --color-scale-purple-=
+3: #c297ff; --color-scale-purple-4: #a475f9; --color-scale-purple-5: #8250d=
+f; --color-scale-purple-6: #6639ba; --color-scale-purple-7: #512a97; --colo=
+r-scale-purple-8: #3e1f79; --color-scale-purple-9: #2e1461; --color-scale-p=
+ink-0: #ffeff7; --color-scale-pink-1: #ffd3eb; --color-scale-pink-2: #ffadd=
+a; --color-scale-pink-3: #ff80c8; --color-scale-pink-4: #e85aad; --color-sc=
+ale-pink-5: #bf3989; --color-scale-pink-6: #99286e; --color-scale-pink-7: #=
+772057; --color-scale-pink-8: #611347; --color-scale-pink-9: #4d0336; --col=
+or-scale-coral-0: #fff0eb; --color-scale-coral-1: #ffd6cc; --color-scale-co=
+ral-2: #ffb4a1; --color-scale-coral-3: #fd8c73; --color-scale-coral-4: #ec6=
+547; --color-scale-coral-5: #c4432b; --color-scale-coral-6: #9e2f1c; --colo=
+r-scale-coral-7: #801f0f; --color-scale-coral-8: #691105; --color-scale-cor=
+al-9: #510901; }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme=3D"light"] { --color-canvas-de=
+fault-transparent: rgba(255,255,255,0); --color-page-header-bg: #f6f8fa; --=
+color-marketing-icon-primary: #218bff; --color-marketing-icon-secondary: #5=
+4aeff; --color-diff-blob-addition-num-text: #24292f; --color-diff-blob-addi=
+tion-fg: #24292f; --color-diff-blob-addition-num-bg: #ccffd8; --color-diff-=
+blob-addition-line-bg: #e6ffec; --color-diff-blob-addition-word-bg: #abf2bc=
+; --color-diff-blob-deletion-num-text: #24292f; --color-diff-blob-deletion-=
+fg: #24292f; --color-diff-blob-deletion-num-bg: #ffd7d5; --color-diff-blob-=
+deletion-line-bg: #ffebe9; --color-diff-blob-deletion-word-bg: rgba(255,129=
+,130,0.4); --color-diff-blob-hunk-num-bg: rgba(84,174,255,0.4); --color-dif=
+f-blob-expander-icon: #57606a; --color-diff-blob-selected-line-highlight-mi=
+x-blend-mode: multiply; --color-diffstat-deletion-border: rgba(27,31,36,0.1=
+5); --color-diffstat-addition-border: rgba(27,31,36,0.15); --color-diffstat=
+-addition-bg: #2da44e; --color-search-keyword-hl: #fff8c5; --color-prettyli=
+ghts-syntax-comment: #6e7781; --color-prettylights-syntax-constant: #0550ae=
+; --color-prettylights-syntax-entity: #8250df; --color-prettylights-syntax-=
+storage-modifier-import: #24292f; --color-prettylights-syntax-entity-tag: #=
+116329; --color-prettylights-syntax-keyword: #cf222e; --color-prettylights-=
+syntax-string: #0a3069; --color-prettylights-syntax-variable: #953800; --co=
+lor-prettylights-syntax-brackethighlighter-unmatched: #82071e; --color-pret=
+tylights-syntax-invalid-illegal-text: #f6f8fa; --color-prettylights-syntax-=
+invalid-illegal-bg: #82071e; --color-prettylights-syntax-carriage-return-te=
+xt: #f6f8fa; --color-prettylights-syntax-carriage-return-bg: #cf222e; --col=
+or-prettylights-syntax-string-regexp: #116329; --color-prettylights-syntax-=
+markup-list: #3b2300; --color-prettylights-syntax-markup-heading: #0550ae; =
+--color-prettylights-syntax-markup-italic: #24292f; --color-prettylights-sy=
+ntax-markup-bold: #24292f; --color-prettylights-syntax-markup-deleted-text:=
+ #82071e; --color-prettylights-syntax-markup-deleted-bg: #ffebe9; --color-p=
+rettylights-syntax-markup-inserted-text: #116329; --color-prettylights-synt=
+ax-markup-inserted-bg: #dafbe1; --color-prettylights-syntax-markup-changed-=
+text: #953800; --color-prettylights-syntax-markup-changed-bg: #ffd8b5; --co=
+lor-prettylights-syntax-markup-ignored-text: #eaeef2; --color-prettylights-=
+syntax-markup-ignored-bg: #0550ae; --color-prettylights-syntax-meta-diff-ra=
+nge: #8250df; --color-prettylights-syntax-brackethighlighter-angle: #57606a=
+; --color-prettylights-syntax-sublimelinter-gutter-mark: #8c959f; --color-p=
+rettylights-syntax-constant-other-reference-link: #0a3069; --color-codemirr=
+or-text: #24292f; --color-codemirror-bg: #ffffff; --color-codemirror-gutter=
+s-bg: #ffffff; --color-codemirror-guttermarker-text: #ffffff; --color-codem=
+irror-guttermarker-subtle-text: #6e7781; --color-codemirror-linenumber-text=
+: #57606a; --color-codemirror-cursor: #24292f; --color-codemirror-selection=
+-bg: rgba(84,174,255,0.4); --color-codemirror-activeline-bg: rgba(234,238,2=
+42,0.5); --color-codemirror-matchingbracket-text: #24292f; --color-codemirr=
+or-lines-bg: #ffffff; --color-codemirror-syntax-comment: #24292f; --color-c=
+odemirror-syntax-constant: #0550ae; --color-codemirror-syntax-entity: #8250=
+df; --color-codemirror-syntax-keyword: #cf222e; --color-codemirror-syntax-s=
+torage: #cf222e; --color-codemirror-syntax-string: #0a3069; --color-codemir=
+ror-syntax-support: #0550ae; --color-codemirror-syntax-variable: #953800; -=
+-color-checks-bg: #24292f; --color-checks-run-border-width: 0px; --color-ch=
+ecks-container-border-width: 0px; --color-checks-text-primary: #f6f8fa; --c=
+olor-checks-text-secondary: #8c959f; --color-checks-text-link: #54aeff; --c=
+olor-checks-btn-icon: #afb8c1; --color-checks-btn-hover-icon: #f6f8fa; --co=
+lor-checks-btn-hover-bg: rgba(255,255,255,0.125); --color-checks-input-text=
+: #eaeef2; --color-checks-input-placeholder-text: #8c959f; --color-checks-i=
+nput-focus-text: #8c959f; --color-checks-input-bg: #32383f; --color-checks-=
+input-shadow: none; --color-checks-donut-error: #fa4549; --color-checks-don=
+ut-pending: #bf8700; --color-checks-donut-success: #2da44e; --color-checks-=
+donut-neutral: #afb8c1; --color-checks-dropdown-text: #afb8c1; --color-chec=
+ks-dropdown-bg: #32383f; --color-checks-dropdown-border: #424a53; --color-c=
+hecks-dropdown-shadow: rgba(27,31,36,0.3); --color-checks-dropdown-hover-te=
+xt: #f6f8fa; --color-checks-dropdown-hover-bg: #424a53; --color-checks-drop=
+down-btn-hover-text: #f6f8fa; --color-checks-dropdown-btn-hover-bg: #32383f=
+; --color-checks-scrollbar-thumb-bg: #57606a; --color-checks-header-label-t=
+ext: #d0d7de; --color-checks-header-label-open-text: #f6f8fa; --color-check=
+s-header-border: #32383f; --color-checks-header-icon: #8c959f; --color-chec=
+ks-line-text: #d0d7de; --color-checks-line-num-text: rgba(140,149,159,0.75)=
+; --color-checks-line-timestamp-text: #8c959f; --color-checks-line-hover-bg=
+: #32383f; --color-checks-line-selected-bg: rgba(33,139,255,0.15); --color-=
+checks-line-selected-num-text: #54aeff; --color-checks-line-dt-fm-text: #24=
+292f; --color-checks-line-dt-fm-bg: #9a6700; --color-checks-gate-bg: rgba(1=
+25,78,0,0.15); --color-checks-gate-text: #d0d7de; --color-checks-gate-waiti=
+ng-text: #d4a72c; --color-checks-step-header-open-bg: #32383f; --color-chec=
+ks-step-error-text: #ff8182; --color-checks-step-warning-text: #d4a72c; --c=
+olor-checks-logline-text: #8c959f; --color-checks-logline-num-text: rgba(14=
+0,149,159,0.75); --color-checks-logline-debug-text: #c297ff; --color-checks=
+-logline-error-text: #d0d7de; --color-checks-logline-error-num-text: #ff818=
+2; --color-checks-logline-error-bg: rgba(164,14,38,0.15); --color-checks-lo=
+gline-warning-text: #d0d7de; --color-checks-logline-warning-num-text: #d4a7=
+2c; --color-checks-logline-warning-bg: rgba(125,78,0,0.15); --color-checks-=
+logline-command-text: #54aeff; --color-checks-logline-section-text: #4ac26b=
+; --color-checks-ansi-black: #24292f; --color-checks-ansi-black-bright: #32=
+383f; --color-checks-ansi-white: #d0d7de; --color-checks-ansi-white-bright:=
+ #d0d7de; --color-checks-ansi-gray: #8c959f; --color-checks-ansi-red: #ff81=
+82; --color-checks-ansi-red-bright: #ffaba8; --color-checks-ansi-green: #4a=
+c26b; --color-checks-ansi-green-bright: #6fdd8b; --color-checks-ansi-yellow=
+: #d4a72c; --color-checks-ansi-yellow-bright: #eac54f; --color-checks-ansi-=
+blue: #54aeff; --color-checks-ansi-blue-bright: #80ccff; --color-checks-ans=
+i-magenta: #c297ff; --color-checks-ansi-magenta-bright: #d8b9ff; --color-ch=
+ecks-ansi-cyan: #76e3ea; --color-checks-ansi-cyan-bright: #b3f0ff; --color-=
+project-header-bg: #24292f; --color-project-sidebar-bg: #ffffff; --color-pr=
+oject-gradient-in: #ffffff; --color-project-gradient-out: rgba(255,255,255,=
+0); --color-mktg-btn-bg: #1b1f23; --color-mktg-btn-shadow-outline: rgb(0 0 =
+0 / 15%) 0 0 0 1px inset; --color-mktg-btn-shadow-focus: rgb(0 0 0 / 15%) 0=
+ 0 0 4px; --color-mktg-btn-shadow-hover: 0 3px 2px rgba(0, 0, 0, 0.07), 0 7=
+px 5px rgba(0, 0, 0, 0.04), 0 12px 10px rgba(0, 0, 0, 0.03), 0 22px 18px rg=
+ba(0, 0, 0, 0.03), 0 42px 33px rgba(0, 0, 0, 0.02), 0 100px 80px rgba(0, 0,=
+ 0, 0.02); --color-mktg-btn-shadow-hover-muted: rgb(0 0 0 / 70%) 0 0 0 2px =
+inset; --color-avatar-bg: #ffffff; --color-avatar-border: rgba(27,31,36,0.1=
+5); --color-avatar-stack-fade: #afb8c1; --color-avatar-stack-fade-more: #d0=
+d7de; --color-avatar-child-shadow: -2px -2px 0 rgba(255,255,255,0.8); --col=
+or-topic-tag-border: rgba(0,0,0,0); --color-counter-border: rgba(0,0,0,0); =
+--color-select-menu-backdrop-border: rgba(0,0,0,0); --color-select-menu-tap=
+-highlight: rgba(175,184,193,0.5); --color-select-menu-tap-focus-bg: #b6e3f=
+f; --color-overlay-shadow: 0 1px 3px rgba(27,31,36,0.12), 0 8px 24px rgba(6=
+6,74,83,0.12); --color-header-text: rgba(255,255,255,0.7); --color-header-b=
+g: #24292f; --color-header-divider: #57606a; --color-header-logo: #ffffff; =
+--color-header-search-bg: #24292f; --color-header-search-border: #57606a; -=
+-color-sidenav-selected-bg: #ffffff; --color-menu-bg-active: rgba(0,0,0,0);=
+ --color-input-disabled-bg: rgba(175,184,193,0.2); --color-timeline-badge-b=
+g: #eaeef2; --color-ansi-black: #24292f; --color-ansi-black-bright: #57606a=
+; --color-ansi-white: #6e7781; --color-ansi-white-bright: #8c959f; --color-=
+ansi-gray: #6e7781; --color-ansi-red: #cf222e; --color-ansi-red-bright: #a4=
+0e26; --color-ansi-green: #116329; --color-ansi-green-bright: #1a7f37; --co=
+lor-ansi-yellow: #4d2d00; --color-ansi-yellow-bright: #633c01; --color-ansi=
+-blue: #0969da; --color-ansi-blue-bright: #218bff; --color-ansi-magenta: #8=
+250df; --color-ansi-magenta-bright: #a475f9; --color-ansi-cyan: #1b7c83; --=
+color-ansi-cyan-bright: #3192aa; --color-btn-text: #24292f; --color-btn-bg:=
+ #f6f8fa; --color-btn-border: rgba(27,31,36,0.15); --color-btn-shadow: 0 1p=
+x 0 rgba(27,31,36,0.04); --color-btn-inset-shadow: inset 0 1px 0 rgba(255,2=
+55,255,0.25); --color-btn-hover-bg: #f3f4f6; --color-btn-hover-border: rgba=
+(27,31,36,0.15); --color-btn-active-bg: hsla(220,14%,93%,1); --color-btn-ac=
+tive-border: rgba(27,31,36,0.15); --color-btn-selected-bg: hsla(220,14%,94%=
+,1); --color-btn-focus-bg: #f6f8fa; --color-btn-focus-border: rgba(27,31,36=
+,0.15); --color-btn-focus-shadow: 0 0 0 3px rgba(9,105,218,0.3); --color-bt=
+n-shadow-active: inset 0 0.15em 0.3em rgba(27,31,36,0.15); --color-btn-shad=
+ow-input-focus: 0 0 0 0.2em rgba(9,105,218,0.3); --color-btn-counter-bg: rg=
+ba(27,31,36,0.08); --color-btn-primary-text: #ffffff; --color-btn-primary-b=
+g: #2da44e; --color-btn-primary-border: rgba(27,31,36,0.15); --color-btn-pr=
+imary-shadow: 0 1px 0 rgba(27,31,36,0.1); --color-btn-primary-inset-shadow:=
+ inset 0 1px 0 rgba(255,255,255,0.03); --color-btn-primary-hover-bg: #2c974=
+b; --color-btn-primary-hover-border: rgba(27,31,36,0.15); --color-btn-prima=
+ry-selected-bg: hsla(137,55%,36%,1); --color-btn-primary-selected-shadow: i=
+nset 0 1px 0 rgba(0,45,17,0.2); --color-btn-primary-disabled-text: rgba(255=
+,255,255,0.8); --color-btn-primary-disabled-bg: #94d3a2; --color-btn-primar=
+y-disabled-border: rgba(27,31,36,0.15); --color-btn-primary-focus-bg: #2da4=
+4e; --color-btn-primary-focus-border: rgba(27,31,36,0.15); --color-btn-prim=
+ary-focus-shadow: 0 0 0 3px rgba(45,164,78,0.4); --color-btn-primary-icon: =
+rgba(255,255,255,0.8); --color-btn-primary-counter-bg: rgba(255,255,255,0.2=
+); --color-btn-outline-text: #0969da; --color-btn-outline-hover-text: #ffff=
+ff; --color-btn-outline-hover-bg: #0969da; --color-btn-outline-hover-border=
+: rgba(27,31,36,0.15); --color-btn-outline-hover-shadow: 0 1px 0 rgba(27,31=
+,36,0.1); --color-btn-outline-hover-inset-shadow: inset 0 1px 0 rgba(255,25=
+5,255,0.03); --color-btn-outline-hover-counter-bg: rgba(255,255,255,0.2); -=
+-color-btn-outline-selected-text: #ffffff; --color-btn-outline-selected-bg:=
+ hsla(212,92%,42%,1); --color-btn-outline-selected-border: rgba(27,31,36,0.=
+15); --color-btn-outline-selected-shadow: inset 0 1px 0 rgba(0,33,85,0.2); =
+--color-btn-outline-disabled-text: rgba(9,105,218,0.5); --color-btn-outline=
+-disabled-bg: #f6f8fa; --color-btn-outline-disabled-counter-bg: rgba(9,105,=
+218,0.05); --color-btn-outline-focus-border: rgba(27,31,36,0.15); --color-b=
+tn-outline-focus-shadow: 0 0 0 3px rgba(5,80,174,0.4); --color-btn-outline-=
+counter-bg: rgba(9,105,218,0.1); --color-btn-danger-text: #cf222e; --color-=
+btn-danger-hover-text: #ffffff; --color-btn-danger-hover-bg: #a40e26; --col=
+or-btn-danger-hover-border: rgba(27,31,36,0.15); --color-btn-danger-hover-s=
+hadow: 0 1px 0 rgba(27,31,36,0.1); --color-btn-danger-hover-inset-shadow: i=
+nset 0 1px 0 rgba(255,255,255,0.03); --color-btn-danger-hover-counter-bg: r=
+gba(255,255,255,0.2); --color-btn-danger-selected-text: #ffffff; --color-bt=
+n-danger-selected-bg: hsla(356,72%,44%,1); --color-btn-danger-selected-bord=
+er: rgba(27,31,36,0.15); --color-btn-danger-selected-shadow: inset 0 1px 0 =
+rgba(76,0,20,0.2); --color-btn-danger-disabled-text: rgba(207,34,46,0.5); -=
+-color-btn-danger-disabled-bg: #f6f8fa; --color-btn-danger-disabled-counter=
+-bg: rgba(207,34,46,0.05); --color-btn-danger-focus-border: rgba(27,31,36,0=
+.15); --color-btn-danger-focus-shadow: 0 0 0 3px rgba(164,14,38,0.4); --col=
+or-btn-danger-counter-bg: rgba(207,34,46,0.1); --color-btn-danger-icon: #cf=
+222e; --color-btn-danger-hover-icon: #ffffff; --color-underlinenav-icon: #6=
+e7781; --color-underlinenav-border-hover: rgba(175,184,193,0.2); --color-ac=
+tion-list-item-inline-divider: rgba(208,215,222,0.48); --color-action-list-=
+item-default-hover-bg: rgba(208,215,222,0.32); --color-action-list-item-def=
+ault-hover-border: rgba(0,0,0,0); --color-action-list-item-default-active-b=
+g: rgba(208,215,222,0.48); --color-action-list-item-default-active-border: =
+rgba(0,0,0,0); --color-action-list-item-default-selected-bg: rgba(208,215,2=
+22,0.24); --color-action-list-item-danger-hover-bg: rgba(255,235,233,0.64);=
+ --color-action-list-item-danger-active-bg: #ffebe9; --color-action-list-it=
+em-danger-hover-text: #cf222e; --color-switch-track-bg: #eaeef2; --color-sw=
+itch-track-border: #afb8c1; --color-switch-track-checked-bg: #ddf4ff; --col=
+or-switch-track-checked-hover-bg: #b6e3ff; --color-switch-track-checked-act=
+ive-bg: #80ccff; --color-switch-track-checked-border: #54aeff; --color-swit=
+ch-knob-checked-bg: #0969da; --color-switch-knob-checked-disabled-bg: #6e77=
+81; --color-segmented-control-bg: #eaeef2; --color-segmented-control-button=
+-hover-bg: rgba(175,184,193,0.2); --color-segmented-control-button-active-b=
+g: rgba(175,184,193,0.4); --color-segmented-control-button-selected-border:=
+ #6e7781; --color-tree-view-item-chevron-hover-bg: rgba(208,215,222,0.32); =
+--color-tree-view-item-directory-fill: #54aeff; --color-fg-default: #24292f=
+; --color-fg-muted: #57606a; --color-fg-subtle: #6e7781; --color-fg-on-emph=
+asis: #ffffff; --color-canvas-default: #ffffff; --color-canvas-overlay: #ff=
+ffff; --color-canvas-inset: #f6f8fa; --color-canvas-subtle: #f6f8fa; --colo=
+r-border-default: #d0d7de; --color-border-muted: hsla(210,18%,87%,1); --col=
+or-border-subtle: rgba(27,31,36,0.15); --color-shadow-small: 0 1px 0 rgba(2=
+7,31,36,0.04); --color-shadow-medium: 0 3px 6px rgba(140,149,159,0.15); --c=
+olor-shadow-large: 0 8px 24px rgba(140,149,159,0.2); --color-shadow-extra-l=
+arge: 0 12px 28px rgba(140,149,159,0.3); --color-neutral-emphasis-plus: #24=
+292f; --color-neutral-emphasis: #6e7781; --color-neutral-muted: rgba(175,18=
+4,193,0.2); --color-neutral-subtle: rgba(234,238,242,0.5); --color-accent-f=
+g: #0969da; --color-accent-emphasis: #0969da; --color-accent-muted: rgba(84=
+,174,255,0.4); --color-accent-subtle: #ddf4ff; --color-success-fg: #1a7f37;=
+ --color-success-emphasis: #2da44e; --color-success-muted: rgba(74,194,107,=
+0.4); --color-success-subtle: #dafbe1; --color-attention-fg: #9a6700; --col=
+or-attention-emphasis: #bf8700; --color-attention-muted: rgba(212,167,44,0.=
+4); --color-attention-subtle: #fff8c5; --color-severe-fg: #bc4c00; --color-=
+severe-emphasis: #bc4c00; --color-severe-muted: rgba(251,143,68,0.4); --col=
+or-severe-subtle: #fff1e5; --color-danger-fg: #cf222e; --color-danger-empha=
+sis: #cf222e; --color-danger-muted: rgba(255,129,130,0.4); --color-danger-s=
+ubtle: #ffebe9; --color-open-fg: #1a7f37; --color-open-emphasis: #2da44e; -=
+-color-open-muted: rgba(74,194,107,0.4); --color-open-subtle: #dafbe1; --co=
+lor-closed-fg: #cf222e; --color-closed-emphasis: #cf222e; --color-closed-mu=
+ted: rgba(255,129,130,0.4); --color-closed-subtle: #ffebe9; --color-done-fg=
+: #8250df; --color-done-emphasis: #8250df; --color-done-muted: rgba(194,151=
+,255,0.4); --color-done-subtle: #fbefff; --color-sponsors-fg: #bf3989; --co=
+lor-sponsors-emphasis: #bf3989; --color-sponsors-muted: rgba(255,128,200,0.=
+4); --color-sponsors-subtle: #ffeff7; --color-primer-fg-disabled: #8c959f; =
+--color-primer-canvas-backdrop: rgba(27,31,36,0.5); --color-primer-canvas-s=
+ticky: rgba(255,255,255,0.95); --color-primer-border-active: #fd8c73; --col=
+or-primer-border-contrast: rgba(27,31,36,0.1); --color-primer-shadow-highli=
+ght: inset 0 1px 0 rgba(255,255,255,0.25); --color-primer-shadow-inset: ins=
+et 0 1px 0 rgba(208,215,222,0.2); --color-primer-shadow-focus: 0 0 0 3px rg=
+ba(9,105,218,0.3); --color-scale-black: #1b1f24; --color-scale-white: #ffff=
+ff; --color-scale-gray-0: #f6f8fa; --color-scale-gray-1: #eaeef2; --color-s=
+cale-gray-2: #d0d7de; --color-scale-gray-3: #afb8c1; --color-scale-gray-4: =
+#8c959f; --color-scale-gray-5: #6e7781; --color-scale-gray-6: #57606a; --co=
+lor-scale-gray-7: #424a53; --color-scale-gray-8: #32383f; --color-scale-gra=
+y-9: #24292f; --color-scale-blue-0: #ddf4ff; --color-scale-blue-1: #b6e3ff;=
+ --color-scale-blue-2: #80ccff; --color-scale-blue-3: #54aeff; --color-scal=
+e-blue-4: #218bff; --color-scale-blue-5: #0969da; --color-scale-blue-6: #05=
+50ae; --color-scale-blue-7: #033d8b; --color-scale-blue-8: #0a3069; --color=
+-scale-blue-9: #002155; --color-scale-green-0: #dafbe1; --color-scale-green=
+-1: #aceebb; --color-scale-green-2: #6fdd8b; --color-scale-green-3: #4ac26b=
+; --color-scale-green-4: #2da44e; --color-scale-green-5: #1a7f37; --color-s=
+cale-green-6: #116329; --color-scale-green-7: #044f1e; --color-scale-green-=
+8: #003d16; --color-scale-green-9: #002d11; --color-scale-yellow-0: #fff8c5=
+; --color-scale-yellow-1: #fae17d; --color-scale-yellow-2: #eac54f; --color=
+-scale-yellow-3: #d4a72c; --color-scale-yellow-4: #bf8700; --color-scale-ye=
+llow-5: #9a6700; --color-scale-yellow-6: #7d4e00; --color-scale-yellow-7: #=
+633c01; --color-scale-yellow-8: #4d2d00; --color-scale-yellow-9: #3b2300; -=
+-color-scale-orange-0: #fff1e5; --color-scale-orange-1: #ffd8b5; --color-sc=
+ale-orange-2: #ffb77c; --color-scale-orange-3: #fb8f44; --color-scale-orang=
+e-4: #e16f24; --color-scale-orange-5: #bc4c00; --color-scale-orange-6: #953=
+800; --color-scale-orange-7: #762c00; --color-scale-orange-8: #5c2200; --co=
+lor-scale-orange-9: #471700; --color-scale-red-0: #ffebe9; --color-scale-re=
+d-1: #ffcecb; --color-scale-red-2: #ffaba8; --color-scale-red-3: #ff8182; -=
+-color-scale-red-4: #fa4549; --color-scale-red-5: #cf222e; --color-scale-re=
+d-6: #a40e26; --color-scale-red-7: #82071e; --color-scale-red-8: #660018; -=
+-color-scale-red-9: #4c0014; --color-scale-purple-0: #fbefff; --color-scale=
+-purple-1: #ecd8ff; --color-scale-purple-2: #d8b9ff; --color-scale-purple-3=
+: #c297ff; --color-scale-purple-4: #a475f9; --color-scale-purple-5: #8250df=
+; --color-scale-purple-6: #6639ba; --color-scale-purple-7: #512a97; --color=
+-scale-purple-8: #3e1f79; --color-scale-purple-9: #2e1461; --color-scale-pi=
+nk-0: #ffeff7; --color-scale-pink-1: #ffd3eb; --color-scale-pink-2: #ffadda=
+; --color-scale-pink-3: #ff80c8; --color-scale-pink-4: #e85aad; --color-sca=
+le-pink-5: #bf3989; --color-scale-pink-6: #99286e; --color-scale-pink-7: #7=
+72057; --color-scale-pink-8: #611347; --color-scale-pink-9: #4d0336; --colo=
+r-scale-coral-0: #fff0eb; --color-scale-coral-1: #ffd6cc; --color-scale-cor=
+al-2: #ffb4a1; --color-scale-coral-3: #fd8c73; --color-scale-coral-4: #ec65=
+47; --color-scale-coral-5: #c4432b; --color-scale-coral-6: #9e2f1c; --color=
+-scale-coral-7: #801f0f; --color-scale-coral-8: #691105; --color-scale-cora=
+l-9: #510901; }
+}
+------MultipartBoundary--Xhr6yb6p8QAUWNcNSMINdGDzY3kbebp99Hpkh4tcTA----
+Content-Type: text/css
+Content-Transfer-Encoding: quoted-printable
+Content-Location: https://github.githubassets.com/assets/dark-0c343b529849.css
+
+@charset "utf-8";
+
+[data-color-mode=3D"light"][data-light-theme=3D"dark"], [data-color-mode=3D=
+"dark"][data-dark-theme=3D"dark"] { --color-canvas-default-transparent: rgb=
+a(13,17,23,0); --color-page-header-bg: #0d1117; --color-marketing-icon-prim=
+ary: #79c0ff; --color-marketing-icon-secondary: #1f6feb; --color-diff-blob-=
+addition-num-text: #c9d1d9; --color-diff-blob-addition-fg: #c9d1d9; --color=
+-diff-blob-addition-num-bg: rgba(63,185,80,0.3); --color-diff-blob-addition=
+-line-bg: rgba(46,160,67,0.15); --color-diff-blob-addition-word-bg: rgba(46=
+,160,67,0.4); --color-diff-blob-deletion-num-text: #c9d1d9; --color-diff-bl=
+ob-deletion-fg: #c9d1d9; --color-diff-blob-deletion-num-bg: rgba(248,81,73,=
+0.3); --color-diff-blob-deletion-line-bg: rgba(248,81,73,0.15); --color-dif=
+f-blob-deletion-word-bg: rgba(248,81,73,0.4); --color-diff-blob-hunk-num-bg=
+: rgba(56,139,253,0.4); --color-diff-blob-expander-icon: #8b949e; --color-d=
+iff-blob-selected-line-highlight-mix-blend-mode: screen; --color-diffstat-d=
+eletion-border: rgba(240,246,252,0.1); --color-diffstat-addition-border: rg=
+ba(240,246,252,0.1); --color-diffstat-addition-bg: #3fb950; --color-search-=
+keyword-hl: rgba(210,153,34,0.4); --color-prettylights-syntax-comment: #8b9=
+49e; --color-prettylights-syntax-constant: #79c0ff; --color-prettylights-sy=
+ntax-entity: #d2a8ff; --color-prettylights-syntax-storage-modifier-import: =
+#c9d1d9; --color-prettylights-syntax-entity-tag: #7ee787; --color-prettylig=
+hts-syntax-keyword: #ff7b72; --color-prettylights-syntax-string: #a5d6ff; -=
+-color-prettylights-syntax-variable: #ffa657; --color-prettylights-syntax-b=
+rackethighlighter-unmatched: #f85149; --color-prettylights-syntax-invalid-i=
+llegal-text: #f0f6fc; --color-prettylights-syntax-invalid-illegal-bg: #8e15=
+19; --color-prettylights-syntax-carriage-return-text: #f0f6fc; --color-pret=
+tylights-syntax-carriage-return-bg: #b62324; --color-prettylights-syntax-st=
+ring-regexp: #7ee787; --color-prettylights-syntax-markup-list: #f2cc60; --c=
+olor-prettylights-syntax-markup-heading: #1f6feb; --color-prettylights-synt=
+ax-markup-italic: #c9d1d9; --color-prettylights-syntax-markup-bold: #c9d1d9=
+; --color-prettylights-syntax-markup-deleted-text: #ffdcd7; --color-prettyl=
+ights-syntax-markup-deleted-bg: #67060c; --color-prettylights-syntax-markup=
+-inserted-text: #aff5b4; --color-prettylights-syntax-markup-inserted-bg: #0=
+33a16; --color-prettylights-syntax-markup-changed-text: #ffdfb6; --color-pr=
+ettylights-syntax-markup-changed-bg: #5a1e02; --color-prettylights-syntax-m=
+arkup-ignored-text: #c9d1d9; --color-prettylights-syntax-markup-ignored-bg:=
+ #1158c7; --color-prettylights-syntax-meta-diff-range: #d2a8ff; --color-pre=
+ttylights-syntax-brackethighlighter-angle: #8b949e; --color-prettylights-sy=
+ntax-sublimelinter-gutter-mark: #484f58; --color-prettylights-syntax-consta=
+nt-other-reference-link: #a5d6ff; --color-codemirror-text: #c9d1d9; --color=
+-codemirror-bg: #0d1117; --color-codemirror-gutters-bg: #0d1117; --color-co=
+demirror-guttermarker-text: #0d1117; --color-codemirror-guttermarker-subtle=
+-text: #6e7681; --color-codemirror-linenumber-text: #8b949e; --color-codemi=
+rror-cursor: #c9d1d9; --color-codemirror-selection-bg: rgba(56,139,253,0.4)=
+; --color-codemirror-activeline-bg: rgba(110,118,129,0.1); --color-codemirr=
+or-matchingbracket-text: #c9d1d9; --color-codemirror-lines-bg: #0d1117; --c=
+olor-codemirror-syntax-comment: #8b949e; --color-codemirror-syntax-constant=
+: #79c0ff; --color-codemirror-syntax-entity: #d2a8ff; --color-codemirror-sy=
+ntax-keyword: #ff7b72; --color-codemirror-syntax-storage: #ff7b72; --color-=
+codemirror-syntax-string: #a5d6ff; --color-codemirror-syntax-support: #79c0=
+ff; --color-codemirror-syntax-variable: #ffa657; --color-checks-bg: #010409=
+; --color-checks-run-border-width: 1px; --color-checks-container-border-wid=
+th: 1px; --color-checks-text-primary: #c9d1d9; --color-checks-text-secondar=
+y: #8b949e; --color-checks-text-link: #58a6ff; --color-checks-btn-icon: #8b=
+949e; --color-checks-btn-hover-icon: #c9d1d9; --color-checks-btn-hover-bg: =
+rgba(110,118,129,0.1); --color-checks-input-text: #8b949e; --color-checks-i=
+nput-placeholder-text: #6e7681; --color-checks-input-focus-text: #c9d1d9; -=
+-color-checks-input-bg: #161b22; --color-checks-input-shadow: 0 0 0 1px (ob=
+j) =3D> (0, get_1.default)(obj, path); --color-checks-donut-error: #f85149;=
+ --color-checks-donut-pending: #d29922; --color-checks-donut-success: #2ea0=
+43; --color-checks-donut-neutral: #8b949e; --color-checks-dropdown-text: #c=
+9d1d9; --color-checks-dropdown-bg: #161b22; --color-checks-dropdown-border:=
+ #30363d; --color-checks-dropdown-shadow: rgba(1,4,9,0.3); --color-checks-d=
+ropdown-hover-text: #c9d1d9; --color-checks-dropdown-hover-bg: rgba(110,118=
+,129,0.1); --color-checks-dropdown-btn-hover-text: #c9d1d9; --color-checks-=
+dropdown-btn-hover-bg: rgba(110,118,129,0.1); --color-checks-scrollbar-thum=
+b-bg: rgba(110,118,129,0.4); --color-checks-header-label-text: #8b949e; --c=
+olor-checks-header-label-open-text: #c9d1d9; --color-checks-header-border: =
+#21262d; --color-checks-header-icon: #8b949e; --color-checks-line-text: #8b=
+949e; --color-checks-line-num-text: #6e7681; --color-checks-line-timestamp-=
+text: #6e7681; --color-checks-line-hover-bg: rgba(110,118,129,0.1); --color=
+-checks-line-selected-bg: rgba(56,139,253,0.15); --color-checks-line-select=
+ed-num-text: #58a6ff; --color-checks-line-dt-fm-text: #ffffff; --color-chec=
+ks-line-dt-fm-bg: #9e6a03; --color-checks-gate-bg: rgba(187,128,9,0.15); --=
+color-checks-gate-text: #8b949e; --color-checks-gate-waiting-text: #d29922;=
+ --color-checks-step-header-open-bg: #161b22; --color-checks-step-error-tex=
+t: #f85149; --color-checks-step-warning-text: #d29922; --color-checks-logli=
+ne-text: #8b949e; --color-checks-logline-num-text: #6e7681; --color-checks-=
+logline-debug-text: #a371f7; --color-checks-logline-error-text: #8b949e; --=
+color-checks-logline-error-num-text: #6e7681; --color-checks-logline-error-=
+bg: rgba(248,81,73,0.15); --color-checks-logline-warning-text: #8b949e; --c=
+olor-checks-logline-warning-num-text: #d29922; --color-checks-logline-warni=
+ng-bg: rgba(187,128,9,0.15); --color-checks-logline-command-text: #58a6ff; =
+--color-checks-logline-section-text: #3fb950; --color-checks-ansi-black: #0=
+d1117; --color-checks-ansi-black-bright: #161b22; --color-checks-ansi-white=
+: #b1bac4; --color-checks-ansi-white-bright: #b1bac4; --color-checks-ansi-g=
+ray: #6e7681; --color-checks-ansi-red: #ff7b72; --color-checks-ansi-red-bri=
+ght: #ffa198; --color-checks-ansi-green: #3fb950; --color-checks-ansi-green=
+-bright: #56d364; --color-checks-ansi-yellow: #d29922; --color-checks-ansi-=
+yellow-bright: #e3b341; --color-checks-ansi-blue: #58a6ff; --color-checks-a=
+nsi-blue-bright: #79c0ff; --color-checks-ansi-magenta: #bc8cff; --color-che=
+cks-ansi-magenta-bright: #d2a8ff; --color-checks-ansi-cyan: #76e3ea; --colo=
+r-checks-ansi-cyan-bright: #b3f0ff; --color-project-header-bg: #0d1117; --c=
+olor-project-sidebar-bg: #161b22; --color-project-gradient-in: #161b22; --c=
+olor-project-gradient-out: rgba(22,27,34,0); --color-mktg-btn-bg: #f6f8fa; =
+--color-mktg-btn-shadow-outline: rgb(255 255 255 / 25%) 0 0 0 1px inset; --=
+color-mktg-btn-shadow-focus: rgb(255 255 255 / 25%) 0 0 0 4px; --color-mktg=
+-btn-shadow-hover: 0 4px 7px rgba(0, 0, 0, 0.15), 0 100px 80px rgba(255, 25=
+5, 255, 0.02), 0 42px 33px rgba(255, 255, 255, 0.024), 0 22px 18px rgba(255=
+, 255, 255, 0.028), 0 12px 10px rgba(255, 255, 255, 0.034), 0 7px 5px rgba(=
+255, 255, 255, 0.04), 0 3px 2px rgba(255, 255, 255, 0.07); --color-mktg-btn=
+-shadow-hover-muted: rgb(255 255 255) 0 0 0 2px inset; --color-avatar-bg: r=
+gba(255,255,255,0.1); --color-avatar-border: rgba(240,246,252,0.1); --color=
+-avatar-stack-fade: #30363d; --color-avatar-stack-fade-more: #21262d; --col=
+or-avatar-child-shadow: -2px -2px 0 #0d1117; --color-topic-tag-border: rgba=
+(0,0,0,0); --color-counter-border: rgba(0,0,0,0); --color-select-menu-backd=
+rop-border: #484f58; --color-select-menu-tap-highlight: rgba(48,54,61,0.5);=
+ --color-select-menu-tap-focus-bg: #0c2d6b; --color-overlay-shadow: 0 0 0 1=
+px #30363d, 0 16px 32px rgba(1,4,9,0.85); --color-header-text: rgba(255,255=
+,255,0.7); --color-header-bg: #161b22; --color-header-divider: #8b949e; --c=
+olor-header-logo: #f0f6fc; --color-header-search-bg: #0d1117; --color-heade=
+r-search-border: #30363d; --color-sidenav-selected-bg: #21262d; --color-men=
+u-bg-active: #161b22; --color-input-disabled-bg: rgba(110,118,129,0); --col=
+or-timeline-badge-bg: #21262d; --color-ansi-black: #484f58; --color-ansi-bl=
+ack-bright: #6e7681; --color-ansi-white: #b1bac4; --color-ansi-white-bright=
+: #ffffff; --color-ansi-gray: #6e7681; --color-ansi-red: #ff7b72; --color-a=
+nsi-red-bright: #ffa198; --color-ansi-green: #3fb950; --color-ansi-green-br=
+ight: #56d364; --color-ansi-yellow: #d29922; --color-ansi-yellow-bright: #e=
+3b341; --color-ansi-blue: #58a6ff; --color-ansi-blue-bright: #79c0ff; --col=
+or-ansi-magenta: #bc8cff; --color-ansi-magenta-bright: #d2a8ff; --color-ans=
+i-cyan: #39c5cf; --color-ansi-cyan-bright: #56d4dd; --color-btn-text: #c9d1=
+d9; --color-btn-bg: #21262d; --color-btn-border: rgba(240,246,252,0.1); --c=
+olor-btn-shadow: 0 0 transparent; --color-btn-inset-shadow: 0 0 transparent=
+; --color-btn-hover-bg: #30363d; --color-btn-hover-border: #8b949e; --color=
+-btn-active-bg: hsla(212,12%,18%,1); --color-btn-active-border: #6e7681; --=
+color-btn-selected-bg: #161b22; --color-btn-focus-bg: #21262d; --color-btn-=
+focus-border: #8b949e; --color-btn-focus-shadow: 0 0 0 3px rgba(139,148,158=
+,0.3); --color-btn-shadow-active: inset 0 0.15em 0.3em rgba(1,4,9,0.15); --=
+color-btn-shadow-input-focus: 0 0 0 0.2em rgba(31,111,235,0.3); --color-btn=
+-counter-bg: #30363d; --color-btn-primary-text: #ffffff; --color-btn-primar=
+y-bg: #238636; --color-btn-primary-border: rgba(240,246,252,0.1); --color-b=
+tn-primary-shadow: 0 0 transparent; --color-btn-primary-inset-shadow: 0 0 t=
+ransparent; --color-btn-primary-hover-bg: #2ea043; --color-btn-primary-hove=
+r-border: rgba(240,246,252,0.1); --color-btn-primary-selected-bg: #238636; =
+--color-btn-primary-selected-shadow: 0 0 transparent; --color-btn-primary-d=
+isabled-text: rgba(255,255,255,0.5); --color-btn-primary-disabled-bg: rgba(=
+35,134,54,0.6); --color-btn-primary-disabled-border: rgba(240,246,252,0.1);=
+ --color-btn-primary-focus-bg: #238636; --color-btn-primary-focus-border: r=
+gba(240,246,252,0.1); --color-btn-primary-focus-shadow: 0 0 0 3px rgba(46,1=
+64,79,0.4); --color-btn-primary-icon: #ffffff; --color-btn-primary-counter-=
+bg: rgba(255,255,255,0.2); --color-btn-outline-text: #58a6ff; --color-btn-o=
+utline-hover-text: #58a6ff; --color-btn-outline-hover-bg: #30363d; --color-=
+btn-outline-hover-border: rgba(240,246,252,0.1); --color-btn-outline-hover-=
+shadow: 0 1px 0 rgba(1,4,9,0.1); --color-btn-outline-hover-inset-shadow: in=
+set 0 1px 0 rgba(255,255,255,0.03); --color-btn-outline-hover-counter-bg: r=
+gba(255,255,255,0.2); --color-btn-outline-selected-text: #ffffff; --color-b=
+tn-outline-selected-bg: #0d419d; --color-btn-outline-selected-border: rgba(=
+240,246,252,0.1); --color-btn-outline-selected-shadow: 0 0 transparent; --c=
+olor-btn-outline-disabled-text: rgba(88,166,255,0.5); --color-btn-outline-d=
+isabled-bg: #0d1117; --color-btn-outline-disabled-counter-bg: rgba(31,111,2=
+35,0.05); --color-btn-outline-focus-border: rgba(240,246,252,0.1); --color-=
+btn-outline-focus-shadow: 0 0 0 3px rgba(17,88,199,0.4); --color-btn-outlin=
+e-counter-bg: rgba(31,111,235,0.1); --color-btn-danger-text: #f85149; --col=
+or-btn-danger-hover-text: #ffffff; --color-btn-danger-hover-bg: #da3633; --=
+color-btn-danger-hover-border: #f85149; --color-btn-danger-hover-shadow: 0 =
+0 transparent; --color-btn-danger-hover-inset-shadow: 0 0 transparent; --co=
+lor-btn-danger-hover-icon: #ffffff; --color-btn-danger-hover-counter-bg: rg=
+ba(255,255,255,0.2); --color-btn-danger-selected-text: #ffffff; --color-btn=
+-danger-selected-bg: #b62324; --color-btn-danger-selected-border: #ff7b72; =
+--color-btn-danger-selected-shadow: 0 0 transparent; --color-btn-danger-dis=
+abled-text: rgba(248,81,73,0.5); --color-btn-danger-disabled-bg: #0d1117; -=
+-color-btn-danger-disabled-counter-bg: rgba(218,54,51,0.05); --color-btn-da=
+nger-focus-border: #f85149; --color-btn-danger-focus-shadow: 0 0 0 3px rgba=
+(248,81,73,0.4); --color-btn-danger-counter-bg: rgba(218,54,51,0.1); --colo=
+r-btn-danger-icon: #f85149; --color-underlinenav-icon: #6e7681; --color-und=
+erlinenav-border-hover: rgba(110,118,129,0.4); --color-action-list-item-inl=
+ine-divider: rgba(48,54,61,0.48); --color-action-list-item-default-hover-bg=
+: rgba(177,186,196,0.12); --color-action-list-item-default-hover-border: rg=
+ba(0,0,0,0); --color-action-list-item-default-active-bg: rgba(177,186,196,0=
+.2); --color-action-list-item-default-active-border: rgba(0,0,0,0); --color=
+-action-list-item-default-selected-bg: rgba(177,186,196,0.08); --color-acti=
+on-list-item-danger-hover-bg: rgba(248,81,73,0.16); --color-action-list-ite=
+m-danger-active-bg: rgba(248,81,73,0.24); --color-action-list-item-danger-h=
+over-text: #ff7b72; --color-switch-track-bg: #010409; --color-switch-track-=
+border: #6e7681; --color-switch-track-checked-bg: rgba(31,111,235,0.35); --=
+color-switch-track-checked-hover-bg: rgba(31,111,235,0.5); --color-switch-t=
+rack-checked-active-bg: rgba(31,111,235,0.65); --color-switch-track-checked=
+-border: #58a6ff; --color-switch-knob-checked-bg: #1f6feb; --color-switch-k=
+nob-checked-disabled-bg: #484f58; --color-segmented-control-bg: #161b22; --=
+color-segmented-control-button-hover-bg: #30363d; --color-segmented-control=
+-button-active-bg: #21262d; --color-segmented-control-button-selected-borde=
+r: #6e7681; --color-tree-view-item-chevron-hover-bg: rgba(177,186,196,0.12)=
+; --color-tree-view-item-directory-fill: #8b949e; --color-fg-default: #c9d1=
+d9; --color-fg-muted: #8b949e; --color-fg-subtle: #6e7681; --color-fg-on-em=
+phasis: #ffffff; --color-canvas-default: #0d1117; --color-canvas-overlay: #=
+161b22; --color-canvas-inset: #010409; --color-canvas-subtle: #161b22; --co=
+lor-border-default: #30363d; --color-border-muted: #21262d; --color-border-=
+subtle: rgba(240,246,252,0.1); --color-shadow-small: 0 0 transparent; --col=
+or-shadow-medium: 0 3px 6px #010409; --color-shadow-large: 0 8px 24px #0104=
+09; --color-shadow-extra-large: 0 12px 48px #010409; --color-neutral-emphas=
+is-plus: #6e7681; --color-neutral-emphasis: #6e7681; --color-neutral-muted:=
+ rgba(110,118,129,0.4); --color-neutral-subtle: rgba(110,118,129,0.1); --co=
+lor-accent-fg: #58a6ff; --color-accent-emphasis: #1f6feb; --color-accent-mu=
+ted: rgba(56,139,253,0.4); --color-accent-subtle: rgba(56,139,253,0.15); --=
+color-success-fg: #3fb950; --color-success-emphasis: #238636; --color-succe=
+ss-muted: rgba(46,160,67,0.4); --color-success-subtle: rgba(46,160,67,0.15)=
+; --color-attention-fg: #d29922; --color-attention-emphasis: #9e6a03; --col=
+or-attention-muted: rgba(187,128,9,0.4); --color-attention-subtle: rgba(187=
+,128,9,0.15); --color-severe-fg: #db6d28; --color-severe-emphasis: #bd561d;=
+ --color-severe-muted: rgba(219,109,40,0.4); --color-severe-subtle: rgba(21=
+9,109,40,0.15); --color-danger-fg: #f85149; --color-danger-emphasis: #da363=
+3; --color-danger-muted: rgba(248,81,73,0.4); --color-danger-subtle: rgba(2=
+48,81,73,0.15); --color-open-fg: #3fb950; --color-open-emphasis: #238636; -=
+-color-open-muted: rgba(46,160,67,0.4); --color-open-subtle: rgba(46,160,67=
+,0.15); --color-closed-fg: #f85149; --color-closed-emphasis: #da3633; --col=
+or-closed-muted: rgba(248,81,73,0.4); --color-closed-subtle: rgba(248,81,73=
+,0.15); --color-done-fg: #a371f7; --color-done-emphasis: #8957e5; --color-d=
+one-muted: rgba(163,113,247,0.4); --color-done-subtle: rgba(163,113,247,0.1=
+5); --color-sponsors-fg: #db61a2; --color-sponsors-emphasis: #bf4b8a; --col=
+or-sponsors-muted: rgba(219,97,162,0.4); --color-sponsors-subtle: rgba(219,=
+97,162,0.15); --color-primer-fg-disabled: #484f58; --color-primer-canvas-ba=
+ckdrop: rgba(1,4,9,0.8); --color-primer-canvas-sticky: rgba(13,17,23,0.95);=
+ --color-primer-border-active: #f78166; --color-primer-border-contrast: rgb=
+a(255,255,255,0.2); --color-primer-shadow-highlight: 0 0 transparent; --col=
+or-primer-shadow-inset: 0 0 transparent; --color-primer-shadow-focus: 0 0 0=
+ 3px #0c2d6b; --color-scale-black: #010409; --color-scale-white: #ffffff; -=
+-color-scale-gray-0: #f0f6fc; --color-scale-gray-1: #c9d1d9; --color-scale-=
+gray-2: #b1bac4; --color-scale-gray-3: #8b949e; --color-scale-gray-4: #6e76=
+81; --color-scale-gray-5: #484f58; --color-scale-gray-6: #30363d; --color-s=
+cale-gray-7: #21262d; --color-scale-gray-8: #161b22; --color-scale-gray-9: =
+#0d1117; --color-scale-blue-0: #cae8ff; --color-scale-blue-1: #a5d6ff; --co=
+lor-scale-blue-2: #79c0ff; --color-scale-blue-3: #58a6ff; --color-scale-blu=
+e-4: #388bfd; --color-scale-blue-5: #1f6feb; --color-scale-blue-6: #1158c7;=
+ --color-scale-blue-7: #0d419d; --color-scale-blue-8: #0c2d6b; --color-scal=
+e-blue-9: #051d4d; --color-scale-green-0: #aff5b4; --color-scale-green-1: #=
+7ee787; --color-scale-green-2: #56d364; --color-scale-green-3: #3fb950; --c=
+olor-scale-green-4: #2ea043; --color-scale-green-5: #238636; --color-scale-=
+green-6: #196c2e; --color-scale-green-7: #0f5323; --color-scale-green-8: #0=
+33a16; --color-scale-green-9: #04260f; --color-scale-yellow-0: #f8e3a1; --c=
+olor-scale-yellow-1: #f2cc60; --color-scale-yellow-2: #e3b341; --color-scal=
+e-yellow-3: #d29922; --color-scale-yellow-4: #bb8009; --color-scale-yellow-=
+5: #9e6a03; --color-scale-yellow-6: #845306; --color-scale-yellow-7: #693e0=
+0; --color-scale-yellow-8: #4b2900; --color-scale-yellow-9: #341a00; --colo=
+r-scale-orange-0: #ffdfb6; --color-scale-orange-1: #ffc680; --color-scale-o=
+range-2: #ffa657; --color-scale-orange-3: #f0883e; --color-scale-orange-4: =
+#db6d28; --color-scale-orange-5: #bd561d; --color-scale-orange-6: #9b4215; =
+--color-scale-orange-7: #762d0a; --color-scale-orange-8: #5a1e02; --color-s=
+cale-orange-9: #3d1300; --color-scale-red-0: #ffdcd7; --color-scale-red-1: =
+#ffc1ba; --color-scale-red-2: #ffa198; --color-scale-red-3: #ff7b72; --colo=
+r-scale-red-4: #f85149; --color-scale-red-5: #da3633; --color-scale-red-6: =
+#b62324; --color-scale-red-7: #8e1519; --color-scale-red-8: #67060c; --colo=
+r-scale-red-9: #490202; --color-scale-purple-0: #eddeff; --color-scale-purp=
+le-1: #e2c5ff; --color-scale-purple-2: #d2a8ff; --color-scale-purple-3: #bc=
+8cff; --color-scale-purple-4: #a371f7; --color-scale-purple-5: #8957e5; --c=
+olor-scale-purple-6: #6e40c9; --color-scale-purple-7: #553098; --color-scal=
+e-purple-8: #3c1e70; --color-scale-purple-9: #271052; --color-scale-pink-0:=
+ #ffdaec; --color-scale-pink-1: #ffbedd; --color-scale-pink-2: #ff9bce; --c=
+olor-scale-pink-3: #f778ba; --color-scale-pink-4: #db61a2; --color-scale-pi=
+nk-5: #bf4b8a; --color-scale-pink-6: #9e3670; --color-scale-pink-7: #7d2457=
+; --color-scale-pink-8: #5e103e; --color-scale-pink-9: #42062a; --color-sca=
+le-coral-0: #ffddd2; --color-scale-coral-1: #ffc2b2; --color-scale-coral-2:=
+ #ffa28b; --color-scale-coral-3: #f78166; --color-scale-coral-4: #ea6045; -=
+-color-scale-coral-5: #cf462d; --color-scale-coral-6: #ac3220; --color-scal=
+e-coral-7: #872012; --color-scale-coral-8: #640d04; --color-scale-coral-9: =
+#460701; }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme=3D"dark"] { --color-canvas-de=
+fault-transparent: rgba(13,17,23,0); --color-page-header-bg: #0d1117; --col=
+or-marketing-icon-primary: #79c0ff; --color-marketing-icon-secondary: #1f6f=
+eb; --color-diff-blob-addition-num-text: #c9d1d9; --color-diff-blob-additio=
+n-fg: #c9d1d9; --color-diff-blob-addition-num-bg: rgba(63,185,80,0.3); --co=
+lor-diff-blob-addition-line-bg: rgba(46,160,67,0.15); --color-diff-blob-add=
+ition-word-bg: rgba(46,160,67,0.4); --color-diff-blob-deletion-num-text: #c=
+9d1d9; --color-diff-blob-deletion-fg: #c9d1d9; --color-diff-blob-deletion-n=
+um-bg: rgba(248,81,73,0.3); --color-diff-blob-deletion-line-bg: rgba(248,81=
+,73,0.15); --color-diff-blob-deletion-word-bg: rgba(248,81,73,0.4); --color=
+-diff-blob-hunk-num-bg: rgba(56,139,253,0.4); --color-diff-blob-expander-ic=
+on: #8b949e; --color-diff-blob-selected-line-highlight-mix-blend-mode: scre=
+en; --color-diffstat-deletion-border: rgba(240,246,252,0.1); --color-diffst=
+at-addition-border: rgba(240,246,252,0.1); --color-diffstat-addition-bg: #3=
+fb950; --color-search-keyword-hl: rgba(210,153,34,0.4); --color-prettylight=
+s-syntax-comment: #8b949e; --color-prettylights-syntax-constant: #79c0ff; -=
+-color-prettylights-syntax-entity: #d2a8ff; --color-prettylights-syntax-sto=
+rage-modifier-import: #c9d1d9; --color-prettylights-syntax-entity-tag: #7ee=
+787; --color-prettylights-syntax-keyword: #ff7b72; --color-prettylights-syn=
+tax-string: #a5d6ff; --color-prettylights-syntax-variable: #ffa657; --color=
+-prettylights-syntax-brackethighlighter-unmatched: #f85149; --color-prettyl=
+ights-syntax-invalid-illegal-text: #f0f6fc; --color-prettylights-syntax-inv=
+alid-illegal-bg: #8e1519; --color-prettylights-syntax-carriage-return-text:=
+ #f0f6fc; --color-prettylights-syntax-carriage-return-bg: #b62324; --color-=
+prettylights-syntax-string-regexp: #7ee787; --color-prettylights-syntax-mar=
+kup-list: #f2cc60; --color-prettylights-syntax-markup-heading: #1f6feb; --c=
+olor-prettylights-syntax-markup-italic: #c9d1d9; --color-prettylights-synta=
+x-markup-bold: #c9d1d9; --color-prettylights-syntax-markup-deleted-text: #f=
+fdcd7; --color-prettylights-syntax-markup-deleted-bg: #67060c; --color-pret=
+tylights-syntax-markup-inserted-text: #aff5b4; --color-prettylights-syntax-=
+markup-inserted-bg: #033a16; --color-prettylights-syntax-markup-changed-tex=
+t: #ffdfb6; --color-prettylights-syntax-markup-changed-bg: #5a1e02; --color=
+-prettylights-syntax-markup-ignored-text: #c9d1d9; --color-prettylights-syn=
+tax-markup-ignored-bg: #1158c7; --color-prettylights-syntax-meta-diff-range=
+: #d2a8ff; --color-prettylights-syntax-brackethighlighter-angle: #8b949e; -=
+-color-prettylights-syntax-sublimelinter-gutter-mark: #484f58; --color-pret=
+tylights-syntax-constant-other-reference-link: #a5d6ff; --color-codemirror-=
+text: #c9d1d9; --color-codemirror-bg: #0d1117; --color-codemirror-gutters-b=
+g: #0d1117; --color-codemirror-guttermarker-text: #0d1117; --color-codemirr=
+or-guttermarker-subtle-text: #6e7681; --color-codemirror-linenumber-text: #=
+8b949e; --color-codemirror-cursor: #c9d1d9; --color-codemirror-selection-bg=
+: rgba(56,139,253,0.4); --color-codemirror-activeline-bg: rgba(110,118,129,=
+0.1); --color-codemirror-matchingbracket-text: #c9d1d9; --color-codemirror-=
+lines-bg: #0d1117; --color-codemirror-syntax-comment: #8b949e; --color-code=
+mirror-syntax-constant: #79c0ff; --color-codemirror-syntax-entity: #d2a8ff;=
+ --color-codemirror-syntax-keyword: #ff7b72; --color-codemirror-syntax-stor=
+age: #ff7b72; --color-codemirror-syntax-string: #a5d6ff; --color-codemirror=
+-syntax-support: #79c0ff; --color-codemirror-syntax-variable: #ffa657; --co=
+lor-checks-bg: #010409; --color-checks-run-border-width: 1px; --color-check=
+s-container-border-width: 1px; --color-checks-text-primary: #c9d1d9; --colo=
+r-checks-text-secondary: #8b949e; --color-checks-text-link: #58a6ff; --colo=
+r-checks-btn-icon: #8b949e; --color-checks-btn-hover-icon: #c9d1d9; --color=
+-checks-btn-hover-bg: rgba(110,118,129,0.1); --color-checks-input-text: #8b=
+949e; --color-checks-input-placeholder-text: #6e7681; --color-checks-input-=
+focus-text: #c9d1d9; --color-checks-input-bg: #161b22; --color-checks-input=
+-shadow: 0 0 0 1px (obj) =3D> (0, get_1.default)(obj, path); --color-checks=
+-donut-error: #f85149; --color-checks-donut-pending: #d29922; --color-check=
+s-donut-success: #2ea043; --color-checks-donut-neutral: #8b949e; --color-ch=
+ecks-dropdown-text: #c9d1d9; --color-checks-dropdown-bg: #161b22; --color-c=
+hecks-dropdown-border: #30363d; --color-checks-dropdown-shadow: rgba(1,4,9,=
+0.3); --color-checks-dropdown-hover-text: #c9d1d9; --color-checks-dropdown-=
+hover-bg: rgba(110,118,129,0.1); --color-checks-dropdown-btn-hover-text: #c=
+9d1d9; --color-checks-dropdown-btn-hover-bg: rgba(110,118,129,0.1); --color=
+-checks-scrollbar-thumb-bg: rgba(110,118,129,0.4); --color-checks-header-la=
+bel-text: #8b949e; --color-checks-header-label-open-text: #c9d1d9; --color-=
+checks-header-border: #21262d; --color-checks-header-icon: #8b949e; --color=
+-checks-line-text: #8b949e; --color-checks-line-num-text: #6e7681; --color-=
+checks-line-timestamp-text: #6e7681; --color-checks-line-hover-bg: rgba(110=
+,118,129,0.1); --color-checks-line-selected-bg: rgba(56,139,253,0.15); --co=
+lor-checks-line-selected-num-text: #58a6ff; --color-checks-line-dt-fm-text:=
+ #ffffff; --color-checks-line-dt-fm-bg: #9e6a03; --color-checks-gate-bg: rg=
+ba(187,128,9,0.15); --color-checks-gate-text: #8b949e; --color-checks-gate-=
+waiting-text: #d29922; --color-checks-step-header-open-bg: #161b22; --color=
+-checks-step-error-text: #f85149; --color-checks-step-warning-text: #d29922=
+; --color-checks-logline-text: #8b949e; --color-checks-logline-num-text: #6=
+e7681; --color-checks-logline-debug-text: #a371f7; --color-checks-logline-e=
+rror-text: #8b949e; --color-checks-logline-error-num-text: #6e7681; --color=
+-checks-logline-error-bg: rgba(248,81,73,0.15); --color-checks-logline-warn=
+ing-text: #8b949e; --color-checks-logline-warning-num-text: #d29922; --colo=
+r-checks-logline-warning-bg: rgba(187,128,9,0.15); --color-checks-logline-c=
+ommand-text: #58a6ff; --color-checks-logline-section-text: #3fb950; --color=
+-checks-ansi-black: #0d1117; --color-checks-ansi-black-bright: #161b22; --c=
+olor-checks-ansi-white: #b1bac4; --color-checks-ansi-white-bright: #b1bac4;=
+ --color-checks-ansi-gray: #6e7681; --color-checks-ansi-red: #ff7b72; --col=
+or-checks-ansi-red-bright: #ffa198; --color-checks-ansi-green: #3fb950; --c=
+olor-checks-ansi-green-bright: #56d364; --color-checks-ansi-yellow: #d29922=
+; --color-checks-ansi-yellow-bright: #e3b341; --color-checks-ansi-blue: #58=
+a6ff; --color-checks-ansi-blue-bright: #79c0ff; --color-checks-ansi-magenta=
+: #bc8cff; --color-checks-ansi-magenta-bright: #d2a8ff; --color-checks-ansi=
+-cyan: #76e3ea; --color-checks-ansi-cyan-bright: #b3f0ff; --color-project-h=
+eader-bg: #0d1117; --color-project-sidebar-bg: #161b22; --color-project-gra=
+dient-in: #161b22; --color-project-gradient-out: rgba(22,27,34,0); --color-=
+mktg-btn-bg: #f6f8fa; --color-mktg-btn-shadow-outline: rgb(255 255 255 / 25=
+%) 0 0 0 1px inset; --color-mktg-btn-shadow-focus: rgb(255 255 255 / 25%) 0=
+ 0 0 4px; --color-mktg-btn-shadow-hover: 0 4px 7px rgba(0, 0, 0, 0.15), 0 1=
+00px 80px rgba(255, 255, 255, 0.02), 0 42px 33px rgba(255, 255, 255, 0.024)=
+, 0 22px 18px rgba(255, 255, 255, 0.028), 0 12px 10px rgba(255, 255, 255, 0=
+.034), 0 7px 5px rgba(255, 255, 255, 0.04), 0 3px 2px rgba(255, 255, 255, 0=
+.07); --color-mktg-btn-shadow-hover-muted: rgb(255 255 255) 0 0 0 2px inset=
+; --color-avatar-bg: rgba(255,255,255,0.1); --color-avatar-border: rgba(240=
+,246,252,0.1); --color-avatar-stack-fade: #30363d; --color-avatar-stack-fad=
+e-more: #21262d; --color-avatar-child-shadow: -2px -2px 0 #0d1117; --color-=
+topic-tag-border: rgba(0,0,0,0); --color-counter-border: rgba(0,0,0,0); --c=
+olor-select-menu-backdrop-border: #484f58; --color-select-menu-tap-highligh=
+t: rgba(48,54,61,0.5); --color-select-menu-tap-focus-bg: #0c2d6b; --color-o=
+verlay-shadow: 0 0 0 1px #30363d, 0 16px 32px rgba(1,4,9,0.85); --color-hea=
+der-text: rgba(255,255,255,0.7); --color-header-bg: #161b22; --color-header=
+-divider: #8b949e; --color-header-logo: #f0f6fc; --color-header-search-bg: =
+#0d1117; --color-header-search-border: #30363d; --color-sidenav-selected-bg=
+: #21262d; --color-menu-bg-active: #161b22; --color-input-disabled-bg: rgba=
+(110,118,129,0); --color-timeline-badge-bg: #21262d; --color-ansi-black: #4=
+84f58; --color-ansi-black-bright: #6e7681; --color-ansi-white: #b1bac4; --c=
+olor-ansi-white-bright: #ffffff; --color-ansi-gray: #6e7681; --color-ansi-r=
+ed: #ff7b72; --color-ansi-red-bright: #ffa198; --color-ansi-green: #3fb950;=
+ --color-ansi-green-bright: #56d364; --color-ansi-yellow: #d29922; --color-=
+ansi-yellow-bright: #e3b341; --color-ansi-blue: #58a6ff; --color-ansi-blue-=
+bright: #79c0ff; --color-ansi-magenta: #bc8cff; --color-ansi-magenta-bright=
+: #d2a8ff; --color-ansi-cyan: #39c5cf; --color-ansi-cyan-bright: #56d4dd; -=
+-color-btn-text: #c9d1d9; --color-btn-bg: #21262d; --color-btn-border: rgba=
+(240,246,252,0.1); --color-btn-shadow: 0 0 transparent; --color-btn-inset-s=
+hadow: 0 0 transparent; --color-btn-hover-bg: #30363d; --color-btn-hover-bo=
+rder: #8b949e; --color-btn-active-bg: hsla(212,12%,18%,1); --color-btn-acti=
+ve-border: #6e7681; --color-btn-selected-bg: #161b22; --color-btn-focus-bg:=
+ #21262d; --color-btn-focus-border: #8b949e; --color-btn-focus-shadow: 0 0 =
+0 3px rgba(139,148,158,0.3); --color-btn-shadow-active: inset 0 0.15em 0.3e=
+m rgba(1,4,9,0.15); --color-btn-shadow-input-focus: 0 0 0 0.2em rgba(31,111=
+,235,0.3); --color-btn-counter-bg: #30363d; --color-btn-primary-text: #ffff=
+ff; --color-btn-primary-bg: #238636; --color-btn-primary-border: rgba(240,2=
+46,252,0.1); --color-btn-primary-shadow: 0 0 transparent; --color-btn-prima=
+ry-inset-shadow: 0 0 transparent; --color-btn-primary-hover-bg: #2ea043; --=
+color-btn-primary-hover-border: rgba(240,246,252,0.1); --color-btn-primary-=
+selected-bg: #238636; --color-btn-primary-selected-shadow: 0 0 transparent;=
+ --color-btn-primary-disabled-text: rgba(255,255,255,0.5); --color-btn-prim=
+ary-disabled-bg: rgba(35,134,54,0.6); --color-btn-primary-disabled-border: =
+rgba(240,246,252,0.1); --color-btn-primary-focus-bg: #238636; --color-btn-p=
+rimary-focus-border: rgba(240,246,252,0.1); --color-btn-primary-focus-shado=
+w: 0 0 0 3px rgba(46,164,79,0.4); --color-btn-primary-icon: #ffffff; --colo=
+r-btn-primary-counter-bg: rgba(255,255,255,0.2); --color-btn-outline-text: =
+#58a6ff; --color-btn-outline-hover-text: #58a6ff; --color-btn-outline-hover=
+-bg: #30363d; --color-btn-outline-hover-border: rgba(240,246,252,0.1); --co=
+lor-btn-outline-hover-shadow: 0 1px 0 rgba(1,4,9,0.1); --color-btn-outline-=
+hover-inset-shadow: inset 0 1px 0 rgba(255,255,255,0.03); --color-btn-outli=
+ne-hover-counter-bg: rgba(255,255,255,0.2); --color-btn-outline-selected-te=
+xt: #ffffff; --color-btn-outline-selected-bg: #0d419d; --color-btn-outline-=
+selected-border: rgba(240,246,252,0.1); --color-btn-outline-selected-shadow=
+: 0 0 transparent; --color-btn-outline-disabled-text: rgba(88,166,255,0.5);=
+ --color-btn-outline-disabled-bg: #0d1117; --color-btn-outline-disabled-cou=
+nter-bg: rgba(31,111,235,0.05); --color-btn-outline-focus-border: rgba(240,=
+246,252,0.1); --color-btn-outline-focus-shadow: 0 0 0 3px rgba(17,88,199,0.=
+4); --color-btn-outline-counter-bg: rgba(31,111,235,0.1); --color-btn-dange=
+r-text: #f85149; --color-btn-danger-hover-text: #ffffff; --color-btn-danger=
+-hover-bg: #da3633; --color-btn-danger-hover-border: #f85149; --color-btn-d=
+anger-hover-shadow: 0 0 transparent; --color-btn-danger-hover-inset-shadow:=
+ 0 0 transparent; --color-btn-danger-hover-icon: #ffffff; --color-btn-dange=
+r-hover-counter-bg: rgba(255,255,255,0.2); --color-btn-danger-selected-text=
+: #ffffff; --color-btn-danger-selected-bg: #b62324; --color-btn-danger-sele=
+cted-border: #ff7b72; --color-btn-danger-selected-shadow: 0 0 transparent; =
+--color-btn-danger-disabled-text: rgba(248,81,73,0.5); --color-btn-danger-d=
+isabled-bg: #0d1117; --color-btn-danger-disabled-counter-bg: rgba(218,54,51=
+,0.05); --color-btn-danger-focus-border: #f85149; --color-btn-danger-focus-=
+shadow: 0 0 0 3px rgba(248,81,73,0.4); --color-btn-danger-counter-bg: rgba(=
+218,54,51,0.1); --color-btn-danger-icon: #f85149; --color-underlinenav-icon=
+: #6e7681; --color-underlinenav-border-hover: rgba(110,118,129,0.4); --colo=
+r-action-list-item-inline-divider: rgba(48,54,61,0.48); --color-action-list=
+-item-default-hover-bg: rgba(177,186,196,0.12); --color-action-list-item-de=
+fault-hover-border: rgba(0,0,0,0); --color-action-list-item-default-active-=
+bg: rgba(177,186,196,0.2); --color-action-list-item-default-active-border: =
+rgba(0,0,0,0); --color-action-list-item-default-selected-bg: rgba(177,186,1=
+96,0.08); --color-action-list-item-danger-hover-bg: rgba(248,81,73,0.16); -=
+-color-action-list-item-danger-active-bg: rgba(248,81,73,0.24); --color-act=
+ion-list-item-danger-hover-text: #ff7b72; --color-switch-track-bg: #010409;=
+ --color-switch-track-border: #6e7681; --color-switch-track-checked-bg: rgb=
+a(31,111,235,0.35); --color-switch-track-checked-hover-bg: rgba(31,111,235,=
+0.5); --color-switch-track-checked-active-bg: rgba(31,111,235,0.65); --colo=
+r-switch-track-checked-border: #58a6ff; --color-switch-knob-checked-bg: #1f=
+6feb; --color-switch-knob-checked-disabled-bg: #484f58; --color-segmented-c=
+ontrol-bg: #161b22; --color-segmented-control-button-hover-bg: #30363d; --c=
+olor-segmented-control-button-active-bg: #21262d; --color-segmented-control=
+-button-selected-border: #6e7681; --color-tree-view-item-chevron-hover-bg: =
+rgba(177,186,196,0.12); --color-tree-view-item-directory-fill: #8b949e; --c=
+olor-fg-default: #c9d1d9; --color-fg-muted: #8b949e; --color-fg-subtle: #6e=
+7681; --color-fg-on-emphasis: #ffffff; --color-canvas-default: #0d1117; --c=
+olor-canvas-overlay: #161b22; --color-canvas-inset: #010409; --color-canvas=
+-subtle: #161b22; --color-border-default: #30363d; --color-border-muted: #2=
+1262d; --color-border-subtle: rgba(240,246,252,0.1); --color-shadow-small: =
+0 0 transparent; --color-shadow-medium: 0 3px 6px #010409; --color-shadow-l=
+arge: 0 8px 24px #010409; --color-shadow-extra-large: 0 12px 48px #010409; =
+--color-neutral-emphasis-plus: #6e7681; --color-neutral-emphasis: #6e7681; =
+--color-neutral-muted: rgba(110,118,129,0.4); --color-neutral-subtle: rgba(=
+110,118,129,0.1); --color-accent-fg: #58a6ff; --color-accent-emphasis: #1f6=
+feb; --color-accent-muted: rgba(56,139,253,0.4); --color-accent-subtle: rgb=
+a(56,139,253,0.15); --color-success-fg: #3fb950; --color-success-emphasis: =
+#238636; --color-success-muted: rgba(46,160,67,0.4); --color-success-subtle=
+: rgba(46,160,67,0.15); --color-attention-fg: #d29922; --color-attention-em=
+phasis: #9e6a03; --color-attention-muted: rgba(187,128,9,0.4); --color-atte=
+ntion-subtle: rgba(187,128,9,0.15); --color-severe-fg: #db6d28; --color-sev=
+ere-emphasis: #bd561d; --color-severe-muted: rgba(219,109,40,0.4); --color-=
+severe-subtle: rgba(219,109,40,0.15); --color-danger-fg: #f85149; --color-d=
+anger-emphasis: #da3633; --color-danger-muted: rgba(248,81,73,0.4); --color=
+-danger-subtle: rgba(248,81,73,0.15); --color-open-fg: #3fb950; --color-ope=
+n-emphasis: #238636; --color-open-muted: rgba(46,160,67,0.4); --color-open-=
+subtle: rgba(46,160,67,0.15); --color-closed-fg: #f85149; --color-closed-em=
+phasis: #da3633; --color-closed-muted: rgba(248,81,73,0.4); --color-closed-=
+subtle: rgba(248,81,73,0.15); --color-done-fg: #a371f7; --color-done-emphas=
+is: #8957e5; --color-done-muted: rgba(163,113,247,0.4); --color-done-subtle=
+: rgba(163,113,247,0.15); --color-sponsors-fg: #db61a2; --color-sponsors-em=
+phasis: #bf4b8a; --color-sponsors-muted: rgba(219,97,162,0.4); --color-spon=
+sors-subtle: rgba(219,97,162,0.15); --color-primer-fg-disabled: #484f58; --=
+color-primer-canvas-backdrop: rgba(1,4,9,0.8); --color-primer-canvas-sticky=
+: rgba(13,17,23,0.95); --color-primer-border-active: #f78166; --color-prime=
+r-border-contrast: rgba(255,255,255,0.2); --color-primer-shadow-highlight: =
+0 0 transparent; --color-primer-shadow-inset: 0 0 transparent; --color-prim=
+er-shadow-focus: 0 0 0 3px #0c2d6b; --color-scale-black: #010409; --color-s=
+cale-white: #ffffff; --color-scale-gray-0: #f0f6fc; --color-scale-gray-1: #=
+c9d1d9; --color-scale-gray-2: #b1bac4; --color-scale-gray-3: #8b949e; --col=
+or-scale-gray-4: #6e7681; --color-scale-gray-5: #484f58; --color-scale-gray=
+-6: #30363d; --color-scale-gray-7: #21262d; --color-scale-gray-8: #161b22; =
+--color-scale-gray-9: #0d1117; --color-scale-blue-0: #cae8ff; --color-scale=
+-blue-1: #a5d6ff; --color-scale-blue-2: #79c0ff; --color-scale-blue-3: #58a=
+6ff; --color-scale-blue-4: #388bfd; --color-scale-blue-5: #1f6feb; --color-=
+scale-blue-6: #1158c7; --color-scale-blue-7: #0d419d; --color-scale-blue-8:=
+ #0c2d6b; --color-scale-blue-9: #051d4d; --color-scale-green-0: #aff5b4; --=
+color-scale-green-1: #7ee787; --color-scale-green-2: #56d364; --color-scale=
+-green-3: #3fb950; --color-scale-green-4: #2ea043; --color-scale-green-5: #=
+238636; --color-scale-green-6: #196c2e; --color-scale-green-7: #0f5323; --c=
+olor-scale-green-8: #033a16; --color-scale-green-9: #04260f; --color-scale-=
+yellow-0: #f8e3a1; --color-scale-yellow-1: #f2cc60; --color-scale-yellow-2:=
+ #e3b341; --color-scale-yellow-3: #d29922; --color-scale-yellow-4: #bb8009;=
+ --color-scale-yellow-5: #9e6a03; --color-scale-yellow-6: #845306; --color-=
+scale-yellow-7: #693e00; --color-scale-yellow-8: #4b2900; --color-scale-yel=
+low-9: #341a00; --color-scale-orange-0: #ffdfb6; --color-scale-orange-1: #f=
+fc680; --color-scale-orange-2: #ffa657; --color-scale-orange-3: #f0883e; --=
+color-scale-orange-4: #db6d28; --color-scale-orange-5: #bd561d; --color-sca=
+le-orange-6: #9b4215; --color-scale-orange-7: #762d0a; --color-scale-orange=
+-8: #5a1e02; --color-scale-orange-9: #3d1300; --color-scale-red-0: #ffdcd7;=
+ --color-scale-red-1: #ffc1ba; --color-scale-red-2: #ffa198; --color-scale-=
+red-3: #ff7b72; --color-scale-red-4: #f85149; --color-scale-red-5: #da3633;=
+ --color-scale-red-6: #b62324; --color-scale-red-7: #8e1519; --color-scale-=
+red-8: #67060c; --color-scale-red-9: #490202; --color-scale-purple-0: #edde=
+ff; --color-scale-purple-1: #e2c5ff; --color-scale-purple-2: #d2a8ff; --col=
+or-scale-purple-3: #bc8cff; --color-scale-purple-4: #a371f7; --color-scale-=
+purple-5: #8957e5; --color-scale-purple-6: #6e40c9; --color-scale-purple-7:=
+ #553098; --color-scale-purple-8: #3c1e70; --color-scale-purple-9: #271052;=
+ --color-scale-pink-0: #ffdaec; --color-scale-pink-1: #ffbedd; --color-scal=
+e-pink-2: #ff9bce; --color-scale-pink-3: #f778ba; --color-scale-pink-4: #db=
+61a2; --color-scale-pink-5: #bf4b8a; --color-scale-pink-6: #9e3670; --color=
+-scale-pink-7: #7d2457; --color-scale-pink-8: #5e103e; --color-scale-pink-9=
+: #42062a; --color-scale-coral-0: #ffddd2; --color-scale-coral-1: #ffc2b2; =
+--color-scale-coral-2: #ffa28b; --color-scale-coral-3: #f78166; --color-sca=
+le-coral-4: #ea6045; --color-scale-coral-5: #cf462d; --color-scale-coral-6:=
+ #ac3220; --color-scale-coral-7: #872012; --color-scale-coral-8: #640d04; -=
+-color-scale-coral-9: #460701; }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme=3D"dark"] { --color-canvas-def=
+ault-transparent: rgba(13,17,23,0); --color-page-header-bg: #0d1117; --colo=
+r-marketing-icon-primary: #79c0ff; --color-marketing-icon-secondary: #1f6fe=
+b; --color-diff-blob-addition-num-text: #c9d1d9; --color-diff-blob-addition=
+-fg: #c9d1d9; --color-diff-blob-addition-num-bg: rgba(63,185,80,0.3); --col=
+or-diff-blob-addition-line-bg: rgba(46,160,67,0.15); --color-diff-blob-addi=
+tion-word-bg: rgba(46,160,67,0.4); --color-diff-blob-deletion-num-text: #c9=
+d1d9; --color-diff-blob-deletion-fg: #c9d1d9; --color-diff-blob-deletion-nu=
+m-bg: rgba(248,81,73,0.3); --color-diff-blob-deletion-line-bg: rgba(248,81,=
+73,0.15); --color-diff-blob-deletion-word-bg: rgba(248,81,73,0.4); --color-=
+diff-blob-hunk-num-bg: rgba(56,139,253,0.4); --color-diff-blob-expander-ico=
+n: #8b949e; --color-diff-blob-selected-line-highlight-mix-blend-mode: scree=
+n; --color-diffstat-deletion-border: rgba(240,246,252,0.1); --color-diffsta=
+t-addition-border: rgba(240,246,252,0.1); --color-diffstat-addition-bg: #3f=
+b950; --color-search-keyword-hl: rgba(210,153,34,0.4); --color-prettylights=
+-syntax-comment: #8b949e; --color-prettylights-syntax-constant: #79c0ff; --=
+color-prettylights-syntax-entity: #d2a8ff; --color-prettylights-syntax-stor=
+age-modifier-import: #c9d1d9; --color-prettylights-syntax-entity-tag: #7ee7=
+87; --color-prettylights-syntax-keyword: #ff7b72; --color-prettylights-synt=
+ax-string: #a5d6ff; --color-prettylights-syntax-variable: #ffa657; --color-=
+prettylights-syntax-brackethighlighter-unmatched: #f85149; --color-prettyli=
+ghts-syntax-invalid-illegal-text: #f0f6fc; --color-prettylights-syntax-inva=
+lid-illegal-bg: #8e1519; --color-prettylights-syntax-carriage-return-text: =
+#f0f6fc; --color-prettylights-syntax-carriage-return-bg: #b62324; --color-p=
+rettylights-syntax-string-regexp: #7ee787; --color-prettylights-syntax-mark=
+up-list: #f2cc60; --color-prettylights-syntax-markup-heading: #1f6feb; --co=
+lor-prettylights-syntax-markup-italic: #c9d1d9; --color-prettylights-syntax=
+-markup-bold: #c9d1d9; --color-prettylights-syntax-markup-deleted-text: #ff=
+dcd7; --color-prettylights-syntax-markup-deleted-bg: #67060c; --color-prett=
+ylights-syntax-markup-inserted-text: #aff5b4; --color-prettylights-syntax-m=
+arkup-inserted-bg: #033a16; --color-prettylights-syntax-markup-changed-text=
+: #ffdfb6; --color-prettylights-syntax-markup-changed-bg: #5a1e02; --color-=
+prettylights-syntax-markup-ignored-text: #c9d1d9; --color-prettylights-synt=
+ax-markup-ignored-bg: #1158c7; --color-prettylights-syntax-meta-diff-range:=
+ #d2a8ff; --color-prettylights-syntax-brackethighlighter-angle: #8b949e; --=
+color-prettylights-syntax-sublimelinter-gutter-mark: #484f58; --color-prett=
+ylights-syntax-constant-other-reference-link: #a5d6ff; --color-codemirror-t=
+ext: #c9d1d9; --color-codemirror-bg: #0d1117; --color-codemirror-gutters-bg=
+: #0d1117; --color-codemirror-guttermarker-text: #0d1117; --color-codemirro=
+r-guttermarker-subtle-text: #6e7681; --color-codemirror-linenumber-text: #8=
+b949e; --color-codemirror-cursor: #c9d1d9; --color-codemirror-selection-bg:=
+ rgba(56,139,253,0.4); --color-codemirror-activeline-bg: rgba(110,118,129,0=
+.1); --color-codemirror-matchingbracket-text: #c9d1d9; --color-codemirror-l=
+ines-bg: #0d1117; --color-codemirror-syntax-comment: #8b949e; --color-codem=
+irror-syntax-constant: #79c0ff; --color-codemirror-syntax-entity: #d2a8ff; =
+--color-codemirror-syntax-keyword: #ff7b72; --color-codemirror-syntax-stora=
+ge: #ff7b72; --color-codemirror-syntax-string: #a5d6ff; --color-codemirror-=
+syntax-support: #79c0ff; --color-codemirror-syntax-variable: #ffa657; --col=
+or-checks-bg: #010409; --color-checks-run-border-width: 1px; --color-checks=
+-container-border-width: 1px; --color-checks-text-primary: #c9d1d9; --color=
+-checks-text-secondary: #8b949e; --color-checks-text-link: #58a6ff; --color=
+-checks-btn-icon: #8b949e; --color-checks-btn-hover-icon: #c9d1d9; --color-=
+checks-btn-hover-bg: rgba(110,118,129,0.1); --color-checks-input-text: #8b9=
+49e; --color-checks-input-placeholder-text: #6e7681; --color-checks-input-f=
+ocus-text: #c9d1d9; --color-checks-input-bg: #161b22; --color-checks-input-=
+shadow: 0 0 0 1px (obj) =3D> (0, get_1.default)(obj, path); --color-checks-=
+donut-error: #f85149; --color-checks-donut-pending: #d29922; --color-checks=
+-donut-success: #2ea043; --color-checks-donut-neutral: #8b949e; --color-che=
+cks-dropdown-text: #c9d1d9; --color-checks-dropdown-bg: #161b22; --color-ch=
+ecks-dropdown-border: #30363d; --color-checks-dropdown-shadow: rgba(1,4,9,0=
+.3); --color-checks-dropdown-hover-text: #c9d1d9; --color-checks-dropdown-h=
+over-bg: rgba(110,118,129,0.1); --color-checks-dropdown-btn-hover-text: #c9=
+d1d9; --color-checks-dropdown-btn-hover-bg: rgba(110,118,129,0.1); --color-=
+checks-scrollbar-thumb-bg: rgba(110,118,129,0.4); --color-checks-header-lab=
+el-text: #8b949e; --color-checks-header-label-open-text: #c9d1d9; --color-c=
+hecks-header-border: #21262d; --color-checks-header-icon: #8b949e; --color-=
+checks-line-text: #8b949e; --color-checks-line-num-text: #6e7681; --color-c=
+hecks-line-timestamp-text: #6e7681; --color-checks-line-hover-bg: rgba(110,=
+118,129,0.1); --color-checks-line-selected-bg: rgba(56,139,253,0.15); --col=
+or-checks-line-selected-num-text: #58a6ff; --color-checks-line-dt-fm-text: =
+#ffffff; --color-checks-line-dt-fm-bg: #9e6a03; --color-checks-gate-bg: rgb=
+a(187,128,9,0.15); --color-checks-gate-text: #8b949e; --color-checks-gate-w=
+aiting-text: #d29922; --color-checks-step-header-open-bg: #161b22; --color-=
+checks-step-error-text: #f85149; --color-checks-step-warning-text: #d29922;=
+ --color-checks-logline-text: #8b949e; --color-checks-logline-num-text: #6e=
+7681; --color-checks-logline-debug-text: #a371f7; --color-checks-logline-er=
+ror-text: #8b949e; --color-checks-logline-error-num-text: #6e7681; --color-=
+checks-logline-error-bg: rgba(248,81,73,0.15); --color-checks-logline-warni=
+ng-text: #8b949e; --color-checks-logline-warning-num-text: #d29922; --color=
+-checks-logline-warning-bg: rgba(187,128,9,0.15); --color-checks-logline-co=
+mmand-text: #58a6ff; --color-checks-logline-section-text: #3fb950; --color-=
+checks-ansi-black: #0d1117; --color-checks-ansi-black-bright: #161b22; --co=
+lor-checks-ansi-white: #b1bac4; --color-checks-ansi-white-bright: #b1bac4; =
+--color-checks-ansi-gray: #6e7681; --color-checks-ansi-red: #ff7b72; --colo=
+r-checks-ansi-red-bright: #ffa198; --color-checks-ansi-green: #3fb950; --co=
+lor-checks-ansi-green-bright: #56d364; --color-checks-ansi-yellow: #d29922;=
+ --color-checks-ansi-yellow-bright: #e3b341; --color-checks-ansi-blue: #58a=
+6ff; --color-checks-ansi-blue-bright: #79c0ff; --color-checks-ansi-magenta:=
+ #bc8cff; --color-checks-ansi-magenta-bright: #d2a8ff; --color-checks-ansi-=
+cyan: #76e3ea; --color-checks-ansi-cyan-bright: #b3f0ff; --color-project-he=
+ader-bg: #0d1117; --color-project-sidebar-bg: #161b22; --color-project-grad=
+ient-in: #161b22; --color-project-gradient-out: rgba(22,27,34,0); --color-m=
+ktg-btn-bg: #f6f8fa; --color-mktg-btn-shadow-outline: rgb(255 255 255 / 25%=
+) 0 0 0 1px inset; --color-mktg-btn-shadow-focus: rgb(255 255 255 / 25%) 0 =
+0 0 4px; --color-mktg-btn-shadow-hover: 0 4px 7px rgba(0, 0, 0, 0.15), 0 10=
+0px 80px rgba(255, 255, 255, 0.02), 0 42px 33px rgba(255, 255, 255, 0.024),=
+ 0 22px 18px rgba(255, 255, 255, 0.028), 0 12px 10px rgba(255, 255, 255, 0.=
+034), 0 7px 5px rgba(255, 255, 255, 0.04), 0 3px 2px rgba(255, 255, 255, 0.=
+07); --color-mktg-btn-shadow-hover-muted: rgb(255 255 255) 0 0 0 2px inset;=
+ --color-avatar-bg: rgba(255,255,255,0.1); --color-avatar-border: rgba(240,=
+246,252,0.1); --color-avatar-stack-fade: #30363d; --color-avatar-stack-fade=
+-more: #21262d; --color-avatar-child-shadow: -2px -2px 0 #0d1117; --color-t=
+opic-tag-border: rgba(0,0,0,0); --color-counter-border: rgba(0,0,0,0); --co=
+lor-select-menu-backdrop-border: #484f58; --color-select-menu-tap-highlight=
+: rgba(48,54,61,0.5); --color-select-menu-tap-focus-bg: #0c2d6b; --color-ov=
+erlay-shadow: 0 0 0 1px #30363d, 0 16px 32px rgba(1,4,9,0.85); --color-head=
+er-text: rgba(255,255,255,0.7); --color-header-bg: #161b22; --color-header-=
+divider: #8b949e; --color-header-logo: #f0f6fc; --color-header-search-bg: #=
+0d1117; --color-header-search-border: #30363d; --color-sidenav-selected-bg:=
+ #21262d; --color-menu-bg-active: #161b22; --color-input-disabled-bg: rgba(=
+110,118,129,0); --color-timeline-badge-bg: #21262d; --color-ansi-black: #48=
+4f58; --color-ansi-black-bright: #6e7681; --color-ansi-white: #b1bac4; --co=
+lor-ansi-white-bright: #ffffff; --color-ansi-gray: #6e7681; --color-ansi-re=
+d: #ff7b72; --color-ansi-red-bright: #ffa198; --color-ansi-green: #3fb950; =
+--color-ansi-green-bright: #56d364; --color-ansi-yellow: #d29922; --color-a=
+nsi-yellow-bright: #e3b341; --color-ansi-blue: #58a6ff; --color-ansi-blue-b=
+right: #79c0ff; --color-ansi-magenta: #bc8cff; --color-ansi-magenta-bright:=
+ #d2a8ff; --color-ansi-cyan: #39c5cf; --color-ansi-cyan-bright: #56d4dd; --=
+color-btn-text: #c9d1d9; --color-btn-bg: #21262d; --color-btn-border: rgba(=
+240,246,252,0.1); --color-btn-shadow: 0 0 transparent; --color-btn-inset-sh=
+adow: 0 0 transparent; --color-btn-hover-bg: #30363d; --color-btn-hover-bor=
+der: #8b949e; --color-btn-active-bg: hsla(212,12%,18%,1); --color-btn-activ=
+e-border: #6e7681; --color-btn-selected-bg: #161b22; --color-btn-focus-bg: =
+#21262d; --color-btn-focus-border: #8b949e; --color-btn-focus-shadow: 0 0 0=
+ 3px rgba(139,148,158,0.3); --color-btn-shadow-active: inset 0 0.15em 0.3em=
+ rgba(1,4,9,0.15); --color-btn-shadow-input-focus: 0 0 0 0.2em rgba(31,111,=
+235,0.3); --color-btn-counter-bg: #30363d; --color-btn-primary-text: #fffff=
+f; --color-btn-primary-bg: #238636; --color-btn-primary-border: rgba(240,24=
+6,252,0.1); --color-btn-primary-shadow: 0 0 transparent; --color-btn-primar=
+y-inset-shadow: 0 0 transparent; --color-btn-primary-hover-bg: #2ea043; --c=
+olor-btn-primary-hover-border: rgba(240,246,252,0.1); --color-btn-primary-s=
+elected-bg: #238636; --color-btn-primary-selected-shadow: 0 0 transparent; =
+--color-btn-primary-disabled-text: rgba(255,255,255,0.5); --color-btn-prima=
+ry-disabled-bg: rgba(35,134,54,0.6); --color-btn-primary-disabled-border: r=
+gba(240,246,252,0.1); --color-btn-primary-focus-bg: #238636; --color-btn-pr=
+imary-focus-border: rgba(240,246,252,0.1); --color-btn-primary-focus-shadow=
+: 0 0 0 3px rgba(46,164,79,0.4); --color-btn-primary-icon: #ffffff; --color=
+-btn-primary-counter-bg: rgba(255,255,255,0.2); --color-btn-outline-text: #=
+58a6ff; --color-btn-outline-hover-text: #58a6ff; --color-btn-outline-hover-=
+bg: #30363d; --color-btn-outline-hover-border: rgba(240,246,252,0.1); --col=
+or-btn-outline-hover-shadow: 0 1px 0 rgba(1,4,9,0.1); --color-btn-outline-h=
+over-inset-shadow: inset 0 1px 0 rgba(255,255,255,0.03); --color-btn-outlin=
+e-hover-counter-bg: rgba(255,255,255,0.2); --color-btn-outline-selected-tex=
+t: #ffffff; --color-btn-outline-selected-bg: #0d419d; --color-btn-outline-s=
+elected-border: rgba(240,246,252,0.1); --color-btn-outline-selected-shadow:=
+ 0 0 transparent; --color-btn-outline-disabled-text: rgba(88,166,255,0.5); =
+--color-btn-outline-disabled-bg: #0d1117; --color-btn-outline-disabled-coun=
+ter-bg: rgba(31,111,235,0.05); --color-btn-outline-focus-border: rgba(240,2=
+46,252,0.1); --color-btn-outline-focus-shadow: 0 0 0 3px rgba(17,88,199,0.4=
+); --color-btn-outline-counter-bg: rgba(31,111,235,0.1); --color-btn-danger=
+-text: #f85149; --color-btn-danger-hover-text: #ffffff; --color-btn-danger-=
+hover-bg: #da3633; --color-btn-danger-hover-border: #f85149; --color-btn-da=
+nger-hover-shadow: 0 0 transparent; --color-btn-danger-hover-inset-shadow: =
+0 0 transparent; --color-btn-danger-hover-icon: #ffffff; --color-btn-danger=
+-hover-counter-bg: rgba(255,255,255,0.2); --color-btn-danger-selected-text:=
+ #ffffff; --color-btn-danger-selected-bg: #b62324; --color-btn-danger-selec=
+ted-border: #ff7b72; --color-btn-danger-selected-shadow: 0 0 transparent; -=
+-color-btn-danger-disabled-text: rgba(248,81,73,0.5); --color-btn-danger-di=
+sabled-bg: #0d1117; --color-btn-danger-disabled-counter-bg: rgba(218,54,51,=
+0.05); --color-btn-danger-focus-border: #f85149; --color-btn-danger-focus-s=
+hadow: 0 0 0 3px rgba(248,81,73,0.4); --color-btn-danger-counter-bg: rgba(2=
+18,54,51,0.1); --color-btn-danger-icon: #f85149; --color-underlinenav-icon:=
+ #6e7681; --color-underlinenav-border-hover: rgba(110,118,129,0.4); --color=
+-action-list-item-inline-divider: rgba(48,54,61,0.48); --color-action-list-=
+item-default-hover-bg: rgba(177,186,196,0.12); --color-action-list-item-def=
+ault-hover-border: rgba(0,0,0,0); --color-action-list-item-default-active-b=
+g: rgba(177,186,196,0.2); --color-action-list-item-default-active-border: r=
+gba(0,0,0,0); --color-action-list-item-default-selected-bg: rgba(177,186,19=
+6,0.08); --color-action-list-item-danger-hover-bg: rgba(248,81,73,0.16); --=
+color-action-list-item-danger-active-bg: rgba(248,81,73,0.24); --color-acti=
+on-list-item-danger-hover-text: #ff7b72; --color-switch-track-bg: #010409; =
+--color-switch-track-border: #6e7681; --color-switch-track-checked-bg: rgba=
+(31,111,235,0.35); --color-switch-track-checked-hover-bg: rgba(31,111,235,0=
+.5); --color-switch-track-checked-active-bg: rgba(31,111,235,0.65); --color=
+-switch-track-checked-border: #58a6ff; --color-switch-knob-checked-bg: #1f6=
+feb; --color-switch-knob-checked-disabled-bg: #484f58; --color-segmented-co=
+ntrol-bg: #161b22; --color-segmented-control-button-hover-bg: #30363d; --co=
+lor-segmented-control-button-active-bg: #21262d; --color-segmented-control-=
+button-selected-border: #6e7681; --color-tree-view-item-chevron-hover-bg: r=
+gba(177,186,196,0.12); --color-tree-view-item-directory-fill: #8b949e; --co=
+lor-fg-default: #c9d1d9; --color-fg-muted: #8b949e; --color-fg-subtle: #6e7=
+681; --color-fg-on-emphasis: #ffffff; --color-canvas-default: #0d1117; --co=
+lor-canvas-overlay: #161b22; --color-canvas-inset: #010409; --color-canvas-=
+subtle: #161b22; --color-border-default: #30363d; --color-border-muted: #21=
+262d; --color-border-subtle: rgba(240,246,252,0.1); --color-shadow-small: 0=
+ 0 transparent; --color-shadow-medium: 0 3px 6px #010409; --color-shadow-la=
+rge: 0 8px 24px #010409; --color-shadow-extra-large: 0 12px 48px #010409; -=
+-color-neutral-emphasis-plus: #6e7681; --color-neutral-emphasis: #6e7681; -=
+-color-neutral-muted: rgba(110,118,129,0.4); --color-neutral-subtle: rgba(1=
+10,118,129,0.1); --color-accent-fg: #58a6ff; --color-accent-emphasis: #1f6f=
+eb; --color-accent-muted: rgba(56,139,253,0.4); --color-accent-subtle: rgba=
+(56,139,253,0.15); --color-success-fg: #3fb950; --color-success-emphasis: #=
+238636; --color-success-muted: rgba(46,160,67,0.4); --color-success-subtle:=
+ rgba(46,160,67,0.15); --color-attention-fg: #d29922; --color-attention-emp=
+hasis: #9e6a03; --color-attention-muted: rgba(187,128,9,0.4); --color-atten=
+tion-subtle: rgba(187,128,9,0.15); --color-severe-fg: #db6d28; --color-seve=
+re-emphasis: #bd561d; --color-severe-muted: rgba(219,109,40,0.4); --color-s=
+evere-subtle: rgba(219,109,40,0.15); --color-danger-fg: #f85149; --color-da=
+nger-emphasis: #da3633; --color-danger-muted: rgba(248,81,73,0.4); --color-=
+danger-subtle: rgba(248,81,73,0.15); --color-open-fg: #3fb950; --color-open=
+-emphasis: #238636; --color-open-muted: rgba(46,160,67,0.4); --color-open-s=
+ubtle: rgba(46,160,67,0.15); --color-closed-fg: #f85149; --color-closed-emp=
+hasis: #da3633; --color-closed-muted: rgba(248,81,73,0.4); --color-closed-s=
+ubtle: rgba(248,81,73,0.15); --color-done-fg: #a371f7; --color-done-emphasi=
+s: #8957e5; --color-done-muted: rgba(163,113,247,0.4); --color-done-subtle:=
+ rgba(163,113,247,0.15); --color-sponsors-fg: #db61a2; --color-sponsors-emp=
+hasis: #bf4b8a; --color-sponsors-muted: rgba(219,97,162,0.4); --color-spons=
+ors-subtle: rgba(219,97,162,0.15); --color-primer-fg-disabled: #484f58; --c=
+olor-primer-canvas-backdrop: rgba(1,4,9,0.8); --color-primer-canvas-sticky:=
+ rgba(13,17,23,0.95); --color-primer-border-active: #f78166; --color-primer=
+-border-contrast: rgba(255,255,255,0.2); --color-primer-shadow-highlight: 0=
+ 0 transparent; --color-primer-shadow-inset: 0 0 transparent; --color-prime=
+r-shadow-focus: 0 0 0 3px #0c2d6b; --color-scale-black: #010409; --color-sc=
+ale-white: #ffffff; --color-scale-gray-0: #f0f6fc; --color-scale-gray-1: #c=
+9d1d9; --color-scale-gray-2: #b1bac4; --color-scale-gray-3: #8b949e; --colo=
+r-scale-gray-4: #6e7681; --color-scale-gray-5: #484f58; --color-scale-gray-=
+6: #30363d; --color-scale-gray-7: #21262d; --color-scale-gray-8: #161b22; -=
+-color-scale-gray-9: #0d1117; --color-scale-blue-0: #cae8ff; --color-scale-=
+blue-1: #a5d6ff; --color-scale-blue-2: #79c0ff; --color-scale-blue-3: #58a6=
+ff; --color-scale-blue-4: #388bfd; --color-scale-blue-5: #1f6feb; --color-s=
+cale-blue-6: #1158c7; --color-scale-blue-7: #0d419d; --color-scale-blue-8: =
+#0c2d6b; --color-scale-blue-9: #051d4d; --color-scale-green-0: #aff5b4; --c=
+olor-scale-green-1: #7ee787; --color-scale-green-2: #56d364; --color-scale-=
+green-3: #3fb950; --color-scale-green-4: #2ea043; --color-scale-green-5: #2=
+38636; --color-scale-green-6: #196c2e; --color-scale-green-7: #0f5323; --co=
+lor-scale-green-8: #033a16; --color-scale-green-9: #04260f; --color-scale-y=
+ellow-0: #f8e3a1; --color-scale-yellow-1: #f2cc60; --color-scale-yellow-2: =
+#e3b341; --color-scale-yellow-3: #d29922; --color-scale-yellow-4: #bb8009; =
+--color-scale-yellow-5: #9e6a03; --color-scale-yellow-6: #845306; --color-s=
+cale-yellow-7: #693e00; --color-scale-yellow-8: #4b2900; --color-scale-yell=
+ow-9: #341a00; --color-scale-orange-0: #ffdfb6; --color-scale-orange-1: #ff=
+c680; --color-scale-orange-2: #ffa657; --color-scale-orange-3: #f0883e; --c=
+olor-scale-orange-4: #db6d28; --color-scale-orange-5: #bd561d; --color-scal=
+e-orange-6: #9b4215; --color-scale-orange-7: #762d0a; --color-scale-orange-=
+8: #5a1e02; --color-scale-orange-9: #3d1300; --color-scale-red-0: #ffdcd7; =
+--color-scale-red-1: #ffc1ba; --color-scale-red-2: #ffa198; --color-scale-r=
+ed-3: #ff7b72; --color-scale-red-4: #f85149; --color-scale-red-5: #da3633; =
+--color-scale-red-6: #b62324; --color-scale-red-7: #8e1519; --color-scale-r=
+ed-8: #67060c; --color-scale-red-9: #490202; --color-scale-purple-0: #eddef=
+f; --color-scale-purple-1: #e2c5ff; --color-scale-purple-2: #d2a8ff; --colo=
+r-scale-purple-3: #bc8cff; --color-scale-purple-4: #a371f7; --color-scale-p=
+urple-5: #8957e5; --color-scale-purple-6: #6e40c9; --color-scale-purple-7: =
+#553098; --color-scale-purple-8: #3c1e70; --color-scale-purple-9: #271052; =
+--color-scale-pink-0: #ffdaec; --color-scale-pink-1: #ffbedd; --color-scale=
+-pink-2: #ff9bce; --color-scale-pink-3: #f778ba; --color-scale-pink-4: #db6=
+1a2; --color-scale-pink-5: #bf4b8a; --color-scale-pink-6: #9e3670; --color-=
+scale-pink-7: #7d2457; --color-scale-pink-8: #5e103e; --color-scale-pink-9:=
+ #42062a; --color-scale-coral-0: #ffddd2; --color-scale-coral-1: #ffc2b2; -=
+-color-scale-coral-2: #ffa28b; --color-scale-coral-3: #f78166; --color-scal=
+e-coral-4: #ea6045; --color-scale-coral-5: #cf462d; --color-scale-coral-6: =
+#ac3220; --color-scale-coral-7: #872012; --color-scale-coral-8: #640d04; --=
+color-scale-coral-9: #460701; }
+}
+------MultipartBoundary--Xhr6yb6p8QAUWNcNSMINdGDzY3kbebp99Hpkh4tcTA----
+Content-Type: text/css
+Content-Transfer-Encoding: quoted-printable
+Content-Location: https://github.githubassets.com/assets/primer-1677771a5a91.css
+
+@charset "utf-8";
+
+:root, [data-color-mode=3D"light"][data-light-theme*=3D"light"], [data-colo=
+r-mode=3D"dark"][data-dark-theme*=3D"light"] { color-scheme: light; }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"light"] { color-scheme: l=
+ight; }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"light"] { color-scheme: li=
+ght; }
+}
+
+[data-color-mode=3D"light"][data-light-theme*=3D"dark"], [data-color-mode=
+=3D"dark"][data-dark-theme*=3D"dark"] { color-scheme: dark; }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] { color-scheme: da=
+rk; }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] { color-scheme: dar=
+k; }
+}
+
+[data-color-mode] { color: var(--color-fg-default); background-color: var(-=
+-color-canvas-default); }
+
+@media (forced-colors: active) {
+  body { --color-accent-emphasis: Highlight; --color-fg-on-emphasis: LinkTe=
+xt; }
+}
+
+.ActionList { padding: 8px; }
+
+.ActionList--full { padding: 0px; }
+
+.ActionList--subGroup { padding: 0px; }
+
+.ActionList--divided .ActionList-item-label::before { position: absolute; t=
+op: -6px; display: block; width: 100%; height: 1px; content: ""; background=
+: var(--color-action-list-item-inline-divider); }
+
+.ActionList--divided .ActionList-item-descriptionWrap--inline::before { pos=
+ition: absolute; top: -6px; display: block; width: 100%; height: 1px; conte=
+nt: ""; background: var(--color-action-list-item-inline-divider); }
+
+.ActionList--divided .ActionList-item-descriptionWrap--inline .ActionList-i=
+tem-label::before { content: unset; }
+
+.ActionList--divided .ActionList-item--navActive .ActionList-item-label::be=
+fore, .ActionList--divided .ActionList-item--navActive + .ActionList-item .=
+ActionList-item-label::before { visibility: hidden; }
+
+.ActionList-item:first-of-type .ActionList-item-label::before, .ActionList-=
+sectionDivider + .ActionList-item .ActionList-item-label::before { visibili=
+ty: hidden; }
+
+.ActionList-item:first-of-type .ActionList-item-descriptionWrap--inline::be=
+fore, .ActionList-sectionDivider + .ActionList-item .ActionList-item-descri=
+ptionWrap--inline::before { visibility: hidden; }
+
+.ActionList--tree { --ActionList-tree-depth: 1; }
+
+.ActionList--tree .ActionList-item--subItem > .ActionList-content { font-si=
+ze: 14px; }
+
+.ActionList--tree .ActionList-item.ActionList-item--singleton .ActionList-c=
+ontent { padding-left: 32px; }
+
+.ActionList--tree .ActionList-item.ActionList-item--navActive:not(.ActionLi=
+st-item--subItem) .ActionList-item-label { font-weight: var(--base-text-wei=
+ght-normal, 400); }
+
+.ActionList--tree .ActionList-content[aria-expanded] + .ActionList--subGrou=
+p { position: relative; }
+
+.ActionList--tree .ActionList-content[aria-expanded] + .ActionList--subGrou=
+p .ActionList-content { padding-left: calc(8px * var(--ActionList-tree-dept=
+h)); }
+
+.ActionList--tree .ActionList-content[aria-expanded=3D"true"] .ActionList-i=
+tem-collapseIcon { transition: transform 120ms linear 0s; transform: rotate=
+(0deg); }
+
+.ActionList--tree .ActionList-content[aria-expanded=3D"true"].ActionList-co=
+ntent--hasActiveSubItem > .ActionList-item-label { font-weight: var(--base-=
+text-weight-normal, 400); }
+
+.ActionList--tree .ActionList-content[aria-expanded=3D"false"] .ActionList-=
+item-collapseIcon { transition: transform 120ms linear 0s; transform: rotat=
+e(-90deg); }
+
+.ActionList--tree .ActionList-content[aria-expanded=3D"false"].ActionList-c=
+ontent--hasActiveSubItem > .ActionList-item-label { font-weight: var(--base=
+-text-weight-normal, 400); }
+
+.ActionList--tree .ActionList-item--hasSubItem .ActionList-item--subItem:no=
+t(.ActionList-item--hasSubItem) .ActionList-content > span:first-child { pa=
+dding-left: 24px; }
+
+.ActionList--tree > [aria-level=3D"1"].ActionList-item--hasSubItem > .Actio=
+nList--subGroup::before { position: absolute; left: 16px; width: 1px; heigh=
+t: 100%; content: ""; background: var(--color-action-list-item-inline-divid=
+er); }
+
+.ActionList--tree .ActionList-item--hasSubItem:not([aria-level=3D"1"]) > .A=
+ctionList--subGroup::before { position: absolute; left: calc(8px * (var(--A=
+ctionList-tree-depth)) + 7px); width: 1px; height: 100%; content: ""; backg=
+round: var(--color-action-list-item-inline-divider); }
+
+.ActionList-item { position: relative; list-style: none; background-color: =
+transparent; border-radius: 6px; }
+
+.ActionList-item:hover, .ActionList-item:active { cursor: pointer; }
+
+@media (hover: hover) {
+  .ActionList-item:not(.ActionList-item--hasSubItem):hover, .ActionList-ite=
+m.ActionList-item--hasSubItem > .ActionList-content:hover { cursor: pointer=
+; background-color: var(--color-action-list-item-default-hover-bg); }
+  .ActionList-item:not(.ActionList-item--hasSubItem):hover:not(.ActionList-=
+item--navActive):not(:focus-visible), .ActionList-item.ActionList-item--has=
+SubItem > .ActionList-content:hover:not(.ActionList-item--navActive):not(:f=
+ocus-visible) { outline: transparent solid 1px; outline-offset: -1px; box-s=
+hadow: inset 0 0 0 1px var(--color-action-list-item-default-active-border);=
+ }
+}
+
+.ActionList-item:not(.ActionList-item--hasSubItem):active, .ActionList-item=
+.ActionList-item--hasSubItem > .ActionList-content:active { background: var=
+(--color-action-list-item-default-active-bg); }
+
+.ActionList-item:not(.ActionList-item--hasSubItem):active:not(.ActionList-i=
+tem--navActive), .ActionList-item.ActionList-item--hasSubItem > .ActionList=
+-content:active:not(.ActionList-item--navActive) { outline: transparent sol=
+id 1px; outline-offset: -1px; box-shadow: inset 0 0 0 1px var(--color-actio=
+n-list-item-default-active-border); }
+
+@media (hover: hover) {
+  .ActionList-item:not(.ActionList-item--hasSubItem):hover .ActionList-item=
+-label::before, .ActionList-item:not(.ActionList-item--hasSubItem):hover + =
+.ActionList-item .ActionList-item-label::before, .ActionList-item.ActionLis=
+t-item--hasSubItem > .ActionList-content:hover .ActionList-item-label::befo=
+re, .ActionList-item.ActionList-item--hasSubItem > .ActionList-content:hove=
+r + .ActionList-item .ActionList-item-label::before { visibility: hidden; }
+  .ActionList-item:not(.ActionList-item--hasSubItem):hover .ActionList-item=
+-descriptionWrap--inline::before, .ActionList-item:not(.ActionList-item--ha=
+sSubItem):hover + .ActionList-item .ActionList-item-descriptionWrap--inline=
+::before, .ActionList-item.ActionList-item--hasSubItem > .ActionList-conten=
+t:hover .ActionList-item-descriptionWrap--inline::before, .ActionList-item.=
+ActionList-item--hasSubItem > .ActionList-content:hover + .ActionList-item =
+.ActionList-item-descriptionWrap--inline::before { visibility: hidden; }
+}
+
+.ActionList-item:not(.ActionList-item--hasSubItem):active .ActionList-item-=
+label::before, .ActionList-item:not(.ActionList-item--hasSubItem):active + =
+.ActionList-item .ActionList-item-label::before, .ActionList-item.ActionLis=
+t-item--hasSubItem > .ActionList-content:active .ActionList-item-label::bef=
+ore, .ActionList-item.ActionList-item--hasSubItem > .ActionList-content:act=
+ive + .ActionList-item .ActionList-item-label::before { visibility: hidden;=
+ }
+
+.ActionList-item.ActionList-item--hasSubItem > .ActionList-content { z-inde=
+x: 1; }
+
+@media (hover: hover) {
+  .ActionList-item.ActionList-item--hasSubItem > .ActionList-content:hover =
+{ background-color: var(--color-action-list-item-default-hover-bg); }
+}
+
+.ActionList-item.ActionList-item--hasSubItem > .ActionList-content:active {=
+ background-color: var(--color-action-list-item-default-active-bg); }
+
+.ActionList-item[hidden] + .ActionList-sectionDivider { display: none; }
+
+.ActionList-item[aria-selected=3D"true"] { font-weight: var(--base-text-wei=
+ght-normal, 400); background: var(--color-action-list-item-default-selected=
+-bg); }
+
+@media (hover: hover) {
+  .ActionList-item[aria-selected=3D"true"]:hover { background-color: var(--=
+color-action-list-item-default-hover-bg); }
+}
+
+.ActionList-item[aria-selected=3D"true"]::before, .ActionList-item[aria-sel=
+ected=3D"true"] + .ActionList-item::before { visibility: hidden; }
+
+.ActionList-item[aria-selected=3D"true"]::after { position: absolute; top: =
+calc(50% - 12px); left: -4px; width: 4px; height: 24px; content: ""; backgr=
+ound: var(--color-accent-fg); border-radius: 6px; }
+
+.ActionList-item.ActionList-item--navActive:not(.ActionList-item--subItem) =
+.ActionList-item-label { font-weight: var(--base-text-weight-semibold, 600)=
+; }
+
+.ActionList-item.ActionList-item--navActive:not(.ActionList-item--danger) {=
+ background: var(--color-action-list-item-default-selected-bg); }
+
+@media (hover: hover) {
+  .ActionList-item.ActionList-item--navActive:not(.ActionList-item--danger)=
+:hover { background-color: var(--color-action-list-item-default-hover-bg); =
+}
+}
+
+.ActionList-item.ActionList-item--navActive:not(.ActionList-item--danger)::=
+before, .ActionList-item.ActionList-item--navActive:not(.ActionList-item--d=
+anger) + .ActionList-item::before { visibility: hidden; }
+
+.ActionList-item.ActionList-item--navActive:not(.ActionList-item--danger)::=
+after { position: absolute; top: calc(50% - 12px); left: -8px; width: 4px; =
+height: 24px; content: ""; background: var(--color-accent-fg); border-radiu=
+s: 6px; }
+
+.ActionList-item[aria-checked=3D"true"] .ActionList-item-multiSelectCheckma=
+rk, .ActionList-item[aria-selected=3D"true"] .ActionList-item-multiSelectCh=
+eckmark { visibility: visible; opacity: 1; }
+
+.ActionList-item[aria-checked=3D"true"] .ActionList-item-singleSelectCheckm=
+ark, .ActionList-item[aria-selected=3D"true"] .ActionList-item-singleSelect=
+Checkmark { visibility: visible; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .ActionList-item[aria-checked=3D"true"] .ActionList-item-singleSelectChec=
+kmark, .ActionList-item[aria-selected=3D"true"] .ActionList-item-singleSele=
+ctCheckmark { animation: 200ms cubic-bezier(0.11, 0, 0.5, 0) 0s 1 normal fo=
+rwards running checkmarkIn; }
+}
+
+.ActionList-item[aria-checked=3D"true"] .ActionList-item-multiSelectIcon .A=
+ctionList-item-multiSelectIconRect, .ActionList-item[aria-selected=3D"true"=
+] .ActionList-item-multiSelectIcon .ActionList-item-multiSelectIconRect { f=
+ill: var(--color-accent-fg); stroke: var(--color-accent-fg); stroke-width: =
+1px; }
+
+.ActionList-item[aria-checked=3D"true"] .ActionList-item-multiSelectIcon .A=
+ctionList-item-multiSelectCheckmark, .ActionList-item[aria-selected=3D"true=
+"] .ActionList-item-multiSelectIcon .ActionList-item-multiSelectCheckmark {=
+ fill: var(--color-fg-on-emphasis); }
+
+.ActionList-item[aria-checked=3D"false"] .ActionList-item-multiSelectCheckm=
+ark, .ActionList-item[aria-selected=3D"false"] .ActionList-item-multiSelect=
+Checkmark { visibility: hidden; opacity: 0; }
+
+.ActionList-item[aria-checked=3D"false"] .ActionList-item-singleSelectCheck=
+mark, .ActionList-item[aria-selected=3D"false"] .ActionList-item-singleSele=
+ctCheckmark { visibility: hidden; transition: visibility 0s linear 200ms; c=
+lip-path: inset(16px 0px 0px); }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .ActionList-item[aria-checked=3D"false"] .ActionList-item-singleSelectChe=
+ckmark, .ActionList-item[aria-selected=3D"false"] .ActionList-item-singleSe=
+lectCheckmark { animation: 200ms cubic-bezier(0.11, 0, 0.5, 0) 0s 1 normal =
+forwards running checkmarkOut; }
+}
+
+.ActionList-item[aria-checked=3D"false"] .ActionList-item-multiSelectIcon .=
+ActionList-item-multiSelectIconRect, .ActionList-item[aria-selected=3D"fals=
+e"] .ActionList-item-multiSelectIcon .ActionList-item-multiSelectIconRect {=
+ fill: var(--color-canvas-default); stroke: var(--color-border-default); st=
+roke-width: 1px; }
+
+.ActionList-item[aria-checked=3D"false"] .ActionList-item-multiSelectIconRe=
+ct, .ActionList-item[aria-selected=3D"false"] .ActionList-item-multiSelectI=
+conRect { fill: var(--color-canvas-default); border: 1px solid var(--color-=
+border-default); }
+
+@keyframes checkmarkIn {=20
+  0% { clip-path: inset(16px 0px 0px); }
+  100% { clip-path: inset(0px); }
+}
+
+@keyframes checkmarkOut {=20
+  0% { clip-path: inset(0px); }
+  100% { clip-path: inset(16px 0px 0px); }
+}
+
+.ActionList-item[aria-disabled=3D"true"] .ActionList-content .ActionList-it=
+em-label, .ActionList-item[aria-disabled=3D"true"] .ActionList-content .Act=
+ionList-item-description { color: var(--color-primer-fg-disabled); }
+
+.ActionList-item[aria-disabled=3D"true"] .ActionList-content .ActionList-it=
+em-visual { fill: var(--color-primer-fg-disabled); }
+
+@media (hover: hover) {
+  .ActionList-item[aria-disabled=3D"true"]:hover { cursor: not-allowed; bac=
+kground-color: transparent; }
+}
+
+.ActionList-item.ActionList-item--danger .ActionList-item-label { color: va=
+r(--color-danger-fg); }
+
+.ActionList-item.ActionList-item--danger .ActionList-item-visual { color: v=
+ar(--color-danger-fg); }
+
+@media (hover: hover) {
+  .ActionList-item.ActionList-item--danger:hover { background: var(--color-=
+action-list-item-danger-hover-bg); }
+  .ActionList-item.ActionList-item--danger:hover .ActionList-item-label { c=
+olor: var(--color-action-list-item-danger-hover-text); }
+}
+
+.ActionList-item.ActionList-item--danger .ActionList-content:active { backg=
+round: var(--color-action-list-item-danger-active-bg); }
+
+.ActionList-item .ActionList { padding: unset; }
+
+.ActionList-content { position: relative; display: grid; width: 100%; paddi=
+ng: 6px 8px; font-size: 14px; font-weight: var(--base-text-weight-normal, 4=
+00); color: var(--color-fg-default); text-align: left; user-select: none; b=
+ackground-color: transparent; border: none; border-radius: 6px; transition:=
+ background 33.333ms linear 0s; touch-action: manipulation; -webkit-tap-hig=
+hlight-color: transparent; grid-template: "leadingAction leadingVisual labe=
+l trailingVisual trailingAction" min-content / min-content min-content minm=
+ax(0px, auto) min-content min-content; align-items: start; }
+
+.ActionList-content > :not(:last-child) { margin-right: 8px; }
+
+.ActionList-content:hover { text-decoration: none; }
+
+.ActionList-content:focus { outline: 2px solid var(--color-accent-fg); outl=
+ine-offset: -2px; box-shadow: none; }
+
+.ActionList-content:focus:not(:focus-visible) { outline: transparent solid =
+1px; }
+
+.ActionList-content:focus-visible { outline: 2px solid var(--color-accent-f=
+g); outline-offset: -2px; box-shadow: none; }
+
+.ActionList-content[aria-disabled=3D"true"] .ActionList-item-label, .Action=
+List-content[aria-disabled=3D"true"] .ActionList-item-description { color: =
+var(--color-primer-fg-disabled); }
+
+.ActionList-content[aria-disabled=3D"true"] .ActionList-item-visual { fill:=
+ var(--color-primer-fg-disabled); }
+
+@media (hover: hover) {
+  .ActionList-content[aria-disabled=3D"true"]:hover { cursor: not-allowed; =
+background-color: transparent; }
+}
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .ActionList-content[aria-expanded] + .ActionList--subGroup { transition: =
+opacity 160ms cubic-bezier(0.25, 1, 0.5, 1) 0s, transform 160ms cubic-bezie=
+r(0.25, 1, 0.5, 1) 0s; }
+}
+
+.ActionList-content[aria-expanded] + .ActionList--subGroup .ActionList-cont=
+ent { padding-left: 24px; }
+
+.ActionList-content[aria-expanded].ActionList-content--visual16 + .ActionLi=
+st--subGroup .ActionList-content { padding-left: 32px; }
+
+.ActionList-content[aria-expanded].ActionList-content--visual20 + .ActionLi=
+st--subGroup .ActionList-content { padding-left: 36px; }
+
+.ActionList-content[aria-expanded].ActionList-content--visual24 + .ActionLi=
+st--subGroup .ActionList-content { padding-left: 40px; }
+
+.ActionList-content[aria-expanded=3D"true"] .ActionList-item-collapseIcon {=
+ transition: transform 120ms linear 0s; transform: scaleY(-1); }
+
+.ActionList-content[aria-expanded=3D"true"] + .ActionList--subGroup { heigh=
+t: auto; overflow: visible; visibility: visible; opacity: 1; transform: tra=
+nslateY(0px); }
+
+.ActionList-content[aria-expanded=3D"true"].ActionList-content--hasActiveSu=
+bItem > .ActionList-item-label { font-weight: var(--base-text-weight-semibo=
+ld, 600); }
+
+.ActionList-content[aria-expanded=3D"false"] .ActionList-item-collapseIcon =
+{ transition: transform 120ms linear 0s; transform: scaleY(1); }
+
+.ActionList-content[aria-expanded=3D"false"] + .ActionList--subGroup { heig=
+ht: 0px; overflow: hidden; visibility: hidden; opacity: 0; transform: trans=
+lateY(-16px); }
+
+.ActionList-content[aria-expanded=3D"false"].ActionList-content--hasActiveS=
+ubItem { background: var(--color-action-list-item-default-selected-bg); }
+
+.ActionList-content[aria-expanded=3D"false"].ActionList-content--hasActiveS=
+ubItem .ActionList-item-label { font-weight: var(--base-text-weight-semibol=
+d, 600); }
+
+.ActionList-content[aria-expanded=3D"false"].ActionList-content--hasActiveS=
+ubItem::before, .ActionList-content[aria-expanded=3D"false"].ActionList-con=
+tent--hasActiveSubItem + .ActionList-item::before { visibility: hidden; }
+
+.ActionList-content[aria-expanded=3D"false"].ActionList-content--hasActiveS=
+ubItem::after { position: absolute; top: calc(50% - 12px); left: -8px; widt=
+h: 4px; height: 24px; content: ""; background: var(--color-accent-fg); bord=
+er-radius: 6px; }
+
+.ActionList-content.ActionList-content--sizeMedium { padding: 10px 8px; }
+
+.ActionList-content.ActionList-content--sizeLarge { padding: 14px 8px; }
+
+.ActionList-content.ActionList-content--fontSmall { font-size: 12px; }
+
+@media (pointer: coarse) {
+  .ActionList-content { padding: 14px 8px; }
+}
+
+.ActionList-content.ActionList-content--blockDescription .ActionList-item-v=
+isual { place-self: start; }
+
+.ActionList-item-action--leading { grid-area: leadingAction / leadingAction=
+ / leadingAction / leadingAction; }
+
+.ActionList-item-visual--leading { grid-area: leadingVisual / leadingVisual=
+ / leadingVisual / leadingVisual; }
+
+.ActionList-item-label { grid-area: label / label / label / label; }
+
+.ActionList-item-visual--trailing { grid-area: trailingVisual / trailingVis=
+ual / trailingVisual / trailingVisual; }
+
+.ActionList-item-action--trailing { grid-area: trailingAction / trailingAct=
+ion / trailingAction / trailingAction; }
+
+.ActionList-item-descriptionWrap { grid-area: label / label / label / label=
+; display: flex; flex-direction: column; }
+
+.ActionList-item-descriptionWrap .ActionList-item-description { margin-top:=
+ 4px; }
+
+.ActionList-item-descriptionWrap .ActionList-item-label { font-weight: var(=
+--base-text-weight-semibold, 600); }
+
+.ActionList-item-descriptionWrap--inline { position: relative; flex-directi=
+on: row; align-items: baseline; }
+
+.ActionList-item-descriptionWrap--inline .ActionList-item-description { mar=
+gin-left: 8px; }
+
+.ActionList-item-description { font-size: 12px; font-weight: var(--base-tex=
+t-weight-normal, 400); line-height: 1.5; color: var(--color-fg-muted); }
+
+.ActionList-item-visual, .ActionList-item-action { display: flex; min-heigh=
+t: 20px; color: var(--color-fg-muted); pointer-events: none; fill: var(--co=
+lor-fg-muted); align-items: center; }
+
+.ActionList-item-label { position: relative; font-weight: var(--base-text-w=
+eight-normal, 400); line-height: 20px; color: var(--color-fg-default); }
+
+.ActionList-item-label--truncate { overflow: hidden; text-overflow: ellipsi=
+s; white-space: nowrap; }
+
+.ActionList-item--subItem > .ActionList-content { font-size: 12px; }
+
+.ActionList-sectionDivider:not(:empty) { display: flex; padding: 6px 8px; f=
+ont-size: 12px; font-weight: var(--base-text-weight-semibold, 600); color: =
+var(--color-fg-muted); flex-direction: column; }
+
+.ActionList-sectionDivider:empty { display: block; height: 1px; padding: 0p=
+x; margin: 7px -8px 8px; list-style: none; background: var(--color-action-l=
+ist-item-inline-divider); border: 0px; }
+
+.ActionList-sectionDivider .ActionList-sectionDivider-title { font-size: 12=
+px; font-weight: var(--base-text-weight-semibold, 600); color: var(--color-=
+fg-muted); }
+
+.ActionList-sectionDivider--filled { margin: 8px -8px; background: var(--co=
+lor-canvas-subtle); border-top: 1px solid var(--color-action-list-item-inli=
+ne-divider); border-bottom: 1px solid var(--color-action-list-item-inline-d=
+ivider); }
+
+.ActionList-sectionDivider--filled:empty { height: 8px; box-sizing: border-=
+box; }
+
+.ActionList-sectionDivider--filled:first-child { margin-top: 0px; }
+
+html { font-family: sans-serif; text-size-adjust: 100%; }
+
+body { margin: 0px; }
+
+article, aside, details, figcaption, figure, footer, header, main, menu, na=
+v, section { display: block; }
+
+summary { display: list-item; }
+
+audio, canvas, progress, video { display: inline-block; }
+
+audio:not([controls]) { display: none; height: 0px; }
+
+progress { vertical-align: baseline; }
+
+template, [hidden] { display: none !important; }
+
+a { background-color: transparent; }
+
+abbr[title] { border-bottom: none; text-decoration: underline dotted; }
+
+b, strong { font-weight: inherit; }
+
+b, strong { font-weight: bolder; }
+
+dfn { font-style: italic; }
+
+h1 { font-size: 2em; margin: 0.67em 0px; }
+
+mark { background-color: var(--color-attention-subtle); color: var(--color-=
+fg-default); }
+
+small { font-size: 80%; }
+
+sub, sup { font-size: 75%; line-height: 0; position: relative; vertical-ali=
+gn: baseline; }
+
+sub { bottom: -0.25em; }
+
+sup { top: -0.5em; }
+
+img { border-style: none; }
+
+svg:not(:root) { overflow: hidden; }
+
+code, kbd, pre, samp { font-family: monospace; font-size: 1em; }
+
+figure { margin: 1em 40px; }
+
+hr { box-sizing: content-box; height: 0px; overflow: visible; }
+
+button, input, select, textarea { font: inherit; margin: 0px; }
+
+optgroup { font-weight: var(--base-text-weight-semibold, 600); }
+
+button, input { overflow: visible; }
+
+button, select { text-transform: none; }
+
+button, html [type=3D"button"], [type=3D"reset"], [type=3D"submit"] { appea=
+rance: button; }
+
+fieldset { border: 1px solid silver; margin: 0px 2px; padding: 0.35em 0.625=
+em 0.75em; }
+
+legend { box-sizing: border-box; color: inherit; display: table; max-width:=
+ 100%; padding: 0px; white-space: normal; }
+
+textarea { overflow: auto; }
+
+[type=3D"checkbox"], [type=3D"radio"] { box-sizing: border-box; padding: 0p=
+x; }
+
+[type=3D"number"]::-webkit-inner-spin-button, [type=3D"number"]::-webkit-ou=
+ter-spin-button { height: auto; }
+
+[type=3D"search"]::-webkit-search-cancel-button, [type=3D"search"]::-webkit=
+-search-decoration { appearance: none; }
+
+::-webkit-input-placeholder { color: inherit; opacity: 0.54; }
+
+::-webkit-file-upload-button { appearance: button; font: inherit; }
+
+* { box-sizing: border-box; }
+
+input, select, textarea, button { font-family: inherit; font-size: inherit;=
+ line-height: inherit; }
+
+body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sa=
+ns", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; f=
+ont-size: var(--body-font-size, 14px); line-height: 1.5; color: var(--color=
+-fg-default); background-color: var(--color-canvas-default); }
+
+a { color: var(--color-accent-fg); text-decoration: none; }
+
+a:hover { text-decoration: underline; }
+
+b, strong { font-weight: var(--base-text-weight-semibold, 600); }
+
+fieldset { padding: 0px; margin: 0px; border: 0px; }
+
+label { font-weight: var(--base-text-weight-semibold, 600); }
+
+::placeholder { color: var(--color-fg-subtle); opacity: 1; }
+
+hr, .rule { height: 0px; margin: 15px 0px; overflow: hidden; background: tr=
+ansparent; border-top: 0px; border-right: 0px; border-left: 0px; border-ima=
+ge: initial; border-bottom: 1px solid var(--color-border-muted); }
+
+hr::before, .rule::before { display: table; content: ""; }
+
+hr::after, .rule::after { display: table; clear: both; content: ""; }
+
+table { border-spacing: 0px; border-collapse: collapse; }
+
+td, th { padding: 0px; }
+
+button { cursor: pointer; border-radius: 0px; }
+
+[hidden][hidden] { display: none !important; }
+
+details summary { cursor: pointer; }
+
+details:not([open]) > :not(summary) { display: none !important; }
+
+a:focus, button:focus, [role=3D"button"]:focus, input[type=3D"radio"]:focus=
+, input[type=3D"checkbox"]:focus { outline: 2px solid var(--color-accent-fg=
+); outline-offset: -2px; box-shadow: none; }
+
+a:focus:not(:focus-visible), button:focus:not(:focus-visible), [role=3D"but=
+ton"]:focus:not(:focus-visible), input[type=3D"radio"]:focus:not(:focus-vis=
+ible), input[type=3D"checkbox"]:focus:not(:focus-visible) { outline: transp=
+arent solid 1px; }
+
+a:focus-visible, button:focus-visible, [role=3D"button"]:focus-visible, inp=
+ut[type=3D"radio"]:focus-visible, input[type=3D"checkbox"]:focus-visible { =
+outline: 2px solid var(--color-accent-fg); outline-offset: -2px; box-shadow=
+: none; }
+
+a:not([class]):focus, a:not([class]):focus-visible, input[type=3D"radio"]:f=
+ocus, input[type=3D"radio"]:focus-visible, input[type=3D"checkbox"]:focus, =
+input[type=3D"checkbox"]:focus-visible { outline-offset: 0px; }
+
+.focus { border-color: var(--color-accent-fg); outline: none; box-shadow: i=
+nset 0 0 0 1px var(--color-accent-fg); }
+
+@media (forced-colors: active) {
+  :focus, :focus-visible { outline: transparent solid 1px; }
+  input:not([type=3D"radio"], [type=3D"checkbox"]):focus, input:not([type=
+=3D"radio"], [type=3D"checkbox"]):focus-visible, textarea:focus, textarea:f=
+ocus-visible, select:focus, select:focus-visible { outline-offset: 2px; }
+}
+
+kbd { display: inline-block; padding: 3px 5px; font: 11px / 10px ui-monospa=
+ce, SFMono-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospac=
+e; color: var(--color-fg-default); vertical-align: middle; background-color=
+: var(--color-canvas-subtle); border-top-color: ; border-top-style: ; borde=
+r-top-width: ; border-right-color: ; border-right-style: ; border-right-wid=
+th: ; border-bottom-style: ; border-bottom-width: ; border-left-color: ; bo=
+rder-left-style: ; border-left-width: ; border-image-source: ; border-image=
+-slice: ; border-image-width: ; border-image-outset: ; border-image-repeat:=
+ ; border-bottom-color: var(--color-neutral-muted); border-radius: 6px; box=
+-shadow: inset 0 -1px 0 var(--color-neutral-muted); }
+
+h1, h2, h3, h4, h5, h6 { margin-top: 0px; margin-bottom: 0px; }
+
+h1 { font-size: var(--h1-size, 32px); font-weight: var(--base-text-weight-s=
+emibold, 600); }
+
+h2 { font-size: var(--h2-size, 24px); font-weight: var(--base-text-weight-s=
+emibold, 600); }
+
+h3 { font-size: var(--h3-size, 20px); font-weight: var(--base-text-weight-s=
+emibold, 600); }
+
+h4 { font-size: var(--h4-size, 16px); font-weight: var(--base-text-weight-s=
+emibold, 600); }
+
+h5 { font-size: var(--h5-size, 14px); font-weight: var(--base-text-weight-s=
+emibold, 600); }
+
+h6 { font-size: var(--h6-size, 12px); font-weight: var(--base-text-weight-s=
+emibold, 600); }
+
+p { margin-top: 0px; margin-bottom: 10px; }
+
+small { font-size: 90%; }
+
+blockquote { margin: 0px; }
+
+ul, ol { padding-left: 0px; margin-top: 0px; margin-bottom: 0px; }
+
+ol ol, ul ol { list-style-type: lower-roman; }
+
+ul ul ol, ul ol ol, ol ul ol, ol ol ol { list-style-type: lower-alpha; }
+
+dd { margin-left: 0px; }
+
+tt, code, samp { font-family: ui-monospace, SFMono-Regular, "SF Mono", Menl=
+o, Consolas, "Liberation Mono", monospace; font-size: 12px; }
+
+pre { margin-top: 0px; margin-bottom: 0px; font-family: ui-monospace, SFMon=
+o-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospace; font-s=
+ize: 12px; }
+
+.octicon { vertical-align: text-bottom; }
+
+.octicon { display: inline-block; vertical-align: text-bottom; fill: curren=
+tcolor; overflow: visible !important; }
+
+.Box--overlay { width: 448px; margin-right: auto; margin-left: auto; backgr=
+ound-color: var(--color-canvas-default); background-clip: padding-box; bord=
+er-color: var(--color-border-default); box-shadow: rgba(0, 0, 0, 0.4) 0px 0=
+px 18px; }
+
+.Box--overlay .Box-header { margin: 0px; border-width: 0px 0px 1px; border-=
+top-left-radius: 6px; border-top-right-radius: 6px; }
+
+.Box-overlay--narrow { width: 320px; }
+
+.Box-overlay--wide { width: 640px; }
+
+.Box-body.scrollable-overlay { max-height: 400px; overflow-y: scroll; }
+
+.Box-body .help { padding-top: 8px; margin: 0px; color: var(--color-fg-mute=
+d); text-align: center; }
+
+.btn { position: relative; display: inline-block; padding: 5px 16px; font-s=
+ize: 14px; font-weight: var(--base-text-weight-medium, 500); line-height: 2=
+0px; white-space: nowrap; vertical-align: middle; cursor: pointer; user-sel=
+ect: none; border: 1px solid; border-radius: 6px; appearance: none; }
+
+.btn:hover { text-decoration: none; }
+
+.btn:disabled, .btn.disabled, .btn[aria-disabled=3D"true"] { cursor: defaul=
+t; }
+
+.btn i { font-style: normal; font-weight: var(--base-text-weight-medium, 50=
+0); opacity: 0.75; }
+
+.btn .octicon { margin-right: 4px; color: var(--color-fg-muted); vertical-a=
+lign: text-bottom; }
+
+.btn .octicon:only-child { margin-right: 0px; }
+
+.btn .Counter { margin-left: 2px; color: inherit; text-shadow: none; vertic=
+al-align: top; background-color: var(--color-btn-counter-bg); }
+
+.btn .dropdown-caret { margin-left: 4px; opacity: 0.8; }
+
+.btn { color: var(--color-btn-text); background-color: var(--color-btn-bg);=
+ border-color: var(--color-btn-border); box-shadow: var(--color-btn-shadow)=
+,var(--color-btn-inset-shadow); transition: color 80ms cubic-bezier(0.33, 1=
+, 0.68, 1) 0s, background-color, box-shadow, border-color; }
+
+.btn:hover, .btn.hover, [open] > .btn { background-color: var(--color-btn-h=
+over-bg); border-color: var(--color-btn-hover-border); transition-duration:=
+ 0.1s; }
+
+.btn:active { background-color: var(--color-btn-active-bg); border-color: v=
+ar(--color-btn-active-border); transition: none 0s ease 0s; }
+
+.btn.selected, .btn[aria-selected=3D"true"] { background-color: var(--color=
+-btn-selected-bg); box-shadow: var(--color-primer-shadow-inset); }
+
+.btn:disabled, .btn.disabled, .btn[aria-disabled=3D"true"] { color: var(--c=
+olor-primer-fg-disabled); background-color: var(--color-btn-bg); border-col=
+or: var(--color-btn-border); }
+
+.btn:disabled .octicon, .btn.disabled .octicon, .btn[aria-disabled=3D"true"=
+] .octicon { color: var(--color-primer-fg-disabled); }
+
+.btn-primary { color: var(--color-btn-primary-text); background-color: var(=
+--color-btn-primary-bg); border-color: var(--color-btn-primary-border); box=
+-shadow: var(--color-btn-primary-shadow),var(--color-btn-primary-inset-shad=
+ow); }
+
+.btn-primary:hover, .btn-primary.hover, [open] > .btn-primary { background-=
+color: var(--color-btn-primary-hover-bg); border-color: var(--color-btn-pri=
+mary-hover-border); }
+
+.btn-primary:focus { outline: 2px solid var(--color-accent-fg); outline-off=
+set: -2px; box-shadow: inset 0 0 0 3px var(--color-fg-on-emphasis); }
+
+.btn-primary:focus:not(:focus-visible) { outline: transparent solid 1px; bo=
+x-shadow: none; }
+
+.btn-primary:focus-visible { outline: 2px solid var(--color-accent-fg); out=
+line-offset: -2px; box-shadow: inset 0 0 0 3px var(--color-fg-on-emphasis);=
+ }
+
+.btn-primary:active, .btn-primary.selected, .btn-primary[aria-selected=3D"t=
+rue"] { background-color: var(--color-btn-primary-selected-bg); box-shadow:=
+ var(--color-btn-primary-selected-shadow); }
+
+.btn-primary:disabled, .btn-primary.disabled, .btn-primary[aria-disabled=3D=
+"true"] { color: var(--color-btn-primary-disabled-text); background-color: =
+var(--color-btn-primary-disabled-bg); border-color: var(--color-btn-primary=
+-disabled-border); }
+
+.btn-primary:disabled .octicon, .btn-primary.disabled .octicon, .btn-primar=
+y[aria-disabled=3D"true"] .octicon { color: var(--color-btn-primary-disable=
+d-text); }
+
+.btn-primary .Counter { color: inherit; background-color: var(--color-btn-p=
+rimary-counter-bg); }
+
+.btn-primary .octicon { color: var(--color-btn-primary-icon); }
+
+a.btn-primary:focus { outline: 2px solid var(--color-accent-fg); outline-of=
+fset: -2px; box-shadow: inset 0 0 0 3px var(--color-fg-on-emphasis); }
+
+a.btn-primary:focus:not(:focus-visible) { outline: transparent solid 1px; b=
+ox-shadow: none; }
+
+a.btn-primary:focus-visible { outline: 2px solid var(--color-accent-fg); ou=
+tline-offset: -2px; box-shadow: inset 0 0 0 3px var(--color-fg-on-emphasis)=
+; }
+
+.btn-outline { color: var(--color-btn-outline-text); }
+
+.btn-outline:hover, [open] > .btn-outline { color: var(--color-btn-outline-=
+hover-text); background-color: var(--color-btn-outline-hover-bg); border-co=
+lor: var(--color-btn-outline-hover-border); box-shadow: var(--color-btn-out=
+line-hover-shadow),var(--color-btn-outline-hover-inset-shadow); }
+
+.btn-outline:hover .Counter, [open] > .btn-outline .Counter { background-co=
+lor: var(--color-btn-outline-hover-counter-bg); }
+
+.btn-outline:hover .octicon, [open] > .btn-outline .octicon { color: inheri=
+t; }
+
+.btn-outline:active, .btn-outline.selected, .btn-outline[aria-selected=3D"t=
+rue"] { color: var(--color-btn-outline-selected-text); background-color: va=
+r(--color-btn-outline-selected-bg); border-color: var(--color-btn-outline-s=
+elected-border); box-shadow: var(--color-btn-outline-selected-shadow); }
+
+.btn-outline:active:focus, .btn-outline.selected:focus, .btn-outline[aria-s=
+elected=3D"true"]:focus { outline: 2px solid var(--color-accent-fg); outlin=
+e-offset: -2px; box-shadow: inset 0 0 0 3px var(--color-fg-on-emphasis); }
+
+.btn-outline:active:focus:not(:focus-visible), .btn-outline.selected:focus:=
+not(:focus-visible), .btn-outline[aria-selected=3D"true"]:focus:not(:focus-=
+visible) { outline: transparent solid 1px; box-shadow: none; }
+
+.btn-outline:active:focus-visible, .btn-outline.selected:focus-visible, .bt=
+n-outline[aria-selected=3D"true"]:focus-visible { outline: 2px solid var(--=
+color-accent-fg); outline-offset: -2px; box-shadow: inset 0 0 0 3px var(--c=
+olor-fg-on-emphasis); }
+
+.btn-outline:disabled, .btn-outline.disabled, .btn-outline[aria-disabled=3D=
+"true"] { color: var(--color-btn-outline-disabled-text); background-color: =
+var(--color-btn-outline-disabled-bg); border-color: var(--color-btn-border)=
+; box-shadow: none; }
+
+.btn-outline:disabled .Counter, .btn-outline.disabled .Counter, .btn-outlin=
+e[aria-disabled=3D"true"] .Counter { background-color: var(--color-btn-outl=
+ine-disabled-counter-bg); }
+
+.btn-outline .Counter { color: inherit; background-color: var(--color-btn-o=
+utline-counter-bg); }
+
+.btn-danger { color: var(--color-btn-danger-text); }
+
+.btn-danger .octicon { color: var(--color-btn-danger-icon); }
+
+.btn-danger:hover, [open] > .btn-danger { color: var(--color-btn-danger-hov=
+er-text); background-color: var(--color-btn-danger-hover-bg); border-color:=
+ var(--color-btn-danger-hover-border); box-shadow: var(--color-btn-danger-h=
+over-shadow),var(--color-btn-danger-hover-inset-shadow); }
+
+.btn-danger:hover .Counter, [open] > .btn-danger .Counter { background-colo=
+r: var(--color-btn-danger-hover-counter-bg); }
+
+.btn-danger:hover .octicon, [open] > .btn-danger .octicon { color: var(--co=
+lor-btn-danger-hover-icon); }
+
+.btn-danger:active, .btn-danger.selected, .btn-danger[aria-selected=3D"true=
+"] { color: var(--color-btn-danger-selected-text); background-color: var(--=
+color-btn-danger-selected-bg); border-color: var(--color-btn-danger-selecte=
+d-border); box-shadow: var(--color-btn-danger-selected-shadow); }
+
+.btn-danger:disabled, .btn-danger.disabled, .btn-danger[aria-disabled=3D"tr=
+ue"] { color: var(--color-btn-danger-disabled-text); background-color: var(=
+--color-btn-danger-disabled-bg); border-color: var(--color-btn-border); box=
+-shadow: none; }
+
+.btn-danger:disabled .Counter, .btn-danger.disabled .Counter, .btn-danger[a=
+ria-disabled=3D"true"] .Counter { background-color: var(--color-btn-danger-=
+disabled-counter-bg); }
+
+.btn-danger:disabled .octicon, .btn-danger.disabled .octicon, .btn-danger[a=
+ria-disabled=3D"true"] .octicon { color: var(--color-btn-danger-disabled-te=
+xt); }
+
+.btn-danger .Counter { color: inherit; background-color: var(--color-btn-da=
+nger-counter-bg); }
+
+.btn-sm { padding: 3px 12px; font-size: 12px; line-height: 20px; }
+
+.btn-sm .octicon { vertical-align: text-top; }
+
+.btn-large { padding: 0.75em 1.5em; font-size: inherit; line-height: 1.5; b=
+order-radius: 0.5em; }
+
+.btn-block { display: block; width: 100%; text-align: center; }
+
+.BtnGroup { display: inline-block; vertical-align: middle; }
+
+.BtnGroup::before { display: table; content: ""; }
+
+.BtnGroup::after { display: table; clear: both; content: ""; }
+
+.BtnGroup + .BtnGroup, .BtnGroup + .btn { margin-left: 4px; }
+
+.BtnGroup-item { position: relative; float: left; border-right-width: 0px; =
+border-radius: 0px; }
+
+.BtnGroup-item:first-child { border-top-left-radius: 6px; border-bottom-lef=
+t-radius: 6px; }
+
+.BtnGroup-item:last-child { border-right-width: 1px; border-top-right-radiu=
+s: 6px; border-bottom-right-radius: 6px; }
+
+.BtnGroup-item.selected, .BtnGroup-item[aria-selected=3D"true"], .BtnGroup-=
+item:focus, .BtnGroup-item:active, .BtnGroup-item:hover { border-right-widt=
+h: 1px; }
+
+.BtnGroup-item.selected + .BtnGroup-item, .BtnGroup-item.selected + .BtnGro=
+up-parent .BtnGroup-item, .BtnGroup-item[aria-selected=3D"true"] + .BtnGrou=
+p-item, .BtnGroup-item[aria-selected=3D"true"] + .BtnGroup-parent .BtnGroup=
+-item, .BtnGroup-item:focus + .BtnGroup-item, .BtnGroup-item:focus + .BtnGr=
+oup-parent .BtnGroup-item, .BtnGroup-item:active + .BtnGroup-item, .BtnGrou=
+p-item:active + .BtnGroup-parent .BtnGroup-item, .BtnGroup-item:hover + .Bt=
+nGroup-item, .BtnGroup-item:hover + .BtnGroup-parent .BtnGroup-item { borde=
+r-left-width: 0px; }
+
+.BtnGroup-parent { float: left; }
+
+.BtnGroup-parent:first-child .BtnGroup-item { border-top-left-radius: 6px; =
+border-bottom-left-radius: 6px; }
+
+.BtnGroup-parent:last-child .BtnGroup-item { border-right-width: 1px; borde=
+r-top-right-radius: 6px; border-bottom-right-radius: 6px; }
+
+.BtnGroup-parent .BtnGroup-item { border-right-width: 0px; border-radius: 0=
+px; }
+
+.BtnGroup-parent.selected .BtnGroup-item, .BtnGroup-parent[aria-selected=3D=
+"true"] .BtnGroup-item, .BtnGroup-parent:focus .BtnGroup-item, .BtnGroup-pa=
+rent:active .BtnGroup-item, .BtnGroup-parent:hover .BtnGroup-item { border-=
+right-width: 1px; }
+
+.BtnGroup-parent.selected + .BtnGroup-item, .BtnGroup-parent.selected + .Bt=
+nGroup-parent .BtnGroup-item, .BtnGroup-parent[aria-selected=3D"true"] + .B=
+tnGroup-item, .BtnGroup-parent[aria-selected=3D"true"] + .BtnGroup-parent .=
+BtnGroup-item, .BtnGroup-parent:focus + .BtnGroup-item, .BtnGroup-parent:fo=
+cus + .BtnGroup-parent .BtnGroup-item, .BtnGroup-parent:active + .BtnGroup-=
+item, .BtnGroup-parent:active + .BtnGroup-parent .BtnGroup-item, .BtnGroup-=
+parent:hover + .BtnGroup-item, .BtnGroup-parent:hover + .BtnGroup-parent .B=
+tnGroup-item { border-left-width: 0px; }
+
+.BtnGroup-item:focus, .BtnGroup-item:active, .BtnGroup-parent:focus, .BtnGr=
+oup-parent:active { z-index: 1; }
+
+.btn-link { display: inline-block; padding: 0px; font-size: inherit; color:=
+ var(--color-accent-fg); text-decoration: none; white-space: nowrap; cursor=
+: pointer; user-select: none; background-color: transparent; border: 0px; a=
+ppearance: none; }
+
+.btn-link:hover { text-decoration: underline; }
+
+.btn-link:disabled, .btn-link:disabled:hover, .btn-link[aria-disabled=3D"tr=
+ue"], .btn-link[aria-disabled=3D"true"]:hover { color: var(--color-primer-f=
+g-disabled); cursor: default; }
+
+.btn-link:not(.dropdown-item):focus, .btn-link:not(.dropdown-item):focus-vi=
+sible { border-radius: 6px; outline-offset: 0px; }
+
+.btn-invisible { color: var(--color-accent-fg); background-color: transpare=
+nt; border: 0px; border-radius: 6px; box-shadow: none; }
+
+.btn-invisible:hover, .btn-invisible.zeroclipboard-is-hover { color: var(--=
+color-accent-fg); background-color: var(--color-btn-hover-bg); outline: non=
+e; box-shadow: none; }
+
+.btn-invisible:active, .btn-invisible.selected, .btn-invisible[aria-selecte=
+d=3D"true"], .btn-invisible.zeroclipboard-is-active { color: var(--color-ac=
+cent-fg); background: none; border-color: var(--color-btn-active-border); o=
+utline: 2px solid var(--color-accent-fg); outline-offset: -2px; box-shadow:=
+ none; }
+
+.btn-invisible:active .btn-invisible.zeroclipboard-is-active { background-c=
+olor: var(--color-btn-selected-bg); }
+
+.btn-invisible:disabled, .btn-invisible.disabled, .btn-invisible[aria-disab=
+led=3D"true"] { color: var(--color-primer-fg-disabled); background-color: t=
+ransparent; }
+
+.btn-octicon { display: inline-block; padding: 5px; margin-left: 5px; line-=
+height: 1; color: var(--color-fg-muted); vertical-align: middle; background=
+: transparent; border: 0px; box-shadow: none; }
+
+.btn-octicon:hover { color: var(--color-accent-fg); }
+
+.btn-octicon:focus, .btn-octicon:focus-visible { border-radius: 6px; }
+
+.btn-octicon.disabled, .btn-octicon[aria-disabled=3D"true"] { color: var(--=
+color-primer-fg-disabled); cursor: default; }
+
+.btn-octicon.disabled:hover, .btn-octicon[aria-disabled=3D"true"]:hover { c=
+olor: var(--color-primer-fg-disabled); }
+
+.btn-octicon-danger:hover { color: var(--color-danger-fg); }
+
+.close-button { padding: 0px; color: var(--color-fg-muted); background: tra=
+nsparent; border: 0px; }
+
+.close-button:hover { color: var(--color-fg-default); }
+
+.close-button:active { outline: 2px solid var(--color-accent-fg); outline-o=
+ffset: -2px; box-shadow: none; }
+
+.hidden-text-expander { display: block; }
+
+.hidden-text-expander.inline { position: relative; top: -1px; display: inli=
+ne-block; margin-left: 5px; line-height: 0; }
+
+.hidden-text-expander a, .ellipsis-expander { display: inline-block; height=
+: 12px; padding: 0px 5px 5px; font-size: 12px; font-weight: var(--base-text=
+-weight-semibold, 600); line-height: 6px; color: var(--color-fg-default); t=
+ext-decoration: none; vertical-align: middle; background: var(--color-neutr=
+al-muted); border: 0px; border-radius: 1px; }
+
+.hidden-text-expander a:hover, .ellipsis-expander:hover { text-decoration: =
+none; background-color: var(--color-accent-muted); }
+
+.hidden-text-expander a:active, .ellipsis-expander:active { color: var(--co=
+lor-fg-on-emphasis); background-color: var(--color-accent-emphasis); }
+
+.btn-with-count { float: left; border-top-right-radius: 0px; border-bottom-=
+right-radius: 0px; }
+
+.btn-with-count:focus { z-index: 1; }
+
+.social-count { position: relative; float: left; padding: 3px 12px; font-si=
+ze: 12px; font-weight: var(--base-text-weight-semibold, 600); line-height: =
+20px; color: var(--color-fg-default); vertical-align: middle; background-co=
+lor: var(--color-canvas-default); border-top-color: ; border-top-style: ; b=
+order-top-width: ; border-right-color: ; border-right-style: ; border-right=
+-width: ; border-bottom-color: ; border-bottom-style: ; border-bottom-width=
+: ; border-image-source: ; border-image-slice: ; border-image-width: ; bord=
+er-image-outset: ; border-image-repeat: ; border-left: 0px; border-top-righ=
+t-radius: 6px; border-bottom-right-radius: 6px; box-shadow: var(--color-sha=
+dow-small),var(--color-primer-shadow-highlight); }
+
+.social-count:hover, .social-count:active { text-decoration: none; }
+
+.social-count:hover { color: var(--color-accent-fg); cursor: pointer; }
+
+.TableObject { display: table; }
+
+.TableObject-item { display: table-cell; width: 1%; white-space: nowrap; ve=
+rtical-align: middle; }
+
+.TableObject-item--primary { width: 99%; }
+
+.form-control, .form-select { padding: 5px 12px; font-size: 14px; line-heig=
+ht: 20px; color: var(--color-fg-default); vertical-align: middle; backgroun=
+d-color: var(--color-canvas-default); background-repeat: no-repeat; backgro=
+und-position: right 8px center; border: 1px solid var(--color-border-defaul=
+t); border-radius: 6px; box-shadow: var(--color-primer-shadow-inset); trans=
+ition: color 80ms cubic-bezier(0.33, 1, 0.68, 1) 0s, background-color, box-=
+shadow, border-color; }
+
+.form-control:focus, .form-select:focus { border-color: var(--color-accent-=
+fg); outline: none; box-shadow: inset 0 0 0 1px var(--color-accent-fg); }
+
+.form-control:focus:not(:focus-visible), .form-select:focus:not(:focus-visi=
+ble) { border-color: var(--color-accent-fg); outline: none; box-shadow: tra=
+nsparent 0px 0px 0px 1px inset; }
+
+.form-control:focus-visible, .form-select:focus-visible { border-color: var=
+(--color-accent-fg); outline: none; box-shadow: inset 0 0 0 1px var(--color=
+-accent-fg); }
+
+.form-control.border-0:focus, .form-control.border-0:focus-visible, .form-s=
+elect.border-0:focus, .form-select.border-0:focus-visible { border: 1px sol=
+id var(--color-accent-fg)  !important; }
+
+.form-control[disabled], fieldset[disabled] .form-control, .form-select[dis=
+abled], fieldset[disabled] .form-select { color: var(--color-primer-fg-disa=
+bled); background-color: var(--color-input-disabled-bg); border-color: var(=
+--color-border-default); -webkit-text-fill-color: var(--color-primer-fg-dis=
+abled); opacity: 1; }
+
+.form-control[disabled]::placeholder, fieldset[disabled] .form-control::pla=
+ceholder, .form-select[disabled]::placeholder, fieldset[disabled] .form-sel=
+ect::placeholder { color: var(--color-primer-fg-disabled); }
+
+@supports (-webkit-touch-callout: none) {
+  .form-control, .form-select { font-size: 16px; }
+  @media (min-width: 768px) {
+  .form-control, .form-select { font-size: 14px; }
+}
+}
+
+textarea.form-control { padding-top: 8px; padding-bottom: 8px; line-height:=
+ 1.5; }
+
+.input-contrast { background-color: var(--color-canvas-inset); }
+
+.input-contrast:focus { background-color: var(--color-canvas-default); }
+
+.input-sm { min-height: 28px; padding-top: 3px; padding-bottom: 3px; font-s=
+ize: 12px; line-height: 20px; }
+
+.input-lg { font-size: 16px; }
+
+.input-block { display: block; width: 100%; }
+
+.input-monospace { font-family: ui-monospace, SFMono-Regular, "SF Mono", Me=
+nlo, Consolas, "Liberation Mono", monospace; }
+
+.input-hide-webkit-autofill::-webkit-contacts-auto-fill-button { position: =
+absolute; right: 0px; pointer-events: none; visibility: hidden; display: no=
+ne !important; }
+
+.form-checkbox { padding-left: 20px; margin: 15px 0px; vertical-align: midd=
+le; }
+
+.form-checkbox label em.highlight { position: relative; left: -4px; padding=
+: 2px 4px; font-style: normal; background: var(--color-attention-subtle); b=
+order-radius: 6px; }
+
+.form-checkbox input[type=3D"checkbox"], .form-checkbox input[type=3D"radio=
+"] { float: left; margin: 5px 0px 0px -20px; vertical-align: middle; }
+
+.form-checkbox .note { display: block; margin: 0px; font-size: 12px; font-w=
+eight: var(--base-text-weight-normal, 400); color: var(--color-fg-muted); }
+
+.form-checkbox-details { display: none; }
+
+.form-checkbox-details-trigger:checked ~ * .form-checkbox-details, .form-ch=
+eckbox-details-trigger:checked ~ .form-checkbox-details { display: block; }
+
+.hfields { margin: 15px 0px; }
+
+.hfields::before { display: table; content: ""; }
+
+.hfields::after { display: table; clear: both; content: ""; }
+
+.hfields .form-group { float: left; margin: 0px 30px 0px 0px; }
+
+.hfields .form-group dt label, .hfields .form-group .form-group-header labe=
+l { display: inline-block; margin: 5px 0px 0px; color: var(--color-fg-muted=
+); }
+
+.hfields .form-group dt img, .hfields .form-group .form-group-header img { =
+position: relative; top: -2px; }
+
+.hfields .btn { float: left; margin: 28px 25px 0px -20px; }
+
+.hfields .form-select { margin-top: 5px; }
+
+input::-webkit-outer-spin-button, input::-webkit-inner-spin-button { margin=
+: 0px; appearance: none; }
+
+.form-actions::before { display: table; content: ""; }
+
+.form-actions::after { display: table; clear: both; content: ""; }
+
+.form-actions .btn { float: right; }
+
+.form-actions .btn + .btn { margin-right: 5px; }
+
+.form-warning { padding: 8px 10px; margin: 10px 0px; font-size: 14px; color=
+: var(--color-attention-fg); background: var(--color-attention-subtle); bor=
+der: 1px solid var(--color-attention-emphasis); border-radius: 6px; }
+
+.form-warning p { margin: 0px; line-height: 1.5; }
+
+.form-warning a { font-weight: var(--base-text-weight-semibold, 600); }
+
+.form-select { display: inline-block; max-width: 100%; height: 32px; paddin=
+g-right: 24px; background-color: var(--color-canvas-default); background-im=
+age: url("data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHZ=
+pZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0iIzU4NjA2OSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3=
+JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNNC40MjcgOS40MjdsMy4zOTYgMy4zOTZhLjI1MS4yNTEgM=
+CAwMC4zNTQgMGwzLjM5Ni0zLjM5NkEuMjUuMjUgMCAwMDExLjM5NiA5SDQuNjA0YS4yNS4yNSAw=
+IDAwLS4xNzcuNDI3ek00LjQyMyA2LjQ3TDcuODIgMy4wNzJhLjI1LjI1IDAgMDEuMzU0IDBMMTE=
+uNTcgNi40N2EuMjUuMjUgMCAwMS0uMTc3LjQyN0g0LjZhLjI1LjI1IDAgMDEtLjE3Ny0uNDI3ei=
+IgLz48L3N2Zz4=3D"); background-repeat: no-repeat; background-position: righ=
+t 4px center; background-size: 16px; appearance: none; }
+
+.form-select[multiple] { height: auto; }
+
+[data-color-mode=3D"light"][data-light-theme*=3D"dark"] .form-select, [data=
+-color-mode=3D"dark"][data-dark-theme*=3D"dark"] .form-select { background-=
+image: url("data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiI=
+HZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0iIzZlNzY4MSIgeG1sbnM9Imh0dHA6Ly93d3cudzMu=
+b3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNNC40MjcgOS40MjdsMy4zOTYgMy4zOTZhLjI1MS4yNTE=
+gMCAwMC4zNTQgMGwzLjM5Ni0zLjM5NkEuMjUuMjUgMCAwMDExLjM5NiA5SDQuNjA0YS4yNS4yNS=
+AwIDAwLS4xNzcuNDI3ek00LjQyMyA2LjQ3TDcuODIgMy4wNzJhLjI1LjI1IDAgMDEuMzU0IDBMM=
+TEuNTcgNi40N2EuMjUuMjUgMCAwMS0uMTc3LjQyN0g0LjZhLjI1LjI1IDAgMDEtLjE3Ny0uNDI3=
+eiIgLz48L3N2Zz4=3D"); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] .form-select { bac=
+kground-image: url("data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdod=
+D0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0iIzZlNzY4MSIgeG1sbnM9Imh0dHA6Ly93=
+d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNNC40MjcgOS40MjdsMy4zOTYgMy4zOTZhLjI=
+1MS4yNTEgMCAwMC4zNTQgMGwzLjM5Ni0zLjM5NkEuMjUuMjUgMCAwMDExLjM5NiA5SDQuNjA0YS=
+4yNS4yNSAwIDAwLS4xNzcuNDI3ek00LjQyMyA2LjQ3TDcuODIgMy4wNzJhLjI1LjI1IDAgMDEuM=
+zU0IDBMMTEuNTcgNi40N2EuMjUuMjUgMCAwMS0uMTc3LjQyN0g0LjZhLjI1LjI1IDAgMDEtLjE3=
+Ny0uNDI3eiIgLz48L3N2Zz4=3D"); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] .form-select { back=
+ground-image: url("data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD=
+0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0iIzZlNzY4MSIgeG1sbnM9Imh0dHA6Ly93d=
+3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNNC40MjcgOS40MjdsMy4zOTYgMy4zOTZhLjI1=
+MS4yNTEgMCAwMC4zNTQgMGwzLjM5Ni0zLjM5NkEuMjUuMjUgMCAwMDExLjM5NiA5SDQuNjA0YS4=
+yNS4yNSAwIDAwLS4xNzcuNDI3ek00LjQyMyA2LjQ3TDcuODIgMy4wNzJhLjI1LjI1IDAgMDEuMz=
+U0IDBMMTEuNTcgNi40N2EuMjUuMjUgMCAwMS0uMTc3LjQyN0g0LjZhLjI1LjI1IDAgMDEtLjE3N=
+y0uNDI3eiIgLz48L3N2Zz4=3D"); }
+}
+
+.select-sm { height: 28px; padding-top: 3px; padding-bottom: 3px; font-size=
+: 12px; }
+
+.select-sm[multiple] { height: auto; min-height: 0px; }
+
+.form-group { margin: 15px 0px; }
+
+.form-group .form-control.autocomplete-embedded-icon-wrap:focus-within { ba=
+ckground-color: var(--color-canvas-default); }
+
+.form-group .form-control { width: 440px; max-width: 100%; margin-right: 5p=
+x; background-color: var(--color-canvas-inset); }
+
+.form-group .form-control:focus { background-color: var(--color-canvas-defa=
+ult); }
+
+.form-group .form-control.shorter { width: 130px; }
+
+.form-group .form-control.short { width: 250px; }
+
+.form-group .form-control.input-block, .form-group .form-control.long { wid=
+th: 100%; }
+
+.form-group textarea.form-control { width: 100%; height: 200px; min-height:=
+ 200px; }
+
+.form-group textarea.form-control.short { height: 50px; min-height: 50px; }
+
+.form-group dt, .form-group .form-group-header { margin: 0px 0px 6px; }
+
+.form-group label { position: relative; }
+
+.form-group.flattened dt, .form-group.flattened .form-group-header { float:=
+ left; margin: 0px; line-height: 32px; }
+
+.form-group.flattened dd, .form-group.flattened .form-group-body { line-hei=
+ght: 32px; }
+
+.form-group dd h4, .form-group .form-group-body h4 { margin: 4px 0px 0px; }
+
+.form-group dd h4.is-error, .form-group .form-group-body h4.is-error { colo=
+r: var(--color-danger-fg); }
+
+.form-group dd h4.is-success, .form-group .form-group-body h4.is-success { =
+color: var(--color-success-fg); }
+
+.form-group dd h4 + .note, .form-group .form-group-body h4 + .note { margin=
+-top: 0px; }
+
+.form-group.required dt label::after, .form-group.required .form-group-head=
+er label::after { padding-left: 5px; color: var(--color-danger-fg); content=
+: "*"; }
+
+.form-group .success, .form-group .error, .form-group .indicator { display:=
+ none; font-size: 12px; font-weight: var(--base-text-weight-semibold, 600);=
+ }
+
+.form-group.loading { opacity: 0.5; }
+
+.form-group.loading .indicator { display: inline; }
+
+.form-group.loading .spinner { display: inline-block; vertical-align: middl=
+e; }
+
+.form-group.successful .success { display: inline; color: var(--color-succe=
+ss-fg); }
+
+.form-group.successed .success, .form-group.successed .warning, .form-group=
+.successed .error, .form-group.warn .success, .form-group.warn .warning, .f=
+orm-group.warn .error, .form-group.errored .success, .form-group.errored .w=
+arning, .form-group.errored .error { position: absolute; z-index: 10; displ=
+ay: block; max-width: 450px; padding: 4px 8px; margin: 8px 0px 0px; font-si=
+ze: 12px; font-weight: var(--base-text-weight-normal, 400); border-style: s=
+olid; border-width: 1px; border-radius: 6px; }
+
+.form-group.successed .success::after, .form-group.successed .success::befo=
+re, .form-group.successed .warning::after, .form-group.successed .warning::=
+before, .form-group.successed .error::after, .form-group.successed .error::=
+before, .form-group.warn .success::after, .form-group.warn .success::before=
+, .form-group.warn .warning::after, .form-group.warn .warning::before, .for=
+m-group.warn .error::after, .form-group.warn .error::before, .form-group.er=
+rored .success::after, .form-group.errored .success::before, .form-group.er=
+rored .warning::after, .form-group.errored .warning::before, .form-group.er=
+rored .error::after, .form-group.errored .error::before { position: absolut=
+e; bottom: 100%; left: 10px; z-index: 15; width: 0px; height: 0px; pointer-=
+events: none; content: " "; border: solid transparent; }
+
+.form-group.successed .success::after, .form-group.successed .warning::afte=
+r, .form-group.successed .error::after, .form-group.warn .success::after, .=
+form-group.warn .warning::after, .form-group.warn .error::after, .form-grou=
+p.errored .success::after, .form-group.errored .warning::after, .form-group=
+.errored .error::after { border-width: 5px; }
+
+.form-group.successed .success::before, .form-group.successed .warning::bef=
+ore, .form-group.successed .error::before, .form-group.warn .success::befor=
+e, .form-group.warn .warning::before, .form-group.warn .error::before, .for=
+m-group.errored .success::before, .form-group.errored .warning::before, .fo=
+rm-group.errored .error::before { margin-left: -1px; border-width: 6px; }
+
+.form-group.successed .success { color: var(--color-fg-default); background=
+-color: var(--color-canvas-default); background-image: linear-gradient(var(=
+--color-success-subtle), var(--color-success-subtle)); border-color: var(--=
+color-success-muted); }
+
+.form-group.successed .success::after { border-bottom-color: var(--color-su=
+ccess-subtle); }
+
+.form-group.successed .success::before { border-bottom-color: var(--color-s=
+uccess-muted); }
+
+.form-group.warn .form-control:not(:focus, :focus-visible) { border-color: =
+var(--color-attention-emphasis); }
+
+.form-group.warn .warning { color: var(--color-fg-default); background-colo=
+r: var(--color-canvas-default); background-image: linear-gradient(var(--col=
+or-attention-subtle), var(--color-attention-subtle)); border-color: var(--c=
+olor-attention-muted); }
+
+.form-group.warn .warning::after { border-bottom-color: var(--color-attenti=
+on-subtle); }
+
+.form-group.warn .warning::before { border-bottom-color: var(--color-attent=
+ion-muted); }
+
+.form-group.errored .form-control:not(:focus, :focus-visible) { border-colo=
+r: var(--color-danger-emphasis); }
+
+.form-group.errored label { color: var(--color-danger-fg); }
+
+.form-group.errored .error { color: var(--color-fg-default); background-col=
+or: var(--color-canvas-default); background-image: linear-gradient(var(--co=
+lor-danger-subtle), var(--color-danger-subtle)); border-color: var(--color-=
+danger-muted); }
+
+.form-group.errored .error::after { border-bottom-color: var(--color-danger=
+-subtle); }
+
+.form-group.errored .error::before { border-bottom-color: var(--color-dange=
+r-muted); }
+
+.note { min-height: 17px; margin: 4px 0px 2px; font-size: 12px; color: var(=
+--color-fg-muted); }
+
+.note .spinner { margin-right: 3px; vertical-align: middle; }
+
+dl.form-group > dd .form-control.is-autocheck-loading, dl.form-group > dd .=
+form-control.is-autocheck-successful, dl.form-group > dd .form-control.is-a=
+utocheck-errored, .form-group > .form-group-body .form-control.is-autocheck=
+-loading, .form-group > .form-group-body .form-control.is-autocheck-success=
+ful, .form-group > .form-group-body .form-control.is-autocheck-errored { pa=
+dding-right: 30px; }
+
+dl.form-group > dd .form-control.is-autocheck-loading, .form-group > .form-=
+group-body .form-control.is-autocheck-loading { background-image: url("/ima=
+ges/spinners/octocat-spinner-16px.gif"); }
+
+dl.form-group > dd .form-control.is-autocheck-successful, .form-group > .fo=
+rm-group-body .form-control.is-autocheck-successful { background-image: url=
+("/images/modules/ajax/success.png"); }
+
+dl.form-group > dd .form-control.is-autocheck-errored, .form-group > .form-=
+group-body .form-control.is-autocheck-errored { background-image: url("/ima=
+ges/modules/ajax/error.png"); }
+
+@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and=
+ (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio:=
+ 2), only screen and (min-resolution: 192dpi), only screen and (min-resolut=
+ion: 2dppx) {
+  dl.form-group > dd .form-control.is-autocheck-loading, dl.form-group > dd=
+ .form-control.is-autocheck-successful, dl.form-group > dd .form-control.is=
+-autocheck-errored, .form-group > .form-group-body .form-control.is-autoche=
+ck-loading, .form-group > .form-group-body .form-control.is-autocheck-succe=
+ssful, .form-group > .form-group-body .form-control.is-autocheck-errored { =
+background-size: 16px 16px; }
+  dl.form-group > dd .form-control.is-autocheck-loading, .form-group > .for=
+m-group-body .form-control.is-autocheck-loading { background-image: url("/i=
+mages/spinners/octocat-spinner-32.gif"); }
+  dl.form-group > dd .form-control.is-autocheck-successful, .form-group > .=
+form-group-body .form-control.is-autocheck-successful { background-image: u=
+rl("/images/modules/ajax/success@2x.png"); }
+  dl.form-group > dd .form-control.is-autocheck-errored, .form-group > .for=
+m-group-body .form-control.is-autocheck-errored { background-image: url("/i=
+mages/modules/ajax/error@2x.png"); }
+}
+
+.status-indicator { display: inline-block; width: 16px; height: 16px; margi=
+n-left: 5px; }
+
+.status-indicator .octicon { display: none; }
+
+.status-indicator-success::before { content: ""; }
+
+.status-indicator-success .octicon-check { display: inline-block; color: va=
+r(--color-success-fg); fill: var(--color-success-fg); }
+
+.status-indicator-success .octicon-x { display: none; }
+
+.status-indicator-failed::before { content: ""; }
+
+.status-indicator-failed .octicon-check { display: none; }
+
+.status-indicator-failed .octicon-x { display: inline-block; color: var(--c=
+olor-danger-fg); fill: var(--color-danger-fg); }
+
+.status-indicator-loading { width: 16px; background-image: url("/images/spi=
+nners/octocat-spinner-32-EAF2F5.gif"); background-repeat: no-repeat; backgr=
+ound-position: 0px 0px; background-size: 16px; }
+
+.inline-form { display: inline-block; }
+
+.inline-form .btn-plain { background-color: transparent; border: 0px; }
+
+.drag-and-drop { padding: 7px 10px; margin: 0px; font-size: 13px; line-heig=
+ht: 16px; color: var(--color-fg-muted); background-color: var(--color-canva=
+s-subtle); border-right-color: ; border-right-style: ; border-right-width: =
+; border-bottom-color: ; border-bottom-style: ; border-bottom-width: ; bord=
+er-left-color: ; border-left-style: ; border-left-width: ; border-image-sou=
+rce: ; border-image-slice: ; border-image-width: ; border-image-outset: ; b=
+order-image-repeat: ; border-top: 0px; border-bottom-right-radius: 6px; bor=
+der-bottom-left-radius: 6px; }
+
+.drag-and-drop .default, .drag-and-drop .loading, .drag-and-drop .error { d=
+isplay: none; }
+
+.drag-and-drop .error { color: var(--color-danger-fg); }
+
+.drag-and-drop img { vertical-align: top; }
+
+.is-default .drag-and-drop .default { display: inline-block; }
+
+.is-uploading .drag-and-drop .loading { display: inline-block; }
+
+.is-bad-file .drag-and-drop .bad-file { display: inline-block; }
+
+.is-duplicate-filename .drag-and-drop .duplicate-filename { display: inline=
+-block; }
+
+.is-too-big .drag-and-drop .too-big { display: inline-block; }
+
+.is-hidden-file .drag-and-drop .hidden-file { display: inline-block; }
+
+.is-empty .drag-and-drop .empty { display: inline-block; }
+
+.is-bad-permissions .drag-and-drop .bad-permissions { display: inline-block=
+; }
+
+.is-repository-required .drag-and-drop .repository-required { display: inli=
+ne-block; }
+
+.drag-and-drop-error-info { font-weight: var(--base-text-weight-normal, 400=
+); color: var(--color-fg-muted); }
+
+.drag-and-drop-error-info a { color: var(--color-accent-fg); }
+
+.is-failed .drag-and-drop .failed-request { display: inline-block; }
+
+.manual-file-chooser { position: absolute; width: 240px; padding: 5px; marg=
+in-left: -80px; cursor: pointer; opacity: 0.0001; }
+
+.manual-file-chooser:hover + .manual-file-chooser-text { text-decoration: u=
+nderline; }
+
+.btn .manual-file-chooser { top: 0px; padding: 0px; line-height: 34px; }
+
+.upload-enabled textarea { display: block; border-bottom: 1px dashed var(--=
+color-border-default); border-bottom-right-radius: 0px; border-bottom-left-=
+radius: 0px; }
+
+.upload-enabled.focused { border-color: var(--color-accent-fg); border-radi=
+us: 6px; outline: none; box-shadow: 0 0 0 2px var(--color-accent-fg); }
+
+.upload-enabled.focused .form-control { border-top-color: transparent; bord=
+er-right-color: transparent; border-left-color: transparent; border-bottom-=
+color: var(--color-accent-fg); box-shadow: none; }
+
+.upload-enabled.focused .drag-and-drop { border-color: transparent; }
+
+.dragover textarea, .dragover .drag-and-drop { box-shadow: rgb(201, 255, 0)=
+ 0px 0px 3px; }
+
+.write-content { position: relative; }
+
+.previewable-comment-form { position: relative; }
+
+.previewable-comment-form .tabnav { position: relative; padding: 8px 8px 0p=
+x; }
+
+.previewable-comment-form .comment { border: 1px solid var(--color-border-d=
+efault); }
+
+.previewable-comment-form .comment-form-error { margin-bottom: 8px; }
+
+.previewable-comment-form .write-content, .previewable-comment-form .previe=
+w-content { display: none; margin: 0px 8px 8px; }
+
+.previewable-comment-form.write-selected .write-content, .previewable-comme=
+nt-form.preview-selected .preview-content { display: block; }
+
+.previewable-comment-form textarea { display: block; width: 100%; min-heigh=
+t: 100px; max-height: 500px; padding: 8px; resize: vertical; }
+
+.form-action-spacious { margin-top: 10px; }
+
+div.composer { margin-top: 0px; border: 0px; }
+
+.composer .comment-form-textarea { height: 200px; min-height: 200px; }
+
+.composer .tabnav { margin: 0px 0px 10px; }
+
+h2.account { margin: 15px 0px 0px; font-size: 18px; font-weight: var(--base=
+-text-weight-normal, 400); color: var(--color-fg-muted); }
+
+p.explain { position: relative; font-size: 12px; color: var(--color-fg-mute=
+d); }
+
+p.explain strong { color: var(--color-fg-default); }
+
+p.explain .octicon { margin-right: 5px; color: var(--color-fg-muted); }
+
+p.explain .minibutton { top: -4px; float: right; }
+
+.form-group label { position: static; }
+
+.input-group { display: table; }
+
+.input-group .form-control { position: relative; width: 100%; }
+
+.input-group .form-control:focus { z-index: 2; }
+
+.input-group .form-control + .btn { margin-left: 0px; }
+
+.input-group.inline { display: inline-table; }
+
+.input-group:focus-within button { outline-offset: 0px; }
+
+.input-group .form-control.autocomplete-embedded-icon-wrap { display: inlin=
+e-flex; padding: 5px 8px; }
+
+.input-group .form-control, .input-group-button { display: table-cell; }
+
+.input-group-button { width: 1%; vertical-align: middle; }
+
+.input-group-button--autocomplete-embedded-icon { vertical-align: bottom; }
+
+.input-group .form-control:first-child, .input-group-button:first-child .bt=
+n { border-top-right-radius: 0px; border-bottom-right-radius: 0px; }
+
+.input-group-button:first-child .btn { margin-right: -1px; }
+
+.input-group .form-control:last-child, .input-group-button:last-child .btn =
+{ border-top-left-radius: 0px; border-bottom-left-radius: 0px; }
+
+.input-group-button:last-child .btn { margin-left: -1px; }
+
+.radio-group::before { display: table; content: ""; }
+
+.radio-group::after { display: table; clear: both; content: ""; }
+
+.radio-label { float: left; padding: 6px 16px 6px 36px; margin-left: -1px; =
+font-size: 14px; line-height: 20px; color: var(--color-fg-default); cursor:=
+ pointer; border: 1px solid var(--color-border-default); }
+
+:checked + .radio-label { position: relative; z-index: 1; border-color: var=
+(--color-accent-emphasis); }
+
+.radio-label:first-of-type { margin-left: 0px; border-top-left-radius: 6px;=
+ border-bottom-left-radius: 6px; }
+
+.radio-label:last-of-type { border-top-right-radius: 6px; border-bottom-rig=
+ht-radius: 6px; }
+
+.radio-label .octicon { margin-left: 4px; color: var(--color-fg-subtle); }
+
+.radio-input { z-index: 3; float: left; margin: 10px -32px 0px 16px; }
+
+.radio-input:disabled { position: relative; }
+
+.radio-input:disabled + .radio-label { color: var(--color-primer-fg-disable=
+d); cursor: default; background-color: var(--color-neutral-subtle); }
+
+.radio-input:disabled + .radio-label .octicon { color: inherit; }
+
+.AppFrame .AppFrame-a11yNav { position: absolute; z-index: 1000; display: f=
+lex; width: 100%; padding: var(--base-size-16, 16px); background: var(--col=
+or-canvas-inset); padding-block-end: calc(var(--base-size-16, 16px) - var(-=
+-primer-borderWidth-thin, 1px)); isolation: isolate; align-items: center; g=
+ap: var(--base-size-8, 8px); }
+
+.AppFrame .AppFrame-a11yNav:not(:focus-within) { width: 1px; height: 1px; p=
+adding: 0px; margin: -1px; overflow: hidden; clip: rect(1px, 1px, 1px, 1px)=
+; border: 0px; }
+
+.AppFrame .AppFrame-a11yNav:focus-within { top: 0px; left: 0px; }
+
+@media (max-width: 767.98px) {
+  .AppFrame .AppFrame-a11yNav:focus-within { justify-content: center; }
+}
+
+.AppFrame .AppFrame-a11yLink { transition: none 0s ease 0s; }
+
+.AppFrame .AppFrame-a11yLink:not(:focus) { display: block; width: var(--bas=
+e-size-8, 8px); height: var(--base-size-8, 8px); overflow: hidden; text-ind=
+ent: var(--base-size-128, 128px); pointer-events: none; background: var(--c=
+olor-border-default); border-radius: var(--primer-borderRadius-full, 100vh)=
+; }
+
+.AppFrame .AppFrame-a11yLink:focus { z-index: 20; display: grid; width: aut=
+o; height: auto; min-height: var(--primer-control-medium-size, 32px); paddi=
+ng: 0 var(--primer-control-medium-paddingInline-spacious, 16px); overflow: =
+auto; color: var(--color-fg-on-emphasis); background: var(--color-accent-em=
+phasis); border-radius: var(--primer-borderRadius-full, 100vh); align-items=
+: center; }
+
+@media (pointer: coarse) {
+  .AppFrame .AppFrame-a11yLink:focus::after { position: absolute; top: 50%;=
+ left: 50%; width: 100%; height: 100%; min-height: var(--primer-control-min=
+Target-coarse, 44px); content: ""; transform: translateX(-50%) translateY(-=
+50%); }
+}
+
+@media (prefers-reduced-motion: no-preference) {
+  .AppFrame .AppFrame-a11yLink:focus { animation: 200ms ease-out 0s 1 norma=
+l none running AppFrame-a11yLink-focus; }
+}
+
+@keyframes AppFrame-a11yLink-focus {=20
+  0% { color: var(--color-accent-emphasis); transform: scale(0.3, 0.25); }
+  50% { color: var(--color-accent-emphasis); transform: scale(1, 1); }
+  55% { color: var(--color-fg-on-emphasis); }
+  100% { transform: scaleX(1); }
+}
+
+.AppFrame .AppFrame-main { display: flex; min-height: 100vh; flex-direction=
+: column; }
+
+@supports (height: 100dvh) {
+  .AppFrame .AppFrame-main { min-height: 100dvh; }
+}
+
+.AppFrame .AppFrame-header-wrapper { position: relative; height: min-conten=
+t; overflow: visible; }
+
+.AppFrame .AppFrame-header-wrapper .AppFrame-header { position: sticky; top=
+: 0px; z-index: 1; }
+
+.AppFrame .AppFrame-header { flex: 0 0 auto; }
+
+.AppFrame .AppFrame-subheader { flex: 0 0 auto; }
+
+.AppFrame .AppFrame-body { flex: 1 0 0%; height: 100%; }
+
+.AppFrame .AppFrame-footer { flex: 0 0 auto; }
+
+.container-sm { max-width: 544px; margin-right: auto; margin-left: auto; }
+
+.container-md { max-width: 768px; margin-right: auto; margin-left: auto; }
+
+.container-lg { max-width: 1012px; margin-right: auto; margin-left: auto; }
+
+.container-xl { max-width: 1280px; margin-right: auto; margin-left: auto; }
+
+.col-1 { width: 8.33333%; }
+
+.col-2 { width: 16.6667%; }
+
+.col-3 { width: 25%; }
+
+.col-4 { width: 33.3333%; }
+
+.col-5 { width: 41.6667%; }
+
+.col-6 { width: 50%; }
+
+.col-7 { width: 58.3333%; }
+
+.col-8 { width: 66.6667%; }
+
+.col-9 { width: 75%; }
+
+.col-10 { width: 83.3333%; }
+
+.col-11 { width: 91.6667%; }
+
+.col-12 { width: 100%; }
+
+@media (min-width: 544px) {
+  .col-sm-1 { width: 8.33333%; }
+  .col-sm-2 { width: 16.6667%; }
+  .col-sm-3 { width: 25%; }
+  .col-sm-4 { width: 33.3333%; }
+  .col-sm-5 { width: 41.6667%; }
+  .col-sm-6 { width: 50%; }
+  .col-sm-7 { width: 58.3333%; }
+  .col-sm-8 { width: 66.6667%; }
+  .col-sm-9 { width: 75%; }
+  .col-sm-10 { width: 83.3333%; }
+  .col-sm-11 { width: 91.6667%; }
+  .col-sm-12 { width: 100%; }
+}
+
+@media (min-width: 768px) {
+  .col-md-1 { width: 8.33333%; }
+  .col-md-2 { width: 16.6667%; }
+  .col-md-3 { width: 25%; }
+  .col-md-4 { width: 33.3333%; }
+  .col-md-5 { width: 41.6667%; }
+  .col-md-6 { width: 50%; }
+  .col-md-7 { width: 58.3333%; }
+  .col-md-8 { width: 66.6667%; }
+  .col-md-9 { width: 75%; }
+  .col-md-10 { width: 83.3333%; }
+  .col-md-11 { width: 91.6667%; }
+  .col-md-12 { width: 100%; }
+}
+
+@media (min-width: 1012px) {
+  .col-lg-1 { width: 8.33333%; }
+  .col-lg-2 { width: 16.6667%; }
+  .col-lg-3 { width: 25%; }
+  .col-lg-4 { width: 33.3333%; }
+  .col-lg-5 { width: 41.6667%; }
+  .col-lg-6 { width: 50%; }
+  .col-lg-7 { width: 58.3333%; }
+  .col-lg-8 { width: 66.6667%; }
+  .col-lg-9 { width: 75%; }
+  .col-lg-10 { width: 83.3333%; }
+  .col-lg-11 { width: 91.6667%; }
+  .col-lg-12 { width: 100%; }
+}
+
+@media (min-width: 1280px) {
+  .col-xl-1 { width: 8.33333%; }
+  .col-xl-2 { width: 16.6667%; }
+  .col-xl-3 { width: 25%; }
+  .col-xl-4 { width: 33.3333%; }
+  .col-xl-5 { width: 41.6667%; }
+  .col-xl-6 { width: 50%; }
+  .col-xl-7 { width: 58.3333%; }
+  .col-xl-8 { width: 66.6667%; }
+  .col-xl-9 { width: 75%; }
+  .col-xl-10 { width: 83.3333%; }
+  .col-xl-11 { width: 91.6667%; }
+  .col-xl-12 { width: 100%; }
+}
+
+.gutter { margin-right: -16px; margin-left: -16px; }
+
+.gutter > [class*=3D"col-"] { padding-right: 16px !important; padding-left:=
+ 16px !important; }
+
+.gutter-condensed { margin-right: -8px; margin-left: -8px; }
+
+.gutter-condensed > [class*=3D"col-"] { padding-right: 8px !important; padd=
+ing-left: 8px !important; }
+
+.gutter-spacious { margin-right: -24px; margin-left: -24px; }
+
+.gutter-spacious > [class*=3D"col-"] { padding-right: 24px !important; padd=
+ing-left: 24px !important; }
+
+@media (min-width: 544px) {
+  .gutter-sm { margin-right: -16px; margin-left: -16px; }
+  .gutter-sm > [class*=3D"col-"] { padding-right: 16px !important; padding-=
+left: 16px !important; }
+  .gutter-sm-condensed { margin-right: -8px; margin-left: -8px; }
+  .gutter-sm-condensed > [class*=3D"col-"] { padding-right: 8px !important;=
+ padding-left: 8px !important; }
+  .gutter-sm-spacious { margin-right: -24px; margin-left: -24px; }
+  .gutter-sm-spacious > [class*=3D"col-"] { padding-right: 24px !important;=
+ padding-left: 24px !important; }
+}
+
+@media (min-width: 768px) {
+  .gutter-md { margin-right: -16px; margin-left: -16px; }
+  .gutter-md > [class*=3D"col-"] { padding-right: 16px !important; padding-=
+left: 16px !important; }
+  .gutter-md-condensed { margin-right: -8px; margin-left: -8px; }
+  .gutter-md-condensed > [class*=3D"col-"] { padding-right: 8px !important;=
+ padding-left: 8px !important; }
+  .gutter-md-spacious { margin-right: -24px; margin-left: -24px; }
+  .gutter-md-spacious > [class*=3D"col-"] { padding-right: 24px !important;=
+ padding-left: 24px !important; }
+}
+
+@media (min-width: 1012px) {
+  .gutter-lg { margin-right: -16px; margin-left: -16px; }
+  .gutter-lg > [class*=3D"col-"] { padding-right: 16px !important; padding-=
+left: 16px !important; }
+  .gutter-lg-condensed { margin-right: -8px; margin-left: -8px; }
+  .gutter-lg-condensed > [class*=3D"col-"] { padding-right: 8px !important;=
+ padding-left: 8px !important; }
+  .gutter-lg-spacious { margin-right: -24px; margin-left: -24px; }
+  .gutter-lg-spacious > [class*=3D"col-"] { padding-right: 24px !important;=
+ padding-left: 24px !important; }
+}
+
+@media (min-width: 1280px) {
+  .gutter-xl { margin-right: -16px; margin-left: -16px; }
+  .gutter-xl > [class*=3D"col-"] { padding-right: 16px !important; padding-=
+left: 16px !important; }
+  .gutter-xl-condensed { margin-right: -8px; margin-left: -8px; }
+  .gutter-xl-condensed > [class*=3D"col-"] { padding-right: 8px !important;=
+ padding-left: 8px !important; }
+  .gutter-xl-spacious { margin-right: -24px; margin-left: -24px; }
+  .gutter-xl-spacious > [class*=3D"col-"] { padding-right: 24px !important;=
+ padding-left: 24px !important; }
+}
+
+.offset-1 { margin-left: 8.33333% !important; }
+
+.offset-2 { margin-left: 16.6667% !important; }
+
+.offset-3 { margin-left: 25% !important; }
+
+.offset-4 { margin-left: 33.3333% !important; }
+
+.offset-5 { margin-left: 41.6667% !important; }
+
+.offset-6 { margin-left: 50% !important; }
+
+.offset-7 { margin-left: 58.3333% !important; }
+
+.offset-8 { margin-left: 66.6667% !important; }
+
+.offset-9 { margin-left: 75% !important; }
+
+.offset-10 { margin-left: 83.3333% !important; }
+
+.offset-11 { margin-left: 91.6667% !important; }
+
+@media (min-width: 544px) {
+  .offset-sm-1 { margin-left: 8.33333% !important; }
+  .offset-sm-2 { margin-left: 16.6667% !important; }
+  .offset-sm-3 { margin-left: 25% !important; }
+  .offset-sm-4 { margin-left: 33.3333% !important; }
+  .offset-sm-5 { margin-left: 41.6667% !important; }
+  .offset-sm-6 { margin-left: 50% !important; }
+  .offset-sm-7 { margin-left: 58.3333% !important; }
+  .offset-sm-8 { margin-left: 66.6667% !important; }
+  .offset-sm-9 { margin-left: 75% !important; }
+  .offset-sm-10 { margin-left: 83.3333% !important; }
+  .offset-sm-11 { margin-left: 91.6667% !important; }
+}
+
+@media (min-width: 768px) {
+  .offset-md-1 { margin-left: 8.33333% !important; }
+  .offset-md-2 { margin-left: 16.6667% !important; }
+  .offset-md-3 { margin-left: 25% !important; }
+  .offset-md-4 { margin-left: 33.3333% !important; }
+  .offset-md-5 { margin-left: 41.6667% !important; }
+  .offset-md-6 { margin-left: 50% !important; }
+  .offset-md-7 { margin-left: 58.3333% !important; }
+  .offset-md-8 { margin-left: 66.6667% !important; }
+  .offset-md-9 { margin-left: 75% !important; }
+  .offset-md-10 { margin-left: 83.3333% !important; }
+  .offset-md-11 { margin-left: 91.6667% !important; }
+}
+
+@media (min-width: 1012px) {
+  .offset-lg-1 { margin-left: 8.33333% !important; }
+  .offset-lg-2 { margin-left: 16.6667% !important; }
+  .offset-lg-3 { margin-left: 25% !important; }
+  .offset-lg-4 { margin-left: 33.3333% !important; }
+  .offset-lg-5 { margin-left: 41.6667% !important; }
+  .offset-lg-6 { margin-left: 50% !important; }
+  .offset-lg-7 { margin-left: 58.3333% !important; }
+  .offset-lg-8 { margin-left: 66.6667% !important; }
+  .offset-lg-9 { margin-left: 75% !important; }
+  .offset-lg-10 { margin-left: 83.3333% !important; }
+  .offset-lg-11 { margin-left: 91.6667% !important; }
+}
+
+@media (min-width: 1280px) {
+  .offset-xl-1 { margin-left: 8.33333% !important; }
+  .offset-xl-2 { margin-left: 16.6667% !important; }
+  .offset-xl-3 { margin-left: 25% !important; }
+  .offset-xl-4 { margin-left: 33.3333% !important; }
+  .offset-xl-5 { margin-left: 41.6667% !important; }
+  .offset-xl-6 { margin-left: 50% !important; }
+  .offset-xl-7 { margin-left: 58.3333% !important; }
+  .offset-xl-8 { margin-left: 66.6667% !important; }
+  .offset-xl-9 { margin-left: 75% !important; }
+  .offset-xl-10 { margin-left: 83.3333% !important; }
+  .offset-xl-11 { margin-left: 91.6667% !important; }
+}
+
+:root { --Layout-pane-width: 220px; --Layout-content-width: 100%; --Layout-=
+template-columns: 1fr var(--Layout-pane-width); --Layout-template-areas: "c=
+ontent pane"; --Layout-column-gap: 16px; --Layout-row-gap: 16px; --Layout-o=
+uter-spacing-x: 0px; --Layout-outer-spacing-y: 0px; --Layout-inner-spacing-=
+min: 0px; --Layout-inner-spacing-max: 0px; }
+
+.PageLayout { display: block; margin: var(--Layout-outer-spacing-y) var(--L=
+ayout-outer-spacing-x); }
+
+@media (min-width: 768px) {
+  .PageLayout.PageLayout--panePos-start { --Layout-template-columns: var(--=
+Layout-pane-width) minmax(0, calc(100% - var(--Layout-pane-width) - var(--L=
+ayout-column-gap))); --Layout-template-areas: "pane content"; }
+  .PageLayout.PageLayout--panePos-end { --Layout-template-columns: minmax(0=
+, calc(100% - var(--Layout-pane-width) - var(--Layout-column-gap))) var(--L=
+ayout-pane-width); --Layout-template-areas: "content pane"; }
+  .PageLayout .PageLayout-header--hasDivider { padding-bottom: max(var(--La=
+yout-row-gap),var(--Layout-inner-spacing-min)); border-bottom: 1px solid va=
+r(--color-border-default); }
+  .PageLayout .PageLayout-footer--hasDivider { padding-top: max(var(--Layou=
+t-row-gap),var(--Layout-inner-spacing-min)); border-top: 1px solid var(--co=
+lor-border-default); }
+  .PageLayout.PageLayout--hasPaneDivider.PageLayout--panePos-start .PageLay=
+out-pane { border-right: 1px solid var(--color-border-default); }
+  .PageLayout.PageLayout--hasPaneDivider.PageLayout--panePos-start:not(.Pag=
+eLayout--columnGap-none) .PageLayout-pane { padding-right: calc(var(--Layou=
+t-column-gap) - 1px); margin-right: calc(var(--Layout-column-gap)*-1); }
+  .PageLayout.PageLayout--hasPaneDivider.PageLayout--panePos-start:not(.Pag=
+eLayout--columnGap-none) .PageLayout-content { margin-left: var(--Layout-co=
+lumn-gap); }
+  .PageLayout.PageLayout--hasPaneDivider.PageLayout--panePos-end .PageLayou=
+t-pane { border-left: 1px solid var(--color-border-default); }
+  .PageLayout.PageLayout--hasPaneDivider.PageLayout--panePos-end:not(.PageL=
+ayout--columnGap-none) .PageLayout-pane { padding-left: calc(var(--Layout-c=
+olumn-gap) - 1px); margin-left: calc(var(--Layout-column-gap)*-1); }
+  .PageLayout.PageLayout--hasPaneDivider.PageLayout--panePos-end:not(.PageL=
+ayout--columnGap-none) .PageLayout-content { margin-right: var(--Layout-col=
+umn-gap); }
+  .PageLayout .PageLayout-pane--sticky { position: sticky; top: 0px; max-he=
+ight: 100vh; overflow: auto; }
+  @supports (max-height: 100dvh) {
+  .PageLayout .PageLayout-pane--sticky { max-height: 100dvh; }
+}
+  .PageLayout [class^=3D"PageLayout-content-centered-"] { max-width: calc(v=
+ar(--Layout-content-width) + var(--Layout-pane-width) + var(--Layout-column=
+-gap)); margin-right: auto; margin-left: auto; }
+  .PageLayout.PageLayout--hasPaneDivider [class^=3D"PageLayout-content-cent=
+ered-"] { max-width: calc(var(--Layout-content-width) + var(--Layout-pane-w=
+idth) + var(--Layout-column-gap)*2); }
+  .PageLayout.PageLayout--panePos-start [class^=3D"PageLayout-content-cente=
+red-"] > [class^=3D"container-"] { margin-left: 0px; }
+  .PageLayout.PageLayout--panePos-end [class^=3D"PageLayout-content-centere=
+d-"] > [class^=3D"container-"] { margin-right: 0px; }
+  .PageLayout .PageLayout-content-centered-sm { --Layout-content-width: 544=
+px; }
+  .PageLayout .PageLayout-content-centered-md { --Layout-content-width: 768=
+px; }
+  .PageLayout .PageLayout-content-centered-lg { --Layout-content-width: 101=
+2px; }
+  .PageLayout .PageLayout-content-centered-xl { --Layout-content-width: 128=
+0px; }
+}
+
+@media (min-width: 768px) and (min-width: 544px) {
+  .PageLayout { --Layout-pane-width: 220px; }
+}
+
+@media (min-width: 768px) and (min-width: 768px) {
+  .PageLayout { --Layout-pane-width: 256px; }
+}
+
+@media (min-width: 768px) and (min-width: 1012px) {
+  .PageLayout { --Layout-pane-width: 296px; }
+}
+
+@media (min-width: 768px) and (min-width: 768px) {
+  .PageLayout.PageLayout--paneWidth-narrow { --Layout-pane-width: 240px; }
+}
+
+@media (min-width: 768px) and (min-width: 1012px) {
+  .PageLayout.PageLayout--paneWidth-narrow { --Layout-pane-width: 256px; }
+}
+
+@media (min-width: 768px) and (min-width: 1012px) {
+  .PageLayout.PageLayout--paneWidth-wide { --Layout-pane-width: 320px; }
+}
+
+@media (min-width: 768px) and (min-width: 1280px) {
+  .PageLayout.PageLayout--paneWidth-wide { --Layout-pane-width: 336px; }
+}
+
+@media (max-width: 767.98px) {
+  .PageLayout.PageLayout--responsive-stackRegions { --Layout-template-colum=
+ns: 1fr; --Layout-template-areas: "content" "pane"; }
+  .PageLayout.PageLayout--responsive-stackRegions.PageLayout--responsive-pa=
+nePos-start { --Layout-template-areas: "pane" "content"; }
+  .PageLayout.PageLayout--responsive-separateRegions { --Layout-template-co=
+lumns: 1fr; --Layout-template-areas: "content"; }
+  .PageLayout.PageLayout--responsive-separateRegions.PageLayout--responsive=
+-primary-content { --Layout-template-areas: "content"; }
+  .PageLayout.PageLayout--responsive-separateRegions.PageLayout--responsive=
+-primary-content .PageLayout-pane { display: none; }
+  .PageLayout.PageLayout--responsive-separateRegions.PageLayout--responsive=
+-primary-pane { --Layout-template-areas: "pane"; }
+  .PageLayout.PageLayout--responsive-separateRegions.PageLayout--responsive=
+-primary-pane .PageLayout-content { display: none; }
+  .PageLayout .PageLayout-region--dividerNarrow-line-before { position: rel=
+ative; margin-top: var(--Layout-row-gap); }
+  .PageLayout .PageLayout-region--dividerNarrow-line-before::before { posit=
+ion: absolute; left: calc(var(--Layout-outer-spacing-x)*-1); display: block=
+; width: calc(100% + var(--Layout-outer-spacing-x)*2); height: 1px; content=
+: ""; background-color: var(--color-border-default); top: calc(-1px - var(-=
+-Layout-row-gap)); }
+  .PageLayout .PageLayout-region--dividerNarrow-line-after { position: rela=
+tive; margin-bottom: var(--Layout-row-gap); }
+  .PageLayout .PageLayout-region--dividerNarrow-line-after::after { positio=
+n: absolute; left: calc(var(--Layout-outer-spacing-x)*-1); display: block; =
+width: calc(100% + var(--Layout-outer-spacing-x)*2); height: 1px; content: =
+""; background-color: var(--color-border-default); bottom: calc(-1px - var(=
+--Layout-row-gap)); }
+  .PageLayout .PageLayout-region--dividerNarrow-filled-before { position: r=
+elative; margin-top: calc(8px + var(--Layout-row-gap)); }
+  .PageLayout .PageLayout-region--dividerNarrow-filled-before::after { posi=
+tion: absolute; bottom: calc(-8px); left: calc(var(--Layout-outer-spacing-x=
+)*-1); display: block; width: calc(100% + var(--Layout-outer-spacing-x)*2);=
+ height: 8px; content: ""; background-color: var(--color-canvas-inset); box=
+-shadow: inset 0 1px var(--color-border-default),inset 0 -1px var(--color-b=
+order-default); top: calc(-8px - var(--Layout-row-gap)); }
+  .PageLayout .PageLayout-region--dividerNarrow-filled-after { position: re=
+lative; margin-bottom: calc(8px + var(--Layout-row-gap)); }
+  .PageLayout .PageLayout-region--dividerNarrow-filled-after::before { posi=
+tion: absolute; left: calc(var(--Layout-outer-spacing-x)*-1); display: bloc=
+k; width: calc(100% + var(--Layout-outer-spacing-x)*2); height: 8px; conten=
+t: ""; background-color: var(--color-canvas-inset); box-shadow: inset 0 1px=
+ var(--color-border-default),inset 0 -1px var(--color-border-default); bott=
+om: calc(-8px - var(--Layout-row-gap)); }
+}
+
+.PageLayout-wrapper { display: grid; grid: auto-flow auto / 1fr; row-gap: v=
+ar(--Layout-row-gap); }
+
+.PageLayout-columns { display: grid; column-gap: var(--Layout-column-gap); =
+row-gap: var(--Layout-row-gap); grid-template-columns: var(--Layout-templat=
+e-columns); grid-template-rows: 1fr; grid-template-areas: var(--Layout-temp=
+late-areas); }
+
+.PageLayout-columns .PageLayout-content { padding-right: var(--Layout-inner=
+-spacing-max); padding-left: var(--Layout-inner-spacing-max); grid-area: co=
+ntent / content / content / content; }
+
+.PageLayout-columns .PageLayout-pane { grid-area: pane / pane / pane / pane=
+; }
+
+.PageLayout--outerSpacing-normal { --Layout-outer-spacing-x: 16px; --Layout=
+-outer-spacing-y: 16px; }
+
+@media (min-width: 1012px) {
+  .PageLayout--outerSpacing-normal { --Layout-outer-spacing-x: 24px; --Layo=
+ut-outer-spacing-y: 24px; }
+}
+
+.PageLayout--outerSpacing-condensed { --Layout-outer-spacing-x: 16px; --Lay=
+out-outer-spacing-y: 16px; }
+
+.PageLayout--innerSpacing-normal { --Layout-inner-spacing-min: 16px; --Layo=
+ut-inner-spacing-max: 16px; }
+
+@media (min-width: 1012px) {
+  .PageLayout--innerSpacing-normal { --Layout-inner-spacing-max: 24px; }
+}
+
+.PageLayout--innerSpacing-condensed { --Layout-inner-spacing-min: 16px; --L=
+ayout-inner-spacing-max: 16px; }
+
+.PageLayout--columnGap-normal { --Layout-column-gap: 16px; }
+
+@media (min-width: 1012px) {
+  .PageLayout--columnGap-normal { --Layout-column-gap: 24px; }
+}
+
+.PageLayout--columnGap-condensed { --Layout-column-gap: 16px; }
+
+.PageLayout--columnGap-none { --Layout-column-gap: 0px; }
+
+.PageLayout--rowGap-normal { --Layout-row-gap: 16px; }
+
+@media (min-width: 1012px) {
+  .PageLayout--rowGap-normal { --Layout-row-gap: 24px; }
+}
+
+.PageLayout--rowGap-none { --Layout-row-gap: 0px; }
+
+.PageLayout--rowGap-condensed { --Layout-row-gap: 16px; }
+
+.PageLayout-header, .PageLayout-content, .PageLayout-pane, .PageLayout-foot=
+er { padding: var(--Layout-inner-spacing-min); }
+
+.Stack { --Stack-gap-whenRegular: var(--primer-stack-gap-normal, 16px); --S=
+tack-gap-whenNarrow: var(--primer-stack-gap-normal, 16px); --Stack-gap-when=
+Wide: var(--Stack-gap-whenRegular); --Stack-divider-color: var(--color-bord=
+er-default); display: flex; flex-flow: column; align-items: stretch; align-=
+content: flex-start; gap: var(--Stack-gap-whenRegular); }
+
+@media (max-width: 767.98px) {
+  .Stack { gap: var(--Stack-gap-whenNarrow); }
+}
+
+@media (min-width: 1400px) {
+  .Stack { gap: var(--Stack-gap-whenWide); }
+}
+
+.Stack-divider { display: none; padding: 0px; margin: 0px; border: 0px; ali=
+gn-self: stretch; }
+
+.Stack-item { flex: 0 1 auto; min-inline-size: 0px; }
+
+@media (max-width: 767.98px) {
+  .Stack--dir-inline-whenNarrow { flex-flow: row; }
+  .Stack--dir-block-whenNarrow { flex-flow: column; }
+  .Stack--gap-none-whenNarrow { --Stack-gap-whenNarrow: 0; }
+  .Stack--gap-condensed-whenNarrow { --Stack-gap-whenNarrow: var(--primer-s=
+tack-gap-condensed, 8px); }
+  .Stack--gap-normal-whenNarrow { --Stack-gap-whenNarrow: var(--primer-stac=
+k-gap-normal, 16px); }
+  .Stack--align-start-whenNarrow { align-items: flex-start; }
+  .Stack--align-center-whenNarrow { align-items: center; }
+  .Stack--align-end-whenNarrow { align-items: flex-end; }
+  .Stack--align-baseline-whenNarrow { align-items: baseline; }
+  .Stack--alignWrap-start-whenNarrow { align-content: flex-start; }
+  .Stack--alignWrap-center-whenNarrow { align-content: center; }
+  .Stack--alignWrap-end-whenNarrow { align-content: flex-end; }
+  .Stack--alignWrap-distribute-whenNarrow { align-content: space-between; }
+  .Stack--alignWrap-distributeEvenly-whenNarrow { align-content: space-even=
+ly; }
+  .Stack--spread-start-whenNarrow { justify-content: flex-start; }
+  .Stack--spread-center-whenNarrow { justify-content: center; }
+  .Stack--spread-end-whenNarrow { justify-content: flex-end; }
+  .Stack--spread-distribute-whenNarrow { justify-content: space-between; }
+  .Stack--spread-distributeEvenly-whenNarrow { justify-content: space-evenl=
+y; }
+  .Stack--wrap-whenNarrow { flex-wrap: wrap; }
+  .Stack--nowrap-whenNarrow { flex-wrap: nowrap; }
+  .Stack--showDividers-whenNarrow > .Stack-divider, .Stack--showDividers-wh=
+enNarrow > .Stack-item > .Stack-divider { display: block; }
+  :not(.Stack--dir-inline-whenNarrow) > .Stack-divider, :not(.Stack--dir-in=
+line-whenNarrow) > .Stack-item > .Stack-divider { border-block-end: var(--p=
+rimer-borderWidth-thin, 1px) solid var(--Stack-divider-color); inline-size:=
+ auto; block-size: 0px; }
+  .Stack--dir-inline-whenNarrow > .Stack-divider, .Stack--dir-inline-whenNa=
+rrow > .Stack-item > .Stack-divider { border-inline-end: var(--primer-borde=
+rWidth-thin, 1px) solid var(--Stack-divider-color); inline-size: 0px; block=
+-size: auto; }
+  .Stack-item--expand-whenNarrow { flex-grow: 1; }
+  .Stack-item--keepSize-whenNarrow { flex-shrink: 0; }
+}
+
+@media (min-width: 768px) {
+  .Stack--dir-inline-whenRegular { flex-flow: row; }
+  .Stack--dir-block-whenRegular { flex-flow: column; }
+  .Stack--gap-none-whenRegular { --Stack-gap-whenRegular: 0; }
+  .Stack--gap-condensed-whenRegular { --Stack-gap-whenRegular: var(--primer=
+-stack-gap-condensed, 8px); }
+  .Stack--gap-normal-whenRegular { --Stack-gap-whenRegular: var(--primer-st=
+ack-gap-normal, 16px); }
+  .Stack--gap-spacious-whenRegular { --Stack-gap-whenRegular: var(--primer-=
+stack-gap-spacious, 24px); }
+  .Stack--align-start-whenRegular { align-items: flex-start; }
+  .Stack--align-center-whenRegular { align-items: center; }
+  .Stack--align-end-whenRegular { align-items: flex-end; }
+  .Stack--align-baseline-whenRegular { align-items: baseline; }
+  .Stack--alignWrap-start-whenRegular { align-content: flex-start; }
+  .Stack--alignWrap-center-whenRegular { align-content: center; }
+  .Stack--alignWrap-end-whenRegular { align-content: flex-end; }
+  .Stack--alignWrap-distribute-whenRegular { align-content: space-between; =
+}
+  .Stack--alignWrap-distributeEvenly-whenRegular { align-content: space-eve=
+nly; }
+  .Stack--spread-start-whenRegular { justify-content: flex-start; }
+  .Stack--spread-center-whenRegular { justify-content: center; }
+  .Stack--spread-end-whenRegular { justify-content: flex-end; }
+  .Stack--spread-distribute-whenRegular { justify-content: space-between; }
+  .Stack--spread-distributeEvenly-whenRegular { justify-content: space-even=
+ly; }
+  .Stack--wrap-whenRegular { flex-wrap: wrap; }
+  .Stack--nowrap-whenRegular { flex-wrap: nowrap; }
+  .Stack--showDividers-whenRegular > .Stack-divider, .Stack--showDividers-w=
+henRegular > .Stack-item > .Stack-divider { display: block; }
+  :not(.Stack--dir-inline-whenRegular) > .Stack-divider, :not(.Stack--dir-i=
+nline-whenRegular) > .Stack-item > .Stack-divider { border-block-end: var(-=
+-primer-borderWidth-thin, 1px) solid var(--Stack-divider-color); inline-siz=
+e: auto; block-size: 0px; }
+  .Stack--dir-inline-whenRegular > .Stack-divider, .Stack--dir-inline-whenR=
+egular > .Stack-item > .Stack-divider { border-inline-end: var(--primer-bor=
+derWidth-thin, 1px) solid var(--Stack-divider-color); inline-size: 0px; blo=
+ck-size: auto; }
+  .Stack-item--expand-whenRegular { flex-grow: 1; }
+  .Stack-item--keepSize-whenRegular { flex-shrink: 0; }
+}
+
+@media (min-width: 1400px) {
+  .Stack--dir-inline-whenWide { flex-flow: row; }
+  .Stack--dir-block-whenWide { flex-flow: column; }
+  .Stack--gap-none-whenWide { --Stack-gap-whenWide: 0; }
+  .Stack--gap-condensed-whenWide { --Stack-gap-whenWide: var(--primer-stack=
+-gap-condensed, 8px); }
+  .Stack--gap-normal-whenWide { --Stack-gap-whenWide: var(--primer-stack-ga=
+p-normal, 16px); }
+  .Stack--gap-spacious-whenWide { --Stack-gap-whenWide: var(--primer-stack-=
+gap-spacious, 24px); }
+  .Stack--align-start-whenWide { align-items: flex-start; }
+  .Stack--align-center-whenWide { align-items: center; }
+  .Stack--align-end-whenWide { align-items: flex-end; }
+  .Stack--align-baseline-whenWide { align-items: baseline; }
+  .Stack--alignWrap-start-whenWide { align-content: flex-start; }
+  .Stack--alignWrap-center-whenWide { align-content: center; }
+  .Stack--alignWrap-end-whenWide { align-content: flex-end; }
+  .Stack--alignWrap-distribute-whenWide { align-content: space-between; }
+  .Stack--alignWrap-distributeEvenly-whenWide { align-content: space-evenly=
+; }
+  .Stack--spread-start-whenWide { justify-content: flex-start; }
+  .Stack--spread-center-whenWide { justify-content: center; }
+  .Stack--spread-end-whenWide { justify-content: flex-end; }
+  .Stack--spread-distribute-whenWide { justify-content: space-between; }
+  .Stack--spread-distributeEvenly-whenWide { justify-content: space-evenly;=
+ }
+  .Stack--wrap-whenWide { flex-wrap: wrap; }
+  .Stack--nowrap-whenWide { flex-wrap: nowrap; }
+  .Stack--showDividers-whenWide > .Stack-divider, .Stack--showDividers-when=
+Wide > .Stack-item > .Stack-divider { display: block; }
+  :not(.Stack--dir-inline-whenWide) > .Stack-divider, :not(.Stack--dir-inli=
+ne-whenWide) > .Stack-item > .Stack-divider { border-block-end: var(--prime=
+r-borderWidth-thin, 1px) solid var(--Stack-divider-color); inline-size: aut=
+o; block-size: 0px; }
+  .Stack--dir-inline-whenWide > .Stack-divider, .Stack--dir-inline-whenWide=
+ > .Stack-item > .Stack-divider { border-inline-end: var(--primer-borderWid=
+th-thin, 1px) solid var(--Stack-divider-color); inline-size: 0px; block-siz=
+e: auto; }
+  .Stack-item--expand-whenWide { flex-grow: 1; }
+  .Stack-item--keepSize-whenWide { flex-shrink: 0; }
+}
+
+.filter-list { list-style-type: none; }
+
+.filter-list.small .filter-item { padding: 6px 12px; font-size: 12px; }
+
+.filter-list.pjax-active .filter-item { color: var(--color-fg-muted); backg=
+round-color: transparent; }
+
+.filter-list.pjax-active .filter-item.pjax-active { color: var(--color-fg-o=
+n-emphasis); background-color: var(--color-accent-emphasis); }
+
+.filter-item { position: relative; display: block; padding: 8px 16px; margi=
+n-bottom: 4px; overflow: hidden; font-size: 14px; color: var(--color-fg-mut=
+ed); text-decoration: none; text-overflow: ellipsis; white-space: nowrap; c=
+ursor: pointer; border-radius: 6px; }
+
+.filter-item:hover { text-decoration: none; background-color: var(--color-c=
+anvas-subtle); }
+
+.filter-item.selected, .filter-item[aria-selected=3D"true"], .filter-item[a=
+ria-current]:not([aria-current=3D"false"]) { color: var(--color-fg-on-empha=
+sis); background-color: var(--color-accent-emphasis); }
+
+.filter-item.selected:focus, .filter-item[aria-selected=3D"true"]:focus, .f=
+ilter-item[aria-current]:not([aria-current=3D"false"]):focus { outline: 2px=
+ solid var(--color-accent-fg); outline-offset: -2px; box-shadow: inset 0 0 =
+0 3px var(--color-fg-on-emphasis); }
+
+.filter-item.selected:focus:not(:focus-visible), .filter-item[aria-selected=
+=3D"true"]:focus:not(:focus-visible), .filter-item[aria-current]:not([aria-=
+current=3D"false"]):focus:not(:focus-visible) { outline: transparent solid =
+1px; box-shadow: none; }
+
+.filter-item.selected:focus-visible, .filter-item[aria-selected=3D"true"]:f=
+ocus-visible, .filter-item[aria-current]:not([aria-current=3D"false"]):focu=
+s-visible { outline: 2px solid var(--color-accent-fg); outline-offset: -2px=
+; box-shadow: inset 0 0 0 3px var(--color-fg-on-emphasis); }
+
+.filter-item .count { float: right; font-weight: var(--base-text-weight-sem=
+ibold, 600); }
+
+.filter-item .bar { position: absolute; top: 2px; right: 0px; bottom: 2px; =
+z-index: -1; display: inline-block; background-color: var(--color-neutral-s=
+ubtle); }
+
+.SideNav { background-color: var(--color-canvas-subtle); }
+
+.SideNav-item { position: relative; display: block; width: 100%; padding: 1=
+2px 16px; color: var(--color-fg-default); text-align: left; background-colo=
+r: transparent; border-right: 0px; border-bottom: 0px; border-left: 0px; bo=
+rder-image: initial; border-top: 1px solid var(--color-border-muted); }
+
+.SideNav-item:first-child { border-top: 0px; }
+
+.SideNav-item:last-child { box-shadow: 0 1px 0 var(--color-border-default);=
+ }
+
+.SideNav-item::before { position: absolute; top: 0px; bottom: 0px; left: 0p=
+x; z-index: 1; width: 2px; pointer-events: none; content: ""; }
+
+.SideNav-item:hover { text-decoration: none; background-color: var(--color-=
+neutral-subtle); }
+
+.SideNav-item:active { background-color: var(--color-canvas-subtle); }
+
+.SideNav-item[aria-current]:not([aria-current=3D"false"]), .SideNav-item[ar=
+ia-selected=3D"true"] { background-color: var(--color-sidenav-selected-bg);=
+ }
+
+.SideNav-item[aria-current]:not([aria-current=3D"false"])::before, .SideNav=
+-item[aria-selected=3D"true"]::before { background-color: var(--color-prime=
+r-border-active); }
+
+.SideNav-icon { width: 16px; margin-right: 8px; color: var(--color-fg-muted=
+); }
+
+.SideNav-subItem { position: relative; display: block; width: 100%; padding=
+: 4px 0px; color: var(--color-accent-fg); text-align: left; background-colo=
+r: transparent; border: 0px; }
+
+.SideNav-subItem:hover { color: var(--color-fg-default); text-decoration: n=
+one; }
+
+.SideNav-subItem[aria-current]:not([aria-current=3D"false"]), .SideNav-subI=
+tem[aria-selected=3D"true"] { font-weight: var(--base-text-weight-medium, 5=
+00); color: var(--color-fg-default); }
+
+.subnav { margin-bottom: 20px; }
+
+.subnav::before { display: table; content: ""; }
+
+.subnav::after { display: table; clear: both; content: ""; }
+
+.subnav-bordered { padding-bottom: 20px; border-bottom: 1px solid var(--col=
+or-border-muted); }
+
+.subnav-flush { margin-bottom: 0px; }
+
+.subnav-item { position: relative; float: left; padding: 5px 16px; font-wei=
+ght: var(--base-text-weight-medium, 500); line-height: 20px; color: var(--c=
+olor-fg-default); border: 1px solid var(--color-border-default); }
+
+.subnav-item + .subnav-item { margin-left: -1px; }
+
+.subnav-item:hover, .subnav-item:focus { text-decoration: none; background-=
+color: var(--color-canvas-subtle); }
+
+.subnav-item.selected, .subnav-item[aria-selected=3D"true"], .subnav-item[a=
+ria-current]:not([aria-current=3D"false"]) { z-index: 2; color: var(--color=
+-fg-on-emphasis); background-color: var(--color-accent-emphasis); border-co=
+lor: var(--color-accent-emphasis); }
+
+.subnav-item.selected:focus, .subnav-item[aria-selected=3D"true"]:focus, .s=
+ubnav-item[aria-current]:not([aria-current=3D"false"]):focus { outline: 2px=
+ solid var(--color-accent-fg); outline-offset: -2px; box-shadow: inset 0 0 =
+0 3px var(--color-fg-on-emphasis); }
+
+.subnav-item.selected:focus:not(:focus-visible), .subnav-item[aria-selected=
+=3D"true"]:focus:not(:focus-visible), .subnav-item[aria-current]:not([aria-=
+current=3D"false"]):focus:not(:focus-visible) { outline: transparent solid =
+1px; box-shadow: none; }
+
+.subnav-item.selected:focus-visible, .subnav-item[aria-selected=3D"true"]:f=
+ocus-visible, .subnav-item[aria-current]:not([aria-current=3D"false"]):focu=
+s-visible { outline: 2px solid var(--color-accent-fg); outline-offset: -2px=
+; box-shadow: inset 0 0 0 3px var(--color-fg-on-emphasis); }
+
+.subnav-item:first-child { border-top-left-radius: 6px; border-bottom-left-=
+radius: 6px; }
+
+.subnav-item:last-child { border-top-right-radius: 6px; border-bottom-right=
+-radius: 6px; }
+
+.subnav-search { position: relative; margin-left: 12px; }
+
+.subnav-search-input { width: 320px; padding-left: 32px; color: var(--color=
+-fg-muted); }
+
+.subnav-search-input-wide { width: 500px; }
+
+.subnav-search-icon { position: absolute; top: 9px; left: 8px; display: blo=
+ck; color: var(--color-fg-muted); text-align: center; pointer-events: none;=
+ }
+
+.subnav-search-context .btn { border-top-right-radius: 0px; border-bottom-r=
+ight-radius: 0px; }
+
+.subnav-search-context .btn:hover, .subnav-search-context .btn:focus, .subn=
+av-search-context .btn:active, .subnav-search-context .btn.selected { z-ind=
+ex: 2; }
+
+.subnav-search-context + .subnav-search { margin-left: -1px; }
+
+.subnav-search-context + .subnav-search .subnav-search-input { border-top-l=
+eft-radius: 0px; border-bottom-left-radius: 0px; }
+
+.subnav-search-context .select-menu-modal-holder { z-index: 30; }
+
+.subnav-search-context .select-menu-modal { width: 220px; }
+
+.subnav-search-context .select-menu-item-icon { color: inherit; }
+
+.subnav-spacer-right { padding-right: 12px; }
+
+.pagination a, .pagination span, .pagination em { min-width: 32px; padding:=
+ 5px 10px; font-style: normal; line-height: 20px; color: var(--color-fg-def=
+ault); text-align: center; white-space: nowrap; vertical-align: middle; cur=
+sor: pointer; user-select: none; border: 1px solid transparent; border-radi=
+us: 6px; transition: border-color 0.2s cubic-bezier(0.3, 0, 0.5, 1) 0s; }
+
+.pagination a:hover, .pagination a:focus, .pagination span:hover, .paginati=
+on span:focus, .pagination em:hover, .pagination em:focus { text-decoration=
+: none; border-color: var(--color-border-default); transition-duration: 0.1=
+s; }
+
+.pagination a:active, .pagination span:active, .pagination em:active { bord=
+er-color: var(--color-border-muted); transition: none 0s ease 0s; }
+
+.pagination .previous_page, .pagination .next_page { color: var(--color-acc=
+ent-fg); }
+
+.pagination .current, .pagination .current:hover, .pagination [aria-current=
+]:not([aria-current=3D"false"]) { color: var(--color-fg-on-emphasis); backg=
+round-color: var(--color-accent-emphasis); border-color: transparent; }
+
+.pagination .gap, .pagination .disabled, .pagination [aria-disabled=3D"true=
+"], .pagination .gap:hover, .pagination .disabled:hover, .pagination [aria-=
+disabled=3D"true"]:hover { color: var(--color-primer-fg-disabled); cursor: =
+default; border-color: transparent; }
+
+@supports (clip-path: polygon(50% 0, 100% 50%, 50% 100%)) {
+  .pagination .previous_page::before, .pagination .next_page::after { displ=
+ay: inline-block; width: 16px; height: 16px; vertical-align: text-bottom; c=
+ontent: ""; background-color: currentcolor; }
+  .pagination .previous_page::before { margin-right: 4px; clip-path: polygo=
+n(9.8px 12.8px, 8.7px 12.8px, 4.5px 8.5px, 4.5px 7.5px, 8.7px 3.2px, 9.8px =
+4.3px, 6.1px 8px, 9.8px 11.7px, 9.8px 12.8px); }
+  .pagination .next_page::after { margin-left: 4px; clip-path: polygon(6.2p=
+x 3.2px, 7.3px 3.2px, 11.5px 7.5px, 11.5px 8.5px, 7.3px 12.8px, 6.2px 11.7p=
+x, 9.9px 8px, 6.2px 4.3px, 6.2px 3.2px); }
+}
+
+.pagination > * { display: none; }
+
+.pagination > :first-child, .pagination > :last-child, .pagination > .previ=
+ous_page, .pagination > .next_page { display: inline-block; }
+
+@media (min-width: 544px) {
+  .pagination > :nth-child(2), .pagination > :nth-last-child(2), .paginatio=
+n > .current, .pagination > .gap { display: inline-block; }
+}
+
+@media (min-width: 768px) {
+  .pagination > * { display: inline-block; }
+}
+
+.paginate-container { margin-top: 16px; margin-bottom: 16px; text-align: ce=
+nter; }
+
+.paginate-container .pagination { display: inline-block; }
+
+.tooltipped { position: relative; }
+
+.tooltipped::after { position: absolute; z-index: 1000000; display: none; p=
+adding: 0.5em 0.75em; font: 11px / 1.5 -apple-system, BlinkMacSystemFont, "=
+Segoe UI", "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", =
+"Segoe UI Emoji"; -webkit-font-smoothing: subpixel-antialiased; color: var(=
+--color-fg-on-emphasis); text-align: center; text-decoration: none; text-sh=
+adow: none; text-transform: none; letter-spacing: normal; overflow-wrap: br=
+eak-word; white-space: pre; pointer-events: none; content: attr(aria-label)=
+; background: var(--color-neutral-emphasis-plus); border-radius: 6px; opaci=
+ty: 0; }
+
+.tooltipped::before { position: absolute; z-index: 1000001; display: none; =
+width: 0px; height: 0px; color: var(--color-neutral-emphasis-plus); pointer=
+-events: none; content: ""; border: 6px solid transparent; opacity: 0; }
+
+@keyframes tooltip-appear {=20
+  0% { opacity: 0; }
+  100% { opacity: 1; }
+}
+
+.tooltipped:hover::before, .tooltipped:hover::after, .tooltipped:active::be=
+fore, .tooltipped:active::after, .tooltipped:focus::before, .tooltipped:foc=
+us::after { display: inline-block; text-decoration: none; animation-name: t=
+ooltip-appear; animation-duration: 0.1s; animation-fill-mode: forwards; ani=
+mation-timing-function: ease-in; animation-delay: 0.4s; }
+
+.tooltipped-no-delay:hover::before, .tooltipped-no-delay:hover::after, .too=
+ltipped-no-delay:active::before, .tooltipped-no-delay:active::after, .toolt=
+ipped-no-delay:focus::before, .tooltipped-no-delay:focus::after { animation=
+-delay: 0s; }
+
+.tooltipped-multiline:hover::after, .tooltipped-multiline:active::after, .t=
+ooltipped-multiline:focus::after { display: table-cell; }
+
+.tooltipped-s::after, .tooltipped-se::after, .tooltipped-sw::after { top: 1=
+00%; right: 50%; margin-top: 6px; }
+
+.tooltipped-s::before, .tooltipped-se::before, .tooltipped-sw::before { top=
+: auto; right: 50%; bottom: -7px; margin-right: -6px; border-bottom-color: =
+var(--color-neutral-emphasis-plus); }
+
+.tooltipped-se::after { right: auto; left: 50%; margin-left: -16px; }
+
+.tooltipped-sw::after { margin-right: -16px; }
+
+.tooltipped-n::after, .tooltipped-ne::after, .tooltipped-nw::after { right:=
+ 50%; bottom: 100%; margin-bottom: 6px; }
+
+.tooltipped-n::before, .tooltipped-ne::before, .tooltipped-nw::before { top=
+: -7px; right: 50%; bottom: auto; margin-right: -6px; border-top-color: var=
+(--color-neutral-emphasis-plus); }
+
+.tooltipped-ne::after { right: auto; left: 50%; margin-left: -16px; }
+
+.tooltipped-nw::after { margin-right: -16px; }
+
+.tooltipped-s::after, .tooltipped-n::after { transform: translateX(50%); }
+
+.tooltipped-w::after { right: 100%; bottom: 50%; margin-right: 6px; transfo=
+rm: translateY(50%); }
+
+.tooltipped-w::before { top: 50%; bottom: 50%; left: -7px; margin-top: -6px=
+; border-left-color: var(--color-neutral-emphasis-plus); }
+
+.tooltipped-e::after { bottom: 50%; left: 100%; margin-left: 6px; transform=
+: translateY(50%); }
+
+.tooltipped-e::before { top: 50%; right: -7px; bottom: 50%; margin-top: -6p=
+x; border-right-color: var(--color-neutral-emphasis-plus); }
+
+.tooltipped-align-right-1::after, .tooltipped-align-right-2::after { right:=
+ 0px; margin-right: 0px; }
+
+.tooltipped-align-right-1::before { right: 10px; }
+
+.tooltipped-align-right-2::before { right: 15px; }
+
+.tooltipped-align-left-1::after, .tooltipped-align-left-2::after { left: 0p=
+x; margin-left: 0px; }
+
+.tooltipped-align-left-1::before { left: 5px; }
+
+.tooltipped-align-left-2::before { left: 10px; }
+
+.tooltipped-multiline::after { width: max-content; max-width: 250px; overfl=
+ow-wrap: break-word; white-space: pre-line; border-collapse: separate; }
+
+.tooltipped-multiline.tooltipped-s::after, .tooltipped-multiline.tooltipped=
+-n::after { right: auto; left: 50%; transform: translateX(-50%); }
+
+.tooltipped-multiline.tooltipped-w::after, .tooltipped-multiline.tooltipped=
+-e::after { right: 100%; }
+
+.tooltipped-sticky::before, .tooltipped-sticky::after { display: inline-blo=
+ck; }
+
+.tooltipped-sticky.tooltipped-multiline::after { display: table-cell; }
+
+.anim-fade-in { animation-name: fade-in; animation-duration: 1s; animation-=
+timing-function: ease-in-out; }
+
+.anim-fade-in.fast { animation-duration: 300ms; }
+
+@keyframes fade-in {=20
+  0% { opacity: 0; }
+  100% { opacity: 1; }
+}
+
+.anim-fade-out { animation-name: fade-out; animation-duration: 1s; animatio=
+n-fill-mode: forwards; animation-timing-function: ease-out; }
+
+.anim-fade-out.fast { animation-duration: 0.3s; }
+
+@keyframes fade-out {=20
+  0% { opacity: 1; }
+  100% { opacity: 0; }
+}
+
+.anim-fade-up { opacity: 0; animation-name: fade-up; animation-duration: 0.=
+3s; animation-fill-mode: forwards; animation-timing-function: ease-out; ani=
+mation-delay: 1s; }
+
+@keyframes fade-up {=20
+  0% { opacity: 0.8; transform: translateY(100%); }
+  100% { opacity: 1; transform: translateY(0px); }
+}
+
+.anim-fade-down { animation-name: fade-down; animation-duration: 0.3s; anim=
+ation-fill-mode: forwards; animation-timing-function: ease-in; }
+
+@keyframes fade-down {=20
+  0% { opacity: 1; transform: translateY(0px); }
+  100% { opacity: 0.5; transform: translateY(100%); }
+}
+
+.anim-grow-x { width: 0%; animation-name: grow-x; animation-duration: 0.3s;=
+ animation-fill-mode: forwards; animation-timing-function: ease; animation-=
+delay: 0.5s; }
+
+@keyframes grow-x {=20
+  100% { width: 100%; }
+}
+
+.anim-shrink-x { animation-name: shrink-x; animation-duration: 0.3s; animat=
+ion-fill-mode: forwards; animation-timing-function: ease-in-out; animation-=
+delay: 0.5s; }
+
+@keyframes shrink-x {=20
+  100% { width: 0%; }
+}
+
+.anim-scale-in { animation-name: scale-in; animation-duration: 0.15s; anima=
+tion-timing-function: cubic-bezier(0.2, 0, 0.13, 1.5); }
+
+@keyframes scale-in {=20
+  0% { opacity: 0; transform: scale(0.5); }
+  100% { opacity: 1; transform: scale(1); }
+}
+
+.anim-pulse { animation-name: pulse; animation-duration: 2s; animation-timi=
+ng-function: linear; animation-iteration-count: infinite; }
+
+@keyframes pulse {=20
+  0% { opacity: 0.3; }
+  10% { opacity: 1; }
+  100% { opacity: 0.3; }
+}
+
+.anim-pulse-in { animation-name: pulse-in; animation-duration: 0.5s; }
+
+@keyframes pulse-in {=20
+  0% { transform: scale3d(1, 1, 1); }
+  50% { transform: scale3d(1.1, 1.1, 1.1); }
+  100% { transform: scale3d(1, 1, 1); }
+}
+
+.hover-grow, .anim-hover-grow { transition: transform 0.3s ease 0s; backfac=
+e-visibility: hidden; }
+
+.hover-grow:hover, .anim-hover-grow:hover { transform: scale(1.025); }
+
+.anim-rotate { animation: 1s linear 0s infinite normal none running rotate-=
+keyframes; }
+
+@keyframes rotate-keyframes {=20
+  100% { transform: rotate(360deg); }
+}
+
+.border-x { border-right: var(--primer-borderWidth-thin, 1px) solid var(--c=
+olor-border-default)  !important; border-left: var(--primer-borderWidth-thi=
+n, 1px) solid var(--color-border-default)  !important; }
+
+.border-y { border-top: var(--primer-borderWidth-thin, 1px) solid var(--col=
+or-border-default)  !important; border-bottom: var(--primer-borderWidth-thi=
+n, 1px) solid var(--color-border-default)  !important; }
+
+.border { border: var(--primer-borderWidth-thin, 1px) solid var(--color-bor=
+der-default)  !important; }
+
+.border-0 { border: 0px !important; }
+
+.border-top { border-top: var(--primer-borderWidth-thin, 1px) solid var(--c=
+olor-border-default)  !important; }
+
+.border-right { border-right: var(--primer-borderWidth-thin, 1px) solid var=
+(--color-border-default)  !important; }
+
+.border-bottom { border-bottom: var(--primer-borderWidth-thin, 1px) solid v=
+ar(--color-border-default)  !important; }
+
+.border-left { border-left: var(--primer-borderWidth-thin, 1px) solid var(-=
+-color-border-default)  !important; }
+
+.border-top-0 { border-top: 0px !important; }
+
+.border-right-0 { border-right: 0px !important; }
+
+.border-bottom-0 { border-bottom: 0px !important; }
+
+.border-left-0 { border-left: 0px !important; }
+
+.rounded { border-radius: var(--primer-borderRadius-medium, 6px)  !importan=
+t; }
+
+.rounded-0 { border-radius: 0px !important; }
+
+.rounded-1 { border-radius: var(--primer-borderRadius-small, 4px)  !importa=
+nt; }
+
+.rounded-2 { border-radius: var(--primer-borderRadius-medium, 6px)  !import=
+ant; }
+
+.rounded-3 { border-radius: var(--primer-borderRadius-large, 8px)  !importa=
+nt; }
+
+.rounded-top-0 { border-top-left-radius: 0px !important; border-top-right-r=
+adius: 0px !important; }
+
+.rounded-top-1 { border-top-left-radius: var(--primer-borderRadius-small, 4=
+px)  !important; border-top-right-radius: var(--primer-borderRadius-small, =
+4px)  !important; }
+
+.rounded-top-2 { border-top-left-radius: var(--primer-borderRadius-medium, =
+6px)  !important; border-top-right-radius: var(--primer-borderRadius-medium=
+, 6px)  !important; }
+
+.rounded-top-3 { border-top-left-radius: var(--primer-borderRadius-medium, =
+8px)  !important; border-top-right-radius: var(--primer-borderRadius-medium=
+, 8px)  !important; }
+
+.rounded-right-0 { border-top-right-radius: 0px !important; border-bottom-r=
+ight-radius: 0px !important; }
+
+.rounded-right-1 { border-top-right-radius: var(--primer-borderRadius-small=
+, 4px)  !important; border-bottom-right-radius: var(--primer-borderRadius-s=
+mall, 4px)  !important; }
+
+.rounded-right-2 { border-top-right-radius: var(--primer-borderRadius-mediu=
+m, 6px)  !important; border-bottom-right-radius: var(--primer-borderRadius-=
+medium, 6px)  !important; }
+
+.rounded-right-3 { border-top-right-radius: var(--primer-borderRadius-mediu=
+m, 8px)  !important; border-bottom-right-radius: var(--primer-borderRadius-=
+medium, 8px)  !important; }
+
+.rounded-bottom-0 { border-bottom-right-radius: 0px !important; border-bott=
+om-left-radius: 0px !important; }
+
+.rounded-bottom-1 { border-bottom-right-radius: var(--primer-borderRadius-s=
+mall, 4px)  !important; border-bottom-left-radius: var(--primer-borderRadiu=
+s-small, 4px)  !important; }
+
+.rounded-bottom-2 { border-bottom-right-radius: var(--primer-borderRadius-m=
+edium, 6px)  !important; border-bottom-left-radius: var(--primer-borderRadi=
+us-medium, 6px)  !important; }
+
+.rounded-bottom-3 { border-bottom-right-radius: var(--primer-borderRadius-m=
+edium, 8px)  !important; border-bottom-left-radius: var(--primer-borderRadi=
+us-medium, 8px)  !important; }
+
+.rounded-left-0 { border-bottom-left-radius: 0px !important; border-top-lef=
+t-radius: 0px !important; }
+
+.rounded-left-1 { border-bottom-left-radius: var(--primer-borderRadius-smal=
+l, 4px)  !important; border-top-left-radius: var(--primer-borderRadius-smal=
+l, 4px)  !important; }
+
+.rounded-left-2 { border-bottom-left-radius: var(--primer-borderRadius-medi=
+um, 6px)  !important; border-top-left-radius: var(--primer-borderRadius-med=
+ium, 6px)  !important; }
+
+.rounded-left-3 { border-bottom-left-radius: var(--primer-borderRadius-medi=
+um, 8px)  !important; border-top-left-radius: var(--primer-borderRadius-med=
+ium, 8px)  !important; }
+
+@media (min-width: 544px) {
+  .border-sm { border: var(--primer-borderWidth-thin, 1px) solid var(--colo=
+r-border-default)  !important; }
+  .border-sm-0 { border: 0px !important; }
+  .border-sm-top { border-top: var(--primer-borderWidth-thin, 1px) solid va=
+r(--color-border-default)  !important; }
+  .border-sm-right { border-right: var(--primer-borderWidth-thin, 1px) soli=
+d var(--color-border-default)  !important; }
+  .border-sm-bottom { border-bottom: var(--primer-borderWidth-thin, 1px) so=
+lid var(--color-border-default)  !important; }
+  .border-sm-left { border-left: var(--primer-borderWidth-thin, 1px) solid =
+var(--color-border-default)  !important; }
+  .border-sm-top-0 { border-top: 0px !important; }
+  .border-sm-right-0 { border-right: 0px !important; }
+  .border-sm-bottom-0 { border-bottom: 0px !important; }
+  .border-sm-left-0 { border-left: 0px !important; }
+  .rounded-sm { border-radius: var(--primer-borderRadius-medium, 6px)  !imp=
+ortant; }
+  .rounded-sm-0 { border-radius: 0px !important; }
+  .rounded-sm-1 { border-radius: var(--primer-borderRadius-small, 4px)  !im=
+portant; }
+  .rounded-sm-2 { border-radius: var(--primer-borderRadius-medium, 6px)  !i=
+mportant; }
+  .rounded-sm-3 { border-radius: var(--primer-borderRadius-large, 8px)  !im=
+portant; }
+  .rounded-sm-top-0 { border-top-left-radius: 0px !important; border-top-ri=
+ght-radius: 0px !important; }
+  .rounded-sm-top-1 { border-top-left-radius: var(--primer-borderRadius-sma=
+ll, 4px)  !important; border-top-right-radius: var(--primer-borderRadius-sm=
+all, 4px)  !important; }
+  .rounded-sm-top-2 { border-top-left-radius: var(--primer-borderRadius-med=
+ium, 6px)  !important; border-top-right-radius: var(--primer-borderRadius-m=
+edium, 6px)  !important; }
+  .rounded-sm-top-3 { border-top-left-radius: var(--primer-borderRadius-med=
+ium, 8px)  !important; border-top-right-radius: var(--primer-borderRadius-m=
+edium, 8px)  !important; }
+  .rounded-sm-right-0 { border-top-right-radius: 0px !important; border-bot=
+tom-right-radius: 0px !important; }
+  .rounded-sm-right-1 { border-top-right-radius: var(--primer-borderRadius-=
+small, 4px)  !important; border-bottom-right-radius: var(--primer-borderRad=
+ius-small, 4px)  !important; }
+  .rounded-sm-right-2 { border-top-right-radius: var(--primer-borderRadius-=
+medium, 6px)  !important; border-bottom-right-radius: var(--primer-borderRa=
+dius-medium, 6px)  !important; }
+  .rounded-sm-right-3 { border-top-right-radius: var(--primer-borderRadius-=
+medium, 8px)  !important; border-bottom-right-radius: var(--primer-borderRa=
+dius-medium, 8px)  !important; }
+  .rounded-sm-bottom-0 { border-bottom-right-radius: 0px !important; border=
+-bottom-left-radius: 0px !important; }
+  .rounded-sm-bottom-1 { border-bottom-right-radius: var(--primer-borderRad=
+ius-small, 4px)  !important; border-bottom-left-radius: var(--primer-border=
+Radius-small, 4px)  !important; }
+  .rounded-sm-bottom-2 { border-bottom-right-radius: var(--primer-borderRad=
+ius-medium, 6px)  !important; border-bottom-left-radius: var(--primer-borde=
+rRadius-medium, 6px)  !important; }
+  .rounded-sm-bottom-3 { border-bottom-right-radius: var(--primer-borderRad=
+ius-medium, 8px)  !important; border-bottom-left-radius: var(--primer-borde=
+rRadius-medium, 8px)  !important; }
+  .rounded-sm-left-0 { border-bottom-left-radius: 0px !important; border-to=
+p-left-radius: 0px !important; }
+  .rounded-sm-left-1 { border-bottom-left-radius: var(--primer-borderRadius=
+-small, 4px)  !important; border-top-left-radius: var(--primer-borderRadius=
+-small, 4px)  !important; }
+  .rounded-sm-left-2 { border-bottom-left-radius: var(--primer-borderRadius=
+-medium, 6px)  !important; border-top-left-radius: var(--primer-borderRadiu=
+s-medium, 6px)  !important; }
+  .rounded-sm-left-3 { border-bottom-left-radius: var(--primer-borderRadius=
+-medium, 8px)  !important; border-top-left-radius: var(--primer-borderRadiu=
+s-medium, 8px)  !important; }
+}
+
+@media (min-width: 768px) {
+  .border-md { border: var(--primer-borderWidth-thin, 1px) solid var(--colo=
+r-border-default)  !important; }
+  .border-md-0 { border: 0px !important; }
+  .border-md-top { border-top: var(--primer-borderWidth-thin, 1px) solid va=
+r(--color-border-default)  !important; }
+  .border-md-right { border-right: var(--primer-borderWidth-thin, 1px) soli=
+d var(--color-border-default)  !important; }
+  .border-md-bottom { border-bottom: var(--primer-borderWidth-thin, 1px) so=
+lid var(--color-border-default)  !important; }
+  .border-md-left { border-left: var(--primer-borderWidth-thin, 1px) solid =
+var(--color-border-default)  !important; }
+  .border-md-top-0 { border-top: 0px !important; }
+  .border-md-right-0 { border-right: 0px !important; }
+  .border-md-bottom-0 { border-bottom: 0px !important; }
+  .border-md-left-0 { border-left: 0px !important; }
+  .rounded-md { border-radius: var(--primer-borderRadius-medium, 6px)  !imp=
+ortant; }
+  .rounded-md-0 { border-radius: 0px !important; }
+  .rounded-md-1 { border-radius: var(--primer-borderRadius-small, 4px)  !im=
+portant; }
+  .rounded-md-2 { border-radius: var(--primer-borderRadius-medium, 6px)  !i=
+mportant; }
+  .rounded-md-3 { border-radius: var(--primer-borderRadius-large, 8px)  !im=
+portant; }
+  .rounded-md-top-0 { border-top-left-radius: 0px !important; border-top-ri=
+ght-radius: 0px !important; }
+  .rounded-md-top-1 { border-top-left-radius: var(--primer-borderRadius-sma=
+ll, 4px)  !important; border-top-right-radius: var(--primer-borderRadius-sm=
+all, 4px)  !important; }
+  .rounded-md-top-2 { border-top-left-radius: var(--primer-borderRadius-med=
+ium, 6px)  !important; border-top-right-radius: var(--primer-borderRadius-m=
+edium, 6px)  !important; }
+  .rounded-md-top-3 { border-top-left-radius: var(--primer-borderRadius-med=
+ium, 8px)  !important; border-top-right-radius: var(--primer-borderRadius-m=
+edium, 8px)  !important; }
+  .rounded-md-right-0 { border-top-right-radius: 0px !important; border-bot=
+tom-right-radius: 0px !important; }
+  .rounded-md-right-1 { border-top-right-radius: var(--primer-borderRadius-=
+small, 4px)  !important; border-bottom-right-radius: var(--primer-borderRad=
+ius-small, 4px)  !important; }
+  .rounded-md-right-2 { border-top-right-radius: var(--primer-borderRadius-=
+medium, 6px)  !important; border-bottom-right-radius: var(--primer-borderRa=
+dius-medium, 6px)  !important; }
+  .rounded-md-right-3 { border-top-right-radius: var(--primer-borderRadius-=
+medium, 8px)  !important; border-bottom-right-radius: var(--primer-borderRa=
+dius-medium, 8px)  !important; }
+  .rounded-md-bottom-0 { border-bottom-right-radius: 0px !important; border=
+-bottom-left-radius: 0px !important; }
+  .rounded-md-bottom-1 { border-bottom-right-radius: var(--primer-borderRad=
+ius-small, 4px)  !important; border-bottom-left-radius: var(--primer-border=
+Radius-small, 4px)  !important; }
+  .rounded-md-bottom-2 { border-bottom-right-radius: var(--primer-borderRad=
+ius-medium, 6px)  !important; border-bottom-left-radius: var(--primer-borde=
+rRadius-medium, 6px)  !important; }
+  .rounded-md-bottom-3 { border-bottom-right-radius: var(--primer-borderRad=
+ius-medium, 8px)  !important; border-bottom-left-radius: var(--primer-borde=
+rRadius-medium, 8px)  !important; }
+  .rounded-md-left-0 { border-bottom-left-radius: 0px !important; border-to=
+p-left-radius: 0px !important; }
+  .rounded-md-left-1 { border-bottom-left-radius: var(--primer-borderRadius=
+-small, 4px)  !important; border-top-left-radius: var(--primer-borderRadius=
+-small, 4px)  !important; }
+  .rounded-md-left-2 { border-bottom-left-radius: var(--primer-borderRadius=
+-medium, 6px)  !important; border-top-left-radius: var(--primer-borderRadiu=
+s-medium, 6px)  !important; }
+  .rounded-md-left-3 { border-bottom-left-radius: var(--primer-borderRadius=
+-medium, 8px)  !important; border-top-left-radius: var(--primer-borderRadiu=
+s-medium, 8px)  !important; }
+}
+
+@media (min-width: 1012px) {
+  .border-lg { border: var(--primer-borderWidth-thin, 1px) solid var(--colo=
+r-border-default)  !important; }
+  .border-lg-0 { border: 0px !important; }
+  .border-lg-top { border-top: var(--primer-borderWidth-thin, 1px) solid va=
+r(--color-border-default)  !important; }
+  .border-lg-right { border-right: var(--primer-borderWidth-thin, 1px) soli=
+d var(--color-border-default)  !important; }
+  .border-lg-bottom { border-bottom: var(--primer-borderWidth-thin, 1px) so=
+lid var(--color-border-default)  !important; }
+  .border-lg-left { border-left: var(--primer-borderWidth-thin, 1px) solid =
+var(--color-border-default)  !important; }
+  .border-lg-top-0 { border-top: 0px !important; }
+  .border-lg-right-0 { border-right: 0px !important; }
+  .border-lg-bottom-0 { border-bottom: 0px !important; }
+  .border-lg-left-0 { border-left: 0px !important; }
+  .rounded-lg { border-radius: var(--primer-borderRadius-medium, 6px)  !imp=
+ortant; }
+  .rounded-lg-0 { border-radius: 0px !important; }
+  .rounded-lg-1 { border-radius: var(--primer-borderRadius-small, 4px)  !im=
+portant; }
+  .rounded-lg-2 { border-radius: var(--primer-borderRadius-medium, 6px)  !i=
+mportant; }
+  .rounded-lg-3 { border-radius: var(--primer-borderRadius-large, 8px)  !im=
+portant; }
+  .rounded-lg-top-0 { border-top-left-radius: 0px !important; border-top-ri=
+ght-radius: 0px !important; }
+  .rounded-lg-top-1 { border-top-left-radius: var(--primer-borderRadius-sma=
+ll, 4px)  !important; border-top-right-radius: var(--primer-borderRadius-sm=
+all, 4px)  !important; }
+  .rounded-lg-top-2 { border-top-left-radius: var(--primer-borderRadius-med=
+ium, 6px)  !important; border-top-right-radius: var(--primer-borderRadius-m=
+edium, 6px)  !important; }
+  .rounded-lg-top-3 { border-top-left-radius: var(--primer-borderRadius-med=
+ium, 8px)  !important; border-top-right-radius: var(--primer-borderRadius-m=
+edium, 8px)  !important; }
+  .rounded-lg-right-0 { border-top-right-radius: 0px !important; border-bot=
+tom-right-radius: 0px !important; }
+  .rounded-lg-right-1 { border-top-right-radius: var(--primer-borderRadius-=
+small, 4px)  !important; border-bottom-right-radius: var(--primer-borderRad=
+ius-small, 4px)  !important; }
+  .rounded-lg-right-2 { border-top-right-radius: var(--primer-borderRadius-=
+medium, 6px)  !important; border-bottom-right-radius: var(--primer-borderRa=
+dius-medium, 6px)  !important; }
+  .rounded-lg-right-3 { border-top-right-radius: var(--primer-borderRadius-=
+medium, 8px)  !important; border-bottom-right-radius: var(--primer-borderRa=
+dius-medium, 8px)  !important; }
+  .rounded-lg-bottom-0 { border-bottom-right-radius: 0px !important; border=
+-bottom-left-radius: 0px !important; }
+  .rounded-lg-bottom-1 { border-bottom-right-radius: var(--primer-borderRad=
+ius-small, 4px)  !important; border-bottom-left-radius: var(--primer-border=
+Radius-small, 4px)  !important; }
+  .rounded-lg-bottom-2 { border-bottom-right-radius: var(--primer-borderRad=
+ius-medium, 6px)  !important; border-bottom-left-radius: var(--primer-borde=
+rRadius-medium, 6px)  !important; }
+  .rounded-lg-bottom-3 { border-bottom-right-radius: var(--primer-borderRad=
+ius-medium, 8px)  !important; border-bottom-left-radius: var(--primer-borde=
+rRadius-medium, 8px)  !important; }
+  .rounded-lg-left-0 { border-bottom-left-radius: 0px !important; border-to=
+p-left-radius: 0px !important; }
+  .rounded-lg-left-1 { border-bottom-left-radius: var(--primer-borderRadius=
+-small, 4px)  !important; border-top-left-radius: var(--primer-borderRadius=
+-small, 4px)  !important; }
+  .rounded-lg-left-2 { border-bottom-left-radius: var(--primer-borderRadius=
+-medium, 6px)  !important; border-top-left-radius: var(--primer-borderRadiu=
+s-medium, 6px)  !important; }
+  .rounded-lg-left-3 { border-bottom-left-radius: var(--primer-borderRadius=
+-medium, 8px)  !important; border-top-left-radius: var(--primer-borderRadiu=
+s-medium, 8px)  !important; }
+}
+
+@media (min-width: 1280px) {
+  .border-xl { border: var(--primer-borderWidth-thin, 1px) solid var(--colo=
+r-border-default)  !important; }
+  .border-xl-0 { border: 0px !important; }
+  .border-xl-top { border-top: var(--primer-borderWidth-thin, 1px) solid va=
+r(--color-border-default)  !important; }
+  .border-xl-right { border-right: var(--primer-borderWidth-thin, 1px) soli=
+d var(--color-border-default)  !important; }
+  .border-xl-bottom { border-bottom: var(--primer-borderWidth-thin, 1px) so=
+lid var(--color-border-default)  !important; }
+  .border-xl-left { border-left: var(--primer-borderWidth-thin, 1px) solid =
+var(--color-border-default)  !important; }
+  .border-xl-top-0 { border-top: 0px !important; }
+  .border-xl-right-0 { border-right: 0px !important; }
+  .border-xl-bottom-0 { border-bottom: 0px !important; }
+  .border-xl-left-0 { border-left: 0px !important; }
+  .rounded-xl { border-radius: var(--primer-borderRadius-medium, 6px)  !imp=
+ortant; }
+  .rounded-xl-0 { border-radius: 0px !important; }
+  .rounded-xl-1 { border-radius: var(--primer-borderRadius-small, 4px)  !im=
+portant; }
+  .rounded-xl-2 { border-radius: var(--primer-borderRadius-medium, 6px)  !i=
+mportant; }
+  .rounded-xl-3 { border-radius: var(--primer-borderRadius-large, 8px)  !im=
+portant; }
+  .rounded-xl-top-0 { border-top-left-radius: 0px !important; border-top-ri=
+ght-radius: 0px !important; }
+  .rounded-xl-top-1 { border-top-left-radius: var(--primer-borderRadius-sma=
+ll, 4px)  !important; border-top-right-radius: var(--primer-borderRadius-sm=
+all, 4px)  !important; }
+  .rounded-xl-top-2 { border-top-left-radius: var(--primer-borderRadius-med=
+ium, 6px)  !important; border-top-right-radius: var(--primer-borderRadius-m=
+edium, 6px)  !important; }
+  .rounded-xl-top-3 { border-top-left-radius: var(--primer-borderRadius-med=
+ium, 8px)  !important; border-top-right-radius: var(--primer-borderRadius-m=
+edium, 8px)  !important; }
+  .rounded-xl-right-0 { border-top-right-radius: 0px !important; border-bot=
+tom-right-radius: 0px !important; }
+  .rounded-xl-right-1 { border-top-right-radius: var(--primer-borderRadius-=
+small, 4px)  !important; border-bottom-right-radius: var(--primer-borderRad=
+ius-small, 4px)  !important; }
+  .rounded-xl-right-2 { border-top-right-radius: var(--primer-borderRadius-=
+medium, 6px)  !important; border-bottom-right-radius: var(--primer-borderRa=
+dius-medium, 6px)  !important; }
+  .rounded-xl-right-3 { border-top-right-radius: var(--primer-borderRadius-=
+medium, 8px)  !important; border-bottom-right-radius: var(--primer-borderRa=
+dius-medium, 8px)  !important; }
+  .rounded-xl-bottom-0 { border-bottom-right-radius: 0px !important; border=
+-bottom-left-radius: 0px !important; }
+  .rounded-xl-bottom-1 { border-bottom-right-radius: var(--primer-borderRad=
+ius-small, 4px)  !important; border-bottom-left-radius: var(--primer-border=
+Radius-small, 4px)  !important; }
+  .rounded-xl-bottom-2 { border-bottom-right-radius: var(--primer-borderRad=
+ius-medium, 6px)  !important; border-bottom-left-radius: var(--primer-borde=
+rRadius-medium, 6px)  !important; }
+  .rounded-xl-bottom-3 { border-bottom-right-radius: var(--primer-borderRad=
+ius-medium, 8px)  !important; border-bottom-left-radius: var(--primer-borde=
+rRadius-medium, 8px)  !important; }
+  .rounded-xl-left-0 { border-bottom-left-radius: 0px !important; border-to=
+p-left-radius: 0px !important; }
+  .rounded-xl-left-1 { border-bottom-left-radius: var(--primer-borderRadius=
+-small, 4px)  !important; border-top-left-radius: var(--primer-borderRadius=
+-small, 4px)  !important; }
+  .rounded-xl-left-2 { border-bottom-left-radius: var(--primer-borderRadius=
+-medium, 6px)  !important; border-top-left-radius: var(--primer-borderRadiu=
+s-medium, 6px)  !important; }
+  .rounded-xl-left-3 { border-bottom-left-radius: var(--primer-borderRadius=
+-medium, 8px)  !important; border-top-left-radius: var(--primer-borderRadiu=
+s-medium, 8px)  !important; }
+}
+
+.circle { border-radius: var(--primer-borderRadius-full, 50%)  !important; =
+}
+
+.border-dashed { border-style: dashed !important; }
+
+.color-shadow-small { box-shadow: var(--color-shadow-small)  !important; }
+
+.color-shadow-medium { box-shadow: var(--color-shadow-medium)  !important; =
+}
+
+.color-shadow-large { box-shadow: var(--color-shadow-large)  !important; }
+
+.color-shadow-extra-large { box-shadow: var(--color-shadow-extra-large)  !i=
+mportant; }
+
+.box-shadow-none { box-shadow: none !important; }
+
+.color-fg-default { color: var(--color-fg-default)  !important; }
+
+.color-fg-muted { color: var(--color-fg-muted)  !important; }
+
+.color-fg-subtle { color: var(--color-fg-subtle)  !important; }
+
+.color-fg-accent { color: var(--color-accent-fg)  !important; }
+
+.color-fg-success { color: var(--color-success-fg)  !important; }
+
+.color-fg-attention { color: var(--color-attention-fg)  !important; }
+
+.color-fg-severe { color: var(--color-severe-fg)  !important; }
+
+.color-fg-danger { color: var(--color-danger-fg)  !important; }
+
+.color-fg-open { color: var(--color-open-fg)  !important; }
+
+.color-fg-closed { color: var(--color-closed-fg)  !important; }
+
+.color-fg-done { color: var(--color-done-fg)  !important; }
+
+.color-fg-sponsors { color: var(--color-sponsors-fg)  !important; }
+
+.color-fg-on-emphasis { color: var(--color-fg-on-emphasis)  !important; }
+
+.color-bg-default { background-color: var(--color-canvas-default)  !importa=
+nt; }
+
+.color-bg-overlay { background-color: var(--color-canvas-overlay)  !importa=
+nt; }
+
+.color-bg-inset { background-color: var(--color-canvas-inset)  !important; =
+}
+
+.color-bg-subtle { background-color: var(--color-canvas-subtle)  !important=
+; }
+
+.color-bg-emphasis { background-color: var(--color-neutral-emphasis-plus)  =
+!important; }
+
+.color-bg-accent { background-color: var(--color-accent-subtle)  !important=
+; }
+
+.color-bg-accent-emphasis { background-color: var(--color-accent-emphasis) =
+ !important; }
+
+.color-bg-success { background-color: var(--color-success-subtle)  !importa=
+nt; }
+
+.color-bg-success-emphasis { background-color: var(--color-success-emphasis=
+)  !important; }
+
+.color-bg-attention { background-color: var(--color-attention-subtle)  !imp=
+ortant; }
+
+.color-bg-attention-emphasis { background-color: var(--color-attention-emph=
+asis)  !important; }
+
+.color-bg-severe { background-color: var(--color-severe-subtle)  !important=
+; }
+
+.color-bg-severe-emphasis { background-color: var(--color-severe-emphasis) =
+ !important; }
+
+.color-bg-danger { background-color: var(--color-danger-subtle)  !important=
+; }
+
+.color-bg-danger-emphasis { background-color: var(--color-danger-emphasis) =
+ !important; }
+
+.color-bg-open { background-color: var(--color-open-subtle)  !important; }
+
+.color-bg-open-emphasis { background-color: var(--color-open-emphasis)  !im=
+portant; }
+
+.color-bg-closed { background-color: var(--color-closed-subtle)  !important=
+; }
+
+.color-bg-closed-emphasis { background-color: var(--color-closed-emphasis) =
+ !important; }
+
+.color-bg-done { background-color: var(--color-done-subtle)  !important; }
+
+.color-bg-done-emphasis { background-color: var(--color-done-emphasis)  !im=
+portant; }
+
+.color-bg-sponsors { background-color: var(--color-sponsors-subtle)  !impor=
+tant; }
+
+.color-bg-sponsors-emphasis { background-color: var(--color-sponsors-emphas=
+is)  !important; }
+
+.color-bg-transparent { background-color: transparent !important; }
+
+.color-border-default { border-color: var(--color-border-default)  !importa=
+nt; }
+
+.color-border-muted { border-color: var(--color-border-muted)  !important; =
+}
+
+.color-border-subtle { border-color: var(--color-border-subtle)  !important=
+; }
+
+.color-border-accent { border-color: var(--color-accent-muted)  !important;=
+ }
+
+.color-border-accent-emphasis { border-color: var(--color-accent-emphasis) =
+ !important; }
+
+.color-border-success { border-color: var(--color-success-muted)  !importan=
+t; }
+
+.color-border-success-emphasis { border-color: var(--color-success-emphasis=
+)  !important; }
+
+.color-border-attention { border-color: var(--color-attention-muted)  !impo=
+rtant; }
+
+.color-border-attention-emphasis { border-color: var(--color-attention-emph=
+asis)  !important; }
+
+.color-border-severe { border-color: var(--color-severe-muted)  !important;=
+ }
+
+.color-border-severe-emphasis { border-color: var(--color-severe-emphasis) =
+ !important; }
+
+.color-border-danger { border-color: var(--color-danger-muted)  !important;=
+ }
+
+.color-border-danger-emphasis { border-color: var(--color-danger-emphasis) =
+ !important; }
+
+.color-border-open { border-color: var(--color-open-muted)  !important; }
+
+.color-border-open-emphasis { border-color: var(--color-open-emphasis)  !im=
+portant; }
+
+.color-border-closed { border-color: var(--color-closed-muted)  !important;=
+ }
+
+.color-border-closed-emphasis { border-color: var(--color-closed-emphasis) =
+ !important; }
+
+.color-border-done { border-color: var(--color-done-muted)  !important; }
+
+.color-border-done-emphasis { border-color: var(--color-done-emphasis)  !im=
+portant; }
+
+.color-border-sponsors { border-color: var(--color-sponsors-muted)  !import=
+ant; }
+
+.color-border-sponsors-emphasis { border-color: var(--color-sponsors-emphas=
+is)  !important; }
+
+.color-fg-inherit { color: inherit !important; }
+
+.details-overlay[open] > summary::before { position: fixed; inset: 0px; z-i=
+ndex: 80; display: block; cursor: default; content: " "; background: transp=
+arent; }
+
+.details-overlay-dark[open] > summary::before { z-index: 111; background: v=
+ar(--color-primer-canvas-backdrop); }
+
+.details-reset > summary { list-style: none; transition: color 80ms cubic-b=
+ezier(0.33, 1, 0.68, 1) 0s, background-color, box-shadow, border-color; }
+
+.details-reset > summary:focus { outline: 2px solid var(--color-accent-fg);=
+ outline-offset: -2px; box-shadow: none; }
+
+.details-reset > summary:focus:not(:focus-visible) { outline: transparent s=
+olid 1px; }
+
+.details-reset > summary:focus-visible { outline: 2px solid var(--color-acc=
+ent-fg); outline-offset: -2px; box-shadow: none; }
+
+.details-reset > summary.btn-primary:focus { outline: 2px solid var(--color=
+-accent-fg); outline-offset: -2px; box-shadow: inset 0 0 0 3px var(--color-=
+fg-on-emphasis); }
+
+.details-reset > summary.btn-primary:focus:not(:focus-visible) { outline: t=
+ransparent solid 1px; box-shadow: none; }
+
+.details-reset > summary.btn-primary:focus-visible { outline: 2px solid var=
+(--color-accent-fg); outline-offset: -2px; box-shadow: inset 0 0 0 3px var(=
+--color-fg-on-emphasis); }
+
+.details-reset > summary::before { display: none; }
+
+.details-reset > summary::-webkit-details-marker { display: none; }
+
+.details-overlay > summary { transition: color 80ms cubic-bezier(0.33, 1, 0=
+.68, 1) 0s, background-color, box-shadow, border-color; }
+
+.details-overlay > summary:focus { outline: 2px solid var(--color-accent-fg=
+); outline-offset: -2px; box-shadow: none; }
+
+.details-overlay > summary:focus:not(:focus-visible) { outline: transparent=
+ solid 1px; }
+
+.details-overlay > summary:focus-visible { outline: 2px solid var(--color-a=
+ccent-fg); outline-offset: -2px; box-shadow: none; }
+
+.details-overlay > summary.btn-primary:focus { outline: 2px solid var(--col=
+or-accent-fg); outline-offset: -2px; box-shadow: inset 0 0 0 3px var(--colo=
+r-fg-on-emphasis); }
+
+.details-overlay > summary.btn-primary:focus:not(:focus-visible) { outline:=
+ transparent solid 1px; box-shadow: none; }
+
+.details-overlay > summary.btn-primary:focus-visible { outline: 2px solid v=
+ar(--color-accent-fg); outline-offset: -2px; box-shadow: inset 0 0 0 3px va=
+r(--color-fg-on-emphasis); }
+
+.flex-row { flex-direction: row !important; }
+
+.flex-row-reverse { flex-direction: row-reverse !important; }
+
+.flex-column { flex-direction: column !important; }
+
+.flex-column-reverse { flex-direction: column-reverse !important; }
+
+.flex-wrap { flex-wrap: wrap !important; }
+
+.flex-nowrap { flex-wrap: nowrap !important; }
+
+.flex-wrap-reverse { flex-wrap: wrap-reverse !important; }
+
+.flex-justify-start { justify-content: flex-start !important; }
+
+.flex-justify-end { justify-content: flex-end !important; }
+
+.flex-justify-center { justify-content: center !important; }
+
+.flex-justify-between { justify-content: space-between !important; }
+
+.flex-justify-around { justify-content: space-around !important; }
+
+.flex-items-start { align-items: flex-start !important; }
+
+.flex-items-end { align-items: flex-end !important; }
+
+.flex-items-center { align-items: center !important; }
+
+.flex-items-baseline { align-items: baseline !important; }
+
+.flex-items-stretch { align-items: stretch !important; }
+
+.flex-content-start { align-content: flex-start !important; }
+
+.flex-content-end { align-content: flex-end !important; }
+
+.flex-content-center { align-content: center !important; }
+
+.flex-content-between { align-content: space-between !important; }
+
+.flex-content-around { align-content: space-around !important; }
+
+.flex-content-stretch { align-content: stretch !important; }
+
+.flex-1 { flex: 1 1 0% !important; }
+
+.flex-auto { flex: 1 1 auto !important; }
+
+.flex-grow-0 { flex-grow: 0 !important; }
+
+.flex-shrink-0 { flex-shrink: 0 !important; }
+
+.flex-self-auto { align-self: auto !important; }
+
+.flex-self-start { align-self: flex-start !important; }
+
+.flex-self-end { align-self: flex-end !important; }
+
+.flex-self-center { align-self: center !important; }
+
+.flex-self-baseline { align-self: baseline !important; }
+
+.flex-self-stretch { align-self: stretch !important; }
+
+.flex-order-1 { order: 1 !important; }
+
+.flex-order-2 { order: 2 !important; }
+
+.flex-order-none { order: inherit !important; }
+
+@media (min-width: 544px) {
+  .flex-sm-row { flex-direction: row !important; }
+  .flex-sm-row-reverse { flex-direction: row-reverse !important; }
+  .flex-sm-column { flex-direction: column !important; }
+  .flex-sm-column-reverse { flex-direction: column-reverse !important; }
+  .flex-sm-wrap { flex-wrap: wrap !important; }
+  .flex-sm-nowrap { flex-wrap: nowrap !important; }
+  .flex-sm-wrap-reverse { flex-wrap: wrap-reverse !important; }
+  .flex-sm-justify-start { justify-content: flex-start !important; }
+  .flex-sm-justify-end { justify-content: flex-end !important; }
+  .flex-sm-justify-center { justify-content: center !important; }
+  .flex-sm-justify-between { justify-content: space-between !important; }
+  .flex-sm-justify-around { justify-content: space-around !important; }
+  .flex-sm-items-start { align-items: flex-start !important; }
+  .flex-sm-items-end { align-items: flex-end !important; }
+  .flex-sm-items-center { align-items: center !important; }
+  .flex-sm-items-baseline { align-items: baseline !important; }
+  .flex-sm-items-stretch { align-items: stretch !important; }
+  .flex-sm-content-start { align-content: flex-start !important; }
+  .flex-sm-content-end { align-content: flex-end !important; }
+  .flex-sm-content-center { align-content: center !important; }
+  .flex-sm-content-between { align-content: space-between !important; }
+  .flex-sm-content-around { align-content: space-around !important; }
+  .flex-sm-content-stretch { align-content: stretch !important; }
+  .flex-sm-1 { flex: 1 1 0% !important; }
+  .flex-sm-auto { flex: 1 1 auto !important; }
+  .flex-sm-grow-0 { flex-grow: 0 !important; }
+  .flex-sm-shrink-0 { flex-shrink: 0 !important; }
+  .flex-sm-self-auto { align-self: auto !important; }
+  .flex-sm-self-start { align-self: flex-start !important; }
+  .flex-sm-self-end { align-self: flex-end !important; }
+  .flex-sm-self-center { align-self: center !important; }
+  .flex-sm-self-baseline { align-self: baseline !important; }
+  .flex-sm-self-stretch { align-self: stretch !important; }
+  .flex-sm-order-1 { order: 1 !important; }
+  .flex-sm-order-2 { order: 2 !important; }
+  .flex-sm-order-none { order: inherit !important; }
+}
+
+@media (min-width: 768px) {
+  .flex-md-row { flex-direction: row !important; }
+  .flex-md-row-reverse { flex-direction: row-reverse !important; }
+  .flex-md-column { flex-direction: column !important; }
+  .flex-md-column-reverse { flex-direction: column-reverse !important; }
+  .flex-md-wrap { flex-wrap: wrap !important; }
+  .flex-md-nowrap { flex-wrap: nowrap !important; }
+  .flex-md-wrap-reverse { flex-wrap: wrap-reverse !important; }
+  .flex-md-justify-start { justify-content: flex-start !important; }
+  .flex-md-justify-end { justify-content: flex-end !important; }
+  .flex-md-justify-center { justify-content: center !important; }
+  .flex-md-justify-between { justify-content: space-between !important; }
+  .flex-md-justify-around { justify-content: space-around !important; }
+  .flex-md-items-start { align-items: flex-start !important; }
+  .flex-md-items-end { align-items: flex-end !important; }
+  .flex-md-items-center { align-items: center !important; }
+  .flex-md-items-baseline { align-items: baseline !important; }
+  .flex-md-items-stretch { align-items: stretch !important; }
+  .flex-md-content-start { align-content: flex-start !important; }
+  .flex-md-content-end { align-content: flex-end !important; }
+  .flex-md-content-center { align-content: center !important; }
+  .flex-md-content-between { align-content: space-between !important; }
+  .flex-md-content-around { align-content: space-around !important; }
+  .flex-md-content-stretch { align-content: stretch !important; }
+  .flex-md-1 { flex: 1 1 0% !important; }
+  .flex-md-auto { flex: 1 1 auto !important; }
+  .flex-md-grow-0 { flex-grow: 0 !important; }
+  .flex-md-shrink-0 { flex-shrink: 0 !important; }
+  .flex-md-self-auto { align-self: auto !important; }
+  .flex-md-self-start { align-self: flex-start !important; }
+  .flex-md-self-end { align-self: flex-end !important; }
+  .flex-md-self-center { align-self: center !important; }
+  .flex-md-self-baseline { align-self: baseline !important; }
+  .flex-md-self-stretch { align-self: stretch !important; }
+  .flex-md-order-1 { order: 1 !important; }
+  .flex-md-order-2 { order: 2 !important; }
+  .flex-md-order-none { order: inherit !important; }
+}
+
+@media (min-width: 1012px) {
+  .flex-lg-row { flex-direction: row !important; }
+  .flex-lg-row-reverse { flex-direction: row-reverse !important; }
+  .flex-lg-column { flex-direction: column !important; }
+  .flex-lg-column-reverse { flex-direction: column-reverse !important; }
+  .flex-lg-wrap { flex-wrap: wrap !important; }
+  .flex-lg-nowrap { flex-wrap: nowrap !important; }
+  .flex-lg-wrap-reverse { flex-wrap: wrap-reverse !important; }
+  .flex-lg-justify-start { justify-content: flex-start !important; }
+  .flex-lg-justify-end { justify-content: flex-end !important; }
+  .flex-lg-justify-center { justify-content: center !important; }
+  .flex-lg-justify-between { justify-content: space-between !important; }
+  .flex-lg-justify-around { justify-content: space-around !important; }
+  .flex-lg-items-start { align-items: flex-start !important; }
+  .flex-lg-items-end { align-items: flex-end !important; }
+  .flex-lg-items-center { align-items: center !important; }
+  .flex-lg-items-baseline { align-items: baseline !important; }
+  .flex-lg-items-stretch { align-items: stretch !important; }
+  .flex-lg-content-start { align-content: flex-start !important; }
+  .flex-lg-content-end { align-content: flex-end !important; }
+  .flex-lg-content-center { align-content: center !important; }
+  .flex-lg-content-between { align-content: space-between !important; }
+  .flex-lg-content-around { align-content: space-around !important; }
+  .flex-lg-content-stretch { align-content: stretch !important; }
+  .flex-lg-1 { flex: 1 1 0% !important; }
+  .flex-lg-auto { flex: 1 1 auto !important; }
+  .flex-lg-grow-0 { flex-grow: 0 !important; }
+  .flex-lg-shrink-0 { flex-shrink: 0 !important; }
+  .flex-lg-self-auto { align-self: auto !important; }
+  .flex-lg-self-start { align-self: flex-start !important; }
+  .flex-lg-self-end { align-self: flex-end !important; }
+  .flex-lg-self-center { align-self: center !important; }
+  .flex-lg-self-baseline { align-self: baseline !important; }
+  .flex-lg-self-stretch { align-self: stretch !important; }
+  .flex-lg-order-1 { order: 1 !important; }
+  .flex-lg-order-2 { order: 2 !important; }
+  .flex-lg-order-none { order: inherit !important; }
+}
+
+@media (min-width: 1280px) {
+  .flex-xl-row { flex-direction: row !important; }
+  .flex-xl-row-reverse { flex-direction: row-reverse !important; }
+  .flex-xl-column { flex-direction: column !important; }
+  .flex-xl-column-reverse { flex-direction: column-reverse !important; }
+  .flex-xl-wrap { flex-wrap: wrap !important; }
+  .flex-xl-nowrap { flex-wrap: nowrap !important; }
+  .flex-xl-wrap-reverse { flex-wrap: wrap-reverse !important; }
+  .flex-xl-justify-start { justify-content: flex-start !important; }
+  .flex-xl-justify-end { justify-content: flex-end !important; }
+  .flex-xl-justify-center { justify-content: center !important; }
+  .flex-xl-justify-between { justify-content: space-between !important; }
+  .flex-xl-justify-around { justify-content: space-around !important; }
+  .flex-xl-items-start { align-items: flex-start !important; }
+  .flex-xl-items-end { align-items: flex-end !important; }
+  .flex-xl-items-center { align-items: center !important; }
+  .flex-xl-items-baseline { align-items: baseline !important; }
+  .flex-xl-items-stretch { align-items: stretch !important; }
+  .flex-xl-content-start { align-content: flex-start !important; }
+  .flex-xl-content-end { align-content: flex-end !important; }
+  .flex-xl-content-center { align-content: center !important; }
+  .flex-xl-content-between { align-content: space-between !important; }
+  .flex-xl-content-around { align-content: space-around !important; }
+  .flex-xl-content-stretch { align-content: stretch !important; }
+  .flex-xl-1 { flex: 1 1 0% !important; }
+  .flex-xl-auto { flex: 1 1 auto !important; }
+  .flex-xl-grow-0 { flex-grow: 0 !important; }
+  .flex-xl-shrink-0 { flex-shrink: 0 !important; }
+  .flex-xl-self-auto { align-self: auto !important; }
+  .flex-xl-self-start { align-self: flex-start !important; }
+  .flex-xl-self-end { align-self: flex-end !important; }
+  .flex-xl-self-center { align-self: center !important; }
+  .flex-xl-self-baseline { align-self: baseline !important; }
+  .flex-xl-self-stretch { align-self: stretch !important; }
+  .flex-xl-order-1 { order: 1 !important; }
+  .flex-xl-order-2 { order: 2 !important; }
+  .flex-xl-order-none { order: inherit !important; }
+}
+
+.position-static { position: static !important; }
+
+.position-relative { position: relative !important; }
+
+.position-absolute { position: absolute !important; }
+
+.position-fixed { position: fixed !important; }
+
+.position-sticky { position: sticky !important; }
+
+@media (min-width: 544px) {
+  .position-sm-static { position: static !important; }
+  .position-sm-relative { position: relative !important; }
+  .position-sm-absolute { position: absolute !important; }
+  .position-sm-fixed { position: fixed !important; }
+  .position-sm-sticky { position: sticky !important; }
+}
+
+@media (min-width: 768px) {
+  .position-md-static { position: static !important; }
+  .position-md-relative { position: relative !important; }
+  .position-md-absolute { position: absolute !important; }
+  .position-md-fixed { position: fixed !important; }
+  .position-md-sticky { position: sticky !important; }
+}
+
+@media (min-width: 1012px) {
+  .position-lg-static { position: static !important; }
+  .position-lg-relative { position: relative !important; }
+  .position-lg-absolute { position: absolute !important; }
+  .position-lg-fixed { position: fixed !important; }
+  .position-lg-sticky { position: sticky !important; }
+}
+
+@media (min-width: 1280px) {
+  .position-xl-static { position: static !important; }
+  .position-xl-relative { position: relative !important; }
+  .position-xl-absolute { position: absolute !important; }
+  .position-xl-fixed { position: fixed !important; }
+  .position-xl-sticky { position: sticky !important; }
+}
+
+.top-0 { top: 0px !important; }
+
+.right-0 { right: 0px !important; }
+
+.bottom-0 { bottom: 0px !important; }
+
+.left-0 { left: 0px !important; }
+
+.top-auto { top: auto !important; }
+
+.right-auto { right: auto !important; }
+
+.bottom-auto { bottom: auto !important; }
+
+.left-auto { left: auto !important; }
+
+@media (min-width: 544px) {
+  .top-sm-0 { top: 0px !important; }
+  .right-sm-0 { right: 0px !important; }
+  .bottom-sm-0 { bottom: 0px !important; }
+  .left-sm-0 { left: 0px !important; }
+  .top-sm-auto { top: auto !important; }
+  .right-sm-auto { right: auto !important; }
+  .bottom-sm-auto { bottom: auto !important; }
+  .left-sm-auto { left: auto !important; }
+}
+
+@media (min-width: 768px) {
+  .top-md-0 { top: 0px !important; }
+  .right-md-0 { right: 0px !important; }
+  .bottom-md-0 { bottom: 0px !important; }
+  .left-md-0 { left: 0px !important; }
+  .top-md-auto { top: auto !important; }
+  .right-md-auto { right: auto !important; }
+  .bottom-md-auto { bottom: auto !important; }
+  .left-md-auto { left: auto !important; }
+}
+
+@media (min-width: 1012px) {
+  .top-lg-0 { top: 0px !important; }
+  .right-lg-0 { right: 0px !important; }
+  .bottom-lg-0 { bottom: 0px !important; }
+  .left-lg-0 { left: 0px !important; }
+  .top-lg-auto { top: auto !important; }
+  .right-lg-auto { right: auto !important; }
+  .bottom-lg-auto { bottom: auto !important; }
+  .left-lg-auto { left: auto !important; }
+}
+
+@media (min-width: 1280px) {
+  .top-xl-0 { top: 0px !important; }
+  .right-xl-0 { right: 0px !important; }
+  .bottom-xl-0 { bottom: 0px !important; }
+  .left-xl-0 { left: 0px !important; }
+  .top-xl-auto { top: auto !important; }
+  .right-xl-auto { right: auto !important; }
+  .bottom-xl-auto { bottom: auto !important; }
+  .left-xl-auto { left: auto !important; }
+}
+
+.v-align-middle { vertical-align: middle !important; }
+
+.v-align-top { vertical-align: top !important; }
+
+.v-align-bottom { vertical-align: bottom !important; }
+
+.v-align-text-top { vertical-align: text-top !important; }
+
+.v-align-text-bottom { vertical-align: text-bottom !important; }
+
+.v-align-baseline { vertical-align: baseline !important; }
+
+.overflow-visible { overflow: visible !important; }
+
+.overflow-x-visible { overflow-x: visible !important; }
+
+.overflow-y-visible { overflow-y: visible !important; }
+
+.overflow-hidden { overflow: hidden !important; }
+
+.overflow-x-hidden { overflow-x: hidden !important; }
+
+.overflow-y-hidden { overflow-y: hidden !important; }
+
+.overflow-auto { overflow: auto !important; }
+
+.overflow-x-auto { overflow-x: auto !important; }
+
+.overflow-y-auto { overflow-y: auto !important; }
+
+.overflow-scroll { overflow: scroll !important; }
+
+.overflow-x-scroll { overflow-x: scroll !important; }
+
+.overflow-y-scroll { overflow-y: scroll !important; }
+
+@media (min-width: 544px) {
+  .overflow-sm-visible { overflow: visible !important; }
+  .overflow-sm-x-visible { overflow-x: visible !important; }
+  .overflow-sm-y-visible { overflow-y: visible !important; }
+  .overflow-sm-hidden { overflow: hidden !important; }
+  .overflow-sm-x-hidden { overflow-x: hidden !important; }
+  .overflow-sm-y-hidden { overflow-y: hidden !important; }
+  .overflow-sm-auto { overflow: auto !important; }
+  .overflow-sm-x-auto { overflow-x: auto !important; }
+  .overflow-sm-y-auto { overflow-y: auto !important; }
+  .overflow-sm-scroll { overflow: scroll !important; }
+  .overflow-sm-x-scroll { overflow-x: scroll !important; }
+  .overflow-sm-y-scroll { overflow-y: scroll !important; }
+}
+
+@media (min-width: 768px) {
+  .overflow-md-visible { overflow: visible !important; }
+  .overflow-md-x-visible { overflow-x: visible !important; }
+  .overflow-md-y-visible { overflow-y: visible !important; }
+  .overflow-md-hidden { overflow: hidden !important; }
+  .overflow-md-x-hidden { overflow-x: hidden !important; }
+  .overflow-md-y-hidden { overflow-y: hidden !important; }
+  .overflow-md-auto { overflow: auto !important; }
+  .overflow-md-x-auto { overflow-x: auto !important; }
+  .overflow-md-y-auto { overflow-y: auto !important; }
+  .overflow-md-scroll { overflow: scroll !important; }
+  .overflow-md-x-scroll { overflow-x: scroll !important; }
+  .overflow-md-y-scroll { overflow-y: scroll !important; }
+}
+
+@media (min-width: 1012px) {
+  .overflow-lg-visible { overflow: visible !important; }
+  .overflow-lg-x-visible { overflow-x: visible !important; }
+  .overflow-lg-y-visible { overflow-y: visible !important; }
+  .overflow-lg-hidden { overflow: hidden !important; }
+  .overflow-lg-x-hidden { overflow-x: hidden !important; }
+  .overflow-lg-y-hidden { overflow-y: hidden !important; }
+  .overflow-lg-auto { overflow: auto !important; }
+  .overflow-lg-x-auto { overflow-x: auto !important; }
+  .overflow-lg-y-auto { overflow-y: auto !important; }
+  .overflow-lg-scroll { overflow: scroll !important; }
+  .overflow-lg-x-scroll { overflow-x: scroll !important; }
+  .overflow-lg-y-scroll { overflow-y: scroll !important; }
+}
+
+@media (min-width: 1280px) {
+  .overflow-xl-visible { overflow: visible !important; }
+  .overflow-xl-x-visible { overflow-x: visible !important; }
+  .overflow-xl-y-visible { overflow-y: visible !important; }
+  .overflow-xl-hidden { overflow: hidden !important; }
+  .overflow-xl-x-hidden { overflow-x: hidden !important; }
+  .overflow-xl-y-hidden { overflow-y: hidden !important; }
+  .overflow-xl-auto { overflow: auto !important; }
+  .overflow-xl-x-auto { overflow-x: auto !important; }
+  .overflow-xl-y-auto { overflow-y: auto !important; }
+  .overflow-xl-scroll { overflow: scroll !important; }
+  .overflow-xl-x-scroll { overflow-x: scroll !important; }
+  .overflow-xl-y-scroll { overflow-y: scroll !important; }
+}
+
+.clearfix::before { display: table; content: ""; }
+
+.clearfix::after { display: table; clear: both; content: ""; }
+
+.float-left { float: left !important; }
+
+.float-right { float: right !important; }
+
+.float-none { float: none !important; }
+
+@media (min-width: 544px) {
+  .float-sm-left { float: left !important; }
+  .float-sm-right { float: right !important; }
+  .float-sm-none { float: none !important; }
+}
+
+@media (min-width: 768px) {
+  .float-md-left { float: left !important; }
+  .float-md-right { float: right !important; }
+  .float-md-none { float: none !important; }
+}
+
+@media (min-width: 1012px) {
+  .float-lg-left { float: left !important; }
+  .float-lg-right { float: right !important; }
+  .float-lg-none { float: none !important; }
+}
+
+@media (min-width: 1280px) {
+  .float-xl-left { float: left !important; }
+  .float-xl-right { float: right !important; }
+  .float-xl-none { float: none !important; }
+}
+
+.width-fit { max-width: 100% !important; }
+
+.width-full { width: 100% !important; }
+
+.height-fit { max-height: 100% !important; }
+
+.height-full { height: 100% !important; }
+
+.min-width-0 { min-width: 0px !important; }
+
+.width-auto { width: auto !important; }
+
+.direction-rtl { direction: rtl !important; }
+
+.direction-ltr { direction: ltr !important; }
+
+@media (min-width: 544px) {
+  .width-sm-auto { width: auto !important; }
+  .direction-sm-rtl { direction: rtl !important; }
+  .direction-sm-ltr { direction: ltr !important; }
+}
+
+@media (min-width: 768px) {
+  .width-md-auto { width: auto !important; }
+  .direction-md-rtl { direction: rtl !important; }
+  .direction-md-ltr { direction: ltr !important; }
+}
+
+@media (min-width: 1012px) {
+  .width-lg-auto { width: auto !important; }
+  .direction-lg-rtl { direction: rtl !important; }
+  .direction-lg-ltr { direction: ltr !important; }
+}
+
+@media (min-width: 1280px) {
+  .width-xl-auto { width: auto !important; }
+  .direction-xl-rtl { direction: rtl !important; }
+  .direction-xl-ltr { direction: ltr !important; }
+}
+
+.m-0 { margin: 0px !important; }
+
+.mt-0 { margin-top: 0px !important; }
+
+.mb-0 { margin-bottom: 0px !important; }
+
+.mr-0 { margin-right: 0px !important; }
+
+.ml-0 { margin-left: 0px !important; }
+
+.mx-0 { margin-right: 0px !important; margin-left: 0px !important; }
+
+.my-0 { margin-top: 0px !important; margin-bottom: 0px !important; }
+
+.m-1 { margin: var(--base-size-4, 4px)  !important; }
+
+.mt-1 { margin-top: var(--base-size-4, 4px)  !important; }
+
+.mb-1 { margin-bottom: var(--base-size-4, 4px)  !important; }
+
+.mr-1 { margin-right: var(--base-size-4, 4px)  !important; }
+
+.ml-1 { margin-left: var(--base-size-4, 4px)  !important; }
+
+.mt-n1 { margin-top: calc(-1*var(--base-size-4, 4px))  !important; }
+
+.mb-n1 { margin-bottom: calc(-1*var(--base-size-4, 4px))  !important; }
+
+.mr-n1 { margin-right: calc(-1*var(--base-size-4, 4px))  !important; }
+
+.ml-n1 { margin-left: calc(-1*var(--base-size-4, 4px))  !important; }
+
+.mx-1 { margin-right: var(--base-size-4, 4px)  !important; margin-left: var=
+(--base-size-4, 4px)  !important; }
+
+.my-1 { margin-top: var(--base-size-4, 4px)  !important; margin-bottom: var=
+(--base-size-4, 4px)  !important; }
+
+.m-2 { margin: var(--base-size-8, 8px)  !important; }
+
+.mt-2 { margin-top: var(--base-size-8, 8px)  !important; }
+
+.mb-2 { margin-bottom: var(--base-size-8, 8px)  !important; }
+
+.mr-2 { margin-right: var(--base-size-8, 8px)  !important; }
+
+.ml-2 { margin-left: var(--base-size-8, 8px)  !important; }
+
+.mt-n2 { margin-top: calc(-1*var(--base-size-8, 8px))  !important; }
+
+.mb-n2 { margin-bottom: calc(-1*var(--base-size-8, 8px))  !important; }
+
+.mr-n2 { margin-right: calc(-1*var(--base-size-8, 8px))  !important; }
+
+.ml-n2 { margin-left: calc(-1*var(--base-size-8, 8px))  !important; }
+
+.mx-2 { margin-right: var(--base-size-8, 8px)  !important; margin-left: var=
+(--base-size-8, 8px)  !important; }
+
+.my-2 { margin-top: var(--base-size-8, 8px)  !important; margin-bottom: var=
+(--base-size-8, 8px)  !important; }
+
+.m-3 { margin: var(--base-size-16, 16px)  !important; }
+
+.mt-3 { margin-top: var(--base-size-16, 16px)  !important; }
+
+.mb-3 { margin-bottom: var(--base-size-16, 16px)  !important; }
+
+.mr-3 { margin-right: var(--base-size-16, 16px)  !important; }
+
+.ml-3 { margin-left: var(--base-size-16, 16px)  !important; }
+
+.mt-n3 { margin-top: calc(-1*var(--base-size-16, 16px))  !important; }
+
+.mb-n3 { margin-bottom: calc(-1*var(--base-size-16, 16px))  !important; }
+
+.mr-n3 { margin-right: calc(-1*var(--base-size-16, 16px))  !important; }
+
+.ml-n3 { margin-left: calc(-1*var(--base-size-16, 16px))  !important; }
+
+.mx-3 { margin-right: var(--base-size-16, 16px)  !important; margin-left: v=
+ar(--base-size-16, 16px)  !important; }
+
+.my-3 { margin-top: var(--base-size-16, 16px)  !important; margin-bottom: v=
+ar(--base-size-16, 16px)  !important; }
+
+.m-4 { margin: var(--base-size-24, 24px)  !important; }
+
+.mt-4 { margin-top: var(--base-size-24, 24px)  !important; }
+
+.mb-4 { margin-bottom: var(--base-size-24, 24px)  !important; }
+
+.mr-4 { margin-right: var(--base-size-24, 24px)  !important; }
+
+.ml-4 { margin-left: var(--base-size-24, 24px)  !important; }
+
+.mt-n4 { margin-top: calc(-1*var(--base-size-24, 24px))  !important; }
+
+.mb-n4 { margin-bottom: calc(-1*var(--base-size-24, 24px))  !important; }
+
+.mr-n4 { margin-right: calc(-1*var(--base-size-24, 24px))  !important; }
+
+.ml-n4 { margin-left: calc(-1*var(--base-size-24, 24px))  !important; }
+
+.mx-4 { margin-right: var(--base-size-24, 24px)  !important; margin-left: v=
+ar(--base-size-24, 24px)  !important; }
+
+.my-4 { margin-top: var(--base-size-24, 24px)  !important; margin-bottom: v=
+ar(--base-size-24, 24px)  !important; }
+
+.m-5 { margin: var(--base-size-32, 32px)  !important; }
+
+.mt-5 { margin-top: var(--base-size-32, 32px)  !important; }
+
+.mb-5 { margin-bottom: var(--base-size-32, 32px)  !important; }
+
+.mr-5 { margin-right: var(--base-size-32, 32px)  !important; }
+
+.ml-5 { margin-left: var(--base-size-32, 32px)  !important; }
+
+.mt-n5 { margin-top: calc(-1*var(--base-size-32, 32px))  !important; }
+
+.mb-n5 { margin-bottom: calc(-1*var(--base-size-32, 32px))  !important; }
+
+.mr-n5 { margin-right: calc(-1*var(--base-size-32, 32px))  !important; }
+
+.ml-n5 { margin-left: calc(-1*var(--base-size-32, 32px))  !important; }
+
+.mx-5 { margin-right: var(--base-size-32, 32px)  !important; margin-left: v=
+ar(--base-size-32, 32px)  !important; }
+
+.my-5 { margin-top: var(--base-size-32, 32px)  !important; margin-bottom: v=
+ar(--base-size-32, 32px)  !important; }
+
+.m-6 { margin: var(--base-size-40, 40px)  !important; }
+
+.mt-6 { margin-top: var(--base-size-40, 40px)  !important; }
+
+.mb-6 { margin-bottom: var(--base-size-40, 40px)  !important; }
+
+.mr-6 { margin-right: var(--base-size-40, 40px)  !important; }
+
+.ml-6 { margin-left: var(--base-size-40, 40px)  !important; }
+
+.mt-n6 { margin-top: calc(-1*var(--base-size-40, 40px))  !important; }
+
+.mb-n6 { margin-bottom: calc(-1*var(--base-size-40, 40px))  !important; }
+
+.mr-n6 { margin-right: calc(-1*var(--base-size-40, 40px))  !important; }
+
+.ml-n6 { margin-left: calc(-1*var(--base-size-40, 40px))  !important; }
+
+.mx-6 { margin-right: var(--base-size-40, 40px)  !important; margin-left: v=
+ar(--base-size-40, 40px)  !important; }
+
+.my-6 { margin-top: var(--base-size-40, 40px)  !important; margin-bottom: v=
+ar(--base-size-40, 40px)  !important; }
+
+.mt-7 { margin-top: var(--base-size-48, 48px)  !important; }
+
+.mb-7 { margin-bottom: var(--base-size-48, 48px)  !important; }
+
+.mt-n7 { margin-top: calc(-1*var(--base-size-48, 48px))  !important; }
+
+.mb-n7 { margin-bottom: calc(-1*var(--base-size-48, 48px))  !important; }
+
+.my-7 { margin-top: var(--base-size-48, 48px)  !important; margin-bottom: v=
+ar(--base-size-48, 48px)  !important; }
+
+.mt-8 { margin-top: var(--base-size-64, 64px)  !important; }
+
+.mb-8 { margin-bottom: var(--base-size-64, 64px)  !important; }
+
+.mt-n8 { margin-top: calc(-1*var(--base-size-64, 64px))  !important; }
+
+.mb-n8 { margin-bottom: calc(-1*var(--base-size-64, 64px))  !important; }
+
+.my-8 { margin-top: var(--base-size-64, 64px)  !important; margin-bottom: v=
+ar(--base-size-64, 64px)  !important; }
+
+.mt-9 { margin-top: var(--base-size-80, 80px)  !important; }
+
+.mb-9 { margin-bottom: var(--base-size-80, 80px)  !important; }
+
+.mt-n9 { margin-top: calc(-1*var(--base-size-80, 80px))  !important; }
+
+.mb-n9 { margin-bottom: calc(-1*var(--base-size-80, 80px))  !important; }
+
+.my-9 { margin-top: var(--base-size-80, 80px)  !important; margin-bottom: v=
+ar(--base-size-80, 80px)  !important; }
+
+.mt-10 { margin-top: var(--base-size-96, 96px)  !important; }
+
+.mb-10 { margin-bottom: var(--base-size-96, 96px)  !important; }
+
+.mt-n10 { margin-top: calc(-1*var(--base-size-96, 96px))  !important; }
+
+.mb-n10 { margin-bottom: calc(-1*var(--base-size-96, 96px))  !important; }
+
+.my-10 { margin-top: var(--base-size-96, 96px)  !important; margin-bottom: =
+var(--base-size-96, 96px)  !important; }
+
+.mt-11 { margin-top: var(--base-size-112, 112px)  !important; }
+
+.mb-11 { margin-bottom: var(--base-size-112, 112px)  !important; }
+
+.mt-n11 { margin-top: calc(-1*var(--base-size-112, 112px))  !important; }
+
+.mb-n11 { margin-bottom: calc(-1*var(--base-size-112, 112px))  !important; =
+}
+
+.my-11 { margin-top: var(--base-size-112, 112px)  !important; margin-bottom=
+: var(--base-size-112, 112px)  !important; }
+
+.mt-12 { margin-top: var(--base-size-128, 128px)  !important; }
+
+.mb-12 { margin-bottom: var(--base-size-128, 128px)  !important; }
+
+.mt-n12 { margin-top: calc(-1*var(--base-size-128, 128px))  !important; }
+
+.mb-n12 { margin-bottom: calc(-1*var(--base-size-128, 128px))  !important; =
+}
+
+.my-12 { margin-top: var(--base-size-128, 128px)  !important; margin-bottom=
+: var(--base-size-128, 128px)  !important; }
+
+.mx-auto { margin-right: auto !important; margin-left: auto !important; }
+
+@media (min-width: 544px) {
+  .m-sm-0 { margin: 0px !important; }
+  .mt-sm-0 { margin-top: 0px !important; }
+  .mb-sm-0 { margin-bottom: 0px !important; }
+  .mr-sm-0 { margin-right: 0px !important; }
+  .ml-sm-0 { margin-left: 0px !important; }
+  .mx-sm-0 { margin-right: 0px !important; margin-left: 0px !important; }
+  .my-sm-0 { margin-top: 0px !important; margin-bottom: 0px !important; }
+  .m-sm-1 { margin: var(--base-size-4, 4px)  !important; }
+  .mt-sm-1 { margin-top: var(--base-size-4, 4px)  !important; }
+  .mb-sm-1 { margin-bottom: var(--base-size-4, 4px)  !important; }
+  .mr-sm-1 { margin-right: var(--base-size-4, 4px)  !important; }
+  .ml-sm-1 { margin-left: var(--base-size-4, 4px)  !important; }
+  .mt-sm-n1 { margin-top: calc(-1*var(--base-size-4, 4px))  !important; }
+  .mb-sm-n1 { margin-bottom: calc(-1*var(--base-size-4, 4px))  !important; =
+}
+  .mr-sm-n1 { margin-right: calc(-1*var(--base-size-4, 4px))  !important; }
+  .ml-sm-n1 { margin-left: calc(-1*var(--base-size-4, 4px))  !important; }
+  .mx-sm-1 { margin-right: var(--base-size-4, 4px)  !important; margin-left=
+: var(--base-size-4, 4px)  !important; }
+  .my-sm-1 { margin-top: var(--base-size-4, 4px)  !important; margin-bottom=
+: var(--base-size-4, 4px)  !important; }
+  .m-sm-2 { margin: var(--base-size-8, 8px)  !important; }
+  .mt-sm-2 { margin-top: var(--base-size-8, 8px)  !important; }
+  .mb-sm-2 { margin-bottom: var(--base-size-8, 8px)  !important; }
+  .mr-sm-2 { margin-right: var(--base-size-8, 8px)  !important; }
+  .ml-sm-2 { margin-left: var(--base-size-8, 8px)  !important; }
+  .mt-sm-n2 { margin-top: calc(-1*var(--base-size-8, 8px))  !important; }
+  .mb-sm-n2 { margin-bottom: calc(-1*var(--base-size-8, 8px))  !important; =
+}
+  .mr-sm-n2 { margin-right: calc(-1*var(--base-size-8, 8px))  !important; }
+  .ml-sm-n2 { margin-left: calc(-1*var(--base-size-8, 8px))  !important; }
+  .mx-sm-2 { margin-right: var(--base-size-8, 8px)  !important; margin-left=
+: var(--base-size-8, 8px)  !important; }
+  .my-sm-2 { margin-top: var(--base-size-8, 8px)  !important; margin-bottom=
+: var(--base-size-8, 8px)  !important; }
+  .m-sm-3 { margin: var(--base-size-16, 16px)  !important; }
+  .mt-sm-3 { margin-top: var(--base-size-16, 16px)  !important; }
+  .mb-sm-3 { margin-bottom: var(--base-size-16, 16px)  !important; }
+  .mr-sm-3 { margin-right: var(--base-size-16, 16px)  !important; }
+  .ml-sm-3 { margin-left: var(--base-size-16, 16px)  !important; }
+  .mt-sm-n3 { margin-top: calc(-1*var(--base-size-16, 16px))  !important; }
+  .mb-sm-n3 { margin-bottom: calc(-1*var(--base-size-16, 16px))  !important=
+; }
+  .mr-sm-n3 { margin-right: calc(-1*var(--base-size-16, 16px))  !important;=
+ }
+  .ml-sm-n3 { margin-left: calc(-1*var(--base-size-16, 16px))  !important; =
+}
+  .mx-sm-3 { margin-right: var(--base-size-16, 16px)  !important; margin-le=
+ft: var(--base-size-16, 16px)  !important; }
+  .my-sm-3 { margin-top: var(--base-size-16, 16px)  !important; margin-bott=
+om: var(--base-size-16, 16px)  !important; }
+  .m-sm-4 { margin: var(--base-size-24, 24px)  !important; }
+  .mt-sm-4 { margin-top: var(--base-size-24, 24px)  !important; }
+  .mb-sm-4 { margin-bottom: var(--base-size-24, 24px)  !important; }
+  .mr-sm-4 { margin-right: var(--base-size-24, 24px)  !important; }
+  .ml-sm-4 { margin-left: var(--base-size-24, 24px)  !important; }
+  .mt-sm-n4 { margin-top: calc(-1*var(--base-size-24, 24px))  !important; }
+  .mb-sm-n4 { margin-bottom: calc(-1*var(--base-size-24, 24px))  !important=
+; }
+  .mr-sm-n4 { margin-right: calc(-1*var(--base-size-24, 24px))  !important;=
+ }
+  .ml-sm-n4 { margin-left: calc(-1*var(--base-size-24, 24px))  !important; =
+}
+  .mx-sm-4 { margin-right: var(--base-size-24, 24px)  !important; margin-le=
+ft: var(--base-size-24, 24px)  !important; }
+  .my-sm-4 { margin-top: var(--base-size-24, 24px)  !important; margin-bott=
+om: var(--base-size-24, 24px)  !important; }
+  .m-sm-5 { margin: var(--base-size-32, 32px)  !important; }
+  .mt-sm-5 { margin-top: var(--base-size-32, 32px)  !important; }
+  .mb-sm-5 { margin-bottom: var(--base-size-32, 32px)  !important; }
+  .mr-sm-5 { margin-right: var(--base-size-32, 32px)  !important; }
+  .ml-sm-5 { margin-left: var(--base-size-32, 32px)  !important; }
+  .mt-sm-n5 { margin-top: calc(-1*var(--base-size-32, 32px))  !important; }
+  .mb-sm-n5 { margin-bottom: calc(-1*var(--base-size-32, 32px))  !important=
+; }
+  .mr-sm-n5 { margin-right: calc(-1*var(--base-size-32, 32px))  !important;=
+ }
+  .ml-sm-n5 { margin-left: calc(-1*var(--base-size-32, 32px))  !important; =
+}
+  .mx-sm-5 { margin-right: var(--base-size-32, 32px)  !important; margin-le=
+ft: var(--base-size-32, 32px)  !important; }
+  .my-sm-5 { margin-top: var(--base-size-32, 32px)  !important; margin-bott=
+om: var(--base-size-32, 32px)  !important; }
+  .m-sm-6 { margin: var(--base-size-40, 40px)  !important; }
+  .mt-sm-6 { margin-top: var(--base-size-40, 40px)  !important; }
+  .mb-sm-6 { margin-bottom: var(--base-size-40, 40px)  !important; }
+  .mr-sm-6 { margin-right: var(--base-size-40, 40px)  !important; }
+  .ml-sm-6 { margin-left: var(--base-size-40, 40px)  !important; }
+  .mt-sm-n6 { margin-top: calc(-1*var(--base-size-40, 40px))  !important; }
+  .mb-sm-n6 { margin-bottom: calc(-1*var(--base-size-40, 40px))  !important=
+; }
+  .mr-sm-n6 { margin-right: calc(-1*var(--base-size-40, 40px))  !important;=
+ }
+  .ml-sm-n6 { margin-left: calc(-1*var(--base-size-40, 40px))  !important; =
+}
+  .mx-sm-6 { margin-right: var(--base-size-40, 40px)  !important; margin-le=
+ft: var(--base-size-40, 40px)  !important; }
+  .my-sm-6 { margin-top: var(--base-size-40, 40px)  !important; margin-bott=
+om: var(--base-size-40, 40px)  !important; }
+  .mt-sm-7 { margin-top: var(--base-size-48, 48px)  !important; }
+  .mb-sm-7 { margin-bottom: var(--base-size-48, 48px)  !important; }
+  .mt-sm-n7 { margin-top: calc(-1*var(--base-size-48, 48px))  !important; }
+  .mb-sm-n7 { margin-bottom: calc(-1*var(--base-size-48, 48px))  !important=
+; }
+  .my-sm-7 { margin-top: var(--base-size-48, 48px)  !important; margin-bott=
+om: var(--base-size-48, 48px)  !important; }
+  .mt-sm-8 { margin-top: var(--base-size-64, 64px)  !important; }
+  .mb-sm-8 { margin-bottom: var(--base-size-64, 64px)  !important; }
+  .mt-sm-n8 { margin-top: calc(-1*var(--base-size-64, 64px))  !important; }
+  .mb-sm-n8 { margin-bottom: calc(-1*var(--base-size-64, 64px))  !important=
+; }
+  .my-sm-8 { margin-top: var(--base-size-64, 64px)  !important; margin-bott=
+om: var(--base-size-64, 64px)  !important; }
+  .mt-sm-9 { margin-top: var(--base-size-80, 80px)  !important; }
+  .mb-sm-9 { margin-bottom: var(--base-size-80, 80px)  !important; }
+  .mt-sm-n9 { margin-top: calc(-1*var(--base-size-80, 80px))  !important; }
+  .mb-sm-n9 { margin-bottom: calc(-1*var(--base-size-80, 80px))  !important=
+; }
+  .my-sm-9 { margin-top: var(--base-size-80, 80px)  !important; margin-bott=
+om: var(--base-size-80, 80px)  !important; }
+  .mt-sm-10 { margin-top: var(--base-size-96, 96px)  !important; }
+  .mb-sm-10 { margin-bottom: var(--base-size-96, 96px)  !important; }
+  .mt-sm-n10 { margin-top: calc(-1*var(--base-size-96, 96px))  !important; =
+}
+  .mb-sm-n10 { margin-bottom: calc(-1*var(--base-size-96, 96px))  !importan=
+t; }
+  .my-sm-10 { margin-top: var(--base-size-96, 96px)  !important; margin-bot=
+tom: var(--base-size-96, 96px)  !important; }
+  .mt-sm-11 { margin-top: var(--base-size-112, 112px)  !important; }
+  .mb-sm-11 { margin-bottom: var(--base-size-112, 112px)  !important; }
+  .mt-sm-n11 { margin-top: calc(-1*var(--base-size-112, 112px))  !important=
+; }
+  .mb-sm-n11 { margin-bottom: calc(-1*var(--base-size-112, 112px))  !import=
+ant; }
+  .my-sm-11 { margin-top: var(--base-size-112, 112px)  !important; margin-b=
+ottom: var(--base-size-112, 112px)  !important; }
+  .mt-sm-12 { margin-top: var(--base-size-128, 128px)  !important; }
+  .mb-sm-12 { margin-bottom: var(--base-size-128, 128px)  !important; }
+  .mt-sm-n12 { margin-top: calc(-1*var(--base-size-128, 128px))  !important=
+; }
+  .mb-sm-n12 { margin-bottom: calc(-1*var(--base-size-128, 128px))  !import=
+ant; }
+  .my-sm-12 { margin-top: var(--base-size-128, 128px)  !important; margin-b=
+ottom: var(--base-size-128, 128px)  !important; }
+  .mx-sm-auto { margin-right: auto !important; margin-left: auto !important=
+; }
+}
+
+@media (min-width: 768px) {
+  .m-md-0 { margin: 0px !important; }
+  .mt-md-0 { margin-top: 0px !important; }
+  .mb-md-0 { margin-bottom: 0px !important; }
+  .mr-md-0 { margin-right: 0px !important; }
+  .ml-md-0 { margin-left: 0px !important; }
+  .mx-md-0 { margin-right: 0px !important; margin-left: 0px !important; }
+  .my-md-0 { margin-top: 0px !important; margin-bottom: 0px !important; }
+  .m-md-1 { margin: var(--base-size-4, 4px)  !important; }
+  .mt-md-1 { margin-top: var(--base-size-4, 4px)  !important; }
+  .mb-md-1 { margin-bottom: var(--base-size-4, 4px)  !important; }
+  .mr-md-1 { margin-right: var(--base-size-4, 4px)  !important; }
+  .ml-md-1 { margin-left: var(--base-size-4, 4px)  !important; }
+  .mt-md-n1 { margin-top: calc(-1*var(--base-size-4, 4px))  !important; }
+  .mb-md-n1 { margin-bottom: calc(-1*var(--base-size-4, 4px))  !important; =
+}
+  .mr-md-n1 { margin-right: calc(-1*var(--base-size-4, 4px))  !important; }
+  .ml-md-n1 { margin-left: calc(-1*var(--base-size-4, 4px))  !important; }
+  .mx-md-1 { margin-right: var(--base-size-4, 4px)  !important; margin-left=
+: var(--base-size-4, 4px)  !important; }
+  .my-md-1 { margin-top: var(--base-size-4, 4px)  !important; margin-bottom=
+: var(--base-size-4, 4px)  !important; }
+  .m-md-2 { margin: var(--base-size-8, 8px)  !important; }
+  .mt-md-2 { margin-top: var(--base-size-8, 8px)  !important; }
+  .mb-md-2 { margin-bottom: var(--base-size-8, 8px)  !important; }
+  .mr-md-2 { margin-right: var(--base-size-8, 8px)  !important; }
+  .ml-md-2 { margin-left: var(--base-size-8, 8px)  !important; }
+  .mt-md-n2 { margin-top: calc(-1*var(--base-size-8, 8px))  !important; }
+  .mb-md-n2 { margin-bottom: calc(-1*var(--base-size-8, 8px))  !important; =
+}
+  .mr-md-n2 { margin-right: calc(-1*var(--base-size-8, 8px))  !important; }
+  .ml-md-n2 { margin-left: calc(-1*var(--base-size-8, 8px))  !important; }
+  .mx-md-2 { margin-right: var(--base-size-8, 8px)  !important; margin-left=
+: var(--base-size-8, 8px)  !important; }
+  .my-md-2 { margin-top: var(--base-size-8, 8px)  !important; margin-bottom=
+: var(--base-size-8, 8px)  !important; }
+  .m-md-3 { margin: var(--base-size-16, 16px)  !important; }
+  .mt-md-3 { margin-top: var(--base-size-16, 16px)  !important; }
+  .mb-md-3 { margin-bottom: var(--base-size-16, 16px)  !important; }
+  .mr-md-3 { margin-right: var(--base-size-16, 16px)  !important; }
+  .ml-md-3 { margin-left: var(--base-size-16, 16px)  !important; }
+  .mt-md-n3 { margin-top: calc(-1*var(--base-size-16, 16px))  !important; }
+  .mb-md-n3 { margin-bottom: calc(-1*var(--base-size-16, 16px))  !important=
+; }
+  .mr-md-n3 { margin-right: calc(-1*var(--base-size-16, 16px))  !important;=
+ }
+  .ml-md-n3 { margin-left: calc(-1*var(--base-size-16, 16px))  !important; =
+}
+  .mx-md-3 { margin-right: var(--base-size-16, 16px)  !important; margin-le=
+ft: var(--base-size-16, 16px)  !important; }
+  .my-md-3 { margin-top: var(--base-size-16, 16px)  !important; margin-bott=
+om: var(--base-size-16, 16px)  !important; }
+  .m-md-4 { margin: var(--base-size-24, 24px)  !important; }
+  .mt-md-4 { margin-top: var(--base-size-24, 24px)  !important; }
+  .mb-md-4 { margin-bottom: var(--base-size-24, 24px)  !important; }
+  .mr-md-4 { margin-right: var(--base-size-24, 24px)  !important; }
+  .ml-md-4 { margin-left: var(--base-size-24, 24px)  !important; }
+  .mt-md-n4 { margin-top: calc(-1*var(--base-size-24, 24px))  !important; }
+  .mb-md-n4 { margin-bottom: calc(-1*var(--base-size-24, 24px))  !important=
+; }
+  .mr-md-n4 { margin-right: calc(-1*var(--base-size-24, 24px))  !important;=
+ }
+  .ml-md-n4 { margin-left: calc(-1*var(--base-size-24, 24px))  !important; =
+}
+  .mx-md-4 { margin-right: var(--base-size-24, 24px)  !important; margin-le=
+ft: var(--base-size-24, 24px)  !important; }
+  .my-md-4 { margin-top: var(--base-size-24, 24px)  !important; margin-bott=
+om: var(--base-size-24, 24px)  !important; }
+  .m-md-5 { margin: var(--base-size-32, 32px)  !important; }
+  .mt-md-5 { margin-top: var(--base-size-32, 32px)  !important; }
+  .mb-md-5 { margin-bottom: var(--base-size-32, 32px)  !important; }
+  .mr-md-5 { margin-right: var(--base-size-32, 32px)  !important; }
+  .ml-md-5 { margin-left: var(--base-size-32, 32px)  !important; }
+  .mt-md-n5 { margin-top: calc(-1*var(--base-size-32, 32px))  !important; }
+  .mb-md-n5 { margin-bottom: calc(-1*var(--base-size-32, 32px))  !important=
+; }
+  .mr-md-n5 { margin-right: calc(-1*var(--base-size-32, 32px))  !important;=
+ }
+  .ml-md-n5 { margin-left: calc(-1*var(--base-size-32, 32px))  !important; =
+}
+  .mx-md-5 { margin-right: var(--base-size-32, 32px)  !important; margin-le=
+ft: var(--base-size-32, 32px)  !important; }
+  .my-md-5 { margin-top: var(--base-size-32, 32px)  !important; margin-bott=
+om: var(--base-size-32, 32px)  !important; }
+  .m-md-6 { margin: var(--base-size-40, 40px)  !important; }
+  .mt-md-6 { margin-top: var(--base-size-40, 40px)  !important; }
+  .mb-md-6 { margin-bottom: var(--base-size-40, 40px)  !important; }
+  .mr-md-6 { margin-right: var(--base-size-40, 40px)  !important; }
+  .ml-md-6 { margin-left: var(--base-size-40, 40px)  !important; }
+  .mt-md-n6 { margin-top: calc(-1*var(--base-size-40, 40px))  !important; }
+  .mb-md-n6 { margin-bottom: calc(-1*var(--base-size-40, 40px))  !important=
+; }
+  .mr-md-n6 { margin-right: calc(-1*var(--base-size-40, 40px))  !important;=
+ }
+  .ml-md-n6 { margin-left: calc(-1*var(--base-size-40, 40px))  !important; =
+}
+  .mx-md-6 { margin-right: var(--base-size-40, 40px)  !important; margin-le=
+ft: var(--base-size-40, 40px)  !important; }
+  .my-md-6 { margin-top: var(--base-size-40, 40px)  !important; margin-bott=
+om: var(--base-size-40, 40px)  !important; }
+  .mt-md-7 { margin-top: var(--base-size-48, 48px)  !important; }
+  .mb-md-7 { margin-bottom: var(--base-size-48, 48px)  !important; }
+  .mt-md-n7 { margin-top: calc(-1*var(--base-size-48, 48px))  !important; }
+  .mb-md-n7 { margin-bottom: calc(-1*var(--base-size-48, 48px))  !important=
+; }
+  .my-md-7 { margin-top: var(--base-size-48, 48px)  !important; margin-bott=
+om: var(--base-size-48, 48px)  !important; }
+  .mt-md-8 { margin-top: var(--base-size-64, 64px)  !important; }
+  .mb-md-8 { margin-bottom: var(--base-size-64, 64px)  !important; }
+  .mt-md-n8 { margin-top: calc(-1*var(--base-size-64, 64px))  !important; }
+  .mb-md-n8 { margin-bottom: calc(-1*var(--base-size-64, 64px))  !important=
+; }
+  .my-md-8 { margin-top: var(--base-size-64, 64px)  !important; margin-bott=
+om: var(--base-size-64, 64px)  !important; }
+  .mt-md-9 { margin-top: var(--base-size-80, 80px)  !important; }
+  .mb-md-9 { margin-bottom: var(--base-size-80, 80px)  !important; }
+  .mt-md-n9 { margin-top: calc(-1*var(--base-size-80, 80px))  !important; }
+  .mb-md-n9 { margin-bottom: calc(-1*var(--base-size-80, 80px))  !important=
+; }
+  .my-md-9 { margin-top: var(--base-size-80, 80px)  !important; margin-bott=
+om: var(--base-size-80, 80px)  !important; }
+  .mt-md-10 { margin-top: var(--base-size-96, 96px)  !important; }
+  .mb-md-10 { margin-bottom: var(--base-size-96, 96px)  !important; }
+  .mt-md-n10 { margin-top: calc(-1*var(--base-size-96, 96px))  !important; =
+}
+  .mb-md-n10 { margin-bottom: calc(-1*var(--base-size-96, 96px))  !importan=
+t; }
+  .my-md-10 { margin-top: var(--base-size-96, 96px)  !important; margin-bot=
+tom: var(--base-size-96, 96px)  !important; }
+  .mt-md-11 { margin-top: var(--base-size-112, 112px)  !important; }
+  .mb-md-11 { margin-bottom: var(--base-size-112, 112px)  !important; }
+  .mt-md-n11 { margin-top: calc(-1*var(--base-size-112, 112px))  !important=
+; }
+  .mb-md-n11 { margin-bottom: calc(-1*var(--base-size-112, 112px))  !import=
+ant; }
+  .my-md-11 { margin-top: var(--base-size-112, 112px)  !important; margin-b=
+ottom: var(--base-size-112, 112px)  !important; }
+  .mt-md-12 { margin-top: var(--base-size-128, 128px)  !important; }
+  .mb-md-12 { margin-bottom: var(--base-size-128, 128px)  !important; }
+  .mt-md-n12 { margin-top: calc(-1*var(--base-size-128, 128px))  !important=
+; }
+  .mb-md-n12 { margin-bottom: calc(-1*var(--base-size-128, 128px))  !import=
+ant; }
+  .my-md-12 { margin-top: var(--base-size-128, 128px)  !important; margin-b=
+ottom: var(--base-size-128, 128px)  !important; }
+  .mx-md-auto { margin-right: auto !important; margin-left: auto !important=
+; }
+}
+
+@media (min-width: 1012px) {
+  .m-lg-0 { margin: 0px !important; }
+  .mt-lg-0 { margin-top: 0px !important; }
+  .mb-lg-0 { margin-bottom: 0px !important; }
+  .mr-lg-0 { margin-right: 0px !important; }
+  .ml-lg-0 { margin-left: 0px !important; }
+  .mx-lg-0 { margin-right: 0px !important; margin-left: 0px !important; }
+  .my-lg-0 { margin-top: 0px !important; margin-bottom: 0px !important; }
+  .m-lg-1 { margin: var(--base-size-4, 4px)  !important; }
+  .mt-lg-1 { margin-top: var(--base-size-4, 4px)  !important; }
+  .mb-lg-1 { margin-bottom: var(--base-size-4, 4px)  !important; }
+  .mr-lg-1 { margin-right: var(--base-size-4, 4px)  !important; }
+  .ml-lg-1 { margin-left: var(--base-size-4, 4px)  !important; }
+  .mt-lg-n1 { margin-top: calc(-1*var(--base-size-4, 4px))  !important; }
+  .mb-lg-n1 { margin-bottom: calc(-1*var(--base-size-4, 4px))  !important; =
+}
+  .mr-lg-n1 { margin-right: calc(-1*var(--base-size-4, 4px))  !important; }
+  .ml-lg-n1 { margin-left: calc(-1*var(--base-size-4, 4px))  !important; }
+  .mx-lg-1 { margin-right: var(--base-size-4, 4px)  !important; margin-left=
+: var(--base-size-4, 4px)  !important; }
+  .my-lg-1 { margin-top: var(--base-size-4, 4px)  !important; margin-bottom=
+: var(--base-size-4, 4px)  !important; }
+  .m-lg-2 { margin: var(--base-size-8, 8px)  !important; }
+  .mt-lg-2 { margin-top: var(--base-size-8, 8px)  !important; }
+  .mb-lg-2 { margin-bottom: var(--base-size-8, 8px)  !important; }
+  .mr-lg-2 { margin-right: var(--base-size-8, 8px)  !important; }
+  .ml-lg-2 { margin-left: var(--base-size-8, 8px)  !important; }
+  .mt-lg-n2 { margin-top: calc(-1*var(--base-size-8, 8px))  !important; }
+  .mb-lg-n2 { margin-bottom: calc(-1*var(--base-size-8, 8px))  !important; =
+}
+  .mr-lg-n2 { margin-right: calc(-1*var(--base-size-8, 8px))  !important; }
+  .ml-lg-n2 { margin-left: calc(-1*var(--base-size-8, 8px))  !important; }
+  .mx-lg-2 { margin-right: var(--base-size-8, 8px)  !important; margin-left=
+: var(--base-size-8, 8px)  !important; }
+  .my-lg-2 { margin-top: var(--base-size-8, 8px)  !important; margin-bottom=
+: var(--base-size-8, 8px)  !important; }
+  .m-lg-3 { margin: var(--base-size-16, 16px)  !important; }
+  .mt-lg-3 { margin-top: var(--base-size-16, 16px)  !important; }
+  .mb-lg-3 { margin-bottom: var(--base-size-16, 16px)  !important; }
+  .mr-lg-3 { margin-right: var(--base-size-16, 16px)  !important; }
+  .ml-lg-3 { margin-left: var(--base-size-16, 16px)  !important; }
+  .mt-lg-n3 { margin-top: calc(-1*var(--base-size-16, 16px))  !important; }
+  .mb-lg-n3 { margin-bottom: calc(-1*var(--base-size-16, 16px))  !important=
+; }
+  .mr-lg-n3 { margin-right: calc(-1*var(--base-size-16, 16px))  !important;=
+ }
+  .ml-lg-n3 { margin-left: calc(-1*var(--base-size-16, 16px))  !important; =
+}
+  .mx-lg-3 { margin-right: var(--base-size-16, 16px)  !important; margin-le=
+ft: var(--base-size-16, 16px)  !important; }
+  .my-lg-3 { margin-top: var(--base-size-16, 16px)  !important; margin-bott=
+om: var(--base-size-16, 16px)  !important; }
+  .m-lg-4 { margin: var(--base-size-24, 24px)  !important; }
+  .mt-lg-4 { margin-top: var(--base-size-24, 24px)  !important; }
+  .mb-lg-4 { margin-bottom: var(--base-size-24, 24px)  !important; }
+  .mr-lg-4 { margin-right: var(--base-size-24, 24px)  !important; }
+  .ml-lg-4 { margin-left: var(--base-size-24, 24px)  !important; }
+  .mt-lg-n4 { margin-top: calc(-1*var(--base-size-24, 24px))  !important; }
+  .mb-lg-n4 { margin-bottom: calc(-1*var(--base-size-24, 24px))  !important=
+; }
+  .mr-lg-n4 { margin-right: calc(-1*var(--base-size-24, 24px))  !important;=
+ }
+  .ml-lg-n4 { margin-left: calc(-1*var(--base-size-24, 24px))  !important; =
+}
+  .mx-lg-4 { margin-right: var(--base-size-24, 24px)  !important; margin-le=
+ft: var(--base-size-24, 24px)  !important; }
+  .my-lg-4 { margin-top: var(--base-size-24, 24px)  !important; margin-bott=
+om: var(--base-size-24, 24px)  !important; }
+  .m-lg-5 { margin: var(--base-size-32, 32px)  !important; }
+  .mt-lg-5 { margin-top: var(--base-size-32, 32px)  !important; }
+  .mb-lg-5 { margin-bottom: var(--base-size-32, 32px)  !important; }
+  .mr-lg-5 { margin-right: var(--base-size-32, 32px)  !important; }
+  .ml-lg-5 { margin-left: var(--base-size-32, 32px)  !important; }
+  .mt-lg-n5 { margin-top: calc(-1*var(--base-size-32, 32px))  !important; }
+  .mb-lg-n5 { margin-bottom: calc(-1*var(--base-size-32, 32px))  !important=
+; }
+  .mr-lg-n5 { margin-right: calc(-1*var(--base-size-32, 32px))  !important;=
+ }
+  .ml-lg-n5 { margin-left: calc(-1*var(--base-size-32, 32px))  !important; =
+}
+  .mx-lg-5 { margin-right: var(--base-size-32, 32px)  !important; margin-le=
+ft: var(--base-size-32, 32px)  !important; }
+  .my-lg-5 { margin-top: var(--base-size-32, 32px)  !important; margin-bott=
+om: var(--base-size-32, 32px)  !important; }
+  .m-lg-6 { margin: var(--base-size-40, 40px)  !important; }
+  .mt-lg-6 { margin-top: var(--base-size-40, 40px)  !important; }
+  .mb-lg-6 { margin-bottom: var(--base-size-40, 40px)  !important; }
+  .mr-lg-6 { margin-right: var(--base-size-40, 40px)  !important; }
+  .ml-lg-6 { margin-left: var(--base-size-40, 40px)  !important; }
+  .mt-lg-n6 { margin-top: calc(-1*var(--base-size-40, 40px))  !important; }
+  .mb-lg-n6 { margin-bottom: calc(-1*var(--base-size-40, 40px))  !important=
+; }
+  .mr-lg-n6 { margin-right: calc(-1*var(--base-size-40, 40px))  !important;=
+ }
+  .ml-lg-n6 { margin-left: calc(-1*var(--base-size-40, 40px))  !important; =
+}
+  .mx-lg-6 { margin-right: var(--base-size-40, 40px)  !important; margin-le=
+ft: var(--base-size-40, 40px)  !important; }
+  .my-lg-6 { margin-top: var(--base-size-40, 40px)  !important; margin-bott=
+om: var(--base-size-40, 40px)  !important; }
+  .mt-lg-7 { margin-top: var(--base-size-48, 48px)  !important; }
+  .mb-lg-7 { margin-bottom: var(--base-size-48, 48px)  !important; }
+  .mt-lg-n7 { margin-top: calc(-1*var(--base-size-48, 48px))  !important; }
+  .mb-lg-n7 { margin-bottom: calc(-1*var(--base-size-48, 48px))  !important=
+; }
+  .my-lg-7 { margin-top: var(--base-size-48, 48px)  !important; margin-bott=
+om: var(--base-size-48, 48px)  !important; }
+  .mt-lg-8 { margin-top: var(--base-size-64, 64px)  !important; }
+  .mb-lg-8 { margin-bottom: var(--base-size-64, 64px)  !important; }
+  .mt-lg-n8 { margin-top: calc(-1*var(--base-size-64, 64px))  !important; }
+  .mb-lg-n8 { margin-bottom: calc(-1*var(--base-size-64, 64px))  !important=
+; }
+  .my-lg-8 { margin-top: var(--base-size-64, 64px)  !important; margin-bott=
+om: var(--base-size-64, 64px)  !important; }
+  .mt-lg-9 { margin-top: var(--base-size-80, 80px)  !important; }
+  .mb-lg-9 { margin-bottom: var(--base-size-80, 80px)  !important; }
+  .mt-lg-n9 { margin-top: calc(-1*var(--base-size-80, 80px))  !important; }
+  .mb-lg-n9 { margin-bottom: calc(-1*var(--base-size-80, 80px))  !important=
+; }
+  .my-lg-9 { margin-top: var(--base-size-80, 80px)  !important; margin-bott=
+om: var(--base-size-80, 80px)  !important; }
+  .mt-lg-10 { margin-top: var(--base-size-96, 96px)  !important; }
+  .mb-lg-10 { margin-bottom: var(--base-size-96, 96px)  !important; }
+  .mt-lg-n10 { margin-top: calc(-1*var(--base-size-96, 96px))  !important; =
+}
+  .mb-lg-n10 { margin-bottom: calc(-1*var(--base-size-96, 96px))  !importan=
+t; }
+  .my-lg-10 { margin-top: var(--base-size-96, 96px)  !important; margin-bot=
+tom: var(--base-size-96, 96px)  !important; }
+  .mt-lg-11 { margin-top: var(--base-size-112, 112px)  !important; }
+  .mb-lg-11 { margin-bottom: var(--base-size-112, 112px)  !important; }
+  .mt-lg-n11 { margin-top: calc(-1*var(--base-size-112, 112px))  !important=
+; }
+  .mb-lg-n11 { margin-bottom: calc(-1*var(--base-size-112, 112px))  !import=
+ant; }
+  .my-lg-11 { margin-top: var(--base-size-112, 112px)  !important; margin-b=
+ottom: var(--base-size-112, 112px)  !important; }
+  .mt-lg-12 { margin-top: var(--base-size-128, 128px)  !important; }
+  .mb-lg-12 { margin-bottom: var(--base-size-128, 128px)  !important; }
+  .mt-lg-n12 { margin-top: calc(-1*var(--base-size-128, 128px))  !important=
+; }
+  .mb-lg-n12 { margin-bottom: calc(-1*var(--base-size-128, 128px))  !import=
+ant; }
+  .my-lg-12 { margin-top: var(--base-size-128, 128px)  !important; margin-b=
+ottom: var(--base-size-128, 128px)  !important; }
+  .mx-lg-auto { margin-right: auto !important; margin-left: auto !important=
+; }
+}
+
+@media (min-width: 1280px) {
+  .m-xl-0 { margin: 0px !important; }
+  .mt-xl-0 { margin-top: 0px !important; }
+  .mb-xl-0 { margin-bottom: 0px !important; }
+  .mr-xl-0 { margin-right: 0px !important; }
+  .ml-xl-0 { margin-left: 0px !important; }
+  .mx-xl-0 { margin-right: 0px !important; margin-left: 0px !important; }
+  .my-xl-0 { margin-top: 0px !important; margin-bottom: 0px !important; }
+  .m-xl-1 { margin: var(--base-size-4, 4px)  !important; }
+  .mt-xl-1 { margin-top: var(--base-size-4, 4px)  !important; }
+  .mb-xl-1 { margin-bottom: var(--base-size-4, 4px)  !important; }
+  .mr-xl-1 { margin-right: var(--base-size-4, 4px)  !important; }
+  .ml-xl-1 { margin-left: var(--base-size-4, 4px)  !important; }
+  .mt-xl-n1 { margin-top: calc(-1*var(--base-size-4, 4px))  !important; }
+  .mb-xl-n1 { margin-bottom: calc(-1*var(--base-size-4, 4px))  !important; =
+}
+  .mr-xl-n1 { margin-right: calc(-1*var(--base-size-4, 4px))  !important; }
+  .ml-xl-n1 { margin-left: calc(-1*var(--base-size-4, 4px))  !important; }
+  .mx-xl-1 { margin-right: var(--base-size-4, 4px)  !important; margin-left=
+: var(--base-size-4, 4px)  !important; }
+  .my-xl-1 { margin-top: var(--base-size-4, 4px)  !important; margin-bottom=
+: var(--base-size-4, 4px)  !important; }
+  .m-xl-2 { margin: var(--base-size-8, 8px)  !important; }
+  .mt-xl-2 { margin-top: var(--base-size-8, 8px)  !important; }
+  .mb-xl-2 { margin-bottom: var(--base-size-8, 8px)  !important; }
+  .mr-xl-2 { margin-right: var(--base-size-8, 8px)  !important; }
+  .ml-xl-2 { margin-left: var(--base-size-8, 8px)  !important; }
+  .mt-xl-n2 { margin-top: calc(-1*var(--base-size-8, 8px))  !important; }
+  .mb-xl-n2 { margin-bottom: calc(-1*var(--base-size-8, 8px))  !important; =
+}
+  .mr-xl-n2 { margin-right: calc(-1*var(--base-size-8, 8px))  !important; }
+  .ml-xl-n2 { margin-left: calc(-1*var(--base-size-8, 8px))  !important; }
+  .mx-xl-2 { margin-right: var(--base-size-8, 8px)  !important; margin-left=
+: var(--base-size-8, 8px)  !important; }
+  .my-xl-2 { margin-top: var(--base-size-8, 8px)  !important; margin-bottom=
+: var(--base-size-8, 8px)  !important; }
+  .m-xl-3 { margin: var(--base-size-16, 16px)  !important; }
+  .mt-xl-3 { margin-top: var(--base-size-16, 16px)  !important; }
+  .mb-xl-3 { margin-bottom: var(--base-size-16, 16px)  !important; }
+  .mr-xl-3 { margin-right: var(--base-size-16, 16px)  !important; }
+  .ml-xl-3 { margin-left: var(--base-size-16, 16px)  !important; }
+  .mt-xl-n3 { margin-top: calc(-1*var(--base-size-16, 16px))  !important; }
+  .mb-xl-n3 { margin-bottom: calc(-1*var(--base-size-16, 16px))  !important=
+; }
+  .mr-xl-n3 { margin-right: calc(-1*var(--base-size-16, 16px))  !important;=
+ }
+  .ml-xl-n3 { margin-left: calc(-1*var(--base-size-16, 16px))  !important; =
+}
+  .mx-xl-3 { margin-right: var(--base-size-16, 16px)  !important; margin-le=
+ft: var(--base-size-16, 16px)  !important; }
+  .my-xl-3 { margin-top: var(--base-size-16, 16px)  !important; margin-bott=
+om: var(--base-size-16, 16px)  !important; }
+  .m-xl-4 { margin: var(--base-size-24, 24px)  !important; }
+  .mt-xl-4 { margin-top: var(--base-size-24, 24px)  !important; }
+  .mb-xl-4 { margin-bottom: var(--base-size-24, 24px)  !important; }
+  .mr-xl-4 { margin-right: var(--base-size-24, 24px)  !important; }
+  .ml-xl-4 { margin-left: var(--base-size-24, 24px)  !important; }
+  .mt-xl-n4 { margin-top: calc(-1*var(--base-size-24, 24px))  !important; }
+  .mb-xl-n4 { margin-bottom: calc(-1*var(--base-size-24, 24px))  !important=
+; }
+  .mr-xl-n4 { margin-right: calc(-1*var(--base-size-24, 24px))  !important;=
+ }
+  .ml-xl-n4 { margin-left: calc(-1*var(--base-size-24, 24px))  !important; =
+}
+  .mx-xl-4 { margin-right: var(--base-size-24, 24px)  !important; margin-le=
+ft: var(--base-size-24, 24px)  !important; }
+  .my-xl-4 { margin-top: var(--base-size-24, 24px)  !important; margin-bott=
+om: var(--base-size-24, 24px)  !important; }
+  .m-xl-5 { margin: var(--base-size-32, 32px)  !important; }
+  .mt-xl-5 { margin-top: var(--base-size-32, 32px)  !important; }
+  .mb-xl-5 { margin-bottom: var(--base-size-32, 32px)  !important; }
+  .mr-xl-5 { margin-right: var(--base-size-32, 32px)  !important; }
+  .ml-xl-5 { margin-left: var(--base-size-32, 32px)  !important; }
+  .mt-xl-n5 { margin-top: calc(-1*var(--base-size-32, 32px))  !important; }
+  .mb-xl-n5 { margin-bottom: calc(-1*var(--base-size-32, 32px))  !important=
+; }
+  .mr-xl-n5 { margin-right: calc(-1*var(--base-size-32, 32px))  !important;=
+ }
+  .ml-xl-n5 { margin-left: calc(-1*var(--base-size-32, 32px))  !important; =
+}
+  .mx-xl-5 { margin-right: var(--base-size-32, 32px)  !important; margin-le=
+ft: var(--base-size-32, 32px)  !important; }
+  .my-xl-5 { margin-top: var(--base-size-32, 32px)  !important; margin-bott=
+om: var(--base-size-32, 32px)  !important; }
+  .m-xl-6 { margin: var(--base-size-40, 40px)  !important; }
+  .mt-xl-6 { margin-top: var(--base-size-40, 40px)  !important; }
+  .mb-xl-6 { margin-bottom: var(--base-size-40, 40px)  !important; }
+  .mr-xl-6 { margin-right: var(--base-size-40, 40px)  !important; }
+  .ml-xl-6 { margin-left: var(--base-size-40, 40px)  !important; }
+  .mt-xl-n6 { margin-top: calc(-1*var(--base-size-40, 40px))  !important; }
+  .mb-xl-n6 { margin-bottom: calc(-1*var(--base-size-40, 40px))  !important=
+; }
+  .mr-xl-n6 { margin-right: calc(-1*var(--base-size-40, 40px))  !important;=
+ }
+  .ml-xl-n6 { margin-left: calc(-1*var(--base-size-40, 40px))  !important; =
+}
+  .mx-xl-6 { margin-right: var(--base-size-40, 40px)  !important; margin-le=
+ft: var(--base-size-40, 40px)  !important; }
+  .my-xl-6 { margin-top: var(--base-size-40, 40px)  !important; margin-bott=
+om: var(--base-size-40, 40px)  !important; }
+  .mt-xl-7 { margin-top: var(--base-size-48, 48px)  !important; }
+  .mb-xl-7 { margin-bottom: var(--base-size-48, 48px)  !important; }
+  .mt-xl-n7 { margin-top: calc(-1*var(--base-size-48, 48px))  !important; }
+  .mb-xl-n7 { margin-bottom: calc(-1*var(--base-size-48, 48px))  !important=
+; }
+  .my-xl-7 { margin-top: var(--base-size-48, 48px)  !important; margin-bott=
+om: var(--base-size-48, 48px)  !important; }
+  .mt-xl-8 { margin-top: var(--base-size-64, 64px)  !important; }
+  .mb-xl-8 { margin-bottom: var(--base-size-64, 64px)  !important; }
+  .mt-xl-n8 { margin-top: calc(-1*var(--base-size-64, 64px))  !important; }
+  .mb-xl-n8 { margin-bottom: calc(-1*var(--base-size-64, 64px))  !important=
+; }
+  .my-xl-8 { margin-top: var(--base-size-64, 64px)  !important; margin-bott=
+om: var(--base-size-64, 64px)  !important; }
+  .mt-xl-9 { margin-top: var(--base-size-80, 80px)  !important; }
+  .mb-xl-9 { margin-bottom: var(--base-size-80, 80px)  !important; }
+  .mt-xl-n9 { margin-top: calc(-1*var(--base-size-80, 80px))  !important; }
+  .mb-xl-n9 { margin-bottom: calc(-1*var(--base-size-80, 80px))  !important=
+; }
+  .my-xl-9 { margin-top: var(--base-size-80, 80px)  !important; margin-bott=
+om: var(--base-size-80, 80px)  !important; }
+  .mt-xl-10 { margin-top: var(--base-size-96, 96px)  !important; }
+  .mb-xl-10 { margin-bottom: var(--base-size-96, 96px)  !important; }
+  .mt-xl-n10 { margin-top: calc(-1*var(--base-size-96, 96px))  !important; =
+}
+  .mb-xl-n10 { margin-bottom: calc(-1*var(--base-size-96, 96px))  !importan=
+t; }
+  .my-xl-10 { margin-top: var(--base-size-96, 96px)  !important; margin-bot=
+tom: var(--base-size-96, 96px)  !important; }
+  .mt-xl-11 { margin-top: var(--base-size-112, 112px)  !important; }
+  .mb-xl-11 { margin-bottom: var(--base-size-112, 112px)  !important; }
+  .mt-xl-n11 { margin-top: calc(-1*var(--base-size-112, 112px))  !important=
+; }
+  .mb-xl-n11 { margin-bottom: calc(-1*var(--base-size-112, 112px))  !import=
+ant; }
+  .my-xl-11 { margin-top: var(--base-size-112, 112px)  !important; margin-b=
+ottom: var(--base-size-112, 112px)  !important; }
+  .mt-xl-12 { margin-top: var(--base-size-128, 128px)  !important; }
+  .mb-xl-12 { margin-bottom: var(--base-size-128, 128px)  !important; }
+  .mt-xl-n12 { margin-top: calc(-1*var(--base-size-128, 128px))  !important=
+; }
+  .mb-xl-n12 { margin-bottom: calc(-1*var(--base-size-128, 128px))  !import=
+ant; }
+  .my-xl-12 { margin-top: var(--base-size-128, 128px)  !important; margin-b=
+ottom: var(--base-size-128, 128px)  !important; }
+  .mx-xl-auto { margin-right: auto !important; margin-left: auto !important=
+; }
+}
+
+.m-auto { margin: auto !important; }
+
+.mt-auto { margin-top: auto !important; }
+
+.mr-auto { margin-right: auto !important; }
+
+.mb-auto { margin-bottom: auto !important; }
+
+.ml-auto { margin-left: auto !important; }
+
+.p-0 { padding: 0px !important; }
+
+.pt-0 { padding-top: 0px !important; }
+
+.pr-0 { padding-right: 0px !important; }
+
+.pb-0 { padding-bottom: 0px !important; }
+
+.pl-0 { padding-left: 0px !important; }
+
+.px-0 { padding-right: 0px !important; padding-left: 0px !important; }
+
+.py-0 { padding-top: 0px !important; padding-bottom: 0px !important; }
+
+.p-1 { padding: var(--base-size-4, 4px)  !important; }
+
+.pt-1 { padding-top: var(--base-size-4, 4px)  !important; }
+
+.pr-1 { padding-right: var(--base-size-4, 4px)  !important; }
+
+.pb-1 { padding-bottom: var(--base-size-4, 4px)  !important; }
+
+.pl-1 { padding-left: var(--base-size-4, 4px)  !important; }
+
+.px-1 { padding-right: var(--base-size-4, 4px)  !important; padding-left: v=
+ar(--base-size-4, 4px)  !important; }
+
+.py-1 { padding-top: var(--base-size-4, 4px)  !important; padding-bottom: v=
+ar(--base-size-4, 4px)  !important; }
+
+.p-2 { padding: var(--base-size-8, 8px)  !important; }
+
+.pt-2 { padding-top: var(--base-size-8, 8px)  !important; }
+
+.pr-2 { padding-right: var(--base-size-8, 8px)  !important; }
+
+.pb-2 { padding-bottom: var(--base-size-8, 8px)  !important; }
+
+.pl-2 { padding-left: var(--base-size-8, 8px)  !important; }
+
+.px-2 { padding-right: var(--base-size-8, 8px)  !important; padding-left: v=
+ar(--base-size-8, 8px)  !important; }
+
+.py-2 { padding-top: var(--base-size-8, 8px)  !important; padding-bottom: v=
+ar(--base-size-8, 8px)  !important; }
+
+.p-3 { padding: var(--base-size-16, 16px)  !important; }
+
+.pt-3 { padding-top: var(--base-size-16, 16px)  !important; }
+
+.pr-3 { padding-right: var(--base-size-16, 16px)  !important; }
+
+.pb-3 { padding-bottom: var(--base-size-16, 16px)  !important; }
+
+.pl-3 { padding-left: var(--base-size-16, 16px)  !important; }
+
+.px-3 { padding-right: var(--base-size-16, 16px)  !important; padding-left:=
+ var(--base-size-16, 16px)  !important; }
+
+.py-3 { padding-top: var(--base-size-16, 16px)  !important; padding-bottom:=
+ var(--base-size-16, 16px)  !important; }
+
+.p-4 { padding: var(--base-size-24, 24px)  !important; }
+
+.pt-4 { padding-top: var(--base-size-24, 24px)  !important; }
+
+.pr-4 { padding-right: var(--base-size-24, 24px)  !important; }
+
+.pb-4 { padding-bottom: var(--base-size-24, 24px)  !important; }
+
+.pl-4 { padding-left: var(--base-size-24, 24px)  !important; }
+
+.px-4 { padding-right: var(--base-size-24, 24px)  !important; padding-left:=
+ var(--base-size-24, 24px)  !important; }
+
+.py-4 { padding-top: var(--base-size-24, 24px)  !important; padding-bottom:=
+ var(--base-size-24, 24px)  !important; }
+
+.p-5 { padding: var(--base-size-32, 32px)  !important; }
+
+.pt-5 { padding-top: var(--base-size-32, 32px)  !important; }
+
+.pr-5 { padding-right: var(--base-size-32, 32px)  !important; }
+
+.pb-5 { padding-bottom: var(--base-size-32, 32px)  !important; }
+
+.pl-5 { padding-left: var(--base-size-32, 32px)  !important; }
+
+.px-5 { padding-right: var(--base-size-32, 32px)  !important; padding-left:=
+ var(--base-size-32, 32px)  !important; }
+
+.py-5 { padding-top: var(--base-size-32, 32px)  !important; padding-bottom:=
+ var(--base-size-32, 32px)  !important; }
+
+.p-6 { padding: var(--base-size-40, 40px)  !important; }
+
+.pt-6 { padding-top: var(--base-size-40, 40px)  !important; }
+
+.pr-6 { padding-right: var(--base-size-40, 40px)  !important; }
+
+.pb-6 { padding-bottom: var(--base-size-40, 40px)  !important; }
+
+.pl-6 { padding-left: var(--base-size-40, 40px)  !important; }
+
+.px-6 { padding-right: var(--base-size-40, 40px)  !important; padding-left:=
+ var(--base-size-40, 40px)  !important; }
+
+.py-6 { padding-top: var(--base-size-40, 40px)  !important; padding-bottom:=
+ var(--base-size-40, 40px)  !important; }
+
+.pt-7 { padding-top: var(--base-size-48, 48px)  !important; }
+
+.pr-7 { padding-right: var(--base-size-48, 48px)  !important; }
+
+.pb-7 { padding-bottom: var(--base-size-48, 48px)  !important; }
+
+.pl-7 { padding-left: var(--base-size-48, 48px)  !important; }
+
+.py-7 { padding-top: var(--base-size-48, 48px)  !important; padding-bottom:=
+ var(--base-size-48, 48px)  !important; }
+
+.pt-8 { padding-top: var(--base-size-64, 64px)  !important; }
+
+.pr-8 { padding-right: var(--base-size-64, 64px)  !important; }
+
+.pb-8 { padding-bottom: var(--base-size-64, 64px)  !important; }
+
+.pl-8 { padding-left: var(--base-size-64, 64px)  !important; }
+
+.py-8 { padding-top: var(--base-size-64, 64px)  !important; padding-bottom:=
+ var(--base-size-64, 64px)  !important; }
+
+.pt-9 { padding-top: var(--base-size-80, 80px)  !important; }
+
+.pr-9 { padding-right: var(--base-size-80, 80px)  !important; }
+
+.pb-9 { padding-bottom: var(--base-size-80, 80px)  !important; }
+
+.pl-9 { padding-left: var(--base-size-80, 80px)  !important; }
+
+.py-9 { padding-top: var(--base-size-80, 80px)  !important; padding-bottom:=
+ var(--base-size-80, 80px)  !important; }
+
+.pt-10 { padding-top: var(--base-size-96, 96px)  !important; }
+
+.pr-10 { padding-right: var(--base-size-96, 96px)  !important; }
+
+.pb-10 { padding-bottom: var(--base-size-96, 96px)  !important; }
+
+.pl-10 { padding-left: var(--base-size-96, 96px)  !important; }
+
+.py-10 { padding-top: var(--base-size-96, 96px)  !important; padding-bottom=
+: var(--base-size-96, 96px)  !important; }
+
+.pt-11 { padding-top: var(--base-size-112, 112px)  !important; }
+
+.pr-11 { padding-right: var(--base-size-112, 112px)  !important; }
+
+.pb-11 { padding-bottom: var(--base-size-112, 112px)  !important; }
+
+.pl-11 { padding-left: var(--base-size-112, 112px)  !important; }
+
+.py-11 { padding-top: var(--base-size-112, 112px)  !important; padding-bott=
+om: var(--base-size-112, 112px)  !important; }
+
+.pt-12 { padding-top: var(--base-size-128, 128px)  !important; }
+
+.pr-12 { padding-right: var(--base-size-128, 128px)  !important; }
+
+.pb-12 { padding-bottom: var(--base-size-128, 128px)  !important; }
+
+.pl-12 { padding-left: var(--base-size-128, 128px)  !important; }
+
+.py-12 { padding-top: var(--base-size-128, 128px)  !important; padding-bott=
+om: var(--base-size-128, 128px)  !important; }
+
+@media (min-width: 544px) {
+  .p-sm-0 { padding: 0px !important; }
+  .pt-sm-0 { padding-top: 0px !important; }
+  .pr-sm-0 { padding-right: 0px !important; }
+  .pb-sm-0 { padding-bottom: 0px !important; }
+  .pl-sm-0 { padding-left: 0px !important; }
+  .px-sm-0 { padding-right: 0px !important; padding-left: 0px !important; }
+  .py-sm-0 { padding-top: 0px !important; padding-bottom: 0px !important; }
+  .p-sm-1 { padding: var(--base-size-4, 4px)  !important; }
+  .pt-sm-1 { padding-top: var(--base-size-4, 4px)  !important; }
+  .pr-sm-1 { padding-right: var(--base-size-4, 4px)  !important; }
+  .pb-sm-1 { padding-bottom: var(--base-size-4, 4px)  !important; }
+  .pl-sm-1 { padding-left: var(--base-size-4, 4px)  !important; }
+  .px-sm-1 { padding-right: var(--base-size-4, 4px)  !important; padding-le=
+ft: var(--base-size-4, 4px)  !important; }
+  .py-sm-1 { padding-top: var(--base-size-4, 4px)  !important; padding-bott=
+om: var(--base-size-4, 4px)  !important; }
+  .p-sm-2 { padding: var(--base-size-8, 8px)  !important; }
+  .pt-sm-2 { padding-top: var(--base-size-8, 8px)  !important; }
+  .pr-sm-2 { padding-right: var(--base-size-8, 8px)  !important; }
+  .pb-sm-2 { padding-bottom: var(--base-size-8, 8px)  !important; }
+  .pl-sm-2 { padding-left: var(--base-size-8, 8px)  !important; }
+  .px-sm-2 { padding-right: var(--base-size-8, 8px)  !important; padding-le=
+ft: var(--base-size-8, 8px)  !important; }
+  .py-sm-2 { padding-top: var(--base-size-8, 8px)  !important; padding-bott=
+om: var(--base-size-8, 8px)  !important; }
+  .p-sm-3 { padding: var(--base-size-16, 16px)  !important; }
+  .pt-sm-3 { padding-top: var(--base-size-16, 16px)  !important; }
+  .pr-sm-3 { padding-right: var(--base-size-16, 16px)  !important; }
+  .pb-sm-3 { padding-bottom: var(--base-size-16, 16px)  !important; }
+  .pl-sm-3 { padding-left: var(--base-size-16, 16px)  !important; }
+  .px-sm-3 { padding-right: var(--base-size-16, 16px)  !important; padding-=
+left: var(--base-size-16, 16px)  !important; }
+  .py-sm-3 { padding-top: var(--base-size-16, 16px)  !important; padding-bo=
+ttom: var(--base-size-16, 16px)  !important; }
+  .p-sm-4 { padding: var(--base-size-24, 24px)  !important; }
+  .pt-sm-4 { padding-top: var(--base-size-24, 24px)  !important; }
+  .pr-sm-4 { padding-right: var(--base-size-24, 24px)  !important; }
+  .pb-sm-4 { padding-bottom: var(--base-size-24, 24px)  !important; }
+  .pl-sm-4 { padding-left: var(--base-size-24, 24px)  !important; }
+  .px-sm-4 { padding-right: var(--base-size-24, 24px)  !important; padding-=
+left: var(--base-size-24, 24px)  !important; }
+  .py-sm-4 { padding-top: var(--base-size-24, 24px)  !important; padding-bo=
+ttom: var(--base-size-24, 24px)  !important; }
+  .p-sm-5 { padding: var(--base-size-32, 32px)  !important; }
+  .pt-sm-5 { padding-top: var(--base-size-32, 32px)  !important; }
+  .pr-sm-5 { padding-right: var(--base-size-32, 32px)  !important; }
+  .pb-sm-5 { padding-bottom: var(--base-size-32, 32px)  !important; }
+  .pl-sm-5 { padding-left: var(--base-size-32, 32px)  !important; }
+  .px-sm-5 { padding-right: var(--base-size-32, 32px)  !important; padding-=
+left: var(--base-size-32, 32px)  !important; }
+  .py-sm-5 { padding-top: var(--base-size-32, 32px)  !important; padding-bo=
+ttom: var(--base-size-32, 32px)  !important; }
+  .p-sm-6 { padding: var(--base-size-40, 40px)  !important; }
+  .pt-sm-6 { padding-top: var(--base-size-40, 40px)  !important; }
+  .pr-sm-6 { padding-right: var(--base-size-40, 40px)  !important; }
+  .pb-sm-6 { padding-bottom: var(--base-size-40, 40px)  !important; }
+  .pl-sm-6 { padding-left: var(--base-size-40, 40px)  !important; }
+  .px-sm-6 { padding-right: var(--base-size-40, 40px)  !important; padding-=
+left: var(--base-size-40, 40px)  !important; }
+  .py-sm-6 { padding-top: var(--base-size-40, 40px)  !important; padding-bo=
+ttom: var(--base-size-40, 40px)  !important; }
+  .pt-sm-7 { padding-top: var(--base-size-48, 48px)  !important; }
+  .pr-sm-7 { padding-right: var(--base-size-48, 48px)  !important; }
+  .pb-sm-7 { padding-bottom: var(--base-size-48, 48px)  !important; }
+  .pl-sm-7 { padding-left: var(--base-size-48, 48px)  !important; }
+  .py-sm-7 { padding-top: var(--base-size-48, 48px)  !important; padding-bo=
+ttom: var(--base-size-48, 48px)  !important; }
+  .pt-sm-8 { padding-top: var(--base-size-64, 64px)  !important; }
+  .pr-sm-8 { padding-right: var(--base-size-64, 64px)  !important; }
+  .pb-sm-8 { padding-bottom: var(--base-size-64, 64px)  !important; }
+  .pl-sm-8 { padding-left: var(--base-size-64, 64px)  !important; }
+  .py-sm-8 { padding-top: var(--base-size-64, 64px)  !important; padding-bo=
+ttom: var(--base-size-64, 64px)  !important; }
+  .pt-sm-9 { padding-top: var(--base-size-80, 80px)  !important; }
+  .pr-sm-9 { padding-right: var(--base-size-80, 80px)  !important; }
+  .pb-sm-9 { padding-bottom: var(--base-size-80, 80px)  !important; }
+  .pl-sm-9 { padding-left: var(--base-size-80, 80px)  !important; }
+  .py-sm-9 { padding-top: var(--base-size-80, 80px)  !important; padding-bo=
+ttom: var(--base-size-80, 80px)  !important; }
+  .pt-sm-10 { padding-top: var(--base-size-96, 96px)  !important; }
+  .pr-sm-10 { padding-right: var(--base-size-96, 96px)  !important; }
+  .pb-sm-10 { padding-bottom: var(--base-size-96, 96px)  !important; }
+  .pl-sm-10 { padding-left: var(--base-size-96, 96px)  !important; }
+  .py-sm-10 { padding-top: var(--base-size-96, 96px)  !important; padding-b=
+ottom: var(--base-size-96, 96px)  !important; }
+  .pt-sm-11 { padding-top: var(--base-size-112, 112px)  !important; }
+  .pr-sm-11 { padding-right: var(--base-size-112, 112px)  !important; }
+  .pb-sm-11 { padding-bottom: var(--base-size-112, 112px)  !important; }
+  .pl-sm-11 { padding-left: var(--base-size-112, 112px)  !important; }
+  .py-sm-11 { padding-top: var(--base-size-112, 112px)  !important; padding=
+-bottom: var(--base-size-112, 112px)  !important; }
+  .pt-sm-12 { padding-top: var(--base-size-128, 128px)  !important; }
+  .pr-sm-12 { padding-right: var(--base-size-128, 128px)  !important; }
+  .pb-sm-12 { padding-bottom: var(--base-size-128, 128px)  !important; }
+  .pl-sm-12 { padding-left: var(--base-size-128, 128px)  !important; }
+  .py-sm-12 { padding-top: var(--base-size-128, 128px)  !important; padding=
+-bottom: var(--base-size-128, 128px)  !important; }
+}
+
+@media (min-width: 768px) {
+  .p-md-0 { padding: 0px !important; }
+  .pt-md-0 { padding-top: 0px !important; }
+  .pr-md-0 { padding-right: 0px !important; }
+  .pb-md-0 { padding-bottom: 0px !important; }
+  .pl-md-0 { padding-left: 0px !important; }
+  .px-md-0 { padding-right: 0px !important; padding-left: 0px !important; }
+  .py-md-0 { padding-top: 0px !important; padding-bottom: 0px !important; }
+  .p-md-1 { padding: var(--base-size-4, 4px)  !important; }
+  .pt-md-1 { padding-top: var(--base-size-4, 4px)  !important; }
+  .pr-md-1 { padding-right: var(--base-size-4, 4px)  !important; }
+  .pb-md-1 { padding-bottom: var(--base-size-4, 4px)  !important; }
+  .pl-md-1 { padding-left: var(--base-size-4, 4px)  !important; }
+  .px-md-1 { padding-right: var(--base-size-4, 4px)  !important; padding-le=
+ft: var(--base-size-4, 4px)  !important; }
+  .py-md-1 { padding-top: var(--base-size-4, 4px)  !important; padding-bott=
+om: var(--base-size-4, 4px)  !important; }
+  .p-md-2 { padding: var(--base-size-8, 8px)  !important; }
+  .pt-md-2 { padding-top: var(--base-size-8, 8px)  !important; }
+  .pr-md-2 { padding-right: var(--base-size-8, 8px)  !important; }
+  .pb-md-2 { padding-bottom: var(--base-size-8, 8px)  !important; }
+  .pl-md-2 { padding-left: var(--base-size-8, 8px)  !important; }
+  .px-md-2 { padding-right: var(--base-size-8, 8px)  !important; padding-le=
+ft: var(--base-size-8, 8px)  !important; }
+  .py-md-2 { padding-top: var(--base-size-8, 8px)  !important; padding-bott=
+om: var(--base-size-8, 8px)  !important; }
+  .p-md-3 { padding: var(--base-size-16, 16px)  !important; }
+  .pt-md-3 { padding-top: var(--base-size-16, 16px)  !important; }
+  .pr-md-3 { padding-right: var(--base-size-16, 16px)  !important; }
+  .pb-md-3 { padding-bottom: var(--base-size-16, 16px)  !important; }
+  .pl-md-3 { padding-left: var(--base-size-16, 16px)  !important; }
+  .px-md-3 { padding-right: var(--base-size-16, 16px)  !important; padding-=
+left: var(--base-size-16, 16px)  !important; }
+  .py-md-3 { padding-top: var(--base-size-16, 16px)  !important; padding-bo=
+ttom: var(--base-size-16, 16px)  !important; }
+  .p-md-4 { padding: var(--base-size-24, 24px)  !important; }
+  .pt-md-4 { padding-top: var(--base-size-24, 24px)  !important; }
+  .pr-md-4 { padding-right: var(--base-size-24, 24px)  !important; }
+  .pb-md-4 { padding-bottom: var(--base-size-24, 24px)  !important; }
+  .pl-md-4 { padding-left: var(--base-size-24, 24px)  !important; }
+  .px-md-4 { padding-right: var(--base-size-24, 24px)  !important; padding-=
+left: var(--base-size-24, 24px)  !important; }
+  .py-md-4 { padding-top: var(--base-size-24, 24px)  !important; padding-bo=
+ttom: var(--base-size-24, 24px)  !important; }
+  .p-md-5 { padding: var(--base-size-32, 32px)  !important; }
+  .pt-md-5 { padding-top: var(--base-size-32, 32px)  !important; }
+  .pr-md-5 { padding-right: var(--base-size-32, 32px)  !important; }
+  .pb-md-5 { padding-bottom: var(--base-size-32, 32px)  !important; }
+  .pl-md-5 { padding-left: var(--base-size-32, 32px)  !important; }
+  .px-md-5 { padding-right: var(--base-size-32, 32px)  !important; padding-=
+left: var(--base-size-32, 32px)  !important; }
+  .py-md-5 { padding-top: var(--base-size-32, 32px)  !important; padding-bo=
+ttom: var(--base-size-32, 32px)  !important; }
+  .p-md-6 { padding: var(--base-size-40, 40px)  !important; }
+  .pt-md-6 { padding-top: var(--base-size-40, 40px)  !important; }
+  .pr-md-6 { padding-right: var(--base-size-40, 40px)  !important; }
+  .pb-md-6 { padding-bottom: var(--base-size-40, 40px)  !important; }
+  .pl-md-6 { padding-left: var(--base-size-40, 40px)  !important; }
+  .px-md-6 { padding-right: var(--base-size-40, 40px)  !important; padding-=
+left: var(--base-size-40, 40px)  !important; }
+  .py-md-6 { padding-top: var(--base-size-40, 40px)  !important; padding-bo=
+ttom: var(--base-size-40, 40px)  !important; }
+  .pt-md-7 { padding-top: var(--base-size-48, 48px)  !important; }
+  .pr-md-7 { padding-right: var(--base-size-48, 48px)  !important; }
+  .pb-md-7 { padding-bottom: var(--base-size-48, 48px)  !important; }
+  .pl-md-7 { padding-left: var(--base-size-48, 48px)  !important; }
+  .py-md-7 { padding-top: var(--base-size-48, 48px)  !important; padding-bo=
+ttom: var(--base-size-48, 48px)  !important; }
+  .pt-md-8 { padding-top: var(--base-size-64, 64px)  !important; }
+  .pr-md-8 { padding-right: var(--base-size-64, 64px)  !important; }
+  .pb-md-8 { padding-bottom: var(--base-size-64, 64px)  !important; }
+  .pl-md-8 { padding-left: var(--base-size-64, 64px)  !important; }
+  .py-md-8 { padding-top: var(--base-size-64, 64px)  !important; padding-bo=
+ttom: var(--base-size-64, 64px)  !important; }
+  .pt-md-9 { padding-top: var(--base-size-80, 80px)  !important; }
+  .pr-md-9 { padding-right: var(--base-size-80, 80px)  !important; }
+  .pb-md-9 { padding-bottom: var(--base-size-80, 80px)  !important; }
+  .pl-md-9 { padding-left: var(--base-size-80, 80px)  !important; }
+  .py-md-9 { padding-top: var(--base-size-80, 80px)  !important; padding-bo=
+ttom: var(--base-size-80, 80px)  !important; }
+  .pt-md-10 { padding-top: var(--base-size-96, 96px)  !important; }
+  .pr-md-10 { padding-right: var(--base-size-96, 96px)  !important; }
+  .pb-md-10 { padding-bottom: var(--base-size-96, 96px)  !important; }
+  .pl-md-10 { padding-left: var(--base-size-96, 96px)  !important; }
+  .py-md-10 { padding-top: var(--base-size-96, 96px)  !important; padding-b=
+ottom: var(--base-size-96, 96px)  !important; }
+  .pt-md-11 { padding-top: var(--base-size-112, 112px)  !important; }
+  .pr-md-11 { padding-right: var(--base-size-112, 112px)  !important; }
+  .pb-md-11 { padding-bottom: var(--base-size-112, 112px)  !important; }
+  .pl-md-11 { padding-left: var(--base-size-112, 112px)  !important; }
+  .py-md-11 { padding-top: var(--base-size-112, 112px)  !important; padding=
+-bottom: var(--base-size-112, 112px)  !important; }
+  .pt-md-12 { padding-top: var(--base-size-128, 128px)  !important; }
+  .pr-md-12 { padding-right: var(--base-size-128, 128px)  !important; }
+  .pb-md-12 { padding-bottom: var(--base-size-128, 128px)  !important; }
+  .pl-md-12 { padding-left: var(--base-size-128, 128px)  !important; }
+  .py-md-12 { padding-top: var(--base-size-128, 128px)  !important; padding=
+-bottom: var(--base-size-128, 128px)  !important; }
+}
+
+@media (min-width: 1012px) {
+  .p-lg-0 { padding: 0px !important; }
+  .pt-lg-0 { padding-top: 0px !important; }
+  .pr-lg-0 { padding-right: 0px !important; }
+  .pb-lg-0 { padding-bottom: 0px !important; }
+  .pl-lg-0 { padding-left: 0px !important; }
+  .px-lg-0 { padding-right: 0px !important; padding-left: 0px !important; }
+  .py-lg-0 { padding-top: 0px !important; padding-bottom: 0px !important; }
+  .p-lg-1 { padding: var(--base-size-4, 4px)  !important; }
+  .pt-lg-1 { padding-top: var(--base-size-4, 4px)  !important; }
+  .pr-lg-1 { padding-right: var(--base-size-4, 4px)  !important; }
+  .pb-lg-1 { padding-bottom: var(--base-size-4, 4px)  !important; }
+  .pl-lg-1 { padding-left: var(--base-size-4, 4px)  !important; }
+  .px-lg-1 { padding-right: var(--base-size-4, 4px)  !important; padding-le=
+ft: var(--base-size-4, 4px)  !important; }
+  .py-lg-1 { padding-top: var(--base-size-4, 4px)  !important; padding-bott=
+om: var(--base-size-4, 4px)  !important; }
+  .p-lg-2 { padding: var(--base-size-8, 8px)  !important; }
+  .pt-lg-2 { padding-top: var(--base-size-8, 8px)  !important; }
+  .pr-lg-2 { padding-right: var(--base-size-8, 8px)  !important; }
+  .pb-lg-2 { padding-bottom: var(--base-size-8, 8px)  !important; }
+  .pl-lg-2 { padding-left: var(--base-size-8, 8px)  !important; }
+  .px-lg-2 { padding-right: var(--base-size-8, 8px)  !important; padding-le=
+ft: var(--base-size-8, 8px)  !important; }
+  .py-lg-2 { padding-top: var(--base-size-8, 8px)  !important; padding-bott=
+om: var(--base-size-8, 8px)  !important; }
+  .p-lg-3 { padding: var(--base-size-16, 16px)  !important; }
+  .pt-lg-3 { padding-top: var(--base-size-16, 16px)  !important; }
+  .pr-lg-3 { padding-right: var(--base-size-16, 16px)  !important; }
+  .pb-lg-3 { padding-bottom: var(--base-size-16, 16px)  !important; }
+  .pl-lg-3 { padding-left: var(--base-size-16, 16px)  !important; }
+  .px-lg-3 { padding-right: var(--base-size-16, 16px)  !important; padding-=
+left: var(--base-size-16, 16px)  !important; }
+  .py-lg-3 { padding-top: var(--base-size-16, 16px)  !important; padding-bo=
+ttom: var(--base-size-16, 16px)  !important; }
+  .p-lg-4 { padding: var(--base-size-24, 24px)  !important; }
+  .pt-lg-4 { padding-top: var(--base-size-24, 24px)  !important; }
+  .pr-lg-4 { padding-right: var(--base-size-24, 24px)  !important; }
+  .pb-lg-4 { padding-bottom: var(--base-size-24, 24px)  !important; }
+  .pl-lg-4 { padding-left: var(--base-size-24, 24px)  !important; }
+  .px-lg-4 { padding-right: var(--base-size-24, 24px)  !important; padding-=
+left: var(--base-size-24, 24px)  !important; }
+  .py-lg-4 { padding-top: var(--base-size-24, 24px)  !important; padding-bo=
+ttom: var(--base-size-24, 24px)  !important; }
+  .p-lg-5 { padding: var(--base-size-32, 32px)  !important; }
+  .pt-lg-5 { padding-top: var(--base-size-32, 32px)  !important; }
+  .pr-lg-5 { padding-right: var(--base-size-32, 32px)  !important; }
+  .pb-lg-5 { padding-bottom: var(--base-size-32, 32px)  !important; }
+  .pl-lg-5 { padding-left: var(--base-size-32, 32px)  !important; }
+  .px-lg-5 { padding-right: var(--base-size-32, 32px)  !important; padding-=
+left: var(--base-size-32, 32px)  !important; }
+  .py-lg-5 { padding-top: var(--base-size-32, 32px)  !important; padding-bo=
+ttom: var(--base-size-32, 32px)  !important; }
+  .p-lg-6 { padding: var(--base-size-40, 40px)  !important; }
+  .pt-lg-6 { padding-top: var(--base-size-40, 40px)  !important; }
+  .pr-lg-6 { padding-right: var(--base-size-40, 40px)  !important; }
+  .pb-lg-6 { padding-bottom: var(--base-size-40, 40px)  !important; }
+  .pl-lg-6 { padding-left: var(--base-size-40, 40px)  !important; }
+  .px-lg-6 { padding-right: var(--base-size-40, 40px)  !important; padding-=
+left: var(--base-size-40, 40px)  !important; }
+  .py-lg-6 { padding-top: var(--base-size-40, 40px)  !important; padding-bo=
+ttom: var(--base-size-40, 40px)  !important; }
+  .pt-lg-7 { padding-top: var(--base-size-48, 48px)  !important; }
+  .pr-lg-7 { padding-right: var(--base-size-48, 48px)  !important; }
+  .pb-lg-7 { padding-bottom: var(--base-size-48, 48px)  !important; }
+  .pl-lg-7 { padding-left: var(--base-size-48, 48px)  !important; }
+  .py-lg-7 { padding-top: var(--base-size-48, 48px)  !important; padding-bo=
+ttom: var(--base-size-48, 48px)  !important; }
+  .pt-lg-8 { padding-top: var(--base-size-64, 64px)  !important; }
+  .pr-lg-8 { padding-right: var(--base-size-64, 64px)  !important; }
+  .pb-lg-8 { padding-bottom: var(--base-size-64, 64px)  !important; }
+  .pl-lg-8 { padding-left: var(--base-size-64, 64px)  !important; }
+  .py-lg-8 { padding-top: var(--base-size-64, 64px)  !important; padding-bo=
+ttom: var(--base-size-64, 64px)  !important; }
+  .pt-lg-9 { padding-top: var(--base-size-80, 80px)  !important; }
+  .pr-lg-9 { padding-right: var(--base-size-80, 80px)  !important; }
+  .pb-lg-9 { padding-bottom: var(--base-size-80, 80px)  !important; }
+  .pl-lg-9 { padding-left: var(--base-size-80, 80px)  !important; }
+  .py-lg-9 { padding-top: var(--base-size-80, 80px)  !important; padding-bo=
+ttom: var(--base-size-80, 80px)  !important; }
+  .pt-lg-10 { padding-top: var(--base-size-96, 96px)  !important; }
+  .pr-lg-10 { padding-right: var(--base-size-96, 96px)  !important; }
+  .pb-lg-10 { padding-bottom: var(--base-size-96, 96px)  !important; }
+  .pl-lg-10 { padding-left: var(--base-size-96, 96px)  !important; }
+  .py-lg-10 { padding-top: var(--base-size-96, 96px)  !important; padding-b=
+ottom: var(--base-size-96, 96px)  !important; }
+  .pt-lg-11 { padding-top: var(--base-size-112, 112px)  !important; }
+  .pr-lg-11 { padding-right: var(--base-size-112, 112px)  !important; }
+  .pb-lg-11 { padding-bottom: var(--base-size-112, 112px)  !important; }
+  .pl-lg-11 { padding-left: var(--base-size-112, 112px)  !important; }
+  .py-lg-11 { padding-top: var(--base-size-112, 112px)  !important; padding=
+-bottom: var(--base-size-112, 112px)  !important; }
+  .pt-lg-12 { padding-top: var(--base-size-128, 128px)  !important; }
+  .pr-lg-12 { padding-right: var(--base-size-128, 128px)  !important; }
+  .pb-lg-12 { padding-bottom: var(--base-size-128, 128px)  !important; }
+  .pl-lg-12 { padding-left: var(--base-size-128, 128px)  !important; }
+  .py-lg-12 { padding-top: var(--base-size-128, 128px)  !important; padding=
+-bottom: var(--base-size-128, 128px)  !important; }
+}
+
+@media (min-width: 1280px) {
+  .p-xl-0 { padding: 0px !important; }
+  .pt-xl-0 { padding-top: 0px !important; }
+  .pr-xl-0 { padding-right: 0px !important; }
+  .pb-xl-0 { padding-bottom: 0px !important; }
+  .pl-xl-0 { padding-left: 0px !important; }
+  .px-xl-0 { padding-right: 0px !important; padding-left: 0px !important; }
+  .py-xl-0 { padding-top: 0px !important; padding-bottom: 0px !important; }
+  .p-xl-1 { padding: var(--base-size-4, 4px)  !important; }
+  .pt-xl-1 { padding-top: var(--base-size-4, 4px)  !important; }
+  .pr-xl-1 { padding-right: var(--base-size-4, 4px)  !important; }
+  .pb-xl-1 { padding-bottom: var(--base-size-4, 4px)  !important; }
+  .pl-xl-1 { padding-left: var(--base-size-4, 4px)  !important; }
+  .px-xl-1 { padding-right: var(--base-size-4, 4px)  !important; padding-le=
+ft: var(--base-size-4, 4px)  !important; }
+  .py-xl-1 { padding-top: var(--base-size-4, 4px)  !important; padding-bott=
+om: var(--base-size-4, 4px)  !important; }
+  .p-xl-2 { padding: var(--base-size-8, 8px)  !important; }
+  .pt-xl-2 { padding-top: var(--base-size-8, 8px)  !important; }
+  .pr-xl-2 { padding-right: var(--base-size-8, 8px)  !important; }
+  .pb-xl-2 { padding-bottom: var(--base-size-8, 8px)  !important; }
+  .pl-xl-2 { padding-left: var(--base-size-8, 8px)  !important; }
+  .px-xl-2 { padding-right: var(--base-size-8, 8px)  !important; padding-le=
+ft: var(--base-size-8, 8px)  !important; }
+  .py-xl-2 { padding-top: var(--base-size-8, 8px)  !important; padding-bott=
+om: var(--base-size-8, 8px)  !important; }
+  .p-xl-3 { padding: var(--base-size-16, 16px)  !important; }
+  .pt-xl-3 { padding-top: var(--base-size-16, 16px)  !important; }
+  .pr-xl-3 { padding-right: var(--base-size-16, 16px)  !important; }
+  .pb-xl-3 { padding-bottom: var(--base-size-16, 16px)  !important; }
+  .pl-xl-3 { padding-left: var(--base-size-16, 16px)  !important; }
+  .px-xl-3 { padding-right: var(--base-size-16, 16px)  !important; padding-=
+left: var(--base-size-16, 16px)  !important; }
+  .py-xl-3 { padding-top: var(--base-size-16, 16px)  !important; padding-bo=
+ttom: var(--base-size-16, 16px)  !important; }
+  .p-xl-4 { padding: var(--base-size-24, 24px)  !important; }
+  .pt-xl-4 { padding-top: var(--base-size-24, 24px)  !important; }
+  .pr-xl-4 { padding-right: var(--base-size-24, 24px)  !important; }
+  .pb-xl-4 { padding-bottom: var(--base-size-24, 24px)  !important; }
+  .pl-xl-4 { padding-left: var(--base-size-24, 24px)  !important; }
+  .px-xl-4 { padding-right: var(--base-size-24, 24px)  !important; padding-=
+left: var(--base-size-24, 24px)  !important; }
+  .py-xl-4 { padding-top: var(--base-size-24, 24px)  !important; padding-bo=
+ttom: var(--base-size-24, 24px)  !important; }
+  .p-xl-5 { padding: var(--base-size-32, 32px)  !important; }
+  .pt-xl-5 { padding-top: var(--base-size-32, 32px)  !important; }
+  .pr-xl-5 { padding-right: var(--base-size-32, 32px)  !important; }
+  .pb-xl-5 { padding-bottom: var(--base-size-32, 32px)  !important; }
+  .pl-xl-5 { padding-left: var(--base-size-32, 32px)  !important; }
+  .px-xl-5 { padding-right: var(--base-size-32, 32px)  !important; padding-=
+left: var(--base-size-32, 32px)  !important; }
+  .py-xl-5 { padding-top: var(--base-size-32, 32px)  !important; padding-bo=
+ttom: var(--base-size-32, 32px)  !important; }
+  .p-xl-6 { padding: var(--base-size-40, 40px)  !important; }
+  .pt-xl-6 { padding-top: var(--base-size-40, 40px)  !important; }
+  .pr-xl-6 { padding-right: var(--base-size-40, 40px)  !important; }
+  .pb-xl-6 { padding-bottom: var(--base-size-40, 40px)  !important; }
+  .pl-xl-6 { padding-left: var(--base-size-40, 40px)  !important; }
+  .px-xl-6 { padding-right: var(--base-size-40, 40px)  !important; padding-=
+left: var(--base-size-40, 40px)  !important; }
+  .py-xl-6 { padding-top: var(--base-size-40, 40px)  !important; padding-bo=
+ttom: var(--base-size-40, 40px)  !important; }
+  .pt-xl-7 { padding-top: var(--base-size-48, 48px)  !important; }
+  .pr-xl-7 { padding-right: var(--base-size-48, 48px)  !important; }
+  .pb-xl-7 { padding-bottom: var(--base-size-48, 48px)  !important; }
+  .pl-xl-7 { padding-left: var(--base-size-48, 48px)  !important; }
+  .py-xl-7 { padding-top: var(--base-size-48, 48px)  !important; padding-bo=
+ttom: var(--base-size-48, 48px)  !important; }
+  .pt-xl-8 { padding-top: var(--base-size-64, 64px)  !important; }
+  .pr-xl-8 { padding-right: var(--base-size-64, 64px)  !important; }
+  .pb-xl-8 { padding-bottom: var(--base-size-64, 64px)  !important; }
+  .pl-xl-8 { padding-left: var(--base-size-64, 64px)  !important; }
+  .py-xl-8 { padding-top: var(--base-size-64, 64px)  !important; padding-bo=
+ttom: var(--base-size-64, 64px)  !important; }
+  .pt-xl-9 { padding-top: var(--base-size-80, 80px)  !important; }
+  .pr-xl-9 { padding-right: var(--base-size-80, 80px)  !important; }
+  .pb-xl-9 { padding-bottom: var(--base-size-80, 80px)  !important; }
+  .pl-xl-9 { padding-left: var(--base-size-80, 80px)  !important; }
+  .py-xl-9 { padding-top: var(--base-size-80, 80px)  !important; padding-bo=
+ttom: var(--base-size-80, 80px)  !important; }
+  .pt-xl-10 { padding-top: var(--base-size-96, 96px)  !important; }
+  .pr-xl-10 { padding-right: var(--base-size-96, 96px)  !important; }
+  .pb-xl-10 { padding-bottom: var(--base-size-96, 96px)  !important; }
+  .pl-xl-10 { padding-left: var(--base-size-96, 96px)  !important; }
+  .py-xl-10 { padding-top: var(--base-size-96, 96px)  !important; padding-b=
+ottom: var(--base-size-96, 96px)  !important; }
+  .pt-xl-11 { padding-top: var(--base-size-112, 112px)  !important; }
+  .pr-xl-11 { padding-right: var(--base-size-112, 112px)  !important; }
+  .pb-xl-11 { padding-bottom: var(--base-size-112, 112px)  !important; }
+  .pl-xl-11 { padding-left: var(--base-size-112, 112px)  !important; }
+  .py-xl-11 { padding-top: var(--base-size-112, 112px)  !important; padding=
+-bottom: var(--base-size-112, 112px)  !important; }
+  .pt-xl-12 { padding-top: var(--base-size-128, 128px)  !important; }
+  .pr-xl-12 { padding-right: var(--base-size-128, 128px)  !important; }
+  .pb-xl-12 { padding-bottom: var(--base-size-128, 128px)  !important; }
+  .pl-xl-12 { padding-left: var(--base-size-128, 128px)  !important; }
+  .py-xl-12 { padding-top: var(--base-size-128, 128px)  !important; padding=
+-bottom: var(--base-size-128, 128px)  !important; }
+}
+
+.p-responsive { padding-right: var(--base-size-16, 16px)  !important; paddi=
+ng-left: var(--base-size-16, 16px)  !important; }
+
+@media (min-width: 544px) {
+  .p-responsive { padding-right: var(--base-size-40, 40px)  !important; pad=
+ding-left: var(--base-size-40, 40px)  !important; }
+}
+
+@media (min-width: 1012px) {
+  .p-responsive { padding-right: var(--base-size-16, 16px)  !important; pad=
+ding-left: var(--base-size-16, 16px)  !important; }
+}
+
+.h1 { font-size: var(--h1-size-mobile, 26px)  !important; }
+
+@media (min-width: 768px) {
+  .h1 { font-size: var(--h1-size, 32px)  !important; }
+}
+
+.h2 { font-size: var(--h2-size-mobile, 22px)  !important; }
+
+@media (min-width: 768px) {
+  .h2 { font-size: var(--h2-size, 24px)  !important; }
+}
+
+.h3 { font-size: var(--h3-size-mobile, 18px)  !important; }
+
+@media (min-width: 768px) {
+  .h3 { font-size: var(--h3-size, 20px)  !important; }
+}
+
+.h4 { font-size: var(--h4-size, 16px)  !important; }
+
+.h5 { font-size: var(--h5-size, 14px)  !important; }
+
+.h6 { font-size: var(--h6-size, 12px)  !important; }
+
+.h1, .h2, .h3, .h4, .h5, .h6 { font-weight: var(--base-text-weight-semibold=
+, var(--base-text-weight-semibold, 600))  !important; }
+
+.f1 { font-size: var(--h1-size-mobile, 26px)  !important; }
+
+@media (min-width: 768px) {
+  .f1 { font-size: var(--h1-size, 32px)  !important; }
+}
+
+.f2 { font-size: var(--h2-size-mobile, 22px)  !important; }
+
+@media (min-width: 768px) {
+  .f2 { font-size: var(--h2-size, 24px)  !important; }
+}
+
+.f3 { font-size: var(--h3-size-mobile, 18px)  !important; }
+
+@media (min-width: 768px) {
+  .f3 { font-size: var(--h3-size, 20px)  !important; }
+}
+
+.f4 { font-size: var(--h4-size, 16px)  !important; }
+
+@media (min-width: 768px) {
+  .f4 { font-size: var(--h4-size, 16px)  !important; }
+}
+
+.f5 { font-size: var(--h5-size, 14px)  !important; }
+
+.f6 { font-size: var(--h6-size, 12px)  !important; }
+
+.f00-light { font-size: var(--h00-size-mobile, 40px)  !important; font-weig=
+ht: var(--base-text-weight-light, var(--base-text-weight-light, 300))  !imp=
+ortant; }
+
+@media (min-width: 768px) {
+  .f00-light { font-size: var(--h00-size, 48px)  !important; }
+}
+
+.f0-light { font-size: var(--h0-size-mobile, 32px)  !important; font-weight=
+: var(--base-text-weight-light, var(--base-text-weight-light, 300))  !impor=
+tant; }
+
+@media (min-width: 768px) {
+  .f0-light { font-size: var(--h0-size, 40px)  !important; }
+}
+
+.f1-light { font-size: var(--h1-size-mobile, 26px)  !important; font-weight=
+: var(--base-text-weight-light, var(--base-text-weight-light, 300))  !impor=
+tant; }
+
+@media (min-width: 768px) {
+  .f1-light { font-size: var(--h1-size, 32px)  !important; }
+}
+
+.f2-light { font-size: var(--h2-size-mobile, 22px)  !important; font-weight=
+: var(--base-text-weight-light, var(--base-text-weight-light, 300))  !impor=
+tant; }
+
+@media (min-width: 768px) {
+  .f2-light { font-size: var(--h2-size, 24px)  !important; }
+}
+
+.f3-light { font-size: var(--h3-size-mobile, 18px)  !important; font-weight=
+: var(--base-text-weight-light, var(--base-text-weight-light, 300))  !impor=
+tant; }
+
+@media (min-width: 768px) {
+  .f3-light { font-size: var(--h3-size, 20px)  !important; }
+}
+
+.text-small { font-size: var(--h6-size, 12px)  !important; }
+
+.lead { margin-bottom: 30px; font-size: var(--h3-size, 20px); font-weight: =
+var(--base-text-weight-light, var(--base-text-weight-light, 300)); }
+
+.lh-condensed-ultra { line-height: 1 !important; }
+
+.lh-condensed { line-height: 1.25 !important; }
+
+.lh-default { line-height: 1.5 !important; }
+
+.lh-0 { line-height: 0 !important; }
+
+@media (min-width: 544px) {
+  .lh-sm-condensed-ultra { line-height: 1 !important; }
+  .lh-sm-condensed { line-height: 1.25 !important; }
+  .lh-sm-default { line-height: 1.5 !important; }
+  .lh-sm-0 { line-height: 0 !important; }
+}
+
+@media (min-width: 768px) {
+  .lh-md-condensed-ultra { line-height: 1 !important; }
+  .lh-md-condensed { line-height: 1.25 !important; }
+  .lh-md-default { line-height: 1.5 !important; }
+  .lh-md-0 { line-height: 0 !important; }
+}
+
+@media (min-width: 1012px) {
+  .lh-lg-condensed-ultra { line-height: 1 !important; }
+  .lh-lg-condensed { line-height: 1.25 !important; }
+  .lh-lg-default { line-height: 1.5 !important; }
+  .lh-lg-0 { line-height: 0 !important; }
+}
+
+@media (min-width: 1280px) {
+  .lh-xl-condensed-ultra { line-height: 1 !important; }
+  .lh-xl-condensed { line-height: 1.25 !important; }
+  .lh-xl-default { line-height: 1.5 !important; }
+  .lh-xl-0 { line-height: 0 !important; }
+}
+
+.text-right { text-align: right !important; }
+
+.text-left { text-align: left !important; }
+
+.text-center { text-align: center !important; }
+
+@media (min-width: 544px) {
+  .text-sm-right { text-align: right !important; }
+  .text-sm-left { text-align: left !important; }
+  .text-sm-center { text-align: center !important; }
+}
+
+@media (min-width: 768px) {
+  .text-md-right { text-align: right !important; }
+  .text-md-left { text-align: left !important; }
+  .text-md-center { text-align: center !important; }
+}
+
+@media (min-width: 1012px) {
+  .text-lg-right { text-align: right !important; }
+  .text-lg-left { text-align: left !important; }
+  .text-lg-center { text-align: center !important; }
+}
+
+@media (min-width: 1280px) {
+  .text-xl-right { text-align: right !important; }
+  .text-xl-left { text-align: left !important; }
+  .text-xl-center { text-align: center !important; }
+}
+
+.text-normal { font-weight: var(--base-text-weight-normal, 400)  !important=
+; }
+
+.text-bold { font-weight: var(--base-text-weight-semibold, 600)  !important=
+; }
+
+.text-semibold { font-weight: var(--base-text-weight-medium, 500)  !importa=
+nt; }
+
+.text-light { font-weight: var(--base-text-weight-light, 300)  !important; =
+}
+
+.text-italic { font-style: italic !important; }
+
+.text-uppercase { text-transform: uppercase !important; }
+
+.text-underline { text-decoration: underline !important; }
+
+.no-underline { text-decoration: none !important; }
+
+.no-wrap { white-space: nowrap !important; }
+
+.ws-normal { white-space: normal !important; }
+
+.wb-break-word { word-break: break-word !important; overflow-wrap: break-wo=
+rd !important; }
+
+.wb-break-all { word-break: break-all !important; }
+
+.text-emphasized { font-weight: var(--base-text-weight-semibold, var(--base=
+-text-weight-semibold, 600)); }
+
+.list-style-none { list-style: none !important; }
+
+.text-mono { font-family: ui-monospace, SFMono-Regular, "SF Mono", Menlo, C=
+onsolas, "Liberation Mono", monospace !important; }
+
+.user-select-none { user-select: none !important; }
+
+.text-capitalize { text-transform: capitalize !important; }
+
+.d-block { display: block !important; }
+
+.d-flex { display: flex !important; }
+
+.d-inline { display: inline !important; }
+
+.d-inline-block { display: inline-block !important; }
+
+.d-inline-flex { display: inline-flex !important; }
+
+.d-none { display: none !important; }
+
+.d-table { display: table !important; }
+
+.d-table-cell { display: table-cell !important; }
+
+@media (min-width: 544px) {
+  .d-sm-block { display: block !important; }
+  .d-sm-flex { display: flex !important; }
+  .d-sm-inline { display: inline !important; }
+  .d-sm-inline-block { display: inline-block !important; }
+  .d-sm-inline-flex { display: inline-flex !important; }
+  .d-sm-none { display: none !important; }
+  .d-sm-table { display: table !important; }
+  .d-sm-table-cell { display: table-cell !important; }
+}
+
+@media (min-width: 768px) {
+  .d-md-block { display: block !important; }
+  .d-md-flex { display: flex !important; }
+  .d-md-inline { display: inline !important; }
+  .d-md-inline-block { display: inline-block !important; }
+  .d-md-inline-flex { display: inline-flex !important; }
+  .d-md-none { display: none !important; }
+  .d-md-table { display: table !important; }
+  .d-md-table-cell { display: table-cell !important; }
+}
+
+@media (min-width: 1012px) {
+  .d-lg-block { display: block !important; }
+  .d-lg-flex { display: flex !important; }
+  .d-lg-inline { display: inline !important; }
+  .d-lg-inline-block { display: inline-block !important; }
+  .d-lg-inline-flex { display: inline-flex !important; }
+  .d-lg-none { display: none !important; }
+  .d-lg-table { display: table !important; }
+  .d-lg-table-cell { display: table-cell !important; }
+}
+
+@media (min-width: 1280px) {
+  .d-xl-block { display: block !important; }
+  .d-xl-flex { display: flex !important; }
+  .d-xl-inline { display: inline !important; }
+  .d-xl-inline-block { display: inline-block !important; }
+  .d-xl-inline-flex { display: inline-flex !important; }
+  .d-xl-none { display: none !important; }
+  .d-xl-table { display: table !important; }
+  .d-xl-table-cell { display: table-cell !important; }
+}
+
+.v-hidden { visibility: hidden !important; }
+
+.v-visible { visibility: visible !important; }
+
+@media (max-width: 543.98px) {
+  .hide-sm { display: none !important; }
+}
+
+@media (min-width: 544px) and (max-width: 767.98px) {
+  .hide-md { display: none !important; }
+}
+
+@media (min-width: 768px) and (max-width: 1011.98px) {
+  .hide-lg { display: none !important; }
+}
+
+@media (min-width: 1012px) {
+  .hide-xl { display: none !important; }
+}
+
+.show-whenNarrow, .show-whenRegular, .show-whenWide, .show-whenRegular.hide=
+-whenWide { display: none !important; }
+
+.hide-whenNarrow, .hide-whenRegular, .hide-whenWide { display: block !impor=
+tant; }
+
+@media (max-width: 767.98px) {
+  .show-whenNarrow { display: block !important; }
+  .hide-whenNarrow { display: none !important; }
+}
+
+@media (min-width: 768px) {
+  .show-whenRegular, .show-whenRegular.hide-whenWide { display: block !impo=
+rtant; }
+  .hide-whenRegular { display: none !important; }
+}
+
+@media (min-width: 1280px) {
+  .show-whenWide { display: block !important; }
+  .hide-whenWide, .show-whenRegular.hide-whenWide { display: none !importan=
+t; }
+}
+
+.table-fixed { table-layout: fixed !important; }
+
+.sr-only { position: absolute; width: 1px; height: 1px; padding: 0px; overf=
+low: hidden; clip: rect(0px, 0px, 0px, 0px); overflow-wrap: normal; border:=
+ 0px; }
+
+.show-on-focus { position: absolute; width: 1px; height: 1px; margin: 0px; =
+overflow: hidden; clip: rect(1px, 1px, 1px, 1px); }
+
+.show-on-focus:focus { z-index: 999; width: auto; height: auto; clip: auto;=
+ }
+
+.suggester { position: relative; top: 0px; left: 0px; min-width: 180px; pad=
+ding: 0px; margin: 24px 0px 0px; list-style: none; cursor: pointer; backgro=
+und: var(--color-canvas-overlay); border: 1px solid var(--color-border-defa=
+ult); border-radius: 6px; box-shadow: var(--color-shadow-medium); }
+
+.suggester li { display: block; padding: 4px 8px; font-weight: var(--base-t=
+ext-weight-medium, 500); border-bottom: 1px solid var(--color-border-muted)=
+; }
+
+.suggester li small { font-weight: var(--base-text-weight-normal, 400); col=
+or: var(--color-fg-muted); }
+
+.suggester li:last-child { border-bottom: 0px; border-bottom-right-radius: =
+6px; border-bottom-left-radius: 6px; }
+
+.suggester li:first-child { border-top-left-radius: 6px; border-top-right-r=
+adius: 6px; }
+
+.suggester li:hover { color: var(--color-fg-on-emphasis); text-decoration: =
+none; background: var(--color-accent-emphasis); }
+
+.suggester li:hover small { color: var(--color-fg-on-emphasis); }
+
+.suggester li:hover .octicon { color: inherit !important; }
+
+.suggester li[aria-selected=3D"true"], .suggester li.navigation-focus { col=
+or: var(--color-fg-on-emphasis); text-decoration: none; background: var(--c=
+olor-accent-emphasis); }
+
+.suggester li[aria-selected=3D"true"] small, .suggester li.navigation-focus=
+ small { color: var(--color-fg-on-emphasis); }
+
+.suggester li[aria-selected=3D"true"] .octicon, .suggester li.navigation-fo=
+cus .octicon { color: inherit !important; }
+
+.suggester-container { position: absolute; top: 0px; left: 0px; z-index: 30=
+; }
+
+@media (max-width: 544px) {
+  .page-responsive .suggester-container { right: 8px !important; left: 8px =
+!important; }
+  .page-responsive .suggester li { padding: 8px 16px; }
+}
+
+.avatar-parent-child { position: relative; }
+
+.avatar-child { position: absolute; right: -15%; bottom: -9%; background-co=
+lor: var(--color-canvas-default); border-radius: 4px; box-shadow: var(--col=
+or-avatar-child-shadow); }
+
+.CircleBadge { display: flex; align-items: center; justify-content: center;=
+ background-color: var(--color-canvas-default); border-radius: 50%; box-sha=
+dow: var(--color-shadow-medium); }
+
+.CircleBadge-icon { max-width: 60% !important; height: auto !important; max=
+-height: 55% !important; }
+
+.CircleBadge--small { width: 56px; height: 56px; }
+
+.CircleBadge--medium { width: 96px; height: 96px; }
+
+.CircleBadge--large { width: 128px; height: 128px; }
+
+.DashedConnection { position: relative; }
+
+.DashedConnection::before { position: absolute; top: 50%; left: 0px; width:=
+ 100%; content: ""; border-bottom: 2px dashed var(--color-border-default); =
+}
+
+.DashedConnection .CircleBadge { position: relative; }
+
+.branch-name { display: inline-block; padding: 2px 6px; font: 12px ui-monos=
+pace, SFMono-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monosp=
+ace; color: var(--color-fg-muted); word-break: break-all; background-color:=
+ var(--color-accent-subtle); border-radius: 6px; }
+
+.branch-name .octicon { margin: 1px -2px 0px 0px; color: var(--color-fg-mut=
+ed); }
+
+a.branch-name { color: var(--color-accent-fg); background-color: var(--colo=
+r-accent-subtle); }
+
+a.branch-name .octicon { color: var(--color-accent-fg); }
+
+.Header { z-index: 32; display: flex; padding: 16px; font-size: 14px; line-=
+height: 1.5; color: var(--color-header-text); background-color: var(--color=
+-header-bg); align-items: center; flex-wrap: nowrap; }
+
+.Header-item { display: flex; margin-right: 16px; align-self: stretch; alig=
+n-items: center; flex-wrap: nowrap; }
+
+.Header-item--full { flex: 1 1 auto; }
+
+.Header-link { font-weight: var(--base-text-weight-semibold, 600); color: v=
+ar(--color-header-logo); white-space: nowrap; }
+
+.Header-link:hover, .Header-link:focus { color: var(--color-header-text); t=
+ext-decoration: none; }
+
+.Header-input { color: var(--color-header-text); background-color: var(--co=
+lor-header-search-bg); border: 1px solid var(--color-header-search-border);=
+ box-shadow: none; }
+
+.Header-input::placeholder { color: rgba(255, 255, 255, 0.75); }
+
+.IssueLabel { display: inline-block; padding: 0px 7px; font-size: 12px; fon=
+t-weight: var(--base-text-weight-medium, 500); line-height: 18px; white-spa=
+ce: nowrap; border: 1px solid transparent; border-radius: 2em; }
+
+.IssueLabel .g-emoji { position: relative; top: -0.05em; display: inline-bl=
+ock; font-size: 1em; line-height: 1; }
+
+.IssueLabel:hover { text-decoration: none; }
+
+.IssueLabel--big { padding-right: 10px; padding-left: 10px; line-height: 22=
+px; }
+
+.diffstat { font-size: 12px; font-weight: var(--base-text-weight-semibold, =
+600); color: var(--color-fg-muted); white-space: nowrap; cursor: default; }
+
+.diffstat-block-deleted, .diffstat-block-added, .diffstat-block-neutral { d=
+isplay: inline-block; width: 8px; height: 8px; margin-left: 1px; outline-of=
+fset: -1px; }
+
+.diffstat-block-deleted { background-color: var(--color-danger-emphasis); o=
+utline: 1px solid var(--color-border-subtle); }
+
+.diffstat-block-added { background-color: var(--color-diffstat-addition-bg)=
+; outline: 1px solid var(--color-border-subtle); }
+
+.diffstat-block-neutral { background-color: var(--color-neutral-muted); out=
+line: 1px solid var(--color-border-subtle); }
+
+.AnimatedEllipsis { display: inline-block; overflow: hidden; vertical-align=
+: bottom; }
+
+.AnimatedEllipsis::after { display: inline-block; content: "..."; animation=
+: 1.2s steps(4, jump-none) 0s infinite normal none running AnimatedEllipsis=
+-keyframes; }
+
+@keyframes AnimatedEllipsis-keyframes {=20
+  0% { transform: translateX(-100%); }
+}
+
+.markdown-body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI"=
+, "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI=
+ Emoji"; font-size: 16px; line-height: 1.5; overflow-wrap: break-word; }
+
+.markdown-body::before { display: table; content: ""; }
+
+.markdown-body::after { display: table; clear: both; content: ""; }
+
+.markdown-body > :first-child { margin-top: 0px !important; }
+
+.markdown-body > :last-child { margin-bottom: 0px !important; }
+
+.markdown-body a:not([href]) { color: inherit; text-decoration: none; }
+
+.markdown-body .absent { color: var(--color-danger-fg); }
+
+.markdown-body .anchor { float: left; padding-right: 4px; margin-left: -20p=
+x; line-height: 1; }
+
+.markdown-body .anchor:focus { outline: none; }
+
+.markdown-body p, .markdown-body blockquote, .markdown-body ul, .markdown-b=
+ody ol, .markdown-body dl, .markdown-body table, .markdown-body pre, .markd=
+own-body details { margin-top: 0px; margin-bottom: 16px; }
+
+.markdown-body hr { height: 0.25em; padding: 0px; margin: 24px 0px; backgro=
+und-color: var(--color-border-default); border: 0px; }
+
+.markdown-body blockquote { padding: 0px 1em; color: var(--color-fg-muted);=
+ border-left: 0.25em solid var(--color-border-default); }
+
+.markdown-body blockquote > :first-child { margin-top: 0px; }
+
+.markdown-body blockquote > :last-child { margin-bottom: 0px; }
+
+.markdown-body h1, .markdown-body h2, .markdown-body h3, .markdown-body h4,=
+ .markdown-body h5, .markdown-body h6 { margin-top: 24px; margin-bottom: 16=
+px; font-weight: var(--base-text-weight-semibold, 600); line-height: 1.25; =
+}
+
+.markdown-body h1 .octicon-link, .markdown-body h2 .octicon-link, .markdown=
+-body h3 .octicon-link, .markdown-body h4 .octicon-link, .markdown-body h5 =
+.octicon-link, .markdown-body h6 .octicon-link { color: var(--color-fg-defa=
+ult); vertical-align: middle; visibility: hidden; }
+
+.markdown-body h1:hover .anchor, .markdown-body h2:hover .anchor, .markdown=
+-body h3:hover .anchor, .markdown-body h4:hover .anchor, .markdown-body h5:=
+hover .anchor, .markdown-body h6:hover .anchor { text-decoration: none; }
+
+.markdown-body h1:hover .anchor .octicon-link, .markdown-body h2:hover .anc=
+hor .octicon-link, .markdown-body h3:hover .anchor .octicon-link, .markdown=
+-body h4:hover .anchor .octicon-link, .markdown-body h5:hover .anchor .octi=
+con-link, .markdown-body h6:hover .anchor .octicon-link { visibility: visib=
+le; }
+
+.markdown-body h1 tt, .markdown-body h1 code, .markdown-body h2 tt, .markdo=
+wn-body h2 code, .markdown-body h3 tt, .markdown-body h3 code, .markdown-bo=
+dy h4 tt, .markdown-body h4 code, .markdown-body h5 tt, .markdown-body h5 c=
+ode, .markdown-body h6 tt, .markdown-body h6 code { padding: 0px 0.2em; fon=
+t-size: inherit; }
+
+.markdown-body h1 { padding-bottom: 0.3em; font-size: 2em; border-bottom: 1=
+px solid var(--color-border-muted); }
+
+.markdown-body h2 { padding-bottom: 0.3em; font-size: 1.5em; border-bottom:=
+ 1px solid var(--color-border-muted); }
+
+.markdown-body h3 { font-size: 1.25em; }
+
+.markdown-body h4 { font-size: 1em; }
+
+.markdown-body h5 { font-size: 0.875em; }
+
+.markdown-body h6 { font-size: 0.85em; color: var(--color-fg-muted); }
+
+.markdown-body summary h1, .markdown-body summary h2, .markdown-body summar=
+y h3, .markdown-body summary h4, .markdown-body summary h5, .markdown-body =
+summary h6 { display: inline-block; }
+
+.markdown-body summary h1 .anchor, .markdown-body summary h2 .anchor, .mark=
+down-body summary h3 .anchor, .markdown-body summary h4 .anchor, .markdown-=
+body summary h5 .anchor, .markdown-body summary h6 .anchor { margin-left: -=
+40px; }
+
+.markdown-body summary h1, .markdown-body summary h2 { padding-bottom: 0px;=
+ border-bottom: 0px; }
+
+.markdown-body ul, .markdown-body ol { padding-left: 2em; }
+
+.markdown-body ul.no-list, .markdown-body ol.no-list { padding: 0px; list-s=
+tyle-type: none; }
+
+.markdown-body ol[type=3D"a"] { list-style-type: lower-alpha; }
+
+.markdown-body ol[type=3D"A"] { list-style-type: upper-alpha; }
+
+.markdown-body ol[type=3D"i"] { list-style-type: lower-roman; }
+
+.markdown-body ol[type=3D"I"] { list-style-type: upper-roman; }
+
+.markdown-body ol[type=3D"1"] { list-style-type: decimal; }
+
+.markdown-body div > ol:not([type]) { list-style-type: decimal; }
+
+.markdown-body ul ul, .markdown-body ul ol, .markdown-body ol ol, .markdown=
+-body ol ul { margin-top: 0px; margin-bottom: 0px; }
+
+.markdown-body li > p { margin-top: 16px; }
+
+.markdown-body li + li { margin-top: 0.25em; }
+
+.markdown-body dl { padding: 0px; }
+
+.markdown-body dl dt { padding: 0px; margin-top: 16px; font-size: 1em; font=
+-style: italic; font-weight: var(--base-text-weight-semibold, 600); }
+
+.markdown-body dl dd { padding: 0px 16px; margin-bottom: 16px; }
+
+.markdown-body table { display: block; width: max-content; max-width: 100%;=
+ overflow: auto; }
+
+.markdown-body table th { font-weight: var(--base-text-weight-semibold, 600=
+); }
+
+.markdown-body table th, .markdown-body table td { padding: 6px 13px; borde=
+r: 1px solid var(--color-border-default); }
+
+.markdown-body table tr { background-color: var(--color-canvas-default); bo=
+rder-top: 1px solid var(--color-border-muted); }
+
+.markdown-body table tr:nth-child(2n) { background-color: var(--color-canva=
+s-subtle); }
+
+.markdown-body table img { background-color: transparent; }
+
+.markdown-body img { max-width: 100%; box-sizing: content-box; background-c=
+olor: var(--color-canvas-default); }
+
+.markdown-body img[align=3D"right"] { padding-left: 20px; }
+
+.markdown-body img[align=3D"left"] { padding-right: 20px; }
+
+.markdown-body .emoji { max-width: none; vertical-align: text-top; backgrou=
+nd-color: transparent; }
+
+.markdown-body span.frame { display: block; overflow: hidden; }
+
+.markdown-body span.frame > span { display: block; float: left; width: auto=
+; padding: 7px; margin: 13px 0px 0px; overflow: hidden; border: 1px solid v=
+ar(--color-border-default); }
+
+.markdown-body span.frame span img { display: block; float: left; }
+
+.markdown-body span.frame span span { display: block; padding: 5px 0px 0px;=
+ clear: both; color: var(--color-fg-default); }
+
+.markdown-body span.align-center { display: block; overflow: hidden; clear:=
+ both; }
+
+.markdown-body span.align-center > span { display: block; margin: 13px auto=
+ 0px; overflow: hidden; text-align: center; }
+
+.markdown-body span.align-center span img { margin: 0px auto; text-align: c=
+enter; }
+
+.markdown-body span.align-right { display: block; overflow: hidden; clear: =
+both; }
+
+.markdown-body span.align-right > span { display: block; margin: 13px 0px 0=
+px; overflow: hidden; text-align: right; }
+
+.markdown-body span.align-right span img { margin: 0px; text-align: right; =
+}
+
+.markdown-body span.float-left { display: block; float: left; margin-right:=
+ 13px; overflow: hidden; }
+
+.markdown-body span.float-left span { margin: 13px 0px 0px; }
+
+.markdown-body span.float-right { display: block; float: right; margin-left=
+: 13px; overflow: hidden; }
+
+.markdown-body span.float-right > span { display: block; margin: 13px auto =
+0px; overflow: hidden; text-align: right; }
+
+.markdown-body code, .markdown-body tt { padding: 0.2em 0.4em; margin: 0px;=
+ font-size: 85%; white-space: break-spaces; background-color: var(--color-n=
+eutral-muted); border-radius: 6px; }
+
+.markdown-body code br, .markdown-body tt br { display: none; }
+
+.markdown-body del code { text-decoration: inherit; }
+
+.markdown-body samp { font-size: 85%; }
+
+.markdown-body pre { overflow-wrap: normal; }
+
+.markdown-body pre code { font-size: 100%; }
+
+.markdown-body pre > code { padding: 0px; margin: 0px; word-break: normal; =
+white-space: pre; background: transparent; border: 0px; }
+
+.markdown-body .highlight { margin-bottom: 16px; }
+
+.markdown-body .highlight pre { margin-bottom: 0px; word-break: normal; }
+
+.markdown-body .highlight pre, .markdown-body pre { padding: 16px; overflow=
+: auto; font-size: 85%; line-height: 1.45; background-color: var(--color-ca=
+nvas-subtle); border-radius: 6px; }
+
+.markdown-body pre code, .markdown-body pre tt { display: inline; padding: =
+0px; margin: 0px; overflow: visible; line-height: inherit; overflow-wrap: n=
+ormal; background-color: transparent; border: 0px; }
+
+.markdown-body .csv-data td, .markdown-body .csv-data th { padding: 5px; ov=
+erflow: hidden; font-size: 12px; line-height: 1; text-align: left; white-sp=
+ace: nowrap; }
+
+.markdown-body .csv-data .blob-num { padding: 10px 8px 9px; text-align: rig=
+ht; background: var(--color-canvas-default); border: 0px; }
+
+.markdown-body .csv-data tr { border-top: 0px; }
+
+.markdown-body .csv-data th { font-weight: var(--base-text-weight-semibold,=
+ 600); background: var(--color-canvas-subtle); border-top: 0px; }
+
+.markdown-body [data-footnote-ref]::before { content: "["; }
+
+.markdown-body [data-footnote-ref]::after { content: "]"; }
+
+.markdown-body .footnotes { font-size: 12px; color: var(--color-fg-muted); =
+border-top: 1px solid var(--color-border-default); }
+
+.markdown-body .footnotes ol { padding-left: 16px; }
+
+.markdown-body .footnotes ol ul { display: inline-block; padding-left: 16px=
+; margin-top: 16px; }
+
+.markdown-body .footnotes li { position: relative; }
+
+.markdown-body .footnotes li:target::before { position: absolute; inset: -8=
+px -8px -8px -24px; pointer-events: none; content: ""; border: 2px solid va=
+r(--color-accent-emphasis); border-radius: 6px; }
+
+.markdown-body .footnotes li:target { color: var(--color-fg-default); }
+
+.markdown-body .footnotes .data-footnote-backref g-emoji { font-family: mon=
+ospace; }
+
+.SelectMenu { position: fixed; inset: 0px; z-index: 99; display: flex; padd=
+ing: 16px; pointer-events: none; flex-direction: column; }
+
+@media (min-width: 544px) {
+  .SelectMenu { position: absolute; inset: auto; padding: 0px; }
+}
+
+.SelectMenu::before { position: absolute; inset: 0px; pointer-events: none;=
+ content: ""; background-color: var(--color-primer-canvas-backdrop); }
+
+@media (min-width: 544px) {
+  .SelectMenu::before { display: none; }
+}
+
+.SelectMenu-modal { position: relative; z-index: 99; display: flex; max-hei=
+ght: 66%; margin: auto 0px; overflow: hidden; pointer-events: auto; flex-di=
+rection: column; background-color: var(--color-canvas-overlay); border: 1px=
+ solid var(--color-select-menu-backdrop-border); border-radius: 12px; box-s=
+hadow: var(--color-shadow-large); animation: 0.12s cubic-bezier(0, 0.1, 0.1=
+, 1) 0s 1 normal backwards running SelectMenu-modal-animation; }
+
+@keyframes SelectMenu-modal-animation {=20
+  0% { opacity: 0; transform: scale(0.9); }
+}
+
+@keyframes SelectMenu-modal-animation--sm {=20
+  0% { opacity: 0; transform: translateY(-16px); }
+}
+
+@media (min-width: 544px) {
+  .SelectMenu-modal { width: 300px; height: auto; max-height: 480px; margin=
+: 8px 0px 16px; font-size: 12px; border-color: var(--color-border-default);=
+ border-radius: 6px; box-shadow: var(--color-shadow-large); animation-name:=
+ SelectMenu-modal-animation--sm; }
+}
+
+.SelectMenu-header { display: flex; padding: 16px; flex: 0 0 auto; align-it=
+ems: center; border-bottom: 1px solid var(--color-border-muted); }
+
+@media (min-width: 544px) {
+  .SelectMenu-header { padding: 7px 7px 7px 16px; }
+}
+
+.SelectMenu-title { flex: 1 1 0%; font-size: 14px; font-weight: var(--base-=
+text-weight-semibold, 600); }
+
+@media (min-width: 544px) {
+  .SelectMenu-title { font-size: inherit; }
+}
+
+.SelectMenu-closeButton { padding: 16px; margin: -16px; line-height: 1; col=
+or: var(--color-fg-muted); background-color: transparent; border: 0px; }
+
+@media (min-width: 544px) {
+  .SelectMenu-closeButton { padding: 8px; margin: -8px -7px; }
+}
+
+.SelectMenu-filter { padding: 16px; margin: 0px; border-bottom: 1px solid v=
+ar(--color-border-muted); }
+
+@media (min-width: 544px) {
+  .SelectMenu-filter { padding: 8px; }
+}
+
+.SelectMenu-input { display: block; width: 100%; }
+
+@media (min-width: 544px) {
+  .SelectMenu-input { font-size: 14px; }
+}
+
+.SelectMenu-list { position: relative; padding: 0px; margin: 0px 0px -1px; =
+flex: 1 1 auto; overflow: hidden auto; background-color: var(--color-canvas=
+-overlay); }
+
+.SelectMenu-item { display: flex; align-items: center; width: 100%; padding=
+: 16px; overflow: hidden; color: var(--color-fg-default); text-align: left;=
+ cursor: pointer; background-color: var(--color-canvas-overlay); border-top=
+: 0px; border-right: 0px; border-left: 0px; border-image: initial; border-b=
+ottom: 1px solid var(--color-border-muted); }
+
+@media (min-width: 544px) {
+  .SelectMenu-item { padding-top: 7px; padding-bottom: 7px; }
+}
+
+.SelectMenu-list--borderless .SelectMenu-item { border-bottom: 0px; }
+
+.SelectMenu-icon { width: 16px; margin-right: 8px; flex-shrink: 0; }
+
+.SelectMenu-icon--check { visibility: hidden; transition: transform 0.12s c=
+ubic-bezier(0.5, 0.1, 1, 0.5) 0s, visibility 0s linear 0.12s; transform: sc=
+ale(0); }
+
+.SelectMenu-tabs { display: flex; flex-shrink: 0; overflow: auto hidden; bo=
+x-shadow: inset 0 -1px 0 var(--color-border-muted); }
+
+.SelectMenu-tabs::-webkit-scrollbar { display: none; }
+
+@media (min-width: 544px) {
+  .SelectMenu-tabs { padding: 8px 8px 0px; }
+}
+
+.SelectMenu-tab { flex: 1 1 0%; padding: 8px 16px; font-size: 12px; font-we=
+ight: var(--base-text-weight-medium, 500); color: var(--color-fg-muted); te=
+xt-align: center; background-color: transparent; border: 0px; box-shadow: i=
+nset 0 -1px 0 var(--color-border-muted); }
+
+@media (min-width: 544px) {
+  .SelectMenu-tab { flex: 0 0 auto; padding: 4px 16px; border-width: 1px 1p=
+x 0px; border-style: solid; border-color: transparent; border-image: initia=
+l; border-top-left-radius: 6px; border-top-right-radius: 6px; }
+}
+
+.SelectMenu-tab[aria-selected=3D"true"] { z-index: 1; color: var(--color-fg=
+-default); cursor: default; background-color: var(--color-canvas-overlay); =
+box-shadow: 0 0 0 1px var(--color-border-muted); }
+
+@media (min-width: 544px) {
+  .SelectMenu-tab[aria-selected=3D"true"] { border-color: var(--color-borde=
+r-muted); box-shadow: none; }
+}
+
+.SelectMenu-message { padding: 7px 16px; text-align: center; background-col=
+or: var(--color-canvas-overlay); border-bottom: 1px solid var(--color-borde=
+r-muted); }
+
+.SelectMenu-blankslate, .SelectMenu-loading { padding: 24px 16px; text-alig=
+n: center; background-color: var(--color-canvas-overlay); }
+
+.SelectMenu-divider { padding: 4px 16px; margin: 0px; font-size: 12px; font=
+-weight: var(--base-text-weight-medium, 500); color: var(--color-fg-muted);=
+ background-color: var(--color-canvas-subtle); border-bottom: 1px solid var=
+(--color-border-muted); }
+
+.SelectMenu-list--borderless .SelectMenu-divider { border-top: 1px solid va=
+r(--color-border-muted); }
+
+.SelectMenu-list--borderless .SelectMenu-divider:empty { padding: 0px; bord=
+er-top: 0px; }
+
+.SelectMenu-footer { z-index: 0; padding: 8px 16px; font-size: 12px; color:=
+ var(--color-fg-muted); text-align: center; border-top: 1px solid var(--col=
+or-border-muted); }
+
+@media (min-width: 544px) {
+  .SelectMenu-footer { padding: 7px 16px; }
+}
+
+.SelectMenu--hasFilter .SelectMenu-modal { height: 80%; max-height: none; m=
+argin-top: 0px; }
+
+@media (min-width: 544px) {
+  .SelectMenu--hasFilter .SelectMenu-modal { height: auto; max-height: 480p=
+x; margin-top: 8px; }
+}
+
+.SelectMenu-tab:focus, .SelectMenu-item:focus { outline: 0px; }
+
+.SelectMenu-item:hover { text-decoration: none; }
+
+.SelectMenu-item[aria-checked=3D"true"] { font-weight: var(--base-text-weig=
+ht-medium, 500); color: var(--color-fg-default); }
+
+.SelectMenu-item[aria-checked=3D"true"] .SelectMenu-icon--check { visibilit=
+y: visible; transition: transform 0.12s cubic-bezier(0, 0, 0.2, 1) 0s, visi=
+bility 0s linear 0s; transform: scale(1); }
+
+.SelectMenu-item:disabled, .SelectMenu-item[aria-disabled=3D"true"] { color=
+: var(--color-primer-fg-disabled); pointer-events: none; }
+
+@media (hover: hover) {
+  body:not(.intent-mouse) .SelectMenu-closeButton:focus, .SelectMenu-closeB=
+utton:hover { color: var(--color-fg-default); }
+  .SelectMenu-closeButton:active { color: var(--color-fg-muted); }
+  body:not(.intent-mouse) .SelectMenu-item:focus, .SelectMenu-item:hover { =
+background-color: var(--color-neutral-subtle); }
+  .SelectMenu-item:active { background-color: var(--color-canvas-subtle); }
+  body:not(.intent-mouse) .SelectMenu-tab:focus { background-color: var(--c=
+olor-select-menu-tap-focus-bg); }
+  .SelectMenu-tab:hover { color: var(--color-fg-default); }
+  .SelectMenu-tab:not([aria-selected=3D"true"]):active { color: var(--color=
+-fg-default); background-color: var(--color-canvas-subtle); }
+}
+
+@media (hover: none) {
+  .SelectMenu-item:focus, .SelectMenu-item:active { background-color: var(-=
+-color-canvas-subtle); }
+  .SelectMenu-item { -webkit-tap-highlight-color: var(--color-select-menu-t=
+ap-highlight); }
+}
+
+.Toast { display: flex; margin: 8px; color: var(--color-fg-default); backgr=
+ound-color: var(--color-canvas-default); border-radius: 6px; box-shadow: in=
+set 0 0 0 1px var(--color-border-default),var(--color-shadow-large); }
+
+@media (min-width: 544px) {
+  .Toast { width: max-content; max-width: 450px; margin: 16px; }
+}
+
+.Toast-icon { display: flex; align-items: center; justify-content: center; =
+width: 48px; flex-shrink: 0; color: var(--color-fg-on-emphasis); background=
+-color: var(--color-accent-emphasis); border-width: 1px 0px 1px 1px; border=
+-top-style: solid; border-bottom-style: solid; border-left-style: solid; bo=
+rder-top-color: transparent; border-bottom-color: transparent; border-left-=
+color: transparent; border-image: initial; border-right-style: initial; bor=
+der-right-color: initial; border-top-left-radius: inherit; border-bottom-le=
+ft-radius: inherit; }
+
+.Toast-content { padding: 16px; }
+
+.Toast-dismissButton { max-height: 54px; padding: 16px; color: inherit; bac=
+kground-color: transparent; border: 0px; }
+
+.Toast-dismissButton:hover { opacity: 0.7; }
+
+.Toast-dismissButton:active { opacity: 0.5; }
+
+.Toast--loading { color: var(--color-fg-default); box-shadow: inset 0 0 0 1=
+px var(--color-border-default),var(--color-shadow-large); }
+
+.Toast--loading .Toast-icon { background-color: var(--color-neutral-emphasi=
+s); }
+
+.Toast--error { color: var(--color-fg-default); box-shadow: inset 0 0 0 1px=
+ var(--color-border-default),var(--color-shadow-large); }
+
+.Toast--error .Toast-icon { background-color: var(--color-danger-emphasis);=
+ }
+
+.Toast--warning { color: var(--color-fg-default); box-shadow: inset 0 0 0 1=
+px var(--color-border-default),var(--color-shadow-large); }
+
+.Toast--warning .Toast-icon { background-color: var(--color-attention-empha=
+sis); }
+
+.Toast--success { color: var(--color-fg-default); box-shadow: inset 0 0 0 1=
+px var(--color-border-default),var(--color-shadow-large); }
+
+.Toast--success .Toast-icon { background-color: var(--color-success-emphasi=
+s); }
+
+.Toast--animateIn { animation: 0.18s cubic-bezier(0.22, 0.61, 0.36, 1) 0s 1=
+ normal backwards running Toast--animateIn; }
+
+@keyframes Toast--animateIn {=20
+  0% { opacity: 0; transform: translateY(100%); }
+}
+
+.Toast--animateOut { animation: 0.18s cubic-bezier(0.55, 0.06, 0.68, 0.19) =
+0s 1 normal forwards running Toast--animateOut; }
+
+@keyframes Toast--animateOut {=20
+  100% { pointer-events: none; opacity: 0; transform: translateY(100%); }
+}
+
+.Toast--spinner { animation: 1000ms linear 0s infinite normal none running =
+Toast--spinner; }
+
+@keyframes Toast--spinner {=20
+  0% { transform: rotate(0deg); }
+  100% { transform: rotate(360deg); }
+}
+
+:root { --primer-actionListContent-paddingBlock:var(--primer-control-medium=
+-paddingBlock,6px); }
+
+.ActionListWrap { list-style: none; }
+
+.ActionListWrap--inset { padding: var(--base-size-8, 8px); }
+
+.ActionListWrap--divided .ActionListItem-label::before { height: 1px; }
+
+.ActionListWrap--divided .ActionListItem-descriptionWrap--inline::before, .=
+ActionListWrap--divided .ActionListItem-label::before { background: var(--c=
+olor-action-list-item-inline-divider); content: ""; display: block; positio=
+n: absolute; top: calc(var(--primer-actionListContent-paddingBlock)*-1); wi=
+dth: 100%; }
+
+.ActionListWrap--divided .ActionListItem-descriptionWrap--inline::before { =
+height: var(--primer-borderWidth-thin, 1px); }
+
+.ActionListWrap--divided .ActionListItem-descriptionWrap--inline .ActionLis=
+tItem-label::before { content: unset; }
+
+.ActionList-sectionDivider + .ActionListItem .ActionListItem-descriptionWra=
+p--inline::before, .ActionList-sectionDivider + .ActionListItem .ActionList=
+Item-label::before, .ActionListItem:first-of-type .ActionListItem-descripti=
+onWrap--inline::before, .ActionListItem:first-of-type .ActionListItem-label=
+::before, .ActionListWrap--divided .ActionListItem--navActive .ActionListIt=
+em-label::before, .ActionListWrap--divided .ActionListItem--navActive + .Ac=
+tionListItem .ActionListItem-label::before { visibility: hidden; }
+
+.ActionListItem { background-color: initial; border-radius: var(--primer-bo=
+rderRadius-medium, 6px); list-style: none; position: relative; }
+
+.ActionListItem:active, .ActionListItem:hover { cursor: pointer; }
+
+@media (hover: hover) {
+  .ActionListItem:hover .ActionListItem-descriptionWrap--inline::before, .A=
+ctionListItem:hover .ActionListItem-label::before, .ActionListItem:hover + =
+.ActionListItem .ActionListItem-descriptionWrap--inline::before, .ActionLis=
+tItem:hover + .ActionListItem .ActionListItem-label::before { visibility: h=
+idden; }
+}
+
+.ActionListItem[hidden] + .ActionList-sectionDivider { display: none; }
+
+.ActionListItem.ActionListItem--hasSubItem > .ActionListContent { z-index: =
+1; }
+
+@media (hover: hover) {
+  .ActionListItem.ActionListItem--hasSubItem > .ActionListContent:hover { b=
+ackground-color: var(--color-action-list-item-default-hover-bg); }
+}
+
+.ActionListItem.ActionListItem--hasSubItem > .ActionListContent:active { ba=
+ckground-color: var(--color-action-list-item-default-active-bg); }
+
+@media (hover: hover) {
+  .ActionListItem.ActionListItem--hasSubItem > .ActionListContent:hover, .A=
+ctionListItem:not(.ActionListItem--hasSubItem):hover { background-color: va=
+r(--color-action-list-item-default-hover-bg); cursor: pointer; }
+  .ActionListItem.ActionListItem--hasSubItem > .ActionListContent:hover:not=
+(.ActionListItem--navActive, :focus-visible), .ActionListItem:not(.ActionLi=
+stItem--hasSubItem):hover:not(.ActionListItem--navActive, :focus-visible) {=
+ box-shadow: var(--primer-borderInset-thin, 1px) var(--color-action-list-it=
+em-default-active-border); outline: solid var(--primer-borderWidth-thin, 1p=
+x) #0000; outline-offset: calc(var(--primer-borderWidth-thin, 1px)*-1); }
+}
+
+.ActionListItem.ActionListItem--hasSubItem > .ActionListContent:active, .Ac=
+tionListItem:not(.ActionListItem--hasSubItem):active { background: var(--co=
+lor-action-list-item-default-active-bg); }
+
+.ActionListItem.ActionListItem--hasSubItem > .ActionListContent:active:not(=
+.ActionListItem--navActive), .ActionListItem:not(.ActionListItem--hasSubIte=
+m):active:not(.ActionListItem--navActive) { box-shadow: var(--primer-border=
+Inset-thin, 1px) var(--color-action-list-item-default-active-border); outli=
+ne: solid var(--primer-borderWidth-thin, 1px) #0000; outline-offset: calc(v=
+ar(--primer-borderWidth-thin, 1px)*-1); }
+
+.ActionListItem.ActionListItem--hasSubItem > .ActionListContent:active .Act=
+ionListItem-label::before, .ActionListItem.ActionListItem--hasSubItem > .Ac=
+tionListContent:active + .ActionListItem .ActionListItem-label::before, .Ac=
+tionListItem:not(.ActionListItem--hasSubItem):active .ActionListItem-label:=
+:before, .ActionListItem:not(.ActionListItem--hasSubItem):active + .ActionL=
+istItem .ActionListItem-label::before { visibility: hidden; }
+
+.ActionListItem[aria-checked=3D"true"] .ActionListItem-multiSelectCheckmark=
+, .ActionListItem[aria-selected=3D"true"] .ActionListItem-multiSelectCheckm=
+ark { opacity: 1; visibility: visible; }
+
+.ActionListItem[aria-checked=3D"true"] .ActionListItem-singleSelectCheckmar=
+k, .ActionListItem[aria-selected=3D"true"] .ActionListItem-singleSelectChec=
+kmark { visibility: visible; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .ActionListItem[aria-checked=3D"true"] .ActionListItem-singleSelectCheckm=
+ark, .ActionListItem[aria-selected=3D"true"] .ActionListItem-singleSelectCh=
+eckmark { animation: 0.2s cubic-bezier(0.11, 0, 0.5, 0) 0s 1 normal forward=
+s running checkmarkIn; }
+}
+
+.ActionListItem[aria-checked=3D"true"] .ActionListItem-multiSelectIcon .Act=
+ionListItem-multiSelectIconRect, .ActionListItem[aria-selected=3D"true"] .A=
+ctionListItem-multiSelectIcon .ActionListItem-multiSelectIconRect { fill: v=
+ar(--color-accent-fg); stroke: var(--color-accent-fg); stroke-width: var(--=
+primer-borderWidth-thin, 1px); }
+
+.ActionListItem[aria-checked=3D"true"] .ActionListItem-multiSelectIcon .Act=
+ionListItem-multiSelectCheckmark, .ActionListItem[aria-selected=3D"true"] .=
+ActionListItem-multiSelectIcon .ActionListItem-multiSelectCheckmark { fill:=
+ var(--color-fg-on-emphasis); }
+
+.ActionListItem[aria-checked=3D"false"] .ActionListItem-multiSelectCheckmar=
+k, .ActionListItem[aria-selected=3D"false"] .ActionListItem-multiSelectChec=
+kmark { opacity: 0; visibility: hidden; }
+
+.ActionListItem[aria-checked=3D"false"] .ActionListItem-singleSelectCheckma=
+rk, .ActionListItem[aria-selected=3D"false"] .ActionListItem-singleSelectCh=
+eckmark { clip-path: inset(16px 0px 0px); transition: visibility 0s linear =
+0.2s; visibility: hidden; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .ActionListItem[aria-checked=3D"false"] .ActionListItem-singleSelectCheck=
+mark, .ActionListItem[aria-selected=3D"false"] .ActionListItem-singleSelect=
+Checkmark { animation: 0.2s cubic-bezier(0.11, 0, 0.5, 0) 0s 1 normal forwa=
+rds running checkmarkOut; }
+}
+
+.ActionListItem[aria-checked=3D"false"] .ActionListItem-multiSelectIcon .Ac=
+tionListItem-multiSelectIconRect, .ActionListItem[aria-selected=3D"false"] =
+.ActionListItem-multiSelectIcon .ActionListItem-multiSelectIconRect { fill:=
+ var(--color-canvas-default); stroke: var(--color-border-default); stroke-w=
+idth: var(--primer-borderWidth-thin, 1px); }
+
+.ActionListItem[aria-checked=3D"false"] .ActionListItem-multiSelectIconRect=
+, .ActionListItem[aria-selected=3D"false"] .ActionListItem-multiSelectIconR=
+ect { fill: var(--color-canvas-default); border: var(--primer-borderWidth-t=
+hin, 1px) solid var(--color-border-default); }
+
+.ActionListItem[aria-selected=3D"true"] { background: var(--color-action-li=
+st-item-default-selected-bg); font-weight: var(--base-text-weight-normal, 4=
+00); }
+
+@media (hover: hover) {
+  .ActionListItem[aria-selected=3D"true"]:hover { background-color: var(--c=
+olor-action-list-item-default-hover-bg); }
+}
+
+.ActionListItem[aria-selected=3D"true"] + .ActionListItem::before, .ActionL=
+istItem[aria-selected=3D"true"]::before { visibility: hidden; }
+
+.ActionListItem[aria-selected=3D"true"]::after { background: var(--color-ac=
+cent-fg); border-radius: var(--primer-borderRadius-medium, 6px); content: "=
+"; height: var(--base-size-24, 24px); left: calc(var(--base-size-4, 4px)*-1=
+); position: absolute; top: calc(50% - 12px); width: var(--base-size-4, 4px=
+); }
+
+.ActionListItem.ActionListItem--navActive:not(.ActionListItem--subItem) .Ac=
+tionListItem-label { font-weight: var(--base-text-weight-semibold, 600); }
+
+.ActionListItem.ActionListItem--navActive:not(.ActionListItem--danger) { ba=
+ckground: var(--color-action-list-item-default-selected-bg); }
+
+@media (hover: hover) {
+  .ActionListItem.ActionListItem--navActive:not(.ActionListItem--danger):ho=
+ver { background-color: var(--color-action-list-item-default-hover-bg); }
+}
+
+.ActionListItem.ActionListItem--navActive:not(.ActionListItem--danger) + .A=
+ctionListItem::before, .ActionListItem.ActionListItem--navActive:not(.Actio=
+nListItem--danger)::before { visibility: hidden; }
+
+.ActionListItem.ActionListItem--navActive:not(.ActionListItem--danger)::aft=
+er { background: var(--color-accent-fg); border-radius: var(--primer-border=
+Radius-medium, 6px); content: ""; height: var(--base-size-24, 24px); left: =
+calc(var(--base-size-8, 8px)*-1); position: absolute; top: calc(50% - 12px)=
+; width: var(--base-size-4, 4px); }
+
+.ActionListItem[aria-disabled=3D"true"] .ActionListContent .ActionListItem-=
+description, .ActionListItem[aria-disabled=3D"true"] .ActionListContent .Ac=
+tionListItem-label { color: var(--color-primer-fg-disabled); }
+
+.ActionListItem[aria-disabled=3D"true"] .ActionListContent .ActionListItem-=
+visual { fill: var(--color-primer-fg-disabled); }
+
+@media (hover: hover) {
+  .ActionListItem[aria-disabled=3D"true"]:hover { background-color: initial=
+; cursor: not-allowed; }
+}
+
+.ActionListItem.ActionListItem--danger .ActionListItem-label, .ActionListIt=
+em.ActionListItem--danger .ActionListItem-visual { color: var(--color-dange=
+r-fg); }
+
+@media (hover: hover) {
+  .ActionListItem.ActionListItem--danger:hover { background: var(--color-ac=
+tion-list-item-danger-hover-bg); }
+  .ActionListItem.ActionListItem--danger:hover .ActionListItem-label { colo=
+r: var(--color-action-list-item-danger-hover-text); }
+}
+
+.ActionListItem.ActionListItem--danger .ActionListContent:active { backgrou=
+nd: var(--color-action-list-item-danger-active-bg); }
+
+.ActionListContent { -webkit-tap-highlight-color: transparent; align-items:=
+ start; background-color: initial; border: none; border-radius: var(--prime=
+r-borderRadius-medium, 6px); color: var(--color-fg-default); display: grid;=
+ grid-template: "leadingAction leadingVisual label trailingVisual trailingA=
+ction" min-content / min-content min-content minmax(0px, auto) min-content =
+min-content; padding-block: var(--primer-actionListContent-paddingBlock); p=
+adding-inline: var(--primer-control-medium-paddingInline-condensed, 8px); p=
+osition: relative; text-align: left; touch-action: manipulation; transition=
+: background 33.333ms linear 0s; user-select: none; width: 100%; }
+
+.ActionListContent > :not(:last-child) { margin-right: var(--primer-control=
+-medium-gap, 8px); }
+
+.ActionListContent:hover { text-decoration: none; }
+
+.ActionListContent[aria-disabled=3D"true"] .ActionListItem-description, .Ac=
+tionListContent[aria-disabled=3D"true"] .ActionListItem-label { color: var(=
+--color-primer-fg-disabled); }
+
+.ActionListContent[aria-disabled=3D"true"] .ActionListItem-visual { fill: v=
+ar(--color-primer-fg-disabled); }
+
+@media (hover: hover) {
+  .ActionListContent[aria-disabled=3D"true"]:hover { background-color: init=
+ial; cursor: not-allowed; }
+}
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .ActionListContent[aria-expanded] + .ActionList--subGroup { transition: o=
+pacity 0.16s cubic-bezier(0.25, 1, 0.5, 1) 0s, transform 0.16s cubic-bezier=
+(0.25, 1, 0.5, 1) 0s; }
+}
+
+.ActionListContent[aria-expanded] + .ActionList--subGroup .ActionListConten=
+t { padding-left: var(--base-size-24, 24px); }
+
+.ActionListContent.ActionListContent--visual16[aria-expanded] + .ActionList=
+--subGroup .ActionListContent { padding-left: var(--base-size-32, 32px); }
+
+.ActionListContent.ActionListContent--visual20[aria-expanded] + .ActionList=
+--subGroup .ActionListContent { padding-left: var(--base-size-36, 36px); }
+
+.ActionListContent.ActionListContent--visual24[aria-expanded] + .ActionList=
+--subGroup .ActionListContent { padding-left: var(--base-size-40, 40px); }
+
+.ActionListContent[aria-expanded=3D"true"] .ActionListItem-collapseIcon { t=
+ransform: scaleY(-1); transition: transform 0.12s linear 0s; }
+
+.ActionListContent[aria-expanded=3D"true"] + .ActionList--subGroup { height=
+: auto; opacity: 1; overflow: visible; transform: translateY(0px); visibili=
+ty: visible; }
+
+.ActionListContent.ActionListContent--hasActiveSubItem[aria-expanded=3D"tru=
+e"] > .ActionListItem-label { font-weight: var(--base-text-weight-semibold,=
+ 600); }
+
+.ActionListContent[aria-expanded=3D"false"] .ActionListItem-collapseIcon { =
+transform: scaleY(1); transition: transform 0.12s linear 0s; }
+
+.ActionListContent[aria-expanded=3D"false"] + .ActionList--subGroup { heigh=
+t: 0px; opacity: 0; overflow: hidden; transform: translateY(calc(var(--base=
+-size-16, 16px) * -1)); visibility: hidden; }
+
+.ActionListContent.ActionListContent--hasActiveSubItem[aria-expanded=3D"fal=
+se"] { background: var(--color-action-list-item-default-selected-bg); }
+
+.ActionListContent.ActionListContent--hasActiveSubItem[aria-expanded=3D"fal=
+se"] .ActionListItem-label { font-weight: var(--base-text-weight-semibold, =
+600); }
+
+.ActionListContent.ActionListContent--hasActiveSubItem[aria-expanded=3D"fal=
+se"] + .ActionListItem::before, .ActionListContent.ActionListContent--hasAc=
+tiveSubItem[aria-expanded=3D"false"]::before { visibility: hidden; }
+
+.ActionListContent.ActionListContent--hasActiveSubItem[aria-expanded=3D"fal=
+se"]::after { background: var(--color-accent-fg); border-radius: var(--prim=
+er-borderRadius-medium, 6px); content: ""; height: var(--base-size-24, 24px=
+); left: calc(var(--base-size-8, 8px)*-1); position: absolute; top: calc(50=
+% - 12px); width: var(--base-size-4, 4px); }
+
+.ActionListContent.ActionListContent--sizeLarge { --primer-actionListConten=
+t-paddingBlock:var(--primer-control-large-paddingBlock,0.625rem); }
+
+.ActionListContent.ActionListContent--sizeXLarge { --primer-actionListConte=
+nt-paddingBlock:var(--primer-control-xlarge-paddingBlock,0.875rem); }
+
+@media (pointer: coarse) {
+  .ActionListContent { --primer-actionListContent-paddingBlock:var(--primer=
+-control-large-paddingBlock,0.625rem); }
+}
+
+.ActionListContent.ActionListContent--blockDescription .ActionListItem-visu=
+al { place-self: start; }
+
+.ActionListItem-action--leading { grid-area: leadingAction / leadingAction =
+/ leadingAction / leadingAction; }
+
+.ActionListItem-visual--leading { grid-area: leadingVisual / leadingVisual =
+/ leadingVisual / leadingVisual; }
+
+.ActionListItem-visual--trailing { grid-area: trailingVisual / trailingVisu=
+al / trailingVisual / trailingVisual; }
+
+.ActionListItem-action--trailing { grid-area: trailingAction / trailingActi=
+on / trailingAction / trailingAction; }
+
+.ActionListItem-descriptionWrap { display: flex; flex-direction: column; ga=
+p: var(--base-size-4, 4px); grid-area: label / label / label / label; }
+
+.ActionListItem-descriptionWrap .ActionListItem-label { font-weight: var(--=
+base-text-weight-semibold, 600); }
+
+.ActionListItem-descriptionWrap--inline { align-items: baseline; flex-direc=
+tion: row; gap: var(--base-size-8, 8px); position: relative; }
+
+.ActionListItem-description { color: var(--color-fg-muted); font-size: var(=
+--primer-text-body-size-small, 12px); font-weight: var(--base-text-weight-n=
+ormal, 400); line-height: var(--primer-text-body-lineHeight-small, 1.66667)=
+; }
+
+.ActionListItem-action, .ActionListItem-visual { fill: var(--color-fg-muted=
+); align-items: center; color: var(--color-fg-muted); display: flex; min-he=
+ight: var(--primer-control-medium-lineBoxHeight, 20px); pointer-events: non=
+e; }
+
+.ActionListItem-label { color: var(--color-fg-default); font-size: var(--pr=
+imer-text-body-size-medium, 14px); font-weight: var(--base-text-weight-norm=
+al, 400); grid-area: label / label / label / label; line-height: var(--prim=
+er-text-body-lineHeight-medium, 1.42857); position: relative; }
+
+.ActionListItem-label--truncate { overflow: hidden; text-overflow: ellipsis=
+; white-space: nowrap; }
+
+.ActionListItem--subItem > .ActionListContent > .ActionListItem-label { fon=
+t-size: var(--primer-text-body-size-small, 12px); line-height: var(--primer=
+-text-body-lineHeight-small, 1.66667); }
+
+.ActionListItem--withActions { align-items: center; display: flex; flex-wra=
+p: nowrap; }
+
+.ActionListItem-trailingAction { border-bottom-left-radius: 0px; border-top=
+-left-radius: 0px; }
+
+.ActionListItem--trailingActionHover .ActionListItem-trailingAction { visib=
+ility: hidden; }
+
+.ActionListItem--trailingActionHover:focus-within .ActionListItem-trailingA=
+ction, .ActionListItem--trailingActionHover:hover .ActionListItem-trailingA=
+ction { visibility: visible; }
+
+.ActionList-sectionDivider:not(:empty) { color: var(--color-fg-muted); disp=
+lay: flex; flex-direction: column; font-size: var(--primer-text-body-size-s=
+mall, 12px); font-weight: var(--base-text-weight-semibold, 600); line-heigh=
+t: var(--primer-text-body-lineHeight-small, 1.66667); padding-block: var(--=
+base-size-8, 8px); padding-inline: var(--primer-actionListContent-paddingBl=
+ock); }
+
+.ActionList-sectionDivider:empty { background: var(--color-action-list-item=
+-inline-divider); border: 0px; display: block; height: var(--primer-borderW=
+idth-thin, 1px); list-style: none; margin-block-end: var(--base-size-8, 8px=
+); margin-block-start: calc(var(--base-size-8, 8px) - var(--primer-borderWi=
+dth-thin, 1px)); margin-inline: calc(var(--base-size-8, 8px)*-1); padding: =
+0px; }
+
+.ActionList-sectionDivider .ActionList-sectionDivider-title { color: var(--=
+color-fg-muted); font-size: var(--primer-text-body-size-small, 12px); font-=
+weight: var(--base-text-weight-semibold, 600); }
+
+.ActionList-sectionDivider--filled { background: var(--color-canvas-subtle)=
+; border-bottom: solid var(--primer-borderWidth-thin, 1px) var(--color-acti=
+on-list-item-inline-divider); border-top: solid var(--primer-borderWidth-th=
+in, 1px) var(--color-action-list-item-inline-divider); margin-block-end: va=
+r(--base-size-8, 8px); margin-block-start: calc(var(--base-size-8, 8px) - v=
+ar(--primer-borderWidth-thin, 1px)); margin-inline: calc(var(--base-size-8,=
+ 8px)*-1); }
+
+.ActionList-sectionDivider--filled:empty { box-sizing: border-box; height: =
+var(--base-size-8, 8px); }
+
+.ActionList-sectionDivider--filled:first-child { margin-block-start: 0px; }
+
+.autocomplete-label-stacked { display: block; margin-bottom: 6px; }
+
+.autocomplete-label-inline { display: inline; margin-right: 6px; }
+
+@media (max-width: 543.98px) {
+  .autocomplete-label-inline { display: block; margin-bottom: 6px; }
+}
+
+.autocomplete-body { display: inline; position: relative; }
+
+.autocomplete-embedded-icon-wrap { align-items: center; display: inline-fle=
+x; padding: 4px 8px; }
+
+.autocomplete-embedded-icon-wrap:focus-within { border-color: var(--color-a=
+ccent-fg); box-shadow: inset 0 0 0 1px var(--color-accent-fg); outline: non=
+e; }
+
+.autocomplete-embedded-icon-wrap .form-control { border: none; box-shadow: =
+none; margin-left: 8px; padding: 0px; }
+
+.autocomplete-embedded-icon-wrap .form-control:focus { box-shadow: none; }
+
+.autocomplete-embedded-icon-wrap .form-control:focus-visible { box-shadow: =
+none; }
+
+.autocomplete-results { background: var(--color-canvas-overlay); border: va=
+r(--primer-borderWidth-thin, 1px) solid var(--color-border-default); border=
+-radius: var(--primer-borderRadius-medium, 6px); box-shadow: var(--color-sh=
+adow-medium); font-size: 13px; left: 0px; list-style: none; max-height: 20e=
+m; min-width: 100%; overflow-y: auto; position: absolute; width: max-conten=
+t; z-index: 99; }
+
+.autocomplete-item { background-color: var(--color-canvas-overlay); border:=
+ 0px; color: var(--color-fg-default); cursor: pointer; display: block; font=
+-weight: var(--base-text-weight-semibold, 600); overflow: hidden; padding: =
+4px 8px; text-align: left; text-decoration: none; text-overflow: ellipsis; =
+white-space: nowrap; width: 100%; }
+
+.autocomplete-item:hover { background-color: var(--color-accent-emphasis); =
+color: var(--color-fg-on-emphasis); text-decoration: none; }
+
+.autocomplete-item:hover * { color: inherit !important; }
+
+.autocomplete-item.navigation-focus, .autocomplete-item.selected, .autocomp=
+lete-item[aria-selected=3D"true"] { background-color: var(--color-accent-em=
+phasis); color: var(--color-fg-on-emphasis); text-decoration: none; }
+
+.autocomplete-item.navigation-focus *, .autocomplete-item.selected *, .auto=
+complete-item[aria-selected=3D"true"] * { color: inherit !important; }
+
+.Banner { background-image: linear-gradient(var(--color-accent-subtle), var=
+(--color-accent-subtle)); border: var(--primer-borderWidth-thin, max(1px, 0=
+.0625rem)) solid var(--color-accent-muted); border-radius: var(--primer-bor=
+derRadius-medium, 6px); color: var(--color-fg-default); display: grid; grid=
+-auto-flow: column; grid-template: "visual message actions close" min-conte=
+nt / min-content 1fr minmax(0px, auto) min-content; padding: var(--base-siz=
+e-8, 8px); position: relative; }
+
+@media (max-width: 543.98px) {
+  .Banner { grid-template: "visual message close" min-content ". actions ac=
+tions" min-content / min-content 1fr min-content; }
+  .Banner .Banner-actions { margin: var(--base-size-8, 8px) 0 0 var(--base-=
+size-8, 8px); }
+}
+
+.Banner .Banner-visual { align-self: start; display: grid; grid-area: visua=
+l / visual / visual / visual; padding: var(--base-size-6, 6px) var(--base-s=
+ize-8, 8px); }
+
+.Banner .Banner-visual > .octicon { margin-block: calc(var(--base-size-4, 4=
+px)/2); }
+
+.Banner .Banner-visual > * { align-self: center; }
+
+.Banner .Banner-message { align-self: center; grid-area: message / message =
+/ message / message; padding: var(--base-size-6, 6px) var(--base-size-8, 8p=
+x); }
+
+.Banner .Banner-message p:last-child { margin-bottom: 0px; }
+
+.Banner .Banner-message .Banner-title:not(:only-child) { font-weight: var(-=
+-base-text-weight-semibold, 600); margin-bottom: 0px; }
+
+.Banner .Banner-actions { grid-area: actions / actions / actions / actions;=
+ }
+
+.Banner .Banner-actions:last-child { align-self: center; }
+
+.Banner .Banner-close { grid-area: close / close / close / close; margin-le=
+ft: var(--primer-controlStack-medium-gap-condensed, 8px); }
+
+.Banner .Banner-visual .octicon { color: var(--color-accent-fg); }
+
+.Banner.Banner--warning { background-image: linear-gradient(var(--color-att=
+ention-subtle), var(--color-attention-subtle)); border-color: var(--color-a=
+ttention-muted); color: var(--color-fg-default); }
+
+.Banner.Banner--warning .Banner-visual .octicon { color: var(--color-attent=
+ion-fg); }
+
+.Banner.Banner--error { background-image: linear-gradient(var(--color-dange=
+r-subtle), var(--color-danger-subtle)); border-color: var(--color-danger-mu=
+ted); color: var(--color-fg-default); }
+
+.Banner.Banner--error .Banner-visual .octicon { color: var(--color-danger-f=
+g); }
+
+.Banner.Banner--success { background-image: linear-gradient(var(--color-suc=
+cess-subtle), var(--color-success-subtle)); border-color: var(--color-succe=
+ss-muted); color: var(--color-fg-default); }
+
+.Banner.Banner--success .Banner-visual .octicon { color: var(--color-succes=
+s-fg); }
+
+.Banner.Banner--full { border-left: 0px; border-radius: 0px; border-right: =
+0px; margin-top: calc(var(--primer-borderWidth-thin, max(1px, 0.0625rem))*-=
+1); }
+
+@media (max-width: 767.98px) {
+  .Banner.Banner--full-whenNarrow { border-left: 0px; border-radius: 0px; b=
+order-right: 0px; margin-top: calc(var(--primer-borderWidth-thin, max(1px, =
+0.0625rem))*-1); }
+}
+
+.Overlay--hidden { display: none !important; }
+
+.Overlay--visibilityHidden { height: 0px; opacity: 0; overflow: hidden; vis=
+ibility: hidden; }
+
+.Overlay { background-color: var(--color-canvas-overlay); border-radius: va=
+r(--primer-borderRadius-large, 12px); box-shadow: var(--color-overlay-shado=
+w); display: flex; flex-direction: column; max-height: min(100vh - 2rem,var=
+(--overlay-height)); min-width: 192px; opacity: 1; white-space: normal; wid=
+th: min(var(--overlay-width),100vw - 2rem); }
+
+.Overlay.Overlay--size-auto { max-height: calc(100vh - 2rem); max-width: ca=
+lc(100vw - 2rem); min-width: 192px; }
+
+.Overlay.Overlay--size-full { height: 100vh; width: 100vw; }
+
+.Overlay.Overlay--size-xsmall { --overlay-width:192px; max-height: calc(100=
+vh - 2rem); }
+
+.Overlay.Overlay--size-small { --overlay-height:256px; --overlay-width:320p=
+x; }
+
+.Overlay.Overlay--size-small-portrait { --overlay-height:432px; --overlay-w=
+idth:320px; }
+
+.Overlay.Overlay--size-medium { --overlay-height:320px; --overlay-width:480=
+px; }
+
+.Overlay.Overlay--size-medium-portrait { --overlay-height:600px; --overlay-=
+width:480px; }
+
+.Overlay.Overlay--size-large { --overlay-height:432px; --overlay-width:640p=
+x; }
+
+.Overlay.Overlay--size-xlarge { --overlay-height:600px; --overlay-width:960=
+px; }
+
+.Overlay.Overlay--height-auto { height: auto; }
+
+.Overlay.Overlay--height-xsmall { height: min(192px, 100vh - 2rem); }
+
+.Overlay.Overlay--height-small { height: min(256px, 100vh - 2rem); }
+
+.Overlay.Overlay--height-medium { height: min(320px, 100vh - 2rem); }
+
+.Overlay.Overlay--height-large { height: min(432px, 100vh - 2rem); }
+
+.Overlay.Overlay--height-xlarge { height: min(600px, 100vh - 2rem); }
+
+.Overlay.Overlay--width-auto { width: auto; }
+
+.Overlay.Overlay--width-small { width: min(256px, 100vw - 2rem); }
+
+.Overlay.Overlay--width-medium { width: min(320px, 100vw - 2rem); }
+
+.Overlay.Overlay--width-large { width: min(480px, 100vw - 2rem); }
+
+.Overlay.Overlay--width-xlarge { width: min(640px, 100vw - 2rem); }
+
+.Overlay.Overlay--width-xxlarge { width: min(960px, 100vw - 2rem); }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .Overlay.Overlay--motion-scaleFade { animation: 0.2s cubic-bezier(0.33, 1=
+, 0.68, 1) 0s 1 normal none running Overlay--motion-scaleFade; }
+}
+
+@keyframes Overlay--motion-scaleFade {=20
+  0% { opacity: 0; transform: scale(0.5); }
+  100% { opacity: 1; transform: scale(1); }
+}
+
+.Overlay-form { flex-grow: 1; overflow: auto; }
+
+.Overlay-form, .Overlay-header { display: flex; flex-direction: column; }
+
+.Overlay-header { z-index: 1; }
+
+.Overlay-header.Overlay-header--divided { box-shadow: inset 0 calc(var(--pr=
+imer-borderWidth-thin, 1px)*-1) var(--color-border-default); padding-bottom=
+: var(--primer-stack-padding-condensed, 8px); }
+
+.Overlay-header.Overlay-header--divided + .Overlay-body { padding-top: var(=
+--primer-stack-padding-normal, 16px); }
+
+.Overlay-header.Overlay-header--large .Overlay-headerContentWrap .Overlay-t=
+itleWrap { gap: var(--primer-stack-gap-condensed, 8px); }
+
+.Overlay-header.Overlay-header--large .Overlay-headerContentWrap .Overlay-t=
+itleWrap .Overlay-title { font-size: var(--primer-text-title-size-medium, 2=
+0px); }
+
+.Overlay-header.Overlay-header--large .Overlay-headerContentWrap .Overlay-t=
+itleWrap .Overlay-description { font-size: var(--primer-text-body-size-medi=
+um, 14px); }
+
+.Overlay-header .Overlay-headerContentWrap { align-items: flex-start; displ=
+ay: flex; gap: var(--primer-stack-gap-condensed, 8px); padding: var(--prime=
+r-stack-gap-condensed, 8px) var(--primer-stack-gap-condensed, 8px) 0 var(--=
+primer-stack-gap-condensed, 8px); }
+
+.Overlay-header .Overlay-headerContentWrap .Overlay-actionWrap { display: f=
+lex; flex-direction: row; gap: var(--primer-stack-gap-condensed, 8px); }
+
+.Overlay-header .Overlay-headerContentWrap .Overlay-titleWrap { display: fl=
+ex; flex-direction: column; flex-grow: 1; gap: var(--primer-control-small-g=
+ap, 4px); padding: calc(var(--primer-stack-gap-condensed, 8px)*0.75) 0 calc=
+(var(--primer-stack-gap-condensed, 8px)*0.75) var(--primer-stack-gap-conden=
+sed, 8px); }
+
+.Overlay-header .Overlay-headerContentWrap .Overlay-titleWrap .Overlay-titl=
+e { font-size: var(--primer-text-body-size-medium, 14px); font-weight: var(=
+--base-text-weight-semibold, 600); margin: 0px; }
+
+.Overlay-header .Overlay-headerContentWrap .Overlay-titleWrap .Overlay-desc=
+ription { color: var(--color-fg-muted); font-size: var(--primer-text-body-s=
+ize-small, 12px); font-weight: var(--base-text-weight-normal, 400); margin:=
+ 0px; }
+
+.Overlay-body { flex-grow: 1; font-size: var(--primer-text-body-size-medium=
+, 14px); overflow-y: auto; padding-right: ; padding-bottom: ; padding-left:=
+ ; padding-top: 0px; }
+
+.Overlay-body.Overlay-body--paddingCondensed { padding-right: ; padding-bot=
+tom: ; padding-left: ; padding-top: 0px; }
+
+.Overlay-body.Overlay-body--paddingNone { padding: 0px; }
+
+.Overlay-footer { display: flex; flex-flow: row wrap; flex-shrink: 0; paddi=
+ng: 0 var(--primer-stack-padding-normal, 16px) var(--primer-stack-padding-n=
+ormal, 16px) var(--primer-stack-padding-normal, 16px); z-index: 1; }
+
+.Overlay-footer.Overlay-footer--divided { box-shadow: inset 0 var(--primer-=
+borderWidth-thin, 1px) var(--color-border-default); padding-top: var(--prim=
+er-stack-padding-normal, 16px); }
+
+.Overlay-footer.Overlay-footer--alignStart { gap: var(--primer-stack-gap-co=
+ndensed, 8px); justify-content: flex-start; }
+
+.Overlay-footer.Overlay-footer--alignCenter { gap: var(--primer-stack-gap-c=
+ondensed, 8px); justify-content: center; }
+
+.Overlay-footer.Overlay-footer--alignEnd { gap: var(--primer-stack-gap-cond=
+ensed, 8px); justify-content: flex-end; }
+
+.Overlay-closeButton { align-self: flex-start; background-color: initial; b=
+order: var(--primer-borderWidth-thin, 1px) solid #0000; border-radius: var(=
+--primer-borderRadius-medium, 6px); color: var(--color-fg-muted); cursor: p=
+ointer; display: grid; flex-shrink: 0; height: var(--base-size-32, 32px); p=
+adding: 0px; place-content: center; position: relative; transition: color 0=
+.2s cubic-bezier(0.3, 0, 0.5, 1) 0s, background-color, border-color; user-s=
+elect: none; width: var(--base-size-32, 32px); }
+
+.Overlay-closeButton:focus, .Overlay-closeButton:hover { background-color: =
+var(--color-btn-hover-bg); border: var(--primer-borderWidth-thin, 1px) soli=
+d var(--color-btn-hover-bg); }
+
+.Overlay-closeButton.close-button { border: var(--primer-borderWidth-thin, =
+1px) solid #0000; }
+
+.Overlay-backdrop--center { align-items: center; background-color: var(--co=
+lor-neutral-muted); inset: 0px; display: flex; justify-content: center; pos=
+ition: fixed; z-index: 999; }
+
+.Overlay-backdrop--anchor { background-color: initial; position: absolute; =
+z-index: 999; }
+
+.Overlay-backdrop--anchor .Overlay { width: auto; }
+
+.Overlay-backdrop--side { background-color: var(--color-neutral-muted); ins=
+et: 0px; display: flex; position: fixed; z-index: 999; }
+
+.Overlay-backdrop--side, .Overlay-backdrop--side.Overlay-backdrop--placemen=
+t-left { align-items: center; justify-content: left; }
+
+.Overlay-backdrop--side.Overlay-backdrop--placement-left > .Overlay { borde=
+r-top-right-radius: ; border-bottom-right-radius: ; border-bottom-left-radi=
+us: 0px; border-top-left-radius: 0px; height: 100vh; max-height: unset; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .Overlay-backdrop--side.Overlay-backdrop--placement-left > .Overlay { ani=
+mation: 0.25s cubic-bezier(0.33, 1, 0.68, 1) 0s 1 normal none running Overl=
+ay--motion-slideInRight; }
+}
+
+.Overlay-backdrop--side.Overlay-backdrop--placement-right { align-items: ce=
+nter; justify-content: right; }
+
+.Overlay-backdrop--side.Overlay-backdrop--placement-right > .Overlay { bord=
+er-top-left-radius: ; border-bottom-left-radius: ; border-bottom-right-radi=
+us: 0px; border-top-right-radius: 0px; height: 100vh; max-height: unset; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .Overlay-backdrop--side.Overlay-backdrop--placement-right > .Overlay { an=
+imation: 0.25s cubic-bezier(0.33, 1, 0.68, 1) 0s 1 normal none running Over=
+lay--motion-slideInLeft; }
+}
+
+.Overlay-backdrop--side.Overlay-backdrop--placement-bottom { align-items: e=
+nd; justify-content: center; }
+
+.Overlay-backdrop--side.Overlay-backdrop--placement-bottom > .Overlay { bor=
+der-top-left-radius: ; border-top-right-radius: ; border-bottom-left-radius=
+: 0px; border-bottom-right-radius: 0px; height: auto; max-height: calc(100v=
+h - 2rem); width: 100vw; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .Overlay-backdrop--side.Overlay-backdrop--placement-bottom > .Overlay { a=
+nimation: 0.25s cubic-bezier(0.33, 1, 0.68, 1) 0s 1 normal none running Ove=
+rlay--motion-slideUp; }
+}
+
+.Overlay-backdrop--side.Overlay-backdrop--placement-top { align-items: star=
+t; justify-content: center; }
+
+.Overlay-backdrop--side.Overlay-backdrop--placement-top > .Overlay { border=
+-bottom-right-radius: ; border-bottom-left-radius: ; border-top-left-radius=
+: 0px; border-top-right-radius: 0px; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .Overlay-backdrop--side.Overlay-backdrop--placement-top > .Overlay { anim=
+ation: 0.25s cubic-bezier(0.33, 1, 0.68, 1) 0s 1 normal none running Overla=
+y--motion-slideDown; }
+}
+
+.Overlay-backdrop--full { background-color: var(--color-neutral-muted); ins=
+et: 0px; display: flex; position: fixed; z-index: 999; }
+
+.Overlay-backdrop--full .Overlay { flex-grow: 1; height: 100%; max-height: =
+100vh; max-width: 100vw; width: 100%; border-radius: unset !important; }
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--center-whenNarrow { align-items: center; background-co=
+lor: var(--color-neutral-muted); inset: 0px; display: flex; justify-content=
+: center; position: fixed; z-index: 999; }
+  .Overlay-backdrop--anchor-whenNarrow { background-color: initial; positio=
+n: absolute; z-index: 999; }
+  .Overlay-backdrop--anchor-whenNarrow .Overlay { width: auto; }
+  .Overlay-backdrop--side-whenNarrow { background-color: var(--color-neutra=
+l-muted); inset: 0px; display: flex; position: fixed; z-index: 999; }
+  .Overlay-backdrop--side-whenNarrow, .Overlay-backdrop--side-whenNarrow.Ov=
+erlay-backdrop--placement-left-whenNarrow { align-items: center; justify-co=
+ntent: left; }
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-left-whenN=
+arrow > .Overlay-whenNarrow { border-top-right-radius: ; border-bottom-righ=
+t-radius: ; border-bottom-left-radius: 0px; border-top-left-radius: 0px; he=
+ight: 100vh; max-height: unset; }
+}
+
+@media screen and (max-width: 767px) and (prefers-reduced-motion: no-prefer=
+ence) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-left-whenN=
+arrow > .Overlay-whenNarrow { animation: 0.25s cubic-bezier(0.33, 1, 0.68, =
+1) 0s 1 normal none running Overlay--motion-slideInRight; }
+}
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-right-when=
+Narrow { align-items: center; justify-content: right; }
+}
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-right-when=
+Narrow > .Overlay-whenNarrow { border-top-left-radius: ; border-bottom-left=
+-radius: ; border-bottom-right-radius: 0px; border-top-right-radius: 0px; h=
+eight: 100vh; max-height: unset; }
+}
+
+@media screen and (max-width: 767px) and (prefers-reduced-motion: no-prefer=
+ence) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-right-when=
+Narrow > .Overlay-whenNarrow { animation: 0.25s cubic-bezier(0.33, 1, 0.68,=
+ 1) 0s 1 normal none running Overlay--motion-slideInLeft; }
+}
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-bottom-whe=
+nNarrow { align-items: end; justify-content: center; }
+}
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-bottom-whe=
+nNarrow > .Overlay-whenNarrow { border-top-left-radius: ; border-top-right-=
+radius: ; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; =
+height: auto; max-height: calc(100vh - 2rem); width: 100vw; }
+}
+
+@media screen and (max-width: 767px) and (prefers-reduced-motion: no-prefer=
+ence) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-bottom-whe=
+nNarrow > .Overlay-whenNarrow { animation: 0.25s cubic-bezier(0.33, 1, 0.68=
+, 1) 0s 1 normal none running Overlay--motion-slideUp; }
+}
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-top-whenNa=
+rrow { align-items: start; justify-content: center; }
+}
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-top-whenNa=
+rrow > .Overlay-whenNarrow { border-bottom-right-radius: ; border-bottom-le=
+ft-radius: ; border-top-left-radius: 0px; border-top-right-radius: 0px; }
+}
+
+@media screen and (max-width: 767px) and (prefers-reduced-motion: no-prefer=
+ence) {
+  .Overlay-backdrop--side-whenNarrow.Overlay-backdrop--placement-top-whenNa=
+rrow > .Overlay-whenNarrow { animation: 0.25s cubic-bezier(0.33, 1, 0.68, 1=
+) 0s 1 normal none running Overlay--motion-slideDown; }
+}
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--full-whenNarrow { background-color: var(--color-neutra=
+l-muted); inset: 0px; display: flex; position: fixed; z-index: 999; }
+}
+
+@media (max-width: 767px) {
+  .Overlay-backdrop--full-whenNarrow .Overlay { flex-grow: 1; height: 100%;=
+ max-height: 100vh; max-width: 100vw; width: 100%; border-radius: unset !im=
+portant; }
+}
+
+@keyframes Overlay--motion-slideDown {=20
+  0% { transform: translateY(-100%); }
+}
+
+@keyframes Overlay--motion-slideUp {=20
+  0% { transform: translateY(100%); }
+}
+
+@keyframes Overlay--motion-slideInRight {=20
+  0% { transform: translateX(-100%); }
+}
+
+@keyframes Overlay--motion-slideInLeft {=20
+  0% { transform: translateX(100%); }
+}
+
+.dropdown { position: relative; }
+
+.dropdown-caret { border-bottom-color: rgba(0, 0, 0, 0); border-left-color:=
+ rgba(0, 0, 0, 0); border-right-color: rgba(0, 0, 0, 0); border-style: soli=
+d; border-width: var(--primer-borderWidth-thicker, 4px) var(--primer-border=
+Width-thicker, 4px) 0; content: ""; display: inline-block; height: 0px; ver=
+tical-align: middle; width: 0px; }
+
+.dropdown-menu { background-clip: padding-box; background-color: var(--colo=
+r-canvas-overlay); border: var(--primer-borderWidth-thin, 1px) solid var(--=
+color-border-default); border-radius: var(--primer-borderRadius-medium, 6px=
+); box-shadow: var(--color-shadow-large); left: 0px; list-style: none; marg=
+in-top: 2px; padding-bottom: var(--primer-control-small-paddingBlock, 4px);=
+ padding-top: var(--primer-control-small-paddingBlock, 4px); position: abso=
+lute; top: 100%; width: 160px; z-index: 100; }
+
+.dropdown-menu::after, .dropdown-menu::before { content: ""; display: inlin=
+e-block; position: absolute; }
+
+.dropdown-menu::before { border-top: 8px solid rgba(0, 0, 0, 0); border-rig=
+ht: 8px solid rgba(0, 0, 0, 0); border-left: 8px solid rgba(0, 0, 0, 0); bo=
+rder-image: initial; border-bottom: 8px solid var(--color-border-default); =
+}
+
+.dropdown-menu::after { border-top: 7px solid rgba(0, 0, 0, 0); border-righ=
+t: 7px solid rgba(0, 0, 0, 0); border-left: 7px solid rgba(0, 0, 0, 0); bor=
+der-image: initial; border-bottom: 7px solid var(--color-canvas-overlay); }
+
+.dropdown-menu > ul { list-style: none; }
+
+.dropdown-menu-no-overflow { width: auto; }
+
+.dropdown-menu-no-overflow .dropdown-item { overflow: visible; padding: var=
+(--primer-control-small-paddingBlock, 4px) var(--primer-control-medium-padd=
+ingInline-spacious, 16px); text-overflow: inherit; }
+
+.dropdown-item { color: var(--color-fg-default); display: block; overflow: =
+hidden; padding: var(--primer-control-small-paddingBlock, 4px) var(--primer=
+-control-medium-paddingInline-condensed, 8px) var(--primer-control-small-pa=
+ddingBlock, 4px) var(--primer-control-medium-paddingInline-spacious, 16px);=
+ text-overflow: ellipsis; white-space: nowrap; }
+
+.dropdown-item:hover { background-color: var(--color-accent-emphasis); colo=
+r: var(--color-fg-on-emphasis); text-decoration: none; }
+
+.dropdown-item:hover > .octicon { color: inherit; opacity: 1; }
+
+.dropdown-item:hover [class*=3D"color-fg-"], .dropdown-item:hover > .Label =
+{ color: inherit !important; }
+
+.dropdown-item:hover > .Label { border-color: currentcolor; }
+
+.dropdown-item.btn-link, .dropdown-signout { text-align: left; width: 100%;=
+ }
+
+.dropdown-signout { background: none; border: 0px; }
+
+.dropdown-divider { border-top: var(--primer-borderWidth-thin, 1px) solid v=
+ar(--color-border-default); display: block; height: 0px; margin: var(--prim=
+er-stack-gap-condensed, 8px) 0; }
+
+.dropdown-header { color: var(--color-fg-muted); font-size: var(--primer-te=
+xt-body-size-small, 12px); padding: var(--primer-control-small-paddingBlock=
+, 4px) var(--primer-control-medium-paddingInline-spacious, 16px); }
+
+.dropdown-item[aria-checked=3D"false"] .octicon-check { display: none; }
+
+.dropdown-menu-w { left: auto; margin-right: 8px; margin-top: 0px; right: 1=
+00%; top: 0px; width: auto; }
+
+.dropdown-menu-w::before { border-top-color: rgba(0, 0, 0, 0); border-right=
+-color: rgba(0, 0, 0, 0); border-bottom-color: rgba(0, 0, 0, 0); border-lef=
+t-color: var(--color-border-default); left: auto; right: -16px; top: 10px; =
+}
+
+.dropdown-menu-w::after { border-top-color: rgba(0, 0, 0, 0); border-right-=
+color: rgba(0, 0, 0, 0); border-bottom-color: rgba(0, 0, 0, 0); border-left=
+-color: var(--color-canvas-overlay); left: auto; right: -14px; top: 11px; }
+
+.dropdown-menu-e { left: 100%; margin-left: 8px; margin-top: 0px; top: 0px;=
+ width: auto; }
+
+.dropdown-menu-e::before { border-top-color: rgba(0, 0, 0, 0); border-botto=
+m-color: rgba(0, 0, 0, 0); border-left-color: rgba(0, 0, 0, 0); border-righ=
+t-color: var(--color-border-default); left: -16px; top: 10px; }
+
+.dropdown-menu-e::after { border-top-color: rgba(0, 0, 0, 0); border-bottom=
+-color: rgba(0, 0, 0, 0); border-left-color: rgba(0, 0, 0, 0); border-right=
+-color: var(--color-canvas-overlay); left: -14px; top: 11px; }
+
+.dropdown-menu-ne { bottom: 100%; left: 0px; margin-bottom: 3px; top: auto;=
+ }
+
+.dropdown-menu-ne::after, .dropdown-menu-ne::before { right: auto; top: aut=
+o; }
+
+.dropdown-menu-ne::before { border-bottom: 0px; border-left: 8px solid rgba=
+(0, 0, 0, 0); border-right: 8px solid rgba(0, 0, 0, 0); border-top: 8px sol=
+id var(--color-border-default); bottom: -8px; left: 9px; }
+
+.dropdown-menu-ne::after { border-bottom: 0px; border-left: 7px solid rgba(=
+0, 0, 0, 0); border-right: 7px solid rgba(0, 0, 0, 0); border-top: 7px soli=
+d var(--color-canvas-overlay); bottom: -7px; left: 10px; }
+
+.dropdown-menu-s { left: auto; right: 50%; transform: translateX(50%); }
+
+.dropdown-menu-s::before { right: 50%; top: -16px; transform: translateX(50=
+%); }
+
+.dropdown-menu-s::after { right: 50%; top: -14px; transform: translateX(50%=
+); }
+
+.dropdown-menu-sw { left: auto; right: 0px; }
+
+.dropdown-menu-sw::before { left: auto; right: 9px; top: -16px; }
+
+.dropdown-menu-sw::after { left: auto; right: 10px; top: -14px; }
+
+.dropdown-menu-se::before { left: 9px; top: -16px; }
+
+.dropdown-menu-se::after { left: 10px; top: -14px; }
+
+.Layout { --Layout-sidebar-width:220px; --Layout-gutter:16px; display: grid=
+; }
+
+@media (max-width: 543.98px) {
+  .Layout { grid-auto-flow: row; grid-template-columns: 1fr !important; }
+  .Layout .Layout-divider, .Layout .Layout-main, .Layout .Layout-sidebar { =
+grid-column: 1 / auto !important; width: 100% !important; }
+  .Layout.Layout--sidebarPosition-flowRow-start .Layout-sidebar { grid-row:=
+ 1 / auto; }
+  .Layout.Layout--sidebarPosition-flowRow-end .Layout-sidebar, .Layout.Layo=
+ut--sidebarPosition-flowRow-start .Layout-main { grid-row: 2 / span 2; }
+  .Layout.Layout--sidebarPosition-flowRow-end .Layout-main { grid-row: 1 / =
+auto; }
+  .Layout.Layout--sidebarPosition-flowRow-none .Layout-sidebar { display: n=
+one; }
+  .Layout.Layout--divided { --Layout-gutter:0; }
+  .Layout.Layout--divided .Layout-divider { grid-row: 2 / auto; height: 1px=
+; }
+  .Layout.Layout--divided .Layout-divider.Layout-divider--flowRow-hidden { =
+display: none; }
+  .Layout.Layout--divided .Layout-divider.Layout-divider--flowRow-shallow {=
+ background: var(--color-canvas-inset); border-color: var(--color-border-de=
+fault); border-style: solid; border-width: var(--primer-borderWidth-thin, 1=
+px) 0; height: 8px; margin-right: 0px; }
+  .Layout.Layout--divided .Layout-main, .Layout.Layout--divided.Layout--sid=
+ebarPosition-flowRow-end .Layout-sidebar { grid-row: 3 / span 1; }
+  .Layout.Layout--divided.Layout--sidebarPosition-flowRow-end .Layout-main =
+{ grid-row: 1 / auto; }
+}
+
+@media (max-width: 767.98px) {
+  .Layout.Layout--flowRow-until-md { grid-auto-flow: row; grid-template-col=
+umns: 1fr !important; }
+  .Layout.Layout--flowRow-until-md .Layout-divider, .Layout.Layout--flowRow=
+-until-md .Layout-main, .Layout.Layout--flowRow-until-md .Layout-sidebar { =
+grid-column: 1 / auto !important; width: 100% !important; }
+  .Layout.Layout--flowRow-until-md.Layout--sidebarPosition-flowRow-start .L=
+ayout-sidebar { grid-row: 1 / auto; }
+  .Layout.Layout--flowRow-until-md.Layout--sidebarPosition-flowRow-end .Lay=
+out-sidebar, .Layout.Layout--flowRow-until-md.Layout--sidebarPosition-flowR=
+ow-start .Layout-main { grid-row: 2 / span 2; }
+  .Layout.Layout--flowRow-until-md.Layout--sidebarPosition-flowRow-end .Lay=
+out-main { grid-row: 1 / auto; }
+  .Layout.Layout--flowRow-until-md.Layout--sidebarPosition-flowRow-none .La=
+yout-sidebar { display: none; }
+  .Layout.Layout--flowRow-until-md.Layout--divided { --Layout-gutter:0; }
+  .Layout.Layout--flowRow-until-md.Layout--divided .Layout-divider { grid-r=
+ow: 2 / auto; height: 1px; }
+  .Layout.Layout--flowRow-until-md.Layout--divided .Layout-divider.Layout-d=
+ivider--flowRow-hidden { display: none; }
+  .Layout.Layout--flowRow-until-md.Layout--divided .Layout-divider.Layout-d=
+ivider--flowRow-shallow { background: var(--color-canvas-inset); border-col=
+or: var(--color-border-default); border-style: solid; border-width: var(--p=
+rimer-borderWidth-thin, 1px) 0; height: 8px; margin-right: 0px; }
+  .Layout.Layout--flowRow-until-md.Layout--divided .Layout-main, .Layout.La=
+yout--flowRow-until-md.Layout--divided.Layout--sidebarPosition-flowRow-end =
+.Layout-sidebar { grid-row: 3 / span 1; }
+  .Layout.Layout--flowRow-until-md.Layout--divided.Layout--sidebarPosition-=
+flowRow-end .Layout-main { grid-row: 1 / auto; }
+}
+
+@media (max-width: 1011.98px) {
+  .Layout.Layout--flowRow-until-lg { grid-auto-flow: row; grid-template-col=
+umns: 1fr !important; }
+  .Layout.Layout--flowRow-until-lg .Layout-divider, .Layout.Layout--flowRow=
+-until-lg .Layout-main, .Layout.Layout--flowRow-until-lg .Layout-sidebar { =
+grid-column: 1 / auto !important; width: 100% !important; }
+  .Layout.Layout--flowRow-until-lg.Layout--sidebarPosition-flowRow-start .L=
+ayout-sidebar { grid-row: 1 / auto; }
+  .Layout.Layout--flowRow-until-lg.Layout--sidebarPosition-flowRow-end .Lay=
+out-sidebar, .Layout.Layout--flowRow-until-lg.Layout--sidebarPosition-flowR=
+ow-start .Layout-main { grid-row: 2 / span 2; }
+  .Layout.Layout--flowRow-until-lg.Layout--sidebarPosition-flowRow-end .Lay=
+out-main { grid-row: 1 / auto; }
+  .Layout.Layout--flowRow-until-lg.Layout--sidebarPosition-flowRow-none .La=
+yout-sidebar { display: none; }
+  .Layout.Layout--flowRow-until-lg.Layout--divided { --Layout-gutter:0; }
+  .Layout.Layout--flowRow-until-lg.Layout--divided .Layout-divider { grid-r=
+ow: 2 / auto; height: 1px; }
+  .Layout.Layout--flowRow-until-lg.Layout--divided .Layout-divider.Layout-d=
+ivider--flowRow-hidden { display: none; }
+  .Layout.Layout--flowRow-until-lg.Layout--divided .Layout-divider.Layout-d=
+ivider--flowRow-shallow { background: var(--color-canvas-inset); border-col=
+or: var(--color-border-default); border-style: solid; border-width: var(--p=
+rimer-borderWidth-thin, 1px) 0; height: 8px; margin-right: 0px; }
+  .Layout.Layout--flowRow-until-lg.Layout--divided .Layout-main, .Layout.La=
+yout--flowRow-until-lg.Layout--divided.Layout--sidebarPosition-flowRow-end =
+.Layout-sidebar { grid-row: 3 / span 1; }
+  .Layout.Layout--flowRow-until-lg.Layout--divided.Layout--sidebarPosition-=
+flowRow-end .Layout-main { grid-row: 1 / auto; }
+}
+
+.Layout { grid-gap: var(--Layout-gutter); grid-auto-flow: column; grid-temp=
+late-columns: auto 0 minmax(0, calc(100% - var(--Layout-sidebar-width) - va=
+r(--Layout-gutter))); }
+
+.Layout .Layout-sidebar { grid-column: 1 / auto; }
+
+.Layout .Layout-divider { display: none; }
+
+.Layout .Layout-main { grid-column: 2 / span 2; }
+
+@media (min-width: 1012px) {
+  .Layout { --Layout-gutter:24px; }
+}
+
+.Layout.Layout--gutter-none { --Layout-gutter:0px; }
+
+.Layout.Layout--gutter-condensed { --Layout-gutter:16px; }
+
+@media (min-width: 1012px) {
+  .Layout.Layout--gutter-spacious { --Layout-gutter:32px; }
+}
+
+@media (min-width: 1280px) {
+  .Layout.Layout--gutter-spacious { --Layout-gutter:40px; }
+}
+
+@media (min-width: 544px) {
+  .Layout { --Layout-sidebar-width:220px; }
+}
+
+@media (min-width: 768px) {
+  .Layout { --Layout-sidebar-width:256px; }
+}
+
+@media (min-width: 1012px) {
+  .Layout { --Layout-sidebar-width:296px; }
+}
+
+@media (min-width: 768px) {
+  .Layout.Layout--sidebar-narrow { --Layout-sidebar-width:240px; }
+}
+
+@media (min-width: 1012px) {
+  .Layout.Layout--sidebar-narrow { --Layout-sidebar-width:256px; }
+  .Layout.Layout--sidebar-wide { --Layout-sidebar-width:320px; }
+}
+
+@media (min-width: 1280px) {
+  .Layout.Layout--sidebar-wide { --Layout-sidebar-width:336px; }
+}
+
+.Layout.Layout--sidebarPosition-start .Layout-sidebar { grid-column: 1 / au=
+to; }
+
+.Layout.Layout--sidebarPosition-start .Layout-main { grid-column: 2 / span =
+2; }
+
+.Layout.Layout--sidebarPosition-end { grid-template-columns: minmax(0, calc=
+(100% - var(--Layout-sidebar-width) - var(--Layout-gutter))) 0 auto; }
+
+.Layout.Layout--sidebarPosition-end .Layout-main { grid-column: 1 / auto; }
+
+.Layout.Layout--sidebarPosition-end .Layout-sidebar { grid-column: 2 / span=
+ 2; }
+
+.Layout.Layout--divided .Layout-divider { background: var(--color-border-de=
+fault); display: block; grid-column: 2 / auto; margin-right: -1px; width: 1=
+px; }
+
+.Layout.Layout--divided .Layout-main, .Layout.Layout--divided.Layout--sideb=
+arPosition-end .Layout-sidebar { grid-column: 3 / span 1; }
+
+.Layout.Layout--divided.Layout--sidebarPosition-end .Layout-main { grid-col=
+umn: 1 / auto; }
+
+.Layout-divider { display: none; width: 1px; }
+
+.Layout-sidebar { width: var(--Layout-sidebar-width); }
+
+.Layout-main { min-width: 0px; }
+
+.Layout-main .Layout-main-centered-lg, .Layout-main .Layout-main-centered-m=
+d, .Layout-main .Layout-main-centered-xl { margin-left: auto; margin-right:=
+ auto; }
+
+.Layout-main .Layout-main-centered-lg > .container-lg, .Layout-main .Layout=
+-main-centered-lg > .container-md, .Layout-main .Layout-main-centered-lg > =
+.container-xl, .Layout-main .Layout-main-centered-md > .container-lg, .Layo=
+ut-main .Layout-main-centered-md > .container-md, .Layout-main .Layout-main=
+-centered-md > .container-xl, .Layout-main .Layout-main-centered-xl > .cont=
+ainer-lg, .Layout-main .Layout-main-centered-xl > .container-md, .Layout-ma=
+in .Layout-main-centered-xl > .container-xl { margin-left: 0px; }
+
+.Layout-main .Layout-main-centered-md { max-width: calc(var(--primer-breakp=
+oint-medium, 768px) + var(--Layout-sidebar-width) + var(--Layout-gutter)); =
+}
+
+.Layout-main .Layout-main-centered-lg { max-width: calc(var(--primer-breakp=
+oint-large, 1012px) + var(--Layout-sidebar-width) + var(--Layout-gutter)); =
+}
+
+.Layout-main .Layout-main-centered-xl { max-width: calc(var(--primer-breakp=
+oint-xlarge, 1280px) + var(--Layout-sidebar-width) + var(--Layout-gutter));=
+ }
+
+.tabnav { border-bottom: var(--primer-borderWidth-thin, 1px) solid var(--co=
+lor-border-default); margin-bottom: var(--primer-stack-gap-normal, 16px); m=
+argin-top: 0px; }
+
+.tabnav-tabs { display: flex; margin-bottom: calc(var(--primer-borderWidth-=
+thin, 1px)*-1); overflow: auto; }
+
+.tabnav-tab { background-color: initial; border-top-color: ; border-top-sty=
+le: ; border-top-width: ; border-right-color: ; border-right-style: ; borde=
+r-right-width: ; border-left-color: ; border-left-style: ; border-left-widt=
+h: ; border-image-source: ; border-image-slice: ; border-image-width: ; bor=
+der-image-outset: ; border-image-repeat: ; border-bottom: 0px; color: var(-=
+-color-fg-muted); display: inline-block; flex-shrink: 0; font-size: var(--p=
+rimer-text-body-size-medium, 14px); line-height: 23px; padding: var(--base-=
+size-8, 8px) var(--primer-control-medium-paddingInline-spacious, 16px); tex=
+t-decoration: none; transition: color 0.2s cubic-bezier(0.3, 0, 0.5, 1) 0s;=
+ }
+
+.tabnav-tab.selected, .tabnav-tab[aria-current]:not([aria-current=3D"false"=
+]), .tabnav-tab[aria-selected=3D"true"] { background-color: var(--color-can=
+vas-default); border-color: var(--color-border-default); border-radius: var=
+(--primer-borderRadius-medium, 6px) var(--primer-borderRadius-medium, 6px) =
+0 0; color: var(--color-fg-default); }
+
+.tabnav-tab.selected .octicon, .tabnav-tab[aria-current]:not([aria-current=
+=3D"false"]) .octicon, .tabnav-tab[aria-selected=3D"true"] .octicon { color=
+: inherit; }
+
+.tabnav-tab:hover { color: var(--color-fg-default); text-decoration: none; =
+transition-duration: 0.1s; }
+
+.tabnav-tab:focus, .tabnav-tab:focus-visible { border-radius: var(--primer-=
+borderRadius-medium, 6px) var(--primer-borderRadius-medium, 6px) 0 0  !impo=
+rtant; }
+
+.tabnav-tab:focus, .tabnav-tab:focus-visible { outline-offset: -6px; }
+
+.tabnav-tab .octicon, .tabnav-tab:active { color: var(--color-fg-muted); }
+
+.tabnav-tab .octicon { margin-right: var(--primer-control-small-gap, 4px); =
+}
+
+.tabnav-tab .Counter { color: inherit; margin-left: var(--primer-control-sm=
+all-gap, 4px); }
+
+.tabnav-extra { color: var(--color-fg-muted); display: inline-block; font-s=
+ize: var(--primer-text-body-size-small, 12px); margin-left: 10px; padding-t=
+op: 10px; }
+
+.tabnav-extra > .octicon { margin-right: 2px; }
+
+a.tabnav-extra:hover { color: var(--color-accent-fg); text-decoration: none=
+; }
+
+.tabnav-btn { margin-left: var(--primer-controlStack-medium-gap-condensed, =
+8px); }
+
+.FormControl { display: inline-flex; flex-direction: column; gap: var(--bas=
+e-size-4, 4px); }
+
+.FormControl--fullWidth { display: flex; }
+
+.FormControl-label { color: var(--color-fg-default); font-size: var(--prime=
+r-text-body-size-medium, 14px); font-weight: var(--base-text-weight-semibol=
+d, 600); line-height: var(--primer-text-body-lineHeight-medium, 1.42857); u=
+ser-select: none; }
+
+.FormControl-caption { color: var(--color-fg-muted); font-size: var(--prime=
+r-text-caption-size, 12px); font-weight: var(--primer-text-caption-weight, =
+400); line-height: var(--primer-text-caption-lineHeight, 1.33333); margin-b=
+ottom: 0px; }
+
+.FormControl-inlineValidation { fill: var(--color-danger-fg); align-items: =
+center; color: var(--color-danger-fg); display: flex; flex-direction: row; =
+font-size: var(--primer-text-caption-size, 12px); font-weight: var(--base-t=
+ext-weight-semibold, 600); gap: var(--base-size-4, 4px); }
+
+.FormControl-inlineValidation p { margin-bottom: 0px; }
+
+.FormControl-spacingWrapper { display: flex; flex-direction: column; row-ga=
+p: 0.5rem; }
+
+.FormControl-horizontalGroup { column-gap: 0.5rem; display: flex; }
+
+.FormControl-input, .FormControl-select, .FormControl-textarea { background=
+-color: var(--color-canvas-default); border: var(--primer-borderWidth-thin,=
+ 1px) solid var(--color-border-default); color: var(--color-fg-default); }
+
+.FormControl-input[disabled], .FormControl-select[disabled], .FormControl-t=
+extarea[disabled] { -webkit-text-fill-color: var(--color-primer-fg-disabled=
+); background-color: var(--color-input-disabled-bg); border-color: var(--co=
+lor-border-default); color: var(--color-primer-fg-disabled); cursor: not-al=
+lowed; opacity: 1; }
+
+.FormControl-input[invalid]:not(:focus), .FormControl-select[invalid]:not(:=
+focus), .FormControl-textarea[invalid]:not(:focus) { border-color: var(--co=
+lor-danger-emphasis); }
+
+.FormControl-input:not([type=3D"checkbox"], [type=3D"radio"]):focus, .FormC=
+ontrol-select:not([type=3D"checkbox"], [type=3D"radio"]):focus, .FormContro=
+l-textarea:not([type=3D"checkbox"], [type=3D"radio"]):focus { border-color:=
+ var(--color-accent-fg); box-shadow: inset 0 0 0 1px var(--color-accent-fg)=
+; outline: none; }
+
+.FormControl-input:not([type=3D"checkbox"], [type=3D"radio"]):focus:not(:fo=
+cus-visible), .FormControl-select:not([type=3D"checkbox"], [type=3D"radio"]=
+):focus:not(:focus-visible), .FormControl-textarea:not([type=3D"checkbox"],=
+ [type=3D"radio"]):focus:not(:focus-visible) { border-color: var(--color-ac=
+cent-fg); box-shadow: inset 0 0 0 1px #0000 var(--color-accent-fg); outline=
+: none; }
+
+.FormControl-input:not([type=3D"checkbox"], [type=3D"radio"]):focus-visible=
+, .FormControl-select:not([type=3D"checkbox"], [type=3D"radio"]):focus-visi=
+ble, .FormControl-textarea:not([type=3D"checkbox"], [type=3D"radio"]):focus=
+-visible { border-color: var(--color-accent-fg); box-shadow: inset 0 0 0 1p=
+x var(--color-accent-fg); outline: none; }
+
+.FormControl-input, .FormControl-select, .FormControl-textarea { border-rad=
+ius: var(--primer-borderRadius-medium, 6px); font-size: var(--primer-text-b=
+ody-size-medium, 14px); line-height: var(--primer-text-body-lineHeight-medi=
+um, 1.42857); padding-block: calc(var(--primer-control-medium-paddingBlock,=
+ 6px) - var(--primer-borderWidth-thin, 1px)); padding-inline: var(--primer-=
+control-medium-paddingInline-condensed, 8px); transition: color 80ms cubic-=
+bezier(0.33, 1, 0.68, 1) 0s, background-color, box-shadow, border-color; wi=
+dth: 100%; }
+
+.FormControl-input[disabled]::placeholder, .FormControl-select[disabled]::p=
+laceholder, .FormControl-textarea[disabled]::placeholder { color: var(--col=
+or-primer-fg-disabled); }
+
+.FormControl-input[readonly], .FormControl-select[readonly], .FormControl-t=
+extarea[readonly] { background-color: var(--color-input-disabled-bg); }
+
+.FormControl-input::placeholder, .FormControl-select::placeholder, .FormCon=
+trol-textarea::placeholder { color: var(--color-fg-subtle); opacity: 1; }
+
+.FormControl-input.FormControl-small, .FormControl-select.FormControl-small=
+, .FormControl-textarea.FormControl-small { font-size: var(--primer-text-bo=
+dy-size-small, 12px); height: var(--primer-control-small-size, 28px); paddi=
+ng-block: var(--primer-control-small-paddingBlock, 4px); padding-inline: va=
+r(--primer-control-small-paddingInline-normal, 8px); }
+
+.FormControl-input.FormControl-medium, .FormControl-select.FormControl-medi=
+um, .FormControl-textarea.FormControl-medium { height: var(--primer-control=
+-medium-size, 32px); }
+
+.FormControl-input.FormControl-large, .FormControl-select.FormControl-large=
+, .FormControl-textarea.FormControl-large { height: var(--primer-control-la=
+rge-size, 40px); padding-block: var(--primer-control-large-paddingBlock, 10=
+px); padding-inline: var(--primer-control-large-paddingInline-normal, 12px)=
+; }
+
+.FormControl-input.FormControl-inset, .FormControl-select.FormControl-inset=
+, .FormControl-textarea.FormControl-inset { background-color: var(--color-c=
+anvas-inset); }
+
+.FormControl-input.FormControl-inset:focus, .FormControl-input.FormControl-=
+inset:focus-visible, .FormControl-select.FormControl-inset:focus, .FormCont=
+rol-select.FormControl-inset:focus-visible, .FormControl-textarea.FormContr=
+ol-inset:focus, .FormControl-textarea.FormControl-inset:focus-visible { bac=
+kground-color: var(--color-canvas-default); }
+
+.FormControl-input.FormControl-monospace, .FormControl-select.FormControl-m=
+onospace, .FormControl-textarea.FormControl-monospace { font-family: var(--=
+primer-fontStack-monospace, "ui-monospace, SFMono-Regular, SF Mono, Menlo, =
+Consolas, Liberation Mono, monospace"); }
+
+.FormControl-input.FormControl-error, .FormControl-select.FormControl-error=
+, .FormControl-textarea.FormControl-error { border-color: var(--color-dange=
+r-emphasis); }
+
+.FormControl-input.FormControl-success, .FormControl-select.FormControl-suc=
+cess, .FormControl-textarea.FormControl-success { border-color: var(--color=
+-success-emphasis); }
+
+.FormControl-input.FormControl-warning, .FormControl-select.FormControl-war=
+ning, .FormControl-textarea.FormControl-warning { border-color: var(--color=
+-attention-emphasis); }
+
+.FormControl-toggleSwitchInput { align-items: flex-start; display: flex; ga=
+p: var(--base-size-16, 16px); }
+
+.FormControl-input-wrap { display: grid; position: relative; }
+
+.FormControl-input-wrap .FormControl-input-leadingVisualWrap { color: var(-=
+-color-fg-muted); display: block; height: var(--base-size-16, 16px); left: =
+var(--base-size-8, 8px); pointer-events: none; position: absolute; top: var=
+(--base-size-8, 8px); width: var(--base-size-16, 16px); }
+
+.FormControl-input-wrap .FormControl-input-leadingVisualWrap .FormControl-i=
+nput-leadingVisual { display: block; user-select: none; }
+
+.FormControl-input-wrap .FormControl-input-trailingAction { align-items: ce=
+nter; background: rgba(0, 0, 0, 0); border: 0px; border-radius: var(--prime=
+r-borderRadius-small); color: var(--color-fg-muted); cursor: pointer; displ=
+ay: grid; height: var(--primer-control-xsmall-size, 24px); justify-content:=
+ center; padding: 0px; position: absolute; right: var(--base-size-4, 4px); =
+top: var(--base-size-4, 4px); transition: color 0.2s cubic-bezier(0.3, 0, 0=
+.5, 1) 0s, background-color, border-color; width: var(--primer-control-xsma=
+ll-size, 24px); z-index: 4; }
+
+.FormControl-input-wrap .FormControl-input-trailingAction svg { user-select=
+: none; }
+
+.FormControl-input-wrap .FormControl-input-trailingAction[disabled] { color=
+: var(--color-primer-fg-disabled); pointer-events: none; }
+
+.FormControl-input-wrap .FormControl-input-trailingAction:hover { backgroun=
+d: var(--color-action-list-item-default-hover-bg); }
+
+.FormControl-input-wrap .FormControl-input-trailingAction:active { backgrou=
+nd: var(--color-action-list-item-default-active-bg); }
+
+.FormControl-input-wrap .FormControl-input-trailingAction.FormControl-input=
+-trailingAction--divider::before { background: var(--color-border-default);=
+ content: ""; display: block; height: var(--base-size-16); left: calc(var(-=
+-base-size-4, 4px)*-1); position: absolute; top: calc((var(--primer-control=
+-xsmall-size) - var(--base-size-16))/2); width: var(--primer-borderWidth-th=
+in); }
+
+.FormControl-input-wrap .FormControl-input-trailingAction::after { content:=
+ ""; height: 100%; left: 50%; min-height: var(--primer-control-medium-size,=
+ 32px) var(--primer-control-medium-size, 32px); position: absolute; top: 50=
+%; transform: translateX(-50%) translateY(-50%); width: 100%; }
+
+@media (pointer: coarse) {
+  .FormControl-input-wrap .FormControl-input-trailingAction::after { min-he=
+ight: var(--primer-control-minTarget-coarse, 44px); min-width: var(--primer=
+-control-minTarget-coarse, 44px); }
+}
+
+.FormControl-input-wrap.FormControl-input-wrap--leadingVisual .FormControl-=
+input { padding-inline-start: calc(var(--primer-control-medium-paddingInlin=
+e-condensed, 8px) + var(--base-size-16, 16px) + var(--primer-control-medium=
+-gap, 8px)); }
+
+.FormControl-input-wrap.FormControl-input-wrap--trailingAction .FormControl=
+-input { padding-inline-end: calc(var(--primer-control-medium-paddingInline=
+-condensed, 8px) + var(--base-size-16, 16px) + var(--primer-control-medium-=
+gap, 8px)); }
+
+.FormControl-input-wrap.FormControl-input-wrap--trailingAction.FormControl-=
+input-wrap-trailingAction--divider .FormControl-input { padding-inline-end:=
+ calc(var(--primer-control-medium-paddingInline-condensed, 8px) + var(--bas=
+e-size-16, 16px) + var(--primer-control-medium-gap, 8px) + var(--primer-bor=
+derWidth-thin, 1px)); }
+
+.FormControl-input-wrap.FormControl-small .FormControl-input-leadingVisualW=
+rap { left: calc(var(--primer-control-medium-paddingInline-condensed, 8px) =
+- 0.125rem); top: calc(var(--primer-control-medium-paddingInline-condensed,=
+ 8px) - 0.125rem); }
+
+.FormControl-input-wrap.FormControl-small.FormControl-input-wrap--trailingA=
+ction .FormControl-input.FormControl-small { padding-inline-end: calc(var(-=
+-primer-control-small-paddingInline-condensed, 8px) + var(--base-size-16, 1=
+6px) + var(--primer-control-small-gap, 8px)); }
+
+.FormControl-input-wrap.FormControl-small.FormControl-input-wrap--trailingA=
+ction.FormControl-input-wrap-trailingAction--divider .FormControl-input.For=
+mControl-small { padding-inline-end: calc(var(--primer-control-small-paddin=
+gInline-condensed, 8px) + var(--base-size-16, 16px) + var(--primer-control-=
+small-gap, 8px) + var(--primer-borderWidth-thin, 1px)); }
+
+.FormControl-input-wrap.FormControl-small .FormControl-input-trailingAction=
+ { height: calc(var(--primer-control-small-size, 28px) - var(--base-size-8,=
+ 8px)); width: calc(var(--primer-control-small-size, 28px) - var(--base-siz=
+e-8, 8px)); }
+
+.FormControl-input-wrap.FormControl-small .FormControl-input-trailingAction=
+::before { top: calc((var(--primer-control-xsmall-size) - var(--base-size-1=
+6))/4); }
+
+.FormControl-input-wrap.FormControl-large .FormControl-input-leadingVisualW=
+rap { left: var(--primer-control-medium-paddingInline-normal, 12px); top: v=
+ar(--primer-control-medium-paddingInline-normal, 12px); }
+
+.FormControl-input-wrap.FormControl-large.FormControl-input-wrap--leadingVi=
+sual .FormControl-input.FormControl-large { padding-inline-start: calc(var(=
+--primer-control-large-paddingInline-normal, 12px) + var(--base-size-16, 16=
+px) + var(--primer-control-large-gap, 8px)); }
+
+.FormControl-input-wrap.FormControl-large.FormControl-input-wrap--trailingA=
+ction .FormControl-input.FormControl-large { padding-inline-end: calc(var(-=
+-primer-control-large-paddingInline-normal, 12px) + var(--base-size-16, 16p=
+x) + var(--primer-control-large-gap, 8px)); }
+
+.FormControl-input-wrap.FormControl-large.FormControl-input-wrap--trailingA=
+ction.FormControl-input-wrap-trailingAction--divider .FormControl-input.For=
+mControl-large { padding-inline-end: calc(var(--primer-control-large-paddin=
+gInline-normal, 12px) + var(--base-size-16, 16px) + var(--primer-control-la=
+rge-gap, 8px) + var(--primer-borderWidth-thin, 1px)); }
+
+.FormControl-input-wrap.FormControl-large .FormControl-input-trailingAction=
+ { height: var(--primer-control-small-size, 28px); right: calc(var(--primer=
+-control-medium-paddingInline-condensed, 8px) - 0.125rem); top: calc(var(--=
+primer-control-medium-paddingInline-condensed, 8px) - 0.125rem); width: var=
+(--primer-control-small-size, 28px); }
+
+.FormControl-input-wrap.FormControl-large .FormControl-input-trailingAction=
+::before { height: var(--base-size-20); top: unset; }
+
+.FormControl-select-wrap { display: grid; grid-template-columns: minmax(0, =
+auto) var(--base-size-16, 16px); }
+
+.FormControl-select-wrap::after { background-color: var(--color-fg-muted); =
+content: ""; grid-area: 1 / 2 / auto / auto; height: var(--base-size-16, 16=
+px); -webkit-mask: url("data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhla=
+WdodD0iMTYiIGZpbGw9IiM1ODYwNjkiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2=
+ZyI+PHBhdGggZD0ibTQuNDI3IDkuNDI3IDMuMzk2IDMuMzk2YS4yNTEuMjUxIDAgMCAwIC4zNTQ=
+gMGwzLjM5Ni0zLjM5NkEuMjUuMjUgMCAwIDAgMTEuMzk2IDlINC42MDRhLjI1LjI1IDAgMCAwLS=
+4xNzcuNDI3ek00LjQyMyA2LjQ3IDcuODIgMy4wNzJhLjI1LjI1IDAgMCAxIC4zNTQgMEwxMS41N=
+yA2LjQ3YS4yNS4yNSAwIDAgMS0uMTc3LjQyN0g0LjZhLjI1LjI1IDAgMCAxLS4xNzctLjQyN3oi=
+Lz48L3N2Zz4=3D") 0% 0% / contain no-repeat; mask: url("data:image/svg+xml;b=
+ase64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIGZpbGw9IiM1ODYwNjkiIHhtbG5zPSJod=
+HRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PHBhdGggZD0ibTQuNDI3IDkuNDI3IDMuMzk2IDMu=
+Mzk2YS4yNTEuMjUxIDAgMCAwIC4zNTQgMGwzLjM5Ni0zLjM5NkEuMjUuMjUgMCAwIDAgMTEuMzk=
+2IDlINC42MDRhLjI1LjI1IDAgMCAwLS4xNzcuNDI3ek00LjQyMyA2LjQ3IDcuODIgMy4wNzJhLj=
+I1LjI1IDAgMCAxIC4zNTQgMEwxMS41NyA2LjQ3YS4yNS4yNSAwIDAgMS0uMTc3LjQyN0g0LjZhL=
+jI1LjI1IDAgMCAxLS4xNzctLjQyN3oiLz48L3N2Zz4=3D"); padding-right: var(--base-=
+size-4, 4px); place-self: center end; pointer-events: none; width: var(--ba=
+se-size-16, 16px); }
+
+.FormControl-select-wrap .FormControl-select { appearance: none; grid-area:=
+ 1 / 1 / auto / -1; padding-right: var(--base-size-20, 20px); }
+
+.FormControl-checkbox-wrap, .FormControl-radio-wrap { display: inline-grid;=
+ gap: var(--base-size-8, 8px); grid-template-columns: min-content auto; }
+
+.FormControl-checkbox-wrap .FormControl-checkbox-labelWrap, .FormControl-ch=
+eckbox-wrap .FormControl-radio-labelWrap, .FormControl-radio-wrap .FormCont=
+rol-checkbox-labelWrap, .FormControl-radio-wrap .FormControl-radio-labelWra=
+p { display: flex; flex-direction: column; gap: var(--base-size-4, 4px); }
+
+.FormControl-checkbox-wrap .FormControl-label, .FormControl-radio-wrap .For=
+mControl-label { cursor: pointer; }
+
+.FormControl-check-group-wrap fieldset, .FormControl-radio-group-wrap field=
+set { border: 0px; margin: 0px; padding: 0px; }
+
+input[type=3D"checkbox"].FormControl-checkbox { background-color: var(--col=
+or-canvas-default); border: var(--primer-borderWidth-thin, 1px) solid var(-=
+-color-border-default); color: var(--color-fg-default); }
+
+input.FormControl-checkbox[type=3D"checkbox"][disabled] { -webkit-text-fill=
+-color: var(--color-primer-fg-disabled); background-color: var(--color-inpu=
+t-disabled-bg); border-color: var(--color-border-default); color: var(--col=
+or-primer-fg-disabled); cursor: not-allowed; opacity: 1; }
+
+input.FormControl-checkbox[type=3D"checkbox"][invalid]:not(:focus) { border=
+-color: var(--color-danger-emphasis); }
+
+input.FormControl-checkbox[type=3D"checkbox"]:not([type=3D"checkbox"], [typ=
+e=3D"radio"]):focus { border-color: var(--color-accent-fg); box-shadow: ins=
+et 0 0 0 1px var(--color-accent-fg); outline: none; }
+
+input.FormControl-checkbox[type=3D"checkbox"]:not([type=3D"checkbox"], [typ=
+e=3D"radio"]):focus:not(:focus-visible) { border-color: var(--color-accent-=
+fg); box-shadow: inset 0 0 0 1px #0000 var(--color-accent-fg); outline: non=
+e; }
+
+input.FormControl-checkbox[type=3D"checkbox"]:not([type=3D"checkbox"], [typ=
+e=3D"radio"]):focus-visible { border-color: var(--color-accent-fg); box-sha=
+dow: inset 0 0 0 1px var(--color-accent-fg); outline: none; }
+
+input[type=3D"checkbox"].FormControl-checkbox { appearance: none; border-co=
+lor: var(--color-neutral-emphasis); border-radius: var(--primer-borderRadiu=
+s-small, 3px); cursor: pointer; display: grid; height: var(--base-size-16, =
+16px); margin: 0.125rem 0px 0px; place-content: center; position: relative;=
+ transition: background-color 0s ease 0s, border-color 80ms cubic-bezier(0.=
+33, 1, 0.68, 1) 0s; width: var(--base-size-16, 16px); }
+
+input.FormControl-checkbox[type=3D"checkbox"]::before { background-color: v=
+ar(--color-fg-on-emphasis); clip-path: inset(var(--base-size-16, 16px) 0 0 =
+0); content: ""; height: var(--base-size-16, 16px); -webkit-mask-image: url=
+("data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTIiIGhlaWdodD0iOSIgZmlsbD0ibm9=
+uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBmaWxsLXJ1bGU9Im=
+V2ZW5vZGQiIGNsaXAtcnVsZT0iZXZlbm9kZCIgZD0iTTExLjc4LjIyYS43NS43NSAwIDAgMSAwI=
+DEuMDYxTDQuNTIgOC41NDFhLjc1Mi43NTIgMCAwIDEtMS4wNjIgMEwuMjAyIDUuMjg1YS43NS43=
+NSAwIDAgMSAxLjA2MS0xLjA2MWwyLjcyNSAyLjcyM0wxMC43MTguMjJhLjc1MS43NTEgMCAwIDE=
+gMS4wNjIgMFoiIGZpbGw9IiNmZmYiLz48L3N2Zz4=3D"); -webkit-mask-position: cente=
+r center; -webkit-mask-repeat: no-repeat; -webkit-mask-size: 75%; transitio=
+n: visibility 0s linear 0.23s; visibility: hidden; width: var(--base-size-1=
+6, 16px); }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  input.FormControl-checkbox[type=3D"checkbox"]::before { animation: 80ms c=
+ubic-bezier(0.65, 0, 0.35, 1) 0s 1 normal forwards running checkmarkOut; }
+}
+
+input.FormControl-checkbox[type=3D"checkbox"]::after { content: ""; height:=
+ 100%; left: 50%; min-height: var(--primer-control-medium-size, 32px) var(-=
+-primer-control-medium-size, 32px); position: absolute; top: 50%; transform=
+: translateX(-50%) translateY(-50%); width: 100%; }
+
+input.FormControl-checkbox[type=3D"checkbox"][disabled] ~ .FormControl-chec=
+kbox-labelWrap .FormControl-label, input.FormControl-checkbox[type=3D"check=
+box"][disabled] ~ .FormControl-radio-labelWrap .FormControl-label { color: =
+var(--color-primer-fg-disabled); cursor: not-allowed; }
+
+input.FormControl-checkbox[type=3D"checkbox"]:checked { background: var(--c=
+olor-accent-fg); border-color: var(--color-accent-fg); transition: backgrou=
+nd-color 0s ease 0s, border-color 80ms cubic-bezier(0.32, 0, 0.67, 0) 0ms; =
+}
+
+input.FormControl-checkbox[type=3D"checkbox"]:checked::before { transition:=
+ visibility 0s linear 0s; visibility: visible; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  input.FormControl-checkbox[type=3D"checkbox"]:checked::before { animation=
+: 80ms cubic-bezier(0.65, 0, 0.35, 1) 80ms 1 normal forwards running checkm=
+arkIn; }
+}
+
+input.FormControl-checkbox[type=3D"checkbox"]:checked:disabled { background=
+-color: var(--color-primer-fg-disabled); border-color: var(--color-primer-f=
+g-disabled); cursor: not-allowed; opacity: 1; }
+
+input.FormControl-checkbox[type=3D"checkbox"]:checked:disabled::before { ba=
+ckground-color: var(--color-fg-on-emphasis); }
+
+@media (forced-colors: active) {
+  input.FormControl-checkbox[type=3D"checkbox"]:checked { background-color:=
+ canvastext; border-color: canvastext; }
+}
+
+input.FormControl-checkbox[type=3D"checkbox"]:focus-visible { box-shadow: n=
+one; outline: 2px solid var(--color-accent-fg); outline-offset: 2px; }
+
+input.FormControl-checkbox[type=3D"checkbox"]:indeterminate::before { -webk=
+it-mask-image: url("data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTAiIGhlaWdod=
+D0iMiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0=
+aCBmaWxsLXJ1bGU9ImV2ZW5vZGQiIGNsaXAtcnVsZT0iZXZlbm9kZCIgZD0iTTAgMWExIDEgMCA=
+wIDEgMS0xaDhhMSAxIDAgMSAxIDAgMkgxYTEgMSAwIDAgMS0xLTFaIiBmaWxsPSIjZmZmIi8+PC=
+9zdmc+"); visibility: visible; }
+
+input[type=3D"radio"].FormControl-radio { background-color: var(--color-can=
+vas-default); border: var(--primer-borderWidth-thin, 1px) solid var(--color=
+-border-default); color: var(--color-fg-default); }
+
+input.FormControl-radio[type=3D"radio"][disabled] { -webkit-text-fill-color=
+: var(--color-primer-fg-disabled); background-color: var(--color-input-disa=
+bled-bg); border-color: var(--color-border-default); color: var(--color-pri=
+mer-fg-disabled); cursor: not-allowed; opacity: 1; }
+
+input.FormControl-radio[type=3D"radio"][invalid]:not(:focus) { border-color=
+: var(--color-danger-emphasis); }
+
+input.FormControl-radio[type=3D"radio"]:not([type=3D"checkbox"], [type=3D"r=
+adio"]):focus { border-color: var(--color-accent-fg); box-shadow: inset 0 0=
+ 0 1px var(--color-accent-fg); outline: none; }
+
+input.FormControl-radio[type=3D"radio"]:not([type=3D"checkbox"], [type=3D"r=
+adio"]):focus:not(:focus-visible) { border-color: var(--color-accent-fg); b=
+ox-shadow: inset 0 0 0 1px #0000 var(--color-accent-fg); outline: none; }
+
+input.FormControl-radio[type=3D"radio"]:not([type=3D"checkbox"], [type=3D"r=
+adio"]):focus-visible { border-color: var(--color-accent-fg); box-shadow: i=
+nset 0 0 0 1px var(--color-accent-fg); outline: none; }
+
+input[type=3D"radio"].FormControl-radio { appearance: none; border-color: v=
+ar(--color-neutral-emphasis); border-radius: var(--primer-borderRadius-full=
+, 100vh); cursor: pointer; height: var(--base-size-16, 16px); margin: 0.125=
+rem 0px 0px; position: relative; transition: background-color 0s ease 0s, b=
+order-color 80ms cubic-bezier(0.33, 1, 0.68, 1) 0s; width: var(--base-size-=
+16, 16px); }
+
+input.FormControl-radio[type=3D"radio"]::after { content: ""; height: 100%;=
+ left: 50%; min-height: var(--primer-control-medium-size, 32px) var(--prime=
+r-control-medium-size, 32px); position: absolute; top: 50%; transform: tran=
+slateX(-50%) translateY(-50%); width: 100%; }
+
+input.FormControl-radio[type=3D"radio"]:checked { border-color: var(--color=
+-accent-fg); border-width: var(--base-size-4, 4px); }
+
+input.FormControl-radio[type=3D"radio"]:checked:disabled { border-color: va=
+r(--color-primer-fg-disabled); cursor: not-allowed; }
+
+input.FormControl-radio[type=3D"radio"]:focus-visible { box-shadow: none; o=
+utline: 2px solid var(--color-accent-fg); outline-offset: 2px; }
+
+@media (forced-colors: active) {
+  input[type=3D"radio"].FormControl-radio { background-color: canvastext; b=
+order-color: canvastext; }
+}
+
+@keyframes checkmarkIn {=20
+  0% { clip-path: inset(var(--base-size-16, 16px) 0 0 0); }
+  100% { clip-path: inset(0px); }
+}
+
+@keyframes checkmarkOut {=20
+  0% { clip-path: inset(0px); }
+  100% { clip-path: inset(var(--base-size-16, 16px) 0 0 0); }
+}
+
+.btn-mktg { border: 0px; border-radius: 0.375rem; color: var(--color-canvas=
+-default); display: inline-block; font-size: 1rem; font-weight: var(--base-=
+text-weight-semibold, 600); line-height: 1; padding: 0.9rem 1.5rem 1.1rem; =
+position: relative; text-align: center; transition: box-shadow 0.2s ease 0s=
+, outline 0.2s ease 0s; user-select: none; vertical-align: middle; white-sp=
+ace: nowrap; z-index: 1; appearance: none !important; background: linear-gr=
+adient(180deg, #ffffff26 0, #fff0 100%),var(--color-mktg-btn-bg)  !importan=
+t; }
+
+.btn-mktg::before { background-blend-mode: normal; border-radius: inherit; =
+inset: 0px; content: ""; opacity: 0; position: absolute; transition: opacit=
+y 0.2s ease 0s; z-index: -1; background: linear-gradient(rgba(255, 255, 255=
+, 0.15), rgba(255, 255, 255, 0)) !important; }
+
+.btn-mktg:hover { text-decoration: none; box-shadow: var(--color-mktg-btn-s=
+hadow-hover)  !important; }
+
+.btn-mktg.focus::before, .btn-mktg:focus-visible::before, .btn-mktg:focus::=
+before, .btn-mktg:hover::before { opacity: 1; }
+
+.btn-mktg:focus { box-shadow: none; outline: 2px solid var(--color-accent-f=
+g); outline-offset: 2px; }
+
+.btn-mktg:focus:not(:focus-visible) { box-shadow: none; outline: rgba(0, 0,=
+ 0, 0) solid 1px; }
+
+.btn-mktg:focus-visible { box-shadow: none; outline: 2px solid var(--color-=
+accent-fg); outline-offset: 2px; }
+
+.btn-mktg:active::before { opacity: 0.5 !important; }
+
+.btn-mktg.disabled, .btn-mktg[disabled] { cursor: default; opacity: 0.5; po=
+inter-events: none; }
+
+.btn-muted-mktg { box-shadow: var(--color-mktg-btn-shadow-outline); backgro=
+und: none !important; color: var(--color-fg-default)  !important; }
+
+.btn-muted-mktg::before { display: none; }
+
+.btn-muted-mktg:hover { box-shadow: var(--color-mktg-btn-shadow-hover-muted=
+)  !important; }
+
+.btn-muted-mktg:active { box-shadow: var(--color-fg-default) 0 0 0 3px inse=
+t  !important; }
+
+.btn-muted-mktg:disabled { box-shadow: var(--color-fg-subtle) 0 0 0 1px ins=
+et  !important; }
+
+.btn-subtle-mktg { box-shadow: none !important; color: var(--color-fg-defau=
+lt)  !important; }
+
+.btn-subtle-mktg, .btn-subtle-mktg::before { background: none !important; }
+
+.btn-subtle-mktg:hover { box-shadow: var(--color-mktg-btn-shadow-hover-mute=
+d)  !important; }
+
+.btn-signup-mktg { color: rgb(255, 255, 255); background: linear-gradient(r=
+gba(52, 183, 89, 0.15), rgba(46, 164, 79, 0)), rgb(46, 164, 79) !important;=
+ }
+
+.btn-signup-mktg::before { background: linear-gradient(rgba(52, 183, 89, 0.=
+15), rgba(46, 164, 79, 0)) !important; }
+
+.btn-signup-mktg:focus { box-shadow: none; outline: 2px solid var(--color-a=
+ccent-fg); outline-offset: 2px; }
+
+.btn-signup-mktg:focus:not(:focus-visible) { box-shadow: none; outline: rgb=
+a(0, 0, 0, 0) solid 1px; }
+
+.btn-signup-mktg:focus-visible { box-shadow: none; outline: 2px solid var(-=
+-color-accent-fg); outline-offset: 2px; }
+
+.btn-small-mktg { padding: 0.625rem 1rem 0.8125rem; }
+
+.btn-large-mktg { font-size: 1.25rem; padding: 16px 30px 20px !important; }
+
+.ToggleSwitch, .ToggleSwitch.ToggleSwitch { display: inline-flex; }
+
+.ToggleSwitch { align-items: center; gap: var(--primer-controlStack-medium-=
+gap-condensed, 8px); }
+
+.ToggleSwitch--checked .ToggleSwitch-statusOn { height: auto; visibility: v=
+isible; }
+
+.ToggleSwitch--checked .ToggleSwitch-statusOff { height: 0px; visibility: h=
+idden; }
+
+.ToggleSwitch-track { appearance: none; background-color: var(--color-switc=
+h-track-bg); border: var(--primer-borderWidth-thin, 1px) solid var(--color-=
+switch-track-border); border-radius: var(--primer-borderRadius-medium, 6px)=
+; cursor: pointer; display: block; height: var(--primer-control-medium-size=
+, 32px); overflow: hidden; padding: 0px; position: relative; text-decoratio=
+n: none; transition-duration: 80ms; transition-property: background-color, =
+border-color; transition-timing-function: cubic-bezier(0.5, 1, 0.89, 1); us=
+er-select: none; width: var(--base-size-64, 64px); }
+
+.ToggleSwitch-track:focus, .ToggleSwitch-track:focus-visible { outline-offs=
+et: 1px; }
+
+.ToggleSwitch-track:hover { background-color: var(--color-switch-track-hove=
+r-bg); }
+
+.ToggleSwitch-track:active { background-color: var(--color-switch-track-act=
+ive-bg); }
+
+@media (pointer: coarse) {
+  .ToggleSwitch-track::before { content: ""; height: 100%; left: 50%; min-h=
+eight: 44px; position: absolute; top: 50%; transform: translateX(-50%) tran=
+slateY(-50%); width: 100%; }
+}
+
+@media (prefers-reduced-motion) {
+  .ToggleSwitch-track, .ToggleSwitch-track * { transition: none 0s ease 0s;=
+ }
+}
+
+.ToggleSwitch-track[aria-checked=3D"true"][aria-disabled=3D"true"] { backgr=
+ound-color: var(--color-switch-track-disabled-bg); border-color: rgba(0, 0,=
+ 0, 0); color: var(--color-switch-track-checked-disabled-fg); }
+
+.ToggleSwitch-track[aria-checked=3D"true"] { background-color: var(--color-=
+switch-track-checked-bg); border-color: var(--color-switch-track-checked-bo=
+rder); }
+
+.ToggleSwitch-track[aria-checked=3D"true"]:not([aria-disabled=3D"true"]):ho=
+ver { background-color: var(--color-switch-track-checked-hover-bg); }
+
+.ToggleSwitch-track[aria-checked=3D"true"]:not([aria-disabled=3D"true"]):ac=
+tive { background-color: var(--color-switch-track-checked-active-bg); }
+
+.ToggleSwitch-track[aria-checked=3D"true"] .ToggleSwitch-knob { background-=
+color: var(--color-switch-knob-checked-bg); border-color: var(--color-switc=
+h-knob-checked-border); transform: translateX(100%); }
+
+.ToggleSwitch-track[aria-checked=3D"true"] .ToggleSwitch-lineIcon { transfo=
+rm: translateX(0px); }
+
+.ToggleSwitch-track[aria-checked=3D"true"] .ToggleSwitch-circleIcon { trans=
+form: translateX(100%); }
+
+.ToggleSwitch-track[aria-disabled=3D"true"] { background-color: var(--color=
+-switch-track-disabled-bg); border-color: rgba(0, 0, 0, 0); cursor: not-all=
+owed; transition-property: none; }
+
+.ToggleSwitch-track[aria-disabled=3D"true"] .ToggleSwitch-knob { border-col=
+or: var(--color-border-default); box-shadow: none; }
+
+.ToggleSwitch-track[aria-disabled=3D"true"] .ToggleSwitch-circleIcon, .Togg=
+leSwitch-track[aria-disabled=3D"true"] .ToggleSwitch-lineIcon { color: var(=
+--color-switch-track-disabled-fg); }
+
+.ToggleSwitch-icons { align-items: center; display: flex; height: 100%; ove=
+rflow: hidden; width: 100%; }
+
+.ToggleSwitch-lineIcon { color: var(--color-switch-track-checked-fg); trans=
+form: translateX(-100%); }
+
+.ToggleSwitch-circleIcon, .ToggleSwitch-lineIcon { flex: 1 0 50%; line-heig=
+ht: 0; transition-duration: 80ms; transition-property: transform; }
+
+.ToggleSwitch-circleIcon { color: var(--color-switch-track-fg); transform: =
+translateX(0px); }
+
+.ToggleSwitch-knob { background-color: var(--color-switch-knob-bg); border:=
+ var(--primer-borderWidth-thin, 1px) solid var(--color-switch-knob-border);=
+ border-radius: var(--primer-borderRadius-medium, 6px); bottom: 0px; box-sh=
+adow: var(--color-shadow-medium),var(--color-btn-inset-shadow); position: a=
+bsolute; top: 0px; transition-duration: 80ms; transition-property: transfor=
+m; transition-timing-function: cubic-bezier(0.5, 1, 0.89, 1); width: 50%; z=
+-index: 1; }
+
+@media (prefers-reduced-motion) {
+  .ToggleSwitch-knob { transition: none 0s ease 0s; }
+}
+
+.ToggleSwitch-status { color: var(--color-fg-default); font-size: var(--pri=
+mer-text-body-size-medium, 14px); line-height: 1.5; position: relative; tex=
+t-align: right; }
+
+.ToggleSwitch-statusIcon { display: flex; margin-top: 0.063rem; width: var(=
+--base-size-16, 16px); }
+
+.ToggleSwitch--small .ToggleSwitch-status { font-size: var(--primer-text-bo=
+dy-size-small, 12px); }
+
+.ToggleSwitch--small .ToggleSwitch-track { height: var(--primer-control-xsm=
+all-size, 24px); width: var(--base-size-48, 48px); }
+
+.ToggleSwitch--disabled .ToggleSwitch-status { color: var(--color-fg-muted)=
+; }
+
+.ToggleSwitch-statusOn { height: 0px; visibility: hidden; }
+
+.ToggleSwitch-statusOff { height: auto; visibility: visible; }
+
+.ToggleSwitch--statusAtEnd { flex-direction: row-reverse; }
+
+.ToggleSwitch--statusAtEnd .ToggleSwitch-status { text-align: left; }
+
+.UnderlineNav { box-shadow: inset 0 -1px 0 var(--color-border-muted); displ=
+ay: flex; justify-content: space-between; min-height: var(--base-size-48, 4=
+8px); overflow: auto hidden; }
+
+.UnderlineNav .Counter { background-color: var(--color-neutral-muted); colo=
+r: var(--color-fg-default); margin-left: var(--primer-control-medium-gap, 8=
+px); }
+
+.UnderlineNav .Counter--primary { background-color: var(--color-neutral-emp=
+hasis); color: var(--color-fg-on-emphasis); }
+
+.UnderlineNav-body { align-items: center; display: flex; gap: var(--primer-=
+control-medium-gap, 8px); list-style: none; }
+
+.UnderlineNav-item { align-items: center; background-color: initial; border=
+: 0px; border-radius: var(--primer-borderRadius-medium, 6px); color: var(--=
+color-fg-default); cursor: pointer; display: flex; font-size: var(--primer-=
+text-body-size-medium, 14px); line-height: 30px; padding: 0 var(--primer-co=
+ntrol-medium-paddingInline-condensed, 8px); position: relative; text-align:=
+ center; white-space: nowrap; }
+
+.UnderlineNav-item:focus, .UnderlineNav-item:focus-visible, .UnderlineNav-i=
+tem:hover { border-bottom-color: var(--color-neutral-muted); color: var(--c=
+olor-fg-default); outline-offset: -2px; text-decoration: none; transition: =
+border-bottom-color 0.12s ease-out 0s; }
+
+.UnderlineNav-item [data-content]::before { content: attr(data-content); di=
+splay: block; font-weight: var(--base-text-weight-semibold, 600); height: 0=
+px; visibility: hidden; }
+
+.UnderlineNav-item::before { content: ""; height: 100%; left: 50%; min-heig=
+ht: 48px; position: absolute; top: 50%; transform: translateX(-50%) transla=
+teY(-50%); width: 100%; }
+
+@media (pointer: fine) {
+  .UnderlineNav-item:hover { background: var(--color-action-list-item-defau=
+lt-hover-bg); color: var(--color-fg-default); text-decoration: none; transi=
+tion: background 0.12s ease-out 0s; }
+}
+
+.UnderlineNav-item.selected, .UnderlineNav-item[aria-current]:not([aria-cur=
+rent=3D"false"]), .UnderlineNav-item[role=3D"tab"][aria-selected=3D"true"] =
+{ border-bottom-color: var(--color-primer-border-active); color: var(--colo=
+r-fg-default); font-weight: var(--base-text-weight-semibold, 600); }
+
+.UnderlineNav-item.selected::after, .UnderlineNav-item[aria-current]:not([a=
+ria-current=3D"false"])::after, .UnderlineNav-item[role=3D"tab"][aria-selec=
+ted=3D"true"]::after { background: var(--color-primer-border-active); borde=
+r-radius: var(--primer-borderRadius-medium, 6px); bottom: calc(50% - 25px);=
+ content: ""; height: 2px; position: absolute; right: 50%; transform: trans=
+late(50%, -50%); width: 100%; }
+
+.UnderlineNav--right { justify-content: flex-end; }
+
+.UnderlineNav--right .UnderlineNav-actions { flex: 1 1 auto; }
+
+.UnderlineNav-actions { align-self: center; }
+
+.UnderlineNav--full { display: block; }
+
+.UnderlineNav--full .UnderlineNav-body { min-height: var(--base-size-48, 48=
+px); }
+
+.UnderlineNav-octicon { fill: var(--color-fg-muted); color: var(--color-fg-=
+muted); margin-right: var(--primer-control-medium-gap, 8px); display: inlin=
+e !important; }
+
+.UnderlineNav-container { display: flex; justify-content: space-between; }
+
+.SegmentedControl { background-color: var(--color-segmented-control-bg); bo=
+rder-radius: var(--primer-borderRadius-medium, 6px); display: inline-flex; =
+list-style: none; }
+
+.SegmentedControl-item { border: var(--primer-borderWidth-thin, max(1px, 0.=
+0625rem)) solid #0000; border-radius: var(--primer-borderRadius-medium, 6px=
+); display: inline-flex; padding: var(--primer-control-xsmall-paddingInline=
+-condensed, 4px); position: relative; }
+
+.SegmentedControl-item.SegmentedControl-item--selected { background-color: =
+var(--color-segmented-control-button-bg); border-color: var(--color-segment=
+ed-control-button-selected-border); }
+
+.SegmentedControl-item.SegmentedControl-item--selected .Button { font-weigh=
+t: var(--base-text-weight-semibold, 600); }
+
+.SegmentedControl-item.SegmentedControl-item--selected .Button:hover { back=
+ground-color: initial; }
+
+.SegmentedControl-item.SegmentedControl-item--selected + .SegmentedControl-=
+item::before, .SegmentedControl-item.SegmentedControl-item--selected::befor=
+e { border-color: rgba(0, 0, 0, 0); }
+
+.SegmentedControl-item .Button-label[data-content]::before { content: attr(=
+data-content); display: block; font-weight: var(--base-text-weight-semibold=
+, 600); height: 0px; visibility: hidden; }
+
+.SegmentedControl-item::before { border-left: var(--primer-borderWidth-thin=
+, max(1px, 0.0625rem)) solid var(--color-border-default); content: ""; inse=
+t: 0px 0px 0px -1px; margin-bottom: var(--primer-control-medium-paddingBloc=
+k, 6px); margin-top: var(--primer-control-medium-paddingBlock, 6px); positi=
+on: absolute; }
+
+.SegmentedControl-item .Button { border: 0px; color: var(--color-btn-text);=
+ font-weight: var(--base-text-weight-normal, 400); transition: none 0s ease=
+ 0s; }
+
+.SegmentedControl-item .Button:focus-visible { border-radius: calc(var(--pr=
+imer-borderRadius-medium, 6px) - 5px); outline-offset: calc(var(--primer-co=
+ntrol-xsmall-paddingInline-condensed, 4px) - var(--primer-borderWidth-thin,=
+ 1px)); }
+
+.SegmentedControl-item .Button--small { height: calc(var(--primer-control-s=
+mall-size, 28px) - var(--primer-control-xsmall-paddingInline-condensed, 4px=
+)*2 - var(--primer-borderWidth-thin, 1px)*2); padding: 0 calc(var(--primer-=
+control-small-paddingInline-condensed, 8px) - var(--primer-control-xsmall-p=
+addingInline-condensed, 4px)); }
+
+.SegmentedControl-item .Button--small.Button--iconOnly { width: calc(var(--=
+primer-control-medium-size, 32px) - var(--primer-control-xsmall-paddingInli=
+ne-condensed, 4px)*2 - var(--primer-borderWidth-thin, 1px)*2); }
+
+.SegmentedControl-item .Button--small.Button--iconOnly::before { content: "=
+"; height: 100%; left: 50%; min-height: var(--primer-control-medium-size, 3=
+2px); min-width: var(--primer-control-medium-size, 32px); position: absolut=
+e; top: 50%; transform: translateX(-50%) translateY(-50%); width: 100%; }
+
+.SegmentedControl-item .Button--medium { height: calc(var(--primer-control-=
+medium-size, 32px) - var(--primer-control-xsmall-paddingInline-condensed, 4=
+px)*2 - var(--primer-borderWidth-thin, 1px)*2); padding: 0 calc(var(--prime=
+r-control-medium-paddingInline-normal, 12px) - var(--primer-control-xsmall-=
+paddingInline-condensed, 4px)); }
+
+.SegmentedControl-item .Button--medium.Button--iconOnly { width: calc(var(-=
+-primer-control-medium-size, 32px) - var(--primer-control-xsmall-paddingInl=
+ine-condensed, 4px)*2 - var(--primer-borderWidth-thin, 1px)*2); }
+
+.SegmentedControl-item .Button--medium.Button--iconOnly::before { content: =
+""; height: 100%; left: 50%; min-height: var(--primer-control-medium-size, =
+32px); min-width: var(--primer-control-medium-size, 32px); position: absolu=
+te; top: 50%; transform: translateX(-50%) translateY(-50%); width: 100%; }
+
+.SegmentedControl-item .Button--large { height: calc(var(--primer-control-l=
+arge-size, 40px) - var(--primer-control-xsmall-paddingInline-condensed, 4px=
+)*2 - var(--primer-borderWidth-thin, 1px)*2); padding: 0 calc(var(--primer-=
+control-large-paddingInline-spacious, 16px) - var(--primer-control-xsmall-p=
+addingInline-condensed, 4px)); }
+
+.SegmentedControl-item .Button--large.Button--iconOnly { width: calc(var(--=
+primer-control-large-size, 40px) - var(--primer-control-xsmall-paddingInlin=
+e-condensed, 4px)*2 - var(--primer-borderWidth-thin, 1px)*2); }
+
+.SegmentedControl-item .Button--large.Button--iconOnly::before { content: "=
+"; height: 100%; left: 50%; min-height: var(--primer-control-large-size, 40=
+px); min-width: var(--primer-control-large-size, 40px); position: absolute;=
+ top: 50%; transform: translateX(-50%) translateY(-50%); width: 100%; }
+
+.SegmentedControl-item .Button--iconOnly { padding: initial; }
+
+.SegmentedControl-item .Button--invisible.Button--invisible-noVisuals .Butt=
+on-label { color: var(--color-btn-text); }
+
+.SegmentedControl--fullWidth { display: flex; }
+
+.SegmentedControl--fullWidth .SegmentedControl-item { flex: 1 1 0%; justify=
+-content: center; }
+
+.SegmentedControl--fullWidth .Button--iconOnly, .SegmentedControl--fullWidt=
+h .Button-withTooltip { width: 100%; }
+
+.menu { background-color: var(--color-canvas-default); border: var(--primer=
+-borderWidth-thin, 1px) solid var(--color-border-default); border-radius: v=
+ar(--primer-borderRadius-medium, 6px); list-style: none; margin-bottom: var=
+(--primer-stack-gap-normal, 16px); }
+
+.menu-item { border-bottom: var(--primer-borderWidth-thin, 1px) solid var(-=
+-color-border-muted); color: var(--color-fg-default); display: block; paddi=
+ng: var(--primer-control-medium-paddingInline-condensed, 8px) var(--primer-=
+control-medium-paddingInline-spacious, 16px); position: relative; }
+
+.menu-item:first-child { border-top: 0px; border-top-right-radius: var(--pr=
+imer-borderRadius-medium, 6px); }
+
+.menu-item:first-child, .menu-item:first-child::before { border-top-left-ra=
+dius: var(--primer-borderRadius-medium, 6px); }
+
+.menu-item:last-child { border-bottom: 0px; border-bottom-right-radius: var=
+(--primer-borderRadius-medium, 6px); }
+
+.menu-item:last-child, .menu-item:last-child::before { border-bottom-left-r=
+adius: var(--primer-borderRadius-medium, 6px); }
+
+.menu-item:hover { background-color: var(--color-neutral-subtle); text-deco=
+ration: none; }
+
+.menu-item:active { background-color: var(--color-canvas-subtle); }
+
+.menu-item.selected, .menu-item[aria-current]:not([aria-current=3D"false"])=
+, .menu-item[aria-selected=3D"true"] { background-color: var(--color-menu-b=
+g-active); cursor: default; }
+
+.menu-item.selected::before, .menu-item[aria-current]:not([aria-current=3D"=
+false"])::before, .menu-item[aria-selected=3D"true"]::before { background-c=
+olor: var(--color-primer-border-active); bottom: 0px; content: ""; left: 0p=
+x; position: absolute; top: 0px; width: 2px; }
+
+.menu-item .octicon { color: var(--color-fg-muted); margin-right: var(--pri=
+mer-control-medium-gap, 8px); text-align: center; width: 16px; }
+
+.menu-item .Counter { float: right; margin-left: var(--primer-control-small=
+-gap, 4px); }
+
+.menu-item .menu-warning { color: var(--color-attention-fg); float: right; =
+}
+
+.menu-item .avatar { float: left; margin-right: var(--primer-control-small-=
+gap, 4px); }
+
+.menu-item.alert .Counter { color: var(--color-danger-fg); }
+
+.menu-heading { border-bottom: var(--primer-borderWidth-thin, 1px) solid va=
+r(--color-border-muted); color: var(--color-fg-default); display: block; fo=
+nt-size: inherit; font-weight: var(--base-text-weight-semibold, 600); margi=
+n-bottom: 0px; margin-top: 0px; padding: var(--primer-control-medium-paddin=
+gInline-condensed, 8px) var(--primer-control-medium-paddingInline-spacious,=
+ 16px); }
+
+.menu-heading:hover { text-decoration: none; }
+
+.menu-heading:first-child { border-top-left-radius: var(--primer-borderRadi=
+us-medium, 6px); border-top-right-radius: var(--primer-borderRadius-medium,=
+ 6px); }
+
+.menu-heading:last-child { border-bottom: 0px; border-bottom-left-radius: v=
+ar(--primer-borderRadius-medium, 6px); border-bottom-right-radius: var(--pr=
+imer-borderRadius-medium, 6px); }
+
+.avatar { background-color: var(--color-avatar-bg); border-radius: var(--pr=
+imer-borderRadius-medium, 6px); box-shadow: 0 0 0 1px var(--color-avatar-bo=
+rder); display: inline-block; flex-shrink: 0; line-height: 1; overflow: hid=
+den; vertical-align: middle; }
+
+.avatar-link { float: left; line-height: 1; }
+
+.avatar-group-item { display: inline-block; margin-bottom: 3px; }
+
+.avatar-1, .avatar-2, .avatar-small { border-radius: var(--primer-borderRad=
+ius-small, 4px); }
+
+.avatar-1 { height: var(--base-size-16, 16px); width: var(--base-size-16, 1=
+6px); }
+
+.avatar-2 { height: var(--base-size-20, 20px); width: var(--base-size-20, 2=
+0px); }
+
+.avatar-3 { height: var(--base-size-24, 24px); width: var(--base-size-24, 2=
+4px); }
+
+.avatar-4 { height: var(--base-size-28, 28px); width: var(--base-size-28, 2=
+8px); }
+
+.avatar-5 { height: var(--base-size-32, 32px); width: var(--base-size-32, 3=
+2px); }
+
+.avatar-6 { height: var(--base-size-40, 40px); width: var(--base-size-40, 4=
+0px); }
+
+.avatar-7 { height: var(--base-size-48, 48px); width: var(--base-size-48, 4=
+8px); }
+
+.avatar-8 { height: var(--base-size-64, 64px); width: var(--base-size-64, 6=
+4px); }
+
+.AvatarStack { height: 20px; min-width: 26px; position: relative; }
+
+.AvatarStack .AvatarStack-body { position: absolute; }
+
+.AvatarStack.AvatarStack--two { min-width: 36px; }
+
+.AvatarStack.AvatarStack--three-plus { min-width: 46px; }
+
+.AvatarStack-body { background: var(--color-canvas-default); border-radius:=
+ 100px; display: flex; }
+
+.AvatarStack-body .avatar { background-color: var(--color-canvas-default); =
+border-radius: var(--primer-borderRadius-small, 4px); border-right: var(--p=
+rimer-borderWidth-thin, 1px) solid var(--color-canvas-default); box-shadow:=
+ none; box-sizing: initial; display: flex; height: 20px; margin-right: -11p=
+x; position: relative; transition: margin 0.1s ease-in-out 0s; width: 20px;=
+ z-index: 2; }
+
+.AvatarStack-body .avatar:first-child { z-index: 3; }
+
+.AvatarStack-body .avatar:last-child { border-right: 0px; z-index: 1; }
+
+.AvatarStack-body .avatar img { border-radius: var(--primer-borderRadius-sm=
+all, 4px); }
+
+.AvatarStack-body .avatar:nth-child(n+4) { display: none; opacity: 0; }
+
+.AvatarStack-body:hover .avatar { margin-right: 3px; }
+
+.AvatarStack-body:hover .avatar:nth-child(n+4) { display: flex; opacity: 1;=
+ }
+
+.AvatarStack-body:hover .avatar-more { display: none !important; }
+
+.avatar.avatar-more { background: var(--color-canvas-subtle); margin-right:=
+ 0px; z-index: 1; }
+
+.avatar.avatar-more::after, .avatar.avatar-more::before { border-radius: 2p=
+x; content: ""; display: block; height: 20px; outline: var(--primer-borderW=
+idth-thin, 1px) solid var(--color-canvas-default); position: absolute; }
+
+.avatar.avatar-more::before { background: var(--color-avatar-stack-fade-mor=
+e); width: 17px; }
+
+.avatar.avatar-more::after { background: var(--color-avatar-stack-fade); wi=
+dth: 14px; }
+
+.AvatarStack--right .AvatarStack-body { flex-direction: row-reverse; right:=
+ 0px; }
+
+.AvatarStack--right .AvatarStack-body:hover .avatar { margin-left: 3px; mar=
+gin-right: 0px; }
+
+.AvatarStack--right .AvatarStack-body .avatar:not(:last-child) { border-lef=
+t: 0px; }
+
+.AvatarStack--right .avatar.avatar-more { background: var(--color-avatar-st=
+ack-fade); }
+
+.AvatarStack--right .avatar.avatar-more::before { width: 5px; }
+
+.AvatarStack--right .avatar.avatar-more::after { background: var(--color-ca=
+nvas-subtle); width: 2px; }
+
+.AvatarStack--right .avatar { border-left: var(--primer-borderWidth-thin, 1=
+px) solid var(--color-canvas-default); border-right: 0px; margin-left: -11p=
+x; margin-right: 0px; }
+
+.Box { background-color: var(--color-canvas-default); border-color: var(--c=
+olor-border-default); border-radius: var(--primer-borderRadius-medium, 6px)=
+; border-style: solid; border-width: var(--primer-borderWidth-thin, 1px); }
+
+.Box--condensed { line-height: 1.25; }
+
+.Box--condensed .Box-body, .Box--condensed .Box-footer, .Box--condensed .Bo=
+x-header { padding: var(--primer-stack-padding-condensed, 8px) var(--primer=
+-stack-padding-normal, 16px); }
+
+.Box--condensed .Box-btn-octicon.btn-octicon { line-height: 1.25; margin: c=
+alc(var(--primer-controlStack-medium-gap-condensed, 8px)*-1) calc(var(--pri=
+mer-controlStack-small-gap-spacious, 16px)*-1); padding: var(--primer-contr=
+ol-medium-paddingInline-condensed, 8px) var(--primer-control-medium-padding=
+Inline-spacious, 16px); }
+
+.Box--condensed .Box-row { padding: var(--primer-stack-padding-condensed, 8=
+px) var(--primer-stack-padding-normal, 16px); }
+
+.Box--spacious .Box-header { line-height: 1.25; padding: var(--primer-stack=
+-padding-spacious, 24px); }
+
+.Box--spacious .Box-title { font-size: var(--primer-text-title-size-medium,=
+ 20px); }
+
+.Box--spacious .Box-body, .Box--spacious .Box-btn-octicon.btn-octicon, .Box=
+--spacious .Box-footer { padding: var(--primer-stack-padding-spacious, 24px=
+); }
+
+.Box--spacious .Box-btn-octicon.btn-octicon { margin: calc(var(--primer-sta=
+ck-gap-spacious, 24px)*-1) calc(var(--primer-stack-gap-spacious, 24px)*-1);=
+ }
+
+.Box--spacious .Box-row { padding: var(--primer-stack-padding-spacious, 24p=
+x); }
+
+.Box-header { background-color: var(--color-canvas-subtle); border-color: v=
+ar(--color-border-default); border-style: solid; border-top-left-radius: va=
+r(--primer-borderRadius-medium, 6px); border-top-right-radius: var(--primer=
+-borderRadius-medium, 6px); border-width: var(--primer-borderWidth-thin, 1p=
+x); margin: calc(var(--primer-borderWidth-thin, 1px)*-1) calc(var(--primer-=
+borderWidth-thin, 1px)*-1) 0; padding: var(--primer-stack-padding-normal, 1=
+6px); }
+
+.Box-title { font-size: var(--primer-text-body-size-medium, 14px); font-wei=
+ght: var(--base-text-weight-semibold, 600); }
+
+.Box-body { border-bottom: var(--primer-borderWidth-thin, 1px) solid var(--=
+color-border-default); padding: var(--primer-stack-padding-normal, 16px); }
+
+.Box-body:last-of-type { border-bottom-left-radius: var(--primer-borderRadi=
+us-medium, 6px); border-bottom-right-radius: var(--primer-borderRadius-medi=
+um, 6px); margin-bottom: calc(var(--primer-borderWidth-thin, 1px)*-1); }
+
+.Box-row { border-top: var(--primer-borderWidth-thin, 1px) solid var(--colo=
+r-border-muted); list-style-type: none; margin-top: calc(var(--primer-borde=
+rWidth-thin, 1px)*-1); padding: var(--primer-stack-padding-normal, 16px); }
+
+.Box-row:first-of-type { border-top-left-radius: var(--primer-borderRadius-=
+medium, 6px); border-top-right-radius: var(--primer-borderRadius-medium, 6p=
+x); }
+
+.Box-row:last-of-type { border-bottom-left-radius: var(--primer-borderRadiu=
+s-medium, 6px); border-bottom-right-radius: var(--primer-borderRadius-mediu=
+m, 6px); }
+
+.Box-row.Box-row--unread, .Box-row.unread { box-shadow: inset 2px 0 0 var(-=
+-color-accent-emphasis); }
+
+.Box-row.navigation-focus .Box-row--drag-button { color: var(--color-accent=
+-fg); cursor: grab; opacity: 1; }
+
+.Box-row.navigation-focus.is-dragging .Box-row--drag-button { cursor: grabb=
+ing; }
+
+.Box-row.navigation-focus.sortable-chosen, .Box-row.navigation-focus.sortab=
+le-ghost { background-color: var(--color-canvas-subtle); }
+
+.Box-row.navigation-focus.sortable-ghost .Box-row--drag-hide { opacity: 0; =
+}
+
+.Box-row--focus-gray.navigation-focus { background-color: var(--color-canva=
+s-subtle); }
+
+.Box-row--focus-blue.navigation-focus { background-color: var(--color-accen=
+t-subtle); }
+
+.Box-row--hover-gray:hover { background-color: var(--color-canvas-subtle); =
+}
+
+.Box-row--hover-blue:hover { background-color: var(--color-accent-subtle); =
+}
+
+@media (min-width: 768px) {
+  .Box-row-link { color: var(--color-fg-default); text-decoration: none; }
+  .Box-row-link:hover { color: var(--color-accent-fg); text-decoration: non=
+e; }
+}
+
+.Box-row--drag-button { opacity: 0; }
+
+.Box-footer { border-radius: 0 0 var(--primer-borderRadius-medium, 6px) var=
+(--primer-borderRadius-medium, 6px); border-top: var(--primer-borderWidth-t=
+hin, 1px) solid var(--color-border-default); margin-top: calc(var(--primer-=
+borderWidth-thin, 1px)*-1); padding: var(--primer-stack-padding-normal, 16p=
+x); }
+
+.Box--scrollable { max-height: 324px; overflow: scroll; }
+
+.Box--blue, .Box--blue .Box-header { border-color: var(--color-accent-muted=
+); }
+
+.Box--blue .Box-header { background-color: var(--color-accent-subtle); }
+
+.Box--blue .Box-body, .Box--blue .Box-footer, .Box--blue .Box-row { border-=
+color: var(--color-accent-muted); }
+
+.Box--danger, .Box--danger .Box-body:last-of-type, .Box--danger .Box-row:fi=
+rst-of-type { border-color: var(--color-danger-emphasis); }
+
+.Box-header--blue { background-color: var(--color-accent-subtle); border-co=
+lor: var(--color-accent-muted); }
+
+.Box-row--yellow { background-color: var(--color-attention-subtle); }
+
+.Box-row--blue { background-color: var(--color-accent-subtle); }
+
+.Box-row--gray { background-color: var(--color-canvas-subtle); }
+
+.Box-btn-octicon.btn-octicon { line-height: 1.5; margin: calc(var(--primer-=
+controlStack-small-gap-spacious, 16px)*-1) calc(var(--primer-controlStack-s=
+mall-gap-spacious, 16px)*-1); padding: var(--primer-control-medium-paddingI=
+nline-spacious, 16px) var(--primer-control-medium-paddingInline-spacious, 1=
+6px); }
+
+.blankslate { padding: var(--base-size-32, 32px); position: relative; text-=
+align: center; }
+
+.blankslate p { color: var(--color-fg-muted); }
+
+.blankslate code { background: var(--color-canvas-default); border: var(--p=
+rimer-borderWidth-thin, 1px) solid var(--color-border-muted); border-radius=
+: var(--primer-borderRadius-medium, 6px); font-size: var(--primer-text-body=
+-size-medium, 14px); padding: 2px 5px 3px; }
+
+.blankslate img { height: 56px; width: 56px; }
+
+.blankslate-icon { color: var(--color-fg-muted); margin-bottom: var(--prime=
+r-stack-gap-condensed, 8px); margin-left: var(--primer-control-small-gap, 4=
+px); margin-right: var(--primer-control-small-gap, 4px); }
+
+.blankslate-image { margin-bottom: var(--primer-stack-gap-normal, 16px); }
+
+.blankslate-heading { margin-bottom: var(--base-size-4, 4px); }
+
+.blankslate-action { margin-top: var(--primer-stack-gap-normal, 16px); }
+
+.blankslate-action:first-of-type { margin-top: var(--primer-stack-gap-spaci=
+ous, 24px); }
+
+.blankslate-action:last-of-type { margin-bottom: var(--primer-stack-gap-con=
+densed, 8px); }
+
+.blankslate-capped { border-radius: 0 0 var(--primer-borderRadius-medium, 6=
+px) var(--primer-borderRadius-medium, 6px); }
+
+.blankslate-spacious { padding: var(--base-size-80, 80px) var(--base-size-4=
+0, 40px); }
+
+.blankslate-narrow { margin: 0px auto; max-width: 485px; }
+
+.blankslate-large img { height: 80px; width: 80px; }
+
+.blankslate-large h3 { font-size: 24px; margin: var(--primer-stack-gap-norm=
+al, 16px) 0; }
+
+.blankslate-large p { font-size: var(--primer-text-body-size-large, 16px); =
+}
+
+.blankslate-clean-background { border: 0px; }
+
+.breadcrumb-item { display: inline-block; list-style: none; margin-left: -0=
+.35em; white-space: nowrap; }
+
+.breadcrumb-item::after { border-right: 0.1em solid var(--color-fg-muted); =
+content: ""; display: inline-block; height: 0.8em; margin: 0px 0.5em; trans=
+form: rotate(15deg); }
+
+.breadcrumb-item:first-child { margin-left: 0px; }
+
+.breadcrumb-item-selected::after, .breadcrumb-item[aria-current]:not([aria-=
+current=3D"false"])::after { content: none; }
+
+.breadcrumb-item-selected a { color: var(--color-fg-default); }
+
+:root { --primer-duration-fast:80ms; --primer-easing-easeInOut:cubic-bezier=
+(0.65,0,0.35,1); }
+
+.Button { align-items: center; background-color: initial; border-top-style:=
+ ; border-top-width: ; border-right-style: ; border-right-width: ; border-b=
+ottom-style: ; border-bottom-width: ; border-left-style: ; border-left-widt=
+h: ; border-image-source: ; border-image-slice: ; border-image-width: ; bor=
+der-image-outset: ; border-image-repeat: ; border-color: rgba(0, 0, 0, 0); =
+border-radius: var(--primer-borderRadius-medium, 6px); color: var(--color-b=
+tn-text); cursor: pointer; display: flex; flex-direction: row; font-size: v=
+ar(--primer-text-body-size-medium, 14px); font-weight: var(--base-text-weig=
+ht-medium, 500); gap: var(--primer-control-medium-gap, 8px); height: var(--=
+primer-control-medium-size, 32px); justify-content: space-between; min-widt=
+h: max-content; padding: 0 var(--primer-control-medium-paddingInline-normal=
+, 12px); position: relative; text-align: center; transition-duration: ; tra=
+nsition-timing-function: ; transition-delay: ; transition-property: color, =
+fill, background-color, border-color; user-select: none; }
+
+@media (pointer: coarse) {
+  .Button::before { content: ""; height: 100%; left: 50%; min-height: 48px;=
+ min-width: 48px; position: absolute; top: 50%; transform: translateX(-50%)=
+ translateY(-50%); width: 100%; }
+}
+
+.Button:hover { transition-duration: var(--primer-duration-fast); }
+
+.Button:active { transition: none 0s ease 0s; }
+
+.Button:disabled, .Button[aria-disabled=3D"true"] { box-shadow: none; curso=
+r: not-allowed; }
+
+.Button-withTooltip { display: inline-block; position: relative; }
+
+a.Button, summary.Button { display: inline-flex; }
+
+a.Button:hover, summary.Button:hover { text-decoration: none; }
+
+.Button-content { align-items: center; display: grid; flex: 1 0 auto; grid-=
+template-areas: "leadingVisual text trailingVisual"; grid-template-columns:=
+ min-content minmax(0px, auto) min-content; place-content: center; }
+
+.Button-content > :not(:last-child) { margin-right: var(--primer-control-me=
+dium-gap, 8px); }
+
+.Button-content--alignStart { justify-content: start; }
+
+.Button-visual { display: flex; pointer-events: none; }
+
+.Button-visual .Counter { background-color: var(--color-btn-counter-bg); co=
+lor: inherit; }
+
+.Button-label { grid-area: text / text / text / text; line-height: var(--pr=
+imer-text-body-lineHeight-medium, 1.42857); white-space: nowrap; }
+
+.Button-leadingVisual { grid-area: leadingVisual / leadingVisual / leadingV=
+isual / leadingVisual; }
+
+.Button-trailingVisual { grid-area: trailingVisual / trailingVisual / trail=
+ingVisual / trailingVisual; }
+
+.Button-trailingAction { margin-right: calc(var(--base-size-4, 4px)*-1); }
+
+.Button--small { font-size: var(--primer-text-body-size-small, 12px); gap: =
+var(--primer-control-small-gap, 4px); height: var(--primer-control-small-si=
+ze, 28px); padding: 0 var(--primer-control-small-paddingInline-condensed, 8=
+px); }
+
+.Button--small .Button-label { line-height: var(--primer-text-body-lineHeig=
+ht-small, 1.66667); }
+
+.Button--small .Button-content > :not(:last-child) { margin-right: var(--pr=
+imer-control-small-gap, 4px); }
+
+.Button--large { gap: var(--primer-control-large-gap, 8px); height: var(--p=
+rimer-control-large-size, 40px); padding: 0 var(--primer-control-large-padd=
+ingInline-spacious, 16px); }
+
+.Button--large .Button-label { line-height: var(--primer-text-body-lineHeig=
+ht-large, 1.5); }
+
+.Button--large .Button-content > :not(:last-child) { margin-right: var(--pr=
+imer-control-large-gap, 8px); }
+
+.Button--fullWidth { width: 100%; }
+
+.Button--primary { fill: var(--color-btn-primary-icon); background-color: v=
+ar(--color-btn-primary-bg); border-color: var(--color-btn-primary-border); =
+box-shadow: var(--color-btn-primary-shadow),var(--color-btn-primary-inset-s=
+hadow); color: var(--color-btn-primary-text); }
+
+.Button--primary:hover:not(:disabled) { background-color: var(--color-btn-p=
+rimary-hover-bg); border-color: var(--color-btn-primary-hover-border); }
+
+.Button--primary:focus { box-shadow: inset 0 0 0 3px var(--color-fg-on-emph=
+asis); outline: 2px solid var(--color-accent-fg); outline-offset: -2px; }
+
+.Button--primary:focus:not(:focus-visible) { box-shadow: none; outline: rgb=
+a(0, 0, 0, 0) solid 1px; }
+
+.Button--primary:focus-visible { box-shadow: inset 0 0 0 3px var(--color-fg=
+-on-emphasis); outline: 2px solid var(--color-accent-fg); outline-offset: -=
+2px; }
+
+.Button--primary:active:not(:disabled), .Button--primary[aria-pressed=3D"tr=
+ue"] { background-color: var(--color-btn-primary-selected-bg); box-shadow: =
+var(--color-btn-primary-selected-shadow); }
+
+.Button--primary:disabled, .Button--primary[aria-disabled=3D"true"] { fill:=
+ var(--color-btn-primary-disabled-text); background-color: var(--color-btn-=
+primary-disabled-bg); border-color: var(--color-btn-primary-disabled-border=
+); color: var(--color-btn-primary-disabled-text); }
+
+.Button--secondary { fill: var(--color-fg-muted); background-color: var(--c=
+olor-btn-bg); border-color: var(--color-btn-border); box-shadow: var(--colo=
+r-btn-shadow),var(--color-btn-inset-shadow); color: var(--color-btn-text); =
+}
+
+.Button--secondary:hover:not(:disabled) { background-color: var(--color-btn=
+-hover-bg); border-color: var(--color-btn-hover-border); }
+
+.Button--secondary:active:not(:disabled) { background-color: var(--color-bt=
+n-active-bg); border-color: var(--color-btn-active-border); }
+
+.Button--secondary[aria-pressed=3D"true"] { background-color: var(--color-b=
+tn-selected-bg); box-shadow: var(--color-primer-shadow-inset); }
+
+.Button--secondary:disabled, .Button--secondary[aria-disabled=3D"true"] { f=
+ill: var(--color-primer-fg-disabled); background-color: var(--color-btn-bg)=
+; border-color: var(--color-btn-border); color: var(--color-primer-fg-disab=
+led); }
+
+.Button--invisible { color: var(--color-btn-text); }
+
+.Button--invisible:hover:not(:disabled) { background-color: var(--color-act=
+ion-list-item-default-hover-bg); }
+
+.Button--invisible:active:not(:disabled), .Button--invisible[aria-pressed=
+=3D"true"] { background-color: var(--color-action-list-item-default-active-=
+bg); }
+
+.Button--invisible:disabled, .Button--invisible[aria-disabled=3D"true"] { f=
+ill: var(--color-primer-fg-disabled); background-color: var(--color-btn-bg)=
+; border-color: var(--color-btn-border); color: var(--color-primer-fg-disab=
+led); }
+
+.Button--invisible.Button--invisible-noVisuals .Button-label { color: var(-=
+-color-accent-fg); }
+
+.Button--invisible .Button-visual { color: var(--color-fg-muted); }
+
+.Button--link { fill: var(--color-accent-fg); border: none; color: var(--co=
+lor-accent-fg); display: inline-block; font-size: inherit; height: unset; p=
+adding: 0px; }
+
+.Button--link:hover:not(:disabled) { text-decoration: underline; }
+
+.Button--link:focus, .Button--link:focus-visible { outline-offset: 2px; }
+
+.Button--link:disabled, .Button--link[aria-disabled=3D"true"] { fill: var(-=
+-color-primer-fg-disabled); background-color: initial; border-color: rgba(0=
+, 0, 0, 0); color: var(--color-primer-fg-disabled); }
+
+.Button--danger { fill: var(--color-btn-danger-icon); background-color: var=
+(--color-btn-bg); border-color: var(--color-btn-border); box-shadow: var(--=
+color-btn-shadow),var(--color-btn-inset-shadow); color: var(--color-btn-dan=
+ger-text); }
+
+.Button--danger:hover:not(:disabled) { fill: var(--color-btn-danger-hover-t=
+ext); background-color: var(--color-btn-danger-hover-bg); border-color: var=
+(--color-btn-danger-hover-border); box-shadow: var(--color-btn-danger-hover=
+-shadow),var(--color-btn-danger-hover-inset-shadow); color: var(--color-btn=
+-danger-hover-text); }
+
+.Button--danger:active:not(:disabled), .Button--danger[aria-pressed=3D"true=
+"] { fill: var(--color-btn-danger-selected-text); background-color: var(--c=
+olor-btn-danger-selected-bg); border-color: var(--color-btn-danger-selected=
+-border); box-shadow: var(--color-btn-danger-selected-shadow); color: var(-=
+-color-btn-danger-selected-text); }
+
+.Button--danger:disabled, .Button--danger[aria-disabled=3D"true"] { fill: v=
+ar(--color-btn-danger-disabled-text); background-color: var(--color-btn-dan=
+ger-disabled-bg); border-color: var(--color-btn-border); color: var(--color=
+-btn-danger-disabled-text); }
+
+.Button--iconOnly { display: grid; padding: unset; place-content: center; w=
+idth: var(--primer-control-medium-size, 32px); }
+
+.Button--iconOnly.Button--small { width: var(--primer-control-small-size, 2=
+8px); }
+
+.Button--iconOnly.Button--large { width: var(--primer-control-large-size, 4=
+0px); }
+
+.Counter { background-color: var(--color-neutral-muted); border: var(--prim=
+er-borderWidth-thin, 1px) solid var(--color-counter-border); border-radius:=
+ 2em; color: var(--color-fg-default); display: inline-block; font-size: var=
+(--primer-text-body-size-small, 12px); font-weight: var(--base-text-weight-=
+medium, 500); line-height: calc(var(--base-size-20, 20px) - var(--primer-bo=
+rderWidth-thin, 1px)*2); min-width: var(--base-size-20, 20px); padding: 0px=
+ 6px; text-align: center; }
+
+.Counter:empty { display: none; }
+
+.Counter .octicon { opacity: 0.8; vertical-align: text-top; }
+
+.Counter--primary { background-color: var(--color-neutral-emphasis); color:=
+ var(--color-fg-on-emphasis); }
+
+.Counter--secondary { background-color: var(--color-neutral-subtle); color:=
+ var(--color-fg-muted); }
+
+.flash:not(.Banner) { background-image: linear-gradient(var(--color-accent-=
+subtle), var(--color-accent-subtle)); border-color: var(--color-accent-mute=
+d); border-radius: var(--primer-borderRadius-medium, 6px); border-style: so=
+lid; border-width: var(--primer-borderWidth-thin, 1px); color: var(--color-=
+fg-default); padding: var(--base-size-20, 20px) var(--primer-control-medium=
+-paddingInline-spacious, 16px); position: relative; }
+
+.flash:not(.Banner) .octicon { color: var(--color-accent-fg); margin-right:=
+ var(--base-size-12, 12px); }
+
+.flash:not(.Banner) p:last-child { margin-bottom: 0px; }
+
+.flash-messages { margin-bottom: var(--primer-stack-gap-spacious, 24px); }
+
+.flash-close:not(.Banner-close) { appearance: none; background: none; borde=
+r: 0px; cursor: pointer; float: right; margin: calc(var(--primer-control-me=
+dium-paddingInline-spacious, 16px)*-1); padding: var(--primer-control-mediu=
+m-paddingInline-spacious, 16px); text-align: center; }
+
+.flash-close:not(.Banner-close):hover { opacity: 0.7; }
+
+.flash-close:not(.Banner-close):active { opacity: 0.5; }
+
+.flash-close:not(.Banner-close) .octicon { margin-right: 0px; }
+
+.flash-action:not(.Banner-actions) { background-clip: padding-box; float: r=
+ight; margin-left: var(--primer-stack-gap-spacious, 24px); margin-top: -3px=
+; }
+
+.flash-action.btn:not(.Banner-actions) .octicon { color: var(--color-fg-mut=
+ed); margin-right: var(--primer-control-small-gap, 4px); }
+
+.flash-action.btn-primary:not(.Banner-actions) { background-clip: initial; =
+}
+
+.flash-action.btn-primary:not(.Banner-actions) .octicon { color: inherit; }
+
+.flash-warn:not(.Banner) { background-image: linear-gradient(var(--color-at=
+tention-subtle), var(--color-attention-subtle)); border-color: var(--color-=
+attention-muted); color: var(--color-fg-default); }
+
+.flash-warn:not(.Banner) .octicon { color: var(--color-attention-fg); }
+
+.flash-error:not(.Banner) { background-image: linear-gradient(var(--color-d=
+anger-subtle), var(--color-danger-subtle)); border-color: var(--color-dange=
+r-muted); color: var(--color-fg-default); }
+
+.flash-error:not(.Banner) .octicon { color: var(--color-danger-fg); }
+
+.flash-success:not(.Banner) { background-image: linear-gradient(var(--color=
+-success-subtle), var(--color-success-subtle)); border-color: var(--color-s=
+uccess-muted); color: var(--color-fg-default); }
+
+.flash-success:not(.Banner) .octicon { color: var(--color-success-fg); }
+
+.flash-full:not(.Banner) { border-radius: 0px; border-width: var(--primer-b=
+orderWidth-thin, 1px) 0; margin-top: calc(var(--primer-borderWidth-thin, 1p=
+x)*-1); }
+
+.flash-banner { border-left: 0px; border-radius: 0px; border-right: 0px; bo=
+rder-top: 0px; position: fixed; top: 0px; width: 100%; z-index: 90; }
+
+.flash-banner, .flash-full { background-color: var(--color-canvas-default);=
+ }
+
+.warning { background-color: var(--color-attention-subtle); font-weight: va=
+r(--base-text-weight-semibold, 600); margin-bottom: 0.8em; padding: 0.5em; =
+}
+
+.labels { position: relative; }
+
+.Label, .label { border: var(--primer-borderWidth-thin, 1px) solid var(--co=
+lor-border-default); border-radius: 2em; display: inline-block; font-size: =
+var(--primer-text-body-size-small, 12px); font-weight: var(--base-text-weig=
+ht-medium, 500); line-height: 18px; padding: 0px 7px; white-space: nowrap; =
+}
+
+.Label:hover, .label:hover { text-decoration: none; }
+
+.Label--large { line-height: 22px; padding-left: 10px; padding-right: 10px;=
+ }
+
+.Label--inline { display: inline; font-size: 85%; padding: 0.12em 0.5em; }
+
+.Label--primary { border-color: var(--color-neutral-emphasis); color: var(-=
+-color-fg-default); }
+
+.Label--secondary { border-color: var(--color-border-default); color: var(-=
+-color-fg-muted); }
+
+.Label--accent, .Label--info { border-color: var(--color-accent-emphasis); =
+color: var(--color-accent-fg); }
+
+.Label--success { border-color: var(--color-success-emphasis); color: var(-=
+-color-success-fg); }
+
+.Label--attention, .Label--warning { border-color: var(--color-attention-em=
+phasis); color: var(--color-attention-fg); }
+
+.Label--severe { border-color: var(--color-severe-emphasis); color: var(--c=
+olor-severe-fg); }
+
+.Label--danger { border-color: var(--color-danger-emphasis); color: var(--c=
+olor-danger-fg); }
+
+.Label--open { border-color: var(--color-open-emphasis); color: var(--color=
+-open-fg); }
+
+.Label--closed { border-color: var(--color-closed-emphasis); color: var(--c=
+olor-closed-fg); }
+
+.Label--done { border-color: var(--color-done-emphasis); color: var(--color=
+-done-fg); }
+
+.Label--sponsors { border-color: var(--color-sponsors-emphasis); color: var=
+(--color-sponsors-fg); }
+
+.Link { color: var(--color-accent-fg); }
+
+.Link:hover { cursor: pointer; text-decoration: underline; }
+
+.Link:focus, .Link:focus-visible { outline-offset: 0px; }
+
+.Link--primary { color: var(--color-fg-default)  !important; }
+
+.Link--primary:hover { color: var(--color-accent-fg)  !important; }
+
+.Link--secondary { color: var(--color-fg-muted)  !important; }
+
+.Link--secondary:hover { color: var(--color-accent-fg)  !important; }
+
+.Link--muted { color: var(--color-fg-muted)  !important; }
+
+.Link--muted:hover { text-decoration: none; }
+
+.Link--muted:hover, .Link--onHover:hover { color: var(--color-accent-fg)  !=
+important; }
+
+.Link--onHover:hover { cursor: pointer; text-decoration: underline; }
+
+.Link--muted:hover [class*=3D"color-fg"], .Link--primary:hover [class*=3D"c=
+olor-fg"], .Link--secondary:hover [class*=3D"color-fg"] { color: inherit !i=
+mportant; }
+
+.Popover { position: absolute; z-index: 100; }
+
+.Popover-message { background-color: var(--color-canvas-overlay); border: v=
+ar(--primer-borderWidth-thin, 1px) solid var(--color-border-default); borde=
+r-radius: var(--primer-borderRadius-medium, 6px); margin-left: auto; margin=
+-right: auto; position: relative; width: 232px; }
+
+.Popover-message::after, .Popover-message::before { content: ""; display: i=
+nline-block; left: 50%; position: absolute; }
+
+.Popover-message::before { border-top: 8px solid rgba(0, 0, 0, 0); border-r=
+ight: 8px solid rgba(0, 0, 0, 0); border-left: 8px solid rgba(0, 0, 0, 0); =
+border-image: initial; border-bottom: 8px solid var(--color-border-default)=
+; margin-left: -9px; top: -16px; }
+
+.Popover-message::after { border-top: 7px solid rgba(0, 0, 0, 0); border-ri=
+ght: 7px solid rgba(0, 0, 0, 0); border-left: 7px solid rgba(0, 0, 0, 0); b=
+order-image: initial; border-bottom: 7px solid var(--color-canvas-overlay);=
+ margin-left: -8px; top: -14px; }
+
+.Popover-message--no-caret::after, .Popover-message--no-caret::before { dis=
+play: none; }
+
+.Popover-message--bottom-left::after, .Popover-message--bottom-left::before=
+, .Popover-message--bottom-right::after, .Popover-message--bottom-right::be=
+fore, .Popover-message--bottom::after, .Popover-message--bottom::before { b=
+order-bottom-color: rgba(0, 0, 0, 0); top: auto; }
+
+.Popover-message--bottom-left::before, .Popover-message--bottom-right::befo=
+re, .Popover-message--bottom::before { border-top-color: var(--color-border=
+-default); bottom: -16px; }
+
+.Popover-message--bottom-left::after, .Popover-message--bottom-right::after=
+, .Popover-message--bottom::after { border-top-color: var(--color-canvas-ov=
+erlay); bottom: -14px; }
+
+.Popover-message--bottom-right, .Popover-message--top-right { margin-right:=
+ 0px; right: -9px; }
+
+.Popover-message--bottom-right::after, .Popover-message--bottom-right::befo=
+re, .Popover-message--top-right::after, .Popover-message--top-right::before=
+ { left: auto; margin-left: 0px; }
+
+.Popover-message--bottom-right::before, .Popover-message--top-right::before=
+ { right: 20px; }
+
+.Popover-message--bottom-right::after, .Popover-message--top-right::after {=
+ right: 21px; }
+
+.Popover-message--bottom-left, .Popover-message--top-left { left: -9px; mar=
+gin-left: 0px; }
+
+.Popover-message--bottom-left::after, .Popover-message--bottom-left::before=
+, .Popover-message--top-left::after, .Popover-message--top-left::before { l=
+eft: 24px; margin-left: 0px; }
+
+.Popover-message--bottom-left::after, .Popover-message--top-left::after { l=
+eft: 25px; }
+
+.Popover-message--left-bottom::after, .Popover-message--left-bottom::before=
+, .Popover-message--left-top::after, .Popover-message--left-top::before, .P=
+opover-message--left::after, .Popover-message--left::before, .Popover-messa=
+ge--right-bottom::after, .Popover-message--right-bottom::before, .Popover-m=
+essage--right-top::after, .Popover-message--right-top::before, .Popover-mes=
+sage--right::after, .Popover-message--right::before { border-bottom-color: =
+rgba(0, 0, 0, 0); left: auto; margin-left: 0px; top: 50%; }
+
+.Popover-message--left-bottom::before, .Popover-message--left-top::before, =
+.Popover-message--left::before, .Popover-message--right-bottom::before, .Po=
+pover-message--right-top::before, .Popover-message--right::before { margin-=
+top: -9px; }
+
+.Popover-message--left-bottom::after, .Popover-message--left-top::after, .P=
+opover-message--left::after, .Popover-message--right-bottom::after, .Popove=
+r-message--right-top::after, .Popover-message--right::after { margin-top: -=
+8px; }
+
+.Popover-message--right-bottom::before, .Popover-message--right-top::before=
+, .Popover-message--right::before { border-left-color: var(--color-border-d=
+efault); right: -16px; }
+
+.Popover-message--right-bottom::after, .Popover-message--right-top::after, =
+.Popover-message--right::after { border-left-color: var(--color-canvas-over=
+lay); right: -14px; }
+
+.Popover-message--left-bottom::before, .Popover-message--left-top::before, =
+.Popover-message--left::before { border-right-color: var(--color-border-def=
+ault); left: -16px; }
+
+.Popover-message--left-bottom::after, .Popover-message--left-top::after, .P=
+opover-message--left::after { border-right-color: var(--color-canvas-overla=
+y); left: -14px; }
+
+.Popover-message--left-top::after, .Popover-message--left-top::before, .Pop=
+over-message--right-top::after, .Popover-message--right-top::before { top: =
+24px; }
+
+.Popover-message--left-bottom::after, .Popover-message--left-bottom::before=
+, .Popover-message--right-bottom::after, .Popover-message--right-bottom::be=
+fore { top: auto; }
+
+.Popover-message--left-bottom::before, .Popover-message--right-bottom::befo=
+re { bottom: 16px; }
+
+.Popover-message--left-bottom::after, .Popover-message--right-bottom::after=
+ { bottom: 17px; }
+
+@media (min-width: 544px) {
+  .Popover-message--large { min-width: 320px; }
+}
+
+@media (max-width: 767.98px) {
+  .Popover { position: fixed; inset: auto 0px 0px !important; }
+  .Popover-message { inset: auto; margin: var(--primer-stack-gap-condensed,=
+ 8px); width: auto !important; }
+  .Popover-message > .btn-octicon { padding: var(--primer-control-medium-pa=
+ddingInline-normal, 12px)  !important; }
+  .Popover-message::after, .Popover-message::before { display: none; }
+}
+
+.Progress { background-color: var(--color-neutral-muted); border-radius: 6p=
+x; display: flex; height: 8px; outline: rgba(0, 0, 0, 0) solid 1px; overflo=
+w: hidden; }
+
+.Progress--large { height: 10px; }
+
+.Progress--small { height: 5px; }
+
+.Progress-item { outline: rgba(0, 0, 0, 0) solid 2px; }
+
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+
+.State, .state { border-radius: 2em; display: inline-block; font-size: var(=
+--primer-text-body-size-medium, 14px); font-weight: var(--base-text-weight-=
+medium, 500); line-height: var(--primer-control-medium-lineBoxHeight, 20px)=
+; padding: 5px var(--primer-control-medium-paddingInline-normal, 12px); tex=
+t-align: center; white-space: nowrap; }
+
+.State, .State--draft, .state { background-color: var(--color-neutral-empha=
+sis); border: var(--primer-borderWidth-thin, 1px) solid #0000; color: var(-=
+-color-fg-on-emphasis); }
+
+.State--open { background-color: var(--color-open-emphasis); }
+
+.State--merged, .State--open { color: var(--color-fg-on-emphasis); }
+
+.State--merged { background-color: var(--color-done-emphasis); }
+
+.State--closed { background-color: var(--color-closed-emphasis); color: var=
+(--color-fg-on-emphasis); }
+
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+eight: var(--base-size-24, 24px); padding: 0px 10px; }
+
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+
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+ex-end; margin-bottom: var(--primer-stack-gap-normal, 16px); padding-bottom=
+: var(--primer-stack-padding-condensed, 8px); }
+
+.Subhead--spacious { margin-top: var(--base-size-40, 40px); }
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+.Subhead-heading { flex: 1 1 auto; font-size: 24px; font-weight: var(--base=
+-text-weight-normal, 400); order: 0; }
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+.Subhead-heading--danger { color: var(--color-danger-fg); font-weight: var(=
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+rder: 1; }
+
+.Subhead-actions + .Subhead-description { margin-top: var(--base-size-4, 4p=
+x); }
+
+.Truncate { display: inline-flex; max-width: 100%; min-width: 0px; }
+
+.Truncate > .Truncate-text { max-width: fit-content; min-width: 1ch; overfl=
+ow: hidden; text-overflow: ellipsis; white-space: nowrap; }
+
+.Truncate > .Truncate-text + .Truncate-text { margin-left: var(--primer-con=
+trol-small-gap, 4px); }
+
+.Truncate > .Truncate-text.Truncate-text--primary { flex-basis: 200%; }
+
+.Truncate > .Truncate-text.Truncate-text--expandable:active, .Truncate > .T=
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+ 100% !important; }
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+.TimelineItem { display: flex; margin-left: var(--primer-stack-gap-normal, =
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+
+.TimelineItem::before { background-color: var(--color-border-muted); bottom=
+: 0px; content: ""; display: block; left: 0px; position: absolute; top: 0px=
+; width: var(--primer-borderWidth-thick, 2px); }
+
+.TimelineItem:target .TimelineItem-badge { border-color: var(--color-accent=
+-emphasis); box-shadow: 0 0 0.2em var(--color-accent-muted); }
+
+.TimelineItem-badge { align-items: center; background-color: var(--color-ti=
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+ndensed, 8px); position: relative; width: var(--primer-control-medium-size,=
+ 32px); z-index: 1; }
+
+.TimelineItem-badge--success { background-color: var(--color-success-emphas=
+is); border: var(--primer-borderWidth-thin, 1px) solid #0000; color: var(--=
+color-fg-on-emphasis); }
+
+.TimelineItem-body { color: var(--color-fg-muted); flex: 1 1 auto; margin-t=
+op: var(--base-size-4, 4px); max-width: 100%; min-width: 0px; }
+
+.TimelineItem-avatar { left: -72px; position: absolute; z-index: 1; }
+
+.TimelineItem-break { background-color: var(--color-canvas-default); border=
+-right: 0px; border-bottom: 0px; border-left: 0px; border-image: initial; b=
+order-top: var(--primer-borderWidth-thicker, 4px) solid var(--color-border-=
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+margin-right: 0px; margin-bottom: calc(var(--primer-stack-gap-normal, 16px)=
+*-1); margin-left: -56px; position: relative; z-index: 1; }
+
+.TimelineItem--condensed { padding-bottom: 0px; padding-top: var(--base-siz=
+e-4, 4px); }
+
+.TimelineItem--condensed:last-child { padding-bottom: var(--primer-stack-ga=
+p-normal, 16px); }
+
+.TimelineItem--condensed .TimelineItem-badge { background-color: var(--colo=
+r-canvas-default); border: 0px; color: var(--color-fg-muted); height: var(-=
+-base-size-16, 16px); margin-bottom: var(--base-size-8, 8px); margin-top: v=
+ar(--base-size-8, 8px); }
+
+.css-truncate .css-truncate-overflow, .css-truncate .css-truncate-target, .=
+css-truncate.css-truncate-overflow, .css-truncate.css-truncate-target { ove=
+rflow: hidden; text-overflow: ellipsis; white-space: nowrap; }
+
+.css-truncate .css-truncate-target, .css-truncate.css-truncate-target { dis=
+play: inline-block; max-width: 125px; vertical-align: top; }
+
+.css-truncate.expandable.css-truncate-target:hover, .css-truncate.expandabl=
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+oclipboard-is-hover.css-truncate-target, .css-truncate.expandable:hover .cs=
+s-truncate-target { max-width: 10000px !important; }
+
+.OrderedList { margin: 8px; }
+
+.OrderedList-type--decimal { list-style-type: decimal; }
+
+.OrderedList-type--upperAlpha { list-style-type: upper-alpha; }
+
+.OrderedList-type--lowerAlpha { list-style-type: lower-alpha; }
+
+.OrderedList-type--upperRoman { list-style-type: upper-roman; }
+
+.OrderedList-type--lowerRoman { list-style-type: lower-roman; }
+
+.UnorderedList { margin: 8px; }
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+Content-Transfer-Encoding: quoted-printable
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+@charset "utf-8";
+
+.color-border-inverse { border-color: var(--color-fg-on-emphasis)  !importa=
+nt; }
+
+.bg-gray-2, .bg-gray-3 { background-color: var(--color-neutral-muted)  !imp=
+ortant; }
+
+.color-text-white { color: var(--color-scale-white)  !important; }
+
+.border-white-fade { border-color: rgba(255, 255, 255, 0.15) !important; }
+
+.lead { color: var(--color-fg-muted); }
+
+.text-emphasized { color: var(--color-fg-default); }
+
+.Label.Label--orange { color: var(--color-severe-fg); border-color: var(--c=
+olor-severe-emphasis); }
+
+.Label.Label--purple { color: var(--color-done-fg); border-color: var(--col=
+or-done-emphasis); }
+
+.Label.Label--pink { color: var(--color-sponsors-fg); border-color: var(--c=
+olor-sponsors-emphasis); }
+
+.pl-c { color: var(--color-prettylights-syntax-comment); }
+
+.pl-c1, .pl-s .pl-v { color: var(--color-prettylights-syntax-constant); }
+
+.pl-e, .pl-en { color: var(--color-prettylights-syntax-entity); }
+
+.pl-smi, .pl-s .pl-s1 { color: var(--color-prettylights-syntax-storage-modi=
+fier-import); }
+
+.pl-ent { color: var(--color-prettylights-syntax-entity-tag); }
+
+.pl-k { color: var(--color-prettylights-syntax-keyword); }
+
+.pl-s, .pl-pds, .pl-s .pl-pse .pl-s1, .pl-sr, .pl-sr .pl-cce, .pl-sr .pl-sr=
+e, .pl-sr .pl-sra { color: var(--color-prettylights-syntax-string); }
+
+.pl-v, .pl-smw { color: var(--color-prettylights-syntax-variable); }
+
+.pl-bu { color: var(--color-prettylights-syntax-brackethighlighter-unmatche=
+d); }
+
+.pl-ii { color: var(--color-prettylights-syntax-invalid-illegal-text); back=
+ground-color: var(--color-prettylights-syntax-invalid-illegal-bg); }
+
+.pl-c2 { color: var(--color-prettylights-syntax-carriage-return-text); back=
+ground-color: var(--color-prettylights-syntax-carriage-return-bg); }
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+.pl-sr .pl-cce { font-weight: bold; color: var(--color-prettylights-syntax-=
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+.pl-ml { color: var(--color-prettylights-syntax-markup-list); }
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+.pl-mh, .pl-mh .pl-en, .pl-ms { font-weight: bold; color: var(--color-prett=
+ylights-syntax-markup-heading); }
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+.pl-mi { font-style: italic; color: var(--color-prettylights-syntax-markup-=
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+.pl-mb { font-weight: bold; color: var(--color-prettylights-syntax-markup-b=
+old); }
+
+.pl-md { color: var(--color-prettylights-syntax-markup-deleted-text); backg=
+round-color: var(--color-prettylights-syntax-markup-deleted-bg); }
+
+.pl-mi1 { color: var(--color-prettylights-syntax-markup-inserted-text); bac=
+kground-color: var(--color-prettylights-syntax-markup-inserted-bg); }
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+.pl-mc { color: var(--color-prettylights-syntax-markup-changed-text); backg=
+round-color: var(--color-prettylights-syntax-markup-changed-bg); }
+
+.pl-mi2 { color: var(--color-prettylights-syntax-markup-ignored-text); back=
+ground-color: var(--color-prettylights-syntax-markup-ignored-bg); }
+
+.pl-mdr { font-weight: bold; color: var(--color-prettylights-syntax-meta-di=
+ff-range); }
+
+.pl-ba { color: var(--color-prettylights-syntax-brackethighlighter-angle); =
+}
+
+.pl-sg { color: var(--color-prettylights-syntax-sublimelinter-gutter-mark);=
+ }
+
+.pl-corl { text-decoration: underline; color: var(--color-prettylights-synt=
+ax-constant-other-reference-link); }
+
+.CodeMirror { font-family: monospace; height: 300px; color: black; directio=
+n: ltr; }
+
+.CodeMirror-lines { padding: 4px 0px; }
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+.CodeMirror pre.CodeMirror-line, .CodeMirror pre.CodeMirror-line-like { pad=
+ding: 0px 4px; }
+
+.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler { background-color:=
+ white; }
+
+.CodeMirror-gutters { border-right: 1px solid rgb(221, 221, 221); backgroun=
+d-color: rgb(247, 247, 247); white-space: nowrap; }
+
+.CodeMirror-linenumber { padding: 0px 3px 0px 5px; min-width: 20px; text-al=
+ign: right; color: rgb(153, 153, 153); white-space: nowrap; }
+
+.CodeMirror-guttermarker { color: black; }
+
+.CodeMirror-guttermarker-subtle { color: rgb(153, 153, 153); }
+
+.CodeMirror-cursor { border-left: 1px solid black; border-right: none; widt=
+h: 0px; }
+
+.CodeMirror div.CodeMirror-secondarycursor { border-left: 1px solid silver;=
+ }
+
+.cm-fat-cursor .CodeMirror-cursor { width: auto; background: rgb(119, 238, =
+119); border: 0px !important; }
+
+.cm-fat-cursor div.CodeMirror-cursors { z-index: 1; }
+
+.cm-fat-cursor-mark { background-color: rgba(20, 255, 20, 0.5); animation: =
+1.06s steps(1) 0s infinite normal none running blink; }
+
+.cm-animate-fat-cursor { width: auto; border: 0px; animation: 1.06s steps(1=
+) 0s infinite normal none running blink; background-color: rgb(119, 238, 11=
+9); }
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+@keyframes blink {=20
+  50% { background-color: transparent; }
+}
+
+.cm-tab { display: inline-block; text-decoration: inherit; }
+
+.CodeMirror-rulers { position: absolute; inset: -50px 0px 0px; overflow: hi=
+dden; }
+
+.CodeMirror-ruler { border-left: 1px solid rgb(204, 204, 204); top: 0px; bo=
+ttom: 0px; position: absolute; }
+
+.cm-s-default .cm-header { color: blue; }
+
+.cm-s-default .cm-quote { color: rgb(0, 153, 0); }
+
+.cm-negative { color: rgb(221, 68, 68); }
+
+.cm-positive { color: rgb(34, 153, 34); }
+
+.cm-header, .cm-strong { font-weight: bold; }
+
+.cm-em { font-style: italic; }
+
+.cm-link { text-decoration: underline; }
+
+.cm-strikethrough { text-decoration: line-through; }
+
+.cm-s-default .cm-keyword { color: rgb(119, 0, 136); }
+
+.cm-s-default .cm-atom { color: rgb(34, 17, 153); }
+
+.cm-s-default .cm-number { color: rgb(17, 102, 68); }
+
+.cm-s-default .cm-def { color: blue; }
+
+.cm-s-default .cm-variable-2 { color: rgb(0, 85, 170); }
+
+.cm-s-default .cm-variable-3, .cm-s-default .cm-type { color: rgb(0, 136, 8=
+5); }
+
+.cm-s-default .cm-comment { color: rgb(170, 85, 0); }
+
+.cm-s-default .cm-string { color: rgb(170, 17, 17); }
+
+.cm-s-default .cm-string-2 { color: rgb(255, 85, 0); }
+
+.cm-s-default .cm-meta { color: rgb(85, 85, 85); }
+
+.cm-s-default .cm-qualifier { color: rgb(85, 85, 85); }
+
+.cm-s-default .cm-builtin { color: rgb(51, 0, 170); }
+
+.cm-s-default .cm-bracket { color: rgb(153, 153, 119); }
+
+.cm-s-default .cm-tag { color: rgb(17, 119, 0); }
+
+.cm-s-default .cm-attribute { color: rgb(0, 0, 204); }
+
+.cm-s-default .cm-hr { color: rgb(153, 153, 153); }
+
+.cm-s-default .cm-link { color: rgb(0, 0, 204); }
+
+.cm-s-default .cm-error { color: red; }
+
+.cm-invalidchar { color: red; }
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+.CodeMirror-composing { border-bottom: 2px solid; }
+
+div.CodeMirror span.CodeMirror-matchingbracket { color: rgb(0, 187, 0); }
+
+div.CodeMirror span.CodeMirror-nonmatchingbracket { color: rgb(170, 34, 34)=
+; }
+
+.CodeMirror-matchingtag { background: rgba(255, 150, 0, 0.3); }
+
+.CodeMirror-activeline-background { background: rgb(232, 242, 255); }
+
+.CodeMirror { position: relative; overflow: hidden; background: white; }
+
+.CodeMirror-scroll { margin-bottom: -50px; margin-right: -50px; padding-bot=
+tom: 50px; height: 100%; outline: none; position: relative; overflow: scrol=
+l !important; }
+
+.CodeMirror-sizer { position: relative; border-right: 50px solid transparen=
+t; }
+
+.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-fille=
+r, .CodeMirror-gutter-filler { position: absolute; z-index: 6; display: non=
+e; outline: none; }
+
+.CodeMirror-vscrollbar { right: 0px; top: 0px; overflow: hidden scroll; }
+
+.CodeMirror-hscrollbar { bottom: 0px; left: 0px; overflow: scroll hidden; }
+
+.CodeMirror-scrollbar-filler { right: 0px; bottom: 0px; }
+
+.CodeMirror-gutter-filler { left: 0px; bottom: 0px; }
+
+.CodeMirror-gutters { position: absolute; left: 0px; top: 0px; min-height: =
+100%; z-index: 3; }
+
+.CodeMirror-gutter { white-space: normal; height: 100%; display: inline-blo=
+ck; vertical-align: top; margin-bottom: -50px; }
+
+.CodeMirror-gutter-wrapper { position: absolute; z-index: 4; background: no=
+ne !important; border: none !important; }
+
+.CodeMirror-gutter-background { position: absolute; top: 0px; bottom: 0px; =
+z-index: 4; }
+
+.CodeMirror-gutter-elt { position: absolute; cursor: default; z-index: 4; }
+
+.CodeMirror-gutter-wrapper ::selection { background-color: transparent; }
+
+.CodeMirror-lines { cursor: text; min-height: 1px; }
+
+.CodeMirror pre.CodeMirror-line, .CodeMirror pre.CodeMirror-line-like { bor=
+der-radius: 0px; border-width: 0px; background: transparent; font-family: i=
+nherit; font-size: inherit; margin: 0px; white-space: pre; overflow-wrap: n=
+ormal; line-height: inherit; color: inherit; z-index: 2; position: relative=
+; overflow: visible; -webkit-tap-highlight-color: transparent; font-variant=
+-ligatures: contextual; }
+
+.CodeMirror-wrap pre.CodeMirror-line, .CodeMirror-wrap pre.CodeMirror-line-=
+like { overflow-wrap: break-word; white-space: pre-wrap; word-break: normal=
+; }
+
+.CodeMirror-linebackground { position: absolute; inset: 0px; z-index: 0; }
+
+.CodeMirror-linewidget { position: relative; z-index: 2; padding: 0.1px; }
+
+.CodeMirror-rtl pre { direction: rtl; }
+
+.CodeMirror-code { outline: none; }
+
+.CodeMirror-scroll, .CodeMirror-sizer, .CodeMirror-gutter, .CodeMirror-gutt=
+ers, .CodeMirror-linenumber { box-sizing: content-box; }
+
+.CodeMirror-measure { position: absolute; width: 100%; height: 0px; overflo=
+w: hidden; visibility: hidden; }
+
+.CodeMirror-cursor { position: absolute; pointer-events: none; }
+
+.CodeMirror-measure pre { position: static; }
+
+div.CodeMirror-cursors { visibility: hidden; position: relative; z-index: 3=
+; }
+
+div.CodeMirror-dragcursors { visibility: visible; }
+
+.CodeMirror-focused div.CodeMirror-cursors { visibility: visible; }
+
+.CodeMirror-selected { background: rgb(217, 217, 217); }
+
+.CodeMirror-focused .CodeMirror-selected { background: rgb(215, 212, 240); =
+}
+
+.CodeMirror-crosshair { cursor: crosshair; }
+
+.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirro=
+r-line > span > span::selection { background: rgb(215, 212, 240); }
+
+.cm-searching { background-color: rgba(255, 255, 0, 0.4); }
+
+.cm-force-border { padding-right: 0.1px; }
+
+@media print {
+  .CodeMirror div.CodeMirror-cursors { visibility: hidden; }
+}
+
+.cm-tab-wrap-hack::after { content: ""; }
+
+span.CodeMirror-selectedtext { background: none; }
+
+.CodeMirror-dialog { position: absolute; left: 0px; right: 0px; background:=
+ inherit; z-index: 15; padding: 0.1em 0.8em; overflow: hidden; color: inher=
+it; }
+
+.CodeMirror-dialog-top { border-bottom: 1px solid rgb(238, 238, 238); top: =
+0px; }
+
+.CodeMirror-dialog-bottom { border-top: 1px solid rgb(238, 238, 238); botto=
+m: 0px; }
+
+.CodeMirror-dialog input { border: none; outline: none; background: transpa=
+rent; width: 20em; color: inherit; font-family: monospace; }
+
+.CodeMirror-dialog button { font-size: 70%; }
+
+.CodeMirror-merge { position: relative; border: 1px solid rgb(221, 221, 221=
+); white-space: pre; }
+
+.CodeMirror-merge, .CodeMirror-merge .CodeMirror { height: 350px; }
+
+.CodeMirror-merge-2pane .CodeMirror-merge-pane { width: 47%; }
+
+.CodeMirror-merge-2pane .CodeMirror-merge-gap { width: 6%; }
+
+.CodeMirror-merge-3pane .CodeMirror-merge-pane { width: 31%; }
+
+.CodeMirror-merge-3pane .CodeMirror-merge-gap { width: 3.5%; }
+
+.CodeMirror-merge-pane { display: inline-block; white-space: normal; vertic=
+al-align: top; }
+
+.CodeMirror-merge-pane-rightmost { position: absolute; right: 0px; z-index:=
+ 1; }
+
+.CodeMirror-merge-gap { z-index: 2; display: inline-block; height: 100%; bo=
+x-sizing: border-box; overflow: hidden; border-left: 1px solid rgb(221, 221=
+, 221); border-right: 1px solid rgb(221, 221, 221); position: relative; bac=
+kground: rgb(248, 248, 248); }
+
+.CodeMirror-merge-scrolllock-wrap { position: absolute; bottom: 0px; left: =
+50%; }
+
+.CodeMirror-merge-scrolllock { position: relative; left: -50%; cursor: poin=
+ter; color: rgb(85, 85, 85); line-height: 1; }
+
+.CodeMirror-merge-scrolllock::after { content: "=E2=87=9B=C2=A0=C2=A0=E2=87=
+=9A"; }
+
+.CodeMirror-merge-scrolllock.CodeMirror-merge-scrolllock-enabled::after { c=
+ontent: "=E2=87=9B=E2=87=9A"; }
+
+.CodeMirror-merge-copybuttons-left, .CodeMirror-merge-copybuttons-right { p=
+osition: absolute; inset: 0px; line-height: 1; }
+
+.CodeMirror-merge-copy { position: absolute; cursor: pointer; color: rgb(68=
+, 68, 204); z-index: 3; }
+
+.CodeMirror-merge-copy-reverse { position: absolute; cursor: pointer; color=
+: rgb(68, 68, 204); }
+
+.CodeMirror-merge-copybuttons-left .CodeMirror-merge-copy { left: 2px; }
+
+.CodeMirror-merge-copybuttons-right .CodeMirror-merge-copy { right: 2px; }
+
+.CodeMirror-merge-r-inserted, .CodeMirror-merge-l-inserted { background-ima=
+ge: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAMAAAACCAYAAACddGYa=
+AAAAGUlEQVQI12MwuCXy3+CWyH8GBgYGJgYkAABZbAQ9ELXurwAAAABJRU5ErkJggg=3D=3D");=
+ background-position: left bottom; background-repeat: repeat-x; }
+
+.CodeMirror-merge-r-deleted, .CodeMirror-merge-l-deleted { background-image=
+: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAMAAAACCAYAAACddGYaAA=
+AAGUlEQVQI12M4Kyb2/6yY2H8GBgYGJgYkAABURgPz6Ks7wQAAAABJRU5ErkJggg=3D=3D"); b=
+ackground-position: left bottom; background-repeat: repeat-x; }
+
+.CodeMirror-merge-r-chunk { background: rgb(255, 255, 224); }
+
+.CodeMirror-merge-r-chunk-start { border-top: 1px solid rgb(238, 238, 136);=
+ }
+
+.CodeMirror-merge-r-chunk-end { border-bottom: 1px solid rgb(238, 238, 136)=
+; }
+
+.CodeMirror-merge-r-connect { fill: rgb(255, 255, 224); stroke: rgb(238, 23=
+8, 136); stroke-width: 1px; }
+
+.CodeMirror-merge-l-chunk { background: rgb(238, 238, 255); }
+
+.CodeMirror-merge-l-chunk-start { border-top: 1px solid rgb(136, 136, 238);=
+ }
+
+.CodeMirror-merge-l-chunk-end { border-bottom: 1px solid rgb(136, 136, 238)=
+; }
+
+.CodeMirror-merge-l-connect { fill: rgb(238, 238, 255); stroke: rgb(136, 13=
+6, 238); stroke-width: 1px; }
+
+.CodeMirror-merge-l-chunk.CodeMirror-merge-r-chunk { background: rgb(221, 2=
+55, 221); }
+
+.CodeMirror-merge-l-chunk-start.CodeMirror-merge-r-chunk-start { border-top=
+: 1px solid rgb(68, 238, 68); }
+
+.CodeMirror-merge-l-chunk-end.CodeMirror-merge-r-chunk-end { border-bottom:=
+ 1px solid rgb(68, 238, 68); }
+
+.CodeMirror-merge-collapsed-widget::before { content: "(...)"; }
+
+.CodeMirror-merge-collapsed-widget { cursor: pointer; color: rgb(136, 136, =
+187); background: rgb(238, 238, 255); border: 1px solid rgb(221, 221, 255);=
+ font-size: 90%; padding: 0px 3px; border-radius: 4px; }
+
+.CodeMirror-merge-collapsed-line .CodeMirror-gutter-elt { display: none; }
+
+.cm-s-github-light.CodeMirror { background: var(--color-codemirror-bg); col=
+or: var(--color-codemirror-text); }
+
+.cm-s-github-light .CodeMirror-gutters { background: var(--color-codemirror=
+-gutters-bg); border-right-width: 0px; }
+
+.cm-s-github-light .CodeMirror-guttermarker { color: var(--color-codemirror=
+-guttermarker-text); }
+
+.cm-s-github-light .CodeMirror-guttermarker-subtle { color: var(--color-cod=
+emirror-guttermarker-subtle-text); }
+
+.cm-s-github-light .CodeMirror-scrollbar-filler, .cm-s-github-light .CodeMi=
+rror-gutter-filler { background-color: transparent; }
+
+.cm-s-github-light .CodeMirror-linenumber { color: var(--color-codemirror-l=
+inenumber-text); padding: 0px 16px; }
+
+.cm-s-github-light .CodeMirror-cursor { border-left: 1px solid var(--color-=
+codemirror-cursor); }
+
+.cm-s-github-light.CodeMirror-focused .CodeMirror-selected, .cm-s-github-li=
+ght .CodeMirror-line::selection, .cm-s-github-light .CodeMirror-line > span=
+::selection, .cm-s-github-light .CodeMirror-line > span > span::selection {=
+ background: var(--color-codemirror-selection-bg, #d7d4f0); }
+
+.cm-s-github-light .CodeMirror-activeline-background { background: var(--co=
+lor-codemirror-activeline-bg); }
+
+.cm-s-github-light .CodeMirror-matchingbracket { text-decoration: underline=
+; color: var(--color-codemirror-matchingbracket-text)  !important; }
+
+.cm-s-github-light .CodeMirror-lines { font-family: SFMono-Regular, Consola=
+s, "Liberation Mono", Menlo, Courier, monospace; font-size: 12px; backgroun=
+d: var(--color-codemirror-lines-bg); line-height: 1.5; }
+
+.cm-s-github-light .cm-comment { color: var(--color-codemirror-syntax-comme=
+nt); }
+
+.cm-s-github-light .cm-constant { color: var(--color-codemirror-syntax-cons=
+tant); }
+
+.cm-s-github-light .cm-entity { font-weight: normal; font-style: normal; te=
+xt-decoration: none; color: var(--color-codemirror-syntax-entity); }
+
+.cm-s-github-light .cm-keyword { font-weight: normal; font-style: normal; t=
+ext-decoration: none; color: var(--color-codemirror-syntax-keyword); }
+
+.cm-s-github-light .cm-storage { color: var(--color-codemirror-syntax-stora=
+ge); }
+
+.cm-s-github-light .cm-string { font-weight: normal; font-style: normal; te=
+xt-decoration: none; color: var(--color-codemirror-syntax-string); }
+
+.cm-s-github-light .cm-support { font-weight: normal; font-style: normal; t=
+ext-decoration: none; color: var(--color-codemirror-syntax-support); }
+
+.cm-s-github-light .cm-variable { font-weight: normal; font-style: normal; =
+text-decoration: none; color: var(--color-codemirror-syntax-variable); }
+
+details-dialog { position: fixed; margin: 10vh auto; top: 0px; left: 50%; t=
+ransform: translateX(-50%); z-index: 999; max-height: 80vh; max-width: 90vw=
+; width: 448px; overflow: auto; }
+
+.user-select-contain { }
+
+.ajax-pagination-form .ajax-pagination-btn { width: 100%; padding: 6px; mar=
+gin-top: 20px; font-weight: var(--base-text-weight-semibold, 600); color: v=
+ar(--color-accent-fg); background: var(--color-canvas-default); border: 1px=
+ solid var(--color-border-default); border-radius: 6px; }
+
+.ajax-pagination-form .ajax-pagination-btn:hover, .ajax-pagination-form .aj=
+ax-pagination-btn:focus { color: var(--color-accent-fg); background-color: =
+var(--color-canvas-subtle); }
+
+.ajax-pagination-form.loading .ajax-pagination-btn { text-indent: -3000px; =
+background-color: var(--color-canvas-subtle); background-image: url("/image=
+s/spinners/octocat-spinner-16px-EAF2F5.gif"); background-repeat: no-repeat;=
+ background-position: center center; border-color: var(--color-border-defau=
+lt); }
+
+@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and=
+ (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio:=
+ 2), only screen and (min-resolution: 192dpi), only screen and (min-resolut=
+ion: 2dppx) {
+  .ajax-pagination-form.loading .ajax-pagination-btn { background-image: ur=
+l("/images/spinners/octocat-spinner-32-EAF2F5.gif"); background-size: 16px;=
+ }
+}
+
+body.intent-mouse [role=3D"button"]:focus, body.intent-mouse [role=3D"tabpa=
+nel"][tabindex=3D"0"]:focus, body.intent-mouse button:focus, body.intent-mo=
+use summary:focus, body.intent-mouse a:focus { outline: none; box-shadow: n=
+one; }
+
+body.intent-mouse [tabindex=3D"0"]:focus, body.intent-mouse details-dialog:=
+focus { outline: none; }
+
+.CodeMirror { height: calc(100vh - 1px); }
+
+.file-editor-upload { height: 100%; }
+
+.issue-template-editor { height: 100%; }
+
+.file-editor-textarea { width: 100%; padding: 5px 4px; font: 12px ui-monosp=
+ace, SFMono-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospa=
+ce; resize: vertical; border: 0px; border-radius: 0px; outline: none; }
+
+.container-preview .tabnav-tabs { margin: -5px 0px -5px -9px; }
+
+.container-preview .tabnav-tabs .tabnav-tab { padding: 12px 16px; border-ra=
+dius: 0px; }
+
+.container-preview .tabnav-tabs > .selected:first-child { border-top-left-r=
+adius: 6px; }
+
+.container-preview .tabnav-tabs .selected { font-weight: var(--base-text-we=
+ight-semibold, 600); }
+
+.container-preview.template-editor .commit-create, .container-preview.templ=
+ate-editor .file-actions { display: block; }
+
+.container-preview.template-editor .show-code, .container-preview.template-=
+editor .commit-preview, .container-preview.template-editor .loading-preview=
+-msg, .container-preview.template-editor .no-changes-preview-msg, .containe=
+r-preview.template-editor .error-preview-msg { display: none !important; }
+
+.container-preview.render-editor .commit-create, .container-preview.render-=
+editor .file-actions { display: block; }
+
+.container-preview.render-editor .template-editor, .container-preview.rende=
+r-editor .show-code, .container-preview.render-editor .commit-preview, .con=
+tainer-preview.render-editor .loading-preview-msg, .container-preview.rende=
+r-editor .no-changes-preview-msg, .container-preview.render-editor .error-p=
+review-msg { display: none !important; }
+
+.container-preview.show-code .commit-create, .container-preview.show-code .=
+file-actions { display: block; }
+
+.container-preview.show-code .template-editor, .container-preview.show-code=
+ .render-editor, .container-preview.show-code .commit-preview, .container-p=
+review.show-code .loading-preview-msg, .container-preview.show-code .no-cha=
+nges-preview-msg, .container-preview.show-code .error-preview-msg { display=
+: none !important; }
+
+.container-preview:not(.show-code) .commit-create, .container-preview:not(.=
+show-code) .file-actions { display: none !important; }
+
+.container-preview.loading-preview .loading-preview-msg { display: block; }
+
+.container-preview.loading-preview .template-editor, .container-preview.loa=
+ding-preview .render-editor, .container-preview.loading-preview .no-changes=
+-preview-msg, .container-preview.loading-preview .error-preview-msg, .conta=
+iner-preview.loading-preview .commit-preview { display: none !important; }
+
+.container-preview.show-preview .commit-preview { display: block; }
+
+.container-preview.show-preview .template-editor, .container-preview.show-p=
+review .render-editor, .container-preview.show-preview .loading-preview-msg=
+, .container-preview.show-preview .no-changes-preview-msg, .container-previ=
+ew.show-preview .error-preview-msg { display: none !important; }
+
+.container-preview.no-changes-preview .no-changes-preview-msg { display: bl=
+ock; }
+
+.container-preview.no-changes-preview .template-editor, .container-preview.=
+no-changes-preview .render-editor, .container-preview.no-changes-preview .l=
+oading-preview-msg, .container-preview.no-changes-preview .error-preview-ms=
+g, .container-preview.no-changes-preview .commit-preview { display: none !i=
+mportant; }
+
+.container-preview.error-preview .error-preview-msg { display: block; }
+
+.container-preview.error-preview .template-editor, .container-preview.error=
+-preview .render-editor, .container-preview.error-preview .loading-preview-=
+msg, .container-preview.error-preview .no-changes-preview-msg, .container-p=
+review.error-preview .commit-preview { display: none !important; }
+
+.container-preview p.preview-msg { padding: 30px; font-size: 16px; }
+
+.CodeMirror-merge-header { height: 30px; }
+
+.CodeMirror-merge-header .CodeMirror-merge-pane { height: 30px; line-height=
+: 30px; }
+
+.cm-s-github-light .merge-gutter { width: 14px; }
+
+.conflict-background + .CodeMirror-gutter-wrapper .CodeMirror-linenumber { =
+background-color: var(--color-attention-subtle); }
+
+.form-group .edit-action { opacity: 0.6; }
+
+.form-group .form-field-hover { border: 1px solid var(--color-border-defaul=
+t); }
+
+.form-group:hover .edit-action { cursor: pointer; opacity: 0.7; }
+
+.form-group:hover .form-field-hover { cursor: pointer; border: 1px solid va=
+r(--color-border-default); }
+
+.placeholder-box { border: 1px solid var(--color-border-default); }
+
+.template-previews { max-width: 768px; }
+
+.template-previews .Box .expand-group { display: none; height: 0px; }
+
+.template-previews .Box .dismiss-preview-button { display: none; }
+
+.template-previews .Box.expand-preview .expand-group { display: block; heig=
+ht: 100%; transition: height 3s ease 0s; }
+
+.template-previews .Box.expand-preview .preview-button { display: none; }
+
+.template-previews .Box.expand-preview .dismiss-preview-button { display: i=
+nline; }
+
+.template-previews .discussion-sidebar-heading { font-size: 14px; color: va=
+r(--color-neutral-emphasis); }
+
+.template-previews .discussion-sidebar-heading:hover { color: var(--color-a=
+ccent-emphasis); }
+
+.edit-labels { display: none; }
+
+.preview-section { display: block; }
+
+.edit-section { display: none; }
+
+.Box .section-focus .preview-section { display: none; }
+
+.Box .section-focus .edit-section { display: block; }
+
+.commit-create .CodeMirror { padding-top: 8px; }
+
+auto-complete, details-dialog, details-menu, file-attachment, filter-input,=
+ remote-input, tab-container, text-expander, turbo-frame, [data-catalyst] {=
+ display: block; }
+
+[data-catalyst-inline] { display: inline; }
+
+.Details--on .Details-content--shown { display: none !important; }
+
+.Details:not(.Details--on) .Details-content--hidden { display: none !import=
+ant; }
+
+.Details:not(.Details--on) .Details-content--hidden-not-important { display=
+: none; }
+
+.Details-element[open] > summary .Details-content--closed { display: none !=
+important; }
+
+.Details-element:not([open]) > summary .Details-content--open { display: no=
+ne !important; }
+
+g-emoji { display: inline-block; min-width: 1ch; font-family: "Apple Color =
+Emoji", "Segoe UI Emoji", "Segoe UI Symbol"; font-size: 1em; font-weight: v=
+ar(--base-text-weight-normal, 400); line-height: 1; vertical-align: -0.075e=
+m; font-style: normal !important; }
+
+g-emoji img { width: 1em; height: 1em; }
+
+.emoji-icon { display: inline-block; width: 20px; height: 20px; vertical-al=
+ign: middle; background-repeat: no-repeat; background-size: 20px 20px; }
+
+.emoji-result { display: inline-block; height: 20px; font-size: 16px; font-=
+weight: var(--base-text-weight-normal, 400); vertical-align: middle; }
+
+.gollum-editor .comment-form-head.tabnav { border: 1px solid var(--color-bo=
+rder-muted); }
+
+.gollum-editor .gollum-editor-body { height: 390px; resize: vertical; }
+
+.active .gollum-editor-function-buttons { display: block !important; }
+
+.auth-form { width: 340px; margin: 0px auto; }
+
+.auth-form .form-group.warn .warning, .auth-form .form-group.warn .error, .=
+auth-form .form-group.errored .warning, .auth-form .form-group.errored .err=
+or { max-width: 274px; }
+
+.auth-form-header { padding: 8px 16px; margin: 0px; color: rgb(255, 255, 25=
+5); text-shadow: rgba(0, 0, 0, 0.3) 0px -1px 0px; background-color: rgb(130=
+, 154, 168); border: 1px solid rgb(118, 137, 149); border-radius: 6px 6px 0=
+px 0px; }
+
+.auth-form-header h1 { font-size: 16px; }
+
+.auth-form-header h1 a { color: rgb(255, 255, 255); }
+
+.auth-form-header .octicon { position: absolute; top: 10px; right: 20px; co=
+lor: rgba(0, 0, 0, 0.4); text-shadow: rgba(255, 255, 255, 0.1) 0px 1px 0px;=
+ }
+
+.auth-form-message { max-height: 140px; padding: 16px 16px 8px; overflow-y:=
+ scroll; border: 1px solid var(--color-border-default); border-radius: 6px;=
+ }
+
+.auth-form-message ol, .auth-form-message ul { padding-left: inherit; margi=
+n-bottom: inherit; }
+
+.auth-form-body { padding: 16px; font-size: 14px; background-color: var(--c=
+olor-canvas-subtle); border-right-color: ; border-right-style: ; border-rig=
+ht-width: ; border-bottom-color: ; border-bottom-style: ; border-bottom-wid=
+th: ; border-left-color: ; border-left-style: ; border-left-width: ; border=
+-image-source: ; border-image-slice: ; border-image-width: ; border-image-o=
+utset: ; border-image-repeat: ; border-top: 0px; border-radius: 0px 0px 6px=
+ 6px; }
+
+.auth-form-body .input-block { margin-top: 4px; margin-bottom: 16px; }
+
+.auth-form-body p { margin-bottom: 0px; }
+
+.auth-form-body ol, .auth-form-body ul { padding-left: inherit; margin-bott=
+om: inherit; }
+
+.two-factor-help { position: relative; padding: 8px 8px 8px 32px; margin: 6=
+0px 0px auto auto; border: 1px solid var(--color-border-muted); border-radi=
+us: 6px; }
+
+.two-factor-help h4 { margin-top: 0px; margin-bottom: 4px; }
+
+.two-factor-help .octicon-device-mobile, .two-factor-help .octicon-key, .tw=
+o-factor-help .octicon-shield-lock, .two-factor-help .octicon-circle-slash =
+{ position: absolute; top: 10px; left: 10px; }
+
+.sms-send-code-spinner { position: relative; bottom: 2px; display: none; ve=
+rtical-align: bottom; }
+
+.loading .sms-send-code-spinner { display: inline; }
+
+.auth-form-body .webauthn-form-body { padding: 0px; }
+
+.webauthn-form-body { padding: 32px 32px 16px; text-align: center; }
+
+.webauthn-form-body button { margin-top: 16px; }
+
+.flash.sms-error, .flash.sms-success { display: none; margin: 0px 0px 8px; =
+}
+
+.is-sent .sms-success { display: block; }
+
+.is-sent .sms-error { display: none; }
+
+.is-not-sent .sms-success { display: none; }
+
+.is-not-sent .sms-error { display: block; }
+
+.session-authentication { background-color: var(--color-canvas-default); }
+
+.session-authentication .header-logged-out { background-color: transparent;=
+ border-bottom: 0px; }
+
+.session-authentication .header-logo { color: var(--color-fg-default); }
+
+.session-authentication .flash { padding: 16px; margin: 0px auto 8px; font-=
+size: 14px; border-style: solid; border-width: 1px; border-radius: 6px; }
+
+.session-authentication .flash .container { width: auto; }
+
+.session-authentication .flash .flash-close { height: 40px; }
+
+.session-authentication .flash.flash-banner { width: 100%; border-top: 0px;=
+ border-right: 0px; border-left: 0px; border-radius: 0px; }
+
+.session-authentication .auth-form label { display: block; margin-bottom: 8=
+px; font-weight: var(--base-text-weight-normal, 400); text-align: left; }
+
+.session-authentication .auth-form .btn { margin-top: 16px; }
+
+.session-authentication .auth-form .webauthn-message { margin-bottom: 0px; =
+}
+
+.session-authentication .label-link { float: right; font-size: 12px; }
+
+.session-authentication .auth-form-header { margin-bottom: 16px; color: var=
+(--color-fg-default); text-align: center; text-shadow: none; background-col=
+or: transparent; border: 0px; }
+
+.session-authentication .auth-form-header h1 { font-size: 24px; font-weight=
+: var(--base-text-weight-light, 300); letter-spacing: -0.5px; }
+
+.session-authentication .auth-form-body { border-top: 1px solid var(--color=
+-border-muted); border-radius: 6px; }
+
+.session-authentication .auth-form-body.webauthn-form-body { padding: 16px;=
+ }
+
+.session-authentication .login-callout { padding: 16px; text-align: center;=
+ border: 1px solid var(--color-border-default); border-radius: 6px; }
+
+.session-authentication .two-factor-help { padding: 0px 0px 0px 16px; margi=
+n-top: 16px; border: 0px; }
+
+.session-authentication .two-factor-help .octicon-device-mobile, .session-a=
+uthentication .two-factor-help .octicon-key, .session-authentication .two-f=
+actor-help .octicon-shield-lock, .session-authentication .two-factor-help .=
+octicon-circle-slash { top: 4px; left: 0px; }
+
+.session-authentication.enterprise .header-logged-out { padding: 48px 0px 2=
+4px; background-color: transparent; }
+
+.session-authentication.hosted .header-logged-out { padding: 40px 0px 16px;=
+ background-color: transparent; }
+
+@media (prefers-reduced-motion: no-preference) {
+  .Header-backdrop, .HeaderMenu--logged-out, .HeaderMenu-link, .HeaderMenu-=
+toggle-bar, .HeaderMenu-icon, .HeaderMenu-dropdown, .HeaderMenu-external-ic=
+on { transition-timing-function: cubic-bezier(0.16, 1, 0.3, 1); transition-=
+duration: 500ms; transition-property: opacity, transform; }
+}
+
+.Header-old { z-index: 32; padding-top: 12px; padding-bottom: 12px; color: =
+rgb(255, 255, 255); background-color: var(--color-header-bg); }
+
+.server-stats + .Header-old { box-shadow: rgba(255, 255, 255, 0.075) 0px 1p=
+x 0px inset; }
+
+.Header-old .dropdown-menu { width: 300px; }
+
+.Header-old .notification-indicator:hover::after { content: none; }
+
+@media (min-width: 1012px) {
+  .Header-old .notification-indicator:hover::after { content: attr(aria-lab=
+el); }
+}
+
+.HeaderMenu-toggle-bar { width: 22px; height: 2px; background-color: var(--=
+color-fg-on-emphasis); }
+
+.HeaderMenu-toggle-bar:nth-of-type(1) { transform-origin: right bottom; }
+
+.HeaderMenu-toggle-bar:nth-of-type(3) { transform-origin: right top; }
+
+.open .HeaderMenu-toggle-bar:nth-of-type(1) { transform: rotate(-45deg) tra=
+nslateY(-3px); }
+
+.open .HeaderMenu-toggle-bar:nth-of-type(2) { opacity: 0; transform: scale(=
+0); }
+
+.open .HeaderMenu-toggle-bar:nth-of-type(3) { transform: rotate(45deg) tran=
+slateY(3px); }
+
+@media (max-width: 1011px) {
+  body:has(.header-logged-out.open) { height: 100%; overflow: hidden; }
+  .Header-backdrop { visibility: hidden; background: var(--color-primer-can=
+vas-backdrop); opacity: 0; }
+  .open .Header-backdrop { visibility: visible; opacity: 1; backdrop-filter=
+: blur(3px); }
+}
+
+.HeaderMenu--logged-out { --header-menu-shadow: 0 5px 30px rgba(27, 31, 35,=
+ 0.1), 0 0 1px rgba(27, 31, 35, 0.4), 0 1px 2px rgba(27, 31, 35, 0.15); con=
+tain: layout; z-index: 100; width: 100%; pointer-events: none; }
+
+@media (prefers-color-scheme: dark) {
+  .HeaderMenu--logged-out { --header-menu-shadow: 0 0 1px #959da5; }
+}
+
+@media (min-width: 1012px) {
+  .HeaderMenu--logged-out { width: auto; transition: none 0s ease 0s; paddi=
+ng: 0px !important; }
+}
+
+@media (max-width: 1011px) {
+  .HeaderMenu--logged-out { position: fixed; display: flex; height: 100%; t=
+ransform-origin: right top; }
+  .header-logged-out:not(.open) .HeaderMenu--logged-out { position: absolut=
+e; visibility: hidden; opacity: 0; transform: scale(0.9) translateY(-24px);=
+ }
+  .HeaderMenu--logged-out .HeaderMenu-link.HeaderMenu-link--sign-up, .Heade=
+rMenu--logged-out .HeaderMenu-link.HeaderMenu-link--sign-up:hover, .HeaderM=
+enu--logged-out .HeaderMenu-link.HeaderMenu-link--sign-in, .HeaderMenu--log=
+ged-out .HeaderMenu-link.HeaderMenu-link--sign-in:hover { background-color:=
+ var(--color-neutral-emphasis-plus); opacity: 1; color: var(--color-fg-on-e=
+mphasis)  !important; border-color: var(--color-neutral-emphasis-plus)  !im=
+portant; }
+}
+
+.HeaderMenu--logged-out .header-menu-wrapper { width: 100%; height: 100%; o=
+verflow: auto; pointer-events: auto; background-color: var(--color-canvas-d=
+efault); box-shadow: var(--header-menu-shadow); }
+
+@media (min-width: 544px) {
+  .HeaderMenu--logged-out .header-menu-wrapper { width: 320px; }
+}
+
+@media (min-width: 1012px) {
+  .HeaderMenu--logged-out .header-menu-wrapper { width: 100%; overflow: vis=
+ible; background-color: transparent; box-shadow: none; }
+}
+
+@media (max-width: 1011px) {
+  .HeaderMenu--logged-out .header-search { margin-right: 0px !important; }
+  .HeaderMenu--logged-out .HeaderMenu-link { font-weight: var(--base-text-w=
+eight-semibold, 600); }
+}
+
+.HeaderMenu--logged-out .jump-to-suggestions { top: 100%; }
+
+.HeaderMenu--logged-out .header-search-key-slash { margin-right: 8px !impor=
+tant; }
+
+@media (max-width: 1012px) {
+  .HeaderMenu--logged-out .header-search-key-slash { display: none; }
+}
+
+.HeaderMenu--logged-out .dropdown-menu { position: static; width: auto; bor=
+der: 0px solid transparent; box-shadow: none; }
+
+.HeaderMenu--logged-out .dropdown-menu::before, .HeaderMenu--logged-out .dr=
+opdown-menu::after { display: none; }
+
+@media (min-width: 1012px) {
+  .HeaderMenu--logged-out .dropdown-menu { position: absolute; width: 300px=
+; border: 0px; box-shadow: var(--header-menu-shadow); }
+  .HeaderMenu--logged-out .dropdown-menu::before, .HeaderMenu--logged-out .=
+dropdown-menu::after { content: ""; }
+  .HeaderMenu--logged-out .dropdown-menu.dropdown-menu-wide { width: 500px;=
+ }
+}
+
+.HeaderMenu--logged-out .dropdown-menu-s { transform: none; }
+
+@media (min-width: 1012px) {
+  .HeaderMenu--logged-out .dropdown-menu-s { transform: translateX(50%); }
+}
+
+.HeaderMenu--logged-out .header-search { width: auto; border-top: 0px; }
+
+@media (min-width: 1012px) {
+  .HeaderMenu--logged-out .header-search { width: 240px; }
+}
+
+.HeaderMenu--logged-out .header-search-wrapper { border-color: var(--color-=
+border-muted); }
+
+@media (min-width: 1012px) {
+  .HeaderMenu--logged-out .header-search-wrapper { border-color: var(--colo=
+r-header-search-border); }
+}
+
+@media (max-width: 1012px) {
+  .HeaderMenu--logged-out .header-search-wrapper { background-color: var(--=
+color-canvas-subtle); }
+}
+
+@media (min-width: 1012px) {
+  .HeaderMenu--logged-out .header-search-input { padding-top: 8px; padding-=
+bottom: 8px; font-size: 14px; appearance: none; }
+}
+
+.HeaderMenu--logged-out .header-search-input::placeholder { color: var(--co=
+lor-scale-gray-4); }
+
+.HeaderMenu-link { color: var(--color-fg-default); white-space: nowrap; bac=
+kground: transparent; }
+
+.HeaderMenu-link:hover { color: var(--color-fg-default); opacity: 0.75; }
+
+@media (min-width: 1012px) {
+  .HeaderMenu-link { color: rgb(255, 255, 255); }
+  .HeaderMenu-link:hover { color: rgb(255, 255, 255); opacity: 0.75; }
+}
+
+.HeaderMenu-item .HeaderMenu-dropdown { visibility: hidden; opacity: 0; tra=
+nsform: scale(0.99) translateY(-0.7em); transform-origin: center top; }
+
+.HeaderMenu-item:hover .HeaderMenu-dropdown, .HeaderMenu-item.open .HeaderM=
+enu-dropdown { visibility: visible; opacity: 1; transform: scale(1) transla=
+teY(0px); }
+
+@media (min-width: 1012px) {
+  .HeaderMenu-item:hover .HeaderMenu-icon, .HeaderMenu-item.open .HeaderMen=
+u-icon { transform: translateY(2px); }
+}
+
+@media (max-width: 1011px) {
+  .HeaderMenu-item .HeaderMenu-dropdown { background-color: transparent; }
+  .HeaderMenu-item .HeaderMenu-icon { transform: scale(1.2); }
+  .HeaderMenu-item:not(.open) .HeaderMenu-dropdown { position: fixed !impor=
+tant; }
+  .HeaderMenu-item:not(.open) .HeaderMenu-icon { transform: rotate(-90deg) =
+scale(1.2); }
+  .HeaderMenu-item .HeaderMenu-link { font-size: 20px; }
+}
+
+.HeaderMenu-dropdown-link:not(:hover):not(:focus) .HeaderMenu-external-icon=
+ { opacity: 0; transform: translateX(-0.5em); }
+
+.header-logo-invertocat { margin: -1px 16px -1px -2px; color: rgb(255, 255,=
+ 255); white-space: nowrap; }
+
+.header-logo-invertocat .octicon-mark-github { float: left; }
+
+.header-logo-invertocat:hover { color: rgb(255, 255, 255); text-decoration:=
+ none; }
+
+.notification-indicator .mail-status { position: absolute; top: -6px; left:=
+ 6px; display: none; width: 14px; height: 14px; color: rgb(255, 255, 255); =
+background-image: linear-gradient(rgb(84, 163, 255), rgb(0, 110, 237)); bac=
+kground-clip: padding-box; border: 2px solid var(--color-header-bg); border=
+-radius: 50%; }
+
+.notification-indicator .mail-status.unread { display: inline-block; }
+
+.notification-indicator:hover .mail-status { text-decoration: none; backgro=
+und-color: var(--color-accent-emphasis); }
+
+.notification-indicator .discussion-status { position: absolute; top: 5px; =
+left: 17px; z-index: 2; display: none; width: 8px; height: 8px; color: rgb(=
+255, 255, 255); background-image: linear-gradient(rgb(84, 163, 255), rgb(0,=
+ 110, 237)); background-clip: padding-box; border: 1px solid var(--color-pa=
+ge-header-bg); border-radius: 50%; }
+
+.notification-indicator .discussion-status.unread { display: inline-block; =
+}
+
+.header-nav-current-user { padding-bottom: 0px; font-size: inherit; }
+
+.header-nav-current-user .css-truncate-target { max-width: 100%; }
+
+.header-nav-current-user .user-profile-link { color: var(--color-fg-default=
+); }
+
+.unread-indicator { position: absolute; top: 0px; left: 13px; z-index: 2; w=
+idth: 14px; height: 14px; color: rgb(255, 255, 255); background-image: line=
+ar-gradient(rgb(84, 163, 255), rgb(0, 110, 237)); background-clip: padding-=
+box; border: 2px solid var(--color-header-bg); border-radius: 50%; }
+
+.unread-indicator--small { width: 10px; height: 10px; border: 0px; }
+
+.unread-indicator-container .unread-indicator { top: 9px; right: 10px; left=
+: inherit; width: 10px; height: 10px; border: 0px; }
+
+.header-search-wrapper { display: table; width: 100%; max-width: 100%; padd=
+ing: 0px; font-size: inherit; font-weight: var(--base-text-weight-normal, 4=
+00); vertical-align: middle; background-color: var(--color-header-search-bg=
+); border: 1px solid var(--color-header-search-border); box-shadow: none; }
+
+@media (min-width: 1012px) {
+  .header-search-wrapper { color: var(--color-scale-white); }
+}
+
+.header-search-wrapper.header-search-wrapper-jump-to .header-search-scope {=
+ width: fit-content; }
+
+.header-search-wrapper.header-search-wrapper-jump-to.search-wrapper-suggest=
+ions-active { color: var(--color-fg-default); background-color: var(--color=
+-canvas-default); border-bottom-right-radius: 0px; border-bottom-left-radiu=
+s: 0px; }
+
+.header-search-wrapper .truncate-repo-scope { max-width: 110px; overflow: h=
+idden; text-overflow: ellipsis; white-space: nowrap; }
+
+.header-search-wrapper.focus { background-color: rgba(255, 255, 255, 0.176)=
+; box-shadow: none; }
+
+.header-search-wrapper.focus .header-search-scope { color: var(--color-scal=
+e-white); background-color: rgba(255, 255, 255, 0.075); border-right-color:=
+ rgb(40, 46, 52); }
+
+.search-input.search-input-absolute { position: absolute; width: calc(100% =
+- 180px); }
+
+.header-search-input { display: table-cell; width: 100%; padding-top: 0px; =
+padding-bottom: 0px; font-size: inherit; color: inherit; background: none; =
+border: 0px; box-shadow: none; }
+
+.header-search-input::placeholder { color: rgba(255, 255, 255, 0.75); }
+
+.header-search-input:focus { border: 0px; box-shadow: none; }
+
+.header-search-input:focus ~ .header-search-key-slash { display: none !impo=
+rtant; }
+
+.header-search-button { display: table-cell; overflow: hidden; font-size: i=
+nherit; color: inherit; word-break: break-word; white-space: pre; backgroun=
+d: none; }
+
+.header-search-button.input-button:focus { border: 0px !important; }
+
+.header-search-button.input-button:focus ~ .header-search-key-slash { displ=
+ay: none !important; }
+
+.header-search-button.input-button:focus-visible { outline: 2px solid var(-=
+-color-accent-fg); outline-offset: -2px; box-shadow: none; }
+
+.Header .header-search-button .input-parsed-symbol { color: rgb(88, 166, 25=
+5); background: rgba(56, 139, 253, 0.15); border-radius: 3px; box-shadow: r=
+gba(56, 139, 253, 0.15) 0px 0px 0px 0.8px; }
+
+.Header .header-search-button .pl-c1 { color: rgb(88, 166, 255); }
+
+.Header .header-search-button .pl-en { color: rgb(88, 166, 255); }
+
+.header-search-button.placeholder { color: rgba(255, 255, 255, 0.75); }
+
+.header-search-scope { display: none; padding-right: 8px; padding-left: 8px=
+; font-size: inherit; line-height: 28px; color: rgba(255, 255, 255, 0.7); w=
+hite-space: nowrap; vertical-align: middle; border-right-width: ; border-ri=
+ght-style: ; border-right-color: rgb(40, 46, 52); border-top-left-radius: 6=
+px; border-bottom-left-radius: 6px; }
+
+.header-search-scope:empty + .header-search-input { width: 100%; }
+
+.header-search-scope:hover { color: var(--color-scale-white); background-co=
+lor: rgba(255, 255, 255, 0.12); }
+
+.scoped-search .header-search-wrapper { display: flex; }
+
+.jump-to-field-active { background-color: var(--color-canvas-default); colo=
+r: var(--color-fg-default)  !important; }
+
+.jump-to-field-active::placeholder { color: var(--color-fg-muted)  !importa=
+nt; }
+
+.jump-to-field-active ~ .header-search-key-slash { display: none; }
+
+.jump-to-field-active.jump-to-dropdown-visible { border-bottom-right-radius=
+: 0px; border-bottom-left-radius: 0px; }
+
+.jump-to-suggestions { top: 100%; left: 0px; z-index: 35; width: 100%; bord=
+er-radius: 0px 0px 6px 6px; box-shadow: rgba(0, 0, 0, 0.1) 0px 4px 10px; }
+
+.jump-to-suggestions-path { min-width: 0px; min-height: 44px; color: var(--=
+color-fg-default); }
+
+.jump-to-suggestions-path .jump-to-octicon { width: 28px; color: var(--colo=
+r-fg-muted); }
+
+.jump-to-suggestions-path .jump-to-suggestion-name { max-width: none; }
+
+.jump-to-suggestions-path mark { font-weight: var(--base-text-weight-semibo=
+ld, 600); background-color: transparent; }
+
+.jump-to-suggestions-results-container .navigation-item { border-bottom: 1p=
+x solid var(--color-border-default); }
+
+.jump-to-suggestions-results-container .navigation-item:last-child { border=
+-bottom: 0px; }
+
+.jump-to-suggestions-results-container .d-on-nav-focus { display: none; }
+
+.jump-to-suggestions-results-container [aria-selected=3D"true"] .jump-to-oc=
+ticon, .jump-to-suggestions-results-container .navigation-focus .jump-to-oc=
+ticon { color: var(--color-fg-on-emphasis); }
+
+.jump-to-suggestions-results-container [aria-selected=3D"true"] .jump-to-su=
+ggestions-path, .jump-to-suggestions-results-container .navigation-focus .j=
+ump-to-suggestions-path { color: var(--color-fg-on-emphasis); background: v=
+ar(--color-accent-emphasis); }
+
+.jump-to-suggestions-results-container [aria-selected=3D"true"] mark, .jump=
+-to-suggestions-results-container .navigation-focus mark { color: var(--col=
+or-fg-on-emphasis); }
+
+.jump-to-suggestions-results-container [aria-selected=3D"true"] .d-on-nav-f=
+ocus, .jump-to-suggestions-results-container .navigation-focus .d-on-nav-fo=
+cus { display: block; }
+
+.header-search { max-width: 100%; transition: max-width 0.2s ease-in-out 0s=
+, padding-bottom, padding-top; }
+
+@media (min-width: 768px) {
+  .header-search { max-width: 272px; }
+}
+
+@media (min-width: 768px) {
+  .header-search:focus-within { max-width: 544px; }
+}
+
+@media (min-width: 768px) {
+  .header-search.fixed-width:focus-within { max-width: 272px; }
+}
+
+.HeaderMenu--logged-out .header-search { min-width: auto; margin-bottom: 0p=
+x !important; }
+
+@media (max-width: 1011px) {
+  .HeaderMenu--logged-out .header-search .header-search-input { min-height:=
+ 40px; }
+}
+
+.search-input { width: 350px; }
+
+@media only screen and (max-width: 768px) {
+  .search-input { width: 100%; }
+  .search-input-container { margin-right: 10px !important; margin-bottom: 1=
+0px !important; margin-left: 10px !important; }
+}
+
+.search-input.expanded { flex: 1 1 0%; }
+
+.search-input.expanded .search-with-dialog { z-index: 35; }
+
+.search-with-dialog { height: 32px; color: var(--color-scale-white); backgr=
+ound-color: var(--color-header-bg); border: 1px solid var(--color-header-se=
+arch-border); }
+
+.search-with-dialog:hover { background-color: var(--color-header-search-bg)=
+; }
+
+.search-with-dialog .input-parsed-symbol { color: var(--color-accent-fg); b=
+ackground-color: var(--color-accent-subtle); border-radius: 3px; box-shadow=
+: 0 0 0 0.8px var(--color-canvas-subtle); }
+
+.query-builder-container { padding-top: 10px; padding-right: 12px; padding-=
+left: 12px; }
+
+.search-suggestions { top: -14px; left: -14px; z-index: 15; max-height: 80v=
+h; padding-top: 12px; border-radius: 12px; width: calc(100% + 26px) !import=
+ant; }
+
+.search-suggestions .suggestion-search-input-wrapper { height: 32px; overfl=
+ow-x: hidden; border: 1px solid var(--color-accent-fg); box-shadow: 0 0 0 1=
+px var(--color-accent-fg); align-items: center; }
+
+.search-suggestions .blackbird-search-button-separator { height: 16px; bord=
+er-right: 1px solid var(--color-border-default); }
+
+.search-suggestions .close-blackbird-search-button { position: relative; di=
+splay: grid; background-color: transparent; border: 0px; box-shadow: none; =
+place-content: center; }
+
+.search-suggestions .close-blackbird-search-button:hover { background-color=
+: var(--color-canvas-subtle); }
+
+.search-suggestions .clear-blackbird-search-button { background-color: tran=
+sparent; border: 0px; box-shadow: none; }
+
+.search-suggestions .clear-blackbird-search-button:hover { background-color=
+: var(--color-canvas-subtle); }
+
+.search-suggestions .suggestion-list { list-style: none; }
+
+.search-suggestions .suggestion-list [aria-selected=3D"true"], .search-sugg=
+estions .suggestion-list .suggestion-item-hover { background-color: var(--c=
+olor-canvas-subtle)  !important; }
+
+.search-suggestions .suggestion-list .suggestion-item-hover::after { positi=
+on: absolute; top: calc(50% - 12px); left: -4px; width: 4px; height: 24px; =
+content: ""; background: var(--color-accent-fg); border-radius: 6px; }
+
+.search-suggestions .suggestion-list .ActionList-item:hover { background-co=
+lor: inherit; box-shadow: inherit; }
+
+.search-suggestions .header-search-input { overflow: hidden; }
+
+.search-suggestions .octicon { pointer-events: none; }
+
+.dark-backdrop { inset: 0px; z-index: 32; height: 100%; background-color: v=
+ar(--color-primer-canvas-backdrop); }
+
+.Header-item--search { flex-grow: 100; }
+
+.styled-text-input { align-items: center; width: 100%; background-repeat: n=
+o-repeat; background-position: right 8px center; }
+
+.styled-text-input.text-input-wrapper { display: inline-flex; min-height: 3=
+0px; color: var(--color-fg-default); cursor: text; background-color: var(--=
+color-canvas-default); outline: none; }
+
+.styled-text-input.text-input-wrapper::placeholder { color: var(--color-fg-=
+subtle); }
+
+.styled-text-input.text-input-wrapper .text-input-icon { display: flex; mar=
+gin-right: 8px; align-self: center; color: var(--color-fg-muted); flex-shri=
+nk: 0; }
+
+.styled-text-input .styled-content-container { font-size: inherit; align-it=
+ems: center; }
+
+.styled-text-input .styled-content-container::-webkit-scrollbar { display: =
+none; }
+
+.styled-text-input .styled-content-container .styled-content { word-break: =
+break-word; white-space: pre; }
+
+.autosize-text-input .input { display: flex; width: 100%; min-width: 100%; =
+height: 100%; color: transparent; resize: none; background: transparent; ou=
+tline: none; caret-color: var(--color-fg-default); }
+
+.autosize-text-input .sizer { height: 0px; white-space: pre; }
+
+.search-query-builder .QueryBuilder-ListWrap { max-height: none; padding-ri=
+ght: 0px; padding-left: 0px; overflow-y: hidden !important; }
+
+.boxed-group { position: relative; margin-bottom: 30px; border-radius: 6px;=
+ }
+
+.boxed-group .Counter { color: var(--color-fg-on-emphasis); background-colo=
+r: var(--color-neutral-emphasis); }
+
+.boxed-group.flush .boxed-group-inner { padding: 0px; }
+
+.boxed-group.condensed .boxed-group-inner { padding: 0px; font-size: 12px; =
+}
+
+.boxed-group > h3, .boxed-group .heading { display: block; padding: 9px 10p=
+x 10px; margin: 0px; font-size: 14px; line-height: 17px; background-color: =
+var(--color-canvas-subtle); border-top-color: ; border-top-style: ; border-=
+top-width: ; border-right-color: ; border-right-style: ; border-right-width=
+: ; border-left-color: ; border-left-style: ; border-left-width: ; border-i=
+mage-source: ; border-image-slice: ; border-image-width: ; border-image-out=
+set: ; border-image-repeat: ; border-bottom: 0px; border-radius: 6px 6px 0p=
+x 0px; }
+
+.boxed-group > h3 a, .boxed-group .heading a { color: inherit; }
+
+.boxed-group > h3 a.boxed-group-breadcrumb, .boxed-group .heading a.boxed-g=
+roup-breadcrumb { font-weight: var(--base-text-weight-normal, 400); color: =
+var(--color-fg-muted); text-decoration: none; }
+
+.boxed-group > h3 .avatar, .boxed-group .heading .avatar { margin-top: -4px=
+; }
+
+.boxed-group .tabnav.heading { padding: 0px; }
+
+.boxed-group .tabnav.heading .tabnav-tab.selected { border-top: 0px; }
+
+.boxed-group .tabnav.heading li:first-child .selected { border-left-color: =
+var(--color-canvas-default); border-top-left-radius: 6px; }
+
+.boxed-group .tabnav-tab { border-top: 0px; border-radius: 0px; }
+
+.boxed-group code.heading { font-size: 12px; }
+
+.boxed-group.dangerzone > h3 { color: var(--color-fg-on-emphasis); backgrou=
+nd-color: var(--color-danger-emphasis); border: 1px solid var(--color-dange=
+r-emphasis); }
+
+.boxed-group.dangerzone .boxed-group-inner { border-top: 0px; }
+
+.boxed-group.condensed > h3 { padding: 6px 6px 7px; font-size: 12px; }
+
+.boxed-group.condensed > h3 .octicon { padding: 0px 6px 0px 2px; }
+
+.dashboard-sidebar .boxed-group { margin-bottom: 20px; }
+
+.boxed-group .bleed-flush { width: 100%; padding: 0px 10px; margin-left: -1=
+0px; }
+
+.boxed-group .compact { margin-top: 10px; margin-bottom: 10px; }
+
+.boxed-group-inner { padding: 10px; color: var(--color-fg-muted); backgroun=
+d: var(--color-canvas-default); border: 1px solid var(--color-border-defaul=
+t); border-bottom-right-radius: 6px; border-bottom-left-radius: 6px; }
+
+.boxed-group-inner .markdown-body { padding: 20px 10px 10px; font-size: 13p=
+x; }
+
+.boxed-group-inner.markdown-body { padding-top: 10px; padding-bottom: 10px;=
+ }
+
+.boxed-group-inner.seamless { padding: 0px; }
+
+.boxed-group-inner .tabnav { padding-right: 10px; padding-left: 10px; margi=
+n-right: -10px; margin-left: -10px; }
+
+.boxed-group-inner .tabnav-tab.selected { border-top: 1px solid var(--color=
+-border-default); }
+
+.boxed-action { float: right; margin-left: 10px; }
+
+.boxed-action .boxed-action { float: none; margin-left: 0px; }
+
+.boxed-group-action { position: relative; z-index: 2; float: right; margin:=
+ 5px 10px 0px 0px; }
+
+.boxed-group-action.flush { margin-top: 0px; margin-right: 0px; }
+
+.field-with-errors { display: inline; }
+
+.boxed-group-list { margin: 0px; list-style: none; }
+
+.boxed-group-list:first-child > li:first-child { border-top: 0px; }
+
+.boxed-group-list > li { display: block; padding: 5px 10px; margin-right: -=
+10px; margin-left: -10px; line-height: 23px; border-bottom: 1px solid var(-=
+-color-border-default); }
+
+.boxed-group-list > li:first-child { border-top: 1px solid var(--color-bord=
+er-default); }
+
+.boxed-group-list > li:last-of-type { border-bottom: 0px; }
+
+.boxed-group-list > li.selected { background: var(--color-success-subtle); =
+}
+
+.boxed-group-list > li.approved .btn-sm, .boxed-group-list > li.rejected .b=
+tn-sm { display: none; }
+
+.boxed-group-list > li.rejected a { text-decoration: line-through; }
+
+.boxed-group-list > li .avatar { margin-top: -2px; margin-right: 4px; }
+
+.boxed-group-list > li .octicon { width: 24px; margin-right: 4px; }
+
+.boxed-group-list > li .btn-sm { float: right; margin: -1px 0px 0px 10px; }
+
+.boxed-group-list > li .BtnGroup { float: right; }
+
+.boxed-group-list > li .BtnGroup .btn-sm { float: left; }
+
+.boxed-group.flush .boxed-group-list li { width: auto; padding-right: 0px; =
+padding-left: 0px; margin-left: 0px; }
+
+.boxed-group-list.standalone { margin-top: -1px; }
+
+.boxed-group-list.standalone > li:first-child { border-top: 0px; }
+
+.boxed-group-table { width: 100%; text-align: left; }
+
+.boxed-group-table tr:last-child td { border-bottom: 0px; }
+
+.boxed-group-table th { padding: 9px; background-color: var(--color-canvas-=
+subtle); border-bottom: 1px solid var(--color-border-muted); }
+
+.boxed-group-table td { padding: 9px; vertical-align: top; border-bottom: 1=
+px solid var(--color-border-muted); }
+
+.ajax-error-message { position: fixed; top: 0px; left: 50%; z-index: 9999; =
+width: 974px; margin: 0px 3px 0px -487px; transition: top 0.5s ease-in-out =
+0s; }
+
+.ajax-error-message > .octicon-alert { vertical-align: text-top; }
+
+.container { width: 980px; margin-right: auto; margin-left: auto; }
+
+.container::before { display: table; content: ""; }
+
+.container::after { display: table; clear: both; content: ""; }
+
+.draft.octicon { color: var(--color-fg-muted); }
+
+.closed.octicon, .reverted.octicon { color: var(--color-closed-fg); }
+
+.open.octicon { color: var(--color-open-fg); }
+
+.closed.octicon.octicon-issue-closed, .merged.octicon { color: var(--color-=
+done-fg); }
+
+.progress-bar { display: block; height: 15px; overflow: hidden; background-=
+color: var(--color-border-muted); border-radius: 6px; }
+
+.progress-bar .progress { display: block; height: 100%; background-color: v=
+ar(--color-success-emphasis); }
+
+.reverse-progress-container { position: relative; height: 3px; background-c=
+olor: var(--color-border-muted); background-image: linear-gradient(to right=
+, var(--color-success-emphasis), var(--color-accent-emphasis), var(--color-=
+done-emphasis), var(--color-danger-emphasis), var(--color-severe-emphasis))=
+; background-size: 100% 3px; }
+
+.reverse-progress-bar { position: absolute; right: 0px; height: 100%; backg=
+round-color: var(--color-border-muted); }
+
+.progress-bar-small { height: 10px; }
+
+.select-menu-button::after { display: inline-block; width: 0px; height: 0px=
+; vertical-align: -2px; content: ""; border-width: 4px; border-style: solid=
+; border-top-color: initial; border-image: initial; border-right-color: tra=
+nsparent; border-bottom-color: transparent; border-left-color: transparent;=
+ }
+
+.select-menu-button.icon-only { padding-left: 7px; }
+
+.select-menu-button.primary::after { border-top-color: var(--color-fg-on-em=
+phasis); }
+
+.select-menu-button-large::after { margin-left: 0.25em; border-width: 0.33e=
+m; }
+
+.select-menu .spinner { float: left; margin: 4px 0px 0px -24px; }
+
+.select-menu.active .select-menu-modal-holder { display: block; }
+
+.select-menu.select-menu-modal-right { position: relative; }
+
+.select-menu.select-menu-modal-right .select-menu-modal-holder { right: 0px=
+; }
+
+.select-menu .select-menu-clear-item { display: block; }
+
+.select-menu .select-menu-clear-item .octicon { color: inherit; }
+
+.select-menu .select-menu-clear-item + .select-menu-no-results { display: n=
+one !important; }
+
+.select-menu.is-loading .select-menu-loading-overlay { display: block; }
+
+.select-menu.is-loading .select-menu-modal { min-height: 200px; }
+
+.select-menu.has-error .select-menu-error { display: block; }
+
+.select-menu-error { display: none; }
+
+.select-menu-loading-overlay { position: absolute; top: 0px; z-index: 5; di=
+splay: none; width: 100%; height: 100%; background-color: var(--color-canva=
+s-overlay); border: 1px solid transparent; border-radius: 5px; }
+
+.select-menu-modal-holder { position: absolute; z-index: 30; display: none;=
+ }
+
+.select-menu-modal { position: relative; width: 300px; margin-top: 4px; mar=
+gin-bottom: 20px; overflow: hidden; font-size: 12px; color: var(--color-fg-=
+default); background-color: var(--color-canvas-overlay); background-clip: p=
+adding-box; border: 1px solid var(--color-border-default); border-radius: 6=
+px; box-shadow: var(--color-shadow-large); }
+
+.select-menu-header, .select-menu-divider { padding: 8px 10px; line-height:=
+ 16px; background: var(--color-canvas-subtle); border-bottom: 1px solid var=
+(--color-border-muted); }
+
+.select-menu-header .select-menu-title, .select-menu-divider { font-weight:=
+ var(--base-text-weight-semibold, 600); color: var(--color-fg-default); }
+
+.select-menu-divider { margin-top: -1px; border-top: 1px solid var(--color-=
+border-muted); }
+
+.select-menu-header .close-button, .select-menu-header .octicon { display: =
+block; float: right; color: var(--color-fg-muted); cursor: pointer; }
+
+.select-menu-header .close-button:hover, .select-menu-header .octicon:hover=
+ { color: var(--color-fg-default); }
+
+.select-menu-header:focus { outline: none; }
+
+.select-menu-filters { background-color: var(--color-canvas-overlay); }
+
+.select-menu-text-filter { padding: 10px 10px 0px; }
+
+.select-menu-text-filter:first-child:last-child { padding-bottom: 10px; bor=
+der-bottom: 1px solid var(--color-border-muted); }
+
+.select-menu-text-filter input { display: block; width: 100%; max-width: 10=
+0%; padding: 5px; border: 1px solid var(--color-border-muted); border-radiu=
+s: 6px; }
+
+.select-menu-text-filter input::placeholder { color: var(--color-fg-subtle)=
+; }
+
+.select-menu-tabs { padding: 10px 10px 0px; border-bottom: 1px solid var(--=
+color-border-muted); }
+
+.select-menu-tabs ul { position: relative; bottom: -1px; }
+
+.select-menu-tabs .select-menu-tab { display: inline-block; }
+
+.select-menu-tabs a, .select-menu-tabs .select-menu-tab-nav { display: inli=
+ne-block; padding: 4px 8px 2px; font-size: 12px; font-weight: var(--base-te=
+xt-weight-semibold, 600); color: var(--color-fg-muted); text-decoration: no=
+ne; cursor: pointer; background: transparent; border: 1px solid transparent=
+; border-radius: 6px 6px 0px 0px; }
+
+.select-menu-tabs a:hover, .select-menu-tabs .select-menu-tab-nav:hover { c=
+olor: var(--color-fg-default); }
+
+.select-menu-tabs a[aria-selected=3D"true"], .select-menu-tabs a.selected, =
+.select-menu-tabs .select-menu-tab-nav[aria-selected=3D"true"], .select-men=
+u-tabs .select-menu-tab-nav.selected { color: var(--color-fg-default); back=
+ground-color: var(--color-canvas-overlay); border-top-color: ; border-right=
+-color: ; border-left-color: ; border-bottom-color: var(--color-canvas-over=
+lay); }
+
+.select-menu-list { position: relative; max-height: 400px; overflow: auto; =
+}
+
+.select-menu-list.is-showing-new-item-form .select-menu-new-item-form { dis=
+play: block; }
+
+.select-menu-list.is-showing-new-item-form .select-menu-no-results, .select=
+-menu-list.is-showing-new-item-form .select-menu-clear-item { display: none=
+; }
+
+.select-menu-blankslate { padding: 16px; text-align: center; }
+
+.select-menu-blankslate svg { display: block; margin-right: auto; margin-bo=
+ttom: 9px; margin-left: auto; fill: var(--color-fg-muted); }
+
+.select-menu-blankslate h3 { font-size: 14px; color: var(--color-fg-default=
+); }
+
+.select-menu-blankslate p { width: 195px; margin-right: auto; margin-bottom=
+: 0px; margin-left: auto; }
+
+.select-menu-item { display: block; padding: 8px 8px 8px 30px; overflow: hi=
+dden; color: inherit; cursor: pointer; border-bottom: 1px solid var(--color=
+-border-muted); }
+
+.select-menu-item .select-menu-item-text .octicon-x { display: none; float:=
+ right; margin: 1px 10px 0px 0px; opacity: 0.6; }
+
+.select-menu-item:hover { text-decoration: none; }
+
+.select-menu-item.disabled, .select-menu-item[disabled], .select-menu-item[=
+aria-disabled=3D"true"], .select-menu-item.disabled.selected { color: var(-=
+-color-fg-muted); cursor: default; }
+
+.select-menu-item.disabled .description, .select-menu-item[disabled] .descr=
+iption, .select-menu-item[aria-disabled=3D"true"] .description, .select-men=
+u-item.disabled.selected .description { color: var(--color-fg-muted); }
+
+.select-menu-item.disabled.opaque, .select-menu-item[disabled].opaque, .sel=
+ect-menu-item[aria-disabled=3D"true"].opaque, .select-menu-item.disabled.se=
+lected.opaque { opacity: 0.7; }
+
+.select-menu-item.disabled .select-menu-item-gravatar, .select-menu-item[di=
+sabled] .select-menu-item-gravatar, .select-menu-item[aria-disabled=3D"true=
+"] .select-menu-item-gravatar, .select-menu-item.disabled.selected .select-=
+menu-item-gravatar { opacity: 0.5; }
+
+.select-menu-item .octicon { vertical-align: middle; }
+
+.select-menu-item .octicon-check, .select-menu-item .octicon-circle-slash, =
+.select-menu-item input[type=3D"radio"]:not(:checked) + .octicon-check, .se=
+lect-menu-item input[type=3D"radio"]:not(:checked) + .octicon-circle-slash =
+{ visibility: hidden; }
+
+.select-menu-item.selected .octicon-circle-slash.select-menu-item-icon { co=
+lor: var(--color-fg-muted)  !important; }
+
+.select-menu-item .octicon-circle-slash { color: var(--color-fg-muted); }
+
+.select-menu-item.excluded { background-color: var(--color-canvas-subtle); =
+}
+
+.select-menu-item input[type=3D"radio"] { display: none; }
+
+.select-menu-item:focus { outline: none; }
+
+.select-menu-item:focus .octicon, .select-menu-item:hover .octicon { color:=
+ inherit !important; }
+
+.select-menu-item:hover, .select-menu-item:hover.selected, .select-menu-ite=
+m:hover.select-menu-action, .select-menu-item:hover .description-inline, .s=
+elect-menu-item:focus, .select-menu-item:focus.selected, .select-menu-item:=
+focus.select-menu-action, .select-menu-item:focus .description-inline, .sel=
+ect-menu-item.navigation-focus, .select-menu-item.navigation-focus.selected=
+, .select-menu-item.navigation-focus.select-menu-action, .select-menu-item.=
+navigation-focus .description-inline, .select-menu-item.navigation-focus[ar=
+ia-checked=3D"true"], .select-menu-item[aria-checked=3D"true"]:focus, .sele=
+ct-menu-item[aria-checked=3D"true"]:hover, .select-menu-item[aria-selected=
+=3D"true"]:hover, .select-menu-item[aria-selected=3D"true"]:focus, .select-=
+menu-item[aria-selected=3D"true"].select-menu-action, .select-menu-item[ari=
+a-selected=3D"true"] .description-inline { color: var(--color-fg-on-emphasi=
+s); background-color: var(--color-accent-emphasis); }
+
+.select-menu-item:hover > .octicon, .select-menu-item:hover.selected > .oct=
+icon, .select-menu-item:hover.select-menu-action > .octicon, .select-menu-i=
+tem:hover .description-inline > .octicon, .select-menu-item:focus > .octico=
+n, .select-menu-item:focus.selected > .octicon, .select-menu-item:focus.sel=
+ect-menu-action > .octicon, .select-menu-item:focus .description-inline > .=
+octicon, .select-menu-item.navigation-focus > .octicon, .select-menu-item.n=
+avigation-focus.selected > .octicon, .select-menu-item.navigation-focus.sel=
+ect-menu-action > .octicon, .select-menu-item.navigation-focus .description=
+-inline > .octicon, .select-menu-item.navigation-focus[aria-checked=3D"true=
+"] > .octicon, .select-menu-item[aria-checked=3D"true"]:focus > .octicon, .=
+select-menu-item[aria-checked=3D"true"]:hover > .octicon, .select-menu-item=
+[aria-selected=3D"true"]:hover > .octicon, .select-menu-item[aria-selected=
+=3D"true"]:focus > .octicon, .select-menu-item[aria-selected=3D"true"].sele=
+ct-menu-action > .octicon, .select-menu-item[aria-selected=3D"true"] .descr=
+iption-inline > .octicon { color: var(--color-fg-on-emphasis); }
+
+.select-menu-item:hover .description, .select-menu-item:hover .description-=
+warning, .select-menu-item:hover.selected .description, .select-menu-item:h=
+over.selected .description-warning, .select-menu-item:hover.select-menu-act=
+ion .description, .select-menu-item:hover.select-menu-action .description-w=
+arning, .select-menu-item:hover .description-inline .description, .select-m=
+enu-item:hover .description-inline .description-warning, .select-menu-item:=
+focus .description, .select-menu-item:focus .description-warning, .select-m=
+enu-item:focus.selected .description, .select-menu-item:focus.selected .des=
+cription-warning, .select-menu-item:focus.select-menu-action .description, =
+.select-menu-item:focus.select-menu-action .description-warning, .select-me=
+nu-item:focus .description-inline .description, .select-menu-item:focus .de=
+scription-inline .description-warning, .select-menu-item.navigation-focus .=
+description, .select-menu-item.navigation-focus .description-warning, .sele=
+ct-menu-item.navigation-focus.selected .description, .select-menu-item.navi=
+gation-focus.selected .description-warning, .select-menu-item.navigation-fo=
+cus.select-menu-action .description, .select-menu-item.navigation-focus.sel=
+ect-menu-action .description-warning, .select-menu-item.navigation-focus .d=
+escription-inline .description, .select-menu-item.navigation-focus .descrip=
+tion-inline .description-warning, .select-menu-item.navigation-focus[aria-c=
+hecked=3D"true"] .description, .select-menu-item.navigation-focus[aria-chec=
+ked=3D"true"] .description-warning, .select-menu-item[aria-checked=3D"true"=
+]:focus .description, .select-menu-item[aria-checked=3D"true"]:focus .descr=
+iption-warning, .select-menu-item[aria-checked=3D"true"]:hover .description=
+, .select-menu-item[aria-checked=3D"true"]:hover .description-warning, .sel=
+ect-menu-item[aria-selected=3D"true"]:hover .description, .select-menu-item=
+[aria-selected=3D"true"]:hover .description-warning, .select-menu-item[aria=
+-selected=3D"true"]:focus .description, .select-menu-item[aria-selected=3D"=
+true"]:focus .description-warning, .select-menu-item[aria-selected=3D"true"=
+].select-menu-action .description, .select-menu-item[aria-selected=3D"true"=
+].select-menu-action .description-warning, .select-menu-item[aria-selected=
+=3D"true"] .description-inline .description, .select-menu-item[aria-selecte=
+d=3D"true"] .description-inline .description-warning { color: var(--color-f=
+g-on-emphasis); }
+
+.select-menu-item:hover.disabled, .select-menu-item[disabled]:hover, .selec=
+t-menu-item[aria-disabled=3D"true"]:hover, .select-menu-item[aria-selected=
+=3D"true"].disabled, .select-menu-item.navigation-focus.disabled { color: v=
+ar(--color-fg-muted); background-color: var(--color-canvas-overlay); }
+
+.select-menu-item:hover.disabled .description, .select-menu-item[disabled]:=
+hover .description, .select-menu-item[aria-disabled=3D"true"]:hover .descri=
+ption, .select-menu-item[aria-selected=3D"true"].disabled .description, .se=
+lect-menu-item.navigation-focus.disabled .description { color: var(--color-=
+fg-muted); }
+
+.select-menu-item > .octicon-dash { display: none; }
+
+.select-menu-item[aria-checked=3D"mixed"] > .octicon-check { display: none;=
+ }
+
+.select-menu-item[aria-checked=3D"mixed"] > .octicon-dash { display: block;=
+ }
+
+.select-menu-item input:checked + .octicon-check { color: inherit; visibili=
+ty: visible; }
+
+details-menu .select-menu-item[aria-checked=3D"true"], details-menu .select=
+-menu-item[aria-selected=3D"true"], .select-menu-item.selected { color: var=
+(--color-fg-default); }
+
+details-menu .select-menu-item[aria-checked=3D"true"] .description, details=
+-menu .select-menu-item[aria-selected=3D"true"] .description, .select-menu-=
+item.selected .description { color: var(--color-fg-muted); }
+
+details-menu .select-menu-item[aria-checked=3D"true"] > .octicon, details-m=
+enu .select-menu-item[aria-selected=3D"true"] > .octicon, .select-menu-item=
+.selected > .octicon { color: var(--color-fg-default); }
+
+details-menu .select-menu-item[aria-checked=3D"true"] .octicon-check, detai=
+ls-menu .select-menu-item[aria-checked=3D"true"] .octicon-circle-slash, det=
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+ect-menu-item.selected .octicon-check, .select-menu-item.selected .octicon-=
+circle-slash { color: inherit; visibility: visible; }
+
+details-menu .select-menu-item[aria-checked=3D"true"] .select-menu-item-tex=
+t .octicon-x, details-menu .select-menu-item[aria-selected=3D"true"] .selec=
+t-menu-item-text .octicon-x, .select-menu-item.selected .select-menu-item-t=
+ext .octicon-x { display: block; color: inherit; }
+
+.select-menu.label-select-menu .select-menu-item:active { background-color:=
+ transparent !important; }
+
+.select-menu-item:hover .Label, .select-menu-item:focus .Label { color: inh=
+erit; border-color: currentcolor; }
+
+.select-menu-item a { color: inherit; text-decoration: none; }
+
+.select-menu-item .hidden-select-button-text { display: none; }
+
+.select-menu-item .css-truncate-target { max-width: 100%; }
+
+.select-menu-item-icon { float: left; margin-left: -20px; }
+
+form.select-menu-item > div:first-child { display: none !important; }
+
+.select-menu-list:last-child .select-menu-item:last-child, .select-menu-ite=
+m.last-visible { border-bottom: 0px; border-radius: 0px 0px 6px 6px; }
+
+.select-menu-action { font-weight: var(--base-text-weight-normal, 400); col=
+or: var(--color-fg-default); }
+
+.select-menu-action > .octicon { color: inherit; }
+
+.select-menu-action:hover { color: var(--color-accent-fg); }
+
+.select-menu-no-results { display: none; padding: 9px; color: var(--color-f=
+g-muted); cursor: auto; }
+
+.select-menu-list.filterable-empty .select-menu-no-results, .select-menu-no=
+-results:only-child { display: block; }
+
+.select-menu-button-gravatar, .select-menu-item-gravatar { width: 20px; ove=
+rflow: hidden; line-height: 0; }
+
+.select-menu-button-gravatar img, .select-menu-item-gravatar img { display:=
+ inline-block; width: 20px; height: 20px; border-radius: 6px; }
+
+.select-menu-item-gravatar { float: left; width: 20px; height: 20px; margin=
+-right: 8px; border-radius: 6px; }
+
+.select-menu-button-gravatar { float: left; margin-right: 5px; }
+
+.select-menu-item-text { display: block; text-align: left; }
+
+.select-menu-item-text .description { display: block; max-width: 265px; fon=
+t-size: 12px; color: var(--color-fg-muted); }
+
+.select-menu-item-text .description-inline { font-size: 12px; color: var(--=
+color-fg-muted); }
+
+.select-menu-item-text .description-warning { color: var(--color-danger-fg)=
+; }
+
+.select-menu-item-text mark { font-weight: var(--base-text-weight-semibold,=
+ 600); color: inherit; background-color: inherit; }
+
+.select-menu-item-heading { display: block; margin-top: 0px; margin-bottom:=
+ 0px; font-size: 14px; font-weight: var(--base-text-weight-semibold, 600); =
+}
+
+.select-menu-item-heading .description { display: inline; font-weight: var(=
+--base-text-weight-normal, 400); }
+
+.select-menu-new-item-form { display: none; }
+
+.select-menu-new-item-form .octicon { color: var(--color-accent-fg); }
+
+.table-list { display: table; width: 100%; color: var(--color-fg-muted); ta=
+ble-layout: fixed; border-bottom: 1px solid var(--color-border-default); }
+
+.table-list ol { list-style-type: decimal; }
+
+.table-list-bordered { border-bottom-color: var(--color-border-default); }
+
+.table-list-bordered .table-list-cell:first-child { border-left: 1px solid =
+var(--color-border-default); }
+
+.table-list-bordered .table-list-cell:last-child { border-right: 1px solid =
+var(--color-border-default); }
+
+.table-list-item { position: relative; display: table-row; list-style: none=
+; }
+
+.table-list-item.unread .table-list-cell:first-child { box-shadow: 2px 0 0 =
+var(--color-accent-emphasis) inset; }
+
+.table-list-cell { position: relative; display: table-cell; padding: 8px 10=
+px; font-size: 12px; vertical-align: top; border-top: 1px solid var(--color=
+-border-default); }
+
+.table-list-cell.flush-left { padding-left: 0px; }
+
+.table-list-cell.flush-right { padding-right: 0px; }
+
+.table-list-header { position: relative; margin-top: 20px; background-color=
+: var(--color-canvas-subtle); border: 1px solid var(--color-border-default)=
+; border-radius: 6px 6px 0px 0px; }
+
+.table-list-header::before { display: table; content: ""; }
+
+.table-list-header::after { display: table; clear: both; content: ""; }
+
+.table-list-header .btn-link { position: relative; display: inline-block; p=
+adding-top: 13px; padding-bottom: 13px; font-weight: var(--base-text-weight=
+-normal, 400); }
+
+.table-list-heading { margin-left: 10px; }
+
+.table-list-header-meta { display: inline-block; padding-top: 13px; padding=
+-bottom: 13px; color: var(--color-fg-muted); }
+
+.table-list-header-toggle h4 { padding: 12px 0px; }
+
+.table-list-filters:first-child .table-list-header-toggle:first-child { pad=
+ding-left: 16px; }
+
+.table-list-header-toggle.states .selected { font-weight: var(--base-text-w=
+eight-semibold, 600); }
+
+.table-list-header-toggle .btn-link { color: var(--color-fg-muted); }
+
+.table-list-header-toggle .btn-link .octicon { margin-right: 4px; }
+
+.table-list-header-toggle .btn-link:hover { color: var(--color-fg-default);=
+ text-decoration: none; }
+
+.table-list-header-toggle .btn-link.selected, .table-list-header-toggle .bt=
+n-link.selected:hover { color: var(--color-fg-default); }
+
+.table-list-header-toggle .btn-link + .btn-link { margin-left: 10px; }
+
+.table-list-header-toggle .btn-link:disabled, .table-list-header-toggle .bt=
+n-link.disabled { pointer-events: none; opacity: 0.5; }
+
+.table-list-header-toggle .select-menu { position: relative; }
+
+.table-list-header-toggle .select-menu-item[aria-checked=3D"true"], .table-=
+list-header-toggle .select-menu-item.selected { font-weight: var(--base-tex=
+t-weight-semibold, 600); }
+
+.table-list-header-toggle .select-menu-button { padding-right: 15px; paddin=
+g-left: 15px; }
+
+.table-list-header-toggle .select-menu-button:hover, .table-list-header-tog=
+gle .select-menu-button.selected, .table-list-header-toggle .select-menu-bu=
+tton.selected:hover { color: var(--color-fg-default); }
+
+.table-list-header-toggle .select-menu-modal-holder { right: 10px; }
+
+.table-list-header-toggle .select-menu-modal-holder .select-menu-modal { ma=
+rgin-top: -1px; }
+
+.table-list-header-next { margin-top: 20px; margin-bottom: -1px; }
+
+.table-list-header-next .table-list-header-select-all { padding-left: 14px;=
+ }
+
+.table-list-header-next .select-all-dropdown { padding-top: 10px; padding-b=
+ottom: 10px; }
+
+.table-list-triage { display: none; }
+
+.triage-mode .table-list-filters { display: none !important; }
+
+.triage-mode .table-list-triage { display: block; }
+
+.breadcrumb { font-size: 16px; color: var(--color-fg-muted); }
+
+.breadcrumb .separator { white-space: pre-wrap; }
+
+.breadcrumb .separator::before, .breadcrumb .separator::after { content: " =
+"; }
+
+.breadcrumb strong.final-path { color: var(--color-fg-default); }
+
+.capped-cards { list-style: none; }
+
+.capped-card-content { display: block; background: var(--color-canvas-subtl=
+e); }
+
+.capped-card-content::before { display: table; content: ""; }
+
+.capped-card-content::after { display: table; clear: both; content: ""; }
+
+.details-collapse .collapse { position: relative; display: none; height: 0p=
+x; overflow: hidden; transition: height 0.35s ease-in-out 0s; }
+
+.details-collapse.open .collapse { display: block; height: auto; overflow: =
+visible; }
+
+.collapsible-sidebar-widget-button { display: flex; padding: 0px; align-ite=
+ms: center; background-color: transparent; border: 0px; justify-content: sp=
+ace-between; }
+
+.collapsible-sidebar-widget-indicator { transition: transform 0.25s ease 0s=
+; transform: translate(0px, 0px) translate3d(0px, 0px, 0px); }
+
+.collapsible-sidebar-widget-loader { display: none; visibility: hidden; opa=
+city: 0; transition: opacity 0.25s ease 0s; animation-play-state: paused; }
+
+.collapsible-sidebar-widget-content { width: 100%; max-height: 0px; overflo=
+w: hidden; opacity: 0; transition: max-height 0.25s ease-in-out 0s, opacity=
+ 0.25s ease-in-out 0s; }
+
+.collapsible-sidebar-widget-loading .collapsible-sidebar-widget-indicator {=
+ display: none; }
+
+.collapsible-sidebar-widget-loading .collapsible-sidebar-widget-loader { di=
+splay: block; visibility: visible; opacity: 1; animation-play-state: runnin=
+g; }
+
+.collapsible-sidebar-widget-active .collapsible-sidebar-widget-content { ma=
+x-height: 100%; overflow: visible; opacity: 1; }
+
+.collapsible-sidebar-widget-active .collapsible-sidebar-widget-indicator { =
+display: block; transform: rotate(180deg); }
+
+.collapsible-sidebar-widget-active .collapsible-sidebar-widget-loader { dis=
+play: none; visibility: hidden; opacity: 0; }
+
+.collapsible-sidebar-widget-active .collapsible-sidebar-widget-active-hidde=
+n { display: none; opacity: 0; }
+
+.comment .email-format { line-height: 1.5; }
+
+.previewable-edit .previewable-comment-form { display: none; }
+
+.previewable-edit .previewable-comment-form::before { display: table; conte=
+nt: ""; }
+
+.previewable-edit .previewable-comment-form::after { display: table; clear:=
+ both; content: ""; }
+
+.previewable-edit .previewable-comment-form .tabnav-tabs { display: inline-=
+block; }
+
+.previewable-edit .previewable-comment-form .form-actions { float: right; m=
+argin-right: 8px; margin-bottom: 8px; }
+
+.previewable-edit.is-comment-editing .timeline-comment-header { display: no=
+ne !important; }
+
+.is-comment-editing .previewable-comment-form { display: block; }
+
+.is-comment-editing .timeline-comment-actions, .is-comment-editing .edit-co=
+mment-hide { display: none; }
+
+.is-comment-loading .previewable-comment-form { opacity: 0.5; }
+
+.comment-show-stale { display: none; }
+
+.is-comment-stale .comment-show-stale { display: block; }
+
+.comment-body { width: 100%; padding: 16px; overflow: visible; font-size: 1=
+4px; color: var(--color-fg-default); }
+
+.comment-body .highlight { background-color: transparent; overflow: visible=
+ !important; }
+
+.comment-form-textarea { width: 100%; max-width: 100%; height: 100px; min-h=
+eight: 100px; margin: 0px; line-height: 1.6; }
+
+.comment-form-textarea.dragover { border: solid 1px var(--color-accent-emph=
+asis); }
+
+.hide-reaction-suggestion:hover::before, .hide-reaction-suggestion:hover::a=
+fter, .hide-reaction-suggestion:active::before, .hide-reaction-suggestion:a=
+ctive::after { display: none; }
+
+.reaction-suggestion[data-reaction-suggestion-message]:hover::before, .reac=
+tion-suggestion[data-reaction-suggestion-message]:hover::after { display: i=
+nline-block; }
+
+.reaction-suggestion[data-reaction-suggestion-message]::before, .reaction-s=
+uggestion[data-reaction-suggestion-message]::after { display: inline-block;=
+ text-decoration: none; animation-name: tooltip-appear; animation-duration:=
+ 0.1s; animation-fill-mode: forwards; animation-timing-function: ease-in; a=
+nimation-delay: 0s; }
+
+.reaction-suggestion[data-reaction-suggestion-message]::after { content: at=
+tr(data-reaction-suggestion-message); }
+
+.discussion-topic-header { position: relative; padding: 8px; overflow-wrap:=
+ break-word; }
+
+.comment-form-error { padding: 16px 8px; margin: 8px; color: var(--color-fg=
+-default); background-color: var(--color-danger-subtle); border: 1px solid =
+var(--color-danger-emphasis); border-radius: 6px; }
+
+.email-format { line-height: 1.5em !important; }
+
+.email-format div { white-space: pre-wrap; }
+
+.email-format .email-hidden-reply { display: none; white-space: pre-wrap; }
+
+.email-format .email-hidden-reply.expanded { display: block; }
+
+.email-format .email-quoted-reply, .email-format .email-signature-reply { p=
+adding: 0px 16px; margin: 16px 0px; color: var(--color-fg-muted); border-le=
+ft: 4px solid var(--color-border-default); }
+
+.email-format .email-hidden-toggle a { display: inline-block; height: 12px;=
+ padding: 0px 8px; font-size: 12px; font-weight: var(--base-text-weight-sem=
+ibold, 600); line-height: 6px; color: var(--color-fg-default); text-decorat=
+ion: none; vertical-align: middle; background: var(--color-neutral-muted); =
+border-radius: 1px; }
+
+.email-format .email-hidden-toggle a:hover { background-color: var(--color-=
+accent-muted); }
+
+.email-format .email-hidden-toggle a:active { color: var(--color-fg-on-emph=
+asis); background-color: var(--color-accent-emphasis); }
+
+.comment-email-format div { white-space: normal; }
+
+.comment-email-format .email-hidden-reply { display: none; white-space: nor=
+mal; }
+
+.comment-email-format .email-hidden-reply.expanded { display: block; }
+
+.comment-email-format blockquote, .comment-email-format p { margin: 0px; }
+
+.locked-conversation .write-tab, .locked-conversation .preview-tab { color:=
+ rgb(198, 203, 209); }
+
+.write-tab:focus, .preview-tab:focus { outline-offset: -6px !important; }
+
+.manual-file-chooser-transparent { min-height: 0px; overflow: hidden; opaci=
+ty: 0.01; }
+
+.manual-file-chooser-transparent::-webkit-file-upload-button { cursor: poin=
+ter; }
+
+.manual-file-chooser-transparent:focus { opacity: 1 !important; }
+
+.markdown-body .highlight:hover .zeroclipboard-container, .markdown-body .s=
+nippet-clipboard-content:hover .zeroclipboard-container { display: block; a=
+nimation: 200ms ease 0s 1 normal both running fade-in; }
+
+.markdown-body .highlight .zeroclipboard-container, .markdown-body .snippet=
+-clipboard-content .zeroclipboard-container { display: none; animation: 200=
+ms ease 0s 1 normal both running fade-out; }
+
+.rich-diff clipboard-copy { display: none; }
+
+.css-overflow-wrap-anywhere { overflow-wrap: anywhere; }
+
+.commit-form { position: relative; padding: 16px; border: 1px solid var(--c=
+olor-border-default); border-radius: 6px; }
+
+.commit-form::after, .commit-form::before { position: absolute; top: 11px; =
+right: 100%; left: -8px; display: block; width: 8px; height: 16px; pointer-=
+events: none; content: " "; clip-path: polygon(0px 50%, 100% 0px, 100% 100%=
+); }
+
+.commit-form::after { margin-left: 2px; background-color: var(--color-canva=
+s-default); background-image: linear-gradient(var(--color-canvas-default), =
+var(--color-canvas-default)); }
+
+.commit-form::before { background-color: var(--color-border-default); }
+
+.commit-form .input-block { margin-top: 8px; margin-bottom: 8px; }
+
+.commit-form-avatar { float: left; margin-left: -64px; border-radius: 6px; =
+}
+
+.commit-form-actions::before { display: table; content: ""; }
+
+.commit-form-actions::after { display: table; clear: both; content: ""; }
+
+.commit-form-actions .BtnGroup { margin-right: 4px; }
+
+.merge-commit-message { resize: vertical; }
+
+@media (max-width: 768px) {
+  .commit-form::after, .commit-form::before { display: none !important; }
+}
+
+.commit-sha { padding: 0.2em 0.4em; font-size: 90%; font-weight: var(--base=
+-text-weight-normal, 400); background-color: var(--color-canvas-subtle); bo=
+rder: 1px solid var(--color-border-muted); border-radius: 0.2em; }
+
+.commit .commit-title, .commit .commit-title a { color: var(--color-fg-defa=
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+
+.commit .commit-title.blank, .commit .commit-title.blank a { color: var(--c=
+olor-fg-muted); }
+
+.commit .commit-title .issue-link { font-weight: var(--base-text-weight-sem=
+ibold, 600); color: var(--color-accent-fg); }
+
+.commit .sha-block, .commit .sha { font-family: ui-monospace, SFMono-Regula=
+r, "SF Mono", Menlo, Consolas, "Liberation Mono", monospace; font-size: 12p=
+x; }
+
+.commit.open .commit-desc { display: block; }
+
+.commit-link { font-weight: var(--base-text-weight-normal, 400); color: var=
+(--color-accent-fg); }
+
+.commit-ref { position: relative; display: inline-block; padding: 0px 4px; =
+font: 0.85em / 1.8 ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas=
+, "Liberation Mono", monospace; color: var(--color-fg-muted); white-space: =
+nowrap; background-color: var(--color-accent-subtle); border-radius: 6px; }
+
+.commit-ref .user { color: var(--color-accent-fg); }
+
+a.commit-ref:hover { color: var(--color-accent-fg); text-decoration: none; =
+background-color: var(--color-accent-subtle); }
+
+.commit-desc { display: none; }
+
+.commit-desc pre { max-width: 700px; margin-top: 8px; font-family: ui-monos=
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+ite-space: pre-wrap; }
+
+.commit-desc + .commit-branches { padding-top: 8px; margin-top: 2px; border=
+-top: solid 1px var(--color-border-subtle); }
+
+.commit-author-section { color: var(--color-fg-default); }
+
+.commit-author-section span.user-mention { font-weight: var(--base-text-wei=
+ght-normal, 400); }
+
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+o-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospace; font-s=
+ize: 90%; color: var(--color-fg-default); }
+
+.commits-list-item[aria-selected=3D"true"], .commits-list-item.navigation-f=
+ocus { background: rgb(246, 251, 255); }
+
+.commits-list-item .commit-title { margin: 0px; font-size: 16px; font-weigh=
+t: var(--base-text-weight-semibold, 600); color: var(--color-fg-default); }
+
+.commits-list-item .commit-meta { margin-top: 1px; font-weight: var(--base-=
+text-weight-normal, 400); color: var(--color-fg-muted); }
+
+.commits-list-item .status .octicon { height: 14px; line-height: 14px; }
+
+.commits-list-item .commit-author { color: var(--color-fg-muted); }
+
+.commits-list-item .octicon-arrow-right { margin: 0px 4px; }
+
+.commits-list-item .btn-outline { margin-top: 2px; }
+
+.commits-list-item .commit-desc pre { margin-top: 4px; margin-bottom: 8px; =
+color: var(--color-fg-muted); }
+
+.commits-list-item .commit-desc pre a { word-break: break-word; }
+
+.commit-indicator { margin-left: 4px; }
+
+.commit-links-group { margin-right: 4px; }
+
+.commits-list-item + .commits-list-item { border-top: 1px solid var(--color=
+-border-default); }
+
+.full-commit { padding: 8px 8px 0px; margin: 8px 0px; font-size: 14px; back=
+ground: var(--color-neutral-subtle); border: 1px solid var(--color-border-d=
+efault); border-radius: 6px; }
+
+.full-commit:first-child { margin-top: 0px; }
+
+.full-commit div.commit-title { font-size: 16px; font-weight: var(--base-te=
+xt-weight-semibold, 600); color: var(--color-fg-default); }
+
+.full-commit .branches-list { display: inline; margin-right: 8px; margin-le=
+ft: 2px; vertical-align: middle; list-style: none; }
+
+.full-commit .branches-list li { display: inline-block; padding-left: 4px; =
+font-weight: var(--base-text-weight-semibold, 600); color: var(--color-fg-d=
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+
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+ar(--base-text-weight-normal, 400); content: "+"; }
+
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+
+.full-commit .branches-list li:first-child::before { padding-right: 0px; co=
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+t-normal, 400); color: var(--color-fg-muted); }
+
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+weight-normal, 400); color: var(--color-fg-muted); }
+
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+ntent: ""; }
+
+.full-commit .branches-list li.pull-request-error { margin-bottom: -1px; }
+
+.full-commit .branches-list li a { color: inherit; }
+
+.full-commit .commit-meta { padding: 8px; margin-right: -8px; margin-left: =
+-8px; background: var(--color-canvas-default); border-top: 1px solid var(--=
+color-border-default); border-bottom-right-radius: 6px; border-bottom-left-=
+radius: 6px; }
+
+.full-commit .sha-block { margin-left: 16px; font-size: 12px; line-height: =
+24px; color: var(--color-fg-muted); }
+
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+-top-right-radius: 6px; }
+
+.signed-commit-header .octicon-verified { color: var(--color-success-fg); }
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+x; margin: 0px auto; }
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+
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+: 1px solid var(--color-border-default); }
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+
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+
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+
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+
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+
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+r: var(--color-accent-muted); }
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+
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+
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+
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+var(--color-accent-subtle); border-width: 1px 0px; }
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+
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+
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+und-color: var(--color-diff-blob-addition-word-bg); }
+
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+
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+lor-danger-emphasis); }
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+ne; content: ""; background: var(--color-attention-subtle); mix-blend-mode:=
+ var(--color-diff-blob-selected-line-highlight-mix-blend-mode); }
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+ { border-left: 1px solid var(--color-attention-muted); }
+
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+ { border-right: 1px solid var(--color-attention-muted); }
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+kground: var(--color-accent-emphasis); }
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+ointer; background-color: var(--color-accent-emphasis); border-radius: 6px;=
+ box-shadow: var(--color-shadow-medium); opacity: 0; transition: transform =
+0.1s ease-in-out 0s; transform: scale(0.8, 0.8); }
+
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+
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+
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+; }
+
+.add-line-comment.octicon-check { background: rgb(51, 51, 51); opacity: 1; =
+}
+
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+ 6px; }
+
+.timeline-inline-comments { width: 100%; table-layout: fixed; }
+
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+ents { display: table-row; }
+
+.inline-comments { display: none; }
+
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+
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+
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+
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+: 1px solid var(--color-border-muted); }
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+ight: 20px; padding-left: 20px; }
+
+body.full-width .repository-content { width: 100%; }
+
+body.full-width .new-pr-form { max-width: 980px; }
+
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+
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+r-border-muted); }
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+
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+or-neutral-subtle); border-right-color: var(--color-border-muted); }
+
+@media (max-width: 1280px) {
+  .file-diff-split .write-selected .comment-form-head.tabnav:not(.CommentBo=
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+  .file-diff-split .tabnav:not(.CommentBox-header) markdown-toolbar { posit=
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+ center !important; }
+}
+
+@media (min-width: 1280px) {
+  .file-diff-split .write-selected .comment-form-head.tabnav:not(.CommentBo=
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+}
+
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+; }
+
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+ }
+
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+
+.submodule-diff-stats .octicon-diff-added { color: var(--color-success-fg);=
+ }
+
+.BlobToolbar { left: -17px; }
+
+.BlobToolbar-dropdown { margin-left: -2px; }
+
+.pl-token:hover, .pl-token.active { cursor: pointer; background: var(--colo=
+r-attention-muted); }
+
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+color: var(--color-border-default); }
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+
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+
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+
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+ text-decoration: none; cursor: pointer; }
+
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+
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+
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+
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+
+.discussion-sidebar-toggle .octicon { float: right; color: var(--color-fg-m=
+uted); }
+
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+ration: none; cursor: pointer; }
+
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+
+button.discussion-sidebar-toggle { display: block; width: 100%; font-weight=
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+
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+ 600); color: var(--color-fg-muted); vertical-align: middle; }
+
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+
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+
+.sidebar-assignee .octicon { margin-top: 2px; }
+
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+or-danger-fg); }
+
+.sidebar-notifications { position: relative; }
+
+.sidebar-notifications .thread-subscription-status { padding: 0px; margin: =
+0px; border: 0px; }
+
+.sidebar-notifications .thread-subscription-status .thread-subscribe-form {=
+ display: block; }
+
+.sidebar-notifications .thread-subscription-status .reason { padding: 0px; =
+margin: 4px 0px 0px; }
+
+.sidebar-notifications .thread-subscription-status .btn-sm { display: block=
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+
+.participation .participant-avatar { float: left; margin: 4px 0px 0px 4px; =
+}
+
+.participation a { color: var(--color-fg-muted); }
+
+.participation a:hover { color: var(--color-accent-fg); text-decoration: no=
+ne; }
+
+.participation-avatars { margin-left: -4px; }
+
+.participation-avatars::before { display: table; content: ""; }
+
+.participation-avatars::after { display: table; clear: both; content: ""; }
+
+.participation-more { float: left; margin: 8px 4px 0px; }
+
+.inline-comment-form .form-actions, .timeline-new-comment .form-actions { p=
+adding: 0px 8px 8px; }
+
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+
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+
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+
+.inline-comment-form .form-actions { float: right; }
+
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+
+.gh-header-actions .btn-sm { float: left; margin-left: 4px; }
+
+.gh-header-actions .btn-sm .octicon { margin-right: 0px; }
+
+.gh-header { background-color: var(--color-canvas-default); }
+
+.gh-header .gh-header-sticky { height: 1px; }
+
+.gh-header .gh-header-sticky .meta { font-size: 12px; }
+
+.gh-header .gh-header-sticky .sticky-content, .gh-header .gh-header-sticky =
+.gh-header-shadow { display: none; }
+
+.gh-header .gh-header-sticky.is-stuck { z-index: 110; height: 60px; }
+
+.gh-header .gh-header-sticky.is-stuck .sticky-content { display: block; }
+
+.gh-header .gh-header-sticky.is-stuck .css-truncate-target { max-width: 150=
+px; }
+
+.gh-header .gh-header-sticky.is-stuck + .gh-header-shadow { position: fixed=
+; top: 0px; right: 0px; left: 0px; z-index: 109; display: block; height: 60=
+px; content: ""; background-color: var(--color-canvas-default); border-bott=
+om: 1px solid var(--color-border-default); }
+
+.gh-header .gh-header-edit { display: none; }
+
+.gh-header .gh-header-meta .base-ref { display: inline-block; }
+
+.gh-header .gh-header-meta .commit-ref-dropdown { display: none; }
+
+.gh-header.open .gh-header-show { display: none; }
+
+.gh-header.open .gh-header-edit { display: block; }
+
+.gh-header.open .gh-header-meta .base-ref { display: none; }
+
+.gh-header.open .gh-header-meta .commit-ref-dropdown { display: inline-bloc=
+k; margin-top: -4px; vertical-align: top; }
+
+.gh-header-title { margin-right: 150px; margin-bottom: 0px; font-weight: va=
+r(--base-text-weight-normal, 400); line-height: 1.125; overflow-wrap: break=
+-word; }
+
+.gh-header-no-access .gh-header-title { margin-right: 0px; }
+
+.gh-header-number { font-weight: var(--base-text-weight-light, 300); color:=
+ var(--color-fg-muted); }
+
+.gh-header-meta { padding-bottom: 8px; margin-top: 8px; font-size: 14px; co=
+lor: var(--color-fg-muted); border-bottom: 1px solid var(--color-border-def=
+ault); }
+
+.gh-header.issue .gh-header-meta { margin-bottom: 16px; }
+
+.gh-header.pull .gh-header-meta { padding-bottom: 0px; border-bottom: 0px; =
+}
+
+.gh-header-meta .commit-ref .css-truncate-target, .gh-header-meta .commit-r=
+ef:hover .css-truncate-target { max-width: 80vw; }
+
+.gh-header-meta .State { margin-right: 8px; }
+
+.gh-header-meta .avatar { float: left; margin-top: -4px; margin-right: 4px;=
+ }
+
+.timeline-comment-wrapper { position: relative; padding-left: 56px; margin-=
+top: 16px; margin-bottom: 16px; }
+
+.timeline-comment-avatar { float: left; margin-left: -56px; border-radius: =
+6px; }
+
+.timeline-comment-avatar .avatar { width: 40px; height: 40px; }
+
+.timeline-comment-avatar .avatar-child { width: 20px; height: 20px; }
+
+.timeline-comment { position: relative; color: var(--color-fg-default); bac=
+kground-color: var(--color-canvas-default); border: 1px solid var(--color-b=
+order-default); border-radius: 6px; }
+
+.timeline-comment.will-transition-once { transition: border-color 0.65s eas=
+e-in-out 0s; }
+
+.timeline-comment.will-transition-once .timeline-comment-header { transitio=
+n: background-color 0.65s ease 0s, border-bottom-color 0.65s ease-in-out 0s=
+; }
+
+.timeline-comment.will-transition-once::before, .timeline-comment.will-tran=
+sition-once::after { transition: border-right-color 0.65s ease-in-out 0s; }
+
+.timeline-comment.current-user { border-color: var(--color-accent-muted); }
+
+.timeline-comment.current-user .timeline-comment-header { background-color:=
+ var(--color-accent-subtle); border-bottom-color: var(--color-accent-muted)=
+; }
+
+.timeline-comment.current-user .Label { border-color: var(--color-accent-mu=
+ted); }
+
+.timeline-comment.current-user .previewable-comment-form .comment-form-head=
+.tabnav { color: var(--color-accent-muted); background-color: var(--color-a=
+ccent-subtle); border-bottom-color: var(--color-accent-muted); }
+
+.timeline-comment.unread-item, .timeline-comment.is-internal { border-color=
+: var(--color-attention-muted); }
+
+.timeline-comment.unread-item .timeline-comment-header, .timeline-comment.i=
+s-internal .timeline-comment-header { background-color: var(--color-attenti=
+on-subtle); border-bottom-color: var(--color-attention-muted); }
+
+.timeline-comment.unread-item .Label, .timeline-comment.is-internal .Label =
+{ border-color: var(--color-attention-muted); }
+
+.timeline-comment.unread-item .previewable-comment-form .comment-form-head.=
+tabnav, .timeline-comment.is-internal .previewable-comment-form .comment-fo=
+rm-head.tabnav { color: var(--color-attention-muted); background-color: var=
+(--color-attention-subtle); border-bottom-color: var(--color-attention-mute=
+d); }
+
+.timeline-comment:empty { display: none; }
+
+.timeline-comment .comment + .comment { border-top: 1px solid var(--color-b=
+order-default); }
+
+.timeline-comment .comment + .comment::before, .timeline-comment .comment +=
+ .comment::after { display: none; }
+
+.timeline-comment .comment + .comment .timeline-comment-header { border-top=
+-left-radius: 0px; border-top-right-radius: 0px; }
+
+.timeline-comment--caret::after, .timeline-comment--caret::before { positio=
+n: absolute; top: 11px; right: 100%; left: -8px; display: block; width: 8px=
+; height: 16px; pointer-events: none; content: " "; clip-path: polygon(0px =
+50%, 100% 0px, 100% 100%); }
+
+.timeline-comment--caret::after { margin-left: 2px; background-color: var(-=
+-color-canvas-default); background-image: linear-gradient(var(--color-canva=
+s-subtle), var(--color-canvas-subtle)); }
+
+.timeline-comment--caret::before { background-color: var(--color-border-def=
+ault); }
+
+.is-pending .timeline-comment--caret::after, .is-pending .timeline-comment-=
+-caret::before { position: absolute; top: 11px; right: 100%; left: -8px; di=
+splay: block; width: 8px; height: 16px; pointer-events: none; content: " ";=
+ clip-path: polygon(0px 50%, 100% 0px, 100% 100%); }
+
+.is-pending .timeline-comment--caret::after { margin-left: 2px; background-=
+color: var(--color-canvas-default); background-image: linear-gradient(var(-=
+-color-attention-subtle), var(--color-attention-subtle)); }
+
+.is-pending .timeline-comment--caret::before { background-color: var(--colo=
+r-attention-emphasis); }
+
+.timeline-comment--caret.current-user::after, .timeline-comment--caret.curr=
+ent-user::before { position: absolute; top: 11px; right: 100%; left: -8px; =
+display: block; width: 8px; height: 16px; pointer-events: none; content: " =
+"; clip-path: polygon(0px 50%, 100% 0px, 100% 100%); }
+
+.timeline-comment--caret.current-user::after { margin-left: 2px; background=
+-color: var(--color-canvas-default); background-image: linear-gradient(var(=
+--color-accent-subtle), var(--color-accent-subtle)); }
+
+.timeline-comment--caret.current-user::before { background-color: var(--col=
+or-accent-muted); }
+
+.timeline-comment--caret.unread-item::after, .timeline-comment--caret.unrea=
+d-item::before, .timeline-comment--caret.is-internal::after, .timeline-comm=
+ent--caret.is-internal::before { position: absolute; top: 11px; right: 100%=
+; left: -8px; display: block; width: 8px; height: 16px; pointer-events: non=
+e; content: " "; clip-path: polygon(0px 50%, 100% 0px, 100% 100%); }
+
+.timeline-comment--caret.unread-item::after, .timeline-comment--caret.is-in=
+ternal::after { margin-left: 2px; background-color: var(--color-canvas-defa=
+ult); background-image: linear-gradient(var(--color-attention-subtle), var(=
+--color-attention-subtle)); }
+
+.timeline-comment--caret.unread-item::before, .timeline-comment--caret.is-i=
+nternal::before { background-color: var(--color-attention-muted); }
+
+.timeline-comment--caret.timeline-comment--caret-nw::before, .timeline-comm=
+ent--caret.timeline-comment--caret-nw::after { transform: rotate(90deg); }
+
+.timeline-comment--caret.timeline-comment--caret-nw::before { top: -12px; l=
+eft: 12px; }
+
+.timeline-comment--caret.timeline-comment--caret-nw::after { top: -10px; le=
+ft: 11px; }
+
+.page-responsive .timeline-comment--caret::before, .page-responsive .timeli=
+ne-comment--caret::after { display: none; }
+
+@media (min-width: 768px) {
+  .page-responsive .timeline-comment--caret::before, .page-responsive .time=
+line-comment--caret::after { display: block; }
+}
+
+:target .timeline-comment--caret::before { background-color: var(--color-ac=
+cent-fg); }
+
+:target .timeline-comment--caret::after { margin-left: 2px !important; }
+
+:target .timeline-comment { border-color: var(--color-accent-fg); outline: =
+none !important; box-shadow: 0 0 0 1px var(--color-accent-fg)  !important; =
+}
+
+.review-comment:target { border-radius: 6px; outline: none !important; box-=
+shadow: 0 0 0 1px var(--color-accent-fg)  !important; }
+
+.timeline-comment-header { display: flex; align-items: center; padding-righ=
+t: 16px; padding-left: 16px; color: var(--color-fg-muted); flex-direction: =
+row-reverse; background-color: var(--color-canvas-subtle); border-bottom: 1=
+px solid var(--color-border-default); border-top-left-radius: 6px; border-t=
+op-right-radius: 6px; }
+
+.timeline-comment-header:only-child { border-bottom: 0px; border-radius: 6p=
+x; }
+
+.timeline-comment-header .author { color: var(--color-fg-muted); }
+
+.timeline-comment-header code { word-break: break-all; }
+
+.timeline-comment-header-text { min-width: 0px; padding-top: 8px; padding-b=
+ottom: 8px; margin-bottom: 1px; flex: 1 1 auto; }
+
+.timeline-comment-header-text code a { color: var(--color-fg-muted); }
+
+.timeline-comment-actions { float: right; margin-left: 8px; }
+
+.timeline-comment-actions .show-more-popover.dropdown-menu-sw { right: -6px=
+; margin-top: -4px; }
+
+.timeline-comment-action { display: inline-block; padding: 8px 4px; color: =
+var(--color-fg-muted); }
+
+.timeline-comment-action:hover, .timeline-comment-action:focus { color: var=
+(--color-accent-fg); text-decoration: none; opacity: 1; }
+
+.timeline-comment-action .octicon-check { height: 16px; }
+
+.timeline-comment-action.disabled { color: var(--color-fg-muted); cursor: d=
+efault; }
+
+.timeline-comment-action.disabled:hover { color: var(--color-fg-muted); }
+
+.timeline-new-comment { margin-bottom: 0px; }
+
+.timeline-new-comment .comment-form-head { margin-bottom: 8px; }
+
+.timeline-new-comment .previewable-comment-form .comment-body { padding-top=
+: 0px; }
+
+.comment-form-head .toolbar-commenting { float: right; }
+
+.discussion-item-icon { float: left; width: 32px; height: 32px; margin-top:=
+ -4px; margin-left: -39px; line-height: 28px; color: var(--color-fg-muted);=
+ text-align: center; background-color: var(--color-timeline-badge-bg); bord=
+er: 2px solid var(--color-canvas-default); border-radius: 50%; }
+
+.discussion-item-header { color: var(--color-fg-muted); overflow-wrap: brea=
+k-word; }
+
+.discussion-item-header .discussion-item-private { vertical-align: -1px; }
+
+.discussion-item-header:last-child { padding-bottom: 0px; }
+
+.discussion-item-header .commit-ref { font-size: 85%; vertical-align: basel=
+ine; }
+
+.discussion-item-header .btn-outline { float: right; padding: 4px 8px; marg=
+in-top: -4px; margin-left: 8px; }
+
+.discussion-item-private { color: var(--color-fg-muted); }
+
+.previewable-comment-form .comment-form-head.tabnav { padding: 8px 8px 0px;=
+ background: var(--color-canvas-subtle); border-radius: 6px 6px 0px 0px; }
+
+.page-responsive .previewable-comment-form .comment-form-head.tabnav .toolb=
+ar-commenting { background: var(--color-canvas-default); }
+
+@media (min-width: 1012px) {
+  .page-responsive .previewable-comment-form .comment-form-head.tabnav .too=
+lbar-commenting { background: transparent; }
+}
+
+@media (min-width: 768px) {
+  .page-responsive .previewable-comment-form .comment-form-head.tabnav { ba=
+ckground: var(--color-canvas-subtle); }
+}
+
+.previewable-comment-form .comment { border: 0px; }
+
+.previewable-comment-form .comment-body { padding: 8px; background-color: t=
+ransparent; border-bottom: 1px solid var(--color-border-default); }
+
+.previewable-comment-form .timeline-comment .timeline-comment-actions { dis=
+play: none; }
+
+.new-discussion-timeline .composer .timeline-comment { margin-bottom: 8px; =
+}
+
+.new-discussion-timeline .composer .comment-form-head.tabnav { padding-top:=
+ 0px; background-color: var(--color-canvas-default); }
+
+.composer.composer-responsive { padding-left: 0px; }
+
+.composer.composer-responsive .discussion-topic-header { padding: 0px; }
+
+.composer.composer-responsive .timeline-comment { border: 0px; }
+
+.composer.composer-responsive .timeline-comment::before, .composer.composer=
+-responsive .timeline-comment::after { display: none; }
+
+.composer.composer-responsive .previewable-comment-form .write-content { ma=
+rgin: 0px; }
+
+@media (min-width: 768px) {
+  .composer.composer-responsive { padding-left: 56px; }
+  .composer.composer-responsive .timeline-comment { border: 1px solid var(-=
+-color-border-default); }
+  .composer.composer-responsive .timeline-comment::after, .composer.compose=
+r-responsive .timeline-comment::before { position: absolute; top: 11px; rig=
+ht: 100%; left: -8px; display: block; width: 8px; height: 16px; pointer-eve=
+nts: none; content: " "; clip-path: polygon(0px 50%, 100% 0px, 100% 100%); =
+}
+  .composer.composer-responsive .timeline-comment::after { margin-left: 2px=
+; background-color: var(--color-canvas-default); background-image: linear-g=
+radient(var(--color-canvas-default), var(--color-canvas-default)); }
+  .composer.composer-responsive .timeline-comment::before { background-colo=
+r: var(--color-border-default); }
+}
+
+.discussion-timeline-actions { background-color: var(--color-canvas-default=
+); border-top: 2px solid var(--color-border-default); }
+
+.discussion-timeline-actions .merge-pr { padding-top: 0px; border-top: 0px;=
+ }
+
+.discussion-timeline-actions .thread-subscription-status { margin-top: 16px=
+; }
+
+.pagination-loader-container { background-color: var(--color-canvas-default=
+); background-image: url("/images/modules/pulls/progressive-disclosure-line=
+.svg"); background-repeat: repeat-x; background-position: center center; ba=
+ckground-size: 16px; }
+
+[data-color-mode=3D"light"][data-light-theme*=3D"dark"] .pagination-loader-=
+container, [data-color-mode=3D"dark"][data-dark-theme*=3D"dark"] .paginatio=
+n-loader-container { background-image: url("/images/modules/pulls/progressi=
+ve-disclosure-line-dark.svg"); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] .pagination-loader=
+-container { background-image: url("/images/modules/pulls/progressive-discl=
+osure-line-dark.svg"); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] .pagination-loader-=
+container { background-image: url("/images/modules/pulls/progressive-disclo=
+sure-line-dark.svg"); }
+}
+
+:target .timeline-comment-group .timeline-comment .timeline-comment-group .=
+timeline-comment { box-shadow: none !important; }
+
+.is-pending .form-actions { margin-right: 8px; margin-bottom: 4px; }
+
+.is-pending .file, .is-pending .file-header, .is-pending .tabnav-tab.select=
+ed, .is-pending .comment-form-head.tabnav { border-color: var(--color-atten=
+tion-emphasis); }
+
+.is-pending .file-header, .is-pending .comment-form-head.tabnav { backgroun=
+d-color: var(--color-attention-subtle); }
+
+.discussion-item-icon-gray { background-color: var(--color-timeline-badge-b=
+g)  !important; }
+
+.footer-octicon { color: var(--color-fg-subtle); }
+
+.footer-octicon:hover { color: var(--color-fg-muted); }
+
+.user-mention, .team-mention { font-weight: var(--base-text-weight-semibold=
+, 600); color: var(--color-fg-default); white-space: nowrap; }
+
+.Popover .user-mention, .Popover .team-mention { white-space: normal; }
+
+@media (max-width: 543px) {
+  .notifications-component-menu-modal { margin: calc(10vh - 16px) 0px; }
+}
+
+@media (min-width: 544px) {
+  .notifications-component-menu-modal, .notifications-component-dialog, .no=
+tifications-component-dialog-modal { width: 100%; }
+}
+
+@media (min-width: 768px) {
+  .notifications-component-menu-modal, .notifications-component-dialog, .no=
+tifications-component-dialog-modal { min-width: 300px; }
+}
+
+.notifications-component-dialog:not([hidden]) + .notifications-component-di=
+alog-overlay { position: fixed; inset: 0px; z-index: 80; display: block; cu=
+rsor: default; content: " "; background: var(--color-primer-canvas-backdrop=
+); }
+
+@media (min-width: 544px) {
+  .notifications-component-dialog:not([hidden]) + .notifications-component-=
+dialog-overlay { display: none; }
+}
+
+.notifications-component-dialog { z-index: 99; animation: 0s ease 0s 1 norm=
+al none running none; }
+
+@keyframes notifications-component-dialog-animation--sm {=20
+  0% { opacity: 0; transform: translateX(16px); }
+}
+
+@media (min-width: 544px) {
+  .notifications-component-dialog { position: absolute; inset: auto; max-he=
+ight: none; padding-top: 0px; margin: 0px; transform: none; }
+}
+
+.notifications-component-dialog .notifications-component-dialog-modal { ani=
+mation: 0s ease 0s 1 normal none running none; }
+
+.pagehead { position: relative; padding-top: 24px; padding-bottom: 24px; ma=
+rgin-bottom: 24px; border-bottom: 1px solid var(--color-border-default); }
+
+.pagehead.admin { background: url("/images/modules/pagehead/background-yell=
+owhatch-v3.png") 0px 0px repeat-x; }
+
+.pagehead ul.pagehead-actions { position: relative; z-index: 31; float: rig=
+ht; margin: 0px; }
+
+.pagehead .path-divider { margin: 0px 0.25em; }
+
+.pagehead h1 { min-height: 32px; margin-top: 0px; margin-bottom: 0px; font-=
+size: 20px; font-weight: var(--base-text-weight-normal, 400); }
+
+.pagehead h1 .avatar { margin-top: -2px; margin-right: 8px; margin-bottom: =
+-2px; }
+
+.pagehead .underline-nav { height: 69px; margin-top: -16px; margin-bottom: =
+-16px; }
+
+.pagehead-heading { color: inherit; }
+
+.pagehead-actions > li { float: left; margin: 0px 8px 0px 0px; font-size: 1=
+2px; color: var(--color-fg-default); list-style-type: none; }
+
+.pagehead-actions > li:last-child { margin-right: 0px; }
+
+.pagehead-actions .octicon-mute { color: var(--color-danger-fg); }
+
+.pagehead-actions .select-menu { position: relative; }
+
+.pagehead-actions .select-menu::before { display: table; content: ""; }
+
+.pagehead-actions .select-menu::after { display: table; clear: both; conten=
+t: ""; }
+
+.pagehead-actions .select-menu-modal-holder { top: 100%; }
+
+.pagehead-tabs-item { float: left; padding: 8px 16px 12px; color: var(--col=
+or-fg-muted); white-space: nowrap; border-style: solid; border-color: trans=
+parent; border-image: initial; border-width: 3px 1px 1px; border-radius: 6p=
+x 6px 0px 0px; }
+
+.pagehead-tabs-item .octicon { color: var(--color-fg-muted); }
+
+.pagehead-tabs-item:hover { color: var(--color-fg-default); text-decoration=
+: none; }
+
+.pagehead-tabs-item.selected { font-weight: var(--base-text-weight-semibold=
+, 600); color: var(--color-fg-default); background-color: var(--color-canva=
+s-default); border-color: var(--color-severe-emphasis) var(--color-border-d=
+efault) transparent; }
+
+.pagehead-tabs-item.selected > .octicon { color: inherit; }
+
+.reponav { position: relative; top: 1px; margin-top: -4px; }
+
+.reponav::before { display: table; content: ""; }
+
+.reponav::after { display: table; clear: both; content: ""; }
+
+.reponav-item { float: left; padding: 8px 16px; color: var(--color-fg-muted=
+); white-space: nowrap; border-style: solid; border-color: transparent; bor=
+der-image: initial; border-width: 3px 1px 1px; border-radius: 6px 6px 0px 0=
+px; }
+
+.reponav-item .octicon { color: var(--color-fg-muted); }
+
+.reponav-item:hover, .reponav-item:focus { color: var(--color-fg-default); =
+text-decoration: none; }
+
+.reponav-item.selected { color: var(--color-fg-default); background-color: =
+var(--color-canvas-default); border-color: var(--color-severe-emphasis) var=
+(--color-border-default) transparent; }
+
+.reponav-item.selected .octicon { color: inherit; }
+
+.reponav-wrapper { position: relative; z-index: 2; overflow-y: hidden; back=
+ground-color: var(--color-neutral-emphasis); }
+
+.reponav-wrapper .reponav { top: 0px; padding-right: 8px; padding-left: 8px=
+; margin-top: 0px; overflow-x: auto; color: rgba(255, 255, 255, 0.75); }
+
+.reponav-wrapper .reponav-item { display: inline-block; float: none; paddin=
+g: 4px 8px 16px; color: var(--color-fg-muted); border: 0px; }
+
+.reponav-wrapper .reponav-item.selected { font-weight: var(--base-text-weig=
+ht-semibold, 600); color: var(--color-fg-default); background-color: transp=
+arent; border: 0px; }
+
+@media (max-width: 768px) {
+  .PageLayout--responsive-separateRegions.PageLayout--responsive-primary-pa=
+ne .ActionList-item.ActionList-item--navActive:not(.ActionList-item--danger=
+) { background-color: transparent; }
+  .PageLayout--responsive-separateRegions.PageLayout--responsive-primary-pa=
+ne .ActionList-item.ActionList-item--navActive:not(.ActionList-item--subIte=
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+); }
+  .PageLayout--responsive-separateRegions.PageLayout--responsive-primary-pa=
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+  .PageLayout--responsive-separateRegions.PageLayout--responsive-primary-pa=
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+ }
+}
+
+.steps { display: table; width: 100%; padding: 0px; margin: 32px auto 0px; =
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+
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+
+.steps li.current { color: var(--color-fg-default); background-color: var(-=
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+
+.steps li.current .octicon { color: var(--color-accent-fg); }
+
+.steps li .octicon { float: left; margin-right: 16px; margin-bottom: 4px; }
+
+.steps li .step { display: block; }
+
+.steps li:first-child { border-left: 0px; }
+
+.steps .complete { color: var(--color-fg-muted); }
+
+.steps .complete .octicon { color: var(--color-success-fg); }
+
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+
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+
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+
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+
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+
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+
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+
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+
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+r-success-muted); }
+
+.prose-diff > .markdown-body .github-user-ins { text-decoration: underline;=
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+
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+; background: var(--color-danger-subtle); }
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+
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+
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+
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+
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+
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+
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+
+.prose-diff > .markdown-body .added, .prose-diff > .markdown-body ins + .ad=
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+
+.prose-diff > .markdown-body .added:not(.github-user-del):not(.github-user-=
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+
+.prose-diff > .markdown-body img.added, .prose-diff > .markdown-body img.re=
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+
+.prose-diff > .markdown-body ins pre:not(.github-user-del):not(.github-user=
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+:not(.github-user-ins) { text-decoration: none; }
+
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+
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+oved > li { margin-top: 0px; margin-bottom: 0px; }
+
+span.changed_tag, em.changed_tag, strong.changed_tag, b.changed_tag, i.chan=
+ged_tag, code.changed_tag { border-bottom: 1px dotted var(--color-border-de=
+fault); border-radius: 0px; }
+
+a.added_href, a.changed_href, span.removed_href { border-bottom: 1px dotted=
+ var(--color-border-default); border-radius: 0px; }
+
+.diff-view .file-type-prose .rich-diff { display: none; }
+
+.diff-view .display-rich-diff .rich-diff { display: block; }
+
+.diff-view .display-rich-diff .file-diff { display: none; }
+
+.protip { margin-top: 16px; color: var(--color-fg-muted); text-align: cente=
+r; }
+
+.protip strong { color: var(--color-fg-default); }
+
+.protip code { padding: 2px; background-color: var(--color-canvas-subtle); =
+border-radius: 6px; }
+
+.add-reactions-options-item { margin-top: -1px; margin-right: -1px; line-he=
+ight: 29px; border: 1px solid transparent; }
+
+.add-reactions-options-item .emoji { display: inline-block; transition: tra=
+nsform 0.15s cubic-bezier(0.2, 0, 0.13, 2) 0s; }
+
+.add-reactions-options-item:hover .emoji, .add-reactions-options-item:focus=
+ .emoji { text-decoration: none !important; transform: scale(1.2) !importan=
+t; }
+
+.add-reactions-options-item:active { background-color: var(--color-accent-s=
+ubtle); }
+
+.page-responsive .add-reactions-options-item { height: 20vw; }
+
+@media (min-width: 544px) {
+  .page-responsive .add-reactions-options-item { height: auto; }
+}
+
+.comment-reactions { display: none; }
+
+.comment-reactions::before { display: table; content: ""; }
+
+.comment-reactions::after { display: table; clear: both; content: ""; }
+
+.page-responsive .comment-reactions { display: none; }
+
+@media (min-width: 768px) {
+  .page-responsive .comment-reactions { display: none; }
+  .page-responsive .comment-reactions.has-reactions { display: flex; }
+}
+
+.comment-reactions.has-reactions { display: flex; }
+
+.comment-reactions.has-reactions:not(.social-reactions) { border-top: 1px s=
+olid var(--color-border-default); }
+
+.comment-reactions .user-has-reacted { background-color: var(--color-accent=
+-subtle); }
+
+.reactions-container .user-has-reacted { background-color: var(--color-acce=
+nt-subtle); }
+
+[data-color-mode=3D"light"][data-light-theme*=3D"dark"], [data-color-mode=
+=3D"dark"][data-dark-theme*=3D"dark"] { --color-social-reaction-bg-hover:va=
+r(--color-scale-gray-7); --color-social-reaction-bg-reacted-hover:var(--col=
+or-scale-blue-8); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] { --color-social-r=
+eaction-bg-hover:var(--color-scale-gray-7); --color-social-reaction-bg-reac=
+ted-hover:var(--color-scale-blue-8); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] { --color-social-re=
+action-bg-hover:var(--color-scale-gray-7); --color-social-reaction-bg-react=
+ed-hover:var(--color-scale-blue-8); }
+}
+
+:root, [data-color-mode=3D"light"][data-light-theme*=3D"light"], [data-colo=
+r-mode=3D"dark"][data-dark-theme*=3D"light"] { --color-social-reaction-bg-h=
+over:var(--color-scale-gray-1); --color-social-reaction-bg-reacted-hover:va=
+r(--color-scale-blue-1); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"light"] { --color-social-=
+reaction-bg-hover:var(--color-scale-gray-1); --color-social-reaction-bg-rea=
+cted-hover:var(--color-scale-blue-1); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"light"] { --color-social-r=
+eaction-bg-hover:var(--color-scale-gray-1); --color-social-reaction-bg-reac=
+ted-hover:var(--color-scale-blue-1); }
+}
+
+.social-reaction-summary-item + .social-reaction-summary-item { margin-left=
+: 8px; }
+
+.social-reactions .comment-body { margin-left: 16px !important; }
+
+.social-button-emoji { display: inline-block; width: 16px; height: 16px; li=
+ne-height: 1.25; vertical-align: -1px; font-size: 1em !important; }
+
+.social-reaction-summary-item { height: 26px; margin-right: 0px; font-size:=
+ 12px; line-height: 26px; background-color: transparent; border: 1px solid =
+var(--color-border-default, #d2dff0); border-radius: 100px; padding: 0px 4p=
+x !important; }
+
+.social-reaction-summary-item:focus, .social-reaction-summary-item:focus-vi=
+sible { border-radius: 100px !important; }
+
+.social-reaction-summary-item:focus { border-color: var(--color-accent-fg);=
+ outline: none; box-shadow: inset 0 0 0 1px var(--color-accent-fg); }
+
+.social-reaction-summary-item:focus:not(:focus-visible) { border-color: var=
+(--color-accent-fg); outline: none; box-shadow: transparent 0px 0px 0px 1px=
+ inset; }
+
+.social-reaction-summary-item:focus-visible { border-color: var(--color-acc=
+ent-fg); outline: none; box-shadow: inset 0 0 0 1px var(--color-accent-fg);=
+ }
+
+.social-reaction-summary-item.user-has-reacted { background-color: var(--co=
+lor-accent-subtle); border: 1px solid var(--color-accent-emphasis)  !import=
+ant; }
+
+.social-reaction-summary-item.user-has-reacted:hover { background-color: va=
+r(--color-social-reaction-bg-reacted-hover)  !important; }
+
+.social-reaction-summary-item > span { height: 24px; padding: 0px 4px; marg=
+in-left: 2px; }
+
+.social-reaction-summary-item:hover { background-color: var(--color-social-=
+reaction-bg-hover); }
+
+.reaction-dropdown-button { color: var(--color-fg-muted); }
+
+.reaction-dropdown-button:hover { color: var(--color-accent-fg); }
+
+.reaction-dropdown-button--inline { width: 26px; height: 26px; }
+
+.reaction-dropdown-button--inline:hover { background-color: var(--color-btn=
+-hover-bg)  !important; border-color: var(--color-btn-hover-border)  !impor=
+tant; }
+
+.reaction-dropdown-button:disabled { pointer-events: none; color: var(--col=
+or-primer-fg-disabled)  !important; }
+
+.reactions-with-gap .comment .comment-reactions { margin-left: 16px; border=
+-top: 0px !important; }
+
+.new-reactions-dropdown .dropdown-menu-reactions { width: auto; padding: 0p=
+x 2px; }
+
+.new-reactions-dropdown .dropdown-menu-reactions::before, .new-reactions-dr=
+opdown .dropdown-menu-reactions::after { background-color: transparent; bor=
+der: 0px; }
+
+.new-reactions-dropdown .dropdown-item-reaction { width: 32px; height: 32px=
+; padding: 4px; margin: 4px 2px; }
+
+.new-reactions-dropdown .dropdown-item-reaction.user-has-reacted { backgrou=
+nd-color: var(--color-accent-subtle); }
+
+.new-reactions-dropdown .dropdown-item-reaction:hover { background-color: v=
+ar(--color-btn-hover-bg); }
+
+.RecentBranches { background-color: var(--color-attention-subtle); border: =
+1px solid var(--color-attention-emphasis); border-radius: 6px; }
+
+.RecentBranches-item { line-height: 28px; color: var(--color-fg-default); }
+
+.RecentBranches-item + .RecentBranches-item { border-top: 1px solid var(--c=
+olor-attention-emphasis); }
+
+.RecentBranches-item-link { color: var(--color-fg-default); }
+
+.RecentBranches-item-link.css-truncate-target { max-width: 400px; }
+
+.render-container { padding: 32px; line-height: 0; text-align: center; back=
+ground: var(--color-canvas-subtle); border-bottom-right-radius: 6px; border=
+-bottom-left-radius: 6px; }
+
+.render-container .render-viewer { display: block; width: 1px; height: 1px;=
+ border: 0px; }
+
+.render-container .octospinner { display: none; }
+
+.render-container .render-viewer-error, .render-container .render-viewer-fa=
+tal, .render-container .render-viewer-invalid, .render-container .render-fu=
+llscreen { display: none; }
+
+.render-container.is-render-automatic .octospinner { display: inline-block;=
+ }
+
+.render-container.is-render-requested .octospinner { display: inline-block;=
+ }
+
+.render-container.is-render-requested.is-render-failed .render-viewer-error=
+ { display: inline-block; }
+
+.render-container.is-render-requested.is-render-failed .render-viewer, .ren=
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+, .render-container.is-render-requested.is-render-failed .octospinner { dis=
+play: none; }
+
+.render-container.is-render-requested.is-render-failed-fatal .render-viewer=
+-fatal { display: inline-block; }
+
+.render-container.is-render-requested.is-render-failed-fatal .render-viewer=
+, .render-container.is-render-requested.is-render-failed-fatal .render-view=
+er-error, .render-container.is-render-requested.is-render-failed-fatal .ren=
+der-viewer-invalid, .render-container.is-render-requested.is-render-failed-=
+fatal .octospinner { display: none; }
+
+.render-container.is-render-requested.is-render-failed-invalid .render-view=
+er-invalid { display: inline-block; }
+
+.render-container.is-render-requested.is-render-failed-invalid .render-view=
+er, .render-container.is-render-requested.is-render-failed-invalid .render-=
+viewer-error, .render-container.is-render-requested.is-render-failed-invali=
+d .render-viewer-fatal, .render-container.is-render-requested.is-render-fai=
+led-invalid .octospinner { display: none; }
+
+.render-container.is-render-ready.is-render-requested:not(.is-render-failed=
+) { height: 500px; padding: 0px; background: none; }
+
+.render-container.is-render-ready.is-render-requested:not(.is-render-failed=
+) .render-viewer { width: 100%; height: 100%; }
+
+.render-container.is-render-ready.is-render-requested:not(.is-render-failed=
+) .render-fullscreen { display: flex; }
+
+.render-container.is-render-ready.is-render-requested:not(.is-render-failed=
+) .render-viewer-error, .render-container.is-render-ready.is-render-request=
+ed:not(.is-render-failed) .render-viewer-fatal, .render-container.is-render=
+-ready.is-render-requested:not(.is-render-failed) .octospinner { display: n=
+one; }
+
+.render-needs-enrichment { margin-bottom: 16px; }
+
+.render-needs-enrichment .render-full-screen { width: 100%; height: auto; p=
+adding: 16px; overflow: auto; }
+
+.render-needs-enrichment .render-full-screen-close { top: 0px; right: 0px; =
+padding: 4px; }
+
+.render-needs-enrichment .details { margin-bottom: 0px; }
+
+.render-needs-enrichment .render-plaintext-hidden { display: none; }
+
+.render-notice { padding: 16px; font-size: 14px; color: var(--color-fg-defa=
+ult); background-color: var(--color-canvas-subtle); border-color: var(--col=
+or-border-subtle); }
+
+relative-time { white-space: nowrap; }
+
+.js-inline-math > mjx-container { overflow: auto hidden; }
+
+math-renderer mjx-labels { right: 0px; left: auto; }
+
+.Skeleton { color: rgba(0, 0, 0, 0); background-image: linear-gradient(270d=
+eg, rgba(0, 0, 0, 0.1), rgba(0, 0, 0, 0.05), rgba(0, 0, 0, 0.05), rgba(0, 0=
+, 0, 0.1)); background-size: 400% 100%; animation: 8s ease-in-out 0s infini=
+te normal none running skeleton-loading; }
+
+.Skeleton * { visibility: hidden; }
+
+.Skeleton--text { clip-path: inset(4px 0px round 3px); }
+
+.is-error .Skeleton { display: none; }
+
+@keyframes skeleton-loading {=20
+  0% { background-position: 200% 0px; }
+  100% { background-position: -200% 0px; }
+}
+
+.authors-2 .AvatarStack { min-width: 36px !important; }
+
+.authors-3 .AvatarStack { min-width: 46px !important; }
+
+[aria-selected=3D"true"] .AvatarStack-body, .navigation-focus .AvatarStack-=
+body { background: rgb(246, 251, 255); }
+
+.tracked-in-parent-pill { position: relative; cursor: default; }
+
+.tracked-in-parent-pill-truncated { position: absolute; left: 100%; display=
+: none; white-space: nowrap; background: var(--color-canvas-default); borde=
+r-left-width: 0px !important; border-top-left-radius: 0px !important; borde=
+r-bottom-left-radius: 0px !important; }
+
+.tracked-in-parent-pill:hover .tracked-in-parent-pill-truncated { display: =
+block; }
+
+.wizard-content.horizontal { flex-direction: column; }
+
+.wizard-content.horizontal .wizard-horizontal-steps { width: 30%; }
+
+.wizard-content.horizontal .wizard-horizontal-steps .wizard-step-badge { ma=
+rgin: 0px; color: var(--color-fg-subtle); background-color: var(--color-can=
+vas-inset); border-color: var(--color-fg-subtle); }
+
+.wizard-content.horizontal .wizard-horizontal-steps .wizard-step-badge.curr=
+ent { color: var(--color-fg-on-emphasis); background-color: var(--color-acc=
+ent-emphasis); border-color: var(--color-accent-emphasis); }
+
+.wizard-content.horizontal .wizard-horizontal-steps .wizard-step-bar { bord=
+er-color: var(--color-fg-subtle); }
+
+.wizard-content.horizontal .wizard-horizontal-steps .wizard-step-bar.comple=
+te { border-color: var(--color-accent-emphasis); }
+
+.wizard-content.horizontal .wizard-horizontal-steps .wizard-step-badge.comp=
+lete { display: none; }
+
+.wizard-content.horizontal .wizard-horizontal-steps .wizard-step-badge-chec=
+k { display: none !important; }
+
+.wizard-content.horizontal .wizard-horizontal-steps .wizard-step-badge-chec=
+k.complete { display: inherit !important; width: 32px !important; height: 3=
+2px !important; }
+
+.wizard-content.horizontal .wizard-step[data-single-page-wizard-last-step=
+=3D"true"] .wizard-step-buttons { align-items: center; justify-content: cen=
+ter; }
+
+.wizard-content.horizontal .wizard-step-item { margin-left: 0px !important;=
+ flex-direction: column !important; }
+
+.wizard-content.horizontal .wizard-step-item::before, .wizard-content.horiz=
+ontal .wizard-step-item .wizard-step-badge, .wizard-content.horizontal .wiz=
+ard-step-item .wizard-step-icon, .wizard-content.horizontal .wizard-step-it=
+em .wizard-step-header { display: none !important; }
+
+.wizard-content.horizontal .wizard-step-item .wizard-step-container::before=
+, .wizard-content.horizontal .wizard-step-item .wizard-step-container::afte=
+r { display: none !important; }
+
+.wizard-step-item { position: relative; padding: 8px 0px; margin-left: 16px=
+; flex-direction: row; }
+
+.wizard-step-item::before { position: absolute; top: 32px; bottom: 0px; lef=
+t: 0px; display: block; width: 2px; height: 100%; content: ""; background-c=
+olor: var(--color-border-default); }
+
+.wizard-step-badge { position: relative; z-index: 1; display: flex; width: =
+32px; height: 32px; margin-right: 8px; margin-left: -16px; color: var(--col=
+or-fg-default); align-items: center; background-color: var(--color-border-d=
+efault); border: 1px solid var(--color-canvas-default); border-radius: 50%;=
+ justify-content: center; flex-shrink: 0; }
+
+.wizard-step-body { min-width: 0px; max-width: 100%; color: var(--color-fg-=
+default); flex: 1 1 auto; }
+
+.wizard-step-body .wizard-step-buttons { display: none; margin-top: 24px; j=
+ustify-content: flex-end; }
+
+.wizard-step-container { border: 0px; }
+
+.wizard-step-container .wizard-step-content { display: none; width: 100%; p=
+adding: 16px 24px 24px; overflow: visible; font-size: 14px; }
+
+.wizard-step-container.wizard-step-container-icon .wizard-step-content { pa=
+dding: 24px; }
+
+.wizard-step-header { padding-top: 4px; padding-left: 8px; }
+
+.wizard-step-header > .wizard-step-title { min-width: 0px; margin-bottom: 4=
+px; flex: 1 1 auto; color: var(--color-fg-muted); }
+
+.wizard-step-icon { display: none; height: 96px; color: var(--color-accent-=
+fg); background-image: linear-gradient(to right, var(--color-accent-subtle)=
+, var(--color-canvas-default)); justify-content: center; align-items: cente=
+r; border-top-left-radius: 6px; border-top-right-radius: 6px; }
+
+.wizard-step[data-single-page-wizard-step-complete=3D"true"] .wizard-step-b=
+adge { color: var(--color-fg-on-emphasis); background-color: var(--color-ac=
+cent-emphasis); }
+
+.wizard-step[data-single-page-wizard-step-complete=3D"true"] .wizard-step-i=
+tem::before { background-color: var(--color-accent-emphasis); }
+
+.wizard-step[data-single-page-wizard-step-complete=3D"true"] .wizard-step-t=
+itle { color: var(--color-fg-default); }
+
+.wizard-step[data-single-page-wizard-last-step=3D"true"] .wizard-step-badge=
+ .wizard-step-check { display: block; color: var(--color-fg-on-emphasis); }
+
+.wizard-step[data-single-page-wizard-last-step=3D"true"] .wizard-step-item:=
+:before { top: 0px; display: block; height: 16px; }
+
+@media (min-width: 768px) {
+  .wizard-step[data-single-page-wizard-last-step=3D"true"] .wizard-step-ite=
+m::before { display: none; }
+}
+
+.wizard-step[data-single-page-wizard-last-step=3D"true"] .wizard-step-icon =
+{ color: var(--color-success-fg); background-image: linear-gradient(to righ=
+t, var(--color-success-subtle), var(--color-canvas-default)); }
+
+.wizard-step:not([data-single-page-wizard-last-step=3D"true"]) .wizard-step=
+-badge .wizard-step-check { display: none; }
+
+.wizard-step:not([data-single-page-wizard-last-step=3D"true"]) .wizard-step=
+-badge::before { color: var(--color-fg-on-emphasis); content: attr(data-sin=
+gle-page-wizard-step); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-ba=
+dge { color: var(--color-fg-on-emphasis); background-color: var(--color-acc=
+ent-emphasis); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"][data-single-pag=
+e-wizard-last-step=3D"true"] .wizard-step-badge { background-color: var(--c=
+olor-success-emphasis); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"][data-single-pag=
+e-wizard-last-step=3D"true"] .wizard-step-item::before { top: 42px; height:=
+ 16px; }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"][data-single-pag=
+e-wizard-last-step=3D"true"] .wizard-step-container-icon::after { backgroun=
+d-image: linear-gradient(var(--color-success-subtle), var(--color-success-s=
+ubtle)); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"]:not([data-singl=
+e-page-wizard-last-step=3D"true"]) .wizard-step-container-icon::after { bac=
+kground-image: linear-gradient(var(--color-accent-subtle), var(--color-acce=
+nt-subtle)); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-ic=
+on { display: flex; }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-it=
+em { flex-direction: column; }
+
+@media (min-width: 768px) {
+  .wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-=
+item { flex-direction: row; }
+}
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-bo=
+dy { margin-top: 16px; margin-left: -16px; }
+
+@media (min-width: 768px) {
+  .wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-=
+body { margin-top: 0px; margin-left: 0px; }
+}
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer { position: relative; background-color: var(--color-canvas-default)=
+; border: 1px solid var(--color-border-default); border-radius: 6px; }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer::after, .wizard-step[data-single-page-wizard-step-current=3D"true"]=
+ .wizard-step-container::before { position: absolute; top: 11px; right: 100=
+%; left: -8px; display: block; width: 8px; height: 16px; pointer-events: no=
+ne; content: " "; clip-path: polygon(0px 50%, 100% 0px, 100% 100%); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer::after { margin-left: 2px; background-color: var(--color-canvas-def=
+ault); background-image: linear-gradient(var(--color-canvas-default), var(-=
+-color-canvas-default)); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer::before { background-color: var(--color-border-default); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer::before, .wizard-step[data-single-page-wizard-step-current=3D"true"=
+] .wizard-step-container::after { transform: rotate(90deg); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer::before { position: absolute; top: -12px; right: 100%; left: 12px; =
+display: block; width: 8px; height: 16px; pointer-events: none; content: " =
+"; clip-path: polygon(0px 50%, 100% 0px, 100% 100%); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer::after { top: -10px; left: 11px; }
+
+@media (min-width: 768px) {
+  .wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-=
+container::before, .wizard-step[data-single-page-wizard-step-current=3D"tru=
+e"] .wizard-step-container::after { top: 11px; left: -8px; transform: rotat=
+e(0deg); }
+  .wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-=
+container::after { margin-left: 1px; }
+}
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer .wizard-step-header { display: none; }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer .wizard-step-content-header { margin-bottom: 16px; }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer .wizard-step-title { color: var(--color-fg-default); }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-co=
+ntainer .wizard-step-content { display: block; }
+
+.wizard-step[data-single-page-wizard-step-current=3D"true"] .wizard-step-bu=
+ttons { display: flex; }
+
+.alert-label { color: var(--color-fg-on-emphasis); }
+
+.graph-canvas .alert-label--critical { fill: var(--color-danger-emphasis); =
+}
+
+.graph-canvas .alert-label--high { fill: var(--color-severe-emphasis); }
+
+.graph-canvas .alert-label--moderate { fill: var(--color-attention-emphasis=
+); }
+
+.graph-canvas .alert-label--low { fill: var(--color-neutral-emphasis); }
+
+.advisory-form { background-color: var(--color-canvas-subtle); border-top: =
+1px solid var(--color-border-default); }
+
+.advisory-form .form-control { background-color: var(--color-canvas-default=
+); }
+
+.advisory-form .form-actions { background-color: var(--color-canvas-default=
+); }
+
+.advisory-form .previewable-comment-form { background-color: var(--color-ca=
+nvas-default); }
+
+.advisory-credit-window-min { min-height: 72px; }
+
+.advisory-credit-window-max { max-height: 370px; }
+
+.AppHeader { --AppHeader-bg: var(--color-canvas-inset); color: var(--color-=
+fg-default); background: var(--AppHeader-bg); box-shadow: inset 0 calc(var(=
+--primer-borderWidth-thin, 1px)*-1) var(--color-border-default); }
+
+.AppHeader .AppHeader-globalBar { display: flex; padding: var(--base-size-1=
+6, 16px); gap: var(--base-size-12, 12px); }
+
+.AppHeader .AppHeader-globalBar.second-row { display: block; }
+
+.AppHeader .AppHeader-globalBar.second-row .AppHeader-search { display: blo=
+ck; }
+
+.AppHeader .AppHeader-globalBar.search-expanded .AppHeader-globalBar-start,=
+ .AppHeader .AppHeader-globalBar.always-expanded .AppHeader-globalBar-start=
+ { flex: 0 0 auto; }
+
+.AppHeader .AppHeader-globalBar.search-expanded .AppHeader-context, .AppHea=
+der .AppHeader-globalBar.always-expanded .AppHeader-context { display: none=
+; }
+
+.AppHeader .AppHeader-globalBar.search-expanded .AppHeader-search .AppHeade=
+r-search-whenRegular, .AppHeader .AppHeader-globalBar.always-expanded .AppH=
+eader-search .AppHeader-search-whenRegular { max-width: 100%; }
+
+.AppHeader .AppHeader-globalBar.search-expanded .AppHeader-globalBar-end, .=
+AppHeader .AppHeader-globalBar.always-expanded .AppHeader-globalBar-end { f=
+lex: 1 1 auto; }
+
+@media (max-width: 767.98px) {
+  .AppHeader .AppHeader-globalBar.search-expanded .AppHeader-search { posit=
+ion: absolute; top: 0px; left: 0px; width: 100%; }
+  .AppHeader .AppHeader-globalBar.search-expanded .AppHeader-globalBar-end =
+{ flex: 0 0 auto; }
+  .AppHeader .AppHeader-globalBar.search-expanded .search-suggestions { lef=
+t: 0px; top: 0px; width: 100% !important; }
+}
+
+.AppHeader .AppHeader-globalBar .AppHeader-globalBar-start { flex: 1 1 auto=
+; display: flex; gap: var(--base-size-8, 8px); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-globalBar-end { flex: 0 1 auto; =
+display: flex; justify-content: flex-end; gap: var(--primer-controlStack-me=
+dium-gap-auto, 8px); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-logo { border: 0px; width: var(-=
+-base-size-32, 32px); height: var(--base-size-32, 32px); outline-offset: 2p=
+x; }
+
+@media (pointer: coarse) {
+  .AppHeader .AppHeader-globalBar .AppHeader-logo::after { position: absolu=
+te; top: 50%; left: 50%; width: 100%; height: 100%; min-height: var(--prime=
+r-control-minTarget-coarse, 44px); content: ""; transform: translateX(-50%)=
+ translateY(-50%); min-width: var(--primer-control-minTarget-coarse, 44px);=
+ }
+}
+
+.AppHeader .AppHeader-globalBar .AppHeader-logo svg { width: var(--base-siz=
+e-32, 32px); height: var(--base-size-32, 32px); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-user { position: relative; backg=
+round: var(--color-neutral-muted); border-radius: 50%; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-user img { position: relative; d=
+isplay: block; border-radius: 50%; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context { min-width: 0px; height=
+: var(--base-size-32, 32px); flex: 1 1 auto; }
+
+@media (min-width: 768px) {
+  .AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-com=
+pact { display: none; }
+}
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-compa=
+ct .AppHeader-context-compact-trigger { display: grid; width: 100%; height:=
+ var(--base-size-48, 48px); color: inherit; text-align: left; cursor: point=
+er; background-color: transparent; border: 0px; border-radius: var(--primer=
+-borderRadius-medium, 6px); margin-block: calc(var(--base-size-8, 8px)*-1);=
+ padding-block: var(--primer-control-medium-paddingBlock, 6px); padding-inl=
+ine: var(--primer-control-medium-paddingInline-condensed, 8px); appearance:=
+ none; align-items: center; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-compa=
+ct .AppHeader-context-compact-trigger:hover { background-color: var(--color=
+-action-list-item-default-hover-bg); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-compa=
+ct .AppHeader-context-compact-trigger:active { background-color: var(--colo=
+r-action-list-item-default-active-bg); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-compa=
+ct .AppHeader-context-compact-lead { display: grid; width: fit-content; fon=
+t-size: var(--primer-text-caption-size, 12px); line-height: var(--primer-te=
+xt-caption-lineHeight, 1.33333); color: var(--color-fg-muted); grid-auto-fl=
+ow: column; align-items: center; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-compa=
+ct .AppHeader-context-compact-parentItem { display: inline; overflow: hidde=
+n; text-overflow: ellipsis; white-space: nowrap; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-compa=
+ct .AppHeader-context-compact-separator { white-space: nowrap; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-compa=
+ct .AppHeader-context-compact-mainItem { display: block; overflow: hidden; =
+line-height: var(--primer-text-body-lineHeight-medium, 1.42857); text-overf=
+low: ellipsis; white-space: nowrap; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-full =
+{ display: inline-flex; width: 100%; min-width: 0px; max-width: 100%; overf=
+low: hidden; }
+
+@media (max-width: 767.98px) {
+  .AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-ful=
+l { display: none; }
+}
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-full =
+nav { width: 100%; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-full =
+ul, .AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-f=
+ull li { list-style: none; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-full =
+ul { display: flex; flex-direction: row; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-full =
+li { display: inline-grid; grid-auto-flow: column; align-items: center; fle=
+x: 0 99999 auto; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-full =
+li:first-child { flex: 0 100 max-content; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-full =
+li:last-child { flex: 0 1 max-content; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-full =
+li:last-child .AppHeader-context-item { font-weight: var(--base-text-weight=
+-semibold, 600); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-item =
+{ display: flex; align-items: center; min-width: 3ch; line-height: var(--pr=
+imer-text-body-lineHeight-medium, 1.42857); color: inherit; border-radius: =
+var(--primer-borderRadius-medium, 6px); padding-inline: var(--primer-contro=
+l-medium-paddingInline-condensed, 8px); padding-block: var(--primer-control=
+-medium-paddingBlock, 6px); text-decoration: none !important; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-item =
+.AppHeader-context-item-label { display: inline-block; overflow: hidden; te=
+xt-overflow: ellipsis; white-space: nowrap; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-item:=
+hover { background: var(--color-action-list-item-default-hover-bg); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-item:=
+active { background: var(--color-action-list-item-default-active-bg); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-context .AppHeader-context-item-=
+separator { color: var(--color-fg-muted); white-space: nowrap; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search { position: relative; dis=
+play: flex; flex: 1 1 auto; justify-content: flex-end; }
+
+@media (min-width: 768px) {
+  .AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-whenN=
+arrow { display: none; }
+}
+
+@media (max-width: 767.98px) {
+  .AppHeader .AppHeader-globalBar .AppHeader-search { flex-grow: 0; }
+  .AppHeader .AppHeader-globalBar .AppHeader-search .search-input { width: =
+auto; }
+  .AppHeader .AppHeader-globalBar .AppHeader-search .search-input-container=
+ { margin: 0px !important; }
+  .AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-whenR=
+egular { display: none; }
+}
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-whenReg=
+ular { min-width: 12rem; max-width: 24rem; flex: 1 1 auto; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-wrap { =
+display: grid; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-wrap.Ap=
+pHeader-search-wrap--hasTrailing input[type=3D"search"] { padding-inline-en=
+d: calc(var(--primer-control-medium-paddingInline-condensed, 8px) + var(--b=
+ase-size-16, 16px) + var(--primer-control-medium-gap, 8px) - var(--primer-b=
+orderWidth-thin, 1px)); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .search-input-container {=
+ height: auto; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-kbd { d=
+isplay: inline-grid; width: var(--base-size-12, 12px); height: var(--base-s=
+ize-16, 16px); padding: 0px; font-size: var(--primer-text-caption-size, 12p=
+x); line-height: var(--primer-text-caption-lineHeight, 1.33333); color: inh=
+erit; vertical-align: baseline; background: transparent; border: var(--prim=
+er-borderWidth-thin, 1px) solid var(--color-fg-subtle); border-radius: var(=
+--primer-borderRadius-small, 3px); box-shadow: none; align-items: center; j=
+ustify-content: center; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-placeho=
+lder { display: block; width: 100%; overflow: hidden; font-weight: var(--ba=
+se-text-weight-normal, 400); line-height: var(--primer-text-body-lineHeight=
+-medium, 20px); color: var(--color-fg-subtle); text-overflow: ellipsis; whi=
+te-space: nowrap; pointer-events: none; grid-area: 1 / 1 / auto / auto; pad=
+ding-block: var(--primer-control-medium-paddingBlock, 6px); padding-inline:=
+ calc(var(--primer-control-medium-paddingInline-condensed, 8px) + var(--bas=
+e-size-16, 16px) + var(--primer-control-medium-gap, 8px)); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-control=
+ { grid-area: 1 / 1 / auto / auto; position: relative; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-visual-=
+-leading { position: absolute; top: var(--base-size-8, 8px); left: var(--ba=
+se-size-8, 8px); display: block; width: var(--base-size-16, 16px); height: =
+var(--base-size-16, 16px); color: var(--color-fg-muted); pointer-events: no=
+ne; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-visual-=
+-leading svg { display: block !important; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-searchButton {=
+ background: transparent; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search input[type=3D"search"], .=
+AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-searchButton { =
+width: 100%; padding-block: calc(var(--primer-control-medium-paddingBlock, =
+6px) - var(--primer-borderWidth-thin, 1px)); padding-inline-start: calc(var=
+(--primer-control-medium-paddingInline-condensed, 8px) + var(--base-size-16=
+, 16px) + var(--primer-control-medium-gap, 8px) - var(--primer-borderWidth-=
+thin, 1px)); padding-inline-end: var(--primer-control-medium-paddingInline-=
+condensed, 8px); transition: none 0s ease 0s; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search input[type=3D"search"]:pl=
+aceholder-shown { background: transparent; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search input[type=3D"search"]:no=
+t(:placeholder-shown) { background: var(--color-canvas-default); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search input[type=3D"search"]::p=
+laceholder { color: transparent; opacity: 1; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search input[type=3D"search"]:fo=
+cus { background: var(--color-canvas-default); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search input[type=3D"search"]:fo=
+cus::placeholder { color: var(--color-fg-subtle); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-action-=
+-trailing { position: absolute; top: var(--base-size-4, 4px); right: var(--=
+base-size-4, 4px); display: grid; width: var(--primer-control-xsmall-size, =
+24px); height: var(--primer-control-xsmall-size, 24px); padding: 0px; color=
+: var(--color-fg-muted); background: transparent; border: none; border-radi=
+us: var(--primer-borderRadius-small, 3px); align-items: center; justify-con=
+tent: center; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-action-=
+-trailing:hover { background: var(--color-action-list-item-default-hover-bg=
+); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-action-=
+-trailing:active { background: var(--color-action-list-item-default-active-=
+bg); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-action-=
+-trailing::before { position: absolute; top: calc((var(--primer-control-xsm=
+all-size, 24px) - var(--base-size-16, 16px))/2); left: calc(var(--base-size=
+-4, 4px)*-1); display: block; width: var(--primer-borderWidth-thin, 1px); h=
+eight: var(--base-size-16, 16px); content: ""; background: var(--color-bord=
+er-default); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-action-=
+-trailing::after { position: absolute; top: 50%; left: 50%; width: 100%; he=
+ight: 100%; min-height: var(--primer-control-medium-size, 32px); content: "=
+"; transform: translateX(-50%) translateY(-50%); min-width: var(--primer-co=
+ntrol-medium-size, 32px); }
+
+@media (pointer: coarse) {
+  .AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-actio=
+n--trailing::after { min-width: var(--primer-control-minTarget-coarse, 44px=
+); min-height: var(--primer-control-minTarget-coarse, 44px); }
+}
+
+.AppHeader .AppHeader-globalBar .AppHeader-search .AppHeader-search-action-=
+-trailing svg { color: inherit; }
+
+.AppHeader .AppHeader-globalBar .AppHeader-divider { width: var(--primer-bo=
+rderWidth-thin, 1px); height: var(--base-size-20, 20px); margin: calc((var(=
+--primer-control-medium-size, 32px) - var(--base-size-20, 20px))/2) 0; back=
+ground: var(--color-border-default); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-actions { display: grid; grid-au=
+to-flow: column; gap: var(--primer-controlStack-medium-gap-auto, 8px); }
+
+.AppHeader .AppHeader-globalBar .AppHeader-actions::before { display: block=
+; width: var(--primer-borderWidth-thin, 1px); height: var(--base-size-20, 2=
+0px); content: ""; background: var(--color-border-default); margin-block: c=
+alc((var(--primer-control-medium-size, 32px) - var(--base-size-20, 20px))/2=
+); }
+
+@media (pointer: fine) {
+  .AppHeader .AppHeader-globalBar .AppHeader-actions::before { margin-inlin=
+e: var(--base-size-4, 4px); }
+}
+
+@media (max-width: 543.98px) {
+  .AppHeader .AppHeader-globalBar .AppHeader-actions { display: none; }
+}
+
+.AppHeader .AppHeader-localBar { padding: 0 var(--base-size-16, 16px); }
+
+.AppHeader .AppHeader-item { flex: 0 0 auto; background: rgb(255, 182, 193)=
+; }
+
+.AppHeader .AppHeader-item--full { flex-grow: 1; }
+
+.AppHeader .AppHeader-button { position: relative; display: grid; width: va=
+r(--base-size-32, 32px); height: var(--base-size-32, 32px); color: var(--co=
+lor-fg-muted); background: transparent; border: var(--primer-borderWidth-th=
+in, 1px) solid var(--color-border-default); border-radius: var(--primer-bor=
+derRadius-medium, 6px); align-items: center; justify-content: center; }
+
+.AppHeader .AppHeader-button svg { width: var(--base-size-16, 16px); height=
+: var(--base-size-16, 16px); display: block !important; color: inherit !imp=
+ortant; }
+
+.AppHeader .AppHeader-button:hover { background: var(--color-action-list-it=
+em-default-hover-bg); }
+
+.AppHeader .AppHeader-button:active { background: var(--color-action-list-i=
+tem-default-active-bg); }
+
+@media (pointer: coarse) {
+  .AppHeader .AppHeader-button::after { position: absolute; top: 50%; left:=
+ 50%; width: 100%; height: 100%; min-height: var(--primer-control-minTarget=
+-coarse, 44px); content: ""; transform: translateX(-50%) translateY(-50%); =
+min-width: var(--primer-control-minTarget-coarse, 44px); }
+}
+
+.AppHeader .AppHeader-button.AppHeader-button--hasIndicator::before { posit=
+ion: absolute; top: calc(var(--base-size-4, 4px)/-2); right: calc(var(--bas=
+e-size-4, 4px)/-2); display: block; width: var(--base-size-8, 8px); height:=
+ var(--base-size-8, 8px); content: ""; background: var(--color-accent-fg); =
+border-radius: 50%; box-shadow: 0 0 0 calc(var(--base-size-4, 4px)/2) var(-=
+-AppHeader-bg); }
+
+[data-target=3D"animated-image.originalImage"], [data-target=3D"animated-im=
+age.replacedImage"], [data-a11y-animated-images=3D"system"] [data-animated-=
+image], [data-a11y-animated-images=3D"disabled"] [data-animated-image] { di=
+splay: none; }
+
+[data-target=3D"animated-image.originalImage"] { width: 100%; }
+
+animated-image[data-catalyst] { display: inline-block; }
+
+animated-image { max-width: 100%; }
+
+.AnimatedImagePlayer { position: relative; display: inline-block; width: 10=
+0%; }
+
+.AnimatedImagePlayer > a:not(.AnimatedImagePlayer-images) { display: none; =
+}
+
+.AnimatedImagePlayer-controls { position: absolute; top: 8px; right: 8px; z=
+-index: 2; display: none; padding: 4px; list-style: none; background: var(-=
+-color-canvas-default); border-radius: 6px; box-shadow: var(--color-overlay=
+-shadow); opacity: 1; transition: opacity 80ms linear 0s; }
+
+.AnimatedImagePlayer-images .AnimatedImagePlayer-animatedImage { cursor: po=
+inter; }
+
+.AnimatedImagePlayer-button { display: flex; align-items: center; justify-c=
+ontent: center; width: 32px; height: 32px; cursor: pointer; background-colo=
+r: var(--color-canvas-default); border: 0px; border-radius: 6px; }
+
+@media (hover: hover) and (pointer: fine) {
+  .AnimatedImagePlayer-button:hover { background-color: var(--color-btn-hov=
+er-bg); transition: background-color 200ms linear 0s; }
+}
+
+.AnimatedImagePlayer-images { display: block; width: 100%; padding: 0px; ma=
+rgin: 0px; background: none; border: 0px; outline: none; outline-offset: 0p=
+x; }
+
+.AnimatedImagePlayer-images:focus-visible { outline: 2px solid var(--color-=
+accent-fg); outline-offset: 0px; }
+
+.AnimatedImagePlayer-button:focus-visible { outline: 2px solid var(--color-=
+accent-fg); outline-offset: -2px; }
+
+.AnimatedImagePlayer-button svg { width: 16px; height: 16px; pointer-events=
+: none; fill: var(--color-fg-muted); }
+
+.AnimatedImagePlayer-button:hover svg { fill: var(--color-fg-default); }
+
+.AnimatedImagePlayer-stillImage { position: absolute; top: 0px; left: 0px; =
+z-index: 1; display: none; width: 100%; height: 100%; pointer-events: none;=
+ }
+
+.AnimatedImagePlayer-animatedImage { width: 100%; max-width: 100%; max-heig=
+ht: 100%; }
+
+.AnimatedImagePlayer.playing .AnimatedImagePlayer-controls { opacity: 0; tr=
+ansition-delay: 1s; }
+
+.AnimatedImagePlayer.enabled .AnimatedImagePlayer-animatedImage { opacity: =
+0 !important; }
+
+.AnimatedImagePlayer.enabled.playing .AnimatedImagePlayer-animatedImage { o=
+pacity: 1 !important; }
+
+.AnimatedImagePlayer.playing.player-focused .AnimatedImagePlayer-controls {=
+ opacity: 1; transition-delay: 0ms; }
+
+@media (hover: hover) and (pointer: fine) {
+  .AnimatedImagePlayer.playing:hover .AnimatedImagePlayer-controls { opacit=
+y: 1; transition-delay: 0s; }
+}
+
+.AnimatedImagePlayer.enabled.playing .AnimatedImagePlayer-stillImage, .Anim=
+atedImagePlayer.enabled.playing .icon-play, .AnimatedImagePlayer .icon-paus=
+e { display: none; }
+
+.AnimatedImagePlayer .icon-play, .AnimatedImagePlayer.enabled.playing .icon=
+-pause, .AnimatedImagePlayer.enabled .AnimatedImagePlayer-stillImage { disp=
+lay: block; }
+
+.AnimatedImagePlayer.enabled .AnimatedImagePlayer-controls { display: flex;=
+ }
+
+.emoji-tab.UnderlineNav-item { margin-right: 4px; }
+
+.emoji-tab[role=3D"tab"][aria-selected=3D"true"] { font-weight: var(--base-=
+text-weight-semibold, 600); color: var(--color-fg-default); border-bottom-c=
+olor: var(--color-severe-emphasis); }
+
+.emoji-tab[role=3D"tab"][aria-selected=3D"true"] .UnderlineNav-octicon { co=
+lor: var(--color-fg-muted); }
+
+.selected-emoji { z-index: 100; background-color: var(--color-accent-emphas=
+is); }
+
+.emoji-picker-container .emoji-picker-tab g-emoji { margin-right: auto; mar=
+gin-left: 4px; }
+
+.emoji-tab .emoji-icon { width: auto; }
+
+.emoji-picker-container { z-index: 1; width: 395px; }
+
+.emoji-picker-tab { height: 136px; padding-top: 8px; }
+
+.emoji-picker-emoji-width { width: 32px; height: 28px; }
+
+.emoji-picker-tab .btn-outline:not(:hover) { background-color: transparent;=
+ }
+
+.emoji-picker-list { list-style: none; }
+
+.notification-shelf { z-index: 34; }
+
+.notification-shelf.is-stuck { z-index: 999; }
+
+@media (max-width: 767px) {
+  .notifications-v2 .commit-ref .css-truncate-target { word-break: break-al=
+l; white-space: normal; }
+}
+
+@media (max-width: 543px) {
+  .notifications-v2 .Box { border-right: 0px; border-left: 0px; border-radi=
+us: 0px; }
+}
+
+@media (max-width: 543px) {
+  .notifications-v2 .Box .Box-header { border-right: 0px !important; border=
+-left: 0px !important; border-radius: 0px !important; }
+}
+
+@media (max-width: 767px) {
+  .notifications-v2 .AvatarStack--right { width: auto !important; min-width=
+: auto !important; margin-left: 53px !important; }
+}
+
+@media (max-width: 767px) {
+  .notifications-v2 .AvatarStack--right .AvatarStack-body { margin-right: 8=
+px; position: relative !important; right: unset !important; flex-direction:=
+ row !important; }
+}
+
+@media (max-width: 767px) {
+  .notifications-v2 .AvatarStack-body .avatar { position: relative !importa=
+nt; margin-right: -12px !important; margin-left: 0px !important; border-rig=
+ht: 1px solid rgb(255, 255, 255) !important; border-left: 0px !important; }
+}
+
+.notifications-v2 .thread-subscription-status { background-color: transpare=
+nt !important; }
+
+.notifications-v2 .notification-action-mark-archived, .notifications-v2 .no=
+tification-action-mark-unread, .notifications-v2 .notification-action-star,=
+ .notifications-v2 .notification-action-unsubscribe { display: block !impor=
+tant; }
+
+.notifications-v2 .notification-action-mark-read, .notifications-v2 .notifi=
+cation-action-mark-unarchived, .notifications-v2 .notification-action-subsc=
+ribe, .notifications-v2 .notification-action-unstar, .notifications-v2 .not=
+ification-is-starred-icon { display: none !important; }
+
+.notifications-v2 .notification-unsubscribed .notification-action-unsubscri=
+be { display: none !important; }
+
+.notifications-v2 .notification-unsubscribed .notification-action-subscribe=
+ { display: block !important; }
+
+.notifications-v2 .notification-unread .notification-action-mark-read { dis=
+play: block !important; }
+
+.notifications-v2 .notification-unread .notification-action-mark-unread { d=
+isplay: none !important; }
+
+.notifications-v2 .notification-archived .notification-action-mark-archived=
+, .notifications-v2 .notification-archived .notification-action-mark-read, =
+.notifications-v2 .notification-archived .notification-action-mark-unread {=
+ display: none !important; }
+
+.notifications-v2 .notification-archived .notification-action-mark-unarchiv=
+ed { display: block !important; }
+
+.notifications-v2 .notification-starred .notification-action-star { display=
+: none !important; }
+
+.notifications-v2 .notification-starred .notification-is-starred-icon { dis=
+play: inline-block !important; }
+
+.notifications-v2 .notification-starred .notification-action-unstar { displ=
+ay: block !important; }
+
+.notifications-v2 .thread-subscribe-form { display: none !important; }
+
+.notifications .read .avatar img { opacity: 0.5; }
+
+.notifications .read .undo { display: block; }
+
+.notifications .read .delete { visibility: hidden; }
+
+.notifications .read[aria-selected=3D"true"], .notifications .read.navigati=
+on-focus { background-color: rgb(245, 249, 252); }
+
+.notifications .muted .unmute { display: block; }
+
+.notifications .muted .mute { display: none; }
+
+.notifications .unmute { display: none; }
+
+.notifications-list { float: left; width: 100%; }
+
+.thread-subscription-status { padding: 8px; margin: 40px 0px 16px; color: v=
+ar(--color-fg-muted); border: 1px solid var(--color-border-default); border=
+-radius: 6px; }
+
+.thread-subscription-status .btn-sm > .octicon { margin-right: 1px; }
+
+.thread-subscription-status .reason { display: inline-block; margin: 0px 8p=
+x; vertical-align: middle; }
+
+.thread-subscription-status .thread-subscribe-form { display: inline-block;=
+ vertical-align: middle; }
+
+.subscription .loading { opacity: 0.5; }
+
+.progress-pjax-loader { z-index: 99999; background: transparent; opacity: 0=
+; transition: opacity 0.4s linear 0.4s; height: 2px !important; }
+
+.progress-pjax-loader.is-loading { opacity: 1; transition: none 0s ease 0s;=
+ }
+
+.progress-pjax-loader > .progress-pjax-loader-bar { background-color: rgb(1=
+21, 184, 255); transition: width 0.4s ease 0s; }
+
+.starred .starred-button-icon { color: var(--color-scale-yellow-2); }
+
+.user-lists-menu-action { color: var(--color-fg-default); }
+
+.user-lists-menu-action:hover:not(:disabled) { color: var(--color-fg-defaul=
+t); background-color: var(--color-canvas-subtle); }
+
+.user-lists-menu-action:focus:not(:disabled) { color: var(--color-fg-defaul=
+t); outline: 2px solid var(--color-accent-emphasis); outline-offset: 2px; }
+
+.starring-container .BtnGroup-parent:active { z-index: auto; }
+
+.shelf { padding-top: 16px; margin-bottom: 16px; background-color: var(--co=
+lor-canvas-default); border-bottom: 1px solid var(--color-border-muted); }
+
+.shelf .container { position: relative; }
+
+.org-sso, .business-sso { width: 340px; margin: 0px auto; }
+
+.org-sso .sso-title, .business-sso .sso-title { font-size: 24px; font-weigh=
+t: var(--base-text-weight-light, 300); letter-spacing: -0.5px; }
+
+.org-sso .org-sso-panel, .org-sso .business-sso-panel, .business-sso .org-s=
+so-panel, .business-sso .business-sso-panel { padding: 16px; background-col=
+or: var(--color-canvas-default); border: 1px solid var(--color-border-defau=
+lt); border-radius: 6px; }
+
+.org-sso .sso-recovery-callout, .business-sso .sso-recovery-callout { paddi=
+ng: 16px 8px; text-align: center; border: 1px solid var(--color-border-mute=
+d); border-radius: 6px; }
+
+.sso-modal { padding: 16px; }
+
+.sso-modal .org-sso, .sso-modal .business-sso { width: auto; }
+
+.sso-modal .org-sso .org-sso-panel, .sso-modal .business-sso .business-sso-=
+panel { border: 0px; }
+
+.sso-modal .sso-prompt-success, .sso-modal .sso-prompt-error { display: non=
+e; }
+
+.sso-modal.success .sso-prompt-default { display: none; }
+
+.sso-modal.success .sso-prompt-success { display: block; }
+
+.sso-modal.error .sso-prompt-default { display: none; }
+
+.sso-modal.error .sso-prompt-error { display: block; }
+
+.sso-modal.error .flash-error { margin-right: -32px; margin-left: -32px; bo=
+rder-right: 0px; border-left: 0px; border-radius: 0px; }
+
+.tag-input-container { position: relative; }
+
+.tag-input-container .focus { border-color: transparent !important; box-sha=
+dow: none !important; }
+
+.tag-input-container .suggester { position: absolute; z-index: 100; width: =
+100%; margin-top: -1px; }
+
+.tag-input-container ul { list-style: none; }
+
+.tag-input input { float: left; padding-left: 2px; margin: 0px; background:=
+ none; border: 0px; box-shadow: none; }
+
+.tag-input input:focus { box-shadow: none; }
+
+.task-list-item { list-style-type: none; }
+
+.task-list-item label { font-weight: var(--base-text-weight-normal, 400); }
+
+.task-list-item.enabled label { cursor: pointer; }
+
+.task-list-item + .task-list-item { margin-top: 4px; }
+
+.task-list-item .handle { display: none; }
+
+.task-list-item-checkbox { margin: 0px 0.2em 0.25em -1.4em; vertical-align:=
+ middle; }
+
+.convert-to-issue-button { top: 2px; right: 4px; padding: 0px 2px; margin-r=
+ight: 8px; user-select: none; background-color: var(--color-canvas-subtle);=
+ }
+
+.convert-to-issue-button .octicon { fill: var(--color-fg-default); }
+
+.convert-to-issue-button:hover .octicon, .convert-to-issue-button:focus .oc=
+ticon { fill: var(--color-accent-fg); }
+
+.reorderable-task-lists .markdown-body .contains-task-list { padding: 0px; =
+}
+
+.reorderable-task-lists .markdown-body li:not(.task-list-item) { margin-lef=
+t: 24px; }
+
+.reorderable-task-lists .markdown-body ol:not(.contains-task-list) li, .reo=
+rderable-task-lists .markdown-body ul:not(.contains-task-list) li { margin-=
+left: 0px; }
+
+.reorderable-task-lists .markdown-body .task-list-item { padding: 2px 15px =
+2px 42px; margin-right: -15px; margin-left: -15px; line-height: 1.5; border=
+: 0px; }
+
+.reorderable-task-lists .markdown-body .task-list-item + .task-list-item { =
+margin-top: 0px; }
+
+.reorderable-task-lists .markdown-body .task-list-item .handle { display: b=
+lock; float: left; width: 20px; padding: 2px 0px 0px 2px; margin-left: -43p=
+x; opacity: 0; }
+
+.reorderable-task-lists .markdown-body .task-list-item .drag-handle { fill:=
+ var(--color-fg-default); }
+
+.reorderable-task-lists .markdown-body .task-list-item.hovered > .handle { =
+opacity: 1; }
+
+.reorderable-task-lists .markdown-body .task-list-item.is-dragging { opacit=
+y: 0; }
+
+.comment-body .reference { font-weight: var(--base-text-weight-semibold, 60=
+0); white-space: nowrap; }
+
+.comment-body .issue-link { white-space: normal; }
+
+.comment-body .issue-link .issue-shorthand { font-weight: var(--base-text-w=
+eight-normal, 400); color: var(--color-fg-muted); }
+
+.comment-body .issue-link:hover .issue-shorthand, .comment-body .issue-link=
+:focus .issue-shorthand { color: var(--color-accent-fg); }
+
+.review-comment-contents .markdown-body .task-list-item { padding-left: 42p=
+x; margin-right: -12px; margin-left: -12px; border-top-left-radius: 6px; bo=
+rder-bottom-left-radius: 6px; }
+
+.convert-to-issue-enabled .task-list-item .contains-task-list { padding: 4p=
+x 15px 0px 43px; margin: 0px -15px 0px -42px; }
+
+.convert-to-issue-enabled .task-list-item.hovered { background-color: var(-=
+-color-canvas-subtle); }
+
+.convert-to-issue-enabled .task-list-item.hovered .contains-task-list { bac=
+kground-color: var(--color-canvas-default); }
+
+.convert-to-issue-enabled .task-list-item.hovered > .convert-to-issue-butto=
+n { z-index: 20; width: auto; height: auto; overflow: visible; clip: auto; =
+}
+
+.convert-to-issue-enabled .task-list-item.hovered > .convert-to-issue-butto=
+n svg { overflow: visible; }
+
+.convert-to-issue-enabled .task-list-item.is-loading { color: var(--color-f=
+g-muted); background-color: var(--color-accent-subtle); border-top: 1px sol=
+id var(--color-accent-subtle); border-bottom: 1px solid var(--color-canvas-=
+default); border-left: 1px solid var(--color-canvas-default); }
+
+.convert-to-issue-enabled .task-list-item.is-loading ul { color: var(--colo=
+r-fg-default); background-color: var(--color-canvas-default); }
+
+.convert-to-issue-enabled .task-list-item.is-loading > .handle { opacity: 0=
+; }
+
+.task-list-item-convert-container { top: calc(100% - 4px); right: 0px; left=
+: 0px; display: none; margin-top: 0px; position: absolute !important; }
+
+.task-list-item-convert-container:hover, .task-list-item-convert-container:=
+focus { display: block; }
+
+.task-list-item-convert-button-container { top: 4px; right: 0px; left: auto=
+; width: auto; }
+
+.contains-task-list { position: relative; }
+
+.contains-task-list:hover .task-list-item-convert-container, .contains-task=
+-list:focus-within .task-list-item-convert-container { display: block; widt=
+h: auto; height: 24px; overflow: visible; clip: auto; }
+
+.convert-to-block-button { margin: 0px 4px; }
+
+.toolbar-commenting .dropdown-menu-s { width: 100px; }
+
+.toolbar-commenting .dropdown-item { font-weight: var(--base-text-weight-se=
+mibold, 600); line-height: 1em; background: none; border: 0px; }
+
+.toolbar-commenting .dropdown-item:hover { color: var(--color-accent-fg); }
+
+.toolbar-commenting .dropdown-item:focus { color: var(--color-accent-fg); o=
+utline: none; }
+
+.toolbar-item { display: block; float: left; padding: 4px; cursor: pointer;=
+ }
+
+.toolbar-item.dropdown, .toolbar-item.select-menu { padding: 0px; }
+
+.toolbar-item .select-menu-modal { margin-top: 2px; }
+
+.toolbar-item .select-menu-item { padding-left: 8px; }
+
+.topic-tag { display: inline-block; padding: 0.3em 0.9em; margin: 0px 0.5em=
+ 0.5em 0px; white-space: nowrap; background-color: var(--color-accent-subtl=
+e); border-radius: 6px; }
+
+.topic-tag-link:hover { text-decoration: none; background-color: rgb(221, 2=
+38, 255); }
+
+.delete-topic-button, .delete-topic-link { display: inline-block; width: 26=
+px; color: var(--color-fg-muted); background-color: var(--color-accent-subt=
+le); border-width: 0px 0px 0px 1px; border-top-style: initial; border-top-c=
+olor: initial; border-right-style: initial; border-right-color: initial; bo=
+rder-bottom-style: initial; border-bottom-color: initial; border-left-style=
+: solid; border-left-color: rgb(180, 217, 255); border-top-right-radius: 6p=
+x; border-bottom-right-radius: 6px; }
+
+.delete-topic-button:hover, .delete-topic-link:hover { background-color: rg=
+b(221, 238, 255); }
+
+.topic-tag-action:hover .delete-topic-link { color: var(--color-fg-on-empha=
+sis); background-color: var(--color-accent-emphasis); }
+
+.topic-tag-outline { background: transparent; box-shadow: rgb(200, 225, 255=
+) 0px 0px 0px 1px inset; }
+
+.delete-topic-link { padding-right: 8px; padding-left: 8px; margin-left: 8p=
+x; line-height: 1.75; }
+
+.delete-topic-link:hover { text-decoration: none; }
+
+.invalid-topic .delete-topic-button { color: var(--color-fg-default); backg=
+round-color: var(--color-danger-subtle); border-left-color: var(--color-dan=
+ger-emphasis); }
+
+.invalid-topic .delete-topic-button:hover { background-color: rgb(255, 200,=
+ 206); }
+
+.topic-tag-action { display: inline-flex; align-items: center; padding-left=
+: 0.8em; margin: 0.4em 0.4em 0px 0px; background-color: var(--color-accent-=
+subtle); border-radius: 6px; }
+
+.topic-tag-action.invalid-topic { color: var(--color-fg-default); backgroun=
+d-color: var(--color-danger-subtle); border-color: var(--color-danger-empha=
+sis); }
+
+.topic-tag-action .add-topic-button, .topic-tag-action .remove-topic-button=
+ { display: inline-block; width: 26px; font-size: 14px; color: var(--color-=
+fg-muted); background-color: var(--color-accent-subtle); border-width: 0px =
+0px 0px 1px; border-top-style: initial; border-top-color: initial; border-r=
+ight-style: initial; border-right-color: initial; border-bottom-style: init=
+ial; border-bottom-color: initial; border-left-style: solid; border-left-co=
+lor: rgb(180, 217, 255); }
+
+.topic-tag-action .add-topic-button:hover, .topic-tag-action .remove-topic-=
+button:hover { color: var(--color-fg-on-emphasis); }
+
+.topic-tag-action .add-topic-button:hover { background-color: var(--color-s=
+uccess-emphasis); }
+
+.topic-tag-action .remove-topic-button { border-right: 0px; border-top-righ=
+t-radius: 6px; border-bottom-right-radius: 6px; }
+
+.topic-tag-action .remove-topic-button:hover { background-color: var(--colo=
+r-danger-emphasis); }
+
+.topic-input-container .tag-input { width: 908px; cursor: text; }
+
+.topic-input-container .tag-input.org-repo-tag-input { width: 100%; }
+
+.topic-input-container .tag-input .tag-input-inner { min-height: 26px; back=
+ground-image: none; }
+
+.topic-input-container .topic-tag { margin-top: 2px; }
+
+.topic .css-truncate-target { max-width: 75%; }
+
+.topic-list .topic-list-item + .topic-list-item { border-top: 1px solid var=
+(--color-border-default); }
+
+.topic-box .starred { color: var(--color-attention-fg); border: 0px; }
+
+.topic-box .unstarred { color: var(--color-fg-muted); border: 0px; }
+
+.user-status-suggestions { height: 98px; transition: height 100ms ease-out =
+0s, opacity 200ms ease-in 0s; }
+
+.user-status-suggestions.collapsed { height: 0px; opacity: 0; }
+
+.user-status-container, .user-status-container .team-mention, .user-status-=
+container .user-mention { white-space: normal; }
+
+.user-status-container { word-break: break-word; overflow-wrap: break-word;=
+ }
+
+.user-status-container .input-group-button .btn { width: 46px; height: 34px=
+; line-height: 0; }
+
+.user-status-container .input-group-button g-emoji { font-size: 1.3em; line=
+-height: 18px; }
+
+.user-status-container .team-mention, .user-status-container .user-mention =
+{ white-space: normal; }
+
+.user-status-container img.emoji { width: 18px; height: 18px; }
+
+.emoji-status-width { width: 20px; }
+
+.user-status-org-button .user-status-org-detail { color: var(--color-fg-mut=
+ed); }
+
+.user-status-org-button:hover .user-status-org-detail, .user-status-org-but=
+ton:focus .user-status-org-detail { color: var(--color-fg-on-emphasis); }
+
+.user-status-org-button.selected { color: var(--color-fg-on-emphasis); back=
+ground-color: var(--color-accent-emphasis); }
+
+.user-status-org-button.selected .user-status-org-detail { color: var(--col=
+or-fg-on-emphasis); }
+
+.user-status-limited-availability-compact { width: 8px; height: 8px; backgr=
+ound-color: var(--color-attention-emphasis); }
+
+.user-status-message-wrapper { color: var(--color-fg-default); }
+
+.toggle-user-status-edit:hover .user-status-message-wrapper, .toggle-user-s=
+tatus-edit:focus .user-status-message-wrapper { color: var(--color-accent-f=
+g); }
+
+.user-status-message-wrapper div { display: inline; }
+
+.user-status-header g-emoji { font-size: 1.25em; }
+
+.user-status-message-wrapper .g-emoji { display: inline-block; }
+
+.user-status-limited-availability-container { margin-top: 16px; margin-bott=
+om: 16px; }
+
+@media only screen and (max-height: 560px) {
+  .user-status-suggestions { display: none; }
+  .user-status-limited-availability-container { margin-top: 8px; margin-bot=
+tom: 8px; }
+}
+
+.user-status-circle-badge { background-color: var(--color-canvas-default); =
+border: 1px solid var(--color-border-default); border-radius: 2em; box-shad=
+ow: var(--color-shadow-small); }
+
+.command-palette { box-shadow: var(--color-overlay-shadow); }
+
+@media (min-width: 768px) {
+  .command-palette-details-dialog { width: 512px; }
+}
+
+@media (min-width: 1012px) {
+  .command-palette-details-dialog { width: 640px; }
+}
+
+@media (min-width: 1280px) {
+  .command-palette-details-dialog { width: 720px; }
+}
+
+.page-stack-transition-height { overflow-y: scroll; transition-timing-funct=
+ion: cubic-bezier(0.25, 0.46, 0.45, 0.94); transition-duration: 0.2s; trans=
+ition-property: max-height, min-height; }
+
+.page-stack-transition-height.no-transition { transition-duration: 0s; }
+
+.command-palette-input-group { position: relative; z-index: 0; padding-left=
+: 0px; color: var(--color-fg-subtle); }
+
+.command-palette-input-group .no-focus-indicator:focus, .command-palette-in=
+put-group .no-focus-indicator:focus-visible { border: 0px !important; outli=
+ne: none !important; }
+
+.command-palette-input-group .command-palette-typeahead { position: absolut=
+e; z-index: 1; padding: inherit; pointer-events: none; }
+
+.command-palette-input-group .command-palette-typeahead .typeahead-segment =
+{ white-space: pre; }
+
+.command-palette-input-group .command-palette-typeahead .typeahead-segment.=
+input-mirror { opacity: 0; }
+
+.command-palette-input-group .typeahead-input { padding: inherit; }
+
+.command-palette-input-clear-button { color: var(--color-fg-subtle); }
+
+.command-palette-input-clear-button:hover { color: var(--color-fg-muted); }
+
+themed-picture { visibility: hidden; }
+
+.SidePanel > .Overlay-header > .Overlay-headerContentWrap > .Overlay-titleW=
+rap { padding-top: 8px; }
+
+.SidePanel > .Overlay-header > .Overlay-headerContentWrap > .Overlay-action=
+Wrap { padding: 8px; }
+
+.is-auto-complete-loading :not(input).form-control { padding-right: 0px; ba=
+ckground-image: none; }
+
+.ActionBar { position: relative; min-width: 0px; align-items: center; flex-=
+grow: 1; flex-shrink: 1; box-sizing: content-box; overflow: hidden; justify=
+-content: flex-end; display: flex !important; }
+
+.ActionBar-item-container { display: flex; box-sizing: content-box; align-i=
+tems: center; flex-shrink: 0; flex-grow: 0; }
+
+.ActionBar-item { position: relative; flex-shrink: 0; }
+
+.ActionBar-more-menu { flex-shrink: 0; }
+
+.ActionBar-divider { height: calc(var(--primer-control-medium-size, 32px)/2=
+); margin: 0 var(--primer-controlStack-medium-gap-condensed, 8px); border-l=
+eft: var(--primer-borderWidth-thin, 1px) solid var(--color-border-subtle); =
+}
+
+.ActionBar--small .ActionBar-divider { margin: 0 var(--primer-controlStack-=
+small-gap-condensed, 8px); }
+
+.ActionBar--large .ActionBar-divider { margin: 0 var(--primer-controlStack-=
+large-gap-condensed, 8px); }
+
+@media (pointer: coarse) {
+  .ActionBar .ActionBar-item-container { gap: calc(var(--primer-control-min=
+Target-coarse, 44px) - var(--primer-control-medium-size, 32px)); }
+  .ActionBar--small .ActionBar-item-container { gap: calc(var(--primer-cont=
+rol-minTarget-coarse, 44px) - var(--primer-control-small-size, 28px)); }
+  .ActionBar--large .ActionBar-item-container { gap: calc(var(--primer-cont=
+rol-minTarget-coarse, 44px) - var(--primer-control-large-size, 40px)); }
+}
+
+.ActionMenu { position: absolute; z-index: 999; display: none; background-c=
+olor: var(--color-canvas-overlay); }
+
+.CommentBox-header { display: flex; background-color: var(--color-canvas-su=
+btle); border-top-left-radius: 6px; border-top-right-radius: 6px; }
+
+.CommentBox-header.CommentBox-header { margin-bottom: 0px; }
+
+.CommentBox-header .tabnav-tabs { margin-top: -1px; margin-left: -1px; flex=
+-shrink: 0; }
+
+.CommentBox-toolbar { display: flex; min-width: 0px; margin-right: var(--ba=
+se-size-4, 4px); flex-shrink: 1; flex-grow: 1; }
+
+.CommentBox-toolbar .Button--invisible { color: var(--color-fg-muted); }
+
+.CommentBox-input { display: block; min-height: 100px; padding: var(--prime=
+r-stack-padding-normal, 16px); line-height: 1.5; color: var(--color-fg-defa=
+ult); resize: vertical; border: 0px; }
+
+.CommentBox-input.CommentBox-input.CommentBox-input:focus { box-shadow: non=
+e; }
+
+.CommentBox-input::placeholder { visibility: hidden; }
+
+.CommentBox-placeholder { position: absolute; top: 0px; left: 0px; display:=
+ none; padding: var(--primer-stack-padding-normal, 16px); color: var(--colo=
+r-fg-subtle); pointer-events: none; }
+
+.CommentBox-input:placeholder-shown + .CommentBox-placeholder { display: bl=
+ock; }
+
+.CommentBox:has(.CommentBox-input:focus, .Button:active) { outline: 2px sol=
+id var(--color-accent-emphasis); outline-offset: -1px; }
+
+@supports not selector(.CommentBox:has(*:focus)) {
+  .CommentBox:focus-within { outline: 2px solid var(--color-accent-emphasis=
+); outline-offset: -1px; }
+}
+
+.CommentBox .dragover .CommentBox-input, .CommentBox .dragover .CommentBox-=
+input:focus { border-radius: 10px; outline: 2px dashed var(--color-border-d=
+efault); outline-offset: -6px; box-shadow: none; }
+
+.PageHeader { display: grid; grid-template-columns: 1fr; grid-template-area=
+s: "contextBar" "titleBar" "description" "navigation"; margin-bottom: 24px;=
+ }
+
+@media (max-width: 768px) {
+  .PageHeader { margin-bottom: 16px; }
+}
+
+.PageHeader .PageHeader-contextBar { display: flex; column-gap: 16px; align=
+-items: center; }
+
+.PageHeader .PageHeader-contextBar .PageHeader-parentLink { flex: 1 1 auto;=
+ }
+
+.PageHeader .PageHeader-parentLink a { display: inline-grid; grid-template-=
+columns: min-content auto; gap: 4px; align-items: center; padding: 4px 8px;=
+ margin-left: -8px; font-size: 14px; line-height: 1.25; color: var(--color-=
+fg-muted); border-radius: 6px; }
+
+.PageHeader .PageHeader-parentLink a:hover { color: var(--color-fg-default)=
+; text-decoration: none; background: var(--color-canvas-subtle); }
+
+.PageHeader .PageHeader-parentLink .PageHeader-parentLink-label { overflow:=
+ hidden; text-overflow: ellipsis; white-space: nowrap; }
+
+.PageHeader .PageHeader-titleBar { display: flex; column-gap: 16px; }
+
+.PageHeader .PageHeader-titleBar .PageHeader-titleWrap { flex: 1 1 auto; }
+
+.PageHeader .PageHeader-titleBar .PageHeader-actions { flex: 0 0 auto; }
+
+.PageHeader .PageHeader-titleWrap { display: flex; column-gap: 8px; }
+
+.PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--large { font: 400 v=
+ar(--primer-text-title-size-large, 2rem) -apple-system,BlinkMacSystemFont,"=
+Segoe UI","Noto Sans",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe=
+ UI Emoji"; }
+
+.PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--large .PageHeader-b=
+ackButton { height: var(--primer-text-title-lineHeight-large, 2.5rem); }
+
+.PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--large .PageHeader-l=
+eadingVisual { max-height: var(--primer-text-title-lineHeight-large, 2.5rem=
+); }
+
+.PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--medium { font: 600 =
+var(--primer-text-title-size-medium, 1.25rem) -apple-system,BlinkMacSystemF=
+ont,"Segoe UI","Noto Sans",Helvetica,Arial,sans-serif,"Apple Color Emoji","=
+Segoe UI Emoji"; }
+
+.PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--subtitle { font: 40=
+0 var(--primer-text-subtitle-size, 1.25rem) -apple-system,BlinkMacSystemFon=
+t,"Segoe UI","Noto Sans",Helvetica,Arial,sans-serif,"Apple Color Emoji","Se=
+goe UI Emoji"; }
+
+@media (max-width: 768px) {
+  .PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--large { font: 600=
+ var(--primer-text-title-size-medium, 1.25rem) -apple-system,BlinkMacSystem=
+Font,"Segoe UI","Noto Sans",Helvetica,Arial,sans-serif,"Apple Color Emoji",=
+"Segoe UI Emoji"; }
+  .PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--medium { font: 60=
+0 var(--primer-text-title-size-medium, 1.25rem) -apple-system,BlinkMacSyste=
+mFont,"Segoe UI","Noto Sans",Helvetica,Arial,sans-serif,"Apple Color Emoji"=
+,"Segoe UI Emoji"; }
+  .PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--subtitle { font: =
+400 var(--primer-text-title-size-medium, 1.25rem) -apple-system,BlinkMacSys=
+temFont,"Segoe UI","Noto Sans",Helvetica,Arial,sans-serif,"Apple Color Emoj=
+i","Segoe UI Emoji"; }
+}
+
+.PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--hasLeadingVisual { =
+display: flex; }
+
+.PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--hasBackButton:first=
+-child { margin-left: -4px; }
+
+@media (min-width: 768px) {
+  .PageHeader .PageHeader-titleWrap.PageHeader-titleWrap--hasBackButton:fir=
+st-child { align-items: center; margin-left: 8px; grid-template-columns: mi=
+n-content auto; display: flex !important; }
+}
+
+.PageHeader .PageHeader-titleWrap h1, .PageHeader .PageHeader-titleWrap h2,=
+ .PageHeader .PageHeader-titleWrap h3 { font-size: inherit; font-weight: in=
+herit; line-height: inherit; }
+
+.PageHeader .PageHeader-titleWrap .PageHeader-title { display: inline-grid;=
+ grid-auto-flow: column; grid-template-columns: auto; align-items: center; =
+gap: 8px; }
+
+.PageHeader .PageHeader-titleWrap .PageHeader-leadingVisual { display: grid=
+; align-items: center; max-height: var(--primer-text-title-lineHeight-large=
+, 1.5); }
+
+.PageHeader .PageHeader-titleWrap .PageHeader-trailingVisual { display: gri=
+d; align-items: center; max-height: var(--primer-text-title-lineHeight-larg=
+e, 1.5); }
+
+.PageHeader .PageHeader-titleWrap .PageHeader-backButton { position: relati=
+ve; display: grid; width: 16px; height: var(--primer-text-title-lineHeight-=
+medium, 1.5rem); align-items: center; padding: 0px; color: inherit; border-=
+radius: 6px; }
+
+@media (max-width: 768px) {
+  .PageHeader .PageHeader-titleWrap .PageHeader-backButton { display: none;=
+ }
+}
+
+.PageHeader .PageHeader-description { margin-top: 8px; overflow: auto; }
+
+.PageHeader .PageHeader-navigation { margin-top: 16px; overflow: auto; }
+
+.QueryBuilder-StyledInput { display: inline-flex; width: 100%; min-height: =
+var(--primer-control-medium-size, 32px); color: var(--color-fg-default); ve=
+rtical-align: middle; cursor: text; background-color: var(--color-canvas-de=
+fault); border: 1px solid var(--color-border-default); border-radius: var(-=
+-primer-borderRadius-medium, 6px); outline: none; align-items: center; gap:=
+ 4px; }
+
+.QueryBuilder-focus { border-color: transparent; outline: 2px solid var(--c=
+olor-accent-fg); box-shadow: transparent 0px 0px 1px inset; }
+
+.QueryBuilder-StyledInputContainer { position: relative; display: flex; ove=
+rflow: auto hidden; font-size: inherit; align-items: center; flex: 1 1 0%; =
+align-self: stretch; }
+
+.QueryBuilder-StyledInputContainer::-webkit-scrollbar { display: none; }
+
+.QueryBuilder-StyledInputContent { position: absolute; display: inline-flex=
+; padding: 0px; word-break: break-word; white-space: pre; user-select: none=
+; flex: 1 1 0%; }
+
+.QueryBuilder-leadingVisualWrap { margin: 4px 4px 4px 8px; color: var(--col=
+or-fg-muted); }
+
+.QueryBuilder-spacer { width: 8px; height: 100%; }
+
+.QueryBuilder-InputWrapper { width: 100%; align-self: stretch; }
+
+.QueryBuilder-Sizer { position: absolute; top: 0px; left: 0px; height: 0px;=
+ overflow: scroll; white-space: pre; visibility: hidden; }
+
+.QueryBuilder-Input { position: relative; display: flex; min-width: 100%; p=
+adding: 0px; overflow: auto hidden; color: transparent; resize: none; backg=
+round: transparent; border: 0px; outline: none; caret-color: var(--color-fg=
+-default); }
+
+.QueryBuilder-Input:focus { border: 0px !important; box-shadow: none !impor=
+tant; }
+
+.QueryBuilder-ListItem { display: grid; grid-template-columns: max-content =
+minmax(0px, auto) max-content; grid-template-areas: "leadingVisual label tr=
+ailingLabel"; user-select: unset; }
+
+.QueryBuilder-ListItem-link { color: inherit; text-decoration: none !import=
+ant; }
+
+.QueryBuilder-ListItem-trailing { grid-area: trailingLabel / trailingLabel =
+/ trailingLabel / trailingLabel; }
+
+.QueryBuilder-ListWrap { max-height: 20em; padding: 8px; overflow-x: hidden=
+; overflow-y: auto !important; }
+
+.QueryBuilder [data-type=3D"filter-value"] { color: var(--color-accent-fg);=
+ background-color: var(--color-accent-subtle); border-radius: var(--primer-=
+borderRadius-small, 3px); }
+
+.QueryBuilder .qb-filter-value { color: var(--color-accent-fg); background-=
+color: var(--color-accent-subtle); border-radius: var(--primer-borderRadius=
+-small, 3px); }
+
+.QueryBuilder .qb-entity { color: var(--color-prettylights-syntax-entity); =
+}
+
+.QueryBuilder .qb-constant { color: var(--color-prettylights-syntax-constan=
+t); }
+
+.QueryBuilder .ActionList-sectionDivider:not(:empty) { padding: 0px; }
+
+@media (min-width: 1012px) {
+  .hx_actions-sidebar { max-width: 320px; }
+}
+
+.hx_anim-fade-out { animation-name: hx-fade-out; animation-duration: 1s; an=
+imation-fill-mode: forwards; animation-timing-function: ease-out; }
+
+@keyframes hx-fade-out {=20
+  0% { opacity: 1; }
+  100% { opacity: 0; }
+}
+
+.AvatarStack--large { min-width: 44px; height: 32px; }
+
+.AvatarStack--large.AvatarStack--two { min-width: 48px; }
+
+.AvatarStack--large.AvatarStack--three-plus { min-width: 52px; }
+
+.AvatarStack--large .AvatarStack-body .avatar { width: 32px; height: 32px; =
+margin-right: -28px; }
+
+.AvatarStack--large .AvatarStack-body:hover .avatar { margin-right: 4px; }
+
+.AvatarStack--large .avatar.avatar-more::before { width: 32px; }
+
+.AvatarStack--large .avatar.avatar-more::after { width: 30px; }
+
+.AvatarStack--large .avatar.avatar-more::after, .AvatarStack--large .avatar=
+.avatar-more::before { height: 32px; }
+
+.hx_avatar_stack_commit .AvatarStack { min-width: 24px; height: 24px; }
+
+.hx_avatar_stack_commit .AvatarStack .avatar { width: 24px; height: 24px; }
+
+.hx_avatar_stack_commit .AvatarStack.AvatarStack--two { min-width: 40px; }
+
+.hx_avatar_stack_commit .AvatarStack.AvatarStack--three-plus { min-width: 4=
+4px; }
+
+.hx_flex-avatar-stack { display: flex; align-items: center; }
+
+.hx_flex-avatar-stack-item { min-width: 0px; max-width: 24px; }
+
+.hx_flex-avatar-stack-item .avatar { display: block; background-color: var(=
+--color-canvas-default); border: 2px solid var(--color-canvas-default); box=
+-shadow: none; }
+
+.hx_flex-avatar-stack-item:last-of-type { flex-shrink: 0; max-width: none; =
+}
+
+.Box-row--focus-gray.navigation-focus .AvatarStack-body { background-color:=
+ var(--color-canvas-subtle); }
+
+.AvatarStack-body:not(:hover) { background-color: transparent; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .avatar-more { display: no=
+ne; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body .avatar:=
+nth-child(n+4) { display: flex; opacity: 1; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body:not(:hov=
+er) .avatar:nth-of-type(n+6) { display: none; opacity: 0; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body > .avata=
+r:nth-of-type(1) { z-index: 5; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body > .avata=
+r:nth-of-type(2) { z-index: 4; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body > .avata=
+r:nth-of-type(3) { z-index: 3; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body > .avata=
+r:nth-of-type(4) { z-index: 2; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body > .avata=
+r:nth-of-type(5) { z-index: 1; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body:not(:hov=
+er) > .avatar-more + .avatar:nth-of-type(3) img { opacity: 0.5; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body:not(:hov=
+er) > .avatar-more ~ .avatar:nth-of-type(4) img { opacity: 0.33; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body:not(:hov=
+er) > .avatar-more ~ .avatar:nth-of-type(5) img { opacity: 0.25; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body:not(:hov=
+er) > .avatar-more + .avatar:nth-of-type(3) { margin-right: 0px; margin-lef=
+t: -6px; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body:not(:hov=
+er) > .avatar-more ~ .avatar:nth-of-type(4) { margin-right: 0px; margin-lef=
+t: -18px; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus .AvatarStack-body:not(:hov=
+er) > .avatar-more ~ .avatar:nth-of-type(5) { margin-right: 0px; margin-lef=
+t: -18px; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus.AvatarStack--right .Avatar=
+Stack-body:not(:hover) > .avatar-more + .avatar:nth-of-type(3) { margin-rig=
+ht: -6px; margin-left: 0px; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus.AvatarStack--right .Avatar=
+Stack-body:not(:hover) > .avatar-more ~ .avatar:nth-of-type(4) { margin-rig=
+ht: -18px; margin-left: 0px; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus.AvatarStack--right .Avatar=
+Stack-body:not(:hover) > .avatar-more ~ .avatar:nth-of-type(5) { margin-rig=
+ht: -18px; margin-left: 0px; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus.AvatarStack--large .Avatar=
+Stack-body:not(:hover) > .avatar-more + .avatar:nth-of-type(3) { margin-rig=
+ht: 0px; margin-left: -2px; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus.AvatarStack--large .Avatar=
+Stack-body:not(:hover) > .avatar-more ~ .avatar:nth-of-type(4) { margin-rig=
+ht: 0px; margin-left: -30px; }
+
+.AvatarStack--three-plus.AvatarStack--three-plus.AvatarStack--large .Avatar=
+Stack-body:not(:hover) > .avatar-more ~ .avatar:nth-of-type(5) { margin-rig=
+ht: 0px; margin-left: -30px; }
+
+.hx_avatar_stack_commit .AvatarStack--three-plus.AvatarStack--three-plus .A=
+vatarStack-body:not(:hover) > .avatar-more + .avatar:nth-of-type(3) { margi=
+n-right: 0px; margin-left: -10px; }
+
+.hx_avatar_stack_commit .AvatarStack--three-plus.AvatarStack--three-plus .A=
+vatarStack-body:not(:hover) > .avatar-more ~ .avatar:nth-of-type(4) { margi=
+n-right: 0px; margin-left: -21px; }
+
+.hx_avatar_stack_commit .AvatarStack--three-plus.AvatarStack--three-plus .A=
+vatarStack-body:not(:hover) > .avatar-more ~ .avatar:nth-of-type(5) { margi=
+n-right: 0px; margin-left: -21px; }
+
+.hx_badge-search-container { cursor: text; }
+
+.hx_badge-search-container .hx_badge-input { border: 0px; outline: 0px; box=
+-shadow: none; }
+
+.hx_badge-search-container .hx_badge-input:focus { border: 0px !important; =
+box-shadow: none !important; }
+
+.hx_badge-search-container .hx_badge-input::placeholder { font-size: 12px; =
+}
+
+.hx_badge-search-container .hx_badge-input-inline { height: 30px; }
+
+.hx_badge { cursor: pointer; }
+
+.hx_badge[aria-pressed=3D"true"] { color: var(--color-fg-on-emphasis)  !imp=
+ortant; background-color: var(--color-accent-emphasis)  !important; border-=
+color: var(--color-accent-emphasis)  !important; }
+
+.hx_Box--firstRowRounded0 .Box-row:first-of-type { border-top-left-radius: =
+0px; border-top-right-radius: 0px; }
+
+.Box-row:first-of-type { border-top-color: transparent; }
+
+.hx_Box-row--with-top-border:first-of-type { border-top-color: inherit; }
+
+.Box--overlay [data-close-dialog], .Box-overlay--narrow [data-close-dialog]=
+, .Box-overlay--wide [data-close-dialog] { z-index: 1; }
+
+.dropdown-item.btn-link:disabled, .dropdown-item.btn-link:disabled:hover, .=
+dropdown-item.btn-link[aria-disabled=3D"true"], .dropdown-item.btn-link[ari=
+a-disabled=3D"true"]:hover { background-color: transparent; }
+
+@media (-webkit-min-device-pixel-ratio: 2) and (-webkit-min-device-pixel-ra=
+tio: 0), (-webkit-min-device-pixel-ratio: 2) and (min-resolution: 0.001dpcm=
+) {
+  g-emoji { font-size: 1.25em; }
+}
+
+.hx_create-pr-button:hover { border-right-width: 0px; }
+
+.hx_create-pr-button:hover + .BtnGroup-parent .BtnGroup-item { border-left-=
+width: 1px; }
+
+summary[type=3D"button"].btn { appearance: none; }
+
+.form-control:-webkit-autofill { -webkit-text-fill-color: var(--color-fg-de=
+fault); box-shadow: inset 0 0 0 32px var(--color-canvas-default)  !importan=
+t; }
+
+.form-control:-webkit-autofill:focus { box-shadow: inset 0 0 0 32px var(--c=
+olor-canvas-default),0 0 0 2px var(--color-accent-fg)  !important; }
+
+::-webkit-calendar-picker-indicator { filter: invert(50%); }
+
+[data-color-mode=3D"light"][data-light-theme*=3D"dark"] ::selection, [data-=
+color-mode=3D"dark"][data-dark-theme*=3D"dark"] ::selection { background-co=
+lor: var(--color-accent-muted); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] ::selection { back=
+ground-color: var(--color-accent-muted); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] ::selection { backg=
+round-color: var(--color-accent-muted); }
+}
+
+@font-face { font-family: "Noto Sans"; src: local("sans-serif"); unicode-ra=
+nge: U+60; }
+
+[data-color-mode=3D"light"][data-light-theme*=3D"dark"], [data-color-mode=
+=3D"dark"][data-dark-theme*=3D"dark"] { --color-workflow-card-connector:var=
+(--color-scale-gray-5); --color-workflow-card-connector-bg:var(--color-scal=
+e-gray-5); --color-workflow-card-connector-inactive:var(--color-border-defa=
+ult); --color-workflow-card-connector-inactive-bg:var(--color-border-defaul=
+t); --color-workflow-card-connector-highlight:var(--color-scale-blue-5); --=
+color-workflow-card-connector-highlight-bg:var(--color-scale-blue-5); --col=
+or-workflow-card-bg:var(--color-scale-gray-7); --color-workflow-card-inacti=
+ve-bg:var(--color-canvas-inset); --color-workflow-card-header-shadow:rgba(2=
+7, 31, 35, 0.04); --color-workflow-card-progress-complete-bg:var(--color-sc=
+ale-blue-5); --color-workflow-card-progress-incomplete-bg:var(--color-scale=
+-gray-6); --color-discussions-state-answered-icon:var(--color-scale-green-3=
+); --color-bg-discussions-row-emoji-box:var(--color-scale-gray-6); --color-=
+notifications-button-text:var(--color-scale-white); --color-notifications-b=
+utton-hover-text:var(--color-scale-white); --color-notifications-button-hov=
+er-bg:var(--color-scale-blue-4); --color-notifications-row-read-bg:var(--co=
+lor-canvas-default); --color-notifications-row-bg:var(--color-canvas-subtle=
+); --color-icon-directory:var(--color-fg-muted); --color-checks-step-error-=
+icon:var(--color-scale-red-4); --color-calendar-halloween-graph-day-L1-bg:#=
+631c03; --color-calendar-halloween-graph-day-L2-bg:#bd561d; --color-calenda=
+r-halloween-graph-day-L3-bg:#fa7a18; --color-calendar-halloween-graph-day-L=
+4-bg:#fddf68; --color-calendar-winter-graph-day-L1-bg:#0A3069; --color-cale=
+ndar-winter-graph-day-L2-bg:#0969DA; --color-calendar-winter-graph-day-L3-b=
+g:#54AEFF; --color-calendar-winter-graph-day-L4-bg:#B6E3FF; --color-calenda=
+r-graph-day-bg:var(--color-scale-gray-8); --color-calendar-graph-day-border=
+:rgba(27, 31, 35, 0.06); --color-calendar-graph-day-L1-bg:#0e4429; --color-=
+calendar-graph-day-L2-bg:#006d32; --color-calendar-graph-day-L3-bg:#26a641;=
+ --color-calendar-graph-day-L4-bg:#39d353; --color-calendar-graph-day-L1-bo=
+rder:rgba(255, 255, 255, 0.05); --color-calendar-graph-day-L2-border:rgba(2=
+55, 255, 255, 0.05); --color-calendar-graph-day-L3-border:rgba(255, 255, 25=
+5, 0.05); --color-calendar-graph-day-L4-border:rgba(255, 255, 255, 0.05); -=
+-color-user-mention-fg:var(--color-scale-yellow-0); --color-user-mention-bg=
+:var(--color-scale-yellow-8); --color-dashboard-feed-bg:var(--color-scale-g=
+ray-9); --color-text-white:var(--color-scale-white); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] { --color-workflow=
+-card-connector:var(--color-scale-gray-5); --color-workflow-card-connector-=
+bg:var(--color-scale-gray-5); --color-workflow-card-connector-inactive:var(=
+--color-border-default); --color-workflow-card-connector-inactive-bg:var(--=
+color-border-default); --color-workflow-card-connector-highlight:var(--colo=
+r-scale-blue-5); --color-workflow-card-connector-highlight-bg:var(--color-s=
+cale-blue-5); --color-workflow-card-bg:var(--color-scale-gray-7); --color-w=
+orkflow-card-inactive-bg:var(--color-canvas-inset); --color-workflow-card-h=
+eader-shadow:rgba(27, 31, 35, 0.04); --color-workflow-card-progress-complet=
+e-bg:var(--color-scale-blue-5); --color-workflow-card-progress-incomplete-b=
+g:var(--color-scale-gray-6); --color-discussions-state-answered-icon:var(--=
+color-scale-green-3); --color-bg-discussions-row-emoji-box:var(--color-scal=
+e-gray-6); --color-notifications-button-text:var(--color-scale-white); --co=
+lor-notifications-button-hover-text:var(--color-scale-white); --color-notif=
+ications-button-hover-bg:var(--color-scale-blue-4); --color-notifications-r=
+ow-read-bg:var(--color-canvas-default); --color-notifications-row-bg:var(--=
+color-canvas-subtle); --color-icon-directory:var(--color-fg-muted); --color=
+-checks-step-error-icon:var(--color-scale-red-4); --color-calendar-hallowee=
+n-graph-day-L1-bg:#631c03; --color-calendar-halloween-graph-day-L2-bg:#bd56=
+1d; --color-calendar-halloween-graph-day-L3-bg:#fa7a18; --color-calendar-ha=
+lloween-graph-day-L4-bg:#fddf68; --color-calendar-winter-graph-day-L1-bg:#0=
+A3069; --color-calendar-winter-graph-day-L2-bg:#0969DA; --color-calendar-wi=
+nter-graph-day-L3-bg:#54AEFF; --color-calendar-winter-graph-day-L4-bg:#B6E3=
+FF; --color-calendar-graph-day-bg:var(--color-scale-gray-8); --color-calend=
+ar-graph-day-border:rgba(27, 31, 35, 0.06); --color-calendar-graph-day-L1-b=
+g:#0e4429; --color-calendar-graph-day-L2-bg:#006d32; --color-calendar-graph=
+-day-L3-bg:#26a641; --color-calendar-graph-day-L4-bg:#39d353; --color-calen=
+dar-graph-day-L1-border:rgba(255, 255, 255, 0.05); --color-calendar-graph-d=
+ay-L2-border:rgba(255, 255, 255, 0.05); --color-calendar-graph-day-L3-borde=
+r:rgba(255, 255, 255, 0.05); --color-calendar-graph-day-L4-border:rgba(255,=
+ 255, 255, 0.05); --color-user-mention-fg:var(--color-scale-yellow-0); --co=
+lor-user-mention-bg:var(--color-scale-yellow-8); --color-dashboard-feed-bg:=
+var(--color-scale-gray-9); --color-text-white:var(--color-scale-white); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] { --color-workflow-=
+card-connector:var(--color-scale-gray-5); --color-workflow-card-connector-b=
+g:var(--color-scale-gray-5); --color-workflow-card-connector-inactive:var(-=
+-color-border-default); --color-workflow-card-connector-inactive-bg:var(--c=
+olor-border-default); --color-workflow-card-connector-highlight:var(--color=
+-scale-blue-5); --color-workflow-card-connector-highlight-bg:var(--color-sc=
+ale-blue-5); --color-workflow-card-bg:var(--color-scale-gray-7); --color-wo=
+rkflow-card-inactive-bg:var(--color-canvas-inset); --color-workflow-card-he=
+ader-shadow:rgba(27, 31, 35, 0.04); --color-workflow-card-progress-complete=
+-bg:var(--color-scale-blue-5); --color-workflow-card-progress-incomplete-bg=
+:var(--color-scale-gray-6); --color-discussions-state-answered-icon:var(--c=
+olor-scale-green-3); --color-bg-discussions-row-emoji-box:var(--color-scale=
+-gray-6); --color-notifications-button-text:var(--color-scale-white); --col=
+or-notifications-button-hover-text:var(--color-scale-white); --color-notifi=
+cations-button-hover-bg:var(--color-scale-blue-4); --color-notifications-ro=
+w-read-bg:var(--color-canvas-default); --color-notifications-row-bg:var(--c=
+olor-canvas-subtle); --color-icon-directory:var(--color-fg-muted); --color-=
+checks-step-error-icon:var(--color-scale-red-4); --color-calendar-halloween=
+-graph-day-L1-bg:#631c03; --color-calendar-halloween-graph-day-L2-bg:#bd561=
+d; --color-calendar-halloween-graph-day-L3-bg:#fa7a18; --color-calendar-hal=
+loween-graph-day-L4-bg:#fddf68; --color-calendar-winter-graph-day-L1-bg:#0A=
+3069; --color-calendar-winter-graph-day-L2-bg:#0969DA; --color-calendar-win=
+ter-graph-day-L3-bg:#54AEFF; --color-calendar-winter-graph-day-L4-bg:#B6E3F=
+F; --color-calendar-graph-day-bg:var(--color-scale-gray-8); --color-calenda=
+r-graph-day-border:rgba(27, 31, 35, 0.06); --color-calendar-graph-day-L1-bg=
+:#0e4429; --color-calendar-graph-day-L2-bg:#006d32; --color-calendar-graph-=
+day-L3-bg:#26a641; --color-calendar-graph-day-L4-bg:#39d353; --color-calend=
+ar-graph-day-L1-border:rgba(255, 255, 255, 0.05); --color-calendar-graph-da=
+y-L2-border:rgba(255, 255, 255, 0.05); --color-calendar-graph-day-L3-border=
+:rgba(255, 255, 255, 0.05); --color-calendar-graph-day-L4-border:rgba(255, =
+255, 255, 0.05); --color-user-mention-fg:var(--color-scale-yellow-0); --col=
+or-user-mention-bg:var(--color-scale-yellow-8); --color-dashboard-feed-bg:v=
+ar(--color-scale-gray-9); --color-text-white:var(--color-scale-white); }
+}
+
+:root, [data-color-mode=3D"light"][data-light-theme*=3D"light"], [data-colo=
+r-mode=3D"dark"][data-dark-theme*=3D"light"] { --color-workflow-card-connec=
+tor:var(--color-scale-gray-3); --color-workflow-card-connector-bg:var(--col=
+or-scale-gray-3); --color-workflow-card-connector-inactive:var(--color-bord=
+er-default); --color-workflow-card-connector-inactive-bg:var(--color-border=
+-default); --color-workflow-card-connector-highlight:var(--color-scale-blue=
+-4); --color-workflow-card-connector-highlight-bg:var(--color-scale-blue-4)=
+; --color-workflow-card-bg:var(--color-scale-white); --color-workflow-card-=
+inactive-bg:var(--color-canvas-inset); --color-workflow-card-header-shadow:=
+rgba(0, 0, 0, 0); --color-workflow-card-progress-complete-bg:var(--color-sc=
+ale-blue-4); --color-workflow-card-progress-incomplete-bg:var(--color-scale=
+-gray-2); --color-discussions-state-answered-icon:var(--color-scale-white);=
+ --color-bg-discussions-row-emoji-box:rgba(209, 213, 218, 0.5); --color-not=
+ifications-button-text:var(--color-fg-muted); --color-notifications-button-=
+hover-text:var(--color-fg-default); --color-notifications-button-hover-bg:v=
+ar(--color-scale-gray-2); --color-notifications-row-read-bg:var(--color-can=
+vas-subtle); --color-notifications-row-bg:var(--color-scale-white); --color=
+-icon-directory:var(--color-scale-blue-3); --color-checks-step-error-icon:v=
+ar(--color-scale-red-4); --color-calendar-halloween-graph-day-L1-bg:#ffee4a=
+; --color-calendar-halloween-graph-day-L2-bg:#ffc501; --color-calendar-hall=
+oween-graph-day-L3-bg:#fe9600; --color-calendar-halloween-graph-day-L4-bg:#=
+03001c; --color-calendar-winter-graph-day-L1-bg:#B6E3FF; --color-calendar-w=
+inter-graph-day-L2-bg:#54AEFF; --color-calendar-winter-graph-day-L3-bg:#096=
+9DA; --color-calendar-winter-graph-day-L4-bg:#0A3069; --color-calendar-grap=
+h-day-bg:#ebedf0; --color-calendar-graph-day-border:rgba(27, 31, 35, 0.06);=
+ --color-calendar-graph-day-L1-bg:#9be9a8; --color-calendar-graph-day-L2-bg=
+:#40c463; --color-calendar-graph-day-L3-bg:#30a14e; --color-calendar-graph-=
+day-L4-bg:#216e39; --color-calendar-graph-day-L1-border:rgba(27, 31, 35, 0.=
+06); --color-calendar-graph-day-L2-border:rgba(27, 31, 35, 0.06); --color-c=
+alendar-graph-day-L3-border:rgba(27, 31, 35, 0.06); --color-calendar-graph-=
+day-L4-border:rgba(27, 31, 35, 0.06); --color-user-mention-fg:var(--color-f=
+g-default); --color-user-mention-bg:var(--color-attention-subtle); --color-=
+dashboard-feed-bg:var(--color-scale-white); --color-text-white:var(--color-=
+scale-white); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"light"] { --color-workflo=
+w-card-connector:var(--color-scale-gray-3); --color-workflow-card-connector=
+-bg:var(--color-scale-gray-3); --color-workflow-card-connector-inactive:var=
+(--color-border-default); --color-workflow-card-connector-inactive-bg:var(-=
+-color-border-default); --color-workflow-card-connector-highlight:var(--col=
+or-scale-blue-4); --color-workflow-card-connector-highlight-bg:var(--color-=
+scale-blue-4); --color-workflow-card-bg:var(--color-scale-white); --color-w=
+orkflow-card-inactive-bg:var(--color-canvas-inset); --color-workflow-card-h=
+eader-shadow:rgba(0, 0, 0, 0); --color-workflow-card-progress-complete-bg:v=
+ar(--color-scale-blue-4); --color-workflow-card-progress-incomplete-bg:var(=
+--color-scale-gray-2); --color-discussions-state-answered-icon:var(--color-=
+scale-white); --color-bg-discussions-row-emoji-box:rgba(209, 213, 218, 0.5)=
+; --color-notifications-button-text:var(--color-fg-muted); --color-notifica=
+tions-button-hover-text:var(--color-fg-default); --color-notifications-butt=
+on-hover-bg:var(--color-scale-gray-2); --color-notifications-row-read-bg:va=
+r(--color-canvas-subtle); --color-notifications-row-bg:var(--color-scale-wh=
+ite); --color-icon-directory:var(--color-scale-blue-3); --color-checks-step=
+-error-icon:var(--color-scale-red-4); --color-calendar-halloween-graph-day-=
+L1-bg:#ffee4a; --color-calendar-halloween-graph-day-L2-bg:#ffc501; --color-=
+calendar-halloween-graph-day-L3-bg:#fe9600; --color-calendar-halloween-grap=
+h-day-L4-bg:#03001c; --color-calendar-winter-graph-day-L1-bg:#B6E3FF; --col=
+or-calendar-winter-graph-day-L2-bg:#54AEFF; --color-calendar-winter-graph-d=
+ay-L3-bg:#0969DA; --color-calendar-winter-graph-day-L4-bg:#0A3069; --color-=
+calendar-graph-day-bg:#ebedf0; --color-calendar-graph-day-border:rgba(27, 3=
+1, 35, 0.06); --color-calendar-graph-day-L1-bg:#9be9a8; --color-calendar-gr=
+aph-day-L2-bg:#40c463; --color-calendar-graph-day-L3-bg:#30a14e; --color-ca=
+lendar-graph-day-L4-bg:#216e39; --color-calendar-graph-day-L1-border:rgba(2=
+7, 31, 35, 0.06); --color-calendar-graph-day-L2-border:rgba(27, 31, 35, 0.0=
+6); --color-calendar-graph-day-L3-border:rgba(27, 31, 35, 0.06); --color-ca=
+lendar-graph-day-L4-border:rgba(27, 31, 35, 0.06); --color-user-mention-fg:=
+var(--color-fg-default); --color-user-mention-bg:var(--color-attention-subt=
+le); --color-dashboard-feed-bg:var(--color-scale-white); --color-text-white=
+:var(--color-scale-white); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"light"] { --color-workflow=
+-card-connector:var(--color-scale-gray-3); --color-workflow-card-connector-=
+bg:var(--color-scale-gray-3); --color-workflow-card-connector-inactive:var(=
+--color-border-default); --color-workflow-card-connector-inactive-bg:var(--=
+color-border-default); --color-workflow-card-connector-highlight:var(--colo=
+r-scale-blue-4); --color-workflow-card-connector-highlight-bg:var(--color-s=
+cale-blue-4); --color-workflow-card-bg:var(--color-scale-white); --color-wo=
+rkflow-card-inactive-bg:var(--color-canvas-inset); --color-workflow-card-he=
+ader-shadow:rgba(0, 0, 0, 0); --color-workflow-card-progress-complete-bg:va=
+r(--color-scale-blue-4); --color-workflow-card-progress-incomplete-bg:var(-=
+-color-scale-gray-2); --color-discussions-state-answered-icon:var(--color-s=
+cale-white); --color-bg-discussions-row-emoji-box:rgba(209, 213, 218, 0.5);=
+ --color-notifications-button-text:var(--color-fg-muted); --color-notificat=
+ions-button-hover-text:var(--color-fg-default); --color-notifications-butto=
+n-hover-bg:var(--color-scale-gray-2); --color-notifications-row-read-bg:var=
+(--color-canvas-subtle); --color-notifications-row-bg:var(--color-scale-whi=
+te); --color-icon-directory:var(--color-scale-blue-3); --color-checks-step-=
+error-icon:var(--color-scale-red-4); --color-calendar-halloween-graph-day-L=
+1-bg:#ffee4a; --color-calendar-halloween-graph-day-L2-bg:#ffc501; --color-c=
+alendar-halloween-graph-day-L3-bg:#fe9600; --color-calendar-halloween-graph=
+-day-L4-bg:#03001c; --color-calendar-winter-graph-day-L1-bg:#B6E3FF; --colo=
+r-calendar-winter-graph-day-L2-bg:#54AEFF; --color-calendar-winter-graph-da=
+y-L3-bg:#0969DA; --color-calendar-winter-graph-day-L4-bg:#0A3069; --color-c=
+alendar-graph-day-bg:#ebedf0; --color-calendar-graph-day-border:rgba(27, 31=
+, 35, 0.06); --color-calendar-graph-day-L1-bg:#9be9a8; --color-calendar-gra=
+ph-day-L2-bg:#40c463; --color-calendar-graph-day-L3-bg:#30a14e; --color-cal=
+endar-graph-day-L4-bg:#216e39; --color-calendar-graph-day-L1-border:rgba(27=
+, 31, 35, 0.06); --color-calendar-graph-day-L2-border:rgba(27, 31, 35, 0.06=
+); --color-calendar-graph-day-L3-border:rgba(27, 31, 35, 0.06); --color-cal=
+endar-graph-day-L4-border:rgba(27, 31, 35, 0.06); --color-user-mention-fg:v=
+ar(--color-fg-default); --color-user-mention-bg:var(--color-attention-subtl=
+e); --color-dashboard-feed-bg:var(--color-scale-white); --color-text-white:=
+var(--color-scale-white); }
+}
+
+.hx_color-icon-directory { color: var(--color-icon-directory); }
+
+.hx_comment-box--tip::after { background-image: linear-gradient(var(--color=
+-canvas-default), var(--color-canvas-default))  !important; }
+
+.hx_keyword-hl { background-color: var(--color-search-keyword-hl); }
+
+.hx_dot-fill-pending-icon { color: var(--color-attention-emphasis)  !import=
+ant; }
+
+@media (max-width: 543px) {
+  [data-color-mode=3D"light"][data-light-theme*=3D"dark"], [data-color-mode=
+=3D"dark"][data-dark-theme*=3D"dark"] { --color-fg-default: var(--color-sca=
+le-gray-0); --color-canvas-default: var(--color-scale-gray-8); }
+}
+
+@media (max-width: 543px) and (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] { --color-fg-defau=
+lt: var(--color-scale-gray-0); --color-canvas-default: var(--color-scale-gr=
+ay-8); }
+}
+
+@media (max-width: 543px) and (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] { --color-fg-defaul=
+t: var(--color-scale-gray-0); --color-canvas-default: var(--color-scale-gra=
+y-8); }
+}
+
+:root[data-color-mode=3D"dark"] .entry-content [href$=3D"#gh-light-mode-onl=
+y"], :root[data-color-mode=3D"dark"] .comment-body [href$=3D"#gh-light-mode=
+-only"], :root[data-color-mode=3D"dark"] .readme [href$=3D"#gh-light-mode-o=
+nly"] { display: none; }
+
+:root[data-color-mode=3D"light"] .entry-content [href$=3D"#gh-dark-mode-onl=
+y"], :root[data-color-mode=3D"light"] .comment-body [href$=3D"#gh-dark-mode=
+-only"], :root[data-color-mode=3D"light"] .readme [href$=3D"#gh-dark-mode-o=
+nly"] { display: none; }
+
+@media (prefers-color-scheme: dark) {
+  :root[data-color-mode=3D"auto"] .entry-content [href$=3D"#gh-light-mode-o=
+nly"], :root[data-color-mode=3D"auto"] .comment-body [href$=3D"#gh-light-mo=
+de-only"], :root[data-color-mode=3D"auto"] .readme [href$=3D"#gh-light-mode=
+-only"] { display: none; }
+}
+
+@media (prefers-color-scheme: light) {
+  :root[data-color-mode=3D"auto"] .entry-content [href$=3D"#gh-dark-mode-on=
+ly"], :root[data-color-mode=3D"auto"] .comment-body [href$=3D"#gh-dark-mode=
+-only"], :root[data-color-mode=3D"auto"] .readme [href$=3D"#gh-dark-mode-on=
+ly"] { display: none; }
+}
+
+.colorblind-themes-v1 { --color-open-fg: var(--color-success-fg); --color-o=
+pen-emphasis: var(--color-success-emphasis); --color-open-muted: var(--colo=
+r-success-muted); --color-open-subtle: var(--color-success-subtle); --color=
+-closed-fg: var(--color-danger-fg); --color-closed-emphasis: var(--color-da=
+nger-emphasis); --color-closed-muted: var(--color-danger-muted); --color-cl=
+osed-subtle: var(--color-danger-subtle); }
+
+.dropdown-item:focus [class*=3D"color-text-"], .dropdown-item:hover [class*=
+=3D"color-text-"] { color: inherit !important; }
+
+.filter-item.selected [class*=3D"color-text-"] { color: inherit !important;=
+ }
+
+body:not(.intent-mouse) .hx_focus-input:focus + .hx_focus-target { box-shad=
+ow: 0 0 0 2px var(--color-accent-fg); }
+
+.reset-btn-override { font-weight: var(--base-text-weight-semibold, 600); c=
+olor: var(--color-fg-muted); }
+
+.reset-btn-override:hover { color: var(--color-accent-fg); text-decoration:=
+ none; }
+
+.reset-btn-override:hover .reset-btn-override-icon { background-color: var(=
+--color-accent-emphasis); }
+
+.reset-btn-override-icon { width: 18px; height: 18px; padding: 1px; margin-=
+right: 3px; color: var(--color-fg-on-emphasis); text-align: center; backgro=
+und-color: var(--color-neutral-emphasis); border-radius: 6px; }
+
+.is-auto-complete-loading .form-control { padding-right: 30px; background-r=
+epeat: no-repeat; background-position: 50% 50%; background-size: 16px; }
+
+[data-color-mode=3D"dark"] .is-auto-complete-loading .form-control { backgr=
+ound-image: url("/images/spinners/octocat-spinner-darkmode.svg") !important=
+; }
+
+[data-color-mode=3D"light"] .is-auto-complete-loading .form-control { backg=
+round-image: url("/images/spinners/octocat-spinner-lightmode.svg") !importa=
+nt; }
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme^=3D"light"] .is-auto-complete-=
+loading .form-control { background-image: url("/images/spinners/octocat-spi=
+nner-lightmode.svg") !important; }
+  [data-color-mode=3D"auto"][data-dark-theme^=3D"dark"] .is-auto-complete-l=
+oading .form-control { background-image: url("/images/spinners/octocat-spin=
+ner-darkmode.svg") !important; }
+}
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme^=3D"light"] .is-auto-complete=
+-loading .form-control { background-image: url("/images/spinners/octocat-sp=
+inner-lightmode.svg") !important; }
+  [data-color-mode=3D"auto"][data-light-theme^=3D"dark"] .is-auto-complete-=
+loading .form-control { background-image: url("/images/spinners/octocat-spi=
+nner-darkmode.svg") !important; }
+}
+
+.hx_breadcrumb-header-crumbs .Header-link { transition: opacity 0.1s ease-o=
+ut 0s; }
+
+.hx_breadcrumb-header-crumbs .Header-link:hover { color: var(--color-header=
+-text); opacity: 0.75; }
+
+.hx_breadcrumb-header-divider { color: var(--color-header-divider); }
+
+.Header-button { background-color: var(--color-scale-gray-8); border: 1px s=
+olid var(--color-scale-gray-6); border-radius: 6px; transition: background-=
+color 0.2s cubic-bezier(0.3, 0, 0.5, 1) 0s; }
+
+.Header-button .octicon { color: var(--color-header-logo); }
+
+.Header-button:hover, .Header-button:focus, .Header-button:active { backgro=
+und-color: transparent; }
+
+.Header-button:hover .octicon, .Header-button:focus .octicon, .Header-butto=
+n:active .octicon { color: var(--color-header-text); box-shadow: none; }
+
+.hx_breadcrumb-header-dropdown::before, .hx_breadcrumb-header-dropdown::aft=
+er { display: none; }
+
+.hx_breadcrumb-header-dropdown .dropdown-item { line-height: 40px; transiti=
+on: background-color 60ms ease-out 0s; }
+
+.hx_breadcrumb-header-dropdown .dropdown-item:hover { color: var(--color-fg=
+-default); background-color: var(--color-canvas-subtle); }
+
+.icon-sponsor, .icon-sponsoring { transition: transform 0.15s cubic-bezier(=
+0.2, 0, 0.13, 2) 0s; transform: scale(1); }
+
+.btn:hover .icon-sponsor, .btn:focus .icon-sponsor, .Label:hover .icon-spon=
+sor, .Label:focus .icon-sponsor, .btn:hover .icon-sponsoring, .btn:focus .i=
+con-sponsoring, .Label:hover .icon-sponsoring, .Label:focus .icon-sponsorin=
+g { transform: scale(1.1); }
+
+.icon-sponsor { overflow: visible !important; }
+
+.hx_kbd { display: inline-block; min-width: 21px; padding: 0px 4px; font-fa=
+mily: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", Helvetica=
+, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; font-size: 12px=
+; font-weight: var(--base-text-weight-normal, 400); line-height: 1.5; color=
+: var(--color-fg-muted); text-align: center; background-color: var(--color-=
+canvas-default); border: 1px solid var(--color-border-default); border-radi=
+us: 6px; box-shadow: none; }
+
+.hx_hit-user em, .hx_hit-package em, .hx_hit-marketplace em, .hx_hit-highli=
+ghting-wrapper em, .hx_hit-commit em, .hx_hit-issue em, .hx_hit-repo em, .h=
+x_hit-wiki em { font-style: normal; font-weight: var(--base-text-weight-sem=
+ibold, 600); }
+
+.SelectMenu-list.select-menu-list { max-height: none; }
+
+@media (max-width: 543px) {
+  .SelectMenu-modal { width: unset !important; }
+}
+
+.SelectMenu--hasFilter .SelectMenu-list { contain: content; }
+
+.SelectMenu-item:disabled, .SelectMenu-item[aria-disabled=3D"true"] { color=
+: var(--color-fg-muted); pointer-events: none; }
+
+.SelectMenu .SelectMenu-item .is-filtering { color: var(--color-fg-muted); =
+}
+
+.SelectMenu .SelectMenu-item .is-filtering b { color: var(--color-fg-defaul=
+t); }
+
+label.SelectMenu-item { font-weight: var(--base-text-weight-normal, 400); }
+
+label.SelectMenu-item[aria-checked=3D"true"] { font-weight: var(--base-text=
+-weight-semibold, 600); }
+
+.hx_SelectMenu-modal-no-animation { animation: 0s ease 0s 1 normal none run=
+ning none; }
+
+.SelectMenu-item.focused { background-color: var(--color-neutral-subtle); }
+
+.Box--responsive { margin-right: -15px; margin-left: -15px; border-right: 0=
+px; border-left: 0px; border-radius: 0px; }
+
+.Box--responsive .Box-row--unread { position: relative; box-shadow: none; }
+
+.Box--responsive .Box-row--unread::before { position: absolute; top: 36px; =
+left: 20px; display: inline-block; width: 8px; height: 8px; color: rgb(255,=
+ 255, 255); content: ""; background-image: linear-gradient(rgb(84, 163, 255=
+), rgb(0, 110, 237)); background-clip: padding-box; border-radius: 50%; }
+
+.Box--responsive .Box-header { border-right-width: 0px; border-left-width: =
+0px; border-radius: 0px; }
+
+@media (min-width: 544px) {
+  .Box--responsive { margin-right: 0px; margin-left: 0px; border: 1px solid=
+ var(--color-border-default); border-radius: 6px; }
+  .Box--responsive .Box-header { border-right-width: 1px; border-left-width=
+: 1px; border-top-left-radius: 6px; border-top-right-radius: 6px; }
+  .Box--responsive .Box-row--unread { box-shadow: 2px 0 0 var(--color-accen=
+t-emphasis) inset; }
+  .Box--responsive .Box-row--unread::before { display: none; }
+}
+
+@media (max-width: 767px) {
+  .page-responsive .dropdown-menu, .page-responsive .dropdown-item { paddin=
+g-top: 8px; padding-bottom: 8px; }
+  .page-responsive .hx_dropdown-fullscreen[open] > summary::before { backgr=
+ound-color: var(--color-primer-canvas-backdrop); }
+  .page-responsive .hx_dropdown-fullscreen .dropdown-menu { position: fixed=
+; top: auto; bottom: 20%; max-height: calc(80% - 16px); overflow-y: auto; t=
+ransform: none; animation: 0.24s cubic-bezier(0, 0.1, 0.1, 1) 0s 1 normal b=
+ackwards running dropdown-menu-animation; right: 16px !important; left: 16p=
+x !important; width: auto !important; max-width: none !important; margin: 0=
+px !important; }
+  .page-responsive .hx_dropdown-fullscreen .dropdown-menu::before, .page-re=
+sponsive .hx_dropdown-fullscreen .dropdown-menu::after { display: none; }
+  @keyframes dropdown-menu-animation {=20
+  0% { opacity: 0; transform: scale(0.9); }
+}
+  .page-responsive .hx_dropdown-fullscreen .dropdown-item { padding-top: 16=
+px; padding-bottom: 16px; }
+}
+
+.hx_rsm-close-button { display: none !important; }
+
+@media (max-width: 767px) {
+  .page-responsive .hx_rsm[open] > summary::before { background-color: var(=
+--color-primer-canvas-backdrop); }
+  .page-responsive .hx_rsm .select-menu-modal, .page-responsive .hx_rsm-mod=
+al { top: 75px; left: 16px; display: flex; width: auto; height: 80%; margin=
+: 0px; flex-direction: column; animation: 0.24s cubic-bezier(0, 0.1, 0.1, 1=
+) 0.12s 1 normal backwards running hx_rsm-modal-animation; position: fixed =
+!important; right: 16px !important; }
+  .page-responsive .hx_rsm--auto-height .select-menu-modal { top: auto; bot=
+tom: 20%; height: auto; max-height: calc(80% - 16px); }
+  .page-responsive .hx_rsm .select-menu-header, .page-responsive .hx_rsm .s=
+elect-menu-text-filter.select-menu-text-filter { padding: 16px; border-top-=
+left-radius: inherit; border-top-right-radius: inherit; }
+  .page-responsive .hx_rsm tab-container, .page-responsive .hx_rsm-content =
+{ display: flex; min-height: 0px; flex-direction: column; flex: 1 1 auto; }
+  .page-responsive .hx_rsm .select-menu-list { flex: 1 1 auto; max-height: =
+none; }
+  .page-responsive .hx_rsm-content > .select-menu-item { flex-shrink: 0; }
+  .page-responsive .hx_rsm .select-menu-item { padding-top: 16px; padding-b=
+ottom: 16px; padding-left: 40px; }
+  .page-responsive .hx_rsm .close-button, .page-responsive .hx_rsm-close-bu=
+tton { position: relative; display: block !important; }
+  .page-responsive .hx_rsm .close-button::before, .page-responsive .hx_rsm-=
+close-button::before { position: absolute; inset: -16px; content: ""; }
+  .page-responsive .hx_rsm .close-button .octicon-x, .page-responsive .hx_r=
+sm-close-button .octicon-x { color: var(--color-fg-muted); }
+  .page-responsive .hx_rsm .select-menu-loading-overlay { animation-delay: =
+1s; }
+  .page-responsive .hx_rsm .select-menu-button::before, .page-responsive .h=
+x_rsm-trigger::before { animation: 0.24s cubic-bezier(0, 0, 0.2, 1) 0s 1 no=
+rmal backwards running hx_rsm-trigger-animation; }
+  @keyframes hx_rsm-trigger-animation {=20
+  0% { opacity: 0; }
+}
+  @keyframes hx_rsm-modal-animation {=20
+  0% { opacity: 0; transform: scale(0.9); }
+}
+  .page-responsive .hx_rsm-dialog { max-width: none; height: auto; max-heig=
+ht: 80%; transform: none; }
+  .page-responsive .hx_rsm-dialog-content { flex: 1 1 0%; min-height: 0px; =
+}
+}
+
+@media (max-width: 767px) and (max-height: 500px) {
+  .page-responsive .hx_rsm .select-menu-modal, .page-responsive .hx_rsm-mod=
+al { bottom: 16px; height: auto; }
+}
+
+.select-menu-modal { border-color: var(--color-border-default); box-shadow:=
+ var(--color-shadow-large); }
+
+.select-menu-header, .select-menu-filters { background: var(--color-canvas-=
+overlay); }
+
+.select-menu-text-filter input { padding: 5px 12px; }
+
+.select-menu-item { text-align: left; background-color: var(--color-canvas-=
+overlay); border-top: 0px; border-right: 0px; border-left: 0px; }
+
+.preview-selected .tabnav--responsive { border-bottom: 1px solid var(--colo=
+r-border-default); }
+
+.tabnav--responsive .tabnav-tabs { z-index: 1; }
+
+@media (max-width: 767px) {
+  .tabnav--responsive .tabnav-tab { background-color: var(--color-canvas-su=
+btle); border-top-color: ; border-top-style: ; border-top-width: ; border-r=
+ight-color: ; border-right-style: ; border-right-width: ; border-bottom-col=
+or: ; border-bottom-style: ; border-bottom-width: ; border-image-source: ; =
+border-image-slice: ; border-image-width: ; border-image-outset: ; border-i=
+mage-repeat: ; border-left: 0px; border-radius: 0px; }
+  .tabnav--responsive .tabnav-tab:first-child { border-left: 1px solid var(=
+--color-border-default); }
+  .tabnav--responsive .tabnav-tab[aria-selected=3D"true"], .tabnav--respons=
+ive .tabnav-tab.selected { background-color: var(--color-canvas-default); b=
+order-bottom: 0px; }
+}
+
+@media (max-width: 767px) {
+  .hx_sm-hide-drag-drop textarea { border-bottom: 1px solid var(--color-bor=
+der-default); border-bottom-right-radius: 6px; border-bottom-left-radius: 6=
+px; }
+  .hx_sm-hide-drag-drop .hx_drag-and-drop { display: none !important; }
+}
+
+@media (hover: none) {
+  .tooltipped:hover::before, .tooltipped:hover::after { display: none; }
+}
+
+@media (hover: none) {
+  .markdown-body h1 .octicon-link, .markdown-body h2 .octicon-link, .markdo=
+wn-body h3 .octicon-link, .markdown-body h4 .octicon-link, .markdown-body h=
+5 .octicon-link, .markdown-body h6 .octicon-link { visibility: visible !imp=
+ortant; }
+}
+
+.min-width-lg { min-width: 1012px; }
+
+.min-width-xl { min-width: 1280px; }
+
+.min-height-0 { min-height: 0px !important; }
+
+.ws-pre-wrap { white-space: pre-wrap; }
+
+.cursor-pointer { cursor: pointer; }
+
+.cursor-default { cursor: default; }
+
+@media screen and (prefers-reduced-motion: no-preference) {
+  .hide-no-pref-motion { visibility: hidden; display: none !important; }
+}
+
+@media screen and (prefers-reduced-motion: reduce) {
+  .hide-reduced-motion { visibility: hidden; display: none !important; }
+}
+
+.line_through { text-decoration: line-through 1%; }
+
+.gap-1 { gap: 4px !important; }
+
+.gap-2 { gap: 8px !important; }
+
+.gap-3 { gap: 16px !important; }
+
+.gap-4 { gap: 24px !important; }
+
+.starring-container .unstarred, .starring-container.on .starred { display: =
+flex; }
+
+.starring-container.on .unstarred, .starring-container .starred { display: =
+none; }
+
+.starring-container.loading { opacity: 0.5; }
+
+.user-following-container .follow, .user-following-container.on .unfollow {=
+ display: inline-block; }
+
+.user-following-container.on .follow, .user-following-container .unfollow {=
+ display: none; }
+
+.user-following-container.loading { opacity: 0.5; }
+
+.hidden-when-empty:empty { display: none !important; }
+
+.cm-number, .cm-atom { color: var(--color-codemirror-syntax-constant); }
+
+[data-color-mode=3D"dark"] auto-check .is-autocheck-loading { background-si=
+ze: 16px 16px; background-image: url("/images/spinners/octocat-spinner-dark=
+mode.svg") !important; }
+
+[data-color-mode=3D"light"] auto-check .is-autocheck-loading { background-s=
+ize: 16px 16px; background-image: url("/images/spinners/octocat-spinner-lig=
+htmode.svg") !important; }
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme^=3D"light"] auto-check .is-aut=
+ocheck-loading { background-size: 16px 16px; background-image: url("/images=
+/spinners/octocat-spinner-lightmode.svg") !important; }
+  [data-color-mode=3D"auto"][data-dark-theme^=3D"dark"] auto-check .is-auto=
+check-loading { background-size: 16px 16px; background-image: url("/images/=
+spinners/octocat-spinner-darkmode.svg") !important; }
+}
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme^=3D"light"] auto-check .is-au=
+tocheck-loading { background-size: 16px 16px; background-image: url("/image=
+s/spinners/octocat-spinner-lightmode.svg") !important; }
+  [data-color-mode=3D"auto"][data-light-theme^=3D"dark"] auto-check .is-aut=
+ocheck-loading { background-size: 16px 16px; background-image: url("/images=
+/spinners/octocat-spinner-darkmode.svg") !important; }
+}
+
+auto-check .is-autocheck-loading, auto-check .is-autocheck-successful, auto=
+-check .is-autocheck-errored { padding-right: 30px; background-repeat: no-r=
+epeat; background-position: right 8px center; }
+
+auto-check .is-autocheck-successful { background-image: url("/images/module=
+s/ajax/success.png"); }
+
+auto-check .is-autocheck-errored { background-image: url("/images/modules/a=
+jax/error.png"); }
+
+@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and=
+ (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio:=
+ 2), only screen and (min-resolution: 192dpi), only screen and (min-resolut=
+ion: 2dppx) {
+  auto-check .is-autocheck-loading, auto-check .is-autocheck-successful, au=
+to-check .is-autocheck-errored { background-size: 16px 16px; }
+  auto-check .is-autocheck-successful { background-image: url("/images/modu=
+les/ajax/success@2x.png"); }
+  auto-check .is-autocheck-errored { background-image: url("/images/modules=
+/ajax/error@2x.png"); }
+}
+
+.hx_text-body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",=
+ "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI =
+Emoji" !important; }
+
+.hx_disabled-form-checkbox-label.form-checkbox.disabled { color: var(--colo=
+r-fg-muted); }
+
+.autocomplete-item { background-color: transparent; }
+
+.ColorSwatch { display: inline-block; width: 1em; height: 1em; vertical-ali=
+gn: middle; border: 1px solid var(--color-border-subtle); border-radius: 6p=
+x; }
+
+.label-select-menu .color, .ColorSwatch { border-radius: 2em; }
+
+.details-overlay[open] > .dropdown-item:hover { color: inherit; background:=
+ var(--color-canvas-default); }
+
+remote-input[loading] .form-control { padding-right: 30px; background-image=
+: url("/images/spinners/octocat-spinner-32.gif"); background-size: 16px; }
+
+.hx_form-control-spinner { position: absolute; top: 24px; right: 24px; disp=
+lay: none; }
+
+@media (min-width: 767px) {
+  .hx_form-control-spinner { top: 18px; right: 18px; }
+}
+
+.hx_form-control-spinner-wrapper { position: relative; }
+
+.hx_form-control-spinner-wrapper .is-loading.form-control { padding-right: =
+28px; }
+
+.hx_form-control-spinner-wrapper .is-loading + .hx_form-control-spinner { d=
+isplay: block; }
+
+.drag-and-drop { border-color: var(--color-border-default); }
+
+.input-sm { min-height: 28px; }
+
+.btn .octicon-triangle-down { margin-right: 0px; }
+
+.UnderlineNav-item.selected .UnderlineNav-octicon, .UnderlineNav-item[aria-=
+current]:not([aria-current=3D"false"]) .UnderlineNav-octicon, .UnderlineNav=
+-item[role=3D"tab"][aria-selected=3D"true"] .UnderlineNav-octicon { color: =
+inherit; }
+
+.break-line-anywhere { line-break: anywhere !important; }
+
+.form-checkbox input[type=3D"checkbox"], .form-checkbox input[type=3D"radio=
+"] { margin-top: 4px; }
+
+.status-indicator-success::before, .status-indicator-failed::before { conte=
+nt: none; }
+
+.markdown-title code { padding: 2px 4px; font-size: 0.9em; line-height: 1; =
+background-color: var(--color-neutral-muted); border-radius: 6px; }
+
+.hx_ActionList-content > .hx_ActionList-item-label, .hx_ActionList-content =
+> .hx_ActionList-item-visual { pointer-events: none; }
+
+.IssueLabel--big.lh-condensed { display: inline-block; padding: 0px 10px; f=
+ont-size: 12px; font-weight: var(--base-text-weight-medium, 500); border: 1=
+px solid transparent; border-radius: 2em; line-height: 22px !important; }
+
+.hx_IssueLabel { --perceived-lightness: calc( ((var(--label-r) * 0.2126) + =
+(var(--label-g) * 0.7152) + (var(--label-b) * 0.0722)) / 255 ); --lightness=
+-switch: max(0, min(calc((var(--perceived-lightness) - var(--lightness-thre=
+shold)) * -1000), 1)); }
+
+:root .hx_IssueLabel, [data-color-mode=3D"light"][data-light-theme*=3D"ligh=
+t"] .hx_IssueLabel, [data-color-mode=3D"dark"][data-dark-theme*=3D"light"] =
+.hx_IssueLabel { --lightness-threshold: 0.453; --border-threshold: 0.96; --=
+border-alpha: max(0, min(calc((var(--perceived-lightness) - var(--border-th=
+reshold)) * 100), 1)); color: hsl(0deg, 0%, calc(var(--lightness-switch) * =
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+rder-color: hsla(var(--label-h), calc(var(--label-s) * 1%), calc((var(--lab=
+el-l) - 25) * 1%), var(--border-alpha)); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"light"] .hx_IssueLabel { =
+--lightness-threshold: 0.453; --border-threshold: 0.96; --border-alpha: max=
+(0, min(calc((var(--perceived-lightness) - var(--border-threshold)) * 100),=
+ 1)); color: hsl(0deg, 0%, calc(var(--lightness-switch) * 100%)); backgroun=
+d: rgb(var(--label-r), var(--label-g), var(--label-b)); border-color: hsla(=
+var(--label-h), calc(var(--label-s) * 1%), calc((var(--label-l) - 25) * 1%)=
+, var(--border-alpha)); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"light"] .hx_IssueLabel { -=
+-lightness-threshold: 0.453; --border-threshold: 0.96; --border-alpha: max(=
+0, min(calc((var(--perceived-lightness) - var(--border-threshold)) * 100), =
+1)); color: hsl(0deg, 0%, calc(var(--lightness-switch) * 100%)); background=
+: rgb(var(--label-r), var(--label-g), var(--label-b)); border-color: hsla(v=
+ar(--label-h), calc(var(--label-s) * 1%), calc((var(--label-l) - 25) * 1%),=
+ var(--border-alpha)); }
+}
+
+[data-color-mode=3D"light"][data-light-theme*=3D"dark"] .hx_IssueLabel, [da=
+ta-color-mode=3D"dark"][data-dark-theme*=3D"dark"] .hx_IssueLabel { --light=
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+en-by: calc(((var(--lightness-threshold) - var(--perceived-lightness)) * 10=
+0) * var(--lightness-switch)); color: hsl(var(--label-h), calc(var(--label-=
+s) * 1%), calc((var(--label-l) + var(--lighten-by)) * 1%)); background: rgb=
+a(var(--label-r), var(--label-g), var(--label-b), var(--background-alpha));=
+ border-color: hsla(var(--label-h), calc(var(--label-s) * 1%), calc((var(--=
+label-l) + var(--lighten-by)) * 1%), var(--border-alpha)); }
+
+@media (prefers-color-scheme: light) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] .hx_IssueLabel { -=
+-lightness-threshold: 0.6; --background-alpha: 0.18; --border-alpha: 0.3; -=
+-lighten-by: calc(((var(--lightness-threshold) - var(--perceived-lightness)=
+) * 100) * var(--lightness-switch)); color: hsl(var(--label-h), calc(var(--=
+label-s) * 1%), calc((var(--label-l) + var(--lighten-by)) * 1%)); backgroun=
+d: rgba(var(--label-r), var(--label-g), var(--label-b), var(--background-al=
+pha)); border-color: hsla(var(--label-h), calc(var(--label-s) * 1%), calc((=
+var(--label-l) + var(--lighten-by)) * 1%), var(--border-alpha)); }
+}
+
+@media (prefers-color-scheme: dark) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] .hx_IssueLabel { --=
+lightness-threshold: 0.6; --background-alpha: 0.18; --border-alpha: 0.3; --=
+lighten-by: calc(((var(--lightness-threshold) - var(--perceived-lightness))=
+ * 100) * var(--lightness-switch)); color: hsl(var(--label-h), calc(var(--l=
+abel-s) * 1%), calc((var(--label-l) + var(--lighten-by)) * 1%)); background=
+: rgba(var(--label-r), var(--label-g), var(--label-b), var(--background-alp=
+ha)); border-color: hsla(var(--label-h), calc(var(--label-s) * 1%), calc((v=
+ar(--label-l) + var(--lighten-by)) * 1%), var(--border-alpha)); }
+}
+
+.signed-commit-badge-small, .signed-commit-badge-medium, .signed-commit-bad=
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+ nowrap; border-width: 1px; border-style: solid; border-image: initial; bor=
+der-radius: 2em; border-color: var(--color-border-default); }
+
+.signed-commit-badge-small { margin-top: 0px; }
+
+.signed-commit-badge-large { padding-right: 10px; padding-left: 10px; line-=
+height: 22px; }
+
+.topic-tag-action, .delete-topic-button, .topic-tag { display: inline-block=
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+edium, 500); white-space: nowrap; border-radius: 2em; line-height: 22px; co=
+lor: var(--color-accent-fg); background-color: var(--color-accent-subtle); =
+border: 1px solid var(--color-topic-tag-border, transparent); }
+
+.topic-tag-action:active, .topic-tag-action:hover, .delete-topic-button:act=
+ive, .delete-topic-button:hover, .topic-tag:active, .topic-tag:hover { colo=
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+is); }
+
+.topic-tag { margin: 0px 0.125em 0.333em 0px; }
+
+.topic-tag-outline { background: transparent; }
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+.topic-tag-action { display: inline-flex; padding-right: 0px; margin: 0.6em=
+ 0.5em 0px 0px; }
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+.delete-topic-button, .topic-tag-action .add-topic-button, .topic-tag-actio=
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+ 0px; color: inherit; border-top-color: transparent; border-right-color: tr=
+ansparent; border-bottom-color: transparent; border-left: 0px; border-radiu=
+s: 2em; align-items: center; justify-content: center; }
+
+.hx_Layout.hx_Layout--sidebar-hidden { grid-auto-flow: row; gap: 0px; grid-=
+template-columns: 1fr; }
+
+.hx_Layout.hx_Layout--sidebar-hidden .Layout-sidebar { display: none; }
+
+.hx_Layout.hx_Layout--sidebar-hidden .Layout-main { grid-column: auto / aut=
+o; }
+
+.hx_Layout--sidebar { top: 60px; box-sizing: border-box; overscroll-behavio=
+r: contain; }
+
+.branch-action-item.color-border-default { border-color: var(--color-border=
+-default)  !important; }
+
+.user-status-container .input-group-button .btn { height: 32px; }
+
+.reponav-item, .pagehead-tabs-item { border-radius: 4px 4px 0px 0px; }
+
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+b(249, 130, 108); }
+
+.auto-search-group > .octicon { top: 8px; }
+
+.subnav-search > button.mt-2 { margin-top: 6px !important; }
+
+.completeness-indicator-success { color: var(--color-fg-on-emphasis); backg=
+round-color: var(--color-btn-primary-bg); }
+
+.pagination-loader-container button.color-bg-default.border-0 { border-top-=
+left-radius: 6px; border-top-right-radius: 6px; }
+
+.avatar-user { border-radius: 50% !important; }
+
+@media (max-width: 543px) {
+  .minimized-comment > details > div { padding-left: 0px !important; }
+}
+
+@media (max-width: 543px) {
+  .minimized-comment > details > summary > div { flex-direction: column; }
+  .minimized-comment > details > summary > div .review-comment-contents { }
+}
+
+.hx_disabled-input { margin-right: -4px !important; margin-left: -4px !impo=
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+
+.hx_disabled-input sidebar-memex-input[disabled] * { cursor: pointer; }
+
+.hx_disabled-input sidebar-memex-input:not([disabled]) .Box-row--hover-gray=
+ { background-color: var(--color-canvas-subtle); }
+
+.hx_disabled-input .Box-row--hover-gray svg.octicon-pencil { visibility: hi=
+dden; opacity: 0; }
+
+.hx_disabled-input .Box-row--hover-gray:hover, .hx_disabled-input .Box-row-=
+-hover-gray:focus { padding-top: 8px !important; padding-bottom: 8px !impor=
+tant; }
+
+.hx_disabled-input .Box-row--hover-gray:hover svg.octicon-pencil, .hx_disab=
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+
+.hx_disabled-input input:not(:disabled) { margin-top: 8px !important; margi=
+n-bottom: 8px !important; }
+
+.hx_disabled-input input[disabled], .hx_disabled-input select[disabled], .h=
+x_disabled-input .form-control[contenteditable=3D"false"] { padding-right: =
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+ 0px; box-shadow: none; opacity: 1; color: var(--color-fg-default)  !import=
+ant; }
+
+.hx_disabled-input text-expander input[type=3D"text"][disabled] { display: =
+none; }
+
+.hx_disabled-input text-expander input[type=3D"text"][disabled] + div.form-=
+control { display: block; }
+
+.hx_disabled-input text-expander input[type=3D"text"] + div.form-control { =
+display: none; }
+
+.hx_disabled-input input[type=3D"number"][disabled] { display: none; }
+
+.hx_disabled-input input[type=3D"number"][disabled] + div.form-control { di=
+splay: block; }
+
+.hx_disabled-input input[type=3D"number"] + div.form-control { display: non=
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+
+.hx_disabled-input input[type=3D"date"][disabled] { display: none; }
+
+.hx_disabled-input input[type=3D"date"][disabled] + div.form-control { disp=
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+
+.hx_disabled-input input[type=3D"date"] + div.form-control { display: none;=
+ }
+
+.hx_disabled-input input[disabled]::placeholder, .hx_disabled-input selecte=
+d[disabled]::placeholder { color: var(--color-fg-default)  !important; }
+
+.hx_disabled-input .form-select { background-image: none !important; }
+
+.hx_disabled-input .Box-row--focus-gray:focus { background: var(--color-can=
+vas-subtle); }
+
+.summary-iteration .inline-status { display: none; }
+
+.summary-iteration .block-status { display: inline-block; }
+
+.list-iteration .inline-status { display: inline; }
+
+.list-iteration .block-status { display: none; }
+
+.hx_tabnav-in-dropdown { border-radius: 5px 5px 0px 0px; }
+
+.hx_tabnav-in-dropdown .tabnav-tabs .hx_tabnav-in-dropdown-wrapper:first-ch=
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+
+.hx_tabnav-in-dropdown .tabnav-tabs .hx_tabnav-in-dropdown-wrapper:last-chi=
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+
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+
+.hx_tabnav-in-dropdown #cloud-tab[aria-selected=3D"false"]::after { positio=
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+ial; border-bottom: 7px solid var(--color-canvas-subtle); }
+
+.details-overlay-dark[open] > summary::before { z-index: 111 !important; }
+
+.turbo-progress-bar { z-index: 2147483647; }
+
+.timeline-comment .previewable-comment-form textarea { max-height: none; }
+
+.truncate-with-responsive-width { width: 50px; min-width: 100%; }
+
+.hx_merge_queue_entry_status_icon { fill: none; background-color: transpare=
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+.marketing-section::before { display: table; content: ""; }
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+.marketing-section::after { display: table; clear: both; content: ""; }
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+.marketing-section h3 { font-size: 20px; font-weight: var(--base-text-weigh=
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+
+.hanging-icon-list .octicon-check { color: var(--color-success-fg); }
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+
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+
+.integrations-hero-octicon.marketing-hero-octicon .octicon { margin-top: 16=
+px; }
+
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+
+.marketing-blue-octicon .octicon { color: rgb(52, 172, 191); }
+
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+
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+
+.marketing-graphic { position: relative; }
+
+.intgrs-dir .marketing-graphic { padding-right: 0px; margin: 0px; }
+
+.intgrs-dir .footer { margin-top: 40px; }
+
+.intgrs-dir-section h2 { margin-top: 0px; margin-bottom: 16px; font-size: 2=
+4px; font-weight: var(--base-text-weight-light, 300); }
+
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+round-image: linear-gradient(-110deg, rgb(72, 34, 125) 0%, rgb(47, 86, 156)=
+ 100%); border-bottom: 0px; }
+
+.pagehead + .intgrs-dir-intro { margin-top: -16px; }
+
+.intgrs-dir-intro .directory-header-back { margin-top: 8px; font-size: 16px=
+; color: rgb(255, 255, 255); }
+
+.intgrs-dir-intro .directory-header-back:hover { color: rgb(215, 222, 241);=
+ text-decoration: none; }
+
+.intgrs-dir-intro .directory-header-back .octicon { vertical-align: middle;=
+ }
+
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+
+.intgrs-dir-intro .directory-tag-line { margin-bottom: 0px; font-size: 32px=
+; font-weight: var(--base-text-weight-normal, 400); color: rgb(255, 255, 25=
+5); }
+
+.intgrs-dir-intro .lead { margin-top: 8px; margin-bottom: 4px; font-size: 1=
+6px; font-weight: var(--base-text-weight-normal, 400); color: rgb(215, 222,=
+ 241); }
+
+.intgrs-lstng-search { display: inline-block; width: 33%; margin-left: 16px=
+; }
+
+.intgrs-lstng-search .subnav-search-input { width: 100%; }
+
+.intgrs-lstng-categories-container { display: inline-block; float: left; wi=
+dth: 20%; }
+
+.intgrs-lstng-categories-container .intgrs-lstng-categories { top: 0px; }
+
+.intgrs-lstng-categories-container .filter-item { padding: 4px 8px; margin-=
+right: -8px; margin-left: -8px; }
+
+.intgrs-lstng-container { display: inline-block; width: 80%; text-align: le=
+ft; }
+
+.intgrs-lstng-item { position: relative; display: inline-flex; width: 30.8%=
+; font-size: 14px; border: 1px solid var(--color-border-muted); border-radi=
+us: 4px; transition: border-color 0.15s ease 0s, transform 0.15s ease 0s, b=
+ox-shadow 0.15s ease 0s, color 0.15s ease 0s; }
+
+.intgrs-lstng-item:hover { border-color: rgb(81, 167, 232); box-shadow: rgb=
+a(81, 167, 232, 0.5) 0px 0px 5px; transform: scale(1.05); }
+
+.intgrs-lstng-item .intgrs-lstng-logo { display: block; margin: 0px auto 8p=
+x; }
+
+.intgrs-lstng-item .draft-tag { position: absolute; top: -1px; left: 10px; =
+}
+
+.intgrs-lstng-item-link { display: block; width: 100%; height: 181px; paddi=
+ng-top: 16px; }
+
+.intgrs-lstng-item-link:hover { text-decoration: none; }
+
+.intgrs-lstng-item-link:hover .intgrs-lstng-item-header { color: var(--colo=
+r-accent-fg); }
+
+.intgrs-lstng-item-header { margin: 16px 8px 0px; font-size: 14px; font-wei=
+ght: var(--base-text-weight-semibold, 600); color: var(--color-fg-default);=
+ }
+
+.intgrs-lstng-item-description { position: relative; height: 2.8em; padding=
+: 0px 8px; margin-top: 4px; overflow: hidden; font-size: 12px; color: var(-=
+-color-fg-muted); }
+
+.intgrs-lstng-item-description::after { position: absolute; right: 0px; bot=
+tom: 0px; padding: 0px 16px; color: transparent; content: "=C2=A0"; backgro=
+und-image: linear-gradient(to right, rgba(255, 255, 255, 0), rgb(255, 255, =
+255) 80%); }
+
+.intgr-admin-link { position: relative; display: inline-block; height: 25px=
+; padding-left: 24px; font-size: 12px; vertical-align: middle; border: 1px =
+solid var(--color-border-muted); border-radius: 6px; }
+
+.intgr-admin-link.draft-tag { padding-left: 24px; border: 0px; }
+
+.intgr-admin-link.draft-tag .octicon, .intgr-admin-link.draft-tag:hover .oc=
+ticon { color: rgb(255, 255, 255); }
+
+.intgr-admin-link.draft-tag:hover { text-decoration: none; background-color=
+: rgb(0, 0, 0); }
+
+.intgr-admin-link:hover .octicon { color: var(--color-accent-fg); }
+
+.intgr-admin-link .octicon { position: absolute; top: 3px; left: 5px; color=
+: var(--color-fg-muted); }
+
+.intgr-feat-header { position: relative; width: 85%; padding: 0px 65px 8px;=
+ color: rgb(215, 222, 241); }
+
+.intgr-feat-header .intgr-admin-link { border-color: rgba(215, 222, 241, 0.=
+6); }
+
+.intgr-feat-header .intgr-admin-link .octicon { color: rgb(215, 222, 241); =
+}
+
+.intgr-feat-header .intgr-admin-link:hover .octicon { color: rgb(255, 255, =
+255); }
+
+.intgr-feat-header .marketing-hero-octicon { position: absolute; top: 0px; =
+left: 5px; width: 50px; height: 50px; border-width: 3px; }
+
+.intgr-feat-header .marketing-hero-octicon .octicon { margin-top: 12px; }
+
+.intgr-feat-header h2 { margin: 0px; font-size: 24px; line-height: 50px; co=
+lor: rgb(255, 255, 255); }
+
+.intgr-feat-header p { max-width: 580px; margin: 0px; font-size: 16px; }
+
+.integrations-breadcrumb { display: inline-block; font-weight: var(--base-t=
+ext-weight-normal, 400); color: var(--color-accent-fg); }
+
+.integrations-breadcrumb-link { line-height: 0; color: rgb(215, 222, 241); =
+}
+
+.integrations-breadcrumb-link:hover { color: rgb(255, 255, 255); text-decor=
+ation: none; }
+
+.integrations-auth-wrapper { max-width: 540px; padding-right: 16px; padding=
+-left: 16px; margin: 60px auto; }
+
+.integrations-auth-header { font-size: 20px; text-align: center; }
+
+.integrations-permissions-group dt { font-size: 16px; font-weight: var(--ba=
+se-text-weight-normal, 400); }
+
+.integrations-permissions-group .integrations-permission { position: relati=
+ve; padding-left: 24px; margin-bottom: 8px; list-style-type: none; }
+
+.integrations-permissions-group .integrations-permission .octicon { positio=
+n: absolute; top: 1px; left: 0px; margin-right: 8px; }
+
+.integrations-install-target .select-menu { vertical-align: middle; }
+
+.integrations-install-target input[type=3D"radio"] { margin-right: 8px; }
+
+.integrations-install-target .flash { background-color: transparent; }
+
+.integrations-install-target .flash-error { background-color: transparent; =
+border: 0px; }
+
+.integrations-install-target .octicon-lock, .integrations-install-target .o=
+cticon-repo, .integrations-install-target .octicon-repo-forked { margin-rig=
+ht: 4px; }
+
+.integrations-install-target .octicon-lock { color: var(--color-attention-f=
+g); }
+
+.integrations-install-target .private { background-color: rgb(255, 249, 234=
+); }
+
+.integrations-install-target [aria-selected=3D"true"].private, .integration=
+s-install-target .navigation-focus.private { background-color: rgb(64, 120,=
+ 192); }
+
+.integrations-install-target [aria-selected=3D"true"].octicon-lock, .integr=
+ations-install-target .navigation-focus .octicon-lock { color: inherit; }
+
+.integrations-setup-note { margin: 8px 0px; }
+
+.listgroup-item { line-height: inherit; }
+
+.listgroup-item.disabled { background-color: var(--color-canvas-subtle); }
+
+.listgroup-item.disabled .listgroup-item-title { color: var(--color-fg-defa=
+ult); }
+
+.link-small { color: var(--color-fg-muted); transition: color 500ms ease 0s=
+; }
+
+.sub-permissions-error { max-width: unset !important; }
+
+.not-found-octocat-wrapper { width: 71px; height: 71px; border-radius: 45px=
+; }
+
+.not-found-octocat-wrapper::after { position: absolute; top: 58px; left: 45=
+px; z-index: -2; display: block; width: 4px; height: 4px; vertical-align: b=
+aseline; content: ""; background: var(--color-canvas-default); border-radiu=
+s: 4px; box-shadow: rgb(255, 255, 255) 0px 4px 0px, rgb(255, 255, 255) 0px =
+8px 0px, rgb(255, 255, 255) 0px 12px 0px, rgb(255, 255, 255) 0px 16px 0px, =
+rgb(255, 255, 255) 0px 20px 0px; animation-name: pull-string; animation-dur=
+ation: 0.75s; animation-fill-mode: forwards; animation-delay: 0.5s; }
+
+@keyframes lightbulb {=20
+  0%, 8%, 14% { opacity: 0.1; }
+  0%, 10%, 25% { opacity: 0.25; }
+  5%, 30%, 50%, 70% { opacity: 0.5; }
+  16%, 60%, 80% { opacity: 0.75; }
+  90% { opacity: 0.8; }
+  94% { opacity: 0.5; }
+  100% { opacity: 1; }
+}
+
+.not-found-lightbulb-ani { z-index: 1; opacity: 0.25; animation-name: light=
+bulb; animation-duration: 2.5s; animation-fill-mode: forwards; animation-de=
+lay: 1.3s; }
+
+@keyframes pull-string {=20
+  50% { transform: translate3d(0px, 12px, 0px); }
+  75% { opacity: 1; transform: none; }
+  100% { opacity: 0; }
+}
+
+.boxed-group-table .toggle-currency { font-size: 12px; font-weight: var(--b=
+ase-text-weight-normal, 400); }
+
+.has-removed-contents { display: none; }
+
+.org-login { margin-top: -32px; margin-bottom: 32px; }
+
+.org-login img { width: 450px; padding: 1px; margin: 8px -24px; border: 1px=
+ solid var(--color-border-default); }
+
+.member-list-item .member-username { display: inline; }
+
+.member-list-item .member-link { display: inline; }
+
+.actor-and-action { font-weight: var(--base-text-weight-semibold, 600); }
+
+.vertical-separator { margin-right: 8px; margin-left: 4px; border-left: 1px=
+ solid var(--color-border-default); }
+
+.audit-log-search .audit-search-form { margin-bottom: 8px; }
+
+.audit-log-search .audit-results-actions { margin: 16px 0px; }
+
+.audit-log-search .audit-search-clear { margin-bottom: 0px; }
+
+.billing-addon-items table input { width: 5em; }
+
+.billing-addon-items td { vertical-align: middle; border-bottom: 0px; }
+
+.billing-addon-items td.fixed { width: 150px; }
+
+.billing-addon-items td.black { color: var(--color-fg-default); }
+
+.billing-addon-items tr { border-bottom: 1px solid var(--color-border-muted=
+); }
+
+.billing-addon-items tr:last-child { border-bottom-width: 0px; }
+
+.billing-addon-items tr:nth-child(2n) { background-color: var(--color-canva=
+s-subtle); }
+
+.billing-addon-items tr.total-row { color: var(--color-danger-fg); backgrou=
+nd-color: var(--color-canvas-default); }
+
+.billing-addon-items .new-addon-items { margin-left: 4px; }
+
+.billing-addon-items .addon-cost { color: var(--color-fg-muted); }
+
+.billing-addon-items .discounted-original-price { color: var(--color-fg-mut=
+ed); }
+
+.billing-addon-items .form-submit, .billing-addon-items .payment-method { m=
+argin-left: 8px; }
+
+.billing-addon-items .payment-summary { margin-right: 8px; margin-left: 8px=
+; }
+
+.billing-credit-card .javascript-disabled-overlay { position: absolute; top=
+: 0px; left: 0px; z-index: 1; display: none; width: 100%; height: 100%; bac=
+kground-color: var(--color-canvas-default); opacity: 0.5; }
+
+.billing-credit-card.disabled .javascript-disabled-overlay { display: block=
+; }
+
+.billing-extra-box { padding-left: 8px; margin: 8px 0px; border-left: 6px s=
+olid var(--color-border-muted); }
+
+.billing-vat-box { padding-left: 8px; margin: 8px 0px; border-left: 6px sol=
+id var(--color-border-muted); }
+
+.billing-section .action-button { float: right; margin-bottom: 4px; margin-=
+left: 8px; }
+
+.billing-section .section-label { position: absolute; width: 85px; font-wei=
+ght: var(--base-text-weight-normal, 400); color: var(--color-fg-muted); tex=
+t-align: right; }
+
+.billing-section .section-content { margin-left: 100px; color: var(--color-=
+fg-default); }
+
+.billing-section:last-child { border-bottom: 0px; }
+
+.billing-section .usage-bar { max-width: 304px; }
+
+.usage-bar { width: 100%; margin: 4px 0px 0px; background: rgb(238, 238, 23=
+8); border-radius: 20px; }
+
+.usage-bar.exceeded .progress { background-color: var(--color-danger-emphas=
+is)  !important; }
+
+.usage-bar .progress { position: relative; max-width: 100%; height: 5px; ba=
+ckground-color: var(--color-success-emphasis); border-radius: 20px; transit=
+ion: width 0.3s ease 0s; }
+
+.usage-bar .progress.no-highlight { background: var(--color-neutral-muted);=
+ }
+
+.usage-bar .progress--orange { background-color: var(--color-severe-emphasi=
+s); }
+
+.usage-bar .progress--purple { background-color: var(--color-done-emphasis)=
+; }
+
+.lfs-data-pack-field { margin: -4px 0px; }
+
+.packs-table .desc { width: 1%; white-space: nowrap; }
+
+.lfs-data-icon { color: var(--color-fg-muted); text-align: center; }
+
+.lfs-data-icon.dark { color: var(--color-fg-default); }
+
+.lfs-data-icon.octicon-database { margin-right: 4px; margin-left: 2px; }
+
+.setup-wrapper .paypal-container { margin-bottom: 32px; }
+
+.setup-wrapper .paypal-logged-in .paypal-container { margin-bottom: 8px; }
+
+.payment-methods { position: relative; }
+
+.payment-methods .selected-payment-method { display: none; }
+
+.payment-methods .selected-payment-method::before { display: table; content=
+: ""; }
+
+.payment-methods .selected-payment-method::after { display: table; clear: b=
+oth; content: ""; }
+
+.payment-methods .selected-payment-method.active { display: block; }
+
+.payment-methods .form-group dd .form-control.short.input-vat { width: 300p=
+x; }
+
+.payment-methods .pay-with-header { margin: 4px 0px; }
+
+.payment-methods .pay-with-paypal .setup-creditcard-form, .payment-methods =
+.pay-with-paypal .paypal-form-actions, .payment-methods .pay-with-paypal .t=
+erms, .payment-methods .pay-with-paypal .paypal-signed-in, .payment-methods=
+ .pay-with-paypal .paypal-down-flash, .payment-methods .pay-with-paypal .lo=
+ading-paypal-spinner { display: none; }
+
+.payment-methods.paypal-loading .loading-paypal-spinner { display: block; }
+
+.payment-methods.paypal-down .paypal-down-flash { display: block; }
+
+.payment-methods.paypal-logged-in .paypal-sign-in { display: none; }
+
+.payment-methods.paypal-logged-in .setup-creditcard-form, .payment-methods.=
+paypal-logged-in .paypal-form-actions, .payment-methods.paypal-logged-in .t=
+erms, .payment-methods.paypal-logged-in .paypal-signed-in { display: block;=
+ }
+
+.payment-methods.has-paypal-account .paypal-sign-in { display: none; }
+
+.payment-methods.has-paypal-account .paypal-signed-in { display: block; }
+
+.paypal-label { margin: 16px 0px 8px; font-weight: var(--base-text-weight-s=
+emibold, 600); }
+
+.paypal-container { display: inline-block; margin-bottom: 16px; vertical-al=
+ign: top; background-color: var(--color-canvas-subtle); border-radius: 4px;=
+ }
+
+.braintree-paypal-loggedin { border-radius: 4px; padding: 12px 16px !import=
+ant; background-position: 12px 50% !important; border: 1px solid var(--colo=
+r-border-muted)  !important; }
+
+.bt-pp-name { margin-left: 16px !important; }
+
+.bt-pp-email { margin-left: 16px !important; }
+
+.bt-pp-cancel { font-size: 0px !important; line-height: 1 !important; color=
+: var(--color-danger-fg)  !important; text-decoration: none !important; }
+
+.payment-history .id, .payment-history .date, .payment-history .receipt, .p=
+ayment-history .status, .payment-history .amount { white-space: nowrap; }
+
+.payment-history .break-all { word-break: break-all; }
+
+.payment-history .receipt { text-align: center; }
+
+.payment-history .currency, .payment-history .status { color: var(--color-f=
+g-muted); }
+
+.payment-history .status-icon { width: 14px; text-align: center; }
+
+.payment-history .succeeded .status { color: var(--color-success-fg); }
+
+.payment-history .refunded, .payment-history .failed { background: var(--co=
+lor-canvas-subtle); }
+
+.payment-history .refunded td, .payment-history .failed td { opacity: 0.5; =
+}
+
+.payment-history .refunded .receipt, .payment-history .refunded .status, .p=
+ayment-history .failed .receipt, .payment-history .failed .status { opacity=
+: 1; }
+
+.payment-history .refunded .status { color: var(--color-fg-muted); }
+
+.payment-history .failed .status { color: var(--color-danger-fg); }
+
+.payment-history .transaction:target { background: var(--color-attention-su=
+btle); border-color: var(--color-attention-emphasis); }
+
+.paypal-icon { margin: 0px 2px 0px 1px; vertical-align: middle; }
+
+.currency-container .local-currency, .currency-container .local-currency-bl=
+ock { display: none; }
+
+.currency-container.open .local-currency { display: inline; }
+
+.currency-container.open .local-currency-block { display: block; }
+
+.currency-container.open .default-currency { display: none; }
+
+.strong-label { display: inline-block; margin-bottom: 4px; font-weight: var=
+(--base-text-weight-semibold, 600); }
+
+.discounted-original-price { font-weight: var(--base-text-weight-normal, 40=
+0); color: var(--color-fg-muted); text-decoration: line-through; }
+
+.billing-manager-input { width: 500px; }
+
+.seats-change-arrow { margin: 0px 8px; }
+
+.plan-choice { position: relative; display: block; padding: 16px 16px 16px =
+40px; font-weight: var(--base-text-weight-normal, 400); background-color: v=
+ar(--color-canvas-subtle); border: 1px solid var(--color-border-default); }
+
+.plan-choice.open, .plan-choice.selected { background-color: var(--color-ca=
+nvas-default); }
+
+.plan-choice--experiment { cursor: pointer; transition: transform 0.3s ease=
+ 0s, box-shadow 0.3s ease 0s, border-color 0.3s ease 0s; }
+
+.plan-choice--experiment.open, .plan-choice--experiment.selected { border-c=
+olor: var(--color-border-default); box-shadow: var(--color-shadow-large); t=
+ransform: scale(1.025); }
+
+.plan-choice--experiment.open .plan-choice-icon, .plan-choice--experiment.s=
+elected .plan-choice-icon { background-color: var(--color-success-emphasis)=
+; box-shadow: var(--color-shadow-small); }
+
+.plan-choice--experiment.open .plan-choice-icon .octicon, .plan-choice--exp=
+eriment.selected .plan-choice-icon .octicon { transform: scale(1); }
+
+.plan-choice--experiment.plan-choice--green.open, .plan-choice--experiment.=
+plan-choice--green.selected { border-color: var(--color-success-emphasis); =
+}
+
+.plan-choice--experiment.plan-choice--green.open .plan-choice-icon, .plan-c=
+hoice--experiment.plan-choice--green.selected .plan-choice-icon { backgroun=
+d-color: var(--color-success-emphasis); }
+
+.plan-choice--experiment.plan-choice--purple.open, .plan-choice--experiment=
+.plan-choice--purple.selected { border-color: var(--color-done-emphasis); }
+
+.plan-choice--experiment.plan-choice--purple.open .plan-choice-icon, .plan-=
+choice--experiment.plan-choice--purple.selected .plan-choice-icon { backgro=
+und-color: var(--color-done-fg); }
+
+.plan-choice-icon { transition: box-shadow 0.3s ease 0s; }
+
+.plan-choice-icon .octicon { transition: transform 0.2s ease 0s; transform:=
+ scale(0.5); }
+
+.plan-choice-radio { position: absolute; top: 18px; left: 15px; }
+
+.plan-choice-exp { margin-top: 4px; font-size: 12px; color: var(--color-fg-=
+muted); }
+
+.seat-field { width: 50px; margin-right: 4px; }
+
+.billing-line-items { margin-top: 8px; }
+
+.billing-line-item { padding: 8px 0px; font-size: 12px; list-style: none; b=
+order-top: 1px solid var(--color-border-default); }
+
+.billing-line-item::before { display: table; content: ""; }
+
+.billing-line-item::after { display: table; clear: both; content: ""; }
+
+.billing-line-item-last { font-weight: var(--base-text-weight-semibold, 600=
+); border-top-width: 3px; }
+
+.line-item-value { float: right; }
+
+.condensed-payment-methods .vat-field { width: 100%; }
+
+.condensed-payment-methods .state-field { width: 30%; }
+
+.condensed-payment-methods .postcode-field { width: 28%; }
+
+.condensed-payment-methods .country-field { width: 42%; }
+
+.condensed-payment-methods .is-international .country-field { width: 72%; }
+
+.condensed-payment-methods .is-international.no-postcodes .country-field { =
+width: 100%; }
+
+.zuora-billing-section.PaymentMethod--creditcard:not(.has-removed-contents)=
+ ~ .SignUpContinueActions { display: none; }
+
+.zuora-billing-section.PaymentMethod--creditcard-added ~ .SignUpContinueAct=
+ions { display: block; }
+
+.zuora-billing-section.PaymentMethod--paypal ~ .SignUpContinueActions { dis=
+play: block; }
+
+.new-org-billing-form .z_hppm_iframe { width: 100% !important; }
+
+.billing-tooltip-underline { cursor: help; border-bottom: 1px dotted; }
+
+.billing-box-accordion[open] .octicon-chevron-right { height: auto; transfo=
+rm: rotate(90deg); }
+
+.billing-box-accordion:hover .billing-box-accordion-state .octicon { color:=
+ var(--color-fg-muted); }
+
+.billing-box-accordion-chevron[open] .octicon-chevron-right { height: auto;=
+ transform: rotate(90deg); }
+
+.billing-box-accordion-state .octicon { color: var(--color-fg-muted); trans=
+ition: transform 0.09s ease-out 0s; }
+
+.billing-box-progress { padding-top: 1px; margin-bottom: 4px; }
+
+.Details-element:focus { outline: none; }
+
+.organization-radio-button-budget-disabled { color: var(--color-fg-muted); =
+background-color: var(--color-canvas-subtle); }
+
+.organization-radio-button-budget-disabled label p { color: var(--color-fg-=
+muted)  !important; }
+
+.blob-interaction-bar { position: relative; background-color: var(--color-c=
+anvas-subtle); border-bottom: 1px solid var(--color-border-default); }
+
+.blob-interaction-bar::before { display: table; content: ""; }
+
+.blob-interaction-bar::after { display: table; clear: both; content: ""; }
+
+.blob-interaction-bar .octicon-search { position: absolute; top: 6px; left:=
+ 10px; font-size: 12px; color: var(--color-fg-muted); }
+
+.blob-filter { width: 100%; padding: 4px 16px 4px 32px; font-size: 12px; bo=
+rder: 0px; border-radius: 0px; outline: none; }
+
+.blob-filter:focus { outline: none; }
+
+.TagsearchPopover { width: inherit; max-width: 600px; }
+
+.TagsearchPopover-content { max-height: 300px; }
+
+.TagsearchPopover-list .TagsearchPopover-list-item:hover { background-color=
+: var(--color-canvas-subtle); }
+
+.TagsearchPopover-list .TagsearchPopover-list-item .TagsearchPopover-item:h=
+over { text-decoration: none; }
+
+.TagsearchPopover-list .blob-code-inner { white-space: pre-wrap; }
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+
+.diff-table .codeowners-error, .blob-code-content .codeowners-error { color=
+: var(--color-danger-fg); }
+
+.diff-table .error-highlight, .blob-code-content .error-highlight { positio=
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+
+.diff-table .blob-num .line-alert { margin: 2px -2px; }
+
+.csv-data .line-alert { position: absolute; margin: 2px 4px; }
+
+.CopyBlock { line-height: 20px; cursor: pointer; }
+
+.CopyBlock .octicon-copy { display: none; }
+
+.CopyBlock:hover, .CopyBlock:focus, .CopyBlock:active { background-color: v=
+ar(--color-canvas-default); outline: none; }
+
+.CopyBlock:hover .octicon-copy, .CopyBlock:focus .octicon-copy, .CopyBlock:=
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+
+.blob-header.is-stuck { border-top: 0px; border-top-left-radius: 0px; borde=
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+
+.commit-form-avatar { margin-left: -64px; }
+
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+
+.commit-form::after { margin-left: 2px; background-color: var(--color-canva=
+s-default); background-image: linear-gradient(var(--color-canvas-default), =
+var(--color-canvas-default)); }
+
+.commit-form::before { background-color: var(--color-border-default); }
+
+.quick-pull-new-branch-icon { top: 9px; left: 10px; }
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+
+.CodeMirror-hint .CodeMirror-hint:last-child { border-bottom-right-radius: =
+6px; border-bottom-left-radius: 6px; }
+
+.CodeMirror-hint-active { color: var(--color-fg-on-emphasis); background-co=
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+ background-clip: ; background-color: ; background-repeat: repeat-x, repeat=
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+
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+g-on-emphasis)  !important; }
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+ar(--color-border-default); }
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+
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+
+.merge-status-list { max-height: 0px; padding: 0px; margin: 16px -16px -16p=
+x -55px; overflow-y: auto; transition: max-height 0.25s ease-in-out 0s; }
+
+.statuses-toggle-opened { display: none; }
+
+.merge-status-item { position: relative; padding: 8px 16px; background-colo=
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+.merge-status-item .css-truncate-target { max-width: 100%; }
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+
+.merge-status-item.open .review-status-item { display: none !important; }
+
+.merge-status-item.open .dismiss-review-form { display: block; }
+
+.status-meta { color: var(--color-fg-muted); }
+
+.status-meta-file-name { padding: 0.2em 0.4em; margin: 0px; font-size: 85%;=
+ background-color: rgba(27, 31, 35, 0.05); border-radius: 6px; }
+
+.status-actions { margin-left: auto; }
+
+.branch-action-item-icon { float: left; margin-left: -40px; }
+
+.merge-status-icon { min-width: 30px; }
+
+.branch-action { padding-left: 55px; margin-top: 16px; margin-bottom: 16px;=
+ }
+
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+
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+-55px; color: var(--color-fg-on-emphasis); border-radius: 6px; }
+
+.branch-action-body { position: relative; background-color: var(--color-can=
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+ 6px; }
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+
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+ .branch-action-body .queue-branch-form { padding: 16px; background-color: =
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+t); border-bottom-right-radius: 6px; border-bottom-left-radius: 6px; }
+
+.post-merge-message { padding: 16px; }
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+.branch-action-item { padding: 16px 16px 16px 55px; font-size: 13px; line-h=
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+.branch-action-item + .branch-action-item, .branch-action-item + .mergeabil=
+ity-details { border-top: 1px solid var(--color-border-default); }
+
+.branch-action-item.open > .merge-status-list-wrapper > .merge-status-list,=
+ .branch-action-item.open > .merge-status-list { max-height: 231px; margin-=
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+border-width: 1px 0px 0px; }
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+
+.branch-action-item.open .statuses-toggle-closed { display: none; }
+
+.branch-action-btn { margin-left: 16px; }
+
+.branch-action-item-simple { padding-left: 16px; }
+
+.branch-action-item-simple .merge-status-list { margin-left: -16px; }
+
+.branch-action-item-simple .merge-status-item { padding-left: 12px; }
+
+.branch-action-state-clean .branch-action-icon { color: var(--color-fg-on-e=
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+
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+
+.branch-action-state-clean .branch-action-body::before { background-color: =
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+
+.branch-action-state-unknown .branch-action-icon, .branch-action-state-unst=
+able .branch-action-icon { color: var(--color-fg-on-emphasis); background-c=
+olor: var(--color-attention-emphasis); border: 1px solid transparent; }
+
+.branch-action-state-unknown .branch-action-body, .branch-action-state-unst=
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+.branch-action-state-unknown .branch-action-body::after, .branch-action-sta=
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+
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+r-canvas-default), var(--color-canvas-default)); }
+
+.branch-action-state-unknown .branch-action-body::before, .branch-action-st=
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+
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+
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+
+.branch-action-state-merged .branch-action-body::after { margin-left: 2px; =
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+dient(var(--color-canvas-default), var(--color-canvas-default)); }
+
+.branch-action-state-merged .branch-action-body::before { background-color:=
+ var(--color-done-emphasis); }
+
+.branch-action-state-dirty .branch-action-icon, .branch-action-state-closed=
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+olor: var(--color-neutral-emphasis); border: 1px solid transparent; }
+
+.branch-action-state-dirty .branch-action-body, .branch-action-state-closed=
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+sing .branch-action-body { border-color: var(--color-border-default); }
+
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+px; display: block; width: 8px; height: 16px; pointer-events: none; content=
+: " "; clip-path: polygon(0px 50%, 100% 0px, 100% 100%); }
+
+.branch-action-state-dirty .branch-action-body::after, .branch-action-state=
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+(--color-canvas-default), var(--color-canvas-default)); }
+
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+color-border-default); }
+
+.branch-action-state-error .branch-action-icon, .is-merging .branch-action-=
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+nsparent; }
+
+.branch-action-state-error .branch-action-body, .is-merging .branch-action-=
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+
+.branch-action-state-error .branch-action-body::after, .branch-action-state=
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+events: none; content: " "; clip-path: polygon(0px 50%, 100% 0px, 100% 100%=
+); }
+
+.branch-action-state-error .branch-action-body::after, .is-merging .branch-=
+action-state-error-if-merging .branch-action-body::after { margin-left: 2px=
+; background-color: var(--color-canvas-default); background-image: linear-g=
+radient(var(--color-canvas-default), var(--color-canvas-default)); }
+
+.branch-action-state-error .branch-action-body::before, .is-merging .branch=
+-action-state-error-if-merging .branch-action-body::before { background-col=
+or: var(--color-danger-emphasis); }
+
+.enqueued-pull-request .branch-action-body::after, .enqueued-pull-request .=
+branch-action-body::before { position: absolute; top: 11px; right: 100%; le=
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+ontent: " "; clip-path: polygon(0px 50%, 100% 0px, 100% 100%); }
+
+.enqueued-pull-request .branch-action-body::after { margin-left: 2px; backg=
+round-color: var(--color-canvas-default); background-image: linear-gradient=
+(var(--color-canvas-default), var(--color-canvas-default)); }
+
+.enqueued-pull-request .branch-action-body::before { background-color: var(=
+--color-attention-emphasis); }
+
+@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and=
+ (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio:=
+ 2), only screen and (min-resolution: 192dpi), only screen and (min-resolut=
+ion: 2dppx) {
+  .branch-action-body .spinner { background-image: url("/images/spinners/oc=
+tocat-spinner-64.gif"); background-size: 32px 32px; }
+}
+
+.merge-branch-form, .queue-branch-form { display: none; margin: 16px 0px; }
+
+.merge-branch-form .commit-form, .queue-branch-form .commit-form { border-c=
+olor: var(--color-success-emphasis); }
+
+.merge-branch-form .commit-form::before, .queue-branch-form .commit-form::b=
+efore { display: none; }
+
+@media (min-width: 768px) {
+  .merge-branch-form .commit-form::before, .queue-branch-form .commit-form:=
+:before { display: block; border-right-color: var(--color-border-default); =
+}
+}
+
+.merge-branch-form .commit-form::after, .queue-branch-form .commit-form::af=
+ter { display: none; }
+
+@media (min-width: 768px) {
+  .merge-branch-form .commit-form::after, .queue-branch-form .commit-form::=
+after { display: block; }
+}
+
+.merge-branch-form.error .commit-form, .merge-branch-form.danger .commit-fo=
+rm, .queue-branch-form.error .commit-form, .queue-branch-form.danger .commi=
+t-form { border-color: var(--color-danger-emphasis); }
+
+.merge-branch-form.error .commit-form::before, .merge-branch-form.danger .c=
+ommit-form::before, .queue-branch-form.error .commit-form::before, .queue-b=
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+nger-emphasis); }
+
+.merge-button-matrix-merge-form .merge-branch-form { display: block; }
+
+.completeness-indicator { display: flex; align-items: center; justify-conte=
+nt: center; width: 30px; height: 30px; }
+
+.completeness-indicator-success { color: var(--color-fg-on-emphasis); backg=
+round-color: var(--color-success-emphasis); border: 1px solid transparent; =
+border-radius: 50%; }
+
+.completeness-indicator-error { color: var(--color-fg-on-emphasis); backgro=
+und-color: var(--color-danger-emphasis); border: 1px solid transparent; bor=
+der-radius: 50%; }
+
+.completeness-indicator-problem { color: var(--color-fg-on-emphasis); backg=
+round-color: var(--color-neutral-emphasis); border: 1px solid transparent; =
+border-radius: 50%; }
+
+.completeness-indicator-warning { color: var(--color-fg-on-emphasis); backg=
+round-color: var(--color-attention-emphasis); border: 1px solid transparent=
+; border-radius: 50%; }
+
+.pull-merging .pull-merging-error { display: none; }
+
+.pull-merging.is-error .pull-merging-error { display: block; }
+
+.pull-merging.is-error .merge-pr { display: none; }
+
+.admin-options-block .admin-option-button { margin-top: 8px; }
+
+.admin-options-block .policy-enforcement { display: inline; margin-left: 8p=
+x; color: var(--color-fg-muted); }
+
+.admin-options-block .policy-enforcement label { font-size: 14px; }
+
+.admin-options-block .disabled { color: var(--color-fg-muted); }
+
+.admin-options-block .disabled .note { color: var(--color-fg-muted); }
+
+.overflow-scroll-y { overflow: hidden scroll !important; }
+
+.business-menu-item:not([aria-current=3D"page"]) + .business-sub-menu { dis=
+play: none; }
+
+.business-menu-icon { width: 16px; margin-right: 8px; }
+
+.deprovisioning-checkbox > .show-if-disabled { display: none; }
+
+.deprovisioning-checkbox.checkbox-disabled { color: var(--color-fg-muted); =
+}
+
+.deprovisioning-checkbox.checkbox-disabled > .show-if-disabled { display: i=
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+
+.dormant-users-recent-reports { gap: 16px; }
+
+.avatar-migration-status-badge { width: 20px; height: 20px; }
+
+body.full-width-p-0 .new-discussion-timeline { padding: 0px !important; }
+
+body.full-width-p-0 .footer .mt-6 { margin-top: 0px !important; border-top:=
+ 0px !important; }
+
+body.full-width-p-0 .tabnav .tabnav-extra { margin-right: 24px; }
+
+body.full-width-p-0 .tabnav .tabnav-tabs { margin-left: 16px; }
+
+.actions-full-screen .pagehead, .actions-full-screen .hide-full-screen, .ac=
+tions-full-screen .Header-old, .actions-full-screen .Header { display: none=
+; }
+
+.checks-list-item.selected .checks-list-item-name { background-color: var(-=
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+
+.checks-list-item.selected .selected-color-white { color: var(--color-fg-on=
+-emphasis)  !important; }
+
+.checks-list-item-icon { width: 16px; }
+
+.check-annotation { border-left: 0px; border-top-left-radius: 0px; border-b=
+ottom-left-radius: 0px; }
+
+.file .check-annotation { border-bottom: 1px solid var(--color-border-defau=
+lt); }
+
+.file .check-annotation:last-child { border-bottom: 0px; }
+
+.check-annotation .annotation-actions { top: 4px; right: 8px; }
+
+.check-annotation .annotation-octicon { width: 16px; }
+
+.check-annotation.Details--on .Details-content--hidden { display: block !im=
+portant; }
+
+.annotation-title { word-break: break-word; }
+
+.check-annotation-failure { box-shadow: inset 4px 0 0 var(--color-danger-em=
+phasis); }
+
+.check-annotation-failure .annotation-title { color: var(--color-danger-fg)=
+; }
+
+.check-annotation-warning { box-shadow: inset 4px 0 0 var(--color-attention=
+-emphasis); }
+
+.check-annotation-warning .octicon-alert { color: var(--color-attention-fg)=
+; }
+
+.check-annotation-warning .annotation-title { color: var(--color-attention-=
+fg); }
+
+.neutral-check { color: var(--color-fg-muted); }
+
+.check-range-menu-loading { display: block; }
+
+.check-range-menu-error { display: none; }
+
+.is-error .check-range-menu-loading { display: none; }
+
+.is-error .check-range-menu-error { display: block; }
+
+.code-frequency .addition { fill: rgb(44, 190, 78); fill-opacity: 1; }
+
+.code-frequency .deletion { fill: var(--color-danger-emphasis); fill-opacit=
+y: 1; }
+
+.code-list .file-box { border: 1px solid var(--color-border-default); borde=
+r-radius: 6px; }
+
+.code-list .title { min-height: 24px; margin: -4px 0px 8px 40px; font-weigh=
+t: var(--base-text-weight-semibold, 600); line-height: 1.2; }
+
+.code-list .repo-specific .title, .code-list .repo-specific .full-path { ma=
+rgin-left: 0px; }
+
+.code-list .match-count, .code-list .updated-at { margin: 0px; font-weight:=
+ var(--base-text-weight-normal, 400); }
+
+.code-list .language { float: right; margin-left: 8px; font-size: 12px; col=
+or: rgba(51, 51, 51, 0.75); }
+
+.code-list .avatar { float: left; }
+
+.code-list .code-list-item + .code-list-item { border-top: 1px solid var(--=
+color-border-muted); }
+
+.code-list .blob-num { padding: 0px; }
+
+.code-list .blob-num::before { content: normal; }
+
+.code-list .blob-num a { padding: 0px 8px; color: inherit; }
+
+.code-list .blob-num a:hover { color: var(--color-accent-fg); }
+
+.code-list .blob-code { white-space: pre-wrap; }
+
+.code-list .divider .blob-num, .code-list .divider .blob-code { padding-top=
+: 0px; padding-bottom: 0px; cursor: default; background-color: var(--color-=
+canvas-subtle); }
+
+.code-list .divider .blob-num { height: 18px; padding: 0px 8px; line-height=
+: 15px; background-color: var(--color-canvas-subtle); }
+
+.code-list .full-path { margin: 0px 0px 0px 40px; }
+
+.code-list .full-path .octicon-repo { color: var(--color-fg-muted); }
+
+.code-list .full-path .octicon-lock { color: var(--color-attention-fg); }
+
+.code-list .full-path a { color: var(--color-fg-muted); }
+
+.code-list-item-private .file-box { border: 1px solid var(--color-attention=
+-muted); }
+
+.code-list-item-private .blob-num { background-color: var(--color-attention=
+-subtle); border-right: 1px solid var(--color-attention-muted); }
+
+.code-list-item-private .blob-num a { color: var(--color-attention-fg); }
+
+.code-list-item-private .divider .blob-num, .code-list-item-private .divide=
+r .blob-code { color: var(--color-attention-fg); background-color: var(--co=
+lor-attention-subtle); }
+
+.code-scanning-alert-warning-message { border-color: var(--color-attention-=
+emphasis)  !important; }
+
+.code-scanning-font-size-inherit { font-size: inherit !important; }
+
+.cs-message .md-list { padding-left: 2em; }
+
+.code-scanning-timeline .TimelineItem:last-of-type.code-scanning-alert-dism=
+issal-comment::before { width: 0px; }
+
+.codesearch-head.pagehead h1 { width: 250px; line-height: 33px; }
+
+@media (min-width: 768px) {
+  .advanced-search-form .flattened dt { width: 230px; }
+  .advanced-search-form .flattened dt label { font-weight: var(--base-text-=
+weight-normal, 400); }
+  .advanced-search-form .flattened dd { margin-left: 250px; }
+  .advanced-search-form .form-checkbox { margin-left: 250px; }
+}
+
+.codesearch-results .code-list .title a { overflow-wrap: break-word; }
+
+.codesearch-results .repo-list-item { border-bottom: 0px; }
+
+.codesearch-results .repo-list-item + .repo-list-item { border-top: 1px sol=
+id var(--color-border-default); }
+
+.search-form-fluid .TableObject-item--primary { position: relative; padding=
+-right: 8px; }
+
+.search-form-fluid .completed-query { position: absolute; z-index: 1; paddi=
+ng: inherit; margin: 0px; overflow: hidden; white-space: nowrap; }
+
+.search-form-fluid .completed-query span { opacity: 0; }
+
+.search-form-fluid .search-page-label { position: relative; display: block;=
+ font-weight: var(--base-text-weight-normal, 400); cursor: text; }
+
+.search-form-fluid .search-page-label.focus .completed-query { opacity: 0.6=
+; }
+
+.search-form-fluid .search-page-input { position: relative; z-index: 2; min=
+-height: 0px; padding: 0px; margin: 0px; background: none; border: 0px; box=
+-shadow: none; }
+
+.search-form-fluid .search-page-input:focus { box-shadow: none; }
+
+.topics-row-container { height: 30px; overflow: hidden; }
+
+@media (max-width: 544px) {
+  .codesearch-pagination-container a:not(.next_page):not(.previous_page), .=
+codesearch-pagination-container .gap { display: none; }
+  .codesearch-pagination-container .previous_page, .codesearch-pagination-c=
+ontainer .next_page { width: 100%; }
+  .codesearch-pagination-container .current { color: var(--color-fg-muted);=
+ background: var(--color-canvas-default); border-color: var(--color-border-=
+default); }
+  .codesearch-pagination-container .current::after { content: " of " attr(d=
+ata-total-pages); }
+}
+
+.codespaces-policy-form details > summary::-webkit-details-marker { display=
+: none; }
+
+.codespaces-wider-select-menu-modal { width: 420px; margin-right: 32px; }
+
+.codespaces-policy-item-no-hover:hover { background-color: var(--color-canv=
+as-overlay); }
+
+.codespaces-policy-constraint-menu-item-muted-hover:hover { color: var(--co=
+lor-fg-default); background-color: var(--color-btn-hover-bg); }
+
+.codespaces-policy-form-button-hover:hover .codespaces-policy-form-button-t=
+ext-hover { border-color: var(--color-btn-hover-bg)  !important; }
+
+.codespaces-policy-form-button-hide-border { border-color: var(--color-scal=
+e-white)  !important; }
+
+.codespaces-policy-form-blue-hover:hover { color: var(--color-accent-fg)  !=
+important; }
+
+.codespaces-policy-form-blue-hover:hover svg { color: var(--color-accent-fg=
+)  !important; }
+
+.codespaces-policy-form-blue-hover:focus svg { color: var(--color-accent-fg=
+)  !important; }
+
+.codespaces-policy-form-danger-hover:hover { background-color: var(--color-=
+danger-subtle)  !important; }
+
+.codespaces-storage-utilization-button:hover { background-color: transparen=
+t !important; }
+
+.commit-activity-master { margin-top: 16px; }
+
+.is-graph-loading .commit-activity-master { display: none; }
+
+rect { shape-rendering: crispedges; }
+
+rect.max { fill: var(--color-attention-fg); }
+
+g.bar { fill: var(--color-success-fg); }
+
+g.mini { fill: var(--color-severe-fg); }
+
+g.active rect { fill: var(--color-danger-fg); }
+
+circle.focus { fill: var(--color-fg-muted); }
+
+.dot text { fill: var(--color-fg-muted); stroke: none; }
+
+span.no-nl-marker { position: relative; color: var(--color-danger-fg); vert=
+ical-align: middle; }
+
+.symlink .no-nl-marker { display: none; }
+
+.range-editor { position: relative; padding: 4px 16px 4px 40px; margin-top:=
+ 16px; margin-bottom: 16px; background-color: var(--color-canvas-subtle); b=
+order: 1px solid var(--color-border-default); border-radius: 6px; }
+
+.range-editor .dots { font-size: 16px; }
+
+.range-editor .select-menu { position: relative; display: inline-block; }
+
+.range-editor .select-menu.fork-suggester { display: none; }
+
+.range-editor .branch-name { line-height: 22px; }
+
+.range-editor .branch .css-truncate-target, .range-editor .fork-suggester .=
+css-truncate-target { max-width: 180px; }
+
+.range-editor .pre-mergability { display: inline-block; padding: 4px; line-=
+height: 26px; vertical-align: middle; }
+
+.range-editor .pre-mergability .octicon { vertical-align: text-bottom; }
+
+.range-editor.is-cross-repo .select-menu.fork-suggester { display: inline-b=
+lock; }
+
+.range-editor-icon { float: left; margin-top: 8px; margin-left: -24px; colo=
+r: var(--color-fg-muted); }
+
+.compare-pr-header { display: none; }
+
+.is-pr-composer-expanded .compare-show-header { display: none; }
+
+.is-pr-composer-expanded .compare-pr-header { display: block; }
+
+.range-cross-repo-pair { display: inline-block; padding: 4px; white-space: =
+nowrap; }
+
+.tint-box { position: relative; margin-bottom: 8px; background: var(--color=
+-canvas-subtle); border-radius: 6px; }
+
+.tint-box.transparent { background: var(--color-canvas-default); }
+
+.tint-box .activity { padding-top: 100px; margin-top: 0px; }
+
+.contrib-person path { fill: var(--color-severe-fg); }
+
+.contrib-person .midlabel { fill: var(--color-neutral-emphasis); }
+
+.coupons .setup-plans td img { margin-top: -2px; vertical-align: middle; }
+
+.coupons .coupon-form-body { width: 270px; padding: 16px; margin: 100px aut=
+o 60px; font-size: 14px; text-align: center; background-color: var(--color-=
+canvas-subtle); border: 1px solid var(--color-border-default); border-radiu=
+s: 6px; }
+
+.coupons .coupon-form-body .input-block { margin-bottom: 16px; }
+
+.coupons .coupon-form-body .btn { display: block; width: 100%; }
+
+.coupon-icon { width: 80px; height: 80px; margin: 0px auto 16px; color: var=
+(--color-accent-fg); border: 1px solid var(--color-border-default); border-=
+radius: 40px; }
+
+.coupon-icon .octicon { margin-top: 16px; margin-right: 2px; }
+
+.coupons-list-options .select-menu { display: inline-block; margin-right: 8=
+px; }
+
+.coupons-list-options .pagination { float: right; margin: 0px; }
+
+.repo-private-icon { fill: var(--color-attention-fg); }
+
+.dashboard-rollup-items > .dashboard-rollup-item { border-top: 1px solid va=
+r(--color-border-default); }
+
+.news-full { float: none; width: auto; }
+
+.pinned-favorites-update-form { height: 200px; }
+
+.dashboard-break-word { hyphens: auto; word-break: break-word; }
+
+.news .bio g-emoji, .news .repo-description g-emoji { display: inline-block=
+; }
+
+.news .feed-item-heading-menu-button { color: var(--color-fg-muted); }
+
+.news .feed-item-heading-menu-button:hover { color: var(--color-fg-default)=
+; }
+
+.suggest-icon { width: 48px; height: 48px; padding: 4px; }
+
+.suggest-icon svg, .suggest-icon path { fill: rgb(255, 255, 255); }
+
+.suggest-icon svg::before, .suggest-icon path::before { bottom: -6px; left:=
+ -4px; background-color: rgb(158, 123, 255); }
+
+.suggest-icon svg::after, .suggest-icon path::after { top: -5px; right: -5p=
+x; width: 5px; height: 5px; background-color: rgb(108, 132, 233); }
+
+.suggest-icon .suggest-icon-bubble { position: absolute; width: 6px; height=
+: 6px; background-color: rgb(108, 132, 233); border-radius: 50%; }
+
+.suggest-icon .suggest-icon-bubble:nth-of-type(2n) { width: 4px; height: 4p=
+x; background-color: rgb(158, 123, 255); }
+
+.suggest-icon .suggest-icon-bubble:nth-of-type(1) { bottom: -7px; left: -7p=
+x; }
+
+.suggest-icon .suggest-icon-bubble:nth-of-type(2) { top: -4px; right: 4px; =
+}
+
+.suggest-icon .suggest-icon-bubble:nth-of-type(3) { top: -7px; right: -8px;=
+ }
+
+.dashboard-notice { position: relative; padding: 16px 16px 16px 55px; margi=
+n-bottom: 16px; font-size: 14px; background-color: var(--color-canvas-subtl=
+e); border: 1px solid var(--color-border-default); border-radius: 6px; }
+
+.dashboard-notice .dismiss { position: absolute; top: 10px; right: 10px; wi=
+dth: 16px; height: 16px; color: var(--color-fg-muted); cursor: pointer; }
+
+.dashboard-notice .dismiss:hover { color: var(--color-fg-muted); }
+
+.dashboard-notice .notice-icon { position: absolute; top: 15px; left: 15px;=
+ }
+
+.dashboard-notice .octicon-organization { color: var(--color-accent-fg); }
+
+.dashboard-notice h2 { margin-top: 8px; margin-bottom: 16px; font-size: 16p=
+x; font-weight: var(--base-text-weight-normal, 400); color: var(--color-fg-=
+default); }
+
+.dashboard-notice p.no-title { padding-right: 4px; }
+
+.dashboard-notice ul { margin-left: 16px; }
+
+.dashboard-notice li { padding-bottom: 16px; }
+
+.dashboard-notice .coupon { padding: 8px; margin: 16px 0px; font-size: 20px=
+; font-weight: var(--base-text-weight-semibold, 600); text-align: center; b=
+ackground: var(--color-canvas-default); border: 1px dashed var(--color-bord=
+er-default); }
+
+.dashboards-overview-lead { width: 700px; }
+
+.dashboards-overview-cards .boxed-group { width: 100%; margin: 8px 0px; }
+
+.dashboards-overview-cards .boxed-group .graph-canvas path { stroke-opacity=
+: 0.5; }
+
+.dashboards-overview-cards .is-no-activity .blankslate { display: block; }
+
+.dashboards-overview-cards .is-no-activity .dashboards-overview-graph { dis=
+play: none; }
+
+.dashboards-overview-cards .blankslate { display: none; padding-top: 47px; =
+background-color: var(--color-canvas-default); border: 0px; box-shadow: non=
+e; }
+
+.dashboards-overview-cards .octicon-arrow-down, .dashboards-overview-cards =
+.octicon-arrow-up { display: none; }
+
+.dashboards-overview-cards .is-increase .octicon-arrow-up { display: inline=
+-block; }
+
+.dashboards-overview-cards .is-decrease .octicon-arrow-down { display: inli=
+ne-block; }
+
+.dashboards-overview-cards .octicon-arrow-down { color: var(--color-danger-=
+fg); }
+
+.dashboards-overview-cards .octicon-arrow-up { color: rgb(29, 179, 79); }
+
+.dashboards-overview-cards .graph-canvas .dots { padding: 43px 0px; }
+
+.dashboards-overview-cards .summary-stats { height: 78px; }
+
+.dashboards-overview-cards .summary-stats .metric-0 { color: rgb(29, 179, 7=
+9); }
+
+.dashboards-overview-cards .summary-stats .metric-1 { color: var(--color-ac=
+cent-fg); }
+
+.dashboards-overview-cards .summary-stats .totals-num { margin: 0px 8px; }
+
+.dashboards-overview-cards .summary-stats .single { width: 100%; }
+
+.dashboards-overview-cards .dashboards-overview-graph { height: 160px; }
+
+.dashboards-overview-cards .dashboards-overview-graph path { fill: none; st=
+roke-width: 2; }
+
+.dashboards-overview-cards .dashboards-overview-graph path.metric-0 { strok=
+e: rgb(29, 179, 79); }
+
+.dashboards-overview-cards .dashboards-overview-graph path.metric-1 { strok=
+e: rgb(29, 127, 179); }
+
+.dashboards-overview-cards .dashboards-overview-graph .y line { stroke: rgb=
+(29, 179, 79); }
+
+.dashboards-overview-cards .dashboards-overview-graph .y.unique line { stro=
+ke: rgb(29, 127, 179); }
+
+.dashboards-overview-cards .dashboards-overview-graph .overlay { fill-opaci=
+ty: 0; }
+
+.dashboards-overview-cards .metric-0 circle { fill: rgb(29, 179, 79); strok=
+e: rgb(255, 255, 255); stroke-width: 2; }
+
+.dashboards-overview-cards .dots.metric-1 circle { fill: rgb(29, 127, 179);=
+ stroke: rgb(255, 255, 255); stroke-width: 2; }
+
+.TimelineItem:last-of-type.dependabot-alert-dismissal-comment::before { wid=
+th: 0px; }
+
+dl.form.developer-select-account { margin-top: 0px; }
+
+.developer-wrapper .setup-info-module .features-list { margin-left: 16px; }
+
+.developer-wrapper .setup-info-module .features-list .octicon { margin-left=
+: -16px; }
+
+.developer-thanks h2 { font-size: 40px; font-weight: var(--base-text-weight=
+-normal, 400); }
+
+.developer-thanks .hook { margin-top: 2px; margin-bottom: 32px; font-size: =
+16px; font-weight: var(--base-text-weight-light, 300); color: var(--color-f=
+g-muted); }
+
+.developer-thanks-image { position: relative; bottom: -45px; float: left; w=
+idth: 400px; }
+
+.developer-thanks-section { margin: 130px 0px 0px 470px; }
+
+.developer-next-steps { font-size: 16px; font-weight: var(--base-text-weigh=
+t-light, 300); list-style: none; }
+
+.developer-next-steps li { margin-top: 8px; }
+
+.developer-next-steps li:first-child { margin-top: 0px; }
+
+.developer-next-steps .octicon { margin-right: 8px; color: var(--color-succ=
+ess-fg); vertical-align: middle; }
+
+development-menu .line-clamp-1 { display: -webkit-box; -webkit-line-clamp: =
+1; -webkit-box-orient: vertical; overflow: hidden; word-break: break-all; }
+
+development-menu .form-control[is-loading] { background: transparent; }
+
+development-menu .development-menu-component-menu-modal, development-menu .=
+development-menu-component-dialog-modal { margin-top: 4px !important; }
+
+development-menu .SelectMenu-item:hover, development-menu .SelectMenu-item:=
+focus-visible { color: var(--color-fg-on-emphasis)  !important; background-=
+color: var(--color-accent-emphasis)  !important; }
+
+development-menu .SelectMenu-item:hover .arrow, development-menu .SelectMen=
+u-item:focus-visible .arrow { visibility: visible !important; }
+
+development-menu .SelectMenu-item:hover svg, development-menu .SelectMenu-i=
+tem:hover span, development-menu .SelectMenu-item:hover p, development-menu=
+ .SelectMenu-item:focus-visible svg, development-menu .SelectMenu-item:focu=
+s-visible span, development-menu .SelectMenu-item:focus-visible p { color: =
+var(--color-fg-on-emphasis)  !important; background-color: var(--color-acce=
+nt-emphasis)  !important; }
+
+development-menu .development-menu-component-menu-modal, development-menu .=
+development-menu-component-dialog, development-menu .development-menu-compo=
+nent-dialog-modal { animation: 0s ease 0s 1 normal none running none; }
+
+@media (min-width: 544px) {
+  development-menu .development-menu-component-menu-modal, development-menu=
+ .development-menu-component-dialog, development-menu .development-menu-com=
+ponent-dialog-modal { max-width: 300px; }
+}
+
+@media (max-width: 544px) {
+  development-menu .hx_rsm-modal-sm { top: 75px; left: 16px; display: flex;=
+ width: auto; height: 80%; margin: 0px; flex-direction: column; animation: =
+0.24s cubic-bezier(0, 0.1, 0.1, 1) 0.12s 1 normal backwards running hx_rsm-=
+modal-animation; position: fixed !important; right: 16px !important; }
+}
+
+development-menu .development-menu-component-dialog:not([hidden]) + .develo=
+pment-menu-component-dialog-overlay { position: fixed; inset: 0px; z-index:=
+ 80; display: block; cursor: default; content: " "; background: var(--color=
+-primer-canvas-backdrop); }
+
+@media (min-width: 544px) {
+  development-menu .development-menu-component-dialog:not([hidden]) + .deve=
+lopment-menu-component-dialog-overlay { display: none; }
+}
+
+development-menu .development-menu-component-dialog { z-index: 99; }
+
+@keyframes development-menu-component-dialog-animation--sm {=20
+  0% { opacity: 0; transform: translateX(16px); }
+}
+
+@media (min-width: 544px) {
+  development-menu .development-menu-component-dialog { position: absolute;=
+ inset: auto; max-height: none; padding-top: 0px; margin: 0px; transform: n=
+one; }
+}
+
+development-menu .development-menu-component-dialog .development-menu-compo=
+nent-dialog-modal { animation: 0s ease 0s 1 normal none running none; }
+
+development-menu .SelectMenu-item[aria-disabled=3D"true"] { opacity: 0.5; }
+
+.file-diff-split[data-lock-side-selection=3D"left"] [data-split-side=3D"rig=
+ht"], .file-diff-split[data-lock-side-selection=3D"right"] [data-split-side=
+=3D"left"] { user-select: none; }
+
+.invisible { position: absolute; opacity: 0; }
+
+.icon-discussion-answered { color: var(--color-success-fg); }
+
+.icon-discussion-answered, .icon-discussion-answered path { fill: var(--col=
+or-success-emphasis); }
+
+.icon-discussion-white { color: var(--color-discussions-state-answered-icon=
+)  !important; }
+
+.icon-discussion-white, .icon-discussion-white path { fill: var(--color-dis=
+cussions-state-answered-icon)  !important; }
+
+.icon-discussion-gray { color: var(--color-fg-default); }
+
+.icon-discussion-gray, .icon-discussion-gray path { fill: var(--color-fg-de=
+fault); }
+
+.label-select-menu .color { display: inline-block; width: 14px; height: 14p=
+x; margin-top: -1px; margin-right: 2px; vertical-align: middle; border-radi=
+us: 7px; }
+
+.label-select-menu .select-menu-item:hover, .label-select-menu .select-menu=
+-item:focus, .label-select-menu .select-menu-item[aria-checked=3D"true"]:ho=
+ver, .label-select-menu .select-menu-item[aria-checked=3D"true"]:focus { co=
+lor: inherit; background-color: var(--color-neutral-subtle); }
+
+.label-select-menu .select-menu-item-icon, .label-select-menu .label-option=
+s-icon { color: inherit !important; }
+
+.user-has-reacted .octicon { fill: var(--color-accent-fg); }
+
+.discussion-feed-body video { max-height: 200px !important; }
+
+.discussions-header-wrap { display: grid; grid-template-columns: minmax(0px=
+, 1fr); gap: 10px; }
+
+@media (min-width: 1012px) {
+  .discussions-header-wrap { grid-template-columns: minmax(0px, 1fr) max-co=
+ntent; }
+}
+
+.discussions-query-builder { order: 2; }
+
+@media (min-width: 1012px) {
+  .discussions-query-builder { order: inherit; }
+}
+
+.discussions-button-wrap { display: flex; align-items: center; order: 1; }
+
+@media (min-width: 1012px) {
+  .discussions-button-wrap { order: inherit; justify-content: flex-end; }
+}
+
+.discussions-select-menu a:focus { outline: 2px solid var(--color-accent-em=
+phasis); }
+
+.donut-chart > .error, .donut-chart > .cancelled, .donut-chart > .action_re=
+quired, .donut-chart > .timed_out, .donut-chart > .failure { fill: var(--co=
+lor-checks-donut-error); }
+
+.donut-chart > .expected, .donut-chart > .queued, .donut-chart > .in_progre=
+ss, .donut-chart > .waiting, .donut-chart > .requested, .donut-chart > .pen=
+ding { fill: var(--color-checks-donut-pending); }
+
+.donut-chart > .success { fill: var(--color-checks-donut-success); }
+
+.donut-chart > .neutral, .donut-chart > .stale, .donut-chart > .skipped { f=
+ill: var(--color-checks-donut-neutral); }
+
+.ds-action-prompt-container { height: auto; background-image: url("/images/=
+modules/dependency-graph/ds-actions-prompt-banner-mobile.png"); background-=
+repeat: no-repeat; background-position: right top; }
+
+.ds-action-prompt-container .content { width: 95%; }
+
+@media (min-width: 768px) {
+  .ds-action-prompt-container { background-image: url("/images/modules/depe=
+ndency-graph/ds-actions-prompt-banner.png"); }
+  .ds-action-prompt-container .content { width: 80%; }
+}
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+e; background-color: var(--color-accent-emphasis); opacity: 1; }
+
+.reaction-sort-item[aria-checked=3D"true"] { background-color: var(--color-=
+accent-subtle); border-color: var(--color-accent-emphasis); opacity: 1; }
+
+.issue-keyword { border-bottom: 1px dotted var(--color-border-default); }
+
+.issue-keyword:hover { border-bottom: 0px; }
+
+.new-label-color-dimensions { width: 24px; height: 24px; }
+
+.select-menu-item[aria-selected=3D"true"] > .octicon.label-options-icon, .s=
+elect-menu-item.navigation-focus > .octicon.label-options-icon { color: var=
+(--color-fg-default); }
+
+.new-label-color-input:invalid { color: var(--color-danger-fg); }
+
+.issue-form-textarea { height: 100px !important; min-height: 100px !importa=
+nt; }
+
+.issue-forms-wysiwyg-container .comment-form-head { background: var(--color=
+-canvas-subtle)  !important; }
+
+.issue-forms-wysiwyg-container .comment-body { border-bottom: 0px !importan=
+t; }
+
+.issue-form-body > :first-child { margin-top: 0px !important; }
+
+.issue-create-branch-menu-action { color: var(--color-fg-default); }
+
+.issue-create-branch-menu-action:hover:not(:disabled) { color: var(--color-=
+fg-default); background-color: var(--color-canvas-subtle); }
+
+.issue-create-branch-menu-action:focus:not(:disabled) { color: var(--color-=
+fg-default); background-color: var(--color-neutral-subtle); }
+
+react-app { display: flex !important; }
+
+react-app > div { width: 100%; }
+
+react-app > div > div { height: 100%; }
+
+.repository-lang-stats { position: relative; }
+
+.repository-lang-stats ol.repository-lang-stats-numbers li { display: table=
+-cell; width: 1%; padding: 8px 4px; text-align: center; white-space: nowrap=
+; border-bottom: 0px; }
+
+.repository-lang-stats ol.repository-lang-stats-numbers li span.percent { f=
+loat: none; }
+
+.repository-lang-stats ol.repository-lang-stats-numbers li > a, .repository=
+-lang-stats ol.repository-lang-stats-numbers li > span { font-weight: var(-=
+-base-text-weight-semibold, 600); color: var(--color-fg-muted); text-decora=
+tion: none; }
+
+.repository-lang-stats ol.repository-lang-stats-numbers li .lang { color: v=
+ar(--color-fg-default); }
+
+.repository-lang-stats ol.repository-lang-stats-numbers li .language-color =
+{ display: inline-block; width: 10px; height: 10px; border-radius: 50%; }
+
+.repository-lang-stats ol.repository-lang-stats-numbers li a:hover { backgr=
+ound: transparent; }
+
+.repository-lang-stats-graph { width: 100%; overflow: hidden; white-space: =
+nowrap; cursor: pointer; user-select: none; border-right-color: ; border-ri=
+ght-style: ; border-right-width: ; border-bottom-color: ; border-bottom-sty=
+le: ; border-bottom-width: ; border-left-color: ; border-left-style: ; bord=
+er-left-width: ; border-image-source: ; border-image-slice: ; border-image-=
+width: ; border-image-outset: ; border-image-repeat: ; border-top: 0px; bor=
+der-bottom-right-radius: 6px; border-bottom-left-radius: 6px; }
+
+.repository-lang-stats-graph .language-color { line-height: 8px; text-inden=
+t: -9999px; }
+
+.repository-lang-stats-graph .language-color:first-child { border-bottom-le=
+ft-radius: 6px; }
+
+.repository-lang-stats-graph .language-color:last-child { border-bottom-rig=
+ht-radius: 6px; }
+
+.repository-lang-stats-graph .language-color:not(:first-child) { border-lef=
+t: 1px solid var(--color-canvas-default); }
+
+.facebox-loading, .octocat-spinner { min-height: 64px; background-image: ur=
+l("/images/spinners/octocat-spinner-64.gif"); background-repeat: no-repeat;=
+ background-position: center center; }
+
+.octocat-spinner-32 { min-height: 32px; background-image: url("/images/spin=
+ners/octocat-spinner-32.gif"); background-repeat: no-repeat; background-pos=
+ition: center center; }
+
+@media only screen and (-webkit-min-device-pixel-ratio: 2), only screen and=
+ (-moz-min-device-pixel-ratio: 2), only screen and (min-device-pixel-ratio:=
+ 2), only screen and (min-resolution: 192dpi), only screen and (min-resolut=
+ion: 2dppx) {
+  .facebox-loading, .octocat-spinner { background-image: url("/images/spinn=
+ers/octocat-spinner-128.gif"); background-size: 64px 64px; }
+  .octocat-spinner-32 { background-image: url("/images/spinners/octocat-spi=
+nner-64.gif"); background-size: 32px 32px; }
+}
+
+.map-container .activity { top: 120px; left: 340px; z-index: 99999; }
+
+.map-container .is-graph-loading .activity { display: block; }
+
+.map { height: 350px; }
+
+.map-background { pointer-events: all; fill: rgb(3, 102, 214); }
+
+.map-background-zoom { cursor: grab; }
+
+.map-land { fill: none; stroke: rgb(37, 106, 174); stroke-width: 2; shape-r=
+endering: crispedges; }
+
+.map-country { fill: rgb(215, 199, 173); shape-rendering: crispedges; curso=
+r: pointer; }
+
+.map-country.hk { stroke: rgb(165, 150, 126); }
+
+.map-country:hover { fill: rgb(200, 178, 142); }
+
+.map-country.active { fill: rgb(246, 229, 202); }
+
+.map-borders { fill: none; stroke: rgb(165, 150, 126); shape-rendering: cri=
+spedges; }
+
+.map-graticule { pointer-events: none; fill: none; stroke: rgb(255, 255, 25=
+5); stroke-opacity: 0.2; shape-rendering: crispedges; }
+
+.map-graticule :nth-child(2n) { stroke-dasharray: 2, 2; }
+
+.map-legend .map-legend-circle { fill-opacity: 0; stroke: rgb(255, 255, 255=
+); stroke-width: 1.5; }
+
+.map-legend .map-legend-text { font-size: 12px; fill: rgb(255, 255, 255); t=
+ext-anchor: end; }
+
+.map-legend .map-legend-link { stroke: rgb(255, 255, 255); stroke-width: 1.=
+5; }
+
+.map-point { pointer-events: none; fill: rgb(246, 106, 10); }
+
+.map-point:hover { fill: rgb(227, 98, 9); }
+
+.map-country-info { top: 8px; right: 8px; pointer-events: none; opacity: 0;=
+ }
+
+.MarketplaceJumbotron { background-color: var(--color-neutral-emphasis); ba=
+ckground-image: url("/images/modules/marketplace/bg-hero.svg"); background-=
+repeat: repeat-y; background-position: center top; background-size: 150%; }
+
+@media (min-width: 768px) {
+  .MarketplaceJumbotron { background-repeat: no-repeat; background-size: co=
+ver; }
+}
+
+.CircleBadge--feature { position: relative; top: 0px; transition: top 0.15s=
+ ease-in 0s, box-shadow 0.12s ease-in 0s; }
+
+.MarketplaceFeature { min-width: 250px; }
+
+.MarketplaceFeature-text { opacity: 0.7; transition: opacity 0.12s ease-in =
+0s; }
+
+.MarketplaceFeature-link:hover .CircleBadge--feature { top: -3px; box-shado=
+w: rgba(0, 0, 0, 0.2) 0px 3px 8px 0px; }
+
+.MarketplaceFeature-link:hover .MarketplaceFeature-text { opacity: 1; }
+
+.MarketplaceFeature-link:active .CircleBadge--feature { top: 0px; }
+
+.MarketplaceSideNav { background-color: var(--color-canvas-subtle); }
+
+@media (min-width: 768px) {
+  .MarketplaceSideNav { background-color: var(--color-canvas-default); bord=
+er-right: 1px solid var(--color-border-default); }
+}
+
+.ScreenshotCarousel { border: 1px solid var(--color-border-default); border=
+-radius: 6px; }
+
+.ScreenshotCarousel-screenshot { padding: 16px; }
+
+.ScreenshotCarousel-nav { display: flex; overflow-x: auto; box-shadow: inse=
+t 0 1px 0 var(--color-border-default); }
+
+.ScreenshotCarousel-navitem { width: 20%; min-width: 120px; padding: 16px; =
+cursor: pointer; border-right: 1px solid var(--color-border-default); }
+
+.ScreenshotCarousel-navitem:last-child { border-right: 0px; }
+
+.ScreenshotCarousel-navitem.selected { background-color: var(--color-canvas=
+-subtle); box-shadow: rgba(36, 41, 46, 0.15) 0px 0px 4px inset; }
+
+.marketplace-listing-screenshot-container { width: 175px; min-height: 175px=
+; background-repeat: no-repeat; background-position: center center; backgro=
+und-size: cover; }
+
+.marketplace-listing-screenshot-zoom { display: none; cursor: move; }
+
+.marketplace-listing-details-sidebar { order: 2; }
+
+@media (min-width: 768px) {
+  .marketplace-listing-details-sidebar { order: 1; }
+}
+
+.marketplace-listing-details-description { order: 1; }
+
+@media (min-width: 768px) {
+  .marketplace-listing-details-description { order: 2; }
+}
+
+.marketplace-listing-screenshot-link { height: 100px; cursor: move; }
+
+.marketplace-listing-screenshot-link:hover .marketplace-listing-screenshot-=
+zoom, .marketplace-listing-screenshot-link:focus .marketplace-listing-scree=
+nshot-zoom { top: 0px; left: 0px; display: block; width: 100%; height: 100%=
+; padding-top: 24px; background-color: rgba(255, 255, 255, 0.75); }
+
+.marketplace-integratable-logo { width: 40px; height: 40px; }
+
+.marketplace-listing-save-notice, .marketplace-listing-save-error { display=
+: none; opacity: 0; transition: opacity 0.15s linear 0s; }
+
+.marketplace-listing-save-notice.visible, .marketplace-listing-save-error.v=
+isible { display: inline-block; opacity: 1; }
+
+.marketplace-listing-screenshot-delete-form { position: absolute; bottom: -=
+24px; width: 100%; text-align: center; }
+
+.marketplace-plan-dollar-field-container .price-note { display: none; }
+
+.marketplace-plan-dollar-field-container.is-errored .price-note { display: =
+block; }
+
+.marketplace-plan-dollar-field-container.is-errored .form-control { border-=
+color: var(--color-danger-emphasis); }
+
+.marketplace-plan-emphasis { color: var(--color-fg-default); }
+
+.selected .marketplace-plan-emphasis { color: var(--color-fg-on-emphasis); =
+}
+
+.marketplace-plan-unit-name-preview::before { content: "per "; }
+
+.marketplace-plan-per-time { clear: right; }
+
+.marketplace-billing-modal { width: 540px; max-height: 90vh; margin-top: 5v=
+h; }
+
+.marketplace-listing-markdown, .marketplace-url-link { overflow-wrap: break=
+-word; white-space: pre-wrap; }
+
+.marketplace-listing-markdown { line-height: 1.4; }
+
+.marketplace-product-callout { border-color: var(--color-border-default)  !=
+important; }
+
+.marketplace-product-callout::before, .marketplace-product-callout::after {=
+ display: none; }
+
+.marketplace-product-callout .branch-action-item-icon { color: var(--color-=
+fg-muted); background-color: var(--color-canvas-subtle); }
+
+.filter-item.selected .Label--secondary { color: var(--color-fg-on-emphasis=
+); border-color: var(--color-fg-on-emphasis); }
+
+.MarketplaceEdit-body { min-height: 570px; }
+
+.MarketplaceEdit-body .pricing-model-selector { width: calc(100% - 12px); m=
+ax-width: 100% !important; }
+
+.MarketplaceEdit-body .menu { border-right: 0px; border-left: 0px; border-r=
+adius: 0px; }
+
+.MarketplaceEdit-body .menu-item { padding: 12px 16px; background: var(--co=
+lor-canvas-subtle); }
+
+.MarketplaceEdit-body .menu-item.selected { background: var(--color-canvas-=
+default); }
+
+.MarketplaceEdit-body .menu-item:hover { background: var(--color-canvas-sub=
+tle); }
+
+.MarketplaceEdit-body .menu-item.selected::before { position: absolute; top=
+: 0px; bottom: 0px; left: 0px; width: 3px; content: ""; background-color: v=
+ar(--color-severe-emphasis); }
+
+.MarketplaceEdit-body .menu-item:first-child::before { border-top-left-radi=
+us: 0px; }
+
+.MarketplaceEdit-body .CircleIcon { display: inline-block; width: 32px; hei=
+ght: 32px; font-weight: var(--base-text-weight-semibold, 600); line-height:=
+ 32px; color: var(--color-fg-muted); text-align: center; background: rgb(23=
+0, 235, 241); border-radius: 50%; }
+
+.MarketplaceEdit-body .CircleIcon .octicon { display: inline-block; }
+
+.MarketplaceInsights-graph .insights-month .tick:nth-child(2n) { visibility=
+: hidden; }
+
+.BarChart { border-radius: 6px; }
+
+.BarChart-bar { height: 10px; border-right: 1px solid var(--color-canvas-de=
+fault); }
+
+.BarChart-bar--green { background-color: var(--color-success-emphasis); }
+
+.BarChart-bar--orange { background-color: var(--color-severe-emphasis); }
+
+.BarChart-bar--yellow { background-color: var(--color-attention-emphasis); =
+}
+
+.CircleBadge--tiny { width: 32px; height: 32px; }
+
+.CircleBadge--github { position: relative; }
+
+.CircleBadge--github.CircleBadge--large::after { right: 5px; bottom: 5px; }
+
+.CircleBadge--github.CircleBadge--small::after { right: -5px; bottom: -5px;=
+ }
+
+.CircleBadge--github::after { position: absolute; right: 0px; bottom: 0px; =
+display: block; width: 22px; height: 22px; padding: 4px; line-height: 0; co=
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+-repeat; border-radius: 100px; }
+
+body.page-responsive .flash-full .container { width: 100%; max-width: 980px=
+; }
+
+.ClipboardButton { position: relative; }
+
+.ClipboardButton.ClipboardButton--success { border-color: var(--color-succe=
+ss-emphasis); box-shadow: rgba(52, 208, 88, 0.4) 0px 0px 0px 0.2em; }
+
+.ClipboardButton.ClipboardButton--success:focus { box-shadow: rgba(52, 208,=
+ 88, 0.4) 0px 0px 0px 0.2em; }
+
+@media (min-width: 768px) {
+  .MarketplacePlan--sticky { position: sticky; top: 24px; z-index: 999; }
+}
+
+@media (max-width: 544px) {
+  .Box--full { right: 0px; bottom: 0px; left: 0px; width: 100%; max-width: =
+none; max-height: none; margin: 0px; border-radius: 0px; transform: none; }
+}
+
+.MarketplaceBackground-wrapper { position: relative; }
+
+.MarketplaceBackground-recommendations { position: relative; top: -90px; wi=
+dth: 313px; margin-top: -150px; margin-bottom: -120px; overflow: hidden; }
+
+.MarketplaceBackground-recommendations img { position: relative; top: 0px; =
+right: 225px; width: 549px; }
+
+@media (min-width: 544px) {
+  .MarketplaceBackground-recommendations { position: relative; width: 463px=
+; margin-top: -180px; margin-bottom: 70px; overflow: hidden; }
+  .MarketplaceBackground-recommendations img { right: 305px; width: 730px; =
+}
+}
+
+@media (min-width: 768px) {
+  .MarketplaceBackground-recommendations { position: absolute; top: -228px;=
+ right: -69px; width: 633px; }
+  .MarketplaceBackground-recommendations img { right: 195px; width: 750px; =
+}
+}
+
+@media (min-width: 1012px) {
+  .MarketplaceBackground-recommendations { top: -268px; right: 0px; width: =
+1040px; }
+  .MarketplaceBackground-recommendations img { right: -115px; width: 900px;=
+ }
+}
+
+@media (min-width: 1280px) {
+  .MarketplaceBackground-recommendations { top: -325px; right: 105px; width=
+: 1040px; }
+  .MarketplaceBackground-recommendations img { right: 0px; width: 1040px; }
+}
+
+.MarketplaceBackground-buffer { padding-top: 40px; margin-top: -146px; back=
+ground: var(--color-canvas-subtle); }
+
+@media (min-width: 544px) {
+  .MarketplaceBackground-buffer { padding-top: 120px; margin-top: -233px; }
+}
+
+@media (min-width: 768px) {
+  .MarketplaceBackground-buffer { margin-top: -109px; }
+}
+
+@media (min-width: 1012px) {
+  .MarketplaceBackground-buffer { margin-top: -89px; }
+}
+
+.MarketplaceHeader { overflow: hidden; }
+
+.Link--muted.filter-item.selected { color: var(--color-fg-on-emphasis)  !im=
+portant; }
+
+.MarketplaceBody { position: relative; }
+
+@media (min-width: 544px) {
+  .MarketplaceBody { top: -72px; z-index: 2; }
+}
+
+.MarketplaceDetails .octicon { transition: transform 200ms linear 0s; trans=
+form: scaleY(1); }
+
+.MarketplaceDetails[open] .octicon { transform: scaleY(-1); }
+
+.MarketplaceAnnouncement { color: rgb(255, 255, 255); background: linear-gr=
+adient(90deg, rgb(37, 123, 249), rgb(36, 38, 202)); }
+
+.MarketplaceAnnouncement-icon { width: 80px; opacity: 0.9; }
+
+.MarketplaceAnnouncement-description { opacity: 0.7; }
+
+.member-list-item .table-list-cell-checkbox { width: 30px; }
+
+.member-list-item.adminable .member-info { padding-left: 4px; }
+
+.member-list-item .member-avatar-cell { width: 64px; }
+
+.triage-mode .none-selected { display: none; }
+
+.merge-branch-heading { margin: 0px; line-height: 1; color: var(--color-fg-=
+default); }
+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+issions .full-access { display: none; }
+
+.oauth-repo-permissions.full .full-access { display: inline; }
+
+.oauth-repo-permissions.limited-repo-invite .limited-repo-invite-access { d=
+isplay: inline; }
+
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+
+.oauth-repo-permissions.default .default-access { display: inline; }
+
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+g); }
+
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+full-access, .oauth-repo-deployment-permissions .no-access, .oauth-repo-dep=
+loyment-permissions .full-access { display: none; }
+
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+
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+
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+
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+
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+e; }
+
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+
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+
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+
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+
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+
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+
+.oauth-no-description { color: var(--color-fg-muted); }
+
+.oauth-org-access-details { background: var(--color-canvas-default); }
+
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+
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+
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+
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+
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+e; margin: 4px; }
+
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+
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+
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+-default); }
+
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+
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+.oauth-org-access-details .oauth-org-item.revoked { background: var(--color=
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+
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+
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+
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+
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+
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+; }
+
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+
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+
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+
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+r-success-fg); }
+
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+
+.oauth-org-access-details .octicon-x.org-access-denied { color: var(--color=
+-danger-fg); }
+
+.permission-title { margin-top: 0px; }
+
+.boxed-group-inner .oauth-application-info { margin-bottom: 8px; }
+
+.oauth-application-info .application-title { font-size: 32px; color: var(--=
+color-fg-default); }
+
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+
+.oauth-application-info .listgroup-item { line-height: inherit; }
+
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+rmal, 400); white-space: nowrap; }
+
+.oauth-application-info .app-approved, .oauth-application-info .octicon-che=
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+
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+olor: var(--color-severe-fg); }
+
+.app-transfer-actions form { display: inline; }
+
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+
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+
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+
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+
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+
+.toggle-secret-field.open .secret-standin { display: none; }
+
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+
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+
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+
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+
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+or: var(--color-fg-default); background-color: var(--color-page-header-bg);=
+ border-bottom: 1px solid var(--color-border-default); }
+
+.orghead .orgnav { position: relative; top: 1px; margin-top: 8px; }
+
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+
+.org-name { font-weight: var(--base-text-weight-normal, 400); color: var(--=
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+
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+
+.audit-log-search .member-info .member-avatar { float: left; margin-right: =
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+
+.audit-log-search .member-info .member-link { display: block; }
+
+.audit-log-search .member-info .member-list-avatar { margin-right: 0px; }
+
+.audit-log-search .member-info .ghost { display: inline-block; color: var(-=
+-color-fg-muted); }
+
+.audit-log-search .blankslate { border-top-left-radius: 0px; border-top-rig=
+ht-radius: 0px; }
+
+.audit-log-search .export-phrase { margin: 4px 0px; }
+
+.audit-results-actions { overflow: auto; }
+
+.audit-search-clear { float: left; margin-bottom: 16px; border: 0px; }
+
+.audit-search-clear .issues-reset-query { margin-bottom: 0px; }
+
+.audit-type { width: 200px; overflow: hidden; text-overflow: ellipsis; whit=
+e-space: nowrap; }
+
+.audit-type .octicon { margin-right: 4px; font-weight: var(--base-text-weig=
+ht-normal, 400); vertical-align: bottom; }
+
+.audit-type .repo { color: var(--color-severe-fg); }
+
+.audit-type .team { color: var(--color-success-fg); }
+
+.audit-type .user { color: var(--color-done-fg); }
+
+.audit-type .oauth_access { color: var(--color-danger-fg); }
+
+.audit-type .hook { color: rgb(225, 191, 78); }
+
+.export-phrase { margin-top: 4px; }
+
+.export-phrase pre { padding-left: 8px; font-family: ui-monospace, SFMono-R=
+egular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospace; white-spa=
+ce: pre-wrap; border-left: 1px solid var(--color-border-muted); }
+
+.two-factor-enforcement-form .loading-spinner { float: left; margin: 0px 0p=
+x 0px -16px; vertical-align: middle; }
+
+.saml-enabled-banner-container { background-color: var(--color-canvas-defau=
+lt); }
+
+.saml-settings-form .test-status-indicator, .oidc-settings-form .test-statu=
+s-indicator { width: 30px; height: 30px; margin-top: -4px; border-radius: 5=
+0%; }
+
+.saml-settings-form .test-status-indicator .octicon, .oidc-settings-form .t=
+est-status-indicator .octicon { display: block; margin-top: 8px; margin-rig=
+ht: auto; margin-left: auto; }
+
+.saml-settings-form .form-group.errored, .oidc-settings-form .form-group.er=
+rored { margin-bottom: 40px; }
+
+.saml-settings-form .test-status-indicator-error, .oidc-settings-form .test=
+-status-indicator-error { color: var(--color-fg-on-emphasis); background-co=
+lor: var(--color-danger-emphasis); }
+
+.saml-settings-form .test-status-indicator-success, .oidc-settings-form .te=
+st-status-indicator-success { color: var(--color-fg-on-emphasis); backgroun=
+d-color: var(--color-success-emphasis); }
+
+.saml-settings-form .details-container .method-field, .oidc-settings-form .=
+details-container .method-field { display: none; }
+
+.saml-settings-form .details-container .method-label, .oidc-settings-form .=
+details-container .method-label { font-weight: var(--base-text-weight-norma=
+l, 400); }
+
+.saml-settings-form .details-container .details-target, .oidc-settings-form=
+ .details-container .details-target { cursor: pointer; }
+
+.saml-settings-form .details-container.open .method-value, .saml-settings-f=
+orm .details-container.open .details-target, .oidc-settings-form .details-c=
+ontainer.open .method-value, .oidc-settings-form .details-container.open .d=
+etails-target { display: none; }
+
+.saml-settings-form .details-container.open .method-field, .oidc-settings-f=
+orm .details-container.open .method-field { display: inline-block; }
+
+.saml-settings-form .saml-enforcement-disabled, .oidc-settings-form .saml-e=
+nforcement-disabled { opacity: 0.5; }
+
+.form-group .form-control.saml-certificate-field { width: 440px; height: 15=
+0px; min-height: 0px; }
+
+.member-avatar { float: left; margin: 1px; }
+
+.member-fullname { color: var(--color-fg-muted); }
+
+.auto-search-group { position: relative; }
+
+.auto-search-group .auto-search-input { padding-left: 32px; }
+
+.auto-search-group .spinner, .auto-search-group > .octicon { position: abso=
+lute; left: 10px; z-index: 5; width: 16px; height: 16px; }
+
+.auto-search-group .spinner { top: 9px; background-color: var(--color-canva=
+s-default); }
+
+.auto-search-group > .octicon { top: 10px; font-size: 14px; color: var(--co=
+lor-fg-muted); text-align: center; }
+
+.org-list .list-item { position: relative; padding-top: 16px; padding-botto=
+m: 16px; border-bottom: 1px solid var(--color-border-muted); }
+
+.org-list .list-item::before { display: table; content: ""; }
+
+.org-list .list-item::after { display: table; clear: both; content: ""; }
+
+.permission-level-cell .select-menu-button { width: 90px; text-align: left;=
+ }
+
+.permission-level-cell .select-menu-button::after { position: absolute; top=
+: 10px; right: 10px; }
+
+.permission-level-cell .spinner { float: none; opacity: 0; transition: opac=
+ity 0.2s ease-in-out 0s; }
+
+.permission-level-cell .is-loading .spinner { opacity: 1; }
+
+.select-menu-option-title { margin-top: 0px; margin-bottom: 0px; }
+
+.reinstate-org-member { position: relative; width: 500px; margin: 40px auto=
+; }
+
+.reinstate-org-member .reinstate-lead { margin-bottom: 32px; font-size: 16p=
+x; }
+
+.reinstate-org-member label { cursor: pointer; }
+
+.reinstate-org-member .reinstate-detail-container { margin: 16px 0px; }
+
+.reinstate-org-member .reinstate-title { color: var(--color-fg-default); }
+
+.reinstate-org-member .reinstate-title .octicon { width: 16px; margin-right=
+: 8px; color: var(--color-fg-muted); }
+
+.permission-title { margin-top: 0px; }
+
+.invite-member-results ul { margin: 0px; }
+
+.team-member-list { list-style: none; }
+
+.team-member-list .table-list-cell { padding-top: 16px; padding-bottom: 16p=
+x; }
+
+.team-member-list .team-member-content { margin-left: 50px; }
+
+.team-member-list .team-member-username { margin: 0px; font-size: 14px; fon=
+t-weight: var(--base-text-weight-semibold, 600); line-height: 20px; }
+
+.team-member-list .Label--secondary { cursor: default; }
+
+.team-member-list .invite-icon { width: 28px; color: var(--color-fg-muted);=
+ }
+
+.menu-item-danger, .menu-item-danger.selected { color: var(--color-danger-f=
+g); }
+
+.menu-item-danger:hover, .menu-item-danger[aria-selected=3D"true"], .menu-i=
+tem-danger.navigation-focus, .menu-item-danger.selected:hover, .menu-item-d=
+anger.selected[aria-selected=3D"true"], .menu-item-danger.selected.navigati=
+on-focus { color: var(--color-fg-on-emphasis); background: var(--color-dang=
+er-emphasis); }
+
+.team-member-list-avatar { float: left; margin-right: 8px; }
+
+.team-member-list-avatar .octicon { width: 40px; color: var(--color-fg-mute=
+d); }
+
+.confirm-removal-list-container { margin-bottom: 16px; border: 1px solid va=
+r(--color-border-default); border-radius: 6px; }
+
+.confirm-removal-list-item { padding: 8px; margin: 0px; font-size: 14px; fo=
+nt-weight: var(--base-text-weight-semibold, 600); border-top: 1px solid var=
+(--color-border-muted); }
+
+.confirm-removal-list-item:first-child { border-top: 0px; }
+
+.manage-member-meta { list-style: none; }
+
+.manage-member-meta-item { margin-top: 12px; color: var(--color-fg-muted); =
+}
+
+.manage-member-meta-item:first-child { margin-top: 0px; }
+
+.manage-member-meta-item .btn-link { color: var(--color-fg-muted); }
+
+.manage-member-meta-item > .octicon { width: 14px; margin-right: 4px; color=
+: var(--color-fg-muted); text-align: center; }
+
+.manage-member-meta-item > .octicon-alert { color: var(--color-severe-fg); =
+}
+
+.ldap-group-dn { display: block; font-weight: var(--base-text-weight-normal=
+, 400); color: var(--color-fg-muted); }
+
+.ldap-import-groups-container .blankslate { display: none; }
+
+.ldap-import-groups-container.is-empty .blankslate { display: block; }
+
+.ldap-import-groups-container.is-empty .ldap-memberships-list { display: no=
+ne; }
+
+.ldap-memberships-list { margin-bottom: 32px; }
+
+.ldap-memberships-list .table-list-cell { padding-top: 8px; padding-bottom:=
+ 8px; font-size: 12px; vertical-align: middle; }
+
+.ldap-memberships-list .table-list-cell:last-child { width: 92px; }
+
+.ldap-memberships-list .ldap-list-team-name { width: 380px; }
+
+.ldap-memberships-list .ldap-group-dn { font-size: 12px; }
+
+.ldap-memberships-list .ldap-mention-as { width: 260px; }
+
+.ldap-memberships-list .edit { position: absolute; padding: 8px; margin-lef=
+t: -32px; color: var(--color-accent-fg); cursor: pointer; }
+
+.ldap-memberships-list .edit-fields { display: none; }
+
+.ldap-memberships-list .is-editing .edit-hide { display: none; }
+
+.ldap-memberships-list .is-editing .edit-fields { display: block; }
+
+.ldap-memberships-list .is-editing .spinner { margin-left: 16px; vertical-a=
+lign: middle; }
+
+.ldap-memberships-list .is-removing { opacity: 0.25; }
+
+.ldap-memberships-list .is-removing .edit { opacity: 0.5; }
+
+.team-name-field { height: 33px; }
+
+.ldap-import-form-actions { margin-top: 32px; }
+
+.invited .team-member-list { margin: -16px 0px; }
+
+.invited .team-member-list .list-item { padding: 8px 0px; border-bottom: 1p=
+x solid var(--color-border-muted); }
+
+.invited .team-member-list .list-item::before { display: table; content: ""=
+; }
+
+.invited .team-member-list .list-item::after { display: table; clear: both;=
+ content: ""; }
+
+.invited .team-member-list .list-item:last-of-type { border: 0px; }
+
+.invited .team-member-list .list-item .edit-invitation { float: right; marg=
+in-top: 4px; }
+
+.invited-banner::before { display: table; content: ""; }
+
+.invited-banner::after { display: table; clear: both; content: ""; }
+
+.invited-banner .btn-sm { float: right; margin-left: 4px; }
+
+.invited-banner p { font-size: 14px; line-height: 1.5; }
+
+.invited-banner .inviter-link { font-weight: var(--base-text-weight-semibol=
+d, 600); }
+
+.manage-member-sso-sessions.has-active-sessions .blankslate { display: none=
+; }
+
+.org-menu-item:not([aria-current=3D"page"]) + .org-sub-menu { display: none=
+; }
+
+.trial-banner-notice { background-image: linear-gradient(rgb(3, 102, 214) 0=
+%, rgb(33, 136, 255) 100%); }
+
+@media (min-width: 768px) {
+  .Popover-message--extra-large { min-width: 544px !important; }
+}
+
+.theme-name { display: inline-block; margin-right: 8px; margin-left: 8px; f=
+ont-size: 20px; line-height: 1; vertical-align: middle; }
+
+.pinned-items-spinner { position: relative; top: 2px; left: 6px; }
+
+.pinned-items-setting-link { font-size: 12px; font-weight: var(--base-text-=
+weight-normal, 400); }
+
+.pinned-item-name { color: var(--color-fg-default); }
+
+.pinned-item-checkbox + .pinned-item-name:hover { cursor: pointer; backgrou=
+nd-color: var(--color-action-list-item-default-hover-bg); border-radius: 6p=
+x; }
+
+.pinned-item-checkbox:disabled + .pinned-item-name { color: var(--color-fg-=
+muted); }
+
+.pinned-item-checkbox:disabled + .pinned-item-name:hover { cursor: default;=
+ }
+
+.pinned-gist-blob-num { min-width: 36px; cursor: default; }
+
+.pinned-gist-blob-num:hover { color: var(--color-fg-muted); cursor: default=
+; }
+
+@media print {
+  #serverstats, .Header-old, .Header, .header-search, .reponav, .comment::b=
+efore, .comment::after, .footer, .pagehead-actions, .discussion-timeline-ac=
+tions, .timeline-comment-actions, .timeline-new-comment, .thread-subscripti=
+on-status, .lock-toggle-link, .header, .file-actions, .js-expandable-line, =
+.gh-header-sticky, .pr-toolbar.is-placeholder, .language-color { display: n=
+one !important; }
+  .repository-lang-stats-graph { height: 0px; }
+  .btn:not(.btn-outline) { background: none; color: var(--color-fg-default)=
+  !important; }
+  p, .comment h2 { break-inside: avoid; }
+  .markdown-body h2 { break-after: avoid; }
+  .topic-tag { padding: 0px; }
+  .topic-tag::before { margin-right: -2px; content: "#"; }
+  .blob-num { border-right: 2px solid var(--color-border-default); }
+  .blob-num-deletion { border-right-color: var(--color-danger-emphasis); }
+  .blob-num-addition { border-right-color: var(--color-success-emphasis); }
+  .blob-code-addition .x { border-bottom: 2px solid var(--color-success-emp=
+hasis); border-radius: 0px; }
+  .blob-code-deletion .x { border-bottom: 2px solid var(--color-danger-emph=
+asis); border-radius: 0px; }
+  .pr-toolbar.is-stuck { position: static !important; width: 100% !importan=
+t; }
+  .diffstat-block-neutral { border: 4px solid var(--color-border-default); =
+}
+  .diffstat-block-deleted { border: 4px solid var(--color-danger-emphasis);=
+ }
+  .diffstat-block-added { border: 4px solid var(--color-success-emphasis); =
+}
+  .State { color: var(--color-fg-default); background: none; border: 1px so=
+lid var(--color-border-default); }
+  .State--open { color: var(--color-success-fg); border: 1px solid rgb(44, =
+190, 78); }
+  .State--merged { color: var(--color-done-fg); border: 1px solid var(--col=
+or-done-emphasis); }
+  .State--closed { color: var(--color-danger-fg); border: 1px solid var(--c=
+olor-danger-emphasis); }
+  .markdown-body pre > code { white-space: pre-wrap; }
+}
+
+.projects-splash-dialog { position: fixed; top: 0px; right: auto; left: 50%=
+; z-index: 999; width: 90vw; max-width: 700px; max-height: 80vh; margin: 10=
+vh auto; transform: translateX(-50%); }
+
+@media (min-width: 544px) {
+  .projects-splash-dialog { margin: 20vh auto; }
+}
+
+.projects-splash-banner { background-image: url("/images/modules/memexes/pr=
+ojects-beta-banner-mobile.png"); background-repeat: no-repeat; background-p=
+osition: left center; background-size: cover; }
+
+@media (min-width: 768px) {
+  .projects-splash-banner { background-image: url("/images/modules/memexes/=
+projects-beta-banner.png"); }
+}
+
+.projects-splash-banner p { max-width: 100%; }
+
+@media (min-width: 768px) {
+  .projects-splash-banner p { max-width: 55%; }
+}
+
+@media (min-width: 768px) {
+  [data-color-mode=3D"light"][data-light-theme*=3D"dark"] .projects-splash-=
+banner, [data-color-mode=3D"dark"][data-dark-theme*=3D"dark"] .projects-spl=
+ash-banner { background-image: url("/images/modules/memexes/projects-beta-b=
+anner-dark.png"); }
+}
+
+@media (prefers-color-scheme: light) and (min-width: 768px) {
+  [data-color-mode=3D"auto"][data-light-theme*=3D"dark"] .projects-splash-b=
+anner { background-image: url("/images/modules/memexes/projects-beta-banner=
+-dark.png"); }
+}
+
+@media (prefers-color-scheme: dark) and (min-width: 768px) {
+  [data-color-mode=3D"auto"][data-dark-theme*=3D"dark"] .projects-splash-ba=
+nner { background-image: url("/images/modules/memexes/projects-beta-banner-=
+dark.png"); }
+}
+
+.project-description p:last-child { margin-bottom: 0px !important; }
+
+.pending-cards-status { top: -2px; right: -9px; width: 14px; height: 14px; =
+background-image: linear-gradient(rgb(84, 163, 255), rgb(0, 110, 237)); bac=
+kground-clip: padding-box; border: 2px solid var(--color-canvas-default); }
+
+.project-column { min-width: 100%; max-width: 100%; background-color: var(-=
+-color-canvas-inset); border-width: 0px !important; border-radius: 0px !imp=
+ortant; }
+
+.project-column:focus { outline: none; }
+
+@media (min-width: 544px) {
+  .project-column { min-width: 355px; max-width: 355px; border-width: 1px !=
+important; border-radius: 6px !important; }
+  .project-column:focus { box-shadow: 0 0 0 2px var(--color-accent-fg); bor=
+der-color: var(--color-accent-fg)  !important; }
+}
+
+.project-column.moving { box-shadow: 0 0 0 2px var(--color-accent-fg); tran=
+sform: translateX(4px) translateY(-4px); background-color: var(--color-acce=
+nt-subtle)  !important; }
+
+.sortable-ghost { background-color: var(--color-canvas-subtle); opacity: 0.=
+5; }
+
+.project-card { background-color: var(--color-canvas-overlay); }
+
+.project-card .project-reference-markdown > p, .project-card:last-child { m=
+argin-bottom: 0px !important; }
+
+.project-card:first-child { margin-top: 8px !important; }
+
+@media (min-width: 544px) {
+  .project-card:first-child { margin-top: 4px !important; }
+}
+
+.project-card ul, .project-card ol { margin-bottom: 8px; margin-left: 16px;=
+ }
+
+.project-card blockquote { padding: 0px 0.75em; color: var(--color-fg-muted=
+); border-left: 0.25em solid var(--color-border-default); }
+
+.project-card .contains-task-list { margin-left: 24px; }
+
+.project-card:hover { border-color: var(--color-border-default)  !important=
+; box-shadow: rgba(106, 115, 125, 0.3) 0px 1px 3px !important; }
+
+.project-card:focus { outline: none; }
+
+@media (min-width: 544px) {
+  .project-card:focus { border-color: var(--color-accent-fg)  !important; b=
+ox-shadow: 0 0 0 2px var(--color-accent-fg)  !important; }
+}
+
+.project-card.moving { transform: translateX(4px) translateY(0px); backgrou=
+nd-color: var(--color-accent-subtle)  !important; box-shadow: 0 0 0 2px var=
+(--color-accent-fg)  !important; }
+
+.issue-card.draggable { cursor: move; }
+
+.issue-card .AvatarStack:hover .from-avatar { margin-right: -4px; }
+
+.issue-card pre { overflow-wrap: break-word; white-space: pre-wrap; }
+
+@keyframes show-pane {=20
+  0% { transform: translateX(390px); }
+  100% { transform: translateX(0px); }
+}
+
+.project-note-form textarea { resize: vertical; }
+
+.card-menu-container .dropdown-menu, .column-menu-container .dropdown-menu =
+{ min-width: 180px; }
+
+.card-octicon { top: 6px; left: 10px; }
+
+.card-note-octicon { top: 8px; }
+
+.is-sending .auto-search-group .chooser-spinner { top: 15px; right: 21px; l=
+eft: auto; }
+
+.card-filter-autocomplete-dropdown { z-index: 500; float: none; min-width: =
+240px; max-height: 270px; cursor: pointer; }
+
+.card-filter-autocomplete-dropdown [aria-selected=3D"true"], .card-filter-a=
+utocomplete-dropdown .navigation-focus { background-color: var(--color-acce=
+nt-emphasis); border-radius: 6px; color: var(--color-fg-on-emphasis)  !impo=
+rtant; }
+
+.card-filter-autocomplete-dropdown [aria-selected=3D"true"] .autocomplete-t=
+ext-qualifier, .card-filter-autocomplete-dropdown .navigation-focus .autoco=
+mplete-text-qualifier { color: var(--color-fg-on-emphasis)  !important; }
+
+.projects-reset-query:hover { color: var(--color-fg-subtle)  !important; }
+
+.projects-reset-query:hover .projects-reset-query-icon { color: var(--color=
+-fg-subtle)  !important; }
+
+.projects-reset-query-icon { width: 18px; height: 18px; padding: 1px; }
+
+.project-small-menu-dropdown::before, .project-small-menu-dropdown::after {=
+ display: none; }
+
+.project-edit-mode .column-menu-container, .project-edit-mode .column-menu-=
+item { display: none !important; }
+
+.project-edit-mode .project-move-actions { display: flex !important; }
+
+.push-board-over { transition: all 0.2s ease 0s; padding-right: 0px !import=
+ant; }
+
+@media (min-width: 544px) {
+  .push-board-over { padding-right: 360px !important; }
+}
+
+.projects-comment-form .comment-md-support-link { float: none; width: 100%;=
+ text-align: center; }
+
+.projects-comment-form .comment-form-actions { width: 100%; padding: 8px 16=
+px; margin: 4px 0px !important; }
+
+.projects-comment-form .comment-form-actions button { width: 100%; margin: =
+4px 0px !important; }
+
+.projects-comment-form .comment-form-head { border-bottom: 0px; padding: 0p=
+x !important; margin: 0px !important; }
+
+.projects-comment-form .comment-form-head .tabnav-tabs { padding: 8px 8px 0=
+px; }
+
+.projects-comment-form .comment-form-head .toolbar-commenting { width: 100%=
+; padding-top: 4px; text-align: center; background-color: var(--color-canva=
+s-default); border-top: 1px solid var(--color-border-default); }
+
+.projects-comment-form .comment-form-head::after { display: block; clear: b=
+oth; content: " "; }
+
+.projects-comment-form .comment-form-textarea { height: 250px !important; }
+
+.projects-comment-form .preview-content { margin: 0px; border-top: 1px soli=
+d var(--color-border-default); }
+
+.projects-comment-form .preview-content .comment-body { padding: 16px; }
+
+.project-issue-body-wrapper { max-height: 200px; overflow: hidden; }
+
+.Details--on .project-issue-body-wrapper { max-height: none; overflow: visi=
+ble; }
+
+.project-issue-body-blur { height: 32px; background: linear-gradient(to top=
+, var(--color-project-gradient-in), var(--color-project-gradient-out)); }
+
+.Details--on .project-issue-body-blur { height: 0px; }
+
+.project-comment-title-hover .comment-action, .project-comment-body-hover .=
+comment-action { opacity: 0; }
+
+.project-comment-title-hover:hover .comment-action, .project-comment-body-h=
+over:hover .comment-action { opacity: 1; }
+
+.project-comment-body-reaction .timeline-comment-action { padding: 4px 8px;=
+ }
+
+.project-name-hover .project-name-edit-action { opacity: 0; }
+
+.project-name-hover:hover .project-name-edit-action { opacity: 1; }
+
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+
+.pull-request-tab-content { display: none; }
+
+.pull-request-tab-content.is-visible { display: block; }
+
+.discussion-timeline p.explain { margin: 0px; font-size: 12px; }
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+.pull-request-ref-restore { display: none; }
+
+.pull-request-ref-restore-text { display: block; }
+
+.pull-discussion-timeline.is-pull-restorable .pull-request-ref-restore.last=
+ { display: block; }
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+
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+
+.pr-toolbar .float-right .diffbar-item + .diffbar-item { margin-left: 16px;=
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+
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+
+@media (min-width: 1150px) {
+  .commit-build-statuses .status-checks-dropdown { width: 500px; }
+}
+
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+ }
+
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+; margin-bottom: 16px; }
+
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+
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+ var(--color-canvas-default); border: 1px solid var(--color-border-default)=
+; border-radius: 6px; }
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+
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+
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+ var(--color-danger-fg); }
+
+.label-edit.loading { display: block; }
+
+.label-characters-remaining { color: var(--color-fg-muted); }
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+: -16px; color: var(--color-fg-muted); }
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+
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+ont-size: 12px; line-height: 21px; color: var(--color-fg-muted); }
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+background-position: center center; background-size: cover; }
+
+.line-clamp-2 { display: -webkit-box; -webkit-line-clamp: 2; -webkit-box-or=
+ient: vertical; overflow: hidden; }
+
+.timeout { width: auto; height: 300px; padding: 0px; margin: 16px 0px; back=
+ground-color: transparent; border: 0px; }
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+
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+ width: 12px; height: 12px; border: 1px solid var(--color-primer-border-con=
+trast); border-radius: 50%; }
+
+.iconbutton .octicon { margin-right: 0px; }
+
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+x 1px 1px 6px; border-style: solid; border-color: rgb(223, 226, 229) rgb(22=
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+order-radius: 6px; }
+
+.prereceive-feedback-heading { margin-top: 0px; margin-bottom: 8px; color: =
+var(--color-attention-fg); }
+
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+r: var(--color-accent-subtle); }
+
+.custom-role-icon { background-color: var(--color-canvas-subtle); }
+
+.profile-picture { margin: 8px 0px 0px; }
+
+.profile-picture > p { float: left; margin: 0px; line-height: 30px; }
+
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+s: 6px; }
+
+.app-owner { margin: 16px 0px 0px; }
+
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+ var(--color-danger-emphasis); box-shadow: none; }
+
+.avatar-upload .upload-state { display: none; padding: 8px 0px; }
+
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+.is-uploading .avatar-upload .loading img { vertical-align: top; }
+
+.is-uploading .avatar-upload .button-change-avatar { display: none; }
+
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+
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+
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+
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+
+.is-empty .avatar-upload .file-empty { display: block; margin: 0px; }
+
+dl.new-email-form { padding: 8px 8px 0px; margin: 0px -8px 8px; border-top:=
+ 1px solid var(--color-border-default); }
+
+.recent-user-key-access { color: rgb(30, 126, 52); }
+
+.oauth-app-info-container .float-left-container { float: left; text-align: =
+left; }
+
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+: right; }
+
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+-base-text-weight-light, 300); color: var(--color-fg-muted); }
+
+.access-token { border-bottom: 1px solid var(--color-border-muted); }
+
+.access-token:last-child { border: 0px; }
+
+.access-token .last-used { margin-right: 8px; }
+
+.access-token.new-token { background-color: rgba(108, 198, 68, 0.1); }
+
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+
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+default); }
+
+.access-token .token { font-size: 14px; }
+
+.access-token .token-type { min-width: 76px; }
+
+table.security-history-detail { width: 100%; font-size: 12px; }
+
+table.security-history-detail td { max-width: 200px; overflow-wrap: break-w=
+ord; }
+
+.u2f-registration { position: relative; padding-bottom: 4px; margin-bottom:=
+ 4px; }
+
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+
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+
+.u2f-registration-icon { position: absolute; left: -24px; color: var(--colo=
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+
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+
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+ display: none; margin-bottom: 8px; }
+
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+ }
+
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+; }
+
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+lock; }
+
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+
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+
+.confirmation-phrase { font-style: italic; font-weight: var(--base-text-wei=
+ght-normal, 400); }
+
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+olor-success-emphasis); box-shadow: rgba(108, 198, 68, 0.5) 0px 0px 10px; }
+
+.session-device .session-state-indicator.revoked { background-color: var(--=
+color-danger-emphasis); box-shadow: rgba(198, 108, 68, 0.5) 0px 0px 10px; }
+
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+ear-gradient(rgb(170, 170, 170), rgb(204, 204, 204)); box-shadow: rgb(255, =
+255, 255) 0px 1px 0px; }
+
+.collaborators .collab-list { border-bottom-width: 0px; }
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+
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+ vertical-align: middle; }
+
+.collaborators .collab-meta { width: 140px; }
+
+.collaborators .collab-remove { padding-right: 16px; text-align: right; }
+
+.collaborators .collab-remove .remove-link { color: var(--color-fg-muted); =
+}
+
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+
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+
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+rgin-right: 8px; }
+
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+
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+ }
+
+.collaborators .collab-info .description { padding-right: 50px; margin-top:=
+ 4px; margin-bottom: 4px; overflow: hidden; text-overflow: ellipsis; white-=
+space: nowrap; }
+
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+ }
+
+.collaborators .collab-info .collab-message { position: relative; top: 25%;=
+ display: block; }
+
+.collaborators .copy-invite-modal { left: 0px; width: 300px; }
+
+@media (min-width: 768px) {
+  .collaborators .copy-invite-modal { right: 0px; left: unset; width: 352px=
+; }
+}
+
+.collaborators .copy-invite-modal::before, .collaborators .copy-invite-moda=
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+
+.access-form-wrapper { padding: 8px; background-color: var(--color-canvas-s=
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+px 0px 6px 6px; }
+
+.access-flash { padding: 8px; margin-right: 8px; margin-bottom: 8px; margin=
+-left: 8px; }
+
+.repo-access-group .blankslate { display: none; }
+
+.repo-access-group.is-empty .blankslate { display: block; }
+
+.boxed-group-list .access-level { color: var(--color-fg-muted); }
+
+.boxed-group-list .access-level.css-truncate-target { max-width: 500px; }
+
+.settings-next { font-size: 14px; line-height: 1.5; }
+
+.settings-next label { font-size: 14px; }
+
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+heckbox input[type=3D"checkbox"] { margin-top: 4px; }
+
+dl.form-group > dd textarea.compact { height: 100px; min-height: 0px; }
+
+.form-hr { margin-top: 16px; margin-bottom: 16px; border-bottom-color: var(=
+--color-border-default, #e5e5e5); }
+
+.listgroup { list-style: none; border: 1px solid var(--color-border-default=
+, #e5e5e5); border-radius: 6px; }
+
+.listgroup-item { min-height: inherit; padding: 8px; font-size: 12px; line-=
+height: 26px; color: var(--color-fg-muted); }
+
+.listgroup-item::before { display: table; content: ""; }
+
+.listgroup-item::after { display: table; clear: both; content: ""; }
+
+.listgroup-item + .listgroup-item { border-top: 1px solid var(--color-borde=
+r-default, #e5e5e5); }
+
+.listgroup-item.listgroup-item-preview { line-height: inherit; }
+
+.listgroup-item.listgroup-item-preview .BtnGroup { margin-top: 4px; }
+
+.listgroup-item .css-truncate-target { max-width: 200px; }
+
+.listgroup-item-title { display: block; font-weight: var(--base-text-weight=
+-semibold, 600); }
+
+.listgroup-item-body { display: block; }
+
+.listgroup-header { border-top: 0px; border-bottom: 1px solid var(--color-b=
+order-default, #e5e5e5); }
+
+.listgroup-overflow { max-height: 240px; overflow-y: auto; background-color=
+: var(--color-canvas-subtle, #f5f5f5); }
+
+.listgroup-sm .listgroup-item { padding-top: 4px; padding-bottom: 4px; }
+
+.protected-branches { margin-top: 16px; margin-bottom: 16px; }
+
+.protected-branch-options { margin-left: 16px; opacity: 0.5; }
+
+.protected-branch-options.active { opacity: 1; }
+
+.protected-branch-reviews.on .require-code-owner-review, .protected-branch-=
+reviews.on .reviews-dismiss-on-push, .protected-branch-reviews.on .reviews-=
+include-dismiss, .protected-branch-reviews.on .ignore-approvals-from-contri=
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+d-branch-reviews.on .allow-force-pushes, .protected-branch-reviews.on .requ=
+ire-approving-reviews { display: block; }
+
+.protected-branch-reviews .require-code-owner-review, .protected-branch-rev=
+iews .reviews-dismiss-on-push, .protected-branch-reviews .reviews-include-d=
+ismiss, .protected-branch-reviews .ignore-approvals-from-contributors, .pro=
+tected-branch-reviews .require-last-push-approval, .protected-branch-review=
+s .allow-force-pushes, .protected-branch-reviews .require-approving-reviews=
+ { display: none; }
+
+.authorized-pushers { width: 440px; }
+
+.authorized-pushers .add-protected-branch-actor { display: block; }
+
+.authorized-pushers .actor-limit-reached { display: none; padding: 8px; fon=
+t-size: 12px; }
+
+.authorized-pushers.at-limit .add-protected-branch-actor { display: none; }
+
+.authorized-pushers.at-limit .actor-limit-reached { display: block; width: =
+440px; }
+
+.protected-branch-authorized-pushers-table, .protected-branch-pushers-table=
+ { margin-top: 8px; }
+
+.protected-branch-authorized-pushers-table .boxed-group-inner, .protected-b=
+ranch-pushers-table .boxed-group-inner { max-height: 350px; overflow-y: aut=
+o; }
+
+.protected-branch-authorized-pushers-table .table-list, .protected-branch-p=
+ushers-table .table-list { border-bottom: 0px; }
+
+.protected-branch-authorized-pushers-table .table-list-cell, .protected-bra=
+nch-pushers-table .table-list-cell { vertical-align: middle; }
+
+.protected-branch-authorized-pushers-table .table-list-cell:first-child, .p=
+rotected-branch-pushers-table .table-list-cell:first-child { width: 100%; }
+
+.protected-branch-authorized-pushers-table .avatar, .protected-branch-autho=
+rized-pushers-table .octicon-jersey, .protected-branch-authorized-pushers-t=
+able .octicon-organization, .protected-branch-pushers-table .avatar, .prote=
+cted-branch-pushers-table .octicon-jersey, .protected-branch-pushers-table =
+.octicon-organization { width: 36px; margin-right: 8px; text-align: center;=
+ }
+
+.user-already-added::after { display: inline-block; padding: 1px 4px; margi=
+n-left: 4px; font-size: 12px; line-height: 1.4; color: var(--color-fg-on-em=
+phasis); content: "Already added"; background: var(--color-severe-emphasis)=
+; border-radius: 6px; }
+
+.protected-branch-admin-permission { padding: 4px; margin: -2px 0px -2px -4=
+px; line-height: normal; border: 1px solid transparent; border-radius: 6px;=
+ }
+
+.protected-branch-admin-permission.active { animation: 1s ease-in-out 0s 1 =
+normal none running toggle-color; }
+
+@keyframes toggle-color {=20
+  0% { background-color: transparent; }
+  50% { color: rgb(76, 74, 66); background-color: rgb(255, 249, 234); borde=
+r-color: rgb(223, 216, 194); }
+  100% { background-color: transparent; }
+}
+
+.automated-check-options { margin-top: 8px; }
+
+.automated-check-options .listgroup-item label { font-size: inherit; }
+
+.automated-check-options .listgroup-item input[type=3D"checkbox"] { float: =
+none; margin-top: -2px; margin-right: 4px; margin-left: 0px; }
+
+.automated-check-options .label { margin-top: 4px; }
+
+.repository-settings-actions [role=3D"tab"][aria-selected=3D"true"] { font-=
+weight: var(--base-text-weight-semibold, 600); color: var(--color-fg-defaul=
+t); border-color: var(--color-severe-emphasis); }
+
+.repository-settings-actions [role=3D"tab"][aria-selected=3D"true"] .Underl=
+ineNav-octicon { color: var(--color-fg-muted); }
+
+.repository-visibility-change-warning::before { display: none; }
+
+.two-factor-recovery-codes { margin: 32px 0px; font-family: ui-monospace, S=
+FMono-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospace; fo=
+nt-size: 20px; }
+
+.two-factor-recovery-code-mark { width: 24px; height: 24px; font-size: 24px=
+; line-height: 16px; color: var(--color-fg-muted); }
+
+.two-factor-recovery-code { display: inline-block; width: 48%; line-height:=
+ 1.6; text-align: center; }
+
+.two-factor-recovery-code::before { position: relative; top: -3px; margin-r=
+ight: 8px; font-size: 12px; color: var(--color-fg-muted); content: "=E2=97=
+=8F"; }
+
+.recovery-codes-saving-options { margin-left: 32px; }
+
+.recovery-codes-saving-options .recovery-code-save-button { width: 115px; m=
+argin-right: 16px; text-align: center; }
+
+.recovery-codes-warning { margin: 0px -16px; }
+
+.two-factor-steps { padding: 16px 16px 0px; margin: 32px 0px; border: 1px s=
+olid var(--color-border-default); border-radius: 6px; }
+
+.setup-wrapper { width: 750px; padding-top: 32px; margin: 0px auto; }
+
+.setup-wrapper::before { display: table; content: ""; }
+
+.setup-wrapper::after { display: table; clear: both; content: ""; }
+
+.setup-header { padding-bottom: 16px; margin: 0px auto 32px; overflow: hidd=
+en; text-align: left; border-bottom: 1px solid var(--color-border-default);=
+ }
+
+.setup-header h1 { margin-top: 0px; margin-bottom: 0px; font-size: 48px; fo=
+nt-weight: var(--base-text-weight-normal, 400); line-height: 1.1; letter-sp=
+acing: -1px; }
+
+.setup-header h1 .octicon { color: var(--color-fg-muted); }
+
+.setup-header .lead { margin-top: 2px; margin-bottom: 0px; font-size: 20px;=
+ }
+
+.setup-header .lead a { color: var(--color-fg-muted); }
+
+.setup-header .lead a:hover { color: var(--color-accent-fg); text-decoratio=
+n: none; }
+
+.setup-org { padding-bottom: 0px; border-bottom: 0px; }
+
+.setup-main { float: left; width: 450px; }
+
+.setup-secondary { float: right; width: 250px; }
+
+.setup-secondary .info { padding-top: 0px; padding-bottom: 0px; margin-top:=
+ -8px; font-size: 12px; line-height: 18px; color: var(--color-fg-muted); te=
+xt-align: center; }
+
+.setup-info-module { margin-bottom: 32px; background-color: var(--color-can=
+vas-default); border: 1px solid var(--color-border-default); border-radius:=
+ 6px; box-shadow: rgba(0, 0, 0, 0.075) 0px 1px 3px; }
+
+.setup-info-module h2 { padding: 16px; margin-bottom: 16px; overflow: hidde=
+n; font-size: 16px; border-bottom: 1px solid var(--color-border-default); }
+
+.setup-info-module h2 .price { float: right; font-weight: var(--base-text-w=
+eight-semibold, 600); color: var(--color-fg-muted); }
+
+.setup-info-module h3 { padding: 0px 16px; margin: 0px 0px -8px; font-size:=
+ 14px; }
+
+.setup-info-module p { padding: 0px 16px; margin: 16px 0px; }
+
+.features-list { padding: 0px 16px 16px; margin: 0px; font-size: 14px; list=
+-style: none; }
+
+.features-list li { margin-top: 8px; }
+
+.features-list li:first-child { margin-top: 0px; }
+
+.features-list .list-divider { margin: 16px -16px; border-top: 1px solid va=
+r(--color-border-muted); }
+
+.features-list .octicon-check { margin-right: 4px; color: var(--color-succe=
+ss-fg); }
+
+.features-list .octicon-question { font-size: 12px; color: var(--color-fg-m=
+uted); }
+
+.features-list .tooltipped::after { width: 250px; white-space: normal; }
+
+.setup-form-container .setup-form-title { font-size: 16px; }
+
+.setup-form-container .secure { float: right; margin-top: 2px; font-size: 1=
+2px; color: var(--color-success-fg); text-transform: uppercase; }
+
+.setup-form-container hr { margin-top: 24px; margin-bottom: 24px; }
+
+.setup-form-container .form-actions { padding-top: 0px; padding-bottom: 0px=
+; text-align: left; }
+
+.team-member-container { margin-bottom: 16px; }
+
+.team-member-container .team-member-username { line-height: 1.2; }
+
+.setup-form { padding-bottom: 16px; }
+
+.setup-form .form-group.successed .error { display: none; }
+
+.setup-form .form-group dd .form-control { width: 100%; }
+
+.setup-form .form-group dd .form-control.short { width: 250px; }
+
+.setup-form dd { position: relative; }
+
+.setup-form dd .octicon { position: absolute; top: 8px; right: 25px; }
+
+.setup-form .octicon-alert { color: var(--color-danger-fg); }
+
+.setup-form .octicon-check { color: var(--color-success-fg); }
+
+.setup-form .tos-info, .setup-form .setup-organization-next { margin: 16px =
+0px; border-top: 1px solid var(--color-border-muted); border-bottom: 1px so=
+lid var(--color-border-muted); }
+
+.setup-form .tos-info { padding: 16px 0px; }
+
+.setup-form .setup-organization-next { padding-top: 16px; padding-bottom: 1=
+6px; }
+
+.setup-form .setup-plans { border-collapse: separate; border: 1px solid var=
+(--color-border-default); }
+
+.setup-form .setup-plans tr.selected { background-color: var(--color-accent=
+-subtle); }
+
+.setup-form .setup-plans .name { font-weight: var(--base-text-weight-semibo=
+ld, 600); }
+
+.setup-form .setup-plans .choose-plan input[type=3D"radio"] { display: none=
+; }
+
+.setup-creditcard-form .country-form, .setup-creditcard-form .state-form { =
+float: left; margin: 0px; overflow-wrap: normal; }
+
+.setup-creditcard-form .country-form, .setup-creditcard-form .postal-code-f=
+orm { margin-top: 0px; margin-bottom: 16px; }
+
+.setup-creditcard-form .form-group select.select-country { width: 182px; ma=
+rgin-right: 4px; }
+
+.setup-creditcard-form .form-group select:invalid { color: var(--color-fg-m=
+uted); }
+
+.setup-creditcard-form .form-group select.select-state { width: 113px; }
+
+.setup-creditcard-form .form-group .input-vat { width: 288px; }
+
+.setup-creditcard-form .form-group input.input-postal-code { width: 180px; =
+}
+
+.setup-creditcard-form.is-vat-country .vat-field { display: block; }
+
+.setup-creditcard-form.is-international .form-group select.select-country {=
+ width: 300px; }
+
+.setup-creditcard-form.is-international .state-form { display: none; }
+
+.setup-creditcard-form.no-postcodes .postal-code-form { display: none; }
+
+.setup-creditcard-form dd .octicon-credit-card { position: inherit; }
+
+.setup-creditcard-form .vat-field { display: none; }
+
+.setup-creditcard-form .vat-field.prefilled { display: block; }
+
+.setup-creditcard-form .help-text { font-size: 80%; font-weight: var(--base=
+-text-weight-normal, 400); color: var(--color-fg-muted); }
+
+.user-identification-questions { float: none; width: auto; margin-top: 40px=
+; }
+
+.user-identification-questions .question { margin-bottom: 32px; }
+
+.user-identification-questions .response-group label { font-weight: var(--b=
+ase-text-weight-normal, 400); }
+
+.user-identification-questions .form-checkbox { margin: 8px 0px; }
+
+.user-identification-questions .disclaimer { margin: 40px 0px 0px; text-ali=
+gn: center; }
+
+.user-identification-questions.redesign .question { margin-bottom: 96px; }
+
+.user-identification-questions.redesign .topic-input-container .tag-input {=
+ width: 100%; border-width: 0px 0px 6px; border-top-style: initial; border-=
+right-style: initial; border-left-style: initial; border-top-color: initial=
+; border-right-color: initial; border-left-color: initial; border-image: in=
+itial; border-bottom-style: solid; border-bottom-color: rgb(0, 0, 0); box-s=
+hadow: 0px 0px 0px; }
+
+.signup-plan-summary-subhead { border-bottom: 6px solid; }
+
+.signup-btn:disabled { opacity: 0.5 !important; }
+
+.collection-search-results em { padding: 0.1em; background-color: rgb(250, =
+255, 166); }
+
+.draft-tag { padding: 4px 8px; font-weight: var(--base-text-weight-semibold=
+, 600); color: rgb(238, 238, 238); background-color: rgb(64, 64, 64); }
+
+.showcase-page-pattern { position: relative; z-index: -1; height: 100px; ma=
+rgin-top: -24px; margin-bottom: -70px; }
+
+.showcase-page-pattern::after { position: absolute; inset: 0px; display: bl=
+ock; content: ""; background-image: linear-gradient(rgba(255, 255, 255, 0.8=
+5), white); }
+
+.showcase-page-repo-list { border-top: 1px solid var(--color-border-muted);=
+ }
+
+.slash-command-menu-item .command-description { color: var(--color-fg-muted=
+); }
+
+.slash-command-menu-item[aria-selected=3D"true"] { color: var(--color-fg-on=
+-emphasis); background-color: var(--color-accent-emphasis); }
+
+.slash-command-menu-item[aria-selected=3D"true"] .command-description { col=
+or: var(--color-fg-on-emphasis); }
+
+.modal-anchor::before { position: fixed; inset: 0px; z-index: 99; display: =
+block; cursor: default; content: " "; background: var(--color-primer-canvas=
+-backdrop); }
+
+.sortable-button-item:first-of-type .sortable-button[data-direction=3D"up"]=
+, .sortable-button-item:last-of-type .sortable-button[data-direction=3D"dow=
+n"] { display: none; }
+
+@keyframes sponsors-progress-animation {=20
+  0% { background-position: 100% center; }
+  100% { background-position: 0% center; }
+}
+
+@keyframes circle-progress {=20
+  0% { stroke-dasharray: 0, 100; }
+}
+
+.sponsors-funded-dependencies-percent-circle-bg, .sponsors-funded-dependenc=
+ies-percent-circle { fill: none; stroke-width: 3; stroke-linecap: round; }
+
+.sponsors-funded-dependencies-percent-circle-bg { stroke: var(--color-done-=
+subtle); }
+
+.sponsors-funded-dependencies-percent-circle { animation: 1s ease-out 0s 1 =
+normal forwards running circle-progress; }
+
+.sponsors-goal-progress-bar { background: rgb(236, 108, 185); transition: w=
+idth 0.5s ease-in 0s; }
+
+@media (prefers-reduced-motion: no-preference) {
+  .sponsors-goal-progress-bar:hover { background: linear-gradient(90deg, rg=
+b(255, 211, 61) 0%, rgb(234, 74, 170) 17%, rgb(179, 75, 255) 34%, rgb(1, 25=
+4, 255) 51%, rgb(255, 211, 61) 68%, rgb(234, 74, 170) 85%, rgb(179, 75, 255=
+) 100%) 0% 0% / 300% 100%; animation: 2s linear 0s infinite normal none run=
+ning sponsors-progress-animation; }
+}
+
+.sponsors-goal-completed-bar { background: linear-gradient(90deg, rgb(255, =
+211, 61) 0%, rgb(234, 74, 170) 17%, rgb(179, 75, 255) 34%, rgb(1, 254, 255)=
+ 51%, rgb(255, 211, 61) 68%, rgb(234, 74, 170) 85%, rgb(179, 75, 255) 100%)=
+ 0% 0% / 300% 100%; }
+
+@media (prefers-reduced-motion: no-preference) {
+  .sponsors-goal-completed-bar { transition: width 0.5s ease-in 0s; animati=
+on: 2s linear 0s infinite normal none running sponsors-progress-animation; =
+}
+}
+
+.sponsors-goals-avatar-border { background-color: var(--color-canvas-defaul=
+t); border: 1px solid var(--color-fg-on-emphasis); }
+
+.bulk-sponsorship-secondary-cell { width: 1%; }
+
+.sponsors-goals-heart-anim { width: 100px; height: 100px; cursor: pointer; =
+background: url("/images/modules/site/sponsors/heart-explosion.png") 0px 0p=
+x / 600px 100px no-repeat; transition: background-position 0s steps(5) 0s; =
+}
+
+.sponsors-goals-heart-anim.is-active { background-position: -500px 0px; tra=
+nsition-duration: 0.3s; }
+
+.open > .sponsors-foldable { max-height: 700px; }
+
+.open .sponsors-foldable-opened { display: block; }
+
+.open .sponsors-foldable-closed { display: none; }
+
+.sponsors-foldable { max-height: 0px; box-sizing: border-box; overflow-y: a=
+uto; transition: max-height 0.25s ease-in-out 0s; }
+
+.sponsors-foldable-opened { display: none; }
+
+.sponsors-foldable-closed { display: block; }
+
+.sponsor-card { width: 100%; height: 450px; border: 0px; }
+
+@media (min-width: 544px) {
+  .sponsor-card { height: 260px; }
+}
+
+.sponsor-cell { padding: 8px; vertical-align: middle; border-right: 1px sol=
+id var(--color-neutral-muted); border-bottom: 1px solid var(--color-neutral=
+-muted); }
+
+.sponsor-cell:first-child { width: 45px; padding-left: 32px; border-right-w=
+idth: 0px; }
+
+.sponsor-cell:last-child { padding-left: 8px; border-right-width: 0px; }
+
+.sponsor-header-cell { padding-right: 16px; font-weight: var(--base-text-we=
+ight-semibold, 600); text-align: left; border-top: 1px solid var(--color-ne=
+utral-muted); }
+
+.sponsor-row-number { color: var(--color-fg-subtle); }
+
+@media (prefers-reduced-motion: no-preference) {
+  .tier-category:hover .tier-emoji { animation: 0.1s ease 0.1s 4 alternate =
+none running wiggle; }
+}
+
+@keyframes wiggle {=20
+  0% { transform: rotate(-25deg); }
+  100% { transform: rotate(15deg) scale(1.2); }
+}
+
+.sponsors-featured-item { width: 100%; }
+
+@media (min-width: 768px) {
+  .sponsors-featured-item { width: calc(50% - 8px); }
+}
+
+.bulk-sponsorship-import-dropzone .bulk-sponsorship-import-outline { inset:=
+ 8px; }
+
+.bulk-sponsorship-import-dropzone .bulk-sponsorship-show-on-dragover { disp=
+lay: none; }
+
+.bulk-sponsorship-import-dropzone.bulk-sponsor-dragover .bulk-sponsorship-s=
+how-on-dragover { display: inline; }
+
+.bulk-sponsorship-import-dropzone.bulk-sponsor-dragover .bulk-sponsorship-h=
+ide-on-dragover { display: none; }
+
+.bulk-sponsorship-import-dropzone.bulk-sponsor-dragover .bulk-sponsorship-d=
+rag-n-drop-instructions { visibility: hidden; }
+
+.bulk-sponsorship-import-dropzone.bulk-sponsor-dragover .bulk-sponsorship-i=
+mport-outline { border: 6px dashed var(--color-border-default); }
+
+.bulk-sponsorship-import-csv { z-index: -1; width: 1px; height: 1px; opacit=
+y: 0; }
+
+.bulk-sponsorship-import-csv:focus + label { outline: 2px solid var(--color=
+-accent-fg); outline-offset: -2px; box-shadow: none; }
+
+.bulk-sponsorship-table { display: block; width: 100%; min-height: 0.01%; o=
+verflow-x: auto; }
+
+@media (min-width: 768px) {
+  .bulk-sponsorship-table { display: table; }
+}
+
+@media (max-width: 768px) {
+  .bulk-sponsorship-amount-cell-non-correctble-error { padding-right: 24px;=
+ }
+}
+
+@media (min-width: 768px) {
+  .bulk-sponsorship-amount-cell-non-correctble-error { padding-right: 8px; =
+}
+}
+
+@media (min-width: 0) {
+  .bulk-sponsorship-amount-column-header { min-width: 150px; }
+}
+
+@media (min-width: 768px) {
+  .bulk-sponsorship-amount-column-header { min-width: 200px; }
+}
+
+.tab-size[data-tab-size=3D"1"] { tab-size: 1; }
+
+.tab-size[data-tab-size=3D"2"] { tab-size: 2; }
+
+.tab-size[data-tab-size=3D"3"] { tab-size: 3; }
+
+.tab-size[data-tab-size=3D"4"] { tab-size: 4; }
+
+.tab-size[data-tab-size=3D"5"] { tab-size: 5; }
+
+.tab-size[data-tab-size=3D"6"] { tab-size: 6; }
+
+.tab-size[data-tab-size=3D"7"] { tab-size: 7; }
+
+.tab-size[data-tab-size=3D"8"] { tab-size: 8; }
+
+.tab-size[data-tab-size=3D"9"] { tab-size: 9; }
+
+.tab-size[data-tab-size=3D"10"] { tab-size: 10; }
+
+.tab-size[data-tab-size=3D"11"] { tab-size: 11; }
+
+.tab-size[data-tab-size=3D"12"] { tab-size: 12; }
+
+.team-label-ldap { display: inline-block; padding: 0px 8px; line-height: 25=
+px; color: var(--color-fg-muted); text-transform: uppercase; cursor: defaul=
+t; border: 1px solid var(--color-border-muted); border-radius: 6px; box-sha=
+dow: none; }
+
+.team-label-ldap.header-label-ldap { padding: 4px; }
+
+.team-member-ellipsis { width: 25px; height: 25px; line-height: 24px; }
+
+.team-member-ellipsis:hover { color: var(--color-accent-fg); background: va=
+r(--color-canvas-subtle); }
+
+.team-listing .nested-teams-checkbox { padding-left: 4px; }
+
+.team-listing .nested-teams-checkbox.show { padding-right: 12px; }
+
+.team-listing .nested-teams-checkbox.indent-1 { padding-left: 30px; }
+
+.team-listing .nested-teams-checkbox.indent-2 { padding-left: 54px; }
+
+.team-listing .nested-teams-checkbox.indent-3 { padding-left: 78px; }
+
+.team-listing .nested-teams-checkbox.indent-4 { padding-left: 102px; }
+
+.team-listing .nested-teams-checkbox.indent-5 { padding-left: 126px; }
+
+.team-listing .nested-teams-checkbox.indent-6 { padding-left: 150px; }
+
+.team-listing .nested-teams-checkbox.indent-7 { padding-left: 174px; }
+
+.team-listing .nested-teams-checkbox.indent-8 { padding-left: 198px; }
+
+.team-listing .nested-teams-checkbox.indent-9 { padding-left: 222px; }
+
+.team-listing .nested-teams-checkbox.indent-10 { padding-left: 246px; }
+
+.team-listing .nested-teams-checkbox.indent-11 { padding-left: 270px; }
+
+.team-listing .nested-teams-checkbox.indent-12 { padding-left: 294px; }
+
+.team-listing .nested-teams-checkbox.indent-13 { padding-left: 318px; }
+
+.team-listing .nested-teams-checkbox.indent-14 { padding-left: 342px; }
+
+.team-listing .nested-teams-checkbox.indent-15 { padding-left: 366px; }
+
+.team-listing .team-info { width: 280px; }
+
+.team-listing .team-short-info { width: 170px; }
+
+.team-listing .nested-team-info { width: 650px; }
+
+.team-listing .nested-team-name { max-width: 268px; }
+
+.team-listing .shortened-teams-avatars { margin-left: auto; }
+
+.team-listing .shortened-teams-avatars.width-0 { width: 300px; }
+
+.team-listing .shortened-teams-avatars.width-1 { width: 233px; }
+
+.team-listing .shortened-teams-avatars.width-2 { width: 167px; }
+
+.team-listing .shortened-teams-avatars.width-3 { width: 99px; }
+
+.team-listing .team-members-count { width: 124px; }
+
+.team-listing .team-show-more-cell { width: 980px; }
+
+.team-listing .team-buttons { width: 150px; }
+
+.team-listing .octicon-wrapper { width: 16px; }
+
+.team-listing .is-open.root-team { background-color: var(--color-canvas-sub=
+tle); }
+
+.team-listing .is-open .expand-nested-team { font-weight: var(--base-text-w=
+eight-semibold, 600); }
+
+.team-listing .is-open .octicon-chevron-down { transform: rotate(180deg); }
+
+.traffic-graph { min-height: 150px; }
+
+.traffic-graph .activity { margin-top: 0px; }
+
+.traffic-graph .activity .dots { margin-top: 40px; }
+
+.traffic-graph .path { fill: none; stroke-width: 2; }
+
+.traffic-graph path.total { stroke: var(--color-success-emphasis); }
+
+.traffic-graph path.unique { stroke: var(--color-accent-emphasis); }
+
+.traffic-graph .axis.x .tick:first-of-type line { stroke: var(--color-succe=
+ss-emphasis); stroke-width: 2px; }
+
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+
+.branch-a-b-count .count-value { position: relative; top: -1px; display: bl=
+ock; padding: 0px 4px; font-size: 12px; }
+
+.branch-a-b-count .bar { position: absolute; min-width: 3px; height: 4px; }
+
+.branch-a-b-count .meter { position: absolute; height: 4px; background-colo=
+r: var(--color-neutral-muted); }
+
+.branch-a-b-count .meter.zero { background-color: transparent; }
+
+.branch-a-b-count .bar-behind { right: 0px; border-radius: 6px 0px 0px 6px;=
+ }
+
+.branch-a-b-count .bar-behind .meter { right: 0px; border-radius: 6px 0px 0=
+px 6px; }
+
+.branch-a-b-count .bar-ahead { left: 0px; border-radius: 0px 6px 6px 0px; }
+
+.branch-a-b-count .bar-ahead .meter { border-radius: 0px 6px 6px 0px; }
+
+.branch-a-b-count .bar-ahead.even, .branch-a-b-count .bar-behind.even { min=
+-width: 2px; background: rgb(234, 236, 239); }
+
+.branches .clear-search { display: none; }
+
+.branches .loading-overlay { position: absolute; top: 0px; z-index: 20; dis=
+play: none; width: 100%; height: 100%; padding-top: 50px; text-align: cente=
+r; }
+
+.branches .loading-overlay::before { position: absolute; inset: 0px; conten=
+t: ""; background-color: var(--color-canvas-default); opacity: 0.7; }
+
+.branches .loading-overlay .spinner { display: inline-block; }
+
+.branches.is-loading .loading-overlay { display: block; }
+
+.branches.is-search-mode .clear-search { display: inline-block; }
+
+.commit-loader .loader-error { display: none; margin: 0px; font-size: 12px;=
+ font-weight: var(--base-text-weight-semibold, 600); color: var(--color-dan=
+ger-fg); }
+
+.commit-loader.is-error .loader-error { display: block; }
+
+.editor-abort { display: inline; font-size: 14px; }
+
+.file-commit-form { padding-left: 64px; }
+
+.file-commit-form--full { position: absolute; bottom: 0px; left: 0px; z-ind=
+ex: 10; width: 100%; padding-top: 16px; padding-left: 0px; margin-top: 16px=
+; margin-bottom: 16px; background: var(--color-canvas-default); }
+
+@media (min-width: 1012px) {
+  .file-commit-form--full { inset: 0px 0px auto auto; width: auto; margin-t=
+op: 0px; margin-bottom: 0px; }
+}
+
+.file-commit-form--full .commit-form { padding: 0px; margin-bottom: 24px; b=
+order: 0px; }
+
+.file-commit-form--full .commit-form::before { display: none; }
+
+.file-commit-form-dropdown { position: fixed; top: 0px; left: 0px; width: 1=
+00%; height: 100%; }
+
+.file-commit-form-dropdown::after { display: none; }
+
+@media (min-width: 1012px) {
+  .file-commit-form-dropdown { position: absolute; top: auto; left: auto; w=
+idth: 420px; height: auto; }
+  .file-commit-form-dropdown::after { display: inline-block; }
+}
+
+.page-blob.height-full .blob-wrapper { overflow-y: auto; }
+
+.file-info-divider { display: inline-block; width: 1px; height: 18px; margi=
+n-right: 4px; margin-left: 4px; vertical-align: middle; border-left: 1px so=
+lid var(--color-border-default); }
+
+.file-mode { text-transform: capitalize; }
+
+.linejump .linejump-input { width: 340px; background-color: var(--color-can=
+vas-subtle); }
+
+.linejump .linejump-input, .linejump .btn { padding: 8px 16px; font-size: 1=
+6px; }
+
+.include-fragment-error { display: none; }
+
+.is-error .include-fragment-error { display: block; }
+
+.html-blob { margin-bottom: 16px; }
+
+.file-sidebar-container .file { border-top-right-radius: 0px; border-bottom=
+-right-radius: 0px; }
+
+.file-navigation::before { display: table; content: ""; }
+
+.file-navigation::after { display: table; clear: both; content: ""; }
+
+.file-navigation .select-menu-button .css-truncate-target { max-width: 200p=
+x; }
+
+.file-navigation .breadcrumb { float: left; margin-top: 0px; margin-left: 4=
+px; font-size: 16px; line-height: 26px; }
+
+.file-navigation + .breadcrumb { margin-bottom: 8px; }
+
+.file-blankslate { border: 0px; border-radius: 0px 0px 6px 6px; }
+
+.add-comment-icon-button { position: absolute; top: 0px; left: -8px; float:=
+ left; padding: 0px; color: var(--color-fg-on-emphasis); background-color: =
+var(--color-accent-emphasis); box-shadow: var(--color-shadow-medium); opaci=
+ty: 0; transition: transform 0.1s ease-in-out 0s; transform: scale(0.8, 0.8=
+); }
+
+.add-comment-icon-button:focus:not([disabled]), .add-comment-icon-button:ho=
+ver:not([disabled]) { opacity: 1; transform: scale(0.9, 0.9); }
+
+.diff-text-cell { position: relative; padding-left: 24px; }
+
+.diff-text-cell:hover .add-comment-icon-button { opacity: 1; }
+
+.diff-text-cell .diff-text::before { position: absolute; top: 1px; left: 8p=
+x; padding-right: 8px; content: attr(data-code-marker); }
+
+.diff-text-cell .diff-text .diff-text-inner { color: var(--color-fg-default=
+); overflow-wrap: break-word; white-space: pre-wrap; }
+
+.diff-text-cell .syntax-highlighted-line.addition .x { background-color: va=
+r(--color-success-muted); }
+
+.diff-text-cell .syntax-highlighted-line.deletion .x { background-color: va=
+r(--color-danger-muted); }
+
+.diff-text-cell .syntax-highlighted-line .x-first { border-top-left-radius:=
+ 4px; border-bottom-left-radius: 4px; }
+
+.diff-text-cell .syntax-highlighted-line .x-last { border-top-right-radius:=
+ 4px; border-bottom-right-radius: 4px; }
+
+.empty-diff-line { background-color: var(--color-neutral-muted); }
+
+.diff-line-number { width: 1%; min-width: 50px; text-align: right; user-sel=
+ect: none; }
+
+.diff-line-number .diff-line-number-button { color: unset; font: unset; fon=
+t-feature-settings: unset; font-kerning: unset; font-optical-sizing: unset;=
+ font-palette: unset; font-synthesis: unset; font-variation-settings: unset=
+; forced-color-adjust: unset; text-orientation: unset; text-rendering: unse=
+t; -webkit-font-smoothing: unset; -webkit-locale: unset; -webkit-text-orien=
+tation: unset; -webkit-writing-mode: unset; writing-mode: unset; zoom: unse=
+t; accent-color: unset; place-content: unset; place-items: unset; place-sel=
+f: unset; alignment-baseline: unset; animation: unset; app-region: unset; a=
+ppearance: unset; aspect-ratio: unset; backdrop-filter: unset; backface-vis=
+ibility: unset; background: unset; background-blend-mode: unset; baseline-s=
+hift: unset; block-size: unset; border-block: unset; border: unset; border-=
+radius: unset; border-collapse: unset; border-end-end-radius: unset; border=
+-end-start-radius: unset; border-inline: unset; border-start-end-radius: un=
+set; border-start-start-radius: unset; inset: unset; box-shadow: unset; box=
+-sizing: unset; break-after: unset; break-before: unset; break-inside: unse=
+t; buffered-rendering: unset; caption-side: unset; caret-color: unset; clea=
+r: unset; clip: unset; clip-path: unset; clip-rule: unset; color-interpolat=
+ion: unset; color-interpolation-filters: unset; color-rendering: unset; col=
+or-scheme: unset; columns: unset; column-fill: unset; gap: unset; column-ru=
+le: unset; column-span: unset; contain: unset; contain-intrinsic-block-size=
+: unset; contain-intrinsic-size: unset; contain-intrinsic-inline-size: unse=
+t; container: unset; content: unset; content-visibility: unset; counter-inc=
+rement: unset; counter-reset: unset; counter-set: unset; cursor: pointer; c=
+x: unset; cy: unset; d: unset; display: unset; dominant-baseline: unset; em=
+pty-cells: unset; fill: unset; fill-opacity: unset; fill-rule: unset; filte=
+r: unset; flex: unset; flex-flow: unset; float: unset; flood-color: unset; =
+flood-opacity: unset; grid: unset; grid-area: unset; height: unset; hyphena=
+te-character: unset; hyphenate-limit-chars: unset; hyphens: unset; image-or=
+ientation: unset; image-rendering: unset; initial-letter: unset; inline-siz=
+e: unset; inset-block: unset; inset-inline: unset; isolation: unset; letter=
+-spacing: unset; lighting-color: unset; line-break: unset; list-style: unse=
+t; margin-block: unset; margin: unset; margin-inline: unset; marker: unset;=
+ mask: unset; mask-type: unset; math-depth: unset; math-shift: unset; math-=
+style: unset; max-block-size: unset; max-height: unset; max-inline-size: un=
+set; max-width: unset; min-block-size: unset; min-height: unset; min-inline=
+-size: unset; min-width: unset; mix-blend-mode: unset; object-fit: unset; o=
+bject-position: unset; object-view-box: unset; offset: unset; opacity: unse=
+t; order: unset; orphans: unset; outline: unset; outline-offset: unset; ove=
+rflow-anchor: unset; overflow-clip-margin: unset; overflow-wrap: unset; ove=
+rflow: unset; overscroll-behavior-block: unset; overscroll-behavior-inline:=
+ unset; overscroll-behavior: unset; padding-block: unset; padding: unset; p=
+adding-inline: unset; page: unset; page-orientation: unset; paint-order: un=
+set; perspective: unset; perspective-origin: unset; pointer-events: unset; =
+position: unset; quotes: unset; r: unset; resize: unset; rotate: unset; rub=
+y-position: unset; rx: unset; ry: unset; scale: unset; scroll-behavior: uns=
+et; scroll-margin-block: unset; scroll-margin: unset; scroll-margin-inline:=
+ unset; scroll-padding-block: unset; scroll-padding: unset; scroll-padding-=
+inline: unset; scroll-snap-align: unset; scroll-snap-stop: unset; scroll-sn=
+ap-type: unset; scrollbar-gutter: unset; shape-image-threshold: unset; shap=
+e-margin: unset; shape-outside: unset; shape-rendering: unset; size: unset;=
+ speak: unset; stop-color: unset; stop-opacity: unset; stroke: unset; strok=
+e-dasharray: unset; stroke-dashoffset: unset; stroke-linecap: unset; stroke=
+-linejoin: unset; stroke-miterlimit: unset; stroke-opacity: unset; stroke-w=
+idth: unset; tab-size: unset; table-layout: unset; text-align: unset; text-=
+align-last: unset; text-anchor: unset; text-combine-upright: unset; text-de=
+coration: unset; text-decoration-skip-ink: unset; text-emphasis: unset; tex=
+t-emphasis-position: unset; text-indent: unset; text-overflow: unset; text-=
+shadow: unset; text-size-adjust: unset; text-transform: unset; text-underli=
+ne-offset: unset; text-underline-position: unset; touch-action: unset; tran=
+sform: unset; transform-box: unset; transform-origin: unset; transform-styl=
+e: unset; transition: unset; translate: unset; user-select: unset; vector-e=
+ffect: unset; vertical-align: unset; visibility: unset; border-spacing: uns=
+et; -webkit-box-align: unset; -webkit-box-decoration-break: unset; -webkit-=
+box-direction: unset; -webkit-box-flex: unset; -webkit-box-ordinal-group: u=
+nset; -webkit-box-orient: unset; -webkit-box-pack: unset; -webkit-box-refle=
+ct: unset; -webkit-highlight: unset; -webkit-line-break: unset; -webkit-lin=
+e-clamp: unset; -webkit-mask-box-image: unset; -webkit-mask: unset; -webkit=
+-mask-composite: unset; -webkit-print-color-adjust: unset; -webkit-rtl-orde=
+ring: unset; -webkit-ruby-position: unset; -webkit-tap-highlight-color: uns=
+et; -webkit-text-combine: unset; -webkit-text-decorations-in-effect: unset;=
+ -webkit-text-fill-color: unset; -webkit-text-security: unset; -webkit-text=
+-stroke: unset; -webkit-user-drag: unset; -webkit-user-modify: unset; white=
+-space: unset; widows: unset; width: 100%; will-change: unset; word-break: =
+unset; word-spacing: unset; x: unset; y: unset; z-index: unset; }
+
+.diff-line-number .diff-line-number-button:hover { font-weight: var(--base-=
+text-weight-semibold, 600); color: var(--color-fg-default); }
+
+.diff-line-number .diff-line-number-button:focus-visible { outline: 2px sol=
+id var(--color-accent-fg); outline-offset: -2px; box-shadow: none; }
+
+.diff-line-number.has-expander { padding-right: 0px; padding-left: 0px; }
+
+.grey-box { background-color: var(--color-neutral-muted); }
+
+.react-code-file-contents { display: flex; }
+
+.react-line-numbers { position: relative; display: flex; width: 60px; min-w=
+idth: 60px; flex-direction: column; align-items: flex-end; }
+
+.react-code-lines { position: relative; width: 100%; }
+
+.react-line-number { position: relative; padding-right: 10px; padding-left:=
+ 16px; color: var(--color-fg-subtle); text-align: right; white-space: nowra=
+p; border: 0px; }
+
+.react-line-number:not(.prevent-click) { cursor: pointer; user-select: none=
+; }
+
+.react-line-number:not(.prevent-click):hover { color: var(--color-fg-defaul=
+t); }
+
+.react-code-line-contents { position: relative; display: flex; width: 100%;=
+ padding-right: 10px; padding-left: 10px; overflow: visible; color: var(--c=
+olor-fg-default); vertical-align: middle; scroll-margin-top: 20vh; }
+
+.react-code-text { font-family: ui-monospace, SFMono-Regular, "SF Mono", Me=
+nlo, Consolas, "Liberation Mono", monospace; font-size: 12px; line-height: =
+20px; overflow-wrap: normal; white-space: pre; }
+
+.react-code-text span { display: inline-block; }
+
+.react-code-text-cell { overflow-wrap: break-word; white-space: pre-wrap; }
+
+.react-code-text-cell span { display: inline; }
+
+.react-code-text-cell span:empty { display: inline-block; }
+
+.react-line-number.virtual, .react-code-line-contents.virtual { position: a=
+bsolute; top: 0px; }
+
+.react-csv-row { background: var(--color-canvas-default); }
+
+.react-csv-line-number { position: relative; padding-left: 4px; }
+
+.react-csv-line-number .react-line-number { padding-left: 12px; }
+
+.react-csv-row--highlighted .react-csv-cell { background: var(--color-atten=
+tion-subtle); }
+
+.react-csv-row--highlighted .react-csv-cell:nth-of-type(2) { box-shadow: in=
+set 2px 0 0 var(--color-attention-fg); }
+
+.react-csv-cell { padding: 8px; font-size: 12px; white-space: nowrap; borde=
+r-top: 1px solid var(--color-border-default); border-left: 1px solid var(--=
+color-border-default); }
+
+.react-csv-cell--header { font-weight: var(--base-text-weight-semibold, 600=
+); text-align: left; background: var(--color-canvas-subtle); border-top: 0p=
+x; }
+
+.react-file-line.html-div { padding-left: 10px; }
+
+.react-last-commit-timestamp { display: none; }
+
+@media (max-width: 768px) {
+  .react-last-commit-timestamp { display: inherit; }
+}
+
+@media (max-width: 375px) {
+  .react-last-commit-timestamp { display: none; }
+}
+
+.react-last-commit-oid-timestamp { display: flex; flex-wrap: nowrap; }
+
+@media (max-width: 768px) {
+  .react-last-commit-oid-timestamp { display: none; }
+}
+
+.react-last-commit-history-group { display: flex; }
+
+@media (max-width: 544px) {
+  .react-last-commit-history-group { display: none; }
+}
+
+.react-last-commit-history-icon { display: none !important; }
+
+@media (max-width: 544px) {
+  .react-last-commit-history-icon { display: inherit !important; }
+}
+
+.react-code-size-details-banner { display: none; }
+
+@media (max-width: 768px) {
+  .react-code-size-details-banner { display: flex !important; }
+}
+
+.react-code-size-details-in-header { display: flex; }
+
+@media (max-width: 768px) {
+  .react-code-size-details-in-header { display: none; }
+}
+
+@media (max-width: 544px) {
+  .react-blob-view-header-sticky { position: relative !important; }
+}
+
+.react-blob-header-edit-and-raw-actions { display: inherit !important; }
+
+@media (max-width: 544px) {
+  .react-blob-header-edit-and-raw-actions { display: none !important; }
+}
+
+.react-blob-header-edit-and-raw-actions-combined { display: none !important=
+; }
+
+@media (max-width: 544px) {
+  .react-blob-header-edit-and-raw-actions-combined { display: inherit !impo=
+rtant; }
+}
+
+@media (max-width: 375px) {
+  .react-contributors-title { display: none; }
+}
+
+@media (max-width: 768px) {
+  .react-blame-for-range { background: var(--color-canvas-subtle); border-b=
+ottom: 1px solid var(--color-border-muted); }
+}
+
+.react-file-upload { display: flex; min-height: 0px; flex-direction: column=
+; }
+
+@media print {
+  .react-blob-print-hide { display: none; }
+}
+
+.react-directory-row { height: 40px; font-size: 14px; }
+
+.react-directory-row td { padding-left: 16px; text-align: left; border-top:=
+ 1px solid var(--color-border-default); }
+
+.react-directory-row:hover { background-color: var(--color-canvas-subtle); =
+}
+
+.react-directory-filename-column { display: flex; height: 40px; padding-rig=
+ht: 16px; align-items: center; gap: 4px 10px; }
+
+.react-directory-filename-column h3 { margin: 0px; font-size: 14px; font-we=
+ight: var(--base-text-weight-normal, 400); }
+
+.react-directory-filename-column .icon-directory { color: var(--color-icon-=
+directory); }
+
+.react-directory-truncate { display: inline-block; max-width: 100%; overflo=
+w: hidden; text-overflow: ellipsis; white-space: pre; vertical-align: top; =
+}
+
+.react-directory-commit-message { max-width: 100%; overflow: hidden; text-o=
+verflow: ellipsis; white-space: nowrap; }
+
+.react-directory-commit-age { padding-right: 16px; color: var(--color-fg-mu=
+ted); text-align: right; }
+
+.react-tree-toggle-button-with-indicator::after { position: absolute; top: =
+3px; right: 2px; display: inline-block; width: var(--base-size-8, 8px); hei=
+ght: var(--base-size-8, 8px); content: ""; background: var(--color-accent-f=
+g); border: 2px solid var(--color-canvas-default); border-radius: 50%; }
+
+@media screen and (min-width: 1280px) {
+  .react-repos-overview-margin { margin-right: calc((100% - 1280px) / 2); }
+  .react-repos-overview-margin.tree-open { margin-right: calc((100% - 1536p=
+x) / 2); }
+}
+
+.manifest-commit-form { margin-top: 16px; }
+
+.repo-file-upload-outline { width: 100%; height: 100%; }
+
+.repo-file-upload-target { position: relative; }
+
+.repo-file-upload-target.is-uploading .repo-file-upload-text.initial-text, =
+.repo-file-upload-target.is-failed .repo-file-upload-text.initial-text, .re=
+po-file-upload-target.is-default .repo-file-upload-text.initial-text { disp=
+lay: none; }
+
+.repo-file-upload-target.is-uploading .repo-file-upload-text.alternate-text=
+, .repo-file-upload-target.is-failed .repo-file-upload-text.alternate-text,=
+ .repo-file-upload-target.is-default .repo-file-upload-text.alternate-text =
+{ display: block; }
+
+.repo-file-upload-target.is-uploading.dragover .repo-file-upload-text, .rep=
+o-file-upload-target.is-failed.dragover .repo-file-upload-text, .repo-file-=
+upload-target.is-default.dragover .repo-file-upload-text { display: none; }
+
+.repo-file-upload-target .repo-file-upload-text.initial-text { display: blo=
+ck; }
+
+.repo-file-upload-target .repo-file-upload-text.alternate-text { display: n=
+one; }
+
+.repo-file-upload-target .repo-file-upload-text, .repo-file-upload-target .=
+repo-file-upload-drop-text { margin-bottom: 4px; }
+
+.repo-file-upload-target .repo-file-upload-choose { display: inline-block; =
+margin-top: 0px; font-size: 16px; }
+
+.repo-file-upload-target .manual-file-chooser { margin-left: 0px; }
+
+.repo-file-upload-target .repo-file-upload-outline { position: absolute; to=
+p: 3%; left: 1%; width: 98%; height: 94%; }
+
+.repo-file-upload-target.is-failed .repo-file-upload-outline, .repo-file-up=
+load-target.is-bad-file .repo-file-upload-outline, .repo-file-upload-target=
+.is-too-big .repo-file-upload-outline, .repo-file-upload-target.is-too-many=
+ .repo-file-upload-outline, .repo-file-upload-target.is-empty .repo-file-up=
+load-outline { height: 85%; }
+
+.repo-file-upload-target.dragover .repo-file-upload-text { display: none; }
+
+.repo-file-upload-target.dragover .repo-file-upload-choose { visibility: hi=
+dden; }
+
+.repo-file-upload-target.dragover .repo-file-upload-drop-text { display: bl=
+ock; }
+
+.repo-file-upload-target.dragover .repo-file-upload-outline { border: 6px d=
+ashed var(--color-border-default); border-radius: 6px; }
+
+.repo-file-upload-target .repo-file-upload-drop-text { display: none; }
+
+.repo-file-upload-errors { display: none; }
+
+.repo-file-upload-errors .error { display: none; }
+
+.is-failed .repo-file-upload-errors, .is-bad-file .repo-file-upload-errors,=
+ .is-too-big .repo-file-upload-errors, .is-too-many .repo-file-upload-error=
+s, .is-hidden-file .repo-file-upload-errors, .is-empty .repo-file-upload-er=
+rors { position: absolute; right: 0px; bottom: 0px; left: 0px; display: blo=
+ck; padding: 4px 8px; line-height: 1.5; text-align: left; background-color:=
+ var(--color-canvas-default); border-top: 1px solid var(--color-border-defa=
+ult); border-bottom-right-radius: 6px; border-bottom-left-radius: 6px; }
+
+.is-file-list .repo-file-upload-errors { border-bottom-right-radius: 0px; b=
+order-bottom-left-radius: 0px; }
+
+.is-failed .repo-file-upload-errors .failed-request, .is-bad-file .repo-fil=
+e-upload-errors .failed-request { display: inline-block; }
+
+.is-too-big .repo-file-upload-errors .too-big { display: inline-block; }
+
+.is-hidden-file .repo-file-upload-errors .hidden-file { display: inline-blo=
+ck; }
+
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+r-spacing: normal; }
+
+#yf-bt-wrapper .t-table .t-header div.t-name { width: auto; text-align: lef=
+t; color: rgb(51, 51, 51); padding: 9px; margin: 0px; display: none; }
+
+#yf-bt-wrapper .t-header .t-more::before { content: ""; background-image: u=
+rl("../img/assets/overflow-icon.svg"); background-repeat: no-repeat; width:=
+ 2px; height: 8px; display: block; margin: 0px auto; }
+
+#yf-bt-wrapper.free .t-body, #yf-bt-wrapper.pro .t-body { background: rgb(2=
+55, 255, 255); min-height: 311px; position: relative; overflow: auto; }
+
+#yf-bt-wrapper.free .t-body { max-height: 311px; }
+
+#yf-bt-wrapper.pro .t-body { max-height: 422px; }
+
+#yf-bt-wrapper .t-body .t-torrent { height: 60px; position: relative; text-=
+align: left; }
+
+#yf-bt-wrapper .t-body .t-row { background-color: rgb(255, 255, 255); borde=
+r-bottom: 2px solid rgb(242, 242, 242); position: relative; }
+
+#yf-bt-wrapper .t-body .t-row:hover { background-color: rgb(241, 241, 241);=
+ }
+
+#yf-bt-wrapper .t-body .t-row:nth-child(2n+1) { background-color: rgb(251, =
+251, 251); }
+
+#yf-bt-wrapper .t-body .t-torrent div { display: inline-block; }
+
+#yf-bt-wrapper .t-body .t-torrent .torrent-info-content { display: inline-b=
+lock; width: 250px; height: 60px; vertical-align: top; }
+
+#yf-bt-wrapper .t-body .t-torrent .torrent-info-list { height: 18px; paddin=
+g: 0px 15px; white-space: nowrap; }
+
+#yf-bt-wrapper .t-body .t-torrent .t-name-text { color: rgb(26, 26, 26); fo=
+nt-size: 12px; letter-spacing: -0.12px; font-weight: 700; font-style: norma=
+l; font-stretch: normal; line-height: 14px; width: 250px; cursor: pointer; =
+vertical-align: middle; white-space: nowrap; }
+
+#yf-bt-wrapper .torrent-link { color: rgb(26, 26, 26); text-decoration: non=
+e; display: inline-block; margin: 11px 0px 6px; line-height: normal; }
+
+#yf-bt-wrapper .t-body .t-torrent .overflow { width: 1px; height: 23px; bac=
+kground-image: url("../img/assets/rectangle.svg"); background-repeat: no-re=
+peat; vertical-align: middle; }
+
+#yf-bt-wrapper .t-body .t-torrent .t-quality-text, #yf-bt-wrapper .t-body .=
+t-torrent .t-type-icon { width: auto; font-size: 10px; line-height: normal;=
+ letter-spacing: normal; font-style: normal; font-stretch: normal; text-ali=
+gn: center; }
+
+#yf-bt-wrapper .t-body .t-torrent .t-type-icon { color: rgb(40, 40, 40); fo=
+nt-weight: 400; }
+
+#yf-bt-wrapper .t-body .t-torrent .t-quality-text { color: rgb(40, 40, 40);=
+ font-weight: 400; margin-right: 7px; vertical-align: middle; }
+
+#yf-bt-wrapper .t-body .t-torrent .t-lang-text, #yf-bt-wrapper .t-body .t-t=
+orrent .t-status-text { width: 67px; color: rgb(40, 40, 40); font-size: 10p=
+x; font-weight: 400; font-style: normal; font-stretch: normal; line-height:=
+ normal; letter-spacing: normal; text-align: center; height: auto; vertical=
+-align: middle; }
+
+#yf-bt-wrapper .t-body .t-torrent .t-arrow { width: 26px; vertical-align: m=
+iddle; text-align: center; height: 45px; float: right; margin-right: 15px; =
+}
+
+#yf-bt-wrapper .t-body .t-torrent .t-arrow .arrow-down, #yf-bt-wrapper .t-b=
+ody .t-torrent .t-arrow .arrow-up { cursor: pointer; z-index: 999; margin-t=
+op: 19px; background-repeat: no-repeat; background-position: center center;=
+ background-size: 13px; width: 27px; height: 27px; display: block; }
+
+#yf-bt-wrapper .t-body .t-torrent .t-arrow .arrow-down { background-image: =
+url("../img/assets/chevron-expand.svg"); }
+
+#yf-bt-wrapper .t-body .t-torrent .t-arrow .arrow-up { background-image: ur=
+l("../img/assets/chevron-collapse.svg"); }
+
+#yf-bt-wrapper .t-row .torrent-download-icon-gif, #yf-bt-wrapper .t-torrent=
+ .torrent-download-icon { background-size: 32px; width: 48px; height: 60px;=
+ background-position: center center; background-repeat: no-repeat; cursor: =
+pointer; }
+
+#yf-bt-wrapper .torrent-title { padding-left: 15px; padding-right: 15px; di=
+splay: inline-block; }
+
+#yf-bt-wrapper .t-torrent .torrent-download-icon { background-image: url(".=
+./img/assets/download-icon.gif"); border-right: 1px solid rgb(222, 222, 222=
+); display: inline-block !important; }
+
+#yf-bt-wrapper.free .t-torrent .torrent-download-icon:hover { background-im=
+age: url("../img/assets/dl-icon-blue-static.gif"); }
+
+#yf-bt-wrapper.pro .t-torrent .torrent-download-icon:hover { background-ima=
+ge: url("../img/assets/dl-icon-green-static.gif"); }
+
+#yf-bt-wrapper .t-row .torrent-download-icon-gif { position: absolute; top:=
+ 0px; left: 0px; z-index: 2; }
+
+#yf-bt-wrapper.free .t-row .torrent-download-icon-gif { background-image: u=
+rl("../img/assets/dl-icon-blue-loop.gif"); }
+
+#yf-bt-wrapper.pro .t-row .torrent-download-icon-gif { background-image: ur=
+l("../img/assets/dl-icon-green-loop.gif"); }
+
+#yf-bt-wrapper .t-row .torrent-download-overlay { position: absolute; width=
+: 100%; height: 100%; background: rgb(255, 255, 255); z-index: 1; top: 0px;=
+ left: 0px; opacity: 0.9; }
+
+#yf-bt-wrapper .t-row .torrent-download-overlay .download-text { text-align=
+: center; letter-spacing: -0.12px; color: rgb(0, 0, 0); font-weight: 700; f=
+ont-size: 12px; padding: 0px; line-height: 60px; margin: 0px 0px 0px 20px; =
+}
+
+#yf-bt-wrapper .tv-icon { width: 14px; height: 45px; vertical-align: middle=
+; }
+
+#yf-bt-wrapper .t-torrent-more { margin: 0px; height: 0px; overflow: hidden=
+; transition: height 0.4s linear 0s; }
+
+#yf-bt-wrapper .show-info { display: block; height: 60px; text-align: left;=
+ }
+
+#yf-bt-wrapper .t-torrent-more button { margin: 0px auto; display: block; }
+
+#yf-bt-wrapper .t-torrent-more .t-info-left { display: inline-block; width:=
+ 30px; margin: 10px 0px 10px 10px; padding-right: 8px; vertical-align: top;=
+ }
+
+#yf-bt-wrapper .t-torrent-more .t-info-right { font-size: 10px; display: in=
+line-block; margin: 0px; padding-left: 15px; padding-right: 15px; vertical-=
+align: top; border-left: 1px solid rgb(226, 226, 226); }
+
+#yf-bt-wrapper .t-torrent-more span { font-weight: 700; }
+
+#yf-bt-wrapper .t-low-availability span { color: rgb(254, 171, 61); vertica=
+l-align: middle; }
+
+#yf-bt-wrapper .t-low-availability img { vertical-align: middle; }
+
+#yf-bt-wrapper .t-low-availability { font-size: 11px; font-weight: 400; fon=
+t-style: normal; font-stretch: normal; letter-spacing: -0.5px; color: rgb(1=
+02, 102, 102); vertical-align: middle; margin-right: 7px; }
+
+#yf-bt-wrapper .t-torrent-secure, #yf-bt-wrapper .t-torrent-size { letter-s=
+pacing: -0.2px; color: rgb(102, 102, 102); margin-right: 7px; vertical-alig=
+n: middle; font-size: 11px; line-height: normal; font-style: normal; font-s=
+tretch: normal; }
+
+#yf-bt-wrapper .t-torrent-secure { font-weight: 700; }
+
+#yf-bt-wrapper .t-torrent-secure::before { content: " "; display: inline-bl=
+ock; background-image: url("../img/assets/icon-secure.png"); background-rep=
+eat: no-repeat; width: 14px; height: 14px; padding-left: 3px; vertical-alig=
+n: middle; }
+
+#yf-bt-wrapper .t-torrent-seeds { font-size: 11px; font-weight: 400; font-s=
+tyle: normal; font-stretch: normal; line-height: normal; letter-spacing: no=
+rmal; color: rgb(118, 184, 63); margin-right: 7px; vertical-align: middle; =
+}
+
+#yf-bt-wrapper .t-torrent-leeches { font-size: 11px; font-weight: 400; font=
+-style: normal; font-stretch: normal; line-height: normal; letter-spacing: =
+normal; color: rgb(0, 153, 255); margin-right: 7px; vertical-align: middle;=
+ }
+
+#yf-bt-wrapper .t-torrent-type { font-size: 11px; font-style: normal; font-=
+stretch: normal; line-height: normal; letter-spacing: -0.2px; color: rgb(10=
+2, 102, 102); vertical-align: middle; margin-right: 7px; }
+
+#yf-bt-wrapper .t-torrent-type b { text-transform: uppercase; }
+
+#yf-bt-wrapper .t-torrent-leeches img, #yf-bt-wrapper .t-torrent-seeds img =
+{ vertical-align: middle; padding-right: 2px; }
+
+#yf-bt-wrapper .t-torrent-filename, #yf-bt-wrapper .t-torrent-lang, #yf-bt-=
+wrapper .t-torrent-path, #yf-bt-wrapper .t-torrent-virus { font-size: 11px;=
+ font-weight: 400; font-style: normal; font-stretch: normal; line-height: n=
+ormal; letter-spacing: normal; color: rgb(102, 102, 102); height: 20px; }
+
+#yf-bt-wrapper .t-torrent-lang img { width: 10px; }
+
+#yf-bt-wrapper .t-torrent-filename img, #yf-bt-wrapper .t-torrent-lang img,=
+ #yf-bt-wrapper .t-torrent-path img, #yf-bt-wrapper .t-torrent-virus img { =
+padding-right: 4px; }
+
+#yf-bt-wrapper .footer { position: absolute; bottom: 0px; color: rgb(102, 1=
+02, 102); width: 365px; height: 25px; margin: 0px auto; padding: 0px; backg=
+round: linear-gradient(rgb(239, 239, 239) 0px, rgb(232, 232, 232) 100%); fo=
+nt-size: 10px; font-weight: 600; font-style: normal; font-stretch: normal; =
+line-height: normal; letter-spacing: normal; text-align: center; }
+
+#yf-bt-wrapper .footer span { padding: 0px 5px; line-height: 25px; font-wei=
+ght: 700; }
+
+#yf-bt-wrapper .feedbackButton { font-size: 9px; font-weight: 600; font-sty=
+le: normal; font-stretch: normal; line-height: normal; letter-spacing: norm=
+al; text-align: center; display: inline-block; cursor: pointer; position: a=
+bsolute; right: 16px; bottom: 5px; color: rgb(118, 184, 63); padding: 3px 5=
+px; border: 1px solid rgb(118, 184, 63); background: 0px 0px; }
+
+#yf-bt-wrapper .feedbackButton:hover { text-decoration: underline; }
+
+@media only screen and (max-height: 670px) {
+  .t-body .t-torrent { height: 36px; position: relative; }
+}
+
+#yf-bt-wrapper .nav-se-container { position: absolute; top: 0px; right: 0px=
+; background: rgb(255, 255, 255); height: 100%; width: 100%; text-align: ce=
+nter; padding: 0px; margin: 0px; display: none; }
+
+#yf-bt-wrapper .expiry-date { font-weight: 700; }
+
+#yf-bt-wrapper .nav-se-content { padding: 34px; }
+
+#yf-bt-wrapper .nav-se-content .settings-title { font-size: 16px; font-weig=
+ht: 700; letter-spacing: 0px; text-align: left; }
+
+#yf-bt-wrapper .nav-se-content .settings-title::before { content: ""; displ=
+ay: inline-block; background-image: url("../img/assets/icon-settings.svg");=
+ width: 20px; height: 20px; vertical-align: sub; margin-right: 5px; }
+
+#yf-bt-wrapper .nav-se-container .nav-se-icon { width: 60px; margin: 15px; =
+}
+
+#yf-bt-wrapper .nav-se-container .nav-se-title { font-size: 22px; font-weig=
+ht: 700; line-height: 28px; padding: 10px 0px; }
+
+#yf-bt-wrapper .nav-se-container .nav-se-text { color: rgb(51, 51, 51); fon=
+t-size: 14px; line-height: 20px; }
+
+#yf-bt-wrapper.free .nav-se-container .nav-se-btn { border-radius: 22px; bo=
+rder: none; padding: 10px 30px; margin: 10px 0px; color: rgb(255, 255, 255)=
+; font-size: 15px; line-height: 18px; background: rgb(19, 153, 252); }
+
+#yf-bt-wrapper.pro .nav-se-container .nav-se-btn { border-radius: 22px; bor=
+der: none; padding: 10px 30px; margin: 10px 0px; color: rgb(255, 255, 255);=
+ font-size: 15px; line-height: 18px; background: rgb(38, 166, 22); }
+
+#yf-bt-wrapper.free .nav-se-container .nav-se-btn:hover { cursor: pointer; =
+background: rgb(3, 117, 193); }
+
+#yf-bt-wrapper.pro .nav-se-container .nav-se-btn:hover { background: rgb(33=
+, 136, 19); }
+
+#yf-bt-wrapper .nav-se-container .license-input-key { color: rgb(51, 51, 51=
+); font-size: 14px; line-height: 20px; width: 100%; padding: 10px 0px; marg=
+in: 10px 0px; text-align: center; border-radius: 6px; border: 1px solid rgb=
+(193, 216, 174); display: block; background-color: rgb(255, 255, 255); }
+
+#yf-bt-wrapper .link, #yf-bt-wrapper .nav-se-container .license-buy-link { =
+color: rgb(57, 143, 230); text-decoration: underline; cursor: pointer; }
+
+#yf-bt-wrapper .torrent-overlay { top: 0px; left: 0px; position: absolute; =
+width: 100%; height: 100%; z-index: 1; overflow: hidden; }
+
+#yf-bt-wrapper .blur-out { filter: blur(2px); display: none; }
+
+#yf-bt-wrapper .table-message-container { display: none; text-align: center=
+; width: 280px; margin: 120px auto 0px; }
+
+#yf-bt-wrapper .table-message-container p { line-height: 25px; }
+
+#yf-bt-wrapper .tooltip { display: none; position: absolute; top: 10px; lef=
+t: 120px; background-color: rgb(255, 255, 255); border-left: 7px solid rgb(=
+19, 153, 252); width: 180px; box-shadow: rgba(100, 100, 111, 0.2) 0px 7px 2=
+9px 0px; }
+
+#yf-bt-wrapper.pro .tooltip { border-left: 7px solid rgb(118, 184, 63); }
+
+#yf-bt-wrapper .tooltip-text { font-size: 11px; line-height: 17px; margin: =
+10px 15px; }
+
+#yf-bt-wrapper .tooltip-text::after { content: ""; position: absolute; top:=
+ -12px; margin-left: -15px; border-width: 6px; border-style: solid; border-=
+color: transparent transparent rgb(255, 255, 255); }
+
+#yf-bt-wrapper { color: rgb(17, 17, 17); font: unset; font-feature-settings=
+: unset; font-kerning: unset; font-optical-sizing: unset; font-palette: uns=
+et; font-synthesis: unset; font-variation-settings: unset; forced-color-adj=
+ust: unset; text-orientation: unset; text-rendering: unset; -webkit-font-sm=
+oothing: unset; -webkit-locale: unset; -webkit-text-orientation: unset; -we=
+bkit-writing-mode: unset; writing-mode: unset; zoom: unset; accent-color: u=
+nset; place-content: unset; place-items: unset; place-self: unset; alignmen=
+t-baseline: unset; animation: unset; app-region: unset; appearance: unset; =
+aspect-ratio: unset; backdrop-filter: unset; backface-visibility: unset; ba=
+ckground: unset; background-blend-mode: unset; baseline-shift: unset; block=
+-size: unset; border-block: unset; border: unset; border-radius: unset; bor=
+der-collapse: unset; border-end-end-radius: unset; border-end-start-radius:=
+ unset; border-inline: unset; border-start-end-radius: unset; border-start-=
+start-radius: unset; inset: unset; box-shadow: unset; box-sizing: unset; br=
+eak-after: unset; break-before: unset; break-inside: unset; buffered-render=
+ing: unset; caption-side: unset; caret-color: unset; clear: unset; clip: un=
+set; clip-path: unset; clip-rule: unset; color-interpolation: unset; color-=
+interpolation-filters: unset; color-rendering: unset; color-scheme: unset; =
+columns: unset; column-fill: unset; gap: unset; column-rule: unset; column-=
+span: unset; contain: unset; contain-intrinsic-block-size: unset; contain-i=
+ntrinsic-size: unset; contain-intrinsic-inline-size: unset; container: unse=
+t; content: unset; content-visibility: unset; counter-increment: unset; cou=
+nter-reset: unset; counter-set: unset; cursor: unset; cx: unset; cy: unset;=
+ d: unset; display: unset; dominant-baseline: unset; empty-cells: unset; fi=
+ll: unset; fill-opacity: unset; fill-rule: unset; filter: unset; flex: unse=
+t; flex-flow: unset; float: unset; flood-color: unset; flood-opacity: unset=
+; grid: unset; grid-area: unset; height: unset; hyphenate-character: unset;=
+ hyphenate-limit-chars: unset; hyphens: unset; image-orientation: unset; im=
+age-rendering: unset; initial-letter: unset; inline-size: unset; inset-bloc=
+k: unset; inset-inline: unset; isolation: unset; letter-spacing: unset; lig=
+hting-color: unset; line-break: unset; list-style: unset; margin-block: uns=
+et; margin: unset; margin-inline: unset; marker: unset; mask: unset; mask-t=
+ype: unset; math-depth: unset; math-shift: unset; math-style: unset; max-bl=
+ock-size: unset; max-height: unset; max-inline-size: unset; max-width: unse=
+t; min-block-size: unset; min-height: unset; min-inline-size: unset; min-wi=
+dth: unset; mix-blend-mode: unset; object-fit: unset; object-position: unse=
+t; object-view-box: unset; offset: unset; opacity: unset; order: unset; orp=
+hans: unset; outline: unset; outline-offset: unset; overflow-anchor: unset;=
+ overflow-clip-margin: unset; overflow-wrap: unset; overflow: unset; oversc=
+roll-behavior-block: unset; overscroll-behavior-inline: unset; overscroll-b=
+ehavior: unset; padding-block: unset; padding: unset; padding-inline: unset=
+; page: unset; page-orientation: unset; paint-order: unset; perspective: un=
+set; perspective-origin: unset; pointer-events: unset; position: unset; quo=
+tes: unset; r: unset; resize: unset; rotate: unset; ruby-position: unset; r=
+x: unset; ry: unset; scale: unset; scroll-behavior: unset; scroll-margin-bl=
+ock: unset; scroll-margin: unset; scroll-margin-inline: unset; scroll-paddi=
+ng-block: unset; scroll-padding: unset; scroll-padding-inline: unset; scrol=
+l-snap-align: unset; scroll-snap-stop: unset; scroll-snap-type: unset; scro=
+llbar-gutter: unset; shape-image-threshold: unset; shape-margin: unset; sha=
+pe-outside: unset; shape-rendering: unset; size: unset; speak: unset; stop-=
+color: unset; stop-opacity: unset; stroke: unset; stroke-dasharray: unset; =
+stroke-dashoffset: unset; stroke-linecap: unset; stroke-linejoin: unset; st=
+roke-miterlimit: unset; stroke-opacity: unset; stroke-width: unset; tab-siz=
+e: unset; table-layout: unset; text-align: unset; text-align-last: unset; t=
+ext-anchor: unset; text-combine-upright: unset; text-decoration: unset; tex=
+t-decoration-skip-ink: unset; text-emphasis: unset; text-emphasis-position:=
+ unset; text-indent: unset; text-overflow: unset; text-shadow: unset; text-=
+size-adjust: unset; text-transform: unset; text-underline-offset: unset; te=
+xt-underline-position: unset; touch-action: unset; transform: unset; transf=
+orm-box: unset; transform-origin: unset; transform-style: unset; transition=
+: unset; translate: unset; user-select: unset; vector-effect: unset; vertic=
+al-align: unset; visibility: unset; border-spacing: unset; -webkit-box-alig=
+n: unset; -webkit-box-decoration-break: unset; -webkit-box-direction: unset=
+; -webkit-box-flex: unset; -webkit-box-ordinal-group: unset; -webkit-box-or=
+ient: unset; -webkit-box-pack: unset; -webkit-box-reflect: unset; -webkit-h=
+ighlight: unset; -webkit-line-break: unset; -webkit-line-clamp: unset; -web=
+kit-mask-box-image: unset; -webkit-mask: unset; -webkit-mask-composite: uns=
+et; -webkit-print-color-adjust: unset; -webkit-rtl-ordering: unset; -webkit=
+-ruby-position: unset; -webkit-tap-highlight-color: unset; -webkit-text-com=
+bine: unset; -webkit-text-decorations-in-effect: unset; -webkit-text-fill-c=
+olor: unset; -webkit-text-security: unset; -webkit-text-stroke: unset; -web=
+kit-user-drag: unset; -webkit-user-modify: unset; white-space: unset; widow=
+s: unset; width: unset; will-change: unset; word-break: unset; word-spacing=
+: unset; x: unset; y: unset; z-index: unset; }
+
+#yf-bt-wrapper .upgradeProPanel { padding: 3px 2px; text-align: center; bac=
+kground: linear-gradient(0deg, rgb(2, 0, 36) 0px, rgb(233, 251, 255) 0px, r=
+gb(255, 255, 255) 100%); box-shadow: rgb(214, 214, 214) 0px 10px 10px -5px;=
+ }
+
+#yf-bt-wrapper .upgradeProPanelTitle { font-weight: 700; font-size: 18px; t=
+ext-align: center; margin: 13px 0px 12px; }
+
+#yf-bt-wrapper .upgradeProPanelList { text-align: left; }
+
+#yf-bt-wrapper .upgradeProPanelList div { padding: 2px 5px 2px 9px; display=
+: inline-block; width: 167px; position: relative; margin: 0px; vertical-ali=
+gn: text-top; }
+
+#yf-bt-wrapper .upgradeProPanelList div > p { font-size: 10px; font-weight:=
+ 700; position: relative; margin: 2px 1px 2px 0px; padding: 0px 0px 0px 20p=
+x; letter-spacing: 0px; line-height: 12px; }
+
+#yf-bt-wrapper .upgradeProPanelList div > p::before { content: ""; backgrou=
+nd-image: url("../img/assets/icon-check.svg"); width: 12px; height: 12px; b=
+ackground-position: center center; display: block; background-repeat: no-re=
+peat; background-size: contain; position: absolute; left: 4px; }
+
+#yf-bt-wrapper .proPanelBanner { width: 100%; background: rgb(255, 255, 255=
+); text-align: center; position: sticky; bottom: 0px; left: 0px; }
+
+#ys-bt-permission-banner { color: unset; font: unset; font-feature-settings=
+: unset; font-kerning: unset; font-optical-sizing: unset; font-palette: uns=
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diff --git a/My_Ustacky_Project/Executive Summary Repository -Template.pdf b/My_Ustacky_Project/Executive Summary Repository -Template.pdf
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index 0000000..9f936a1
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diff --git a/My_Ustacky_Project/F5_-_IMPLEMENTATION_GUIDELINES_Ako.pdf b/My_Ustacky_Project/F5_-_IMPLEMENTATION_GUIDELINES_Ako.pdf
new file mode 100644
index 0000000..1e50bef
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diff --git a/My_Ustacky_Project/Lagos_Branch.csv b/My_Ustacky_Project/Lagos_Branch.csv
new file mode 100644
index 0000000..e2e3017
--- /dev/null
+++ b/My_Ustacky_Project/Lagos_Branch.csv
@@ -0,0 +1,341 @@
+Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
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+765-26-6951,A,Lagos,Normal,Male,Sports and travel,26139.6,6,7841.88,164679.48,1/1/2019,10:39,Card,156837.6,4.7619047619999995,7841.88,6.9
+329-62-1586,A,Lagos,Normal,Male,Food and beverages,19681.2,3,2952.18,61995.78,1/21/2019,18:00,Card,59043.6,4.7619047619999995,2952.18,8.6
+636-48-8204,A,Lagos,Normal,Male,Electronic accessories,12441.6,5,3110.4,65318.4,2/17/2019,11:15,Epay,62208.00000000001,4.7619047619999995,3110.4,9.9
+549-59-1358,A,Lagos,Member,Male,Sports and travel,31906.8,3,4786.0199999999995,100506.42,3/2/2019,17:36,Epay,95720.4,4.7619047619999995,4786.0199999999995,6.0
+227-03-5010,A,Lagos,Member,Female,Home and lifestyle,18932.4,8,7572.959999999999,159032.16,3/22/2019,19:20,Card,151459.2,4.7619047619999995,7572.959999999999,8.5
+189-17-4241,A,Lagos,Normal,Female,Fashion accessories,31561.2,2,3156.1200000000003,66278.52,3/10/2019,12:17,Card,63122.4,4.7619047619999995,3156.1200000000003,7.7
+848-62-7243,A,Lagos,Normal,Male,Health and beauty,8960.4,9,4032.18,84675.78,3/15/2019,15:36,Cash,80643.59999999999,4.7619047619999995,4032.18,7.4
+595-11-5460,A,Lagos,Normal,Male,Health and beauty,34768.8,2,3476.8799999999997,73014.48,3/15/2019,10:12,Card,69537.6,4.7619047619999995,3476.8799999999997,5.1
+129-29-8530,A,Lagos,Member,Male,Sports and travel,22543.2,5,5635.8,118351.8,3/10/2019,19:15,Epay,112716.00000000001,4.7619047619999995,5635.8,7.0
+272-65-1806,A,Lagos,Normal,Female,Electronic accessories,21916.8,9,9862.56,207113.76,1/15/2019,17:17,Epay,197251.19999999998,4.7619047619999995,9862.56,4.7
+162-48-8011,A,Lagos,Member,Female,Food and beverages,16052.400000000001,5,4013.1000000000004,84275.1,2/10/2019,15:10,Cash,80262.0,4.7619047619999995,4013.1000000000004,8.5
+106-35-6779,A,Lagos,Member,Male,Home and lifestyle,15962.400000000001,2,1596.24,33521.03999999999,3/27/2019,11:26,Cash,31924.800000000003,4.7619047619999995,1596.24,5.8
+635-40-6220,A,Lagos,Normal,Male,Health and beauty,32255.999999999996,8,12902.400000000001,270950.4,2/7/2019,11:28,Epay,258047.99999999997,4.7619047619999995,12902.400000000001,6.6
+817-48-8732,A,Lagos,Member,Female,Home and lifestyle,26045.999999999996,10,13022.999999999998,273483.0,1/20/2019,15:55,Cash,260460.0,4.7619047619999995,13022.999999999998,5.4
+199-75-8169,A,Lagos,Member,Male,Sports and travel,5691.6,10,2845.8,59761.799999999996,3/6/2019,12:27,Card,56916.0,4.7619047619999995,2845.8,8.6
+877-22-3308,A,Lagos,Member,Male,Health and beauty,5713.2,10,2856.6,59988.6,3/13/2019,16:40,Cash,57131.99999999999,4.7619047619999995,2856.6,5.8
+232-11-3025,A,Lagos,Normal,Male,Sports and travel,28357.199999999997,10,14178.599999999999,297750.60000000003,1/24/2019,10:04,Cash,283572.0,4.7619047619999995,14178.599999999999,6.4
+382-03-4532,A,Lagos,Member,Female,Health and beauty,6598.799999999999,1,329.94,6928.740000000001,2/2/2019,18:50,Cash,6598.799999999999,4.7619047619999995,329.94,4.3
+287-21-9091,A,Lagos,Normal,Male,Home and lifestyle,26881.2,9,12096.54,254027.34,1/22/2019,10:55,Epay,241930.8,4.7619047619999995,12096.54,9.4
+381-20-0914,A,Lagos,Member,Female,Fashion accessories,7203.6,9,3241.62,68074.02,1/12/2019,15:48,Card,64832.4,4.7619047619999995,3241.62,5.7
+633-44-8566,A,Lagos,Member,Male,Food and beverages,17776.8,7,6221.88,130659.48000000001,3/27/2019,20:35,Card,124437.6,4.7619047619999995,6221.88,7.3
+504-35-8843,A,Lagos,Normal,Male,Sports and travel,15289.199999999999,1,764.46,16053.66,1/2/2019,16:57,Cash,15289.199999999999,4.7619047619999995,764.46,5.7
+873-51-0671,A,Lagos,Member,Female,Sports and travel,7912.8,7,2769.48,58159.08,1/10/2019,16:42,Epay,55389.600000000006,4.7619047619999995,2769.48,5.1
+594-34-4444,A,Lagos,Normal,Male,Electronic accessories,34977.6,1,1748.88,36726.479999999996,3/8/2019,20:38,Epay,34977.6,4.7619047619999995,1748.88,7.2
+865-92-6136,A,Lagos,Normal,Male,Food and beverages,18990.0,3,2848.5,59818.5,3/23/2019,10:16,Epay,56970.0,4.7619047619999995,2848.5,9.3
+212-62-1842,A,Lagos,Normal,Male,Food and beverages,20973.6,6,6292.079999999999,132133.68,3/28/2019,16:44,Cash,125841.6,4.7619047619999995,6292.079999999999,9.9
+704-48-3927,A,Lagos,Member,Male,Electronic accessories,31921.2,10,15960.6,335172.6,1/12/2019,14:50,Epay,319212.0,4.7619047619999995,15960.6,7.3
+630-74-5166,A,Lagos,Normal,Male,Sports and travel,22366.8,6,6710.04,140910.84000000003,3/22/2019,20:19,Cash,134200.8,4.7619047619999995,6710.04,7.4
+645-44-1170,A,Lagos,Member,Male,Home and lifestyle,20905.2,9,9407.34,197554.13999999998,1/19/2019,20:07,Epay,188146.8,4.7619047619999995,9407.34,4.3
+642-32-2990,A,Lagos,Normal,Female,Food and beverages,3945.6000000000004,10,1972.8000000000002,41428.8,2/2/2019,20:48,Epay,39456.0,4.7619047619999995,1972.8000000000002,6.0
+638-60-7125,A,Lagos,Normal,Female,Electronic accessories,35841.6,8,14336.64,301069.44,2/14/2019,17:03,Card,286732.8,4.7619047619999995,14336.64,5.2
+668-90-8900,A,Lagos,Normal,Female,Home and lifestyle,33728.4,7,11804.94,247903.74,3/10/2019,18:44,Card,236098.80000000002,4.7619047619999995,11804.94,4.5
+870-54-3162,A,Lagos,Normal,Female,Sports and travel,11610.0,5,2902.5,60952.5,1/27/2019,13:26,Cash,58050.0,4.7619047619999995,2902.5,9.0
+802-70-5316,A,Lagos,Member,Female,Sports and travel,33166.799999999996,6,9950.039999999999,208950.84,3/6/2019,20:34,Cash,199000.8,4.7619047619999995,9950.039999999999,8.3
+700-81-1757,A,Lagos,Normal,Female,Electronic accessories,9471.6,5,2367.9,49725.9,1/18/2019,20:59,Card,47358.00000000001,4.7619047619999995,2367.9,8.8
+354-39-5160,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.3599999999997,78064.56,2/18/2019,15:39,Cash,74347.2,4.7619047619999995,3717.3599999999997,9.8
+575-30-8091,A,Lagos,Normal,Male,Sports and travel,26100.0,8,10440.0,219240.0,3/16/2019,19:25,Epay,208800.0,4.7619047619999995,10440.0,9.2
+239-10-7476,A,Lagos,Normal,Female,Home and lifestyle,28062.0,6,8418.6,176790.6,1/21/2019,16:37,Epay,168372.0,4.7619047619999995,8418.6,8.0
+685-64-1609,A,Lagos,Member,Female,Fashion accessories,10850.4,10,5425.2,113929.20000000001,2/10/2019,12:28,Epay,108503.99999999999,4.7619047619999995,5425.2,9.2
+238-49-0436,A,Lagos,Normal,Male,Health and beauty,11685.6,8,4674.240000000001,98159.04,3/27/2019,13:48,Card,93484.8,4.7619047619999995,4674.240000000001,4.9
+746-94-0204,A,Lagos,Normal,Male,Fashion accessories,29966.399999999998,9,13484.88,283182.48000000004,1/29/2019,11:56,Card,269697.6,4.7619047619999995,13484.88,7.4
+782-95-9291,A,Lagos,Member,Male,Food and beverages,33224.4,5,8306.1,174428.1,2/20/2019,15:55,Card,166122.0,4.7619047619999995,8306.1,9.0
+275-28-0149,A,Lagos,Normal,Male,Sports and travel,22928.399999999998,1,1146.42,24074.82,2/25/2019,16:21,Cash,22928.399999999998,4.7619047619999995,1146.42,6.0
+101-17-6199,A,Lagos,Normal,Male,Food and beverages,16484.4,7,5769.539999999999,121160.34000000001,3/13/2019,19:44,Card,115390.79999999999,4.7619047619999995,5769.539999999999,7.0
+687-47-8271,A,Lagos,Normal,Male,Fashion accessories,35632.8,10,17816.4,374144.39999999997,2/8/2019,16:20,Card,356328.0,4.7619047619999995,17816.4,8.7
+796-32-9050,A,Lagos,Normal,Male,Food and beverages,18460.8,6,5538.24,116303.04000000001,1/19/2019,16:31,Cash,110764.8,4.7619047619999995,5538.24,6.5
+105-31-1824,A,Lagos,Member,Male,Sports and travel,25027.199999999997,7,8759.519999999999,183949.91999999998,2/1/2019,15:10,Card,175190.4,4.7619047619999995,8759.519999999999,8.5
+249-42-3782,A,Lagos,Normal,Male,Health and beauty,25203.600000000002,5,6300.900000000001,132318.9,1/3/2019,11:36,Epay,126018.0,4.7619047619999995,6300.900000000001,5.5
+827-26-2100,A,Lagos,Member,Male,Home and lifestyle,12182.400000000001,9,5482.08,115123.68000000001,3/21/2019,16:21,Epay,109641.6,4.7619047619999995,5482.08,8.8
+175-54-2529,A,Lagos,Member,Male,Food and beverages,7981.200000000001,8,3192.48,67042.07999999999,3/3/2019,17:01,Card,63849.600000000006,4.7619047619999995,3192.48,9.6
+407-63-8975,A,Lagos,Normal,Male,Food and beverages,26596.8,6,7979.040000000001,167559.84,3/23/2019,19:16,Epay,159580.8,4.7619047619999995,7979.040000000001,4.4
+851-28-6367,A,Lagos,Member,Male,Sports and travel,5580.0,10,2790.0,58590.0,3/23/2019,10:55,Epay,55800.0,4.7619047619999995,2790.0,8.0
+586-25-0848,A,Lagos,Normal,Female,Sports and travel,4442.4,7,1554.84,32651.64,3/4/2019,11:19,Card,31096.8,4.7619047619999995,1554.84,6.7
+400-60-7251,A,Lagos,Normal,Male,Home and lifestyle,26665.199999999997,1,1333.26,27998.46,2/10/2019,12:50,Epay,26665.199999999997,4.7619047619999995,1333.26,9.9
+831-07-6050,A,Lagos,Normal,Male,Electronic accessories,11775.6,5,2943.9,61821.899999999994,3/19/2019,11:30,Card,58878.00000000001,4.7619047619999995,2943.9,9.9
+856-22-8149,A,Lagos,Normal,Female,Home and lifestyle,9104.4,1,455.21999999999997,9559.62,3/23/2019,10:13,Epay,9104.4,4.7619047619999995,455.21999999999997,6.1
+749-24-1565,A,Lagos,Normal,Female,Health and beauty,8290.800000000001,9,3730.86,78348.06,1/3/2019,12:02,Epay,74617.2,4.7619047619999995,3730.86,7.9
+888-02-0338,A,Lagos,Normal,Male,Electronic accessories,9442.8,9,4249.26,89234.46,1/25/2019,20:24,Epay,84985.2,4.7619047619999995,4249.26,5.9
+802-43-8934,A,Lagos,Normal,Male,Home and lifestyle,6580.8,1,329.04,6909.840000000001,3/22/2019,15:05,Card,6580.8,4.7619047619999995,329.04,8.3
+319-74-2561,A,Lagos,Member,Female,Electronic accessories,34070.4,3,5110.5599999999995,107321.76,2/21/2019,16:55,Cash,102211.20000000001,4.7619047619999995,5110.5599999999995,5.5
+213-72-6612,A,Lagos,Normal,Male,Food and beverages,15570.0,2,1557.0,32697.0,3/20/2019,15:56,Cash,31140.0,4.7619047619999995,1557.0,6.2
+721-86-6247,A,Lagos,Normal,Female,Home and lifestyle,22831.2,8,9132.480000000001,191782.08000000002,3/11/2019,12:55,Epay,182649.6,4.7619047619999995,9132.480000000001,7.4
+157-13-5295,A,Lagos,Member,Male,Health and beauty,18698.399999999998,10,9349.199999999999,196333.2,3/9/2019,18:24,Epay,186984.0,4.7619047619999995,9349.199999999999,6.5
+645-78-8093,A,Lagos,Normal,Female,Sports and travel,33530.4,2,3353.04,70413.84000000001,1/20/2019,18:09,Epay,67060.8,4.7619047619999995,3353.04,4.1
+478-06-7835,A,Lagos,Normal,Male,Fashion accessories,32288.399999999998,1,1614.4199999999998,33902.82,1/11/2019,11:20,Epay,32288.399999999998,4.7619047619999995,1614.4199999999998,4.9
+540-11-4336,A,Lagos,Normal,Male,Food and beverages,8978.4,9,4040.2799999999997,84845.88,1/11/2019,16:49,Card,80805.6,4.7619047619999995,4040.2799999999997,5.6
+448-81-5016,A,Lagos,Normal,Male,Health and beauty,21517.2,2,2151.7200000000003,45186.12,3/11/2019,12:01,Card,43034.4,4.7619047619999995,2151.7200000000003,5.8
+217-58-1179,A,Lagos,Member,Male,Home and lifestyle,22554.0,4,4510.8,94726.8,1/5/2019,11:25,Cash,90216.0,4.7619047619999995,4510.8,4.2
+530-90-9855,A,Lagos,Member,Male,Home and lifestyle,17132.4,8,6852.959999999999,143912.16,1/1/2019,14:47,Cash,137059.2,4.7619047619999995,6852.959999999999,5.7
+604-70-6476,A,Lagos,Member,Male,Fashion accessories,6458.400000000001,5,1614.6000000000001,33906.6,1/23/2019,14:04,Epay,32292.0,4.7619047619999995,1614.6000000000001,6.8
+799-71-1548,A,Lagos,Member,Male,Electronic accessories,27979.2,4,5595.84,117512.64000000001,1/7/2019,16:11,Card,111916.8,4.7619047619999995,5595.84,8.8
+290-68-2984,A,Lagos,Normal,Male,Home and lifestyle,8550.0,4,1710.0,35910.0,3/16/2019,11:22,Cash,34200.0,4.7619047619999995,1710.0,5.2
+704-11-6354,A,Lagos,Member,Male,Home and lifestyle,21204.0,8,8481.6,178113.6,1/6/2019,11:23,Cash,169632.0,4.7619047619999995,8481.6,8.9
+366-93-0948,A,Lagos,Member,Male,Electronic accessories,23885.999999999996,1,1194.3,25080.300000000003,1/31/2019,10:46,Card,23885.999999999996,4.7619047619999995,1194.3,9.7
+729-09-9681,A,Lagos,Member,Male,Home and lifestyle,9327.6,6,2798.2799999999997,58763.880000000005,2/5/2019,10:16,Epay,55965.600000000006,4.7619047619999995,2798.2799999999997,8.7
+151-16-1484,A,Lagos,Member,Male,Electronic accessories,11610.0,4,2322.0,48761.99999999999,2/13/2019,12:38,Epay,46440.0,4.7619047619999995,2322.0,6.5
+850-41-9669,A,Lagos,Normal,Female,Electronic accessories,27021.600000000002,9,12159.720000000001,255354.12,3/19/2019,13:25,Epay,243194.4,4.7619047619999995,12159.720000000001,6.2
+447-15-7839,A,Lagos,Member,Female,Sports and travel,8006.4,10,4003.2,84067.2,2/9/2019,11:00,Cash,80064.0,4.7619047619999995,4003.2,4.2
+253-12-6086,A,Lagos,Member,Female,Sports and travel,35424.0,7,12398.4,260366.4,3/12/2019,12:43,Card,247967.99999999997,4.7619047619999995,12398.4,8.7
+144-51-6085,A,Lagos,Member,Male,Home and lifestyle,25466.399999999998,4,5093.28,106958.88,1/5/2019,16:05,Card,101865.59999999999,4.7619047619999995,5093.28,4.4
+731-14-2199,A,Lagos,Member,Female,Home and lifestyle,12794.4,10,6397.2,134341.2,1/4/2019,13:34,Epay,127943.99999999999,4.7619047619999995,6397.2,7.0
+126-54-1082,A,Lagos,Member,Female,Home and lifestyle,7754.4,9,3489.48,73279.08,1/7/2019,11:44,Card,69789.6,4.7619047619999995,3489.48,8.8
+633-91-1052,A,Lagos,Normal,Female,Home and lifestyle,4330.8,2,433.08000000000004,9094.68,1/27/2019,15:51,Cash,8661.6,4.7619047619999995,433.08000000000004,5.1
+828-61-5674,A,Lagos,Member,Male,Sports and travel,15847.2,10,7923.6,166395.6,3/20/2019,19:57,Card,158472.0,4.7619047619999995,7923.6,9.6
+136-08-6195,A,Lagos,Normal,Female,Home and lifestyle,25185.6,8,10074.24,211559.04,2/15/2019,17:01,Card,201484.8,4.7619047619999995,10074.24,6.4
+490-29-1201,A,Lagos,Normal,Female,Sports and travel,5522.4,1,276.12,5798.5199999999995,1/6/2019,11:09,Cash,5522.4,4.7619047619999995,276.12,6.5
+667-92-0055,A,Lagos,Member,Male,Health and beauty,35938.8,6,10781.640000000001,226414.44,3/4/2019,15:02,Epay,215632.80000000002,4.7619047619999995,10781.640000000001,8.5
+565-17-3836,A,Lagos,Member,Female,Health and beauty,17161.2,4,3432.24,72077.04000000001,3/12/2019,14:21,Cash,68644.8,4.7619047619999995,3432.24,9.1
+430-60-3493,A,Lagos,Member,Female,Home and lifestyle,34156.799999999996,7,11954.88,251052.48,2/3/2019,14:38,Cash,239097.59999999998,4.7619047619999995,11954.88,4.2
+278-97-7759,A,Lagos,Member,Female,Electronic accessories,22492.8,1,1124.6399999999999,23617.44,2/18/2019,20:29,Cash,22492.8,4.7619047619999995,1124.6399999999999,4.7
+316-68-6352,A,Lagos,Member,Female,Food and beverages,13089.6,2,1308.96,27488.160000000003,1/21/2019,10:00,Cash,26179.2,4.7619047619999995,1308.96,7.1
+744-02-5987,A,Lagos,Member,Male,Home and lifestyle,28216.8,6,8465.039999999999,177765.84000000003,1/10/2019,14:16,Epay,169300.8,4.7619047619999995,8465.039999999999,5.8
+307-83-9164,A,Lagos,Member,Male,Home and lifestyle,21603.6,4,4320.72,90735.12,1/25/2019,15:54,Cash,86414.4,4.7619047619999995,4320.72,4.5
+439-54-7422,A,Lagos,Normal,Female,Electronic accessories,18428.399999999998,4,3685.68,77399.28,3/18/2019,17:15,Card,73713.59999999999,4.7619047619999995,3685.68,4.7
+411-77-0180,A,Lagos,Member,Male,Electronic accessories,25992.0,7,9097.2,191041.19999999998,3/26/2019,20:14,Epay,181944.0,4.7619047619999995,9097.2,4.3
+286-01-5402,A,Lagos,Normal,Female,Sports and travel,14482.8,7,5068.9800000000005,106448.58,3/30/2019,13:22,Cash,101379.6,4.7619047619999995,5068.9800000000005,9.6
+803-17-8013,A,Lagos,Member,Female,Home and lifestyle,31964.4,8,12785.76,268500.96,2/17/2019,17:09,Cash,255715.2,4.7619047619999995,12785.76,4.1
+512-98-1403,A,Lagos,Member,Female,Electronic accessories,9532.8,3,1429.92,30028.320000000003,3/21/2019,10:40,Epay,28598.399999999998,4.7619047619999995,1429.92,4.7
+848-42-2560,A,Lagos,Normal,Female,Fashion accessories,29487.6,2,2948.76,61923.96,3/5/2019,17:43,Cash,58975.2,4.7619047619999995,2948.76,7.8
+870-76-1733,A,Lagos,Member,Female,Food and beverages,5122.8,5,1280.7,26894.699999999997,2/1/2019,10:08,Card,25614.000000000004,4.7619047619999995,1280.7,4.4
+423-64-4619,A,Lagos,Member,Female,Health and beauty,5598.0,9,2519.1,52901.1,3/7/2019,13:12,Cash,50381.99999999999,4.7619047619999995,2519.1,5.0
+372-94-8041,A,Lagos,Normal,Male,Health and beauty,5493.6,6,1648.0800000000002,34609.68,2/15/2019,18:03,Epay,32961.6,4.7619047619999995,1648.0800000000002,9.8
+563-91-7120,A,Lagos,Normal,Female,Fashion accessories,22237.2,5,5559.3,116745.3,3/8/2019,13:21,Cash,111186.00000000001,4.7619047619999995,5559.3,6.7
+746-54-5508,A,Lagos,Normal,Male,Home and lifestyle,7747.2,6,2324.1600000000003,48807.36,1/17/2019,12:48,Card,46483.200000000004,4.7619047619999995,2324.1600000000003,9.4
+815-11-1168,A,Lagos,Member,Male,Food and beverages,35920.8,5,8980.2,188584.2,3/9/2019,19:09,Cash,179604.0,4.7619047619999995,8980.2,5.4
+340-66-0321,A,Lagos,Member,Male,Electronic accessories,13089.6,4,2617.92,54976.32000000001,3/25/2019,13:07,Cash,52358.4,4.7619047619999995,2617.92,7.6
+634-97-8956,A,Lagos,Normal,Male,Food and beverages,11844.0,3,1776.6,37308.6,2/17/2019,17:27,Card,35532.0,4.7619047619999995,1776.6,9.1
+566-71-1091,A,Lagos,Normal,Male,Fashion accessories,27727.199999999997,5,6931.799999999999,145567.80000000002,2/3/2019,15:59,Cash,138636.0,4.7619047619999995,6931.799999999999,5.5
+442-48-3607,A,Lagos,Member,Male,Food and beverages,8452.8,2,845.2800000000001,17750.88,3/14/2019,11:21,Card,16905.6,4.7619047619999995,845.2800000000001,7.9
+527-09-6272,A,Lagos,Member,Female,Electronic accessories,10242.0,5,2560.5,53770.50000000001,3/21/2019,10:17,Card,51210.0,4.7619047619999995,2560.5,9.1
+898-04-2717,A,Lagos,Normal,Male,Fashion accessories,27504.000000000004,9,12376.800000000001,259912.80000000002,3/19/2019,15:49,Epay,247536.0,4.7619047619999995,12376.800000000001,7.5
+150-89-8043,A,Lagos,Normal,Male,Sports and travel,16074.0,3,2411.1,50633.100000000006,2/14/2019,15:04,Cash,48221.99999999999,4.7619047619999995,2411.1,6.2
+135-84-8019,A,Lagos,Normal,Female,Fashion accessories,28054.800000000003,9,12624.66,265117.86,2/27/2019,16:10,Epay,252493.2,4.7619047619999995,12624.66,7.6
+441-94-7118,A,Lagos,Member,Male,Electronic accessories,25902.0,1,1295.1000000000001,27197.1,2/4/2019,12:14,Cash,25902.0,4.7619047619999995,1295.1000000000001,7.3
+531-80-1784,A,Lagos,Normal,Male,Electronic accessories,9367.2,7,3278.5200000000004,68848.92,3/28/2019,17:38,Cash,65570.4,4.7619047619999995,3278.5200000000004,5.1
+834-61-8124,A,Lagos,Normal,Male,Electronic accessories,18608.399999999998,7,6512.94,136771.74,1/26/2019,18:22,Cash,130258.79999999999,4.7619047619999995,6512.94,5.5
+612-36-5536,A,Lagos,Member,Male,Food and beverages,29145.6,8,11658.24,244823.03999999998,2/17/2019,11:12,Card,233164.8,4.7619047619999995,11658.24,7.4
+462-67-9126,A,Lagos,Normal,Male,Home and lifestyle,26359.2,6,7907.759999999999,166062.96,1/21/2019,17:44,Cash,158155.2,4.7619047619999995,7907.759999999999,7.2
+468-88-0009,A,Lagos,Member,Male,Home and lifestyle,23738.399999999998,4,4747.679999999999,99701.28,3/24/2019,10:29,Cash,94953.59999999999,4.7619047619999995,4747.679999999999,6.0
+254-31-0042,A,Lagos,Member,Male,Electronic accessories,7740.0,9,3483.0000000000005,73143.0,3/6/2019,12:46,Card,69660.0,4.7619047619999995,3483.0000000000005,7.8
+422-29-8786,A,Lagos,Normal,Female,Home and lifestyle,24152.4,5,6038.1,126800.1,1/3/2019,16:47,Card,120762.0,4.7619047619999995,6038.1,9.1
+667-23-5919,A,Lagos,Member,Female,Fashion accessories,34812.0,5,8703.0,182763.0,1/14/2019,12:52,Epay,174060.0,4.7619047619999995,8703.0,7.0
+289-15-7034,A,Lagos,Member,Male,Sports and travel,29638.8,4,5927.759999999999,124482.96,1/11/2019,10:37,Card,118555.2,4.7619047619999995,5927.759999999999,7.5
+662-72-2873,A,Lagos,Normal,Female,Food and beverages,14738.4,5,3684.6,77376.6,1/6/2019,13:58,Epay,73692.0,4.7619047619999995,3684.6,9.9
+725-56-0833,A,Lagos,Normal,Female,Health and beauty,11635.2,10,5817.6,122169.6,2/20/2019,16:49,Card,116352.0,4.7619047619999995,5817.6,10.0
+563-36-9814,A,Lagos,Member,Male,Electronic accessories,27655.199999999997,1,1382.76,29037.96,2/13/2019,18:27,Epay,27655.199999999997,4.7619047619999995,1382.76,7.2
+308-47-4913,A,Lagos,Member,Female,Sports and travel,18813.6,10,9406.8,197542.80000000002,3/9/2019,12:45,Card,188136.0,4.7619047619999995,9406.8,6.2
+885-17-6250,A,Lagos,Normal,Female,Health and beauty,28706.399999999998,1,1435.32,30141.72,3/6/2019,10:36,Epay,28706.399999999998,4.7619047619999995,1435.32,7.3
+726-27-2396,A,Lagos,Normal,Female,Health and beauty,27900.0,5,6975.0,146475.0,1/24/2019,20:36,Epay,139500.0,4.7619047619999995,6975.0,4.3
+316-01-3952,A,Lagos,Normal,Female,Food and beverages,19537.2,5,4884.3,102570.3,3/13/2019,14:16,Epay,97686.00000000001,4.7619047619999995,4884.3,4.6
+882-40-4577,A,Lagos,Member,Male,Sports and travel,24213.600000000002,4,4842.72,101697.12000000001,1/19/2019,15:28,Card,96854.40000000001,4.7619047619999995,4842.72,8.0
+732-67-5346,A,Lagos,Normal,Male,Food and beverages,4964.4,5,1241.1,26063.1,1/11/2019,19:07,Card,24822.0,4.7619047619999995,1241.1,7.8
+256-08-8343,A,Lagos,Normal,Female,Home and lifestyle,20350.8,4,4070.16,85473.36,3/4/2019,19:48,Epay,81403.2,4.7619047619999995,4070.16,5.5
+132-23-6451,A,Lagos,Member,Male,Health and beauty,7549.2,5,1887.3,39633.3,1/4/2019,13:21,Cash,37746.0,4.7619047619999995,1887.3,7.8
+696-90-2548,A,Lagos,Normal,Male,Sports and travel,9302.4,3,1395.3600000000001,29302.56,3/10/2019,18:55,Epay,27907.199999999997,4.7619047619999995,1395.3600000000001,6.6
+472-15-9636,A,Lagos,Normal,Male,Home and lifestyle,18334.8,8,7333.92,154012.31999999998,3/22/2019,19:36,Epay,146678.4,4.7619047619999995,7333.92,9.2
+745-71-3520,A,Lagos,Member,Female,Electronic accessories,9079.199999999999,7,3177.72,66732.12000000001,2/4/2019,10:23,Cash,63554.399999999994,4.7619047619999995,3177.72,8.2
+269-10-8440,A,Lagos,Member,Male,Health and beauty,19141.2,7,6699.42,140687.82,1/21/2019,18:01,Cash,133988.4,4.7619047619999995,6699.42,8.9
+325-77-6186,A,Lagos,Member,Female,Home and lifestyle,32634.000000000004,10,16317.000000000002,342657.0,3/8/2019,10:53,Epay,326340.0,4.7619047619999995,16317.000000000002,7.3
+459-50-7686,A,Lagos,Normal,Female,Electronic accessories,8445.6,6,2533.68,53207.28,1/13/2019,19:14,Epay,50673.6,4.7619047619999995,2533.68,6.4
+345-08-4992,A,Lagos,Normal,Male,Home and lifestyle,12236.400000000001,6,3670.9200000000005,77089.32,3/8/2019,15:37,Card,73418.4,4.7619047619999995,3670.9200000000005,7.7
+138-17-5109,A,Lagos,Member,Female,Home and lifestyle,32115.6,9,14452.02,303492.42,1/15/2019,15:42,Card,289040.4,4.7619047619999995,14452.02,6.5
+301-11-9629,A,Lagos,Normal,Female,Sports and travel,6876.000000000001,7,2406.6,50538.6,1/15/2019,10:43,Cash,48131.99999999999,4.7619047619999995,2406.6,9.7
+727-17-0390,A,Lagos,Normal,Female,Food and beverages,22899.6,5,5724.9,120222.9,3/16/2019,12:43,Epay,114498.0,4.7619047619999995,5724.9,4.8
+568-88-3448,A,Lagos,Normal,Male,Health and beauty,9000.0,1,450.0,9450.0,3/3/2019,15:09,Epay,9000.0,4.7619047619999995,450.0,5.5
+187-83-5490,A,Lagos,Member,Male,Electronic accessories,7477.2,4,1495.44,31404.239999999998,1/31/2019,13:47,Cash,29908.8,4.7619047619999995,1495.44,4.7
+470-32-9057,A,Lagos,Member,Male,Food and beverages,18482.4,5,4620.6,97032.6,3/28/2019,15:31,Card,92412.0,4.7619047619999995,4620.6,9.1
+340-21-9136,A,Lagos,Member,Female,Sports and travel,14417.999999999998,4,2883.6,60555.600000000006,1/25/2019,11:40,Cash,57671.99999999999,4.7619047619999995,2883.6,9.7
+405-31-3305,A,Lagos,Member,Male,Fashion accessories,15526.800000000001,10,7763.400000000001,163031.4,2/2/2019,18:31,Card,155268.0,4.7619047619999995,7763.400000000001,5.5
+676-39-6028,A,Lagos,Member,Female,Electronic accessories,23198.399999999998,5,5799.599999999999,121791.6,3/30/2019,17:04,Cash,115992.0,4.7619047619999995,5799.599999999999,6.6
+502-05-1910,A,Lagos,Normal,Male,Health and beauty,23464.800000000003,3,3519.7200000000003,73914.12,2/25/2019,20:35,Card,70394.4,4.7619047619999995,3519.7200000000003,6.3
+485-30-8700,A,Lagos,Normal,Female,Sports and travel,11973.599999999999,5,2993.3999999999996,62861.4,3/18/2019,16:10,Card,59868.00000000001,4.7619047619999995,2993.3999999999996,4.2
+575-67-1508,A,Lagos,Normal,Male,Electronic accessories,13896.0,1,694.8,14590.800000000001,1/29/2019,11:26,Epay,13896.0,4.7619047619999995,694.8,6.7
+674-15-9296,A,Lagos,Normal,Male,Sports and travel,13370.4,5,3342.6,70194.6,1/8/2019,13:05,Epay,66852.0,4.7619047619999995,3342.6,5.0
+795-49-7276,A,Lagos,Normal,Male,Fashion accessories,13374.0,4,2674.7999999999997,56170.8,3/23/2019,18:59,Epay,53496.0,4.7619047619999995,2674.7999999999997,8.3
+510-09-5628,A,Lagos,Member,Female,Fashion accessories,7077.6,10,3538.8,74314.8,3/15/2019,18:20,Card,70776.0,4.7619047619999995,3538.8,7.2
+420-18-8989,A,Lagos,Member,Female,Sports and travel,18547.2,8,7418.88,155796.48,2/2/2019,15:47,Cash,148377.6,4.7619047619999995,7418.88,9.6
+726-29-6793,A,Lagos,Member,Male,Electronic accessories,8704.8,8,3481.92,73120.31999999999,1/28/2019,20:54,Epay,69638.4,4.7619047619999995,3481.92,9.8
+209-61-0206,A,Lagos,Normal,Female,Home and lifestyle,15447.599999999999,5,3861.8999999999996,81099.9,1/5/2019,17:29,Epay,77238.0,4.7619047619999995,3861.8999999999996,6.1
+595-27-4851,A,Lagos,Normal,Female,Fashion accessories,19540.8,7,6839.280000000001,143624.88,1/27/2019,18:05,Epay,136785.6,4.7619047619999995,6839.280000000001,9.3
+189-52-0236,A,Lagos,Normal,Male,Electronic accessories,35838.0,7,12543.300000000001,263409.3,3/14/2019,12:07,Cash,250866.0,4.7619047619999995,12543.300000000001,7.6
+220-28-1851,A,Lagos,Normal,Male,Home and lifestyle,12502.8,2,1250.2800000000002,26255.88,3/1/2019,18:14,Epay,25005.6,4.7619047619999995,1250.2800000000002,9.7
+609-81-8548,A,Lagos,Member,Female,Home and lifestyle,13478.4,6,4043.5200000000004,84913.92,2/6/2019,13:55,Card,80870.4,4.7619047619999995,4043.5200000000004,5.9
+534-01-4457,A,Lagos,Normal,Male,Food and beverages,29415.6,6,8824.68,185318.28,1/27/2019,14:36,Card,176493.6,4.7619047619999995,8824.68,8.0
+719-89-8991,A,Lagos,Member,Female,Sports and travel,32907.6,5,8226.9,172764.9,2/25/2019,16:03,Epay,164538.0,4.7619047619999995,8226.9,7.1
+827-77-7633,A,Lagos,Normal,Male,Sports and travel,35312.4,9,15890.580000000002,333702.18,2/17/2019,19:41,Cash,317811.6,4.7619047619999995,15890.580000000002,9.3
+287-83-1405,A,Lagos,Normal,Male,Health and beauty,9154.8,6,2746.44,57675.24,2/12/2019,19:01,Epay,54928.8,4.7619047619999995,2746.44,7.0
+435-13-4908,A,Lagos,Member,Male,Fashion accessories,31204.800000000003,8,12481.919999999998,262120.31999999998,1/24/2019,18:04,Card,249638.40000000002,4.7619047619999995,12481.919999999998,7.2
+892-05-6689,A,Lagos,Normal,Female,Home and lifestyle,10195.2,5,2548.8,53524.8,3/11/2019,13:28,Epay,50976.0,4.7619047619999995,2548.8,6.2
+643-38-7867,A,Lagos,Normal,Male,Home and lifestyle,35258.4,1,1762.92,37021.32,3/7/2019,11:44,Epay,35258.4,4.7619047619999995,1762.92,6.9
+308-81-0538,A,Lagos,Normal,Female,Fashion accessories,26298.0,4,5259.599999999999,110451.6,2/25/2019,17:16,Card,105192.0,4.7619047619999995,5259.599999999999,4.9
+460-35-4390,A,Lagos,Normal,Male,Home and lifestyle,11044.8,3,1656.72,34791.12,1/22/2019,11:00,Epay,33134.4,4.7619047619999995,1656.72,9.1
+647-50-1224,A,Lagos,Normal,Female,Fashion accessories,10591.2,10,5295.6,111207.6,1/12/2019,16:23,Epay,105912.0,4.7619047619999995,5295.6,8.9
+541-48-8554,A,Lagos,Normal,Male,Sports and travel,21942.0,9,9873.9,207351.9,1/7/2019,12:08,Card,197477.99999999997,4.7619047619999995,9873.9,6.0
+213-32-1216,A,Lagos,Normal,Female,Electronic accessories,23781.600000000002,6,7134.48,149824.08000000002,1/23/2019,10:28,Cash,142689.6,4.7619047619999995,7134.48,7.3
+134-75-2619,A,Lagos,Member,Male,Electronic accessories,6955.2,7,2434.32,51120.72,3/25/2019,18:51,Cash,48686.4,4.7619047619999995,2434.32,6.9
+712-39-0363,A,Lagos,Member,Male,Food and beverages,14997.599999999999,6,4499.28,94484.87999999999,1/2/2019,15:24,Epay,89985.6,4.7619047619999995,4499.28,5.6
+218-59-9410,A,Lagos,Member,Female,Home and lifestyle,26071.2,3,3910.68,82124.28,3/29/2019,16:54,Epay,78213.59999999999,4.7619047619999995,3910.68,8.2
+760-90-2357,A,Lagos,Member,Male,Electronic accessories,26823.600000000002,6,8047.080000000001,168988.68,3/20/2019,15:08,Epay,160941.6,4.7619047619999995,8047.080000000001,5.0
+698-98-5964,A,Lagos,Normal,Female,Food and beverages,29235.6,10,14617.8,306973.8,1/17/2019,13:01,Card,292356.0,4.7619047619999995,14617.8,6.3
+651-88-7328,A,Lagos,Normal,Female,Fashion accessories,23666.399999999998,9,10649.880000000001,223647.48,1/1/2019,13:55,Cash,212997.59999999998,4.7619047619999995,10649.880000000001,7.7
+239-48-4278,A,Lagos,Member,Male,Food and beverages,3646.8,7,1276.38,26803.98,3/10/2019,19:35,Epay,25527.6,4.7619047619999995,1276.38,8.3
+550-84-8664,A,Lagos,Normal,Male,Sports and travel,30927.6,5,7731.9,162369.9,3/22/2019,14:33,Card,154638.0,4.7619047619999995,7731.9,8.6
+797-88-0493,A,Lagos,Normal,Female,Health and beauty,23137.199999999997,4,4627.4400000000005,97176.24,3/26/2019,13:54,Cash,92548.79999999999,4.7619047619999995,4627.4400000000005,7.7
+443-82-0585,A,Lagos,Member,Female,Health and beauty,27964.800000000003,4,5592.96,117452.15999999999,2/1/2019,19:54,Cash,111859.20000000001,4.7619047619999995,5592.96,8.4
+127-47-6963,A,Lagos,Normal,Male,Health and beauty,18615.6,4,3723.1200000000003,78185.52,3/9/2019,13:53,Card,74462.4,4.7619047619999995,3723.1200000000003,9.8
+278-86-2735,A,Lagos,Normal,Female,Food and beverages,18842.4,3,2826.36,59353.55999999999,3/27/2019,14:03,Cash,56527.200000000004,4.7619047619999995,2826.36,9.2
+695-28-6250,A,Lagos,Normal,Female,Sports and travel,15501.6,5,3875.4,81383.4,2/4/2019,16:38,Epay,77508.0,4.7619047619999995,3875.4,7.7
+227-50-3718,A,Lagos,Normal,Male,Health and beauty,5263.2,5,1315.8,27631.8,3/4/2019,12:23,Cash,26315.999999999996,4.7619047619999995,1315.8,4.4
+560-49-6611,A,Lagos,Member,Female,Sports and travel,16408.8,1,820.4399999999999,17229.239999999998,2/7/2019,14:13,Cash,16408.8,4.7619047619999995,820.4399999999999,9.8
+880-35-0356,A,Lagos,Member,Female,Sports and travel,27072.0,3,4060.7999999999997,85276.8,2/5/2019,11:51,Epay,81216.0,4.7619047619999995,4060.7999999999997,4.8
+152-68-2907,A,Lagos,Normal,Male,Food and beverages,18792.0,3,2818.8,59194.8,2/15/2019,13:30,Card,56376.0,4.7619047619999995,2818.8,9.5
+334-64-2006,A,Lagos,Member,Female,Home and lifestyle,25315.199999999997,2,2531.52,53161.92,3/24/2019,14:22,Epay,50630.399999999994,4.7619047619999995,2531.52,9.6
+559-98-9873,A,Lagos,Member,Female,Fashion accessories,19314.0,7,6759.9,141957.9,2/10/2019,12:56,Epay,135198.0,4.7619047619999995,6759.9,5.2
+318-12-0304,A,Lagos,Normal,Male,Fashion accessories,11019.6,1,550.98,11570.580000000002,1/23/2019,12:20,Epay,11019.6,4.7619047619999995,550.98,5.2
+421-95-9805,A,Lagos,Normal,Female,Electronic accessories,10425.6,1,521.2800000000001,10946.880000000001,2/7/2019,10:18,Card,10425.6,4.7619047619999995,521.2800000000001,6.2
+443-59-0061,A,Lagos,Member,Male,Food and beverages,24282.0,10,12141.0,254961.0,2/3/2019,11:25,Epay,242820.0,4.7619047619999995,12141.0,4.2
+509-29-3912,A,Lagos,Member,Female,Sports and travel,13939.199999999999,9,6272.639999999999,131725.44,3/20/2019,12:24,Epay,125452.8,4.7619047619999995,6272.639999999999,4.2
+828-46-6863,A,Lagos,Member,Male,Food and beverages,35470.8,6,10641.24,223466.04,1/23/2019,11:22,Card,212824.8,4.7619047619999995,10641.24,4.0
+420-97-3340,A,Lagos,Normal,Female,Food and beverages,25804.800000000003,3,3870.7200000000003,81285.12,3/28/2019,15:30,Card,77414.4,4.7619047619999995,3870.7200000000003,9.2
+436-54-4512,A,Lagos,Member,Female,Food and beverages,32979.6,1,1648.98,34628.58,3/20/2019,19:44,Cash,32979.6,4.7619047619999995,1648.98,9.8
+816-57-2053,A,Lagos,Normal,Male,Sports and travel,21913.2,2,2191.32,46017.72,3/9/2019,12:37,Epay,43826.4,4.7619047619999995,2191.32,8.7
+856-66-2701,A,Lagos,Member,Male,Home and lifestyle,19188.0,3,2878.2,60442.200000000004,1/25/2019,14:19,Epay,57564.0,4.7619047619999995,2878.2,7.5
+308-39-1707,A,Lagos,Normal,Female,Fashion accessories,4352.4,1,217.62,4570.0199999999995,1/26/2019,18:19,Card,4352.4,4.7619047619999995,217.62,8.2
+149-61-1929,A,Lagos,Normal,Male,Sports and travel,23108.399999999998,10,11554.199999999999,242638.2,1/19/2019,14:08,Card,231084.0,4.7619047619999995,11554.199999999999,6.7
+655-07-2265,A,Lagos,Normal,Male,Electronic accessories,28191.600000000002,3,4228.74,88803.54000000001,3/5/2019,16:38,Epay,84574.8,4.7619047619999995,4228.74,5.4
+589-02-8023,A,Lagos,Member,Male,Food and beverages,30157.199999999997,2,3015.7200000000003,63330.12,1/15/2019,10:54,Card,60314.399999999994,4.7619047619999995,3015.7200000000003,7.0
+610-46-4100,A,Lagos,Normal,Male,Health and beauty,10422.0,7,3647.7000000000003,76601.7,3/3/2019,20:31,Card,72954.0,4.7619047619999995,3647.7000000000003,6.0
+706-36-6154,A,Lagos,Member,Male,Home and lifestyle,6969.599999999999,9,3136.3199999999997,65862.72,1/18/2019,18:43,Epay,62726.4,4.7619047619999995,3136.3199999999997,8.7
+742-04-5161,A,Lagos,Member,Male,Home and lifestyle,26200.8,10,13100.4,275108.4,2/3/2019,17:24,Cash,262007.99999999997,4.7619047619999995,13100.4,7.3
+169-52-4504,A,Lagos,Normal,Female,Electronic accessories,5648.4,3,847.26,17792.46,3/14/2019,14:13,Card,16945.2,4.7619047619999995,847.26,5.8
+562-12-5430,A,Lagos,Member,Female,Fashion accessories,31734.000000000004,3,4760.1,99962.1,1/18/2019,10:11,Epay,95202.0,4.7619047619999995,4760.1,7.9
+816-72-8853,A,Lagos,Member,Female,Sports and travel,10054.8,5,2513.7,52787.7,1/29/2019,15:48,Cash,50274.0,4.7619047619999995,2513.7,5.9
+491-38-3499,A,Lagos,Member,Male,Fashion accessories,19962.0,1,998.1,20960.1,2/26/2019,17:46,Card,19962.0,4.7619047619999995,998.1,4.9
+518-17-2983,A,Lagos,Normal,Female,Fashion accessories,17506.8,4,3501.36,73528.56,2/4/2019,15:44,Epay,70027.2,4.7619047619999995,3501.36,7.6
+588-47-8641,A,Lagos,Member,Male,Fashion accessories,20174.4,10,10087.2,211831.19999999998,1/14/2019,19:30,Epay,201744.0,4.7619047619999995,10087.2,4.4
+811-03-8790,A,Lagos,Normal,Female,Electronic accessories,16372.8,10,8186.4,171914.4,3/1/2019,10:22,Card,163728.0,4.7619047619999995,8186.4,4.8
+274-05-5470,A,Lagos,Member,Female,Food and beverages,26449.2,4,5289.84,111086.64,2/23/2019,18:30,Cash,105796.8,4.7619047619999995,5289.84,6.0
+130-67-4723,A,Lagos,Member,Male,Food and beverages,17460.0,6,5238.0,109998.0,1/11/2019,13:57,Epay,104760.0,4.7619047619999995,5238.0,9.4
+105-10-6182,A,Lagos,Member,Male,Fashion accessories,7732.8,2,773.2800000000001,16238.880000000001,2/27/2019,12:22,Epay,15465.6,4.7619047619999995,773.2800000000001,6.6
+648-83-1321,A,Lagos,Member,Male,Home and lifestyle,22881.600000000002,10,11440.800000000001,240256.8,1/16/2019,17:59,Cash,228816.0,4.7619047619999995,11440.800000000001,4.3
+305-03-2383,A,Lagos,Normal,Female,Food and beverages,24155.999999999996,3,3623.3999999999996,76091.40000000001,2/15/2019,10:36,Cash,72468.0,4.7619047619999995,3623.3999999999996,7.5
+689-05-1884,A,Lagos,Member,Male,Health and beauty,17506.8,10,8753.4,183821.4,3/4/2019,12:44,Cash,175068.0,4.7619047619999995,8753.4,8.8
+800-09-8606,A,Lagos,Member,Female,Home and lifestyle,31453.2,5,7863.3,165129.3,1/29/2019,19:45,Cash,157266.0,4.7619047619999995,7863.3,6.6
+182-52-7000,A,Lagos,Member,Female,Sports and travel,9734.4,4,1946.8799999999999,40884.479999999996,1/1/2019,20:26,Epay,38937.6,4.7619047619999995,1946.8799999999999,6.9
+868-06-0466,A,Lagos,Member,Male,Electronic accessories,25048.8,9,11271.96,236711.16000000003,2/19/2019,19:38,Card,225439.2,4.7619047619999995,11271.96,7.8
+445-30-9252,A,Lagos,Normal,Male,Sports and travel,9252.0,3,1387.8,29143.8,1/17/2019,17:59,Epay,27755.999999999996,4.7619047619999995,1387.8,6.1
+786-94-2700,A,Lagos,Member,Male,Food and beverages,29023.2,6,8706.96,182846.16,2/28/2019,20:18,Cash,174139.2,4.7619047619999995,8706.96,9.1
+258-92-7466,A,Lagos,Normal,Female,Health and beauty,12844.8,5,3211.2,67435.2,2/6/2019,18:33,Card,64224.0,4.7619047619999995,3211.2,6.6
+857-16-3520,A,Lagos,Member,Female,Fashion accessories,25725.6,7,9003.96,189083.16,3/28/2019,16:06,Epay,180079.2,4.7619047619999995,9003.96,4.5
+482-17-1179,A,Lagos,Member,Male,Electronic accessories,4298.4,3,644.76,13539.96,1/19/2019,12:47,Card,12895.2,4.7619047619999995,644.76,8.1
+788-21-5741,A,Lagos,Normal,Male,Fashion accessories,16336.800000000001,3,2450.52,51460.92,2/17/2019,13:34,Card,49010.399999999994,4.7619047619999995,2450.52,7.2
+247-11-2470,A,Lagos,Member,Female,Fashion accessories,8035.2,4,1607.0399999999997,33747.84,3/1/2019,16:23,Card,32140.8,4.7619047619999995,1607.0399999999997,4.4
+635-28-5728,A,Lagos,Normal,Male,Health and beauty,20160.0,3,3024.0,63504.0,2/28/2019,19:33,Epay,60480.0,4.7619047619999995,3024.0,4.8
+756-49-0168,A,Lagos,Member,Male,Fashion accessories,7092.0,1,354.6,7446.599999999999,2/8/2019,11:39,Epay,7092.0,4.7619047619999995,354.6,9.5
+805-86-0265,A,Lagos,Normal,Male,Home and lifestyle,33825.6,9,15221.519999999999,319651.92,3/20/2019,11:32,Cash,304430.4,4.7619047619999995,15221.519999999999,9.8
+373-14-0504,A,Lagos,Member,Female,Sports and travel,25786.8,2,2578.68,54152.28,2/12/2019,14:33,Epay,51573.6,4.7619047619999995,2578.68,8.8
+546-80-2899,A,Lagos,Member,Male,Home and lifestyle,13568.4,2,1356.84,28493.64,2/20/2019,15:29,Epay,27136.8,4.7619047619999995,1356.84,9.5
+585-86-8361,A,Lagos,Normal,Female,Food and beverages,9820.800000000001,5,2455.2000000000003,51559.2,2/3/2019,10:31,Card,49104.0,4.7619047619999995,2455.2000000000003,8.6
+807-14-7833,A,Lagos,Member,Female,Electronic accessories,6271.200000000001,10,3135.6000000000004,65847.6,2/22/2019,12:30,Epay,62711.99999999999,4.7619047619999995,3135.6000000000004,7.0
+652-43-6591,A,Lagos,Normal,Female,Fashion accessories,35024.4,8,14009.759999999998,294204.96,3/9/2019,13:18,Card,280195.2,4.7619047619999995,14009.759999999998,6.2
+406-46-7107,A,Lagos,Normal,Female,Home and lifestyle,34747.2,6,10424.16,218907.36000000002,1/11/2019,11:52,Cash,208483.2,4.7619047619999995,10424.16,4.5
+250-17-5703,A,Lagos,Member,Male,Food and beverages,6786.000000000001,10,3393.0000000000005,71253.0,2/27/2019,18:24,Epay,67860.0,4.7619047619999995,3393.0000000000005,5.6
+156-95-3964,A,Lagos,Normal,Female,Food and beverages,19940.4,4,3988.08,83749.68000000001,3/25/2019,15:19,Epay,79761.6,4.7619047619999995,3988.08,8.0
+410-67-1709,A,Lagos,Member,Female,Fashion accessories,22996.8,8,9198.72,193173.12,1/20/2019,17:48,Epay,183974.4,4.7619047619999995,9198.72,9.9
+587-73-4862,A,Lagos,Member,Female,Health and beauty,3848.3999999999996,5,962.0999999999999,20204.100000000002,3/26/2019,11:07,Epay,19242.0,4.7619047619999995,962.0999999999999,7.6
+787-87-2010,A,Lagos,Member,Male,Health and beauty,19980.0,4,3996.0,83916.0,1/20/2019,15:48,Card,79920.0,4.7619047619999995,3996.0,6.6
+886-54-6089,A,Lagos,Normal,Female,Home and lifestyle,4114.8,6,1234.4399999999998,25923.24,1/15/2019,17:24,Cash,24688.8,4.7619047619999995,1234.4399999999998,7.7
+534-53-3526,A,Lagos,Normal,Female,Sports and travel,34113.6,4,6822.72,143277.12,2/11/2019,16:06,Epay,136454.4,4.7619047619999995,6822.72,7.8
+307-04-2070,A,Lagos,Member,Female,Fashion accessories,11023.2,1,551.16,11574.359999999999,2/5/2019,14:14,Card,11023.2,4.7619047619999995,551.16,4.1
+404-91-5964,A,Lagos,Normal,Male,Electronic accessories,26848.8,7,9397.08,197338.68,2/4/2019,16:09,Card,187941.59999999998,4.7619047619999995,9397.08,9.0
+497-37-6538,A,Lagos,Normal,Male,Sports and travel,21207.6,7,7422.660000000001,155875.86,1/17/2019,15:15,Epay,148453.2,4.7619047619999995,7422.660000000001,9.7
+651-96-5970,A,Lagos,Normal,Male,Fashion accessories,16707.6,1,835.38,17542.98,3/3/2019,20:06,Card,16707.6,4.7619047619999995,835.38,4.0
+263-12-5321,A,Lagos,Member,Male,Electronic accessories,33336.0,7,11667.599999999999,245019.6,2/27/2019,12:52,Card,233352.00000000003,4.7619047619999995,11667.599999999999,9.3
+702-72-0487,A,Lagos,Normal,Female,Electronic accessories,16779.6,2,1677.9600000000003,35237.16,2/26/2019,12:28,Card,33559.2,4.7619047619999995,1677.9600000000003,6.6
+864-24-7918,A,Lagos,Member,Female,Sports and travel,8816.4,10,4408.2,92572.2,2/22/2019,15:15,Cash,88164.0,4.7619047619999995,4408.2,8.1
+759-29-9521,A,Lagos,Member,Female,Fashion accessories,17625.6,9,7931.519999999999,166561.91999999998,3/4/2019,11:27,Cash,158630.4,4.7619047619999995,7931.519999999999,8.0
+220-68-6701,A,Lagos,Normal,Female,Home and lifestyle,27889.2,4,5577.84,117134.64000000001,3/17/2019,16:36,Cash,111556.8,4.7619047619999995,5577.84,4.2
+618-34-8551,A,Lagos,Normal,Female,Sports and travel,33544.8,2,3354.48,70444.08,1/16/2019,18:41,Card,67089.6,4.7619047619999995,3354.48,8.5
+257-60-7754,A,Lagos,Normal,Female,Electronic accessories,18082.8,4,3616.56,75947.76,1/8/2019,17:12,Cash,72331.2,4.7619047619999995,3616.56,9.0
+380-60-5336,A,Lagos,Normal,Female,Electronic accessories,14493.599999999999,10,7246.799999999999,152182.80000000002,2/24/2019,18:06,Card,144936.0,4.7619047619999995,7246.799999999999,5.0
+674-56-6360,A,Lagos,Normal,Male,Electronic accessories,34254.0,1,1712.7,35966.7,3/22/2019,14:00,Cash,34254.0,4.7619047619999995,1712.7,6.0
+778-34-2523,A,Lagos,Member,Female,Electronic accessories,17503.2,8,7001.28,147026.88,1/24/2019,10:57,Cash,140025.6,4.7619047619999995,7001.28,5.0
+832-51-6761,A,Lagos,Normal,Male,Food and beverages,12196.800000000001,8,4878.72,102453.12,1/19/2019,20:29,Epay,97574.40000000001,4.7619047619999995,4878.72,9.6
+186-43-8965,A,Lagos,Member,Female,Home and lifestyle,17164.8,2,1716.48,36046.079999999994,2/24/2019,10:10,Card,34329.6,4.7619047619999995,1716.48,4.1
+276-75-6884,A,Lagos,Normal,Female,Health and beauty,24735.6,3,3710.34,77917.14,3/4/2019,10:05,Cash,74206.8,4.7619047619999995,3710.34,8.7
+569-76-2760,A,Lagos,Member,Female,Sports and travel,7923.6,4,1584.72,33279.12,1/29/2019,18:15,Card,31694.4,4.7619047619999995,1584.72,6.6
+760-53-9233,A,Lagos,Member,Male,Fashion accessories,14860.800000000001,3,2229.12,46811.520000000004,3/26/2019,18:37,Card,44582.4,4.7619047619999995,2229.12,8.5
+416-17-9926,A,Lagos,Member,Female,Electronic accessories,26719.2,10,13359.6,280551.6,1/1/2019,14:42,Card,267192.0,4.7619047619999995,13359.6,4.3
+237-44-6163,A,Lagos,Normal,Male,Electronic accessories,3801.6000000000004,8,1520.64,31933.44,1/24/2019,17:43,Cash,30412.800000000003,4.7619047619999995,1520.64,7.6
+528-14-9470,A,Lagos,Member,Male,Health and beauty,32868.0,1,1643.4,34511.4,2/14/2019,14:42,Epay,32868.0,4.7619047619999995,1643.4,9.2
+807-34-3742,A,Lagos,Normal,Male,Fashion accessories,18856.8,1,942.8399999999999,19799.64,3/26/2019,19:44,Cash,18856.8,4.7619047619999995,942.8399999999999,5.8
+288-62-1085,A,Lagos,Member,Male,Fashion accessories,13874.4,5,3468.6,72840.6,1/9/2019,13:34,Epay,69372.0,4.7619047619999995,3468.6,5.6
+497-36-0989,A,Lagos,Normal,Male,Fashion accessories,18698.399999999998,3,2804.76,58899.96,2/15/2019,15:21,Cash,56095.2,4.7619047619999995,2804.76,7.9
+860-73-6466,A,Lagos,Member,Female,Sports and travel,14209.199999999999,2,1420.92,29839.32,3/2/2019,16:16,Card,28418.399999999998,4.7619047619999995,1420.92,5.0
+896-34-0956,A,Lagos,Normal,Male,Fashion accessories,7675.2,1,383.75999999999993,8058.96,1/26/2019,12:43,Cash,7675.2,4.7619047619999995,383.75999999999993,5.9
+804-38-3935,A,Lagos,Member,Male,Electronic accessories,33760.8,3,5064.12,106346.51999999999,1/30/2019,11:32,Card,101282.4,4.7619047619999995,5064.12,5.9
+585-90-0249,A,Lagos,Member,Male,Electronic accessories,26373.600000000002,1,1318.68,27692.28,1/27/2019,18:08,Epay,26373.600000000002,4.7619047619999995,1318.68,9.7
+125-45-2293,A,Lagos,Normal,Female,Fashion accessories,35676.0,6,10702.8,224758.80000000002,1/19/2019,13:11,Cash,214056.0,4.7619047619999995,10702.8,4.2
+843-73-4724,A,Lagos,Normal,Male,Fashion accessories,26675.999999999996,1,1333.8,28009.800000000003,1/25/2019,11:05,Cash,26675.999999999996,4.7619047619999995,1333.8,9.2
+409-33-9708,A,Lagos,Normal,Female,Fashion accessories,35452.8,2,3545.2799999999997,74450.88,2/19/2019,10:12,Epay,70905.6,4.7619047619999995,3545.2799999999997,9.2
+160-22-2687,A,Lagos,Member,Female,Health and beauty,34542.0,5,8635.5,181345.5,1/23/2019,14:21,Epay,172710.0,4.7619047619999995,8635.5,8.8
+748-45-2862,A,Lagos,Member,Female,Home and lifestyle,10191.6,4,2038.3200000000004,42804.72,3/7/2019,18:35,Cash,40766.4,4.7619047619999995,2038.3200000000004,8.2
+316-66-3011,A,Lagos,Member,Female,Food and beverages,17146.8,9,7716.0599999999995,162037.26,1/23/2019,12:35,Cash,154321.2,4.7619047619999995,7716.0599999999995,5.0
+840-76-5966,A,Lagos,Member,Male,Sports and travel,4593.6,2,459.36,9646.56,1/8/2019,18:06,Epay,9187.2,4.7619047619999995,459.36,7.8
+124-31-1458,A,Lagos,Member,Female,Electronic accessories,28652.4,3,4297.86,90255.06,1/8/2019,14:30,Cash,85957.2,4.7619047619999995,4297.86,6.6
+852-82-2749,A,Lagos,Normal,Male,Sports and travel,23252.4,4,4650.48,97660.08,1/6/2019,13:35,Epay,93009.6,4.7619047619999995,4650.48,9.3
+873-14-6353,A,Lagos,Member,Male,Food and beverages,8935.2,7,3127.3200000000006,65673.72,2/16/2019,10:33,Card,62546.4,4.7619047619999995,3127.3200000000006,7.1
+166-19-2553,A,Lagos,Member,Male,Sports and travel,32061.600000000002,6,9618.480000000001,201988.08,1/18/2019,17:26,Cash,192369.6,4.7619047619999995,9618.480000000001,9.9
+737-88-5876,A,Lagos,Member,Male,Home and lifestyle,8384.4,4,1676.88,35214.479999999996,3/19/2019,11:52,Card,33537.6,4.7619047619999995,1676.88,5.9
+448-61-3783,A,Lagos,Normal,Female,Electronic accessories,32407.199999999997,8,12962.880000000001,272220.48,3/21/2019,16:08,Card,259257.59999999998,4.7619047619999995,12962.880000000001,4.5
+291-55-6563,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.3599999999997,78064.56,3/30/2019,12:45,Epay,74347.2,4.7619047619999995,3717.3599999999997,7.5
+548-48-3156,A,Lagos,Member,Female,Food and beverages,30002.4,2,3000.24,63005.04,3/19/2019,13:37,Cash,60004.8,4.7619047619999995,3000.24,7.6
+460-93-5834,A,Lagos,Normal,Male,Sports and travel,16408.8,7,5743.08,120604.68000000001,1/13/2019,10:03,Cash,114861.6,4.7619047619999995,5743.08,5.0
+325-89-4209,A,Lagos,Member,Male,Food and beverages,31644.000000000004,1,1582.1999999999998,33226.2,2/5/2019,19:42,Epay,31644.000000000004,4.7619047619999995,1582.1999999999998,6.7
+884-80-6021,A,Lagos,Member,Female,Electronic accessories,26449.2,10,13224.6,277716.6,3/23/2019,13:14,Epay,264492.0,4.7619047619999995,13224.6,9.5
+880-46-5796,A,Lagos,Member,Male,Sports and travel,27691.2,10,13845.6,290757.6,3/17/2019,19:53,Epay,276912.0,4.7619047619999995,13845.6,5.6
+146-09-5432,A,Lagos,Member,Male,Food and beverages,12614.4,9,5676.48,119206.08000000002,2/9/2019,19:17,Epay,113529.6,4.7619047619999995,5676.48,4.6
+595-94-9924,A,Lagos,Member,Female,Health and beauty,9982.8,5,2495.7,52409.700000000004,3/26/2019,20:21,Card,49914.0,4.7619047619999995,2495.7,4.2
+865-41-9075,A,Lagos,Normal,Male,Food and beverages,4150.8,7,1452.78,30508.38,1/28/2019,17:35,Cash,29055.6,4.7619047619999995,1452.78,8.1
+186-71-5196,A,Lagos,Member,Female,Food and beverages,28634.4,2,2863.44,60132.240000000005,3/27/2019,16:30,Epay,57268.8,4.7619047619999995,2863.44,6.2
+453-33-6436,A,Lagos,Normal,Female,Home and lifestyle,33523.200000000004,8,13409.280000000002,281594.88,2/7/2019,10:09,Cash,268185.60000000003,4.7619047619999995,13409.280000000002,6.8
+522-57-8364,A,Lagos,Member,Male,Fashion accessories,18482.4,8,7392.959999999999,155252.16,1/31/2019,10:00,Epay,147859.2,4.7619047619999995,7392.959999999999,7.6
+459-45-2396,A,Lagos,Member,Female,Food and beverages,35856.0,3,5378.4,112946.40000000001,2/25/2019,18:45,Cash,107568.0,4.7619047619999995,5378.4,5.8
+749-81-8133,A,Lagos,Normal,Female,Fashion accessories,34081.2,4,6816.240000000001,143141.04,3/11/2019,12:04,Cash,136324.8,4.7619047619999995,6816.240000000001,6.8
+397-25-8725,A,Lagos,Member,Female,Health and beauty,14263.199999999999,9,6418.4400000000005,134787.24,1/13/2019,17:54,Card,128368.79999999999,4.7619047619999995,6418.4400000000005,6.8
+243-55-8457,A,Lagos,Normal,Female,Food and beverages,26798.399999999998,10,13399.199999999999,281383.2,2/27/2019,11:40,Epay,267984.0,4.7619047619999995,13399.199999999999,5.1
+361-85-2571,A,Lagos,Normal,Female,Sports and travel,32212.800000000003,5,8053.200000000001,169117.19999999998,3/30/2019,10:18,Cash,161064.0,4.7619047619999995,8053.200000000001,7.4
+131-70-8179,A,Lagos,Member,Female,Health and beauty,33152.4,3,4972.86,104430.06000000001,2/17/2019,16:27,Cash,99457.2,4.7619047619999995,4972.86,4.2
+720-72-2436,A,Lagos,Normal,Male,Food and beverages,23947.199999999997,4,4789.4400000000005,100578.24,3/2/2019,18:14,Epay,95788.79999999999,4.7619047619999995,4789.4400000000005,6.9
+809-69-9497,A,Lagos,Normal,Female,Home and lifestyle,16444.8,10,8222.4,172670.4,1/19/2019,19:30,Epay,164448.0,4.7619047619999995,8222.4,5.7
+449-16-6770,A,Lagos,Normal,Male,Health and beauty,18284.4,5,4571.1,95993.09999999999,2/19/2019,14:53,Card,91422.0,4.7619047619999995,4571.1,5.3
+333-23-2632,A,Lagos,Member,Male,Health and beauty,3628.8,7,1270.08,26671.680000000004,3/28/2019,20:14,Cash,25401.600000000002,4.7619047619999995,1270.08,4.2
+489-82-1237,A,Lagos,Normal,Female,Electronic accessories,33796.799999999996,7,11828.880000000001,248406.48,1/5/2019,11:51,Card,236577.59999999998,4.7619047619999995,11828.880000000001,7.3
+846-10-0341,A,Lagos,Normal,Female,Fashion accessories,15325.2,7,5363.82,112640.22,1/6/2019,11:51,Cash,107276.40000000001,4.7619047619999995,5363.82,6.8
+384-59-6655,A,Lagos,Member,Female,Food and beverages,35517.6,9,15982.92,335641.32,2/19/2019,15:07,Cash,319658.4,4.7619047619999995,15982.92,8.4
+324-92-3863,A,Lagos,Member,Male,Electronic accessories,7520.400000000001,2,752.04,15792.84,2/5/2019,18:45,Cash,15040.800000000001,4.7619047619999995,752.04,9.8
+593-08-5916,A,Lagos,Normal,Female,Fashion accessories,5580.0,1,279.0,5858.999999999999,3/19/2019,15:23,Card,5580.0,4.7619047619999995,279.0,7.4
+558-60-5016,A,Lagos,Normal,Female,Home and lifestyle,11987.999999999998,9,5394.599999999999,113286.6,3/4/2019,15:27,Epay,107892.0,4.7619047619999995,5394.599999999999,7.2
+195-06-0432,A,Lagos,Member,Male,Home and lifestyle,29163.600000000002,3,4374.54,91865.34,1/13/2019,12:55,Card,87490.8,4.7619047619999995,4374.54,9.3
+605-03-2706,A,Lagos,Normal,Female,Health and beauty,5688.0,3,853.2,17917.2,3/25/2019,18:02,Cash,17064.0,4.7619047619999995,853.2,9.5
+531-56-4728,A,Lagos,Normal,Male,Home and lifestyle,28828.8,3,4324.32,90810.72,2/11/2019,15:29,Cash,86486.40000000001,4.7619047619999995,4324.32,5.4
+221-25-5073,A,Lagos,Normal,Female,Food and beverages,26877.6,4,5375.52,112885.92,3/4/2019,10:39,Cash,107510.4,4.7619047619999995,5375.52,8.5
+809-46-1866,A,Lagos,Normal,Male,Health and beauty,20934.0,4,4186.8,87922.8,1/23/2019,17:44,Cash,83736.0,4.7619047619999995,4186.8,8.4
+139-32-4183,A,Lagos,Member,Female,Sports and travel,35092.8,9,15791.760000000002,331626.96,3/14/2019,14:19,Epay,315835.2,4.7619047619999995,15791.760000000002,7.4
+886-18-2897,A,Lagos,Normal,Female,Food and beverages,20361.600000000002,5,5090.400000000001,106898.4,3/22/2019,19:06,Card,101808.0,4.7619047619999995,5090.400000000001,4.5
+745-74-0715,A,Lagos,Normal,Male,Electronic accessories,20890.8,2,2089.08,43870.68,3/10/2019,20:46,Epay,41781.6,4.7619047619999995,2089.08,8.8
+727-02-1313,A,Lagos,Member,Male,Food and beverages,11462.4,1,573.12,12035.519999999999,2/9/2019,13:22,Cash,11462.4,4.7619047619999995,573.12,7.7
+347-56-2442,A,Lagos,Normal,Male,Home and lifestyle,23695.199999999997,1,1184.7600000000002,24879.960000000003,2/22/2019,15:33,Cash,23695.199999999997,4.7619047619999995,1184.7600000000002,4.1
+849-09-3807,A,Lagos,Member,Female,Fashion accessories,31802.4,7,11130.84,233747.63999999998,2/18/2019,13:28,Cash,222616.8,4.7619047619999995,11130.84,6.6
diff --git a/My_Ustacky_Project/Market_data.csv b/My_Ustacky_Project/Market_data.csv
new file mode 100644
index 0000000..5ee47ef
--- /dev/null
+++ b/My_Ustacky_Project/Market_data.csv
@@ -0,0 +1,1001 @@
+Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
+750-67-8428,A,Lagos,Member,Female,Health and beauty,26888.4,7,9410.94,197629.74,1/5/2019,13:08,Epay,188218.8,4.761904762,9410.94,9.1
+631-41-3108,A,Lagos,Normal,Male,Home and lifestyle,16678.8,7,5837.58,122589.18,3/3/2019,13:23,Card,116751.6,4.761904762,5837.58,7.4
+123-19-1176,A,Lagos,Member,Male,Health and beauty,20959.2,8,8383.68,176057.28,1/27/2019,20:33,Epay,167673.6,4.761904762,8383.68,8.4
+373-73-7910,A,Lagos,Normal,Male,Sports and travel,31071.6,7,10875.06,228376.26,2/8/2019,10:37,Epay,217501.2,4.761904762,10875.06,5.3
+355-53-5943,A,Lagos,Member,Female,Electronic accessories,24782.4,6,7434.719999999999,156129.12,2/25/2019,14:36,Epay,148694.4,4.761904762,7434.719999999999,5.8
+665-32-9167,A,Lagos,Member,Female,Health and beauty,13053.6,2,1305.36,27412.56,1/10/2019,17:15,Card,26107.2,4.761904762,1305.36,7.2
+365-64-0515,A,Lagos,Normal,Female,Electronic accessories,16902.0,5,4225.5,88735.5,2/12/2019,10:25,Epay,84510.0,4.761904762,4225.5,7.1
+252-56-2699,A,Lagos,Normal,Male,Food and beverages,15548.4,10,7774.2,163258.2,2/7/2019,16:48,Epay,155484.0,4.761904762,7774.2,8.2
+829-34-3910,A,Lagos,Normal,Female,Health and beauty,25696.8,10,12848.4,269816.4,3/29/2019,19:21,Cash,256968.0,4.761904762,12848.4,5.7
+656-95-9349,A,Lagos,Member,Female,Health and beauty,24814.800000000003,7,8685.18,182388.78,3/11/2019,11:03,Card,173703.6,4.761904762,8685.18,4.6
+765-26-6951,A,Lagos,Normal,Male,Sports and travel,26139.6,6,7841.88,164679.48,1/1/2019,10:39,Card,156837.6,4.761904762,7841.88,6.9
+329-62-1586,A,Lagos,Normal,Male,Food and beverages,19681.2,3,2952.18,61995.78,1/21/2019,18:00,Card,59043.6,4.761904762,2952.18,8.6
+636-48-8204,A,Lagos,Normal,Male,Electronic accessories,12441.6,5,3110.4,65318.4,2/17/2019,11:15,Epay,62208.00000000001,4.761904762,3110.4,9.9
+549-59-1358,A,Lagos,Member,Male,Sports and travel,31906.8,3,4786.02,100506.42,3/2/2019,17:36,Epay,95720.4,4.761904762,4786.02,6.0
+227-03-5010,A,Lagos,Member,Female,Home and lifestyle,18932.4,8,7572.959999999999,159032.16,3/22/2019,19:20,Card,151459.2,4.761904762,7572.959999999999,8.5
+189-17-4241,A,Lagos,Normal,Female,Fashion accessories,31561.2,2,3156.1200000000003,66278.52,3/10/2019,12:17,Card,63122.4,4.761904762,3156.1200000000003,7.7
+848-62-7243,A,Lagos,Normal,Male,Health and beauty,8960.4,9,4032.18,84675.78,3/15/2019,15:36,Cash,80643.59999999999,4.761904762,4032.18,7.4
+595-11-5460,A,Lagos,Normal,Male,Health and beauty,34768.8,2,3476.88,73014.48,3/15/2019,10:12,Card,69537.6,4.761904762,3476.88,5.1
+129-29-8530,A,Lagos,Member,Male,Sports and travel,22543.2,5,5635.8,118351.8,3/10/2019,19:15,Epay,112716.0,4.761904762,5635.8,7.0
+272-65-1806,A,Lagos,Normal,Female,Electronic accessories,21916.8,9,9862.56,207113.76,1/15/2019,17:17,Epay,197251.2,4.761904762,9862.56,4.7
+162-48-8011,A,Lagos,Member,Female,Food and beverages,16052.4,5,4013.1,84275.1,2/10/2019,15:10,Cash,80262.0,4.761904762,4013.1,8.5
+106-35-6779,A,Lagos,Member,Male,Home and lifestyle,15962.4,2,1596.24,33521.03999999999,3/27/2019,11:26,Cash,31924.800000000003,4.761904762,1596.24,5.8
+635-40-6220,A,Lagos,Normal,Male,Health and beauty,32256.0,8,12902.4,270950.4,2/7/2019,11:28,Epay,258048.0,4.761904762,12902.4,6.6
+817-48-8732,A,Lagos,Member,Female,Home and lifestyle,26046.0,10,13022.999999999998,273483.0,1/20/2019,15:55,Cash,260460.0,4.761904762,13022.999999999998,5.4
+199-75-8169,A,Lagos,Member,Male,Sports and travel,5691.6,10,2845.8,59761.8,3/6/2019,12:27,Card,56916.0,4.761904762,2845.8,8.6
+877-22-3308,A,Lagos,Member,Male,Health and beauty,5713.2,10,2856.6,59988.6,3/13/2019,16:40,Cash,57131.99999999999,4.761904762,2856.6,5.8
+232-11-3025,A,Lagos,Normal,Male,Sports and travel,28357.2,10,14178.6,297750.60000000003,1/24/2019,10:04,Cash,283572.0,4.761904762,14178.6,6.4
+382-03-4532,A,Lagos,Member,Female,Health and beauty,6598.799999999999,1,329.94,6928.740000000001,2/2/2019,18:50,Cash,6598.799999999999,4.761904762,329.94,4.3
+287-21-9091,A,Lagos,Normal,Male,Home and lifestyle,26881.2,9,12096.54,254027.34,1/22/2019,10:55,Epay,241930.8,4.761904762,12096.54,9.4
+381-20-0914,A,Lagos,Member,Female,Fashion accessories,7203.6,9,3241.62,68074.02,1/12/2019,15:48,Card,64832.4,4.761904762,3241.62,5.7
+633-44-8566,A,Lagos,Member,Male,Food and beverages,17776.8,7,6221.88,130659.48,3/27/2019,20:35,Card,124437.6,4.761904762,6221.88,7.3
+504-35-8843,A,Lagos,Normal,Male,Sports and travel,15289.2,1,764.46,16053.66,1/2/2019,16:57,Cash,15289.2,4.761904762,764.46,5.7
+873-51-0671,A,Lagos,Member,Female,Sports and travel,7912.8,7,2769.48,58159.08,1/10/2019,16:42,Epay,55389.600000000006,4.761904762,2769.48,5.1
+594-34-4444,A,Lagos,Normal,Male,Electronic accessories,34977.6,1,1748.88,36726.48,3/8/2019,20:38,Epay,34977.6,4.761904762,1748.88,7.2
+865-92-6136,A,Lagos,Normal,Male,Food and beverages,18990.0,3,2848.5,59818.5,3/23/2019,10:16,Epay,56970.0,4.761904762,2848.5,9.3
+212-62-1842,A,Lagos,Normal,Male,Food and beverages,20973.6,6,6292.079999999999,132133.68,3/28/2019,16:44,Cash,125841.6,4.761904762,6292.079999999999,9.9
+704-48-3927,A,Lagos,Member,Male,Electronic accessories,31921.2,10,15960.6,335172.6,1/12/2019,14:50,Epay,319212.0,4.761904762,15960.6,7.3
+630-74-5166,A,Lagos,Normal,Male,Sports and travel,22366.8,6,6710.04,140910.84000000003,3/22/2019,20:19,Cash,134200.8,4.761904762,6710.04,7.4
+645-44-1170,A,Lagos,Member,Male,Home and lifestyle,20905.2,9,9407.34,197554.14,1/19/2019,20:07,Epay,188146.8,4.761904762,9407.34,4.3
+642-32-2990,A,Lagos,Normal,Female,Food and beverages,3945.6,10,1972.8,41428.8,2/2/2019,20:48,Epay,39456.0,4.761904762,1972.8,6.0
+638-60-7125,A,Lagos,Normal,Female,Electronic accessories,35841.6,8,14336.64,301069.44,2/14/2019,17:03,Card,286732.8,4.761904762,14336.64,5.2
+668-90-8900,A,Lagos,Normal,Female,Home and lifestyle,33728.4,7,11804.94,247903.74,3/10/2019,18:44,Card,236098.8,4.761904762,11804.94,4.5
+870-54-3162,A,Lagos,Normal,Female,Sports and travel,11610.0,5,2902.5,60952.5,1/27/2019,13:26,Cash,58050.0,4.761904762,2902.5,9.0
+802-70-5316,A,Lagos,Member,Female,Sports and travel,33166.8,6,9950.04,208950.84,3/6/2019,20:34,Cash,199000.8,4.761904762,9950.04,8.3
+700-81-1757,A,Lagos,Normal,Female,Electronic accessories,9471.6,5,2367.9,49725.9,1/18/2019,20:59,Card,47358.00000000001,4.761904762,2367.9,8.8
+354-39-5160,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.36,78064.56,2/18/2019,15:39,Cash,74347.2,4.761904762,3717.36,9.8
+575-30-8091,A,Lagos,Normal,Male,Sports and travel,26100.0,8,10440.0,219240.0,3/16/2019,19:25,Epay,208800.0,4.761904762,10440.0,9.2
+239-10-7476,A,Lagos,Normal,Female,Home and lifestyle,28062.0,6,8418.6,176790.6,1/21/2019,16:37,Epay,168372.0,4.761904762,8418.6,8.0
+685-64-1609,A,Lagos,Member,Female,Fashion accessories,10850.4,10,5425.2,113929.2,2/10/2019,12:28,Epay,108504.0,4.761904762,5425.2,9.2
+238-49-0436,A,Lagos,Normal,Male,Health and beauty,11685.6,8,4674.240000000001,98159.04,3/27/2019,13:48,Card,93484.8,4.761904762,4674.240000000001,4.9
+746-94-0204,A,Lagos,Normal,Male,Fashion accessories,29966.4,9,13484.88,283182.48000000004,1/29/2019,11:56,Card,269697.6,4.761904762,13484.88,7.4
+782-95-9291,A,Lagos,Member,Male,Food and beverages,33224.4,5,8306.1,174428.1,2/20/2019,15:55,Card,166122.0,4.761904762,8306.1,9.0
+275-28-0149,A,Lagos,Normal,Male,Sports and travel,22928.4,1,1146.42,24074.82,2/25/2019,16:21,Cash,22928.4,4.761904762,1146.42,6.0
+101-17-6199,A,Lagos,Normal,Male,Food and beverages,16484.4,7,5769.539999999999,121160.34,3/13/2019,19:44,Card,115390.8,4.761904762,5769.539999999999,7.0
+687-47-8271,A,Lagos,Normal,Male,Fashion accessories,35632.8,10,17816.4,374144.4,2/8/2019,16:20,Card,356328.0,4.761904762,17816.4,8.7
+796-32-9050,A,Lagos,Normal,Male,Food and beverages,18460.8,6,5538.24,116303.04,1/19/2019,16:31,Cash,110764.8,4.761904762,5538.24,6.5
+105-31-1824,A,Lagos,Member,Male,Sports and travel,25027.2,7,8759.519999999999,183949.92,2/1/2019,15:10,Card,175190.4,4.761904762,8759.519999999999,8.5
+249-42-3782,A,Lagos,Normal,Male,Health and beauty,25203.6,5,6300.900000000001,132318.9,1/3/2019,11:36,Epay,126018.0,4.761904762,6300.900000000001,5.5
+827-26-2100,A,Lagos,Member,Male,Home and lifestyle,12182.4,9,5482.08,115123.68,3/21/2019,16:21,Epay,109641.6,4.761904762,5482.08,8.8
+175-54-2529,A,Lagos,Member,Male,Food and beverages,7981.200000000001,8,3192.48,67042.07999999999,3/3/2019,17:01,Card,63849.600000000006,4.761904762,3192.48,9.6
+407-63-8975,A,Lagos,Normal,Male,Food and beverages,26596.8,6,7979.040000000001,167559.84,3/23/2019,19:16,Epay,159580.8,4.761904762,7979.040000000001,4.4
+851-28-6367,A,Lagos,Member,Male,Sports and travel,5580.0,10,2790.0,58590.0,3/23/2019,10:55,Epay,55800.0,4.761904762,2790.0,8.0
+586-25-0848,A,Lagos,Normal,Female,Sports and travel,4442.4,7,1554.84,32651.64,3/4/2019,11:19,Card,31096.8,4.761904762,1554.84,6.7
+400-60-7251,A,Lagos,Normal,Male,Home and lifestyle,26665.2,1,1333.26,27998.46,2/10/2019,12:50,Epay,26665.2,4.761904762,1333.26,9.9
+831-07-6050,A,Lagos,Normal,Male,Electronic accessories,11775.6,5,2943.9,61821.9,3/19/2019,11:30,Card,58878.00000000001,4.761904762,2943.9,9.9
+856-22-8149,A,Lagos,Normal,Female,Home and lifestyle,9104.4,1,455.22,9559.62,3/23/2019,10:13,Epay,9104.4,4.761904762,455.22,6.1
+749-24-1565,A,Lagos,Normal,Female,Health and beauty,8290.800000000001,9,3730.86,78348.06,1/3/2019,12:02,Epay,74617.2,4.761904762,3730.86,7.9
+888-02-0338,A,Lagos,Normal,Male,Electronic accessories,9442.8,9,4249.26,89234.46,1/25/2019,20:24,Epay,84985.2,4.761904762,4249.26,5.9
+802-43-8934,A,Lagos,Normal,Male,Home and lifestyle,6580.8,1,329.04,6909.840000000001,3/22/2019,15:05,Card,6580.8,4.761904762,329.04,8.3
+319-74-2561,A,Lagos,Member,Female,Electronic accessories,34070.4,3,5110.56,107321.76,2/21/2019,16:55,Cash,102211.2,4.761904762,5110.56,5.5
+213-72-6612,A,Lagos,Normal,Male,Food and beverages,15570.0,2,1557.0,32697.0,3/20/2019,15:56,Cash,31140.0,4.761904762,1557.0,6.2
+721-86-6247,A,Lagos,Normal,Female,Home and lifestyle,22831.2,8,9132.48,191782.08,3/11/2019,12:55,Epay,182649.6,4.761904762,9132.48,7.4
+157-13-5295,A,Lagos,Member,Male,Health and beauty,18698.4,10,9349.2,196333.2,3/9/2019,18:24,Epay,186984.0,4.761904762,9349.2,6.5
+645-78-8093,A,Lagos,Normal,Female,Sports and travel,33530.4,2,3353.04,70413.84000000001,1/20/2019,18:09,Epay,67060.8,4.761904762,3353.04,4.1
+478-06-7835,A,Lagos,Normal,Male,Fashion accessories,32288.4,1,1614.4199999999998,33902.82,1/11/2019,11:20,Epay,32288.4,4.761904762,1614.4199999999998,4.9
+540-11-4336,A,Lagos,Normal,Male,Food and beverages,8978.4,9,4040.28,84845.88,1/11/2019,16:49,Card,80805.6,4.761904762,4040.28,5.6
+448-81-5016,A,Lagos,Normal,Male,Health and beauty,21517.2,2,2151.7200000000003,45186.12,3/11/2019,12:01,Card,43034.4,4.761904762,2151.7200000000003,5.8
+217-58-1179,A,Lagos,Member,Male,Home and lifestyle,22554.0,4,4510.8,94726.8,1/5/2019,11:25,Cash,90216.0,4.761904762,4510.8,4.2
+530-90-9855,A,Lagos,Member,Male,Home and lifestyle,17132.4,8,6852.959999999999,143912.16,1/1/2019,14:47,Cash,137059.2,4.761904762,6852.959999999999,5.7
+604-70-6476,A,Lagos,Member,Male,Fashion accessories,6458.400000000001,5,1614.6,33906.6,1/23/2019,14:04,Epay,32292.0,4.761904762,1614.6,6.8
+799-71-1548,A,Lagos,Member,Male,Electronic accessories,27979.2,4,5595.84,117512.64,1/7/2019,16:11,Card,111916.8,4.761904762,5595.84,8.8
+290-68-2984,A,Lagos,Normal,Male,Home and lifestyle,8550.0,4,1710.0,35910.0,3/16/2019,11:22,Cash,34200.0,4.761904762,1710.0,5.2
+704-11-6354,A,Lagos,Member,Male,Home and lifestyle,21204.0,8,8481.6,178113.6,1/6/2019,11:23,Cash,169632.0,4.761904762,8481.6,8.9
+366-93-0948,A,Lagos,Member,Male,Electronic accessories,23886.0,1,1194.3,25080.300000000003,1/31/2019,10:46,Card,23886.0,4.761904762,1194.3,9.7
+729-09-9681,A,Lagos,Member,Male,Home and lifestyle,9327.6,6,2798.28,58763.880000000005,2/5/2019,10:16,Epay,55965.600000000006,4.761904762,2798.28,8.7
+151-16-1484,A,Lagos,Member,Male,Electronic accessories,11610.0,4,2322.0,48761.99999999999,2/13/2019,12:38,Epay,46440.0,4.761904762,2322.0,6.5
+850-41-9669,A,Lagos,Normal,Female,Electronic accessories,27021.6,9,12159.72,255354.12,3/19/2019,13:25,Epay,243194.4,4.761904762,12159.72,6.2
+447-15-7839,A,Lagos,Member,Female,Sports and travel,8006.4,10,4003.2,84067.2,2/9/2019,11:00,Cash,80064.0,4.761904762,4003.2,4.2
+253-12-6086,A,Lagos,Member,Female,Sports and travel,35424.0,7,12398.4,260366.4,3/12/2019,12:43,Card,247968.0,4.761904762,12398.4,8.7
+144-51-6085,A,Lagos,Member,Male,Home and lifestyle,25466.4,4,5093.28,106958.88,1/5/2019,16:05,Card,101865.6,4.761904762,5093.28,4.4
+731-14-2199,A,Lagos,Member,Female,Home and lifestyle,12794.4,10,6397.2,134341.2,1/4/2019,13:34,Epay,127944.0,4.761904762,6397.2,7.0
+126-54-1082,A,Lagos,Member,Female,Home and lifestyle,7754.4,9,3489.48,73279.08,1/7/2019,11:44,Card,69789.6,4.761904762,3489.48,8.8
+633-91-1052,A,Lagos,Normal,Female,Home and lifestyle,4330.8,2,433.08,9094.68,1/27/2019,15:51,Cash,8661.6,4.761904762,433.08,5.1
+828-61-5674,A,Lagos,Member,Male,Sports and travel,15847.2,10,7923.6,166395.6,3/20/2019,19:57,Card,158472.0,4.761904762,7923.6,9.6
+136-08-6195,A,Lagos,Normal,Female,Home and lifestyle,25185.6,8,10074.24,211559.04,2/15/2019,17:01,Card,201484.8,4.761904762,10074.24,6.4
+490-29-1201,A,Lagos,Normal,Female,Sports and travel,5522.4,1,276.12,5798.52,1/6/2019,11:09,Cash,5522.4,4.761904762,276.12,6.5
+667-92-0055,A,Lagos,Member,Male,Health and beauty,35938.8,6,10781.64,226414.44,3/4/2019,15:02,Epay,215632.8,4.761904762,10781.64,8.5
+565-17-3836,A,Lagos,Member,Female,Health and beauty,17161.2,4,3432.24,72077.04000000001,3/12/2019,14:21,Cash,68644.8,4.761904762,3432.24,9.1
+430-60-3493,A,Lagos,Member,Female,Home and lifestyle,34156.8,7,11954.88,251052.48,2/3/2019,14:38,Cash,239097.6,4.761904762,11954.88,4.2
+278-97-7759,A,Lagos,Member,Female,Electronic accessories,22492.8,1,1124.64,23617.44,2/18/2019,20:29,Cash,22492.8,4.761904762,1124.64,4.7
+316-68-6352,A,Lagos,Member,Female,Food and beverages,13089.6,2,1308.96,27488.160000000003,1/21/2019,10:00,Cash,26179.2,4.761904762,1308.96,7.1
+744-02-5987,A,Lagos,Member,Male,Home and lifestyle,28216.8,6,8465.039999999999,177765.84000000003,1/10/2019,14:16,Epay,169300.8,4.761904762,8465.039999999999,5.8
+307-83-9164,A,Lagos,Member,Male,Home and lifestyle,21603.6,4,4320.72,90735.12,1/25/2019,15:54,Cash,86414.4,4.761904762,4320.72,4.5
+439-54-7422,A,Lagos,Normal,Female,Electronic accessories,18428.4,4,3685.68,77399.28,3/18/2019,17:15,Card,73713.59999999999,4.761904762,3685.68,4.7
+411-77-0180,A,Lagos,Member,Male,Electronic accessories,25992.0,7,9097.2,191041.2,3/26/2019,20:14,Epay,181944.0,4.761904762,9097.2,4.3
+286-01-5402,A,Lagos,Normal,Female,Sports and travel,14482.8,7,5068.9800000000005,106448.58,3/30/2019,13:22,Cash,101379.6,4.761904762,5068.9800000000005,9.6
+803-17-8013,A,Lagos,Member,Female,Home and lifestyle,31964.4,8,12785.76,268500.96,2/17/2019,17:09,Cash,255715.2,4.761904762,12785.76,4.1
+512-98-1403,A,Lagos,Member,Female,Electronic accessories,9532.8,3,1429.92,30028.320000000003,3/21/2019,10:40,Epay,28598.4,4.761904762,1429.92,4.7
+848-42-2560,A,Lagos,Normal,Female,Fashion accessories,29487.6,2,2948.76,61923.96,3/5/2019,17:43,Cash,58975.2,4.761904762,2948.76,7.8
+870-76-1733,A,Lagos,Member,Female,Food and beverages,5122.8,5,1280.7,26894.7,2/1/2019,10:08,Card,25614.000000000004,4.761904762,1280.7,4.4
+423-64-4619,A,Lagos,Member,Female,Health and beauty,5598.0,9,2519.1,52901.1,3/7/2019,13:12,Cash,50381.99999999999,4.761904762,2519.1,5.0
+372-94-8041,A,Lagos,Normal,Male,Health and beauty,5493.6,6,1648.0800000000002,34609.68,2/15/2019,18:03,Epay,32961.6,4.761904762,1648.0800000000002,9.8
+563-91-7120,A,Lagos,Normal,Female,Fashion accessories,22237.2,5,5559.3,116745.3,3/8/2019,13:21,Cash,111186.0,4.761904762,5559.3,6.7
+746-54-5508,A,Lagos,Normal,Male,Home and lifestyle,7747.2,6,2324.1600000000003,48807.36,1/17/2019,12:48,Card,46483.2,4.761904762,2324.1600000000003,9.4
+815-11-1168,A,Lagos,Member,Male,Food and beverages,35920.8,5,8980.2,188584.2,3/9/2019,19:09,Cash,179604.0,4.761904762,8980.2,5.4
+340-66-0321,A,Lagos,Member,Male,Electronic accessories,13089.6,4,2617.92,54976.32000000001,3/25/2019,13:07,Cash,52358.4,4.761904762,2617.92,7.6
+634-97-8956,A,Lagos,Normal,Male,Food and beverages,11844.0,3,1776.6,37308.6,2/17/2019,17:27,Card,35532.0,4.761904762,1776.6,9.1
+566-71-1091,A,Lagos,Normal,Male,Fashion accessories,27727.2,5,6931.799999999999,145567.80000000002,2/3/2019,15:59,Cash,138636.0,4.761904762,6931.799999999999,5.5
+442-48-3607,A,Lagos,Member,Male,Food and beverages,8452.8,2,845.2800000000001,17750.88,3/14/2019,11:21,Card,16905.6,4.761904762,845.2800000000001,7.9
+527-09-6272,A,Lagos,Member,Female,Electronic accessories,10242.0,5,2560.5,53770.50000000001,3/21/2019,10:17,Card,51210.0,4.761904762,2560.5,9.1
+898-04-2717,A,Lagos,Normal,Male,Fashion accessories,27504.000000000004,9,12376.8,259912.8,3/19/2019,15:49,Epay,247536.0,4.761904762,12376.8,7.5
+150-89-8043,A,Lagos,Normal,Male,Sports and travel,16074.0,3,2411.1,50633.100000000006,2/14/2019,15:04,Cash,48221.99999999999,4.761904762,2411.1,6.2
+135-84-8019,A,Lagos,Normal,Female,Fashion accessories,28054.800000000003,9,12624.66,265117.86,2/27/2019,16:10,Epay,252493.2,4.761904762,12624.66,7.6
+441-94-7118,A,Lagos,Member,Male,Electronic accessories,25902.0,1,1295.1,27197.1,2/4/2019,12:14,Cash,25902.0,4.761904762,1295.1,7.3
+531-80-1784,A,Lagos,Normal,Male,Electronic accessories,9367.2,7,3278.5200000000004,68848.92,3/28/2019,17:38,Cash,65570.4,4.761904762,3278.5200000000004,5.1
+834-61-8124,A,Lagos,Normal,Male,Electronic accessories,18608.4,7,6512.94,136771.74,1/26/2019,18:22,Cash,130258.8,4.761904762,6512.94,5.5
+612-36-5536,A,Lagos,Member,Male,Food and beverages,29145.6,8,11658.24,244823.04,2/17/2019,11:12,Card,233164.8,4.761904762,11658.24,7.4
+462-67-9126,A,Lagos,Normal,Male,Home and lifestyle,26359.2,6,7907.759999999999,166062.96,1/21/2019,17:44,Cash,158155.2,4.761904762,7907.759999999999,7.2
+468-88-0009,A,Lagos,Member,Male,Home and lifestyle,23738.4,4,4747.679999999999,99701.28,3/24/2019,10:29,Cash,94953.6,4.761904762,4747.679999999999,6.0
+254-31-0042,A,Lagos,Member,Male,Electronic accessories,7740.0,9,3483.0000000000005,73143.0,3/6/2019,12:46,Card,69660.0,4.761904762,3483.0000000000005,7.8
+422-29-8786,A,Lagos,Normal,Female,Home and lifestyle,24152.4,5,6038.1,126800.1,1/3/2019,16:47,Card,120762.0,4.761904762,6038.1,9.1
+667-23-5919,A,Lagos,Member,Female,Fashion accessories,34812.0,5,8703.0,182763.0,1/14/2019,12:52,Epay,174060.0,4.761904762,8703.0,7.0
+289-15-7034,A,Lagos,Member,Male,Sports and travel,29638.8,4,5927.759999999999,124482.96,1/11/2019,10:37,Card,118555.2,4.761904762,5927.759999999999,7.5
+662-72-2873,A,Lagos,Normal,Female,Food and beverages,14738.4,5,3684.6,77376.6,1/6/2019,13:58,Epay,73692.0,4.761904762,3684.6,9.9
+725-56-0833,A,Lagos,Normal,Female,Health and beauty,11635.2,10,5817.6,122169.6,2/20/2019,16:49,Card,116352.0,4.761904762,5817.6,10.0
+563-36-9814,A,Lagos,Member,Male,Electronic accessories,27655.2,1,1382.76,29037.96,2/13/2019,18:27,Epay,27655.2,4.761904762,1382.76,7.2
+308-47-4913,A,Lagos,Member,Female,Sports and travel,18813.6,10,9406.8,197542.8,3/9/2019,12:45,Card,188136.0,4.761904762,9406.8,6.2
+885-17-6250,A,Lagos,Normal,Female,Health and beauty,28706.4,1,1435.32,30141.72,3/6/2019,10:36,Epay,28706.4,4.761904762,1435.32,7.3
+726-27-2396,A,Lagos,Normal,Female,Health and beauty,27900.0,5,6975.0,146475.0,1/24/2019,20:36,Epay,139500.0,4.761904762,6975.0,4.3
+316-01-3952,A,Lagos,Normal,Female,Food and beverages,19537.2,5,4884.3,102570.3,3/13/2019,14:16,Epay,97686.0,4.761904762,4884.3,4.6
+882-40-4577,A,Lagos,Member,Male,Sports and travel,24213.6,4,4842.72,101697.12,1/19/2019,15:28,Card,96854.4,4.761904762,4842.72,8.0
+732-67-5346,A,Lagos,Normal,Male,Food and beverages,4964.4,5,1241.1,26063.1,1/11/2019,19:07,Card,24822.0,4.761904762,1241.1,7.8
+256-08-8343,A,Lagos,Normal,Female,Home and lifestyle,20350.8,4,4070.16,85473.36,3/4/2019,19:48,Epay,81403.2,4.761904762,4070.16,5.5
+132-23-6451,A,Lagos,Member,Male,Health and beauty,7549.2,5,1887.3,39633.3,1/4/2019,13:21,Cash,37746.0,4.761904762,1887.3,7.8
+696-90-2548,A,Lagos,Normal,Male,Sports and travel,9302.4,3,1395.36,29302.56,3/10/2019,18:55,Epay,27907.2,4.761904762,1395.36,6.6
+472-15-9636,A,Lagos,Normal,Male,Home and lifestyle,18334.8,8,7333.92,154012.31999999998,3/22/2019,19:36,Epay,146678.4,4.761904762,7333.92,9.2
+745-71-3520,A,Lagos,Member,Female,Electronic accessories,9079.2,7,3177.72,66732.12000000001,2/4/2019,10:23,Cash,63554.4,4.761904762,3177.72,8.2
+269-10-8440,A,Lagos,Member,Male,Health and beauty,19141.2,7,6699.42,140687.82,1/21/2019,18:01,Cash,133988.4,4.761904762,6699.42,8.9
+325-77-6186,A,Lagos,Member,Female,Home and lifestyle,32634.000000000004,10,16317.000000000002,342657.0,3/8/2019,10:53,Epay,326340.0,4.761904762,16317.000000000002,7.3
+459-50-7686,A,Lagos,Normal,Female,Electronic accessories,8445.6,6,2533.68,53207.28,1/13/2019,19:14,Epay,50673.6,4.761904762,2533.68,6.4
+345-08-4992,A,Lagos,Normal,Male,Home and lifestyle,12236.4,6,3670.920000000001,77089.32,3/8/2019,15:37,Card,73418.4,4.761904762,3670.920000000001,7.7
+138-17-5109,A,Lagos,Member,Female,Home and lifestyle,32115.6,9,14452.02,303492.42,1/15/2019,15:42,Card,289040.4,4.761904762,14452.02,6.5
+301-11-9629,A,Lagos,Normal,Female,Sports and travel,6876.000000000001,7,2406.6,50538.6,1/15/2019,10:43,Cash,48131.99999999999,4.761904762,2406.6,9.7
+727-17-0390,A,Lagos,Normal,Female,Food and beverages,22899.6,5,5724.9,120222.9,3/16/2019,12:43,Epay,114498.0,4.761904762,5724.9,4.8
+568-88-3448,A,Lagos,Normal,Male,Health and beauty,9000.0,1,450.0,9450.0,3/3/2019,15:09,Epay,9000.0,4.761904762,450.0,5.5
+187-83-5490,A,Lagos,Member,Male,Electronic accessories,7477.2,4,1495.44,31404.24,1/31/2019,13:47,Cash,29908.8,4.761904762,1495.44,4.7
+470-32-9057,A,Lagos,Member,Male,Food and beverages,18482.4,5,4620.6,97032.6,3/28/2019,15:31,Card,92412.0,4.761904762,4620.6,9.1
+340-21-9136,A,Lagos,Member,Female,Sports and travel,14417.999999999998,4,2883.6,60555.600000000006,1/25/2019,11:40,Cash,57671.99999999999,4.761904762,2883.6,9.7
+405-31-3305,A,Lagos,Member,Male,Fashion accessories,15526.8,10,7763.400000000001,163031.4,2/2/2019,18:31,Card,155268.0,4.761904762,7763.400000000001,5.5
+676-39-6028,A,Lagos,Member,Female,Electronic accessories,23198.4,5,5799.599999999999,121791.6,3/30/2019,17:04,Cash,115992.0,4.761904762,5799.599999999999,6.6
+502-05-1910,A,Lagos,Normal,Male,Health and beauty,23464.800000000003,3,3519.7200000000003,73914.12,2/25/2019,20:35,Card,70394.4,4.761904762,3519.7200000000003,6.3
+485-30-8700,A,Lagos,Normal,Female,Sports and travel,11973.6,5,2993.4,62861.4,3/18/2019,16:10,Card,59868.00000000001,4.761904762,2993.4,4.2
+575-67-1508,A,Lagos,Normal,Male,Electronic accessories,13896.0,1,694.8,14590.8,1/29/2019,11:26,Epay,13896.0,4.761904762,694.8,6.7
+674-15-9296,A,Lagos,Normal,Male,Sports and travel,13370.4,5,3342.6,70194.6,1/8/2019,13:05,Epay,66852.0,4.761904762,3342.6,5.0
+795-49-7276,A,Lagos,Normal,Male,Fashion accessories,13374.0,4,2674.8,56170.8,3/23/2019,18:59,Epay,53496.0,4.761904762,2674.8,8.3
+510-09-5628,A,Lagos,Member,Female,Fashion accessories,7077.6,10,3538.8,74314.8,3/15/2019,18:20,Card,70776.0,4.761904762,3538.8,7.2
+420-18-8989,A,Lagos,Member,Female,Sports and travel,18547.2,8,7418.88,155796.48,2/2/2019,15:47,Cash,148377.6,4.761904762,7418.88,9.6
+726-29-6793,A,Lagos,Member,Male,Electronic accessories,8704.8,8,3481.92,73120.31999999999,1/28/2019,20:54,Epay,69638.4,4.761904762,3481.92,9.8
+209-61-0206,A,Lagos,Normal,Female,Home and lifestyle,15447.6,5,3861.9,81099.9,1/5/2019,17:29,Epay,77238.0,4.761904762,3861.9,6.1
+595-27-4851,A,Lagos,Normal,Female,Fashion accessories,19540.8,7,6839.280000000001,143624.88,1/27/2019,18:05,Epay,136785.6,4.761904762,6839.280000000001,9.3
+189-52-0236,A,Lagos,Normal,Male,Electronic accessories,35838.0,7,12543.3,263409.3,3/14/2019,12:07,Cash,250866.0,4.761904762,12543.3,7.6
+220-28-1851,A,Lagos,Normal,Male,Home and lifestyle,12502.8,2,1250.2800000000002,26255.88,3/1/2019,18:14,Epay,25005.6,4.761904762,1250.2800000000002,9.7
+609-81-8548,A,Lagos,Member,Female,Home and lifestyle,13478.4,6,4043.52,84913.92,2/6/2019,13:55,Card,80870.4,4.761904762,4043.52,5.9
+534-01-4457,A,Lagos,Normal,Male,Food and beverages,29415.6,6,8824.68,185318.28,1/27/2019,14:36,Card,176493.6,4.761904762,8824.68,8.0
+719-89-8991,A,Lagos,Member,Female,Sports and travel,32907.6,5,8226.9,172764.9,2/25/2019,16:03,Epay,164538.0,4.761904762,8226.9,7.1
+827-77-7633,A,Lagos,Normal,Male,Sports and travel,35312.4,9,15890.580000000002,333702.18,2/17/2019,19:41,Cash,317811.6,4.761904762,15890.580000000002,9.3
+287-83-1405,A,Lagos,Normal,Male,Health and beauty,9154.8,6,2746.44,57675.24,2/12/2019,19:01,Epay,54928.8,4.761904762,2746.44,7.0
+435-13-4908,A,Lagos,Member,Male,Fashion accessories,31204.800000000003,8,12481.919999999998,262120.32,1/24/2019,18:04,Card,249638.4,4.761904762,12481.919999999998,7.2
+892-05-6689,A,Lagos,Normal,Female,Home and lifestyle,10195.2,5,2548.8,53524.8,3/11/2019,13:28,Epay,50976.0,4.761904762,2548.8,6.2
+643-38-7867,A,Lagos,Normal,Male,Home and lifestyle,35258.4,1,1762.92,37021.32,3/7/2019,11:44,Epay,35258.4,4.761904762,1762.92,6.9
+308-81-0538,A,Lagos,Normal,Female,Fashion accessories,26298.0,4,5259.599999999999,110451.6,2/25/2019,17:16,Card,105192.0,4.761904762,5259.599999999999,4.9
+460-35-4390,A,Lagos,Normal,Male,Home and lifestyle,11044.8,3,1656.72,34791.12,1/22/2019,11:00,Epay,33134.4,4.761904762,1656.72,9.1
+647-50-1224,A,Lagos,Normal,Female,Fashion accessories,10591.2,10,5295.6,111207.6,1/12/2019,16:23,Epay,105912.0,4.761904762,5295.6,8.9
+541-48-8554,A,Lagos,Normal,Male,Sports and travel,21942.0,9,9873.9,207351.9,1/7/2019,12:08,Card,197478.0,4.761904762,9873.9,6.0
+213-32-1216,A,Lagos,Normal,Female,Electronic accessories,23781.6,6,7134.48,149824.08000000002,1/23/2019,10:28,Cash,142689.6,4.761904762,7134.48,7.3
+134-75-2619,A,Lagos,Member,Male,Electronic accessories,6955.2,7,2434.32,51120.72,3/25/2019,18:51,Cash,48686.4,4.761904762,2434.32,6.9
+712-39-0363,A,Lagos,Member,Male,Food and beverages,14997.6,6,4499.28,94484.88,1/2/2019,15:24,Epay,89985.6,4.761904762,4499.28,5.6
+218-59-9410,A,Lagos,Member,Female,Home and lifestyle,26071.2,3,3910.68,82124.28,3/29/2019,16:54,Epay,78213.59999999999,4.761904762,3910.68,8.2
+760-90-2357,A,Lagos,Member,Male,Electronic accessories,26823.6,6,8047.080000000001,168988.68,3/20/2019,15:08,Epay,160941.6,4.761904762,8047.080000000001,5.0
+698-98-5964,A,Lagos,Normal,Female,Food and beverages,29235.6,10,14617.8,306973.8,1/17/2019,13:01,Card,292356.0,4.761904762,14617.8,6.3
+651-88-7328,A,Lagos,Normal,Female,Fashion accessories,23666.4,9,10649.88,223647.48,1/1/2019,13:55,Cash,212997.6,4.761904762,10649.88,7.7
+239-48-4278,A,Lagos,Member,Male,Food and beverages,3646.8,7,1276.38,26803.98,3/10/2019,19:35,Epay,25527.6,4.761904762,1276.38,8.3
+550-84-8664,A,Lagos,Normal,Male,Sports and travel,30927.6,5,7731.9,162369.9,3/22/2019,14:33,Card,154638.0,4.761904762,7731.9,8.6
+797-88-0493,A,Lagos,Normal,Female,Health and beauty,23137.2,4,4627.4400000000005,97176.24,3/26/2019,13:54,Cash,92548.8,4.761904762,4627.4400000000005,7.7
+443-82-0585,A,Lagos,Member,Female,Health and beauty,27964.800000000003,4,5592.96,117452.16,2/1/2019,19:54,Cash,111859.2,4.761904762,5592.96,8.4
+127-47-6963,A,Lagos,Normal,Male,Health and beauty,18615.6,4,3723.12,78185.52,3/9/2019,13:53,Card,74462.4,4.761904762,3723.12,9.8
+278-86-2735,A,Lagos,Normal,Female,Food and beverages,18842.4,3,2826.36,59353.55999999999,3/27/2019,14:03,Cash,56527.2,4.761904762,2826.36,9.2
+695-28-6250,A,Lagos,Normal,Female,Sports and travel,15501.6,5,3875.4,81383.4,2/4/2019,16:38,Epay,77508.0,4.761904762,3875.4,7.7
+227-50-3718,A,Lagos,Normal,Male,Health and beauty,5263.2,5,1315.8,27631.8,3/4/2019,12:23,Cash,26316.0,4.761904762,1315.8,4.4
+560-49-6611,A,Lagos,Member,Female,Sports and travel,16408.8,1,820.4399999999999,17229.239999999998,2/7/2019,14:13,Cash,16408.8,4.761904762,820.4399999999999,9.8
+880-35-0356,A,Lagos,Member,Female,Sports and travel,27072.0,3,4060.8,85276.8,2/5/2019,11:51,Epay,81216.0,4.761904762,4060.8,4.8
+152-68-2907,A,Lagos,Normal,Male,Food and beverages,18792.0,3,2818.8,59194.8,2/15/2019,13:30,Card,56376.0,4.761904762,2818.8,9.5
+334-64-2006,A,Lagos,Member,Female,Home and lifestyle,25315.2,2,2531.52,53161.92,3/24/2019,14:22,Epay,50630.4,4.761904762,2531.52,9.6
+559-98-9873,A,Lagos,Member,Female,Fashion accessories,19314.0,7,6759.9,141957.9,2/10/2019,12:56,Epay,135198.0,4.761904762,6759.9,5.2
+318-12-0304,A,Lagos,Normal,Male,Fashion accessories,11019.6,1,550.98,11570.580000000002,1/23/2019,12:20,Epay,11019.6,4.761904762,550.98,5.2
+421-95-9805,A,Lagos,Normal,Female,Electronic accessories,10425.6,1,521.2800000000001,10946.88,2/7/2019,10:18,Card,10425.6,4.761904762,521.2800000000001,6.2
+443-59-0061,A,Lagos,Member,Male,Food and beverages,24282.0,10,12141.0,254961.0,2/3/2019,11:25,Epay,242820.0,4.761904762,12141.0,4.2
+509-29-3912,A,Lagos,Member,Female,Sports and travel,13939.2,9,6272.639999999999,131725.44,3/20/2019,12:24,Epay,125452.8,4.761904762,6272.639999999999,4.2
+828-46-6863,A,Lagos,Member,Male,Food and beverages,35470.8,6,10641.24,223466.04,1/23/2019,11:22,Card,212824.8,4.761904762,10641.24,4.0
+420-97-3340,A,Lagos,Normal,Female,Food and beverages,25804.800000000003,3,3870.72,81285.12,3/28/2019,15:30,Card,77414.4,4.761904762,3870.72,9.2
+436-54-4512,A,Lagos,Member,Female,Food and beverages,32979.6,1,1648.98,34628.58,3/20/2019,19:44,Cash,32979.6,4.761904762,1648.98,9.8
+816-57-2053,A,Lagos,Normal,Male,Sports and travel,21913.2,2,2191.32,46017.72,3/9/2019,12:37,Epay,43826.4,4.761904762,2191.32,8.7
+856-66-2701,A,Lagos,Member,Male,Home and lifestyle,19188.0,3,2878.2,60442.2,1/25/2019,14:19,Epay,57564.0,4.761904762,2878.2,7.5
+308-39-1707,A,Lagos,Normal,Female,Fashion accessories,4352.4,1,217.62,4570.02,1/26/2019,18:19,Card,4352.4,4.761904762,217.62,8.2
+149-61-1929,A,Lagos,Normal,Male,Sports and travel,23108.4,10,11554.2,242638.2,1/19/2019,14:08,Card,231084.0,4.761904762,11554.2,6.7
+655-07-2265,A,Lagos,Normal,Male,Electronic accessories,28191.6,3,4228.74,88803.54000000001,3/5/2019,16:38,Epay,84574.8,4.761904762,4228.74,5.4
+589-02-8023,A,Lagos,Member,Male,Food and beverages,30157.2,2,3015.7200000000003,63330.12,1/15/2019,10:54,Card,60314.4,4.761904762,3015.7200000000003,7.0
+610-46-4100,A,Lagos,Normal,Male,Health and beauty,10422.0,7,3647.7,76601.7,3/3/2019,20:31,Card,72954.0,4.761904762,3647.7,6.0
+706-36-6154,A,Lagos,Member,Male,Home and lifestyle,6969.599999999999,9,3136.32,65862.72,1/18/2019,18:43,Epay,62726.4,4.761904762,3136.32,8.7
+742-04-5161,A,Lagos,Member,Male,Home and lifestyle,26200.8,10,13100.4,275108.4,2/3/2019,17:24,Cash,262008.0,4.761904762,13100.4,7.3
+169-52-4504,A,Lagos,Normal,Female,Electronic accessories,5648.4,3,847.26,17792.46,3/14/2019,14:13,Card,16945.2,4.761904762,847.26,5.8
+562-12-5430,A,Lagos,Member,Female,Fashion accessories,31734.000000000004,3,4760.1,99962.1,1/18/2019,10:11,Epay,95202.0,4.761904762,4760.1,7.9
+816-72-8853,A,Lagos,Member,Female,Sports and travel,10054.8,5,2513.7,52787.7,1/29/2019,15:48,Cash,50274.0,4.761904762,2513.7,5.9
+491-38-3499,A,Lagos,Member,Male,Fashion accessories,19962.0,1,998.1,20960.1,2/26/2019,17:46,Card,19962.0,4.761904762,998.1,4.9
+518-17-2983,A,Lagos,Normal,Female,Fashion accessories,17506.8,4,3501.36,73528.56,2/4/2019,15:44,Epay,70027.2,4.761904762,3501.36,7.6
+588-47-8641,A,Lagos,Member,Male,Fashion accessories,20174.4,10,10087.2,211831.2,1/14/2019,19:30,Epay,201744.0,4.761904762,10087.2,4.4
+811-03-8790,A,Lagos,Normal,Female,Electronic accessories,16372.8,10,8186.4,171914.4,3/1/2019,10:22,Card,163728.0,4.761904762,8186.4,4.8
+274-05-5470,A,Lagos,Member,Female,Food and beverages,26449.2,4,5289.84,111086.64,2/23/2019,18:30,Cash,105796.8,4.761904762,5289.84,6.0
+130-67-4723,A,Lagos,Member,Male,Food and beverages,17460.0,6,5238.0,109998.0,1/11/2019,13:57,Epay,104760.0,4.761904762,5238.0,9.4
+105-10-6182,A,Lagos,Member,Male,Fashion accessories,7732.8,2,773.2800000000001,16238.88,2/27/2019,12:22,Epay,15465.6,4.761904762,773.2800000000001,6.6
+648-83-1321,A,Lagos,Member,Male,Home and lifestyle,22881.6,10,11440.8,240256.8,1/16/2019,17:59,Cash,228816.0,4.761904762,11440.8,4.3
+305-03-2383,A,Lagos,Normal,Female,Food and beverages,24156.0,3,3623.4,76091.40000000001,2/15/2019,10:36,Cash,72468.0,4.761904762,3623.4,7.5
+689-05-1884,A,Lagos,Member,Male,Health and beauty,17506.8,10,8753.4,183821.4,3/4/2019,12:44,Cash,175068.0,4.761904762,8753.4,8.8
+800-09-8606,A,Lagos,Member,Female,Home and lifestyle,31453.2,5,7863.3,165129.3,1/29/2019,19:45,Cash,157266.0,4.761904762,7863.3,6.6
+182-52-7000,A,Lagos,Member,Female,Sports and travel,9734.4,4,1946.88,40884.48,1/1/2019,20:26,Epay,38937.6,4.761904762,1946.88,6.9
+868-06-0466,A,Lagos,Member,Male,Electronic accessories,25048.8,9,11271.96,236711.16000000003,2/19/2019,19:38,Card,225439.2,4.761904762,11271.96,7.8
+445-30-9252,A,Lagos,Normal,Male,Sports and travel,9252.0,3,1387.8,29143.8,1/17/2019,17:59,Epay,27756.0,4.761904762,1387.8,6.1
+786-94-2700,A,Lagos,Member,Male,Food and beverages,29023.2,6,8706.96,182846.16,2/28/2019,20:18,Cash,174139.2,4.761904762,8706.96,9.1
+258-92-7466,A,Lagos,Normal,Female,Health and beauty,12844.8,5,3211.2,67435.2,2/6/2019,18:33,Card,64224.0,4.761904762,3211.2,6.6
+857-16-3520,A,Lagos,Member,Female,Fashion accessories,25725.6,7,9003.96,189083.16,3/28/2019,16:06,Epay,180079.2,4.761904762,9003.96,4.5
+482-17-1179,A,Lagos,Member,Male,Electronic accessories,4298.4,3,644.76,13539.96,1/19/2019,12:47,Card,12895.2,4.761904762,644.76,8.1
+788-21-5741,A,Lagos,Normal,Male,Fashion accessories,16336.8,3,2450.52,51460.92,2/17/2019,13:34,Card,49010.4,4.761904762,2450.52,7.2
+247-11-2470,A,Lagos,Member,Female,Fashion accessories,8035.2,4,1607.0399999999995,33747.84,3/1/2019,16:23,Card,32140.8,4.761904762,1607.0399999999995,4.4
+635-28-5728,A,Lagos,Normal,Male,Health and beauty,20160.0,3,3024.0,63504.0,2/28/2019,19:33,Epay,60480.0,4.761904762,3024.0,4.8
+756-49-0168,A,Lagos,Member,Male,Fashion accessories,7092.0,1,354.6,7446.599999999999,2/8/2019,11:39,Epay,7092.0,4.761904762,354.6,9.5
+805-86-0265,A,Lagos,Normal,Male,Home and lifestyle,33825.6,9,15221.52,319651.92,3/20/2019,11:32,Cash,304430.4,4.761904762,15221.52,9.8
+373-14-0504,A,Lagos,Member,Female,Sports and travel,25786.8,2,2578.68,54152.28,2/12/2019,14:33,Epay,51573.6,4.761904762,2578.68,8.8
+546-80-2899,A,Lagos,Member,Male,Home and lifestyle,13568.4,2,1356.84,28493.64,2/20/2019,15:29,Epay,27136.8,4.761904762,1356.84,9.5
+585-86-8361,A,Lagos,Normal,Female,Food and beverages,9820.8,5,2455.2000000000003,51559.2,2/3/2019,10:31,Card,49104.0,4.761904762,2455.2000000000003,8.6
+807-14-7833,A,Lagos,Member,Female,Electronic accessories,6271.200000000001,10,3135.6000000000004,65847.6,2/22/2019,12:30,Epay,62711.99999999999,4.761904762,3135.6000000000004,7.0
+652-43-6591,A,Lagos,Normal,Female,Fashion accessories,35024.4,8,14009.759999999998,294204.96,3/9/2019,13:18,Card,280195.2,4.761904762,14009.759999999998,6.2
+406-46-7107,A,Lagos,Normal,Female,Home and lifestyle,34747.2,6,10424.16,218907.36,1/11/2019,11:52,Cash,208483.2,4.761904762,10424.16,4.5
+250-17-5703,A,Lagos,Member,Male,Food and beverages,6786.000000000001,10,3393.0000000000005,71253.0,2/27/2019,18:24,Epay,67860.0,4.761904762,3393.0000000000005,5.6
+156-95-3964,A,Lagos,Normal,Female,Food and beverages,19940.4,4,3988.08,83749.68000000001,3/25/2019,15:19,Epay,79761.6,4.761904762,3988.08,8.0
+410-67-1709,A,Lagos,Member,Female,Fashion accessories,22996.8,8,9198.72,193173.12,1/20/2019,17:48,Epay,183974.4,4.761904762,9198.72,9.9
+587-73-4862,A,Lagos,Member,Female,Health and beauty,3848.4,5,962.1,20204.1,3/26/2019,11:07,Epay,19242.0,4.761904762,962.1,7.6
+787-87-2010,A,Lagos,Member,Male,Health and beauty,19980.0,4,3996.0,83916.0,1/20/2019,15:48,Card,79920.0,4.761904762,3996.0,6.6
+886-54-6089,A,Lagos,Normal,Female,Home and lifestyle,4114.8,6,1234.4399999999998,25923.24,1/15/2019,17:24,Cash,24688.8,4.761904762,1234.4399999999998,7.7
+534-53-3526,A,Lagos,Normal,Female,Sports and travel,34113.6,4,6822.72,143277.12,2/11/2019,16:06,Epay,136454.4,4.761904762,6822.72,7.8
+307-04-2070,A,Lagos,Member,Female,Fashion accessories,11023.2,1,551.16,11574.36,2/5/2019,14:14,Card,11023.2,4.761904762,551.16,4.1
+404-91-5964,A,Lagos,Normal,Male,Electronic accessories,26848.8,7,9397.08,197338.68,2/4/2019,16:09,Card,187941.6,4.761904762,9397.08,9.0
+497-37-6538,A,Lagos,Normal,Male,Sports and travel,21207.6,7,7422.660000000001,155875.86,1/17/2019,15:15,Epay,148453.2,4.761904762,7422.660000000001,9.7
+651-96-5970,A,Lagos,Normal,Male,Fashion accessories,16707.6,1,835.38,17542.98,3/3/2019,20:06,Card,16707.6,4.761904762,835.38,4.0
+263-12-5321,A,Lagos,Member,Male,Electronic accessories,33336.0,7,11667.6,245019.6,2/27/2019,12:52,Card,233352.00000000003,4.761904762,11667.6,9.3
+702-72-0487,A,Lagos,Normal,Female,Electronic accessories,16779.6,2,1677.9600000000005,35237.16,2/26/2019,12:28,Card,33559.2,4.761904762,1677.9600000000005,6.6
+864-24-7918,A,Lagos,Member,Female,Sports and travel,8816.4,10,4408.2,92572.2,2/22/2019,15:15,Cash,88164.0,4.761904762,4408.2,8.1
+759-29-9521,A,Lagos,Member,Female,Fashion accessories,17625.6,9,7931.519999999999,166561.91999999998,3/4/2019,11:27,Cash,158630.4,4.761904762,7931.519999999999,8.0
+220-68-6701,A,Lagos,Normal,Female,Home and lifestyle,27889.2,4,5577.84,117134.64,3/17/2019,16:36,Cash,111556.8,4.761904762,5577.84,4.2
+618-34-8551,A,Lagos,Normal,Female,Sports and travel,33544.8,2,3354.48,70444.08,1/16/2019,18:41,Card,67089.6,4.761904762,3354.48,8.5
+257-60-7754,A,Lagos,Normal,Female,Electronic accessories,18082.8,4,3616.56,75947.76,1/8/2019,17:12,Cash,72331.2,4.761904762,3616.56,9.0
+380-60-5336,A,Lagos,Normal,Female,Electronic accessories,14493.6,10,7246.799999999999,152182.80000000002,2/24/2019,18:06,Card,144936.0,4.761904762,7246.799999999999,5.0
+674-56-6360,A,Lagos,Normal,Male,Electronic accessories,34254.0,1,1712.7,35966.7,3/22/2019,14:00,Cash,34254.0,4.761904762,1712.7,6.0
+778-34-2523,A,Lagos,Member,Female,Electronic accessories,17503.2,8,7001.28,147026.88,1/24/2019,10:57,Cash,140025.6,4.761904762,7001.28,5.0
+832-51-6761,A,Lagos,Normal,Male,Food and beverages,12196.8,8,4878.72,102453.12,1/19/2019,20:29,Epay,97574.4,4.761904762,4878.72,9.6
+186-43-8965,A,Lagos,Member,Female,Home and lifestyle,17164.8,2,1716.48,36046.08,2/24/2019,10:10,Card,34329.6,4.761904762,1716.48,4.1
+276-75-6884,A,Lagos,Normal,Female,Health and beauty,24735.6,3,3710.34,77917.14,3/4/2019,10:05,Cash,74206.8,4.761904762,3710.34,8.7
+569-76-2760,A,Lagos,Member,Female,Sports and travel,7923.6,4,1584.72,33279.12,1/29/2019,18:15,Card,31694.4,4.761904762,1584.72,6.6
+760-53-9233,A,Lagos,Member,Male,Fashion accessories,14860.8,3,2229.12,46811.52,3/26/2019,18:37,Card,44582.4,4.761904762,2229.12,8.5
+416-17-9926,A,Lagos,Member,Female,Electronic accessories,26719.2,10,13359.6,280551.6,1/1/2019,14:42,Card,267192.0,4.761904762,13359.6,4.3
+237-44-6163,A,Lagos,Normal,Male,Electronic accessories,3801.6,8,1520.64,31933.44,1/24/2019,17:43,Cash,30412.800000000003,4.761904762,1520.64,7.6
+528-14-9470,A,Lagos,Member,Male,Health and beauty,32868.0,1,1643.4,34511.4,2/14/2019,14:42,Epay,32868.0,4.761904762,1643.4,9.2
+807-34-3742,A,Lagos,Normal,Male,Fashion accessories,18856.8,1,942.84,19799.64,3/26/2019,19:44,Cash,18856.8,4.761904762,942.84,5.8
+288-62-1085,A,Lagos,Member,Male,Fashion accessories,13874.4,5,3468.6,72840.6,1/9/2019,13:34,Epay,69372.0,4.761904762,3468.6,5.6
+497-36-0989,A,Lagos,Normal,Male,Fashion accessories,18698.4,3,2804.76,58899.96,2/15/2019,15:21,Cash,56095.2,4.761904762,2804.76,7.9
+860-73-6466,A,Lagos,Member,Female,Sports and travel,14209.2,2,1420.92,29839.32,3/2/2019,16:16,Card,28418.4,4.761904762,1420.92,5.0
+896-34-0956,A,Lagos,Normal,Male,Fashion accessories,7675.2,1,383.75999999999993,8058.96,1/26/2019,12:43,Cash,7675.2,4.761904762,383.75999999999993,5.9
+804-38-3935,A,Lagos,Member,Male,Electronic accessories,33760.8,3,5064.12,106346.52,1/30/2019,11:32,Card,101282.4,4.761904762,5064.12,5.9
+585-90-0249,A,Lagos,Member,Male,Electronic accessories,26373.6,1,1318.68,27692.28,1/27/2019,18:08,Epay,26373.6,4.761904762,1318.68,9.7
+125-45-2293,A,Lagos,Normal,Female,Fashion accessories,35676.0,6,10702.8,224758.8,1/19/2019,13:11,Cash,214056.0,4.761904762,10702.8,4.2
+843-73-4724,A,Lagos,Normal,Male,Fashion accessories,26676.0,1,1333.8,28009.800000000003,1/25/2019,11:05,Cash,26676.0,4.761904762,1333.8,9.2
+409-33-9708,A,Lagos,Normal,Female,Fashion accessories,35452.8,2,3545.28,74450.88,2/19/2019,10:12,Epay,70905.6,4.761904762,3545.28,9.2
+160-22-2687,A,Lagos,Member,Female,Health and beauty,34542.0,5,8635.5,181345.5,1/23/2019,14:21,Epay,172710.0,4.761904762,8635.5,8.8
+748-45-2862,A,Lagos,Member,Female,Home and lifestyle,10191.6,4,2038.3200000000004,42804.72,3/7/2019,18:35,Cash,40766.4,4.761904762,2038.3200000000004,8.2
+316-66-3011,A,Lagos,Member,Female,Food and beverages,17146.8,9,7716.059999999999,162037.26,1/23/2019,12:35,Cash,154321.2,4.761904762,7716.059999999999,5.0
+840-76-5966,A,Lagos,Member,Male,Sports and travel,4593.6,2,459.36,9646.56,1/8/2019,18:06,Epay,9187.2,4.761904762,459.36,7.8
+124-31-1458,A,Lagos,Member,Female,Electronic accessories,28652.4,3,4297.86,90255.06,1/8/2019,14:30,Cash,85957.2,4.761904762,4297.86,6.6
+852-82-2749,A,Lagos,Normal,Male,Sports and travel,23252.4,4,4650.48,97660.08,1/6/2019,13:35,Epay,93009.6,4.761904762,4650.48,9.3
+873-14-6353,A,Lagos,Member,Male,Food and beverages,8935.2,7,3127.3200000000006,65673.72,2/16/2019,10:33,Card,62546.4,4.761904762,3127.3200000000006,7.1
+166-19-2553,A,Lagos,Member,Male,Sports and travel,32061.6,6,9618.48,201988.08,1/18/2019,17:26,Cash,192369.6,4.761904762,9618.48,9.9
+737-88-5876,A,Lagos,Member,Male,Home and lifestyle,8384.4,4,1676.88,35214.48,3/19/2019,11:52,Card,33537.6,4.761904762,1676.88,5.9
+448-61-3783,A,Lagos,Normal,Female,Electronic accessories,32407.2,8,12962.88,272220.48,3/21/2019,16:08,Card,259257.6,4.761904762,12962.88,4.5
+291-55-6563,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.36,78064.56,3/30/2019,12:45,Epay,74347.2,4.761904762,3717.36,7.5
+548-48-3156,A,Lagos,Member,Female,Food and beverages,30002.4,2,3000.24,63005.04,3/19/2019,13:37,Cash,60004.8,4.761904762,3000.24,7.6
+460-93-5834,A,Lagos,Normal,Male,Sports and travel,16408.8,7,5743.08,120604.68,1/13/2019,10:03,Cash,114861.6,4.761904762,5743.08,5.0
+325-89-4209,A,Lagos,Member,Male,Food and beverages,31644.000000000004,1,1582.1999999999998,33226.2,2/5/2019,19:42,Epay,31644.000000000004,4.761904762,1582.1999999999998,6.7
+884-80-6021,A,Lagos,Member,Female,Electronic accessories,26449.2,10,13224.6,277716.6,3/23/2019,13:14,Epay,264492.0,4.761904762,13224.6,9.5
+880-46-5796,A,Lagos,Member,Male,Sports and travel,27691.2,10,13845.6,290757.6,3/17/2019,19:53,Epay,276912.0,4.761904762,13845.6,5.6
+146-09-5432,A,Lagos,Member,Male,Food and beverages,12614.4,9,5676.48,119206.08000000002,2/9/2019,19:17,Epay,113529.6,4.761904762,5676.48,4.6
+595-94-9924,A,Lagos,Member,Female,Health and beauty,9982.8,5,2495.7,52409.7,3/26/2019,20:21,Card,49914.0,4.761904762,2495.7,4.2
+865-41-9075,A,Lagos,Normal,Male,Food and beverages,4150.8,7,1452.78,30508.38,1/28/2019,17:35,Cash,29055.6,4.761904762,1452.78,8.1
+186-71-5196,A,Lagos,Member,Female,Food and beverages,28634.4,2,2863.44,60132.240000000005,3/27/2019,16:30,Epay,57268.8,4.761904762,2863.44,6.2
+453-33-6436,A,Lagos,Normal,Female,Home and lifestyle,33523.200000000004,8,13409.280000000002,281594.88,2/7/2019,10:09,Cash,268185.60000000003,4.761904762,13409.280000000002,6.8
+522-57-8364,A,Lagos,Member,Male,Fashion accessories,18482.4,8,7392.959999999999,155252.16,1/31/2019,10:00,Epay,147859.2,4.761904762,7392.959999999999,7.6
+459-45-2396,A,Lagos,Member,Female,Food and beverages,35856.0,3,5378.4,112946.4,2/25/2019,18:45,Cash,107568.0,4.761904762,5378.4,5.8
+749-81-8133,A,Lagos,Normal,Female,Fashion accessories,34081.2,4,6816.240000000001,143141.04,3/11/2019,12:04,Cash,136324.8,4.761904762,6816.240000000001,6.8
+397-25-8725,A,Lagos,Member,Female,Health and beauty,14263.2,9,6418.4400000000005,134787.24,1/13/2019,17:54,Card,128368.8,4.761904762,6418.4400000000005,6.8
+243-55-8457,A,Lagos,Normal,Female,Food and beverages,26798.4,10,13399.2,281383.2,2/27/2019,11:40,Epay,267984.0,4.761904762,13399.2,5.1
+361-85-2571,A,Lagos,Normal,Female,Sports and travel,32212.800000000003,5,8053.200000000001,169117.19999999998,3/30/2019,10:18,Cash,161064.0,4.761904762,8053.200000000001,7.4
+131-70-8179,A,Lagos,Member,Female,Health and beauty,33152.4,3,4972.86,104430.06,2/17/2019,16:27,Cash,99457.2,4.761904762,4972.86,4.2
+720-72-2436,A,Lagos,Normal,Male,Food and beverages,23947.2,4,4789.4400000000005,100578.24,3/2/2019,18:14,Epay,95788.8,4.761904762,4789.4400000000005,6.9
+809-69-9497,A,Lagos,Normal,Female,Home and lifestyle,16444.8,10,8222.4,172670.4,1/19/2019,19:30,Epay,164448.0,4.761904762,8222.4,5.7
+449-16-6770,A,Lagos,Normal,Male,Health and beauty,18284.4,5,4571.1,95993.1,2/19/2019,14:53,Card,91422.0,4.761904762,4571.1,5.3
+333-23-2632,A,Lagos,Member,Male,Health and beauty,3628.8,7,1270.08,26671.680000000004,3/28/2019,20:14,Cash,25401.6,4.761904762,1270.08,4.2
+489-82-1237,A,Lagos,Normal,Female,Electronic accessories,33796.8,7,11828.88,248406.48,1/5/2019,11:51,Card,236577.6,4.761904762,11828.88,7.3
+846-10-0341,A,Lagos,Normal,Female,Fashion accessories,15325.2,7,5363.82,112640.22,1/6/2019,11:51,Cash,107276.4,4.761904762,5363.82,6.8
+384-59-6655,A,Lagos,Member,Female,Food and beverages,35517.6,9,15982.92,335641.32,2/19/2019,15:07,Cash,319658.4,4.761904762,15982.92,8.4
+324-92-3863,A,Lagos,Member,Male,Electronic accessories,7520.400000000001,2,752.04,15792.84,2/5/2019,18:45,Cash,15040.8,4.761904762,752.04,9.8
+593-08-5916,A,Lagos,Normal,Female,Fashion accessories,5580.0,1,279.0,5858.999999999999,3/19/2019,15:23,Card,5580.0,4.761904762,279.0,7.4
+558-60-5016,A,Lagos,Normal,Female,Home and lifestyle,11987.999999999998,9,5394.599999999999,113286.6,3/4/2019,15:27,Epay,107892.0,4.761904762,5394.599999999999,7.2
+195-06-0432,A,Lagos,Member,Male,Home and lifestyle,29163.6,3,4374.54,91865.34,1/13/2019,12:55,Card,87490.8,4.761904762,4374.54,9.3
+605-03-2706,A,Lagos,Normal,Female,Health and beauty,5688.0,3,853.2,17917.2,3/25/2019,18:02,Cash,17064.0,4.761904762,853.2,9.5
+531-56-4728,A,Lagos,Normal,Male,Home and lifestyle,28828.8,3,4324.32,90810.72,2/11/2019,15:29,Cash,86486.40000000001,4.761904762,4324.32,5.4
+221-25-5073,A,Lagos,Normal,Female,Food and beverages,26877.6,4,5375.52,112885.92,3/4/2019,10:39,Cash,107510.4,4.761904762,5375.52,8.5
+809-46-1866,A,Lagos,Normal,Male,Health and beauty,20934.0,4,4186.8,87922.8,1/23/2019,17:44,Cash,83736.0,4.761904762,4186.8,8.4
+139-32-4183,A,Lagos,Member,Female,Sports and travel,35092.8,9,15791.760000000002,331626.96,3/14/2019,14:19,Epay,315835.2,4.761904762,15791.760000000002,7.4
+886-18-2897,A,Lagos,Normal,Female,Food and beverages,20361.6,5,5090.400000000001,106898.4,3/22/2019,19:06,Card,101808.0,4.761904762,5090.400000000001,4.5
+745-74-0715,A,Lagos,Normal,Male,Electronic accessories,20890.8,2,2089.08,43870.68,3/10/2019,20:46,Epay,41781.6,4.761904762,2089.08,8.8
+727-02-1313,A,Lagos,Member,Male,Food and beverages,11462.4,1,573.12,12035.52,2/9/2019,13:22,Cash,11462.4,4.761904762,573.12,7.7
+347-56-2442,A,Lagos,Normal,Male,Home and lifestyle,23695.2,1,1184.7600000000002,24879.960000000003,2/22/2019,15:33,Cash,23695.2,4.761904762,1184.7600000000002,4.1
+849-09-3807,A,Lagos,Member,Female,Fashion accessories,31802.4,7,11130.84,233747.64,2/18/2019,13:28,Cash,222616.8,4.761904762,11130.84,6.6
+692-92-5582,B,Abuja,Member,Female,Food and beverages,19742.4,3,2961.36,62188.55999999999,2/20/2019,13:27,Card,59227.2,4.761904762,2961.36,5.9
+351-62-0822,B,Abuja,Member,Female,Fashion accessories,5212.8,4,1042.5600000000002,21893.76,2/6/2019,18:07,Epay,20851.2,4.761904762,1042.5600000000002,4.5
+529-56-3974,B,Abuja,Member,Male,Electronic accessories,9183.6,4,1836.72,38571.12,3/9/2019,17:03,Cash,36734.4,4.761904762,1836.72,6.8
+299-46-1805,B,Abuja,Member,Female,Sports and travel,33739.2,6,10121.76,212556.96,1/15/2019,16:19,Cash,202435.2,4.761904762,10121.76,4.5
+319-50-3348,B,Abuja,Normal,Female,Home and lifestyle,14507.999999999998,2,1450.8000000000002,30466.8,3/11/2019,15:30,Epay,29016.0,4.761904762,1450.8000000000002,4.4
+371-85-5789,B,Abuja,Normal,Male,Health and beauty,31672.800000000003,3,4750.92,99769.32,3/5/2019,10:40,Epay,95018.4,4.761904762,4750.92,5.1
+273-16-6619,B,Abuja,Normal,Male,Home and lifestyle,11952.000000000002,2,1195.2,25099.2,3/15/2019,12:20,Card,23904.000000000004,4.761904762,1195.2,4.4
+649-29-6775,B,Abuja,Normal,Male,Fashion accessories,12067.2,1,603.36,12670.56,2/8/2019,15:31,Cash,12067.2,4.761904762,603.36,6.7
+145-94-9061,B,Abuja,Normal,Female,Food and beverages,31809.6,5,7952.4,167000.4,1/25/2019,19:48,Cash,159048.0,4.761904762,7952.4,9.6
+871-79-8483,B,Abuja,Normal,Male,Fashion accessories,33886.8,5,8471.699999999999,177905.7,2/25/2019,19:39,Card,169434.0,4.761904762,8471.699999999999,4.8
+149-71-6266,B,Abuja,Member,Male,Sports and travel,28105.2,9,12647.34,265594.14,1/28/2019,12:43,Cash,252946.8,4.761904762,12647.34,4.5
+640-49-2076,B,Abuja,Normal,Male,Sports and travel,30160.8,8,12064.32,253350.72,1/10/2019,14:49,Cash,241286.4,4.761904762,12064.32,5.1
+777-82-7220,B,Abuja,Member,Male,Home and lifestyle,10843.2,8,4337.28,91082.88,3/3/2019,13:01,Cash,86745.6,4.761904762,4337.28,7.7
+280-35-5823,B,Abuja,Member,Female,Home and lifestyle,31219.2,1,1560.96,32780.16,1/17/2019,18:45,Epay,31219.2,4.761904762,1560.96,7.9
+354-25-5821,B,Abuja,Member,Female,Sports and travel,24883.2,6,7464.96,156764.15999999997,2/8/2019,13:03,Cash,149299.2,4.761904762,7464.96,5.6
+132-32-9879,B,Abuja,Member,Female,Electronic accessories,33825.6,4,6765.120000000001,142067.52,3/9/2019,18:00,Cash,135302.4,4.761904762,6765.120000000001,9.5
+370-41-7321,B,Abuja,Member,Male,Health and beauty,20408.4,9,9183.78,192859.38,2/27/2019,17:24,Card,183675.6,4.761904762,9183.78,8.4
+727-46-3608,B,Abuja,Member,Female,Food and beverages,7203.6,9,3241.62,68074.02,2/6/2019,15:47,Epay,64832.4,4.761904762,3241.62,4.1
+669-54-1719,B,Abuja,Member,Male,Electronic accessories,6814.8,6,2044.44,42933.24,2/10/2019,12:45,Card,40888.8,4.761904762,2044.44,8.1
+616-24-2851,B,Abuja,Member,Female,Fashion accessories,6433.200000000001,4,1286.64,27019.44,3/22/2019,14:42,Epay,25732.800000000003,4.761904762,1286.64,6.5
+242-55-6721,B,Abuja,Normal,Male,Home and lifestyle,5817.6,2,581.76,12216.96,3/7/2019,11:49,Epay,11635.2,4.761904762,581.76,6.5
+347-34-2234,B,Abuja,Member,Female,Sports and travel,19825.2,9,8921.34,187348.14,2/3/2019,13:40,Epay,178426.8,4.761904762,8921.34,10.0
+853-23-2453,B,Abuja,Member,Male,Health and beauty,27266.4,4,5453.28,114518.88,2/14/2019,14:35,Cash,109065.6,4.761904762,5453.28,7.6
+109-28-2512,B,Abuja,Member,Female,Fashion accessories,35139.6,6,10541.88,221379.48,1/7/2019,15:01,Epay,210837.6,4.761904762,10541.88,9.9
+510-95-6347,B,Abuja,Member,Female,Food and beverages,17467.2,3,2620.08,55021.68,3/5/2019,18:17,Epay,52401.6,4.761904762,2620.08,4.0
+847-38-7188,B,Abuja,Normal,Female,Food and beverages,34804.8,3,5220.72,109635.12,1/26/2019,19:56,Epay,104414.4,4.761904762,5220.72,6.4
+318-68-5053,B,Abuja,Normal,Female,Health and beauty,27716.4,6,8314.92,174613.31999999998,2/27/2019,17:55,Cash,166298.4,4.761904762,8314.92,6.1
+152-08-9985,B,Abuja,Member,Male,Health and beauty,23169.6,9,10426.32,218952.72,3/12/2019,12:09,Card,208526.4,4.761904762,10426.32,8.6
+766-85-7061,B,Abuja,Normal,Male,Health and beauty,31633.2,10,15816.6,332148.6,3/29/2019,10:25,Epay,316332.0,4.761904762,15816.6,5.1
+733-01-9107,B,Abuja,Normal,Male,Home and lifestyle,29772.0,6,8931.6,187563.6,3/5/2019,18:14,Cash,178632.0,4.761904762,8931.6,7.4
+716-39-1409,B,Abuja,Normal,Male,Health and beauty,10926.0,7,3824.1,80306.09999999999,3/19/2019,18:19,Cash,76482.0,4.761904762,3824.1,8.0
+479-26-8945,B,Abuja,Member,Female,Sports and travel,5936.4,2,593.64,12466.44,2/5/2019,11:32,Epay,11872.8,4.761904762,593.64,4.6
+227-78-1148,B,Abuja,Normal,Female,Fashion accessories,26222.4,7,9177.84,192734.64,2/15/2019,12:44,Cash,183556.8,4.761904762,9177.84,8.4
+291-32-1427,B,Abuja,Member,Male,Fashion accessories,7898.400000000001,5,1974.6,41466.6,3/5/2019,12:29,Epay,39492.0,4.761904762,1974.6,5.3
+659-65-8956,B,Abuja,Member,Male,Fashion accessories,18489.6,1,924.48,19414.08,1/16/2019,15:26,Epay,18489.6,4.761904762,924.48,5.2
+378-24-2715,B,Abuja,Normal,Male,Home and lifestyle,19238.4,2,1923.84,40400.64,1/20/2019,20:38,Epay,38476.8,4.761904762,1923.84,4.1
+219-22-9386,B,Abuja,Member,Male,Sports and travel,35985.6,9,16193.52,340063.92,3/9/2019,17:26,Card,323870.4,4.761904762,16193.52,4.2
+268-27-6179,B,Abuja,Member,Female,Fashion accessories,20329.2,8,8131.68,170765.28,3/9/2019,14:57,Epay,162633.6,4.761904762,8131.68,7.3
+549-84-7482,B,Abuja,Normal,Female,Sports and travel,32500.8,9,14625.36,307132.56,2/8/2019,11:15,Epay,292507.2,4.761904762,14625.36,7.2
+191-10-6171,B,Abuja,Normal,Female,Fashion accessories,14263.2,7,4992.12,104834.52,1/25/2019,13:18,Cash,99842.4,4.761904762,4992.12,7.5
+695-51-0018,B,Abuja,Normal,Female,Sports and travel,12542.4,4,2508.48,52678.08,2/10/2019,18:36,Cash,50169.600000000006,4.761904762,2508.48,7.4
+590-83-4591,B,Abuja,Member,Male,Electronic accessories,31482.0,6,9444.6,198336.6,2/17/2019,14:40,Card,188892.00000000003,4.761904762,9444.6,8.8
+241-72-9525,B,Abuja,Normal,Male,Sports and travel,18687.6,10,9343.8,196219.8,2/16/2019,12:21,Cash,186876.0,4.761904762,9343.8,8.2
+262-47-2794,B,Abuja,Member,Male,Home and lifestyle,25869.6,8,10347.84,217304.64,3/6/2019,15:07,Card,206956.8,4.761904762,10347.84,6.2
+608-96-3517,B,Abuja,Member,Female,Fashion accessories,32954.4,4,6590.88,138408.48,3/23/2019,19:20,Card,131817.6,4.761904762,6590.88,4.8
+279-74-2924,B,Abuja,Member,Male,Electronic accessories,25981.2,1,1299.06,27280.26,1/4/2019,19:40,Cash,25981.2,4.761904762,1299.06,6.1
+307-85-2293,B,Abuja,Normal,Male,Home and lifestyle,18100.8,5,4525.2,95029.2,3/7/2019,13:58,Epay,90504.0,4.761904762,4525.2,9.7
+743-04-1105,B,Abuja,Member,Male,Health and beauty,34999.2,9,15749.639999999998,330742.44,3/30/2019,14:43,Epay,314992.8,4.761904762,15749.639999999998,6.0
+423-57-2993,B,Abuja,Normal,Male,Sports and travel,33620.4,6,10086.12,211808.52,3/27/2019,19:18,Epay,201722.4,4.761904762,10086.12,10.0
+548-46-9322,B,Abuja,Normal,Male,Food and beverages,14364.0,10,7182.0,150822.0,2/20/2019,15:24,Card,143640.0,4.761904762,7182.0,5.9
+505-02-0892,B,Abuja,Member,Male,Health and beauty,15325.2,8,6130.08,128731.68,2/25/2019,14:12,Epay,122601.6,4.761904762,6130.08,5.6
+316-55-4634,B,Abuja,Member,Male,Food and beverages,28818.0,5,7204.5,151294.5,1/26/2019,12:45,Card,144090.0,4.761904762,7204.5,9.4
+608-27-6295,B,Abuja,Member,Male,Electronic accessories,19040.4,6,5712.12,119954.52,1/19/2019,17:34,Card,114242.4,4.761904762,5712.12,9.8
+414-12-7047,B,Abuja,Normal,Male,Food and beverages,7124.4,8,2849.76,59844.96,1/18/2019,12:04,Epay,56995.2,4.761904762,2849.76,8.7
+895-66-0685,B,Abuja,Member,Male,Food and beverages,6508.799999999999,3,976.32,20502.72,3/5/2019,19:46,Epay,19526.4,4.761904762,976.32,8.0
+305-14-0245,B,Abuja,Member,Female,Home and lifestyle,34016.4,8,13606.56,285737.76,3/3/2019,19:00,Epay,272131.2,4.761904762,13606.56,7.5
+732-04-5373,B,Abuja,Member,Male,Home and lifestyle,16729.2,4,3345.84,70262.64,2/8/2019,10:53,Cash,66916.8,4.761904762,3345.84,7.0
+284-34-9626,B,Abuja,Normal,Female,Home and lifestyle,27734.4,3,4160.16,87363.36,2/11/2019,10:39,Card,83203.2,4.761904762,4160.16,7.2
+437-58-8131,B,Abuja,Normal,Female,Fashion accessories,26467.2,2,2646.7200000000003,55581.12,1/15/2019,13:41,Epay,52934.4,4.761904762,2646.7200000000003,4.6
+641-43-2399,B,Abuja,Normal,Male,Home and lifestyle,9198.0,4,1839.6,38631.6,1/26/2019,20:23,Epay,36792.0,4.761904762,1839.6,5.7
+542-41-0513,B,Abuja,Member,Female,Electronic accessories,20696.4,4,4139.28,86924.88,3/15/2019,11:57,Cash,82785.6,4.761904762,4139.28,6.6
+875-46-5808,B,Abuja,Member,Male,Health and beauty,9324.0,10,4662.0,97902.0,2/6/2019,14:51,Epay,93240.0,4.761904762,4662.0,8.7
+394-43-4238,B,Abuja,Member,Male,Home and lifestyle,6397.2,5,1599.3,33585.3,2/15/2019,12:42,Card,31986.0,4.761904762,1599.3,5.4
+573-58-9734,B,Abuja,Normal,Female,Fashion accessories,10933.2,3,1639.98,34439.579999999994,3/28/2019,13:41,Epay,32799.6,4.761904762,1639.98,5.1
+817-69-8206,B,Abuja,Normal,Female,Electronic accessories,35902.8,9,16156.26,339281.46,3/2/2019,19:42,Card,323125.2,4.761904762,16156.26,6.5
+142-63-6033,B,Abuja,Normal,Male,Home and lifestyle,33249.6,5,8312.4,174560.4,3/20/2019,19:17,Epay,166248.0,4.761904762,8312.4,4.9
+656-16-1063,B,Abuja,Normal,Male,Sports and travel,16711.2,3,2506.68,52640.27999999999,1/4/2019,13:24,Card,50133.6,4.761904762,2506.68,4.4
+891-58-8335,B,Abuja,Member,Female,Sports and travel,10659.6,7,3730.86,78348.06,3/11/2019,15:53,Cash,74617.2,4.761904762,3730.86,6.5
+560-30-5617,B,Abuja,Normal,Female,Sports and travel,8917.2,5,2229.3,46815.3,3/24/2019,18:27,Cash,44586.0,4.761904762,2229.3,8.5
+549-03-9315,B,Abuja,Normal,Male,Fashion accessories,34153.200000000004,8,13661.28,286886.88,2/12/2019,12:58,Epay,273225.60000000003,4.761904762,13661.28,8.7
+790-29-1172,B,Abuja,Normal,Female,Food and beverages,20642.4,3,3096.36,65023.55999999999,3/10/2019,18:59,Card,61927.2,4.761904762,3096.36,7.9
+239-36-3640,B,Abuja,Normal,Male,Electronic accessories,16326.0,6,4897.8,102853.8,1/31/2019,13:44,Epay,97956.0,4.761904762,4897.8,6.1
+468-01-2051,B,Abuja,Normal,Male,Food and beverages,22348.8,7,7822.080000000001,164263.68,3/6/2019,13:46,Epay,156441.6,4.761904762,7822.080000000001,5.4
+836-82-5858,B,Abuja,Member,Male,Health and beauty,24973.2,9,11237.94,235996.74,1/26/2019,19:14,Epay,224758.8,4.761904762,11237.94,4.0
+466-61-5506,B,Abuja,Member,Female,Electronic accessories,32652.0,6,9795.6,205707.6,2/26/2019,10:52,Cash,195912.00000000003,4.761904762,9795.6,5.3
+289-65-5721,B,Abuja,Normal,Female,Fashion accessories,29293.2,2,2929.32,61515.72,1/26/2019,19:28,Cash,58586.4,4.761904762,2929.32,6.5
+545-46-3100,B,Abuja,Member,Female,Electronic accessories,3812.4,3,571.86,12009.06,3/12/2019,13:52,Card,11437.2,4.761904762,571.86,8.7
+418-02-5978,B,Abuja,Normal,Female,Health and beauty,30272.4,9,13622.58,286074.18,2/11/2019,10:54,Cash,272451.6,4.761904762,13622.58,8.0
+269-04-5750,B,Abuja,Member,Male,Fashion accessories,26575.2,4,5315.04,111615.84,2/21/2019,18:31,Cash,106300.8,4.761904762,5315.04,6.7
+346-84-3103,B,Abuja,Member,Female,Electronic accessories,4759.2,5,1189.8,24985.8,3/2/2019,19:26,Cash,23796.0,4.761904762,1189.8,4.3
+376-02-8238,B,Abuja,Normal,Male,Home and lifestyle,33793.200000000004,8,13517.28,283862.88,2/2/2019,18:42,Card,270345.60000000003,4.761904762,13517.28,8.3
+866-05-7563,B,Abuja,Member,Female,Electronic accessories,29304.000000000004,3,4395.6,92307.6,2/9/2019,19:43,Cash,87912.0,4.761904762,4395.6,4.8
+785-13-7708,B,Abuja,Normal,Male,Food and beverages,26301.6,7,9205.56,193316.76,1/14/2019,19:06,Card,184111.2,4.761904762,9205.56,4.2
+845-51-0542,B,Abuja,Member,Male,Food and beverages,16758.0,9,7541.1,158363.1,2/2/2019,15:34,Epay,150822.0,4.761904762,7541.1,6.4
+110-48-7033,B,Abuja,Member,Male,Fashion accessories,11743.2,4,2348.64,49321.44,1/29/2019,14:12,Cash,46972.8,4.761904762,2348.64,9.0
+655-85-5130,B,Abuja,Member,Female,Fashion accessories,13787.999999999998,4,2757.6,57909.600000000006,3/13/2019,19:22,Cash,55151.99999999999,4.761904762,2757.6,5.7
+154-74-7179,B,Abuja,Normal,Male,Sports and travel,19602.0,1,980.1,20582.1,2/26/2019,19:24,Epay,19602.0,4.761904762,980.1,7.9
+571-94-0759,B,Abuja,Member,Female,Food and beverages,26856.0,10,13427.999999999998,281988.0,1/8/2019,20:55,Cash,268560.0,4.761904762,13427.999999999998,9.5
+783-09-1637,B,Abuja,Normal,Female,Sports and travel,24274.800000000003,5,6068.700000000001,127442.7,3/6/2019,18:13,Epay,121374.0,4.761904762,6068.700000000001,6.3
+477-24-6490,B,Abuja,Normal,Female,Health and beauty,35895.6,6,10768.68,226142.28,2/26/2019,16:52,Epay,215373.6,4.761904762,10768.68,7.9
+566-19-5475,B,Abuja,Normal,Male,Fashion accessories,17269.2,7,6044.22,126928.62,1/7/2019,20:52,Cash,120884.4,4.761904762,6044.22,6.2
+498-41-1961,B,Abuja,Normal,Male,Health and beauty,24004.800000000003,5,6001.200000000001,126025.2,2/20/2019,18:01,Cash,120024.0,4.761904762,6001.200000000001,7.6
+283-79-9594,B,Abuja,Normal,Female,Food and beverages,17463.6,7,6112.26,128357.46,1/25/2019,13:30,Card,122245.2,4.761904762,6112.26,5.2
+139-20-0155,B,Abuja,Member,Male,Electronic accessories,14507.999999999998,10,7253.999999999999,152334.0,1/24/2019,17:37,Card,145080.0,4.761904762,7253.999999999999,7.0
+585-03-5943,B,Abuja,Normal,Male,Health and beauty,6519.599999999999,10,3259.8,68455.8,3/13/2019,11:46,Epay,65196.0,4.761904762,3259.8,5.9
+573-10-3877,B,Abuja,Member,Male,Health and beauty,14043.6,1,702.18,14745.78,3/12/2019,16:46,Card,14043.6,4.761904762,702.18,4.7
+396-90-2219,B,Abuja,Normal,Female,Electronic accessories,5385.6,8,2154.24,45239.04,2/23/2019,12:29,Cash,43084.8,4.761904762,2154.24,8.6
+532-59-7201,B,Abuja,Member,Male,Sports and travel,28774.800000000003,6,8632.44,181281.24,1/31/2019,14:04,Cash,172648.8,4.761904762,8632.44,5.5
+276-54-0879,B,Abuja,Normal,Male,Sports and travel,35186.4,4,7037.28,147782.88,3/12/2019,19:53,Epay,140745.6,4.761904762,7037.28,6.4
+730-61-8757,B,Abuja,Member,Male,Health and beauty,18406.8,4,3681.36,77308.56,1/25/2019,10:11,Card,73627.2,4.761904762,3681.36,4.0
+868-81-1752,B,Abuja,Normal,Male,Home and lifestyle,7927.2,9,3567.24,74912.04000000001,2/7/2019,18:48,Cash,71344.8,4.761904762,3567.24,6.8
+692-27-8933,B,Abuja,Normal,Female,Sports and travel,20862.0,6,6258.6,131430.6,2/24/2019,13:02,Cash,125172.0,4.761904762,6258.6,5.2
+374-17-3652,B,Abuja,Member,Female,Food and beverages,15415.2,9,6936.840000000001,145673.64,2/5/2019,15:26,Card,138736.8,4.761904762,6936.840000000001,8.9
+378-07-7001,B,Abuja,Member,Male,Electronic accessories,17312.4,3,2596.86,54534.06,2/10/2019,18:23,Card,51937.2,4.761904762,2596.86,7.8
+433-75-6987,B,Abuja,Member,Female,Health and beauty,20149.2,7,7052.22,148096.62,3/5/2019,19:06,Epay,141044.4,4.761904762,7052.22,8.9
+873-95-4984,B,Abuja,Member,Female,Health and beauty,27684.000000000004,7,9689.4,203477.4,2/15/2019,20:21,Cash,193788.0,4.761904762,9689.4,7.7
+400-45-1220,B,Abuja,Normal,Female,Health and beauty,4860.0,10,2430.0,51030.0,2/27/2019,11:06,Card,48600.0,4.761904762,2430.0,4.8
+115-99-4379,B,Abuja,Member,Female,Fashion accessories,19702.8,7,6895.98,144815.58,3/14/2019,19:02,Card,137919.6,4.761904762,6895.98,8.5
+565-67-6697,B,Abuja,Member,Male,Home and lifestyle,9720.0,9,4374.0,91854.0,3/2/2019,14:16,Cash,87480.0,4.761904762,4374.0,4.8
+889-04-9723,B,Abuja,Member,Female,Food and beverages,32090.4,4,6418.08,134779.68000000002,1/7/2019,12:20,Card,128361.6,4.761904762,6418.08,7.8
+453-63-6187,B,Abuja,Normal,Male,Electronic accessories,9900.0,3,1485.0,31185.0,3/1/2019,15:40,Epay,29700.0,4.761904762,1485.0,6.5
+578-80-7669,B,Abuja,Normal,Male,Sports and travel,26989.2,1,1349.46,28338.660000000003,3/16/2019,16:58,Cash,26989.2,4.761904762,1349.46,5.6
+201-86-2184,B,Abuja,Member,Female,Electronic accessories,9453.6,7,3308.76,69483.95999999999,2/2/2019,19:40,Cash,66175.2,4.761904762,3308.76,9.9
+261-12-8671,B,Abuja,Normal,Female,Fashion accessories,21945.6,2,2194.56,46085.76,1/25/2019,19:39,Card,43891.2,4.761904762,2194.56,4.9
+843-01-4703,B,Abuja,Member,Female,Home and lifestyle,12736.8,9,5731.56,120362.76,1/5/2019,19:50,Card,114631.2,4.761904762,5731.56,9.6
+182-69-8360,B,Abuja,Normal,Female,Electronic accessories,8514.0,4,1702.8000000000002,35758.8,1/30/2019,13:32,Card,34056.0,4.761904762,1702.8000000000002,4.0
+868-52-7573,B,Abuja,Normal,Female,Food and beverages,35888.4,5,8972.1,188414.1,1/14/2019,12:09,Cash,179442.0,4.761904762,8972.1,9.9
+525-88-7307,B,Abuja,Member,Male,Sports and travel,27295.2,1,1364.7600000000002,28659.960000000003,1/31/2019,13:19,Cash,27295.2,4.761904762,1364.7600000000002,5.8
+596-42-3999,B,Abuja,Normal,Male,Food and beverages,6559.2,7,2295.7200000000003,48210.12,3/10/2019,14:04,Card,45914.4,4.761904762,2295.7200000000003,6.6
+173-82-9529,B,Abuja,Normal,Female,Fashion accessories,13662.000000000002,10,6831.000000000001,143451.0,1/26/2019,14:51,Cash,136620.0,4.761904762,6831.000000000001,9.7
+760-54-1821,B,Abuja,Normal,Male,Home and lifestyle,4892.4,9,2201.58,46233.18,3/15/2019,10:26,Cash,44031.6,4.761904762,2201.58,5.8
+793-10-3222,B,Abuja,Member,Female,Health and beauty,14781.6,6,4434.48,93124.08,3/5/2019,13:30,Card,88689.6,4.761904762,4434.48,8.3
+346-12-3257,B,Abuja,Member,Male,Electronic accessories,6926.4,9,3116.88,65454.48,3/4/2019,16:28,Cash,62337.6,4.761904762,3116.88,8.0
+831-64-0259,B,Abuja,Normal,Female,Fashion accessories,14310.0,5,3577.5,75127.5,2/22/2019,10:43,Epay,71550.0,4.761904762,3577.5,9.6
+725-32-9708,B,Abuja,Member,Female,Fashion accessories,24735.6,4,4947.12,103889.52,1/4/2019,19:01,Cash,98942.4,4.761904762,4947.12,4.1
+244-08-0162,B,Abuja,Normal,Female,Health and beauty,12315.6,10,6157.8,129313.8,1/2/2019,13:00,Cash,123156.0,4.761904762,6157.8,5.1
+569-71-4390,B,Abuja,Normal,Male,Sports and travel,7873.200000000001,2,787.3199999999999,16533.72,1/25/2019,14:29,Epay,15746.4,4.761904762,787.3199999999999,6.9
+268-03-6164,B,Abuja,Normal,Male,Health and beauty,34599.6,1,1729.98,36329.58,1/25/2019,16:28,Epay,34599.6,4.761904762,1729.98,7.8
+848-07-1692,B,Abuja,Normal,Female,Health and beauty,20599.2,2,2059.92,43258.32,1/12/2019,17:13,Epay,41198.4,4.761904762,2059.92,8.3
+301-81-8610,B,Abuja,Member,Male,Fashion accessories,9151.2,8,3660.48,76870.08,3/19/2019,19:42,Card,73209.6,4.761904762,3660.48,6.7
+198-84-7132,B,Abuja,Member,Male,Fashion accessories,14619.6,9,6578.82,138155.22,1/2/2019,13:40,Cash,131576.4,4.761904762,6578.82,7.0
+650-98-6268,B,Abuja,Member,Female,Food and beverages,7513.200000000001,3,1126.98,23666.58,3/20/2019,13:53,Card,22539.6,4.761904762,1126.98,8.0
+741-73-3559,B,Abuja,Normal,Male,Sports and travel,24217.2,5,6054.299999999999,127140.3,2/27/2019,17:27,Cash,121086.0,4.761904762,6054.299999999999,6.9
+286-75-7818,B,Abuja,Normal,Male,Fashion accessories,24868.8,2,2486.88,52224.48,1/31/2019,19:48,Card,49737.6,4.761904762,2486.88,6.9
+616-87-0016,B,Abuja,Normal,Male,Fashion accessories,34394.4,7,12038.04,252798.84,3/9/2019,14:36,Card,240760.8,4.761904762,12038.04,9.6
+837-55-7229,B,Abuja,Normal,Female,Fashion accessories,17078.399999999998,1,853.92,17932.32,2/22/2019,18:19,Card,17078.399999999998,4.761904762,853.92,6.8
+394-30-3170,B,Abuja,Member,Female,Sports and travel,31834.800000000003,8,12733.92,267412.32,3/22/2019,19:35,Card,254678.4,4.761904762,12733.92,4.3
+390-80-5128,B,Abuja,Member,Female,Health and beauty,6893.999999999999,1,344.7,7238.700000000001,1/28/2019,17:58,Card,6893.999999999999,4.761904762,344.7,9.5
+296-11-7041,B,Abuja,Member,Female,Health and beauty,9745.2,1,487.26,10232.46,1/12/2019,20:07,Card,9745.2,4.761904762,487.26,5.3
+449-27-2918,B,Abuja,Member,Female,Sports and travel,14083.2,1,704.16,14787.36,3/26/2019,11:02,Card,14083.2,4.761904762,704.16,9.6
+891-01-7034,B,Abuja,Normal,Female,Electronic accessories,26895.6,6,8068.68,169442.28,1/1/2019,19:07,Cash,161373.6,4.761904762,8068.68,6.7
+744-09-5786,B,Abuja,Normal,Male,Electronic accessories,7923.6,6,2377.08,49918.67999999999,1/2/2019,18:50,Cash,47541.6,4.761904762,2377.08,7.6
+767-54-1907,B,Abuja,Member,Female,Fashion accessories,10641.6,5,2660.4,55868.4,2/13/2019,16:59,Cash,53208.00000000001,4.761904762,2660.4,6.9
+710-46-4433,B,Abuja,Member,Female,Food and beverages,27864.000000000004,9,12538.8,263314.8,2/15/2019,14:15,Card,250776.0,4.761904762,12538.8,4.5
+533-33-5337,B,Abuja,Normal,Male,Electronic accessories,28580.4,10,14290.2,300094.2,2/7/2019,20:24,Cash,285804.0,4.761904762,14290.2,6.2
+234-03-4040,B,Abuja,Member,Female,Food and beverages,26298.0,10,13149.0,276129.0,3/3/2019,12:25,Card,262980.0,4.761904762,13149.0,8.7
+554-53-3790,B,Abuja,Normal,Male,Sports and travel,13327.2,6,3998.16,83961.36,3/22/2019,18:33,Cash,79963.2,4.761904762,3998.16,4.5
+731-59-7531,B,Abuja,Member,Male,Health and beauty,26125.2,8,10450.08,219451.68,3/30/2019,17:58,Cash,209001.6,4.761904762,10450.08,4.6
+701-69-8742,B,Abuja,Normal,Male,Sports and travel,12373.2,10,6186.599999999999,129918.6,3/16/2019,10:11,Epay,123732.0,4.761904762,6186.599999999999,6.7
+305-18-3552,B,Abuja,Member,Male,Home and lifestyle,21736.8,10,10868.4,228236.4,2/12/2019,16:19,Cash,217368.0,4.761904762,10868.4,6.0
+438-01-4015,B,Abuja,Member,Female,Sports and travel,17816.4,4,3563.28,74828.88,3/21/2019,15:25,Epay,71265.6,4.761904762,3563.28,6.6
+709-58-4068,B,Abuja,Normal,Female,Fashion accessories,14792.4,10,7396.200000000001,155320.2,2/28/2019,14:42,Cash,147924.0,4.761904762,7396.200000000001,7.3
+627-95-3243,B,Abuja,Member,Female,Home and lifestyle,27964.800000000003,9,12584.159999999998,264267.36,2/4/2019,13:21,Epay,251683.2,4.761904762,12584.159999999998,9.8
+686-41-0932,B,Abuja,Normal,Female,Fashion accessories,12492.000000000002,2,1249.2,26233.2,3/13/2019,19:48,Epay,24984.000000000004,4.761904762,1249.2,8.2
+608-04-3797,B,Abuja,Member,Female,Health and beauty,9115.2,8,3646.08,76567.68,3/5/2019,20:24,Epay,72921.6,4.761904762,3646.08,8.7
+437-53-3084,B,Abuja,Normal,Male,Fashion accessories,35960.4,2,3596.0399999999995,75516.84,2/26/2019,11:48,Epay,71920.8,4.761904762,3596.0399999999995,7.1
+632-32-4574,B,Abuja,Normal,Male,Sports and travel,27331.2,8,10932.48,229582.08,3/20/2019,14:14,Cash,218649.6,4.761904762,10932.48,5.5
+401-18-8016,B,Abuja,Member,Female,Sports and travel,35326.8,1,1766.34,37093.14,1/21/2019,17:36,Cash,35326.8,4.761904762,1766.34,8.9
+277-63-2961,B,Abuja,Member,Male,Sports and travel,26629.2,1,1331.46,27960.66,2/3/2019,15:53,Card,26629.2,4.761904762,1331.46,5.4
+282-35-2475,B,Abuja,Normal,Female,Sports and travel,33591.6,2,3359.16,70542.36,3/25/2019,17:53,Cash,67183.2,4.761904762,3359.16,6.3
+511-54-3087,B,Abuja,Normal,Male,Sports and travel,31842.0,1,1592.1,33434.1,2/25/2019,16:36,Card,31842.0,4.761904762,1592.1,9.5
+387-49-4215,B,Abuja,Member,Female,Sports and travel,17460.0,3,2619.0,54999.0,1/8/2019,12:50,Cash,52380.0,4.761904762,2619.0,6.7
+862-17-9201,B,Abuja,Normal,Female,Food and beverages,30258.0,6,9077.4,190625.4,1/29/2019,10:48,Card,181548.0,4.761904762,9077.4,7.7
+291-21-5991,B,Abuja,Member,Male,Health and beauty,22064.4,5,5516.1,115838.1,3/29/2019,14:28,Cash,110322.0,4.761904762,5516.1,7.0
+347-72-6115,B,Abuja,Member,Female,Sports and travel,32666.4,7,11433.24,240098.04,1/16/2019,18:03,Card,228664.8,4.761904762,11433.24,6.2
+425-85-2085,B,Abuja,Member,Male,Health and beauty,19749.6,5,4937.4,103685.4,3/29/2019,16:48,Epay,98748.0,4.761904762,4937.4,9.8
+734-91-1155,B,Abuja,Normal,Female,Electronic accessories,16455.6,3,2468.34,51835.14,3/26/2019,10:34,Card,49366.8,4.761904762,2468.34,7.7
+286-62-6248,B,Abuja,Normal,Male,Fashion accessories,14115.6,4,2823.1200000000003,59285.52,1/16/2019,20:03,Card,56462.4,4.761904762,2823.1200000000003,9.0
+339-38-9982,B,Abuja,Member,Male,Fashion accessories,21549.6,2,2154.96,45254.16,1/13/2019,14:55,Epay,43099.2,4.761904762,2154.96,6.7
+827-44-5872,B,Abuja,Member,Female,Food and beverages,19569.6,10,9784.8,205480.8,2/7/2019,11:28,Card,195696.0,4.761904762,9784.8,6.1
+857-67-9057,B,Abuja,Normal,Male,Electronic accessories,8262.0,10,4131.0,86751.0,2/6/2019,19:20,Epay,82620.0,4.761904762,4131.0,8.2
+339-12-4827,B,Abuja,Member,Female,Fashion accessories,26625.6,1,1331.28,27956.88,1/5/2019,11:32,Card,26625.6,4.761904762,1331.28,5.0
+173-50-1108,B,Abuja,Member,Female,Sports and travel,7264.8,4,1452.9600000000005,30512.16,2/13/2019,12:14,Card,29059.2,4.761904762,1452.9600000000005,5.0
+841-18-8232,B,Abuja,Normal,Female,Food and beverages,25632.0,1,1281.6,26913.6,1/5/2019,20:40,Card,25632.0,4.761904762,1281.6,9.2
+701-23-5550,B,Abuja,Member,Male,Home and lifestyle,13971.6,4,2794.32,58680.72,3/19/2019,13:40,Epay,55886.4,4.761904762,2794.32,4.9
+539-21-7227,B,Abuja,Normal,Female,Sports and travel,18554.4,5,4638.6,97410.6,1/26/2019,17:45,Cash,92772.0,4.761904762,4638.6,4.2
+747-58-7183,B,Abuja,Normal,Male,Fashion accessories,20617.2,3,3092.5800000000004,64944.18,2/9/2019,20:31,Epay,61851.6,4.761904762,3092.5800000000004,6.5
+582-52-8065,B,Abuja,Normal,Female,Fashion accessories,19551.6,9,8798.22,184762.62,2/22/2019,10:49,Cash,175964.4,4.761904762,8798.22,8.9
+210-57-1719,B,Abuja,Normal,Female,Health and beauty,20966.4,9,9434.88,198132.48,2/5/2019,12:34,Cash,188697.6,4.761904762,9434.88,9.7
+356-44-8813,B,Abuja,Normal,Male,Home and lifestyle,13492.8,3,2023.9200000000003,42502.32000000001,1/20/2019,13:45,Card,40478.4,4.761904762,2023.9200000000003,7.7
+198-66-9832,B,Abuja,Member,Female,Fashion accessories,25934.4,2,2593.44,54462.24,2/4/2019,19:38,Cash,51868.8,4.761904762,2593.44,9.5
+174-75-0888,B,Abuja,Normal,Male,Electronic accessories,7768.799999999999,9,3495.96,73415.15999999999,3/14/2019,12:32,Cash,69919.2,4.761904762,3495.96,7.3
+134-54-4720,B,Abuja,Normal,Female,Electronic accessories,15271.2,8,6108.48,128278.08000000002,1/30/2019,13:58,Epay,122169.6,4.761904762,6108.48,5.7
+514-37-2845,B,Abuja,Normal,Male,Fashion accessories,35730.0,2,3573.0000000000005,75033.0,3/20/2019,13:02,Cash,71460.0,4.761904762,3573.0000000000005,9.0
+241-11-2261,B,Abuja,Normal,Female,Fashion accessories,28749.6,7,10062.36,211309.56,1/10/2019,10:33,Card,201247.2,4.761904762,10062.36,5.5
+834-83-1826,B,Abuja,Member,Female,Home and lifestyle,29534.4,5,7383.6,155055.6,2/25/2019,17:16,Card,147672.0,4.761904762,7383.6,7.6
+343-61-3544,B,Abuja,Member,Male,Sports and travel,9601.2,10,4800.6,100812.6,1/29/2019,11:48,Cash,96012.0,4.761904762,4800.6,8.6
+355-34-6244,B,Abuja,Normal,Male,Food and beverages,26060.4,2,2606.04,54726.84,1/13/2019,19:55,Card,52120.8,4.761904762,2606.04,8.1
+339-96-8318,B,Abuja,Member,Male,Fashion accessories,29271.6,7,10245.06,215146.26,3/1/2019,19:49,Epay,204901.2,4.761904762,10245.06,6.3
+458-61-0011,B,Abuja,Normal,Male,Food and beverages,21708.0,4,4341.6,91173.6,2/20/2019,18:43,Cash,86832.0,4.761904762,4341.6,5.8
+207-73-1363,B,Abuja,Normal,Male,Health and beauty,25023.6,2,2502.36,52549.56,3/1/2019,12:15,Epay,50047.2,4.761904762,2502.36,8.1
+359-90-3665,B,Abuja,Member,Female,Fashion accessories,6508.799999999999,4,1301.76,27336.960000000003,1/14/2019,18:03,Card,26035.2,4.761904762,1301.76,9.5
+375-72-3056,B,Abuja,Normal,Male,Sports and travel,22701.6,3,3405.24,71510.04000000001,1/19/2019,15:58,Epay,68104.8,4.761904762,3405.24,7.0
+585-11-6748,B,Abuja,Member,Male,Sports and travel,34848.0,3,5227.2,109771.2,3/15/2019,13:05,Cash,104544.0,4.761904762,5227.2,5.3
+470-31-3286,B,Abuja,Normal,Male,Health and beauty,5335.2,3,800.2800000000001,16805.88,3/1/2019,11:30,Card,16005.6,4.761904762,800.2800000000001,8.7
+642-30-6693,B,Abuja,Normal,Female,Sports and travel,19623.6,6,5887.079999999999,123628.68,3/17/2019,13:54,Epay,117741.6,4.761904762,5887.079999999999,7.8
+830-58-2383,B,Abuja,Normal,Male,Home and lifestyle,11430.0,4,2286.0,48006.0,2/8/2019,15:26,Cash,45720.0,4.761904762,2286.0,8.6
+349-97-8902,B,Abuja,Member,Male,Food and beverages,20840.4,2,2084.04,43764.84,1/17/2019,10:37,Epay,41680.8,4.761904762,2084.04,8.9
+789-23-8625,B,Abuja,Member,Male,Fashion accessories,33559.2,3,5033.879999999999,105711.48,1/24/2019,11:45,Cash,100677.6,4.761904762,5033.879999999999,7.2
+327-40-9673,B,Abuja,Member,Male,Sports and travel,26136.0,6,7840.8,164656.8,1/13/2019,19:51,Cash,156816.0,4.761904762,7840.8,6.9
+670-79-6321,B,Abuja,Member,Female,Home and lifestyle,34052.4,7,11918.339999999998,250285.14,1/17/2019,15:27,Card,238366.8,4.761904762,11918.339999999998,4.9
+852-62-7105,B,Abuja,Normal,Female,Fashion accessories,29970.0,10,14985.0,314685.0,1/12/2019,11:25,Card,299700.0,4.761904762,14985.0,4.4
+598-06-7312,B,Abuja,Member,Male,Fashion accessories,32886.0,1,1644.3,34530.3,2/16/2019,15:42,Cash,32886.0,4.761904762,1644.3,6.8
+135-13-8269,B,Abuja,Member,Female,Food and beverages,28396.8,2,2839.68,59633.28,1/26/2019,16:04,Cash,56793.6,4.761904762,2839.68,9.1
+628-90-8624,B,Abuja,Member,Male,Health and beauty,29728.8,10,14864.4,312152.4,3/14/2019,14:41,Cash,297288.0,4.761904762,14864.4,5.0
+420-04-7590,B,Abuja,Normal,Male,Home and lifestyle,35892.0,3,5383.8,113059.8,3/18/2019,11:29,Epay,107676.0,4.761904762,5383.8,4.7
+182-88-2763,B,Abuja,Member,Male,Food and beverages,28767.6,3,4315.139999999999,90617.94,3/20/2019,19:28,Card,86302.8,4.761904762,4315.139999999999,5.0
+188-55-0967,B,Abuja,Member,Male,Health and beauty,23929.2,10,11964.6,251256.6,1/15/2019,15:01,Card,239292.00000000003,4.761904762,11964.6,5.0
+364-33-8584,B,Abuja,Member,Female,Food and beverages,6346.799999999999,5,1586.6999999999998,33320.700000000004,3/8/2019,15:27,Cash,31734.000000000004,4.761904762,1586.6999999999998,8.5
+665-63-9737,B,Abuja,Normal,Male,Fashion accessories,18871.2,3,2830.68,59444.28,2/27/2019,17:36,Epay,56613.6,4.761904762,2830.68,7.5
+695-09-5146,B,Abuja,Member,Female,Food and beverages,35564.4,3,5334.66,112027.86,2/23/2019,20:00,Epay,106693.2,4.761904762,5334.66,6.4
+794-32-2436,B,Abuja,Member,Male,Electronic accessories,20041.2,2,2004.12,42086.52,3/27/2019,15:08,Epay,40082.4,4.761904762,2004.12,6.0
+574-31-8277,B,Abuja,Member,Male,Fashion accessories,12106.8,1,605.34,12712.14,3/20/2019,19:55,Cash,12106.8,4.761904762,605.34,5.6
+369-82-2676,B,Abuja,Normal,Male,Electronic accessories,27237.6,5,6809.4,142997.4,1/15/2019,18:22,Epay,136188.0,4.761904762,6809.4,7.8
+563-47-4072,B,Abuja,Normal,Female,Health and beauty,20091.6,6,6027.48,126577.08,1/22/2019,11:52,Cash,120549.6,4.761904762,6027.48,9.9
+149-15-7606,B,Abuja,Member,Male,Sports and travel,13435.2,9,6045.84,126962.64,3/6/2019,15:31,Epay,120916.8,4.761904762,6045.84,5.1
+133-77-3154,B,Abuja,Member,Male,Fashion accessories,21664.8,4,4332.96,90992.16,2/16/2019,18:04,Card,86659.2,4.761904762,4332.96,9.4
+322-02-2271,B,Abuja,Normal,Female,Sports and travel,15469.2,3,2320.38,48727.98,2/3/2019,11:46,Cash,46407.6,4.761904762,2320.38,9.3
+725-67-2480,B,Abuja,Member,Female,Fashion accessories,21150.0,6,6345.0,133245.0,3/24/2019,18:14,Card,126900.0,4.761904762,6345.0,5.9
+779-42-2410,B,Abuja,Member,Male,Food and beverages,20786.4,3,3117.96,65477.15999999999,2/20/2019,13:06,Epay,62359.2,4.761904762,3117.96,7.7
+190-14-3147,B,Abuja,Normal,Female,Health and beauty,6469.2,4,1293.84,27170.64,2/23/2019,20:43,Epay,25876.8,4.761904762,1293.84,6.4
+679-22-6530,B,Abuja,Normal,Female,Sports and travel,14623.2,2,1462.3200000000002,30708.72,1/17/2019,10:01,Card,29246.4,4.761904762,1462.3200000000002,4.1
+642-61-4706,B,Abuja,Member,Male,Food and beverages,33624.0,2,3362.4,70610.4,3/30/2019,16:34,Cash,67248.0,4.761904762,3362.4,5.5
+576-31-4774,B,Abuja,Normal,Female,Health and beauty,26427.6,3,3964.14,83246.94,3/2/2019,13:10,Epay,79282.8,4.761904762,3964.14,4.0
+242-11-3142,B,Abuja,Member,Male,Fashion accessories,30157.2,2,3015.7200000000003,63330.12,2/24/2019,19:57,Cash,60314.4,4.761904762,3015.7200000000003,4.6
+752-23-3760,B,Abuja,Member,Female,Sports and travel,23068.8,7,8074.08,169555.68000000002,2/19/2019,19:29,Card,161481.6,4.761904762,8074.08,7.3
+528-87-5606,B,Abuja,Member,Female,Electronic accessories,14212.8,1,710.64,14923.439999999997,2/12/2019,19:43,Cash,14212.8,4.761904762,710.64,6.5
+320-85-2052,B,Abuja,Normal,Female,Sports and travel,12531.6,1,626.5799999999999,13158.18,1/14/2019,10:11,Card,12531.6,4.761904762,626.5799999999999,7.0
+510-79-0415,B,Abuja,Member,Female,Sports and travel,8308.8,6,2492.64,52345.44,1/24/2019,19:20,Epay,49852.8,4.761904762,2492.64,4.9
+241-96-5076,B,Abuja,Member,Female,Home and lifestyle,17676.0,2,1767.6,37119.6,1/8/2019,12:58,Card,35352.0,4.761904762,1767.6,6.4
+767-97-4650,B,Abuja,Member,Female,Sports and travel,23338.8,2,2333.88,49011.48,1/8/2019,11:59,Card,46677.6,4.761904762,2333.88,8.0
+826-58-8051,B,Abuja,Normal,Male,Home and lifestyle,22388.4,4,4477.679999999999,94031.28,1/6/2019,19:46,Epay,89553.59999999999,4.761904762,4477.679999999999,4.3
+176-64-7711,B,Abuja,Normal,Male,Food and beverages,11635.2,3,1745.28,36650.88,3/27/2019,19:11,Card,34905.6,4.761904762,1745.28,4.3
+191-29-0321,B,Abuja,Member,Female,Fashion accessories,7117.2,10,3558.6,74730.6,2/27/2019,18:57,Card,71172.0,4.761904762,3558.6,5.0
+729-06-2010,B,Abuja,Member,Male,Health and beauty,28969.2,9,13036.14,273758.94,1/6/2019,11:18,Cash,260722.8,4.761904762,13036.14,9.2
+640-48-5028,B,Abuja,Member,Female,Home and lifestyle,31820.4,9,14319.18,300702.78,3/2/2019,12:40,Cash,286383.6,4.761904762,14319.18,6.3
+186-79-9562,B,Abuja,Normal,Male,Health and beauty,25837.2,7,9043.02,189903.42,3/29/2019,14:06,Cash,180860.4,4.761904762,9043.02,8.9
+834-45-5519,B,Abuja,Normal,Female,Electronic accessories,15480.0,4,3096.0,65016.0,1/31/2019,20:48,Epay,61920.0,4.761904762,3096.0,7.6
+821-14-9046,B,Abuja,Member,Female,Fashion accessories,6292.8,6,1887.84,39644.64,1/18/2019,15:04,Card,37756.8,4.761904762,1887.84,6.1
+418-05-0656,B,Abuja,Normal,Female,Fashion accessories,9201.6,7,3220.56,67631.76,2/2/2019,20:42,Cash,64411.2,4.761904762,3220.56,7.1
+776-68-1096,B,Abuja,Normal,Male,Home and lifestyle,15883.2,3,2382.48,50032.08,3/18/2019,13:45,Card,47649.600000000006,4.761904762,2382.48,7.9
+434-35-9162,B,Abuja,Member,Male,Food and beverages,8402.4,4,1680.48,35290.079999999994,2/4/2019,18:53,Epay,33609.6,4.761904762,1680.48,7.4
+174-64-0215,B,Abuja,Normal,Male,Sports and travel,25106.4,10,12553.2,263617.2,3/5/2019,17:49,Card,251064.0,4.761904762,12553.2,8.9
+299-29-0180,B,Abuja,Member,Female,Home and lifestyle,18784.8,7,6574.679999999999,138068.28,3/9/2019,10:54,Cash,131493.6,4.761904762,6574.679999999999,9.3
+438-23-1242,B,Abuja,Normal,Male,Electronic accessories,27316.8,7,9560.88,200778.48000000004,1/24/2019,10:38,Epay,191217.6,4.761904762,9560.88,8.9
+238-45-6950,B,Abuja,Member,Male,Food and beverages,19339.2,1,966.9600000000002,20306.160000000003,3/1/2019,20:03,Epay,19339.2,4.761904762,966.9600000000002,6.4
+197-77-7132,B,Abuja,Member,Male,Electronic accessories,32961.6,8,13184.64,276877.44,1/12/2019,18:22,Epay,263692.8,4.761904762,13184.64,6.0
+457-13-1708,B,Abuja,Member,Male,Fashion accessories,23482.800000000003,10,11741.4,246569.4,1/8/2019,19:07,Card,234828.0,4.761904762,11741.4,5.2
+487-79-6868,B,Abuja,Member,Female,Home and lifestyle,4424.4,9,1990.98,41810.58,3/26/2019,19:28,Card,39819.6,4.761904762,1990.98,8.0
+210-30-7976,B,Abuja,Member,Female,Fashion accessories,8035.2,4,1607.0399999999995,33747.84,3/14/2019,11:16,Epay,32140.8,4.761904762,1607.0399999999995,4.1
+775-72-1988,B,Abuja,Normal,Male,Home and lifestyle,26380.8,5,6595.2,138499.2,1/24/2019,15:05,Epay,131904.0,4.761904762,6595.2,8.4
+785-96-0615,B,Abuja,Member,Female,Electronic accessories,12866.4,8,5146.56,108077.76,2/17/2019,15:28,Epay,102931.2,4.761904762,5146.56,4.9
+842-40-8179,B,Abuja,Member,Female,Food and beverages,27792.0,10,13896.0,291816.0,2/11/2019,10:38,Card,277920.0,4.761904762,13896.0,5.6
+525-09-8450,B,Abuja,Normal,Male,Electronic accessories,25966.8,10,12983.4,272651.4,1/31/2019,15:12,Card,259668.0,4.761904762,12983.4,4.2
+593-14-4239,B,Abuja,Normal,Female,Home and lifestyle,34365.6,8,13746.24,288671.04,3/5/2019,19:40,Epay,274924.8,4.761904762,13746.24,4.7
+388-76-2555,B,Abuja,Normal,Male,Sports and travel,4928.4,6,1478.52,31048.92000000001,2/13/2019,13:59,Cash,29570.4,4.761904762,1478.52,6.3
+711-31-1234,B,Abuja,Normal,Female,Electronic accessories,34430.4,4,6886.08,144607.68000000002,3/16/2019,18:51,Cash,137721.6,4.761904762,6886.08,7.9
+707-32-7409,B,Abuja,Member,Female,Sports and travel,34394.4,4,6878.88,144456.48,2/26/2019,11:58,Epay,137577.6,4.761904762,6878.88,4.5
+120-54-2248,B,Abuja,Normal,Female,Food and beverages,10389.6,5,2597.4,54545.4,1/22/2019,18:08,Card,51948.00000000001,4.761904762,2597.4,8.0
+875-31-8302,B,Abuja,Normal,Male,Sports and travel,33616.8,1,1680.84,35297.64,1/3/2019,13:07,Cash,33616.8,4.761904762,1680.84,9.6
+457-94-0464,B,Abuja,Member,Male,Electronic accessories,31633.2,9,14234.94,298933.74,1/31/2019,20:32,Epay,284698.8,4.761904762,14234.94,5.6
+744-16-7898,B,Abuja,Normal,Female,Home and lifestyle,35053.200000000004,10,17526.600000000002,368058.6,1/15/2019,13:48,Card,350532.0,4.761904762,17526.600000000002,4.9
+605-83-1050,B,Abuja,Normal,Male,Fashion accessories,9784.8,2,978.48,20548.08,3/15/2019,16:26,Epay,19569.6,4.761904762,978.48,4.3
+359-94-5395,B,Abuja,Normal,Male,Health and beauty,33400.8,1,1670.0399999999995,35070.84,3/15/2019,10:50,Card,33400.8,4.761904762,1670.0399999999995,9.8
+751-15-6198,B,Abuja,Normal,Male,Sports and travel,8283.6,6,2485.08,52186.68,1/12/2019,16:45,Epay,49701.6,4.761904762,2485.08,7.9
+831-81-6575,B,Abuja,Member,Female,Electronic accessories,27212.4,9,12245.580000000002,257157.18,2/23/2019,11:12,Cash,244911.6,4.761904762,12245.580000000002,8.0
+559-61-5987,B,Abuja,Normal,Female,Health and beauty,6390.0,1,319.5,6709.5,1/14/2019,10:38,Cash,6390.0,4.761904762,319.5,8.6
+565-91-4567,B,Abuja,Normal,Male,Health and beauty,3870.0,8,1548.0,32508.0,3/15/2019,14:38,Epay,30960.0,4.761904762,1548.0,6.2
+499-27-7781,B,Abuja,Normal,Female,Food and beverages,19155.6,8,7662.240000000001,160907.04,3/14/2019,16:45,Epay,153244.8,4.761904762,7662.240000000001,5.0
+869-11-3082,B,Abuja,Member,Male,Health and beauty,34617.6,4,6923.52,145393.91999999998,1/27/2019,20:03,Card,138470.4,4.761904762,6923.52,8.4
+190-59-3964,B,Abuja,Member,Male,Food and beverages,16977.6,5,4244.4,89132.4,2/3/2019,14:35,Card,84888.0,4.761904762,4244.4,6.0
+366-43-6862,B,Abuja,Normal,Male,Electronic accessories,19040.4,4,3808.08,79969.68000000001,3/25/2019,16:32,Epay,76161.6,4.761904762,3808.08,6.7
+109-86-4363,B,Abuja,Member,Female,Sports and travel,21628.8,7,7570.080000000001,158971.68000000002,2/14/2019,11:36,Card,151401.6,4.761904762,7570.080000000001,4.5
+222-42-0244,B,Abuja,Member,Female,Health and beauty,25959.6,9,11681.82,245318.22,1/28/2019,13:53,Card,233636.4,4.761904762,11681.82,7.7
+636-17-0325,B,Abuja,Normal,Male,Health and beauty,22525.2,4,4505.04,94605.84,2/25/2019,18:37,Cash,90100.8,4.761904762,4505.04,9.5
+343-75-9322,B,Abuja,Member,Female,Sports and travel,4266.0,8,1706.4,35834.4,1/9/2019,16:34,Cash,34128.0,4.761904762,1706.4,4.1
+427-45-9297,B,Abuja,Member,Female,Home and lifestyle,14662.8,7,5131.98,107771.58,3/12/2019,11:01,Epay,102639.6,4.761904762,5131.98,5.4
+670-71-7306,B,Abuja,Normal,Male,Sports and travel,16066.8,6,4820.04,101220.84,1/2/2019,20:08,Card,96400.8,4.761904762,4820.04,5.1
+291-59-1384,B,Abuja,Normal,Male,Electronic accessories,21708.0,1,1085.4,22793.4,2/28/2019,17:38,Cash,21708.0,4.761904762,1085.4,6.0
+866-70-2814,B,Abuja,Normal,Female,Electronic accessories,19004.4,10,9502.2,199546.2,2/25/2019,11:58,Epay,190044.0,4.761904762,9502.2,10.0
+895-03-6665,B,Abuja,Normal,Female,Fashion accessories,13143.6,9,5914.62,124207.02,2/16/2019,10:52,Cash,118292.4,4.761904762,5914.62,4.2
+770-42-8960,B,Abuja,Normal,Male,Food and beverages,7603.200000000001,8,3041.28,63866.88,1/1/2019,19:31,Cash,60825.600000000006,4.761904762,3041.28,6.3
+234-36-2483,B,Abuja,Normal,Male,Health and beauty,20732.4,6,6219.72,130614.11999999998,2/15/2019,13:51,Cash,124394.4,4.761904762,6219.72,5.1
+152-03-4217,B,Abuja,Normal,Female,Home and lifestyle,4060.8,9,1827.36,38374.56,3/17/2019,11:55,Card,36547.2,4.761904762,1827.36,4.3
+533-66-5566,B,Abuja,Normal,Female,Home and lifestyle,18385.2,7,6434.820000000001,135131.22,1/12/2019,11:42,Cash,128696.4,4.761904762,6434.820000000001,7.0
+361-59-0574,B,Abuja,Member,Male,Sports and travel,32590.8,8,13036.319999999998,273762.72,3/15/2019,14:48,Card,260726.4,4.761904762,13036.319999999998,6.5
+544-55-9589,B,Abuja,Member,Female,Electronic accessories,7714.8,10,3857.4,81005.4,1/28/2019,11:51,Cash,77148.0,4.761904762,3857.4,6.2
+608-05-3804,B,Abuja,Member,Male,Electronic accessories,14310.0,1,715.5,15025.499999999998,2/25/2019,20:19,Cash,14310.0,4.761904762,715.5,6.1
+761-49-0439,B,Abuja,Member,Female,Electronic accessories,4356.0,8,1742.4,36590.4,1/19/2019,10:17,Epay,34848.0,4.761904762,1742.4,8.6
+490-95-0021,B,Abuja,Member,Female,Food and beverages,11955.6,10,5977.8,125533.8,1/8/2019,14:25,Epay,119556.0,4.761904762,5977.8,6.0
+311-13-6971,B,Abuja,Member,Male,Sports and travel,11516.4,10,5758.2,120922.2,2/20/2019,15:18,Card,115164.0,4.761904762,5758.2,9.9
+114-35-5271,B,Abuja,Normal,Female,Electronic accessories,20847.6,8,8339.04,175119.84,2/7/2019,15:06,Cash,166780.8,4.761904762,8339.04,8.1
+715-20-1673,B,Abuja,Normal,Male,Electronic accessories,10216.8,5,2554.2,53638.2,3/6/2019,20:57,Cash,51084.0,4.761904762,2554.2,9.4
+811-35-1094,B,Abuja,Member,Male,Electronic accessories,18162.0,6,5448.6,114420.6,2/6/2019,15:16,Card,108972.0,4.761904762,5448.6,8.9
+699-88-1972,B,Abuja,Normal,Male,Health and beauty,35697.6,8,14279.04,299859.84,1/28/2019,17:47,Card,285580.8,4.761904762,14279.04,4.2
+509-10-0516,B,Abuja,Normal,Male,Home and lifestyle,16549.2,4,3309.84,69506.64,2/9/2019,12:02,Epay,66196.8,4.761904762,3309.84,5.1
+851-98-3555,B,Abuja,Normal,Female,Health and beauty,29836.8,5,7459.2,156643.2,3/24/2019,14:08,Card,149184.0,4.761904762,7459.2,6.6
+624-01-8356,B,Abuja,Normal,Female,Home and lifestyle,17643.6,10,8821.8,185257.8,1/27/2019,10:44,Card,176436.0,4.761904762,8821.8,4.2
+313-66-9943,B,Abuja,Member,Female,Food and beverages,10494.0,3,1574.1,33056.1,3/27/2019,20:29,Card,31482.0,4.761904762,1574.1,7.3
+777-67-2495,B,Abuja,Normal,Male,Home and lifestyle,24829.2,3,3724.38,78211.98000000001,2/22/2019,11:26,Epay,74487.6,4.761904762,3724.38,8.7
+636-98-3364,B,Abuja,Member,Female,Electronic accessories,9453.6,3,1418.04,29778.84,3/2/2019,12:36,Epay,28360.8,4.761904762,1418.04,6.3
+181-82-6255,B,Abuja,Normal,Female,Home and lifestyle,5893.200000000001,6,1767.9600000000005,37127.16,2/8/2019,10:58,Cash,35359.2,4.761904762,1767.9600000000005,7.0
+226-34-0034,B,Abuja,Normal,Female,Electronic accessories,4960.8,4,992.16,20835.36,1/10/2019,11:10,Epay,19843.2,4.761904762,992.16,9.0
+321-49-7382,B,Abuja,Member,Male,Sports and travel,31791.6,1,1589.58,33381.18,2/15/2019,17:38,Card,31791.6,4.761904762,1589.58,5.2
+431-66-2305,B,Abuja,Normal,Female,Electronic accessories,31770.0,9,14296.5,300226.5,2/15/2019,20:51,Card,285930.0,4.761904762,14296.5,7.6
+825-94-5922,B,Abuja,Normal,Male,Sports and travel,9111.6,2,911.16,19134.36,3/2/2019,19:26,Epay,18223.2,4.761904762,911.16,7.2
+641-62-7288,B,Abuja,Normal,Male,Home and lifestyle,35971.2,6,10791.36,226618.56,3/24/2019,13:33,Epay,215827.2,4.761904762,10791.36,7.1
+501-61-1753,B,Abuja,Normal,Female,Home and lifestyle,22734.0,6,6820.2,143224.2,1/3/2019,20:24,Epay,136404.0,4.761904762,6820.2,9.8
+676-10-2200,B,Abuja,Member,Male,Fashion accessories,19360.8,1,968.0399999999998,20328.84,2/3/2019,20:13,Epay,19360.8,4.761904762,968.0399999999998,4.7
+365-16-4334,B,Abuja,Normal,Female,Food and beverages,9514.8,8,3805.920000000001,79924.32,2/24/2019,14:26,Epay,76118.4,4.761904762,3805.920000000001,8.9
+503-21-4385,B,Abuja,Member,Male,Health and beauty,14367.6,3,2155.1400000000003,45257.94,2/21/2019,12:40,Epay,43102.8,4.761904762,2155.1400000000003,9.3
+305-89-2768,B,Abuja,Member,Female,Home and lifestyle,7883.999999999999,3,1182.6,24834.6,1/9/2019,18:43,Epay,23652.0,4.761904762,1182.6,4.7
+574-80-1489,B,Abuja,Member,Female,Food and beverages,22626.0,4,4525.2,95029.2,2/25/2019,13:22,Epay,90504.0,4.761904762,4525.2,8.7
+200-40-6154,B,Abuja,Member,Male,Home and lifestyle,23727.6,6,7118.28,149483.88,2/9/2019,11:45,Cash,142365.6,4.761904762,7118.28,5.7
+430-02-3888,B,Abuja,Normal,Male,Electronic accessories,16567.2,6,4970.16,104373.36,2/7/2019,15:55,Cash,99403.2,4.761904762,4970.16,7.1
+794-42-3736,B,Abuja,Normal,Male,Food and beverages,11998.8,2,1199.88,25197.48,1/26/2019,14:41,Card,23997.6,4.761904762,1199.88,6.4
+172-42-8274,B,Abuja,Normal,Female,Electronic accessories,13777.2,2,1377.72,28932.120000000003,3/2/2019,18:18,Card,27554.4,4.761904762,1377.72,5.8
+214-30-2776,B,Abuja,Member,Female,Electronic accessories,12416.4,5,3104.1000000000004,65186.1,3/11/2019,19:44,Card,62081.99999999999,4.761904762,3104.1000000000004,9.0
+746-04-1077,B,Abuja,Member,Female,Food and beverages,30466.8,10,15233.4,319901.4,1/1/2019,11:36,Card,304668.0,4.761904762,15233.4,9.0
+448-34-8700,B,Abuja,Member,Male,Home and lifestyle,13287.6,7,4650.66,97663.86,2/10/2019,13:51,Epay,93013.2,4.761904762,4650.66,6.7
+452-04-8808,B,Abuja,Normal,Male,Electronic accessories,31348.8,7,10972.08,230413.68,1/26/2019,15:17,Cash,219441.6,4.761904762,10972.08,5.5
+883-69-1285,B,Abuja,Member,Male,Fashion accessories,17971.2,2,1797.12,37739.52,3/6/2019,11:55,Card,35942.4,4.761904762,1797.12,7.0
+518-71-6847,B,Abuja,Member,Male,Food and beverages,9576.0,6,2872.8,60328.8,2/26/2019,15:10,Epay,57456.0,4.761904762,2872.8,4.9
+156-20-0370,B,Abuja,Normal,Female,Electronic accessories,9162.0,1,458.1,9620.1,3/10/2019,18:10,Card,9162.0,4.761904762,458.1,5.1
+151-33-7434,B,Abuja,Normal,Female,Food and beverages,24397.2,1,1219.86,25617.06,2/4/2019,20:43,Card,24397.2,4.761904762,1219.86,6.5
+374-38-5555,B,Abuja,Normal,Female,Fashion accessories,22935.6,5,5733.9,120411.9,2/7/2019,19:30,Epay,114678.0,4.761904762,5733.9,8.5
+764-44-8999,B,Abuja,Normal,Female,Health and beauty,5313.6,2,531.36,11158.56,2/18/2019,14:42,Epay,10627.2,4.761904762,531.36,4.3
+552-44-5977,B,Abuja,Member,Male,Health and beauty,22320.0,8,8928.0,187488.0,1/3/2019,19:08,Card,178560.0,4.761904762,8928.0,6.2
+430-53-4718,B,Abuja,Member,Male,Health and beauty,27133.2,8,10853.28,227918.88000000003,1/28/2019,15:46,Card,217065.6,4.761904762,10853.28,8.4
+602-16-6955,B,Abuja,Normal,Female,Sports and travel,27576.0,10,13787.999999999998,289548.0,1/24/2019,18:10,Epay,275760.0,4.761904762,13787.999999999998,6.0
+690-01-6631,B,Abuja,Normal,Male,Fashion accessories,6296.4,10,3148.2,66112.2,2/22/2019,18:35,Epay,62964.0,4.761904762,3148.2,6.6
+303-96-2227,B,Abuja,Normal,Female,Home and lifestyle,35056.8,10,17528.399999999998,368096.4,3/2/2019,17:16,Epay,350568.0,4.761904762,17528.399999999998,4.4
+226-31-3081,C,Port Harcourt,Normal,Female,Electronic accessories,5500.8,5,1375.2,28879.2,3/8/2019,10:29,Cash,27504.000000000004,4.761904762,1375.2,9.6
+699-14-3026,C,Port Harcourt,Normal,Male,Electronic accessories,30740.4,7,10759.14,225941.94,3/25/2019,18:30,Epay,215182.8,4.761904762,10759.14,4.1
+315-22-5665,C,Port Harcourt,Normal,Female,Home and lifestyle,26481.6,10,13240.8,278056.8,2/24/2019,11:38,Epay,264816.0,4.761904762,13240.8,8.0
+300-71-4605,C,Port Harcourt,Member,Male,Electronic accessories,30974.4,5,7743.6,162615.6,2/25/2019,11:24,Epay,154872.0,4.761904762,7743.6,4.8
+183-56-6882,C,Port Harcourt,Member,Female,Food and beverages,35791.2,4,7158.24,150323.04,2/6/2019,10:42,Epay,143164.8,4.761904762,7158.24,7.5
+232-16-2483,C,Port Harcourt,Member,Female,Sports and travel,24523.2,1,1226.16,25749.360000000004,1/7/2019,12:28,Epay,24523.2,4.761904762,1226.16,6.8
+333-73-7901,C,Port Harcourt,Normal,Female,Health and beauty,19771.2,8,7908.480000000001,166078.08000000002,3/23/2019,13:24,Epay,158169.6,4.761904762,7908.480000000001,7.6
+554-53-8700,C,Port Harcourt,Member,Male,Home and lifestyle,20199.6,2,2019.9600000000005,42419.16,2/2/2019,10:11,Cash,40399.2,4.761904762,2019.9600000000005,6.3
+228-96-1411,C,Port Harcourt,Member,Female,Food and beverages,35532.0,8,14212.8,298468.8,3/4/2019,20:39,Cash,284256.0,4.761904762,14212.8,7.6
+617-15-4209,C,Port Harcourt,Member,Male,Health and beauty,5533.2,2,553.3199999999999,11619.72,3/16/2019,19:47,Cash,11066.4,4.761904762,553.3199999999999,7.2
+574-22-5561,C,Port Harcourt,Member,Female,Fashion accessories,29746.8,10,14873.4,312341.4,3/19/2019,17:08,Epay,297468.0,4.761904762,14873.4,7.9
+326-78-5178,C,Port Harcourt,Member,Male,Food and beverages,32904.0,7,11516.4,241844.4,2/3/2019,10:19,Cash,230328.0,4.761904762,11516.4,9.5
+778-71-5554,C,Port Harcourt,Member,Male,Fashion accessories,5554.8,1,277.74,5832.54,1/25/2019,15:46,Card,5554.8,4.761904762,277.74,6.1
+399-46-5918,C,Port Harcourt,Normal,Female,Electronic accessories,30952.800000000003,8,12381.12,260003.52,2/28/2019,19:01,Cash,247622.4,4.761904762,12381.12,8.2
+120-06-4233,C,Port Harcourt,Normal,Male,Electronic accessories,11019.6,6,3305.88,69423.48,3/12/2019,20:36,Cash,66117.6,4.761904762,3305.88,9.3
+285-68-5083,C,Port Harcourt,Member,Female,Sports and travel,8906.4,3,1335.96,28055.16,2/15/2019,17:47,Card,26719.2,4.761904762,1335.96,10.0
+803-83-5989,C,Port Harcourt,Normal,Male,Home and lifestyle,20062.8,6,6018.84,126395.64,2/24/2019,10:55,Epay,120376.8,4.761904762,6018.84,7.0
+838-78-4295,C,Port Harcourt,Normal,Female,Health and beauty,12049.2,2,1204.92,25303.32,2/10/2019,15:43,Epay,24098.4,4.761904762,1204.92,6.7
+393-65-2792,C,Port Harcourt,Normal,Male,Food and beverages,32212.800000000003,10,16106.4,338234.4,1/6/2019,12:46,Card,322128.0,4.761904762,16106.4,9.6
+796-12-2025,C,Port Harcourt,Normal,Male,Fashion accessories,22363.2,10,11181.6,234813.6,2/11/2019,16:19,Cash,223632.00000000003,4.761904762,11181.6,5.9
+841-35-6630,C,Port Harcourt,Normal,Female,Electronic accessories,27327.6,6,8198.28,172163.88,3/9/2019,18:21,Cash,163965.6,4.761904762,8198.28,8.7
+732-94-0499,C,Port Harcourt,Normal,Female,Electronic accessories,14994.0,10,7497.0,157437.0,1/13/2019,17:04,Card,149940.0,4.761904762,7497.0,5.4
+263-10-3913,C,Port Harcourt,Member,Male,Fashion accessories,17654.4,9,7944.48,166834.08000000002,1/9/2019,14:20,Card,158889.6,4.761904762,7944.48,8.6
+829-49-1914,C,Port Harcourt,Member,Female,Food and beverages,28191.6,10,14095.8,296011.8,3/5/2019,16:24,Epay,281916.0,4.761904762,14095.8,6.6
+756-01-7507,C,Port Harcourt,Normal,Female,Health and beauty,7336.799999999999,5,1834.2,38518.2,1/22/2019,18:56,Cash,36684.0,4.761904762,1834.2,6.0
+870-72-4431,C,Port Harcourt,Normal,Female,Health and beauty,35708.4,6,10712.52,224962.92,1/21/2019,14:42,Card,214250.4,4.761904762,10712.52,5.5
+480-63-2856,C,Port Harcourt,Normal,Male,Food and beverages,6930.0,8,2772.0,58211.99999999999,1/23/2019,18:37,Epay,55440.0,4.761904762,2772.0,6.6
+787-56-0757,C,Port Harcourt,Member,Female,Food and beverages,28929.6,4,5785.92,121504.31999999998,2/23/2019,18:45,Card,115718.4,4.761904762,5785.92,8.3
+360-39-5055,C,Port Harcourt,Member,Male,Sports and travel,17607.6,5,4401.9,92439.9,3/9/2019,10:17,Cash,88038.0,4.761904762,4401.9,6.6
+730-50-9884,C,Port Harcourt,Normal,Female,Sports and travel,29901.6,7,10465.56,219776.76,3/5/2019,14:31,Epay,209311.2,4.761904762,10465.56,4.0
+362-58-8315,C,Port Harcourt,Normal,Male,Fashion accessories,27547.2,5,6886.799999999999,144622.80000000002,3/25/2019,10:23,Cash,137736.0,4.761904762,6886.799999999999,9.9
+565-80-5980,C,Port Harcourt,Member,Female,Home and lifestyle,17056.8,4,3411.36,71638.56,1/23/2019,10:25,Cash,68227.2,4.761904762,3411.36,7.1
+225-32-0908,C,Port Harcourt,Normal,Female,Sports and travel,16149.6,10,8074.8,169570.8,1/26/2019,19:54,Epay,161496.0,4.761904762,8074.8,8.2
+512-91-0811,C,Port Harcourt,Normal,Male,Health and beauty,32310.0,1,1615.5,33925.5,2/6/2019,20:05,Card,32310.0,4.761904762,1615.5,6.6
+871-39-9221,C,Port Harcourt,Normal,Female,Electronic accessories,4482.0,6,1344.6,28236.6,2/9/2019,13:11,Cash,26892.0,4.761904762,1344.6,4.1
+163-56-7055,C,Port Harcourt,Member,Male,Fashion accessories,17535.6,1,876.7800000000001,18412.38,3/26/2019,19:20,Cash,17535.6,4.761904762,876.7800000000001,4.1
+189-98-2939,C,Port Harcourt,Normal,Male,Fashion accessories,28278.0,9,12725.1,267227.1,3/1/2019,13:22,Cash,254502.00000000003,4.761904762,12725.1,7.2
+551-21-3069,C,Port Harcourt,Normal,Female,Electronic accessories,8305.2,9,3737.34,78484.14,2/1/2019,11:27,Cash,74746.8,4.761904762,3737.34,4.9
+628-34-3388,C,Port Harcourt,Normal,Male,Fashion accessories,9856.8,6,2957.04,62097.84000000001,1/5/2019,20:54,Card,59140.8,4.761904762,2957.04,7.9
+588-01-7461,C,Port Harcourt,Normal,Female,Food and beverages,12232.8,9,5504.759999999999,115599.96,3/24/2019,10:43,Cash,110095.2,4.761904762,5504.759999999999,4.2
+861-77-0145,C,Port Harcourt,Member,Male,Electronic accessories,29509.2,10,14754.6,309846.6,3/3/2019,14:30,Cash,295092.0,4.761904762,14754.6,9.2
+210-67-5886,C,Port Harcourt,Member,Female,Health and beauty,35355.6,3,5303.34,111370.13999999998,2/5/2019,10:41,Card,106066.8,4.761904762,5303.34,7.8
+237-01-6122,C,Port Harcourt,Member,Female,Home and lifestyle,29084.4,9,13087.98,274847.58,2/1/2019,20:31,Card,261759.6,4.761904762,13087.98,9.5
+225-98-1496,C,Port Harcourt,Normal,Female,Fashion accessories,9727.2,3,1459.08,30640.68,3/2/2019,13:01,Card,29181.6,4.761904762,1459.08,7.1
+659-36-1684,C,Port Harcourt,Member,Male,Sports and travel,20563.2,7,7197.12,151139.52,1/12/2019,12:02,Card,143942.4,4.761904762,7197.12,6.5
+336-78-2147,C,Port Harcourt,Member,Male,Home and lifestyle,23007.6,8,9203.04,193263.84000000003,3/13/2019,19:52,Card,184060.8,4.761904762,9203.04,4.6
+189-08-9157,C,Port Harcourt,Normal,Female,Fashion accessories,11422.8,9,5140.259999999999,107945.46,1/8/2019,16:17,Card,102805.2,4.761904762,5140.259999999999,5.9
+663-86-9076,C,Port Harcourt,Member,Female,Food and beverages,24674.4,8,9869.76,207264.96,1/8/2019,15:57,Epay,197395.2,4.761904762,9869.76,8.5
+483-71-1164,C,Port Harcourt,Normal,Female,Health and beauty,29268.0,6,8780.4,184388.4,3/8/2019,16:43,Epay,175608.0,4.761904762,8780.4,5.3
+597-78-7908,C,Port Harcourt,Normal,Male,Fashion accessories,32479.2,3,4871.88,102309.48,2/18/2019,19:39,Cash,97437.6,4.761904762,4871.88,6.2
+731-81-9469,C,Port Harcourt,Member,Female,Sports and travel,32328.0,10,16164.0,339444.0,1/23/2019,13:00,Card,323280.0,4.761904762,16164.0,5.4
+280-17-4359,C,Port Harcourt,Member,Male,Health and beauty,32580.0,10,16290.0,342090.0,1/25/2019,13:48,Cash,325800.0,4.761904762,16290.0,8.1
+338-65-2210,C,Port Harcourt,Member,Female,Health and beauty,24696.0,10,12347.999999999998,259308.0,2/5/2019,19:57,Cash,246960.0,4.761904762,12347.999999999998,9.1
+488-25-4221,C,Port Harcourt,Member,Female,Food and beverages,10947.6,1,547.38,11494.98,2/22/2019,10:36,Card,10947.6,4.761904762,547.38,8.4
+458-41-1477,C,Port Harcourt,Normal,Female,Health and beauty,16653.6,6,4996.08,104917.68,3/8/2019,17:11,Card,99921.6,4.761904762,4996.08,9.5
+568-90-5112,C,Port Harcourt,Normal,Male,Health and beauty,23810.4,4,4762.08,100003.68,3/19/2019,12:46,Card,95241.6,4.761904762,4762.08,5.6
+584-86-7256,C,Port Harcourt,Member,Male,Sports and travel,12441.6,7,4354.56,91445.76,3/11/2019,16:07,Card,87091.2,4.761904762,4354.56,7.3
+214-17-6927,C,Port Harcourt,Normal,Female,Food and beverages,5932.8,6,1779.84,37376.64,2/7/2019,18:23,Epay,35596.8,4.761904762,1779.84,9.9
+400-89-4171,C,Port Harcourt,Normal,Female,Sports and travel,29149.2,8,11659.680000000002,244853.28,1/28/2019,13:05,Cash,233193.6,4.761904762,11659.680000000002,9.3
+894-41-5205,C,Port Harcourt,Normal,Female,Food and beverages,15544.8,8,6217.920000000001,130576.32,1/19/2019,19:39,Card,124358.4,4.761904762,6217.920000000001,8.3
+423-80-0988,C,Port Harcourt,Normal,Male,Sports and travel,27504.000000000004,2,2750.4,57758.4,1/30/2019,19:42,Epay,55008.00000000001,4.761904762,2750.4,6.5
+234-65-2137,C,Port Harcourt,Normal,Male,Home and lifestyle,34408.8,10,17204.4,361292.4,1/16/2019,13:32,Cash,344088.0,4.761904762,17204.4,4.8
+733-33-4967,C,Port Harcourt,Normal,Male,Electronic accessories,7506.000000000001,8,3002.4,63050.4,3/3/2019,19:17,Cash,60048.00000000001,4.761904762,3002.4,6.3
+139-52-2867,C,Port Harcourt,Normal,Female,Fashion accessories,8103.6,7,2836.26,59561.46,2/13/2019,10:50,Card,56725.2,4.761904762,2836.26,4.8
+342-65-4817,C,Port Harcourt,Member,Male,Health and beauty,31248.0,3,4687.2,98431.2,1/28/2019,16:47,Epay,93744.0,4.761904762,4687.2,9.9
+130-98-8941,C,Port Harcourt,Normal,Male,Fashion accessories,23133.6,7,8096.76,170031.96,2/9/2019,10:00,Cash,161935.2,4.761904762,8096.76,5.7
+434-83-9547,C,Port Harcourt,Member,Male,Food and beverages,13849.2,8,5539.68,116333.27999999998,1/23/2019,11:51,Cash,110793.6,4.761904762,5539.68,7.7
+824-88-3614,C,Port Harcourt,Normal,Male,Health and beauty,12351.6,8,4940.64,103753.44,1/25/2019,15:00,Epay,98812.8,4.761904762,4940.64,5.7
+593-65-1552,C,Port Harcourt,Normal,Female,Home and lifestyle,25131.6,4,5026.32,105552.72,1/28/2019,20:50,Card,100526.4,4.761904762,5026.32,5.9
+286-43-6208,C,Port Harcourt,Normal,Female,Food and beverages,31608.0,9,14223.6,298695.60000000003,3/16/2019,19:08,Cash,284472.0,4.761904762,14223.6,9.2
+556-86-3144,C,Port Harcourt,Member,Female,Fashion accessories,26744.4,1,1337.22,28081.62,1/13/2019,19:30,Cash,26744.4,4.761904762,1337.22,5.0
+848-24-9445,C,Port Harcourt,Member,Male,Health and beauty,15732.000000000002,2,1573.2,33037.2,3/26/2019,18:03,Cash,31464.000000000004,4.761904762,1573.2,4.9
+699-01-4164,C,Port Harcourt,Normal,Male,Health and beauty,14940.0,4,2988.0000000000005,62748.00000000001,3/12/2019,19:58,Card,59760.0,4.761904762,2988.0000000000005,8.2
+420-11-4919,C,Port Harcourt,Member,Female,Food and beverages,25700.4,5,6425.1,134927.1,2/17/2019,19:57,Card,128502.0,4.761904762,6425.1,5.5
+606-80-4905,C,Port Harcourt,Member,Female,Sports and travel,6893.999999999999,6,2068.2,43432.2,1/29/2019,10:01,Card,41364.0,4.761904762,2068.2,6.8
+426-39-2418,C,Port Harcourt,Normal,Male,Electronic accessories,22107.6,7,7737.660000000001,162490.86,1/14/2019,10:02,Cash,154753.2,4.761904762,7737.660000000001,9.8
+672-51-8681,C,Port Harcourt,Member,Female,Electronic accessories,23994.000000000004,9,10797.3,226743.3,1/4/2019,18:19,Card,215946.0,4.761904762,10797.3,9.7
+263-87-5680,C,Port Harcourt,Member,Female,Home and lifestyle,10270.8,10,5135.400000000001,107843.4,3/18/2019,17:38,Epay,102708.0,4.761904762,5135.400000000001,7.8
+677-11-0152,C,Port Harcourt,Normal,Female,Food and beverages,33573.6,9,15108.12,317270.52,1/16/2019,18:08,Cash,302162.4,4.761904762,15108.12,8.8
+389-25-3394,C,Port Harcourt,Normal,Male,Electronic accessories,4251.6,5,1062.9,22320.9,2/17/2019,18:06,Cash,21258.0,4.761904762,1062.9,9.4
+279-62-1445,C,Port Harcourt,Member,Female,Fashion accessories,4514.4,1,225.72,4740.12,2/21/2019,12:38,Cash,4514.4,4.761904762,225.72,8.2
+746-68-6593,C,Port Harcourt,Member,Female,Sports and travel,31377.6,2,3137.76,65892.95999999999,1/11/2019,14:29,Card,62755.2,4.761904762,3137.76,9.7
+583-72-1480,C,Port Harcourt,Member,Male,Electronic accessories,13341.6,4,2668.32,56034.72000000001,1/31/2019,16:24,Epay,53366.4,4.761904762,2668.32,9.7
+211-30-9270,C,Port Harcourt,Normal,Male,Health and beauty,6267.6,5,1566.9,32904.9,1/28/2019,15:16,Card,31338.0,4.761904762,1566.9,4.9
+755-12-3214,C,Port Harcourt,Member,Female,Fashion accessories,15919.2,5,3979.8,83575.8,3/5/2019,17:07,Card,79596.0,4.761904762,3979.8,8.6
+142-72-4741,C,Port Harcourt,Member,Male,Fashion accessories,33552.0,2,3355.2000000000003,70459.2,2/28/2019,18:37,Card,67104.0,4.761904762,3355.2000000000003,6.0
+662-47-5456,C,Port Harcourt,Member,Male,Fashion accessories,12668.4,10,6334.2,133018.2,3/17/2019,19:06,Card,126684.0,4.761904762,6334.2,8.4
+883-17-4236,C,Port Harcourt,Normal,Female,Sports and travel,5180.400000000001,2,518.04,10878.84,3/2/2019,19:44,Card,10360.8,4.761904762,518.04,7.2
+380-94-4661,C,Port Harcourt,Member,Male,Electronic accessories,23738.4,4,4747.679999999999,99701.28,2/7/2019,13:05,Card,94953.6,4.761904762,4747.679999999999,6.9
+821-07-3596,C,Port Harcourt,Normal,Female,Fashion accessories,5922.0,4,1184.4,24872.4,3/7/2019,14:53,Epay,23688.0,4.761904762,1184.4,5.6
+808-65-0703,C,Port Harcourt,Normal,Male,Home and lifestyle,12769.2,4,2553.84,53630.64,3/14/2019,17:22,Card,51076.8,4.761904762,2553.84,6.9
+687-15-1097,C,Port Harcourt,Member,Female,Health and beauty,7603.200000000001,2,760.32,15966.72,1/3/2019,19:17,Cash,15206.4,4.761904762,760.32,9.7
+526-86-8552,C,Port Harcourt,Member,Female,Home and lifestyle,7855.2,10,3927.6,82479.6,1/7/2019,17:36,Cash,78552.0,4.761904762,3927.6,7.1
+376-56-3573,C,Port Harcourt,Normal,Female,Fashion accessories,34351.2,4,6870.24,144275.04,2/2/2019,13:23,Epay,137404.8,4.761904762,6870.24,6.4
+537-72-0426,C,Port Harcourt,Member,Male,Fashion accessories,25556.4,10,12778.2,268342.2,3/20/2019,16:28,Cash,255564.0,4.761904762,12778.2,5.7
+523-38-0215,C,Port Harcourt,Normal,Male,Home and lifestyle,13320.0,1,666.0,13986.0,3/6/2019,13:29,Card,13320.0,4.761904762,666.0,7.9
+593-95-4461,C,Port Harcourt,Member,Male,Home and lifestyle,26949.6,1,1347.48,28297.08,3/24/2019,14:49,Cash,26949.6,4.761904762,1347.48,6.9
+226-71-3580,C,Port Harcourt,Normal,Female,Sports and travel,8550.0,9,3847.5,80797.5,1/31/2019,12:02,Cash,76950.0,4.761904762,3847.5,9.5
+558-80-4082,C,Port Harcourt,Normal,Male,Electronic accessories,10026.0,7,3509.1000000000004,73691.09999999999,3/14/2019,17:20,Epay,70182.0,4.761904762,3509.1000000000004,6.0
+211-05-0490,C,Port Harcourt,Member,Female,Electronic accessories,18691.2,5,4672.8,98128.8,3/3/2019,13:42,Cash,93456.0,4.761904762,4672.8,7.5
+727-75-6477,C,Port Harcourt,Normal,Male,Electronic accessories,10382.4,4,2076.48,43606.08,3/29/2019,14:44,Cash,41529.6,4.761904762,2076.48,6.4
+779-06-0012,C,Port Harcourt,Member,Female,Home and lifestyle,31899.6,1,1594.98,33494.579999999994,1/19/2019,10:21,Cash,31899.6,4.761904762,1594.98,7.7
+446-47-6729,C,Port Harcourt,Normal,Male,Fashion accessories,35935.2,2,3593.5200000000004,75463.92,1/2/2019,18:09,Card,71870.4,4.761904762,3593.5200000000004,6.7
+735-06-4124,C,Port Harcourt,Normal,Male,Food and beverages,17499.6,1,874.9799999999999,18374.58,2/25/2019,15:31,Cash,17499.6,4.761904762,874.9799999999999,4.4
+181-94-6432,C,Port Harcourt,Member,Male,Fashion accessories,24958.8,2,2495.88,52413.48,2/5/2019,19:05,Epay,49917.6,4.761904762,2495.88,9.7
+227-07-4446,C,Port Harcourt,Member,Female,Electronic accessories,28126.8,10,14063.4,295331.4,2/10/2019,20:51,Cash,281268.0,4.761904762,14063.4,4.4
+174-36-3675,C,Port Harcourt,Member,Male,Food and beverages,35773.200000000004,2,3577.3200000000006,75123.72,2/14/2019,17:29,Cash,71546.40000000001,4.761904762,3577.3200000000006,5.2
+428-83-5800,C,Port Harcourt,Member,Female,Food and beverages,7588.799999999999,3,1138.32,23904.72,2/9/2019,10:25,Cash,22766.4,4.761904762,1138.32,7.3
+603-07-0961,C,Port Harcourt,Member,Male,Electronic accessories,26924.4,5,6731.1,141353.1,1/10/2019,11:34,Cash,134622.0,4.761904762,6731.1,4.9
+704-20-4138,C,Port Harcourt,Member,Female,Health and beauty,10681.2,7,3738.42,78506.82,3/11/2019,18:58,Card,74768.4,4.761904762,3738.42,8.1
+787-15-1757,C,Port Harcourt,Member,Male,Health and beauty,15865.2,4,3173.04,66633.84000000001,2/18/2019,16:28,Epay,63460.8,4.761904762,3173.04,8.4
+649-11-3678,C,Port Harcourt,Normal,Female,Food and beverages,8254.8,9,3714.66,78007.86,2/26/2019,20:26,Cash,74293.2,4.761904762,3714.66,5.5
+622-20-1945,C,Port Harcourt,Normal,Female,Health and beauty,14191.2,1,709.56,14900.760000000002,1/18/2019,15:08,Cash,14191.2,4.761904762,709.56,8.4
+719-76-3868,C,Port Harcourt,Member,Male,Food and beverages,33933.6,4,6786.72,142521.12,3/12/2019,16:30,Cash,135734.4,4.761904762,6786.72,8.6
+835-16-0096,C,Port Harcourt,Member,Male,Sports and travel,5292.0,5,1323.0,27783.0,3/24/2019,13:48,Epay,26460.0,4.761904762,1323.0,8.5
+633-09-3463,C,Port Harcourt,Normal,Female,Electronic accessories,17154.0,3,2573.1,54035.1,3/28/2019,12:58,Card,51461.99999999999,4.761904762,2573.1,9.5
+416-13-5917,C,Port Harcourt,Normal,Female,Food and beverages,34930.8,5,8732.7,183386.7,1/30/2019,16:24,Epay,174654.0,4.761904762,8732.7,9.3
+725-96-3778,C,Port Harcourt,Member,Female,Home and lifestyle,32130.0,8,12852.000000000002,269892.0,1/20/2019,10:13,Cash,257040.0,4.761904762,12852.000000000002,4.7
+860-79-0874,C,Port Harcourt,Member,Female,Fashion accessories,35748.0,10,17874.0,375354.00000000006,2/15/2019,14:53,Card,357480.0,4.761904762,17874.0,6.6
+320-49-6392,C,Port Harcourt,Normal,Female,Electronic accessories,10886.4,1,544.3199999999999,11430.72,3/4/2019,15:44,Cash,10886.4,4.761904762,544.3199999999999,8.4
+632-90-0281,C,Port Harcourt,Normal,Female,Fashion accessories,13517.999999999998,10,6758.999999999999,141939.0,3/8/2019,20:01,Card,135180.0,4.761904762,6758.999999999999,9.3
+554-42-2417,C,Port Harcourt,Normal,Female,Sports and travel,34358.4,10,17179.2,360763.2,1/9/2019,13:45,Cash,343584.0,4.761904762,17179.2,5.2
+605-72-4132,C,Port Harcourt,Normal,Female,Food and beverages,34009.2,8,13603.680000000002,285677.28,2/27/2019,15:12,Cash,272073.6,4.761904762,13603.680000000002,9.1
+471-41-2823,C,Port Harcourt,Normal,Male,Food and beverages,35924.4,2,3592.44,75441.24,3/7/2019,20:37,Epay,71848.8,4.761904762,3592.44,8.0
+272-27-9238,C,Port Harcourt,Normal,Female,Food and beverages,14846.4,4,2969.28,62354.88,2/19/2019,16:23,Cash,59385.600000000006,4.761904762,2969.28,7.1
+834-25-9262,C,Port Harcourt,Normal,Female,Fashion accessories,29404.800000000003,4,5880.959999999999,123500.16,1/6/2019,12:12,Cash,117619.2,4.761904762,5880.959999999999,9.1
+122-61-9553,C,Port Harcourt,Normal,Female,Electronic accessories,18475.2,9,8313.84,174590.63999999998,3/14/2019,19:33,Cash,166276.8,4.761904762,8313.84,5.6
+613-59-9758,C,Port Harcourt,Normal,Female,Sports and travel,5169.599999999999,10,2584.8,54280.8,1/27/2019,14:28,Cash,51696.0,4.761904762,2584.8,5.4
+730-70-9830,C,Port Harcourt,Normal,Female,Home and lifestyle,25239.6,6,7571.88,159009.48,3/14/2019,17:54,Epay,151437.6,4.761904762,7571.88,5.2
+382-25-8917,C,Port Harcourt,Normal,Male,Fashion accessories,15148.8,6,4544.64,95437.44000000002,1/29/2019,12:25,Cash,90892.8,4.761904762,4544.64,8.9
+743-88-1662,C,Port Harcourt,Normal,Male,Sports and travel,34376.4,7,12031.74,252666.54,2/22/2019,18:17,Epay,240634.8,4.761904762,12031.74,8.7
+595-86-2894,C,Port Harcourt,Member,Male,Fashion accessories,34912.8,4,6982.56,146633.75999999998,2/6/2019,17:20,Epay,139651.2,4.761904762,6982.56,9.4
+462-78-5240,C,Port Harcourt,Normal,Female,Electronic accessories,9579.6,2,957.96,20117.16,3/19/2019,14:35,Cash,19159.2,4.761904762,957.96,4.2
+153-58-4872,C,Port Harcourt,Member,Female,Food and beverages,26960.4,4,5392.08,113233.68,3/1/2019,15:32,Epay,107841.6,4.761904762,5392.08,4.2
+689-16-9784,C,Port Harcourt,Normal,Male,Food and beverages,16837.2,6,5051.16,106074.36,3/11/2019,13:37,Cash,101023.2,4.761904762,5051.16,6.0
+394-41-0748,C,Port Harcourt,Member,Female,Fashion accessories,19465.2,9,8759.34,183946.14,1/27/2019,14:55,Epay,175186.8,4.761904762,8759.34,9.5
+541-89-9860,C,Port Harcourt,Member,Female,Fashion accessories,28972.800000000003,3,4345.92,91264.32,2/15/2019,12:31,Cash,86918.4,4.761904762,4345.92,8.1
+110-05-6330,C,Port Harcourt,Normal,Female,Food and beverages,14194.8,6,4258.4400000000005,89427.24,3/25/2019,20:18,Card,85168.8,4.761904762,4258.4400000000005,9.4
+651-61-0874,C,Port Harcourt,Normal,Male,Home and lifestyle,16639.2,4,3327.84,69884.64000000001,3/12/2019,20:04,Card,66556.8,4.761904762,3327.84,6.2
+236-86-3015,C,Port Harcourt,Member,Male,Home and lifestyle,5032.8,1,251.64,5284.4400000000005,2/4/2019,13:38,Epay,5032.8,4.761904762,251.64,9.8
+587-03-7455,C,Port Harcourt,Member,Female,Fashion accessories,35204.4,7,12321.54,258752.34,2/16/2019,17:30,Epay,246430.8,4.761904762,12321.54,4.9
+372-26-1506,C,Port Harcourt,Normal,Female,Fashion accessories,8575.2,5,2143.8,45019.8,1/28/2019,19:24,Epay,42876.0,4.761904762,2143.8,5.4
+750-57-9686,C,Port Harcourt,Normal,Female,Home and lifestyle,16336.8,4,3267.36,68614.56,1/8/2019,13:48,Card,65347.2,4.761904762,3267.36,8.7
+186-09-3669,C,Port Harcourt,Member,Female,Health and beauty,29343.6,1,1467.18,30810.78,1/22/2019,10:57,Epay,29343.6,4.761904762,1467.18,9.2
+266-76-6436,C,Port Harcourt,Member,Female,Food and beverages,13896.0,3,2084.4,43772.4,3/28/2019,13:57,Epay,41688.0,4.761904762,2084.4,7.5
+740-22-2500,C,Port Harcourt,Normal,Female,Electronic accessories,30258.0,3,4538.7,95312.7,1/23/2019,13:29,Cash,90774.0,4.761904762,4538.7,9.8
+271-88-8734,C,Port Harcourt,Member,Female,Fashion accessories,34995.6,10,17497.8,367453.8,2/8/2019,13:00,Card,349956.0,4.761904762,17497.8,8.7
+489-64-4354,C,Port Harcourt,Normal,Male,Fashion accessories,5860.8,1,293.04,6153.84,3/9/2019,15:36,Cash,5860.8,4.761904762,293.04,5.0
+574-57-9721,C,Port Harcourt,Normal,Male,Food and beverages,15577.2,2,1557.72,32712.120000000003,3/8/2019,16:53,Epay,31154.4,4.761904762,1557.72,5.7
+751-69-0068,C,Port Harcourt,Normal,Male,Sports and travel,35726.4,9,16076.88,337614.48,3/19/2019,19:09,Epay,321537.6,4.761904762,16076.88,9.0
+257-73-1380,C,Port Harcourt,Member,Male,Sports and travel,29854.800000000003,4,5970.959999999999,125390.16,1/20/2019,16:51,Epay,119419.2,4.761904762,5970.959999999999,9.6
+549-96-4200,C,Port Harcourt,Member,Male,Food and beverages,6134.4,4,1226.88,25764.48,3/8/2019,20:15,Epay,24537.6,4.761904762,1226.88,7.0
+810-60-6344,C,Port Harcourt,Normal,Female,Electronic accessories,14709.6,8,5883.84,123560.64,2/7/2019,14:38,Card,117676.8,4.761904762,5883.84,6.5
+450-28-2866,C,Port Harcourt,Member,Male,Food and beverages,6278.400000000001,5,1569.6,32961.6,1/15/2019,19:25,Cash,31392.0,4.761904762,1569.6,8.1
+192-98-7397,C,Port Harcourt,Normal,Male,Fashion accessories,4600.8,1,230.04,4830.84,1/8/2019,14:11,Epay,4600.8,4.761904762,230.04,9.5
+235-46-8343,C,Port Harcourt,Member,Male,Food and beverages,9957.6,10,4978.8,104554.8,2/14/2019,11:26,Card,99576.0,4.761904762,4978.8,8.9
+453-12-7053,C,Port Harcourt,Normal,Male,Fashion accessories,16466.4,3,2469.96,51869.16,3/10/2019,17:38,Card,49399.2,4.761904762,2469.96,6.5
+325-90-8763,C,Port Harcourt,Member,Female,Electronic accessories,16765.2,10,8382.6,176034.6,1/27/2019,13:58,Cash,167652.0,4.761904762,8382.6,7.6
+729-46-7422,C,Port Harcourt,Normal,Male,Food and beverages,12920.4,1,646.02,13566.42,2/23/2019,16:52,Card,12920.4,4.761904762,646.02,7.9
+639-76-1242,C,Port Harcourt,Normal,Male,Food and beverages,14587.2,5,3646.8,76582.8,2/3/2019,15:19,Cash,72936.0,4.761904762,3646.8,4.5
+326-71-2155,C,Port Harcourt,Normal,Female,Sports and travel,26622.0,4,5324.4,111812.4,2/3/2019,10:02,Cash,106488.0,4.761904762,5324.4,6.1
+320-32-8842,C,Port Harcourt,Member,Female,Food and beverages,8143.200000000001,1,407.16,8550.359999999999,3/17/2019,18:58,Cash,8143.200000000001,4.761904762,407.16,6.4
+878-30-2331,C,Port Harcourt,Member,Female,Sports and travel,19638.0,10,9819.0,206199.0,3/2/2019,11:22,Card,196380.0,4.761904762,9819.0,7.1
+440-59-5691,C,Port Harcourt,Member,Female,Health and beauty,13374.0,7,4680.9,98298.9,2/8/2019,13:12,Card,93618.0,4.761904762,4680.9,7.7
+746-19-0921,C,Port Harcourt,Normal,Male,Food and beverages,7768.799999999999,1,388.44,8157.240000000001,2/9/2019,10:02,Epay,7768.799999999999,4.761904762,388.44,7.2
+233-34-0817,C,Port Harcourt,Member,Female,Electronic accessories,35582.4,1,1779.12,37361.52,2/15/2019,11:21,Cash,35582.4,4.761904762,1779.12,8.4
+767-05-1286,C,Port Harcourt,Member,Female,Home and lifestyle,30157.2,6,9047.16,189990.36,1/23/2019,12:10,Epay,180943.2,4.761904762,9047.16,5.4
+598-47-9715,C,Port Harcourt,Normal,Male,Electronic accessories,30265.2,4,6053.04,127113.84,3/7/2019,16:54,Epay,121060.8,4.761904762,6053.04,4.4
+541-08-3113,C,Port Harcourt,Normal,Male,Food and beverages,23749.2,8,9499.68,199493.28,2/2/2019,20:29,Cash,189993.6,4.761904762,9499.68,8.4
+246-11-3901,C,Port Harcourt,Normal,Female,Electronic accessories,11807.999999999998,10,5903.999999999999,123984.0,2/15/2019,12:12,Cash,118080.0,4.761904762,5903.999999999999,6.2
+493-65-6248,C,Port Harcourt,Member,Female,Sports and travel,13312.8,10,6656.4,139784.4,1/1/2019,19:48,Card,133128.0,4.761904762,6656.4,7.0
+556-72-8512,C,Port Harcourt,Normal,Male,Home and lifestyle,8265.6,1,413.28,8678.880000000001,1/30/2019,20:47,Cash,8265.6,4.761904762,413.28,4.3
+148-82-2527,C,Port Harcourt,Member,Female,Home and lifestyle,4363.2,10,2181.6,45813.6,3/5/2019,13:44,Card,43632.0,4.761904762,2181.6,8.4
+556-97-7101,C,Port Harcourt,Normal,Female,Electronic accessories,22759.2,2,2275.92,47794.32,1/1/2019,15:51,Cash,45518.4,4.761904762,2275.92,8.5
+862-59-8517,C,Port Harcourt,Normal,Female,Food and beverages,32486.4,6,9745.92,204664.32,1/27/2019,11:17,Cash,194918.4,4.761904762,9745.92,6.2
+573-98-8548,C,Port Harcourt,Member,Female,Fashion accessories,11484.0,1,574.2,12058.2,1/5/2019,12:40,Epay,11484.0,4.761904762,574.2,9.1
+620-02-2046,C,Port Harcourt,Normal,Male,Home and lifestyle,24984.000000000004,2,2498.4,52466.4,1/27/2019,19:48,Epay,49968.00000000001,4.761904762,2498.4,9.0
+602-80-9671,C,Port Harcourt,Member,Female,Home and lifestyle,5742.0,6,1722.6,36174.6,2/9/2019,17:15,Card,34452.0,4.761904762,1722.6,5.1
+503-07-0930,C,Port Harcourt,Member,Male,Sports and travel,21020.4,7,7357.139999999999,154499.94,2/23/2019,19:49,Card,147142.80000000002,4.761904762,7357.139999999999,8.2
+413-20-6708,C,Port Harcourt,Member,Female,Fashion accessories,18529.2,1,926.46,19455.66,3/18/2019,15:52,Epay,18529.2,4.761904762,926.46,8.5
+521-18-7827,C,Port Harcourt,Member,Male,Home and lifestyle,14180.4,5,3545.1,74447.1,1/22/2019,20:46,Card,70902.0,4.761904762,3545.1,8.7
+600-38-9738,C,Port Harcourt,Member,Male,Sports and travel,25891.2,5,6472.8,135928.8,1/17/2019,15:05,Card,129456.0,4.761904762,6472.8,4.3
+451-28-5717,C,Port Harcourt,Member,Female,Home and lifestyle,29941.2,6,8982.36,188629.56,3/20/2019,11:23,Cash,179647.19999999998,4.761904762,8982.36,7.3
+133-14-7229,C,Port Harcourt,Normal,Male,Health and beauty,22633.2,2,2263.32,47529.72000000001,1/1/2019,11:43,Cash,45266.4,4.761904762,2263.32,5.0
+236-27-1144,C,Port Harcourt,Normal,Female,Food and beverages,5871.599999999999,9,2642.2200000000003,55486.62,3/26/2019,10:31,Epay,52844.4,4.761904762,2642.2200000000003,8.4
+583-41-4548,C,Port Harcourt,Normal,Male,Home and lifestyle,6001.200000000001,7,2100.42,44108.82,2/7/2019,11:36,Epay,42008.4,4.761904762,2100.42,7.4
+358-88-9262,C,Port Harcourt,Member,Female,Food and beverages,31492.800000000003,6,9447.84,198404.64,2/1/2019,18:43,Epay,188956.8,4.761904762,9447.84,5.1
+343-87-0864,C,Port Harcourt,Member,Male,Health and beauty,27316.8,1,1365.84,28682.64,1/3/2019,10:30,Card,27316.8,4.761904762,1365.84,7.1
+243-47-2663,C,Port Harcourt,Member,Male,Electronic accessories,6757.2,6,2027.16,42570.36,1/28/2019,16:43,Card,40543.2,4.761904762,2027.16,5.5
+399-69-4630,C,Port Harcourt,Normal,Male,Electronic accessories,7995.6,6,2398.68,50372.28,3/7/2019,10:23,Card,47973.6,4.761904762,2398.68,8.6
+283-26-5248,C,Port Harcourt,Member,Female,Food and beverages,35467.2,10,17733.6,372405.6,1/30/2019,20:23,Epay,354672.0,4.761904762,17733.6,4.5
+866-99-7614,C,Port Harcourt,Normal,Male,Food and beverages,32112.0,10,16056.0,337176.0,2/11/2019,15:42,Card,321120.0,4.761904762,16056.0,4.4
+718-57-9773,C,Port Harcourt,Normal,Female,Sports and travel,17758.8,10,8879.4,186467.4,2/3/2019,16:40,Card,177588.0,4.761904762,8879.4,9.4
+408-26-9866,C,Port Harcourt,Normal,Female,Sports and travel,26632.800000000003,7,9321.48,195751.08,3/2/2019,16:42,Epay,186429.6,4.761904762,9321.48,4.1
+592-34-6155,C,Port Harcourt,Normal,Male,Food and beverages,11437.2,4,2287.44,48036.24,1/14/2019,14:43,Epay,45748.8,4.761904762,2287.44,6.2
+390-31-6381,C,Port Harcourt,Normal,Male,Food and beverages,9799.2,3,1469.88,30867.48,1/7/2019,12:37,Cash,29397.6,4.761904762,1469.88,7.3
+339-18-7061,C,Port Harcourt,Member,Female,Fashion accessories,33472.8,2,3347.28,70292.87999999999,2/13/2019,15:06,Card,66945.6,4.761904762,3347.28,8.0
+379-17-6588,C,Port Harcourt,Normal,Male,Fashion accessories,21459.6,10,10729.8,225325.8,3/14/2019,11:07,Cash,214596.0,4.761904762,10729.8,5.3
+302-15-2162,C,Port Harcourt,Member,Male,Health and beauty,16750.8,6,5025.240000000001,105530.04,3/3/2019,10:54,Card,100504.8,4.761904762,5025.240000000001,4.3
+788-07-8452,C,Port Harcourt,Member,Female,Home and lifestyle,8726.4,7,3054.24,64139.04000000001,1/27/2019,17:38,Epay,61084.8,4.761904762,3054.24,9.4
+123-35-4896,C,Port Harcourt,Normal,Female,Sports and travel,16797.6,9,7558.92,158737.31999999998,2/17/2019,19:11,Epay,151178.4,4.761904762,7558.92,5.3
+258-69-7810,C,Port Harcourt,Normal,Female,Fashion accessories,13266.0,5,3316.5,69646.5,1/26/2019,18:53,Cash,66330.0,4.761904762,3316.5,9.2
+219-61-4139,C,Port Harcourt,Normal,Male,Electronic accessories,29908.8,1,1495.44,31404.24,1/23/2019,17:16,Epay,29908.8,4.761904762,1495.44,6.4
+881-41-7302,C,Port Harcourt,Normal,Female,Fashion accessories,23396.4,1,1169.82,24566.22,1/26/2019,10:06,Card,23396.4,4.761904762,1169.82,4.5
+373-09-4567,C,Port Harcourt,Normal,Male,Food and beverages,27921.6,10,13960.8,293176.8,3/14/2019,20:35,Epay,279216.0,4.761904762,13960.8,6.9
+484-22-8230,C,Port Harcourt,Member,Female,Fashion accessories,18680.4,7,6538.14,137300.94,1/8/2019,20:08,Cash,130762.8,4.761904762,6538.14,4.5
+544-32-5024,C,Port Harcourt,Member,Female,Food and beverages,17924.4,4,3584.88,75282.48,3/28/2019,19:16,Card,71697.6,4.761904762,3584.88,6.4
+277-35-5865,C,Port Harcourt,Member,Female,Food and beverages,35629.2,9,16033.14,336695.94,3/9/2019,11:23,Cash,320662.8,4.761904762,16033.14,6.7
+284-54-4231,C,Port Harcourt,Member,Male,Sports and travel,29134.800000000003,1,1456.74,30591.54,1/19/2019,16:08,Card,29134.800000000003,4.761904762,1456.74,9.0
+840-19-2096,C,Port Harcourt,Member,Male,Electronic accessories,31647.6,5,7911.9,166149.9,3/14/2019,18:10,Epay,158238.0,4.761904762,7911.9,4.4
+641-96-3695,C,Port Harcourt,Member,Female,Fashion accessories,15645.6,6,4693.68,98567.28,2/7/2019,17:55,Epay,93873.6,4.761904762,4693.68,8.5
+318-81-2368,C,Port Harcourt,Normal,Female,Electronic accessories,16632.0,1,831.6,17463.6,3/19/2019,12:16,Cash,16632.0,4.761904762,831.6,6.3
+155-45-3814,C,Port Harcourt,Member,Female,Electronic accessories,31878.0,8,12751.2,267775.2,3/19/2019,15:29,Epay,255024.0,4.761904762,12751.2,4.7
+131-15-8856,C,Port Harcourt,Member,Female,Food and beverages,26107.2,8,10442.88,219300.48,3/30/2019,19:26,Card,208857.6,4.761904762,10442.88,4.0
+273-84-2164,C,Port Harcourt,Member,Male,Electronic accessories,4338.0,5,1084.5,22774.5,2/16/2019,15:53,Epay,21690.0,4.761904762,1084.5,5.5
+778-89-7974,C,Port Harcourt,Normal,Male,Health and beauty,25275.6,6,7582.680000000001,159236.28,3/30/2019,14:58,Cash,151653.6,4.761904762,7582.680000000001,7.4
+859-71-0933,C,Port Harcourt,Member,Female,Sports and travel,5576.4,2,557.6400000000001,11710.44,1/16/2019,15:10,Cash,11152.8,4.761904762,557.6400000000001,6.3
+740-11-5257,C,Port Harcourt,Normal,Male,Electronic accessories,8906.4,10,4453.2,93517.2,2/24/2019,16:44,Cash,89064.0,4.761904762,4453.2,7.1
+250-81-7186,C,Port Harcourt,Normal,Female,Electronic accessories,35888.4,1,1794.4199999999998,37682.82,2/27/2019,10:23,Card,35888.4,4.761904762,1794.4199999999998,8.0
+842-29-4695,C,Port Harcourt,Member,Male,Sports and travel,6170.400000000001,7,2159.64,45352.44,1/16/2019,12:07,Card,43192.8,4.761904762,2159.64,7.9
+641-51-2661,C,Port Harcourt,Member,Female,Food and beverages,31356.0,10,15677.999999999998,329238.0,2/12/2019,14:45,Card,313560.0,4.761904762,15677.999999999998,9.9
+714-02-3114,C,Port Harcourt,Normal,Female,Sports and travel,35568.0,2,3556.8,74692.8,2/21/2019,11:39,Cash,71136.0,4.761904762,3556.8,7.7
+408-66-6712,C,Port Harcourt,Member,Female,Health and beauty,17175.6,6,5152.679999999999,108206.28,2/16/2019,14:19,Epay,103053.6,4.761904762,5152.679999999999,4.4
+556-41-6224,C,Port Harcourt,Normal,Male,Health and beauty,12110.4,8,4844.16,101727.36,2/15/2019,17:10,Card,96883.2,4.761904762,4844.16,9.3
+648-94-3045,C,Port Harcourt,Normal,Male,Health and beauty,21222.0,10,10611.0,222831.0,2/7/2019,14:27,Epay,212220.0,4.761904762,10611.0,8.1
+370-96-0655,C,Port Harcourt,Normal,Female,Fashion accessories,17755.2,6,5326.56,111857.76,1/9/2019,13:46,Epay,106531.2,4.761904762,5326.56,7.1
+173-57-2300,C,Port Harcourt,Member,Male,Sports and travel,26236.8,2,2623.68,55097.28,3/13/2019,12:51,Cash,52473.6,4.761904762,2623.68,6.1
+394-55-6384,C,Port Harcourt,Member,Female,Sports and travel,25268.4,9,11370.78,238786.38,1/25/2019,13:38,Cash,227415.6,4.761904762,11370.78,6.7
+266-20-6657,C,Port Harcourt,Member,Male,Food and beverages,19814.4,7,6935.04,145635.84000000003,3/12/2019,19:39,Epay,138700.8,4.761904762,6935.04,5.2
+196-01-2849,C,Port Harcourt,Member,Female,Fashion accessories,26416.8,7,9245.88,194163.48000000004,2/10/2019,13:56,Cash,184917.6,4.761904762,9245.88,9.5
+372-62-5264,C,Port Harcourt,Normal,Female,Food and beverages,18936.0,9,8521.2,178945.2,1/16/2019,14:42,Cash,170424.0,4.761904762,8521.2,7.6
+751-41-9720,C,Port Harcourt,Normal,Male,Home and lifestyle,35100.0,10,17550.0,368550.0,1/12/2019,16:18,Epay,351000.0,4.761904762,17550.0,8.0
+626-43-7888,C,Port Harcourt,Normal,Female,Fashion accessories,21747.6,8,8699.04,182679.84,2/7/2019,12:23,Epay,173980.8,4.761904762,8699.04,9.6
+162-65-8559,C,Port Harcourt,Member,Male,Food and beverages,24832.800000000003,1,1241.64,26074.44,1/21/2019,20:13,Cash,24832.800000000003,4.761904762,1241.64,4.8
+760-27-5490,C,Port Harcourt,Normal,Male,Fashion accessories,5623.2,8,2249.28,47234.88,1/20/2019,20:37,Epay,44985.6,4.761904762,2249.28,9.1
+728-88-7867,C,Port Harcourt,Member,Female,Home and lifestyle,27190.8,4,5438.16,114201.36,3/19/2019,15:52,Epay,108763.2,4.761904762,5438.16,8.3
+183-21-3799,C,Port Harcourt,Normal,Female,Electronic accessories,27946.8,9,12576.06,264097.26,2/19/2019,15:14,Epay,251521.2,4.761904762,12576.06,7.2
+268-20-3585,C,Port Harcourt,Normal,Female,Health and beauty,4986.0,9,2243.7,47117.7,2/4/2019,12:50,Epay,44874.0,4.761904762,2243.7,6.0
+735-32-9839,C,Port Harcourt,Member,Male,Fashion accessories,35532.0,8,14212.8,298468.8,1/31/2019,10:36,Epay,284256.0,4.761904762,14212.8,8.5
+678-79-0726,C,Port Harcourt,Member,Female,Sports and travel,32626.8,9,14682.06,308323.26,1/18/2019,15:28,Cash,293641.2,4.761904762,14682.06,5.1
+592-46-1692,C,Port Harcourt,Member,Female,Food and beverages,13237.2,7,4633.02,97293.42,1/11/2019,20:10,Cash,92660.4,4.761904762,4633.02,7.4
+149-14-0304,C,Port Harcourt,Member,Female,Health and beauty,10260.0,8,4104.0,86184.0,2/6/2019,14:24,Cash,82080.0,4.761904762,4104.0,6.6
+442-44-6497,C,Port Harcourt,Member,Male,Home and lifestyle,20005.2,3,3000.78,63016.380000000005,1/8/2019,11:42,Card,60015.600000000006,4.761904762,3000.78,5.9
+210-74-9613,C,Port Harcourt,Normal,Male,Fashion accessories,35013.6,4,7002.72,147057.12,3/16/2019,15:33,Epay,140054.4,4.761904762,7002.72,6.8
+607-65-2441,C,Port Harcourt,Member,Male,Health and beauty,29502.0,10,14751.0,309771.0,3/10/2019,12:39,Card,295020.0,4.761904762,14751.0,6.0
+386-27-7606,C,Port Harcourt,Member,Female,Home and lifestyle,29232.0,7,10231.2,214855.2,3/23/2019,15:59,Card,204624.0,4.761904762,10231.2,8.1
+137-63-5492,C,Port Harcourt,Normal,Male,Electronic accessories,21153.6,10,10576.8,222112.8,1/29/2019,14:26,Epay,211536.0,4.761904762,10576.8,9.0
+733-29-1227,C,Port Harcourt,Normal,Male,Home and lifestyle,20019.6,7,7006.86,147144.06,3/23/2019,12:41,Cash,140137.19999999998,4.761904762,7006.86,8.5
+451-73-2711,C,Port Harcourt,Normal,Male,Food and beverages,30538.8,1,1526.94,32065.74,1/14/2019,15:20,Epay,30538.8,4.761904762,1526.94,8.8
+345-68-9016,C,Port Harcourt,Member,Female,Sports and travel,11401.2,8,4560.48,95770.08,1/2/2019,16:19,Card,91209.6,4.761904762,4560.48,5.6
+390-17-5806,C,Port Harcourt,Member,Female,Food and beverages,13831.2,1,691.5600000000001,14522.76,2/2/2019,16:33,Cash,13831.2,4.761904762,691.5600000000001,8.6
+664-14-2882,C,Port Harcourt,Member,Female,Home and lifestyle,3790.8,5,947.7,19901.7,1/30/2019,14:43,Card,18954.0,4.761904762,947.7,5.8
+314-23-4520,C,Port Harcourt,Member,Male,Health and beauty,29242.800000000003,7,10234.98,214934.58,1/15/2019,20:44,Cash,204699.6,4.761904762,10234.98,9.0
+288-38-3758,C,Port Harcourt,Member,Female,Fashion accessories,30553.2,3,4582.98,96242.58,1/25/2019,18:30,Epay,91659.6,4.761904762,4582.98,7.4
+801-88-0346,C,Port Harcourt,Normal,Female,Fashion accessories,27381.6,3,4107.240000000001,86252.04000000001,1/5/2019,20:30,Card,82144.8,4.761904762,4107.240000000001,9.8
+759-98-4285,C,Port Harcourt,Member,Female,Health and beauty,30913.2,7,10819.62,227212.02,2/27/2019,19:01,Card,216392.4,4.761904762,10819.62,8.0
+201-63-8275,C,Port Harcourt,Member,Female,Sports and travel,24476.4,7,8566.74,179901.54,2/17/2019,16:50,Epay,171334.8,4.761904762,8566.74,5.7
+471-06-8611,C,Port Harcourt,Normal,Female,Food and beverages,18871.2,1,943.5600000000002,19814.76,2/6/2019,10:22,Card,18871.2,4.761904762,943.5600000000002,6.3
+200-16-5952,C,Port Harcourt,Member,Male,Food and beverages,23634.000000000004,2,2363.4,49631.4,1/17/2019,16:46,Cash,47268.00000000001,4.761904762,2363.4,6.0
+102-77-2261,C,Port Harcourt,Member,Male,Health and beauty,23511.6,7,8229.06,172810.26,3/5/2019,18:02,Card,164581.2,4.761904762,8229.06,4.2
+102-06-2002,C,Port Harcourt,Member,Male,Sports and travel,9090.0,5,2272.5,47722.5,3/20/2019,17:52,Cash,45450.0,4.761904762,2272.5,6.1
+629-42-4133,C,Port Harcourt,Normal,Male,Health and beauty,7848.0,8,3139.2000000000003,65923.2,2/19/2019,19:24,Cash,62784.0,4.761904762,3139.2000000000003,8.3
+468-99-7231,C,Port Harcourt,Normal,Female,Home and lifestyle,15843.6,8,6337.44,133086.24000000002,3/3/2019,17:36,Cash,126748.8,4.761904762,6337.44,8.8
+516-77-6464,C,Port Harcourt,Member,Female,Health and beauty,3657.6,5,914.4,19202.4,2/24/2019,13:08,Epay,18288.0,4.761904762,914.4,4.1
+886-77-9084,C,Port Harcourt,Normal,Male,Electronic accessories,25880.4,8,10352.16,217395.36,2/19/2019,11:33,Epay,207043.2,4.761904762,10352.16,5.5
+790-38-4466,C,Port Harcourt,Normal,Female,Health and beauty,3956.4,5,989.1,20771.1,1/23/2019,10:18,Card,19782.0,4.761904762,989.1,9.3
+704-10-4056,C,Port Harcourt,Member,Male,Health and beauty,21769.2,3,3265.38,68572.98,1/14/2019,10:55,Card,65307.6,4.761904762,3265.38,5.6
+400-80-4065,C,Port Harcourt,Member,Male,Health and beauty,24678.0,4,4935.6,103647.6,2/15/2019,20:21,Card,98712.0,4.761904762,4935.6,9.2
+443-60-9639,C,Port Harcourt,Member,Female,Home and lifestyle,21913.2,1,1095.66,23008.86,1/24/2019,13:24,Cash,21913.2,4.761904762,1095.66,5.5
+401-09-4232,C,Port Harcourt,Member,Male,Home and lifestyle,31208.4,5,7802.099999999999,163844.1,2/11/2019,18:38,Epay,156042.0,4.761904762,7802.099999999999,9.4
+324-41-6833,C,Port Harcourt,Member,Female,Electronic accessories,10872.0,8,4348.8,91324.8,3/3/2019,19:30,Epay,86976.0,4.761904762,4348.8,5.1
+474-33-8305,C,Port Harcourt,Member,Male,Fashion accessories,24260.4,7,8491.14,178313.94,3/23/2019,13:23,Epay,169822.80000000002,4.761904762,8491.14,6.9
+189-55-2313,C,Port Harcourt,Normal,Female,Fashion accessories,22384.8,10,11192.4,235040.4,1/31/2019,10:33,Epay,223848.0,4.761904762,11192.4,6.0
+815-04-6282,C,Port Harcourt,Member,Female,Sports and travel,23389.2,5,5847.3,122793.3,2/8/2019,12:52,Card,116946.0,4.761904762,5847.3,6.5
+477-59-2456,C,Port Harcourt,Normal,Female,Fashion accessories,16358.4,7,5725.4400000000005,120234.24000000002,1/23/2019,11:15,Cash,114508.8,4.761904762,5725.4400000000005,9.2
+784-21-9238,C,Port Harcourt,Member,Male,Sports and travel,3661.2,1,183.06,3844.26,2/7/2019,14:15,Cash,3661.2,4.761904762,183.06,5.9
+538-22-0304,C,Port Harcourt,Normal,Male,Electronic accessories,23382.0,10,11691.0,245511.0,3/24/2019,18:27,Cash,233820.0,4.761904762,11691.0,5.2
+660-29-7083,C,Port Harcourt,Normal,Male,Electronic accessories,20113.2,10,10056.6,211188.6,1/15/2019,15:01,Cash,201132.00000000003,4.761904762,10056.6,5.8
+271-77-8740,C,Port Harcourt,Member,Female,Sports and travel,10519.2,6,3155.76,66270.95999999999,1/1/2019,11:40,Epay,63115.2,4.761904762,3155.76,5.0
+549-23-9016,C,Port Harcourt,Member,Female,Food and beverages,5353.2,2,535.3199999999999,11241.72,2/13/2019,18:15,Card,10706.4,4.761904762,535.3199999999999,8.9
+862-29-5914,C,Port Harcourt,Normal,Female,Sports and travel,8056.799999999999,1,402.84,8459.640000000001,1/30/2019,17:08,Card,8056.799999999999,4.761904762,402.84,8.6
+845-94-6841,C,Port Harcourt,Member,Female,Food and beverages,26236.8,9,11806.56,247937.76,1/8/2019,19:38,Cash,236131.2,4.761904762,11806.56,4.0
+658-66-3967,C,Port Harcourt,Normal,Male,Health and beauty,19148.4,7,6701.94,140740.74,1/14/2019,15:42,Epay,134038.8,4.761904762,6701.94,5.0
+848-95-6252,C,Port Harcourt,Member,Female,Home and lifestyle,31057.2,1,1552.86,32610.06,2/20/2019,13:24,Epay,31057.2,4.761904762,1552.86,7.0
+176-78-1170,C,Port Harcourt,Member,Male,Health and beauty,12171.6,3,1825.74,38340.54,1/26/2019,15:11,Epay,36514.8,4.761904762,1825.74,7.3
+101-81-4070,C,Port Harcourt,Member,Female,Health and beauty,22615.2,2,2261.52,47491.92,1/17/2019,12:36,Epay,45230.4,4.761904762,2261.52,4.9
+631-34-1880,C,Port Harcourt,Member,Male,Food and beverages,8751.6,3,1312.74,27567.54,1/8/2019,19:09,Card,26254.800000000003,4.761904762,1312.74,4.3
+584-66-4073,C,Port Harcourt,Normal,Male,Fashion accessories,20340.0,1,1017.0,21357.0,3/13/2019,15:45,Epay,20340.0,4.761904762,1017.0,9.6
+154-87-7367,C,Port Harcourt,Normal,Male,Home and lifestyle,23493.6,8,9397.44,197346.24,3/15/2019,14:04,Epay,187948.8,4.761904762,9397.44,6.3
+885-56-0389,C,Port Harcourt,Member,Male,Fashion accessories,18846.0,1,942.3,19788.3,2/12/2019,17:49,Cash,18846.0,4.761904762,942.3,4.0
+115-38-7388,C,Port Harcourt,Member,Female,Fashion accessories,3664.8,8,1465.92,30784.32,3/30/2019,12:51,Card,29318.4,4.761904762,1465.92,9.5
+137-74-8729,C,Port Harcourt,Normal,Female,Fashion accessories,4388.4,8,1755.36,36862.56,3/13/2019,12:47,Epay,35107.2,4.761904762,1755.36,6.8
+389-70-2397,C,Port Harcourt,Normal,Female,Health and beauty,30117.6,5,7529.4,158117.4,2/21/2019,10:26,Cash,150588.0,4.761904762,7529.4,7.2
+607-76-6216,C,Port Harcourt,Member,Female,Fashion accessories,33296.4,5,8324.1,174806.1,3/2/2019,16:35,Card,166482.0,4.761904762,8324.1,8.6
+781-84-8059,C,Port Harcourt,Normal,Male,Fashion accessories,21866.4,7,7653.24,160718.04,1/18/2019,16:23,Epay,153064.8,4.761904762,7653.24,5.0
+409-49-6995,C,Port Harcourt,Member,Female,Food and beverages,17017.2,6,5105.16,107208.36,2/5/2019,10:17,Cash,102103.2,4.761904762,5105.16,8.8
+725-54-0677,C,Port Harcourt,Member,Male,Health and beauty,30816.0,7,10785.6,226497.6,3/2/2019,13:50,Cash,215712.00000000003,4.761904762,10785.6,5.3
+377-79-7592,C,Port Harcourt,Member,Female,Electronic accessories,16142.4,9,7264.08,152545.68000000002,1/14/2019,14:00,Card,145281.6,4.761904762,7264.08,7.5
+545-07-8534,C,Port Harcourt,Normal,Female,Health and beauty,20995.2,2,2099.5200000000004,44089.920000000006,2/14/2019,12:42,Epay,41990.4,4.761904762,2099.5200000000004,6.0
+118-62-1812,C,Port Harcourt,Member,Female,Home and lifestyle,28216.8,4,5643.36,118510.55999999998,3/24/2019,17:56,Cash,112867.2,4.761904762,5643.36,7.9
+450-42-3339,C,Port Harcourt,Normal,Male,Health and beauty,30459.6,10,15229.8,319825.8,2/9/2019,18:58,Card,304596.0,4.761904762,15229.8,8.8
+151-27-8496,C,Port Harcourt,Normal,Female,Electronic accessories,20206.8,4,4041.36,84868.56,1/19/2019,11:43,Epay,80827.2,4.761904762,4041.36,8.6
+717-96-4189,C,Port Harcourt,Normal,Female,Electronic accessories,12776.4,6,3832.92,80491.32,2/2/2019,12:40,Cash,76658.4,4.761904762,3832.92,4.1
+722-13-2115,C,Port Harcourt,Member,Male,Sports and travel,15426.0,1,771.3000000000001,16197.3,3/14/2019,15:36,Card,15426.0,4.761904762,771.3000000000001,9.3
+246-55-6923,C,Port Harcourt,Member,Female,Home and lifestyle,12884.4,9,5797.98,121757.58,3/10/2019,15:06,Card,115959.6,4.761904762,5797.98,5.1
+838-02-1821,C,Port Harcourt,Member,Female,Home and lifestyle,4582.8,2,458.28,9623.88,2/22/2019,12:10,Card,9165.6,4.761904762,458.28,5.2
+887-42-0517,C,Port Harcourt,Normal,Female,Sports and travel,29930.4,7,10475.64,219988.44000000003,1/10/2019,10:31,Card,209512.8,4.761904762,10475.64,6.6
+457-12-0244,C,Port Harcourt,Member,Female,Sports and travel,12679.2,6,3803.76,79878.95999999999,3/14/2019,13:49,Epay,76075.2,4.761904762,3803.76,6.5
+756-93-1854,C,Port Harcourt,Member,Female,Fashion accessories,30006.0,2,3000.6000000000004,63012.6,2/2/2019,14:05,Card,60011.99999999999,4.761904762,3000.6000000000004,9.5
+458-10-8612,C,Port Harcourt,Normal,Male,Health and beauty,23068.8,7,8074.08,169555.68000000002,1/20/2019,12:27,Epay,161481.6,4.761904762,8074.08,7.6
+235-06-8510,C,Port Harcourt,Member,Male,Home and lifestyle,30859.2,3,4628.879999999999,97206.48,1/24/2019,20:59,Epay,92577.6,4.761904762,4628.879999999999,5.1
+433-08-7822,C,Port Harcourt,Normal,Female,Health and beauty,28400.4,7,9940.14,208742.94,1/5/2019,19:48,Epay,198802.8,4.761904762,9940.14,7.5
+500-02-2261,C,Port Harcourt,Normal,Female,Food and beverages,20624.4,6,6187.320000000001,129933.72,3/21/2019,17:04,Epay,123746.4,4.761904762,6187.320000000001,5.9
+702-83-5291,C,Port Harcourt,Member,Male,Fashion accessories,35935.2,9,16170.839999999998,339587.64,3/27/2019,10:43,Cash,323416.8,4.761904762,16170.839999999998,6.6
+859-97-6048,C,Port Harcourt,Member,Male,Electronic accessories,30330.0,2,3033.0000000000005,63693.00000000001,3/26/2019,14:13,Card,60660.0,4.761904762,3033.0000000000005,5.3
+373-88-1424,C,Port Harcourt,Member,Male,Home and lifestyle,12891.6,5,3222.9,67680.9,2/6/2019,18:44,Epay,64458.00000000001,4.761904762,3222.9,7.9
+784-08-0310,C,Port Harcourt,Member,Female,Food and beverages,7574.4,4,1514.88,31812.48,1/13/2019,13:58,Cash,30297.6,4.761904762,1514.88,7.6
+577-34-7579,C,Port Harcourt,Member,Male,Food and beverages,18176.4,9,8179.38,171766.97999999998,1/10/2019,17:16,Cash,163587.6,4.761904762,8179.38,5.4
+867-47-1948,C,Port Harcourt,Normal,Female,Home and lifestyle,5688.0,10,2844.0,59724.0,1/9/2019,12:07,Cash,56880.0,4.761904762,2844.0,7.8
+256-58-3609,C,Port Harcourt,Member,Male,Fashion accessories,33112.8,1,1655.64,34768.439999999995,3/18/2019,15:29,Cash,33112.8,4.761904762,1655.64,9.8
+364-34-2972,C,Port Harcourt,Member,Male,Electronic accessories,34855.2,3,5228.28,109793.88,3/30/2019,20:37,Cash,104565.6,4.761904762,5228.28,6.7
+744-82-9138,C,Port Harcourt,Normal,Male,Fashion accessories,31006.8,2,3100.68,65114.28,2/7/2019,17:59,Cash,62013.6,4.761904762,3100.68,8.2
+728-47-9078,C,Port Harcourt,Member,Male,Food and beverages,21452.4,4,4290.48,90100.08,1/19/2019,12:46,Cash,85809.6,4.761904762,4290.48,9.8
+148-41-7930,C,Port Harcourt,Normal,Male,Health and beauty,35985.6,7,12594.96,264494.16000000003,1/23/2019,10:33,Cash,251899.2,4.761904762,12594.96,6.1
+189-40-5216,C,Port Harcourt,Normal,Male,Electronic accessories,34693.200000000004,7,12142.62,254995.02,1/9/2019,11:40,Cash,242852.4,4.761904762,12142.62,6.0
+267-62-7380,C,Port Harcourt,Member,Male,Electronic accessories,29642.4,10,14821.2,311245.2,3/29/2019,19:12,Epay,296424.0,4.761904762,14821.2,4.3
+652-49-6720,C,Port Harcourt,Member,Female,Electronic accessories,21942.0,1,1097.1,23039.1,2/18/2019,11:40,Epay,21942.0,4.761904762,1097.1,5.9
+233-67-5758,C,Port Harcourt,Normal,Male,Health and beauty,14526.0,1,726.3000000000001,15252.3,1/29/2019,13:46,Epay,14526.0,4.761904762,726.3000000000001,6.2
diff --git a/My_Ustacky_Project/My Project.ipynb b/My_Ustacky_Project/My Project.ipynb
new file mode 100644
index 0000000..e10fbf9
--- /dev/null
+++ b/My_Ustacky_Project/My Project.ipynb	
@@ -0,0 +1,1395 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7c0676cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import pandas as pd\n",
+    "os.chdir(r\"C:\\Users\\Chizy\\Desktop\\MyContent\\Ustacky\\My project\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d99ac026",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extension = 'csv'\n",
+    "file_1 =  [i for i in glob.glob('*.{}'.format(extension))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "37838777",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja_Branch.csv',\n",
+       " 'Lagos_Branch.csv',\n",
+       " 'Market_data.csv',\n",
+       " 'Port_Harcourt_Branch.csv']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c20066af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read each dataframe\n",
+    "df_A = pd.read_csv('Abuja_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b3211088",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "1  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "2  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "3  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "4  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
+       "1      5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
+       "2      9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
+       "3     33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
+       "4     14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0   59227.2                 4.761905       2961.36     5.9  \n",
+       "1   20851.2                 4.761905       1042.56     4.5  \n",
+       "2   36734.4                 4.761905       1836.72     6.8  \n",
+       "3  202435.2                 4.761905      10121.76     4.5  \n",
+       "4   29016.0                 4.761905       1450.80     4.4  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_A.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a303bab3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_L = pd.read_csv('Lagos_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "42599319",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1     16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2     20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3     31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4     24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0  188218.8                 4.761905       9410.94     9.1  \n",
+       "1  116751.6                 4.761905       5837.58     7.4  \n",
+       "2  167673.6                 4.761905       8383.68     8.4  \n",
+       "3  217501.2                 4.761905      10875.06     5.3  \n",
+       "4  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_L.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "304da609",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_p = pd.read_csv('Port_Harcourt_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c1440da4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>226-31-3081</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>5500.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>28879.20</td>\n",
+       "      <td>3/8/2019</td>\n",
+       "      <td>10:29</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>27504.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699-14-3026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30740.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>225941.94</td>\n",
+       "      <td>3/25/2019</td>\n",
+       "      <td>18:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>215182.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>315-22-5665</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>26481.6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>278056.80</td>\n",
+       "      <td>2/24/2019</td>\n",
+       "      <td>11:38</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>264816.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>300-71-4605</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30974.4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>162615.60</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>11:24</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>154872.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>4.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>183-56-6882</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>35791.2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>150323.04</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>10:42</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>143164.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0  226-31-3081      C  Port Harcourt        Normal  Female   \n",
+       "1  699-14-3026      C  Port Harcourt        Normal    Male   \n",
+       "2  315-22-5665      C  Port Harcourt        Normal  Female   \n",
+       "3  300-71-4605      C  Port Harcourt        Member    Male   \n",
+       "4  183-56-6882      C  Port Harcourt        Member  Female   \n",
+       "\n",
+       "             Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0  Electronic accessories      5500.8         5   1375.20   28879.20   \n",
+       "1  Electronic accessories     30740.4         7  10759.14  225941.94   \n",
+       "2      Home and lifestyle     26481.6        10  13240.80  278056.80   \n",
+       "3  Electronic accessories     30974.4         5   7743.60  162615.60   \n",
+       "4      Food and beverages     35791.2         4   7158.24  150323.04   \n",
+       "\n",
+       "        Date   Time Payment      cogs  gross margin percentage  gross income  \\\n",
+       "0   3/8/2019  10:29    Cash   27504.0                 4.761905       1375.20   \n",
+       "1  3/25/2019  18:30    Epay  215182.8                 4.761905      10759.14   \n",
+       "2  2/24/2019  11:38    Epay  264816.0                 4.761905      13240.80   \n",
+       "3  2/25/2019  11:24    Epay  154872.0                 4.761905       7743.60   \n",
+       "4   2/6/2019  10:42    Epay  143164.8                 4.761905       7158.24   \n",
+       "\n",
+       "   Rating  \n",
+       "0     9.6  \n",
+       "1     4.1  \n",
+       "2     8.0  \n",
+       "3     4.8  \n",
+       "4     7.5  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_p.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9147fea6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a list of the dataframes\n",
+    "pd_list = [df_L, df_A, df_p]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b96a216a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine all the df\n",
+    "df_ABC = pd.concat(pd_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "99822910",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ABC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c71bf693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Export combined data as csv\n",
+    "df_ABC.to_csv('Market_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "34a6a9dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>323</td>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>324</td>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>325</td>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>326</td>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>327</td>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 18 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0   Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0             0  750-67-8428      A          Lagos        Member  Female   \n",
+       "1             1  631-41-3108      A          Lagos        Normal    Male   \n",
+       "2             2  123-19-1176      A          Lagos        Member    Male   \n",
+       "3             3  373-73-7910      A          Lagos        Normal    Male   \n",
+       "4             4  355-53-5943      A          Lagos        Member  Female   \n",
+       "..          ...          ...    ...            ...           ...     ...   \n",
+       "995         323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996         324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997         325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998         326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999         327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "995      12594.96     6.1  \n",
+       "996      12142.62     6.0  \n",
+       "997      14821.20     4.3  \n",
+       "998       1097.10     5.9  \n",
+       "999        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('Market_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7ade140",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2a445c3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/My_Ustacky_Project/MyProject_step2.ipynb b/My_Ustacky_Project/MyProject_step2.ipynb
new file mode 100644
index 0000000..503b7da
--- /dev/null
+++ b/My_Ustacky_Project/MyProject_step2.ipynb
@@ -0,0 +1,6196 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "01a24aab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import glob\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import datetime as dt\n",
+    "os.chdir(r\"C:\\Users\\Chizy\\Desktop\\MyContent\\Ustacky\\My project\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d5bf8d2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extension = 'csv'\n",
+    "file_1 =  [i for i in glob.glob('*.{}'.format(extension))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "735f6be0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja_Branch.csv',\n",
+       " 'Lagos_Branch.csv',\n",
+       " 'Market_data.csv',\n",
+       " 'Port_Harcourt_Branch.csv']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f6ac03d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read each dataframe\n",
+    "df_A = pd.read_csv('Abuja_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "ab893353",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>552-44-5977</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>22320.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8928.00</td>\n",
+       "      <td>187488.00</td>\n",
+       "      <td>1/3/2019</td>\n",
+       "      <td>19:08</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>178560.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8928.00</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>430-53-4718</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>27133.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10853.28</td>\n",
+       "      <td>227918.88</td>\n",
+       "      <td>1/28/2019</td>\n",
+       "      <td>15:46</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>217065.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10853.28</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>602-16-6955</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>27576.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13788.00</td>\n",
+       "      <td>289548.00</td>\n",
+       "      <td>1/24/2019</td>\n",
+       "      <td>18:10</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>275760.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13788.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>690-01-6631</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>6296.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3148.20</td>\n",
+       "      <td>66112.20</td>\n",
+       "      <td>2/22/2019</td>\n",
+       "      <td>18:35</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>62964.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>3148.20</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>303-96-2227</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>35056.8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>17528.40</td>\n",
+       "      <td>368096.40</td>\n",
+       "      <td>3/2/2019</td>\n",
+       "      <td>17:16</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>350568.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>17528.40</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>332 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0    692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "1    351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "2    529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "3    299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "4    319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "..           ...    ...    ...           ...     ...                     ...   \n",
+       "327  552-44-5977      B  Abuja        Member    Male       Health and beauty   \n",
+       "328  430-53-4718      B  Abuja        Member    Male       Health and beauty   \n",
+       "329  602-16-6955      B  Abuja        Normal  Female       Sports and travel   \n",
+       "330  690-01-6631      B  Abuja        Normal    Male     Fashion accessories   \n",
+       "331  303-96-2227      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0       19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
+       "1        5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
+       "2        9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
+       "3       33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
+       "4       14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
+       "..          ...       ...       ...        ...        ...    ...     ...   \n",
+       "327     22320.0         8   8928.00  187488.00   1/3/2019  19:08    Card   \n",
+       "328     27133.2         8  10853.28  227918.88  1/28/2019  15:46    Card   \n",
+       "329     27576.0        10  13788.00  289548.00  1/24/2019  18:10    Epay   \n",
+       "330      6296.4        10   3148.20   66112.20  2/22/2019  18:35    Epay   \n",
+       "331     35056.8        10  17528.40  368096.40   3/2/2019  17:16    Epay   \n",
+       "\n",
+       "         cogs  gross margin percentage  gross income  Rating  \n",
+       "0     59227.2                 4.761905       2961.36     5.9  \n",
+       "1     20851.2                 4.761905       1042.56     4.5  \n",
+       "2     36734.4                 4.761905       1836.72     6.8  \n",
+       "3    202435.2                 4.761905      10121.76     4.5  \n",
+       "4     29016.0                 4.761905       1450.80     4.4  \n",
+       "..        ...                      ...           ...     ...  \n",
+       "327  178560.0                 4.761905       8928.00     6.2  \n",
+       "328  217065.6                 4.761905      10853.28     8.4  \n",
+       "329  275760.0                 4.761905      13788.00     6.0  \n",
+       "330   62964.0                 4.761905       3148.20     6.6  \n",
+       "331  350568.0                 4.761905      17528.40     4.4  \n",
+       "\n",
+       "[332 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aff26318",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_L = pd.read_csv('Lagos_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "f81b0249",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>886-18-2897</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>20361.6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5090.40</td>\n",
+       "      <td>106898.40</td>\n",
+       "      <td>3/22/2019</td>\n",
+       "      <td>19:06</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>101808.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5090.40</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>745-74-0715</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>20890.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2089.08</td>\n",
+       "      <td>43870.68</td>\n",
+       "      <td>3/10/2019</td>\n",
+       "      <td>20:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>41781.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2089.08</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>727-02-1313</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>11462.4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>573.12</td>\n",
+       "      <td>12035.52</td>\n",
+       "      <td>2/9/2019</td>\n",
+       "      <td>13:22</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>11462.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>573.12</td>\n",
+       "      <td>7.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>347-56-2442</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>23695.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1184.76</td>\n",
+       "      <td>24879.96</td>\n",
+       "      <td>2/22/2019</td>\n",
+       "      <td>15:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>23695.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1184.76</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>849-09-3807</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>31802.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11130.84</td>\n",
+       "      <td>233747.64</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>13:28</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>222616.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>11130.84</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>340 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0    750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1    631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2    123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3    373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4    355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "..           ...    ...    ...           ...     ...                     ...   \n",
+       "335  886-18-2897      A  Lagos        Normal  Female      Food and beverages   \n",
+       "336  745-74-0715      A  Lagos        Normal    Male  Electronic accessories   \n",
+       "337  727-02-1313      A  Lagos        Member    Male      Food and beverages   \n",
+       "338  347-56-2442      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "339  849-09-3807      A  Lagos        Member  Female     Fashion accessories   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0       26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1       16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2       20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3       31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4       24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "..          ...       ...       ...        ...        ...    ...     ...   \n",
+       "335     20361.6         5   5090.40  106898.40  3/22/2019  19:06    Card   \n",
+       "336     20890.8         2   2089.08   43870.68  3/10/2019  20:46    Epay   \n",
+       "337     11462.4         1    573.12   12035.52   2/9/2019  13:22    Cash   \n",
+       "338     23695.2         1   1184.76   24879.96  2/22/2019  15:33    Cash   \n",
+       "339     31802.4         7  11130.84  233747.64  2/18/2019  13:28    Cash   \n",
+       "\n",
+       "         cogs  gross margin percentage  gross income  Rating  \n",
+       "0    188218.8                 4.761905       9410.94     9.1  \n",
+       "1    116751.6                 4.761905       5837.58     7.4  \n",
+       "2    167673.6                 4.761905       8383.68     8.4  \n",
+       "3    217501.2                 4.761905      10875.06     5.3  \n",
+       "4    148694.4                 4.761905       7434.72     5.8  \n",
+       "..        ...                      ...           ...     ...  \n",
+       "335  101808.0                 4.761905       5090.40     4.5  \n",
+       "336   41781.6                 4.761905       2089.08     8.8  \n",
+       "337   11462.4                 4.761905        573.12     7.7  \n",
+       "338   23695.2                 4.761905       1184.76     4.1  \n",
+       "339  222616.8                 4.761905      11130.84     6.6  \n",
+       "\n",
+       "[340 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4e90494b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_p = pd.read_csv('Port_Harcourt_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "7bd0e3d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>226-31-3081</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>5500.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>28879.20</td>\n",
+       "      <td>3/8/2019</td>\n",
+       "      <td>10:29</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>27504.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699-14-3026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30740.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>225941.94</td>\n",
+       "      <td>3/25/2019</td>\n",
+       "      <td>18:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>215182.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>315-22-5665</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>26481.6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>278056.80</td>\n",
+       "      <td>2/24/2019</td>\n",
+       "      <td>11:38</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>264816.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>300-71-4605</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30974.4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>162615.60</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>11:24</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>154872.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>4.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>183-56-6882</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>35791.2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>150323.04</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>10:42</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>143164.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>328 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    226-31-3081      C  Port Harcourt        Normal  Female   \n",
+       "1    699-14-3026      C  Port Harcourt        Normal    Male   \n",
+       "2    315-22-5665      C  Port Harcourt        Normal  Female   \n",
+       "3    300-71-4605      C  Port Harcourt        Member    Male   \n",
+       "4    183-56-6882      C  Port Harcourt        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0    Electronic accessories      5500.8         5   1375.20   28879.20   \n",
+       "1    Electronic accessories     30740.4         7  10759.14  225941.94   \n",
+       "2        Home and lifestyle     26481.6        10  13240.80  278056.80   \n",
+       "3    Electronic accessories     30974.4         5   7743.60  162615.60   \n",
+       "4        Food and beverages     35791.2         4   7158.24  150323.04   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     3/8/2019  10:29    Cash   27504.0                 4.761905   \n",
+       "1    3/25/2019  18:30    Epay  215182.8                 4.761905   \n",
+       "2    2/24/2019  11:38    Epay  264816.0                 4.761905   \n",
+       "3    2/25/2019  11:24    Epay  154872.0                 4.761905   \n",
+       "4     2/6/2019  10:42    Epay  143164.8                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         1375.20     9.6  \n",
+       "1        10759.14     4.1  \n",
+       "2        13240.80     8.0  \n",
+       "3         7743.60     4.8  \n",
+       "4         7158.24     7.5  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[328 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e3802334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a list of the dataframes\n",
+    "pd_list = [df_L, df_A, df_p]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0db90e96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine all the df\n",
+    "df_ABC = pd.concat(pd_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1b43e9eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ABC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "09a3d601",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Export combined data as csv\n",
+    "df_ABC.to_csv('Market_data.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "dc2f293c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "995      12594.96     6.1  \n",
+       "996      12142.62     6.0  \n",
+       "997      14821.20     4.3  \n",
+       "998       1097.10     5.9  \n",
+       "999        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('Market_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "66c0ffff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1     16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2     20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3     31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4     24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0  188218.8                 4.761905       9410.94     9.1  \n",
+       "1  116751.6                 4.761905       5837.58     7.4  \n",
+       "2  167673.6                 4.761905       8383.68     8.4  \n",
+       "3  217501.2                 4.761905      10875.06     5.3  \n",
+       "4  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View the first 5rows of the dataset\n",
+    "df = pd.read_csv('Market_data.csv')\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "022eba6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 17)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To check no of rows and column\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6ff0da19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',\n",
+       "       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',\n",
+       "       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',\n",
+       "       'Rating'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To check various column name in the dataset\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401ddd35",
+   "metadata": {},
+   "source": [
+    "# Statistical summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6185861a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1.000000e+03</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>20041.966800</td>\n",
+       "      <td>5.510000</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>116268.029640</td>\n",
+       "      <td>110731.456800</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>6.97270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9538.066205</td>\n",
+       "      <td>2.923431</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>88518.720636</td>\n",
+       "      <td>84303.543463</td>\n",
+       "      <td>6.131498e-14</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>1.71858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3628.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>3844.260000</td>\n",
+       "      <td>3661.200000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>11835.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>44792.055000</td>\n",
+       "      <td>42659.100000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>5.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>19882.800000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>91385.280000</td>\n",
+       "      <td>87033.600000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>7.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>28056.600000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>169686.090000</td>\n",
+       "      <td>161605.800000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>8.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>35985.600000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>375354.000000</td>\n",
+       "      <td>357480.000000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>10.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unit price     Quantity        Tax 5%          Total           cogs  \\\n",
+       "count   1000.000000  1000.000000   1000.000000    1000.000000    1000.000000   \n",
+       "mean   20041.966800     5.510000   5536.572840  116268.029640  110731.456800   \n",
+       "std     9538.066205     2.923431   4215.177173   88518.720636   84303.543463   \n",
+       "min     3628.800000     1.000000    183.060000    3844.260000    3661.200000   \n",
+       "25%    11835.000000     3.000000   2132.955000   44792.055000   42659.100000   \n",
+       "50%    19882.800000     5.000000   4351.680000   91385.280000   87033.600000   \n",
+       "75%    28056.600000     8.000000   8080.290000  169686.090000  161605.800000   \n",
+       "max    35985.600000    10.000000  17874.000000  375354.000000  357480.000000   \n",
+       "\n",
+       "       gross margin percentage  gross income      Rating  \n",
+       "count             1.000000e+03   1000.000000  1000.00000  \n",
+       "mean              4.761905e+00   5536.572840     6.97270  \n",
+       "std               6.131498e-14   4215.177173     1.71858  \n",
+       "min               4.761905e+00    183.060000     4.00000  \n",
+       "25%               4.761905e+00   2132.955000     5.50000  \n",
+       "50%               4.761905e+00   4351.680000     7.00000  \n",
+       "75%               4.761905e+00   8080.290000     8.50000  \n",
+       "max               4.761905e+00  17874.000000    10.00000  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To get statistical surmary\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a5e9895",
+   "metadata": {},
+   "source": [
+    "# Deductions:\n",
+    "There are 1000 rows.\n",
+    "\n",
+    "The mean, std, min, max and various percentage of the total summation of values for each columns with numeric values were provided with a single command (Describe) which shows the Standard Deviation (STD) of the unit price and gross income to be 9538.066205 and 4215.177173 respectively. Also, the mean value of the unit price and gross income is 20041.966800 and 5536.572840 respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "eaf11085",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
+       "0         False   False  False          False   False         False   \n",
+       "1         False   False  False          False   False         False   \n",
+       "2         False   False  False          False   False         False   \n",
+       "3         False   False  False          False   False         False   \n",
+       "4         False   False  False          False   False         False   \n",
+       "..          ...     ...    ...            ...     ...           ...   \n",
+       "995       False   False  False          False   False         False   \n",
+       "996       False   False  False          False   False         False   \n",
+       "997       False   False  False          False   False         False   \n",
+       "998       False   False  False          False   False         False   \n",
+       "999       False   False  False          False   False         False   \n",
+       "\n",
+       "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
+       "0         False     False   False  False  False  False    False  False   \n",
+       "1         False     False   False  False  False  False    False  False   \n",
+       "2         False     False   False  False  False  False    False  False   \n",
+       "3         False     False   False  False  False  False    False  False   \n",
+       "4         False     False   False  False  False  False    False  False   \n",
+       "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
+       "995       False     False   False  False  False  False    False  False   \n",
+       "996       False     False   False  False  False  False    False  False   \n",
+       "997       False     False   False  False  False  False    False  False   \n",
+       "998       False     False   False  False  False  False    False  False   \n",
+       "999       False     False   False  False  False  False    False  False   \n",
+       "\n",
+       "     gross margin percentage  gross income  Rating  \n",
+       "0                      False         False   False  \n",
+       "1                      False         False   False  \n",
+       "2                      False         False   False  \n",
+       "3                      False         False   False  \n",
+       "4                      False         False   False  \n",
+       "..                       ...           ...     ...  \n",
+       "995                    False         False   False  \n",
+       "996                    False         False   False  \n",
+       "997                    False         False   False  \n",
+       "998                    False         False   False  \n",
+       "999                    False         False   False  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Missing value:\n",
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "30d2d644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 0\n",
+       "Branch                     0\n",
+       "City                       0\n",
+       "Customer type              0\n",
+       "Gender                     0\n",
+       "Product line               0\n",
+       "Unit price                 0\n",
+       "Quantity                   0\n",
+       "Tax 5%                     0\n",
+       "Total                      0\n",
+       "Date                       0\n",
+       "Time                       0\n",
+       "Payment                    0\n",
+       "cogs                       0\n",
+       "gross margin percentage    0\n",
+       "gross income               0\n",
+       "Rating                     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "72bdce31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                     1000\n",
+       "City                       1000\n",
+       "Customer type              1000\n",
+       "Gender                     1000\n",
+       "Product line               1000\n",
+       "Unit price                 1000\n",
+       "Quantity                   1000\n",
+       "Tax 5%                     1000\n",
+       "Total                      1000\n",
+       "Date                       1000\n",
+       "Time                       1000\n",
+       "Payment                    1000\n",
+       "cogs                       1000\n",
+       "gross margin percentage    1000\n",
+       "gross income               1000\n",
+       "Rating                     1000\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.notnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26717e81",
+   "metadata": {},
+   "source": [
+    "# There are no missing values in the set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7810eb93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   Invoice ID               1000 non-null   object \n",
+      " 1   Branch                   1000 non-null   object \n",
+      " 2   City                     1000 non-null   object \n",
+      " 3   Customer type            1000 non-null   object \n",
+      " 4   Gender                   1000 non-null   object \n",
+      " 5   Product line             1000 non-null   object \n",
+      " 6   Unit price               1000 non-null   float64\n",
+      " 7   Quantity                 1000 non-null   int64  \n",
+      " 8   Tax 5%                   1000 non-null   float64\n",
+      " 9   Total                    1000 non-null   float64\n",
+      " 10  Date                     1000 non-null   object \n",
+      " 11  Time                     1000 non-null   object \n",
+      " 12  Payment                  1000 non-null   object \n",
+      " 13  cogs                     1000 non-null   float64\n",
+      " 14  gross margin percentage  1000 non-null   float64\n",
+      " 15  gross income             1000 non-null   float64\n",
+      " 16  Rating                   1000 non-null   float64\n",
+      "dtypes: float64(7), int64(1), object(9)\n",
+      "memory usage: 132.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the information of the dataframe\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f670b3ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Converting to datetime:\n",
+    "df['Date'] = pd.to_datetime(df['Date'])\n",
+    "df['Time'] = pd.to_datetime(df['Time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "15cdbef0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[ns]')"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Date'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "1dbc2015",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[ns]')"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Time'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f94d858f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>2019-01-05</td>\n",
+       "      <td>2023-07-17 13:08:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>2019-03-03</td>\n",
+       "      <td>2023-07-17 13:23:00</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>2019-01-27</td>\n",
+       "      <td>2023-07-17 20:33:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2019-02-08</td>\n",
+       "      <td>2023-07-17 10:37:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2019-02-25</td>\n",
+       "      <td>2023-07-17 14:36:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date                Time  \\\n",
+       "0     26888.4         7   9410.94  197629.74 2019-01-05 2023-07-17 13:08:00   \n",
+       "1     16678.8         7   5837.58  122589.18 2019-03-03 2023-07-17 13:23:00   \n",
+       "2     20959.2         8   8383.68  176057.28 2019-01-27 2023-07-17 20:33:00   \n",
+       "3     31071.6         7  10875.06  228376.26 2019-02-08 2023-07-17 10:37:00   \n",
+       "4     24782.4         6   7434.72  156129.12 2019-02-25 2023-07-17 14:36:00   \n",
+       "\n",
+       "  Payment      cogs  gross margin percentage  gross income  Rating  \n",
+       "0    Epay  188218.8                 4.761905       9410.94     9.1  \n",
+       "1    Card  116751.6                 4.761905       5837.58     7.4  \n",
+       "2    Epay  167673.6                 4.761905       8383.68     8.4  \n",
+       "3    Epay  217501.2                 4.761905      10875.06     5.3  \n",
+       "4    Epay  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1d8ff8ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extract features from date & time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "79154e07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['day'] = df['Date'].dt.day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1280e135",
+   "metadata": {},
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b0c8b6ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['month'] = df['Date'].dt.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "dc123e2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019     0   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019     0   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019     0   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019     0   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019     0   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "f1628da8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['year'] = df['Date'].dt.year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "f4cfc25c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
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+       "      <th>Tax 5%</th>\n",
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+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
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+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019     0   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019     0   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019     0   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019     0   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019     0   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "458d83be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['hour'] = df['Time'].dt.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "c3db5ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019    13   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019    13   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019    20   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019    10   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019    14   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "23ca4780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Minute'] = df['Time'].dt.minute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "0078b0cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019    13   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019    13   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019    20   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019    10   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019    14   \n",
+       "\n",
+       "   Minute  \n",
+       "0       8  \n",
+       "1      23  \n",
+       "2      33  \n",
+       "3      37  \n",
+       "4      36  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "d5ee1db5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "194e69de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_hour = pd.Series(df.nunique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "3f8951f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "5c9bd0e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique(axis = 'columns' == 22)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "496b249f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      21\n",
+       "1      20\n",
+       "2      21\n",
+       "3      21\n",
+       "4      21\n",
+       "       ..\n",
+       "995    21\n",
+       "996    21\n",
+       "997    21\n",
+       "998    20\n",
+       "999    19\n",
+       "Length: 1000, dtype: int64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique(axis = 'columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "72fca3a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['2023-07-17T13:08:00.000000000', '2023-07-17T13:23:00.000000000',\n",
+       "       '2023-07-17T20:33:00.000000000', '2023-07-17T10:37:00.000000000',\n",
+       "       '2023-07-17T14:36:00.000000000', '2023-07-17T17:15:00.000000000',\n",
+       "       '2023-07-17T10:25:00.000000000', '2023-07-17T16:48:00.000000000',\n",
+       "       '2023-07-17T19:21:00.000000000', '2023-07-17T11:03:00.000000000',\n",
+       "       '2023-07-17T10:39:00.000000000', '2023-07-17T18:00:00.000000000',\n",
+       "       '2023-07-17T11:15:00.000000000', '2023-07-17T17:36:00.000000000',\n",
+       "       '2023-07-17T19:20:00.000000000', '2023-07-17T12:17:00.000000000',\n",
+       "       '2023-07-17T15:36:00.000000000', '2023-07-17T10:12:00.000000000',\n",
+       "       '2023-07-17T19:15:00.000000000', '2023-07-17T17:17:00.000000000',\n",
+       "       '2023-07-17T15:10:00.000000000', '2023-07-17T11:26:00.000000000',\n",
+       "       '2023-07-17T11:28:00.000000000', '2023-07-17T15:55:00.000000000',\n",
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+       "       '2023-07-17T20:51:00.000000000', '2023-07-17T13:33:00.000000000',\n",
+       "       '2023-07-17T20:13:00.000000000', '2023-07-17T14:26:00.000000000',\n",
+       "       '2023-07-17T18:18:00.000000000', '2023-07-17T15:17:00.000000000',\n",
+       "       '2023-07-17T18:10:00.000000000', '2023-07-17T19:08:00.000000000',\n",
+       "       '2023-07-17T15:46:00.000000000', '2023-07-17T11:38:00.000000000',\n",
+       "       '2023-07-17T11:24:00.000000000', '2023-07-17T10:42:00.000000000',\n",
+       "       '2023-07-17T20:39:00.000000000', '2023-07-17T19:47:00.000000000',\n",
+       "       '2023-07-17T17:08:00.000000000', '2023-07-17T10:19:00.000000000',\n",
+       "       '2023-07-17T15:43:00.000000000', '2023-07-17T18:21:00.000000000',\n",
+       "       '2023-07-17T14:20:00.000000000', '2023-07-17T16:24:00.000000000',\n",
+       "       '2023-07-17T18:56:00.000000000', '2023-07-17T14:31:00.000000000',\n",
+       "       '2023-07-17T20:05:00.000000000', '2023-07-17T10:41:00.000000000',\n",
+       "       '2023-07-17T19:52:00.000000000', '2023-07-17T16:17:00.000000000',\n",
+       "       '2023-07-17T15:57:00.000000000', '2023-07-17T16:43:00.000000000',\n",
+       "       '2023-07-17T17:11:00.000000000', '2023-07-17T16:07:00.000000000',\n",
+       "       '2023-07-17T15:00:00.000000000', '2023-07-17T20:50:00.000000000',\n",
+       "       '2023-07-17T19:58:00.000000000', '2023-07-17T10:02:00.000000000',\n",
+       "       '2023-07-17T17:07:00.000000000', '2023-07-17T17:22:00.000000000',\n",
+       "       '2023-07-17T13:29:00.000000000', '2023-07-17T17:20:00.000000000',\n",
+       "       '2023-07-17T13:42:00.000000000', '2023-07-17T14:44:00.000000000',\n",
+       "       '2023-07-17T10:21:00.000000000', '2023-07-17T19:05:00.000000000',\n",
+       "       '2023-07-17T11:34:00.000000000', '2023-07-17T18:58:00.000000000',\n",
+       "       '2023-07-17T20:01:00.000000000', '2023-07-17T20:37:00.000000000',\n",
+       "       '2023-07-17T12:12:00.000000000', '2023-07-17T15:32:00.000000000',\n",
+       "       '2023-07-17T12:31:00.000000000', '2023-07-17T20:04:00.000000000',\n",
+       "       '2023-07-17T13:38:00.000000000', '2023-07-17T17:30:00.000000000',\n",
+       "       '2023-07-17T16:53:00.000000000', '2023-07-17T16:51:00.000000000',\n",
+       "       '2023-07-17T20:15:00.000000000', '2023-07-17T14:11:00.000000000',\n",
+       "       '2023-07-17T12:10:00.000000000', '2023-07-17T20:47:00.000000000',\n",
+       "       '2023-07-17T11:17:00.000000000', '2023-07-17T15:52:00.000000000',\n",
+       "       '2023-07-17T11:43:00.000000000', '2023-07-17T10:30:00.000000000',\n",
+       "       '2023-07-17T10:06:00.000000000', '2023-07-17T12:16:00.000000000',\n",
+       "       '2023-07-17T14:58:00.000000000', '2023-07-17T14:45:00.000000000',\n",
+       "       '2023-07-17T17:10:00.000000000', '2023-07-17T14:27:00.000000000',\n",
+       "       '2023-07-17T12:51:00.000000000', '2023-07-17T13:56:00.000000000',\n",
+       "       '2023-07-17T16:18:00.000000000', '2023-07-17T15:14:00.000000000',\n",
+       "       '2023-07-17T20:10:00.000000000', '2023-07-17T14:24:00.000000000',\n",
+       "       '2023-07-17T12:39:00.000000000', '2023-07-17T12:41:00.000000000',\n",
+       "       '2023-07-17T15:20:00.000000000', '2023-07-17T16:33:00.000000000',\n",
+       "       '2023-07-17T20:44:00.000000000', '2023-07-17T20:30:00.000000000',\n",
+       "       '2023-07-17T16:50:00.000000000', '2023-07-17T17:52:00.000000000',\n",
+       "       '2023-07-17T11:33:00.000000000', '2023-07-17T18:38:00.000000000',\n",
+       "       '2023-07-17T15:11:00.000000000', '2023-07-17T15:45:00.000000000',\n",
+       "       '2023-07-17T16:35:00.000000000', '2023-07-17T13:50:00.000000000',\n",
+       "       '2023-07-17T17:56:00.000000000', '2023-07-17T13:49:00.000000000',\n",
+       "       '2023-07-17T14:05:00.000000000', '2023-07-17T19:12:00.000000000'],\n",
+       "      dtype='datetime64[ns]')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(df['Time'], name = 'hour').unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d564b9cc",
+   "metadata": {},
+   "source": [
+    "# Examples on Unique Values in Columns eg Branch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "64651e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Invoice ID',\n",
+       " 'Branch',\n",
+       " 'City',\n",
+       " 'Customer type',\n",
+       " 'Gender',\n",
+       " 'Product line',\n",
+       " 'Payment']"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical_columns = [col for col in df.columns if df[col].dtype == \"object\" ]\n",
+    "categorical_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "df0c3d1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A', 'B', 'C']"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Branch'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "64a7a37a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Branch Column : 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Number of unique values in the Branch Column : {}\". format(len(df['Branch'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c991f6c",
+   "metadata": {},
+   "source": [
+    "# Unique Values in Columns - Payment, Product line, Gender and Customer type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "3c560e79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Epay', 'Card', 'Cash']"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "46c1d063",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Health and beauty',\n",
+       " 'Home and lifestyle',\n",
+       " 'Sports and travel',\n",
+       " 'Electronic accessories',\n",
+       " 'Food and beverages',\n",
+       " 'Fashion accessories']"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "02857072",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Product line Column : 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Number of unique values in the Product line Column : {}\". format(len(df['Product line'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "0fd80ec0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Female', 'Male']"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "0bbf9240",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Member', 'Normal']"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Customer type'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e358b2fb",
+   "metadata": {},
+   "source": [
+    "# Value_Count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "18d19007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fashion accessories       178\n",
+       "Food and beverages        174\n",
+       "Electronic accessories    170\n",
+       "Sports and travel         166\n",
+       "Home and lifestyle        160\n",
+       "Health and beauty         152\n",
+       "Name: Product line, dtype: int64"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "07448b7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "3448491c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Lagos            340\n",
+       "Abuja            332\n",
+       "Port Harcourt    328\n",
+       "Name: City, dtype: int64"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['City'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "3fcb5833",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Epay    345\n",
+       "Cash    344\n",
+       "Card    311\n",
+       "Name: Payment, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8a1b933",
+   "metadata": {},
+   "source": [
+    "# Groupby City column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "185c52ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "City = df.groupby('City')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "59eee3e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.groupby.generic.DataFrameGroupBy"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(City)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "7ee2f6e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Abuja': [340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, ...], 'Lagos': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], 'Port Harcourt': [672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, ...]}"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "5626b15d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            6652396.8\n",
+       "Lagos            6705176.4\n",
+       "Port Harcourt    6684393.6\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['Unit price'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "487b1c9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            20037.339759\n",
+       "Lagos            19721.107059\n",
+       "Port Harcourt    20379.248780\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['Unit price'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "2e398df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>18</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>16</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>15</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "340  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "341  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "342  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "343  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "344  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "340     19742.4         3   2961.36   62188.56  ...    Card   59227.2   \n",
+       "341      5212.8         4   1042.56   21893.76  ...    Epay   20851.2   \n",
+       "342      9183.6         4   1836.72   38571.12  ...    Cash   36734.4   \n",
+       "343     33739.2         6  10121.76  212556.96  ...    Cash  202435.2   \n",
+       "344     14508.0         2   1450.80   30466.80  ...    Epay   29016.0   \n",
+       "\n",
+       "    gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "340                4.761905       2961.36     5.9   20      2  2019    13   \n",
+       "341                4.761905       1042.56     4.5    6      2  2019    18   \n",
+       "342                4.761905       1836.72     6.8    9      3  2019    17   \n",
+       "343                4.761905      10121.76     4.5   15      1  2019    16   \n",
+       "344                4.761905       1450.80     4.4   11      3  2019    15   \n",
+       "\n",
+       "     Minute  \n",
+       "340      27  \n",
+       "341       7  \n",
+       "342       3  \n",
+       "343      19  \n",
+       "344      30  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Abuja').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "c3775a60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1820531.52"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Abuja')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "1caaa895",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            1820531.52\n",
+       "Lagos            1820577.78\n",
+       "Port Harcourt    1895463.54\n",
+       "Name: gross income, dtype: float64"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "05fe8fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1820577.78"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Lagos')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "688e914b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1895463.54"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Port Harcourt')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "cc493a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_Aggregate = City.agg({'gross income': 'sum'})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "5cb92d4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gross income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>City</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Abuja</th>\n",
+       "      <td>1820531.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lagos</th>\n",
+       "      <td>1820577.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Port Harcourt</th>\n",
+       "      <td>1895463.54</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               gross income\n",
+       "City                       \n",
+       "Abuja            1820531.52\n",
+       "Lagos            1820577.78\n",
+       "Port Harcourt    1895463.54"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_Aggregate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "29d2b82a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gross income    1895463.54\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_Aggregate.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "66b1220f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            17528.4\n",
+       "Lagos            17816.4\n",
+       "Port Harcourt    17874.0\n",
+       "Name: gross income, dtype: float64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['gross income'].max('City')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "fc4ee498",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_with_highest_income = city_Aggregate['gross income'].idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "ab710a2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Port Harcourt'"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_with_highest_income"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60754059",
+   "metadata": {},
+   "source": [
+    "# The city with the higehest gross income is Port Harcout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e53b88a7",
+   "metadata": {},
+   "source": [
+    "# Data Visualization using seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "ffc9432a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Data = df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "1bf2829b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>19</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  ...  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74  ...   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18  ...   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28  ...   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26  ...   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12  ...   \n",
+       "..                      ...         ...       ...       ...        ...  ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16  ...   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02  ...   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20  ...   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10  ...   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30  ...   \n",
+       "\n",
+       "    Payment      cogs gross margin percentage  gross income  Rating  day  \\\n",
+       "0      Epay  188218.8                4.761905       9410.94     9.1    5   \n",
+       "1      Card  116751.6                4.761905       5837.58     7.4    3   \n",
+       "2      Epay  167673.6                4.761905       8383.68     8.4   27   \n",
+       "3      Epay  217501.2                4.761905      10875.06     5.3    8   \n",
+       "4      Epay  148694.4                4.761905       7434.72     5.8   25   \n",
+       "..      ...       ...                     ...           ...     ...  ...   \n",
+       "995    Cash  251899.2                4.761905      12594.96     6.1   23   \n",
+       "996    Cash  242852.4                4.761905      12142.62     6.0    9   \n",
+       "997    Epay  296424.0                4.761905      14821.20     4.3   29   \n",
+       "998    Epay   21942.0                4.761905       1097.10     5.9   18   \n",
+       "999    Epay   14526.0                4.761905        726.30     6.2   29   \n",
+       "\n",
+       "     month  year  hour  Minute  \n",
+       "0        1  2019    13       8  \n",
+       "1        3  2019    13      23  \n",
+       "2        1  2019    20      33  \n",
+       "3        2  2019    10      37  \n",
+       "4        2  2019    14      36  \n",
+       "..     ...   ...   ...     ...  \n",
+       "995      1  2019    10      33  \n",
+       "996      1  2019    11      40  \n",
+       "997      3  2019    19      12  \n",
+       "998      2  2019    11      40  \n",
+       "999      1  2019    13      46  \n",
+       "\n",
+       "[1000 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "cd9e9763",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'City with max Gross income'}, xlabel='City', ylabel='gross income'>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "city_Aggregate.plot(kind='bar', ylabel = 'gross income', title = 'City with max Gross income', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "caa28f73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Customer_type_counts = Data['Customer type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "69c66158",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Member    501\n",
+       "Normal    499\n",
+       "Name: Customer type, dtype: int64"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Customer_type_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "68cf8924",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Customer_type_counts')"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(Customer_type_counts.values, labels=Customer_type_counts.index, autopct='%1.1f%%')\n",
+    "plt.title('Customer_type_counts')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "id": "96ab0009",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gender_counts = Data['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "38c9b442",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "dcf83ead",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Gender_counts.values')"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(gender_counts.values, labels=gender_counts.index, autopct='%1.1f%%')\n",
+    "plt.title('Gender_counts.values')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "c6911fd2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Hours spent')"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'City', y = 'hour', hue = 'Gender', aspect = 4)\n",
+    "plt.title('Hours spent')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75c59cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "a30ec3cd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_Sales Record')]"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, x = 'Branch').set(title = 'Highest_Sales Record')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aef1baed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "e817ede3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_Payment')]"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Payment').set(title = 'Highest_Payment')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb2b6f6f",
+   "metadata": {},
+   "source": [
+    "# Highest product line sold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "c19b150c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_productline')]"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Product line', hue = 'City').set(title = 'Highest_productline')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "9150e7bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Most used_Payment_type')]"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Product line', hue = 'Payment').set(title = 'Most used_Payment_type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "7726fd8a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Payment/Branch')]"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Payment', hue = 'Branch').set(title = 'Payment/Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35f6f49e",
+   "metadata": {},
+   "source": [
+    "# Branch with lowest rating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "480fddfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Branch_lowest rating')]"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(orient = 'v', x = 'City', y = 'Rating', data = Data).set(title = 'Branch_lowest rating')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "8f40e524",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Gender/Branch')]"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Gender', hue = 'Branch').set(title = 'Gender/Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c69c2742",
+   "metadata": {},
+   "source": [
+    "# Using Catplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "ac69c43b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc8074aca60>"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(data = Data, x = 'Product line', y = 'Quantity', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b909985",
+   "metadata": {},
+   "source": [
+    "# Using Catplot on total column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "2e377480",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc806fdbdc0>"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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txXFxNzuVHAEAABrBkwsAADqYgkqf/rS2UrPya4JaenRShkMPDYpR305UCGitOtnNuqF3pG7oHalKb0CfFrk0v8ClT4pdqvaF5q2rLWU+bSmr1FPrKpURZamrLDGka5jMJEsAAAAAHV6ZO6DcPbXJEItKXNpRGaLM7nagwmPoPzud+s9Op8ym2taHE9JqEyb6xFpJUAdQT9ybu5sUV3Zrt2aeCQCEFgkSAAB0EHtr/Hp6XaX+sa1a3iC+cDMmxa6HB8docNew4O0UzS7aZtYPMiP0g8wIOX2GPi+pTZb4X6FTZZ7QJEsUVPn14sYqvbixSskRZl2WHq7LMsI1LClMVsrCAgAAAB2Cy2foy30e5e5xaVGJW3kHvOpYTTOaR8CQVpR6tKLUo0dXVah7tEXjUx26JN2hoYl2hVm45gIAAB2TuaUn0JhZs2bpnnvuUXZ2thISEhQXF6eZM2c2Gjtjxgxde+21GjBggFJSUpSenq5hw4bpD3/4gw4fPnzMMd577z2NGTNGKSkpysjI0NVXX628vLxjxufn52vq1Knq2bOnkpKSNHToUL322msKBBp/wuR2u/Xkk08qKytLiYmJ6tOnj+666y6VlpYec4ycnBxNnDhRaWlpSk1N1cSJE5WTk3PMeAAAmuKwO6DHVpVr0L9L9fctwUuOOD8hTPMndNF/xnchOaKNC7eadGl6uF4e0UnfXJ+s98fF6//6RCohPHSnintqAvr7lmpd8fEBnfGvvbpz6WF9XOSSu4P1EwYAAADau4BhaN1Bj57/ulI/+PiAeryzR1d8fEDPrK/SmhZOjrCYpE729pk4sKvSr1c3V2vyxwfV8909uuXzg3rnm2odcFGhAwAAdCymsrKyVnfXuX///ioqKlJ8fLwiIiJUVFSkl156STfeeGOD2EsuuUTl5eXq37+/kpKS5Ha7tWrVKq1atUqpqanKyclRYmJivW2efvppTZs2TampqbriiitUXV2t//znP3K5XJozZ45GjBhRL37Lli0aN26cnE6npkyZouTkZH366afatGmTbrnlFj333HP14gOBgK6++mrl5ORoyJAhGj58uHbu3Kn58+crJSWl0TnNnj1bt912m+Lj4/WDH/xAJpNJc+fO1b59+/Taa6/pmmuuCdKnC5xY1C3ZTYqrmrGoWecB4PRUegN6ZWOVXthQpQpv8L7uz+5s0+8Gx+jiVHurL89JqcDT4w8Y+mq/R/N2OTW/wKXi6tDfOIu2mTQ+zaFJGeG6qJtdkbZWmd8LNCvOzQC0FxzPgI6roNKn3D1ufb7brdw9bh1yB7Gs4WnqE2vVqBS7RqfYNSzJrhqfoQveLw1JZT2HReoRbdXmMl+zj3UsJknndg3ThHSHxqc6dFYnWnEAHQX3zQB0VK0yQWLRokXKzMxUenq6/vrXv+rxxx8/ZoKEy+WSw+FosHz69Ol66qmndNddd2natGl1y/Pz83X++eere/fuysnJUWxsrCRp8+bNGjt2rBITE7Vy5UpZrd92H7n00ku1fPlyzZ49W+PGjZMkeb1eXXXVVcrNzdW8efM0cuTIuvi3335bd955p6688kq9/vrrdSeUR5dfd911euWVV+riy8rKdM4558hqtSo3N1epqamSpL1792rUqFFyuVxat26d4uLiTuNTBZqOm1ZA2+byGXpja7WeWV+pA67g3XTqFWPVbwdH64ru4TK3kZslXOgFj2EYWnvQq/kFTs3b5dL2itDfwHNYpLHdHLq8e7jGpzoUZydZAh0D52YA2guOZ0DHcdgd0OI9bi0qqW2bsbOy9VQpSAo3a1SKXdkpDo1Ktisl0tIgZs6OGv0o99jViYPl/43qpCszI7S72q9PilxaUORU7h63WrKoQ1qURRNSHRqf5tDwJLsc1rZx/Q/g5HHfDEBH1SrvKmdnZys9Pb1JsY0lR0jS5MmTJUk7duyot3zmzJny+Xy677776pIjJKlv37667rrrtHPnTi1evLhu+fbt27V8+XKNGDGiLjlCkmw2mx555BFJ0ltvvVVvjKM/P/roo/WybW+66Sb16dNH77//viorK+uWz507V+Xl5brtttvqkiMkKSkpSXfccYfKy8s1d+7cpnwcAIAOzBsw9Na2amXNKdVDX5UHLTkiNdKiF4bF6YspCZrSI6LNJEcguEwmkwZ1CdPvsmK18gcJWjE5QQ8NitbZnW0hm4PLL31Y6NLtiw+r97/26KpPDmjG1mrtd7aem60AAABAR+TyGcotcenxVeUaPX+fMt/Zo1s+P6Q3t9a0eHJElLW2Kt0T58VqxeQEbb42Sa+O7Kzre0U0mhwhSVdmRujXA6ObdV6/GRitKzMjJEndIi269cxIzbq4i3bckKx3x3bW1DMilBwR+tv3RVV+/X1Lta76tLYVx405B/XPbdUqreG6CwAAtA/WE4e0TZ988omk2sSH71q6dKkkacyYMQ22GTNmjN544w0tW7asbv3x4rOyshQbG6tly5bVLXO5XFq1apV69+7daJLH6NGj9corr2jVqlUaPXp0k+b02GOPadmyZZo6deoJf2+Xy3XCGOBEopoYx783oHUIGIbmFXr0569rtKMyeBUjuthNuqdfuH7YyyG7xSSfx62WK/rZvDienbwe4dIvzgzTL84M065Kvz4q9ujDIo9WHwzNvxJvQFq4262Fu9365Qrp/K5WTUwN0yWpYep2jJucQFvFuRmA9oLjGdB+BAxDGw77tXivV4v3evXVAW+LVj34LotJyoq3akSSTSOTbBocb5XNfDTR3y+3u2kTvedMm/y+cD21wRn0Of7q7HDdfaat0eOdWdLoBJNGJ4TricEOfX3Yr092e/RpiUfrDoX2Q672Gfqw0KUPC2vnOSjeqotTbBrXLUz94iy04gA6CM7NEGzHevkdCJV2kyAxc+ZMFRYWqqqqSuvWrdPSpUs1YMAA3XnnnfXi8vPzFRUVpcTExAb76NmzZ13Md+MlKTMzs0G8yWRSZmam8vLyVFNTo4iICO3cuVOBQKDR+O+PcTRB4ugYR9edaE7HU1JSIr+/lVyNoM3q0sS4oqKiZp0HgOMzDGnpYbNe3hWmb2qC91ZJlMXQzaleXZviU4SlWvtKgrbrFhDRpCiOZ6fHImlSlDSpr7TPbdKigxZ9ftCiNeVmBdT8N8wChrRin08r9vn08Joa9Yvya3QXv8bE+5UW3uq6yQEnjXMzAO0FxzOgbSt2mfRVmVkryyxaWWZRua/1PBzvERHQebF+nd/Jr0ExAUUdvevtkvY2rYJ8o66Nkzr1sejJ/DBVBOH3jbEa+nVPj8bF1ai4uGnbdDoyj2vjpP1uk5YdNmvJIYu+LLPIHQjt30HeQZ/yDvr056+dSggLaERnv4Z39mtIbEAO8tSBNoj7ZmgZvXv3bukpoINrNwkS77zzTr1KDmPGjNGrr76quLi4enEVFRXq2rVro/uIjo6ui/luvKR67TiOtU1ERMRJxX9/jJiYmAbxkZGRslgs9eKPJyUlpUlxQDCkpaW19BSADmtpqVdPrKsJ6tv64RbpJ33C9bO+DsWFtcouXKfgYJOiOJ4FT5qkrF7SfZIOuAL6ZLdHHxZ7tHivV97gFTg5ro1VFm2ssujFXVLfWIsmpoVpYlqYzozlDSe0bxzLALQXHM+A1uGQO6ClpV4t2evV4lKvCqpCdELfBInhJo1MDNPIJJtGJNqU1IytKH6UJl3WN6CHVlXrw2LPKe9nYmqY/jgkUonhpz7XNEmDJd0lyekztGyfV5/u9mhhiVe7a0L797PPY9acvWbN2WtTuEUakWTTuJQwXZQS1qx/HwCCiftmADqmdpMg8eGHH0qSDh48qFWrVunRRx/VqFGjNHv2bJ199tktPLvQoSwNQol/b0Dord7v0bQ1FVpU4g7aPm1m6dY+kbpvQLQSIzrmKx8cz5pHqkP6v7gI/V8/qdwT0CdFLs0rcGphsVtOf2gqO2wu92tzuVNPbXCqZ4xFkzLCNSkjXIO72EiWQLvDsQxAe8HxDGgZTp+hL/e5tajErc9L3Fp/0KvWUo8t2mbSsCS7slNq//SJtYb0fD7DIc28OEJfH/Lq/22u0j+21TRpuwirSddkhutHfaPUv7MtqHNySLosKlyXZUqGYWjDYZ8+LnJpQZFTq/eH9u/O6Zc+2e3VJ7u9kqo1MN6m8WkOXZLm0IB4m8xcewFtGudmANqbdpMgcVR8fLzGjx+vfv36KSsrS3fffbdycnLq1sfExByzGkNlZWVdzHfjJam8vPy42xytDNHU+MbGqKioUOfOnevFV1dXy+/3N1pdAgDQcWw67NUf1lTU9f0MBrNJur5XhH49MFrpUe3ulACtTGyYWVf3jNDVPSNU4wsoZ7db83c5taDIpQpvaG7d5Vf49ezXVXr26yqlRlo0Md2hy7uH64KEMFnM3LADAABAx+IPGPr6kFefl9QmRXyxzy13K+ncazVJ5yaE1SZEJNs1uGuYbK3gnL1/Z5ueHdapyQkS269PUoS1+aspmEwm9e9sU//ONv3qnGjtc/r1SbFLCwpd+rzErWpfaFNd1h70au1Br55cW6mkcLPGpzk0Ps2h7BR7SD4PAACA42m3T0NSU1N1xhlnaM2aNaqpqVFERG0vpZ49e+qrr75SaWmpEhMT622Tn59fF3PU0f/esWNHgzEMw9COHTuUnJysyMhISVKPHj1kNpsbjT/eGHl5ecrPz2+QINFYPACg49hZ4dMTayv0Xr4zqG9/TO4erocGReuMuOC+wQI0RYTVXFfJweM3tHiPW/MKnPqwwKWD7tCUhS2u9uvVzdV6dXO1ujjMdckSI5LsCrO0/I1XAAAAoDnsrPBpUYlbi/a4tHiPW4fdraVGhHRWnFWjUuzKTnFoaFKYom1t/0F6SyUDJIRbdFPvSN3UO1Juv6Gle91aUOTSgiKXiqpCmwWz1xnQjG01mrGtRg6LNDLZrglp4RqXalcqL2sAAIAW0K7PQEpLS2UymWSxfFsufNiwYfrqq6/02Wef6frrr68X/9lnn9XFHDV8+PC6db/85S/rxa9evVrl5eW6+OKL65Y5HA5lZWVp5cqVKiwsVHp6er1tPv/8c9ntdmVlZdWb07///W999tlnOvfcc084JwBA+1dS7ddT6yr11rZqBfNFj3Gpdv12cIzOiQ8L3k6B0xBmMemiVIcuSnXomQsNrSj1aH6BUx8UOFUSoh66B1zf3rCLDTNpQppDkzLCNbabQ+FWkiUAAADQdh10+bV4T22FiEUlbhWE+OH48SRHmJWdUltVYFSyXUkdtOVjc7NbTBrbzaGx3Rz68/mGNpcdbcXh0lf7PCFtxeHyS58Uu/VJcW3b0P6da1txTEhzaHAXWnEAAIDQaNMJEocOHVJpaan69u1bb7lhGPrTn/6kffv2adSoUbLb7XXrbrzxRr3wwgt6+umndemllyo2NlaStHnzZv3rX/9Sjx49NHLkyLr4Xr16aejQoVqyZIk++eQTjRs3TpLk9Xo1ffp0SdLNN99cb/xbbrlFK1eu1OOPP67XX3+9rh/e22+/ra1bt+q6666r1zJjypQpevTRR/Xaa6/pxhtvVGpqqiRp7969evnllxUbG6vJkycH6VMDALRmB121LQD+vrlKriDet7owMUy/y4rRhYn2EwcDLcRqNmlEsl0jku360/mxWnPAq3m7nJpX4NSuytDcyC33GJqV79SsfKcirCZdnGrXpIxwjUt1KCas7b/BBgAAgPbN6TP0Ram7rm3G+kPelp5SnWibScOT7MpOsWt0il29Y611900RGiaTSWd1sumsTjb9ckC0Drj8+rTYrY+LXMrZ7VJliNofHvX1Ia++PuTVU+sqlRBu1rjU2lYco1PsimoHFUQAAEDrZCorK2s9ddSOeOutt7RixQpJ0qZNm7Ru3TpdcMEF6tGjhyRp4sSJuuyyy7R+/XqNHDlSWVlZ6tOnjxITE3Xw4EGtWLFC33zzjRITE/XBBx+od+/e9fb/1FNPafr06UpNTdUVV1yhmpoazZkzR06nU3PmzKmXICFJW7Zs0bhx4+RyuTR58mQlJydr4cKF2rhxo26++WY9//zz9eL9fr+uueYa5eTkaMiQIRo+fLh27dqlefPmKSUlRTk5OQ3ae8yaNUu333674uPj9YMf/EBms1nvv/++9u3bp1dffVXXXnttsD9m4JiibsluUlzVjEXNOg+gI6nwBPTSxir9bWNVUG9InBNv0++yYjQmxd4hbzzFvbm7SXFlt3Zr5pngdBiGoY2HfZpX4NQHu5zaVOYL+RzCzNLoFLsmdQ/XpWkOdXbwdhtCh3MzAO0FxzMg+PwBQ+sOerXoSJWIL/e55W4lRSKsJunchDCNTqlNihjcJUxWc/u4Lm2P15oev6EVpW7970h1iVAlqTcmzCyNSLZrQlptwkQ6rTiAZtEej2UA0BStMkHijjvu0LvvvnvM9b/+9a/14IMPqqysTM8//7yWLl2qnTt36vDhw3I4HMrMzNS4ceP0s5/9TJ07d250H7Nnz9bLL7+sLVu2yGaz6bzzztNDDz2kwYMHNxq/fft2TZs2TUuWLFF1dbUyMzM1depU/eQnP5HZ3DCb1e1269lnn9WsWbNUXFysuLg4jRs3Tg8//LCSkpIaHWPhwoV65plntH79eknSgAEDdN9992ns2LEn+siAoGryTavX/ifZw5t3MkA75/QZen1zlf76dZUOuYPXTuCMWKt+OzhGl2c4OmRixFFc6LVP28u9ml/g0vwCp9YcCP0bcRaTNCzJrsszHJqYEa5kSgGjmfFAEUB7wfEMOH2GYWhnpf9IywyXFu9xq8zTem7vntXJquwUu7KTHRqaFNZuqwC092tNwzC0rby2Fcf/ilz6cp9HgRb8Z3ZWJ6smHGnFkdUlTJZ2kmgDtLT2fiwDgGNplQkSAFqGuXC7bAvnypb7QZPijTCHfBdeJO9FkxVI79XMswPaF2/A0D+31egv6yq0pyZ4iRFpURY9ODBa1/aM4IaBuNDrCIqqfPqgwKV5BU59URra/rlHnZ8QpssyHJqUEa7u0bzZhODjgSKA9oLjGXBqDrj8Wlzi1qI9ta0ziqpaSYkISSkRZmWnOJSdYteoZLsSO0jycEe71jzsDujTYpc+LnLp090uVbRgUk683ayLU+26JD1co1PstEIETkNHO5YBwFEkSACQqeyg7P98TtZVi095H74hI+X+4d0y4uKDODOg/fEHDP17p1NP5FUEtVxlQrhZ958TrZvPiJTdQmLEUVzodSz7nH59VOjSvF1OLd7jlq8FznIHdLZpUoZDl3cPV584W+gngHaJB4oA2guOZ0DT1PgCWlHqOVIlwq2vD4W+atqxxNhMGp5sr2ub0SvG2iGrFnbka01vwNAXpR4tKKpNmNheEfoWiEfZzLXV/canOnRJuoOEdeAkdeRjGYCOjQQJoIOzfpEj+1vPylRdedr7MiKj5b75HvkuoC0M8H2GYejDQpf+sKZCm8uCd/MgLsyke/pH6yd9IxXZTkuXng4u9DquMndA/yuqbcPx2W6XXC3wkt0ZsVZNOlJZ4px4W4e8cYzg4IEigPaC4xnQOH/A0NqD3rq2GV/u88gTvEKDp8Vmls5LCFN2sl3ZKQ4N6mKTlWqFXGt+x/ZyrxYUubSgyKUVpR75W/BpQ5/Y2lYc49McOi8hjH+rwAlwLAPQUZFSCXRUhqGwuf9Q2NwZQdulqbpSjpenybOnUJ7JUyUeBAEyDEO5e9z6/eoKrTkQvLd+Iq0m/axflH7eL0pxdhIjgO+Ls5t1fa8IXd8rQlXegBYWuzW/wKmPi1yqClFpiW3lPj29vkpPr69SepRFkzLCNSmj9kadme9IAACADsswDO2o8GvRHpcWlbi1eI9b5S3YsuD7+nWy1rXNuDAxTFEk4+M4esXadGesTXeeHa0yd0A5u2srS3xS7FJZiP9dby33aWt5lZ7bUKVOdpMu7ubQhDSHxnRzcO8EAADUIUEC6KCCnRxRf98zZMgk75SpzbJ/oK34ap9b01ZXaMleT9D2abdIPzozUr/sH62u4R2jtytwuqJsZk3uEa7JPcLl8hlatMel+QUufVTo1GF3aG7YFVb59dLGKr20sUqJ4WZddiRZYliSXTbeagIAAGj39jv9yt3jrmubUVzdAiXOjqFbhEXZ3ezKTrZrVIpdCVxr4hTF2c26MjNCV2ZGyBcw9NU+jz4+Ul1ia3loW3EcdhuavcOp2TucspqkCxPDND7NoUvSwtUzlsciAAB0ZJwJAB2QdUVOsyVHHGWf+w8ZyWm020CHtOGQV9PWVOjjIlfQ9mkxSTf1jtD950QrNYqvb+BUOawmTUgL14S0cHkDcVq+1615BS59UOBUqTM0dYxLnQH9vy3V+n9bqtXJbtIlaeG6vLtD2ckOOawkSwAAALQH1d6AVpR6ahMi9ri14VDwKgqerpgwk0Ym2ZWdUvunZ4yVdnAIOqvZpKFJdg1Nsuvxc2O1s8KnBUUufVzs0rK9bnlD2EbGZ0hL9nq0ZK9HD6+sUK+Yb1txXJAYRtI6AAAdDE9YgA7GVHZQ9n8+G5Kx7G89K/+ZA2XExYdkPKCl5Zf79MTaCv17hzOo+70qM1wPDozhDQcgyGxmk0alODQqxaG/XBCrlfs8mlfg0vwCpwqrQvNG32G3oXe21+id7TWKspo0Ls2hyzPCdVGqnVLGAAAAbYgvYGjtQe+RChEufbXPI08IHwAfj80snZ8QVtc2Y2C8TVYeCCPEesRYdUe/KN3RL0oVnoA+L3Hrf4VOfVrs1kF3aP9n2V7h04sbq/TixirFhpl00ZFWHBelOtSJVhwAALR7PGkBOhj7P5+TqboyJGOZqitl/+dzct31+5CMB7SU4iqf/rKuUm9/UyN/EKv1T0hz6LeDY9S/sy14OwXQKLPJpPMT7To/0a7p58Zo3UGvPihwaV6BU9tCVAq2ymfoPzud+s9Op+wWaUyKQ5d3D9clafTLBQAAaG0Mw1B+hU+LStz6vMStJXvdqvCEpn1bU5zd2abs5NoKERcmhimS5Fu0IjFhZl3RPVxXdA+XP2Bo1X6PPi52aUGhS5vKQtuKo9xjaM5Op+bsdMpiqk0mmpBWmzDRO5bqKgAAtEckSAAdiLngG1lXLQ7pmNZVi2Uu3K5Aeq+QjguEwn6nX8+sr9QbW6vlDuLL5sOTwvS7rBidl2AP3k4BNJnJZNLALmEa2CVMD2fFaGuZV/MLXJq3y6n1ISqN7PZL/yty6X9FLllN0shkuyZlhGtihoOe0AAAAC1kn9Ov3CMtM3JL3CquDk3VsaZIjbQoO8Wu0Sl2jUy2qyvnjGgjLOZvk9V/lxWrgkqfPj7SimPJHndIK7H4DWl5qUfLSz363aoK9Yi2aHyaQ5ekOXRhol1hFpIlAABoD0iQADoQW85/W2xc9633tcjYQHMocwf04sYqvbyxStW+4L0hNLiLTb/LitGoZDtvKACtSJ84m/rE2fSrc6K1q9KnDwqcml/g0pf7PCEZ32dIn5W49VmJW/eukC5IDNPlGeG6LMOhtChO5wEAAJpLtTeg5aWeurYZGw+H9s3244kNM2nkkQoR2ckOZcZYuI5Eu5ARbdVtZ0XptrOiVOkNaFGJWwuKXPqkyKX9rtC24thZ6dcrm6r1yqZqxdhMGtPNofFpDo1LtSveQRISAABtFXdUgY7C7ZR1xcIWGdq6/FO5b/i5ZHe0yPhAsNT4AnptU7We/bpSZUEsndo3zqrfDo7RxHQHN7SAVq57tFV3nh2tO8+O1p4avz48kiyxdK87qC12jsWQtKLUoxWlHj34VbkGdbHp8oxwTcpwqFcs7XgAAABOhy9gKO+AV4tKXPq8xK2V+z3yhvZ57DGFmWtL/2enOJSdYtfAeJssZq4f0b5F28yalBGuSRnhChiG1hzwakGRSwuKXNoQoup+R1V4Dc3d5dTcXU6ZJJ13pBXH+DSH+sbRigMAgLaEBAmgg7Bs3yiTx9UiY5s8Llm2b5C/35AWGR84XR6/oRnbqvXUukqVOoN3d6x7tEUPDorRVT3CubEFtEHJERb9uG+Uftw3Sgddfv2vyKX5BS59vtsVsjKweQe8yjvg1eOrK9Q3zqpJ3WtvHp7diRt0AAAAJ2IYhrZX+PT57tq2GUv3uFXhDUHWaxP172yra5txQWKYIqzmlp4S0GLMJpOGdA3TkK5henhwjIqrfPq42KWPi1zK3eMOauvTEzEkfbnPoy/3efT46gqlR33bimNYkl12WnEAANCqkSABdBDmnVtbdvxd20iQQJvjDxialV+jP62tVGFV8K60kyPMeuCcGN10RoRsJEYA7UK8w6Kbekfqpt6RqvAE9GmxS/MKnPq02K2aILbiOZ7NZT5tXlupP6+tVI9oiyZlhOvy7uEa3MUmM8kSHYPbKdnDW3oWAAC0aqU1fuXucWtRiVu5JW7trgnhU9UTSIuyaHSKXdnJdo1MsasLJfyBY0qNsupHZ0bpR2dGqdobUO4etz4uqk2Y2BvEl1uaorDKr79vrtbfN1crymrS6G52jU9zaHyqQ13D+f8YAIDWhgQJoIMwF25v2fELvmnR8YGTYRiG5hW49Mc1FdpaHrwes53tZv1yQJR+fGaUwq08rATaq5gws67MjNCVmRFy+gx9trs2WeJ/RS5VBLE9z/HsrPTr+Q1Ven5DlVIizJqYEa7LM8J1YWKYrCRmtRnmwu2yLZzb5PjIO6fId+FF8l40WYH0Xs03MQAA2pAqb0DL93q0aI9Li3a7takseNd4pysuzKSRyfa6thk9oi1UAQNOQaTNrEvTw3Vpem0rjvUHvfrfkWSJtQdD24qjymdofkFtdUGTpKyuNk1IC9f4NAeV/tDq+Bbd2KS4qlsXNe9EACDESJAAOghz2aEWHd+ydb0sG1bJf+Y5kpUe6WidDMNQzm63pq+pCOoFdLTNpJ/3i9LP+kUpJoySqEBHEm41aWJGuCZmhMvjN7Rkr1vzdzn1YaFL+12heauppCZQ9zZTvN2siRkOTcoI18hkSr+2Vqayg7L/8zlZVy0+ue08LtlyP5At9wP5hoyU+4d3y4iLb6ZZAgDQOvkChtYc8OjzktoqESv3eRSigl4nFGaWLki0K/tIlYhz4m20WwSCzGwyaWCXMA3sEqYHB8WopNqvT4td+l+RS7klbjn9oTsgGJJW7fdq1X6vpq+pUGpkbSuOCWkOjUiyy8HLMwAAtAhTWVlZK7lEANCcwn//M1nyN7X0NGRERMp3zoXyDR4uf//zpPCIlp4SIElaUerW71dXaEWpJ2j7dFikn/SN0j39oxRPadQWEffm7ibFld3arZlnAtTnDxj6Yp9H8wuc+qDApeLq0Jd2jrGZNCHNocsywnVRqp2e1q2E9Ysc2d96VqbqytPelxEZLffN98h3wdggzAwATl/ULdlNiquasahZ54H2xTAMbSv3adGRhIhle92q8Lae250DOttq22ak2HV+YhjnXO0E15ptk9NnaPGRVhwLipwqqQltK47virCalJ1i14Q0h8alOpQUwX0jhB7nZgA6KhIkgA4i/I93y7J1XUtPox7DapO/X1ZtssSgoTJiO7f0lNABrTvo0fTVFfp0tzto+7SapJvPiNSvzolWSiQXuC2Jm1ZoCwzDUN4Br+YVODVvl1M7KkOfLBFuMemiVLsmZdSWfo2l2k3oGYbC5v5DYXNnBH3Xnsm3yDN5qkQ5XwAtjJvwCJa9NX7l7qlNiMgtcbXoQ87vS4+y1CVEjEy2kyzfTnGt2fYZhqGvD3m14EgrjtUHQtuK4/sGdbFpQppD41MdOifeRisOhATnZgA6KlpsAB1EIK6zWtslucnnlXXdF7Ku+0LGP0wK9Oon3+Dh8mUNl5GY2tLTQzu3rcyrP+ZVau4uZ9D2aZJ0dc9wPTgwRj1i+IoF0DQmk0mDu4ZpcNcwPZoVo81lPs3b5dT8Aqc2Hg5Nj2yn/9s+uTazlJ1s16Tu4bo03aEu3NQPieZKjqjd9wwZMsk7ZWqz7B8AgOZW6Q1o+V6PFpW4tKjErc1loTlHaoq4MJNGpdiVnezQ6G52dY/mWhBoC0wmkwbEh2lAfJgeGBij0hq/Pil2aUFR7XGmOsS9efIOeJV3wKsn8iqVHGHW+FSHxqc5NCqFan8AAAQbFSSADsL2wUzZ3/t7S0+jyfzdust/JFki0L0PbzwiaAqrfHpybaXe3V6jQBC/AS9Ld+ihwTE6q5MteDvFaeOtHrR1Oyp8ml9Qmyyxan/o32gym6RhiWGalBGuyzLCqYrTTKwrcuR4ZVqzj+O64xHabQBoUbyliKbyBgyt2e/R5yVu5e5xa+U+j0L8rPKY7BbpggR7XZWI/p1tspi5Z9HRcK3Zvrl8hpburW3F8b+ilmmJeJTDIo1KtmtCWm21P67JEEycmwHoqJqUIHHOOeec2s5NJq1du/aUtgUQXJaNqxT+51+19DROSaBzV/kGDZM/a7j8fQZKVt7GwMkrrfHr6fWVenNrtbxBrL46OsWuhwfHKKtrWPB2iqDhphXak+Iqnz4sdGlegVMrSj1BTfJqqnO72jQpI1yTMsKplBMkprKDinhoqkzVlc0+lhEZrZo//kNGXHyzjwUAjeEmPI7FMAxtLfdpUUlt24xle92q9LaOjAiTpHPibco+khBxfoJd4VYSIjo6rjU7DsMwtOmwr64Vx8r9HrXk0WlAZ5smpDs0IdWhgV1sMvNSGU4D52YAOqom3dUMBAKn1PPKMFrHhQwAyd+rn4wwh0weV0tP5aSZD+1XWM5cKWeujIgo+QZeKN/g4fL3P1dyRLT09NDKlbkDen5DpV7ZVK2aIL5ydF7XMD2cFaORyfag7RMAjic1yqrbz4rS7WdFab/Tr48KXZpf4FTuHndQE7+OZ+V+r1bu9+p3qyp0dmebJmU4dHlGuM6Ms9Ij9xTZ//lcSJIjJMlUXSn7P5+T667fh2Q8AACOZ0+NX7klbi0qcSl3j1t7akJ0QtME3aMtyk62KzvFoRHJYYqn5RjQYZlMJvXrbFO/zjbdd0609jv9+vRIK47PdrtVFeLyNusPebX+kFd/XlupxHCzxh1pxTE6xa5IG604AABoClpsAB2I/Y2nZMv9oKWnETSGzSZ/vyHyDR4u36BhUkxcS08JrUiVN6BXN1XruQ2VqvAE76uuXyerHsmK0fhUBw8D2wAy4dERlLkD+rjYpfm7nMrZ7ZbTH/rT+14xVl3e3aFJGeEaGG/j+NhE5oJvFPG7n4R83JppryuQ3ivk4wIA52YdW6U3oKV7aitE5O5xa0uZr6WnVKeT3aRRyY66KhHdo6mUheOjggQkye03tHyvWwuKahMmCqparhWH3SKNSLJrQlptwkRaFMcxnBjnZgA6KhIkgA7EXLhdEY/8uKWn0SwMk1mB3mfLlzVcvsHDZSSktPSU0ELcfkNvbq3W0+sqtd8VvDeQMqMt+u3gGE3pEU75wjaECz10NNXegBbuduuDAqcWFLlapDR1aqRFkzJqkyXOTwijJ/dxtFTyqjd7kty33hfycQGAc7OOxRswtGq/pzYhosStVfs9CvGL1sdkt0gXJto1OsWuUcl2DYinTD1ODgkS+L6jrYIWFLr0cbFLX+5rmbaIR/XrZNWENIcmpIVrcBcb12VoFOdmADoqEiSADsbxwu9kXbW4pafR7PypmfIfSZYIZPSWuNHR7vkCht7ZXqM/r61UcXXwMva7RVj060HRur5XhGxcTLY5XOihI3P7DeWWuDWvwKmPCl065A592eqEcLMmptcmS4xItnMc/S63U5F3TmmR9mdGmEPVL86V7I6Qjw2gY+PcrH0zDENbynxaVOLWoj1uLdsT+tLzx2KSNLCL7UjbDLvOS7Ar3Mp5CU4dCRI4kYMuvxbudmtBoUs5u12qaIHk9aO6OL5txTGmm13RtOLAEZybAeioTqvO0u7du7VkyRLt3btXbre7wXqTyaQHHnjgdIYAEGTuH94ty+a8kPS6NiKi5brll7JsWy/r6qUylx1o9jGPshTvkKV4h8L++5YC8YnyDR4uf9Zw+c/oL1koMdeeBAxDc3c69ce8Sm2vCF6J1i4Os+4dEK3/6xMpBzfOALRBdotJ49IcGpfmkC9gaHmpR/N3OfVBoTNkPb73OQN6c2uN3txao9gwky5Jc+jy7uEaneLo8A8lLNs3tkhyhCSZPC5Ztm+Qv9+QFhkfANB+lFT7lbvHrc9LXMotcavUGfqEzGPpEW050jLDoRFJYerssLT0lAB0IPEOi67tGaFre0bIGzC0otSjBUVOLSh0aUdlaFtxHHAF9M72Gr2zvUY2szT8O604aCkEAOiITrmCxCOPPKJXXnlFfv+3X+aGYdT1Gz7634cOHQrOTAEEjfWLHDlentbs47jueES+C8bW/hAIyLxrm6xrlsqyeqksJbuaffzGGJEx8g28sDZhov8QyR7eIvPA6TMMQ58UuzVtTYU2HPIGbb8xNpPuOjtKP+0XRUZ9O0AmPNBQwKgtdz2/wKV5u5wt0ic30mrSxakOXZ7h0MVpjg55vLV9MFP29/7eYuP7Bg2Td8zlCiR0k9ElSbJyYxRA8+PcrO2r8AS0dK+7rm3G1vLgJamfrs52s0YdqRAxKsXOQz80KypI4HR8U+7VgiKXFhS59EWpR/4WLLZzZtzRVhwOnduVFokdDedmQMcxceJELVu2TGVlZS09lVbhlK4UZsyYoRdffFGjR4/Wrbfeqptvvlk33HCDxo4dq+XLl+utt97SpZdeqh//+MfBni+AIPBdMFaePYUKmzuj2cZwT576bXKEJJnNCmSeKU/mmdJVP5Zpb5Gsq5fKumapzPmbZDJCcyVgqq6QbdnHsi37WIYtTP6zz5Vv8HD5Bl0oRceFZA44fUv3ujVtdYW+3OcJ2j7DLSb99KxI/aJ/tDrZO96DOgAdh9lk0nkJtaWlfz8kRl8f8mp+gUvzC5zaUhaahxzVPkNzdzk1d5dTdos0OsWhSRkOXZLm6DBvd5oLt7fo+Na8ZbLmLZMkGSazjPgEBRK7yUhIUSChmwIJKUf+O0VyRLToXAEALcfjr02sXLSnNiFi1f6WfZD3XQ6LNDTx24SI/p1tMtNeE0Ab0DvWpt6xNt11drTK3AEt3O3Sx0UufVrsUpkntAfZLWU+bSmr0rNfV6mz3ayLU2urS4zp5lBsGPfHAOB41q9frzfffFPLly9XSUmJXC6XOnfurLPOOksXX3yxrrvuOnXu3Lmlp4lGnFIFidGjR+vQoUPKy8uT2WxWp06d9Jvf/Ea//vWvJUlLly7VlClT9Prrr+uKK64I+qQBBIFhyDZ3huxz/xH0XbsnT5V38i1SE29MmMoOypK3vLa6xKY1MvmCVw2gqQyTWYE+/WuTJQYPl9E1OeRzwInlHfBo2uoKfVbSsK3TqbKZpal9InXfgGglRXSMh3IdCZnwwMnZVubVB4W1lSXWHgz997HFJI1ItmtShkOXpYcrsT0elwMBmYvy5XjhdzLv39PSs2mSQEwnGUeSJuoSJ44kUxjRcU0+5wMAzs1aP8MwtLnMd6RChEtL93pU7WsdGREmSYO62GoTIpIdOj8hjHaIaDFUkEBz8AUMfbnPowVFtQkT21qwSo/VJA090opjQppDmTFU5WmPODcDTk0gENDvfvc7vfjii7JarRo6dKjOOussRUREaP/+/frqq6+0detWRUdHa+3atYqPj2/pKVNB4ntO6Vvtm2++0bXXXiuz+dsMQp/v2y/r4cOHa9y4cXrhhRdIkABaK5NJ3ilTZSSnyf7WszJVV572Lo3IaLlvvqd+5YimbBcXL9/oSfKNniQ5q2Vd/5Usq5fIuu4LmVw1pz2vpjAZAVm2rJNlyzrZ33lJ/vSe8h9Jlgik9+LGfwvbUubVH9ZUaH5B8Hq1m03SdT0j9OuB0cqg9CoASJLOiLPp3jib7h0QrcIqn+YXuPRBgVNflHoUikcjfkNaVFJbtvtXK8p1fkKYJnUP12XpjjZ9rDYdLJVlwypZNq6WZdMamSvLWnpKJ8VccViqOCzL9g0N1hmOCAUSko8kUNSvPGHEJ0jmdpjkAgDtzO5qvxaVuJRb4lbuHrdKnYGWnlKdzGiLslMcyk6xa0SynWp/ANo1q9mkYUl2DUuya9q5sdpR4atrxbF8r1uhzFfzGdLiPW4t3uPWQ1+Vq3dsbSuO8WkOXZAQJiutOAB0YNOmTdOLL76oQYMG6Y033lCPHj0axKxZs0a/+93v5HIF75kGgueU7zLGxsbW/XdkZKQOHz5cb33v3r2Vm5t76jMDEBK+C8bKf+ZA2f/5nKyrFp/6foaMlPuHd8uIO81MuPBI+c4fLd/5o+X2emTZslbW1UtlyVsmc9nB09v3SbAU5stSmK+wuTMU6JJUW1kia7gCvc+WLG33AU1bs6vSpyfyKjQ73xnUB3NXdHfooUEx6hNnC+JeAaB9SY+y6uf9ovTzflHaW+PXR4W1bTgW73GHpLS2IemLfR59sc+j335VrnPibbo8I1yTMhw6o7Ufv6srZdmyVpaNq2XduFrmvUUtPaNmY3LVyFKYLxXmN1hnWKwyuiQpkPjdxIkj/901WQqzt8CMAQDlnoCW7nHXtc1oyTeUvy/ebq5rmTEq2d6mEyQB4HRlxlj1s35R+lm/KJV7Avpsd22yxKfFbh1yhzaZ7Ztyn74pr9ILG6oUG2bSxam1lSUu6uZQHMlrADqQ/Px8Pf/88+ratav+/e9/H7M6xODBgzV//nwFAvWP1xs2bNAzzzyjZcuW6dChQ0pMTNQll1yiBx98sF47joKCAp1zzjm6/vrr9etf/1q/+93vlJubK6/Xq3PPPVfTp09X//79G4y7YsUKTZs2TWvXrpXdbld2dramTZt2zN/HMAy9/fbbevvtt7Vp0yb5fD716dNHP/rRj/TDH/6wXuwTTzyhJ598UvPnz1dRUZFeffVVbdu2TYMHD9aHH354Mh9jizulq4zk5GTt3v1tGbHu3btr1apV9WI2b96siAh61QJtgREXL9ddv5e5cLtsOf+VbdH8pm0X5pBv6MXyjr2itspCsNnC5O9/nvz9z5NuvkfmnVtkXb1U1jVLZN4TugcN5gN7FfbJvxX2yb9lRMXIN3CofFnD5e83RLI7QjaPjmRPjV9PravUjK3VQc2Ov6ibXQ8PjtHALmHB2ykAdABJERb935mR+r8zI3XYHdD/Cp2aX+DSZyUuuf2hmcO6g16tO+jVtDUV6hNr1aTutckSAzrbZGrpSk8+r8zbN8m6abUsG1bJvGOLTEbrefu2pZj8PplKi2UuLW50faBTFxmJDStPBBK6SZHRIZ4tALRfHr+hlfs9R6o0ubTmgDckyY5NEW4xaWhSmLKTa5Mizu5sk7mlv9cBoBWKDTNrSo8ITekRIX+g9rh+tBXH5rLQJrqVewz9e4dT/97hlMUkXZAYpgmpDk1Id6hXjLXlr88AoBm988478vv9mjp16glbZ5hMJlks31bW/Oijj3TrrbfKYrHokksuUbdu3bR161b9/e9/12effaacnBzFxcXV20dhYaHGjh2rPn366KabbtLOnTv10UcfadKkSfrqq6+UkJBQF5ubm6urrrpKZrNZU6ZMUXJysnJzczVhwoR6hQ+OMgxDt912m9577z316tVLV111lWw2mxYtWqS77rpLW7du1fTp0xts98ILL2jJkiW65JJLNHr0aFmtbS+p+ZRmfP7552vFihV1P1966aV66qmn9Mtf/lITJkzQF198oU8//VSXX3550CYKoPkF0nvJfet9TU6QqH5xbugSBMxmBXqeJU/Ps+S55jaZSgpkXbNU1jVLZcnfHJo5SDJVVci2dIFsSxfICLPLf/a58mWNkG/gBVJUwy8YnJxDLr+e/bpKr22ukiuID9wuTAzTw4NjNCyJN1UB4HR1spt1Q+9I3dA7UpXegBYWuzRvl0ufFLtC1qN8a7lPW9dV6ql1lcqIsmhSRrgu7+7QkK5hoXmoYhgy794ly8YjbTO2rJXJTcnEk2U+fEA6fECWLesarDMio7+XOPHtfxtx8ZKZt9QA4FgMw9Cmwz59fqRtxrJSj2pCWZf9OMwmaVC87UiVCIfOTwiT3cKDNAA4GRazSRck2nVBol2PDYnVrkqfPj7SimPpXre8IczV9hvSsr0eLdvr0SOrKpQZbdGEdIfGp4ZraFKYbLTiANDOfPXVV5KkESNGnNR2hw4d0k9/+lN16dJFCxYsUFpaWt26f//73/rxj3+sP/zhD/rLX/5Sb7tly5bpscce0z333FO3bPr06Xrqqac0c+ZM/fKXv5QkBQIB3X333fL5fProo4904YUXSqqfBPF9b731lt577z398Ic/1F//+te6RAePx6Obb75ZL774oq666ioNHDiwwZwWLlyofv36ndRn0JqYysrKTvoKacmSJXruuef0zDPPKD09XVVVVZo4caLWr18vk8kkwzCUnp6u+fPnKz09vTnmDaAZRd2S3aS4qhmLmnUeTWU6fECWvGW1yRKb8mTyh748qGE2y9/nHPkHD5dv8DAZXZJCPoe2rNIb0N82VumlDVWq8Abvxt2AzjY9khWji7rZyV7voNra8Qxoy5w+Q4tKXJpX4NL/Cp0q84T+QUxSuFmXZYRrUka4hiUFty+u6fABWTatkWXDKlk2rQ5p6y/UZ9jCjiRLNKw8YXRJktrgmwtAR8G5WfMprvLVtczI3ePWPmfrqWTUM8ai7BSHslPsGpFkpxQ72oW4N3efOEhS2a3dmnkmQH2V3oA+3+3WgqLaJPYDrpb7PoixmTS2W21liYu72dXZYTnxRggpzs2Ak3f++edr69atWrlypXr37l1vXW5urpYvX15vWXZ2ti688EK99NJL+u1vf6tXX31V1157bYP9jho1SsXFxcrPr21herTFRkZGhvLy8mT+zosiR9dNmjRJ//znPyXVJi1MnDhREyZM0L/+9a96+y4sLNSgQYPk9/tVVlZWt3zYsGHatWuX8vPz5XDUfxl648aNGjZsmO688866KhJHW2z87Gc/0x//+MeT/ORal1O6czRixIh6mTFRUVFauHChPvroI+3cuVNpaWmaMGGCIiMjgzZRADgWo1MX+cZcId+YK6SaKlnXfynL6qWyrv9CJpczJHMwBQKybs6TdXOe7DNfkD+jt3yDh8s/eLgCaZkSD+cb5fQZ+n9bqvTX9VU6GMTeib1jrXp4cIwmZTgozwoAIRJuNemS9HBdkh4ubyBOS/e4Nb/ApQ8KnSF7SLPXGdDrW6r1+pZqdbabdWm6Q5MywpWdYj/5t1NdNbJsWVdbIWLjKll272qWOePkmbye2r+PRv5ODJNZRpfERitPBBJSJAdtIAG0D2XugJburU2IWLTHrW/KQ/+iwLF0cZhrK0QcaZuRHkXiGgCESrTNrMu7h+vy7uEKGIZW7/dqQZFTC4pc2ng4tN8VFV5D7+9y6v1dTplN0vkJYRp/pBVHn1hacQBomwzj2C8ELVmyRE899VS9ZQ6HQxdeeKFWrVolSVq1apV27NjRYFu3262DBw/q4MGD9Vp3nH322fWSIySpW7faBMzy8vK6ZRs2bJAkDR06tMG+09PT1a1bNxUWFtYtq6mp0aZNm5ScnKy//vWvDbbx+Wq/M7755psG67Kyshosa2uCdoVis9l0xRVXBGt3AHBqIqLku2CsfBeMldvrkWVTXm1libylMpcfDtk0LAXfyFLwjfT+mwp0TZEva7h8g4cr0LufZCZb2hswNPObGv15bYVKaoL30Cw10qIHB0Xr2p4RQX1rGABwcmxmk0Z3c2h0N4f+ckGsvtrv0fwCp+btcqm4Oog9lI7jkDugt7+p0dvf1CjaZtL4tNpkiYu62RVpa+TNVb9P5p1bZdm4WtYNq2TO3yiTPzRzRfCYjIBM+/fIvH+PtHF1g/WB2E4yunY7UnHiSOJEYrfa1h3RcSS1Ami13P7afvOLStxaVOLSmgNeBVpH1wyFW0walhSmUSl2Zac41K+TlUR1AGgFzCaTzk0I07kJYXokK1aFVT59cqQVx+I9bnlCWFwiYEgrSj1aUerRY6srlBFl0YQ0hyakOTQsya4w2i0BaCO6du2qbdu2qaSkpEEFiYcfflgPP/ywJGnmzJn6+c9/Xrfu8OHa51N///vfj7v/6urqegkSMTExDWKOtsLwf+e+VUVFhSSpS5cuje43ISGhXoJEWVmZDMNQSUmJnnzyyePO5/u6du163N+hLTilBIlzzjlHd9xxh376058eM+aNN97Qc889p3XrGvaTPZFZs2ZpxYoVWrt2rTZt2iSPx6OXXnpJN954Y704r9erjz76SAsWLNDq1atVXFwss9msPn366Prrr9ett94qi6XxB5HvvfeeXn75ZW3ZskU2m03nnXeeHnroIQ0aNKjR+Pz8fE2bNk1LlixRdXW1MjMzNXXqVP34xz9ukLkj1Wb6PPvss5o9e7aKi4sVFxencePG6eGHH1ZiYmKjY+Tk5OiZZ57R+vXrZRiGzjnnHN17770aO3bsSX6CACRJtjD5zzlf/nPOl275pcw7Nsu6eqmsq5fIXFocsmmY95cobMFshS2YLSM6Vr5Bw2qrS/TLksLsIZtHaxAwDM3Z4dQf8yq0szJ4D50Sws361YBo3dInkv61ANDKWMwmXZho14WJdv3hXEPrDno170iyxPaK0LzBVOk19O8dTv17h1MOizS2m0OXZzg0MeyA4r7Jk3XDKlm25MlU0/Cirz3yDh8vk8sp077dMpfulsntaukphYy5/LBUfliW7RsarDMcEcesPGHEJ5DkCiCkAoahTYd9+rzEpdwSt5aXelTjax0ZEWaTNLiLTdnJDo1Kseu8hDCuwwCgDUiPsurHfaP0475RqvIGlFtS24rj42JXyFszFVT59ermar26uVrRNpNGp9g1Ic2hi1Md6hrOeTeA1uv888/XsmXLtGTJEo0aNarJ20VHR0uSli9frrPOOivo8zqaSHHgwIFG1+/bt6/R+QwcOFCLFi06qbHaQwWgU0qQKCwsrFe2ozEVFRUqKio6pUlNnz5dRUVFio+PV2Ji4jH3s3PnTt1yyy2Kjo7WiBEjdMkll6iiokILFizQr371Ky1cuFDvvvtug7+op59+WtOmTVNqaqpuvfVWVVdX6z//+Y/Gjx+vOXPm1GsfIklbtmzRuHHj5HQ6NWXKFCUnJ+vTTz/VAw88oI0bN+q5556rFx8IBHTDDTcoJydHQ4YM0aRJk7Rz507NnDlTn3/+uXJychokScyePVu33Xab4uPjdd1118lkMmnu3Lm68sor9dprr+maa645pc8SwBFmswK9+snTq58819wm055CWVcvkXX1Ull2bgnZNEyV5bIt/ki2xR/JCHPIP+A8+QYPl2/ghVJkdMjmEWqGYeijQpf+sKZCm8qC9zAsNsyke/pH67a+kY2/DQwAaFVMJpMGdgnTwC5hemRwjLaW+zRvl1PzC1z6+pC32cfv4qnQmMMbNXbjBl10+GsluQ82+5itjW/ISLl/8uC3CwxDporDMu0rkXlficz7dtf9t6l0t8yVZS0211AzuWpkKdwuFW5vsM6wWGV0Ta5feeJoEkXX5A6X9AqgeRRV+bSoxK3cPbWtM/a3YN/47+sda1X2kZYZw5PsirNz/QUAbVmUzayJGeGamFHbimPtAa8WFLu0oNCl9SG4NvuuSq+heQUuzStwySTp3K5hGp/m0Pi02qpE7eFBHID24/rrr9ezzz6rGTNm6I477qhX7eF4hgwZovnz52vlypXNkiBx9tlnS6pNwPjFL35Rb11hYaF2795db1l0dLT69Omjbdu2qaysTHFxcUGfU2vWbE0AKyoqZLef2k2iF154QZmZmUpPT9df//pXPf74443GRUVF6emnn9b111+viIhve8lOnz5dl112mRYsWKD//ve/mjx5ct26/Px8PfHEE+rVq5dycnIUGxsrSbr99ts1duxY/eIXv9DKlSvrypNI0r333quKigrNnj1b48aNk1RbJuWqq67SjBkzdOWVV2rkyJF18e+8845ycnJ05ZVX6vXXX6/7An/77bd155136tFHH9Urr7xSF19WVqb7779f8fHxys3NVWpqat24o0aN0v33369x48Z1uH+cQLMxmWSkZMibkiHvpJtkOrRPlrzltckSW/JCVk7b5HHJumqxrKsWyzCb5T9zoPyDa1txGPEJIZlDKOSWuDVtTblW7Q/exVWk1aQ7zorSnWdHcWMOANook8mkM+NsOnOgTQ8MjNHOCp8+KKhNlvhqvycoYzj8Hg0v36qLDn+tMYc3anDVrqDsNxgCcV3k75clf48+sv/nDZlqqpp9TCMyWu4f3l1/ockkI7azjNjOCvQ+u+FGzpp6SRPmfSW1lSf27Zbp4H6ZjNbz8K45mfw+mfYWyby38eT9QOeujVaeCCR0a9dJsABOT5k7oCV7a5MhFpW4Q1ZZqSm6OszKTqlNiBiVbFdaVLPdQgQAtDCzyaTBXcM0uGuYHhoUo93V/tpWHMUu5Za45Aph50FD0lf7Pfpqv0fT1lQoNfLbVhzDk+xyWEmWANCyevfurZ///Od64YUXdNVVV+mNN95Qjx49GsR9v9DAjTfeqKeeekrTpk3Teeedp759+9ZbX1NTo40bN+rcc889pXldeOGFysjI0Mcff6wVK1bowgsvlFT78uq0adPqteM46vbbb9e9996ru+++W3/7298UGRlZb/2uXbtkMpmUkZFxSnNqzZp8dbNs2bJ6PxcWFjZYJtX2OykpKdGsWbPUs2fPU5pUdnZ2k+JSUlL0ox/9qMHyyMhI/fznP9ePf/xjLVu2rF6CxMyZM+Xz+XTffffVJUdIUt++fXXdddfpjTfe0OLFizVmzBhJ0vbt27V8+XKNGDGiLjlCkmw2mx555BHl5ubqrbfeqpcg8dZbb0mSHn300XrZjTfddJNeeOEFvf/++/rLX/5SV75k7ty5Ki8v14MPPliXHCFJSUlJuuOOO/TYY49p7ty5mjp1apM+FwAnx+icIN/YyfKNnSxVV8q67gtZ1yyVZf2XISs5bQoEZN20RtZNa2R/+3n5u59R24Yja4QC3bq3yX7YK/fVXsgs3uMO2j7DzNL/nRmpewdEK4FyewDQrvSIsequ/tG6q3+0Sqr9+rDQqXm7nFpW6mlyj3WTEdCgqgKNPbxBFx36WsPKt8lhhPbtp2MxHOG1yZD9suTrN0RGSsa33+/RsXK8PK3Z5+C++R4ZcU17s6FOeIQCGb2ljN5qcCnt88p0YK/Mpd+tPLG7Nolif4lM3tbx2YeC+dB+6dB+WbY0bDFpRMY0XnkisVvt30cbPM8DcGrcfkNf7fNoUYlLi0rcyjvobfJ3XHOLsJo0LDFMo1LsGp3i0Fm8sQsAHVa3SItuPTNSt54ZqRpfQIv3uLWgsLYVx56a0CZIF1f79fqWar2+pVqRVpOyU+y11SVSHUqM4N4ggJbx2GOPyev16pVXXtGQIUM0bNgw9evXTxEREdq/f7++/vpr5eXlKSYmRv369ZMkdenSRa+//rqmTp2q4cOH66KLLlLv3r3ldrtVWFio5cuX67zzztOcOXNOaU5ms1nPPfecrr76ak2ePLmuI8LixYtVWlqqfv36aePGjfW2ufXWW7Vy5Uq9++67+vLLLzVq1CglJydr3759+uabb7Rq1Sq9/vrrHTtB4rLLLqu7MDKZTHr33Xf17rvvNhprGIZMJpMeeeSR4MzyFNhsNkmSxVL/S3Lp0qWSVJcA8V1jxozRG2+8oWXLltWtP158VlaWYmNj6yWKuFwurVq1Sr1791Z6enqDbUaPHq1XXnlFq1at0ujRo5s0p8cee0zLli0jQQIIhcho+YZeLN/QiyWPW5ZNa2qTJdYsC2mJacuubbLs2ib95w0FErvVtuEYPFyBXme1+v7XGw95NX1Nhf5XFLzkEotJuqFXhB4YGM2bSwDQAaREWvSTvlH6Sd8oHXT59WGhSx8UOPV5iVve792Py3Du10WHv9bYwxs05vBGdfE1fyWGpjDMZgUy+8rfb4h8Z2cpkHmWZG38O8x3wVh59hQqbO6MZpuPe/JU+S4YG9ydWm0yktLkT0prmDwRCMhUdqBh5YnSIwkUIaiY0VqYqitk2VnRaFs3I8yuQNdkGd+vPJHYTUZ80jH/zQBoGwKGoQ2HvLUVIva4tXyvR05/68iIMJukrC42Zac4lJ1i17ldwxRmISECAFBfhNWsCWnhmpAWLsMwtO6gVx8Xu7SgyKW8A6FNiK72Gfqw0KUPC2vvOWZ1sdW14hjQ2UZiH4CQsVgs+tOf/lT34v3y5cu1evVqeTwederUSWeddZb+8Ic/6LrrrqvXgmP8+PFavHixnn/+eS1atEiff/65IiIilJKSohtuuEHXXnvtac0rOztb//3vfzV9+nT997//lcPh0KhRo/SPf/xDP/3pTxvEm0wmvfzyyxo3bpxmzJihjz/+WNXV1eratasyMzM1bdq0Jhc1aGuafLflgQcekMlkkmEY+vOf/6xhw4Zp+PDhDeIsFos6deqkESNGqE+fPkGd7Ml4++23JTVMOsjPz1dUVJQSExMbbHO04kV+fn69eEnKzMxsEG8ymZSZmam8vDzV1NQoIiJCO3fuVCAQaDT++2McTZA4OkZjFTcam9PxuFyhedsd7VtUE+M6xL+3MwfV/rnuZ7Lu2KKwtctlX7tclv17QjYFc+luhf1vlsL+N0uB6Dh5zrlA7oEXynvmIMkWFrJ5nMjOSr/+8nWN3i/wKJi3/K5ID9P9/SPUK8YiySeXq/WUnUXrx/EMaPsiJV2TbtE16VGq8ERocf5h7cnLU9fteco+uEG9XKUtPcU6vsRUefsOkrfvYHn7DJAR/p3ShD5f7Z9jcE24TuE+vyI/eDvo86q+7CY5J1wrhfpYFxEjdY+Rup/ZYJWpulKW/SUy798jy9E/+0pkPrBHlrKDoZ1nCzJ53LLs3iXt3tVgnWE2K9A5Qf6uyfJ3TVagS/KR/06Rv2uy5AgP+XyB09URzs2Kqv1asterxXu9WlLq1UF360iIkKTeMRaNSLRpZJJNQxOsigk72q7QUMDrlqvjFP4BQqYtH8+AxpwZJZ15ZpjuPjNMpc6AFpZ49Oluj3L3euUMYSsOSVp9wKvVB7z6Y16lksPNuribTRenhGl4ok3htOJoko5wbobWyeFwtPQUgmLgwIF6/vnnT2qb3r1764UXXjhhXEZGhsrKyo65/ljrhg4dqo8++qjB8g8//PCY+5oyZYqmTJlywjk9+OCDevDBB08Y1xY0OUHiu7/wsmXLdOONN+r6669vlkmdrn/84x/69NNPNXLkyHptMSSpoqJCXbt2bXS7oy0vKioq6sVLqteO41jbREREnFT898eIiYlpEB8ZGSmLxVIv/nhKSkoa7SMDnIwuTYwrKmq8B3O7ZY+Rzp8gnTdejv0lit2ap7htaxWxpyBkUzBXlsmxdIEcSxfIH2ZXRc+zVd5nkCp69ZffERGyeXxXqduk/1dk1by9VvkVvIuP4Z38+mmGR32iaqTyMhWVn3gb4Ps4ngFtn8nnVWTxDkXv3KTEnZt1y55dMhmt42HTAVu0tqX0k6/XmYrre6b8321fceCQpEMnt8OBoxRnC1fa/2bK6qo57fn5HBEquuRGlfU7TyouPu39BZ0lQkrqWfvnO0xet+xlBxR2eL/sR/8c2lf7c/lBmQId43rHFAjIcmCvLAf2SpvzGqz3RsbI3amrPJ26yt2pq9ydEo78nCBfRBStO9AqtcdzswqftKrMoq/KzPqqzKIil/nEG4VIZ5uh8+L8Oi/Or3PjAkqyH/n+NKTyUolLLOB0NO0eTFs6ngGnYmSYNLKH5EqXVpebteSQRUsPW1TqDu334R5nQG9td+ut7W7ZzbXff8M7+zWiU0Bd7a3j+rE1ao/nZmgbevfu3dJTQAd3SvU6P/jgg2DPI2g+/vhj3X///UpLS9Nrr73W0tMJuZSUlJaeAjqQtLS0lp5Cy0lPl7IuUI0k16F9Clv3hcLWLpdt23qZAqHpxWfxuNVp82p12rxahtkib59z5Bl4oTznXKhAp6ae3p66A66AXtjk1D++cckdxF/5wgSrHhoQoXO72oK3U+AEOvTxDGhtDEOWkl2ybVqjsC1ra79bPe6WnpUkyWm2aUnsmVrYqb9yOvXT+qh0GSaz5JfivzHpktQwXZpa+8bSKZcpT0tT/sBROvj6Cxpa9OUpz3V52vmK//Fdik/qouhT3ktL6lXvJ8+RP1V+v8yH98uyv0SW/XtqK1DsK5HlQG0VCpO747zZZKuukK26QipuWG0w4IhQ4EjlCf+RyhOBrsnyJ6TUnie28pZtQGs+N3P5Da3a79PiUq8W7/Vo/WG/Aq3kuUuEVbowwaaRR6pEnBlrodw40GyaVvGqNR/PgGDrLek61bZg31Tm16clHn2y26u8g76gVps9EXfApCWHrFpyyKonJA3oZNHF3cJ0cUqYBnS2yMx340njWAagvTmthqbV1dX66KOP9PXXX6uiokLR0dHq37+/Jk6cqMjIyBPvIMhycnJ08803KyEhQfPnz1dSUlKDmJiYmGNWY6isrKyL+W68JJWXN55Xf3Sbo5Uhmhrf2BgVFRXq3Llzvfjq6mr5/f5Gq0s0pr2UpUHbwL+3I1LSpZR0eS65Rp6qClnXfSHrmqWyrP9KJk9obtKbAn6FbV6jsM1rpHdfkr/HmfJlDZdv8HAZKRlBfYOw3BPQSxur9LcNVaryBe/yZlAXm343OEbZKXZu4iHkOJ4BLct0aL8sm1bLsmGVLJtWy1x+uKWnJEkKyKTV0T30Wad+Wtipv5bH9Jbb0nh7q4NuQ2/nu/V2vlsxYSZNSHPo8oxwjelmV4S16W9PzdlRo/tWmFXW8xcakFign+5eqNv2fNakbavNds1MHKZXul2k9VEZiltm0tMXBnRlZstUmWo2kZFSandJUuDIH68kGYZMFYdl2lci874SmUt3y7Rvt8z7SmqXVZa12JRDzeyqkbkoX9aihskThsUqo2uyAgkpCiSkyEhIUSChmwKJ3WR0SZLC7C0wY6C+1nRuFjAMfX3Iq9wStxaVuLWi1COnv3VkRFhMUlaXMGV3sys72a4hXcNOPUEPQLNoTcczIJSywqWsZOk3WdI+p1+fFLv0cZFLn+92B/V+YlOsP+zX+sNOPb3BqaRws8alOTQhzaFR/5+9Ow+PqjzfB36fc2Yyk31IAlnYQwKEsGQTWSKyCK4IiAuKIv7Utvar1daqQLXWYrVqsai1WLWtImpFUYRqXQiLBFBICEtCAiSBsAQSCEz22c45vz8mGRISIMDMnMnk/lxXLsjMO+d9iPFkcuae54k1IFjvO52ffBnPZUTkby45IPHf//4Xv/rVr2A2m6G2aHErCALCw8Px2muv4eabb3ZLkR2xZs0a3H333YiMjMTq1avRr1+/dtcNGDAAW7duRUVFBaKjo1vdV1JS4lrTcj0AlJaWtjmWqqooLS1FbGysKxDSv39/iKLY7vrz7ZGXl4eSkpI2AYn21hORDwsJg2PsFDjGTgFsVkgFudBtz4YubxOEWu81MJUOFEE6UATDZ+9CiekNR1omHOmZUOKTAPHSnvg3OBS8U1iPxbtrcdqNc3QHm3T4XVoYbupjZDCCiKiraGyAVLTD+XOyIAdiuffGVV1IqbF7U4eIoVjXbQhO6S++/0KNTcXykkYsL2lEkE7A5F4GTO0biCm9jC3mvbemqir+vKMWL+2odd22K6Qvfjno/g4HJGLGLkGjdObFbbNNxf0bTmNftQPzUkL9/+esIEANj4AaHgElcWjb+xvrz4QlKo9CrCiHcML5d6HqBATVO13AtCbIDgjHD0M83rZNrioIULtFnQlNtAxQ9IgDgjtnPxLqfOrtiqYvWJTVOrDhmDMQsaHciip3tsy7TIPCdbg6zoDxcQaMjTEg/Bw/V4iIiHxFj0AJdycG4+7EYFhlFZuOW/HNYQu+OWzBoTrvjs873qhg6b4GLN3XAIMEXB1rwLW9jbi2lxG9Qi7r/cRERNSJXNIZf+vWrbjvvvsgSRLmzp2LzMxM9OjRAydOnEB2djY++ugj3H///fjqq68wcuRId9fcxpo1azB79mx069YNq1evRnx8/DnXjh07Flu3bsXatWtx5513trpv7dq1rjXNMjMzXff9+te/brU+NzcX1dXVmDx5sus2o9GI9PR0bNu2DYcOHUKfPn1aPWbdunUwGAxIT09vVdNnn32GtWvX4oorrrhgTUTUSQQYIKeOgZw6BlZFhri/wBmWyN0I8cQxr5UhHj+MgK8/RsDXH0MJ7wY51dlZQh6SCujbfxdsSzZZxdJ99XhlZy0qGt13YbBviIT5qWG4LT4QkujnL9gQEXV1DgfEA0WQ8nOg25MLsWQPBNm7F8LO5ZQuGOtMQ7AmYhjWdBuGA4E93Hr8BoeKLw9a8OVBCwJEYEKcATf1DcQNfYyINJ4Zc3B2OOJStAxHtPTSjloIAOaldqwrnd8KDIbSNxHom4g2330OO4STxyFWNAUmmjpPiJXOEIVgt2tRsdcJqgrh1Ang1AlIRTvb3K8Gh7XfeaJHHFRTpFu7lpF/2X3KjncL6/DPDq5P/M9x3BYfiAeSQjAswvOj905bFfxwzIr15RasL7fiQK1v/IwCgOhA0RmIiDXg6jgjegZzRA4REXVeBknAxJ5GTOxpxEtXqigyO1xhia2VNu+O4pCB745Y8d0RKx5HNYZG6HFdU3eJtCg9R3EQEfkxwWw2X/TPnDvuuAObNm3Cd999hyFDhrS5f8+ePZgyZQrGjh2LTz755LIK/Otf/4rnnnsOb775JmbPnt3m/uZwhMlkwn//+18kJiae93jFxcUYNWoU+vXrh6ysLISHhwMACgsLMWnSJERHR2Pbtm3Q6c5kR2644QZs3rwZy5cvx5QpUwAAdrsdt912G9avX49Vq1Zh3LhxrvXLli3Dww8/jJkzZ+Ldd991vVOr+fZZs2bhrbfecq03m80YPnw49Ho9NmzYgF69egEAjh8/jquvvhoWiwU7d+6EyWS6tC8i0UUKuXd8h9bVvb/eo3X4LVWFeLgU0vZs5yiOsv3alGEMgmPElZDTMuEYfiUQFNLqfllRsby0ES/m1bg1zR0TKOKJlFDckxjM9q/kcTyfEWlEVSEcOwRdQS6kglxIhXkQLA1aVwUAUHV6yIlDUTkgBV+HD8W7lp7IqfL+C2GSAIyNMWBqXyMEAL/98dydphzr2/4e1B7d+A/Pe/8/r+7mf+M2vEFRIJhPnjW6o6kLRWU5hIY6rSv0CWqAAUr3WKgtO09EN/09MgbQ8R15XdHxBhlP/GjG6jLn6MFLOZ9N7WvEK6NMiAlyXzDA4lDxU6WzQ8T6Y1bsOGn36gsy5xOsE5AZE4DxcUaMjzNgsEnn/x2AiDoh07+Pdmid+b6eHq6EyH9UWWR8d8SKbw9bkHXUglq7dj+duxvPjOKYEGdAiJ+O4uB1M+rq6u0Ktp2wIe+kHbtP2XG8QYZdUaEXBcQESRgWoUdqlB5XdA/gSB4/c0kBif79++Omm27CG2+8cc41Dz/8ML766iscOHDgootaunQptmzZAsAZtti5cydGjRqF/v37AwBuvPFG3HTTTdi3bx+uuuoqWK1WzJw5EwkJCW2O1adPnzbBir/85S94/vnn0atXL0ybNg0NDQ1YsWIFGhsbsWLFilZhBwAoKirClClTYLFYMH36dMTGxmLNmjUoKCjAnDlz8Prrr7daL8sybr/9dmRlZSEjIwOZmZk4ePAgVq1ahbi4OGRlZbUZ7/HJJ5/g5z//OSIjI3HLLbdAFEV88cUXqKysxD/+8Q/ccccdF/11JLpUfGLkXcLJ49Bt3wRpezakvTshKN5v36pKOshDUp2jOFLGYFVNCF7Iq0GR2eG2PboZBPx6WCgeSAq+qFnsRJeD5zMi7xFqTjvDEAW5kApyIJ46oXVJLnLvAZCT050fg4YDhsBW9x+uc+C/ZRasLmvElgrvvmupI9wVkDAFCPhxRrRbX2QkAHU1LYITLTpPVJZDNJ/UujqfoIoi1MjoNiM7nGGKWMDI4I4/WlHagMe3mGG2nTmrXur5zBQgYNFo0yWHvBRVxa4qu2tsxpYKKyw+0iRCEoAruge4xmakRwUwSE7UCTAgQeRZNlnFloozozi07O4UIAJXxRpwbS8jru1tRN9Q/wn+8roZdVXNHe4+LW1Eg+PCV4GCdIJXO9yR511SQCImJga//OUv8fvf//6ca5577jksWbIEx48fv+iiHnroIXz88cfnvP+pp57C/PnzsXHjRkydOvW8xxo7diy++uqrNrcvX74cS5YsQVFREfR6PUaOHIkFCxYgLS2t3eMUFxdj4cKF2LhxI+rr6xEfH4+5c+fiwQcfhCi2faHParVi8eLF+OSTT3DkyBGYTCZMmTIFTz/9NGJiYtrdY82aNXj11Vexa9cuAMDw4cPx+OOPY9KkSef9NxK5G58YaaiuGrodW6DLzYaUvw2CzapJGT+FDsDK7lfgy6h07AuKu6xjhegE/HJoCP4vOYTzccnreD4j8iCrBdK+XZDycyDtyYV0qETrilyUblGQkzMgD82APCQNanhEhx9b2Sjj60POsMSGcis68Huyx7krIAE434n9wcTIyy2JOspqgXjimCs40arzxMnjPjNqRmtKeDeo3Xu2Ht8R3RNKj55AaDhHd3Qyqqqec2zQ5Z7PnkoJxbyU0A51VDhY68CGcivWlVvxwzErTlm9H0Q/l8EmHa6OdQYixsYYEMbfk4g6HQYkiLxHVVXsrz4ziuPHShsUDX9PG2LS4dqm7hIZ3QM69ehgXjejrubsDneXwhMd7sj7LikgMXLkSAQHB2PdunXnXDNx4kTU1dVh69atl1UgEXkfnxj5CKsFUkEOdLnZ0O3YDKGuRpMyCoPisCoqHV9GZWBbaDxUoWMX7wwS8ODgEDw2PARRRj5ZIG3wfEbkRooM8eB+V4cIaX8+BIdd66oAOMdGyUmpkJPT4UhOhxrbxy0vqJqtCr45bMGqskasPWrR7N3G7gxIAMDGaT34jgdfIDsgnDrRfueJyqMQrJd+wcafqMagtp0nmkd3RHQHRD7P9DUv5tW0G44A3HM+m5cSinmpYW1uP2WR8cMxG9aXW7D+mBUHNXyn6dliAkVcHWfAhDgjro4zIJYXU4k6PQYkiLRz2qpgzRFnWOL7oxbU2LRLS0QaREzuZcB1vQMxsWfnCz3yuhl1Je11uLtUl9vhjrR3Sb2AZsyYgZdffhm/+MUv8OyzzyI2NtZ13/Hjx/Hcc89hx44deOKJJ9xWKBFRl2MwQk7LhJyWCavsgLg/H7rcjdBtz4Z4ssJrZSQ1lCPpUDmeOrQa5QEmrIpKx6qoDKwzDYFdbPtjRBKAOQOD8NsRYegZzAt/RESdmVBZ7gzrFeRC2pMHoV6bsN7ZVFGEMmAIHMkZkJPTocQnATr3tzk1GUTMSgjCrIQg1NkVZB21YtXBRnx72II6X2gtcYn+WViHxWO7aV0GSTqo3WMhd48FkNH6PlWFUHMaQkXL4ESLLhS1Zi0q1oRgaYB0qBg4VNzmPlWnhxoV027nCTUqBggwaFBx1/ZZacM5wxHu8ucdtUgM1+GGPoH4qdI5MmN9uRU7q+w+MyIpRCcgs6lDxPg4AwaF6zrU9YKIiIgurJtBxG0DgnDbgCDYFRU/VtjwbVN3ieIa940L7ogqq4L/lDTiPyWN0IvAmGgDrmvqLtE/zH9GcRB1ZufrcHepzDYV9284jX3Vjg53uCPf0uEOEhEREZg3bx6efPJJNDY24uabb0ZOTg4CAgIQHx+P7t2748SJEygtLYXNZkN6ejpWr16NwMDACx+ciHwKk6M+TlUhHiqGbns2pO3ZmrU0r5YC8XVkCr6MysC3EcNRpwvCbfGBmJcahnj+AkA+guczootUVwOpcDt0+bmQCnIhnijXuiIXJbYPHEMznKMzBo8AAoM1q8XiULH+mAWryyz4+lAjTls9+5KcuztI6EXgiRGhMEoCRAGQBAGSAEii8+/O287crhMBsXlN8+2i8+9tbhcAUQB0Ytv1rfYSWjy+6Vi6Fo/nxYULaKxvFZoQK8ohnGjqPFF1AoLqO6MEtKIKAtRuUa07T/Q4M8YDwaFal+h3jjfIGPVFxXnfkeWu85mu6Vxh85FvdZ0AXNEjwDU2I717APSduN02EV0YO0gQ+abiaju+OWzBt4ct2FJh03Rk4qDwM6M4RvYIgM4Hnxvwuhl1BefrcOcO5+pwR76tw69gqaoKVXX+NAkMDMRXX32FxYsX46OPPkJRURGKiooAAP369cOdd96JRx99FAYD361BROR2ggClbyJsfROBGfdBOHEMuu3Zzs4Se3d77YJ4uNyIOyu34M7KLbCLOtQPSoWx3zjIyhio4GxzIqJOwW6DtD/fNTZDPLgPguob779VwrtBHpIOOdn5oUb00LokF6NOwHW9A3Fd70DYFRM2H7didZkF/y1rxPFGH3m17jzsCvBCnmff4X25xBYhipbBi5bhjOYwh04AJPGsNRcKZ5wd4HAFNtre1zIwomsvGHKBvc/eQxLbhlDODos0B0ba1tNckwFSZH9I3eMhDT1rP4cN+qoK6E+UQ3eiHOKJ8jNdKE6UQ7D7xmgcTxNUFcKpE8CpE5CKdra5Xw0Oa7/zRI84qKZIt4zp6Wqe+NE97Wo7wqECWreLSDLpcHVTh4ixMQaE6jtXS20iIiJ/lBCux8Phejw8NBRmq4K1R52dJb47YvHa85Rme6sd2Ftdh9fz69DNIGByTyOu7W3EpJ5GmAx83kDkDd7scMdxG51LhztIdOvWDfPmzcNTTz3V5r7a2lrU1tYiNDQUoaF8FwZRZ8fkaCdWa4Yub4uzu0T+Ngh2m9dLUAXB2fY8LROO9KugxvTyeg1EzXg+IzqLokA8UgopPwfSnlxIe3dBsFm1rgoAoAYYIA8aAXloBuQh6VB6x3e6FygVVcW2ShtWl1mwqqwRh+pktxzX3R0kqGsR0BzMAHRQ0ct2GgMaKxDfWIn4xgr0a6hAv8ZK9Ks/jjBHg9bl+gRFb4DcPRZyjzio3eOgxjiDE4juCTUyxiMjfTq7XVU2jFt14oLrOvP5LDZIxPg4I8bHGXB1rAExQRwnSNSVsYMEUefiUFRsrTwzimNvtXdHcbQkCcDo6ADXKI6EcL1mtfC6GfmzjnS4cxdTgIAfZ0Tzd4QO2rhxI6ZOnYqnnnoK8+fP16QGt/xWz2AEEZGPCDXBMe56OMZdD1gbYcnbirJ16xFfvBXdHPVeKUFQVUjFBZCKC2BY/g/Icf0gp2fCkZYJpf+gTvdiFxFRZyecqoSU7+wQIe3ZDrHmtNYlAQBUQYTSf5CrQ4SckAzoA7Qu67KIgoArow24MtqAhVeEYdcpO1YftGB1WaOmF+Coa1PhfLe9QwasELBXisDekAggJOmshSoiHHUY0BScGNBYgXhLpStM0dPmG+cObxDtVojlB6EvP9jmPocg4rAhEgeConEwMBoHg6NRFhSNw8HROBwUDZs+EKLYuuPHuTqgtNfRpL1xNi27k+jO04VEEoQWe7fujHJm7E3Les6/98V0TPnrrjrv/4fysFC9gMwYZ4eI8XEGDAzXcfwPERFRJ6UTBYyJMWBMjAHPXRGOAzUO5yiOIxZsOm6F3YuNAGUVyD5uQ/ZxG57eVoOEsDOjOEZF++aYrnq7gmB2y6JOxpsd7sw2FU/8aMYHEz3TWbusrAwjRow4fw1ms0f29lcXFZDgL4JERJ1DvV3BP/Y68FpJIqpjEqDrMReZ1Xsx7WQOpp3MRR9rlddqkcoPQio/iIDVy6B0i4IjLRNyeibkQSl89x0RkSc01kMqzINUkAtdQQ7EY4e1rshF6REHOTkDjuR0yEPSgGD/DVkLgoARkQEYERmAp9PDsNdsx+oyZ1hiZ1XXGHFAnYwg4JQ+FKf0odgWNqDN3YGy1dl1wlLRIkTh/Lyf5ST0qns6pvg6naqgv+UE+ltOAMhvc/9xfThKA3ugJDDa9VFqdH5+Uh/KsHAHBckWNEhGr+6pE4AregQ4AxGxBqR1980XKIiIiOjy9Q/T4aHkEDyUHIIam4J15VbnKI7DFlRZvTs2sbjGgeKCOrxZUIfwAAHXNI3imNzLiG4eGsWx+5Qd7xbW4Z8dXJ/4n+O4LT4QDySFYFiEdh0viDpqV5Wzu6c3rS6zYPcpu0f/H+nfvz9uv/12jx2/K7moV6b+/ve/48MPO97mUBAE7Nix42JrIiKiS2SVVby3tx6LdtWissUMdIeow/puyVjfLRm/TpiD1LqDmHYyFzefzMHweu+9cCaePomArJVA1kqoQcFwjBgNR3om5GEjASNndBERXRKHA2LpHujycyEV5EIs3QNB8e4FnXNRg8PgGJLmHJuRnA61e6zWJWlmkEmPQSY9fjsiFAdrHZj6v5M4XN81XlAm/9AoGVAQ0hsFIb3b3CcpMvpYq5q6TVRggMUZoIhvrERCYwWCFd8Y5eMNMfZqxNirMaZmf5v7aiRji8BEDEoCe6A0MBolgT1wxBAJRfDfd+UNryvDQ0e/7/D6Y5t+iY+ix2BJz8nYFdLXY3UNMelwdZwBE+KMGBMTgBC+M5KIiKjLCQsQMa1fIKb1C4SsqMg9acM3TaM49pz2bifAapuKFQcaseJAI0QBuLLHmVEc7uhmdbxBxhM/ml0vHHc0INHgUPH+vga8v68BU/sa8cooE0cJkE/7Z5F3umm32bewDovHdvPY8ePj4zUbSeFvLiogUV1djerqak/VQkREl8ihqPhPSQP+nFeLIxd6sUUQkBfaH3mh/fGH/rcivrECN5/MxfSTORhTvQ8ivNN2Smioh37LGui3rIGq10Meku7sLpE6Bmp4hFdqICLqlFQVQnkZdAVNYzOKdkCwNGpdFQBA1ekhDxzWNDYjA0rfBEDkRZOz9QvVoXeIxIAE+Q1ZlHAgsAcOBPYAMKz1naqKaFs1BlicgYnmkR0DLM4xHt3ttZrUrIUw2YLUujKk1pW1uc8q6HDQ2B2lgT1QHBiN0hadJw4Yu8Mqdc4RRDHW03h9//u45eS2i3pcsGLFg8fW4cFj6/B51BX4VeK9OG64/AuNcUEixscZMT7OgHGxBl7YJyIiolYkUcDIHgaM7GHA79PDUVbrwHdHnGGJjcessHnxvQiKCmypsGFLhQ3P5tSgX6jkCkuMiTYgQLq4sMSK0gY8vuXyRw6sLrNg47EKLBptwsx4vuGNfE+9XcGnpdpcJ1te2ogXrgxHkE674HV+fj5effVVbNq0CadOnUJ0dDSuv/56zJ8/HxERZ153aR7bceedd+Kxxx7D73//e2zZsgWCIOCaa67Byy+/jMjISOTk5OD5559Hbm4uRFHEtGnT8OKLLyI4ONh1LJvNhn//+9/49ttvsXfvXpw4cQJhYWEYNWoUnnjiiQuOB2npxIkTePXVV/HNN9/g6NGjCAkJwdixYzF//nwMGTLEbV+niwpIzJs3D0899ZTbNiciosujqCpWHbTgT3k12H+Js81LA6OxuPcNWNz7BnS3VeOmqjzcdioXE0/thk72TgtwwW6HbueP0O38Eep7ApSEZDjSr4IjbSzU6F5eqYGIyJcJ5ipIBbmQ9jR1iTh9UuuSXOQ+Cc4OEUPSIQ8cBhi82xK9s+KLctRlCAIqDCZUGEzYHD6ozd2hjoYzIzssZ0Z3DGg8jt7WU14L72rNoDowqPEYBjUea3OfAgFHDd1QaoxuCk84gxOlgdEoMfZAtT64nSNq7/aKLfjb/n8jwnF579665eQ2jDfvwcOJ92F59OiLemyYXkBmrHNkxvg4AxLd8M5LIiIi6jr6hurwYFIIHkwKQZ3dOYrj28MWfHvYghMW73ZuPFgr46099XhrTz1C9QIm9jTgut6BmNzLgCjjuX+/VFUVf95Ri5d2uC+YbLapuH/DaeyrdmBeSiifX5FP2XbChgaHNr9HNjhUbK20YXycNtfGvv76a9x3332QJAnXX389evbsib179+Kdd97B2rVrkZWVBZPJ1OoxZWVlmDJlClJTUzFnzhzk5eVhxYoVOHr0KP7whz9gxowZGD9+PO69915kZ2dj6dKlAIDXX3/ddYzTp09j/vz5GD16NCZPngyTyYSDBw/if//7H9asWYOvv/4aaWlpF6z/wIEDuOmmm1BeXo6JEyfixhtvxIkTJ7B69WqsXbsWX375JTIyMtzyteLwdyKiTkhVVaw5asXC3BrsOuW+EIM12ITYK6diWPKdsMgWSLu3Qbc9G7odWyA01Lltn/MRVBXS/nxI+/Nh+M8SyL36Q07LhCMtE0q/gZzbTERdg7URUtEuZ4eIglxIR0q1rshFiejhGpkhD0mDGua51oH+bFiEHp8f8I3OH0RaqtUFYUdoP+wI7dfmvgDFjn6WE64ARcvOE/0bT8CoeifMqzURKnpbT6G39RSuri5sc3+VLqRV54kSY/PojmgcCzB5//mzquL3Bz/H78s+d9shIxz1+KjwbxjcUI4/9rvlnP8mvQhc0T0A45vGZqRG6aET+fsDERERXb4QvYipfQMxtW8gFFVF3kk7/tcUltjtxuuzHVFrV/HlQQu+PGiBAGBkjwBc29RdIsnUOhDq7nBESy/tqIUAYF5qmEeOT3Qp8k5q+3vijpN2jwUkSktL8eKLL7a5/ZprrsGAAQPwi1/8AlFRUfjmm2/Qu/eZ8ZifffYZHnjgAfzpT3/CK6+80uqxmzdvxosvvoiHHnoIgPO1pzvuuAPfffcd7rjjDrz77ru48cYbAQB2ux3jx4/Hxx9/jKeffho9evQAAJhMJuTn5yMuLq7VsQsLCzF58mT88Y9/xMqVKy/47/vFL36BiooKfP7555gwYYLr9ieeeAITJkzAr371K2zevLljX6wLYECCiKiT2XTciue312BLhc1txwyUBPwsKRiPDgtBRHPiWB8E+YqrIV9xNawOB6S9OyHlboRue7ZX37ksHTkA6cgBBKz6AEpEDzjSxkJOy4Q8aASg448xIvITigzxwF5IBbnQFeRA3F8AQfburNNzUQODISelQk5Oh2NohrOzD8Nqly01Sq91CUQ+zybqsS8oDvuC4trcJ6gKelpPO0d2WJpHdzjDEwMaK2GSGzSoWBuRjjpE1tbhitq2YboGMcA1quPszhNlxig4RPc/n3Z3OKLVscs+hyoAC/vNdN0WKAH/b3AIxscZMDo6ACF67drZEhERUdcgCgLSuwcgvXsAnk4Lw5E6B747YsU3hxux4ZgVVi9OU1QB/FRpw0+VNvwxtwZ9QiRXWKKyUfZYOKLZn3fUIjFcx3Eb5DO8HVg6mzvf0Hq2AwcO4KWXXmpze3h4OLZu3Yqamhq88sorrcIRAHDrrbfijTfewOeff94mINGvXz/8/Oc/d30uCAJuueUWfPfddxg+fLgrHAEAer0e06ZNwwsvvIC9e/e6AhIGg6FNOAIAkpKSkJmZibVr18Jut0OvP/e1sJ07d+Knn37CPffc0yocAQAJCQmYM2cO3nzzTezZs8ctozb4yhIRUSex46QNC7fXIOuo1W3H1AnA3EHBeHxEKGLP1+pbp2uaJ58O2z2PQjy4F7rcbEjbsyEdPei2ei5EPFWJgDVfAGu+gBocCseI0XCkZ0IedgVgCPRaHUREl01VIVQehZSfC92eXEh7tnutU8+FqJIEZUAyHE1dIpT+gwCJvza42xXdAxCkEzRp+6gTgJ8lBUMUBMiqCofqnG8rKypkFU0fqvO2pr/LivPviupc42ixXmle41oPKGcdq3mdowN7dI2BCnS5VEHEEWMkjhgjsQFnXRxRVUQ46s6M7mgKUSQ0hSjibGZNatZCkGLD0IYjGNpwpM19Dog4ZIxEaWDT6A5jNEoCm7tP9ECDdPHverqjYrPHwhHNnj34OfYGxrnGbaR1D8CfRoZ7dE8iIiKi8+kVosP/G6zD/xscjHq7gg3HzoziON7o3VEch+pkvFNYj3cKL2/M2cV4fIsZY2MMHCVJPuF4gxcTSu2oaPTc/pMmTcKKFSvave++++4DAOTk5KC0tG143mq1oqqqClVVVYiMjHTdPnToUIhi65B5TEwMAGDYsGFtjtN837FjrcdD7tq1C6+//jp+/PFHVFRUwG5vHRSpqqpyPbY9OTk5AIDKysp2u2Ts37/f9ScDEkREXcBesx1/2l6DVWUWtx1TAHDHgEDMSw1Dv9CL/FEgCFD6D4at/2Dg1gcgHD/iHMOxPRticQEE1Tsvawj1tdBv/g76zd9B1QdATs6AIz0TjpQxQJjJKzUQEV2UumroCra7xmaIJ49rXZGLHNcP8tB0yMkZzg49gXznh6cF60XcFh+I9/d5/13udycG4YUrTV7ft6NUtW24whmeaHH7ecIZsuIMXjharT8T7jg7tHF2MKTd/VqGOVqsb3lMx7lqUloGQVrfpyjOgEqrPTq499m1Ku0dv6umTQQBp/ShOKUPxbawAW3uDpStiG+sRLylwhWiSGga4dHXehJ6VdsLat6ig4J4ywnEW07gmtP5be4/rg9vGt0Rc6b7RFM3ipP60DbdhGKsp/HG/ve8Uvvf9v8bP5gG47ihG6IDeSGeiIiIfEewXsQNfQJxQx/nKI5dVWdGceyo8s8RcWabiid+NOODiZEXXkzkYXaNfxG2ydrsf/r0aQDAO++8c9519fX1rQISoaGhbdZIknTB+1oGIH766SfcfPPNAIAJEyZg2rRpCA4OhiAI+Oqrr5Cfnw+r9fxv/G2u/9tvv8W333573vrdocOvijUXRkRE3nGw1oGXdtTik5IGt17cntrXiAWpYUjq5p7W3mpML9hvmAX7DbMgmKsg7dgC3fZsSAW5EBzeedIv2G3Q7dgM3Y7NUAURSuJQONKvgiNtLNQebVs7ERF5hc0Kaf9uSAW5kPJzIR7a77UQ2YUo4RFNnYGcXSLUblFal9QlPZAUoklA4v6kEK/veTEEQYBOaP5lleNcLoeqtg5nOJo6dShnh0ouEM5wKGcHL9oPkihnBUNc3UladAiR1fYDI0rTsRwt1rcX+jhnrefoWtJ+kEQHi9oPu9V+yDvr6wHZgdjGk+hbX4m+DRXo11iB+IYK9G8a3RGsuK+bm6+LsVcjxl6NMTX729xXIxlREhjdFJpwdpy4uyIbEQ7vvFMxwlGP1/e/j9uHPobhERxZRERERL5JFASkRAUgJSoA81PDcKxBxneHLfjmsAXry61o1OiFVE9YXWbB7lN2DONzM9KYXtT2OkKApM3+zWGGzZs3u6XDwsVYtGgRrFYrvvnmG4waNarVfTk5OcjPbxvIP1tz/S+//DJ+9rOfeaTOlthBgojIxxxvkLFoZy3e21cPuxs7sE3qacDTaWFIjQpw30HPopoi4Rh/ExzjbwIaGyDt3ursLrFjC4RG71wsFVQF0r5dkPbtguHjNyH3HgA5LROO9EwofRLavNONiMhtFAXi4RJI+c4OEdK+XRDsNq2rAgCoBiPkwSmucUlKz/48H/qAYRF6TO1rxGo3dom6kKl9jbxg1YUIggBJAM68v57/33dM73ZvlRUF5urTUI8fhVBZDrHyKKTKcuhOOD+kumov16mdMNmC1LoypNaVaVbDLSe3YXhdGVKi0jSrgYiIiOhixAZJuHdQMO4dFIxGh4ofWoziOKrxWAB3+GdhHRaP7aZ1GdTFaT3qRasOdxkZGVi9ejW2bdvm9YDEgQMH0K1btzbhiIaGBuzcubNDx8jIyAAAbNu2jQEJIqKu5LRVwWu7a/GPPfVuTQ9f2SMAz6SHITPG4LZjdkhgEOSR4yGPHA+rww6pcIezs8T2TRDNJ71WhnS4BNLhEgR8+T6UyGg40jIhp2dCHjgMkPhjkIguj3DyuDMMUZAD3Z7tEGp948UxVRChxA9yjh9KzoCSMATQ8UVxX/TKKBM2HquA2eb5dw6ZAgS8Msrk8X2I/JUoihC7RQLdIoGk4QAAFYC96QON9RAryyFUHoVYWQ6xojlIUQ7hVKXPdBHyJw8fW4ORPUZdeCERERGRjwnUCbi2txHX9jZCVVXsPmXHN01hidyTnXMUx/LSRrxwZTiCdKLWpVAXNixCj88PNGq2v1Yd7mbPno2//OUvWLhwIUaOHImkpKRW9zc0NKCgoABXXHGF2/fu3bs3iouLUVhY6NpXlmU888wzOHmyY68FpaenIyMjA5999hmuv/563HLLLa3uVxQFmzdvRmZmpltq5itDREQaq7UreKugDm/k16HG7r6LpsMi9HgmLQyTexkgaP0uYZ0e8rArIA+7ArjnUYgH9jo7S2zPhljuvXediVUVCPh+BfD9CqjBYXCkjnYGJoZeARiMXquDiDqx+lpIhTsg7cmFLj8HYsURrStyUaJ7wTE0A/KQdMhJKUBw2zmB5HtigiQsGm3C/Rs8P9Jw0WiT5u+kIPJrgcFQ+iYCfRPR5v1/dhuEk8edwYnKcggVRyE2BSmEk8cg2DvnRXCtza7YBLtsA3R8Lk9ERESdlyAIGB4ZgOGRAXgyJQwVDTK+O+IMS6wrt6Le0TmCtg0OFVsrbRgfx+dmpJ3UKG3fIJSi0f5RUVF49913MXfuXGRmZuKaa65BYmIirFYrDh06hM2bN2PkyJFYsWKF2/f+2c9+hrVr1+K6667DjBkzYDAYkJ2djWPHjiEzMxPZ2dkdOs67776LqVOn4v/9v/+HJUuWICUlBQaDAUeOHMG2bdtw8uRJVFRUuKVmBiSIiDRicaj45956vLqzFlVW983SSAjT4XdpoZjWLxCi1sGI9ogilAFJsA1Igu22ByEcOwTd9k3Q5W6EVLLHa2UI9TXQZ38Lffa3UAMMkIdmwJGWCUfKaCDU5LU6iMjHOewQi/dAV+AcmyGWFkFQ3Tj/6DKoIWFwJKdDTs6AnJwONSpG65LoEs2MD8K+agde2lHrsT3mpYRiZnyQx45PRBegD4Aa2wdybJ+24QlFgXD6JMTKpo4TLTpPiCeOQmjwzqi6zsjgsEIpzoecnKF1KURERERuEx0k4Z6BwbhnYDAsDhWbKqz45pAF/ztswZF63x7FseOknQEJ0tQV3QMQpBPQoEGwKEgnYGQPz404v5Brr70WP/zwA15//XWsX78e69atQ1BQEOLi4nDXXXfhjjvu8Mi+1113Hd5//328+uqrWL58OQIDAzFu3Dh8+OGHeOmllzp8nH79+mHjxo3429/+hq+//hrLli2DJEmIjo7GmDFjcPPNN7utZsFsNneO6BkReU3IveM7tK7u/fUercNf2RUVH+1vwMs7at06W65XsISnUkJxZ0IQdKIPBiM6QDBXQcrbBF1uNqQ92yHIDq/XoAoi5EHDIadnwpE6Fmr3WK/XQO7D8xldNFWFePQgpKZAhFS0A4LVonVVAABVr4c8cLgzEDE0A0rvAYDItpX+QlVVvLSjFn9uJyThWD+7Q8fQjf+w3dvnpYTiqZRQ7TtKEdHFU1WgvgZiRXmL8R1NnScqyyGaq7SuUHPW238G+413aV0GEXUxpn8f7dA68309PVwJEXUlqqpiz2kHvj1iwTeHLNh2wobLeYHvcn/XbM8t/QPxr/ERl1oSkVs8uuk03t/X4PV95w4MwuKx3by+L108dpAgIvISRVXx+YFGvLC9BqW17gtGdDeKeHxEKO4bFAyD1Llf+FBNkXBMuBmOCTcDjfXQ7foJUm42dDt/hGDxzhMaQVWgK9oBXdEOGD78G+Q+Cc4xHOmZzhcj+eISkd8RTp90hiEKciHtyfWZF5tUQYDSJxFycjrkoemQE4cBAQatyyIPEQQB81LDkBiuw+NbzDDbLj/HbgoQsGi0iZ0jiDozQQBCwqGEhEMZkNT2fqsF4onmkR3lZ4ITlUchnDwOQfGNrkeepNv2AxwjJzDYTERe1dEXFevuW+/ZQoioSxEEAckReiRH6PGb4aE4aZHx3WELvj1iwdqjVtS6cXzzpapo9O0OF9Q1PJAUoklA4v6kEK/vSZeGAQkiIg9TVRXfHLbg+e01KDjtvo4I4QECHh0Wip8lBSNE74fvIA4MhuPKiXBcORFWuw1SYR5027Mhbd8EsfqU18qQDhVDOlQMrHwPSlSMcwxHeiaUxKGAxB+jRJ2SpQFS0c4zXSKOHtS6IhclKrppZEYGHENSOfKnC5oZH4SxMQY88aMZq8suvXvJ1L5GvDLKhJggyY3VEZHPMRih9IoHesW3Hd0hOyBUVbYY2dGi80RlOQSbb3RIulzSgSIE//ZO58/QwSmuDwYmiIiIyN9FGSXclRiMuxKDYZNVbK6w4mc/nEZlo3YhWZusfUiDaFiEHlP7Gi/rusrFmtrXiGEReq/tR5eHr+wQEXnQD8esWJhbjW0n7G47ZpBOwC+GBONXQ0NhMvhhMKI9+gDIw6+EPPxKYM6vIZYWQrc9G7rcbIjHD3utDPHkcQR89xkCvvsMamg4HCljnN0lhmbwXd1Evkx2QDywF1JBLnT5ORBLCiDIvvGOBjUoGHJSGhzJGZCHpkPt0ZOdaggxQRI+mBiJ3afs+GdhHbC+Y48L0gm4PT4Q9yeF8JdyIgIkHdQecZB7xLW9T1UhVJ9qGtnRuvOEWFkOobba+/VeJvFkBcTsb6HP/hYAzgQmklKdgYmoGI0rJCIiIvKcAEnA+DgjEsJ0qGy0aVoHkS94ZZQJG49VuKVD54WYAgS8Msrk8X3IfRiQICLygNwTNizcXoP15Va3HTNABO4bFIzfDA9FdFd+N6goQklIhi0hGbbbfw6hvMwZltieDamk0GtlCLXV0G/8H/Qb/wc1wAh52BXO7hIpo4GQMK/VQUTtUFUIFUegy2/qEFGUB6GhXuuqAACqpIOSmOwMRCSnQ+k3kN1o6JyGReidsyvf7tj64jtjEKTrIuFJIro8ggDVFAnVFAll4PC29zfUQTxxrGl0x1mdJ05VQlB9/52BbQMTMU2BiRQGJoiIiMhvad1FMDqwC1+3Jp8SEyRh0WgT7t9w2uN7LRrNDp6dDa/GEhG50Z7Tdvxpew2+OuS+1k2iANyVEIQnU0LRJ4Sn7bOpcX1hj+sL+02zIZw6ASlvszMsUbjda+8QF2wW6HI3Qpe7EaooOi+8pmXCkTYWamS0V2og6vJqzNDtyXUGIgpyIVZVaF2Ri9yrv2tshjxoGGAM0rok8lMMRxCR2wSFQOmbCPRNbDu6w26DcPI4xApncEK35gtIFUe0qPKiiCePQ8z+BvrsbwC0DEykQk5K4fN2IiIi8gvDIvT4/ECjZvsPZzdD8iEz44Owr9qBl3bUemyPeSmhmBnPa32dDV9pIyJygwM1Dry4owafljTCne+lmtEvEPNTQzHQxCeWHaFGdIdj0jQ4Jk0D6muh27UV0vZs6Hb9CMHinV8MBEWBbs926PZsh2HZ65D7DoQjPRNyWiaUXv3ZOp/IXWxWSPt2QyrIgZSfA+lQsdYVuSimKMjJ6a4P1RSpdUlERETuow+AGtsHcmwfZ3jCZoH06TtaV3XR2gQmusc6AxNNXSYYmCAiIqLOKDVK2+vIKRrvT3S2eSmhEAD82QMhiXkpoXgqJdTtxyXPY0CCiOgylNfLeGVnDT7Y1wCHG5MRU3oZ8Lu0MIyIDHDfQbua4FA4Rk+CY/QkWG1WSIV50OVmQ8rbBLHG8221mkll+yCV7QM+/xeUHnHOMRzpmVASkgGRbbeIOkxRIJbtdwYiCnIh7d8NwW7XuioAgGoMdL6YkpwOR3IG1Li+DEMREVGXofQfpHUJbiGeOAbxxDHoN/4PQIvARFKqcyRHZA+NKyQiIiK6sCu6ByBIJ6DBnRerOyhIJ2BkD17PJt8iCALmpYYhMVyHx7eYYbad+X8jSLZgdHUx0mtLMaKuDLE2MwJUB2yCDscCTNgZ0he5ofHYEp6ABsnoepwpQMCi0SZ2jujEGJAgIroEVRYZf91Vh3eL6mBx4xSH0dEB+H16GEZHG9x3UAICDJBHjII8YhSg/BpiSSF027Ohy90IseKo18oQK8sR8M1yBHyzHEqoCXLqGGd3iSHpQAD/mxOdTThxzBmGyM+BrnA7hLoarUsCAKiiCCU+CXJyBhxD06HEDwF0fFpNRERdk5yQDDXACMHmvjGDvqBtYCIOclKKq8sEAxNERETki4L1Im6LD8T7+xq8vvft8YEc/Ug+a2Z8EMbGGPDEj2aUFezFQ0e/x10VmxGsWM/5mDtO/AgAqBcN+Ch6DJb0nIy+yYPwyigTYoL45sfOjFdyiYguQo1NwZsFdfh7QR1q7e5L4Y6I1OP36WGYGGeAwHcde5YoQUkcClviUNhu/zmE8jLocjdCtz0b0oG93iuj1gzxh6+h/+FrqAYj5GEjnd0lUkYDwWzLRV1UfS2kPduhK8iFVJADsbJc64pclNjecAxJhzw0A/LgFCAoROuSiIiIfIMhEI7R10C/4b9aV+JR4olyiCfKof/hawCA0iOu9UiOCAYmiIiIyDc8kBSiSUDi/iReKyHfFmsz49OC16DL+eGiHhesWPHgsXV48Ng6ODAO1rRHoQZxpG5nxoAEEVEHNDpUvFtYh7/ursMpq+K24w4K12FBWhhu7mtkMEILggC1Zz/Ye/aD/eZ7IFRVQpe3CdL2bEhFOyDIbmwPcr4yrBbocn6ALucHqJLkvMialglH2lheaCX/ZrdBKi5wdokoyIV4YC8E1X3n2MuhhJogJ6e7PjiHnIiI6Nzs10z3+4DE2cTKcoiVDEwQERGR7xkWocfUvkasLvNeh6+pfY0YFqH32n5EF0v3YxYMSxdDqK+9vOPk/ACpMA/WOY/BMWqSm6ojb2NAgojoPGyyimX7G/DKzhoca3Dfi3Z9QiTMTw3D7fGBkEQGI3yFGtkD9mtmwH7NDKC+FrqdPzo7S+z6CYLVO79QCLIMXUEudAW5MHzwGuT+g+BIy4SclgmlZz+AQRrqzFQV4pEDkApyIOXnQNq7y2facav6AMiDRrgCEUrvAYDItpBEREQdofRJgCNj3EW/E+tyyL0HQO0RB6loJ4R67cdwtQlMRPc8E5gYnAI1orvGFRIREVFX8sooEzYeq4DZ5r4uyOdiChDwyiiTx/chuiSqioCV7yFg5ftuO6RQXwvjkoWwHTsE2/S5vGbfCTEgQUTUDllR8dmBRryYV4ODte7rIhAdKOKJEaGYMzAYARJ/aPq04FA4xkyGY8xkwGaFtCcXutxsSHmbIdaavVaGdGCvc/THin9Cie4JR/pVcKRlQhkwhC/eUqcgnDoBaU+uMxCxJxdi9WmtSwIAqIIApd9AyM1jMxKSgQCD1mURERF1WtZ7HoVUmHfZ78jqCDU4FJbfvgzVFAkoCsQjpZCKdkAq3AFp706v1HAhYsVRiBVHod/wFQAGJoiIiMi7YoIkLBptwv0bPH8dZtFoE2KCJI/vQ3Qp3B2OaH3s96FCgH3GXI8cnzyHAQkiohZUVcV/D1nwwvYaFJodbjuuKUDAr4eH4sGkYATp+KJ2pxNggJwyBnLKGECRIRYXQJebDV1uNsQT5V4rQ6w4ioCv/4OAr/8DJbwb5JSxcKRnQk5K5Qu75DsaG5wvUBTkQFeQC7G8TOuKXJTusZCTM+BIToc8JBUICde6JCIiIr+hmiJhnfMYjEsWenwv65zHnOEIABBFKH0SoPRJgH3KrZ0oMNHLNY5DHjSCgQkiIiJyu5nxQdhX7cBLOzz3XGheSihmxgd57PhEl0O3Jctj4YhmhpXvQY3tzXEbnQwDEkREcAYj1pdbsXB7DbaftLvtuME6Ab9MDsHDQ0MQHsBghF8QJSgDh8M2cDhssx5yjgvY7gxLSGX7vFdG9WmIG/4L/Yb/QjUGwjHsSsjpV8Ex4kogKMRrdRDB4YB4oAhSfg50BTkQSwshyO7rvHM51OBQyEmpcAzNgJycAbVHnNYlERER+TXHqEmwHTvk0YuQ1ulzz3/xsb3AxOESZ2CiaIdzJEdDncfq6yix4gjEiiPQb/gvgLMCE4NToHaL0rhCIiIi8gfzUkIhAPizB0IS81JC8VRKqNuPS+QOgrkKhg8We2Uvw9LFzufwzSHuTu7FF1/ESy+9hNWrV+Oqq67SuhyPYECCiLq8rZVW/DG3BtnHbW47pkEC7h8cjF8PC0X3QLYX81uCAKV3PJTe8bBPmwOhqgK67Zsgbc+GVLQDgqJ4pwxLI/Tb1kO/bT1USed8QTgtE3LqGL4TjdxPVSEcOwRdQS6kglxnK21Lg9ZVAQBUnR5y4lDIyemQkzOg9EsERJ6DiYiIvMk2fS5UCDCsfM/tx7ZOnwv79Hsv7kGiCKVvIpS+ibBfe5uzI9zhUt8PTMT0bjGSYwQDE0RERHRJBEHAvNQwJIbr8PgWM8w29bKPaQoQsGi0iZ0jyKcZPnjNa53khPpaGD54DZZH/uiR45eVlWHEiBEAgNjYWOTn50OS2l7zLCgowNixYwEAiYmJ2LZtm0fq8QcMSBBRl7X7lB3Pb6/Bt4ctbjumJAB3JwbhiRGh6BXCU2xXo0ZGwz75Ftgn3wLU1UC380focjdC2r0Vgs3qlRoE2QFd/jbo8rcBS/8KOT4JjrRMONIzocb19UoN5H+E6lOQ9myHlJ8DaU8uxFMntC7JRe49wBmIGJoBeeAwwBCodUlERERdm+CcwavG9oZh6WK3XJRUg0NhnfOYe9rWilL7gYnCpsDE3h0QGuovf5/LLfP4YYjHD0O/fjWAFoGJ5g4TfvLuNCIiIvKOmfFBGBtjwBM/mrG67NKvh0/ta8Qro0yICeIbUsh3iWX7ocv5wat76nJ+gHioGEqfBM/todPh2LFjyMrKwpQpU9rc/8EHH0Cn08HhcN/4eH/lk6/effLJJ9iyZQt27NiBPXv2wGaz4c0338Ts2bPbrN21axdWrlyJHTt2YOfOnaiqqsLYsWPx1VdfnXePTz/9FEuWLEFRURH0ej1GjhyJBQsWIDU1td31JSUlWLhwITZu3Ij6+nrEx8dj7ty5eOCBByCKbdvmW61WLF68GMuXL8eRI0dgMpkwZcoUPP3004iOjm53j6ysLLz66qvYtWsXVFXFiBEj8Jvf/AaTJnFuDZE7FVfb8WJeLVYcaHTrcW+ND8T8lDAMCPfJUyt5W0gYHGOnwDF2CmC1QCrIhW57NnR5myDU1XitDKm0EFJpIQyfvQMltrczLJGWCSU+CWjn5xcRAOf37L5dzkBEQS6kwyVaV+SidIuCnJzhDEQMSYMaHqF1SURERNQOx6hJkAenwPDBa5d1cdKRMQ7Wex71XCCgZWDiupaBiTxnaGLfTt8MTMS27DDBwAQRERFdWEyQhA8mRmL3KTv+WVgHrO/Y44J0Am6PD8T9SSEYFqH3aI1E7qDP+lKzfa33Pe6x448cORL5+flYtmxZm4CEzWbDp59+ismTJ+N///ufx2rwFz75Kt7zzz+Pw4cPIzIyEtHR0Th8+PA513711Vd49dVXERAQgISEBFRVVV3w+IsWLcLChQvRq1cv3Hfffaivr8fnn3+Oa6+9FitWrGgzT6WoqAhTpkxBY2MjZsyYgdjYWHz//fd48sknUVBQgNdee63VekVRcNdddyErKwsZGRmYOnUqDhw4gA8//BDr1q1DVlZWm5DE8uXL8bOf/QyRkZGYNWsWBEHAypUrMXPmTLz99tu4/fbbL+IrSETtOVLnwMs7a/Hh/gbIl99JzOW63kY8nRaGoXxySOdiMEJOGws5bSyssgPi/gJnWCJ3I8STx71WhnjsMAK++hgBX30MJTwCctpY5yiOpFRAH+C1OsgHKTLEg/udYYiCHEj78yE47FpXBQBQjUGQk1IhJ6fDkZwONbYPIAhal0VEREQdoJoiYXnkjxAPFUOf9aXrxf0LPi7ACMeYybBPmubRd2C1q1Vg4nbn86RDJc7uEr4UmDh2GOKxw9CvOyswkZQKedAIBiaIiIjonIZF6LF4bDfg7Y6tL74zBkE6vtGKOglrI3Rb1miytW7z97De9X+AweiR4wcGBuKWW27BRx99hKqqKkRGnnnO/7///Q9VVVWYPXt2m4DEsWPH8O9//xtr167FwYMHUVNTg+joaEyZMgXz5s1D9+4dH9Odn5+PV199FZs2bcKpU6cQHR2N66+/HvPnz0dEROd5I5tPBiTeeOMNxMfHo0+fPvjrX/+K55577pxrp0+fjuuvvx7Jyck4deoUBg0adN5jl5SU4MUXX0RCQgKysrIQHh4OAPj5z3+OSZMm4Ve/+hW2bdsGne7Ml+Y3v/kNampqsHz5clci5+mnn8att96K999/HzNnzsS4ceNc6z/66CNkZWVh5syZePfddyE0XcRftmwZHn74YTz77LN46623XOvNZjOeeOIJREZGYsOGDejVq5dr36uvvhpPPPEEpkyZApPJdHFfSCICAJxolPHqrlr8s6geNsV9x82MCcDv08MwsofBfQcl/yfpoAweAdvgEbDd+UuIh0sg5WZDtz0b0qFir5UhVp+CuG419OtWQzUGwTHiSshpmXCMGAUEBnutDtKOUFkOqSAHuvwcSIV5XpvJdyGqKEIZMASO5AzIyenObic6n3zKSkRERB2k9EmA9b7HOxyQqP/bSo9dVLxoogSl30Ao/Qa2DkwU5jWN5NgFodEXAxN9zozkYGCCiIiILgPDEdSZSMUFEGzuG6t+MQSbBVJxPuTkDI/tcffdd+O9997D8uXL8dBDD7luX7ZsGbp3747rrruuzWM2b96MN998E+PGjUN6ejr0ej127dqFf/7zn8jKysKGDRtcr5efz9dff4377rsPkiTh+uuvR8+ePbF371688847WLt2LbKysjrNa9k+ebV5/PjxHV6blJR0Ucf+8MMP4XA48Pjjj7f6j52UlIRZs2bhX//6F3744QdMnDgRAFBcXIzNmzfjqquuatWuRK/X45lnnsGGDRuwdOnSVgGJpUuXAgCeffZZVzgCcH7TvvHGG/jiiy/wyiuvIDQ0FACwcuVKVFdXY/78+a5wBADExMTgoYcewh/+8AesXLkSc+fOvah/K1FXZ7Yq+FtBHZYU1KHe4b6WEelRejyTHoarYw2t/h8numiCAKVPApQ+CbDPmAvhxDHo8jZBys12XmhV3ZjoOV8Zlgbof1oH/U/roEo6yEPS4EjPhJw6lhdS/UldDaTC7dDlO7tEiCeOaV2RixLXF47kdOfojMEjGNIhIiLq6nwlHNGeloGJ6+9wBibKip1hCZ8KTByCeOwQ9OtWAWgZmEiFPHgEx5QRERERkV8SD+zVdv+D+zwakMjIyEBSUhKWLVvmCkiUl5dj7dq1eOihh1o1AGg2btw47N27FyEhIa1u//jjj/HQQw/hnXfewW9/+9vz7nvq1Cn84he/QFRUFL755hv07t3bdd9nn32GBx54AH/605/wyiuvuOFf6Xk+GZDwpOzsbABwBSBamjhxIv71r39h06ZNrvvPtz49PR3h4eHYtGmT6zaLxYKcnBwkJiaiT58+bR4zYcIEvPXWW8jJycGECRM6VNMf/vAHbNq0iQEJog6qtyt4p7Aei3fXwmxzXzAiyaTD02lhuKGPkcEI8gi1eyzsU26FfcqtQK0Zup0/QpebDSl/GwSb1Ss1CLIDut1bodu9FXjvVcgDhsCRnglH+lVQY3pf+ADkO+w2SPvzIeXnQNqTC/HgPgiqG+cLXQYlvBvkIemQk50fakQPrUsiIiIiujSiBKX/ICj9B7UOTDR3mNi32zcDE3F9nYGJwSkMTBARERGR3xC92KW53f3L9nt8j7vuugvPPPMMduzYgZSUFHz00UeQZRl33313u+vPNUJj1qxZeOqpp7B+/foLBiQ+/vhj1NTU4JVXXmkVjgCAW2+9FW+88QY+//xzBiR8VUlJCUJCQhAdHd3mvgEDBrjWtFwPAPHx8W3WC4KA+Ph45OXloaGhAUFBQThw4AAURWl3/dl7NAckmvdovu9CNZ2PxaJN2xjyLyEXXgLA977frLKKZSUWvFbQiEqL+14E7Bsi4slhQZjeJwCSKMBq9c4L1dTF6Y1Axnjnh82CgD3bEbBjMwJ2/QTRi6MQpJI9kEr2wLD8bThi+8A2YjRsKWPg6JsIiL7fXq+zns8uiaJAOnoQAYXboS/cDv3+Agh23zhfqXoD7AOHwZaUCntSKuSe/YGWQTN/+PoTeVCXOpcRkV/rMuez2L7Oj4nTAVmG7nAJ9Pt2Qb9vF3T78yFaGrSuEGJ5GcTyMujXfgkAcMT2gX3gcNgHDYc9cTjUMJO2BRL5uC5zPiMiv8ZzGWnFaPRcxzjRfMpjx+7Q/tWe33/WrFn44x//iGXLlrkCEhkZGRg8ePA5H7Nq1Sq899572LlzJ8xmM2RZdt13/PjxC+6Zk5Pj+rO0tLTN/VarFVVVVaiqqkJkpO93pe5yAYmamppzJmWaR17U1NS0Wg/gnLNXWj4mKCjootafvUdYWFib9cHBwZAkqdX68ykvL2/1TU10KaI6uO7w4cMeraOjHCrwv0oJ7xzS45jVfS/Ydg9Q8EBvB26OdkAn1qH8qNsOTXTxInsDk+4AJtyKkEPFCN+XB1NRHgJqvPeET3fsEHTHDiHom09gCzWhemAKqgemoK7fIKiSbz6l6Gzns4ulrz6F0AN7EHqgEKEHCqFv8F545nxUQUBDbD/U9k9Cbf8hqO8VD1Wnb7oTwJEjmtZH1Nn4+7mMiLqOLns+kwKBpCudH4qMwOOHEVq2FyFlexFyaD8kjWYkt9T8XD9ww38BAI1RsajrO6jpYyAcwW2vWRF1ZV32fEZEfoXnMtJKYmKi5w7usHvu2B1h9/z+3bt3x+TJk/HZZ5/h+uuvR2lpKR555JFzrn/jjTfwzDPPICoqChMnTkRcXJwrpLJkyZIOvSn49OnTAIB33nnnvOvq6+sZkCDvi4uL07oE6kLObqPjbYqq4qvDNry8uxH7a9wXDIoIEPDIkEDMTTQiUMdRGuSD+vYDrroGNaoK6XAJDDu2IGDnZuiOHPBaCQG1ZnTPXY/uueuhBAbDNmwkbCmjYU/OgGoM8lod7qL1+ayjhMZ66PfucnaIKMyDrsJ3ggZy91jYktJgT0qFfdAIqMGh0AHo1vRBRJ7XWc5lREQX4vfns779gCuvgh3AaVmG7nCx8znevl3QFRf4RIeJwJPHEHjyGLrnrgcAOOL6OjtMDBwO+8BhUENNmtZH1Fn4/fmMiLoEnsuoU2l+k5ZW9N7Z/+6778bXX3+NRx55BIGBgZg5c2a76xwOB1555RXExsZi48aNiIo6E41SVRWvv/56h/ZrbgCwefNmDBky5PL/ARrrcgGJsLCwc3ZjqK2tda1puR4Aqqurz/uY5m+Mjq5vb4+amhpERLSe+VhfXw9ZltvtLtEeT7alITqbVt9vqqoi66gVC7fXYmeV+9J4oXoB/5ccgl8mhyAswPdHBxABAAYOhTJwKCy3Pwihshy67Zug254Ncd9uCKrilRLExnoYt66Dces6qDo95OR0ONIyIaeO6TSzjH3256fDAbF0D3T5uZAKciCWFkJQvPPf9ULU4DA4hqRBHpoBOTkdavdYAIAIwKBtaURdls+ey4iILlKXO58NHgF18AjYANhkB8SD+yEV7XB+7NsFwdKodYXQlZdBV16GwPWrAQByz36QB6dATkqBPCgF4EgOonZ1ufMZEfklnsuoM1FMEZC03N9L18OnTJmC6OholJeX4/bbbz/n68hVVVWoqanB1Vdf3SocAQB5eXlobOzY7xoZGRlYvXo1tm3bxoBEZzRgwABs3boVFRUViI6ObnVfSUmJa03L9QDanaeiqipKS0sRGxuL4OBgAED//v0himK768+3R15eHkpKStoEJNpbT9SVbT5uxcLtNdhSYXPbMY0S8GBSCB4bFoJIo5Y/Ookuj9ojDvbrboP9utuAGjN0OzZDl5sNqWAbBC+09gIAwWGHbueP0O38Eep7ApQByXCkZ8KRlgk1ppdXaujUVBVCeRl0Bc5AhFS0wycuiAOAqtdDThwGOTkdcnIGlL6JgMgwGREREZFbSTooA5KgDEiC/cY7AR8NTEhHD0I6ehDIWgkAkHv1dwYmBqdAHjSCgQkiIiIi0oTSJwH4aZ12+/f14PiQFnQ6HT7++GOUl5djxIgR51zXvXt3BAYGYufOnWhoaEBQkLP7s9lsxpNPPtnh/WbPno2//OUvWLhwIUaOHImkpKRW9zc0NKCgoABXXHHFpf2DvKzLBSTGjh2LrVu3Yu3atbjzzjtb3bd27VrXmmaZmZmu+37961+3Wp+bm4vq6mpMnjzZdZvRaER6ejq2bduGQ4cOoU+fPq0es27dOhgMBqSnp7eq6bPPPsPatWvbfOO0VxNRV7TjpA1/2l6D749eeBZSR+kE4N5BwfjtiFDEBjEYQX4mzATHuBvgGHcDYGmAlJ8DXW42dDu3QKiv9UoJgqpCKs6HVJwPwydvOd9llpYJR3omlH6DAMGHRthYGwFDoCZbC+YqSAW5rg/RfFKTOtoj90lwdogYkg554DDAwHcMEBEREXnV2YEJhwNi2T5nWKKwKTBhtWhdJaQjByAdOQCs+QJAi8BEUirkQcMBjuQgIiIiIi9Q+g/Sdv9+A722V1paGtLS0s67RhRF3H///fjb3/6GzMxMXHfddaitrcWaNWvQu3dvxMbGdmivqKgovPvuu5g7dy4yMzNxzTXXIDExEVarFYcOHcLmzZsxcuRIrFixwh3/NI/rcgGJ2bNn44033sCiRYtwww03IDw8HABQWFiI//znP+jfvz/GjRvnWp+QkIAxY8Zg48aN+O677zBlyhQAgN1ux/PPPw8AmDNnTqs97r33Xmzbtg3PPfcc3n33XQhNLwAtW7YMe/fuxaxZs1q1OpkxYwaeffZZvP3225g9ezZ69XK+w/b48eNYsmQJwsPDMX36dI99TYh82T6zHS/k1WLlQfe9Q0YAcPuAQMxPDUO/0C53GqSuyBgEOWMc5IxxsDockPbtgrQ9G7rcbIinKr1WRvO7zAJWL4MS0R2O1LGQ0zOdLXl17v1/UTxUDP2alR1eH/zwDDhGXwP7NdOdKWNPsjZCKtrl7BBRkOO8kOwjlIgerpEZ8pA0qGHdtC6JiIiIiFrS6aAMGAJlwBDYb7yrEwUm4p3jOAanMDBBRERERB4jJyRDDTBCsHn/ObEaYIScMNTr+17Is88+i27duuGjjz7CP//5T3Tv3h233HIL5s+fj9GjR3f4ONdeey1++OEHvP7661i/fj3WrVuHoKAgxMXF4a677sIdd9zhwX+Fewlms1nVuoizLV26FFu2bAEA7NmzBzt37sSoUaPQv39/AMCNN96Im266CQCwb98+/PWvfwUAWCwWfPHFF+jRowcmTZoEAIiMjHQFGZr95S9/wfPPP49evXph2rRpaGhowIoVK9DY2IgVK1a0CkgAQFFREaZMmQKLxYLp06cjNjYWa9asQUFBAebMmYPXX3+91XpZlnH77bcjKysLGRkZyMzMxMGDB7Fq1SrExcUhKyurzXiPTz75BD//+c8RGRmJW265BaIo4osvvkBlZSX+8Y9/dKpvKur8Qu4d36F1de+v91gNZbUOvLSjFv8paYDixrPUTX2MWJAWhiHd9O47KFFnpaoQD+6Dbns2pO3Zmr1QrwaFwDFiFBzpV0EedgVgDLrkYwnmKhg+eA26nB8u+RiOjHGw3vMoVFPkJR+jFUWGeGCvs4vHnlyI+wsgyA73HPsyqYHBznf1JafDMTQDanQv3+rsQUQAfOO5GRGRO/B85gUOB8SDe1uM5NjtE4GJs50JTKRCHjwcCAnXuiSii8LzGRH5A57LyF8Z/vUX6Df81+v72sdPhfW+x72+L108nwxIPPTQQ/j444/Pef9TTz2F+fPnAwA2btyIqVOnnnNt7969sXv37ja3L1++HEuWLEFRURH0ej1GjhyJBQsWnLMVSXFxMRYuXIiNGzeivr4e8fHxmDt3Lh588EGI7czftlqtWLx4MT755BMcOXIEJpMJU6ZMwdNPP42YmJh291izZg1effVV7Nq1CwAwfPhwPP74466wB5G3aPnEqKJBxl921eK9vfWwK+477oQ4A55JC0Na9wD3HZTIzwgVR6Dbvgm67dkQ9+dDUL3/FEHV6yEnZ8CRlgk5dcxFdTDQ/ZgFw9LFbhkhogaHwjrnMThGXcLPYFWFUHkUUn4udAU5kArzIDTUXXZN7qBKEpQByXA0dYlQ+g8CJHbSIfJ1vGhFRP6C5zMNdJbARO8Bzu4Sg1MYmKBOgeczIvIHPJeRvxIPFSPomQe8vm/Dwnc9352Y3MInAxJEpC0tnhidtip4fXct/lFYjwaH+05LI7sH4On0MIyLNbjtmERdgVB9ClLeZmd3iT25EOx2r9egCgKUxKFwpF8FR+pYqNE9z7FQRcDK9xCw8n2312Cbfi9s0+deuKtCrRm6PXmusRniyQq313Kp5J79nCMzkjMgDxoBBF56hw4i0gYvWhGRv+D5zAc0ByYKWwQmNGg/fCGuwERSivM5bEjYhR9E5EU8nxGRP+C5jPyZ8Y3fX1aX4YvlyBgHyyN/9Np+dHn4lkEi0lSdXcFbe+rxen4tamzuC0Ykd9PhmfQwXNvLCIHt4okumhoeAcf4m+AYfxPQ2AApfyt0udnQ7dwCoaHeKzUIqgpp325I+3bD8PHfnW140zPhSMuE0jfRFVrwVDjCeez3oUKAfcbc1nfYrJD274ZUkAspPxfiof2adNxojxIe4QxDJKdDTk6H2i1K65KIiIiIyFfodFASkqEkJMM+dTbgsEM8uA9SYZ4zNLE/3ycCE9LhEkiHS4DvVziD073jz3SYYGCCiIjIu6yNgCFQ6yqILor1nkednX3d0G34QtTgUFjvedTj+5D7MCBBRJqwOFT8e289Fu2qxUmL+2ZpDAiT8LvUMEzvHwiRwQgi9wgMgnzFeMhXjIfVYYe0dyek3GznKI7TJ71WhnSkFNKRUgR8uRRKRA840jMBYxACVi/z6L6Gle9BjekFJa4vpHxnhwhp324IdptH9+0o1WB0XihuCkQoPftfuOMFEREREREA6PQtAhN3OwMTB5pGcvhIYEJQVUiHSiAdKgG+aw5MtOgwMXA4AxNEREQXQTxUDP2alR1eH/zwDDhGXwP7NdM5PoA6DdUUCeucx2BcstDje1nnPAbVFOnxfch9OGKDiNroaGutinfXIlgvXtSxHYqKj4ob8PKOWhyply+huvb1DJLwVGoo7koIgk7kC4NEXqEoEA/uc47hyM2GVH5Q64o8RoUAAb7xlEkVRCjxgyAnZ8CRnAElYQig02tdFhF5ENueEpG/4PmsE2oOTBTmOUMT+/Mh2KxaV9VKm8DEoBFAcKjWZZGf4/mMiDojwVwFwwevXdbYAUfGOFjveZQvBlOnEfDFvz3WfRgArNPntu0+TD6PHSSIyGX3KTveLazDPzu4PvE/x3FbfCAeSArBsIjzvzinqCpWHmjEC3m1KK5xXH6xTaKMIh4fHor7BgXDqGMwgsirRBFK/GDY4gcDtz4A4fhh6LZvgi43G2JJgc+MnHAHrcMRSnQvOIZmQB6SDjkphRd8iYiIiMg7dHooiUOhJA6F/eZ7nIGJ0iJnWMJHAhPODhPFkA4VA9995gxM9EloMZJjOJ8/ExFRl6f7MQuGpYsve9yALucHSIV5sM55DI5Rk9xUHZHn2KbPhQoBhpXvuf3Y1ulzYZ9+r9uPS57HDhJEhOMNMp740YzVZc62mY71szv0ON34D11/n9rXiFdGmRATJLVao6oqvjtixcLtNcg/ZXdbzWEBAn41NBS/GBKMkIvsYkFEnieYqyDlbXZ2l9izHYLDff//dwVqaDgcQ9IgJ2dATk6HGhWjdUlEpCG+Q5GI/AXPZ36oOTDR3GGiuEDzwMTZGJggT+D5jIg6DVVFwMr3PPIOetv0e2GbPpejXqlTcFdICADU4FCGhDo5dpAg6uJWlDbg8S1mmG2Xl5VaXWbBxmMVWDTahJnxQQCA7ONWLMytwU+VNneUCgAIlAT8YkgwfjUsFN0MDEYQ+SrVFAnHhKlwTJgKNNZDt2srpO3Z0O38EUJjvdbl+RxVHwB54HDIyemQh2ZA6T0AEHmOIyIiIiIfp9NDGTgMysBhsE+bA9htEA8UQSps0WHC7r5rApdCUFVIZfshle0Hvv20KTCR6BzHMTgF8sBhDEwQEZHf8lQ4wnns96FC4HgB6hQcoyZBHpzCMTMEgAEJoi5LVVX8eUctXtpx+Wm5Zmabivs3nMaGcisO1Tmw/pj7LoLoRWDuoGA8Pjy0TZcKIvJxgcFwXDkBjisnwOqwQyrc4ewssT0borlK6+o04booOzTdGYpIHAYEGLQui4iIiIjo8ugDoAwcDmXgcB8PTOyDVLYP+GY5AxNEROS3dFuyPBaOaGZY+R7U2N58Jz11CqopEpZH/gjxUDH0WV9Ct/l7CDbLhR8XYIRjzGTYJ02D0ifBC5WSp3HEBlEX9WJejVvDEZ4iCsCsAUF4KiUUfUOZ6SLyK4oC8UARdLnZ0G3PhnjskNYVeZQSFd00MiMDjiGpQKhJ65KIqJNgC2ci8hc8nxHsNudIjiLfCUycTRVEKH2bRnIkpUAeOBwICtG6LPIxPJ8Rka8TzFUIWjDXLeMELkQNDkXDC+/xHfXU+VgtkIrzIR7cB7FsP8TqU4DdDuj1UMIjoPRNhNJvIOSEoYDBqHW15EYMSBB1QZ+VNuCBDae1LuOCpvUzYkFqGAaZ9FqXQkReIJSXQZe3CbrcjZBKCrUu57KpQcGQk9LgSM6APDQdao+enMlIRJeEF+CJyF/wfEZt2KytAxPF+RDsdq2raqV1YCLV2WGCgYkuj+czIvJ1xjd+f1ljBC6WI2McLI/80Wv7ERFdDr4dm6iLOd4g47dbzFqXcV6Texrwu7QwpEQFaF0KEXmRGtcX9ri+sN94F4TTJyHlbXKO4tiTB0F2aF3eBamSDkpisjMQkZwOpd9AQOJTLSIiIiKicwowQBk8AsrgEbDjXp8MTAiqAungPkgHm0dyiFD6JToDE80jORiYICIiHyKW7fdqOAIAdDk/QDxUzPEDRNQp8Ko9URfzxI9mmG2+2ThmdHQAnkkLw5gYg9alEJHG1G5RcEycBsfEaUBDHXS7foK0PRu6nT9BsDRoXZ6L3Ku/a2yGPGgYYAzSuiQiIiIios6r3cBEIaTCpsBESYFvBCYO7IV0YC/wv09aByaaO0wEBmtaIxERdW36rC8129d63+Oa7E1EdDEYkCDqQnZV2bC6zKJ1GW0Mj9Dj9+lhmNTTAIHt54nobEEhcIyaBMeoSbDabQh65gGIxw5pVo4SGg7bnf8HOTmdsxWJiIiIiDwpwABlcAqUwSmwA50oMDEQclKLDhMMTBARkbdYG6HbskaTrXWbv4f1rv8DDEZN9ici6igGJIi6kH8W1WtdQisDw3X4XVoYpvY1QmQwgog6Qh8ANawboGFAQu3ZD46xUzTbn4iIiIioy2onMCGV7HGN5BCL90Bw+EJgogjSgSLg6/+cFZhIhTxwKAMTRETkGbIDUt4WCDZt3iQp2CyQivMhJ2dosj8RUUcxIEHURdTbFXxa2qh1GQCA3iES5qWE4o4BQdCJDEYQ0cVRTBGQtNw/PELD3YmIiIiIyCXA4BxrkZTq/LwzBCZEEUq/Qc7uEq4OExzVR0RE56EoQF0NRPNJCKerIJhPQjBXQTx9otXnQvVpCKqiaaniwX0MSBCRz2NAgqiL2HbChgaHqmkNPQJF/HZ4KO4dFAyDxGAEEV0apU8C8NM67fbvm6jZ3kREREREdB7nCkw0jeQQS3wgMKEokEoLIZUWAl9/7AxM9G8RmEhkYIKIqMtQVaChzhl2aBl+OH0SorkKwumm4IO5CoLs0LraDhHL9mtdAhHRBTEgQdRF5J3U9gLApJ4GLJ0QgWC9qGkdRNT5Kf0Habt/v4Ga7k9ERERERB10zsBEHqTCpsCExi84CYoCqaQQUkkh8BUDE0REfsPa6Ao8iC2CD60+N1dBsFm1rtStxOpTWpdARHRBDEgQdRG7T2kbkAgPEBmOICK3kBOSoQYYNZmnqAYYIScM9fq+RERERETkBmcHJqyWMyM5fDowMdgZlkhKgZw4FDAyMEFEpBm77UxXhzbhhyqIzV0fGuu1rlQbdm1fhyAi6ggGJIi6iOMNsqb7VzRquz8R+RFDIByjr4F+w3+9vrVjzGTAYPT6vkRERERE5AEGI+QhaZCHpDk/bw5MFOY1jeQo9JHAxB5IJXuArz5iYIKIyFNkB4Tq067gg3C6eezFydaf19VoXalv0+u1roCI6IIYkCDqIuyKqun+Nlnb/YnIv9ivma5JQMI+aZrX9yQiIiIiIi9pLzBRXODsMOGrgQlJOhOYGJwCOTGZgQkiopYUBUJdtavDg6vTQ3P4obkDRM1pCCqvYV8uJTxC6xKIiC6IAQmiLkIvCpruHyBpuz8R+RelTwIcGeOgy/nBa3s6MsZB6ZPgtf2IiIiIiEhjBiPk5HTIyenOz5sDE80dJkqLtA9MyLKzpuIC4L8ftg1MDBwKGAI1rZGIyCNUFWioOzPSwnwm7CC26PogVFdBkNnd2FuUvolal0BEdEEMSBB1ETFBkqb7Rwdquz8R+R/rPY9CKsyDUF/r8b3U4FBY73nU4/sQEREREZEPaxOYaGwKTDR1mCgt1PxFuPYDE0nOcRzNHSYYmCAiX2dpcHZ5OKvrgyv80Nz1wW7TulI6i9JvoNYlEBFdEAMSRF3EsAg9Pj/QqNn+wyM4e4yI3Es1RcI65zEYlyz0+F7WOY9BNUV6fB8iIiIiIupEDIGQkzMgJ2c4P28ZmCjcAfGArwQm8iEV5wOrl7UOTCSlQE5gYIKIvMhmhVB96qwxF83dH06e+dzSoHWldAnUACPkhKFal0FEdEEMSBB1EalR2gYUUjTen4j8k2PUJNiOHULAyvc9tod1+lw4Rk3y2PGJiIiIiMhPtBeY2F8AqciXAxM6KPFNIzmSUpwvbBmMmtZIRJ2QwwGh5tSZsEOb7g8nIZ6uglBfo3Wlfk81GAFRgtBY7/W9HWMm82cIEXUKDEgQdRFXdA9AkE5Ag0P1+t5BOgEjewR4fV8i6hps0+dChQDDyvfcfmzr9LmwT7/X7cclIiIiIqIuwBAIeWgG5KFNgQlLw5nARNEOiAeKfCAw4YC0Px/S/vYCE6lNHSb4YhdRl6UoEGrNrcdcnD4J0Xym64NgPgmhxgxB9f51565E1euhmqKgmiKhmKKgdot0fa52i4LS9CcCgyEeKkbQMw94vUb7pGle35OI6FIwIEHURQTrRdwWH4j393m/Pdnt8YEI0ole35eIughBgH3GXKixvWFYuhhCfe1lH1INDoV1zmPsHEFERERERO5jDII87ArIw65wft5ZAhMDkpyBicEpDEwQ+QtVBeprW4+4aBl+aA4+VJ/S/Lzk71RRdIYcmsMP3aLOfN4UglC6RQLBYYAgdOiYSp8EODLGQZfzg4erP8ORMQ5KnwSv7UdEdDkYkCDqQh5ICtEkIHF/UojX9ySirscxahLkwSkwfPDaZf0C6MgYB+s9j0I1RbqxOiKiS6cb/2GH1pk9WwYRERG5W7uBiXxIhS0CE4qiaYmC7IC0bzekfbuBVR+cCUwkpZ4JTAQYNK2RiM7S2OAcaXFW1wfBXNUUiGgKP9jtWlfq11RBgBpmOtPloSnw0Kb7Q5gJECW372+951FIhXlueSPRhajBobDe86jH9yEichcGJIi6kGERekzta8TqMovX9pza14hhEXqv7UdEXZtqioTlkT9CPFQMfdaX0K9f3bHHBRjhGDMZ9knTmHYnIiIiIiJtGIMgDxsJedhI5+eNDZCKfTgw8eVSqDo9lPgkyEkpDEwQeZrN6gw7mE9CPKvrg2CugtgcfLA0al2p31ODw6CcNeKizeiL8AhAp91LcKopEtY5j8G4ZKHH97LOeYxvNCKiToUBCaIu5pVRJmw8VgGzzfMz4UwBAl4ZZfL4PkREZ1P6JMB63+MdDkjU/20l28QSEREREZFvCWwnMLF/d4uRHHu1D0w47JD27YK0b9eZwMSAIc6wRFIK5AFDGJgguhCHwznKomnchXhW8MEZiDjplU4AXZ1qDDrT5aFF8KF5zEVzIKKznNccoybBduwQAla+77E9rNPnckQtEXU6DEgQdTExQRIWjTbh/g2nPb7XotEmxAS5vz0YEZHbMRxBRERERES+LjAI8vArIQ+/0vm5rwYm9u6EtHcn8OX7DExQ16bIEGrMrpCDK/xwdveHWjME1fNvZuvKVL0eqql7++GHblFQmkIQCAzSulS3s02fCxUCDCvfc/uxrdPnwj79Xrcfl4jI0xiQIOqCZsYHYV+1Ay/t8FzqeF5KKGbG+98TSiIiIiIiIiIin9AmMFEPaV/+mcDEQR8MTOhbBCYGMzBBnZSqAvU1Z8ZctAg8iM1dH06fcHaF0Pj/QX+nShLU8EjXWAvlrK4PzYEIBIcCgqB1udoQBNhnzIUa2xuGpYvd0olEDQ6Fdc5j7BxBRJ0WAxJEXdS8lFAIAP7sgZDEvJRQPJUS6vbjEhERERERERHROQQGQx5xJeQR7QQmCvMgHtwHQdU4MGG3QyraCaloJwAGJsjHqCpgaYBw+iTEs0ZctBp9YT4JwW7Xulq/pgoC1LBurpBDm/BD85+hJkAUtS63U3CMmgR5cAoMH7wGXc4Pl36cjHGw3vOoc9QIEVEnxYAEURclCALmpYYhMVyHx7eYYbZdfhs3U4CARaNN7BxBRERERERERKS1dgMTTSM5Cnf4bGBCHpDsGsmhxCcxMEHuYbO6Ag/tjbkQm7s+WC1aV+r31JAw55iLpuCDaoqEclbXBzUsAtDx5St3U02RsDzyR4iHiqHP+hL69as79rgAIxxjJsM+aRqUPgkerpKIyPP4E4aoi5sZH4SxMQY88aMZq8su/ReAqX2NeGWUCTFBkhurIyIiIiIiIiIitwgMhjxiFOQRo5yfN9RB2p8PqTCvaSTHfp8ITOiKdkBXtANYibMCE6lQBiQB+gBNa7wo1kbAEKh1Ff7NYXeOsjh91oiLFiEI8fRJCA11Wlfq99TA4HbDDoqpRdeH8AiGnnyA0icB1vse73BAov5vKwGD0bNFERF5EQMSRISYIAkfTIzE7lN2/LOwDu/ta+jQ44J0Am6PD8T9SSEYFqH3cJVEREREREREROQ2QSFtAxPNHSZ8MjDxHlR9AOSEZNdIDm8HJsRDxdCvWdnh9cEPz4Bj9DWwXzOd77q+WIoMocbcasyFeFbXB+H0SYi1Zq0r9XuqPqBppEUUlBZdH5rHXDQHImBkV2G/xXAEEfkZBiSIyGVYhB6Lx3brcECi+M4YBOk4442IiIiIiIiIqNMLCoGcMhpyymjn582BieYOE2XFPhCYsEFXmAddYR4AtA5MJKVCiR/skcCEYK6C4YPXoMv54eIeZ7NAv+G/0G/4LxwZ42C951HnC8ldmaoCddVtwg7iWV0fhOpTEBRtv9/8nSpJrcIOSssRFy1GXyAoBBAErcslIiJyG58MSHzyySfYsmULduzYgT179sBms+HNN9/E7Nmz211fU1ODP//5z1i1ahUqKyvRo0cP3HzzzZg3bx7CwsLafcynn36KJUuWoKioCHq9HiNHjsSCBQuQmpra7vqSkhIsXLgQGzduRH19PeLj4zF37lw88MADEMW2LxBbrVYsXrwYy5cvx5EjR2AymTBlyhQ8/fTTiI6ObnePrKwsvPrqq9i1axdUVcWIESPwm9/8BpMmTergV47IuxiOICIiIiIiIiLyU2cHJuprIe3fDalwh28GJr74tzMwkTj0TIcJNwQmdD9mwbB0MYT62ss7Ts4PkArzYJ3zGByj/PB6r6oCjfXtjrkQWwYfzFUQHHatq/VrqiBCDe927jEXzX+GhAPtvLZBRETk73wyIPH888/j8OHDiIyMRHR0NA4fPnzOtfX19bjxxhuxe/duTJgwAbfeeivy8/Px97//HRs3bsQ333yD4ODgVo9ZtGgRFi5ciF69euG+++5DfX09Pv/8c1x77bVYsWIFrrrqqlbri4qKMGXKFDQ2NmLGjBmIjY3F999/jyeffBIFBQV47bXXWq1XFAV33XUXsrKykJGRgalTp+LAgQP48MMPsW7dOmRlZbUJSSxfvhw/+9nPEBkZiVmzZkEQBKxcuRIzZ87E22+/jdtvv/0yv6pERERERERERERElyg4FHLKGMgpY5yftwxMFO6AeGg/BFXVtETBboNuz3bo9mwHgNaBiaQUKP0vIjChqghY+R4CVr7vvvrqa2FcshC2Y4dgmz6387wr32ppHXZoHnFhPiv8YLVoXanfU0PD24QdlLO6Pqjh3QDJJ1/6ISIi8gmC2WzW9llrO9avX4/4+Hj06dMHf/3rX/Hcc8+ds4PECy+8gJdffhmPPvoonnvuuTa3P/nkk1iwYIHr9pKSElx55ZXo168fsrKyEB4eDgAoLCzEpEmTEB0djW3btkGnO/ME4oYbbsDmzZuxfPlyTJkyBQBgt9tx6623YsOGDVi1ahXGjRvnWr9s2TI8/PDDmDlzJt59910ITU90m2+fNWsW3nrrLdd6s9mMESNGQKfTYcOGDejVqxcA4Pjx47j66qthsViwc+dOmEwmN3x1iS7M9O+jHVpnvq+nhyshIro8IfeO79C6uvfXe7QOIqLLwedmROQv+NyMyM/V1zpHchT5TmDibGqAocVIjhQo8UmATt/u2oAv/u3WcMTZrNPnwj5jrseO3yEOe1OnhxZjLtrr/tBQp22dXYAaFAzV1HbMhdIy+GCK9MgIGeq6+NyMiLoqn4wRjh8/vkPrVFXFBx98gJCQEDz55JOt7vvNb36Dt99+G8uWLcP8+fNdIYUPP/wQDocDjz/+uCscAQBJSUmYNWsW/vWvf+GHH37AxIkTAQDFxcXYvHkzrrrqKlc4AgD0ej2eeeYZbNiwAUuXLm0VkFi6dCkA4Nlnn3XtCwB333033njjDXzxxRd45ZVXEBoaCgBYuXIlqqurMX/+fFc4AgBiYmLw0EMP4Q9/+ANWrlyJuXPndujrQkRERERERERERORVwaGQU8dATm3RYWLvLmdgomgHxEPFmgcmBJu1dYeJ5sBEUuqZkRw6PXRbsjwajgAAw8r3oMb29sy4DUWGUH3aFXIQzOcIP9Sa3b83taIGGJo6PTSFH5pHXLQMP3SLBAyBWpdKRETUZfhkQKKjSkpKcOzYMUyaNKnNGA2j0YgxY8bg66+/RmlpKQYMGAAAyM7OBgBXAKKliRMn4l//+hc2bdrkuv9869PT0xEeHo5Nmza5brNYLMjJyUFiYiL69OnT5jETJkzAW2+9hZycHEyYMKFDNf3hD3/Apk2bOhSQsFjYxoy8h99vROTrQjq4juczIvIHPJcRka/jczOiLkbSA0PSnR9wjpfQF+dDv3cX9Pt2QTpS6nuBCb0B9r6J0Jft88r+Ae//FfX9B0MNj+zYA1QVQl01RPMpiNVVEM1VTX+2/PwUxOrTEFTFs8V3caqkg2KKgBIeCcUUCSU8ounPyDO3d4uCagy68CgVFQB/9pEG+NyMtGI0GrUugbq4Th+QAID4+Ph2728ORZSUlLT6e0hICKKjo8+7viN7CIKA+Ph45OXloaGhAUFBQThw4AAURelQTc0BieY9mu+7UE3nU15eDlmWO7SW6NyCOrTq8OHDHq6DiOjyRHVwHc9nROTb+NyMiPwDn5sRESJ6AaN7AaNvgNRYj5BD+xFSthchZXsRWHEEAjQOTNitCCjO99p+YkMdpH8twoGZv4BobURArRn6WjP0dWboa6uhq2v+vNr5Z60ZosJrv56kCgLsIeHOj1AT7CGmpj/D4Qg1uW5zBAUDgnjuA8kATp4CcMpbpRNdND43I60kJiZqXQJ1cZ06IFFTUwMArUZltNQ8wqJ5XfPfu3fvflHrO7pHUFDQJdcEAGFhYW3WBwcHQ5KkVuvPJy4urkPriM6vqkOrevfu7eE6iIi8g+czIvJtfG5GRF0Lz2dEXcjAwQCmogFAY30t9Pvzod/nOx0mvMFUtB0pL/0fBIdd61L8nhIafv6OD6ZIKGEmQJRcjxEABDR9EHVVfG5GRP6mUwckqC22pSFv4vcbEfkLns+IyB/wXEZE/oLnM6IuymgEIidAHjUBMgDU1UDauxNS0Q7nx6GOddjtjBiOuDxqUAgUUxTUbpFQTVFQTZFQu0VBafpTNUVCNUUCOn27jxebPoiofXxuRkT+plMHJJo7LlRXV7d7f21tbat1zX8/VzeGc63vyB7NnSEutSbA2UkiIiKi1fr6+nrIstxudwkiIiIiIiIiIiIivxQSBjn9KsjpVzk/r6uGtHdXlwhMkJMaYHQGHLpFOgMQLQIPLQMRMPDFWyIiIuq4Th2QGDBgAACgtLS03ftLSkparWv++9atW1FRUYHo6OgOrT/XHqqqorS0FLGxsQgODgYA9O/fH6IoXnRNeXl5KCkpaROQaG89ERERERF1Heb7empdAhEREZH2QsLbBiaKWgQmDjMw0VmoOn1TV4cW4YcW3R+UblFQu0UBxiBAELQul4iIiPxMpw9IxMbG4qeffkJ9fb0rpAAAFosFmzdvRmxsLOLj4123jx07Flu3bsXatWtx5513tjre2rVrXWuaZWZmuu779a9/3Wp9bm4uqqurMXnyZNdtRqMR6enp2LZtGw4dOoQ+ffq0esy6detgMBiQnp7eqqbPPvsMa9euxRVXXHHBmoiIiIiIiIiIiIi6tJBwyBlXQc44OzCRB6lwB6Qj7b+BjTxHFUWo4RGu4INqOnvMRRSUiCggOIzBByIiItJMpw5ICIKAe+65By+//DJefvllPPfcc677Xn31VZjNZvzsZz+D0OLJ1uzZs/HGG29g0aJFuOGGGxAeHg4AKCwsxH/+8x/0798f48aNc61PSEjAmDFjsHHjRnz33XeYMmUKAMBut+P5558HAMyZM6dVXffeey+2bduG5557Du+++65r/2XLlmHv3r2YNWtWq5EZM2bMwLPPPou3334bs2fPRq9evQAAx48fx5IlSxAeHo7p06e78StHRERERERERERE5EfODkzUms+M5GBg4rIpYd1ajbhQTVFQWnR9ULtFQQ0zAaKkdalERERE5yWYzWZV6yLOtnTpUmzZsgUAsGfPHuzcuROjRo1C//79AQA33ngjbrrpJgBAfX09rrvuOuzevRsTJkxASkoK8vPz8f3332PYsGH45ptvWnWWAIC//OUveP7559GrVy9MmzYNDQ0NWLFiBRobG7FixYpWAQkAKCoqwpQpU2CxWDB9+nTExsZizZo1KCgowJw5c/D666+3Wi/LMm6//XZkZWUhIyMDmZmZOHjwIFatWoW4uDhkZWW1Ge/xySef4Oc//zkiIyNxyy23QBRFfPHFF6isrMQ//vEP3HHHHW79GhOdj+nfRzu0ju2eicjXhdw7vkPr6t5f79E6iIiIiIjPzYhIY82BicI8Z2jiyAGtK/IJanCos8tDe2MumoMP4RGATq91qUTkZnxuRkRdlU8GJB566CF8/PHH57z/qaeewvz5812fV1dX46WXXsKqVatQUVGB6Oho3HzzzXjqqadcHSLOtnz5cixZsgRFRUXQ6/UYOXIkFixYgLS0tHbXFxcXY+HChdi4cSPq6+sRHx+PuXPn4sEHH4Qoim3WW61WLF68GJ988gmOHDkCk8mEKVOm4Omnn0ZMTEy7e6xZswavvvoqdu3aBQAYPnw4Hn/8cUyaNOmcXwsiT2BAgoj8BX/RIyIiIvIdfG5GRD6lxgxp705nWMIPAxOqwQi1W/c2Iy7UbpFQWnR9QIBB61KJSCN8bkZEXZVPBiSISFsMSBCRv+AvekRERES+g8/NiMiXBS78P0jFBVqXcUGqXn+my8NZXR/UblFNgYjuQGCQ1qUSkY/jczMi6qp0WhdARERERERERERERKQlJbKHpgEJFYAa0b3tiIsWIQilWxQQHAoIgmZ1EhEREXV2DEgQERERERERERERUZem9EkAflqn2f62Wx+Aferdmu1PRERE1FWIWhdARERERERERERERKQlpf8gbfePH6zp/kRERERdBQMSRERERERERERERNSlyQnJUAOMmuytBhghJwzVZG8iIiKiroYBCSIiIiIiIiIiIiLq2gyBcIy+RpOtHWMmAwZtwhlEREREXQ0DEkRERERERERERETU5dmvma7NvpOmabIvERERUVfEgAQRERERERERERERdXlKnwQ4MsZ5dU9HxjgofRK8uicRERFRV8aABBERERERERERERERAOs9j0INDvXKXmpwKKz3POqVvYiIiIjIiQEJIiIiIiIiIiIiIiIAqikS1jmPeWUv65zHoJoivbIXERERETkxIEFERERERERERERE1MQxahJs0+/16B7W6XPhGDXJo3sQERERUVsMSBARERERERERERERtWCbPhfW6XM9cmzr9LmweziAQURERETt02ldABERERERERER+b+699drXQIRUccJAuwz5kKN7Q3D0sUQ6msv+5BqcCiscx5j5wgiIiIiDTEgQURERERERERERETUDseoSZAHp8DwwWvQ5fxw6cfJGAfrPY9CNUW6sToiIiIiulgMSBARERERERERERERnYNqioTlkT9CPFQMfdaX0K9f3bHHBRjhGDMZ9knToPRJ8HCVRERERNQRDEgQEREREREREREREV2A0icB1vse73BAov5vKwGD0bNFEREREdFFEbUugIiIiIiIiIiIiIjI7zAcQURERORzGJAgIiIiIiIiIiIiIiIiIiIiv8eABBEREREREREREREREREREfk9BiSIiIiIiIiIiIiIiIiIiIjI7zEgQURERERERERERERERERERH6PAQkiIiIiIiIiIiIiIiIiIiLyewxIEBERERERERERERERERERkd9jQIKIiIiIiIiIiIiIiIiIiIj8HgMSRERERERERERERERERERE5PcYkCAiIiIiIiIiIiIiIiIiIiK/x4AEERERERERERERERERERER+T0GJIiIiIiIiIiIiIiIiIiIiMjvMSBBREREREREREREREREREREfo8BCSIiIiIiIiIiIiIiIiIiIvJ7DEgQERERERERERERERERERGR32NAgoiIiIiIiIiIiIiIiIiIiPweAxJERERERERERERERERERETk9xiQICIiIiIiIiIiIiIiIiIiIr/HgAQRERERERERERERERERERH5Pb8ISCiKgrfffhvjxo1DbGwsevfujRtuuAFff/11u+tramqwYMECDB06FD169MDQoUOxYMEC1NTUnHOPTz/9FBMnTkRcXBz69u2L2267DXl5eedcX1JSgrlz52LAgAGIiYnBmDFj8Pbbb0NRlHbXW61WvPTSS0hPT0d0dDQGDRqERx55BBUVFRf3xSAiIiIiIiIiIiIiIiIiIqI2On1AQlVVzJ07F08++SRqa2tx991345ZbbsH+/ftx11134e233261vr6+HjfeeCP+/ve/IzExEb/85S8xePBg/P3vf8eNN96I+vr6NnssWrQIDz74ICorK3HfffdhxowZ+Omnn3Dttddi48aNbdYXFRVhwoQJ+OqrrzBp0iT8/Oc/BwA8+eST+PWvf91mvaIouOuuu/Diiy+iW7dueOihhzBq1Ch8+OGHmDRpEkMSREREREREREREREREREREl0kwm82q1kVcji+//BL33nsvRo0ahS+++AKBgYEAgKqqKowfPx6VlZXYunUr+vbtCwB44YUX8PLLL+PRRx/Fc8895zpO8+1PPvkkFixY4Lq9pKQEV155Jfr164esrCyEh4cDAAoLCzFp0iRER0dj27Zt0Ol0rsfccMMN2Lx5M5YvX44pU6YAAOx2O2699VZs2LABq1atwrhx41zrly1bhocffhgzZ87Eu+++C0EQWt0+a9YsvPXWWx76ChK1Zfr30Q6tM9/X08OVEBFdnpB7x3doXd376z1aBxEREREREfkP/q5JRP6A5zIi6qo6fQeJr776CgDwm9/8xhWOAIDIyEj88pe/hNVqxYcffgjA2W3igw8+QEhICJ588slWx/nNb34Dk8mEZcuWQVXPZEY+/PBDOBwOPP74465wBAAkJSVh1qxZOHDgAH744QfX7cXFxdi8eTOuuuoqVzgCAPR6PZ555hkAwNKlS1vt3fz5s88+6wpHAMDdd9+NQYMG4YsvvkBtbe2lfYGIiIiIiIiIiIiIiIiIiIio8wckKisrAcDVIaKl5tuax2CUlJTg2LFjuPLKKxEcHNxqrdFoxJgxY1BeXo7S0lLX7dnZ2QCAiRMntjl+822bNm3q0Pr09HSEh4e3Wm+xWJCTk4PExET06dOnzWMmTJgAq9WKnJyc9v75RERERERERERERERERERE1AG6Cy/xbVFRUQCAsrIyDBo0qNV9ZWVlAJxdHQBnQAIA4uPj2z3WgAEDXOta/j0kJATR0dHnXd/sfHsIgoD4+Hjk5eWhoaEBQUFBOHDgABRF6VBNEyZMaHdNSxaL5YJriNyF329E5OtCOriO5zMiIiIiIiLqKP6uSUT+gOcy0orRaNS6BOriOn1A4pprrsFnn32Gv/71rxg3bpzrf6pTp05hyZIlAIDq6moAQE1NDQC0GpXRUmhoaKt1zX/v3r37Ra3v6B5BQUGXVNP5lJeXQ5blDq0lOregDq06fPiwh+sgIro8UR1cx/MZERERERERdRR/1yQif8BzGWklMTFR6xKoi+v0AYlbb70VH374ITZu3IgxY8Zg0qRJcDgc+Oqrr1zBBkmSNK7Se+Li4rQugfxCVYdW9e7d28N1EBF5B89nRERERERE5G78XZOI/AHPZUTkbzp9QEKn07k6SHz22Wd4//33ERYWhptuugmPPPII0tPTERkZCQAICwsDcKajxNlqa2tbrWv++7m6N5xrfUf2aO4McSk1nQ/b0pA38fuNiPwFz2dERERERETkbvxdk4j8Ac9lRORvOn1AAgAMBgPmzZuHefPmtbp948aNAIDU1FQAwIABAwAApaWl7R6npKSk1brmv2/duhUVFRWIjo7u0Ppz7aGqKkpLSxEbG4vg4GAAQP/+/SGK4kXVRERERB1T9/56rUsgIiIiIiIiIiIiIiIfIWpdgCd9+umnAICZM2cCcIYMYmNj8dNPP6G+vr7VWovFgs2bNyM2Nhbx8fGu28eOHQsAWLt2bZvjN9/WvAYAMjMzz7k+NzcX1dXVrdYbjUakp6dj//79OHToUJvHrFu3DgaDAenp6R37RxMREREREREREREREREREVEbfhGQaG8Expdffolly5YhLS0NU6dOBQAIgoB77rkHdXV1ePnll1utf/XVV2E2m3HPPfdAEATX7bNnz4ZOp8OiRYtajcEoLCzEf/7zH/Tv3x/jxo1z3Z6QkIAxY8Zg48aN+O6771y32+12PP/88wCAOXPmtNr73nvvBQA899xzUFXVdfuyZcuwd+9ezJgxo8MjNoiIiIiIiIiIiIiIiIiIiKgtwWw2qxde5ttGjhyJnj17YuDAgTAajcjNzUV2djb69euHVatWoU+fPq619fX1uO6667B7925MmDABKSkpyM/Px/fff49hw4bhm2++cY2/aPaXv/wFzz//PHr16oVp06ahoaEBK1asQGNjI1asWNEqIAEARUVFmDJlCiwWC6ZPn47Y2FisWbMGBQUFmDNnDl5//fVW62VZxu23346srCxkZGQgMzMTBw8exKpVqxAXF4esrKw24z2IPMn076MdWme+r6eHKyEiIiIiIiIiIvItIfeO79A6jn0kIl/GcxkRdVV+EZB48cUXsXr1ahw+fBh2ux19+/bF1KlT8atf/ardzgvV1dV46aWXsGrVKlRUVCA6Oho333wznnrqKYSHh7e7x/Lly7FkyRIUFRVBr9dj5MiRWLBgAdLS0tpdX1xcjIULF2Ljxo2or69HfHw85s6diwcffBCi2LZxh9VqxeLFi/HJJ5/gyJEjMJlMmDJlCp5++mnExMRc3heI6CIxIEFERERERERERNQ+vqhIRP6A5zIi6qr8IiBBRO7FgAQREREREREREVH7+KIiEfkDnsuIqKtq28qAiIiIiIiIiIiIiIiIiIiIyM8wIEFERERERERERERERERERER+jwEJIiIiIiIiIiIiIiIiIiIi8nsMSBAREREREREREREREREREZHfY0CCiIiIiIiIiIiIiIiIiIiI/B4DEkREREREREREREREREREROT3GJAgIiIiIiIiIiIiIiIiIiIiv8eABBEREREREREREREREREREfk9BiSIiIiIiIiIiIiIiIiIiIjI7zEgQURERERERERERERERERERH6PAQkiIiIiIiIiIiIiIiIiIiLyezqtCyAiIiIiIiIiIiIiIiIi76l7f73WJRARaYIdJIiIiIiIiIiIiIiIiIiIiMjvMSBBREREREREREREREREREREfo8jNoioDfN9PbUugYiIiIiIiIiIiIiIiIjIrdhBgoiIiIiIiIiIiIiIiIiIiPweAxJERERERERERERERERERETk9xiQICIiIiIiIiIiIiIiIiIiIr/HgAQRERERERERERERERERERH5PQYkiIiIiIiIiIiIiIiIiIiIyO8xIEFERERERERERERERERERER+jwEJIiIiIiIiIiIiIiIiIiIi8nsMSBAREREREREREREREREREZHfY0CCiIiIiIiIiIiIiIiIiIiI/B4DEkREREREREREREREREREROT3BLPZrGpdBBEREREREREREREREREREZEnsYMEERERERERERERERERERER+T0GJIiIiIiIiIiIiIiIiIiIiMjvMSBBREREREREREREREREREREfo8BCSIiIiIiIiIiIiIiIiIiIvJ7DEgQERERERERERERERERERGR32NAgoiIiIiIiIiIiIiIiIiIiPweAxJERERERERERERERERERETk9xiQICIiIiIiIiIiIiIiIiIiIr/HgAQRERERERERERERERERERH5Pb8ISKiqilWrVuGmm27CoEGDEBsbi4yMDDz22GM4ePBgm/U1NTVYsGABhg4dih49emDo0KFYsGABampqzrnHp59+iokTJyIuLg59+/bFbbfdhry8vHOuLykpwdy5czFgwADExMRgzJgxePvtt6EoSrvrrVYrXnrpJaSnpyM6OhqDBg3CI488goqKiov+ehAREREREREREREREREREVFrgtlsVrUu4nL97ne/w5tvvomYmBjccMMNCA0NRX5+PtauXYuQkBB8++23GDJkCACgvr4e1113HXbv3o0JEyZgxIgRyM/Px5o1azBs2DB88803CA4ObnX8RYsWYeHChejVqxemTZuG+vp6fP7557BYLFixYgWuuuqqVuuLioowZcoUNDY2YsaMGYiNjcX333+PPXv24N5778Vrr73War2iKLjtttuQlZWFjIwMZGZm4sCBA1i9ejXi4uKQlZWF6Ohoz34RiYiIiIiIiIiIiIiIiIiI/FinD0hUVFQgKSkJvXr1QnZ2NsLCwlz3/f3vf8eCBQswe/ZsvPnmmwCAF154AS+//DIeffRRPPfcc661zbc/+eSTWLBggev2kpISXHnllejXrx+ysrIQHh4OACgsLMSkSZMQHR2Nbdu2/f/27jyu5uz/A/irDdFyLe2pRJZUtixtKCEtUpZkicaMdexDw9jHMgwxthoMClkry0ghSymMMPZtkC1MlhYRqX5/9Lufb9e91b1ppsHr+XjMY3TuuZ/PuZ/7uedzzvm8P+dAVVVVeI+7uzuSk5OxY8cOdOnSBQCQl5eHXr164fjx49i7dy/at28v5N+8eTO+/fZb9OzZE+vWrYOSkpJEet++fREaGvoPHD0iIiIiIiIiIiIiIiIiIqIvwye/xMb9+/dRUFCAdu3aSQRHAEDXrl0BAM+ePQNQtBTHpk2boKGhgcmTJ0vknTBhAkQiETZv3ozCwv/FjGzZsgXv37/HxIkTheAIAGjSpAn69u2Lu3fvIiEhQUj/66+/kJycDCcnJyE4AgDU1NQwffp0AEB4eLjEvsV/z5w5UwiOAIABAwagUaNGiI6ORnZ2tuIHh4iIiIiIiIiIiIiIiIiIiAAAqmVn+W+rX78+qlSpglOnTiE7OxuamprCawcPHgQAYQmM27dv4/Hjx+jUqZPUMhrVqlWDvb09YmJicOfOHdSvXx8AcOLECQCAi4uL1L5dXFywfv16JCUlCa+Xlr9Vq1bQ1tZGUlKSkJabm4uUlBRYWFjAxMRE6j3Ozs4IDQ1FSkoKnJ2dyzweubm5ZeYhIiIiIiIiIiIiIiIiIvq3VatWrbKLQF+4Tz5AolatWpg+fTqmT5+Otm3bolu3btDQ0MDVq1dx7NgxDB48GMOGDQNQFCABAObm5jK3JQ6KuH37tsS/NTQ0oKenV2p+sdL2oaSkBHNzc5w/fx6vX79G9erVcffuXRQUFMhVJnkCJNLS0pCfn19mPiIiIiIiIiIiIiIiIiKif5OFhUVlF4G+cJ98gAQAjB49Gvr6+hg/fjx+++03Ib1t27bo06cP1NTUAABZWVkAILFURnHi2SfE+cT/1tHRUSi/vPuoGL7vCQAAQRlJREFUXr16ucpUGkNDQ7nyERERERERERERERERERERfUk+iwCJn3/+GYsWLcL333+Pvn37QiQS4dKlS/jhhx/g5eWFDRs2oHv37pVdzH8Fp6UhIiIiIiIiIiIiIiIiIiKSplzZBfhYx48fx7x58/DNN99g4sSJMDIyQo0aNdCuXTts374d6urqmDp1KgBAS0sLAJCZmSlzW9nZ2RL5xP8uafaGkvLLsw/xzBDlKRMREREREREREREREREREREp5pMPkDh48CAAwMnJSeq1OnXqwNLSEg8fPsTz589Rv359AMCdO3dkbuv27dsAIOQT//vVq1d4+vSp3PlL2kdhYSHu3LkDAwMD1KhRAwBQr149KCsrK1QmIiIiIiIiIiIiIiIiIiIiUswnHyDx7t07AMCzZ89kvi5Or1KlCurXrw8DAwOcPn0aOTk5Evlyc3ORnJwMAwMDmJubC+kODg4AgCNHjkhtW5wmzgMAjo6OJeY/e/YsMjMzJfJXq1YNrVq1wq1bt3D//n2p9xw9ehRVq1ZFq1atZH4+IiIiIiIiIiIiIiIiIiIiKtsnHyDRrl07AMDq1aullqmIiIjAnTt30Lx5c2hqakJJSQkDBw7Eq1evsGjRIom8wcHByMjIwMCBA6GkpCSk9+/fH6qqqliyZInE9q9du4Zt27ahXr16aN++vZDeoEED2NvbIzExUZjdAgDy8vIwd+5cAEBAQIDEvgcNGgQAmD17NgoLC4X0zZs348aNG/Dx8eESG0RERERERERERERERERERB9BKSMjo7DsbP9d+fn58Pb2xokTJ1CnTh1069YNIpEIly9fFmZf2L17N+zs7AAAOTk5cHNzw6VLl+Ds7IzmzZvj8uXLOHToEKytrREbGyssfyG2ePFizJ07F8bGxvD29sbr168RGRmJN2/eIDIyUiJAAgCuX7+OLl26IDc3Fz169ICBgQEOHz6MK1euICAgAMuXL5f6DH369EF8fDxsbW3h6OiI1NRU7N27F4aGhoiPj4eent4/eyCJiIiIiIiIiIiIiIiIiIg+Y598gAQAvH37FqGhoYiKisKtW7fw7t076OrqwsHBAePHj4elpaVE/szMTCxcuBB79+7F06dPoaenh+7duyMoKAja2toy97Fjxw6EhITg+vXrUFNTQ5s2bTB16lS0bNlSZv6//voLP/74IxITE5GTkwNzc3MMHjwY33zzDZSVpSfuePv2LZYtW4bt27fj4cOHEIlE6NKlC6ZNmwZ9ff2PP0hERERERERERERERERERERfsM8iQILov8zDwwNJSUnIyMj4R9/zX3Pv3j00a9YM/v7+CAkJKTP/5/CZiejTtWDBAixcuBD79u2Dk5NTmflFIhEcHBywf/9+ubaxatUqhIWF4f79+8jNzcWqVavQv3//Cv8c/wRra2sAwKVLlyq5JERU0RITE+Hl5YWgoCBMmTKlsovzrxoxYgS2bt2KCxcuwNTUtLKLQ/8RvOZJktXeof8unr/0KfhUxn44pkVEil5XFR1Xok8T21tEnw/pqQyIPkH37t2DSCRCz549S8xz5swZiEQijBgx4l8smWxbtmyBSCTCli1bKrsoVEESExMhEomwYMGCyi4KfcI+tbqM5LNz50788MMPUFdXx8iRIxEUFCR0qP4J4vOI5wj9V71+/RpLlixB+/btYWRkBD09PVhaWqJbt26YPXs27t69W9lFlMB22/9YW1v/o/UXUUUSXw9L+8/R0bFSy+jh4QGRSFSpZSCiz4M8dR4R0aeE9RoREf2TVCu7AERERESfg6FDh6Jnz54wNjaWSD906BCAouW69PT0KqNoRP8Z2dnZcHNzw5UrV2Bubo4+ffpAW1sbjx49wrVr17B06VLUq1cP9erVq+yiEtFnol69eujTp4/M13hdLt0ff/wBdXX1yi4GyWnv3r2VXQT6DyitziMi+hR9KvVaSWNC9Hlhe4vo88EACSIiIqIKULt2bdSuXVsq/fHjxwB4E4YIAEJCQnDlyhUMHDgQy5cvh5KSksTrqampePfuXSWVjog+R+bm5l/cEjIVpWHDhpVdBFIAgwsJYJ1HRJ+fT6VeK2lMiD4vbG8RfT64xAYRip5mnD9/Ptq1awd9fX2YmJigZ8+eOHnypFTeP//8E5MmTYKdnR1MTEygr68Pe3t7LF26FHl5eWXua8SIERg1ahQAYNSoUaVOC/b+/XssWrQINjY20NXVRatWrbBu3Tq5P1dmZiaWLVsGd3d3NG7cGDo6OmjcuDGGDRsmc/rqBQsWQCQSITExEdHR0Wjfvj309fXRqFEjTJ48GW/evJF6T35+PpYtW4YWLVpAT08PLVq0QHBwMAoLC+UuZ3G5ubmYMWMGmjZtCj09Pdjb22PTpk0l5t+/fz+6d+8OU1NT6Onpwc7ODitWrEB+fv5HHYsRI0ZAJBLh3r17Uq8VP07iv728vAAACxculPhO7927h+HDh0MkEuHcuXMyP8OMGTMgEomwb98+uY8TkSwPHjzAt99+iyZNmkBHRweWlpb49ttv8fDhQ6m84imd3759izlz5sDKygr6+vro0KEDjh07BqCobpw8eTKaNGkCPT09dO7cGefPn5e57/T0dEyZMgUtWrSArq4uzM3NMXDgQFy9elXu8itav4qnes/JycHUqVPRpEkT6Orqwt7eHnv27JG5j4cPH2LIkCEwMzODkZER3N3dkZSUJHcZS/Nh3SCell/8t7he+HB6+qSkJPj5+cHc3By6urpo2bIl5s6di9evX0vtY8+ePXB3d0eDBg2gp6eHpk2bomfPnvj999+FfTZr1gwAsHXrVon6KDExEfPnz4dIJMLu3btlfoZ169ZBJBJh1apVZX7ewsJCbNq0CV27dkXdunVhYGCAjh07llpnE505cwYA8M0330gFRwCAmZmZ1A058W89IyMDY8aMgYWFBfT19eHi4oKYmBiZ+3n9+jUWLFiA1q1bQ09PD2ZmZujTpw9Onz4tlbf4bzciIgIdOnSAgYEBPDw85Gq3PXnyBEFBQWjZsiX09fVhZmYGe3t7TJw4EVlZWWUek8ePH2P+/PlwdXVFgwYNoKurC2tra0ycOBHp6elS+Yu3UdatW4c2bdpAT08PVlZW+Omnn1BQUCD1njdv3mDWrFlC+8rOzg5hYWFllk1MPL3tgwcP8ODBA4njIF5erPhyY3/88Qd8fX1hYmIicaw2bdoEf39/WFtbC9+Lr68vEhISJPaXlJQEkUiEb7/9VmZ5Hj16hFq1aqF79+4S6Yq06YkUoeg1r7CwEBEREejWrRtMTExgYGCAli1bYvz48Xjw4AGAonaBuA1S/DclXiKr+JJZN2/exIABA2Bubi7RR3n//j1WrVoFBwcH4Zz39PREXFycVJmKLxd0/PhxuLm5wdDQEPXq1cPw4cPx4sULqfeIRCJ4eHhIpb979w4hISFwcXGBsbExjIyM0LZtW0ydOhUZGRllHs/y9KnT09Mxbdo02NraCvWHq6srVqxYIZX38uXLGDp0KCwtLaGrq4tGjRqhZ8+eOHDggFReefuTBQUFCA8Ph4uLC8zMzGBgYABra2v069dPqi1ZVnutuNjYWHh6egrHwdHREatXr5bavzznQ0nLICly/ubm5mLFihVwcHCAiYkJjIyM0Lx5cwwZMgRXrlyR8c3Qp+ratWsIDAwU2h42NjaYMmUKXr58WSH5T548CXd3d6GeCQwMlNkvLQ3HtIqI+3WLFi2SuY1Tp07JbDcp0kcv3t6ePHkymjZtitq1awtLzJWn3j5x4gS6desmdQ6UtLwU6yoqr4SEBIwaNQq2trYwMjKCkZEROnbsiI0bN8rM/+effyIgIABWVlbQ1dWFhYUFOnfujKVLl8rML++Y04djQsWV53qfmpqKgIAAmJqawtDQEN7e3rh06ZLcx4Xtrf9he4uIZOEMEvTFe/nyJdzd3XHt2jXY2dnBxcUFWVlZiImJgZeXFzZu3AhPT08hf1hYGGJjY2Fvb4/OnTvjzZs3OHHiBGbPno1z586VeWPIw8MDmZmZiImJgbu7e6nrOA8ZMgRnz56Fq6srVFRUEB0dje+++w5qamoYNGhQmZ/t5s2bmD9/PpycnODp6Ynq1avj5s2b2LVrFw4ePIjjx4/DxMRE6n3r1q3D4cOH4e7uDkdHR8THx2PNmjV4+fIl1q5dK5F37Nix2Lx5M0xNTfH111/j7du3WLVqlcwbEPIYNGgQrly5Am9vb+Tl5WH37t0YPXo00tPTMWHCBIm8c+bMQXBwMIyMjNC9e3doamoiOTkZ06dPR0pKisTAf3mPhTwcHR1x//59bN26FQ4ODhJrGWtrayMwMBDbtm1DWFgYWrZsKfHevLw8bNu2DXp6eujWrVu59k8EALdv34abmxvS09Ph5uaGJk2a4Nq1a9i8eTPi4uIQFxcHc3NzqfcFBgbi6tWr6NatG968eYOdO3fCz88PcXFxGD9+PN6+fYvu3bvj+fPniIqKgq+vLy5cuAAtLS1hG3fv3oWnpyfS0tLg4uICDw8PpKenY9++fThy5Aj27NkDW1vbMj9DeerX9+/fw9fXFy9fvoSnpyfevHmDqKgoDB48GJGRkXBxcRHyPnnyBF26dEFaWho6deqEZs2a4caNG/Dx8YGTk1M5j3zJrK2tERQUhIiICDx48ABBQUEAiuoFsfXr12PixIkQiURwc3NDnTp1cO7cOSxevBiJiYnYt28fqlSpAgD47bffMHHiROjr68PT0xO1atXCkydPcO7cOezfvx+enp6wtrbG8OHDERoaCisrK4mbGiYmJggICMCSJUsQFhaGHj16SJU5PDwcVapUgb+/f6mfrbCwEEOHDsXOnTvRoEED9OrVC2pqajh27BhGjx6NGzduYO7cuRVwFOlzU7NmTQBF9YaNjY3c78vLy4O3tzdyc3Ph7++PzMxMREVFoX///vj1118lplx9+/YtvL29cebMGTRr1gwjRoxAeno6oqOjceTIEaxfv17qxjoArFixAomJiejWrRucnZ2hqqqK5s2bl9pue/36Nbp27Yr79+/DxcUFnp6eePfuHVJTUxEREYExY8ZI1JeyJCcnY9WqVWjfvj1atWoFNTU1XLx4Eb/99hvi4+Nx/PhxiXpDbMaMGThx4gS6du0KZ2dn7N+/Hz/99BPy8vIwffp0IV9BQQH8/f1x7NgxWFpaolevXnjx4gWmTp0q0WYpjba2NoKCghASEgIAwg1cAFLb+OOPPxAcHAwnJycMHjxY4mbIpEmTYGVlhY4dO6JOnTpIS0tDTEwMevTogU2bNgl1lr29PUxMTLB3714sXrwY1apVk9jHzp07UVBQAD8/PyFN0TY9kbwUveYVFhZiyJAhiIqKgqGhIXr16gVNTU3cv38fUVFR6NSpE+rWrSuzjQBAqp65e/cuXF1d0aRJE/j7++Ply5eoUqUKCgsLERgYiH379qFBgwb4+uuv8fr1a0RHR8PPzw8//fQThg8fLvV5YmNjERcXBzc3N3z11VdITk7Gtm3bkJqaitjY2DKPR25uLnx9fZGcnIz69eujX79+qFq1Km7fvo0NGzagb9++Za4Jrmib7/bt2/Dy8kJaWhrs7Ozg4eGB169f4+rVq1iyZAlGjx4t5N23bx++/vprFBQUwM3NDRYWFkhPT8fZs2exadMmiT6XIv3J2bNn45dffkG9evXQu3dvaGhoIC0tDSdPnkRCQgIcHBwAyNdeEwsJCcGUKVNQs2ZN9OrVC9WrV0dsbCymTp2KkydPIjw8XCqYsKTzoSSKnr8jRoxAdHQ0mjZtKny3Dx8+RGJiIlxcXNC0adNSv1v6NJw+fRq+vr5Cm8nExARnzpxBSEgIDh48iEOHDqFWrVrlzn/8+HH06tULysrK8PHxgYGBgRCYJatNUxKOaRXVQV5eXpg4cSJ27tyJyZMnS+1zx44dACDRLipPH/3du3fo3r07Xr16BTc3N6ipqUFXVxeA4vX2kSNH0KdPH6iqqsLHxwf6+vo4ceIE3NzcSgyOYF1F5fXLL7/gzp07aN26NQwNDZGZmYnDhw9j3LhxuHXrFubNmyfkvXjxIrp27QoVFRW4u7ujbt26yMzMxLVr1xAWFobx48dLbFuRMaeSlOd6f//+fXTq1AmNGjXCgAEDcPfuXaFf88cffwi/zdKwvVX+48/2FtGXgQES9Fm5c+eO8BTbh9LS0mSmT548GdeuXcPKlSsxYMAAIf3vv/+Gi4sLxo0bB1dXV2Fgdvz48Vi8eDFUVFSEvIWFhRg9ejQ2b96MU6dOoV27diWW0dPTUxho9/DwQP/+/UvM++jRIyQnJwsD68OHD4ednR1WrlwpV4BEw4YNcePGDeFmhFhCQgJ69OiBxYsXY/ny5VLvO3r0KI4dOwYLCwsARU8dOjk5YdeuXZgzZw4MDAwAFD0puHnzZlhZWSEuLg41atQAAEyYMKHcNxvv3buHkydPQlNTEwDw3XffoUOHDpg/fz58fX1hZmYmlDE4OBidO3dGWFgYqlevDqDou5g4cSLWr1+PPXv2wNvb+6OOhTzEn3Xr1q1wdHSUmvatbdu2sLS0RFRUFObPny8cJ6BogPLvv//GuHHjoKrKKpmKlKcuGz9+PNLT07Fs2TIMHjxYSN+4cSPGjRuH8ePHy4xwf/HiBZKSkoTz0sXFBV999RW8vb3h7OyMdevWCeemtbU1Zs6ciU2bNglPVANFddPTp08RFRUFZ2dnIX3SpElwdnbGmDFjkJycXObnLk/9+vjxY7Ro0UIikKB3797w9vbGqlWrJDqrs2fPRlpaGqZNm4bvvvtO6hhVNBsbG9jY2ODEiRN48OCBVN1w/fp1TJ48GdbW1tizZ49E/bR06VLMnj0bv/76q9AJFQcvnDhxAnXq1JHYlvipTxsbG2hrayM0NBTW1tYyp6F0dXXFwYMHce/ePZiamgrpFy9exMWLF+Hr6ysxwClLeHg4du7ciYEDB2Lp0qXCOfLu3TsEBARg5cqV6NWrF5o3by7/AaMvgre3N3bs2IHRo0fj/PnzcHZ2RrNmzcq8mfbkyRM0bNgQhw8fhpqaGoCiGR06dOiASZMmoVu3bkLb4ZdffsGZM2fQp08f/Prrr8Jgx4gRI9CpUyeMGTMGzs7OQn6xpKQkHD58WGoworR22/Hjx3Hv3j2MHDkS8+fPl3gtOzsbVatWLfOYtG/fHjdu3ICGhoZE+tatWzFixAisXbtWos4S+/PPP5GUlAR9fX0ARW3ali1bYs2aNQgKChLqxK1bt+LYsWNwdXXF9u3bhTp2+PDhEnV2aUQiEaZMmYKIiAgAKHWK26NHj2LFihUYOHCg1GunTp0S2nJiT548gbOzM2bMmCEESCgpKaFPnz5YvHgxYmNjpQK6duzYAXV1dYlAF0Xb9PTlKK1d1bp1a7i6upb6fkWveb/99huioqLQoUMHbNu2Derq6sK23rx5g9zcXABFv6OS2gjFnTp1CpMmTcIPP/wgkb5t2zbs27cPDg4OiI6OFn7zEydORMeOHTF9+nS4ublJ/eYOHDiA33//XWhT5efnw9vbGydOnMCZM2fQunXrUo/H/PnzkZycDD8/P6xevVqi3ZaZmSnxd0kUbfMNHToUaWlp+OWXX6T6wY8ePRL+nZ6ejhEjRkBVVRUxMTHCrFqy8iranwwPD4ehoSGSkpKEvOL8xWfNkKe9BhQtKTV9+nTo6Ojg6NGjwnrlM2bMgI+PD/bt24cdO3ZI3PAESj4fSqLI+ZuZmYndu3ejRYsWOHz4sMT3k5+fj+zsbLn2SZWrpDrP1dUVrVu3RkFBAUaOHImcnBxERkaiU6dOQh7xTayZM2cKTwuXJ//YsWPx/v17xMTEwM7ODoDkzSN5cUyrqA7S0NCAp6cnduzYgXPnzkk8eCMOwjA2NpYIWi1PH/3p06do2rQp4uLiJK5dgGL1dn5+PsaOHYuCggL8/vvvEoEYI0eOFNqTxbGuotKUVa8tWbJEqr3z/v179O7dG6GhoRg+fDjq1q0LANi+fTvevn2LiIgIuLu7S7xH1mxaiow5yVLe631SUhJmzZolMVY1d+5cLF68GFu2bJEK5JCF7S22t4iodFxigz4rd+/excKFC2X+J+vJY/ET0R06dJAYSAUAXV1djB49Gs+ePROmmweKnsD9cNBHSUkJX3/9NQBI5P1YM2bMkHjq0MLCAm3btsWtW7fkulhqa2tLdSSBooH4xo0bl1jW4cOHCx1JAFBXV0fPnj1RWFiIP//8U0jftm0bgKIB6eI3/Q0NDWU+rSSPiRMnStyw0NPTw6hRo/D+/XuJjvSaNWsAFN1ELN5gUlJSwsyZM6GkpITIyEghvbzHoqIMGjQI2dnZiIqKkkjftGkTlJSUEBAQ8I/unz4titZlDx8+REJCAho3bizViRk0aBAaNWqE48ePy5zSdNq0aRK/X29vb6ipqSEzMxM//vijROBOz549ARRNoyd24cIFnD59Gv7+/lI32ho0aICAgABcvXpVrqU2ylu/zp8/XyKSu0OHDqhbt67Esjbv3r1DdHQ0dHR0pKYeDQgIQIMGDcosX0XbsGED3r9/j4ULF0rVT2PHjkWdOnUk6jEAUFNTkxlMVVZAQ3GDBw9GYWEhNm/eLJEeHh4OAHIF4K1ZswY1atTAzz//LFGeKlWqCE+u79q1S+4y0ZfDw8MDc+bMQUFBAZYtWwZvb2+YmZmhRYsWmDRpEm7fvl3ie3/44QchOAIoGjQfMGCAEMAgFhERATU1NaE9IGZlZYV+/fohIyND5tIcgwYNKveTGh8OIgOApqZmqU+ZiOno6EgFRwBA3759oaWlVWLdN2nSJCE4Aiha89bd3R3Z2dm4deuWkC5ur02bNk2ijm3atKnUQFBFsLGxkRkcAUBq4BIA9PX14eXlhdu3b+P+/ftCurhs27dvl8h/6dIlXL16Fe7u7kKbsTxtevpylNauOnz4cJnvV/Sat27dOqioqCA4OFiqblBXV5fZJymNnp4eJk2aJJUuvsE0Z84cibrGyMgII0eORF5enswbkb169ZIYDFdRURFmjippSUCx/Px8bNy4EVpaWli4cKFUu01bW1tmffYhRdp8586dw9mzZ2Fvby+zjWJkZCT8e+vWrXj16hVGjRolNVj/YV5F+5OA7HaYkpKS1HcqT3ttx44deP/+Pb799lthsB4oOq9mzZoFADJvIpZ0PpREkfNXSUkJhYWFqFq1qtT3o6KiUmYwI/03lFTniZc5O3XqFG7fvo3OnTtLBDsAReMxtWrVwq5du/Du3bty5T958iRSU1PRtWtXITgCKDq/pk+fLlcQlRjHtP5XB4lnS/uwXXTw4EG8ePECvXv3Ftq9H9NHnzNnjsx2rSL19smTJ/HgwQO4u7tLzVLxww8/yDwHWFdRacqq12T1MVRVVREYGIj8/HyZS17IOs9LGleRZ8ypJOW93puammLMmDESaeI+ljz7BdjeAtjeIqLS8XFl+qx06tRJ6qIqdubMGXTu3Fki7dy5c8jPz8fbt29lRqLeuXMHAHDr1i24ubkBKLrJtmbNGkRFReHWrVt49eqVxNqET548qaiPU2ojIzMzU+rJR1kSExMREhKCs2fP4vnz53j//r3wWkmD9mXtV0x8k9Te3l4qf/GOsCJkvU+cVnydtZSUFNSoUaPEJU3U1dUlbg4A5TsWFcXPzw+zZs3Cpk2bhAZtWloa4uPj4eDgIHPpA/pyKVqXXbx4EQDg4OAgNS2ckpIS7O3tcePGDVy+fFmiQwBAaop7FRUV6OjoICcnR4iwFxPfiHv8+LGQlpKSAqDoCV1Z9aj4d3jr1i1YWlrK/sD/rzz1q7a2tszOsJGREf744w+JcuTm5qJ9+/ZSTw8rKyujTZs2+Ouvv0otX0UTH7v4+HiZg3tqamoS9ZiPjw9mzZoFe3t7+Pr6wtHREe3atVO489SlSxcYGRkhIiICU6ZMgbKyMnJzc7Fz506YmZmhffv2pb5fPMWigYGBzDU6xXXrh3UwkdiYMWMQGBiI+Ph4nD59Gn/++SdSUlKwdu1abNq0CevXr5d6mkdNTU3mU812dnZYu3YtLl26BD8/P2RlZSE1NRWNGjWSGJgRc3R0xIYNG4T8xbVq1Urhz2Jvbw89PT0EBwfj0qVL6NKlC9q1a4emTZtK1cel2bt3LzZu3IgLFy4gIyNDYi3UktqWirTXqlevLnNGFzs7uzKXh1NUaccxNTUVwcHBSEhIwOPHj/H27VuJ1588eSJMlW1hYSE8VfPy5UthQEx8Y6D491eeNj19OUprV5VF0WteTk4Orl+/DnNzc9SvX7/8hS7GyspKZl/l4sWLUFdXl/mbEz9FLGudannrDllu3ryJrKwsdOzY8aMGbxVp8509exYA5JrCWpG8ivYnfXx8sH79etjZ2cHHxweOjo5o3bq1xE1VcT552mvi9ruspY5at24NdXV1md9fSeeDLIqev1paWnB1dcXhw4fRvn17eHt7w87ODra2tv94f5kqTll1XmnnXo0aNdCiRQvEx8fjr7/+gqWlpcL5SxsnEq+zXjwgsiwc0yri7OwMPT09YWZS8U01We2i8vbRq1WrVmKwsCL1tvi4tm3bVmo7RkZGMDY2xr1794Q01lVUlrLqtezsbKxYsQL79+9HamoqcnJyJF4vfn56e3sjJCQE/fv3R48ePeDs7Ix27dpJjX+JyTvmVJKPud4rK0s+2yxve02M7S22t4iodAyQoC/ay5cvARRFxJ86darEfMUbVgEBAYiNjUWDBg3g4+MDHR0dqKqqIjMzE6GhoVIDvR9D1tqM4k5Q8YHzkuzevRuBgYHQ0NCAi4sLTExMoK6uDiUlJWG9W1lkrZUta79ZWVlQVlZG7dq1pfLLsxaaLDo6OiWmZWVlCWkvX74UnrwuSfHvrbzHoqKIRCL06NEDW7duxfXr19G4cWNs2bIF+fn5cj2tTVQa8Ywysn4/wP9+j8V/Q2Il/d5lpYujoPPy8oQ0cT0aFxeHuLi4Esv4YQdVlvLUr7LKKf4MBQUFwt/iz/7h1Hti5a2zPob42C1evFiu/GPHjkWtWrWwfv16rFq1CitXroSqqio6d+6MBQsWyOy0y6KiooIBAwYIT8526dIFe/bsQWZmJsaMGVPmTd2MjAwUFhYiLS1N7jqY6EOampro0aOHsHSCeNaadevWYfTo0XB1dZUYHKhVq5bUABEg3Ub4mPqwpPeURltbGwcPHsSCBQsQGxuLgwcPAigavBo/frzwdE5pVqxYgenTp6NOnTpwcXGBoaGhEMgVEhJSYttSkfaarGAR4J+p+0o6jnfu3IGLiwuys7Ph5OQENzc3aGpqQllZGSdOnEBSUpLUZ/Xz88P58+cRHR2Nr776CgUFBYiMjISOjo7EgFx52vRE8lD0micesBZP314RSvpNZWdnl/nbVqTtB5Tdx6yoz6dIm0+RfSqSV9H+5MKFC2FmZoaIiAgsXrwYixcvRrVq1dCjRw/MmzdP6BPL214r63pVp04diaBkMUWuVeVps4WFhSE4OBi7du3Cjz/+CKDomt2/f3/MmDFD4ulP+jQp2lZSNL88/S55AyQ4pvW/36aKigp69uyJ1atX4+jRo3B1dUVmZiYOHjyIZs2aoXHjxhLbBhTvo9epU6fEvqAi9bb4nJF1XIGiY1s8QIJ1FX2Md+/ewdPTExcuXICNjQ38/PxQq1YtqKio4P79+9i6davE+dmmTRvs3bsXS5cuRWRkpDB7QPPmzTFnzhypB0bkHXMqSXmv96WNyclzTwBgewtge4uISscACfqiiWdg+PbbbzF37twy8587dw6xsbHo1KkTduzYITEN0pkzZxAaGvqPlbU8fvrpJ1SrVg3Hjh2TeoLpw6UeykNLSwsFBQV4/vy5VOf377//Ltc209PTpZ5wT09PF/YnpqmpCSUlJeGJwLIoeizEN2FkNTplDTTKIzAwEFu3bkV4eDjmzZuHLVu2oGbNmvDy8irX9ojExHWZ+LfyIXG6PLPOlHffixYtwtChQ8u9nX+6fhXXH8+ePZP5ennrrI8hPnYPHjyQ67sRL8cTEBCAFy9eIDk5GZGRkYiOjsadO3eQnJws95S1AQEBWLx4McLDw9GlSxeEh4dDVVUV/fr1k7vczZs353T1VGG0tbXx888/Iy4uDg8ePMDVq1clZjx48eIFCgoKpIIkPmwjfEx9qMiMD8WZmpoiNDQU+fn5uHLlCo4ePYpff/0V3333HUQiEXr16lXie9+/f4+ff/4ZBgYGSExMlGhPFRYWylxXW1FaWlr/at1X0nFcvXo1MjIysGbNGmGaaLHx48cjKSlJ6j09e/bEtGnTsGPHDnz11VfCzBPDhw+XmLpU0TY9kbwUveaJ6yJZA63lVdJvSlNT819v+4kD+D/m8yna5lNkn8XzmpqalppX0f6kmpoaxowZgzFjxuDx48dISkrCli1bsG3bNvz9999Cn1Le9lrx65V45pzinj179tHXqvK02WrUqIHp06dj+vTpSE1NRWJiIjZs2IDQ0FDk5uZi2bJlcu+f/psUbSspmr8i+10c05Lk5+eH1atXY8eOHXB1dcWePXuQm5srNStaefvoJdUvitbbxZdAk+XDY8u6ij5GTEwMLly4gICAAKm+U2RkJLZu3Sr1HkdHRzg6OuLNmzdISUlBbGwsfvvtN/j5+SE5ORn16tWrsPKV93r/sdjeYnuLiMom/RgW0RekZcuWUFJSEtYsK8vdu3cBFE1P/uFNqJMnT8q9X0VmgfgYd+/eRcOGDaU6ko8fPxY+y8ewsrICACQnJ0u9psjxKOt94jRra2shzdbWFi9evCh1rfLiFD0W4im50tLSpF4TT89VnDzfaZs2bWBpaYnt27fj0KFDSE1NRZ8+faSm+ydSlPi3kZycLDFdHlB0g03Wb6iiiNcUlbceLUlF1a8lsbCwQLVq1XD+/Hnk5uZKvFZQUCDX1IgVTXzsxFOgKqJWrVrw9PTEhg0b0L59e9y4cUPocMpTHxkZGcHV1RVxcXE4deoUkpOT0blzZ7meANDU1ESjRo1w8+ZNZGRkKFx2opIoKSmV+KREXl6ezHrmw/pNS0sLZmZmuHPnjsxruPgmvCL1obztNhUVFdjY2GDs2LFYt24dAODAgQOlvuf58+fIysqCra2t1MD8+fPn8ebNG7nLWRIrKyu8fv1aYs1tMUXrV3mflJJFXM9369ZNIr2goACnT5+W+R7xTBGnT59GamqqzGmkAcXb9ETyUvSap6GhgcaNG+PevXty9VM+pl9oY2ODN2/eCNMcF1eeuk4eFhYW0NLSwrlz58rdBlC0zSdeQuTIkSNlbluRvIr2J4szMDBAr169EBkZifr16+PYsWMy6+vS2mviZe5OnDgh9b6zZ8/izZs3H/39fWybzczMDAMHDsT+/fuhoaFR5jWNPg2lnXuvX7/G+fPnoa6uDgsLi3LlL22c6P79+3j06JHcZeWYliTxTBH79+9HTk4Otm/fDhUVFalg3Irqo4spWm+Lj6us9t2jR4+kzgHWVfQxSupjAGX/jtXV1eHk5IR58+ZhwoQJePPmTYU/BPJvXO9lYXuL7S0iKhsDJOiLpqenBx8fH5w+fRrLly+XurEIFN24ev36NQAI65F9OHXvtWvXEBwcLPd+xWsoyxq4r0h169bF3bt3JaKzc3NzMWHCBIl1G8urb9++AIqi0otPE5WWllbup72XLFkiTH8FFEWWr1q1Cqqqqujdu7eQPmzYMABFTwq+ePFCajtPnz7FjRs3hL8VPRYtWrQAAGGqNbE9e/bIfMJR3u908ODBeP78OcaOHQug6Cluoo9Vt25dODk54dq1a1Lr+m3atAnXrl1D+/btpZ5kqQitWrWCra0tdu3aJfMpnoKCApkdkQ9VVP1akipVqqBHjx5IT0/HypUrJV4LDw/HX3/99dH7UNSQIUOgqqqKyZMn4+HDh1KvZ2Rk4MKFC8Lf8fHxUvVVXl6eMIWqONhKJBJBSUmpzPooMDAQeXl5CAwMRGFhoULL/QwbNgyvX7/G2LFjZU5Zn5qaKjFtKpHYhg0bcO7cOZmv7d27Fzdv3oS2tjaaNGki9fq8efMklvi5efMmNm/eDC0tLbi7uwvp/v7+yMvLw+zZsyXadlevXsWWLVugpaUFDw8Puctc2jX+6tWrMqeJFj8pWFYQpI6ODtTV1XHhwgWhvQkU/f4nT54sdxlLIw4mmDt3rsRN2CtXrggBB/KqWbMmnj9/LhVoJo+S6vlly5bh6tWrJb7Pz88PhYWF2LRpE37//Xc0bNhQaKeJKdqmJ1KEote8r7/+Gvn5+Zg4caLUIG5ubq5w3Qb+V78ocsNQzN/fHwAwe/ZsiboxLS1N6D99OFvLx1JVVcXgwYORlZWFoKAgqcCOzMxMvHr1qtRtKNrma9myJVq1aoXk5GSEhYVJvV68bvb394eGhgZWrVolM6i9eF5F+pNv377F8ePHpeqWnJwcvHr1CmpqasLNB3nba71794aqqipWrVol8bRmXl4eZs2aBQByzexVFkXO32fPnskMuMnIyMDbt28Z2P+ZaNeuHerVq4dDhw5J3QgMDg7G8+fP0bNnT2GpM0Xz29nZwdTUFHFxcRI34QoLC/Hjjz8qFBDGMa0bUul+fn7IyclBaGgokpOT4ezsLLUcSEX10cUUrbft7OxgbGyMmJgYqTpl3rx5Mr871lVUXiWdnydOnJDZbkhOTpY5M7C8/TdF/VvX+w+xvcX2FhGVjUts0BdvyZIluHXrFmbMmIFt27ahTZs20NLSwqNHj/Dnn3/i9u3buHHjBqpXr45WrVqhVatWiI6OxpMnT9C6dWs8fPgQBw4cENZwl0ebNm2grq6OkJAQZGdnC08Mjh8/vkI/29ChQzF58mS0b98e3bt3R35+Po4ePYrCwkJYWVnh8uXLH7V9Jycn9O/fH1u2bIG9vT08PT3x7t07REVFwdbWttS1DktiamoKOzs7dO/eHXl5edi9ezfS09Mxffp0Yf0wAHB1dcWkSZPw888/o0WLFnB1dUXdunXx4sUL3LlzBydPnsS0adPQqFGjch0LDw8PmJqaIiIiAo8ePYKNjQ1u3ryJhIQEdOnSRVhjXKxhw4YwMDBAVFQUqlevDkNDQygpKeGrr74Sph4Dijqzs2bNwuPHj2Fra4umTZsqfIyIZAkODoabmxvGjh2L2NhYNG7cGNevX8eBAwdQp06dCgkyKMm6devg5eWFr776CiEhIWjevDmqVq2Khw8f4syZM3j27BmePn1a6jYqqn4tzaxZs5CQkIC5c+fi1KlTsLGxwY0bN3Do0CG4uLjIFf1ekSwtLbFkyRJMmDABrVu3RufOnVGvXj1kZ2cjNTUVSUlJ6NevH5YuXQqgKKChevXqaNeuHerWrYu8vDwcO3YM169fh6+vr9AB1tDQQMuWLZGcnIyRI0eifv36UFZWRq9evYQ8QNGTBMbGxnj48CEMDQ3RuXNnucseGBiIM2fOYOvWrTh9+jQ6dOgAAwMD/P3337h16xZSUlKwbt26MqdbpC/PoUOHMH78eJibm6Nt27YwMDDAq1evcOnSJZw8eRLKyspYsmQJqlatKvE+fX19ZGZmwsnJCV26dEFWVhYiIyORm5uLX375RWJazLFjx+LgwYPYvn07bt68iQ4dOuDZs2eIjo5GXl4eQkNDFZrGtLR227FjxzBt2jS0bdsWDRs2RK1atZCamooDBw5AXV0d33zzTanbVlZWxpAhQ7By5Uo4OjrCzc0N2dnZOHz4MOrWrSvXrC5l6devH3bt2oXDhw/DyckJnTt3xsuXLxEZGQlnZ2eF2mvt27fH+fPn0bdvX9jZ2aFKlSpo164d7OzsynxvYGAgtmzZgoEDB8LHxwe1atVCSkoKLly4gK5du5ZYDnd3d2hpaWH58uXIy8vDuHHjZOZTpE1PX5Y7d+5gwYIFJb4+ZcqUUt+v6DVvyJAhSEpKQnR0NFq1aoVu3bpBU1MTDx8+RHx8PFasWAFPT08ARb+pPXv2YPDgwejcuTOqVasGS0tLdO3atczP1bdvX+zbtw8xMTFwcHBA165d8fr1a0RHR+PFixeYO3euRP+pokydOhVnzpzB9u3bkZKSAldXV1StWhWpqamIj4/HgQMHhKf1ZClPm2/NmjXw9PTE2LFjhd93bm4url+/josXLwpPSero6CAkJARDhgxBp06d0K1bN1hYWOD58+dISUmBiYmJEACvSH/yzZs38Pb2hpmZGWxtbWFsbIycnBzExsbi6dOnGDdunHBzWN72Wr169TBr1ixMmzYNDg4O8PHxQfXq1REXF4ebN2/C3d1darac8lDk/E1LS0OnTp3QpEkT2NjYwNDQEC9evEBMTAzy8vKEIH/6tCkrK2P16tXo2bMnevfujR49eqBu3bpISUlBQkKCcG5+TP5ffvlFyOvj4wMDAwMkJCTg6dOnaNq0Ka5cuSJXWTmm9b8xLbHevXtjzpw5+Omnn1BYWFhiPVERfXQxRettFRUVLF26FP7+/vDw8ICvry/09PSQlJSEtLQ0WFlZSZ0DrKuovNzc3GBiYoJffvkF165dQ5MmTXDr1i3ExcXBw8MDe/fulci/cuVKHDt2DE5OTjA1NUW1atVw4cIFHD9+HObm5kIbraL8W9f7D7G9xfYWEZWNARL0xatZsyYOHjyItWvXIioqCjt37kRBQQF0dXVhZWWFSZMmoXbt2gCKGvnbt2/HrFmzEB8fj/Pnz8Pc3Bw//vijsP6fvPsMCwvDTz/9hPXr1wtPFlV0gMQ333wDNTU1rFmzBuHh4dDW1kaXLl0wY8YMDB48uEL2sXz5cjRo0ABhYWFYu3YtDA0NMWrUKPj4+JSrM7lx40bMnz8fkZGRePbsGerXr4/p06fLnGnhhx9+gIODA0JDQ3H8+HFkZmaiVq1aMDU1xffffy8Rna/osVBXV8eePXswdepUJCYmIiUlBba2toiJiUFsbKxUgISKigo2bdqEmTNnYvv27cITA76+vhIBEtra2nB3d8euXbs4ewRVKAsLCxw9ehQLFy5EfHw8Dh48iDp16qBfv34ICgqSudZeRTEzM0NiYiJWrlyJmJgYbN68GSoqKtDT04O9vT26d+9e5jYqqn4tjb6+PuLi4jBz5kzEx8cjOTkZzZo1Q3R0NBISEv71AAkAGDRoEKytrbFq1SokJyfjwIED0NLSgrGxMUaOHCk8HQoAM2fOxOHDh3H27FnExsaievXqMDc3x7JlyzBgwACJ7f7666+YOnUq9u/fj6ysLBQWFsLW1lYiQEJZWRl9+vRBcHAw+vXrJzXtYmmUlJQQEhKCLl26ICwsDHFxccjJyYGOjo7wvXXs2PGjjw99fmbPno127drh6NGjSE5OFgZmDQwM4O/vj2HDhqF58+ZS71NTU8Pu3bsxc+ZMbN26FVlZWbC0tMSkSZMkZo8Aip4W2bt3L5YtW4bo6GisXr0a6urqsLe3x4QJE+S6mV9cae22Tp064f79+0hOTsa+ffuQk5MDAwMD+Pr6YuzYsVKD2rLMnDkTNWvWREREBH777Tfo6OjA19cXU6ZMUbissigrKyMiIgILFy7Ezp07ERoainr16mHevHmoX7++Qu21SZMmISMjA3FxcUhISEBBQQGCgoLkKmezZs0QFRWFefPm4ffff4eysjLatm2L2NhYHDhwoMRyqKurw8vLC1u2bIGSkpJE+644Rdr09GW5e/cuFi5cWOLrZQVIKHrNU1JSwvr16+Hs7IxNmzZh27ZtKCwshIGBAXx8fCTquEGDBuH+/fuIjIzEkiVL8P79e/j7+8sVIKGkpITw8HCEhIRg69atWLNmDapUqQIbGxuMGjVKqm6sKNWqVcPu3buxZs0a7NixA+Hh4VBRUYGxsTECAwPLbHOWp81Xv359HD9+HMHBwYiNjUVISAhq1KiB+vXr47vvvpPI6+XlhcOHD2Pp0qVISkrCgQMHULt2bVhbW0vNliVvf7JGjRqYPXs2jh8/jpMnTyI9PR0ikQgWFhaYPXs2fH19hW0q0l779ttvYW5ujlWrVmHHjh149+4d6tevj7lz52L48OEKrX9dEkXOXxMTE3z//fdISEjA8ePH8eLFC9SuXRvNmjXDyJEj4eLi8tHlof8GOzs7HDp0CIsWLcKRI0eQlZUFfX19DBs2DJMnT5a6Xiqav2PHjtizZw/mzp2LPXv2oFq1aujQoQM2btyI4cOHy11OjmlJt3mMjY3h6OiIxMREaGholDgrWkX00cXKU2937twZUVFRWLBgAaKjo4VzYP369ejTp49UsDLrKiovDQ0N7N27FzNmzEBycjJOnDiBxo0bY+3atdDR0ZEKkBgyZAi0tLRw9uxZYZlaY2NjfPfddxg5cqRCgfTy+jeu9x9ie+t/2N4iopIoZWRkSM8/SkRE/5h27drh4cOHuH79OjQ0NCq7OET0BevduzcOHz6MP//8k7M90H+WeE3QS5cuVXJJiIiIiIiovLKzs9GwYUNYWloiPj6+sotDREREXzDlyi4AEdGX5ODBg7h+/Tr8/PwYHEFEleratWs4fPgwXF1dGRxBREREREREFSInJ0eYWVUsPz8fM2bMwJs3b0qc+YKIiIjo38IlNoiI/gW//fYbHj16hLCwMKirq2PMmDGVXSQi+kLt3LkTt27dwrZt2wAUTZlPREREREREVBFu376Nbt26wcXFBWZmZnj16hVOnjyJ69evo0mTJhg2bFhlF5GIiIi+cAyQICL6FyxbtgxpaWmwsLDArFmz+LQ2EVWajRs34uTJk6hbty5WrFiBNm3aVHaRiIiIiIiI6DNhaGgIb29vJCUlIT4+Hu/fv4exsTFGjx6NiRMnokaNGpVdRCIiIvrCKWVkZBRWdiGIiIiIiIiIiIiIiIiIiIiI/knKlV0AIiIiIiIiIiIiIiIiIiIion8aAySIiIiIiIiIiIiIiIiIiIjos8cACSIiIiIiIiIiIiIiIiIiIvrsMUCCiIiIiIiIiIiIiIiIiIiIPnsMkCAiIiIiIiIiIiIiIiIiIqLPHgMkiIiIiIiIiD4j9+7dg0gkwogRIyq7KGUSiUTw8PCQSFuwYAFEIhESExMrqVRERERERERE9LligAQRERERERFROYgDEYr/p6Ojg6ZNm+Lrr7/G5cuXK7uI/xpra2tYW1tXdjGIiIiIiIiIiEqlWtkFICIiIiIiIvqU1atXD3369AEA5OTkICUlBbt27cK+ffuwd+9etG3btpJL+GkZOnQoevbsCWNj48ouChERERERERF9ZhggQURERERERPQRzM3NMWXKFIm0uXPnYvHixfjxxx/x+++/V1LJPk21a9dG7dq1K7sYRERERERERPQZ4hIbRERERERERBVs6NChAIDz588LaSKRCB4eHkhLS8OIESPQsGFD1KxZE4mJiUKeiIgIuLq6wsjICEZGRnB1dUVERITMfeTn52PZsmVo0aIF9PT00KJFCwQHB6OwsFBmfvH+ZSlpiYx3794hJCQELi4uMDY2hpGREdq2bYupU6ciIyNDWGbkwYMHePDggcRyIwsWLJD7eBW3YMECiEQiieMi3s+IESOQmpqKgIAAmJqawtDQEN7e3rh06ZLMbaWnp2PKlClo0aIFdHV1YW5ujoEDB+Lq1avlKhsRERERERERfdo4gwQRERERERFRBVNSUpKZ/vLlS3Tp0gUikQg+Pj7Iy8uDpqYmAGDKlCkICQmBoaEhBgwYACUlJezbtw8jR47E5cuXMX/+fIltjR07Fps3b4apqSm+/vprvH37FqtWrcLp06cr5DPk5ubC19cXycnJqF+/Pvr164eqVavi9u3b2LBhA/r27QsTExMEBQUhJCQEADBixAjh/Y6OjhVSjuLu37+PTp06oVGjRhgwYADu3r2LmJgYeHl54Y8//oCurq6Q9+7du/D09ERaWhpcXFzg4eGB9PR07Nu3D0eOHMGePXtga2tb4WUkIiIiIiIiov8uBkgQERERERERVbBff/0VANCiRQuJ9KtXr6J///5Yvnw5VFRUhPTk5GSEhISgUaNGOHjwILS1tQEUBU107twZq1evhpeXF+zs7AAAiYmJ2Lx5M6ysrBAXF4caNWoAACZMmAAnJ6cK+Qzz589HcnIy/Pz8sHr1aonyZmZmQkVFBRoaGpgyZYowy8WHS41UtKSkJMyaNQvjxo0T0sTLmWzZsgXjx48X0ocPH46nT58iKioKzs7OQvqkSZPg7OyMMWPGIDk5+R8tLxERERERERH9t3CJDSIiIiIiIqKPcOfOHSxYsAALFizAtGnT4ObmhsWLF6NatWqYMWOGRN4qVapgzpw5EsEGAIQAg++//14IjgAAbW1tBAUFSeQBgG3btgEAJk+eLARHAIChoSGGDx/+0Z8pPz8fGzduhJaWFhYuXChVXm1tbWhoaHz0fhRlamqKMWPGSKQNHDgQAHDu3Dkh7cKFCzh9+jT8/f0lgiMAoEGDBggICMDVq1e51AYRERERERHRF4YzSBARERERERF9hLt372LhwoUAADU1Nejq6qJ3794YN24cmjZtKpHX1NQUtWvXltrGxYsXAchelkKcdunSJSHt8uXLAAB7e3up/OJZJj7GzZs3kZWVhY4dO0IkEn309iqKlZUVlJUln/UwMjICUDSrhVhKSgoA4O+//8aCBQuktnPr1i3h/5aWlv9UcYmIiIiIiIjoP4YBEkREREREREQfoVOnToiMjJQrr46Ojsz07OxsKCsro06dOlKv6erqQllZGVlZWUJaVlYWlJWVZQZb6OrqylnykomDDQwMDD56WxVJS0tLKk1VtWhoIz8/X0h7+fIlACAuLg5xcXElbi8nJ6eCS0hERERERERE/2UMkCAiIiIiIiL6lygpKclM19TUREFBAZ49eyYVRJGeno6CggJoamoKaVpaWigoKMDz58+lgir+/vvvEvddPIiguKysLIngA/EyH48fPy77Q/0HiY/VokWLMHTo0EouDRERERERERH9VyiXnYWIiIiIiIiI/kk2NjYAgBMnTki9lpSUBACwtrYW0qysrAAAycnJUvlPnjwpcx8ikQhpaWlS6ffu3ZNYngIALCwsoKWlhXPnziEjI6PM8quoqKCgoKDMfP8WW1tbAMCZM2cquSRERERERERE9F/CAAkiIiIiIiKiSubv7w8AWLhwodRSGgsXLpTIAwB9+/YFUDRDQvFlItLS0hAaGipzHy1atMD9+/eRmJgopL179w4//PCDVF5VVVUMHjwYWVlZCAoKkpp5IjMzE69evRL+rlmzJp4/f47c3Fy5P/M/qVWrVrC1tcWuXbsQFRUl9XpBQYHMYBQiIiIiIiIi+rxxiQ0iIiIiIiKiSubg4IChQ4dizZo1sLe3h5eXFwoLC/H777/j4cOHGDZsGBwcHIT8Tk5O6N+/P7Zs2QJ7e3t4enri3bt3iIqKgq2tLeLi4qT2MXLkSBw5cgR+fn7o2bMn1NXVcezYMWhra0NfX18q/9SpU3HmzBls374dKSkpcHV1RdWqVZGamor4+HgcOHBAmPmiffv2OH/+PPr27Qs7OztUqVIF7dq1g52d3T930Mqwbt06eHl54auvvkJISAiaN2+OqlWr4uHDhzhz5gyePXuGp0+fVlr5iIiIiIiIiOjfxwAJIiIiIiIiov+ARYsWwcbGBuvXr0dYWBgAoHHjxvj+++8xYMAAqfzLly9HgwYNEBYWhrVr18LQ0BCjRo2Cj4+PzAAJV1dXbNiwAT///DO2b9+OmjVrwtvbGzNmzJAZyFCtWjXs3r0ba9aswY4dOxAeHg4VFRUYGxsjMDAQJiYmQt5JkyYhIyMDcXFxSEhIQEFBAYKCgio1QMLMzAyJiYlYuXIlYmJisHnzZqioqEBPTw/29vbo3r17pZWNiIiIiIiIiCqHUkZGRmFlF4KIiIiIiIiIiIiIiIiIiIjon6Rc2QUgIiIiIiIiIiIiIiIiIiIi+qcxQIKIiIiIiIiIiIiIiIiIiIg+ewyQICIiIiIiIiIiIiIiIiIios8eAySIiIiIiIiIiIiIiIiIiIjos8cACSIiIiIiIiIiIiIiIiIiIvrsMUCCiIiIiIiIiIiIiIiIiIiIPnsMkCAiIiIiIiIiIiIiIiIiIqLPHgMkiIiIiIiIiIiIiIiIiIiI6LPHAAkiIiIiIiIiIiIiIiIiIiL67DFAgoiIiIiIiIiIiIiIiIiIiD57DJAgIiIiIiIiIiIiIiIiIiKizx4DJIiIiIiIiIiIiIiIiIiIiOiz93+fP4c1YYmLhwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Total', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "b4a4684f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc8070a6800>"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AAAAAAAAAZAoESQAAAAAAAAAAAAAAgEyBIAkAAAAAAAAAAAAAAJApECQBAAAAAAAAAAAAAAAyBYIkAAAAAAAAAAAAAABApkCQBAAAAAAAAAAAAAAAyBQIkgAAAAAAAAAAAAAAAJkCQRIAAAAAAAAAAAAAACBTIEgCAAAAAAAAAAAAAABkCgRJAAAAAAAAAAAAAACATIEgCQAAAAAAAAAAAAAAkCn8P7KvI8BxUm4vAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Unit price', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "f50c5fe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc807e24e50>"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Quantity', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b24cf5c",
+   "metadata": {},
+   "source": [
+    "# Summary of Insight\n",
+    " It was observed that the city with the highest gross income is 'Port Harcourt'.\n",
+    " \n",
+    " It can be observed that the most frequent customers are of the female gender and the most bought product is the 'Home and     lifestyle'. Also, the use of catplot with kind = 'point' is an easy to read representation.\n",
+    "\n",
+    " The most used payment method with respect to 'Product line' is Cash\n",
+    " \n",
+    " The branch with the lowest rating is \"B\" while 'C' has the highest\n",
+    " \n",
+    " It is observed that the most frequent customers are of the female gender and the most bought product is the 'Home and lifestyle'.\n",
+    " \n",
+    " It is observed that the most expensive 'product line' is the 'food and beverages' and it is mostly bought by the female gender."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dd96b18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8343c810",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main.zip b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main.zip
new file mode 100644
index 0000000..e433214
Binary files /dev/null and b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main.zip differ
diff --git a/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Budget data.csv b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Budget data.csv
new file mode 100644
index 0000000..8cd642c
--- /dev/null
+++ b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Budget data.csv	
@@ -0,0 +1,38 @@
+states,Initial_budget (Bn),Revised_budget (Bn)
+Abia,136.6,102.7
+Adamawa,183.3,139.31
+Akwa-Ibom,597.73,366
+Anambra,137.1,112.8
+Bauchi,167.2,128
+Bayelsa,242.18,183.15
+Benue,189,119
+Borno,146.8,108.8
+Cross River,1100,147.1
+Delta,395.5,282.3
+Ebonyi,178.4,131.8
+Edo,179.2,128.8
+Ekiti,124.5,91.1
+Enugu,169.56,146.4
+Gombe,130.83,107.4
+Imo,197.6,108.3
+Jigawa,152.92,124
+Kaduna,259.25,223.6
+Kano,200,138
+Katsina,244,213
+Kebbi,138,99.6
+Kogi,176,102
+Kwara,160,120
+Lagos,1680,920.5
+Nasarawa,108.4,62.96
+Niger,155,98
+Ogun,449.9,280
+Ondo,187.8,151.4
+Osun,119.6,82.2
+Oyo,213,174
+Plateau,177.3,122
+Rivers,530.8,300.4
+Sokoto,202.4,153
+Taraba,215,150.5
+Yobe,108,86
+Zamfara,188.5,127.3
+FCT,278.78,199
\ No newline at end of file
diff --git a/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/README.md b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/README.md
new file mode 100644
index 0000000..643a0e9
--- /dev/null
+++ b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/README.md
@@ -0,0 +1,2 @@
+# Nigeria-COVID-19-Data-Analysis-Using-Python
+Data Scientist Microdegree Capstone Project
diff --git a/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/RealGDP.csv b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/RealGDP.csv
new file mode 100644
index 0000000..e8d780e
--- /dev/null
+++ b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/RealGDP.csv
@@ -0,0 +1,8 @@
+Year,Q1,Q2,Q3,Q4
+2014,15438679.5,16084622.31,17479127.58,18150356.45
+2015,16050601.38,16463341.91,17976234.59,18533752.07
+2016,15943714.54,16218542.41,17555441.69,18213537.29
+2017,15797965.83,16334719.27,17760228.17,18598067.07
+2018,16096654.19,16580508.07,18081342.1,19041437.59
+2019,16434552.65,16931434.89,18494114.17,19530000
+2020,16740000,15890000,17820000,0
\ No newline at end of file
diff --git a/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covid_external.csv b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covid_external.csv
new file mode 100644
index 0000000..c20cce6
--- /dev/null
+++ b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covid_external.csv
@@ -0,0 +1,38 @@
+states,region,Population,Overall CCVI Index,Age,Epidemiological,Fragility,Health System,Population Density,Socio-Economic, Transport Availability,Acute IHR
+FCT,North Central,4865000,0.3,0,0.9,0.4,0.6,0.9,0.6,0.2,0.79
+Plateau,North Central,4766000,0.4,0.5,0.4,0.8,0.3,0.3,0.5,0.3,0.93
+Kwara,North Central,3524000,0.3,0.4,0.3,0.2,0.4,0.2,0.6,0.7,0.93
+Nassarawa,North Central,2783000,0.1,0.3,0.5,0.9,0,0.1,0.6,0.5,0.85
+Niger,North Central,6260000,0.6,0,0.6,0.3,0.7,0.1,0.8,0.8,0.84
+Benue,North Central,6376000,0.5,0.7,0.5,0.7,0.4,0.4,0.3,0.5,0.91
+Kogi,North Central,4970000,0.1,0.3,0.2,0.5,0.5,0.4,0.3,0.6,0.87
+Bauchi,North East,7270000,0.8,0.1,0.2,0.8,0.8,0.2,0.8,0.8,0.85
+Gombe,North East,3692000,1,0.4,0.4,0.9,0.9,0.3,0.8,0.7,0.83
+Borno,North East,6651000,0.9,0.3,0.1,1,1,0.1,0.7,0.9,0.89
+Adamawa,North East,4672000,0.8,0.5,0.6,0.9,0.7,0.3,0.7,0.6,0.94
+Yobe,North East,3723000,1,0.6,0.3,1,0.5,0,0.9,1,0.87
+Taraba,North East,3387000,0.8,0.6,0.8,0.9,0.5,0,0.7,0.9,0.86
+Kano,North West,14726000,0.6,0.2,0.1,0.3,0.4,0.8,0.8,0.5,0.87
+Kaduna,North West,9227000,0.7,0.1,0.9,0.8,0.9,0.4,0.6,0.4,0.86
+Katsina,North West,8713000,0.7,0.1,0.1,0.3,0.2,0.6,0.9,0.8,0.85
+Jigawa,North West,6435000,0.9,0.4,0,0.7,0.6,0.5,0.9,0.8,0.92
+Sokoto,North West,5612000,0.9,0.3,0.2,0.4,0.7,0.3,1,1,0.89
+Kebbi,North West,4968000,0.8,0.2,0.3,0.4,0.9,0.1,0.9,0.9,0.85
+Zamfara,North West,4974000,0.9,0.2,0.1,0.7,0.8,0.2,1,0.9,0.87
+Imo,South East,6018000,0.4,1,0.9,0,0.4,0.9,0.4,0,1.12
+Abia,South East,4190000,0.2,0.9,0.6,0.1,0.3,0.9,0.2,0,1.14
+Enugu,South East,4801000,0.2,0.9,0.4,0,0.3,0.7,0.4,0.1,1.14
+Akwa Ibom,South East,6093000,0.7,0.8,0.7,0.6,0.8,0.8,0.5,0.1,1.01
+Anambra,South East,6050000,0.6,0.9,1,0.6,0.2,1,0,0.1,1.08
+Edo,South South,4705000,0.1,0.9,0.8,0.1,0.1,0.5,0.4,0.3,1.09
+Rivers,South South,7763000,0.4,0.5,0.9,0.6,0.6,0.9,0.1,0.1,1
+Delta,South South,6303000,0.4,0.6,0.7,0.2,1,0.6,0.5,0.4,1.08
+Ebonyi,South South,3192000,0.6,1,0,0.6,0.1,0.7,0.3,0.6,1
+Bayelsa,South South,2606000,0.5,0.8,0.6,0.1,0.9,0.5,0.2,0.7,1
+Cross river,South South,4272000,0.5,0.4,0.7,0.8,0.1,0.4,0.4,0.6,0.98
+Lagos,South West,13992000,0,0.1,1,0.3,0.1,1,0.1,0.4,0.93
+Oyo,South West,8737000,0.2,0.7,0.8,0.2,0.8,0.6,0.2,0.3,1.06
+Ogun,South West,5878000,0.3,0.6,0.7,0.5,0.6,0.6,0,0.2,1.07
+Ondo,South West,5185000,0.1,0.8,0.5,0.1,0.3,0.6,0.3,0.3,1.04
+Osun,South West,5252000,0,0.7,0.4,0.4,0,0.8,0.1,0.2,1.06
+Ekiti,South West,3593000,0.3,0.8,0.3,0.5,0.2,0.8,0.1,0.4,1.03
\ No newline at end of file
diff --git a/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covidnig.csv b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covidnig.csv
new file mode 100644
index 0000000..b04fb21
--- /dev/null
+++ b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covidnig.csv
@@ -0,0 +1,38 @@
+States Affected,No. of Cases (Lab Confirmed),No. of Cases (on admission),No. Discharged,No. of Deaths
+Lagos,"26,708","2,435","24,037",236
+FCT,"9,627","2,840","6,694",93
+Kaduna,"4,504",579,"3,877",48
+Plateau,"4,262",280,"3,948",34
+Oyo,"3,788",368,"3,374",46
+Rivers,"3,279",232 ,"2,987",60
+Edo,"2,768",52,"2,603",113
+Ogun,"2,382",174,"2,175",33
+Kano,"2,032",198,"1,778",56
+Delta,"1,843",57,"1,737",49
+Ondo,"1,793",62,"1,690",41
+Katsina,"1,405",214,"1,167",24
+Enugu,"1,376",34,"1,321",21
+Kwara,"1,296",171,"1,094",31
+Gombe,"1,164",183,950,31
+Ebonyi,"1,091",24,"1,037",30
+Abia,980,25,945,10
+Osun,979 ,20,937,22
+Bauchi,897,108,775,14
+Borno,778,37,705,36
+Imo,734,61,661,12
+Nasarawa,600,262,325,13
+Benue,515,44,460,11
+Bayelsa,497,55,421,21
+Ekiti,405,24,375,6
+Akwa Ibom,395,44,342,9
+Jigawa,386,57,318,11
+Niger,381,69,300,12
+Adamawa,355,96,238,21
+Anambra,299,6,274,19
+Sokoto,228,40,171,17
+Taraba,203,19,177,7
+Yobe,164,33,123,8
+Kebbi,143,5,130,8
+Cross River,93,1,81,11
+Zamfara ,79,1,73,5
+Kogi,5,0,3,2
diff --git a/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covidproject.ipynb b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covidproject.ipynb
new file mode 100644
index 0000000..01d1faf
--- /dev/null
+++ b/My_Ustacky_Project/Nigeria-COVID-19-Data-Analysis-Using-Python-main/Nigeria-COVID-19-Data-Analysis-Using-Python-main/covidproject.ipynb
@@ -0,0 +1,319 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "### Task 1 - Data Collection\n",
+    "Here you will obtain the required data for the analysis. As described in the project instructions, you will perform a web scrap to obtain data from the NCDC website, import data from the John Hopkins repository, and import the provided external data.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "source": [
+    "### A - NCDC Website scrap\n",
+    "Website - https://covid19.ncdc.gov.ng/"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write Your Code Below\n",
+    "# Import all libraries in this cell\n",
+    "import requests\n",
+    "import numpy as np\n",
+    "import urllib.request\n",
+    "import pandas as pd\n",
+    "import csv\n",
+    "from bs4 import BeautifulSoup\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"darkgrid\")\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')  \n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "source": [
+    "# Save the data to a DataFrame object.\n"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 7,
+   "outputs": []
+  },
+  {
+   "source": [
+    "### B - John Hopkins Data Repository\n",
+    "Here you will obtain data from the John Hopkins repository. Your task here involves saving the data from the GitHub repo link to DataFrame for further analysis. Find the links below. \n",
+    "* Global Daily Confirmed Cases - Click [Here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv)\n",
+    "* Global Daily Recovered Cases - Click [Here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv)\n",
+    "* Global Daily Death Cases - Click [Here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv)"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#[Write Your Code Here]\n",
+    "\n"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 8,
+   "outputs": []
+  },
+  {
+   "source": [
+    "### C - External Data \n",
+    "* Save the external data to a DataFrame\n",
+    "* External Data includes but not limited to: `covid_external.csv`, `Budget data.csv`, `RealGDP.csv`"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write Your Code Here]\n"
+   ]
+  },
+  {
+   "source": [
+    "### Task 2 - View the data\n",
+    "Obtain basic information about the data using the `head()` and `info()` method."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write Your Code Here]\n"
+   ]
+  },
+  {
+   "source": [
+    "### Task 3 - Data Cleaning and Preparation\n",
+    "From the information obtained above, you will need to fix the data format. \n",
+    "<br>\n",
+    "Examples: \n",
+    "* Convert to appropriate data type.\n",
+    "* Rename the columns of the scraped data.\n",
+    "* Remove comma(,) in numerical data\n",
+    "* Extract daily data for Nigeria from the Global daily cases data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO A - Clean the scraped data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write Your Code Here]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "source": [
+    "TODO B - Get a Pandas DataFrame for Daily Confirmed Cases in Nigeria. Columns are Date and Cases"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO C - Get a Pandas DataFrame for Daily Recovered Cases in Nigeria. Columns are Date and Cases"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO D - Get a Pandas DataFrame for Daily Death Cases in Nigeria. Columns are Date and Cases"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "### Task 4 - Analysis\n",
+    "Here you will perform some analyses on the datasets. You are welcome to communicate findings in charts and summary. \n",
+    "<br>\n",
+    "We have included a few TODOs to help with your analysis. However, do not let this limit your approach, feel free to include more, and be sure to support your findings with chart and summary "
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO A - Generate a plot that shows the Top 10 states in terms of Confirmed Covid cases by Laboratory test"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO B - Generate a plot that shows the Top 10 states in terms of Discharged Covid cases. Hint - Sort the values"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO D - Plot the top 10 Death cases"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO E - Generate a line plot for the total daily confirmed, recovered and death cases in Nigeria"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO F - \n",
+    "* Determine the daily infection rate, you can use the Pandas `diff` method to find the derivate of the total cases.\n",
+    "* Generate a line plot for the above"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO G - \n",
+    "* Calculate maximum infection rate for a day (Number of new cases)\n",
+    "* Find the date"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO H - Determine the relationship between the external dataset and the NCDC COVID-19 dataset. \n",
+    "Here you will generate a line plot of top 10 confirmed cases and the overall community vulnerability index on the same axis. From the graph, explain your observation.\n",
+    "<br>\n",
+    "Steps\n",
+    "* Combine the two dataset together on a common column(states)\n",
+    "* Create a new dataframe for plotting. This DataFrame will contain top 10 states in terms of confirmed cases i.e sort by confirmed cases. ** Hint: Check out Pandas [nlargest](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nlargest.html) function. This [tutorial](https://cmdlinetips.com/2019/03/how-to-select-top-n-rows-with-the-largest-values-in-a-columns-in-pandas/) can help out ** \n",
+    "* Plot both variable on the same axis. Check out this [tutorial](http://kitchingroup.cheme.cmu.edu/blog/2013/09/13/Plotting-two-datasets-with-very-different-scales/)"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO I - Determine the relationship between the external dataset and the NCDC COVID-19 dataset. \n",
+    "* Here you will generate a regression plot between two variables to visualize the linear relationships - Confirmed Cases and Population Density.\n",
+    "Hint: Check out Seaborn [Regression Plot](https://seaborn.pydata.org/generated/seaborn.regplot.html).\n",
+    "* Provide a summary of your observation"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO J - \n",
+    "* Provide more analyses by extending TODO G & H. Meaning, determine relationships between more features.\n",
+    "* Provide a detailed summary of your findings. \n",
+    "* Note that you can have as many as possible."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "### TODO L - \n",
+    "Determine the effect of the Pandemic on the economy. To do this, you will compare the Real GDP value Pre-COVID-19 with Real GDP in 2020 (COVID-19 Period, especially Q2 2020)\n",
+    "<br>\n",
+    "Steps\n",
+    "* From the Real GDP Data, generate a `barplot` using the GDP values for each year & quarters. For example: On x-axis you will have year 2017 and the bars will be values of each quarters(Q1-Q4). You expected to have subplots of each quarters on one graph.\n",
+    "<br>\n",
+    "Hint: Use [Pandas.melt](https://pandas.pydata.org/docs/reference/api/pandas.melt.html) to create your plot DataFrame \n",
+    "* Set your quarter legend to lower left.\n",
+    "* Using `axhline`, draw a horizontal line through the graph at the value of Q2 2020.\n",
+    "* Write out your observation"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "### Note: Do not limit your analysis to the provided TODOs. Perform more analyses e.g \n",
+    "* Check for more external dataset\n",
+    "* Ask more questions & find the right answers by exploring the data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/My_Ustacky_Project/Pandas Analytics_Project Executive Summary.pdf b/My_Ustacky_Project/Pandas Analytics_Project Executive Summary.pdf
new file mode 100644
index 0000000..7d3f0bd
Binary files /dev/null and b/My_Ustacky_Project/Pandas Analytics_Project Executive Summary.pdf differ
diff --git a/My_Ustacky_Project/Port_Harcourt_Branch.csv b/My_Ustacky_Project/Port_Harcourt_Branch.csv
new file mode 100644
index 0000000..9c09251
--- /dev/null
+++ b/My_Ustacky_Project/Port_Harcourt_Branch.csv
@@ -0,0 +1,329 @@
+Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
+226-31-3081,C,Port Harcourt,Normal,Female,Electronic accessories,5500.8,5,1375.2,28879.2,3/8/2019,10:29,Cash,27504.000000000004,4.7619047619999995,1375.2,9.6
+699-14-3026,C,Port Harcourt,Normal,Male,Electronic accessories,30740.4,7,10759.140000000001,225941.94,3/25/2019,18:30,Epay,215182.80000000002,4.7619047619999995,10759.140000000001,4.1
+315-22-5665,C,Port Harcourt,Normal,Female,Home and lifestyle,26481.600000000002,10,13240.800000000001,278056.8,2/24/2019,11:38,Epay,264816.0,4.7619047619999995,13240.800000000001,8.0
+300-71-4605,C,Port Harcourt,Member,Male,Electronic accessories,30974.4,5,7743.6,162615.6,2/25/2019,11:24,Epay,154872.0,4.7619047619999995,7743.6,4.8
+183-56-6882,C,Port Harcourt,Member,Female,Food and beverages,35791.2,4,7158.24,150323.04,2/6/2019,10:42,Epay,143164.8,4.7619047619999995,7158.24,7.5
+232-16-2483,C,Port Harcourt,Member,Female,Sports and travel,24523.2,1,1226.16,25749.360000000004,1/7/2019,12:28,Epay,24523.2,4.7619047619999995,1226.16,6.8
+333-73-7901,C,Port Harcourt,Normal,Female,Health and beauty,19771.2,8,7908.480000000001,166078.08000000002,3/23/2019,13:24,Epay,158169.6,4.7619047619999995,7908.480000000001,7.6
+554-53-8700,C,Port Harcourt,Member,Male,Home and lifestyle,20199.6,2,2019.9600000000003,42419.16,2/2/2019,10:11,Cash,40399.2,4.7619047619999995,2019.9600000000003,6.3
+228-96-1411,C,Port Harcourt,Member,Female,Food and beverages,35532.0,8,14212.8,298468.8,3/4/2019,20:39,Cash,284256.0,4.7619047619999995,14212.8,7.6
+617-15-4209,C,Port Harcourt,Member,Male,Health and beauty,5533.2,2,553.3199999999999,11619.720000000001,3/16/2019,19:47,Cash,11066.4,4.7619047619999995,553.3199999999999,7.2
+574-22-5561,C,Port Harcourt,Member,Female,Fashion accessories,29746.8,10,14873.4,312341.4,3/19/2019,17:08,Epay,297468.0,4.7619047619999995,14873.4,7.9
+326-78-5178,C,Port Harcourt,Member,Male,Food and beverages,32904.0,7,11516.4,241844.4,2/3/2019,10:19,Cash,230327.99999999997,4.7619047619999995,11516.4,9.5
+778-71-5554,C,Port Harcourt,Member,Male,Fashion accessories,5554.8,1,277.74,5832.54,1/25/2019,15:46,Card,5554.8,4.7619047619999995,277.74,6.1
+399-46-5918,C,Port Harcourt,Normal,Female,Electronic accessories,30952.800000000003,8,12381.119999999999,260003.52,2/28/2019,19:01,Cash,247622.40000000002,4.7619047619999995,12381.119999999999,8.2
+120-06-4233,C,Port Harcourt,Normal,Male,Electronic accessories,11019.6,6,3305.88,69423.48,3/12/2019,20:36,Cash,66117.6,4.7619047619999995,3305.88,9.3
+285-68-5083,C,Port Harcourt,Member,Female,Sports and travel,8906.4,3,1335.96,28055.16,2/15/2019,17:47,Card,26719.2,4.7619047619999995,1335.96,10.0
+803-83-5989,C,Port Harcourt,Normal,Male,Home and lifestyle,20062.8,6,6018.84,126395.64,2/24/2019,10:55,Epay,120376.8,4.7619047619999995,6018.84,7.0
+838-78-4295,C,Port Harcourt,Normal,Female,Health and beauty,12049.199999999999,2,1204.92,25303.319999999996,2/10/2019,15:43,Epay,24098.399999999998,4.7619047619999995,1204.92,6.7
+393-65-2792,C,Port Harcourt,Normal,Male,Food and beverages,32212.800000000003,10,16106.400000000001,338234.39999999997,1/6/2019,12:46,Card,322128.0,4.7619047619999995,16106.400000000001,9.6
+796-12-2025,C,Port Harcourt,Normal,Male,Fashion accessories,22363.2,10,11181.6,234813.6,2/11/2019,16:19,Cash,223632.00000000003,4.7619047619999995,11181.6,5.9
+841-35-6630,C,Port Harcourt,Normal,Female,Electronic accessories,27327.6,6,8198.28,172163.88,3/9/2019,18:21,Cash,163965.6,4.7619047619999995,8198.28,8.7
+732-94-0499,C,Port Harcourt,Normal,Female,Electronic accessories,14994.0,10,7497.0,157437.0,1/13/2019,17:04,Card,149940.0,4.7619047619999995,7497.0,5.4
+263-10-3913,C,Port Harcourt,Member,Male,Fashion accessories,17654.4,9,7944.4800000000005,166834.08000000002,1/9/2019,14:20,Card,158889.6,4.7619047619999995,7944.4800000000005,8.6
+829-49-1914,C,Port Harcourt,Member,Female,Food and beverages,28191.600000000002,10,14095.800000000001,296011.8,3/5/2019,16:24,Epay,281916.0,4.7619047619999995,14095.800000000001,6.6
+756-01-7507,C,Port Harcourt,Normal,Female,Health and beauty,7336.799999999999,5,1834.1999999999998,38518.200000000004,1/22/2019,18:56,Cash,36684.0,4.7619047619999995,1834.1999999999998,6.0
+870-72-4431,C,Port Harcourt,Normal,Female,Health and beauty,35708.4,6,10712.519999999999,224962.91999999998,1/21/2019,14:42,Card,214250.4,4.7619047619999995,10712.519999999999,5.5
+480-63-2856,C,Port Harcourt,Normal,Male,Food and beverages,6930.0,8,2772.0,58211.99999999999,1/23/2019,18:37,Epay,55440.0,4.7619047619999995,2772.0,6.6
+787-56-0757,C,Port Harcourt,Member,Female,Food and beverages,28929.6,4,5785.92,121504.31999999998,2/23/2019,18:45,Card,115718.4,4.7619047619999995,5785.92,8.3
+360-39-5055,C,Port Harcourt,Member,Male,Sports and travel,17607.6,5,4401.9,92439.9,3/9/2019,10:17,Cash,88038.0,4.7619047619999995,4401.9,6.6
+730-50-9884,C,Port Harcourt,Normal,Female,Sports and travel,29901.600000000002,7,10465.56,219776.76,3/5/2019,14:31,Epay,209311.19999999998,4.7619047619999995,10465.56,4.0
+362-58-8315,C,Port Harcourt,Normal,Male,Fashion accessories,27547.199999999997,5,6886.799999999999,144622.80000000002,3/25/2019,10:23,Cash,137736.0,4.7619047619999995,6886.799999999999,9.9
+565-80-5980,C,Port Harcourt,Member,Female,Home and lifestyle,17056.8,4,3411.36,71638.56,1/23/2019,10:25,Cash,68227.2,4.7619047619999995,3411.36,7.1
+225-32-0908,C,Port Harcourt,Normal,Female,Sports and travel,16149.6,10,8074.8,169570.8,1/26/2019,19:54,Epay,161496.0,4.7619047619999995,8074.8,8.2
+512-91-0811,C,Port Harcourt,Normal,Male,Health and beauty,32310.0,1,1615.5,33925.5,2/6/2019,20:05,Card,32310.0,4.7619047619999995,1615.5,6.6
+871-39-9221,C,Port Harcourt,Normal,Female,Electronic accessories,4482.0,6,1344.6,28236.600000000002,2/9/2019,13:11,Cash,26892.0,4.7619047619999995,1344.6,4.1
+163-56-7055,C,Port Harcourt,Member,Male,Fashion accessories,17535.6,1,876.7800000000001,18412.38,3/26/2019,19:20,Cash,17535.6,4.7619047619999995,876.7800000000001,4.1
+189-98-2939,C,Port Harcourt,Normal,Male,Fashion accessories,28278.0,9,12725.099999999999,267227.1,3/1/2019,13:22,Cash,254502.00000000003,4.7619047619999995,12725.099999999999,7.2
+551-21-3069,C,Port Harcourt,Normal,Female,Electronic accessories,8305.2,9,3737.34,78484.14,2/1/2019,11:27,Cash,74746.8,4.7619047619999995,3737.34,4.9
+628-34-3388,C,Port Harcourt,Normal,Male,Fashion accessories,9856.8,6,2957.04,62097.84000000001,1/5/2019,20:54,Card,59140.8,4.7619047619999995,2957.04,7.9
+588-01-7461,C,Port Harcourt,Normal,Female,Food and beverages,12232.8,9,5504.759999999999,115599.95999999999,3/24/2019,10:43,Cash,110095.2,4.7619047619999995,5504.759999999999,4.2
+861-77-0145,C,Port Harcourt,Member,Male,Electronic accessories,29509.2,10,14754.6,309846.6,3/3/2019,14:30,Cash,295092.0,4.7619047619999995,14754.6,9.2
+210-67-5886,C,Port Harcourt,Member,Female,Health and beauty,35355.6,3,5303.34,111370.13999999998,2/5/2019,10:41,Card,106066.8,4.7619047619999995,5303.34,7.8
+237-01-6122,C,Port Harcourt,Member,Female,Home and lifestyle,29084.4,9,13087.98,274847.58,2/1/2019,20:31,Card,261759.6,4.7619047619999995,13087.98,9.5
+225-98-1496,C,Port Harcourt,Normal,Female,Fashion accessories,9727.2,3,1459.08,30640.68,3/2/2019,13:01,Card,29181.600000000002,4.7619047619999995,1459.08,7.1
+659-36-1684,C,Port Harcourt,Member,Male,Sports and travel,20563.2,7,7197.12,151139.52,1/12/2019,12:02,Card,143942.4,4.7619047619999995,7197.12,6.5
+336-78-2147,C,Port Harcourt,Member,Male,Home and lifestyle,23007.6,8,9203.04,193263.84000000003,3/13/2019,19:52,Card,184060.8,4.7619047619999995,9203.04,4.6
+189-08-9157,C,Port Harcourt,Normal,Female,Fashion accessories,11422.8,9,5140.259999999999,107945.46,1/8/2019,16:17,Card,102805.2,4.7619047619999995,5140.259999999999,5.9
+663-86-9076,C,Port Harcourt,Member,Female,Food and beverages,24674.4,8,9869.76,207264.96,1/8/2019,15:57,Epay,197395.2,4.7619047619999995,9869.76,8.5
+483-71-1164,C,Port Harcourt,Normal,Female,Health and beauty,29268.0,6,8780.4,184388.40000000002,3/8/2019,16:43,Epay,175608.0,4.7619047619999995,8780.4,5.3
+597-78-7908,C,Port Harcourt,Normal,Male,Fashion accessories,32479.2,3,4871.88,102309.48,2/18/2019,19:39,Cash,97437.6,4.7619047619999995,4871.88,6.2
+731-81-9469,C,Port Harcourt,Member,Female,Sports and travel,32328.0,10,16164.0,339444.0,1/23/2019,13:00,Card,323280.0,4.7619047619999995,16164.0,5.4
+280-17-4359,C,Port Harcourt,Member,Male,Health and beauty,32580.0,10,16290.0,342090.0,1/25/2019,13:48,Cash,325800.0,4.7619047619999995,16290.0,8.1
+338-65-2210,C,Port Harcourt,Member,Female,Health and beauty,24695.999999999996,10,12347.999999999998,259307.99999999997,2/5/2019,19:57,Cash,246960.0,4.7619047619999995,12347.999999999998,9.1
+488-25-4221,C,Port Harcourt,Member,Female,Food and beverages,10947.6,1,547.38,11494.98,2/22/2019,10:36,Card,10947.6,4.7619047619999995,547.38,8.4
+458-41-1477,C,Port Harcourt,Normal,Female,Health and beauty,16653.6,6,4996.08,104917.68,3/8/2019,17:11,Card,99921.6,4.7619047619999995,4996.08,9.5
+568-90-5112,C,Port Harcourt,Normal,Male,Health and beauty,23810.4,4,4762.08,100003.68000000001,3/19/2019,12:46,Card,95241.6,4.7619047619999995,4762.08,5.6
+584-86-7256,C,Port Harcourt,Member,Male,Sports and travel,12441.6,7,4354.5599999999995,91445.76,3/11/2019,16:07,Card,87091.2,4.7619047619999995,4354.5599999999995,7.3
+214-17-6927,C,Port Harcourt,Normal,Female,Food and beverages,5932.8,6,1779.84,37376.64,2/7/2019,18:23,Epay,35596.799999999996,4.7619047619999995,1779.84,9.9
+400-89-4171,C,Port Harcourt,Normal,Female,Sports and travel,29149.2,8,11659.680000000002,244853.28,1/28/2019,13:05,Cash,233193.6,4.7619047619999995,11659.680000000002,9.3
+894-41-5205,C,Port Harcourt,Normal,Female,Food and beverages,15544.8,8,6217.920000000001,130576.31999999999,1/19/2019,19:39,Card,124358.4,4.7619047619999995,6217.920000000001,8.3
+423-80-0988,C,Port Harcourt,Normal,Male,Sports and travel,27504.000000000004,2,2750.4,57758.4,1/30/2019,19:42,Epay,55008.00000000001,4.7619047619999995,2750.4,6.5
+234-65-2137,C,Port Harcourt,Normal,Male,Home and lifestyle,34408.8,10,17204.4,361292.4,1/16/2019,13:32,Cash,344088.0,4.7619047619999995,17204.4,4.8
+733-33-4967,C,Port Harcourt,Normal,Male,Electronic accessories,7506.000000000001,8,3002.4,63050.399999999994,3/3/2019,19:17,Cash,60048.00000000001,4.7619047619999995,3002.4,6.3
+139-52-2867,C,Port Harcourt,Normal,Female,Fashion accessories,8103.6,7,2836.2599999999998,59561.46,2/13/2019,10:50,Card,56725.2,4.7619047619999995,2836.2599999999998,4.8
+342-65-4817,C,Port Harcourt,Member,Male,Health and beauty,31248.0,3,4687.2,98431.20000000001,1/28/2019,16:47,Epay,93743.99999999999,4.7619047619999995,4687.2,9.9
+130-98-8941,C,Port Harcourt,Normal,Male,Fashion accessories,23133.600000000002,7,8096.76,170031.96,2/9/2019,10:00,Cash,161935.2,4.7619047619999995,8096.76,5.7
+434-83-9547,C,Port Harcourt,Member,Male,Food and beverages,13849.199999999999,8,5539.68,116333.27999999998,1/23/2019,11:51,Cash,110793.59999999999,4.7619047619999995,5539.68,7.7
+824-88-3614,C,Port Harcourt,Normal,Male,Health and beauty,12351.6,8,4940.64,103753.44,1/25/2019,15:00,Epay,98812.8,4.7619047619999995,4940.64,5.7
+593-65-1552,C,Port Harcourt,Normal,Female,Home and lifestyle,25131.600000000002,4,5026.32,105552.72,1/28/2019,20:50,Card,100526.40000000001,4.7619047619999995,5026.32,5.9
+286-43-6208,C,Port Harcourt,Normal,Female,Food and beverages,31608.0,9,14223.599999999999,298695.60000000003,3/16/2019,19:08,Cash,284472.0,4.7619047619999995,14223.599999999999,9.2
+556-86-3144,C,Port Harcourt,Member,Female,Fashion accessories,26744.4,1,1337.22,28081.62,1/13/2019,19:30,Cash,26744.4,4.7619047619999995,1337.22,5.0
+848-24-9445,C,Port Harcourt,Member,Male,Health and beauty,15732.000000000002,2,1573.2,33037.2,3/26/2019,18:03,Cash,31464.000000000004,4.7619047619999995,1573.2,4.9
+699-01-4164,C,Port Harcourt,Normal,Male,Health and beauty,14940.0,4,2988.0000000000005,62748.00000000001,3/12/2019,19:58,Card,59760.0,4.7619047619999995,2988.0000000000005,8.2
+420-11-4919,C,Port Harcourt,Member,Female,Food and beverages,25700.4,5,6425.1,134927.1,2/17/2019,19:57,Card,128502.0,4.7619047619999995,6425.1,5.5
+606-80-4905,C,Port Harcourt,Member,Female,Sports and travel,6893.999999999999,6,2068.2,43432.2,1/29/2019,10:01,Card,41364.0,4.7619047619999995,2068.2,6.8
+426-39-2418,C,Port Harcourt,Normal,Male,Electronic accessories,22107.6,7,7737.660000000001,162490.86,1/14/2019,10:02,Cash,154753.2,4.7619047619999995,7737.660000000001,9.8
+672-51-8681,C,Port Harcourt,Member,Female,Electronic accessories,23994.000000000004,9,10797.3,226743.3,1/4/2019,18:19,Card,215946.0,4.7619047619999995,10797.3,9.7
+263-87-5680,C,Port Harcourt,Member,Female,Home and lifestyle,10270.800000000001,10,5135.400000000001,107843.4,3/18/2019,17:38,Epay,102708.0,4.7619047619999995,5135.400000000001,7.8
+677-11-0152,C,Port Harcourt,Normal,Female,Food and beverages,33573.6,9,15108.119999999999,317270.52,1/16/2019,18:08,Cash,302162.4,4.7619047619999995,15108.119999999999,8.8
+389-25-3394,C,Port Harcourt,Normal,Male,Electronic accessories,4251.6,5,1062.9,22320.899999999998,2/17/2019,18:06,Cash,21258.0,4.7619047619999995,1062.9,9.4
+279-62-1445,C,Port Harcourt,Member,Female,Fashion accessories,4514.4,1,225.72,4740.12,2/21/2019,12:38,Cash,4514.4,4.7619047619999995,225.72,8.2
+746-68-6593,C,Port Harcourt,Member,Female,Sports and travel,31377.6,2,3137.76,65892.95999999999,1/11/2019,14:29,Card,62755.2,4.7619047619999995,3137.76,9.7
+583-72-1480,C,Port Harcourt,Member,Male,Electronic accessories,13341.6,4,2668.32,56034.72000000001,1/31/2019,16:24,Epay,53366.4,4.7619047619999995,2668.32,9.7
+211-30-9270,C,Port Harcourt,Normal,Male,Health and beauty,6267.6,5,1566.9,32904.9,1/28/2019,15:16,Card,31338.0,4.7619047619999995,1566.9,4.9
+755-12-3214,C,Port Harcourt,Member,Female,Fashion accessories,15919.199999999999,5,3979.7999999999997,83575.8,3/5/2019,17:07,Card,79596.0,4.7619047619999995,3979.7999999999997,8.6
+142-72-4741,C,Port Harcourt,Member,Male,Fashion accessories,33552.0,2,3355.2000000000003,70459.2,2/28/2019,18:37,Card,67104.0,4.7619047619999995,3355.2000000000003,6.0
+662-47-5456,C,Port Harcourt,Member,Male,Fashion accessories,12668.4,10,6334.2,133018.2,3/17/2019,19:06,Card,126683.99999999999,4.7619047619999995,6334.2,8.4
+883-17-4236,C,Port Harcourt,Normal,Female,Sports and travel,5180.400000000001,2,518.04,10878.84,3/2/2019,19:44,Card,10360.800000000001,4.7619047619999995,518.04,7.2
+380-94-4661,C,Port Harcourt,Member,Male,Electronic accessories,23738.399999999998,4,4747.679999999999,99701.28,2/7/2019,13:05,Card,94953.59999999999,4.7619047619999995,4747.679999999999,6.9
+821-07-3596,C,Port Harcourt,Normal,Female,Fashion accessories,5922.0,4,1184.4,24872.4,3/7/2019,14:53,Epay,23688.0,4.7619047619999995,1184.4,5.6
+808-65-0703,C,Port Harcourt,Normal,Male,Home and lifestyle,12769.199999999999,4,2553.8399999999997,53630.64,3/14/2019,17:22,Card,51076.799999999996,4.7619047619999995,2553.8399999999997,6.9
+687-15-1097,C,Port Harcourt,Member,Female,Health and beauty,7603.200000000001,2,760.32,15966.72,1/3/2019,19:17,Cash,15206.400000000001,4.7619047619999995,760.32,9.7
+526-86-8552,C,Port Harcourt,Member,Female,Home and lifestyle,7855.2,10,3927.6,82479.6,1/7/2019,17:36,Cash,78552.0,4.7619047619999995,3927.6,7.1
+376-56-3573,C,Port Harcourt,Normal,Female,Fashion accessories,34351.2,4,6870.24,144275.04,2/2/2019,13:23,Epay,137404.8,4.7619047619999995,6870.24,6.4
+537-72-0426,C,Port Harcourt,Member,Male,Fashion accessories,25556.399999999998,10,12778.199999999999,268342.2,3/20/2019,16:28,Cash,255564.0,4.7619047619999995,12778.199999999999,5.7
+523-38-0215,C,Port Harcourt,Normal,Male,Home and lifestyle,13320.0,1,666.0,13986.0,3/6/2019,13:29,Card,13320.0,4.7619047619999995,666.0,7.9
+593-95-4461,C,Port Harcourt,Member,Male,Home and lifestyle,26949.6,1,1347.48,28297.08,3/24/2019,14:49,Cash,26949.6,4.7619047619999995,1347.48,6.9
+226-71-3580,C,Port Harcourt,Normal,Female,Sports and travel,8550.0,9,3847.5,80797.5,1/31/2019,12:02,Cash,76950.0,4.7619047619999995,3847.5,9.5
+558-80-4082,C,Port Harcourt,Normal,Male,Electronic accessories,10026.0,7,3509.1000000000004,73691.09999999999,3/14/2019,17:20,Epay,70182.0,4.7619047619999995,3509.1000000000004,6.0
+211-05-0490,C,Port Harcourt,Member,Female,Electronic accessories,18691.2,5,4672.8,98128.79999999999,3/3/2019,13:42,Cash,93456.00000000001,4.7619047619999995,4672.8,7.5
+727-75-6477,C,Port Harcourt,Normal,Male,Electronic accessories,10382.4,4,2076.48,43606.079999999994,3/29/2019,14:44,Cash,41529.6,4.7619047619999995,2076.48,6.4
+779-06-0012,C,Port Harcourt,Member,Female,Home and lifestyle,31899.6,1,1594.98,33494.579999999994,1/19/2019,10:21,Cash,31899.6,4.7619047619999995,1594.98,7.7
+446-47-6729,C,Port Harcourt,Normal,Male,Fashion accessories,35935.2,2,3593.5200000000004,75463.92,1/2/2019,18:09,Card,71870.4,4.7619047619999995,3593.5200000000004,6.7
+735-06-4124,C,Port Harcourt,Normal,Male,Food and beverages,17499.6,1,874.9799999999999,18374.58,2/25/2019,15:31,Cash,17499.6,4.7619047619999995,874.9799999999999,4.4
+181-94-6432,C,Port Harcourt,Member,Male,Fashion accessories,24958.8,2,2495.88,52413.479999999996,2/5/2019,19:05,Epay,49917.6,4.7619047619999995,2495.88,9.7
+227-07-4446,C,Port Harcourt,Member,Female,Electronic accessories,28126.8,10,14063.4,295331.4,2/10/2019,20:51,Cash,281268.0,4.7619047619999995,14063.4,4.4
+174-36-3675,C,Port Harcourt,Member,Male,Food and beverages,35773.200000000004,2,3577.3200000000006,75123.72,2/14/2019,17:29,Cash,71546.40000000001,4.7619047619999995,3577.3200000000006,5.2
+428-83-5800,C,Port Harcourt,Member,Female,Food and beverages,7588.799999999999,3,1138.32,23904.72,2/9/2019,10:25,Cash,22766.4,4.7619047619999995,1138.32,7.3
+603-07-0961,C,Port Harcourt,Member,Male,Electronic accessories,26924.4,5,6731.1,141353.1,1/10/2019,11:34,Cash,134622.0,4.7619047619999995,6731.1,4.9
+704-20-4138,C,Port Harcourt,Member,Female,Health and beauty,10681.2,7,3738.4199999999996,78506.82,3/11/2019,18:58,Card,74768.4,4.7619047619999995,3738.4199999999996,8.1
+787-15-1757,C,Port Harcourt,Member,Male,Health and beauty,15865.2,4,3173.04,66633.84000000001,2/18/2019,16:28,Epay,63460.8,4.7619047619999995,3173.04,8.4
+649-11-3678,C,Port Harcourt,Normal,Female,Food and beverages,8254.8,9,3714.66,78007.86,2/26/2019,20:26,Cash,74293.2,4.7619047619999995,3714.66,5.5
+622-20-1945,C,Port Harcourt,Normal,Female,Health and beauty,14191.2,1,709.56,14900.760000000002,1/18/2019,15:08,Cash,14191.2,4.7619047619999995,709.56,8.4
+719-76-3868,C,Port Harcourt,Member,Male,Food and beverages,33933.6,4,6786.72,142521.12,3/12/2019,16:30,Cash,135734.4,4.7619047619999995,6786.72,8.6
+835-16-0096,C,Port Harcourt,Member,Male,Sports and travel,5292.0,5,1323.0,27783.0,3/24/2019,13:48,Epay,26460.0,4.7619047619999995,1323.0,8.5
+633-09-3463,C,Port Harcourt,Normal,Female,Electronic accessories,17154.0,3,2573.1,54035.1,3/28/2019,12:58,Card,51461.99999999999,4.7619047619999995,2573.1,9.5
+416-13-5917,C,Port Harcourt,Normal,Female,Food and beverages,34930.8,5,8732.7,183386.7,1/30/2019,16:24,Epay,174654.0,4.7619047619999995,8732.7,9.3
+725-96-3778,C,Port Harcourt,Member,Female,Home and lifestyle,32130.0,8,12852.000000000002,269892.0,1/20/2019,10:13,Cash,257040.0,4.7619047619999995,12852.000000000002,4.7
+860-79-0874,C,Port Harcourt,Member,Female,Fashion accessories,35748.0,10,17874.0,375354.00000000006,2/15/2019,14:53,Card,357480.0,4.7619047619999995,17874.0,6.6
+320-49-6392,C,Port Harcourt,Normal,Female,Electronic accessories,10886.4,1,544.3199999999999,11430.72,3/4/2019,15:44,Cash,10886.4,4.7619047619999995,544.3199999999999,8.4
+632-90-0281,C,Port Harcourt,Normal,Female,Fashion accessories,13517.999999999998,10,6758.999999999999,141939.0,3/8/2019,20:01,Card,135180.0,4.7619047619999995,6758.999999999999,9.3
+554-42-2417,C,Port Harcourt,Normal,Female,Sports and travel,34358.4,10,17179.2,360763.2,1/9/2019,13:45,Cash,343584.0,4.7619047619999995,17179.2,5.2
+605-72-4132,C,Port Harcourt,Normal,Female,Food and beverages,34009.2,8,13603.680000000002,285677.28,2/27/2019,15:12,Cash,272073.6,4.7619047619999995,13603.680000000002,9.1
+471-41-2823,C,Port Harcourt,Normal,Male,Food and beverages,35924.4,2,3592.4399999999996,75441.24,3/7/2019,20:37,Epay,71848.8,4.7619047619999995,3592.4399999999996,8.0
+272-27-9238,C,Port Harcourt,Normal,Female,Food and beverages,14846.400000000001,4,2969.2799999999997,62354.88,2/19/2019,16:23,Cash,59385.600000000006,4.7619047619999995,2969.2799999999997,7.1
+834-25-9262,C,Port Harcourt,Normal,Female,Fashion accessories,29404.800000000003,4,5880.959999999999,123500.15999999999,1/6/2019,12:12,Cash,117619.20000000001,4.7619047619999995,5880.959999999999,9.1
+122-61-9553,C,Port Harcourt,Normal,Female,Electronic accessories,18475.2,9,8313.84,174590.63999999998,3/14/2019,19:33,Cash,166276.8,4.7619047619999995,8313.84,5.6
+613-59-9758,C,Port Harcourt,Normal,Female,Sports and travel,5169.599999999999,10,2584.7999999999997,54280.8,1/27/2019,14:28,Cash,51696.0,4.7619047619999995,2584.7999999999997,5.4
+730-70-9830,C,Port Harcourt,Normal,Female,Home and lifestyle,25239.6,6,7571.88,159009.48,3/14/2019,17:54,Epay,151437.6,4.7619047619999995,7571.88,5.2
+382-25-8917,C,Port Harcourt,Normal,Male,Fashion accessories,15148.8,6,4544.64,95437.44000000002,1/29/2019,12:25,Cash,90892.8,4.7619047619999995,4544.64,8.9
+743-88-1662,C,Port Harcourt,Normal,Male,Sports and travel,34376.4,7,12031.74,252666.54,2/22/2019,18:17,Epay,240634.8,4.7619047619999995,12031.74,8.7
+595-86-2894,C,Port Harcourt,Member,Male,Fashion accessories,34912.8,4,6982.56,146633.75999999998,2/6/2019,17:20,Epay,139651.2,4.7619047619999995,6982.56,9.4
+462-78-5240,C,Port Harcourt,Normal,Female,Electronic accessories,9579.6,2,957.96,20117.16,3/19/2019,14:35,Cash,19159.2,4.7619047619999995,957.96,4.2
+153-58-4872,C,Port Harcourt,Member,Female,Food and beverages,26960.4,4,5392.08,113233.68000000001,3/1/2019,15:32,Epay,107841.6,4.7619047619999995,5392.08,4.2
+689-16-9784,C,Port Harcourt,Normal,Male,Food and beverages,16837.2,6,5051.16,106074.36,3/11/2019,13:37,Cash,101023.2,4.7619047619999995,5051.16,6.0
+394-41-0748,C,Port Harcourt,Member,Female,Fashion accessories,19465.2,9,8759.34,183946.14,1/27/2019,14:55,Epay,175186.8,4.7619047619999995,8759.34,9.5
+541-89-9860,C,Port Harcourt,Member,Female,Fashion accessories,28972.800000000003,3,4345.92,91264.32,2/15/2019,12:31,Cash,86918.4,4.7619047619999995,4345.92,8.1
+110-05-6330,C,Port Harcourt,Normal,Female,Food and beverages,14194.8,6,4258.4400000000005,89427.24,3/25/2019,20:18,Card,85168.8,4.7619047619999995,4258.4400000000005,9.4
+651-61-0874,C,Port Harcourt,Normal,Male,Home and lifestyle,16639.2,4,3327.84,69884.64000000001,3/12/2019,20:04,Card,66556.8,4.7619047619999995,3327.84,6.2
+236-86-3015,C,Port Harcourt,Member,Male,Home and lifestyle,5032.8,1,251.64000000000001,5284.4400000000005,2/4/2019,13:38,Epay,5032.8,4.7619047619999995,251.64000000000001,9.8
+587-03-7455,C,Port Harcourt,Member,Female,Fashion accessories,35204.4,7,12321.54,258752.34,2/16/2019,17:30,Epay,246430.8,4.7619047619999995,12321.54,4.9
+372-26-1506,C,Port Harcourt,Normal,Female,Fashion accessories,8575.2,5,2143.8,45019.8,1/28/2019,19:24,Epay,42876.0,4.7619047619999995,2143.8,5.4
+750-57-9686,C,Port Harcourt,Normal,Female,Home and lifestyle,16336.800000000001,4,3267.36,68614.56,1/8/2019,13:48,Card,65347.200000000004,4.7619047619999995,3267.36,8.7
+186-09-3669,C,Port Harcourt,Member,Female,Health and beauty,29343.600000000002,1,1467.18,30810.78,1/22/2019,10:57,Epay,29343.600000000002,4.7619047619999995,1467.18,9.2
+266-76-6436,C,Port Harcourt,Member,Female,Food and beverages,13896.0,3,2084.4,43772.4,3/28/2019,13:57,Epay,41688.0,4.7619047619999995,2084.4,7.5
+740-22-2500,C,Port Harcourt,Normal,Female,Electronic accessories,30258.0,3,4538.7,95312.7,1/23/2019,13:29,Cash,90774.0,4.7619047619999995,4538.7,9.8
+271-88-8734,C,Port Harcourt,Member,Female,Fashion accessories,34995.6,10,17497.8,367453.8,2/8/2019,13:00,Card,349956.0,4.7619047619999995,17497.8,8.7
+489-64-4354,C,Port Harcourt,Normal,Male,Fashion accessories,5860.8,1,293.04,6153.84,3/9/2019,15:36,Cash,5860.8,4.7619047619999995,293.04,5.0
+574-57-9721,C,Port Harcourt,Normal,Male,Food and beverages,15577.2,2,1557.72,32712.120000000003,3/8/2019,16:53,Epay,31154.4,4.7619047619999995,1557.72,5.7
+751-69-0068,C,Port Harcourt,Normal,Male,Sports and travel,35726.4,9,16076.880000000001,337614.48,3/19/2019,19:09,Epay,321537.6,4.7619047619999995,16076.880000000001,9.0
+257-73-1380,C,Port Harcourt,Member,Male,Sports and travel,29854.800000000003,4,5970.959999999999,125390.15999999999,1/20/2019,16:51,Epay,119419.20000000001,4.7619047619999995,5970.959999999999,9.6
+549-96-4200,C,Port Harcourt,Member,Male,Food and beverages,6134.4,4,1226.8799999999999,25764.48,3/8/2019,20:15,Epay,24537.6,4.7619047619999995,1226.8799999999999,7.0
+810-60-6344,C,Port Harcourt,Normal,Female,Electronic accessories,14709.6,8,5883.84,123560.64,2/7/2019,14:38,Card,117676.8,4.7619047619999995,5883.84,6.5
+450-28-2866,C,Port Harcourt,Member,Male,Food and beverages,6278.400000000001,5,1569.6000000000001,32961.6,1/15/2019,19:25,Cash,31392.0,4.7619047619999995,1569.6000000000001,8.1
+192-98-7397,C,Port Harcourt,Normal,Male,Fashion accessories,4600.8,1,230.04,4830.84,1/8/2019,14:11,Epay,4600.8,4.7619047619999995,230.04,9.5
+235-46-8343,C,Port Harcourt,Member,Male,Food and beverages,9957.6,10,4978.8,104554.8,2/14/2019,11:26,Card,99576.00000000001,4.7619047619999995,4978.8,8.9
+453-12-7053,C,Port Harcourt,Normal,Male,Fashion accessories,16466.4,3,2469.96,51869.16,3/10/2019,17:38,Card,49399.2,4.7619047619999995,2469.96,6.5
+325-90-8763,C,Port Harcourt,Member,Female,Electronic accessories,16765.2,10,8382.6,176034.6,1/27/2019,13:58,Cash,167652.0,4.7619047619999995,8382.6,7.6
+729-46-7422,C,Port Harcourt,Normal,Male,Food and beverages,12920.4,1,646.02,13566.42,2/23/2019,16:52,Card,12920.4,4.7619047619999995,646.02,7.9
+639-76-1242,C,Port Harcourt,Normal,Male,Food and beverages,14587.2,5,3646.8,76582.8,2/3/2019,15:19,Cash,72936.0,4.7619047619999995,3646.8,4.5
+326-71-2155,C,Port Harcourt,Normal,Female,Sports and travel,26622.0,4,5324.4,111812.4,2/3/2019,10:02,Cash,106488.0,4.7619047619999995,5324.4,6.1
+320-32-8842,C,Port Harcourt,Member,Female,Food and beverages,8143.200000000001,1,407.16,8550.359999999999,3/17/2019,18:58,Cash,8143.200000000001,4.7619047619999995,407.16,6.4
+878-30-2331,C,Port Harcourt,Member,Female,Sports and travel,19638.0,10,9819.0,206199.0,3/2/2019,11:22,Card,196380.0,4.7619047619999995,9819.0,7.1
+440-59-5691,C,Port Harcourt,Member,Female,Health and beauty,13374.0,7,4680.9,98298.90000000001,2/8/2019,13:12,Card,93618.0,4.7619047619999995,4680.9,7.7
+746-19-0921,C,Port Harcourt,Normal,Male,Food and beverages,7768.799999999999,1,388.44,8157.240000000001,2/9/2019,10:02,Epay,7768.799999999999,4.7619047619999995,388.44,7.2
+233-34-0817,C,Port Harcourt,Member,Female,Electronic accessories,35582.4,1,1779.1200000000001,37361.520000000004,2/15/2019,11:21,Cash,35582.4,4.7619047619999995,1779.1200000000001,8.4
+767-05-1286,C,Port Harcourt,Member,Female,Home and lifestyle,30157.199999999997,6,9047.16,189990.36,1/23/2019,12:10,Epay,180943.2,4.7619047619999995,9047.16,5.4
+598-47-9715,C,Port Harcourt,Normal,Male,Electronic accessories,30265.199999999997,4,6053.04,127113.84,3/7/2019,16:54,Epay,121060.79999999999,4.7619047619999995,6053.04,4.4
+541-08-3113,C,Port Harcourt,Normal,Male,Food and beverages,23749.2,8,9499.68,199493.28,2/2/2019,20:29,Cash,189993.6,4.7619047619999995,9499.68,8.4
+246-11-3901,C,Port Harcourt,Normal,Female,Electronic accessories,11807.999999999998,10,5903.999999999999,123983.99999999999,2/15/2019,12:12,Cash,118080.0,4.7619047619999995,5903.999999999999,6.2
+493-65-6248,C,Port Harcourt,Member,Female,Sports and travel,13312.8,10,6656.4,139784.4,1/1/2019,19:48,Card,133128.0,4.7619047619999995,6656.4,7.0
+556-72-8512,C,Port Harcourt,Normal,Male,Home and lifestyle,8265.6,1,413.28,8678.880000000001,1/30/2019,20:47,Cash,8265.6,4.7619047619999995,413.28,4.3
+148-82-2527,C,Port Harcourt,Member,Female,Home and lifestyle,4363.2,10,2181.6,45813.6,3/5/2019,13:44,Card,43632.0,4.7619047619999995,2181.6,8.4
+556-97-7101,C,Port Harcourt,Normal,Female,Electronic accessories,22759.2,2,2275.92,47794.32,1/1/2019,15:51,Cash,45518.4,4.7619047619999995,2275.92,8.5
+862-59-8517,C,Port Harcourt,Normal,Female,Food and beverages,32486.399999999998,6,9745.92,204664.31999999998,1/27/2019,11:17,Cash,194918.40000000002,4.7619047619999995,9745.92,6.2
+573-98-8548,C,Port Harcourt,Member,Female,Fashion accessories,11484.0,1,574.2,12058.199999999999,1/5/2019,12:40,Epay,11484.0,4.7619047619999995,574.2,9.1
+620-02-2046,C,Port Harcourt,Normal,Male,Home and lifestyle,24984.000000000004,2,2498.4,52466.4,1/27/2019,19:48,Epay,49968.00000000001,4.7619047619999995,2498.4,9.0
+602-80-9671,C,Port Harcourt,Member,Female,Home and lifestyle,5742.0,6,1722.6000000000001,36174.6,2/9/2019,17:15,Card,34452.0,4.7619047619999995,1722.6000000000001,5.1
+503-07-0930,C,Port Harcourt,Member,Male,Sports and travel,21020.4,7,7357.139999999999,154499.94,2/23/2019,19:49,Card,147142.80000000002,4.7619047619999995,7357.139999999999,8.2
+413-20-6708,C,Port Harcourt,Member,Female,Fashion accessories,18529.2,1,926.46,19455.66,3/18/2019,15:52,Epay,18529.2,4.7619047619999995,926.46,8.5
+521-18-7827,C,Port Harcourt,Member,Male,Home and lifestyle,14180.4,5,3545.1,74447.1,1/22/2019,20:46,Card,70902.0,4.7619047619999995,3545.1,8.7
+600-38-9738,C,Port Harcourt,Member,Male,Sports and travel,25891.2,5,6472.8,135928.8,1/17/2019,15:05,Card,129456.00000000001,4.7619047619999995,6472.8,4.3
+451-28-5717,C,Port Harcourt,Member,Female,Home and lifestyle,29941.2,6,8982.36,188629.56,3/20/2019,11:23,Cash,179647.19999999998,4.7619047619999995,8982.36,7.3
+133-14-7229,C,Port Harcourt,Normal,Male,Health and beauty,22633.2,2,2263.32,47529.72000000001,1/1/2019,11:43,Cash,45266.4,4.7619047619999995,2263.32,5.0
+236-27-1144,C,Port Harcourt,Normal,Female,Food and beverages,5871.599999999999,9,2642.2200000000003,55486.62,3/26/2019,10:31,Epay,52844.399999999994,4.7619047619999995,2642.2200000000003,8.4
+583-41-4548,C,Port Harcourt,Normal,Male,Home and lifestyle,6001.200000000001,7,2100.42,44108.82,2/7/2019,11:36,Epay,42008.4,4.7619047619999995,2100.42,7.4
+358-88-9262,C,Port Harcourt,Member,Female,Food and beverages,31492.800000000003,6,9447.84,198404.64,2/1/2019,18:43,Epay,188956.8,4.7619047619999995,9447.84,5.1
+343-87-0864,C,Port Harcourt,Member,Male,Health and beauty,27316.8,1,1365.84,28682.639999999996,1/3/2019,10:30,Card,27316.8,4.7619047619999995,1365.84,7.1
+243-47-2663,C,Port Harcourt,Member,Male,Electronic accessories,6757.2,6,2027.16,42570.36,1/28/2019,16:43,Card,40543.200000000004,4.7619047619999995,2027.16,5.5
+399-69-4630,C,Port Harcourt,Normal,Male,Electronic accessories,7995.6,6,2398.68,50372.28,3/7/2019,10:23,Card,47973.6,4.7619047619999995,2398.68,8.6
+283-26-5248,C,Port Harcourt,Member,Female,Food and beverages,35467.2,10,17733.6,372405.60000000003,1/30/2019,20:23,Epay,354672.0,4.7619047619999995,17733.6,4.5
+866-99-7614,C,Port Harcourt,Normal,Male,Food and beverages,32112.0,10,16056.0,337176.0,2/11/2019,15:42,Card,321120.0,4.7619047619999995,16056.0,4.4
+718-57-9773,C,Port Harcourt,Normal,Female,Sports and travel,17758.8,10,8879.4,186467.40000000002,2/3/2019,16:40,Card,177588.0,4.7619047619999995,8879.4,9.4
+408-26-9866,C,Port Harcourt,Normal,Female,Sports and travel,26632.800000000003,7,9321.48,195751.08000000002,3/2/2019,16:42,Epay,186429.6,4.7619047619999995,9321.48,4.1
+592-34-6155,C,Port Harcourt,Normal,Male,Food and beverages,11437.2,4,2287.44,48036.24,1/14/2019,14:43,Epay,45748.8,4.7619047619999995,2287.44,6.2
+390-31-6381,C,Port Harcourt,Normal,Male,Food and beverages,9799.199999999999,3,1469.88,30867.48,1/7/2019,12:37,Cash,29397.6,4.7619047619999995,1469.88,7.3
+339-18-7061,C,Port Harcourt,Member,Female,Fashion accessories,33472.8,2,3347.28,70292.87999999999,2/13/2019,15:06,Card,66945.6,4.7619047619999995,3347.28,8.0
+379-17-6588,C,Port Harcourt,Normal,Male,Fashion accessories,21459.6,10,10729.8,225325.8,3/14/2019,11:07,Cash,214596.0,4.7619047619999995,10729.8,5.3
+302-15-2162,C,Port Harcourt,Member,Male,Health and beauty,16750.8,6,5025.240000000001,105530.04000000001,3/3/2019,10:54,Card,100504.8,4.7619047619999995,5025.240000000001,4.3
+788-07-8452,C,Port Harcourt,Member,Female,Home and lifestyle,8726.4,7,3054.24,64139.04000000001,1/27/2019,17:38,Epay,61084.8,4.7619047619999995,3054.24,9.4
+123-35-4896,C,Port Harcourt,Normal,Female,Sports and travel,16797.6,9,7558.92,158737.31999999998,2/17/2019,19:11,Epay,151178.4,4.7619047619999995,7558.92,5.3
+258-69-7810,C,Port Harcourt,Normal,Female,Fashion accessories,13266.0,5,3316.5,69646.5,1/26/2019,18:53,Cash,66330.0,4.7619047619999995,3316.5,9.2
+219-61-4139,C,Port Harcourt,Normal,Male,Electronic accessories,29908.8,1,1495.44,31404.239999999998,1/23/2019,17:16,Epay,29908.8,4.7619047619999995,1495.44,6.4
+881-41-7302,C,Port Harcourt,Normal,Female,Fashion accessories,23396.399999999998,1,1169.82,24566.22,1/26/2019,10:06,Card,23396.399999999998,4.7619047619999995,1169.82,4.5
+373-09-4567,C,Port Harcourt,Normal,Male,Food and beverages,27921.600000000002,10,13960.800000000001,293176.8,3/14/2019,20:35,Epay,279216.0,4.7619047619999995,13960.800000000001,6.9
+484-22-8230,C,Port Harcourt,Member,Female,Fashion accessories,18680.4,7,6538.14,137300.94,1/8/2019,20:08,Cash,130762.8,4.7619047619999995,6538.14,4.5
+544-32-5024,C,Port Harcourt,Member,Female,Food and beverages,17924.4,4,3584.88,75282.48,3/28/2019,19:16,Card,71697.6,4.7619047619999995,3584.88,6.4
+277-35-5865,C,Port Harcourt,Member,Female,Food and beverages,35629.2,9,16033.14,336695.94,3/9/2019,11:23,Cash,320662.8,4.7619047619999995,16033.14,6.7
+284-54-4231,C,Port Harcourt,Member,Male,Sports and travel,29134.800000000003,1,1456.74,30591.54,1/19/2019,16:08,Card,29134.800000000003,4.7619047619999995,1456.74,9.0
+840-19-2096,C,Port Harcourt,Member,Male,Electronic accessories,31647.6,5,7911.9,166149.9,3/14/2019,18:10,Epay,158238.0,4.7619047619999995,7911.9,4.4
+641-96-3695,C,Port Harcourt,Member,Female,Fashion accessories,15645.6,6,4693.68,98567.28,2/7/2019,17:55,Epay,93873.59999999999,4.7619047619999995,4693.68,8.5
+318-81-2368,C,Port Harcourt,Normal,Female,Electronic accessories,16632.0,1,831.6,17463.6,3/19/2019,12:16,Cash,16632.0,4.7619047619999995,831.6,6.3
+155-45-3814,C,Port Harcourt,Member,Female,Electronic accessories,31878.0,8,12751.2,267775.2,3/19/2019,15:29,Epay,255024.0,4.7619047619999995,12751.2,4.7
+131-15-8856,C,Port Harcourt,Member,Female,Food and beverages,26107.199999999997,8,10442.880000000001,219300.48,3/30/2019,19:26,Card,208857.59999999998,4.7619047619999995,10442.880000000001,4.0
+273-84-2164,C,Port Harcourt,Member,Male,Electronic accessories,4338.0,5,1084.5,22774.5,2/16/2019,15:53,Epay,21690.0,4.7619047619999995,1084.5,5.5
+778-89-7974,C,Port Harcourt,Normal,Male,Health and beauty,25275.6,6,7582.680000000001,159236.28,3/30/2019,14:58,Cash,151653.6,4.7619047619999995,7582.680000000001,7.4
+859-71-0933,C,Port Harcourt,Member,Female,Sports and travel,5576.4,2,557.6400000000001,11710.44,1/16/2019,15:10,Cash,11152.8,4.7619047619999995,557.6400000000001,6.3
+740-11-5257,C,Port Harcourt,Normal,Male,Electronic accessories,8906.4,10,4453.2,93517.2,2/24/2019,16:44,Cash,89064.0,4.7619047619999995,4453.2,7.1
+250-81-7186,C,Port Harcourt,Normal,Female,Electronic accessories,35888.4,1,1794.4199999999998,37682.82,2/27/2019,10:23,Card,35888.4,4.7619047619999995,1794.4199999999998,8.0
+842-29-4695,C,Port Harcourt,Member,Male,Sports and travel,6170.400000000001,7,2159.64,45352.44,1/16/2019,12:07,Card,43192.8,4.7619047619999995,2159.64,7.9
+641-51-2661,C,Port Harcourt,Member,Female,Food and beverages,31355.999999999996,10,15677.999999999998,329238.0,2/12/2019,14:45,Card,313560.0,4.7619047619999995,15677.999999999998,9.9
+714-02-3114,C,Port Harcourt,Normal,Female,Sports and travel,35568.0,2,3556.8,74692.8,2/21/2019,11:39,Cash,71136.0,4.7619047619999995,3556.8,7.7
+408-66-6712,C,Port Harcourt,Member,Female,Health and beauty,17175.6,6,5152.679999999999,108206.28,2/16/2019,14:19,Epay,103053.59999999999,4.7619047619999995,5152.679999999999,4.4
+556-41-6224,C,Port Harcourt,Normal,Male,Health and beauty,12110.4,8,4844.16,101727.35999999999,2/15/2019,17:10,Card,96883.2,4.7619047619999995,4844.16,9.3
+648-94-3045,C,Port Harcourt,Normal,Male,Health and beauty,21222.0,10,10611.0,222831.0,2/7/2019,14:27,Epay,212220.0,4.7619047619999995,10611.0,8.1
+370-96-0655,C,Port Harcourt,Normal,Female,Fashion accessories,17755.2,6,5326.5599999999995,111857.76000000001,1/9/2019,13:46,Epay,106531.20000000001,4.7619047619999995,5326.5599999999995,7.1
+173-57-2300,C,Port Harcourt,Member,Male,Sports and travel,26236.8,2,2623.68,55097.28,3/13/2019,12:51,Cash,52473.6,4.7619047619999995,2623.68,6.1
+394-55-6384,C,Port Harcourt,Member,Female,Sports and travel,25268.399999999998,9,11370.78,238786.37999999998,1/25/2019,13:38,Cash,227415.6,4.7619047619999995,11370.78,6.7
+266-20-6657,C,Port Harcourt,Member,Male,Food and beverages,19814.4,7,6935.04,145635.84000000003,3/12/2019,19:39,Epay,138700.8,4.7619047619999995,6935.04,5.2
+196-01-2849,C,Port Harcourt,Member,Female,Fashion accessories,26416.8,7,9245.880000000001,194163.48000000004,2/10/2019,13:56,Cash,184917.59999999998,4.7619047619999995,9245.880000000001,9.5
+372-62-5264,C,Port Harcourt,Normal,Female,Food and beverages,18936.0,9,8521.2,178945.2,1/16/2019,14:42,Cash,170424.0,4.7619047619999995,8521.2,7.6
+751-41-9720,C,Port Harcourt,Normal,Male,Home and lifestyle,35100.0,10,17550.0,368550.0,1/12/2019,16:18,Epay,351000.0,4.7619047619999995,17550.0,8.0
+626-43-7888,C,Port Harcourt,Normal,Female,Fashion accessories,21747.6,8,8699.04,182679.84,2/7/2019,12:23,Epay,173980.8,4.7619047619999995,8699.04,9.6
+162-65-8559,C,Port Harcourt,Member,Male,Food and beverages,24832.800000000003,1,1241.6399999999999,26074.440000000002,1/21/2019,20:13,Cash,24832.800000000003,4.7619047619999995,1241.6399999999999,4.8
+760-27-5490,C,Port Harcourt,Normal,Male,Fashion accessories,5623.2,8,2249.2799999999997,47234.88,1/20/2019,20:37,Epay,44985.6,4.7619047619999995,2249.2799999999997,9.1
+728-88-7867,C,Port Harcourt,Member,Female,Home and lifestyle,27190.8,4,5438.16,114201.36,3/19/2019,15:52,Epay,108763.2,4.7619047619999995,5438.16,8.3
+183-21-3799,C,Port Harcourt,Normal,Female,Electronic accessories,27946.8,9,12576.060000000001,264097.26,2/19/2019,15:14,Epay,251521.19999999998,4.7619047619999995,12576.060000000001,7.2
+268-20-3585,C,Port Harcourt,Normal,Female,Health and beauty,4986.0,9,2243.7,47117.7,2/4/2019,12:50,Epay,44874.0,4.7619047619999995,2243.7,6.0
+735-32-9839,C,Port Harcourt,Member,Male,Fashion accessories,35532.0,8,14212.8,298468.8,1/31/2019,10:36,Epay,284256.0,4.7619047619999995,14212.8,8.5
+678-79-0726,C,Port Harcourt,Member,Female,Sports and travel,32626.8,9,14682.06,308323.26,1/18/2019,15:28,Cash,293641.2,4.7619047619999995,14682.06,5.1
+592-46-1692,C,Port Harcourt,Member,Female,Food and beverages,13237.2,7,4633.02,97293.42,1/11/2019,20:10,Cash,92660.4,4.7619047619999995,4633.02,7.4
+149-14-0304,C,Port Harcourt,Member,Female,Health and beauty,10260.0,8,4104.0,86184.0,2/6/2019,14:24,Cash,82080.0,4.7619047619999995,4104.0,6.6
+442-44-6497,C,Port Harcourt,Member,Male,Home and lifestyle,20005.2,3,3000.7799999999997,63016.380000000005,1/8/2019,11:42,Card,60015.600000000006,4.7619047619999995,3000.7799999999997,5.9
+210-74-9613,C,Port Harcourt,Normal,Male,Fashion accessories,35013.6,4,7002.72,147057.12,3/16/2019,15:33,Epay,140054.4,4.7619047619999995,7002.72,6.8
+607-65-2441,C,Port Harcourt,Member,Male,Health and beauty,29502.0,10,14751.0,309771.0,3/10/2019,12:39,Card,295020.0,4.7619047619999995,14751.0,6.0
+386-27-7606,C,Port Harcourt,Member,Female,Home and lifestyle,29232.0,7,10231.2,214855.2,3/23/2019,15:59,Card,204624.0,4.7619047619999995,10231.2,8.1
+137-63-5492,C,Port Harcourt,Normal,Male,Electronic accessories,21153.6,10,10576.8,222112.80000000002,1/29/2019,14:26,Epay,211536.0,4.7619047619999995,10576.8,9.0
+733-29-1227,C,Port Harcourt,Normal,Male,Home and lifestyle,20019.6,7,7006.86,147144.06,3/23/2019,12:41,Cash,140137.19999999998,4.7619047619999995,7006.86,8.5
+451-73-2711,C,Port Harcourt,Normal,Male,Food and beverages,30538.8,1,1526.94,32065.74,1/14/2019,15:20,Epay,30538.8,4.7619047619999995,1526.94,8.8
+345-68-9016,C,Port Harcourt,Member,Female,Sports and travel,11401.2,8,4560.48,95770.08,1/2/2019,16:19,Card,91209.6,4.7619047619999995,4560.48,5.6
+390-17-5806,C,Port Harcourt,Member,Female,Food and beverages,13831.2,1,691.5600000000001,14522.76,2/2/2019,16:33,Cash,13831.2,4.7619047619999995,691.5600000000001,8.6
+664-14-2882,C,Port Harcourt,Member,Female,Home and lifestyle,3790.7999999999997,5,947.6999999999999,19901.7,1/30/2019,14:43,Card,18954.0,4.7619047619999995,947.6999999999999,5.8
+314-23-4520,C,Port Harcourt,Member,Male,Health and beauty,29242.800000000003,7,10234.98,214934.58,1/15/2019,20:44,Cash,204699.6,4.7619047619999995,10234.98,9.0
+288-38-3758,C,Port Harcourt,Member,Female,Fashion accessories,30553.2,3,4582.98,96242.58,1/25/2019,18:30,Epay,91659.6,4.7619047619999995,4582.98,7.4
+801-88-0346,C,Port Harcourt,Normal,Female,Fashion accessories,27381.600000000002,3,4107.240000000001,86252.04000000001,1/5/2019,20:30,Card,82144.8,4.7619047619999995,4107.240000000001,9.8
+759-98-4285,C,Port Harcourt,Member,Female,Health and beauty,30913.2,7,10819.62,227212.02,2/27/2019,19:01,Card,216392.40000000002,4.7619047619999995,10819.62,8.0
+201-63-8275,C,Port Harcourt,Member,Female,Sports and travel,24476.399999999998,7,8566.74,179901.54,2/17/2019,16:50,Epay,171334.8,4.7619047619999995,8566.74,5.7
+471-06-8611,C,Port Harcourt,Normal,Female,Food and beverages,18871.2,1,943.5600000000002,19814.760000000002,2/6/2019,10:22,Card,18871.2,4.7619047619999995,943.5600000000002,6.3
+200-16-5952,C,Port Harcourt,Member,Male,Food and beverages,23634.000000000004,2,2363.4,49631.4,1/17/2019,16:46,Cash,47268.00000000001,4.7619047619999995,2363.4,6.0
+102-77-2261,C,Port Harcourt,Member,Male,Health and beauty,23511.600000000002,7,8229.06,172810.26,3/5/2019,18:02,Card,164581.2,4.7619047619999995,8229.06,4.2
+102-06-2002,C,Port Harcourt,Member,Male,Sports and travel,9090.0,5,2272.5,47722.5,3/20/2019,17:52,Cash,45450.0,4.7619047619999995,2272.5,6.1
+629-42-4133,C,Port Harcourt,Normal,Male,Health and beauty,7848.0,8,3139.2000000000003,65923.2,2/19/2019,19:24,Cash,62784.0,4.7619047619999995,3139.2000000000003,8.3
+468-99-7231,C,Port Harcourt,Normal,Female,Home and lifestyle,15843.599999999999,8,6337.44,133086.24000000002,3/3/2019,17:36,Cash,126748.79999999999,4.7619047619999995,6337.44,8.8
+516-77-6464,C,Port Harcourt,Member,Female,Health and beauty,3657.6,5,914.4,19202.4,2/24/2019,13:08,Epay,18288.0,4.7619047619999995,914.4,4.1
+886-77-9084,C,Port Harcourt,Normal,Male,Electronic accessories,25880.4,8,10352.16,217395.36,2/19/2019,11:33,Epay,207043.2,4.7619047619999995,10352.16,5.5
+790-38-4466,C,Port Harcourt,Normal,Female,Health and beauty,3956.4,5,989.1,20771.1,1/23/2019,10:18,Card,19782.0,4.7619047619999995,989.1,9.3
+704-10-4056,C,Port Harcourt,Member,Male,Health and beauty,21769.2,3,3265.3799999999997,68572.98,1/14/2019,10:55,Card,65307.6,4.7619047619999995,3265.3799999999997,5.6
+400-80-4065,C,Port Harcourt,Member,Male,Health and beauty,24678.0,4,4935.6,103647.6,2/15/2019,20:21,Card,98712.0,4.7619047619999995,4935.6,9.2
+443-60-9639,C,Port Harcourt,Member,Female,Home and lifestyle,21913.2,1,1095.6599999999999,23008.86,1/24/2019,13:24,Cash,21913.2,4.7619047619999995,1095.6599999999999,5.5
+401-09-4232,C,Port Harcourt,Member,Male,Home and lifestyle,31208.399999999998,5,7802.099999999999,163844.1,2/11/2019,18:38,Epay,156042.0,4.7619047619999995,7802.099999999999,9.4
+324-41-6833,C,Port Harcourt,Member,Female,Electronic accessories,10872.0,8,4348.8,91324.8,3/3/2019,19:30,Epay,86976.0,4.7619047619999995,4348.8,5.1
+474-33-8305,C,Port Harcourt,Member,Male,Fashion accessories,24260.4,7,8491.14,178313.94,3/23/2019,13:23,Epay,169822.80000000002,4.7619047619999995,8491.14,6.9
+189-55-2313,C,Port Harcourt,Normal,Female,Fashion accessories,22384.8,10,11192.4,235040.4,1/31/2019,10:33,Epay,223847.99999999997,4.7619047619999995,11192.4,6.0
+815-04-6282,C,Port Harcourt,Member,Female,Sports and travel,23389.2,5,5847.3,122793.29999999999,2/8/2019,12:52,Card,116946.00000000001,4.7619047619999995,5847.3,6.5
+477-59-2456,C,Port Harcourt,Normal,Female,Fashion accessories,16358.4,7,5725.4400000000005,120234.24000000002,1/23/2019,11:15,Cash,114508.79999999999,4.7619047619999995,5725.4400000000005,9.2
+784-21-9238,C,Port Harcourt,Member,Male,Sports and travel,3661.2,1,183.05999999999997,3844.2599999999998,2/7/2019,14:15,Cash,3661.2,4.7619047619999995,183.05999999999997,5.9
+538-22-0304,C,Port Harcourt,Normal,Male,Electronic accessories,23382.0,10,11691.0,245511.0,3/24/2019,18:27,Cash,233820.0,4.7619047619999995,11691.0,5.2
+660-29-7083,C,Port Harcourt,Normal,Male,Electronic accessories,20113.2,10,10056.6,211188.6,1/15/2019,15:01,Cash,201132.00000000003,4.7619047619999995,10056.6,5.8
+271-77-8740,C,Port Harcourt,Member,Female,Sports and travel,10519.199999999999,6,3155.76,66270.95999999999,1/1/2019,11:40,Epay,63115.2,4.7619047619999995,3155.76,5.0
+549-23-9016,C,Port Harcourt,Member,Female,Food and beverages,5353.2,2,535.3199999999999,11241.72,2/13/2019,18:15,Card,10706.4,4.7619047619999995,535.3199999999999,8.9
+862-29-5914,C,Port Harcourt,Normal,Female,Sports and travel,8056.799999999999,1,402.84,8459.640000000001,1/30/2019,17:08,Card,8056.799999999999,4.7619047619999995,402.84,8.6
+845-94-6841,C,Port Harcourt,Member,Female,Food and beverages,26236.8,9,11806.56,247937.76,1/8/2019,19:38,Cash,236131.19999999998,4.7619047619999995,11806.56,4.0
+658-66-3967,C,Port Harcourt,Normal,Male,Health and beauty,19148.399999999998,7,6701.94,140740.74,1/14/2019,15:42,Epay,134038.8,4.7619047619999995,6701.94,5.0
+848-95-6252,C,Port Harcourt,Member,Female,Home and lifestyle,31057.199999999997,1,1552.8600000000001,32610.06,2/20/2019,13:24,Epay,31057.199999999997,4.7619047619999995,1552.8600000000001,7.0
+176-78-1170,C,Port Harcourt,Member,Male,Health and beauty,12171.6,3,1825.7400000000002,38340.54,1/26/2019,15:11,Epay,36514.8,4.7619047619999995,1825.7400000000002,7.3
+101-81-4070,C,Port Harcourt,Member,Female,Health and beauty,22615.2,2,2261.52,47491.92,1/17/2019,12:36,Epay,45230.4,4.7619047619999995,2261.52,4.9
+631-34-1880,C,Port Harcourt,Member,Male,Food and beverages,8751.6,3,1312.74,27567.539999999997,1/8/2019,19:09,Card,26254.800000000003,4.7619047619999995,1312.74,4.3
+584-66-4073,C,Port Harcourt,Normal,Male,Fashion accessories,20340.0,1,1017.0000000000001,21357.0,3/13/2019,15:45,Epay,20340.0,4.7619047619999995,1017.0000000000001,9.6
+154-87-7367,C,Port Harcourt,Normal,Male,Home and lifestyle,23493.600000000002,8,9397.44,197346.24,3/15/2019,14:04,Epay,187948.80000000002,4.7619047619999995,9397.44,6.3
+885-56-0389,C,Port Harcourt,Member,Male,Fashion accessories,18846.0,1,942.3000000000001,19788.3,2/12/2019,17:49,Cash,18846.0,4.7619047619999995,942.3000000000001,4.0
+115-38-7388,C,Port Harcourt,Member,Female,Fashion accessories,3664.7999999999997,8,1465.92,30784.32,3/30/2019,12:51,Card,29318.399999999998,4.7619047619999995,1465.92,9.5
+137-74-8729,C,Port Harcourt,Normal,Female,Fashion accessories,4388.4,8,1755.3600000000001,36862.56,3/13/2019,12:47,Epay,35107.2,4.7619047619999995,1755.3600000000001,6.8
+389-70-2397,C,Port Harcourt,Normal,Female,Health and beauty,30117.6,5,7529.4,158117.4,2/21/2019,10:26,Cash,150588.0,4.7619047619999995,7529.4,7.2
+607-76-6216,C,Port Harcourt,Member,Female,Fashion accessories,33296.4,5,8324.1,174806.1,3/2/2019,16:35,Card,166482.0,4.7619047619999995,8324.1,8.6
+781-84-8059,C,Port Harcourt,Normal,Male,Fashion accessories,21866.4,7,7653.24,160718.04,1/18/2019,16:23,Epay,153064.8,4.7619047619999995,7653.24,5.0
+409-49-6995,C,Port Harcourt,Member,Female,Food and beverages,17017.2,6,5105.16,107208.36,2/5/2019,10:17,Cash,102103.2,4.7619047619999995,5105.16,8.8
+725-54-0677,C,Port Harcourt,Member,Male,Health and beauty,30815.999999999996,7,10785.6,226497.59999999998,3/2/2019,13:50,Cash,215712.00000000003,4.7619047619999995,10785.6,5.3
+377-79-7592,C,Port Harcourt,Member,Female,Electronic accessories,16142.400000000001,9,7264.08,152545.68000000002,1/14/2019,14:00,Card,145281.6,4.7619047619999995,7264.08,7.5
+545-07-8534,C,Port Harcourt,Normal,Female,Health and beauty,20995.2,2,2099.5200000000004,44089.920000000006,2/14/2019,12:42,Epay,41990.4,4.7619047619999995,2099.5200000000004,6.0
+118-62-1812,C,Port Harcourt,Member,Female,Home and lifestyle,28216.8,4,5643.36,118510.55999999998,3/24/2019,17:56,Cash,112867.2,4.7619047619999995,5643.36,7.9
+450-42-3339,C,Port Harcourt,Normal,Male,Health and beauty,30459.6,10,15229.8,319825.8,2/9/2019,18:58,Card,304596.0,4.7619047619999995,15229.8,8.8
+151-27-8496,C,Port Harcourt,Normal,Female,Electronic accessories,20206.8,4,4041.3599999999997,84868.56,1/19/2019,11:43,Epay,80827.2,4.7619047619999995,4041.3599999999997,8.6
+717-96-4189,C,Port Harcourt,Normal,Female,Electronic accessories,12776.400000000001,6,3832.92,80491.32,2/2/2019,12:40,Cash,76658.4,4.7619047619999995,3832.92,4.1
+722-13-2115,C,Port Harcourt,Member,Male,Sports and travel,15426.0,1,771.3000000000001,16197.3,3/14/2019,15:36,Card,15426.0,4.7619047619999995,771.3000000000001,9.3
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diff --git a/My_Ustacky_Project/README.md b/My_Ustacky_Project/README.md
new file mode 100644
index 0000000..f0748dd
--- /dev/null
+++ b/My_Ustacky_Project/README.md
@@ -0,0 +1,3 @@
+# Nigeria-COVID-19-Data-Analysis-Using-Python
+Data Scientist Microdegree Capstone Project
+htyhtynh
\ No newline at end of file
diff --git a/My_Ustacky_Project/Untitled.ipynb b/My_Ustacky_Project/Untitled.ipynb
new file mode 100644
index 0000000..d5663b2
--- /dev/null
+++ b/My_Ustacky_Project/Untitled.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "142012ab",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/My_Ustacky_Project/Updated_PANDAS ANALYTICS ExECUTIVE SUMMARY - Google Docs.pdf b/My_Ustacky_Project/Updated_PANDAS ANALYTICS ExECUTIVE SUMMARY - Google Docs.pdf
new file mode 100644
index 0000000..96a6a52
Binary files /dev/null and b/My_Ustacky_Project/Updated_PANDAS ANALYTICS ExECUTIVE SUMMARY - Google Docs.pdf differ
diff --git a/My_Ustacky_Project/covidproject.ipynb b/My_Ustacky_Project/covidproject.ipynb
new file mode 100644
index 0000000..01d1faf
--- /dev/null
+++ b/My_Ustacky_Project/covidproject.ipynb
@@ -0,0 +1,319 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "### Task 1 - Data Collection\n",
+    "Here you will obtain the required data for the analysis. As described in the project instructions, you will perform a web scrap to obtain data from the NCDC website, import data from the John Hopkins repository, and import the provided external data.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "source": [
+    "### A - NCDC Website scrap\n",
+    "Website - https://covid19.ncdc.gov.ng/"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write Your Code Below\n",
+    "# Import all libraries in this cell\n",
+    "import requests\n",
+    "import numpy as np\n",
+    "import urllib.request\n",
+    "import pandas as pd\n",
+    "import csv\n",
+    "from bs4 import BeautifulSoup\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"darkgrid\")\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')  \n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "source": [
+    "# Save the data to a DataFrame object.\n"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 7,
+   "outputs": []
+  },
+  {
+   "source": [
+    "### B - John Hopkins Data Repository\n",
+    "Here you will obtain data from the John Hopkins repository. Your task here involves saving the data from the GitHub repo link to DataFrame for further analysis. Find the links below. \n",
+    "* Global Daily Confirmed Cases - Click [Here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv)\n",
+    "* Global Daily Recovered Cases - Click [Here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv)\n",
+    "* Global Daily Death Cases - Click [Here](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv)"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#[Write Your Code Here]\n",
+    "\n"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 8,
+   "outputs": []
+  },
+  {
+   "source": [
+    "### C - External Data \n",
+    "* Save the external data to a DataFrame\n",
+    "* External Data includes but not limited to: `covid_external.csv`, `Budget data.csv`, `RealGDP.csv`"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write Your Code Here]\n"
+   ]
+  },
+  {
+   "source": [
+    "### Task 2 - View the data\n",
+    "Obtain basic information about the data using the `head()` and `info()` method."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write Your Code Here]\n"
+   ]
+  },
+  {
+   "source": [
+    "### Task 3 - Data Cleaning and Preparation\n",
+    "From the information obtained above, you will need to fix the data format. \n",
+    "<br>\n",
+    "Examples: \n",
+    "* Convert to appropriate data type.\n",
+    "* Rename the columns of the scraped data.\n",
+    "* Remove comma(,) in numerical data\n",
+    "* Extract daily data for Nigeria from the Global daily cases data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO A - Clean the scraped data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write Your Code Here]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "source": [
+    "TODO B - Get a Pandas DataFrame for Daily Confirmed Cases in Nigeria. Columns are Date and Cases"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO C - Get a Pandas DataFrame for Daily Recovered Cases in Nigeria. Columns are Date and Cases"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO D - Get a Pandas DataFrame for Daily Death Cases in Nigeria. Columns are Date and Cases"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "### Task 4 - Analysis\n",
+    "Here you will perform some analyses on the datasets. You are welcome to communicate findings in charts and summary. \n",
+    "<br>\n",
+    "We have included a few TODOs to help with your analysis. However, do not let this limit your approach, feel free to include more, and be sure to support your findings with chart and summary "
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO A - Generate a plot that shows the Top 10 states in terms of Confirmed Covid cases by Laboratory test"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO B - Generate a plot that shows the Top 10 states in terms of Discharged Covid cases. Hint - Sort the values"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO D - Plot the top 10 Death cases"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO E - Generate a line plot for the total daily confirmed, recovered and death cases in Nigeria"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO F - \n",
+    "* Determine the daily infection rate, you can use the Pandas `diff` method to find the derivate of the total cases.\n",
+    "* Generate a line plot for the above"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO G - \n",
+    "* Calculate maximum infection rate for a day (Number of new cases)\n",
+    "* Find the date"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO H - Determine the relationship between the external dataset and the NCDC COVID-19 dataset. \n",
+    "Here you will generate a line plot of top 10 confirmed cases and the overall community vulnerability index on the same axis. From the graph, explain your observation.\n",
+    "<br>\n",
+    "Steps\n",
+    "* Combine the two dataset together on a common column(states)\n",
+    "* Create a new dataframe for plotting. This DataFrame will contain top 10 states in terms of confirmed cases i.e sort by confirmed cases. ** Hint: Check out Pandas [nlargest](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nlargest.html) function. This [tutorial](https://cmdlinetips.com/2019/03/how-to-select-top-n-rows-with-the-largest-values-in-a-columns-in-pandas/) can help out ** \n",
+    "* Plot both variable on the same axis. Check out this [tutorial](http://kitchingroup.cheme.cmu.edu/blog/2013/09/13/Plotting-two-datasets-with-very-different-scales/)"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO I - Determine the relationship between the external dataset and the NCDC COVID-19 dataset. \n",
+    "* Here you will generate a regression plot between two variables to visualize the linear relationships - Confirmed Cases and Population Density.\n",
+    "Hint: Check out Seaborn [Regression Plot](https://seaborn.pydata.org/generated/seaborn.regplot.html).\n",
+    "* Provide a summary of your observation"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "TODO J - \n",
+    "* Provide more analyses by extending TODO G & H. Meaning, determine relationships between more features.\n",
+    "* Provide a detailed summary of your findings. \n",
+    "* Note that you can have as many as possible."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "### TODO L - \n",
+    "Determine the effect of the Pandemic on the economy. To do this, you will compare the Real GDP value Pre-COVID-19 with Real GDP in 2020 (COVID-19 Period, especially Q2 2020)\n",
+    "<br>\n",
+    "Steps\n",
+    "* From the Real GDP Data, generate a `barplot` using the GDP values for each year & quarters. For example: On x-axis you will have year 2017 and the bars will be values of each quarters(Q1-Q4). You expected to have subplots of each quarters on one graph.\n",
+    "<br>\n",
+    "Hint: Use [Pandas.melt](https://pandas.pydata.org/docs/reference/api/pandas.melt.html) to create your plot DataFrame \n",
+    "* Set your quarter legend to lower left.\n",
+    "* Using `axhline`, draw a horizontal line through the graph at the value of Q2 2020.\n",
+    "* Write out your observation"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "### Note: Do not limit your analysis to the provided TODOs. Perform more analyses e.g \n",
+    "* Check for more external dataset\n",
+    "* Ask more questions & find the right answers by exploring the data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/Nigeria-COVID-19-Data-Analysis-Using-Python b/Nigeria-COVID-19-Data-Analysis-Using-Python
new file mode 160000
index 0000000..100fa3b
--- /dev/null
+++ b/Nigeria-COVID-19-Data-Analysis-Using-Python
@@ -0,0 +1 @@
+Subproject commit 100fa3bf55fe66a2244db5672a064362f3256e46
diff --git a/Note.txt b/Note.txt
index e69de29..6b63cc6 100644
--- a/Note.txt
+++ b/Note.txt
@@ -0,0 +1,16 @@
+Common Pandas attributes:
+Df.index: to view all index of a dataframe
+df.columns
+df.axes: used to combine the index and columns value in the same field
+df.dtypes: to display all column names with its various datatypes
+Notes: Keeping track of data consistency is the main goal of a data analyst
+df.values: delivers a 2-dimensional numpy array representation of the object
+You can use df.to_numpy() instead
+df.shape: Obtain a tuple that shows the dimesionality of the object and the number of element there are in each dimension
+Data Selection:
+Nested list to display column values: df[["Name"]]
+Working with iloc indexing
+Note: iloc is an integer location based property used for slection by position, it is also known as iloc indexer or iloc accessor
+E.g: df.iloc[1,3] fetches info from row1 column3, df.iloc[1,:] fetches an entire column
+df.iloc[[1, 3]: ] fetches the entire 2nd and 4th columns
+.loc[] is a tool that allows us to subselect info from a df by referring to its index label ie. column label
\ No newline at end of file
diff --git a/Notkagg;e.ipynb b/Notkagg;e.ipynb
new file mode 100644
index 0000000..0c93dc3
--- /dev/null
+++ b/Notkagg;e.ipynb
@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Update the weights based on the perceptron learning rule code  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_perceptron(X, y, eta, n_iter):\n",
+    "    \"\"\"Train a perceptron.\"\"\"\n",
+    "    # Initialize weights and bias\n",
+    "    w = np.zeros(X.shape[1])\n",
+    "    b = 0\n",
+    "\n",
+    "    # Iterate over the training examples\n",
+    "    for _ in range(n_iter):\n",
+    "        for i in range(X.shape[0]):\n",
+    "            # Compute the output\n",
+    "            y_hat = 1 if np.dot(w, X[i]) + b >= 0 else 0\n",
+    "\n",
+    "            # Update the weights and bias where eta is learning rate default = 0.1\n",
+    "            w = w + eta * (y[i] - y_hat) * X[i]\n",
+    "            b = b + eta * (y[i] - y_hat)\n",
+    "\n",
+    "    return w, b\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Perceptron Trick, Mathematical intuition\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VIl++fNSoUSPDfUivv/463333Hffffz9Dhw4lX758TJ8+ncOHDzNv3rw0e6acYTSG9LRo0YJnn32WkSNHsnXrVtq1a0dQUBD79+9nzpw5fPHFF3Tr1i3D8SNHjqRDhw40bdqUvn37cunSJb788kuqV6+eKhHu0KEDn332Gffddx/du3fn3LlzjB07lgoVKvDvv/+mes66deuyfPlyPvvsM4oVK0bZsmVp2LAhHTt2ZObMmURFRVGtWjXWrVvH8uXLyZ8/f6rx1apVo2XLltStW5d8+fKxadMm5s6dy+DBg1OOGTt2LE2bNqVmzZo888wzlCtXjrNnz7Ju3TpOnDjBtm3bAKhduzZWq5UPP/yQ6OhoQkJCaNWqVYZ7voQQwq28WKFPCCECxi+//KL37dtXr1Klih4REaEHBwfrFSpU0IcMGaKfPXs2zfFff/21XqdOHT0kJETPmzev3qJFC33ZsmUpj69Zs0Zv1KiRHhYWphcrViylTDl3lFq+du2a3r17dz1PnjxpSkovWrRIr1atmm6z2dKUGP/nn3/0hx56SM+fP78eEhKily5dWn/00Uf133//PeWY5LLi58+fd+o9SC4rvmrVKr1///563rx59YiICL1Hjx76xYsXUx17Z1lxXdf1s2fP6n369NELFCigBwcH6zVr1kxTFl3XdX3t2rV63bp19eDgYKdKjB88eFDv1q2bnidPHj00NFRv0KCB/vPPP6c5DifLimcWQ69evfTw8PA0xye/l3eaNGmSXrduXT0sLEyPjIzUa9asqb/66qv6qVOnsoxh3rx5etWqVfWQkBC9WrVq+vz58/VevXqlKSs+ZcoUvWLFinpISIhepUoVferUqenGs2fPHr158+Z6WFiYDqSUGL98+XLK9yUiIkJv3769vmfPHr106dKpypD/3//9n96gQQM9T548elhYmF6lShX9/fffT1Na/+DBg/pTTz2lFylSRA8KCtKLFy+ud+zYUZ87d26q47766iu9XLlyutVqlRLjQgiv0nTdiS6EQgghRBaSm7Bu3LgxZe+WEEII4e9kD5MQQgghhBBCZEASJiGEEEIIIYTIgCRMQgghhBBCCJEB2cMkhBBCCCGEEBmQGSYhhBBCCCGEyIAkTEIIIYQQQgiRgRzVuNbhcHDq1CkiIyPRNM3b4QghhBBCCCG8RNd1rl69SrFixTJtZJ6jEqZTp05RsmRJb4chhBBCCCGE8BHHjx+nRIkSGT6eoxKmyMhIQL0puXPn9nI0QgghhBBCCG+JiYmhZMmSKTlCRnJUwpS8DC937tySMAkhhBBCCCGy3KojRR+EEEIIIYQQIgOSMAkhhBBCCCFEBiRhEkIIIYQQQogMSMIkhBBCCCGEEBmQhEkIIYQQQgghMiAJkxBCCCGEEEJkQBImIYQQQgghhMiAJExCCCGEEEIIkQFJmIQQQgghhBAiA5IwCSGEEEIIIUQGbN4OQAghhBDuc4IYJrGZH9jJReIIxkJVCjKAenSmMkFYvR2iEEL4NEmYhBBCiAB0gyQGspgZbEMD7Ogpj53lOr9zmEKEM4VOdKSS9wIVQggfJ0vyhBBCiAATTxL38Q0z2IYDPVWyBLeSp/NcpxPfMZsd3ghTCCH8giRMQgghRIB5nl9ZzTEcdyRKd0p+tCcL2MYZ9wcmhBB+SBImIYQQIoCc4zpT+CfLZClZ8lGj+Nt9QQkhhB+ThEkIIYQIIFPY4nSylCwJB9+yg0vEuSkqIYTwX5IwCSGEEAFkIXsNJ0wACdhZziE3RCSEEP5NEiYhhBAigFwg1vRYmWESQoi0JGESQgghAkhYNjqGZGesEEIEKr9JmEaOHEn9+vWJjIykUKFCdOnShb1793o7LCGEEMKn1KIwNpP/vFejoIujEUII/+c3CdOqVasYNGgQf//9N8uWLSMxMZF27dpx/fp1b4cmhBBC+IxnqUcSDkNjLEBNClGPYu4JSggh/JjfzL3/+uuvqW5PmzaNQoUKsXnzZpo3b+6lqIQQQgjf0oxSVKEA+7jodPEHBzCUhmho7g1OCCH8kN/MMN0pOjoagHz58mV4THx8PDExMam+hBBCiECmoTGDLgRhweJEAmRFox3l6EUtD0QnhBD+xy8TJofDwbBhw2jSpAk1atTI8LiRI0cSFRWV8lWyZEkPRimEEEJ4R32K8ws9CMOGNYOkKTmZak1Z5vEYQVg9GaIQQvgNTdd1480avGzgwIH88ssv/PXXX5QoUSLD4+Lj44mPj0+5HRMTQ8mSJYmOjiZ37tyeCFUIIYTwmmNEM5YNTGQz0cSneqwRxRlMAx6jhukiEUII4c9iYmKIiorKMjfwu4Rp8ODBLFq0iD///JOyZcsaGuvsmyKEEEIEkhsk8RfHuEgswVipRH6qU8jbYQkhhFc5mxv4TdEHXdcZMmQICxYsYOXKlYaTJSGEECKnCsVGG8p5OwyXusINDnOZeOzkI4yK5JOiFUIIt/CbhGnQoEF8++23LFq0iMjISM6cOQNAVFQUYWFhXo5OCCGEEJ6wgZOMYQOz2UHibeXTy5OXITSgF7XJQ6gXIxRCBBq/WZKnael/ajR16lR69+7t1HPIkjwhhBDCP9lx8DJLGcV6bFjS9JpKvkrITy5+pQd1paeUECILAbkkTwghhBA5j47OEH5hApsA0m3Mm3yVcJk4WjKddTxNDdmnJYRwASmLI4QQQgif9isHGM8mp9rw2tGJI5HHmYvuZONeIYTIjCRMQgghhPBpo1mfYT+p9NjR2cl51nDcjVEJIXIKSZiEEEII4bOOcIXfOIjd4GyRDQvj2OimqIQQOYkkTEIIIYTwWVs4bWphXRIO/uaEy+MRQuQ8kjAJIYQQwmfFkmh67PVsjBVCiGSSMAkhhBDCZ0UR4pWxQgiRTBImIYQQQvisxpQkyMTlig0L91HBDREJIXIaSZiEEEII4bPyk4snqInN4CVLEg6eo76bohJC5CSSMAkhhBDCpz1PQ+zpNKvNiBWNdpSnCgXcGJUQIqeQhEkIIYQQPu1uijKODk4da8NCKaKYxUNujkoIkVNIwiSEEEIInzeAesygC6HY0CBNG9vkJXv1Kcbf9KMAuTweoxAiMNm8HYAQQgghhDN6UotOVGYG2xjLRvZzCQc6YdjoQhUGUZ/GlERLk04JIYR5kjAJIYQQwm9EEcoQGjKEhjjQScJBMFZvhyWECGCyJE8IIYQQfsmCJsmSEMLtJGESQgghhBBCiAzIkjwhhBB+y46D1RzjFFfRgJJE0ZiSWGQPixBCCBeRhEkIIYTfuUQcE9jEWDZyiqupHitFFENowDPcTRShXopQCCFEoJCESQghhF/ZwwXaMpNTXMWBnubxY0TzGsuZyGaW0ZMy5PF8kEIIIQKG7GESQgjhN04QQ0umcTqDZCmZA50jXKEl0zjPdQ9GKIQQItBIwiSEEMJvvMoyLhKLPZNkKVkSDk4QwzusdH9gQgghApYkTMKrdB0uXYIjR+D8eXVbCCHSc5ZrzGEXSU4kS8ns6ExjKzHEuzEyIYQQgUwSJuEVMTEwbhxUqwb580PZslCokPr/J5/AxYvejlAI4Wumsy3TZXgZuUES37HdDREJIYTICSRhEh63ahWUKgWDB8PevakfO3oUXnsNSpaERYu8E58Qwjft5oKpcuE2LOzmghsiEkIIkRNIwiQ8avVqaNsWrl5Vy+/SW4LncMCNG9C1KyxY4PkYhRC+KZ4kUzNMoGaZhBBCCDMkYRIeExsLXbqA3a6SoswkJ1JPPAFnz7o9NCF82jUS2MMF/uVsmp5DOUk+wkzNMOk3xwohhBBmSMIkPGb2bFXgIatkKZmuQ2IiTJni3riE8FWbOEUfFpKfj6jKWGoxgeJ8RgO+YibbctysyYNUIgkn/wK5TRIOOlPZDREJIYTICSRhEh4zejRYDP7EORwwZoyalRIip0jCwUAWU5+v+IbtJJD6F2Azp3mKhVRnHAe55KUoPa8t5Q03odWAuyhMA4q7JSYhhBCBTxIm4RHx8bBtm/OzS7c7fRqOH3d9TEL4Ih2d/vzERDYBpDujkryP5xjRNOFrThDj0Ri9xYLGf2lmaIwOvEkzNBNL+YQQQgiQhEl4yNVsbrvI7ngh/MUP7GQqW50qbZCEg4vE0YuF7g7LZzxNHZ6nodPHv0MLHqG6GyMSQggR6CRhEh4RGend8UL4i1GsN1TYIAkHf3CYvTmkbLaGxue05yPakIsgNEj1biW/d5EEM5YHGE5Lb4QphBAigEjCJDwiJARq1TK+hwmgaFHVl0mIQLeds/zNCcOls21oTLi5hC8n0NB4hSac4SXG0YEGFKc4kZQgN/dQgil04gwv8xz1vR2qEEKIAGDzdgAi5xg6FJ5+2tgYi0U1uLVa3ROTEL5kAydNjUtCZw05b6NfJCEMoB4DqOftUIQQQgQwmWESHvP445Avn/OzTJoGQUHGkywh/NU1Ekz1GQK4SoKLoxFCCCEEyAyT8KBcuWDhQmjdWt3OrGKedvOacfZsKFw44+OOHYNDhyAhAQoWNL/sTwhfkJsQw8vxkuUl1MXRCCGEEAJkhkl4WLNmsHy5KuKgabcSo9tZLGrP04IF0KVL2scdDvjxR2jbFkqXhnvvhfbt4e67oVw5+OQTuHzZ7S9FCJdrTmlT46xotKasi6MRQgghBEjCJLygeXPVV2nsWKhcOfVjpUvDRx/BiRPQuXPasbGx0LWremzFirSPHz0Kr70GVaqovk9C+JPy5KMd5bAaXJbnQKc/dd0UlRBCCJGzabqum1v/4YdiYmKIiooiOjqa3LlzezscAei6mg2KiYGICMifP/1ZJ4CkJJUo/fpr1g1wrVY1i7V+PVSq5Pq4hXCX3zjAfcxy+ngrGl2pyhwecWNUQgghROBxNjeQGSbhVZqmCkGUKQMFCmScLAF8/TX88kvWyRKA3a6a3fbt67JQhfCI9lTgLZo7dawVjfLkYxId3RyVEEIIkXNJwiT8gq7DF18YG2O3w5o1sH27e2ISwl1G0JL/0RoLWrpV82w3/+puSHHW0Je8hHk6RCGEECLHkIRJ+IW//4Zdu1TiZITNBhMmuCcmIdxFQ+M1mnKMYbxFcwoRnvKYDQtdqcIqevMXfSlALi9GKoQQQgQ+KSsu/MK2bWq5ntGEKSkJtmxxT0xCuFtxcjOclgynJXEkkoCdSEJM92oSQgghhHGSMAm/EBuryo3b7cbHXr/u+niE8LQwgggjyNthCCGEEDmOLMkTfiFPHnPJEqiiEkIIIYQQQpghM0zCL7RubW5JnsUCDzzgnpiE7zp2DI4cUUsyCxeGatUyr8AohBBCCJERmWESfqF0aejQQRVxMMJqldLiOYXdDgsXquS6dGlo0UL9uUYNqFoVxo1TpeaFEEIIIYyQhEn4jVdeMbYsz2KBPn1UfycR2K5dUwl1166walXax/ftg8GDoWZN2L/f8/EJIYQQwn9JwiT8RvPm8Nlnzh1rsUDDhjBqlFtDEj4gIQEefBCWL1e300uqdV19nTwJzZrBiROejVEIIYQQ/ksSJuFXhg2DyZMhLEztSblzX0rykr1u3dQFdJj08wx4X36pZpWcmX1MSoKLF2HAAPfHJYQQQojAIAmT8DtPPw1nz8LYsWp/Sng4BAdDkSJq2dWePfD995BL+nkGPIcDRo82VgwkKQmWLFFFIYQQQgghsiJV8oRfioyEgQPVl8i5li9XFfGMsljgq6/g/fddH5MQQgghAovMMAkh/NbWraoSolF2uxorhBBCCJEVmWESQvit69fVbJGZpsZSYlwIYdQprvI9OzjFVTQ0SpKbx6hBIcK9HZoQwo0kYRJC+K08ecwlS5oG+fO7PBwhRIDazllGsIoF7AHAiqo4ZEfnRZbyCNV4hxZURvpYCBGIZEmeEMJvtWunCj+YHSuEEFn5jQM0YDIL2YMDHQc6iThIxIEDnSQczGEX9fiK1Rz1drhCCDfQdN1IfSn/FhMTQ1RUFNHR0eTOndvb4QghXKBpU/j7b2MzTWFhqtJiZKT74hJCeFcidn5kL6s4SgzxhBNEA4rzKNUJI8ip59jMKZrwNQnYyepiyYJGLoLYQD+qUjD7L8CEGyQxh51s5BTXSCCKEFpShg5UwiafkQuRhrO5gSzJE0L4tVdfhc6dnT/eYoHnnpNkSYhAlYSDj1nDKNZzjusEYUEHNGAcm3ieX3mWugynZZaJ02ssJwlHlskSgAOdOBJ5mxXM4VFXvBSnxZHIu6xiPJuIJj7Vax7FeooSwQs04kXuwSqJkxCGyQyTEMLvvf02vPde1sdZLNCiBfz6q+rdJYQILAnYeZgfWMy+TJMcCxp1KcoyehJFaLrH7OMilRljOAYrGsd4gWJ45lOZaG7Qjpls4nSmqZ0GdKYKP9CNIEyUFxUiADmbG8jHDEIIvzdiBHz6qUqCLOn8rZZcerx7d9W0VpIlIQLTs/zEEvZnOSPkQGcLp+nK9xkmGTPYllLcwQgdmMW/hseZ4UCnK9+zOYtkKTmuRezhOZZ4JDYhAokkTEIIv6dp8OKLcPo0fPwxVKoEISFgs0HhwjBsGOzbBzNnQmj6HyYLIfzcTs4xjW1OLqBTFe5WcISlHEz38WNEm4pDzTCZG2vUrxxgBUec2GGl6MBktrCHC+4NTIgAIwmTECJg5MunEqe9e+HGDUhMhDNn4JNPoGJFb0cnhHCn8WwyXNjAhsYYNqT7mLOJV3qcTWCyawwbDM+C2dCYwCY3RSREYJKESQghhBB+TUdnGltJwlifgSR0lrCfi8SmeawIEWgmluQ50ClKhOFxRl0gll85YDg5S0JnKlvRPZTUCREIJGESQgghhF+7RgLXSTQ1VgfOcC3N/d2paTgBAzW79Dg1TMVixCmumk55YognjiSXxiNEIJOESQS8hAT4/nto3hyiotTeloIFoU8f2CSrEoQQQqTjbopSl6JYDMwyWdFoQ1kqkt+NkQkhPE0SJhHQfv8diheHxx+HtWshJkYlUBcuwDffQP36KpE6d87bkQohhDArgmDCnWxGeycNtfwuPR/Q2tDSNQ2NEdxrKg6jihJhYsGgEkkwYdKKUwinScIkAtbixdC+PVy6pG7b7akfT7q5GmHdOmjYEM6e9Wx8QgghXENDoze1TRV9eICK5CdXuo+3ozyT6YQGmc40WdCwYWEWD9GYkoZiMKsg4bSjvImiDxZ6U9vU/iwhcipJmERAOnoUunUDh0N9ZSYpCY4fh0ce8UxsQgghXG8g9UwVfRhMg0yP6UsdltCD6hQEVMKhoWamkhO0uhRlOT15lOpmQjdtMA1MFH1wMIB6bopIiMAk87EiIE2YoEpK607+O2K3w+rVak9TPfl3RAgh/E51CtGbWszgX6dKglvRaE5p2lE+y2PvowLtKc8GTvIN/3KSq2holCQ3T1GLuynqipdg2P1UoBmlWMtxpxInCxpPcRfVbiZ/QgjnaLru7CWl/4uJiSEqKoro6Ghy587t7XCEm8THQ5EicOWKsXE2G/TsCV9/7ZawhBBCuFkCdh7mBxazL9P0wYJGPYqylJ5E4d/drC8TRxtmspUzmSaKGtCBiszjMYKxei5AIXyYs7mBLMkTAWfzZuPJEqileT//7PJwhBBCeEgwVhbyGCNpTWHCAQjCgg0LQTcveaII4RUas5Lefp8sAeQljD/pzQs0IpJgQL1m6819VQCFCOd9WrGQxyVZEsIEmWESAWfJEujQwdzY0FCIi3NtPL7Iboe//oJTp0DToGRJuOcesMhHKEKIAJGEg5/YywqOEEM8EQTTgOI8SnVCA3RHQiyJfM8ONnCSayQSRQgtKUNnKhMkiZIQaTibGwTm3xgiRwsLMz82JMR1cfiiS5fU/q6xY1WydLuyZWHIEOjXDyIjvROfEK50kVhO32xIWoQICmRQCU0EJhsWulKVrlT1digek4sg+lCHPtTxdihCBBRJmETAqVYNrNa0ZcSzYrVCrVruickX7NkDbdrA6dPpVw48cgReekklVMuWQalSHg9RiGxzoPMbBxjDBn7hQMqODg1oT3kG04D7qIBVVqQLIYRwkl/9i/Hnn3/y4IMPUqxYMTRNY+HChd4OSfigwoWha1dVxMEIux0GD3ZPTN524gS0bAlnzmRcZl3X1dehQ3DvvXDxokdDFCLbYoinHTN5gG9ZysFU2991YBmH6Mh3tGIGV7jhrTCFEEL4Gb9KmK5fv06tWrUYO3ast0MRPm7w4FuNaZ1hsUDBgtCli9tC8qqXX1YJkDOzbklJqo/ViBHuj0sIV4kjkXbMZCVHANVf507JZZfXcIw2zOA6CZ4MUQghhJ/y26IPmqaxYMECuhi4wpWiD65nt8PatXDypLpdvDg0bqyWt3nbsGEwenTWvZg0TcX722/QqpVHQvOoM2dUUQcjCSRAeLgaGxHhnriEcKX/8Dsfssap/jugykq/QCM+oZ2bIxNCCOGrpOgDEB8fT3x8fMrtmJgYL0YTWK5cga++gi+/hOPHUz9WsqSa4enfH/Lk8UZ0yqefqmRo1Ci1PC+9hMFqheBgmDs3MJMlgGnTMl6Gl5nYWPj+e3j6aZeHJIRL3SCJ8WxyOlkCtddpEpt5l3vJRZAboxPC83R0/uQof3CYaOIJJ4h6FONBKqeUGhdCOC+gE6aRI0cyQtYVudyhQ6p4wNGj6V+IHz8Ob7wB48fD8uVQPusm6m5htcLnn6tldmPHwvz5qZek5c0LAwaor0AucLBrl0ocjbLZ1Fh/FRurZg1Pn1ZLLkuXVj+3QXJtHHDmscvUnqSrJDCbHfSVimIiQOjoTOEfPmYt+7iIDQvJf/0n4qAw4QymAa/QmJDAvgQUwqUC+rfljTfe4MUXX0y5HRMTQ8mSJb0Ykf87exZatMi8eACox06cUMdu2gRFinguxju1aKG+zp5VleJiY9XM1913B34ZcYD4+KyXJWbkhh/uiz9+XCXKkyfD1asqWUx+/YUKwXPPqfLp+fJ5N07hOus5SRAWEjE2lRqEhY2clIRJBAQHOs/wE1/zT0qSlHTH78RZrvMOK1nKQRbTnUhywD+CQrhAQCdMISEhhOSEK2IP+s9/VLLkzH6YpCSVpPznP/D11+6PLSuFC6uvnCZfPjXDYnRZnq77X1Kxfj3cfz/ExNyaTbw9WTx3Dt57Ty1T9Obsp3Ct6yQYWIx3iwOdayS6PB4hvOE1lvE1/wBk+vvgQGctx+nGD/zCk7fNQQkhMiILWYXTLl+GWbOMFQ9ISlJjLl92X1wicw8+aLzgA6gxnTu7Ph532bMH2raF6OjMqwHa7WoWqlUrlUAJ/5ebEFOXfBoauQl2eTxCeNoRrvAp65w+3o7OUg7xGwfcGJUQgcOvEqZr166xdetWtm7dCsDhw4fZunUrx44d825gOcQ330CCiSq8iYkwc6br4xHOad9eFeIwwmKBOnWgXj33xOQOw4ap5ZbOzKTZ7aqy43vvuT0s4QEtKWN4OR6o5UotKeP6gITwsElsNjxTZEPjSza4KSIhAotfJUybNm2iTp061Kmj1pu/+OKL1KlTh7ffftvLkeUMu3YZbwYL/l88wF0cDli6VF20v/YavP8+/PWX+f1GGbFa4b//NR6b0THedPCgei+d6TOVzG6HqVPh2jX3xSU8owOVKIrx+vcFyUUXqrghIiE8azJbUvqMOSsJnV85wDmuuykqIQKHX+1hatmyJX7aNiogmC0eoOv+WTzAXZKSYMwYVe786FGVUGqaSlLsdqhaVTWa7dPHXHW79PTvD9u3q2qBzhg+HB5+2DXn9oSvv1azYkYSJlAzUnPmqPda+C8bFvpSh/dZ7fQYDRhGI4LwgaZxQmRDInbOE2tqrA6cJIZChLs2KCECjF8lTMK7slMAwN+KB7jLjRsqEfnll1vJ5537i/bsUb2PVq9Wld5c0QRY01TPrBIlYMQIlfzCrRiSi0JERsKHH8LAgdk/pyft328umbfZ1FjhvxKwM5glTGaL02MsaNxPBV6liRsjE8IzsvsxstGZKSFyIr9akie8K6cUD3AXXYdeveDXXzO/uE9+bPp0NdPkKpoGr7+uKhd++aXan1SsGBQvDg0bwpQpqgKivyVLoPbWmWnOC7eSR+F/ErHTkW+Zwj9OXfIl7/HoyV3M5zGvNfA8yhV+ZC+z2cEv7DfVQ0qIZMFYicpGefAiJpazCpHTyAyTcFrz5lCpkrFP8zUNKlZUY3O6v/6CH35w/nhdV8v2nntOvYeukjs3DBqkvgJFgQJqtshoQu9wqLHCP73B7yznkNOfjzvQmU5nnqK2O8NKl47ObxxkNOv5lQOpYg7FSk9qMYQG1CQH9j4Q2daTu5jA5jR9lzJjRaMRJShBbjdGJkRgkBkm4TRNg7feMrb0SddV8QBX7cVxxo0b8NNPMGmSmjVZvtz43hZ3GDvWeNEMqxUmTHBPPIHkoYfMzX7a7Wqs8D/R3GAsGw0tJrKiMZfdbospI3YcDGIJ9zOLpRxME/MN7ExlK3WYyHS2ejw+4f8GUt9QsgRqKd5QGropIiECi6bnoCoKMTExREVFER0dTe7c8omKWa+9Bh995Nyxr7zi/LHZdeIEfPEFfPWV6sVzu2LF1IzKc89Bnjyeied2MTGQP7+5i/rcueHKFc8mnf7GboeyZVV/JWdZrdCsGaxY4b64hPuMYQND+cXw7gsNOMIwShHljrDSNYQlhpK77+nGo1R3a0zCsxKxs5A9/M0JrpNIJMG0oAz3UwGriz677sF8ZrMDhxM/aTYs1KQQf9OPYCl8InIwZ3MDSZiEYclLxd56S1UZS74Pbl3Uh4WpctkvvOCZC/0NG+C++1RiktFsksUC5crBsmVQpoz7Y7rd3r1QJRvVi2NiVEEGd9F1WLdOzWb9+6+apStQALp0gb59/aNox+TJ8Mwzzh+vaepnoXVr98Uk3Kc937AsndkaZ0yiI89Q1+UxpWcNx2jKVKeP14BcBHGGl4mQprp+LwE7/+MvvmQDF4gl6LbkKBEHxYnkBRoxjEbZTpxukMSDfMfvWSxTtaJRnnysorfsXxI5niRM6ZCEybWuXYNZs1QvmxMn1EV3iRLqArtHD4hw4u/h3bth4kTYskU9X/78cP/9qsxz3rzOxbFnjypacP161kvvbDbVxHXjRnUuT9m1C6pn4wPjS5ecfz+M2rRJvd87dqTdB2SxqPuefRY++QSCffz67ZVXVJzOGDfOPwtcGKWjs4Ij/MUxrhJPJCE0piStKGu40aUvqcckNnPa8DgrGh/Q2mMV8rozjznsMrRcSgMm0JH+HkrqhHtcJ4EOfMtqjmU666MBnajMDzyS7dmeROy8yyq+ZAPRxGNFw46OFQ0HOkFYeZK7+JR25CE0W+cSIhBIwpQOSZh8x8GDqnT2qlVpL9I1TV2Y9+unLn5Ds/g7vV07+OMP5/cpWa0wdCh89pn5+I26eNF8cYHgYIiLU8mLq61cqRLUxMTM3z+LBVq1gsWLfTtp0nVVAfDtt9WyzORy6aC+73Y7FC2qlm4+8oh3Y3U3BzoT2MTn/M0BLmG7mR7pQBIOypGXF2jEc9T3y8SpGV/zFwbWYN5kQWMU7Rnigb0bF4mlCJ8a3luiATUpzDYGuCcw4XY6Op34jiUccGqJnAb0pjZf45qSsnEkModd/MFhrnCDCIKpRzF6UYu8hLnkHEIEAkmY0iEJk2/YsUNVzcts+Ryoi90mTeC339QSv/QcPAgVKhiPIXduOH0acuUyPtasNm1UgmKkAIXNpmbrpk1zfTzHjqlZr9hY50pyWyxqyZs/FKG4cUM1pP3mGzX7abWqPU59+0KHDreKb+i6+p6MG6f6Xl2/rmZGW7ZUe96aNPHPvWOJ2HmS+fzArpQk6U7JL6sLVZhNN7/bxzCEJYargiX7nadoRVk3RJXaek7QiCmmxgZhIYG3XByR8JSVHOFephset52B1KCQGyISQqTH2dxAquQJj7pyBdq2zTpZAnURv2aNmonKyJQp5hq7xsTAvHnGx2XH4MHGq/UlJalCFe4wZoyauXK2f5HDofYJnTa+CsrjQkOhZ0+VbO/cqfZlLVqk+oElJ0u7d0O1amrmbOFC1Z/q2jXVi2ruXFUQok4dOHTIqy/FlOdYwhx2ARk3tdRvfi1iL8/wo6dCc5n+1DU1c1OOvLSkjFtiulM85stzJuJwamZC+KYxbDDc58uGhfFsdFNEQojskIRJeNTUqerC1NnEweGA775TvZ/Ss3evuYalQUGwb5/xcdnRsaNqFutsgme1qtmQ+vVdH0tcnCq7bjSB03WVNPm77duhUaNbP1d3Vi9Mvr1zp3r/Pf2zkh3bOMNkthjqTTSDf9nEKbfG5Wo1KUxjSmA1uJxwCA08tgQxbzb2iEQS7JdLJQXEEM8C9hhO6JNwMI1tkigL4YMkYRIe43Co/SVGZdaLKD7eWF+oO8d6ks2m9gBVqJB10mSxqORq9mz3LAlbvTpt6XVnOBzw/feuj8eT4uKgfXvnioQkJan36b771D4vfzCeTaY+2R7nh59sT6YTuQhyKmmyotGUUgykngciU6pR0FRTUBsWupCNsprCq85wzXTSE0si0dxwcURCiOyShEl4zL//wuHDxhMcux2+/Tb9xwoUMN4MFtSFv9kiDNlRqBD8/bdaLhYUpBKj5IQo+c9hYTBkiOoP5EylQTMuXDA/9vx518XhDd9/r5YVOju7Zrern9tFi9wblyskYmcG20x9sj2L7dzARKMwL6pKQVbQizyEZjgbk3x/c0rzM90JwcRfGCZZsTDYRFGNJBwMwvmp5Rsk8Q3/0pYZVOJLKvElrZnBTLb53fc0EOjZnCGS+SVz4kliNUdZyB5+5QCHueztkEQA8dy/HMJtEhPVbEl4uGtnI3RdlZw+eVLdLlEC6tY1f47sXGhfupT+/V27wnTj+2qx29V+Fm/Ik0ctTfzkE/X/9evh6lWIilLFMHr2VEUp3Ck7le7i4lwXhzeMHp26ep4zrFY1O9qtm/vicoWLxBFn8gI5ATvnuU5JDzZ0dYW6FGMXg5jEZsaykTNcS/V4fYoxmAY8RnWCvFDY4mnu5n+s4Srx2J1qKKrRiBI0oLhTzz+JzbzGcq5wA8vN0tEAB7nMHxxmCL/wP9owwIMzazldYSKwgIlyJBCKjShCXB1SQDtBDOPZyAQ2c4nU/0C1oSyDacCDVJYlriJbJGHyU+fPq4IH48bB8ZuVdYOCoFMnVVygRQvzic3Vq+pC/ssv4cCB1I9VrKhmP/r0MT77YWYmKKuxHTqoMtFGChFYrWpDf+XK5uNxhfz54eWXvXPuihXNj71+XRXN8MdCk+fPwz//GB9nt8Off6qKgp6srGhUdvc+OHNB74sKEc6bNOd1mrKeE5zjOsFYKUdeqlLQq7EVIBdL6E5rZpCAPdP32IpGKfIwj8fQnLi4e49VvM3KlNu3f/+T/xxNPANZzBmuMZyWpl+HcF4eQnmASvzCfkO/UzYsdKdmthvY5iR/cJhOfMcNktJ9r1dwhOUc5jGqM50uHp1hFoFFfiv90PjxULw4/Pe/t5IlUDNNixbBvfeqRq5nzxp/7iNH4O67YdgwVbL7TgcOwPPPq/01x44Ze+7y5Y3HAyrxK1cu/cdsNnjLYOVdhwPefNNcLIGiVi2oWdPcWIdDlev2R5ezuULDzL4vT8pHmOEiCMksaBTAh7NBJ9iw0IRSdKUqHajk9WQp2T2UZA19KYvqPn3nHrPk260py3r6UYjwLJ9zDjtTJUtZGcEqZrPD+aBFtgymvuEPIIwuxczp/uYE9zOLuAySJbj1IdAcdtGTBVJQQ5gmCZOf+fRTVWY6MTH9JUXJ1b22bIF77jG2V+X8eTUzdeSIWo6X3l6j5PsPHlS9aow8f6lSqheRmTLgAzLp3zhggJr1ctaYMdC6tfEYAs0995gbp2meL8nuKhn18/LUeHcLxUZXqpgo+qDRkUpE4MNdif1cHYqyj8H8zlN0ojIlyU1+wihPXgZQl508x2/0dCpp1dF5lz8NpcYa8C6rsr2/RjinLeVpSRmnP8CwoNGNatxNUTdHFhgc6PRgPklOlt93oDOHXcxhpweiE4FIEiY/snat80u47HY1A/TUU84//+uvq/1Kd5ZYTk9Sknp+o7M7ZnoRJffUyYimwRdfwMcfQ2TkrfuSJSdohQurDf/u6mvkb6qYLMKl6+ZmL31BkSJqD5nZsf6wDHEQDUwUfdAZLJ9su52GRivKMo9HOcYLXOBVDjCUL3mAagZmw9Zxgh2cM5T66MBuLrCG41keK7LPgsYCHqM2RbJMmixotKA0M+jimeACwO8c4hCXDc0YWdH4kg1ujEoEMkmY/MioUcb2Adnt8MsvGfcwut3lyzBrlrFkxm6HadPUfhZnPfig2ndkZJZp1KisL1Q1TSWTZ86ovV0tWkClSlC1qiohPW8enDgBjz7q/HkDXXZmS0LNt5fxqqAg6N/f+CynxQKDBqn/+7oWlOZeA59sJ5fbbk0G616Fz1nIHsOziKCW/i1gtxsiEunJQyir6M1QGqbM3tqwYEFL+f7lIZT/0JRfeZIwgrwZrl9R7ROMLT+2o7OG4+zGz0u9Cq/wg3/+BaiiBvPnOzf7czurVe15yso330BCgvG44uNVouUsi0XN8rRurZKcjApTJF/QfvyxusB1Vq5c0LevKsm9dy/s2qV6Hz30UPaKTgSiSpXMjbPZVCLqrwYMMF7a3mKBfv3cE4+raTc/2a5F4SyTJisa1SnEjzwuFaT8yEViTY3T0bmIn5e59DPhBPMZ7TnDS3zFg/SiFo9QjT7U5hu6coaXeI9WBHuhgqM/28oZkkwuL90pCZMwQRImP7FmjfGlbKDGLF2a9XE7dpjbW2SzwU6DS4LDw+Hnn9VeouRqbZp26/yaBm3bwu+/e6+KXE7QsiWUKWN8XFKSsSTW15QtCx99ZGzMmDFqSZ6/iCKUP+nDAOoRevNz2OSEyHKz/looVp7hbtbQl7z4+OYskUqwydIeGhohcmHuFeEE04+7mUwnZtONSTxID+6Sqm0mZae/WCx+0oVc+BT5TfUT2anO5cySuRs3jH/qDmqMmb48QUFqL9HAgbB6tWpqGxur9pe0aZNxVTzhOhaLKpbx8svOf+8tFjUz1bSpe2NztxdfVInf66+rpD+9mVubTX3gMHo0PPus52PMrnCCGcMDvE8rZvIvf3GMaOLJTQhNKMlT1CIPfrq2MoerTAFTJeAd6FTGCx27hUjHJeKYzQ6OcgU7OkWI4FGqU8qJXnB5CeP0HT3XnJVX/t4TJkjC5CeM9jwyOjZvXvN9m/LmNTcO1DmbN1dfwvMGDoTvvlO9ibKawUyeBZw82bUNkr1B0+C116BVK9VvbPZsVXkyWUgI9OihEsratb0WpktEEcpgGjCYBt4ORbhIT+7iVZYZLpFsQaMnd7kpKiGcc5jLvMsqvmUHidhT9nPZ0XmVZXSgEu/QgnoUy/A5OlOZvVww/MFBKDaaUzpb8YucSZbk+Ym6dc2Ns9mgUaOsj+vQwfj+KFBjOnY0Pk74hrAwVRikTp3M95TZbCqJWLQImjTxbIzuVL8+zJih9gguW6b2CS5frm5PmeL/yZIITPnJxRPUNLQwz4rGY1SnoBM9noRwl02coi6T+IbtJNxMdxJxkHizPLgO/MJ+GjMl0wIlz1LX8AcGNiz0ohZRMsMkTNB03cxCLP8UExNDVFQU0dHR5PaH+sB3aNtWFTMwupdp06asEy6HAypUuNWDyRmapsbs3ev/Mw6uduMGzJkDS5bApUuqGEWNGvDMM6ofla+Ji4NJk9Tys0OHUj8WFga9eqlmxpUreyU8IcQdThJDPb7iPNez/JTdikZ+crGZ/pTA//7tE4HhMJepyyRiiM/yZ1YDrFj4g6dolsGM0OPMZS67nJplSn6+rTxLdQqZiF4EKmdzA0mY/MhPP0GnTs4fb7OpT8g3bnTu+MmT1QW9EVOnQu/exsYEMrsdRo5UDYavXFFL2Ox2lVBaLCox7dhRFRHwxcRJ1+Gvv+DAAVUBsWBBlaj74a+LEAFvDxdowwzOcC3Di0YrGoWJYBk9DfV6EsLV+rKImWxzurqdBY06FGET6VcZukYCzZjKds5mmjQlf577HQ/zGDWMhi0CnCRM6fD3hEnXVS+YCROyngWyWtVF7oYNahbI2ecfMgTGjnXu+GHD4LPPZHYpWVISPP64WtaV2ffHalX7vv7807/LcwshvO8CsYxlA+PYxDmup3qsILl4jvoMor4sxRNedZk4ivApCRgv97uJZ6ibwX6mq8TTm4XMZw9WtFSJkwUNBzr5CWMaXeiIyV4aIqA5mxtI0Qc/omlqg3pIyK0mtnfuO0qexShcWJUTdzZZuv35ixaF99671Zfp9ot/TVPnf+cdtWlekqVbXn4562QJ1IzT5ctq5mb79uwVzRBC5GwFyMU7tOQ/NOMPDnOSq+joFCc3rSlLkJQRFz7gB3aSaCJZsmFhOtsyTJgiCWEej7GPi0xgE/PYzSXiCMVGNQrwHPXpSlXpcyWyTWaY/NTmzTBunGoaGx9/6/4aNWDoUOjeXfU7MuvyZZg+XS25O3FCJUYlSqimsE89pcp/i1tOn4aSJY3tL7NYVD+gl15yX1xCCCEC1xZOs5bjXCOBCIJpSilq43tN4/7L73zMWhJxGB7bmcos5HE3RCWELMlLVyAlTMmuXoXjx1WRgfz51b4YmfXxvPfeg+HD1eyeEaVLqyILFqlXKYQQAtDR+YPDLOcQ0cQTThD1KJYyU6Kj8x07+Ix1bOZ0SkED+826cfUpxkvcw6NUv9mm2vveYDmfss5UwtSBivxMdzdE5Rw7DnZzgcs3Z67Kk4980uw7YMiSvBwiMhKqVfN2FGLmTOPJEsDRo6qKYYMAapGj62rWMyREknchhHCWjs4kNvMxaznIZWxYUtKdRBzkI4wB1OUEV5nBtpRHdSDptkRkM6d5nHms4Ajj6HDbs3hPESJMNVu2YaEo2WhEmQ0XiOVr/mEMGzhOTKqYHqM6g6jPPZT0SmzC8+RzbSFc4Nw582PPnMn6mEuXVA+kGTNg7lxVxc6XJCWp/Vv33gvBwaoUeXIPsG++Sb1sVAghRGp2HPTlRwawmENcBlQSlNyjCOAScYzkL2awDSDDPkTJ909kM6+xzAPRZ+0Rqpsal4SDHl5otryW41TkS97g91TJUnJM37OTxnzNMH7FbmLWTPgfSZhEQNu5EwYPhurV1XLF6tXhuedUsQVXys6SOmsme1H/+Qf69FGFOLp0Uf2QHnkEKlaEVq1UEuXtRbXbtkG5cvDww7B69a1CJA6HKmnfs6fa//bXX96NUwghfNVrLGc6WwEynYcx+tf9J6xjDxfMhuUyxYikK1UMNVvWgIrko0UGfZjcZQunacMMYojPMClNntEbzXqG8dvNlrsikMkeJhGQTp2CJ59UjX7vrCaYfLt5czX7UdIFM+p33w1bt5pLXnbsUIncnaZMgf79VTJ2ZzVEuNXjqU8f1XTW5oUFttu2QZMmag9dZgUvLBYV79Kl0LKlx8ITQgDnuM42znCdRKIIoR7FiCTE0HNcJo7pbGMSmznMFew4yE8uelCTAdSjAvncFH3gO8oVyvKFWy65bWgMogGjuM8Nz27MVs7QiMkkGFicN49HeQjP9d9woFOJLznCFUNLCH/jSdpR3o2RCXeRog/pkIQpZzh2DO65Ry2TSy/RSGazqUIZ69ZB2bLZO+f48apHlpHfJotFNRbevDntY7NnwxNPOPc8mgb9+sHEiZ7dM5SQoN63s2edqw5osajKjUePSil1ITzhL47xJeuZz55Ue1zCsNGb2gymgVPNbL9kPa+wLKWHzu1/zSX3vnmSu/iKBwmVrdGG/Zff+ZA1pvb4OCOCYC7zGjYfWFS0mH08xA8k4chw9kZD/YyNoj3P08ij8S3jIO34xtAYGxbuowI/4eQ/2sKnOJsbeP+3RwgXSkqC++7LOllKPvbiRWjX7lbPKbOefFLt2zHC4VAl4O907ZpKgJxNfnQdvvpKNcL1pPnz1Uyes6XUHQ712qZPd29cQuR0Ojr/5XeaMTVNsgQQRxJfsYW7GJ+yDCwj77GKofxK/M3L+TsvcZMv8r9lOw8wy1Rj0pxuCv+4LVkCuEYCl4hz2/Mb0YFK/Elv7qEEQEphC+3mnwEqkZ/5POrxZAlgHJuwGSySkYSDxezjONFuikr4AkmYRED5+WfYvTvrZClZUpIqoLBwYfbOGxkJH3/s/PE2GzRsCI+n01pi1iyIjTU2W2Wzwdixzh/vCl9+mfn+q4yMHm2uoqAQwjnvsJIPUJsG70yWkiXhwI5ObxYxmx3pHrOE/bzNSqfO6UBnFUd5g+WmYs6p7Dg4y3W3n8eXEtmGlOAv+rKdgTxPQx6mGp2pwjPczZ/0ZjeD6OrBZXi328hJkkwkrzrwL2ddH5DwGTJ3LgLKmDG39vY4y2pV4x59NHvnfu45Vc3urbfU8rOMkgKLBe66SyV3IelsIxg3zvi5k6vUnT8PBbNeYZNtCQmwdq3xcboOhw/DyZOu2TsmhEjtX87yHs5PN2vA0yziASqS+459TR+xJmXJnTMc6IxnE+/QMs1z+ZrrJLCIvRwjGgc6xYikM5XJ6+H+Op7YE2FBI78P9g2qQSE+oZ23w0gllkTTY6+RzaUqwqdJwiQCRmws/P678XF2u6ruduUK5MmTvRjefBNq1YL//U8lFFbrrQp6iYkqmXnuOXjlFbWfJz1795orHmG3w8GDnkmYYmKyPiYz0dGSMAmRmQNcYj67Oc91bFioQD4epXqWxRrGsREblgxnlu6ko5bozWAbg7nVEG4351nFUcNx3yCJmWxjEL7ZXO4kMXzEGr5mK9dISFkGZsdBMFa6U5PXaUol8nskHhsW8hHmtiVzyftrwghyy/MHmihCuMwNU2PzEOriaIQvkYRJBIxLl7I/PrsJE8CDD6qv7dthyRKViIWFQY0a6v6gTP7d0vXs7ac6dAj+/hsuXFD9kCpVUuXIQ13893hGyZ6zIrzTh1AIn/cnR3mPP1nOIaxoKU1Hk3AwhF/oQ23eogVF0mnmeY0EprPN6WTpdmPYkCphWsYhLGgZbszPzG8c9MmEaTtnacNMLhGX8h7d/l7FY2cm/zKHXfzME7SgjEfi6kUtRrPeLfuYknAwmPouf95AdT8V+Yothn+HQrHR6Oa+LBGYJGESASO95W2eHH+nmjXVlxGaBlFRKskyo0ePWyW8Qc1qRUWp8uSvvw75XFT5NzRUJWP79xufDcuXD4oXd00cQgSSaWzlaX5M2XJuR091ER1HEhPZzEL28gdPUZkCqcYf4Qo3cHID5210YB8XsePAenPG5Qo3sJpImHTwmQIDtztONK2YwWXiMk1MknAQSyL3M4u/6cddFHZ7bAOox+f87fLntaFRh6K0lXLXThtIPcazydAYGxZ6UYsomWEKaFL0QQSMfPnMl6vOnRsKFXJtPGZ17Zq9nkoOh0qUEm8uxY6Ohs8+gwYNVMl1V9A0GDLE+DirFQYOzHyWTYic6Gf20ZdFOO5Iku5kR+cs12jDTM7fUSwgLhv7L3TUDEuyXASZml0CVcba1wxnFVe44dQsjgOdBOy8xG8eiExVhetL7ZTZRFewolGWvPxMd5c+b6CrSWFaU9ZQpTwdPdXsrAhMkjCJgGG1wrPPGq/cZrWqMt6+chE/aJDzVf6cZber/kdt2qgEyhV69lRLDY32furf3zXnFyJQONAZwi9OH29H5zRX+Yx1qe7PTsGCICyE3bbopBaFTS0Rs6JRywOzMkZcJo5Z/GtomZUdneUcZj8X3RjZLePpyANUyPIy3YpGBfLRkUpY0LDeMcJ6875HqMZ6+lGIbK6fzoG+42FKEJXmvb1T8qNf05ka+MgnrsJtJGESAeXZZ42XrLbbYcAA98RjRt260KqVuZLdmUlKUkUhXFV+PCoKvvtO/dnZpGniRChVyjXnFyJQLOcQR7hiKD2xozORzcTftgSvHHkpR17D8wk2NB6gItptI1tTjjLkMfhMKvl7lnqGx7nTbHaYKqttw8LULPpUuUowVhbyOO9yL/luJr42LFjQsN38zoRiox93s5Fn+IknOMYw3qQ5rShLXYrSmrK8TQuO8QLf0c3jFf8CRUHC+ZunaYBaO35nw9/kvlG5COJ7uvEUtTwfpPA4TdfN1OPyT8528xX+beRI+M9/nD/+nXdg+HC3hWPKpUvQuLHqEWWkRLozihVTS/NclZAtWKD6Sdnt6cdqtap9ThMnqpk8IURqTzCPOew0NaMzn0dT9awZxd+8yG+Gn2kZPWlDuVT3fc46XmKp089lRaMt5fiFJw2e3b1eYSlfsJ5Egxv5LWg8SnW+42E3RZa+BOzMZze/c4ho4gknmLoUpSd3yT4ZD9LRWccJxrKRH9nLNRIIwkIVCjCI+vTgLp9cfiqMcTY3kKIPIuC8/jrcuAHvvqv2AqW3vC35/tdeUwmTr8mXD9atg6eeUv2aXOnUKVV+vZ2L2l907aqq802apHpIXbhw67GoKHjmGTWDV172HQuRrkNcNpUsWdA4Suo1tr2oxXusIpp4p57ThoWqFKAVZdM8NogG/Mg+VnM0y+eyYSEvoUzkQWMvwgOMJkrJdHQSPdzwNQE7C9nDWo5jR6cIETSmJA9RlWBcvOxAZEpDozElaYzqgeFAl/1gOZgsyRMBR9NgxAhYvhzuvz/95WIREVC7Nly9Clu3ejpC5+TNCz/9pL5c7cgR1z5f8eLqPT91CnbuVMne9u1w5gx8/LEkS0Jkxm7ygl5LZ2xewlhMD4KxZrkHw4ZGQXKxOIPCAMFY+YknaHtz5im950tenlSUCFbTh1JEmXot7lSYcFMFLKxYPLYHKBE777GK4nzGY8xlApuYyb9MYBNPMI+ifMpwVppaWihcQ5KlnE0SJhGwWreGH3+Ew4ehe3fIlUvdb7Gost1bt6pZkbvvVhXktmzxZrQZW7w4e1Xz7qRprl/mlywoCKpVg0aNVN8pV/d/EiIQlSC3qYsxOzpFiUxzfyNK8Bd9U5KXO/dgJN+uSzE28AwlM0lyIgjmZ7qzgMfS7UtUgXyM5n528FyaMue+4mGqmZrBS8LBo1R3Q0SpxZHIA8xiOKu4QCygZsWScKTMjl0ijvf4k3bMJDYb1RCFEObIHiYR0HRd7Zv5+uvMj7Na1cX+kiVw772eic1ZVarA3r2ufc558+Chh1z7nEIIc35gJ48x1/C4MGyc4WVyk34TOQc6yzjIGDaygZPEkkAkIbSjPIOoT32MN0Q7yhWOEk0idgoRTg0KpSoW4avaMIOVHHE6cdJQyeBeBrv19enoPMpc5rPbqVkwCxoPUokFPOYX77sQvk72MAkBfPBB1skSqBkXXYcHH4Rt23xrCdnVq659vogIuO8+1z6nEMK8LlShALlSZhecYcNCb2pnmCyBurhuTwXaU8EVYQJQmjyUNlE9z9vepgUrOOL08Trwf7Rye1KykVPMZZfTxzvQWcRe1nKcJkjJUSE8RZbkiYB17ZqqmOcshwPi42HUKLeFZEpk2hU3plmt8PTTt5YnCiG8Lxgr79PK6eMtaIRh4yXucWNUgaU5pZlK55Q9V1kZSWuPLMcby8Y0SyazYsPCODa5KSIhRHokYRIB69tvIdb5D2wBVTnv669VsuUrWrRwzR4mi0XNLg0blv3nCiTnz8OHH0KdOqp4RalS0LYtzJkDibJVQHhIf+ryJs2yPM6KRig2FtOd8uTzQGSB4ylq8TPdqXDzfbs9UUkuaFGcSGbSlddp6vZ44kniO7YbaqgLam/VD+wkTvYyCeExsodJBKx774VVq9RSO6PmzIFu3Vwfkxlbt6qL+ewKCVHlxJs0yf5zBYKkJHjlFRgzRs0u3t7w2GpVyzQLFlT9o7p29V6cImeZzlbeYSVHicaGhSQcaKhZJTs691KGUdzHXRT2dqh+S0dnFUeZxlaOcAUHOsXJTQ9qUp2CrOYY0dxI6X9UiyJuieMUVynOZ6bHH2WYT1YlFMKfyB4mkeOdOWMuWdI0OHfO9fGYVbu2SnLWr0+/p5SzXn1VkqVkdrtKiH/8Mf2fkeQqghcuwMMPw5Qp0KePZ2MUOVMvatOTWizjIHPYxXmuE4SV8uTlae6mEvm9HaLf09BoSRla3lb1byVH+JR1LGYfOipBTS7CUJ9iDKMRT1BDCi0IkUNJwiQCltllbLquKub5km+/hXr11PIxs0rJ/uAUb7+dcbJ0u+TH+/WDqlVVuXQh3M0dxRr8wR4usINzxJFIXsJoRimicH9vgg/5i9f5HRuWlDp1t1es28xpejCfxexjGl0IclED2fyEEYTFVGNdGxYKIJtRhfAUSZhEwKpUCXbvNtdzqGzapvdeVaqUagZ7113G92UlK13atTH5q6tXVWEPI7OPFgt88gnMvaPyc3S0atZrt0PhwmoJnxDCeQ505rOb0axnNcdSPRaKjV7UYigNqYZ7frnGsoHX+R0gw71EycnTd+wgGBtf08klM00J2ClLXvZx0dA4Gxa6UZVc+Ngne0IEMCn6IAJWv37mkqUSJXyvFxOoUufTppkbW6wYtHK+CFdAmzUL4uKMjUlKggULVHKk67ByJTzyCOTPrxr11qwJhQqp93jBguwtnQxEh7jMZLbwKWsZz0bWcwLdRCNREVjiSaI783iEOazleJrHb5DEFP6hFhP4nh0uP/9FYnmB35w+XgemsTVNYmf23I35mgMGkyVQid0gGmQ7BiGE82SGSQSs9u2hZEk4ccL52QSLBQYPVpv+fVHXrlC0KJw+7fwYX39NnrZokblxDgcsXAjLl6ukyGZLm5D/+SesWKGWTy5erJKonGw5h/iEtSzlIDqqEpnjZqpUk0I8T0N6UxurfHaX4+jo9GYhc272IMqooWxy0YsnmEc4wXSkkstimMpWpxvZJrNhYSwbaI75KXs7Dh7kO3Zz3vBiPCsa91OBJpQ0fX4hhHHyr5QIWBYLTJjg/PE2G1SpAs89576Ysstmgy+/NHZ8+fK+/Zo87dw5c8VArFb44otbCVd6s0jJCdTWrdC8OVy5YjZK//cBq2nLTJZzKOWS1H7bvNJOztGPn+jGHOKRKbmc5mf2MZudqfYKZST5iF4sJAETywYyMJ5NTp3/dkk4mMduLhpoMnynxexnHScMJ2sa0JiSzKabFJ8QwsMkYRIB7YEHYOZMdbGbWREIiwUqVoSlS13bKNYdHn5YlbrWtMxnjaxWtW9p+XKIksqzKcLCzI1zOGDfvtTlxzOSlAQHDsCLL5o7l78bwwb+yx9AxjMHyW/jj+ylNwtliV4OM5oNKb2PnKEDl4hj3s0ZqexyoHOYy6bG2tE5SrTpc39p8LUnq00RltGTcIJNn1sIYY4kTCLg9egBa9dCp04qMdI0lTwlJxsFCsB//wt//60al/qD/v3V0q/kfUkWi6rsl1zdLyoKXngBNmxwvjqerqv3YNAgtfSva1cYMgQ2bnTPa/CWmjXNVVA0Oitlt6v9UpcuGT+XP7tILC8a2BfiQGc2O1nOITdGJXzJEa6wnEOGZ1isaIzDNX8h2Q3PLaVmdqbrArGmXjuoKoLBLqrQJ4QwRvYwiRyhQQOYNw9OnoSfflL9dUJC1KxShw6+V0bcGS1aqK8DB9QysYsXIThYVQfs2tXYTMpvv6kmrtu3p96bY7Wqxq61a6sqca1bu+WleNQzzxhbqpkdSUmqUEdOmmkyvy9kI20p76aohC/ZywVT4+zo7DI59k5BWAkniOskmhqfH3NT1We4ZmocQBxJXCOBSEJMP4cQwhxJmESOUrw4DBjg7SjSd/06nD2rZjIKFXJ+aWCFCvDSS+bPO3UqPP20mnmD1Htzkv/877/Qrp1a3ti9u/lz+YK774b69WHLFuerKGqa+nJmOd6d1q83PsafTTS5L+Qn9nGO6xQi3E2RCV8Rn419SAku3O/2CNX5hn8zLCeeHg2oQgEqkM/UOS3Z3Hske5eE8A5JmITwIl2Hv/6CceNUj5/kBMVigc6dVXW7e++9lcy42u+/q2RJ1zNfcpacKDz1lKo82KyZe+LxlK++gsaN4caNrJMgq1X1V7pwwXjC5HDANfMfKPsdHZ1DXDE11oHOEa6kJEx7uMBsdnCGa1jRKE0enuQuiuHjmwxvc50E5rKLA1wiEQcFyUVXqlKOvN4OzavymZydAciTjbF3GkR9prHV8LihNDSduBQlAsvNapFGRRJMuPReEsIrJGESwktiY9VszaJFahnc7TM7DodaOrhgAbRpo5YT5s7t+hheeMH43py33lJ9iPxZrVqqwEeHDiqhSW+mKTlJrV9fLX389FPj57Fac1bBDQd6tnaGJGBnNUd5ixWs4ihWtJRP5O3ovMHvdKEy79OaKhRwVdgud47rvM+fTOEfrpNI0M3twnZ0XmEZ7SjPmzSnKU5uMAww9SlGPsK4hLGGaDY0Hqaqy+KoRzFaU5aVHHFqGakVjSJE8CR3mT5nXsJ4kEosZr+hmS0bFp6mjswwCeElUvRBCC9ISICOHVVSBOmXqE6+b8UKtRzOaLPVrAwfrvYsGWG3w6pVsHu3a2PxhiZNYMcOtXcrT560j1erpvY6rVypqi2aaUbrcKhkK6ewYiEqG/sr/uYE9zKdv242BrWjk4iDRBwpydgi9lKfr1jjguah7nCAS9RjEmPZmLI/5vbXoKP6U7VgGtNNzG4EghBsDKSe4UpxSegMpJ5LY5nLo1SlYJax2G7+bC+lJxHZrFI3mAaGkiVQy1YHUj9b5xVCmCcJkxBe8OGH6kLcmSVedruqVPfOO647/7ffwogR5sZarTB7tuti8aYSJWDkSDhzRhW+mDlTvbZNm1Qy2b+/Kg7SrBlUrmx8aWRYmKrSmJM8RnVsBv9p0YCS5OY1lmNHz/TTfjs6sSRyP7PYx8VsRutaF4ilNTM4zbUsX4MDnT4s4mf2eTBC3zGAeoQR5PSeHisanalMZRfPLOYhlDX05RGqYUFLkzgl/yw3ojgb6U81Cmb7nK0pywNUcDph1ICB1KMS+bN9biGEOZqum2nh6J9iYmKIiooiOjqa3O5Y3ySEExITVfGJ8+eNjQsNVZXwcuXK3vmvXIFixczPWAUFQZ8+qhdUTjJ5sqqw5yyLBYYNM7eUz59t5Qx1MP7DUZxITnPV6c/dbVh4nBrMpKvhc7nLm/zB//jL6SqBGlCaPBxkaLaLAfijFRzmPmZhx5Hpe2ZFowaFWE0ft1aIO0kMk9jMbxzkCjeIIJiGFGcg9alBIZee6zoJdOBbVnMsw2WsGqr/1ONUZyYPGf4gQgiRNWdzA/ntE8LDFi0yniyBKlAwbFj2zz99unqu7MisYW6gevpp6NXLuVkmiwXuuQfef9/9cfma2hShIxWd/vTcikZBcnHSQLIEaonSbHZwgVhzgbpYAnbGs8lQSXWdWz2JcqJ7Kcuf9KY0eQDSJATWmzt2HqKq25MlgOLkZgT38jf92MNgNtGfsXRwebIEEE4wS+nJCFqmFDoJwkIQlpT3oQx5GMP9zOJhSZaE8DIp+iCEh61bp2ZpEk20//j6a/joo/T33Dhr7FjzY0EtI3S2GW4g0TSYMgXy5oVRo1TSeGexiOTiHQ8+qJrWhoZ6JVSv+5aHuZfpbOVMljMHeQilDeWYwy7D+zrsOPiBnTznA3s7fmG/4SIGoN6Dr/mHdjm0B1VDSrCfIfzBYcaxka2cIY4k8hJKZyrzLPUoczOhCjTBWHmT5rxOU35iL+s5yTUSyE0ILSlDG8rlyJlHIXyRJExCeFh2ykzb7WqG6PnnzY/fv9/8+UFV1Xvyyew9h7+yWuHzz2HQIFUQYvJkiI5Wj4WGwhNPqMfq1vVunN4WSQir6M1QfmEG/6apnmdDIwmdZpRmKp15nl8MJ0vqeSycIMaVoZt2lGhT5aLt6Bzispui8g8WNNpQjjaU83YoXmHDQleq0tWFFQCFEK4lCZMQHhYZabyU9+3GjDGfMGV3KZ7FAp06qWIJOVmFCvDJJ/Dxx6o8vN2uvq/u6pflj8IJZgqdGUkbprCF3zjIJeKIIJgGFGcA9VxSGlzPRhlzV7LjMD0XYCZZFEII4TmSMAnhYc2aZa8QwIED6gLdzD6iXLnMLwcEVTEuJ+7LyYimQXi4t6PwbYUI5w2a8QYZdzsuRiQ2LCaW5Ok+08i2CBGG9i8ls6BRHClCJHzDDs6xk3PcIIm8hNGC0kSRQ9cWC3Ebv9tFOHbsWMqUKUNoaCgNGzZkw4YN3g5JCEM6dIAC2fxg3exMkabBffepvTZmxv7yi+pPJIQrPcldpmZZLGg8SnU3RGRcByoRauIzSAc6PajphoiEcI4dB7P4l0ZMpibjeZx59GYRnZlNET7lWX5iFyYqFQkRQPwqYfr+++958cUXeeedd9iyZQu1atWiffv2nDt3ztuhCeE0mw369s3e+OyUFh8yxFwT1jfeyFlNWIXnNKYk1ShoaEmbFY1uVKUwEW6Ly4jchNCbWoarmeUjjIdk74rwkjgSeYgfeJIFbORUmsdvkMTXbKU2E5jLLi9EKIRv8Ks+TA0bNqR+/fqMGTMGAIfDQcmSJRkyZAivv/56muPj4+OJj49PuR0TE0PJkiWlD5OLJSbCjz/CmjWqoEFEBDRpova6BAV5OzrflJioKt3FGqyIbLPBAw+o0uRmORxQuzbs3u1c4mS1QlQUHDyYvep8QmRmCfvpyLdOLWrTgFBsbOQZqruh5LNZB7lEbSYSS6LTxR/G8oBPVPkTOY8DnYf5gR/Zm+XPqwZoaCymO/dRwTMBCuEBAdeHKSEhgc2bN9OmTZuU+ywWC23atGHdunXpjhk5ciRRUVEpXyVLlvRUuDlCYiJ88IEqANCtmypGMHWq+n+3bur+9983v18mkAUFwXvvGR+XlASDB2fv3BYLLF4MBQtmvTTPaoXgYLUUT5Il4U4PUJFJPIgGmZZStqIRio2feMKnkiWA8uRjMd0JxeZUH6rXaMJA6nkgMiHSmscuFrLHqeRev/nfXiwkEXsWRwsRePwmYbpw4QJ2u53ChQunur9w4cKcOXMm3TFvvPEG0dHRKV/Hjx/3RKg5wo0b0LEjvPkmJK+ITExUF/TJCdK5c/DWW2pGJM54exK3sdvhp59U+efu3VVD0tGj4bKHK/s+/TTkz68SGGfYbHDXXdC6dfbPXbIkbNwI9evfeu47zwVQpgysXQsNGmT/nEJkpR938ytPUpeigCq3bEVL+b8GtKEc63ia1j5agro5pVlLX5qgmpXZbkv/bDf/VJxIvuJB/kebm69KCM/7kg1ON5gGcADnuM6P7HVfUEL4qICukhcSEkJIiHs7g+dEug69esHy5VmXx9Z1+OMP6NkT5szxbtllXYcvv4QPP4RTp1RS4HCohMVuh1dfVf2FPvxQJTLuFhWlZm5atoT4+LRNUG9ns6kZocWLnU+wslK8uEqGNm1SzWyXLoWrV1XVt4YNVULZurXrzhfodu6Er76CHTvUBwSFCqkGto8/nr09ZzlNO8rTjvJs4TSz2cFprmHDQmmieIpalCOvt0PMUi2KsIre7OY8X/MPB7hMPEkUIpxuVON+KmDN5ueVsSSyhP2c4ioAJcjNA1Q0VXhC5Dz7uchqjhkeZ0VjPJt4GKn+I3IWv9nDlJCQQK5cuZg7dy5dunRJub9Xr15cuXKFRU5s6nB2naLI3IYN6oLaqL//NjfOFRwO6NMHZszI/DirFUqVgj//9FyvoW3b1BLGAwdUYnT7vqLk2/fcoxLO4sU9E5Nw3q5dMGAArF6d+vunaSpJz50bXnxRzcaaKQUvxO1OEsOnrGMyW7hKQsr8lQOdKELoT11eoBFFfaTcuvBNP7GXTsw2NbYoEZziJRdHJIR3BNwepuDgYOrWrcvvv/+ecp/D4eD333/nnnvu8WJkOc+4ccbLUttsahbDW/7zn6yTJVCzPMePQ9u2xgsymFWrFuzbp2bsOnaEokXVRXbx4mpmbtMmNRMkyZLv2bABGjVS3x9InewmfxQVEwMjRqiZpsxmEYXIyhZOU4sJjGY9V0kAVKKUvAclmng+Yx11mMh2znozVOHjbmCiVKoLxgrhr/xmhglUWfFevXoxceJEGjRowKhRo/jhhx/Ys2dPmr1N6ZEZpuyLj4fISHOFHIKC1MVjqId74J05o2aLjFysahpMnAjPPOO+uIR/O30aqldXP9PO/GxpGrz0Enz8sftjE4HnIJeoz1fEEJ9lg1wrGnkJYxPPUJo8nglQ+JU/OExrnPgUMR3lycsBhro4IiG8I+BmmAAee+wxPvnkE95++21q167N1q1b+fXXX51KloRrXLhgvupdYqIa72mTJ2e91yo9o0ebG+cJug5//QXTp8OUKaqsu6dmxIQybpzzyRKo79kXX3jnd0D4v9dYzlUSskyWAOzoXOEGb7LCA5EJf9SYkkRhfI+36n8m+5dEzuNXM0zZJTNM2XfihKqwZtaxY9kbb0aFCqqHkBnbt0ONGq6NJztiY1UCOHp02tcUGakq7z3/vKpuJ9wnIQGKFYOLF42Ns1jgf/+DV15xT1wiMJ3iKiX53OneTslsWDjNSxRAqo6ItF5lGZ+xzqkkPJkGHGQoZf2g+IrwLds4wzg2spxDXCWBcIJoQikGUZ9GlPBaxVC3zDDFxcXx119/sWtX2m7PN27cYIYzm0SEXytQwHwzWptNjfe006fNjz2VtvG515w7B82awbBhcOhQ2sevXlU9sGrWhFWrPB6eU3RdlTP/4gvVo2v0aNi61dtRGffnn8aTJVDFR2bNcn08IrBNZ6upSwkHOjPZ5vJ4RGAYRH1CUhW+z5wFjceoLsmSMOQUV2nBVGozka/ZyiGucJ5YjhDN9+ykMV9Th4nsw8Q/qh7kdMK0b98+qlatSvPmzalZsyYtWrTg9G1XotHR0fTp08ctQQrfERqqKrqZKfrQrRuEhbknrsxkp5S5N8ug3+76dWjXDv79VyUdGc0LJyWpWaj77oMtWzwbY2Z0XRXdqFNH9XR68UUYPhxeeOHWfbNn++4SyDsl9x4z46zsxRcGHeCSqU9frWgcxMMN5oTfKE0eFvE4NixZJk0WNOpRlCl09lB0IhAcJ5r6fMVaTgCQhCPV48m3d3COhkxmJ9n4x9XNnE6YXnvtNWrUqMG5c+fYu3cvkZGRNGnShGPHjNfxF/5t0KDU1cCckZSkxnlDdopMeKq0eFZGj1bLA5153x0OtV/MVwpWJCWpvl29eqnXACrGpCT1f4DNm+GJJ2DgwFv3+TKjHxjczuwMrci5EnCgG1yOB6ADCUhpRpGxNpRjBb0oc7M4iO2Oy0IrGhY0ulOTFfQmF/IXmHCOA50H+JZzXEuTKN3Jjs5V4mnHN8RicqO8mzn9z/7atWtZvnw5BQoUoECBAvz0008899xzNGvWjBUrVhAeHu7OOIUPadxYlb/+5RfnNrxbrdC+PTRp4v7Y7rRqlbmlUxaLKvddtarrYzLKbldL7YwkEna7mmHavBnq1nVfbM4YMgS++Ub9OaPXkHz/xImqoe+HH3omNrPKlTM3zmqF8uVdG4sIfAXJdXOGyXjSJPuXjDlONJPYzBqOE0M8UYTSgtI8w90B29uqMSXZzxD+4DDj2cQ2znCDJPISxkNU4RnqUgLZ9y2M+Y0D7DAwY2RH5xRXmc0O+lLHjZGZ43TRh9y5c7N+/Xqq3nEFOXjwYBYtWsS3335Ly5YtsftwoxEp+uA616+rZV9r12Z+IW+xqKarv/4KERGeiy9Zly7w88/m+t9MnQq9e7s6IuN++QUeeMD4OJsN+vZVSYi3bN2qltwZtW8fVKzo8nBcRtdVMZDdu40vI/z2WzWbJoSz1nCMpkw1NXYz/bmboi6OKPCc5zrP8jML2YMFLVUhBMvNdPURqjOeDuTBw70xhPBDDzCLpRw0VFTEAtSgMFt51mNFIFxe9KFKlSps2rQpzf1jxoyhc+fOdOrUyVykwi+Fh6tGq6+/rmYEQF2gW623litFRcGrr8Lvv3snWTpxQpXbNpMsFSyoGo36gr17VeJpVFKSuqD3pjFjjC9fs1phwgT3xOMqmgZDDbYh0TTIlw8eesg9MYnA1ZiSVKOgocuH5D0nkixl7RRXacBkfmQvOqS5wHOgY0dnDjtpxGQuID0chMhMEg5+M5gsATiAfznLaa65J7BscPoyrGvXrnz33XfpPjZmzBieeOIJclCFcgGEhKhKZ2fOqH5AffvCo4+q/0+bpqrTjRypjvOGNWvMFxHIn9/zDXYzEh9vLmECuHHDtbEYERenluIZ3e9mt6vS6T48WQ2oPVn16qkEzxm6DmPHeu/3QfgvDY0RtDR06eFA5x1auiukgJGEg/uZxQmis7y4s6NzgEt04jtTe8qEyCliiDfcBuF2l4hzYTSu4fRl2BtvvMGSJUsyfHzcuHE4/GG3tnC50FB46im19Ovbb9X/e/XyTkW828XEmB/rS01g8+c3nnSAmtEoWND18Tjr7FmV7JkREwNXrrg0HJcLDYUlS9Ret8wSWqtVfS/Gj/edWUvhf7pRjZG0dvr4z2hHRyq5MaLA8DP7+JezJDl5cWdHZx0n+IPDbo5MCP8VgpOfJGYg1PkSCx5j8nNrIXxfduqQRPrQ3t4OHZyfxbidrnt3+ZeZJO92ib5ZKCeVAgVUT6Z334WiN1c+JS9L1TT11b49rFwJAwZ4NVQRAF6nKdPoTCHUX27W2xbpJf+5KBF8y0O8wD1eidHfjGZ9qvfRGTYsjGGjmyISwv/lIohiJouk5CKI4j5YYMX3UjghXKR+fXPjbDZo2tS1sWRH0aLQtSssXGgsCYmI8G5xgew0KbZYIK+f9EYMD4f//hdee00VN9m9Wy2FzJdPJbtlyng7QhFIelGb7tTkJ/Yxg20cJRoNKEMeelGLDlRKUxpapO8KN1jBEcPjknDwI3uJJ4kQuYwSIg0NjUHU5y1WGFqaZ0OjL7UJ88Hy9fKbLgJWxYrQurX6dN/IfpikJNUPyJe8+iosWGBszMsvQy4vVhTOkwfatIEVK4y9/zYbdOrkf3t9bDZVbr9jR29HIgJdEFYeoioP4QN9D3xYconwBezhEnGEYKMWhRlAPdpRPlvFGxzoXOYGRfBCRaNMOND5jQOMZxNbOUMcSeQllC5U4VnqUp583g5R5BBPU4fhrDSUMCWhMxCTn3a7mXwMJQLa888bv1i/5x61L8WX1K+vCmkkL/PKjKap4htvveWR0DI1eLDx4g3ebHIshPB/sSTSkwWUZhQj+YudnOc01zjCFX5mH/czi3J8wT+cztZ5gnzsEmoNxyjHFzzAt/zCfo4TwwVi2c8lPmMdFfmSbvxADCY3lwphQGEiGM39hsa8RXOq4cXN15nwrd92IVysY0fnL75tNlUK/dtv3RuTWU8+qcqk375XJllyIhUaCm+8oV6D2cp6rtShg0o+nS0tbrWqBsf33uveuIQQgek6CbRiOt+yPd0S4cm3jxNDD+abTnpyE+JT/ZiWcZB7mc5xVLWjO4tY2G/W9VvIHpoxVZIm4REDqMdntEODDJcKJ9//Gk0Y4cOVPU39TTFz5kyaNGlCsWLFOHr0KACjRo1i0aJFLg1OiOzSNBg9Gl56Sd1O78I9ObEoVkw14vXlPScdO8KxYypxuv9+qFRJxduoEYwapUq5v/++uSIR7mCzqX09pUplnTRZrer1LFqU9SyaEEKkpz8/sZFTWS4DUr2VHDjQDRd9sKLxDHdjvXkJdZk45rObKWzhG/7N9syVUceIpgvfp7yezNjR2ck5nsLgGm8hTHqBe/ibfjxG9TRJkwZ0oCK/8xT/o43HmtWaoekGmyeNHz+et99+m2HDhvH++++zY8cOypUrx7Rp05g+fTorVqxwV6zZ5mw3XxGYtm2DceNgxozU/Ynuvls1IX30Ue+XQg9UFy/Cc8/B3Lnq9u0dCCwW9fX446rRbXIjZCGEMOIIVyjHF4a6v1jRDDfXBNjPEG6QxOf8zSz+JZ7Ua4/rUITnaUgP7nJ7EY43WM7HrDX8OvYymErkd1NUQqR1juus5wQxxBNBMHUpRgm8ez3ubG5gOGGqVq0aH3zwAV26dCEyMpJt27ZRrlw5duzYQcuWLblw4UK2g3cXSZgEwPXrcOqU6hGUL5+aWRKecfKkakq7Zg1ER6vCEM2bQ79+ULiwt6MTQviz//A7H7HGcOIQhIVEnO8j+TpNqENRejAfUFXz7mRBw4HO/VRgLo+Sy01Vv+JJogifcgVjXcptaAymAZ9zn1viEsJfOJsbGK6Sd/jwYerUqZPm/pCQEK5fv2706YTwuPBwVUFPeF7x4vDOO96OQggRiOax29RsUSIO2lGepRxMSXTulHz/c9SnKaXoxGz0m/uC0pP8HEs5yKPMYRGPpyzhc6X1nDScLIHa4zSfPZIwCeEkw7+9ZcuWZevWrWnu//XXX6laVUqcCuEpDgdcuQJxcapJrRBC5GSXiDM9tje1+Iau1KEIQMom9eQdFQ0pzg9043Pa0ZcfM02WbmdHZzH7+YGdpmPLTHZe8+VsjBUipzE8w/Tiiy8yaNAgbty4ga7rbNiwge+++46RI0cyefJkd8QohLhJ1+GPP9Ren59+ulWyu2hRtUeoXz8oUsS7MQohhDeEYL7aTS6C6EwVenAXmznF35zgKglEEkwzSnMXas3wPHZxDmOraaxojGEDT1DTdHwZCc1GO01puiuE8wz/tvTr14+wsDDefPNNYmNj6d69O8WKFeOLL77g8ccfd0eMQgjgzBlVJW/zZlVx7vb+RqdPq6Vuw4fDp5+q/lNCCJGTVKcQZ7hmalleFQqk/LkuxahL+ptbx7PJcKEIOzprOcFuzlPVxT1mbo/bCCsaNSjk0liECGSGluQlJSUxY8YM2rRpw/79+7l27RpnzpzhxIkTPP300+6KUYgc7/x51VB32zZ1Oykp7TEOh0qihg2DkSM9Gp4QQnjdQOoZTpasaDSlJJWdTDx2c95UQgawj4umxmWmDHloSznDpdHt6DxHPZfHI0SgMpQw2Ww2BgwYwI2bNZlz5cpFoULyCYUQ7tarFxw/nn6ilJ7//Af+/NO9MQkhhC/pSCWKEnHbzqOs2dEZTAOnj7+zfLgR2RmbmSE0MJTEWYCC5KILVdwSjxCByHDRhwYNGvDPP/+4IxYhRDr274dffkm9BC8rNht8/rn7YhJCCF9jw8J0uqCBUymTBY1OVKYb1Zw+R35ymY4vH+5p9NeRSnSnhlOJonbzvzPoSlA29nwJkdMY3sP03HPP8dJLL3HixAnq1q1LeHh4qsfvuusulwUnhIAJE8BqNZYwJSXBjz/CiRNQooT7YhNCCF/SlvLM5VEeYy4OHCRlUiK8I5WYzcOGyn0/QjX+x1+Gl+VFEUJTShka4ywNjal0wYHObHZmuMfKhgULGt/xMPdRwS2xCBGoDDeutVjS/sWiaRq6rqNpGnYjV3UeJo1rb0lKgk2b4MIFCA6GChWgXDlvRyXSc9ddsH27ubGzZ8Njj7k2HiGE8HV7uMCXrGcqW4kj9VrmRpRgCA14jOqGeyMdI5oyjDKULlnReJF7+Ii2hs5llAOdhezhSzawkiOpHstFEH2ozRAaOL1fS4icwK2Na4X/On8eJk2CsWNVZbXbtWgBQ4ZAly5qRkP4hpgY74wVQgh/VYUCjKUD/6MNqzjKJeIIw0Y1ClI9G9XhShHFk9zFLLan2+D2ThY0QrExiPqmz+ksCxoPUZWHqMp+LrKT88SRSB5CaUopIglxewxCBCrDCVPp0qXdEYfwgE2boH171ezU4Uj7+F9/wapV8MAD8MMPcMdqS+ElERHeGSuEEP4ukhA6UsmlzzmRjhziMus4kWnSZEHDhoVFPE5p8rg0hqxUJD8Vye/RcwoRyAwnTDNmzMj08aeeesp0MMJ9du2CVq0gNjb9ZAlu7ZH57Tfo1k01RrVJXzuva9oU9u51vkLe7eq7/0NNIYTIUcIIYjlPMZglTGMrOqRKnGxYSMJBefIyg640QjaSCuHvDO9hyps3b6rbiYmJxMbGEhwcTK5cubh06ZJLA3SlnLyHqUED2LLF+cIBmgbjx8Ozz7o3LpG1rVuhTh1jY6xWuPdeWLbMLSEJIYQAznCNyWxhAXu4SCxh2LiLwgykPi0ojWawP5IQwrOczQ0MJ0zp2b9/PwMHDuSVV16hffv22X06t/GFhMluhzVrVPUygOLF1QyCO/cMbdkCdesaG6NpULUq7Nih/iy8q3Fj2LjR2CzTjz/Cgw+6LyYhhBBCCH/mbG5guA9TeipWrMj//vc/nn/+eVc8XUC6cgU+/BDKlFHFFXr0UF8tW0Lp0vC//8Hly+4596RJxpfW6bpaxvf33+6JSRgzaxbkzu3893HQIOjY0b0xCSGEEELkBC5JmABsNhunTp1y1dMFlAMH1JKq//zn1szS7U6ehP/+Vx2zf7/rz79li7n9L2C+nLVwrbJlYe1aKFpU3U6nun9KMvXiizB6tG/MDMbEwMKFMGUKfPONmiXL/py2EEIIIYTnGN7S/+OPP6a6res6p0+fZsyYMTRp0sRlgQWKM2fULNLZsxkXWwD12MmT6thNm25dGLtCbKy5cRaL+bHC9SpXVsUfvv8evvhC7W1KFhoKPXvCc89B7dreivCWvXth1CiYNg1u3Ej9WI0aMHQo9OqleoAJIYQQQviybDeu1TSNggUL0qpVKz799FOKuvJK38W8sYepb1+YOdP5GR6bDZ58EqZOdV0MLVrAn3+aGzttmrqwFb7n5Em4dAlCQtReOF8pA//zz6rKot2e/s+9pqlZphYt1D6rHFZ/RQghhBA+wqNFH/yFpxOmy5ehSBFISDA2LjgYTp2C/C5qofDRR/DGG5nPcKXHYoFjx9TFuBDOWLUK2rRRyVJWf7NYrdCsmSpjLzNNQmTPFW5wlCvEYyc/YZQjr1RoE0KILLit6MO7775LbDrrtOLi4nj33XeNPl1A++YbSEw0Pi4pSc1KuUrfvsar8Nls0KWLJEvCeQ4H9O6t/u/MxzB2O6xcCdOnuzsyIQLX35ygJwsoxMfUZiINmUwFvqQa4xjPRq4S7+0QhRDC7xlOmEaMGMG1a9fS3B8bG8uIESNcElSg2L3bXONXqxX27HFdHAUKwNNPp18oICN2O7z0kutiEIFv+XI4csTYTKbFogpU5Jx5biFcIwkHz7GYe5jCbHaQSOpfvL1cYBBLqMwYtnPWS1EKIURgMJww6bqOlk75rW3btpEvXz6XBBUobtwwdyGo62k3ymfX55+rXj7OJk1jx6rjhXDWhAnGPyBwOFSvr82b3ROTEIFIR2cgi5nAJkAlT2mPUV/nuE5zprGfi54NUgghAojTlzd58+ZF0zQ0TaNSpUqpkia73c61a9cYMGCAW4L0V9nZg+Tq3DM0FJYuhX794LvvVOJkt9963GJRF6+RkTBunCo8IXKWU6fgr78gOloVkKhbV1Xmc9bOnebL1+/ZA/XqmRsrRE6zkD1MZotTx9rRuUo8TzCPTfR3c2RCCBGYnE6YRo0aha7r9O3blxEjRhAVFZXyWHBwMGXKlOGee+5xS5D+6sEH4ZNPjI9LSoLOnV0fT1iYaoD67rswcSLMnq2qrAUFQYUKMHAgPP445Mrl+nML37VmjSoBvmBB6iQaVCW7559X+9my6uuUnVnReNlmIYTTvmA9VjTsOLeEwY7OZk6ziVPUo5iboxNCiMBjuEreqlWraNy4MUFBQe6KyW08XSVP16FqVdi3z/mleZoGFSuqT9x9ofGoCGyffab2qtls6c8OWa0qierXTy25y6x4SP36qoeYGQsWqKRMCJG5vVygCmMNj7Nh4SlqMYVObohKCCH8k9uq5LVo0SIlWbpx4wYxMTGpvsQtmgZvvWVsH5Ouw5tvSrIk3G/ChFuFPTJaSpc84zRlCgwZkvnzPfywscIiycLCoFUr4+OEyIk2c9rUuCQc/M1xF0cjhBA5g+HLm9jYWAYPHkyhQoUIDw8nb968qb5Eaj16wOuvO3/8q69Cz57ui0cIgAsXYOhQ54/XdRg/Hv7+O+Nj+vY1njDZbKoUuTSvFcI51zHY2O8217IxVgghcjLDCdMrr7zCH3/8wfjx4wkJCWHy5MmMGDGCYsWKMWPGDHfE6Pc++EDtEYmIULdvv6jUNPUVHq6WR/3vf14JUeQwU6em3a+UFZtNFQTJSKFC0L+/80mTpqnnfP55Y3EI/3OJOLZxhk2c4hjR3g7Hr0URanpsHsJcGIkQQuQchvcwlSpVihkzZtCyZUty587Nli1bqFChAjNnzuS7775jyZIl7oo12zy9h+lO16/Dt9+qi9XjN1dGlCypPmHv3v1WQiWEu5UrB4cPGx8XFATnzkGePOk/npAAHTrAH39k3o/JYlFfCxZAx47G4xC+T0fnT44ylo3MZ3eqAgV1KMJQGvIY1QnD//bDetNprlKSz50u+JDMisaL3MNHtHVTZEII4X+czQ0MJ0wRERHs2rWLUqVKUaJECebPn0+DBg04fPgwNWvWTLepra/wdsIkhC+w2801VE62bRvcdVfGjyckwAsvwKRJKmm6PXFKLi5Rpoz64KBlS/NxCN8VTxJP8yOz2I4NS5o+QRY0HOiUJy9L6Uk5Ml/OfZV4tnKGGOKJIJhaFCGPwZmWq8RznBjiSSI/uShJbjT8c7PoI/zAQvam238pIxqwnyGUR/olCiFEMmdzA8OXTeXKlePw4cOUKlWKKlWq8MMPP9CgQQN++ukn8mT0sbMQwmckJmZvfFYlwIODVePjd96Br7+GuXPVnqnQUFU1cuBAaNPGXIEI4fsc6DzBPBaxF0i/qarj5uzIUaJpwtds4hmKk/Yfql2cZywbmMpW4rhVmSQEKz24i8HUpw5FM41nM6cYy0a+ZTvx3FqHWpNCDKUhT1CDcIJNvVZveYF7mMdup4+3ovEAFSVZEkIIkwzPMH3++edYrVaGDh3K8uXLefDBB9F1ncTERD777DOe9+ENCTLDJIQSFma+b9LBg2pJnxDpmcwWnuEnp4+3YaEd5VhMj1T3f8VmBrAYC1q6SVfyzNXHtOVlGqd53I6Dl1nKKNZnMMsFDqA4kSylJ9Uo6HTMvuBz1vEiS7M8zopGOfLyN/3IJ3uYhA/T0f121lf4L7ctybvT0aNH2bx5MxUqVOCuzNbp+ABJmIRQuneHOXMyLieeHk2DKlVg504pey/Sp6NTg/Hs5ryhHTYacIChKUvzprOV3ixyevzntGcYjVLF8RxLmMimLOOwopGbEDbyjN/NwExkE0P5lSQcKbN2yZKTxCaUZCGPU4DA6UieiJ1EHIRhkwtsPxZHIt+zkzFsYAfnSMBOBMF0oCKDaEATSsr3V7idRxKmGzduEBpqvmKPp0nCJISyZg00bWpsjKapKnkDBrgnJuH/1nGcxnxteJwVjZe4hw9py3muU5zPSDSwP8eCxgGGUPZmwvUje+nMbKfH29C4m2Ksp5/h2L3tIrFMZSvj2MhhrgAQio2uVGEQ9WkcIBedZ7nGZLYwgc2cQPV8DMXGw1RlEPVpRImAeJ05xRL204P5XOFGyp7GZMnJfj2KsYjHKUakFyMVgc5te5jsdjsffPABEyZM4OzZs+zbt49y5crx1ltvUaZMGZ5++ulsBS6EO125AqdPq75CRYpAPv/6QNk0XYe//oKJE2H7doiLg8hIuHbNucbKVqsqG96jR9bHipxrE6fSXPw4w47ORk4BMIV/DFeA04AJbOLDmxXgvmA9VjSnnycJnQ2cZAunuTuLPVG+Jj+5eJnGvExj7DhIxEGo8X/afZaOzuf8zWssx4Ge6mfrBkl8z05msZ2WlGE+j5JXlh36vPnsphs/pNy+8++L5OWzWzlDQyazgX4UlaRJeJnhbdfvv/8+06ZN46OPPiI4+NZG2Ro1ajB58mSXBieEK+g6LF8OXbpA/vxQrRpUrw4FCqgS2L/8knkJbH+3fr16vc2bw/ffw7//wv79cPWqc8mSzaaSq6VL1f+FyMh1ErGY/JQ/hnh0dMay0VTCNZHNJGBnPxf5g8OGky4bFsax0dAYX2PFElDJEsD/8ScvsTTdZYdw6+J6NUdpylSiMbk5U3jEIS7zBPMAsvwNTcLBGa7x8G3JlRDeYjhhmjFjBpMmTaJHjx5YrdaU+2vVqsWePXtcGpwQ2XXtGjzwALRtC4sXp06MdB1++009fu+9avYp0CxfDi1awL596raRPUvJpcdr14YNG6BGDZeHJwJMbkKwG1hKd7u8hHGVhJTlVkZFE89prrLp5kyVUUk4WMdxU2OFeyzjIG+z0qlj7ejs5QID+Nm9QYlsGcdG7AY+ElG/lyfYyEm3xiVEVgwnTCdPnqRChQpp7nc4HCRmt16xEC4UHw/33w/Llqnb6SUL9ptVhtesgdatVXPhQHH4MHTurMqI2+1ZHw9qBq50aTUj1asXbNoEGzdCxYrujVUEhnspY3BeR7Gg0ZqyxJG9f0PiSOJ6Np7jKgnZOr9wrc9Yh9XAjKUdnR/YxUmTSbdwrzgS+YotOXL2V/g/wwlTtWrVWL16dZr7586dS506dVwSVE6RvfqEOZfdri7kf/sNVqyAEyfSP27kSFi71rlkwW5XDVnfesu1sXrT6NGqiayR5YbR0Wo2accOmDwZ6tZ1X3wi8FSlIM0pbegiF9QepN7UIspgM9o75SGU3ISYHp/d8wvXOcRlfuOg4YtrgElsdkNEIrs2c5oYsmjkl44kHCxmvxsiEsJ5hhOmt99+m8GDB/Phhx/icDiYP38+zzzzDO+//z5vv/22O2IMGLoOK1fCI49A7txqI31YGDRuDLNmZd0QNKc7fx4++ABKlYL69eG++6BVK3X7gQdgyZJbSWhCAowZYyxZsNvhq68CY5bp+nWV8BhZggfq/ZoyxT0xiZzhZe4xfJFrR6cBkxnNehpR3FTCVZ2CFCacppQytY/KikZ7yhseJ9xjFUdMzVY60FnGIZfHI7IvO/vLZPZXeJvhhKlz58789NNPLF++nPDwcN5++212797NTz/9RNu2bd0RY0A4dgzq1FF7ZRYuvLXh/sYNtSn/ySeheHH4/XdvR+qbNmyAypXVDNCpO7Yo6LoqSNChAzzxhEo8Fy6EixeNn+faNfjuO5eE7FWrVqnXYpTDoQpDCGHWg1TmDQzWrAeOE8Mb/M4OzpuaVXiehmhoFCOSrlTBZjBpsqMzgHqGzyvcI4Z40wVErkjhB5+UiyDTY8MCrJiJ8D9O/wQeOnSIsmXLomkazZo1Y1nyxhCRpWPHoEGDWxfwd37qnzwLcvmymjVZtEjNmAjl339Vohkfn/GMUfKyuzlz1J9LlICgILV/xwibTSWw/fyvHUsqFy6YH3v+vOviEDnT+7QiNyG8yR/opC0bnBEHOnEkoqH2NTmTOFnRyEcY3amZct8wGjGP3U7Hq2aXKlDBzxrXBrJwgg1XS0wWmY1lmcJ9qlMopceSEVY0vyv3LwKP0zNMFStW5PxtV1KPPfYYZ8+edUtQgUTXoVMnlSxltTzK4VAX+926wUkpCAOo969nT5UsObMXyeGAuXPVfiQze8QcDnMzM77mtor/Hh0rBICGxus05QQvMoKW5DWwN8iOnjKzkNXSPCsaIdj4lScJ59YPblNK8RFtnDqfDQulycN0ujgdo3C/ehQzNc6GhUYUd3E0whUKEc7DVMVmcHGTHZ3BNHBTVEI4x+mfWv2Oq88lS5ZwPRA2e7jZqlXq4t3ZvSS6rvbfTJrk3rj8xfr1aobJ2SpvABYL7NkDmonVHBaL2l/m7ypVMjfOalV9qoRwhSJE8CbNqUwBQ8ur7OjY0SlFFECaC6zkRKo0eVjH0+l++vwKTRjD/QRhSffcyc95N0VZS18KkMvp+IT71aYI9SlmeFleEg5ZWunDBtPA0AyTBY2iRNARk/+oCeEihvcwCWPGjbvVz8ZZdrsalyB7HBk/3vj753DA6dPGl+OBSmxbtDA+ztfUqQN33aUSQCPsdnj2WffEJHKm7Zzlb04YXl5lw0IzSrGCXnShMvkIIwQreQnlfiryKz3YzxDuonCGzzGIBpzgRd6nFSW59UlIEBa6UJkV9OJvnqYwEaZfn3CfYTQy9HNjw0JLylCVgm6MSmRHU0rxCo2dOtaChhWNOTxieFZKCFdz+lJU0zS0Oz6yv/O2SOvXX41XKgO1B2XnTnXhm5Nt2mTu/QPIm1ftCzMiMhIeftjc+XyJpsHQocb2YmkaFCmiimcI4SrbMLd0OwkHmznNdLrSkjKmz1+IcF6nKa/TlATsJGAnnCA0kwUFhOc8QQ1+YT+z2J5l2mRFIy+hsrTSD/yPNljQ+JA1Ge5psqARho1FPE4TSnkhSiFSczph0nWd3r17ExKiNlPeuHGDAQMGEB4enuq4+fPnuzZCP6br2dsPc+WKy0LxW7Gx5se2bg3z5hnby3TtGvz1lxrr73r2hBkzVFPerJY0apr6+vprtSxPCFeJzUYj2ew0oU1PMFaCkR9wf6Gh8TWdCcHGFP5J9+LagoYDnRLkZik9U5ZxCt9lQeN/tKErVRjLRmazg8Tbvq+FCWcQ9XmGuhSR2V/hI5xOmHr16pXq9pNPPunyYAKNpkFIiCodbsYduWiOlC8fHDlibuzDD0NcHCxebGzcQw+pZriRkebO6yuCg+HHH1XRkeRe0+klj8lLHmfNUlUahXClPNloBmukWIQITEFY+YoH6UsdxrKRH9iZKmmqQSGepyGPUyNbZauF5zWkBA0pwSjuYz8XiSOJPIRS42Y1PSF8idMJ09SpU90ZR8CqU0cVLjDSQBXUxa7ZjfuBpGNHVTTDSNEHUElA69Zw6JCxhEnXVY+sWbNgwABj5/RFUVGwbJlqYjt6NOzdq+7XNPVag4Ohe3d44QW150kIV2tBadOlhDtQ0U1RCX+iodGYkjSmJGN5gONEk4CdAuSiFFGyvNLP5SOMhpTwdhhCZErT7yx/F8BiYmKIiooiOjqa3B4qhTZrlmpKa4TNppZTff21e2LyJydOQOnSxhJOmw0efRRmzlRjT5wwdk5NgypVYNcuY+N8na7DunWqgmBcHOTPD23bqv8L4U5PMJe57DaUNGnAUYZRUpZYCSGEcBNncwNJmNwsPh6KFVP7kYxc9G/aBHXrui0sv9KjB3z/vfOzTBaLmtUrWlQ1sDXr6lWIkOXTQmTbBk7SiMlO1zuzotGNasymm1vjEkIIkbM5mxvIIlE3CwlRF/vJm+qd8c47kizdbuJEqFUr62IEye/vxIlQr172G9BevZq98UIIpQHFGY9z5RetaFSnEF/xoJujEkIIIZwjCZMHtGmjNt+HhmZ80Z+88f6dd9SXuCUiAlauvFXu+s6+TMm9hiIi4LvvbpXSzu7skL8XfQhkx4/DW29B7dpQqpRaQtm9O/z5p7GqiMJznqUe39D1Zklv0uw6Sd7kfR8VWE0fIgnxeIxCCCFEemRJngf9f3t3Hh5Vfeh//D2TQBYgQSBI0QBB6tKCgAhWrBWqt2Jdilaq1RaxXLSKW7VL7NNCabU8rVgtKq73gr3Fq7YWvKXPdalVXLooKP6u1lgBLRpckCUBhIRk5vfHSDSFAzNhZs7M5P16nvOQOXNO/EAn6Xzme873+9ZbcMcdicVY33//o/1duyYuO5s+3ZGlvfn73+G22+D++xOXOXbpkpgcY/p0OPtsKC//6NhYDAYNSry5TkUkAocdllgHS7llyxa44AK4995EUf74ZZrFxYk1uw49NHHv4BFHhJdTwbbQzD38H/N4jtfYwA5aqaSUSXyKiziSYXtYiFaFbyPbuJsXuZPl/JMGWolTRTlf53Au5EinDZeUVt7DtBthF6admpsTb8Y3bUpMHX7wwdCzZ2hxCtp110FtbWr3j0UiiVJ2wQWZy6XUbd4M48btfdbEoqLEhxCPPgrHHJO1eJL2QZw4v+Rv1PJHmmn9cN9Hij5cb+kCRnETJ9HF9bQkpYGFaTdypTApe95/P3HJ1vbtyV2qFY0mLsV76y0nfMg1Z5yRuLQ1mck/dv7v+Morick/JOW2GTzOT3hyr8dFgJP5JIs427V6JO2zgpv04dprr2Xs2LGUl5fT0+EYJalPn8Tle8lMuhGNJrbFiy1LuaauDhYtSn6mxFgsMSJ1xx2ZzSVp3y2mLqmyBIlRpz/wGj9maWZDSdLH5E1ham5uZtKkSVx00UVhR1GeOeUU+P3voaxs98Vp577u3ROXcY0bF0pM7cFtt+19lsR/FYvBvHmwY0dmMklKj5/xNNEUFp+NA7/kb2zDH25J2ZE3hWnWrFl861vfYtiwYWFHUR764hehvh5uuAFqato/d8ghcMsticvwLEu56Te/SX506ePeew+WL09/Hknp8SLv8FfqiSW9SldCI03chzPzSMqO4r0fkr+amppoampqe9zY2BhiGoWtZ0+4/HK47DLYuDFxyVZFRWJ/smtkKRwbN3b83PXr05dDUno9zCqKiNCaYmGKEuEhVjKFEZkJptA8z9v8lbfYSjMVlHAcgziUPmHHUidX0IVp9uzZzJo1K+wYyjGRCPTqldiUH7p2hW3bOnZuicv5SDlrI9uIdqAwxYizgQ7+UlDOiRPn1/w/buRvPM/bRKDd6+JzDOQqjuY0Dgk3qDqtUC/Jq62tJRKJ7HGrq6vr8Pe/+uqraWhoaNveTHVBHkk54dBDP1qgOBWRSGLafkm5qZwuKValhAjQja7pjqMQtBBjMouYzGJW8A6QuE/t4yX6GdbwJe7luzxKvEOvGGnfhDrCdNVVVzFlypQ9HjN48OAOf/+SkhJK/HhZynsXXwznnZfaOUVFcMIJiWnlJeWm4fSjhRQWyvtQlAiH0zcDiZRtF/MHFvJ/AIH3su0sT9fxZ3rQlR9yXNbySRByYaqqqqKqqirMCJLywFe+krj/rKEhufW0IDFJxPTpmc0lad98kU/Sj+68w5aUzosD0xiVmVDKmmep506eT+mcH/EE5zGCAVRmKJW0q7yZJW/NmjWsWLGCNWvW0NrayooVK1ixYgVbtqT2S1ZS/ikthf/4j+SPj0bhrLPg5JMzl0nSvismynRGpzSteDFRTuVgDsQF6PPdrTyX8gLEESLcidOfKrsi8Xiyn9eGa8qUKdx999277H/88ccZl+Rc0Mmu5ispN/3qV/CNbyTuTWpp2fX5aDSx/tKZZ8Kvf+2ED1I+2MYOxnM3y1i718kfionSmzKWcYGFKc9toZne/JxmUl8zog/lvMe3iaRQtKXdSbYb5M0I04IFC4jH47tsyZYlSflv8mRYtgy+9rXEzHn/aswYWLgQ7rvPsiTlizK68BBfYyzVABTt5k1w5MPtAHrwFOdblgpAPY0dKksA7/MBH7hwsbKooKcVl1R4RoyA+fNhzhxYujSxRlN5OQwbBkOHhp1OUkf0pJTHmMwDvMJN/I0/81a75w+iF5cxhvMYQQV+GlIIdnRgso+Pa6aVbmnKIu2NhUlSXurdG844I+wUktKlC0WczVDOZigr2cA/2UQLMfrRncPZ38uvCkwV5R0+t5golZSmMY20ZxYmSVKn8w/WcxvLWMI/2MR2yujCKD7BxYzmeGp8cx6yIfRiCK4uXsj2pztHcyDPUp/SwsXFRPkyh6U0UYi0ryxMkqROYyPbmMxilvAPioi0e6O2ls0soo4h9OK/+TJH0j/EpFLhu5QxnMPvUjqnhRjTGZ2hRNLu5c2kD5Ik7YsNbGMs/8n/8hrALp9q71xA9XU2cizzeZo1Wc8odSZncBiH0JviJEeLiojwOQbyWVyRXNllYZIkdQpn8RteY/1eL/9pJU4zrZzCPbyb4oKqkpJXQjGP8HX60n2v6zEVEeFgerOIs7xkVllnYZIkFbwXeJs/8nrS90rEiLOZZu7i+Qwnkzq3AVSyjGmcyEFE2HVa+SgRiojwFT7NX5hKL8rCCapOzXuYJEkFbx7PUUy07bK7ZMSIczPP8T0+u9dPvyV13CfowRLOYTUbuZ1l/IW3aKSJ/ShlPDVM4wg+QY+wY6oTszBJkgreIupSKks7vcMWXuQdRjkBhJRxg9mPn/FvYceQduFHZpKkgtdIU4fP3cC2NCaRJOUbC5MkqeB1oajD55Z6MYYkdWoWJklSwTuMPh1a6DJKhE/SOwOJJEn5wsIkSSp40xlNLMkZ8nYqJspEDqEf3TOUSpKUDyxMkqSCdxZDqaAkpTGmFmJMZ0zGMkmS8oOFSZJU8Mrpwny+lPTxEWAqIxnPoIxlkiTlBwuTJKlTOIPD+BWnU0w0cF2lnYtmTmEEt3EKkQ7c9yRJKiwWJklSp/E1DucFLuQbjNjt7HfjqeFBzuY/OM3FaiVJAETi8Xhqd8HmscbGRiorK2loaKCioiLsOJKkEDWwnadZQwNNlNOFYfTlIHqFHUuSlCXJdgMXl5AkdUqVlHIyB4cdQ5KU47zeQJIkSZICWJgkSZIkKYCFSZIkSZICWJgkSZIkKYCFSZIkSZICWJgkSZIkKYCFSVJOaWiAuXNh5EioqkpsI0bAL38JmzaFnU6SJHU2LlwrKSfE4/Dzn8OPfgRNTR/tA4hEEn+WlMCMGVBb+9E+SUqXRpp4lFW8zwd0pYhP0ptjqCaCv3CkQuTCtZLyRjwOV10FN9wQ/DzA9u3w/e/DO+/AjTdamiSlx6u8z438lQW8yHZa2j03hF5cxhimMYpS3zZJnZKX5EkK3d13B5el3Zk7F+bPz1weSZ3H//IaI7idu3hhl7IEsIoNXM5DHMcC1vNBCAklhc3CJClU8Thce23qo0U//elHI0+S1BHPsIYvcS9NtNBCbLfHxD/clrOWL3IP29iR1YySwmdhkhSqJ56AlStTLz+rVsGf/pSRSJI6gThxvsH/0EqcZH79tBLnOeq5neUZzyYpt1iYJIXqD3+A4g7cFlBcDEuWpD+PpM7hSf7JP1hPLKm69JGbeDbJiiWpUFiYJIVqw4aOnRePw8aN6c0iqfO4g+UUp/g2KA6sZiNP8s/MhJKUkyxMkkJVWtqx2e6iUSgrS38eSZ3Dy6wLvG9pb/7B+jSnkZTLLEySQjV0KLTsOjHVXrW0wKc+lf48kjqHJlo7dF5kH86VlJ8sTJJCde65iQVpU9W1K3z96+nPI6lz6Eu3Di1HGwd64/C21JlYmCSFqrIyUXyKipI/p6gIvvY16NkzY7EkFbgzOaxD55VQxASGpDmNpFxmYZIUup/+FKqrk5str7gYDjggcY4kddRkhlNCalN0FhPlqwxjP0eYpE7FwiQpdH36JNZjqqlJTACxu0kgdu4fNAiWLoW+fbOdUlIhqaSUyzkq6cvyIkCUCFdxdCZjScpBFiZJOWHgQFi+HG68EQYP3vX5mhq44YbEMYMGZTudpEJ0DZ/nSxy619IUJUKUCPfyZYbipzVSZxOJx+OdZvW1xsZGKisraWhooKKiIuw4kgLE4/Dcc1Bfn/j6gANg9OjEVOKSlE4txLiaP3ITz9JMa7slaYuJ0kKMQfTkLk7leHbzaY6kvJVsN7AwSZKkTm8j27ibF7mfl3mPrZRQxGFUcQGjOIHBRDs0p56kXGZh2g0LkyRJkiRIvht4gYskSZIkBbAwSZIkSVIAC5MkSZIkBbAwSZIkSVIAC5MkSZIkBbAwSZIkSVIAC5MkSZIkBbAwSZIkSVIAC5MkSZIkBbAwSZIkSVKA4rADSJIK02o2cjvL+D3/YBPbKaOYI+nPRYzmOAYSIRJ2REmS9srCJElKqwa2M5X/4Xe8QpQIrcTbnltDI/fzdw6mN/dwBqPoH2JSSZL2zkvyJElps4ntfJb5LKaOOLQrSwAtxABYxQY+y3yeYU0IKSVJSp6FSZKUNufwAK+wbpei9K9aidNMKydzD++yJUvpJElKnYVJkpQW/8e7/C8r91qWdooRZzPN3MnzGU4mSVLHWZgkSWlxK8soTvH/VmLEuYXn2i7VkyQp11iYJElp8QCvdKj4vMMWXuDtDCSSJGnfWZgkSWmxie0dPnc929KYRJKk9LEwSZLSoitFHT63zFUuJEk5ysIkSUqLw+hDtAOL0UaAT9I7/YEkSUoDC5MkKS0uZjSxJGfI26mYKKdwMP3pkaFUkiTtGwuTJCktzuLTVFKS0hhTCzGmMzpjmSRJ2lcWJklSWpTRhf/kS0kfHwEmM5wvcFDmQkmStI8sTJKktDmDw1jARIqIBK7JVPThGNRXGcqdnEqkA/c9SZKULRYmSVJaTWY4z3MhkxlOyW5mzjuGAfyWSfyaM/ZpZj1JkrIhEo/HU7tDN481NjZSWVlJQ0MDFRUVYceRpIK3kW08xRo2sZ0yijmc/TmEPmHHkiQp6W7gwhdShrW0wO9/D089BY2N0KMHHH00TJwIXbuGnU7KrP0o4zQOCTuGJEkdZmGSMqSlBebMgRtvhHffhS5dIB6HSCSxr08fuOQSuPpqi5MkSVKusjBJGdDUBKefDg89lChJADt2tD/m/ffhxz+GJ56AJUugW7esx5QkSdJe5MWkD2+88QZTp06lpqaGsrIyDjroIGbOnElzc3PY0aTdmjIFHn74o7IUJBaDJ5+Er35178dKkiQp+/JihKmuro5YLMbtt9/OkCFDeOmll5g2bRpbt25lzpw5YceT2lm+HO69N/njY7HEPU5PPw3HHpu5XJIkSUpd3s6Sd91113HrrbeyevXqpM9xljxlw9Sp8KtfJe5hSlZxMXz5y6kVLUmSJHVcwc+S19DQQK9evfZ4TFNTE01NTW2PGxsbMx1LndyOHbBwYWplCRLHP/AAbN3qvUySJEm5JC/uYfpXK1eu5KabbuLCCy/c43GzZ8+msrKybauurs5SQnVWGzcmJnzoiJYWeO+99OaRJEnSvgm1MNXW1hKJRPa41dXVtTunvr6eCRMmMGnSJKZNm7bH73/11VfT0NDQtr355puZ/OtI+yw/L5CVJEkqXKHew7Ru3TrWr1+/x2MGDx5M1w8XqVm7di3jxo3jM5/5DAsWLCAaTa3veQ+TMm3HDujeHToygWNREWzalDhfkiRJmZUX9zBVVVVRVVWV1LH19fWMHz+eUaNGMX/+/JTLkpQNXbrA2WfDPfekPunDxImWJUmSpFyTF5M+1NfXM27cOAYOHMicOXNYt25d23P9+vULMZmyJR6H556Dxx+HhoZEsRg9Go4/HnKtO0+fnpglLxUtLYnzJEmSlFvyojA9+uijrFy5kpUrV3LggQe2ey5PZ0VXkuLxxKxzc+bAiy8mLluLRhP7W1pg0CC4/HK45JLEKE0uGD0aTj8dHnwwscbS3hQVJYrfccdlPpskSZJSk7frMHWE9zDll1gsUYRuvTVRknZXPiKRxJ8TJiSm5S4ry27GINu2wRe/CEuX7nkih2gUxoyBRx6BHj2yl0+SJKmzS7Yb5NjFTNJHfvCDRFmC4JGaeDyxPfwwnHtu7swyV1aWyPSDH8B++yX2demSGE3aORJWWQnf+U7iMkPLkiRJUm5yhEk56Y03YPDg1AvQn/4E48dnJFKHNTUlRr+eegoaGxPlaOxYmDQpd0bEJEmSOpu8mCVPCnLHHYnL1Vpbkz+nuBhuuSX3ClNJCZxzTmKTJElSfvGSPOWkO+5IrSxBYhKIRYtgw4bMZJIkSVLnY2FSztm+HfaynnGgWAzq69ObR5IkSZ2XhUk5J9WRpXSfL0mSJO1kYVLOKS/ft8kQ+vZNXxZJkiR1bhYm5ZxIBL761dQXoi0qgmOOgf79M5NLkiRJnY+FSTlp+vTEJA6paG2FSy/NTB5JkiR1ThYm5aQjjoDTTkuMGiWjuBiGDoXTT89sLkmSJHUuFiblrHvugTFjEusx7UlREQwYAA8/DF27ZiebJEmSOgcLk3JWt27wpz8lLrMrLU3c27SzPO38urgYzjoLnn3We5ckSZKUfpF4PB4PO0S2NDY2UllZSUNDAxUVFWHHUQoaG+G//gsefxw2boSKChg9GqZOhf33DzudJEmS8k2y3cDCJEmSJKnTSbYbeEmeJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSAAuTJEmSJAWwMEmSJElSgOKwA0jKbS0t8NZbsHUr9OgBBx4IUT9qkSRJnYRveyTt1tq1MGsWHHAA1NTA0KEwcGDi6+uug/Xrw04oSZKUeRYmSbu45x4YNAh+/GN47732z61ZA7W1MGAAPPxwKPEkSZKyxkvyJLXz3/8N556752NiMdi+HU4+GR56CE44ITvZpDCtYgO3sYzFvMpGtlFGMcPpx0UcyQSGUORnkJJUkCLxeDwedohsaWxspLKykoaGBioqKsKOI+WctWsTI0stLZDMb4ZoFLp3h/r6xJ9SIdpME+fzIA/wCkVEaOWjH46dj6upYCFncCwDQ0wqSUpFst3Aj8MktbnzTmhtTa4sQWKkafPmxCV8UiFqpIljmc9i6gDalaWPP65nM5/nVzzMyqxnlCRlloVJEpAYVZo3L1GCUjV3bvrzSLlgCot5ifd2KUr/KkacGDFO5z7+yabshJMkZYWFSRIAb7656wQPyYjH4eWXoakp/ZmkMP2D9Syibq9laacY0Ewr83gus8EkSVmVN4XptNNOY8CAAZSWlvKJT3yCr3/966xduzbsWFLB2Lp1387fvDk9OaRccRvLKCKS0jmtxLmd5WynJUOpJEnZljeFafz48dx///28+uqrPPDAA6xatYozzzwz7FhSwejRY9/Odx4VFZpURpc+roEm/spbGUgkSQpD3kwr/q1vfavt64EDB1JbW8vEiRPZsWMHXbp0CTGZVBiqqxPbm2+mdl40CqNGQdeumcklhWUj2zp87oZ9OFeSlFvyZoTp4zZs2MDChQsZO3bsHstSU1MTjY2N7TZJuxeNwiWXJP5MRSwGl16amUxSmEr24TPF0vz5PFKStBd5VZi+973v0a1bN3r37s2aNWt48MEH93j87NmzqaysbNuqq6uzlFTKT9/4BpSWJl+aioqgqgomTcpsLikMw+ib8j1MOx1GnzSnkSSFJdTCVFtbSyQS2eNWV1fXdvx3vvMdXnjhBR555BGKioqYPHkye1p39+qrr6ahoaFtezPVa42kTqZPH/jtbyES2XtpKiqCLl1gyZJEyZIKzUUcmfI9TEVEOIHB1LBfhlJJkrItEt9T48iwdevWsX79+j0eM3jwYLru5uaIt956i+rqav785z9z9NFHJ/XfS3Y1X6mze/jhxKjRli2Jxx//LRGNJi7Dq6pKlKUxY8LJKGXaDlqp5gbW8QGxFIrTg5zNaRySwWSSpHRIthuEepF1VVUVVVVVHTo39uHqmk0u/iKl3YknQn09LFyYWJT2lVc+em7ECLj8cvjKVxxZUmHrQhH/xemcxEIisNfKFCXCWXyaUzk4G/EkSVkS6ghTsv72t7/x3HPP8dnPfpb99tuPVatW8cMf/pB3332Xl19+mZKSkqS+jyNMUsds25YYbaqogCR/3KSCsZg6zuK3xIjTQmyX54uI0Eqcs/k0C5i4T5NFSJKyJ9lukBeTPpSXl/O73/2O448/nkMOOYSpU6dy+OGHs3Tp0qTLkqSOKytLXILnj5s6o4kcyktcxMUcSTd2nZn1WAbyAF9hIV+2LElSAcqLEaZ0cYRJkrQvttDMM6xhI9spo5hPUcUn6R12LElSB+TFPUySJOWT7nTlRIaEHUOSlEV5cUmeJEmSJIXBwiRJkiRJASxMkiRJkhTAwiRJkiRJASxMkiRJkhTAwiRJkiRJASxMkiRJkhTAwiRJkiRJASxMkiRJkhTAwiRJkiRJASxMkiRJkhSgOOwA2RSPxwFobGwMOYkkSZKkMO3sBDs7QpBOVZg2b94MQHV1dchJJEmSJOWCzZs3U1lZGfh8JL63SlVAYrEYa9eupUePHkQikbDjdEqNjY1UV1fz5ptvUlFREXYcFTBfa8oWX2vKFl9rypbO8lqLx+Ns3ryZ/v37E40G36nUqUaYotEoBx54YNgxBFRUVBT0D6Byh681ZYuvNWWLrzVlS2d4re1pZGknJ32QJEmSpAAWJkmSJEkKYGFSVpWUlDBz5kxKSkrCjqIC52tN2eJrTdnia03Z4mutvU416YMkSZIkpcIRJkmSJEkKYGGSJEmSpAAWJkmSJEkKYGGSJEmSpAAWJoXijTfeYOrUqdTU1FBWVsZBBx3EzJkzaW5uDjuaCtC1117L2LFjKS8vp2fPnmHHUYG55ZZbGDRoEKWlpRx11FE8++yzYUdSgXnyySc59dRT6d+/P5FIhMWLF4cdSQVq9uzZjB49mh49etC3b18mTpzIq6++Gnas0FmYFIq6ujpisRi33347L7/8MjfccAO33XYb3//+98OOpgLU3NzMpEmTuOiii8KOogJz3333ceWVVzJz5kyef/55hg8fzoknnsh7770XdjQVkK1btzJ8+HBuueWWsKOowC1dupTp06fz17/+lUcffZQdO3bwhS98ga1bt4YdLVROK66ccd1113HrrbeyevXqsKOoQC1YsIArrriCTZs2hR1FBeKoo45i9OjR3HzzzQDEYjGqq6u59NJLqa2tDTmdClEkEmHRokVMnDgx7CjqBNatW0ffvn1ZunQpn/vc58KOExpHmJQzGhoa6NWrV9gxJCkpzc3NLF++nBNOOKFtXzQa5YQTTuAvf/lLiMkkKT0aGhoAOv37MwuTcsLKlSu56aabuPDCC8OOIklJef/992ltbWX//fdvt3///ffnnXfeCSmVJKVHLBbjiiuu4JhjjmHo0KFhxwmVhUlpVVtbSyQS2eNWV1fX7pz6+nomTJjApEmTmDZtWkjJlW868lqTJEnJmT59Oi+99BL33ntv2FFCVxx2ABWWq666iilTpuzxmMGDB7d9vXbtWsaPH8/YsWO54447MpxOhSTV15qUbn369KGoqIh333233f53332Xfv36hZRKkvbdJZdcwpIlS3jyySc58MADw44TOguT0qqqqoqqqqqkjq2vr2f8+PGMGjWK+fPnE4064KnkpfJakzKha9eujBo1iscee6ztBvxYLMZjjz3GJZdcEm44SeqAeDzOpZdeyqJFi3jiiSeoqakJO1JOsDApFPX19YwbN46BAwcyZ84c1q1b1/acn8wq3dasWcOGDRtYs2YNra2trFixAoAhQ4bQvXv3cMMpr1155ZWcd955HHnkkYwZM4Ybb7yRrVu3cv7554cdTQVky5YtrFy5su3x66+/zooVK+jVqxcDBgwIMZkKzfTp07nnnnt48MEH6dGjR9v9mJWVlZSVlYWcLjxOK65QLFiwIPANhS9JpduUKVO4++67d9n/+OOPM27cuOwHUkG5+eabue6663jnnXcYMWIEc+fO5aijjgo7lgrIE088wfjx43fZf95557FgwYLsB1LBikQiu90/f/78vV4GX8gsTJIkSZIUwJtGJEmSJCmAhUmSJEmSAliYJEmSJCmAhUmSJEmSAliYJEmSJCmAhUmSJEmSAliYJEmSJCmAhUmSJEmSAliYJEmSJCmAhUmSlHVTpkwhEonssq1cuTIt33/BggX07NkzLd+ro5588klOPfVU+vfvTyQSYfHixaHmkSR1jIVJkhSKCRMm8Pbbb7fbampqwo61ix07dnTovK1btzJ8+HBuueWWNCeSJGWThUmSFIqSkhL69evXbisqKgLgwQcf5IgjjqC0tJTBgwcza9YsWlpa2s79xS9+wbBhw+jWrRvV1dVcfPHFbNmyBYAnnniC888/n4aGhraRqx/96EcAux3p6dmzJwsWLADgjTfeIBKJcN9993HcccdRWlrKwoULAbjrrrs47LDDKC0t5dBDD2XevHl7/PuddNJJXHPNNZx++ulp+NeSJIWlOOwAkiR93FNPPcXkyZOZO3cuxx57LKtWreKCCy4AYObMmQBEo1Hmzp1LTU0Nq1ev5uKLL+a73/0u8+bNY+zYsdx4443MmDGDV199FYDu3bunlKG2tpbrr7+ekSNHtpWmGTNmcPPNNzNy5EheeOEFpk2bRrdu3TjvvPPS+w8gScopFiZJUiiWLFnSrsicdNJJ/OY3v2HWrFnU1ta2FZHBgwfzk5/8hO9+97tthemKK65oO2/QoEFcc801fPOb32TevHl07dqVyspKIpEI/fr161C2K664gjPOOKPt8cyZM7n++uvb9tXU1PD3v/+d22+/3cIkSQXOwiRJCsX48eO59dZb2x5369YNgBdffJFnnnmGa6+9tu251tZWtm/fzgcffEB5eTl//OMfmT17NnV1dTQ2NtLS0tLu+X115JFHtn29detWVq1axdSpU5k2bVrb/paWFiorK/f5vyVJym0WJklSKLp168aQIUN22b9lyxZmzZrVboRnp9LSUt544w1OOeUULrroIq699lp69erF008/zdSpU2lubt5jYYpEIsTj8Xb7djepw87ytjMPwJ133slRRx3V7rid91xJkgqXhUmSlFOOOOIIXn311d2WKYDly5cTi8W4/vrriUYTcxfdf//97Y7p2rUrra2tu5xbVVXF22+/3fb4tdde44MPPthjnv3335/+/fuzevVqzj333FT/OpKkPGdhkiTllBkzZnDKKacwYMAAzjzzTKLRKC+++CIvvfQS11xzDUOGDGHHjh3cdNNNnHrqqTzzzDPcdttt7b7HoEGD2LJlC4899hjDhw+nvLyc8vJyPv/5z3PzzTdz9NFH09rayve+9z26dOmy10yzZs3isssuo7KykgkTJtDU1MSyZcvYuHEjV1555W7P2bJlS7t1pV5//XVWrFhBr169GDBgwL79I0mSssZpxSVJOeXEE09kyZIlPPLII4wePZrPfOYz3HDDDQwcOBCA4cOH84tf/IKf/exnDB06lIULFzJ79ux232Ps2LF885vf5KyzzqKqqoqf//znAFx//fVUV1dz7LHHcs455/Dtb387qXue/v3f/5277rqL+fPnM2zYMI477jgWLFiwx3Wjli1bxsiRIxk5ciQAV155JSNHjmTGjBkd/aeRJIUgEv/Xi7klSZIkSYAjTJIkSZIUyMIkSZIkSQEsTJIkSZIUwMIkSZIkSQEsTJIkSZIUwMIkSZIkSQEsTJIkSZIUwMIkSZIkSQEsTJIkSZIUwMIkSZIkSQEsTJIkSZIU4P8D8Uw9SSJgaxAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "[1.37849446 0.06691885]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "\n",
+    "# Generate a random n-class classification problem\n",
+    "X, y = make_classification(n_samples=100, n_features=2, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=41,hypercube=False, class_sep=10)\n",
+    " \n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='winter', s = 100)\n",
+    "\n",
+    "# Set the title and labels\n",
+    "plt.title('Scatter plot of the dataset')\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "\n",
+    "import numpy as np\n",
+    "def Perceptron(X,y):\n",
+    "\n",
+    "   # Insert a column of ones at the beginning of X to account for the bias term (intercept).\n",
+    "    X = np.insert(X, 0, 1, axis=1)\n",
+    "# Initialize the weights for the perceptron model to ones.\n",
+    "\n",
+    "    weight = np.ones(X.shape[1])\n",
+    "# set learning rate, which determines the step size for updating the weights.\n",
+    "    learning_rate = 0.1\n",
+    "# Iterate 1000 times to update the weights based on the perceptron learning rule.\n",
+    "    for i in range(1000):\n",
+    "    # Select a random index from the range of sample indices (0 to 99).\n",
+    "        j = np.random.randint(0, 100)\n",
+    "  \n",
+    "        y_hat = step(np.dot(X[j], weight))\n",
+    "\n",
+    "      # Update the weights based on the perceptron learning rule.\n",
+    "        # The update is proportional to the learning rate (lr), the difference between the\n",
+    "        # actual label (y[j]) and the predicted label (y_hat), and the feature vector X[j].\n",
+    "    # Apply the Heaviside step function\n",
+    "        weight = weight + learning_rate * (y[j] - y_hat) * X[j]\n",
+    "  \n",
+    "    # Return the bias term (weights[0]) and the remaining weights (weights[1:]).\n",
+    "    return weight[0], weight[1:]\n",
+    "# Apply the step function used in the perceptron algorithm.\n",
+    "# This function returns 1 if the input is greater than or equal to 0, and 0 otherwise.\n",
+    "def step(z):\n",
+    "    return 1 if z > 0 else 0\n",
+    "intercept_, coef_ = Perceptron(X,y)\n",
+    "print(intercept_)\n",
+    "print(coef_)\n",
+    "# Calculate the slope of the decision boundary line.\n",
+    "m = -(coef_[0] / coef_[1])\n",
+    "\n",
+    "# Calculate the y-intercept of the decision boundary line.\n",
+    "b = -(intercept_ / coef_[1])\n",
+    "# Generate 100 evenly spaced values between -3 and 3 for the x-axis input.\n",
+    "x_input = np.linspace(-3, 3, 100)\n",
+    "# Calculate the corresponding y-axis values using the equation of the decision boundary line.\n",
+    "y_input = m * x_input + b\n",
+    "# Create a new figure with a specific size using the figure function from matplotlib.pyplot.\n",
+    "# figsize=(10,6) specifies that the figure size is 10 inches wide and 6 inches tall.\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot the decision boundary line using the plot function from matplotlib.pyplot.\n",
+    "# The line is defined by the x_input values on the x-axis and the corresponding y_input values on the y-axis.\n",
+    "# color='red' sets the color of the line to red.\n",
+    "# linewidth=3 sets the width of the line to 3.\n",
+    "plt.plot(x_input, y_input, color='red', linewidth=3)\n",
+    "# Create a scatter plot of the data points using the scatter function from matplotlib.pyplot.\n",
+    "# X[:,0] represents all values in the first feature column (x-axis values).\n",
+    "# X[:,1] represents all values in the second feature column (y-axis values).\n",
+    "# c=y specifies that the color of each point in the scatter plot should correspond to its class label.\n",
+    "# cmap='winter' sets the colormap to 'winter', which will be used to map class labels to colors.\n",
+    "# s=100 sets the size of each scatter plot point to 100.\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='winter', s=100)\n",
+    "\n",
+    "# Set the y-axis limits to -3 and 2.\n",
+    "plt.ylim(-4, 2)\n",
+    "plt.show()\n",
+    "\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Online Sales Data.csv b/Online Sales Data.csv
new file mode 100644
index 0000000..7ecd8cc
--- /dev/null
+++ b/Online Sales Data.csv	
@@ -0,0 +1,241 @@
+Transaction ID,Date,Product Category,Product Name,Units Sold,Unit Price,Total Revenue,Region,Payment Method
+10001,2024-01-01,Electronics,iPhone 14 Pro,2,999.99,1999.98,North America,Credit Card
+10002,2024-01-02,Home Appliances,Dyson V11 Vacuum,1,499.99,499.99,Europe,PayPal
+10003,2024-01-03,Clothing,Levi's 501 Jeans,3,69.99,209.97,Asia,Debit Card
+10004,2024-01-04,Books,The Da Vinci Code,4,15.99,63.96,North America,Credit Card
+10005,2024-01-05,Beauty Products,Neutrogena Skincare Set,1,89.99,89.99,Europe,PayPal
+10006,2024-01-06,Sports,Wilson Evolution Basketball,5,29.99,149.95,Asia,Credit Card
+10007,2024-01-07,Electronics,MacBook Pro 16-inch,1,2499.99,2499.99,North America,Credit Card
+10008,2024-01-08,Home Appliances,Blueair Classic 480i,2,599.99,1199.98,Europe,PayPal
+10009,2024-01-09,Clothing,Nike Air Force 1,6,89.99,539.94,Asia,Debit Card
+10010,2024-01-10,Books,Dune by Frank Herbert,2,25.99,51.98,North America,Credit Card
+10011,2024-01-11,Beauty Products,Chanel No. 5 Perfume,1,129.99,129.99,Europe,PayPal
+10012,2024-01-12,Sports,Babolat Pure Drive Tennis Racket,3,199.99,599.97,Asia,Credit Card
+10013,2024-01-13,Electronics,Samsung Galaxy Tab S8,2,749.99,1499.98,North America,Credit Card
+10014,2024-01-14,Home Appliances,Keurig K-Elite Coffee Maker,1,189.99,189.99,Europe,PayPal
+10015,2024-01-15,Clothing,North Face Down Jacket,2,249.99,499.98,Asia,Debit Card
+10016,2024-01-16,Books,"Salt, Fat, Acid, Heat by Samin Nosrat",3,35.99,107.97,North America,Credit Card
+10017,2024-01-17,Beauty Products,Dyson Supersonic Hair Dryer,1,399.99,399.99,Europe,PayPal
+10018,2024-01-18,Sports,Manduka PRO Yoga Mat,4,119.99,479.96,Asia,Credit Card
+10019,2024-01-19,Electronics,Garmin Forerunner 945,2,499.99,999.98,North America,Credit Card
+10020,2024-01-20,Home Appliances,Ninja Professional Blender,1,99.99,99.99,Europe,PayPal
+10021,2024-01-21,Clothing,Zara Summer Dress,3,59.99,179.97,Asia,Debit Card
+10022,2024-01-22,Books,Gone Girl by Gillian Flynn,2,22.99,45.98,North America,Credit Card
+10023,2024-01-23,Beauty Products,Olay Regenerist Face Cream,1,49.99,49.99,Europe,PayPal
+10024,2024-01-24,Sports,Adidas FIFA World Cup Football,3,29.99,89.97,Asia,Credit Card
+10025,2024-01-25,Electronics,Bose QuietComfort 35 Headphones,1,299.99,299.99,North America,Credit Card
+10026,2024-01-26,Home Appliances,Panasonic NN-SN966S Microwave,1,179.99,179.99,Europe,PayPal
+10027,2024-01-27,Clothing,Adidas Ultraboost Shoes,2,179.99,359.98,Asia,Debit Card
+10028,2024-01-28,Books,Pride and Prejudice by Jane Austen,3,12.99,38.97,North America,Credit Card
+10029,2024-01-29,Beauty Products,MAC Ruby Woo Lipstick,1,29.99,29.99,Europe,PayPal
+10030,2024-01-30,Sports,Nike Air Zoom Pegasus 37,2,129.99,259.98,Asia,Credit Card
+10031,2024-01-31,Electronics,Sony WH-1000XM4 Headphones,2,349.99,699.98,North America,Credit Card
+10032,2024-02-01,Home Appliances,Instant Pot Duo,3,89.99,269.97,Europe,PayPal
+10033,2024-02-02,Clothing,Under Armour HeatGear T-Shirt,5,29.99,149.95,Asia,Debit Card
+10034,2024-02-03,Books,1984 by George Orwell,4,19.99,79.96,North America,Credit Card
+10035,2024-02-04,Beauty Products,L'Oreal Revitalift Serum,2,39.99,79.98,Europe,PayPal
+10036,2024-02-05,Sports,Peloton Bike,1,1895,1895,Asia,Credit Card
+10037,2024-02-06,Electronics,Apple Watch Series 8,3,399.99,1199.97,North America,Credit Card
+10038,2024-02-07,Home Appliances,Roomba i7+,2,799.99,1599.98,Europe,PayPal
+10039,2024-02-08,Clothing,Columbia Fleece Jacket,4,59.99,239.96,Asia,Debit Card
+10040,2024-02-09,Books,Harry Potter and the Sorcerer's Stone,3,24.99,74.97,North America,Credit Card
+10041,2024-02-10,Beauty Products,Estee Lauder Advanced Night Repair,1,105,105,Europe,PayPal
+10042,2024-02-11,Sports,Fitbit Charge 5,2,129.99,259.98,Asia,Credit Card
+10043,2024-02-12,Electronics,GoPro HERO10 Black,3,399.99,1199.97,North America,Credit Card
+10044,2024-02-13,Home Appliances,Nespresso VertuoPlus,1,199.99,199.99,Europe,PayPal
+10045,2024-02-14,Clothing,Patagonia Better Sweater,2,139.99,279.98,Asia,Debit Card
+10046,2024-02-15,Books,Becoming by Michelle Obama,4,32.5,130,North America,Credit Card
+10047,2024-02-16,Beauty Products,Clinique Moisture Surge,1,52,52,Europe,PayPal
+10048,2024-02-17,Sports,Yeti Rambler Tumbler,6,39.99,239.94,Asia,Credit Card
+10049,2024-02-18,Electronics,Kindle Paperwhite,2,129.99,259.98,North America,Credit Card
+10050,2024-02-19,Home Appliances,Breville Smart Oven,1,299.99,299.99,Europe,PayPal
+10051,2024-02-20,Clothing,Ray-Ban Aviator Sunglasses,3,154.99,464.97,Asia,Debit Card
+10052,2024-02-21,Books,The Silent Patient by Alex Michaelides,2,26.99,53.98,North America,Credit Card
+10053,2024-02-22,Beauty Products,Shiseido Ultimate Sun Protector,1,49,49,Europe,PayPal
+10054,2024-02-23,Sports,Titleist Pro V1 Golf Balls,5,49.99,249.95,Asia,Credit Card
+10055,2024-02-24,Electronics,Anker PowerCore Portable Charger,4,59.99,239.96,North America,Credit Card
+10056,2024-02-25,Home Appliances,KitchenAid Artisan Stand Mixer,1,499.99,499.99,Europe,PayPal
+10057,2024-02-26,Clothing,Calvin Klein Boxer Briefs,5,29.99,149.95,Asia,Debit Card
+10058,2024-02-27,Books,Educated by Tara Westover,3,28,84,North America,Credit Card
+10059,2024-02-28,Beauty Products,Anastasia Beverly Hills Brow Wiz,2,23,46,Europe,PayPal
+10060,2024-02-29,Sports,Hyperice Hypervolt Massager,1,349,349,Asia,Credit Card
+10061,2024-03-01,Electronics,Nintendo Switch,3,299.99,899.97,North America,Credit Card
+10062,2024-03-02,Home Appliances,Philips Airfryer XXL,2,199.99,399.98,Europe,PayPal
+10063,2024-03-03,Clothing,Hanes ComfortSoft T-Shirt,10,9.99,99.9,Asia,Debit Card
+10064,2024-03-04,Books,Where the Crawdads Sing by Delia Owens,4,18.99,75.96,North America,Credit Card
+10065,2024-03-05,Beauty Products,Lancome La Vie Est Belle,1,102,102,Europe,PayPal
+10066,2024-03-06,Sports,Garmin Edge 530,2,299.99,599.98,Asia,Credit Card
+10067,2024-03-07,Electronics,Samsung QLED 4K TV,1,1199.99,1199.99,North America,Credit Card
+10068,2024-03-08,Home Appliances,Eufy RoboVac 11S,3,219.99,659.97,Europe,PayPal
+10069,2024-03-09,Clothing,Puma Suede Classic Sneakers,4,59.99,239.96,Asia,Debit Card
+10070,2024-03-10,Books,The Great Gatsby by F. Scott Fitzgerald,2,10.99,21.98,North America,Credit Card
+10071,2024-03-11,Beauty Products,Drunk Elephant C-Firma Day Serum,1,78,78,Europe,PayPal
+10072,2024-03-12,Sports,Nike Metcon 6,3,129.99,389.97,Asia,Credit Card
+10073,2024-03-13,Electronics,HP Spectre x360 Laptop,1,1599.99,1599.99,North America,Credit Card
+10074,2024-03-14,Home Appliances,De'Longhi Magnifica Espresso Machine,1,899.99,899.99,Europe,PayPal
+10075,2024-03-15,Clothing,Tommy Hilfiger Polo Shirt,5,49.99,249.95,Asia,Debit Card
+10076,2024-03-16,Books,To Kill a Mockingbird by Harper Lee,4,14.99,59.96,North America,Credit Card
+10077,2024-03-17,Beauty Products,Glossier Boy Brow,2,16,32,Europe,PayPal
+10078,2024-03-18,Sports,Rogue Fitness Kettlebell,3,69.99,209.97,Asia,Credit Card
+10079,2024-03-19,Electronics,Apple AirPods Pro,2,249.99,499.98,North America,Credit Card
+10080,2024-03-20,Home Appliances,Dyson Pure Cool Link,1,499.99,499.99,Europe,PayPal
+10081,2024-03-21,Clothing,Levi's Trucker Jacket,2,89.99,179.98,Asia,Debit Card
+10082,2024-03-22,Books,The Hobbit by J.R.R. Tolkien,3,12.99,38.97,North America,Credit Card
+10083,2024-03-23,Beauty Products,Charlotte Tilbury Magic Cream,1,100,100,Europe,PayPal
+10084,2024-03-24,Sports,Spalding NBA Street Basketball,6,24.99,149.94,Asia,Credit Card
+10085,2024-03-25,Electronics,Ring Video Doorbell,1,99.99,99.99,North America,Credit Card
+10086,2024-03-26,Home Appliances,LG OLED TV,2,1299.99,2599.98,Europe,PayPal
+10087,2024-03-27,Clothing,Uniqlo Ultra Light Down Jacket,3,79.99,239.97,Asia,Debit Card
+10088,2024-03-28,Books,The Catcher in the Rye by J.D. Salinger,4,13.99,55.96,North America,Credit Card
+10089,2024-03-29,Beauty Products,Sunday Riley Good Genes,1,105,105,Europe,PayPal
+10090,2024-03-30,Sports,On Running Cloud Shoes,2,129.99,259.98,Asia,Credit Card
+10091,2024-03-31,Electronics,Logitech MX Master 3 Mouse,2,99.99,199.98,North America,Credit Card
+10092,2024-04-01,Home Appliances,Instant Pot Duo Crisp,1,179.99,179.99,Europe,PayPal
+10093,2024-04-02,Clothing,Adidas Originals Superstar Sneakers,4,79.99,319.96,Asia,Debit Card
+10094,2024-04-03,Books,The Alchemist by Paulo Coelho,3,14.99,44.97,North America,Credit Card
+10095,2024-04-04,Beauty Products,Tatcha The Water Cream,1,68,68,Europe,PayPal
+10096,2024-04-05,Sports,Garmin Fenix 6X Pro,1,999.99,999.99,Asia,Credit Card
+10097,2024-04-06,Electronics,Bose SoundLink Revolve+ Speaker,3,299.99,899.97,North America,Credit Card
+10098,2024-04-07,Home Appliances,Vitamix Explorian Blender,1,349.99,349.99,Europe,PayPal
+10099,2024-04-08,Clothing,Gap Essential Crewneck T-Shirt,6,19.99,119.94,Asia,Debit Card
+10100,2024-04-09,Books,The Power of Now by Eckhart Tolle,2,12.99,25.98,North America,Credit Card
+10101,2024-04-10,Beauty Products,Kiehl's Midnight Recovery Concentrate,1,82,82,Europe,PayPal
+10102,2024-04-11,Sports,Under Armour HOVR Sonic 4 Shoes,2,109.99,219.98,Asia,Credit Card
+10103,2024-04-12,Electronics,Canon EOS R5 Camera,1,3899.99,3899.99,North America,Credit Card
+10104,2024-04-13,Home Appliances,Shark IQ Robot Vacuum,2,349.99,699.98,Europe,PayPal
+10105,2024-04-14,Clothing,H&M Slim Fit Jeans,3,39.99,119.97,Asia,Debit Card
+10106,2024-04-15,Books,The Girl on the Train by Paula Hawkins,4,10.99,43.96,North America,Credit Card
+10107,2024-04-16,Beauty Products,The Ordinary Niacinamide Serum,1,6.5,6.5,Europe,PayPal
+10108,2024-04-17,Sports,Bowflex SelectTech 552 Dumbbells,1,399.99,399.99,Asia,Credit Card
+10109,2024-04-18,Electronics,Google Nest Hub Max,2,229.99,459.98,North America,Credit Card
+10110,2024-04-19,Home Appliances,Cuisinart Griddler Deluxe,1,159.99,159.99,Europe,PayPal
+10111,2024-04-20,Clothing,Old Navy Relaxed-Fit T-Shirt,4,14.99,59.96,Asia,Debit Card
+10112,2024-04-21,Books,Sapiens: A Brief History of Humankind by Yuval Noah Harari,2,18.99,37.98,North America,Credit Card
+10113,2024-04-22,Beauty Products,Biore UV Aqua Rich Watery Essence Sunscreen,1,15,15,Europe,PayPal
+10114,2024-04-23,Sports,Fitbit Versa 3,3,229.95,689.85,Asia,Credit Card
+10115,2024-04-24,Electronics,Amazon Echo Show 10,1,249.99,249.99,North America,Credit Card
+10116,2024-04-25,Home Appliances,Breville Smart Grill,2,299.95,599.9,Europe,PayPal
+10117,2024-04-26,Clothing,Gap High Rise Skinny Jeans,3,49.99,149.97,Asia,Debit Card
+10118,2024-04-27,Books,Atomic Habits by James Clear,4,16.99,67.96,North America,Credit Card
+10119,2024-04-28,Beauty Products,CeraVe Hydrating Facial Cleanser,2,14.99,29.98,Europe,PayPal
+10120,2024-04-29,Sports,YETI Hopper Flip Portable Cooler,1,249.99,249.99,Asia,Credit Card
+10121,2024-04-30,Electronics,Apple iPad Air,2,599.99,1199.98,North America,Credit Card
+10122,2024-05-01,Home Appliances,Hamilton Beach FlexBrew Coffee Maker,1,89.99,89.99,Europe,PayPal
+10123,2024-05-02,Clothing,Forever 21 Graphic Tee,5,12.99,64.95,Asia,Debit Card
+10124,2024-05-03,Books,The Subtle Art of Not Giving a F*ck by Mark Manson,3,14.99,44.97,North America,Credit Card
+10125,2024-05-04,Beauty Products,NARS Radiant Creamy Concealer,1,30,30,Europe,PayPal
+10126,2024-05-05,Sports,Yeti Roadie 24 Cooler,1,199.99,199.99,Asia,Credit Card
+10127,2024-05-06,Electronics,Sony PlayStation 5,1,499.99,499.99,North America,Credit Card
+10128,2024-05-07,Home Appliances,Dyson Supersonic Hair Dryer,2,399.99,799.98,Europe,PayPal
+10129,2024-05-08,Clothing,Lululemon Align Leggings,3,98,294,Asia,Debit Card
+10130,2024-05-09,Books,The Four Agreements by Don Miguel Ruiz,2,8.99,17.98,North America,Credit Card
+10131,2024-05-10,Beauty Products,Fenty Beauty Killawatt Highlighter,1,36,36,Europe,PayPal
+10132,2024-05-11,Sports,Hydro Flask Wide Mouth Water Bottle,4,39.95,159.8,Asia,Credit Card
+10133,2024-05-12,Electronics,Microsoft Surface Laptop 4,1,1299.99,1299.99,North America,Credit Card
+10134,2024-05-13,Home Appliances,Keurig K-Mini Coffee Maker,2,79.99,159.98,Europe,PayPal
+10135,2024-05-14,Clothing,Gap Crewneck Sweatshirt,4,34.99,139.96,Asia,Debit Card
+10136,2024-05-15,Books,Think and Grow Rich by Napoleon Hill,3,9.99,29.97,North America,Credit Card
+10137,2024-05-16,Beauty Products,The Ordinary Hyaluronic Acid Serum,1,6.8,6.8,Europe,PayPal
+10138,2024-05-17,Sports,Fitbit Inspire 2,2,99.95,199.9,Asia,Credit Card
+10139,2024-05-18,Electronics,Samsung Odyssey G9 Gaming Monitor,1,1499.99,1499.99,North America,Credit Card
+10140,2024-05-19,Home Appliances,Instant Pot Ultra,1,139.99,139.99,Europe,PayPal
+10141,2024-05-20,Clothing,Adidas Essential Track Pants,3,44.99,134.97,Asia,Debit Card
+10142,2024-05-21,Books,The Power of Habit by Charles Duhigg,2,11.99,23.98,North America,Credit Card
+10143,2024-05-22,Beauty Products,Clinique Dramatically Different Moisturizing Lotion,1,29.5,29.5,Europe,PayPal
+10144,2024-05-23,Sports,YETI Tundra 45 Cooler,1,299.99,299.99,Asia,Credit Card
+10145,2024-05-24,Electronics,Apple AirPods Max,1,549,549,North America,Credit Card
+10146,2024-05-25,Home Appliances,Cuisinart Coffee Center,2,199.95,399.9,Europe,PayPal
+10147,2024-05-26,Clothing,Levi's Sherpa Trucker Jacket,2,98,196,Asia,Debit Card
+10148,2024-05-27,Books,The Outsiders by S.E. Hinton,3,10.99,32.97,North America,Credit Card
+10149,2024-05-28,Beauty Products,Laneige Water Sleeping Mask,1,25,25,Europe,PayPal
+10150,2024-05-29,Sports,Bose SoundSport Wireless Earbuds,2,149.99,299.98,Asia,Credit Card
+10151,2024-05-30,Electronics,Sony WH-1000XM4 Headphones,1,349.99,349.99,North America,Credit Card
+10152,2024-05-31,Home Appliances,Ninja Foodi Pressure Cooker,2,199.99,399.98,Europe,PayPal
+10153,2024-06-01,Clothing,Nike Sportswear Club Fleece Hoodie,3,54.99,164.97,Asia,Debit Card
+10154,2024-06-02,Books,The Night Circus by Erin Morgenstern,2,16.99,33.98,North America,Credit Card
+10155,2024-06-03,Beauty Products,GlamGlow Supermud Clearing Treatment,1,59,59,Europe,PayPal
+10156,2024-06-04,Sports,Garmin Forerunner 245,1,299.99,299.99,Asia,Credit Card
+10157,2024-06-05,Electronics,Google Pixel 6 Pro,1,899.99,899.99,North America,Credit Card
+10158,2024-06-06,Home Appliances,Breville Nespresso Creatista Plus,1,499.95,499.95,Europe,PayPal
+10159,2024-06-07,Clothing,Under Armour Tech 2.0 T-Shirt,4,24.99,99.96,Asia,Debit Card
+10160,2024-06-08,Books,The Art of War by Sun Tzu,3,7.99,23.97,North America,Credit Card
+10161,2024-06-09,Beauty Products,Youth to the People Superfood Antioxidant Cleanser,1,36,36,Europe,PayPal
+10162,2024-06-10,Sports,TriggerPoint GRID Foam Roller,2,34.99,69.98,Asia,Credit Card
+10163,2024-06-11,Electronics,Apple MacBook Air,1,1199.99,1199.99,North America,Credit Card
+10164,2024-06-12,Home Appliances,Cuisinart Custom 14-Cup Food Processor,1,199.99,199.99,Europe,PayPal
+10165,2024-06-13,Clothing,Adidas 3-Stripes Shorts,5,29.99,149.95,Asia,Debit Card
+10166,2024-06-14,Books,The Hunger Games by Suzanne Collins,4,8.99,35.96,North America,Credit Card
+10167,2024-06-15,Beauty Products,Neutrogena Hydro Boost Water Gel,1,16.99,16.99,Europe,PayPal
+10168,2024-06-16,Sports,Yeti Rambler Bottle,3,49.99,149.97,Asia,Credit Card
+10169,2024-06-17,Electronics,Samsung Odyssey G7 Gaming Monitor,1,699.99,699.99,North America,Credit Card
+10170,2024-06-18,Home Appliances,Instant Pot Duo Evo Plus,2,139.99,279.98,Europe,PayPal
+10171,2024-06-19,Clothing,Nike Tempo Running Shorts,3,34.99,104.97,Asia,Debit Card
+10172,2024-06-20,Books,The Girl with the Dragon Tattoo by Stieg Larsson,2,9.99,19.98,North America,Credit Card
+10173,2024-06-21,Beauty Products,Paula's Choice Skin Perfecting 2% BHA Liquid Exfoliant,1,29.5,29.5,Europe,PayPal
+10174,2024-06-22,Sports,Bowflex SelectTech 1090 Adjustable Dumbbells,1,699.99,699.99,Asia,Credit Card
+10175,2024-06-23,Electronics,Amazon Fire TV Stick 4K,3,49.99,149.97,North America,Credit Card
+10176,2024-06-24,Home Appliances,Crock-Pot 6-Quart Slow Cooker,2,49.99,99.98,Europe,PayPal
+10177,2024-06-25,Clothing,Uniqlo Airism Mesh Boxer Briefs,4,14.9,59.6,Asia,Debit Card
+10178,2024-06-26,Books,The Sun Also Rises by Ernest Hemingway,3,11.99,35.97,North America,Credit Card
+10179,2024-06-27,Beauty Products,First Aid Beauty Ultra Repair Cream,2,34,68,Europe,PayPal
+10180,2024-06-28,Sports,Oakley Holbrook Sunglasses,1,146,146,Asia,Credit Card
+10181,2024-06-29,Electronics,Google Pixelbook Go,1,649.99,649.99,North America,Credit Card
+10182,2024-06-30,Home Appliances,Dyson V8 Absolute,1,399.99,399.99,Europe,PayPal
+10183,2024-07-01,Clothing,Levi's 511 Slim Fit Jeans,3,59.99,179.97,Asia,Debit Card
+10184,2024-07-02,Books,The Martian by Andy Weir,2,12.99,25.98,North America,Credit Card
+10185,2024-07-03,Beauty Products,La Mer Crème de la Mer Moisturizer,1,190,190,Europe,PayPal
+10186,2024-07-04,Sports,Polar Vantage V2,1,499.95,499.95,Asia,Credit Card
+10187,2024-07-05,Electronics,Sonos Beam Soundbar,1,399,399,North America,Credit Card
+10188,2024-07-06,Home Appliances,Anova Precision Cooker,2,199,398,Europe,PayPal
+10189,2024-07-07,Clothing,Nike Dri-FIT Training Shorts,4,34.99,139.96,Asia,Debit Card
+10190,2024-07-08,Books,The Catcher in the Rye by J.D. Salinger,3,10.99,32.97,North America,Credit Card
+10191,2024-07-09,Beauty Products,Glossier Cloud Paint,1,18,18,Europe,PayPal
+10192,2024-07-10,Sports,TRX All-in-One Suspension Training System,1,169.95,169.95,Asia,Credit Card
+10193,2024-07-11,Electronics,Logitech G Pro X Wireless Gaming Headset,1,199.99,199.99,North America,Credit Card
+10194,2024-07-12,Home Appliances,Breville Smart Coffee Grinder Pro,1,199.95,199.95,Europe,PayPal
+10195,2024-07-13,Clothing,Adidas Ultraboost Running Shoes,2,179.99,359.98,Asia,Debit Card
+10196,2024-07-14,Books,The Road by Cormac McCarthy,2,11.99,23.98,North America,Credit Card
+10197,2024-07-15,Beauty Products,Tom Ford Black Orchid Perfume,1,125,125,Europe,PayPal
+10198,2024-07-16,Sports,GoPro HERO9 Black,1,449.99,449.99,Asia,Credit Card
+10199,2024-07-17,Electronics,Apple TV 4K,2,179,358,North America,Credit Card
+10200,2024-07-18,Home Appliances,Instant Pot Duo Nova,1,99.95,99.95,Europe,PayPal
+10201,2024-07-19,Clothing,Gap 1969 Original Fit Jeans,3,59.99,179.97,Asia,Debit Card
+10202,2024-07-20,Books,The Goldfinch by Donna Tartt,2,14.99,29.98,North America,Credit Card
+10203,2024-07-21,Beauty Products,Dr. Jart+ Cicapair Tiger Grass Color Correcting Treatment,1,52,52,Europe,PayPal
+10204,2024-07-22,Sports,Yeti Tundra Haul Portable Wheeled Cooler,1,399.99,399.99,Asia,Credit Card
+10205,2024-07-23,Electronics,Samsung Galaxy Watch 4,1,299.99,299.99,North America,Credit Card
+10206,2024-07-24,Home Appliances,KitchenAid Stand Mixer,1,379.99,379.99,Europe,PayPal
+10207,2024-07-25,Clothing,Lululemon Wunder Under High-Rise Leggings,2,98,196,Asia,Debit Card
+10208,2024-07-26,Books,The Great Alone by Kristin Hannah,3,16.99,50.97,North America,Credit Card
+10209,2024-07-27,Beauty Products,Caudalie Vinoperfect Radiance Serum,1,79,79,Europe,PayPal
+10210,2024-07-28,Sports,Bose SoundLink Color Bluetooth Speaker II,1,129,129,Asia,Credit Card
+10211,2024-07-29,Electronics,Canon EOS Rebel T7i DSLR Camera,1,749.99,749.99,North America,Credit Card
+10212,2024-07-30,Home Appliances,Keurig K-Elite Coffee Maker,2,169.99,339.98,Europe,PayPal
+10213,2024-07-31,Clothing,Uniqlo Airism Seamless Boxer Briefs,4,9.9,39.6,Asia,Debit Card
+10214,2024-08-01,Books,The Girl with the Dragon Tattoo by Stieg Larsson,3,10.99,32.97,North America,Credit Card
+10215,2024-08-02,Beauty Products,L'Occitane Shea Butter Hand Cream,2,29,58,Europe,PayPal
+10216,2024-08-03,Sports,YETI Tundra 65 Cooler,1,349.99,349.99,Asia,Credit Card
+10217,2024-08-04,Electronics,Apple MacBook Pro 16-inch,1,2399,2399,North America,Credit Card
+10218,2024-08-05,Home Appliances,iRobot Braava Jet M6,1,449.99,449.99,Europe,PayPal
+10219,2024-08-06,Clothing,Champion Reverse Weave Hoodie,3,49.99,149.97,Asia,Debit Card
+10220,2024-08-07,Books,The Nightingale by Kristin Hannah,2,12.99,25.98,North America,Credit Card
+10221,2024-08-08,Beauty Products,Tarte Shape Tape Concealer,1,27,27,Europe,PayPal
+10222,2024-08-09,Sports,Garmin Forerunner 945,1,599.99,599.99,Asia,Credit Card
+10223,2024-08-10,Electronics,Amazon Echo Dot (4th Gen),4,49.99,199.96,North America,Credit Card
+10224,2024-08-11,Home Appliances,Philips Sonicare DiamondClean Toothbrush,2,229.99,459.98,Europe,PayPal
+10225,2024-08-12,Clothing,Old Navy Mid-Rise Rockstar Super Skinny Jeans,2,44.99,89.98,Asia,Debit Card
+10226,2024-08-13,Books,The Silent Patient by Alex Michaelides,3,26.99,80.97,North America,Credit Card
+10227,2024-08-14,Beauty Products,The Ordinary Caffeine Solution 5% + EGCG,1,6.7,6.7,Europe,PayPal
+10228,2024-08-15,Sports,Fitbit Luxe,2,149.95,299.9,Asia,Credit Card
+10229,2024-08-16,Electronics,Google Nest Wifi Router,1,169,169,North America,Credit Card
+10230,2024-08-17,Home Appliances,Anova Precision Oven,1,599,599,Europe,PayPal
+10231,2024-08-18,Clothing,Adidas Originals Trefoil Hoodie,4,64.99,259.96,Asia,Debit Card
+10232,2024-08-19,Books,Dune by Frank Herbert,2,9.99,19.98,North America,Credit Card
+10233,2024-08-20,Beauty Products,Fresh Sugar Lip Treatment,1,24,24,Europe,PayPal
+10234,2024-08-21,Sports,Hydro Flask Standard Mouth Water Bottle,3,32.95,98.85,Asia,Credit Card
+10235,2024-08-22,Electronics,Bose QuietComfort 35 II Wireless Headphones,1,299,299,North America,Credit Card
+10236,2024-08-23,Home Appliances,Nespresso Vertuo Next Coffee and Espresso Maker,1,159.99,159.99,Europe,PayPal
+10237,2024-08-24,Clothing,Nike Air Force 1 Sneakers,3,90,270,Asia,Debit Card
+10238,2024-08-25,Books,The Handmaid's Tale by Margaret Atwood,3,10.99,32.97,North America,Credit Card
+10239,2024-08-26,Beauty Products,Sunday Riley Luna Sleeping Night Oil,1,55,55,Europe,PayPal
+10240,2024-08-27,Sports,Yeti Rambler 20 oz Tumbler,2,29.99,59.98,Asia,Credit Card
\ No newline at end of file
diff --git a/Online_sales_EDA.ipynb b/Online_sales_EDA.ipynb
new file mode 100644
index 0000000..e69de29
diff --git a/Pandas_Analytics_project/Abuja_Branch.csv b/Pandas_Analytics_project/Abuja_Branch.csv
new file mode 100644
index 0000000..4bce687
--- /dev/null
+++ b/Pandas_Analytics_project/Abuja_Branch.csv
@@ -0,0 +1,333 @@
+Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
+692-92-5582,B,Abuja,Member,Female,Food and beverages,19742.4,3,2961.36,62188.55999999999,2/20/2019,13:27,Card,59227.200000000004,4.7619047619999995,2961.36,5.9
+351-62-0822,B,Abuja,Member,Female,Fashion accessories,5212.8,4,1042.5600000000002,21893.760000000002,2/6/2019,18:07,Epay,20851.2,4.7619047619999995,1042.5600000000002,4.5
+529-56-3974,B,Abuja,Member,Male,Electronic accessories,9183.6,4,1836.72,38571.12,3/9/2019,17:03,Cash,36734.4,4.7619047619999995,1836.72,6.8
+299-46-1805,B,Abuja,Member,Female,Sports and travel,33739.2,6,10121.76,212556.96000000002,1/15/2019,16:19,Cash,202435.2,4.7619047619999995,10121.76,4.5
+319-50-3348,B,Abuja,Normal,Female,Home and lifestyle,14507.999999999998,2,1450.8000000000002,30466.8,3/11/2019,15:30,Epay,29015.999999999996,4.7619047619999995,1450.8000000000002,4.4
+371-85-5789,B,Abuja,Normal,Male,Health and beauty,31672.800000000003,3,4750.92,99769.32,3/5/2019,10:40,Epay,95018.4,4.7619047619999995,4750.92,5.1
+273-16-6619,B,Abuja,Normal,Male,Home and lifestyle,11952.000000000002,2,1195.2,25099.2,3/15/2019,12:20,Card,23904.000000000004,4.7619047619999995,1195.2,4.4
+649-29-6775,B,Abuja,Normal,Male,Fashion accessories,12067.2,1,603.36,12670.56,2/8/2019,15:31,Cash,12067.2,4.7619047619999995,603.36,6.7
+145-94-9061,B,Abuja,Normal,Female,Food and beverages,31809.6,5,7952.4,167000.4,1/25/2019,19:48,Cash,159048.0,4.7619047619999995,7952.4,9.6
+871-79-8483,B,Abuja,Normal,Male,Fashion accessories,33886.799999999996,5,8471.699999999999,177905.7,2/25/2019,19:39,Card,169434.0,4.7619047619999995,8471.699999999999,4.8
+149-71-6266,B,Abuja,Member,Male,Sports and travel,28105.199999999997,9,12647.34,265594.13999999996,1/28/2019,12:43,Cash,252946.8,4.7619047619999995,12647.34,4.5
+640-49-2076,B,Abuja,Normal,Male,Sports and travel,30160.8,8,12064.32,253350.71999999997,1/10/2019,14:49,Cash,241286.4,4.7619047619999995,12064.32,5.1
+777-82-7220,B,Abuja,Member,Male,Home and lifestyle,10843.2,8,4337.28,91082.87999999999,3/3/2019,13:01,Cash,86745.6,4.7619047619999995,4337.28,7.7
+280-35-5823,B,Abuja,Member,Female,Home and lifestyle,31219.2,1,1560.96,32780.159999999996,1/17/2019,18:45,Epay,31219.2,4.7619047619999995,1560.96,7.9
+354-25-5821,B,Abuja,Member,Female,Sports and travel,24883.2,6,7464.96,156764.15999999997,2/8/2019,13:03,Cash,149299.2,4.7619047619999995,7464.96,5.6
+132-32-9879,B,Abuja,Member,Female,Electronic accessories,33825.6,4,6765.120000000001,142067.52,3/9/2019,18:00,Cash,135302.4,4.7619047619999995,6765.120000000001,9.5
+370-41-7321,B,Abuja,Member,Male,Health and beauty,20408.399999999998,9,9183.78,192859.38,2/27/2019,17:24,Card,183675.6,4.7619047619999995,9183.78,8.4
+727-46-3608,B,Abuja,Member,Female,Food and beverages,7203.6,9,3241.62,68074.02,2/6/2019,15:47,Epay,64832.4,4.7619047619999995,3241.62,4.1
+669-54-1719,B,Abuja,Member,Male,Electronic accessories,6814.8,6,2044.4399999999998,42933.24,2/10/2019,12:45,Card,40888.8,4.7619047619999995,2044.4399999999998,8.1
+616-24-2851,B,Abuja,Member,Female,Fashion accessories,6433.200000000001,4,1286.6399999999999,27019.440000000002,3/22/2019,14:42,Epay,25732.800000000003,4.7619047619999995,1286.6399999999999,6.5
+242-55-6721,B,Abuja,Normal,Male,Home and lifestyle,5817.6,2,581.76,12216.96,3/7/2019,11:49,Epay,11635.2,4.7619047619999995,581.76,6.5
+347-34-2234,B,Abuja,Member,Female,Sports and travel,19825.2,9,8921.34,187348.14,2/3/2019,13:40,Epay,178426.8,4.7619047619999995,8921.34,10.0
+853-23-2453,B,Abuja,Member,Male,Health and beauty,27266.399999999998,4,5453.28,114518.88,2/14/2019,14:35,Cash,109065.59999999999,4.7619047619999995,5453.28,7.6
+109-28-2512,B,Abuja,Member,Female,Fashion accessories,35139.6,6,10541.880000000001,221379.47999999998,1/7/2019,15:01,Epay,210837.59999999998,4.7619047619999995,10541.880000000001,9.9
+510-95-6347,B,Abuja,Member,Female,Food and beverages,17467.2,3,2620.08,55021.68,3/5/2019,18:17,Epay,52401.6,4.7619047619999995,2620.08,4.0
+847-38-7188,B,Abuja,Normal,Female,Food and beverages,34804.8,3,5220.72,109635.12000000001,1/26/2019,19:56,Epay,104414.40000000001,4.7619047619999995,5220.72,6.4
+318-68-5053,B,Abuja,Normal,Female,Health and beauty,27716.399999999998,6,8314.92,174613.31999999998,2/27/2019,17:55,Cash,166298.4,4.7619047619999995,8314.92,6.1
+152-08-9985,B,Abuja,Member,Male,Health and beauty,23169.6,9,10426.32,218952.72,3/12/2019,12:09,Card,208526.4,4.7619047619999995,10426.32,8.6
+766-85-7061,B,Abuja,Normal,Male,Health and beauty,31633.2,10,15816.6,332148.6,3/29/2019,10:25,Epay,316332.0,4.7619047619999995,15816.6,5.1
+733-01-9107,B,Abuja,Normal,Male,Home and lifestyle,29772.0,6,8931.6,187563.6,3/5/2019,18:14,Cash,178632.0,4.7619047619999995,8931.6,7.4
+716-39-1409,B,Abuja,Normal,Male,Health and beauty,10926.0,7,3824.1000000000004,80306.09999999999,3/19/2019,18:19,Cash,76482.0,4.7619047619999995,3824.1000000000004,8.0
+479-26-8945,B,Abuja,Member,Female,Sports and travel,5936.4,2,593.64,12466.439999999999,2/5/2019,11:32,Epay,11872.8,4.7619047619999995,593.64,4.6
+227-78-1148,B,Abuja,Normal,Female,Fashion accessories,26222.4,7,9177.84,192734.64,2/15/2019,12:44,Cash,183556.8,4.7619047619999995,9177.84,8.4
+291-32-1427,B,Abuja,Member,Male,Fashion accessories,7898.400000000001,5,1974.6000000000001,41466.6,3/5/2019,12:29,Epay,39492.0,4.7619047619999995,1974.6000000000001,5.3
+659-65-8956,B,Abuja,Member,Male,Fashion accessories,18489.6,1,924.48,19414.08,1/16/2019,15:26,Epay,18489.6,4.7619047619999995,924.48,5.2
+378-24-2715,B,Abuja,Normal,Male,Home and lifestyle,19238.399999999998,2,1923.8399999999997,40400.64,1/20/2019,20:38,Epay,38476.799999999996,4.7619047619999995,1923.8399999999997,4.1
+219-22-9386,B,Abuja,Member,Male,Sports and travel,35985.6,9,16193.52,340063.92,3/9/2019,17:26,Card,323870.4,4.7619047619999995,16193.52,4.2
+268-27-6179,B,Abuja,Member,Female,Fashion accessories,20329.2,8,8131.68,170765.28,3/9/2019,14:57,Epay,162633.6,4.7619047619999995,8131.68,7.3
+549-84-7482,B,Abuja,Normal,Female,Sports and travel,32500.8,9,14625.359999999999,307132.56,2/8/2019,11:15,Epay,292507.2,4.7619047619999995,14625.359999999999,7.2
+191-10-6171,B,Abuja,Normal,Female,Fashion accessories,14263.199999999999,7,4992.12,104834.52,1/25/2019,13:18,Cash,99842.4,4.7619047619999995,4992.12,7.5
+695-51-0018,B,Abuja,Normal,Female,Sports and travel,12542.400000000001,4,2508.48,52678.08,2/10/2019,18:36,Cash,50169.600000000006,4.7619047619999995,2508.48,7.4
+590-83-4591,B,Abuja,Member,Male,Electronic accessories,31482.0,6,9444.6,198336.59999999998,2/17/2019,14:40,Card,188892.00000000003,4.7619047619999995,9444.6,8.8
+241-72-9525,B,Abuja,Normal,Male,Sports and travel,18687.6,10,9343.8,196219.8,2/16/2019,12:21,Cash,186876.0,4.7619047619999995,9343.8,8.2
+262-47-2794,B,Abuja,Member,Male,Home and lifestyle,25869.6,8,10347.84,217304.64,3/6/2019,15:07,Card,206956.8,4.7619047619999995,10347.84,6.2
+608-96-3517,B,Abuja,Member,Female,Fashion accessories,32954.4,4,6590.88,138408.48,3/23/2019,19:20,Card,131817.6,4.7619047619999995,6590.88,4.8
+279-74-2924,B,Abuja,Member,Male,Electronic accessories,25981.2,1,1299.06,27280.26,1/4/2019,19:40,Cash,25981.2,4.7619047619999995,1299.06,6.1
+307-85-2293,B,Abuja,Normal,Male,Home and lifestyle,18100.8,5,4525.2,95029.20000000001,3/7/2019,13:58,Epay,90504.0,4.7619047619999995,4525.2,9.7
+743-04-1105,B,Abuja,Member,Male,Health and beauty,34999.2,9,15749.639999999998,330742.44,3/30/2019,14:43,Epay,314992.8,4.7619047619999995,15749.639999999998,6.0
+423-57-2993,B,Abuja,Normal,Male,Sports and travel,33620.4,6,10086.119999999999,211808.52,3/27/2019,19:18,Epay,201722.40000000002,4.7619047619999995,10086.119999999999,10.0
+548-46-9322,B,Abuja,Normal,Male,Food and beverages,14364.0,10,7182.0,150822.0,2/20/2019,15:24,Card,143640.0,4.7619047619999995,7182.0,5.9
+505-02-0892,B,Abuja,Member,Male,Health and beauty,15325.2,8,6130.08,128731.68000000001,2/25/2019,14:12,Epay,122601.6,4.7619047619999995,6130.08,5.6
+316-55-4634,B,Abuja,Member,Male,Food and beverages,28818.0,5,7204.5,151294.5,1/26/2019,12:45,Card,144090.0,4.7619047619999995,7204.5,9.4
+608-27-6295,B,Abuja,Member,Male,Electronic accessories,19040.4,6,5712.12,119954.52,1/19/2019,17:34,Card,114242.4,4.7619047619999995,5712.12,9.8
+414-12-7047,B,Abuja,Normal,Male,Food and beverages,7124.4,8,2849.76,59844.96,1/18/2019,12:04,Epay,56995.2,4.7619047619999995,2849.76,8.7
+895-66-0685,B,Abuja,Member,Male,Food and beverages,6508.799999999999,3,976.3199999999999,20502.72,3/5/2019,19:46,Epay,19526.4,4.7619047619999995,976.3199999999999,8.0
+305-14-0245,B,Abuja,Member,Female,Home and lifestyle,34016.4,8,13606.56,285737.76,3/3/2019,19:00,Epay,272131.2,4.7619047619999995,13606.56,7.5
+732-04-5373,B,Abuja,Member,Male,Home and lifestyle,16729.2,4,3345.84,70262.64,2/8/2019,10:53,Cash,66916.8,4.7619047619999995,3345.84,7.0
+284-34-9626,B,Abuja,Normal,Female,Home and lifestyle,27734.4,3,4160.16,87363.36,2/11/2019,10:39,Card,83203.2,4.7619047619999995,4160.16,7.2
+437-58-8131,B,Abuja,Normal,Female,Fashion accessories,26467.199999999997,2,2646.7200000000003,55581.119999999995,1/15/2019,13:41,Epay,52934.399999999994,4.7619047619999995,2646.7200000000003,4.6
+641-43-2399,B,Abuja,Normal,Male,Home and lifestyle,9198.0,4,1839.6000000000001,38631.6,1/26/2019,20:23,Epay,36792.0,4.7619047619999995,1839.6000000000001,5.7
+542-41-0513,B,Abuja,Member,Female,Electronic accessories,20696.4,4,4139.28,86924.88,3/15/2019,11:57,Cash,82785.6,4.7619047619999995,4139.28,6.6
+875-46-5808,B,Abuja,Member,Male,Health and beauty,9324.0,10,4662.0,97902.0,2/6/2019,14:51,Epay,93240.0,4.7619047619999995,4662.0,8.7
+394-43-4238,B,Abuja,Member,Male,Home and lifestyle,6397.2,5,1599.3,33585.3,2/15/2019,12:42,Card,31985.999999999996,4.7619047619999995,1599.3,5.4
+573-58-9734,B,Abuja,Normal,Female,Fashion accessories,10933.2,3,1639.98,34439.579999999994,3/28/2019,13:41,Epay,32799.6,4.7619047619999995,1639.98,5.1
+817-69-8206,B,Abuja,Normal,Female,Electronic accessories,35902.8,9,16156.26,339281.45999999996,3/2/2019,19:42,Card,323125.2,4.7619047619999995,16156.26,6.5
+142-63-6033,B,Abuja,Normal,Male,Home and lifestyle,33249.6,5,8312.4,174560.4,3/20/2019,19:17,Epay,166248.0,4.7619047619999995,8312.4,4.9
+656-16-1063,B,Abuja,Normal,Male,Sports and travel,16711.2,3,2506.68,52640.27999999999,1/4/2019,13:24,Card,50133.6,4.7619047619999995,2506.68,4.4
+891-58-8335,B,Abuja,Member,Female,Sports and travel,10659.6,7,3730.86,78348.06,3/11/2019,15:53,Cash,74617.2,4.7619047619999995,3730.86,6.5
+560-30-5617,B,Abuja,Normal,Female,Sports and travel,8917.2,5,2229.3,46815.299999999996,3/24/2019,18:27,Cash,44586.0,4.7619047619999995,2229.3,8.5
+549-03-9315,B,Abuja,Normal,Male,Fashion accessories,34153.200000000004,8,13661.28,286886.88,2/12/2019,12:58,Epay,273225.60000000003,4.7619047619999995,13661.28,8.7
+790-29-1172,B,Abuja,Normal,Female,Food and beverages,20642.4,3,3096.36,65023.55999999999,3/10/2019,18:59,Card,61927.200000000004,4.7619047619999995,3096.36,7.9
+239-36-3640,B,Abuja,Normal,Male,Electronic accessories,16326.0,6,4897.8,102853.79999999999,1/31/2019,13:44,Epay,97956.00000000001,4.7619047619999995,4897.8,6.1
+468-01-2051,B,Abuja,Normal,Male,Food and beverages,22348.8,7,7822.080000000001,164263.68,3/6/2019,13:46,Epay,156441.6,4.7619047619999995,7822.080000000001,5.4
+836-82-5858,B,Abuja,Member,Male,Health and beauty,24973.2,9,11237.94,235996.74000000002,1/26/2019,19:14,Epay,224758.80000000002,4.7619047619999995,11237.94,4.0
+466-61-5506,B,Abuja,Member,Female,Electronic accessories,32652.0,6,9795.6,205707.59999999998,2/26/2019,10:52,Cash,195912.00000000003,4.7619047619999995,9795.6,5.3
+289-65-5721,B,Abuja,Normal,Female,Fashion accessories,29293.2,2,2929.32,61515.72,1/26/2019,19:28,Cash,58586.4,4.7619047619999995,2929.32,6.5
+545-46-3100,B,Abuja,Member,Female,Electronic accessories,3812.4,3,571.86,12009.06,3/12/2019,13:52,Card,11437.2,4.7619047619999995,571.86,8.7
+418-02-5978,B,Abuja,Normal,Female,Health and beauty,30272.4,9,13622.58,286074.18,2/11/2019,10:54,Cash,272451.6,4.7619047619999995,13622.58,8.0
+269-04-5750,B,Abuja,Member,Male,Fashion accessories,26575.199999999997,4,5315.04,111615.84000000001,2/21/2019,18:31,Cash,106300.79999999999,4.7619047619999995,5315.04,6.7
+346-84-3103,B,Abuja,Member,Female,Electronic accessories,4759.2,5,1189.8,24985.8,3/2/2019,19:26,Cash,23795.999999999996,4.7619047619999995,1189.8,4.3
+376-02-8238,B,Abuja,Normal,Male,Home and lifestyle,33793.200000000004,8,13517.28,283862.88,2/2/2019,18:42,Card,270345.60000000003,4.7619047619999995,13517.28,8.3
+866-05-7563,B,Abuja,Member,Female,Electronic accessories,29304.000000000004,3,4395.6,92307.6,2/9/2019,19:43,Cash,87912.0,4.7619047619999995,4395.6,4.8
+785-13-7708,B,Abuja,Normal,Male,Food and beverages,26301.600000000002,7,9205.56,193316.76,1/14/2019,19:06,Card,184111.2,4.7619047619999995,9205.56,4.2
+845-51-0542,B,Abuja,Member,Male,Food and beverages,16758.0,9,7541.1,158363.1,2/2/2019,15:34,Epay,150822.0,4.7619047619999995,7541.1,6.4
+110-48-7033,B,Abuja,Member,Male,Fashion accessories,11743.199999999999,4,2348.64,49321.439999999995,1/29/2019,14:12,Cash,46972.799999999996,4.7619047619999995,2348.64,9.0
+655-85-5130,B,Abuja,Member,Female,Fashion accessories,13787.999999999998,4,2757.6,57909.600000000006,3/13/2019,19:22,Cash,55151.99999999999,4.7619047619999995,2757.6,5.7
+154-74-7179,B,Abuja,Normal,Male,Sports and travel,19602.0,1,980.1,20582.1,2/26/2019,19:24,Epay,19602.0,4.7619047619999995,980.1,7.9
+571-94-0759,B,Abuja,Member,Female,Food and beverages,26855.999999999996,10,13427.999999999998,281988.0,1/8/2019,20:55,Cash,268560.0,4.7619047619999995,13427.999999999998,9.5
+783-09-1637,B,Abuja,Normal,Female,Sports and travel,24274.800000000003,5,6068.700000000001,127442.7,3/6/2019,18:13,Epay,121373.99999999999,4.7619047619999995,6068.700000000001,6.3
+477-24-6490,B,Abuja,Normal,Female,Health and beauty,35895.6,6,10768.68,226142.28,2/26/2019,16:52,Epay,215373.6,4.7619047619999995,10768.68,7.9
+566-19-5475,B,Abuja,Normal,Male,Fashion accessories,17269.2,7,6044.22,126928.62,1/7/2019,20:52,Cash,120884.40000000001,4.7619047619999995,6044.22,6.2
+498-41-1961,B,Abuja,Normal,Male,Health and beauty,24004.800000000003,5,6001.200000000001,126025.2,2/20/2019,18:01,Cash,120023.99999999999,4.7619047619999995,6001.200000000001,7.6
+283-79-9594,B,Abuja,Normal,Female,Food and beverages,17463.6,7,6112.26,128357.45999999999,1/25/2019,13:30,Card,122245.2,4.7619047619999995,6112.26,5.2
+139-20-0155,B,Abuja,Member,Male,Electronic accessories,14507.999999999998,10,7253.999999999999,152334.0,1/24/2019,17:37,Card,145080.0,4.7619047619999995,7253.999999999999,7.0
+585-03-5943,B,Abuja,Normal,Male,Health and beauty,6519.599999999999,10,3259.7999999999997,68455.8,3/13/2019,11:46,Epay,65196.0,4.7619047619999995,3259.7999999999997,5.9
+573-10-3877,B,Abuja,Member,Male,Health and beauty,14043.599999999999,1,702.18,14745.78,3/12/2019,16:46,Card,14043.599999999999,4.7619047619999995,702.18,4.7
+396-90-2219,B,Abuja,Normal,Female,Electronic accessories,5385.6,8,2154.24,45239.04,2/23/2019,12:29,Cash,43084.8,4.7619047619999995,2154.24,8.6
+532-59-7201,B,Abuja,Member,Male,Sports and travel,28774.800000000003,6,8632.44,181281.24000000002,1/31/2019,14:04,Cash,172648.8,4.7619047619999995,8632.44,5.5
+276-54-0879,B,Abuja,Normal,Male,Sports and travel,35186.4,4,7037.28,147782.88,3/12/2019,19:53,Epay,140745.6,4.7619047619999995,7037.28,6.4
+730-61-8757,B,Abuja,Member,Male,Health and beauty,18406.8,4,3681.36,77308.56,1/25/2019,10:11,Card,73627.2,4.7619047619999995,3681.36,4.0
+868-81-1752,B,Abuja,Normal,Male,Home and lifestyle,7927.2,9,3567.24,74912.04000000001,2/7/2019,18:48,Cash,71344.8,4.7619047619999995,3567.24,6.8
+692-27-8933,B,Abuja,Normal,Female,Sports and travel,20862.0,6,6258.6,131430.6,2/24/2019,13:02,Cash,125172.0,4.7619047619999995,6258.6,5.2
+374-17-3652,B,Abuja,Member,Female,Food and beverages,15415.2,9,6936.840000000001,145673.64,2/5/2019,15:26,Card,138736.8,4.7619047619999995,6936.840000000001,8.9
+378-07-7001,B,Abuja,Member,Male,Electronic accessories,17312.4,3,2596.86,54534.06,2/10/2019,18:23,Card,51937.200000000004,4.7619047619999995,2596.86,7.8
+433-75-6987,B,Abuja,Member,Female,Health and beauty,20149.2,7,7052.22,148096.62,3/5/2019,19:06,Epay,141044.4,4.7619047619999995,7052.22,8.9
+873-95-4984,B,Abuja,Member,Female,Health and beauty,27684.000000000004,7,9689.4,203477.40000000002,2/15/2019,20:21,Cash,193787.99999999997,4.7619047619999995,9689.4,7.7
+400-45-1220,B,Abuja,Normal,Female,Health and beauty,4860.0,10,2430.0,51030.0,2/27/2019,11:06,Card,48600.0,4.7619047619999995,2430.0,4.8
+115-99-4379,B,Abuja,Member,Female,Fashion accessories,19702.8,7,6895.98,144815.58,3/14/2019,19:02,Card,137919.6,4.7619047619999995,6895.98,8.5
+565-67-6697,B,Abuja,Member,Male,Home and lifestyle,9720.0,9,4374.0,91854.0,3/2/2019,14:16,Cash,87480.0,4.7619047619999995,4374.0,4.8
+889-04-9723,B,Abuja,Member,Female,Food and beverages,32090.4,4,6418.08,134779.68000000002,1/7/2019,12:20,Card,128361.6,4.7619047619999995,6418.08,7.8
+453-63-6187,B,Abuja,Normal,Male,Electronic accessories,9900.0,3,1485.0,31185.0,3/1/2019,15:40,Epay,29700.0,4.7619047619999995,1485.0,6.5
+578-80-7669,B,Abuja,Normal,Male,Sports and travel,26989.2,1,1349.46,28338.660000000003,3/16/2019,16:58,Cash,26989.2,4.7619047619999995,1349.46,5.6
+201-86-2184,B,Abuja,Member,Female,Electronic accessories,9453.6,7,3308.76,69483.95999999999,2/2/2019,19:40,Cash,66175.2,4.7619047619999995,3308.76,9.9
+261-12-8671,B,Abuja,Normal,Female,Fashion accessories,21945.6,2,2194.56,46085.759999999995,1/25/2019,19:39,Card,43891.2,4.7619047619999995,2194.56,4.9
+843-01-4703,B,Abuja,Member,Female,Home and lifestyle,12736.800000000001,9,5731.5599999999995,120362.76000000001,1/5/2019,19:50,Card,114631.20000000001,4.7619047619999995,5731.5599999999995,9.6
+182-69-8360,B,Abuja,Normal,Female,Electronic accessories,8514.0,4,1702.8000000000002,35758.8,1/30/2019,13:32,Card,34056.0,4.7619047619999995,1702.8000000000002,4.0
+868-52-7573,B,Abuja,Normal,Female,Food and beverages,35888.4,5,8972.1,188414.09999999998,1/14/2019,12:09,Cash,179442.0,4.7619047619999995,8972.1,9.9
+525-88-7307,B,Abuja,Member,Male,Sports and travel,27295.199999999997,1,1364.7600000000002,28659.960000000003,1/31/2019,13:19,Cash,27295.199999999997,4.7619047619999995,1364.7600000000002,5.8
+596-42-3999,B,Abuja,Normal,Male,Food and beverages,6559.2,7,2295.7200000000003,48210.12,3/10/2019,14:04,Card,45914.4,4.7619047619999995,2295.7200000000003,6.6
+173-82-9529,B,Abuja,Normal,Female,Fashion accessories,13662.000000000002,10,6831.000000000001,143451.0,1/26/2019,14:51,Cash,136620.0,4.7619047619999995,6831.000000000001,9.7
+760-54-1821,B,Abuja,Normal,Male,Home and lifestyle,4892.4,9,2201.58,46233.18,3/15/2019,10:26,Cash,44031.6,4.7619047619999995,2201.58,5.8
+793-10-3222,B,Abuja,Member,Female,Health and beauty,14781.6,6,4434.48,93124.08,3/5/2019,13:30,Card,88689.6,4.7619047619999995,4434.48,8.3
+346-12-3257,B,Abuja,Member,Male,Electronic accessories,6926.4,9,3116.8799999999997,65454.479999999996,3/4/2019,16:28,Cash,62337.6,4.7619047619999995,3116.8799999999997,8.0
+831-64-0259,B,Abuja,Normal,Female,Fashion accessories,14310.0,5,3577.5,75127.5,2/22/2019,10:43,Epay,71550.0,4.7619047619999995,3577.5,9.6
+725-32-9708,B,Abuja,Member,Female,Fashion accessories,24735.6,4,4947.12,103889.52,1/4/2019,19:01,Cash,98942.4,4.7619047619999995,4947.12,4.1
+244-08-0162,B,Abuja,Normal,Female,Health and beauty,12315.6,10,6157.8,129313.79999999999,1/2/2019,13:00,Cash,123156.00000000001,4.7619047619999995,6157.8,5.1
+569-71-4390,B,Abuja,Normal,Male,Sports and travel,7873.200000000001,2,787.3199999999999,16533.72,1/25/2019,14:29,Epay,15746.400000000001,4.7619047619999995,787.3199999999999,6.9
+268-03-6164,B,Abuja,Normal,Male,Health and beauty,34599.6,1,1729.98,36329.579999999994,1/25/2019,16:28,Epay,34599.6,4.7619047619999995,1729.98,7.8
+848-07-1692,B,Abuja,Normal,Female,Health and beauty,20599.2,2,2059.92,43258.32,1/12/2019,17:13,Epay,41198.4,4.7619047619999995,2059.92,8.3
+301-81-8610,B,Abuja,Member,Male,Fashion accessories,9151.2,8,3660.4799999999996,76870.08,3/19/2019,19:42,Card,73209.6,4.7619047619999995,3660.4799999999996,6.7
+198-84-7132,B,Abuja,Member,Male,Fashion accessories,14619.6,9,6578.82,138155.22,1/2/2019,13:40,Cash,131576.4,4.7619047619999995,6578.82,7.0
+650-98-6268,B,Abuja,Member,Female,Food and beverages,7513.200000000001,3,1126.98,23666.579999999998,3/20/2019,13:53,Card,22539.6,4.7619047619999995,1126.98,8.0
+741-73-3559,B,Abuja,Normal,Male,Sports and travel,24217.199999999997,5,6054.299999999999,127140.3,2/27/2019,17:27,Cash,121086.00000000001,4.7619047619999995,6054.299999999999,6.9
+286-75-7818,B,Abuja,Normal,Male,Fashion accessories,24868.8,2,2486.8799999999997,52224.479999999996,1/31/2019,19:48,Card,49737.6,4.7619047619999995,2486.8799999999997,6.9
+616-87-0016,B,Abuja,Normal,Male,Fashion accessories,34394.4,7,12038.04,252798.83999999997,3/9/2019,14:36,Card,240760.8,4.7619047619999995,12038.04,9.6
+837-55-7229,B,Abuja,Normal,Female,Fashion accessories,17078.399999999998,1,853.92,17932.32,2/22/2019,18:19,Card,17078.399999999998,4.7619047619999995,853.92,6.8
+394-30-3170,B,Abuja,Member,Female,Sports and travel,31834.800000000003,8,12733.92,267412.32,3/22/2019,19:35,Card,254678.40000000002,4.7619047619999995,12733.92,4.3
+390-80-5128,B,Abuja,Member,Female,Health and beauty,6893.999999999999,1,344.7,7238.700000000001,1/28/2019,17:58,Card,6893.999999999999,4.7619047619999995,344.7,9.5
+296-11-7041,B,Abuja,Member,Female,Health and beauty,9745.2,1,487.26,10232.460000000001,1/12/2019,20:07,Card,9745.2,4.7619047619999995,487.26,5.3
+449-27-2918,B,Abuja,Member,Female,Sports and travel,14083.199999999999,1,704.16,14787.36,3/26/2019,11:02,Card,14083.199999999999,4.7619047619999995,704.16,9.6
+891-01-7034,B,Abuja,Normal,Female,Electronic accessories,26895.6,6,8068.68,169442.28,1/1/2019,19:07,Cash,161373.6,4.7619047619999995,8068.68,6.7
+744-09-5786,B,Abuja,Normal,Male,Electronic accessories,7923.6,6,2377.08,49918.67999999999,1/2/2019,18:50,Cash,47541.6,4.7619047619999995,2377.08,7.6
+767-54-1907,B,Abuja,Member,Female,Fashion accessories,10641.6,5,2660.4,55868.4,2/13/2019,16:59,Cash,53208.00000000001,4.7619047619999995,2660.4,6.9
+710-46-4433,B,Abuja,Member,Female,Food and beverages,27864.000000000004,9,12538.8,263314.8,2/15/2019,14:15,Card,250776.0,4.7619047619999995,12538.8,4.5
+533-33-5337,B,Abuja,Normal,Male,Electronic accessories,28580.4,10,14290.2,300094.2,2/7/2019,20:24,Cash,285804.0,4.7619047619999995,14290.2,6.2
+234-03-4040,B,Abuja,Member,Female,Food and beverages,26298.0,10,13149.0,276129.0,3/3/2019,12:25,Card,262980.0,4.7619047619999995,13149.0,8.7
+554-53-3790,B,Abuja,Normal,Male,Sports and travel,13327.2,6,3998.16,83961.36,3/22/2019,18:33,Cash,79963.2,4.7619047619999995,3998.16,4.5
+731-59-7531,B,Abuja,Member,Male,Health and beauty,26125.199999999997,8,10450.08,219451.68000000002,3/30/2019,17:58,Cash,209001.59999999998,4.7619047619999995,10450.08,4.6
+701-69-8742,B,Abuja,Normal,Male,Sports and travel,12373.199999999999,10,6186.599999999999,129918.59999999999,3/16/2019,10:11,Epay,123732.0,4.7619047619999995,6186.599999999999,6.7
+305-18-3552,B,Abuja,Member,Male,Home and lifestyle,21736.8,10,10868.4,228236.4,2/12/2019,16:19,Cash,217367.99999999997,4.7619047619999995,10868.4,6.0
+438-01-4015,B,Abuja,Member,Female,Sports and travel,17816.4,4,3563.2799999999997,74828.88,3/21/2019,15:25,Epay,71265.6,4.7619047619999995,3563.2799999999997,6.6
+709-58-4068,B,Abuja,Normal,Female,Fashion accessories,14792.400000000001,10,7396.200000000001,155320.2,2/28/2019,14:42,Cash,147924.0,4.7619047619999995,7396.200000000001,7.3
+627-95-3243,B,Abuja,Member,Female,Home and lifestyle,27964.800000000003,9,12584.159999999998,264267.36,2/4/2019,13:21,Epay,251683.2,4.7619047619999995,12584.159999999998,9.8
+686-41-0932,B,Abuja,Normal,Female,Fashion accessories,12492.000000000002,2,1249.2,26233.2,3/13/2019,19:48,Epay,24984.000000000004,4.7619047619999995,1249.2,8.2
+608-04-3797,B,Abuja,Member,Female,Health and beauty,9115.2,8,3646.08,76567.68,3/5/2019,20:24,Epay,72921.6,4.7619047619999995,3646.08,8.7
+437-53-3084,B,Abuja,Normal,Male,Fashion accessories,35960.4,2,3596.0399999999995,75516.84,2/26/2019,11:48,Epay,71920.8,4.7619047619999995,3596.0399999999995,7.1
+632-32-4574,B,Abuja,Normal,Male,Sports and travel,27331.2,8,10932.480000000001,229582.08000000002,3/20/2019,14:14,Cash,218649.6,4.7619047619999995,10932.480000000001,5.5
+401-18-8016,B,Abuja,Member,Female,Sports and travel,35326.799999999996,1,1766.3400000000001,37093.14,1/21/2019,17:36,Cash,35326.799999999996,4.7619047619999995,1766.3400000000001,8.9
+277-63-2961,B,Abuja,Member,Male,Sports and travel,26629.2,1,1331.46,27960.659999999996,2/3/2019,15:53,Card,26629.2,4.7619047619999995,1331.46,5.4
+282-35-2475,B,Abuja,Normal,Female,Sports and travel,33591.6,2,3359.16,70542.36,3/25/2019,17:53,Cash,67183.2,4.7619047619999995,3359.16,6.3
+511-54-3087,B,Abuja,Normal,Male,Sports and travel,31842.0,1,1592.1000000000001,33434.1,2/25/2019,16:36,Card,31842.0,4.7619047619999995,1592.1000000000001,9.5
+387-49-4215,B,Abuja,Member,Female,Sports and travel,17460.0,3,2619.0,54999.0,1/8/2019,12:50,Cash,52380.0,4.7619047619999995,2619.0,6.7
+862-17-9201,B,Abuja,Normal,Female,Food and beverages,30258.0,6,9077.4,190625.4,1/29/2019,10:48,Card,181548.0,4.7619047619999995,9077.4,7.7
+291-21-5991,B,Abuja,Member,Male,Health and beauty,22064.4,5,5516.1,115838.09999999999,3/29/2019,14:28,Cash,110322.0,4.7619047619999995,5516.1,7.0
+347-72-6115,B,Abuja,Member,Female,Sports and travel,32666.399999999998,7,11433.24,240098.03999999998,1/16/2019,18:03,Card,228664.8,4.7619047619999995,11433.24,6.2
+425-85-2085,B,Abuja,Member,Male,Health and beauty,19749.6,5,4937.4,103685.4,3/29/2019,16:48,Epay,98748.0,4.7619047619999995,4937.4,9.8
+734-91-1155,B,Abuja,Normal,Female,Electronic accessories,16455.6,3,2468.3399999999997,51835.14,3/26/2019,10:34,Card,49366.799999999996,4.7619047619999995,2468.3399999999997,7.7
+286-62-6248,B,Abuja,Normal,Male,Fashion accessories,14115.6,4,2823.1200000000003,59285.520000000004,1/16/2019,20:03,Card,56462.4,4.7619047619999995,2823.1200000000003,9.0
+339-38-9982,B,Abuja,Member,Male,Fashion accessories,21549.6,2,2154.96,45254.16,1/13/2019,14:55,Epay,43099.2,4.7619047619999995,2154.96,6.7
+827-44-5872,B,Abuja,Member,Female,Food and beverages,19569.6,10,9784.8,205480.8,2/7/2019,11:28,Card,195696.0,4.7619047619999995,9784.8,6.1
+857-67-9057,B,Abuja,Normal,Male,Electronic accessories,8262.0,10,4131.0,86751.0,2/6/2019,19:20,Epay,82620.0,4.7619047619999995,4131.0,8.2
+339-12-4827,B,Abuja,Member,Female,Fashion accessories,26625.6,1,1331.28,27956.88,1/5/2019,11:32,Card,26625.6,4.7619047619999995,1331.28,5.0
+173-50-1108,B,Abuja,Member,Female,Sports and travel,7264.8,4,1452.9600000000003,30512.16,2/13/2019,12:14,Card,29059.2,4.7619047619999995,1452.9600000000003,5.0
+841-18-8232,B,Abuja,Normal,Female,Food and beverages,25632.0,1,1281.6,26913.600000000002,1/5/2019,20:40,Card,25632.0,4.7619047619999995,1281.6,9.2
+701-23-5550,B,Abuja,Member,Male,Home and lifestyle,13971.6,4,2794.32,58680.72,3/19/2019,13:40,Epay,55886.4,4.7619047619999995,2794.32,4.9
+539-21-7227,B,Abuja,Normal,Female,Sports and travel,18554.4,5,4638.6,97410.59999999999,1/26/2019,17:45,Cash,92772.0,4.7619047619999995,4638.6,4.2
+747-58-7183,B,Abuja,Normal,Male,Fashion accessories,20617.2,3,3092.5800000000004,64944.18,2/9/2019,20:31,Epay,61851.6,4.7619047619999995,3092.5800000000004,6.5
+582-52-8065,B,Abuja,Normal,Female,Fashion accessories,19551.600000000002,9,8798.22,184762.62000000002,2/22/2019,10:49,Cash,175964.4,4.7619047619999995,8798.22,8.9
+210-57-1719,B,Abuja,Normal,Female,Health and beauty,20966.4,9,9434.880000000001,198132.48,2/5/2019,12:34,Cash,188697.59999999998,4.7619047619999995,9434.880000000001,9.7
+356-44-8813,B,Abuja,Normal,Male,Home and lifestyle,13492.8,3,2023.9200000000003,42502.32000000001,1/20/2019,13:45,Card,40478.4,4.7619047619999995,2023.9200000000003,7.7
+198-66-9832,B,Abuja,Member,Female,Fashion accessories,25934.4,2,2593.44,54462.24,2/4/2019,19:38,Cash,51868.8,4.7619047619999995,2593.44,9.5
+174-75-0888,B,Abuja,Normal,Male,Electronic accessories,7768.799999999999,9,3495.96,73415.15999999999,3/14/2019,12:32,Cash,69919.2,4.7619047619999995,3495.96,7.3
+134-54-4720,B,Abuja,Normal,Female,Electronic accessories,15271.2,8,6108.48,128278.08000000002,1/30/2019,13:58,Epay,122169.6,4.7619047619999995,6108.48,5.7
+514-37-2845,B,Abuja,Normal,Male,Fashion accessories,35730.0,2,3573.0000000000005,75033.0,3/20/2019,13:02,Cash,71460.0,4.7619047619999995,3573.0000000000005,9.0
+241-11-2261,B,Abuja,Normal,Female,Fashion accessories,28749.6,7,10062.36,211309.56,1/10/2019,10:33,Card,201247.19999999998,4.7619047619999995,10062.36,5.5
+834-83-1826,B,Abuja,Member,Female,Home and lifestyle,29534.4,5,7383.6,155055.6,2/25/2019,17:16,Card,147672.0,4.7619047619999995,7383.6,7.6
+343-61-3544,B,Abuja,Member,Male,Sports and travel,9601.2,10,4800.6,100812.6,1/29/2019,11:48,Cash,96012.0,4.7619047619999995,4800.6,8.6
+355-34-6244,B,Abuja,Normal,Male,Food and beverages,26060.4,2,2606.04,54726.840000000004,1/13/2019,19:55,Card,52120.8,4.7619047619999995,2606.04,8.1
+339-96-8318,B,Abuja,Member,Male,Fashion accessories,29271.600000000002,7,10245.06,215146.26,3/1/2019,19:49,Epay,204901.19999999998,4.7619047619999995,10245.06,6.3
+458-61-0011,B,Abuja,Normal,Male,Food and beverages,21708.0,4,4341.6,91173.59999999999,2/20/2019,18:43,Cash,86832.0,4.7619047619999995,4341.6,5.8
+207-73-1363,B,Abuja,Normal,Male,Health and beauty,25023.600000000002,2,2502.36,52549.56,3/1/2019,12:15,Epay,50047.200000000004,4.7619047619999995,2502.36,8.1
+359-90-3665,B,Abuja,Member,Female,Fashion accessories,6508.799999999999,4,1301.76,27336.960000000003,1/14/2019,18:03,Card,26035.199999999997,4.7619047619999995,1301.76,9.5
+375-72-3056,B,Abuja,Normal,Male,Sports and travel,22701.600000000002,3,3405.24,71510.04000000001,1/19/2019,15:58,Epay,68104.8,4.7619047619999995,3405.24,7.0
+585-11-6748,B,Abuja,Member,Male,Sports and travel,34848.0,3,5227.2,109771.20000000001,3/15/2019,13:05,Cash,104543.99999999999,4.7619047619999995,5227.2,5.3
+470-31-3286,B,Abuja,Normal,Male,Health and beauty,5335.2,3,800.2800000000001,16805.88,3/1/2019,11:30,Card,16005.6,4.7619047619999995,800.2800000000001,8.7
+642-30-6693,B,Abuja,Normal,Female,Sports and travel,19623.6,6,5887.079999999999,123628.68000000001,3/17/2019,13:54,Epay,117741.6,4.7619047619999995,5887.079999999999,7.8
+830-58-2383,B,Abuja,Normal,Male,Home and lifestyle,11430.0,4,2286.0,48006.0,2/8/2019,15:26,Cash,45720.0,4.7619047619999995,2286.0,8.6
+349-97-8902,B,Abuja,Member,Male,Food and beverages,20840.4,2,2084.04,43764.840000000004,1/17/2019,10:37,Epay,41680.8,4.7619047619999995,2084.04,8.9
+789-23-8625,B,Abuja,Member,Male,Fashion accessories,33559.2,3,5033.879999999999,105711.48,1/24/2019,11:45,Cash,100677.6,4.7619047619999995,5033.879999999999,7.2
+327-40-9673,B,Abuja,Member,Male,Sports and travel,26135.999999999996,6,7840.8,164656.8,1/13/2019,19:51,Cash,156816.0,4.7619047619999995,7840.8,6.9
+670-79-6321,B,Abuja,Member,Female,Home and lifestyle,34052.4,7,11918.339999999998,250285.13999999998,1/17/2019,15:27,Card,238366.8,4.7619047619999995,11918.339999999998,4.9
+852-62-7105,B,Abuja,Normal,Female,Fashion accessories,29970.0,10,14985.0,314685.0,1/12/2019,11:25,Card,299700.0,4.7619047619999995,14985.0,4.4
+598-06-7312,B,Abuja,Member,Male,Fashion accessories,32886.0,1,1644.3,34530.3,2/16/2019,15:42,Cash,32886.0,4.7619047619999995,1644.3,6.8
+135-13-8269,B,Abuja,Member,Female,Food and beverages,28396.8,2,2839.68,59633.28,1/26/2019,16:04,Cash,56793.6,4.7619047619999995,2839.68,9.1
+628-90-8624,B,Abuja,Member,Male,Health and beauty,29728.8,10,14864.4,312152.4,3/14/2019,14:41,Cash,297288.0,4.7619047619999995,14864.4,5.0
+420-04-7590,B,Abuja,Normal,Male,Home and lifestyle,35892.0,3,5383.8,113059.8,3/18/2019,11:29,Epay,107676.00000000001,4.7619047619999995,5383.8,4.7
+182-88-2763,B,Abuja,Member,Male,Food and beverages,28767.6,3,4315.139999999999,90617.94,3/20/2019,19:28,Card,86302.8,4.7619047619999995,4315.139999999999,5.0
+188-55-0967,B,Abuja,Member,Male,Health and beauty,23929.2,10,11964.6,251256.59999999998,1/15/2019,15:01,Card,239292.00000000003,4.7619047619999995,11964.6,5.0
+364-33-8584,B,Abuja,Member,Female,Food and beverages,6346.799999999999,5,1586.6999999999998,33320.700000000004,3/8/2019,15:27,Cash,31734.000000000004,4.7619047619999995,1586.6999999999998,8.5
+665-63-9737,B,Abuja,Normal,Male,Fashion accessories,18871.2,3,2830.68,59444.28,2/27/2019,17:36,Epay,56613.6,4.7619047619999995,2830.68,7.5
+695-09-5146,B,Abuja,Member,Female,Food and beverages,35564.4,3,5334.66,112027.85999999999,2/23/2019,20:00,Epay,106693.2,4.7619047619999995,5334.66,6.4
+794-32-2436,B,Abuja,Member,Male,Electronic accessories,20041.2,2,2004.1200000000001,42086.520000000004,3/27/2019,15:08,Epay,40082.4,4.7619047619999995,2004.1200000000001,6.0
+574-31-8277,B,Abuja,Member,Male,Fashion accessories,12106.800000000001,1,605.34,12712.140000000001,3/20/2019,19:55,Cash,12106.800000000001,4.7619047619999995,605.34,5.6
+369-82-2676,B,Abuja,Normal,Male,Electronic accessories,27237.6,5,6809.4,142997.4,1/15/2019,18:22,Epay,136188.0,4.7619047619999995,6809.4,7.8
+563-47-4072,B,Abuja,Normal,Female,Health and beauty,20091.600000000002,6,6027.48,126577.08,1/22/2019,11:52,Cash,120549.6,4.7619047619999995,6027.48,9.9
+149-15-7606,B,Abuja,Member,Male,Sports and travel,13435.2,9,6045.84,126962.64000000001,3/6/2019,15:31,Epay,120916.8,4.7619047619999995,6045.84,5.1
+133-77-3154,B,Abuja,Member,Male,Fashion accessories,21664.8,4,4332.96,90992.15999999999,2/16/2019,18:04,Card,86659.2,4.7619047619999995,4332.96,9.4
+322-02-2271,B,Abuja,Normal,Female,Sports and travel,15469.199999999999,3,2320.38,48727.98,2/3/2019,11:46,Cash,46407.6,4.7619047619999995,2320.38,9.3
+725-67-2480,B,Abuja,Member,Female,Fashion accessories,21150.0,6,6345.0,133245.0,3/24/2019,18:14,Card,126900.0,4.7619047619999995,6345.0,5.9
+779-42-2410,B,Abuja,Member,Male,Food and beverages,20786.4,3,3117.96,65477.15999999999,2/20/2019,13:06,Epay,62359.2,4.7619047619999995,3117.96,7.7
+190-14-3147,B,Abuja,Normal,Female,Health and beauty,6469.2,4,1293.84,27170.639999999996,2/23/2019,20:43,Epay,25876.8,4.7619047619999995,1293.84,6.4
+679-22-6530,B,Abuja,Normal,Female,Sports and travel,14623.199999999999,2,1462.3200000000002,30708.72,1/17/2019,10:01,Card,29246.399999999998,4.7619047619999995,1462.3200000000002,4.1
+642-61-4706,B,Abuja,Member,Male,Food and beverages,33624.0,2,3362.4,70610.4,3/30/2019,16:34,Cash,67248.0,4.7619047619999995,3362.4,5.5
+576-31-4774,B,Abuja,Normal,Female,Health and beauty,26427.6,3,3964.14,83246.94,3/2/2019,13:10,Epay,79282.8,4.7619047619999995,3964.14,4.0
+242-11-3142,B,Abuja,Member,Male,Fashion accessories,30157.199999999997,2,3015.7200000000003,63330.12,2/24/2019,19:57,Cash,60314.399999999994,4.7619047619999995,3015.7200000000003,4.6
+752-23-3760,B,Abuja,Member,Female,Sports and travel,23068.8,7,8074.08,169555.68000000002,2/19/2019,19:29,Card,161481.6,4.7619047619999995,8074.08,7.3
+528-87-5606,B,Abuja,Member,Female,Electronic accessories,14212.8,1,710.64,14923.439999999997,2/12/2019,19:43,Cash,14212.8,4.7619047619999995,710.64,6.5
+320-85-2052,B,Abuja,Normal,Female,Sports and travel,12531.6,1,626.5799999999999,13158.18,1/14/2019,10:11,Card,12531.6,4.7619047619999995,626.5799999999999,7.0
+510-79-0415,B,Abuja,Member,Female,Sports and travel,8308.8,6,2492.64,52345.44,1/24/2019,19:20,Epay,49852.799999999996,4.7619047619999995,2492.64,4.9
+241-96-5076,B,Abuja,Member,Female,Home and lifestyle,17676.0,2,1767.6000000000001,37119.6,1/8/2019,12:58,Card,35352.0,4.7619047619999995,1767.6000000000001,6.4
+767-97-4650,B,Abuja,Member,Female,Sports and travel,23338.8,2,2333.8799999999997,49011.48,1/8/2019,11:59,Card,46677.6,4.7619047619999995,2333.8799999999997,8.0
+826-58-8051,B,Abuja,Normal,Male,Home and lifestyle,22388.399999999998,4,4477.679999999999,94031.28,1/6/2019,19:46,Epay,89553.59999999999,4.7619047619999995,4477.679999999999,4.3
+176-64-7711,B,Abuja,Normal,Male,Food and beverages,11635.2,3,1745.28,36650.88,3/27/2019,19:11,Card,34905.6,4.7619047619999995,1745.28,4.3
+191-29-0321,B,Abuja,Member,Female,Fashion accessories,7117.2,10,3558.6,74730.6,2/27/2019,18:57,Card,71172.0,4.7619047619999995,3558.6,5.0
+729-06-2010,B,Abuja,Member,Male,Health and beauty,28969.2,9,13036.14,273758.94,1/6/2019,11:18,Cash,260722.80000000002,4.7619047619999995,13036.14,9.2
+640-48-5028,B,Abuja,Member,Female,Home and lifestyle,31820.4,9,14319.18,300702.77999999997,3/2/2019,12:40,Cash,286383.6,4.7619047619999995,14319.18,6.3
+186-79-9562,B,Abuja,Normal,Male,Health and beauty,25837.199999999997,7,9043.019999999999,189903.42,3/29/2019,14:06,Cash,180860.4,4.7619047619999995,9043.019999999999,8.9
+834-45-5519,B,Abuja,Normal,Female,Electronic accessories,15480.0,4,3096.0,65016.0,1/31/2019,20:48,Epay,61920.0,4.7619047619999995,3096.0,7.6
+821-14-9046,B,Abuja,Member,Female,Fashion accessories,6292.8,6,1887.84,39644.64,1/18/2019,15:04,Card,37756.799999999996,4.7619047619999995,1887.84,6.1
+418-05-0656,B,Abuja,Normal,Female,Fashion accessories,9201.6,7,3220.56,67631.76,2/2/2019,20:42,Cash,64411.2,4.7619047619999995,3220.56,7.1
+776-68-1096,B,Abuja,Normal,Male,Home and lifestyle,15883.199999999999,3,2382.48,50032.079999999994,3/18/2019,13:45,Card,47649.600000000006,4.7619047619999995,2382.48,7.9
+434-35-9162,B,Abuja,Member,Male,Food and beverages,8402.4,4,1680.48,35290.079999999994,2/4/2019,18:53,Epay,33609.6,4.7619047619999995,1680.48,7.4
+174-64-0215,B,Abuja,Normal,Male,Sports and travel,25106.399999999998,10,12553.199999999999,263617.2,3/5/2019,17:49,Card,251064.0,4.7619047619999995,12553.199999999999,8.9
+299-29-0180,B,Abuja,Member,Female,Home and lifestyle,18784.8,7,6574.679999999999,138068.28,3/9/2019,10:54,Cash,131493.6,4.7619047619999995,6574.679999999999,9.3
+438-23-1242,B,Abuja,Normal,Male,Electronic accessories,27316.8,7,9560.880000000001,200778.48000000004,1/24/2019,10:38,Epay,191217.59999999998,4.7619047619999995,9560.880000000001,8.9
+238-45-6950,B,Abuja,Member,Male,Food and beverages,19339.2,1,966.9600000000002,20306.160000000003,3/1/2019,20:03,Epay,19339.2,4.7619047619999995,966.9600000000002,6.4
+197-77-7132,B,Abuja,Member,Male,Electronic accessories,32961.6,8,13184.640000000001,276877.44,1/12/2019,18:22,Epay,263692.8,4.7619047619999995,13184.640000000001,6.0
+457-13-1708,B,Abuja,Member,Male,Fashion accessories,23482.800000000003,10,11741.400000000001,246569.4,1/8/2019,19:07,Card,234827.99999999997,4.7619047619999995,11741.400000000001,5.2
+487-79-6868,B,Abuja,Member,Female,Home and lifestyle,4424.4,9,1990.98,41810.58,3/26/2019,19:28,Card,39819.6,4.7619047619999995,1990.98,8.0
+210-30-7976,B,Abuja,Member,Female,Fashion accessories,8035.2,4,1607.0399999999997,33747.84,3/14/2019,11:16,Epay,32140.8,4.7619047619999995,1607.0399999999997,4.1
+775-72-1988,B,Abuja,Normal,Male,Home and lifestyle,26380.8,5,6595.2,138499.2,1/24/2019,15:05,Epay,131904.0,4.7619047619999995,6595.2,8.4
+785-96-0615,B,Abuja,Member,Female,Electronic accessories,12866.400000000001,8,5146.5599999999995,108077.76000000001,2/17/2019,15:28,Epay,102931.20000000001,4.7619047619999995,5146.5599999999995,4.9
+842-40-8179,B,Abuja,Member,Female,Food and beverages,27792.0,10,13896.0,291816.0,2/11/2019,10:38,Card,277920.0,4.7619047619999995,13896.0,5.6
+525-09-8450,B,Abuja,Normal,Male,Electronic accessories,25966.8,10,12983.4,272651.4,1/31/2019,15:12,Card,259667.99999999997,4.7619047619999995,12983.4,4.2
+593-14-4239,B,Abuja,Normal,Female,Home and lifestyle,34365.6,8,13746.24,288671.04,3/5/2019,19:40,Epay,274924.8,4.7619047619999995,13746.24,4.7
+388-76-2555,B,Abuja,Normal,Male,Sports and travel,4928.4,6,1478.52,31048.920000000006,2/13/2019,13:59,Cash,29570.4,4.7619047619999995,1478.52,6.3
+711-31-1234,B,Abuja,Normal,Female,Electronic accessories,34430.4,4,6886.08,144607.68000000002,3/16/2019,18:51,Cash,137721.6,4.7619047619999995,6886.08,7.9
+707-32-7409,B,Abuja,Member,Female,Sports and travel,34394.4,4,6878.88,144456.48,2/26/2019,11:58,Epay,137577.6,4.7619047619999995,6878.88,4.5
+120-54-2248,B,Abuja,Normal,Female,Food and beverages,10389.6,5,2597.4,54545.399999999994,1/22/2019,18:08,Card,51948.00000000001,4.7619047619999995,2597.4,8.0
+875-31-8302,B,Abuja,Normal,Male,Sports and travel,33616.799999999996,1,1680.84,35297.64,1/3/2019,13:07,Cash,33616.799999999996,4.7619047619999995,1680.84,9.6
+457-94-0464,B,Abuja,Member,Male,Electronic accessories,31633.2,9,14234.94,298933.74,1/31/2019,20:32,Epay,284698.8,4.7619047619999995,14234.94,5.6
+744-16-7898,B,Abuja,Normal,Female,Home and lifestyle,35053.200000000004,10,17526.600000000002,368058.6,1/15/2019,13:48,Card,350532.0,4.7619047619999995,17526.600000000002,4.9
+605-83-1050,B,Abuja,Normal,Male,Fashion accessories,9784.8,2,978.48,20548.08,3/15/2019,16:26,Epay,19569.6,4.7619047619999995,978.48,4.3
+359-94-5395,B,Abuja,Normal,Male,Health and beauty,33400.8,1,1670.0399999999997,35070.84,3/15/2019,10:50,Card,33400.8,4.7619047619999995,1670.0399999999997,9.8
+751-15-6198,B,Abuja,Normal,Male,Sports and travel,8283.6,6,2485.08,52186.68,1/12/2019,16:45,Epay,49701.6,4.7619047619999995,2485.08,7.9
+831-81-6575,B,Abuja,Member,Female,Electronic accessories,27212.4,9,12245.580000000002,257157.18000000002,2/23/2019,11:12,Cash,244911.59999999998,4.7619047619999995,12245.580000000002,8.0
+559-61-5987,B,Abuja,Normal,Female,Health and beauty,6390.0,1,319.5,6709.5,1/14/2019,10:38,Cash,6390.0,4.7619047619999995,319.5,8.6
+565-91-4567,B,Abuja,Normal,Male,Health and beauty,3870.0,8,1548.0,32508.0,3/15/2019,14:38,Epay,30960.0,4.7619047619999995,1548.0,6.2
+499-27-7781,B,Abuja,Normal,Female,Food and beverages,19155.6,8,7662.240000000001,160907.04,3/14/2019,16:45,Epay,153244.8,4.7619047619999995,7662.240000000001,5.0
+869-11-3082,B,Abuja,Member,Male,Health and beauty,34617.6,4,6923.5199999999995,145393.91999999998,1/27/2019,20:03,Card,138470.4,4.7619047619999995,6923.5199999999995,8.4
+190-59-3964,B,Abuja,Member,Male,Food and beverages,16977.6,5,4244.4,89132.4,2/3/2019,14:35,Card,84888.0,4.7619047619999995,4244.4,6.0
+366-43-6862,B,Abuja,Normal,Male,Electronic accessories,19040.4,4,3808.08,79969.68000000001,3/25/2019,16:32,Epay,76161.6,4.7619047619999995,3808.08,6.7
+109-86-4363,B,Abuja,Member,Female,Sports and travel,21628.8,7,7570.080000000001,158971.68000000002,2/14/2019,11:36,Card,151401.6,4.7619047619999995,7570.080000000001,4.5
+222-42-0244,B,Abuja,Member,Female,Health and beauty,25959.6,9,11681.82,245318.21999999997,1/28/2019,13:53,Card,233636.4,4.7619047619999995,11681.82,7.7
+636-17-0325,B,Abuja,Normal,Male,Health and beauty,22525.2,4,4505.04,94605.84000000001,2/25/2019,18:37,Cash,90100.8,4.7619047619999995,4505.04,9.5
+343-75-9322,B,Abuja,Member,Female,Sports and travel,4266.0,8,1706.4,35834.4,1/9/2019,16:34,Cash,34128.0,4.7619047619999995,1706.4,4.1
+427-45-9297,B,Abuja,Member,Female,Home and lifestyle,14662.8,7,5131.98,107771.58,3/12/2019,11:01,Epay,102639.6,4.7619047619999995,5131.98,5.4
+670-71-7306,B,Abuja,Normal,Male,Sports and travel,16066.800000000001,6,4820.04,101220.84000000001,1/2/2019,20:08,Card,96400.79999999999,4.7619047619999995,4820.04,5.1
+291-59-1384,B,Abuja,Normal,Male,Electronic accessories,21708.0,1,1085.4,22793.399999999998,2/28/2019,17:38,Cash,21708.0,4.7619047619999995,1085.4,6.0
+866-70-2814,B,Abuja,Normal,Female,Electronic accessories,19004.4,10,9502.2,199546.19999999998,2/25/2019,11:58,Epay,190044.0,4.7619047619999995,9502.2,10.0
+895-03-6665,B,Abuja,Normal,Female,Fashion accessories,13143.599999999999,9,5914.62,124207.02,2/16/2019,10:52,Cash,118292.4,4.7619047619999995,5914.62,4.2
+770-42-8960,B,Abuja,Normal,Male,Food and beverages,7603.200000000001,8,3041.28,63866.88,1/1/2019,19:31,Cash,60825.600000000006,4.7619047619999995,3041.28,6.3
+234-36-2483,B,Abuja,Normal,Male,Health and beauty,20732.4,6,6219.72,130614.11999999998,2/15/2019,13:51,Cash,124394.40000000001,4.7619047619999995,6219.72,5.1
+152-03-4217,B,Abuja,Normal,Female,Home and lifestyle,4060.7999999999997,9,1827.3600000000001,38374.56,3/17/2019,11:55,Card,36547.2,4.7619047619999995,1827.3600000000001,4.3
+533-66-5566,B,Abuja,Normal,Female,Home and lifestyle,18385.2,7,6434.820000000001,135131.22,1/12/2019,11:42,Cash,128696.40000000001,4.7619047619999995,6434.820000000001,7.0
+361-59-0574,B,Abuja,Member,Male,Sports and travel,32590.8,8,13036.319999999998,273762.72,3/15/2019,14:48,Card,260726.4,4.7619047619999995,13036.319999999998,6.5
+544-55-9589,B,Abuja,Member,Female,Electronic accessories,7714.8,10,3857.4,81005.4,1/28/2019,11:51,Cash,77148.0,4.7619047619999995,3857.4,6.2
+608-05-3804,B,Abuja,Member,Male,Electronic accessories,14310.0,1,715.5,15025.499999999998,2/25/2019,20:19,Cash,14310.0,4.7619047619999995,715.5,6.1
+761-49-0439,B,Abuja,Member,Female,Electronic accessories,4356.0,8,1742.3999999999999,36590.4,1/19/2019,10:17,Epay,34848.0,4.7619047619999995,1742.3999999999999,8.6
+490-95-0021,B,Abuja,Member,Female,Food and beverages,11955.6,10,5977.8,125533.79999999999,1/8/2019,14:25,Epay,119556.00000000001,4.7619047619999995,5977.8,6.0
+311-13-6971,B,Abuja,Member,Male,Sports and travel,11516.4,10,5758.2,120922.2,2/20/2019,15:18,Card,115163.99999999999,4.7619047619999995,5758.2,9.9
+114-35-5271,B,Abuja,Normal,Female,Electronic accessories,20847.6,8,8339.04,175119.84,2/7/2019,15:06,Cash,166780.8,4.7619047619999995,8339.04,8.1
+715-20-1673,B,Abuja,Normal,Male,Electronic accessories,10216.8,5,2554.2,53638.200000000004,3/6/2019,20:57,Cash,51084.0,4.7619047619999995,2554.2,9.4
+811-35-1094,B,Abuja,Member,Male,Electronic accessories,18162.0,6,5448.6,114420.59999999999,2/6/2019,15:16,Card,108972.0,4.7619047619999995,5448.6,8.9
+699-88-1972,B,Abuja,Normal,Male,Health and beauty,35697.6,8,14279.04,299859.83999999997,1/28/2019,17:47,Card,285580.8,4.7619047619999995,14279.04,4.2
+509-10-0516,B,Abuja,Normal,Male,Home and lifestyle,16549.2,4,3309.8399999999997,69506.64,2/9/2019,12:02,Epay,66196.8,4.7619047619999995,3309.8399999999997,5.1
+851-98-3555,B,Abuja,Normal,Female,Health and beauty,29836.8,5,7459.2,156643.2,3/24/2019,14:08,Card,149184.0,4.7619047619999995,7459.2,6.6
+624-01-8356,B,Abuja,Normal,Female,Home and lifestyle,17643.6,10,8821.8,185257.80000000002,1/27/2019,10:44,Card,176436.0,4.7619047619999995,8821.8,4.2
+313-66-9943,B,Abuja,Member,Female,Food and beverages,10494.0,3,1574.1,33056.1,3/27/2019,20:29,Card,31482.0,4.7619047619999995,1574.1,7.3
+777-67-2495,B,Abuja,Normal,Male,Home and lifestyle,24829.2,3,3724.3799999999997,78211.98000000001,2/22/2019,11:26,Epay,74487.6,4.7619047619999995,3724.3799999999997,8.7
+636-98-3364,B,Abuja,Member,Female,Electronic accessories,9453.6,3,1418.04,29778.839999999997,3/2/2019,12:36,Epay,28360.8,4.7619047619999995,1418.04,6.3
+181-82-6255,B,Abuja,Normal,Female,Home and lifestyle,5893.200000000001,6,1767.9600000000003,37127.16,2/8/2019,10:58,Cash,35359.2,4.7619047619999995,1767.9600000000003,7.0
+226-34-0034,B,Abuja,Normal,Female,Electronic accessories,4960.8,4,992.1600000000001,20835.36,1/10/2019,11:10,Epay,19843.2,4.7619047619999995,992.1600000000001,9.0
+321-49-7382,B,Abuja,Member,Male,Sports and travel,31791.600000000002,1,1589.58,33381.18,2/15/2019,17:38,Card,31791.600000000002,4.7619047619999995,1589.58,5.2
+431-66-2305,B,Abuja,Normal,Female,Electronic accessories,31770.0,9,14296.5,300226.5,2/15/2019,20:51,Card,285930.0,4.7619047619999995,14296.5,7.6
+825-94-5922,B,Abuja,Normal,Male,Sports and travel,9111.6,2,911.1600000000001,19134.36,3/2/2019,19:26,Epay,18223.2,4.7619047619999995,911.1600000000001,7.2
+641-62-7288,B,Abuja,Normal,Male,Home and lifestyle,35971.2,6,10791.36,226618.56,3/24/2019,13:33,Epay,215827.19999999998,4.7619047619999995,10791.36,7.1
+501-61-1753,B,Abuja,Normal,Female,Home and lifestyle,22734.0,6,6820.2,143224.2,1/3/2019,20:24,Epay,136404.0,4.7619047619999995,6820.2,9.8
+676-10-2200,B,Abuja,Member,Male,Fashion accessories,19360.8,1,968.0399999999998,20328.839999999997,2/3/2019,20:13,Epay,19360.8,4.7619047619999995,968.0399999999998,4.7
+365-16-4334,B,Abuja,Normal,Female,Food and beverages,9514.8,8,3805.9200000000005,79924.32,2/24/2019,14:26,Epay,76118.4,4.7619047619999995,3805.9200000000005,8.9
+503-21-4385,B,Abuja,Member,Male,Health and beauty,14367.599999999999,3,2155.1400000000003,45257.94,2/21/2019,12:40,Epay,43102.8,4.7619047619999995,2155.1400000000003,9.3
+305-89-2768,B,Abuja,Member,Female,Home and lifestyle,7883.999999999999,3,1182.6000000000001,24834.6,1/9/2019,18:43,Epay,23652.0,4.7619047619999995,1182.6000000000001,4.7
+574-80-1489,B,Abuja,Member,Female,Food and beverages,22626.0,4,4525.2,95029.20000000001,2/25/2019,13:22,Epay,90504.0,4.7619047619999995,4525.2,8.7
+200-40-6154,B,Abuja,Member,Male,Home and lifestyle,23727.6,6,7118.28,149483.88,2/9/2019,11:45,Cash,142365.6,4.7619047619999995,7118.28,5.7
+430-02-3888,B,Abuja,Normal,Male,Electronic accessories,16567.2,6,4970.16,104373.36,2/7/2019,15:55,Cash,99403.2,4.7619047619999995,4970.16,7.1
+794-42-3736,B,Abuja,Normal,Male,Food and beverages,11998.8,2,1199.88,25197.48,1/26/2019,14:41,Card,23997.6,4.7619047619999995,1199.88,6.4
+172-42-8274,B,Abuja,Normal,Female,Electronic accessories,13777.2,2,1377.72,28932.120000000003,3/2/2019,18:18,Card,27554.4,4.7619047619999995,1377.72,5.8
+214-30-2776,B,Abuja,Member,Female,Electronic accessories,12416.400000000001,5,3104.1000000000004,65186.1,3/11/2019,19:44,Card,62081.99999999999,4.7619047619999995,3104.1000000000004,9.0
+746-04-1077,B,Abuja,Member,Female,Food and beverages,30466.8,10,15233.4,319901.4,1/1/2019,11:36,Card,304668.0,4.7619047619999995,15233.4,9.0
+448-34-8700,B,Abuja,Member,Male,Home and lifestyle,13287.599999999999,7,4650.66,97663.86,2/10/2019,13:51,Epay,93013.2,4.7619047619999995,4650.66,6.7
+452-04-8808,B,Abuja,Normal,Male,Electronic accessories,31348.8,7,10972.08,230413.68,1/26/2019,15:17,Cash,219441.59999999998,4.7619047619999995,10972.08,5.5
+883-69-1285,B,Abuja,Member,Male,Fashion accessories,17971.2,2,1797.12,37739.520000000004,3/6/2019,11:55,Card,35942.4,4.7619047619999995,1797.12,7.0
+518-71-6847,B,Abuja,Member,Male,Food and beverages,9576.0,6,2872.8,60328.8,2/26/2019,15:10,Epay,57456.0,4.7619047619999995,2872.8,4.9
+156-20-0370,B,Abuja,Normal,Female,Electronic accessories,9162.0,1,458.09999999999997,9620.1,3/10/2019,18:10,Card,9162.0,4.7619047619999995,458.09999999999997,5.1
+151-33-7434,B,Abuja,Normal,Female,Food and beverages,24397.199999999997,1,1219.8600000000001,25617.06,2/4/2019,20:43,Card,24397.199999999997,4.7619047619999995,1219.8600000000001,6.5
+374-38-5555,B,Abuja,Normal,Female,Fashion accessories,22935.6,5,5733.9,120411.90000000001,2/7/2019,19:30,Epay,114678.0,4.7619047619999995,5733.9,8.5
+764-44-8999,B,Abuja,Normal,Female,Health and beauty,5313.6,2,531.36,11158.56,2/18/2019,14:42,Epay,10627.2,4.7619047619999995,531.36,4.3
+552-44-5977,B,Abuja,Member,Male,Health and beauty,22320.0,8,8928.0,187487.99999999997,1/3/2019,19:08,Card,178560.0,4.7619047619999995,8928.0,6.2
+430-53-4718,B,Abuja,Member,Male,Health and beauty,27133.2,8,10853.28,227918.88000000003,1/28/2019,15:46,Card,217065.6,4.7619047619999995,10853.28,8.4
+602-16-6955,B,Abuja,Normal,Female,Sports and travel,27575.999999999996,10,13787.999999999998,289548.0,1/24/2019,18:10,Epay,275760.0,4.7619047619999995,13787.999999999998,6.0
+690-01-6631,B,Abuja,Normal,Male,Fashion accessories,6296.4,10,3148.2,66112.2,2/22/2019,18:35,Epay,62964.0,4.7619047619999995,3148.2,6.6
+303-96-2227,B,Abuja,Normal,Female,Home and lifestyle,35056.799999999996,10,17528.399999999998,368096.4,3/2/2019,17:16,Epay,350568.0,4.7619047619999995,17528.399999999998,4.4
diff --git a/Pandas_Analytics_project/Executive Summary.pdf b/Pandas_Analytics_project/Executive Summary.pdf
new file mode 100644
index 0000000..96a6a52
Binary files /dev/null and b/Pandas_Analytics_project/Executive Summary.pdf differ
diff --git a/Pandas_Analytics_project/Lagos_Branch.csv b/Pandas_Analytics_project/Lagos_Branch.csv
new file mode 100644
index 0000000..e2e3017
--- /dev/null
+++ b/Pandas_Analytics_project/Lagos_Branch.csv
@@ -0,0 +1,341 @@
+Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
+750-67-8428,A,Lagos,Member,Female,Health and beauty,26888.399999999998,7,9410.94,197629.74,1/5/2019,13:08,Epay,188218.80000000002,4.7619047619999995,9410.94,9.1
+631-41-3108,A,Lagos,Normal,Male,Home and lifestyle,16678.8,7,5837.58,122589.18000000001,3/3/2019,13:23,Card,116751.6,4.7619047619999995,5837.58,7.4
+123-19-1176,A,Lagos,Member,Male,Health and beauty,20959.2,8,8383.68,176057.28,1/27/2019,20:33,Epay,167673.6,4.7619047619999995,8383.68,8.4
+373-73-7910,A,Lagos,Normal,Male,Sports and travel,31071.600000000002,7,10875.06,228376.26,2/8/2019,10:37,Epay,217501.19999999998,4.7619047619999995,10875.06,5.3
+355-53-5943,A,Lagos,Member,Female,Electronic accessories,24782.4,6,7434.719999999999,156129.12,2/25/2019,14:36,Epay,148694.4,4.7619047619999995,7434.719999999999,5.8
+665-32-9167,A,Lagos,Member,Female,Health and beauty,13053.599999999999,2,1305.3600000000001,27412.56,1/10/2019,17:15,Card,26107.199999999997,4.7619047619999995,1305.3600000000001,7.2
+365-64-0515,A,Lagos,Normal,Female,Electronic accessories,16902.0,5,4225.5,88735.5,2/12/2019,10:25,Epay,84510.0,4.7619047619999995,4225.5,7.1
+252-56-2699,A,Lagos,Normal,Male,Food and beverages,15548.4,10,7774.2,163258.2,2/7/2019,16:48,Epay,155484.0,4.7619047619999995,7774.2,8.2
+829-34-3910,A,Lagos,Normal,Female,Health and beauty,25696.8,10,12848.4,269816.4,3/29/2019,19:21,Cash,256967.99999999997,4.7619047619999995,12848.4,5.7
+656-95-9349,A,Lagos,Member,Female,Health and beauty,24814.800000000003,7,8685.18,182388.78,3/11/2019,11:03,Card,173703.6,4.7619047619999995,8685.18,4.6
+765-26-6951,A,Lagos,Normal,Male,Sports and travel,26139.6,6,7841.88,164679.48,1/1/2019,10:39,Card,156837.6,4.7619047619999995,7841.88,6.9
+329-62-1586,A,Lagos,Normal,Male,Food and beverages,19681.2,3,2952.18,61995.78,1/21/2019,18:00,Card,59043.6,4.7619047619999995,2952.18,8.6
+636-48-8204,A,Lagos,Normal,Male,Electronic accessories,12441.6,5,3110.4,65318.4,2/17/2019,11:15,Epay,62208.00000000001,4.7619047619999995,3110.4,9.9
+549-59-1358,A,Lagos,Member,Male,Sports and travel,31906.8,3,4786.0199999999995,100506.42,3/2/2019,17:36,Epay,95720.4,4.7619047619999995,4786.0199999999995,6.0
+227-03-5010,A,Lagos,Member,Female,Home and lifestyle,18932.4,8,7572.959999999999,159032.16,3/22/2019,19:20,Card,151459.2,4.7619047619999995,7572.959999999999,8.5
+189-17-4241,A,Lagos,Normal,Female,Fashion accessories,31561.2,2,3156.1200000000003,66278.52,3/10/2019,12:17,Card,63122.4,4.7619047619999995,3156.1200000000003,7.7
+848-62-7243,A,Lagos,Normal,Male,Health and beauty,8960.4,9,4032.18,84675.78,3/15/2019,15:36,Cash,80643.59999999999,4.7619047619999995,4032.18,7.4
+595-11-5460,A,Lagos,Normal,Male,Health and beauty,34768.8,2,3476.8799999999997,73014.48,3/15/2019,10:12,Card,69537.6,4.7619047619999995,3476.8799999999997,5.1
+129-29-8530,A,Lagos,Member,Male,Sports and travel,22543.2,5,5635.8,118351.8,3/10/2019,19:15,Epay,112716.00000000001,4.7619047619999995,5635.8,7.0
+272-65-1806,A,Lagos,Normal,Female,Electronic accessories,21916.8,9,9862.56,207113.76,1/15/2019,17:17,Epay,197251.19999999998,4.7619047619999995,9862.56,4.7
+162-48-8011,A,Lagos,Member,Female,Food and beverages,16052.400000000001,5,4013.1000000000004,84275.1,2/10/2019,15:10,Cash,80262.0,4.7619047619999995,4013.1000000000004,8.5
+106-35-6779,A,Lagos,Member,Male,Home and lifestyle,15962.400000000001,2,1596.24,33521.03999999999,3/27/2019,11:26,Cash,31924.800000000003,4.7619047619999995,1596.24,5.8
+635-40-6220,A,Lagos,Normal,Male,Health and beauty,32255.999999999996,8,12902.400000000001,270950.4,2/7/2019,11:28,Epay,258047.99999999997,4.7619047619999995,12902.400000000001,6.6
+817-48-8732,A,Lagos,Member,Female,Home and lifestyle,26045.999999999996,10,13022.999999999998,273483.0,1/20/2019,15:55,Cash,260460.0,4.7619047619999995,13022.999999999998,5.4
+199-75-8169,A,Lagos,Member,Male,Sports and travel,5691.6,10,2845.8,59761.799999999996,3/6/2019,12:27,Card,56916.0,4.7619047619999995,2845.8,8.6
+877-22-3308,A,Lagos,Member,Male,Health and beauty,5713.2,10,2856.6,59988.6,3/13/2019,16:40,Cash,57131.99999999999,4.7619047619999995,2856.6,5.8
+232-11-3025,A,Lagos,Normal,Male,Sports and travel,28357.199999999997,10,14178.599999999999,297750.60000000003,1/24/2019,10:04,Cash,283572.0,4.7619047619999995,14178.599999999999,6.4
+382-03-4532,A,Lagos,Member,Female,Health and beauty,6598.799999999999,1,329.94,6928.740000000001,2/2/2019,18:50,Cash,6598.799999999999,4.7619047619999995,329.94,4.3
+287-21-9091,A,Lagos,Normal,Male,Home and lifestyle,26881.2,9,12096.54,254027.34,1/22/2019,10:55,Epay,241930.8,4.7619047619999995,12096.54,9.4
+381-20-0914,A,Lagos,Member,Female,Fashion accessories,7203.6,9,3241.62,68074.02,1/12/2019,15:48,Card,64832.4,4.7619047619999995,3241.62,5.7
+633-44-8566,A,Lagos,Member,Male,Food and beverages,17776.8,7,6221.88,130659.48000000001,3/27/2019,20:35,Card,124437.6,4.7619047619999995,6221.88,7.3
+504-35-8843,A,Lagos,Normal,Male,Sports and travel,15289.199999999999,1,764.46,16053.66,1/2/2019,16:57,Cash,15289.199999999999,4.7619047619999995,764.46,5.7
+873-51-0671,A,Lagos,Member,Female,Sports and travel,7912.8,7,2769.48,58159.08,1/10/2019,16:42,Epay,55389.600000000006,4.7619047619999995,2769.48,5.1
+594-34-4444,A,Lagos,Normal,Male,Electronic accessories,34977.6,1,1748.88,36726.479999999996,3/8/2019,20:38,Epay,34977.6,4.7619047619999995,1748.88,7.2
+865-92-6136,A,Lagos,Normal,Male,Food and beverages,18990.0,3,2848.5,59818.5,3/23/2019,10:16,Epay,56970.0,4.7619047619999995,2848.5,9.3
+212-62-1842,A,Lagos,Normal,Male,Food and beverages,20973.6,6,6292.079999999999,132133.68,3/28/2019,16:44,Cash,125841.6,4.7619047619999995,6292.079999999999,9.9
+704-48-3927,A,Lagos,Member,Male,Electronic accessories,31921.2,10,15960.6,335172.6,1/12/2019,14:50,Epay,319212.0,4.7619047619999995,15960.6,7.3
+630-74-5166,A,Lagos,Normal,Male,Sports and travel,22366.8,6,6710.04,140910.84000000003,3/22/2019,20:19,Cash,134200.8,4.7619047619999995,6710.04,7.4
+645-44-1170,A,Lagos,Member,Male,Home and lifestyle,20905.2,9,9407.34,197554.13999999998,1/19/2019,20:07,Epay,188146.8,4.7619047619999995,9407.34,4.3
+642-32-2990,A,Lagos,Normal,Female,Food and beverages,3945.6000000000004,10,1972.8000000000002,41428.8,2/2/2019,20:48,Epay,39456.0,4.7619047619999995,1972.8000000000002,6.0
+638-60-7125,A,Lagos,Normal,Female,Electronic accessories,35841.6,8,14336.64,301069.44,2/14/2019,17:03,Card,286732.8,4.7619047619999995,14336.64,5.2
+668-90-8900,A,Lagos,Normal,Female,Home and lifestyle,33728.4,7,11804.94,247903.74,3/10/2019,18:44,Card,236098.80000000002,4.7619047619999995,11804.94,4.5
+870-54-3162,A,Lagos,Normal,Female,Sports and travel,11610.0,5,2902.5,60952.5,1/27/2019,13:26,Cash,58050.0,4.7619047619999995,2902.5,9.0
+802-70-5316,A,Lagos,Member,Female,Sports and travel,33166.799999999996,6,9950.039999999999,208950.84,3/6/2019,20:34,Cash,199000.8,4.7619047619999995,9950.039999999999,8.3
+700-81-1757,A,Lagos,Normal,Female,Electronic accessories,9471.6,5,2367.9,49725.9,1/18/2019,20:59,Card,47358.00000000001,4.7619047619999995,2367.9,8.8
+354-39-5160,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.3599999999997,78064.56,2/18/2019,15:39,Cash,74347.2,4.7619047619999995,3717.3599999999997,9.8
+575-30-8091,A,Lagos,Normal,Male,Sports and travel,26100.0,8,10440.0,219240.0,3/16/2019,19:25,Epay,208800.0,4.7619047619999995,10440.0,9.2
+239-10-7476,A,Lagos,Normal,Female,Home and lifestyle,28062.0,6,8418.6,176790.6,1/21/2019,16:37,Epay,168372.0,4.7619047619999995,8418.6,8.0
+685-64-1609,A,Lagos,Member,Female,Fashion accessories,10850.4,10,5425.2,113929.20000000001,2/10/2019,12:28,Epay,108503.99999999999,4.7619047619999995,5425.2,9.2
+238-49-0436,A,Lagos,Normal,Male,Health and beauty,11685.6,8,4674.240000000001,98159.04,3/27/2019,13:48,Card,93484.8,4.7619047619999995,4674.240000000001,4.9
+746-94-0204,A,Lagos,Normal,Male,Fashion accessories,29966.399999999998,9,13484.88,283182.48000000004,1/29/2019,11:56,Card,269697.6,4.7619047619999995,13484.88,7.4
+782-95-9291,A,Lagos,Member,Male,Food and beverages,33224.4,5,8306.1,174428.1,2/20/2019,15:55,Card,166122.0,4.7619047619999995,8306.1,9.0
+275-28-0149,A,Lagos,Normal,Male,Sports and travel,22928.399999999998,1,1146.42,24074.82,2/25/2019,16:21,Cash,22928.399999999998,4.7619047619999995,1146.42,6.0
+101-17-6199,A,Lagos,Normal,Male,Food and beverages,16484.4,7,5769.539999999999,121160.34000000001,3/13/2019,19:44,Card,115390.79999999999,4.7619047619999995,5769.539999999999,7.0
+687-47-8271,A,Lagos,Normal,Male,Fashion accessories,35632.8,10,17816.4,374144.39999999997,2/8/2019,16:20,Card,356328.0,4.7619047619999995,17816.4,8.7
+796-32-9050,A,Lagos,Normal,Male,Food and beverages,18460.8,6,5538.24,116303.04000000001,1/19/2019,16:31,Cash,110764.8,4.7619047619999995,5538.24,6.5
+105-31-1824,A,Lagos,Member,Male,Sports and travel,25027.199999999997,7,8759.519999999999,183949.91999999998,2/1/2019,15:10,Card,175190.4,4.7619047619999995,8759.519999999999,8.5
+249-42-3782,A,Lagos,Normal,Male,Health and beauty,25203.600000000002,5,6300.900000000001,132318.9,1/3/2019,11:36,Epay,126018.0,4.7619047619999995,6300.900000000001,5.5
+827-26-2100,A,Lagos,Member,Male,Home and lifestyle,12182.400000000001,9,5482.08,115123.68000000001,3/21/2019,16:21,Epay,109641.6,4.7619047619999995,5482.08,8.8
+175-54-2529,A,Lagos,Member,Male,Food and beverages,7981.200000000001,8,3192.48,67042.07999999999,3/3/2019,17:01,Card,63849.600000000006,4.7619047619999995,3192.48,9.6
+407-63-8975,A,Lagos,Normal,Male,Food and beverages,26596.8,6,7979.040000000001,167559.84,3/23/2019,19:16,Epay,159580.8,4.7619047619999995,7979.040000000001,4.4
+851-28-6367,A,Lagos,Member,Male,Sports and travel,5580.0,10,2790.0,58590.0,3/23/2019,10:55,Epay,55800.0,4.7619047619999995,2790.0,8.0
+586-25-0848,A,Lagos,Normal,Female,Sports and travel,4442.4,7,1554.84,32651.64,3/4/2019,11:19,Card,31096.8,4.7619047619999995,1554.84,6.7
+400-60-7251,A,Lagos,Normal,Male,Home and lifestyle,26665.199999999997,1,1333.26,27998.46,2/10/2019,12:50,Epay,26665.199999999997,4.7619047619999995,1333.26,9.9
+831-07-6050,A,Lagos,Normal,Male,Electronic accessories,11775.6,5,2943.9,61821.899999999994,3/19/2019,11:30,Card,58878.00000000001,4.7619047619999995,2943.9,9.9
+856-22-8149,A,Lagos,Normal,Female,Home and lifestyle,9104.4,1,455.21999999999997,9559.62,3/23/2019,10:13,Epay,9104.4,4.7619047619999995,455.21999999999997,6.1
+749-24-1565,A,Lagos,Normal,Female,Health and beauty,8290.800000000001,9,3730.86,78348.06,1/3/2019,12:02,Epay,74617.2,4.7619047619999995,3730.86,7.9
+888-02-0338,A,Lagos,Normal,Male,Electronic accessories,9442.8,9,4249.26,89234.46,1/25/2019,20:24,Epay,84985.2,4.7619047619999995,4249.26,5.9
+802-43-8934,A,Lagos,Normal,Male,Home and lifestyle,6580.8,1,329.04,6909.840000000001,3/22/2019,15:05,Card,6580.8,4.7619047619999995,329.04,8.3
+319-74-2561,A,Lagos,Member,Female,Electronic accessories,34070.4,3,5110.5599999999995,107321.76,2/21/2019,16:55,Cash,102211.20000000001,4.7619047619999995,5110.5599999999995,5.5
+213-72-6612,A,Lagos,Normal,Male,Food and beverages,15570.0,2,1557.0,32697.0,3/20/2019,15:56,Cash,31140.0,4.7619047619999995,1557.0,6.2
+721-86-6247,A,Lagos,Normal,Female,Home and lifestyle,22831.2,8,9132.480000000001,191782.08000000002,3/11/2019,12:55,Epay,182649.6,4.7619047619999995,9132.480000000001,7.4
+157-13-5295,A,Lagos,Member,Male,Health and beauty,18698.399999999998,10,9349.199999999999,196333.2,3/9/2019,18:24,Epay,186984.0,4.7619047619999995,9349.199999999999,6.5
+645-78-8093,A,Lagos,Normal,Female,Sports and travel,33530.4,2,3353.04,70413.84000000001,1/20/2019,18:09,Epay,67060.8,4.7619047619999995,3353.04,4.1
+478-06-7835,A,Lagos,Normal,Male,Fashion accessories,32288.399999999998,1,1614.4199999999998,33902.82,1/11/2019,11:20,Epay,32288.399999999998,4.7619047619999995,1614.4199999999998,4.9
+540-11-4336,A,Lagos,Normal,Male,Food and beverages,8978.4,9,4040.2799999999997,84845.88,1/11/2019,16:49,Card,80805.6,4.7619047619999995,4040.2799999999997,5.6
+448-81-5016,A,Lagos,Normal,Male,Health and beauty,21517.2,2,2151.7200000000003,45186.12,3/11/2019,12:01,Card,43034.4,4.7619047619999995,2151.7200000000003,5.8
+217-58-1179,A,Lagos,Member,Male,Home and lifestyle,22554.0,4,4510.8,94726.8,1/5/2019,11:25,Cash,90216.0,4.7619047619999995,4510.8,4.2
+530-90-9855,A,Lagos,Member,Male,Home and lifestyle,17132.4,8,6852.959999999999,143912.16,1/1/2019,14:47,Cash,137059.2,4.7619047619999995,6852.959999999999,5.7
+604-70-6476,A,Lagos,Member,Male,Fashion accessories,6458.400000000001,5,1614.6000000000001,33906.6,1/23/2019,14:04,Epay,32292.0,4.7619047619999995,1614.6000000000001,6.8
+799-71-1548,A,Lagos,Member,Male,Electronic accessories,27979.2,4,5595.84,117512.64000000001,1/7/2019,16:11,Card,111916.8,4.7619047619999995,5595.84,8.8
+290-68-2984,A,Lagos,Normal,Male,Home and lifestyle,8550.0,4,1710.0,35910.0,3/16/2019,11:22,Cash,34200.0,4.7619047619999995,1710.0,5.2
+704-11-6354,A,Lagos,Member,Male,Home and lifestyle,21204.0,8,8481.6,178113.6,1/6/2019,11:23,Cash,169632.0,4.7619047619999995,8481.6,8.9
+366-93-0948,A,Lagos,Member,Male,Electronic accessories,23885.999999999996,1,1194.3,25080.300000000003,1/31/2019,10:46,Card,23885.999999999996,4.7619047619999995,1194.3,9.7
+729-09-9681,A,Lagos,Member,Male,Home and lifestyle,9327.6,6,2798.2799999999997,58763.880000000005,2/5/2019,10:16,Epay,55965.600000000006,4.7619047619999995,2798.2799999999997,8.7
+151-16-1484,A,Lagos,Member,Male,Electronic accessories,11610.0,4,2322.0,48761.99999999999,2/13/2019,12:38,Epay,46440.0,4.7619047619999995,2322.0,6.5
+850-41-9669,A,Lagos,Normal,Female,Electronic accessories,27021.600000000002,9,12159.720000000001,255354.12,3/19/2019,13:25,Epay,243194.4,4.7619047619999995,12159.720000000001,6.2
+447-15-7839,A,Lagos,Member,Female,Sports and travel,8006.4,10,4003.2,84067.2,2/9/2019,11:00,Cash,80064.0,4.7619047619999995,4003.2,4.2
+253-12-6086,A,Lagos,Member,Female,Sports and travel,35424.0,7,12398.4,260366.4,3/12/2019,12:43,Card,247967.99999999997,4.7619047619999995,12398.4,8.7
+144-51-6085,A,Lagos,Member,Male,Home and lifestyle,25466.399999999998,4,5093.28,106958.88,1/5/2019,16:05,Card,101865.59999999999,4.7619047619999995,5093.28,4.4
+731-14-2199,A,Lagos,Member,Female,Home and lifestyle,12794.4,10,6397.2,134341.2,1/4/2019,13:34,Epay,127943.99999999999,4.7619047619999995,6397.2,7.0
+126-54-1082,A,Lagos,Member,Female,Home and lifestyle,7754.4,9,3489.48,73279.08,1/7/2019,11:44,Card,69789.6,4.7619047619999995,3489.48,8.8
+633-91-1052,A,Lagos,Normal,Female,Home and lifestyle,4330.8,2,433.08000000000004,9094.68,1/27/2019,15:51,Cash,8661.6,4.7619047619999995,433.08000000000004,5.1
+828-61-5674,A,Lagos,Member,Male,Sports and travel,15847.2,10,7923.6,166395.6,3/20/2019,19:57,Card,158472.0,4.7619047619999995,7923.6,9.6
+136-08-6195,A,Lagos,Normal,Female,Home and lifestyle,25185.6,8,10074.24,211559.04,2/15/2019,17:01,Card,201484.8,4.7619047619999995,10074.24,6.4
+490-29-1201,A,Lagos,Normal,Female,Sports and travel,5522.4,1,276.12,5798.5199999999995,1/6/2019,11:09,Cash,5522.4,4.7619047619999995,276.12,6.5
+667-92-0055,A,Lagos,Member,Male,Health and beauty,35938.8,6,10781.640000000001,226414.44,3/4/2019,15:02,Epay,215632.80000000002,4.7619047619999995,10781.640000000001,8.5
+565-17-3836,A,Lagos,Member,Female,Health and beauty,17161.2,4,3432.24,72077.04000000001,3/12/2019,14:21,Cash,68644.8,4.7619047619999995,3432.24,9.1
+430-60-3493,A,Lagos,Member,Female,Home and lifestyle,34156.799999999996,7,11954.88,251052.48,2/3/2019,14:38,Cash,239097.59999999998,4.7619047619999995,11954.88,4.2
+278-97-7759,A,Lagos,Member,Female,Electronic accessories,22492.8,1,1124.6399999999999,23617.44,2/18/2019,20:29,Cash,22492.8,4.7619047619999995,1124.6399999999999,4.7
+316-68-6352,A,Lagos,Member,Female,Food and beverages,13089.6,2,1308.96,27488.160000000003,1/21/2019,10:00,Cash,26179.2,4.7619047619999995,1308.96,7.1
+744-02-5987,A,Lagos,Member,Male,Home and lifestyle,28216.8,6,8465.039999999999,177765.84000000003,1/10/2019,14:16,Epay,169300.8,4.7619047619999995,8465.039999999999,5.8
+307-83-9164,A,Lagos,Member,Male,Home and lifestyle,21603.6,4,4320.72,90735.12,1/25/2019,15:54,Cash,86414.4,4.7619047619999995,4320.72,4.5
+439-54-7422,A,Lagos,Normal,Female,Electronic accessories,18428.399999999998,4,3685.68,77399.28,3/18/2019,17:15,Card,73713.59999999999,4.7619047619999995,3685.68,4.7
+411-77-0180,A,Lagos,Member,Male,Electronic accessories,25992.0,7,9097.2,191041.19999999998,3/26/2019,20:14,Epay,181944.0,4.7619047619999995,9097.2,4.3
+286-01-5402,A,Lagos,Normal,Female,Sports and travel,14482.8,7,5068.9800000000005,106448.58,3/30/2019,13:22,Cash,101379.6,4.7619047619999995,5068.9800000000005,9.6
+803-17-8013,A,Lagos,Member,Female,Home and lifestyle,31964.4,8,12785.76,268500.96,2/17/2019,17:09,Cash,255715.2,4.7619047619999995,12785.76,4.1
+512-98-1403,A,Lagos,Member,Female,Electronic accessories,9532.8,3,1429.92,30028.320000000003,3/21/2019,10:40,Epay,28598.399999999998,4.7619047619999995,1429.92,4.7
+848-42-2560,A,Lagos,Normal,Female,Fashion accessories,29487.6,2,2948.76,61923.96,3/5/2019,17:43,Cash,58975.2,4.7619047619999995,2948.76,7.8
+870-76-1733,A,Lagos,Member,Female,Food and beverages,5122.8,5,1280.7,26894.699999999997,2/1/2019,10:08,Card,25614.000000000004,4.7619047619999995,1280.7,4.4
+423-64-4619,A,Lagos,Member,Female,Health and beauty,5598.0,9,2519.1,52901.1,3/7/2019,13:12,Cash,50381.99999999999,4.7619047619999995,2519.1,5.0
+372-94-8041,A,Lagos,Normal,Male,Health and beauty,5493.6,6,1648.0800000000002,34609.68,2/15/2019,18:03,Epay,32961.6,4.7619047619999995,1648.0800000000002,9.8
+563-91-7120,A,Lagos,Normal,Female,Fashion accessories,22237.2,5,5559.3,116745.3,3/8/2019,13:21,Cash,111186.00000000001,4.7619047619999995,5559.3,6.7
+746-54-5508,A,Lagos,Normal,Male,Home and lifestyle,7747.2,6,2324.1600000000003,48807.36,1/17/2019,12:48,Card,46483.200000000004,4.7619047619999995,2324.1600000000003,9.4
+815-11-1168,A,Lagos,Member,Male,Food and beverages,35920.8,5,8980.2,188584.2,3/9/2019,19:09,Cash,179604.0,4.7619047619999995,8980.2,5.4
+340-66-0321,A,Lagos,Member,Male,Electronic accessories,13089.6,4,2617.92,54976.32000000001,3/25/2019,13:07,Cash,52358.4,4.7619047619999995,2617.92,7.6
+634-97-8956,A,Lagos,Normal,Male,Food and beverages,11844.0,3,1776.6,37308.6,2/17/2019,17:27,Card,35532.0,4.7619047619999995,1776.6,9.1
+566-71-1091,A,Lagos,Normal,Male,Fashion accessories,27727.199999999997,5,6931.799999999999,145567.80000000002,2/3/2019,15:59,Cash,138636.0,4.7619047619999995,6931.799999999999,5.5
+442-48-3607,A,Lagos,Member,Male,Food and beverages,8452.8,2,845.2800000000001,17750.88,3/14/2019,11:21,Card,16905.6,4.7619047619999995,845.2800000000001,7.9
+527-09-6272,A,Lagos,Member,Female,Electronic accessories,10242.0,5,2560.5,53770.50000000001,3/21/2019,10:17,Card,51210.0,4.7619047619999995,2560.5,9.1
+898-04-2717,A,Lagos,Normal,Male,Fashion accessories,27504.000000000004,9,12376.800000000001,259912.80000000002,3/19/2019,15:49,Epay,247536.0,4.7619047619999995,12376.800000000001,7.5
+150-89-8043,A,Lagos,Normal,Male,Sports and travel,16074.0,3,2411.1,50633.100000000006,2/14/2019,15:04,Cash,48221.99999999999,4.7619047619999995,2411.1,6.2
+135-84-8019,A,Lagos,Normal,Female,Fashion accessories,28054.800000000003,9,12624.66,265117.86,2/27/2019,16:10,Epay,252493.2,4.7619047619999995,12624.66,7.6
+441-94-7118,A,Lagos,Member,Male,Electronic accessories,25902.0,1,1295.1000000000001,27197.1,2/4/2019,12:14,Cash,25902.0,4.7619047619999995,1295.1000000000001,7.3
+531-80-1784,A,Lagos,Normal,Male,Electronic accessories,9367.2,7,3278.5200000000004,68848.92,3/28/2019,17:38,Cash,65570.4,4.7619047619999995,3278.5200000000004,5.1
+834-61-8124,A,Lagos,Normal,Male,Electronic accessories,18608.399999999998,7,6512.94,136771.74,1/26/2019,18:22,Cash,130258.79999999999,4.7619047619999995,6512.94,5.5
+612-36-5536,A,Lagos,Member,Male,Food and beverages,29145.6,8,11658.24,244823.03999999998,2/17/2019,11:12,Card,233164.8,4.7619047619999995,11658.24,7.4
+462-67-9126,A,Lagos,Normal,Male,Home and lifestyle,26359.2,6,7907.759999999999,166062.96,1/21/2019,17:44,Cash,158155.2,4.7619047619999995,7907.759999999999,7.2
+468-88-0009,A,Lagos,Member,Male,Home and lifestyle,23738.399999999998,4,4747.679999999999,99701.28,3/24/2019,10:29,Cash,94953.59999999999,4.7619047619999995,4747.679999999999,6.0
+254-31-0042,A,Lagos,Member,Male,Electronic accessories,7740.0,9,3483.0000000000005,73143.0,3/6/2019,12:46,Card,69660.0,4.7619047619999995,3483.0000000000005,7.8
+422-29-8786,A,Lagos,Normal,Female,Home and lifestyle,24152.4,5,6038.1,126800.1,1/3/2019,16:47,Card,120762.0,4.7619047619999995,6038.1,9.1
+667-23-5919,A,Lagos,Member,Female,Fashion accessories,34812.0,5,8703.0,182763.0,1/14/2019,12:52,Epay,174060.0,4.7619047619999995,8703.0,7.0
+289-15-7034,A,Lagos,Member,Male,Sports and travel,29638.8,4,5927.759999999999,124482.96,1/11/2019,10:37,Card,118555.2,4.7619047619999995,5927.759999999999,7.5
+662-72-2873,A,Lagos,Normal,Female,Food and beverages,14738.4,5,3684.6,77376.6,1/6/2019,13:58,Epay,73692.0,4.7619047619999995,3684.6,9.9
+725-56-0833,A,Lagos,Normal,Female,Health and beauty,11635.2,10,5817.6,122169.6,2/20/2019,16:49,Card,116352.0,4.7619047619999995,5817.6,10.0
+563-36-9814,A,Lagos,Member,Male,Electronic accessories,27655.199999999997,1,1382.76,29037.96,2/13/2019,18:27,Epay,27655.199999999997,4.7619047619999995,1382.76,7.2
+308-47-4913,A,Lagos,Member,Female,Sports and travel,18813.6,10,9406.8,197542.80000000002,3/9/2019,12:45,Card,188136.0,4.7619047619999995,9406.8,6.2
+885-17-6250,A,Lagos,Normal,Female,Health and beauty,28706.399999999998,1,1435.32,30141.72,3/6/2019,10:36,Epay,28706.399999999998,4.7619047619999995,1435.32,7.3
+726-27-2396,A,Lagos,Normal,Female,Health and beauty,27900.0,5,6975.0,146475.0,1/24/2019,20:36,Epay,139500.0,4.7619047619999995,6975.0,4.3
+316-01-3952,A,Lagos,Normal,Female,Food and beverages,19537.2,5,4884.3,102570.3,3/13/2019,14:16,Epay,97686.00000000001,4.7619047619999995,4884.3,4.6
+882-40-4577,A,Lagos,Member,Male,Sports and travel,24213.600000000002,4,4842.72,101697.12000000001,1/19/2019,15:28,Card,96854.40000000001,4.7619047619999995,4842.72,8.0
+732-67-5346,A,Lagos,Normal,Male,Food and beverages,4964.4,5,1241.1,26063.1,1/11/2019,19:07,Card,24822.0,4.7619047619999995,1241.1,7.8
+256-08-8343,A,Lagos,Normal,Female,Home and lifestyle,20350.8,4,4070.16,85473.36,3/4/2019,19:48,Epay,81403.2,4.7619047619999995,4070.16,5.5
+132-23-6451,A,Lagos,Member,Male,Health and beauty,7549.2,5,1887.3,39633.3,1/4/2019,13:21,Cash,37746.0,4.7619047619999995,1887.3,7.8
+696-90-2548,A,Lagos,Normal,Male,Sports and travel,9302.4,3,1395.3600000000001,29302.56,3/10/2019,18:55,Epay,27907.199999999997,4.7619047619999995,1395.3600000000001,6.6
+472-15-9636,A,Lagos,Normal,Male,Home and lifestyle,18334.8,8,7333.92,154012.31999999998,3/22/2019,19:36,Epay,146678.4,4.7619047619999995,7333.92,9.2
+745-71-3520,A,Lagos,Member,Female,Electronic accessories,9079.199999999999,7,3177.72,66732.12000000001,2/4/2019,10:23,Cash,63554.399999999994,4.7619047619999995,3177.72,8.2
+269-10-8440,A,Lagos,Member,Male,Health and beauty,19141.2,7,6699.42,140687.82,1/21/2019,18:01,Cash,133988.4,4.7619047619999995,6699.42,8.9
+325-77-6186,A,Lagos,Member,Female,Home and lifestyle,32634.000000000004,10,16317.000000000002,342657.0,3/8/2019,10:53,Epay,326340.0,4.7619047619999995,16317.000000000002,7.3
+459-50-7686,A,Lagos,Normal,Female,Electronic accessories,8445.6,6,2533.68,53207.28,1/13/2019,19:14,Epay,50673.6,4.7619047619999995,2533.68,6.4
+345-08-4992,A,Lagos,Normal,Male,Home and lifestyle,12236.400000000001,6,3670.9200000000005,77089.32,3/8/2019,15:37,Card,73418.4,4.7619047619999995,3670.9200000000005,7.7
+138-17-5109,A,Lagos,Member,Female,Home and lifestyle,32115.6,9,14452.02,303492.42,1/15/2019,15:42,Card,289040.4,4.7619047619999995,14452.02,6.5
+301-11-9629,A,Lagos,Normal,Female,Sports and travel,6876.000000000001,7,2406.6,50538.6,1/15/2019,10:43,Cash,48131.99999999999,4.7619047619999995,2406.6,9.7
+727-17-0390,A,Lagos,Normal,Female,Food and beverages,22899.6,5,5724.9,120222.9,3/16/2019,12:43,Epay,114498.0,4.7619047619999995,5724.9,4.8
+568-88-3448,A,Lagos,Normal,Male,Health and beauty,9000.0,1,450.0,9450.0,3/3/2019,15:09,Epay,9000.0,4.7619047619999995,450.0,5.5
+187-83-5490,A,Lagos,Member,Male,Electronic accessories,7477.2,4,1495.44,31404.239999999998,1/31/2019,13:47,Cash,29908.8,4.7619047619999995,1495.44,4.7
+470-32-9057,A,Lagos,Member,Male,Food and beverages,18482.4,5,4620.6,97032.6,3/28/2019,15:31,Card,92412.0,4.7619047619999995,4620.6,9.1
+340-21-9136,A,Lagos,Member,Female,Sports and travel,14417.999999999998,4,2883.6,60555.600000000006,1/25/2019,11:40,Cash,57671.99999999999,4.7619047619999995,2883.6,9.7
+405-31-3305,A,Lagos,Member,Male,Fashion accessories,15526.800000000001,10,7763.400000000001,163031.4,2/2/2019,18:31,Card,155268.0,4.7619047619999995,7763.400000000001,5.5
+676-39-6028,A,Lagos,Member,Female,Electronic accessories,23198.399999999998,5,5799.599999999999,121791.6,3/30/2019,17:04,Cash,115992.0,4.7619047619999995,5799.599999999999,6.6
+502-05-1910,A,Lagos,Normal,Male,Health and beauty,23464.800000000003,3,3519.7200000000003,73914.12,2/25/2019,20:35,Card,70394.4,4.7619047619999995,3519.7200000000003,6.3
+485-30-8700,A,Lagos,Normal,Female,Sports and travel,11973.599999999999,5,2993.3999999999996,62861.4,3/18/2019,16:10,Card,59868.00000000001,4.7619047619999995,2993.3999999999996,4.2
+575-67-1508,A,Lagos,Normal,Male,Electronic accessories,13896.0,1,694.8,14590.800000000001,1/29/2019,11:26,Epay,13896.0,4.7619047619999995,694.8,6.7
+674-15-9296,A,Lagos,Normal,Male,Sports and travel,13370.4,5,3342.6,70194.6,1/8/2019,13:05,Epay,66852.0,4.7619047619999995,3342.6,5.0
+795-49-7276,A,Lagos,Normal,Male,Fashion accessories,13374.0,4,2674.7999999999997,56170.8,3/23/2019,18:59,Epay,53496.0,4.7619047619999995,2674.7999999999997,8.3
+510-09-5628,A,Lagos,Member,Female,Fashion accessories,7077.6,10,3538.8,74314.8,3/15/2019,18:20,Card,70776.0,4.7619047619999995,3538.8,7.2
+420-18-8989,A,Lagos,Member,Female,Sports and travel,18547.2,8,7418.88,155796.48,2/2/2019,15:47,Cash,148377.6,4.7619047619999995,7418.88,9.6
+726-29-6793,A,Lagos,Member,Male,Electronic accessories,8704.8,8,3481.92,73120.31999999999,1/28/2019,20:54,Epay,69638.4,4.7619047619999995,3481.92,9.8
+209-61-0206,A,Lagos,Normal,Female,Home and lifestyle,15447.599999999999,5,3861.8999999999996,81099.9,1/5/2019,17:29,Epay,77238.0,4.7619047619999995,3861.8999999999996,6.1
+595-27-4851,A,Lagos,Normal,Female,Fashion accessories,19540.8,7,6839.280000000001,143624.88,1/27/2019,18:05,Epay,136785.6,4.7619047619999995,6839.280000000001,9.3
+189-52-0236,A,Lagos,Normal,Male,Electronic accessories,35838.0,7,12543.300000000001,263409.3,3/14/2019,12:07,Cash,250866.0,4.7619047619999995,12543.300000000001,7.6
+220-28-1851,A,Lagos,Normal,Male,Home and lifestyle,12502.8,2,1250.2800000000002,26255.88,3/1/2019,18:14,Epay,25005.6,4.7619047619999995,1250.2800000000002,9.7
+609-81-8548,A,Lagos,Member,Female,Home and lifestyle,13478.4,6,4043.5200000000004,84913.92,2/6/2019,13:55,Card,80870.4,4.7619047619999995,4043.5200000000004,5.9
+534-01-4457,A,Lagos,Normal,Male,Food and beverages,29415.6,6,8824.68,185318.28,1/27/2019,14:36,Card,176493.6,4.7619047619999995,8824.68,8.0
+719-89-8991,A,Lagos,Member,Female,Sports and travel,32907.6,5,8226.9,172764.9,2/25/2019,16:03,Epay,164538.0,4.7619047619999995,8226.9,7.1
+827-77-7633,A,Lagos,Normal,Male,Sports and travel,35312.4,9,15890.580000000002,333702.18,2/17/2019,19:41,Cash,317811.6,4.7619047619999995,15890.580000000002,9.3
+287-83-1405,A,Lagos,Normal,Male,Health and beauty,9154.8,6,2746.44,57675.24,2/12/2019,19:01,Epay,54928.8,4.7619047619999995,2746.44,7.0
+435-13-4908,A,Lagos,Member,Male,Fashion accessories,31204.800000000003,8,12481.919999999998,262120.31999999998,1/24/2019,18:04,Card,249638.40000000002,4.7619047619999995,12481.919999999998,7.2
+892-05-6689,A,Lagos,Normal,Female,Home and lifestyle,10195.2,5,2548.8,53524.8,3/11/2019,13:28,Epay,50976.0,4.7619047619999995,2548.8,6.2
+643-38-7867,A,Lagos,Normal,Male,Home and lifestyle,35258.4,1,1762.92,37021.32,3/7/2019,11:44,Epay,35258.4,4.7619047619999995,1762.92,6.9
+308-81-0538,A,Lagos,Normal,Female,Fashion accessories,26298.0,4,5259.599999999999,110451.6,2/25/2019,17:16,Card,105192.0,4.7619047619999995,5259.599999999999,4.9
+460-35-4390,A,Lagos,Normal,Male,Home and lifestyle,11044.8,3,1656.72,34791.12,1/22/2019,11:00,Epay,33134.4,4.7619047619999995,1656.72,9.1
+647-50-1224,A,Lagos,Normal,Female,Fashion accessories,10591.2,10,5295.6,111207.6,1/12/2019,16:23,Epay,105912.0,4.7619047619999995,5295.6,8.9
+541-48-8554,A,Lagos,Normal,Male,Sports and travel,21942.0,9,9873.9,207351.9,1/7/2019,12:08,Card,197477.99999999997,4.7619047619999995,9873.9,6.0
+213-32-1216,A,Lagos,Normal,Female,Electronic accessories,23781.600000000002,6,7134.48,149824.08000000002,1/23/2019,10:28,Cash,142689.6,4.7619047619999995,7134.48,7.3
+134-75-2619,A,Lagos,Member,Male,Electronic accessories,6955.2,7,2434.32,51120.72,3/25/2019,18:51,Cash,48686.4,4.7619047619999995,2434.32,6.9
+712-39-0363,A,Lagos,Member,Male,Food and beverages,14997.599999999999,6,4499.28,94484.87999999999,1/2/2019,15:24,Epay,89985.6,4.7619047619999995,4499.28,5.6
+218-59-9410,A,Lagos,Member,Female,Home and lifestyle,26071.2,3,3910.68,82124.28,3/29/2019,16:54,Epay,78213.59999999999,4.7619047619999995,3910.68,8.2
+760-90-2357,A,Lagos,Member,Male,Electronic accessories,26823.600000000002,6,8047.080000000001,168988.68,3/20/2019,15:08,Epay,160941.6,4.7619047619999995,8047.080000000001,5.0
+698-98-5964,A,Lagos,Normal,Female,Food and beverages,29235.6,10,14617.8,306973.8,1/17/2019,13:01,Card,292356.0,4.7619047619999995,14617.8,6.3
+651-88-7328,A,Lagos,Normal,Female,Fashion accessories,23666.399999999998,9,10649.880000000001,223647.48,1/1/2019,13:55,Cash,212997.59999999998,4.7619047619999995,10649.880000000001,7.7
+239-48-4278,A,Lagos,Member,Male,Food and beverages,3646.8,7,1276.38,26803.98,3/10/2019,19:35,Epay,25527.6,4.7619047619999995,1276.38,8.3
+550-84-8664,A,Lagos,Normal,Male,Sports and travel,30927.6,5,7731.9,162369.9,3/22/2019,14:33,Card,154638.0,4.7619047619999995,7731.9,8.6
+797-88-0493,A,Lagos,Normal,Female,Health and beauty,23137.199999999997,4,4627.4400000000005,97176.24,3/26/2019,13:54,Cash,92548.79999999999,4.7619047619999995,4627.4400000000005,7.7
+443-82-0585,A,Lagos,Member,Female,Health and beauty,27964.800000000003,4,5592.96,117452.15999999999,2/1/2019,19:54,Cash,111859.20000000001,4.7619047619999995,5592.96,8.4
+127-47-6963,A,Lagos,Normal,Male,Health and beauty,18615.6,4,3723.1200000000003,78185.52,3/9/2019,13:53,Card,74462.4,4.7619047619999995,3723.1200000000003,9.8
+278-86-2735,A,Lagos,Normal,Female,Food and beverages,18842.4,3,2826.36,59353.55999999999,3/27/2019,14:03,Cash,56527.200000000004,4.7619047619999995,2826.36,9.2
+695-28-6250,A,Lagos,Normal,Female,Sports and travel,15501.6,5,3875.4,81383.4,2/4/2019,16:38,Epay,77508.0,4.7619047619999995,3875.4,7.7
+227-50-3718,A,Lagos,Normal,Male,Health and beauty,5263.2,5,1315.8,27631.8,3/4/2019,12:23,Cash,26315.999999999996,4.7619047619999995,1315.8,4.4
+560-49-6611,A,Lagos,Member,Female,Sports and travel,16408.8,1,820.4399999999999,17229.239999999998,2/7/2019,14:13,Cash,16408.8,4.7619047619999995,820.4399999999999,9.8
+880-35-0356,A,Lagos,Member,Female,Sports and travel,27072.0,3,4060.7999999999997,85276.8,2/5/2019,11:51,Epay,81216.0,4.7619047619999995,4060.7999999999997,4.8
+152-68-2907,A,Lagos,Normal,Male,Food and beverages,18792.0,3,2818.8,59194.8,2/15/2019,13:30,Card,56376.0,4.7619047619999995,2818.8,9.5
+334-64-2006,A,Lagos,Member,Female,Home and lifestyle,25315.199999999997,2,2531.52,53161.92,3/24/2019,14:22,Epay,50630.399999999994,4.7619047619999995,2531.52,9.6
+559-98-9873,A,Lagos,Member,Female,Fashion accessories,19314.0,7,6759.9,141957.9,2/10/2019,12:56,Epay,135198.0,4.7619047619999995,6759.9,5.2
+318-12-0304,A,Lagos,Normal,Male,Fashion accessories,11019.6,1,550.98,11570.580000000002,1/23/2019,12:20,Epay,11019.6,4.7619047619999995,550.98,5.2
+421-95-9805,A,Lagos,Normal,Female,Electronic accessories,10425.6,1,521.2800000000001,10946.880000000001,2/7/2019,10:18,Card,10425.6,4.7619047619999995,521.2800000000001,6.2
+443-59-0061,A,Lagos,Member,Male,Food and beverages,24282.0,10,12141.0,254961.0,2/3/2019,11:25,Epay,242820.0,4.7619047619999995,12141.0,4.2
+509-29-3912,A,Lagos,Member,Female,Sports and travel,13939.199999999999,9,6272.639999999999,131725.44,3/20/2019,12:24,Epay,125452.8,4.7619047619999995,6272.639999999999,4.2
+828-46-6863,A,Lagos,Member,Male,Food and beverages,35470.8,6,10641.24,223466.04,1/23/2019,11:22,Card,212824.8,4.7619047619999995,10641.24,4.0
+420-97-3340,A,Lagos,Normal,Female,Food and beverages,25804.800000000003,3,3870.7200000000003,81285.12,3/28/2019,15:30,Card,77414.4,4.7619047619999995,3870.7200000000003,9.2
+436-54-4512,A,Lagos,Member,Female,Food and beverages,32979.6,1,1648.98,34628.58,3/20/2019,19:44,Cash,32979.6,4.7619047619999995,1648.98,9.8
+816-57-2053,A,Lagos,Normal,Male,Sports and travel,21913.2,2,2191.32,46017.72,3/9/2019,12:37,Epay,43826.4,4.7619047619999995,2191.32,8.7
+856-66-2701,A,Lagos,Member,Male,Home and lifestyle,19188.0,3,2878.2,60442.200000000004,1/25/2019,14:19,Epay,57564.0,4.7619047619999995,2878.2,7.5
+308-39-1707,A,Lagos,Normal,Female,Fashion accessories,4352.4,1,217.62,4570.0199999999995,1/26/2019,18:19,Card,4352.4,4.7619047619999995,217.62,8.2
+149-61-1929,A,Lagos,Normal,Male,Sports and travel,23108.399999999998,10,11554.199999999999,242638.2,1/19/2019,14:08,Card,231084.0,4.7619047619999995,11554.199999999999,6.7
+655-07-2265,A,Lagos,Normal,Male,Electronic accessories,28191.600000000002,3,4228.74,88803.54000000001,3/5/2019,16:38,Epay,84574.8,4.7619047619999995,4228.74,5.4
+589-02-8023,A,Lagos,Member,Male,Food and beverages,30157.199999999997,2,3015.7200000000003,63330.12,1/15/2019,10:54,Card,60314.399999999994,4.7619047619999995,3015.7200000000003,7.0
+610-46-4100,A,Lagos,Normal,Male,Health and beauty,10422.0,7,3647.7000000000003,76601.7,3/3/2019,20:31,Card,72954.0,4.7619047619999995,3647.7000000000003,6.0
+706-36-6154,A,Lagos,Member,Male,Home and lifestyle,6969.599999999999,9,3136.3199999999997,65862.72,1/18/2019,18:43,Epay,62726.4,4.7619047619999995,3136.3199999999997,8.7
+742-04-5161,A,Lagos,Member,Male,Home and lifestyle,26200.8,10,13100.4,275108.4,2/3/2019,17:24,Cash,262007.99999999997,4.7619047619999995,13100.4,7.3
+169-52-4504,A,Lagos,Normal,Female,Electronic accessories,5648.4,3,847.26,17792.46,3/14/2019,14:13,Card,16945.2,4.7619047619999995,847.26,5.8
+562-12-5430,A,Lagos,Member,Female,Fashion accessories,31734.000000000004,3,4760.1,99962.1,1/18/2019,10:11,Epay,95202.0,4.7619047619999995,4760.1,7.9
+816-72-8853,A,Lagos,Member,Female,Sports and travel,10054.8,5,2513.7,52787.7,1/29/2019,15:48,Cash,50274.0,4.7619047619999995,2513.7,5.9
+491-38-3499,A,Lagos,Member,Male,Fashion accessories,19962.0,1,998.1,20960.1,2/26/2019,17:46,Card,19962.0,4.7619047619999995,998.1,4.9
+518-17-2983,A,Lagos,Normal,Female,Fashion accessories,17506.8,4,3501.36,73528.56,2/4/2019,15:44,Epay,70027.2,4.7619047619999995,3501.36,7.6
+588-47-8641,A,Lagos,Member,Male,Fashion accessories,20174.4,10,10087.2,211831.19999999998,1/14/2019,19:30,Epay,201744.0,4.7619047619999995,10087.2,4.4
+811-03-8790,A,Lagos,Normal,Female,Electronic accessories,16372.8,10,8186.4,171914.4,3/1/2019,10:22,Card,163728.0,4.7619047619999995,8186.4,4.8
+274-05-5470,A,Lagos,Member,Female,Food and beverages,26449.2,4,5289.84,111086.64,2/23/2019,18:30,Cash,105796.8,4.7619047619999995,5289.84,6.0
+130-67-4723,A,Lagos,Member,Male,Food and beverages,17460.0,6,5238.0,109998.0,1/11/2019,13:57,Epay,104760.0,4.7619047619999995,5238.0,9.4
+105-10-6182,A,Lagos,Member,Male,Fashion accessories,7732.8,2,773.2800000000001,16238.880000000001,2/27/2019,12:22,Epay,15465.6,4.7619047619999995,773.2800000000001,6.6
+648-83-1321,A,Lagos,Member,Male,Home and lifestyle,22881.600000000002,10,11440.800000000001,240256.8,1/16/2019,17:59,Cash,228816.0,4.7619047619999995,11440.800000000001,4.3
+305-03-2383,A,Lagos,Normal,Female,Food and beverages,24155.999999999996,3,3623.3999999999996,76091.40000000001,2/15/2019,10:36,Cash,72468.0,4.7619047619999995,3623.3999999999996,7.5
+689-05-1884,A,Lagos,Member,Male,Health and beauty,17506.8,10,8753.4,183821.4,3/4/2019,12:44,Cash,175068.0,4.7619047619999995,8753.4,8.8
+800-09-8606,A,Lagos,Member,Female,Home and lifestyle,31453.2,5,7863.3,165129.3,1/29/2019,19:45,Cash,157266.0,4.7619047619999995,7863.3,6.6
+182-52-7000,A,Lagos,Member,Female,Sports and travel,9734.4,4,1946.8799999999999,40884.479999999996,1/1/2019,20:26,Epay,38937.6,4.7619047619999995,1946.8799999999999,6.9
+868-06-0466,A,Lagos,Member,Male,Electronic accessories,25048.8,9,11271.96,236711.16000000003,2/19/2019,19:38,Card,225439.2,4.7619047619999995,11271.96,7.8
+445-30-9252,A,Lagos,Normal,Male,Sports and travel,9252.0,3,1387.8,29143.8,1/17/2019,17:59,Epay,27755.999999999996,4.7619047619999995,1387.8,6.1
+786-94-2700,A,Lagos,Member,Male,Food and beverages,29023.2,6,8706.96,182846.16,2/28/2019,20:18,Cash,174139.2,4.7619047619999995,8706.96,9.1
+258-92-7466,A,Lagos,Normal,Female,Health and beauty,12844.8,5,3211.2,67435.2,2/6/2019,18:33,Card,64224.0,4.7619047619999995,3211.2,6.6
+857-16-3520,A,Lagos,Member,Female,Fashion accessories,25725.6,7,9003.96,189083.16,3/28/2019,16:06,Epay,180079.2,4.7619047619999995,9003.96,4.5
+482-17-1179,A,Lagos,Member,Male,Electronic accessories,4298.4,3,644.76,13539.96,1/19/2019,12:47,Card,12895.2,4.7619047619999995,644.76,8.1
+788-21-5741,A,Lagos,Normal,Male,Fashion accessories,16336.800000000001,3,2450.52,51460.92,2/17/2019,13:34,Card,49010.399999999994,4.7619047619999995,2450.52,7.2
+247-11-2470,A,Lagos,Member,Female,Fashion accessories,8035.2,4,1607.0399999999997,33747.84,3/1/2019,16:23,Card,32140.8,4.7619047619999995,1607.0399999999997,4.4
+635-28-5728,A,Lagos,Normal,Male,Health and beauty,20160.0,3,3024.0,63504.0,2/28/2019,19:33,Epay,60480.0,4.7619047619999995,3024.0,4.8
+756-49-0168,A,Lagos,Member,Male,Fashion accessories,7092.0,1,354.6,7446.599999999999,2/8/2019,11:39,Epay,7092.0,4.7619047619999995,354.6,9.5
+805-86-0265,A,Lagos,Normal,Male,Home and lifestyle,33825.6,9,15221.519999999999,319651.92,3/20/2019,11:32,Cash,304430.4,4.7619047619999995,15221.519999999999,9.8
+373-14-0504,A,Lagos,Member,Female,Sports and travel,25786.8,2,2578.68,54152.28,2/12/2019,14:33,Epay,51573.6,4.7619047619999995,2578.68,8.8
+546-80-2899,A,Lagos,Member,Male,Home and lifestyle,13568.4,2,1356.84,28493.64,2/20/2019,15:29,Epay,27136.8,4.7619047619999995,1356.84,9.5
+585-86-8361,A,Lagos,Normal,Female,Food and beverages,9820.800000000001,5,2455.2000000000003,51559.2,2/3/2019,10:31,Card,49104.0,4.7619047619999995,2455.2000000000003,8.6
+807-14-7833,A,Lagos,Member,Female,Electronic accessories,6271.200000000001,10,3135.6000000000004,65847.6,2/22/2019,12:30,Epay,62711.99999999999,4.7619047619999995,3135.6000000000004,7.0
+652-43-6591,A,Lagos,Normal,Female,Fashion accessories,35024.4,8,14009.759999999998,294204.96,3/9/2019,13:18,Card,280195.2,4.7619047619999995,14009.759999999998,6.2
+406-46-7107,A,Lagos,Normal,Female,Home and lifestyle,34747.2,6,10424.16,218907.36000000002,1/11/2019,11:52,Cash,208483.2,4.7619047619999995,10424.16,4.5
+250-17-5703,A,Lagos,Member,Male,Food and beverages,6786.000000000001,10,3393.0000000000005,71253.0,2/27/2019,18:24,Epay,67860.0,4.7619047619999995,3393.0000000000005,5.6
+156-95-3964,A,Lagos,Normal,Female,Food and beverages,19940.4,4,3988.08,83749.68000000001,3/25/2019,15:19,Epay,79761.6,4.7619047619999995,3988.08,8.0
+410-67-1709,A,Lagos,Member,Female,Fashion accessories,22996.8,8,9198.72,193173.12,1/20/2019,17:48,Epay,183974.4,4.7619047619999995,9198.72,9.9
+587-73-4862,A,Lagos,Member,Female,Health and beauty,3848.3999999999996,5,962.0999999999999,20204.100000000002,3/26/2019,11:07,Epay,19242.0,4.7619047619999995,962.0999999999999,7.6
+787-87-2010,A,Lagos,Member,Male,Health and beauty,19980.0,4,3996.0,83916.0,1/20/2019,15:48,Card,79920.0,4.7619047619999995,3996.0,6.6
+886-54-6089,A,Lagos,Normal,Female,Home and lifestyle,4114.8,6,1234.4399999999998,25923.24,1/15/2019,17:24,Cash,24688.8,4.7619047619999995,1234.4399999999998,7.7
+534-53-3526,A,Lagos,Normal,Female,Sports and travel,34113.6,4,6822.72,143277.12,2/11/2019,16:06,Epay,136454.4,4.7619047619999995,6822.72,7.8
+307-04-2070,A,Lagos,Member,Female,Fashion accessories,11023.2,1,551.16,11574.359999999999,2/5/2019,14:14,Card,11023.2,4.7619047619999995,551.16,4.1
+404-91-5964,A,Lagos,Normal,Male,Electronic accessories,26848.8,7,9397.08,197338.68,2/4/2019,16:09,Card,187941.59999999998,4.7619047619999995,9397.08,9.0
+497-37-6538,A,Lagos,Normal,Male,Sports and travel,21207.6,7,7422.660000000001,155875.86,1/17/2019,15:15,Epay,148453.2,4.7619047619999995,7422.660000000001,9.7
+651-96-5970,A,Lagos,Normal,Male,Fashion accessories,16707.6,1,835.38,17542.98,3/3/2019,20:06,Card,16707.6,4.7619047619999995,835.38,4.0
+263-12-5321,A,Lagos,Member,Male,Electronic accessories,33336.0,7,11667.599999999999,245019.6,2/27/2019,12:52,Card,233352.00000000003,4.7619047619999995,11667.599999999999,9.3
+702-72-0487,A,Lagos,Normal,Female,Electronic accessories,16779.6,2,1677.9600000000003,35237.16,2/26/2019,12:28,Card,33559.2,4.7619047619999995,1677.9600000000003,6.6
+864-24-7918,A,Lagos,Member,Female,Sports and travel,8816.4,10,4408.2,92572.2,2/22/2019,15:15,Cash,88164.0,4.7619047619999995,4408.2,8.1
+759-29-9521,A,Lagos,Member,Female,Fashion accessories,17625.6,9,7931.519999999999,166561.91999999998,3/4/2019,11:27,Cash,158630.4,4.7619047619999995,7931.519999999999,8.0
+220-68-6701,A,Lagos,Normal,Female,Home and lifestyle,27889.2,4,5577.84,117134.64000000001,3/17/2019,16:36,Cash,111556.8,4.7619047619999995,5577.84,4.2
+618-34-8551,A,Lagos,Normal,Female,Sports and travel,33544.8,2,3354.48,70444.08,1/16/2019,18:41,Card,67089.6,4.7619047619999995,3354.48,8.5
+257-60-7754,A,Lagos,Normal,Female,Electronic accessories,18082.8,4,3616.56,75947.76,1/8/2019,17:12,Cash,72331.2,4.7619047619999995,3616.56,9.0
+380-60-5336,A,Lagos,Normal,Female,Electronic accessories,14493.599999999999,10,7246.799999999999,152182.80000000002,2/24/2019,18:06,Card,144936.0,4.7619047619999995,7246.799999999999,5.0
+674-56-6360,A,Lagos,Normal,Male,Electronic accessories,34254.0,1,1712.7,35966.7,3/22/2019,14:00,Cash,34254.0,4.7619047619999995,1712.7,6.0
+778-34-2523,A,Lagos,Member,Female,Electronic accessories,17503.2,8,7001.28,147026.88,1/24/2019,10:57,Cash,140025.6,4.7619047619999995,7001.28,5.0
+832-51-6761,A,Lagos,Normal,Male,Food and beverages,12196.800000000001,8,4878.72,102453.12,1/19/2019,20:29,Epay,97574.40000000001,4.7619047619999995,4878.72,9.6
+186-43-8965,A,Lagos,Member,Female,Home and lifestyle,17164.8,2,1716.48,36046.079999999994,2/24/2019,10:10,Card,34329.6,4.7619047619999995,1716.48,4.1
+276-75-6884,A,Lagos,Normal,Female,Health and beauty,24735.6,3,3710.34,77917.14,3/4/2019,10:05,Cash,74206.8,4.7619047619999995,3710.34,8.7
+569-76-2760,A,Lagos,Member,Female,Sports and travel,7923.6,4,1584.72,33279.12,1/29/2019,18:15,Card,31694.4,4.7619047619999995,1584.72,6.6
+760-53-9233,A,Lagos,Member,Male,Fashion accessories,14860.800000000001,3,2229.12,46811.520000000004,3/26/2019,18:37,Card,44582.4,4.7619047619999995,2229.12,8.5
+416-17-9926,A,Lagos,Member,Female,Electronic accessories,26719.2,10,13359.6,280551.6,1/1/2019,14:42,Card,267192.0,4.7619047619999995,13359.6,4.3
+237-44-6163,A,Lagos,Normal,Male,Electronic accessories,3801.6000000000004,8,1520.64,31933.44,1/24/2019,17:43,Cash,30412.800000000003,4.7619047619999995,1520.64,7.6
+528-14-9470,A,Lagos,Member,Male,Health and beauty,32868.0,1,1643.4,34511.4,2/14/2019,14:42,Epay,32868.0,4.7619047619999995,1643.4,9.2
+807-34-3742,A,Lagos,Normal,Male,Fashion accessories,18856.8,1,942.8399999999999,19799.64,3/26/2019,19:44,Cash,18856.8,4.7619047619999995,942.8399999999999,5.8
+288-62-1085,A,Lagos,Member,Male,Fashion accessories,13874.4,5,3468.6,72840.6,1/9/2019,13:34,Epay,69372.0,4.7619047619999995,3468.6,5.6
+497-36-0989,A,Lagos,Normal,Male,Fashion accessories,18698.399999999998,3,2804.76,58899.96,2/15/2019,15:21,Cash,56095.2,4.7619047619999995,2804.76,7.9
+860-73-6466,A,Lagos,Member,Female,Sports and travel,14209.199999999999,2,1420.92,29839.32,3/2/2019,16:16,Card,28418.399999999998,4.7619047619999995,1420.92,5.0
+896-34-0956,A,Lagos,Normal,Male,Fashion accessories,7675.2,1,383.75999999999993,8058.96,1/26/2019,12:43,Cash,7675.2,4.7619047619999995,383.75999999999993,5.9
+804-38-3935,A,Lagos,Member,Male,Electronic accessories,33760.8,3,5064.12,106346.51999999999,1/30/2019,11:32,Card,101282.4,4.7619047619999995,5064.12,5.9
+585-90-0249,A,Lagos,Member,Male,Electronic accessories,26373.600000000002,1,1318.68,27692.28,1/27/2019,18:08,Epay,26373.600000000002,4.7619047619999995,1318.68,9.7
+125-45-2293,A,Lagos,Normal,Female,Fashion accessories,35676.0,6,10702.8,224758.80000000002,1/19/2019,13:11,Cash,214056.0,4.7619047619999995,10702.8,4.2
+843-73-4724,A,Lagos,Normal,Male,Fashion accessories,26675.999999999996,1,1333.8,28009.800000000003,1/25/2019,11:05,Cash,26675.999999999996,4.7619047619999995,1333.8,9.2
+409-33-9708,A,Lagos,Normal,Female,Fashion accessories,35452.8,2,3545.2799999999997,74450.88,2/19/2019,10:12,Epay,70905.6,4.7619047619999995,3545.2799999999997,9.2
+160-22-2687,A,Lagos,Member,Female,Health and beauty,34542.0,5,8635.5,181345.5,1/23/2019,14:21,Epay,172710.0,4.7619047619999995,8635.5,8.8
+748-45-2862,A,Lagos,Member,Female,Home and lifestyle,10191.6,4,2038.3200000000004,42804.72,3/7/2019,18:35,Cash,40766.4,4.7619047619999995,2038.3200000000004,8.2
+316-66-3011,A,Lagos,Member,Female,Food and beverages,17146.8,9,7716.0599999999995,162037.26,1/23/2019,12:35,Cash,154321.2,4.7619047619999995,7716.0599999999995,5.0
+840-76-5966,A,Lagos,Member,Male,Sports and travel,4593.6,2,459.36,9646.56,1/8/2019,18:06,Epay,9187.2,4.7619047619999995,459.36,7.8
+124-31-1458,A,Lagos,Member,Female,Electronic accessories,28652.4,3,4297.86,90255.06,1/8/2019,14:30,Cash,85957.2,4.7619047619999995,4297.86,6.6
+852-82-2749,A,Lagos,Normal,Male,Sports and travel,23252.4,4,4650.48,97660.08,1/6/2019,13:35,Epay,93009.6,4.7619047619999995,4650.48,9.3
+873-14-6353,A,Lagos,Member,Male,Food and beverages,8935.2,7,3127.3200000000006,65673.72,2/16/2019,10:33,Card,62546.4,4.7619047619999995,3127.3200000000006,7.1
+166-19-2553,A,Lagos,Member,Male,Sports and travel,32061.600000000002,6,9618.480000000001,201988.08,1/18/2019,17:26,Cash,192369.6,4.7619047619999995,9618.480000000001,9.9
+737-88-5876,A,Lagos,Member,Male,Home and lifestyle,8384.4,4,1676.88,35214.479999999996,3/19/2019,11:52,Card,33537.6,4.7619047619999995,1676.88,5.9
+448-61-3783,A,Lagos,Normal,Female,Electronic accessories,32407.199999999997,8,12962.880000000001,272220.48,3/21/2019,16:08,Card,259257.59999999998,4.7619047619999995,12962.880000000001,4.5
+291-55-6563,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.3599999999997,78064.56,3/30/2019,12:45,Epay,74347.2,4.7619047619999995,3717.3599999999997,7.5
+548-48-3156,A,Lagos,Member,Female,Food and beverages,30002.4,2,3000.24,63005.04,3/19/2019,13:37,Cash,60004.8,4.7619047619999995,3000.24,7.6
+460-93-5834,A,Lagos,Normal,Male,Sports and travel,16408.8,7,5743.08,120604.68000000001,1/13/2019,10:03,Cash,114861.6,4.7619047619999995,5743.08,5.0
+325-89-4209,A,Lagos,Member,Male,Food and beverages,31644.000000000004,1,1582.1999999999998,33226.2,2/5/2019,19:42,Epay,31644.000000000004,4.7619047619999995,1582.1999999999998,6.7
+884-80-6021,A,Lagos,Member,Female,Electronic accessories,26449.2,10,13224.6,277716.6,3/23/2019,13:14,Epay,264492.0,4.7619047619999995,13224.6,9.5
+880-46-5796,A,Lagos,Member,Male,Sports and travel,27691.2,10,13845.6,290757.6,3/17/2019,19:53,Epay,276912.0,4.7619047619999995,13845.6,5.6
+146-09-5432,A,Lagos,Member,Male,Food and beverages,12614.4,9,5676.48,119206.08000000002,2/9/2019,19:17,Epay,113529.6,4.7619047619999995,5676.48,4.6
+595-94-9924,A,Lagos,Member,Female,Health and beauty,9982.8,5,2495.7,52409.700000000004,3/26/2019,20:21,Card,49914.0,4.7619047619999995,2495.7,4.2
+865-41-9075,A,Lagos,Normal,Male,Food and beverages,4150.8,7,1452.78,30508.38,1/28/2019,17:35,Cash,29055.6,4.7619047619999995,1452.78,8.1
+186-71-5196,A,Lagos,Member,Female,Food and beverages,28634.4,2,2863.44,60132.240000000005,3/27/2019,16:30,Epay,57268.8,4.7619047619999995,2863.44,6.2
+453-33-6436,A,Lagos,Normal,Female,Home and lifestyle,33523.200000000004,8,13409.280000000002,281594.88,2/7/2019,10:09,Cash,268185.60000000003,4.7619047619999995,13409.280000000002,6.8
+522-57-8364,A,Lagos,Member,Male,Fashion accessories,18482.4,8,7392.959999999999,155252.16,1/31/2019,10:00,Epay,147859.2,4.7619047619999995,7392.959999999999,7.6
+459-45-2396,A,Lagos,Member,Female,Food and beverages,35856.0,3,5378.4,112946.40000000001,2/25/2019,18:45,Cash,107568.0,4.7619047619999995,5378.4,5.8
+749-81-8133,A,Lagos,Normal,Female,Fashion accessories,34081.2,4,6816.240000000001,143141.04,3/11/2019,12:04,Cash,136324.8,4.7619047619999995,6816.240000000001,6.8
+397-25-8725,A,Lagos,Member,Female,Health and beauty,14263.199999999999,9,6418.4400000000005,134787.24,1/13/2019,17:54,Card,128368.79999999999,4.7619047619999995,6418.4400000000005,6.8
+243-55-8457,A,Lagos,Normal,Female,Food and beverages,26798.399999999998,10,13399.199999999999,281383.2,2/27/2019,11:40,Epay,267984.0,4.7619047619999995,13399.199999999999,5.1
+361-85-2571,A,Lagos,Normal,Female,Sports and travel,32212.800000000003,5,8053.200000000001,169117.19999999998,3/30/2019,10:18,Cash,161064.0,4.7619047619999995,8053.200000000001,7.4
+131-70-8179,A,Lagos,Member,Female,Health and beauty,33152.4,3,4972.86,104430.06000000001,2/17/2019,16:27,Cash,99457.2,4.7619047619999995,4972.86,4.2
+720-72-2436,A,Lagos,Normal,Male,Food and beverages,23947.199999999997,4,4789.4400000000005,100578.24,3/2/2019,18:14,Epay,95788.79999999999,4.7619047619999995,4789.4400000000005,6.9
+809-69-9497,A,Lagos,Normal,Female,Home and lifestyle,16444.8,10,8222.4,172670.4,1/19/2019,19:30,Epay,164448.0,4.7619047619999995,8222.4,5.7
+449-16-6770,A,Lagos,Normal,Male,Health and beauty,18284.4,5,4571.1,95993.09999999999,2/19/2019,14:53,Card,91422.0,4.7619047619999995,4571.1,5.3
+333-23-2632,A,Lagos,Member,Male,Health and beauty,3628.8,7,1270.08,26671.680000000004,3/28/2019,20:14,Cash,25401.600000000002,4.7619047619999995,1270.08,4.2
+489-82-1237,A,Lagos,Normal,Female,Electronic accessories,33796.799999999996,7,11828.880000000001,248406.48,1/5/2019,11:51,Card,236577.59999999998,4.7619047619999995,11828.880000000001,7.3
+846-10-0341,A,Lagos,Normal,Female,Fashion accessories,15325.2,7,5363.82,112640.22,1/6/2019,11:51,Cash,107276.40000000001,4.7619047619999995,5363.82,6.8
+384-59-6655,A,Lagos,Member,Female,Food and beverages,35517.6,9,15982.92,335641.32,2/19/2019,15:07,Cash,319658.4,4.7619047619999995,15982.92,8.4
+324-92-3863,A,Lagos,Member,Male,Electronic accessories,7520.400000000001,2,752.04,15792.84,2/5/2019,18:45,Cash,15040.800000000001,4.7619047619999995,752.04,9.8
+593-08-5916,A,Lagos,Normal,Female,Fashion accessories,5580.0,1,279.0,5858.999999999999,3/19/2019,15:23,Card,5580.0,4.7619047619999995,279.0,7.4
+558-60-5016,A,Lagos,Normal,Female,Home and lifestyle,11987.999999999998,9,5394.599999999999,113286.6,3/4/2019,15:27,Epay,107892.0,4.7619047619999995,5394.599999999999,7.2
+195-06-0432,A,Lagos,Member,Male,Home and lifestyle,29163.600000000002,3,4374.54,91865.34,1/13/2019,12:55,Card,87490.8,4.7619047619999995,4374.54,9.3
+605-03-2706,A,Lagos,Normal,Female,Health and beauty,5688.0,3,853.2,17917.2,3/25/2019,18:02,Cash,17064.0,4.7619047619999995,853.2,9.5
+531-56-4728,A,Lagos,Normal,Male,Home and lifestyle,28828.8,3,4324.32,90810.72,2/11/2019,15:29,Cash,86486.40000000001,4.7619047619999995,4324.32,5.4
+221-25-5073,A,Lagos,Normal,Female,Food and beverages,26877.6,4,5375.52,112885.92,3/4/2019,10:39,Cash,107510.4,4.7619047619999995,5375.52,8.5
+809-46-1866,A,Lagos,Normal,Male,Health and beauty,20934.0,4,4186.8,87922.8,1/23/2019,17:44,Cash,83736.0,4.7619047619999995,4186.8,8.4
+139-32-4183,A,Lagos,Member,Female,Sports and travel,35092.8,9,15791.760000000002,331626.96,3/14/2019,14:19,Epay,315835.2,4.7619047619999995,15791.760000000002,7.4
+886-18-2897,A,Lagos,Normal,Female,Food and beverages,20361.600000000002,5,5090.400000000001,106898.4,3/22/2019,19:06,Card,101808.0,4.7619047619999995,5090.400000000001,4.5
+745-74-0715,A,Lagos,Normal,Male,Electronic accessories,20890.8,2,2089.08,43870.68,3/10/2019,20:46,Epay,41781.6,4.7619047619999995,2089.08,8.8
+727-02-1313,A,Lagos,Member,Male,Food and beverages,11462.4,1,573.12,12035.519999999999,2/9/2019,13:22,Cash,11462.4,4.7619047619999995,573.12,7.7
+347-56-2442,A,Lagos,Normal,Male,Home and lifestyle,23695.199999999997,1,1184.7600000000002,24879.960000000003,2/22/2019,15:33,Cash,23695.199999999997,4.7619047619999995,1184.7600000000002,4.1
+849-09-3807,A,Lagos,Member,Female,Fashion accessories,31802.4,7,11130.84,233747.63999999998,2/18/2019,13:28,Cash,222616.8,4.7619047619999995,11130.84,6.6
diff --git a/Pandas_Analytics_project/Market_data.csv b/Pandas_Analytics_project/Market_data.csv
new file mode 100644
index 0000000..657baba
--- /dev/null
+++ b/Pandas_Analytics_project/Market_data.csv
@@ -0,0 +1,1001 @@
+,Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
+0,750-67-8428,A,Lagos,Member,Female,Health and beauty,26888.4,7,9410.94,197629.74,1/5/2019,13:08,Epay,188218.8,4.761904762,9410.94,9.1
+1,631-41-3108,A,Lagos,Normal,Male,Home and lifestyle,16678.8,7,5837.58,122589.18,3/3/2019,13:23,Card,116751.6,4.761904762,5837.58,7.4
+2,123-19-1176,A,Lagos,Member,Male,Health and beauty,20959.2,8,8383.68,176057.28,1/27/2019,20:33,Epay,167673.6,4.761904762,8383.68,8.4
+3,373-73-7910,A,Lagos,Normal,Male,Sports and travel,31071.6,7,10875.06,228376.26,2/8/2019,10:37,Epay,217501.2,4.761904762,10875.06,5.3
+4,355-53-5943,A,Lagos,Member,Female,Electronic accessories,24782.4,6,7434.719999999999,156129.12,2/25/2019,14:36,Epay,148694.4,4.761904762,7434.719999999999,5.8
+5,665-32-9167,A,Lagos,Member,Female,Health and beauty,13053.6,2,1305.36,27412.56,1/10/2019,17:15,Card,26107.2,4.761904762,1305.36,7.2
+6,365-64-0515,A,Lagos,Normal,Female,Electronic accessories,16902.0,5,4225.5,88735.5,2/12/2019,10:25,Epay,84510.0,4.761904762,4225.5,7.1
+7,252-56-2699,A,Lagos,Normal,Male,Food and beverages,15548.4,10,7774.2,163258.2,2/7/2019,16:48,Epay,155484.0,4.761904762,7774.2,8.2
+8,829-34-3910,A,Lagos,Normal,Female,Health and beauty,25696.8,10,12848.4,269816.4,3/29/2019,19:21,Cash,256968.0,4.761904762,12848.4,5.7
+9,656-95-9349,A,Lagos,Member,Female,Health and beauty,24814.800000000003,7,8685.18,182388.78,3/11/2019,11:03,Card,173703.6,4.761904762,8685.18,4.6
+10,765-26-6951,A,Lagos,Normal,Male,Sports and travel,26139.6,6,7841.88,164679.48,1/1/2019,10:39,Card,156837.6,4.761904762,7841.88,6.9
+11,329-62-1586,A,Lagos,Normal,Male,Food and beverages,19681.2,3,2952.18,61995.78,1/21/2019,18:00,Card,59043.6,4.761904762,2952.18,8.6
+12,636-48-8204,A,Lagos,Normal,Male,Electronic accessories,12441.6,5,3110.4,65318.4,2/17/2019,11:15,Epay,62208.00000000001,4.761904762,3110.4,9.9
+13,549-59-1358,A,Lagos,Member,Male,Sports and travel,31906.8,3,4786.02,100506.42,3/2/2019,17:36,Epay,95720.4,4.761904762,4786.02,6.0
+14,227-03-5010,A,Lagos,Member,Female,Home and lifestyle,18932.4,8,7572.959999999999,159032.16,3/22/2019,19:20,Card,151459.2,4.761904762,7572.959999999999,8.5
+15,189-17-4241,A,Lagos,Normal,Female,Fashion accessories,31561.2,2,3156.1200000000003,66278.52,3/10/2019,12:17,Card,63122.4,4.761904762,3156.1200000000003,7.7
+16,848-62-7243,A,Lagos,Normal,Male,Health and beauty,8960.4,9,4032.18,84675.78,3/15/2019,15:36,Cash,80643.59999999999,4.761904762,4032.18,7.4
+17,595-11-5460,A,Lagos,Normal,Male,Health and beauty,34768.8,2,3476.88,73014.48,3/15/2019,10:12,Card,69537.6,4.761904762,3476.88,5.1
+18,129-29-8530,A,Lagos,Member,Male,Sports and travel,22543.2,5,5635.8,118351.8,3/10/2019,19:15,Epay,112716.0,4.761904762,5635.8,7.0
+19,272-65-1806,A,Lagos,Normal,Female,Electronic accessories,21916.8,9,9862.56,207113.76,1/15/2019,17:17,Epay,197251.2,4.761904762,9862.56,4.7
+20,162-48-8011,A,Lagos,Member,Female,Food and beverages,16052.4,5,4013.1,84275.1,2/10/2019,15:10,Cash,80262.0,4.761904762,4013.1,8.5
+21,106-35-6779,A,Lagos,Member,Male,Home and lifestyle,15962.4,2,1596.24,33521.03999999999,3/27/2019,11:26,Cash,31924.800000000003,4.761904762,1596.24,5.8
+22,635-40-6220,A,Lagos,Normal,Male,Health and beauty,32256.0,8,12902.4,270950.4,2/7/2019,11:28,Epay,258048.0,4.761904762,12902.4,6.6
+23,817-48-8732,A,Lagos,Member,Female,Home and lifestyle,26046.0,10,13022.999999999998,273483.0,1/20/2019,15:55,Cash,260460.0,4.761904762,13022.999999999998,5.4
+24,199-75-8169,A,Lagos,Member,Male,Sports and travel,5691.6,10,2845.8,59761.8,3/6/2019,12:27,Card,56916.0,4.761904762,2845.8,8.6
+25,877-22-3308,A,Lagos,Member,Male,Health and beauty,5713.2,10,2856.6,59988.6,3/13/2019,16:40,Cash,57131.99999999999,4.761904762,2856.6,5.8
+26,232-11-3025,A,Lagos,Normal,Male,Sports and travel,28357.2,10,14178.6,297750.60000000003,1/24/2019,10:04,Cash,283572.0,4.761904762,14178.6,6.4
+27,382-03-4532,A,Lagos,Member,Female,Health and beauty,6598.799999999999,1,329.94,6928.740000000001,2/2/2019,18:50,Cash,6598.799999999999,4.761904762,329.94,4.3
+28,287-21-9091,A,Lagos,Normal,Male,Home and lifestyle,26881.2,9,12096.54,254027.34,1/22/2019,10:55,Epay,241930.8,4.761904762,12096.54,9.4
+29,381-20-0914,A,Lagos,Member,Female,Fashion accessories,7203.6,9,3241.62,68074.02,1/12/2019,15:48,Card,64832.4,4.761904762,3241.62,5.7
+30,633-44-8566,A,Lagos,Member,Male,Food and beverages,17776.8,7,6221.88,130659.48,3/27/2019,20:35,Card,124437.6,4.761904762,6221.88,7.3
+31,504-35-8843,A,Lagos,Normal,Male,Sports and travel,15289.2,1,764.46,16053.66,1/2/2019,16:57,Cash,15289.2,4.761904762,764.46,5.7
+32,873-51-0671,A,Lagos,Member,Female,Sports and travel,7912.8,7,2769.48,58159.08,1/10/2019,16:42,Epay,55389.600000000006,4.761904762,2769.48,5.1
+33,594-34-4444,A,Lagos,Normal,Male,Electronic accessories,34977.6,1,1748.88,36726.48,3/8/2019,20:38,Epay,34977.6,4.761904762,1748.88,7.2
+34,865-92-6136,A,Lagos,Normal,Male,Food and beverages,18990.0,3,2848.5,59818.5,3/23/2019,10:16,Epay,56970.0,4.761904762,2848.5,9.3
+35,212-62-1842,A,Lagos,Normal,Male,Food and beverages,20973.6,6,6292.079999999999,132133.68,3/28/2019,16:44,Cash,125841.6,4.761904762,6292.079999999999,9.9
+36,704-48-3927,A,Lagos,Member,Male,Electronic accessories,31921.2,10,15960.6,335172.6,1/12/2019,14:50,Epay,319212.0,4.761904762,15960.6,7.3
+37,630-74-5166,A,Lagos,Normal,Male,Sports and travel,22366.8,6,6710.04,140910.84000000003,3/22/2019,20:19,Cash,134200.8,4.761904762,6710.04,7.4
+38,645-44-1170,A,Lagos,Member,Male,Home and lifestyle,20905.2,9,9407.34,197554.14,1/19/2019,20:07,Epay,188146.8,4.761904762,9407.34,4.3
+39,642-32-2990,A,Lagos,Normal,Female,Food and beverages,3945.6,10,1972.8,41428.8,2/2/2019,20:48,Epay,39456.0,4.761904762,1972.8,6.0
+40,638-60-7125,A,Lagos,Normal,Female,Electronic accessories,35841.6,8,14336.64,301069.44,2/14/2019,17:03,Card,286732.8,4.761904762,14336.64,5.2
+41,668-90-8900,A,Lagos,Normal,Female,Home and lifestyle,33728.4,7,11804.94,247903.74,3/10/2019,18:44,Card,236098.8,4.761904762,11804.94,4.5
+42,870-54-3162,A,Lagos,Normal,Female,Sports and travel,11610.0,5,2902.5,60952.5,1/27/2019,13:26,Cash,58050.0,4.761904762,2902.5,9.0
+43,802-70-5316,A,Lagos,Member,Female,Sports and travel,33166.8,6,9950.04,208950.84,3/6/2019,20:34,Cash,199000.8,4.761904762,9950.04,8.3
+44,700-81-1757,A,Lagos,Normal,Female,Electronic accessories,9471.6,5,2367.9,49725.9,1/18/2019,20:59,Card,47358.00000000001,4.761904762,2367.9,8.8
+45,354-39-5160,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.36,78064.56,2/18/2019,15:39,Cash,74347.2,4.761904762,3717.36,9.8
+46,575-30-8091,A,Lagos,Normal,Male,Sports and travel,26100.0,8,10440.0,219240.0,3/16/2019,19:25,Epay,208800.0,4.761904762,10440.0,9.2
+47,239-10-7476,A,Lagos,Normal,Female,Home and lifestyle,28062.0,6,8418.6,176790.6,1/21/2019,16:37,Epay,168372.0,4.761904762,8418.6,8.0
+48,685-64-1609,A,Lagos,Member,Female,Fashion accessories,10850.4,10,5425.2,113929.2,2/10/2019,12:28,Epay,108504.0,4.761904762,5425.2,9.2
+49,238-49-0436,A,Lagos,Normal,Male,Health and beauty,11685.6,8,4674.240000000001,98159.04,3/27/2019,13:48,Card,93484.8,4.761904762,4674.240000000001,4.9
+50,746-94-0204,A,Lagos,Normal,Male,Fashion accessories,29966.4,9,13484.88,283182.48000000004,1/29/2019,11:56,Card,269697.6,4.761904762,13484.88,7.4
+51,782-95-9291,A,Lagos,Member,Male,Food and beverages,33224.4,5,8306.1,174428.1,2/20/2019,15:55,Card,166122.0,4.761904762,8306.1,9.0
+52,275-28-0149,A,Lagos,Normal,Male,Sports and travel,22928.4,1,1146.42,24074.82,2/25/2019,16:21,Cash,22928.4,4.761904762,1146.42,6.0
+53,101-17-6199,A,Lagos,Normal,Male,Food and beverages,16484.4,7,5769.539999999999,121160.34,3/13/2019,19:44,Card,115390.8,4.761904762,5769.539999999999,7.0
+54,687-47-8271,A,Lagos,Normal,Male,Fashion accessories,35632.8,10,17816.4,374144.4,2/8/2019,16:20,Card,356328.0,4.761904762,17816.4,8.7
+55,796-32-9050,A,Lagos,Normal,Male,Food and beverages,18460.8,6,5538.24,116303.04,1/19/2019,16:31,Cash,110764.8,4.761904762,5538.24,6.5
+56,105-31-1824,A,Lagos,Member,Male,Sports and travel,25027.2,7,8759.519999999999,183949.92,2/1/2019,15:10,Card,175190.4,4.761904762,8759.519999999999,8.5
+57,249-42-3782,A,Lagos,Normal,Male,Health and beauty,25203.6,5,6300.900000000001,132318.9,1/3/2019,11:36,Epay,126018.0,4.761904762,6300.900000000001,5.5
+58,827-26-2100,A,Lagos,Member,Male,Home and lifestyle,12182.4,9,5482.08,115123.68,3/21/2019,16:21,Epay,109641.6,4.761904762,5482.08,8.8
+59,175-54-2529,A,Lagos,Member,Male,Food and beverages,7981.200000000001,8,3192.48,67042.07999999999,3/3/2019,17:01,Card,63849.600000000006,4.761904762,3192.48,9.6
+60,407-63-8975,A,Lagos,Normal,Male,Food and beverages,26596.8,6,7979.040000000001,167559.84,3/23/2019,19:16,Epay,159580.8,4.761904762,7979.040000000001,4.4
+61,851-28-6367,A,Lagos,Member,Male,Sports and travel,5580.0,10,2790.0,58590.0,3/23/2019,10:55,Epay,55800.0,4.761904762,2790.0,8.0
+62,586-25-0848,A,Lagos,Normal,Female,Sports and travel,4442.4,7,1554.84,32651.64,3/4/2019,11:19,Card,31096.8,4.761904762,1554.84,6.7
+63,400-60-7251,A,Lagos,Normal,Male,Home and lifestyle,26665.2,1,1333.26,27998.46,2/10/2019,12:50,Epay,26665.2,4.761904762,1333.26,9.9
+64,831-07-6050,A,Lagos,Normal,Male,Electronic accessories,11775.6,5,2943.9,61821.9,3/19/2019,11:30,Card,58878.00000000001,4.761904762,2943.9,9.9
+65,856-22-8149,A,Lagos,Normal,Female,Home and lifestyle,9104.4,1,455.22,9559.62,3/23/2019,10:13,Epay,9104.4,4.761904762,455.22,6.1
+66,749-24-1565,A,Lagos,Normal,Female,Health and beauty,8290.800000000001,9,3730.86,78348.06,1/3/2019,12:02,Epay,74617.2,4.761904762,3730.86,7.9
+67,888-02-0338,A,Lagos,Normal,Male,Electronic accessories,9442.8,9,4249.26,89234.46,1/25/2019,20:24,Epay,84985.2,4.761904762,4249.26,5.9
+68,802-43-8934,A,Lagos,Normal,Male,Home and lifestyle,6580.8,1,329.04,6909.840000000001,3/22/2019,15:05,Card,6580.8,4.761904762,329.04,8.3
+69,319-74-2561,A,Lagos,Member,Female,Electronic accessories,34070.4,3,5110.56,107321.76,2/21/2019,16:55,Cash,102211.2,4.761904762,5110.56,5.5
+70,213-72-6612,A,Lagos,Normal,Male,Food and beverages,15570.0,2,1557.0,32697.0,3/20/2019,15:56,Cash,31140.0,4.761904762,1557.0,6.2
+71,721-86-6247,A,Lagos,Normal,Female,Home and lifestyle,22831.2,8,9132.48,191782.08,3/11/2019,12:55,Epay,182649.6,4.761904762,9132.48,7.4
+72,157-13-5295,A,Lagos,Member,Male,Health and beauty,18698.4,10,9349.2,196333.2,3/9/2019,18:24,Epay,186984.0,4.761904762,9349.2,6.5
+73,645-78-8093,A,Lagos,Normal,Female,Sports and travel,33530.4,2,3353.04,70413.84000000001,1/20/2019,18:09,Epay,67060.8,4.761904762,3353.04,4.1
+74,478-06-7835,A,Lagos,Normal,Male,Fashion accessories,32288.4,1,1614.4199999999998,33902.82,1/11/2019,11:20,Epay,32288.4,4.761904762,1614.4199999999998,4.9
+75,540-11-4336,A,Lagos,Normal,Male,Food and beverages,8978.4,9,4040.28,84845.88,1/11/2019,16:49,Card,80805.6,4.761904762,4040.28,5.6
+76,448-81-5016,A,Lagos,Normal,Male,Health and beauty,21517.2,2,2151.7200000000003,45186.12,3/11/2019,12:01,Card,43034.4,4.761904762,2151.7200000000003,5.8
+77,217-58-1179,A,Lagos,Member,Male,Home and lifestyle,22554.0,4,4510.8,94726.8,1/5/2019,11:25,Cash,90216.0,4.761904762,4510.8,4.2
+78,530-90-9855,A,Lagos,Member,Male,Home and lifestyle,17132.4,8,6852.959999999999,143912.16,1/1/2019,14:47,Cash,137059.2,4.761904762,6852.959999999999,5.7
+79,604-70-6476,A,Lagos,Member,Male,Fashion accessories,6458.400000000001,5,1614.6,33906.6,1/23/2019,14:04,Epay,32292.0,4.761904762,1614.6,6.8
+80,799-71-1548,A,Lagos,Member,Male,Electronic accessories,27979.2,4,5595.84,117512.64,1/7/2019,16:11,Card,111916.8,4.761904762,5595.84,8.8
+81,290-68-2984,A,Lagos,Normal,Male,Home and lifestyle,8550.0,4,1710.0,35910.0,3/16/2019,11:22,Cash,34200.0,4.761904762,1710.0,5.2
+82,704-11-6354,A,Lagos,Member,Male,Home and lifestyle,21204.0,8,8481.6,178113.6,1/6/2019,11:23,Cash,169632.0,4.761904762,8481.6,8.9
+83,366-93-0948,A,Lagos,Member,Male,Electronic accessories,23886.0,1,1194.3,25080.300000000003,1/31/2019,10:46,Card,23886.0,4.761904762,1194.3,9.7
+84,729-09-9681,A,Lagos,Member,Male,Home and lifestyle,9327.6,6,2798.28,58763.880000000005,2/5/2019,10:16,Epay,55965.600000000006,4.761904762,2798.28,8.7
+85,151-16-1484,A,Lagos,Member,Male,Electronic accessories,11610.0,4,2322.0,48761.99999999999,2/13/2019,12:38,Epay,46440.0,4.761904762,2322.0,6.5
+86,850-41-9669,A,Lagos,Normal,Female,Electronic accessories,27021.6,9,12159.72,255354.12,3/19/2019,13:25,Epay,243194.4,4.761904762,12159.72,6.2
+87,447-15-7839,A,Lagos,Member,Female,Sports and travel,8006.4,10,4003.2,84067.2,2/9/2019,11:00,Cash,80064.0,4.761904762,4003.2,4.2
+88,253-12-6086,A,Lagos,Member,Female,Sports and travel,35424.0,7,12398.4,260366.4,3/12/2019,12:43,Card,247968.0,4.761904762,12398.4,8.7
+89,144-51-6085,A,Lagos,Member,Male,Home and lifestyle,25466.4,4,5093.28,106958.88,1/5/2019,16:05,Card,101865.6,4.761904762,5093.28,4.4
+90,731-14-2199,A,Lagos,Member,Female,Home and lifestyle,12794.4,10,6397.2,134341.2,1/4/2019,13:34,Epay,127944.0,4.761904762,6397.2,7.0
+91,126-54-1082,A,Lagos,Member,Female,Home and lifestyle,7754.4,9,3489.48,73279.08,1/7/2019,11:44,Card,69789.6,4.761904762,3489.48,8.8
+92,633-91-1052,A,Lagos,Normal,Female,Home and lifestyle,4330.8,2,433.08,9094.68,1/27/2019,15:51,Cash,8661.6,4.761904762,433.08,5.1
+93,828-61-5674,A,Lagos,Member,Male,Sports and travel,15847.2,10,7923.6,166395.6,3/20/2019,19:57,Card,158472.0,4.761904762,7923.6,9.6
+94,136-08-6195,A,Lagos,Normal,Female,Home and lifestyle,25185.6,8,10074.24,211559.04,2/15/2019,17:01,Card,201484.8,4.761904762,10074.24,6.4
+95,490-29-1201,A,Lagos,Normal,Female,Sports and travel,5522.4,1,276.12,5798.52,1/6/2019,11:09,Cash,5522.4,4.761904762,276.12,6.5
+96,667-92-0055,A,Lagos,Member,Male,Health and beauty,35938.8,6,10781.64,226414.44,3/4/2019,15:02,Epay,215632.8,4.761904762,10781.64,8.5
+97,565-17-3836,A,Lagos,Member,Female,Health and beauty,17161.2,4,3432.24,72077.04000000001,3/12/2019,14:21,Cash,68644.8,4.761904762,3432.24,9.1
+98,430-60-3493,A,Lagos,Member,Female,Home and lifestyle,34156.8,7,11954.88,251052.48,2/3/2019,14:38,Cash,239097.6,4.761904762,11954.88,4.2
+99,278-97-7759,A,Lagos,Member,Female,Electronic accessories,22492.8,1,1124.64,23617.44,2/18/2019,20:29,Cash,22492.8,4.761904762,1124.64,4.7
+100,316-68-6352,A,Lagos,Member,Female,Food and beverages,13089.6,2,1308.96,27488.160000000003,1/21/2019,10:00,Cash,26179.2,4.761904762,1308.96,7.1
+101,744-02-5987,A,Lagos,Member,Male,Home and lifestyle,28216.8,6,8465.039999999999,177765.84000000003,1/10/2019,14:16,Epay,169300.8,4.761904762,8465.039999999999,5.8
+102,307-83-9164,A,Lagos,Member,Male,Home and lifestyle,21603.6,4,4320.72,90735.12,1/25/2019,15:54,Cash,86414.4,4.761904762,4320.72,4.5
+103,439-54-7422,A,Lagos,Normal,Female,Electronic accessories,18428.4,4,3685.68,77399.28,3/18/2019,17:15,Card,73713.59999999999,4.761904762,3685.68,4.7
+104,411-77-0180,A,Lagos,Member,Male,Electronic accessories,25992.0,7,9097.2,191041.2,3/26/2019,20:14,Epay,181944.0,4.761904762,9097.2,4.3
+105,286-01-5402,A,Lagos,Normal,Female,Sports and travel,14482.8,7,5068.9800000000005,106448.58,3/30/2019,13:22,Cash,101379.6,4.761904762,5068.9800000000005,9.6
+106,803-17-8013,A,Lagos,Member,Female,Home and lifestyle,31964.4,8,12785.76,268500.96,2/17/2019,17:09,Cash,255715.2,4.761904762,12785.76,4.1
+107,512-98-1403,A,Lagos,Member,Female,Electronic accessories,9532.8,3,1429.92,30028.320000000003,3/21/2019,10:40,Epay,28598.4,4.761904762,1429.92,4.7
+108,848-42-2560,A,Lagos,Normal,Female,Fashion accessories,29487.6,2,2948.76,61923.96,3/5/2019,17:43,Cash,58975.2,4.761904762,2948.76,7.8
+109,870-76-1733,A,Lagos,Member,Female,Food and beverages,5122.8,5,1280.7,26894.7,2/1/2019,10:08,Card,25614.000000000004,4.761904762,1280.7,4.4
+110,423-64-4619,A,Lagos,Member,Female,Health and beauty,5598.0,9,2519.1,52901.1,3/7/2019,13:12,Cash,50381.99999999999,4.761904762,2519.1,5.0
+111,372-94-8041,A,Lagos,Normal,Male,Health and beauty,5493.6,6,1648.0800000000002,34609.68,2/15/2019,18:03,Epay,32961.6,4.761904762,1648.0800000000002,9.8
+112,563-91-7120,A,Lagos,Normal,Female,Fashion accessories,22237.2,5,5559.3,116745.3,3/8/2019,13:21,Cash,111186.0,4.761904762,5559.3,6.7
+113,746-54-5508,A,Lagos,Normal,Male,Home and lifestyle,7747.2,6,2324.1600000000003,48807.36,1/17/2019,12:48,Card,46483.2,4.761904762,2324.1600000000003,9.4
+114,815-11-1168,A,Lagos,Member,Male,Food and beverages,35920.8,5,8980.2,188584.2,3/9/2019,19:09,Cash,179604.0,4.761904762,8980.2,5.4
+115,340-66-0321,A,Lagos,Member,Male,Electronic accessories,13089.6,4,2617.92,54976.32000000001,3/25/2019,13:07,Cash,52358.4,4.761904762,2617.92,7.6
+116,634-97-8956,A,Lagos,Normal,Male,Food and beverages,11844.0,3,1776.6,37308.6,2/17/2019,17:27,Card,35532.0,4.761904762,1776.6,9.1
+117,566-71-1091,A,Lagos,Normal,Male,Fashion accessories,27727.2,5,6931.799999999999,145567.80000000002,2/3/2019,15:59,Cash,138636.0,4.761904762,6931.799999999999,5.5
+118,442-48-3607,A,Lagos,Member,Male,Food and beverages,8452.8,2,845.2800000000001,17750.88,3/14/2019,11:21,Card,16905.6,4.761904762,845.2800000000001,7.9
+119,527-09-6272,A,Lagos,Member,Female,Electronic accessories,10242.0,5,2560.5,53770.50000000001,3/21/2019,10:17,Card,51210.0,4.761904762,2560.5,9.1
+120,898-04-2717,A,Lagos,Normal,Male,Fashion accessories,27504.000000000004,9,12376.8,259912.8,3/19/2019,15:49,Epay,247536.0,4.761904762,12376.8,7.5
+121,150-89-8043,A,Lagos,Normal,Male,Sports and travel,16074.0,3,2411.1,50633.100000000006,2/14/2019,15:04,Cash,48221.99999999999,4.761904762,2411.1,6.2
+122,135-84-8019,A,Lagos,Normal,Female,Fashion accessories,28054.800000000003,9,12624.66,265117.86,2/27/2019,16:10,Epay,252493.2,4.761904762,12624.66,7.6
+123,441-94-7118,A,Lagos,Member,Male,Electronic accessories,25902.0,1,1295.1,27197.1,2/4/2019,12:14,Cash,25902.0,4.761904762,1295.1,7.3
+124,531-80-1784,A,Lagos,Normal,Male,Electronic accessories,9367.2,7,3278.5200000000004,68848.92,3/28/2019,17:38,Cash,65570.4,4.761904762,3278.5200000000004,5.1
+125,834-61-8124,A,Lagos,Normal,Male,Electronic accessories,18608.4,7,6512.94,136771.74,1/26/2019,18:22,Cash,130258.8,4.761904762,6512.94,5.5
+126,612-36-5536,A,Lagos,Member,Male,Food and beverages,29145.6,8,11658.24,244823.04,2/17/2019,11:12,Card,233164.8,4.761904762,11658.24,7.4
+127,462-67-9126,A,Lagos,Normal,Male,Home and lifestyle,26359.2,6,7907.759999999999,166062.96,1/21/2019,17:44,Cash,158155.2,4.761904762,7907.759999999999,7.2
+128,468-88-0009,A,Lagos,Member,Male,Home and lifestyle,23738.4,4,4747.679999999999,99701.28,3/24/2019,10:29,Cash,94953.6,4.761904762,4747.679999999999,6.0
+129,254-31-0042,A,Lagos,Member,Male,Electronic accessories,7740.0,9,3483.0000000000005,73143.0,3/6/2019,12:46,Card,69660.0,4.761904762,3483.0000000000005,7.8
+130,422-29-8786,A,Lagos,Normal,Female,Home and lifestyle,24152.4,5,6038.1,126800.1,1/3/2019,16:47,Card,120762.0,4.761904762,6038.1,9.1
+131,667-23-5919,A,Lagos,Member,Female,Fashion accessories,34812.0,5,8703.0,182763.0,1/14/2019,12:52,Epay,174060.0,4.761904762,8703.0,7.0
+132,289-15-7034,A,Lagos,Member,Male,Sports and travel,29638.8,4,5927.759999999999,124482.96,1/11/2019,10:37,Card,118555.2,4.761904762,5927.759999999999,7.5
+133,662-72-2873,A,Lagos,Normal,Female,Food and beverages,14738.4,5,3684.6,77376.6,1/6/2019,13:58,Epay,73692.0,4.761904762,3684.6,9.9
+134,725-56-0833,A,Lagos,Normal,Female,Health and beauty,11635.2,10,5817.6,122169.6,2/20/2019,16:49,Card,116352.0,4.761904762,5817.6,10.0
+135,563-36-9814,A,Lagos,Member,Male,Electronic accessories,27655.2,1,1382.76,29037.96,2/13/2019,18:27,Epay,27655.2,4.761904762,1382.76,7.2
+136,308-47-4913,A,Lagos,Member,Female,Sports and travel,18813.6,10,9406.8,197542.8,3/9/2019,12:45,Card,188136.0,4.761904762,9406.8,6.2
+137,885-17-6250,A,Lagos,Normal,Female,Health and beauty,28706.4,1,1435.32,30141.72,3/6/2019,10:36,Epay,28706.4,4.761904762,1435.32,7.3
+138,726-27-2396,A,Lagos,Normal,Female,Health and beauty,27900.0,5,6975.0,146475.0,1/24/2019,20:36,Epay,139500.0,4.761904762,6975.0,4.3
+139,316-01-3952,A,Lagos,Normal,Female,Food and beverages,19537.2,5,4884.3,102570.3,3/13/2019,14:16,Epay,97686.0,4.761904762,4884.3,4.6
+140,882-40-4577,A,Lagos,Member,Male,Sports and travel,24213.6,4,4842.72,101697.12,1/19/2019,15:28,Card,96854.4,4.761904762,4842.72,8.0
+141,732-67-5346,A,Lagos,Normal,Male,Food and beverages,4964.4,5,1241.1,26063.1,1/11/2019,19:07,Card,24822.0,4.761904762,1241.1,7.8
+142,256-08-8343,A,Lagos,Normal,Female,Home and lifestyle,20350.8,4,4070.16,85473.36,3/4/2019,19:48,Epay,81403.2,4.761904762,4070.16,5.5
+143,132-23-6451,A,Lagos,Member,Male,Health and beauty,7549.2,5,1887.3,39633.3,1/4/2019,13:21,Cash,37746.0,4.761904762,1887.3,7.8
+144,696-90-2548,A,Lagos,Normal,Male,Sports and travel,9302.4,3,1395.36,29302.56,3/10/2019,18:55,Epay,27907.2,4.761904762,1395.36,6.6
+145,472-15-9636,A,Lagos,Normal,Male,Home and lifestyle,18334.8,8,7333.92,154012.31999999998,3/22/2019,19:36,Epay,146678.4,4.761904762,7333.92,9.2
+146,745-71-3520,A,Lagos,Member,Female,Electronic accessories,9079.2,7,3177.72,66732.12000000001,2/4/2019,10:23,Cash,63554.4,4.761904762,3177.72,8.2
+147,269-10-8440,A,Lagos,Member,Male,Health and beauty,19141.2,7,6699.42,140687.82,1/21/2019,18:01,Cash,133988.4,4.761904762,6699.42,8.9
+148,325-77-6186,A,Lagos,Member,Female,Home and lifestyle,32634.000000000004,10,16317.000000000002,342657.0,3/8/2019,10:53,Epay,326340.0,4.761904762,16317.000000000002,7.3
+149,459-50-7686,A,Lagos,Normal,Female,Electronic accessories,8445.6,6,2533.68,53207.28,1/13/2019,19:14,Epay,50673.6,4.761904762,2533.68,6.4
+150,345-08-4992,A,Lagos,Normal,Male,Home and lifestyle,12236.4,6,3670.920000000001,77089.32,3/8/2019,15:37,Card,73418.4,4.761904762,3670.920000000001,7.7
+151,138-17-5109,A,Lagos,Member,Female,Home and lifestyle,32115.6,9,14452.02,303492.42,1/15/2019,15:42,Card,289040.4,4.761904762,14452.02,6.5
+152,301-11-9629,A,Lagos,Normal,Female,Sports and travel,6876.000000000001,7,2406.6,50538.6,1/15/2019,10:43,Cash,48131.99999999999,4.761904762,2406.6,9.7
+153,727-17-0390,A,Lagos,Normal,Female,Food and beverages,22899.6,5,5724.9,120222.9,3/16/2019,12:43,Epay,114498.0,4.761904762,5724.9,4.8
+154,568-88-3448,A,Lagos,Normal,Male,Health and beauty,9000.0,1,450.0,9450.0,3/3/2019,15:09,Epay,9000.0,4.761904762,450.0,5.5
+155,187-83-5490,A,Lagos,Member,Male,Electronic accessories,7477.2,4,1495.44,31404.24,1/31/2019,13:47,Cash,29908.8,4.761904762,1495.44,4.7
+156,470-32-9057,A,Lagos,Member,Male,Food and beverages,18482.4,5,4620.6,97032.6,3/28/2019,15:31,Card,92412.0,4.761904762,4620.6,9.1
+157,340-21-9136,A,Lagos,Member,Female,Sports and travel,14417.999999999998,4,2883.6,60555.600000000006,1/25/2019,11:40,Cash,57671.99999999999,4.761904762,2883.6,9.7
+158,405-31-3305,A,Lagos,Member,Male,Fashion accessories,15526.8,10,7763.400000000001,163031.4,2/2/2019,18:31,Card,155268.0,4.761904762,7763.400000000001,5.5
+159,676-39-6028,A,Lagos,Member,Female,Electronic accessories,23198.4,5,5799.599999999999,121791.6,3/30/2019,17:04,Cash,115992.0,4.761904762,5799.599999999999,6.6
+160,502-05-1910,A,Lagos,Normal,Male,Health and beauty,23464.800000000003,3,3519.7200000000003,73914.12,2/25/2019,20:35,Card,70394.4,4.761904762,3519.7200000000003,6.3
+161,485-30-8700,A,Lagos,Normal,Female,Sports and travel,11973.6,5,2993.4,62861.4,3/18/2019,16:10,Card,59868.00000000001,4.761904762,2993.4,4.2
+162,575-67-1508,A,Lagos,Normal,Male,Electronic accessories,13896.0,1,694.8,14590.8,1/29/2019,11:26,Epay,13896.0,4.761904762,694.8,6.7
+163,674-15-9296,A,Lagos,Normal,Male,Sports and travel,13370.4,5,3342.6,70194.6,1/8/2019,13:05,Epay,66852.0,4.761904762,3342.6,5.0
+164,795-49-7276,A,Lagos,Normal,Male,Fashion accessories,13374.0,4,2674.8,56170.8,3/23/2019,18:59,Epay,53496.0,4.761904762,2674.8,8.3
+165,510-09-5628,A,Lagos,Member,Female,Fashion accessories,7077.6,10,3538.8,74314.8,3/15/2019,18:20,Card,70776.0,4.761904762,3538.8,7.2
+166,420-18-8989,A,Lagos,Member,Female,Sports and travel,18547.2,8,7418.88,155796.48,2/2/2019,15:47,Cash,148377.6,4.761904762,7418.88,9.6
+167,726-29-6793,A,Lagos,Member,Male,Electronic accessories,8704.8,8,3481.92,73120.31999999999,1/28/2019,20:54,Epay,69638.4,4.761904762,3481.92,9.8
+168,209-61-0206,A,Lagos,Normal,Female,Home and lifestyle,15447.6,5,3861.9,81099.9,1/5/2019,17:29,Epay,77238.0,4.761904762,3861.9,6.1
+169,595-27-4851,A,Lagos,Normal,Female,Fashion accessories,19540.8,7,6839.280000000001,143624.88,1/27/2019,18:05,Epay,136785.6,4.761904762,6839.280000000001,9.3
+170,189-52-0236,A,Lagos,Normal,Male,Electronic accessories,35838.0,7,12543.3,263409.3,3/14/2019,12:07,Cash,250866.0,4.761904762,12543.3,7.6
+171,220-28-1851,A,Lagos,Normal,Male,Home and lifestyle,12502.8,2,1250.2800000000002,26255.88,3/1/2019,18:14,Epay,25005.6,4.761904762,1250.2800000000002,9.7
+172,609-81-8548,A,Lagos,Member,Female,Home and lifestyle,13478.4,6,4043.52,84913.92,2/6/2019,13:55,Card,80870.4,4.761904762,4043.52,5.9
+173,534-01-4457,A,Lagos,Normal,Male,Food and beverages,29415.6,6,8824.68,185318.28,1/27/2019,14:36,Card,176493.6,4.761904762,8824.68,8.0
+174,719-89-8991,A,Lagos,Member,Female,Sports and travel,32907.6,5,8226.9,172764.9,2/25/2019,16:03,Epay,164538.0,4.761904762,8226.9,7.1
+175,827-77-7633,A,Lagos,Normal,Male,Sports and travel,35312.4,9,15890.580000000002,333702.18,2/17/2019,19:41,Cash,317811.6,4.761904762,15890.580000000002,9.3
+176,287-83-1405,A,Lagos,Normal,Male,Health and beauty,9154.8,6,2746.44,57675.24,2/12/2019,19:01,Epay,54928.8,4.761904762,2746.44,7.0
+177,435-13-4908,A,Lagos,Member,Male,Fashion accessories,31204.800000000003,8,12481.919999999998,262120.32,1/24/2019,18:04,Card,249638.4,4.761904762,12481.919999999998,7.2
+178,892-05-6689,A,Lagos,Normal,Female,Home and lifestyle,10195.2,5,2548.8,53524.8,3/11/2019,13:28,Epay,50976.0,4.761904762,2548.8,6.2
+179,643-38-7867,A,Lagos,Normal,Male,Home and lifestyle,35258.4,1,1762.92,37021.32,3/7/2019,11:44,Epay,35258.4,4.761904762,1762.92,6.9
+180,308-81-0538,A,Lagos,Normal,Female,Fashion accessories,26298.0,4,5259.599999999999,110451.6,2/25/2019,17:16,Card,105192.0,4.761904762,5259.599999999999,4.9
+181,460-35-4390,A,Lagos,Normal,Male,Home and lifestyle,11044.8,3,1656.72,34791.12,1/22/2019,11:00,Epay,33134.4,4.761904762,1656.72,9.1
+182,647-50-1224,A,Lagos,Normal,Female,Fashion accessories,10591.2,10,5295.6,111207.6,1/12/2019,16:23,Epay,105912.0,4.761904762,5295.6,8.9
+183,541-48-8554,A,Lagos,Normal,Male,Sports and travel,21942.0,9,9873.9,207351.9,1/7/2019,12:08,Card,197478.0,4.761904762,9873.9,6.0
+184,213-32-1216,A,Lagos,Normal,Female,Electronic accessories,23781.6,6,7134.48,149824.08000000002,1/23/2019,10:28,Cash,142689.6,4.761904762,7134.48,7.3
+185,134-75-2619,A,Lagos,Member,Male,Electronic accessories,6955.2,7,2434.32,51120.72,3/25/2019,18:51,Cash,48686.4,4.761904762,2434.32,6.9
+186,712-39-0363,A,Lagos,Member,Male,Food and beverages,14997.6,6,4499.28,94484.88,1/2/2019,15:24,Epay,89985.6,4.761904762,4499.28,5.6
+187,218-59-9410,A,Lagos,Member,Female,Home and lifestyle,26071.2,3,3910.68,82124.28,3/29/2019,16:54,Epay,78213.59999999999,4.761904762,3910.68,8.2
+188,760-90-2357,A,Lagos,Member,Male,Electronic accessories,26823.6,6,8047.080000000001,168988.68,3/20/2019,15:08,Epay,160941.6,4.761904762,8047.080000000001,5.0
+189,698-98-5964,A,Lagos,Normal,Female,Food and beverages,29235.6,10,14617.8,306973.8,1/17/2019,13:01,Card,292356.0,4.761904762,14617.8,6.3
+190,651-88-7328,A,Lagos,Normal,Female,Fashion accessories,23666.4,9,10649.88,223647.48,1/1/2019,13:55,Cash,212997.6,4.761904762,10649.88,7.7
+191,239-48-4278,A,Lagos,Member,Male,Food and beverages,3646.8,7,1276.38,26803.98,3/10/2019,19:35,Epay,25527.6,4.761904762,1276.38,8.3
+192,550-84-8664,A,Lagos,Normal,Male,Sports and travel,30927.6,5,7731.9,162369.9,3/22/2019,14:33,Card,154638.0,4.761904762,7731.9,8.6
+193,797-88-0493,A,Lagos,Normal,Female,Health and beauty,23137.2,4,4627.4400000000005,97176.24,3/26/2019,13:54,Cash,92548.8,4.761904762,4627.4400000000005,7.7
+194,443-82-0585,A,Lagos,Member,Female,Health and beauty,27964.800000000003,4,5592.96,117452.16,2/1/2019,19:54,Cash,111859.2,4.761904762,5592.96,8.4
+195,127-47-6963,A,Lagos,Normal,Male,Health and beauty,18615.6,4,3723.12,78185.52,3/9/2019,13:53,Card,74462.4,4.761904762,3723.12,9.8
+196,278-86-2735,A,Lagos,Normal,Female,Food and beverages,18842.4,3,2826.36,59353.55999999999,3/27/2019,14:03,Cash,56527.2,4.761904762,2826.36,9.2
+197,695-28-6250,A,Lagos,Normal,Female,Sports and travel,15501.6,5,3875.4,81383.4,2/4/2019,16:38,Epay,77508.0,4.761904762,3875.4,7.7
+198,227-50-3718,A,Lagos,Normal,Male,Health and beauty,5263.2,5,1315.8,27631.8,3/4/2019,12:23,Cash,26316.0,4.761904762,1315.8,4.4
+199,560-49-6611,A,Lagos,Member,Female,Sports and travel,16408.8,1,820.4399999999999,17229.239999999998,2/7/2019,14:13,Cash,16408.8,4.761904762,820.4399999999999,9.8
+200,880-35-0356,A,Lagos,Member,Female,Sports and travel,27072.0,3,4060.8,85276.8,2/5/2019,11:51,Epay,81216.0,4.761904762,4060.8,4.8
+201,152-68-2907,A,Lagos,Normal,Male,Food and beverages,18792.0,3,2818.8,59194.8,2/15/2019,13:30,Card,56376.0,4.761904762,2818.8,9.5
+202,334-64-2006,A,Lagos,Member,Female,Home and lifestyle,25315.2,2,2531.52,53161.92,3/24/2019,14:22,Epay,50630.4,4.761904762,2531.52,9.6
+203,559-98-9873,A,Lagos,Member,Female,Fashion accessories,19314.0,7,6759.9,141957.9,2/10/2019,12:56,Epay,135198.0,4.761904762,6759.9,5.2
+204,318-12-0304,A,Lagos,Normal,Male,Fashion accessories,11019.6,1,550.98,11570.580000000002,1/23/2019,12:20,Epay,11019.6,4.761904762,550.98,5.2
+205,421-95-9805,A,Lagos,Normal,Female,Electronic accessories,10425.6,1,521.2800000000001,10946.88,2/7/2019,10:18,Card,10425.6,4.761904762,521.2800000000001,6.2
+206,443-59-0061,A,Lagos,Member,Male,Food and beverages,24282.0,10,12141.0,254961.0,2/3/2019,11:25,Epay,242820.0,4.761904762,12141.0,4.2
+207,509-29-3912,A,Lagos,Member,Female,Sports and travel,13939.2,9,6272.639999999999,131725.44,3/20/2019,12:24,Epay,125452.8,4.761904762,6272.639999999999,4.2
+208,828-46-6863,A,Lagos,Member,Male,Food and beverages,35470.8,6,10641.24,223466.04,1/23/2019,11:22,Card,212824.8,4.761904762,10641.24,4.0
+209,420-97-3340,A,Lagos,Normal,Female,Food and beverages,25804.800000000003,3,3870.72,81285.12,3/28/2019,15:30,Card,77414.4,4.761904762,3870.72,9.2
+210,436-54-4512,A,Lagos,Member,Female,Food and beverages,32979.6,1,1648.98,34628.58,3/20/2019,19:44,Cash,32979.6,4.761904762,1648.98,9.8
+211,816-57-2053,A,Lagos,Normal,Male,Sports and travel,21913.2,2,2191.32,46017.72,3/9/2019,12:37,Epay,43826.4,4.761904762,2191.32,8.7
+212,856-66-2701,A,Lagos,Member,Male,Home and lifestyle,19188.0,3,2878.2,60442.2,1/25/2019,14:19,Epay,57564.0,4.761904762,2878.2,7.5
+213,308-39-1707,A,Lagos,Normal,Female,Fashion accessories,4352.4,1,217.62,4570.02,1/26/2019,18:19,Card,4352.4,4.761904762,217.62,8.2
+214,149-61-1929,A,Lagos,Normal,Male,Sports and travel,23108.4,10,11554.2,242638.2,1/19/2019,14:08,Card,231084.0,4.761904762,11554.2,6.7
+215,655-07-2265,A,Lagos,Normal,Male,Electronic accessories,28191.6,3,4228.74,88803.54000000001,3/5/2019,16:38,Epay,84574.8,4.761904762,4228.74,5.4
+216,589-02-8023,A,Lagos,Member,Male,Food and beverages,30157.2,2,3015.7200000000003,63330.12,1/15/2019,10:54,Card,60314.4,4.761904762,3015.7200000000003,7.0
+217,610-46-4100,A,Lagos,Normal,Male,Health and beauty,10422.0,7,3647.7,76601.7,3/3/2019,20:31,Card,72954.0,4.761904762,3647.7,6.0
+218,706-36-6154,A,Lagos,Member,Male,Home and lifestyle,6969.599999999999,9,3136.32,65862.72,1/18/2019,18:43,Epay,62726.4,4.761904762,3136.32,8.7
+219,742-04-5161,A,Lagos,Member,Male,Home and lifestyle,26200.8,10,13100.4,275108.4,2/3/2019,17:24,Cash,262008.0,4.761904762,13100.4,7.3
+220,169-52-4504,A,Lagos,Normal,Female,Electronic accessories,5648.4,3,847.26,17792.46,3/14/2019,14:13,Card,16945.2,4.761904762,847.26,5.8
+221,562-12-5430,A,Lagos,Member,Female,Fashion accessories,31734.000000000004,3,4760.1,99962.1,1/18/2019,10:11,Epay,95202.0,4.761904762,4760.1,7.9
+222,816-72-8853,A,Lagos,Member,Female,Sports and travel,10054.8,5,2513.7,52787.7,1/29/2019,15:48,Cash,50274.0,4.761904762,2513.7,5.9
+223,491-38-3499,A,Lagos,Member,Male,Fashion accessories,19962.0,1,998.1,20960.1,2/26/2019,17:46,Card,19962.0,4.761904762,998.1,4.9
+224,518-17-2983,A,Lagos,Normal,Female,Fashion accessories,17506.8,4,3501.36,73528.56,2/4/2019,15:44,Epay,70027.2,4.761904762,3501.36,7.6
+225,588-47-8641,A,Lagos,Member,Male,Fashion accessories,20174.4,10,10087.2,211831.2,1/14/2019,19:30,Epay,201744.0,4.761904762,10087.2,4.4
+226,811-03-8790,A,Lagos,Normal,Female,Electronic accessories,16372.8,10,8186.4,171914.4,3/1/2019,10:22,Card,163728.0,4.761904762,8186.4,4.8
+227,274-05-5470,A,Lagos,Member,Female,Food and beverages,26449.2,4,5289.84,111086.64,2/23/2019,18:30,Cash,105796.8,4.761904762,5289.84,6.0
+228,130-67-4723,A,Lagos,Member,Male,Food and beverages,17460.0,6,5238.0,109998.0,1/11/2019,13:57,Epay,104760.0,4.761904762,5238.0,9.4
+229,105-10-6182,A,Lagos,Member,Male,Fashion accessories,7732.8,2,773.2800000000001,16238.88,2/27/2019,12:22,Epay,15465.6,4.761904762,773.2800000000001,6.6
+230,648-83-1321,A,Lagos,Member,Male,Home and lifestyle,22881.6,10,11440.8,240256.8,1/16/2019,17:59,Cash,228816.0,4.761904762,11440.8,4.3
+231,305-03-2383,A,Lagos,Normal,Female,Food and beverages,24156.0,3,3623.4,76091.40000000001,2/15/2019,10:36,Cash,72468.0,4.761904762,3623.4,7.5
+232,689-05-1884,A,Lagos,Member,Male,Health and beauty,17506.8,10,8753.4,183821.4,3/4/2019,12:44,Cash,175068.0,4.761904762,8753.4,8.8
+233,800-09-8606,A,Lagos,Member,Female,Home and lifestyle,31453.2,5,7863.3,165129.3,1/29/2019,19:45,Cash,157266.0,4.761904762,7863.3,6.6
+234,182-52-7000,A,Lagos,Member,Female,Sports and travel,9734.4,4,1946.88,40884.48,1/1/2019,20:26,Epay,38937.6,4.761904762,1946.88,6.9
+235,868-06-0466,A,Lagos,Member,Male,Electronic accessories,25048.8,9,11271.96,236711.16000000003,2/19/2019,19:38,Card,225439.2,4.761904762,11271.96,7.8
+236,445-30-9252,A,Lagos,Normal,Male,Sports and travel,9252.0,3,1387.8,29143.8,1/17/2019,17:59,Epay,27756.0,4.761904762,1387.8,6.1
+237,786-94-2700,A,Lagos,Member,Male,Food and beverages,29023.2,6,8706.96,182846.16,2/28/2019,20:18,Cash,174139.2,4.761904762,8706.96,9.1
+238,258-92-7466,A,Lagos,Normal,Female,Health and beauty,12844.8,5,3211.2,67435.2,2/6/2019,18:33,Card,64224.0,4.761904762,3211.2,6.6
+239,857-16-3520,A,Lagos,Member,Female,Fashion accessories,25725.6,7,9003.96,189083.16,3/28/2019,16:06,Epay,180079.2,4.761904762,9003.96,4.5
+240,482-17-1179,A,Lagos,Member,Male,Electronic accessories,4298.4,3,644.76,13539.96,1/19/2019,12:47,Card,12895.2,4.761904762,644.76,8.1
+241,788-21-5741,A,Lagos,Normal,Male,Fashion accessories,16336.8,3,2450.52,51460.92,2/17/2019,13:34,Card,49010.4,4.761904762,2450.52,7.2
+242,247-11-2470,A,Lagos,Member,Female,Fashion accessories,8035.2,4,1607.0399999999995,33747.84,3/1/2019,16:23,Card,32140.8,4.761904762,1607.0399999999995,4.4
+243,635-28-5728,A,Lagos,Normal,Male,Health and beauty,20160.0,3,3024.0,63504.0,2/28/2019,19:33,Epay,60480.0,4.761904762,3024.0,4.8
+244,756-49-0168,A,Lagos,Member,Male,Fashion accessories,7092.0,1,354.6,7446.599999999999,2/8/2019,11:39,Epay,7092.0,4.761904762,354.6,9.5
+245,805-86-0265,A,Lagos,Normal,Male,Home and lifestyle,33825.6,9,15221.52,319651.92,3/20/2019,11:32,Cash,304430.4,4.761904762,15221.52,9.8
+246,373-14-0504,A,Lagos,Member,Female,Sports and travel,25786.8,2,2578.68,54152.28,2/12/2019,14:33,Epay,51573.6,4.761904762,2578.68,8.8
+247,546-80-2899,A,Lagos,Member,Male,Home and lifestyle,13568.4,2,1356.84,28493.64,2/20/2019,15:29,Epay,27136.8,4.761904762,1356.84,9.5
+248,585-86-8361,A,Lagos,Normal,Female,Food and beverages,9820.8,5,2455.2000000000003,51559.2,2/3/2019,10:31,Card,49104.0,4.761904762,2455.2000000000003,8.6
+249,807-14-7833,A,Lagos,Member,Female,Electronic accessories,6271.200000000001,10,3135.6000000000004,65847.6,2/22/2019,12:30,Epay,62711.99999999999,4.761904762,3135.6000000000004,7.0
+250,652-43-6591,A,Lagos,Normal,Female,Fashion accessories,35024.4,8,14009.759999999998,294204.96,3/9/2019,13:18,Card,280195.2,4.761904762,14009.759999999998,6.2
+251,406-46-7107,A,Lagos,Normal,Female,Home and lifestyle,34747.2,6,10424.16,218907.36,1/11/2019,11:52,Cash,208483.2,4.761904762,10424.16,4.5
+252,250-17-5703,A,Lagos,Member,Male,Food and beverages,6786.000000000001,10,3393.0000000000005,71253.0,2/27/2019,18:24,Epay,67860.0,4.761904762,3393.0000000000005,5.6
+253,156-95-3964,A,Lagos,Normal,Female,Food and beverages,19940.4,4,3988.08,83749.68000000001,3/25/2019,15:19,Epay,79761.6,4.761904762,3988.08,8.0
+254,410-67-1709,A,Lagos,Member,Female,Fashion accessories,22996.8,8,9198.72,193173.12,1/20/2019,17:48,Epay,183974.4,4.761904762,9198.72,9.9
+255,587-73-4862,A,Lagos,Member,Female,Health and beauty,3848.4,5,962.1,20204.1,3/26/2019,11:07,Epay,19242.0,4.761904762,962.1,7.6
+256,787-87-2010,A,Lagos,Member,Male,Health and beauty,19980.0,4,3996.0,83916.0,1/20/2019,15:48,Card,79920.0,4.761904762,3996.0,6.6
+257,886-54-6089,A,Lagos,Normal,Female,Home and lifestyle,4114.8,6,1234.4399999999998,25923.24,1/15/2019,17:24,Cash,24688.8,4.761904762,1234.4399999999998,7.7
+258,534-53-3526,A,Lagos,Normal,Female,Sports and travel,34113.6,4,6822.72,143277.12,2/11/2019,16:06,Epay,136454.4,4.761904762,6822.72,7.8
+259,307-04-2070,A,Lagos,Member,Female,Fashion accessories,11023.2,1,551.16,11574.36,2/5/2019,14:14,Card,11023.2,4.761904762,551.16,4.1
+260,404-91-5964,A,Lagos,Normal,Male,Electronic accessories,26848.8,7,9397.08,197338.68,2/4/2019,16:09,Card,187941.6,4.761904762,9397.08,9.0
+261,497-37-6538,A,Lagos,Normal,Male,Sports and travel,21207.6,7,7422.660000000001,155875.86,1/17/2019,15:15,Epay,148453.2,4.761904762,7422.660000000001,9.7
+262,651-96-5970,A,Lagos,Normal,Male,Fashion accessories,16707.6,1,835.38,17542.98,3/3/2019,20:06,Card,16707.6,4.761904762,835.38,4.0
+263,263-12-5321,A,Lagos,Member,Male,Electronic accessories,33336.0,7,11667.6,245019.6,2/27/2019,12:52,Card,233352.00000000003,4.761904762,11667.6,9.3
+264,702-72-0487,A,Lagos,Normal,Female,Electronic accessories,16779.6,2,1677.9600000000005,35237.16,2/26/2019,12:28,Card,33559.2,4.761904762,1677.9600000000005,6.6
+265,864-24-7918,A,Lagos,Member,Female,Sports and travel,8816.4,10,4408.2,92572.2,2/22/2019,15:15,Cash,88164.0,4.761904762,4408.2,8.1
+266,759-29-9521,A,Lagos,Member,Female,Fashion accessories,17625.6,9,7931.519999999999,166561.91999999998,3/4/2019,11:27,Cash,158630.4,4.761904762,7931.519999999999,8.0
+267,220-68-6701,A,Lagos,Normal,Female,Home and lifestyle,27889.2,4,5577.84,117134.64,3/17/2019,16:36,Cash,111556.8,4.761904762,5577.84,4.2
+268,618-34-8551,A,Lagos,Normal,Female,Sports and travel,33544.8,2,3354.48,70444.08,1/16/2019,18:41,Card,67089.6,4.761904762,3354.48,8.5
+269,257-60-7754,A,Lagos,Normal,Female,Electronic accessories,18082.8,4,3616.56,75947.76,1/8/2019,17:12,Cash,72331.2,4.761904762,3616.56,9.0
+270,380-60-5336,A,Lagos,Normal,Female,Electronic accessories,14493.6,10,7246.799999999999,152182.80000000002,2/24/2019,18:06,Card,144936.0,4.761904762,7246.799999999999,5.0
+271,674-56-6360,A,Lagos,Normal,Male,Electronic accessories,34254.0,1,1712.7,35966.7,3/22/2019,14:00,Cash,34254.0,4.761904762,1712.7,6.0
+272,778-34-2523,A,Lagos,Member,Female,Electronic accessories,17503.2,8,7001.28,147026.88,1/24/2019,10:57,Cash,140025.6,4.761904762,7001.28,5.0
+273,832-51-6761,A,Lagos,Normal,Male,Food and beverages,12196.8,8,4878.72,102453.12,1/19/2019,20:29,Epay,97574.4,4.761904762,4878.72,9.6
+274,186-43-8965,A,Lagos,Member,Female,Home and lifestyle,17164.8,2,1716.48,36046.08,2/24/2019,10:10,Card,34329.6,4.761904762,1716.48,4.1
+275,276-75-6884,A,Lagos,Normal,Female,Health and beauty,24735.6,3,3710.34,77917.14,3/4/2019,10:05,Cash,74206.8,4.761904762,3710.34,8.7
+276,569-76-2760,A,Lagos,Member,Female,Sports and travel,7923.6,4,1584.72,33279.12,1/29/2019,18:15,Card,31694.4,4.761904762,1584.72,6.6
+277,760-53-9233,A,Lagos,Member,Male,Fashion accessories,14860.8,3,2229.12,46811.52,3/26/2019,18:37,Card,44582.4,4.761904762,2229.12,8.5
+278,416-17-9926,A,Lagos,Member,Female,Electronic accessories,26719.2,10,13359.6,280551.6,1/1/2019,14:42,Card,267192.0,4.761904762,13359.6,4.3
+279,237-44-6163,A,Lagos,Normal,Male,Electronic accessories,3801.6,8,1520.64,31933.44,1/24/2019,17:43,Cash,30412.800000000003,4.761904762,1520.64,7.6
+280,528-14-9470,A,Lagos,Member,Male,Health and beauty,32868.0,1,1643.4,34511.4,2/14/2019,14:42,Epay,32868.0,4.761904762,1643.4,9.2
+281,807-34-3742,A,Lagos,Normal,Male,Fashion accessories,18856.8,1,942.84,19799.64,3/26/2019,19:44,Cash,18856.8,4.761904762,942.84,5.8
+282,288-62-1085,A,Lagos,Member,Male,Fashion accessories,13874.4,5,3468.6,72840.6,1/9/2019,13:34,Epay,69372.0,4.761904762,3468.6,5.6
+283,497-36-0989,A,Lagos,Normal,Male,Fashion accessories,18698.4,3,2804.76,58899.96,2/15/2019,15:21,Cash,56095.2,4.761904762,2804.76,7.9
+284,860-73-6466,A,Lagos,Member,Female,Sports and travel,14209.2,2,1420.92,29839.32,3/2/2019,16:16,Card,28418.4,4.761904762,1420.92,5.0
+285,896-34-0956,A,Lagos,Normal,Male,Fashion accessories,7675.2,1,383.75999999999993,8058.96,1/26/2019,12:43,Cash,7675.2,4.761904762,383.75999999999993,5.9
+286,804-38-3935,A,Lagos,Member,Male,Electronic accessories,33760.8,3,5064.12,106346.52,1/30/2019,11:32,Card,101282.4,4.761904762,5064.12,5.9
+287,585-90-0249,A,Lagos,Member,Male,Electronic accessories,26373.6,1,1318.68,27692.28,1/27/2019,18:08,Epay,26373.6,4.761904762,1318.68,9.7
+288,125-45-2293,A,Lagos,Normal,Female,Fashion accessories,35676.0,6,10702.8,224758.8,1/19/2019,13:11,Cash,214056.0,4.761904762,10702.8,4.2
+289,843-73-4724,A,Lagos,Normal,Male,Fashion accessories,26676.0,1,1333.8,28009.800000000003,1/25/2019,11:05,Cash,26676.0,4.761904762,1333.8,9.2
+290,409-33-9708,A,Lagos,Normal,Female,Fashion accessories,35452.8,2,3545.28,74450.88,2/19/2019,10:12,Epay,70905.6,4.761904762,3545.28,9.2
+291,160-22-2687,A,Lagos,Member,Female,Health and beauty,34542.0,5,8635.5,181345.5,1/23/2019,14:21,Epay,172710.0,4.761904762,8635.5,8.8
+292,748-45-2862,A,Lagos,Member,Female,Home and lifestyle,10191.6,4,2038.3200000000004,42804.72,3/7/2019,18:35,Cash,40766.4,4.761904762,2038.3200000000004,8.2
+293,316-66-3011,A,Lagos,Member,Female,Food and beverages,17146.8,9,7716.059999999999,162037.26,1/23/2019,12:35,Cash,154321.2,4.761904762,7716.059999999999,5.0
+294,840-76-5966,A,Lagos,Member,Male,Sports and travel,4593.6,2,459.36,9646.56,1/8/2019,18:06,Epay,9187.2,4.761904762,459.36,7.8
+295,124-31-1458,A,Lagos,Member,Female,Electronic accessories,28652.4,3,4297.86,90255.06,1/8/2019,14:30,Cash,85957.2,4.761904762,4297.86,6.6
+296,852-82-2749,A,Lagos,Normal,Male,Sports and travel,23252.4,4,4650.48,97660.08,1/6/2019,13:35,Epay,93009.6,4.761904762,4650.48,9.3
+297,873-14-6353,A,Lagos,Member,Male,Food and beverages,8935.2,7,3127.3200000000006,65673.72,2/16/2019,10:33,Card,62546.4,4.761904762,3127.3200000000006,7.1
+298,166-19-2553,A,Lagos,Member,Male,Sports and travel,32061.6,6,9618.48,201988.08,1/18/2019,17:26,Cash,192369.6,4.761904762,9618.48,9.9
+299,737-88-5876,A,Lagos,Member,Male,Home and lifestyle,8384.4,4,1676.88,35214.48,3/19/2019,11:52,Card,33537.6,4.761904762,1676.88,5.9
+300,448-61-3783,A,Lagos,Normal,Female,Electronic accessories,32407.2,8,12962.88,272220.48,3/21/2019,16:08,Card,259257.6,4.761904762,12962.88,4.5
+301,291-55-6563,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.36,78064.56,3/30/2019,12:45,Epay,74347.2,4.761904762,3717.36,7.5
+302,548-48-3156,A,Lagos,Member,Female,Food and beverages,30002.4,2,3000.24,63005.04,3/19/2019,13:37,Cash,60004.8,4.761904762,3000.24,7.6
+303,460-93-5834,A,Lagos,Normal,Male,Sports and travel,16408.8,7,5743.08,120604.68,1/13/2019,10:03,Cash,114861.6,4.761904762,5743.08,5.0
+304,325-89-4209,A,Lagos,Member,Male,Food and beverages,31644.000000000004,1,1582.1999999999998,33226.2,2/5/2019,19:42,Epay,31644.000000000004,4.761904762,1582.1999999999998,6.7
+305,884-80-6021,A,Lagos,Member,Female,Electronic accessories,26449.2,10,13224.6,277716.6,3/23/2019,13:14,Epay,264492.0,4.761904762,13224.6,9.5
+306,880-46-5796,A,Lagos,Member,Male,Sports and travel,27691.2,10,13845.6,290757.6,3/17/2019,19:53,Epay,276912.0,4.761904762,13845.6,5.6
+307,146-09-5432,A,Lagos,Member,Male,Food and beverages,12614.4,9,5676.48,119206.08000000002,2/9/2019,19:17,Epay,113529.6,4.761904762,5676.48,4.6
+308,595-94-9924,A,Lagos,Member,Female,Health and beauty,9982.8,5,2495.7,52409.7,3/26/2019,20:21,Card,49914.0,4.761904762,2495.7,4.2
+309,865-41-9075,A,Lagos,Normal,Male,Food and beverages,4150.8,7,1452.78,30508.38,1/28/2019,17:35,Cash,29055.6,4.761904762,1452.78,8.1
+310,186-71-5196,A,Lagos,Member,Female,Food and beverages,28634.4,2,2863.44,60132.240000000005,3/27/2019,16:30,Epay,57268.8,4.761904762,2863.44,6.2
+311,453-33-6436,A,Lagos,Normal,Female,Home and lifestyle,33523.200000000004,8,13409.280000000002,281594.88,2/7/2019,10:09,Cash,268185.60000000003,4.761904762,13409.280000000002,6.8
+312,522-57-8364,A,Lagos,Member,Male,Fashion accessories,18482.4,8,7392.959999999999,155252.16,1/31/2019,10:00,Epay,147859.2,4.761904762,7392.959999999999,7.6
+313,459-45-2396,A,Lagos,Member,Female,Food and beverages,35856.0,3,5378.4,112946.4,2/25/2019,18:45,Cash,107568.0,4.761904762,5378.4,5.8
+314,749-81-8133,A,Lagos,Normal,Female,Fashion accessories,34081.2,4,6816.240000000001,143141.04,3/11/2019,12:04,Cash,136324.8,4.761904762,6816.240000000001,6.8
+315,397-25-8725,A,Lagos,Member,Female,Health and beauty,14263.2,9,6418.4400000000005,134787.24,1/13/2019,17:54,Card,128368.8,4.761904762,6418.4400000000005,6.8
+316,243-55-8457,A,Lagos,Normal,Female,Food and beverages,26798.4,10,13399.2,281383.2,2/27/2019,11:40,Epay,267984.0,4.761904762,13399.2,5.1
+317,361-85-2571,A,Lagos,Normal,Female,Sports and travel,32212.800000000003,5,8053.200000000001,169117.19999999998,3/30/2019,10:18,Cash,161064.0,4.761904762,8053.200000000001,7.4
+318,131-70-8179,A,Lagos,Member,Female,Health and beauty,33152.4,3,4972.86,104430.06,2/17/2019,16:27,Cash,99457.2,4.761904762,4972.86,4.2
+319,720-72-2436,A,Lagos,Normal,Male,Food and beverages,23947.2,4,4789.4400000000005,100578.24,3/2/2019,18:14,Epay,95788.8,4.761904762,4789.4400000000005,6.9
+320,809-69-9497,A,Lagos,Normal,Female,Home and lifestyle,16444.8,10,8222.4,172670.4,1/19/2019,19:30,Epay,164448.0,4.761904762,8222.4,5.7
+321,449-16-6770,A,Lagos,Normal,Male,Health and beauty,18284.4,5,4571.1,95993.1,2/19/2019,14:53,Card,91422.0,4.761904762,4571.1,5.3
+322,333-23-2632,A,Lagos,Member,Male,Health and beauty,3628.8,7,1270.08,26671.680000000004,3/28/2019,20:14,Cash,25401.6,4.761904762,1270.08,4.2
+323,489-82-1237,A,Lagos,Normal,Female,Electronic accessories,33796.8,7,11828.88,248406.48,1/5/2019,11:51,Card,236577.6,4.761904762,11828.88,7.3
+324,846-10-0341,A,Lagos,Normal,Female,Fashion accessories,15325.2,7,5363.82,112640.22,1/6/2019,11:51,Cash,107276.4,4.761904762,5363.82,6.8
+325,384-59-6655,A,Lagos,Member,Female,Food and beverages,35517.6,9,15982.92,335641.32,2/19/2019,15:07,Cash,319658.4,4.761904762,15982.92,8.4
+326,324-92-3863,A,Lagos,Member,Male,Electronic accessories,7520.400000000001,2,752.04,15792.84,2/5/2019,18:45,Cash,15040.8,4.761904762,752.04,9.8
+327,593-08-5916,A,Lagos,Normal,Female,Fashion accessories,5580.0,1,279.0,5858.999999999999,3/19/2019,15:23,Card,5580.0,4.761904762,279.0,7.4
+328,558-60-5016,A,Lagos,Normal,Female,Home and lifestyle,11987.999999999998,9,5394.599999999999,113286.6,3/4/2019,15:27,Epay,107892.0,4.761904762,5394.599999999999,7.2
+329,195-06-0432,A,Lagos,Member,Male,Home and lifestyle,29163.6,3,4374.54,91865.34,1/13/2019,12:55,Card,87490.8,4.761904762,4374.54,9.3
+330,605-03-2706,A,Lagos,Normal,Female,Health and beauty,5688.0,3,853.2,17917.2,3/25/2019,18:02,Cash,17064.0,4.761904762,853.2,9.5
+331,531-56-4728,A,Lagos,Normal,Male,Home and lifestyle,28828.8,3,4324.32,90810.72,2/11/2019,15:29,Cash,86486.40000000001,4.761904762,4324.32,5.4
+332,221-25-5073,A,Lagos,Normal,Female,Food and beverages,26877.6,4,5375.52,112885.92,3/4/2019,10:39,Cash,107510.4,4.761904762,5375.52,8.5
+333,809-46-1866,A,Lagos,Normal,Male,Health and beauty,20934.0,4,4186.8,87922.8,1/23/2019,17:44,Cash,83736.0,4.761904762,4186.8,8.4
+334,139-32-4183,A,Lagos,Member,Female,Sports and travel,35092.8,9,15791.760000000002,331626.96,3/14/2019,14:19,Epay,315835.2,4.761904762,15791.760000000002,7.4
+335,886-18-2897,A,Lagos,Normal,Female,Food and beverages,20361.6,5,5090.400000000001,106898.4,3/22/2019,19:06,Card,101808.0,4.761904762,5090.400000000001,4.5
+336,745-74-0715,A,Lagos,Normal,Male,Electronic accessories,20890.8,2,2089.08,43870.68,3/10/2019,20:46,Epay,41781.6,4.761904762,2089.08,8.8
+337,727-02-1313,A,Lagos,Member,Male,Food and beverages,11462.4,1,573.12,12035.52,2/9/2019,13:22,Cash,11462.4,4.761904762,573.12,7.7
+338,347-56-2442,A,Lagos,Normal,Male,Home and lifestyle,23695.2,1,1184.7600000000002,24879.960000000003,2/22/2019,15:33,Cash,23695.2,4.761904762,1184.7600000000002,4.1
+339,849-09-3807,A,Lagos,Member,Female,Fashion accessories,31802.4,7,11130.84,233747.64,2/18/2019,13:28,Cash,222616.8,4.761904762,11130.84,6.6
+0,692-92-5582,B,Abuja,Member,Female,Food and beverages,19742.4,3,2961.36,62188.55999999999,2/20/2019,13:27,Card,59227.2,4.761904762,2961.36,5.9
+1,351-62-0822,B,Abuja,Member,Female,Fashion accessories,5212.8,4,1042.5600000000002,21893.76,2/6/2019,18:07,Epay,20851.2,4.761904762,1042.5600000000002,4.5
+2,529-56-3974,B,Abuja,Member,Male,Electronic accessories,9183.6,4,1836.72,38571.12,3/9/2019,17:03,Cash,36734.4,4.761904762,1836.72,6.8
+3,299-46-1805,B,Abuja,Member,Female,Sports and travel,33739.2,6,10121.76,212556.96,1/15/2019,16:19,Cash,202435.2,4.761904762,10121.76,4.5
+4,319-50-3348,B,Abuja,Normal,Female,Home and lifestyle,14507.999999999998,2,1450.8000000000002,30466.8,3/11/2019,15:30,Epay,29016.0,4.761904762,1450.8000000000002,4.4
+5,371-85-5789,B,Abuja,Normal,Male,Health and beauty,31672.800000000003,3,4750.92,99769.32,3/5/2019,10:40,Epay,95018.4,4.761904762,4750.92,5.1
+6,273-16-6619,B,Abuja,Normal,Male,Home and lifestyle,11952.000000000002,2,1195.2,25099.2,3/15/2019,12:20,Card,23904.000000000004,4.761904762,1195.2,4.4
+7,649-29-6775,B,Abuja,Normal,Male,Fashion accessories,12067.2,1,603.36,12670.56,2/8/2019,15:31,Cash,12067.2,4.761904762,603.36,6.7
+8,145-94-9061,B,Abuja,Normal,Female,Food and beverages,31809.6,5,7952.4,167000.4,1/25/2019,19:48,Cash,159048.0,4.761904762,7952.4,9.6
+9,871-79-8483,B,Abuja,Normal,Male,Fashion accessories,33886.8,5,8471.699999999999,177905.7,2/25/2019,19:39,Card,169434.0,4.761904762,8471.699999999999,4.8
+10,149-71-6266,B,Abuja,Member,Male,Sports and travel,28105.2,9,12647.34,265594.14,1/28/2019,12:43,Cash,252946.8,4.761904762,12647.34,4.5
+11,640-49-2076,B,Abuja,Normal,Male,Sports and travel,30160.8,8,12064.32,253350.72,1/10/2019,14:49,Cash,241286.4,4.761904762,12064.32,5.1
+12,777-82-7220,B,Abuja,Member,Male,Home and lifestyle,10843.2,8,4337.28,91082.88,3/3/2019,13:01,Cash,86745.6,4.761904762,4337.28,7.7
+13,280-35-5823,B,Abuja,Member,Female,Home and lifestyle,31219.2,1,1560.96,32780.16,1/17/2019,18:45,Epay,31219.2,4.761904762,1560.96,7.9
+14,354-25-5821,B,Abuja,Member,Female,Sports and travel,24883.2,6,7464.96,156764.15999999997,2/8/2019,13:03,Cash,149299.2,4.761904762,7464.96,5.6
+15,132-32-9879,B,Abuja,Member,Female,Electronic accessories,33825.6,4,6765.120000000001,142067.52,3/9/2019,18:00,Cash,135302.4,4.761904762,6765.120000000001,9.5
+16,370-41-7321,B,Abuja,Member,Male,Health and beauty,20408.4,9,9183.78,192859.38,2/27/2019,17:24,Card,183675.6,4.761904762,9183.78,8.4
+17,727-46-3608,B,Abuja,Member,Female,Food and beverages,7203.6,9,3241.62,68074.02,2/6/2019,15:47,Epay,64832.4,4.761904762,3241.62,4.1
+18,669-54-1719,B,Abuja,Member,Male,Electronic accessories,6814.8,6,2044.44,42933.24,2/10/2019,12:45,Card,40888.8,4.761904762,2044.44,8.1
+19,616-24-2851,B,Abuja,Member,Female,Fashion accessories,6433.200000000001,4,1286.64,27019.44,3/22/2019,14:42,Epay,25732.800000000003,4.761904762,1286.64,6.5
+20,242-55-6721,B,Abuja,Normal,Male,Home and lifestyle,5817.6,2,581.76,12216.96,3/7/2019,11:49,Epay,11635.2,4.761904762,581.76,6.5
+21,347-34-2234,B,Abuja,Member,Female,Sports and travel,19825.2,9,8921.34,187348.14,2/3/2019,13:40,Epay,178426.8,4.761904762,8921.34,10.0
+22,853-23-2453,B,Abuja,Member,Male,Health and beauty,27266.4,4,5453.28,114518.88,2/14/2019,14:35,Cash,109065.6,4.761904762,5453.28,7.6
+23,109-28-2512,B,Abuja,Member,Female,Fashion accessories,35139.6,6,10541.88,221379.48,1/7/2019,15:01,Epay,210837.6,4.761904762,10541.88,9.9
+24,510-95-6347,B,Abuja,Member,Female,Food and beverages,17467.2,3,2620.08,55021.68,3/5/2019,18:17,Epay,52401.6,4.761904762,2620.08,4.0
+25,847-38-7188,B,Abuja,Normal,Female,Food and beverages,34804.8,3,5220.72,109635.12,1/26/2019,19:56,Epay,104414.4,4.761904762,5220.72,6.4
+26,318-68-5053,B,Abuja,Normal,Female,Health and beauty,27716.4,6,8314.92,174613.31999999998,2/27/2019,17:55,Cash,166298.4,4.761904762,8314.92,6.1
+27,152-08-9985,B,Abuja,Member,Male,Health and beauty,23169.6,9,10426.32,218952.72,3/12/2019,12:09,Card,208526.4,4.761904762,10426.32,8.6
+28,766-85-7061,B,Abuja,Normal,Male,Health and beauty,31633.2,10,15816.6,332148.6,3/29/2019,10:25,Epay,316332.0,4.761904762,15816.6,5.1
+29,733-01-9107,B,Abuja,Normal,Male,Home and lifestyle,29772.0,6,8931.6,187563.6,3/5/2019,18:14,Cash,178632.0,4.761904762,8931.6,7.4
+30,716-39-1409,B,Abuja,Normal,Male,Health and beauty,10926.0,7,3824.1,80306.09999999999,3/19/2019,18:19,Cash,76482.0,4.761904762,3824.1,8.0
+31,479-26-8945,B,Abuja,Member,Female,Sports and travel,5936.4,2,593.64,12466.44,2/5/2019,11:32,Epay,11872.8,4.761904762,593.64,4.6
+32,227-78-1148,B,Abuja,Normal,Female,Fashion accessories,26222.4,7,9177.84,192734.64,2/15/2019,12:44,Cash,183556.8,4.761904762,9177.84,8.4
+33,291-32-1427,B,Abuja,Member,Male,Fashion accessories,7898.400000000001,5,1974.6,41466.6,3/5/2019,12:29,Epay,39492.0,4.761904762,1974.6,5.3
+34,659-65-8956,B,Abuja,Member,Male,Fashion accessories,18489.6,1,924.48,19414.08,1/16/2019,15:26,Epay,18489.6,4.761904762,924.48,5.2
+35,378-24-2715,B,Abuja,Normal,Male,Home and lifestyle,19238.4,2,1923.84,40400.64,1/20/2019,20:38,Epay,38476.8,4.761904762,1923.84,4.1
+36,219-22-9386,B,Abuja,Member,Male,Sports and travel,35985.6,9,16193.52,340063.92,3/9/2019,17:26,Card,323870.4,4.761904762,16193.52,4.2
+37,268-27-6179,B,Abuja,Member,Female,Fashion accessories,20329.2,8,8131.68,170765.28,3/9/2019,14:57,Epay,162633.6,4.761904762,8131.68,7.3
+38,549-84-7482,B,Abuja,Normal,Female,Sports and travel,32500.8,9,14625.36,307132.56,2/8/2019,11:15,Epay,292507.2,4.761904762,14625.36,7.2
+39,191-10-6171,B,Abuja,Normal,Female,Fashion accessories,14263.2,7,4992.12,104834.52,1/25/2019,13:18,Cash,99842.4,4.761904762,4992.12,7.5
+40,695-51-0018,B,Abuja,Normal,Female,Sports and travel,12542.4,4,2508.48,52678.08,2/10/2019,18:36,Cash,50169.600000000006,4.761904762,2508.48,7.4
+41,590-83-4591,B,Abuja,Member,Male,Electronic accessories,31482.0,6,9444.6,198336.6,2/17/2019,14:40,Card,188892.00000000003,4.761904762,9444.6,8.8
+42,241-72-9525,B,Abuja,Normal,Male,Sports and travel,18687.6,10,9343.8,196219.8,2/16/2019,12:21,Cash,186876.0,4.761904762,9343.8,8.2
+43,262-47-2794,B,Abuja,Member,Male,Home and lifestyle,25869.6,8,10347.84,217304.64,3/6/2019,15:07,Card,206956.8,4.761904762,10347.84,6.2
+44,608-96-3517,B,Abuja,Member,Female,Fashion accessories,32954.4,4,6590.88,138408.48,3/23/2019,19:20,Card,131817.6,4.761904762,6590.88,4.8
+45,279-74-2924,B,Abuja,Member,Male,Electronic accessories,25981.2,1,1299.06,27280.26,1/4/2019,19:40,Cash,25981.2,4.761904762,1299.06,6.1
+46,307-85-2293,B,Abuja,Normal,Male,Home and lifestyle,18100.8,5,4525.2,95029.2,3/7/2019,13:58,Epay,90504.0,4.761904762,4525.2,9.7
+47,743-04-1105,B,Abuja,Member,Male,Health and beauty,34999.2,9,15749.639999999998,330742.44,3/30/2019,14:43,Epay,314992.8,4.761904762,15749.639999999998,6.0
+48,423-57-2993,B,Abuja,Normal,Male,Sports and travel,33620.4,6,10086.12,211808.52,3/27/2019,19:18,Epay,201722.4,4.761904762,10086.12,10.0
+49,548-46-9322,B,Abuja,Normal,Male,Food and beverages,14364.0,10,7182.0,150822.0,2/20/2019,15:24,Card,143640.0,4.761904762,7182.0,5.9
+50,505-02-0892,B,Abuja,Member,Male,Health and beauty,15325.2,8,6130.08,128731.68,2/25/2019,14:12,Epay,122601.6,4.761904762,6130.08,5.6
+51,316-55-4634,B,Abuja,Member,Male,Food and beverages,28818.0,5,7204.5,151294.5,1/26/2019,12:45,Card,144090.0,4.761904762,7204.5,9.4
+52,608-27-6295,B,Abuja,Member,Male,Electronic accessories,19040.4,6,5712.12,119954.52,1/19/2019,17:34,Card,114242.4,4.761904762,5712.12,9.8
+53,414-12-7047,B,Abuja,Normal,Male,Food and beverages,7124.4,8,2849.76,59844.96,1/18/2019,12:04,Epay,56995.2,4.761904762,2849.76,8.7
+54,895-66-0685,B,Abuja,Member,Male,Food and beverages,6508.799999999999,3,976.32,20502.72,3/5/2019,19:46,Epay,19526.4,4.761904762,976.32,8.0
+55,305-14-0245,B,Abuja,Member,Female,Home and lifestyle,34016.4,8,13606.56,285737.76,3/3/2019,19:00,Epay,272131.2,4.761904762,13606.56,7.5
+56,732-04-5373,B,Abuja,Member,Male,Home and lifestyle,16729.2,4,3345.84,70262.64,2/8/2019,10:53,Cash,66916.8,4.761904762,3345.84,7.0
+57,284-34-9626,B,Abuja,Normal,Female,Home and lifestyle,27734.4,3,4160.16,87363.36,2/11/2019,10:39,Card,83203.2,4.761904762,4160.16,7.2
+58,437-58-8131,B,Abuja,Normal,Female,Fashion accessories,26467.2,2,2646.7200000000003,55581.12,1/15/2019,13:41,Epay,52934.4,4.761904762,2646.7200000000003,4.6
+59,641-43-2399,B,Abuja,Normal,Male,Home and lifestyle,9198.0,4,1839.6,38631.6,1/26/2019,20:23,Epay,36792.0,4.761904762,1839.6,5.7
+60,542-41-0513,B,Abuja,Member,Female,Electronic accessories,20696.4,4,4139.28,86924.88,3/15/2019,11:57,Cash,82785.6,4.761904762,4139.28,6.6
+61,875-46-5808,B,Abuja,Member,Male,Health and beauty,9324.0,10,4662.0,97902.0,2/6/2019,14:51,Epay,93240.0,4.761904762,4662.0,8.7
+62,394-43-4238,B,Abuja,Member,Male,Home and lifestyle,6397.2,5,1599.3,33585.3,2/15/2019,12:42,Card,31986.0,4.761904762,1599.3,5.4
+63,573-58-9734,B,Abuja,Normal,Female,Fashion accessories,10933.2,3,1639.98,34439.579999999994,3/28/2019,13:41,Epay,32799.6,4.761904762,1639.98,5.1
+64,817-69-8206,B,Abuja,Normal,Female,Electronic accessories,35902.8,9,16156.26,339281.46,3/2/2019,19:42,Card,323125.2,4.761904762,16156.26,6.5
+65,142-63-6033,B,Abuja,Normal,Male,Home and lifestyle,33249.6,5,8312.4,174560.4,3/20/2019,19:17,Epay,166248.0,4.761904762,8312.4,4.9
+66,656-16-1063,B,Abuja,Normal,Male,Sports and travel,16711.2,3,2506.68,52640.27999999999,1/4/2019,13:24,Card,50133.6,4.761904762,2506.68,4.4
+67,891-58-8335,B,Abuja,Member,Female,Sports and travel,10659.6,7,3730.86,78348.06,3/11/2019,15:53,Cash,74617.2,4.761904762,3730.86,6.5
+68,560-30-5617,B,Abuja,Normal,Female,Sports and travel,8917.2,5,2229.3,46815.3,3/24/2019,18:27,Cash,44586.0,4.761904762,2229.3,8.5
+69,549-03-9315,B,Abuja,Normal,Male,Fashion accessories,34153.200000000004,8,13661.28,286886.88,2/12/2019,12:58,Epay,273225.60000000003,4.761904762,13661.28,8.7
+70,790-29-1172,B,Abuja,Normal,Female,Food and beverages,20642.4,3,3096.36,65023.55999999999,3/10/2019,18:59,Card,61927.2,4.761904762,3096.36,7.9
+71,239-36-3640,B,Abuja,Normal,Male,Electronic accessories,16326.0,6,4897.8,102853.8,1/31/2019,13:44,Epay,97956.0,4.761904762,4897.8,6.1
+72,468-01-2051,B,Abuja,Normal,Male,Food and beverages,22348.8,7,7822.080000000001,164263.68,3/6/2019,13:46,Epay,156441.6,4.761904762,7822.080000000001,5.4
+73,836-82-5858,B,Abuja,Member,Male,Health and beauty,24973.2,9,11237.94,235996.74,1/26/2019,19:14,Epay,224758.8,4.761904762,11237.94,4.0
+74,466-61-5506,B,Abuja,Member,Female,Electronic accessories,32652.0,6,9795.6,205707.6,2/26/2019,10:52,Cash,195912.00000000003,4.761904762,9795.6,5.3
+75,289-65-5721,B,Abuja,Normal,Female,Fashion accessories,29293.2,2,2929.32,61515.72,1/26/2019,19:28,Cash,58586.4,4.761904762,2929.32,6.5
+76,545-46-3100,B,Abuja,Member,Female,Electronic accessories,3812.4,3,571.86,12009.06,3/12/2019,13:52,Card,11437.2,4.761904762,571.86,8.7
+77,418-02-5978,B,Abuja,Normal,Female,Health and beauty,30272.4,9,13622.58,286074.18,2/11/2019,10:54,Cash,272451.6,4.761904762,13622.58,8.0
+78,269-04-5750,B,Abuja,Member,Male,Fashion accessories,26575.2,4,5315.04,111615.84,2/21/2019,18:31,Cash,106300.8,4.761904762,5315.04,6.7
+79,346-84-3103,B,Abuja,Member,Female,Electronic accessories,4759.2,5,1189.8,24985.8,3/2/2019,19:26,Cash,23796.0,4.761904762,1189.8,4.3
+80,376-02-8238,B,Abuja,Normal,Male,Home and lifestyle,33793.200000000004,8,13517.28,283862.88,2/2/2019,18:42,Card,270345.60000000003,4.761904762,13517.28,8.3
+81,866-05-7563,B,Abuja,Member,Female,Electronic accessories,29304.000000000004,3,4395.6,92307.6,2/9/2019,19:43,Cash,87912.0,4.761904762,4395.6,4.8
+82,785-13-7708,B,Abuja,Normal,Male,Food and beverages,26301.6,7,9205.56,193316.76,1/14/2019,19:06,Card,184111.2,4.761904762,9205.56,4.2
+83,845-51-0542,B,Abuja,Member,Male,Food and beverages,16758.0,9,7541.1,158363.1,2/2/2019,15:34,Epay,150822.0,4.761904762,7541.1,6.4
+84,110-48-7033,B,Abuja,Member,Male,Fashion accessories,11743.2,4,2348.64,49321.44,1/29/2019,14:12,Cash,46972.8,4.761904762,2348.64,9.0
+85,655-85-5130,B,Abuja,Member,Female,Fashion accessories,13787.999999999998,4,2757.6,57909.600000000006,3/13/2019,19:22,Cash,55151.99999999999,4.761904762,2757.6,5.7
+86,154-74-7179,B,Abuja,Normal,Male,Sports and travel,19602.0,1,980.1,20582.1,2/26/2019,19:24,Epay,19602.0,4.761904762,980.1,7.9
+87,571-94-0759,B,Abuja,Member,Female,Food and beverages,26856.0,10,13427.999999999998,281988.0,1/8/2019,20:55,Cash,268560.0,4.761904762,13427.999999999998,9.5
+88,783-09-1637,B,Abuja,Normal,Female,Sports and travel,24274.800000000003,5,6068.700000000001,127442.7,3/6/2019,18:13,Epay,121374.0,4.761904762,6068.700000000001,6.3
+89,477-24-6490,B,Abuja,Normal,Female,Health and beauty,35895.6,6,10768.68,226142.28,2/26/2019,16:52,Epay,215373.6,4.761904762,10768.68,7.9
+90,566-19-5475,B,Abuja,Normal,Male,Fashion accessories,17269.2,7,6044.22,126928.62,1/7/2019,20:52,Cash,120884.4,4.761904762,6044.22,6.2
+91,498-41-1961,B,Abuja,Normal,Male,Health and beauty,24004.800000000003,5,6001.200000000001,126025.2,2/20/2019,18:01,Cash,120024.0,4.761904762,6001.200000000001,7.6
+92,283-79-9594,B,Abuja,Normal,Female,Food and beverages,17463.6,7,6112.26,128357.46,1/25/2019,13:30,Card,122245.2,4.761904762,6112.26,5.2
+93,139-20-0155,B,Abuja,Member,Male,Electronic accessories,14507.999999999998,10,7253.999999999999,152334.0,1/24/2019,17:37,Card,145080.0,4.761904762,7253.999999999999,7.0
+94,585-03-5943,B,Abuja,Normal,Male,Health and beauty,6519.599999999999,10,3259.8,68455.8,3/13/2019,11:46,Epay,65196.0,4.761904762,3259.8,5.9
+95,573-10-3877,B,Abuja,Member,Male,Health and beauty,14043.6,1,702.18,14745.78,3/12/2019,16:46,Card,14043.6,4.761904762,702.18,4.7
+96,396-90-2219,B,Abuja,Normal,Female,Electronic accessories,5385.6,8,2154.24,45239.04,2/23/2019,12:29,Cash,43084.8,4.761904762,2154.24,8.6
+97,532-59-7201,B,Abuja,Member,Male,Sports and travel,28774.800000000003,6,8632.44,181281.24,1/31/2019,14:04,Cash,172648.8,4.761904762,8632.44,5.5
+98,276-54-0879,B,Abuja,Normal,Male,Sports and travel,35186.4,4,7037.28,147782.88,3/12/2019,19:53,Epay,140745.6,4.761904762,7037.28,6.4
+99,730-61-8757,B,Abuja,Member,Male,Health and beauty,18406.8,4,3681.36,77308.56,1/25/2019,10:11,Card,73627.2,4.761904762,3681.36,4.0
+100,868-81-1752,B,Abuja,Normal,Male,Home and lifestyle,7927.2,9,3567.24,74912.04000000001,2/7/2019,18:48,Cash,71344.8,4.761904762,3567.24,6.8
+101,692-27-8933,B,Abuja,Normal,Female,Sports and travel,20862.0,6,6258.6,131430.6,2/24/2019,13:02,Cash,125172.0,4.761904762,6258.6,5.2
+102,374-17-3652,B,Abuja,Member,Female,Food and beverages,15415.2,9,6936.840000000001,145673.64,2/5/2019,15:26,Card,138736.8,4.761904762,6936.840000000001,8.9
+103,378-07-7001,B,Abuja,Member,Male,Electronic accessories,17312.4,3,2596.86,54534.06,2/10/2019,18:23,Card,51937.2,4.761904762,2596.86,7.8
+104,433-75-6987,B,Abuja,Member,Female,Health and beauty,20149.2,7,7052.22,148096.62,3/5/2019,19:06,Epay,141044.4,4.761904762,7052.22,8.9
+105,873-95-4984,B,Abuja,Member,Female,Health and beauty,27684.000000000004,7,9689.4,203477.4,2/15/2019,20:21,Cash,193788.0,4.761904762,9689.4,7.7
+106,400-45-1220,B,Abuja,Normal,Female,Health and beauty,4860.0,10,2430.0,51030.0,2/27/2019,11:06,Card,48600.0,4.761904762,2430.0,4.8
+107,115-99-4379,B,Abuja,Member,Female,Fashion accessories,19702.8,7,6895.98,144815.58,3/14/2019,19:02,Card,137919.6,4.761904762,6895.98,8.5
+108,565-67-6697,B,Abuja,Member,Male,Home and lifestyle,9720.0,9,4374.0,91854.0,3/2/2019,14:16,Cash,87480.0,4.761904762,4374.0,4.8
+109,889-04-9723,B,Abuja,Member,Female,Food and beverages,32090.4,4,6418.08,134779.68000000002,1/7/2019,12:20,Card,128361.6,4.761904762,6418.08,7.8
+110,453-63-6187,B,Abuja,Normal,Male,Electronic accessories,9900.0,3,1485.0,31185.0,3/1/2019,15:40,Epay,29700.0,4.761904762,1485.0,6.5
+111,578-80-7669,B,Abuja,Normal,Male,Sports and travel,26989.2,1,1349.46,28338.660000000003,3/16/2019,16:58,Cash,26989.2,4.761904762,1349.46,5.6
+112,201-86-2184,B,Abuja,Member,Female,Electronic accessories,9453.6,7,3308.76,69483.95999999999,2/2/2019,19:40,Cash,66175.2,4.761904762,3308.76,9.9
+113,261-12-8671,B,Abuja,Normal,Female,Fashion accessories,21945.6,2,2194.56,46085.76,1/25/2019,19:39,Card,43891.2,4.761904762,2194.56,4.9
+114,843-01-4703,B,Abuja,Member,Female,Home and lifestyle,12736.8,9,5731.56,120362.76,1/5/2019,19:50,Card,114631.2,4.761904762,5731.56,9.6
+115,182-69-8360,B,Abuja,Normal,Female,Electronic accessories,8514.0,4,1702.8000000000002,35758.8,1/30/2019,13:32,Card,34056.0,4.761904762,1702.8000000000002,4.0
+116,868-52-7573,B,Abuja,Normal,Female,Food and beverages,35888.4,5,8972.1,188414.1,1/14/2019,12:09,Cash,179442.0,4.761904762,8972.1,9.9
+117,525-88-7307,B,Abuja,Member,Male,Sports and travel,27295.2,1,1364.7600000000002,28659.960000000003,1/31/2019,13:19,Cash,27295.2,4.761904762,1364.7600000000002,5.8
+118,596-42-3999,B,Abuja,Normal,Male,Food and beverages,6559.2,7,2295.7200000000003,48210.12,3/10/2019,14:04,Card,45914.4,4.761904762,2295.7200000000003,6.6
+119,173-82-9529,B,Abuja,Normal,Female,Fashion accessories,13662.000000000002,10,6831.000000000001,143451.0,1/26/2019,14:51,Cash,136620.0,4.761904762,6831.000000000001,9.7
+120,760-54-1821,B,Abuja,Normal,Male,Home and lifestyle,4892.4,9,2201.58,46233.18,3/15/2019,10:26,Cash,44031.6,4.761904762,2201.58,5.8
+121,793-10-3222,B,Abuja,Member,Female,Health and beauty,14781.6,6,4434.48,93124.08,3/5/2019,13:30,Card,88689.6,4.761904762,4434.48,8.3
+122,346-12-3257,B,Abuja,Member,Male,Electronic accessories,6926.4,9,3116.88,65454.48,3/4/2019,16:28,Cash,62337.6,4.761904762,3116.88,8.0
+123,831-64-0259,B,Abuja,Normal,Female,Fashion accessories,14310.0,5,3577.5,75127.5,2/22/2019,10:43,Epay,71550.0,4.761904762,3577.5,9.6
+124,725-32-9708,B,Abuja,Member,Female,Fashion accessories,24735.6,4,4947.12,103889.52,1/4/2019,19:01,Cash,98942.4,4.761904762,4947.12,4.1
+125,244-08-0162,B,Abuja,Normal,Female,Health and beauty,12315.6,10,6157.8,129313.8,1/2/2019,13:00,Cash,123156.0,4.761904762,6157.8,5.1
+126,569-71-4390,B,Abuja,Normal,Male,Sports and travel,7873.200000000001,2,787.3199999999999,16533.72,1/25/2019,14:29,Epay,15746.4,4.761904762,787.3199999999999,6.9
+127,268-03-6164,B,Abuja,Normal,Male,Health and beauty,34599.6,1,1729.98,36329.58,1/25/2019,16:28,Epay,34599.6,4.761904762,1729.98,7.8
+128,848-07-1692,B,Abuja,Normal,Female,Health and beauty,20599.2,2,2059.92,43258.32,1/12/2019,17:13,Epay,41198.4,4.761904762,2059.92,8.3
+129,301-81-8610,B,Abuja,Member,Male,Fashion accessories,9151.2,8,3660.48,76870.08,3/19/2019,19:42,Card,73209.6,4.761904762,3660.48,6.7
+130,198-84-7132,B,Abuja,Member,Male,Fashion accessories,14619.6,9,6578.82,138155.22,1/2/2019,13:40,Cash,131576.4,4.761904762,6578.82,7.0
+131,650-98-6268,B,Abuja,Member,Female,Food and beverages,7513.200000000001,3,1126.98,23666.58,3/20/2019,13:53,Card,22539.6,4.761904762,1126.98,8.0
+132,741-73-3559,B,Abuja,Normal,Male,Sports and travel,24217.2,5,6054.299999999999,127140.3,2/27/2019,17:27,Cash,121086.0,4.761904762,6054.299999999999,6.9
+133,286-75-7818,B,Abuja,Normal,Male,Fashion accessories,24868.8,2,2486.88,52224.48,1/31/2019,19:48,Card,49737.6,4.761904762,2486.88,6.9
+134,616-87-0016,B,Abuja,Normal,Male,Fashion accessories,34394.4,7,12038.04,252798.84,3/9/2019,14:36,Card,240760.8,4.761904762,12038.04,9.6
+135,837-55-7229,B,Abuja,Normal,Female,Fashion accessories,17078.399999999998,1,853.92,17932.32,2/22/2019,18:19,Card,17078.399999999998,4.761904762,853.92,6.8
+136,394-30-3170,B,Abuja,Member,Female,Sports and travel,31834.800000000003,8,12733.92,267412.32,3/22/2019,19:35,Card,254678.4,4.761904762,12733.92,4.3
+137,390-80-5128,B,Abuja,Member,Female,Health and beauty,6893.999999999999,1,344.7,7238.700000000001,1/28/2019,17:58,Card,6893.999999999999,4.761904762,344.7,9.5
+138,296-11-7041,B,Abuja,Member,Female,Health and beauty,9745.2,1,487.26,10232.46,1/12/2019,20:07,Card,9745.2,4.761904762,487.26,5.3
+139,449-27-2918,B,Abuja,Member,Female,Sports and travel,14083.2,1,704.16,14787.36,3/26/2019,11:02,Card,14083.2,4.761904762,704.16,9.6
+140,891-01-7034,B,Abuja,Normal,Female,Electronic accessories,26895.6,6,8068.68,169442.28,1/1/2019,19:07,Cash,161373.6,4.761904762,8068.68,6.7
+141,744-09-5786,B,Abuja,Normal,Male,Electronic accessories,7923.6,6,2377.08,49918.67999999999,1/2/2019,18:50,Cash,47541.6,4.761904762,2377.08,7.6
+142,767-54-1907,B,Abuja,Member,Female,Fashion accessories,10641.6,5,2660.4,55868.4,2/13/2019,16:59,Cash,53208.00000000001,4.761904762,2660.4,6.9
+143,710-46-4433,B,Abuja,Member,Female,Food and beverages,27864.000000000004,9,12538.8,263314.8,2/15/2019,14:15,Card,250776.0,4.761904762,12538.8,4.5
+144,533-33-5337,B,Abuja,Normal,Male,Electronic accessories,28580.4,10,14290.2,300094.2,2/7/2019,20:24,Cash,285804.0,4.761904762,14290.2,6.2
+145,234-03-4040,B,Abuja,Member,Female,Food and beverages,26298.0,10,13149.0,276129.0,3/3/2019,12:25,Card,262980.0,4.761904762,13149.0,8.7
+146,554-53-3790,B,Abuja,Normal,Male,Sports and travel,13327.2,6,3998.16,83961.36,3/22/2019,18:33,Cash,79963.2,4.761904762,3998.16,4.5
+147,731-59-7531,B,Abuja,Member,Male,Health and beauty,26125.2,8,10450.08,219451.68,3/30/2019,17:58,Cash,209001.6,4.761904762,10450.08,4.6
+148,701-69-8742,B,Abuja,Normal,Male,Sports and travel,12373.2,10,6186.599999999999,129918.6,3/16/2019,10:11,Epay,123732.0,4.761904762,6186.599999999999,6.7
+149,305-18-3552,B,Abuja,Member,Male,Home and lifestyle,21736.8,10,10868.4,228236.4,2/12/2019,16:19,Cash,217368.0,4.761904762,10868.4,6.0
+150,438-01-4015,B,Abuja,Member,Female,Sports and travel,17816.4,4,3563.28,74828.88,3/21/2019,15:25,Epay,71265.6,4.761904762,3563.28,6.6
+151,709-58-4068,B,Abuja,Normal,Female,Fashion accessories,14792.4,10,7396.200000000001,155320.2,2/28/2019,14:42,Cash,147924.0,4.761904762,7396.200000000001,7.3
+152,627-95-3243,B,Abuja,Member,Female,Home and lifestyle,27964.800000000003,9,12584.159999999998,264267.36,2/4/2019,13:21,Epay,251683.2,4.761904762,12584.159999999998,9.8
+153,686-41-0932,B,Abuja,Normal,Female,Fashion accessories,12492.000000000002,2,1249.2,26233.2,3/13/2019,19:48,Epay,24984.000000000004,4.761904762,1249.2,8.2
+154,608-04-3797,B,Abuja,Member,Female,Health and beauty,9115.2,8,3646.08,76567.68,3/5/2019,20:24,Epay,72921.6,4.761904762,3646.08,8.7
+155,437-53-3084,B,Abuja,Normal,Male,Fashion accessories,35960.4,2,3596.0399999999995,75516.84,2/26/2019,11:48,Epay,71920.8,4.761904762,3596.0399999999995,7.1
+156,632-32-4574,B,Abuja,Normal,Male,Sports and travel,27331.2,8,10932.48,229582.08,3/20/2019,14:14,Cash,218649.6,4.761904762,10932.48,5.5
+157,401-18-8016,B,Abuja,Member,Female,Sports and travel,35326.8,1,1766.34,37093.14,1/21/2019,17:36,Cash,35326.8,4.761904762,1766.34,8.9
+158,277-63-2961,B,Abuja,Member,Male,Sports and travel,26629.2,1,1331.46,27960.66,2/3/2019,15:53,Card,26629.2,4.761904762,1331.46,5.4
+159,282-35-2475,B,Abuja,Normal,Female,Sports and travel,33591.6,2,3359.16,70542.36,3/25/2019,17:53,Cash,67183.2,4.761904762,3359.16,6.3
+160,511-54-3087,B,Abuja,Normal,Male,Sports and travel,31842.0,1,1592.1,33434.1,2/25/2019,16:36,Card,31842.0,4.761904762,1592.1,9.5
+161,387-49-4215,B,Abuja,Member,Female,Sports and travel,17460.0,3,2619.0,54999.0,1/8/2019,12:50,Cash,52380.0,4.761904762,2619.0,6.7
+162,862-17-9201,B,Abuja,Normal,Female,Food and beverages,30258.0,6,9077.4,190625.4,1/29/2019,10:48,Card,181548.0,4.761904762,9077.4,7.7
+163,291-21-5991,B,Abuja,Member,Male,Health and beauty,22064.4,5,5516.1,115838.1,3/29/2019,14:28,Cash,110322.0,4.761904762,5516.1,7.0
+164,347-72-6115,B,Abuja,Member,Female,Sports and travel,32666.4,7,11433.24,240098.04,1/16/2019,18:03,Card,228664.8,4.761904762,11433.24,6.2
+165,425-85-2085,B,Abuja,Member,Male,Health and beauty,19749.6,5,4937.4,103685.4,3/29/2019,16:48,Epay,98748.0,4.761904762,4937.4,9.8
+166,734-91-1155,B,Abuja,Normal,Female,Electronic accessories,16455.6,3,2468.34,51835.14,3/26/2019,10:34,Card,49366.8,4.761904762,2468.34,7.7
+167,286-62-6248,B,Abuja,Normal,Male,Fashion accessories,14115.6,4,2823.1200000000003,59285.52,1/16/2019,20:03,Card,56462.4,4.761904762,2823.1200000000003,9.0
+168,339-38-9982,B,Abuja,Member,Male,Fashion accessories,21549.6,2,2154.96,45254.16,1/13/2019,14:55,Epay,43099.2,4.761904762,2154.96,6.7
+169,827-44-5872,B,Abuja,Member,Female,Food and beverages,19569.6,10,9784.8,205480.8,2/7/2019,11:28,Card,195696.0,4.761904762,9784.8,6.1
+170,857-67-9057,B,Abuja,Normal,Male,Electronic accessories,8262.0,10,4131.0,86751.0,2/6/2019,19:20,Epay,82620.0,4.761904762,4131.0,8.2
+171,339-12-4827,B,Abuja,Member,Female,Fashion accessories,26625.6,1,1331.28,27956.88,1/5/2019,11:32,Card,26625.6,4.761904762,1331.28,5.0
+172,173-50-1108,B,Abuja,Member,Female,Sports and travel,7264.8,4,1452.9600000000005,30512.16,2/13/2019,12:14,Card,29059.2,4.761904762,1452.9600000000005,5.0
+173,841-18-8232,B,Abuja,Normal,Female,Food and beverages,25632.0,1,1281.6,26913.6,1/5/2019,20:40,Card,25632.0,4.761904762,1281.6,9.2
+174,701-23-5550,B,Abuja,Member,Male,Home and lifestyle,13971.6,4,2794.32,58680.72,3/19/2019,13:40,Epay,55886.4,4.761904762,2794.32,4.9
+175,539-21-7227,B,Abuja,Normal,Female,Sports and travel,18554.4,5,4638.6,97410.6,1/26/2019,17:45,Cash,92772.0,4.761904762,4638.6,4.2
+176,747-58-7183,B,Abuja,Normal,Male,Fashion accessories,20617.2,3,3092.5800000000004,64944.18,2/9/2019,20:31,Epay,61851.6,4.761904762,3092.5800000000004,6.5
+177,582-52-8065,B,Abuja,Normal,Female,Fashion accessories,19551.6,9,8798.22,184762.62,2/22/2019,10:49,Cash,175964.4,4.761904762,8798.22,8.9
+178,210-57-1719,B,Abuja,Normal,Female,Health and beauty,20966.4,9,9434.88,198132.48,2/5/2019,12:34,Cash,188697.6,4.761904762,9434.88,9.7
+179,356-44-8813,B,Abuja,Normal,Male,Home and lifestyle,13492.8,3,2023.9200000000003,42502.32000000001,1/20/2019,13:45,Card,40478.4,4.761904762,2023.9200000000003,7.7
+180,198-66-9832,B,Abuja,Member,Female,Fashion accessories,25934.4,2,2593.44,54462.24,2/4/2019,19:38,Cash,51868.8,4.761904762,2593.44,9.5
+181,174-75-0888,B,Abuja,Normal,Male,Electronic accessories,7768.799999999999,9,3495.96,73415.15999999999,3/14/2019,12:32,Cash,69919.2,4.761904762,3495.96,7.3
+182,134-54-4720,B,Abuja,Normal,Female,Electronic accessories,15271.2,8,6108.48,128278.08000000002,1/30/2019,13:58,Epay,122169.6,4.761904762,6108.48,5.7
+183,514-37-2845,B,Abuja,Normal,Male,Fashion accessories,35730.0,2,3573.0000000000005,75033.0,3/20/2019,13:02,Cash,71460.0,4.761904762,3573.0000000000005,9.0
+184,241-11-2261,B,Abuja,Normal,Female,Fashion accessories,28749.6,7,10062.36,211309.56,1/10/2019,10:33,Card,201247.2,4.761904762,10062.36,5.5
+185,834-83-1826,B,Abuja,Member,Female,Home and lifestyle,29534.4,5,7383.6,155055.6,2/25/2019,17:16,Card,147672.0,4.761904762,7383.6,7.6
+186,343-61-3544,B,Abuja,Member,Male,Sports and travel,9601.2,10,4800.6,100812.6,1/29/2019,11:48,Cash,96012.0,4.761904762,4800.6,8.6
+187,355-34-6244,B,Abuja,Normal,Male,Food and beverages,26060.4,2,2606.04,54726.84,1/13/2019,19:55,Card,52120.8,4.761904762,2606.04,8.1
+188,339-96-8318,B,Abuja,Member,Male,Fashion accessories,29271.6,7,10245.06,215146.26,3/1/2019,19:49,Epay,204901.2,4.761904762,10245.06,6.3
+189,458-61-0011,B,Abuja,Normal,Male,Food and beverages,21708.0,4,4341.6,91173.6,2/20/2019,18:43,Cash,86832.0,4.761904762,4341.6,5.8
+190,207-73-1363,B,Abuja,Normal,Male,Health and beauty,25023.6,2,2502.36,52549.56,3/1/2019,12:15,Epay,50047.2,4.761904762,2502.36,8.1
+191,359-90-3665,B,Abuja,Member,Female,Fashion accessories,6508.799999999999,4,1301.76,27336.960000000003,1/14/2019,18:03,Card,26035.2,4.761904762,1301.76,9.5
+192,375-72-3056,B,Abuja,Normal,Male,Sports and travel,22701.6,3,3405.24,71510.04000000001,1/19/2019,15:58,Epay,68104.8,4.761904762,3405.24,7.0
+193,585-11-6748,B,Abuja,Member,Male,Sports and travel,34848.0,3,5227.2,109771.2,3/15/2019,13:05,Cash,104544.0,4.761904762,5227.2,5.3
+194,470-31-3286,B,Abuja,Normal,Male,Health and beauty,5335.2,3,800.2800000000001,16805.88,3/1/2019,11:30,Card,16005.6,4.761904762,800.2800000000001,8.7
+195,642-30-6693,B,Abuja,Normal,Female,Sports and travel,19623.6,6,5887.079999999999,123628.68,3/17/2019,13:54,Epay,117741.6,4.761904762,5887.079999999999,7.8
+196,830-58-2383,B,Abuja,Normal,Male,Home and lifestyle,11430.0,4,2286.0,48006.0,2/8/2019,15:26,Cash,45720.0,4.761904762,2286.0,8.6
+197,349-97-8902,B,Abuja,Member,Male,Food and beverages,20840.4,2,2084.04,43764.84,1/17/2019,10:37,Epay,41680.8,4.761904762,2084.04,8.9
+198,789-23-8625,B,Abuja,Member,Male,Fashion accessories,33559.2,3,5033.879999999999,105711.48,1/24/2019,11:45,Cash,100677.6,4.761904762,5033.879999999999,7.2
+199,327-40-9673,B,Abuja,Member,Male,Sports and travel,26136.0,6,7840.8,164656.8,1/13/2019,19:51,Cash,156816.0,4.761904762,7840.8,6.9
+200,670-79-6321,B,Abuja,Member,Female,Home and lifestyle,34052.4,7,11918.339999999998,250285.14,1/17/2019,15:27,Card,238366.8,4.761904762,11918.339999999998,4.9
+201,852-62-7105,B,Abuja,Normal,Female,Fashion accessories,29970.0,10,14985.0,314685.0,1/12/2019,11:25,Card,299700.0,4.761904762,14985.0,4.4
+202,598-06-7312,B,Abuja,Member,Male,Fashion accessories,32886.0,1,1644.3,34530.3,2/16/2019,15:42,Cash,32886.0,4.761904762,1644.3,6.8
+203,135-13-8269,B,Abuja,Member,Female,Food and beverages,28396.8,2,2839.68,59633.28,1/26/2019,16:04,Cash,56793.6,4.761904762,2839.68,9.1
+204,628-90-8624,B,Abuja,Member,Male,Health and beauty,29728.8,10,14864.4,312152.4,3/14/2019,14:41,Cash,297288.0,4.761904762,14864.4,5.0
+205,420-04-7590,B,Abuja,Normal,Male,Home and lifestyle,35892.0,3,5383.8,113059.8,3/18/2019,11:29,Epay,107676.0,4.761904762,5383.8,4.7
+206,182-88-2763,B,Abuja,Member,Male,Food and beverages,28767.6,3,4315.139999999999,90617.94,3/20/2019,19:28,Card,86302.8,4.761904762,4315.139999999999,5.0
+207,188-55-0967,B,Abuja,Member,Male,Health and beauty,23929.2,10,11964.6,251256.6,1/15/2019,15:01,Card,239292.00000000003,4.761904762,11964.6,5.0
+208,364-33-8584,B,Abuja,Member,Female,Food and beverages,6346.799999999999,5,1586.6999999999998,33320.700000000004,3/8/2019,15:27,Cash,31734.000000000004,4.761904762,1586.6999999999998,8.5
+209,665-63-9737,B,Abuja,Normal,Male,Fashion accessories,18871.2,3,2830.68,59444.28,2/27/2019,17:36,Epay,56613.6,4.761904762,2830.68,7.5
+210,695-09-5146,B,Abuja,Member,Female,Food and beverages,35564.4,3,5334.66,112027.86,2/23/2019,20:00,Epay,106693.2,4.761904762,5334.66,6.4
+211,794-32-2436,B,Abuja,Member,Male,Electronic accessories,20041.2,2,2004.12,42086.52,3/27/2019,15:08,Epay,40082.4,4.761904762,2004.12,6.0
+212,574-31-8277,B,Abuja,Member,Male,Fashion accessories,12106.8,1,605.34,12712.14,3/20/2019,19:55,Cash,12106.8,4.761904762,605.34,5.6
+213,369-82-2676,B,Abuja,Normal,Male,Electronic accessories,27237.6,5,6809.4,142997.4,1/15/2019,18:22,Epay,136188.0,4.761904762,6809.4,7.8
+214,563-47-4072,B,Abuja,Normal,Female,Health and beauty,20091.6,6,6027.48,126577.08,1/22/2019,11:52,Cash,120549.6,4.761904762,6027.48,9.9
+215,149-15-7606,B,Abuja,Member,Male,Sports and travel,13435.2,9,6045.84,126962.64,3/6/2019,15:31,Epay,120916.8,4.761904762,6045.84,5.1
+216,133-77-3154,B,Abuja,Member,Male,Fashion accessories,21664.8,4,4332.96,90992.16,2/16/2019,18:04,Card,86659.2,4.761904762,4332.96,9.4
+217,322-02-2271,B,Abuja,Normal,Female,Sports and travel,15469.2,3,2320.38,48727.98,2/3/2019,11:46,Cash,46407.6,4.761904762,2320.38,9.3
+218,725-67-2480,B,Abuja,Member,Female,Fashion accessories,21150.0,6,6345.0,133245.0,3/24/2019,18:14,Card,126900.0,4.761904762,6345.0,5.9
+219,779-42-2410,B,Abuja,Member,Male,Food and beverages,20786.4,3,3117.96,65477.15999999999,2/20/2019,13:06,Epay,62359.2,4.761904762,3117.96,7.7
+220,190-14-3147,B,Abuja,Normal,Female,Health and beauty,6469.2,4,1293.84,27170.64,2/23/2019,20:43,Epay,25876.8,4.761904762,1293.84,6.4
+221,679-22-6530,B,Abuja,Normal,Female,Sports and travel,14623.2,2,1462.3200000000002,30708.72,1/17/2019,10:01,Card,29246.4,4.761904762,1462.3200000000002,4.1
+222,642-61-4706,B,Abuja,Member,Male,Food and beverages,33624.0,2,3362.4,70610.4,3/30/2019,16:34,Cash,67248.0,4.761904762,3362.4,5.5
+223,576-31-4774,B,Abuja,Normal,Female,Health and beauty,26427.6,3,3964.14,83246.94,3/2/2019,13:10,Epay,79282.8,4.761904762,3964.14,4.0
+224,242-11-3142,B,Abuja,Member,Male,Fashion accessories,30157.2,2,3015.7200000000003,63330.12,2/24/2019,19:57,Cash,60314.4,4.761904762,3015.7200000000003,4.6
+225,752-23-3760,B,Abuja,Member,Female,Sports and travel,23068.8,7,8074.08,169555.68000000002,2/19/2019,19:29,Card,161481.6,4.761904762,8074.08,7.3
+226,528-87-5606,B,Abuja,Member,Female,Electronic accessories,14212.8,1,710.64,14923.439999999997,2/12/2019,19:43,Cash,14212.8,4.761904762,710.64,6.5
+227,320-85-2052,B,Abuja,Normal,Female,Sports and travel,12531.6,1,626.5799999999999,13158.18,1/14/2019,10:11,Card,12531.6,4.761904762,626.5799999999999,7.0
+228,510-79-0415,B,Abuja,Member,Female,Sports and travel,8308.8,6,2492.64,52345.44,1/24/2019,19:20,Epay,49852.8,4.761904762,2492.64,4.9
+229,241-96-5076,B,Abuja,Member,Female,Home and lifestyle,17676.0,2,1767.6,37119.6,1/8/2019,12:58,Card,35352.0,4.761904762,1767.6,6.4
+230,767-97-4650,B,Abuja,Member,Female,Sports and travel,23338.8,2,2333.88,49011.48,1/8/2019,11:59,Card,46677.6,4.761904762,2333.88,8.0
+231,826-58-8051,B,Abuja,Normal,Male,Home and lifestyle,22388.4,4,4477.679999999999,94031.28,1/6/2019,19:46,Epay,89553.59999999999,4.761904762,4477.679999999999,4.3
+232,176-64-7711,B,Abuja,Normal,Male,Food and beverages,11635.2,3,1745.28,36650.88,3/27/2019,19:11,Card,34905.6,4.761904762,1745.28,4.3
+233,191-29-0321,B,Abuja,Member,Female,Fashion accessories,7117.2,10,3558.6,74730.6,2/27/2019,18:57,Card,71172.0,4.761904762,3558.6,5.0
+234,729-06-2010,B,Abuja,Member,Male,Health and beauty,28969.2,9,13036.14,273758.94,1/6/2019,11:18,Cash,260722.8,4.761904762,13036.14,9.2
+235,640-48-5028,B,Abuja,Member,Female,Home and lifestyle,31820.4,9,14319.18,300702.78,3/2/2019,12:40,Cash,286383.6,4.761904762,14319.18,6.3
+236,186-79-9562,B,Abuja,Normal,Male,Health and beauty,25837.2,7,9043.02,189903.42,3/29/2019,14:06,Cash,180860.4,4.761904762,9043.02,8.9
+237,834-45-5519,B,Abuja,Normal,Female,Electronic accessories,15480.0,4,3096.0,65016.0,1/31/2019,20:48,Epay,61920.0,4.761904762,3096.0,7.6
+238,821-14-9046,B,Abuja,Member,Female,Fashion accessories,6292.8,6,1887.84,39644.64,1/18/2019,15:04,Card,37756.8,4.761904762,1887.84,6.1
+239,418-05-0656,B,Abuja,Normal,Female,Fashion accessories,9201.6,7,3220.56,67631.76,2/2/2019,20:42,Cash,64411.2,4.761904762,3220.56,7.1
+240,776-68-1096,B,Abuja,Normal,Male,Home and lifestyle,15883.2,3,2382.48,50032.08,3/18/2019,13:45,Card,47649.600000000006,4.761904762,2382.48,7.9
+241,434-35-9162,B,Abuja,Member,Male,Food and beverages,8402.4,4,1680.48,35290.079999999994,2/4/2019,18:53,Epay,33609.6,4.761904762,1680.48,7.4
+242,174-64-0215,B,Abuja,Normal,Male,Sports and travel,25106.4,10,12553.2,263617.2,3/5/2019,17:49,Card,251064.0,4.761904762,12553.2,8.9
+243,299-29-0180,B,Abuja,Member,Female,Home and lifestyle,18784.8,7,6574.679999999999,138068.28,3/9/2019,10:54,Cash,131493.6,4.761904762,6574.679999999999,9.3
+244,438-23-1242,B,Abuja,Normal,Male,Electronic accessories,27316.8,7,9560.88,200778.48000000004,1/24/2019,10:38,Epay,191217.6,4.761904762,9560.88,8.9
+245,238-45-6950,B,Abuja,Member,Male,Food and beverages,19339.2,1,966.9600000000002,20306.160000000003,3/1/2019,20:03,Epay,19339.2,4.761904762,966.9600000000002,6.4
+246,197-77-7132,B,Abuja,Member,Male,Electronic accessories,32961.6,8,13184.64,276877.44,1/12/2019,18:22,Epay,263692.8,4.761904762,13184.64,6.0
+247,457-13-1708,B,Abuja,Member,Male,Fashion accessories,23482.800000000003,10,11741.4,246569.4,1/8/2019,19:07,Card,234828.0,4.761904762,11741.4,5.2
+248,487-79-6868,B,Abuja,Member,Female,Home and lifestyle,4424.4,9,1990.98,41810.58,3/26/2019,19:28,Card,39819.6,4.761904762,1990.98,8.0
+249,210-30-7976,B,Abuja,Member,Female,Fashion accessories,8035.2,4,1607.0399999999995,33747.84,3/14/2019,11:16,Epay,32140.8,4.761904762,1607.0399999999995,4.1
+250,775-72-1988,B,Abuja,Normal,Male,Home and lifestyle,26380.8,5,6595.2,138499.2,1/24/2019,15:05,Epay,131904.0,4.761904762,6595.2,8.4
+251,785-96-0615,B,Abuja,Member,Female,Electronic accessories,12866.4,8,5146.56,108077.76,2/17/2019,15:28,Epay,102931.2,4.761904762,5146.56,4.9
+252,842-40-8179,B,Abuja,Member,Female,Food and beverages,27792.0,10,13896.0,291816.0,2/11/2019,10:38,Card,277920.0,4.761904762,13896.0,5.6
+253,525-09-8450,B,Abuja,Normal,Male,Electronic accessories,25966.8,10,12983.4,272651.4,1/31/2019,15:12,Card,259668.0,4.761904762,12983.4,4.2
+254,593-14-4239,B,Abuja,Normal,Female,Home and lifestyle,34365.6,8,13746.24,288671.04,3/5/2019,19:40,Epay,274924.8,4.761904762,13746.24,4.7
+255,388-76-2555,B,Abuja,Normal,Male,Sports and travel,4928.4,6,1478.52,31048.92000000001,2/13/2019,13:59,Cash,29570.4,4.761904762,1478.52,6.3
+256,711-31-1234,B,Abuja,Normal,Female,Electronic accessories,34430.4,4,6886.08,144607.68000000002,3/16/2019,18:51,Cash,137721.6,4.761904762,6886.08,7.9
+257,707-32-7409,B,Abuja,Member,Female,Sports and travel,34394.4,4,6878.88,144456.48,2/26/2019,11:58,Epay,137577.6,4.761904762,6878.88,4.5
+258,120-54-2248,B,Abuja,Normal,Female,Food and beverages,10389.6,5,2597.4,54545.4,1/22/2019,18:08,Card,51948.00000000001,4.761904762,2597.4,8.0
+259,875-31-8302,B,Abuja,Normal,Male,Sports and travel,33616.8,1,1680.84,35297.64,1/3/2019,13:07,Cash,33616.8,4.761904762,1680.84,9.6
+260,457-94-0464,B,Abuja,Member,Male,Electronic accessories,31633.2,9,14234.94,298933.74,1/31/2019,20:32,Epay,284698.8,4.761904762,14234.94,5.6
+261,744-16-7898,B,Abuja,Normal,Female,Home and lifestyle,35053.200000000004,10,17526.600000000002,368058.6,1/15/2019,13:48,Card,350532.0,4.761904762,17526.600000000002,4.9
+262,605-83-1050,B,Abuja,Normal,Male,Fashion accessories,9784.8,2,978.48,20548.08,3/15/2019,16:26,Epay,19569.6,4.761904762,978.48,4.3
+263,359-94-5395,B,Abuja,Normal,Male,Health and beauty,33400.8,1,1670.0399999999995,35070.84,3/15/2019,10:50,Card,33400.8,4.761904762,1670.0399999999995,9.8
+264,751-15-6198,B,Abuja,Normal,Male,Sports and travel,8283.6,6,2485.08,52186.68,1/12/2019,16:45,Epay,49701.6,4.761904762,2485.08,7.9
+265,831-81-6575,B,Abuja,Member,Female,Electronic accessories,27212.4,9,12245.580000000002,257157.18,2/23/2019,11:12,Cash,244911.6,4.761904762,12245.580000000002,8.0
+266,559-61-5987,B,Abuja,Normal,Female,Health and beauty,6390.0,1,319.5,6709.5,1/14/2019,10:38,Cash,6390.0,4.761904762,319.5,8.6
+267,565-91-4567,B,Abuja,Normal,Male,Health and beauty,3870.0,8,1548.0,32508.0,3/15/2019,14:38,Epay,30960.0,4.761904762,1548.0,6.2
+268,499-27-7781,B,Abuja,Normal,Female,Food and beverages,19155.6,8,7662.240000000001,160907.04,3/14/2019,16:45,Epay,153244.8,4.761904762,7662.240000000001,5.0
+269,869-11-3082,B,Abuja,Member,Male,Health and beauty,34617.6,4,6923.52,145393.91999999998,1/27/2019,20:03,Card,138470.4,4.761904762,6923.52,8.4
+270,190-59-3964,B,Abuja,Member,Male,Food and beverages,16977.6,5,4244.4,89132.4,2/3/2019,14:35,Card,84888.0,4.761904762,4244.4,6.0
+271,366-43-6862,B,Abuja,Normal,Male,Electronic accessories,19040.4,4,3808.08,79969.68000000001,3/25/2019,16:32,Epay,76161.6,4.761904762,3808.08,6.7
+272,109-86-4363,B,Abuja,Member,Female,Sports and travel,21628.8,7,7570.080000000001,158971.68000000002,2/14/2019,11:36,Card,151401.6,4.761904762,7570.080000000001,4.5
+273,222-42-0244,B,Abuja,Member,Female,Health and beauty,25959.6,9,11681.82,245318.22,1/28/2019,13:53,Card,233636.4,4.761904762,11681.82,7.7
+274,636-17-0325,B,Abuja,Normal,Male,Health and beauty,22525.2,4,4505.04,94605.84,2/25/2019,18:37,Cash,90100.8,4.761904762,4505.04,9.5
+275,343-75-9322,B,Abuja,Member,Female,Sports and travel,4266.0,8,1706.4,35834.4,1/9/2019,16:34,Cash,34128.0,4.761904762,1706.4,4.1
+276,427-45-9297,B,Abuja,Member,Female,Home and lifestyle,14662.8,7,5131.98,107771.58,3/12/2019,11:01,Epay,102639.6,4.761904762,5131.98,5.4
+277,670-71-7306,B,Abuja,Normal,Male,Sports and travel,16066.8,6,4820.04,101220.84,1/2/2019,20:08,Card,96400.8,4.761904762,4820.04,5.1
+278,291-59-1384,B,Abuja,Normal,Male,Electronic accessories,21708.0,1,1085.4,22793.4,2/28/2019,17:38,Cash,21708.0,4.761904762,1085.4,6.0
+279,866-70-2814,B,Abuja,Normal,Female,Electronic accessories,19004.4,10,9502.2,199546.2,2/25/2019,11:58,Epay,190044.0,4.761904762,9502.2,10.0
+280,895-03-6665,B,Abuja,Normal,Female,Fashion accessories,13143.6,9,5914.62,124207.02,2/16/2019,10:52,Cash,118292.4,4.761904762,5914.62,4.2
+281,770-42-8960,B,Abuja,Normal,Male,Food and beverages,7603.200000000001,8,3041.28,63866.88,1/1/2019,19:31,Cash,60825.600000000006,4.761904762,3041.28,6.3
+282,234-36-2483,B,Abuja,Normal,Male,Health and beauty,20732.4,6,6219.72,130614.11999999998,2/15/2019,13:51,Cash,124394.4,4.761904762,6219.72,5.1
+283,152-03-4217,B,Abuja,Normal,Female,Home and lifestyle,4060.8,9,1827.36,38374.56,3/17/2019,11:55,Card,36547.2,4.761904762,1827.36,4.3
+284,533-66-5566,B,Abuja,Normal,Female,Home and lifestyle,18385.2,7,6434.820000000001,135131.22,1/12/2019,11:42,Cash,128696.4,4.761904762,6434.820000000001,7.0
+285,361-59-0574,B,Abuja,Member,Male,Sports and travel,32590.8,8,13036.319999999998,273762.72,3/15/2019,14:48,Card,260726.4,4.761904762,13036.319999999998,6.5
+286,544-55-9589,B,Abuja,Member,Female,Electronic accessories,7714.8,10,3857.4,81005.4,1/28/2019,11:51,Cash,77148.0,4.761904762,3857.4,6.2
+287,608-05-3804,B,Abuja,Member,Male,Electronic accessories,14310.0,1,715.5,15025.499999999998,2/25/2019,20:19,Cash,14310.0,4.761904762,715.5,6.1
+288,761-49-0439,B,Abuja,Member,Female,Electronic accessories,4356.0,8,1742.4,36590.4,1/19/2019,10:17,Epay,34848.0,4.761904762,1742.4,8.6
+289,490-95-0021,B,Abuja,Member,Female,Food and beverages,11955.6,10,5977.8,125533.8,1/8/2019,14:25,Epay,119556.0,4.761904762,5977.8,6.0
+290,311-13-6971,B,Abuja,Member,Male,Sports and travel,11516.4,10,5758.2,120922.2,2/20/2019,15:18,Card,115164.0,4.761904762,5758.2,9.9
+291,114-35-5271,B,Abuja,Normal,Female,Electronic accessories,20847.6,8,8339.04,175119.84,2/7/2019,15:06,Cash,166780.8,4.761904762,8339.04,8.1
+292,715-20-1673,B,Abuja,Normal,Male,Electronic accessories,10216.8,5,2554.2,53638.2,3/6/2019,20:57,Cash,51084.0,4.761904762,2554.2,9.4
+293,811-35-1094,B,Abuja,Member,Male,Electronic accessories,18162.0,6,5448.6,114420.6,2/6/2019,15:16,Card,108972.0,4.761904762,5448.6,8.9
+294,699-88-1972,B,Abuja,Normal,Male,Health and beauty,35697.6,8,14279.04,299859.84,1/28/2019,17:47,Card,285580.8,4.761904762,14279.04,4.2
+295,509-10-0516,B,Abuja,Normal,Male,Home and lifestyle,16549.2,4,3309.84,69506.64,2/9/2019,12:02,Epay,66196.8,4.761904762,3309.84,5.1
+296,851-98-3555,B,Abuja,Normal,Female,Health and beauty,29836.8,5,7459.2,156643.2,3/24/2019,14:08,Card,149184.0,4.761904762,7459.2,6.6
+297,624-01-8356,B,Abuja,Normal,Female,Home and lifestyle,17643.6,10,8821.8,185257.8,1/27/2019,10:44,Card,176436.0,4.761904762,8821.8,4.2
+298,313-66-9943,B,Abuja,Member,Female,Food and beverages,10494.0,3,1574.1,33056.1,3/27/2019,20:29,Card,31482.0,4.761904762,1574.1,7.3
+299,777-67-2495,B,Abuja,Normal,Male,Home and lifestyle,24829.2,3,3724.38,78211.98000000001,2/22/2019,11:26,Epay,74487.6,4.761904762,3724.38,8.7
+300,636-98-3364,B,Abuja,Member,Female,Electronic accessories,9453.6,3,1418.04,29778.84,3/2/2019,12:36,Epay,28360.8,4.761904762,1418.04,6.3
+301,181-82-6255,B,Abuja,Normal,Female,Home and lifestyle,5893.200000000001,6,1767.9600000000005,37127.16,2/8/2019,10:58,Cash,35359.2,4.761904762,1767.9600000000005,7.0
+302,226-34-0034,B,Abuja,Normal,Female,Electronic accessories,4960.8,4,992.16,20835.36,1/10/2019,11:10,Epay,19843.2,4.761904762,992.16,9.0
+303,321-49-7382,B,Abuja,Member,Male,Sports and travel,31791.6,1,1589.58,33381.18,2/15/2019,17:38,Card,31791.6,4.761904762,1589.58,5.2
+304,431-66-2305,B,Abuja,Normal,Female,Electronic accessories,31770.0,9,14296.5,300226.5,2/15/2019,20:51,Card,285930.0,4.761904762,14296.5,7.6
+305,825-94-5922,B,Abuja,Normal,Male,Sports and travel,9111.6,2,911.16,19134.36,3/2/2019,19:26,Epay,18223.2,4.761904762,911.16,7.2
+306,641-62-7288,B,Abuja,Normal,Male,Home and lifestyle,35971.2,6,10791.36,226618.56,3/24/2019,13:33,Epay,215827.2,4.761904762,10791.36,7.1
+307,501-61-1753,B,Abuja,Normal,Female,Home and lifestyle,22734.0,6,6820.2,143224.2,1/3/2019,20:24,Epay,136404.0,4.761904762,6820.2,9.8
+308,676-10-2200,B,Abuja,Member,Male,Fashion accessories,19360.8,1,968.0399999999998,20328.84,2/3/2019,20:13,Epay,19360.8,4.761904762,968.0399999999998,4.7
+309,365-16-4334,B,Abuja,Normal,Female,Food and beverages,9514.8,8,3805.920000000001,79924.32,2/24/2019,14:26,Epay,76118.4,4.761904762,3805.920000000001,8.9
+310,503-21-4385,B,Abuja,Member,Male,Health and beauty,14367.6,3,2155.1400000000003,45257.94,2/21/2019,12:40,Epay,43102.8,4.761904762,2155.1400000000003,9.3
+311,305-89-2768,B,Abuja,Member,Female,Home and lifestyle,7883.999999999999,3,1182.6,24834.6,1/9/2019,18:43,Epay,23652.0,4.761904762,1182.6,4.7
+312,574-80-1489,B,Abuja,Member,Female,Food and beverages,22626.0,4,4525.2,95029.2,2/25/2019,13:22,Epay,90504.0,4.761904762,4525.2,8.7
+313,200-40-6154,B,Abuja,Member,Male,Home and lifestyle,23727.6,6,7118.28,149483.88,2/9/2019,11:45,Cash,142365.6,4.761904762,7118.28,5.7
+314,430-02-3888,B,Abuja,Normal,Male,Electronic accessories,16567.2,6,4970.16,104373.36,2/7/2019,15:55,Cash,99403.2,4.761904762,4970.16,7.1
+315,794-42-3736,B,Abuja,Normal,Male,Food and beverages,11998.8,2,1199.88,25197.48,1/26/2019,14:41,Card,23997.6,4.761904762,1199.88,6.4
+316,172-42-8274,B,Abuja,Normal,Female,Electronic accessories,13777.2,2,1377.72,28932.120000000003,3/2/2019,18:18,Card,27554.4,4.761904762,1377.72,5.8
+317,214-30-2776,B,Abuja,Member,Female,Electronic accessories,12416.4,5,3104.1000000000004,65186.1,3/11/2019,19:44,Card,62081.99999999999,4.761904762,3104.1000000000004,9.0
+318,746-04-1077,B,Abuja,Member,Female,Food and beverages,30466.8,10,15233.4,319901.4,1/1/2019,11:36,Card,304668.0,4.761904762,15233.4,9.0
+319,448-34-8700,B,Abuja,Member,Male,Home and lifestyle,13287.6,7,4650.66,97663.86,2/10/2019,13:51,Epay,93013.2,4.761904762,4650.66,6.7
+320,452-04-8808,B,Abuja,Normal,Male,Electronic accessories,31348.8,7,10972.08,230413.68,1/26/2019,15:17,Cash,219441.6,4.761904762,10972.08,5.5
+321,883-69-1285,B,Abuja,Member,Male,Fashion accessories,17971.2,2,1797.12,37739.52,3/6/2019,11:55,Card,35942.4,4.761904762,1797.12,7.0
+322,518-71-6847,B,Abuja,Member,Male,Food and beverages,9576.0,6,2872.8,60328.8,2/26/2019,15:10,Epay,57456.0,4.761904762,2872.8,4.9
+323,156-20-0370,B,Abuja,Normal,Female,Electronic accessories,9162.0,1,458.1,9620.1,3/10/2019,18:10,Card,9162.0,4.761904762,458.1,5.1
+324,151-33-7434,B,Abuja,Normal,Female,Food and beverages,24397.2,1,1219.86,25617.06,2/4/2019,20:43,Card,24397.2,4.761904762,1219.86,6.5
+325,374-38-5555,B,Abuja,Normal,Female,Fashion accessories,22935.6,5,5733.9,120411.9,2/7/2019,19:30,Epay,114678.0,4.761904762,5733.9,8.5
+326,764-44-8999,B,Abuja,Normal,Female,Health and beauty,5313.6,2,531.36,11158.56,2/18/2019,14:42,Epay,10627.2,4.761904762,531.36,4.3
+327,552-44-5977,B,Abuja,Member,Male,Health and beauty,22320.0,8,8928.0,187488.0,1/3/2019,19:08,Card,178560.0,4.761904762,8928.0,6.2
+328,430-53-4718,B,Abuja,Member,Male,Health and beauty,27133.2,8,10853.28,227918.88000000003,1/28/2019,15:46,Card,217065.6,4.761904762,10853.28,8.4
+329,602-16-6955,B,Abuja,Normal,Female,Sports and travel,27576.0,10,13787.999999999998,289548.0,1/24/2019,18:10,Epay,275760.0,4.761904762,13787.999999999998,6.0
+330,690-01-6631,B,Abuja,Normal,Male,Fashion accessories,6296.4,10,3148.2,66112.2,2/22/2019,18:35,Epay,62964.0,4.761904762,3148.2,6.6
+331,303-96-2227,B,Abuja,Normal,Female,Home and lifestyle,35056.8,10,17528.399999999998,368096.4,3/2/2019,17:16,Epay,350568.0,4.761904762,17528.399999999998,4.4
+0,226-31-3081,C,Port Harcourt,Normal,Female,Electronic accessories,5500.8,5,1375.2,28879.2,3/8/2019,10:29,Cash,27504.000000000004,4.761904762,1375.2,9.6
+1,699-14-3026,C,Port Harcourt,Normal,Male,Electronic accessories,30740.4,7,10759.14,225941.94,3/25/2019,18:30,Epay,215182.8,4.761904762,10759.14,4.1
+2,315-22-5665,C,Port Harcourt,Normal,Female,Home and lifestyle,26481.6,10,13240.8,278056.8,2/24/2019,11:38,Epay,264816.0,4.761904762,13240.8,8.0
+3,300-71-4605,C,Port Harcourt,Member,Male,Electronic accessories,30974.4,5,7743.6,162615.6,2/25/2019,11:24,Epay,154872.0,4.761904762,7743.6,4.8
+4,183-56-6882,C,Port Harcourt,Member,Female,Food and beverages,35791.2,4,7158.24,150323.04,2/6/2019,10:42,Epay,143164.8,4.761904762,7158.24,7.5
+5,232-16-2483,C,Port Harcourt,Member,Female,Sports and travel,24523.2,1,1226.16,25749.360000000004,1/7/2019,12:28,Epay,24523.2,4.761904762,1226.16,6.8
+6,333-73-7901,C,Port Harcourt,Normal,Female,Health and beauty,19771.2,8,7908.480000000001,166078.08000000002,3/23/2019,13:24,Epay,158169.6,4.761904762,7908.480000000001,7.6
+7,554-53-8700,C,Port Harcourt,Member,Male,Home and lifestyle,20199.6,2,2019.9600000000005,42419.16,2/2/2019,10:11,Cash,40399.2,4.761904762,2019.9600000000005,6.3
+8,228-96-1411,C,Port Harcourt,Member,Female,Food and beverages,35532.0,8,14212.8,298468.8,3/4/2019,20:39,Cash,284256.0,4.761904762,14212.8,7.6
+9,617-15-4209,C,Port Harcourt,Member,Male,Health and beauty,5533.2,2,553.3199999999999,11619.72,3/16/2019,19:47,Cash,11066.4,4.761904762,553.3199999999999,7.2
+10,574-22-5561,C,Port Harcourt,Member,Female,Fashion accessories,29746.8,10,14873.4,312341.4,3/19/2019,17:08,Epay,297468.0,4.761904762,14873.4,7.9
+11,326-78-5178,C,Port Harcourt,Member,Male,Food and beverages,32904.0,7,11516.4,241844.4,2/3/2019,10:19,Cash,230328.0,4.761904762,11516.4,9.5
+12,778-71-5554,C,Port Harcourt,Member,Male,Fashion accessories,5554.8,1,277.74,5832.54,1/25/2019,15:46,Card,5554.8,4.761904762,277.74,6.1
+13,399-46-5918,C,Port Harcourt,Normal,Female,Electronic accessories,30952.800000000003,8,12381.12,260003.52,2/28/2019,19:01,Cash,247622.4,4.761904762,12381.12,8.2
+14,120-06-4233,C,Port Harcourt,Normal,Male,Electronic accessories,11019.6,6,3305.88,69423.48,3/12/2019,20:36,Cash,66117.6,4.761904762,3305.88,9.3
+15,285-68-5083,C,Port Harcourt,Member,Female,Sports and travel,8906.4,3,1335.96,28055.16,2/15/2019,17:47,Card,26719.2,4.761904762,1335.96,10.0
+16,803-83-5989,C,Port Harcourt,Normal,Male,Home and lifestyle,20062.8,6,6018.84,126395.64,2/24/2019,10:55,Epay,120376.8,4.761904762,6018.84,7.0
+17,838-78-4295,C,Port Harcourt,Normal,Female,Health and beauty,12049.2,2,1204.92,25303.32,2/10/2019,15:43,Epay,24098.4,4.761904762,1204.92,6.7
+18,393-65-2792,C,Port Harcourt,Normal,Male,Food and beverages,32212.800000000003,10,16106.4,338234.4,1/6/2019,12:46,Card,322128.0,4.761904762,16106.4,9.6
+19,796-12-2025,C,Port Harcourt,Normal,Male,Fashion accessories,22363.2,10,11181.6,234813.6,2/11/2019,16:19,Cash,223632.00000000003,4.761904762,11181.6,5.9
+20,841-35-6630,C,Port Harcourt,Normal,Female,Electronic accessories,27327.6,6,8198.28,172163.88,3/9/2019,18:21,Cash,163965.6,4.761904762,8198.28,8.7
+21,732-94-0499,C,Port Harcourt,Normal,Female,Electronic accessories,14994.0,10,7497.0,157437.0,1/13/2019,17:04,Card,149940.0,4.761904762,7497.0,5.4
+22,263-10-3913,C,Port Harcourt,Member,Male,Fashion accessories,17654.4,9,7944.48,166834.08000000002,1/9/2019,14:20,Card,158889.6,4.761904762,7944.48,8.6
+23,829-49-1914,C,Port Harcourt,Member,Female,Food and beverages,28191.6,10,14095.8,296011.8,3/5/2019,16:24,Epay,281916.0,4.761904762,14095.8,6.6
+24,756-01-7507,C,Port Harcourt,Normal,Female,Health and beauty,7336.799999999999,5,1834.2,38518.2,1/22/2019,18:56,Cash,36684.0,4.761904762,1834.2,6.0
+25,870-72-4431,C,Port Harcourt,Normal,Female,Health and beauty,35708.4,6,10712.52,224962.92,1/21/2019,14:42,Card,214250.4,4.761904762,10712.52,5.5
+26,480-63-2856,C,Port Harcourt,Normal,Male,Food and beverages,6930.0,8,2772.0,58211.99999999999,1/23/2019,18:37,Epay,55440.0,4.761904762,2772.0,6.6
+27,787-56-0757,C,Port Harcourt,Member,Female,Food and beverages,28929.6,4,5785.92,121504.31999999998,2/23/2019,18:45,Card,115718.4,4.761904762,5785.92,8.3
+28,360-39-5055,C,Port Harcourt,Member,Male,Sports and travel,17607.6,5,4401.9,92439.9,3/9/2019,10:17,Cash,88038.0,4.761904762,4401.9,6.6
+29,730-50-9884,C,Port Harcourt,Normal,Female,Sports and travel,29901.6,7,10465.56,219776.76,3/5/2019,14:31,Epay,209311.2,4.761904762,10465.56,4.0
+30,362-58-8315,C,Port Harcourt,Normal,Male,Fashion accessories,27547.2,5,6886.799999999999,144622.80000000002,3/25/2019,10:23,Cash,137736.0,4.761904762,6886.799999999999,9.9
+31,565-80-5980,C,Port Harcourt,Member,Female,Home and lifestyle,17056.8,4,3411.36,71638.56,1/23/2019,10:25,Cash,68227.2,4.761904762,3411.36,7.1
+32,225-32-0908,C,Port Harcourt,Normal,Female,Sports and travel,16149.6,10,8074.8,169570.8,1/26/2019,19:54,Epay,161496.0,4.761904762,8074.8,8.2
+33,512-91-0811,C,Port Harcourt,Normal,Male,Health and beauty,32310.0,1,1615.5,33925.5,2/6/2019,20:05,Card,32310.0,4.761904762,1615.5,6.6
+34,871-39-9221,C,Port Harcourt,Normal,Female,Electronic accessories,4482.0,6,1344.6,28236.6,2/9/2019,13:11,Cash,26892.0,4.761904762,1344.6,4.1
+35,163-56-7055,C,Port Harcourt,Member,Male,Fashion accessories,17535.6,1,876.7800000000001,18412.38,3/26/2019,19:20,Cash,17535.6,4.761904762,876.7800000000001,4.1
+36,189-98-2939,C,Port Harcourt,Normal,Male,Fashion accessories,28278.0,9,12725.1,267227.1,3/1/2019,13:22,Cash,254502.00000000003,4.761904762,12725.1,7.2
+37,551-21-3069,C,Port Harcourt,Normal,Female,Electronic accessories,8305.2,9,3737.34,78484.14,2/1/2019,11:27,Cash,74746.8,4.761904762,3737.34,4.9
+38,628-34-3388,C,Port Harcourt,Normal,Male,Fashion accessories,9856.8,6,2957.04,62097.84000000001,1/5/2019,20:54,Card,59140.8,4.761904762,2957.04,7.9
+39,588-01-7461,C,Port Harcourt,Normal,Female,Food and beverages,12232.8,9,5504.759999999999,115599.96,3/24/2019,10:43,Cash,110095.2,4.761904762,5504.759999999999,4.2
+40,861-77-0145,C,Port Harcourt,Member,Male,Electronic accessories,29509.2,10,14754.6,309846.6,3/3/2019,14:30,Cash,295092.0,4.761904762,14754.6,9.2
+41,210-67-5886,C,Port Harcourt,Member,Female,Health and beauty,35355.6,3,5303.34,111370.13999999998,2/5/2019,10:41,Card,106066.8,4.761904762,5303.34,7.8
+42,237-01-6122,C,Port Harcourt,Member,Female,Home and lifestyle,29084.4,9,13087.98,274847.58,2/1/2019,20:31,Card,261759.6,4.761904762,13087.98,9.5
+43,225-98-1496,C,Port Harcourt,Normal,Female,Fashion accessories,9727.2,3,1459.08,30640.68,3/2/2019,13:01,Card,29181.6,4.761904762,1459.08,7.1
+44,659-36-1684,C,Port Harcourt,Member,Male,Sports and travel,20563.2,7,7197.12,151139.52,1/12/2019,12:02,Card,143942.4,4.761904762,7197.12,6.5
+45,336-78-2147,C,Port Harcourt,Member,Male,Home and lifestyle,23007.6,8,9203.04,193263.84000000003,3/13/2019,19:52,Card,184060.8,4.761904762,9203.04,4.6
+46,189-08-9157,C,Port Harcourt,Normal,Female,Fashion accessories,11422.8,9,5140.259999999999,107945.46,1/8/2019,16:17,Card,102805.2,4.761904762,5140.259999999999,5.9
+47,663-86-9076,C,Port Harcourt,Member,Female,Food and beverages,24674.4,8,9869.76,207264.96,1/8/2019,15:57,Epay,197395.2,4.761904762,9869.76,8.5
+48,483-71-1164,C,Port Harcourt,Normal,Female,Health and beauty,29268.0,6,8780.4,184388.4,3/8/2019,16:43,Epay,175608.0,4.761904762,8780.4,5.3
+49,597-78-7908,C,Port Harcourt,Normal,Male,Fashion accessories,32479.2,3,4871.88,102309.48,2/18/2019,19:39,Cash,97437.6,4.761904762,4871.88,6.2
+50,731-81-9469,C,Port Harcourt,Member,Female,Sports and travel,32328.0,10,16164.0,339444.0,1/23/2019,13:00,Card,323280.0,4.761904762,16164.0,5.4
+51,280-17-4359,C,Port Harcourt,Member,Male,Health and beauty,32580.0,10,16290.0,342090.0,1/25/2019,13:48,Cash,325800.0,4.761904762,16290.0,8.1
+52,338-65-2210,C,Port Harcourt,Member,Female,Health and beauty,24696.0,10,12347.999999999998,259308.0,2/5/2019,19:57,Cash,246960.0,4.761904762,12347.999999999998,9.1
+53,488-25-4221,C,Port Harcourt,Member,Female,Food and beverages,10947.6,1,547.38,11494.98,2/22/2019,10:36,Card,10947.6,4.761904762,547.38,8.4
+54,458-41-1477,C,Port Harcourt,Normal,Female,Health and beauty,16653.6,6,4996.08,104917.68,3/8/2019,17:11,Card,99921.6,4.761904762,4996.08,9.5
+55,568-90-5112,C,Port Harcourt,Normal,Male,Health and beauty,23810.4,4,4762.08,100003.68,3/19/2019,12:46,Card,95241.6,4.761904762,4762.08,5.6
+56,584-86-7256,C,Port Harcourt,Member,Male,Sports and travel,12441.6,7,4354.56,91445.76,3/11/2019,16:07,Card,87091.2,4.761904762,4354.56,7.3
+57,214-17-6927,C,Port Harcourt,Normal,Female,Food and beverages,5932.8,6,1779.84,37376.64,2/7/2019,18:23,Epay,35596.8,4.761904762,1779.84,9.9
+58,400-89-4171,C,Port Harcourt,Normal,Female,Sports and travel,29149.2,8,11659.680000000002,244853.28,1/28/2019,13:05,Cash,233193.6,4.761904762,11659.680000000002,9.3
+59,894-41-5205,C,Port Harcourt,Normal,Female,Food and beverages,15544.8,8,6217.920000000001,130576.32,1/19/2019,19:39,Card,124358.4,4.761904762,6217.920000000001,8.3
+60,423-80-0988,C,Port Harcourt,Normal,Male,Sports and travel,27504.000000000004,2,2750.4,57758.4,1/30/2019,19:42,Epay,55008.00000000001,4.761904762,2750.4,6.5
+61,234-65-2137,C,Port Harcourt,Normal,Male,Home and lifestyle,34408.8,10,17204.4,361292.4,1/16/2019,13:32,Cash,344088.0,4.761904762,17204.4,4.8
+62,733-33-4967,C,Port Harcourt,Normal,Male,Electronic accessories,7506.000000000001,8,3002.4,63050.4,3/3/2019,19:17,Cash,60048.00000000001,4.761904762,3002.4,6.3
+63,139-52-2867,C,Port Harcourt,Normal,Female,Fashion accessories,8103.6,7,2836.26,59561.46,2/13/2019,10:50,Card,56725.2,4.761904762,2836.26,4.8
+64,342-65-4817,C,Port Harcourt,Member,Male,Health and beauty,31248.0,3,4687.2,98431.2,1/28/2019,16:47,Epay,93744.0,4.761904762,4687.2,9.9
+65,130-98-8941,C,Port Harcourt,Normal,Male,Fashion accessories,23133.6,7,8096.76,170031.96,2/9/2019,10:00,Cash,161935.2,4.761904762,8096.76,5.7
+66,434-83-9547,C,Port Harcourt,Member,Male,Food and beverages,13849.2,8,5539.68,116333.27999999998,1/23/2019,11:51,Cash,110793.6,4.761904762,5539.68,7.7
+67,824-88-3614,C,Port Harcourt,Normal,Male,Health and beauty,12351.6,8,4940.64,103753.44,1/25/2019,15:00,Epay,98812.8,4.761904762,4940.64,5.7
+68,593-65-1552,C,Port Harcourt,Normal,Female,Home and lifestyle,25131.6,4,5026.32,105552.72,1/28/2019,20:50,Card,100526.4,4.761904762,5026.32,5.9
+69,286-43-6208,C,Port Harcourt,Normal,Female,Food and beverages,31608.0,9,14223.6,298695.60000000003,3/16/2019,19:08,Cash,284472.0,4.761904762,14223.6,9.2
+70,556-86-3144,C,Port Harcourt,Member,Female,Fashion accessories,26744.4,1,1337.22,28081.62,1/13/2019,19:30,Cash,26744.4,4.761904762,1337.22,5.0
+71,848-24-9445,C,Port Harcourt,Member,Male,Health and beauty,15732.000000000002,2,1573.2,33037.2,3/26/2019,18:03,Cash,31464.000000000004,4.761904762,1573.2,4.9
+72,699-01-4164,C,Port Harcourt,Normal,Male,Health and beauty,14940.0,4,2988.0000000000005,62748.00000000001,3/12/2019,19:58,Card,59760.0,4.761904762,2988.0000000000005,8.2
+73,420-11-4919,C,Port Harcourt,Member,Female,Food and beverages,25700.4,5,6425.1,134927.1,2/17/2019,19:57,Card,128502.0,4.761904762,6425.1,5.5
+74,606-80-4905,C,Port Harcourt,Member,Female,Sports and travel,6893.999999999999,6,2068.2,43432.2,1/29/2019,10:01,Card,41364.0,4.761904762,2068.2,6.8
+75,426-39-2418,C,Port Harcourt,Normal,Male,Electronic accessories,22107.6,7,7737.660000000001,162490.86,1/14/2019,10:02,Cash,154753.2,4.761904762,7737.660000000001,9.8
+76,672-51-8681,C,Port Harcourt,Member,Female,Electronic accessories,23994.000000000004,9,10797.3,226743.3,1/4/2019,18:19,Card,215946.0,4.761904762,10797.3,9.7
+77,263-87-5680,C,Port Harcourt,Member,Female,Home and lifestyle,10270.8,10,5135.400000000001,107843.4,3/18/2019,17:38,Epay,102708.0,4.761904762,5135.400000000001,7.8
+78,677-11-0152,C,Port Harcourt,Normal,Female,Food and beverages,33573.6,9,15108.12,317270.52,1/16/2019,18:08,Cash,302162.4,4.761904762,15108.12,8.8
+79,389-25-3394,C,Port Harcourt,Normal,Male,Electronic accessories,4251.6,5,1062.9,22320.9,2/17/2019,18:06,Cash,21258.0,4.761904762,1062.9,9.4
+80,279-62-1445,C,Port Harcourt,Member,Female,Fashion accessories,4514.4,1,225.72,4740.12,2/21/2019,12:38,Cash,4514.4,4.761904762,225.72,8.2
+81,746-68-6593,C,Port Harcourt,Member,Female,Sports and travel,31377.6,2,3137.76,65892.95999999999,1/11/2019,14:29,Card,62755.2,4.761904762,3137.76,9.7
+82,583-72-1480,C,Port Harcourt,Member,Male,Electronic accessories,13341.6,4,2668.32,56034.72000000001,1/31/2019,16:24,Epay,53366.4,4.761904762,2668.32,9.7
+83,211-30-9270,C,Port Harcourt,Normal,Male,Health and beauty,6267.6,5,1566.9,32904.9,1/28/2019,15:16,Card,31338.0,4.761904762,1566.9,4.9
+84,755-12-3214,C,Port Harcourt,Member,Female,Fashion accessories,15919.2,5,3979.8,83575.8,3/5/2019,17:07,Card,79596.0,4.761904762,3979.8,8.6
+85,142-72-4741,C,Port Harcourt,Member,Male,Fashion accessories,33552.0,2,3355.2000000000003,70459.2,2/28/2019,18:37,Card,67104.0,4.761904762,3355.2000000000003,6.0
+86,662-47-5456,C,Port Harcourt,Member,Male,Fashion accessories,12668.4,10,6334.2,133018.2,3/17/2019,19:06,Card,126684.0,4.761904762,6334.2,8.4
+87,883-17-4236,C,Port Harcourt,Normal,Female,Sports and travel,5180.400000000001,2,518.04,10878.84,3/2/2019,19:44,Card,10360.8,4.761904762,518.04,7.2
+88,380-94-4661,C,Port Harcourt,Member,Male,Electronic accessories,23738.4,4,4747.679999999999,99701.28,2/7/2019,13:05,Card,94953.6,4.761904762,4747.679999999999,6.9
+89,821-07-3596,C,Port Harcourt,Normal,Female,Fashion accessories,5922.0,4,1184.4,24872.4,3/7/2019,14:53,Epay,23688.0,4.761904762,1184.4,5.6
+90,808-65-0703,C,Port Harcourt,Normal,Male,Home and lifestyle,12769.2,4,2553.84,53630.64,3/14/2019,17:22,Card,51076.8,4.761904762,2553.84,6.9
+91,687-15-1097,C,Port Harcourt,Member,Female,Health and beauty,7603.200000000001,2,760.32,15966.72,1/3/2019,19:17,Cash,15206.4,4.761904762,760.32,9.7
+92,526-86-8552,C,Port Harcourt,Member,Female,Home and lifestyle,7855.2,10,3927.6,82479.6,1/7/2019,17:36,Cash,78552.0,4.761904762,3927.6,7.1
+93,376-56-3573,C,Port Harcourt,Normal,Female,Fashion accessories,34351.2,4,6870.24,144275.04,2/2/2019,13:23,Epay,137404.8,4.761904762,6870.24,6.4
+94,537-72-0426,C,Port Harcourt,Member,Male,Fashion accessories,25556.4,10,12778.2,268342.2,3/20/2019,16:28,Cash,255564.0,4.761904762,12778.2,5.7
+95,523-38-0215,C,Port Harcourt,Normal,Male,Home and lifestyle,13320.0,1,666.0,13986.0,3/6/2019,13:29,Card,13320.0,4.761904762,666.0,7.9
+96,593-95-4461,C,Port Harcourt,Member,Male,Home and lifestyle,26949.6,1,1347.48,28297.08,3/24/2019,14:49,Cash,26949.6,4.761904762,1347.48,6.9
+97,226-71-3580,C,Port Harcourt,Normal,Female,Sports and travel,8550.0,9,3847.5,80797.5,1/31/2019,12:02,Cash,76950.0,4.761904762,3847.5,9.5
+98,558-80-4082,C,Port Harcourt,Normal,Male,Electronic accessories,10026.0,7,3509.1000000000004,73691.09999999999,3/14/2019,17:20,Epay,70182.0,4.761904762,3509.1000000000004,6.0
+99,211-05-0490,C,Port Harcourt,Member,Female,Electronic accessories,18691.2,5,4672.8,98128.8,3/3/2019,13:42,Cash,93456.0,4.761904762,4672.8,7.5
+100,727-75-6477,C,Port Harcourt,Normal,Male,Electronic accessories,10382.4,4,2076.48,43606.08,3/29/2019,14:44,Cash,41529.6,4.761904762,2076.48,6.4
+101,779-06-0012,C,Port Harcourt,Member,Female,Home and lifestyle,31899.6,1,1594.98,33494.579999999994,1/19/2019,10:21,Cash,31899.6,4.761904762,1594.98,7.7
+102,446-47-6729,C,Port Harcourt,Normal,Male,Fashion accessories,35935.2,2,3593.5200000000004,75463.92,1/2/2019,18:09,Card,71870.4,4.761904762,3593.5200000000004,6.7
+103,735-06-4124,C,Port Harcourt,Normal,Male,Food and beverages,17499.6,1,874.9799999999999,18374.58,2/25/2019,15:31,Cash,17499.6,4.761904762,874.9799999999999,4.4
+104,181-94-6432,C,Port Harcourt,Member,Male,Fashion accessories,24958.8,2,2495.88,52413.48,2/5/2019,19:05,Epay,49917.6,4.761904762,2495.88,9.7
+105,227-07-4446,C,Port Harcourt,Member,Female,Electronic accessories,28126.8,10,14063.4,295331.4,2/10/2019,20:51,Cash,281268.0,4.761904762,14063.4,4.4
+106,174-36-3675,C,Port Harcourt,Member,Male,Food and beverages,35773.200000000004,2,3577.3200000000006,75123.72,2/14/2019,17:29,Cash,71546.40000000001,4.761904762,3577.3200000000006,5.2
+107,428-83-5800,C,Port Harcourt,Member,Female,Food and beverages,7588.799999999999,3,1138.32,23904.72,2/9/2019,10:25,Cash,22766.4,4.761904762,1138.32,7.3
+108,603-07-0961,C,Port Harcourt,Member,Male,Electronic accessories,26924.4,5,6731.1,141353.1,1/10/2019,11:34,Cash,134622.0,4.761904762,6731.1,4.9
+109,704-20-4138,C,Port Harcourt,Member,Female,Health and beauty,10681.2,7,3738.42,78506.82,3/11/2019,18:58,Card,74768.4,4.761904762,3738.42,8.1
+110,787-15-1757,C,Port Harcourt,Member,Male,Health and beauty,15865.2,4,3173.04,66633.84000000001,2/18/2019,16:28,Epay,63460.8,4.761904762,3173.04,8.4
+111,649-11-3678,C,Port Harcourt,Normal,Female,Food and beverages,8254.8,9,3714.66,78007.86,2/26/2019,20:26,Cash,74293.2,4.761904762,3714.66,5.5
+112,622-20-1945,C,Port Harcourt,Normal,Female,Health and beauty,14191.2,1,709.56,14900.760000000002,1/18/2019,15:08,Cash,14191.2,4.761904762,709.56,8.4
+113,719-76-3868,C,Port Harcourt,Member,Male,Food and beverages,33933.6,4,6786.72,142521.12,3/12/2019,16:30,Cash,135734.4,4.761904762,6786.72,8.6
+114,835-16-0096,C,Port Harcourt,Member,Male,Sports and travel,5292.0,5,1323.0,27783.0,3/24/2019,13:48,Epay,26460.0,4.761904762,1323.0,8.5
+115,633-09-3463,C,Port Harcourt,Normal,Female,Electronic accessories,17154.0,3,2573.1,54035.1,3/28/2019,12:58,Card,51461.99999999999,4.761904762,2573.1,9.5
+116,416-13-5917,C,Port Harcourt,Normal,Female,Food and beverages,34930.8,5,8732.7,183386.7,1/30/2019,16:24,Epay,174654.0,4.761904762,8732.7,9.3
+117,725-96-3778,C,Port Harcourt,Member,Female,Home and lifestyle,32130.0,8,12852.000000000002,269892.0,1/20/2019,10:13,Cash,257040.0,4.761904762,12852.000000000002,4.7
+118,860-79-0874,C,Port Harcourt,Member,Female,Fashion accessories,35748.0,10,17874.0,375354.00000000006,2/15/2019,14:53,Card,357480.0,4.761904762,17874.0,6.6
+119,320-49-6392,C,Port Harcourt,Normal,Female,Electronic accessories,10886.4,1,544.3199999999999,11430.72,3/4/2019,15:44,Cash,10886.4,4.761904762,544.3199999999999,8.4
+120,632-90-0281,C,Port Harcourt,Normal,Female,Fashion accessories,13517.999999999998,10,6758.999999999999,141939.0,3/8/2019,20:01,Card,135180.0,4.761904762,6758.999999999999,9.3
+121,554-42-2417,C,Port Harcourt,Normal,Female,Sports and travel,34358.4,10,17179.2,360763.2,1/9/2019,13:45,Cash,343584.0,4.761904762,17179.2,5.2
+122,605-72-4132,C,Port Harcourt,Normal,Female,Food and beverages,34009.2,8,13603.680000000002,285677.28,2/27/2019,15:12,Cash,272073.6,4.761904762,13603.680000000002,9.1
+123,471-41-2823,C,Port Harcourt,Normal,Male,Food and beverages,35924.4,2,3592.44,75441.24,3/7/2019,20:37,Epay,71848.8,4.761904762,3592.44,8.0
+124,272-27-9238,C,Port Harcourt,Normal,Female,Food and beverages,14846.4,4,2969.28,62354.88,2/19/2019,16:23,Cash,59385.600000000006,4.761904762,2969.28,7.1
+125,834-25-9262,C,Port Harcourt,Normal,Female,Fashion accessories,29404.800000000003,4,5880.959999999999,123500.16,1/6/2019,12:12,Cash,117619.2,4.761904762,5880.959999999999,9.1
+126,122-61-9553,C,Port Harcourt,Normal,Female,Electronic accessories,18475.2,9,8313.84,174590.63999999998,3/14/2019,19:33,Cash,166276.8,4.761904762,8313.84,5.6
+127,613-59-9758,C,Port Harcourt,Normal,Female,Sports and travel,5169.599999999999,10,2584.8,54280.8,1/27/2019,14:28,Cash,51696.0,4.761904762,2584.8,5.4
+128,730-70-9830,C,Port Harcourt,Normal,Female,Home and lifestyle,25239.6,6,7571.88,159009.48,3/14/2019,17:54,Epay,151437.6,4.761904762,7571.88,5.2
+129,382-25-8917,C,Port Harcourt,Normal,Male,Fashion accessories,15148.8,6,4544.64,95437.44000000002,1/29/2019,12:25,Cash,90892.8,4.761904762,4544.64,8.9
+130,743-88-1662,C,Port Harcourt,Normal,Male,Sports and travel,34376.4,7,12031.74,252666.54,2/22/2019,18:17,Epay,240634.8,4.761904762,12031.74,8.7
+131,595-86-2894,C,Port Harcourt,Member,Male,Fashion accessories,34912.8,4,6982.56,146633.75999999998,2/6/2019,17:20,Epay,139651.2,4.761904762,6982.56,9.4
+132,462-78-5240,C,Port Harcourt,Normal,Female,Electronic accessories,9579.6,2,957.96,20117.16,3/19/2019,14:35,Cash,19159.2,4.761904762,957.96,4.2
+133,153-58-4872,C,Port Harcourt,Member,Female,Food and beverages,26960.4,4,5392.08,113233.68,3/1/2019,15:32,Epay,107841.6,4.761904762,5392.08,4.2
+134,689-16-9784,C,Port Harcourt,Normal,Male,Food and beverages,16837.2,6,5051.16,106074.36,3/11/2019,13:37,Cash,101023.2,4.761904762,5051.16,6.0
+135,394-41-0748,C,Port Harcourt,Member,Female,Fashion accessories,19465.2,9,8759.34,183946.14,1/27/2019,14:55,Epay,175186.8,4.761904762,8759.34,9.5
+136,541-89-9860,C,Port Harcourt,Member,Female,Fashion accessories,28972.800000000003,3,4345.92,91264.32,2/15/2019,12:31,Cash,86918.4,4.761904762,4345.92,8.1
+137,110-05-6330,C,Port Harcourt,Normal,Female,Food and beverages,14194.8,6,4258.4400000000005,89427.24,3/25/2019,20:18,Card,85168.8,4.761904762,4258.4400000000005,9.4
+138,651-61-0874,C,Port Harcourt,Normal,Male,Home and lifestyle,16639.2,4,3327.84,69884.64000000001,3/12/2019,20:04,Card,66556.8,4.761904762,3327.84,6.2
+139,236-86-3015,C,Port Harcourt,Member,Male,Home and lifestyle,5032.8,1,251.64,5284.4400000000005,2/4/2019,13:38,Epay,5032.8,4.761904762,251.64,9.8
+140,587-03-7455,C,Port Harcourt,Member,Female,Fashion accessories,35204.4,7,12321.54,258752.34,2/16/2019,17:30,Epay,246430.8,4.761904762,12321.54,4.9
+141,372-26-1506,C,Port Harcourt,Normal,Female,Fashion accessories,8575.2,5,2143.8,45019.8,1/28/2019,19:24,Epay,42876.0,4.761904762,2143.8,5.4
+142,750-57-9686,C,Port Harcourt,Normal,Female,Home and lifestyle,16336.8,4,3267.36,68614.56,1/8/2019,13:48,Card,65347.2,4.761904762,3267.36,8.7
+143,186-09-3669,C,Port Harcourt,Member,Female,Health and beauty,29343.6,1,1467.18,30810.78,1/22/2019,10:57,Epay,29343.6,4.761904762,1467.18,9.2
+144,266-76-6436,C,Port Harcourt,Member,Female,Food and beverages,13896.0,3,2084.4,43772.4,3/28/2019,13:57,Epay,41688.0,4.761904762,2084.4,7.5
+145,740-22-2500,C,Port Harcourt,Normal,Female,Electronic accessories,30258.0,3,4538.7,95312.7,1/23/2019,13:29,Cash,90774.0,4.761904762,4538.7,9.8
+146,271-88-8734,C,Port Harcourt,Member,Female,Fashion accessories,34995.6,10,17497.8,367453.8,2/8/2019,13:00,Card,349956.0,4.761904762,17497.8,8.7
+147,489-64-4354,C,Port Harcourt,Normal,Male,Fashion accessories,5860.8,1,293.04,6153.84,3/9/2019,15:36,Cash,5860.8,4.761904762,293.04,5.0
+148,574-57-9721,C,Port Harcourt,Normal,Male,Food and beverages,15577.2,2,1557.72,32712.120000000003,3/8/2019,16:53,Epay,31154.4,4.761904762,1557.72,5.7
+149,751-69-0068,C,Port Harcourt,Normal,Male,Sports and travel,35726.4,9,16076.88,337614.48,3/19/2019,19:09,Epay,321537.6,4.761904762,16076.88,9.0
+150,257-73-1380,C,Port Harcourt,Member,Male,Sports and travel,29854.800000000003,4,5970.959999999999,125390.16,1/20/2019,16:51,Epay,119419.2,4.761904762,5970.959999999999,9.6
+151,549-96-4200,C,Port Harcourt,Member,Male,Food and beverages,6134.4,4,1226.88,25764.48,3/8/2019,20:15,Epay,24537.6,4.761904762,1226.88,7.0
+152,810-60-6344,C,Port Harcourt,Normal,Female,Electronic accessories,14709.6,8,5883.84,123560.64,2/7/2019,14:38,Card,117676.8,4.761904762,5883.84,6.5
+153,450-28-2866,C,Port Harcourt,Member,Male,Food and beverages,6278.400000000001,5,1569.6,32961.6,1/15/2019,19:25,Cash,31392.0,4.761904762,1569.6,8.1
+154,192-98-7397,C,Port Harcourt,Normal,Male,Fashion accessories,4600.8,1,230.04,4830.84,1/8/2019,14:11,Epay,4600.8,4.761904762,230.04,9.5
+155,235-46-8343,C,Port Harcourt,Member,Male,Food and beverages,9957.6,10,4978.8,104554.8,2/14/2019,11:26,Card,99576.0,4.761904762,4978.8,8.9
+156,453-12-7053,C,Port Harcourt,Normal,Male,Fashion accessories,16466.4,3,2469.96,51869.16,3/10/2019,17:38,Card,49399.2,4.761904762,2469.96,6.5
+157,325-90-8763,C,Port Harcourt,Member,Female,Electronic accessories,16765.2,10,8382.6,176034.6,1/27/2019,13:58,Cash,167652.0,4.761904762,8382.6,7.6
+158,729-46-7422,C,Port Harcourt,Normal,Male,Food and beverages,12920.4,1,646.02,13566.42,2/23/2019,16:52,Card,12920.4,4.761904762,646.02,7.9
+159,639-76-1242,C,Port Harcourt,Normal,Male,Food and beverages,14587.2,5,3646.8,76582.8,2/3/2019,15:19,Cash,72936.0,4.761904762,3646.8,4.5
+160,326-71-2155,C,Port Harcourt,Normal,Female,Sports and travel,26622.0,4,5324.4,111812.4,2/3/2019,10:02,Cash,106488.0,4.761904762,5324.4,6.1
+161,320-32-8842,C,Port Harcourt,Member,Female,Food and beverages,8143.200000000001,1,407.16,8550.359999999999,3/17/2019,18:58,Cash,8143.200000000001,4.761904762,407.16,6.4
+162,878-30-2331,C,Port Harcourt,Member,Female,Sports and travel,19638.0,10,9819.0,206199.0,3/2/2019,11:22,Card,196380.0,4.761904762,9819.0,7.1
+163,440-59-5691,C,Port Harcourt,Member,Female,Health and beauty,13374.0,7,4680.9,98298.9,2/8/2019,13:12,Card,93618.0,4.761904762,4680.9,7.7
+164,746-19-0921,C,Port Harcourt,Normal,Male,Food and beverages,7768.799999999999,1,388.44,8157.240000000001,2/9/2019,10:02,Epay,7768.799999999999,4.761904762,388.44,7.2
+165,233-34-0817,C,Port Harcourt,Member,Female,Electronic accessories,35582.4,1,1779.12,37361.52,2/15/2019,11:21,Cash,35582.4,4.761904762,1779.12,8.4
+166,767-05-1286,C,Port Harcourt,Member,Female,Home and lifestyle,30157.2,6,9047.16,189990.36,1/23/2019,12:10,Epay,180943.2,4.761904762,9047.16,5.4
+167,598-47-9715,C,Port Harcourt,Normal,Male,Electronic accessories,30265.2,4,6053.04,127113.84,3/7/2019,16:54,Epay,121060.8,4.761904762,6053.04,4.4
+168,541-08-3113,C,Port Harcourt,Normal,Male,Food and beverages,23749.2,8,9499.68,199493.28,2/2/2019,20:29,Cash,189993.6,4.761904762,9499.68,8.4
+169,246-11-3901,C,Port Harcourt,Normal,Female,Electronic accessories,11807.999999999998,10,5903.999999999999,123984.0,2/15/2019,12:12,Cash,118080.0,4.761904762,5903.999999999999,6.2
+170,493-65-6248,C,Port Harcourt,Member,Female,Sports and travel,13312.8,10,6656.4,139784.4,1/1/2019,19:48,Card,133128.0,4.761904762,6656.4,7.0
+171,556-72-8512,C,Port Harcourt,Normal,Male,Home and lifestyle,8265.6,1,413.28,8678.880000000001,1/30/2019,20:47,Cash,8265.6,4.761904762,413.28,4.3
+172,148-82-2527,C,Port Harcourt,Member,Female,Home and lifestyle,4363.2,10,2181.6,45813.6,3/5/2019,13:44,Card,43632.0,4.761904762,2181.6,8.4
+173,556-97-7101,C,Port Harcourt,Normal,Female,Electronic accessories,22759.2,2,2275.92,47794.32,1/1/2019,15:51,Cash,45518.4,4.761904762,2275.92,8.5
+174,862-59-8517,C,Port Harcourt,Normal,Female,Food and beverages,32486.4,6,9745.92,204664.32,1/27/2019,11:17,Cash,194918.4,4.761904762,9745.92,6.2
+175,573-98-8548,C,Port Harcourt,Member,Female,Fashion accessories,11484.0,1,574.2,12058.2,1/5/2019,12:40,Epay,11484.0,4.761904762,574.2,9.1
+176,620-02-2046,C,Port Harcourt,Normal,Male,Home and lifestyle,24984.000000000004,2,2498.4,52466.4,1/27/2019,19:48,Epay,49968.00000000001,4.761904762,2498.4,9.0
+177,602-80-9671,C,Port Harcourt,Member,Female,Home and lifestyle,5742.0,6,1722.6,36174.6,2/9/2019,17:15,Card,34452.0,4.761904762,1722.6,5.1
+178,503-07-0930,C,Port Harcourt,Member,Male,Sports and travel,21020.4,7,7357.139999999999,154499.94,2/23/2019,19:49,Card,147142.80000000002,4.761904762,7357.139999999999,8.2
+179,413-20-6708,C,Port Harcourt,Member,Female,Fashion accessories,18529.2,1,926.46,19455.66,3/18/2019,15:52,Epay,18529.2,4.761904762,926.46,8.5
+180,521-18-7827,C,Port Harcourt,Member,Male,Home and lifestyle,14180.4,5,3545.1,74447.1,1/22/2019,20:46,Card,70902.0,4.761904762,3545.1,8.7
+181,600-38-9738,C,Port Harcourt,Member,Male,Sports and travel,25891.2,5,6472.8,135928.8,1/17/2019,15:05,Card,129456.0,4.761904762,6472.8,4.3
+182,451-28-5717,C,Port Harcourt,Member,Female,Home and lifestyle,29941.2,6,8982.36,188629.56,3/20/2019,11:23,Cash,179647.19999999998,4.761904762,8982.36,7.3
+183,133-14-7229,C,Port Harcourt,Normal,Male,Health and beauty,22633.2,2,2263.32,47529.72000000001,1/1/2019,11:43,Cash,45266.4,4.761904762,2263.32,5.0
+184,236-27-1144,C,Port Harcourt,Normal,Female,Food and beverages,5871.599999999999,9,2642.2200000000003,55486.62,3/26/2019,10:31,Epay,52844.4,4.761904762,2642.2200000000003,8.4
+185,583-41-4548,C,Port Harcourt,Normal,Male,Home and lifestyle,6001.200000000001,7,2100.42,44108.82,2/7/2019,11:36,Epay,42008.4,4.761904762,2100.42,7.4
+186,358-88-9262,C,Port Harcourt,Member,Female,Food and beverages,31492.800000000003,6,9447.84,198404.64,2/1/2019,18:43,Epay,188956.8,4.761904762,9447.84,5.1
+187,343-87-0864,C,Port Harcourt,Member,Male,Health and beauty,27316.8,1,1365.84,28682.64,1/3/2019,10:30,Card,27316.8,4.761904762,1365.84,7.1
+188,243-47-2663,C,Port Harcourt,Member,Male,Electronic accessories,6757.2,6,2027.16,42570.36,1/28/2019,16:43,Card,40543.2,4.761904762,2027.16,5.5
+189,399-69-4630,C,Port Harcourt,Normal,Male,Electronic accessories,7995.6,6,2398.68,50372.28,3/7/2019,10:23,Card,47973.6,4.761904762,2398.68,8.6
+190,283-26-5248,C,Port Harcourt,Member,Female,Food and beverages,35467.2,10,17733.6,372405.6,1/30/2019,20:23,Epay,354672.0,4.761904762,17733.6,4.5
+191,866-99-7614,C,Port Harcourt,Normal,Male,Food and beverages,32112.0,10,16056.0,337176.0,2/11/2019,15:42,Card,321120.0,4.761904762,16056.0,4.4
+192,718-57-9773,C,Port Harcourt,Normal,Female,Sports and travel,17758.8,10,8879.4,186467.4,2/3/2019,16:40,Card,177588.0,4.761904762,8879.4,9.4
+193,408-26-9866,C,Port Harcourt,Normal,Female,Sports and travel,26632.800000000003,7,9321.48,195751.08,3/2/2019,16:42,Epay,186429.6,4.761904762,9321.48,4.1
+194,592-34-6155,C,Port Harcourt,Normal,Male,Food and beverages,11437.2,4,2287.44,48036.24,1/14/2019,14:43,Epay,45748.8,4.761904762,2287.44,6.2
+195,390-31-6381,C,Port Harcourt,Normal,Male,Food and beverages,9799.2,3,1469.88,30867.48,1/7/2019,12:37,Cash,29397.6,4.761904762,1469.88,7.3
+196,339-18-7061,C,Port Harcourt,Member,Female,Fashion accessories,33472.8,2,3347.28,70292.87999999999,2/13/2019,15:06,Card,66945.6,4.761904762,3347.28,8.0
+197,379-17-6588,C,Port Harcourt,Normal,Male,Fashion accessories,21459.6,10,10729.8,225325.8,3/14/2019,11:07,Cash,214596.0,4.761904762,10729.8,5.3
+198,302-15-2162,C,Port Harcourt,Member,Male,Health and beauty,16750.8,6,5025.240000000001,105530.04,3/3/2019,10:54,Card,100504.8,4.761904762,5025.240000000001,4.3
+199,788-07-8452,C,Port Harcourt,Member,Female,Home and lifestyle,8726.4,7,3054.24,64139.04000000001,1/27/2019,17:38,Epay,61084.8,4.761904762,3054.24,9.4
+200,123-35-4896,C,Port Harcourt,Normal,Female,Sports and travel,16797.6,9,7558.92,158737.31999999998,2/17/2019,19:11,Epay,151178.4,4.761904762,7558.92,5.3
+201,258-69-7810,C,Port Harcourt,Normal,Female,Fashion accessories,13266.0,5,3316.5,69646.5,1/26/2019,18:53,Cash,66330.0,4.761904762,3316.5,9.2
+202,219-61-4139,C,Port Harcourt,Normal,Male,Electronic accessories,29908.8,1,1495.44,31404.24,1/23/2019,17:16,Epay,29908.8,4.761904762,1495.44,6.4
+203,881-41-7302,C,Port Harcourt,Normal,Female,Fashion accessories,23396.4,1,1169.82,24566.22,1/26/2019,10:06,Card,23396.4,4.761904762,1169.82,4.5
+204,373-09-4567,C,Port Harcourt,Normal,Male,Food and beverages,27921.6,10,13960.8,293176.8,3/14/2019,20:35,Epay,279216.0,4.761904762,13960.8,6.9
+205,484-22-8230,C,Port Harcourt,Member,Female,Fashion accessories,18680.4,7,6538.14,137300.94,1/8/2019,20:08,Cash,130762.8,4.761904762,6538.14,4.5
+206,544-32-5024,C,Port Harcourt,Member,Female,Food and beverages,17924.4,4,3584.88,75282.48,3/28/2019,19:16,Card,71697.6,4.761904762,3584.88,6.4
+207,277-35-5865,C,Port Harcourt,Member,Female,Food and beverages,35629.2,9,16033.14,336695.94,3/9/2019,11:23,Cash,320662.8,4.761904762,16033.14,6.7
+208,284-54-4231,C,Port Harcourt,Member,Male,Sports and travel,29134.800000000003,1,1456.74,30591.54,1/19/2019,16:08,Card,29134.800000000003,4.761904762,1456.74,9.0
+209,840-19-2096,C,Port Harcourt,Member,Male,Electronic accessories,31647.6,5,7911.9,166149.9,3/14/2019,18:10,Epay,158238.0,4.761904762,7911.9,4.4
+210,641-96-3695,C,Port Harcourt,Member,Female,Fashion accessories,15645.6,6,4693.68,98567.28,2/7/2019,17:55,Epay,93873.6,4.761904762,4693.68,8.5
+211,318-81-2368,C,Port Harcourt,Normal,Female,Electronic accessories,16632.0,1,831.6,17463.6,3/19/2019,12:16,Cash,16632.0,4.761904762,831.6,6.3
+212,155-45-3814,C,Port Harcourt,Member,Female,Electronic accessories,31878.0,8,12751.2,267775.2,3/19/2019,15:29,Epay,255024.0,4.761904762,12751.2,4.7
+213,131-15-8856,C,Port Harcourt,Member,Female,Food and beverages,26107.2,8,10442.88,219300.48,3/30/2019,19:26,Card,208857.6,4.761904762,10442.88,4.0
+214,273-84-2164,C,Port Harcourt,Member,Male,Electronic accessories,4338.0,5,1084.5,22774.5,2/16/2019,15:53,Epay,21690.0,4.761904762,1084.5,5.5
+215,778-89-7974,C,Port Harcourt,Normal,Male,Health and beauty,25275.6,6,7582.680000000001,159236.28,3/30/2019,14:58,Cash,151653.6,4.761904762,7582.680000000001,7.4
+216,859-71-0933,C,Port Harcourt,Member,Female,Sports and travel,5576.4,2,557.6400000000001,11710.44,1/16/2019,15:10,Cash,11152.8,4.761904762,557.6400000000001,6.3
+217,740-11-5257,C,Port Harcourt,Normal,Male,Electronic accessories,8906.4,10,4453.2,93517.2,2/24/2019,16:44,Cash,89064.0,4.761904762,4453.2,7.1
+218,250-81-7186,C,Port Harcourt,Normal,Female,Electronic accessories,35888.4,1,1794.4199999999998,37682.82,2/27/2019,10:23,Card,35888.4,4.761904762,1794.4199999999998,8.0
+219,842-29-4695,C,Port Harcourt,Member,Male,Sports and travel,6170.400000000001,7,2159.64,45352.44,1/16/2019,12:07,Card,43192.8,4.761904762,2159.64,7.9
+220,641-51-2661,C,Port Harcourt,Member,Female,Food and beverages,31356.0,10,15677.999999999998,329238.0,2/12/2019,14:45,Card,313560.0,4.761904762,15677.999999999998,9.9
+221,714-02-3114,C,Port Harcourt,Normal,Female,Sports and travel,35568.0,2,3556.8,74692.8,2/21/2019,11:39,Cash,71136.0,4.761904762,3556.8,7.7
+222,408-66-6712,C,Port Harcourt,Member,Female,Health and beauty,17175.6,6,5152.679999999999,108206.28,2/16/2019,14:19,Epay,103053.6,4.761904762,5152.679999999999,4.4
+223,556-41-6224,C,Port Harcourt,Normal,Male,Health and beauty,12110.4,8,4844.16,101727.36,2/15/2019,17:10,Card,96883.2,4.761904762,4844.16,9.3
+224,648-94-3045,C,Port Harcourt,Normal,Male,Health and beauty,21222.0,10,10611.0,222831.0,2/7/2019,14:27,Epay,212220.0,4.761904762,10611.0,8.1
+225,370-96-0655,C,Port Harcourt,Normal,Female,Fashion accessories,17755.2,6,5326.56,111857.76,1/9/2019,13:46,Epay,106531.2,4.761904762,5326.56,7.1
+226,173-57-2300,C,Port Harcourt,Member,Male,Sports and travel,26236.8,2,2623.68,55097.28,3/13/2019,12:51,Cash,52473.6,4.761904762,2623.68,6.1
+227,394-55-6384,C,Port Harcourt,Member,Female,Sports and travel,25268.4,9,11370.78,238786.38,1/25/2019,13:38,Cash,227415.6,4.761904762,11370.78,6.7
+228,266-20-6657,C,Port Harcourt,Member,Male,Food and beverages,19814.4,7,6935.04,145635.84000000003,3/12/2019,19:39,Epay,138700.8,4.761904762,6935.04,5.2
+229,196-01-2849,C,Port Harcourt,Member,Female,Fashion accessories,26416.8,7,9245.88,194163.48000000004,2/10/2019,13:56,Cash,184917.6,4.761904762,9245.88,9.5
+230,372-62-5264,C,Port Harcourt,Normal,Female,Food and beverages,18936.0,9,8521.2,178945.2,1/16/2019,14:42,Cash,170424.0,4.761904762,8521.2,7.6
+231,751-41-9720,C,Port Harcourt,Normal,Male,Home and lifestyle,35100.0,10,17550.0,368550.0,1/12/2019,16:18,Epay,351000.0,4.761904762,17550.0,8.0
+232,626-43-7888,C,Port Harcourt,Normal,Female,Fashion accessories,21747.6,8,8699.04,182679.84,2/7/2019,12:23,Epay,173980.8,4.761904762,8699.04,9.6
+233,162-65-8559,C,Port Harcourt,Member,Male,Food and beverages,24832.800000000003,1,1241.64,26074.44,1/21/2019,20:13,Cash,24832.800000000003,4.761904762,1241.64,4.8
+234,760-27-5490,C,Port Harcourt,Normal,Male,Fashion accessories,5623.2,8,2249.28,47234.88,1/20/2019,20:37,Epay,44985.6,4.761904762,2249.28,9.1
+235,728-88-7867,C,Port Harcourt,Member,Female,Home and lifestyle,27190.8,4,5438.16,114201.36,3/19/2019,15:52,Epay,108763.2,4.761904762,5438.16,8.3
+236,183-21-3799,C,Port Harcourt,Normal,Female,Electronic accessories,27946.8,9,12576.06,264097.26,2/19/2019,15:14,Epay,251521.2,4.761904762,12576.06,7.2
+237,268-20-3585,C,Port Harcourt,Normal,Female,Health and beauty,4986.0,9,2243.7,47117.7,2/4/2019,12:50,Epay,44874.0,4.761904762,2243.7,6.0
+238,735-32-9839,C,Port Harcourt,Member,Male,Fashion accessories,35532.0,8,14212.8,298468.8,1/31/2019,10:36,Epay,284256.0,4.761904762,14212.8,8.5
+239,678-79-0726,C,Port Harcourt,Member,Female,Sports and travel,32626.8,9,14682.06,308323.26,1/18/2019,15:28,Cash,293641.2,4.761904762,14682.06,5.1
+240,592-46-1692,C,Port Harcourt,Member,Female,Food and beverages,13237.2,7,4633.02,97293.42,1/11/2019,20:10,Cash,92660.4,4.761904762,4633.02,7.4
+241,149-14-0304,C,Port Harcourt,Member,Female,Health and beauty,10260.0,8,4104.0,86184.0,2/6/2019,14:24,Cash,82080.0,4.761904762,4104.0,6.6
+242,442-44-6497,C,Port Harcourt,Member,Male,Home and lifestyle,20005.2,3,3000.78,63016.380000000005,1/8/2019,11:42,Card,60015.600000000006,4.761904762,3000.78,5.9
+243,210-74-9613,C,Port Harcourt,Normal,Male,Fashion accessories,35013.6,4,7002.72,147057.12,3/16/2019,15:33,Epay,140054.4,4.761904762,7002.72,6.8
+244,607-65-2441,C,Port Harcourt,Member,Male,Health and beauty,29502.0,10,14751.0,309771.0,3/10/2019,12:39,Card,295020.0,4.761904762,14751.0,6.0
+245,386-27-7606,C,Port Harcourt,Member,Female,Home and lifestyle,29232.0,7,10231.2,214855.2,3/23/2019,15:59,Card,204624.0,4.761904762,10231.2,8.1
+246,137-63-5492,C,Port Harcourt,Normal,Male,Electronic accessories,21153.6,10,10576.8,222112.8,1/29/2019,14:26,Epay,211536.0,4.761904762,10576.8,9.0
+247,733-29-1227,C,Port Harcourt,Normal,Male,Home and lifestyle,20019.6,7,7006.86,147144.06,3/23/2019,12:41,Cash,140137.19999999998,4.761904762,7006.86,8.5
+248,451-73-2711,C,Port Harcourt,Normal,Male,Food and beverages,30538.8,1,1526.94,32065.74,1/14/2019,15:20,Epay,30538.8,4.761904762,1526.94,8.8
+249,345-68-9016,C,Port Harcourt,Member,Female,Sports and travel,11401.2,8,4560.48,95770.08,1/2/2019,16:19,Card,91209.6,4.761904762,4560.48,5.6
+250,390-17-5806,C,Port Harcourt,Member,Female,Food and beverages,13831.2,1,691.5600000000001,14522.76,2/2/2019,16:33,Cash,13831.2,4.761904762,691.5600000000001,8.6
+251,664-14-2882,C,Port Harcourt,Member,Female,Home and lifestyle,3790.8,5,947.7,19901.7,1/30/2019,14:43,Card,18954.0,4.761904762,947.7,5.8
+252,314-23-4520,C,Port Harcourt,Member,Male,Health and beauty,29242.800000000003,7,10234.98,214934.58,1/15/2019,20:44,Cash,204699.6,4.761904762,10234.98,9.0
+253,288-38-3758,C,Port Harcourt,Member,Female,Fashion accessories,30553.2,3,4582.98,96242.58,1/25/2019,18:30,Epay,91659.6,4.761904762,4582.98,7.4
+254,801-88-0346,C,Port Harcourt,Normal,Female,Fashion accessories,27381.6,3,4107.240000000001,86252.04000000001,1/5/2019,20:30,Card,82144.8,4.761904762,4107.240000000001,9.8
+255,759-98-4285,C,Port Harcourt,Member,Female,Health and beauty,30913.2,7,10819.62,227212.02,2/27/2019,19:01,Card,216392.4,4.761904762,10819.62,8.0
+256,201-63-8275,C,Port Harcourt,Member,Female,Sports and travel,24476.4,7,8566.74,179901.54,2/17/2019,16:50,Epay,171334.8,4.761904762,8566.74,5.7
+257,471-06-8611,C,Port Harcourt,Normal,Female,Food and beverages,18871.2,1,943.5600000000002,19814.76,2/6/2019,10:22,Card,18871.2,4.761904762,943.5600000000002,6.3
+258,200-16-5952,C,Port Harcourt,Member,Male,Food and beverages,23634.000000000004,2,2363.4,49631.4,1/17/2019,16:46,Cash,47268.00000000001,4.761904762,2363.4,6.0
+259,102-77-2261,C,Port Harcourt,Member,Male,Health and beauty,23511.6,7,8229.06,172810.26,3/5/2019,18:02,Card,164581.2,4.761904762,8229.06,4.2
+260,102-06-2002,C,Port Harcourt,Member,Male,Sports and travel,9090.0,5,2272.5,47722.5,3/20/2019,17:52,Cash,45450.0,4.761904762,2272.5,6.1
+261,629-42-4133,C,Port Harcourt,Normal,Male,Health and beauty,7848.0,8,3139.2000000000003,65923.2,2/19/2019,19:24,Cash,62784.0,4.761904762,3139.2000000000003,8.3
+262,468-99-7231,C,Port Harcourt,Normal,Female,Home and lifestyle,15843.6,8,6337.44,133086.24000000002,3/3/2019,17:36,Cash,126748.8,4.761904762,6337.44,8.8
+263,516-77-6464,C,Port Harcourt,Member,Female,Health and beauty,3657.6,5,914.4,19202.4,2/24/2019,13:08,Epay,18288.0,4.761904762,914.4,4.1
+264,886-77-9084,C,Port Harcourt,Normal,Male,Electronic accessories,25880.4,8,10352.16,217395.36,2/19/2019,11:33,Epay,207043.2,4.761904762,10352.16,5.5
+265,790-38-4466,C,Port Harcourt,Normal,Female,Health and beauty,3956.4,5,989.1,20771.1,1/23/2019,10:18,Card,19782.0,4.761904762,989.1,9.3
+266,704-10-4056,C,Port Harcourt,Member,Male,Health and beauty,21769.2,3,3265.38,68572.98,1/14/2019,10:55,Card,65307.6,4.761904762,3265.38,5.6
+267,400-80-4065,C,Port Harcourt,Member,Male,Health and beauty,24678.0,4,4935.6,103647.6,2/15/2019,20:21,Card,98712.0,4.761904762,4935.6,9.2
+268,443-60-9639,C,Port Harcourt,Member,Female,Home and lifestyle,21913.2,1,1095.66,23008.86,1/24/2019,13:24,Cash,21913.2,4.761904762,1095.66,5.5
+269,401-09-4232,C,Port Harcourt,Member,Male,Home and lifestyle,31208.4,5,7802.099999999999,163844.1,2/11/2019,18:38,Epay,156042.0,4.761904762,7802.099999999999,9.4
+270,324-41-6833,C,Port Harcourt,Member,Female,Electronic accessories,10872.0,8,4348.8,91324.8,3/3/2019,19:30,Epay,86976.0,4.761904762,4348.8,5.1
+271,474-33-8305,C,Port Harcourt,Member,Male,Fashion accessories,24260.4,7,8491.14,178313.94,3/23/2019,13:23,Epay,169822.80000000002,4.761904762,8491.14,6.9
+272,189-55-2313,C,Port Harcourt,Normal,Female,Fashion accessories,22384.8,10,11192.4,235040.4,1/31/2019,10:33,Epay,223848.0,4.761904762,11192.4,6.0
+273,815-04-6282,C,Port Harcourt,Member,Female,Sports and travel,23389.2,5,5847.3,122793.3,2/8/2019,12:52,Card,116946.0,4.761904762,5847.3,6.5
+274,477-59-2456,C,Port Harcourt,Normal,Female,Fashion accessories,16358.4,7,5725.4400000000005,120234.24000000002,1/23/2019,11:15,Cash,114508.8,4.761904762,5725.4400000000005,9.2
+275,784-21-9238,C,Port Harcourt,Member,Male,Sports and travel,3661.2,1,183.06,3844.26,2/7/2019,14:15,Cash,3661.2,4.761904762,183.06,5.9
+276,538-22-0304,C,Port Harcourt,Normal,Male,Electronic accessories,23382.0,10,11691.0,245511.0,3/24/2019,18:27,Cash,233820.0,4.761904762,11691.0,5.2
+277,660-29-7083,C,Port Harcourt,Normal,Male,Electronic accessories,20113.2,10,10056.6,211188.6,1/15/2019,15:01,Cash,201132.00000000003,4.761904762,10056.6,5.8
+278,271-77-8740,C,Port Harcourt,Member,Female,Sports and travel,10519.2,6,3155.76,66270.95999999999,1/1/2019,11:40,Epay,63115.2,4.761904762,3155.76,5.0
+279,549-23-9016,C,Port Harcourt,Member,Female,Food and beverages,5353.2,2,535.3199999999999,11241.72,2/13/2019,18:15,Card,10706.4,4.761904762,535.3199999999999,8.9
+280,862-29-5914,C,Port Harcourt,Normal,Female,Sports and travel,8056.799999999999,1,402.84,8459.640000000001,1/30/2019,17:08,Card,8056.799999999999,4.761904762,402.84,8.6
+281,845-94-6841,C,Port Harcourt,Member,Female,Food and beverages,26236.8,9,11806.56,247937.76,1/8/2019,19:38,Cash,236131.2,4.761904762,11806.56,4.0
+282,658-66-3967,C,Port Harcourt,Normal,Male,Health and beauty,19148.4,7,6701.94,140740.74,1/14/2019,15:42,Epay,134038.8,4.761904762,6701.94,5.0
+283,848-95-6252,C,Port Harcourt,Member,Female,Home and lifestyle,31057.2,1,1552.86,32610.06,2/20/2019,13:24,Epay,31057.2,4.761904762,1552.86,7.0
+284,176-78-1170,C,Port Harcourt,Member,Male,Health and beauty,12171.6,3,1825.74,38340.54,1/26/2019,15:11,Epay,36514.8,4.761904762,1825.74,7.3
+285,101-81-4070,C,Port Harcourt,Member,Female,Health and beauty,22615.2,2,2261.52,47491.92,1/17/2019,12:36,Epay,45230.4,4.761904762,2261.52,4.9
+286,631-34-1880,C,Port Harcourt,Member,Male,Food and beverages,8751.6,3,1312.74,27567.54,1/8/2019,19:09,Card,26254.800000000003,4.761904762,1312.74,4.3
+287,584-66-4073,C,Port Harcourt,Normal,Male,Fashion accessories,20340.0,1,1017.0,21357.0,3/13/2019,15:45,Epay,20340.0,4.761904762,1017.0,9.6
+288,154-87-7367,C,Port Harcourt,Normal,Male,Home and lifestyle,23493.6,8,9397.44,197346.24,3/15/2019,14:04,Epay,187948.8,4.761904762,9397.44,6.3
+289,885-56-0389,C,Port Harcourt,Member,Male,Fashion accessories,18846.0,1,942.3,19788.3,2/12/2019,17:49,Cash,18846.0,4.761904762,942.3,4.0
+290,115-38-7388,C,Port Harcourt,Member,Female,Fashion accessories,3664.8,8,1465.92,30784.32,3/30/2019,12:51,Card,29318.4,4.761904762,1465.92,9.5
+291,137-74-8729,C,Port Harcourt,Normal,Female,Fashion accessories,4388.4,8,1755.36,36862.56,3/13/2019,12:47,Epay,35107.2,4.761904762,1755.36,6.8
+292,389-70-2397,C,Port Harcourt,Normal,Female,Health and beauty,30117.6,5,7529.4,158117.4,2/21/2019,10:26,Cash,150588.0,4.761904762,7529.4,7.2
+293,607-76-6216,C,Port Harcourt,Member,Female,Fashion accessories,33296.4,5,8324.1,174806.1,3/2/2019,16:35,Card,166482.0,4.761904762,8324.1,8.6
+294,781-84-8059,C,Port Harcourt,Normal,Male,Fashion accessories,21866.4,7,7653.24,160718.04,1/18/2019,16:23,Epay,153064.8,4.761904762,7653.24,5.0
+295,409-49-6995,C,Port Harcourt,Member,Female,Food and beverages,17017.2,6,5105.16,107208.36,2/5/2019,10:17,Cash,102103.2,4.761904762,5105.16,8.8
+296,725-54-0677,C,Port Harcourt,Member,Male,Health and beauty,30816.0,7,10785.6,226497.6,3/2/2019,13:50,Cash,215712.00000000003,4.761904762,10785.6,5.3
+297,377-79-7592,C,Port Harcourt,Member,Female,Electronic accessories,16142.4,9,7264.08,152545.68000000002,1/14/2019,14:00,Card,145281.6,4.761904762,7264.08,7.5
+298,545-07-8534,C,Port Harcourt,Normal,Female,Health and beauty,20995.2,2,2099.5200000000004,44089.920000000006,2/14/2019,12:42,Epay,41990.4,4.761904762,2099.5200000000004,6.0
+299,118-62-1812,C,Port Harcourt,Member,Female,Home and lifestyle,28216.8,4,5643.36,118510.55999999998,3/24/2019,17:56,Cash,112867.2,4.761904762,5643.36,7.9
+300,450-42-3339,C,Port Harcourt,Normal,Male,Health and beauty,30459.6,10,15229.8,319825.8,2/9/2019,18:58,Card,304596.0,4.761904762,15229.8,8.8
+301,151-27-8496,C,Port Harcourt,Normal,Female,Electronic accessories,20206.8,4,4041.36,84868.56,1/19/2019,11:43,Epay,80827.2,4.761904762,4041.36,8.6
+302,717-96-4189,C,Port Harcourt,Normal,Female,Electronic accessories,12776.4,6,3832.92,80491.32,2/2/2019,12:40,Cash,76658.4,4.761904762,3832.92,4.1
+303,722-13-2115,C,Port Harcourt,Member,Male,Sports and travel,15426.0,1,771.3000000000001,16197.3,3/14/2019,15:36,Card,15426.0,4.761904762,771.3000000000001,9.3
+304,246-55-6923,C,Port Harcourt,Member,Female,Home and lifestyle,12884.4,9,5797.98,121757.58,3/10/2019,15:06,Card,115959.6,4.761904762,5797.98,5.1
+305,838-02-1821,C,Port Harcourt,Member,Female,Home and lifestyle,4582.8,2,458.28,9623.88,2/22/2019,12:10,Card,9165.6,4.761904762,458.28,5.2
+306,887-42-0517,C,Port Harcourt,Normal,Female,Sports and travel,29930.4,7,10475.64,219988.44000000003,1/10/2019,10:31,Card,209512.8,4.761904762,10475.64,6.6
+307,457-12-0244,C,Port Harcourt,Member,Female,Sports and travel,12679.2,6,3803.76,79878.95999999999,3/14/2019,13:49,Epay,76075.2,4.761904762,3803.76,6.5
+308,756-93-1854,C,Port Harcourt,Member,Female,Fashion accessories,30006.0,2,3000.6000000000004,63012.6,2/2/2019,14:05,Card,60011.99999999999,4.761904762,3000.6000000000004,9.5
+309,458-10-8612,C,Port Harcourt,Normal,Male,Health and beauty,23068.8,7,8074.08,169555.68000000002,1/20/2019,12:27,Epay,161481.6,4.761904762,8074.08,7.6
+310,235-06-8510,C,Port Harcourt,Member,Male,Home and lifestyle,30859.2,3,4628.879999999999,97206.48,1/24/2019,20:59,Epay,92577.6,4.761904762,4628.879999999999,5.1
+311,433-08-7822,C,Port Harcourt,Normal,Female,Health and beauty,28400.4,7,9940.14,208742.94,1/5/2019,19:48,Epay,198802.8,4.761904762,9940.14,7.5
+312,500-02-2261,C,Port Harcourt,Normal,Female,Food and beverages,20624.4,6,6187.320000000001,129933.72,3/21/2019,17:04,Epay,123746.4,4.761904762,6187.320000000001,5.9
+313,702-83-5291,C,Port Harcourt,Member,Male,Fashion accessories,35935.2,9,16170.839999999998,339587.64,3/27/2019,10:43,Cash,323416.8,4.761904762,16170.839999999998,6.6
+314,859-97-6048,C,Port Harcourt,Member,Male,Electronic accessories,30330.0,2,3033.0000000000005,63693.00000000001,3/26/2019,14:13,Card,60660.0,4.761904762,3033.0000000000005,5.3
+315,373-88-1424,C,Port Harcourt,Member,Male,Home and lifestyle,12891.6,5,3222.9,67680.9,2/6/2019,18:44,Epay,64458.00000000001,4.761904762,3222.9,7.9
+316,784-08-0310,C,Port Harcourt,Member,Female,Food and beverages,7574.4,4,1514.88,31812.48,1/13/2019,13:58,Cash,30297.6,4.761904762,1514.88,7.6
+317,577-34-7579,C,Port Harcourt,Member,Male,Food and beverages,18176.4,9,8179.38,171766.97999999998,1/10/2019,17:16,Cash,163587.6,4.761904762,8179.38,5.4
+318,867-47-1948,C,Port Harcourt,Normal,Female,Home and lifestyle,5688.0,10,2844.0,59724.0,1/9/2019,12:07,Cash,56880.0,4.761904762,2844.0,7.8
+319,256-58-3609,C,Port Harcourt,Member,Male,Fashion accessories,33112.8,1,1655.64,34768.439999999995,3/18/2019,15:29,Cash,33112.8,4.761904762,1655.64,9.8
+320,364-34-2972,C,Port Harcourt,Member,Male,Electronic accessories,34855.2,3,5228.28,109793.88,3/30/2019,20:37,Cash,104565.6,4.761904762,5228.28,6.7
+321,744-82-9138,C,Port Harcourt,Normal,Male,Fashion accessories,31006.8,2,3100.68,65114.28,2/7/2019,17:59,Cash,62013.6,4.761904762,3100.68,8.2
+322,728-47-9078,C,Port Harcourt,Member,Male,Food and beverages,21452.4,4,4290.48,90100.08,1/19/2019,12:46,Cash,85809.6,4.761904762,4290.48,9.8
+323,148-41-7930,C,Port Harcourt,Normal,Male,Health and beauty,35985.6,7,12594.96,264494.16000000003,1/23/2019,10:33,Cash,251899.2,4.761904762,12594.96,6.1
+324,189-40-5216,C,Port Harcourt,Normal,Male,Electronic accessories,34693.200000000004,7,12142.62,254995.02,1/9/2019,11:40,Cash,242852.4,4.761904762,12142.62,6.0
+325,267-62-7380,C,Port Harcourt,Member,Male,Electronic accessories,29642.4,10,14821.2,311245.2,3/29/2019,19:12,Epay,296424.0,4.761904762,14821.2,4.3
+326,652-49-6720,C,Port Harcourt,Member,Female,Electronic accessories,21942.0,1,1097.1,23039.1,2/18/2019,11:40,Epay,21942.0,4.761904762,1097.1,5.9
+327,233-67-5758,C,Port Harcourt,Normal,Male,Health and beauty,14526.0,1,726.3000000000001,15252.3,1/29/2019,13:46,Epay,14526.0,4.761904762,726.3000000000001,6.2
diff --git a/Pandas_Analytics_project/MyProject_step2.ipynb b/Pandas_Analytics_project/MyProject_step2.ipynb
new file mode 100644
index 0000000..da2a967
--- /dev/null
+++ b/Pandas_Analytics_project/MyProject_step2.ipynb
@@ -0,0 +1,6204 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "01a24aab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import glob\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import datetime as dt\n",
+    "os.chdir(r\"C:\\Users\\Chizy\\Desktop\\MyContent\\Ustacky\\My project\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d5bf8d2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extension = 'csv'\n",
+    "file_1 =  [i for i in glob.glob('*.{}'.format(extension))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "735f6be0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja_Branch.csv',\n",
+       " 'Lagos_Branch.csv',\n",
+       " 'Market_data.csv',\n",
+       " 'Port_Harcourt_Branch.csv']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f6ac03d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read each dataframe\n",
+    "df_A = pd.read_csv('Abuja_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ab893353",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>552-44-5977</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>22320.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8928.00</td>\n",
+       "      <td>187488.00</td>\n",
+       "      <td>1/3/2019</td>\n",
+       "      <td>19:08</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>178560.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8928.00</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>430-53-4718</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>27133.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10853.28</td>\n",
+       "      <td>227918.88</td>\n",
+       "      <td>1/28/2019</td>\n",
+       "      <td>15:46</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>217065.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10853.28</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>602-16-6955</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>27576.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13788.00</td>\n",
+       "      <td>289548.00</td>\n",
+       "      <td>1/24/2019</td>\n",
+       "      <td>18:10</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>275760.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13788.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>690-01-6631</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>6296.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3148.20</td>\n",
+       "      <td>66112.20</td>\n",
+       "      <td>2/22/2019</td>\n",
+       "      <td>18:35</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>62964.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>3148.20</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>303-96-2227</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>35056.8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>17528.40</td>\n",
+       "      <td>368096.40</td>\n",
+       "      <td>3/2/2019</td>\n",
+       "      <td>17:16</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>350568.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>17528.40</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>332 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0    692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "1    351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "2    529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "3    299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "4    319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "..           ...    ...    ...           ...     ...                     ...   \n",
+       "327  552-44-5977      B  Abuja        Member    Male       Health and beauty   \n",
+       "328  430-53-4718      B  Abuja        Member    Male       Health and beauty   \n",
+       "329  602-16-6955      B  Abuja        Normal  Female       Sports and travel   \n",
+       "330  690-01-6631      B  Abuja        Normal    Male     Fashion accessories   \n",
+       "331  303-96-2227      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0       19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
+       "1        5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
+       "2        9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
+       "3       33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
+       "4       14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
+       "..          ...       ...       ...        ...        ...    ...     ...   \n",
+       "327     22320.0         8   8928.00  187488.00   1/3/2019  19:08    Card   \n",
+       "328     27133.2         8  10853.28  227918.88  1/28/2019  15:46    Card   \n",
+       "329     27576.0        10  13788.00  289548.00  1/24/2019  18:10    Epay   \n",
+       "330      6296.4        10   3148.20   66112.20  2/22/2019  18:35    Epay   \n",
+       "331     35056.8        10  17528.40  368096.40   3/2/2019  17:16    Epay   \n",
+       "\n",
+       "         cogs  gross margin percentage  gross income  Rating  \n",
+       "0     59227.2                 4.761905       2961.36     5.9  \n",
+       "1     20851.2                 4.761905       1042.56     4.5  \n",
+       "2     36734.4                 4.761905       1836.72     6.8  \n",
+       "3    202435.2                 4.761905      10121.76     4.5  \n",
+       "4     29016.0                 4.761905       1450.80     4.4  \n",
+       "..        ...                      ...           ...     ...  \n",
+       "327  178560.0                 4.761905       8928.00     6.2  \n",
+       "328  217065.6                 4.761905      10853.28     8.4  \n",
+       "329  275760.0                 4.761905      13788.00     6.0  \n",
+       "330   62964.0                 4.761905       3148.20     6.6  \n",
+       "331  350568.0                 4.761905      17528.40     4.4  \n",
+       "\n",
+       "[332 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aff26318",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_L = pd.read_csv('Lagos_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f81b0249",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>886-18-2897</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>20361.6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5090.40</td>\n",
+       "      <td>106898.40</td>\n",
+       "      <td>3/22/2019</td>\n",
+       "      <td>19:06</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>101808.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5090.40</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>745-74-0715</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>20890.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2089.08</td>\n",
+       "      <td>43870.68</td>\n",
+       "      <td>3/10/2019</td>\n",
+       "      <td>20:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>41781.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2089.08</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>727-02-1313</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>11462.4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>573.12</td>\n",
+       "      <td>12035.52</td>\n",
+       "      <td>2/9/2019</td>\n",
+       "      <td>13:22</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>11462.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>573.12</td>\n",
+       "      <td>7.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>347-56-2442</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>23695.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1184.76</td>\n",
+       "      <td>24879.96</td>\n",
+       "      <td>2/22/2019</td>\n",
+       "      <td>15:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>23695.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1184.76</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>849-09-3807</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>31802.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11130.84</td>\n",
+       "      <td>233747.64</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>13:28</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>222616.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>11130.84</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>340 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0    750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1    631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2    123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3    373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4    355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "..           ...    ...    ...           ...     ...                     ...   \n",
+       "335  886-18-2897      A  Lagos        Normal  Female      Food and beverages   \n",
+       "336  745-74-0715      A  Lagos        Normal    Male  Electronic accessories   \n",
+       "337  727-02-1313      A  Lagos        Member    Male      Food and beverages   \n",
+       "338  347-56-2442      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "339  849-09-3807      A  Lagos        Member  Female     Fashion accessories   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0       26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1       16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2       20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3       31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4       24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "..          ...       ...       ...        ...        ...    ...     ...   \n",
+       "335     20361.6         5   5090.40  106898.40  3/22/2019  19:06    Card   \n",
+       "336     20890.8         2   2089.08   43870.68  3/10/2019  20:46    Epay   \n",
+       "337     11462.4         1    573.12   12035.52   2/9/2019  13:22    Cash   \n",
+       "338     23695.2         1   1184.76   24879.96  2/22/2019  15:33    Cash   \n",
+       "339     31802.4         7  11130.84  233747.64  2/18/2019  13:28    Cash   \n",
+       "\n",
+       "         cogs  gross margin percentage  gross income  Rating  \n",
+       "0    188218.8                 4.761905       9410.94     9.1  \n",
+       "1    116751.6                 4.761905       5837.58     7.4  \n",
+       "2    167673.6                 4.761905       8383.68     8.4  \n",
+       "3    217501.2                 4.761905      10875.06     5.3  \n",
+       "4    148694.4                 4.761905       7434.72     5.8  \n",
+       "..        ...                      ...           ...     ...  \n",
+       "335  101808.0                 4.761905       5090.40     4.5  \n",
+       "336   41781.6                 4.761905       2089.08     8.8  \n",
+       "337   11462.4                 4.761905        573.12     7.7  \n",
+       "338   23695.2                 4.761905       1184.76     4.1  \n",
+       "339  222616.8                 4.761905      11130.84     6.6  \n",
+       "\n",
+       "[340 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e90494b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_p = pd.read_csv('Port_Harcourt_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bd0e3d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>226-31-3081</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>5500.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>28879.20</td>\n",
+       "      <td>3/8/2019</td>\n",
+       "      <td>10:29</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>27504.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699-14-3026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30740.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>225941.94</td>\n",
+       "      <td>3/25/2019</td>\n",
+       "      <td>18:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>215182.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>315-22-5665</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>26481.6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>278056.80</td>\n",
+       "      <td>2/24/2019</td>\n",
+       "      <td>11:38</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>264816.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>300-71-4605</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30974.4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>162615.60</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>11:24</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>154872.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>4.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>183-56-6882</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>35791.2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>150323.04</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>10:42</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>143164.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>328 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    226-31-3081      C  Port Harcourt        Normal  Female   \n",
+       "1    699-14-3026      C  Port Harcourt        Normal    Male   \n",
+       "2    315-22-5665      C  Port Harcourt        Normal  Female   \n",
+       "3    300-71-4605      C  Port Harcourt        Member    Male   \n",
+       "4    183-56-6882      C  Port Harcourt        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0    Electronic accessories      5500.8         5   1375.20   28879.20   \n",
+       "1    Electronic accessories     30740.4         7  10759.14  225941.94   \n",
+       "2        Home and lifestyle     26481.6        10  13240.80  278056.80   \n",
+       "3    Electronic accessories     30974.4         5   7743.60  162615.60   \n",
+       "4        Food and beverages     35791.2         4   7158.24  150323.04   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     3/8/2019  10:29    Cash   27504.0                 4.761905   \n",
+       "1    3/25/2019  18:30    Epay  215182.8                 4.761905   \n",
+       "2    2/24/2019  11:38    Epay  264816.0                 4.761905   \n",
+       "3    2/25/2019  11:24    Epay  154872.0                 4.761905   \n",
+       "4     2/6/2019  10:42    Epay  143164.8                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         1375.20     9.6  \n",
+       "1        10759.14     4.1  \n",
+       "2        13240.80     8.0  \n",
+       "3         7743.60     4.8  \n",
+       "4         7158.24     7.5  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[328 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3802334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a list of the dataframes\n",
+    "pd_list = [df_L, df_A, df_p]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0db90e96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine all the df\n",
+    "df_ABC = pd.concat(pd_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b43e9eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ABC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09a3d601",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Export combined data as csv\n",
+    "df_ABC.to_csv('Market_data.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc2f293c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "995      12594.96     6.1  \n",
+       "996      12142.62     6.0  \n",
+       "997      14821.20     4.3  \n",
+       "998       1097.10     5.9  \n",
+       "999        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('Market_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66c0ffff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1     16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2     20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3     31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4     24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0  188218.8                 4.761905       9410.94     9.1  \n",
+       "1  116751.6                 4.761905       5837.58     7.4  \n",
+       "2  167673.6                 4.761905       8383.68     8.4  \n",
+       "3  217501.2                 4.761905      10875.06     5.3  \n",
+       "4  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View the first 5rows of the dataset\n",
+    "df = pd.read_csv('Market_data.csv')\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "022eba6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 17)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To check no of rows and column\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ff0da19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',\n",
+       "       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',\n",
+       "       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',\n",
+       "       'Rating'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To check various column name in the dataset\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401ddd35",
+   "metadata": {},
+   "source": [
+    "# Statistical summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6185861a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1.000000e+03</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>20041.966800</td>\n",
+       "      <td>5.510000</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>116268.029640</td>\n",
+       "      <td>110731.456800</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>6.97270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9538.066205</td>\n",
+       "      <td>2.923431</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>88518.720636</td>\n",
+       "      <td>84303.543463</td>\n",
+       "      <td>6.131498e-14</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>1.71858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3628.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>3844.260000</td>\n",
+       "      <td>3661.200000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>11835.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>44792.055000</td>\n",
+       "      <td>42659.100000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>5.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>19882.800000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>91385.280000</td>\n",
+       "      <td>87033.600000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>7.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>28056.600000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>169686.090000</td>\n",
+       "      <td>161605.800000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>8.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>35985.600000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>375354.000000</td>\n",
+       "      <td>357480.000000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>10.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unit price     Quantity        Tax 5%          Total           cogs  \\\n",
+       "count   1000.000000  1000.000000   1000.000000    1000.000000    1000.000000   \n",
+       "mean   20041.966800     5.510000   5536.572840  116268.029640  110731.456800   \n",
+       "std     9538.066205     2.923431   4215.177173   88518.720636   84303.543463   \n",
+       "min     3628.800000     1.000000    183.060000    3844.260000    3661.200000   \n",
+       "25%    11835.000000     3.000000   2132.955000   44792.055000   42659.100000   \n",
+       "50%    19882.800000     5.000000   4351.680000   91385.280000   87033.600000   \n",
+       "75%    28056.600000     8.000000   8080.290000  169686.090000  161605.800000   \n",
+       "max    35985.600000    10.000000  17874.000000  375354.000000  357480.000000   \n",
+       "\n",
+       "       gross margin percentage  gross income      Rating  \n",
+       "count             1.000000e+03   1000.000000  1000.00000  \n",
+       "mean              4.761905e+00   5536.572840     6.97270  \n",
+       "std               6.131498e-14   4215.177173     1.71858  \n",
+       "min               4.761905e+00    183.060000     4.00000  \n",
+       "25%               4.761905e+00   2132.955000     5.50000  \n",
+       "50%               4.761905e+00   4351.680000     7.00000  \n",
+       "75%               4.761905e+00   8080.290000     8.50000  \n",
+       "max               4.761905e+00  17874.000000    10.00000  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To get statistical surmary\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a5e9895",
+   "metadata": {},
+   "source": [
+    "# Deductions:\n",
+    "There are 1000 rows.\n",
+    "\n",
+    "The mean, std, min, max and various percentage of the total summation of values for each columns with numeric values were provided with a single command (Describe) which shows the Standard Deviation (STD) of the unit price and gross income to be 9538.066205 and 4215.177173 respectively. Also, the mean value of the unit price and gross income is 20041.966800 and 5536.572840 respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eaf11085",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
+       "0         False   False  False          False   False         False   \n",
+       "1         False   False  False          False   False         False   \n",
+       "2         False   False  False          False   False         False   \n",
+       "3         False   False  False          False   False         False   \n",
+       "4         False   False  False          False   False         False   \n",
+       "..          ...     ...    ...            ...     ...           ...   \n",
+       "995       False   False  False          False   False         False   \n",
+       "996       False   False  False          False   False         False   \n",
+       "997       False   False  False          False   False         False   \n",
+       "998       False   False  False          False   False         False   \n",
+       "999       False   False  False          False   False         False   \n",
+       "\n",
+       "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
+       "0         False     False   False  False  False  False    False  False   \n",
+       "1         False     False   False  False  False  False    False  False   \n",
+       "2         False     False   False  False  False  False    False  False   \n",
+       "3         False     False   False  False  False  False    False  False   \n",
+       "4         False     False   False  False  False  False    False  False   \n",
+       "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
+       "995       False     False   False  False  False  False    False  False   \n",
+       "996       False     False   False  False  False  False    False  False   \n",
+       "997       False     False   False  False  False  False    False  False   \n",
+       "998       False     False   False  False  False  False    False  False   \n",
+       "999       False     False   False  False  False  False    False  False   \n",
+       "\n",
+       "     gross margin percentage  gross income  Rating  \n",
+       "0                      False         False   False  \n",
+       "1                      False         False   False  \n",
+       "2                      False         False   False  \n",
+       "3                      False         False   False  \n",
+       "4                      False         False   False  \n",
+       "..                       ...           ...     ...  \n",
+       "995                    False         False   False  \n",
+       "996                    False         False   False  \n",
+       "997                    False         False   False  \n",
+       "998                    False         False   False  \n",
+       "999                    False         False   False  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Missing value:\n",
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30d2d644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 0\n",
+       "Branch                     0\n",
+       "City                       0\n",
+       "Customer type              0\n",
+       "Gender                     0\n",
+       "Product line               0\n",
+       "Unit price                 0\n",
+       "Quantity                   0\n",
+       "Tax 5%                     0\n",
+       "Total                      0\n",
+       "Date                       0\n",
+       "Time                       0\n",
+       "Payment                    0\n",
+       "cogs                       0\n",
+       "gross margin percentage    0\n",
+       "gross income               0\n",
+       "Rating                     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72bdce31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                     1000\n",
+       "City                       1000\n",
+       "Customer type              1000\n",
+       "Gender                     1000\n",
+       "Product line               1000\n",
+       "Unit price                 1000\n",
+       "Quantity                   1000\n",
+       "Tax 5%                     1000\n",
+       "Total                      1000\n",
+       "Date                       1000\n",
+       "Time                       1000\n",
+       "Payment                    1000\n",
+       "cogs                       1000\n",
+       "gross margin percentage    1000\n",
+       "gross income               1000\n",
+       "Rating                     1000\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.notnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26717e81",
+   "metadata": {},
+   "source": [
+    "# There are no missing values in the set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7810eb93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   Invoice ID               1000 non-null   object \n",
+      " 1   Branch                   1000 non-null   object \n",
+      " 2   City                     1000 non-null   object \n",
+      " 3   Customer type            1000 non-null   object \n",
+      " 4   Gender                   1000 non-null   object \n",
+      " 5   Product line             1000 non-null   object \n",
+      " 6   Unit price               1000 non-null   float64\n",
+      " 7   Quantity                 1000 non-null   int64  \n",
+      " 8   Tax 5%                   1000 non-null   float64\n",
+      " 9   Total                    1000 non-null   float64\n",
+      " 10  Date                     1000 non-null   object \n",
+      " 11  Time                     1000 non-null   object \n",
+      " 12  Payment                  1000 non-null   object \n",
+      " 13  cogs                     1000 non-null   float64\n",
+      " 14  gross margin percentage  1000 non-null   float64\n",
+      " 15  gross income             1000 non-null   float64\n",
+      " 16  Rating                   1000 non-null   float64\n",
+      "dtypes: float64(7), int64(1), object(9)\n",
+      "memory usage: 132.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the information of the dataframe\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f670b3ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Converting to datetime:\n",
+    "df['Date'] = pd.to_datetime(df['Date'])\n",
+    "df['Time'] = pd.to_datetime(df['Time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15cdbef0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[ns]')"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Date'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1dbc2015",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[ns]')"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Time'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f94d858f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>2019-01-05</td>\n",
+       "      <td>2023-07-17 13:08:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>2019-03-03</td>\n",
+       "      <td>2023-07-17 13:23:00</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>2019-01-27</td>\n",
+       "      <td>2023-07-17 20:33:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2019-02-08</td>\n",
+       "      <td>2023-07-17 10:37:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2019-02-25</td>\n",
+       "      <td>2023-07-17 14:36:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date                Time  \\\n",
+       "0     26888.4         7   9410.94  197629.74 2019-01-05 2023-07-17 13:08:00   \n",
+       "1     16678.8         7   5837.58  122589.18 2019-03-03 2023-07-17 13:23:00   \n",
+       "2     20959.2         8   8383.68  176057.28 2019-01-27 2023-07-17 20:33:00   \n",
+       "3     31071.6         7  10875.06  228376.26 2019-02-08 2023-07-17 10:37:00   \n",
+       "4     24782.4         6   7434.72  156129.12 2019-02-25 2023-07-17 14:36:00   \n",
+       "\n",
+       "  Payment      cogs  gross margin percentage  gross income  Rating  \n",
+       "0    Epay  188218.8                 4.761905       9410.94     9.1  \n",
+       "1    Card  116751.6                 4.761905       5837.58     7.4  \n",
+       "2    Epay  167673.6                 4.761905       8383.68     8.4  \n",
+       "3    Epay  217501.2                 4.761905      10875.06     5.3  \n",
+       "4    Epay  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d8ff8ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extract features from date & time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79154e07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['day'] = df['Date'].dt.day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1280e135",
+   "metadata": {},
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0c8b6ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['month'] = df['Date'].dt.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc123e2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019     0   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019     0   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019     0   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019     0   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019     0   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1628da8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['year'] = df['Date'].dt.year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4cfc25c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019     0   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019     0   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019     0   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019     0   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019     0   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "458d83be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['hour'] = df['Time'].dt.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3db5ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019    13   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019    13   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019    20   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019    10   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019    14   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23ca4780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Minute'] = df['Time'].dt.minute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0078b0cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019    13   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019    13   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019    20   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019    10   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019    14   \n",
+       "\n",
+       "   Minute  \n",
+       "0       8  \n",
+       "1      23  \n",
+       "2      33  \n",
+       "3      37  \n",
+       "4      36  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5ee1db5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "194e69de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_hour = pd.Series(df.nunique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f8951f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c9bd0e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique(axis = 'columns' == 22)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "496b249f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      21\n",
+       "1      20\n",
+       "2      21\n",
+       "3      21\n",
+       "4      21\n",
+       "       ..\n",
+       "995    21\n",
+       "996    21\n",
+       "997    21\n",
+       "998    20\n",
+       "999    19\n",
+       "Length: 1000, dtype: int64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique(axis = 'columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72fca3a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['2023-07-17T13:08:00.000000000', '2023-07-17T13:23:00.000000000',\n",
+       "       '2023-07-17T20:33:00.000000000', '2023-07-17T10:37:00.000000000',\n",
+       "       '2023-07-17T14:36:00.000000000', '2023-07-17T17:15:00.000000000',\n",
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+       "       '2023-07-17T16:32:00.000000000', '2023-07-17T11:01:00.000000000',\n",
+       "       '2023-07-17T20:08:00.000000000', '2023-07-17T19:31:00.000000000',\n",
+       "       '2023-07-17T13:51:00.000000000', '2023-07-17T11:55:00.000000000',\n",
+       "       '2023-07-17T11:42:00.000000000', '2023-07-17T14:48:00.000000000',\n",
+       "       '2023-07-17T14:25:00.000000000', '2023-07-17T15:18:00.000000000',\n",
+       "       '2023-07-17T15:06:00.000000000', '2023-07-17T20:57:00.000000000',\n",
+       "       '2023-07-17T15:16:00.000000000', '2023-07-17T17:47:00.000000000',\n",
+       "       '2023-07-17T10:44:00.000000000', '2023-07-17T12:36:00.000000000',\n",
+       "       '2023-07-17T10:58:00.000000000', '2023-07-17T11:10:00.000000000',\n",
+       "       '2023-07-17T20:51:00.000000000', '2023-07-17T13:33:00.000000000',\n",
+       "       '2023-07-17T20:13:00.000000000', '2023-07-17T14:26:00.000000000',\n",
+       "       '2023-07-17T18:18:00.000000000', '2023-07-17T15:17:00.000000000',\n",
+       "       '2023-07-17T18:10:00.000000000', '2023-07-17T19:08:00.000000000',\n",
+       "       '2023-07-17T15:46:00.000000000', '2023-07-17T11:38:00.000000000',\n",
+       "       '2023-07-17T11:24:00.000000000', '2023-07-17T10:42:00.000000000',\n",
+       "       '2023-07-17T20:39:00.000000000', '2023-07-17T19:47:00.000000000',\n",
+       "       '2023-07-17T17:08:00.000000000', '2023-07-17T10:19:00.000000000',\n",
+       "       '2023-07-17T15:43:00.000000000', '2023-07-17T18:21:00.000000000',\n",
+       "       '2023-07-17T14:20:00.000000000', '2023-07-17T16:24:00.000000000',\n",
+       "       '2023-07-17T18:56:00.000000000', '2023-07-17T14:31:00.000000000',\n",
+       "       '2023-07-17T20:05:00.000000000', '2023-07-17T10:41:00.000000000',\n",
+       "       '2023-07-17T19:52:00.000000000', '2023-07-17T16:17:00.000000000',\n",
+       "       '2023-07-17T15:57:00.000000000', '2023-07-17T16:43:00.000000000',\n",
+       "       '2023-07-17T17:11:00.000000000', '2023-07-17T16:07:00.000000000',\n",
+       "       '2023-07-17T15:00:00.000000000', '2023-07-17T20:50:00.000000000',\n",
+       "       '2023-07-17T19:58:00.000000000', '2023-07-17T10:02:00.000000000',\n",
+       "       '2023-07-17T17:07:00.000000000', '2023-07-17T17:22:00.000000000',\n",
+       "       '2023-07-17T13:29:00.000000000', '2023-07-17T17:20:00.000000000',\n",
+       "       '2023-07-17T13:42:00.000000000', '2023-07-17T14:44:00.000000000',\n",
+       "       '2023-07-17T10:21:00.000000000', '2023-07-17T19:05:00.000000000',\n",
+       "       '2023-07-17T11:34:00.000000000', '2023-07-17T18:58:00.000000000',\n",
+       "       '2023-07-17T20:01:00.000000000', '2023-07-17T20:37:00.000000000',\n",
+       "       '2023-07-17T12:12:00.000000000', '2023-07-17T15:32:00.000000000',\n",
+       "       '2023-07-17T12:31:00.000000000', '2023-07-17T20:04:00.000000000',\n",
+       "       '2023-07-17T13:38:00.000000000', '2023-07-17T17:30:00.000000000',\n",
+       "       '2023-07-17T16:53:00.000000000', '2023-07-17T16:51:00.000000000',\n",
+       "       '2023-07-17T20:15:00.000000000', '2023-07-17T14:11:00.000000000',\n",
+       "       '2023-07-17T12:10:00.000000000', '2023-07-17T20:47:00.000000000',\n",
+       "       '2023-07-17T11:17:00.000000000', '2023-07-17T15:52:00.000000000',\n",
+       "       '2023-07-17T11:43:00.000000000', '2023-07-17T10:30:00.000000000',\n",
+       "       '2023-07-17T10:06:00.000000000', '2023-07-17T12:16:00.000000000',\n",
+       "       '2023-07-17T14:58:00.000000000', '2023-07-17T14:45:00.000000000',\n",
+       "       '2023-07-17T17:10:00.000000000', '2023-07-17T14:27:00.000000000',\n",
+       "       '2023-07-17T12:51:00.000000000', '2023-07-17T13:56:00.000000000',\n",
+       "       '2023-07-17T16:18:00.000000000', '2023-07-17T15:14:00.000000000',\n",
+       "       '2023-07-17T20:10:00.000000000', '2023-07-17T14:24:00.000000000',\n",
+       "       '2023-07-17T12:39:00.000000000', '2023-07-17T12:41:00.000000000',\n",
+       "       '2023-07-17T15:20:00.000000000', '2023-07-17T16:33:00.000000000',\n",
+       "       '2023-07-17T20:44:00.000000000', '2023-07-17T20:30:00.000000000',\n",
+       "       '2023-07-17T16:50:00.000000000', '2023-07-17T17:52:00.000000000',\n",
+       "       '2023-07-17T11:33:00.000000000', '2023-07-17T18:38:00.000000000',\n",
+       "       '2023-07-17T15:11:00.000000000', '2023-07-17T15:45:00.000000000',\n",
+       "       '2023-07-17T16:35:00.000000000', '2023-07-17T13:50:00.000000000',\n",
+       "       '2023-07-17T17:56:00.000000000', '2023-07-17T13:49:00.000000000',\n",
+       "       '2023-07-17T14:05:00.000000000', '2023-07-17T19:12:00.000000000'],\n",
+       "      dtype='datetime64[ns]')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(df['Time'], name = 'hour').unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d564b9cc",
+   "metadata": {},
+   "source": [
+    "# Examples on Unique Values in Columns eg Branch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64651e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Invoice ID',\n",
+       " 'Branch',\n",
+       " 'City',\n",
+       " 'Customer type',\n",
+       " 'Gender',\n",
+       " 'Product line',\n",
+       " 'Payment']"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical_columns = [col for col in df.columns if df[col].dtype == \"object\" ]\n",
+    "categorical_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df0c3d1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A', 'B', 'C']"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Branch'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64a7a37a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Branch Column : 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Number of unique values in the Branch Column : {}\". format(len(df['Branch'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c991f6c",
+   "metadata": {},
+   "source": [
+    "# Unique Values in Columns - Payment, Product line, Gender and Customer type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c560e79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Epay', 'Card', 'Cash']"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46c1d063",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Health and beauty',\n",
+       " 'Home and lifestyle',\n",
+       " 'Sports and travel',\n",
+       " 'Electronic accessories',\n",
+       " 'Food and beverages',\n",
+       " 'Fashion accessories']"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02857072",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Product line Column : 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Number of unique values in the Product line Column : {}\". format(len(df['Product line'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fd80ec0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Female', 'Male']"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bbf9240",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Member', 'Normal']"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Customer type'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e358b2fb",
+   "metadata": {},
+   "source": [
+    "# Value_Count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18d19007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fashion accessories       178\n",
+       "Food and beverages        174\n",
+       "Electronic accessories    170\n",
+       "Sports and travel         166\n",
+       "Home and lifestyle        160\n",
+       "Health and beauty         152\n",
+       "Name: Product line, dtype: int64"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07448b7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3448491c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Lagos            340\n",
+       "Abuja            332\n",
+       "Port Harcourt    328\n",
+       "Name: City, dtype: int64"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['City'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fcb5833",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Epay    345\n",
+       "Cash    344\n",
+       "Card    311\n",
+       "Name: Payment, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8a1b933",
+   "metadata": {},
+   "source": [
+    "# Groupby City column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "185c52ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "City = df.groupby('City')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59eee3e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.groupby.generic.DataFrameGroupBy"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(City)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ee2f6e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Abuja': [340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, ...], 'Lagos': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], 'Port Harcourt': [672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, ...]}"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5626b15d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            6652396.8\n",
+       "Lagos            6705176.4\n",
+       "Port Harcourt    6684393.6\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['Unit price'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "487b1c9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            20037.339759\n",
+       "Lagos            19721.107059\n",
+       "Port Harcourt    20379.248780\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['Unit price'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e398df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>18</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>16</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>15</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "340  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "341  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "342  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "343  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "344  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "340     19742.4         3   2961.36   62188.56  ...    Card   59227.2   \n",
+       "341      5212.8         4   1042.56   21893.76  ...    Epay   20851.2   \n",
+       "342      9183.6         4   1836.72   38571.12  ...    Cash   36734.4   \n",
+       "343     33739.2         6  10121.76  212556.96  ...    Cash  202435.2   \n",
+       "344     14508.0         2   1450.80   30466.80  ...    Epay   29016.0   \n",
+       "\n",
+       "    gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "340                4.761905       2961.36     5.9   20      2  2019    13   \n",
+       "341                4.761905       1042.56     4.5    6      2  2019    18   \n",
+       "342                4.761905       1836.72     6.8    9      3  2019    17   \n",
+       "343                4.761905      10121.76     4.5   15      1  2019    16   \n",
+       "344                4.761905       1450.80     4.4   11      3  2019    15   \n",
+       "\n",
+       "     Minute  \n",
+       "340      27  \n",
+       "341       7  \n",
+       "342       3  \n",
+       "343      19  \n",
+       "344      30  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Abuja').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3775a60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1820531.52"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Abuja')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1caaa895",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            1820531.52\n",
+       "Lagos            1820577.78\n",
+       "Port Harcourt    1895463.54\n",
+       "Name: gross income, dtype: float64"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05fe8fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1820577.78"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Lagos')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "688e914b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1895463.54"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Port Harcourt')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc493a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_Aggregate = City.agg({'gross income': 'sum'})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5cb92d4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gross income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>City</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Abuja</th>\n",
+       "      <td>1820531.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lagos</th>\n",
+       "      <td>1820577.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Port Harcourt</th>\n",
+       "      <td>1895463.54</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               gross income\n",
+       "City                       \n",
+       "Abuja            1820531.52\n",
+       "Lagos            1820577.78\n",
+       "Port Harcourt    1895463.54"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_Aggregate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29d2b82a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gross income    1895463.54\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_Aggregate.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66b1220f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            17528.4\n",
+       "Lagos            17816.4\n",
+       "Port Harcourt    17874.0\n",
+       "Name: gross income, dtype: float64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['gross income'].max('City')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc4ee498",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_with_highest_income = city_Aggregate['gross income'].idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab710a2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Port Harcourt'"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_with_highest_income"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60754059",
+   "metadata": {},
+   "source": [
+    "# The city with the higehest gross income is Port Harcout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e53b88a7",
+   "metadata": {},
+   "source": [
+    "# Data Visualization using seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffc9432a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Data = df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bf2829b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>19</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  ...  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74  ...   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18  ...   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28  ...   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26  ...   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12  ...   \n",
+       "..                      ...         ...       ...       ...        ...  ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16  ...   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02  ...   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20  ...   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10  ...   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30  ...   \n",
+       "\n",
+       "    Payment      cogs gross margin percentage  gross income  Rating  day  \\\n",
+       "0      Epay  188218.8                4.761905       9410.94     9.1    5   \n",
+       "1      Card  116751.6                4.761905       5837.58     7.4    3   \n",
+       "2      Epay  167673.6                4.761905       8383.68     8.4   27   \n",
+       "3      Epay  217501.2                4.761905      10875.06     5.3    8   \n",
+       "4      Epay  148694.4                4.761905       7434.72     5.8   25   \n",
+       "..      ...       ...                     ...           ...     ...  ...   \n",
+       "995    Cash  251899.2                4.761905      12594.96     6.1   23   \n",
+       "996    Cash  242852.4                4.761905      12142.62     6.0    9   \n",
+       "997    Epay  296424.0                4.761905      14821.20     4.3   29   \n",
+       "998    Epay   21942.0                4.761905       1097.10     5.9   18   \n",
+       "999    Epay   14526.0                4.761905        726.30     6.2   29   \n",
+       "\n",
+       "     month  year  hour  Minute  \n",
+       "0        1  2019    13       8  \n",
+       "1        3  2019    13      23  \n",
+       "2        1  2019    20      33  \n",
+       "3        2  2019    10      37  \n",
+       "4        2  2019    14      36  \n",
+       "..     ...   ...   ...     ...  \n",
+       "995      1  2019    10      33  \n",
+       "996      1  2019    11      40  \n",
+       "997      3  2019    19      12  \n",
+       "998      2  2019    11      40  \n",
+       "999      1  2019    13      46  \n",
+       "\n",
+       "[1000 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd9e9763",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'City with max Gross income'}, xlabel='City', ylabel='gross income'>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "city_Aggregate.plot(kind='bar', ylabel = 'gross income', title = 'City with max Gross income', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caa28f73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Customer_type_counts = Data['Customer type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69c66158",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Member    501\n",
+       "Normal    499\n",
+       "Name: Customer type, dtype: int64"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Customer_type_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68cf8924",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Customer_type_counts')"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(Customer_type_counts.values, labels=Customer_type_counts.index, autopct='%1.1f%%')\n",
+    "plt.title('Customer_type_counts')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96ab0009",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gender_counts = Data['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38c9b442",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcf83ead",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Gender_counts.values')"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(gender_counts.values, labels=gender_counts.index, autopct='%1.1f%%')\n",
+    "plt.title('Gender_counts.values')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6911fd2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Hours spent')"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'City', y = 'hour', hue = 'Gender', aspect = 4)\n",
+    "plt.title('Hours spent')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75c59cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a30ec3cd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_Sales Record')]"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, x = 'Branch').set(title = 'Highest_Sales Record')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aef1baed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e817ede3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_Payment')]"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Payment').set(title = 'Highest_Payment')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb2b6f6f",
+   "metadata": {},
+   "source": [
+    "# Highest product line sold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c19b150c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_productline')]"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Product line', hue = 'City').set(title = 'Highest_productline')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9150e7bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Most used_Payment_type')]"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Product line', hue = 'Payment').set(title = 'Most used_Payment_type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7726fd8a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Payment/Branch')]"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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wtM7InyVb3Puiz4ULFx7ptYCcgpdjABhy6NAhSXdfJslsrp7RL2SkLlmza9cuHTx4UCtXrtT58+cl3X2jNjtIfZFl+/btio2NdXA1xq1evVoJCQkqWLCg3b4rfe/0BKNTFTLy119/SbobajMKjSkpKdqzZ49N18lK5cqVLSONvO0MGENwBGBI6qPbjObvSXdHbVIfkWalXbt2lvlwM2fOtDy6LFq0qGUpHUdLXew6Pj5eEydOdHA1srw5nNmn+KT/PaZu1KhRlnPvjNq1a5fl56VLl7bpXKnBM7M/S0uWLHnko4BFixa1LMf0+++/W76OAyBjBEcAhpQtW1bS3RdYUpcwudft27f15ptvZvkiQ6oCBQpYFveeM2eOVqxYIUnq1KlTumvhOULv3r0tb0ePGTNGixYtyrT9gQMHDAfnh5H6AseJEycybJOcnKyVK1dKst9j6vj4eIWGhlp+ndVi4llJ/bO0fPnyNJ9xlO7e30cffWTTNYx67733JN29x9deey3delIZ+VY1kNMxxxGAIV26dNHkyZNlNpvVqVMnDRo0SHXq1JGrq6t2796tiRMn6vjx46pbt662bNli6Jw9e/bUlClTrP6xzi6PqaW7S8v8+9//Vvv27ZWYmKjXXntNLVq0UPv27VW2bFm5uLjo8uXL2rt3r5YvX67t27fr3XfftVtgu1+dOnV06tQpLVu2TP/+978t/S9Jbm5u8vDw0NatW3Xt2jU5OTk9UMC7/8sxKSkpunnzpvbs2aNp06ZZHi8/9dRT6tWrl0330bVrV40cOVLnz5/XCy+8oEGDBum5555TXFyc1q9frx9//FEJCQmqUqXKI39c3bx5c/Xq1UvTp0/Xn3/+qdq1a+uNN95QvXr1LJ8c3Ldvn37//Xe5uLhkuRg5kNMRHAEYUr16dQ0bNkyhoaGKiYlJdy2/d999VxUqVDAcHAMDA1WtWjXLvMiqVasqMDDQrnXbKigoSIsWLdIbb7yhs2fPaunSpel+FSdVRl+XsYd3331XCxcuVHx8vN5//32rfV27dtWPP/5oWYanevXqD7TETGaLqaeqUqWKZs6cmeFi20a98847+uOPP7RmzRodO3YszWcMCxQooEmTJmnFihWPPDhKd78eU6hQIU2cOFGXL1+2Gl29V4MGDR55LUB2R3AEYNjQoUNVrVo1TZo0Sbt27dLt27fl4eGh6tWr6/XXX1eTJk2y/OTe/Tp37mwJjll9KcZR6tWrp507d+qXX37RsmXLtG/fPssagMWKFVP58uVVt25dtWzZ0vKFkEehcuXKioiI0HfffaetW7fq0qVLaeY72utrMZJUqFAheXh4qEqVKmrXrp3atGljWSrHFnnz5tW8efP0888/65dfftHhw4eVkpKikiVLqnHjxnrnnXf09NNPW6YvPGrOzs766quv1LVrV02fPl0bNmzQuXPnlJSUJC8vL5UpU8Yy0gzkdk7R0dGPfoEsAMjAwIEDFR4ergIFCujQoUOGv9CCtI4fP64aNWpIkjZu3Gj12UUAsAdejgHgMDdv3tSCBQsk3f1WMKHRNqmP0H19fQmNAB4JHlUDcJipU6davlF876cP8XB8fHw0dOjQJ2qxcgBPFh5VA3hskpKSdPr0acXHxysyMlIjR47UnTt31KBBAy1ZssTR5QEAssCII4DH5uzZs6pevbrVtoIFC+pf//qXgyoCADwI5jgCcIgSJUqoefPmWr58OY9WAeAJwaNqAAAAGMKIIwAAAAwhOAIAAMAQgiMAAAAMITjiiREXF6eoqCjFxcU5upQcgf60L/rTvuhP+6I/7Ss39yfBEU+U5ORkR5eQo9Cf9kV/2hf9aV/0p33l1v4kOAIAAMAQgiMAAAAMITgCAADAEIIjAAAADCE4AgAAwBCCIwAAAAwhOAIAAMAQgiMAAAAMITgCAADAEKfo6OgURxeR0wVN26XrcUmOLgMAADxhDvSr7egSrDDiCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABD8ji6gNxgedQkpdy64egyAADIkdzGzXms14uLi9OZM2fk6+srV1fXx3ptR2PEEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhmS7b1WfOnVKVapUybRNpUqVtHHjxsdUEQAAAKRsGBxTBQQEqFOnTunu8/LyeszVAAAAINsGx7Jly2rYsGGOLgMAAAD/3xM/x9FkMqlly5b6+++/1bt3bwUEBKhUqVJq2bKltm7dmqb9sWPH9Omnnyo4OFgBAQHy8vJSjRo1NGrUKN28edOq7UsvvaQSJUrowoUL6V67V69eMplM2rNnzyO5NwAAgOzkiQ+OkhQdHa0XX3xRp0+fVq9evdS6dWtt375drVu31oYNG6zaLl68WDNmzJC/v7+6du2q3r17q2jRovr222/Vvn17JSYmWtr27t1bSUlJmjVrVpprXr16VUuXLlXVqlWznJMJAACQEzhFR0enOLqIe6W+HJPZHMdatWrp+eefl3R3xFGSOnfurEmTJsnJyUmStHHjRrVu3VoBAQHasWOHnJ3vZuRz586pRIkSypcvn9U5w8LCFBoaqsmTJ1uuGx8frwoVKqhIkSLatWuX5dyS9MMPP+iTTz7RN998o9dffz3Te7o54i2l3Lrx4J0BAACy5DZuzmO9XlxcnM6cOSNfX1+5uro+1ms7Wrad43jixAmFhYWlu++dd96xBEdJcnFx0ciRI62CXVBQkEJCQrRixQpt3bpV9erVkySVKlUq3XO+9dZbCg0N1dq1ay3BMX/+/Oratat++OEHrV+/Xo0aNbK0nzlzpgoWLKiOHTvafK8AAABPgmz7qLpZs2aKjo5O98fo0aOt2vr6+srHxyfNOVLD4r59+yzbUlJSNGPGDLVo0UL+/v4qVqyYTCaTAgICJCnNfMZevXpJkmbMmGHZtn37dh06dEjt2rWTu7u7Xe4XAAAgu8u2I44PwsPDI9PtsbGxlm1DhgzRlClT5OPjoxYtWsjb29vy2DosLEzx8fFW5yhfvrwaNGigxYsX6/r16ypatKjCw8MlSa+99tqjuB0AAIBsKUcEx8uXL2e6PXVU8PLly5o6daoqVqyolStXqmDBgpa2Fy9ezPDReO/evRUZGam5c+fq1Vdf1YIFC/Tss8+qTp06dr4TAACA7CtHBMczZ87o77//TvO4evPmzZKkwMBASdLJkyeVkpKixo0bW4XGe9ump02bNipevLjCw8NVsGBB3bx5Uz169LDzXQAAAGRv2XaO44NITk7WF198oZSU/70gvnHjRkVERKhs2bKWkUFfX19J0rZt22Q2my1tz549q1GjRmV4/nz58qlr1646ePCgQkNDLb8GAADITbLtiGNUVJRCQ0Mz3H/vV2UqVqyoyMhIPf/88woODtb58+f122+/KW/evBo/frxlKR5vb2+1adNGixYtUuPGjdWoUSNdunRJK1asUHBwsE6ePJnh9Xr16qUJEybo/Pnz6tChg4oVK2a3ewUAAHgSZNvgmNlyPJJ1cDSZTPrll180cuRI/fvf/1Z8fLxq1qypTz/9VHXr1rU6buLEifLz89OiRYs0efJk+fj4qH///nrvvfcyfMlGkp566inVrl1b27Zt46UYAACQK2W7BcAflMlkUoMGDbRkyZJHep24uDjLYuB//vmn1ZqRWWEBcAAAHh0WAH98csQcx8dh5syZun79unr37v1AoREAACCnyLaPqrOLcePG6cqVK5o+fbo8PDzUu3dvR5cEAADgEATHLHz22WfKly+fKlWqpLCwML4UAwAAcq0nPjhGR0c/0ecHAAB4UjDHEQAAAIYQHAEAAGAIwREAAACGEBwBAABgyBP/csyT4GrjvDIn5HV0GQAAPHIBL4U7ugQ8Qow4AgAAwBCCIwAAAAwhOAIAAMAQgiMAAAAMITgCAADAEIIjAAAADCE4AgAAwBCCIwAAAAwhOAIAAMAQgiMAAAAMITgCAADAEIIjAAAADCE4AgAAwBCCIwAAAAwhOAIAAMAQgiMAAAAMITgCAADAEIIjAAAADCE4AgAAwBCCIwAAAAwhOAIAAMAQgiMAAAAMITgCAADAEIIjAAAADCE4AgAAwBCCIwAAAAwhOAIAAMAQgiMAAAAMITgCAADAEIIjAAAADCE4AgAAwBCCIwAAAAwhOAIAAMCQPI4uIDco8/wPji4hR4iLi9OZM2fk6+srV1dXR5fzxKM/7Yv+tC/6077oT9gLI44AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADAkj6MLAAAAuJfZbNatW7cUFxfn6FLSZTablS9fPsXExOjGjRuOLiddzs7OcnNzU758+ex6XoIjAADINsxms65evarChQurRIkScnJycnRJaZjNZiUkJChfvnxyds6eD2+Tk5N19epVlShRwq41Zs+7BQAAudKtW7dUuHBhFShQIFuGxieFi4uL3N3dFRsba9fzEhwBAEC2ERcXJ1dXV0eXkSPkz59fiYmJdj0nwREAAGQrjDTax6PoR4IjAAAADCE4AgAAwBCCIwAAAAxhOZ7HIGjaLl2PS3J0GTnIdUcXkMPQn/ZFf9oX/WlftvXngX617VQHsmIymdSgQQMtWbLE0aVYITgCAIAnzqlTp1SlSpU02wsWLCh/f3+1adNG7777rgoXLuyA6nIugiMAAHhiBQQEqFOnTpKklJQUXb16VStXrtTo0aO1Zs0aLVu2TC4uLg6uMucgOAIAgCdW2bJlNWzYMKtt8fHxeuGFF7Rt2zZFRkYqODjYQdXlPLwcAwAAcpT8+fOrYcOGkqSrV69atptMJrVs2VLnzp1T37599fTTT6to0aLasGGDJGn9+vXq37+/atasqdKlS6t06dJq3Lixpk+fnu51ihUrppYtW+rKlSvq37+/nnrqKXl7e+v555+3nPN+N27cUFhYmOrXr69SpUrJz89PDRs21JdffpnuYt0Pcu7HgRFHAACQoyQkJGjjxo1ycnJSYGCg1b7r168rJCREJpNJ7du3V2Jiotzc3CRJ48ePV1RUlGrVqqVSpUopJiZGq1at0nvvvaejR4/qq6++SnOtmJgYNW/eXG5ubnrllVd05coV/fbbb3r55Ze1du1aPffcc5a2V69eVcuWLfXXX38pMDBQvXv3ltls1tGjRzV+/Hi9++67MplMD3Xux4XgCAAAnlhRUVEKDQ2VdHeO47Vr17R69WqdP39en3/+uZ566imr9gcPHlT37t313XffpZn7OHbsWPn7+1ttS0pK0iuvvKJJkybpnXfeka+vr9X+/fv364033tA///lPOTvffZDbsGFDDRw4UFOmTNG4ceMsbQcPHqy//vpLgwcP1siRI63Oc+nSpTQv8jzIuR8Xp+jo6JTHftVchuV4AAA5weNYjufy5cvy8PDIsl1Gb1WnatGihUaNGqVnnnnGss1kMilfvnw6dOiQihcvbrimRYsWqWfPnpo4caK6desms9mshIQEeXt7q1ChQjp8+LBV6EtKSpK3t7cqVqyodevWSbobDJ955hn5+/tr27Ztyps3b6bXNJlMhs+dGaP9aRRzHAEAwBOrWbNmio6Otvw4evSopk6dqm3btikkJETHjh2zal+mTJkMQ+ONGzf09ddfq0GDBipdurRMJpNMJpN69uwpSbpw4UKaY8qWLZtmpDBPnjzy9PRUTEyMZduff/6plJQUNWzYMMvQ+KDnfpx4VA0AAHIMDw8PdezYUXfu3NGAAQM0btw4/fDDD1b705OQkKBWrVppz549qly5sjp37qxixYrJxcVFp0+f1pw5cxQfH5/mOHd393TP5+LiouTkZMuvU4NeyZIlDd+L0XM/TgRHAACQ49SoUUOStGfPHqvtTk5O6bZfunSp9uzZo549e+q7776z2jd//nzNmTPHpnqKFCkiSTp//rxN53E0HlUDAIAc5/r1u59XNJvNhtqfOHFC0t25kffbvHmzzfVUq1ZNzs7O2rBhQ7rL7jwpCI4AACBHMZvNmjx5siSpfv36ho5JfVt6y5YtVts3btyo//znPzbX5OnpqTZt2ujEiRMKCwtLs//y5ctKSsr+L9La9Kh6zpw5CgwMVKVKlTJsc/DgQe3Zs0ddu3a15VIAAABp3Lscj3R3rcQNGzbo8OHD8vHx0YcffmjoPC+++KL8/Pw0fvx4HTp0SBUqVNDRo0e1YsUKtWzZUosWLbK51rFjx+rQoUMaM2aMIiIiFBwcrJSUFB07dkx//PGHjhw5YrWOY3ZkU3Ds16+fPv7440yDY0REhD7//HOCIwAAsLv7R/Dy588vPz8/9e/fXx988IHhZXcKFy6sRYsW6dNPP9WmTZu0ceNGPfvss5oyZYo8PDzsEhyLFy+ulStX6vvvv9fChQs1ZcoU5c+fX2XKlNF7772nQoUK2XyNR+2RvxyTnJxsWbQSAADAHsqUKaPo6OgHOiar9v7+/goPDzd87LVr1zLMOPv27Ut3u7u7uz755BN98sknD11rRud+HB55otu7d6+KFi36qC8DAACAR+yBRxxbt25t9evZs2dr48aNadolJyfr3LlzOn36tNq3b//wFQIAACBbeODgeG9IdHJy0unTp3X69Ok07ZydnVW0aFG1a9fOatIqAAAAnkwPHBxT10WSpKJFi+rjjz/W0KFD7VoUAAAAsh+bXo5ZvHix/Pz87FULAAAAsjGbgmNQUJC96gAAAEA2Z/NyPAkJCVqyZIl27dqlmJiYdD+67eTkpAkTJth6KQAAADiQTcEx9Y3pEydOKCUlJcN2BEcAAIAnn03Bcfjw4YqKilLnzp316quvqnTp0nJxcbFXbQAAAMhGbAqO69evV6NGjTRp0iR71QMAAIBsyqYvx6SkpKhy5cr2qgUAAADZmE3BsVatWjp8+LC9agEAAEA2ZlNw/Mc//qGNGzdq4cKF9qoHAAAA2ZRNcxyXL1+uoKAg9e7dWw0aNFDlypXl7u6epp2Tk5OGDBliy6UAAADgYE7R0dEZr6OThaJFixq7iJOTrl279rCXeeIFTdul63FJji4DAACbHOhX+5Ff4/Lly/Lw8Mhwf8WJ2x55DQ/rUfTP22+/rblz58rT01MHDx5UnjwPNuaXVX8+KJs/OQgAAAD7i42N1eLFi+Xk5KRLly5pxYoVatmypUNr4pODAAAA2dD8+fN1+/ZtDRgwQBMmTNCMGTMcHhxtejkGAAAAj8aMGTOUL18+ffDBB6pbt65WrlypCxcuOLQmm4NjUlKSfvjhBzVt2lS+vr4qXry4Zd/evXs1ePBgHTt2zNbLAAAA5BoHDhzQrl27FBISoqJFi6pLly5KTk7WnDlzHFqXTY+q79y5ow4dOmjr1q0qXry43NzcdOvWLcv+MmXKaNasWSpatKhGjBhhc7FPquVRk5Ry64ajywAAwCY33rf/Od3GOTYIZVczZsyQJHXu3FmS1K5dOw0dOlQzZ87U++8/gt8Ig2wacRw7dqy2bNmif/zjHzpy5Ih69Ohhtb9IkSJq0KCBVq9ebVORAAAAuUVCQoLmzZsnk8mk5s2bS7qbqV566SUdP35ckZGRDqvNpuC4YMECBQUFadCgQXJycpKTk1OaNv7+/vr7779tuQwAAECusWTJEl27dk0dOnRQvnz5LNu7dOkiSZo5c6ajSrMtOP7999+qXr16pm3c3NwUGxtry2UAAAByjdRgmPqYOlWzZs3k5eWlhQsXOixb2TTHsXDhwrpy5UqmbU6cOKESJUrYchkAAIBc4e+//9Yff/whSZbH1On57bff1KtXr8dU1f/YFBxr1qyp5cuXKyYmRkWKFEmz/+zZs1q5cqXD1xwCAAB4EsyaNUtms1n16tXTU089lWZ/QkKC5s6dqxkzZjx5wXHgwIFq3bq12rVrp9GjRysp6e5n9W7fvq3t27dryJAhSkxMVP/+/e1SLAAAQE6VkpKiWbNmycnJST/++KP8/f3TbXfo0CHt3LlTBw8e1HPPPfdYa7RpjmODBg00ZswY7d+/Xy1atNA333wjSfLx8VH79u0VFRWlsWPHqmrVqvaoFQAAIMdat26dTp8+raCgoAxDoyR1795d0v+W7HmcnKKjo1NsPcnhw4c1bdo07dy5U9evX5ebm5tq1qypPn36qEKFCvao84l2c8RbrOMIAEA67l/H8fLly/Lw8HBQNcaYzWYlJCQoX758cna230f4+vTpo/nz5+unn35K82LMva5du6Znn31Wbm5uOnTokNWb1/ezd3/aJTgicwRHAADSR3B8tOzdn9n7brOxwMBABQYGOroMAACAx8aml2NSXblyRYcPH9b58+eVmJiYbpuuXbs+9Pl3796tqVOnatOmTbpw4YLMZrO8vb1Vu3Ztde3aVU2aNHnocwMAAMAYm79VPWzYMM2ZMyfDwJiSkiInJ6eHCo5ms1kjRozQxIkTlSdPHgUHB6tFixbKmzevTp48qYiICM2bN0/Dhw/XkCFDbLkVAAAAZMGm4Pjxxx8rPDxcFStWVNu2beXl5aU8eewyiClJ+vLLLzVx4kQFBgYqPDxcAQEBVvvv3LmjKVOm6Nq1a3a7JgAAANJnU8pbuHChqlWrppUrV8rFxcVeNUmSoqKiNH78eBUrVkzz58+Xp6dnmjYFChTQwIEDFR8fL0k6duyYwsPDtXbtWp05c0a3b9+Wj4+PWrdurQ8//FCFCxe2Or5ly5aKjIzUxYsXNWbMGP3f//2fzpw5o8GDB2vYsGGS7n4vcsyYMTp06JDc3NzUokULff7553a9VwAAgCeBTcHRbDYrKCjI7qFRkmbPnq3k5GT17t073dB4r/z580uSFi9erBkzZqhhw4YKCgqS2WzWjh079O233yoyMlJLly5V3rx50xzfo0cP7d+/X02bNlXRokUtayfNmTNHffv2lbu7uzp37qwiRYpoxYoVatu2rRITE9M9FwAAQE5lU3CsUaOGjh8/bq9arGzZskWSFBwcbPiYzp07q3///mnWMwoLC1NoaKgWLFigTp06pTnu/PnzioyMVNGiRS3bYmNjNXToUBUqVEhr1qyxfPZn5MiRatu2rS5cuCBfX9+HuTUAAIAnkk3L8XzyySf6448/tHz5cnvVY3Hp0iVJUqlSpQwfU6pUqXQXwXzrrbckSWvXrk33uGHDhlmFRunuI+rY2Fh1797d6luRefPm1ciRIw3XBAAAkFPYNOJYs2ZNLViwQF26dFGVKlVUqVIlubm5pWnn5OT0WN56TklJ0cyZMzV79mwdOnRIsbGxMpvNlv0XLlxI97gaNWqk2bZ//35JUv369dPsq127tl1fAgIAAHgS2JR+rl27plGjRik6Olrr1q3TunXr0m33MMHR09NTR44c0blz51S+fHlDxwwZMkRTpkyRj4+PWrRoIW9vb8sIZFhYmOUlmvSudb/Y2FhJUokSJdLsc3FxUbFixYzeCgAAQI5gU3AcMmSItmzZopCQEHXo0EHe3t52e1Gmbt262rhxo9avX69GjRpl2f7y5cuaOnWqKlasqJUrV6pgwYKWfRcvXlRYWFiGxzo5OaXZ5u7uLunu4ub3S05O1rVr11SyZEkjtwIAAJAj2BQcV61apaCgIM2dO9de9Vh069ZN48aN0/Tp09W3b990R/5SxcfH6+TJk0pJSVHjxo2tQqMkbd68+YGvX6lSJUnSpk2b1K5dO6t927ZtU1JS0gOfEwAA4Elm08sxKSkpqlatmr1qsVK2bFkNGjRIV69eVceOHXXy5Mk0beLi4jRhwgSNHj3a8obztm3brOY1nj17VqNGjXrg67/00ktyd3fXrFmzdOzYMcv2xMREffnllw98PgAAgCedTSOOdevWtbxE8iiMGDFCcXFxmjhxomrVqqXg4GBVqFBBefPm1alTp7R27Vpdu3ZNI0aMkLe3t9q0aaNFixapcePGatSokS5duqQVK1YoODg43eCZmSJFimj06NHq16+fmjZtqg4dOsjd3V0rVqyQq6urvL29H81NAwCADN14/8E/Yfyo3P+xZbdxcxxSx+NkU3D84osvFBISosmTJ1uWvLEnZ2dnff3113rllVf0888/a9OmTdq0aZPMZrO8vLzUtGlTde/eXU2aNJEkTZw4UX5+flq0aJEmT54sHx8f9e/fX++99548PDwe+PrdunWTu7u7xowZozlz5sjd3d3y5ZiGDRva+3YBAAB06tQpValSJc32ggULyt/fX23atNG7776b5ot4j4NTdHR0ysMe3L9/f508eVKbN2+Wv79/psvxTJgwwaZCn2Q3R7yllFs3HF0GAADZzv2jdJcvX850sCc7jTjez14jjqnBMSAgwPLhkpSUFF29elUrV67U6dOnVbt2bS1btizLl5Kz6s8HZdOI4+zZsy0/P3HihE6cOJFuu9weHAEAAB5U2bJlNWzYMKtt8fHxeuGFF7Rt2zZFRkY+0Bf27MGm4Lhnzx571QEAAIAs5M+fXw0bNtTevXt19erVx359m4Kjn5+fveoAAABAFhISErRx40Y5OTkpMDDwsV/fpuC4ZMkStWjRQs7ONq3qAwAAgPtERUUpNDRU0t05jteuXdPq1at1/vx5ff7553rqqacee002BcdXX31VJUuWVNeuXdWjRw/5+/vbqSwAAIDc7cSJE+l++a5FixYKCQlxQEU2LgA+YMAAJScn65tvvlGNGjXUtm1b/fbbb0pISLBXfQAAALlSs2bNFB0dbflx9OhRTZ06Vdu2bVNISIjVB0oeF5uC4+eff64DBw4oPDxczZo1U2RkpN544w1VqFBBw4cP16FDh+xVJwAAQK7m4eGhjh07atSoUYqJidG4ceMeew02T07MkyePWrdurXnz5mnfvn0aNmyY3Nzc9OOPP6pBgwYKCQnRrFmzdPv2bXvUCwAAkKvVqFFDkmNWt7FpjuP9SpYsqY8++kgfffSRVq5cqUGDBmnHjh3asWOHhg0bps6dO2vgwIGW70rnFlcb55U5Ia+jywAA5GABL4VnuC8uLk5nzpyRr6+vXF1dH2NVeBSuX78uSTKbzY/92nZ/HfrIkSMaMWKE+vXrp/Pnz6tAgQJ6+eWXVbp0aU2dOlV169bVypUr7X1ZAACAHM9sNmvy5MmSpPr16z/269tlxPHOnTv67bffNGPGDG3btk0pKSl67rnnNGTIEHXu3Fnu7u6SpIiICPXr10//+Mc/9MILL9jj0gAAADnSvcvxSNLVq1e1YcMGHT58WD4+Pvrwww8fe002Bcfdu3frP//5j+bPn6+bN2/K1dVVXbp0Ue/evVWrVq007UNCQtSzZ099//33tlwWAADkUvb6HrQtzGazEhISlC9fvke6lvX9y/Hkz59ffn5+6t+/vz744AMVL178kV07IzYFxyZNmkiSKlSooF69eqlz584qUqRIpsf4+PioZMmStlwWAAAgxypTpoyio6MdXUa6bAqOqaOLtWvXNnzM66+/rtdff92WywIAAMABbAqOP/74o73qAAAAQDbHR6YBAABgiM1vVd+4cUNTpkzR2rVrdeHCBcXHx6dp4+TkpN27d9t6KQAAADiQTcHxypUrCgkJ0YkTJ+Tm5qYbN27I3d1diYmJunPnjqS7i4LnyWPXdcYBAADgADY9qh49erROnDihSZMm6dSpU5Kkfv366dy5c1q9erVq1KghPz8/bdmyxS7FAgAAwHFsCo4RERFq1KiROnfuLCcnJ6t91atX1//93//p9OnTGj16tE1FAgAAwPFsCo4XL15U5cqVLb92cXFRXFyc5dcmk0nPP/+8/vvf/9pyGQAAAGQDNgXH1PmMqUwmk86ePWvVxs3NTZcvX7blMgAAAMgGbAqO/v7+On36tOXXlStX1tq1a3X9+nVJd79hvXz5cvn4+NhWJQAAABzOpuDYpEkTrV+/Xrdv35Yk9erVS5cvX1ZQUJBee+011atXTydOnFC3bt3sUiwAAAAcx6bg2KdPH40fP96y9E6bNm30+eef6+bNm1q0aJEuXbqk/v37a+DAgXYpFgAAAI7zUAssbt++XV988YX+/PNPSXffoP70009Vo0YNDRgwQP369dPVq1fl4eGR5m1rAAAAPJkeODgeOHBAbdq0sXp7ev369WrdurVWr16tChUqyMXFRZ6ennYtFAAA4MTSno4uIUMBL4Xb9Xy7d+/W1KlTtWnTJl24cEFms1ne3t6qXbu2unbtqiZNmtj1ekY88KPqb7/9VnFxcRo8eLCOHDmio0ePavDgwbpz546+/fbbR1AiAABA7mE2mzV8+HA1btxYv/zyi/z9/dW7d2+98847qlq1qiIiItS+fXv985//fOy1PfCI4+bNm1W3bl2NGDHCsm3EiBGKjIxUZGSkXYsDAADIbb788ktNnDhRgYGBCg8PV0BAgNX+O3fuaMqUKbp27dpjr+2Bg+OlS5fUoUOHNNtr1qypXbt22aUoAACA3CgqKkrjx49XsWLFNH/+/HSn/hUoUEADBw5UfHz8Y6/vgR9VJyYmqlChQmm2FypUyGoxcAAAADyY2bNnKzk5Wb17987yfZH8+fM/pqr+x6bleAAAAGA/W7ZskSQFBwc7uJL0PdRyPPPmzdOOHTustkVFRUmSXnnllTTtnZycNG/evIe5FAAAQK5x6dIlSVKpUqUcXEn6Hio4RkVFWYLi/VatWpVmG2s5AgAAPPkeODju2bPnUdQBAACQ63l6eurIkSM6d+6cypcv7+hy0njg4Ojn5/co6gAAAMj16tatq40bN2r9+vVq1KiRo8tJg5djAAAAsolu3brJxcVF06dP15UrVzJt+0QsxwMAAIBHo2zZsho0aJCuXr2qjh076uTJk2naxMXFacKECRo9evRjr++hXo4BAADAozFixAjFxcVp4sSJqlWrloKDg1WhQgXlzZtXp06d0tq1a3Xt2jWrr/g9LgRHAACAbMTZ2Vlff/21XnnlFf3888/atGmTNm3aJLPZLC8vLzVt2lTdu3dXkyZNHnttBEcAAPDECHgp3NElyGw2KyEhQfny5ZOz86Ob9VetWjVNmDDhkZ3/YTDHEQAAAIYQHAEAAGAIj6ofgzLP/+DoEnKEuLg4nTlzRr6+vnJ1dXV0OU88+tO+6E/7oj+B7IkRRwAAABhCcAQAAIAhBEcAAAAYQnAEAACAIQRHAAAAGEJwBAAA2UpKSoqjS8gRHkU/EhwBAEC24erqqri4OEeXkSPEx8crb968dj0nwREAAGQbhQoV0s2bN3Xnzh1GHm2QnJys2NhYubu72/W8LAAOAACyDWdnZxUvXly3bt3SlStXHF1Ousxms+Li4uTq6vpIv1VtC2dnZ5lMJrvXR3AEAADZirOzs9zc3OTm5uboUtIVFxen2NhYeXl55bovG2XPmAwAAIBsh+AIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMCSPowvIDYKm7dL1uCRHl5GDXHd0ATkM/Wlf9Kd90Z/2lf3780C/2o4uAZlgxBEAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcAQAAYEgeRxeQGyyPmqSUWzccXQYAAE+AOY4uAJlgxBEAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcM9G3b1+ZTCadOnXK0aUAAAA4XLb+5ODu3bs1depUbdq0SRcuXJDZbJa3t7dq166trl27qkmTJo4uEQAAINfIlsHRbDZrxIgRmjhxovLkyaPg4GC1aNFCefPm1cmTJxUREaF58+Zp+PDhGjJkiKPLBQAAyBWyZXD88ssvNXHiRAUGBio8PFwBAQFW++/cuaMpU6bo2rVrDqoQAAAg98l2cxyjoqI0fvx4FStWTPPnz08TGiWpQIECGjhwoIYNGyZJOnbsmD799FMFBwcrICBAXl5eqlGjhkaNGqWbN2+mOf7ChQsaOnSoqlevLm9vb/n7+6t+/foaPHiwYmNj061r6tSpql27try8vFSpUiWNHj1aZrPZvjcPAACQjWW7EcfZs2crOTlZvXv3lqenZ6Zt8+fPL0lavHixZsyYoYYNGyooKEhms1k7duzQt99+q8jISC1dulR58+aVJN2+fVvNmzfX6dOn1bRpU7Vq1UoJCQk6efKkZs+erYEDB8rd3d3qOp9++qk2btyo5s2bq0mTJlqyZIlGjx6txMREjRw58tF0BAAAQDaT7YLjli1bJEnBwcGGj+ncubP69++vfPnyWW0PCwtTaGioFixYoE6dOkmS1q1bp1OnTqlfv376+uuvrdrfuHHDEkbvtXv3bkVGRsrb21uSNGTIEFWvXl2TJ0/W0KFD01wXAAAgJ8p2j6ovXbokSSpVqpThY0qVKpVueHvrrbckSWvXrk2zr0CBAmm2ubm5pXuejz76yBIaJal48eJ66aWXdOPGDR09etRwnQAAAE+ybDfi+DBSUlI0c+ZMzZ49W4cOHVJsbKzV/MMLFy5Yfl6/fn15eXnpm2++0b59+xQSEqK6deuqYsWKcnJySvf8VapUSbOtdOnSkqSYmBg73w0AAED2lO2Co6enp44cOaJz586pfPnyho4ZMmSIpkyZIh8fH7Vo0ULe3t6WkcOwsDDFx8db2hYpUkQREREKDQ3V8uXLFRERIeluEHz//ff1xhtvpDn//XMeJcnFxUWSlJyc/MD3CAAA8CTKdsGxbt262rhxo9avX69GjRpl2f7y5cuaOnWqKlasqJUrV6pgwYKWfRcvXlRYWFiaY8qUKaNJkyYpOTlZBw4c0B9//KGffvpJH374oUwmkzp27GjXewIAAMgJst0cx27dusnFxUXTp0/XlStXMm0bHx+vkydPKiUlRY0bN7YKjZK0efPmTI93cXFR5cqVNWjQIE2dOlWStGzZMttuAAAAIIfKdsGxbNmyGjRokK5evaqOHTvq5MmTadrExcVpwoQJGj16tHx9fSVJ27Zts5rXePbsWY0aNSrNsQcPHtTp06fTbL98+bIkydXV1T43AgAAkMNku0fVkjRixAjFxcVp4sSJqlWrloKDg1WhQgXlzZtXp06d0tq1a3Xt2jWNGDFC3t7eatOmjRYtWqTGjRurUaNGunTpklasWKHg4OA0wXPt2rUaMWKE6tSpo6efflrFihXTyZMntWzZMhUoUEBvvvmmY24aAAAgm8uWwdHZ2Vlff/21XnnlFf3888/atGmTNm3aJLPZLC8vLzVt2lTdu3dXkyZNJEkTJ06Un5+fFi1apMmTJ8vHx0f9+/fXe++9Jw8PD6tzN2vWTKdPn9amTZu0ePFi3bp1SyVLllSHDh00aNAgPfPMM464ZQAAgGzPKTo6OsXRReR0N0e8pZRbNxxdBgAA2Z7buDmOLiFLcXFxOnPmjHx9fXPdFLdsN8cRAAAA2RPBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIawAPhjcGpVf5kTWAAcAAD8T8BL4Y4u4YEx4ggAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMMQpOjo6xdFFAEbExcXpzJkz8vX1laurq6PLeeLRn/ZFf9oX/Wlf9Kd95eb+ZMQRAAAAhhAcAQAAYAjBEQAAAIYQHAEAAGAIwREAAACGEBwBAABgCMERAAAAhhAcAQAAYAjBEQAAAIYQHPFEcXFxcXQJOQr9aV/0p33Rn/ZFf9pXbu1PPjkIAAAAQxhxBAAAgCEERwAAABhCcAQAAIAhBEcAAAAYQnAEAACAIQRHAAAAGEJwBAAAgCEERzvbtWuXXnnlFZUpU0alSpVS06ZN9euvvzq6rGzt3Llzmjhxotq3b69KlSrJw8NDTz/9tHr06KEdO3ake0xsbKyGDx+uSpUqydPTU5UqVdLw4cMVGxv7mKvP/saPHy+TySSTyaTt27en24b+NGbx4sVq166dAgIC5O3trcqVK6tPnz76+++/rdrRn5lLSUnRokWL1KpVKz3zzDMqWbKkatasqffee08nT55M057+vGvu3Ll677331LhxY3l6espkMmnWrFkZtn+Yfvv111/VtGlTlSpVSmXKlNErr7yiP//881HcjsMZ7c/ExEQtXLhQffv2Ve3atVWqVCn5+PioWbNmmjp1qpKTkzO8Rk7sTxYAt6MNGzbo5ZdfVr58+dShQwe5u7tr8eLFOnXqlEaOHKnBgwc7usRsadSoUfr2228VEBCgBg0ayMPDQ8ePH9eSJUuUkpKin3/+We3bt7e0v3Xrll588UXt27dPTZo0UZUqVbR//36tWrVKgYGBWr58uQoVKuTAO8o+Dh8+rODgYOXJk0e3bt3SypUrVatWLas29GfWUlJS9P7772v69OkKCAhQs2bNVLhwYZ0/f16RkZGaMmWK6tWrJ4n+NOKTTz7RDz/8IG9vb7300ktyc3PT/v37tWbNGhUuXFgrVqzQc889J4n+vFdgYKDOnDmj4sWLq2DBgjpz5ox++OEHde/ePU3bh+m3sWPH6osvvpCPj4/atm2rW7du6bffflNcXJzmz5+vhg0bPq5bfSyM9ueRI0dUu3Ztubm5qWHDhipfvrxiY2O1fPlynT9/Xi+++KLmzJkjJycnq+Nyan8SHO0kKSlJtWrV0rlz5xQREaEqVapIkm7cuKGQkBAdPXpUW7duVbly5RxcafazaNEilShRQvXr17favmnTJrVt21aFCxfWX3/9pfz580uSvv76a/3zn//UoEGD9Nlnn1nap24fMmSIhg8f/ljvITtKTk7WCy+8ICcnJ5UrV07z5s1LNzjSn1mbNGmSPv74Y7355psaPXp0mk+NJSUlKU+ePJLoz6xcvHhRFSpUkI+PjzZu3Ch3d3fLvokTJ2r48OHq3r27fvjhB0n0573Wrl2rsmXLys/PT+PGjdNnn32WYXB80H47fvy46tSpI39/f61evVpFihSRJB06dEjNmjWTl5eXtm/fbvlznhMY7c9z585p2bJl6tq1qwoWLGjZfuvWLbVq1Up//vmnpk+frnbt2ln25eT+5FG1naxfv14nTpxQx44dLaFRktzc3PTRRx8pKSkp00cKuVmbNm3ShEZJql+/vho2bKjr16/r4MGDku6O/MyYMUOFCxfWkCFDrNp/8MEHMplMmjlzplJS+P/Qt99+q/3792vChAkZflOV/szanTt3FBYWJn9/f4WGhqbbl6l/+dOfWTt9+rTMZrPq1q1rFRolqXnz5pKkK1euSKI/79e4cWP5+fll2e5h+m3WrFlKSkrS4MGDLSFHkipUqKAuXbroxIkTWr9+vf1uJhsw2p+lSpVSnz59rEKjJBUqVEj9+/eXJEVGRlrty8n9SXC0k40bN0qSmjZtmmZf6rb7/2Aha3nz5pX0v4/JHz9+XOfPn1edOnXSPGZxdXVV/fr1de7cOUVFRT32WrOTgwcPKiwsTB9++KEqVKiQYTv6M2t//PGHrl+/rpYtWyo5OVmLFi3SuHHjNG3atDT9Qn9mrVy5csqXL5+2bNmiGzduWO2LiIiQJMsjPPrz4TxMv/Fv2MO5/9+oVDm5P5+8MdJs6vjx45KU7qNok8mk4sWLW9rAmDNnzmjt2rXy8vJSxYoVJf2vn8uWLZvuMan9f/z48Vw7LSApKUn9+vXT008/rffffz/TtvRn1lInsufJk0dBQUE6evSoZZ+zs7P69eunL7/8UhL9aUSxYsU0cuRIjRw5UnXq1FGLFi1UuHBhHTx4UGvXrlWvXr309ttvS6I/H9bD9Nvx48dVuHBheXl5Zdoe1mbOnCkpbUDMyf1JcLST1LfU7n/0ksrNzU3nzp17nCU90RITE/X2228rPj5en332meV/c6n9fO/Q/73c3Nys2uVGY8eOtUyCT/3fcEboz6ylPjadMGGCqlSpojVr1ujpp5/W3r179d5772nChAkKCAhQnz596E+DBgwYIG9vb73//vv6+eefLdvr1KmjTp06Wf7c0p8P52H6LTY2Vh4eHobbQ5o+fbpWrlyp4OBghYSEWO3Lyf3Jo2pkO2azWf3799emTZv02muvqUuXLo4u6Ymxb98+jRkzRgMGDFDVqlUdXU6OYDabJUn58uXTrFmzVL16dRUuXFj169fXf/7zHzk7O2vChAkOrvLJ8q9//Uv9+vXT+++/rwMHDujs2bNavny5kpKS1Lp1ay1atMjRJQKZWrFihT766CP5+vpq8uTJji7nsSI42knqSGNG/4O4ceNGhqOR+J+UlBQNHDhQ8+bNU6dOnTRu3Dir/al9GBMTk+7xqXOmcmtf9+3bVwEBAfr4448Ntac/s5Z671WrVlXJkiWt9lWoUEH+/v46ceKEoqOj6U8D1q1bp6+++kpvvvmmBg8erNKlS6tQoUKqW7eu5s6dqwIFClje9qU/H87D9Ju7u3um/37d3z43W716tXr27ClPT08tXrxY3t7eadrk5P4kONpJZnMWoqOjdfXqVebgZMFsNuvdd9/VzJkz1bFjR/34449ydrb+I5rahxlNhs9srmlusH//fh05ckReXl6WRb9NJpPmzJkjSXrhhRdkMpn0+++/S6I/jShfvrykjB/7pW6Pi4ujPw24/wWYe5UoUULPPfec/v77b6u/M+nPB/Mw/VauXDndvHlTFy9eNNQ+t1q1apW6d++u4sWLa/HixfL390+3XU7uT4KjnTRo0ECStGbNmjT7UreltkFaZrNZAwYM0KxZs9ShQwf99NNP6S57Uq5cOZUsWVJbt27VrVu3rPbFxcVp06ZNKlmyZIaTwnO6Hj16pPsj9S+oFi1aqEePHpYlKOjPrKUGnCNHjqTZl5iYqKioKBUqVEglSpSgPw1ISEiQ9L+5o/dL3Z4vXz768yE9TL/xb1jWUkOjyWTS4sWLM/1zl5P7k+BoJ40aNZK/v7/+7//+T3v37rVsv3Hjhv71r38pT5486tatmwMrzL5SRxpnzZqldu3aafLkyRmuO+jk5KQePXro5s2b+uc//2m175tvvlF0dLR69OiRZgX/3OL7779P90ft2rUl3V3D7fvvv1flypUl0Z9GBAQEqGnTpoqKilJ4eLjVvnHjxikmJkYtW7ZUnjx56E8D6tatK+nuYt/3P0qdPXu2oqKiVLVqVbm5udGfD+lh+q179+7KkyePxo4da/X7cujQIf3yyy8KCAhQcHDwY7uH7Ob+0JjVaGFO7k++HGNH69ev18svv6z8+fPr5Zdflpubm+WTgyNGjNCHH37o6BKzpdDQUIWFhalw4cJ655130g2NLVu2tISd+z+lVbVqVe3fv18rV67MdZ8gM6pv376aM2eOoU8O0p9pnThxQiEhIbp8+bKaN2+u8uXLa+/evVq/fr18fX21atUqy7Ib9GfmkpOT1bZtW23cuFElSpRQixYtZDKZtH//fv3xxx/Knz+//vvf/2b4Ccfc3J/h4eHavHmzpLtrte7Zs0d169ZVQECApLt/T7Zq1UrSw/XbmDFj9OWXX1o+kXf79m3Nnz9fd+7c0fz585/YoJMRo/155MgRNWzYUPHx8Xr55Zf11FNPpTmXn59fmi/O5NT+JDja2c6dOxUaGqpt27YpMTFRzz77rPr27atOnTo5urRsKzXUZOb+z0DFxMQoLCxMixYt0sWLF+Xl5aU2bdpo6NChGc5Fy80yC44S/WnE33//ra+//lqrV6/WtWvX5OXlpRYtWmjIkCFplt2gPzMXHx+vSZMm6bffftPRo0eVkJAgT09PNWjQQO+//77lO9Wp6M+7svq7cujQoRo2bJjl1w/Tb/PmzdOPP/6ov/76S3nz5lXt2rU1fPhwVa9e3e7342hG+3PDhg1q3bp1pudq0KCBlixZkmZ7TuxPgiMAAAAMYY4jAAAADCE4AgAAwBCCIwAAAAwhOAIAAMAQgiMAAAAMITgCAADAEIIjAAAADCE4AgAAwBCCIwAAAAwhOAIAAMAQgiMAQCaTSS1btnR0GQCyOYIjAAAADCE4AgAAwBCCIwDY2aZNm9S9e3eVL19enp6eqlixol599VVt3rzZ0ub27dsKDQ1VrVq15OXlJX9/f3Xq1Elbt25Nc76+ffvKZDLp1KlTafaFhobKZDJpw4YNlm0bNmyQyWRSaGiodu/erQ4dOsjHx0d+fn7q3r271XlS20pSZGSkTCaT5cesWbPs2CsAcoI8ji4AAHKSKVOmaMiQISpQoIBatWolHx8fnTt3Tlu2bNHChQtVr149xcfHq23bttq+fbuqVKmivn376vLly1qwYIHWrFmjadOmqU2bNjbXsnv3bn3//fcKCgpSr169tHfvXi1ZskQHDx7U5s2b5erqKj8/Pw0dOlRhYWHy9fVVt27dLMcHBgbaXAOAnIXgCAB2cuDAAX388cfy9vbW8uXLVaZMGcu+lJQUXbhwQZI0fvx4bd++XZ06ddJPP/0kJycnSXdHFps1a6aBAweqSZMmcnNzs6meFStWaNq0aerQoYNl29tvv625c+dqyZIlevnll1WmTBkNGzZMYWFh8vPz07Bhw2y6JoCcjUfVAGAn//73v5WcnKxPPvnEKjRKkpOTk0qWLClJmj17tvLmzat//OMfltAoSZUqVVK3bt0UHR2tpUuX2lxP/fr1rUKjJL366quSpF27dtl8fgC5D8ERAOxk586dkqSmTZtm2CY2NlYnT55U2bJlVbp06TT7g4KCJEn79u2zuZ4qVaqk2ZZ6zZiYGJvPDyD3ITgCgJ3ExMTIyclJ3t7eGba5ceOGJMnDwyPd/Z6enpLuBkxbubu7p9nm4uIiSUpOTrb5/AByH4IjANhJkSJFrOYypid13uLly5fT3Z+6/d75jc7Od/+qTi/s2SNgAoBRBEcAsJMaNWpIktasWZNhG3d3d/n7+ysqKkrnzp1Lsz8yMlKS9RvNqcvlpNd+7969tpRs4ezsLLPZbJdzAci5CI4AYCe9e/eWi4uLvvrqK50+fdpq370jkV27dlViYqI+++wzpaSkWNocPHhQs2bNkru7u9Xn/6pVqybp7ks191q4cKElaNqqaNGiOnv2rF3OBSDnYjkeALCTihUrKjQ0VEOHDlW9evXUsmVL+fr66uLFi9q0aZNCQkI0evRoDRo0SBEREZo7d66OHDmiRo0a6cqVK1qwYIESExM1adIkq0fVLVu2VJkyZTR79mydPXtWlStX1pEjR7R+/XqFhIQoIiLC5tqDg4O1YMEC9ezZU5UrV5aLi4tCQkJUsWJFm88NIOcgOAKAHb311luqUKGCJkyYoJUrV+rWrVvy8PBQjRo11L59e0mSq6urFi1apG+//VYLFizQxIkTVaBAAdWvX18ffPCB6tWrZ3XOAgUKaOHChRo+fLg2bNigHTt2qGbNmlq6dKmWL19ul+A4evRoSdL69ev1+++/y2w2W756AwCpnKKjo1OybgYAAIDcjjmOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQwiOAAAAMITgCAAAAEMIjgAAADCE4AgAAABDCI4AAAAwhOAIAAAAQ/4fbvsdpwVCf+UAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Payment', hue = 'Branch').set(title = 'Payment/Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35f6f49e",
+   "metadata": {},
+   "source": [
+    "# Branch with lowest rating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "480fddfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Branch_lowest rating')]"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(orient = 'v', x = 'City', y = 'Rating', data = Data).set(title = 'Branch_lowest rating')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f40e524",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Gender/Branch')]"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Gender', hue = 'Branch').set(title = 'Gender/Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c69c2742",
+   "metadata": {},
+   "source": [
+    "# Using Catplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac69c43b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc8074aca60>"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(data = Data, x = 'Product line', y = 'Quantity', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b909985",
+   "metadata": {},
+   "source": [
+    "# Using Catplot on total column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e377480",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc806fdbdc0>"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Total', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4a4684f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc8070a6800>"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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d7DTRp78UXX8zvgEAQOjYjqPP9lbq1bxSrSyqCskeo1I9eiwjXiNTPTLO1+ErRBpSQEKSokxD1yZ7dG2yR//RXyr32/qyqEqLT3Wa2HDUJzuCt1FWBBwt3O/Vwv1eScGxKEPbe6rHc/ROsiLy/yPQWLmKj0V2/xOR3R8A6oqQBAAAAIDGw1sha+1SWcs+k7llvQwn9Fd07YQk+a8dI//QG4KhhHq+SGt37iHvAz+pc0ii7PezJE90vdZwPk5yR/mSO8p33YTgOJPtucHARO4amfsKwlbHmVyH9ysqZ5aUM0uOaSmQnhUMTGRmy+7Yrd7/PwIAAJen0u/ob/nl+v2mUu044a/39zcN6bZuMZqSEa+s1ozoOp9Yy6VRHaI1qkPw82Sx19byg97q8Rxbiuv//5+LcbLK0ad7KvXpnmAwt12Mq7rLxPAUj7om8JUGcF4RGJ1Ygy/C+wNAHfGJAgAAAEDDZtsyt30ta9k8WWsXy6isCPmWjuVWoP+18g29QYHMbMlqQKdOYQxI1OKOUqDfAAX6DZAmPSzj2CGZeWuDozk2rZVRXhr2koyAX9bmdbI2r5P+9obspDYKZF4dHM3Rb4AU3yLsNQEAgKDjXlvvbC3TW1tKdaii/mc8xFuG7kuP1cN949UpvgF9XmtEkjwu3dQlRjd1iZEkFZUHtPSM0MSukkBE6ztUYeujggp9VBA8B+gcb2p4SrDTxLAUj9rHmhGtD2gwbFtm7mq5Doany+I5ud2R3R8A6ohPjgAAAAAaJOPgPrmXz5O14nO5jhSFZc9AWh/5ho6Tf9BovlyvA6dVO/mH3yj/8BvlDfjlKtgq61SXCVfh1rB0+vhnruIjci39h9xL/yHHcMnu3kf+010muvWSXFxIBwAg1HaX+PXaplJN3VGuMn/9fx5IjnHph33j9UB6nJI8DWu8RWOXHGtqYlqsJqYFx5ntLvFXByaWHPCqKARhl4uxpzSgqTvKNXVHuSSpd5KlYae6TAxr7+HPA5qfshK5l/5D7gWz5Dq8P9LVyE5sFekSAKBOCEkAAAAAaDjKSmStXiT3snkyd+aFZUu7VVv5B4+Vb8hYOaldwrJnk2RasntmqKpnhvS9f5VKimXlfRUczZG3Wq4Tx8NekuHYMndukrlzkzTzj3LiWsifMVCBrGwFMq6Wk9Q67DUBANCUbThSpVfzSjVrV4UCIchKpidampIRrzu6x8pjMl4rHLokWLo3wdK9veLkOI62n/Br8f5gYGLpQa9OVIU/FHumrcV+bS3263+3lMmQdEVrt0akeDQ81aNr2kUpzk1oAk2Ta89OuRfMkrVyvowqb6TLqWZ36RnpEgCgTghJAAAAAIisgF9m3trgOI31y2SEYYapExUt/8Bh8g8dp0CfK+kuEAoJSfJfO0b+a8dIti3X3vzgWI681XLtyJMRCH/rZqPspNxfLpT7y4WSpEDnHsGxHFnZsnv0kyxawwIAcLEcx1HON169kleqJQdC80Xd4OQoPZYZr7Edo+UyCEdEimEYSk9yKz3JrX/rG6+A7Sj3mE9LDni1+IBXK4uqVB6CziF15UjacNSnDUd9ejmvVG6XNLBtVPV4joFtoxRFuAaNmd8v66ulci+YKXP7xkhXc1Z2116RLgEA6oSQBAAAAICIcO0tkLXsM1krF8h14lhY9vT37i//0HHyDxwhxcSGZU9Icrlkd+kpu0tP+W6+W6ook7l5XXA0x8bVch0NzziVf2bu2Slzz05Fzf1ATnSMAn2vqh7N4bRNiUhNAAA0FlUBRzMKK/RqXok2H/fX+/u7DOnmLtF6LCNBA9pG1fv74/KZLkP920Spf5soPZaZoKqAo7WHq6pHc6w5XCVfBKdz+GxpZVGVVhZV6bkNJYq1DF3TLkojUoPjObJauWW6CE2g4TOKj8pa9IncX8yWq/hopMs5JycqWoEeGZEuAwDqhJAEAAAAgLAxTh6XtXKBrGXzZO7ZGZY97eQO8g0ZJ//g6/niu6GIiVNgwDAFBgyTHEfGgT3BwETuaplbv5bhqwp7SUZlhax1y2WtWy5JslM6VQcmAr37S1GesNcEAEBDdLLK1nvbyvT65lLtL6//b8BjTEN394zVI/3i1a0Fl68bkyjT0OD2Hg1u79ETV0plPltfHqoKjuc46NWGIz5FcjhHud/Rwv1eLdwf7HiSGGVoaPtgYGJEqkfpiZYMOpWgoXAcuXZuknvBTFlrFssI1H8Yrb75B18veaIjXQYA1EmD+5S5f/9+zZo1S/Pnz9eOHTtUVFSkli1batCgQXr88cc1cODAGus3btyoWbNmacOGDfr666919OhRDRkyRHPnzj3vPh9++KFef/11bd26VW63W9nZ2XryySd15ZVXnnV9fn6+nnnmGS1dulRlZWVKS0vT/fffrx/84AdyuWrPNfN6vXrppZc0ffp07du3T0lJSRo7dqyeeuopJScnX/oPCAAAAGhsfFUyN6yUe9k8mblfhmXMghMbJ3/2aPmGjguOUeBiZ8NlGHJSu8iX2kW+cbdLVV6Z274OjubIXS3XgT0RKct1YK+iDuyVPp8hxx2lQO8rFMgIjuZwUjrzZwoA0OzsLwvojc2l+tO2Mp301f9X3a09Lj3YJ04P9olT62hGoTUFcW6XRneI1ugOwS9Ni722lh30Vnea2Foc2S99T1Q5mrunUnP3VEqSkmNcGp7i0bCUYHCia0KD+/oEzUGVV9aqnOBIjd07Il3NRfGNuTXSJQBAnTW4v+XfeustvfTSS+rWrZtGjhyptm3bKj8/X3PnztXcuXP1zjvvaMKECdXr586dq9/97neKiopSjx49dPTohVsNvfDCC3rmmWfUsWNHPfDAAyorK9Pf//53jRs3TjNmzNCwYcNqrN+6davGjh2riooKTZgwQSkpKZo/f75+/vOfa9OmTXr55ZdrrLdtW3fddZdycnI0cOBA3XzzzSosLNS0adP0xRdfKCcnh6AEAAAAmjbHkatgi9zL5sn6cqGMspLQb2m4FMi8OjhO48oh3PnfWEV5gt0bMrNVJck4fEBm3hpZG1fL3LxORmV52EsyfFWyctfIyl0jz1/+ILt1sgKZ2cFOE/2ukmLiwl4TAADhsvm4T6/mleqjgvKQjE5ISzA1JSNBk3rEKNaqfTMamo4kj0vf7RKj73aJkSQVlQe09KA32GnigFe7S0Mfpj6fogpbHxZU6MOCCklSl3izusvEsPYeJccS3kHoGIcPyL3wY7kXfyqj7GSky7lo/oHDZXfuEekyAKDOjOLi4kh2uKpl9uzZatOmjQYPHlzj8RUrVujWW29VfHy8tm7dKo8neMFzy5Yt8nq96tevn44dO6b09PTzdpLIz8/XoEGD1LVrV+Xk5CgxMbH6fcaMGaPk5GStWbNGlvVtfuTGG2/UihUrNH36dI0dO1aS5PP5NHHiRC1evFizZ8/W8OHDq9dPnTpVU6ZM0W233aa33367ukXX6ccnTZqkN954o/5+aEAdxE8eWad1pe8tCmkdAFBfOK4BDZNx9JCsFfPlXv6ZXAf2hmXPQMdu8p8ep5HUOix7hgLHtTrw++Xauenb0RwN4M4qxzRl98iQP/NqBTKzgxcGz9JtEGhuOKYBjZvjOFp6sEqv5pZo/jfekOxxdVu3Hs1I0E2do2W6Gn6HpqQ/flOndcUPdAhxJU3XrhK/lhzwaukBrxYf8OpQRQhSOZehd5Kl4ae6TAxt71GSh898uEy2LXPTWrkXzJL59UoZToP6uq7OnLgElf+/PzXq83EAzU+D6yRxyy23nPXxwYMHa9iwYVq4cKE2b95cPRajT58+F/X+06ZNk9/v109+8pPqgMTp95k0aZLeffddLVmyRKNHj5Yk7dy5UytWrNCwYcOqAxKS5Ha79Ytf/EKLFy/W+++/XyMk8f7770uSfvWrX9WYYXbPPffo1Vdf1cyZM/Xb3/5WCQkJF1U7EBbeCskTE+kqAABAY+KtkLV2qaxln8ncsj4sF3bshCT5rx0j/9Abgl9KM/qgebAs2b2vUFXvK6TbH5RRfFRm3lqZuatl5a2RURr+O66MQCA4HmTb19JHb8tu0VKBjKsVyMqWv99AqUVS2GsCAOBS+W1Hs3dV6JW8Um046gvJHt/pFK3HMuN1TbuoGtdOga4JlromWLqvV5wcx9G2E/7qLhPLDnp1oiqyXyBvLfZra7Ffb20pk8uQrmjt1vD2wU4Tg9pFKc5NaAJ1VF4q97J5cufMkutg6G4usBMS5So5EbL3P817348ISABodBpcSOJ83G63JMk0L72t1bJlyySpOgRxptGjR+vdd9/V8uXLq58/3/oBAwYoMTFRy5cvr36ssrJSa9euVc+ePdW5c+darxk1apTeeOMNrV27VqNGjbrkfw+grlx7dsq9YFad18dNmSD/tdfJd9142mMBAIBzs22Z276WtWyerLWLZVRWhHxLx7QUuHKwfEPGKZA1SLIa1ekMQsBJah0crzJ0nLx2QK5dO4KBiY2r5crfLMMJ/92HrpPH5VrxudwrPpdjGLK7pgfHwGRmy+7eRzL5cwsAaHjKfLam7ijXHzaVak8IRh5EuaRJPWI1pV+8eiW56/390fQYhqHeSW71TnLrob7xCtiONh7zacmB4HiOlUVVqghELjRhO9L6Iz6tP+LTy3mlcrukq9tGVXeaGNg2SlEmISDU5NpXKHfOLFnL58nwVoZsH3/G1fJdP0GBrEGK+vh9Rc16L2R7ecffL/81Y0L2/gAQKo3m6szevXu1aNEiJScnq1+/fpf8Pvn5+YqPj1dycnKt57p371695sz1kpSWllZrvWEYSktL0/r161VeXq7Y2FgVFhbKtu2zrv/nPS4UkqisDN1fkmj6jBNHFf+X1+RZv/zCi898XVWl3Is/kXvxJ/JeOUSld/67nERSoAAanvg6ruPvU6B+uQ7tV/TK+fJ8mSPz6KGw7Onrmi7vtdfJO3CEnPgWwQf9/uCvJoTjWj1I7Rr8Ne4OGWUlcm9dr6hNa+Xe9JXM4qNhL8dwHJmFW2UWblXU7D/LjomTr8+Vquo3UL5+A2S3bBv2moBw4ZgGNA6HK2y9u6NSf9pRqeMhuEs/0W3o/p7R+n6vaLWLcUkKqLKy/kMYDQnHtdDpEy/16enWQz3d8gYcrT/q19Iin5YX+fTVUb98EZzO4bOlFUVVWlFUpd9sKFGMKV3T1q2h7d0amuxWRpLZKMbKIAQCAUV9vVLRi+YoatvXIdvGjo6Vd/D1qhzxXQXadwo+WOVT5Q2TFOMPKO6TqfW+Z9l371HFDf8icdzDJYiOjo50CWjmGkVIwufz6aGHHpLX69XTTz99WZ0kTp48qbZtz34h6vT4i5MnT9ZYL6nGaI5zvSY2Nvai1l/I/v37FQg07ZMGhEbSptXq9I9psirLL+t9POuXy9yyXnu/c7eK+2XXU3UAUD/a1HHd3r2ha1sINBdmZbmSNq9Vq40rFL8v/8IvqAdVCUk6lnmtjmVdK2+blOCDx08EfzVRHNdCoF234K+RExV9eL9a5OepRf4mxe3dIVcg/CEbV0WZPOuWybMu2LGwom0HnezeTyVp/VTauacciztr0XRwTAMatl3lhj7Yb2lukaUqp/6/uE3x2Loz1a9b2/sVa5bJe0Rq/P+1x9ZpFce18OkgaVJS8FdFQNpw0qW1xabWnHBpa6lLjiIXSqgISF8c9OmLg8GxNS0sR1clBnR1oq2rkwLqGuMwMbCJs8pOqvX6pWrz1WJFlRwP2T4VbVN1ZOAoHcu4RrYnWvJJ+ufjUP8RSnLH1Mt3BpLkj4799juDffsu+/3QPPXs2TPSJaCZa/AhCdu29cgjj2jFihWaPHmyJk2aFOmSwiY1NTXSJaCxcRzFfjJVsZ9Mq7e3tCrL1W3m/6rcV67y797DvG8AjU6nTp0iXQLQOAUCcm/+StGrchS1YYUMf2hmUp/JifLIe+UQea+9Tr70K2S5TLUL+a6ND8e1S9S5szTgGnkleSsr5N6+UVGb1ipq01qZhw9EpKSYw98o5vA3Sl71uZwoj3zpV6iq30BV9Rsoux3ng2geOKYB4bX6sE+vbanQvG98CsWggsyWpv69d4xu7hwlq8ndNV+3rlQc1yKnl6Q7Tv1zcZWtFUU+LSvya1mRT9tPRvZmxJN+Q4uOWlp06o9Ru2hDQ5Pd1b86x1/6jaFoWKzCbYpeNFuetUtCdh7tuFyq6j9YlSNvlq9XlmIMQx0u9KJOnXTi2pGX1H36TKe7TycktlbCJb8LAERegw5JOI6jxx57TNOnT9cdd9yhF1988bLfs0WLFufs4lBSUlK95sz1knTixNnvWDv9mtMdIuq6/sw9zoVWM7hYUTP/qKh6DEicKfaTaTItt3wT7g/J+wNAqPD3KXBxXHsLZC37TNbKBXKdOBaWPf29+8s/ZJz8V4+QYmJlSuIS4blxXKsH0dFS9ggFskeoQpJRtE9W7hqZuatlbl4voyr87WKNKq+iclcrKne1JMlulyp/ZrYCmdkK9OkvRdft7lWgseGYBoSe7Tiau6dSr+aWavXhqpDsMaaDR49lxGt4ikdGM7/BhuNaw9A+WvpeC+l7p25UPlge0NIDXi0+9WtvaWRDE4cqHf19d5X+vjv432TXBFPDUzzVv9rFcEbUqFR5Za3+Qu4FM2UWbgvZNnZCkvwjvyvfqFvktG538efO0R3k+9H/KLBnp9w5H8u9aE6dXuZERcs/+Hr5xtwqu3MPeS6leABoYBpsSMK2bT366KOaNm2aJk6cqNdff10ul+uy37d79+5avXq1ioqKlJycXOO5/Pz86jVnrpekgoKCWu/lOI4KCgqUkpKiuLg4SVK3bt3kcrnOuv5cewD1wVqZo6hZ74V0D8+sP8lJ6ST/NWNCug8A1Kcyn6049+V/hgCatJPFcq9aIGvZPJm7d4RlS7tdqnxDb5B/8PVy2qaEZU/gXJzkjvIld5TvugmSr0rm9txgYCJ3tcx9hRGpyXVov6JyZkk5s+SYlgLpWcHARGa27I7d6PAGALigSr+jv+aX6/d5pdp5sv7HTFmGdFtajB7NSFBGK0ZGoWFrH2vq9u6xur17MHi6q8SvJQe81b8OVdgRrW9XSUC7Ssr1/vbgKIQ+SVZ1YGJIe4+SPFzXaIiMo0VyL5wt9+JPZJSEbjRkoHsf+cZMkD97pOSOuuz3szv3kPeBn9Q5JFH2+1mShwAYgKalQYYkzgxIfO9739Obb74p06yf5OSQIUO0evVqLVy4UHfeeWeN5xYuXFi95rShQ4dWP/fjH/+4xvqvvvpKJ06c0PXXX1/9WHR0tAYMGKA1a9Zoz5496ty5c43XfPHFF/J4PBowYEC9/PsAkmQUH5Xnzy+FZS/P+y8p0Lu/nKTWYdkPAP5Z7jGf3t5SqnfquL7nXw/q9rQY/aBPvDK5cAd8y1clc8NKuZfNk5n7pYxA6O+kcmLi5M8eJd/QcbJ7ZvAlLxomd5QC/QYo0G+ANOlhGccOycxdIyt3tcxNX8koLw17SUbAL2vzOlmb10l/e0N2UhsFsrKDnSb6DZDiaHQLAPjWscqA3t5apv/dUqbDlfX/xW+C29DkXnH6Yd84dYxvkJeXgQvqmmCpa4Kl+3rFyXEcbS0OhiYWH/Bq2UGvTlaFYiBN3W0p9mtLsV9vbimTy5D6t3ZreIpHI1I8GpQcpViL0ETEOI7MLeuDXSPWLZfhhCZg41hu+QeNlu+6CbLTeodkjzojIAGgCWpwn2Jt29aUKVP0wQcfaPz48XrrrbfqLSAhSXfffbdeffVVvfDCC7rxxhuVmJgoSdqyZYv++te/qlu3bho+fHj1+h49emjw4MFaunSpPv/8c40dO1aS5PP59Oyzz0qS7rvvvhp7TJ48WWvWrNHTTz+tt99+u7rF3NSpU7Vt2zZNmjSpTuM2gLry/PllGWUlYdnLKCuR588vq/LR/w7LfgBw2sHygH62qlhzdgdboNc1JFHud/Te9nK9t71cN3eJ1m+vSVL7WNpWoplyHLkKtsha/rncq3LC8vnBMVwKZF4t/9Bx8l85RIqiMScaF6dVO/lH3CT/iJukgF+ugq3BwMTG1XLt2ibDCf8FdFfxEbmWfCr3kk/lGC7Z3ftUj+awu/WSXPw9BwDN0a4Sv/6wqVTTdpSr3F//fz+lxLr0w77xuj89TolRfEGLpsMwDPVp6Vaflm491DdeAdvR10d91V0mVhZVqSIQudCE7Ujrjvi07ohPL+WWyu2Srm4bpRGpwU4TA9pEKcokgB5yFeWyVnyuqAUz5dq/O2Tb2K3ayTf6VvlG3CS1SArZPgDQ3BnFxcWRjUT+k1//+td67rnnFB8frx/+8IdnDUjcdNNNysrKkiRt375dL774oiSpsrJSM2fOVLt27TRmTHAcQOvWravDDKc9//zzevbZZ9WxY0fdeuutKi8v14wZM1RRUaEZM2bUCElI0tatWzV27FhVVlZq/PjxSklJ0YIFC7Rp0ybdd999euWVV2qsDwQCuuOOO5STk6OBAwdq6NCh2rVrl2bPnq3U1FTl5OTUGvUBXCrX7h2K/eWDYd+3/Jm3ZXfuEfZ9ATRPMwrK9ZOVxSo+404O/6K76/Raa+S0Gr9PijL0wrVJui2N2e5oPoxjh2Qtny/38nlyHdgTlj0DHbvJP2RccJwGHajqJH7yyDqtK31vUUjrwEU4WSxr01qZG1fLzFsj18njka5ITnwL+TOuDo7myBjIf3+IGI5pQPisP1KlV3JL9fHuCtkhuNLbJ8nSlIx43Z4W26y/iE364zd1Wlf8QIcQV4Jw8wYcrT1cpcUHvFp6wKs1h6oUghzSJYuzDF2bHFU9niOzlVumq/n+t1rfjP275V74sdxLP5NRWR6yffx9r5LvugkK9L9WMsNzfzOf1wA0Z5cVkigqKtKcOXO0fft2VVRU6NVXX5UkHTlyRLt371bfvn0VExNzUe/58MMP6y9/+ct51/zhD3/Q3XcHvxhZunSpbr755nOu7dSpk3Jzc2s9Pn36dL3++uvaunWr3G63srOz9eSTT+qqq6466/vs3LlTzzzzjJYuXaqysjKlpaXp/vvv14MPPiiXq3Zy2uv16qWXXtLf/vY37du3T0lJSRo7dqyeeuoptW/f/rz/fsDF8Lz7vNyLPwn7vr6RN8v7wE/Cvi+A5sVxHP1mQ4me21D7bvdLDUmc9h/9E/RE/4Tqjk9Ak+OtkLV2qazl82RuXheWu92dhET5rr1O/iHjZHfpyTiNi8QFqkbOtuXamy9z42pZuavl2pkXljE2FxLo3EOBzGz5s7Jl98iQrAbXUBJNFMc0ILQcx9H8fV69kleiZQerQrLH0PZReiwjQdd39HDeJEIS+Fapz9aqoqrq8Rwbj/rUgDITSooyNOxUYGJ4ike9Ei3+G75YdiA4nnLBTFmbvgrZNk50jHxDxsk3ZrycDl1Dts+58HkNQHN2ySGJt99+W0899ZS8Xm/wjQxDx44dkxQcXTFkyBC9+OKLmjx5cv1VC6Amb4XipkyQUVUZ9q2dqGiV/X4W88gAhNSv1588a0BCuvyQhCQ90T9BT1zJCCw0IbYtc9vXspbNk7V2sYzKipBv6ZiWAlcOlm/IOAWyBvEF7GXgAlUTU14qc/P64GiO3NVyHS2KdEVyomMV6HuV/FnZCmRcLadtSqRLQhPGMQ0IjaqAow8LyvX7vFJtKfbX+/u7DOnWLjF6LDNeV7aJqvf3b8wISeBcjnttLTvo1ZL9wfEc207U/3+bl6N9jCsYmDg1nqNzPOds51R6Qu7Fn8q9cJZcR0L3+d1O6STfmAnyDR0nxcSFbJ8L4fMagObskv42/Mc//qGf/exnuvLKK/Xzn/9cCxYs0Lvvvlv9fJ8+fdSvXz/NnTuXkAQQQubOTREJSEiSUVUpc2eeAv0GRmR/AE3fRwXl5wxI1JffbChRz0SL0Rto9IyifXIv/1zW8s/lOnIwLHsG0vrIN3Sc/INGSfGJYdkTaFRi4xUYOEyBgcMkx5FxYE91YMLcukGGzxf2kozKclnrlslat0xS8OKsPzM7OJqjd38pyhP2mgAAdXOiytaftpXpjc2lOlBu1/v7x1qG7ukZq3/vF6+uCXyBClyMlh6Xbu4So5u7BLtqHygPaOkBb3Wnib2lke0udrDC1vSCCk0vCIbouyaYGnFGp4m2MbVHnjc3rl3bg10jVuXI8IWmO49jGAr0Hyzf9RMU6DuAzosAEGGX9In3lVdeUceOHTVnzhzFxcVpw4YNtdb07dtXK1euvNz6AJyHq3BbZPfftZ2QBICQOFge0E9XFodlr5+sLNaQ9h61j+WiABqZshJZqxfJvWyezJ15YdnSbtlG/sFj5Rs6Tk5ql7DsCTQJhiEntYt8qV3kG3e75K2Uue1rmbmrZeWukevAnoiU5TqwV1EH9kqfz5DjjlKg9xXB0RyZ2XJSOnPhFgAagG/KAnp9U6ne216mEl/9N/RvG+3Sv/WJ0/d7x6lVNOdEQH1IiTV1R/dY3dE9Vo7jaHdpQItPdZlYcsCrw5X1H3S6GLtKAtpVUq73tpdLkvomWRqW4tGIVI8GJ3uU5Kk9XrxJ8vtkrVks94KZMnduCtk2TlwL+UbcJN/oW+jkBgANyCWFJPLy8vQv//Ivios7dxug1NRUHT58+JILA3Bhrj07I7q/mbtGvutv444zAPXuZ6uKVVwVnomexVWOfraqWH8e3Tos+wGXJeCXmfeVrOWfyVq3LCx3ojtRHvkHDpd/6DgF+lwpubh4Dlw2T7QCWYMUyBqkKknG4QMy89bI2rha5uavwjIq558ZvipZuWtk5a6RR3+Q3Tq5OjAR6HdVRNsAA0BzlHfMp1fzSjSjoEL+EJwa9WhhaUpGvP6le6xiLEJxQKgYhqGuCZa6pluanB4nx3G0pdgf7DKx36vlB706GYIA1MXYXOzX5mK/3txSJpchXdnaXd1lYlBylGKtphWaMI4dlnvRHFmL5sh14njI9gl06SXfdRPkv2Y0188BoAG6pJCEbduyLjBr+MiRI4qKYm4dEEqu4mMR3d/asl5xP7xJdrfeCvTKDP7qmSHFt4hoXQAat41HqzRnd3hHCc3ZXancYz5ltnKHdV+grlx7C2QtnydrxXy5ToTn7/9A7yvkG3KD/FePkGIYSQOEktM2Rf5Rt8g/6hbJ75Nr56ZgYCJvjczdOyJSk+tokVyL5si9aI4c05TdIyMYmMjKlt2pu+RqWhfLAaAhcBxHSw549UpeqXK+8YZkj0HtovRoRrxu7BwtFx2DgLAzDEN9W7rVt6VbP+wbL7/taONRnxaf6jKxqqhKFYHIhSZsR/rqiE9fHfHpxdxSRbmkq9tFVY/nGNA2Sm5XIzx2OI5c276We8EsWV8tkWGHppuHY1ryZ4+U77oJsrv3pTMbADRglxSS6NGjh1atWnXO5/1+v5YvX66+fftecmEA6sAf/jnG/8wI+GXuzAu2+f70L5KkQIeusntlBUMT6VlyWidHuEoAjck7W8sis++WUr00pGVE9gbO6mSx3KsWyFo2L2xfktrtUuUbMk7+IWNpAwpEiuWW3bu/qnr3l+74NxnFR2XmrQ2O5shbI6P0ZNhLMgKB4HiQbV9LH/2v7BYtFci4WoGsbPn7DZRaJIW9JgBoSvy2o1m7KvRKbqk2Hqv/az2GpBs7R+uxjHgNSuZuZqAhsVyGrmobpavaRunHWQnyBhytOVwVHM2x36u1h6tC0k2mrqpsafnBKi0/WKX/t75EcZahwclRwU4TqR5ltnI37MCVt0LWivnBkRr7CkO2jZ3URr7Rt8g/8rtyEluFbB8AQP25pJDE7bffrl/84hf67W9/q5/97Gc1ngsEAnrqqae0a9cu/ehHP6qPGgGci9Uw73g2v9kl85tdcn8xW5Jkt2p3qtNEluxembI7dOXOMwBnVeaz9WFB+FuMS9L0ggr9v0GJTa6NJBoZX5XMDSvlXjZPZu6XMgKBkG/pxMTJnz1KvqHjZPfM4E4XoIFxklrLP3Sc/EPHyWsH5CrcHgxM5K6WK3+LDCf8M61dJ4/LteJzuVd8LscwZHdNDwYmMrNlp/WWzEu61AAAzU6pz9aft5frtc2l2lta/5/7PKZ0Z/dYPZIRr56JDfMaEoCaPKahoe09GtreoyevDB4nVhadCk0c8GrjUZ8iOZyjzO9o/jdezT/V7aalJ1jv6U4TPRMtGQ3gnNIo2if3gllyL/uHjPLQ3YwT6H2Fqq6boMCVQ6ULdF8HADQsl3TUfuihh/TZZ5/p17/+tf72t79Vj9W4//77tX79eu3Zs0ejR4/WvffeW6/FAqjJTmqlxjAV3HXskFyrcuRelSNJcmLjFeiZoUB6lgI9M2V3S5fcjOcBIK05XKXyCN0iUe53tPpQlUamRkdkfzRjjiNXwVZZy+fJvWqhjLLQ3yXuGC4FMgYGv3i9aijzUYHGwmXK7t5Hdvc+8o2fLJWelLl5nazc1TI3rpar+EjYSzIcR2bhVpmFWxX18fvBz/r9BgRHc2ReLadVu7DXBAANXVF5QG9uKdU7W8t0oqr+z39aegx9v3e8/q1PnNrFNIYrRwDOJd7t0vUdo3V9x+C1imOVAS07+G1oYvsJf0TrO+51NGd3ZfXY1JRYl4adCkyMSPGoU3wYgwO2LTN3tdwLZsra+GXItnGiouUfcr18YybI7pQWsn0AAKF1SX9Dud1u/f3vf9dzzz2nd999V8XFxZKkjz/+WAkJCfrRj36k//zP/2wQiUGgKbM795C+/CLSZVw0o7xU1terZH0dHNvjuN2yu/WpDk0EevaTYuMjXCWASFh/JLJjhBbsq9TQ9h5ZjXG+Jhod49ghWcvny718nlwH9oRlz0DHbvIPGSf/4OvlJLUOy54AQii+hQLZIxXIHhkMXO0rlJm7Ovhr20YZgfBfNDfKS2WtWSxrzWJJweNOIDM7+KtXJuFoAM3a9mKffr+pVH/dWa6qEDQC6hxv6pF+8bqnZ6zi3HTIA5qiVtGmbukao1u6xkiSDpQHqgMTi/d7ta8s9N0Iz+dAua3p+RWanh/sEtotwazuMjEsxaO2oQhulZXIvfQfci+YJdfh/fX//qfY7VLlGzNBvmE3SHEJIdsHABAeRnFx8WXFlR3H0Y4dO3T8+HElJCQoPT1dpklCGQgHc9Naxfx/P410GfXOMQzZndKCXSZOBSecVm0jXRaAMPjXRcf098K6jdvwL7q7TuuskdMuqoYol9Qj0VLvJLfSk4L/2yvRUvcWlqJMwhO4TN4KWWuXylo+T+bmdTKc0HdOcRIS5bvmOvmHjpPdpSfjNBqw+Mkj67Su9L1FIa0DTURlucytG2RuXC1r4+qQXjCuKycqWoE+/RXIzJY/K1tOcsdIl4QQ4pgGBDmOo1WHqvRKbqn+sbcyJHv0b+3WYxnxuqVrDIHvEEr64zd1Wlf8QIcQVwKcneM42lUSDE0sPhWcOFIZ/tFs59O3pVXdZWJwe48Soy490OXas1PuBbNkrZwvo8pbj1V+yzEMBbIGyXfdBAUyrm5yI6T5vAagObvsXkeGYahXr171UQuAixTo0U9OVLSMqtCcZEeK4Tgy9+TL3JMv5cySJNltU4JdJnplKpCeJSelM1/yAE3QwfLI3vEgSVW2tPm4X5uP17z71jSk7i0spSdZSk9yq3eSpV6JlnomuhVjcTzCedi2zG1fy1r+uaw1i2RU1i0IdDkc01LgysHyDRmnQFa2ZDGDGmh2omMV6D9Ygf6DVaXgXGZr46kuE1s2ROQcwqiqrO4o51Hwbjx/ZrYCWdkK9O4vRceGvSYACJWA7eiTPZX6fV6J1hwOTce86zt49Ghmgoa1j6KjLwAZhqFuLSx1a2FpcnqcHMfR5uP+6k4Tyw96ddIXmRGnp52+3vLG5jK5DOnK1m6NSA12mhjUznPh6yt+v6yvlsi9YKbM7bkhq9OJjZdv+I3yjb5VTjLBJwBoii4pJLF161YtWrRIEydOVJs2bWo9f/jwYc2YMUOjRo1Senr6ZRcJ4Bw8MfJfe53ciz+JdCUh5zp8QK7DB+Re8bkkyYlvEQxM9AyGJuwuPfkCCGjESny2Pt1TqU3HIztu43wCjrT9hF/bT/irZ21KkiGpa4JZHZxIT3IrPdFSryRL8bS4bdaMon1yL/9c1vLP5TpyMCx7BtL6yD9krHzXjJbiE8OyJ4DGwUnuKN/1HeW7/ntSlVfmjlyZuWuCoYl9hRGpyXVov6JyZkk5s+RY7uDn+1OjOeyO3QhFA2iUKvyOPthZpj/klaqgpP5D4G6XNDEtVo9mxKtvS66DADg3wzDUr5Vb/Vq59XC/ePltR18f9VV3mVhV5FVlBO9VsR3pqyM+fXXEp99tLFWUS8puF1XdaeKqtlFyn+qOYxQflfXFHLkXzZGr+GjIagp06i7fdRPkv/Y6yRMdsn0AAJF3SeM2HnroIS1ZskSbNm2S6yzthQKBgLKysjRixAi99tpr9VIogLNz7dmp2F/8INJlRJwT5VGge1/ZvTIV6JWlQPe+Ugx3ogENmTfgaMG+Sn1UUKHP9laqInBxH0lCNW6jPnWMM78NTiRZSk8M/nOSh/BEk1VeKmv1IrmXfSZzR15YtrRbtpF/8Fj5ho6Tk9olLHsiNGh1ikgxjh2SmbtGVu5qmZvWyigvi3RJspPaKJCVHew00W8Ac58bIY5paG6OVgb0v1vK9L9bynTUW//t7Vu4Dd2fHqcf9o1XahyjjiOB4xqaGm/A0epDVdWdJr46XCV/ZBtN1BBvSt+3CvX93Z+r9/blctmhSXQ4pin/wOHyXTdBds/MZhXU5bgGoDm7pE4SK1eu1PDhw88akJAk0zQ1fPhwrVix4rKKA3Bhduce8g8cLmvtkvDt2S5VTmy8XLt3ynAaxlw7o8ora8t6act6SZJjuGR37qFAenBEh90zU05S6whXCSBgO1p2sEofFZRr9u4KnahqQGffIbCvLKB9ZQEt+KbmbMz2Ma7q4ETvJLd6JVnqnWSpTTQXOxulgF9m3leyln8ma91yGb6qkG/pRHnkHzBM/qE3KND3SsnFnx0Al85p1U7+ETfJP+ImKeCXK3+LrLw1MjeulmvXNhlO+P++dhUfkWvJp3Iv+TT42b57X/mzshXIuFp2t14c9wA0GIUn/frDplJN21F+0cHvukiNdenhvvGanB6nFlGErQHUH49paFiKR8NSPPq/Cnb5XFVUpcX7g6GJ3GM+ReKqTXSgSpMOrdC/fzNfV5XuCtk+dmIr+UfdLN/Im+W0rN0xHQDQtF1SSOLQoUPq2LHjedekpqaqqKjokooCcHG89z4uc8t6GWUlId/LiUtQxf99NRg4qCiXmb9Z5vaNcm3PlZm/WUaV98JvEgaGY8vcvV3m7u3S5zMkSXZyh2CXiV7B4IST3LFZJYOBSHEcR+uO+PRRQblmFlboYEXDCFdF0sEKWwcrvFp8oOYxs7XHVR2cSE+yTv1yq32MixnDDZBrb4Gs5fNkrZgv14ljYdkz0PsK+YbcIP/Vw6WYuLDsCaCZMS3ZvTJV1StT+t6/SieLZW1aK3Pjapl5a+Q6eTzsJRmOLXNnnsydedLf35UT30L+jKuDozkyBhKGBhARXx2u0it5JZqzu1J2CL5F7NvS0mMZCfpetxhFmZwLAAi9BLdL13eM1vUdg2MmjlUGtPRglZYeCF6/2HHCH9L9u1Yc0g/3L9ADBxartb80ZPsEembIN2ZC8Lya8c0A0GxdUkgiNjZWhw8fPu+aw4cPy+PxXFJRAC6Ok9Ra3vt+pOjXnwn5Xt77fvTtRciYWAUyBiqQMTD4e79Prt07ZG7Plbl9o8ztuTJKT4a8prpyFX0jV9E3ci/9hyTJbtHy1HiOU90mOveQzEs6LAI4i23FPn1YUKEZBeUqDMEs3qboqNfWiqIqrSiq2YmgRZSh3ok1gxPpSZY6xplyEZ4Ir5PFcq9aIGvZPJm7d4RlS7tdqnxDxsk/ZKyctilh2RMAqrVIkv/a64JzmW1brj07q0dzuHbkyrDDH340Sk/KvSpH7lU5kqRAl54KZGbLn3m17B4ZksVnegChYTuOPt9XqVdyS2t9Zq8vI1I8eiwzXqNTPQSlAURUq2hTt3aN0a1dYyRJ+8sC1aM5lhzwal/Z5V/rMRxb1x/P08PfzNdNR9fLFaLeFY47Sv5rrwuO1OjSMyR7AAAal0u6ctC/f3/NnTtX//3f/62kpKRazxcXF+uTTz7RFVdccbn1Aagj/zVjVHVgj6JmvReyPbzj75f/mjHnXmC5ZXfvK7t7X/m+8y+S48g4sCcYmti2UeaOXLkOHwhZfRfLdfK4XGuXVI8qcTzRCvTop0CvrGB4onsfyRMT4SqBxmVvqV9/L6zQhwUVyjvmi3Q5TcbJKkerD1dp9eGaF2LjLEO9kiylJ545tsOtLvGmTBcXVOuNr0rm16vkXjZP5sZVMgKhD/04MXHyZ4+Sb+jYZjcTFUAD5nLJ7tpLdtde8t18t1ReKnPzelm5q2XmrpbraGS6SZq7d8jcvUNRn0yTEx2rQN+rqkdzEC4DUB+8AUfT88v1+7xSbQvBndSmIU3oFqMp/eLVv01Uvb8/ANSH1DhTk3rEalKPWDmOo8KSYGji9HiOo966h2db+Ms1+eASPfzNfPWqOBiymo8mtNO+wTer3Q3fVUKrliHbBwDQ+FxSSOIHP/iB7r77bt188836zW9+oyFDhlQ/t2zZMj3xxBMqLi7Wgw8+WG+FAriwqvH3y5Ehz6w/1ft7e8ffL9/4yRf3IsOQk9pF/tQu8o/8bvChY4eDYYnToYm9BRGZcXw2hrdS1qavZG36SpLkmKbsLr2qO00EemZKLZIiWyTQAB2pDGhWYYVmFFZoZYjupsLZlfkdrT/i0/ojPkkV1Y97TKlnolu9TwUoTneeSGthyU14om4cR66CrbKWz5N71UIZZaHvjOQYLgUyBso/dJz8Vw2VoujKBqCBi41XYOAwBQYOqw5IWxuDgQlz2wYZvvAHJo3Kclnrlslat0ySZKd0lj8zW4HMqxXo3Z9jK4CLUuy19cdtZXpzc2lIxgbGWYbu7RWrh/vGq0sCXXAANB6GYSitRfA6w/3pcbIdR1uO+7X4VJeJFQe9Oumrfc23X+lePbx/vu45uEzxdujGNs9rmaXXOlyvf7TuL9vrkjm7XFe28WlEikfDUzzKbudRjMX1EQBozozi4uJL+nbyl7/8pV599VUZhiGPx6N27drp0KFD8nq9chxHjz32mJ5++un6rhdAHVircuR5/yUZZSWX/V5OXIK89/3o/B0kLkd5qcwdm2TuCHabcBVuicjF1LqyUzqfCk1kKdArM3hnGnf3ohkq8dmau7tSHxWU64v9XgUikHXyL7q7TuuskdPqtC4twdS+soCqwt81PGwsQ+qReMbIjlMBih4tLEVzcUCSZBw7JGvFfLmXzZPrwJ6w7Bno0FX+oTfIf+11clq2CcueaJjiJ4+s07rS9xaFtA6gXngrZW77Wmbu6uBojgN7I12RHHeUAr2vODWaI1tOSmc+y4cQxzQ0ZntL/Xp9c6ne31auUn/9n+y0i3HpoT7x+tfecWrpcdX7+yM0OK4Bdee3HW046tOSA14t3VeudptX6MG9n2tk8ZaQ7XnCjNGf2o/QGx2u047Y83cT85hSdtsoDT8VmriqbVSzvKmE4xqA5uySQxKS9Pnnn+vtt9/WunXrdOLECSUmJmrAgAH6wQ9+oOuvv74+6wRwkYzio/L8+eXqURKXwj9wuLz3Pi4nqXU9VnYBviq5CrcFQxPbg7+M8tLw7X+R7KQ2CvTKDI7n6JUpu1Oa5DIjXRYQEt6Ao/n7KvVRQYU+21uhytBPHDiv+gxJ3NwlWn8e3Vp+29GuEr+2Fvu1rdivbcU+bS32a8cJvyoikQQJE5chdUswlZ50qvvEqQBFz0RLce5mcNHWWyFr7VJZy+fJ3LwuLB2OnIRE+a65Tv6h44LzUPmSDuICFZo24/CB6sCEuXmdjMqKC78oxOw2ycHAREa2Av2ukmLiIl1Sk8IxDY1R7jGfXs0t0YzCipAEwXsmWno0I153pMUSUm6EOK4BF8c4eVzW4rlyL/xYrmOHQ7ZPXmxHvdbhek1LHqoyK/qS3iPeMjS4/behiYxWbrmawXk6xzUAzdllhSQANHyuPTvlzvlY7kVz6rTeiYqWf/D18o25VXbnHiGurg5sW679u+Talvttt4ljhyJd1Tk5MXEK9OhX3W3CTutNS180agHb0dKDXn1UUKHZuyt0sip8HxtchmSfZ7v6CkkkRRlaNSFZ7WPPHXCyHUd7SgM1ghPbin3afsKvkrO0j2xKOseb3wYnkiylJ7rVK8lSYlQjD0/YtlzbN8q9bJ6sNYvC8mWdY1oKXDlYviHjFMjKlix3yPdE48IFKjQbfp9cOzd9O5pjz85IVxQctdcjIziaIytbdqfukquR/10XYRzT0Fg4jqNF+716Ja9UX+wPTev3a5Oj9GhGvG7oFN0svnRrqjiuAXXjyt8i94KZslZ/IcMfmo7Bfrn0cZsBeq3DWC1O6lPvNx608rg0LCUYmhiR4lH3FpaMJnj85rgGoDkjJAE0E3X+wPPWZ5Ln0hK34WIcLZK5baPM7bly7ciVua8w0iWdk2Nasrv1PhWayFSgZ4YU3yLSZQHn5TiOvjri04f55Zq1q0JFIZi9ey4d40x9r1uMJqbFaHuxXz9Ycvyca+srJPHOiJa6LS32ouo8zXEc7S+3awQnthX7tbXYp+IwBkoiITXWVR2c6J3kVq9ES72TLLWKbtjddIyifXIvny9r+Ty5jhwMy56Bbr3lHzpOvmtGS/GJYdkTjRMXqNBcGcVHZeatkblxtay8tTLKTka6JNmJLRXIyFYg82r5MwZKCUmRLqnR4ZiGhs5nO5pZWKFX80qVe6z+v8QzJH23S7Qey0jQ1e2i6v39EX4c14DzqPLKWv2F3AtmyizcFrJtDrlb6O2UUXordYz2RYev+3FqrEvDTgUmhqd41DHeCtveocRxDUBz1jSO5ADqTwMPSEiS0zpZ/sHXyz/41Fif0pMyd+bJ3BYcz+Eq3Coj4I9skacYAX+wtp150qd/kRScPW/3ygqGJtKz5LROjnCVQNCW4z7NKKjQR4Xl2lUSvlkarTwuTegWo9u6xeia5KjqO6uyWkdpx0m/nttQErK9n+ifcMkBCUkyDEMd4kx1iDM1usO3jzuOo8OVdq3gxLZivw5Xhi90Ekr7y23tL/fWutuubbSrOjiRnmSpV2JwhEe7GFfk7rooL5W1epHcyz6TuSMvLFvaLdsEOzMNvUFOapew7AkAjZWT1Fr+oTfIP/QGee2AXIXbq0dzuPK3yHDC/3en68RxuZbPk3v5PDmGIbtrugJZ2fJnZge7xZlcTgEaqxKfrfe3l+v1TaXaV1b/5z3RpnRXjzg90i9e3RM5VgBo2owjB+VeOFvuJXNllJwI2T5fJnTXHzqM1UftBqnKFf6ujPvLbf0tv0J/yw92oezewqwezTEsxaM2DfyGEQBAbXX6pN6yZUu5XC59+eWX6tGjh1q2bFmni9yGYejo0aOXXSQAnFd8CwX6D1ag/+Dg76u8chVslbk92G3C3LlJRkVZZGs8g/nNLpnf7JL7i9mSJLtVu2/Hc/TKlN2hK619ETa7S/z6e2GFPioo16bj4QsXxVmGbuoSrYndYjWqg0du19k/VzzRP0GGpN+EICjxRP8E/Uf/hHp/Xyn4GahdjKl2McGT5jMdqwxo2wl/jeDE9mK/vikPXzAllA5X2jp8sErLDlbVeDwpyqgOTnw7usNShzgzNOGJgF9m3leyln8ma91yGb6qC7/mMjlRHvkHDJN/6A0K9L1ScnGRBAAumsuU3b2P7O595Bs/WSo9KWvTV8GxHLlr5Co+EvaSDMeRWbhVZuFWRX38vpzYePn7DVQgK1uBjKvltGob9poAXLyD5QG9ublU72wrC8kYwVYel37QJ04P9o5T2xg+BwJowhxH5uZ1wa4R61eELNDquN3yDxqtqtHjFd2mp64+4FXFAa+WHPDqqDeyN6Dknwwo/2S5/ritXJLUr6WlEanB0MTgZI9aNPYRpQDQDNQpJDF48GAZhqGYmJgavweABinKI7v3FbJ7XyGfJNkBufYWBLtMbM+VuX2jXMUNJ8DlOnZIrlU5cq/KkSQ5sfEK9MxQID1LgZ6ZsrulS25ac6L+HK4IaNauCn1UUKEvD4X+i+PTolzSdR2jdXtajMZ1ilasdeETRsMw9MSVLdQz0dJPVhbXywiLpChDL1ybdFkdJC5Hq2hT10abuja5ZnjiRJWtHSe+DU6cHuGxp7RphCeKqxytOlSlVf/0Zy7BbahXYjA40fuMAEXnePOS5jW79hXIWjZP1or5cp04Vl/ln1cg/Qr5ho6T/+oRUkxcWPYEgGYjvoX8g0bJP2iU5Dhy7Ss8FZhYLXPbxoh0kDPKS+Ves0juNYskSYGO3RTIzA7+6pXJZ3eggdla7NPv80o1Pb9cVSH4Tq1rgqlH+sXr7p6xdTrHAYBGq6Jc7uXz5M6ZJdf+3SHbxm7VTr4xt8o3/CapRZIkqbuk7omWHugdJ9txtPm4X4tPBSZWHPSqxBfZkaebjvu16bhfr20qk2lIV7Vxn+o0Ea3sdlGKsfg+DQAaGqO4uLhpD8wGIIn5YjU4jozDB4JdJk6HJg7siXRV5+S43bK79anuNhHo0VeKC83d72i6TlbZmrunUh8VlGvRfq8CYfrb35A0PMWj29JidEuXGCV5Lv2i4cHygH62qlhzdldKkvyL7q7T66yR06r/+eYu0frtNUlqH9t47uwq8wXDE8HuE77qER6FJQHZTfhTXIxpqGeiVSM4kZ5kqVuCJeufO4+cLJZ71QJZyz6XuXt7WOqz26YGgxFDxsppmxKWPdH08XkNuEiV5TK3bAiO5ti4Wq7D+yNdkZyoaAX6XqlAZnA0h5Pc4cIvaqI4piGSHMfR8qIqvZpbonn7vBd+wSW4qo1bj2Uk6OYu0TLP0RkPTQvHNTRXxv7dci/8WO6ln8moLA/ZPv6+V8l33QQF+l97UaPN/Laj9Ud8WnIqNPHlIa8qG9D9Jh5Tym4bpRGp0RqeEqWr2kTVvq4RIXU9rhW9vVBxboKAAJqWSwpJLF++XAkJCcrKygpFTQBCgBO5CzhZLHNHbnVwwrV7u4xAA/o0fQbHMGR3Sgt2mTjVbYIWvzibSr+jz/dVakZhuebtrQzrCeKANm5NTIvVhG4x9R5IyD3m0ztbSvX2WxPqtL7FdR/ojrQYfb9PvDJbhX9uZahU+h3lnzwVnDgVoNhW7Ff+Sb98ke06GVJRLqlHC0v9EqSbj6/XsB2L1GHHGrns0P8Bd2Li5M8eKd/QcbJ7Zkp0VkM94/MacHmMon2yNp7qMrFlvYyq0HwxejHsdqnyZ2YHR3P0uVLyxES6pLDhmIZICNiO5uyu1Ct5JVp3xBeSPcZ19OjRzAQNSY6i024zw3ENzYodkLlhpdwLZsra9FXItnGiY+QbMk6+MePldOhaL+9Z6Xe0+nBVMDSx36uvjlSF7WahukhwGxqcHKVhKR6NSI1Wv5bWJXXSvBy5x3x6e0up3rmIa2u3p8XoB03s2hqA5u2SQhKtWrXSv/7rv+r5558PRU0AQoATuYvkrZCZv6V6PIe5c5MMb2Wkqzonu22KAj0zg90m0rPkpHTmy7tmym87WnrAq48KKzRnV4VOhrHdYHqipYlpMbotLVZpLeqe+L9UdT2uHXpnYbNqe+uzHRWe9Fd3nNh2IvjPO0/4GtSdFJfEcXR1SYHuObhUkw6tVGt/aei3NFwKZAyUf+g4+a8aKkV5Lvwi4BLxeQ2oR1XeYAj6dGjim12RrkiO5Q5+Xj8VmrA7dGvSn9k5piGcyv22pu0o1x82lWpXSf1/6HW7pDu6x+rRjHj1TuLLoeaK4xqahZJiuZd8KvfCj+U6UhSybeyUTvKNmSDf0HEhH1tZ4rO14mBVdaeJ3GOhCdFdqtYel4aleE6N54hS9xZWyEJ4zbVLKwCczSV9g9G2bVu53ZwQAGjCPDEK9L1Kgb5XySdJAb9ce/KDgYntuXJtz5Xr5PFIV1nNdfiAXIcPyL3ic0mSE98ieAG2ZzA0YXfpKVkct5sqx3G09rBPHxaUa9auCh2qCF8bgY5xZnUwIqNl6E7iLkdzCkhIkttlqFeSW72S3JK+vVs1YDvaUxrQ1lMdJ7aeClBsL/arzN+Abqk4iw6VR3V30XLdU7RUfcvD00o90KGr/ENvkP/a6+S0bBOWPQEA9SjKo0C/gQr0Gyjd+e8yjh4KjuXIWyNz01oZ5WVhL8nw+2RtXidr8zrpb2/IbtmmeixHoN8ARuoBl+BIZUBvbSnT21vKdMxb/+dBLaIMfT89Tv/WN14pfBkEoAlzFW4Ldo34MkeGLzQhAsdwKXDltcGRGn0HhC0smuB2aVynaI3rFC1JOloZ0LKDVVq8Pxia2HnSH5Y6zuWo19asXRWatatCktQh1tSwlNPjOTzqEFc/f//MKCjXT1YWq7jq8q4BzdldqaUHivTCtUm6LS22XmoDgEi4pJDE6NGjtXz5cjmO0yC/DAGAemdasruly+6WLt+42yXHkVH0TXVowtyeK1fRvkhXWc0oPSlr3XJZ65ZLkpwojwLd+8o+HZzo0U+K4UNsY7f5uE8zCsr1UUGFdpeGr0VAa49LE7rF6La0GA1qFxX2loC4NKbLULcWlrq1sPSdzt8+bjuOvikLfBucKPYH//mETycv88T5csQEvBp/ZK3uO7hEY45vkkuhr6UqLlHeQWNkjrghGC7jzzYANBlO63byj/yu/CO/GwxA52+RlXuqy0ThtojU5Dp+RK4ln8q95FM5hkt2977yZ2UrkJktu2svydW8gp7AxSg46dfv80r1wc6ykHRL6xhn6uF+8bqvV6wSmMEOoKny+2StXiT3gpky8zeHbBsnroV8I2+Sb9QtctqmhGyfumodberWrjG6tWvwxpJvygLVXSaW7Pfqm/LItuH8pjygv+ZX6K/5wdBE9xamRqQEAxPDUqLUOvriQhOO4+g3G0r03IaSequxuMrR9xcf1/YTfj3RP4HvCYFG4qabbtLy5ctVXFwc6VIahEsKSfzyl7/U9ddfr8cff1xPP/20WrZsWd91AUDDZhhy2neUv31H+YffGHyo+KhcO/K+7Taxe6cMJ3x39J+PUeWVtWW9tGW9pGBy2+7cQ4H04IgOu2emnKTWEa4SdbG7xK8ZhRX6qKBcm4+HL+kebxm6qUu0bk+L1YhUj9wuTn6aCpdhqFO8pU7xlq7rGF39uOM4Kqqwta3Yd2p0RzBEsf2EX0cqQ3NsMxxbw05s070Hl2ri4S+VEAj9mKMqw9Tc1lfq/fbD9Fmr/vLZlpJXuZS+9ajSkyz1TrLUK9Gt3kmW2kS7OPEHgKbAtGT3ylRVr0zptu/LOHlcZt5amblrZOatiUjHOMOxZe7Mk7kzT/r7u3ISEuXvNzA4miNjIJ/VgVPWHKrSK3kl+mR3ZUgitBmt3HosI14TusVwzgOgyTKOHZZ70RxZi+bIdSJ0n3sCXXrJd/0E+QeNbtCjKzvEmbqzR6zu7BErx3FUcDIYmlh8wKulB7w6GoJORRcj/2RA+SfL9O62YCe0jFZuDU+J0oiUaF2bHKUWUecP89V3QOJMz20okSHpiStbhOT9gcZg48aN+uMf/6gVK1Zo//79qqysVKtWrdS3b19df/31mjRpklq1ahXpMnEWRnFx8UWfU9x88806duyYtmzZoqioKHXp0kVt27atddHYMAzNnj273ooFcOmYmxgBFeUy8zefGs+xUWb+ZhlV3khXdU52cgcFemUFx3T0ypST3JG7qBuIQxUBzSqs0EcFFVp9uCps+0a5pLEdozUxLVbjOkUrxmpYfx44rkXOkcrAtx0nTnWf2H7CpwPll3bhoHv5Qd1TtEz3FC1Tt8rD9Vzt2a1JSNP77Yfpb+2u1TF33dqbt/K4qoMT6UlupScG/zcllvAE6gfHNaABsG259uwMjubYuFqunXky7MgHnwNdelaP5rB79JOsS7rnJaw4pqG+2I6jz/ZW6tW8Uq0sCs350KhUjx7LiNfIVA+f63BOHNfQqDmOXNu+lnvBLFlfLQnZ5xvHtOTPHinfdRNkd+/b6K8t2o6jTcf91Z0mVhz0qsTXcEaWmoZ0VRu3RqREa1iKR4PaRSn6jOt3HxWU6weLzx2E8S+6u077WCOnnff5d0a0ZPQGmh3btvXLX/5Sv//972VZlgYPHqy+ffsqNjZWhw8f1urVq7Vt2zYlJCRow4YNat068sF3OknUdEln1cuWLav+Z6/Xq+3bt2v79u211nFSAaBZi4lVIGOgAhkDg7/3++Xavf3UeI5gtwmj9GRkazyDq+gbuYq+kXvpPyRJdouWwfEcp37ZnXtIZsO/GNtUnKiy9cnuCs0oqNCiA17ZYTr/chnS8BSPJqbF6LudY5TkobUsamsTbapNe1ND2te8E6TYa2v7iW87T5zuQrGvrHaryhb+ct1+aJXuO7hUQ07W/hwZCt9EtdS05KH6c/uh2hLX8aJff8xra2VRVa2L8y3chtJPByeSLPVOcqtXoqVO8SbjaACgsXG5ZHftJbtrL/luvkcqL5W5eZ2s3DUyN34p17FDESnL3L1D5u4divpkmpzoWAX6XlU9msNp0z4iNQGhVul3NL2gXL/PK9X2E/XfRc80pNu6xWhKRryyWkfV+/sDQIPgrZC1Yn5wpMa+wpBtYye1kW/0LfKP/K6cxKZzx7TLMJTZyq3MVm490i9ePtvRhiM+LT4VmvjykFfeCE7nCDjSmsM+rTns0/MbS+QxpUHtPBqe4lFGS0s/XVkcljp+srJYQ9p71D724kaBAI3ZM888o9///ve68sor9e6776pbt2611qxbt06//OUvVVkZ+m65uHiX1EkCQOND2r0BchwZB/YEQxPbNsrckSvX4QORruqcHE+0Aj36KdArKxie6N5H8sREuqwmpdLvaN6+Ss0oKNe8fZVhPcm6uq1bt6XFakLXGCU3khMajmuNR6nP1o4Tfm075pWTu1a9Ni7QoD2rFW37Qr53uStKM9tcranthyqnZYZsI3zBn1jLUK9Eq0ZwoneSW10TTJm0b8ZZcFwDGrhTn9+tjatl5q6WuW2DDF/o/y67EDuls/yZ2QpkZSuQfkWDaWfNMQ2Xqthr652tZXpzS6kOVdT/nc7xlqH70mP1cN94dYrnRgDUHcc1NCbGwX1y58ySe9k/ZJSXhWyfQO8rVHXdBAWuHNooOl3Vt0q/oy8PVWnpAa8WH6jUuiM+BRrRN2711UlCkm7uEq0/j478nfJAOOTn5ys7O1utWrXSqlWrztslwnEc2bYt0/z2mnteXp5+97vfafny5Tp27JiSk5P1ne98R//5n/9ZYzTH7t27dcUVV+jOO+/Uf/zHf+iXv/ylFi9eLJ/Pp6uvvlrPPvusMjMza+25cuVKPfPMM9qwYYM8Ho9GjhypZ555Rg899NBZO0k4jqOpU6dq6tSp2rx5s/x+v9LT0/X9739f9957b421v/71r/Xcc89pzpw52rt3r958801t375dV111lebOnXuJP9HIaH5/awFAQ2EYclK7yJ/aRf6R3w0+dOxwMCxxqtuEa2+BDKdhfLI2vJWyNn0la9NXkiTHNGV36VXdaSLQM1NqkRTZIhshv+1oyQGvPiyo0NzdFToZxpZ9vZMs3Z4Wq9vSYtQ1gY8ECJ0WRbs0aNk8DVm5QK7io2HZc0lib73ffphmtM1WiRWZlo/lfkcbjvq04ahPUkX14x5T6tEiGJg4swNFWoKlKJPwBAA0WKc+v/tSu8h3w+2St1Lm1q9l5q2WlbtargN7I1KW68AeRR3YI33+kRx3lAK9r6gezeGkdG74ba69FYSvIUnaU+rXa5tK9eft5Srz1/95UXKMSz/sG68H0uPomAegabJtmRu/lHvBTFm5q0O2jRMVLf+Q6+UbM0F2p7SQ7dMYRFuGRqR6NCLVo6fUQiergh0oFx+o1JIDVco7FvlAbbjM2V2p3GM+ZbZyR7oUIOQ++OADBQIB3X///Rcco2EYRo2AxKeffqoHHnhApmnqO9/5jjp06KBt27bpf//3f7Vw4ULl5OQoKSmpxnvs2bNHY8aMUXp6uu655x4VFhbq008/1c0336zVq1erXbt21WsXL16siRMnyuVyacKECUpJSdHixYt1ww03KDExsVZ9juPo3/7t3/Thhx+qR48emjhxotxutxYtWqRHH31U27Zt07PPPlvrda+++qqWLl2q73znOxo1apSsRhiUq5eKy8rKVFJSooSEBMXFxV3We+3fv1+zZs3S/PnztWPHDhUVFally5YaNGiQHn/8cQ0cOLDWa06ePKnf/OY3mj17tg4dOqR27drplltu0RNPPKEWLVqcdZ8PP/xQr7/+urZu3Sq3263s7Gw9+eSTuvLKK8+6Pj8/X88884yWLl2qsrIypaWl6f7779cPfvADuVy1T6y8Xq9eeuklTZ8+Xfv27VNSUpLGjh2rp556SsnJyZf1MwLQdDmt2so/aLQ0aHTwgfJSmTs2ydyRK3N7rlwFmxvE3WqSZAQCMgu2yCzYIn02XVLwLrZgaCJLgV6ZctqmNPyLshHgOI5WH6rSR4UVmlVYocOV4Ztz3Sne1MRuMbotLVb9WlqMxkLonCyWe1WOrGXzZO4OzziN/Oh2mtp+qKYmD1NhTLsLvyBCvAFp03G/Nh2v2TbaMqTuLazq4ETvJEu9ktzq2cKqMVMUANBAeKIVuGKQAlcMUpUk4/ABmbnBwIS5eZ2MyooLvkV9M3xVsnLXyMpdI4/+ILtNcnVgItD3Kinm8q7Z1IVrz065F8yq8/q4KRPkv/Y6+a4bHxzxh2bn66NVejWvVDMLK0Jy9216oqUpGfG6o3usPARSATRFZSVyL/lU7pyP5Tq8P2Tb2Mkd5BszXr6hN0hxCSHbpzFrEeXSuE7RGtcpWpJ0pDKgZQeqtORUp4n8kxGczREG72wp1UtDWka6DCDkVq8OBtGGDRt2Ua87duyYfvjDH6pNmzb67LPP1KlTp+rnPvroI/3gBz/Q//zP/+i3v/1tjdctX75c//Vf/6Uf/ehH1Y89++yzev755zVt2jT9+Mc/liTZtq3HH39cfr9fn376qa699lpJNYMQ/+z999/Xhx9+qHvvvVcvvvhiddihqqpK9913n37/+99r4sSJ6t+/f62aFixYoH79+l3Uz6AhueRxG16vV6+++qr+8pe/qLDw21lW3bp101133aUpU6bI47n4Fo//9V//pZdeekndunXTkCFD1LZtW+Xn52vu3LlyHEfvvPOOJkyYUL2+rKxMN9xwg3JzczVq1ChdccUVysvL04IFC5SZmanPPvusVnDjhRde0DPPPKOOHTvq1ltvVVlZmf7+97+rsrJSM2bMqPWHeuvWrRo7dqwqKiqqUzfz58/X5s2bNXnyZL388ss11tu2rdtvv105OTkaOHCghg4dqsLCQs2ZM0epqanKyckhKIGwoyVgE+GrkmvXdpnbNwbHdGzPlVFeGumqzslOaqNAr8zgeI5emcF0uatxjHIIhU3H/n/27js+qjrt///rnDOTKamUkARIISR0kCYCQaRjBwR7w3vXXV3bdnXX/e3X1V3Xdd3bsq67a7nt7irYsKH0LqCgCSW9AKFD6iSTmXPO749JBmIChJCZSbmej0ceiWc+cz6fxHAyc877XJeHRQUuFhfWUFIVvDdFPe0q81IcLEh1MK5XWKcJRshxrR3yetC2b8S6binad5tQ9MD/nldZHCzqNZ7/63Uh66MHdMpglqpASoTmD040VJ5Ij7YQYZW7IDsTOa4J0Yl4PWi5WWiZW3ytOUryQr0iXyW49GG+wMTwcRiJ/aGZmz5aSyk7iu31p7FsXdPqfXjHTsZ9832YMVKqubMzTZMVpW6eyaxi9X53QOaYGBfGvcMjmNXXjtoJXyOK4JPXaqK9aQgmWjZ+iVIXmGOpqSjoIy7AM2Me+rDz2/S1Q1e0t8rLmv1u/0epK3g3TjWnLdttgK/taN718Tgt8nsiOrcLLriA7OxstmzZQnp6eqPHVq9ezYYNGxptmzJlChMmTOC5557jt7/9Lf/617+49tprm+z3oosuYu/eveTn5wMn2m0kJyezbdu2RjftNzx2xRVX8PrrrwO+4MJll13GxRdfzH/+859G+y4pKWHUqFHout6o3UZGRgZFRUXk5+djt9sbPWfHjh1kZGRw9913+6tJNLTb+MlPfsKf/vSns/zJtS+tqiRRVlbGlVdeSVZWFhaLhfT0dGJjYzl8+DCFhYX88Y9/5KOPPuLDDz9sUhLkTEaPHs2nn37KxIkTG23fsGEDc+bM4ec//zmXXnqpP4Dx9NNPk5mZyX333cfDDz/sH/+nP/2Jv/zlLzz99NP85je/8W/Pz8/nscceIy0tjeXLl/tLi/z4xz9m+vTp3HvvvWzZsqVRWZCf//znVFRU8M477zBr1iwAHnroIRYsWMCrr77K/PnzmTx5sn/8W2+9xfLly5k/fz4vvvii/2LUG2+8wd13383vf/97/vnPf57Vz0UIIQCwhmGkD8NIH4bnMsAwUEuLULMzfdUmsr9DPXYo1Kv0U8uOoG5eCZtXAmA6wtHThvqrTRipg9pNz+RAKar0srighkUFLnaVec/8hDYSaVW4PNkXjLgowYZFlZOCIkBME7UwG8u6z7FuWoFSXRH4KRUVfdhYvBmzYcwkrrKGcX6VTnaZl+wyD7vLfZ+zy7xUBrGFTaAYJhRU6hRU6nz2vUruiREag6JPBCcGxlgYEG2VEtJCCBFqFiv64FHog0fBNT9CKTuKlrUF7bvNWLK2BuXv5fcpuu5rD7L7W3j3BYzobujDfIEJ77AxEBnT6n1bNi3H9tpTKNWV57RGy9Y1aLu24b7lp3jHTz+nfYn2yWOYLC6o4dmsyiZVtdqCqvh6ot87LJIxsWFtvn8hhAg5rxfL12uwLnsfLSczYNOYzgg8ky/FM20OZlyfgM3T1fSNsHBDuoUb0sMxTZP8Ci9r9vvac6zdX8cxd2hDE+fK5fVVz53S237mwUJ0YOZpWqSvXbuWv/71r4222e12JkyYwNatWwHYunUrBQUFTZ7rdrs5evQoR48ebdTGY9iwYU26GvTp4zs2l5eX+7dlZWUBNLnGDpCUlESfPn0oKSnxb3O5XOzcuZOEhAT+93//t8lzvF7f6/Xc3Nwmj40ZM6bJto6mVSGJRx55hMzMTG688UYeeugh4uPj/Y8dOHCARx55hLfeestf6uNsXHnllc1unzhxIhdeeCErVqxg586djBo1CtM0ef3114mIiODXv/51o/E///nP+fe//80bb7zBgw8+6A8qvPnmm3i9Xn7xi1806r0yePBgrrvuOl5++WXWrFnDtGm+Uvd5eXls2LCBCy+80B+QALBarfzud79j9erVvPbaa41CEq+99hoAv//97xvdrXvTTTfx7LPP8v777/PEE08QGSklqYQQ50hVMfqmYvRNxTt9DgDK0YO+KhPZ36HmZqLtLTzDToJHqanGUl+CGMDULBj9BtWHJoajpw+DiObbJHUkh2p03i/0BSO2HA5eexSbBrP62lmQ6mRWXzsOKc8vAkg5dhjLxi+xrluKWloclDn1Pil4J12Md8IMzG49/dtVIDnSQnKkhVmJJ96Im6bJfpfhC07UByiyy73sLvNw3N3xwxMAe6p09lTpfLmv8V1DCU7VF5yItjAoxsqAGAuDYiz0sHfdaj5CCBFKZkwP39+wSRfjNnTUwmxfYCJzM2rBbhQz+CfE1fLjqOuXYl2/FFNRfK/Lh4/DO/x8X5hZa8EpI9Mk7INXCPvg1TZbl1Jdif35R6jbX0Ld3IWdskpUV1RRZ/BqTjX/3FHNPlfbVxtzaAo3pju5a2gE/aI6Xj9kIYQ4E6XsKJaVS7CuWoJadjRg8+iJ/fHM8LXCwiYXugNJURTSoq2kRVv5n0HhGKbJjuNeVpfWsna/m/UH6qjydrxzF9uPeCQkITq92NhYcnJyKC0tbVJJ4qGHHuKhhx4CfNek77rrLv9jx48fB+CFF1447f6rq6sbhSSioppeM2m42V8/qZJvRYUvjN+zZ88m4wF69erVKCRRVlaGaZqUlpby+OOPn3Y93xcbG3va76EjaNW7ho8//pgLLriAv//9700ei4+P57nnniM3N5clS5acdUjidKxWKwCa5ju5m5+fz/79+5k+fXqTlhp2u52JEyfy6aefUlBQQP/+/QFYt24dgD8EcbJp06bx8ssvs379ev/jpxs/ZswYoqOjWb9+vX9bbW0tW7duJT09naSkpCbPmTp1Kv/85z/ZunUrU6dOPeufgRBCnInZIw7vhDjfmxmAqgq0vKz64EQmauFuFD14FQ1OR9G9vrXlZcGnbwO+i6DGgBG+0MTAEZg9OkZ7ovI6gyXFNSwuqGH1fjdGkN7DqApclGBjQaqDy5MdRIfJ3eMigNy1WL5ei2XdUrSdX6OcJjXdVsyIKDwTZuDNmI2RcnbtNBRFoXe4Ru9wjakn3fhimiZHao0TwYkyX3Aiu9zLoZqOfddGg/0ug/0uN6tKG4cnetpVBsb4ghMDoy311SesxDnUTtOKRwgh2j1Vw+g/BKP/EDzzFkJVBZYdX/vacmRuDuiFj1NRTBOtYBdawS7CPnwV0xmBd+hY9BHj0Iedj9m9+RNgbR2QaLzvVzFRfD8j0WGVVuv8c2cVr2RXUxGACl89bCq3Dw7n9sHhEgYVQnQ+pomam4V12ftYtq4OWEtLU9Pwjp2MZ8Y8jPThElAMEVVRGN7dyvDuVu4eFonHMNl2pM5XaaK0ls2H63AHr3tvq313LHg3jAkRKhdccAHr169n7dq1XHTRRS1+XsPN8xs2bGDIkCFtvq6GMMWRI0eaffzQocZVyBvWM3LkSFatWnVWc3WG84itCklUVlaSkZFx2jGTJk3yl/VoC3v27GHVqlXExcUxdOhQAH9PltTU1Gaf0xCMyM/Pb/R1REQEcXFNL7qdPKbB6eZQFIXU1FS2bduGy+XC6XRSWFiIYRgtWtOZQhK1tbWnfVyIsxHRwnHye9cJWcJg0GjfB0CdG0tRDta8HVjzsrDk70StdYV2jSfR9hWh7SvCuvIjAPRusXjShuJNH4YnbSh6QnK76X9Y4zVZVlrH+8V1LC+tI5gV8cb2tDA3OYw5iTZiHfU/D6OOrvRPWI5rQWIYWPKysG9aRtjX64JyvDA1C3XDx+GeMJO6YWPB4gvK4m67HquRCpzfDc7vZsH3kth3l8Nxt0FuhU5OuU7OSZ9D3Se0rRypNThyoI71B+oabY+2KgyI1nwfURrpUb6v+zglPBFMclwToouyhMF5E3wfpom2r5CwHV9j3bEVa96OkAScFVcV1i2rsG5ZBYC3Twp1Q8fiGTIGT9pQsIYRtnllwAISDWwfvIK7Zzx1508J6Dyi7e0q8/L87lreL3bjCcDLqJQIlTsGObimnw2nRQE81NbKRRkRWPJaTQRNXS22zatwrFqCZU/+mce3khHVjdrJl1J74aUYMfV3LLfh+25x7kZEwYgoK3cPtFLjNdl6xMu6gx7WHfSw/ZgXvR0Wmthf7ZHjoGgRu73jVhy5/vrreeqpp3j11Ve58847G1V9OJ2xY8eyZMkStmzZEpCQxLBhwwBfCOPee+9t9FhJSQn79u1rtC0yMpKBAweSk5NDWVkZMTExbb6m9qxVIYlBgwaxd+/e047Zu3cvgwYNatWivs/j8fDjH/8Yt9vNww8/7K8k0VA25OS2GSdrSMA0jGv4+lQlQE41vqVzOJ3OVq3pVEpLSxuVSRHiXDRfXKepPXv2nHmQ6PgcMTA8w/dhGDgO7SV8Tx4Re3KJKMnFWlV+xl0Ei3b8MNqWVdBwgtbupLpvf6qS0qlOTMeVkIzZcAE1CLwmbC5T+eKwhVVHNar14F087O80mB3rZVasTh+7711Q7RHoqv9q5bgWWGHHDtE9cyPdv9uIrTw4d7RWJyRzbMREjg89H91Z35Zs/4GgzH2yBCDBBhfFAvUvG6u8UFyjUuBSKHSpFLp8X+93K5h0/BBBucdkyxEvW440vhDn1ExSHAb9nCapToMUh+9zgt1E6/jfdrsjxzUhhI8VhoyHIeNR62qJKMomKj+LqPwsbGXN3xEUaJZ9RVj2FcEXi9CtYVT37U/43sBdtDmZ841nKA7vgTcyJijzidYzTfi6XOX1fVY2HA9MVYdhkTo39/FyUQ8dTani6H4Ifu0V0VXJazURaGHHD9Pz61X02L4OSwBvUKjqm8bh86dSPmg0pmaBSpfvQ7R7KUBKd7ipu+88xbYKlS1lGlvLNHJd7eOmsqoatxwHRYt8v01FR5Kens5dd93Fs88+y4IFC3j55Zfp169fk3Hl5Y2vs9x444389a9/5ZFHHmHcuHEMHjy40eMul4sdO3Zw/vnnt2pdEyZMIDk5maVLl7Jx40YmTJgA+KrqPvLII81ec/7xj3/Mz3/+c+677z7+8Y9/NOncUFRUhKIoJCcnt2pN7VmrQhI/+9nPuP3227npppu48MILmzy+evVqPvzwQ15++eVzXqBhGNx1111s2LCBW2+9leuuu+6c99lR9O7dO9RLEF1QYmJiqJcgQiE5Gc7PQAfKTRP16EGsuVlY83ZgycvCcqD9vLC11LqIzsskOi8TANNixZsyEE/aUDzpw/CmDsZ0tvT+jpYxTF9S+71iN0tK6jjqDl5MOzFcZV6yjXnJYQyOkd66rSHHtZZTaqoJ27oG+6ZlWPN2BGVOPaYH7gum4x4/Hb13Mg7AEZSZz97gZra5vCb5FSdXnfCSU65TVGW0yzs6zpZLV9hZpbGzqvF2uwZpkVrj6hPRFlIiVKyqpCcCTY5rQnQx/dNh+uVUmibVh0oJ27GVsB1bsWZ/h+IJ/t2emqeOqMJdQZvPUutiwJoPqLzjd0GbU5wdr2Hy8Z46nt9dw7fHAnOzz+w+Vn4y2MG4nhapciXaPXmtJs6KYWDd9Q32VUsIy9wcsLaWpjUM97ip1Ey5Aj0pjUggMiAziWAaDNxQ//WRWoP1hzz8f19Xc7A2dCckIhw2EhN7hWx+IYLl//2//4fH4+Gf//wnY8eOJSMjg6FDh+J0Ojl8+DCZmZls27aNqKgof4eEnj178uKLL7Jw4UImTZrEjBkzSE9Px+12U1JSwoYNGxg3bhyLFy9u1ZpUVeXpp5/m6quvZu7cucybN4+EhATWrFnDwYMHGTp0KDt2ND7ne9ttt7FlyxbefvttvvrqKy666CISEhI4dOgQubm5bN26lRdffFFCEg2qqqq46KKLmDNnDtOmTWP8+PHExsZy+PBhNm7cyMqVK5k1axYVFRW8/fbbjZ57/fXXt3ge0zS59957eeedd7jmmmv43//930aPN/RW+X4Sp0FlZWWjcQ1fn6qKw6nGt2SOhgoRrVnTqXTkUjOi45LfOwFA3xTom4J36uV4ASrK0HIz0XIy0XIzUYtyAtYH8WwpXg/WvCyseVnw+X8xFQUjMRU9fTjGwBHo6cNP2UP5dEzTJOu4l8UFLhYV1LC3Onjfb6xdZW4/B1enOjg/NkxOAp4jOa6dgaGjZW3Fsm4plm/WoXjqzvycc2SG2fCOuRBvxmz0oaNB1bACwasJ03bsQPcIOP972Va3bpJf4SW7zMPuMi/ZZb6v8yq8ASk5HWy1OmSV6WSVNT42WlVIi7IwMMbKwBgLA6N9X6dFW7BJ6Yk2I8c1Ibqw5P6Q3J+6S6+lrs7te43+3Wa0zM1o+4pCvbqAsW1bj35oL0ZSWqiXIk5S7TF4I9fFP3ZUUVzV9u+XwlS4Ls3J3UMjGBDTEV8piq5KXquJFnFVYV33OdZlH6AePH3V7nNh9IzHM30unsmXQER0h33vLc6srx2ujYGlpTrvFdaEbB0J4VY5DoouQdM0/vznP3Pdddfx8ssvs2HDBr7++mvq6uro1q0bQ4YM4Y9//CPXXXddo3Ycs2fPZs2aNTzzzDOsWrWKlStX4nQ66d27NzfccAPXXnvtOa1rypQpfPjhhzz66KN8+OGH2O12LrroIl555RXuuOOOJuMVReH5559n1qxZvPrqqyxdupTq6mpiY2NJTU3lkUceYcqUKee0pvaqVSGJn/zkJyiKgmmaLF++nOXLlzcZs3TpUr744gv/f5umiaIoLQ5JGIbBPffcw5tvvsmCBQt4/vnnUb/Xg75///4AFBQUNLuP/Pz8RuMavt68eTMHDx4kLi6uReNPNYdpmhQUFJCQkOAvP9KvXz9UVT2rNQkhRLsXFYM+5kL0MfXVg9w1aAW7UbO/8wUn8rJQ3O2j15ximmgl+Wgl+bD8AwCM2AT09OHoA4ajDxyBmZAEpwgeFFV6WVRQw6ICF7vLgtf/OcqqcHmygwWpDiYn2LDIndgiwNS9BVjWf4Flw5eoZcEpUqwPPA/PpNl4z78IHOFnfkIHZtMUhnSzMqRb49NPHsOkqNLbKDixu8xLbrmH2vaRPTsnHsPXg3zX946fmgL9Ii0MjLEwKMYXnBgQbWFAjAWnpX2UBBVCiA4nzIY+dCz60LFw/U9Qjh5Cy9yMJXMz2s6vUVzVoV5hm7Iu/xD3bb8I9TIEcLhG51+7qnlpdxXHA1BlLzpM4YeDwvnR4AjinIFp2yGEEKGi7i3AuuwDLBu+COi5NO/w8/HMmIc+4gJQ5VjalQzvbg1pSGJEd4nhiK5l5MiRPPPMM2f1nPT0dJ599tkzjktOTqasrOyUj5/qsYkTJ/Lpp5822f7JJ5+ccl/z5s1j3rx5Z1zTgw8+yIMPPnjGcR1Bq0ISzz33XFuvo5GTAxJXXXUV//rXv9C0pn/I+/fvT0JCAl999RXV1dWN+qTU1tayYcMGEhISSE1N9W/PyMhg8+bNrFixoklgY8WKFf4xDSZNmuR/7Gc/+1mj8V9//TXl5eXMnDnTv81utzNmzBi2bNlCSUkJSUlJjZ6zcuVKbDYbY8aMOdsfixBCtB82B/rgUeiDR+EB0L2oJfloOb7QhJqTiVpxPNSr9FMP70c9vB/rBl94z4yI8gUm0n2hidLYVN7f42VxoYuthz1BW5dNg9l97SxIdTKrrx27RYIRIsAqyrBuWo5l3VK04pygTGnE9saTMQtvxizMXtJKzKoqpEdbSY+2csVJVep0w2RPtc7uMg/ZZQ0hCg85ZV6qvB2/b4duQl6Fl7wKL5+UnNiuAEkRmj84MfCkAEVUmIQnhBDibJg9euGdcjneKZf7Xp/n7/IFJjI3oxVmh3p558yy4UvcN9wFNrkzMVTyyj38PauKt/NduAMQ7kyM0PjJkAhuHuAkwiqvA4QQnYjuRftmPdZl72PZvT1g05iOcDwXXoxn+lzMeGn70lWN6hnakMLIEM8vhBAt1aqQxA033HDmQa1kGAZ33303b731FnPnzuXf//53swEJ8JUAufnmm/nLX/7CX/7yFx5++GH/Y3/7298oKyvjRz/6UaMy5TfeeCPPPvssTz75JJdeeinR0dEA7Nq1i//85z/069ePyZMn+8enpaUxceJE1q5dyxdffMGsWbMA8Hg8PProowDccsstjdZ16623smXLFh5++GFefPFF//xvvPEG2dnZXHfddS1qtyGEEB2GZsHoNxCj30A8s68G00Q5uM9XZaIhOBHA0oFnS6mqwPLNeizfrAegtxrG2Kg03NED6RY9gI1R6VRZHAGZW1NgSm8b8/s5uCzZQbRcBBSB5vWgbd+Edf3naN9uCkqrHNMRjnfcFDwZszEGDD9l5RZxgqYqpERaSIm0cPFJ57JM02RftU52+YnghC9E4aG8ruOHJ0yguEqnuEpn6V53o8f6OLX60ISFQTFWBtR/7maT42a1xyBcLh4JIU5Hs2AMGE7dgOEw/wcoFcfRsrb6AhOZW1Ary0K9wrOm1NWi5WX5KmeIoPrqoJtnsqr4tKSWQLz6GNHdyr3DI5ib4pCKekKITkWpOI5l1cdYV36EeuxwwObR+6TgmTEP78SZYHcGbB7RMZwfG4bTouAKwQ0XTovCuF5hQZ9XCCFaQykrK2tXZ1cfe+wxHn/8cSIiIrjjjjuaDUhcdtlljBgxAoDq6mouvvhiMjMzmTp1KiNHjiQrK4svv/yS4cOH8/nnnzeqMAHw17/+lUcffZS+ffsyZ84cXC4XixcvpqamhsWLFzcKSQDs3r2bWbNmUVtby9y5c0lISGDZsmXs2LGDW265pUkZFV3Xueaaa1i+fDljx45l0qRJFBUV8dFHH9G7d2+WL1/epNWHEIEWceuUFo2renVVQNchui6l7ChqbtaJ0ERxHopphHpZzdJR2B6RzLroQayPHsD66IEctMWc0z4v6BXGglQHc1McxDqkzGFbkOPaaZgmamE2lnWfY920AqW6IvBTKir6sDF4My7GO2YShNkCPmdXZpomh2qME8GJcq+/CsWR2vZ5bG0rvRwqA6N9gYmBMRYGxFgZFGMh1q42Ckd3JJnHPLy4q4qX/n3msoYAUTPe4upUBz8cHMFwKWUqhDhbhoFakudrzfHdZtS8LBSjY/ztcF/zIzyXBe7GHXGCYZp8WlLLs1lVfHWoLiBzTO9j495hEUxOsHXYv+Gia5H3oKKl1PydvqoRm1eheANTsdRUVfTRk3wtNQaNlJsTRCP3rT/OqzmuM47zrrqxRfuzTHmzReMWDnDyVEa3Fo0VQohQa3chiTvvvJO33377tGOee+45brzxxMG7vLycxx9/nI8++oiDBw8SFxfHlVdeyf333++vFPF977zzDs8//zy7d+/GarUybtw4fvOb3zB69Ohmx+fl5fHII4+wdu1aqqurSU1NZeHChdx+++2oatO7uNxuN0899RT//e9/2bt3LzExMcyaNYuHHnqI+Pj4s/iJCNE25I2caHdqXGj5O+vbc3yHlr8Tpc595ueFSK4jrlFoItcRf8Y3oEO6Wbg61clV/RwkR7aqeJM4DTmuNaUcO4xl45dY1y1FLS0Oypx6nxS8ky7GO2EGZreeQZlTnN7RWp3sMq+/4kR2uZecMg+lro5xAay1utkUX3AiunHrjt7O9hueOODS+dWmMpYU+3oRt+YE1RXJdp4YH0O89GwXQrSWqwpt5zdYvvO15lCPHQr1ik7JiE2g7pJrMZLTMRL7S+uNAKj1mvwn38Xfs6rIq/C2+f4tCsxPdXDPsEiGSdBPdDDyHlScVp0by+aVWJe9H9A2V0ZkDN4pl+OZeiVmj14Bm0d0bJnHPFz44Zlf07V1SGLtnF4S5BdCdBjtLiQhhAgMeSMn2j2vF7U4p75FRyZabiZKZXmoV3VKB61RrI8eyLrogayPHsj2iGR0VSM5QmNBqoP5qU6GdJM3BYEkx7V67losX6/Fsv4LtB1fB6VCixkRhWfCDLwZszFSBsgdKx1EeZ1BTtmJihPZZR52l3vZUxX4FiyhFGlV/IGJQdG+yhMDYywkRWioIfzdXVzg4hcbyyg7qW1Ka09QxYQpPDkhhvmpUlpXCHGOTBOltBhLfVsOLXs7iicwd8CeK1NRMRMS0ZPTMZLSMFIGoCelQYS0N22NY7U6L+2u5t+7qjkcgKpUkVaFWweEc8eQcPpGSIBcdEzyHlQ0RzlyAOuKj7Cu+SSg57H0/oPxTJ+Hd9wUsEo7A3FmN6846g/kn0pbhiSuSLbz+rQeLdqfEEK0B/KuRAghRPtgsWD0H4LRfwieS671naDdX3IiNJHzHerh/aFepV+cp4KrjmzhqiNbAHBb7FT3G0z4sPMwHCPQnYMBCUmIADEM1JxMrOuX+sp31p65hOK5MjUL+sgJeDJmoZ83Hizy+93RRIepnN8rjPO/1x+0ymOQV+71t+5o+FxUpWN0gjh1pcdk62EPWw83vsjn0BQGxFgYGONr3TEg2sKgGAspkZaA9kM3TZM/b6/k8e2VbbbPsjqTH6w+Tk65lwdGRrbbyhlCiA5AUTD7pODpk4Ln4mvAXYu2+1tfa47MzagH9oR6hX6KaaCUFvuqZ21c5t9u9IzDSEr3hSdS0jGS0n3VruTY2KyiSi//2FHFG7mugPQuT3Cq3DEkgoUDw4kOa1qJVQghOiTTRNv5ja9qxLYNAbtZwbRa8V4wDc/0eRipgwIyh+i8nhgfw9r9BxsF8wMlJkzhifExAZ9HCCHakoQkhBBCtE+Kgtk7GW/vZLxTLvdtOnYYLTcTtSE0sacAxWwfV/Bs3lpsudsgdxsApqZhJA9AHzDc95E+HKJiQrtI0eEpB/dhXf8Flg1fBC00pPcbiDdjNp7x0yAyJihziuCKsKqM7BnGyJ6NwxO1XpO8isbBiewyL/kVXgJwDSXoanSTb496+PaoB6jxbw9TIS3aF5wYGGNhYLTvc/8oC2HauV9ga+uAxMke316JAjwwSu6iFkK0EZsd/bwL0M+7gDrA8fAdaAW7Q72q01KPHEQ9chDLN+v824zIGF+LjuQ0jOR09OQBmL16QzPtU7uK7UfqeCarig+KagISihwcY+HuYRFcnepsk7+fQgjRLtS4sK5finX5BwFtcWl074Vn+hw8ky+Tc0mi1eKdGk9OiOEHq48HfK4nJ0gLSNFxVXsMthyuY9sRD5nHPBxw6XgME6uqEO/UGN7dyqieVs6PDSPc2nXfP3RGEpIQQgjRYZjdY/FeMI0Dwy/ivcIaPss+grNgJ5PKs8koz2ZcRT52s32UA1Z0Ha1gF1rBLvj8HQCMhKT60MQI9AHDMWMT5I42cWauKiybV2FdvxQtJzMoUxoxPfFOnIln0mzMPilBmVO0P3aLwrDu1ib9wj2GSUGFt1FwYneZh7wKL+5O0LmjzoCdx73sPN64D7umQP8oi791x8Bo39fp0VYclpYdyxcVuAIWkGjw5+2VpEdbpPWGECIgjNiEdh+SaI5aWYaatQWytvi3mXYnRlL/+nYd9VUneqeApfOeKjNNk2X73DyTWcnaA3UBmWNSfBj3DotkZl+bVDYSQnQaSmkx1uUfYF23NKCVHL1DRuOZMQ995ATQOu/fIxE881Od5JR7A/o+9IGRkfL+U3RImcc8vLirincLak5bUe29Qt+NNU6LwtWpDn44OILh3aXCbmfQqr+069evJykpicTExFOO2bdvH0VFRWRkZLR6cUIIIUSDMrfBR8U1LCqoYe1+N76XLVbocR5Le5wHQJjhYUxloT80MbE8h+7e6lAuuxF1fwnq/hKsqz8BfBei9QHDMeqrTRiJqaBK6loAho6WtRXLuqVYvlmH4gnMSeyTmWE2vGMuxJsxG33oaPldFKdkVRVfSCDGCjj823XDpLhKZ/dJwYnsMi855d6AlO8ONt2EnHLf93NyX1cFSInUGBhjZVCMpb5th5UBMRYiTrrD4IBL55cby4Ky1l9sLCMj3iZ38ggh2pyRlAZfrQz1MtqEUuvyt/ZrYFqsGH36nag4kTLA9xrd5jjNntq/Ot1kUYGLv2dVsbPMe+YnnCVVgTnJDu4dHsGo71WmEkKIDsvQ0bZvxLrsfSw7vg7YNKbdgSdjNp7pc+UmBREQD4yMRMEXqA/Evu8fGdnm+xUikA64dH61qazRuZ2WcHlNXs1x8WqOiyuS7TwxXiqodHStCklcccUV3H///dx///2nHPPuu+/yhz/8gWPHjrV6cUIIIbo2l9fg85Ja3i2oYdm+WjxnaPFYp1rZGD2AjdEDeIIrUEyDIdX7yCjP9gcnkt1Hg7P4FlDLjqBuXgmbfSeaTUc4etpQf4sOI3UwhNlCvEoRTOreQizrl2LZ8CVqWXB+V/WB5+HJmIV33BRwhAdlTtE5aapCapSF1CgLlyad2G6YJnur9UbBiYYKFBWejh+eMIHCSp3CSp3P9zR+rG+4xqD6yhPrDriD0gsWoKzO5Febynh9Wo+gzCeE6DqMfgNDvYSAUrwetOIctOIc/zZTUTDjE30VJ+o/9OR0iGj/rY3K6wxeya7mnzur2O86w5upVnBaFG5Md3LX0AhSIuWOZyFEJ1FZhnXNp1hXfIh65GDApjESEvFMn4dn0mx5Ly4CSlEUHhgVRXq0hV9sLGuT96UxYQpPToiRChKiw1lc4GqTfwdLimtZu/+g/Dvo4Fr1DsZsQf93wzCkrJ4QQoiz5jFMVuxzs7jAxScltVSfw93HpqKyIyKRHRGJ/LvPDAASa4+QUZ5NRnkOk8p3M7x6b1st/ZwpNdVYMjdjydwMgKlZMPoN9Lfn0NOHdYiTseIsVZZh3bQCy7rP0Ypyzjy+DRixvX3BiIxZvn7cQgSQqigkRVhIirAws6/dv900TQ7UGGSXefytO3yfvRxzt/2FnFDYW62zt1pn2T530OdeUlxL5jGPlIAUQrQpPW0oZpgdpe7s7rrqyBTTRKmvCMem5f7tRo84jOQ09KR0jJQBGMlpmN1i20U7vX3VOv/cWcUr2dVUBiCQGGtX+dHgcH4wKJzudrl7TgjROaiF2b6qEV8tR/EEppWrqajooyb4WmoMGdMu/maIrmN+qpOMeFur7qA/mdxBLzoi0zT58/bKNm09U1Zn8oPVx8kp9/oqtsgxvcMJWMw7Pz+fqCi5kCOEEOLMDNNk48E6FhfU8EFRTUAvju2x9+Q/9p78J87XDuo8Ww33hBUz25VDr+IdqIW7UfS2L0HbGoruRcvbgZa3Az59GwC9TwpGQ2hi4AjMHnEhXqVoFa8HbfsmrOs/R/t2E4quB3xK0+7EO24KnkkXYwwYLidjRMgpikKCUyPBqTHle1mdI7V6k+BETpmHAzWdIzwRLC/tquKpjG6hXoYQojOxOfBOmIF19cehXknIqUcPoh49iOWb9f5tZmS0LzSRnO4LUKQMwOzVB1T1NHtqO1nHPDybVcnighoC0ekqLcrC3cMiuLa/E4dFXksKIToBTx2WLauxLnsfLX9nwKYxw6PwTLkMz9QrMWMTAjaPEGcS79R4fVoPMo95eGlXFaxq2fOcFoVrUh38YHCEBPFFh9TWAYmTPb69EgV4YJRcE+9oWhySuOuuuxr99yeffEJJSUmTcbquU1payoYNG5g+ffq5r1AIIUSnZJom3x3zsKighvcKatjnCvxF4gZxDpV5/RxcnepkdE8ripIGTKcGoM6NWrAbLTcTLfs7tLwdKDXVQVvbmWj7itD2FWFd+REARvde9e05RmAMGI7RJyVoJ2HFWTJN1KJsLOuWYt20HKWqIvBTKir6sDF4My7GOzoDbPYzP0mIdqCnXWNSvMak+MYth8rcvsoT2eUnt+7wsrc6eH9DAsky5c023d87BTX86YJonBb5uyCEaDueGXMlJHEKSmU5lh1bYcdW/zbT7sBI7N+oXYfRJwUsbXOBwTRN1ux380xWFcsDVLnogl5h3DMsgkuT7KgStBVCdALKsUNYVy7Bsupj1IrjAZtHTx6AZ+Y8vBdMk3aqol0Z3t3qC9T/u2Xj866Pl/eVosNaVOAKWECiwZ+3V5IebZHWGx1Mi0MSb731lv9rRVHIzMwkMzOz2bGKojB69Ggee+yxc1+hEEKITiW/3MuiQheLCmrILQ9exYaoMIU5yQ4WpDqYFG9DU09xci/MhjHoPIxB5+G5AjB01L2FaNnfoeZmomVnopYdCdq6z0Q9dgh103Ks9aV/TWcEevow9IEj0NOH+/pGW8NCvMquTTl2GMvGL7Gs+wKttCgoc+q9U/BOmo134kzMbj2DMqcQwRBjU7kgzsYFcY1PMFZ6DHLLTgQndpf7Kk8UVeoE4EbaDsPlNdlwoI4ZfSUgJYRoO0ZSGt6xk7FsXRO0Ob2DR+EdPx21JA+tOBe1JL/DtPxQamvQcrPQcrP820zNgtG3ny8wkeSrOGEkpoK95SdVvYbJB0U1PJtVxbdH274svAJcmmTn3mERTf7uCiFEh2SaqNnfErbsfbSv16IYgalSZ2oWXxXHGfMw+g+RKo6iU5CAhOioDrh0frmxLChz/WJjGRnxNmlF00Jr167liiuu4P777+fBBx8MyRpaHJL49ttvAV9CfeTIkdx5553ccccdTcZpmkZMTAzh4eFtt0ohhBAdWmm1znuFLhYX1rDtSGD6OjbHrsElib5gxIy+dmxaK96YqhpGUhpGUhrMvApME+XIAV+ViZxMtNxM1NLitl98KymuKizfbsLy7SYATKsVo99gf7UJPW0IhEeGeJVdgLsWy9drsaz/Am3H1yhm4FsEmBFReCbMwJsxGyNlgJyIEV1KpFVldGwYo2Mbh8JqvCa55ScqTuyur0JRUOFF7yLpiau/PEpShEZKpIXkSN/nlAiN5EgLKZEa3W2q9M0UQpw19833oe3ahlId2DuyAMzwSNx3PIQZ0+PERkNHObDXF5io/9CK81CqA1+pqy0ouhetOBetONe/zVQUzPi+J9p1pKSjJ6dDRHSj51Z5DF7PcfGPnVXsqWr7Sko2Da7v7+SuYRGkR0s5bSFEJ+CuwbLhS19Ljb2FAZvG6NYTz9Qr8U65HDO6e8DmEUII0XK/2lRGWV1wTgCV1Zn8alMZr0/rcebBrVBcXMx55513+jWUlQVk7s6qxSGJpKQk/9fPPfccw4cPb7RNCCGEONlxt8FHRTUsKnCx7kBd0O7k1RSY1tvGgv5OLk2yE2lt46SzomDGJuCNTcA7abZvW0UZWl6WLziRm4lalIOit4/S74rHg5bzHVrOd8CbmIqCkZjqqzJRX23C7B4b6mV2DoaBmpOJdf1SLJtXodS6Aj6lqVnQzxuPZ9Js9PPGt1nZZiE6C4dFYUSPMEb0aByeqNNN8itOCk6Uecku95BX7qUu8JmmoDKB4iqd4iod9jd9PNKq+EMUKZEWkv1fayRFWLBLz3khRDPMmB64b/kp9ucfCfhc7lt+2jggAaBqmL2T8fZOhgkz6hdlohw7hFqUg1ac5wtPlOSiHjsc8DW2BcU0UfbvQd2/B75a4d9udO+FkZxORe/+fKT05W8VCexUurV5IDYmTOGHgyP40eBwejnk7jchRMenHNiLdfkHWNd9huIKXBtVfdB51M2Yhz5qElhafLlFCCFEgH13tI4lxcGtPrekuJbMYx6Gdw/cOdp+/fpxzTXXBGz/XUmr/mrfcMMNbb0OIYQQnUC1x+DzPbW8W1DD8n21eIJ4oWlCXBgLUh3MSXHQ0x7kk3pRMeijJ6GPnuT7b3cNWsFu1IZqE/k7UGprgrumU1BME60kH60kH5Z/AIARm4CePtxXbWLgCMyEJKlCcBaUg/uwbvgCy/ovUA83cwUyAPR+A/FmzMYzfhpExgRlTiE6kzBNYXA3K4O7WQGHf7vXMCmq9LK7vvJEdpmH3WVecsu91HTS0hOVHpMdx73sON58C6wEp+oPTyTXBylSIjWSIyzEO1XpTS9EF+YdP526/SWEffBqwOZwz12Id/z0lg1WFMweceg94tDHXHhie0VZfYuOkypOHNyLYnaM47p67BDqsUN037aehcBC4Iglgu2RKWyPSOabiH5sj0wm1xGPqZx9QDwpQuOuoRHclO4kvK0D5kIIEWyGgfbdV1iXvY8lc3PApjHD7HgzZuKZPs/XLkkIIUS789LuwAXkTjvvriqeyugWsP2npqaGrD1FZ9OikMT69esBGDNmDHa73f/fLZGRkdG6lQkhhOgQ6nSTFaW1LC6o4dOSWqq9wTvZOLy7latTHczr5yAxoh2l9W0O9MGj0AePwgOge1FL8tFyM9Gyv0PNzUQtPx7qVfqph/ejHt6PdcMXgK9tgz5guC84MXAERnJ6x65Q4K4Bm+PM486GqwrL5lVY1y9Fy8ls232fghHTE+/EmXgzZmH07ReUOYXoaiyqQlq0lbRoK5cnn9humCYlVXqj4ER2mYecci+Vno5xka219rsM9rvq2Hiw6WM2DZIjToQmGtp5JNeHKqLC5GKbEJ1d3dyFmCjYPnilzfftnrsQz9xbz31HUTHow89HH37+iW01LtQ9eScqThTnou4rQtGbD4y1Nz29Vcw4nsWM41n+bVWqje8iktgWkcL2yBS2RaSwI7wvHrX590kje1i5d1gEV6Y4sKgSeBNCdHBVFVjXfoZ1+Yeoh0sDNo0R1wfP9Ll4Jl0srUyFEKIdq/YYvFsQmpsW3ymo4U8XROO0hO6cSFZWFn/7299Yv349x44dIy4ujksuuYQHH3yQ7t1PtIRqaOFx/fXX89Of/pT/7//7/9i4cSOKojBjxgz+8pe/0KNHD7Zu3cqjjz7K119/jaqqzJkzh8cee4zw8HD/vurq6vi///s/li5dSnZ2NocPHyYqKorx48fzq1/96oytQk52+PBh/va3v/H555+zb98+IiIiyMjI4MEHH2TIkCFt9nNq0RWlyy+/HEVR2Lx5M2lpaf7/boljx46d0wKFEEK0P4ZpsuFgHYvyXXxYXMNxd/AuEKVGasxPdbIg1cHAmA5y4V6zYPQbiNFvIJ5ZC3ylgA/u81WZaAhOHNwb6lX6KVUVWL5Zj+UbXyjSDLOhpw72t+fQ04aCwxmy9akleViXfdDi8eF3z8M7YQaeGXMxktJaP7Gho+34Gsu6pVi+XoviqWv9vlrIDLPhHXMh3oxZ6EPHgCqlj4UIBVVR/O0oZifa/dtN06TUZTQKTjS08AhWz8tQcuuQU+4lp9wLuJs83sOm+oMTDUGKlEhfRYq+4ZpclBOiM1AUPPMWYiYkYnvtKZTqynPepRkeifuWn7a8gkRrOJwYA0ZgDBhxYpunDrW0GLUox19xQt2Th+IObone1oow3EysyGViRa5/W52isSO8L9sjfFUntkWm0CM9ndtH9+LC+LAWn9sTQoj2ynd+4H0sG5eh1DV9PdoWTEVBH3EBnhnz0IedD6oEgYUQor3bcrgOVxBv5jyZy2uy+VAdU3rbzzw4AD799FNuu+02NE3jkksuoU+fPmRnZ/PCCy+wYsUKli9fTkxMTKPnFBcXM2vWLEaNGsUtt9zCtm3bWLx4Mfv27eP//b//x7x585gyZQq33nor69at47XXXgPgmWee8e/j+PHjPPjgg0yYMIGZM2cSExNDUVERn332GcuWLePTTz9l9OjRZ1x/YWEhl19+OaWlpUybNo3LLruMw4cPs2TJElasWMGHH37I2LFj2+Rn1aKQxK9//WsURaFHjx6N/lsIIUTXYZom3x71sKighvcKXZS6gtdLI96hclWqgwX9nIzqae34f4MUBTO+L974vngnX+LbVH4MNSfTF5zI+Q61OA/FDGK/ktNQ6txYdm+H3dsBMBUVIykNfaCvRYeRPrxpn+hArKPsKLbXn8aydc3ZPa+uFuvqj7Gu/hjv2Mm4b77vrNar7i3Esn4plg1fopYdPdtlt4o+YASeSbPxjpsCjvAzjhdChIaiKPQJ1+gTrjGtz4ntpmlyuNZgd5mX320p59ujntAtMoSOug2Oug2+OdL0+9cU6Bt+UoAi0kJKREMlCo3uNrXj/70Xogvxjp+OPmhkq16rNdpPK16rtRlrGEZyuq+KWgND94Wbi05u15GLUlUR/PW1QpipM6qqmFFVxf5t5nYFc01f9OS0+u93AHpymrRwE0J0HF4vlq/XYF32fkArO5rOCDyTL8UzbQ5mXJ8zP0EIIUS7sa2Z8xDBtP2IJ2AhiYKCAh577LEm22fMmEH//v2544476NmzJ59//jmJiYn+xxctWsQPf/hD/vjHP/LEE080eu6GDRt47LHHuPPOOwHfea1rr72WL774gmuvvZYXX3yRyy67DACPx8OUKVN4++23eeihh+jVqxcAMTExZGVl0bt370b73rVrFzNnzuQPf/gDH3zwwRm/vzvuuIODBw/y3nvvMXXqVP/2X/3qV0ydOpV7772XDRs2tOyHdQYtCkl8v7eJ9DoRQoiuI6/cF4xYVFBDXkXwys9GhynMSXGwINVJRlwYWie/29SM7o5+/kXo51/k21DjQsvfiZaTiZrzHVr+zoDdFXG2FNNAK85BK86BLxYDvpKT+oARvjYdA4ZjxvWFNry4Zdm0vE3uTrRsXYO2a9uZ706sLMO6aQWWdZ+jFeWc05wtZcT2xpMxC2/GLMxevc/8BCFEu6UoCr0cGr0cGnNTHF02JHE6ugnFVTrFVTqr9zd9PNKq+Nt2NAQpUur/OynCgt3SuV8XCNERmTE9qL3nD767epd/iHXVkpY9L8yOd+JMPNPnnFvVr0BQNcyEJLwJSTCh/rWjaaIcO4xanINWnIva0LLj2KHQrrWFFNNEObAH9cAe+Gqlf7vRPRYjyRcS0VN8n83uvdr0Nb0QQpwLpewolpVLsK5aEtAbGPTE/nhm+CpSYgvNXcBCCCHOTeax0J6H+S6A8xcWFvL444832R4dHc3mzZupqKjgiSeeaBSQAFiwYAHPPvss7733XpOQREpKCj/+8Y/9/60oCldddRVffPEFI0aM8AckAKxWK3PmzOFPf/oT2dnZ/pCEzWZrEpAAGDx4MJMmTWLFihV4PB6s1lNXB//222/56quvuPnmmxsFJADS0tK45ZZbeO6559i5c2ebtN1oRw3chRBCtBf7qnXeK3SxuKCG7UG8sOPQFC5JsrMg1cH0PnZsWhc+Iedwog8biz6svnSU1+u7cy3nO3+bDqWyPLRrPIl6cB/qwX1Y134GgBHVDaM+MKEPGO474a214mWHaRL2wSuEffBqm61Vqa7E/vwj1O0voW7uwhMnfr0etO2bsK5fivbtpqD0pDbtTrzjpuCZdDHGgOFyElqITmhUzw7SGqqdqfSYZB3zkHWKEwu9nWqjEEXySUGKOIeKKsdTIULGSErDfdsvWhySqP77Bx3rIpSiYPbohd6jF/roSSe2V5b5WnQU11ecKMlFObAXxewY7ZfUY4dRjx2G7SfuyjLDo5pUnDDj+0oLOCFE8Jgmam6Wr6XG1tUouh6YaTQN79jJeGbMw0iX9+ZCCNHRHXAF5u9FSx2sCdz806dPZ/Hixc0+dttttwGwdetWCgoKmjzudrs5evQoR48e9XePABg2bBjq99pJxcfHAzB8+PAm+2l4bP/+xne9fPfddzzzzDNs2rSJgwcP4vE0Pqdz9OhR/3Obs3XrVgAOHTrUbLWM3Nxc/2cJSQghhGgzx2p1Piqu5d0CFxsO1BGsU3kWBab3sTE/1cmlSXYirNLbsVkWC0b/wRj9B+O55FrfHWz7S+rbc/g+1MOloV6ln1pxHHXrGn+5ZdNmR08bip4+HGPgCPT+g8HmOON+2jog0Xjfr2KioI8cj2XdUqyblgeldLKpqOjDxuDNmI139KSOdVFACHHWzo8Nw2lRQtYLs7MqdRmUuurYeLDpY3YNkiJOtPE4UY3C18ojUl5rCNG+dJbXQpExjUPOALUu1JJ8X8WJkjxq83Ow7y/CagSvQt+5UKorsOz8BnZ+499mhtkxkvqj17cmMZLTMfqkgDUsdAsVQnQ+7losm5b7WmqU5AVsGiO6O96pV+CZcgVmt54Bm0cIIURweYzQnoOp00Mz//HjxwF44YUXTjuuurq6UUgiMjKyyRhN08742MkhiK+++oorr7wSgKlTpzJnzhzCw8NRFIVPPvmErKws3O7TV8puWP/SpUtZunTpadffFlodkli1ahXPPfcc33zzDeXl5RhG077piqJw9GhwencLIYQ4e1Ueg89KallUWMPyvbUE8/rNxLgwrk51cmWKnR52uRPprCkKZu9kvL2T8U653Lfp2GG03Cxfe46cTNQ9+e3mzjXFXYtlx9dYdnwN+O7SMJIH+CtN6OnDISqm0XMsG5cHLCDRwPbBK/DBKwGdo4HeOwXvpNl4J8zA7B4blDmFEKEXblW5OtXBqzmuoM9tUQjq3/b2olaHnHIvOeVeoOkb8B421R+gONHGwxeg6BuuYenkLb6EEEFkd+JNH8ZnjnSe9lSyQa3D2t/L0Oq9jKwqYmRVMaMqizivqpgIo3201jsTpa4WLW8HWt4O/zZT0zD6pPjadaT4Kk4YiWngcIZwpUKIjkg5VIp1xYdY13x6zu02T0dPH+ZrqTF2Mlik8psQQnQ21hC/rw8LUYXshkDDhg0b2qTSwtl48skncbvdfP7554wfP77RY1u3biUrK+uM+2hY/1/+8hd+9KMfBWSdJ2tVSOLDDz/kf/7nfzAMg8TERAYMGOBPjQghhGjf6nST5ftqWVRQw2d7aoN6Z+t5Paws6OdgXj8HfSOkmFFbM7vH4r1gKlxQ36/LVeU7gdlQaaJgJ4ontP3YGii6jlawC61gF3z+DgBGQlJ9aGIERnxfbK8/FdpFtgEzIgrP+Ol4J83GSBkoJTuF6KJ+ODgiJCGJFVfEEu/UKK7UKar0UlTppbiq4WudUpdOiG+uCImjboOjboOvjzT9m6gpkBihkRxxcoCioRKFRjebiiLHciFEC7h1k3cLXPw9q4rdZScqR3hUC9sjU9gemeLfppgG6TUHGFlZzKiqIn+AItYTuAuEbUnRdbSSfLSSfFj3OQCmomD26uOrOJGSjpGUjp6c3iQYLYQQGAbajq2+qhHfbgrYzR6mNQzvhBm+lhrJ6QGZQwghRPsQ7wztNes4R2jmHzt2LEuWLGHLli1BD0kUFhbSrVu3JgEJl8vFt99+26J9jB3rq8q3ZcuW9huS+Mtf/oLD4eDNN9/koosuaus1CSGEaGO6YbLhYB2LClx8WFRDWV3wroj0j9JYkOpkQaqD9GhJ5weVMwJ9xAXoIy7w/benDrUopz408R1ablZA78w4W+r+EtT9JVhXfxLqpZwTU9PQz5uAZ9Js9PPGy10pQgiGd7dyRbKdJcW1QZvzimQ7I3r4Sp/3cmic36tpGfQ63WRv9YnQRHGll6KqE18H8/VCe6GbUFSpU1Sps3p/08cjrYqvAkV9cCI58kSAIjHcgt0iAQohuroyt8H/ZVfzr51VHKhpWnW1OaaikuPsTY6zN+/ETajfaNLXfYyRVUW+4ERlMSOrikh2d4yKrYppohzci3pwL2xe6d9udOtZ36ajvuJEcjpmjzgJEwvRFbmqsK77HOuyD3zHigAxYhPwTJuDZ/KlEBEVsHmEEEK0H8O7W3mvsCZk84/oHprzwTfeeCN//etfeeSRRxg3bhyDBw9u9LjL5WLHjh2cf/75bT53YmIieXl57Nq1yz+vruv87ne/48iRIy3ax5gxYxg7diyLFi3ikksu4aqrrmr0uGEYbNiwgUmTJrXJmlsVksjLy+Paa6+VgIQQQrRjpmmy/aiHRQU1vFfoYr+rZSfo2kKCU+Wqfk6uTnVwXg+r3HHZXljDMNKHYaQPw3PZ9WAYqKVFqPWVJrScTNSjzTR3Fy2ipwzAO+liPOOnQWRMqJcjhGhnnhgfw9r9B4MSPIgJU3hifMwZx4VpCqlRFlKjmn9bWOY2/NUnihuCFFW+ihQlVTqe4L20aDcqPSZZxzxkHWu+MlNvp0pyo+oTFn9rjziHiiqviYTotPZUeXl+ZxWvZbuoaotqfYrCXnsP9tp78HHPMfRyqPxocAS39/XS/UA+anEeanEuWnEuyoE97abN3pmox4+gHj8C2zf6t5nhUf7AhJHsqzhhxvcFVarWCtEZqXsLsC77AMuGL1DcgQsRe4efj2fGPN+NI3I8EUKILmVUz9DetDYyRPP37NmTF198kYULFzJp0iRmzJhBeno6brebkpISNmzYwLhx41i8eHGbz/2jH/2IFStWcPHFFzNv3jxsNhvr1q1j//79TJo0iXXr1rVoPy+++CJXXHEF//M//8Pzzz/PyJEjsdls7N27ly1btnDkyBEOHmybaxitCkn06NEDh8PRJgsQQgjRtnLKPCwqrGFxgYv8Cj1o88aEKcxJcbAg1cnEuDA06efd/qkqRt9UjL6peKfNAUA5evBEYCLnO7S9hSFeZPtmxPTEO3Em3oxZGH37hXo5Qoh2LN6p8eSEGH6w+njA53pyQkyblJaMsamMtIUxsmfTx3TDpNSlN2rfUVL/uajKy6EW3j3d2ZS6DEpddWxs5v26XYPkCF/1ieT6AEXySRUpIq1q8BcshDhnmcc8PJtZyXuFNQSik2F6tIV7hkVwTarTX61G7zEWfejYE4NqXah7CtCKc1GLc30Bin2FKN720WrvTJTqCiw7v4Gd3/i3mWF2jMRUf2jCSE73vd62Nq2MJIToAHQv2jfrsS57H8vu7QGbxnSE47nwYjzT52LGJwZsHiGEEO3b+bFhOC1KUFuNN3BaFMY1U80zWGbPns2aNWt45plnWLVqFStXrsTpdNK7d29uuOEGrr322oDMe/HFF/Pqq6/yt7/9jXfeeQeHw8HkyZN58803efzxx1u8n5SUFNauXcvf//53Pv30U9544w00TSMuLo6JEydy5ZVXttmalbKysrP+DfnNb37DihUrWLduHRaL9JQXoiOIuHVKi8ZVvboqoOsQgbG3ysv7hTW8W1DDd6e4szEQnBaFS5PszO/nYHofO2GaBCM6naoKtLysE8GJwuwOc7I1UExrGN4xF+KdNBt96Bi5I0UIcVYe21bB49sD1+rogZGRPDAq9GWEqz0GJfUBipODFMX1/x2KExXtXQ+bSspJ7Tt8FSl8X/cJ17BIAFV0MJ35PahpmqwqdfNMVhUrS90BmWNCXBj3DIvg4kR766rQeD2o+4p8gYkSX8UJtSQPpTZ0ZYfPlalpGL1TMJLT6tt1pGMk9QdHeKiXJrqIznxcCxSl4jiWVR9jXfkR6rHDAZtH75OCZ8Y8vBNngt0ZsHmE6GzkuCY6s/vWH+fVHFfQ5104wMlTGd2CPq84e60KSbhcLq666ipiY2P505/+RGKipDKFaO/kBU/nc7RW58OiWhYVuNhwsC5o81oUmN7XztWpDi5JtBMudz12LXVu1MJstJzvfMGJ3CyUmupQryoo9AEj8EyajXfcFDkRK4RoNdM0eXx7JX8OQFDigZGR3D8yst23uTJNk8O19a08KusDFCe19NhXrSMRisYsCvRtqDoRcVKQoj5E0c2mtvv/76Lr6YzvQT2GyfuFNTybVUVmAMLpCnB5sp17h0VyfiDuPjMMlEP7GlecKM5FrSxr+7mCyIjrg56UjpGS7m/ZYUbJiWnR9jrjcS0gTBO1YJevasTmVQG70cJUVfTRk3wtNQaNBHktJMRZk+Oa6Mwyj3m48MNDQZ937ZxeDO8e2nYfomVaVQZiwoQJeL1eNm/ezCeffEJ0dDRRUU3vVlIUhe3bt5/rGoUQQtSr8hh8WlLL4gIXy/e5A1LOtTkKkBEfxoJUJ1cm2+lulzvnu6wwG8bAERgDR+ABMHTUvYVo2d+h5maiZWeilh0J9SrbjBGbgDdjFp6M2Zi9eod6OUKITkBRFB4YFUV6tIVfbCyjrO7c/5jHhCk8OSGG+akd4645RVHo5dDo5dAY16vp43W6yZ4qneKq+vYdld5GX5e3wc+so/Ga1H//zbdSi7Iq9ZUnGleiSInUSAy3+Ev0CyFap9Jj8FqOi+d3VLG3uu1bGto1uCEtnLuGRtA/OoAVW1UVMz4Rb3wiXDDNt800UY4f9gcmfBUnclGPtE2f32BQD+5DPbgPtqzybzNievoqTqQM8AUoktMwe8bLRVQhAqnOjWXzSqzL3kcrzA7YNEZkDN4pl+OZeiVmj2ZeTAohhBDA8O5Wrki2s6S4NmhzXpFsl4BEB9Kqd16GYaBpGn379vVvM82mJ6qa2yaEEOLsuHWT5ftqWVRQw2cltdTowTu2juxhZUGqg6v6OekdLsEI0QxVw0hKw0hKg5lX+U6yHjngqzKR/R1abiZqaXGoV3lWTLsT77gpeCZdjJE+DFSpliKEaHvzU51kxNv41aayc3rDfkWynSfGxxDv7Dx/p8M0hf7RllNeKCxzG/42Hg3VJ3ztPLzsqdbxGEFecDtQ4THJPOZp9s52BUhwqvWhicYVKFIiLcQ5pAqFEKdywKXzr51VvJxdHZCAVnebyg8Hh3P7oHBiHSE6jisKZvde6N17oY+aiP8oUlWO1lBpoiQPrTgXZf8eFLNjHGTVsiO+8Pa3m/zbzPBI9KQ0f7UJPTkdMyFR2ucJcY6UIwewrvgI6+qPUaoqAjaP3n8wnunzfNUdraHr9S6EEKLjeGJ8DGv3H2yTG1TOJCZM4YnxMQGfR7SdVoUkMjMz23odQgghTqIbJusO1LG40MWHRTVBvWMyLcrCglQHC1IdpEVL6lGcJUXBjE3AW1+BAYDKMrTcrBOhiaIcFL3t78BrC0ZCEq6H/w02e6iXIoToAuKdGq9P60HmMQ8v7arilRb2ynRaFK5JdfCDwRFd8g6FGJvKSFsYI3s2fUw3TEpdvooLDdUnihvaelR5OVTTMS7utSUTKHUZlLrq2NhMiza7hj80kdQQpIjwVaJIjtSIlNZqogvKLvPwbFYV7+S7qAvAYSMlUuOuoRHcmO7EaWmn/8YiotGHjkEfOubENncN6p6CExUninNR9xYGrJR+W1OqK7Hs2ga7tvm3mWE2jMRUjOQB6Mm+ihNGn34QZgvhSoXoAEwTbec3vqoR2zYELEBlWq14L5iGZ/o8jNRBAZlDCCFE5xXv1HhyQgw/WH084HM9OaFz3cDSFQSwhp8QQoizYZom2454eLfAxfuFNRwI4kn83k6Vq/o5WZDq4LweVrmbULStyBj00ZPQR0/y/be7Fq1gF2pDtYn8HSi1NaFdYz3TGSEBCSFE0A3vbuWpjG4tDknkXR/ffi+qhZimKiRGWEiMsHAhTS9wVXsMSqoaKk/oTSpSBLNiV3tRq0N2uZfsci/gbvJ4T7vaqPqEr62H7+s+4RoWVV43is7BNE02HKzjmawqlu4JTEne0T2t3DsskiuS7Wgd8d+OzYGRNhQjbSjehm1eL2ppkS8wUZxX364jD6W2ZX/TQk2pc6Pl70LL30VD7NDUNIyEZF/FiZR0f7sOHOEhXasQ7UKNC+v6pViXfxDQqpFG9154ps/BM/kyiIoJ2DxCdHWWKW+2aFxZYJchREDNT3WSU+7l8e2VAZvjgZGRHaYFqjhBQhJCCBFi2WUeFhXUsLjARcEp+kwHQjebwtwUB/NTnUyMC0OVYIQIFpsdffAo9MGjfOV8dS9qST5abiZhH72OUlkeurVZu94d2UKIjkcCEq0XblUZ3E1lcLemx3vTNDlca/gDFMWVXorqAxXFlTr7qnW6XoQCjtQaHKk12Hq46Z3iFgX6RmiNqk80tPFIibQQE6ZI+Fa0e7ph8nFJLc9kVvL1kcBURJjd18Y9wyPJiAvrfP8mLJYT7fcurN9mGCiHSk9UmyjJRS3KRa0sC+VKW0zRdbS9BWh7C2D9Uv92o1fv+jYdA3wVJ5LTMaO7h3ClQgSPUlqMdfkHWNctDWgIyjtkNJ4Z89BHTgBNLl0IIYRoGw+MjEQB/hyAoMQDIyO5f2Rkm+9XBF6LX2ncddddp31cVVWioqIYOnQol19+OVFRUee8OCGE6Kz2VHl5r7CGRQU1zfaODpRwi8KlSXYWpDqZ2ttGmNbJTtCJjkmzYPQbiNFvIGreDqxfrQzZUgw5ySmEEF2Woij0cmj0cmiM69X0cbdusrfK17ajuLKhGoWvEkVhpZeKILZHay+8JvUVOZoP+kZZFX9woqH6REp9G4+kCAs2eS0qQsjlNXgr18VzO6ooDEBY3arCNf2d3DMsgkExXSyIq6qY8X3xxveFC6b6tpkmyvEjvsBEQ8WJ4lzUIwdCu9azoB4qRT1UimXLav82I6aHr+JEcnp9u450zJ7x0NnCMKJrMnS07RuxLnsfy46vAzaNaXfgyZiNZ/pczD4pAZtHCCFE16UoCg+MiiI92sIvNpZRdtL7d6dey4TyPMZUFnBeVTEJdWWEmV7qFAv7w2L4NiKZryNT2Ridhks7UYE4JkzhyQkxUkGiA2txSOKtt95q0ThFUXjwwQd59tlnufLKK1u9MCGE6GyO1up8UOQLRjTXDzpQrCrM6GNnQaqDixPthEtfadGOGUlpEMqQRHJ6yOYWQgjRvtk0hf7RFvpHN/82usxt+EMT/gBFfZiipErH2/UyFFR4TDKPeZoNBStAb6dGcmTjChTJ9ZUp4hxq57vjXrQLR2p1XthVzYu7qjnqbvsWh1FhCv8zMJwfD4kgQXoSn6AomN1j0bvHoo+ciP+oUFWBVpJX367DF6BQ95egmMFrP3ku1LKjqGVH4dtN/m2mMwI9KQ0jZYCvykZyOkZCotwVLzqOyjKsaz7FuuJD1CMHAzaNkZCIZ/o8PJNmSzsbIYQQQTE/1UlGvI1fbSqjeEc2d+77khsObiDcaNp6ssG1h32v86pVG2/FTeT5PjNJHjqQJ8bHEC+v9zu0Fr86X7JkyWkfNwyDY8eO8dVXX/HGG29w++23069fP4YPH37OixRCiI6q0mPwaUkti/JdrCx1B+3kuAJMig/j6v5Orkh20M0mwQjRMRj9BoZ2/pQBIZ1fCCFExxVjUxlpC2Nkz6aP6YZJqUuvr7rgC1IUn1SJ4lBNx7gY2JZMYJ9LZ59LZ0MzAWKHppAUofmqUERafEGKiBOVKCIk+CvOUkGFl+d2VPFmbjW1Aehy2Ddc444h4dw6MJxI+f1suYgo9CGj0YeMPrHNXYu6J/9ExYmSXNS9BSie4FVhPBeKqwrL7u2we7t/m2kNw0js36jihNG3H4TZQrZOIb5PLcz2VY34annA/r2Zioo+aoKvpcaQMVJ1RQghRNAl1JXx7o6nsWxdc1bPCzfc3L5/JbfvX4mXybhH34fp7BGgVYpgaHFIYtKkSS0aN3fuXG666SamTZvG3//+d/71r3+1enFCCNERuXWTL/fWsrighs/31FKjB++2wVE9rSxIdTIvxUHvcEkxio5HTxuKGWZHqasN+txmmB09bVjQ5xVCCNH5aapCYoSFxAgLFyY0vSBW7TH8FSj8rTyqdEoqvRRV6kF9Pdle1Ogm2eVessu9QNO7enra1UbVJ3zVKHxf9wnXsKhy0UX4bD1cxzOZlSwpriUQ/5KGdbdy77AI5vVzYJXfu7Zhs2OkDcVIG4q3YZvXi1paXN+uIxetOA+1JA+lpjqUK20xxVOHVrALrWAXDc1XTFXF6J2MkTwAIznNF55ISgNnREjXKroYTx2WLauxLnsfLX9nwKYxw6PwTLkMz9QrMWMTAjaPEEIIcTqWTcuxvfYUSnXlue1n6xq0Xdtw3/JTvOOnt9HqRLAFpM7b0KFDueSSS9iwYUOrnv/f//6XjRs3sn37dnbu3EldXR3PPfccN954Y7Pjt27dypNPPslXX31FVVUViYmJXHXVVfz85z/H4XA0+5x3332X559/nt27d2O1Whk3bhy/+c1vGDVqVLPj8/PzeeSRR1i7di3V1dWkpqaycOFCfvjDH6KqTe8QcLvdPPXUU7zzzjvs3buXmJgYZs2axUMPPURcXFyrfi5CiPZLN0zWHXDzbkENHxXXBLUndXq0hQWpDhb0c56y/LMQHYbNgXfCDKyrPw761N6JM8FmP/NAIYQQoo2FW1WGdFMZ0s3a5DHTNDlUY1Bc5fVXoiiq1Cmu8gUq9lXrAbnw294dqTU4Umuw9XDTO10tCiRGNG7f0VCBIiXSQkyYIq08OjnDNFm6p5ZnsqoC1upwSm8b9w2LYEpvm/w+BYPFgpHUHyOpP0y62LfNMFAOl56oONHwUXE8tGttIcUw0PYWou0thPVL/duN2N6+0ETKAIykdIzkNMwYuUtRtC3l2CGsK5dgWfVxQP/N6MkD8Mych/eCaVI5RQghROiYJmEfvELYB6+22S6V6krszz9C3f4S6uYulOpIHVDArqalpaXx2Wefteq5jz76KHv27KFHjx7ExcWxZ8+eU4796KOP+J//+R80TePKK6+kV69efPXVVzzxxBOsXbuWDz/8EJut8QuwJ598kkceeYS+ffty2223UV1dzXvvvcfs2bNZvHgxF154YaPxu3fvZtasWdTU1DBv3jwSEhL48ssv+fWvf82OHTt4+umnG403DIMbbriB5cuXM3bsWK644goKCwt58803WblyJcuXL5eghBCdgGmafH3Ew6ICF+8X1nAwiGWS+zg15qc6mJ/qYER3q5yUE52KZ8bckIQkPNPnBH1OIYQQ4kwURSHOqRHn1BjXq+njbt1kb5VOUZW3cSWKSt+2YIZ32wuvCYWVOoWVzfdUiApTSI6wNKpE4QtSaCRGWLBp8tq6o6r1mrxT4OLvWVXklHvP/ISzpCkwv5+Du4dFMKJHWJvvX5wlVcWM64se1xd93BTfNtNEKTvqD0xoJXm+rw/vD+lSz4Z6uBT1cGmjEtBGdHdfi46T2nWYsQlyMl6cHdNEzf6WsGXvo329FsUIzHksU7PgHTcFz4x5GP2HyO+pEEKIkGvrgETjfb+KiYJn3sKA7F8ETsBCErW1tYSFte4N47PPPktqaipJSUn87//+Lw8//HCz42pqavjZz36GoigsXbqUkSNHAr4Ll7/+9a954YUX+Mc//sHPfvYz/3Py8/N57LHHSEtLY/ny5URHRwPw4x//mOnTp3PvvfeyZcsWLJYTP5qf//znVFRU8M477zBr1iwAHnroIRYsWMCrr77K/PnzmTx5sn/8W2+9xfLly5k/fz4vvvii/+LlG2+8wd13383vf/97/vnPf7bqZyOECL3dZR4W5dewqNBF0SlOvAZCd5vK3BRfMGJCXBiqvMkUnZSRlIZ37OSz7gt3LrxjJ/tK2wohhBAdjE1T6B9tOWVFsTK30aj6hP/rSi8lVTrerpehoKLOJPOYh8xjTatQKEBvp+avOtHwOaW+pUecQ5WAcjtU5jZ4aXc1/9pVxaEAhNcjLAq3DHRy55AIEiOkel+7piiY3Xqid+uJPnIC/n/l1ZUnAhMNH6UlKGbwbnY4F2r5MdTvvoLvvvJvM53hGElp6MkD6gMUaRgJSaDJ76j4nloXlo3LfC019hYGbBqjW088U6/EO+VyzOjuAZtHCCGEOBuWjcsDFpBoYPvgFcyExE7VeuOxxx7j8ccfZ8mSJU2KC3QWAXvVvH79evr169eq506ZMqVF47766iuOHj3K3Llz/QEJ8N1p89vf/pYXXniBl19+mZ/+9Kf+kxhvvvkmXq+XX/ziF/6ABMDgwYO57rrrePnll1mzZg3Tpk0DIC8vjw0bNnDhhRf6AxIAVquV3/3ud6xevZrXXnutUUjitddeA+D3v/99o5MnN910E88++yzvv/8+TzzxBJGRkWf9sxFChEZJlZf3CmpYVFhDVjMnUwMl3KJwWZKdBalOpvaxSY9b0WW4b74Pbde2c+4P1xJmeCTum+8L+DxCCCFEKMTYVEbawhjZs+ljumGyz6X7q08Ufy9Icbi2Y1w8bEsmsM+ls8+ls6GZNg0OTSE5UiO5PjTRUIEiOcIXqIiwNm3HKU6v2mMQ3sqfW0mVl3/sqOL1HBfVAUj8xDlU7hgSwW0Dw4mxyf/bDi08En3wKPTBJ7XZddei7i3wVZworg9Q7M1H8QTvPf+5UFzVaLu/Rdv9rX+baQ3D6JuKkVJfcSIpHSMxVdocdFHKgb1Yl3+Add1nKK7qgM2jDzqPuhnz0EdNAouEdIQQQrQfStlRbK8/FZS5bK89hT5oZMDapBUXF3PeeecBkJCQQFZWFpqmNRm3Y8cOMjIyAEhPT2fLli0BWU9n0OavWgzD4C9/+Qvffvst999/f1vvvpFDhw4BkJyc3OSxmJgYYmJi2LNnD0VFRf7Axrp16wD8IYiTTZs2jZdffpn169f7Hz/d+DFjxhAdHc369ev922pra9m6dSvp6ekkJSU1ec7UqVP55z//ydatW5k6derZfstCiCA6UqvzQWENiwpq2HQoMH1sm2NVYWZfOwv6Obg4yY7TIifjRNdjxvTAfctPsT//SMDnct/yU+nxK4QQokvSVIWkCAtJERYuTGh6Aa3KY1BSpTeqPlFU5ftcXKlTo3e9MhQ1usnuMi+7y7yAu8njsXb1pOoTFpIiT7Ty6OPU0LpA6DnzmIcXd1XxUgvHp//nAFenOvjh4AiGd7e26DnfHq3j2awq3i+sIRC/hgOjLdw9LIJr+jul/UpnZrNj9B+C0X8I/uYsXi/q/pKT2nXkohbnodQE7gJzW1I8dWiFu9EKd9Pwr8lUVYyEJIyTKk7oSWkQLjdvdUqGgfbdV1iXvY8lc3PApjHD7HgzZuKZPs8XxBFCCCHaIdvrTwflJjwApboS2+tPU3vPHwI6j8ViYf/+/SxfvrzRzf0NXn/9dSwWC15v27cf7GxaHJK46667Tvu4YRgcO3aMbdu2ceTIEfr3789PfvKTc17g6fTs6bsVpri4uMlj5eXllJWVAb5qEA0hifz8fCIiIoiLi2vynP79+/vHNGj4OjW16Ys9RVFITU1l27ZtuFwunE4nhYWFGIbR7Pjvz3GmkERtbe1pHxfibES0cFxX/72r9Bh8ttfD+8Vu1hzwBOSEW3MUICPOwlXJNi5NDCMmrD4Y4a2jVv6Wia5qZAbG5Tfi/PjNgE1RfflN1IzMgC5+7BNCdCxd/fWaCB4LkOqAVIcCvSycfArBNE0O15oUV+kUVxmUVPs+F1fp7Kk2KHUZdL0IBRyuNThca7D1cNM70S0K9A1XfVUoIlQSw32fkyM0ksJVYsKUDt3K42CNwW+2VvPJXl/AvKUhCZfX5NUcF6/muLisbxh/GhtOnKNpUNw0TVYd8PCPXbWsPRiYO/3Hx1r4yWAHM3pbURUF0+OmtmMUFRBtKba372PsRb7/NgzUowewlORj2VP/UZKHWnE8tOtsIcUw0PYVoe0rgg1f+LfrPePxJqXhTezv+0jqjxkt4fHTac/n1pTqSuwbvsC+6mO0I/sDNo/eqzc1F12Be+JMTGf9T0RemwrR6cl7UBEIdrs9oPtXi3OD2s4ZwLJ1DWpJXkDbOo8bN46srCzeeOONJiGJuro63n33XWbOnMlnn30WsDV0Fi0OSbz11lstGhcWFsbVV1/NH//4x4C3kxg3bhxRUVF88sknfPvtt/4yIwB//OMf/V+Xl5f7v66oqCA2NrbZ/TWst6KiotF4oFFrjlM9x+l0ntX4MyktLUXX9TOOE6Ilmqmu26w9e/YEdB3tkduADcc0lh7WWHdcw20E78TkkAid2bE6M3vqxNp8p5ErD0Jwso1CdADnXUR8RQUJa5a0+a73T76CA+dNhi543BNCtFfOFo3qiq/XRPsVD8Rb4IJo4KS3wXUG7HcrlNYq7KtV2Vd74uu9tQrVescNA7SW14SiKoOiqubbmERoJn3sJr3tBn3sZv2HQW+7SYLNJKwdF5j74rDG4/lhVHjP7f/rJ3vrWHvAzf3965gV6zsf4jXgiyMab+y1kutq+x+CisnUHjo39/UyNNIAo4J9e9t8GtEZ9ErxfYzx9Zq2VJbhPLgHx4ESHAdKcB7Yg63scEiXeDa0IwfQjhzA9s06/zZPeBQ18Um44pN8n+MSqesWCx04wNWW2uO5NcfBPfTcspJuWV+hegNTBdVEoSJtGIfPn0Zl6hBQVDh63PchhOjg5D2oCJ309PSA7t+6/MOA7v9087pv+0XA9u9wOLjqqqt46623OHr0KD16nAi5fvbZZxw9epQbb7yxSUhi//79/N///R8rVqygqKiIiooK4uLimDVrFg888MApr503Jysri7/97W+sX7+eY8eOERcXxyWXXMKDDz5I9+7d2+x7DbQWhySWLDn9xQlVVYmKiiI9PR2bLTh97iIiInj00Ue59957mTVrFnPmzKFXr15s3ryZ7du3M2DAAHJycprtydIR9O7dO9RLEF1QYmJiqJcQFF7DZP1BD+8X1/HJ3joqPcG7zy09SmNechjzkm30i+yYxychgurGu6gYMJSIt/6O6qo6590Zzgiqbrgb6/lT6BpHPCFEx3G0RaO6yus10fH1P8V20zQpqzMpqTb8lSj2nFSJYm+1gbcLlqGo0hWyqxWyq5sGARSgt1Ml8aRKFEknVaKItYemCoVpmvw1q4Yns2vabJ8VXoXfZts4oNrpFqbwYq6bUlfzwZJz4dDg2lQbdwx0kCLvy0SrJMKQ4QDo+G52qHJVYdmTj+avOJGPdqAExWj73+FAsFZXYM3PIio/y7/NsDvRE/ufqDqR1B89Pgk66PnWYAj4azXdS9g363Gs+ghr3o6ATWM4I6jNmE3tRZdhxPYmBogJ2GxCiNCQ96Cik3LXYNm4LCRTWzZ8ifuGu8AWuEoZN910E6+88grvvPMOd955p3/7G2+8QWxsLBdffHGT52zYsIHnnnuOyZMnM2bMGKxWK9999x0vvfQSy5cvZ/Xq1acsAHCyTz/9lNtuuw1N07jkkkvo06cP2dnZvPDCC6xYsYLly5cTExPTlt9uwLQ4JDFp0qRArqPVbrnlFhISEnj66af59NNP0XWdkSNH8uGHH/LUU0+Rk5PTKEUTFRV1yioOlZWV/jEnj4fG1Siae05DhYiWjj95jlMJdKkZIZrTmX/vTNNk62EP7xa4+KCohkM1wTtJ0TdcY34/B/NTHQzvbu3QZXSFCIkLL6Zm+PnYXn/6nMqkecdOxn3zfagxPei8RzshRGfXmV+via7D4YCEaLigmce8hkmpS6eoUqe40ktxpU5RlZei+q8P13aMi41tyQT2uQz2uQw2HW7aj8+hKSRHaiRHWkiJqP8cqZESaSE5QiPcGpgyFI9tq+DJrLYLSJzs2V2BKevcw6Zy++Bwbh8cTg+7XOQVbcxuh+494bwL8AJegDo36p4C1JJctOJc1OI81D35KJ7A3PXf1tRaF2puJtbcTP8202rF6JuKkTwAPTkNIzkdI7E/hAXnxrn2LlCv1ZSyo1hWLsG6aglqWcsubLaGntQfz/R5eCfMAJudsIDNJIToKOQ9qOhotLwdKHWhaROj1NWi5WWhDx0bsDnGjh3L4MGDeeONN/whidLSUlasWMGdd96JxdL08v/kyZPJzs4mIqJxA7G3336bO++8kxdeeIFf/vKXp5332LFj3HHHHfTs2ZPPP/+8UYBq0aJF/PCHP+SPf/wjTzzxRBt8l4HX4pBEezZz5kxmzpzZZPuPf/xjVFVt1Iajf//+bN68mYMHDxIXF9dofH5+vn/MyeMBCgoKmuzfNE0KCgpISEggPDwcgH79+qGqarPjTzWHECKwdh33sKjAxaKCGoqrgtfCpodNZW4/BwtSHVzQKwxVghFCnBMzpge19/wBtSQP6/IPsa5qWQsOM8yOd+JMPNPnBLQfnBBCCCHahkVVSIqwkBRhgYSmF9yqPAbFlTrFVV6KKnVfeKLKF6goqvRS2wW7VtboJrvLvOwuaxqgAIi1q6T4QxQWkhsCFJEafZwamnr271UWFbh4fHvHaRTYL1Lj7mERXJ/mxGlpx71LROcTZsPoPxij/2D8/0J1L+r+El9gojgXtTgXrSQXxVUdypW2mOLxoBVmoxVmY63fZioqRkISRkq6LzSRnI6elAbhgW3H3OmZJmpuFtZl72PZuholQK2ZTU3DO3YynhnzMNKHS4sVIYQQHZpamB3a+YtyAhqSALjhhhv43e9+x/bt2xk5ciRvvfUWuq5z0003NTv+VO00rrvuOu6//35WrVp1xpDE22+/TUVFBU888USTCjMLFizg2Wef5b333pOQRKht2rSJkpISZs2a1ag8SEZGBps3b2bFihVcf/31jZ6zYsUK/5gGDRU0VqxYwc9+9rNG47/++mvKy8sbBTTsdjtjxoxhy5YtlJSUkJSU1Og5K1euxGazMWbMmLb5RoUQzSqu9PJeYQ3vFrjYebz5E4WBEGFRuCzZzoJUJ1N627C24mSjEOL0jKQ03Lf9osUhieq/fxDQ8mZCCCGECK4Iq8rQ7ipDu1ubPGaaJgdrDF9gokr3V59o+Fzq0umCnTw4XGtwuNZgy2FPk8esKiSGf7/6xImvY2xNAwUHXDq/3FgWhJWfu7GxVu4ZFsnlSfZWhUGECAjN4qvE0DcVMmb5tpkmyuH9vsBEcS5qSX2AIoAVA9qSYhpopUVopUWw4Uv/dqNnPEbKAPSkNH94wozp0bkvwrtrwOY4x33UYtm0HOuy99FK8tpmXc0worvjnXoFnilXYHbrGbB5hBBCiGBSA/i3s0XzF+cGfI7rrruOP/zhD7zxxhv+kMTYsWMZNGjQKZ/z0Ucf8corr/Dtt99SVlaGflL48sCBA2ecc+vWrf7PzRULcLvdHD16lKNHjzbq8tBedfiQREVFRZPWFfv37+fee+/FYrHwm9/8ptFjN954I88++yxPPvkkl156qT9AsWvXLv7zn//Qr18/Jk+e7B+flpbGxIkTWbt2LV988QWzZvneuHg8Hh599FHA1/LjZLfeeitbtmzh4Ycf5sUXX/SX1X/jjTfIzs7muuuua1G7DSHE2Tlco/NBUQ2LCmr46lDwylaGqTCzr52rU53MSrTJXUlCtDcSkBBCCCG6DEVRiHdqxDs1Lohr+rhbN9lTX4Hi5EoUDa09KjxdL0LhMaCgUqegsvm7k6PCFFJOCk0kR2q8k19DWV37/lldnGjn3mERTIgLk3aHomNQFMxevdF79UY//6ITm8uO+ipO+Nt15KIeKg3hQs+OeuQA6pEDjdomGlHdTlSbSE7HSE7DjO0Navs8n6KW5GFd9kGLx4ff7WtV4Zkx96yrGSqHSrGu+BDrmk9RqgNXrUdPH4Znxjy8YyeDpWnoUAghhOjI1LJjoZ2/PPDzx8bGMnPmTBYtWsQll1xCQUEB99xzzynHP/vss/zud7+jZ8+eTJs2jd69e/tb6Tz//PO43e4zznn8+HEAXnjhhdOOq66ulpBEa7322mts3LgRgJ07dwLw+uuvs27dOgAuu+wyLr/8cgD+9a9/8c477zB+/HhiY2PZu3cvn332GS6Xi2effZaRI0c22ndaWhoPPPAAjz76KBkZGcyZMweXy8XixYvxeDw8/fTTTXq1/O1vf2PWrFncdNNNzJ07l4SEBJYtW8aOHTu45ZZbGoUqAK6//nref/99Fi9eTHFxMZMmTaKoqIiPPvqIvn378vDDDwfixyZEl1RRZ/BxcQ2LC2tYVepGD9J5OlWBC+NtLEh1cEWyo9m7q4QQQgghhBDti01TSIu2khbdfBWKsjqzUfWJhlYeRZVe9lTpeNt3LiAgKupMvjvm4btjTatQtDdhKlzb38ndwyIYGCMX/UTnYMb0QI/pgX7eBfj/FbqqUEvy6kMT9RUnSotQDCOUS20xteI4auZmyNzs32banfWhifqKE0npGL2ToZme2sGilB3F9vrTjQIeLXpeXS3W1R9jXf0x3rGTcd98n696xqkYBtqOrb6qEd9uQjED88fGtIbVhzfmYSSnB2QOIYQQol3whvi9iyc489900018+umn3HPPPTgcDubPn9/sOK/XyxNPPEFCQgJr166lZ88T1aNM0+SZZ55p0XyRkb42ahs2bGDIkCHn/g2EWLsMSWzcuJG333670bZNmzaxadMmAJKSkvwhiXHjxrF+/Xo+//xzysrK6N69OzNnzuS+++7jvPPOa3b/v/zlL0lKSuL555/n5Zdfxmq1Mm7cOH7zm98wevToJuMHDRrEihUreOSRR1i2bBnV1dWkpqby+OOPc/vttzcZr2kab731Fk899RT//e9/+cc//kFMTAw33HADDz30EHFxzdzOIkSAWaa82aJxZYFdRpuo9Zp8sbeWRQUulu6txR3EnsNjY63M7+dkXj8H8U4teBMLIYQQQgghAkpRFLrZFLrZwhjVTMVxr2Gyr1r3hyaKTwpQFFXqHKntGBcnO6PoMIUfDgrnR4MjiJP3aaIrcEZgDBqJMWjkiW11btS9hSe168hFLclH8QSv0ua5UGpdaNnfomV/699mWq2+tiRJJypOGIn9g1It0LJpObbXnjrnag6WrWvQdm3DfctP8Y6f3vhBVxXWdZ9jXfYB6sG95zTP6RixCXimzcEz+VKIkOrGQgghuoBQV0myBmf+WbNmERcXR2lpKddcc80puxgcPXqUiooKLrrookYBCYBt27ZRU1PTovnGjh3LkiVL2LJli4QkAuX555/n+eefb9HYiy66iIsuuujMA7/nmmuu4Zprrmnx+LS0NF599dUWj7fZbNx///3cf//9Z702IURTXsNkzX43iwpq+Li4JqhlcAfFWFiQ6mR+Pwf9otrlYVMIIYQQQggRYBZVITnSQnKkhckJtiaPV3kMfwWKk4MUDa09aoMY7u4q+oZr/GRoBDcPcBJplep+oosLs2GkDsJIHYS3YZvuRd2/x1dpoiTPH6BQXFWhXGmLKR4PWmE2WmE2DZcaTEXFSEjyBSZOatlBeGTbTGqahH3wCmEftPw88Jko1ZXYn3+Euv0l1M1diLqvEOuyD7Bs+ALFXdtm83yfd/j5eGbMQx9xAagSIBNCCNF1GDHdCeVfPiO6e1DmsVgsvP3225SWlp6ycAD4WnM4HA6+/fZbXC4XTqcTgLKyMn7961+3eL4bb7yRv/71rzzyyCOMGzeOwYMHN3rc5XKxY8cOzj///NZ9Q0EmV/uEEO2WaZpsOVzHuwU1fFBYw+Eg3pnVN1xjQaqDBalOhnazSA9bIYQQQgghxGlFWFWGdlcZ2r35Vh4Ha4xGAYqiSt1XjaJSp9Sl0wU7ebTaiO5W7h0ewZwUB1ZV3qsJcUqaBaNvP4y+/SBjlm+baaIcOXCi4kRDy46yI6FdawsppoFWWoRWWgQbl/m3Gz3j/YEJIzkNIykds1tPOMvzOW0dkGi871fR1i1FO3IgIPsHMB3heC68GM/0uZjxiQGbRwghhGjPjKQ0+Gpl6OYPYlur0aNHN9sl4WSqqvKDH/yAv//970yaNImLL76YyspKli1bRmJiIgkJCS2aq2fPnrz44ossXLiQSZMmMWPGDNLT03G73ZSUlLBhwwbGjRvH4sWL2+JbCzgJSQgh2p2dxz0sKnCxqKCGkqrg3W7V064yL8XB/FQH43qFoUowQgghhBBCCNEGFEUh3qkR79QY30wHTrdusqfKF5woOqn6REOQIpiV9Nqz/lEaf5sQw+QEmwTZhWgtRcGMTUCPTUAfO/nE5vJjJwITxbloJbmoB/eFcKFnRz1yAPXIASxfr/VvM6K6+QMTDQEKs1dvUJuvPGPZuDxgAYkGgQpI6H1S8MyYh3fiTLA7AzKHEEII0VEY/QaGdv6UASGdvzm///3v6datG2+99RYvvfQSsbGxXHXVVTz44INMmDChxfuZPXs2a9as4ZlnnmHVqlWsXLkSp9NJ7969ueGGG7j22msD+F20LaWsrEzeaQvRBcT8X8ve2Jbd1ifAK2leUaWX9wprWJTvYmeZ98xPaCORVoXLkuxc3d/JRQk2LHIXkhAdRsStU1o0rurVVQFdhxBCtJX2/npNCBEapmlSVmfWhye8/pYeRfUVKfZW6Xjb4Zkd76obWzTOMuXNFu/zqn4OXp4SnNK1QgjAVYVaku8LTDQEKPYVohjBq/TZ1ky7EyMp7UTFieR0jN4pKFXlOH+zEKW6MtRLbDFTVdFHT/K11Bg08qyrZgghhLwHFZ2Wu4bwu+eh1AWurdWpmGF2qv/+AdjsQZ9bnB2pJCGECJlDNTrvF9awuKCGzYfrgjZvmAqz+vqCEbP62nFY5E2kEEIIIYQQon1SFIVuNoVutjBG9Qxr8rjXMNlXrfurTxR/rxLFkSC2LQy0gzXBqzQohACcERiDzsMYdFKP6zo36r5C1OK8E+069uSj1LlDt86zoNS60HK+Q8v5zr/NtFghzIbiqgrhylrOiIzBO+VyPFOvxOzRK9TLEUIIIdofmwPvhBlYV38c9Km9E2dKQKKDkJCEECKoyusMPi6uYVFBDav3uzGCdMeTqsBFCTbmpzq4PMlBjK350opCCCGEEEII0ZFYVIXkSAvJkRbA1uTxSo9ByfeqT5ScFKSo7UC5gzq9HZbMEKKrCbNh9BuE0W8Q/jqgho6yf8+J0ERJHlpRTocJHSheD3g9oV7GGen9B+OZPg/vuClgbRqaE0IIIcQJnhlzQxKS8EyfE/Q5RetISEII0Ui1xyDc2rYBghqvyRd7a1lU4OKLvbW4g3gS7vxYKwtSncxNcRDn1II3sRBCCCGEEEK0A5FWlaHdVYZ2tzZ5zDBNDtUY9a08TlSf8AUpdEpdOu0plhCmSRVAIdolVcPsk4K3TwpMnOnbZpooRw74Kk7423Xkoh4/EtKldjSm1Yr3gml4ps/DSB0U6uUIIYQQHYaRlIZ37GQsW9cEbU7v2MkYSWlBm0+cGwlJCNHJZR7z8OKulif30/9zgKtTHfxwcATDmzmJ1lJew2T1fjfv5rv4pKSWSk/wTq0NjrGwINXJ/FQHKZFymBNCCCGEEEKI5qiKQrxTI96pMT6u6eO1XpM91fUBikov/9pZTW6Ft+nAIIlzSPBdiA5DUTBjE9BjE9DHXnhic8XxE4GJhpYdB/eGcKHtk9EjDs+0K/FMvgyiYkK9HCGEEKJDct98H9qubSjVlQGfywyPxH3zfQGfR7QduXooRCd1wKXzq01lLCmuPavnubwmr+a4eDXHxRXJdp4YH0N8CyswmKbJ5kN1LCqo4f2imqD2vk2M0Lg61cH8fs5m75ASQgghhBBCCHF27BaF9Ggr6dG+91iVHpOHv64I2XpGyHs9ITo8M6ob+vBx6MPHndhYU41akn9Su45c1H1FKHoH6gfURrxDRuOZMQ995ATQ5NS9EEIIcS7MmB64b/kp9ucfCfhc7lt+ihnTI+DziLYjr7SE6IQWF7j4xcYyyurOrXrDkuJa1u4/yJMTYpif6mx2jGma7DjuZXGBi0WFNeypCt4b2J52lXn9HFyd6uD82DAURUqvCiGEEEIIIUSgjOoZ2pDCyBDPL4QIEEc4xsARGANHnNjmqUPdW1gfmqivOFGSj1J3djcDdQSm3YEnYzae6XMx+6SEejlCCCFEp+IdP526/SWEffBqwOZwz12Id/z0gO1fBIaEJIToREzT5M/bK3l8e9uVDiqrM/nB6uPklHt5YGSkP4hQVOllUUENiwtc7CoLXrnVSKvC5cm+YMTkBBsWVYIRQgghhBBCCBEM58eG4bQouLzBa6fYwGlRGNcrLOjzCiFCxBqG0W8gRr+BJ7YZOsqBvScqThTnohXnoVSHrsLNudKT06l58ClwhId6KUIIIUSnVTd3ISYKtg9eafN9u+cuxDP31jbfrwg8CUkI0Ym0dUDiZI9vr8TlMekTobGowMXWw56AzNMcmwaz+9qZn+pkVl87DosEI4QQQgghhBAi2MKtKlenOng1xxX0ua9JdeC0qEGfVwjRjqgaZu9kvL2TYcIM3zbTRDl60B+Y8LfrOHY4tGttKYdTAhJCCCFEoCkKnnkLMRMSsb32FEr1uV9HM8Mjcd/yU6kg0YFJSEKITmJRgStgAYkGz+6oCuj+T6YqMCXBxoJUB5clO4gOk5NhQgghhBBCCBFqPxwcEZKQxA8GRwR9TiFEB6AomD3j0XvGo4+58MT2irL6Fh0nKk6oB/aEbp2n4gneTUhCCCFEV+cdPx190Ehsrz+NZeua1u9n7GTcN9+HGdOjDVcngk1CEkJ0AgdcOr/cWBbqZbSJC3qFMb+fg7n9HPRyaKFejhBCCCGEEEKIkwzvbuWKZDtLimuDNucVyXaGd7cGbT4hRCcQFYM+/Hz04eef2FbjQt2Td6LiRHEuakkeIa1XapVjmxBCCBFMZkwPau/5A2pJHtblH2LZ8CVK3Znf25hhdrwTZ+KZPgcjKS0IKxWBJiEJITqBX20qo6wu+D1h28qQbhauTnVyVT8HyZFyWBJCCCGEEEKI9uyJ8TGs3X8wKO9DY8IUnhgfE/B5hBBdgMOJMWAExoAR/k22v/8e65bVIVuSEd09ZHMLIYQQXZmRlIb7tl/gvuEutLws1KIcX4Cy/Jiv0pPVihHdHSM5HSNlAHraMLDZQ71s0YbkaqQQHdx3R+uCegdPW0mK0Lg61cH8VCdDuklqXgghhBBCCCE6ininxpMTYvjB6uMBn+vJCTHEO6XKoBAiMIyUARDKkERyesjmFkIIIQRgs6MPHYs+dGyoVyKCTEISQnRwL+2uDvUSWqyXQ2VuioOrU52MjbWiKCEtaCiEEEIIIYQQopXmpzrJKffy+PbKgM3xwMhI5qc6A7Z/IYQw+g0M7fwpA0I6vxBCCCFEVyUhCSE6sGqPwbsFNaFexmlFWRWuSHGwoJ+DCxNsWFQJRgghhBBCCCFEZ/DAyEgU4M8BCEo8MDKS+0dGtvl+hRDiZHraUMwwe4t6kbc1M8zuK90thBBCCCGCTkISQnRgWw7X4fIGvgfs2bJrMDvRzoJUJzP72LFbJBghhBBCCCGEEJ2Noig8MCqK9GgLv9hYRlndub8/jQlTeHJCjFSQEEIEh82Bd8IMrKs/DvrU3okzpbe5EEIIIUSISEhCiA5s2xFPqJfgpykwtbeN+alOLkuyExWmhnpJQgghhBBCCCGCYH6qk4x4G7/aVMaS4tbfjX1Fsp0nxscQ79TacHVCCHF6nhlzQxKS8EyfE/Q5hRBCCCGEj4QkhOjAMo+FPiQxvlcYC1IdzElxEOuQE1lCCCGEEEII0RXFOzVen9aDzGMeXtpVBata9jynReGaVAc/GBzB8O7WgK5RCCGaYySl4R07GcvWNUGb0zt2MkZSWtDmE0IIIYQQjUlIQogO7IBLD+n8Y2OtfH5ZbEjXIIQQQgghhBCi/Rje3cpTGd3g3y0bn3d9PE6LVCIUQoSW++b70HZtQ6muDPhcZngk7pvvC/g8QgghhBDi1ORdqBAdmMc4936v50IJ6exCCCGEEEIIITo6CUgIIdoDM6YH7lt+GpS53Lf8FDOmR1DmEkIIIYQQzZN3okJ0YFY1tDGFME1iEkIIIYQQQgghhBCi4/OOn07d3FsDOod77kK846cHdA4hhBBCCHFmEpIQogOLd2ohnT/OEdr5hRBCCCGEEEIIIYRoK3VzF+KeuzAg+3bPXYgnwCEMIYQQQgjRMhKSEKIDG97dGtL5R4R4fiGEEEIIIYQQQggh2oyi4Jm3kNo7f4cZHtkmuzTDI6m983d45i0ERaqyCiGEEEK0B5ZQL0AI0XqjeoY2pDAyxPMLIYQQQgghhBBCCNHWvOOnow8aie31p7FsXdP6/YydjPvm+zBjerTh6oQQQgghxLmSkIQQHdj5sWE4LQourxn0uZ0WhXG9woI+rxBCCCGEEEIIIYQQgWbG9KD2nj+gluRhXf4h1lVLWva8MDveiTPxTJ+DkZQW4FUKIYQQQojWkHYbQnRg4VaVq1MdIZn7mlQHToscQoQQQgghhBBCCCFE52UkpeG+7RctHl/99w9w3/YLCUgIIYQQQrRjcoVTiA7uh4MjQjLvD0I0rxBCCCGEEEIIIYQQ7ZbNHuoVCCGEEEKIM5CQhBAd3PDuVq5IDu6bryuS7Qzvbg3qnEIIIYQQQgghhBBCCCGEEEIIca4kJCFEJ/DE+BhiwpSgzBUTpvDE+JigzCWEEEIIIYQQQgghhBBCCCGEEG1JQhJCdALxTo0nJ8QEZa4nJ8QQ79SCMpcQQgghhBBCCCGEEEIIIYQQQrQlS6gXIIRoG/NTneSUe3l8e2XA5nhgZCTzU50B278QQgghhBBCCCGEEEIIIQKv7LY+oV6CEEKETLusJPHf//6Xn/70p0yZMoVevXoRExPDm2++ecrx+fn5/OQnP2H06NHEx8czePBg5s6dy6effnrK57z77rtMmzaN3r17k5yczNVXX822bdtOO8fChQvp378/8fHxTJw4kX//+98YhtHseLfbzeOPP86YMWOIi4tj4MCB3HPPPRw8eLDlPwghztIDIyN5YGRkwPZ9f4D2LYQQQgghhBBCCCGEEEIIIYQQwdAuQxKPPvoor7zyCnv27CEuLu60Y7du3cqkSZN45513GDp0KHfccQfTpk3jm2++4YYbbuDPf/5zk+c8+eST3H777Rw6dIjbbruNefPm8dVXXzF79mzWrl3bZPzu3buZOnUqn3zyCdOnT+fHP/4xAL/+9a/52c9+1mS8YRjccMMNPPbYY3Tr1o0777yT8ePH8+abbzJ9+nQJSoiAURSFB0ZF8dJF3YgJU9pknzFhCi9d1I0HRkWhKG2zTyGEEEI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AAAAAAAAAZAoESQAAAAAAAAAAAAAAgEyBIAkAAAAAAAAAAAAAAJApECQBAAAAAAAAAAAAAAAyBYIkAAAAAAAAAAAAAABApkCQBAAAAAAAAAAAAAAAyBQIkgAAAAAAAAAAAAAAAJkCQRIAAAAAAAAAAAAAACBTIEgCAAAAAAAAAAAAAABkCgRJAAAAAAAAAAAAAACATIEgCQAAAAAAAAAAAAAAkCn8P7KvI8BxUm4vAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Unit price', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f50c5fe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc807e24e50>"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Quantity', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "8b24cf5c",
+   "metadata": {},
+   "source": [
+    "# Summary of Insight\n",
+    " The combined dataset consists of 1000 rows and 17 columns.\n",
+    "\n",
+    " The dataset contains no null values\n",
+    "\n",
+    " The dataset contains records of 501 female customers and 499 male customers\n",
+    "\n",
+    " It was observed that the city with the highest gross income is 'Port Harcourt'.\n",
+    " \n",
+    " It can be observed that the most frequent customers are of the female gender and the most bought product is the 'Home and     lifestyle'. Also, the use of catplot with kind = 'point' is an easy to read representation.\n",
+    "\n",
+    " The most used payment method with respect to 'Product line' is Cash\n",
+    " \n",
+    " The branch with the lowest rating is \"B\" while 'C' has the highest\n",
+    " \n",
+    " It is observed that the most frequent customers are of the female gender and the most bought product is the 'Home and lifestyle'.\n",
+    " \n",
+    " It is observed that the most expensive 'product line' is the 'food and beverages' and it is mostly bought by the female gender."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dd96b18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8343c810",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Pandas_Analytics_project/Port_Harcourt_Branch.csv b/Pandas_Analytics_project/Port_Harcourt_Branch.csv
new file mode 100644
index 0000000..9c09251
--- /dev/null
+++ b/Pandas_Analytics_project/Port_Harcourt_Branch.csv
@@ -0,0 +1,329 @@
+Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
+226-31-3081,C,Port Harcourt,Normal,Female,Electronic accessories,5500.8,5,1375.2,28879.2,3/8/2019,10:29,Cash,27504.000000000004,4.7619047619999995,1375.2,9.6
+699-14-3026,C,Port Harcourt,Normal,Male,Electronic accessories,30740.4,7,10759.140000000001,225941.94,3/25/2019,18:30,Epay,215182.80000000002,4.7619047619999995,10759.140000000001,4.1
+315-22-5665,C,Port Harcourt,Normal,Female,Home and lifestyle,26481.600000000002,10,13240.800000000001,278056.8,2/24/2019,11:38,Epay,264816.0,4.7619047619999995,13240.800000000001,8.0
+300-71-4605,C,Port Harcourt,Member,Male,Electronic accessories,30974.4,5,7743.6,162615.6,2/25/2019,11:24,Epay,154872.0,4.7619047619999995,7743.6,4.8
+183-56-6882,C,Port Harcourt,Member,Female,Food and beverages,35791.2,4,7158.24,150323.04,2/6/2019,10:42,Epay,143164.8,4.7619047619999995,7158.24,7.5
+232-16-2483,C,Port Harcourt,Member,Female,Sports and travel,24523.2,1,1226.16,25749.360000000004,1/7/2019,12:28,Epay,24523.2,4.7619047619999995,1226.16,6.8
+333-73-7901,C,Port Harcourt,Normal,Female,Health and beauty,19771.2,8,7908.480000000001,166078.08000000002,3/23/2019,13:24,Epay,158169.6,4.7619047619999995,7908.480000000001,7.6
+554-53-8700,C,Port Harcourt,Member,Male,Home and lifestyle,20199.6,2,2019.9600000000003,42419.16,2/2/2019,10:11,Cash,40399.2,4.7619047619999995,2019.9600000000003,6.3
+228-96-1411,C,Port Harcourt,Member,Female,Food and beverages,35532.0,8,14212.8,298468.8,3/4/2019,20:39,Cash,284256.0,4.7619047619999995,14212.8,7.6
+617-15-4209,C,Port Harcourt,Member,Male,Health and beauty,5533.2,2,553.3199999999999,11619.720000000001,3/16/2019,19:47,Cash,11066.4,4.7619047619999995,553.3199999999999,7.2
+574-22-5561,C,Port Harcourt,Member,Female,Fashion accessories,29746.8,10,14873.4,312341.4,3/19/2019,17:08,Epay,297468.0,4.7619047619999995,14873.4,7.9
+326-78-5178,C,Port Harcourt,Member,Male,Food and beverages,32904.0,7,11516.4,241844.4,2/3/2019,10:19,Cash,230327.99999999997,4.7619047619999995,11516.4,9.5
+778-71-5554,C,Port Harcourt,Member,Male,Fashion accessories,5554.8,1,277.74,5832.54,1/25/2019,15:46,Card,5554.8,4.7619047619999995,277.74,6.1
+399-46-5918,C,Port Harcourt,Normal,Female,Electronic accessories,30952.800000000003,8,12381.119999999999,260003.52,2/28/2019,19:01,Cash,247622.40000000002,4.7619047619999995,12381.119999999999,8.2
+120-06-4233,C,Port Harcourt,Normal,Male,Electronic accessories,11019.6,6,3305.88,69423.48,3/12/2019,20:36,Cash,66117.6,4.7619047619999995,3305.88,9.3
+285-68-5083,C,Port Harcourt,Member,Female,Sports and travel,8906.4,3,1335.96,28055.16,2/15/2019,17:47,Card,26719.2,4.7619047619999995,1335.96,10.0
+803-83-5989,C,Port Harcourt,Normal,Male,Home and lifestyle,20062.8,6,6018.84,126395.64,2/24/2019,10:55,Epay,120376.8,4.7619047619999995,6018.84,7.0
+838-78-4295,C,Port Harcourt,Normal,Female,Health and beauty,12049.199999999999,2,1204.92,25303.319999999996,2/10/2019,15:43,Epay,24098.399999999998,4.7619047619999995,1204.92,6.7
+393-65-2792,C,Port Harcourt,Normal,Male,Food and beverages,32212.800000000003,10,16106.400000000001,338234.39999999997,1/6/2019,12:46,Card,322128.0,4.7619047619999995,16106.400000000001,9.6
+796-12-2025,C,Port Harcourt,Normal,Male,Fashion accessories,22363.2,10,11181.6,234813.6,2/11/2019,16:19,Cash,223632.00000000003,4.7619047619999995,11181.6,5.9
+841-35-6630,C,Port Harcourt,Normal,Female,Electronic accessories,27327.6,6,8198.28,172163.88,3/9/2019,18:21,Cash,163965.6,4.7619047619999995,8198.28,8.7
+732-94-0499,C,Port Harcourt,Normal,Female,Electronic accessories,14994.0,10,7497.0,157437.0,1/13/2019,17:04,Card,149940.0,4.7619047619999995,7497.0,5.4
+263-10-3913,C,Port Harcourt,Member,Male,Fashion accessories,17654.4,9,7944.4800000000005,166834.08000000002,1/9/2019,14:20,Card,158889.6,4.7619047619999995,7944.4800000000005,8.6
+829-49-1914,C,Port Harcourt,Member,Female,Food and beverages,28191.600000000002,10,14095.800000000001,296011.8,3/5/2019,16:24,Epay,281916.0,4.7619047619999995,14095.800000000001,6.6
+756-01-7507,C,Port Harcourt,Normal,Female,Health and beauty,7336.799999999999,5,1834.1999999999998,38518.200000000004,1/22/2019,18:56,Cash,36684.0,4.7619047619999995,1834.1999999999998,6.0
+870-72-4431,C,Port Harcourt,Normal,Female,Health and beauty,35708.4,6,10712.519999999999,224962.91999999998,1/21/2019,14:42,Card,214250.4,4.7619047619999995,10712.519999999999,5.5
+480-63-2856,C,Port Harcourt,Normal,Male,Food and beverages,6930.0,8,2772.0,58211.99999999999,1/23/2019,18:37,Epay,55440.0,4.7619047619999995,2772.0,6.6
+787-56-0757,C,Port Harcourt,Member,Female,Food and beverages,28929.6,4,5785.92,121504.31999999998,2/23/2019,18:45,Card,115718.4,4.7619047619999995,5785.92,8.3
+360-39-5055,C,Port Harcourt,Member,Male,Sports and travel,17607.6,5,4401.9,92439.9,3/9/2019,10:17,Cash,88038.0,4.7619047619999995,4401.9,6.6
+730-50-9884,C,Port Harcourt,Normal,Female,Sports and travel,29901.600000000002,7,10465.56,219776.76,3/5/2019,14:31,Epay,209311.19999999998,4.7619047619999995,10465.56,4.0
+362-58-8315,C,Port Harcourt,Normal,Male,Fashion accessories,27547.199999999997,5,6886.799999999999,144622.80000000002,3/25/2019,10:23,Cash,137736.0,4.7619047619999995,6886.799999999999,9.9
+565-80-5980,C,Port Harcourt,Member,Female,Home and lifestyle,17056.8,4,3411.36,71638.56,1/23/2019,10:25,Cash,68227.2,4.7619047619999995,3411.36,7.1
+225-32-0908,C,Port Harcourt,Normal,Female,Sports and travel,16149.6,10,8074.8,169570.8,1/26/2019,19:54,Epay,161496.0,4.7619047619999995,8074.8,8.2
+512-91-0811,C,Port Harcourt,Normal,Male,Health and beauty,32310.0,1,1615.5,33925.5,2/6/2019,20:05,Card,32310.0,4.7619047619999995,1615.5,6.6
+871-39-9221,C,Port Harcourt,Normal,Female,Electronic accessories,4482.0,6,1344.6,28236.600000000002,2/9/2019,13:11,Cash,26892.0,4.7619047619999995,1344.6,4.1
+163-56-7055,C,Port Harcourt,Member,Male,Fashion accessories,17535.6,1,876.7800000000001,18412.38,3/26/2019,19:20,Cash,17535.6,4.7619047619999995,876.7800000000001,4.1
+189-98-2939,C,Port Harcourt,Normal,Male,Fashion accessories,28278.0,9,12725.099999999999,267227.1,3/1/2019,13:22,Cash,254502.00000000003,4.7619047619999995,12725.099999999999,7.2
+551-21-3069,C,Port Harcourt,Normal,Female,Electronic accessories,8305.2,9,3737.34,78484.14,2/1/2019,11:27,Cash,74746.8,4.7619047619999995,3737.34,4.9
+628-34-3388,C,Port Harcourt,Normal,Male,Fashion accessories,9856.8,6,2957.04,62097.84000000001,1/5/2019,20:54,Card,59140.8,4.7619047619999995,2957.04,7.9
+588-01-7461,C,Port Harcourt,Normal,Female,Food and beverages,12232.8,9,5504.759999999999,115599.95999999999,3/24/2019,10:43,Cash,110095.2,4.7619047619999995,5504.759999999999,4.2
+861-77-0145,C,Port Harcourt,Member,Male,Electronic accessories,29509.2,10,14754.6,309846.6,3/3/2019,14:30,Cash,295092.0,4.7619047619999995,14754.6,9.2
+210-67-5886,C,Port Harcourt,Member,Female,Health and beauty,35355.6,3,5303.34,111370.13999999998,2/5/2019,10:41,Card,106066.8,4.7619047619999995,5303.34,7.8
+237-01-6122,C,Port Harcourt,Member,Female,Home and lifestyle,29084.4,9,13087.98,274847.58,2/1/2019,20:31,Card,261759.6,4.7619047619999995,13087.98,9.5
+225-98-1496,C,Port Harcourt,Normal,Female,Fashion accessories,9727.2,3,1459.08,30640.68,3/2/2019,13:01,Card,29181.600000000002,4.7619047619999995,1459.08,7.1
+659-36-1684,C,Port Harcourt,Member,Male,Sports and travel,20563.2,7,7197.12,151139.52,1/12/2019,12:02,Card,143942.4,4.7619047619999995,7197.12,6.5
+336-78-2147,C,Port Harcourt,Member,Male,Home and lifestyle,23007.6,8,9203.04,193263.84000000003,3/13/2019,19:52,Card,184060.8,4.7619047619999995,9203.04,4.6
+189-08-9157,C,Port Harcourt,Normal,Female,Fashion accessories,11422.8,9,5140.259999999999,107945.46,1/8/2019,16:17,Card,102805.2,4.7619047619999995,5140.259999999999,5.9
+663-86-9076,C,Port Harcourt,Member,Female,Food and beverages,24674.4,8,9869.76,207264.96,1/8/2019,15:57,Epay,197395.2,4.7619047619999995,9869.76,8.5
+483-71-1164,C,Port Harcourt,Normal,Female,Health and beauty,29268.0,6,8780.4,184388.40000000002,3/8/2019,16:43,Epay,175608.0,4.7619047619999995,8780.4,5.3
+597-78-7908,C,Port Harcourt,Normal,Male,Fashion accessories,32479.2,3,4871.88,102309.48,2/18/2019,19:39,Cash,97437.6,4.7619047619999995,4871.88,6.2
+731-81-9469,C,Port Harcourt,Member,Female,Sports and travel,32328.0,10,16164.0,339444.0,1/23/2019,13:00,Card,323280.0,4.7619047619999995,16164.0,5.4
+280-17-4359,C,Port Harcourt,Member,Male,Health and beauty,32580.0,10,16290.0,342090.0,1/25/2019,13:48,Cash,325800.0,4.7619047619999995,16290.0,8.1
+338-65-2210,C,Port Harcourt,Member,Female,Health and beauty,24695.999999999996,10,12347.999999999998,259307.99999999997,2/5/2019,19:57,Cash,246960.0,4.7619047619999995,12347.999999999998,9.1
+488-25-4221,C,Port Harcourt,Member,Female,Food and beverages,10947.6,1,547.38,11494.98,2/22/2019,10:36,Card,10947.6,4.7619047619999995,547.38,8.4
+458-41-1477,C,Port Harcourt,Normal,Female,Health and beauty,16653.6,6,4996.08,104917.68,3/8/2019,17:11,Card,99921.6,4.7619047619999995,4996.08,9.5
+568-90-5112,C,Port Harcourt,Normal,Male,Health and beauty,23810.4,4,4762.08,100003.68000000001,3/19/2019,12:46,Card,95241.6,4.7619047619999995,4762.08,5.6
+584-86-7256,C,Port Harcourt,Member,Male,Sports and travel,12441.6,7,4354.5599999999995,91445.76,3/11/2019,16:07,Card,87091.2,4.7619047619999995,4354.5599999999995,7.3
+214-17-6927,C,Port Harcourt,Normal,Female,Food and beverages,5932.8,6,1779.84,37376.64,2/7/2019,18:23,Epay,35596.799999999996,4.7619047619999995,1779.84,9.9
+400-89-4171,C,Port Harcourt,Normal,Female,Sports and travel,29149.2,8,11659.680000000002,244853.28,1/28/2019,13:05,Cash,233193.6,4.7619047619999995,11659.680000000002,9.3
+894-41-5205,C,Port Harcourt,Normal,Female,Food and beverages,15544.8,8,6217.920000000001,130576.31999999999,1/19/2019,19:39,Card,124358.4,4.7619047619999995,6217.920000000001,8.3
+423-80-0988,C,Port Harcourt,Normal,Male,Sports and travel,27504.000000000004,2,2750.4,57758.4,1/30/2019,19:42,Epay,55008.00000000001,4.7619047619999995,2750.4,6.5
+234-65-2137,C,Port Harcourt,Normal,Male,Home and lifestyle,34408.8,10,17204.4,361292.4,1/16/2019,13:32,Cash,344088.0,4.7619047619999995,17204.4,4.8
+733-33-4967,C,Port Harcourt,Normal,Male,Electronic accessories,7506.000000000001,8,3002.4,63050.399999999994,3/3/2019,19:17,Cash,60048.00000000001,4.7619047619999995,3002.4,6.3
+139-52-2867,C,Port Harcourt,Normal,Female,Fashion accessories,8103.6,7,2836.2599999999998,59561.46,2/13/2019,10:50,Card,56725.2,4.7619047619999995,2836.2599999999998,4.8
+342-65-4817,C,Port Harcourt,Member,Male,Health and beauty,31248.0,3,4687.2,98431.20000000001,1/28/2019,16:47,Epay,93743.99999999999,4.7619047619999995,4687.2,9.9
+130-98-8941,C,Port Harcourt,Normal,Male,Fashion accessories,23133.600000000002,7,8096.76,170031.96,2/9/2019,10:00,Cash,161935.2,4.7619047619999995,8096.76,5.7
+434-83-9547,C,Port Harcourt,Member,Male,Food and beverages,13849.199999999999,8,5539.68,116333.27999999998,1/23/2019,11:51,Cash,110793.59999999999,4.7619047619999995,5539.68,7.7
+824-88-3614,C,Port Harcourt,Normal,Male,Health and beauty,12351.6,8,4940.64,103753.44,1/25/2019,15:00,Epay,98812.8,4.7619047619999995,4940.64,5.7
+593-65-1552,C,Port Harcourt,Normal,Female,Home and lifestyle,25131.600000000002,4,5026.32,105552.72,1/28/2019,20:50,Card,100526.40000000001,4.7619047619999995,5026.32,5.9
+286-43-6208,C,Port Harcourt,Normal,Female,Food and beverages,31608.0,9,14223.599999999999,298695.60000000003,3/16/2019,19:08,Cash,284472.0,4.7619047619999995,14223.599999999999,9.2
+556-86-3144,C,Port Harcourt,Member,Female,Fashion accessories,26744.4,1,1337.22,28081.62,1/13/2019,19:30,Cash,26744.4,4.7619047619999995,1337.22,5.0
+848-24-9445,C,Port Harcourt,Member,Male,Health and beauty,15732.000000000002,2,1573.2,33037.2,3/26/2019,18:03,Cash,31464.000000000004,4.7619047619999995,1573.2,4.9
+699-01-4164,C,Port Harcourt,Normal,Male,Health and beauty,14940.0,4,2988.0000000000005,62748.00000000001,3/12/2019,19:58,Card,59760.0,4.7619047619999995,2988.0000000000005,8.2
+420-11-4919,C,Port Harcourt,Member,Female,Food and beverages,25700.4,5,6425.1,134927.1,2/17/2019,19:57,Card,128502.0,4.7619047619999995,6425.1,5.5
+606-80-4905,C,Port Harcourt,Member,Female,Sports and travel,6893.999999999999,6,2068.2,43432.2,1/29/2019,10:01,Card,41364.0,4.7619047619999995,2068.2,6.8
+426-39-2418,C,Port Harcourt,Normal,Male,Electronic accessories,22107.6,7,7737.660000000001,162490.86,1/14/2019,10:02,Cash,154753.2,4.7619047619999995,7737.660000000001,9.8
+672-51-8681,C,Port Harcourt,Member,Female,Electronic accessories,23994.000000000004,9,10797.3,226743.3,1/4/2019,18:19,Card,215946.0,4.7619047619999995,10797.3,9.7
+263-87-5680,C,Port Harcourt,Member,Female,Home and lifestyle,10270.800000000001,10,5135.400000000001,107843.4,3/18/2019,17:38,Epay,102708.0,4.7619047619999995,5135.400000000001,7.8
+677-11-0152,C,Port Harcourt,Normal,Female,Food and beverages,33573.6,9,15108.119999999999,317270.52,1/16/2019,18:08,Cash,302162.4,4.7619047619999995,15108.119999999999,8.8
+389-25-3394,C,Port Harcourt,Normal,Male,Electronic accessories,4251.6,5,1062.9,22320.899999999998,2/17/2019,18:06,Cash,21258.0,4.7619047619999995,1062.9,9.4
+279-62-1445,C,Port Harcourt,Member,Female,Fashion accessories,4514.4,1,225.72,4740.12,2/21/2019,12:38,Cash,4514.4,4.7619047619999995,225.72,8.2
+746-68-6593,C,Port Harcourt,Member,Female,Sports and travel,31377.6,2,3137.76,65892.95999999999,1/11/2019,14:29,Card,62755.2,4.7619047619999995,3137.76,9.7
+583-72-1480,C,Port Harcourt,Member,Male,Electronic accessories,13341.6,4,2668.32,56034.72000000001,1/31/2019,16:24,Epay,53366.4,4.7619047619999995,2668.32,9.7
+211-30-9270,C,Port Harcourt,Normal,Male,Health and beauty,6267.6,5,1566.9,32904.9,1/28/2019,15:16,Card,31338.0,4.7619047619999995,1566.9,4.9
+755-12-3214,C,Port Harcourt,Member,Female,Fashion accessories,15919.199999999999,5,3979.7999999999997,83575.8,3/5/2019,17:07,Card,79596.0,4.7619047619999995,3979.7999999999997,8.6
+142-72-4741,C,Port Harcourt,Member,Male,Fashion accessories,33552.0,2,3355.2000000000003,70459.2,2/28/2019,18:37,Card,67104.0,4.7619047619999995,3355.2000000000003,6.0
+662-47-5456,C,Port Harcourt,Member,Male,Fashion accessories,12668.4,10,6334.2,133018.2,3/17/2019,19:06,Card,126683.99999999999,4.7619047619999995,6334.2,8.4
+883-17-4236,C,Port Harcourt,Normal,Female,Sports and travel,5180.400000000001,2,518.04,10878.84,3/2/2019,19:44,Card,10360.800000000001,4.7619047619999995,518.04,7.2
+380-94-4661,C,Port Harcourt,Member,Male,Electronic accessories,23738.399999999998,4,4747.679999999999,99701.28,2/7/2019,13:05,Card,94953.59999999999,4.7619047619999995,4747.679999999999,6.9
+821-07-3596,C,Port Harcourt,Normal,Female,Fashion accessories,5922.0,4,1184.4,24872.4,3/7/2019,14:53,Epay,23688.0,4.7619047619999995,1184.4,5.6
+808-65-0703,C,Port Harcourt,Normal,Male,Home and lifestyle,12769.199999999999,4,2553.8399999999997,53630.64,3/14/2019,17:22,Card,51076.799999999996,4.7619047619999995,2553.8399999999997,6.9
+687-15-1097,C,Port Harcourt,Member,Female,Health and beauty,7603.200000000001,2,760.32,15966.72,1/3/2019,19:17,Cash,15206.400000000001,4.7619047619999995,760.32,9.7
+526-86-8552,C,Port Harcourt,Member,Female,Home and lifestyle,7855.2,10,3927.6,82479.6,1/7/2019,17:36,Cash,78552.0,4.7619047619999995,3927.6,7.1
+376-56-3573,C,Port Harcourt,Normal,Female,Fashion accessories,34351.2,4,6870.24,144275.04,2/2/2019,13:23,Epay,137404.8,4.7619047619999995,6870.24,6.4
+537-72-0426,C,Port Harcourt,Member,Male,Fashion accessories,25556.399999999998,10,12778.199999999999,268342.2,3/20/2019,16:28,Cash,255564.0,4.7619047619999995,12778.199999999999,5.7
+523-38-0215,C,Port Harcourt,Normal,Male,Home and lifestyle,13320.0,1,666.0,13986.0,3/6/2019,13:29,Card,13320.0,4.7619047619999995,666.0,7.9
+593-95-4461,C,Port Harcourt,Member,Male,Home and lifestyle,26949.6,1,1347.48,28297.08,3/24/2019,14:49,Cash,26949.6,4.7619047619999995,1347.48,6.9
+226-71-3580,C,Port Harcourt,Normal,Female,Sports and travel,8550.0,9,3847.5,80797.5,1/31/2019,12:02,Cash,76950.0,4.7619047619999995,3847.5,9.5
+558-80-4082,C,Port Harcourt,Normal,Male,Electronic accessories,10026.0,7,3509.1000000000004,73691.09999999999,3/14/2019,17:20,Epay,70182.0,4.7619047619999995,3509.1000000000004,6.0
+211-05-0490,C,Port Harcourt,Member,Female,Electronic accessories,18691.2,5,4672.8,98128.79999999999,3/3/2019,13:42,Cash,93456.00000000001,4.7619047619999995,4672.8,7.5
+727-75-6477,C,Port Harcourt,Normal,Male,Electronic accessories,10382.4,4,2076.48,43606.079999999994,3/29/2019,14:44,Cash,41529.6,4.7619047619999995,2076.48,6.4
+779-06-0012,C,Port Harcourt,Member,Female,Home and lifestyle,31899.6,1,1594.98,33494.579999999994,1/19/2019,10:21,Cash,31899.6,4.7619047619999995,1594.98,7.7
+446-47-6729,C,Port Harcourt,Normal,Male,Fashion accessories,35935.2,2,3593.5200000000004,75463.92,1/2/2019,18:09,Card,71870.4,4.7619047619999995,3593.5200000000004,6.7
+735-06-4124,C,Port Harcourt,Normal,Male,Food and beverages,17499.6,1,874.9799999999999,18374.58,2/25/2019,15:31,Cash,17499.6,4.7619047619999995,874.9799999999999,4.4
+181-94-6432,C,Port Harcourt,Member,Male,Fashion accessories,24958.8,2,2495.88,52413.479999999996,2/5/2019,19:05,Epay,49917.6,4.7619047619999995,2495.88,9.7
+227-07-4446,C,Port Harcourt,Member,Female,Electronic accessories,28126.8,10,14063.4,295331.4,2/10/2019,20:51,Cash,281268.0,4.7619047619999995,14063.4,4.4
+174-36-3675,C,Port Harcourt,Member,Male,Food and beverages,35773.200000000004,2,3577.3200000000006,75123.72,2/14/2019,17:29,Cash,71546.40000000001,4.7619047619999995,3577.3200000000006,5.2
+428-83-5800,C,Port Harcourt,Member,Female,Food and beverages,7588.799999999999,3,1138.32,23904.72,2/9/2019,10:25,Cash,22766.4,4.7619047619999995,1138.32,7.3
+603-07-0961,C,Port Harcourt,Member,Male,Electronic accessories,26924.4,5,6731.1,141353.1,1/10/2019,11:34,Cash,134622.0,4.7619047619999995,6731.1,4.9
+704-20-4138,C,Port Harcourt,Member,Female,Health and beauty,10681.2,7,3738.4199999999996,78506.82,3/11/2019,18:58,Card,74768.4,4.7619047619999995,3738.4199999999996,8.1
+787-15-1757,C,Port Harcourt,Member,Male,Health and beauty,15865.2,4,3173.04,66633.84000000001,2/18/2019,16:28,Epay,63460.8,4.7619047619999995,3173.04,8.4
+649-11-3678,C,Port Harcourt,Normal,Female,Food and beverages,8254.8,9,3714.66,78007.86,2/26/2019,20:26,Cash,74293.2,4.7619047619999995,3714.66,5.5
+622-20-1945,C,Port Harcourt,Normal,Female,Health and beauty,14191.2,1,709.56,14900.760000000002,1/18/2019,15:08,Cash,14191.2,4.7619047619999995,709.56,8.4
+719-76-3868,C,Port Harcourt,Member,Male,Food and beverages,33933.6,4,6786.72,142521.12,3/12/2019,16:30,Cash,135734.4,4.7619047619999995,6786.72,8.6
+835-16-0096,C,Port Harcourt,Member,Male,Sports and travel,5292.0,5,1323.0,27783.0,3/24/2019,13:48,Epay,26460.0,4.7619047619999995,1323.0,8.5
+633-09-3463,C,Port Harcourt,Normal,Female,Electronic accessories,17154.0,3,2573.1,54035.1,3/28/2019,12:58,Card,51461.99999999999,4.7619047619999995,2573.1,9.5
+416-13-5917,C,Port Harcourt,Normal,Female,Food and beverages,34930.8,5,8732.7,183386.7,1/30/2019,16:24,Epay,174654.0,4.7619047619999995,8732.7,9.3
+725-96-3778,C,Port Harcourt,Member,Female,Home and lifestyle,32130.0,8,12852.000000000002,269892.0,1/20/2019,10:13,Cash,257040.0,4.7619047619999995,12852.000000000002,4.7
+860-79-0874,C,Port Harcourt,Member,Female,Fashion accessories,35748.0,10,17874.0,375354.00000000006,2/15/2019,14:53,Card,357480.0,4.7619047619999995,17874.0,6.6
+320-49-6392,C,Port Harcourt,Normal,Female,Electronic accessories,10886.4,1,544.3199999999999,11430.72,3/4/2019,15:44,Cash,10886.4,4.7619047619999995,544.3199999999999,8.4
+632-90-0281,C,Port Harcourt,Normal,Female,Fashion accessories,13517.999999999998,10,6758.999999999999,141939.0,3/8/2019,20:01,Card,135180.0,4.7619047619999995,6758.999999999999,9.3
+554-42-2417,C,Port Harcourt,Normal,Female,Sports and travel,34358.4,10,17179.2,360763.2,1/9/2019,13:45,Cash,343584.0,4.7619047619999995,17179.2,5.2
+605-72-4132,C,Port Harcourt,Normal,Female,Food and beverages,34009.2,8,13603.680000000002,285677.28,2/27/2019,15:12,Cash,272073.6,4.7619047619999995,13603.680000000002,9.1
+471-41-2823,C,Port Harcourt,Normal,Male,Food and beverages,35924.4,2,3592.4399999999996,75441.24,3/7/2019,20:37,Epay,71848.8,4.7619047619999995,3592.4399999999996,8.0
+272-27-9238,C,Port Harcourt,Normal,Female,Food and beverages,14846.400000000001,4,2969.2799999999997,62354.88,2/19/2019,16:23,Cash,59385.600000000006,4.7619047619999995,2969.2799999999997,7.1
+834-25-9262,C,Port Harcourt,Normal,Female,Fashion accessories,29404.800000000003,4,5880.959999999999,123500.15999999999,1/6/2019,12:12,Cash,117619.20000000001,4.7619047619999995,5880.959999999999,9.1
+122-61-9553,C,Port Harcourt,Normal,Female,Electronic accessories,18475.2,9,8313.84,174590.63999999998,3/14/2019,19:33,Cash,166276.8,4.7619047619999995,8313.84,5.6
+613-59-9758,C,Port Harcourt,Normal,Female,Sports and travel,5169.599999999999,10,2584.7999999999997,54280.8,1/27/2019,14:28,Cash,51696.0,4.7619047619999995,2584.7999999999997,5.4
+730-70-9830,C,Port Harcourt,Normal,Female,Home and lifestyle,25239.6,6,7571.88,159009.48,3/14/2019,17:54,Epay,151437.6,4.7619047619999995,7571.88,5.2
+382-25-8917,C,Port Harcourt,Normal,Male,Fashion accessories,15148.8,6,4544.64,95437.44000000002,1/29/2019,12:25,Cash,90892.8,4.7619047619999995,4544.64,8.9
+743-88-1662,C,Port Harcourt,Normal,Male,Sports and travel,34376.4,7,12031.74,252666.54,2/22/2019,18:17,Epay,240634.8,4.7619047619999995,12031.74,8.7
+595-86-2894,C,Port Harcourt,Member,Male,Fashion accessories,34912.8,4,6982.56,146633.75999999998,2/6/2019,17:20,Epay,139651.2,4.7619047619999995,6982.56,9.4
+462-78-5240,C,Port Harcourt,Normal,Female,Electronic accessories,9579.6,2,957.96,20117.16,3/19/2019,14:35,Cash,19159.2,4.7619047619999995,957.96,4.2
+153-58-4872,C,Port Harcourt,Member,Female,Food and beverages,26960.4,4,5392.08,113233.68000000001,3/1/2019,15:32,Epay,107841.6,4.7619047619999995,5392.08,4.2
+689-16-9784,C,Port Harcourt,Normal,Male,Food and beverages,16837.2,6,5051.16,106074.36,3/11/2019,13:37,Cash,101023.2,4.7619047619999995,5051.16,6.0
+394-41-0748,C,Port Harcourt,Member,Female,Fashion accessories,19465.2,9,8759.34,183946.14,1/27/2019,14:55,Epay,175186.8,4.7619047619999995,8759.34,9.5
+541-89-9860,C,Port Harcourt,Member,Female,Fashion accessories,28972.800000000003,3,4345.92,91264.32,2/15/2019,12:31,Cash,86918.4,4.7619047619999995,4345.92,8.1
+110-05-6330,C,Port Harcourt,Normal,Female,Food and beverages,14194.8,6,4258.4400000000005,89427.24,3/25/2019,20:18,Card,85168.8,4.7619047619999995,4258.4400000000005,9.4
+651-61-0874,C,Port Harcourt,Normal,Male,Home and lifestyle,16639.2,4,3327.84,69884.64000000001,3/12/2019,20:04,Card,66556.8,4.7619047619999995,3327.84,6.2
+236-86-3015,C,Port Harcourt,Member,Male,Home and lifestyle,5032.8,1,251.64000000000001,5284.4400000000005,2/4/2019,13:38,Epay,5032.8,4.7619047619999995,251.64000000000001,9.8
+587-03-7455,C,Port Harcourt,Member,Female,Fashion accessories,35204.4,7,12321.54,258752.34,2/16/2019,17:30,Epay,246430.8,4.7619047619999995,12321.54,4.9
+372-26-1506,C,Port Harcourt,Normal,Female,Fashion accessories,8575.2,5,2143.8,45019.8,1/28/2019,19:24,Epay,42876.0,4.7619047619999995,2143.8,5.4
+750-57-9686,C,Port Harcourt,Normal,Female,Home and lifestyle,16336.800000000001,4,3267.36,68614.56,1/8/2019,13:48,Card,65347.200000000004,4.7619047619999995,3267.36,8.7
+186-09-3669,C,Port Harcourt,Member,Female,Health and beauty,29343.600000000002,1,1467.18,30810.78,1/22/2019,10:57,Epay,29343.600000000002,4.7619047619999995,1467.18,9.2
+266-76-6436,C,Port Harcourt,Member,Female,Food and beverages,13896.0,3,2084.4,43772.4,3/28/2019,13:57,Epay,41688.0,4.7619047619999995,2084.4,7.5
+740-22-2500,C,Port Harcourt,Normal,Female,Electronic accessories,30258.0,3,4538.7,95312.7,1/23/2019,13:29,Cash,90774.0,4.7619047619999995,4538.7,9.8
+271-88-8734,C,Port Harcourt,Member,Female,Fashion accessories,34995.6,10,17497.8,367453.8,2/8/2019,13:00,Card,349956.0,4.7619047619999995,17497.8,8.7
+489-64-4354,C,Port Harcourt,Normal,Male,Fashion accessories,5860.8,1,293.04,6153.84,3/9/2019,15:36,Cash,5860.8,4.7619047619999995,293.04,5.0
+574-57-9721,C,Port Harcourt,Normal,Male,Food and beverages,15577.2,2,1557.72,32712.120000000003,3/8/2019,16:53,Epay,31154.4,4.7619047619999995,1557.72,5.7
+751-69-0068,C,Port Harcourt,Normal,Male,Sports and travel,35726.4,9,16076.880000000001,337614.48,3/19/2019,19:09,Epay,321537.6,4.7619047619999995,16076.880000000001,9.0
+257-73-1380,C,Port Harcourt,Member,Male,Sports and travel,29854.800000000003,4,5970.959999999999,125390.15999999999,1/20/2019,16:51,Epay,119419.20000000001,4.7619047619999995,5970.959999999999,9.6
+549-96-4200,C,Port Harcourt,Member,Male,Food and beverages,6134.4,4,1226.8799999999999,25764.48,3/8/2019,20:15,Epay,24537.6,4.7619047619999995,1226.8799999999999,7.0
+810-60-6344,C,Port Harcourt,Normal,Female,Electronic accessories,14709.6,8,5883.84,123560.64,2/7/2019,14:38,Card,117676.8,4.7619047619999995,5883.84,6.5
+450-28-2866,C,Port Harcourt,Member,Male,Food and beverages,6278.400000000001,5,1569.6000000000001,32961.6,1/15/2019,19:25,Cash,31392.0,4.7619047619999995,1569.6000000000001,8.1
+192-98-7397,C,Port Harcourt,Normal,Male,Fashion accessories,4600.8,1,230.04,4830.84,1/8/2019,14:11,Epay,4600.8,4.7619047619999995,230.04,9.5
+235-46-8343,C,Port Harcourt,Member,Male,Food and beverages,9957.6,10,4978.8,104554.8,2/14/2019,11:26,Card,99576.00000000001,4.7619047619999995,4978.8,8.9
+453-12-7053,C,Port Harcourt,Normal,Male,Fashion accessories,16466.4,3,2469.96,51869.16,3/10/2019,17:38,Card,49399.2,4.7619047619999995,2469.96,6.5
+325-90-8763,C,Port Harcourt,Member,Female,Electronic accessories,16765.2,10,8382.6,176034.6,1/27/2019,13:58,Cash,167652.0,4.7619047619999995,8382.6,7.6
+729-46-7422,C,Port Harcourt,Normal,Male,Food and beverages,12920.4,1,646.02,13566.42,2/23/2019,16:52,Card,12920.4,4.7619047619999995,646.02,7.9
+639-76-1242,C,Port Harcourt,Normal,Male,Food and beverages,14587.2,5,3646.8,76582.8,2/3/2019,15:19,Cash,72936.0,4.7619047619999995,3646.8,4.5
+326-71-2155,C,Port Harcourt,Normal,Female,Sports and travel,26622.0,4,5324.4,111812.4,2/3/2019,10:02,Cash,106488.0,4.7619047619999995,5324.4,6.1
+320-32-8842,C,Port Harcourt,Member,Female,Food and beverages,8143.200000000001,1,407.16,8550.359999999999,3/17/2019,18:58,Cash,8143.200000000001,4.7619047619999995,407.16,6.4
+878-30-2331,C,Port Harcourt,Member,Female,Sports and travel,19638.0,10,9819.0,206199.0,3/2/2019,11:22,Card,196380.0,4.7619047619999995,9819.0,7.1
+440-59-5691,C,Port Harcourt,Member,Female,Health and beauty,13374.0,7,4680.9,98298.90000000001,2/8/2019,13:12,Card,93618.0,4.7619047619999995,4680.9,7.7
+746-19-0921,C,Port Harcourt,Normal,Male,Food and beverages,7768.799999999999,1,388.44,8157.240000000001,2/9/2019,10:02,Epay,7768.799999999999,4.7619047619999995,388.44,7.2
+233-34-0817,C,Port Harcourt,Member,Female,Electronic accessories,35582.4,1,1779.1200000000001,37361.520000000004,2/15/2019,11:21,Cash,35582.4,4.7619047619999995,1779.1200000000001,8.4
+767-05-1286,C,Port Harcourt,Member,Female,Home and lifestyle,30157.199999999997,6,9047.16,189990.36,1/23/2019,12:10,Epay,180943.2,4.7619047619999995,9047.16,5.4
+598-47-9715,C,Port Harcourt,Normal,Male,Electronic accessories,30265.199999999997,4,6053.04,127113.84,3/7/2019,16:54,Epay,121060.79999999999,4.7619047619999995,6053.04,4.4
+541-08-3113,C,Port Harcourt,Normal,Male,Food and beverages,23749.2,8,9499.68,199493.28,2/2/2019,20:29,Cash,189993.6,4.7619047619999995,9499.68,8.4
+246-11-3901,C,Port Harcourt,Normal,Female,Electronic accessories,11807.999999999998,10,5903.999999999999,123983.99999999999,2/15/2019,12:12,Cash,118080.0,4.7619047619999995,5903.999999999999,6.2
+493-65-6248,C,Port Harcourt,Member,Female,Sports and travel,13312.8,10,6656.4,139784.4,1/1/2019,19:48,Card,133128.0,4.7619047619999995,6656.4,7.0
+556-72-8512,C,Port Harcourt,Normal,Male,Home and lifestyle,8265.6,1,413.28,8678.880000000001,1/30/2019,20:47,Cash,8265.6,4.7619047619999995,413.28,4.3
+148-82-2527,C,Port Harcourt,Member,Female,Home and lifestyle,4363.2,10,2181.6,45813.6,3/5/2019,13:44,Card,43632.0,4.7619047619999995,2181.6,8.4
+556-97-7101,C,Port Harcourt,Normal,Female,Electronic accessories,22759.2,2,2275.92,47794.32,1/1/2019,15:51,Cash,45518.4,4.7619047619999995,2275.92,8.5
+862-59-8517,C,Port Harcourt,Normal,Female,Food and beverages,32486.399999999998,6,9745.92,204664.31999999998,1/27/2019,11:17,Cash,194918.40000000002,4.7619047619999995,9745.92,6.2
+573-98-8548,C,Port Harcourt,Member,Female,Fashion accessories,11484.0,1,574.2,12058.199999999999,1/5/2019,12:40,Epay,11484.0,4.7619047619999995,574.2,9.1
+620-02-2046,C,Port Harcourt,Normal,Male,Home and lifestyle,24984.000000000004,2,2498.4,52466.4,1/27/2019,19:48,Epay,49968.00000000001,4.7619047619999995,2498.4,9.0
+602-80-9671,C,Port Harcourt,Member,Female,Home and lifestyle,5742.0,6,1722.6000000000001,36174.6,2/9/2019,17:15,Card,34452.0,4.7619047619999995,1722.6000000000001,5.1
+503-07-0930,C,Port Harcourt,Member,Male,Sports and travel,21020.4,7,7357.139999999999,154499.94,2/23/2019,19:49,Card,147142.80000000002,4.7619047619999995,7357.139999999999,8.2
+413-20-6708,C,Port Harcourt,Member,Female,Fashion accessories,18529.2,1,926.46,19455.66,3/18/2019,15:52,Epay,18529.2,4.7619047619999995,926.46,8.5
+521-18-7827,C,Port Harcourt,Member,Male,Home and lifestyle,14180.4,5,3545.1,74447.1,1/22/2019,20:46,Card,70902.0,4.7619047619999995,3545.1,8.7
+600-38-9738,C,Port Harcourt,Member,Male,Sports and travel,25891.2,5,6472.8,135928.8,1/17/2019,15:05,Card,129456.00000000001,4.7619047619999995,6472.8,4.3
+451-28-5717,C,Port Harcourt,Member,Female,Home and lifestyle,29941.2,6,8982.36,188629.56,3/20/2019,11:23,Cash,179647.19999999998,4.7619047619999995,8982.36,7.3
+133-14-7229,C,Port Harcourt,Normal,Male,Health and beauty,22633.2,2,2263.32,47529.72000000001,1/1/2019,11:43,Cash,45266.4,4.7619047619999995,2263.32,5.0
+236-27-1144,C,Port Harcourt,Normal,Female,Food and beverages,5871.599999999999,9,2642.2200000000003,55486.62,3/26/2019,10:31,Epay,52844.399999999994,4.7619047619999995,2642.2200000000003,8.4
+583-41-4548,C,Port Harcourt,Normal,Male,Home and lifestyle,6001.200000000001,7,2100.42,44108.82,2/7/2019,11:36,Epay,42008.4,4.7619047619999995,2100.42,7.4
+358-88-9262,C,Port Harcourt,Member,Female,Food and beverages,31492.800000000003,6,9447.84,198404.64,2/1/2019,18:43,Epay,188956.8,4.7619047619999995,9447.84,5.1
+343-87-0864,C,Port Harcourt,Member,Male,Health and beauty,27316.8,1,1365.84,28682.639999999996,1/3/2019,10:30,Card,27316.8,4.7619047619999995,1365.84,7.1
+243-47-2663,C,Port Harcourt,Member,Male,Electronic accessories,6757.2,6,2027.16,42570.36,1/28/2019,16:43,Card,40543.200000000004,4.7619047619999995,2027.16,5.5
+399-69-4630,C,Port Harcourt,Normal,Male,Electronic accessories,7995.6,6,2398.68,50372.28,3/7/2019,10:23,Card,47973.6,4.7619047619999995,2398.68,8.6
+283-26-5248,C,Port Harcourt,Member,Female,Food and beverages,35467.2,10,17733.6,372405.60000000003,1/30/2019,20:23,Epay,354672.0,4.7619047619999995,17733.6,4.5
+866-99-7614,C,Port Harcourt,Normal,Male,Food and beverages,32112.0,10,16056.0,337176.0,2/11/2019,15:42,Card,321120.0,4.7619047619999995,16056.0,4.4
+718-57-9773,C,Port Harcourt,Normal,Female,Sports and travel,17758.8,10,8879.4,186467.40000000002,2/3/2019,16:40,Card,177588.0,4.7619047619999995,8879.4,9.4
+408-26-9866,C,Port Harcourt,Normal,Female,Sports and travel,26632.800000000003,7,9321.48,195751.08000000002,3/2/2019,16:42,Epay,186429.6,4.7619047619999995,9321.48,4.1
+592-34-6155,C,Port Harcourt,Normal,Male,Food and beverages,11437.2,4,2287.44,48036.24,1/14/2019,14:43,Epay,45748.8,4.7619047619999995,2287.44,6.2
+390-31-6381,C,Port Harcourt,Normal,Male,Food and beverages,9799.199999999999,3,1469.88,30867.48,1/7/2019,12:37,Cash,29397.6,4.7619047619999995,1469.88,7.3
+339-18-7061,C,Port Harcourt,Member,Female,Fashion accessories,33472.8,2,3347.28,70292.87999999999,2/13/2019,15:06,Card,66945.6,4.7619047619999995,3347.28,8.0
+379-17-6588,C,Port Harcourt,Normal,Male,Fashion accessories,21459.6,10,10729.8,225325.8,3/14/2019,11:07,Cash,214596.0,4.7619047619999995,10729.8,5.3
+302-15-2162,C,Port Harcourt,Member,Male,Health and beauty,16750.8,6,5025.240000000001,105530.04000000001,3/3/2019,10:54,Card,100504.8,4.7619047619999995,5025.240000000001,4.3
+788-07-8452,C,Port Harcourt,Member,Female,Home and lifestyle,8726.4,7,3054.24,64139.04000000001,1/27/2019,17:38,Epay,61084.8,4.7619047619999995,3054.24,9.4
+123-35-4896,C,Port Harcourt,Normal,Female,Sports and travel,16797.6,9,7558.92,158737.31999999998,2/17/2019,19:11,Epay,151178.4,4.7619047619999995,7558.92,5.3
+258-69-7810,C,Port Harcourt,Normal,Female,Fashion accessories,13266.0,5,3316.5,69646.5,1/26/2019,18:53,Cash,66330.0,4.7619047619999995,3316.5,9.2
+219-61-4139,C,Port Harcourt,Normal,Male,Electronic accessories,29908.8,1,1495.44,31404.239999999998,1/23/2019,17:16,Epay,29908.8,4.7619047619999995,1495.44,6.4
+881-41-7302,C,Port Harcourt,Normal,Female,Fashion accessories,23396.399999999998,1,1169.82,24566.22,1/26/2019,10:06,Card,23396.399999999998,4.7619047619999995,1169.82,4.5
+373-09-4567,C,Port Harcourt,Normal,Male,Food and beverages,27921.600000000002,10,13960.800000000001,293176.8,3/14/2019,20:35,Epay,279216.0,4.7619047619999995,13960.800000000001,6.9
+484-22-8230,C,Port Harcourt,Member,Female,Fashion accessories,18680.4,7,6538.14,137300.94,1/8/2019,20:08,Cash,130762.8,4.7619047619999995,6538.14,4.5
+544-32-5024,C,Port Harcourt,Member,Female,Food and beverages,17924.4,4,3584.88,75282.48,3/28/2019,19:16,Card,71697.6,4.7619047619999995,3584.88,6.4
+277-35-5865,C,Port Harcourt,Member,Female,Food and beverages,35629.2,9,16033.14,336695.94,3/9/2019,11:23,Cash,320662.8,4.7619047619999995,16033.14,6.7
+284-54-4231,C,Port Harcourt,Member,Male,Sports and travel,29134.800000000003,1,1456.74,30591.54,1/19/2019,16:08,Card,29134.800000000003,4.7619047619999995,1456.74,9.0
+840-19-2096,C,Port Harcourt,Member,Male,Electronic accessories,31647.6,5,7911.9,166149.9,3/14/2019,18:10,Epay,158238.0,4.7619047619999995,7911.9,4.4
+641-96-3695,C,Port Harcourt,Member,Female,Fashion accessories,15645.6,6,4693.68,98567.28,2/7/2019,17:55,Epay,93873.59999999999,4.7619047619999995,4693.68,8.5
+318-81-2368,C,Port Harcourt,Normal,Female,Electronic accessories,16632.0,1,831.6,17463.6,3/19/2019,12:16,Cash,16632.0,4.7619047619999995,831.6,6.3
+155-45-3814,C,Port Harcourt,Member,Female,Electronic accessories,31878.0,8,12751.2,267775.2,3/19/2019,15:29,Epay,255024.0,4.7619047619999995,12751.2,4.7
+131-15-8856,C,Port Harcourt,Member,Female,Food and beverages,26107.199999999997,8,10442.880000000001,219300.48,3/30/2019,19:26,Card,208857.59999999998,4.7619047619999995,10442.880000000001,4.0
+273-84-2164,C,Port Harcourt,Member,Male,Electronic accessories,4338.0,5,1084.5,22774.5,2/16/2019,15:53,Epay,21690.0,4.7619047619999995,1084.5,5.5
+778-89-7974,C,Port Harcourt,Normal,Male,Health and beauty,25275.6,6,7582.680000000001,159236.28,3/30/2019,14:58,Cash,151653.6,4.7619047619999995,7582.680000000001,7.4
+859-71-0933,C,Port Harcourt,Member,Female,Sports and travel,5576.4,2,557.6400000000001,11710.44,1/16/2019,15:10,Cash,11152.8,4.7619047619999995,557.6400000000001,6.3
+740-11-5257,C,Port Harcourt,Normal,Male,Electronic accessories,8906.4,10,4453.2,93517.2,2/24/2019,16:44,Cash,89064.0,4.7619047619999995,4453.2,7.1
+250-81-7186,C,Port Harcourt,Normal,Female,Electronic accessories,35888.4,1,1794.4199999999998,37682.82,2/27/2019,10:23,Card,35888.4,4.7619047619999995,1794.4199999999998,8.0
+842-29-4695,C,Port Harcourt,Member,Male,Sports and travel,6170.400000000001,7,2159.64,45352.44,1/16/2019,12:07,Card,43192.8,4.7619047619999995,2159.64,7.9
+641-51-2661,C,Port Harcourt,Member,Female,Food and beverages,31355.999999999996,10,15677.999999999998,329238.0,2/12/2019,14:45,Card,313560.0,4.7619047619999995,15677.999999999998,9.9
+714-02-3114,C,Port Harcourt,Normal,Female,Sports and travel,35568.0,2,3556.8,74692.8,2/21/2019,11:39,Cash,71136.0,4.7619047619999995,3556.8,7.7
+408-66-6712,C,Port Harcourt,Member,Female,Health and beauty,17175.6,6,5152.679999999999,108206.28,2/16/2019,14:19,Epay,103053.59999999999,4.7619047619999995,5152.679999999999,4.4
+556-41-6224,C,Port Harcourt,Normal,Male,Health and beauty,12110.4,8,4844.16,101727.35999999999,2/15/2019,17:10,Card,96883.2,4.7619047619999995,4844.16,9.3
+648-94-3045,C,Port Harcourt,Normal,Male,Health and beauty,21222.0,10,10611.0,222831.0,2/7/2019,14:27,Epay,212220.0,4.7619047619999995,10611.0,8.1
+370-96-0655,C,Port Harcourt,Normal,Female,Fashion accessories,17755.2,6,5326.5599999999995,111857.76000000001,1/9/2019,13:46,Epay,106531.20000000001,4.7619047619999995,5326.5599999999995,7.1
+173-57-2300,C,Port Harcourt,Member,Male,Sports and travel,26236.8,2,2623.68,55097.28,3/13/2019,12:51,Cash,52473.6,4.7619047619999995,2623.68,6.1
+394-55-6384,C,Port Harcourt,Member,Female,Sports and travel,25268.399999999998,9,11370.78,238786.37999999998,1/25/2019,13:38,Cash,227415.6,4.7619047619999995,11370.78,6.7
+266-20-6657,C,Port Harcourt,Member,Male,Food and beverages,19814.4,7,6935.04,145635.84000000003,3/12/2019,19:39,Epay,138700.8,4.7619047619999995,6935.04,5.2
+196-01-2849,C,Port Harcourt,Member,Female,Fashion accessories,26416.8,7,9245.880000000001,194163.48000000004,2/10/2019,13:56,Cash,184917.59999999998,4.7619047619999995,9245.880000000001,9.5
+372-62-5264,C,Port Harcourt,Normal,Female,Food and beverages,18936.0,9,8521.2,178945.2,1/16/2019,14:42,Cash,170424.0,4.7619047619999995,8521.2,7.6
+751-41-9720,C,Port Harcourt,Normal,Male,Home and lifestyle,35100.0,10,17550.0,368550.0,1/12/2019,16:18,Epay,351000.0,4.7619047619999995,17550.0,8.0
+626-43-7888,C,Port Harcourt,Normal,Female,Fashion accessories,21747.6,8,8699.04,182679.84,2/7/2019,12:23,Epay,173980.8,4.7619047619999995,8699.04,9.6
+162-65-8559,C,Port Harcourt,Member,Male,Food and beverages,24832.800000000003,1,1241.6399999999999,26074.440000000002,1/21/2019,20:13,Cash,24832.800000000003,4.7619047619999995,1241.6399999999999,4.8
+760-27-5490,C,Port Harcourt,Normal,Male,Fashion accessories,5623.2,8,2249.2799999999997,47234.88,1/20/2019,20:37,Epay,44985.6,4.7619047619999995,2249.2799999999997,9.1
+728-88-7867,C,Port Harcourt,Member,Female,Home and lifestyle,27190.8,4,5438.16,114201.36,3/19/2019,15:52,Epay,108763.2,4.7619047619999995,5438.16,8.3
+183-21-3799,C,Port Harcourt,Normal,Female,Electronic accessories,27946.8,9,12576.060000000001,264097.26,2/19/2019,15:14,Epay,251521.19999999998,4.7619047619999995,12576.060000000001,7.2
+268-20-3585,C,Port Harcourt,Normal,Female,Health and beauty,4986.0,9,2243.7,47117.7,2/4/2019,12:50,Epay,44874.0,4.7619047619999995,2243.7,6.0
+735-32-9839,C,Port Harcourt,Member,Male,Fashion accessories,35532.0,8,14212.8,298468.8,1/31/2019,10:36,Epay,284256.0,4.7619047619999995,14212.8,8.5
+678-79-0726,C,Port Harcourt,Member,Female,Sports and travel,32626.8,9,14682.06,308323.26,1/18/2019,15:28,Cash,293641.2,4.7619047619999995,14682.06,5.1
+592-46-1692,C,Port Harcourt,Member,Female,Food and beverages,13237.2,7,4633.02,97293.42,1/11/2019,20:10,Cash,92660.4,4.7619047619999995,4633.02,7.4
+149-14-0304,C,Port Harcourt,Member,Female,Health and beauty,10260.0,8,4104.0,86184.0,2/6/2019,14:24,Cash,82080.0,4.7619047619999995,4104.0,6.6
+442-44-6497,C,Port Harcourt,Member,Male,Home and lifestyle,20005.2,3,3000.7799999999997,63016.380000000005,1/8/2019,11:42,Card,60015.600000000006,4.7619047619999995,3000.7799999999997,5.9
+210-74-9613,C,Port Harcourt,Normal,Male,Fashion accessories,35013.6,4,7002.72,147057.12,3/16/2019,15:33,Epay,140054.4,4.7619047619999995,7002.72,6.8
+607-65-2441,C,Port Harcourt,Member,Male,Health and beauty,29502.0,10,14751.0,309771.0,3/10/2019,12:39,Card,295020.0,4.7619047619999995,14751.0,6.0
+386-27-7606,C,Port Harcourt,Member,Female,Home and lifestyle,29232.0,7,10231.2,214855.2,3/23/2019,15:59,Card,204624.0,4.7619047619999995,10231.2,8.1
+137-63-5492,C,Port Harcourt,Normal,Male,Electronic accessories,21153.6,10,10576.8,222112.80000000002,1/29/2019,14:26,Epay,211536.0,4.7619047619999995,10576.8,9.0
+733-29-1227,C,Port Harcourt,Normal,Male,Home and lifestyle,20019.6,7,7006.86,147144.06,3/23/2019,12:41,Cash,140137.19999999998,4.7619047619999995,7006.86,8.5
+451-73-2711,C,Port Harcourt,Normal,Male,Food and beverages,30538.8,1,1526.94,32065.74,1/14/2019,15:20,Epay,30538.8,4.7619047619999995,1526.94,8.8
+345-68-9016,C,Port Harcourt,Member,Female,Sports and travel,11401.2,8,4560.48,95770.08,1/2/2019,16:19,Card,91209.6,4.7619047619999995,4560.48,5.6
+390-17-5806,C,Port Harcourt,Member,Female,Food and beverages,13831.2,1,691.5600000000001,14522.76,2/2/2019,16:33,Cash,13831.2,4.7619047619999995,691.5600000000001,8.6
+664-14-2882,C,Port Harcourt,Member,Female,Home and lifestyle,3790.7999999999997,5,947.6999999999999,19901.7,1/30/2019,14:43,Card,18954.0,4.7619047619999995,947.6999999999999,5.8
+314-23-4520,C,Port Harcourt,Member,Male,Health and beauty,29242.800000000003,7,10234.98,214934.58,1/15/2019,20:44,Cash,204699.6,4.7619047619999995,10234.98,9.0
+288-38-3758,C,Port Harcourt,Member,Female,Fashion accessories,30553.2,3,4582.98,96242.58,1/25/2019,18:30,Epay,91659.6,4.7619047619999995,4582.98,7.4
+801-88-0346,C,Port Harcourt,Normal,Female,Fashion accessories,27381.600000000002,3,4107.240000000001,86252.04000000001,1/5/2019,20:30,Card,82144.8,4.7619047619999995,4107.240000000001,9.8
+759-98-4285,C,Port Harcourt,Member,Female,Health and beauty,30913.2,7,10819.62,227212.02,2/27/2019,19:01,Card,216392.40000000002,4.7619047619999995,10819.62,8.0
+201-63-8275,C,Port Harcourt,Member,Female,Sports and travel,24476.399999999998,7,8566.74,179901.54,2/17/2019,16:50,Epay,171334.8,4.7619047619999995,8566.74,5.7
+471-06-8611,C,Port Harcourt,Normal,Female,Food and beverages,18871.2,1,943.5600000000002,19814.760000000002,2/6/2019,10:22,Card,18871.2,4.7619047619999995,943.5600000000002,6.3
+200-16-5952,C,Port Harcourt,Member,Male,Food and beverages,23634.000000000004,2,2363.4,49631.4,1/17/2019,16:46,Cash,47268.00000000001,4.7619047619999995,2363.4,6.0
+102-77-2261,C,Port Harcourt,Member,Male,Health and beauty,23511.600000000002,7,8229.06,172810.26,3/5/2019,18:02,Card,164581.2,4.7619047619999995,8229.06,4.2
+102-06-2002,C,Port Harcourt,Member,Male,Sports and travel,9090.0,5,2272.5,47722.5,3/20/2019,17:52,Cash,45450.0,4.7619047619999995,2272.5,6.1
+629-42-4133,C,Port Harcourt,Normal,Male,Health and beauty,7848.0,8,3139.2000000000003,65923.2,2/19/2019,19:24,Cash,62784.0,4.7619047619999995,3139.2000000000003,8.3
+468-99-7231,C,Port Harcourt,Normal,Female,Home and lifestyle,15843.599999999999,8,6337.44,133086.24000000002,3/3/2019,17:36,Cash,126748.79999999999,4.7619047619999995,6337.44,8.8
+516-77-6464,C,Port Harcourt,Member,Female,Health and beauty,3657.6,5,914.4,19202.4,2/24/2019,13:08,Epay,18288.0,4.7619047619999995,914.4,4.1
+886-77-9084,C,Port Harcourt,Normal,Male,Electronic accessories,25880.4,8,10352.16,217395.36,2/19/2019,11:33,Epay,207043.2,4.7619047619999995,10352.16,5.5
+790-38-4466,C,Port Harcourt,Normal,Female,Health and beauty,3956.4,5,989.1,20771.1,1/23/2019,10:18,Card,19782.0,4.7619047619999995,989.1,9.3
+704-10-4056,C,Port Harcourt,Member,Male,Health and beauty,21769.2,3,3265.3799999999997,68572.98,1/14/2019,10:55,Card,65307.6,4.7619047619999995,3265.3799999999997,5.6
+400-80-4065,C,Port Harcourt,Member,Male,Health and beauty,24678.0,4,4935.6,103647.6,2/15/2019,20:21,Card,98712.0,4.7619047619999995,4935.6,9.2
+443-60-9639,C,Port Harcourt,Member,Female,Home and lifestyle,21913.2,1,1095.6599999999999,23008.86,1/24/2019,13:24,Cash,21913.2,4.7619047619999995,1095.6599999999999,5.5
+401-09-4232,C,Port Harcourt,Member,Male,Home and lifestyle,31208.399999999998,5,7802.099999999999,163844.1,2/11/2019,18:38,Epay,156042.0,4.7619047619999995,7802.099999999999,9.4
+324-41-6833,C,Port Harcourt,Member,Female,Electronic accessories,10872.0,8,4348.8,91324.8,3/3/2019,19:30,Epay,86976.0,4.7619047619999995,4348.8,5.1
+474-33-8305,C,Port Harcourt,Member,Male,Fashion accessories,24260.4,7,8491.14,178313.94,3/23/2019,13:23,Epay,169822.80000000002,4.7619047619999995,8491.14,6.9
+189-55-2313,C,Port Harcourt,Normal,Female,Fashion accessories,22384.8,10,11192.4,235040.4,1/31/2019,10:33,Epay,223847.99999999997,4.7619047619999995,11192.4,6.0
+815-04-6282,C,Port Harcourt,Member,Female,Sports and travel,23389.2,5,5847.3,122793.29999999999,2/8/2019,12:52,Card,116946.00000000001,4.7619047619999995,5847.3,6.5
+477-59-2456,C,Port Harcourt,Normal,Female,Fashion accessories,16358.4,7,5725.4400000000005,120234.24000000002,1/23/2019,11:15,Cash,114508.79999999999,4.7619047619999995,5725.4400000000005,9.2
+784-21-9238,C,Port Harcourt,Member,Male,Sports and travel,3661.2,1,183.05999999999997,3844.2599999999998,2/7/2019,14:15,Cash,3661.2,4.7619047619999995,183.05999999999997,5.9
+538-22-0304,C,Port Harcourt,Normal,Male,Electronic accessories,23382.0,10,11691.0,245511.0,3/24/2019,18:27,Cash,233820.0,4.7619047619999995,11691.0,5.2
+660-29-7083,C,Port Harcourt,Normal,Male,Electronic accessories,20113.2,10,10056.6,211188.6,1/15/2019,15:01,Cash,201132.00000000003,4.7619047619999995,10056.6,5.8
+271-77-8740,C,Port Harcourt,Member,Female,Sports and travel,10519.199999999999,6,3155.76,66270.95999999999,1/1/2019,11:40,Epay,63115.2,4.7619047619999995,3155.76,5.0
+549-23-9016,C,Port Harcourt,Member,Female,Food and beverages,5353.2,2,535.3199999999999,11241.72,2/13/2019,18:15,Card,10706.4,4.7619047619999995,535.3199999999999,8.9
+862-29-5914,C,Port Harcourt,Normal,Female,Sports and travel,8056.799999999999,1,402.84,8459.640000000001,1/30/2019,17:08,Card,8056.799999999999,4.7619047619999995,402.84,8.6
+845-94-6841,C,Port Harcourt,Member,Female,Food and beverages,26236.8,9,11806.56,247937.76,1/8/2019,19:38,Cash,236131.19999999998,4.7619047619999995,11806.56,4.0
+658-66-3967,C,Port Harcourt,Normal,Male,Health and beauty,19148.399999999998,7,6701.94,140740.74,1/14/2019,15:42,Epay,134038.8,4.7619047619999995,6701.94,5.0
+848-95-6252,C,Port Harcourt,Member,Female,Home and lifestyle,31057.199999999997,1,1552.8600000000001,32610.06,2/20/2019,13:24,Epay,31057.199999999997,4.7619047619999995,1552.8600000000001,7.0
+176-78-1170,C,Port Harcourt,Member,Male,Health and beauty,12171.6,3,1825.7400000000002,38340.54,1/26/2019,15:11,Epay,36514.8,4.7619047619999995,1825.7400000000002,7.3
+101-81-4070,C,Port Harcourt,Member,Female,Health and beauty,22615.2,2,2261.52,47491.92,1/17/2019,12:36,Epay,45230.4,4.7619047619999995,2261.52,4.9
+631-34-1880,C,Port Harcourt,Member,Male,Food and beverages,8751.6,3,1312.74,27567.539999999997,1/8/2019,19:09,Card,26254.800000000003,4.7619047619999995,1312.74,4.3
+584-66-4073,C,Port Harcourt,Normal,Male,Fashion accessories,20340.0,1,1017.0000000000001,21357.0,3/13/2019,15:45,Epay,20340.0,4.7619047619999995,1017.0000000000001,9.6
+154-87-7367,C,Port Harcourt,Normal,Male,Home and lifestyle,23493.600000000002,8,9397.44,197346.24,3/15/2019,14:04,Epay,187948.80000000002,4.7619047619999995,9397.44,6.3
+885-56-0389,C,Port Harcourt,Member,Male,Fashion accessories,18846.0,1,942.3000000000001,19788.3,2/12/2019,17:49,Cash,18846.0,4.7619047619999995,942.3000000000001,4.0
+115-38-7388,C,Port Harcourt,Member,Female,Fashion accessories,3664.7999999999997,8,1465.92,30784.32,3/30/2019,12:51,Card,29318.399999999998,4.7619047619999995,1465.92,9.5
+137-74-8729,C,Port Harcourt,Normal,Female,Fashion accessories,4388.4,8,1755.3600000000001,36862.56,3/13/2019,12:47,Epay,35107.2,4.7619047619999995,1755.3600000000001,6.8
+389-70-2397,C,Port Harcourt,Normal,Female,Health and beauty,30117.6,5,7529.4,158117.4,2/21/2019,10:26,Cash,150588.0,4.7619047619999995,7529.4,7.2
+607-76-6216,C,Port Harcourt,Member,Female,Fashion accessories,33296.4,5,8324.1,174806.1,3/2/2019,16:35,Card,166482.0,4.7619047619999995,8324.1,8.6
+781-84-8059,C,Port Harcourt,Normal,Male,Fashion accessories,21866.4,7,7653.24,160718.04,1/18/2019,16:23,Epay,153064.8,4.7619047619999995,7653.24,5.0
+409-49-6995,C,Port Harcourt,Member,Female,Food and beverages,17017.2,6,5105.16,107208.36,2/5/2019,10:17,Cash,102103.2,4.7619047619999995,5105.16,8.8
+725-54-0677,C,Port Harcourt,Member,Male,Health and beauty,30815.999999999996,7,10785.6,226497.59999999998,3/2/2019,13:50,Cash,215712.00000000003,4.7619047619999995,10785.6,5.3
+377-79-7592,C,Port Harcourt,Member,Female,Electronic accessories,16142.400000000001,9,7264.08,152545.68000000002,1/14/2019,14:00,Card,145281.6,4.7619047619999995,7264.08,7.5
+545-07-8534,C,Port Harcourt,Normal,Female,Health and beauty,20995.2,2,2099.5200000000004,44089.920000000006,2/14/2019,12:42,Epay,41990.4,4.7619047619999995,2099.5200000000004,6.0
+118-62-1812,C,Port Harcourt,Member,Female,Home and lifestyle,28216.8,4,5643.36,118510.55999999998,3/24/2019,17:56,Cash,112867.2,4.7619047619999995,5643.36,7.9
+450-42-3339,C,Port Harcourt,Normal,Male,Health and beauty,30459.6,10,15229.8,319825.8,2/9/2019,18:58,Card,304596.0,4.7619047619999995,15229.8,8.8
+151-27-8496,C,Port Harcourt,Normal,Female,Electronic accessories,20206.8,4,4041.3599999999997,84868.56,1/19/2019,11:43,Epay,80827.2,4.7619047619999995,4041.3599999999997,8.6
+717-96-4189,C,Port Harcourt,Normal,Female,Electronic accessories,12776.400000000001,6,3832.92,80491.32,2/2/2019,12:40,Cash,76658.4,4.7619047619999995,3832.92,4.1
+722-13-2115,C,Port Harcourt,Member,Male,Sports and travel,15426.0,1,771.3000000000001,16197.3,3/14/2019,15:36,Card,15426.0,4.7619047619999995,771.3000000000001,9.3
+246-55-6923,C,Port Harcourt,Member,Female,Home and lifestyle,12884.4,9,5797.98,121757.58,3/10/2019,15:06,Card,115959.6,4.7619047619999995,5797.98,5.1
+838-02-1821,C,Port Harcourt,Member,Female,Home and lifestyle,4582.8,2,458.28,9623.880000000001,2/22/2019,12:10,Card,9165.6,4.7619047619999995,458.28,5.2
+887-42-0517,C,Port Harcourt,Normal,Female,Sports and travel,29930.4,7,10475.64,219988.44000000003,1/10/2019,10:31,Card,209512.80000000002,4.7619047619999995,10475.64,6.6
+457-12-0244,C,Port Harcourt,Member,Female,Sports and travel,12679.199999999999,6,3803.7599999999998,79878.95999999999,3/14/2019,13:49,Epay,76075.2,4.7619047619999995,3803.7599999999998,6.5
+756-93-1854,C,Port Harcourt,Member,Female,Fashion accessories,30005.999999999996,2,3000.6000000000004,63012.6,2/2/2019,14:05,Card,60011.99999999999,4.7619047619999995,3000.6000000000004,9.5
+458-10-8612,C,Port Harcourt,Normal,Male,Health and beauty,23068.8,7,8074.08,169555.68000000002,1/20/2019,12:27,Epay,161481.6,4.7619047619999995,8074.08,7.6
+235-06-8510,C,Port Harcourt,Member,Male,Home and lifestyle,30859.2,3,4628.879999999999,97206.48,1/24/2019,20:59,Epay,92577.6,4.7619047619999995,4628.879999999999,5.1
+433-08-7822,C,Port Harcourt,Normal,Female,Health and beauty,28400.4,7,9940.14,208742.94,1/5/2019,19:48,Epay,198802.80000000002,4.7619047619999995,9940.14,7.5
+500-02-2261,C,Port Harcourt,Normal,Female,Food and beverages,20624.4,6,6187.320000000001,129933.71999999999,3/21/2019,17:04,Epay,123746.40000000001,4.7619047619999995,6187.320000000001,5.9
+702-83-5291,C,Port Harcourt,Member,Male,Fashion accessories,35935.2,9,16170.839999999998,339587.64,3/27/2019,10:43,Cash,323416.8,4.7619047619999995,16170.839999999998,6.6
+859-97-6048,C,Port Harcourt,Member,Male,Electronic accessories,30330.0,2,3033.0000000000005,63693.00000000001,3/26/2019,14:13,Card,60660.0,4.7619047619999995,3033.0000000000005,5.3
+373-88-1424,C,Port Harcourt,Member,Male,Home and lifestyle,12891.6,5,3222.9,67680.9,2/6/2019,18:44,Epay,64458.00000000001,4.7619047619999995,3222.9,7.9
+784-08-0310,C,Port Harcourt,Member,Female,Food and beverages,7574.4,4,1514.88,31812.48,1/13/2019,13:58,Cash,30297.6,4.7619047619999995,1514.88,7.6
+577-34-7579,C,Port Harcourt,Member,Male,Food and beverages,18176.4,9,8179.38,171766.97999999998,1/10/2019,17:16,Cash,163587.6,4.7619047619999995,8179.38,5.4
+867-47-1948,C,Port Harcourt,Normal,Female,Home and lifestyle,5688.0,10,2844.0,59724.0,1/9/2019,12:07,Cash,56880.0,4.7619047619999995,2844.0,7.8
+256-58-3609,C,Port Harcourt,Member,Male,Fashion accessories,33112.8,1,1655.64,34768.439999999995,3/18/2019,15:29,Cash,33112.8,4.7619047619999995,1655.64,9.8
+364-34-2972,C,Port Harcourt,Member,Male,Electronic accessories,34855.2,3,5228.28,109793.88,3/30/2019,20:37,Cash,104565.59999999999,4.7619047619999995,5228.28,6.7
+744-82-9138,C,Port Harcourt,Normal,Male,Fashion accessories,31006.8,2,3100.68,65114.28,2/7/2019,17:59,Cash,62013.6,4.7619047619999995,3100.68,8.2
+728-47-9078,C,Port Harcourt,Member,Male,Food and beverages,21452.4,4,4290.48,90100.08,1/19/2019,12:46,Cash,85809.6,4.7619047619999995,4290.48,9.8
+148-41-7930,C,Port Harcourt,Normal,Male,Health and beauty,35985.6,7,12594.96,264494.16000000003,1/23/2019,10:33,Cash,251899.2,4.7619047619999995,12594.96,6.1
+189-40-5216,C,Port Harcourt,Normal,Male,Electronic accessories,34693.200000000004,7,12142.62,254995.02,1/9/2019,11:40,Cash,242852.40000000002,4.7619047619999995,12142.62,6.0
+267-62-7380,C,Port Harcourt,Member,Male,Electronic accessories,29642.4,10,14821.2,311245.2,3/29/2019,19:12,Epay,296424.0,4.7619047619999995,14821.2,4.3
+652-49-6720,C,Port Harcourt,Member,Female,Electronic accessories,21942.0,1,1097.1,23039.100000000002,2/18/2019,11:40,Epay,21942.0,4.7619047619999995,1097.1,5.9
+233-67-5758,C,Port Harcourt,Normal,Male,Health and beauty,14526.0,1,726.3000000000001,15252.3,1/29/2019,13:46,Epay,14526.0,4.7619047619999995,726.3000000000001,6.2
diff --git a/Pandas_Analytics_project/README.md b/Pandas_Analytics_project/README.md
new file mode 100644
index 0000000..cbf697c
--- /dev/null
+++ b/Pandas_Analytics_project/README.md
@@ -0,0 +1,68 @@
+Project title:
+# Analyze Supermarket Data Across the Country - Company XYZ
+# Project description:
+      Company XYZ owns a supermarket chain across the country. Each major branch located in 3 cities across the country recorded sales information for 3 months, to help the company understand sales trends and determine its growth, as the rise of supermarkets competition is seen.
+# Project Steps
+      * Various libraries were imported such as os, glob, numpy, pandas, matplotlib.pyplot etc
+      * The data containing information of each branch in the 3 major cities were loaded and combined using pd.concat to form a single dataframe with csv extension and then save as a new file in the local directory containing the project.
+ # 1. The first major task was data exploration
+       * The first 5 rows of the newly created file 'Market_data.csv' was displayed
+       * no of rows and columns was checked using df.shape
+       * check various column names in the dataset using df.columns 
+       * statistical summary was checked using df.describe()
+       * Missing data was checked using the isnull
+       * General Information of the data was checked using .info
+ # 2. The second: Dealing with datetime features
+       * Convert the date column to datetime
+       * The above was also done for the time column
+       *  * Extract year, month, and day from the date column and also hour from the time column
+       * Determine the unique ours of sales in the supermarket and return the result as an array.
+ # 3. Check for unique values in columns
+       * Get a list of the categorical column in the dataset, and check if each element in the column is of type object
+       * generate the unique values in the categorical columns in this case, Payment, product line, gender, and customer type
+       * Get a Series containing counts of unique values for various categories
+ # 4.  Aggregation with GroupBy
+       * Create a groupby object with the "City Column", and aggregation function of sum and mean.
+       * Display a table that shows the gross income of each city, and determine the city with the highest total gross income.
+ # 5. Data Visualization
+       * Determine the branch with the highest sales record using countplot.
+       * Determine the highest & lowest sold product line, using Countplot
+       * Determine the Payment channel used by most customers to pay for each product line.
+       * Determine the Payment channel for each branch.
+       * Determine the branch with the lowest rating
+       * Generate visualization for the "product line" per gender
+       * Generate visualization for the "product line" per "Total" column
+       * plot Product line per unit price, and Product line per Quantity
+# Insights
+      There are 3 different datasets (csv files): Dataset for Abuja_Branch, Lagos Branch, and Port Harcourt Branch
+            * Lagos branch (A) consists of 340 rows and 17 columns
+            * Abuja branch (B) consists of 332 rows and 17 columns
+            * Port Harcourt branch (C) consists of 328 rows and 17 columns
+      The datasets were combined to generate a single dataframe (dataset) which consists of 1000 rows and 17 columns
+      The statistical summary shows the Standard Deviation (STD) of the unit price and gross income to be 9538.066205 and 4215.177173 respectively and also, the mean value of the unit price   and the gross income is 20041.966800 and 5536.572840 respectively.
+      The dataset contains no null values
+      The dataset contains records of 501 female customers and 499 male customers
+      Port-Harcourt (C) records the highest revenue(gross income) while Lagos (A) follows next and then Abuja (B) as shown in Figure 1
+      There are more member customers than normal customers which correspond to females and males in the gender count as shown in Figure 6 and Figure 7 respectively.
+      The female spent more time in the City of Port Harcourt as shown in Figure 8
+      'A' has the highest no of customers/sales followed by 'B' and the least is 'C' as can be seen in Figure 2.
+      The most used payment method, in general, is EBAY and the least is Card
+      The most sold product line in all 3 branches is 'food and beverages' in 'C', next is 'Home and lifestyle' in 'A' and the 3rd most sold product line is 'fashion and accessories' in 'C'.  However, in 'B', the most sold is 'Travel and sport' as can be seen in figure 3.
+      The most used payment method for 'Product line' is Cash
+      The branch with the lowest rating is "B" while 'C' has the highest as can be seen in Figure 4.
+      It is observed that the most frequent customers are of the female gender and the most bought product is the 'Home and lifestyle' as can be seen in Figure 5.
+      It is also observed that the most expensive 'product line' is the 'food and beverages' and it is mainly bought by the female gender.
+# Future Work
+      Explore the  dataset for types of customers to be sure that both new and old customers are maintained.
+      Check for specific sales per month to know the monthly trend of the market
+      Explore other data visualization framework to get a more understandable presentation.  
+
+# Standout Section
+      Inserted all required libraries in the first cell of my work for the purpose of orderliness unlike the import instruction stated in the notebook.
+      Extracted the minute feature from the Time column and saved it to a new minute column
+      Presented the percentage of normal customers Vs member customers.
+      Presented the percentage of female Vs male
+      Gave a visualization of the gender that spent the highest number time in each city
+
+
+# Executive Summary.
diff --git a/Pandas_Analytics_project/Starter_notebook.ipynb b/Pandas_Analytics_project/Starter_notebook.ipynb
new file mode 100644
index 0000000..b2abd74
--- /dev/null
+++ b/Pandas_Analytics_project/Starter_notebook.ipynb
@@ -0,0 +1,765 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyze Supermarket Data Across the Country - Company XYZ\n",
+    "\n",
+    "Company XYZ owns a supermarket chain across the country. Each major branch located in 3 cities across the country recorded sales information for 3 months, to help the company understand sales trends and determine its growth, as the rise of supermarkets competition is seen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will apply learnings to analyse the dataset in the data folder, and the description of each feature can be found in this [link](https://docs.google.com/document/d/1Sv-DlynHpOBAs5qKokn5MtbzqZcumTSlSI4-wQ0kf0w/edit?usp=sharing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Loading the Dataset\n",
+    "In this step, you will combine the dataset from each branch (3 branches) into one dataset for easy analysis. You expected to write the syntaxes that will read multiple files from your current working directory and export a CSV file after combining. \n",
+    "The learning from this step is the ability to automate reading and combining multiple CSV files, because as a Data Professional, you will saddled with the responsibility of reading data from different sources, and this is one of the use case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import pandas as pd\n",
+    "os.chdir(\"/mydir\") #Current working directory that contains your dataset and code file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Use glob to match the pattern \"csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extension = 'csv'\n",
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Combine all the files in the generated list above and export to a CSV\n",
+    "Tips - Use the `pd.concat` method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]\n",
+    "#Export to csv\n",
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do -Read the CSV file using `pd.read_csv` method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Data Exploration\n",
+    "In this step you will explore the loaded dataset using some built-in Pandas function. You expected to begin to think about your analytical approach from here. \n",
+    "You can include other methods that you might think will make your exploration process easier. For Instance, you can write a function that print out a summary of the missing data occurence in the data. Be sure to document these methods using `markdown` and in-line `comments`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')  \n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Use the head() method to view first few rows of the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Check the number of rows and columns present in the data using the shape attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Generate the names of the columns using the columns attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Statiscal Summary\n",
+    "The describe method provides the data statistical summary for the dataset, use the method to explore the statistical summary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Below this cell write in few sentences what you can derive from the data statistical summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Missing values\n",
+    "The `isnull`, and `notna` methods can provide a quick overview of the missing data occurence, use that to observe the presence of missing data in the dataset. You can add the sum method to get a summary and not boolean values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Data Information\n",
+    "The `info()` function is used to print a concise summary of a DataFrame. This method prints information about a DataFrame including the index dtype and column dtypes, non-null values and memory usage. Use this method to observe the data type of each feature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Dealing with DateTime Features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the summary above, you can observe that the `date` and `time` columns are not in the appropriate data type\n",
+    "you can convert to `datetime` datatype using the `to_datetime()` method. After that is done, use the type attribute \n",
+    "to confirm the datatype. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Use `to_datetime()` to convert the `date` column to `datetime`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Check the datatype to confirm if it's in `datetime`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Repeat the two steps above to the `time` column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract Features from date & time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Extract the Day feature from the `Date` column, and save to a new `Day` column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Extract the Month feature from the `Date` column, and save to a new `Month` column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do -Extract the Year feature from the `Date` column, and save to a new `Year` column  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Extract the `Hour` feature from the `Time` column and save to a new `Hour` column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pandas provide the `nunique()` method to get a count of unique values, while the `unique()` function is used to get unique values of Series object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do -  From the hours information, determine the numbers of unique hours of sales in the supermarket, and return an array of the hours using the unique() method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Unique Values in Columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the line of code below, we can get a list of the categorical column in the dataset. We iterate through the columns and check if each element is an object datatype. The result is saved to the \"categorical_columns\" variable\n",
+    "as a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Uncomment the code and Run it\n",
+    "#categorical_columns = [col for col in df.columns if df[col].dtype == \"object\" ]\n",
+    "#categorical_columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do- From the information above, generate the unique values in the categorical columns (apart from the example - Branch column).\n",
+    "For example, the unique values of the \"Branch\" column is displayed in list, as shown below.\n",
+    "<br/>\n",
+    "`df['Branch'].unique().tolist()\n",
+    "['B', 'C', 'A']`\n",
+    "</br>\n",
+    "<br/>\n",
+    "You can add print formating syntax to print out the total numbers of these values in a particular column. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Example - Uncomment the Code and Run it\n",
+    "#df['Branch'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(\"Total Number of unique values in the Branch Column : {}\". format(len(df['Branch'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write Your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do: The `value_counts()` function is used to get a Series containing counts of unique values. For the categorical columns above, generate the count figure of the values using the `value_counts() ` method.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Example\n",
+    "#df['City'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Aggregration with GroupBy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Your task here, will be to create a groupby object with the \"City Column\", and aggregation function of sum and mean. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Using the groupby object, display a table that shows the gross income of each city, and determine the city with the highest total gross income."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# [Write your Answer Here]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Optional - You can explore other columns such as \"Unit Price\", \"Quantity\" etc. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section, you will provide answer to some questions by generating charts and making use of different plotting styles. You will use the `Seaborn visualization` library to generate your plots. For all visualizations, include a chart title by using the seaborn `set_title` method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do- Using `countplot`, determine the branch with the highest sales record. Optional - You can extend this to determine - most used payment method, city with the most sales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Explore a countplot for the `Payment` and `City` Column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - Determine the highest & lowest sold product line, using Countplot \n",
+    "Count Plot Tips- Set the `Product line` column to y-axis to prevent congestion on the x-axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do -  Determine the Payment channel used by most customer to pay for each product line. Count Plot Tips- Set the \"product line\" column to y-axis to prevent congestion on the x-axis, and use the \"hue\" parameter for the \"Payment\" column. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do- Determine the Payment channel for each branch.\n",
+    "Count Plot Tips - Use the hue parameter for the \"branch\" column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do- Determine the branch with the lowest rating. This you can determine using abox plot which gives a statistical summary of the plotted features, and you can pick out the branch with the lowest rating from the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your answer here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do - The gender type often affects the kind of products being purchased at the supermarket. \n",
+    "* Using a catplot() generate visualization for the \"product line\" on x-axis, quantity on the y-axis, and hue as gender. \n",
+    "<br/>\n",
+    "\n",
+    "#### Set the aspect parameter to 4, so can you can effectively space out each product line. \n",
+    "\n",
+    "\n",
+    "\n",
+    "* Plot the same chart, but `Total Column` as the y-axis\n",
+    "\n",
+    "* Write a summary of the insights you can pick from this chart."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To-Do -  An interesting insight to explore is the interaction of Unit price on the Quantity of goods purchased. To achieve this: \n",
+    "* Use the `catplot()` to plot `Product line per unit price`, and `Product line per Quantity`. Set the kind parameter to `point` \n",
+    "* In a new cell, Write a summary of the insights you uncovered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#[Write your Code Here]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7 - StandOut Section\n",
+    "To make your submission standout, \n",
+    "* You can continue to explore more insights that can be uncovered with different plotting methods.\n",
+    "* Write a detailed summary on your approach, methods and insights. \n",
+    "* Visit the [link](https://seaborn.pydata.org/tutorial/categorical.html) for more Seaborn visualization plots.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8 - Documentation.\n",
+    "\n",
+    "* Write a detailed `ReadMe` that describes the project, explains important approach you followed to solve the tasks. Find a template in the starter file that can get you started quickly. Note: Do limit yourself to the template, you can include any interesting step that is worth sharing. \n",
+    "* Write an Executive summary that you can present to `Company XYZ` management. The summary will explain your processes and insights you uncovered that can help the company "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/Pandas_tips.ipynb b/Pandas_tips.ipynb
new file mode 100644
index 0000000..8c7eb3b
--- /dev/null
+++ b/Pandas_tips.ipynb
@@ -0,0 +1,633 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating dateframe using a dictionary with list being its values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 Name  Age  Working Experience (Yrs.)\n",
+      "0           Amy White   50                          5\n",
+      "1        Jack Stewart   53                          8\n",
+      "2  Richard Lauderdale   35                          3\n",
+      "3        Sara Johnson   43                         10\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "data = {'Name': ['Amy White', 'Jack Stewart', 'Richard Lauderdale', 'Sara Johnson'], 'Age': [50, 53, 35, 43], 'Working Experience (Yrs.)': [5, 8, 3, 10]}\n",
+    "df = pd.DataFrame(data)\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create a datafram of a list named data containing dictionaries as its elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 Name  Age  Working Experience (Yrs.)\n",
+      "0           Amy White   50                          5\n",
+      "1        Jack Stewart   53                          8\n",
+      "2  Richard Lauderdale   35                          3\n",
+      "3        Sara Johnson   43                         10\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "data = [{'Name': 'Amy White', 'Age': 50, 'Working Experience (Yrs.)': 5}, \n",
+    "        {'Name': 'Jack Stewart', 'Age': 53, 'Working Experience (Yrs.)': 8},\n",
+    "        {'Name': 'Richard Lauderdale', 'Age': 35, 'Working Experience (Yrs.)': 3},\n",
+    "        {'Name': 'Sara Johnson', 'Age': 43, 'Working Experience (Yrs.)': 10}]\n",
+    "df = pd.DataFrame(data)\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create a dictionary called data where the values should be pandas Series. Store the pandas Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 Name  Age  Working Experience (Yrs.)\n",
+      "0           Amy White   50                          5\n",
+      "1        Jack Stewart   53                          8\n",
+      "2  Richard Lauderdale   35                          3\n",
+      "3        Sara Johnson   43                         10\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "names = pd.Series(['Amy White', 'Jack Stewart', 'Richard Lauderdale', 'Sara Johnson'])\n",
+    "age = pd.Series([50, 53, 35, 43])\n",
+    "working_experience_yrs = pd.Series([5, 8, 3, 10])\n",
+    "data = {'Name': names, 'Age': age, 'Working Experience (Yrs.)': working_experience_yrs}\n",
+    "df = pd.DataFrame(data)\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a Dataframe: create a list called data where the values should be lists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 Name  Age  Working Experience (Yrs.)\n",
+      "0           Amy White   50                          5\n",
+      "1        Jack Stewart   53                          8\n",
+      "2  Richard Lauderdale   35                          3\n",
+      "3        Sara Johnson   43                         10\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "List1 = [\"Amy White\", 50, 5]\n",
+    "List2 = [\"Jack Stewart\", 53, 8]\n",
+    "List3 =  [\"Richard Lauderdale\", 35, 3]\n",
+    "List4 = [\"Sara Johnson\", 43, 10]\n",
+    "\n",
+    "data = [List1, List2, List3, List4]\n",
+    "df = pd.DataFrame(data, columns= ['Name', 'Age', 'Working Experience (Yrs.)'])\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modify the code below to add integers starting from 1 in ascending order as index values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 Name  Age  Working Experience (Yrs.)\n",
+      "1           Amy White   50                          5\n",
+      "2        Jack Stewart   53                          8\n",
+      "3  Richard Lauderdale   35                          3\n",
+      "4        Sara Johnson   43                         10\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "data = {\n",
+    "    \"Name\":['Amy White', 'Jack Stewart', 'Richard Lauderdale', 'Sara Johnson'], \n",
+    "    \"Age\":[50, 53, 35, 43], \n",
+    "    \"Working Experience (Yrs.)\":[5,8,3,10]}\n",
+    "df = pd.DataFrame(data, index= range(1, len(data['Name'])+1))\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "one\n",
+      "\t two\n",
+      "\t\t three\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('one\\n\\t', 'two\\n\\t\\t', 'three\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pandas Attributes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "17                         ;40;365;3500;4400;15600\n",
+       "18                        ;40;365;8200;13200;16600\n",
+       "19                         ;40;365;3080;4600;16600\n",
+       "20                                 ;50;365;;;20250\n",
+       "21                         ;40;365;3440;3440;15600\n",
+       "22                                        ;;365;;;\n",
+       "23                         ;40;365;2120;3280;15600\n",
+       "24                                ;50;;;6900;22250\n",
+       "25                         ;40;365;4900;4900;16600\n",
+       "26                         ;40;365;4000;5000;16600\n",
+       "27                         ;50;365;3400;5000;20250\n",
+       "28                                   ;125;;;;54625\n",
+       "29                            ;40;;3441;4541;16600"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy  as np\n",
+    "import pandas as pd\n",
+    "data_numpy = pd.read_csv('Data_preprocessing_Numpy.csv')\n",
+    "data_numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_data = np.genfromtxt('Data_preprocessing_Numpy.csv', delimiter=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True,  True,  True,  True,  True],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True,  True,  True, False, False, False],\n",
+       "       [ True,  True,  True, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False,  True,  True],\n",
+       "       [ True, False, False, False,  True,  True],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True,  True,  True,  True,  True,  True],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False,  True,  True, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True,  True, False,  True,  True,  True],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False,  True,  True, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False, False, False, False, False],\n",
+       "       [ True, False,  True,  True,  True, False],\n",
+       "       [ True, False,  True, False, False, False]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isnan(my_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isnan(my_data).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Could not convert [';50;365;2801;3626;4676' ';40;365;3121;5381;15281'\n ';40;365;3121;4501;16381' ';50;365;1700;3200;16550'\n ';40;365;3401;4421;13101' ';50;365;-1850;-530;1480'\n ';50;365;3750;6250;20250' ';50;365;3751;5251;20250' ';;;5453;6953;22250'\n ';;;5100;6400;22250' ';40;365;3400;5400;16600' ';40;365;3360;;'\n ';40;365;1940;;' ';50;365;5500;7500;22250' ';;;;;'\n ';50;365;1250;2650;19250' ';50;365;5500;5500;22250'\n ';40;365;3500;4400;15600' ';40;365;8200;13200;16600'\n ';40;365;3080;4600;16600' ';50;365;;;20250' ';40;365;3440;3440;15600'\n ';;365;;;' ';40;365;2120;3280;15600' ';50;;;6900;22250'\n ';40;365;4900;4900;16600' ';40;365;4000;5000;16600'\n ';50;365;3400;5000;20250' ';125;;;;54625' ';40;;3441;4541;16600'] to numeric",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[18], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_numpy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m   \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mround (\u001b[38;5;241m2\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3502\u001b[0m, in \u001b[0;36mmean\u001b[1;34m(a, axis, dtype, out, keepdims, where)\u001b[0m\n\u001b[0;32m   3500\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m   3501\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 3502\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _methods\u001b[38;5;241m.\u001b[39m_mean(a, axis\u001b[38;5;241m=\u001b[39maxis, dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[0;32m   3505\u001b[0m                       out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11693\u001b[0m, in \u001b[0;36mDataFrame.mean\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11685\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[0;32m  11686\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[0;32m  11687\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  11691\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  11692\u001b[0m ):\n\u001b[1;32m> 11693\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m  11694\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, Series):\n\u001b[0;32m  11695\u001b[0m         result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:12420\u001b[0m, in \u001b[0;36mNDFrame.mean\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  12413\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[0;32m  12414\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  12415\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  12418\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  12419\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[1;32m> 12420\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  12421\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m  12422\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:12377\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[1;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  12373\u001b[0m nv\u001b[38;5;241m.\u001b[39mvalidate_func(name, (), kwargs)\n\u001b[0;32m  12375\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m> 12377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  12378\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[0;32m  12379\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11562\u001b[0m, in \u001b[0;36mDataFrame._reduce\u001b[1;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[0;32m  11558\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m  11560\u001b[0m \u001b[38;5;66;03m# After possibly _get_data and transposing, we are now in the\u001b[39;00m\n\u001b[0;32m  11561\u001b[0m \u001b[38;5;66;03m#  simple case where we can use BlockManager.reduce\u001b[39;00m\n\u001b[1;32m> 11562\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblk_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m  11563\u001b[0m out \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(res, axes\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39maxes)\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m  11564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m out\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mboolean\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1500\u001b[0m, in \u001b[0;36mBlockManager.reduce\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m   1498\u001b[0m res_blocks: \u001b[38;5;28mlist\u001b[39m[Block] \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m   1499\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks:\n\u001b[1;32m-> 1500\u001b[0m     nbs \u001b[38;5;241m=\u001b[39m \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1501\u001b[0m     res_blocks\u001b[38;5;241m.\u001b[39mextend(nbs)\n\u001b[0;32m   1503\u001b[0m index \u001b[38;5;241m=\u001b[39m Index([\u001b[38;5;28;01mNone\u001b[39;00m])  \u001b[38;5;66;03m# placeholder\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:404\u001b[0m, in \u001b[0;36mBlock.reduce\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m    398\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreduce\u001b[39m(\u001b[38;5;28mself\u001b[39m, func) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[0;32m    400\u001b[0m     \u001b[38;5;66;03m# We will apply the function and reshape the result into a single-row\u001b[39;00m\n\u001b[0;32m    401\u001b[0m     \u001b[38;5;66;03m#  Block with the same mgr_locs; squeezing will be done at a higher level\u001b[39;00m\n\u001b[0;32m    402\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m--> 404\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    406\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    407\u001b[0m         res_values \u001b[38;5;241m=\u001b[39m result\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11481\u001b[0m, in \u001b[0;36mDataFrame._reduce.<locals>.blk_func\u001b[1;34m(values, axis)\u001b[0m\n\u001b[0;32m  11479\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([result])\n\u001b[0;32m  11480\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m> 11481\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[1;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[0;32m    145\u001b[0m         result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 147\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat.<locals>.new_func\u001b[1;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[0;32m    401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    402\u001b[0m     mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[1;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[0;32m    407\u001b[0m     result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:720\u001b[0m, in \u001b[0;36mnanmean\u001b[1;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[0;32m    718\u001b[0m count \u001b[38;5;241m=\u001b[39m _get_counts(values\u001b[38;5;241m.\u001b[39mshape, mask, axis, dtype\u001b[38;5;241m=\u001b[39mdtype_count)\n\u001b[0;32m    719\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39msum(axis, dtype\u001b[38;5;241m=\u001b[39mdtype_sum)\n\u001b[1;32m--> 720\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthe_sum\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    722\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(the_sum, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m    723\u001b[0m     count \u001b[38;5;241m=\u001b[39m cast(np\u001b[38;5;241m.\u001b[39mndarray, count)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1686\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1683\u001b[0m inferred \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39minfer_dtype(x)\n\u001b[0;32m   1684\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inferred \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstring\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m   1685\u001b[0m     \u001b[38;5;66;03m# GH#44008, GH#36703 avoid casting e.g. strings to numeric\u001b[39;00m\n\u001b[1;32m-> 1686\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not convert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1687\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1688\u001b[0m     x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mcomplex128)\n",
+      "\u001b[1;31mTypeError\u001b[0m: Could not convert [';50;365;2801;3626;4676' ';40;365;3121;5381;15281'\n ';40;365;3121;4501;16381' ';50;365;1700;3200;16550'\n ';40;365;3401;4421;13101' ';50;365;-1850;-530;1480'\n ';50;365;3750;6250;20250' ';50;365;3751;5251;20250' ';;;5453;6953;22250'\n ';;;5100;6400;22250' ';40;365;3400;5400;16600' ';40;365;3360;;'\n ';40;365;1940;;' ';50;365;5500;7500;22250' ';;;;;'\n ';50;365;1250;2650;19250' ';50;365;5500;5500;22250'\n ';40;365;3500;4400;15600' ';40;365;8200;13200;16600'\n ';40;365;3080;4600;16600' ';50;365;;;20250' ';40;365;3440;3440;15600'\n ';;365;;;' ';40;365;2120;3280;15600' ';50;;;6900;22250'\n ';40;365;4900;4900;16600' ';40;365;4000;5000;16600'\n ';50;365;3400;5000;20250' ';125;;;;54625' ';40;;3441;4541;16600'] to numeric"
+     ]
+    }
+   ],
+   "source": [
+    "np.mean(data_numpy, axis= 1).round (2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_numpy.isnull().values.any()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[';50;365;2801;3626;4676'],\n",
+       "       [';40;365;3121;5381;15281'],\n",
+       "       [';40;365;3121;4501;16381'],\n",
+       "       [';50;365;1700;3200;16550'],\n",
+       "       [';40;365;3401;4421;13101'],\n",
+       "       [';50;365;-1850;-530;1480'],\n",
+       "       [';50;365;3750;6250;20250'],\n",
+       "       [';50;365;3751;5251;20250'],\n",
+       "       [';;;5453;6953;22250'],\n",
+       "       [';;;5100;6400;22250'],\n",
+       "       [';40;365;3400;5400;16600'],\n",
+       "       [';40;365;3360;;'],\n",
+       "       [';40;365;1940;;'],\n",
+       "       [';50;365;5500;7500;22250'],\n",
+       "       [';;;;;'],\n",
+       "       [';50;365;1250;2650;19250'],\n",
+       "       [';50;365;5500;5500;22250'],\n",
+       "       [';40;365;3500;4400;15600'],\n",
+       "       [';40;365;8200;13200;16600'],\n",
+       "       [';40;365;3080;4600;16600'],\n",
+       "       [';50;365;;;20250'],\n",
+       "       [';40;365;3440;3440;15600'],\n",
+       "       [';;365;;;'],\n",
+       "       [';40;365;2120;3280;15600'],\n",
+       "       [';50;;;6900;22250'],\n",
+       "       [';40;365;4900;4900;16600'],\n",
+       "       [';40;365;4000;5000;16600'],\n",
+       "       [';50;365;3400;5000;20250'],\n",
+       "       [';125;;;;54625'],\n",
+       "       [';40;;3441;4541;16600']], dtype=object)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data_numpy.to_numpy()\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'numpy.ndarray' object has no attribute 'null'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[15], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnull\u001b[49m()\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'null'"
+     ]
+    }
+   ],
+   "source": [
+    "data.null()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Properties+analysis.ipynb b/Properties+analysis.ipynb
new file mode 100644
index 0000000..ad72c72
--- /dev/null
+++ b/Properties+analysis.ipynb
@@ -0,0 +1,412 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(   PropertyID     Location  Size_sqft  SalePrice\n",
+       " 0        2001       Suburb       2996     355372\n",
+       " 1        2002     Downtown       2887     505830\n",
+       " 2        2003       Suburb        957     338789\n",
+       " 3        2004  Countryside       1814     419335\n",
+       " 4        2005       Suburb        850     357301,\n",
+       "          PropertyID Location    Size_sqft      SalePrice\n",
+       " count    100.000000      100   100.000000     100.000000\n",
+       " unique          NaN        3          NaN            NaN\n",
+       " top             NaN   Suburb          NaN            NaN\n",
+       " freq            NaN       40          NaN            NaN\n",
+       " mean    2050.500000      NaN  1832.410000  401866.300000\n",
+       " std       29.011492      NaN   695.535926  116274.958848\n",
+       " min     2001.000000      NaN   806.000000  205943.000000\n",
+       " 25%     2025.750000      NaN  1154.000000  303015.500000\n",
+       " 50%     2050.500000      NaN  1816.000000  403301.500000\n",
+       " 75%     2075.250000      NaN  2438.000000  506435.250000\n",
+       " max     2100.000000      NaN  2996.000000  598378.000000)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "# Load the dataset\n",
+    "properties_df = pd.read_csv('properties.csv')\n",
+    "\n",
+    "# Display the first few rows of the dataset and its summary\n",
+    "properties_head = properties_df.head()\n",
+    "properties_summary = properties_df.describe(include='all')\n",
+    "\n",
+    "properties_head, properties_summary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "colors = sns.color_palette('viridis', 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_15436\\2591743356.py:24: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.countplot(data=properties_df, x='Location', palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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z50yZMiWvvfZali5dmr333jvNmzdPoVBImzZt0qZNmzRp0iQvv/xy7r333sycOTNHHXVUevXqlVtvvTVvvvlmaV7mT+b485//nD59+myV7f/PUVZWllNOOSUHHnhgrrzyyqxbty6PP/74rjmpfKUtXbo0VVVV6dq166fusz3X2/bq2bNnJk2alE6dOmXMmDE5+OCDS/Omt2rVKsnHRX2bNm1Ky8nHU7LMmTMnffr0Sc+ePdOwYcP84Ac/yKxZs0r73HLLLdlvv/1Kd7e//vrrGTJkSLp27ZpOnTrl+9//fnr16rXNXNOmTcvYsWMzduzYdOnSJZdffnkOOuigavtMmjQp11xzTU466aQccMABOemkk3LuuefmV7/61Q6dAwAAgNpIac5OM3LkyLz11lu59957c+yxx2bevHn51re+VZo+Ynv179+/9OeWLVumS5cuqaio2KExevTosc05Xj859pblHR0baouqqqoUCoVUVFSkffv2pelVkuSggw5KixYtSq/vgQMH5qGHHsqmTZsyf/78DBo0qFRWv/XWW1m6dGm1qSOSjwu9LbZMofL2229/ap6KiorUq1cv/fr1K63bZ599ql3DAwcOzPPPP5933nlnqxwbNmzII4888pk5GjdunGbNmn1mDvh7VVVVfe4+23O9ba9PvraTj6+z7Xlt77///tVK9CQZN25c/vCHP+TNN99M8vGUK1t+SDhJJkyYkNNPPz1DhgzJ1VdfnWXLln3q+BUVFdWu46T6++f69euzbNmyjB07tvRNsyZNmuTyyy//zHEBAAB2F0pzdqoGDRrkmGOOycSJE/PII4+krKwskyZNSp06H7/UPllI7OiUK0lSp06drUqNbY2zra/Vw56moqIiBxxwwHbtO2DAgHz44Yd56qmn8uc//7laWT1//vy0a9cunTp1qvacT07dsKV427x58xfK3KNHj7Rs2TLz58+vVppvuUt3w4YNOfzwwz81x5YsXzQHbEunTp1SKBS+8I99bu971d/72t7We1yfPn3Sq1evzJkzJ08++WSee+65lJWVlbZPnjw5zz33XI4//vj86U9/ykEHHbTD3+LaYsu0TjfeeGMWLVpUeixZsqQ07zsAAMDuTGnOLnXQQQdl/fr1pTviVqxYUdr2yR8F/aRP/od79erVeemll9KtW7ckH39dfeXKldXKiE8b5/PG3rK8ZWzYnfzpT3/K4sWLM3LkyHTr1i3Lly/P8uXLS9uff/75rFmzpjSlQosWLdKzZ89cf/312WuvvdK1a9cMGDAgTz/9dO67776tpkT5PHvvvXc2bdpUbV23bt2ycePGPPbYY6V17733Xl588cVSjkKhkKOOOir33HNPnnvuuRx55JHp2bNnKisr86tf/SoHH3ywD72oMS1btszQoUMzY8aMrF+/fqvta9as2a7rrVWrVtXe75Ide6/aYq+99trqOvssp59+embPnp1Zs2ZlyJAh1e6GT5LOnTvn3HPPzR/+8IecdNJJ1aZz+aRu3bpVu46T6u+frVu3Trt27fKXv/wlBx54YLXH9n6QBwAAUJspzdkp3nvvvRx99NG55ZZb8uyzz+aVV17Jb37zm0ydOjXDhw9Pw4YNc9hhh+Xqq69ORUVF5s+fn0suuWSbY1122WUpLy/PkiVLUlZWlq9//esZMWJEko/nQ37nnXcyderULFu2LDNmzMjvfve77c758MMPZ+rUqXnppZcyY8aM/OY3v8n48eN3ximAXaaysjIrV67Mm2++maeeeipXXnllhg8fnu985zsZM2ZMhgwZkh49emT06NF56qmn8vjjj2fMmDEZOHBgDj744NI4gwYNyq233loqyFu2bJlu3brl9ttv3+HSvEOHDlm3bl3Ky8vz7rvv5q9//Ws6deqU4cOHZ9y4cXnooYfyzDPP5B//8R+z7777Zvjw4dVy/Od//md69+6dJk2apE6dOhkwYEC1bFBTZsyYkU2bNuXQQw/NHXfckZdffjkVFRWZPn16+vfvv13X29FHH52FCxdmzpw5efnllzNp0qQsWbJkh7N06NAh5eXlWblyZVavXv25+//gBz/IG2+8kRtvvLH0A6BJ8re//S1nnXVW5s2bl9deey0PP/xwnnjiiU/90Hj8+PG56aabMmvWrLz00kuZNGlSnnvuuWr7TJkyJVdddVWmT5+el156KYsXL86sWbNy7bXX7vDfEwAAoLZRmrNTNGnSJP369ct1112XAQMG5Jvf/GYmTpyYcePG5frrr0+S3HTTTdm4cWP69u2bc845J5dffvk2x7r66qszfvz49O3bNytXrsx//dd/leYn79atW2644YbMmDEjvXr1yuOPP57zzjtvu3P+5Cc/ycKFC9OnT59cfvnlufbaazN06NAvfgJgF5o7d27atm2bDh065Nhjj82DDz6Y6dOn55577kndunVTKBRyzz335Gtf+1oGDBiQIUOGpGPHjrn99turjTNw4MBs2rSp2pzhgwYN2mrd9jj88MPzox/9KKNGjUqrVq0yderUJMmsWbPSt2/ffOc730n//v1TVVWV+++/xfn2TAAABmBJREFUv9o0FDszB+xsHTt2zFNPPZXBgwfnJz/5Sb75zW/mmGOOSXl5ef71X/91u663oUOHZuLEiTn//PNzyCGH5MMPP8yYMWN2OMs111yTBx54IO3bt0+fPn0+d//mzZtn5MiRadKkSenD5iSpW7du3nvvvYwZMyadO3fOySefnGHDhmXKlCnbHGfUqFGl/H379s1rr72WH//4x9X2Of300zNz5szMmjUrPXr0yMCBAzN79mx3mgMAAHuEQtX2/OoVAAC13re//e10794906dPr+koAAAAuy2lOQDAbm716tWZN29evve97+X5559Ply5dajoSAADAbqteTQcAAOCL6dOnT1avXp2f//znCnMAAIAvyJ3mAAAAAABQ5IdAAQAAAACgSGkOAAAAAABFSnMAAAAAAChSmgMAAAAAQJHSHAAAAAAAipTmAOw0hUIhd999d03HAAAAAPi7Kc0BdlNlZWUZMWJEjRx78uTJ6d2791brV6xYkWHDhn35gQAAAAB2kno1HQCAPUebNm1qOgIAAADAF+JOc4A90Pz583PooYemfv36adu2bS688MJs3LixtH3z5s2ZOnVqDjzwwNSvXz/77bdfrrjiitL2Cy64IJ07d06jRo3SsWPHTJw4MRs2bEiSzJ49O1OmTMkzzzyTQqGQQqGQ2bNnJ9l6epbFixfn6KOPTsOGDbPPPvvkjDPOyLp160rbt9wt/8tf/jJt27bNPvvskzPPPLN0LAAAAIAvmzvNAfYwb775Zo477riUlZVlzpw5eeGFFzJu3Lg0aNAgkydPTpJcdNFFufHGG3PdddflyCOPzIoVK/LCCy+UxmjatGlmz56ddu3aZfHixRk3blyaNm2a888/P6NGjcqSJUsyd+7c/PGPf0ySNG/efKsc69evz9ChQ9O/f/888cQTefvtt3P66afnrLPOKpXsSfLggw+mbdu2efDBB7N06dKMGjUqvXv3zrhx43bpeQIAAADYlkJVVVVVTYcAYMeVlZVlzZo1W/3w5sUXX5w77rgjFRUVKRQKSZIbbrghF1xwQdauXZv169enVatWuf7663P66adv17F++ctf5rbbbsvChQuTfDyn+d13351FixZV269QKOSuu+7KiBEjcuONN+aCCy7I8uXL07hx4yTJ/fffnxNOOCFvvfVWWrdunbKyssybNy/Lli1L3bp1kyQnn3xy6tSpk9tuu+0LnB0AAACAv487zQH2MBUVFenfv3+pME+SI444IuvWrcsbb7yRlStXprKyMt/+9rc/dYzbb78906dPz7Jly7Ju3bps3LgxzZo12+EcvXr1KhXmW3Js3rw5L774Ylq3bp0k6d69e6kwT5K2bdtm8eLFO3QsAAAAgJ3FnOYAXzENGzb8zO0LFizI6NGjc9xxx+W+++7L008/nYsvvjgfffTRLsmz1157VVsuFArZvHnzLjkWAAAAwOdRmgPsYbp165YFCxbkk7NvPfzww2natGm+8Y1vpFOnTmnYsGHKy8u3+fxHHnkk+++/fy6++OIcfPDB6dSpU1577bVq++y9997ZtGnT5+Z45plnsn79+mo56tSpky5dunyBvyEAAADArqM0B9iNrV27NosWLar2OOOMM7J8+fKcffbZeeGFF3LPPfdk0qRJmTBhQurUqZMGDRrkggsuyPnnn585c+Zk2bJlefTRR/Pv//7vSZJOnTrl9ddfz2233ZZly5Zl+vTpueuuu6odt0OHDnnllVeyaNGivPvuu6msrNwq2+jRo9OgQYOceuqpWbJkSR588MGcffbZ+eEPf1iamgUAAACgtjGnOcBubN68eenTp0+1dWPHjs3999+fn/70p+nVq1datmyZsWPH5pJLLintM3HixNSrVy+XXnpp3nrrrbRt2zY/+tGPkiTf/e53c+655+ass85KZWVljj/++EycODGTJ08uPX/kyJG58847M3jw4KxZsyazZs1KWVlZtRyNGjXK73//+4wfPz6HHHJIGjVqlJEjR+baa6/dZecDAAAA4IsqVH3y+/sAAAAAAPAVZnoWAAAAAAAoUpoDAAAAAECR0hwAAAAAAIqU5gAAAAAAUKQ0BwAAAACAIqU5AAAAAAAUKc0BAAAAAKBIaQ4AAAAAAEVKcwAAAAAAKFKaAwAAAABAkdIcAAAAAACKlOYAAAAAAFD0fwFn0kVIlEuP2QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Adjusting the color scheme for all three charts based on the 'viridis' palette\n",
+    "\n",
+    "# Initialize the figure\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# Setting up the color palette\n",
+    "colors = sns.color_palette('viridis', 10)\n",
+    "\n",
+    "# Plotting histograms with adjusted color scheme for Size_sqft and SalePrice\n",
+    "plt.subplot(2, 2, 1)\n",
+    "sns.histplot(properties_df['Size_sqft'], kde=True, color=colors[4], bins=10)\n",
+    "plt.title('Distribution of Property Size (sqft)')\n",
+    "plt.xlabel('Size (sqft)')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "sns.histplot(properties_df['SalePrice'], kde=True, color=colors[7], bins=10)\n",
+    "plt.title('Distribution of Sale Price')\n",
+    "plt.xlabel('Sale Price')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "# Plotting a bar chart for Location with the same color scheme\n",
+    "plt.subplot(2, 2, 3)\n",
+    "sns.countplot(data=properties_df, x='Location', palette='viridis')\n",
+    "plt.title('Distribution of Properties by Location')\n",
+    "plt.xlabel('Location')\n",
+    "plt.ylabel('Number of Properties')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relationship Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_15436\\643855216.py:15: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(data=properties_df, x='Location', y='SalePrice', palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Proceeding with Relationship Analysis\n",
+    "\n",
+    "# Initialize the figure\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# Scatter plot between Size_sqft and SalePrice\n",
+    "plt.subplot(2, 2, 1)\n",
+    "sns.scatterplot(data=properties_df, x='Size_sqft', y='SalePrice', hue='Location', palette='viridis', s=80)\n",
+    "plt.title('Relationship between Property Size and Sale Price')\n",
+    "plt.xlabel('Size (sqft)')\n",
+    "plt.ylabel('Sale Price')\n",
+    "\n",
+    "# Box plots to compare SalePrice across different Location categories\n",
+    "plt.subplot(2, 2, 2)\n",
+    "sns.boxplot(data=properties_df, x='Location', y='SalePrice', palette='viridis')\n",
+    "plt.title('Sale Price Distribution by Location')\n",
+    "plt.xlabel('Location')\n",
+    "plt.ylabel('Sale Price')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Correlation Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Proceeding with Correlation Analysis\n",
+    "\n",
+    "# Calculate and visualize the correlation between Size_sqft and SalePrice\n",
+    "correlation = properties_df[['Size_sqft', 'SalePrice']].corr()\n",
+    "\n",
+    "# Initialize the figure\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# Plotting the heatmap for correlation\n",
+    "sns.heatmap(correlation, annot=True, cmap='viridis', vmin=-1, vmax=1)\n",
+    "plt.title('Correlation between Property Size and Sale Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_15436\\361190972.py:8: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(data=properties_df, y='Size_sqft', palette='viridis')\n",
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_15436\\361190972.py:13: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(data=properties_df, y='SalePrice', palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Proceeding with Outlier Detection\n",
+    "\n",
+    "# Initialize the figure\n",
+    "plt.figure(figsize=(15, 7))\n",
+    "\n",
+    "# Box plots for Size_sqft and SalePrice to detect outliers\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.boxplot(data=properties_df, y='Size_sqft', palette='viridis')\n",
+    "plt.title('Box Plot for Property Size (sqft)')\n",
+    "plt.ylabel('Size (sqft)')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(data=properties_df, y='SalePrice', palette='viridis')\n",
+    "plt.title('Box Plot for Sale Price')\n",
+    "plt.ylabel('Sale Price')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Identifying potential outliers for SalePrice based on IQR\n",
+    "Q1 = properties_df['SalePrice'].quantile(0.25)\n",
+    "Q3 = properties_df['SalePrice'].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "lower_bound = Q1 - 1.5 * IQR\n",
+    "upper_bound = Q3 + 1.5 * IQR\n",
+    "\n",
+    "# Filter the potential outliers\n",
+    "outliers = properties_df[(properties_df['SalePrice'] < lower_bound) | (properties_df['SalePrice'] > upper_bound)]\n",
+    "\n",
+    "# Plotting the scatter plot with potential outliers highlighted\n",
+    "plt.figure(figsize=(15, 7))\n",
+    "\n",
+    "# Scatter plot for regular data points\n",
+    "for location, color in zip(['Suburb', 'Downtown', 'Countryside'], [colors[4], colors[7], colors[2]]):\n",
+    "    subset = properties_df[properties_df['Location'] == location]\n",
+    "    plt.scatter(subset['Size_sqft'], subset['SalePrice'], color=color, label=location, marker='^', alpha=0.7)\n",
+    "\n",
+    "# Highlighting the outliers\n",
+    "plt.scatter(outliers['Size_sqft'], outliers['SalePrice'], color='red', label='Potential Outliers', s=150, edgecolors='black')\n",
+    "\n",
+    "plt.title('Relationship between Property Size and Sale Price with Potential Outliers Highlighted')\n",
+    "plt.xlabel('Size (sqft)')\n",
+    "plt.ylabel('Sale Price')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_15436\\652623439.py:21: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.countplot(data=properties_df, x='Location', palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Adjusting the scatter plot to use circle markers in the consolidated view using a different approach\n",
+    "\n",
+    "plt.figure(figsize=(20, 15))\n",
+    "\n",
+    "# 1. Distribution of Property Size (sqft)\n",
+    "plt.subplot(2, 2, 1)\n",
+    "sns.histplot(properties_df['Size_sqft'], kde=True, color=colors[4], bins=10)\n",
+    "plt.title('Distribution of Property Size (sqft)')\n",
+    "plt.xlabel('Size (sqft)')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "# 2. Distribution of Sale Price\n",
+    "plt.subplot(2, 2, 2)\n",
+    "sns.histplot(properties_df['SalePrice'], kde=True, color=colors[7], bins=10)\n",
+    "plt.title('Distribution of Sale Price')\n",
+    "plt.xlabel('Sale Price')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "# 3. Distribution of Properties by Location\n",
+    "plt.subplot(2, 2, 3)\n",
+    "sns.countplot(data=properties_df, x='Location', palette='viridis')\n",
+    "plt.title('Distribution of Properties by Location')\n",
+    "plt.xlabel('Location')\n",
+    "plt.ylabel('Number of Properties')\n",
+    "\n",
+    "# 4. Relationship between Property Size and Sale Price with location as hue (circle markers)\n",
+    "plt.subplot(2, 2, 4)\n",
+    "for location, color in zip(['Suburb', 'Downtown', 'Countryside'], [colors[4], colors[7], colors[2]]):\n",
+    "    subset = properties_df[properties_df['Location'] == location]\n",
+    "    plt.scatter(subset['Size_sqft'], subset['SalePrice'], color=color, label=location, alpha=0.7)\n",
+    "\n",
+    "plt.title('Relationship between Property Size and Sale Price')\n",
+    "plt.xlabel('Size (sqft)')\n",
+    "plt.ylabel('Sale Price')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Simple+linear+regression_with_comments.ipynb b/Simple+linear+regression_with_comments.ipynb
new file mode 100644
index 0000000..7e7e818
--- /dev/null
+++ b/Simple+linear+regression_with_comments.ipynb
@@ -0,0 +1,182 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "# We can override the default matplotlib styles with those of Seaborn\n",
+    "#import seaborn as sns\n",
+    "#sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "data = pd.read_csv('1.01. Simple linear regression.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's check what's inside this data frame\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# This method gives us very nice descriptive statistics. We don't need this as of now, but will later on!\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create your first regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Following the regression equation, our dependent variable (y) is the GPA\n",
+    "y = data ['GPA']\n",
+    "# Similarly, our independent variable (x) is the SAT score\n",
+    "x1 = data ['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot a scatter plot (first we put the horizontal axis, then the vertical axis)\n",
+    "plt.scatter(x1,y)\n",
+    "# Name the axes\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Add a constant. Essentially, we are adding a new column (equal in lenght to x), which consists only of 1s\n",
+    "x = sm.add_constant(x1)\n",
+    "# Fit the model, according to the OLS (ordinary least squares) method with a dependent variable y and an idependent x\n",
+    "results = sm.OLS(y,x).fit()\n",
+    "# Print a nice summary of the regression. That's one of the strong points of statsmodels -> the summaries\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a scatter plot\n",
+    "plt.scatter(x1,y)\n",
+    "# Define the regression equation, so we can plot it later\n",
+    "yhat = 0.0017*x1 + 0.275\n",
+    "# Plot the regression line against the independent variable (SAT)\n",
+    "fig = plt.plot(x1,yhat, lw=4, c='orange', label ='regression line')\n",
+    "# Label the axes\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/## To check versions of Tensorflow.md b/Udemy DataScience Workbook/## To check versions of Tensorflow.md
new file mode 100644
index 0000000..9f0cfbe
--- /dev/null
+++ b/Udemy DataScience Workbook/## To check versions of Tensorflow.md	
@@ -0,0 +1,2 @@
+## To check versions of Tensorflow
+#### pip list | findstr tensorflow
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/For Loops-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/For Loops-checkpoint.ipynb
new file mode 100644
index 0000000..66b9cd6
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/For Loops-checkpoint.ipynb	
@@ -0,0 +1,600 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "71b995d9",
+   "metadata": {},
+   "source": [
+    "# For Loops - Exercise #1\n",
+    "Create a For loop that prints every digit on a new line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "528e5bb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = [0,1,2,3,4,5,6,7,8,9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6811ad2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "for n in digits:\n",
+    "    print (n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "096bf234",
+   "metadata": {},
+   "source": [
+    "Adjust the code, so the digits are all printed on the same line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "28282536",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 5 6 7 8 9 "
+     ]
+    }
+   ],
+   "source": [
+    "for n in digits:\n",
+    "    print (n, end = \" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc553117",
+   "metadata": {},
+   "source": [
+    "# While Loops and Incrementing - Exercise #1\n",
+    "Create a while loop that will print all odd numbers from 0 to 30 on the same row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9b9441dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 "
+     ]
+    }
+   ],
+   "source": [
+    "x = 1\n",
+    "while x<=30:\n",
+    "    print(x, end = \" \")\n",
+    "    x = x+2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47dcfad3",
+   "metadata": {},
+   "source": [
+    "# Lists with the range() Function - Exercise #1\n",
+    "Use the range() function to create a list with all numbers from 1 to 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "42862846",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_list = range(1, 11, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "09b582e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = list(range(1, 11, 1))\n",
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1ed60059",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "range(1, 11)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "672df6f9",
+   "metadata": {},
+   "source": [
+    "Use the range() function to create a list with all even numbers from 0 to 30 included."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d2f3ef8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = list(range(0, 31, 2))\n",
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c45b70d8",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements and Loops - Exercise #1\n",
+    "Create a For loop that will print all the variables from a given list multiplied by 2. Let the list contain all numbers from 1 to 10. Create it with the help of the range() function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ef0d3fd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 4 6 8 10 12 14 16 18 20 "
+     ]
+    }
+   ],
+   "source": [
+    "my_list = list(range(1,11))\n",
+    "for n in my_list:\n",
+    "    n = n * 2\n",
+    "    print(n,end = \" \")\n",
+    "    n = n+1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "579cab8b",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements and Loops - Exercise #2\n",
+    "Create a little program that runs a loop over all values from 1 to 30. Let it print all odd numbers, and in the place of the even numbers, it should print \"Even\". Help yourself with the range() function to solve this exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "8289bfe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_list = list(range(1,31))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "481458ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] "
+     ]
+    }
+   ],
+   "source": [
+    "print(my_list, end = \" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0f69b56d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 even 3 even 5 even 7 even 9 even 11 even 13 even 15 even 17 even 19 even 21 even 23 even 25 even 27 even 29 even "
+     ]
+    }
+   ],
+   "source": [
+    "for n in range(1,31):\n",
+    "    if n % 2 == 1:\n",
+    "        print(n, end = \" \")\n",
+    "    else:\n",
+    "        print(\"even\", end = \" \")\n",
+    "        n = n + 1\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f39ef12",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements and Loops - Exercise #3\n",
+    "You have the following list of numbers. Iterate over this list, printing out each list value multiplied by 10. Find two solutions of this problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "fd73c62c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_num = [1,2,3,4,5,6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f47cb115",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 20 30 40 50 60 "
+     ]
+    }
+   ],
+   "source": [
+    "for n in my_num:\n",
+    "    n = n * 10\n",
+    "    print(n, end = \" \")\n",
+    "    n = n+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "03b6f2d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 20 30 40 50 60 "
+     ]
+    }
+   ],
+   "source": [
+    "for n in range(1,7):\n",
+    "    n = n * 10\n",
+    "    print(n, end = \" \")\n",
+    "    n = n+1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc811bb7",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements, Functions, and Loops - Exercise #1\n",
+    "You are provided with the nums list. Complete the code in the cell that follows. Use a while loop to count the number of values lower than 20."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c721ca91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1,35,12,24,31,51,70,100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "df16eff6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count(nums):\n",
+    "    Total = 0\n",
+    "    index = 0\n",
+    "    while index < len(nums):\n",
+    "        if nums[index] > 20:\n",
+    "            Total += 1\n",
+    "        index +=1\n",
+    "    return Total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "939c2541",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1,35,12,24,31,51,70,100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d5842c7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "57d653d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The number of values lower than 20 is: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "def count_values_below_20(nums):\n",
+    "    count = 0\n",
+    "    index = 0\n",
+    "\n",
+    "    while index < len(nums):\n",
+    "        if nums[index] < 20:\n",
+    "            count += 1\n",
+    "        index += 1\n",
+    "\n",
+    "    return count\n",
+    "\n",
+    "nums = [15, 25, 10, 5, 30, 18, 9, 24, 22]\n",
+    "\n",
+    "result = count_values_below_20(nums)\n",
+    "print(\"The number of values lower than 20 is:\", result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bd1aec5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1,35,12,24,31,51,70,100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1ce2a4a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count(nums):\n",
+    "    Total = 0\n",
+    "    for X in nums:\n",
+    "        if X <= 19:\n",
+    "            Total += 1\n",
+    "    return Total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "cb9cec5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1,35,12,24,31,51,70,100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "dfbdd79b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79eb1fc5",
+   "metadata": {},
+   "source": [
+    "# How to Iterate over Dictionaries - Exercise #1\n",
+    "In this exercise you will use the same dictionaries as the ones we used in the lesson - prices and quantity. This time, don't just calculate all the money Jan spent. Calculate how much she spent on products with a price of 5 dollars or more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "279f5a11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prices = {\n",
+    "    \"box_of_spaghetti\" : 4,\n",
+    "    \"lasagna\"  : 5,\n",
+    "    \"hamburger\" : 2\n",
+    "   }\n",
+    "quantity = {\n",
+    "    \"box_of_spaghetti\" : 6,\n",
+    "    \"lasagna\"  : 10,\n",
+    "    \"hamburger\" : 0\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4a3be27f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "74\n"
+     ]
+    }
+   ],
+   "source": [
+    "money_spent = 0\n",
+    "for i in prices:\n",
+    "    money_spent = money_spent + (prices[i] * quantity[i])\n",
+    "print(money_spent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e3c7c1d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50\n"
+     ]
+    }
+   ],
+   "source": [
+    "money_spent = 0\n",
+    "for i in prices:\n",
+    "    if prices[i] >= 5:\n",
+    "        money_spent = money_spent + (prices[i] * quantity[i])\n",
+    "print(money_spent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d91a4a1b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/Functions-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/Functions-checkpoint.ipynb
new file mode 100644
index 0000000..30a4599
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/Functions-checkpoint.ipynb	
@@ -0,0 +1,515 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6bb5e767",
+   "metadata": {},
+   "source": [
+    "How to Create a Function with a Parameter - Exercise #1\n",
+    "Define a function called multiplication_by_2(x) that returns a value equal to its argument multiplied by 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2572d938",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def multiplication_by(a):\n",
+    "    return a * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b7009699",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multiplication_by(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0bb0dba",
+   "metadata": {},
+   "source": [
+    "# How to Create a Function with a Parameter - Exercise #2\n",
+    "Define a function called division_by_2(x) that returns a float value equal to its argument divided by 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fd5b1125",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def division_by(x):\n",
+    "    return x/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3bfbf419",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "division_by(8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "378d0926",
+   "metadata": {},
+   "source": [
+    "# Defining a Function in Python - Exercise #1\n",
+    "Define a function called exponentiation_exp_2(x) that states the value of the argument accompanied by the phrase \"Raised to the power of 2:\" and returns a value equal to its argument raised to the power of 2. This time, use a new variable, called result, in the body of the Function. Call the function with some argument to verify it works properly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c032fe28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def exponentiation_exp(a):\n",
+    "    result = a ** 2\n",
+    "    print(\"result = \", result)\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8aae8fbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "result =  16\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exponentiation_exp(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff56fcc9",
+   "metadata": {},
+   "source": [
+    "# How to Use a Function within a Function - Exercise #1\n",
+    "Define a function called plus_five() that adds 5 to its argument. Then, define another function named m_by_3() that multiplies the argument (the result obtained from plus_five()) by 3. Verify your code was correct by calling the second function with an argument of 5. Was your output equal to 30?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "df8a4325",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plus_five(x):\n",
+    "    return x + 5\n",
+    "def m_by_3(x):\n",
+    "    return plus_five(x) * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6e97cd4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m_by_3(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b900d918",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements and Functions - Exercise #1\n",
+    "Define a function, called compare_the_two(), with two arguments. If the first one is greater than the second one, let it print \"Greater\". If the second one is greater, it should print \"Less\". Let it print \"Equal\" if the two values are the same number.\n",
+    "\n",
+    "This topic is covered in"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e58757ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_the_two(a,b):\n",
+    "    if a>b:\n",
+    "        print(\"Greater\")\n",
+    "    elif a<b:\n",
+    "        print(\"Less\")\n",
+    "    else:\n",
+    "        print(\"Equal\")\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d3a11c5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Greater\n"
+     ]
+    }
+   ],
+   "source": [
+    "compare_the_two(9,7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6836e757",
+   "metadata": {},
+   "source": [
+    "# Obtain the maximum number\n",
+    "among the values 25, 65, 890, and 15."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f2b9b6b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "890"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(25,65,890,15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef702643",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Obtain the minimum number among the values 25, 65, 890, and 15.\n",
+    "min(25,65,890,15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d8bedb72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find the absolute value of -100.\n",
+    "abs(-100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "43ef1a12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "56"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Round the value of 55.5.\n",
+    "round(55.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "25a59d93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35.568"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Round 35.56789 to the third digit.\n",
+    "round(35.56789,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d5cc9e63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Find the sum of all elements in the provided list, called \"Numbers\".\n",
+    "Numbers = [1, 5, 64, 24.5]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a5e3d54a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Built-in Functions in Python - Exercise #7\n",
+    "#Use a built-in function to raise 10 to the power of 3.\n",
+    "pow(10,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "633daf1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#In one line of code, find how many characters there are in the word \"Elephant\"?\n",
+    "len(\"Elephant\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8043cca3",
+   "metadata": {},
+   "source": [
+    "# Built-in Functions in Python - Exercise #9\n",
+    "Create a function, called distance_from_zero(), that returns the absolute value of a provided single argument and prints a statement \"Not Possible\" if the argument provided is not a number. To solve the task, use the type() function in the body of distance_from_zero().\n",
+    "\n",
+    "Call the function with the values of -10 and \"cat\" to verify it works correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "58900c38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def distance_from_zero(a):\n",
+    "    if isinstance (a, (int,float)):\n",
+    "        return abs(a)\n",
+    "    else:\n",
+    "        print(\" not Possible\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "606f23f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " not Possible\n"
+     ]
+    }
+   ],
+   "source": [
+    "distance_from_zero('cat')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "544013a6",
+   "metadata": {},
+   "source": [
+    "# Lists - Exercise #1\n",
+    "Create a list, called Numbers. Let it contain the numbers 10, 25, 40, and 50.\n",
+    "\n",
+    "Print the element at index 2 from the list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "49a95585",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40\n"
+     ]
+    }
+   ],
+   "source": [
+    "Numbers = [10,25,40,50]\n",
+    "print(Numbers[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "82d1dfcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers[0] = 15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d15ed41",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "raw",
+   "id": "1cf36d75",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/Improved_model.html-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/Improved_model.html-checkpoint.ipynb
new file mode 100644
index 0000000..297326c
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/Improved_model.html-checkpoint.ipynb	
@@ -0,0 +1,19919 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Improving the model that predicts the price of a fairly used car"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import statsmodels.api as sum\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "sns.set()\n",
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('Real-life+example.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preprocessing the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "Model             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4025         4025  4025.000000     4025  \n",
+       "unique           4            2          NaN      306  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          1861         3654          NaN      188  \n",
+       "mean           NaN          NaN  2006.379627      NaN  \n",
+       "std            NaN          NaN     6.695595      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2007.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_2108\\4196895882.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Outliers are not removed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we decided to use some matplotlib code, without explaining it\n",
+    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
+    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1,3, sharey=True, figsize = (15,3))\n",
+    "ax1.scatter(data['Year'], data['Price'])\n",
+    "ax1.set_title('Year and Price')\n",
+    "ax2.scatter(data['EngineV'],data['Price'])\n",
+    "ax2.set_title('Engine and Price')\n",
+    "ax3.scatter(data['Mileage'], data['Price'])\n",
+    "ax3.set_title('Mileage and Price')\n",
+    "#ax4.scatter(data['Brand'], data['Price'])\n",
+    "#ax4.set_title('Brand and Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 3))\n",
+    "\n",
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=data['Year'], y=data['Price'], ax=ax1, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax1.set_title('Year and Price')\n",
+    "\n",
+    "# Scatter plot for EngineV and log_price with regression line\n",
+    "sns.regplot(x=data['EngineV'], y=data['Price'], ax=ax2, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax2.set_title('EngineV and Price')\n",
+    "\n",
+    "# Scatter plot for Mileage and log_price with regression line\n",
+    "sns.regplot(x=data['Mileage'], y=data['Price'], ax=ax3, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax3.set_title('Mileage and Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_2108\\1987317337.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  data['log_price'] = log_price\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4339</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>Corolla</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4340</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>S 350</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4341</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>535</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4342</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>8000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>520</td>\n",
+       "      <td>8.987197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4344</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>T5 (Transporter)</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4025 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "4339         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "4340  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "4341            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "4342            BMW    8000.0      sedan      194      2.0      Petrol   \n",
+       "4344     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year              Model  log_price  \n",
+       "0             yes  1991                320   8.342840  \n",
+       "1             yes  1999       Sprinter 212   8.974618  \n",
+       "2             yes  2003              S 500   9.495519  \n",
+       "3             yes  2007                 Q7  10.043249  \n",
+       "4             yes  2011              Rav 4   9.814656  \n",
+       "...           ...   ...                ...        ...  \n",
+       "4339          yes  2014            Corolla   9.792556  \n",
+       "4340          yes  2014              S 350  11.736069  \n",
+       "4341          yes  1999                535   8.779557  \n",
+       "4342          yes  1985                520   8.987197  \n",
+       "4344          yes  2013  T5 (Transporter)    9.510445  \n",
+       "\n",
+       "[4025 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#Transform the 'Price' log transformation\n",
+    "log_price = np.log(data['Price'])\n",
+    "#Add log_price to the dataframe\n",
+    "\n",
+    "data['log_price'] = log_price\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1 , ax2, ax3) = plt.subplots(1,3, sharey=True, figsize= (15,3))\n",
+    "ax1.scatter(data['Year'],data['log_price'])\n",
+    "ax1.set_title('Year and log_price')\n",
+    "ax2.scatter(data['EngineV'], data['log_price'])\n",
+    "ax2.set_title('EngineV and log_price')\n",
+    "ax3.scatter(data['Mileage'],data['log_price'])\n",
+    "ax3.set_title('Mileage and log_price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 3))\n",
+    "\n",
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=data['Year'], y=data['log_price'], ax=ax1, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax1.set_title('Year and log_price')\n",
+    "\n",
+    "# Scatter plot for EngineV and log_price with regression line\n",
+    "sns.regplot(x=data['EngineV'], y=data['log_price'], ax=ax2, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax2.set_title('EngineV and log_price')\n",
+    "\n",
+    "# Scatter plot for Mileage and log_price with regression line\n",
+    "sns.regplot(x=data['Mileage'], y=data['log_price'], ax=ax3, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax3.set_title('Mileage and log_price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>306</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>188</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.409665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.933136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.396930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.853523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.994242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.611538</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand   Body      Mileage      EngineV Engine Type Registration  \\\n",
+       "count         4025   4025  4025.000000  4025.000000        4025         4025   \n",
+       "unique           7      6          NaN          NaN           4            2   \n",
+       "top     Volkswagen  sedan          NaN          NaN      Diesel          yes   \n",
+       "freq           880   1534          NaN          NaN        1861         3654   \n",
+       "mean           NaN    NaN   163.572174     2.764586         NaN          NaN   \n",
+       "std            NaN    NaN   103.394703     4.935941         NaN          NaN   \n",
+       "min            NaN    NaN     0.000000     0.600000         NaN          NaN   \n",
+       "25%            NaN    NaN    90.000000     1.800000         NaN          NaN   \n",
+       "50%            NaN    NaN   158.000000     2.200000         NaN          NaN   \n",
+       "75%            NaN    NaN   230.000000     3.000000         NaN          NaN   \n",
+       "max            NaN    NaN   980.000000    99.990000         NaN          NaN   \n",
+       "\n",
+       "               Year    Model    log_price  \n",
+       "count   4025.000000     4025  4025.000000  \n",
+       "unique          NaN      306          NaN  \n",
+       "top             NaN  E-Class          NaN  \n",
+       "freq            NaN      188          NaN  \n",
+       "mean    2006.379627      NaN     9.409665  \n",
+       "std        6.695595      NaN     0.933136  \n",
+       "min     1969.000000      NaN     6.396930  \n",
+       "25%     2003.000000      NaN     8.853523  \n",
+       "50%     2007.000000      NaN     9.350102  \n",
+       "75%     2012.000000      NaN     9.994242  \n",
+       "max     2016.000000      NaN    12.611538  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'Model', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn does not have a built-in way to check for multicollinearity\n",
+    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
+    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
+    "\n",
+    "# Here's the relevant module\n",
+    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# To make this as easy as possible to use, we declare a variable where we put\n",
+    "# all features where we want to check for multicollinearity\n",
+    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
+    "variables = data_cleaned[['Mileage', 'Year', 'EngineV']]\n",
+    "\n",
+    "# we create a new data frame which will include all the VIFs\n",
+    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
+    "vif = pd.DataFrame()\n",
+    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
+    "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range (variables.shape[1])]\n",
+    "# Finally, I like to include names so it is easier to explore the result\n",
+    "vif['Features'] = variables.columns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.481307</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.748134</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.314535</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        VIF Features\n",
+       "0  3.481307  Mileage\n",
+       "1  3.748134     Year\n",
+       "2  1.314535  EngineV"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With all vifs less than 5, It means that the correlation between your independent variables (Mileage, Year, and EngineV) is not excessively high.\n",
+    "So, it looks like your dataset is behaving well in terms of multicollinearity. You can proceed with your regression analysis without major concerns about the variables playing too nicely with each other. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To include the categorical data in the regression, let's create dummies\n",
+    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
+    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
+    "data_with_dummies = pd.get_dummies(data_cleaned,drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  Year  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0  1991   8.342840       True                False   \n",
+       "1      427      2.9  1999   8.974618      False                 True   \n",
+       "2      358      5.0  2003   9.495519      False                 True   \n",
+       "3      240      4.2  2007  10.043249      False                False   \n",
+       "4      120      2.0  2011   9.814656      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  Model_100  \\\n",
+       "0              False                True              True      False   \n",
+       "1              False               False              True      False   \n",
+       "2              False               False              True      False   \n",
+       "3              False                True              True      False   \n",
+       "4              False                True              True      False   \n",
+       "\n",
+       "   Model_11  Model_116  Model_118  Model_120  Model_19  Model_190  Model_200  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_21  Model_210  Model_220  Model_230  Model_25  Model_250  Model_300  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_316  Model_318  Model_320  Model_323  Model_324  Model_325  \\\n",
+       "0      False      False       True      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_328  Model_330  Model_335  Model_4 Series Gran Coupe  Model_428  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_4Runner  Model_5  Model_5 Series  Model_5 Series GT  Model_520  \\\n",
+       "0          False    False           False              False      False   \n",
+       "1          False    False           False              False      False   \n",
+       "2          False    False           False              False      False   \n",
+       "3          False    False           False              False      False   \n",
+       "4          False    False           False              False      False   \n",
+       "\n",
+       "   Model_523  Model_524  Model_525  Model_528  Model_530  Model_535  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_540  Model_545  Model_550  Model_6 Series Gran Coupe  Model_630  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_640  Model_645  Model_650  Model_730  Model_735  Model_740  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_745  Model_750  Model_760  Model_80  Model_9  Model_90  Model_A 140  \\\n",
+       "0      False      False      False     False    False     False        False   \n",
+       "1      False      False      False     False    False     False        False   \n",
+       "2      False      False      False     False    False     False        False   \n",
+       "3      False      False      False     False    False     False        False   \n",
+       "4      False      False      False     False    False     False        False   \n",
+       "\n",
+       "   Model_A 150  Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0        False        False        False     False     False     False   \n",
+       "1        False        False        False     False     False     False   \n",
+       "2        False        False        False     False     False     False   \n",
+       "3        False        False        False     False     False     False   \n",
+       "4        False        False        False     False     False     False   \n",
+       "\n",
+       "   Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  Model_A8  \\\n",
+       "0             False     False     False             False     False     False   \n",
+       "1             False     False     False             False     False     False   \n",
+       "2             False     False     False             False     False     False   \n",
+       "3             False     False     False             False     False     False   \n",
+       "4             False     False     False             False     False     False   \n",
+       "\n",
+       "   Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  Model_Avensis  \\\n",
+       "0      False         False        False         False          False   \n",
+       "1      False         False        False         False          False   \n",
+       "2      False         False        False         False          False   \n",
+       "3      False         False        False         False          False   \n",
+       "4      False         False        False         False          False   \n",
+       "\n",
+       "   Model_Aygo  Model_B 170  Model_B 180  Model_B 200  Model_Beetle  \\\n",
+       "0       False        False        False        False         False   \n",
+       "1       False        False        False        False         False   \n",
+       "2       False        False        False        False         False   \n",
+       "3       False        False        False        False         False   \n",
+       "4       False        False        False        False         False   \n",
+       "\n",
+       "   Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  Model_CL 55 AMG  \\\n",
+       "0       False          False         False         False            False   \n",
+       "1       False          False         False         False            False   \n",
+       "2       False          False         False         False            False   \n",
+       "3       False          False         False         False            False   \n",
+       "4       False          False         False         False            False   \n",
+       "\n",
+       "   Model_CL 550  Model_CL 63 AMG  Model_CLA 200  Model_CLA 220  \\\n",
+       "0         False            False          False          False   \n",
+       "1         False            False          False          False   \n",
+       "2         False            False          False          False   \n",
+       "3         False            False          False          False   \n",
+       "4         False            False          False          False   \n",
+       "\n",
+       "   Model_CLA-Class  Model_CLC 180  Model_CLC 200  Model_CLK 200  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 220  Model_CLK 230  Model_CLK 240  Model_CLK 280  Model_CLK 320  \\\n",
+       "0          False          False          False          False          False   \n",
+       "1          False          False          False          False          False   \n",
+       "2          False          False          False          False          False   \n",
+       "3          False          False          False          False          False   \n",
+       "4          False          False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 430  Model_CLS 350  Model_CLS 400  Model_CLS 500  \\\n",
+       "0          False          False          False          False   \n",
+       "1          False          False          False          False   \n",
+       "2          False          False          False          False   \n",
+       "3          False          False          False          False   \n",
+       "4          False          False          False          False   \n",
+       "\n",
+       "   Model_CLS 63 AMG  Model_Caddy  Model_Camry  Model_Captur  Model_Caravelle  \\\n",
+       "0             False        False        False         False            False   \n",
+       "1             False        False        False         False            False   \n",
+       "2             False        False        False         False            False   \n",
+       "3             False        False        False         False            False   \n",
+       "4             False        False        False         False            False   \n",
+       "\n",
+       "   Model_Carina  Model_Carisma  Model_Celica  Model_Clio  Model_Colt  \\\n",
+       "0         False          False         False       False       False   \n",
+       "1         False          False         False       False       False   \n",
+       "2         False          False         False       False       False   \n",
+       "3         False          False         False       False       False   \n",
+       "4         False          False         False       False       False   \n",
+       "\n",
+       "   Model_Corolla  Model_Corolla Verso  Model_Cross Touran  Model_Dokker  \\\n",
+       "0          False                False               False         False   \n",
+       "1          False                False               False         False   \n",
+       "2          False                False               False         False   \n",
+       "3          False                False               False         False   \n",
+       "4          False                False               False         False   \n",
+       "\n",
+       "   Model_Duster  Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  \\\n",
+       "0         False          False          False      False         False   \n",
+       "1         False          False          False      False         False   \n",
+       "2         False          False          False      False         False   \n",
+       "3         False          False          False      False         False   \n",
+       "4         False          False          False      False         False   \n",
+       "\n",
+       "   Model_FJ Cruiser  Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  \\\n",
+       "0             False          False           False        False        False   \n",
+       "1             False          False           False        False        False   \n",
+       "2             False          False           False        False        False   \n",
+       "3             False          False           False        False        False   \n",
+       "4             False          False           False        False        False   \n",
+       "\n",
+       "   Model_G 500  Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0        False           False           False         False         False   \n",
+       "1        False           False           False         False         False   \n",
+       "2        False           False           False         False         False   \n",
+       "3        False           False           False         False         False   \n",
+       "4        False           False           False         False         False   \n",
+       "\n",
+       "   Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  Model_Golf GTI  \\\n",
+       "0          False          False         False         False           False   \n",
+       "1          False          False         False         False           False   \n",
+       "2          False          False         False         False           False   \n",
+       "3          False          False         False         False           False   \n",
+       "4          False          False         False         False           False   \n",
+       "\n",
+       "   Model_Golf II  Model_Golf III  Model_Golf IV  Model_Golf Plus  \\\n",
+       "0          False           False          False            False   \n",
+       "1          False           False          False            False   \n",
+       "2          False           False          False            False   \n",
+       "3          False           False          False            False   \n",
+       "4          False           False          False            False   \n",
+       "\n",
+       "   Model_Golf V  Model_Golf VI  Model_Golf VII  Model_Golf Variant  \\\n",
+       "0         False          False           False               False   \n",
+       "1         False          False           False               False   \n",
+       "2         False          False           False               False   \n",
+       "3         False          False           False               False   \n",
+       "4         False          False           False               False   \n",
+       "\n",
+       "   Model_Grand Scenic  Model_Grandis  Model_Hiace  Model_Highlander  \\\n",
+       "0               False          False        False             False   \n",
+       "1               False          False        False             False   \n",
+       "2               False          False        False             False   \n",
+       "3               False          False        False             False   \n",
+       "4               False          False        False             False   \n",
+       "\n",
+       "   Model_Hilux  Model_I3  Model_IQ  Model_Jetta  Model_Kangoo  Model_Koleos  \\\n",
+       "0        False     False     False        False         False         False   \n",
+       "1        False     False     False        False         False         False   \n",
+       "2        False     False     False        False         False         False   \n",
+       "3        False     False     False        False         False         False   \n",
+       "4        False     False     False        False         False         False   \n",
+       "\n",
+       "   Model_L 200  Model_LT  Model_Laguna  Model_Lancer  Model_Lancer Evolution  \\\n",
+       "0        False     False         False         False                   False   \n",
+       "1        False     False         False         False                   False   \n",
+       "2        False     False         False         False                   False   \n",
+       "3        False     False         False         False                   False   \n",
+       "4        False     False         False         False                   False   \n",
+       "\n",
+       "   Model_Lancer X  Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0           False                     False                   False   \n",
+       "1           False                     False                   False   \n",
+       "2           False                     False                   False   \n",
+       "3           False                     False                   False   \n",
+       "4           False                     False                   False   \n",
+       "\n",
+       "   Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                   False                   False                  False   \n",
+       "1                   False                   False                  False   \n",
+       "2                   False                   False                  False   \n",
+       "3                   False                   False                  False   \n",
+       "4                   False                   False                  False   \n",
+       "\n",
+       "   Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                  False                     False           False   \n",
+       "1                  False                     False           False   \n",
+       "2                  False                     False           False   \n",
+       "3                  False                     False           False   \n",
+       "4                  False                     False           False   \n",
+       "\n",
+       "   Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0           False        False       False     False     False     False   \n",
+       "1           False        False       False     False     False     False   \n",
+       "2           False        False       False     False     False     False   \n",
+       "3           False        False       False     False     False     False   \n",
+       "4           False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0         False         False         False         False         False   \n",
+       "1         False         False         False         False         False   \n",
+       "2         False         False         False         False         False   \n",
+       "3         False         False         False         False         False   \n",
+       "4         False         False         False         False         False   \n",
+       "\n",
+       "   Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0          False         False         False         False        False   \n",
+       "1          False         False         False         False        False   \n",
+       "2          False         False         False         False        False   \n",
+       "3          False         False         False         False        False   \n",
+       "4          False         False         False         False        False   \n",
+       "\n",
+       "   Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0           False             False            False               False   \n",
+       "1           False             False            False               False   \n",
+       "2           False             False            False               False   \n",
+       "3           False             False            False               False   \n",
+       "4           False             False            False               False   \n",
+       "\n",
+       "   Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  Model_Pajero Wagon  \\\n",
+       "0         False               False               False               False   \n",
+       "1         False               False               False               False   \n",
+       "2         False               False               False               False   \n",
+       "3         False               False               False               False   \n",
+       "4         False               False               False               False   \n",
+       "\n",
+       "   Model_Passat B2  Model_Passat B3  Model_Passat B4  Model_Passat B5  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Passat B6  Model_Passat B7  Model_Passat B8  Model_Passat CC  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  Model_Prius  \\\n",
+       "0          False          False       False         False        False   \n",
+       "1          False          False       False         False        False   \n",
+       "2          False          False       False         False        False   \n",
+       "3          False          False       False         False        False   \n",
+       "4          False          False       False         False        False   \n",
+       "\n",
+       "   Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  Model_Rav 4  \\\n",
+       "0     False     False     False        False     False        False   \n",
+       "1     False     False     False        False     False        False   \n",
+       "2     False     False     False        False     False        False   \n",
+       "3     False     False      True        False     False        False   \n",
+       "4     False     False     False        False     False         True   \n",
+       "\n",
+       "   Model_S 140  Model_S 250  Model_S 280  Model_S 300  Model_S 320  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False        False   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 350  Model_S 400  Model_S 420  Model_S 430  Model_S 500  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False         True   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  Model_S 65 AMG  \\\n",
+       "0       False        False        False           False           False   \n",
+       "1       False        False        False           False           False   \n",
+       "2       False        False        False           False           False   \n",
+       "3       False        False        False           False           False   \n",
+       "4       False        False        False           False           False   \n",
+       "\n",
+       "   Model_S-Guard  Model_S4  Model_S5  Model_S8  Model_SL 500 (550)  \\\n",
+       "0          False     False     False     False               False   \n",
+       "1          False     False     False     False               False   \n",
+       "2          False     False     False     False               False   \n",
+       "3          False     False     False     False               False   \n",
+       "4          False     False     False     False               False   \n",
+       "\n",
+       "   Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  Model_Sandero  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_Sandero StepWay  Model_Scenic  Model_Scion  Model_Scirocco  \\\n",
+       "0                  False         False        False           False   \n",
+       "1                  False         False        False           False   \n",
+       "2                  False         False        False           False   \n",
+       "3                  False         False        False           False   \n",
+       "4                  False         False        False           False   \n",
+       "\n",
+       "   Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  Model_Space Star  \\\n",
+       "0          False         False         False        False             False   \n",
+       "1          False         False         False        False             False   \n",
+       "2          False         False         False        False             False   \n",
+       "3          False         False         False        False             False   \n",
+       "4          False         False         False        False             False   \n",
+       "\n",
+       "   Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  Model_Sprinter 210  \\\n",
+       "0              False           False               False               False   \n",
+       "1              False           False               False               False   \n",
+       "2              False           False               False               False   \n",
+       "3              False           False               False               False   \n",
+       "4              False           False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 211  Model_Sprinter 212  Model_Sprinter 213  \\\n",
+       "0               False               False               False   \n",
+       "1               False                True               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 311  Model_Sprinter 312  Model_Sprinter 313  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 315  Model_Sprinter 316  Model_Sprinter 318  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 319  Model_Sprinter 324  Model_Symbol  Model_Syncro  \\\n",
+       "0               False               False         False         False   \n",
+       "1               False               False         False         False   \n",
+       "2               False               False         False         False   \n",
+       "3               False               False         False         False   \n",
+       "4               False               False         False         False   \n",
+       "\n",
+       "   Model_T2 (Transporter)  Model_T3 (Transporter)  Model_T4 (Transporter)  \\\n",
+       "0                   False                   False                   False   \n",
+       "1                   False                   False                   False   \n",
+       "2                   False                   False                   False   \n",
+       "3                   False                   False                   False   \n",
+       "4                   False                   False                   False   \n",
+       "\n",
+       "   Model_T4 (Transporter)   Model_T5 (Transporter)  Model_T5 (Transporter)   \\\n",
+       "0                    False                   False                    False   \n",
+       "1                    False                   False                    False   \n",
+       "2                    False                   False                    False   \n",
+       "3                    False                   False                    False   \n",
+       "4                    False                   False                    False   \n",
+       "\n",
+       "   Model_T6 (Transporter)  Model_T6 (Transporter)   Model_TT  Model_Tacoma  \\\n",
+       "0                   False                    False     False         False   \n",
+       "1                   False                    False     False         False   \n",
+       "2                   False                    False     False         False   \n",
+       "3                   False                    False     False         False   \n",
+       "4                   False                    False     False         False   \n",
+       "\n",
+       "   Model_Tiguan  Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  \\\n",
+       "0         False          False         False         False         False   \n",
+       "1         False          False         False         False         False   \n",
+       "2         False          False         False         False         False   \n",
+       "3         False          False         False         False         False   \n",
+       "4         False          False         False         False         False   \n",
+       "\n",
+       "   Model_Up  Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0     False        False        False        False        False        False   \n",
+       "1     False        False        False        False        False        False   \n",
+       "2     False        False        False        False        False        False   \n",
+       "3     False        False        False        False        False        False   \n",
+       "4     False        False        False        False        False        False   \n",
+       "\n",
+       "   Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0         False        False       False     False     False     False   \n",
+       "1         False        False       False     False     False     False   \n",
+       "2         False        False       False     False     False     False   \n",
+       "3         False        False       False     False     False     False   \n",
+       "4         False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0       False     False       False        False     False     False  \n",
+       "1       False     False       False        False     False     False  \n",
+       "2       False     False       False        False     False     False  \n",
+       "3       False     False       False        False     False     False  \n",
+       "4       False     False       False        False     False     False  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Rearrange to have the dependent variable on first column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'Year', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes',\n",
+       "       'Model_100', 'Model_11', 'Model_116', 'Model_118', 'Model_120',\n",
+       "       'Model_19', 'Model_190', 'Model_200', 'Model_21', 'Model_210',\n",
+       "       'Model_220', 'Model_230', 'Model_25', 'Model_250', 'Model_300',\n",
+       "       'Model_316', 'Model_318', 'Model_320', 'Model_323', 'Model_324',\n",
+       "       'Model_325', 'Model_328', 'Model_330', 'Model_335',\n",
+       "       'Model_4 Series Gran Coupe', 'Model_428', 'Model_4Runner',\n",
+       "       'Model_5', 'Model_5 Series', 'Model_5 Series GT', 'Model_520',\n",
+       "       'Model_523', 'Model_524', 'Model_525', 'Model_528', 'Model_530',\n",
+       "       'Model_535', 'Model_540', 'Model_545', 'Model_550',\n",
+       "       'Model_6 Series Gran Coupe', 'Model_630', 'Model_640', 'Model_645',\n",
+       "       'Model_650', 'Model_730', 'Model_735', 'Model_740', 'Model_745',\n",
+       "       'Model_750', 'Model_760', 'Model_80', 'Model_9', 'Model_90',\n",
+       "       'Model_A 140', 'Model_A 150', 'Model_A 170', 'Model_A 180',\n",
+       "       'Model_A1', 'Model_A3', 'Model_A4', 'Model_A4 Allroad', 'Model_A5',\n",
+       "       'Model_A6', 'Model_A6 Allroad', 'Model_A7', 'Model_A8',\n",
+       "       'Model_ASX', 'Model_Amarok', 'Model_Auris', 'Model_Avalon',\n",
+       "       'Model_Avensis', 'Model_Aygo', 'Model_B 170', 'Model_B 180',\n",
+       "       'Model_B 200', 'Model_Beetle', 'Model_Bora', 'Model_C-Class',\n",
+       "       'Model_CL 180', 'Model_CL 500', 'Model_CL 55 AMG', 'Model_CL 550',\n",
+       "       'Model_CL 63 AMG', 'Model_CLA 200', 'Model_CLA 220',\n",
+       "       'Model_CLA-Class', 'Model_CLC 180', 'Model_CLC 200',\n",
+       "       'Model_CLK 200', 'Model_CLK 220', 'Model_CLK 230', 'Model_CLK 240',\n",
+       "       'Model_CLK 280', 'Model_CLK 320', 'Model_CLK 430', 'Model_CLS 350',\n",
+       "       'Model_CLS 400', 'Model_CLS 500', 'Model_CLS 63 AMG',\n",
+       "       'Model_Caddy', 'Model_Camry', 'Model_Captur', 'Model_Caravelle',\n",
+       "       'Model_Carina', 'Model_Carisma', 'Model_Celica', 'Model_Clio',\n",
+       "       'Model_Colt', 'Model_Corolla', 'Model_Corolla Verso',\n",
+       "       'Model_Cross Touran', 'Model_Dokker', 'Model_Duster',\n",
+       "       'Model_E-Class', 'Model_Eclipse', 'Model_Eos', 'Model_Espace',\n",
+       "       'Model_FJ Cruiser', 'Model_Fluence', 'Model_Fortuner',\n",
+       "       'Model_G 320', 'Model_G 350', 'Model_G 500', 'Model_G 55 AMG',\n",
+       "       'Model_G 63 AMG', 'Model_GL 320', 'Model_GL 350', 'Model_GL 420',\n",
+       "       'Model_GL 450', 'Model_GL 500', 'Model_GL 550', 'Model_GLC-Class',\n",
+       "       'Model_GLE-Class', 'Model_GLK 220', 'Model_GLK 300',\n",
+       "       'Model_GLS 350', 'Model_GLS 400', 'Model_GLS 500', 'Model_GLS 63',\n",
+       "       'Model_Galant', 'Model_Golf GTI', 'Model_Golf II',\n",
+       "       'Model_Golf III', 'Model_Golf IV', 'Model_Golf Plus',\n",
+       "       'Model_Golf V', 'Model_Golf VI', 'Model_Golf VII',\n",
+       "       'Model_Golf Variant', 'Model_Grand Scenic', 'Model_Grandis',\n",
+       "       'Model_Hiace', 'Model_Highlander', 'Model_Hilux', 'Model_I3',\n",
+       "       'Model_IQ', 'Model_Jetta', 'Model_Kangoo', 'Model_Koleos',\n",
+       "       'Model_L 200', 'Model_LT', 'Model_Laguna', 'Model_Lancer',\n",
+       "       'Model_Lancer Evolution', 'Model_Lancer X',\n",
+       "       'Model_Lancer X Sportback', 'Model_Land Cruiser 100',\n",
+       "       'Model_Land Cruiser 105', 'Model_Land Cruiser 200',\n",
+       "       'Model_Land Cruiser 76', 'Model_Land Cruiser 80',\n",
+       "       'Model_Land Cruiser Prado', 'Model_Latitude', 'Model_Lite Ace',\n",
+       "       'Model_Logan', 'Model_Lupo', 'Model_M5', 'Model_M6', 'Model_MB',\n",
+       "       'Model_ML 250', 'Model_ML 270', 'Model_ML 280', 'Model_ML 320',\n",
+       "       'Model_ML 350', 'Model_ML 400', 'Model_ML 430', 'Model_ML 500',\n",
+       "       'Model_ML 550', 'Model_ML 63 AMG', 'Model_Mark II', 'Model_Master',\n",
+       "       'Model_Matrix', 'Model_Megane', 'Model_Modus', 'Model_Multivan',\n",
+       "       'Model_New Beetle', 'Model_Outlander', 'Model_Outlander XL',\n",
+       "       'Model_Pajero', 'Model_Pajero Pinin', 'Model_Pajero Sport',\n",
+       "       'Model_Pajero Wagon', 'Model_Passat B2', 'Model_Passat B3',\n",
+       "       'Model_Passat B4', 'Model_Passat B5', 'Model_Passat B6',\n",
+       "       'Model_Passat B7', 'Model_Passat B8', 'Model_Passat CC',\n",
+       "       'Model_Phaeton', 'Model_Pointer', 'Model_Polo', 'Model_Previa',\n",
+       "       'Model_Prius', 'Model_Q3', 'Model_Q5', 'Model_Q7', 'Model_R 320',\n",
+       "       'Model_R8', 'Model_Rav 4', 'Model_S 140', 'Model_S 250',\n",
+       "       'Model_S 280', 'Model_S 300', 'Model_S 320', 'Model_S 350',\n",
+       "       'Model_S 400', 'Model_S 420', 'Model_S 430', 'Model_S 500',\n",
+       "       'Model_S 55', 'Model_S 550', 'Model_S 600', 'Model_S 63 AMG',\n",
+       "       'Model_S 65 AMG', 'Model_S-Guard', 'Model_S4', 'Model_S5',\n",
+       "       'Model_S8', 'Model_SL 500 (550)', 'Model_SL 55 AMG',\n",
+       "       'Model_SLK 200', 'Model_SLK 350', 'Model_Sandero',\n",
+       "       'Model_Sandero StepWay', 'Model_Scenic', 'Model_Scion',\n",
+       "       'Model_Scirocco', 'Model_Sequoia', 'Model_Sharan', 'Model_Sienna',\n",
+       "       'Model_Smart', 'Model_Space Star', 'Model_Space Wagon',\n",
+       "       'Model_Sprinter', 'Model_Sprinter 208', 'Model_Sprinter 210',\n",
+       "       'Model_Sprinter 211', 'Model_Sprinter 212', 'Model_Sprinter 213',\n",
+       "       'Model_Sprinter 311', 'Model_Sprinter 312', 'Model_Sprinter 313',\n",
+       "       'Model_Sprinter 315', 'Model_Sprinter 316', 'Model_Sprinter 318',\n",
+       "       'Model_Sprinter 319', 'Model_Sprinter 324', 'Model_Symbol',\n",
+       "       'Model_Syncro', 'Model_T2 (Transporter)', 'Model_T3 (Transporter)',\n",
+       "       'Model_T4 (Transporter)', 'Model_T4 (Transporter) ',\n",
+       "       'Model_T5 (Transporter)', 'Model_T5 (Transporter) ',\n",
+       "       'Model_T6 (Transporter)', 'Model_T6 (Transporter) ', 'Model_TT',\n",
+       "       'Model_Tacoma', 'Model_Tiguan', 'Model_Touareg', 'Model_Touran',\n",
+       "       'Model_Trafic', 'Model_Tundra', 'Model_Up', 'Model_V 250',\n",
+       "       'Model_Vaneo', 'Model_Vento', 'Model_Venza', 'Model_Viano',\n",
+       "       'Model_Virage', 'Model_Vista', 'Model_Vito', 'Model_X1',\n",
+       "       'Model_X3', 'Model_X5', 'Model_X5 M', 'Model_X6', 'Model_X6 M',\n",
+       "       'Model_Yaris', 'Model_Z3', 'Model_Z4'], dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df\n",
+    "# Since each problem is different, that must be done manually\n",
+    "# We can display all possible features and then choose the desired order\n",
+    "pd.options.display.max_columns = 999\n",
+    "data_with_dummies.columns.values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['log_price' 'Mileage' 'EngineV' 'Year' 'Brand_BMW' 'Brand_Mercedes-Benz'\n",
+      " 'Brand_Mitsubishi' 'Brand_Renault' 'Brand_Toyota' 'Brand_Volkswagen'\n",
+      " 'Body_hatch' 'Body_other' 'Body_sedan' 'Body_vagon' 'Body_van'\n",
+      " 'Engine Type_Gas' 'Engine Type_Other' 'Engine Type_Petrol'\n",
+      " 'Registration_yes' 'Model_100' 'Model_11' 'Model_116' 'Model_118'\n",
+      " 'Model_120' 'Model_19' 'Model_190' 'Model_200' 'Model_21' 'Model_210'\n",
+      " 'Model_220' 'Model_230' 'Model_25' 'Model_250' 'Model_300' 'Model_316'\n",
+      " 'Model_318' 'Model_320' 'Model_323' 'Model_324' 'Model_325' 'Model_328'\n",
+      " 'Model_330' 'Model_335' 'Model_4 Series Gran Coupe' 'Model_428'\n",
+      " 'Model_4Runner' 'Model_5' 'Model_5 Series' 'Model_5 Series GT'\n",
+      " 'Model_520' 'Model_523' 'Model_524' 'Model_525' 'Model_528' 'Model_530'\n",
+      " 'Model_535' 'Model_540' 'Model_545' 'Model_550'\n",
+      " 'Model_6 Series Gran Coupe' 'Model_630' 'Model_640' 'Model_645'\n",
+      " 'Model_650' 'Model_730' 'Model_735' 'Model_740' 'Model_745' 'Model_750'\n",
+      " 'Model_760' 'Model_80' 'Model_9' 'Model_90' 'Model_A 140' 'Model_A 150'\n",
+      " 'Model_A 170' 'Model_A 180' 'Model_A1' 'Model_A3' 'Model_A4'\n",
+      " 'Model_A4 Allroad' 'Model_A5' 'Model_A6' 'Model_A6 Allroad' 'Model_A7'\n",
+      " 'Model_A8' 'Model_ASX' 'Model_Amarok' 'Model_Auris' 'Model_Avalon'\n",
+      " 'Model_Avensis' 'Model_Aygo' 'Model_B 170' 'Model_B 180' 'Model_B 200'\n",
+      " 'Model_Beetle' 'Model_Bora' 'Model_C-Class' 'Model_CL 180' 'Model_CL 500'\n",
+      " 'Model_CL 55 AMG' 'Model_CL 550' 'Model_CL 63 AMG' 'Model_CLA 200'\n",
+      " 'Model_CLA 220' 'Model_CLA-Class' 'Model_CLC 180' 'Model_CLC 200'\n",
+      " 'Model_CLK 200' 'Model_CLK 220' 'Model_CLK 230' 'Model_CLK 240'\n",
+      " 'Model_CLK 280' 'Model_CLK 320' 'Model_CLK 430' 'Model_CLS 350'\n",
+      " 'Model_CLS 400' 'Model_CLS 500' 'Model_CLS 63 AMG' 'Model_Caddy'\n",
+      " 'Model_Camry' 'Model_Captur' 'Model_Caravelle' 'Model_Carina'\n",
+      " 'Model_Carisma' 'Model_Celica' 'Model_Clio' 'Model_Colt' 'Model_Corolla'\n",
+      " 'Model_Corolla Verso' 'Model_Cross Touran' 'Model_Dokker' 'Model_Duster'\n",
+      " 'Model_E-Class' 'Model_Eclipse' 'Model_Eos' 'Model_Espace'\n",
+      " 'Model_FJ Cruiser' 'Model_Fluence' 'Model_Fortuner' 'Model_G 320'\n",
+      " 'Model_G 350' 'Model_G 500' 'Model_G 55 AMG' 'Model_G 63 AMG'\n",
+      " 'Model_GL 320' 'Model_GL 350' 'Model_GL 420' 'Model_GL 450'\n",
+      " 'Model_GL 500' 'Model_GL 550' 'Model_GLC-Class' 'Model_GLE-Class'\n",
+      " 'Model_GLK 220' 'Model_GLK 300' 'Model_GLS 350' 'Model_GLS 400'\n",
+      " 'Model_GLS 500' 'Model_GLS 63' 'Model_Galant' 'Model_Golf GTI'\n",
+      " 'Model_Golf II' 'Model_Golf III' 'Model_Golf IV' 'Model_Golf Plus'\n",
+      " 'Model_Golf V' 'Model_Golf VI' 'Model_Golf VII' 'Model_Golf Variant'\n",
+      " 'Model_Grand Scenic' 'Model_Grandis' 'Model_Hiace' 'Model_Highlander'\n",
+      " 'Model_Hilux' 'Model_I3' 'Model_IQ' 'Model_Jetta' 'Model_Kangoo'\n",
+      " 'Model_Koleos' 'Model_L 200' 'Model_LT' 'Model_Laguna' 'Model_Lancer'\n",
+      " 'Model_Lancer Evolution' 'Model_Lancer X' 'Model_Lancer X Sportback'\n",
+      " 'Model_Land Cruiser 100' 'Model_Land Cruiser 105'\n",
+      " 'Model_Land Cruiser 200' 'Model_Land Cruiser 76' 'Model_Land Cruiser 80'\n",
+      " 'Model_Land Cruiser Prado' 'Model_Latitude' 'Model_Lite Ace'\n",
+      " 'Model_Logan' 'Model_Lupo' 'Model_M5' 'Model_M6' 'Model_MB'\n",
+      " 'Model_ML 250' 'Model_ML 270' 'Model_ML 280' 'Model_ML 320'\n",
+      " 'Model_ML 350' 'Model_ML 400' 'Model_ML 430' 'Model_ML 500'\n",
+      " 'Model_ML 550' 'Model_ML 63 AMG' 'Model_Mark II' 'Model_Master'\n",
+      " 'Model_Matrix' 'Model_Megane' 'Model_Modus' 'Model_Multivan'\n",
+      " 'Model_New Beetle' 'Model_Outlander' 'Model_Outlander XL' 'Model_Pajero'\n",
+      " 'Model_Pajero Pinin' 'Model_Pajero Sport' 'Model_Pajero Wagon'\n",
+      " 'Model_Passat B2' 'Model_Passat B3' 'Model_Passat B4' 'Model_Passat B5'\n",
+      " 'Model_Passat B6' 'Model_Passat B7' 'Model_Passat B8' 'Model_Passat CC'\n",
+      " 'Model_Phaeton' 'Model_Pointer' 'Model_Polo' 'Model_Previa' 'Model_Prius'\n",
+      " 'Model_Q3' 'Model_Q5' 'Model_Q7' 'Model_R 320' 'Model_R8' 'Model_Rav 4'\n",
+      " 'Model_S 140' 'Model_S 250' 'Model_S 280' 'Model_S 300' 'Model_S 320'\n",
+      " 'Model_S 350' 'Model_S 400' 'Model_S 420' 'Model_S 430' 'Model_S 500'\n",
+      " 'Model_S 55' 'Model_S 550' 'Model_S 600' 'Model_S 63 AMG'\n",
+      " 'Model_S 65 AMG' 'Model_S-Guard' 'Model_S4' 'Model_S5' 'Model_S8'\n",
+      " 'Model_SL 500 (550)' 'Model_SL 55 AMG' 'Model_SLK 200' 'Model_SLK 350'\n",
+      " 'Model_Sandero' 'Model_Sandero StepWay' 'Model_Scenic' 'Model_Scion'\n",
+      " 'Model_Scirocco' 'Model_Sequoia' 'Model_Sharan' 'Model_Sienna'\n",
+      " 'Model_Smart' 'Model_Space Star' 'Model_Space Wagon' 'Model_Sprinter'\n",
+      " 'Model_Sprinter 208' 'Model_Sprinter 210' 'Model_Sprinter 211'\n",
+      " 'Model_Sprinter 212' 'Model_Sprinter 213' 'Model_Sprinter 311'\n",
+      " 'Model_Sprinter 312' 'Model_Sprinter 313' 'Model_Sprinter 315'\n",
+      " 'Model_Sprinter 316' 'Model_Sprinter 318' 'Model_Sprinter 319'\n",
+      " 'Model_Sprinter 324' 'Model_Symbol' 'Model_Syncro'\n",
+      " 'Model_T2 (Transporter)' 'Model_T3 (Transporter)'\n",
+      " 'Model_T4 (Transporter)' 'Model_T4 (Transporter) '\n",
+      " 'Model_T5 (Transporter)' 'Model_T5 (Transporter) '\n",
+      " 'Model_T6 (Transporter)' 'Model_T6 (Transporter) ' 'Model_TT'\n",
+      " 'Model_Tacoma' 'Model_Tiguan' 'Model_Touareg' 'Model_Touran'\n",
+      " 'Model_Trafic' 'Model_Tundra' 'Model_Up' 'Model_V 250' 'Model_Vaneo'\n",
+      " 'Model_Vento' 'Model_Venza' 'Model_Viano' 'Model_Virage' 'Model_Vista'\n",
+      " 'Model_Vito' 'Model_X1' 'Model_X3' 'Model_X5' 'Model_X5 M' 'Model_X6'\n",
+      " 'Model_X6 M' 'Model_Yaris' 'Model_Z3' 'Model_Z4']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a list of column names with 'log_price' at the beginning\n",
+    "new_order = ['log_price'] + [col for col in data_with_dummies.columns if col != 'log_price']\n",
+    "\n",
+    "# Reorder the columns in the DataFrame\n",
+    "data_with_dummies = data_with_dummies[new_order]\n",
+    "\n",
+    "# Now, when you print the column names, 'log_price' should be the first column\n",
+    "print(data_with_dummies.columns.values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Year  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0  1991       True                False   \n",
+       "1   8.974618      427      2.9  1999      False                 True   \n",
+       "2   9.495519      358      5.0  2003      False                 True   \n",
+       "3  10.043249      240      4.2  2007      False                False   \n",
+       "4   9.814656      120      2.0  2011      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  Model_100  \\\n",
+       "0              False                True              True      False   \n",
+       "1              False               False              True      False   \n",
+       "2              False               False              True      False   \n",
+       "3              False                True              True      False   \n",
+       "4              False                True              True      False   \n",
+       "\n",
+       "   Model_11  Model_116  Model_118  Model_120  Model_19  Model_190  Model_200  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_21  Model_210  Model_220  Model_230  Model_25  Model_250  Model_300  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_316  Model_318  Model_320  Model_323  Model_324  Model_325  \\\n",
+       "0      False      False       True      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_328  Model_330  Model_335  Model_4 Series Gran Coupe  Model_428  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_4Runner  Model_5  Model_5 Series  Model_5 Series GT  Model_520  \\\n",
+       "0          False    False           False              False      False   \n",
+       "1          False    False           False              False      False   \n",
+       "2          False    False           False              False      False   \n",
+       "3          False    False           False              False      False   \n",
+       "4          False    False           False              False      False   \n",
+       "\n",
+       "   Model_523  Model_524  Model_525  Model_528  Model_530  Model_535  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_540  Model_545  Model_550  Model_6 Series Gran Coupe  Model_630  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_640  Model_645  Model_650  Model_730  Model_735  Model_740  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_745  Model_750  Model_760  Model_80  Model_9  Model_90  Model_A 140  \\\n",
+       "0      False      False      False     False    False     False        False   \n",
+       "1      False      False      False     False    False     False        False   \n",
+       "2      False      False      False     False    False     False        False   \n",
+       "3      False      False      False     False    False     False        False   \n",
+       "4      False      False      False     False    False     False        False   \n",
+       "\n",
+       "   Model_A 150  Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0        False        False        False     False     False     False   \n",
+       "1        False        False        False     False     False     False   \n",
+       "2        False        False        False     False     False     False   \n",
+       "3        False        False        False     False     False     False   \n",
+       "4        False        False        False     False     False     False   \n",
+       "\n",
+       "   Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  Model_A8  \\\n",
+       "0             False     False     False             False     False     False   \n",
+       "1             False     False     False             False     False     False   \n",
+       "2             False     False     False             False     False     False   \n",
+       "3             False     False     False             False     False     False   \n",
+       "4             False     False     False             False     False     False   \n",
+       "\n",
+       "   Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  Model_Avensis  \\\n",
+       "0      False         False        False         False          False   \n",
+       "1      False         False        False         False          False   \n",
+       "2      False         False        False         False          False   \n",
+       "3      False         False        False         False          False   \n",
+       "4      False         False        False         False          False   \n",
+       "\n",
+       "   Model_Aygo  Model_B 170  Model_B 180  Model_B 200  Model_Beetle  \\\n",
+       "0       False        False        False        False         False   \n",
+       "1       False        False        False        False         False   \n",
+       "2       False        False        False        False         False   \n",
+       "3       False        False        False        False         False   \n",
+       "4       False        False        False        False         False   \n",
+       "\n",
+       "   Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  Model_CL 55 AMG  \\\n",
+       "0       False          False         False         False            False   \n",
+       "1       False          False         False         False            False   \n",
+       "2       False          False         False         False            False   \n",
+       "3       False          False         False         False            False   \n",
+       "4       False          False         False         False            False   \n",
+       "\n",
+       "   Model_CL 550  Model_CL 63 AMG  Model_CLA 200  Model_CLA 220  \\\n",
+       "0         False            False          False          False   \n",
+       "1         False            False          False          False   \n",
+       "2         False            False          False          False   \n",
+       "3         False            False          False          False   \n",
+       "4         False            False          False          False   \n",
+       "\n",
+       "   Model_CLA-Class  Model_CLC 180  Model_CLC 200  Model_CLK 200  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 220  Model_CLK 230  Model_CLK 240  Model_CLK 280  Model_CLK 320  \\\n",
+       "0          False          False          False          False          False   \n",
+       "1          False          False          False          False          False   \n",
+       "2          False          False          False          False          False   \n",
+       "3          False          False          False          False          False   \n",
+       "4          False          False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 430  Model_CLS 350  Model_CLS 400  Model_CLS 500  \\\n",
+       "0          False          False          False          False   \n",
+       "1          False          False          False          False   \n",
+       "2          False          False          False          False   \n",
+       "3          False          False          False          False   \n",
+       "4          False          False          False          False   \n",
+       "\n",
+       "   Model_CLS 63 AMG  Model_Caddy  Model_Camry  Model_Captur  Model_Caravelle  \\\n",
+       "0             False        False        False         False            False   \n",
+       "1             False        False        False         False            False   \n",
+       "2             False        False        False         False            False   \n",
+       "3             False        False        False         False            False   \n",
+       "4             False        False        False         False            False   \n",
+       "\n",
+       "   Model_Carina  Model_Carisma  Model_Celica  Model_Clio  Model_Colt  \\\n",
+       "0         False          False         False       False       False   \n",
+       "1         False          False         False       False       False   \n",
+       "2         False          False         False       False       False   \n",
+       "3         False          False         False       False       False   \n",
+       "4         False          False         False       False       False   \n",
+       "\n",
+       "   Model_Corolla  Model_Corolla Verso  Model_Cross Touran  Model_Dokker  \\\n",
+       "0          False                False               False         False   \n",
+       "1          False                False               False         False   \n",
+       "2          False                False               False         False   \n",
+       "3          False                False               False         False   \n",
+       "4          False                False               False         False   \n",
+       "\n",
+       "   Model_Duster  Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  \\\n",
+       "0         False          False          False      False         False   \n",
+       "1         False          False          False      False         False   \n",
+       "2         False          False          False      False         False   \n",
+       "3         False          False          False      False         False   \n",
+       "4         False          False          False      False         False   \n",
+       "\n",
+       "   Model_FJ Cruiser  Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  \\\n",
+       "0             False          False           False        False        False   \n",
+       "1             False          False           False        False        False   \n",
+       "2             False          False           False        False        False   \n",
+       "3             False          False           False        False        False   \n",
+       "4             False          False           False        False        False   \n",
+       "\n",
+       "   Model_G 500  Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0        False           False           False         False         False   \n",
+       "1        False           False           False         False         False   \n",
+       "2        False           False           False         False         False   \n",
+       "3        False           False           False         False         False   \n",
+       "4        False           False           False         False         False   \n",
+       "\n",
+       "   Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  Model_Golf GTI  \\\n",
+       "0          False          False         False         False           False   \n",
+       "1          False          False         False         False           False   \n",
+       "2          False          False         False         False           False   \n",
+       "3          False          False         False         False           False   \n",
+       "4          False          False         False         False           False   \n",
+       "\n",
+       "   Model_Golf II  Model_Golf III  Model_Golf IV  Model_Golf Plus  \\\n",
+       "0          False           False          False            False   \n",
+       "1          False           False          False            False   \n",
+       "2          False           False          False            False   \n",
+       "3          False           False          False            False   \n",
+       "4          False           False          False            False   \n",
+       "\n",
+       "   Model_Golf V  Model_Golf VI  Model_Golf VII  Model_Golf Variant  \\\n",
+       "0         False          False           False               False   \n",
+       "1         False          False           False               False   \n",
+       "2         False          False           False               False   \n",
+       "3         False          False           False               False   \n",
+       "4         False          False           False               False   \n",
+       "\n",
+       "   Model_Grand Scenic  Model_Grandis  Model_Hiace  Model_Highlander  \\\n",
+       "0               False          False        False             False   \n",
+       "1               False          False        False             False   \n",
+       "2               False          False        False             False   \n",
+       "3               False          False        False             False   \n",
+       "4               False          False        False             False   \n",
+       "\n",
+       "   Model_Hilux  Model_I3  Model_IQ  Model_Jetta  Model_Kangoo  Model_Koleos  \\\n",
+       "0        False     False     False        False         False         False   \n",
+       "1        False     False     False        False         False         False   \n",
+       "2        False     False     False        False         False         False   \n",
+       "3        False     False     False        False         False         False   \n",
+       "4        False     False     False        False         False         False   \n",
+       "\n",
+       "   Model_L 200  Model_LT  Model_Laguna  Model_Lancer  Model_Lancer Evolution  \\\n",
+       "0        False     False         False         False                   False   \n",
+       "1        False     False         False         False                   False   \n",
+       "2        False     False         False         False                   False   \n",
+       "3        False     False         False         False                   False   \n",
+       "4        False     False         False         False                   False   \n",
+       "\n",
+       "   Model_Lancer X  Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0           False                     False                   False   \n",
+       "1           False                     False                   False   \n",
+       "2           False                     False                   False   \n",
+       "3           False                     False                   False   \n",
+       "4           False                     False                   False   \n",
+       "\n",
+       "   Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                   False                   False                  False   \n",
+       "1                   False                   False                  False   \n",
+       "2                   False                   False                  False   \n",
+       "3                   False                   False                  False   \n",
+       "4                   False                   False                  False   \n",
+       "\n",
+       "   Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                  False                     False           False   \n",
+       "1                  False                     False           False   \n",
+       "2                  False                     False           False   \n",
+       "3                  False                     False           False   \n",
+       "4                  False                     False           False   \n",
+       "\n",
+       "   Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0           False        False       False     False     False     False   \n",
+       "1           False        False       False     False     False     False   \n",
+       "2           False        False       False     False     False     False   \n",
+       "3           False        False       False     False     False     False   \n",
+       "4           False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0         False         False         False         False         False   \n",
+       "1         False         False         False         False         False   \n",
+       "2         False         False         False         False         False   \n",
+       "3         False         False         False         False         False   \n",
+       "4         False         False         False         False         False   \n",
+       "\n",
+       "   Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0          False         False         False         False        False   \n",
+       "1          False         False         False         False        False   \n",
+       "2          False         False         False         False        False   \n",
+       "3          False         False         False         False        False   \n",
+       "4          False         False         False         False        False   \n",
+       "\n",
+       "   Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0           False             False            False               False   \n",
+       "1           False             False            False               False   \n",
+       "2           False             False            False               False   \n",
+       "3           False             False            False               False   \n",
+       "4           False             False            False               False   \n",
+       "\n",
+       "   Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  Model_Pajero Wagon  \\\n",
+       "0         False               False               False               False   \n",
+       "1         False               False               False               False   \n",
+       "2         False               False               False               False   \n",
+       "3         False               False               False               False   \n",
+       "4         False               False               False               False   \n",
+       "\n",
+       "   Model_Passat B2  Model_Passat B3  Model_Passat B4  Model_Passat B5  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Passat B6  Model_Passat B7  Model_Passat B8  Model_Passat CC  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  Model_Prius  \\\n",
+       "0          False          False       False         False        False   \n",
+       "1          False          False       False         False        False   \n",
+       "2          False          False       False         False        False   \n",
+       "3          False          False       False         False        False   \n",
+       "4          False          False       False         False        False   \n",
+       "\n",
+       "   Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  Model_Rav 4  \\\n",
+       "0     False     False     False        False     False        False   \n",
+       "1     False     False     False        False     False        False   \n",
+       "2     False     False     False        False     False        False   \n",
+       "3     False     False      True        False     False        False   \n",
+       "4     False     False     False        False     False         True   \n",
+       "\n",
+       "   Model_S 140  Model_S 250  Model_S 280  Model_S 300  Model_S 320  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False        False   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 350  Model_S 400  Model_S 420  Model_S 430  Model_S 500  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False         True   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  Model_S 65 AMG  \\\n",
+       "0       False        False        False           False           False   \n",
+       "1       False        False        False           False           False   \n",
+       "2       False        False        False           False           False   \n",
+       "3       False        False        False           False           False   \n",
+       "4       False        False        False           False           False   \n",
+       "\n",
+       "   Model_S-Guard  Model_S4  Model_S5  Model_S8  Model_SL 500 (550)  \\\n",
+       "0          False     False     False     False               False   \n",
+       "1          False     False     False     False               False   \n",
+       "2          False     False     False     False               False   \n",
+       "3          False     False     False     False               False   \n",
+       "4          False     False     False     False               False   \n",
+       "\n",
+       "   Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  Model_Sandero  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_Sandero StepWay  Model_Scenic  Model_Scion  Model_Scirocco  \\\n",
+       "0                  False         False        False           False   \n",
+       "1                  False         False        False           False   \n",
+       "2                  False         False        False           False   \n",
+       "3                  False         False        False           False   \n",
+       "4                  False         False        False           False   \n",
+       "\n",
+       "   Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  Model_Space Star  \\\n",
+       "0          False         False         False        False             False   \n",
+       "1          False         False         False        False             False   \n",
+       "2          False         False         False        False             False   \n",
+       "3          False         False         False        False             False   \n",
+       "4          False         False         False        False             False   \n",
+       "\n",
+       "   Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  Model_Sprinter 210  \\\n",
+       "0              False           False               False               False   \n",
+       "1              False           False               False               False   \n",
+       "2              False           False               False               False   \n",
+       "3              False           False               False               False   \n",
+       "4              False           False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 211  Model_Sprinter 212  Model_Sprinter 213  \\\n",
+       "0               False               False               False   \n",
+       "1               False                True               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 311  Model_Sprinter 312  Model_Sprinter 313  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 315  Model_Sprinter 316  Model_Sprinter 318  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 319  Model_Sprinter 324  Model_Symbol  Model_Syncro  \\\n",
+       "0               False               False         False         False   \n",
+       "1               False               False         False         False   \n",
+       "2               False               False         False         False   \n",
+       "3               False               False         False         False   \n",
+       "4               False               False         False         False   \n",
+       "\n",
+       "   Model_T2 (Transporter)  Model_T3 (Transporter)  Model_T4 (Transporter)  \\\n",
+       "0                   False                   False                   False   \n",
+       "1                   False                   False                   False   \n",
+       "2                   False                   False                   False   \n",
+       "3                   False                   False                   False   \n",
+       "4                   False                   False                   False   \n",
+       "\n",
+       "   Model_T4 (Transporter)   Model_T5 (Transporter)  Model_T5 (Transporter)   \\\n",
+       "0                    False                   False                    False   \n",
+       "1                    False                   False                    False   \n",
+       "2                    False                   False                    False   \n",
+       "3                    False                   False                    False   \n",
+       "4                    False                   False                    False   \n",
+       "\n",
+       "   Model_T6 (Transporter)  Model_T6 (Transporter)   Model_TT  Model_Tacoma  \\\n",
+       "0                   False                    False     False         False   \n",
+       "1                   False                    False     False         False   \n",
+       "2                   False                    False     False         False   \n",
+       "3                   False                    False     False         False   \n",
+       "4                   False                    False     False         False   \n",
+       "\n",
+       "   Model_Tiguan  Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  \\\n",
+       "0         False          False         False         False         False   \n",
+       "1         False          False         False         False         False   \n",
+       "2         False          False         False         False         False   \n",
+       "3         False          False         False         False         False   \n",
+       "4         False          False         False         False         False   \n",
+       "\n",
+       "   Model_Up  Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0     False        False        False        False        False        False   \n",
+       "1     False        False        False        False        False        False   \n",
+       "2     False        False        False        False        False        False   \n",
+       "3     False        False        False        False        False        False   \n",
+       "4     False        False        False        False        False        False   \n",
+       "\n",
+       "   Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0         False        False       False     False     False     False   \n",
+       "1         False        False       False     False     False     False   \n",
+       "2         False        False       False     False     False     False   \n",
+       "3         False        False       False     False     False     False   \n",
+       "4         False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0       False     False       False        False     False     False  \n",
+       "1       False     False       False        False     False     False  \n",
+       "2       False     False       False        False     False     False  \n",
+       "3       False     False       False        False     False     False  \n",
+       "4       False     False       False        False     False     False  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed = data_with_dummies\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Declare the inputs and the targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = data_preprocessed['log_price'] #  Dependent variable\n",
+    "#Input are everything but the dependent variable, so i'll drop 'log_price'\n",
+    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4339</th>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4340</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4341</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4342</th>\n",
+       "      <td>194</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4344</th>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4025 rows × 323 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Mileage  EngineV  Year  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0         277      2.0  1991       True                False   \n",
+       "1         427      2.9  1999      False                 True   \n",
+       "2         358      5.0  2003      False                 True   \n",
+       "3         240      4.2  2007      False                False   \n",
+       "4         120      2.0  2011      False                False   \n",
+       "...       ...      ...   ...        ...                  ...   \n",
+       "4339       35      1.6  2014      False                False   \n",
+       "4340        9      3.0  2014      False                 True   \n",
+       "4341        1      3.5  1999       True                False   \n",
+       "4342      194      2.0  1985       True                False   \n",
+       "4344      124      2.0  2013      False                False   \n",
+       "\n",
+       "      Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                False          False         False             False   \n",
+       "1                False          False         False             False   \n",
+       "2                False          False         False             False   \n",
+       "3                False          False         False             False   \n",
+       "4                False          False          True             False   \n",
+       "...                ...            ...           ...               ...   \n",
+       "4339             False          False          True             False   \n",
+       "4340             False          False         False             False   \n",
+       "4341             False          False         False             False   \n",
+       "4342             False          False         False             False   \n",
+       "4344             False          False         False              True   \n",
+       "\n",
+       "      Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  \\\n",
+       "0          False       False        True       False     False   \n",
+       "1          False       False       False       False      True   \n",
+       "2          False       False        True       False     False   \n",
+       "3          False       False       False       False     False   \n",
+       "4          False       False       False       False     False   \n",
+       "...          ...         ...         ...         ...       ...   \n",
+       "4339       False       False        True       False     False   \n",
+       "4340       False       False        True       False     False   \n",
+       "4341       False       False        True       False     False   \n",
+       "4342       False       False        True       False     False   \n",
+       "4344       False       False       False       False      True   \n",
+       "\n",
+       "      Engine Type_Gas  Engine Type_Other  Engine Type_Petrol  \\\n",
+       "0               False              False                True   \n",
+       "1               False              False               False   \n",
+       "2                True              False               False   \n",
+       "3               False              False                True   \n",
+       "4               False              False                True   \n",
+       "...               ...                ...                 ...   \n",
+       "4339            False              False                True   \n",
+       "4340            False              False               False   \n",
+       "4341            False              False                True   \n",
+       "4342            False              False                True   \n",
+       "4344            False              False               False   \n",
+       "\n",
+       "      Registration_yes  Model_100  Model_11  Model_116  Model_118  Model_120  \\\n",
+       "0                 True      False     False      False      False      False   \n",
+       "1                 True      False     False      False      False      False   \n",
+       "2                 True      False     False      False      False      False   \n",
+       "3                 True      False     False      False      False      False   \n",
+       "4                 True      False     False      False      False      False   \n",
+       "...                ...        ...       ...        ...        ...        ...   \n",
+       "4339              True      False     False      False      False      False   \n",
+       "4340              True      False     False      False      False      False   \n",
+       "4341              True      False     False      False      False      False   \n",
+       "4342              True      False     False      False      False      False   \n",
+       "4344              True      False     False      False      False      False   \n",
+       "\n",
+       "      Model_19  Model_190  Model_200  Model_21  Model_210  Model_220  \\\n",
+       "0        False      False      False     False      False      False   \n",
+       "1        False      False      False     False      False      False   \n",
+       "2        False      False      False     False      False      False   \n",
+       "3        False      False      False     False      False      False   \n",
+       "4        False      False      False     False      False      False   \n",
+       "...        ...        ...        ...       ...        ...        ...   \n",
+       "4339     False      False      False     False      False      False   \n",
+       "4340     False      False      False     False      False      False   \n",
+       "4341     False      False      False     False      False      False   \n",
+       "4342     False      False      False     False      False      False   \n",
+       "4344     False      False      False     False      False      False   \n",
+       "\n",
+       "      Model_230  Model_25  Model_250  Model_300  Model_316  Model_318  \\\n",
+       "0         False     False      False      False      False      False   \n",
+       "1         False     False      False      False      False      False   \n",
+       "2         False     False      False      False      False      False   \n",
+       "3         False     False      False      False      False      False   \n",
+       "4         False     False      False      False      False      False   \n",
+       "...         ...       ...        ...        ...        ...        ...   \n",
+       "4339      False     False      False      False      False      False   \n",
+       "4340      False     False      False      False      False      False   \n",
+       "4341      False     False      False      False      False      False   \n",
+       "4342      False     False      False      False      False      False   \n",
+       "4344      False     False      False      False      False      False   \n",
+       "\n",
+       "      Model_320  Model_323  Model_324  Model_325  Model_328  Model_330  \\\n",
+       "0          True      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False      False      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_335  Model_4 Series Gran Coupe  Model_428  Model_4Runner  Model_5  \\\n",
+       "0         False                      False      False          False    False   \n",
+       "1         False                      False      False          False    False   \n",
+       "2         False                      False      False          False    False   \n",
+       "3         False                      False      False          False    False   \n",
+       "4         False                      False      False          False    False   \n",
+       "...         ...                        ...        ...            ...      ...   \n",
+       "4339      False                      False      False          False    False   \n",
+       "4340      False                      False      False          False    False   \n",
+       "4341      False                      False      False          False    False   \n",
+       "4342      False                      False      False          False    False   \n",
+       "4344      False                      False      False          False    False   \n",
+       "\n",
+       "      Model_5 Series  Model_5 Series GT  Model_520  Model_523  Model_524  \\\n",
+       "0              False              False      False      False      False   \n",
+       "1              False              False      False      False      False   \n",
+       "2              False              False      False      False      False   \n",
+       "3              False              False      False      False      False   \n",
+       "4              False              False      False      False      False   \n",
+       "...              ...                ...        ...        ...        ...   \n",
+       "4339           False              False      False      False      False   \n",
+       "4340           False              False      False      False      False   \n",
+       "4341           False              False      False      False      False   \n",
+       "4342           False              False       True      False      False   \n",
+       "4344           False              False      False      False      False   \n",
+       "\n",
+       "      Model_525  Model_528  Model_530  Model_535  Model_540  Model_545  \\\n",
+       "0         False      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False       True      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_550  Model_6 Series Gran Coupe  Model_630  Model_640  Model_645  \\\n",
+       "0         False                      False      False      False      False   \n",
+       "1         False                      False      False      False      False   \n",
+       "2         False                      False      False      False      False   \n",
+       "3         False                      False      False      False      False   \n",
+       "4         False                      False      False      False      False   \n",
+       "...         ...                        ...        ...        ...        ...   \n",
+       "4339      False                      False      False      False      False   \n",
+       "4340      False                      False      False      False      False   \n",
+       "4341      False                      False      False      False      False   \n",
+       "4342      False                      False      False      False      False   \n",
+       "4344      False                      False      False      False      False   \n",
+       "\n",
+       "      Model_650  Model_730  Model_735  Model_740  Model_745  Model_750  \\\n",
+       "0         False      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False      False      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_760  Model_80  Model_9  Model_90  Model_A 140  Model_A 150  \\\n",
+       "0         False     False    False     False        False        False   \n",
+       "1         False     False    False     False        False        False   \n",
+       "2         False     False    False     False        False        False   \n",
+       "3         False     False    False     False        False        False   \n",
+       "4         False     False    False     False        False        False   \n",
+       "...         ...       ...      ...       ...          ...          ...   \n",
+       "4339      False     False    False     False        False        False   \n",
+       "4340      False     False    False     False        False        False   \n",
+       "4341      False     False    False     False        False        False   \n",
+       "4342      False     False    False     False        False        False   \n",
+       "4344      False     False    False     False        False        False   \n",
+       "\n",
+       "      Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0           False        False     False     False     False   \n",
+       "1           False        False     False     False     False   \n",
+       "2           False        False     False     False     False   \n",
+       "3           False        False     False     False     False   \n",
+       "4           False        False     False     False     False   \n",
+       "...           ...          ...       ...       ...       ...   \n",
+       "4339        False        False     False     False     False   \n",
+       "4340        False        False     False     False     False   \n",
+       "4341        False        False     False     False     False   \n",
+       "4342        False        False     False     False     False   \n",
+       "4344        False        False     False     False     False   \n",
+       "\n",
+       "      Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  \\\n",
+       "0                False     False     False             False     False   \n",
+       "1                False     False     False             False     False   \n",
+       "2                False     False     False             False     False   \n",
+       "3                False     False     False             False     False   \n",
+       "4                False     False     False             False     False   \n",
+       "...                ...       ...       ...               ...       ...   \n",
+       "4339             False     False     False             False     False   \n",
+       "4340             False     False     False             False     False   \n",
+       "4341             False     False     False             False     False   \n",
+       "4342             False     False     False             False     False   \n",
+       "4344             False     False     False             False     False   \n",
+       "\n",
+       "      Model_A8  Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  \\\n",
+       "0        False      False         False        False         False   \n",
+       "1        False      False         False        False         False   \n",
+       "2        False      False         False        False         False   \n",
+       "3        False      False         False        False         False   \n",
+       "4        False      False         False        False         False   \n",
+       "...        ...        ...           ...          ...           ...   \n",
+       "4339     False      False         False        False         False   \n",
+       "4340     False      False         False        False         False   \n",
+       "4341     False      False         False        False         False   \n",
+       "4342     False      False         False        False         False   \n",
+       "4344     False      False         False        False         False   \n",
+       "\n",
+       "      Model_Avensis  Model_Aygo  Model_B 170  Model_B 180  Model_B 200  \\\n",
+       "0             False       False        False        False        False   \n",
+       "1             False       False        False        False        False   \n",
+       "2             False       False        False        False        False   \n",
+       "3             False       False        False        False        False   \n",
+       "4             False       False        False        False        False   \n",
+       "...             ...         ...          ...          ...          ...   \n",
+       "4339          False       False        False        False        False   \n",
+       "4340          False       False        False        False        False   \n",
+       "4341          False       False        False        False        False   \n",
+       "4342          False       False        False        False        False   \n",
+       "4344          False       False        False        False        False   \n",
+       "\n",
+       "      Model_Beetle  Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  \\\n",
+       "0            False       False          False         False         False   \n",
+       "1            False       False          False         False         False   \n",
+       "2            False       False          False         False         False   \n",
+       "3            False       False          False         False         False   \n",
+       "4            False       False          False         False         False   \n",
+       "...            ...         ...            ...           ...           ...   \n",
+       "4339         False       False          False         False         False   \n",
+       "4340         False       False          False         False         False   \n",
+       "4341         False       False          False         False         False   \n",
+       "4342         False       False          False         False         False   \n",
+       "4344         False       False          False         False         False   \n",
+       "\n",
+       "      Model_CL 55 AMG  Model_CL 550  Model_CL 63 AMG  Model_CLA 200  \\\n",
+       "0               False         False            False          False   \n",
+       "1               False         False            False          False   \n",
+       "2               False         False            False          False   \n",
+       "3               False         False            False          False   \n",
+       "4               False         False            False          False   \n",
+       "...               ...           ...              ...            ...   \n",
+       "4339            False         False            False          False   \n",
+       "4340            False         False            False          False   \n",
+       "4341            False         False            False          False   \n",
+       "4342            False         False            False          False   \n",
+       "4344            False         False            False          False   \n",
+       "\n",
+       "      Model_CLA 220  Model_CLA-Class  Model_CLC 180  Model_CLC 200  \\\n",
+       "0             False            False          False          False   \n",
+       "1             False            False          False          False   \n",
+       "2             False            False          False          False   \n",
+       "3             False            False          False          False   \n",
+       "4             False            False          False          False   \n",
+       "...             ...              ...            ...            ...   \n",
+       "4339          False            False          False          False   \n",
+       "4340          False            False          False          False   \n",
+       "4341          False            False          False          False   \n",
+       "4342          False            False          False          False   \n",
+       "4344          False            False          False          False   \n",
+       "\n",
+       "      Model_CLK 200  Model_CLK 220  Model_CLK 230  Model_CLK 240  \\\n",
+       "0             False          False          False          False   \n",
+       "1             False          False          False          False   \n",
+       "2             False          False          False          False   \n",
+       "3             False          False          False          False   \n",
+       "4             False          False          False          False   \n",
+       "...             ...            ...            ...            ...   \n",
+       "4339          False          False          False          False   \n",
+       "4340          False          False          False          False   \n",
+       "4341          False          False          False          False   \n",
+       "4342          False          False          False          False   \n",
+       "4344          False          False          False          False   \n",
+       "\n",
+       "      Model_CLK 280  Model_CLK 320  Model_CLK 430  Model_CLS 350  \\\n",
+       "0             False          False          False          False   \n",
+       "1             False          False          False          False   \n",
+       "2             False          False          False          False   \n",
+       "3             False          False          False          False   \n",
+       "4             False          False          False          False   \n",
+       "...             ...            ...            ...            ...   \n",
+       "4339          False          False          False          False   \n",
+       "4340          False          False          False          False   \n",
+       "4341          False          False          False          False   \n",
+       "4342          False          False          False          False   \n",
+       "4344          False          False          False          False   \n",
+       "\n",
+       "      Model_CLS 400  Model_CLS 500  Model_CLS 63 AMG  Model_Caddy  \\\n",
+       "0             False          False             False        False   \n",
+       "1             False          False             False        False   \n",
+       "2             False          False             False        False   \n",
+       "3             False          False             False        False   \n",
+       "4             False          False             False        False   \n",
+       "...             ...            ...               ...          ...   \n",
+       "4339          False          False             False        False   \n",
+       "4340          False          False             False        False   \n",
+       "4341          False          False             False        False   \n",
+       "4342          False          False             False        False   \n",
+       "4344          False          False             False        False   \n",
+       "\n",
+       "      Model_Camry  Model_Captur  Model_Caravelle  Model_Carina  Model_Carisma  \\\n",
+       "0           False         False            False         False          False   \n",
+       "1           False         False            False         False          False   \n",
+       "2           False         False            False         False          False   \n",
+       "3           False         False            False         False          False   \n",
+       "4           False         False            False         False          False   \n",
+       "...           ...           ...              ...           ...            ...   \n",
+       "4339        False         False            False         False          False   \n",
+       "4340        False         False            False         False          False   \n",
+       "4341        False         False            False         False          False   \n",
+       "4342        False         False            False         False          False   \n",
+       "4344        False         False            False         False          False   \n",
+       "\n",
+       "      Model_Celica  Model_Clio  Model_Colt  Model_Corolla  \\\n",
+       "0            False       False       False          False   \n",
+       "1            False       False       False          False   \n",
+       "2            False       False       False          False   \n",
+       "3            False       False       False          False   \n",
+       "4            False       False       False          False   \n",
+       "...            ...         ...         ...            ...   \n",
+       "4339         False       False       False           True   \n",
+       "4340         False       False       False          False   \n",
+       "4341         False       False       False          False   \n",
+       "4342         False       False       False          False   \n",
+       "4344         False       False       False          False   \n",
+       "\n",
+       "      Model_Corolla Verso  Model_Cross Touran  Model_Dokker  Model_Duster  \\\n",
+       "0                   False               False         False         False   \n",
+       "1                   False               False         False         False   \n",
+       "2                   False               False         False         False   \n",
+       "3                   False               False         False         False   \n",
+       "4                   False               False         False         False   \n",
+       "...                   ...                 ...           ...           ...   \n",
+       "4339                False               False         False         False   \n",
+       "4340                False               False         False         False   \n",
+       "4341                False               False         False         False   \n",
+       "4342                False               False         False         False   \n",
+       "4344                False               False         False         False   \n",
+       "\n",
+       "      Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  Model_FJ Cruiser  \\\n",
+       "0             False          False      False         False             False   \n",
+       "1             False          False      False         False             False   \n",
+       "2             False          False      False         False             False   \n",
+       "3             False          False      False         False             False   \n",
+       "4             False          False      False         False             False   \n",
+       "...             ...            ...        ...           ...               ...   \n",
+       "4339          False          False      False         False             False   \n",
+       "4340          False          False      False         False             False   \n",
+       "4341          False          False      False         False             False   \n",
+       "4342          False          False      False         False             False   \n",
+       "4344          False          False      False         False             False   \n",
+       "\n",
+       "      Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  Model_G 500  \\\n",
+       "0             False           False        False        False        False   \n",
+       "1             False           False        False        False        False   \n",
+       "2             False           False        False        False        False   \n",
+       "3             False           False        False        False        False   \n",
+       "4             False           False        False        False        False   \n",
+       "...             ...             ...          ...          ...          ...   \n",
+       "4339          False           False        False        False        False   \n",
+       "4340          False           False        False        False        False   \n",
+       "4341          False           False        False        False        False   \n",
+       "4342          False           False        False        False        False   \n",
+       "4344          False           False        False        False        False   \n",
+       "\n",
+       "      Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0              False           False         False         False   \n",
+       "1              False           False         False         False   \n",
+       "2              False           False         False         False   \n",
+       "3              False           False         False         False   \n",
+       "4              False           False         False         False   \n",
+       "...              ...             ...           ...           ...   \n",
+       "4339           False           False         False         False   \n",
+       "4340           False           False         False         False   \n",
+       "4341           False           False         False         False   \n",
+       "4342           False           False         False         False   \n",
+       "4344           False           False         False         False   \n",
+       "\n",
+       "      Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0            False         False         False         False            False   \n",
+       "1            False         False         False         False            False   \n",
+       "2            False         False         False         False            False   \n",
+       "3            False         False         False         False            False   \n",
+       "4            False         False         False         False            False   \n",
+       "...            ...           ...           ...           ...              ...   \n",
+       "4339         False         False         False         False            False   \n",
+       "4340         False         False         False         False            False   \n",
+       "4341         False         False         False         False            False   \n",
+       "4342         False         False         False         False            False   \n",
+       "4344         False         False         False         False            False   \n",
+       "\n",
+       "      Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0               False          False          False          False   \n",
+       "1               False          False          False          False   \n",
+       "2               False          False          False          False   \n",
+       "3               False          False          False          False   \n",
+       "4               False          False          False          False   \n",
+       "...               ...            ...            ...            ...   \n",
+       "4339            False          False          False          False   \n",
+       "4340            False          False          False          False   \n",
+       "4341            False          False          False          False   \n",
+       "4342            False          False          False          False   \n",
+       "4344            False          False          False          False   \n",
+       "\n",
+       "      Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  \\\n",
+       "0             False          False         False         False   \n",
+       "1             False          False         False         False   \n",
+       "2             False          False         False         False   \n",
+       "3             False          False         False         False   \n",
+       "4             False          False         False         False   \n",
+       "...             ...            ...           ...           ...   \n",
+       "4339          False          False         False         False   \n",
+       "4340          False          False         False         False   \n",
+       "4341          False          False         False         False   \n",
+       "4342          False          False         False         False   \n",
+       "4344          False          False         False         False   \n",
+       "\n",
+       "      Model_Golf GTI  Model_Golf II  Model_Golf III  Model_Golf IV  \\\n",
+       "0              False          False           False          False   \n",
+       "1              False          False           False          False   \n",
+       "2              False          False           False          False   \n",
+       "3              False          False           False          False   \n",
+       "4              False          False           False          False   \n",
+       "...              ...            ...             ...            ...   \n",
+       "4339           False          False           False          False   \n",
+       "4340           False          False           False          False   \n",
+       "4341           False          False           False          False   \n",
+       "4342           False          False           False          False   \n",
+       "4344           False          False           False          False   \n",
+       "\n",
+       "      Model_Golf Plus  Model_Golf V  Model_Golf VI  Model_Golf VII  \\\n",
+       "0               False         False          False           False   \n",
+       "1               False         False          False           False   \n",
+       "2               False         False          False           False   \n",
+       "3               False         False          False           False   \n",
+       "4               False         False          False           False   \n",
+       "...               ...           ...            ...             ...   \n",
+       "4339            False         False          False           False   \n",
+       "4340            False         False          False           False   \n",
+       "4341            False         False          False           False   \n",
+       "4342            False         False          False           False   \n",
+       "4344            False         False          False           False   \n",
+       "\n",
+       "      Model_Golf Variant  Model_Grand Scenic  Model_Grandis  Model_Hiace  \\\n",
+       "0                  False               False          False        False   \n",
+       "1                  False               False          False        False   \n",
+       "2                  False               False          False        False   \n",
+       "3                  False               False          False        False   \n",
+       "4                  False               False          False        False   \n",
+       "...                  ...                 ...            ...          ...   \n",
+       "4339               False               False          False        False   \n",
+       "4340               False               False          False        False   \n",
+       "4341               False               False          False        False   \n",
+       "4342               False               False          False        False   \n",
+       "4344               False               False          False        False   \n",
+       "\n",
+       "      Model_Highlander  Model_Hilux  Model_I3  Model_IQ  Model_Jetta  \\\n",
+       "0                False        False     False     False        False   \n",
+       "1                False        False     False     False        False   \n",
+       "2                False        False     False     False        False   \n",
+       "3                False        False     False     False        False   \n",
+       "4                False        False     False     False        False   \n",
+       "...                ...          ...       ...       ...          ...   \n",
+       "4339             False        False     False     False        False   \n",
+       "4340             False        False     False     False        False   \n",
+       "4341             False        False     False     False        False   \n",
+       "4342             False        False     False     False        False   \n",
+       "4344             False        False     False     False        False   \n",
+       "\n",
+       "      Model_Kangoo  Model_Koleos  Model_L 200  Model_LT  Model_Laguna  \\\n",
+       "0            False         False        False     False         False   \n",
+       "1            False         False        False     False         False   \n",
+       "2            False         False        False     False         False   \n",
+       "3            False         False        False     False         False   \n",
+       "4            False         False        False     False         False   \n",
+       "...            ...           ...          ...       ...           ...   \n",
+       "4339         False         False        False     False         False   \n",
+       "4340         False         False        False     False         False   \n",
+       "4341         False         False        False     False         False   \n",
+       "4342         False         False        False     False         False   \n",
+       "4344         False         False        False     False         False   \n",
+       "\n",
+       "      Model_Lancer  Model_Lancer Evolution  Model_Lancer X  \\\n",
+       "0            False                   False           False   \n",
+       "1            False                   False           False   \n",
+       "2            False                   False           False   \n",
+       "3            False                   False           False   \n",
+       "4            False                   False           False   \n",
+       "...            ...                     ...             ...   \n",
+       "4339         False                   False           False   \n",
+       "4340         False                   False           False   \n",
+       "4341         False                   False           False   \n",
+       "4342         False                   False           False   \n",
+       "4344         False                   False           False   \n",
+       "\n",
+       "      Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0                        False                   False   \n",
+       "1                        False                   False   \n",
+       "2                        False                   False   \n",
+       "3                        False                   False   \n",
+       "4                        False                   False   \n",
+       "...                        ...                     ...   \n",
+       "4339                     False                   False   \n",
+       "4340                     False                   False   \n",
+       "4341                     False                   False   \n",
+       "4342                     False                   False   \n",
+       "4344                     False                   False   \n",
+       "\n",
+       "      Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                      False                   False                  False   \n",
+       "1                      False                   False                  False   \n",
+       "2                      False                   False                  False   \n",
+       "3                      False                   False                  False   \n",
+       "4                      False                   False                  False   \n",
+       "...                      ...                     ...                    ...   \n",
+       "4339                   False                   False                  False   \n",
+       "4340                   False                   False                  False   \n",
+       "4341                   False                   False                  False   \n",
+       "4342                   False                   False                  False   \n",
+       "4344                   False                   False                  False   \n",
+       "\n",
+       "      Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                     False                     False           False   \n",
+       "1                     False                     False           False   \n",
+       "2                     False                     False           False   \n",
+       "3                     False                     False           False   \n",
+       "4                     False                     False           False   \n",
+       "...                     ...                       ...             ...   \n",
+       "4339                  False                     False           False   \n",
+       "4340                  False                     False           False   \n",
+       "4341                  False                     False           False   \n",
+       "4342                  False                     False           False   \n",
+       "4344                  False                     False           False   \n",
+       "\n",
+       "      Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0              False        False       False     False     False     False   \n",
+       "1              False        False       False     False     False     False   \n",
+       "2              False        False       False     False     False     False   \n",
+       "3              False        False       False     False     False     False   \n",
+       "4              False        False       False     False     False     False   \n",
+       "...              ...          ...         ...       ...       ...       ...   \n",
+       "4339           False        False       False     False     False     False   \n",
+       "4340           False        False       False     False     False     False   \n",
+       "4341           False        False       False     False     False     False   \n",
+       "4342           False        False       False     False     False     False   \n",
+       "4344           False        False       False     False     False     False   \n",
+       "\n",
+       "      Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0            False         False         False         False         False   \n",
+       "1            False         False         False         False         False   \n",
+       "2            False         False         False         False         False   \n",
+       "3            False         False         False         False         False   \n",
+       "4            False         False         False         False         False   \n",
+       "...            ...           ...           ...           ...           ...   \n",
+       "4339         False         False         False         False         False   \n",
+       "4340         False         False         False         False         False   \n",
+       "4341         False         False         False         False         False   \n",
+       "4342         False         False         False         False         False   \n",
+       "4344         False         False         False         False         False   \n",
+       "\n",
+       "      Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0            False         False         False         False            False   \n",
+       "1            False         False         False         False            False   \n",
+       "2            False         False         False         False            False   \n",
+       "3            False         False         False         False            False   \n",
+       "4            False         False         False         False            False   \n",
+       "...            ...           ...           ...           ...              ...   \n",
+       "4339         False         False         False         False            False   \n",
+       "4340         False         False         False         False            False   \n",
+       "4341         False         False         False         False            False   \n",
+       "4342         False         False         False         False            False   \n",
+       "4344         False         False         False         False            False   \n",
+       "\n",
+       "      Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0             False         False         False         False        False   \n",
+       "1             False         False         False         False        False   \n",
+       "2             False         False         False         False        False   \n",
+       "3             False         False         False         False        False   \n",
+       "4             False         False         False         False        False   \n",
+       "...             ...           ...           ...           ...          ...   \n",
+       "4339          False         False         False         False        False   \n",
+       "4340          False         False         False         False        False   \n",
+       "4341          False         False         False         False        False   \n",
+       "4342          False         False         False         False        False   \n",
+       "4344          False         False         False         False        False   \n",
+       "\n",
+       "      Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0              False             False            False               False   \n",
+       "1              False             False            False               False   \n",
+       "2              False             False            False               False   \n",
+       "3              False             False            False               False   \n",
+       "4              False             False            False               False   \n",
+       "...              ...               ...              ...                 ...   \n",
+       "4339           False             False            False               False   \n",
+       "4340           False             False            False               False   \n",
+       "4341           False             False            False               False   \n",
+       "4342           False             False            False               False   \n",
+       "4344           False             False            False               False   \n",
+       "\n",
+       "      Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  \\\n",
+       "0            False               False               False   \n",
+       "1            False               False               False   \n",
+       "2            False               False               False   \n",
+       "3            False               False               False   \n",
+       "4            False               False               False   \n",
+       "...            ...                 ...                 ...   \n",
+       "4339         False               False               False   \n",
+       "4340         False               False               False   \n",
+       "4341         False               False               False   \n",
+       "4342         False               False               False   \n",
+       "4344         False               False               False   \n",
+       "\n",
+       "      Model_Pajero Wagon  Model_Passat B2  Model_Passat B3  Model_Passat B4  \\\n",
+       "0                  False            False            False            False   \n",
+       "1                  False            False            False            False   \n",
+       "2                  False            False            False            False   \n",
+       "3                  False            False            False            False   \n",
+       "4                  False            False            False            False   \n",
+       "...                  ...              ...              ...              ...   \n",
+       "4339               False            False            False            False   \n",
+       "4340               False            False            False            False   \n",
+       "4341               False            False            False            False   \n",
+       "4342               False            False            False            False   \n",
+       "4344               False            False            False            False   \n",
+       "\n",
+       "      Model_Passat B5  Model_Passat B6  Model_Passat B7  Model_Passat B8  \\\n",
+       "0               False            False            False            False   \n",
+       "1               False            False            False            False   \n",
+       "2               False            False            False            False   \n",
+       "3               False            False            False            False   \n",
+       "4               False            False            False            False   \n",
+       "...               ...              ...              ...              ...   \n",
+       "4339            False            False            False            False   \n",
+       "4340            False            False            False            False   \n",
+       "4341            False            False            False            False   \n",
+       "4342            False            False            False            False   \n",
+       "4344            False            False            False            False   \n",
+       "\n",
+       "      Model_Passat CC  Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  \\\n",
+       "0               False          False          False       False         False   \n",
+       "1               False          False          False       False         False   \n",
+       "2               False          False          False       False         False   \n",
+       "3               False          False          False       False         False   \n",
+       "4               False          False          False       False         False   \n",
+       "...               ...            ...            ...         ...           ...   \n",
+       "4339            False          False          False       False         False   \n",
+       "4340            False          False          False       False         False   \n",
+       "4341            False          False          False       False         False   \n",
+       "4342            False          False          False       False         False   \n",
+       "4344            False          False          False       False         False   \n",
+       "\n",
+       "      Model_Prius  Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  \\\n",
+       "0           False     False     False     False        False     False   \n",
+       "1           False     False     False     False        False     False   \n",
+       "2           False     False     False     False        False     False   \n",
+       "3           False     False     False      True        False     False   \n",
+       "4           False     False     False     False        False     False   \n",
+       "...           ...       ...       ...       ...          ...       ...   \n",
+       "4339        False     False     False     False        False     False   \n",
+       "4340        False     False     False     False        False     False   \n",
+       "4341        False     False     False     False        False     False   \n",
+       "4342        False     False     False     False        False     False   \n",
+       "4344        False     False     False     False        False     False   \n",
+       "\n",
+       "      Model_Rav 4  Model_S 140  Model_S 250  Model_S 280  Model_S 300  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4            True        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False        False        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_S 320  Model_S 350  Model_S 400  Model_S 420  Model_S 430  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4           False        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False         True        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_S 500  Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  \\\n",
+       "0           False       False        False        False           False   \n",
+       "1           False       False        False        False           False   \n",
+       "2            True       False        False        False           False   \n",
+       "3           False       False        False        False           False   \n",
+       "4           False       False        False        False           False   \n",
+       "...           ...         ...          ...          ...             ...   \n",
+       "4339        False       False        False        False           False   \n",
+       "4340        False       False        False        False           False   \n",
+       "4341        False       False        False        False           False   \n",
+       "4342        False       False        False        False           False   \n",
+       "4344        False       False        False        False           False   \n",
+       "\n",
+       "      Model_S 65 AMG  Model_S-Guard  Model_S4  Model_S5  Model_S8  \\\n",
+       "0              False          False     False     False     False   \n",
+       "1              False          False     False     False     False   \n",
+       "2              False          False     False     False     False   \n",
+       "3              False          False     False     False     False   \n",
+       "4              False          False     False     False     False   \n",
+       "...              ...            ...       ...       ...       ...   \n",
+       "4339           False          False     False     False     False   \n",
+       "4340           False          False     False     False     False   \n",
+       "4341           False          False     False     False     False   \n",
+       "4342           False          False     False     False     False   \n",
+       "4344           False          False     False     False     False   \n",
+       "\n",
+       "      Model_SL 500 (550)  Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  \\\n",
+       "0                  False            False          False          False   \n",
+       "1                  False            False          False          False   \n",
+       "2                  False            False          False          False   \n",
+       "3                  False            False          False          False   \n",
+       "4                  False            False          False          False   \n",
+       "...                  ...              ...            ...            ...   \n",
+       "4339               False            False          False          False   \n",
+       "4340               False            False          False          False   \n",
+       "4341               False            False          False          False   \n",
+       "4342               False            False          False          False   \n",
+       "4344               False            False          False          False   \n",
+       "\n",
+       "      Model_Sandero  Model_Sandero StepWay  Model_Scenic  Model_Scion  \\\n",
+       "0             False                  False         False        False   \n",
+       "1             False                  False         False        False   \n",
+       "2             False                  False         False        False   \n",
+       "3             False                  False         False        False   \n",
+       "4             False                  False         False        False   \n",
+       "...             ...                    ...           ...          ...   \n",
+       "4339          False                  False         False        False   \n",
+       "4340          False                  False         False        False   \n",
+       "4341          False                  False         False        False   \n",
+       "4342          False                  False         False        False   \n",
+       "4344          False                  False         False        False   \n",
+       "\n",
+       "      Model_Scirocco  Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  \\\n",
+       "0              False          False         False         False        False   \n",
+       "1              False          False         False         False        False   \n",
+       "2              False          False         False         False        False   \n",
+       "3              False          False         False         False        False   \n",
+       "4              False          False         False         False        False   \n",
+       "...              ...            ...           ...           ...          ...   \n",
+       "4339           False          False         False         False        False   \n",
+       "4340           False          False         False         False        False   \n",
+       "4341           False          False         False         False        False   \n",
+       "4342           False          False         False         False        False   \n",
+       "4344           False          False         False         False        False   \n",
+       "\n",
+       "      Model_Space Star  Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  \\\n",
+       "0                False              False           False               False   \n",
+       "1                False              False           False               False   \n",
+       "2                False              False           False               False   \n",
+       "3                False              False           False               False   \n",
+       "4                False              False           False               False   \n",
+       "...                ...                ...             ...                 ...   \n",
+       "4339             False              False           False               False   \n",
+       "4340             False              False           False               False   \n",
+       "4341             False              False           False               False   \n",
+       "4342             False              False           False               False   \n",
+       "4344             False              False           False               False   \n",
+       "\n",
+       "      Model_Sprinter 210  Model_Sprinter 211  Model_Sprinter 212  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False                True   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 213  Model_Sprinter 311  Model_Sprinter 312  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 313  Model_Sprinter 315  Model_Sprinter 316  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 318  Model_Sprinter 319  Model_Sprinter 324  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Symbol  Model_Syncro  Model_T2 (Transporter)  \\\n",
+       "0            False         False                   False   \n",
+       "1            False         False                   False   \n",
+       "2            False         False                   False   \n",
+       "3            False         False                   False   \n",
+       "4            False         False                   False   \n",
+       "...            ...           ...                     ...   \n",
+       "4339         False         False                   False   \n",
+       "4340         False         False                   False   \n",
+       "4341         False         False                   False   \n",
+       "4342         False         False                   False   \n",
+       "4344         False         False                   False   \n",
+       "\n",
+       "      Model_T3 (Transporter)  Model_T4 (Transporter)  Model_T4 (Transporter)   \\\n",
+       "0                      False                   False                    False   \n",
+       "1                      False                   False                    False   \n",
+       "2                      False                   False                    False   \n",
+       "3                      False                   False                    False   \n",
+       "4                      False                   False                    False   \n",
+       "...                      ...                     ...                      ...   \n",
+       "4339                   False                   False                    False   \n",
+       "4340                   False                   False                    False   \n",
+       "4341                   False                   False                    False   \n",
+       "4342                   False                   False                    False   \n",
+       "4344                   False                   False                    False   \n",
+       "\n",
+       "      Model_T5 (Transporter)  Model_T5 (Transporter)   Model_T6 (Transporter)  \\\n",
+       "0                      False                    False                   False   \n",
+       "1                      False                    False                   False   \n",
+       "2                      False                    False                   False   \n",
+       "3                      False                    False                   False   \n",
+       "4                      False                    False                   False   \n",
+       "...                      ...                      ...                     ...   \n",
+       "4339                   False                    False                   False   \n",
+       "4340                   False                    False                   False   \n",
+       "4341                   False                    False                   False   \n",
+       "4342                   False                    False                   False   \n",
+       "4344                   False                     True                   False   \n",
+       "\n",
+       "      Model_T6 (Transporter)   Model_TT  Model_Tacoma  Model_Tiguan  \\\n",
+       "0                       False     False         False         False   \n",
+       "1                       False     False         False         False   \n",
+       "2                       False     False         False         False   \n",
+       "3                       False     False         False         False   \n",
+       "4                       False     False         False         False   \n",
+       "...                       ...       ...           ...           ...   \n",
+       "4339                    False     False         False         False   \n",
+       "4340                    False     False         False         False   \n",
+       "4341                    False     False         False         False   \n",
+       "4342                    False     False         False         False   \n",
+       "4344                    False     False         False         False   \n",
+       "\n",
+       "      Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  Model_Up  \\\n",
+       "0             False         False         False         False     False   \n",
+       "1             False         False         False         False     False   \n",
+       "2             False         False         False         False     False   \n",
+       "3             False         False         False         False     False   \n",
+       "4             False         False         False         False     False   \n",
+       "...             ...           ...           ...           ...       ...   \n",
+       "4339          False         False         False         False     False   \n",
+       "4340          False         False         False         False     False   \n",
+       "4341          False         False         False         False     False   \n",
+       "4342          False         False         False         False     False   \n",
+       "4344          False         False         False         False     False   \n",
+       "\n",
+       "      Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4           False        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False        False        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0            False        False       False     False     False     False   \n",
+       "1            False        False       False     False     False     False   \n",
+       "2            False        False       False     False     False     False   \n",
+       "3            False        False       False     False     False     False   \n",
+       "4            False        False       False     False     False     False   \n",
+       "...            ...          ...         ...       ...       ...       ...   \n",
+       "4339         False        False       False     False     False     False   \n",
+       "4340         False        False       False     False     False     False   \n",
+       "4341         False        False       False     False     False     False   \n",
+       "4342         False        False       False     False     False     False   \n",
+       "4344         False        False       False     False     False     False   \n",
+       "\n",
+       "      Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0          False     False       False        False     False     False  \n",
+       "1          False     False       False        False     False     False  \n",
+       "2          False     False       False        False     False     False  \n",
+       "3          False     False       False        False     False     False  \n",
+       "4          False     False       False        False     False     False  \n",
+       "...          ...       ...         ...          ...       ...       ...  \n",
+       "4339       False     False       False        False     False     False  \n",
+       "4340       False     False       False        False     False     False  \n",
+       "4341       False     False       False        False     False     False  \n",
+       "4342       False     False       False        False     False     False  \n",
+       "4344       False     False       False        False     False     False  \n",
+       "\n",
+       "[4025 rows x 323 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Train Test Spilt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the module for the split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# split the variables with an 80 - 20 split and some random state for example use random_state = 365\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(inputs, target, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create the regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create the regression object\n",
+    "reg = LinearRegression()\n",
+    "# fit the the regression with the trained inputs and the target\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's check the outputs of the regression\n",
+    "# I'll store them in y_hat as this is the 'theoretical' name of the predictions\n",
+    "y_hat = reg.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot\n",
+    "# The closer the points to the 45-degree line, the better the prediction\n",
+    "plt.scatter(y_train, y_hat, alpha= 0.2)\n",
+    "plt.xlabel('Target(y_train)', size= 18)\n",
+    "plt.ylabel('Prediction(Y_hat)', size= 18)\n",
+    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
+    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
+    "# We want the x-axis and the y-axis to be the same\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6.0, 13.0)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=y_train, y=y_hat, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "plt.xlabel('Target(y_train)', size= 18)\n",
+    "plt.ylabel('Prediction(Y_hat)', size= 18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check for residue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_2108\\2934438678.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(y_train-y_hat)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Residuals PDF')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Another useful check of our model is a residual plot\n",
+    "# We can plot the PDF of the residuals and check for anomalies\n",
+    "sns.distplot(y_train-y_hat)\n",
+    "plt.title(\"Residuals PDF\", size=18)\n",
+    "\n",
+    "# In the best case scenario this plot should be normally distributed\n",
+    "#And our graph happened to be nornally distributed with the midpoint at zero, meaning that our prediction fits our target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# R-Squared of our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9410857085028603"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_train, y_train)\n",
+    "# Note that this is NOT the adjusted R-squared\n",
+    "# in other words... find the Adjusted R-squared to have the appropriate measure :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adjusted R2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3220, 323)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the shape of x, to facilitate the creation of the Adjusted R^2 metric\n",
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9345148120409901"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If we want to find the Adjusted R-squared we can do so by knowing the r2, the # observations, the # features\n",
+    "r2 = reg.score(x_train, y_train)\n",
+    "# Number of observations is the shape along axis 0\n",
+    "n = x_train.shape[0]\n",
+    "# Number of features (predictors, p) is the shape along axis 1\n",
+    "p = x_train.shape[1]\n",
+    "\n",
+    "# We find the Adjusted R-squared using the formula\n",
+    "Adj_r2 = 1-(1-r2)* (n-1)/(n-p-1)\n",
+    "Adj_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.77503789e+03, 4.35676244e+00, 4.13082009e+03, 1.81202877e+01,\n",
+       "        9.46723457e+01, 1.90844651e+01, 1.30693718e+02, 5.91312636e+01,\n",
+       "        5.68044564e+01, 7.32783831e+01, 7.93890307e+00, 7.67267201e+01,\n",
+       "        9.95728691e+01, 4.19460543e+01, 3.00296891e+01, 6.02422631e+00,\n",
+       "        1.40106743e+01, 7.37501879e+02, 2.17908132e+01, 0.00000000e+00,\n",
+       "        8.57016665e-03, 2.52183205e-04, 1.77368305e+00, 2.85631239e+00,\n",
+       "        1.92264268e+01, 5.00553865e+00, 5.50707175e+00, 5.34654600e+00,\n",
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+       "        7.40340068e-001, 8.90623082e-001, 2.67587449e-001, 6.90853933e-001,\n",
+       "        1.00754811e-002, 6.89055296e-008, 4.32934445e-001, 2.04960304e-002,\n",
+       "        4.43239721e-002, 6.51183356e-001, 8.06907592e-008, 5.31985601e-001,\n",
+       "        6.63600657e-004, 2.22523086e-002, 4.31816253e-002, 1.00000000e+000,\n",
+       "        6.98283335e-001, 6.94986205e-002, 4.39863254e-003, 4.00579913e-004,\n",
+       "        6.26179589e-019, 2.36500444e-007, 3.39282802e-015, 4.84506304e-003,\n",
+       "        3.37943093e-002, 3.41070801e-001, 1.03219301e-001]))"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import the feature selection module from sklearn\n",
+    "# This module allows us to select the most appopriate features for our regression\n",
+    "# There exist many different approaches to feature selection, however, we will use one of the simplest\n",
+    "\n",
+    "from sklearn.feature_selection import f_regression\n",
+    "\n",
+    "# f_regression finds the F-statistics for the *simple* regressions created with each of the independent variables\n",
+    "# In our case, this would mean running a simple linear regression on GPA where SAT is the independent variable\n",
+    "# and a simple linear regression on GPA where Rand 1,2,3 is the indepdent variable\n",
+    "# The limitation of this approach is that it does not take into account the mutual effect of the two features\n",
+    "f_regression(x_train,y_train)\n",
+    "\n",
+    "#There are two output arrays\n",
+    "# The first one contains the F-statistics for each of the regressions\n",
+    "# The second one contains the p-values of these F-statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       4.13817142e-001, 5.91417515e-001, 1.25528377e-001, 8.14801678e-012,\n",
+       "       1.68556062e-001, 2.42028095e-001, 4.33383053e-001, 1.39233795e-001,\n",
+       "       6.48887602e-001, 1.63039120e-003, 3.85561860e-002, 5.17179158e-001,\n",
+       "       4.41821584e-004, 3.71343697e-001, 5.32761253e-001, 7.20978206e-001,\n",
+       "       4.09527800e-001, 8.64753022e-001, 5.63591273e-001, 1.42773118e-003,\n",
+       "       4.71586242e-001, 5.30703625e-009, 1.34748018e-002, 1.83021249e-001,\n",
+       "       2.16459985e-002, 7.34813292e-001, 9.89122411e-003, 3.68574535e-001,\n",
+       "       6.66441099e-001, 5.51175993e-001, 4.43305980e-004, 2.86342637e-001,\n",
+       "       3.35086125e-003, 8.80657206e-008, 7.35425272e-001, 2.50471868e-002,\n",
+       "       3.80894623e-002, 4.01958798e-001, 3.85931106e-001, 8.29302428e-001,\n",
+       "       8.27233532e-001, 6.71061839e-001, 5.46268783e-001, 9.68555856e-001,\n",
+       "       2.97385141e-001, 6.83120185e-001, 2.24054490e-001, 7.98829944e-002,\n",
+       "       7.74621412e-001, 1.00000000e+000, 4.64693438e-001, 1.07626058e-001,\n",
+       "       1.00000000e+000, 6.47070340e-003, 1.44611890e-001, 1.89630151e-001,\n",
+       "       1.00000000e+000, 1.00000000e+000, 6.81585635e-001, 7.89753766e-002,\n",
+       "       5.52414839e-001, 1.26031599e-001, 8.12262775e-001, 5.32761253e-001,\n",
+       "       6.20087988e-001, 2.44760711e-001, 9.28392404e-002, 1.00000000e+000,\n",
+       "       9.07988454e-002, 3.19204353e-002, 3.41680389e-004, 5.19563591e-002,\n",
+       "       1.00000000e+000, 4.42762754e-001, 4.06122042e-003, 3.99665611e-003,\n",
+       "       5.57008646e-001, 6.74717576e-002, 9.78408658e-003, 2.77099111e-003,\n",
+       "       6.02532109e-001, 1.00000000e+000, 7.11148676e-001, 5.01945642e-001,\n",
+       "       9.27493882e-004, 6.72904741e-001, 1.00000000e+000, 6.02532109e-001,\n",
+       "       3.24910587e-002, 8.26249124e-001, 8.34188757e-001, 1.00000000e+000,\n",
+       "       4.40746655e-004, 2.99839421e-006, 5.56621416e-004, 3.76191257e-010,\n",
+       "       1.01276166e-002, 1.68808863e-009, 4.34900697e-001, 1.31882699e-001,\n",
+       "       7.35283039e-002, 2.08717435e-001, 9.42660004e-002, 4.58042029e-009,\n",
+       "       2.91378789e-001, 3.42031569e-001, 1.58497540e-018, 4.88624552e-004,\n",
+       "       2.23462920e-004, 4.10323481e-011, 7.55702579e-009, 6.43900200e-001,\n",
+       "       9.50372855e-009, 6.07613924e-009, 2.96823255e-010, 5.09345354e-001,\n",
+       "       1.78926525e-001, 9.06121568e-001, 4.51607438e-001, 7.24165492e-001,\n",
+       "       5.91496727e-001, 9.04286267e-001, 3.01764465e-001, 4.36726460e-003,\n",
+       "       1.45529256e-003, 2.18784422e-001, 1.00000000e+000, 5.99223423e-002,\n",
+       "       9.74787951e-016, 9.07064650e-001, 7.03783041e-001, 2.06076823e-001,\n",
+       "       8.72593903e-008, 6.03304420e-006, 5.22102438e-001, 5.54278957e-002,\n",
+       "       7.34813292e-001, 7.61703682e-002, 1.00000000e+000, 6.12517681e-028,\n",
+       "       8.13086341e-002, 9.09954117e-001, 1.01974973e-009, 9.95704967e-001,\n",
+       "       1.03579279e-002, 8.84971529e-002, 3.29403271e-002, 2.66984999e-004,\n",
+       "       5.03783861e-002, 6.07751197e-002, 6.69335455e-002, 7.86322149e-001,\n",
+       "       6.08838773e-001, 7.57552813e-001, 1.82214428e-004, 7.23171120e-001,\n",
+       "       1.00000000e+000, 1.33597912e-001, 3.93444560e-001, 1.68935551e-002,\n",
+       "       1.00000000e+000, 3.60467380e-001, 7.43784280e-001, 1.68285534e-003,\n",
+       "       1.00000000e+000, 1.00901157e-005, 5.26457888e-001, 8.00001965e-001,\n",
+       "       4.62580458e-001, 1.11882697e-001, 6.41575085e-001, 1.88970570e-001,\n",
+       "       3.44770075e-002, 1.69454213e-002, 7.59920516e-007, 8.47328813e-006,\n",
+       "       1.34741492e-013, 3.35770762e-001, 3.63941304e-003, 1.96044222e-002,\n",
+       "       6.37819043e-003, 4.76202033e-002, 2.44760711e-001, 2.65676768e-002,\n",
+       "       6.01376927e-001, 6.85024613e-001, 7.95535047e-002, 1.75529365e-004,\n",
+       "       1.39028045e-014, 6.81975855e-001, 2.28028853e-004, 1.26486512e-001,\n",
+       "       3.73764587e-001, 1.09905717e-001, 1.33851176e-001, 6.70212905e-001,\n",
+       "       4.49327808e-001, 6.73575171e-011, 3.52668313e-002, 4.69537352e-001,\n",
+       "       9.67432151e-001, 6.31620457e-011, 1.00000000e+000, 4.65496452e-002,\n",
+       "       1.21390210e-004, 2.02025339e-008, 2.57302704e-001, 7.04803460e-004,\n",
+       "       8.91981750e-001, 3.40248085e-001, 6.66724461e-001, 5.83221602e-001,\n",
+       "       1.00000000e+000, 6.03919769e-001, 6.43900200e-001, 2.95109444e-001,\n",
+       "       9.28112666e-001, 2.15910154e-004, 1.00000000e+000, 3.75678769e-001,\n",
+       "       2.31400817e-001, 1.07557544e-001, 6.69175228e-002, 2.13495231e-001,\n",
+       "       1.77244755e-002, 2.55315670e-001, 1.00000000e+000, 2.62329963e-001,\n",
+       "       3.62648525e-001, 6.98283335e-001, 8.67631905e-001, 8.41694589e-001,\n",
+       "       5.82654840e-001, 2.25752785e-001, 9.54668885e-001, 8.12262775e-001,\n",
+       "       7.45977702e-001, 3.11392793e-001, 2.77867310e-002, 2.94550329e-003,\n",
+       "       5.15234482e-002, 8.70599844e-001, 1.70456776e-002, 2.32418760e-002,\n",
+       "       2.66085948e-003, 3.25914134e-002, 3.87907274e-001, 7.92210128e-001,\n",
+       "       7.40340068e-001, 8.90623082e-001, 2.67587449e-001, 6.90853933e-001,\n",
+       "       1.00754811e-002, 6.89055296e-008, 4.32934445e-001, 2.04960304e-002,\n",
+       "       4.43239721e-002, 6.51183356e-001, 8.06907592e-008, 5.31985601e-001,\n",
+       "       6.63600657e-004, 2.22523086e-002, 4.31816253e-002, 1.00000000e+000,\n",
+       "       6.98283335e-001, 6.94986205e-002, 4.39863254e-003, 4.00579913e-004,\n",
+       "       6.26179589e-019, 2.36500444e-007, 3.39282802e-015, 4.84506304e-003,\n",
+       "       3.37943093e-002, 3.41070801e-001, 1.03219301e-001])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Since we are more interested in the latter (p-values), we can just take the second array\n",
+    "p_values = f_regression(x_train,y_train)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.037, 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,\n",
+       "       0.   , 0.005, 0.   , 0.   , 0.   , 0.   , 0.014, 0.   , 0.   ,\n",
+       "       0.   , 1.   , 0.926, 0.987, 0.183, 0.091, 0.   , 0.025, 0.019,\n",
+       "       0.021, 0.487, 0.057, 0.001, 0.406, 0.108, 0.005, 0.   , 0.   ,\n",
+       "       0.491, 0.068, 0.827, 0.398, 0.232, 0.433, 0.123, 1.   , 0.19 ,\n",
+       "       1.   , 0.601, 0.038, 0.414, 0.591, 0.126, 0.   , 0.169, 0.242,\n",
+       "       0.433, 0.139, 0.649, 0.002, 0.039, 0.517, 0.   , 0.371, 0.533,\n",
+       "       0.721, 0.41 , 0.865, 0.564, 0.001, 0.472, 0.   , 0.013, 0.183,\n",
+       "       0.022, 0.735, 0.01 , 0.369, 0.666, 0.551, 0.   , 0.286, 0.003,\n",
+       "       0.   , 0.735, 0.025, 0.038, 0.402, 0.386, 0.829, 0.827, 0.671,\n",
+       "       0.546, 0.969, 0.297, 0.683, 0.224, 0.08 , 0.775, 1.   , 0.465,\n",
+       "       0.108, 1.   , 0.006, 0.145, 0.19 , 1.   , 1.   , 0.682, 0.079,\n",
+       "       0.552, 0.126, 0.812, 0.533, 0.62 , 0.245, 0.093, 1.   , 0.091,\n",
+       "       0.032, 0.   , 0.052, 1.   , 0.443, 0.004, 0.004, 0.557, 0.067,\n",
+       "       0.01 , 0.003, 0.603, 1.   , 0.711, 0.502, 0.001, 0.673, 1.   ,\n",
+       "       0.603, 0.032, 0.826, 0.834, 1.   , 0.   , 0.   , 0.001, 0.   ,\n",
+       "       0.01 , 0.   , 0.435, 0.132, 0.074, 0.209, 0.094, 0.   , 0.291,\n",
+       "       0.342, 0.   , 0.   , 0.   , 0.   , 0.   , 0.644, 0.   , 0.   ,\n",
+       "       0.   , 0.509, 0.179, 0.906, 0.452, 0.724, 0.591, 0.904, 0.302,\n",
+       "       0.004, 0.001, 0.219, 1.   , 0.06 , 0.   , 0.907, 0.704, 0.206,\n",
+       "       0.   , 0.   , 0.522, 0.055, 0.735, 0.076, 1.   , 0.   , 0.081,\n",
+       "       0.91 , 0.   , 0.996, 0.01 , 0.088, 0.033, 0.   , 0.05 , 0.061,\n",
+       "       0.067, 0.786, 0.609, 0.758, 0.   , 0.723, 1.   , 0.134, 0.393,\n",
+       "       0.017, 1.   , 0.36 , 0.744, 0.002, 1.   , 0.   , 0.526, 0.8  ,\n",
+       "       0.463, 0.112, 0.642, 0.189, 0.034, 0.017, 0.   , 0.   , 0.   ,\n",
+       "       0.336, 0.004, 0.02 , 0.006, 0.048, 0.245, 0.027, 0.601, 0.685,\n",
+       "       0.08 , 0.   , 0.   , 0.682, 0.   , 0.126, 0.374, 0.11 , 0.134,\n",
+       "       0.67 , 0.449, 0.   , 0.035, 0.47 , 0.967, 0.   , 1.   , 0.047,\n",
+       "       0.   , 0.   , 0.257, 0.001, 0.892, 0.34 , 0.667, 0.583, 1.   ,\n",
+       "       0.604, 0.644, 0.295, 0.928, 0.   , 1.   , 0.376, 0.231, 0.108,\n",
+       "       0.067, 0.213, 0.018, 0.255, 1.   , 0.262, 0.363, 0.698, 0.868,\n",
+       "       0.842, 0.583, 0.226, 0.955, 0.812, 0.746, 0.311, 0.028, 0.003,\n",
+       "       0.052, 0.871, 0.017, 0.023, 0.003, 0.033, 0.388, 0.792, 0.74 ,\n",
+       "       0.891, 0.268, 0.691, 0.01 , 0.   , 0.433, 0.02 , 0.044, 0.651,\n",
+       "       0.   , 0.532, 0.001, 0.022, 0.043, 1.   , 0.698, 0.069, 0.004,\n",
+       "       0.   , 0.   , 0.   , 0.   , 0.005, 0.034, 0.341, 0.103])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Finding weight and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-271886118.8957569"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the bias (intercept) of the regression\n",
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-7.11168307e-04, -2.35976356e-03,  8.68792315e-02,  2.45087768e+07,\n",
+       "        2.45087768e+07,  3.35460065e+08,  2.30199764e+09,  1.45542358e+09,\n",
+       "        4.62245051e+08,  9.66255861e-03,  1.23547420e-02, -1.06618585e-01,\n",
+       "       -1.31590684e-01, -5.92526948e-02, -9.57940713e-02, -4.74364368e-02,\n",
+       "       -6.14403758e-02,  8.89392304e-01,  2.71885954e+08, -7.18996649e+09,\n",
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+       "        2.47377177e+08,  2.47377177e+08, -2.03011169e+09,  2.47377176e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08, -2.03011169e+09,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.59254546e+09,\n",
+       "       -1.18353763e+09, -2.65819554e+07,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377178e+08,  2.47377177e+08,\n",
+       "        2.47377178e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.71885953e+08, -2.03011169e+09,  2.71885954e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08,  2.47377176e+08,  2.47377176e+08,\n",
+       "        2.71885953e+08,  2.71885953e+08,  2.71885953e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08,  2.71885954e+08,  2.71885954e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08, -6.35741121e+07, -1.90359097e+08, -1.18353763e+09,\n",
+       "       -1.18353763e+09, -1.18353763e+09, -1.18353763e+09,  2.47377176e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08, -1.90359097e+08, -1.90359098e+08,\n",
+       "        2.47377177e+08, -8.93153042e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -7.09605288e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -1.08592764e+09, -7.86250753e+08,  2.47377177e+08,  2.47377176e+08,\n",
+       "        2.47377177e+08,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  1.82078318e+09,\n",
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+       "       -1.46384430e+09, -1.90359097e+08, -1.18353763e+09, -6.35741122e+07,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -6.35741125e+07, -1.18353763e+09,\n",
+       "       -1.18353763e+09, -1.58640664e+08, -2.03011169e+09, -2.03011169e+09,\n",
+       "        2.47377177e+08, -6.35741116e+07,  1.06343391e+09, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -1.18353763e+09,  5.60770216e+07,\n",
+       "        2.47377178e+08,  2.47377178e+08,  2.47377178e+08,  2.47377178e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377178e+08,  2.47377178e+08,\n",
+       "        2.47377178e+08,  2.47377178e+08, -6.35741121e+07, -1.90359098e+08,\n",
+       "       -1.90359097e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -6.35741119e+07, -1.18353763e+09, -1.18353763e+09,\n",
+       "       -1.18353763e+09,  2.47377177e+08, -2.56326823e+08, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -2.03011169e+09, -6.35741122e+07, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -6.35741124e+07, -6.35741114e+07, -6.35741123e+07,\n",
+       "       -6.35741125e+07, -1.18353763e+09, -4.21205487e+03, -1.18353762e+09,\n",
+       "       -1.18353763e+09, -1.18353763e+09, -1.18353763e+09, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -1.90359098e+08,  2.47377178e+08,\n",
+       "        2.47377178e+08,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -1.69340410e-07,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -2.77046898e-07, -2.03011169e+09, -1.18353763e+09, -2.03011169e+09,\n",
+       "        1.42292599e-06, -1.90359097e+08, -1.90359098e+08, -6.35741121e+07,\n",
+       "       -6.35741119e+07, -6.35741112e+07, -6.35741120e+07, -6.35741119e+07,\n",
+       "       -6.35741117e+07, -1.90359097e+08, -1.90359097e+08, -1.90359097e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08, -1.90359098e+08, -1.90359097e+08,\n",
+       "       -1.90359097e+08, -1.90359097e+08, -1.90359099e+08, -1.90359098e+08,\n",
+       "       -1.18353763e+09, -1.18353763e+09,  2.71885954e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08,  2.47377177e+08,  2.71885955e+08, -1.18353763e+09,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377178e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  6.17630946e-14,  2.47377177e+08,\n",
+       "        2.47377178e+08,  2.47377178e+08,  2.47377178e+08,  2.47377179e+08,\n",
+       "        2.71885954e+08,  2.71885954e+08,  2.71885954e+08,  2.47377177e+08,\n",
+       "       -1.00867231e-13,  2.47377177e+08,  2.47377177e+08, -2.03011169e+09,\n",
+       "       -2.03011169e+09, -2.03011169e+09, -1.57485136e-13, -1.90359098e+08,\n",
+       "       -1.18353763e+09, -1.90359097e+08, -1.18353763e+09,  2.47377176e+08,\n",
+       "       -6.35741123e+07, -6.35741115e+07,  2.27283470e-14,  2.47377177e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08,  2.47377177e+08,  2.47377176e+08,\n",
+       "        2.47377176e+08,  2.47377177e+08,  2.47377176e+08,  2.47377177e+08,\n",
+       "        2.47377176e+08,  2.47377177e+08,  2.47377177e+08,  2.47377179e+08,\n",
+       "       -2.03011169e+09, -1.90359097e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359097e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08,  2.71885954e+08, -1.18353763e+09,\n",
+       "       -1.90359098e+08, -1.90359097e+08, -1.90359098e+08, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -1.90359099e+08,  2.47377178e+08,  2.47377176e+08,\n",
+       "       -1.90359098e+08, -1.18353763e+09,  2.47377177e+08,  1.11022302e-15,\n",
+       "       -1.18353763e+09,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377178e+08,\n",
+       "       -1.18353763e+09,  2.47377177e+08,  2.47377177e+08])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the weights (coefficients) of the regression\n",
+    "reg.coef_\n",
+    "# Note that they are barely interpretable if at all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-7.111683e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>-2.359764e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Year</td>\n",
+       "      <td>8.687923e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>Model_X6</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>Model_X6 M</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>Model_Yaris</td>\n",
+       "      <td>-1.183538e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>Model_Z3</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>Model_Z4</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>323 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Features       Weights\n",
+       "0                Mileage -7.111683e-04\n",
+       "1                EngineV -2.359764e-03\n",
+       "2                   Year  8.687923e-02\n",
+       "3              Brand_BMW  2.450878e+07\n",
+       "4    Brand_Mercedes-Benz  2.450878e+07\n",
+       "..                   ...           ...\n",
+       "318             Model_X6  2.473772e+08\n",
+       "319           Model_X6 M  2.473772e+08\n",
+       "320          Model_Yaris -1.183538e+09\n",
+       "321             Model_Z3  2.473772e+08\n",
+       "322             Model_Z4  2.473772e+08\n",
+       "\n",
+       "[323 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a regression summary where we can compare them with one another\n",
+    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
+    "\n",
+    "reg_summary['Weights'] = reg.coef_\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-7.111683e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>-2.359764e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Year</td>\n",
+       "      <td>8.687923e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>3.354601e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>2.301998e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>1.455424e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>4.622451e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>9.662559e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>1.235474e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-1.066186e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-1.315907e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-5.925269e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-9.579407e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-4.743644e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-6.144038e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>8.893923e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Model_100</td>\n",
+       "      <td>2.718860e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Model_11</td>\n",
+       "      <td>-7.189966e+09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features       Weights\n",
+       "0               Mileage -7.111683e-04\n",
+       "1               EngineV -2.359764e-03\n",
+       "2                  Year  8.687923e-02\n",
+       "3             Brand_BMW  2.450878e+07\n",
+       "4   Brand_Mercedes-Benz  2.450878e+07\n",
+       "5      Brand_Mitsubishi  3.354601e+08\n",
+       "6         Brand_Renault  2.301998e+09\n",
+       "7          Brand_Toyota  1.455424e+09\n",
+       "8      Brand_Volkswagen  4.622451e+08\n",
+       "9            Body_hatch  9.662559e-03\n",
+       "10           Body_other  1.235474e-02\n",
+       "11           Body_sedan -1.066186e-01\n",
+       "12           Body_vagon -1.315907e-01\n",
+       "13             Body_van -5.925269e-02\n",
+       "14      Engine Type_Gas -9.579407e-02\n",
+       "15    Engine Type_Other -4.743644e-02\n",
+       "16   Engine Type_Petrol -6.144038e-02\n",
+       "17     Registration_yes  8.893923e-01\n",
+       "18            Model_100  2.718860e+08\n",
+       "19             Model_11 -7.189966e+09"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the different categories in the 'Brand' variable\n",
+    "data['Brand'].unique()\n",
+    "# In this way we can see which 'Brand' is actually the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brand missing: Audi\n",
+      "Body missing: crossover\n",
+      "Engine Type missing: Diesel\n",
+      "Registration missing: no\n"
+     ]
+    }
+   ],
+   "source": [
+    "brands= data['Brand'].unique()\n",
+    "\n",
+    "bodies= data['Body'].unique()\n",
+    "\n",
+    "engine_types= data['Engine Type'].unique()\n",
+    "\n",
+    "registrations= data['Registration'].unique()\n",
+    "\n",
+    "dummy_values=[brands,bodies,engine_types,registrations]\n",
+    "\n",
+    "dummy_strings=['Brand','Body','Engine Type','Registration']\n",
+    "\n",
+    "dummy_cols= data_preprocessed.columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "for vals, label in zip(dummy_values,dummy_strings):\n",
+    "\n",
+    "    for val in vals:\n",
+    "\n",
+    "        found=False\n",
+    "\n",
+    "        for dummy_col in dummy_cols:\n",
+    "\n",
+    "            if dummy_col==label + '_' + val:\n",
+    "\n",
+    "                found=True\n",
+    "                break  # exit the inner loop when a match is found\n",
+    "\n",
+    "        if found==False:\n",
+    "\n",
+    "            print(label +' missing:',val)\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Once we have trained and fine-tuned our model, we can proceed to testing it\n",
+    "# Testing is done on a dataset that the algorithm has never seen\n",
+    "# Luckily we have prepared such a dataset\n",
+    "# Our test inputs are 'x_test', while the outputs: 'y_test' \n",
+    "# We SHOULD NOT TRAIN THE MODEL ON THEM, we just feed them and find the predictions\n",
+    "# If the predictions are far off, we will know that our model overfitted\n",
+    "y_hat_test = reg.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot with the test targets and the test predictions\n",
+    "# You can include the argument 'alpha' which will introduce opacity to the graph\n",
+    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
+    "plt.xlabel('Target(y_test)', size= 18)\n",
+    "plt.ylabel('Prediction(y_hat_test', size= 18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6.13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_2108\\2906630971.py:3: RuntimeWarning: overflow encountered in exp\n",
+      "  df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction\n",
+       "0  30228.855333\n",
+       "1   9383.112639\n",
+       "2   6003.067434\n",
+       "3   8074.467696\n",
+       "4  11280.346946"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, let's manually check these predictions\n",
+    "# To obtain the actual prices, we take the exponential of the log_price\n",
+    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>7900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction  Target\n",
+       "0  30228.855333     NaN\n",
+       "1   9383.112639  7900.0\n",
+       "2   6003.067434     NaN\n",
+       "3   8074.467696     NaN\n",
+       "4  11280.346946     NaN"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can also include the test targets in that data frame (so we can manually compare them)\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf.head()\n",
+    "# Note that we have a lot of missing values\n",
+    "# There is no reason to have ANY missing values, though\n",
+    "# This suggests that something is wrong with the data frame / indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    10.257659\n",
+       "1     9.093807\n",
+       "2     8.787220\n",
+       "3     8.985946\n",
+       "4     9.323669\n",
+       "Name: log_price, dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# After displaying y_test, we find what the issue is\n",
+    "# The old indexes are preserved\n",
+    "# Therefore, to get a proper result, we must reset the index and drop the old indexing\n",
+    "y_test = y_test.reset_index(drop=True)\n",
+    "y_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>28500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>8900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>6550.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>7990.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>11200.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction   Target\n",
+       "0  30228.855333  28500.0\n",
+       "1   9383.112639   8900.0\n",
+       "2   6003.067434   6550.0\n",
+       "3   8074.467696   7990.0\n",
+       "4  11280.346946  11200.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's overwrite the 'Target' column with the appropriate values\n",
+    "# Again, we need the exponential of the test log price\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Additionally, we can calculate the difference between the targets and the predictions\n",
+    "# Note that this is actually the residual (we already plotted the residuals)\n",
+    "df_pf['Residual'] = df_pf['Target'] - df_pf['Prediction']\n",
+    "# Since OLS is basically an algorithm which minimizes the total sum of squared errors (residuals),\n",
+    "# this comparison makes a lot of sense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "        vertical-align: top;\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>28500.0</td>\n",
+       "      <td>-1728.855333</td>\n",
+       "      <td>6.066159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>8900.0</td>\n",
+       "      <td>-483.112639</td>\n",
+       "      <td>5.428232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>6550.0</td>\n",
+       "      <td>546.932566</td>\n",
+       "      <td>8.350116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>7990.0</td>\n",
+       "      <td>-84.467696</td>\n",
+       "      <td>1.057168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>11200.0</td>\n",
+       "      <td>-80.346946</td>\n",
+       "      <td>0.717383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4280.485476</td>\n",
+       "      <td>4600.0</td>\n",
+       "      <td>319.514524</td>\n",
+       "      <td>6.945968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11427.087287</td>\n",
+       "      <td>8350.0</td>\n",
+       "      <td>-3077.087287</td>\n",
+       "      <td>36.851345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2895.962467</td>\n",
+       "      <td>3300.0</td>\n",
+       "      <td>404.037533</td>\n",
+       "      <td>12.243562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>39228.722376</td>\n",
+       "      <td>43500.0</td>\n",
+       "      <td>4271.277624</td>\n",
+       "      <td>9.819029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22173.636762</td>\n",
+       "      <td>25500.0</td>\n",
+       "      <td>3326.363238</td>\n",
+       "      <td>13.044562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4899.198732</td>\n",
+       "      <td>5200.0</td>\n",
+       "      <td>300.801268</td>\n",
+       "      <td>5.784640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13110.051878</td>\n",
+       "      <td>15300.0</td>\n",
+       "      <td>2189.948122</td>\n",
+       "      <td>14.313386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20551.654463</td>\n",
+       "      <td>22500.0</td>\n",
+       "      <td>1948.345537</td>\n",
+       "      <td>8.659313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13015.274654</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>484.725346</td>\n",
+       "      <td>3.590558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2718.022542</td>\n",
+       "      <td>2600.0</td>\n",
+       "      <td>-118.022542</td>\n",
+       "      <td>4.539329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3900.793694</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>-1000.793694</td>\n",
+       "      <td>34.510127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10857.592502</td>\n",
+       "      <td>9900.0</td>\n",
+       "      <td>-957.592502</td>\n",
+       "      <td>9.672652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>4330.566126</td>\n",
+       "      <td>4800.0</td>\n",
+       "      <td>469.433874</td>\n",
+       "      <td>9.779872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10710.004512</td>\n",
+       "      <td>13100.0</td>\n",
+       "      <td>2389.995488</td>\n",
+       "      <td>18.244240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9723.514589</td>\n",
+       "      <td>12650.0</td>\n",
+       "      <td>2926.485411</td>\n",
+       "      <td>23.134272</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Prediction   Target     Residual  Difference%\n",
+       "0   30228.855333  28500.0 -1728.855333     6.066159\n",
+       "1    9383.112639   8900.0  -483.112639     5.428232\n",
+       "2    6003.067434   6550.0   546.932566     8.350116\n",
+       "3    8074.467696   7990.0   -84.467696     1.057168\n",
+       "4   11280.346946  11200.0   -80.346946     0.717383\n",
+       "5    4280.485476   4600.0   319.514524     6.945968\n",
+       "6   11427.087287   8350.0 -3077.087287    36.851345\n",
+       "7    2895.962467   3300.0   404.037533    12.243562\n",
+       "8   39228.722376  43500.0  4271.277624     9.819029\n",
+       "9   22173.636762  25500.0  3326.363238    13.044562\n",
+       "10   4899.198732   5200.0   300.801268     5.784640\n",
+       "11  13110.051878  15300.0  2189.948122    14.313386\n",
+       "12  20551.654463  22500.0  1948.345537     8.659313\n",
+       "13  13015.274654  13500.0   484.725346     3.590558\n",
+       "14   2718.022542   2600.0  -118.022542     4.539329\n",
+       "15   3900.793694   2900.0 -1000.793694    34.510127\n",
+       "16  10857.592502   9900.0  -957.592502     9.672652\n",
+       "17   4330.566126   4800.0   469.433874     9.779872\n",
+       "18  10710.004512  13100.0  2389.995488    18.244240\n",
+       "19   9723.514589  12650.0  2926.485411    23.134272"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, it makes sense to see how far off we are from the result percentage-wise\n",
+    "# Here, we take the absolute difference in %, so we can easily order the data frame\n",
+    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
+    "df_pf.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>8.050000e+02</td>\n",
+       "      <td>805.000000</td>\n",
+       "      <td>8.050000e+02</td>\n",
+       "      <td>805.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>18297.659888</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20081.200958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1150.000000</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>0.072466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>6.702651e+03</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>-1.574397e+03</td>\n",
+       "      <td>5.661504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.194286e+04</td>\n",
+       "      <td>11450.000000</td>\n",
+       "      <td>-1.598741e+01</td>\n",
+       "      <td>13.313279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.270802e+04</td>\n",
+       "      <td>22500.000000</td>\n",
+       "      <td>1.351802e+03</td>\n",
+       "      <td>24.983670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>199999.000000</td>\n",
+       "      <td>7.291851e+04</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Prediction         Target      Residual  Difference%\n",
+       "count  8.050000e+02     805.000000  8.050000e+02   805.000000\n",
+       "mean            inf   18297.659888          -inf          inf\n",
+       "std             NaN   20081.200958           NaN          NaN\n",
+       "min    0.000000e+00    1150.000000          -inf     0.072466\n",
+       "25%    6.702651e+03    6999.000000 -1.574397e+03     5.661504\n",
+       "50%    1.194286e+04   11450.000000 -1.598741e+01    13.313279\n",
+       "75%    2.270802e+04   22500.000000  1.351802e+03    24.983670\n",
+       "max             inf  199999.000000  7.291851e+04          inf"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>15211.01</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>-11.01</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>6305.83</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>-5.83</td>\n",
+       "      <td>0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>6706.65</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-6.65</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>12486.63</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>13.37</td>\n",
+       "      <td>0.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>8715.33</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-15.33</td>\n",
+       "      <td>0.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>3206.26</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-6.26</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>6513.15</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-13.15</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>17863.45</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>36.55</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>6715.99</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-15.99</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>8826.34</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-26.34</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>51340.32</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>159.68</td>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>14051.69</td>\n",
+       "      <td>14100.00</td>\n",
+       "      <td>48.31</td>\n",
+       "      <td>0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>2107.62</td>\n",
+       "      <td>2100.00</td>\n",
+       "      <td>-7.62</td>\n",
+       "      <td>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>5828.76</td>\n",
+       "      <td>5850.00</td>\n",
+       "      <td>21.24</td>\n",
+       "      <td>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>12653.40</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>46.60</td>\n",
+       "      <td>0.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>12447.09</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>52.91</td>\n",
+       "      <td>0.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>2837.86</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>12.14</td>\n",
+       "      <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>13541.54</td>\n",
+       "      <td>13600.00</td>\n",
+       "      <td>58.46</td>\n",
+       "      <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>7734.58</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-34.58</td>\n",
+       "      <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>12836.82</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>63.18</td>\n",
+       "      <td>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>30492.98</td>\n",
+       "      <td>30650.00</td>\n",
+       "      <td>157.02</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>9551.54</td>\n",
+       "      <td>9499.00</td>\n",
+       "      <td>-52.54</td>\n",
+       "      <td>0.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>14318.96</td>\n",
+       "      <td>14400.00</td>\n",
+       "      <td>81.04</td>\n",
+       "      <td>0.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>781</th>\n",
+       "      <td>7543.03</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-43.03</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25045.23</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>-145.23</td>\n",
+       "      <td>0.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>6538.30</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-38.30</td>\n",
+       "      <td>0.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>109346.97</td>\n",
+       "      <td>110000.00</td>\n",
+       "      <td>653.03</td>\n",
+       "      <td>0.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>5633.89</td>\n",
+       "      <td>5600.00</td>\n",
+       "      <td>-33.89</td>\n",
+       "      <td>0.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>7352.50</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>47.50</td>\n",
+       "      <td>0.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>15607.46</td>\n",
+       "      <td>15499.00</td>\n",
+       "      <td>-108.46</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.35</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-80.35</td>\n",
+       "      <td>0.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>11077.66</td>\n",
+       "      <td>10990.00</td>\n",
+       "      <td>-87.66</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>801</th>\n",
+       "      <td>39657.24</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>341.76</td>\n",
+       "      <td>0.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>23106.56</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-206.56</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>2219.87</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-19.87</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>25027.42</td>\n",
+       "      <td>24800.00</td>\n",
+       "      <td>-227.42</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>27755.46</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>-255.46</td>\n",
+       "      <td>0.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>20195.56</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-195.56</td>\n",
+       "      <td>0.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>7978.67</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-78.67</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>9897.28</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>101.72</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>23557.83</td>\n",
+       "      <td>23800.00</td>\n",
+       "      <td>242.17</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>8182.71</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-82.71</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>10094.92</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>105.08</td>\n",
+       "      <td>1.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.47</td>\n",
+       "      <td>7990.00</td>\n",
+       "      <td>-84.47</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>16675.21</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>-175.21</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>7883.21</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-83.21</td>\n",
+       "      <td>1.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>9394.45</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>105.55</td>\n",
+       "      <td>1.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>17194.49</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-195.49</td>\n",
+       "      <td>1.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>7407.42</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>92.58</td>\n",
+       "      <td>1.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>10863.14</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>135.86</td>\n",
+       "      <td>1.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>2227.46</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-27.46</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>109352.13</td>\n",
+       "      <td>107999.00</td>\n",
+       "      <td>-1353.13</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>9014.15</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-114.15</td>\n",
+       "      <td>1.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>65500.00</td>\n",
+       "      <td>850.28</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>6807.32</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>92.68</td>\n",
+       "      <td>1.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11436.89</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>163.11</td>\n",
+       "      <td>1.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>688</th>\n",
+       "      <td>2464.71</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>35.29</td>\n",
+       "      <td>1.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>9947.38</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-147.38</td>\n",
+       "      <td>1.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>33474.09</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>525.91</td>\n",
+       "      <td>1.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>21559.42</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>340.58</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9104.53</td>\n",
+       "      <td>9250.00</td>\n",
+       "      <td>145.47</td>\n",
+       "      <td>1.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>12087.91</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-187.91</td>\n",
+       "      <td>1.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>8939.11</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-139.11</td>\n",
+       "      <td>1.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>35453.02</td>\n",
+       "      <td>34899.14</td>\n",
+       "      <td>-553.88</td>\n",
+       "      <td>1.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>34436.11</td>\n",
+       "      <td>34999.00</td>\n",
+       "      <td>562.89</td>\n",
+       "      <td>1.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>2897.71</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>-47.71</td>\n",
+       "      <td>1.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>3440.49</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>59.51</td>\n",
+       "      <td>1.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>33320.06</td>\n",
+       "      <td>33900.00</td>\n",
+       "      <td>579.94</td>\n",
+       "      <td>1.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>784</th>\n",
+       "      <td>18817.25</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>-317.25</td>\n",
+       "      <td>1.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>13857.09</td>\n",
+       "      <td>14100.00</td>\n",
+       "      <td>242.91</td>\n",
+       "      <td>1.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>20040.20</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>-340.20</td>\n",
+       "      <td>1.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>35262.29</td>\n",
+       "      <td>35900.00</td>\n",
+       "      <td>637.71</td>\n",
+       "      <td>1.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>24059.43</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>440.57</td>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>63500.00</td>\n",
+       "      <td>-1149.72</td>\n",
+       "      <td>1.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>19627.25</td>\n",
+       "      <td>19990.00</td>\n",
+       "      <td>362.75</td>\n",
+       "      <td>1.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>26993.16</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>506.84</td>\n",
+       "      <td>1.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>32584.87</td>\n",
+       "      <td>31990.00</td>\n",
+       "      <td>-594.87</td>\n",
+       "      <td>1.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>3056.57</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-56.57</td>\n",
+       "      <td>1.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>98085.79</td>\n",
+       "      <td>99999.00</td>\n",
+       "      <td>1913.21</td>\n",
+       "      <td>1.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>19170.78</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>-370.78</td>\n",
+       "      <td>1.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>10782.03</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>216.97</td>\n",
+       "      <td>1.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>17047.74</td>\n",
+       "      <td>17394.09</td>\n",
+       "      <td>346.35</td>\n",
+       "      <td>1.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1958.31</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>40.69</td>\n",
+       "      <td>2.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>29500.27</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>-600.27</td>\n",
+       "      <td>2.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>23990.17</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>509.83</td>\n",
+       "      <td>2.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>30909.33</td>\n",
+       "      <td>31600.00</td>\n",
+       "      <td>690.67</td>\n",
+       "      <td>2.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>11959.28</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-259.28</td>\n",
+       "      <td>2.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>11042.29</td>\n",
+       "      <td>10799.00</td>\n",
+       "      <td>-243.29</td>\n",
+       "      <td>2.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>56673.17</td>\n",
+       "      <td>58000.00</td>\n",
+       "      <td>1326.83</td>\n",
+       "      <td>2.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>43942.54</td>\n",
+       "      <td>45000.00</td>\n",
+       "      <td>1057.46</td>\n",
+       "      <td>2.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>11224.40</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>275.60</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>12978.30</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>321.70</td>\n",
+       "      <td>2.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>9166.68</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>233.32</td>\n",
+       "      <td>2.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>8478.83</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>221.17</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>11184.55</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>-284.55</td>\n",
+       "      <td>2.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>8524.29</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-224.29</td>\n",
+       "      <td>2.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>14494.88</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>405.12</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>37938.31</td>\n",
+       "      <td>39000.00</td>\n",
+       "      <td>1061.69</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>18384.89</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>515.11</td>\n",
+       "      <td>2.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>23828.27</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>671.73</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>11570.15</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>329.85</td>\n",
+       "      <td>2.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>25592.54</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>-692.54</td>\n",
+       "      <td>2.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>2004.72</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>-54.72</td>\n",
+       "      <td>2.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6374.72</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>-174.72</td>\n",
+       "      <td>2.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>194242.37</td>\n",
+       "      <td>199999.00</td>\n",
+       "      <td>5756.63</td>\n",
+       "      <td>2.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>58649.23</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>-1649.23</td>\n",
+       "      <td>2.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>3398.26</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>101.74</td>\n",
+       "      <td>2.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>12864.69</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-364.69</td>\n",
+       "      <td>2.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>64695.71</td>\n",
+       "      <td>66666.00</td>\n",
+       "      <td>1970.29</td>\n",
+       "      <td>2.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>29563.28</td>\n",
+       "      <td>28700.00</td>\n",
+       "      <td>-863.28</td>\n",
+       "      <td>3.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>6787.74</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>211.26</td>\n",
+       "      <td>3.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>5769.23</td>\n",
+       "      <td>5600.00</td>\n",
+       "      <td>-169.23</td>\n",
+       "      <td>3.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>23184.61</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-684.61</td>\n",
+       "      <td>3.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>10824.58</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-324.58</td>\n",
+       "      <td>3.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>22372.19</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>-672.19</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>15019.12</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>480.88</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>10058.12</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>-308.12</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>8769.20</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-269.20</td>\n",
+       "      <td>3.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>10844.83</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>355.17</td>\n",
+       "      <td>3.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>20522.18</td>\n",
+       "      <td>21200.00</td>\n",
+       "      <td>677.82</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>23223.72</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-723.72</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>1934.64</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>64.36</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>11770.33</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-370.33</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>43881.06</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>-1381.06</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>17450.09</td>\n",
+       "      <td>16900.00</td>\n",
+       "      <td>-550.09</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>23341.27</td>\n",
+       "      <td>22600.00</td>\n",
+       "      <td>-741.27</td>\n",
+       "      <td>3.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>10919.19</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>380.81</td>\n",
+       "      <td>3.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>21192.26</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-692.26</td>\n",
+       "      <td>3.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>12749.66</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>450.34</td>\n",
+       "      <td>3.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>20283.08</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>716.92</td>\n",
+       "      <td>3.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>16201.55</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>598.45</td>\n",
+       "      <td>3.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>7974.91</td>\n",
+       "      <td>7699.00</td>\n",
+       "      <td>-275.91</td>\n",
+       "      <td>3.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13015.27</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>484.73</td>\n",
+       "      <td>3.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>6890.35</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>259.65</td>\n",
+       "      <td>3.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>5483.10</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>216.90</td>\n",
+       "      <td>3.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>20200.76</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>799.24</td>\n",
+       "      <td>3.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>13917.01</td>\n",
+       "      <td>14474.00</td>\n",
+       "      <td>556.99</td>\n",
+       "      <td>3.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>11016.60</td>\n",
+       "      <td>10600.00</td>\n",
+       "      <td>-416.60</td>\n",
+       "      <td>3.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>3840.88</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>158.12</td>\n",
+       "      <td>3.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>8524.71</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-324.71</td>\n",
+       "      <td>3.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>11551.56</td>\n",
+       "      <td>11111.00</td>\n",
+       "      <td>-440.56</td>\n",
+       "      <td>3.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>17687.48</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-688.48</td>\n",
+       "      <td>4.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>33095.36</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>1404.64</td>\n",
+       "      <td>4.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>26864.09</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>-1064.09</td>\n",
+       "      <td>4.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>27698.29</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>1201.71</td>\n",
+       "      <td>4.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>31149.15</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>-1249.15</td>\n",
+       "      <td>4.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>12454.14</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>545.86</td>\n",
+       "      <td>4.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>35396.30</td>\n",
+       "      <td>33950.00</td>\n",
+       "      <td>-1446.30</td>\n",
+       "      <td>4.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>20103.15</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>896.85</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>4170.89</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-170.89</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>62000.00</td>\n",
+       "      <td>-2649.72</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>11964.00</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>536.00</td>\n",
+       "      <td>4.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>25045.23</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-1046.23</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>3442.95</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>157.05</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>29647.28</td>\n",
+       "      <td>31000.00</td>\n",
+       "      <td>1352.72</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>15012.39</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>687.61</td>\n",
+       "      <td>4.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>8143.20</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-343.20</td>\n",
+       "      <td>4.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>25063.05</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-1064.05</td>\n",
+       "      <td>4.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>12010.10</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-510.10</td>\n",
+       "      <td>4.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>11176.69</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-476.69</td>\n",
+       "      <td>4.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>17286.75</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>813.25</td>\n",
+       "      <td>4.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>34276.07</td>\n",
+       "      <td>35900.00</td>\n",
+       "      <td>1623.93</td>\n",
+       "      <td>4.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>25088.99</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-1088.99</td>\n",
+       "      <td>4.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2718.02</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-118.02</td>\n",
+       "      <td>4.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>3770.21</td>\n",
+       "      <td>3950.00</td>\n",
+       "      <td>179.79</td>\n",
+       "      <td>4.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>53853.04</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>-2353.04</td>\n",
+       "      <td>4.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>10463.07</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-464.07</td>\n",
+       "      <td>4.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>8008.22</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>391.78</td>\n",
+       "      <td>4.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>13401.92</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>-601.92</td>\n",
+       "      <td>4.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>13616.65</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>-616.65</td>\n",
+       "      <td>4.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>19805.25</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>994.75</td>\n",
+       "      <td>4.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>11005.68</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-505.68</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>7517.14</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>382.86</td>\n",
+       "      <td>4.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>17220.82</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>879.18</td>\n",
+       "      <td>4.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>2307.20</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-107.20</td>\n",
+       "      <td>4.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>9130.01</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>469.99</td>\n",
+       "      <td>4.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>109346.97</td>\n",
+       "      <td>114999.00</td>\n",
+       "      <td>5652.03</td>\n",
+       "      <td>4.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>4748.92</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>250.08</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>2677.67</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>-127.67</td>\n",
+       "      <td>5.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>25642.46</td>\n",
+       "      <td>26999.00</td>\n",
+       "      <td>1356.54</td>\n",
+       "      <td>5.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>11203.43</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>596.57</td>\n",
+       "      <td>5.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>3987.44</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>212.56</td>\n",
+       "      <td>5.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>6834.18</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-334.18</td>\n",
+       "      <td>5.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>54169.36</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>-2669.36</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>6540.19</td>\n",
+       "      <td>6899.00</td>\n",
+       "      <td>358.81</td>\n",
+       "      <td>5.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>5996.99</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>-296.99</td>\n",
+       "      <td>5.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19982.41</td>\n",
+       "      <td>18990.00</td>\n",
+       "      <td>-992.41</td>\n",
+       "      <td>5.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3001.45</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>-151.45</td>\n",
+       "      <td>5.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>8216.39</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-416.39</td>\n",
+       "      <td>5.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>11638.91</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>661.09</td>\n",
+       "      <td>5.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>77117.34</td>\n",
+       "      <td>81500.00</td>\n",
+       "      <td>4382.66</td>\n",
+       "      <td>5.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.11</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-483.11</td>\n",
+       "      <td>5.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>6961.07</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-361.07</td>\n",
+       "      <td>5.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>27883.22</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>1616.78</td>\n",
+       "      <td>5.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>92087.91</td>\n",
+       "      <td>97500.00</td>\n",
+       "      <td>5412.09</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>14038.48</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-738.48</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>17416.25</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>-916.25</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>802</th>\n",
+       "      <td>12088.25</td>\n",
+       "      <td>11450.00</td>\n",
+       "      <td>-638.25</td>\n",
+       "      <td>5.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>40127.52</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>2372.48</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>16792.66</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-892.66</td>\n",
+       "      <td>5.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>18167.73</td>\n",
+       "      <td>17200.00</td>\n",
+       "      <td>-967.73</td>\n",
+       "      <td>5.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>27263.83</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>1636.17</td>\n",
+       "      <td>5.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>6555.59</td>\n",
+       "      <td>6950.00</td>\n",
+       "      <td>394.41</td>\n",
+       "      <td>5.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>11415.57</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-615.57</td>\n",
+       "      <td>5.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4899.20</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>300.80</td>\n",
+       "      <td>5.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>11642.12</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>-643.12</td>\n",
+       "      <td>5.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>59312.24</td>\n",
+       "      <td>63000.00</td>\n",
+       "      <td>3687.76</td>\n",
+       "      <td>5.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>5458.98</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>341.02</td>\n",
+       "      <td>5.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>10059.50</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-559.50</td>\n",
+       "      <td>5.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>15882.42</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-883.42</td>\n",
+       "      <td>5.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>6869.42</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>430.58</td>\n",
+       "      <td>5.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>9004.49</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-504.49</td>\n",
+       "      <td>5.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>776</th>\n",
+       "      <td>6112.31</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>387.69</td>\n",
+       "      <td>5.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>10057.03</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>642.97</td>\n",
+       "      <td>6.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>5450.83</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>349.17</td>\n",
+       "      <td>6.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.86</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>-1728.86</td>\n",
+       "      <td>6.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>30536.34</td>\n",
+       "      <td>28787.85</td>\n",
+       "      <td>-1748.49</td>\n",
+       "      <td>6.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>10987.90</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>712.10</td>\n",
+       "      <td>6.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>8262.86</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>537.14</td>\n",
+       "      <td>6.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>28542.74</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>-1642.74</td>\n",
+       "      <td>6.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>4886.77</td>\n",
+       "      <td>4600.00</td>\n",
+       "      <td>-286.77</td>\n",
+       "      <td>6.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>10622.75</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-623.75</td>\n",
+       "      <td>6.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>1967.13</td>\n",
+       "      <td>2099.00</td>\n",
+       "      <td>131.87</td>\n",
+       "      <td>6.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>672</th>\n",
+       "      <td>17804.56</td>\n",
+       "      <td>19000.00</td>\n",
+       "      <td>1195.44</td>\n",
+       "      <td>6.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>5902.43</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>397.57</td>\n",
+       "      <td>6.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>58999.17</td>\n",
+       "      <td>62999.00</td>\n",
+       "      <td>3999.83</td>\n",
+       "      <td>6.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>46824.03</td>\n",
+       "      <td>43999.00</td>\n",
+       "      <td>-2825.03</td>\n",
+       "      <td>6.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>2432.69</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>167.31</td>\n",
+       "      <td>6.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>9305.11</td>\n",
+       "      <td>9950.00</td>\n",
+       "      <td>644.89</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>4067.90</td>\n",
+       "      <td>4350.00</td>\n",
+       "      <td>282.10</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>37165.88</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>-2265.88</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>12248.10</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-748.10</td>\n",
+       "      <td>6.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7572.68</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>527.32</td>\n",
+       "      <td>6.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>21837.03</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-1337.03</td>\n",
+       "      <td>6.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>9919.12</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>-619.12</td>\n",
+       "      <td>6.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>96042.20</td>\n",
+       "      <td>89999.00</td>\n",
+       "      <td>-6043.20</td>\n",
+       "      <td>6.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>7739.78</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>560.22</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10247.89</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>751.11</td>\n",
+       "      <td>6.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>13573.72</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>-873.72</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>8274.39</td>\n",
+       "      <td>8888.00</td>\n",
+       "      <td>613.61</td>\n",
+       "      <td>6.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>2887.13</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-187.13</td>\n",
+       "      <td>6.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4280.49</td>\n",
+       "      <td>4600.00</td>\n",
+       "      <td>319.51</td>\n",
+       "      <td>6.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>16367.58</td>\n",
+       "      <td>15300.00</td>\n",
+       "      <td>-1067.58</td>\n",
+       "      <td>6.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6741.83</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>-441.83</td>\n",
+       "      <td>7.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>10274.74</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-674.74</td>\n",
+       "      <td>7.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>46581.86</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>-3081.86</td>\n",
+       "      <td>7.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>22293.90</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>1706.10</td>\n",
+       "      <td>7.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8035.09</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-535.09</td>\n",
+       "      <td>7.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>3712.23</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>287.77</td>\n",
+       "      <td>7.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>13456.31</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>1043.69</td>\n",
+       "      <td>7.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>6673.96</td>\n",
+       "      <td>7199.00</td>\n",
+       "      <td>525.04</td>\n",
+       "      <td>7.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>17166.98</td>\n",
+       "      <td>15999.00</td>\n",
+       "      <td>-1167.98</td>\n",
+       "      <td>7.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>20763.53</td>\n",
+       "      <td>22400.00</td>\n",
+       "      <td>1636.47</td>\n",
+       "      <td>7.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>4634.43</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>365.57</td>\n",
+       "      <td>7.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>22540.34</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>-1540.34</td>\n",
+       "      <td>7.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>33345.57</td>\n",
+       "      <td>36000.00</td>\n",
+       "      <td>2654.43</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>6205.20</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>494.80</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>13749.47</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>-949.47</td>\n",
+       "      <td>7.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>4906.11</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>393.89</td>\n",
+       "      <td>7.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>21489.76</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-1490.76</td>\n",
+       "      <td>7.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>64619.43</td>\n",
+       "      <td>69999.00</td>\n",
+       "      <td>5379.57</td>\n",
+       "      <td>7.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>20892.73</td>\n",
+       "      <td>19400.00</td>\n",
+       "      <td>-1492.73</td>\n",
+       "      <td>7.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>8491.05</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>708.95</td>\n",
+       "      <td>7.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>3137.24</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>262.76</td>\n",
+       "      <td>7.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>61406.17</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>-4406.17</td>\n",
+       "      <td>7.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>7008.81</td>\n",
+       "      <td>7600.00</td>\n",
+       "      <td>591.19</td>\n",
+       "      <td>7.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>17794.23</td>\n",
+       "      <td>19300.00</td>\n",
+       "      <td>1505.77</td>\n",
+       "      <td>7.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>778</th>\n",
+       "      <td>2913.09</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-213.09</td>\n",
+       "      <td>7.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>13922.03</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-1022.03</td>\n",
+       "      <td>7.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>25294.20</td>\n",
+       "      <td>23400.00</td>\n",
+       "      <td>-1894.20</td>\n",
+       "      <td>8.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>59458.46</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-4458.46</td>\n",
+       "      <td>8.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>20486.18</td>\n",
+       "      <td>18950.00</td>\n",
+       "      <td>-1536.18</td>\n",
+       "      <td>8.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>13047.55</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>1152.45</td>\n",
+       "      <td>8.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>9352.13</td>\n",
+       "      <td>8650.00</td>\n",
+       "      <td>-702.13</td>\n",
+       "      <td>8.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>792</th>\n",
+       "      <td>32994.80</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>-2494.80</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>793</th>\n",
+       "      <td>6702.65</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>597.35</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>23852.98</td>\n",
+       "      <td>26000.00</td>\n",
+       "      <td>2147.02</td>\n",
+       "      <td>8.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>5963.16</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>536.84</td>\n",
+       "      <td>8.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>38422.39</td>\n",
+       "      <td>41900.00</td>\n",
+       "      <td>3477.61</td>\n",
+       "      <td>8.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.07</td>\n",
+       "      <td>6550.00</td>\n",
+       "      <td>546.93</td>\n",
+       "      <td>8.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>665</th>\n",
+       "      <td>6688.38</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>611.62</td>\n",
+       "      <td>8.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>779</th>\n",
+       "      <td>19054.71</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>1745.29</td>\n",
+       "      <td>8.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>3360.19</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>-260.19</td>\n",
+       "      <td>8.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>14929.05</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>1370.95</td>\n",
+       "      <td>8.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>22027.55</td>\n",
+       "      <td>20300.00</td>\n",
+       "      <td>-1727.55</td>\n",
+       "      <td>8.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>22027.55</td>\n",
+       "      <td>20300.00</td>\n",
+       "      <td>-1727.55</td>\n",
+       "      <td>8.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>8357.21</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-657.21</td>\n",
+       "      <td>8.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>22153.23</td>\n",
+       "      <td>20400.00</td>\n",
+       "      <td>-1753.23</td>\n",
+       "      <td>8.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>17054.19</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-1354.19</td>\n",
+       "      <td>8.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>10417.13</td>\n",
+       "      <td>11403.99</td>\n",
+       "      <td>986.86</td>\n",
+       "      <td>8.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20551.65</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>1948.35</td>\n",
+       "      <td>8.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>26009.78</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>2490.22</td>\n",
+       "      <td>8.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>12731.48</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-1031.48</td>\n",
+       "      <td>8.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>18506.54</td>\n",
+       "      <td>17000.00</td>\n",
+       "      <td>-1506.54</td>\n",
+       "      <td>8.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>3592.48</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-292.48</td>\n",
+       "      <td>8.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>23222.71</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>2277.29</td>\n",
+       "      <td>8.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>6826.47</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>673.53</td>\n",
+       "      <td>8.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>9482.59</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-782.59</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>6975.75</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-575.75</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>18326.60</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-1526.60</td>\n",
+       "      <td>9.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>27001.23</td>\n",
+       "      <td>24750.00</td>\n",
+       "      <td>-2251.23</td>\n",
+       "      <td>9.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>24552.29</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-2052.29</td>\n",
+       "      <td>9.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>25017.11</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-2117.11</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>14066.37</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>1433.63</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>25215.17</td>\n",
+       "      <td>27800.00</td>\n",
+       "      <td>2584.83</td>\n",
+       "      <td>9.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>52390.49</td>\n",
+       "      <td>57777.00</td>\n",
+       "      <td>5386.51</td>\n",
+       "      <td>9.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>24614.93</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-2114.93</td>\n",
+       "      <td>9.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>31509.05</td>\n",
+       "      <td>28800.00</td>\n",
+       "      <td>-2709.05</td>\n",
+       "      <td>9.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10857.59</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-957.59</td>\n",
+       "      <td>9.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>4605.75</td>\n",
+       "      <td>5100.00</td>\n",
+       "      <td>494.25</td>\n",
+       "      <td>9.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>7679.11</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-680.11</td>\n",
+       "      <td>9.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>13449.96</td>\n",
+       "      <td>14899.00</td>\n",
+       "      <td>1449.04</td>\n",
+       "      <td>9.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>19098.09</td>\n",
+       "      <td>17400.00</td>\n",
+       "      <td>-1698.09</td>\n",
+       "      <td>9.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>4330.57</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>469.43</td>\n",
+       "      <td>9.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>39228.72</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>4271.28</td>\n",
+       "      <td>9.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>782</th>\n",
+       "      <td>2911.16</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>-261.16</td>\n",
+       "      <td>9.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>13959.83</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>1540.17</td>\n",
+       "      <td>9.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>4184.64</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-384.64</td>\n",
+       "      <td>10.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>12670.45</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-1170.45</td>\n",
+       "      <td>10.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5386.74</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>613.26</td>\n",
+       "      <td>10.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>39141.03</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>-3641.03</td>\n",
+       "      <td>10.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>10476.67</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-976.67</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>4483.97</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>516.03</td>\n",
+       "      <td>10.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>8696.33</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>1003.67</td>\n",
+       "      <td>10.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>8964.70</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1035.30</td>\n",
+       "      <td>10.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>5739.40</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>-539.40</td>\n",
+       "      <td>10.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>8512.76</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>987.24</td>\n",
+       "      <td>10.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4305.44</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>-405.44</td>\n",
+       "      <td>10.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>33929.47</td>\n",
+       "      <td>37900.00</td>\n",
+       "      <td>3970.53</td>\n",
+       "      <td>10.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>5997.22</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>702.78</td>\n",
+       "      <td>10.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>27739.55</td>\n",
+       "      <td>31000.00</td>\n",
+       "      <td>3260.45</td>\n",
+       "      <td>10.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>9395.10</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-895.10</td>\n",
+       "      <td>10.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>23054.57</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>2745.43</td>\n",
+       "      <td>10.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>25323.00</td>\n",
+       "      <td>28350.00</td>\n",
+       "      <td>3027.00</td>\n",
+       "      <td>10.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>12411.94</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>1488.06</td>\n",
+       "      <td>10.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>30347.45</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>3652.55</td>\n",
+       "      <td>10.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>17165.39</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-1665.39</td>\n",
+       "      <td>10.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>3927.19</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>472.81</td>\n",
+       "      <td>10.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>2228.68</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>270.32</td>\n",
+       "      <td>10.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>2584.33</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>315.67</td>\n",
+       "      <td>10.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>9974.83</td>\n",
+       "      <td>8990.00</td>\n",
+       "      <td>-984.83</td>\n",
+       "      <td>10.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>50737.64</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>6262.36</td>\n",
+       "      <td>10.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>4181.54</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>518.46</td>\n",
+       "      <td>11.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>11824.42</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>1475.58</td>\n",
+       "      <td>11.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>34352.54</td>\n",
+       "      <td>30900.00</td>\n",
+       "      <td>-3452.54</td>\n",
+       "      <td>11.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>31072.01</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>3927.99</td>\n",
+       "      <td>11.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>9901.71</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1001.71</td>\n",
+       "      <td>11.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>8123.03</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-823.03</td>\n",
+       "      <td>11.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>13300.81</td>\n",
+       "      <td>11950.00</td>\n",
+       "      <td>-1350.81</td>\n",
+       "      <td>11.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>8867.20</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>1131.80</td>\n",
+       "      <td>11.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>13213.07</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>1686.93</td>\n",
+       "      <td>11.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>44560.45</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>-4561.45</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>44560.45</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>-4561.45</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>11484.51</td>\n",
+       "      <td>10300.00</td>\n",
+       "      <td>-1184.51</td>\n",
+       "      <td>11.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>7804.78</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-805.78</td>\n",
+       "      <td>11.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>22287.84</td>\n",
+       "      <td>25200.00</td>\n",
+       "      <td>2912.16</td>\n",
+       "      <td>11.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>24321.68</td>\n",
+       "      <td>21800.00</td>\n",
+       "      <td>-2521.68</td>\n",
+       "      <td>11.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>2343.59</td>\n",
+       "      <td>2100.00</td>\n",
+       "      <td>-243.59</td>\n",
+       "      <td>11.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>11932.57</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>1567.43</td>\n",
+       "      <td>11.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>10054.06</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-1054.06</td>\n",
+       "      <td>11.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>40781.07</td>\n",
+       "      <td>36500.00</td>\n",
+       "      <td>-4281.07</td>\n",
+       "      <td>11.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>2779.76</td>\n",
+       "      <td>3149.25</td>\n",
+       "      <td>369.49</td>\n",
+       "      <td>11.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>7056.08</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>943.92</td>\n",
+       "      <td>11.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>13196.07</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-1396.07</td>\n",
+       "      <td>11.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>8365.18</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1134.82</td>\n",
+       "      <td>11.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>2729.63</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>370.37</td>\n",
+       "      <td>11.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>7279.35</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-779.35</td>\n",
+       "      <td>11.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>6600.31</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>899.69</td>\n",
+       "      <td>12.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>8577.61</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>1172.39</td>\n",
+       "      <td>12.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>13331.21</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1431.21</td>\n",
+       "      <td>12.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>3074.51</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>425.49</td>\n",
+       "      <td>12.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2020.09</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>279.91</td>\n",
+       "      <td>12.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2895.96</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>404.04</td>\n",
+       "      <td>12.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>7808.12</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>1091.88</td>\n",
+       "      <td>12.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>21139.89</td>\n",
+       "      <td>24100.00</td>\n",
+       "      <td>2960.11</td>\n",
+       "      <td>12.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>24999.38</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>3500.62</td>\n",
+       "      <td>12.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>2718.83</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>381.17</td>\n",
+       "      <td>12.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>42942.54</td>\n",
+       "      <td>38233.00</td>\n",
+       "      <td>-4709.54</td>\n",
+       "      <td>12.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11131.10</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>1568.90</td>\n",
+       "      <td>12.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>7447.84</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1052.16</td>\n",
+       "      <td>12.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>4248.90</td>\n",
+       "      <td>4850.00</td>\n",
+       "      <td>601.10</td>\n",
+       "      <td>12.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>2802.47</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>397.53</td>\n",
+       "      <td>12.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>7351.03</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1048.97</td>\n",
+       "      <td>12.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>14528.10</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-1628.10</td>\n",
+       "      <td>12.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>22532.79</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-2533.79</td>\n",
+       "      <td>12.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>3832.33</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>-432.33</td>\n",
+       "      <td>12.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>19197.99</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>2802.01</td>\n",
+       "      <td>12.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>18942.17</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-2142.17</td>\n",
+       "      <td>12.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>19190.84</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>2809.16</td>\n",
+       "      <td>12.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>73324.12</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>-8324.12</td>\n",
+       "      <td>12.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>12141.67</td>\n",
+       "      <td>10750.00</td>\n",
+       "      <td>-1391.67</td>\n",
+       "      <td>12.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>13444.25</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1544.25</td>\n",
+       "      <td>12.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>32212.96</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>-3712.96</td>\n",
+       "      <td>13.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>10173.73</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-1173.73</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22173.64</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>3326.36</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>3216.30</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>483.70</td>\n",
+       "      <td>13.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>13061.64</td>\n",
+       "      <td>11550.00</td>\n",
+       "      <td>-1511.64</td>\n",
+       "      <td>13.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>7640.17</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1159.83</td>\n",
+       "      <td>13.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>8941.66</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-1041.66</td>\n",
+       "      <td>13.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8848.20</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1351.80</td>\n",
+       "      <td>13.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>37294.36</td>\n",
+       "      <td>43000.00</td>\n",
+       "      <td>5705.64</td>\n",
+       "      <td>13.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>28900.44</td>\n",
+       "      <td>33333.00</td>\n",
+       "      <td>4432.56</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>10084.88</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1184.88</td>\n",
+       "      <td>13.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>10766.99</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-1266.99</td>\n",
+       "      <td>13.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>19048.58</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-2248.58</td>\n",
+       "      <td>13.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>23296.66</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>3603.34</td>\n",
+       "      <td>13.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>27706.06</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>4293.94</td>\n",
+       "      <td>13.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>2856.79</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>443.21</td>\n",
+       "      <td>13.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>8400.37</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>-1000.37</td>\n",
+       "      <td>13.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>14874.09</td>\n",
+       "      <td>17200.00</td>\n",
+       "      <td>2325.91</td>\n",
+       "      <td>13.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>2853.47</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>446.53</td>\n",
+       "      <td>13.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>1929.25</td>\n",
+       "      <td>1699.00</td>\n",
+       "      <td>-230.25</td>\n",
+       "      <td>13.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>6740.68</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>1059.32</td>\n",
+       "      <td>13.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>15113.32</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-1813.32</td>\n",
+       "      <td>13.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>8146.17</td>\n",
+       "      <td>9450.00</td>\n",
+       "      <td>1303.83</td>\n",
+       "      <td>13.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>16956.75</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>-2056.75</td>\n",
+       "      <td>13.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>8616.78</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1383.22</td>\n",
+       "      <td>13.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>27560.75</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>4439.25</td>\n",
+       "      <td>13.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>11782.11</td>\n",
+       "      <td>13700.00</td>\n",
+       "      <td>1917.89</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>6047.87</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-747.87</td>\n",
+       "      <td>14.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>3263.77</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>536.23</td>\n",
+       "      <td>14.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>25652.89</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>4247.11</td>\n",
+       "      <td>14.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13110.05</td>\n",
+       "      <td>15300.00</td>\n",
+       "      <td>2189.95</td>\n",
+       "      <td>14.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>6154.50</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1045.50</td>\n",
+       "      <td>14.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>85955.91</td>\n",
+       "      <td>74999.00</td>\n",
+       "      <td>-10956.91</td>\n",
+       "      <td>14.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>13772.47</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>-1773.47</td>\n",
+       "      <td>14.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>7923.76</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>1376.24</td>\n",
+       "      <td>14.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>32350.70</td>\n",
+       "      <td>37990.00</td>\n",
+       "      <td>5639.30</td>\n",
+       "      <td>14.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>3043.80</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>-393.80</td>\n",
+       "      <td>14.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>9993.19</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1293.19</td>\n",
+       "      <td>14.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>24702.61</td>\n",
+       "      <td>21500.00</td>\n",
+       "      <td>-3202.61</td>\n",
+       "      <td>14.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>63213.55</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-8213.55</td>\n",
+       "      <td>14.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>12647.28</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>-1648.28</td>\n",
+       "      <td>14.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>11812.55</td>\n",
+       "      <td>13899.00</td>\n",
+       "      <td>2086.45</td>\n",
+       "      <td>15.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>3398.91</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>601.09</td>\n",
+       "      <td>15.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>12482.76</td>\n",
+       "      <td>10850.00</td>\n",
+       "      <td>-1632.76</td>\n",
+       "      <td>15.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>6099.48</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-799.48</td>\n",
+       "      <td>15.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>5248.40</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>951.60</td>\n",
+       "      <td>15.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>6052.23</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>1097.77</td>\n",
+       "      <td>15.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>9809.02</td>\n",
+       "      <td>8499.00</td>\n",
+       "      <td>-1310.02</td>\n",
+       "      <td>15.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>6174.68</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>1125.32</td>\n",
+       "      <td>15.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>3635.06</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>664.94</td>\n",
+       "      <td>15.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>3347.79</td>\n",
+       "      <td>2899.00</td>\n",
+       "      <td>-448.79</td>\n",
+       "      <td>15.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>20557.05</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>-2757.05</td>\n",
+       "      <td>15.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>28877.80</td>\n",
+       "      <td>25000.00</td>\n",
+       "      <td>-3877.80</td>\n",
+       "      <td>15.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>5082.76</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-682.76</td>\n",
+       "      <td>15.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>17104.46</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-2304.46</td>\n",
+       "      <td>15.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>14842.29</td>\n",
+       "      <td>17595.00</td>\n",
+       "      <td>2752.71</td>\n",
+       "      <td>15.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>786</th>\n",
+       "      <td>3817.05</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-517.05</td>\n",
+       "      <td>15.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>9373.98</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-1273.98</td>\n",
+       "      <td>15.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>6888.50</td>\n",
+       "      <td>5950.00</td>\n",
+       "      <td>-938.50</td>\n",
+       "      <td>15.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>6903.35</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>1296.65</td>\n",
+       "      <td>15.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>34332.85</td>\n",
+       "      <td>40800.00</td>\n",
+       "      <td>6467.15</td>\n",
+       "      <td>15.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>16799.16</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>-2299.16</td>\n",
+       "      <td>15.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>98480.67</td>\n",
+       "      <td>85000.00</td>\n",
+       "      <td>-13480.67</td>\n",
+       "      <td>15.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>6139.72</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>1160.28</td>\n",
+       "      <td>15.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>15437.68</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-2137.68</td>\n",
+       "      <td>16.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>4064.76</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-564.76</td>\n",
+       "      <td>16.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>14087.55</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>2712.45</td>\n",
+       "      <td>16.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>26137.11</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-3637.11</td>\n",
+       "      <td>16.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>27304.45</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-3804.45</td>\n",
+       "      <td>16.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>9637.17</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>1862.83</td>\n",
+       "      <td>16.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>33099.94</td>\n",
+       "      <td>39500.00</td>\n",
+       "      <td>6400.06</td>\n",
+       "      <td>16.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>36023.63</td>\n",
+       "      <td>30990.00</td>\n",
+       "      <td>-5033.63</td>\n",
+       "      <td>16.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>37352.82</td>\n",
+       "      <td>44600.00</td>\n",
+       "      <td>7247.18</td>\n",
+       "      <td>16.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>10462.54</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-1463.54</td>\n",
+       "      <td>16.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>31976.86</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>-4476.86</td>\n",
+       "      <td>16.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>20915.39</td>\n",
+       "      <td>24996.00</td>\n",
+       "      <td>4080.61</td>\n",
+       "      <td>16.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>1673.12</td>\n",
+       "      <td>2000.00</td>\n",
+       "      <td>326.88</td>\n",
+       "      <td>16.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>14221.55</td>\n",
+       "      <td>17000.00</td>\n",
+       "      <td>2778.45</td>\n",
+       "      <td>16.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>34297.36</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>6702.64</td>\n",
+       "      <td>16.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>19907.26</td>\n",
+       "      <td>17100.00</td>\n",
+       "      <td>-2807.26</td>\n",
+       "      <td>16.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>10362.29</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1462.29</td>\n",
+       "      <td>16.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>23291.25</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-3291.25</td>\n",
+       "      <td>16.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>2213.58</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-313.58</td>\n",
+       "      <td>16.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>8738.60</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-1238.60</td>\n",
+       "      <td>16.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>6877.84</td>\n",
+       "      <td>5900.00</td>\n",
+       "      <td>-977.84</td>\n",
+       "      <td>16.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>10377.09</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1477.09</td>\n",
+       "      <td>16.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>21349.94</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>-3049.94</td>\n",
+       "      <td>16.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>2858.60</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-408.60</td>\n",
+       "      <td>16.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>11676.82</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-1677.82</td>\n",
+       "      <td>16.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>7892.51</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1607.49</td>\n",
+       "      <td>16.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>5399.74</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1100.26</td>\n",
+       "      <td>16.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>25408.55</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>-3708.55</td>\n",
+       "      <td>17.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>10609.93</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>2190.07</td>\n",
+       "      <td>17.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>28114.25</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-4114.25</td>\n",
+       "      <td>17.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>39356.61</td>\n",
+       "      <td>47500.00</td>\n",
+       "      <td>8143.39</td>\n",
+       "      <td>17.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>50516.19</td>\n",
+       "      <td>61000.00</td>\n",
+       "      <td>10483.81</td>\n",
+       "      <td>17.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>9481.20</td>\n",
+       "      <td>11450.00</td>\n",
+       "      <td>1968.80</td>\n",
+       "      <td>17.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>13594.21</td>\n",
+       "      <td>16450.00</td>\n",
+       "      <td>2855.79</td>\n",
+       "      <td>17.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>12675.07</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1875.07</td>\n",
+       "      <td>17.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>11505.09</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-1705.09</td>\n",
+       "      <td>17.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>9082.32</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>1916.68</td>\n",
+       "      <td>17.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>31495.17</td>\n",
+       "      <td>26800.00</td>\n",
+       "      <td>-4695.17</td>\n",
+       "      <td>17.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>11282.67</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1682.67</td>\n",
+       "      <td>17.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>19281.74</td>\n",
+       "      <td>16400.00</td>\n",
+       "      <td>-2881.74</td>\n",
+       "      <td>17.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>7992.08</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>1707.92</td>\n",
+       "      <td>17.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>5881.36</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-881.36</td>\n",
+       "      <td>17.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>18117.61</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>3882.39</td>\n",
+       "      <td>17.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>11412.71</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-1712.71</td>\n",
+       "      <td>17.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>15774.43</td>\n",
+       "      <td>13400.00</td>\n",
+       "      <td>-2374.43</td>\n",
+       "      <td>17.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>4357.87</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>942.13</td>\n",
+       "      <td>17.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8126.96</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1226.96</td>\n",
+       "      <td>17.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>11434.04</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-1734.04</td>\n",
+       "      <td>17.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>74745.66</td>\n",
+       "      <td>63333.00</td>\n",
+       "      <td>-11412.66</td>\n",
+       "      <td>18.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>18302.49</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-2802.49</td>\n",
+       "      <td>18.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>3679.35</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>820.65</td>\n",
+       "      <td>18.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10710.00</td>\n",
+       "      <td>13100.00</td>\n",
+       "      <td>2390.00</td>\n",
+       "      <td>18.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>6293.06</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>1406.94</td>\n",
+       "      <td>18.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>13370.87</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>-2070.87</td>\n",
+       "      <td>18.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>9355.15</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-1455.15</td>\n",
+       "      <td>18.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>26420.53</td>\n",
+       "      <td>32400.00</td>\n",
+       "      <td>5979.47</td>\n",
+       "      <td>18.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>3790.91</td>\n",
+       "      <td>4650.00</td>\n",
+       "      <td>859.09</td>\n",
+       "      <td>18.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>5459.66</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1240.34</td>\n",
+       "      <td>18.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>4480.19</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1019.81</td>\n",
+       "      <td>18.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>8536.82</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1336.82</td>\n",
+       "      <td>18.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>40274.34</td>\n",
+       "      <td>49500.00</td>\n",
+       "      <td>9225.66</td>\n",
+       "      <td>18.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>89582.34</td>\n",
+       "      <td>75500.00</td>\n",
+       "      <td>-14082.34</td>\n",
+       "      <td>18.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>6884.55</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>-1084.55</td>\n",
+       "      <td>18.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>51073.39</td>\n",
+       "      <td>43000.00</td>\n",
+       "      <td>-8073.39</td>\n",
+       "      <td>18.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>6090.18</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>1409.82</td>\n",
+       "      <td>18.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>19364.44</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>-3064.44</td>\n",
+       "      <td>18.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>8280.51</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1919.49</td>\n",
+       "      <td>18.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>5598.65</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>1301.35</td>\n",
+       "      <td>18.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>85858.88</td>\n",
+       "      <td>105999.00</td>\n",
+       "      <td>20140.12</td>\n",
+       "      <td>19.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>5506.55</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>1293.45</td>\n",
+       "      <td>19.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>8817.95</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2082.05</td>\n",
+       "      <td>19.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>11319.17</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-1819.17</td>\n",
+       "      <td>19.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>11445.18</td>\n",
+       "      <td>9599.00</td>\n",
+       "      <td>-1846.18</td>\n",
+       "      <td>19.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>5650.29</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1349.71</td>\n",
+       "      <td>19.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>4433.53</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1066.47</td>\n",
+       "      <td>19.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>12724.62</td>\n",
+       "      <td>15800.00</td>\n",
+       "      <td>3075.38</td>\n",
+       "      <td>19.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>2092.87</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>507.13</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36593.81</td>\n",
+       "      <td>45500.00</td>\n",
+       "      <td>8906.19</td>\n",
+       "      <td>19.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>60274.39</td>\n",
+       "      <td>75000.00</td>\n",
+       "      <td>14725.61</td>\n",
+       "      <td>19.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>46983.85</td>\n",
+       "      <td>58500.00</td>\n",
+       "      <td>11516.15</td>\n",
+       "      <td>19.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>8747.37</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2152.63</td>\n",
+       "      <td>19.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>22708.02</td>\n",
+       "      <td>18950.00</td>\n",
+       "      <td>-3758.02</td>\n",
+       "      <td>19.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13185.60</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2185.60</td>\n",
+       "      <td>19.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>9594.67</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1594.67</td>\n",
+       "      <td>19.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>16676.56</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-2776.56</td>\n",
+       "      <td>19.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>5399.84</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-899.84</td>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>6639.69</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>1660.31</td>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>5676.99</td>\n",
+       "      <td>7100.00</td>\n",
+       "      <td>1423.01</td>\n",
+       "      <td>20.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>34847.46</td>\n",
+       "      <td>28950.00</td>\n",
+       "      <td>-5897.46</td>\n",
+       "      <td>20.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>3010.71</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-511.71</td>\n",
+       "      <td>20.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>5315.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1384.73</td>\n",
+       "      <td>20.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>6940.02</td>\n",
+       "      <td>8750.00</td>\n",
+       "      <td>1809.98</td>\n",
+       "      <td>20.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>6056.80</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>1593.20</td>\n",
+       "      <td>20.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>8708.11</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1508.11</td>\n",
+       "      <td>20.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>16175.59</td>\n",
+       "      <td>20477.54</td>\n",
+       "      <td>4301.95</td>\n",
+       "      <td>21.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>5290.04</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1409.96</td>\n",
+       "      <td>21.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>1973.68</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>526.32</td>\n",
+       "      <td>21.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>4181.72</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>1118.28</td>\n",
+       "      <td>21.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>4732.08</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>1267.92</td>\n",
+       "      <td>21.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12222.81</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>3277.19</td>\n",
+       "      <td>21.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>32724.25</td>\n",
+       "      <td>41500.00</td>\n",
+       "      <td>8775.75</td>\n",
+       "      <td>21.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>13328.75</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2328.75</td>\n",
+       "      <td>21.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>36828.78</td>\n",
+       "      <td>46800.00</td>\n",
+       "      <td>9971.22</td>\n",
+       "      <td>21.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>6920.60</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1879.40</td>\n",
+       "      <td>21.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>19020.64</td>\n",
+       "      <td>24193.65</td>\n",
+       "      <td>5173.01</td>\n",
+       "      <td>21.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>24740.92</td>\n",
+       "      <td>31500.00</td>\n",
+       "      <td>6759.08</td>\n",
+       "      <td>21.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>18255.49</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-3255.49</td>\n",
+       "      <td>21.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>775</th>\n",
+       "      <td>11692.08</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-2092.08</td>\n",
+       "      <td>21.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>17173.49</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>4826.51</td>\n",
+       "      <td>21.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>11700.38</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>3299.62</td>\n",
+       "      <td>22.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>2623.69</td>\n",
+       "      <td>2150.00</td>\n",
+       "      <td>-473.69</td>\n",
+       "      <td>22.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>2026.96</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>573.04</td>\n",
+       "      <td>22.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>8434.23</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1534.23</td>\n",
+       "      <td>22.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>2717.11</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>782.89</td>\n",
+       "      <td>22.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>2713.87</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>786.13</td>\n",
+       "      <td>22.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>6200.75</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>1799.25</td>\n",
+       "      <td>22.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>10783.10</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-1983.10</td>\n",
+       "      <td>22.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>22566.89</td>\n",
+       "      <td>18400.00</td>\n",
+       "      <td>-4166.89</td>\n",
+       "      <td>22.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>18486.02</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>5413.98</td>\n",
+       "      <td>22.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>42831.62</td>\n",
+       "      <td>55555.00</td>\n",
+       "      <td>12723.38</td>\n",
+       "      <td>22.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>18116.63</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>5383.37</td>\n",
+       "      <td>22.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>13535.73</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2535.73</td>\n",
+       "      <td>23.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9723.51</td>\n",
+       "      <td>12650.00</td>\n",
+       "      <td>2926.49</td>\n",
+       "      <td>23.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>14919.17</td>\n",
+       "      <td>12114.35</td>\n",
+       "      <td>-2804.82</td>\n",
+       "      <td>23.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>17573.11</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>5326.89</td>\n",
+       "      <td>23.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>20558.01</td>\n",
+       "      <td>16672.50</td>\n",
+       "      <td>-3885.51</td>\n",
+       "      <td>23.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>16030.55</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-3031.55</td>\n",
+       "      <td>23.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>17535.66</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>5364.34</td>\n",
+       "      <td>23.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>2977.77</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>922.23</td>\n",
+       "      <td>23.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>22121.67</td>\n",
+       "      <td>29000.00</td>\n",
+       "      <td>6878.33</td>\n",
+       "      <td>23.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>29567.87</td>\n",
+       "      <td>38764.00</td>\n",
+       "      <td>9196.13</td>\n",
+       "      <td>23.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>22081.93</td>\n",
+       "      <td>28999.00</td>\n",
+       "      <td>6917.07</td>\n",
+       "      <td>23.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>8174.40</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-1574.40</td>\n",
+       "      <td>23.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>2813.36</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>886.64</td>\n",
+       "      <td>23.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>9728.36</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>3071.64</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>23236.27</td>\n",
+       "      <td>18700.00</td>\n",
+       "      <td>-4536.27</td>\n",
+       "      <td>24.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>14539.53</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-2839.53</td>\n",
+       "      <td>24.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>4915.54</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1584.46</td>\n",
+       "      <td>24.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>22276.96</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-4376.96</td>\n",
+       "      <td>24.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>10082.85</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-1982.85</td>\n",
+       "      <td>24.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>3113.25</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-613.25</td>\n",
+       "      <td>24.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>30902.33</td>\n",
+       "      <td>24800.00</td>\n",
+       "      <td>-6102.33</td>\n",
+       "      <td>24.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>12346.01</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-2446.01</td>\n",
+       "      <td>24.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>6161.68</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>2038.32</td>\n",
+       "      <td>24.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>21017.12</td>\n",
+       "      <td>28000.00</td>\n",
+       "      <td>6982.88</td>\n",
+       "      <td>24.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>3748.29</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-748.29</td>\n",
+       "      <td>24.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>3061.17</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-611.17</td>\n",
+       "      <td>24.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>9998.69</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1998.69</td>\n",
+       "      <td>24.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>3749.11</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>1250.89</td>\n",
+       "      <td>25.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>5390.58</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1809.42</td>\n",
+       "      <td>25.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>9709.07</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>3290.93</td>\n",
+       "      <td>25.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>803</th>\n",
+       "      <td>3760.79</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-760.79</td>\n",
+       "      <td>25.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>4400.37</td>\n",
+       "      <td>5900.00</td>\n",
+       "      <td>1499.63</td>\n",
+       "      <td>25.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>3203.55</td>\n",
+       "      <td>4299.00</td>\n",
+       "      <td>1095.45</td>\n",
+       "      <td>25.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>18827.10</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-3828.10</td>\n",
+       "      <td>25.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>9674.76</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>3325.24</td>\n",
+       "      <td>25.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>5205.98</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1794.02</td>\n",
+       "      <td>25.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>18944.86</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>6555.14</td>\n",
+       "      <td>25.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>31179.60</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>10820.40</td>\n",
+       "      <td>25.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>804</th>\n",
+       "      <td>26319.25</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-5419.25</td>\n",
+       "      <td>25.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>2959.68</td>\n",
+       "      <td>2350.00</td>\n",
+       "      <td>-609.68</td>\n",
+       "      <td>25.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>8863.85</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>3136.15</td>\n",
+       "      <td>26.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>38039.83</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>13460.17</td>\n",
+       "      <td>26.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>13761.17</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>-2861.17</td>\n",
+       "      <td>26.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>22356.85</td>\n",
+       "      <td>17700.00</td>\n",
+       "      <td>-4656.85</td>\n",
+       "      <td>26.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>10767.98</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-2267.98</td>\n",
+       "      <td>26.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>37385.04</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>-7885.04</td>\n",
+       "      <td>26.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>10013.18</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-2113.18</td>\n",
+       "      <td>26.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>6589.41</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>2409.59</td>\n",
+       "      <td>26.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>24995.51</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>-5295.51</td>\n",
+       "      <td>26.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>7821.99</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>2878.01</td>\n",
+       "      <td>26.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>6736.55</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-1436.55</td>\n",
+       "      <td>27.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>11445.85</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-2445.85</td>\n",
+       "      <td>27.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>2909.80</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>1090.20</td>\n",
+       "      <td>27.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>24627.39</td>\n",
+       "      <td>33900.00</td>\n",
+       "      <td>9272.61</td>\n",
+       "      <td>27.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>82760.24</td>\n",
+       "      <td>64900.00</td>\n",
+       "      <td>-17860.24</td>\n",
+       "      <td>27.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>5787.88</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>2212.12</td>\n",
+       "      <td>27.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>19048.95</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>-4148.95</td>\n",
+       "      <td>27.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>4668.85</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-1018.85</td>\n",
+       "      <td>27.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>7564.39</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>2935.61</td>\n",
+       "      <td>27.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>30477.38</td>\n",
+       "      <td>23800.00</td>\n",
+       "      <td>-6677.38</td>\n",
+       "      <td>28.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>11154.62</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-2454.62</td>\n",
+       "      <td>28.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>21802.49</td>\n",
+       "      <td>30385.00</td>\n",
+       "      <td>8582.51</td>\n",
+       "      <td>28.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>65745.19</td>\n",
+       "      <td>92222.00</td>\n",
+       "      <td>26476.81</td>\n",
+       "      <td>28.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>21739.14</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>8760.86</td>\n",
+       "      <td>28.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>25245.24</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>10254.76</td>\n",
+       "      <td>28.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>15218.48</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-3418.48</td>\n",
+       "      <td>28.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>55229.59</td>\n",
+       "      <td>77777.00</td>\n",
+       "      <td>22547.41</td>\n",
+       "      <td>28.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>55229.59</td>\n",
+       "      <td>77777.00</td>\n",
+       "      <td>22547.41</td>\n",
+       "      <td>28.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>8665.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-1965.27</td>\n",
+       "      <td>29.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>11514.96</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-2614.96</td>\n",
+       "      <td>29.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>2951.62</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>1248.38</td>\n",
+       "      <td>29.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>5449.56</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-1249.56</td>\n",
+       "      <td>29.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>39888.50</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>17111.50</td>\n",
+       "      <td>30.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>8386.38</td>\n",
+       "      <td>6450.00</td>\n",
+       "      <td>-1936.38</td>\n",
+       "      <td>30.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>5242.13</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>2257.87</td>\n",
+       "      <td>30.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>17055.42</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>7444.58</td>\n",
+       "      <td>30.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>783</th>\n",
+       "      <td>18140.29</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-4240.29</td>\n",
+       "      <td>30.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15633.25</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>6866.75</td>\n",
+       "      <td>30.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>10150.80</td>\n",
+       "      <td>7777.00</td>\n",
+       "      <td>-2373.80</td>\n",
+       "      <td>30.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774</th>\n",
+       "      <td>2485.85</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-585.85</td>\n",
+       "      <td>30.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>2073.96</td>\n",
+       "      <td>2999.00</td>\n",
+       "      <td>925.04</td>\n",
+       "      <td>30.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>19812.54</td>\n",
+       "      <td>28713.75</td>\n",
+       "      <td>8901.21</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>12983.41</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-3083.41</td>\n",
+       "      <td>31.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>7869.15</td>\n",
+       "      <td>5999.00</td>\n",
+       "      <td>-1870.15</td>\n",
+       "      <td>31.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>12071.60</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>-2871.60</td>\n",
+       "      <td>31.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>8941.06</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>4058.94</td>\n",
+       "      <td>31.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>13336.31</td>\n",
+       "      <td>10150.00</td>\n",
+       "      <td>-3186.31</td>\n",
+       "      <td>31.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>14200.19</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-3400.19</td>\n",
+       "      <td>31.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3105.70</td>\n",
+       "      <td>4550.00</td>\n",
+       "      <td>1444.30</td>\n",
+       "      <td>31.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>682</th>\n",
+       "      <td>4624.41</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>2175.59</td>\n",
+       "      <td>31.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>3511.87</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>1688.13</td>\n",
+       "      <td>32.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>4779.20</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-1179.20</td>\n",
+       "      <td>32.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>11942.86</td>\n",
+       "      <td>17777.00</td>\n",
+       "      <td>5834.14</td>\n",
+       "      <td>32.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>16542.07</td>\n",
+       "      <td>12450.00</td>\n",
+       "      <td>-4092.07</td>\n",
+       "      <td>32.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>15827.38</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-3927.38</td>\n",
+       "      <td>33.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>4216.67</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>2083.33</td>\n",
+       "      <td>33.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>9333.37</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-2333.37</td>\n",
+       "      <td>33.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>7732.20</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>3867.80</td>\n",
+       "      <td>33.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>12012.45</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-3012.45</td>\n",
+       "      <td>33.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>19812.54</td>\n",
+       "      <td>29800.00</td>\n",
+       "      <td>9987.46</td>\n",
+       "      <td>33.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>5317.17</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>2682.83</td>\n",
+       "      <td>33.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>46006.01</td>\n",
+       "      <td>34419.45</td>\n",
+       "      <td>-11586.56</td>\n",
+       "      <td>33.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>36381.57</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>18618.43</td>\n",
+       "      <td>33.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>23207.21</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>-5907.21</td>\n",
+       "      <td>34.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>12083.74</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-3083.74</td>\n",
+       "      <td>34.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>1642.87</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>857.13</td>\n",
+       "      <td>34.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3900.79</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>-1000.79</td>\n",
+       "      <td>34.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>67578.74</td>\n",
+       "      <td>49900.00</td>\n",
+       "      <td>-17678.74</td>\n",
+       "      <td>35.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>5030.79</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>2769.21</td>\n",
+       "      <td>35.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>12682.44</td>\n",
+       "      <td>9350.00</td>\n",
+       "      <td>-3332.44</td>\n",
+       "      <td>35.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>44889.92</td>\n",
+       "      <td>69900.00</td>\n",
+       "      <td>25010.08</td>\n",
+       "      <td>35.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>5702.84</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>3297.16</td>\n",
+       "      <td>36.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11427.09</td>\n",
+       "      <td>8350.00</td>\n",
+       "      <td>-3077.09</td>\n",
+       "      <td>36.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>13828.77</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>8071.23</td>\n",
+       "      <td>36.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>5591.66</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>3308.34</td>\n",
+       "      <td>37.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>16260.94</td>\n",
+       "      <td>11850.00</td>\n",
+       "      <td>-4410.94</td>\n",
+       "      <td>37.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>19627.05</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-5327.05</td>\n",
+       "      <td>37.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>2005.13</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>1194.87</td>\n",
+       "      <td>37.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>46345.58</td>\n",
+       "      <td>74000.00</td>\n",
+       "      <td>27654.42</td>\n",
+       "      <td>37.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>777</th>\n",
+       "      <td>51526.76</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-14026.76</td>\n",
+       "      <td>37.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>785</th>\n",
+       "      <td>29732.92</td>\n",
+       "      <td>47700.00</td>\n",
+       "      <td>17967.08</td>\n",
+       "      <td>37.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>46378.55</td>\n",
+       "      <td>74500.00</td>\n",
+       "      <td>28121.45</td>\n",
+       "      <td>37.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>16572.98</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>-4573.98</td>\n",
+       "      <td>38.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>6644.61</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1844.61</td>\n",
+       "      <td>38.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>12197.10</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-3397.10</td>\n",
+       "      <td>38.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>64451.49</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>40547.51</td>\n",
+       "      <td>38.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>39888.50</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>25111.50</td>\n",
+       "      <td>38.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>17656.32</td>\n",
+       "      <td>28793.04</td>\n",
+       "      <td>11136.72</td>\n",
+       "      <td>38.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>15936.61</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-4536.61</td>\n",
+       "      <td>39.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>71675.00</td>\n",
+       "      <td>121500.00</td>\n",
+       "      <td>49825.00</td>\n",
+       "      <td>41.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>16226.28</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-4726.28</td>\n",
+       "      <td>41.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>8889.98</td>\n",
+       "      <td>6299.00</td>\n",
+       "      <td>-2590.98</td>\n",
+       "      <td>41.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>9669.09</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>6830.91</td>\n",
+       "      <td>41.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>14822.55</td>\n",
+       "      <td>10450.00</td>\n",
+       "      <td>-4372.55</td>\n",
+       "      <td>41.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>16930.40</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>12569.60</td>\n",
+       "      <td>42.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>5700.21</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>4299.79</td>\n",
+       "      <td>43.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>32880.90</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-9980.90</td>\n",
+       "      <td>43.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>6477.68</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-1977.68</td>\n",
+       "      <td>43.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>10016.88</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>7983.12</td>\n",
+       "      <td>44.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>11415.61</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-3515.61</td>\n",
+       "      <td>44.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>2050.79</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>1649.21</td>\n",
+       "      <td>44.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>13804.82</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-4304.82</td>\n",
+       "      <td>45.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>24080.07</td>\n",
+       "      <td>16555.00</td>\n",
+       "      <td>-7525.07</td>\n",
+       "      <td>45.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>8443.28</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>7056.72</td>\n",
+       "      <td>45.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>17048.89</td>\n",
+       "      <td>11707.80</td>\n",
+       "      <td>-5341.09</td>\n",
+       "      <td>45.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>8419.88</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>7080.12</td>\n",
+       "      <td>45.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>31124.28</td>\n",
+       "      <td>21300.00</td>\n",
+       "      <td>-9824.28</td>\n",
+       "      <td>46.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>13157.10</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-4157.10</td>\n",
+       "      <td>46.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>1733.85</td>\n",
+       "      <td>1185.60</td>\n",
+       "      <td>-548.25</td>\n",
+       "      <td>46.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>38632.53</td>\n",
+       "      <td>73500.00</td>\n",
+       "      <td>34867.47</td>\n",
+       "      <td>47.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>62100.23</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>-20100.23</td>\n",
+       "      <td>47.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>1196.37</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>1103.63</td>\n",
+       "      <td>47.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>794</th>\n",
+       "      <td>65898.02</td>\n",
+       "      <td>129222.00</td>\n",
+       "      <td>63323.98</td>\n",
+       "      <td>49.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>2313.02</td>\n",
+       "      <td>1550.00</td>\n",
+       "      <td>-763.02</td>\n",
+       "      <td>49.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>2972.03</td>\n",
+       "      <td>1990.00</td>\n",
+       "      <td>-982.03</td>\n",
+       "      <td>49.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>1417.83</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>1382.17</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>22471.17</td>\n",
+       "      <td>44700.00</td>\n",
+       "      <td>22228.83</td>\n",
+       "      <td>49.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>71981.49</td>\n",
+       "      <td>144900.00</td>\n",
+       "      <td>72918.51</td>\n",
+       "      <td>50.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>50745.07</td>\n",
+       "      <td>33715.50</td>\n",
+       "      <td>-17029.57</td>\n",
+       "      <td>50.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>7343.52</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>7655.48</td>\n",
+       "      <td>51.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>26609.57</td>\n",
+       "      <td>17600.00</td>\n",
+       "      <td>-9009.57</td>\n",
+       "      <td>51.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>17888.88</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-6088.88</td>\n",
+       "      <td>51.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>20479.37</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-6979.37</td>\n",
+       "      <td>51.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>12461.52</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-4261.52</td>\n",
+       "      <td>51.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>20828.11</td>\n",
+       "      <td>13700.00</td>\n",
+       "      <td>-7128.11</td>\n",
+       "      <td>52.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>18980.92</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>20919.08</td>\n",
+       "      <td>52.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>780</th>\n",
+       "      <td>28362.55</td>\n",
+       "      <td>60500.00</td>\n",
+       "      <td>32137.45</td>\n",
+       "      <td>53.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>3521.95</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1221.95</td>\n",
+       "      <td>53.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>9427.42</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>11072.58</td>\n",
+       "      <td>54.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>5408.75</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-1908.75</td>\n",
+       "      <td>54.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>10854.85</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>13145.15</td>\n",
+       "      <td>54.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>65041.89</td>\n",
+       "      <td>41900.00</td>\n",
+       "      <td>-23141.89</td>\n",
+       "      <td>55.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>7715.56</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-2815.56</td>\n",
+       "      <td>57.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>30316.04</td>\n",
+       "      <td>19200.00</td>\n",
+       "      <td>-11116.04</td>\n",
+       "      <td>57.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>20712.46</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>-7712.46</td>\n",
+       "      <td>59.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>2719.53</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-1019.53</td>\n",
+       "      <td>59.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>3906.56</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-1506.56</td>\n",
+       "      <td>62.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>5055.93</td>\n",
+       "      <td>3099.00</td>\n",
+       "      <td>-1956.93</td>\n",
+       "      <td>63.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>4243.79</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-1643.79</td>\n",
+       "      <td>63.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>10676.78</td>\n",
+       "      <td>6499.00</td>\n",
+       "      <td>-4177.78</td>\n",
+       "      <td>64.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>16658.12</td>\n",
+       "      <td>10100.00</td>\n",
+       "      <td>-6558.12</td>\n",
+       "      <td>64.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>6656.03</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-2656.03</td>\n",
+       "      <td>66.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>2486.42</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>5513.58</td>\n",
+       "      <td>68.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>13706.76</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-5706.76</td>\n",
+       "      <td>71.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>6524.07</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-2725.07</td>\n",
+       "      <td>71.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>41367.65</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>-17467.65</td>\n",
+       "      <td>73.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>60487.88</td>\n",
+       "      <td>34600.00</td>\n",
+       "      <td>-25887.88</td>\n",
+       "      <td>74.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>9215.35</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-4215.35</td>\n",
+       "      <td>84.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>6870.33</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-3270.33</td>\n",
+       "      <td>90.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>9213.19</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-4413.19</td>\n",
+       "      <td>91.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>1444.13</td>\n",
+       "      <td>34999.00</td>\n",
+       "      <td>33554.87</td>\n",
+       "      <td>95.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>5130.28</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-2530.28</td>\n",
+       "      <td>97.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>44000.00</td>\n",
+       "      <td>44000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>29058.70</td>\n",
+       "      <td>14250.00</td>\n",
+       "      <td>-14808.70</td>\n",
+       "      <td>103.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>20157.28</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-11157.28</td>\n",
+       "      <td>123.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>9463.58</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-5813.58</td>\n",
+       "      <td>159.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>4534.02</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-2834.02</td>\n",
+       "      <td>166.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>3200.86</td>\n",
+       "      <td>1200.00</td>\n",
+       "      <td>-2000.86</td>\n",
+       "      <td>166.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>3875.31</td>\n",
+       "      <td>1450.00</td>\n",
+       "      <td>-2425.31</td>\n",
+       "      <td>167.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>35212.93</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-22312.93</td>\n",
+       "      <td>172.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>14347.91</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-10147.91</td>\n",
+       "      <td>241.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>8975.55</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-6575.55</td>\n",
+       "      <td>273.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>48602.40</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-37402.40</td>\n",
+       "      <td>333.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>28240.95</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-22740.95</td>\n",
+       "      <td>413.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>99000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>20996.24</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>1150.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>17639.47</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>683</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction    Target  Residual  Difference%\n",
+       "681    15211.01  15200.00    -11.01         0.07\n",
+       "700     6305.83   6300.00     -5.83         0.09\n",
+       "540     6706.65   6700.00     -6.65         0.10\n",
+       "399    12486.63  12500.00     13.37         0.11\n",
+       "374     8715.33   8700.00    -15.33         0.18\n",
+       "74      3206.26   3200.00     -6.26         0.20\n",
+       "49      6513.15   6500.00    -13.15         0.20\n",
+       "397    17863.45  17900.00     36.55         0.20\n",
+       "216     6715.99   6700.00    -15.99         0.24\n",
+       "725     8826.34   8800.00    -26.34         0.30\n",
+       "26     51340.32  51500.00    159.68         0.31\n",
+       "615    14051.69  14100.00     48.31         0.34\n",
+       "536     2107.62   2100.00     -7.62         0.36\n",
+       "413     5828.76   5850.00     21.24         0.36\n",
+       "27     12653.40  12700.00     46.60         0.37\n",
+       "714    12447.09  12500.00     52.91         0.42\n",
+       "193     2837.86   2850.00     12.14         0.43\n",
+       "53     13541.54  13600.00     58.46         0.43\n",
+       "757     7734.58   7700.00    -34.58         0.45\n",
+       "706    12836.82  12900.00     63.18         0.49\n",
+       "618    30492.98  30650.00    157.02         0.51\n",
+       "259     9551.54   9499.00    -52.54         0.55\n",
+       "417    14318.96  14400.00     81.04         0.56\n",
+       "781     7543.03   7500.00    -43.03         0.57\n",
+       "466    25045.23  24900.00   -145.23         0.58\n",
+       "250     6538.30   6500.00    -38.30         0.59\n",
+       "448   109346.97 110000.00    653.03         0.59\n",
+       "461     5633.89   5600.00    -33.89         0.61\n",
+       "525     7352.50   7400.00     47.50         0.64\n",
+       "96     15607.46  15499.00   -108.46         0.70\n",
+       "4      11280.35  11200.00    -80.35         0.72\n",
+       "463    11077.66  10990.00    -87.66         0.80\n",
+       "801    39657.24  39999.00    341.76         0.85\n",
+       "298    23106.56  22900.00   -206.56         0.90\n",
+       "256     2219.87   2200.00    -19.87         0.90\n",
+       "344    25027.42  24800.00   -227.42         0.92\n",
+       "79     27755.46  27500.00   -255.46         0.93\n",
+       "471    20195.56  20000.00   -195.56         0.98\n",
+       "292     7978.67   7900.00    -78.67         1.00\n",
+       "733     9897.28   9999.00    101.72         1.02\n",
+       "21     23557.83  23800.00    242.17         1.02\n",
+       "521     8182.71   8100.00    -82.71         1.02\n",
+       "98     10094.92  10200.00    105.08         1.03\n",
+       "3       8074.47   7990.00    -84.47         1.06\n",
+       "546    16675.21  16500.00   -175.21         1.06\n",
+       "224     7883.21   7800.00    -83.21         1.07\n",
+       "59      9394.45   9500.00    105.55         1.11\n",
+       "324    17194.49  16999.00   -195.49         1.15\n",
+       "416     7407.42   7500.00     92.58         1.23\n",
+       "768    10863.14  10999.00    135.86         1.24\n",
+       "638     2227.46   2200.00    -27.46         1.25\n",
+       "231   109352.13 107999.00  -1353.13         1.25\n",
+       "673     9014.15   8900.00   -114.15         1.28\n",
+       "544    64649.72  65500.00    850.28         1.30\n",
+       "326     6807.32   6900.00     92.68         1.34\n",
+       "418    11436.89  11600.00    163.11         1.41\n",
+       "688     2464.71   2500.00     35.29         1.41\n",
+       "720     9947.38   9800.00   -147.38         1.50\n",
+       "797    33474.09  34000.00    525.91         1.55\n",
+       "360    21559.42  21900.00    340.58         1.56\n",
+       "562     9104.53   9250.00    145.47         1.57\n",
+       "503    12087.91  11900.00   -187.91         1.58\n",
+       "384     8939.11   8800.00   -139.11         1.58\n",
+       "713    35453.02  34899.14   -553.88         1.59\n",
+       "257    34436.11  34999.00    562.89         1.61\n",
+       "272     2897.71   2850.00    -47.71         1.67\n",
+       "671     3440.49   3500.00     59.51         1.70\n",
+       "735    33320.06  33900.00    579.94         1.71\n",
+       "784    18817.25  18500.00   -317.25         1.71\n",
+       "697    13857.09  14100.00    242.91         1.72\n",
+       "381    20040.20  19700.00   -340.20         1.73\n",
+       "565    35262.29  35900.00    637.71         1.78\n",
+       "278    24059.43  24500.00    440.57         1.80\n",
+       "576    64649.72  63500.00  -1149.72         1.81\n",
+       "363    19627.25  19990.00    362.75         1.81\n",
+       "484    26993.16  27500.00    506.84         1.84\n",
+       "213    32584.87  31990.00   -594.87         1.86\n",
+       "632     3056.57   3000.00    -56.57         1.89\n",
+       "509    98085.79  99999.00   1913.21         1.91\n",
+       "336    19170.78  18800.00   -370.78         1.97\n",
+       "166    10782.03  10999.00    216.97         1.97\n",
+       "480    17047.74  17394.09    346.35         1.99\n",
+       "165     1958.31   1999.00     40.69         2.04\n",
+       "660    29500.27  28900.00   -600.27         2.08\n",
+       "712    23990.17  24500.00    509.83         2.08\n",
+       "422    30909.33  31600.00    690.67         2.19\n",
+       "455    11959.28  11700.00   -259.28         2.22\n",
+       "653    11042.29  10799.00   -243.29         2.25\n",
+       "800    56673.17  58000.00   1326.83         2.29\n",
+       "212    43942.54  45000.00   1057.46         2.35\n",
+       "58     11224.40  11500.00    275.60         2.40\n",
+       "230    12978.30  13300.00    321.70         2.42\n",
+       "528     9166.68   9400.00    233.32         2.48\n",
+       "260     8478.83   8700.00    221.17         2.54\n",
+       "232    11184.55  10900.00   -284.55         2.61\n",
+       "130     8524.29   8300.00   -224.29         2.70\n",
+       "684    14494.88  14900.00    405.12         2.72\n",
+       "582    37938.31  39000.00   1061.69         2.72\n",
+       "663    18384.89  18900.00    515.11         2.73\n",
+       "287    23828.27  24500.00    671.73         2.74\n",
+       "376    11570.15  11900.00    329.85         2.77\n",
+       "532    25592.54  24900.00   -692.54         2.78\n",
+       "67      2004.72   1950.00    -54.72         2.81\n",
+       "211     6374.72   6200.00   -174.72         2.82\n",
+       "449   194242.37 199999.00   5756.63         2.88\n",
+       "76     58649.23  57000.00  -1649.23         2.89\n",
+       "274     3398.26   3500.00    101.74         2.91\n",
+       "711    12864.69  12500.00   -364.69         2.92\n",
+       "267    64695.71  66666.00   1970.29         2.96\n",
+       "284    29563.28  28700.00   -863.28         3.01\n",
+       "726     6787.74   6999.00    211.26         3.02\n",
+       "308     5769.23   5600.00   -169.23         3.02\n",
+       "762    23184.61  22500.00   -684.61         3.04\n",
+       "769    10824.58  10500.00   -324.58         3.09\n",
+       "566    22372.19  21700.00   -672.19         3.10\n",
+       "575    15019.12  15500.00    480.88         3.10\n",
+       "354    10058.12   9750.00   -308.12         3.16\n",
+       "36      8769.20   8500.00   -269.20         3.17\n",
+       "708    10844.83  11200.00    355.17         3.17\n",
+       "742    20522.18  21200.00    677.82         3.20\n",
+       "157    23223.72  22500.00   -723.72         3.22\n",
+       "537     1934.64   1999.00     64.36         3.22\n",
+       "61     11770.33  11400.00   -370.33         3.25\n",
+       "190    43881.06  42500.00  -1381.06         3.25\n",
+       "489    17450.09  16900.00   -550.09         3.25\n",
+       "285    23341.27  22600.00   -741.27         3.28\n",
+       "514    10919.19  11300.00    380.81         3.37\n",
+       "659    21192.26  20500.00   -692.26         3.38\n",
+       "592    12749.66  13200.00    450.34         3.41\n",
+       "262    20283.08  21000.00    716.92         3.41\n",
+       "419    16201.55  16800.00    598.45         3.56\n",
+       "555     7974.91   7699.00   -275.91         3.58\n",
+       "13     13015.27  13500.00    484.73         3.59\n",
+       "247     6890.35   7150.00    259.65         3.63\n",
+       "557     5483.10   5700.00    216.90         3.81\n",
+       "585    20200.76  21000.00    799.24         3.81\n",
+       "332    13917.01  14474.00    556.99         3.85\n",
+       "60     11016.60  10600.00   -416.60         3.93\n",
+       "487     3840.88   3999.00    158.12         3.95\n",
+       "134     8524.71   8200.00   -324.71         3.96\n",
+       "745    11551.56  11111.00   -440.56         3.97\n",
+       "759    17687.48  16999.00   -688.48         4.05\n",
+       "482    33095.36  34500.00   1404.64         4.07\n",
+       "655    26864.09  25800.00  -1064.09         4.12\n",
+       "385    27698.29  28900.00   1201.71         4.16\n",
+       "590    31149.15  29900.00  -1249.15         4.18\n",
+       "752    12454.14  13000.00    545.86         4.20\n",
+       "158    35396.30  33950.00  -1446.30         4.26\n",
+       "32     20103.15  21000.00    896.85         4.27\n",
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+       "407    10173.73   9000.00  -1173.73        13.04\n",
+       "9      22173.64  25500.00   3326.36        13.04\n",
+       "675     3216.30   3700.00    483.70        13.07\n",
+       "270    13061.64  11550.00  -1511.64        13.09\n",
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+       "517    19048.58  16800.00  -2248.58        13.38\n",
+       "606    23296.66  26900.00   3603.34        13.40\n",
+       "580    27706.06  32000.00   4293.94        13.42\n",
+       "717     2856.79   3300.00    443.21        13.43\n",
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+       "173    14874.09  17200.00   2325.91        13.52\n",
+       "595     2853.47   3300.00    446.53        13.53\n",
+       "133     1929.25   1699.00   -230.25        13.55\n",
+       "240     6740.68   7800.00   1059.32        13.58\n",
+       "542    15113.32  13300.00  -1813.32        13.63\n",
+       "764     8146.17   9450.00   1303.83        13.80\n",
+       "432    16956.75  14900.00  -2056.75        13.80\n",
+       "138     8616.78  10000.00   1383.22        13.83\n",
+       "97     27560.75  32000.00   4439.25        13.87\n",
+       "568    11782.11  13700.00   1917.89        14.00\n",
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+       "198     3263.77   3800.00    536.23        14.11\n",
+       "368    25652.89  29900.00   4247.11        14.20\n",
+       "11     13110.05  15300.00   2189.95        14.31\n",
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+       "127    32350.70  37990.00   5639.30        14.84\n",
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+       "508     9993.19   8700.00  -1293.19        14.86\n",
+       "375    24702.61  21500.00  -3202.61        14.90\n",
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+       "716     3398.91   4000.00    601.09        15.03\n",
+       "340    12482.76  10850.00  -1632.76        15.05\n",
+       "153     6099.48   5300.00   -799.48        15.08\n",
+       "347     5248.40   6200.00    951.60        15.35\n",
+       "104     6052.23   7150.00   1097.77        15.35\n",
+       "83      9809.02   8499.00  -1310.02        15.41\n",
+       "450     6174.68   7300.00   1125.32        15.42\n",
+       "593     3635.06   4300.00    664.94        15.46\n",
+       "135     3347.79   2899.00   -448.79        15.48\n",
+       "93     20557.05  17800.00  -2757.05        15.49\n",
+       "392    28877.80  25000.00  -3877.80        15.51\n",
+       "529     5082.76   4400.00   -682.76        15.52\n",
+       "159    17104.46  14800.00  -2304.46        15.57\n",
+       "579    14842.29  17595.00   2752.71        15.64\n",
+       "786     3817.05   3300.00   -517.05        15.67\n",
+       "42      9373.98   8100.00  -1273.98        15.73\n",
+       "702     6888.50   5950.00   -938.50        15.77\n",
+       "206     6903.35   8200.00   1296.65        15.81\n",
+       "43     34332.85  40800.00   6467.15        15.85\n",
+       "387    16799.16  14500.00  -2299.16        15.86\n",
+       "492    98480.67  85000.00 -13480.67        15.86\n",
+       "414     6139.72   7300.00   1160.28        15.89\n",
+       "325    15437.68  13300.00  -2137.68        16.07\n",
+       "322     4064.76   3500.00   -564.76        16.14\n",
+       "265    14087.55  16800.00   2712.45        16.15\n",
+       "208    26137.11  22500.00  -3637.11        16.16\n",
+       "72     27304.45  23500.00  -3804.45        16.19\n",
+       "383     9637.17  11500.00   1862.83        16.20\n",
+       "605    33099.94  39500.00   6400.06        16.20\n",
+       "218    36023.63  30990.00  -5033.63        16.24\n",
+       "429    37352.82  44600.00   7247.18        16.25\n",
+       "753    10462.54   8999.00  -1463.54        16.26\n",
+       "126    31976.86  27500.00  -4476.86        16.28\n",
+       "253    20915.39  24996.00   4080.61        16.33\n",
+       "587     1673.12   2000.00    326.88        16.34\n",
+       "570    14221.55  17000.00   2778.45        16.34\n",
+       "625    34297.36  41000.00   6702.64        16.35\n",
+       "707    19907.26  17100.00  -2807.26        16.42\n",
+       "686    10362.29   8900.00  -1462.29        16.43\n",
+       "219    23291.25  20000.00  -3291.25        16.46\n",
+       "747     2213.58   1900.00   -313.58        16.50\n",
+       "406     8738.60   7500.00  -1238.60        16.51\n",
+       "770     6877.84   5900.00   -977.84        16.57\n",
+       "427    10377.09   8900.00  -1477.09        16.60\n",
+       "380    21349.94  18300.00  -3049.94        16.67\n",
+       "386     2858.60   2450.00   -408.60        16.68\n",
+       "493    11676.82   9999.00  -1677.82        16.78\n",
+       "251     7892.51   9500.00   1607.49        16.92\n",
+       "303     5399.74   6500.00   1100.26        16.93\n",
+       "434    25408.55  21700.00  -3708.55        17.09\n",
+       "255    10609.93  12800.00   2190.07        17.11\n",
+       "648    28114.25  24000.00  -4114.25        17.14\n",
+       "47     39356.61  47500.00   8143.39        17.14\n",
+       "321    50516.19  61000.00  10483.81        17.19\n",
+       "316     9481.20  11450.00   1968.80        17.19\n",
+       "390    13594.21  16450.00   2855.79        17.36\n",
+       "349    12675.07  10800.00  -1875.07        17.36\n",
+       "789    11505.09   9800.00  -1705.09        17.40\n",
+       "602     9082.32  10999.00   1916.68        17.43\n",
+       "203    31495.17  26800.00  -4695.17        17.52\n",
+       "748    11282.67   9600.00  -1682.67        17.53\n",
+       "109    19281.74  16400.00  -2881.74        17.57\n",
+       "658     7992.08   9700.00   1707.92        17.61\n",
+       "758     5881.36   5000.00   -881.36        17.63\n",
+       "168    18117.61  22000.00   3882.39        17.65\n",
+       "746    11412.71   9700.00  -1712.71        17.66\n",
+       "119    15774.43  13400.00  -2374.43        17.72\n",
+       "496     4357.87   5300.00    942.13        17.78\n",
+       "415     8126.96   6900.00  -1226.96        17.78\n",
+       "309    11434.04   9700.00  -1734.04        17.88\n",
+       "421    74745.66  63333.00 -11412.66        18.02\n",
+       "796    18302.49  15500.00  -2802.49        18.08\n",
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+       "18     10710.00  13100.00   2390.00        18.24\n",
+       "637     6293.06   7700.00   1406.94        18.27\n",
+       "154    13370.87  11300.00  -2070.87        18.33\n",
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+       "704     5459.66   6700.00   1240.34        18.51\n",
+       "485     4480.19   5500.00   1019.81        18.54\n",
+       "333     8536.82   7200.00  -1336.82        18.57\n",
+       "420    40274.34  49500.00   9225.66        18.64\n",
+       "674    89582.34  75500.00 -14082.34        18.65\n",
+       "366     6884.55   5800.00  -1084.55        18.70\n",
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+       "490     6090.18   7500.00   1409.82        18.80\n",
+       "642    19364.44  16300.00  -3064.44        18.80\n",
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+       "192    11319.17   9500.00  -1819.17        19.15\n",
+       "180    11445.18   9599.00  -1846.18        19.23\n",
+       "699     5650.29   7000.00   1349.71        19.28\n",
+       "634     4433.53   5500.00   1066.47        19.39\n",
+       "545    12724.62  15800.00   3075.38        19.46\n",
+       "771     2092.87   2600.00    507.13        19.50\n",
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+       "337    60274.39  75000.00  14725.61        19.63\n",
+       "184    46983.85  58500.00  11516.15        19.69\n",
+       "724     8747.37  10900.00   2152.63        19.75\n",
+       "129    22708.02  18950.00  -3758.02        19.83\n",
+       "773    13185.60  11000.00  -2185.60        19.87\n",
+       "305     9594.67   8000.00  -1594.67        19.93\n",
+       "678    16676.56  13900.00  -2776.56        19.98\n",
+       "718     5399.84   4500.00   -899.84        20.00\n",
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+       "591    34847.46  28950.00  -5897.46        20.37\n",
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+       "404     6940.02   8750.00   1809.98        20.69\n",
+       "574     6056.80   7650.00   1593.20        20.83\n",
+       "599     8708.11   7200.00  -1508.11        20.95\n",
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+       "656     1973.68   2500.00    526.32        21.05\n",
+       "277     4181.72   5300.00   1118.28        21.10\n",
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+       "20     13328.75  11000.00  -2328.75        21.17\n",
+       "64     36828.78  46800.00   9971.22        21.31\n",
+       "494     6920.60   8800.00   1879.40        21.36\n",
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+       "775    11692.08   9600.00  -2092.08        21.79\n",
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+       "342    11700.38  15000.00   3299.62        22.00\n",
+       "799     2623.69   2150.00   -473.69        22.03\n",
+       "507     2026.96   2600.00    573.04        22.04\n",
+       "183     8434.23   6900.00  -1534.23        22.24\n",
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+       "741     6200.75   8000.00   1799.25        22.49\n",
+       "371    10783.10   8800.00  -1983.10        22.54\n",
+       "511    22566.89  18400.00  -4166.89        22.65\n",
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+       "113    13535.73  11000.00  -2535.73        23.05\n",
+       "19      9723.51  12650.00   2926.49        23.13\n",
+       "479    14919.17  12114.35  -2804.82        23.15\n",
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+       "312    20558.01  16672.50  -3885.51        23.30\n",
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+       "533     2977.77   3900.00    922.23        23.65\n",
+       "139    22121.67  29000.00   6878.33        23.72\n",
+       "40     29567.87  38764.00   9196.13        23.72\n",
+       "358    22081.93  28999.00   6917.07        23.85\n",
+       "611     8174.40   6600.00  -1574.40        23.85\n",
+       "456     2813.36   3700.00    886.64        23.96\n",
+       "629     9728.36  12800.00   3071.64        24.00\n",
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+       "696    14539.53  11700.00  -2839.53        24.27\n",
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+       "505    22276.96  17900.00  -4376.96        24.45\n",
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+       "258    12346.01   9900.00  -2446.01        24.71\n",
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+       "616    21017.12  28000.00   6982.88        24.94\n",
+       "241     3748.29   3000.00   -748.29        24.94\n",
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+       "111     9998.69   8000.00  -1998.69        24.98\n",
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+       "803     3760.79   3000.00   -760.79        25.36\n",
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+       "436    18827.10  14999.00  -3828.10        25.52\n",
+       "635     9674.76  13000.00   3325.24        25.58\n",
+       "350     5205.98   7000.00   1794.02        25.63\n",
+       "751    18944.86  25500.00   6555.14        25.71\n",
+       "341    31179.60  42000.00  10820.40        25.76\n",
+       "804    26319.25  20900.00  -5419.25        25.93\n",
+       "172     2959.68   2350.00   -609.68        25.94\n",
+       "214     8863.85  12000.00   3136.15        26.13\n",
+       "652    38039.83  51500.00  13460.17        26.14\n",
+       "112    13761.17  10900.00  -2861.17        26.25\n",
+       "687    22356.85  17700.00  -4656.85        26.31\n",
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+       "108    10013.18   7900.00  -2113.18        26.75\n",
+       "37      6589.41   8999.00   2409.59        26.78\n",
+       "311    24995.51  19700.00  -5295.51        26.88\n",
+       "677     7821.99  10700.00   2878.01        26.90\n",
+       "279     6736.55   5300.00  -1436.55        27.10\n",
+       "307    11445.85   9000.00  -2445.85        27.18\n",
+       "361     2909.80   4000.00   1090.20        27.26\n",
+       "175    24627.39  33900.00   9272.61        27.35\n",
+       "293    82760.24  64900.00 -17860.24        27.52\n",
+       "497     5787.88   8000.00   2212.12        27.65\n",
+       "527    19048.95  14900.00  -4148.95        27.85\n",
+       "730     4668.85   3650.00  -1018.85        27.91\n",
+       "561     7564.39  10500.00   2935.61        27.96\n",
+       "670    30477.38  23800.00  -6677.38        28.06\n",
+       "296    11154.62   8700.00  -2454.62        28.21\n",
+       "80     21802.49  30385.00   8582.51        28.25\n",
+       "151    65745.19  92222.00  26476.81        28.71\n",
+       "633    21739.14  30500.00   8760.86        28.72\n",
+       "162    25245.24  35500.00  10254.76        28.89\n",
+       "598    15218.48  11800.00  -3418.48        28.97\n",
+       "470    55229.59  77777.00  22547.41        28.99\n",
+       "283    55229.59  77777.00  22547.41        28.99\n",
+       "249     8665.27   6700.00  -1965.27        29.33\n",
+       "327    11514.96   8900.00  -2614.96        29.38\n",
+       "186     2951.62   4200.00   1248.38        29.72\n",
+       "355     5449.56   4200.00  -1249.56        29.75\n",
+       "70     39888.50  57000.00  17111.50        30.02\n",
+       "443     8386.38   6450.00  -1936.38        30.02\n",
+       "176     5242.13   7500.00   2257.87        30.10\n",
+       "693    17055.42  24500.00   7444.58        30.39\n",
+       "783    18140.29  13900.00  -4240.29        30.51\n",
+       "477    15633.25  22500.00   6866.75        30.52\n",
+       "640    10150.80   7777.00  -2373.80        30.52\n",
+       "774     2485.85   1900.00   -585.85        30.83\n",
+       "763     2073.96   2999.00    925.04        30.84\n",
+       "132    19812.54  28713.75   8901.21        31.00\n",
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+       "46      7869.15   5999.00  -1870.15        31.17\n",
+       "710    12071.60   9200.00  -2871.60        31.21\n",
+       "534     8941.06  13000.00   4058.94        31.22\n",
+       "499    13336.31  10150.00  -3186.31        31.39\n",
+       "30     14200.19  10800.00  -3400.19        31.48\n",
+       "66      3105.70   4550.00   1444.30        31.74\n",
+       "682     4624.41   6800.00   2175.59        31.99\n",
+       "441     3511.87   5200.00   1688.13        32.46\n",
+       "520     4779.20   3600.00  -1179.20        32.76\n",
+       "644    11942.86  17777.00   5834.14        32.82\n",
+       "81     16542.07  12450.00  -4092.07        32.87\n",
+       "400    15827.38  11900.00  -3927.38        33.00\n",
+       "71      4216.67   6300.00   2083.33        33.07\n",
+       "147     9333.37   7000.00  -2333.37        33.33\n",
+       "559     7732.20  11600.00   3867.80        33.34\n",
+       "766    12012.45   9000.00  -3012.45        33.47\n",
+       "728    19812.54  29800.00   9987.46        33.51\n",
+       "245     5317.17   8000.00   2682.83        33.54\n",
+       "114    46006.01  34419.45 -11586.56        33.66\n",
+       "543    36381.57  55000.00  18618.43        33.85\n",
+       "703    23207.21  17300.00  -5907.21        34.15\n",
+       "438    12083.74   9000.00  -3083.74        34.26\n",
+       "646     1642.87   2500.00    857.13        34.29\n",
+       "15      3900.79   2900.00  -1000.79        34.51\n",
+       "431    67578.74  49900.00 -17678.74        35.43\n",
+       "273     5030.79   7800.00   2769.21        35.50\n",
+       "692    12682.44   9350.00  -3332.44        35.64\n",
+       "25     44889.92  69900.00  25010.08        35.78\n",
+       "335     5702.84   9000.00   3297.16        36.64\n",
+       "6      11427.09   8350.00  -3077.09        36.85\n",
+       "310    13828.77  21900.00   8071.23        36.85\n",
+       "600     5591.66   8900.00   3308.34        37.17\n",
+       "617    16260.94  11850.00  -4410.94        37.22\n",
+       "209    19627.05  14300.00  -5327.05        37.25\n",
+       "394     2005.13   3200.00   1194.87        37.34\n",
+       "727    46345.58  74000.00  27654.42        37.37\n",
+       "777    51526.76  37500.00 -14026.76        37.40\n",
+       "785    29732.92  47700.00  17967.08        37.67\n",
+       "515    46378.55  74500.00  28121.45        37.75\n",
+       "31     16572.98  11999.00  -4573.98        38.12\n",
+       "772     6644.61   4800.00  -1844.61        38.43\n",
+       "460    12197.10   8800.00  -3397.10        38.60\n",
+       "304    64451.49 104999.00  40547.51        38.62\n",
+       "367    39888.50  65000.00  25111.50        38.63\n",
+       "220    17656.32  28793.04  11136.72        38.68\n",
+       "50     15936.61  11400.00  -4536.61        39.79\n",
+       "388    71675.00 121500.00  49825.00        41.01\n",
+       "551    16226.28  11500.00  -4726.28        41.10\n",
+       "302     8889.98   6299.00  -2590.98        41.13\n",
+       "235     9669.09  16500.00   6830.91        41.40\n",
+       "535    14822.55  10450.00  -4372.55        41.84\n",
+       "601    16930.40  29500.00  12569.60        42.61\n",
+       "73      5700.21  10000.00   4299.79        43.00\n",
+       "701    32880.90  22900.00  -9980.90        43.58\n",
+       "137     6477.68   4500.00  -1977.68        43.95\n",
+       "756    10016.88  18000.00   7983.12        44.35\n",
+       "237    11415.61   7900.00  -3515.61        44.50\n",
+       "539     2050.79   3700.00   1649.21        44.57\n",
+       "328    13804.82   9500.00  -4304.82        45.31\n",
+       "676    24080.07  16555.00  -7525.07        45.45\n",
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+       "444    17048.89  11707.80  -5341.09        45.62\n",
+       "69      8419.88  15500.00   7080.12        45.68\n",
+       "221    31124.28  21300.00  -9824.28        46.12\n",
+       "428    13157.10   9000.00  -4157.10        46.19\n",
+       "643     1733.85   1185.60   -548.25        46.24\n",
+       "41     38632.53  73500.00  34867.47        47.44\n",
+       "612    62100.23  42000.00 -20100.23        47.86\n",
+       "628     1196.37   2300.00   1103.63        47.98\n",
+       "794    65898.02 129222.00  63323.98        49.00\n",
+       "738     2313.02   1550.00   -763.02        49.23\n",
+       "164     2972.03   1990.00   -982.03        49.35\n",
+       "228     1417.83   2800.00   1382.17        49.36\n",
+       "502    22471.17  44700.00  22228.83        49.73\n",
+       "721    71981.49 144900.00  72918.51        50.32\n",
+       "481    50745.07  33715.50 -17029.57        50.51\n",
+       "577     7343.52  14999.00   7655.48        51.04\n",
+       "519    26609.57  17600.00  -9009.57        51.19\n",
+       "275    17888.88  11800.00  -6088.88        51.60\n",
+       "457    20479.37  13500.00  -6979.37        51.70\n",
+       "603    12461.52   8200.00  -4261.52        51.97\n",
+       "401    20828.11  13700.00  -7128.11        52.03\n",
+       "362    18980.92  39900.00  20919.08        52.43\n",
+       "780    28362.55  60500.00  32137.45        53.12\n",
+       "99      3521.95   2300.00  -1221.95        53.13\n",
+       "102     9427.42  20500.00  11072.58        54.01\n",
+       "370     5408.75   3500.00  -1908.75        54.54\n",
+       "516    10854.85  24000.00  13145.15        54.77\n",
+       "48     65041.89  41900.00 -23141.89        55.23\n",
+       "491     7715.56   4900.00  -2815.56        57.46\n",
+       "318    30316.04  19200.00 -11116.04        57.90\n",
+       "34     20712.46  13000.00  -7712.46        59.33\n",
+       "122     2719.53   1700.00  -1019.53        59.97\n",
+       "39      3906.56   2400.00  -1506.56        62.77\n",
+       "790     5055.93   3099.00  -1956.93        63.15\n",
+       "200     4243.79   2600.00  -1643.79        63.22\n",
+       "788    10676.78   6499.00  -4177.78        64.28\n",
+       "86     16658.12  10100.00  -6558.12        64.93\n",
+       "662     6656.03   4000.00  -2656.03        66.40\n",
+       "425     2486.42   8000.00   5513.58        68.92\n",
+       "661    13706.76   8000.00  -5706.76        71.33\n",
+       "23      6524.07   3799.00  -2725.07        71.73\n",
+       "423    41367.65  23900.00 -17467.65        73.09\n",
+       "238    60487.88  34600.00 -25887.88        74.82\n",
+       "524     9215.35   5000.00  -4215.35        84.31\n",
+       "636     6870.33   3600.00  -3270.33        90.84\n",
+       "506     9213.19   4800.00  -4413.19        91.94\n",
+       "613     1444.13  34999.00  33554.87        95.87\n",
+       "791     5130.28   2600.00  -2530.28        97.32\n",
+       "623        0.00   1800.00   1800.00       100.00\n",
+       "306        0.00  44000.00  44000.00       100.00\n",
+       "286        0.00  35000.00  35000.00       100.00\n",
+       "121        0.00   3800.00   3800.00       100.00\n",
+       "396        0.00  38500.00  38500.00       100.00\n",
+       "124        0.00   4100.00   4100.00       100.00\n",
+       "690        0.00   6500.00   6500.00       100.00\n",
+       "24         0.00  32000.00  32000.00       100.00\n",
+       "301        0.00   8500.00   8500.00       100.00\n",
+       "351        0.00  18800.00  18800.00       100.00\n",
+       "723        0.00   1650.00   1650.00       100.00\n",
+       "54     29058.70  14250.00 -14808.70       103.92\n",
+       "266    20157.28   9000.00 -11157.28       123.97\n",
+       "594     9463.58   3650.00  -5813.58       159.28\n",
+       "143     4534.02   1700.00  -2834.02       166.71\n",
+       "131     3200.86   1200.00  -2000.86       166.74\n",
+       "452     3875.31   1450.00  -2425.31       167.26\n",
+       "685    35212.93  12900.00 -22312.93       172.97\n",
+       "604    14347.91   4200.00 -10147.91       241.62\n",
+       "737     8975.55   2400.00  -6575.55       273.98\n",
+       "28     48602.40  11200.00 -37402.40       333.95\n",
+       "538    28240.95   5500.00 -22740.95       413.47\n",
+       "290         inf  19999.00      -inf          inf\n",
+       "731         inf  99000.00      -inf          inf\n",
+       "282         inf  20996.24      -inf          inf\n",
+       "679         inf   1150.00      -inf          inf\n",
+       "120         inf  17639.47      -inf          inf\n",
+       "204         inf  12900.00      -inf          inf\n",
+       "683         inf  20000.00      -inf          inf\n",
+       "650         inf  11300.00      -inf          inf\n",
+       "458         inf   7450.00      -inf          inf\n",
+       "314         inf  10200.00      -inf          inf\n",
+       "63          inf  37500.00      -inf          inf\n",
+       "698         inf  11700.00      -inf          inf\n",
+       "33          inf   6000.00      -inf          inf\n",
+       "82          inf   4000.00      -inf          inf"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sometimes it is useful to check these outputs manually\n",
+    "# To see all rows, we use the relevant pandas syntax\n",
+    "pd.options.display.max_rows = 999\n",
+    "# Moreover, to make the dataset clear, we can display the result with only 2 digits after the dot \n",
+    "\n",
+    "pd.set_option('display.float_format', lambda x: '%.2f' %x)\n",
+    "# Finally, we sort by difference in % and manually check the model\n",
+    "df_pf.sort_values(by=['Difference%'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/List slicing-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/List slicing-checkpoint.ipynb
new file mode 100644
index 0000000..ffcc402
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/List slicing-checkpoint.ipynb	
@@ -0,0 +1,294 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "14e35bf1",
+   "metadata": {},
+   "source": [
+    "# List Slicing - Exercise #1\n",
+    "Using list slicing, obtain the numbers 100 and 115."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e033baf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers = [15, 40, 50, 100, 115, 140]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "182882c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 115]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers[3:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c83bc03",
+   "metadata": {},
+   "source": [
+    "Using slicing, extract the first four elements from the list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "167b0556",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[15, 40, 50, 100]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers[:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d00f2088",
+   "metadata": {},
+   "source": [
+    "Using slicing, extract all the elements from the list from the 3rd position onwards."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2b53232e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 100, 115, 140]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers[2:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acdaf6af",
+   "metadata": {},
+   "source": [
+    "Using slicing, extract the last 4 elements from the list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f7dcf33c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 100, 115, 140]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers[2:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "915934af",
+   "metadata": {},
+   "source": [
+    "Which is the position of the value 15?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "499691e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The value 15 is in the 0 th position\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The value 15 is in the\", Numbers.index(15),'th', \"position\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80271dc3",
+   "metadata": {},
+   "source": [
+    "# Create a list, called Two_Numbers. Let its elements be the values 1 and 2. Then, create a new one, named All_Numbers, that will contain both the Two_Numbers and the Numbers lists in the given order.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "71080ff3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers = [15, 40, 50, 100, 115, 140]\n",
+    "Two_Numbers = [1,2]\n",
+    "All_Numbers = [Numbers,Two_Numbers]\n",
+    "All_Numbers.sort(reverse = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3fd391bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2], [15, 40, 50, 100, 115, 140]]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "All_Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "686b2df8",
+   "metadata": {},
+   "source": [
+    "Sort all the numbers in the Numbers list from the largest to the smallest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "74abf734",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers = [15, 40, 50, 100, 115, 140]\n",
+    "Numbers.sort(reverse = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "5db03823",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[140, 115, 100, 50, 40, 15]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3186b355",
+   "metadata": {},
+   "source": [
+    "# Tuples - Exercise #1\n",
+    "Create a tuple, called Cars, with elements \"BMW\", \"Dodge\", and \"Ford\" in the given order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b4a2b60b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Cars = ('BMW','Dodge','Ford')"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "cc25439a",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15b70a6d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/Modules & Packages-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/Modules & Packages-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/Modules & Packages-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/My_MNIST_part2-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/My_MNIST_part2-checkpoint.ipynb
new file mode 100644
index 0000000..d1ea786
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/My_MNIST_part2-checkpoint.ipynb	
@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tensorflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow_datasets\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtfds\u001b[39;00m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/Python basics-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/Python basics-checkpoint.ipynb
new file mode 100644
index 0000000..bb1e674
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/Python basics-checkpoint.ipynb	
@@ -0,0 +1,506 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9bc6a2b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99.0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = 99\n",
+    "float(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "60bafe0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "531a5223",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = 0.99\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "87b50f4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6ae1d262",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cbe5dd26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 days\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(str(m) + \" days\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "17b342b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "str(m)  days\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"str(m)  days\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d6493f80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'Big Houses'\n",
+      "Big  Houses\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"'Big\" + \" Houses'\")\n",
+    "print(\"Big\", \" Houses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7f78d10f",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "unterminated string literal (detected at line 1) (4111028140.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[12], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    print('Aren't you hungry?')\u001b[0m\n\u001b[1;37m                             ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated string literal (detected at line 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Aren't you hungry?')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e7d3fe98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 22/4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d72f4be5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9128f7db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "22%4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e52d6a4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "True and not False and True or not False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "386f85c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "True or False and False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fa5af17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "False and True or False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bbda09fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 != 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "24bc95d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "50 ==50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cf1fe8bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The condition has been satisfied\n"
+     ]
+    }
+   ],
+   "source": [
+    "if 5> 2:\n",
+    "    print('The condition has been satisfied')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a45e352",
+   "metadata": {},
+   "source": [
+    "Assign 10 to the variable x and 25 to the variable y. In the same cell, create 2 conditional statements. Let the first one print \"Both conditions are correct\" if x is greater then 3 and y is greater than 13. Let the second one print \"At least one of the conditions is false\" if x is less than or equal to 3 or y is less than or equal to 13. Change the values assigned to x and y and re-run the cell to verify your code still works.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0e595a9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Both conditions are correct\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 10\n",
+    "y = 25\n",
+    "if x>3 and y>13:\n",
+    "    print('Both conditions are correct')\n",
+    "if x<=3 or y<=13:\n",
+    "    print('At least one of the conditions is false')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8761411c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 10\n",
+    "y = 25\n",
+    "if x<=3 or y<=13:\n",
+    "    print('At least one of the conditions is false')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85cd4a58",
+   "metadata": {},
+   "source": [
+    "Let x represent the number of orders received during a certain day. Create a program that prints \"A busy day\" if x is greater than 100, and \"A calm day\" otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6513794e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A busy day\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 103\n",
+    "if x > 100:\n",
+    "    print ('A busy day')\n",
+    "else:\n",
+    "    print('A calm day')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca8d3610",
+   "metadata": {},
+   "source": [
+    "Create the following piece of code: If x > 200, print out \"Big\"; If x > 100 and x <= 200, print out \"Average\"; and If x <= 100, print out \"Small\". Use the if, elif, and else keywords in your code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0bddb03a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "samll\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 95\n",
+    "if x > 200:\n",
+    "    print(\"Big\")\n",
+    "elif 100 < x <= 200:\n",
+    "    print(\"Average\")\n",
+    "else:\n",
+    "    print(\"samll\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b95b1bb4",
+   "metadata": {},
+   "source": [
+    "Add a new elif statement, so that, eventually, the program prints \"Small\" if x >= 0 and x <= 100, and \"Negative\" if x < 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4b8b51bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Negative\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = -95\n",
+    "if x > 200:\n",
+    "    print(\"Big\")\n",
+    "elif 100 < x <= 200:\n",
+    "    print(\"Average\")\n",
+    "elif x >=0 and x<= 100:\n",
+    "    print(\"Small\")\n",
+    "else:\n",
+    "    print(\"Negative\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/Simple+Linear+Regression+Exercise-Copy1-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/Simple+Linear+Regression+Exercise-Copy1-checkpoint.ipynb
new file mode 100644
index 0000000..570b51c
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/Simple+Linear+Regression+Exercise-Copy1-checkpoint.ipynb	
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression - exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size.csv'. \n",
+    "\n",
+    "You are expected to create a simple linear regression (similar to the one in the lecture), using the new data.\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables is 'size'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the regression line on the initial scatter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/Simple+Linear+Regression+Exercise-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/Simple+Linear+Regression+Exercise-checkpoint.ipynb
new file mode 100644
index 0000000..570b51c
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/Simple+Linear+Regression+Exercise-checkpoint.ipynb	
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression - exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size.csv'. \n",
+    "\n",
+    "You are expected to create a simple linear regression (similar to the one in the lecture), using the new data.\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables is 'size'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the regression line on the initial scatter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/Untitled-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/.ipynb_checkpoints/Untitled1-checkpoint.ipynb b/Udemy DataScience Workbook/.ipynb_checkpoints/Untitled1-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/Udemy DataScience Workbook/.ipynb_checkpoints/Untitled1-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/.vscode/launch.json b/Udemy DataScience Workbook/.vscode/launch.json
new file mode 100644
index 0000000..306f58e
--- /dev/null
+++ b/Udemy DataScience Workbook/.vscode/launch.json	
@@ -0,0 +1,16 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": true
+        }
+    ]
+}
\ No newline at end of file
diff --git a/Udemy DataScience Workbook/1.+TensorFlow_MNIST_Width_Solution.ipynb b/Udemy DataScience Workbook/1.+TensorFlow_MNIST_Width_Solution.ipynb
new file mode 100644
index 0000000..e5c6077
--- /dev/null
+++ b/Udemy DataScience Workbook/1.+TensorFlow_MNIST_Width_Solution.ipynb	
@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "### 1. The *width* (the hidden layer size) of the algorithm. Try a hidden layer size of 200. How does the validation accuracy of the model change? What about the time it took the algorithm to train? Can you find a hidden layer size that does better?\n",
+    "\n",
+    "**Solution**\n",
+    "\n",
+    "Find the variable: \"hidden_layer_size\" and change it to 200.\n",
+    "\n",
+    "The validation accuracy is significantly higher (as the algorithm with 50 hidden units was too simple of a model).\n",
+    "\n",
+    "Naturally, it takes the algorithm much longer to train (unless early stopping is triggered too soon).\n",
+    "\n",
+    "A hidden layer size of 500 (and not only) works even better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 200\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "540/540 - 7s - loss: 0.2710 - accuracy: 0.9214 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "540/540 - 7s - loss: 0.1043 - accuracy: 0.9689 - val_loss: 0.0907 - val_accuracy: 0.9727\n",
+      "Epoch 3/5\n",
+      "540/540 - 6s - loss: 0.0694 - accuracy: 0.9788 - val_loss: 0.0672 - val_accuracy: 0.9785\n",
+      "Epoch 4/5\n",
+      "540/540 - 6s - loss: 0.0511 - accuracy: 0.9842 - val_loss: 0.0481 - val_accuracy: 0.9847\n",
+      "Epoch 5/5\n",
+      "540/540 - 7s - loss: 0.0383 - accuracy: 0.9878 - val_loss: 0.0441 - val_accuracy: 0.9858\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1df626b76d8>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 1s 1s/step - loss: 0.0733 - accuracy: 0.97 - 1s 1s/step - loss: 0.0733 - accuracy: 0.9774"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.07. Test accuracy: 97.74%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/1.01.+Simple+linear+regression.csv b/Udemy DataScience Workbook/1.01.+Simple+linear+regression.csv
new file mode 100644
index 0000000..8fec0c0
--- /dev/null
+++ b/Udemy DataScience Workbook/1.01.+Simple+linear+regression.csv	
@@ -0,0 +1,85 @@
+SAT,GPA
+1714,2.4
+1664,2.52
+1760,2.54
+1685,2.74
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+1934,3.54
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diff --git a/Udemy DataScience Workbook/1.02.+Multiple+linear+regression.csv b/Udemy DataScience Workbook/1.02.+Multiple+linear+regression.csv
new file mode 100644
index 0000000..7f990d3
--- /dev/null
+++ b/Udemy DataScience Workbook/1.02.+Multiple+linear+regression.csv	
@@ -0,0 +1,85 @@
+SAT,"Rand 1,2,3",GPA
+1714,1,2.4
+1664,3,2.52
+1760,3,2.54
+1685,3,2.74
+1693,2,2.83
+1670,1,2.91
+1764,2,3
+1764,1,3
+1792,2,3.01
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+1674,2,3.17
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+1786,3,3.19
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+1722,3,3.19
+1663,3,3.2
+1687,1,3.21
+1974,1,3.24
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+1821,3,3.28
+2020,1,3.28
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diff --git a/Udemy DataScience Workbook/2.+TensorFlow_MNIST_Depth_Solution.ipynb b/Udemy DataScience Workbook/2.+TensorFlow_MNIST_Depth_Solution.ipynb
new file mode 100644
index 0000000..aec6f3b
--- /dev/null
+++ b/Udemy DataScience Workbook/2.+TensorFlow_MNIST_Depth_Solution.ipynb	
@@ -0,0 +1,377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "### 2. The *depth* of the algorithm. Add another hidden layer to the algorithm. This is an extremely important exercise! How does the validation accuracy change? What about the time it took the algorithm to train?\n",
+    "Hint: Be careful with the shapes of the weights and the biases.\n",
+    "\n",
+    "**Solution**\n",
+    "\n",
+    "Adding another hidden layer to the algorithm is done in the same way as in the lecture.\n",
+    "\n",
+    "We simply add a new line in Sequential:\n",
+    "\n",
+    "tf.keras.layers.Dense(hidden_layer_size, activation='relu')\n",
+    "\n",
+    "We can see that the accuracy of the model does not necessarily improve. This is an important lesson for us. Fiddling with a single hyperparameter may not be enough. Sometimes, a deeper net needs to also be wider in order to have higher accuracy. Maybe you need more epochs?\n",
+    "\n",
+    "**ADDITIONAL TASK: Try this new model, but with a wider one (200-500 hidden units). Basically, combine this and the previous exercises**\n",
+    "\n",
+    "In any case, it takes longer for the algorithm to train."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 3rd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "540/540 - 6s - loss: 0.4014 - accuracy: 0.8783 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "540/540 - 5s - loss: 0.1696 - accuracy: 0.9499 - val_loss: 0.1544 - val_accuracy: 0.9577\n",
+      "Epoch 3/5\n",
+      "540/540 - 5s - loss: 0.1301 - accuracy: 0.9610 - val_loss: 0.1302 - val_accuracy: 0.9633\n",
+      "Epoch 4/5\n",
+      "540/540 - 5s - loss: 0.1071 - accuracy: 0.9677 - val_loss: 0.1084 - val_accuracy: 0.9673\n",
+      "Epoch 5/5\n",
+      "540/540 - 5s - loss: 0.0940 - accuracy: 0.9715 - val_loss: 0.1089 - val_accuracy: 0.9687\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x2946f136080>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 1s 1s/step - loss: 0.1128 - accuracy: 0.96 - 1s 1s/step - loss: 0.1128 - accuracy: 0.9655"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.11. Test accuracy: 96.55%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/2.01.+Admittance.csv b/Udemy DataScience Workbook/2.01.+Admittance.csv
new file mode 100644
index 0000000..fef66ad
--- /dev/null
+++ b/Udemy DataScience Workbook/2.01.+Admittance.csv	
@@ -0,0 +1,169 @@
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diff --git a/Udemy DataScience Workbook/2.02.+Binary+predictors.csv b/Udemy DataScience Workbook/2.02.+Binary+predictors.csv
new file mode 100644
index 0000000..2508c45
--- /dev/null
+++ b/Udemy DataScience Workbook/2.02.+Binary+predictors.csv	
@@ -0,0 +1,169 @@
+SAT,Admitted,Gender
+1363,No,Male
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+1987,Yes,Female
+1997,Yes,Male
+1422,No,Female
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diff --git a/Udemy DataScience Workbook/2.03.+Test+dataset.csv b/Udemy DataScience Workbook/2.03.+Test+dataset.csv
new file mode 100644
index 0000000..4f8b42a
--- /dev/null
+++ b/Udemy DataScience Workbook/2.03.+Test+dataset.csv	
@@ -0,0 +1,20 @@
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diff --git a/Udemy DataScience Workbook/3.+TensorFlow_MNIST_Width_and_Depth_Solution.ipynb b/Udemy DataScience Workbook/3.+TensorFlow_MNIST_Width_and_Depth_Solution.ipynb
new file mode 100644
index 0000000..b037b2a
--- /dev/null
+++ b/Udemy DataScience Workbook/3.+TensorFlow_MNIST_Width_and_Depth_Solution.ipynb	
@@ -0,0 +1,375 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "### 3. The *width and depth* of the algorithm. Add as many additional layers as you need to reach 5 hidden layers. Moreover, adjust the width of the algorithm as you find suitable. How does the validation accuracy change? What about the time it took the algorithm to train?\n",
+    "\n",
+    "**Solution**\n",
+    "\n",
+    "This exercise is pretty much the same as the previous one. However, it will get us to a much deeper net. As we noted in the previous exercise, you a deeeper net may need to be wider to produce better results.\n",
+    "\n",
+    "We tried with 1000 hidden units in each layer and 5 hidden layers.\n",
+    "\n",
+    "The result (as you can see below) is that our model's training was going very well, until it overfit. It did so by quite a lot.\n",
+    "\n",
+    "It took my personal computer around 5-6 minutes to train the model. \n",
+    "\n",
+    "What if you have more epochs?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 1000\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 3nd hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 4nd hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 5nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "540/540 - 33s - loss: 0.2382 - accuracy: 0.9291 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "540/540 - 41s - loss: 0.1084 - accuracy: 0.9703 - val_loss: 0.1113 - val_accuracy: 0.9712\n",
+      "Epoch 3/5\n",
+      "540/540 - 33s - loss: 0.0784 - accuracy: 0.9779 - val_loss: 0.0897 - val_accuracy: 0.9740\n",
+      "Epoch 4/5\n",
+      "540/540 - 31s - loss: 0.0675 - accuracy: 0.9814 - val_loss: 0.0938 - val_accuracy: 0.9757\n",
+      "Epoch 5/5\n",
+      "540/540 - 30s - loss: 0.0550 - accuracy: 0.9847 - val_loss: 0.0654 - val_accuracy: 0.9815\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1ac02204a20>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 2s 2s/step - loss: 0.1002 - accuracy: 0.97 - 2s 2s/step - loss: 0.1002 - accuracy: 0.9747"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.10. Test accuracy: 97.47%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/3.01.+Country+clusters.csv b/Udemy DataScience Workbook/3.01.+Country+clusters.csv
new file mode 100644
index 0000000..c9327f1
--- /dev/null
+++ b/Udemy DataScience Workbook/3.01.+Country+clusters.csv	
@@ -0,0 +1,7 @@
+Country,Latitude,Longitude,Language
+USA,44.97,-103.77,English
+Canada,62.4,-96.8,English
+France,46.75,2.4,French
+UK,54.01,-2.53,English
+Germany,51.15,10.4,German
+Australia,-25.45,133.11,English
diff --git a/Udemy DataScience Workbook/3.12.+Example.csv b/Udemy DataScience Workbook/3.12.+Example.csv
new file mode 100644
index 0000000..84ca79c
--- /dev/null
+++ b/Udemy DataScience Workbook/3.12.+Example.csv	
@@ -0,0 +1,31 @@
+Satisfaction,Loyalty
+4,-1.33
+6,-0.28
+5,-0.99
+7,-0.29
+4,1.06
+1,-1.66
+10,-0.97
+8,-0.32
+8,1.02
+8,0.68
+10,-0.34
+5,0.39
+5,-1.69
+2,0.67
+7,0.27
+9,1.36
+8,1.38
+7,1.36
+7,-0.34
+9,0.67
+10,1.18
+3,-1.69
+4,1.04
+3,-0.96
+6,1.03
+9,-0.99
+10,0.37
+9,0.03
+3,-1.36
+5,0.73
diff --git a/Udemy DataScience Workbook/4.+TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb b/Udemy DataScience Workbook/4.+TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb
new file mode 100644
index 0000000..fea3f25
--- /dev/null
+++ b/Udemy DataScience Workbook/4.+TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb	
@@ -0,0 +1,373 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "### 4. Fiddle with the activation functions. Try applying sigmoid transformation to both layers. The sigmoid activation is given by the method: tf.nn.sigmoid()\n",
+    "\n",
+    "**Solution**\n",
+    "\n",
+    "Find the part where we stack layers (Sequential()).\n",
+    "\n",
+    "Adjust the activations from 'relu' to 'sigmoid'.\n",
+    "    \n",
+    "Generally, we should reach an inferior solution. That is because relu 'cleans' the noise in the data (think about it - if a value is negative, relu filters it out, while if it is positive, it takes it into account). For the MNIST dataset, we care only about the intensely black and white parts in the images of the digits, so such filtering proves beneficial.\n",
+    "\n",
+    "The sigmoid does not filter the signals as well as relu, but still reaches a respectable result (around 95%).\n",
+    "\n",
+    "**Try using softmax activations for all layers. How does the result change? Can you explain why that happens?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "540/540 - 7s - loss: 1.0357 - accuracy: 0.7668 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "540/540 - 6s - loss: 0.3254 - accuracy: 0.9140 - val_loss: 0.2827 - val_accuracy: 0.9228\n",
+      "Epoch 3/5\n",
+      "540/540 - 5s - loss: 0.2406 - accuracy: 0.9315 - val_loss: 0.2300 - val_accuracy: 0.9360\n",
+      "Epoch 4/5\n",
+      "540/540 - 6s - loss: 0.1974 - accuracy: 0.9429 - val_loss: 0.1960 - val_accuracy: 0.9460\n",
+      "Epoch 5/5\n",
+      "540/540 - 6s - loss: 0.1696 - accuracy: 0.9507 - val_loss: 0.1720 - val_accuracy: 0.9530\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x28bd67035c0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 1s 1s/step - loss: 0.1659 - accuracy: 0.95 - 1s 1s/step - loss: 0.1659 - accuracy: 0.9514"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.17. Test accuracy: 95.14%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/5.+TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb b/Udemy DataScience Workbook/5.+TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb
new file mode 100644
index 0000000..94aa0b9
--- /dev/null
+++ b/Udemy DataScience Workbook/5.+TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb	
@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "### 5. Fiddle with the activation functions. Try applying a ReLu to the first hidden layer and tanh to the second one. The tanh activation is given by the string: 'tanh'\n",
+    "\n",
+    "**Solution** \n",
+    "\n",
+    "Analogically to the previous lecture, we can change the activation functions. This time though, we will use different activators for the different layers.\n",
+    "\n",
+    "The result should not be significantly different. However, with different width and depth, that may change.\n",
+    "\n",
+    "*Additional exercise: Try to find a better combination of activation functions*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='tanh'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "540/540 - 6s - loss: 0.4009 - accuracy: 0.8913 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "540/540 - 5s - loss: 0.1712 - accuracy: 0.9492 - val_loss: 0.1565 - val_accuracy: 0.9533\n",
+      "Epoch 3/5\n",
+      "540/540 - 6s - loss: 0.1288 - accuracy: 0.9617 - val_loss: 0.1352 - val_accuracy: 0.9588\n",
+      "Epoch 4/5\n",
+      "540/540 - 6s - loss: 0.1048 - accuracy: 0.9689 - val_loss: 0.1123 - val_accuracy: 0.9663\n",
+      "Epoch 5/5\n",
+      "540/540 - 6s - loss: 0.0880 - accuracy: 0.9738 - val_loss: 0.0978 - val_accuracy: 0.9688\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1fa2e5576d8>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 2s 2s/step - loss: 0.1045 - accuracy: 0.96 - 2s 2s/step - loss: 0.1045 - accuracy: 0.9672"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.10. Test accuracy: 96.72%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/6.+TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb b/Udemy DataScience Workbook/6.+TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb
new file mode 100644
index 0000000..f69c7fb
--- /dev/null
+++ b/Udemy DataScience Workbook/6.+TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb	
@@ -0,0 +1,375 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "### 6. Adjust the batch size. Try a batch size of 10000. How does the required time change? What about the accuracy?\n",
+    "\n",
+    "**Solution**\n",
+    "\n",
+    "Find the line that declares the batch size.\n",
+    "\n",
+    "Change batch_size from 100 to 10000.\n",
+    "\n",
+    "    BATCH_SIZE = 10000\n",
+    "    \n",
+    "A bigger batch size results in slower training. That's what we expected from the theory. We are taking advantage of batching because of the amazing speed increase.\n",
+    "\n",
+    "Notice that the validation accuracy starts from a low number and with 5 epochs actually **finishes** at a lower number. That's because there are **fewer** updates in a single epoch.\n",
+    "\n",
+    "*Try a batch size of 30,000 or 50,000. That's very close to single batch GD for this problem. What do you think about the speed?You will need to change the max epochs to 100 (for instance), as 5 epochs won't be enough to train the model. What do you think about the speed of optimization?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 10000\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "6/6 - 6s - loss: 2.2121 - accuracy: 0.2268 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "6/6 - 5s - loss: 1.9340 - accuracy: 0.4355 - val_loss: 1.7433 - val_accuracy: 0.5355\n",
+      "Epoch 3/5\n",
+      "6/6 - 5s - loss: 1.6016 - accuracy: 0.5867 - val_loss: 1.3985 - val_accuracy: 0.6718\n",
+      "Epoch 4/5\n",
+      "6/6 - 5s - loss: 1.2563 - accuracy: 0.7183 - val_loss: 1.0648 - val_accuracy: 0.7685\n",
+      "Epoch 5/5\n",
+      "6/6 - 5s - loss: 0.9441 - accuracy: 0.7928 - val_loss: 0.8017 - val_accuracy: 0.8153\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1ef6c686e80>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 2s 2s/step - loss: 0.7766 - accuracy: 0.82 - 2s 2s/step - loss: 0.7766 - accuracy: 0.8248"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.78. Test accuracy: 82.48%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/7.+TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb b/Udemy DataScience Workbook/7.+TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb
new file mode 100644
index 0000000..caf9bb1
--- /dev/null
+++ b/Udemy DataScience Workbook/7.+TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb	
@@ -0,0 +1,373 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "### 7. Adjust the batch size. Try a batch size of 1. How does the required time change? What about the accuracy?\n",
+    "\n",
+    "**Solution**\n",
+    "\n",
+    "Find the line that declares the batch size.\n",
+    "\n",
+    "Change batch_size from 100 to 1.\n",
+    "\n",
+    "    batch_size = 1\n",
+    "    \n",
+    "A batch size of 1 results in the SGD. It takes the algorithm very little time to process a single batch (as it is one data point), but there are thousands of batches (54000 to be precise), thus the algorithm is actually slow. Remember that this depends on the number of cores that you train on. If you are using a CPU with 4 or 8 cores, you can only train 4 or 8 batches at once. The middle ground (mini-batching such as 100 samples per batch) is optimal.\n",
+    "\n",
+    "Notice that the validation accuracy starts from a high number. That's because there are **lots** updates in a single epoch. Once the training is over, the accuracy is lower than all other batch sizes (SGD was an approximation)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 1\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "54000/54000 - 91s - loss: 0.2509 - accuracy: 0.9264 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "54000/54000 - 101s - loss: 0.1581 - accuracy: 0.9566 - val_loss: 0.1594 - val_accuracy: 0.9588\n",
+      "Epoch 3/5\n",
+      "54000/54000 - 109s - loss: 0.1462 - accuracy: 0.9617 - val_loss: 0.1455 - val_accuracy: 0.9623\n",
+      "Epoch 4/5\n",
+      "54000/54000 - 92s - loss: 0.1399 - accuracy: 0.9636 - val_loss: 0.1702 - val_accuracy: 0.9583\n",
+      "Epoch 5/5\n",
+      "54000/54000 - 165s - loss: 0.1377 - accuracy: 0.9654 - val_loss: 0.1625 - val_accuracy: 0.9660\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1d911786358>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 4s 4s/step - loss: 0.1719 - accuracy: 0.96 - 4s 4s/step - loss: 0.1719 - accuracy: 0.9607"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.17. Test accuracy: 96.07%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/A+Simple+Example+of+Clustering+-+Exercise.ipynb b/Udemy DataScience Workbook/A+Simple+Example+of+Clustering+-+Exercise.ipynb
new file mode 100644
index 0000000..f5e48be
--- /dev/null
+++ b/Udemy DataScience Workbook/A+Simple+Example+of+Clustering+-+Exercise.ipynb	
@@ -0,0 +1,690 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A Simple Example of Clustering "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given much more country data. Using the same methodology as the one in the lecture, group all the countries in 2 clusters. \n",
+    "\n",
+    "Try with other numbers of clusters and see if they match your expectations. Maybe 7 is going to be a cool one!\n",
+    "\n",
+    "Plot the data using the <i> c </i> parameter to separate the data by the clusters we defined.  \n",
+    "\n",
+    "<i> Note: c stands for color <i>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'Countries.csv'</i>.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude\n",
+       "0           Aruba  -69.982677  12.520880\n",
+       "1     Afghanistan   66.004734  33.835231\n",
+       "2          Angola   17.537368 -12.293361\n",
+       "3        Anguilla  -63.064989  18.223959\n",
+       "4         Albania   20.049834  41.142450\n",
+       "..            ...         ...        ...\n",
+       "236         Samoa -172.164851 -13.753243\n",
+       "237         Yemen   47.586762  15.909280\n",
+       "238  South Africa   25.083901 -29.000341\n",
+       "239        Zambia   27.774759 -13.458242\n",
+       "240      Zimbabwe   29.851441 -19.004204\n",
+       "\n",
+       "[241 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Countries-exercise.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the <i>'Longtitude'</i> and <i>'Latitude'</i> columns. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Note that in order to reach a result resembling the world map, we must use the longitude as y, and the latitude as x\n",
+    "plt.scatter(raw_data['Longitude'], raw_data['Latitude'])\n",
+    "# Set limits of the axes, again to resemble the world map\n",
+    "plt.xlim(-180, 180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a copy of that data and remove all parameters apart from <i>Longitude</i> and <i>Latitude</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-69.98</td>\n",
+       "      <td>12.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>66.00</td>\n",
+       "      <td>33.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.54</td>\n",
+       "      <td>-12.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-63.06</td>\n",
+       "      <td>18.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.05</td>\n",
+       "      <td>41.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>-172.16</td>\n",
+       "      <td>-13.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>47.59</td>\n",
+       "      <td>15.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>25.08</td>\n",
+       "      <td>-29.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>27.77</td>\n",
+       "      <td>-13.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>29.85</td>\n",
+       "      <td>-19.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Longitude  Latitude\n",
+       "0       -69.98     12.52\n",
+       "1        66.00     33.84\n",
+       "2        17.54    -12.29\n",
+       "3       -63.06     18.22\n",
+       "4        20.05     41.14\n",
+       "..         ...       ...\n",
+       "236    -172.16    -13.75\n",
+       "237      47.59     15.91\n",
+       "238      25.08    -29.00\n",
+       "239      27.77    -13.46\n",
+       "240      29.85    -19.00\n",
+       "\n",
+       "[241 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# iloc is a method used to 'slice' data \n",
+    "# 'slice' is not technically correct as there are methods 'slice' which are a bit different\n",
+    "# The term used by pandas is 'selection by position'\n",
+    "# The first argument of identifies the rows we want to keep\n",
+    "# The second - the columns\n",
+    "# When choosing the columns, e.g. a:b, we will keep columns a,a+1,a+2,...,b-1 ; so column b is excluded\n",
+    "data = raw_data.iloc[:,1:3]\n",
+    "pd.set_option('display.float_format', lambda x: '%.2f' %x)\n",
+    "# for this particular case, we are choosing columns 1 and 2\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assume there are only two clusters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " #Create an object (which we would call kmeans)\n",
+    "# The number in the brackets is K, or the number of clusters we are aiming for\n",
+    "kmeans = KMeans(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=5)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=5)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit the input data, i.e. cluster the data in X in K clusters\n",
+    "kmeans.fit(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clustering Resutls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 1, 2, 2, 2, 0, 1, 0, 3, 0, 4, 0, 1, 4, 2, 0, 0, 2, 2, 2,\n",
+       "       4, 2, 0, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 4, 4, 0, 0, 2, 1, 4, 2, 0,\n",
+       "       0, 0, 3, 1, 0, 2, 1, 1, 1, 1, 2, 2, 2, 2, 0, 1, 2, 1, 2, 1, 0, 0,\n",
+       "       2, 2, 0, 2, 4, 1, 2, 2, 4, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 1, 2, 1,\n",
+       "       4, 1, 4, 0, 1, 2, 1, 2, 4, 2, 0, 4, 0, 2, 0, 0, 2, 0, 2, 1, 2, 0,\n",
+       "       4, 0, 0, 0, 0, 4, 1, 1, 4, 2, 0, 4, 0, 2, 2, 1, 2, 0, 0, 2, 2, 2,\n",
+       "       4, 1, 2, 2, 2, 0, 0, 1, 4, 2, 2, 2, 4, 2, 4, 4, 0, 2, 1, 0, 0, 4,\n",
+       "       0, 4, 2, 4, 2, 1, 3, 2, 2, 0, 4, 4, 0, 0, 1, 3, 1, 4, 4, 4, 2, 1,\n",
+       "       4, 2, 1, 0, 3, 0, 2, 4, 0, 2, 0, 0, 0, 2, 4, 1, 2, 4, 2, 1, 2, 0,\n",
+       "       0, 1, 2, 0, 1, 2, 2, 2, 0, 1, 0, 0, 1, 0, 2, 4, 0, 0, 4, 3, 1, 2,\n",
+       "       2, 4, 0, 0, 2, 1, 1, 0, 2, 1, 1, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a variable which will contain the predicted clusters for each observation\n",
+    "identified_clusters = kmeans.fit_predict(data)\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Clusters</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.98</td>\n",
+       "      <td>12.52</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.00</td>\n",
+       "      <td>33.84</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.54</td>\n",
+       "      <td>-12.29</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.06</td>\n",
+       "      <td>18.22</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.05</td>\n",
+       "      <td>41.14</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.16</td>\n",
+       "      <td>-13.75</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.59</td>\n",
+       "      <td>15.91</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.08</td>\n",
+       "      <td>-29.00</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.77</td>\n",
+       "      <td>-13.46</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>-19.00</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name  Longitude  Latitude  Clusters\n",
+       "0           Aruba     -69.98     12.52         1\n",
+       "1     Afghanistan      66.00     33.84         0\n",
+       "2          Angola      17.54    -12.29         0\n",
+       "3        Anguilla     -63.06     18.22         1\n",
+       "4         Albania      20.05     41.14         2\n",
+       "..            ...        ...       ...       ...\n",
+       "236         Samoa    -172.16    -13.75         3\n",
+       "237         Yemen      47.59     15.91         0\n",
+       "238  South Africa      25.08    -29.00         0\n",
+       "239        Zambia      27.77    -13.46         0\n",
+       "240      Zimbabwe      29.85    -19.00         0\n",
+       "\n",
+       "[241 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a copy of the data\n",
+    "data_with_clusters = raw_data.copy()\n",
+    "# Create a new Series, containing the identified cluster for each observation\n",
+    "data_with_clusters['Clusters'] = identified_clusters\n",
+    "data_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data using the longitude and the latitude\n",
+    "# c (color) is an argument which could be coded with a variable \n",
+    "# The variable in this case has values 0,1,2, indicating to plt.scatter, that there are three colors (0,1,2)\n",
+    "# All points in cluster 0 will be the same colour, all points in cluster 1 - another one, etc.\n",
+    "# cmap is the color map. Rainbow is a nice one, but you can check others here: https://matplotlib.org/users/colormaps.html\n",
+    "plt.scatter(data_with_clusters['Longitude'], data_with_clusters['Latitude'],c=data_with_clusters['Clusters'], cmap='rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Did you remember to use the <i> c </i> parameter to separate the data by the clusters we defined?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you haven't, go back and play around with the number of clusters. \n",
+    "\n",
+    "Try 3, 7 and 8 and see if the results match your expectations!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/A+Simple+Example+of+Clustering+-+Solution.ipynb b/Udemy DataScience Workbook/A+Simple+Example+of+Clustering+-+Solution.ipynb
new file mode 100644
index 0000000..fc054be
--- /dev/null
+++ b/Udemy DataScience Workbook/A+Simple+Example+of+Clustering+-+Solution.ipynb	
@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A Simple Example of Clustering "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given much more country data. Using the same methodology as the one in the lecture, group all the countries in 2 clusters. \n",
+    "\n",
+    "Try with other numbers of clusters and see if they match your expectations. Maybe 7 is going to be a cool one!\n",
+    "\n",
+    "Plot the data using the <i> c </i> parameter to separate the data by the clusters we defined.  \n",
+    "\n",
+    "<i> Note: c stands for color <i>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'Countries_exercise.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load the data\n",
+    "raw_data = pd.read_csv('Countries_exercise.csv')\n",
+    "# Check the data\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remove the duplicate index column from the dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the <i>'Longtitude'</i> and <i>'Latitude'</i> columns. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(data['Longitude'], data['Latitude'])\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90, 90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a copy of that data and remove all parameters apart from <i>Longitude</i> and <i>Latitude</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "x = data.iloc[:,1:3]\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the actual solution: \n",
+    "\n",
+    "Simply change <i> kmeans = KMeans(2) </i> to <i> kmeans = KMeans(3) </i>. \n",
+    "\n",
+    "Then run the remaining kernels until the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clustering Resutls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "identified_clusters = kmeans.fit_predict(x)\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data_with_clusters = data.copy()\n",
+    "data_with_clusters['Cluster'] = identified_clusters\n",
+    "data_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(data['Longitude'], data['Latitude'],c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90, 90)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
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+}
diff --git a/Udemy DataScience Workbook/Absenteeism_data.csv b/Udemy DataScience Workbook/Absenteeism_data.csv
new file mode 100644
index 0000000..0a8e3a9
--- /dev/null
+++ b/Udemy DataScience Workbook/Absenteeism_data.csv	
@@ -0,0 +1,701 @@
+ID,Reason for Absence,Date,Transportation Expense,Distance to Work,Age,Daily Work Load Average,Body Mass Index,Education,Children,Pets,Absenteeism Time in Hours
+11,26,07/07/2015,289,36,33,239.554,30,1,2,1,4
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+7,7,16/07/2015,279,5,39,239.554,24,1,2,0,4
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+10,22,17/07/2015,361,52,28,239.554,27,1,1,4,8
+20,23,24/07/2015,260,50,36,239.554,23,1,4,0,4
+14,19,06/07/2015,155,12,34,239.554,25,1,2,0,40
+1,22,13/07/2015,235,11,37,239.554,29,3,1,1,8
+20,1,20/07/2015,260,50,36,239.554,23,1,4,0,8
+20,1,14/07/2015,260,50,36,239.554,23,1,4,0,8
+20,11,15/07/2015,260,50,36,239.554,23,1,4,0,8
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+3,23,15/07/2015,179,51,38,239.554,31,1,0,0,4
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+3,23,17/07/2015,179,51,38,239.554,31,1,0,0,2
+3,21,27/07/2015,179,51,38,239.554,31,1,0,0,8
+6,11,30/07/2015,189,29,33,239.554,25,1,2,2,8
+33,23,05/08/2015,248,25,47,205.917,32,1,2,1,2
+18,10,12/08/2015,330,16,28,205.917,25,2,0,0,8
+3,11,03/08/2015,179,51,38,205.917,31,1,0,0,1
+10,13,10/08/2015,361,52,28,205.917,27,1,1,4,40
+20,28,14/08/2015,260,50,36,205.917,23,1,4,0,4
+11,18,17/08/2015,289,36,33,205.917,30,1,2,1,8
+10,25,24/08/2015,361,52,28,205.917,27,1,1,4,7
+11,23,04/08/2015,289,36,33,205.917,30,1,2,1,1
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+11,24,04/08/2015,289,36,33,205.917,30,1,2,1,8
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+34,23,07/09/2015,118,10,37,241.476,28,1,0,0,4
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+5,19,08/09/2015,235,20,43,241.476,38,1,1,0,8
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+33,23,01/12/2015,248,25,47,261.306,32,1,2,1,1
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+34,19,04/01/2016,118,10,37,308.593,28,1,0,0,1
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diff --git a/Udemy DataScience Workbook/Accuracy.ipynb b/Udemy DataScience Workbook/Accuracy.ipynb
new file mode 100644
index 0000000..862837f
--- /dev/null
+++ b/Udemy DataScience Workbook/Accuracy.ipynb	
@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "#Apply a fix to the statsmodels library\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('2.02. Binary predictors.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data = raw_data\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "data['Gender'] = data['Gender'].map({'Female': 1, 'Male': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data[['SAT','Gender']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "np.exp(1.9449)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)})\n",
+    "results_log.predict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.array(data['Admitted'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_log.pred_table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cm_df = pd.DataFrame(results_log.pred_table())\n",
+    "cm_df.columns = ['Predicted 0','Predicted 1']\n",
+    "cm_df = cm_df.rename(index={0:'Actual 0',1:'Actual 1'})\n",
+    "cm_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "cm = np.array(cm_df)\n",
+    "accuracy_train = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "accuracy_train"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Accuracy_with_comments.ipynb b/Udemy DataScience Workbook/Accuracy_with_comments.ipynb
new file mode 100644
index 0000000..4de858e
--- /dev/null
+++ b/Udemy DataScience Workbook/Accuracy_with_comments.ipynb	
@@ -0,0 +1,682 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "#Apply a fix to the statsmodels library\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1363</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1954</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1653</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1593</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>1722</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1555</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>1524</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>1461</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>168 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      SAT Admitted  Gender\n",
+       "0    1363       No    Male\n",
+       "1    1792      Yes  Female\n",
+       "2    1954      Yes  Female\n",
+       "3    1653       No    Male\n",
+       "4    1593       No    Male\n",
+       "..    ...      ...     ...\n",
+       "163  1722      Yes  Female\n",
+       "164  1750      Yes    Male\n",
+       "165  1555       No    Male\n",
+       "166  1524       No    Male\n",
+       "167  1461       No    Male\n",
+       "\n",
+       "[168 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('2.02.+Binary+predictors.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1363</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1954</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1653</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>1722</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1555</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>1524</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>1461</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>168 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      SAT  Admitted  Gender\n",
+       "0    1363         0       0\n",
+       "1    1792         1       1\n",
+       "2    1954         1       1\n",
+       "3    1653         0       0\n",
+       "4    1593         0       0\n",
+       "..    ...       ...     ...\n",
+       "163  1722         1       1\n",
+       "164  1750         1       0\n",
+       "165  1555         0       0\n",
+       "166  1524         0       0\n",
+       "167  1461         0       0\n",
+       "\n",
+       "[168 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "data['Gender'] = data['Gender'].map({'Female': 1, 'Male': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data[['SAT','Gender']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.120117\n",
+      "         Iterations 10\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>       <td>Admitted</td>     <th>  No. Observations:  </th>  <td>   168</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   165</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Fri, 10 Nov 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.8249</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>12:09:23</td>     <th>  Log-Likelihood:    </th> <td> -20.180</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -115.26</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.118e-42</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>  <td>  -68.3489</td> <td>   16.454</td> <td>   -4.154</td> <td> 0.000</td> <td> -100.598</td> <td>  -36.100</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>SAT</th>    <td>    0.0406</td> <td>    0.010</td> <td>    4.129</td> <td> 0.000</td> <td>    0.021</td> <td>    0.060</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Gender</th> <td>    1.9449</td> <td>    0.846</td> <td>    2.299</td> <td> 0.022</td> <td>    0.287</td> <td>    3.603</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Possibly complete quasi-separation: A fraction 0.27 of observations can be<br/>perfectly predicted. This might indicate that there is complete<br/>quasi-separation. In this case some parameters will not be identified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &     Admitted     & \\textbf{  No. Observations:  } &      168    \\\\\n",
+       "\\textbf{Model:}           &      Logit       & \\textbf{  Df Residuals:      } &      165    \\\\\n",
+       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        2    \\\\\n",
+       "\\textbf{Date:}            & Fri, 10 Nov 2023 & \\textbf{  Pseudo R-squ.:     } &   0.8249    \\\\\n",
+       "\\textbf{Time:}            &     12:09:23     & \\textbf{  Log-Likelihood:    } &   -20.180   \\\\\n",
+       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -115.26   \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 5.118e-42   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}  &     -68.3489  &       16.454     &    -4.154  &         0.000        &     -100.598    &      -36.100     \\\\\n",
+       "\\textbf{SAT}    &       0.0406  &        0.010     &     4.129  &         0.000        &        0.021    &        0.060     \\\\\n",
+       "\\textbf{Gender} &       1.9449  &        0.846     &     2.299  &         0.022        &        0.287    &        3.603     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Logit Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Possibly complete quasi-separation: A fraction 0.27 of observations can be \\newline\n",
+       " perfectly predicted. This might indicate that there is complete \\newline\n",
+       " quasi-separation. In this case some parameters will not be identified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:               Admitted   No. Observations:                  168\n",
+       "Model:                          Logit   Df Residuals:                      165\n",
+       "Method:                           MLE   Df Model:                            2\n",
+       "Date:                Fri, 10 Nov 2023   Pseudo R-squ.:                  0.8249\n",
+       "Time:                        12:09:23   Log-Likelihood:                -20.180\n",
+       "converged:                       True   LL-Null:                       -115.26\n",
+       "Covariance Type:            nonrobust   LLR p-value:                 5.118e-42\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const        -68.3489     16.454     -4.154      0.000    -100.598     -36.100\n",
+       "SAT            0.0406      0.010      4.129      0.000       0.021       0.060\n",
+       "Gender         1.9449      0.846      2.299      0.022       0.287       3.603\n",
+       "==============================================================================\n",
+       "\n",
+       "Possibly complete quasi-separation: A fraction 0.27 of observations can be\n",
+       "perfectly predicted. This might indicate that there is complete\n",
+       "quasi-separation. In this case some parameters will not be identified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "# Get the regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.992932526814459"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(1.9449)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.00, 1.00, 1.00, 0.23, 0.02, 0.99, 1.00, 1.00, 1.00, 0.01, 1.00,\n",
+       "       1.00, 0.76, 0.00, 0.60, 1.00, 0.11, 0.12, 0.51, 1.00, 1.00, 1.00,\n",
+       "       0.00, 0.01, 0.97, 1.00, 0.48, 0.99, 1.00, 0.99, 0.00, 0.83, 0.25,\n",
+       "       1.00, 1.00, 1.00, 0.31, 1.00, 0.23, 0.00, 0.02, 0.45, 1.00, 0.00,\n",
+       "       0.99, 0.00, 0.99, 0.00, 0.00, 0.01, 0.00, 1.00, 0.92, 0.02, 1.00,\n",
+       "       0.00, 0.37, 0.98, 0.12, 1.00, 0.00, 0.78, 1.00, 1.00, 0.98, 0.00,\n",
+       "       0.00, 0.00, 1.00, 0.00, 0.78, 0.12, 0.00, 0.99, 1.00, 1.00, 0.00,\n",
+       "       0.30, 1.00, 1.00, 0.00, 1.00, 1.00, 0.85, 1.00, 1.00, 0.00, 1.00,\n",
+       "       1.00, 0.89, 0.83, 0.00, 0.98, 0.97, 0.00, 1.00, 1.00, 0.03, 0.99,\n",
+       "       0.96, 1.00, 0.00, 1.00, 0.01, 0.01, 1.00, 1.00, 1.00, 0.00, 0.00,\n",
+       "       0.02, 0.33, 0.00, 1.00, 0.09, 0.00, 0.97, 0.00, 0.75, 1.00, 1.00,\n",
+       "       0.01, 0.01, 0.00, 1.00, 0.00, 0.99, 0.57, 0.54, 0.87, 0.83, 0.00,\n",
+       "       1.00, 0.00, 0.00, 0.00, 1.00, 0.04, 0.00, 0.01, 1.00, 0.99, 0.52,\n",
+       "       1.00, 1.00, 0.05, 0.00, 0.00, 0.00, 0.68, 1.00, 1.00, 1.00, 1.00,\n",
+       "       1.00, 0.00, 1.00, 1.00, 0.04, 1.00, 0.02, 1.00, 0.99, 0.97, 0.94,\n",
+       "       0.01, 0.00, 0.00])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This is a method to change the formatting of np arrays\n",
+    "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)})\n",
+    "# Should you want to go back to the default formatting, uncomment and execute the line below\n",
+    "#np.set_printoptions(formatter=None)\n",
+    "results_log.predict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1,\n",
+       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
+       "       1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0,\n",
+       "       0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
+       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0,\n",
+       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
+       "       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
+       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# An array containing the TRUE (actual) values\n",
+    "np.array(data['Admitted'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[69.00, 5.00],\n",
+       "       [4.00, 90.00]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# A prediction table (confusion matrix) showing the \n",
+    "results_log.pred_table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predicted 0</th>\n",
+       "      <th>Predicted 1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Actual 0</th>\n",
+       "      <td>69.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Actual 1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>90.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Predicted 0  Predicted 1\n",
+       "Actual 0         69.0          5.0\n",
+       "Actual 1          4.0         90.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Some neat formatting to read the table (better when seeing it for the first time)\n",
+    "cm_df = pd.DataFrame(results_log.pred_table())\n",
+    "cm_df.columns = ['Predicted 0','Predicted 1']\n",
+    "cm_df = cm_df.rename(index={0: 'Actual 0',1:'Actual 1'})\n",
+    "cm_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9464285714285714"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an array (so it is easier to calculate the accuracy)\n",
+    "cm = np.array(cm_df)\n",
+    "# Calculate the accuracy of the model\n",
+    "accuracy_train = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "accuracy_train"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Adding+and+subtracting+matrices.ipynb b/Udemy DataScience Workbook/Adding+and+subtracting+matrices.ipynb
new file mode 100644
index 0000000..9a72921
--- /dev/null
+++ b/Udemy DataScience Workbook/Adding+and+subtracting+matrices.ipynb	
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Addition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 = np.array([[5,12,6],[-3,0,14]])\n",
+    "m1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m2 = np.array([[9,8,7],[1,3,-5]])\n",
+    "m2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "m1 + m2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Difference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m3 = np.array([[5,3],[-2,4]])\n",
+    "m4 = np.array([[7,-5],[3,8]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m3 - m4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m5 = np.array([[22.33,-4.73],[-203.14,1200.02],[4.22,234.1]])\n",
+    "m5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m6 = np.array([[131.13,448.29],[-340.21,1.06],[30.41,424.99]])\n",
+    "m6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "m5 - m6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding vectors together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1 = np.array([1,2,3,4,5])\n",
+    "v2 = np.array([5,4,3,2,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "v1 + v2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1 - v2"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Admittance+regression+tables_fixed_error.ipynb b/Udemy DataScience Workbook/Admittance+regression+tables_fixed_error.ipynb
new file mode 100644
index 0000000..9b7c8f0
--- /dev/null
+++ b/Udemy DataScience Workbook/Admittance+regression+tables_fixed_error.ipynb	
@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of logistic regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "#sns.set()\n",
+    "\n",
+    "#Apply a fix to the statsmodels library\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('2.01. Admittance.csv')\n",
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = raw_data['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Get the regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Looking into LL-null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a variable only of 1s\n",
+    "const = np.ones(168)\n",
+    "const"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg_null = sm.Logit(y,const)\n",
+    "results_null = reg_null.fit()\n",
+    "results_null.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot a logistic regression curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a logit regression (we will discuss this in another notebook)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "# Fitting the regression\n",
+    "results_log = reg_log.fit()\n",
+    "\n",
+    "# Creating a logit function, depending on the input and coefficients\n",
+    "def f(x,b0,b1):\n",
+    "    return np.array(np.exp(b0+x*b1) / (1 + np.exp(b0+x*b1)))\n",
+    "\n",
+    "# Sorting the y and x, so we can plot the curve\n",
+    "f_sorted = np.sort(f(x1,results_log.params[0],results_log.params[1]))\n",
+    "x_sorted = np.sort(np.array(x1))\n",
+    "ax = plt.scatter(x1,y,color='C0')\n",
+    "#plt.xlabel('SAT', fontsize = 20)\n",
+    "#plt.ylabel('Admitted', fontsize = 20)\n",
+    "# Plotting the curve\n",
+    "ax2 = plt.plot(x_sorted,f_sorted,color='red')\n",
+    "plt.figure(figsize=(20,20))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.exp(4.20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Admittance+regression.ipynb b/Udemy DataScience Workbook/Admittance+regression.ipynb
new file mode 100644
index 0000000..5b14cf3
--- /dev/null
+++ b/Udemy DataScience Workbook/Admittance+regression.ipynb	
@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of logistic regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('2.01. Admittance.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes':1,'No':0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Admittance+regression_summary_error.ipynb b/Udemy DataScience Workbook/Admittance+regression_summary_error.ipynb
new file mode 100644
index 0000000..55eab81
--- /dev/null
+++ b/Udemy DataScience Workbook/Admittance+regression_summary_error.ipynb	
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of logistic regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "#Apply a fix to the statsmodels library\n",
+    "#from scipy import stats\n",
+    "#stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('2.01. Admittance.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes':1,'No':0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_log.summary()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Admittance.ipynb b/Udemy DataScience Workbook/Admittance.ipynb
new file mode 100644
index 0000000..67fbb07
--- /dev/null
+++ b/Udemy DataScience Workbook/Admittance.ipynb	
@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of logistic regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('2.01. Admittance.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes':1,'No':0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(x1,y,color='C0')\n",
+    "plt.xlabel('SAT',fontsize=20)\n",
+    "plt.ylabel('Admitted',fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot with a regression line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_lin = sm.OLS(y,x)\n",
+    "results_lin = reg_lin.fit()\n",
+    "\n",
+    "plt.scatter(x1,y,color = 'C0')\n",
+    "y_hat = x1*results_lin.params[1]+results_lin.params[0]\n",
+    "\n",
+    "plt.plot(x1,y_hat,lw=2.5,color='C8')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('Admitted', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot with a logistic regression curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "\n",
+    "def f(x,b0,b1):\n",
+    "    return np.array(np.exp(b0+x*b1) / (1 + np.exp(b0+x*b1)))\n",
+    "\n",
+    "f_sorted = np.sort(f(x1,results_log.params[0],results_log.params[1]))\n",
+    "x_sorted = np.sort(np.array(x1))\n",
+    "\n",
+    "plt.scatter(x1,y,color='C0')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('Admitted', fontsize = 20)\n",
+    "plt.plot(x_sorted,f_sorted,color='C8')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Admittance_with_comments.ipynb b/Udemy DataScience Workbook/Admittance_with_comments.ipynb
new file mode 100644
index 0000000..eb1aa05
--- /dev/null
+++ b/Udemy DataScience Workbook/Admittance_with_comments.ipynb	
@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of logistic regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1363</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1954</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1653</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1593</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>1722</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1555</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>1524</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>1461</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>168 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      SAT Admitted\n",
+       "0    1363       No\n",
+       "1    1792      Yes\n",
+       "2    1954      Yes\n",
+       "3    1653       No\n",
+       "4    1593       No\n",
+       "..    ...      ...\n",
+       "163  1722      Yes\n",
+       "164  1750      Yes\n",
+       "165  1555       No\n",
+       "166  1524       No\n",
+       "167  1461       No\n",
+       "\n",
+       "[168 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('2.01.+Admittance.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1363</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1954</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1653</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1593</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>1722</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1555</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>1524</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>1461</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>168 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      SAT  Admitted\n",
+       "0    1363         0\n",
+       "1    1792         1\n",
+       "2    1954         1\n",
+       "3    1653         0\n",
+       "4    1593         0\n",
+       "..    ...       ...\n",
+       "163  1722         1\n",
+       "164  1750         1\n",
+       "165  1555         0\n",
+       "166  1524         0\n",
+       "167  1461         0\n",
+       "\n",
+       "[168 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Replace all No entries with 0, and all Yes entries with 1\n",
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the dependent and independent variables\n",
+    "y = data['Admitted']\n",
+    "x1 = data['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SUqccDrsyMk40uOuvQUF2xcZGUDu1NxjUTu3UHhiaNInw+uZrvzpjBAAAUJcIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAliBvGs2YMaNWN/rnP/+5VtcHAABQG7wORjabrcYbM8bIZrMRjAAAQL3kVTBq0aJFhdONMTp8+LD7382aNdPvfvc7RUVFqaioSKmpqdq/f7+MMQoKClLPnj1rJWABAADUBa+C0RdffFHh9Pvvv1+ffvqpEhMTNXnyZF188cXl2qSmpuqJJ57Ql19+qeDgYL366qs16zEAAEAd8fnm6w8//NAdit55550KQ5EktWrVSjNnzlSfPn20du1avf322z53FgAAoC75HIzmz58vm82mCRMmKCQkpMq2rvuKjDFatGiRr5sEAACoUz4Hoz179kiSEhISvGrfpk0bSdKvv/7q6yYBAADqlM/BqFGjRpKko0ePetU+NTVVkhQdHe3rJgEAAOqUz8Gobdu2kqQ5c+Z41f61116TzWZTx44dfd0kAABAnfI5GN18880yxuiDDz7Q888/r6Kiogrb5ebm6m9/+5tWrlwpSfrDH/7g6yYBAADqlFeP61fk6quv1kcffaQ1a9bojTfe0Pz585WcnKzWrVsrPDxc+fn52rt3r7799lsVFBRIkkaOHKnevXvXWucBAABqk8/ByGaz6YUXXtDDDz+spUuXKicnp8LxjlyDO9555536y1/+UqPOAgAA1CWfg5EkhYWF6amnntLo0aP16aef6ptvvlFaWpqys7MVExOjFi1aqF+/fvr973+v1q1b11afAQAA6kSNgpFLp06d1KlTp9pYVZWcTqdmzJihBQsWKCcnR0lJSZo6dapatWpVYfvi4mK99NJLWrJkiXJyctS5c2f9/e9/10UXXVTnfQUAAP7H55uvz4aZM2dq7ty5euyxxzR//nw5nU6NGTOm0hu/H374YS1atEhPPPGEFi5cqCZNmmjs2LHKyck5wz0HAAD+oFaC0bFjx/TGG29o3Lhxuv766zVo0CD3vGnTpmnZsmU13kZRUZHeeustjR8/Xv3791dCQoKmT5+utLQ0rVixolz71NRULVy4UP/zP/+jvn37qm3btnr88ccVEhKi//u//6txfwAAQOCp8aW02bNn6+WXX1ZxcbGksputbTabe/4nn3yi2bNna968eXrppZcUGxvr03ZSUlJ04sQJj6faoqOj1bFjR23atElDhgzxaP/VV18pKipK/fr182hf2RfiAgAA1CgYPf/883rjjTdkjFFYWJg6dOigrVu3erQpKCiQMUabN2/WuHHjNG/ePI/g5K20tDRJUvPmzT2mx8XFueed7JdfflGrVq20YsUKvf766zpy5Ig6duyohx56yD04pS9cXXc4/OoqZK1w1UztDQu1U3tDQ+0Ns3YXn4PR1q1bNXv2bNlsNo0aNUoTJkyQJHXv3t2j3apVqzRt2jS9++672rp1qxYtWqQbbrih2tvLz8+XpHJfWBsaGqqsrKxy7XNzc7V//37NnDlTkyZNUnR0tF599VWNHDlSy5YtU9OmTavdB0nuUBcdHe7T8oGA2hsmam+YqL1hasi1+xyMXF8Fcs0112jKlCmSpLy8vHLtGjVqpClTpignJ0dLly7Vxx9/7FMwCgsLk1R2r5HrZ0kqLCxUeHj5HRgUFKTc3FxNnz7dfYZo+vTpuuyyy7R48WKNGTOm2n2Q/nOpMDs7X6WlTp/W4a8cDruio8OpndobDGqndmoPDNHR4V6fBfM5GG3evFk2m83rr/gYPXq0li5dqpSUFJ+257qElp6e7jEmUnp6ujp06FCufXx8vIKCgjwum4WFhalVq1Y6cOCAT32QJGPK/i4tdaqkJHAOmuqgdmpvaKid2huahly7zxcRjx07Jkm64IILvGrfsmVLSWWXuHyRkJCgyMhIbdy40T0tOztb27ZtU1JSUrn2SUlJKikp0U8//eSeVlBQoNTUVJ1//vk+9QEAAAQ2n4NRRESEJCkzM9Or9kePHpUkRUVF+bS9kJAQjRo1Ss8995xWr16tlJQUTZgwQfHx8Ro0aJBKS0t19OhR9/ey9ejRQ5deeqkefPBBbd68Wbt379akSZPkcDh03XXX+dQHAAAQ2HwORhdeeKEkaeXKlV61X7x4sSSpffv2vm5S48eP14gRIzRlyhTdcsstcjgcevPNNxUcHKzDhw+rT58+HmMmvfzyy0pOTtaf//xnjRgxQrm5uXrnnXfUpEkTn/sAAAACl8/3GF1zzTXatGmTXnnlFSUnJ6tLly6Vtl2+fLnefvtt2Ww2j8Efq8vhcGjixImaOHFiuXktW7bUjh07PKZFRkbq4Ycf1sMPP+zzNgEAQMPhczC68cYbNXfuXO3atUsjR47UkCFDlJCQ4J7/1Vdf6cCBA1q9erXWr18vY4wuuOAC3XjjjbXScQAAgNrmczAKCgrS7Nmzdfvtt+uXX37RkiVLJP1nnJ+TH4c3xqh58+aaNWtWuXGIAAAA6osaDW0ZHx+vRYsWady4cWrSpImMMeX+RERE6P/9v/+nxYsXe/0EGwAAwNlQ4+9KCw8P11//+lf99a9/1e7du5Wamqrc3FyFh4erefPmSkhIkMPhqI2+AgAA1Cmfg9GhQ4ckSS1atHBPa9eundq1a1dh+5KSEn3//fc6evSorrnmGl83CwAAUGd8DkYDBw6U3W7Xli1bKvxKjlPl5uZq9OjRatasGcEIAADUSzW6x8j1vWHecJ1hys7OrskmAQAA6sxpzxg5nU5NnDjRPXL1qcaMGSO7vep8VVxcrJ07d8pms3lcegMAAKhPThuM7Ha7Bg4cqPvvv7/cPGOMNm/eXK0NevulswAAAGeaV/cYXXvttdq5c6fS09Pd0xYvXiybzaYhQ4YoKKjq1QQFBSkmJkY9e/ZUnz59atZjAACAOuL1zdcTJkzw+Lfru88effRRr26+BgAAqO98firtz3/+syQpODi41joDAABwNtXoqbTqyM3N1eTJk/XXv/71TG0SAACgWnw+YzRjxgzZ7Xbdcccdp73HSCq7UXvx4sVq1KiRr5sEAACoU6dNNMYYHT58uMp5YWFhVa6juLhYn3zyiSR5Pe4RAADAmXbaYGSz2TRlyhT9+9//LjddKntizVs2m01dunSpZhcBAADODK/uMfrnP/+p4OBgGWNq9Cc+Pl5/+9vf6romAAAAn3h1j9H555+vBQsWKCsrS1LZJbQ//OEPstlseu2116q8lGaz2eRwOBQTE6Pzzz9fDoejdnoOAABQy7y++bpDhw4VTk9KSmIcIwAAEBB8fipt9erVkkQoAgAAAcPnYHTeeefVZj8AAADOOq+C0YwZMyRJMTExGjVqlMc0X7hGzQYAAKhPvA5GNptNrVu39ghGvo5JRDACAAD1kdeX0owxXk0DAADwV14Fo5SUFK+mAQAA+LMz9iWyAAAA9R3BCAAAwOLz4/ouRUVF+vHHH5Wenq6ioiKvlhk2bFhNNwsAAFDrahSM3nvvPb344ovKycnxehmbzUYwAgAA9ZLPwejzzz/XY489Vu3leJINAADUVz4Ho3/961+SpBYtWujhhx9Wt27dFBkZ6fPYRgAAAGebz8Fox44dstlseuSRR9S3b9/a7BMAAMBZ4fNTaXZ72aJdu3attc4AAACcTT4HowsvvFCSlJ6eXmudAQAAOJt8DkY333yzjDF6++23a7M/AAAAZ43P9xgNGzZMGzZs0MKFCxUSEqI77rhD5513Xm32DQAA4Iyq0ThGDz/8sH7++WfNmzdP8+bNU3R0tCIjI6tcxmazadWqVTXZLAAAQJ3wORgdP35ct9xyi3799VdJZeMTZWVlKSsrq8rleJwfAADUVz4Ho1deeUX79++XJMXExKhz585q2rSp+2k1AAAAf+NzMPryyy9ls9l0zTXX6Mknn1RISEht9gsAAOCM8/n0zm+//SZJuvvuuwlFAAAgIPgcjJo0aSJJio6OrrXOAAAAnE0+B6M+ffpIkr766qta6wwAAMDZ5HMwuuuuu9SoUSM9/fTTSklJqc0+AQAAnBU+33ztcDg0depUTZ06VTfccIP69Omjrl27KjY2Vo0aNapy2WHDhvm6WQAAgDrjczC6/PLL3T8bY7Ru3TqtW7futMvZbDaCEQAAqJd8DkbGmCr/DQAA4G98DkarV6+uzX4AAACcdT4HI74wFgAABBq+vwMAAMBCMAIAALB4dSntoosuqrUN2mw2bdu2rdbWBwAAUFu8Cka1+cQZT68BAID6yqtg9OSTT1Y4fevWrZo/f76ksq8IueKKK9S2bVtFRUWpqKhIqampWrdunT799FNJ0r333qukpKRa6joAAEDtshkfT+EcOHBA1113nUpKSvT888/riiuuqLTtDz/8oDFjxqikpEQLFizQhRde6HOHz7bSUqccDrsyMk6opMR5trtzRgUF2RUbG0Ht1N5gUDu1U3tgaNIkQg6Hd7dV+3zz9SuvvKK8vDzdfffdVYYiSUpMTNS9996rgoICvfLKK75uUk6nUy+99JL69u2rxMREjR07VqmpqV4t+9FHH6lDhw46cOCAz9sHAACBzedgtGHDBknSkCFDvGp/5ZVXSpI2bdrk6yY1c+ZMzZ07V4899pjmz58vp9OpMWPGqKioqMrlDh48qEcffdTn7QIAgIbB52CUk5MjSQoPD/duQ/ayTeXl5fm0vaKiIr311lsaP368+vfvr4SEBE2fPl1paWlasWJFpcs5nU5NnDhRnTp18mm7AACg4fA5GLVo0UKS92eA1qxZI0lq1aqVT9tLSUnRiRMn1Lt3b/e06OhodezYsco+zJo1S8XFxbrrrrt82i4AAGg4fP5KkEsvvVR79+7V008/rW7duikuLq7Stnv27NHzzz8vm82mQYMG+bS9tLQ0SVLz5s09psfFxbnnnerHH3/UW2+9pQ8//FBHjhzxabunstnK/vb2Jq5A4qqZ2hsWaqf2hobaG2btLj4Hoz/+8Y9auHChDh8+rN///vf64x//qH79+ql169YKDw9XXl6e9u7dq1WrVmnOnDnKy8tTXFycRo0a5dP28vPzJUkhISEe00NDQ5WVlVWufV5enh544AE98MADuuCCC2oxGJUlo+ho7y4hBiJqb5iovWGi9oapIdfuczBq0aKFXnzxRd1zzz3KzMzU9OnTNX369ArbGmMUExOj119/XTExMT5tLywsTFLZvUaunyWpsLCwwvucHn/8cbVp00Y333yzT9urjDFGNptN2dn5Ki0NnEcZveFw2BUdHU7t1N5gUDu1U3tgiI4O9/osmM/BSJL69eunjz/+WE8//bTWr1+vkpKScm3Cw8N19dVX64EHHlCTJk183pbrElp6erpat27tnp6enq4OHTqUa79w4UKFhISoW7dukqTS0lJJZU/R/elPf9Kf/vQnn/rhGvWptNQZUGM8VAe1U3tDQ+3U3tA05NprFIwk6YILLtCrr76q3Nxcbdq0SYcPH1ZOTo4aN26sFi1aKDk52X2Gp6SkRCtWrNA111xT7e0kJCQoMjJSGzdudAej7Oxsbdu2rcLLc6c+qbZ161ZNnDhRr7/+utq3b+9DpQAAINDVOBi5REZGasCAARXOO3jwoN5//30tWrRIx48f9ykYhYSEaNSoUXruuefUpEkTnXfeeXr22WcVHx+vQYMGqbS0VMePH1dUVJTCwsJ0/vnneyzvukG7RYsWPl/OAwAAga3WgtGpjDH64osvNH/+fH311Vcyxrjvz/HV+PHjVVJSoilTpqigoEBJSUl68803FRwcrAMHDujyyy/Xk08+qeHDh9diJQAAoKGo9WCUnp6uDz74wOMRedfXsYWFhWnw4ME+r9vhcGjixImaOHFiuXktW7bUjh07Kl22Z8+eVc4HAACotWC0YcMGzZ8/X2vWrFFpaalO/m7aTp066cYbb9SQIUMUGRlZW5sEAACoVTUKRsePH9fChQv1wQcfuL+c1RWIbDabbr31Vo0YMUIJCQk17ykAAEAd8ykYbdq0SfPnz9fKlStVXFzsDkNBQUHq3LmzfvjhB0nSlClTaq2jAAAAdc3rYJSTk6PFixfr/fff1969eyX95+xQQkKChg0bpt///vc6duyYhg4dWje9BQAAqENeBaPJkyfr888/V0FBgTsMNWvWTEOHDtV1113ncans2LFjddNTAACAOuZVMFq8eLFsNptiYmI0cOBAXX311br00ktltzfcL5kDAACBp1rJJjo6Wna7XSdOnFBRUVFd9QkAAOCs8OqM0U033aRly5Zp//79+vXXX/Xhhx8qNDRUAwYM0PXXX6++ffvWaOBGAACA+sCrM0aPPvqovvrqKz311FPq0aOHJKmgoECff/657rrrLvXr10/PPvusdu3aVaedBQAAqEs2c/JIjF46cOCAFi5cqKVLl+rQoUNlK7LOGLVs2VKpqamy2Wzavn177fa2HigtdcrhsCsj40SD++bhoCC7YmMjqJ3aGwxqp3ZqDwxNmkTI4fDu7iGf7p5u2bKl/vrXv2r16tV68803dfXVVys4OFjGGHcokqTRo0dr0aJFys3N9WUzAAAAZ5RPZ4wqkp2drY8//lgLFy7Utm3bylZuBaTQ0FANHDhQ1113nfr27evXT7Nxxijw/ifhDWqndmpvOKg98GqvzhmjWgtGJ9uxY4cWLFigTz75RJmZmWUbskJS06ZNtWHDhtre5BlDMAq8N4w3qJ3aqb3hoPbAq73OL6WdTocOHTRlyhStX79eL774ovr16ye73S5jDANAAgCAeqtGXyJ7OsHBwbrqqqt01VVX6ciRI1q8eLEWL15cl5sEAADw2Rm72efcc8/Vn/70Jy1fvvxMbRIAAKBa/PcuaAAAgFpGMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACx+FYycTqdeeukl9e3bV4mJiRo7dqxSU1Mrbb9r1y7deeed6tmzp3r37q3x48fr0KFDZ7DHAADAn/hVMJo5c6bmzp2rxx57TPPnz5fT6dSYMWNUVFRUrm1GRoZuv/12hYWFac6cOZo9e7aOHz+uMWPGqLCw8Cz0HgAA1Hd+E4yKior01ltvafz48erfv78SEhI0ffp0paWlacWKFeXar1q1Snl5eXrmmWfUvn17de7cWc8++6z27Nmj77777ixUAAAA6ju/CUYpKSk6ceKEevfu7Z4WHR2tjh07atOmTeXa9+7dWzNnzlRYWJh7mt1eVm52dnbddxgAAPidoLPdAW+lpaVJkpo3b+4xPS4uzj3vZC1btlTLli09pr3++usKCwtTUlKSz/2w2cr+djj8JlPWGlfN1N6wUDu1NzTU3jBrd/GbYJSfny9JCgkJ8ZgeGhqqrKys0y4/Z84cvfvuu5oyZYqaNGnicz9sVjKKjg73eR3+jtobJmpvmKi9YWrItftNMHJdEisqKvK4PFZYWKjw8Mp3oDFGL774ol599VWNGzdOo0ePrlE/jDGy2WzKzs5XaamzRuvyNw6HXdHR4dRO7Q0GtVM7tQeG6Ohwr8+C+U0wcl1CS09PV+vWrd3T09PT1aFDhwqXKS4u1uTJk/XJJ59o8uTJuu2222rcD2PK/i4tdaqkJHAOmuqgdmpvaKid2huahly731xETEhIUGRkpDZu3Oielp2drW3btlV6z9CkSZP0+eef6/nnn6+VUAQAAAKb35wxCgkJ0ahRo/Tcc8+pSZMmOu+88/Tss88qPj5egwYNUmlpqY4fP66oqCiFhYVp0aJFWrZsmSZNmqTk5GQdPXrUvS5XGwAAgJP5zRkjSRo/frxGjBihKVOm6JZbbpHD4dCbb76p4OBgHT58WH369NGyZcskSZ988okk6ZlnnlGfPn08/rjaAAAAnMxmjOuuGXijtNQph8OujIwTDe76a1CQXbGxEdRO7Q0GtVM7tQeGJk0ivL752q/OGAEAANQlghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYAk62x2oDqfTqRkzZmjBggXKyclRUlKSpk6dqlatWlXYPiMjQ48//rjWrVsnm82ma6+9VpMmTVJ4ePgZ7nnVnE6jnamZyjxRqJiIULVvFSO73ebTelL2ZyglNUMycq8nO69I0eEhkk3Kzisqt41Tl0s4P1btW8Zo98Esjz45nUY/7f5NqYezFBUeXH4dv2YoZX+GZJMSWsUq4fxYSSpX28nTosKClXo0V79lFSguJlwDL2mpoKCq8/rJr1d0oxA5S412HsyUs9Qor7BENpt0Tky4Wp4Tqez8ImXmFOrAkRMqKC6RMVJMZLDsdrvatmismKhQ9/IyUodWMTJW/06u49Taf9c8Wmt+OKgjGXmSpLYtGqtJdJj7NXH18XhugXJPFCsyPFi5+cWKjAhWdHiIDlRSc0mJU198d0DpmfmKiwlX/8TzlPJrhpZvTlVeQYnOj4vQOTGNtPdwtkJDHOp10bkKctiVmVeo3BPFimoUotio8q9zTESo2p3XWDsPZHrso/at/rOfo8NDVFrq1L+3HVFhcakubNlYV3RvVW5/VOd4raitJG375bi+3pamwqLKt3OykhKnVm1J1a4DWQoNdujSzvHqeEETd40Vvc4xjULdx3x0oxDJSNn5FR//O1MzlZFTqJy8IjVqFKS8vBJFNQpRs5gw9WzcqMrjEUBgsRljzNnuhLdmzJihd999V0899ZTi4+P17LPP6sCBA/r4448VEhJSrv3o0aOVn5+vRx55RNnZ2fr73/+upKQkPf300z73obTUKYfDroyMEyopcdakHEnSlh3pmrtqlzJyCt3TYqNCNfKKC9W9Q1y11vOvz1J0oqDEq/aubUiqcDmbpJMPjIiwINlsNuXmF3u9jtBgu4Icdo/pEWFlWbyyftps0lVJrXTTwAsrrfPU16uuhQbbFRzk8Ki9MrFRoep5UZw2bk/3uo+umiVp+aZU1cY7sqLX+dR96lXfJA3ufb7uHpGojIwT2vhzmtfHa0X7KiIsSIXFpSop9eyJTdJVyRXv9w++2KXl36aW63uQw6bQYIfXx/zJTj52T3c8NW0cppFXtle3ds2qvR1/FhRkV2xsRK191vkTag+82ps0iZDD4d1FMr8JRkVFRerVq5ceeOABjRw5UpKUnZ2tvn376n/+5380ZMgQj/bff/+9br75Zi1btkxt27aVJG3YsEFjxozR2rVrde655/rUj9oMRlt2pOuVxf9X6fx7ru/sVTg63Xr80eAKfkkGYp3+Ynj/tjqvWYRe/vDHStucfLz6uq9O3e8ffLFLn3+bWv0O1wFv34+BIlB/QXqD2gOv9uoEI7+5xyglJUUnTpxQ79693dOio6PVsWNHbdq0qVz7zZs365xzznGHIklKTk6WzWbTli1bzkifq+J0Gs1dtavKNvNW7ZLTWXVudTqN3lu5sza7Vi8s35Tq8ab05vVC3Vm8do/mfJ5SZRvX8VqTfXXyfi8pcWr5pvoRiiTv3o8A/J/f3GOUlpYmSWrevLnH9Li4OPe8kx05cqRc25CQEMXExOjw4cM+98Nm3UrhbfKszPZ9x097qeV4TqH2HMrSRda9FJWtJzO3qEZ9qY+Mkdb8cFCDe50vybvXC3XHGJ32OHMdr5J83lcn7/dVm2vnsmJt8eb9GEhcn3E1/azzR9TeMGt38ZtglJ+fL0nl7iUKDQ1VVlZWhe0ruu8oNDRUhYW+/4K1WckoOrpmN3AX/5LhXTtjU2xsRI3X44+y8kvctQdynYGk2FT/oYFTufZ7Vn717x2qa6d7Pwaimn7W+TNqb5j8JhiFhYVJKrvXyPWzJBUWFlb4lFlYWJiKisr/D7ewsFCNGvn+lIkxRjabTdnZ+Sot9f36a7DNu/8KB9uMMjJO1Hg9/qhxeJC79kCuM5DUxn5y7ffG4fXv4+l078dA4nDYFR0dXuPPOn9E7YFXe3R0uNdnwerfJ08lXJfF0tPT1bp1a/f09PR0dejQoVz7+Ph4rVq1ymNaUVGRMjMzFRfn+w2UrlP7paXOGt2Y1rZFY8VGhVZ5yaFJVKjatmhc5XbatmismMiQgLucZrNJ/RPPc9fuzeuFumOzSY0jqj7OXMerJJ/31cn7vX/ieZq3ele9uZzmzfsxENX0s86fUXvDrN1vLiImJCQoMjJSGzdudE/Lzs7Wtm3blJSUVK59UlKS0tLStH//fve0b7/9VpLUvXv3uu/wadjtNvfjwpW55YoLTzuekd1u061Xtq/NrtULVyV5jmvjzeuFunP9ZW01enBClW1cx2tN9tXJ+z0oyO4exqA+8Ob9CMD/+U0wCgkJ0ahRo/Tcc89p9erVSklJ0YQJExQfH69BgwaptLRUR48eVUFBgSTp4osv1iWXXKIJEyboxx9/1DfffKOpU6dq2LBhPj+qX9u6d4jTPdd3VmxUqMf0JlGh1Xo02LUe19g13nBto7LlTv34jwwLUmR4cLXWERbiKDc9Miyoyn7abBU/qi9V/nrVtbBge7naK9MkKlSDk1tVq4+umgcnt3Lf3F9TkeHB5V5nX1Ztk3R17/N1+9DOSkrw/nitbF9FhgcryFG+JzZVvN9vGnhh2etSQd+Cg+zVOuYr6rM3x1OzmHD9ZUTXBvWoPtCQ+c04RpJUWlqqadOmadGiRSooKHCPfN2yZUsdOHBAl19+uZ588kkNHz5cknTs2DE98sgjWr9+vUJDQzV48GBNnjxZoaG+/2Kt7QEeJf8Z+TooyK5DGQUNcuTrU2tvSCNfh4UFeYxr0uBGvr64pbKz8hrcZYVAHc/GG9QeeLUH5ACP9UVdBCN/EahvGG9QO7VTe8NB7YFXe0AO8AgAAFDXCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWBj5upqMMbLZbCotDZwRQavD4bBTewNE7dTe0FB7YNVut9tk8/LLKAlGAAAAFi6lAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGkl577TWNHj3aY9qyZcs0dOhQde3aVVdccYVmz54tY4x7vtPp1EsvvaS+ffsqMTFRY8eOVWpqqsc6tm/frlGjRikxMVEDBw7UO++8c0bqqY6Kaj/ZlClTNHDgQI9pgVz7lClT1KFDB48/J9cfyLWnp6frvvvuU48ePdSzZ0/df//9On78uEeb9957T5dffrm6du2qkSNHatu2bR7zDxw4oLvuukuXXHKJ+vTpoxdeeEGlpaV1Xk91nFr76NGjy+1z158lS5a42wVi7ZL0888/a/To0erWrZv69++v5557TkVFRe75gXzMr1+/XjfccIO6deumoUOH6pNPPvGYX1hYqEceeUS9e/dWt27dKnxP/Pvf/9bw4cN18cUXa/Dgwfr000/rvBZvZGZmaurUqerXr58uueQS3XLLLdq8ebN7/un67c+115hp4N59912TkJBgRo0a5Z62bt06c9FFF5l33nnH/Prrr2b58uUmMTHR/Otf/3K3efnll03Pnj3Nl19+abZv327++Mc/mkGDBpnCwkJjjDHHjx83PXv2NJMnTza7d+82H374oenSpYv58MMPz3iNlamo9pOtXLnStG/f3gwYMMBjeiDXPmLECDNt2jSTnp7u/nPs2DH3/ECtvbCw0Fx77bXmv//7v83PP/9sfvjhB3PNNdeYMWPGuNssWrTIdO3a1SxdutTs2rXLTJw40SQnJ7tfn6KiIjNo0CBz5513mh07dpiVK1ea5ORk8+KLL57xGitTUe0ZGRke+/vIkSNm5MiR5tprrzW5ubnGmMCt/fjx4yY5OdlMnTrV7Nu3z6xbt8707t3bPP300+42gXrMb9682XTo0ME8+uijZvfu3eaTTz4x3bp1M4sXL3a3eeihh8wVV1xhNm3aZLZu3WqGDRtmbr31Vvf83bt3my5duphp06aZ3bt3mzfeeMN07NjRfP3112eyvArdfvvtZsiQIWbTpk1m79695pFHHjFdu3Y1e/bs8arf/lx7TTXYYJSWlmbuuusuk5iYaAYPHuzxhlm4cKGZPn26R/u7777bjB071hhT9kukW7du5r333nPPz8rKMl27djUff/yxMcaYWbNmmT59+pji4mJ3m+eff94MGjSoDqvyTlW1uxw5csT06tXLjBo1yiMYBXLtTqfTJCYmmhUrVlS4bCDXvnDhQpOYmGiOHj3qnrZu3Tpz+eWXm5ycHGOMMYMGDTLPPPOMe35xcbG57LLLzKxZs4wxxnz88cemc+fOJjMz091m/vz55pJLLnH/Ej1bvDnmXebMmWM6d+5s9uzZ454WqLW7/vPj2sfGGPPEE0+YIUOGGGMC+5gfN26cufHGGz3az5w50/15l5aWZhISEsyaNWvc8/fu3Wvat29vvvvuO2OMMf/4xz/MiBEjPNZx3333mT/+8Y91VZJX9u3bZ9q3b282b97snuZ0Os0VV1xhXnjhhdP2259rrw0N9lLazz//rODgYH300Ue6+OKLPeYNHz5c9957r6Sy08hff/21Nm3apP/6r/+SJKWkpOjEiRPq3bu3e5no6Gh17NhRmzZtkiRt3rxZycnJCgoKcrfp1auX9u3bp99++62Oq6taVbVLkjFGDz30kK677jolJyd7zAvk2n/99Vfl5eXpd7/7XYXLBnLtGzZsUK9evdSsWTP3tL59+2rVqlWKjIzUsWPHtG/fPo/ag4KC1KNHD4/aO3XqpMaNG7vb9OrVS7m5udq+fXsdV1e10x3zLsePH9cLL7ygcePGuY+DQK69SZMmkqR58+aptLRUBw4c0Nq1a93tAvmY379/v7p37+4xrWPHjjp48KAOHTqkLVu2SCqrxaVNmzY699xzPWo/+bVxtd+yZYvHrRdnWmxsrF5//XV16dLFPc1ms8lmsyk7O/u0/fbn2mtD0OmbBKaBAweWu3fmVIcOHdKVV16pkpIS9enTR7fccoskKS0tTZLUvHlzj/ZxcXHueWlpaWrfvn25+ZJ0+PBhj19AZ9rpav/Xv/6lo0ePatasWXrttdc85gVy7Tt37pQkzZkzR+vWrZPdble/fv00YcIERUVFBXTtv/zyi3r06KFXXnlFS5YscR/zEydOVHR0dJW1p6SkSCqrPT4+vtx8qaz2qgJJXfPm/S5Js2fPVlhYmO644w73tECu/ZJLLtG4ceP04osvavr06SotLVWvXr00depUSYH9fo+Li9Phw4c9ph04cEBSWRg+cuSIYmNjFRoaWm65k2uvaL/n5+crIyPDHTzPtOjoaF122WUe05YvX679+/frb3/7mxYvXlxlv/259trQYM8YeSM6OloLFizQCy+8oJSUFE2aNEmSlJ+fL0kKCQnxaB8aGqrCwkJJUkFBQYXzJbnb1EcpKSmaMWOGnn322XL9lwK79p07d8putysuLk6zZs3SQw89pA0bNujuu++W0+kM6Npzc3O1ZMkS7dixQ88//7weffRRbdmyRXfffbeMMQFdu0tubq4++OAD3XHHHR6/EAK59tzcXO3du1e33nqrFixYoBdffFH79u3TP/7xD0mBXft1112nFStW6KOPPlJJSYm2b9+ut956S5JUXFys/Pz8Cj8DT1e7698n38B+tn333XeaPHmyBg0apP79+5+234FUuy8a7Bkjb0RGRqpjx47q2LGjSktLdf/992vixIkKCwuTVLbzXT9LZR8C4eHhkqSwsLByB4frgGrUqNEZqqB6CgsL9cADD2jcuHFKSEiosE2g1i5J48aN08iRIxUbGytJat++vc455xzddNNN+umnnwK69qCgIDVq1EjPP/+8goODJUmNGzfWjTfeWK72kwVC7S6rVq1SUVGRbrjhBo/pgVz7s88+q6ysLL300kuS5L4ceNttt+m2224L6GN+2LBhOnjwoP7xj3/owQcfVPPmzTV27Fg9/PDDioqKqrAuybP20NDQcm1c/3a1OdtWrVqlBx54QJdccomee+45Safvd6DU7ivOGFVg8+bN+vHHHz2mdejQQVLZI82u08rp6ekebdLT03XuuedKkuLj4yucL8ndpr7ZunWrdu3apRkzZqhbt27q1q2bXnvtNR06dEjdunXT5s2bA7Z2SbLb7e5Q5HLhhRdKKjttHMi1x8fHq02bNu5QJP2n9gMHDgR07S6rVq3SZZddpujoaI/pgVz7li1bPO5DkeS+7Ldv376Arl2S7rnnHn333Xdas2aNVq1apRYtWsjhcKhFixaKj49XZmZmuV/+J9fevHnzCmtv1KiRoqKizlgdlXn33Xf1l7/8RQMGDNCsWbPcZ/JO1+9AqL0mCEYVeOedd/TEE094TNu6dauCgoJ0wQUXKCEhQZGRkdq4caN7fnZ2trZt26akpCRJUlJSkrZs2eIxjsk333yjNm3aqGnTpmemkGrq2rWrVqxYoaVLl2rJkiVasmSJbr75ZsXFxWnJkiXq3LlzwNYuSZMmTdJtt93mMe2nn36SJLVr1y6ga09KSlJKSooKCgrc01z3XJ1//vlq2rSp2rRp41F7SUmJNm/e7FH7tm3blJub627zzTffKCIiotIzkPVJRTeTSgro2s8991zt2LHDY5rr323atAnoY/7dd9/VY489JofDoXPPPVd2u13Lly9Xt27dFBERoe7du8vpdLpvRJbK7sU7cuSIu/YePXro22+/9VjvN998o0suuUR2+9n99Tp37lw99thjuvXWWzVt2jSPy16n67e/115jZ/WZuHriwQcf9HiMc8uWLeaiiy4y06ZNM/v27TPLli0zycnJ5qmnnnK3mTZtmklOTjarVq3yGNujqKjIGGPMb7/9ZpKSksyDDz5odu3aZRYuXGi6dOliFi1adMbrq8qptZ/qpZdeKjeOUaDWvmrVKtO+fXvz8ssvm/3795s1a9aYgQMHmvvuu8/dJlBrP3bsmLn00kvN3XffbXbu3Gk2b95shgwZYkaPHu1u8/7775uuXbuaRYsWucfy6dmzp3ssn4KCAnPFFVeYO+64w2zfvt09ls/LL798xuurSkXH/KFDh8o93nyyQK193bp1pkOHDmb69Olm//795uuvvzZXXXWVufPOO91tAvWY//rrr03Hjh3N4sWLTWpqqnnttddMp06dzMaNG91t7rvvPjNw4EDzzTffuMfyOXkdO3fuNJ06dTLPPvus2b17t3nzzTfrxVg+e/fuNZ06dTL33HOPxxhd6enpJjs726t++2vttYFgZCr+oFy3bp0ZPny46dq1q+nfv7+ZNWuWKS0tdc8vKSkxzzzzjOnVq5dJTEw0Y8eONampqR7r2Lp1q7nppptM586dzYABA8ycOXPOSD3V4UswCuTaly1bZoYNG2a6du1q/uu//ss89dRTpqCgwD0/kGv/5ZdfzJ133mkuvvhik5SUZCZPnmyysrI82rzxxhumX79+pmvXrmbkyJFm27ZtHvP37dtnbr/9dtOlSxfTp08f88ILL3i8b+qDimrfunWrad++vdm9e3elywVq7WvWrDE33nijSUxMNAMGDDBPPPGEOXHihHt+IB/zCxYsMFdeeaXp2rWrGT58uFm3bp3H/BMnTpi///3vpkePHqZHjx7mvvvuM8ePH/dos3btWjNkyBDTuXNnM3jwYPPpp5/WeS2n8+qrr5r27dtX+OfBBx80xpy+3/5ae22wGePnAw4AAADUEj+/EAgAAFB7CEYAAAAWghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIUvkQXgd0pKSrRs2TJ98cUX+umnn3T8+HFJUmxsrNq1a6e+ffvq+uuvV2RkpFfrO3LkiPr37y+n0ymbzaaVK1eqVatW5dotWrRIkydP9rnfTz75pIYPH+7z8gDqHsEIgF/ZsWOH7rvvPu3evbvcvLy8PB08eFBr167VzJkzNXXqVF199dWnXeeiRYvkdDolScYYvf/++3rggQdqve8A6j9GvgbgN44cOaIbbrhBR48eVbNmzTR27Fj17t1bcXFxstlsOnLkiL766iu9/vrrysjIkMPh0MyZM9W/f/8q1zto0CDt379fiYmJ+uGHH9S0aVOtWbPG44s3pbIzVYWFhRWuY8iQITp06JC6d++u2bNnV9gmNDRUQUH8fxSoz3iHAvAbs2fP1tGjRxUTE6MFCxaoRYsWHvNjYmLUoUMHDRw4UCNGjFBOTo6efPJJ9evXr9Jv/P7222+1f/9+SdKkSZM0cuRIHTt2TCtXrtS1117r0TYoKKjSYGOz2SRJDodDERERNS0VwFnCzdcA/MaXX34pSRo6dGi5UHSyCy64QH/6058kSfv27dPPP/9cadtFixZJktq1a6fu3bsrMTFRkjR//vxa6jUAf0IwAuA3jh49KkkqKCg4bdv+/furQ4cO6tWrl0pKSipsk5ubq+XLl0uSrrzySknS73//e0llZ5L27NlTG90G4EcIRgD8RsuWLSVJn376qbZt21Zl23bt2umjjz7S22+/rW7dulXY5rPPPlNeXp6ksnuEJOmaa65RcHCwJM4aAQ0RwQiA37j++usllT19dtNNN+nee+/VZ599poyMDJ/Wt3DhQklSly5d1K5dO0llj/xfdtllkqSlS5d6dXYKQOAgGAHwG7fffrv69esnSSouLtZnn32me++9V71799bQoUP18MMP67PPPlNOTs5p17Vnzx59//33kv4TuFxc/87KytKyZctquQoA9RnBCIDfCAoK0qxZs/TAAw8oOjraPd0Yo507d2revHnuoDR+/Hj302YVcd10HRwcXO7ps8suu0yxsbGSuJwGNDQEIwB+xeFwaOzYsVq/fr1efvll3XDDDTrvvPM82hQXF2v58uUaMmSIPv7443LrKC0t1dKlSyVJAwYMUExMjMf84OBg9z1HW7du1fbt2+umGAD1DuMYAfBLYWFhGjRokAYNGiRJOnz4sDZu3Kj169friy++UF5enoqKivTQQw+pdevWuvjii93Lrl271v2EW2Jionbu3Flu/Z07d3b/PG/ePD366KN1XBGA+oCRrwEEnMzMTD399NPuy2UDBw7Uq6++6p5/zz33aNWqVV6vr1GjRlq/fn2V3702cOBAHTx4UMnJyZozZ47vnQdwVnEpDYBfWLZsmZ555hnNmDHjtG1jYmL05JNPqkuXLpKkH3/80T3v2LFjWrt2bbW2nZeXV+ElOQCBh0tpAPzC559/ruXLl6tRo0a68847y32PWUWSkpL0008/eTxy/9FHH6m4uFiS9Pbbb6tXr16VLv/bb7/psssuU0lJid5//33dcsstNS8EQL3GGSMAfqF79+6Sys7euC6Rnc6vv/4qSbrwwgvd01zLtmjRQj179qxy+WbNmrm/gHb79u364YcfqtlrAP6GYATAL1x33XVq3LixJOmpp57Shg0bqmy/du1affHFF5Kkm2++WVLZJTXXjdZDhw51f/FrVUaMGOH+mUf3gcBHMALgF2JiYjRt2jQFBwcrPz9fd9xxh+6++24tW7ZM+/btU1ZWlg4dOqS1a9dq0qRJGjdunJxOpwYMGKChQ4dKkj788EP3+q677jqvttuvXz/FxcVJKvsKkaysrNovDkC9wT1GAPxGnz59NHv2bP3zn//U/v37tXr1aq1evbrCtjabTTfeeKOmTJkih8OhgoIC9yjWnTt3Vtu2bb3apsPh0PDhwzVr1iwVFBRo8eLFuu2222qrJAD1DMEIgF/p3bu3PvnkE61cuVLr16/XTz/9pOPHjysnJ0cRERGKj493f0VIp06d3MutWLHC/VUh3p4tcrnhhhv02muvyRij999/n2AEBDDGMQIAALBwjxEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFj+P7xw2VGF4lU3AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot of x1 (SAT, no constant) and y (Admitted)\n",
+    "plt.scatter(x1,y, color='C0')\n",
+    "# Don't forget to label your axes!\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('Admitted', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot with a regression line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_1632\\4235221287.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  y_hat = x1*results_lin.params[1]+results_lin.params[0]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a linear regression on the data in order to estimate the coefficients and be able to plot a regression line\n",
+    "# The data is not linear, so the linear regression doesn't make much sense\n",
+    "x = sm.add_constant(x1)\n",
+    "# I'll call it reg_lin, instead of reg, as we will be dealing with logistic regressions later on\n",
+    "reg_lin = sm.OLS(y,x)\n",
+    "# I'll segment it into regression and fitted regression (results) as I can use the results as an object for some operations\n",
+    "results_lin = reg_lin.fit()\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "plt.scatter(x1,y,color = 'C0')\n",
+    "# Plot the regression line. The coefficients are coming from results_lin.params\n",
+    "y_hat = x1*results_lin.params[1]+results_lin.params[0]\n",
+    "plt.plot(x1,y_hat,lw=2.5,color='C8')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('Admitted', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot a logistic regression curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.137766\n",
+      "         Iterations 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_1632\\1901913320.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  f_sorted = np.sort(f(x1,results_log.params[0],results_log.params[1]))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "\n",
+    "def f(x,b0,b1):\n",
+    "    return np.array(np.exp(b0+x*b1) / (1 + np.exp(b0+x*b1)))\n",
+    "\n",
+    "f_sorted = np.sort(f(x1,results_log.params[0],results_log.params[1]))\n",
+    "x_sorted = np.sort(np.array(x1))\n",
+    "\n",
+    "plt.scatter(x1,y,color='C0')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('Admitted', fontsize = 20)\n",
+    "plt.plot(x_sorted,f_sorted,color='C8')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.137766\n",
+      "         Iterations 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_1632\\3106139542.py:11: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  f_sorted = np.sort(f(x1,results_log.params[0],results_log.params[1]))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a logit regression (we will discuss this in another notebook)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "# Fitting the regression\n",
+    "results_log = reg_log.fit()\n",
+    "\n",
+    "# Creating a logit function, depending on the input and coefficients\n",
+    "def f(x,b0,b1):\n",
+    "    return np.array(np.exp(b0+x*b1) / (1 + np.exp(b0+x*b1)))\n",
+    "\n",
+    "# Sorting the y and x, so we can plot the curve\n",
+    "f_sorted = np.sort(f(x1,results_log.params[0],results_log.params[1]))\n",
+    "x_sorted = np.sort(np.array(x1))\n",
+    "plt.scatter(x1,y,color='C0')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('Admitted', fontsize = 20)\n",
+    "# Plotting the curve\n",
+    "plt.plot(x_sorted,f_sorted,color='C8')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final exercise\n",
+    "\n",
+    "Based on everything you have seen in the course (including the preprocessing exercise in this section), try to achieve a better accuracy. Obviously, there is no 'correct' answer to this exercise. It depends solely on your own motivation and willingness to become better at machine learning. \n",
+    "\n",
+    "Don't be shy to post your result in the Q&A section, where you can discuss your results with other students.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problem\n",
+    "\n",
+    "You are given data from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If we can focus our efforts SOLELY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) ,Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (0 or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing Libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Create a class that will batch the data\n",
+    "\n",
+    "Whenever you want to batch the data you need to have appropriate methods. There are some batching methods integrated in TensorFlow and sklearn, but some problems may need specific coding. \n",
+    "\n",
+    "Here we show how these methods look like. You can use them for any machine learning framework you need (directly or after little fine-tuning)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Create a class that will do the batching for the algorithm\n",
+    "# This code is extremely reusable. You should just change Audiobooks_data everywhere in the code\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    # Dataset is a mandatory arugment, while the batch_size is optional\n",
+    "    # If you don't input batch_size, it will automatically take the value: None\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "    \n",
+    "        # The dataset that loads is one of \"train\", \"validation\", \"test\".\n",
+    "        # e.g. if I call this class with x('train',5), it will load 'Audiobooks_data_train.npz' with a batch size of 5.\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        \n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n",
+    "        \n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "    \n",
+    "    # A method which loads the next batch\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "            \n",
+    "        # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "        \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already but we're giving you the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "        \n",
+    "        # The function will return the inputs batch and the one-hot encoded targets\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "    \n",
+    "        \n",
+    "    # A method needed for iterating over the batches, as we will put them in a loop\n",
+    "    # This tells Python that the class we're defining is iterable, i.e. that we can use it like:\n",
+    "    # for input, output in data: \n",
+    "        # do things\n",
+    "    # An iterator in Python is a class with a method __next__ that defines exactly how to iterate through its objects\n",
+    "    def __iter__(self):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n",
+    "We will create an algorithm which is essentially copy-pasting the MNIST code and we will simply adjust where needed.\n",
+    "Once more, I will put the whole code in one piece as we can simply rerun the cell and train a new model. That's because the whole algorithm is contained in the cell and we have the tf.reset_default_graph() function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "`tape` is required when a `Tensor` loss is passed. Received: loss=KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='tf.math.reduce_mean_2/Mean:0', description=\"created by layer 'tf.math.reduce_mean_2'\"), tape=None.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\Audiobooks_Machine_learning_Homework.ipynb Cell 9\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m accuracy \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mreduce_mean(tf\u001b[39m.\u001b[39mcast(out_equals_target, tf\u001b[39m.\u001b[39mfloat32))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m \u001b[39m# Optimization Operation:\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m \u001b[39m# Define the optimization operation using the Adam optimizer.\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m optimizer \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39;49mkeras\u001b[39m.\u001b[39;49moptimizers\u001b[39m.\u001b[39;49mAdam(learning_rate\u001b[39m=\u001b[39;49m\u001b[39m0.0001\u001b[39;49m)\u001b[39m.\u001b[39;49mminimize(mean_loss, var_list\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m \u001b[39m'''\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m \u001b[39m#optimize = optimizer.minimize(mean_loss)\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m \u001b[39mgrads_and_vars = optimize.compute_gradients(mean_loss)\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=50'>51</a>\u001b[0m \u001b[39m#opt.apply_gradients(zip(grads, [var1, var2]))\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=51'>52</a>\u001b[0m \u001b[39m# Session Initialization:'''\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Audiobooks_Machine_learning_Homework.ipynb#X11sZmlsZQ%3D%3D?line=52'>53</a>\u001b[0m \u001b[39m# Create a TensorFlow session and initialize the variables.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\optimizers\\optimizer.py:543\u001b[0m, in \u001b[0;36m_BaseOptimizer.minimize\u001b[1;34m(self, loss, var_list, tape)\u001b[0m\n\u001b[0;32m    522\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mminimize\u001b[39m(\u001b[39mself\u001b[39m, loss, var_list, tape\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    523\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Minimize `loss` by updating `var_list`.\u001b[39;00m\n\u001b[0;32m    524\u001b[0m \n\u001b[0;32m    525\u001b[0m \u001b[39m    This method simply computes gradient using `tf.GradientTape` and calls\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    541\u001b[0m \u001b[39m      None\u001b[39;00m\n\u001b[0;32m    542\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 543\u001b[0m     grads_and_vars \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcompute_gradients(loss, var_list, tape)\n\u001b[0;32m    544\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_gradients(grads_and_vars)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\optimizers\\optimizer.py:262\u001b[0m, in \u001b[0;36m_BaseOptimizer.compute_gradients\u001b[1;34m(self, loss, var_list, tape)\u001b[0m\n\u001b[0;32m    244\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Compute gradients of loss on trainable variables.\u001b[39;00m\n\u001b[0;32m    245\u001b[0m \n\u001b[0;32m    246\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[39m  gradient can be `None`.\u001b[39;00m\n\u001b[0;32m    260\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    261\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mcallable\u001b[39m(loss) \u001b[39mand\u001b[39;00m tape \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 262\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    263\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m`tape` is required when a `Tensor` loss is passed. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    264\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mReceived: loss=\u001b[39m\u001b[39m{\u001b[39;00mloss\u001b[39m}\u001b[39;00m\u001b[39m, tape=\u001b[39m\u001b[39m{\u001b[39;00mtape\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    265\u001b[0m     )\n\u001b[0;32m    266\u001b[0m \u001b[39mif\u001b[39;00m tape \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    267\u001b[0m     tape \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mGradientTape()\n",
+      "\u001b[1;31mValueError\u001b[0m: `tape` is required when a `Tensor` loss is passed. Received: loss=KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='tf.math.reduce_mean_2/Mean:0', description=\"created by layer 'tf.math.reduce_mean_2'\"), tape=None."
+     ]
+    }
+   ],
+   "source": [
+    "# Defining Hyperparameters\n",
+    "# Set the input size, output size, and the size of the hidden layer\n",
+    "input_size = 10\n",
+    "output_size = 2\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Creating Input Placeholders:\n",
+    "# Create input placeholders using tf.keras.Input. This is part of the TensorFlow 2.x Keras API.\n",
+    "inputs = tf.keras.Input(shape=(input_size,), dtype=tf.float32)\n",
+    "targets = tf.keras.Input(shape=(output_size,), dtype=tf.int32)\n",
+    "\n",
+    "# Defining Neural Network Architecture:\n",
+    "# Define a neural network with two hidden layers using the Keras functional API.\n",
+    "hidden1 = tf.keras.layers.Dense(hidden_layer_size, activation='relu')(inputs)\n",
+    "hidden2 = tf.keras.layers.Dense(hidden_layer_size, activation='relu')(hidden1)\n",
+    "output_layer = tf.keras.layers.Dense(output_size, activation='softmax')(hidden2)\n",
+    "\n",
+    "# Compiling the Model:\n",
+    "# Create and compile the model using the Adam optimizer and sparse categorical crossentropy loss.\n",
+    "model = tf.keras.Model(inputs=[inputs, targets], outputs=output_layer)\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Defining Loss and Accuracy Operations\n",
+    "\n",
+    "loss = tf.nn.softmax_cross_entropy_with_logits(logits=output_layer, labels=targets)\n",
+    "mean_loss = tf.reduce_mean(loss)\n",
+    "\n",
+    "out_equals_target = tf.equal(tf.argmax(output_layer, 1), tf.argmax(targets, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(out_equals_target, tf.float32))\n",
+    "\n",
+    "# Optimization Operation:\n",
+    "# Define the optimization operation using the Adam optimizer.\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001).minimize(mean_loss, var_list=None)\n",
+    "\n",
+    "'''\n",
+    "#optimize = optimizer.minimize(mean_loss)\n",
+    "grads_and_vars = optimize.compute_gradients(mean_loss)\n",
+    "optimize = optimize.apply_gradients(grads_and_vars)\n",
+    "#Create an optimizer.\n",
+    "opt = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
+    "out_equals_target, accuracy = tf.Variable(1.0), tf.Variable(2.0)\n",
+    "# Compute the gradients for a list of variables.\n",
+    "with tf.GradientTape() as tape:\n",
+    "loss = 3 * var1 * var1 + 2 * var2 * var2\n",
+    "grads = tape.gradient(loss, [var1, var2])\n",
+    "\n",
+    "# Process the gradients.\n",
+    "grads[0] = grads[0] + 1\n",
+    "\n",
+    "# Ask the optimizer to apply the gradients on variables.\n",
+    "#opt.apply_gradients(zip(grads, [var1, var2]))\n",
+    "# Session Initialization:'''\n",
+    "# Create a TensorFlow session and initialize the variables.\n",
+    "\n",
+    "sess = tf.InteractiveSession()\n",
+    "initializer = tf.global_variables_initializer()\n",
+    "sess.run(initializer)\n",
+    "\n",
+    "# choose the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "prev_validation_loss = 9999999\n",
+    "\n",
+    "# Load the first batch of training and validation, using the class we created. \n",
+    "\n",
+    "train_data = Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data = Audiobooks_Data_Reader('validation')\n",
+    "\n",
+    "# Assuming train_data and validation_data are TensorFlow datasets or generators\n",
+    "model.fit(train_data, epochs=max_epochs, validation_data=validation_data)\n",
+    "\n",
+    "# Training Loop:\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
+    "\n",
+    "for epoch in range(max_epochs):\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        with tf.GradientTape() as tape:\n",
+    "            predictions = model(input_batch)\n",
+    "            loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target_batch, logits=predictions))\n",
+    "\n",
+    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
+    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
+    "\n",
+    "'''for epoch_counter in range(max_epochs):\n",
+    "    curr_epoch_loss = 0\n",
+    "    # Training loop\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        batch_loss = sess.run([optimize, mean_loss], feed_dict={inputs: input_batch, targets: target_batch})\n",
+    "        curr_epoch_loss += batch_loss\n",
+    "\n",
+    "    # Validation loop\n",
+    "    for input_batch, target_batch in validation_data:\n",
+    "        validation_loss, validation_accuracy = sess.run([mean_loss, accuracy], feed_dict={inputs: input_batch, targets: target_batch})\n",
+    "\n",
+    "    # Print statistics for the current epoch\n",
+    "    print('Epoch '+str(epoch_counter+1)+\n",
+    "          '. Training loss: '+'{0:.3f}'.format(curr_epoch_loss / train_data.batch_count)+\n",
+    "          '. Validation loss: '+'{0:.3f}'.format(validation_loss)+\n",
+    "          '. Validation accuracy: '+'{0:.2f}'.format(validation_accuracy * 100.)+'%')\n",
+    "\n",
+    "    # Trigger early stopping if validation loss begins increasing.\n",
+    "    if validation_loss > prev_validation_loss:\n",
+    "        break\n",
+    "    prev_validation_loss = validation_loss\n",
+    "# End of Training:\n",
+    "print('End of training.')\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load the test data, following the same logic as we did for the train_data and validation data\n",
+    "test_data = Audiobooks_Data_Reader('test')\n",
+    "\n",
+    "# Forward propagate through the training set. This time we only need the accuracy\n",
+    "for inputs_batch, targets_batch in test_data:\n",
+    "    test_accuracy = sess.run([accuracy],\n",
+    "                     feed_dict={inputs: inputs_batch, targets: targets_batch})\n",
+    "\n",
+    "# Get the test accuracy in percentages\n",
+    "# When sess.run is has a single output, we get a list (that's how it was coded by Google), rather than a float.\n",
+    "# Therefore, we must take the first value from the list (the value at position 0)\n",
+    "test_accuracy_percent = test_accuracy[0] * 100.\n",
+    "\n",
+    "# Print the test accuracy\n",
+    "print('Test accuracy: '+'{0:.2f}'.format(test_accuracy_percent)+'%')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Audiobooks_data.csv b/Udemy DataScience Workbook/Audiobooks_data.csv
new file mode 100644
index 0000000..b5be725
--- /dev/null
+++ b/Udemy DataScience Workbook/Audiobooks_data.csv	
@@ -0,0 +1,14084 @@
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new file mode 100644
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diff --git a/Udemy DataScience Workbook/Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf b/Udemy DataScience Workbook/Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf
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diff --git a/Udemy DataScience Workbook/Bank_data.csv b/Udemy DataScience Workbook/Bank_data.csv
new file mode 100644
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diff --git a/Udemy DataScience Workbook/Bank_data_testing.csv b/Udemy DataScience Workbook/Bank_data_testing.csv
new file mode 100644
index 0000000..f1c9084
--- /dev/null
+++ b/Udemy DataScience Workbook/Bank_data_testing.csv	
@@ -0,0 +1,223 @@
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diff --git a/Udemy DataScience Workbook/Basic NN Exercises.ipynb b/Udemy DataScience Workbook/Basic NN Exercises.ipynb
new file mode 100644
index 0000000..da38e52
--- /dev/null
+++ b/Udemy DataScience Workbook/Basic NN Exercises.ipynb	
@@ -0,0 +1,1486 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate random input number to trian on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# declare a variable containing the size of the training set we want to generate.\n",
+    "obeservations = 1000000\n",
+    "# Declare 2 inputs to work with X1 and X2\n",
+    "# Generate them randomly from a uniform distrinbution. There are 3 arguments of this method (low, high, size).\n",
+    "X1 = np.random.uniform(low=-10, high=10, size=(obeservations,1))\n",
+    "X2 = np.random.uniform(-10,10,(obeservations,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000000, 2)"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((X1, X2))\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "inputs.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate to aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000000, 1)"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1,1,(obeservations,1))\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 13*X1 - 7*X2 - 12 + noise\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "targets.shape\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "plt.rcParams['agg.path.chunksize'] = 200\n",
+    "\n",
+    "targets = targets.reshape(obeservations,1)\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "# Call a method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection= '3d')\n",
+    "\n",
+    "# Choose the axes\n",
+    "ax.plot(X1,X2,targets)\n",
+    "\n",
+    "#set labels\n",
+    "ax.set_xlabel('X1')\n",
+    "ax.set_ylabel('X2')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(obeservations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.03344725]\n",
+      " [-0.03423104]]\n",
+      "[0.01568644]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "init_range = 0.1\n",
+    "#Note: High initial range may prevent the ML algorothm from learning\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (X1 and X2) and one output (y)\n",
+    "weights = np.random.uniform(-init_range, init_range, size=(2,1))\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "\n",
+    "biases = np.random.uniform(-init_range, init_range, size=1)\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print(weights)\n",
+    "print(biases)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (eta). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "35.936595591267285\n",
+      "12.705863980834138\n",
+      "6.233809279202317\n",
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+     ]
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+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (1000):\n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs-targets\n",
+    "    \n",
+    "      # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(np.absolute(deltas))/2/ obeservations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print(loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    \n",
+    "    deltas_scaled = deltas / obeservations\n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    \n",
+    "    weights = weights- learning_rate * np.dot(inputs.T, deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[12.99993649]\n",
+      " [-6.99996782]] [-12.00060163]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print(weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot last output Vs targets\n",
+    "\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "\n",
+    "plt.plot(outputs, targets)\n",
+    "plt.xlabel('output')\n",
+    "plt.ylabel('Targets')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. It takes the algorithm much more time to solve the problem.\n",
+    "2. The result is the same as we SCALED the deltas, so the number of observations doesn't matter.\n",
+    "3. matplotlib cannot plot the data, as there are too many points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. It takes the algorithm much more time to solve the problem.\n",
+    "2. The result is the same as we SCALED the deltas, so the number of observations doesn't matter.\n",
+    "3. matplotlib cannot plot the data, as there are too many points. and gave an overflow error, however, \n",
+    "plt.rcParams['agg.path.chunksize'] = 200 was used to increase the chunksize to allow it run successfully\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. It takes the algorithm the same time to finish working.\n",
+    "2. The loss is not minimized.\n",
+    "3. The weights and biases are far from what we want them to be.\n",
+    "4. More iterations are needed for this learning rate to solve the problem. And when 10000 iteration was carried out, it took about 9m 43s of the wait time and still gave dissimilar results to that of 100 iterations\n",
+    "5. The problem IS NOT SOLVED.\n",
+    "6. The final graph looks like a 45-degree line, but look at the axes -> it is not.\n",
+    "\n",
+    "With \"learning_rate\" equal to 0.1.\n",
+    "\n",
+    "1. It takes the algorithm the same time to finish working.\n",
+    "2. The loss DIVERGES TO INFINITY.\n",
+    "3. The weights and biases are completely random and extremely big.\n",
+    "4. More iterations would not solve the issue, as the loss is not converging.\n",
+    "5. The problem IS NOT SOLVED.\n",
+    "6. The final graph looks like a 45-degree line, but look at the axes -> it is not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 4\n",
+    " Change the loss function. L2-norm loss (without dividing by 2) \n",
+    "\n",
+    " 1. It takes the algorithm the same time to finish working.\n",
+    "2. The loss is as minimized as the one in the lecture.\n",
+    "3. The weights and biases are almost the same as the ones in the lecture.\n",
+    "4. The problem is as solved as that in the lecture.\n",
+    "5. More iterations would yield a better result.    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 5\n",
+    " 5. Тry with the L1-norm loss, given by the sum of the ABSOLUTE value of yj - tj. The L1-norm loss is given by:\n",
+    "## $$ \\Sigma_i = |y_i-t_i| $$\n",
+    "\n",
+    "1. Any function that has the property to be lower for better results and higher for worse results can be a loss function.\n",
+    "This includes the L1-norm loss.\n",
+    "2. Almost everything seems identical.\n",
+    "3. The values of the loss we generally lower (as we don't elevate to the second power).\n",
+    "4. For our problem, both L1-norm and L2-norm work equally well. \n",
+    "5. Generally, L1-norm has more limitations than L2-norm. You can read more about that online. Overall, L2-norm loss is much more stable and computationally efficient. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Solution  6. \n",
+    "\n",
+    "Create a function f(x,z) = 13*xs + 7*zs - 12. Does the algorithm work in the same way?\n",
+    "\n",
+    "1. The solution is almost found, judging by the values of the loss and those of the weights and biases.\n",
+    "2. I would say this algorithm is AS effective as that in the lecture. Still needs more iterations.\n",
+    "3. All else equal, the values of the loss are higher, as the values of the targets are higher.\n",
+    "4. The same algorithm and methodology solved a completely different problem. That's machine learning for you"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Binary+Predictors+in+a+Logistic+Regression+-+Exercise.ipynb b/Udemy DataScience Workbook/Binary+Predictors+in+a+Logistic+Regression+-+Exercise.ipynb
new file mode 100644
index 0000000..07a9c58
--- /dev/null
+++ b/Udemy DataScience Workbook/Binary+Predictors+in+a+Logistic+Regression+-+Exercise.ipynb	
@@ -0,0 +1,982 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Binary Predictors in a Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same code as in the previous exercise, find the odds of 'duration'. \n",
+    "\n",
+    "What do they tell you?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.767</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>no</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <th>513</th>\n",
+       "      <td>513</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>514</td>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>515</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>516</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>517</td>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  interest_rate  credit  march  may  previous  duration    y\n",
+       "0             0          1.334     0.0    1.0  0.0       0.0     117.0   no\n",
+       "1             1          0.767     0.0    0.0  2.0       1.0     274.0  yes\n",
+       "2             2          4.858     0.0    1.0  0.0       0.0     167.0   no\n",
+       "3             3          4.120     0.0    0.0  0.0       0.0     686.0  yes\n",
+       "4             4          4.856     0.0    1.0  0.0       0.0     157.0   no\n",
+       "..          ...            ...     ...    ...  ...       ...       ...  ...\n",
+       "513         513          1.334     0.0    1.0  0.0       0.0     204.0   no\n",
+       "514         514          0.861     0.0    0.0  2.0       1.0     806.0  yes\n",
+       "515         515          0.879     0.0    0.0  0.0       0.0     290.0   no\n",
+       "516         516          0.877     0.0    0.0  5.0       1.0     473.0  yes\n",
+       "517         517          4.965     0.0    0.0  0.0       0.0     142.0   no\n",
+       "\n",
+       "[518 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Bank_data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <th>514</th>\n",
+       "      <td>0.861</td>\n",
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+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration    y\n",
+       "0            1.334     0.0    1.0  0.0       0.0     117.0   no\n",
+       "1            0.767     0.0    0.0  2.0       1.0     274.0  yes\n",
+       "2            4.858     0.0    1.0  0.0       0.0     167.0   no\n",
+       "3            4.120     0.0    0.0  0.0       0.0     686.0  yes\n",
+       "4            4.856     0.0    1.0  0.0       0.0     157.0   no\n",
+       "..             ...     ...    ...  ...       ...       ...  ...\n",
+       "513          1.334     0.0    1.0  0.0       0.0     204.0   no\n",
+       "514          0.861     0.0    0.0  2.0       1.0     806.0  yes\n",
+       "515          0.879     0.0    0.0  0.0       0.0     290.0   no\n",
+       "516          0.877     0.0    0.0  5.0       1.0     473.0  yes\n",
+       "517          4.965     0.0    0.0  0.0       0.0     142.0   no\n",
+       "\n",
+       "[518 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data = data.drop(['Unnamed: 0'], axis=1)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration  y\n",
+       "0            1.334     0.0    1.0  0.0       0.0     117.0  0\n",
+       "1            0.767     0.0    0.0  2.0       1.0     274.0  1\n",
+       "2            4.858     0.0    1.0  0.0       0.0     167.0  0\n",
+       "3            4.120     0.0    0.0  0.0       0.0     686.0  1\n",
+       "4            4.856     0.0    1.0  0.0       0.0     157.0  0\n",
+       "..             ...     ...    ...  ...       ...       ... ..\n",
+       "513          1.334     0.0    1.0  0.0       0.0     204.0  0\n",
+       "514          0.861     0.0    0.0  2.0       1.0     806.0  1\n",
+       "515          0.879     0.0    0.0  0.0       0.0     290.0  0\n",
+       "516          0.877     0.0    0.0  5.0       1.0     473.0  1\n",
+       "517          4.965     0.0    0.0  0.0       0.0     142.0  0\n",
+       "\n",
+       "[518 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['y']=data['y'].map({'yes':1, 'no': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.835776</td>\n",
+       "      <td>0.034749</td>\n",
+       "      <td>0.266409</td>\n",
+       "      <td>0.388031</td>\n",
+       "      <td>0.127413</td>\n",
+       "      <td>382.177606</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.876903</td>\n",
+       "      <td>0.183321</td>\n",
+       "      <td>0.442508</td>\n",
+       "      <td>0.814527</td>\n",
+       "      <td>0.333758</td>\n",
+       "      <td>344.295990</td>\n",
+       "      <td>0.500483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.635000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.042750</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.466000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.956500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>482.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.970000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2653.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       interest_rate      credit       march         may    previous  \\\n",
+       "count     518.000000  518.000000  518.000000  518.000000  518.000000   \n",
+       "mean        2.835776    0.034749    0.266409    0.388031    0.127413   \n",
+       "std         1.876903    0.183321    0.442508    0.814527    0.333758   \n",
+       "min         0.635000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%         1.042750    0.000000    0.000000    0.000000    0.000000   \n",
+       "50%         1.466000    0.000000    0.000000    0.000000    0.000000   \n",
+       "75%         4.956500    0.000000    1.000000    0.000000    0.000000   \n",
+       "max         4.970000    1.000000    1.000000    5.000000    1.000000   \n",
+       "\n",
+       "          duration           y  \n",
+       "count   518.000000  518.000000  \n",
+       "mean    382.177606    0.500000  \n",
+       "std     344.295990    0.500483  \n",
+       "min       9.000000    0.000000  \n",
+       "25%     155.000000    0.000000  \n",
+       "50%     266.500000    0.500000  \n",
+       "75%     482.750000    1.000000  \n",
+       "max    2653.000000    1.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 'duration' as the independet variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.546118\n",
+      "         Iterations 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Fri, 10 Nov 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>11:27:56</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &      518    \\\\\n",
+       "\\textbf{Model:}           &      Logit       & \\textbf{  Df Residuals:      } &      516    \\\\\n",
+       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        1    \\\\\n",
+       "\\textbf{Date:}            & Fri, 10 Nov 2023 & \\textbf{  Pseudo R-squ.:     } &   0.2121    \\\\\n",
+       "\\textbf{Time:}            &     11:27:56     & \\textbf{  Log-Likelihood:    } &   -282.89   \\\\\n",
+       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -359.05   \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 5.387e-35   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}    &      -1.7001  &        0.192     &    -8.863  &         0.000        &       -2.076    &       -1.324     \\\\\n",
+       "\\textbf{duration} &       0.0051  &        0.001     &     9.159  &         0.000        &        0.004    &        0.006     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Logit Regression Results}\n",
+       "\\end{center}"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   No. Observations:                  518\n",
+       "Model:                          Logit   Df Residuals:                      516\n",
+       "Method:                           MLE   Df Model:                            1\n",
+       "Date:                Fri, 10 Nov 2023   Pseudo R-squ.:                  0.2121\n",
+       "Time:                        11:27:56   Log-Likelihood:                -282.89\n",
+       "converged:                       True   LL-Null:                       -359.05\n",
+       "Covariance Type:            nonrobust   LLR p-value:                 5.387e-35\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
+       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
+       "==============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the odds of duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.005113027136717"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the odds of duration are the exponential of the log odds from the summary table\n",
+    "np.exp(0.0051)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The odds of duration are pretty close to 1. This tells us that although duration is a significant predictor, a change in 1 day would barely affect the regression. \n",
+    "\n",
+    "Note that we could have inferred that from the coefficient itself.\n",
+    "\n",
+    "Finally, note that the data is not standardized (scaled) and duration is a feature of a relatively big order of magnitude."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Binary+Predictors+in+a+Logistic+Regression+-+Solution.ipynb b/Udemy DataScience Workbook/Binary+Predictors+in+a+Logistic+Regression+-+Solution.ipynb
new file mode 100644
index 0000000..b3e6755
--- /dev/null
+++ b/Udemy DataScience Workbook/Binary+Predictors+in+a+Logistic+Regression+-+Solution.ipynb	
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Binary Predictors in a Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same code as in the previous exercise, find the odds of 'duration'. \n",
+    "\n",
+    "What do they tell you?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('Bank_data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# We make sure to create a copy of the data before we start altering it. Note that we don't change the original data we loaded.\n",
+    "data = raw_data.copy()\n",
+    "# Removes the index column thata comes with the data\n",
+    "data = data.drop(['Unnamed: 0'], axis = 1)\n",
+    "# We use the map function to change any 'yes' values to 1 and 'no'values to 0. \n",
+    "data['y'] = data['y'].map({'yes':1, 'no':0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 'duration' as the independet variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "# Get the regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a scatter plot of x1 (Duration, no constant) and y (Subscribed)\n",
+    "plt.scatter(x1,y,color = 'C0')\n",
+    "# Don't forget to label your axes!\n",
+    "plt.xlabel('Duration', fontsize = 20)\n",
+    "plt.ylabel('Subscription', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the odds of duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the odds of duration are the exponential of the log odds from the summary table\n",
+    "np.exp(0.0051)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The odds of duration are pretty close to 1. This tells us that although duration is a significant predictor, a change in 1 day would barely affect the regression. \n",
+    "\n",
+    "Note that we could have inferred that from the coefficient itself.\n",
+    "\n",
+    "Finally, note that the data is not standardized (scaled) and duration is a feature of a relatively big order of magnitude."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Binary+predictors.ipynb b/Udemy DataScience Workbook/Binary+predictors.ipynb
new file mode 100644
index 0000000..d731d9a
--- /dev/null
+++ b/Udemy DataScience Workbook/Binary+predictors.ipynb	
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "#Apply a fix to the statsmodels library\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('2.02. Binary predictors.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "data['Gender'] = data['Gender'].map({'Female': 1, 'Male': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data[['SAT','Gender']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.exp(1.9449)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Building+a+Logistic+Regression+-+Exercise.ipynb b/Udemy DataScience Workbook/Building+a+Logistic+Regression+-+Exercise.ipynb
new file mode 100644
index 0000000..4966495
--- /dev/null
+++ b/Udemy DataScience Workbook/Building+a+Logistic+Regression+-+Exercise.ipynb	
@@ -0,0 +1,634 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Building a Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a logistic regression based on the bank data provided. \n",
+    "\n",
+    "The data is based on the marketing campaign efforts of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).\n",
+    "\n",
+    "Note that the first column of the dataset is the index.\n",
+    "\n",
+    "Source: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Example_bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>117</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>274</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>167</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>686</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>157</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>513</td>\n",
+       "      <td>204</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>514</td>\n",
+       "      <td>806</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>515</td>\n",
+       "      <td>290</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>516</td>\n",
+       "      <td>473</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>517</td>\n",
+       "      <td>142</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  duration    y\n",
+       "0             0       117   no\n",
+       "1             1       274  yes\n",
+       "2             2       167   no\n",
+       "3             3       686  yes\n",
+       "4             4       157   no\n",
+       "..          ...       ...  ...\n",
+       "513         513       204   no\n",
+       "514         514       806  yes\n",
+       "515         515       290   no\n",
+       "516         516       473  yes\n",
+       "517         517       142   no\n",
+       "\n",
+       "[518 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Example_bank_data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to know whether the bank marketing strategy was successful, so we need to transform the outcome variable into 0s and 1s in order to perform a logistic regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>117</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>274</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>167</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>686</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>157</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>204</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>806</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>290</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>473</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>142</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     duration  y\n",
+       "0         117  0\n",
+       "1         274  1\n",
+       "2         167  0\n",
+       "3         686  1\n",
+       "4         157  0\n",
+       "..        ... ..\n",
+       "513       204  0\n",
+       "514       806  1\n",
+       "515       290  0\n",
+       "516       473  1\n",
+       "517       142  0\n",
+       "\n",
+       "[518 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['y'] = data['y'].map({'yes':1, 'no': 0})\n",
+    "New_data = data.drop(['Unnamed: 0'], axis=1)\n",
+    "New_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>382.177606</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>344.295990</td>\n",
+       "      <td>0.500483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>266.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>482.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2653.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          duration           y\n",
+       "count   518.000000  518.000000\n",
+       "mean    382.177606    0.500000\n",
+       "std     344.295990    0.500483\n",
+       "min       9.000000    0.000000\n",
+       "25%     155.000000    0.000000\n",
+       "50%     266.500000    0.500000\n",
+       "75%     482.750000    1.000000\n",
+       "max    2653.000000    1.000000"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "New_data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = New_data['y']\n",
+    "x1 = New_data['duration']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x1,y)\n",
+    "plt.xlabel('duration', size =20)\n",
+    "plt.ylabel('Subscribed')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression and visualize it on a scatter plot (no need to plot the line)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.546118\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Fri, 10 Nov 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>10:24:17</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &      518    \\\\\n",
+       "\\textbf{Model:}           &      Logit       & \\textbf{  Df Residuals:      } &      516    \\\\\n",
+       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        1    \\\\\n",
+       "\\textbf{Date:}            & Fri, 10 Nov 2023 & \\textbf{  Pseudo R-squ.:     } &   0.2121    \\\\\n",
+       "\\textbf{Time:}            &     10:24:17     & \\textbf{  Log-Likelihood:    } &   -282.89   \\\\\n",
+       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -359.05   \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 5.387e-35   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}    &      -1.7001  &        0.192     &    -8.863  &         0.000        &       -2.076    &       -1.324     \\\\\n",
+       "\\textbf{duration} &       0.0051  &        0.001     &     9.159  &         0.000        &        0.004    &        0.006     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Logit Regression Results}\n",
+       "\\end{center}"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   No. Observations:                  518\n",
+       "Model:                          Logit   Df Residuals:                      516\n",
+       "Method:                           MLE   Df Model:                            1\n",
+       "Date:                Fri, 10 Nov 2023   Pseudo R-squ.:                  0.2121\n",
+       "Time:                        10:24:17   Log-Likelihood:                -282.89\n",
+       "converged:                       True   LL-Null:                       -359.05\n",
+       "Covariance Type:            nonrobust   LLR p-value:                 5.387e-35\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
+       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
+       "==============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y, x)\n",
+    "results_log = reg_log.fit()\n",
+    "\n",
+    "# Get regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a scatter plot of x1 (Duration, no constant) and y (Subscribed)\n",
+    "plt.scatter(x1,y)\n",
+    "plt.xlabel('duration', size =20)\n",
+    "plt.ylabel('Subscribed')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Building+a+Logistic+Regression+-+Solution.ipynb b/Udemy DataScience Workbook/Building+a+Logistic+Regression+-+Solution.ipynb
new file mode 100644
index 0000000..e624138
--- /dev/null
+++ b/Udemy DataScience Workbook/Building+a+Logistic+Regression+-+Solution.ipynb	
@@ -0,0 +1,639 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Building a Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a logistic regression based on the bank data provided. \n",
+    "\n",
+    "The data is based on the marketing campaign efforts of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).\n",
+    "\n",
+    "Note that the first column of the dataset is the index.\n",
+    "\n",
+    "Source: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "# this part not be needed after the latests updates of the library\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Example_bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>117</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>274</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>167</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>686</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>157</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>513</td>\n",
+       "      <td>204</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>514</td>\n",
+       "      <td>806</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>515</td>\n",
+       "      <td>290</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>516</td>\n",
+       "      <td>473</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>517</td>\n",
+       "      <td>142</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  duration    y\n",
+       "0             0       117   no\n",
+       "1             1       274  yes\n",
+       "2             2       167   no\n",
+       "3             3       686  yes\n",
+       "4             4       157   no\n",
+       "..          ...       ...  ...\n",
+       "513         513       204   no\n",
+       "514         514       806  yes\n",
+       "515         515       290   no\n",
+       "516         516       473  yes\n",
+       "517         517       142   no\n",
+       "\n",
+       "[518 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Example_bank_data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to know whether the bank marketing strategy was successful, so we need to transform the outcome variable into 0s and 1s in order to perform a logistic regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>117</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>274</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>167</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>686</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>157</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>204</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>806</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>290</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>473</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>142</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     duration  y\n",
+       "0         117  0\n",
+       "1         274  1\n",
+       "2         167  0\n",
+       "3         686  1\n",
+       "4         157  0\n",
+       "..        ... ..\n",
+       "513       204  0\n",
+       "514       806  1\n",
+       "515       290  0\n",
+       "516       473  1\n",
+       "517       142  0\n",
+       "\n",
+       "[518 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We make sure to create a copy of the data before we start altering it. Note that we don't change the original data we loaded.\n",
+    "data = raw_data.copy()\n",
+    "\n",
+    "# Removes the index column that came with the data\n",
+    "data = data.drop(['Unnamed: 0'], axis = 1)\n",
+    "\n",
+    "# We use the map function to change any 'yes' values to 1 and 'no' values to 0. \n",
+    "data['y'] = data['y'].map({'yes':1, 'no':0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>382.177606</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>344.295990</td>\n",
+       "      <td>0.500483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>266.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>482.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2653.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          duration           y\n",
+       "count   518.000000  518.000000\n",
+       "mean    382.177606    0.500000\n",
+       "std     344.295990    0.500483\n",
+       "min       9.000000    0.000000\n",
+       "25%     155.000000    0.000000\n",
+       "50%     266.500000    0.500000\n",
+       "75%     482.750000    1.000000\n",
+       "max    2653.000000    1.000000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the descriptive statistics\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x1, y)\n",
+    "plt.xlabel('duration', size= 20)\n",
+    "plt.ylabel('Y', size=20)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression and visualize it on a scatter plot (no need to plot the line)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.546118\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Fri, 10 Nov 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>10:13:49</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &      518    \\\\\n",
+       "\\textbf{Model:}           &      Logit       & \\textbf{  Df Residuals:      } &      516    \\\\\n",
+       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        1    \\\\\n",
+       "\\textbf{Date:}            & Fri, 10 Nov 2023 & \\textbf{  Pseudo R-squ.:     } &   0.2121    \\\\\n",
+       "\\textbf{Time:}            &     10:13:49     & \\textbf{  Log-Likelihood:    } &   -282.89   \\\\\n",
+       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -359.05   \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 5.387e-35   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}    &      -1.7001  &        0.192     &    -8.863  &         0.000        &       -2.076    &       -1.324     \\\\\n",
+       "\\textbf{duration} &       0.0051  &        0.001     &     9.159  &         0.000        &        0.004    &        0.006     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Logit Regression Results}\n",
+       "\\end{center}"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   No. Observations:                  518\n",
+       "Model:                          Logit   Df Residuals:                      516\n",
+       "Method:                           MLE   Df Model:                            1\n",
+       "Date:                Fri, 10 Nov 2023   Pseudo R-squ.:                  0.2121\n",
+       "Time:                        10:13:49   Log-Likelihood:                -282.89\n",
+       "converged:                       True   LL-Null:                       -359.05\n",
+       "Covariance Type:            nonrobust   LLR p-value:                 5.387e-35\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
+       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
+       "==============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "\n",
+    "# Get the regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot of x1 (Duration, no constant) and y (Subscribed)\n",
+    "plt.scatter(x1,y,color = 'C0')\n",
+    "\n",
+    "# Don't forget to label your axes!\n",
+    "plt.xlabel('Duration', fontsize = 20)\n",
+    "plt.ylabel('Subscription', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Calculating+the+Accuracy+of+the+Model+-+Exercise.ipynb b/Udemy DataScience Workbook/Calculating+the+Accuracy+of+the+Model+-+Exercise.ipynb
new file mode 100644
index 0000000..bb9f2ab
--- /dev/null
+++ b/Udemy DataScience Workbook/Calculating+the+Accuracy+of+the+Model+-+Exercise.ipynb	
@@ -0,0 +1,1075 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculating the Accuracy of the Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same dataset, expand the model by including all other features into the regression. \n",
+    "\n",
+    "Moreover, calculate the accuracy of the model and create a confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "\n",
+    "#Apply a fix to the statsmodels library\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>513</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>514</td>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>515</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>516</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>517</td>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  interest_rate  credit  march  may  previous  duration    y\n",
+       "0             0          1.334     0.0    1.0  0.0       0.0     117.0   no\n",
+       "1             1          0.767     0.0    0.0  2.0       1.0     274.0  yes\n",
+       "2             2          4.858     0.0    1.0  0.0       0.0     167.0   no\n",
+       "3             3          4.120     0.0    0.0  0.0       0.0     686.0  yes\n",
+       "4             4          4.856     0.0    1.0  0.0       0.0     157.0   no\n",
+       "..          ...            ...     ...    ...  ...       ...       ...  ...\n",
+       "513         513          1.334     0.0    1.0  0.0       0.0     204.0   no\n",
+       "514         514          0.861     0.0    0.0  2.0       1.0     806.0  yes\n",
+       "515         515          0.879     0.0    0.0  0.0       0.0     290.0   no\n",
+       "516         516          0.877     0.0    0.0  5.0       1.0     473.0  yes\n",
+       "517         517          4.965     0.0    0.0  0.0       0.0     142.0   no\n",
+       "\n",
+       "[518 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Bank_data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration    y\n",
+       "0            1.334     0.0    1.0  0.0       0.0     117.0   no\n",
+       "1            0.767     0.0    0.0  2.0       1.0     274.0  yes\n",
+       "2            4.858     0.0    1.0  0.0       0.0     167.0   no\n",
+       "3            4.120     0.0    0.0  0.0       0.0     686.0  yes\n",
+       "4            4.856     0.0    1.0  0.0       0.0     157.0   no\n",
+       "..             ...     ...    ...  ...       ...       ...  ...\n",
+       "513          1.334     0.0    1.0  0.0       0.0     204.0   no\n",
+       "514          0.861     0.0    0.0  2.0       1.0     806.0  yes\n",
+       "515          0.879     0.0    0.0  0.0       0.0     290.0   no\n",
+       "516          0.877     0.0    0.0  5.0       1.0     473.0  yes\n",
+       "517          4.965     0.0    0.0  0.0       0.0     142.0   no\n",
+       "\n",
+       "[518 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data = data.drop(['Unnamed: 0'], axis=1)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['y']=data['y'].map({'yes':1, 'no': 0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.835776</td>\n",
+       "      <td>0.034749</td>\n",
+       "      <td>0.266409</td>\n",
+       "      <td>0.388031</td>\n",
+       "      <td>0.127413</td>\n",
+       "      <td>382.177606</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.876903</td>\n",
+       "      <td>0.183321</td>\n",
+       "      <td>0.442508</td>\n",
+       "      <td>0.814527</td>\n",
+       "      <td>0.333758</td>\n",
+       "      <td>344.295990</td>\n",
+       "      <td>0.500483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.635000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.042750</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.466000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.956500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>482.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.970000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2653.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       interest_rate      credit       march         may    previous  \\\n",
+       "count     518.000000  518.000000  518.000000  518.000000  518.000000   \n",
+       "mean        2.835776    0.034749    0.266409    0.388031    0.127413   \n",
+       "std         1.876903    0.183321    0.442508    0.814527    0.333758   \n",
+       "min         0.635000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%         1.042750    0.000000    0.000000    0.000000    0.000000   \n",
+       "50%         1.466000    0.000000    0.000000    0.000000    0.000000   \n",
+       "75%         4.956500    0.000000    1.000000    0.000000    0.000000   \n",
+       "max         4.970000    1.000000    1.000000    5.000000    1.000000   \n",
+       "\n",
+       "          duration           y  \n",
+       "count   518.000000  518.000000  \n",
+       "mean    382.177606    0.500000  \n",
+       "std     344.295990    0.500483  \n",
+       "min       9.000000    0.000000  \n",
+       "25%     155.000000    0.000000  \n",
+       "50%     266.500000    0.500000  \n",
+       "75%     482.750000    1.000000  \n",
+       "max    2653.000000    1.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 'duration' as the independet variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression and graph the scatter plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.546118\n",
+      "         Iterations 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Fri, 10 Nov 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>13:07:58</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &      518    \\\\\n",
+       "\\textbf{Model:}           &      Logit       & \\textbf{  Df Residuals:      } &      516    \\\\\n",
+       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        1    \\\\\n",
+       "\\textbf{Date:}            & Fri, 10 Nov 2023 & \\textbf{  Pseudo R-squ.:     } &   0.2121    \\\\\n",
+       "\\textbf{Time:}            &     13:07:58     & \\textbf{  Log-Likelihood:    } &   -282.89   \\\\\n",
+       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -359.05   \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 5.387e-35   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}    &      -1.7001  &        0.192     &    -8.863  &         0.000        &       -2.076    &       -1.324     \\\\\n",
+       "\\textbf{duration} &       0.0051  &        0.001     &     9.159  &         0.000        &        0.004    &        0.006     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Logit Regression Results}\n",
+       "\\end{center}"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   No. Observations:                  518\n",
+       "Model:                          Logit   Df Residuals:                      516\n",
+       "Method:                           MLE   Df Model:                            1\n",
+       "Date:                Fri, 10 Nov 2023   Pseudo R-squ.:                  0.2121\n",
+       "Time:                        13:07:58   Log-Likelihood:                -282.89\n",
+       "converged:                       True   LL-Null:                       -359.05\n",
+       "Covariance Type:            nonrobust   LLR p-value:                 5.387e-35\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
+       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
+       "==============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot of x1 (Duration, no constant) and y (Subscribed)\n",
+    "plt.scatter(x1, y,color= 'C0')\n",
+    "plt.xlabel('Duration', fontsize=20)\n",
+    "plt.ylabel('Subscription', fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Expand the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can be omitting many causal factors in our simple logistic model, so we instead switch to a multivariate logistic regression model. Add the ‘interest_rate’, ‘march’, ‘credit’ and ‘previous’ estimators to our model and run the regression again. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the independent variable(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['interest_rate', 'credit', 'march', 'may', 'previous', 'duration', 'y'], dtype='object')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To avoid writing them out every time, we save the names of the estimators of our model in a list. \n",
+    "estimators=['interest_rate','march','credit','previous','duration']\n",
+    "X1 = data[estimators]\n",
+    "y = data['y']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.336664\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td>Model:</td>              <td>Logit</td>           <td>Method:</td>          <td>MLE</td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <td>Dependent Variable:</td>         <td>y</td>        <td>Pseudo R-squared:</td>    <td>0.514</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "         <td>Date:</td>        <td>2023-11-10 13:11</td>       <td>AIC:</td>         <td>360.7836</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "   <td>No. Observations:</td>         <td>518</td>             <td>BIC:</td>         <td>386.2834</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "       <td>Df Model:</td>              <td>5</td>         <td>Log-Likelihood:</td>    <td>-174.39</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "     <td>Df Residuals:</td>           <td>512</td>           <td>LL-Null:</td>        <td>-359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "      <td>Converged:</td>           <td>1.0000</td>        <td>LLR p-value:</td>    <td>1.2114e-77</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "    <td>No. Iterations:</td>        <td>7.0000</td>           <td>Scale:</td>         <td>1.0000</td>  \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td></td>         <th>Coef.</th>  <th>Std.Err.</th>    <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>         <td>-0.0211</td>  <td>0.3113</td>  <td>-0.0677</td> <td>0.9460</td> <td>-0.6313</td> <td>0.5891</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>interest_rate</th> <td>-0.8001</td>  <td>0.0895</td>  <td>-8.9434</td> <td>0.0000</td> <td>-0.9755</td> <td>-0.6248</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>march</th>         <td>-1.8322</td>  <td>0.3297</td>  <td>-5.5563</td> <td>0.0000</td> <td>-2.4785</td> <td>-1.1859</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>credit</th>        <td>2.3585</td>   <td>1.0875</td>  <td>2.1688</td>  <td>0.0301</td> <td>0.2271</td>  <td>4.4900</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>previous</th>      <td>1.5363</td>   <td>0.5010</td>  <td>3.0666</td>  <td>0.0022</td> <td>0.5544</td>  <td>2.5182</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th>      <td>0.0070</td>   <td>0.0007</td>  <td>9.3810</td>  <td>0.0000</td> <td>0.0055</td>  <td>0.0084</td> \n",
+       "</tr>\n",
+       "</table><br/>\n"
+      ],
+      "text/latex": [
+       "\\begin{table}\n",
+       "\\caption{Results: Logit}\n",
+       "\\label{}\n",
+       "\\begin{center}\n",
+       "\\begin{tabular}{llll}\n",
+       "\\hline\n",
+       "Model:              & Logit            & Method:           & MLE         \\\\\n",
+       "Dependent Variable: & y                & Pseudo R-squared: & 0.514       \\\\\n",
+       "Date:               & 2023-11-10 13:11 & AIC:              & 360.7836    \\\\\n",
+       "No. Observations:   & 518              & BIC:              & 386.2834    \\\\\n",
+       "Df Model:           & 5                & Log-Likelihood:   & -174.39     \\\\\n",
+       "Df Residuals:       & 512              & LL-Null:          & -359.05     \\\\\n",
+       "Converged:          & 1.0000           & LLR p-value:      & 1.2114e-77  \\\\\n",
+       "No. Iterations:     & 7.0000           & Scale:            & 1.0000      \\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n",
+       "\\end{center}\n",
+       "\n",
+       "\\begin{center}\n",
+       "\\begin{tabular}{lrrrrrr}\n",
+       "\\hline\n",
+       "               &   Coef. & Std.Err. &       z & P$> |$z$|$ &  [0.025 &  0.975]  \\\\\n",
+       "\\hline\n",
+       "const          & -0.0211 &   0.3113 & -0.0677 &      0.9460 & -0.6313 &  0.5891  \\\\\n",
+       "interest\\_rate & -0.8001 &   0.0895 & -8.9434 &      0.0000 & -0.9755 & -0.6248  \\\\\n",
+       "march          & -1.8322 &   0.3297 & -5.5563 &      0.0000 & -2.4785 & -1.1859  \\\\\n",
+       "credit         &  2.3585 &   1.0875 &  2.1688 &      0.0301 &  0.2271 &  4.4900  \\\\\n",
+       "previous       &  1.5363 &   0.5010 &  3.0666 &      0.0022 &  0.5544 &  2.5182  \\\\\n",
+       "duration       &  0.0070 &   0.0007 &  9.3810 &      0.0000 &  0.0055 &  0.0084  \\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n",
+       "\\end{center}\n",
+       "\\end{table}\n",
+       "\\bigskip\n"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary2.Summary'>\n",
+       "\"\"\"\n",
+       "                         Results: Logit\n",
+       "=================================================================\n",
+       "Model:              Logit            Method:           MLE       \n",
+       "Dependent Variable: y                Pseudo R-squared: 0.514     \n",
+       "Date:               2023-11-10 13:11 AIC:              360.7836  \n",
+       "No. Observations:   518              BIC:              386.2834  \n",
+       "Df Model:           5                Log-Likelihood:   -174.39   \n",
+       "Df Residuals:       512              LL-Null:          -359.05   \n",
+       "Converged:          1.0000           LLR p-value:      1.2114e-77\n",
+       "No. Iterations:     7.0000           Scale:            1.0000    \n",
+       "-----------------------------------------------------------------\n",
+       "                   Coef.  Std.Err.    z    P>|z|   [0.025  0.975]\n",
+       "-----------------------------------------------------------------\n",
+       "const             -0.0211   0.3113 -0.0677 0.9460 -0.6313  0.5891\n",
+       "interest_rate     -0.8001   0.0895 -8.9434 0.0000 -0.9755 -0.6248\n",
+       "march             -1.8322   0.3297 -5.5563 0.0000 -2.4785 -1.1859\n",
+       "credit             2.3585   1.0875  2.1688 0.0301  0.2271  4.4900\n",
+       "previous           1.5363   0.5010  3.0666 0.0022  0.5544  2.5182\n",
+       "duration           0.0070   0.0007  9.3810 0.0000  0.0055  0.0084\n",
+       "=================================================================\n",
+       "\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = sm.add_constant(X1)\n",
+    "reg_logit = sm.Logit(y,X)\n",
+    "results_logit = reg_logit.fit()\n",
+    "results_logit.summary2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the confusion matrix of the model and estimate its accuracy. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<i> For convenience we have already provided you with a function that finds the confusion matrix and the model accuracy.</i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def confusion_matrix(data,actual_values,model):\n",
+    "        \n",
+    "        # Confusion matrix \n",
+    "        \n",
+    "        # Parameters\n",
+    "        # ----------\n",
+    "        # data: data frame or array\n",
+    "            # data is a data frame formatted in the same way as your input data (without the actual values)\n",
+    "            # e.g. const, var1, var2, etc. Order is very important!\n",
+    "        # actual_values: data frame or array\n",
+    "            # These are the actual values from the test_data\n",
+    "            # In the case of a logistic regression, it should be a single column with 0s and 1s\n",
+    "            \n",
+    "        # model: a LogitResults object\n",
+    "            # this is the variable where you have the fitted model \n",
+    "            # e.g. results_log in this course\n",
+    "        # ----------\n",
+    "        \n",
+    "        #Predict the values using the Logit model\n",
+    "        pred_values = model.predict(data)\n",
+    "        # Specify the bins \n",
+    "        bins=np.array([0,0.5,1])\n",
+    "        # Create a histogram, where if values are between 0 and 0.5 tell will be considered 0\n",
+    "        # if they are between 0.5 and 1, they will be considered 1\n",
+    "        cm = np.histogram2d(actual_values, pred_values, bins=bins)[0]\n",
+    "        # Calculate the accuracy\n",
+    "        accuracy = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "        # Return the confusion matrix and \n",
+    "        return cm, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[218.,  41.],\n",
+       "        [ 30., 229.]]),\n",
+       " 0.862934362934363)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(X,y,results_logit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Calculating+the+Accuracy+of+the+Model+-+Solution.ipynb b/Udemy DataScience Workbook/Calculating+the+Accuracy+of+the+Model+-+Solution.ipynb
new file mode 100644
index 0000000..a5f3453
--- /dev/null
+++ b/Udemy DataScience Workbook/Calculating+the+Accuracy+of+the+Model+-+Solution.ipynb	
@@ -0,0 +1,2130 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculating the Accuracy of the Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same dataset, expand the model by including all other features into the regression. \n",
+    "\n",
+    "Moreover, calculate the accuracy of the model and create a confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>704.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>4.865</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>185.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.365</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>374.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.773</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0.714</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>169.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>4.864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>4.966</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>215.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.904</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>0.849</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>1.811</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1.264</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>337.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>4.076</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>20</td>\n",
+       "      <td>1.262</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>663.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>21</td>\n",
+       "      <td>0.695</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>22</td>\n",
+       "      <td>4.960</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>23</td>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>255.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>24</td>\n",
+       "      <td>1.354</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>25</td>\n",
+       "      <td>1.244</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>26</td>\n",
+       "      <td>0.748</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>27</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>272.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>28</td>\n",
+       "      <td>0.644</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>398.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>29</td>\n",
+       "      <td>4.968</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>488</td>\n",
+       "      <td>0.885</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>608.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>489</td>\n",
+       "      <td>4.153</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>490</td>\n",
+       "      <td>0.735</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>491</td>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>716.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>492</td>\n",
+       "      <td>4.859</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>619.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>493</td>\n",
+       "      <td>4.864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>406.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>494</td>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>495</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>496</td>\n",
+       "      <td>4.153</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>497</td>\n",
+       "      <td>0.646</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>498</td>\n",
+       "      <td>1.266</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>326.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>499</td>\n",
+       "      <td>1.405</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>500</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>470.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>501</td>\n",
+       "      <td>4.076</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>502</td>\n",
+       "      <td>1.050</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>503</td>\n",
+       "      <td>1.029</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>504</td>\n",
+       "      <td>0.748</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>171.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>505</td>\n",
+       "      <td>4.961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>498.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>506</td>\n",
+       "      <td>1.268</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>507</td>\n",
+       "      <td>4.959</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>508</td>\n",
+       "      <td>4.021</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>796.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>509</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>279.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>510</td>\n",
+       "      <td>1.327</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>476.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>511</td>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>479.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>512</td>\n",
+       "      <td>1.266</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>513</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>514</td>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>515</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>516</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>517</td>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  interest_rate  credit  march  may  previous  duration    y\n",
+       "0             0          1.334     0.0    1.0  0.0       0.0     117.0   no\n",
+       "1             1          0.767     0.0    0.0  2.0       1.0     274.0  yes\n",
+       "2             2          4.858     0.0    1.0  0.0       0.0     167.0   no\n",
+       "3             3          4.120     0.0    0.0  0.0       0.0     686.0  yes\n",
+       "4             4          4.856     0.0    1.0  0.0       0.0     157.0   no\n",
+       "5             5          0.899     0.0    0.0  1.0       0.0     126.0   no\n",
+       "6             6          4.962     0.0    0.0  0.0       0.0      84.0   no\n",
+       "7             7          4.858     0.0    1.0  0.0       0.0      17.0   no\n",
+       "8             8          4.962     0.0    0.0  0.0       0.0     704.0  yes\n",
+       "9             9          4.865     0.0    0.0  0.0       0.0     185.0   no\n",
+       "10           10          1.365     0.0    0.0  1.0       1.0     374.0   no\n",
+       "11           11          0.773     0.0    0.0  0.0       0.0      91.0  yes\n",
+       "12           12          0.714     0.0    0.0  2.0       1.0     169.0  yes\n",
+       "13           13          4.864     0.0    0.0  0.0       0.0     249.0   no\n",
+       "14           14          4.966     0.0    0.0  0.0       0.0     215.0   no\n",
+       "15           15          0.904     0.0    0.0  0.0       0.0     324.0  yes\n",
+       "16           16          0.849     0.0    0.0  2.0       1.0     159.0  yes\n",
+       "17           17          1.811     1.0    0.0  0.0       0.0     120.0  yes\n",
+       "18           18          1.264     0.0    1.0  0.0       0.0     337.0  yes\n",
+       "19           19          4.076     0.0    0.0  0.0       0.0     640.0   no\n",
+       "20           20          1.262     0.0    0.0  0.0       0.0     663.0  yes\n",
+       "21           21          0.695     0.0    1.0  2.0       1.0     403.0  yes\n",
+       "22           22          4.960     0.0    0.0  0.0       0.0     300.0   no\n",
+       "23           23          4.963     0.0    0.0  0.0       0.0     255.0  yes\n",
+       "24           24          1.354     0.0    1.0  1.0       1.0     293.0  yes\n",
+       "25           25          1.244     0.0    1.0  0.0       0.0      68.0   no\n",
+       "26           26          0.748     0.0    0.0  1.0       0.0     266.0  yes\n",
+       "27           27          0.878     0.0    0.0  0.0       0.0     272.0  yes\n",
+       "28           28          0.644     0.0    0.0  0.0       0.0     398.0  yes\n",
+       "29           29          4.968     0.0    0.0  0.0       0.0     126.0   no\n",
+       "..          ...            ...     ...    ...  ...       ...       ...  ...\n",
+       "488         488          0.885     0.0    0.0  2.0       1.0     608.0  yes\n",
+       "489         489          4.153     0.0    0.0  0.0       0.0     250.0   no\n",
+       "490         490          0.735     0.0    0.0  0.0       0.0     107.0  yes\n",
+       "491         491          4.965     0.0    0.0  0.0       0.0     716.0  yes\n",
+       "492         492          4.859     0.0    1.0  0.0       0.0     619.0  yes\n",
+       "493         493          4.864     0.0    0.0  0.0       0.0     406.0   no\n",
+       "494         494          4.963     0.0    0.0  0.0       0.0      73.0   no\n",
+       "495         495          1.334     0.0    1.0  0.0       0.0     127.0   no\n",
+       "496         496          4.153     0.0    0.0  0.0       0.0     187.0   no\n",
+       "497         497          0.646     1.0    0.0  1.0       1.0     180.0  yes\n",
+       "498         498          1.266     0.0    1.0  0.0       0.0     326.0  yes\n",
+       "499         499          1.405     0.0    0.0  1.0       0.0     109.0  yes\n",
+       "500         500          0.900     0.0    0.0  2.0       1.0     470.0  yes\n",
+       "501         501          4.076     0.0    0.0  0.0       0.0      73.0   no\n",
+       "502         502          1.050     0.0    0.0  0.0       0.0     141.0   no\n",
+       "503         503          1.029     0.0    0.0  1.0       0.0     115.0   no\n",
+       "504         504          0.748     0.0    0.0  1.0       1.0     171.0   no\n",
+       "505         505          4.961     0.0    0.0  0.0       0.0     498.0   no\n",
+       "506         506          1.268     0.0    0.0  0.0       0.0     365.0  yes\n",
+       "507         507          4.959     0.0    0.0  0.0       0.0      10.0   no\n",
+       "508         508          4.021     0.0    0.0  1.0       0.0     796.0  yes\n",
+       "509         509          0.877     0.0    0.0  2.0       1.0     279.0  yes\n",
+       "510         510          1.327     0.0    1.0  0.0       0.0     476.0  yes\n",
+       "511         511          4.965     0.0    0.0  0.0       0.0     479.0  yes\n",
+       "512         512          1.266     0.0    1.0  1.0       0.0     225.0   no\n",
+       "513         513          1.334     0.0    1.0  0.0       0.0     204.0   no\n",
+       "514         514          0.861     0.0    0.0  2.0       1.0     806.0  yes\n",
+       "515         515          0.879     0.0    0.0  0.0       0.0     290.0   no\n",
+       "516         516          0.877     0.0    0.0  5.0       1.0     473.0  yes\n",
+       "517         517          4.965     0.0    0.0  0.0       0.0     142.0   no\n",
+       "\n",
+       "[518 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Bank-data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.962</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.962</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>704.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4.865</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.365</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>374.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.773</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.714</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>169.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>4.864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>4.966</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>215.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.904</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.849</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.811</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.264</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>337.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>4.076</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.262</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>663.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.695</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4.960</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>255.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1.354</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1.244</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.748</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>272.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.644</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>398.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>4.968</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>0.885</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>608.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>4.153</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>0.735</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>716.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>4.859</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>619.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>4.864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>406.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>4.153</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>0.646</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>1.266</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>326.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>1.405</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>470.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>4.076</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>1.050</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>1.029</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>0.748</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>171.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>4.961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>498.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>1.268</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>4.959</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>4.021</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>796.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>279.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1.327</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>476.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>479.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>1.266</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration  y\n",
+       "0            1.334     0.0    1.0  0.0       0.0     117.0  0\n",
+       "1            0.767     0.0    0.0  2.0       1.0     274.0  1\n",
+       "2            4.858     0.0    1.0  0.0       0.0     167.0  0\n",
+       "3            4.120     0.0    0.0  0.0       0.0     686.0  1\n",
+       "4            4.856     0.0    1.0  0.0       0.0     157.0  0\n",
+       "5            0.899     0.0    0.0  1.0       0.0     126.0  0\n",
+       "6            4.962     0.0    0.0  0.0       0.0      84.0  0\n",
+       "7            4.858     0.0    1.0  0.0       0.0      17.0  0\n",
+       "8            4.962     0.0    0.0  0.0       0.0     704.0  1\n",
+       "9            4.865     0.0    0.0  0.0       0.0     185.0  0\n",
+       "10           1.365     0.0    0.0  1.0       1.0     374.0  0\n",
+       "11           0.773     0.0    0.0  0.0       0.0      91.0  1\n",
+       "12           0.714     0.0    0.0  2.0       1.0     169.0  1\n",
+       "13           4.864     0.0    0.0  0.0       0.0     249.0  0\n",
+       "14           4.966     0.0    0.0  0.0       0.0     215.0  0\n",
+       "15           0.904     0.0    0.0  0.0       0.0     324.0  1\n",
+       "16           0.849     0.0    0.0  2.0       1.0     159.0  1\n",
+       "17           1.811     1.0    0.0  0.0       0.0     120.0  1\n",
+       "18           1.264     0.0    1.0  0.0       0.0     337.0  1\n",
+       "19           4.076     0.0    0.0  0.0       0.0     640.0  0\n",
+       "20           1.262     0.0    0.0  0.0       0.0     663.0  1\n",
+       "21           0.695     0.0    1.0  2.0       1.0     403.0  1\n",
+       "22           4.960     0.0    0.0  0.0       0.0     300.0  0\n",
+       "23           4.963     0.0    0.0  0.0       0.0     255.0  1\n",
+       "24           1.354     0.0    1.0  1.0       1.0     293.0  1\n",
+       "25           1.244     0.0    1.0  0.0       0.0      68.0  0\n",
+       "26           0.748     0.0    0.0  1.0       0.0     266.0  1\n",
+       "27           0.878     0.0    0.0  0.0       0.0     272.0  1\n",
+       "28           0.644     0.0    0.0  0.0       0.0     398.0  1\n",
+       "29           4.968     0.0    0.0  0.0       0.0     126.0  0\n",
+       "..             ...     ...    ...  ...       ...       ... ..\n",
+       "488          0.885     0.0    0.0  2.0       1.0     608.0  1\n",
+       "489          4.153     0.0    0.0  0.0       0.0     250.0  0\n",
+       "490          0.735     0.0    0.0  0.0       0.0     107.0  1\n",
+       "491          4.965     0.0    0.0  0.0       0.0     716.0  1\n",
+       "492          4.859     0.0    1.0  0.0       0.0     619.0  1\n",
+       "493          4.864     0.0    0.0  0.0       0.0     406.0  0\n",
+       "494          4.963     0.0    0.0  0.0       0.0      73.0  0\n",
+       "495          1.334     0.0    1.0  0.0       0.0     127.0  0\n",
+       "496          4.153     0.0    0.0  0.0       0.0     187.0  0\n",
+       "497          0.646     1.0    0.0  1.0       1.0     180.0  1\n",
+       "498          1.266     0.0    1.0  0.0       0.0     326.0  1\n",
+       "499          1.405     0.0    0.0  1.0       0.0     109.0  1\n",
+       "500          0.900     0.0    0.0  2.0       1.0     470.0  1\n",
+       "501          4.076     0.0    0.0  0.0       0.0      73.0  0\n",
+       "502          1.050     0.0    0.0  0.0       0.0     141.0  0\n",
+       "503          1.029     0.0    0.0  1.0       0.0     115.0  0\n",
+       "504          0.748     0.0    0.0  1.0       1.0     171.0  0\n",
+       "505          4.961     0.0    0.0  0.0       0.0     498.0  0\n",
+       "506          1.268     0.0    0.0  0.0       0.0     365.0  1\n",
+       "507          4.959     0.0    0.0  0.0       0.0      10.0  0\n",
+       "508          4.021     0.0    0.0  1.0       0.0     796.0  1\n",
+       "509          0.877     0.0    0.0  2.0       1.0     279.0  1\n",
+       "510          1.327     0.0    1.0  0.0       0.0     476.0  1\n",
+       "511          4.965     0.0    0.0  0.0       0.0     479.0  1\n",
+       "512          1.266     0.0    1.0  1.0       0.0     225.0  0\n",
+       "513          1.334     0.0    1.0  0.0       0.0     204.0  0\n",
+       "514          0.861     0.0    0.0  2.0       1.0     806.0  1\n",
+       "515          0.879     0.0    0.0  0.0       0.0     290.0  0\n",
+       "516          0.877     0.0    0.0  5.0       1.0     473.0  1\n",
+       "517          4.965     0.0    0.0  0.0       0.0     142.0  0\n",
+       "\n",
+       "[518 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We make sure to create a copy of the data before we start altering it. Note that we don't change the original data we loaded.\n",
+    "data = raw_data.copy()\n",
+    "# Removes the index column thata comes with the data\n",
+    "data = data.drop(['Unnamed: 0'], axis = 1)\n",
+    "# We use the map function to change any 'yes' values to 1 and 'no'values to 0. \n",
+    "data['y'] = data['y'].map({'yes':1, 'no':0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.835776</td>\n",
+       "      <td>0.034749</td>\n",
+       "      <td>0.266409</td>\n",
+       "      <td>0.388031</td>\n",
+       "      <td>0.127413</td>\n",
+       "      <td>382.177606</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.876903</td>\n",
+       "      <td>0.183321</td>\n",
+       "      <td>0.442508</td>\n",
+       "      <td>0.814527</td>\n",
+       "      <td>0.333758</td>\n",
+       "      <td>344.295990</td>\n",
+       "      <td>0.500483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.635000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.042750</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.466000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.956500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>482.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.970000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2653.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       interest_rate      credit       march         may    previous  \\\n",
+       "count     518.000000  518.000000  518.000000  518.000000  518.000000   \n",
+       "mean        2.835776    0.034749    0.266409    0.388031    0.127413   \n",
+       "std         1.876903    0.183321    0.442508    0.814527    0.333758   \n",
+       "min         0.635000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%         1.042750    0.000000    0.000000    0.000000    0.000000   \n",
+       "50%         1.466000    0.000000    0.000000    0.000000    0.000000   \n",
+       "75%         4.956500    0.000000    1.000000    0.000000    0.000000   \n",
+       "max         4.970000    1.000000    1.000000    5.000000    1.000000   \n",
+       "\n",
+       "          duration           y  \n",
+       "count   518.000000  518.000000  \n",
+       "mean    382.177606    0.500000  \n",
+       "std     344.295990    0.500483  \n",
+       "min       9.000000    0.000000  \n",
+       "25%     155.000000    0.000000  \n",
+       "50%     266.500000    0.500000  \n",
+       "75%     482.750000    1.000000  \n",
+       "max    2653.000000    1.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 'duration' as the independet variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression and graph the scatter plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.546118\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>          <td>Wed, 30 Jan 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>              <td>17:58:51</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   No. Observations:                  518\n",
+       "Model:                          Logit   Df Residuals:                      516\n",
+       "Method:                           MLE   Df Model:                            1\n",
+       "Date:                Wed, 30 Jan 2019   Pseudo R-squ.:                  0.2121\n",
+       "Time:                        17:58:51   Log-Likelihood:                -282.89\n",
+       "converged:                       True   LL-Null:                       -359.05\n",
+       "                                        LLR p-value:                 5.387e-35\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
+       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
+       "==============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "# Get the regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ys8PM7HGC6cIz26qAp8xsjZmNtyiRiIiUiVhJwswOAG4GdgJ6snY1AL8G3gjcb2ZvK1iEIiJSNHHvJL4OdAB7uPvPMhvdvc/dP0Gw8FAfr831JCIiZSxuktgDuMrdn8m1M9x+NbkXIxIRkTITN0nUAo3jHJMmmAlWRETKXNwk8ThwdNSIazObDxwN/GWygYmISPHFHSdxMXAdcI+ZfRNYRdBGkQDeDpwDLAY+V8ggRUSkOOKOk7jBzM4hWFfiqhyHpIFz3f3aQgQnIiLFFfdOAnf/tpldC3wA2B1oAbqANQSN2l7YEEVEpFhiJwkAd38auLDAsYiISInJK0mY2a7u/tes12cABxDMCnupu28qTHgiIlJMcUdczzGzO4Enwp5MmNkFBLPDngh8BXjEzJYUPFIREZl2cbvAngMcQjA1B2bWBPwbkAIOBD4GJAlGZouISJmL+7jpfcAf3f1oADM7FphN8IhpBbDCzI4EjixsmCIiUgxxk8T2wO+zXh9J0O31lqxt64F/nkhhZlZN8KhqOdAPnObu63IccxNwg7v/JGa8IiIyCXEfN20m6PKacSTQDTyQtW0X4MUJlncs0Oju+wFfAi7KccwFwPyYcYqISAHEvZNYDRxvZr8B9geWANe4+xCAmb2f4IP/NxMsb3/gVgB3XzV6LYqwvBG2vlOJ1NLSRG1tzQRPvbVkMpHX+8pNJdSzEuoIlVHPSqgjlHY94yaJc4E7gXsIJvHrA74FYGaXECxE1Ap8c4LlNQPtWa+HzazW3YfM7C0EPabezwQbwtvaesY/KIdkMkEq1ZnXe8tJJdSzEuoIlVHPSqgjlEY9x0pSsR43ufujBHM0/SD82tfd14S7HwZ+DLwtHGw3EZl5n16NJ3NXApwEbAfcDXwcOMvM3hMnXhERmZx8puV4GvhCju1XAlfGLG4lwayxvzWzfQmm9siUd3bmZzM7D9jk7rfGjVdERPKX74jrZoK2h+XAHIIG7UeAP7h7f4yirgMON7MHCB5fnWxmZwHr3P3GfGITEZHCiZ0kzOx04HsE4yOyFxdKAykzO9ndJ9TQ7O4jwCdHbX4qx3HnxY1TREQmL+60HMcBPyGY9fUrwKEEbRTHAt8FZgHXm9l+BY5TRESKIO6dxNkEvZf2dfe/j9p3o5ldQbAQ0QUECURERMpY3MF0uwHX5kgQALj7UwQjsveZbGAiIlJ8cZNEKzDeaLUeYOZ3bhYRqQBxk8RPgQ+b2R65dprZTgQD4H4x2cBERKT4xmyTMLMzR23qIhgA96CZXUkwZ9NLwDzgbQQD4FLAY4UPVUREptt4DdcXE3RtzXR1zf75lPArHb7ObJ8HXMP4j6VERKTEjZckTp6WKEREpCSNmSTc/ZfTFYiIiJSeuA3XIiJSQWINpjOzzRM8NO3u2+QRj4iIlJC4I647eK2hOlsTsA3Bncka4NlJxiUiIiUgVpJw96VR+8xsLnAOcCpwwuTCEhGRUlCwNgl3bw/XgPgf4DuFKldERIpnKhquHwAOnIJyRURkmk1FktiD3O0WIiJSZuL2bnpvxK4qghXq/gk4nGDFORERKXNxezddz9h3CVXARuCLeUckIiIlI26S+Aa5k0QaGCBYevQmdx+cbGAiIlJ8cbvAnjd6m5k1AkuATe7eVaC4RESkBEyo4drMjjazX5jZ7qO2f5tgISIH2szsGjPTSGsRkRli3CRhZj8haIs4Cdgla/u3CNoeZgF3AHcBxwH3mVnDlEQrIiLTaswkYWZHA6cDjwPvAW4Kt28HfJ6gLeIT7v4ed38P8D7gH4DRixWJiEgZGu9O4lTgFeBgd7/D3fvC7e8H6oB17n555mB3vxFYCRw/FcGKiMj0Gi9JvJ2gt1LnqO2HE9xF3JjjPQ+S9VhKRETK13hJYj6wIXuDmVUD+4cv78rxnkGgfvKhiYhIsY2XJNqBBaO2vR1oJkgGf8zxnmUEPZ5ERKTMjZckHgYOC+8eMk4Mv9/l7j3ZB5vZtsARwEOFC1FERIplvMF0PyXo/vobM/sRsBtBb6c0cGn2gWaWBH5L0CX214UPVUREptuYdxJhb6UfEfRWuge4hKC94cfufkvmODO7EXiOoK3iWne/YcoiFhGRaTPutBzu/q9mdi3BDK91wB3uftOow3YFOoFvAxdO9OThY6zLgOVAP3Cau6/L2v854IPhy5vd/fyJli0iIpM3obmb3P0+4L4xDtkrRzfZiTgWaHT3/cxsX+Ai4BgAM9sR+DCwD8HjrfvN7Dp3/0se5xERkTwUZNGhPBMEBI+nbg3LWAXsnbXveeA97j7s7iMEdzF9ry9CRESmStypwgutmaCbbcawmdW6+1A43XirmVUB3wVWu/vasQpraWmitrYmr0CSyURe7ys3lVDPSqgjVEY9K6GOUNr1LHaS6ACyfzvV7j6UeRFOQ345QXvHp8YrrK2tZ7xDckomE6RS+d4MlY9KqGcl1BEqo56VUEcojXqOlaSmYo3rOFYCRwGEbRJrMjvCO4gbgD+7+xnuPlycEEVEKlex7ySuAw43swcIlj492czOAtYBNcBBQIOZHRke/2V3/1NxQhURqTxFTRJhg/QnR21+KuvnxmkMR0RERin24yYRESlhShIiIhJJSUJERCIpSYiISCQlCRERiaQkISIikZQkREQkkpKEiIhEUpIQEZFIShIiIhJJSUJERCIpSYiISCQlCRERiaQkISIikZQkREQkkpKEiIhEUpIQEZFIShIiIhJJSUJERCIpSYiISCQlCRERiaQkISIikZQkREQkkpKEiIhEUpIQEZFIShIiIhJJSUJERCIpSYiISCQlCRERiVRb7ABKQXtXPyv+spGXNnfzxoXN9PQN8OLmHnoHBnnx5W56BwbpHUjTUFfN0kXNrN/QTlf/EM2Jero6+ximmsHBEYZGoGVOLXMaG0g01dLdN0xLcwOd3QM01NWyzbxG3jB/Fpvbe1m/qZORkTR1tdUkW5rYcbu5zG6oZd0LWxgeTrN0cTNvXDiHR9e28vxLHSxOJnjTwgStW3rpHxwiTZqqqiqaGuvY2xYyp6me9q5+ZjXU8tymTtZv6uDN28+jp2+IObPrSc6dxVBrN709A7R3D0A6zayGWjakukk01bE4OYeGuhr6B4dp7+qnprqKl9t6Wdgyi97+IQaHRl6NtaGuZqvfX//gMKktvZBO59w/GZl45s5p2Krc/sFhUm09UFVFct6s1+3L9R4Ria8qnU4X7eRmVg1cBiwH+oHT3H1d1v5PAGcAQ8AF7v6HscpLpTpjVWZgaIhvXPEIG1t7Ysc+01RXweIFs+npH2JzR3/kcQ111bxz90V86NBdALj6rqdZuWYTfQPDADTWV/OO3YL9NdX536gOj4xwzd3rWL02xeaOfuY3N7DnsiTvP3hHfnvPMzyw5kX6BkbCc9bwzt225fh37cTND77Ayj9v2Oo9Jxyy86RiKUXJZIJUqrPYYUypSqgjlEY9k8lEVdS+Yt9JHAs0uvt+ZrYvcBFwDICZbQucCewNNAIrzOwOd4/+BIvpwl89pgQRGknDC6nucY/rHxzh7kc3UF0V/E3d9eiGrfb3Dby2/8TDluUdzzV3r+POR1549fUrHf3c+cgL+N+38PzLXaPOOcxdj25g7fPtW+3LvAeYVCwilazY/73aH7gVwN1XESSEjLcDK929393bgXXA7oU6cWfPAC+M+rCRiXv0qZd51FOR+x/zFP2Dw3mV3T84zOq1ucvekIq+ZlH7Vq9tzTsWkUpX7DuJZqA96/WwmdW6+1COfZ3A3LEKa2lporZ2Ys+gNz6dongP2spfW9cAkfenQFtnPzX1dSQXzI5d9out3WzuzH3DODLGRYva19bZl3cspSyZTBQ7hClXCXWE0q5nsZNEB5D926kOE0SufQlgy1iFtbVN/NFRor6aKlCiyFPLnHqoqqIt4sO8JdHA8MBgXs9ahweHmZ9o4JUcbSPVVdHJIGpfS6Ix71hKVSk8x55qlVBHKI16jpWkiv24aSVwFEDYJrEma99DwAFm1mhmc4FdgScKdeJEUz1LFs4pVHEV561vXshbLRm5fy9L5t2zqKGuhj2X5S57u2T0NYvat+eyBerlJJKnYt9JXAccbmYPAFXAyWZ2FrDO3W80s0uA+wmS2Tnu3lfIk59z0l7q3RTK9G7q7R/K+T/4jEzvphMO2RmAdDo9qndTDe/YbdtX9+cr8/7Va1tp6+yjJdHInssWZPVu2vqcW/du2rjVeyYbi0glK2oX2EKL2wU2o35WPXc/uH7Gj5OYNbuR3u6+GT1OIplM8MLGLTN+nEQpPKKYapVQRyiNeo7VBVZJgtK4SNOhEupZCXWEyqhnJdQRSqOeYyWJYrdJiIhICVOSEBGRSEoSIiISSUlCREQiKUmIiEgkJQkREYmkJCEiIpFm1DgJEREpLN1JiIhIJCUJERGJpCQhIiKRlCRERCSSkoSIiERSkhARkUhKEiIiEqnYK9MVlZlVA5cBy4F+4DR3X1fcqCbHzFYD7eHLvwH/D/ghMATc7u7nl2u9zWwf4D/c/WAz2xm4gmCZ8ieAT7v7iJmdC/wjQX0/6+4PRR1bjDpMxKh67gX8N/B0uPvH7n5NOdfTzOqAy4GlQANwAfAkM+h6RtTxBcrwWlb6ncSxQKO77wd8CbioyPFMipk1Arj7weHXycBPgBOB/YF9wg+dsqu3mZ0N/BxoDDd9H/iqux9AsPTtMWHdDgL2AT4I/Cjq2OmMPY4c9dwL+H7WNb1mBtTzI8ArYZxHApcy865nrjqW5bWs6DsJgg/OWwHcfZWZ7V3keCZrOdBkZrcTXNvzgAZ3fwbAzG4DDgUWUX71fgZ4H3Bl+PqtwH3hz7cA7wac4G4pDfzdzGrNLBlx7HXTFXhMueppZnYMwf9AP0vwd1vO9fwdcG3W6yFm3vWMqmPZXctKv5No5rVHMwDDZlbOibMH+B5wBPBJ4BfhtoxOYC5lWG93/z0wmLWpKvyHBdH1ymzPdWxJylHPh4B/d/cDgWeBcynzerp7l7t3mlmC4IP0q8yw6xlRx7K8lpWeJDqARNbrancfKlYwBbAW+LW7p919LcEf3/ys/QlgCzOj3tnPZ6Pqldme69hycZ27P5r5GdiTGVBPM3sjcA9wpbtfxQy8njnqWJbXstKTxErgKAAz2xdYU9xwJu0UwvYFM1sMNAHdZraTmVUR3GHcz8yo92ozOzj8+Uheq9cRZlZtZtsTJL/WiGPLxW1m9vbw50OBRynzeprZG4DbgS+6++Xh5hl1PSPqWJbXsqQfMUyD64DDzewBgsahk4scz2T9J3CFma0g6BVxCsH/SP4LqCF49vmgmT1M+df788DPzKwe+CtwrbsPm9n9wJ8I/gP06ahjixFwnv4FuNTMBoBNwOnu3lHm9fwK0AJ8zcy+Fm77N+CSGXQ9c9XxLODicruWmipcREQiVfrjJhERGYOShIiIRFKSEBGRSEoSIiISSUlCREQiVXoXWJmhzOw8ghGt2dJAL7CRYJDT9939qWkO7XXMbC7wUXe/NGvbvQRz+rS4e0kOFpPKoCQhM90NwOPhz9UE0yAsBz4BfMTMjnf3m4oVXGgt8CLBJHAZVwD3An1FiEfkVUoSMtNd7+5XjN5oZkcRDKa8xsz2KPJU6QsJksSrcsUsUgxqk5CK5O43A18DZoffRSQH3UlIJbsUOB84zsxOJZi2+R7gh+7+2ewDzewK4GPAnu7+uJktJVjU6ZvAPOBUgvaOf3H335nZAuCLwD8BbwqL+RvBFCnfcfehcG6ee8J9y80sDZzv7uflapMIF4s6AzgdeDMwQDCz6Hfc/Y6sWDOxnQ88RjAD6W4Es4neAHw5nB9IZFy6k5CK5e49BB+is4E98izmdOADwI+BVcCqsCH6QYL1Ap4kWBnwKoJ1PC4E/m/43vUEH+QAL4U/35vrJGGCuJpgRcFmglXPrgfeRjBx3KdyvO1ogkdqLwKXABuA08JyRCZEdxJS6TaE3xcR/E87roUEdxd/zmwwsy8BOwKfcPefZ20/n2CxmROBL7j7euC8cPnKTe5+3hjn+TBwPHAbcJy7d4dl7gisAH5oZre6+7NZ79kL+IC7/y489qvAauBQM9spsxiVyFh0JyGVrj8RQ2PtAAAChklEQVT83pzn+5/OThCh2wgWffpl9kZ3f55gsZmFeZzn4+H3T2USRFjmswR3J7XASaPe82wmQYTHDgJ3hi93ySMGqUC6k5BKl1nwpSvP968fvcHdVxOsBzAnXK9jZ2AZwaOhXQimbY9rD2DDqDuFjBXh9+Wjtq/NcWxmFbSGPGKQCqQkIZVuafj9WWCbPN7fO3qDmTUC3yJoZG4KN28A/gikCB5txdVMsAZBLhvD702jtvePPpBgQCEE64iIjEuPm6RimVkL8H8IloZ8krE/QEd/AI/lIuBzwM3Au4Bt3H2Ju5/I1usZx9EJLI7Y1xJ+fyXPskUiKUlIJTuD4G76GncfJuhSCjAnx7E7xSj3ROBlgkbje919M4CZzSLsDhsuJxvH48A8M3tLjn0Hht//J2aZIuNSkpCKZGaHAF8naIv4drh5HTAMHGJmTVnH/iNBT6GJ6gMaCcZPZMqoIegKOyvcVJd1/CBQP06ZV4Tff2hms7PK3SGsxyDq2ipTQG0SMtMdGw4ug+Ax0lyCD/wDCNoTPujuzwG4e8rMrgeOAx4ys5sIurIeQ9A4vP8Ez/lr4AvAI2F5tcARgBG0SSQJ2j8yU3FsAN5sZj8Gbnb3/85R5pXAe8PY/mJmtxDc8RwT1ukz6tIqU0FJQma6Y8KvjB6CHkmXAhfn+GA9haAh+HjgTOAJgg/mnZh4kjiHoA3ho8CnCBLDk2F5uwIXA0cB/xke/xmCwW6nENxhvC5JuHvazD4AfJpgdPepYV3+BHzX3e8Z/R6RQqhKp9PjHyUiIhVJbRIiIhJJSUJERCIpSYiISCQlCRERiaQkISIikZQkREQkkpKEiIhEUpIQEZFIShIiIhLpfwEQSNCOQ/dCjQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x28ec539a8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot of x1 (Duration, no constant) and y (Subscribed)\n",
+    "plt.scatter(x1,y,color = 'C0')\n",
+    "# Don't forget to label your axes!\n",
+    "plt.xlabel('Duration', fontsize = 20)\n",
+    "plt.ylabel('Subscription', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Expand the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can be omitting many causal factors in our simple logistic model, so we instead switch to a multivariate logistic regression model. Add the ‘interest_rate’, ‘march’, ‘credit’ and ‘previous’ estimators to our model and run the regression again. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the independent variable(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To avoid writing them out every time, we save the names of the estimators of our model in a list. \n",
+    "estimators=['interest_rate','march','credit','previous','duration']\n",
+    "\n",
+    "X1 = data[estimators]\n",
+    "y = data['y']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.336664\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.514</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <td>Dependent Variable:</td>         <td>y</td>              <td>AIC:</td>         <td>360.7836</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "         <td>Date:</td>        <td>2019-01-30 17:58</td>       <td>BIC:</td>         <td>386.2834</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "   <td>No. Observations:</td>         <td>518</td>        <td>Log-Likelihood:</td>    <td>-174.39</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "       <td>Df Model:</td>              <td>5</td>            <td>LL-Null:</td>        <td>-359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "     <td>Df Residuals:</td>           <td>512</td>         <td>LLR p-value:</td>    <td>1.2114e-77</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>         <td>1.0000</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "    <td>No. Iterations:</td>        <td>7.0000</td>              <td></td>               <td></td>     \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td></td>         <th>Coef.</th>  <th>Std.Err.</th>    <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>         <td>-0.0211</td>  <td>0.3113</td>  <td>-0.0677</td> <td>0.9460</td> <td>-0.6313</td> <td>0.5891</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>interest_rate</th> <td>-0.8001</td>  <td>0.0895</td>  <td>-8.9434</td> <td>0.0000</td> <td>-0.9755</td> <td>-0.6248</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>march</th>         <td>-1.8322</td>  <td>0.3297</td>  <td>-5.5563</td> <td>0.0000</td> <td>-2.4785</td> <td>-1.1859</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>credit</th>        <td>2.3585</td>   <td>1.0875</td>  <td>2.1688</td>  <td>0.0301</td> <td>0.2271</td>  <td>4.4900</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>previous</th>      <td>1.5363</td>   <td>0.5010</td>  <td>3.0666</td>  <td>0.0022</td> <td>0.5544</td>  <td>2.5182</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th>      <td>0.0070</td>   <td>0.0007</td>  <td>9.3810</td>  <td>0.0000</td> <td>0.0055</td>  <td>0.0084</td> \n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary2.Summary'>\n",
+       "\"\"\"\n",
+       "                         Results: Logit\n",
+       "=================================================================\n",
+       "Model:              Logit            Pseudo R-squared: 0.514     \n",
+       "Dependent Variable: y                AIC:              360.7836  \n",
+       "Date:               2019-01-30 17:58 BIC:              386.2834  \n",
+       "No. Observations:   518              Log-Likelihood:   -174.39   \n",
+       "Df Model:           5                LL-Null:          -359.05   \n",
+       "Df Residuals:       512              LLR p-value:      1.2114e-77\n",
+       "Converged:          1.0000           Scale:            1.0000    \n",
+       "No. Iterations:     7.0000                                       \n",
+       "-----------------------------------------------------------------\n",
+       "                   Coef.  Std.Err.    z    P>|z|   [0.025  0.975]\n",
+       "-----------------------------------------------------------------\n",
+       "const             -0.0211   0.3113 -0.0677 0.9460 -0.6313  0.5891\n",
+       "interest_rate     -0.8001   0.0895 -8.9434 0.0000 -0.9755 -0.6248\n",
+       "march             -1.8322   0.3297 -5.5563 0.0000 -2.4785 -1.1859\n",
+       "credit             2.3585   1.0875  2.1688 0.0301  0.2271  4.4900\n",
+       "previous           1.5363   0.5010  3.0666 0.0022  0.5544  2.5182\n",
+       "duration           0.0070   0.0007  9.3810 0.0000  0.0055  0.0084\n",
+       "=================================================================\n",
+       "\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = sm.add_constant(X1)\n",
+    "reg_logit = sm.Logit(y,X)\n",
+    "results_logit = reg_logit.fit()\n",
+    "results_logit.summary2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the confusion matrix of the model and estimate its accuracy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def confusion_matrix(data,actual_values,model):\n",
+    "        \n",
+    "        # Confusion matrix \n",
+    "        \n",
+    "        # Parameters\n",
+    "        # ----------\n",
+    "        # data: data frame or array\n",
+    "            # data is a data frame formatted in the same way as your input data (without the actual values)\n",
+    "            # e.g. const, var1, var2, etc. Order is very important!\n",
+    "        # actual_values: data frame or array\n",
+    "            # These are the actual values from the test_data\n",
+    "            # In the case of a logistic regression, it should be a single column with 0s and 1s\n",
+    "            \n",
+    "        # model: a LogitResults object\n",
+    "            # this is the variable where you have the fitted model \n",
+    "            # e.g. results_log in this course\n",
+    "        # ----------\n",
+    "        \n",
+    "        #Predict the values using the Logit model\n",
+    "        pred_values = model.predict(data)\n",
+    "        # Specify the bins \n",
+    "        bins=np.array([0,0.5,1])\n",
+    "        # Create a histogram, where if values are between 0 and 0.5 tell will be considered 0\n",
+    "        # if they are between 0.5 and 1, they will be considered 1\n",
+    "        cm = np.histogram2d(actual_values, pred_values, bins=bins)[0]\n",
+    "        # Calculate the accuracy\n",
+    "        accuracy = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "        # Return the confusion matrix and \n",
+    "        return cm, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[218.,  41.],\n",
+       "        [ 30., 229.]]), 0.862934362934363)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(X,y,results_logit)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Categorical+data_with_comments.ipynb b/Udemy DataScience Workbook/Categorical+data_with_comments.ipynb
new file mode 100644
index 0000000..7aef7b9
--- /dev/null
+++ b/Udemy DataScience Workbook/Categorical+data_with_comments.ipynb	
@@ -0,0 +1,641 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of cluster analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we explore how to include categorical data into a clustering algorithm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "# Set the styles to Seaborn\n",
+    "sns.set()\n",
+    "# Import the KMeans module so we can perform k-means clustering with sklearn\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the country clusters data\n",
+    "data = pd.read_csv('3.01.+Country+clusters.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Language</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.97</td>\n",
+       "      <td>-103.77</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>62.40</td>\n",
+       "      <td>-96.80</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>France</td>\n",
+       "      <td>46.75</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>French</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UK</td>\n",
+       "      <td>54.01</td>\n",
+       "      <td>-2.53</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>51.15</td>\n",
+       "      <td>10.40</td>\n",
+       "      <td>German</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>-25.45</td>\n",
+       "      <td>133.11</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Country  Latitude  Longitude Language\n",
+       "0        USA     44.97    -103.77  English\n",
+       "1     Canada     62.40     -96.80  English\n",
+       "2     France     46.75       2.40   French\n",
+       "3         UK     54.01      -2.53  English\n",
+       "4    Germany     51.15      10.40   German\n",
+       "5  Australia    -25.45     133.11  English"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check out the data manually \n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Map the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Language</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.97</td>\n",
+       "      <td>-103.77</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>62.40</td>\n",
+       "      <td>-96.80</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>France</td>\n",
+       "      <td>46.75</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UK</td>\n",
+       "      <td>54.01</td>\n",
+       "      <td>-2.53</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>51.15</td>\n",
+       "      <td>10.40</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>-25.45</td>\n",
+       "      <td>133.11</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Country  Latitude  Longitude  Language\n",
+       "0        USA     44.97    -103.77         0\n",
+       "1     Canada     62.40     -96.80         0\n",
+       "2     France     46.75       2.40         1\n",
+       "3         UK     54.01      -2.53         0\n",
+       "4    Germany     51.15      10.40         2\n",
+       "5  Australia    -25.45     133.11         0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a copy of the original dataset\n",
+    "data_mapped = data.copy()\n",
+    "# Map languages with 0, 1, and 2. Note that this is not the best way to do that, but for now we will use it \n",
+    "data_mapped['Language']=data_mapped['Language'].map({'English':0,'French':1,'German':2})\n",
+    "# Check if we did it correctly\n",
+    "data_mapped"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iloc is a method used to 'slice' data \n",
+    "# 'slice' is not technically correct as there are methods 'slice' which are a bit different\n",
+    "# The term used by pandas is 'selection by position'\n",
+    "# The first argument of identifies the rows we want to keep\n",
+    "# The second - the columns\n",
+    "# When choosing the columns, e.g. a:b, we will keep columns a,a+1,a+2,...,b-1 ; so column b is excluded\n",
+    "x = data_mapped.iloc[:,1:4]\n",
+    "# for this particular case, we are choosing columns 1 and 2\n",
+    "# Note column indices in Python start from 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Language</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>44.97</td>\n",
+       "      <td>-103.77</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>62.40</td>\n",
+       "      <td>-96.80</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>46.75</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54.01</td>\n",
+       "      <td>-2.53</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>51.15</td>\n",
+       "      <td>10.40</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-25.45</td>\n",
+       "      <td>133.11</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Latitude  Longitude  Language\n",
+       "0     44.97    -103.77         0\n",
+       "1     62.40     -96.80         0\n",
+       "2     46.75       2.40         1\n",
+       "3     54.01      -2.53         0\n",
+       "4     51.15      10.40         2\n",
+       "5    -25.45     133.11         0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check if we worked correctly\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an object (which we would call kmeans)\n",
+    "# The number in the brackets is K, or the number of clusters we are aiming for\n",
+    "kmeans = KMeans(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit the input data, i.e. cluster the data in X in K clusters\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are many ways to do this part, we found this to be the most illustrative one"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, 1, 1, 0])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a variable which will contain the predicted clusters for each observation\n",
+    "identified_clusters = kmeans.fit_predict(x)\n",
+    "# Check the result\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Language</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.97</td>\n",
+       "      <td>-103.77</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>62.40</td>\n",
+       "      <td>-96.80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>France</td>\n",
+       "      <td>46.75</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UK</td>\n",
+       "      <td>54.01</td>\n",
+       "      <td>-2.53</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>51.15</td>\n",
+       "      <td>10.40</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>-25.45</td>\n",
+       "      <td>133.11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Country  Latitude  Longitude  Language  Cluster\n",
+       "0        USA     44.97    -103.77         0        1\n",
+       "1     Canada     62.40     -96.80         0        1\n",
+       "2     France     46.75       2.40         1        1\n",
+       "3         UK     54.01      -2.53         0        1\n",
+       "4    Germany     51.15      10.40         2        1\n",
+       "5  Australia    -25.45     133.11         0        0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a copy of the mapped data\n",
+    "data_with_clusters = data_mapped.copy()\n",
+    "# Create a new Series, containing the identified cluster for each observation\n",
+    "data_with_clusters['Cluster'] = identified_clusters\n",
+    "# Check the result\n",
+    "data_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data using the longitude and the latitude\n",
+    "# c (color) is an argument which could be coded with a variable \n",
+    "# The variable in this case has values 0,1,2, indicating to plt.scatter, that there are three colors (0,1,2)\n",
+    "# All points in cluster 0 will be the same colour, all points in cluster 1 - another one, etc.\n",
+    "# cmap is the color map. Rainbow is a nice one, but you can check others here: https://matplotlib.org/users/colormaps.html\n",
+    "plt.scatter(data_with_clusters['Longitude'],data_with_clusters['Latitude'],c=data_with_clusters['Cluster'],cmap='rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Categorical.csv b/Udemy DataScience Workbook/Categorical.csv
new file mode 100644
index 0000000..51cea6c
--- /dev/null
+++ b/Udemy DataScience Workbook/Categorical.csv	
@@ -0,0 +1,242 @@
+name,Longitude,Latitude,continent
+Aruba,-69.98267711,12.52088038,North America
+Afghanistan,66.00473366,33.83523073,Asia
+Angola,17.53736768,-12.29336054,Africa
+Anguilla,-63.06498927,18.2239595,North America
+Albania,20.04983396,41.14244989,Europe
+Aland,19.95328768,60.21488688,Europe
+Andorra,1.56054378,42.54229102,Europe
+United Arab Emirates,54.3001671,23.90528188,Asia
+Argentina,-65.17980692,-35.3813488,South America
+Armenia,44.92993276,40.28952569,Asia
+American Samoa,-170.7180258,-14.30445997,Oceania
+Antarctica,19.92108951,-80.50857913,Antarctica
+Ashmore and Cartier Is.,123.5838379,-12.42993164,Oceania
+Fr. S. Antarctic Lands,69.22666758,-49.24895485,Seven seas (open ocean)
+Antigua and Barb.,-61.79469343,17.2774996,North America
+Australia,134.4910001,-25.73288704,Oceania
+Austria,14.1264761,47.58549439,Europe
+Azerbaijan,47.54599879,40.28827235,Asia
+Burundi,29.87512156,-3.35939666,Africa
+Belgium,4.64065114,50.63981576,Europe
+Benin,2.32785254,9.6417597,Africa
+Burkina Faso,-1.75456601,12.26953846,Africa
+Bangladesh,90.23812743,23.86731158,Asia
+Bulgaria,25.21552909,42.76890318,Europe
+Bahrain,50.54196932,26.04205135,Asia
+Canada,-98.30777028,61.36206324,North America
+Bahamas,-76.62843038,24.29036702,North America
+Bosnia and Herz.,17.76876733,44.17450125,Europe
+St-Barthélemy,-62.84067779,17.89880451,North America
+Belarus,28.03209307,53.53131377,Europe
+Belize,-88.71010486,17.20027509,North America
+Bermuda,-64.7545589,32.31367802,North America
+Bolivia,-64.68538645,-16.70814787,South America
+Brazil,-53.09783113,-10.78777702,South America
+Barbados,-59.559797,13.18145428,North America
+Brunei,114.7220304,4.51968958,Asia
+Bhutan,90.40188155,27.41106589,Asia
+Botswana,23.79853368,-22.18403213,Africa
+Central African Rep.,20.46826831,6.56823297,Africa
+Switzerland,8.20867471,46.79785878,Europe
+Chile,-71.38256213,-37.73070989,South America
+China,103.8190735,36.56176546,Asia
+Côte d'Ivoire,-5.5692157,7.6284262,Africa
+Cameroon,12.73964156,5.69109849,Africa
+Dem. Rep. Congo,23.64396107,-2.87746289,Africa
+Congo,15.21965762,-0.83787463,Africa
+Cook Is.,-159.7872422,-21.21927288,Oceania
+Colombia,-73.08114582,3.91383431,South America
+Comoros,43.68253968,-11.87783444,Africa
+Cape Verde,-23.9598882,15.95523324,Africa
+Costa Rica,-84.19208768,9.97634464,North America
+Cuba,-79.01605384,21.62289528,North America
+Curaçao,-68.97119369,12.19551675,North America
+Cayman Is.,-80.91213321,19.42896497,North America
+N. Cyprus,33.5684813,35.26277486,Asia
+Cyprus,33.0060022,34.91667211,Asia
+Czech Rep.,15.31240163,49.73341233,Europe
+Germany,10.38578051,51.10698181,Europe
+Djibouti,42.5606754,11.74871806,Africa
+Dominica,-61.357726,15.4394702,North America
+Denmark,10.02800992,55.98125296,Europe
+Dominican Rep.,-70.50568896,18.89433082,North America
+Algeria,2.61732301,28.15893849,Africa
+Ecuador,-78.75201922,-1.42381612,South America
+Egypt,29.86190099,26.49593311,Africa
+Eritrea,38.84617011,15.36186618,Africa
+Spain,-3.64755047,40.24448698,Europe
+Estonia,25.54248537,58.67192972,Europe
+Ethiopia,39.60080098,8.62278679,Africa
+Finland,26.2746656,64.49884603,Europe
+Fiji,165.4519543,-17.42858032,Oceania
+Falkland Is.,-59.35238956,-51.74483954,South America
+France,-2.76172945,42.17344011,Europe
+Faeroe Is.,-6.88095423,62.05385403,Europe
+Micronesia,153.2394379,7.45246814,Oceania
+Gabon,11.7886287,-0.58660025,Africa
+United Kingdom,-2.86563164,54.12387156,Europe
+Georgia,43.50780252,42.16855755,Asia
+Guernsey,-2.57239064,49.46809761,Europe
+Ghana,-1.21676566,7.95345644,Africa
+Guinea,-10.94066612,10.43621593,Africa
+Gambia,-15.39601295,13.44965244,Africa
+Guinea-Bissau,-14.94972445,12.04744948,Africa
+Eq. Guinea,10.34137924,1.70555135,Africa
+Greece,22.95555794,39.07469623,Europe
+Grenada,-61.68220189,12.11725044,North America
+Greenland,-41.34191127,74.71051289,North America
+Guatemala,-90.36482009,15.69403664,North America
+Guam,144.7679102,13.44165626,Oceania
+Guyana,-58.98202459,4.79378034,South America
+Hong Kong,114.1138045,22.39827737,Asia
+Heard I. and McDonald Is.,73.5205171,-53.08724656,Seven seas (open ocean)
+Honduras,-86.6151661,14.82688165,North America
+Croatia,16.40412899,45.08047631,Europe
+Haiti,-72.68527509,18.93502563,North America
+Hungary,19.39559116,47.16277506,Europe
+Indonesia,117.2401137,-2.21505456,Asia
+Isle of Man,-4.53873952,54.22418911,Europe
+India,79.6119761,22.88578212,Asia
+Indian Ocean Ter.,104.851898,-10.6478515,Asia
+Br. Indian Ocean Ter.,72.44541229,-7.33059751,Seven seas (open ocean)
+Ireland,-8.13793569,53.1754487,Europe
+Iran,54.27407004,32.57503292,Asia
+Iraq,43.74353149,33.03970582,Asia
+Iceland,-18.57396167,64.99575386,Europe
+Israel,35.00444693,31.46110101,Asia
+Italy,12.07001339,42.79662641,Europe
+Jamaica,-77.31482593,18.15694878,North America
+Jersey,-2.12689938,49.21837377,Europe
+Jordan,36.77136104,31.24579091,Asia
+Japan,138.0308956,37.59230135,Asia
+Siachen Glacier,77.18011865,35.39236325,Asia
+Kazakhstan,67.29149357,48.15688067,Asia
+Kenya,37.79593973,0.59988022,Africa
+Kyrgyzstan,74.54165513,41.46221943,Asia
+Cambodia,104.9069433,12.72004786,Asia
+Kiribati,-45.61110513,0.86001503,Oceania
+St. Kitts and Nevis,-62.68755265,17.2645995,North America
+Korea,127.8391609,36.38523983,Asia
+Kosovo,20.87249811,42.57078707,Europe
+Kuwait,47.58700459,29.33431262,Asia
+Lao PDR,103.7377241,18.50217433,Asia
+Lebanon,35.88016072,33.92306631,Asia
+Liberia,-9.32207573,6.45278492,Africa
+Libya,18.00866169,27.03094495,Africa
+Saint Lucia,-60.96969923,13.89479481,North America
+Liechtenstein,9.53574312,47.13665835,Europe
+Sri Lanka,80.70108238,7.61266509,Asia
+Lesotho,28.22723131,-29.58003188,Africa
+Lithuania,23.88719355,55.32610984,Europe
+Luxembourg,6.07182201,49.76725361,Europe
+Latvia,24.91235983,56.85085163,Europe
+Macao,113.5093212,22.22311688,Asia
+St-Martin,-63.05972851,18.08888611,North America
+Morocco,-8.45615795,29.83762955,Africa
+Monaco,7.40627677,43.75274627,Europe
+Moldova,28.45673372,47.19498804,Europe
+Madagascar,46.70473674,-19.37189587,Africa
+Maldives,73.45713004,3.7287092,Seven seas (open ocean)
+Mexico,-102.5234517,23.94753724,North America
+Marshall Is.,170.3397612,7.00376358,Oceania
+Macedonia,21.68211346,41.59530893,Europe
+Mali,-3.54269065,17.34581581,Africa
+Malta,14.40523316,35.92149632,Europe
+Myanmar,96.48843321,21.18566599,Asia
+Montenegro,19.23883939,42.78890259,Europe
+Mongolia,103.0529977,46.82681544,Asia
+N. Mariana Is.,145.6196965,15.82927563,Oceania
+Mozambique,35.53367543,-17.27381643,Africa
+Mauritania,-10.34779815,20.25736706,Africa
+Montserrat,-62.18518546,16.73941406,North America
+Mauritius,57.57120551,-20.27768704,Seven seas (open ocean)
+Malawi,34.28935599,-13.21808088,Africa
+Malaysia,109.6976228,3.78986846,Asia
+Namibia,17.20963567,-22.13032568,Africa
+New Caledonia,165.6849237,-21.29991806,Oceania
+Niger,9.38545882,17.41912493,Africa
+Norfolk Island,167.9492168,-29.0514609,Oceania
+Nigeria,8.08943895,9.59411452,Africa
+Nicaragua,-85.0305297,12.84709429,North America
+Niue,-169.8699468,-19.04945708,Oceania
+Netherlands,5.28144793,52.1007899,Europe
+Norway,15.34834656,68.75015572,Europe
+Nepal,83.9158264,28.24891365,Asia
+Nauru,166.9325682,-0.51912639,Oceania
+New Zealand,171.4849235,-41.81113557,Oceania
+Oman,56.09166155,20.60515333,Asia
+Pakistan,69.33957937,29.9497515,Asia
+Panama,-80.11915156,8.51750797,North America
+Pitcairn Is.,-128.317042,-24.36500535,Oceania
+Peru,-74.38242685,-9.15280381,South America
+Philippines,122.8839325,11.77536778,Asia
+Palau,134.4080797,7.28742784,Oceania
+Papua New Guinea,145.2074475,-6.46416646,Oceania
+Poland,19.39012835,52.12759564,Europe
+Puerto Rico,-66.47307604,18.22813055,North America
+Dem. Rep. Korea,127.1924797,40.15350311,Asia
+Portugal,-8.50104361,39.59550671,Europe
+Paraguay,-58.40013703,-23.22823913,South America
+Palestine,35.19628705,31.91613893,Asia
+Fr. Polynesia,-144.9049439,-14.72227409,Oceania
+Qatar,51.18479632,25.30601188,Asia
+Romania,24.97293039,45.85243127,Europe
+Russia,96.68656112,61.98052209,Europe
+Rwanda,29.91988515,-1.99033832,Africa
+W. Sahara,-12.21982755,24.22956739,Africa
+Saudi Arabia,44.53686271,24.12245841,Asia
+Sudan,29.94046812,15.99035669,Africa
+S. Sudan,30.24790002,7.30877945,Africa
+Senegal,-14.4734924,14.36624173,Africa
+Singapore,103.8172559,1.35876087,Asia
+S. Geo. and S. Sandw. Is.,-36.43318388,-54.46488248,Seven seas (open ocean)
+Saint Helena,-9.54779416,-12.40355951,Seven seas (open ocean)
+Solomon Is.,159.6328767,-8.92178022,Oceania
+Sierra Leone,-11.79271247,8.56329593,Africa
+El Salvador,-88.87164469,13.73943744,North America
+San Marino,12.45922334,43.94186747,Europe
+Somaliland,46.25198395,9.73345496,Africa
+Somalia,45.70714487,4.75062876,Africa
+St. Pierre and Miquelon,-56.30319779,46.91918789,North America
+Serbia,20.78958334,44.2215032,Europe
+São Tomé and Principe,6.72429658,0.44391445,Africa
+Suriname,-55.9123457,4.13055413,South America
+Slovakia,19.47905218,48.70547528,Europe
+Slovenia,14.80444238,46.11554772,Europe
+Sweden,16.74558049,62.77966519,Europe
+Swaziland,31.4819369,-26.55843045,Africa
+Sint Maarten,-63.05713363,18.05081728,North America
+Seychelles,55.47603279,-4.66099094,Seven seas (open ocean)
+Syria,38.50788204,35.02547389,Asia
+Turks and Caicos Is.,-71.97387881,21.83047572,North America
+Chad,18.64492513,15.33333758,Africa
+Togo,0.96232845,8.52531356,Africa
+Thailand,101.0028813,15.11815794,Asia
+Tajikistan,71.01362631,38.5304539,Asia
+Turkmenistan,59.37100021,39.11554137,Asia
+Timor-Leste,125.8443898,-8.82889162,Asia
+Tonga,-174.8098734,-20.42843174,Oceania
+Trinidad and Tobago,-61.26567923,10.45733408,North America
+Tunisia,9.55288359,34.11956246,Africa
+Turkey,35.16895346,39.0616029,Asia
+Taiwan,120.9542728,23.7539928,Asia
+Tanzania,34.81309981,-6.27565408,Africa
+Uganda,32.36907971,1.27469299,Africa
+Ukraine,31.38326469,48.99656673,Europe
+Uruguay,-56.01807053,-32.79951534,South America
+United States,-112.4616737,45.6795472,North America
+Uzbekistan,63.14001528,41.75554225,Asia
+Vatican,12.43387177,41.90174985,Europe
+St. Vin. and Gren.,-61.20129695,13.22472269,North America
+Venezuela,-66.18184123,7.12422421,South America
+British Virgin Is.,-64.47146992,18.52585755,North America
+U.S. Virgin Is.,-64.80301538,17.95500624,North America
+Vietnam,106.299147,16.6460167,Asia
+Vanuatu,167.6864464,-16.22640909,Oceania
+Wallis and Futuna Is.,-177.3483483,-13.88737039,Oceania
+Samoa,-172.1648506,-13.75324346,Oceania
+Yemen,47.58676189,15.90928005,Asia
+South Africa,25.08390093,-29.00034095,Africa
+Zambia,27.77475946,-13.45824152,Africa
+Zimbabwe,29.8514412,-19.00420419,Africa
diff --git a/Udemy DataScience Workbook/Clustering+Categorical+Data+-+Exercise.ipynb b/Udemy DataScience Workbook/Clustering+Categorical+Data+-+Exercise.ipynb
new file mode 100644
index 0000000..cc6f602
--- /dev/null
+++ b/Udemy DataScience Workbook/Clustering+Categorical+Data+-+Exercise.ipynb	
@@ -0,0 +1,749 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clustering Categorical Data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given much more country data. Using the same methodology as the one in the lecture, group all the countries in 2 clusters. \n",
+    "\n",
+    "<b> Already done that? Okay! </b>\n",
+    "    \n",
+    "There are other features: name and continent. \n",
+    "\n",
+    "Encode the continent one and use it in the clustering solution. Think about the difference with the previous exercise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'Categorical.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "      <td>North America</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "      <td>Asia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "      <td>Africa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "      <td>North America</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "      <td>Oceania</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "      <td>Asia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "      <td>Africa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "      <td>Africa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "      <td>Africa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude      continent\n",
+       "0           Aruba  -69.982677  12.520880  North America\n",
+       "1     Afghanistan   66.004734  33.835231           Asia\n",
+       "2          Angola   17.537368 -12.293361         Africa\n",
+       "3        Anguilla  -63.064989  18.223959  North America\n",
+       "4         Albania   20.049834  41.142450         Europe\n",
+       "..            ...         ...        ...            ...\n",
+       "236         Samoa -172.164851 -13.753243        Oceania\n",
+       "237         Yemen   47.586762  15.909280           Asia\n",
+       "238  South Africa   25.083901 -29.000341         Africa\n",
+       "239        Zambia   27.774759 -13.458242         Africa\n",
+       "240      Zimbabwe   29.851441 -19.004204         Africa\n",
+       "\n",
+       "[241 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Categorical.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      North America\n",
+       "1               Asia\n",
+       "2             Africa\n",
+       "3      North America\n",
+       "4             Europe\n",
+       "           ...      \n",
+       "236          Oceania\n",
+       "237             Asia\n",
+       "238           Africa\n",
+       "239           Africa\n",
+       "240           Africa\n",
+       "Name: continent, Length: 241, dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data['continent']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remove the duplicate index column from the dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Map the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the <i>'continent'</i> category for this analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a copy of the original dataset\n",
+    "data_mapped = raw_data.copy()\n",
+    "data_mapped['continent']= data_mapped['continent'].map({'North America':0, 'Europe':1, 'Asia':2, 'Africa':3, 'South America': 4, 'Oceania':5, 'Seven seas (open ocean)':6, 'Antarctica': 7})\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude  continent\n",
+       "0           Aruba  -69.982677  12.520880          0\n",
+       "1     Afghanistan   66.004734  33.835231          2\n",
+       "2          Angola   17.537368 -12.293361          3\n",
+       "3        Anguilla  -63.064989  18.223959          0\n",
+       "4         Albania   20.049834  41.142450          1\n",
+       "..            ...         ...        ...        ...\n",
+       "236         Samoa -172.164851 -13.753243          5\n",
+       "237         Yemen   47.586762  15.909280          2\n",
+       "238  South Africa   25.083901 -29.000341          3\n",
+       "239        Zambia   27.774759 -13.458242          3\n",
+       "240      Zimbabwe   29.851441 -19.004204          3\n",
+       "\n",
+       "[241 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_mapped['continent'] = data_mapped['continent'].astype(int)\n",
+    "data_mapped"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data_mapped.iloc[:,3:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 4 clusters initially."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=4)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(4)\n",
+    "kmeans.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 3, 0, 1, 1, 1, 1, 3, 0, 3, 2, 2, 2, 2, 1, 2, 1, 3, 0, 1, 0, 0,\n",
+       "       3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 3, 3, 0, 0, 1, 0, 3, 0, 0,\n",
+       "       0, 0, 2, 0, 0, 0, 1, 1, 1, 1, 3, 3, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0,\n",
+       "       1, 1, 0, 1, 2, 0, 1, 1, 2, 0, 1, 3, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
+       "       2, 0, 3, 2, 1, 1, 1, 1, 3, 1, 3, 3, 2, 1, 3, 3, 1, 3, 1, 1, 1, 3,\n",
+       "       3, 3, 3, 0, 3, 3, 2, 1, 3, 1, 3, 3, 3, 0, 0, 1, 1, 3, 0, 1, 1, 1,\n",
+       "       3, 1, 0, 1, 1, 0, 2, 1, 2, 1, 0, 1, 3, 1, 3, 2, 0, 0, 1, 2, 0, 3,\n",
+       "       0, 2, 0, 2, 0, 1, 2, 1, 1, 3, 2, 2, 3, 3, 1, 2, 0, 3, 2, 2, 1, 1,\n",
+       "       3, 1, 0, 3, 2, 3, 1, 1, 0, 0, 3, 0, 0, 0, 3, 2, 2, 2, 0, 1, 1, 0,\n",
+       "       0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 2, 3, 1, 0, 0, 3, 3, 3, 3, 2, 1, 0,\n",
+       "       3, 3, 0, 0, 1, 0, 1, 3, 1, 1, 0, 1, 1, 3, 2, 2, 2, 3, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "identified_clusters = kmeans.fit_predict(X)\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude  continent  Cluster\n",
+       "0           Aruba  -69.982677  12.520880          0        1\n",
+       "1     Afghanistan   66.004734  33.835231          2        3\n",
+       "2          Angola   17.537368 -12.293361          3        0\n",
+       "3        Anguilla  -63.064989  18.223959          0        1\n",
+       "4         Albania   20.049834  41.142450          1        1\n",
+       "..            ...         ...        ...        ...      ...\n",
+       "236         Samoa -172.164851 -13.753243          5        2\n",
+       "237         Yemen   47.586762  15.909280          2        3\n",
+       "238  South Africa   25.083901 -29.000341          3        0\n",
+       "239        Zambia   27.774759 -13.458242          3        0\n",
+       "240      Zimbabwe   29.851441 -19.004204          3        0\n",
+       "\n",
+       "[241 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_clusters = data_mapped.copy()\n",
+    "data_with_clusters['Cluster'] = identified_clusters\n",
+    "data_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(raw_data['Longitude'], raw_data['Latitude'], c=data_with_clusters['Cluster'], cmap= 'rainbow')\n",
+    "plt.xlim(-180, 180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since you already have all the code necessary, go back and play around with the number of clusters. Try 3, 7 and 8 and see if the results match your expectations. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Simply go back to the beggining of the <b> Clustering </b> section and change <i> kmeans = KMeans(4) </i> to <i> kmeans = KMeans(3) </i>. Then run the remaining cells until the end."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Clustering+Categorical+Data+-+Solution.ipynb b/Udemy DataScience Workbook/Clustering+Categorical+Data+-+Solution.ipynb
new file mode 100644
index 0000000..656d30d
--- /dev/null
+++ b/Udemy DataScience Workbook/Clustering+Categorical+Data+-+Solution.ipynb	
@@ -0,0 +1,712 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clustering Categorical Data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given much more country data. Using the same methodology as the one in the lecture, group all the countries in 2 clusters. \n",
+    "\n",
+    "<b> Already done that? Okay! </b>\n",
+    "    \n",
+    "There are other features: name and continent. \n",
+    "\n",
+    "Encode the continent one and use it in the clustering solution. Think about the difference with the previous exercise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'Categorical.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "      <td>North America</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "      <td>Asia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "      <td>Africa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "      <td>North America</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "      <td>Oceania</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "      <td>Asia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "      <td>Africa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "      <td>Africa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "      <td>Africa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude      continent\n",
+       "0           Aruba  -69.982677  12.520880  North America\n",
+       "1     Afghanistan   66.004734  33.835231           Asia\n",
+       "2          Angola   17.537368 -12.293361         Africa\n",
+       "3        Anguilla  -63.064989  18.223959  North America\n",
+       "4         Albania   20.049834  41.142450         Europe\n",
+       "..            ...         ...        ...            ...\n",
+       "236         Samoa -172.164851 -13.753243        Oceania\n",
+       "237         Yemen   47.586762  15.909280           Asia\n",
+       "238  South Africa   25.083901 -29.000341         Africa\n",
+       "239        Zambia   27.774759 -13.458242         Africa\n",
+       "240      Zimbabwe   29.851441 -19.004204         Africa\n",
+       "\n",
+       "[241 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data\n",
+    "raw_data = pd.read_csv('Categorical.csv')\n",
+    "# Check the data\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remove the duplicate index column from the dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()\n",
+    "#data = data.drop(['Unnamed: 0'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Map the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the <i>'continent'</i> category for this analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude  continent\n",
+       "0           Aruba  -69.982677  12.520880          0\n",
+       "1     Afghanistan   66.004734  33.835231          2\n",
+       "2          Angola   17.537368 -12.293361          3\n",
+       "3        Anguilla  -63.064989  18.223959          0\n",
+       "4         Albania   20.049834  41.142450          1\n",
+       "..            ...         ...        ...        ...\n",
+       "236         Samoa -172.164851 -13.753243          5\n",
+       "237         Yemen   47.586762  15.909280          2\n",
+       "238  South Africa   25.083901 -29.000341          3\n",
+       "239        Zambia   27.774759 -13.458242          3\n",
+       "240      Zimbabwe   29.851441 -19.004204          3\n",
+       "\n",
+       "[241 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_mapped = data.copy()\n",
+    "data_mapped['continent'] = data_mapped['continent'].map({'North America':0,'Europe':1,'Asia':2,'Africa':3,'South America':4, 'Oceania':5,'Seven seas (open ocean)':6, 'Antarctica':7})\n",
+    "data_mapped"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data_mapped.iloc[:,3:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 4 clusters initially."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=4)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(4)\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 3, 1, 0, 3, 3, 3, 3, 1, 3, 2, 2, 2, 2, 0, 2, 3, 3, 1, 3, 1, 1,\n",
+       "       3, 3, 3, 0, 0, 3, 0, 3, 0, 0, 1, 1, 0, 3, 3, 1, 1, 3, 1, 3, 1, 1,\n",
+       "       1, 1, 2, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3, 1, 0, 3, 0, 1, 1, 1, 1,\n",
+       "       3, 3, 1, 3, 2, 1, 3, 3, 2, 1, 3, 3, 3, 1, 1, 1, 1, 1, 3, 0, 0, 0,\n",
+       "       2, 1, 3, 2, 0, 3, 0, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 0, 3, 3,\n",
+       "       3, 3, 3, 1, 3, 3, 2, 0, 3, 3, 3, 3, 3, 1, 1, 0, 3, 3, 1, 3, 3, 3,\n",
+       "       3, 0, 1, 3, 3, 1, 2, 0, 2, 3, 1, 3, 3, 3, 3, 2, 1, 1, 0, 2, 1, 3,\n",
+       "       1, 2, 1, 2, 1, 0, 2, 3, 3, 3, 2, 2, 3, 3, 0, 2, 1, 3, 2, 2, 3, 0,\n",
+       "       3, 3, 1, 3, 2, 3, 3, 3, 1, 1, 3, 1, 1, 1, 3, 2, 2, 2, 1, 0, 3, 1,\n",
+       "       1, 0, 3, 1, 1, 3, 3, 3, 1, 0, 2, 3, 0, 1, 1, 3, 3, 3, 3, 2, 0, 1,\n",
+       "       3, 3, 1, 1, 3, 1, 0, 3, 3, 0, 1, 0, 0, 3, 2, 2, 2, 3, 1, 1, 1])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "identified_clusters = kmeans.fit_predict(x)\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude  continent  Cluster\n",
+       "0           Aruba  -69.982677  12.520880          0        0\n",
+       "1     Afghanistan   66.004734  33.835231          2        3\n",
+       "2          Angola   17.537368 -12.293361          3        1\n",
+       "3        Anguilla  -63.064989  18.223959          0        0\n",
+       "4         Albania   20.049834  41.142450          1        3\n",
+       "..            ...         ...        ...        ...      ...\n",
+       "236         Samoa -172.164851 -13.753243          5        2\n",
+       "237         Yemen   47.586762  15.909280          2        3\n",
+       "238  South Africa   25.083901 -29.000341          3        1\n",
+       "239        Zambia   27.774759 -13.458242          3        1\n",
+       "240      Zimbabwe   29.851441 -19.004204          3        1\n",
+       "\n",
+       "[241 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_clusters = data_mapped.copy()\n",
+    "data_with_clusters['Cluster'] = identified_clusters\n",
+    "data_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data['Longitude'], data['Latitude'], c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90, 90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since you already have all the code necessary, go back and play around with the number of clusters. Try 3, 7 and 8 and see if the results match your expectations. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Simply go back to the beggining of the <b> Clustering </b> section and change <i> kmeans = KMeans(4) </i> to <i> kmeans = KMeans(3) </i>. Then run the remaining cells until the end."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Convert_txt_to_pandas_table.ipynb b/Udemy DataScience Workbook/Convert_txt_to_pandas_table.ipynb
new file mode 100644
index 0000000..aeed9a3
--- /dev/null
+++ b/Udemy DataScience Workbook/Convert_txt_to_pandas_table.ipynb	
@@ -0,0 +1,10811 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read the input file\n",
+    "with open('passwords_random.txt', 'r') as file:\n",
+    "    data = file.read()\n",
+    "\n",
+    "# Replace spaces with commas\n",
+    "data = data.replace(' ', ',')\n",
+    "\n",
+    "# Write the data to a new file\n",
+    "with open('output.csv', 'w') as file:\n",
+    "   file.write(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       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+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6568,\n",
+      "5877,\n",
+      "9559,\n",
+      "7219,\n",
+      "4537,\n",
+      "2002,\n",
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+      "2481,\n",
+      "9566,\n",
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+      "1440,\n",
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+      "1972,\n",
+      "3692,\n",
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+      "4590,\n",
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+      "2717,\n",
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+      "4123,\n",
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+      "6,\n",
+      "9813,\n",
+      "4858,\n",
+      "82,\n",
+      "4712,\n",
+      "2433,\n",
+      "9230,\n",
+      "6821,\n",
+      "9483,\n",
+      "3502,\n",
+      "2440,\n",
+      "3338,\n",
+      "514,\n",
+      "7330,\n",
+      "7528,\n",
+      "935,\n",
+      "6480,\n",
+      "1404,\n",
+      "8247,\n",
+      "6556,\n",
+      "8170,\n",
+      "3062,\n",
+      "4398,\n",
+      "2471,\n",
+      "8991,\n",
+      "3544,\n",
+      "1700,\n",
+      "2767,\n",
+      "4711,\n",
+      "7668,\n",
+      "783,\n",
+      "7044,\n",
+      "390,\n",
+      "5354,\n",
+      "7831,\n",
+      "2200,\n",
+      "7564,\n",
+      "605,\n",
+      "6230,\n",
+      "3769,\n",
+      "6832,\n",
+      "6594,\n",
+      "7779,\n",
+      "7672,\n",
+      "234,\n",
+      "3870,\n",
+      "9683,\n",
+      "5299,\n",
+      "6932,\n",
+      "2519,\n",
+      "1359,\n",
+      "9463,\n",
+      "7972,\n",
+      "7072,\n",
+      "967,\n",
+      "2170,\n",
+      "9525,\n",
+      "7016,\n",
+      "9263,\n",
+      "8715,\n",
+      "5698,\n",
+      "8735,\n",
+      "1340,\n",
+      "2890,\n",
+      "1247,\n",
+      "3980,\n",
+      "7179,\n",
+      "3134,\n",
+      "4930,\n",
+      "7483,\n",
+      "2020,\n",
+      "5145,\n",
+      "7847,\n",
+      "3695,\n",
+      "3173,\n",
+      "7930,\n",
+      "9988,\n",
+      "3699,\n",
+      "5124,\n",
+      "5560,\n",
+      "1850,\n",
+      "4954,\n",
+      "4255,\n",
+      "6262,\n",
+      "6492,\n",
+      "4144,\n",
+      "5384\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Replace all commas followed by a newline character\n",
+    "modified_data = data.replace(',', ',\\n')\n",
+    "\n",
+    "print(modified_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6568, 5877, 9559, 7219, 4537, 2002, 6059, 8199, 5804, 1389, 4847, 6528, 8518, 9541, 606, 8488, 3643, 7665, 8390, 1339, 9434, 6792, 6690, 6944, 7984, 3803, 5458, 6503, 4880, 9844, 3303, 2795, 3973, 4753, 6534, 6903, 1041, 5328, 8467, 2626, 6817, 5204, 5489, 5868, 5576, 9770, 9178, 162, 9857, 8347, 9745, 8666, 9592, 9510, 53, 1013, 4080, 7982, 9389, 7520, 348, 3276, 4570, 8245, 7962, 3589, 9435, 462, 2350, 6665, 5185, 142, 5760, 5765, 6060, 8990, 1436, 2180, 6147, 3076, 6448, 7616, 9125, 6168, 831, 8007, 1029, 5528, 5598, 7607, 5606, 9689, 5950, 7481, 8794, 6308, 4556, 2488, 8505, 7470, 3191, 5541, 7438, 5825, 2863, 5025, 4461, 9873, 9236, 2327, 8482, 3890, 908, 2060, 4062, 5897, 6237, 9958, 6127, 3710, 9544, 2339, 723, 4386, 5432, 2046, 9485, 2734, 9885, 224, 6470, 7104, 4254, 6254, 6306, 5034, 1710, 1108, 7823, 7687, 7107, 1864, 6336, 7360, 9072, 1802, 1549, 1375, 9382, 3838, 4877, 4508, 5279, 1571, 2386, 4248, 9521, 6834, 9035, 4189, 5131, 7194, 4391, 1981, 1349, 1225, 1848, 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109, 9701, 2374, 3097, 9871, 3075, 9336, 5350, 6493, 1405, 4076, 540, 8704, 635, 6352, 3498, 3885, 3140, 8872, 1858, 244, 8661, 7080, 2542, 8392, 840, 4903, 7928, 3369, 3959, 7601, 8371, 6577, 9309, 1755, 6424, 7549, 5642, 6553, 9964, 4120, 2468, 2054, 1591, 7735, 3370, 9291, 1788, 8292, 8407, 5799, 7898, 3266, 1024, 1963, 4175, 1397, 745, 2401, 394, 4996, 1288, 3492, 4717, 1319, 7250, 5187, 4671, 7946, 8556, 1427, 682, 4086, 4497, 656, 383, 3396, 6167, 8697, 2527, 6740, 5314, 7807, 7932, 5189, 7705, 5420, 4321, 8530, 296, 9247, 9171, 3289, 2916, 3132, 2822, 9311, 1575, 5920, 3125, 3159, 89, 6084, 2070, 5428, 4173, 1445, 6161, 4025, 2605, 2197, 9877, 1722, 3961, 3279, 9999, 9909, 7426, 564, 4247, 2426, 8788, 992, 6670, 8521, 4282, 8769, 4720, 8593, 5175, 4392, 4156, 1000, 2510, 2421, 1021, 3916, 7643, 386, 4287, 8632, 1712, 3482, 7140, 1576, 2586, 8383, 1524, 2834, 3811, 8413, 7455, 7753, 8642, 5934, 8192, 9815, 4502, 1590, 375, 4471, 6634, 5111, 7796, 2011, 2007, 4075, 7241, 1066, 7541, 4258, 1726, 6675, 4266, 2100, 1635, 7555, 4533, 1151, 918, 8452, 9151, 333, 1793, 986, 7685, 7654, 493, 3978, 409, 1565, 9109, 1185, 7212, 5459, 2479, 1064, 1300, 7645, 7068, 7422, 8868, 5588, 3264, 8946, 1097, 8449, 6386, 8176, 8796, 7196, 1201, 7902, 7887, 2713, 6947, 1768, 3800, 642, 2862, 9672, 5592, 4395, 6220, 696, 4054, 6280, 8525, 4043, 1134, 7530, 6420, 3623, 8324, 3090, 8570, 7292, 6609, 8364, 6401, 5865, 6375, 4704, 5014, 7800, 488, 4588, 1807, 8852, 3243, 1903, 6693, 284, 6201, 8880, 7606, 6752, 7987, 2352, 6928, 3508, 3677, 1353, 6735, 6867, 7397, 3493, 1245, 3924, 4104, 1506, 1279, 2836, 5938, 991, 2214, 4976, 1711, 2086, 9056, 4787, 9576, 2764, 6516, 7639, 7802, 7421, 4693, 1118, 1111, 584, 9969, 9710, 5313, 4966, 9414, 7433, 4024, 3557, 1645, 2415, 3727, 1938, 1699, 5737, 2546, 9399, 9159, 8180, 9222, 1543, 8342, 6734, 7834, 2500, 2914, 5297, 6259, 4947, 6325, 4042, 580, 5771, 1203, 6397, 4220, 9642, 3474, 698, 2581, 1220, 7572, 4421, 2358, 4422, 7699, 1810, 599, 1925, 9439, 6394, 7337, 6497, 5159, 5325, 2393, 2996, 3225, 6490, 5888, 444, 159, 8435, 6941, 5932, 4283, 6478, 7367, 6767, 5691, 250, 6182, 4401, 3735, 3136, 6908, 4780, 2023, 9927, 2066, 3111, 4177, 6562, 9861, 9448, 7244, 1470, 5326, 7577, 4915, 7553, 5579, 6035, 4707, 6513, 6714, 1685, 3581, 6937, 7936, 6871, 2858, 471, 3998, 8183, 2668, 5882, 4853, 6415, 7327, 6793, 9203, 5492, 7788, 1671, 3821, 3399, 990, 559, 3975, 197, 6227, 3141, 9460, 1321, 5051, 9000, 9479, 7428, 1707, 4893, 9572, 1261, 9898, 4101, 9410, 5856, 3455, 740, 3433, 6125, 1235, 4297, 5681, 5262, 2637, 6298, 2231, 3945, 2439, 3373, 3127, 2578, 1913, 996, 8037, 1208, 7038, 6274, 6234, 8825, 8376, 8233, 1147, 8353, 4473, 1792, 2375, 69, 4881, 110, 4513, 7015, 8327, 9366, 472, 3067, 9396, 1684, 8014, 4507, 1379, 8178, 8381, 1132, 6742, 6769, 1849, 1893, 1943, 4434, 7134, 5666, 3886, 1241, 532, 4600, 6275, 6781, 4223, 9598, 7372, 1478, 9998, 2223, 8770, 1345, 4094, 1264, 3951, 3153, 9777, 7960, 7197, 9107, 11, 2465, 8191, 9507, 3833, 9322, 8494, 6469, 3152, 2294, 913, 2456, 534, 8873, 3999, 7392, 9360, 2632, 5485, 1281, 1561, 7627, 604, 3376, 9906, 3969, 7503, 2920, 1324, 5300, 9210, 3936, 9892, 5733, 9040, 4833, 7744, 7624, 5883, 8420, 8523, 5484, 6276, 8337, 9472, 3524, 1306, 9992, 1166, 9319, 545, 5467, 6839, 6581, 8489, 2517, 2448, 3921, 5232, 198, 7658, 2230, 4841, 9249, 519, 6894, 9895, 6257, 9854, 5359, 1336, 1763, 5332, 4183, 1854, 7857, 7018, 9662, 4194, 2188, 5669, 5378, 7181, 2688, 3239, 7638, 2013, 2342, 7777, 962, 9352, 932, 8772, 6395, 7724, 7493, 9781, 6342, 292, 2272, 6823, 5179, 7733, 1200, 449, 954, 9099, 4860, 3187, 4250, 2689, 5369, 728, 8277, 1546, 6442, 4596, 2910, 3470, 5629, 4459, 9038, 2758, 308, 8597, 2663, 4167, 1145, 1740, 4012, 3114, 787, 2785, 6744, 8154, 5803, 6614, 5686, 477, 6755, 889, 5776, 9196, 8006, 8845, 1105, 4876, 714, 9136, 3405, 4178, 8149, 1679, 6465, 8111, 120, 995, 602, 8982, 9852, 5706, 5321, 8712, 1579, 3354, 6300, 459, 695, 9647, 4988, 9274, 48, 9084, 2219, 6678, 3430, 3010, 511, 6378, 8531, 3693, 1299, 9539, 3162, 5501, 3868, 924, 999, 1081, 7911, 9617, 4405, 143, 461, 7205, 6260, 7958, 5519, 4828, 9951, 6621, 6341, 4744, 4300, 5344, 9220, 8022, 7264, 6135, 3539, 6912, 1246, 8121, 5242, 7804, 1226, 3020, 8677, 5499, 6920, 8251, 9103, 3381, 2259, 4134, 3334, 6689, 9667, 4887, 2431, 6134, 9946, 2962, 1411, 3467, 4788, 3996, 968, 5261, 4114, 8609, 5683, 177, 5447, 7578, 3516, 748, 8790, 1717, 4935, 5382, 5388, 7373, 3586, 6844, 4046, 1977, 3221, 2631, 2320, 8941, 6249, 2886, 7106, 3662, 2155, 2261, 359, 553, 62, 9054, 8020, 5368, 2119, 9817, 845, 7755, 3420, 403, 5972, 3590, 4336, 9398, 2056, 3792, 3004, 5281, 4212, 2884, 2145, 8402, 4883, 4603, 6297, 3570, 470, 313, 6403, 9061, 3976, 2987, 3675, 5477, 36, 687, 285, 1270, 5498, 6774, 2372, 9499, 8720, 3597, 6659, 855, 6968, 2073, 6105, 3281, 9762, 60, 6344, 3449, 9450, 3156, 4830, 2300, 8631, 5619, 6253, 2576, 6180, 876, 3340, 3828, 6044, 9828, 6319, 944, 2590, 666, 4446, 5097, 9052, 2005, 8115, 3174, 3882, 2220, 4447, 5532, 5670, 1586, 6302, 8789, 1062, 5006, 294, 6546, 1416, 6686, 5974, 6791, 3240, 7655, 3535, 4951, 3002, 4362, 2588, 8463, 7590, 9574, 3726, 7214, 7519, 1995, 7189, 972, 3021, 8036, 4146, 7418, 4271, 1468, 5480, 6616, 1179, 2709, 6559, 1733, 3974, 9137, 2971, 3245, 7752, 9975, 3462, 4407, 1325, 3632, 7027, 2248, 9907, 9177, 8078, 1289, 5294, 7310, 387, 3851, 9819, 4208, 4148, 9940, 2224, 9278, 3451, 9119, 8861, 325, 2166, 3708, 9681, 2617, 8218, 9338, 9413, 1331, 8837, 658, 1027, 7374, 441, 5963, 7763, 4041, 207, 6589, 2781, 638, 8782, 1127, 640, 9794, 2163, 6266, 3742, 4653, 3588, 3531, 2896, 2633, 8012, 5260, 7925, 3072, 343, 2445, 4626, 3468, 3018, 377, 4523, 3051, 2123, 1818, 8450, 9838, 766, 4737, 3877, 4856, 288, 436, 5144, 9743, 749, 8140, 5019, 2677, 9392, 2676, 5373, 3304, 955, 3262, 9593, 290, 6033, 9295, 427, 2575, 4518, 2435, 9102, 9297, 9019, 9562, 3367, 2823, 8972, 8603, 4360, 5577, 5568, 7132, 1310, 5454, 9332, 9553, 2606, 2563, 1057, 7375, 7486, 4701, 5092, 3352, 9259, 7341, 6811, 5867, 6732, 4429, 2055, 2146, 8351, 2437, 4209, 388, 7559, 8925, 4675, 7243, 9416, 4803, 8308, 8911, 6789, 4387, 9386, 6042, 3545, 4269, 5080, 7491, 9628, 2341, 8953, 9948, 4337, 5687, 6652, 2871, 4657, 9674, 5125, 6327, 872, 1123, 6329, 2196, 3941, 8271, 9286, 1819, 9715, 1058, 3115, 7790, 798, 4344, 7404, 7837, 9960, 9932, 4599, 7063, 8943, 6295, 7400, 1451, 1703, 7511, 9455, 7516, 6247, 2703, 5031, 7953, 5887, 2928, 3121, 8090, 9734, 4462, 5983, 5256, 1296, 8768, 1036, 3579, 2696, 9901, 9643, 5224, 1172, 9938, 9188, 1626, 7760, 5, 7411, 6624, 7669, 3593, 3560, 47, 4069, 5778, 6484, 4870, 3795, 8665, 4642, 896, 5819, 5460, 7230, 4500, 3526, 5896, 7285, 1573, 5989, 6953, 3213, 242, 6518, 9186, 679, 4084, 7100, 6845, 1049, 2109, 3569, 4517, 9826, 7343, 6393, 4351, 337, 8671, 3113, 2807, 1116, 3679, 1787, 7395, 5358, 5637, 3327, 9812, 3928, 5311, 7684, 1751, 4373, 2391, 4531, 1469, 8380, 4306, 6396, 20, 8440, 7248, 1932, 9379, 6468, 2868, 1860, 1971, 1799, 2594, 6120, 2561, 931, 7103, 8676, 9182, 7393, 3086, 9615, 2124, 2748, 1072, 4999, 8640, 4730, 8928, 8112, 7299, 1425, 9584, 1006, 3927, 8153, 5689, 4825, 3268, 746, 1884, 5597, 5654, 1622, 8244, 3392, 7242, 5448, 1253, 3952, 7766, 7611, 6131, 2652, 8851, 2428, 5773, 9919, 1267, 9501, 9947, 31, 5987, 9174, 7652, 3922, 5793, 7584, 5857, 6410, 8601, 8700, 8476, 8330, 3371, 4334, 2361, 1617, 9987, 9007, 3269, 4679, 7563, 9347, 5096, 90, 9346, 3038, 9372, 6316, 8767, 1931, 2028, 2574, 9573, 2609, 5650, 9560, 8877, 8021, 8306, 4747, 5802, 9191, 1290, 2857, 5120, 437, 5782, 8714, 1419, 9285, 1009, 8113, 79, 8046, 1822, 3486, 4001, 6558, 8895, 3908, 5317, 3397, 8809, 7407, 6289, 6282, 8382, 1446, 5361, 812, 1537, 1559, 23, 370, 3920, 5180, 7153, 7990, 1011, 8227, 3696, 6174, 8580, 4016, 3831, 7233, 151, 7817, 5604, 7757, 4210, 8047, 3940, 4235, 3719, 3380, 9851, 763, 4560, 5233, 861, 7859, 208, 6768, 7560, 3546, 3137, 8458, 9409, 4268, 5910, 6121, 1133, 2876, 800, 318, 1244, 7880, 9935, 4804, 804, 5044, 8448, 4027, 9330, 218, 2378, 6417, 8497, 1256, 6144, 3823, 1146, 9978, 5747, 839, 6058, 4172, 5264, 6244, 3139, 2685, 1093, 8901, 6334, 5259, 2895, 4863, 2267, 7300, 2305, 7612, 9702, 5172, 6152, 5884, 5335, 1237, 4854, 6571, 8106, 74, 3452, 4725, 9790, 6389, 6797, 2566, 8146, 6082, 8003, 4760, 5702, 9288, 9403, 543, 9729, 3898, 5968, 1103, 3319, 1619, 5027, 7573, 5054, 7957, 4499, 6896, 6036, 3107, 5520, 6565, 5542, 6338, 7302, 8065, 77, 9839, 1355, 8513, 2496, 247, 4646, 4969, 6620, 2638, 8434, 6718, 4467, 2253, 4490, 2789, 3985, 1173, 9353, 2447, 2460, 6778, 2768, 1838, 1053, 4253, 336, 4372, 2906, 4379, 3147, 9864, 3164, 9334, 6660, 2776, 7625, 105, 6799, 8089, 3292, 2771, 7780, 4196, 5531, 3144, 5937, 904, 413, 5739, 6683, 4053, 354, 5611, 6758, 3883, 616, 9668, 6239, 3681, 6114, 6408, 9965, 6402, 4630, 9939, 8548, 3377, 6022, 4628, 9481, 7, 6554, 4618, 50, 7457, 5895, 7998, 5112, 1110, 2647, 7391, 8328, 3364, 5132, 8980, 2360, 9368, 4842, 2820, 3120, 2160, 5914, 44, 960, 5207, 7137, 581, 4563, 2311, 4137, 9760, 1074, 1820, 9774, 1046, 6519, 6984, 5122, 6506, 6390, 521, 2240, 7731, 2337, 2122, 9090, 363, 3595, 3044, 5578, 9520, 9804, 8053, 4819, 5012, 4553, 9764, 1956, 577, 796, 4689, 7026, 7597, 673, 5925, 5086, 127, 5601, 4423, 6647, 6429, 6905, 6942, 3633, 6904, 4702, 4430, 8354, 9112, 1851, 9673, 8346, 8854, 1183, 4844, 9605, 7477, 3091, 65, 7193, 465, 1776, 5408, 1682, 583, 304, 6458, 3915, 6057, 850, 9719, 8296, 5469, 2731, 769, 2877, 9597, 3884, 9305, 2625, 1900, 2469, 3172, 7864, 7587, 8594, 9778, 7005, 4489, 8649, 937, 7814, 7828, 9126, 6872, 2998, 1043, 5538, 1240, 6208, 3779, 9400, 335, 6092, 4031, 3641, 1501, 1519, 974, 7868, 1070, 3527, 8902, 3070, 7144, 9333, 802, 8116, 8298, 670, 3783, 2983, 5599, 930, 648, 4230, 9586, 5304, 6895, 9432, 9462, 5424, 6878, 1794, 777, 705, 948, 1376, 5638, 6404, 832, 4705, 977, 9704, 5421, 1387, 6910, 7854, 6439, 57, 6922, 2380, 6110, 596, 1459, 6019, 3314, 1001, 8206, 5677, 2698, 5095, 6875, 6487, 2761, 7278, 4403, 736, 1385, 270, 5543, 9944, 6043, 5353, 6766, 8744, 644, 7848, 8103, 9161, 719, 3808, 4229, 941, 6924, 5423, 5038, 3812, 320, 6578, 6387, 2152, 4060, 820, 146, 4225, 3864, 3725, 3857, 3092, 4911, 8281, 613, 6809, 7593, 7801, 6597, 9397, 4219, 6628, 6056, 442, 4724, 4312, 7143, 9284, 3891, 1998, 2, 4213, 8693, 6600, 9616, 9920, 4660, 8290, 1560, 7704, 5221, 4739, 4233, 8043, 9388, 4493, 9799, 7592, 8496, 3630, 408, 3763, 7173, 7659, 6991, 6426, 7507, 3437, 9111, 6542, 5208, 8321, 9950, 8892, 7446, 1099, 314, 9442, 3658, 3481, 7113, 5907, 9363, 5559, 6015, 2889, 5685, 2783, 6992, 8406, 1135, 2860, 1990, 556, 8614, 3056, 6606, 6407, 5917, 4905, 1615, 3298, 7164, 1348, 4284, 1088, 7495, 2228, 6664, 3236, 5021, 9184, 4440, 245, 5218, 567, 9680, 9827, 327, 186, 1012, 4165, 781, 3408, 9301, 1620, 4582, 4768, 9169, 5119, 1198, 2683, 2548, 6745, 7379, 7245, 9073, 1507, 1588, 7764, 5273, 7313, 9660, 7794, 6772, 1277, 2891, 4048, 1527, 7305, 8667, 662, 4790, 4716, 7355, 5468, 1061, 9787, 1332, 3627, 9621, 3168, 5508, 9114, 2601, 9532, 9216, 9176, 3176, 950, 3208, 821, 2227, 3906, 4997, 1520, 8443, 871, 715, 9856, 7085, 9937, 7322, 5059, 3839, 3620, 2147, 882, 3301, 8386, 3671, 8914, 6172, 1654, 5648, 7294, 4567, 8918, 4482, 4960, 8576, 7289, 3789, 8472, 140, 2821, 2721, 3619, 4549, 7478, 6163, 4047, 3200, 4633, 7209, 8881, 4566, 9751, 1144, 4115, 2520, 1790, 6673, 4572, 8650, 5083, 8325, 3494, 2063, 3888, 1542, 249, 6918, 1388, 7177, 6576, 1989, 9912, 2376, 1284, 2466, 3963, 3151, 9534, 8077, 6870, 3871, 42, 4510, 2003, 5126, 5324, 4914, 2817, 379, 6412, 4191, 546, 8126, 7405, 2804, 1905, 6836, 5435, 1689, 7924, 3724, 6184, 9766, 9049, 1169, 231, 4775, 17, 911, 8811, 5005, 9607, 1067, 7353, 3622, 1566, 9779, 7180, 7977, 9215, 7352, 9144, 5436, 9170, 7882, 6482, 6523, 8692, 2427, 4895, 8034, 2292, 9949, 4237, 8225, 1657, 1494, 1059, 7131, 2001, 5632, 8538, 5057, 7059, 7793, 623, 1294, 2450, 5892, 9785, 8499, 5375, 2649, 3344, 8165, 2314, 701, 6566, 2953, 2859, 9157, 8270, 6078, 1122, 1660, 2565, 739, 5287, 478, 4281, 3739, 9419, 3407, 4174, 1413, 209, 1747, 1069, 3512, 8628, 7937, 4655, 467, 3702, 2128, 1541, 7224, 2344, 4261, 8136, 5348, 2719, 6854, 4595, 1540, 2326, 5832, 1705, 1153, 5584, 9133, 1357, 5163, 3846, 8295, 8041, 9320, 7836, 2499, 9879, 9536, 8262, 6107, 9115, 8318, 9496, 9307, 3273, 4305, 1552, 8026, 3610, 7537, 3747, 3443, 9834, 3387, 4557, 1714, 2875, 1640, 6228, 530, 9189, 6488, 7750, 495, 8634, 860, 7133, 7055, 2602, 7467, 7748, 111, 8132, 2312, 2049, 3644, 562, 8080, 778, 8124, 8361, 3081, 6409, 1647, 2892, 5449, 9782, 4011, 8424, 7873, 898, 7939, 7938, 9609, 2752, 8025, 4869, 8750, 8847, 526, 8816, 3323, 9548, 7856, 542, 5169, 4978, 4150, 2031, 392, 1958, 533, 6790, 5679, 8110, 5272, 9801, 1280, 3993, 3612, 4007, 2707, 2356, 5759, 5634, 9733, 3294, 4832, 9922, 7437, 9180, 7087, 1777, 9200, 5715, 8722, 7331, 9459, 8332, 3257, 5834, 1692, 3414, 6866, 9390, 2992, 3701, 3628, 2658, 6406, 2436, 2508, 2949, 3718, 5596, 8208, 7722, 6575, 3723, 6028, 4772, 8285, 727, 9091, 1737, 5400, 8539, 9420, 5015, 9831, 4641, 468, 8694, 9158, 3205, 9002, 5995, 8375, 7145, 8606, 6515, 6998, 4778, 5170, 1564, 7076, 2911, 6934, 758, 4109, 1629, 9313, 7257, 4335, 2090, 239, 4394, 8857, 7551, 4681, 5270, 7311, 4818, 5824, 7349, 7586, 659, 8562, 6481, 7172, 7225, 8242, 647, 9443, 9356, 9757, 8032, 2779, 4840, 827, 4820, 1486, 8698, 1770, 66, 196, 236, 3193, 6143, 6695, 7989, 3351, 8377, 9533, 6012, 1100, 994, 1337, 3861, 7710, 4539, 5661, 1509, 9074, 5040, 1936, 7532, 6141, 4472, 9929, 7954, 3094, 8323, 7424, 2657, 6950, 7271, 6340, 9567, 1168, 8841, 5094, 3199, 5104, 9193, 1839, 4051, 7489, 4835, 6430, 7390, 5033, 1902, 5570, 6960, 6933, 2656, 222, 5414, 9373, 2743, 6522, 5965, 2105, 117, 6422, 4926, 307, 970, 5798, 2083, 3832, 6132, 2363, 430, 1082, 8414, 2324, 6382, 424, 4327, 9518, 4127, 3171, 9129, 28, 6032, 6459, 4750, 5947, 667, 3219, 4457, 9716, 4652, 8196, 194, 1433, 1304, 8999, 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5384]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split the modified text by newlines first to get rows, and then by commas to get individual values\n",
+    "rows = modified_data.split('\\n')\n",
+    "int_list = []\n",
+    "\n",
+    "for row in rows:\n",
+    "    # Split each row by commas to get individual values\n",
+    "    values = row.split(',')\n",
+    "    \n",
+    "    for value in values:\n",
+    "        # Check if the value is not an empty string\n",
+    "        if value.strip():\n",
+    "            # Convert each value to an integer and append to the int_list\n",
+    "            int_list.append(int(value.strip()))\n",
+    "\n",
+    "print(int_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          \n",
+      "0     6568\n",
+      "1     5877\n",
+      "2     9559\n",
+      "3     7219\n",
+      "4     4537\n",
+      "...    ...\n",
+      "9995  4255\n",
+      "9996  6262\n",
+      "9997  6492\n",
+      "9998  4144\n",
+      "9999  5384\n",
+      "\n",
+      "[10000 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(int_list, columns=[\"\"])\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([''], dtype='object')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = ['Numbers']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Numbers'], dtype='object')"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Numbers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Numbers\n",
+       "0        6568\n",
+       "1        5877\n",
+       "2        9559\n",
+       "3        7219\n",
+       "4        4537\n",
+       "...       ...\n",
+       "9995     4255\n",
+       "9996     6262\n",
+       "9997     6492\n",
+       "9998     4144\n",
+       "9999     5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Numbers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Numbers\n",
+       "0        6568\n",
+       "1        5877\n",
+       "2        9559\n",
+       "3        7219\n",
+       "4        4537\n",
+       "...       ...\n",
+       "9995     4255\n",
+       "9996     6262\n",
+       "9997     6492\n",
+       "9998     4144\n",
+       "9999     5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Numbers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Numbers\n",
+       "0        6568\n",
+       "1        5877\n",
+       "2        9559\n",
+       "3        7219\n",
+       "4        4537\n",
+       "...       ...\n",
+       "9995     4255\n",
+       "9996     6262\n",
+       "9997     6492\n",
+       "9998     4144\n",
+       "9999     5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Numbers'], dtype='object')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Numbers\"] = df[\"Numbers\"].astype(str).str.zfill(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Numbers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Numbers\n",
+       "0       6568\n",
+       "1       5877\n",
+       "2       9559\n",
+       "3       7219\n",
+       "4       4537\n",
+       "...      ...\n",
+       "9995    4255\n",
+       "9996    6262\n",
+       "9997    6492\n",
+       "9998    4144\n",
+       "9999    5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the column labels\n",
+    "df.columns = [\"\" for _ in df.columns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          \n",
+       "0     6568\n",
+       "1     5877\n",
+       "2     9559\n",
+       "3     7219\n",
+       "4     4537\n",
+       "...    ...\n",
+       "9995  4255\n",
+       "9996  6262\n",
+       "9997  6492\n",
+       "9998  4144\n",
+       "9999  5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = [None] * len(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Save the DataFrame to an Excel file\n",
+    "df.to_csv('Pasword_Numbers.txt', index=False)  # 'numbers.xlsx' is the file name you can change\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Countries-exercise.csv b/Udemy DataScience Workbook/Countries-exercise.csv
new file mode 100644
index 0000000..7c60331
--- /dev/null
+++ b/Udemy DataScience Workbook/Countries-exercise.csv	
@@ -0,0 +1,242 @@
+name,Longitude,Latitude
+Aruba,-69.98267711,12.52088038
+Afghanistan,66.00473366,33.83523073
+Angola,17.53736768,-12.29336054
+Anguilla,-63.06498927,18.2239595
+Albania,20.04983396,41.14244989
+Aland,19.95328768,60.21488688
+Andorra,1.56054378,42.54229102
+United Arab Emirates,54.3001671,23.90528188
+Argentina,-65.17980692,-35.3813488
+Armenia,44.92993276,40.28952569
+American Samoa,-170.7180258,-14.30445997
+Antarctica,19.92108951,-80.50857913
+Ashmore and Cartier Is.,123.5838379,-12.42993164
+Fr. S. Antarctic Lands,69.22666758,-49.24895485
+Antigua and Barb.,-61.79469343,17.2774996
+Australia,134.4910001,-25.73288704
+Austria,14.1264761,47.58549439
+Azerbaijan,47.54599879,40.28827235
+Burundi,29.87512156,-3.35939666
+Belgium,4.64065114,50.63981576
+Benin,2.32785254,9.6417597
+Burkina Faso,-1.75456601,12.26953846
+Bangladesh,90.23812743,23.86731158
+Bulgaria,25.21552909,42.76890318
+Bahrain,50.54196932,26.04205135
+Canada,-98.30777028,61.36206324
+Bahamas,-76.62843038,24.29036702
+Bosnia and Herz.,17.76876733,44.17450125
+St-Barthélemy,-62.84067779,17.89880451
+Belarus,28.03209307,53.53131377
+Belize,-88.71010486,17.20027509
+Bermuda,-64.7545589,32.31367802
+Bolivia,-64.68538645,-16.70814787
+Brazil,-53.09783113,-10.78777702
+Barbados,-59.559797,13.18145428
+Brunei,114.7220304,4.51968958
+Bhutan,90.40188155,27.41106589
+Botswana,23.79853368,-22.18403213
+Central African Rep.,20.46826831,6.56823297
+Switzerland,8.20867471,46.79785878
+Chile,-71.38256213,-37.73070989
+China,103.8190735,36.56176546
+Côte d'Ivoire,-5.5692157,7.6284262
+Cameroon,12.73964156,5.69109849
+Dem. Rep. Congo,23.64396107,-2.87746289
+Congo,15.21965762,-0.83787463
+Cook Is.,-159.7872422,-21.21927288
+Colombia,-73.08114582,3.91383431
+Comoros,43.68253968,-11.87783444
+Cape Verde,-23.9598882,15.95523324
+Costa Rica,-84.19208768,9.97634464
+Cuba,-79.01605384,21.62289528
+Curaçao,-68.97119369,12.19551675
+Cayman Is.,-80.91213321,19.42896497
+N. Cyprus,33.5684813,35.26277486
+Cyprus,33.0060022,34.91667211
+Czech Rep.,15.31240163,49.73341233
+Germany,10.38578051,51.10698181
+Djibouti,42.5606754,11.74871806
+Dominica,-61.357726,15.4394702
+Denmark,10.02800992,55.98125296
+Dominican Rep.,-70.50568896,18.89433082
+Algeria,2.61732301,28.15893849
+Ecuador,-78.75201922,-1.42381612
+Egypt,29.86190099,26.49593311
+Eritrea,38.84617011,15.36186618
+Spain,-3.64755047,40.24448698
+Estonia,25.54248537,58.67192972
+Ethiopia,39.60080098,8.62278679
+Finland,26.2746656,64.49884603
+Fiji,165.4519543,-17.42858032
+Falkland Is.,-59.35238956,-51.74483954
+France,-2.76172945,42.17344011
+Faeroe Is.,-6.88095423,62.05385403
+Micronesia,153.2394379,7.45246814
+Gabon,11.7886287,-0.58660025
+United Kingdom,-2.86563164,54.12387156
+Georgia,43.50780252,42.16855755
+Guernsey,-2.57239064,49.46809761
+Ghana,-1.21676566,7.95345644
+Guinea,-10.94066612,10.43621593
+Gambia,-15.39601295,13.44965244
+Guinea-Bissau,-14.94972445,12.04744948
+Eq. Guinea,10.34137924,1.70555135
+Greece,22.95555794,39.07469623
+Grenada,-61.68220189,12.11725044
+Greenland,-41.34191127,74.71051289
+Guatemala,-90.36482009,15.69403664
+Guam,144.7679102,13.44165626
+Guyana,-58.98202459,4.79378034
+Hong Kong,114.1138045,22.39827737
+Heard I. and McDonald Is.,73.5205171,-53.08724656
+Honduras,-86.6151661,14.82688165
+Croatia,16.40412899,45.08047631
+Haiti,-72.68527509,18.93502563
+Hungary,19.39559116,47.16277506
+Indonesia,117.2401137,-2.21505456
+Isle of Man,-4.53873952,54.22418911
+India,79.6119761,22.88578212
+Indian Ocean Ter.,104.851898,-10.6478515
+Br. Indian Ocean Ter.,72.44541229,-7.33059751
+Ireland,-8.13793569,53.1754487
+Iran,54.27407004,32.57503292
+Iraq,43.74353149,33.03970582
+Iceland,-18.57396167,64.99575386
+Israel,35.00444693,31.46110101
+Italy,12.07001339,42.79662641
+Jamaica,-77.31482593,18.15694878
+Jersey,-2.12689938,49.21837377
+Jordan,36.77136104,31.24579091
+Japan,138.0308956,37.59230135
+Siachen Glacier,77.18011865,35.39236325
+Kazakhstan,67.29149357,48.15688067
+Kenya,37.79593973,0.59988022
+Kyrgyzstan,74.54165513,41.46221943
+Cambodia,104.9069433,12.72004786
+Kiribati,-45.61110513,0.86001503
+St. Kitts and Nevis,-62.68755265,17.2645995
+Korea,127.8391609,36.38523983
+Kosovo,20.87249811,42.57078707
+Kuwait,47.58700459,29.33431262
+Lao PDR,103.7377241,18.50217433
+Lebanon,35.88016072,33.92306631
+Liberia,-9.32207573,6.45278492
+Libya,18.00866169,27.03094495
+Saint Lucia,-60.96969923,13.89479481
+Liechtenstein,9.53574312,47.13665835
+Sri Lanka,80.70108238,7.61266509
+Lesotho,28.22723131,-29.58003188
+Lithuania,23.88719355,55.32610984
+Luxembourg,6.07182201,49.76725361
+Latvia,24.91235983,56.85085163
+Macao,113.5093212,22.22311688
+St-Martin,-63.05972851,18.08888611
+Morocco,-8.45615795,29.83762955
+Monaco,7.40627677,43.75274627
+Moldova,28.45673372,47.19498804
+Madagascar,46.70473674,-19.37189587
+Maldives,73.45713004,3.7287092
+Mexico,-102.5234517,23.94753724
+Marshall Is.,170.3397612,7.00376358
+Macedonia,21.68211346,41.59530893
+Mali,-3.54269065,17.34581581
+Malta,14.40523316,35.92149632
+Myanmar,96.48843321,21.18566599
+Montenegro,19.23883939,42.78890259
+Mongolia,103.0529977,46.82681544
+N. Mariana Is.,145.6196965,15.82927563
+Mozambique,35.53367543,-17.27381643
+Mauritania,-10.34779815,20.25736706
+Montserrat,-62.18518546,16.73941406
+Mauritius,57.57120551,-20.27768704
+Malawi,34.28935599,-13.21808088
+Malaysia,109.6976228,3.78986846
+Namibia,17.20963567,-22.13032568
+New Caledonia,165.6849237,-21.29991806
+Niger,9.38545882,17.41912493
+Norfolk Island,167.9492168,-29.0514609
+Nigeria,8.08943895,9.59411452
+Nicaragua,-85.0305297,12.84709429
+Niue,-169.8699468,-19.04945708
+Netherlands,5.28144793,52.1007899
+Norway,15.34834656,68.75015572
+Nepal,83.9158264,28.24891365
+Nauru,166.9325682,-0.51912639
+New Zealand,171.4849235,-41.81113557
+Oman,56.09166155,20.60515333
+Pakistan,69.33957937,29.9497515
+Panama,-80.11915156,8.51750797
+Pitcairn Is.,-128.317042,-24.36500535
+Peru,-74.38242685,-9.15280381
+Philippines,122.8839325,11.77536778
+Palau,134.4080797,7.28742784
+Papua New Guinea,145.2074475,-6.46416646
+Poland,19.39012835,52.12759564
+Puerto Rico,-66.47307604,18.22813055
+Dem. Rep. Korea,127.1924797,40.15350311
+Portugal,-8.50104361,39.59550671
+Paraguay,-58.40013703,-23.22823913
+Palestine,35.19628705,31.91613893
+Fr. Polynesia,-144.9049439,-14.72227409
+Qatar,51.18479632,25.30601188
+Romania,24.97293039,45.85243127
+Russia,96.68656112,61.98052209
+Rwanda,29.91988515,-1.99033832
+W. Sahara,-12.21982755,24.22956739
+Saudi Arabia,44.53686271,24.12245841
+Sudan,29.94046812,15.99035669
+S. Sudan,30.24790002,7.30877945
+Senegal,-14.4734924,14.36624173
+Singapore,103.8172559,1.35876087
+S. Geo. and S. Sandw. Is.,-36.43318388,-54.46488248
+Saint Helena,-9.54779416,-12.40355951
+Solomon Is.,159.6328767,-8.92178022
+Sierra Leone,-11.79271247,8.56329593
+El Salvador,-88.87164469,13.73943744
+San Marino,12.45922334,43.94186747
+Somaliland,46.25198395,9.73345496
+Somalia,45.70714487,4.75062876
+St. Pierre and Miquelon,-56.30319779,46.91918789
+Serbia,20.78958334,44.2215032
+São Tomé and Principe,6.72429658,0.44391445
+Suriname,-55.9123457,4.13055413
+Slovakia,19.47905218,48.70547528
+Slovenia,14.80444238,46.11554772
+Sweden,16.74558049,62.77966519
+Swaziland,31.4819369,-26.55843045
+Sint Maarten,-63.05713363,18.05081728
+Seychelles,55.47603279,-4.66099094
+Syria,38.50788204,35.02547389
+Turks and Caicos Is.,-71.97387881,21.83047572
+Chad,18.64492513,15.33333758
+Togo,0.96232845,8.52531356
+Thailand,101.0028813,15.11815794
+Tajikistan,71.01362631,38.5304539
+Turkmenistan,59.37100021,39.11554137
+Timor-Leste,125.8443898,-8.82889162
+Tonga,-174.8098734,-20.42843174
+Trinidad and Tobago,-61.26567923,10.45733408
+Tunisia,9.55288359,34.11956246
+Turkey,35.16895346,39.0616029
+Taiwan,120.9542728,23.7539928
+Tanzania,34.81309981,-6.27565408
+Uganda,32.36907971,1.27469299
+Ukraine,31.38326469,48.99656673
+Uruguay,-56.01807053,-32.79951534
+United States,-112.4616737,45.6795472
+Uzbekistan,63.14001528,41.75554225
+Vatican,12.43387177,41.90174985
+St. Vin. and Gren.,-61.20129695,13.22472269
+Venezuela,-66.18184123,7.12422421
+British Virgin Is.,-64.47146992,18.52585755
+U.S. Virgin Is.,-64.80301538,17.95500624
+Vietnam,106.299147,16.6460167
+Vanuatu,167.6864464,-16.22640909
+Wallis and Futuna Is.,-177.3483483,-13.88737039
+Samoa,-172.1648506,-13.75324346
+Yemen,47.58676189,15.90928005
+South Africa,25.08390093,-29.00034095
+Zambia,27.77475946,-13.45824152
+Zimbabwe,29.8514412,-19.00420419
diff --git a/Udemy DataScience Workbook/Countries_exercise.csv b/Udemy DataScience Workbook/Countries_exercise.csv
new file mode 100644
index 0000000..7c60331
--- /dev/null
+++ b/Udemy DataScience Workbook/Countries_exercise.csv	
@@ -0,0 +1,242 @@
+name,Longitude,Latitude
+Aruba,-69.98267711,12.52088038
+Afghanistan,66.00473366,33.83523073
+Angola,17.53736768,-12.29336054
+Anguilla,-63.06498927,18.2239595
+Albania,20.04983396,41.14244989
+Aland,19.95328768,60.21488688
+Andorra,1.56054378,42.54229102
+United Arab Emirates,54.3001671,23.90528188
+Argentina,-65.17980692,-35.3813488
+Armenia,44.92993276,40.28952569
+American Samoa,-170.7180258,-14.30445997
+Antarctica,19.92108951,-80.50857913
+Ashmore and Cartier Is.,123.5838379,-12.42993164
+Fr. S. Antarctic Lands,69.22666758,-49.24895485
+Antigua and Barb.,-61.79469343,17.2774996
+Australia,134.4910001,-25.73288704
+Austria,14.1264761,47.58549439
+Azerbaijan,47.54599879,40.28827235
+Burundi,29.87512156,-3.35939666
+Belgium,4.64065114,50.63981576
+Benin,2.32785254,9.6417597
+Burkina Faso,-1.75456601,12.26953846
+Bangladesh,90.23812743,23.86731158
+Bulgaria,25.21552909,42.76890318
+Bahrain,50.54196932,26.04205135
+Canada,-98.30777028,61.36206324
+Bahamas,-76.62843038,24.29036702
+Bosnia and Herz.,17.76876733,44.17450125
+St-Barthélemy,-62.84067779,17.89880451
+Belarus,28.03209307,53.53131377
+Belize,-88.71010486,17.20027509
+Bermuda,-64.7545589,32.31367802
+Bolivia,-64.68538645,-16.70814787
+Brazil,-53.09783113,-10.78777702
+Barbados,-59.559797,13.18145428
+Brunei,114.7220304,4.51968958
+Bhutan,90.40188155,27.41106589
+Botswana,23.79853368,-22.18403213
+Central African Rep.,20.46826831,6.56823297
+Switzerland,8.20867471,46.79785878
+Chile,-71.38256213,-37.73070989
+China,103.8190735,36.56176546
+Côte d'Ivoire,-5.5692157,7.6284262
+Cameroon,12.73964156,5.69109849
+Dem. Rep. Congo,23.64396107,-2.87746289
+Congo,15.21965762,-0.83787463
+Cook Is.,-159.7872422,-21.21927288
+Colombia,-73.08114582,3.91383431
+Comoros,43.68253968,-11.87783444
+Cape Verde,-23.9598882,15.95523324
+Costa Rica,-84.19208768,9.97634464
+Cuba,-79.01605384,21.62289528
+Curaçao,-68.97119369,12.19551675
+Cayman Is.,-80.91213321,19.42896497
+N. Cyprus,33.5684813,35.26277486
+Cyprus,33.0060022,34.91667211
+Czech Rep.,15.31240163,49.73341233
+Germany,10.38578051,51.10698181
+Djibouti,42.5606754,11.74871806
+Dominica,-61.357726,15.4394702
+Denmark,10.02800992,55.98125296
+Dominican Rep.,-70.50568896,18.89433082
+Algeria,2.61732301,28.15893849
+Ecuador,-78.75201922,-1.42381612
+Egypt,29.86190099,26.49593311
+Eritrea,38.84617011,15.36186618
+Spain,-3.64755047,40.24448698
+Estonia,25.54248537,58.67192972
+Ethiopia,39.60080098,8.62278679
+Finland,26.2746656,64.49884603
+Fiji,165.4519543,-17.42858032
+Falkland Is.,-59.35238956,-51.74483954
+France,-2.76172945,42.17344011
+Faeroe Is.,-6.88095423,62.05385403
+Micronesia,153.2394379,7.45246814
+Gabon,11.7886287,-0.58660025
+United Kingdom,-2.86563164,54.12387156
+Georgia,43.50780252,42.16855755
+Guernsey,-2.57239064,49.46809761
+Ghana,-1.21676566,7.95345644
+Guinea,-10.94066612,10.43621593
+Gambia,-15.39601295,13.44965244
+Guinea-Bissau,-14.94972445,12.04744948
+Eq. Guinea,10.34137924,1.70555135
+Greece,22.95555794,39.07469623
+Grenada,-61.68220189,12.11725044
+Greenland,-41.34191127,74.71051289
+Guatemala,-90.36482009,15.69403664
+Guam,144.7679102,13.44165626
+Guyana,-58.98202459,4.79378034
+Hong Kong,114.1138045,22.39827737
+Heard I. and McDonald Is.,73.5205171,-53.08724656
+Honduras,-86.6151661,14.82688165
+Croatia,16.40412899,45.08047631
+Haiti,-72.68527509,18.93502563
+Hungary,19.39559116,47.16277506
+Indonesia,117.2401137,-2.21505456
+Isle of Man,-4.53873952,54.22418911
+India,79.6119761,22.88578212
+Indian Ocean Ter.,104.851898,-10.6478515
+Br. Indian Ocean Ter.,72.44541229,-7.33059751
+Ireland,-8.13793569,53.1754487
+Iran,54.27407004,32.57503292
+Iraq,43.74353149,33.03970582
+Iceland,-18.57396167,64.99575386
+Israel,35.00444693,31.46110101
+Italy,12.07001339,42.79662641
+Jamaica,-77.31482593,18.15694878
+Jersey,-2.12689938,49.21837377
+Jordan,36.77136104,31.24579091
+Japan,138.0308956,37.59230135
+Siachen Glacier,77.18011865,35.39236325
+Kazakhstan,67.29149357,48.15688067
+Kenya,37.79593973,0.59988022
+Kyrgyzstan,74.54165513,41.46221943
+Cambodia,104.9069433,12.72004786
+Kiribati,-45.61110513,0.86001503
+St. Kitts and Nevis,-62.68755265,17.2645995
+Korea,127.8391609,36.38523983
+Kosovo,20.87249811,42.57078707
+Kuwait,47.58700459,29.33431262
+Lao PDR,103.7377241,18.50217433
+Lebanon,35.88016072,33.92306631
+Liberia,-9.32207573,6.45278492
+Libya,18.00866169,27.03094495
+Saint Lucia,-60.96969923,13.89479481
+Liechtenstein,9.53574312,47.13665835
+Sri Lanka,80.70108238,7.61266509
+Lesotho,28.22723131,-29.58003188
+Lithuania,23.88719355,55.32610984
+Luxembourg,6.07182201,49.76725361
+Latvia,24.91235983,56.85085163
+Macao,113.5093212,22.22311688
+St-Martin,-63.05972851,18.08888611
+Morocco,-8.45615795,29.83762955
+Monaco,7.40627677,43.75274627
+Moldova,28.45673372,47.19498804
+Madagascar,46.70473674,-19.37189587
+Maldives,73.45713004,3.7287092
+Mexico,-102.5234517,23.94753724
+Marshall Is.,170.3397612,7.00376358
+Macedonia,21.68211346,41.59530893
+Mali,-3.54269065,17.34581581
+Malta,14.40523316,35.92149632
+Myanmar,96.48843321,21.18566599
+Montenegro,19.23883939,42.78890259
+Mongolia,103.0529977,46.82681544
+N. Mariana Is.,145.6196965,15.82927563
+Mozambique,35.53367543,-17.27381643
+Mauritania,-10.34779815,20.25736706
+Montserrat,-62.18518546,16.73941406
+Mauritius,57.57120551,-20.27768704
+Malawi,34.28935599,-13.21808088
+Malaysia,109.6976228,3.78986846
+Namibia,17.20963567,-22.13032568
+New Caledonia,165.6849237,-21.29991806
+Niger,9.38545882,17.41912493
+Norfolk Island,167.9492168,-29.0514609
+Nigeria,8.08943895,9.59411452
+Nicaragua,-85.0305297,12.84709429
+Niue,-169.8699468,-19.04945708
+Netherlands,5.28144793,52.1007899
+Norway,15.34834656,68.75015572
+Nepal,83.9158264,28.24891365
+Nauru,166.9325682,-0.51912639
+New Zealand,171.4849235,-41.81113557
+Oman,56.09166155,20.60515333
+Pakistan,69.33957937,29.9497515
+Panama,-80.11915156,8.51750797
+Pitcairn Is.,-128.317042,-24.36500535
+Peru,-74.38242685,-9.15280381
+Philippines,122.8839325,11.77536778
+Palau,134.4080797,7.28742784
+Papua New Guinea,145.2074475,-6.46416646
+Poland,19.39012835,52.12759564
+Puerto Rico,-66.47307604,18.22813055
+Dem. Rep. Korea,127.1924797,40.15350311
+Portugal,-8.50104361,39.59550671
+Paraguay,-58.40013703,-23.22823913
+Palestine,35.19628705,31.91613893
+Fr. Polynesia,-144.9049439,-14.72227409
+Qatar,51.18479632,25.30601188
+Romania,24.97293039,45.85243127
+Russia,96.68656112,61.98052209
+Rwanda,29.91988515,-1.99033832
+W. Sahara,-12.21982755,24.22956739
+Saudi Arabia,44.53686271,24.12245841
+Sudan,29.94046812,15.99035669
+S. Sudan,30.24790002,7.30877945
+Senegal,-14.4734924,14.36624173
+Singapore,103.8172559,1.35876087
+S. Geo. and S. Sandw. Is.,-36.43318388,-54.46488248
+Saint Helena,-9.54779416,-12.40355951
+Solomon Is.,159.6328767,-8.92178022
+Sierra Leone,-11.79271247,8.56329593
+El Salvador,-88.87164469,13.73943744
+San Marino,12.45922334,43.94186747
+Somaliland,46.25198395,9.73345496
+Somalia,45.70714487,4.75062876
+St. Pierre and Miquelon,-56.30319779,46.91918789
+Serbia,20.78958334,44.2215032
+São Tomé and Principe,6.72429658,0.44391445
+Suriname,-55.9123457,4.13055413
+Slovakia,19.47905218,48.70547528
+Slovenia,14.80444238,46.11554772
+Sweden,16.74558049,62.77966519
+Swaziland,31.4819369,-26.55843045
+Sint Maarten,-63.05713363,18.05081728
+Seychelles,55.47603279,-4.66099094
+Syria,38.50788204,35.02547389
+Turks and Caicos Is.,-71.97387881,21.83047572
+Chad,18.64492513,15.33333758
+Togo,0.96232845,8.52531356
+Thailand,101.0028813,15.11815794
+Tajikistan,71.01362631,38.5304539
+Turkmenistan,59.37100021,39.11554137
+Timor-Leste,125.8443898,-8.82889162
+Tonga,-174.8098734,-20.42843174
+Trinidad and Tobago,-61.26567923,10.45733408
+Tunisia,9.55288359,34.11956246
+Turkey,35.16895346,39.0616029
+Taiwan,120.9542728,23.7539928
+Tanzania,34.81309981,-6.27565408
+Uganda,32.36907971,1.27469299
+Ukraine,31.38326469,48.99656673
+Uruguay,-56.01807053,-32.79951534
+United States,-112.4616737,45.6795472
+Uzbekistan,63.14001528,41.75554225
+Vatican,12.43387177,41.90174985
+St. Vin. and Gren.,-61.20129695,13.22472269
+Venezuela,-66.18184123,7.12422421
+British Virgin Is.,-64.47146992,18.52585755
+U.S. Virgin Is.,-64.80301538,17.95500624
+Vietnam,106.299147,16.6460167
+Vanuatu,167.6864464,-16.22640909
+Wallis and Futuna Is.,-177.3483483,-13.88737039
+Samoa,-172.1648506,-13.75324346
+Yemen,47.58676189,15.90928005
+South Africa,25.08390093,-29.00034095
+Zambia,27.77475946,-13.45824152
+Zimbabwe,29.8514412,-19.00420419
diff --git a/Udemy DataScience Workbook/Country+clusters+standardized.csv b/Udemy DataScience Workbook/Country+clusters+standardized.csv
new file mode 100644
index 0000000..09dad09
--- /dev/null
+++ b/Udemy DataScience Workbook/Country+clusters+standardized.csv	
@@ -0,0 +1,7 @@
+Country,Latitude,Longitude,Language
+USA,0.186539588,-1.089159514,0
+Canada,0.728587658,-1.008603741,0
+France,0.241895042,0.137900235,1
+UK,0.467670658,0.080921761,0
+Germany,0.378728749,0.230360233,2
+Australia,-2.003421694,1.648581027,0
diff --git a/Udemy DataScience Workbook/Country+clusters_with_comments.ipynb b/Udemy DataScience Workbook/Country+clusters_with_comments.ipynb
new file mode 100644
index 0000000..97532f9
--- /dev/null
+++ b/Udemy DataScience Workbook/Country+clusters_with_comments.ipynb	
@@ -0,0 +1,574 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of cluster analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we explore the very basics of cluster analysis with k-means"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "# Set the styles to Seaborn\n",
+    "sns.set()\n",
+    "# Import the KMeans module so we can perform k-means clustering with sklearn\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the country clusters data\n",
+    "data = pd.read_csv('3.01.+Country+clusters.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Language</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.97</td>\n",
+       "      <td>-103.77</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>62.40</td>\n",
+       "      <td>-96.80</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>France</td>\n",
+       "      <td>46.75</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>French</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UK</td>\n",
+       "      <td>54.01</td>\n",
+       "      <td>-2.53</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>51.15</td>\n",
+       "      <td>10.40</td>\n",
+       "      <td>German</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>-25.45</td>\n",
+       "      <td>133.11</td>\n",
+       "      <td>English</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Country  Latitude  Longitude Language\n",
+       "0        USA     44.97    -103.77  English\n",
+       "1     Canada     62.40     -96.80  English\n",
+       "2     France     46.75       2.40   French\n",
+       "3         UK     54.01      -2.53  English\n",
+       "4    Germany     51.15      10.40   German\n",
+       "5  Australia    -25.45     133.11  English"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check out the data manually \n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use the simplest code possible to create a scatter plot using the longitude and latitude\n",
+    "# Note that in order to reach a result resembling the world map, we must use the longitude as y, and the latitude as x\n",
+    "plt.scatter(data['Longitude'],data['Latitude'])\n",
+    "# Set limits of the axes, again to resemble the world map\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iloc is a method used to 'slice' data \n",
+    "# 'slice' is not technically correct as there are methods 'slice' which are a bit different\n",
+    "# The term used by pandas is 'selection by position'\n",
+    "# The first argument of identifies the rows we want to keep\n",
+    "# The second - the columns\n",
+    "# When choosing the columns, e.g. a:b, we will keep columns a,a+1,a+2,...,b-1 ; so column b is excluded\n",
+    "x = data.iloc[:,1:3]\n",
+    "# for this particular case, we are choosing columns 1 and 2\n",
+    "# Note column indices in Python start from 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>44.97</td>\n",
+       "      <td>-103.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>62.40</td>\n",
+       "      <td>-96.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>46.75</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54.01</td>\n",
+       "      <td>-2.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>51.15</td>\n",
+       "      <td>10.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-25.45</td>\n",
+       "      <td>133.11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Latitude  Longitude\n",
+       "0     44.97    -103.77\n",
+       "1     62.40     -96.80\n",
+       "2     46.75       2.40\n",
+       "3     54.01      -2.53\n",
+       "4     51.15      10.40\n",
+       "5    -25.45     133.11"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check if we worked correctly\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the part of the sheet which deals with the actual clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an object (which we would call kmeans)\n",
+    "# The number in the brackets is K, or the number of clusters we are aiming for\n",
+    "kmeans = KMeans(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=3)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit the input data, i.e. cluster the data in X in K clusters\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are many ways to do this part, we found this to be the most illustrative one"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0, 0, 0, 2])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a variable which will contain the predicted clusters for each observation\n",
+    "identified_clusters = kmeans.fit_predict(x)\n",
+    "# Check the result\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Language</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>44.97</td>\n",
+       "      <td>-103.77</td>\n",
+       "      <td>English</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>62.40</td>\n",
+       "      <td>-96.80</td>\n",
+       "      <td>English</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>France</td>\n",
+       "      <td>46.75</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>French</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UK</td>\n",
+       "      <td>54.01</td>\n",
+       "      <td>-2.53</td>\n",
+       "      <td>English</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>51.15</td>\n",
+       "      <td>10.40</td>\n",
+       "      <td>German</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>-25.45</td>\n",
+       "      <td>133.11</td>\n",
+       "      <td>English</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Country  Latitude  Longitude Language  Cluster\n",
+       "0        USA     44.97    -103.77  English        1\n",
+       "1     Canada     62.40     -96.80  English        1\n",
+       "2     France     46.75       2.40   French        0\n",
+       "3         UK     54.01      -2.53  English        0\n",
+       "4    Germany     51.15      10.40   German        0\n",
+       "5  Australia    -25.45     133.11  English        2"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a copy of the data\n",
+    "data_with_clusters = data.copy()\n",
+    "# Create a new Series, containing the identified cluster for each observation\n",
+    "data_with_clusters['Cluster'] = identified_clusters\n",
+    "# Check the result\n",
+    "data_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data using the longitude and the latitude\n",
+    "# c (color) is an argument which could be coded with a variable \n",
+    "# The variable in this case has values 0,1,2, indicating to plt.scatter, that there are three colors (0,1,2)\n",
+    "# All points in cluster 0 will be the same colour, all points in cluster 1 - another one, etc.\n",
+    "# cmap is the color map. Rainbow is a nice one, but you can check others here: https://matplotlib.org/users/colormaps.html\n",
+    "plt.scatter(data_with_clusters['Longitude'],data_with_clusters['Latitude'],c=data_with_clusters['Cluster'],cmap='rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Course+Notes+-+Section+2.pdf b/Udemy DataScience Workbook/Course+Notes+-+Section+2.pdf
new file mode 100644
index 0000000..1194f01
Binary files /dev/null and b/Udemy DataScience Workbook/Course+Notes+-+Section+2.pdf differ
diff --git a/Udemy DataScience Workbook/Course_Notes_Logistic_Regression.pdf b/Udemy DataScience Workbook/Course_Notes_Logistic_Regression.pdf
new file mode 100644
index 0000000..ff31ba5
Binary files /dev/null and b/Udemy DataScience Workbook/Course_Notes_Logistic_Regression.pdf differ
diff --git a/Udemy DataScience Workbook/Data_Files/1.01.+Simple+linear+regression.csv b/Udemy DataScience Workbook/Data_Files/1.01.+Simple+linear+regression.csv
new file mode 100644
index 0000000..8fec0c0
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/1.01.+Simple+linear+regression.csv	
@@ -0,0 +1,85 @@
+SAT,GPA
+1714,2.4
+1664,2.52
+1760,2.54
+1685,2.74
+1693,2.83
+1670,2.91
+1764,3
+1764,3
+1792,3.01
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+1786,3.19
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+1826,3.28
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+2020,3.28
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+1778,3.59
+1975,3.6
+1934,3.6
+2021,3.61
+2015,3.62
+1997,3.64
+2020,3.65
+1843,3.71
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+1987,3.73
+1962,3.76
+2050,3.81
diff --git a/Udemy DataScience Workbook/Data_Files/1.02.+Multiple+linear+regression.csv b/Udemy DataScience Workbook/Data_Files/1.02.+Multiple+linear+regression.csv
new file mode 100644
index 0000000..7f990d3
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/1.02.+Multiple+linear+regression.csv	
@@ -0,0 +1,85 @@
+SAT,"Rand 1,2,3",GPA
+1714,1,2.4
+1664,3,2.52
+1760,3,2.54
+1685,3,2.74
+1693,2,2.83
+1670,1,2.91
+1764,2,3
+1764,1,3
+1792,2,3.01
+1850,3,3.01
+1735,3,3.02
+1775,2,3.07
+1735,1,3.08
+1712,3,3.08
+1773,2,3.12
+1872,2,3.17
+1755,3,3.17
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+1722,3,3.19
+1663,3,3.2
+1687,1,3.21
+1974,1,3.24
+1826,1,3.28
+1787,1,3.28
+1821,3,3.28
+2020,1,3.28
+1794,2,3.28
+1769,1,3.28
+1934,1,3.28
+1775,1,3.29
+1855,3,3.29
+1880,2,3.29
+1849,2,3.31
+1808,3,3.32
+1954,3,3.34
+1777,3,3.37
+1831,2,3.37
+1865,3,3.37
+1850,1,3.38
+1966,3,3.38
+1702,1,3.39
+1990,1,3.39
+1925,2,3.4
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+1857,1,3.41
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+2021,3,3.61
+2015,3,3.62
+1997,1,3.64
+2020,2,3.65
+1843,3,3.71
+1936,3,3.71
+1810,1,3.71
+1987,3,3.73
+1962,1,3.76
+2050,2,3.81
diff --git a/Udemy DataScience Workbook/Data_Files/2.01.+Admittance.csv b/Udemy DataScience Workbook/Data_Files/2.01.+Admittance.csv
new file mode 100644
index 0000000..fef66ad
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/2.01.+Admittance.csv	
@@ -0,0 +1,169 @@
+SAT,Admitted
+1363,No
+1792,Yes
+1954,Yes
+1653,No
+1593,No
+1755,Yes
+1775,Yes
+1887,Yes
+1893,Yes
+1580,No
+1857,Yes
+1880,Yes
+1664,Yes
+1364,No
+1693,No
+1850,Yes
+1633,No
+1634,No
+1636,Yes
+1855,Yes
+1987,Yes
+1997,Yes
+1422,No
+1508,No
+1720,Yes
+1879,Yes
+1634,Yes
+1802,Yes
+1849,Yes
+1764,Yes
+1460,No
+1675,Yes
+1656,No
+2020,Yes
+1850,Yes
+1865,Yes
+1664,No
+1872,Yes
+1654,No
+1393,No
+1587,No
+1631,Yes
+1931,Yes
+1370,No
+1810,Yes
+1414,No
+1761,Yes
+1477,No
+1486,No
+1561,No
+1549,No
+2050,Yes
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diff --git a/Udemy DataScience Workbook/Data_Files/2.02.+Binary+predictors.csv b/Udemy DataScience Workbook/Data_Files/2.02.+Binary+predictors.csv
new file mode 100644
index 0000000..2508c45
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/2.02.+Binary+predictors.csv	
@@ -0,0 +1,169 @@
+SAT,Admitted,Gender
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diff --git a/Udemy DataScience Workbook/Data_Files/2.03.+Test+dataset.csv b/Udemy DataScience Workbook/Data_Files/2.03.+Test+dataset.csv
new file mode 100644
index 0000000..4f8b42a
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/2.03.+Test+dataset.csv	
@@ -0,0 +1,20 @@
+SAT,Admitted,Gender
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diff --git a/Udemy DataScience Workbook/Data_Files/3.01.+Country+clusters.csv b/Udemy DataScience Workbook/Data_Files/3.01.+Country+clusters.csv
new file mode 100644
index 0000000..c9327f1
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/3.01.+Country+clusters.csv	
@@ -0,0 +1,7 @@
+Country,Latitude,Longitude,Language
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+Canada,62.4,-96.8,English
+France,46.75,2.4,French
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diff --git a/Udemy DataScience Workbook/Data_Files/3.12.+Example.csv b/Udemy DataScience Workbook/Data_Files/3.12.+Example.csv
new file mode 100644
index 0000000..84ca79c
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/3.12.+Example.csv	
@@ -0,0 +1,31 @@
+Satisfaction,Loyalty
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diff --git a/Udemy DataScience Workbook/Data_Files/Absenteeism_data.csv b/Udemy DataScience Workbook/Data_Files/Absenteeism_data.csv
new file mode 100644
index 0000000..0a8e3a9
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Absenteeism_data.csv	
@@ -0,0 +1,701 @@
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diff --git a/Udemy DataScience Workbook/Data_Files/Audiobooks_data.csv b/Udemy DataScience Workbook/Data_Files/Audiobooks_data.csv
new file mode 100644
index 0000000..b5be725
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Audiobooks_data.csv	
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diff --git a/Udemy DataScience Workbook/Data_Files/Bank_data_testing.csv b/Udemy DataScience Workbook/Data_Files/Bank_data_testing.csv
new file mode 100644
index 0000000..f1c9084
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Bank_data_testing.csv	
@@ -0,0 +1,223 @@
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diff --git a/Udemy DataScience Workbook/Data_Files/Categorical.csv b/Udemy DataScience Workbook/Data_Files/Categorical.csv
new file mode 100644
index 0000000..51cea6c
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Categorical.csv	
@@ -0,0 +1,242 @@
+name,Longitude,Latitude,continent
+Aruba,-69.98267711,12.52088038,North America
+Afghanistan,66.00473366,33.83523073,Asia
+Angola,17.53736768,-12.29336054,Africa
+Anguilla,-63.06498927,18.2239595,North America
+Albania,20.04983396,41.14244989,Europe
+Aland,19.95328768,60.21488688,Europe
+Andorra,1.56054378,42.54229102,Europe
+United Arab Emirates,54.3001671,23.90528188,Asia
+Argentina,-65.17980692,-35.3813488,South America
+Armenia,44.92993276,40.28952569,Asia
+American Samoa,-170.7180258,-14.30445997,Oceania
+Antarctica,19.92108951,-80.50857913,Antarctica
+Ashmore and Cartier Is.,123.5838379,-12.42993164,Oceania
+Fr. S. Antarctic Lands,69.22666758,-49.24895485,Seven seas (open ocean)
+Antigua and Barb.,-61.79469343,17.2774996,North America
+Australia,134.4910001,-25.73288704,Oceania
+Austria,14.1264761,47.58549439,Europe
+Azerbaijan,47.54599879,40.28827235,Asia
+Burundi,29.87512156,-3.35939666,Africa
+Belgium,4.64065114,50.63981576,Europe
+Benin,2.32785254,9.6417597,Africa
+Burkina Faso,-1.75456601,12.26953846,Africa
+Bangladesh,90.23812743,23.86731158,Asia
+Bulgaria,25.21552909,42.76890318,Europe
+Bahrain,50.54196932,26.04205135,Asia
+Canada,-98.30777028,61.36206324,North America
+Bahamas,-76.62843038,24.29036702,North America
+Bosnia and Herz.,17.76876733,44.17450125,Europe
+St-Barthélemy,-62.84067779,17.89880451,North America
+Belarus,28.03209307,53.53131377,Europe
+Belize,-88.71010486,17.20027509,North America
+Bermuda,-64.7545589,32.31367802,North America
+Bolivia,-64.68538645,-16.70814787,South America
+Brazil,-53.09783113,-10.78777702,South America
+Barbados,-59.559797,13.18145428,North America
+Brunei,114.7220304,4.51968958,Asia
+Bhutan,90.40188155,27.41106589,Asia
+Botswana,23.79853368,-22.18403213,Africa
+Central African Rep.,20.46826831,6.56823297,Africa
+Switzerland,8.20867471,46.79785878,Europe
+Chile,-71.38256213,-37.73070989,South America
+China,103.8190735,36.56176546,Asia
+Côte d'Ivoire,-5.5692157,7.6284262,Africa
+Cameroon,12.73964156,5.69109849,Africa
+Dem. Rep. Congo,23.64396107,-2.87746289,Africa
+Congo,15.21965762,-0.83787463,Africa
+Cook Is.,-159.7872422,-21.21927288,Oceania
+Colombia,-73.08114582,3.91383431,South America
+Comoros,43.68253968,-11.87783444,Africa
+Cape Verde,-23.9598882,15.95523324,Africa
+Costa Rica,-84.19208768,9.97634464,North America
+Cuba,-79.01605384,21.62289528,North America
+Curaçao,-68.97119369,12.19551675,North America
+Cayman Is.,-80.91213321,19.42896497,North America
+N. Cyprus,33.5684813,35.26277486,Asia
+Cyprus,33.0060022,34.91667211,Asia
+Czech Rep.,15.31240163,49.73341233,Europe
+Germany,10.38578051,51.10698181,Europe
+Djibouti,42.5606754,11.74871806,Africa
+Dominica,-61.357726,15.4394702,North America
+Denmark,10.02800992,55.98125296,Europe
+Dominican Rep.,-70.50568896,18.89433082,North America
+Algeria,2.61732301,28.15893849,Africa
+Ecuador,-78.75201922,-1.42381612,South America
+Egypt,29.86190099,26.49593311,Africa
+Eritrea,38.84617011,15.36186618,Africa
+Spain,-3.64755047,40.24448698,Europe
+Estonia,25.54248537,58.67192972,Europe
+Ethiopia,39.60080098,8.62278679,Africa
+Finland,26.2746656,64.49884603,Europe
+Fiji,165.4519543,-17.42858032,Oceania
+Falkland Is.,-59.35238956,-51.74483954,South America
+France,-2.76172945,42.17344011,Europe
+Faeroe Is.,-6.88095423,62.05385403,Europe
+Micronesia,153.2394379,7.45246814,Oceania
+Gabon,11.7886287,-0.58660025,Africa
+United Kingdom,-2.86563164,54.12387156,Europe
+Georgia,43.50780252,42.16855755,Asia
+Guernsey,-2.57239064,49.46809761,Europe
+Ghana,-1.21676566,7.95345644,Africa
+Guinea,-10.94066612,10.43621593,Africa
+Gambia,-15.39601295,13.44965244,Africa
+Guinea-Bissau,-14.94972445,12.04744948,Africa
+Eq. Guinea,10.34137924,1.70555135,Africa
+Greece,22.95555794,39.07469623,Europe
+Grenada,-61.68220189,12.11725044,North America
+Greenland,-41.34191127,74.71051289,North America
+Guatemala,-90.36482009,15.69403664,North America
+Guam,144.7679102,13.44165626,Oceania
+Guyana,-58.98202459,4.79378034,South America
+Hong Kong,114.1138045,22.39827737,Asia
+Heard I. and McDonald Is.,73.5205171,-53.08724656,Seven seas (open ocean)
+Honduras,-86.6151661,14.82688165,North America
+Croatia,16.40412899,45.08047631,Europe
+Haiti,-72.68527509,18.93502563,North America
+Hungary,19.39559116,47.16277506,Europe
+Indonesia,117.2401137,-2.21505456,Asia
+Isle of Man,-4.53873952,54.22418911,Europe
+India,79.6119761,22.88578212,Asia
+Indian Ocean Ter.,104.851898,-10.6478515,Asia
+Br. Indian Ocean Ter.,72.44541229,-7.33059751,Seven seas (open ocean)
+Ireland,-8.13793569,53.1754487,Europe
+Iran,54.27407004,32.57503292,Asia
+Iraq,43.74353149,33.03970582,Asia
+Iceland,-18.57396167,64.99575386,Europe
+Israel,35.00444693,31.46110101,Asia
+Italy,12.07001339,42.79662641,Europe
+Jamaica,-77.31482593,18.15694878,North America
+Jersey,-2.12689938,49.21837377,Europe
+Jordan,36.77136104,31.24579091,Asia
+Japan,138.0308956,37.59230135,Asia
+Siachen Glacier,77.18011865,35.39236325,Asia
+Kazakhstan,67.29149357,48.15688067,Asia
+Kenya,37.79593973,0.59988022,Africa
+Kyrgyzstan,74.54165513,41.46221943,Asia
+Cambodia,104.9069433,12.72004786,Asia
+Kiribati,-45.61110513,0.86001503,Oceania
+St. Kitts and Nevis,-62.68755265,17.2645995,North America
+Korea,127.8391609,36.38523983,Asia
+Kosovo,20.87249811,42.57078707,Europe
+Kuwait,47.58700459,29.33431262,Asia
+Lao PDR,103.7377241,18.50217433,Asia
+Lebanon,35.88016072,33.92306631,Asia
+Liberia,-9.32207573,6.45278492,Africa
+Libya,18.00866169,27.03094495,Africa
+Saint Lucia,-60.96969923,13.89479481,North America
+Liechtenstein,9.53574312,47.13665835,Europe
+Sri Lanka,80.70108238,7.61266509,Asia
+Lesotho,28.22723131,-29.58003188,Africa
+Lithuania,23.88719355,55.32610984,Europe
+Luxembourg,6.07182201,49.76725361,Europe
+Latvia,24.91235983,56.85085163,Europe
+Macao,113.5093212,22.22311688,Asia
+St-Martin,-63.05972851,18.08888611,North America
+Morocco,-8.45615795,29.83762955,Africa
+Monaco,7.40627677,43.75274627,Europe
+Moldova,28.45673372,47.19498804,Europe
+Madagascar,46.70473674,-19.37189587,Africa
+Maldives,73.45713004,3.7287092,Seven seas (open ocean)
+Mexico,-102.5234517,23.94753724,North America
+Marshall Is.,170.3397612,7.00376358,Oceania
+Macedonia,21.68211346,41.59530893,Europe
+Mali,-3.54269065,17.34581581,Africa
+Malta,14.40523316,35.92149632,Europe
+Myanmar,96.48843321,21.18566599,Asia
+Montenegro,19.23883939,42.78890259,Europe
+Mongolia,103.0529977,46.82681544,Asia
+N. Mariana Is.,145.6196965,15.82927563,Oceania
+Mozambique,35.53367543,-17.27381643,Africa
+Mauritania,-10.34779815,20.25736706,Africa
+Montserrat,-62.18518546,16.73941406,North America
+Mauritius,57.57120551,-20.27768704,Seven seas (open ocean)
+Malawi,34.28935599,-13.21808088,Africa
+Malaysia,109.6976228,3.78986846,Asia
+Namibia,17.20963567,-22.13032568,Africa
+New Caledonia,165.6849237,-21.29991806,Oceania
+Niger,9.38545882,17.41912493,Africa
+Norfolk Island,167.9492168,-29.0514609,Oceania
+Nigeria,8.08943895,9.59411452,Africa
+Nicaragua,-85.0305297,12.84709429,North America
+Niue,-169.8699468,-19.04945708,Oceania
+Netherlands,5.28144793,52.1007899,Europe
+Norway,15.34834656,68.75015572,Europe
+Nepal,83.9158264,28.24891365,Asia
+Nauru,166.9325682,-0.51912639,Oceania
+New Zealand,171.4849235,-41.81113557,Oceania
+Oman,56.09166155,20.60515333,Asia
+Pakistan,69.33957937,29.9497515,Asia
+Panama,-80.11915156,8.51750797,North America
+Pitcairn Is.,-128.317042,-24.36500535,Oceania
+Peru,-74.38242685,-9.15280381,South America
+Philippines,122.8839325,11.77536778,Asia
+Palau,134.4080797,7.28742784,Oceania
+Papua New Guinea,145.2074475,-6.46416646,Oceania
+Poland,19.39012835,52.12759564,Europe
+Puerto Rico,-66.47307604,18.22813055,North America
+Dem. Rep. Korea,127.1924797,40.15350311,Asia
+Portugal,-8.50104361,39.59550671,Europe
+Paraguay,-58.40013703,-23.22823913,South America
+Palestine,35.19628705,31.91613893,Asia
+Fr. Polynesia,-144.9049439,-14.72227409,Oceania
+Qatar,51.18479632,25.30601188,Asia
+Romania,24.97293039,45.85243127,Europe
+Russia,96.68656112,61.98052209,Europe
+Rwanda,29.91988515,-1.99033832,Africa
+W. Sahara,-12.21982755,24.22956739,Africa
+Saudi Arabia,44.53686271,24.12245841,Asia
+Sudan,29.94046812,15.99035669,Africa
+S. Sudan,30.24790002,7.30877945,Africa
+Senegal,-14.4734924,14.36624173,Africa
+Singapore,103.8172559,1.35876087,Asia
+S. Geo. and S. Sandw. Is.,-36.43318388,-54.46488248,Seven seas (open ocean)
+Saint Helena,-9.54779416,-12.40355951,Seven seas (open ocean)
+Solomon Is.,159.6328767,-8.92178022,Oceania
+Sierra Leone,-11.79271247,8.56329593,Africa
+El Salvador,-88.87164469,13.73943744,North America
+San Marino,12.45922334,43.94186747,Europe
+Somaliland,46.25198395,9.73345496,Africa
+Somalia,45.70714487,4.75062876,Africa
+St. Pierre and Miquelon,-56.30319779,46.91918789,North America
+Serbia,20.78958334,44.2215032,Europe
+São Tomé and Principe,6.72429658,0.44391445,Africa
+Suriname,-55.9123457,4.13055413,South America
+Slovakia,19.47905218,48.70547528,Europe
+Slovenia,14.80444238,46.11554772,Europe
+Sweden,16.74558049,62.77966519,Europe
+Swaziland,31.4819369,-26.55843045,Africa
+Sint Maarten,-63.05713363,18.05081728,North America
+Seychelles,55.47603279,-4.66099094,Seven seas (open ocean)
+Syria,38.50788204,35.02547389,Asia
+Turks and Caicos Is.,-71.97387881,21.83047572,North America
+Chad,18.64492513,15.33333758,Africa
+Togo,0.96232845,8.52531356,Africa
+Thailand,101.0028813,15.11815794,Asia
+Tajikistan,71.01362631,38.5304539,Asia
+Turkmenistan,59.37100021,39.11554137,Asia
+Timor-Leste,125.8443898,-8.82889162,Asia
+Tonga,-174.8098734,-20.42843174,Oceania
+Trinidad and Tobago,-61.26567923,10.45733408,North America
+Tunisia,9.55288359,34.11956246,Africa
+Turkey,35.16895346,39.0616029,Asia
+Taiwan,120.9542728,23.7539928,Asia
+Tanzania,34.81309981,-6.27565408,Africa
+Uganda,32.36907971,1.27469299,Africa
+Ukraine,31.38326469,48.99656673,Europe
+Uruguay,-56.01807053,-32.79951534,South America
+United States,-112.4616737,45.6795472,North America
+Uzbekistan,63.14001528,41.75554225,Asia
+Vatican,12.43387177,41.90174985,Europe
+St. Vin. and Gren.,-61.20129695,13.22472269,North America
+Venezuela,-66.18184123,7.12422421,South America
+British Virgin Is.,-64.47146992,18.52585755,North America
+U.S. Virgin Is.,-64.80301538,17.95500624,North America
+Vietnam,106.299147,16.6460167,Asia
+Vanuatu,167.6864464,-16.22640909,Oceania
+Wallis and Futuna Is.,-177.3483483,-13.88737039,Oceania
+Samoa,-172.1648506,-13.75324346,Oceania
+Yemen,47.58676189,15.90928005,Asia
+South Africa,25.08390093,-29.00034095,Africa
+Zambia,27.77475946,-13.45824152,Africa
+Zimbabwe,29.8514412,-19.00420419,Africa
diff --git a/Udemy DataScience Workbook/Data_Files/Countries-exercise.csv b/Udemy DataScience Workbook/Data_Files/Countries-exercise.csv
new file mode 100644
index 0000000..7c60331
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Countries-exercise.csv	
@@ -0,0 +1,242 @@
+name,Longitude,Latitude
+Aruba,-69.98267711,12.52088038
+Afghanistan,66.00473366,33.83523073
+Angola,17.53736768,-12.29336054
+Anguilla,-63.06498927,18.2239595
+Albania,20.04983396,41.14244989
+Aland,19.95328768,60.21488688
+Andorra,1.56054378,42.54229102
+United Arab Emirates,54.3001671,23.90528188
+Argentina,-65.17980692,-35.3813488
+Armenia,44.92993276,40.28952569
+American Samoa,-170.7180258,-14.30445997
+Antarctica,19.92108951,-80.50857913
+Ashmore and Cartier Is.,123.5838379,-12.42993164
+Fr. S. Antarctic Lands,69.22666758,-49.24895485
+Antigua and Barb.,-61.79469343,17.2774996
+Australia,134.4910001,-25.73288704
+Austria,14.1264761,47.58549439
+Azerbaijan,47.54599879,40.28827235
+Burundi,29.87512156,-3.35939666
+Belgium,4.64065114,50.63981576
+Benin,2.32785254,9.6417597
+Burkina Faso,-1.75456601,12.26953846
+Bangladesh,90.23812743,23.86731158
+Bulgaria,25.21552909,42.76890318
+Bahrain,50.54196932,26.04205135
+Canada,-98.30777028,61.36206324
+Bahamas,-76.62843038,24.29036702
+Bosnia and Herz.,17.76876733,44.17450125
+St-Barthélemy,-62.84067779,17.89880451
+Belarus,28.03209307,53.53131377
+Belize,-88.71010486,17.20027509
+Bermuda,-64.7545589,32.31367802
+Bolivia,-64.68538645,-16.70814787
+Brazil,-53.09783113,-10.78777702
+Barbados,-59.559797,13.18145428
+Brunei,114.7220304,4.51968958
+Bhutan,90.40188155,27.41106589
+Botswana,23.79853368,-22.18403213
+Central African Rep.,20.46826831,6.56823297
+Switzerland,8.20867471,46.79785878
+Chile,-71.38256213,-37.73070989
+China,103.8190735,36.56176546
+Côte d'Ivoire,-5.5692157,7.6284262
+Cameroon,12.73964156,5.69109849
+Dem. Rep. Congo,23.64396107,-2.87746289
+Congo,15.21965762,-0.83787463
+Cook Is.,-159.7872422,-21.21927288
+Colombia,-73.08114582,3.91383431
+Comoros,43.68253968,-11.87783444
+Cape Verde,-23.9598882,15.95523324
+Costa Rica,-84.19208768,9.97634464
+Cuba,-79.01605384,21.62289528
+Curaçao,-68.97119369,12.19551675
+Cayman Is.,-80.91213321,19.42896497
+N. Cyprus,33.5684813,35.26277486
+Cyprus,33.0060022,34.91667211
+Czech Rep.,15.31240163,49.73341233
+Germany,10.38578051,51.10698181
+Djibouti,42.5606754,11.74871806
+Dominica,-61.357726,15.4394702
+Denmark,10.02800992,55.98125296
+Dominican Rep.,-70.50568896,18.89433082
+Algeria,2.61732301,28.15893849
+Ecuador,-78.75201922,-1.42381612
+Egypt,29.86190099,26.49593311
+Eritrea,38.84617011,15.36186618
+Spain,-3.64755047,40.24448698
+Estonia,25.54248537,58.67192972
+Ethiopia,39.60080098,8.62278679
+Finland,26.2746656,64.49884603
+Fiji,165.4519543,-17.42858032
+Falkland Is.,-59.35238956,-51.74483954
+France,-2.76172945,42.17344011
+Faeroe Is.,-6.88095423,62.05385403
+Micronesia,153.2394379,7.45246814
+Gabon,11.7886287,-0.58660025
+United Kingdom,-2.86563164,54.12387156
+Georgia,43.50780252,42.16855755
+Guernsey,-2.57239064,49.46809761
+Ghana,-1.21676566,7.95345644
+Guinea,-10.94066612,10.43621593
+Gambia,-15.39601295,13.44965244
+Guinea-Bissau,-14.94972445,12.04744948
+Eq. Guinea,10.34137924,1.70555135
+Greece,22.95555794,39.07469623
+Grenada,-61.68220189,12.11725044
+Greenland,-41.34191127,74.71051289
+Guatemala,-90.36482009,15.69403664
+Guam,144.7679102,13.44165626
+Guyana,-58.98202459,4.79378034
+Hong Kong,114.1138045,22.39827737
+Heard I. and McDonald Is.,73.5205171,-53.08724656
+Honduras,-86.6151661,14.82688165
+Croatia,16.40412899,45.08047631
+Haiti,-72.68527509,18.93502563
+Hungary,19.39559116,47.16277506
+Indonesia,117.2401137,-2.21505456
+Isle of Man,-4.53873952,54.22418911
+India,79.6119761,22.88578212
+Indian Ocean Ter.,104.851898,-10.6478515
+Br. Indian Ocean Ter.,72.44541229,-7.33059751
+Ireland,-8.13793569,53.1754487
+Iran,54.27407004,32.57503292
+Iraq,43.74353149,33.03970582
+Iceland,-18.57396167,64.99575386
+Israel,35.00444693,31.46110101
+Italy,12.07001339,42.79662641
+Jamaica,-77.31482593,18.15694878
+Jersey,-2.12689938,49.21837377
+Jordan,36.77136104,31.24579091
+Japan,138.0308956,37.59230135
+Siachen Glacier,77.18011865,35.39236325
+Kazakhstan,67.29149357,48.15688067
+Kenya,37.79593973,0.59988022
+Kyrgyzstan,74.54165513,41.46221943
+Cambodia,104.9069433,12.72004786
+Kiribati,-45.61110513,0.86001503
+St. Kitts and Nevis,-62.68755265,17.2645995
+Korea,127.8391609,36.38523983
+Kosovo,20.87249811,42.57078707
+Kuwait,47.58700459,29.33431262
+Lao PDR,103.7377241,18.50217433
+Lebanon,35.88016072,33.92306631
+Liberia,-9.32207573,6.45278492
+Libya,18.00866169,27.03094495
+Saint Lucia,-60.96969923,13.89479481
+Liechtenstein,9.53574312,47.13665835
+Sri Lanka,80.70108238,7.61266509
+Lesotho,28.22723131,-29.58003188
+Lithuania,23.88719355,55.32610984
+Luxembourg,6.07182201,49.76725361
+Latvia,24.91235983,56.85085163
+Macao,113.5093212,22.22311688
+St-Martin,-63.05972851,18.08888611
+Morocco,-8.45615795,29.83762955
+Monaco,7.40627677,43.75274627
+Moldova,28.45673372,47.19498804
+Madagascar,46.70473674,-19.37189587
+Maldives,73.45713004,3.7287092
+Mexico,-102.5234517,23.94753724
+Marshall Is.,170.3397612,7.00376358
+Macedonia,21.68211346,41.59530893
+Mali,-3.54269065,17.34581581
+Malta,14.40523316,35.92149632
+Myanmar,96.48843321,21.18566599
+Montenegro,19.23883939,42.78890259
+Mongolia,103.0529977,46.82681544
+N. Mariana Is.,145.6196965,15.82927563
+Mozambique,35.53367543,-17.27381643
+Mauritania,-10.34779815,20.25736706
+Montserrat,-62.18518546,16.73941406
+Mauritius,57.57120551,-20.27768704
+Malawi,34.28935599,-13.21808088
+Malaysia,109.6976228,3.78986846
+Namibia,17.20963567,-22.13032568
+New Caledonia,165.6849237,-21.29991806
+Niger,9.38545882,17.41912493
+Norfolk Island,167.9492168,-29.0514609
+Nigeria,8.08943895,9.59411452
+Nicaragua,-85.0305297,12.84709429
+Niue,-169.8699468,-19.04945708
+Netherlands,5.28144793,52.1007899
+Norway,15.34834656,68.75015572
+Nepal,83.9158264,28.24891365
+Nauru,166.9325682,-0.51912639
+New Zealand,171.4849235,-41.81113557
+Oman,56.09166155,20.60515333
+Pakistan,69.33957937,29.9497515
+Panama,-80.11915156,8.51750797
+Pitcairn Is.,-128.317042,-24.36500535
+Peru,-74.38242685,-9.15280381
+Philippines,122.8839325,11.77536778
+Palau,134.4080797,7.28742784
+Papua New Guinea,145.2074475,-6.46416646
+Poland,19.39012835,52.12759564
+Puerto Rico,-66.47307604,18.22813055
+Dem. Rep. Korea,127.1924797,40.15350311
+Portugal,-8.50104361,39.59550671
+Paraguay,-58.40013703,-23.22823913
+Palestine,35.19628705,31.91613893
+Fr. Polynesia,-144.9049439,-14.72227409
+Qatar,51.18479632,25.30601188
+Romania,24.97293039,45.85243127
+Russia,96.68656112,61.98052209
+Rwanda,29.91988515,-1.99033832
+W. Sahara,-12.21982755,24.22956739
+Saudi Arabia,44.53686271,24.12245841
+Sudan,29.94046812,15.99035669
+S. Sudan,30.24790002,7.30877945
+Senegal,-14.4734924,14.36624173
+Singapore,103.8172559,1.35876087
+S. Geo. and S. Sandw. Is.,-36.43318388,-54.46488248
+Saint Helena,-9.54779416,-12.40355951
+Solomon Is.,159.6328767,-8.92178022
+Sierra Leone,-11.79271247,8.56329593
+El Salvador,-88.87164469,13.73943744
+San Marino,12.45922334,43.94186747
+Somaliland,46.25198395,9.73345496
+Somalia,45.70714487,4.75062876
+St. Pierre and Miquelon,-56.30319779,46.91918789
+Serbia,20.78958334,44.2215032
+São Tomé and Principe,6.72429658,0.44391445
+Suriname,-55.9123457,4.13055413
+Slovakia,19.47905218,48.70547528
+Slovenia,14.80444238,46.11554772
+Sweden,16.74558049,62.77966519
+Swaziland,31.4819369,-26.55843045
+Sint Maarten,-63.05713363,18.05081728
+Seychelles,55.47603279,-4.66099094
+Syria,38.50788204,35.02547389
+Turks and Caicos Is.,-71.97387881,21.83047572
+Chad,18.64492513,15.33333758
+Togo,0.96232845,8.52531356
+Thailand,101.0028813,15.11815794
+Tajikistan,71.01362631,38.5304539
+Turkmenistan,59.37100021,39.11554137
+Timor-Leste,125.8443898,-8.82889162
+Tonga,-174.8098734,-20.42843174
+Trinidad and Tobago,-61.26567923,10.45733408
+Tunisia,9.55288359,34.11956246
+Turkey,35.16895346,39.0616029
+Taiwan,120.9542728,23.7539928
+Tanzania,34.81309981,-6.27565408
+Uganda,32.36907971,1.27469299
+Ukraine,31.38326469,48.99656673
+Uruguay,-56.01807053,-32.79951534
+United States,-112.4616737,45.6795472
+Uzbekistan,63.14001528,41.75554225
+Vatican,12.43387177,41.90174985
+St. Vin. and Gren.,-61.20129695,13.22472269
+Venezuela,-66.18184123,7.12422421
+British Virgin Is.,-64.47146992,18.52585755
+U.S. Virgin Is.,-64.80301538,17.95500624
+Vietnam,106.299147,16.6460167
+Vanuatu,167.6864464,-16.22640909
+Wallis and Futuna Is.,-177.3483483,-13.88737039
+Samoa,-172.1648506,-13.75324346
+Yemen,47.58676189,15.90928005
+South Africa,25.08390093,-29.00034095
+Zambia,27.77475946,-13.45824152
+Zimbabwe,29.8514412,-19.00420419
diff --git a/Udemy DataScience Workbook/Data_Files/Countries_exercise.csv b/Udemy DataScience Workbook/Data_Files/Countries_exercise.csv
new file mode 100644
index 0000000..7c60331
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Countries_exercise.csv	
@@ -0,0 +1,242 @@
+name,Longitude,Latitude
+Aruba,-69.98267711,12.52088038
+Afghanistan,66.00473366,33.83523073
+Angola,17.53736768,-12.29336054
+Anguilla,-63.06498927,18.2239595
+Albania,20.04983396,41.14244989
+Aland,19.95328768,60.21488688
+Andorra,1.56054378,42.54229102
+United Arab Emirates,54.3001671,23.90528188
+Argentina,-65.17980692,-35.3813488
+Armenia,44.92993276,40.28952569
+American Samoa,-170.7180258,-14.30445997
+Antarctica,19.92108951,-80.50857913
+Ashmore and Cartier Is.,123.5838379,-12.42993164
+Fr. S. Antarctic Lands,69.22666758,-49.24895485
+Antigua and Barb.,-61.79469343,17.2774996
+Australia,134.4910001,-25.73288704
+Austria,14.1264761,47.58549439
+Azerbaijan,47.54599879,40.28827235
+Burundi,29.87512156,-3.35939666
+Belgium,4.64065114,50.63981576
+Benin,2.32785254,9.6417597
+Burkina Faso,-1.75456601,12.26953846
+Bangladesh,90.23812743,23.86731158
+Bulgaria,25.21552909,42.76890318
+Bahrain,50.54196932,26.04205135
+Canada,-98.30777028,61.36206324
+Bahamas,-76.62843038,24.29036702
+Bosnia and Herz.,17.76876733,44.17450125
+St-Barthélemy,-62.84067779,17.89880451
+Belarus,28.03209307,53.53131377
+Belize,-88.71010486,17.20027509
+Bermuda,-64.7545589,32.31367802
+Bolivia,-64.68538645,-16.70814787
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diff --git a/Udemy DataScience Workbook/Data_Files/Country+clusters+standardized.csv b/Udemy DataScience Workbook/Data_Files/Country+clusters+standardized.csv
new file mode 100644
index 0000000..09dad09
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Country+clusters+standardized.csv	
@@ -0,0 +1,7 @@
+Country,Latitude,Longitude,Language
+USA,0.186539588,-1.089159514,0
+Canada,0.728587658,-1.008603741,0
+France,0.241895042,0.137900235,1
+UK,0.467670658,0.080921761,0
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+Australia,-2.003421694,1.648581027,0
diff --git a/Udemy DataScience Workbook/Data_Files/Course+Notes+-+Section+2.pdf b/Udemy DataScience Workbook/Data_Files/Course+Notes+-+Section+2.pdf
new file mode 100644
index 0000000..1194f01
Binary files /dev/null and b/Udemy DataScience Workbook/Data_Files/Course+Notes+-+Section+2.pdf differ
diff --git a/Udemy DataScience Workbook/Data_Files/Course_Notes_Logistic_Regression.pdf b/Udemy DataScience Workbook/Data_Files/Course_Notes_Logistic_Regression.pdf
new file mode 100644
index 0000000..ff31ba5
Binary files /dev/null and b/Udemy DataScience Workbook/Data_Files/Course_Notes_Logistic_Regression.pdf differ
diff --git a/Udemy DataScience Workbook/Data_Files/Example_bank_data.csv b/Udemy DataScience Workbook/Data_Files/Example_bank_data.csv
new file mode 100644
index 0000000..dd45fff
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Example_bank_data.csv	
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diff --git a/Udemy DataScience Workbook/Data_Files/Pasword_Numbers.xlsx b/Udemy DataScience Workbook/Data_Files/Pasword_Numbers.xlsx
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diff --git a/Udemy DataScience Workbook/Data_Files/Real-life+example.csv b/Udemy DataScience Workbook/Data_Files/Real-life+example.csv
new file mode 100644
index 0000000..7dbe08c
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/Real-life+example.csv	
@@ -0,0 +1,4346 @@
+Brand,Price,Body,Mileage,EngineV,Engine Type,Registration,Year,Model
+BMW,4200,sedan,277,2,Petrol,yes,1991,320
+Mercedes-Benz,7900,van,427,2.9,Diesel,yes,1999,Sprinter 212
+Mercedes-Benz,13300,sedan,358,5,Gas,yes,2003,S 500
+Audi,23000,crossover,240,4.2,Petrol,yes,2007,Q7
+Toyota,18300,crossover,120,2,Petrol,yes,2011,Rav 4
+Mercedes-Benz,199999,crossover,0,5.5,Petrol,yes,2016,GLS 63
+BMW,6100,sedan,438,2,Gas,yes,1997,320
+Audi,14200,vagon,200,2.7,Diesel,yes,2006,A6
+Renault,10799,vagon,193,1.5,Diesel,yes,2012,Megane
+Volkswagen,1400,other,212,1.8,Gas,no,1999,Golf IV
+Renault,11950,vagon,177,1.5,Diesel,yes,2011,Megane
+Renault,2500,sedan,260,1.79,Petrol,yes,1994,19
+Audi,9500,vagon,165,2.7,Gas,yes,2003,A6 Allroad
+Volkswagen,10500,sedan,100,1.8,Petrol,yes,2008,Passat B6
+Toyota,16000,crossover,250,4.7,Gas,yes,2001,Land Cruiser 100
+Renault,8600,hatch,84,1.5,Diesel,yes,2012,Clio
+BMW,2990,other,203,2,Petrol,no,2001,318
+Mercedes-Benz,NA,van,240,2.2,Diesel,yes,2007,Vito
+Toyota,26500,crossover,21,2,Petrol,yes,2013,Rav 4
+Audi,3500,vagon,250,2.5,Diesel,no,1998,A6
+Toyota,38233,other,0,2.4,Diesel,yes,2016,Hilux
+Volkswagen,7500,hatch,132,1.4,Diesel,yes,2006,Polo
+Audi,6800,sedan,225,2.4,Gas,yes,1998,A6
+Mitsubishi,10500,crossover,130,2.4,Gas,yes,2006,Outlander
+Audi,24900,sedan,163,4.2,Diesel,yes,2008,A8
+Volkswagen,20800,crossover,151,3,Diesel,yes,2008,Touareg
+Audi,6500,sedan,330,2.4,Petrol,yes,1999,A6
+Mercedes-Benz,13566,other,171,2.2,Other,no,2011,Vito
+Mitsubishi,8500,hatch,65,1.3,Petrol,yes,2010,Colt
+Mercedes-Benz,NA,crossover,0,4.6,Petrol,yes,2016,GLS 500
+Audi,2900,sedan,1,2.3,Gas,yes,1989,100
+BMW,21500,other,72,3,Petrol,yes,2007,Z4
+Mitsubishi,17900,crossover,87,3.8,Gas,yes,2008,Pajero Wagon
+BMW,28500,crossover,160,4.8,Gas,yes,2008,X5
+Volkswagen,9800,van,160,1.9,Diesel,yes,2009,Caddy
+Toyota,14500,sedan,147,2.4,Gas,yes,2006,Camry
+BMW,6000,sedan,300,2.8,Petrol,yes,1996,528
+BMW,67500,crossover,1,2.5,Diesel,yes,2016,X5
+Audi,33900,other,14,1.8,Petrol,yes,2014,TT
+Audi,2500,sedan,180,NA,Petrol,no,2000,A4
+Mercedes-Benz,9800,van,230,2.15,Diesel,yes,2004,Vito
+Mercedes-Benz,63000,crossover,86,5.5,Petrol,yes,2009,G 55 AMG
+BMW,7700,sedan,326,2.8,Petrol,yes,1998,528
+Mercedes-Benz,9600,van,30,2.2,Diesel,yes,2006,Vito
+Toyota,12800,sedan,181,2.4,Petrol,yes,2008,Camry
+BMW,41000,crossover,80,3,Petrol,yes,2012,X6
+Mitsubishi,2500,sedan,220,2.4,Petrol,yes,1987,Galant
+BMW,3700,sedan,231,2.5,Petrol,yes,1990,525
+Mercedes-Benz,29500,sedan,134,5,Petrol,yes,2008,S 500
+BMW,6500,sedan,335,2.8,Petrol,yes,1996,528
+Mercedes-Benz,NA,sedan,145,3.2,Diesel,yes,2007,E-Class
+Renault,8250,van,66,1.5,Diesel,yes,2014,Kangoo
+Mercedes-Benz,19500,crossover,172,3.5,Other,yes,2008,ML 350
+Mercedes-Benz,13500,sedan,317,5,Petrol,yes,2001,S 500
+Mercedes-Benz,9200,van,266,2.2,Diesel,yes,2008,Vito
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLS 350
+Toyota,10900,sedan,156,NA,Gas,yes,2005,Camry
+Volkswagen,5000,vagon,265,2,Diesel,no,2006,Passat B6
+BMW,23900,crossover,162,4.8,Petrol,yes,2007,X5
+BMW,36900,sedan,81,3,Diesel,yes,2010,730
+BMW,8900,other,253,1.8,Petrol,yes,1995,318
+Renault,7800,van,200,1.9,Diesel,yes,2002,Trafic
+BMW,133000,crossover,7,4.4,Petrol,yes,2015,X5 M
+BMW,1650,sedan,270,1.8,Petrol,no,1995,318
+Mercedes-Benz,50000,sedan,240,3.5,Petrol,yes,2006,S 350
+Renault,9700,van,245,2,Diesel,yes,2008,Trafic
+Mercedes-Benz,9999,van,127,2.2,Diesel,yes,2005,Vito
+Audi,69999,crossover,21,3,Petrol,yes,2015,A6 Allroad
+Mitsubishi,6500,sedan,152,1.6,Petrol,yes,2007,Lancer
+Audi,4300,sedan,267,1.9,Diesel,no,2003,A6
+Audi,3000,sedan,267,2.5,Diesel,no,1999,A6
+Mercedes-Benz,8700,sedan,240,2.2,Diesel,yes,2000,E-Class
+Renault,9000,vagon,178,2,Petrol,yes,2008,Scenic
+Mercedes-Benz,20400,sedan,190,1.8,Gas,yes,2011,E-Class
+BMW,9000,sedan,175,3,Petrol,no,2006,330
+Mercedes-Benz,13400,sedan,105,1.8,Petrol,yes,2006,E-Class
+Volkswagen,3700,sedan,195,1.9,Diesel,no,2001,Passat B5
+Volkswagen,9850,van,126,2,Diesel,yes,2012,Caddy
+Audi,3700,hatch,420,2,Diesel,no,2004,A3
+Toyota,15700,crossover,195,4,Gas,yes,2003,Land Cruiser Prado
+Mercedes-Benz,24500,sedan,180,2.2,Diesel,yes,2012,E-Class
+Mitsubishi,7500,sedan,195,1.6,Gas,yes,2007,Lancer
+Volkswagen,11990,van,231,2.5,Diesel,yes,2002,Caravelle
+Volkswagen,13708.5,hatch,51,1.4,Petrol,yes,2014,Polo
+BMW,8200,sedan,287,2.5,Gas,yes,1999,525
+Renault,6999,van,222,1.5,Diesel,yes,2009,Kangoo
+Renault,6750,vagon,214,1.5,Diesel,yes,2004,Megane
+Audi,18500,sedan,245,3,Diesel,yes,2006,A8
+Toyota,14299,sedan,83,2,Petrol,yes,2009,Avensis
+Mercedes-Benz,28500,crossover,210,3.2,Diesel,yes,2007,GL 320
+Mitsubishi,17000,crossover,89,3.8,Petrol,yes,2008,Pajero Wagon
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GL 350
+Mercedes-Benz,25700,crossover,130,4.7,Gas,yes,2008,GL 450
+Audi,2650,vagon,333,2.5,Diesel,no,2000,A6
+Mitsubishi,8500,sedan,116,1.5,Petrol,yes,2008,Lancer X
+Mercedes-Benz,4000,sedan,405,2,Diesel,yes,1992,200
+Renault,11000,other,131,1.5,Diesel,yes,2011,Kangoo
+Mercedes-Benz,14500,sedan,138,2,Petrol,yes,2007,E-Class
+BMW,4099,sedan,50,2.5,Petrol,yes,1988,520
+Volkswagen,31500,crossover,1,2,Petrol,yes,2014,Tiguan
+Renault,4500,van,207,1.5,Diesel,yes,2003,Kangoo
+Volkswagen,6700,other,207,1.9,Diesel,yes,2004,Caddy
+Renault,NA,van,85,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,20800,crossover,31,2.4,Petrol,yes,2011,Outlander XL
+Mercedes-Benz,107999,crossover,0,2.98,Diesel,yes,2016,GLS 350
+BMW,3500,sedan,334,2,Diesel,no,1999,320
+Audi,39000,sedan,18,1.8,Other,yes,2015,A5
+Toyota,13500,crossover,150,4.7,Gas,yes,2002,Land Cruiser 100
+Mitsubishi,6150,sedan,175,1.6,Petrol,yes,2007,Lancer
+BMW,45500,crossover,76,4.4,Petrol,yes,2010,X6 M
+BMW,4900,sedan,295,2,Petrol,yes,1994,520
+BMW,47000,other,5,2,Petrol,yes,2014,Z4
+Toyota,19200,crossover,190,2.7,Gas,yes,2005,Land Cruiser Prado
+Renault,NA,vagon,180,1.5,Diesel,yes,2011,Megane
+Mitsubishi,11100,crossover,119,2.4,Gas,yes,2008,Outlander
+Mitsubishi,8200,sedan,139,1.5,Other,yes,2012,Lancer X
+Volkswagen,10300,sedan,235,2,Petrol,yes,2007,Passat B6
+Renault,11990,van,210,1.1,Diesel,yes,2010,Scenic
+Mitsubishi,800,sedan,340,NA,Other,yes,1986,Galant
+BMW,46800,sedan,33,2,Diesel,yes,2014,520
+Volkswagen,1500,other,260,1.8,Gas,no,1994,Golf III
+BMW,6350,crossover,156,4.4,Gas,no,2002,X5
+Mercedes-Benz,NA,sedan,16,3,Diesel,yes,2014,S 350
+Mercedes-Benz,8900,hatch,155,1.5,Petrol,yes,2005,A 150
+Mercedes-Benz,12700,van,160,3,Diesel,yes,2007,Vito
+Mitsubishi,8000,sedan,207,1.6,Gas,yes,2007,Lancer
+Toyota,29500,crossover,63,4,Petrol,yes,2008,FJ Cruiser
+Volkswagen,NA,hatch,100,2,Petrol,yes,2013,Golf GTI
+Renault,7100,van,208,1.5,Diesel,yes,2008,Kangoo
+Renault,10850,crossover,103,2.5,Gas,yes,2010,Koleos
+Volkswagen,NA,van,277,2.5,Diesel,yes,2001,T4 (Transporter)
+Toyota,195000,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,27000,sedan,57,2,Diesel,yes,2013,Passat B7
+Volkswagen,18100,other,64,2,Petrol,yes,2011,Scirocco
+BMW,27000,sedan,137,5,Other,yes,2005,M5
+BMW,8990,other,300,2.2,Petrol,yes,2002,320
+Toyota,32900,crossover,82,2.7,Petrol,yes,2013,Venza
+BMW,25500,other,163,4.8,Petrol,yes,2008,X5
+Toyota,8900,crossover,153,2,Petrol,yes,2003,Rav 4
+Audi,11950,sedan,190,3,Petrol,yes,2006,A6
+Mercedes-Benz,22700,sedan,125,2.2,Diesel,yes,2010,E-Class
+Audi,600,sedan,112,1.6,Petrol,yes,1980,80
+BMW,13000,sedan,165,2.2,Gas,yes,2003,520
+Audi,25000,other,60,2,Petrol,yes,2011,A5
+Toyota,26900,crossover,119,4,Gas,yes,2008,Land Cruiser Prado
+Mercedes-Benz,87777,van,0,2.2,Diesel,yes,2016,V 250
+Volkswagen,6000,van,200,2,Diesel,yes,2008,Caddy
+Audi,9800,vagon,257,2.5,Diesel,yes,2003,A6
+Audi,13900,other,197,2,Diesel,yes,2006,A6
+Renault,12200,van,164,2.5,Diesel,yes,2009,Trafic
+Mercedes-Benz,13500,crossover,205,2.7,Diesel,yes,2005,ML 270
+BMW,18100,crossover,200,4.8,Petrol,yes,2005,X5
+BMW,33000,crossover,200,3,Diesel,yes,2010,X5
+Volkswagen,16900,crossover,145,1.4,Petrol,yes,2011,Tiguan
+Mercedes-Benz,22000,sedan,110,1.8,Petrol,yes,2011,C-Class
+Volkswagen,10700,sedan,195,3.2,Petrol,yes,2007,Passat B6
+Mercedes-Benz,7800,sedan,226,2.2,Diesel,yes,2000,E-Class
+Mercedes-Benz,17300,sedan,184,1.8,Gas,yes,2008,E-Class
+BMW,70000,sedan,16,3,Diesel,yes,2014,730
+Volkswagen,2200,hatch,118,1.4,Petrol,no,2001,Lupo
+BMW,8900,sedan,256,2.2,Petrol,yes,2002,5 Series
+Mercedes-Benz,31600,sedan,33,1.6,Petrol,yes,2013,CLA 200
+Volkswagen,7900,other,168,1.9,Diesel,yes,2006,T5 (Transporter)
+Toyota,28500,crossover,97,3.5,Petrol,yes,2012,Highlander
+Renault,6900,van,140,1.5,Diesel,yes,2012,Kangoo
+BMW,8850,vagon,280,2.5,Diesel,yes,2000,525
+Renault,3100,van,164,1.4,Petrol,yes,1999,Kangoo
+BMW,8900,sedan,250,2.5,Petrol,yes,2000,525
+Renault,6100,sedan,130,1.4,Petrol,yes,2005,Megane
+BMW,1400,sedan,260,1.8,Petrol,no,1996,318
+Volkswagen,8800,van,93,1.6,Diesel,yes,2012,Caddy
+Renault,10299,other,176,2,Diesel,yes,2013,Trafic
+Renault,6500,sedan,170,1.6,Petrol,yes,2004,Megane
+Mercedes-Benz,169000,other,1,4.7,Petrol,yes,2015,S 500
+Toyota,9100,sedan,151,1.6,Gas,yes,2007,Corolla
+Volkswagen,9950,hatch,95,1.4,Petrol,yes,2010,Polo
+Toyota,24500,crossover,260,4.7,Gas,yes,2003,Land Cruiser 100
+Audi,51500,crossover,27,3,Diesel,yes,2013,Q7
+Volkswagen,12900,van,290,1.9,Diesel,yes,2008,T5 (Transporter)
+Mercedes-Benz,8000,van,250,2.2,Diesel,yes,2006,Vito
+Mitsubishi,8100,sedan,112,2.4,Petrol,yes,2007,Galant
+Renault,10100,van,164,2,Diesel,yes,2012,Trafic
+BMW,1900,sedan,300,2.5,Diesel,no,1997,5 Series
+Renault,10800,van,187,2,Diesel,yes,2012,Trafic
+Audi,8000,sedan,234,4.2,Gas,no,2004,A8
+Mercedes-Benz,15300,other,120,2.6,Petrol,yes,2004,CLK 240
+Volkswagen,2350,hatch,182,1.9,Diesel,no,1998,Golf IV
+Volkswagen,23999,sedan,88,NA,Other,yes,2012,Passat CC
+BMW,38888,vagon,5,0.65,Other,yes,2015,I3
+Mitsubishi,7300,hatch,126,NA,Other,yes,2004,Colt
+Toyota,99999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,17650,sedan,110,1.8,Petrol,yes,2012,Passat B7
+Volkswagen,12999,van,237,2.5,Diesel,yes,2005,Multivan
+Mercedes-Benz,18500,other,90,1.8,Petrol,yes,2008,C-Class
+Volkswagen,9200,sedan,195,2,Petrol,yes,2006,Passat B6
+Volkswagen,14500,sedan,1,1.6,Other,yes,2016,Polo
+Volkswagen,18000,vagon,18,1.6,Diesel,yes,2011,Passat B7
+Mercedes-Benz,6800,van,250,1.7,Diesel,yes,2003,Vaneo
+BMW,20500,crossover,73,2,Petrol,yes,2011,X1
+BMW,28000,other,100,5,Petrol,yes,2005,M5
+Volkswagen,10650,van,130,2,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,20300,other,30,1.4,Petrol,yes,2013,Scirocco
+Volkswagen,3000,vagon,267,1.9,Diesel,no,1999,Passat B5
+Toyota,25250,crossover,112,4.5,Gas,yes,2007,Land Cruiser 100
+Mitsubishi,1389,hatch,245,1.3,Petrol,no,1996,Colt
+Mercedes-Benz,17600,sedan,174,4,Diesel,yes,2005,S 400
+Mercedes-Benz,14200,van,180,2.2,Diesel,yes,2007,Vito
+BMW,13700,crossover,270,4.6,Gas,yes,2004,X5
+Mercedes-Benz,23900,sedan,187,5.5,Petrol,yes,2007,S 550
+Volkswagen,3700,crossover,267,NA,Diesel,no,2000,Golf IV
+Volkswagen,32000,sedan,144,3.6,Petrol,yes,2013,Passat CC
+BMW,4599,sedan,267,2,Gas,yes,1991,520
+Volkswagen,2350,sedan,300,1.9,Diesel,no,1998,Passat B5
+Audi,19999,vagon,160,2,Petrol,yes,2010,A4 Allroad
+BMW,28900,sedan,78,2,Petrol,yes,2012,528
+Volkswagen,3800,sedan,300,2.8,Gas,yes,1996,Passat B4
+Audi,4649,vagon,350,NA,Diesel,yes,2000,A6
+Volkswagen,1800,other,300,1.3,Petrol,yes,1989,Golf II
+Toyota,135000,crossover,1,4.5,Diesel,yes,2011,Land Cruiser 200
+Volkswagen,9700,sedan,201,2,Petrol,yes,2007,Passat B6
+Mitsubishi,25342.2,other,1,2.5,Diesel,yes,2016,L 200
+Volkswagen,24900,sedan,1,1.6,Diesel,yes,2016,Jetta
+Renault,4999,sedan,140,1.4,Petrol,yes,2011,Logan
+Volkswagen,NA,other,142,2.5,Diesel,yes,2007,Multivan
+BMW,4700,sedan,390,2.5,Petrol,yes,1991,525
+Mercedes-Benz,4999,sedan,1,2.8,Petrol,yes,1997,E-Class
+Mercedes-Benz,9299,sedan,350,2.7,Diesel,yes,2000,E-Class
+Mercedes-Benz,25000,sedan,130,5.5,Petrol,yes,2007,S 550
+Mitsubishi,12900,crossover,148,3,Gas,yes,2008,Pajero Sport
+Audi,9700,vagon,189,2.5,Diesel,yes,2003,A6
+Renault,10100,van,64,2,Diesel,yes,2014,Trafic
+BMW,2800,sedan,296,3,Diesel,no,2001,730
+Mercedes-Benz,13500,van,350,2.9,Diesel,yes,2000,Sprinter 312
+Mitsubishi,8900,hatch,125,1.8,Petrol,yes,2009,Lancer X Sportback
+Mercedes-Benz,109999,crossover,0,3,Diesel,yes,2016,GLS 350
+Mercedes-Benz,11500,sedan,180,3.2,Gas,yes,2004,E-Class
+BMW,36900,crossover,82,3,Petrol,yes,2010,X6
+Toyota,33200,crossover,150,4,Gas,yes,2010,Land Cruiser Prado
+Volkswagen,10200,van,253,2.5,Diesel,yes,2008,T5 (Transporter)
+Volkswagen,16999,sedan,0,1.6,Petrol,yes,2016,Jetta
+Toyota,10800,sedan,179,3,Gas,yes,2005,Camry
+Mercedes-Benz,35500,other,99,2.99,Diesel,yes,2011,E-Class
+Volkswagen,9900,hatch,156,1.6,Petrol,yes,2007,Golf Plus
+Renault,7900,vagon,91,1.5,Diesel,yes,2008,Scenic
+Mercedes-Benz,13900,sedan,216,2.2,Diesel,yes,2007,E-Class
+Volkswagen,20000,sedan,102,1.8,Petrol,yes,2011,Passat CC
+Toyota,28500,crossover,38,2.2,Diesel,yes,2015,Rav 4
+Mitsubishi,7200,sedan,178,1.6,Petrol,yes,2007,Lancer
+Mitsubishi,9000,crossover,200,2,Gas,yes,2006,Outlander
+Audi,36500,crossover,115,3,Diesel,yes,2010,Q7
+Volkswagen,8000,hatch,41,1,Petrol,yes,2012,Up
+Audi,43000,other,11,1.8,Petrol,yes,2015,A6
+Toyota,13700,sedan,48,1.4,Diesel,yes,2013,Corolla
+Toyota,7899,sedan,165,1.8,Gas,yes,2006,Avensis
+Renault,13999,van,180,2,Diesel,yes,2012,Trafic
+Volkswagen,10500,van,176,1.6,Diesel,yes,2012,Caddy
+Mercedes-Benz,5500,sedan,100,99.99,Petrol,yes,2007,S 550
+Volkswagen,20800,other,160,2,Diesel,yes,2012,Amarok
+Volkswagen,2500,sedan,488,2,Petrol,yes,1991,Passat B3
+Mercedes-Benz,32000,crossover,176,3.2,Gas,yes,2003,G 320
+Mercedes-Benz,14500,sedan,220,5,Petrol,yes,2004,S 500
+Toyota,16900,hatch,20,1.6,Petrol,yes,2014,Auris
+Mercedes-Benz,13600,van,172,2.2,Diesel,yes,2013,Vito
+Audi,2600,vagon,273,2.5,Diesel,no,1999,A6
+Mercedes-Benz,NA,van,0,2.2,Diesel,yes,2016,V 250
+BMW,12000,sedan,237,3,Diesel,yes,2003,530
+Volkswagen,NA,sedan,118,1.6,Gas,yes,2008,Jetta
+Volkswagen,18800,vagon,106,1.6,Diesel,yes,2013,Passat B7
+Toyota,35500,crossover,85,3,Diesel,yes,2012,Land Cruiser Prado
+Mercedes-Benz,29900,other,71,5.5,Petrol,yes,2007,CL 500
+Renault,6750,other,110,1.5,Diesel,yes,2012,Kangoo
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+BMW,34900,crossover,117,3,Petrol,yes,2011,X5
+Volkswagen,7700,van,177,1.9,Diesel,yes,2008,T5 (Transporter) 
+Renault,9250,vagon,220,1.5,Diesel,yes,2010,Megane
+Renault,8400,hatch,84,1.5,Diesel,yes,2011,Kangoo
+Toyota,11750,sedan,93,1.6,Petrol,yes,2013,Corolla
+Mercedes-Benz,5800,van,368,2.2,Diesel,yes,2000,Vito
+Toyota,5900,crossover,180,3,Diesel,no,2003,Land Cruiser Prado
+BMW,44999,crossover,87,3,Diesel,yes,2011,X6
+Volkswagen,10500,van,170,1.6,Diesel,yes,2013,Caddy
+Mercedes-Benz,17900,sedan,190,3,Diesel,yes,2007,E-Class
+Audi,9999,sedan,190,3,Petrol,yes,2007,A6
+Audi,9999,sedan,190,2.5,Diesel,yes,2003,A6
+Renault,4900,van,472,2.5,Diesel,yes,2000,Master
+Volkswagen,61000,crossover,2,3,Diesel,yes,2016,Touareg
+Toyota,38300,crossover,130,5.7,Gas,yes,2009,Land Cruiser 200
+Toyota,17700,sedan,30,1.6,Petrol,yes,2014,Corolla
+Toyota,600,sedan,10,1.5,Petrol,yes,1979,Corolla
+Volkswagen,55555,van,50,2,Diesel,yes,2013,Multivan
+BMW,18000,crossover,280,3,Diesel,yes,2005,X5
+Renault,7600,other,125,1.5,Diesel,yes,2013,Kangoo
+Renault,6900,sedan,68,1.5,Diesel,yes,2013,Logan
+Renault,15635.1,vagon,0,1.5,Diesel,yes,2016,Dokker
+Renault,10550,van,195,2,Diesel,yes,2011,Trafic
+Volkswagen,12500,van,190,1.9,Diesel,yes,2008,T5 (Transporter)
+Audi,13000,sedan,157,3,Petrol,yes,2006,A6
+BMW,7400,sedan,285,4.4,Gas,yes,1997,540
+Audi,64400,other,41,NA,Other,yes,2014,A8
+Renault,9500,hatch,100,1.5,Diesel,yes,2009,Megane
+Audi,18800,crossover,197,4.2,Petrol,yes,2007,Q7
+Toyota,8000,sedan,176,3,Petrol,yes,2002,Camry
+Volkswagen,8700,vagon,250,1.9,Diesel,yes,2002,Passat B5
+Mercedes-Benz,9999,crossover,300,3.2,Petrol,yes,1998,ML 320
+Volkswagen,16700,sedan,175,2,Petrol,yes,2011,Passat CC
+Toyota,13900,hatch,90,1.6,Petrol,yes,2012,Auris
+Volkswagen,18700,other,145,2,Diesel,yes,2011,T5 (Transporter) 
+Mitsubishi,6200,sedan,152,1.6,Petrol,yes,2006,Lancer
+Mercedes-Benz,NA,van,450,2.2,Diesel,yes,2000,Vito
+Renault,3600,van,22,1.9,Diesel,yes,2001,Kangoo
+Renault,7500,vagon,98,1.4,Petrol,yes,2007,Kangoo
+Renault,2300,van,200,1.9,Diesel,yes,2002,Kangoo
+Volkswagen,16900,crossover,133,2.5,Diesel,yes,2007,Touareg
+Mitsubishi,13499,crossover,116,2.4,Petrol,yes,2008,Outlander XL
+Renault,5700,van,180,1.5,Diesel,yes,2007,Kangoo
+Renault,7250,van,83,NA,Other,yes,2013,Kangoo
+Mitsubishi,17900,crossover,140,3.8,Gas,yes,2007,Pajero Wagon
+Volkswagen,7800,vagon,289,NA,Diesel,yes,2003,Passat B5
+Volkswagen,13900,other,170,1.9,Diesel,yes,2006,Multivan
+Audi,7950,vagon,167,2.5,Diesel,yes,2000,A6
+BMW,9700,sedan,250,2,Petrol,yes,2003,318
+Volkswagen,7500,sedan,240,1.8,Petrol,yes,2002,Passat B5
+Audi,19999,crossover,117,4.2,Petrol,yes,2007,Q7
+BMW,9100,sedan,230,2,Gas,yes,2004,318
+Renault,8100,van,135,NA,Diesel,yes,2008,Kangoo
+Volkswagen,13300,hatch,73,1.4,Petrol,yes,2011,Golf VI
+Volkswagen,21000,hatch,22,1.2,Other,yes,2014,Golf VII
+Audi,6600,sedan,360,1.8,Petrol,yes,1998,A6
+Audi,7600,vagon,226,3,Diesel,no,2006,A6
+Mercedes-Benz,3500,hatch,258,1.7,Diesel,no,2002,A 170
+Volkswagen,3500,sedan,3,1.8,Petrol,yes,1991,Passat B3
+Mercedes-Benz,17900,van,167,2.2,Diesel,yes,2012,Vito
+Renault,1950,vagon,286,1.9,Diesel,no,2001,Laguna
+Volkswagen,7990,sedan,90,1.4,Petrol,yes,2003,New Beetle
+Volkswagen,65000,crossover,24,NA,Other,yes,2015,Touareg
+Volkswagen,6500,sedan,320,1.9,Diesel,yes,2001,Passat B5
+Mitsubishi,3500,sedan,300,2,Diesel,yes,1996,Galant
+Toyota,14100,sedan,113,2.4,Gas,yes,2009,Camry
+Toyota,2999,other,300,1.6,Petrol,yes,1991,Corolla
+BMW,66900,crossover,70,3,Diesel,yes,2014,X5
+Toyota,13100,sedan,200,3.5,Gas,yes,2006,Camry
+Volkswagen,6900,sedan,300,1.8,Petrol,yes,1998,Passat B5
+Volkswagen,23999,sedan,80,2,Diesel,yes,2014,Passat B7
+Mitsubishi,6990,crossover,225,2.5,Petrol,yes,1997,Pajero
+Toyota,13999,sedan,198,2.4,Gas,yes,2009,Camry
+Renault,7600,van,108,1,Diesel,yes,2013,Kangoo
+Renault,11100,vagon,180,1.5,Diesel,yes,2012,Megane
+Mercedes-Benz,16500,other,260,2.2,Diesel,yes,2011,Sprinter 319
+Mercedes-Benz,199999,crossover,0,5.5,Petrol,yes,2016,GLS 63
+Mercedes-Benz,44000,other,82,5.5,Petrol,yes,2010,S 500
+Audi,39900,other,12,1.8,Petrol,yes,2014,A5
+Mercedes-Benz,20500,sedan,173,1.8,Gas,yes,2011,E-Class
+BMW,8000,crossover,214,4.4,Petrol,no,2000,X5
+BMW,11600,sedan,142,2,Petrol,yes,2005,320
+Audi,3500,sedan,265,2,Diesel,yes,1986,100
+Renault,11500,vagon,170,1.5,Diesel,yes,2012,Megane
+Volkswagen,13700,other,103,2,Diesel,yes,2011,Passat B6
+Mercedes-Benz,13700,van,198,3.2,Gas,yes,2006,Viano
+Renault,7700,vagon,200,1.5,Diesel,yes,2007,Megane
+Mercedes-Benz,5800,sedan,231,2.2,Petrol,yes,1994,E-Class
+Volkswagen,9500,sedan,95,2,Petrol,yes,2007,Passat B6
+Mercedes-Benz,24900,sedan,140,5.5,Petrol,yes,2009,S 550
+Volkswagen,55500,crossover,47,3,Diesel,yes,2016,Touareg
+Volkswagen,6500,van,186,2.8,Petrol,yes,2000,Sharan
+Mercedes-Benz,8300,sedan,175,3.2,Diesel,no,2006,E-Class
+Mitsubishi,12000,crossover,130,3,Gas,yes,2007,Pajero Sport
+Mercedes-Benz,15000,crossover,185,3.7,Gas,yes,2005,ML 350
+BMW,8100,sedan,246,3,Diesel,yes,2000,530
+Volkswagen,11200,van,178,NA,Diesel,yes,2008,T5 (Transporter)
+Renault,8000,sedan,175,2,Gas,yes,2005,Megane
+Mercedes-Benz,5500,van,285,2.3,Diesel,yes,1999,Vito
+Mercedes-Benz,NA,van,0,2.2,Diesel,yes,2016,V 250
+Renault,9600,vagon,184,1.5,Diesel,yes,2011,Megane
+Volkswagen,12500,vagon,96,NA,Petrol,yes,2010,Golf VI
+BMW,10800,sedan,180,2,Petrol,yes,2002,318
+Mercedes-Benz,10999,van,263,2.2,Diesel,yes,2005,Vito
+BMW,44000,crossover,80,3,Diesel,yes,2012,X5
+Toyota,15700,sedan,108,NA,Petrol,yes,2008,Camry
+Toyota,19200,hatch,54,1.4,Other,yes,2012,Prius
+Mercedes-Benz,19600,van,127,1.8,Diesel,yes,2013,B 180
+Volkswagen,9000,sedan,135,1.6,Gas,yes,2011,Polo
+BMW,10900,sedan,245,3,Diesel,yes,2003,530
+Volkswagen,12900,van,180,2,Diesel,yes,2012,T5 (Transporter) 
+BMW,6600,sedan,285,2.5,Petrol,yes,1999,525
+Renault,15820.35,sedan,0,1.5,Diesel,yes,2016,Fluence
+Toyota,10200,sedan,180,2.4,Petrol,yes,2003,Camry
+Mercedes-Benz,6000,sedan,366,3.2,Gas,yes,1994,E-Class
+Audi,3500,sedan,1,2,Other,yes,1988,90
+Volkswagen,7800,van,380,2.5,Diesel,yes,1999,T4 (Transporter)
+Toyota,35900,crossover,84,3,Diesel,yes,2013,Land Cruiser Prado
+Mercedes-Benz,NA,crossover,0,4.6,Petrol,yes,2016,GLS 500
+Renault,11700,crossover,90,1.5,Diesel,yes,2011,Duster
+Volkswagen,15200,crossover,58,2,Petrol,yes,2008,Tiguan
+Mercedes-Benz,2500,sedan,111,2.3,Gas,yes,1992,E-Class
+Volkswagen,16800,other,164,2,Diesel,yes,2010,T5 (Transporter)
+BMW,50499,sedan,128,3,Diesel,yes,2013,730
+Mercedes-Benz,2650,sedan,470,2,Gas,yes,1985,190
+BMW,2250,sedan,300,2.5,Petrol,no,1997,523
+Mercedes-Benz,9300,sedan,270,2.7,Diesel,yes,2001,E-Class
+Toyota,11500,van,161,2.5,Diesel,yes,2002,Hiace
+BMW,13000,sedan,304,4.4,Other,yes,2002,745
+Mercedes-Benz,7500,sedan,319,3.2,Gas,yes,1997,S 320
+Mercedes-Benz,17999,other,38,2,Petrol,yes,2009,CLC 200
+Renault,7300,vagon,162,1.5,Diesel,yes,2006,Megane
+Toyota,29900,crossover,117,3,Diesel,yes,2008,Land Cruiser Prado
+Renault,4950,vagon,170,1.5,Diesel,yes,2004,Kangoo
+Audi,36000,vagon,40,3,Diesel,yes,2010,Q7
+Audi,1700,sedan,177,1.6,Petrol,no,1996,A4
+Audi,12450,vagon,1,2,Diesel,yes,2008,A4
+Volkswagen,13893.75,sedan,0,1.6,Petrol,yes,2016,Polo
+Volkswagen,44600,crossover,60,3,Diesel,yes,2013,Touareg
+Toyota,35138.29,crossover,0,2,Petrol,yes,2016,Rav 4
+BMW,3500,sedan,313,2.5,Diesel,yes,1994,325
+Mercedes-Benz,12800,other,260,2.7,Diesel,yes,2005,Sprinter 316
+Volkswagen,27000,sedan,85,2,Diesel,yes,2013,Passat CC
+Volkswagen,14800,sedan,143,2,Diesel,yes,2010,Passat B6
+Renault,2200,other,155,1.6,Petrol,no,2004,Scenic
+Volkswagen,2900,van,433,2.4,Diesel,no,1995,T4 (Transporter)
+Audi,12000,sedan,200,6,Petrol,yes,2004,A8
+Volkswagen,41000,crossover,180,3,Diesel,yes,2013,Touareg
+Audi,16300,vagon,180,3.2,Petrol,yes,2006,A6 Allroad
+Renault,5300,van,145,1.5,Other,yes,2010,Kangoo
+Mercedes-Benz,40500,sedan,39,2.2,Diesel,yes,2015,E-Class
+Mercedes-Benz,4300,van,300,2.2,Diesel,no,2005,Vito
+Volkswagen,4000,crossover,137,2.5,Diesel,no,2004,Touareg
+Volkswagen,9600,hatch,132,1.6,Petrol,yes,2007,Golf V
+Mitsubishi,18000,crossover,112,3,Gas,yes,2008,Pajero Wagon
+Renault,11300,vagon,184,1.5,Diesel,yes,2011,Megane
+Volkswagen,4999,hatch,131,1.4,Petrol,yes,2002,Polo
+Mercedes-Benz,24999,sedan,112,2.2,Diesel,yes,2012,C-Class
+Toyota,23300,crossover,93,2.7,Other,yes,2011,Venza
+Toyota,14900,crossover,124,4,Petrol,yes,2006,Fortuner
+Audi,22800,sedan,184,2.7,Diesel,yes,2010,A6
+Mercedes-Benz,35500,sedan,137,3,Diesel,yes,2011,E-Class
+Volkswagen,15000,other,280,2.8,Diesel,yes,2006,LT
+Toyota,8650,sedan,132,1.6,Petrol,yes,2008,Corolla
+Mitsubishi,22400,crossover,96,2.5,Diesel,yes,2011,Pajero Sport
+Mitsubishi,10400,sedan,123,2,Petrol,yes,2009,Lancer X
+Mercedes-Benz,195000,crossover,2,5.5,Petrol,yes,2016,GLS 63
+Audi,10500,vagon,198,4.2,Gas,yes,2005,A6 Allroad
+Renault,11900,crossover,68,1.5,Diesel,yes,2011,Duster
+BMW,36900,sedan,17,2,Petrol,yes,2013,520
+BMW,24500,crossover,170,3,Diesel,yes,2006,X5
+Volkswagen,4700,van,400,2.4,Diesel,yes,1999,T4 (Transporter)
+Toyota,18100,hatch,30,1.6,Petrol,yes,2013,Auris
+Mercedes-Benz,115000,sedan,47,4.6,Petrol,yes,2013,S 500
+Mercedes-Benz,10900,sedan,217,3.2,Diesel,yes,2002,E-Class
+BMW,7000,sedan,280,3,Petrol,yes,2001,530
+Mercedes-Benz,NA,sedan,20,3,Diesel,yes,2015,S 350
+Volkswagen,9999,vagon,161,2,Diesel,yes,2008,Passat B6
+Mitsubishi,12300,other,160,2.5,Diesel,yes,2008,L 200
+Volkswagen,67622.55,crossover,0,3,Diesel,yes,2016,Touareg
+Volkswagen,18900,other,45,2,Diesel,yes,2013,Passat B7
+Volkswagen,6999,sedan,248,1.8,Petrol,yes,2002,Passat B5
+Mercedes-Benz,10250,sedan,261,1.8,Petrol,yes,2003,E-Class
+Toyota,3500,sedan,286,1.6,Petrol,yes,2000,Corolla
+BMW,13500,crossover,250,3,Diesel,no,2006,X5
+BMW,2450,sedan,328,2.5,Diesel,no,1996,525
+Volkswagen,7500,van,180,2.5,Diesel,yes,2004,T5 (Transporter) 
+Volkswagen,4900,vagon,263,2,Diesel,no,2007,Passat B6
+Audi,4600,vagon,242,NA,Diesel,no,2004,A6
+Mercedes-Benz,5000,vagon,248,2,Gas,yes,1996,E-Class
+BMW,29900,crossover,235,3,Diesel,yes,2009,X5
+Renault,7000,sedan,110,1.4,Petrol,yes,2012,Symbol
+Mitsubishi,11800,sedan,1,1.5,Gas,yes,2010,Lancer X
+Mercedes-Benz,16500,van,223,2.2,Diesel,yes,2009,Vito
+BMW,5300,sedan,147,2.2,Gas,no,2001,320
+Mercedes-Benz,7200,van,200,2.2,Diesel,yes,2004,Vito
+Volkswagen,8500,van,230,1.9,Diesel,yes,2008,Caddy
+BMW,39000,crossover,180,3,Diesel,yes,2011,X5
+Volkswagen,1850,vagon,270,1.9,Diesel,yes,2000,Passat B5
+Audi,36100,sedan,75,3,Diesel,yes,2011,A7
+Mitsubishi,3149.25,hatch,330,2,Gas,yes,1993,Galant
+Renault,5800,van,159,1.5,Diesel,yes,2007,Kangoo
+BMW,6700,sedan,370,2,Petrol,yes,1997,320
+Renault,18988.13,crossover,0,1.5,Diesel,yes,2016,Duster
+Mitsubishi,6300,sedan,128,NA,Petrol,yes,2007,Colt
+BMW,7500,sedan,425,NA,Gas,yes,1996,535
+Volkswagen,28660,van,0,1.6,Diesel,yes,2016,Caddy
+BMW,2999,sedan,210,2,Other,no,1999,320
+BMW,64500,crossover,1,2,Diesel,yes,2016,X5
+Toyota,21500,crossover,135,2,Gas,yes,2007,Land Cruiser Prado
+BMW,85555,sedan,10,3,Diesel,yes,2015,640
+Volkswagen,8650,van,108,1.6,Diesel,yes,2013,Caddy
+BMW,10600,sedan,1,3,Diesel,yes,2000,530
+Toyota,6900,hatch,109,1.3,Gas,yes,2007,Yaris
+Mercedes-Benz,2900,other,248,2,Petrol,no,1999,CLK 200
+Mercedes-Benz,12250,other,185,2.2,Diesel,yes,2012,Vito
+BMW,21700,crossover,101,2.5,Gas,yes,2007,X3
+Mercedes-Benz,2000,sedan,320,2.2,Diesel,no,1998,E-Class
+Audi,9900,vagon,284,2.5,Diesel,yes,2004,A6
+Mercedes-Benz,5850,van,240,2.2,Diesel,yes,2001,Vito
+Toyota,8200,vagon,204,1.4,Diesel,yes,2005,Corolla
+Volkswagen,NA,sedan,381,2.5,Diesel,yes,2000,T4 (Transporter) 
+Renault,7300,vagon,200,1.5,Diesel,yes,2008,Megane
+Mitsubishi,8000,sedan,145,2,Gas,yes,2007,Lancer
+Renault,6600,van,184,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,22500,sedan,164,1.8,Gas,yes,2012,E-Class
+Mercedes-Benz,13200,other,196,2.2,Other,yes,2012,Sprinter 313
+Volkswagen,1200,other,300,1.4,Gas,yes,2000,Caddy
+Volkswagen,8900,van,264,2.5,Diesel,yes,2003,T4 (Transporter)
+Mitsubishi,9200,sedan,163,2,Petrol,yes,2008,Lancer X
+Mercedes-Benz,10200,sedan,154,2.5,Petrol,yes,2006,C-Class
+Mercedes-Benz,2700,sedan,121,2.5,Diesel,yes,1986,E-Class
+Mercedes-Benz,9500,sedan,246,6,Petrol,yes,1997,S 600
+Mitsubishi,26850,crossover,84,3.2,Diesel,yes,2012,Pajero Wagon
+Audi,21900,crossover,183,4.2,Petrol,yes,2007,Q7
+Volkswagen,1700,hatch,250,1.4,Petrol,no,1998,Polo
+Mercedes-Benz,NA,crossover,0,2.2,Diesel,yes,2016,GLC-Class
+Toyota,NA,crossover,49,4.5,Diesel,yes,2014,Land Cruiser 200
+Volkswagen,6500,hatch,225,1.4,Petrol,yes,2000,Golf IV
+Mitsubishi,10600,crossover,85,NA,Gas,yes,2006,Outlander
+Renault,5000,vagon,34,1.5,Diesel,yes,2013,Logan
+BMW,38000,sedan,74,2,Diesel,yes,2014,520
+Volkswagen,9599,sedan,140,1.8,Petrol,yes,2008,Passat B6
+Mercedes-Benz,10800,other,230,1.8,Petrol,yes,2006,C-Class
+Toyota,18800,crossover,105,2,Gas,yes,2011,Rav 4
+Mitsubishi,7650,sedan,133,1.5,Gas,yes,2008,Lancer
+Renault,6300,vagon,125,2,Petrol,yes,2003,Laguna
+BMW,5000,other,75,1.9,Petrol,no,1998,Z3
+Toyota,28500,crossover,107,3.5,Gas,yes,2011,Highlander
+BMW,13000,sedan,193,3.6,Petrol,yes,2003,735
+Toyota,16999,crossover,157,2.7,Gas,yes,2007,Land Cruiser Prado
+BMW,34500,other,160,3,Diesel,yes,2011,X5
+Volkswagen,10900,vagon,204,2,Diesel,yes,2007,Passat B6
+Mitsubishi,18999,crossover,121,3.8,Gas,yes,2008,Pajero Wagon
+BMW,9800,sedan,350,3,Diesel,yes,2001,530
+Volkswagen,4300,vagon,298,1.8,Petrol,yes,1998,Golf III
+Audi,3900,vagon,260,2.5,Diesel,no,2003,A6
+Volkswagen,13499,van,154,2,Diesel,yes,2013,T5 (Transporter) 
+Audi,19990,sedan,127,2.8,Petrol,yes,2010,A6
+Renault,3400,van,260,2.2,Diesel,no,2005,Espace
+Toyota,27800,sedan,157,3.5,Petrol,yes,2011,Camry
+Volkswagen,2300,hatch,320,1.6,Gas,yes,1989,Golf II
+BMW,30500,sedan,630,NA,Petrol,yes,2010,523
+Renault,7650,van,151,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,10000,van,220,1.9,Diesel,yes,2008,Caddy
+Volkswagen,9700,vagon,130,1.6,Gas,yes,2008,Golf Variant
+Audi,1950,sedan,320,19,Other,no,1996,A4
+Audi,3800,sedan,270,2.5,Diesel,no,2000,A6
+Toyota,26900,crossover,115,4,Gas,yes,2008,Land Cruiser Prado
+BMW,NA,other,35,3,Petrol,yes,2008,630
+Toyota,12600,sedan,205,2.4,Gas,yes,2006,Camry
+Volkswagen,14899,van,158,2,Diesel,yes,2012,T5 (Transporter) 
+Renault,4999,vagon,252,1.5,Diesel,yes,2007,Kangoo
+BMW,57777,other,40,4,Diesel,yes,2012,X6
+Volkswagen,12350,van,167,1.4,Petrol,yes,2011,Touran
+Audi,7999,sedan,177,2.7,Gas,yes,2003,A6
+BMW,21600,crossover,200,4.8,Other,yes,2007,X5
+Mitsubishi,8000,sedan,67,1.5,Petrol,yes,2009,Lancer X
+Volkswagen,6799,hatch,1,1.4,Gas,yes,1998,Golf IV
+Toyota,25749.75,sedan,0,2.5,Petrol,yes,2016,Camry
+Volkswagen,8899,vagon,198,1.9,Other,yes,2004,Passat B5
+Mercedes-Benz,26800,sedan,162,5.5,Petrol,yes,2007,S 500
+Toyota,25555,crossover,128,4.7,Gas,yes,2004,Land Cruiser 100
+Audi,25000,crossover,112,3.2,Petrol,yes,2010,Q5
+Volkswagen,15800,van,194,2,Diesel,yes,2012,T5 (Transporter) 
+BMW,3150,sedan,200,2,Diesel,no,1999,320
+Toyota,12300,sedan,157,2.4,Gas,yes,2007,Camry
+Toyota,45500,crossover,45,3.5,Petrol,yes,2015,Highlander
+Renault,6500,other,406,2.5,Diesel,yes,2004,Master
+Toyota,18999,crossover,95,2,Petrol,yes,2011,Rav 4
+BMW,8500,sedan,260,2.2,Gas,yes,2001,520
+Mercedes-Benz,8500,van,350,2.2,Diesel,yes,2004,Sprinter 211
+Toyota,13500,crossover,126,4,Petrol,no,2011,Land Cruiser Prado
+Volkswagen,2200,sedan,180,1.9,Diesel,no,2003,Passat B5
+Volkswagen,18500,vagon,125,2,Diesel,yes,2011,Passat B7
+Toyota,8420,other,150,2.7,Gas,yes,2005,Tacoma
+Audi,17500,vagon,144,2,Diesel,yes,2010,A4
+Audi,9000,sedan,158,2,Gas,yes,2003,A4
+Volkswagen,4400,hatch,238,1.6,Petrol,yes,1994,Golf III
+BMW,NA,crossover,0,3,Diesel,yes,2016,X5
+Renault,7500,van,200,2.2,Diesel,yes,2002,Espace
+Toyota,17950,sedan,87,2.4,Petrol,yes,2010,Camry
+BMW,51500,crossover,23,3,Petrol,yes,2013,X6
+Mitsubishi,10800,crossover,165,3,Gas,yes,2007,Pajero Sport
+Volkswagen,33700,van,11,1.4,Petrol,yes,2016,Touran
+Volkswagen,17000,crossover,158,2.5,Diesel,yes,2007,Touareg
+Volkswagen,11200,crossover,1,4.2,Petrol,yes,2004,Touareg
+BMW,4500,sedan,140,3,Diesel,no,2000,530
+Toyota,43999,other,0,2.8,Diesel,yes,2016,Hilux
+Audi,39800,sedan,180,4.2,Other,yes,2012,A8
+BMW,6500,sedan,272,2.8,Petrol,yes,1996,328
+Volkswagen,18700,vagon,196,2,Diesel,yes,2011,Passat B7
+Audi,38500,vagon,75,2,Diesel,yes,2013,Q3
+BMW,8200,sedan,241,2,Petrol,yes,1998,320
+Audi,7500,vagon,240,2,Petrol,yes,2002,A4
+Toyota,11550,sedan,95,2.4,Petrol,yes,2007,Camry
+Mercedes-Benz,39500,sedan,30,1.6,Petrol,yes,2014,C-Class
+Volkswagen,6200,sedan,161,4.2,Gas,no,2004,Phaeton
+BMW,17200,crossover,308,4.6,Gas,yes,2003,X5
+BMW,2900,sedan,280,2.5,Diesel,no,1997,525
+Renault,14600,other,169,2.3,Diesel,yes,2013,Master
+BMW,34900,crossover,110,4.4,Petrol,yes,2010,X6
+Toyota,164500,crossover,0,4.5,Diesel,yes,2015,Land Cruiser 200
+Audi,NA,crossover,1,3,Diesel,yes,2016,A6 Allroad
+BMW,15299,other,136,NA,Petrol,yes,2008,523
+Mercedes-Benz,7000,other,170,2.2,Petrol,no,2001,CLK 220
+Mercedes-Benz,32000,van,255,3,Diesel,yes,2008,Sprinter 318
+Renault,7500,vagon,168,1.5,Diesel,yes,2005,Grand Scenic
+Volkswagen,9000,other,217,2.5,Diesel,yes,2005,T5 (Transporter)
+Mitsubishi,7000,sedan,180,1.6,Gas,yes,2004,Lancer
+Mercedes-Benz,199999,crossover,0,5.5,Petrol,yes,2016,GLS 63
+Audi,2700,sedan,234,2.5,Other,no,2000,A6
+Volkswagen,10950,van,165,2,Diesel,yes,2012,Caddy
+Toyota,15500,sedan,120,3.5,Petrol,yes,2008,Camry
+Mercedes-Benz,8700,sedan,210,5,Petrol,yes,1997,S 500
+Mercedes-Benz,6000,van,200,2.2,Diesel,yes,2000,Vito
+Mitsubishi,16950,crossover,135,3,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,15000,crossover,400,3,Diesel,yes,1997,Sprinter 212
+Toyota,26500,crossover,102,2.5,Diesel,yes,2013,Hilux
+Mercedes-Benz,3100,vagon,400,2.2,Diesel,no,2002,C-Class
+Toyota,59900,crossover,90,4.5,Diesel,yes,2012,Land Cruiser 200
+Toyota,63000,crossover,3,4.2,Diesel,yes,2013,Land Cruiser 76
+Mercedes-Benz,19800,crossover,218,3,Diesel,yes,2006,ML 280
+Volkswagen,13000,van,110,1.9,Diesel,yes,2009,T5 (Transporter)
+Audi,12200,sedan,130,2.4,Petrol,yes,2006,A6
+Mitsubishi,5700,other,170,1.3,Petrol,yes,2006,Colt
+Audi,2550,sedan,370,2.5,Diesel,no,1998,A6
+Toyota,13500,sedan,144,3.5,Gas,yes,2007,Camry
+Renault,10200,van,183,2,Diesel,yes,2012,Trafic
+Audi,13000,other,161,1.8,Gas,yes,2001,TT
+Volkswagen,10500,sedan,210,5,Diesel,yes,2004,Phaeton
+BMW,3100,sedan,315,2.5,Diesel,no,1998,525
+BMW,9400,sedan,221,4.4,Gas,yes,2004,745
+Audi,10000,sedan,206,2.5,Diesel,yes,2003,A6
+Mitsubishi,4200,sedan,270,2,Gas,yes,1998,Galant
+Renault,7500,sedan,131,1.6,Petrol,yes,2009,Megane
+Mercedes-Benz,38600,other,12,1.8,Petrol,yes,2012,E-Class
+Toyota,8900,hatch,140,1.6,Petrol,yes,2008,Auris
+Renault,4800,van,113,15,Diesel,yes,2006,Kangoo
+Toyota,24000,crossover,270,4.2,Diesel,yes,1994,Land Cruiser 80
+Mercedes-Benz,51500,other,75,6.3,Petrol,yes,2009,CL 63 AMG
+Mercedes-Benz,5100,sedan,270,3.2,Diesel,no,2001,S 320
+Mitsubishi,4999,sedan,60,1.6,Petrol,yes,2007,Lancer
+Mitsubishi,6666,sedan,162,NA,Petrol,yes,2007,Lancer
+Mitsubishi,9450,sedan,125,2,Gas,yes,2008,Lancer X
+Audi,3850,vagon,215,2.5,Diesel,no,2002,A6
+Renault,7800,vagon,170,1.5,Diesel,no,2013,Megane
+Audi,49700,crossover,80,3,Diesel,yes,2013,Q7
+Mitsubishi,9000,sedan,78,2.4,Gas,yes,2006,Galant
+Toyota,18990,crossover,95,2,Petrol,yes,2011,Rav 4
+Mitsubishi,7200,sedan,102,1.6,Petrol,yes,2007,Lancer
+BMW,117000,crossover,1,NA,Other,yes,2016,X6 M
+Volkswagen,11900,other,112,2,Diesel,yes,2011,T6 (Transporter) 
+Toyota,2800,sedan,287,1.3,Petrol,yes,1995,Corolla
+Volkswagen,2200,sedan,272,1.9,Diesel,no,1998,Passat B5
+Mitsubishi,17500,crossover,170,3.8,Gas,yes,2008,Pajero Wagon
+Audi,49900,other,79,4.2,Petrol,yes,2007,R8
+Volkswagen,8650,van,238,2.5,Diesel,yes,2002,T4 (Transporter)
+Audi,8800,sedan,205,1.8,Petrol,yes,2003,A4
+Mercedes-Benz,69999,crossover,0,2.2,Diesel,yes,2016,GLE-Class
+Volkswagen,8910.53,hatch,103,1.4,Petrol,yes,2012,Polo
+Toyota,15500,crossover,165,2.7,Gas,yes,2005,Land Cruiser Prado
+Audi,19500,crossover,175,3.6,Petrol,yes,2007,Q7
+Toyota,27700,sedan,21,2.5,Petrol,yes,2015,Camry
+Volkswagen,9600,van,255,1.9,Diesel,yes,2004,T5 (Transporter)
+BMW,12500,crossover,176,3,Gas,yes,2002,X5
+Audi,4799,vagon,260,2.5,Diesel,no,2002,A6 Allroad
+Toyota,20900,sedan,70,2.5,Petrol,yes,2012,Camry
+Audi,14900,sedan,203,2.8,Petrol,yes,2008,A6
+Toyota,12750,hatch,164,1.4,Diesel,yes,2012,Auris
+BMW,5900,hatch,243,2,Petrol,no,2006,116
+Mercedes-Benz,2700,sedan,400,2.9,Diesel,yes,1997,E-Class
+Audi,4700,sedan,400,2.6,Petrol,yes,1996,A6
+Audi,9500,other,0,NA,Other,yes,2003,A6
+BMW,77000,other,64,4.4,Petrol,yes,2012,M6
+Renault,5900,van,250,1.9,Diesel,yes,2005,Trafic
+Volkswagen,24000,sedan,67,1.8,Petrol,yes,2012,Passat CC
+Audi,18400,vagon,151,2,Diesel,yes,2010,A4
+Mercedes-Benz,56800,sedan,74,6.3,Petrol,yes,2010,S 63 AMG
+Renault,8000,hatch,50,1.6,Petrol,yes,2012,Sandero
+Mercedes-Benz,5000,sedan,382,2,Petrol,yes,1994,E-Class
+Mitsubishi,9900,sedan,98,2,Petrol,yes,2009,Lancer X
+Mercedes-Benz,18900,other,111,2.5,Petrol,yes,2009,C-Class
+Toyota,20500,crossover,139,4,Gas,yes,2005,Land Cruiser Prado
+Volkswagen,2500,vagon,232,1.6,Petrol,no,2001,Golf IV
+Renault,9000,vagon,179,1.5,Diesel,yes,2010,Megane
+Renault,7700,sedan,100,1.6,Gas,yes,2008,Megane
+Audi,32300,hatch,54,2.8,Petrol,yes,2011,A6
+Mercedes-Benz,72700,van,42,2.2,Diesel,yes,2014,V 250
+Renault,6200,sedan,246,1.4,Petrol,yes,2005,Megane
+Audi,4000,sedan,400,2,Petrol,yes,1992,100
+Toyota,9003.15,sedan,43,1.3,Petrol,yes,2014,Corolla
+Mercedes-Benz,14900,other,195,3.2,Gas,yes,1999,CLK 320
+Mercedes-Benz,NA,van,0,2.2,Diesel,yes,2016,V 250
+Audi,2200,sedan,250,NA,Diesel,no,2001,A6
+Volkswagen,NA,van,1,NA,Diesel,yes,2005,Caddy
+BMW,65393.25,crossover,0,2,Diesel,yes,2016,X5
+Renault,9000,van,158,2,Diesel,yes,2012,Trafic
+Audi,NA,sedan,60,6.3,Petrol,yes,2013,A8
+Audi,8500,sedan,253,2.5,Diesel,yes,2001,A6
+Renault,5900,hatch,171,1.5,Other,yes,2005,Megane
+Mitsubishi,5100,hatch,250,1.3,Gas,yes,2006,Colt
+Mercedes-Benz,40500,sedan,108,5.5,Petrol,yes,2010,S 500
+Audi,8600,sedan,154,1.8,Petrol,yes,2001,A6
+Mitsubishi,5900,sedan,142,1.6,Gas,yes,2005,Lancer
+Volkswagen,17900,other,123,1.8,Petrol,yes,2010,Passat CC
+BMW,34400,crossover,37,2,Diesel,yes,2013,X1
+BMW,13500,sedan,138,2.5,Petrol,yes,2004,525
+Renault,9600,van,175,2,Diesel,yes,2007,Trafic
+Mercedes-Benz,15000,other,82,1.8,Petrol,yes,2004,SLK 200
+Mercedes-Benz,22000,other,53,3,Petrol,yes,2007,CLK 280
+Volkswagen,13700,other,200,2.5,Diesel,yes,2005,Touareg
+BMW,38000,sedan,61,NA,Petrol,yes,2013,535
+Toyota,18800,sedan,47,2.4,Petrol,yes,2009,Camry
+Volkswagen,7600,hatch,127,1.4,Petrol,yes,2007,Polo
+Toyota,11950,hatch,120,1.4,Petrol,yes,2013,Auris
+Audi,11300,sedan,202,2.7,Diesel,no,2008,A6
+Volkswagen,13500,van,43,2,Diesel,yes,2014,T5 (Transporter) 
+Toyota,11990,sedan,129,2.4,Petrol,yes,2007,Camry
+Toyota,16500,other,130,2.7,Gas,yes,2004,Land Cruiser Prado
+BMW,19000,crossover,215,3,Diesel,yes,2006,X5
+Renault,5500,van,160,1.5,Diesel,yes,2005,Kangoo
+BMW,39999,sedan,45,3,Petrol,yes,2011,535
+Toyota,15500,sedan,140,2.4,Gas,yes,2007,Camry
+Mitsubishi,11700,crossover,197,3,Gas,yes,2006,Pajero Sport
+Volkswagen,2750,vagon,1,1.9,Diesel,no,2001,Passat B5
+Renault,7150,vagon,1,1.5,Diesel,yes,2005,Megane
+BMW,10500,hatch,220,2,Petrol,yes,2005,118
+Mercedes-Benz,24900,sedan,97,1.8,Petrol,yes,2012,E-Class
+Toyota,NA,sedan,24,2.5,Petrol,yes,2015,Camry
+Mercedes-Benz,26000,other,22,1.8,Petrol,yes,2014,C-Class
+Mercedes-Benz,21500,crossover,148,6.2,Petrol,yes,2008,ML 63 AMG
+BMW,2222,sedan,268,1.8,Petrol,no,1996,318
+BMW,2900,vagon,236,2,Diesel,no,2001,320
+Volkswagen,4600,hatch,1,1.8,Gas,yes,1996,Golf III
+Mercedes-Benz,3200,sedan,198,2,Gas,yes,1992,190
+Mitsubishi,19400,crossover,78,2.4,Petrol,yes,2012,Outlander XL
+Renault,4000,sedan,215,1.9,Diesel,no,2005,Laguna
+BMW,65099,crossover,0,2,Diesel,yes,2016,X5
+BMW,53999,sedan,80,2.99,Diesel,yes,2012,550
+Renault,9200,vagon,155,1.5,Diesel,yes,2010,Clio
+Mitsubishi,19400,crossover,78,NA,Petrol,yes,2012,Outlander XL
+Mercedes-Benz,109999,van,0,2.2,Diesel,yes,2016,V 250
+Mercedes-Benz,23500,van,170,2.2,Diesel,yes,2011,Vito
+Audi,4800,sedan,390,2.6,Petrol,yes,1993,100
+Toyota,13500,sedan,100,2.4,Petrol,yes,2007,Camry
+Toyota,8700,sedan,131,1.6,Gas,yes,2007,Corolla
+Mercedes-Benz,12300,sedan,423,5,Gas,yes,1997,S 500
+Mitsubishi,7900,other,148,1.8,Petrol,yes,2003,Pajero Pinin
+Volkswagen,10700,van,153,2,Diesel,yes,2010,Caddy
+Renault,6800,van,185,1.6,Other,yes,2006,Kangoo
+Renault,8500,van,157,1.5,Diesel,no,2013,Scenic
+Mitsubishi,18700,crossover,97,3.2,Diesel,yes,2009,Pajero Wagon
+BMW,1900,vagon,285,2.5,Diesel,no,1997,525
+Mercedes-Benz,15500,van,75,2,Diesel,yes,2011,Vito
+Volkswagen,8700,hatch,148,1.4,Other,yes,2008,Polo
+Mercedes-Benz,47300,sedan,121,3,Diesel,yes,2012,S 350
+BMW,5000,sedan,400,2.5,Petrol,yes,1991,525
+Toyota,24000,crossover,73,4,Petrol,yes,2008,Land Cruiser Prado
+Volkswagen,14800,sedan,85,1.4,Petrol,yes,2013,Jetta
+Mitsubishi,5500,crossover,1,2,Petrol,yes,1986,Pajero
+Mercedes-Benz,6400,van,290,2.2,Diesel,yes,1999,Vito
+Mitsubishi,19000,crossover,139,2.4,Gas,yes,2011,Outlander XL
+Mercedes-Benz,11700,van,211,2.2,Diesel,yes,2007,Vito
+Volkswagen,6500,vagon,101,2,Diesel,yes,2011,Passat B6
+Volkswagen,4999,sedan,145,2,Petrol,yes,2000,Bora
+Volkswagen,1900,hatch,100,1.6,Petrol,yes,1989,Golf II
+Renault,8999,vagon,100,1.5,Diesel,yes,2009,Kangoo
+Volkswagen,12700,van,164,1.6,Diesel,yes,2012,Touran
+Mercedes-Benz,2200,sedan,168,2,Petrol,no,1994,E-Class
+BMW,32500,vagon,65,2,Petrol,yes,2014,328
+Volkswagen,16800,vagon,149,1.4,Gas,yes,2013,Passat B7
+Mercedes-Benz,8999,van,360,2.2,Diesel,yes,2006,Vito
+Audi,2500,sedan,407,2.8,Gas,no,1999,A6
+BMW,37000,sedan,95,4.4,Petrol,yes,2011,750
+Volkswagen,19500,sedan,82,1.8,Petrol,yes,2011,Passat B7
+Volkswagen,10900,van,240,2.5,Diesel,yes,2007,T5 (Transporter)
+BMW,36500,sedan,33,2,Diesel,yes,2014,328
+Renault,7300,van,154,1.5,Diesel,yes,2012,Kangoo
+BMW,17500,vagon,125,3,Diesel,yes,2006,530
+Audi,24500,sedan,122,4,Diesel,yes,2008,A8
+Toyota,15500,crossover,146,2.4,Gas,yes,2008,Rav 4
+Mercedes-Benz,4050,sedan,200,NA,Diesel,yes,2001,S 320
+Mitsubishi,25000,crossover,0,2,Petrol,yes,2016,Outlander
+Mercedes-Benz,11900,sedan,167,1.8,Petrol,yes,2005,C-Class
+Mercedes-Benz,8000,van,40,2.9,Diesel,yes,1999,Sprinter 212
+Mitsubishi,16900,crossover,126,3,Gas,yes,2008,Pajero Wagon
+Toyota,8800,crossover,205,2,Petrol,yes,2002,Rav 4
+BMW,13000,sedan,220,2.5,Petrol,yes,2005,523
+Renault,7500,van,43,1.5,Diesel,yes,2013,Kangoo
+Toyota,8300,sedan,140,3,Petrol,yes,2002,Camry
+Toyota,18700,crossover,250,4.7,Gas,yes,2004,Land Cruiser 100
+Toyota,28787.85,sedan,0,2.5,Petrol,yes,2016,Camry
+Mercedes-Benz,16500,sedan,190,2.2,Diesel,yes,2007,C-Class
+Mitsubishi,13900,crossover,127,3,Petrol,yes,2008,Outlander XL
+Audi,1350,vagon,400,1.9,Diesel,no,1994,80
+Toyota,20859.15,crossover,1,2,Petrol,yes,2016,Rav 4
+Mitsubishi,28800,crossover,100,3.2,Diesel,yes,2013,Pajero Wagon
+Mercedes-Benz,34900,crossover,64,2.2,Diesel,yes,2013,GLK 220
+BMW,37500,other,45,2,Petrol,yes,2013,520
+Mercedes-Benz,19700,sedan,220,2.2,Diesel,yes,2009,E-Class
+Mercedes-Benz,12800,other,116,2.2,Diesel,yes,2013,Vito
+Mercedes-Benz,8000,van,293,NA,Diesel,yes,1999,Vito
+Renault,4600,van,265,1.5,Diesel,yes,2008,Kangoo
+Mercedes-Benz,17977,van,322,3,Diesel,yes,2007,Sprinter 318
+Volkswagen,11962,sedan,0,1.6,Petrol,yes,2015,Polo
+Volkswagen,12500,vagon,195,1.6,Diesel,yes,2013,Golf VI
+Volkswagen,9200,van,268,2.5,Diesel,yes,2003,T4 (Transporter)
+Mercedes-Benz,28500,sedan,148,2.2,Diesel,yes,2011,E-Class
+Volkswagen,16950,sedan,38,2,Diesel,yes,2014,Jetta
+Mercedes-Benz,24750,sedan,212,5.5,Petrol,yes,2006,S 500
+Mercedes-Benz,57000,crossover,3,2.2,Diesel,yes,2015,ML 250
+Toyota,11300,hatch,87,1,Petrol,yes,2009,IQ
+Mercedes-Benz,51000,sedan,119,4.7,Petrol,yes,2012,S 500
+Audi,NA,sedan,2,3,Diesel,yes,2015,A8
+Audi,3000,vagon,270,1.9,Diesel,no,2001,A4
+Toyota,9800,other,250,1.8,Petrol,yes,2000,Celica
+Volkswagen,12850,crossover,200,2.5,Diesel,yes,2006,Touareg
+Mercedes-Benz,34999,sedan,150,2.8,Petrol,yes,1969,S 280
+BMW,52300,sedan,55,3,Diesel,yes,2013,5 Series GT
+BMW,3650,sedan,300,2,Petrol,yes,1990,520
+BMW,39900,crossover,82,3,Petrol,yes,2011,X6
+Renault,7400,vagon,200,1.5,Diesel,yes,2007,Megane
+Volkswagen,13999,van,175,2,Diesel,yes,2012,T5 (Transporter) 
+Mercedes-Benz,15500,sedan,270,3.2,Diesel,no,2007,S 320
+Renault,8500,vagon,130,1.5,Diesel,yes,2012,Clio
+BMW,32500,other,86,3,Petrol,yes,2010,5 Series GT
+Toyota,14700,sedan,131,3.5,Gas,yes,2007,Camry
+Volkswagen,6500,van,310,1.9,Diesel,yes,1997,T4 (Transporter)
+Renault,7800,van,139,1.5,Diesel,yes,2013,Kangoo
+Mercedes-Benz,8700,van,290,2.2,Diesel,yes,2005,Vito
+Renault,9000,hatch,56,1.6,Petrol,yes,2011,Megane
+Mercedes-Benz,14800,van,139,3,Diesel,yes,2008,Vito
+Toyota,13400,sedan,125,2.4,Petrol,yes,2008,Camry
+Volkswagen,2650,hatch,280,1.8,Petrol,yes,1987,Golf II
+BMW,7777,other,375,4,Diesel,yes,2000,740
+Toyota,6900,hatch,92,1.3,Petrol,yes,2006,Yaris
+Mercedes-Benz,1850,sedan,281,2,Diesel,yes,1985,190
+Volkswagen,10500,other,270,2.5,Diesel,yes,2007,T5 (Transporter)
+Toyota,NA,crossover,140,4.2,Diesel,yes,1993,Land Cruiser 80
+Mercedes-Benz,NA,crossover,154,3,Diesel,yes,2009,ML 320
+Audi,7800,sedan,90,2.2,Gas,yes,1994,S4
+Mercedes-Benz,4100,sedan,215,4.3,Petrol,no,2001,CLK 430
+BMW,4400,sedan,260,2,Gas,yes,1990,520
+BMW,41500,crossover,176,3,Diesel,yes,2012,X5
+Mercedes-Benz,13999,van,98,2.2,Diesel,yes,2011,Vito
+BMW,NA,crossover,0,3,Diesel,yes,2016,X5
+Mercedes-Benz,16800,van,116,2.2,Diesel,yes,2013,Vito
+BMW,58500,crossover,80,3,Diesel,yes,2013,X5 M
+Volkswagen,10200,sedan,280,1.8,Petrol,yes,2006,Passat B5
+Mitsubishi,13300,crossover,110,2.4,Gas,yes,2008,Outlander XL
+Renault,6300,van,180,1.5,Diesel,yes,2012,Kangoo
+Audi,16999,hatch,69,1.4,Petrol,yes,2012,A1
+Toyota,20100,crossover,48,2,Petrol,yes,2012,Rav 4
+Volkswagen,10800,sedan,100,1.8,Petrol,yes,2008,Passat B6
+Mercedes-Benz,129999,sedan,37,4.7,Petrol,yes,2014,S 500
+Mercedes-Benz,14200,sedan,245,2.2,Diesel,yes,2007,E-Class
+BMW,4700,sedan,333,3,Petrol,yes,1994,730
+BMW,5700,sedan,252,2.5,Diesel,yes,1997,525
+BMW,11900,sedan,300,2.5,Gas,yes,2004,525
+Mercedes-Benz,3750,vagon,330,2.2,Gas,yes,1995,E-Class
+Mercedes-Benz,2700,sedan,350,3,Diesel,yes,1988,300
+Volkswagen,2999,vagon,239,1.9,Diesel,no,2003,Passat B5
+Mercedes-Benz,8600,sedan,240,3.2,Diesel,yes,2001,E-Class
+Toyota,6950,sedan,192,2,Petrol,yes,2003,Avensis
+Mitsubishi,22900,crossover,69,2.4,Petrol,yes,2014,Outlander XL
+Volkswagen,8900,sedan,185,1.6,Petrol,yes,2007,Jetta
+Volkswagen,7500,van,317,1.9,Diesel,yes,2008,T5 (Transporter) 
+BMW,28700,sedan,160,3,Petrol,yes,2011,528
+Mercedes-Benz,12000,other,218,2.2,Diesel,yes,2007,Vito
+BMW,10999,sedan,150,2,Gas,yes,2003,320
+Audi,23000,crossover,167,3.6,Petrol,yes,2008,Q7
+Volkswagen,16500,sedan,87,NA,Other,yes,2012,Passat B7
+Audi,2550,vagon,1,2.5,Diesel,no,1998,A6
+Toyota,8800,crossover,388,4.2,Diesel,yes,1997,Land Cruiser 80
+Renault,10000,van,220,2,Diesel,yes,2007,Trafic
+Renault,6300,van,73,1.5,Diesel,yes,2008,Kangoo
+Toyota,13800,other,160,3.5,Gas,yes,2006,Camry
+Volkswagen,10750,van,176,2,Diesel,yes,2012,T5 (Transporter) 
+Volkswagen,12500,van,207,1.9,Diesel,yes,2007,Sharan
+Mercedes-Benz,5500,sedan,250,2.2,Diesel,no,2003,E-Class
+Volkswagen,13300,van,250,2.5,Diesel,yes,2008,T5 (Transporter)
+Audi,NA,crossover,65,3,Diesel,yes,2013,A6
+Volkswagen,20000,crossover,115,2.5,Diesel,yes,2008,Touareg
+Volkswagen,2500,sedan,324,1.6,Diesel,yes,1984,Jetta
+Renault,3500,van,30,1.4,Gas,yes,2006,Kangoo
+Renault,9300,sedan,94,1.5,Diesel,yes,2011,Fluence
+Audi,30500,sedan,110,3,Petrol,yes,2011,A6
+Audi,18300,vagon,190,2,Diesel,yes,2011,A6
+Audi,41000,crossover,88,4.2,Diesel,yes,2010,Q7
+BMW,4100,hatch,160,1.6,Petrol,yes,2004,1 Series
+Volkswagen,22500,van,62,2,Diesel,yes,2011,Caravelle
+Volkswagen,8900,van,272,1.9,Diesel,yes,2005,Caddy
+BMW,13950,crossover,278,3,Diesel,yes,2003,X5
+Volkswagen,8999,van,125,1.6,Diesel,yes,2011,Caddy
+Toyota,14100,hatch,67,1.6,Petrol,yes,2012,Auris
+Mercedes-Benz,NA,sedan,240,2.2,Diesel,yes,2001,E-Class
+Volkswagen,6950,sedan,132,1.6,Other,yes,2004,Bora
+Volkswagen,7350,hatch,180,1.4,Petrol,yes,2003,Golf IV
+Mercedes-Benz,17500,other,180,3,Diesel,yes,2008,Vito
+Mercedes-Benz,20500,sedan,222,5.5,Petrol,yes,2006,S 500
+Renault,6700,van,1,NA,Diesel,yes,2012,Kangoo
+Mercedes-Benz,115000,sedan,49,4.7,Petrol,yes,2014,S 500
+Mitsubishi,11100,vagon,132,2.4,Gas,yes,2006,Grandis
+BMW,29900,sedan,175,2,Diesel,yes,2011,520
+Toyota,17595,sedan,131,2,Petrol,yes,2011,Avensis
+Volkswagen,6800,van,170,2,Gas,no,2011,Caddy
+Volkswagen,6500,other,547,1.9,Diesel,yes,1998,T4 (Transporter)
+Audi,4150,vagon,249,2.5,Diesel,no,2004,A6
+Toyota,32950,other,50,5.7,Petrol,yes,2013,Tundra
+Mercedes-Benz,7500,other,406,2.2,Diesel,yes,2003,Vito
+BMW,33700,crossover,134,4.4,Petrol,yes,2009,X6
+Mitsubishi,8350,sedan,98,1.5,Petrol,yes,2010,Lancer X
+BMW,4100,sedan,350,2.4,Diesel,yes,1992,525
+BMW,4700,sedan,230,2.5,Petrol,yes,1990,525
+Mercedes-Benz,14500,van,207,2.2,Diesel,yes,2008,Vito
+Mercedes-Benz,9600,van,184,2.2,Diesel,yes,2012,Vito
+Mercedes-Benz,17800,van,162,1.8,Diesel,yes,2012,B 180
+Mercedes-Benz,40000,crossover,152,5,Gas,yes,2006,G 500
+Toyota,24000,crossover,152,4,Gas,yes,2006,Land Cruiser Prado
+Volkswagen,8300,other,151,2,Petrol,yes,2005,T5 (Transporter)
+BMW,3800,sedan,278,2.9,Diesel,no,2001,530
+Audi,27500,sedan,150,2,Petrol,yes,2013,A6
+Volkswagen,5200,vagon,248,2,Diesel,no,2005,Passat B6
+Volkswagen,NA,vagon,217,2,Diesel,no,2008,Passat B6
+Mercedes-Benz,NA,sedan,245,2.2,Diesel,yes,2002,E-Class
+Mercedes-Benz,14200,crossover,210,NA,Gas,yes,2004,ML 350
+Volkswagen,8500,sedan,220,2,Other,yes,2005,Passat B5
+Mercedes-Benz,43500,sedan,59,4.6,Petrol,yes,2011,S 500
+BMW,14000,crossover,240,4.4,Gas,yes,2002,X5
+Volkswagen,5200,van,442,2.4,Diesel,yes,1992,T4 (Transporter)
+Mercedes-Benz,10000,van,450,NA,Diesel,yes,1998,Sprinter 312
+Mitsubishi,11500,crossover,191,3,Gas,yes,2006,Pajero Sport
+BMW,41000,sedan,97,4.4,Petrol,yes,2011,750
+Mercedes-Benz,62000,crossover,150,3,Diesel,yes,2013,GL 350
+Audi,35900,crossover,143,3,Diesel,yes,2010,Q7
+Mercedes-Benz,8200,other,199,2.2,Diesel,yes,2001,Vito
+Mercedes-Benz,17300,sedan,107,1.8,Petrol,yes,2008,C-Class
+BMW,6800,crossover,242,99.99,Diesel,no,2005,X3
+Audi,30999,sedan,2,1.4,Petrol,yes,2016,A3
+BMW,3099,sedan,230,3,Diesel,no,2003,530
+Audi,5700,vagon,250,2,Diesel,no,2006,A4
+Volkswagen,7400,sedan,178,1.8,Gas,yes,2001,Passat B5
+Toyota,14500,other,180,2.5,Diesel,yes,2007,Hilux
+Volkswagen,7300,van,214,1.9,Diesel,yes,2001,T4 (Transporter)
+Mercedes-Benz,33800,crossover,120,3,Diesel,yes,2010,ML 350
+BMW,3900,sedan,330,3,Diesel,no,2000,530
+Mercedes-Benz,NA,sedan,322,2.7,Diesel,no,2000,E-Class
+Volkswagen,10700,van,274,2.5,Diesel,yes,2001,T4 (Transporter)
+Volkswagen,NA,hatch,128,1.6,Petrol,yes,2008,Golf V
+Audi,17800,sedan,177,4.2,Petrol,yes,2008,A8
+Mitsubishi,14500,crossover,167,3,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,29900,sedan,83,2.2,Diesel,yes,2012,E-Class
+Mercedes-Benz,8100,van,330,2.2,Diesel,yes,2001,Vito
+Mercedes-Benz,29500,sedan,64,1.6,Petrol,yes,2013,CLA 200
+Mercedes-Benz,12700,other,198,2.2,Diesel,yes,2011,Vito
+Volkswagen,1199,hatch,250,1.9,Diesel,no,1994,Golf III
+Volkswagen,12999,van,250,1.9,Diesel,yes,2009,T5 (Transporter)
+Audi,34419.45,sedan,0,1.4,Petrol,yes,2016,Q3
+Mitsubishi,8600,sedan,145,2,Petrol,yes,2009,Lancer X
+Renault,10450,van,106,2,Diesel,yes,2013,Trafic
+Mercedes-Benz,17800,van,145,2.2,Diesel,yes,2010,Vito
+Audi,3550,sedan,215,2.5,Diesel,no,2001,A6
+Mercedes-Benz,114999,crossover,0,2.99,Diesel,yes,2016,GLS 350
+Renault,13400,vagon,190,1.5,Diesel,yes,2013,Laguna
+BMW,2250,sedan,350,2.5,Diesel,yes,1998,525
+Mitsubishi,14999,crossover,115,3,Petrol,yes,2008,Outlander XL
+Volkswagen,10600,van,140,2,Diesel,yes,2011,T6 (Transporter) 
+Audi,34000,crossover,119,2,Diesel,yes,2013,Q5
+BMW,33500,sedan,65,2,Petrol,yes,2013,520
+Audi,22800,sedan,122,2,Petrol,yes,2011,A6
+BMW,66666,crossover,0,2,Diesel,yes,2016,X5
+Audi,18500,crossover,185,3.6,Petrol,yes,2008,Q7
+Volkswagen,17900,sedan,192,2.5,Diesel,yes,2007,Multivan
+Mercedes-Benz,6950,other,190,2.2,Diesel,yes,1999,Vito
+Mercedes-Benz,105000,crossover,1,3,Diesel,yes,2016,GLS 350
+Renault,11999,van,107,2,Diesel,yes,2013,Trafic
+Renault,8000,van,189,1.9,Diesel,yes,2005,Trafic
+BMW,4600,sedan,280,3,Other,no,1999,730
+Renault,7400,vagon,242,1.5,Diesel,yes,2007,Megane
+Audi,27500,crossover,147,3,Diesel,yes,2008,Q7
+Toyota,NA,crossover,33,2.7,Gas,yes,2012,Venza
+Toyota,27500,crossover,85,4,Petrol,yes,2007,Land Cruiser Prado
+Volkswagen,9800,van,196,1.6,Diesel,yes,2011,Caddy
+Volkswagen,21000,crossover,173,3,Diesel,yes,2008,Touareg
+Audi,9000,vagon,199,2.7,Petrol,yes,2003,A6 Allroad
+Audi,55000,crossover,100,6,Diesel,yes,2009,Q7
+Mercedes-Benz,38900,crossover,65,4.5,Petrol,yes,2010,GL 450
+Renault,4000,sedan,180,1.4,Gas,yes,2006,Logan
+Toyota,13300,sedan,152,2,Petrol,yes,2009,Avensis
+Volkswagen,34500,crossover,108,3,Diesel,yes,2012,Touareg
+BMW,24999,vagon,24,2,Petrol,yes,2012,X1
+Volkswagen,9750,hatch,130,1.4,Petrol,yes,2005,Golf V
+BMW,5900,sedan,390,1.8,Gas,yes,1996,318
+Volkswagen,6500,van,290,1.9,Diesel,yes,2004,T5 (Transporter) 
+Volkswagen,11000,vagon,127,1.6,Diesel,yes,2012,Golf VI
+Mercedes-Benz,18800,sedan,320,5,Petrol,yes,1998,S 500
+Volkswagen,13700,van,222,2,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,8500,van,184,2,Diesel,yes,2007,Caddy
+Volkswagen,19100,sedan,4,1.6,Other,yes,2016,Jetta
+Renault,10200,hatch,70,1.5,Diesel,yes,2013,Sandero
+Mercedes-Benz,173333,other,0,3,Petrol,yes,2016,S 400
+BMW,7800,sedan,310,2,Gas,yes,2000,520
+Mercedes-Benz,9150,sedan,273,2.7,Diesel,yes,2000,E-Class
+Renault,7900,van,268,1.5,Diesel,yes,2008,Grand Scenic
+Toyota,72000,crossover,40,4.5,Diesel,yes,2014,Land Cruiser 200
+Mercedes-Benz,49500,crossover,52,3,Diesel,yes,2013,ML 350
+Volkswagen,45000,sedan,90,3,Diesel,yes,2011,Phaeton
+Renault,8300,van,141,2,Diesel,yes,2011,Trafic
+Renault,5100,vagon,156,1.5,Diesel,yes,2006,Kangoo
+Toyota,15300,other,116,2.4,Petrol,yes,2009,Camry
+Volkswagen,7300,van,142,2,Diesel,yes,2006,Caddy
+Toyota,14700,hatch,98,1.6,Gas,yes,2013,Auris
+Mercedes-Benz,7900,other,197,2.2,Diesel,yes,2003,Vito
+Volkswagen,2900,vagon,243,1.9,Diesel,no,1999,Passat B5
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLE-Class
+BMW,3200,sedan,237,2.5,Petrol,yes,1990,525
+Mercedes-Benz,4400,sedan,160,2.7,Diesel,no,2000,E-Class
+Toyota,20800,crossover,107,4,Petrol,yes,2006,Land Cruiser Prado
+Toyota,27800,crossover,98,2.2,Diesel,yes,2013,Rav 4
+Audi,16500,vagon,171,2,Diesel,yes,2008,A6
+Mercedes-Benz,NA,van,190,2.2,Diesel,yes,2007,Vito
+Renault,10500,van,67,1.6,Petrol,yes,2012,Scenic
+Mitsubishi,16999,crossover,107,3,Gas,yes,2008,Pajero Wagon
+Audi,35000,sedan,92,2.8,Petrol,yes,2013,A6
+Mercedes-Benz,21500,crossover,142,3.5,Petrol,yes,2008,ML 350
+Mercedes-Benz,15800,other,264,2.2,Other,yes,2009,Vito
+BMW,75555,sedan,30,4.4,Petrol,yes,2014,550
+Renault,NA,van,159,1.5,Diesel,yes,2011,Kangoo
+Toyota,8900,other,180,2,Petrol,yes,2003,Rav 4
+Renault,6300,vagon,189,1.5,Diesel,yes,2008,Kangoo
+Mitsubishi,20500,crossover,109,3.2,Diesel,yes,2009,Pajero Wagon
+Volkswagen,11300,van,165,1.9,Diesel,yes,2010,Caddy
+Mercedes-Benz,2300,sedan,261,2.3,Gas,yes,1978,E-Class
+Volkswagen,42500,crossover,63,3,Diesel,yes,2014,Touareg
+Toyota,28000,crossover,95,3.5,Gas,yes,2011,Highlander
+Mitsubishi,17500,crossover,38,1.8,Petrol,yes,2012,ASX
+Volkswagen,7800,van,282,2.5,Diesel,yes,2000,T4 (Transporter)
+Mercedes-Benz,74999,crossover,1,2.2,Diesel,yes,2016,GLE-Class
+Renault,10200,other,39,1.5,Diesel,yes,2014,Kangoo
+BMW,22500,sedan,179,3,Diesel,yes,2007,730
+Mercedes-Benz,52777,sedan,60,3,Diesel,yes,2012,CLS 350
+Toyota,14250,van,127,3.5,Other,yes,2007,Sienna
+Toyota,26200,crossover,156,4,Gas,yes,2007,Land Cruiser Prado
+Volkswagen,9800,van,299,2.5,Diesel,yes,2002,T4 (Transporter)
+Mercedes-Benz,47600,van,0,2.2,Diesel,yes,2016,Vito
+Volkswagen,8700,other,357,2.5,Diesel,yes,2003,T4 (Transporter) 
+BMW,30500,crossover,147,3,Diesel,yes,2008,X5
+Renault,11700,vagon,177,1.5,Diesel,yes,2012,Laguna
+BMW,29500,sedan,850,NA,Petrol,yes,2011,523
+Mercedes-Benz,6400,hatch,177,2,Diesel,no,2008,B 200
+BMW,36400,crossover,80,3,Petrol,yes,2011,X5
+Audi,75000,crossover,0,3,Diesel,yes,2015,Q7
+Volkswagen,6000,hatch,195,1.2,Gas,yes,2004,Polo
+Mercedes-Benz,4900,van,248,2.2,Diesel,yes,2003,Vito
+Mercedes-Benz,6999,sedan,288,3.2,Gas,yes,2001,E-Class
+Volkswagen,9900,other,171,1.6,Diesel,yes,2011,Caddy
+Volkswagen,3500,sedan,189,1.8,Petrol,yes,1993,Vento
+Volkswagen,NA,van,162,2,Diesel,yes,2011,T5 (Transporter) 
+BMW,1750,vagon,200,2,Petrol,no,2000,520
+Renault,4600,sedan,230,1.5,Diesel,yes,2007,Kangoo
+Mercedes-Benz,NA,other,0,3,Other,yes,2016,S 400
+Volkswagen,9800,van,220,1.9,Diesel,yes,2008,Caddy
+Renault,5950,van,135,1.5,Diesel,yes,2012,Kangoo
+Renault,7400,van,120,1.5,Diesel,yes,2012,Kangoo
+Audi,12800,sedan,217,1.8,Petrol,yes,2007,A4
+BMW,7300,sedan,350,NA,Petrol,yes,1997,520
+Volkswagen,9000,sedan,59,1.6,Gas,yes,2013,Polo
+Volkswagen,1550,hatch,130,1.3,Petrol,yes,1986,Golf II
+Mitsubishi,18600,crossover,52,2,Petrol,yes,2013,Outlander
+BMW,3900,sedan,340,2.5,Diesel,no,2000,525
+Audi,92222,crossover,0,3,Diesel,yes,2016,Q7
+Volkswagen,5400,other,314,2.4,Diesel,yes,1993,T4 (Transporter)
+Audi,16000,sedan,250,2,Diesel,yes,2008,A6
+Volkswagen,3500,other,304,1.9,Diesel,no,2006,Touran
+Mercedes-Benz,6500,sedan,270,2.8,Gas,yes,1999,E-Class
+Mercedes-Benz,7000,sedan,300,4.3,Gas,yes,2000,E-Class
+Renault,10499,vagon,121,1.5,Diesel,yes,2013,Megane
+Renault,13900,crossover,111,2.5,Gas,yes,2010,Koleos
+BMW,6400,other,220,2.5,Petrol,yes,1997,323
+BMW,10800,sedan,246,3,Diesel,yes,2003,530
+BMW,12700,hatch,130,2,Petrol,yes,2007,118
+BMW,38500,sedan,140,2,Diesel,yes,2012,520
+Mitsubishi,5999,sedan,150,1.6,Petrol,yes,2005,Lancer
+Renault,9500,hatch,77,1.6,Petrol,yes,2011,Megane
+Volkswagen,12000,van,196,2,Diesel,yes,2011,T5 (Transporter) 
+Mercedes-Benz,38700,sedan,117,5.5,Petrol,yes,2010,S 550
+Mercedes-Benz,40000,other,119,6.3,Petrol,yes,2008,ML 63 AMG
+Mercedes-Benz,6900,other,311,2.2,Diesel,yes,1999,Vito
+Audi,24300,sedan,43,1.8,Petrol,yes,2012,A4
+Mitsubishi,17700,crossover,165,3,Gas,yes,2007,Pajero Wagon
+Toyota,18999,crossover,153,2.7,Gas,yes,2006,Land Cruiser Prado
+BMW,8300,sedan,200,4.4,Petrol,yes,2001,740
+Toyota,25000,crossover,80,4,Gas,yes,2008,FJ Cruiser
+Volkswagen,NA,vagon,161,1.4,Petrol,yes,2011,Passat B7
+Mitsubishi,12499,crossover,142,2.4,Gas,yes,2008,Outlander XL
+BMW,2899,sedan,340,2.4,Diesel,yes,1988,524
+BMW,19900,other,95,4.5,Petrol,yes,2005,645
+Audi,51000,crossover,44,3,Diesel,yes,2014,Q7
+Audi,81999,crossover,0,2.98,Diesel,yes,2016,Q7
+BMW,9500,sedan,400,3.5,Gas,yes,1999,735
+Mitsubishi,10200,crossover,99,2,Gas,yes,2008,Outlander
+Mercedes-Benz,5500,van,260,2.2,Diesel,no,2004,Vito
+Volkswagen,18500,sedan,74,1.8,Petrol,yes,2011,Passat CC
+Volkswagen,11200,van,200,2.5,Diesel,yes,2003,T4 (Transporter)
+BMW,3099,sedan,320,2,Diesel,no,2000,320
+Audi,27300,other,65,2,Petrol,yes,2011,TT
+Volkswagen,1450,other,200,1.8,Gas,yes,1987,Jetta
+Mitsubishi,9700,sedan,120,2.4,Gas,yes,2006,Galant
+Mitsubishi,18900,crossover,127,3,Gas,yes,2008,Pajero Wagon
+Audi,33500,crossover,67,2,Petrol,yes,2011,Q5
+Volkswagen,5500,van,400,1.9,Diesel,yes,1995,T4 (Transporter)
+Volkswagen,5300,sedan,270,1.8,Gas,yes,1997,Passat B5
+Toyota,25500,crossover,200,4,Petrol,yes,2007,Land Cruiser Prado
+Toyota,23660,crossover,125,3.5,Gas,yes,2013,Highlander
+Volkswagen,3100,hatch,311,1.6,Gas,yes,1986,Golf II
+Mitsubishi,5900,sedan,170,1.6,Petrol,yes,2007,Lancer
+Volkswagen,15900,sedan,150,2,Diesel,yes,2012,Passat B7
+Mercedes-Benz,105999,crossover,0,2.98,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,18500,van,170,2.2,Diesel,yes,2010,Vito
+BMW,86999,crossover,0,3,Diesel,yes,2016,X6
+BMW,27800,sedan,170,2,Diesel,yes,2011,520
+Volkswagen,9000,van,275,2.5,Diesel,yes,2002,T4 (Transporter)
+Toyota,12200,sedan,185,3.5,Petrol,yes,2008,Camry
+Toyota,5800,sedan,200,2.2,Petrol,yes,1999,Camry
+Volkswagen,14499,van,164,2,Diesel,yes,2013,T5 (Transporter) 
+Audi,37000,other,170,3,Diesel,yes,2010,Q7
+Volkswagen,4500,hatch,199,1.8,Petrol,yes,1996,Golf III
+Audi,6557.85,sedan,327,4.2,Gas,yes,2000,A8
+Renault,6800,sedan,219,1.6,Gas,yes,2007,Megane
+BMW,37900,crossover,97,3,Diesel,yes,2010,X6
+Audi,14200,sedan,176,3,Diesel,yes,2005,A6
+Mitsubishi,9350,sedan,94,1.5,Gas,yes,2010,Lancer X
+Volkswagen,2200,vagon,150,1.6,Petrol,yes,1986,Passat B2
+Toyota,3100,hatch,283,1.3,Petrol,yes,1994,Corolla
+Mitsubishi,8400,crossover,100,3.2,Diesel,yes,2011,Pajero Wagon
+Audi,30650,vagon,35,2,Petrol,yes,2013,A4 Allroad
+Audi,8800,sedan,298,2.4,Gas,yes,1999,A6
+Mercedes-Benz,NA,other,25,4.7,Petrol,yes,2014,S 500
+Mitsubishi,7699,crossover,254,3,Petrol,yes,2002,Pajero Sport
+Renault,6400,van,136,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,16200,hatch,44,1.4,Petrol,yes,2012,Golf VI
+Volkswagen,4100,sedan,88,1.8,Gas,yes,1993,Vento
+Mercedes-Benz,6800,sedan,235,2.2,Diesel,yes,1999,C-Class
+Volkswagen,10600,sedan,75,1.6,Petrol,yes,2012,Polo
+Toyota,13000,crossover,149,2,Petrol,yes,2006,Rav 4
+Toyota,6500,sedan,248,2,Petrol,yes,2001,Avensis
+Audi,25500,vagon,181,3,Petrol,yes,2009,A6 Allroad
+Volkswagen,28000,crossover,132,2,Diesel,yes,2011,Amarok
+Toyota,9800,sedan,123,NA,Petrol,yes,2008,Corolla
+Audi,3500,sedan,200,2.5,Diesel,no,2001,A8
+Volkswagen,14899,van,165,2,Diesel,yes,2013,Caddy
+BMW,3500,sedan,300,3,Diesel,no,1999,530
+BMW,3500,sedan,272,2,Diesel,yes,2000,320
+Volkswagen,71138,crossover,0,3,Diesel,yes,2016,Touareg
+Renault,9658.94,sedan,1,1.2,Petrol,yes,2016,Logan
+Volkswagen,13000,van,178,2.5,Diesel,yes,2007,T6 (Transporter)
+Toyota,8000,hatch,115,1.6,Petrol,yes,2007,Corolla
+BMW,56500,crossover,1,2,Diesel,yes,2016,X5
+Renault,9700,vagon,114,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,7300,hatch,176,1.4,Petrol,yes,2004,A 140
+Renault,9000,vagon,190,1.5,Diesel,yes,2009,Megane
+Volkswagen,17900,sedan,96,2.5,Petrol,yes,2013,Passat B7
+Volkswagen,10700,sedan,147,2,Petrol,yes,2007,Passat B6
+Mercedes-Benz,11600,sedan,186,3.2,Petrol,yes,2003,E-Class
+Volkswagen,15000,van,275,2.5,Diesel,yes,2008,T5 (Transporter)
+Volkswagen,8350,hatch,200,1.4,Petrol,yes,2005,Golf V
+Mercedes-Benz,7700,sedan,199,5,Petrol,yes,1996,S 500
+BMW,47000,other,5,2,Petrol,yes,2014,Z4
+Audi,72000,sedan,55,6.3,Petrol,yes,2012,A8
+Mitsubishi,17499,crossover,149,3,Gas,yes,2008,Pajero Wagon
+Renault,11300,vagon,141,1.5,Diesel,yes,2013,Megane
+Toyota,8500,sedan,168,1.6,Petrol,yes,2008,Corolla
+Mercedes-Benz,14490,vagon,212,2.2,Diesel,yes,2008,E-Class
+Volkswagen,11999,van,176,2,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,4300,hatch,217,1.6,Gas,yes,1997,Golf III
+BMW,35500,crossover,33,2,Diesel,yes,2014,X1
+Toyota,2800,other,421,1.3,Gas,yes,1988,Corolla
+BMW,37976.25,sedan,0,2,Petrol,yes,2016,520
+Renault,10500,van,183,2.5,Diesel,yes,2006,Trafic
+Toyota,74999,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,7300,van,125,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,12200,crossover,140,2.4,Petrol,yes,2007,Outlander XL
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,13600,van,199,2.2,Diesel,yes,2009,Vito
+Audi,5000,sedan,280,2.3,Gas,yes,1992,100
+Renault,12800,other,188,2,Diesel,yes,2008,Trafic
+Volkswagen,12499,other,184,2,Diesel,yes,2012,T6 (Transporter) 
+Mitsubishi,2800,sedan,150,2,Gas,yes,1996,Galant
+Renault,7300,van,200,1.5,Diesel,yes,2007,Megane
+Mitsubishi,6800,sedan,82,2,Petrol,yes,2007,Lancer
+Mitsubishi,9500,crossover,225,2.4,Gas,yes,2004,Outlander
+Audi,15900,hatch,160,1.4,Petrol,yes,2011,A3
+Volkswagen,8900,other,180,1.9,Diesel,yes,2010,Caddy
+Toyota,16030,sedan,90,2.5,Gas,yes,2012,Camry
+BMW,28000,sedan,170,2,Diesel,yes,2011,520
+Audi,10700,vagon,222,2.5,Diesel,yes,2005,A6
+BMW,25000,crossover,150,30,Petrol,yes,2007,X5
+Mercedes-Benz,7000,sedan,230,2,Gas,yes,2000,E-Class
+Volkswagen,8800,van,260,2.5,Diesel,yes,2008,T5 (Transporter) 
+Volkswagen,35000,crossover,20,2,Diesel,yes,2014,Tiguan
+Audi,8000,vagon,200,2.7,Diesel,yes,2006,A6
+Mercedes-Benz,15400,sedan,260,2.2,Diesel,yes,2008,E-Class
+Volkswagen,7650,sedan,325,1.8,Gas,yes,2003,Passat B5
+Mitsubishi,10600,crossover,52,2,Petrol,yes,2008,Outlander
+Renault,8200,vagon,192,1.5,Diesel,yes,2010,Logan
+Volkswagen,8700,other,520,2.5,Diesel,yes,1999,T4 (Transporter)
+BMW,45000,sedan,29,2,Diesel,yes,2016,520
+Volkswagen,5699,van,258,2.5,Diesel,yes,2001,T4 (Transporter) 
+Mercedes-Benz,13200,van,217,3.7,Gas,yes,2005,Viano
+Volkswagen,11500,van,70,2,Diesel,yes,2013,Caddy
+BMW,3300,sedan,233,28,Petrol,no,1997,528
+Volkswagen,12700,van,213,2,Diesel,yes,2012,T5 (Transporter) 
+Mercedes-Benz,NA,other,176,3,Diesel,yes,2008,E-Class
+Volkswagen,12300,hatch,62,1.4,Petrol,yes,2012,Polo
+Mercedes-Benz,35500,sedan,100,3.5,Petrol,yes,2010,S 350
+Audi,15500,sedan,210,2,Diesel,yes,2008,A4
+Mercedes-Benz,33000,crossover,109,2.2,Diesel,yes,2012,GLK 220
+Mercedes-Benz,NA,van,173,2.2,Diesel,yes,2011,Vito
+BMW,6500,sedan,280,2,Petrol,yes,1997,320
+Volkswagen,2350,sedan,11,1.9,Other,no,2000,Passat B5
+Volkswagen,17900,crossover,130,3.6,Gas,yes,2007,Touareg
+BMW,34500,other,160,3,Diesel,yes,2010,X5
+Renault,12300,sedan,99,2.5,Petrol,yes,2011,Latitude
+Mercedes-Benz,20500,sedan,193,3.5,Other,yes,2006,CLS 350
+BMW,75555,sedan,30,4.4,Petrol,yes,2014,M5
+Toyota,12800,sedan,145,2.4,Petrol,yes,2008,Camry
+Mercedes-Benz,12800,other,360,2.2,Diesel,yes,2008,Vito
+Audi,6900,sedan,327,3.7,Petrol,yes,1997,A8
+Volkswagen,17200,crossover,220,2.5,Diesel,yes,2006,Touareg
+Renault,6800,sedan,87,1.4,Petrol,yes,2013,Symbol
+Audi,14300,other,80,4.2,Petrol,yes,2004,A8
+Volkswagen,11600,sedan,169,1.4,Gas,no,2013,Passat B7
+Volkswagen,11990,van,231,2.5,Diesel,yes,2002,T4 (Transporter)
+BMW,77777,sedan,8,4.4,Petrol,yes,2014,750
+Mercedes-Benz,6800,sedan,186,NA,Petrol,yes,1998,C-Class
+BMW,2800,sedan,300,1.8,Other,yes,1987,318
+Mercedes-Benz,28500,other,65,1.8,Petrol,yes,2011,E-Class
+Audi,42700,crossover,75,3,Diesel,yes,2013,Q7
+Volkswagen,6950,other,217,1.9,Diesel,yes,2006,Caddy
+Volkswagen,7300,hatch,117,1.4,Petrol,yes,2007,Polo
+Mercedes-Benz,NA,sedan,25,3,Diesel,yes,2014,S 350
+Mitsubishi,27565.2,crossover,1,2.5,Diesel,yes,2016,Pajero Sport
+Volkswagen,12900,vagon,145,1.6,Diesel,yes,2011,Golf Variant
+Volkswagen,5900,sedan,217,2.3,Petrol,yes,1999,Bora
+Mercedes-Benz,2600,hatch,111,1.4,Petrol,no,1999,A 140
+Audi,2799,vagon,390,1.9,Diesel,no,1998,A6
+Mitsubishi,21000,crossover,98,2.5,Diesel,yes,2011,Pajero Sport
+Audi,4999,sedan,231,1.9,Diesel,no,2002,A6
+Volkswagen,6900,sedan,160,1.6,Petrol,yes,2003,Bora
+Mercedes-Benz,5800,sedan,366,3.2,Gas,yes,1991,S 320
+Mitsubishi,15700,other,68,1.8,Petrol,yes,2011,ASX
+Volkswagen,74500,crossover,0,3,Diesel,yes,2015,Touareg
+Mercedes-Benz,22900,sedan,131,5.5,Gas,yes,2008,S 500
+Mercedes-Benz,32000,crossover,96,3.5,Petrol,yes,2010,ML 350
+Mercedes-Benz,35300,sedan,3,1.6,Petrol,yes,2014,CLA 200
+Volkswagen,6900,sedan,320,1.8,Gas,yes,2002,Passat B5
+BMW,7000,sedan,286,2.8,Petrol,yes,2001,520
+Volkswagen,35800,crossover,92,3,Diesel,yes,2011,Touareg
+Volkswagen,10700,hatch,60,1.4,Petrol,yes,2010,Polo
+BMW,23999,crossover,194,3,Petrol,yes,2007,X5
+Volkswagen,2700,hatch,300,1.9,Diesel,no,2000,Golf IV
+Mercedes-Benz,10000,sedan,80,NA,Diesel,yes,2010,S 350
+Toyota,18200,crossover,146,2.7,Gas,yes,2006,Land Cruiser Prado
+Audi,2450,vagon,325,2.5,Diesel,no,2000,A6
+Audi,15800,hatch,123,1.4,Petrol,yes,2010,A3
+Volkswagen,9400,vagon,176,1.9,Diesel,yes,2005,Passat B5
+Mitsubishi,5800,vagon,230,1.6,Petrol,yes,2005,Space Star
+Volkswagen,13499,sedan,162,1.4,Petrol,yes,2012,Passat B7
+Volkswagen,39615,sedan,0,2,Diesel,yes,2016,Passat B8
+Toyota,23300,crossover,40,2,Petrol,yes,2014,Rav 4
+Audi,2100,sedan,217,1.9,Diesel,no,1996,A4
+BMW,3000,sedan,350,99.99,Other,yes,1992,520
+Mercedes-Benz,2700,sedan,337,2.3,Gas,yes,1988,230
+Renault,NA,van,153,2,Diesel,yes,2012,Trafic
+Volkswagen,6700,van,16,2,Diesel,yes,2005,Caddy
+BMW,43163.25,crossover,0,2,Diesel,yes,2016,X1
+Renault,2750,hatch,186,22,Diesel,no,2005,Laguna
+Toyota,28350,crossover,92,2.2,Diesel,yes,2013,Rav 4
+Toyota,29500,crossover,90,2.7,Gas,yes,2010,Land Cruiser Prado
+BMW,NA,crossover,15,4.39,Petrol,yes,2015,X5 M
+Volkswagen,8200,sedan,258,1.9,Diesel,yes,2004,Passat B5
+Mercedes-Benz,10999,other,269,2.2,Diesel,yes,2006,Sprinter 313
+Mercedes-Benz,NA,crossover,1,3,Diesel,yes,2016,GLS 350
+Renault,8299,sedan,19,1.2,Petrol,yes,2013,Logan
+Toyota,20500,sedan,105,2.5,Petrol,yes,2013,Camry
+Volkswagen,14500,van,168,2,Diesel,yes,2012,T5 (Transporter) 
+Toyota,17300,other,135,3.5,Gas,yes,2009,Camry
+Mercedes-Benz,18700,sedan,105,5.5,Petrol,yes,2006,S 500
+Toyota,13200,sedan,38,1.33,Petrol,yes,2014,Corolla
+Volkswagen,2450,sedan,200,1.3,Gas,yes,1991,Jetta
+Audi,55000,hatch,24,2.8,Other,yes,2014,A7
+Toyota,20000,van,135,2.4,Gas,yes,2006,Previa
+Volkswagen,4000,hatch,266,1.9,Diesel,yes,1993,Golf III
+Mitsubishi,16900,other,120,3,Petrol,yes,2008,Pajero Wagon
+Mitsubishi,11450,sedan,60,1.6,Gas,yes,2012,Lancer X
+Mercedes-Benz,24500,crossover,236,5,Petrol,yes,2000,G 500
+Renault,12350,sedan,64,2,Gas,yes,2011,Fluence
+Mercedes-Benz,6900,sedan,239,2.3,Petrol,yes,1996,E-Class
+Toyota,30500,crossover,40,2.7,Petrol,yes,2011,Land Cruiser Prado
+Mitsubishi,8800,sedan,105,2,Gas,yes,2008,Lancer X
+Audi,9700,sedan,249,2.5,Diesel,yes,2004,A6
+BMW,63500,crossover,33,3,Petrol,yes,2015,X5
+Mitsubishi,8900,sedan,96,2,Petrol,yes,2008,Lancer X
+Audi,1950,vagon,210,1.8,Gas,no,1996,A4
+Audi,4400,sedan,190,2.5,Diesel,no,2003,A6
+Toyota,12450,sedan,162,3.5,Gas,yes,2007,Camry
+Audi,3200,sedan,295,2.5,Diesel,no,2000,A6
+Audi,12600,sedan,294,3,Gas,yes,2004,A8
+Renault,NA,vagon,137,1.9,Diesel,yes,2008,Kangoo
+Mercedes-Benz,4400,sedan,225,2,Gas,yes,1995,E-Class
+Mercedes-Benz,3900,sedan,111,2.5,Diesel,yes,1989,E-Class
+Mercedes-Benz,154999,crossover,0,4.6,Petrol,yes,2016,GLS 500
+Volkswagen,8499,sedan,101,1.6,Petrol,yes,2012,Polo
+Mercedes-Benz,8000,other,230,2.2,Diesel,yes,2001,Vito
+Volkswagen,3500,hatch,250,1.9,Diesel,no,2000,Beetle
+Audi,11900,sedan,195,2.4,Gas,yes,2005,A6
+Toyota,24800,sedan,43,2.5,Gas,yes,2015,Camry
+Audi,3800,vagon,190,2.5,Diesel,no,2002,A6
+Volkswagen,4800,sedan,190,99.99,Petrol,yes,1998,Passat B5
+Volkswagen,7500,van,230,1.9,Diesel,yes,2006,Caddy
+BMW,8999,sedan,270,2.8,Petrol,yes,1998,528
+Volkswagen,5000,van,326,2.4,Diesel,yes,1995,T4 (Transporter)
+Mercedes-Benz,21000,crossover,135,3.5,Petrol,yes,2007,ML 350
+Renault,4200,van,142,1.9,Diesel,yes,2007,Kangoo
+Mitsubishi,18000,crossover,160,3.2,Diesel,yes,2007,Pajero Wagon
+Volkswagen,8500,van,220,2.5,Diesel,yes,2003,T4 (Transporter)
+Renault,3750,hatch,232,1.8,Petrol,yes,1997,Laguna
+Volkswagen,9000,other,212,2,Gas,yes,2007,Caddy
+Mitsubishi,6300,crossover,235,3.2,Diesel,no,2001,Pajero
+BMW,8400,sedan,297,2.2,Petrol,yes,2000,320
+Volkswagen,5000,hatch,190,NA,Petrol,yes,1998,Golf IV
+Volkswagen,17000,van,157,2,Diesel,yes,2010,T5 (Transporter)
+Volkswagen,3000,sedan,240,1.8,Petrol,yes,1991,Jetta
+Mercedes-Benz,21300,crossover,114,4.7,Petrol,yes,2007,GL 450
+BMW,16500,crossover,230,3,Gas,yes,2004,X5
+Mercedes-Benz,7300,other,250,2.2,Diesel,yes,2005,Sprinter 311
+Mercedes-Benz,NA,van,300,2.3,Diesel,yes,1996,Vito
+Mercedes-Benz,2300,sedan,300,3,Diesel,yes,1999,E-Class
+BMW,8200,sedan,250,4.4,Petrol,yes,1997,540
+Audi,35000,sedan,61,2.8,Petrol,yes,2012,A6
+Volkswagen,7800,vagon,193,1.9,Diesel,yes,2002,Passat B5
+Volkswagen,8200,hatch,157,1.6,Petrol,yes,2008,Golf Plus
+Audi,39999,crossover,45,2,Diesel,yes,2014,Q5
+Renault,11900,other,173,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,3600,sedan,276,2.7,Diesel,no,2001,E-Class
+Volkswagen,3399,sedan,201,1.8,Petrol,yes,1991,Passat B3
+BMW,2650,vagon,335,2,Petrol,no,2000,320
+Mercedes-Benz,11700,hatch,103,1.5,Petrol,yes,2007,A 150
+Mercedes-Benz,1600,hatch,180,NA,Petrol,no,2000,A 160
+BMW,2550,vagon,312,2.5,Diesel,no,1999,525
+Renault,NA,van,220,2,Diesel,yes,2008,Trafic
+Mercedes-Benz,2550,vagon,367,3.2,Diesel,no,2000,E-Class
+Audi,31700,sedan,32,1.8,Petrol,yes,2014,A4
+Mercedes-Benz,125000,sedan,18,3,Diesel,yes,2015,S 350
+BMW,20500,crossover,285,4.8,Petrol,yes,2006,X5
+Mitsubishi,2600,other,200,2.4,Petrol,no,2002,Lancer
+BMW,2500,vagon,250,2.5,Diesel,no,2000,525
+Mercedes-Benz,13200,van,182,2.2,Diesel,yes,2011,Vito
+Mercedes-Benz,31000,crossover,220,3.2,Diesel,yes,2008,GL 320
+Audi,30900,vagon,118,2,Petrol,yes,2013,A4 Allroad
+Mitsubishi,4750,sedan,167,1.6,Petrol,yes,2003,Carisma
+Volkswagen,NA,van,160,2,Diesel,yes,2011,T6 (Transporter) 
+Audi,NA,sedan,62,3,Petrol,yes,2013,A8
+Toyota,8950,hatch,118,1.6,Petrol,yes,2008,Auris
+Renault,8300,van,177,1.5,Diesel,yes,2007,Grand Scenic
+Audi,6900,sedan,360,2.5,Diesel,yes,1997,A6
+Volkswagen,5000,sedan,81,NA,Other,no,2013,Passat B7
+BMW,69999,crossover,71,4.4,Petrol,yes,2011,X5 M
+Volkswagen,3250,sedan,255,1.9,Diesel,no,2003,Passat B5
+Volkswagen,11900,hatch,91,1.4,Petrol,yes,2010,Golf VI
+Mitsubishi,9600,other,114,2,Petrol,yes,1999,Eclipse
+Mercedes-Benz,21200,vagon,206,2.1,Diesel,yes,2011,C-Class
+Toyota,102999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,11900,van,277,2.2,Diesel,yes,2007,Vito
+Renault,9999,van,185,1.5,Diesel,yes,2008,Kangoo
+Renault,8500,sedan,57,1.2,Gas,yes,2013,Logan
+Volkswagen,6500,other,385,2.4,Diesel,yes,1997,T4 (Transporter)
+Mitsubishi,8700,sedan,80,1.5,Petrol,yes,2008,Lancer X
+Volkswagen,8599,van,169,1.6,Diesel,yes,2011,Caddy
+Volkswagen,12700,hatch,156,1.6,Diesel,yes,2011,Golf VI
+Mitsubishi,7700,sedan,135,2,Gas,yes,2007,Lancer
+Mitsubishi,2020,other,90,NA,Other,no,2008,L 200
+Volkswagen,NA,van,123,1.2,Petrol,yes,2013,Caddy
+Mitsubishi,8700,sedan,150,2.4,Gas,yes,2007,Galant
+Mitsubishi,9300,sedan,95,2,Petrol,yes,2007,Lancer X
+Mercedes-Benz,3550,sedan,238,2.2,Diesel,no,2001,E-Class
+Audi,29800,crossover,151,3,Diesel,yes,2007,Q7
+Renault,3900,van,202,1.9,Diesel,yes,2001,Kangoo
+BMW,NA,crossover,200,3,Diesel,yes,2009,X5
+Volkswagen,7000,van,345,1.9,Diesel,yes,2006,T5 (Transporter) 
+Audi,17700,other,70,2,Petrol,yes,2009,TT
+Toyota,9999,sedan,225,3,Gas,yes,2004,Camry
+Audi,25300,hatch,180,2,Diesel,yes,2011,A5
+Audi,22500,vagon,175,2,Gas,yes,2010,A4 Allroad
+Volkswagen,18600,sedan,2,1.6,Petrol,yes,2015,Jetta
+Mitsubishi,6999,sedan,160,2,Petrol,yes,2007,Lancer
+Toyota,16200,crossover,281,4.2,Diesel,yes,1999,Land Cruiser 100
+Mercedes-Benz,29999,hatch,9,NA,Other,yes,2015,B-Class Electric Drive
+Toyota,18000,crossover,320,4.2,Diesel,yes,1999,Land Cruiser 100
+Mercedes-Benz,27300,sedan,144,2.14,Diesel,yes,2011,E-Class
+Toyota,2700,sedan,550,1.6,Gas,yes,1989,Carina
+BMW,12000,crossover,218,3,Diesel,no,2006,X5
+Mercedes-Benz,39900,hatch,2,NA,Other,yes,2015,B-Class Electric Drive
+Volkswagen,1800,other,280,1.9,Diesel,no,1997,Passat B5
+Audi,20500,sedan,106,1.8,Petrol,yes,2011,A4
+Volkswagen,10493,other,1,1.4,Petrol,yes,2014,Polo
+BMW,16800,sedan,154,3,Petrol,yes,2006,330
+Volkswagen,12500,sedan,177,NA,Petrol,yes,2008,Passat B6
+BMW,8200,sedan,238,3,Diesel,no,2004,730
+BMW,8000,sedan,330,NA,Diesel,yes,1997,525
+Mercedes-Benz,14500,van,171,2.2,Diesel,yes,2011,Vito
+Volkswagen,24500,crossover,62,2,Diesel,yes,2012,Passat CC
+Toyota,13250,sedan,96,2.4,Gas,yes,2008,Rav 4
+Toyota,6400,sedan,250,3,Gas,yes,1998,Camry
+Mercedes-Benz,29500,sedan,135,5.5,Petrol,yes,2008,S 550
+Volkswagen,69990,van,1,5,Diesel,yes,2015,Multivan
+Audi,2600,vagon,110,2.5,Diesel,no,2002,A6
+Volkswagen,4900,sedan,500,1.9,Diesel,yes,1995,Passat B4
+Renault,10500,vagon,185,1.5,Diesel,yes,2011,Megane
+Volkswagen,NA,crossover,90,3.6,Petrol,yes,2008,Touareg
+Volkswagen,27200,crossover,1,2,Diesel,yes,2015,Tiguan
+Mercedes-Benz,1350,sedan,360,2.5,Diesel,no,1989,E-Class
+Mercedes-Benz,NA,sedan,24,3,Diesel,yes,2015,S 350
+Toyota,13900,sedan,139,2.4,Other,yes,2008,Camry
+BMW,12800,sedan,194,4.4,Gas,yes,2002,745
+Toyota,21200,crossover,135,4,Gas,yes,2007,Land Cruiser Prado
+Mercedes-Benz,8700,sedan,196,2,Gas,yes,2000,E-Class
+Toyota,8800,sedan,165,1.8,Petrol,yes,2007,Avensis
+Toyota,12900,sedan,129,1.8,Petrol,yes,2009,Avensis
+BMW,9500,sedan,339,3,Diesel,yes,2001,530
+BMW,10700,sedan,243,3,Diesel,yes,2002,530
+Volkswagen,10800,van,156,1.6,Diesel,yes,2012,Caddy
+Mitsubishi,2400,hatch,350,2,Gas,yes,1989,Galant
+Renault,13300,other,132,1.5,Diesel,yes,2012,Megane
+BMW,34500,crossover,85,3,Petrol,yes,2010,X5
+BMW,57000,other,20,3.5,Petrol,yes,2013,X6
+Renault,7200,van,146,1.5,Diesel,yes,2012,Kangoo
+Toyota,23400,sedan,69,2.5,Gas,yes,2014,Camry
+Mercedes-Benz,6500,sedan,215,55,Petrol,no,2001,S 55
+Toyota,48000,crossover,59,3,Diesel,yes,2014,Land Cruiser Prado
+BMW,6800,sedan,325,NA,Other,no,2005,525
+Volkswagen,4200,sedan,1,1.4,Petrol,yes,2012,Golf VI
+Mercedes-Benz,6200,other,340,2.4,Gas,yes,1998,E-Class
+BMW,137000,crossover,2,4.4,Petrol,yes,2016,X5 M
+Audi,1300,sedan,250,NA,Gas,no,1994,80
+BMW,2900,sedan,225,2.5,Petrol,no,1997,523
+Mercedes-Benz,12700,other,199,2.2,Diesel,yes,2011,Vito
+Audi,16999,vagon,140,4.2,Petrol,yes,2007,A6 Allroad
+Volkswagen,13972.07,sedan,0,1.6,Petrol,yes,2016,Polo
+Mercedes-Benz,41900,sedan,95,6.2,Petrol,yes,2011,S 500
+Renault,6650,vagon,227,1.5,Diesel,yes,2005,Megane
+Mercedes-Benz,9200,other,280,2.4,Petrol,yes,2003,E-Class
+BMW,15300,sedan,250,2.5,Petrol,yes,2006,525
+Volkswagen,1450,van,100,1.6,Diesel,yes,1988,T3 (Transporter)
+Volkswagen,5900,van,278,1.9,Diesel,yes,2005,Caddy
+Mercedes-Benz,4100,sedan,400,2,Diesel,yes,1987,190
+Mercedes-Benz,4900,sedan,282,3.2,Gas,yes,1993,E-Class
+BMW,12900,vagon,260,3,Diesel,yes,2004,330
+Toyota,33333,vagon,73,4,Gas,yes,2009,FJ Cruiser
+Volkswagen,7200,hatch,127,1.39,Petrol,yes,2007,Polo
+BMW,77777,crossover,1,3,Diesel,yes,2014,X6
+Mercedes-Benz,2300,hatch,175,1.4,Petrol,no,2000,A 140
+Volkswagen,13000,crossover,235,3.2,Gas,yes,2005,Touareg
+Volkswagen,3000,van,318,1.9,Diesel,yes,1993,T4 (Transporter) 
+Renault,7999,van,92,1.5,Diesel,yes,2013,Kangoo
+Mercedes-Benz,11000,van,250,2.2,Diesel,yes,2005,Vito
+BMW,81000,sedan,23,4.4,Petrol,yes,2014,M5
+BMW,3800,sedan,304,1.9,Gas,no,2000,318
+BMW,4300,other,280,2.5,Petrol,yes,1985,325
+Audi,4400,sedan,340,2.8,Gas,yes,1992,100
+Audi,3150,vagon,320,2.5,Diesel,no,2002,A6
+Volkswagen,10900,other,287,2.5,Other,yes,2004,T5 (Transporter)
+Volkswagen,6400,van,305,NA,Diesel,yes,2004,Caddy
+Audi,3050,vagon,250,2.5,Other,no,2001,A6
+Volkswagen,30385,vagon,0,2,Diesel,yes,2014,Touran
+BMW,20999,crossover,170,4.8,Petrol,yes,2008,X5
+Volkswagen,1500,sedan,255,1.9,Diesel,no,1998,Polo
+Mitsubishi,NA,crossover,120,2,Gas,yes,2008,Outlander
+Toyota,8500,vagon,200,2,Petrol,yes,1999,Vista
+Toyota,9200,sedan,115,1.6,Gas,yes,2008,Corolla
+Toyota,20800,sedan,49,3.5,Petrol,yes,2011,Camry
+Renault,8100,van,206,1.9,Diesel,yes,2005,Trafic
+Volkswagen,13500,sedan,104,1.8,Petrol,yes,2010,Passat B6
+Renault,4900,van,218,1.5,Diesel,yes,2006,Kangoo
+Volkswagen,6600,hatch,136,1.4,Gas,yes,2001,Golf IV
+Mitsubishi,1450,crossover,245,2.8,Diesel,no,1996,Pajero
+Volkswagen,3600,sedan,320,1.8,Gas,yes,1989,Passat B3
+Toyota,24000,sedan,51,2.5,Gas,yes,2014,Camry
+Toyota,12700,sedan,130,2.4,Petrol,yes,2008,Camry
+Mercedes-Benz,14500,other,315,5,Gas,yes,2004,CL 500
+BMW,2299,sedan,200,1.8,Petrol,no,1999,318
+BMW,8900,sedan,234,3.5,Gas,yes,1999,735
+Audi,30500,vagon,152,3,Diesel,yes,2012,A6
+Mercedes-Benz,4000,sedan,370,3.2,Diesel,no,2002,E-Class
+Toyota,12900,sedan,137,2.4,Petrol,yes,2008,Camry
+Volkswagen,3300,sedan,200,1.8,Petrol,yes,1989,Passat B3
+Toyota,9800,sedan,178,1.8,Petrol,yes,2007,Avensis
+Renault,15200,other,173,2.3,Diesel,yes,2013,Master
+Volkswagen,4950,vagon,290,1.8,Petrol,yes,1996,Passat B4
+Volkswagen,55500,crossover,36,3,Diesel,yes,2015,Touareg
+Volkswagen,17900,van,188,2.5,Diesel,yes,2008,Multivan
+Mercedes-Benz,5900,crossover,271,2.7,Diesel,no,2003,ML 270
+Toyota,20500,crossover,225,4.7,Gas,yes,2003,Land Cruiser 100
+BMW,5000,other,300,1.8,Petrol,yes,1994,318
+Volkswagen,11500,van,100,2,Diesel,yes,2010,T5 (Transporter) 
+Mercedes-Benz,115000,other,35,NA,Diesel,yes,2015,G 350
+Volkswagen,2900,hatch,230,1.6,Petrol,yes,2000,Golf IV
+Mitsubishi,4800,sedan,247,2,Gas,yes,1998,Galant
+Renault,1550,vagon,235,2,Petrol,no,1998,Laguna
+Mitsubishi,16000,crossover,147,2.5,Diesel,yes,2008,Pajero Sport
+Volkswagen,6400,van,312,2.5,Diesel,yes,2000,T4 (Transporter) 
+BMW,5800,other,400,2,Petrol,yes,1998,520
+Mercedes-Benz,11000,sedan,263,3.2,Diesel,yes,2001,E-Class
+Renault,4500,sedan,305,1.5,Diesel,yes,2004,Megane
+Renault,14200,crossover,83,2,Diesel,yes,2012,Koleos
+BMW,71500,crossover,19,2.5,Diesel,yes,2015,X5
+Audi,6200,sedan,200,1.8,Petrol,yes,2003,A4
+Volkswagen,10500,van,130,1.9,Diesel,yes,2010,Caddy
+Toyota,12700,sedan,136,3.5,Petrol,yes,2007,Camry
+Audi,7000,other,126,2.6,Petrol,yes,1998,80
+BMW,4500,sedan,230,4,Gas,yes,1994,540
+BMW,4800,vagon,400,2.5,Diesel,yes,1994,525
+Renault,11500,van,170,1.5,Diesel,yes,2012,Grand Scenic
+BMW,42000,crossover,33,2,Petrol,yes,2014,X3
+Audi,2500,sedan,371,2.2,Gas,yes,1987,100
+Mercedes-Benz,22600,crossover,165,3,Diesel,yes,2007,ML 320
+Volkswagen,9900,sedan,80,1.6,Petrol,yes,2011,Polo
+Mercedes-Benz,13500,van,259,3,Diesel,yes,2007,Vito
+Volkswagen,10600,other,140,2,Other,yes,2007,Passat B6
+Mercedes-Benz,12500,van,242,2.2,Diesel,yes,2008,Vito
+Renault,7499,van,123,1.5,Diesel,yes,2009,Kangoo
+Volkswagen,9999,hatch,118,1.2,Diesel,yes,2011,Polo
+Toyota,18600,hatch,73,1.6,Petrol,yes,2013,Auris
+Volkswagen,7600,hatch,225,1.4,Petrol,yes,2004,Golf V
+Volkswagen,NA,other,201,1.9,Diesel,yes,2007,T5 (Transporter)
+Volkswagen,9200,hatch,127,1.6,Petrol,yes,2007,Golf V
+Audi,3700,vagon,290,1.9,Diesel,no,2002,A4
+BMW,26000,crossover,270,4.8,Petrol,yes,2008,X5
+BMW,12950,sedan,268,4,Diesel,yes,2004,740
+Mitsubishi,15200,crossover,193,3,Gas,yes,2008,Pajero Wagon
+Audi,62000,vagon,24,3,Diesel,yes,2015,A6 Allroad
+Toyota,68500,van,1,3.5,Petrol,yes,2016,Sienna
+Volkswagen,2500,hatch,111,1.6,Gas,yes,1999,Golf IV
+Volkswagen,2600,other,296,1.9,Diesel,no,2000,Golf IV
+Mercedes-Benz,12250,van,300,2.2,Diesel,yes,2007,Vito
+Toyota,39900,crossover,94,4,Gas,yes,2010,Land Cruiser Prado
+Volkswagen,1500,other,160,1.4,Petrol,no,1999,Golf IV
+Mercedes-Benz,3700,sedan,270,3.5,Petrol,no,2004,S 350
+BMW,8999,other,160,1.8,Petrol,yes,1997,318
+Mitsubishi,9900,other,200,2.5,Diesel,yes,2007,L 200
+Volkswagen,16800,crossover,249,5,Diesel,yes,2005,Touareg
+Volkswagen,23999,sedan,0,1.6,Diesel,yes,2016,Jetta
+Mercedes-Benz,36000,crossover,162,3,Diesel,yes,2009,GL 320
+Mitsubishi,15800,other,146,2.5,Diesel,yes,2013,L 200
+Volkswagen,19300,sedan,136,2,Diesel,yes,2011,Passat B7
+Renault,6450,van,148,1.5,Diesel,yes,2012,Kangoo
+Renault,4200,van,220,1.5,Diesel,yes,2004,Kangoo
+Mercedes-Benz,NA,sedan,29,6,Petrol,yes,2011,S-Guard
+BMW,32000,crossover,52,3,Petrol,yes,2012,X5
+Toyota,12500,sedan,110,2.4,Gas,yes,2007,Camry
+BMW,47900,crossover,178,4,Diesel,yes,2012,X5
+Mercedes-Benz,21999,sedan,200,3,Diesel,yes,2008,E-Class
+Audi,15000,sedan,168,NA,Other,yes,2007,A6
+Toyota,7500,hatch,74,1.3,Petrol,yes,2008,Yaris
+Mercedes-Benz,125000,sedan,17,3,Diesel,yes,2015,S 350
+BMW,30500,hatch,110,4.4,Petrol,yes,2008,X6
+Volkswagen,17191.2,sedan,0,1.6,Petrol,yes,2016,Jetta
+Mercedes-Benz,45555,sedan,42,3,Diesel,yes,2011,S 350
+BMW,15200,sedan,227,2.5,Petrol,yes,2008,525
+Volkswagen,8700,sedan,270,NA,Diesel,yes,2009,Caddy
+Volkswagen,7800,van,226,1.9,Diesel,yes,2005,T5 (Transporter) 
+Volkswagen,22000,hatch,40,1.4,Petrol,yes,2013,Golf VII
+Mitsubishi,9300,crossover,156,2.4,Gas,yes,2006,Outlander
+Mercedes-Benz,31900,sedan,97,2.2,Diesel,yes,2011,E-Class
+Mercedes-Benz,9350,van,160,2.2,Diesel,yes,2008,Vito
+BMW,32500,sedan,134,2,Diesel,yes,2010,520
+Renault,10200,vagon,151,1.5,Diesel,yes,2011,Megane
+Toyota,8200,crossover,200,2,Gas,yes,2002,Rav 4
+Mercedes-Benz,7400,sedan,190,2,Petrol,yes,2002,C-Class
+Mitsubishi,14200,crossover,155,3,Gas,yes,2007,Outlander XL
+Volkswagen,24900,sedan,1,1.6,Diesel,yes,2016,Jetta
+Mercedes-Benz,24300,sedan,150,3.5,Gas,yes,2008,S 350
+Volkswagen,9300,van,176,1.9,Diesel,yes,2005,T5 (Transporter)
+Volkswagen,21000,crossover,130,2.5,Diesel,yes,2008,Touareg
+Audi,36000,crossover,145,3,Petrol,yes,2010,Q7
+Mitsubishi,22900,crossover,69,2.4,Petrol,yes,2014,Outlander
+Mercedes-Benz,9900,sedan,275,2.2,Other,yes,2005,E-Class
+Volkswagen,3950,hatch,288,1.8,Gas,yes,1996,Golf III
+Toyota,28700,crossover,100,3.5,Petrol,yes,2012,Highlander
+Volkswagen,16900,van,121,2,Diesel,yes,2014,T5 (Transporter) 
+Mercedes-Benz,7300,sedan,245,2.8,Gas,yes,1998,E-Class
+Mercedes-Benz,2200,sedan,400,2,Petrol,yes,1988,190
+Toyota,25500,other,103,3.5,Petrol,yes,2010,Venza
+Volkswagen,7650,other,260,2,Diesel,yes,2005,Caddy
+Toyota,22800,other,55,2.5,Petrol,yes,2013,Camry
+Renault,8900,van,170,1.9,Diesel,yes,2005,Trafic
+Renault,10990,sedan,118,2,Petrol,yes,2012,Fluence
+Mercedes-Benz,107777,crossover,0,3,Diesel,yes,2016,GLS 350
+Toyota,17000,sedan,95,2,Gas,yes,2011,Avensis
+Renault,999,sedan,430,2,Gas,yes,1988,25
+Audi,12800,other,0,NA,Other,yes,2005,A6
+BMW,66500,crossover,0,2,Other,yes,2016,X5
+Volkswagen,4300,vagon,289,2,Petrol,yes,1994,Passat B4
+BMW,3350,vagon,295,2,Diesel,no,2002,320
+Toyota,10400,sedan,87,1.6,Petrol,yes,2008,Corolla
+BMW,22700,crossover,216,4.8,Petrol,yes,2007,X5
+Renault,6700,van,102,1.5,Diesel,yes,2012,Kangoo
+Audi,3250,sedan,285,NA,Gas,no,1996,A8
+Audi,3800,sedan,257,2,Petrol,yes,1993,80
+Volkswagen,10900,vagon,185,1.4,Petrol,yes,2011,Golf VII
+Mercedes-Benz,11000,van,300,2.2,Diesel,yes,2007,Vito
+Volkswagen,51900,crossover,30,3,Diesel,yes,2015,Touareg
+Mercedes-Benz,18900,sedan,232,3,Diesel,yes,2007,E-Class
+BMW,25500,sedan,148,3,Diesel,yes,2007,535
+Renault,14500,sedan,98,1.5,Diesel,yes,2014,Fluence
+BMW,7900,sedan,260,3.5,Petrol,yes,1997,535
+Audi,44800,crossover,95,3,Diesel,yes,2014,Q7
+Toyota,26200,other,73,2.5,Diesel,yes,2012,Hilux
+BMW,14000,crossover,164,3,Diesel,no,2006,X5
+Renault,7900,van,300,1.9,Diesel,yes,2006,Trafic
+Renault,1350,sedan,100,1.7,Petrol,yes,1986,21
+Volkswagen,10777,van,157,1.6,Diesel,yes,2012,Caddy
+Renault,8300,van,217,1.9,Diesel,yes,2004,Trafic
+Volkswagen,40000,sedan,1,1.4,Petrol,yes,2015,Passat B8
+Mercedes-Benz,16800,other,266,3,Diesel,yes,2007,E-Class
+Audi,43000,crossover,1,2,Petrol,yes,2016,Q3
+Toyota,22900,sedan,60,2.5,Petrol,yes,2012,Camry
+Audi,10900,sedan,225,2.4,Petrol,yes,2005,A6
+BMW,2550,sedan,280,1.6,Petrol,no,2000,316
+Mercedes-Benz,18700,other,142,2.2,Diesel,yes,2007,Viano
+Toyota,3800,sedan,220,1.6,Petrol,yes,1994,Carina
+Mercedes-Benz,7700,van,290,2.2,Diesel,yes,2004,Vito
+BMW,112000,sedan,16,3,Diesel,yes,2015,730
+Mercedes-Benz,2600,van,280,2.9,Diesel,no,1997,Sprinter 312
+Volkswagen,14900,vagon,165,1.4,Petrol,yes,2013,Passat B7
+Mitsubishi,11960,other,140,2.5,Diesel,yes,2007,L 200
+Mitsubishi,19800,crossover,130,2.5,Diesel,yes,2011,Pajero Sport
+Mercedes-Benz,12800,van,116,2.2,Diesel,yes,2012,Vito
+Mercedes-Benz,34000,sedan,69,2,Petrol,yes,2013,E-Class
+Audi,29999,sedan,97,3,Petrol,yes,2011,S5
+Renault,10499,other,160,NA,Diesel,yes,2013,Trafic
+Mitsubishi,4900,vagon,250,2.5,Petrol,yes,2001,Galant
+BMW,12900,sedan,200,2.5,Petrol,yes,2006,523
+Mercedes-Benz,12600,sedan,300,2.7,Diesel,yes,2003,E-Class
+Volkswagen,9800,other,117,1.6,Diesel,yes,2014,Caddy
+Audi,3650,sedan,270,1.9,Diesel,yes,2002,A6
+Renault,1150,hatch,250,1.4,Petrol,yes,1987,5
+Volkswagen,37900,crossover,73,3,Diesel,yes,2012,Touareg
+Volkswagen,17900,crossover,145,3,Diesel,yes,2007,Touareg
+BMW,37500,crossover,110,4.4,Petrol,yes,2011,X6
+BMW,27900,sedan,170,3,Petrol,yes,2011,530
+Mercedes-Benz,177000,crossover,0,5.5,Petrol,yes,2016,GLS 63
+Volkswagen,10600,van,153,1.6,Diesel,yes,2012,Caddy
+Mercedes-Benz,39900,crossover,127,5.5,Petrol,yes,2010,GL 550
+Volkswagen,5200,van,260,1.9,Diesel,no,2005,Touran
+Volkswagen,16999,sedan,105,1.8,Petrol,yes,2011,Passat B7
+Toyota,10200,sedan,122,7.2,Petrol,yes,2011,Corolla
+Renault,8400,van,260,1.9,Diesel,yes,2005,Trafic
+Audi,30900,crossover,27,2,Petrol,yes,2011,Q5
+Mercedes-Benz,9500,van,248,2.2,Diesel,yes,2005,Vito
+Mitsubishi,12800,crossover,153,1.6,Gas,yes,2011,ASX
+BMW,48900,crossover,60,3,Diesel,yes,2011,X3
+Renault,11600,vagon,165,1.5,Diesel,yes,2012,Megane
+Volkswagen,20200,hatch,22,1.2,Petrol,yes,2014,Golf VII
+Mercedes-Benz,12700,hatch,140,1.7,Gas,yes,2007,B 170
+Mercedes-Benz,144900,sedan,16,3,Diesel,yes,2014,S 350
+Toyota,17835,crossover,240,2.7,Gas,yes,2007,Land Cruiser Prado
+Toyota,NA,vagon,160,2.4,Gas,yes,2007,Rav 4
+Mercedes-Benz,30900,sedan,71,2.2,Diesel,yes,2012,E-Class
+Renault,9500,sedan,88,1.6,Gas,yes,2012,Fluence
+Volkswagen,11500,van,192,1.9,Diesel,yes,2008,Caddy
+Renault,18932.55,hatch,0,1.5,Diesel,yes,2016,Megane
+Mercedes-Benz,33000,vagon,91,NA,Other,yes,2013,E-Class
+Renault,11700,vagon,155,1.5,Diesel,yes,2012,Megane
+Renault,9900,crossover,201,1.9,Diesel,yes,2006,Kangoo
+Mercedes-Benz,19500,sedan,180,3,Diesel,yes,2008,E-Class
+Mercedes-Benz,6000,sedan,350,2,Diesel,yes,1985,200
+Audi,3400,vagon,261,2.5,Diesel,no,2004,A6
+Volkswagen,8200,other,102,1.6,Diesel,yes,2012,Caddy
+Toyota,15600,sedan,165,3.5,Gas,yes,2008,Camry
+BMW,NA,sedan,43,3,Petrol,yes,2011,535
+Toyota,47500,crossover,45,3,Diesel,yes,2014,Land Cruiser Prado
+Audi,33900,sedan,58,2,Petrol,yes,2014,A5
+Toyota,97500,crossover,2,4.6,Petrol,yes,2016,Land Cruiser 200
+Renault,15000,sedan,21,1.5,Diesel,yes,2014,Fluence
+Toyota,28999,sedan,86,3.5,Petrol,yes,2013,Camry
+Audi,15000,hatch,66,1.2,Petrol,yes,2011,A1
+Toyota,36000,crossover,66,3,Diesel,yes,2010,Land Cruiser Prado
+Toyota,19500,crossover,150,4,Petrol,yes,2005,Land Cruiser Prado
+Mercedes-Benz,5000,sedan,350,1.8,Other,yes,1995,C-Class
+Mercedes-Benz,17800,sedan,73,3,Petrol,yes,2008,C-Class
+Mercedes-Benz,5000,sedan,215,2.6,Gas,yes,1992,E-Class
+Renault,3100,hatch,275,1.2,Petrol,yes,1991,Clio
+Renault,1200,sedan,240,1.7,Gas,yes,1988,9
+BMW,3800,sedan,100,1.8,Petrol,yes,1992,318
+Renault,1999,vagon,260,1.9,Diesel,no,2001,Laguna
+Mercedes-Benz,42900,hatch,3,NA,Other,yes,2016,B-Class Electric Drive
+Mercedes-Benz,8800,sedan,330,5,Petrol,yes,2000,S 500
+Renault,8800,van,250,1.9,Diesel,yes,2006,Trafic
+Mercedes-Benz,15300,other,168,3.2,Petrol,yes,2003,CLK 320
+BMW,39900,sedan,5,2,Petrol,yes,2016,520
+Renault,7400,van,181,2,Diesel,yes,2007,Trafic
+Volkswagen,28000,van,163,2,Diesel,yes,2010,Multivan
+Volkswagen,12300,van,182,2,Diesel,yes,2011,T6 (Transporter) 
+Renault,3600,sedan,101,2,Petrol,yes,1997,Megane
+Toyota,8450,sedan,193,1.6,Gas,yes,2007,Corolla
+Volkswagen,2500,hatch,297,1.6,Petrol,yes,1995,Golf III
+Toyota,9950,sedan,106,1.6,Gas,yes,2008,Corolla
+Mercedes-Benz,23200,sedan,58,1.8,Petrol,yes,2010,C-Class
+Toyota,10500,sedan,124,1.8,Petrol,yes,2008,Avensis
+Mercedes-Benz,11200,sedan,280,2.2,Diesel,yes,2005,E-Class
+Volkswagen,9750,van,1,1.9,Diesel,yes,2004,T5 (Transporter)
+Toyota,20800,crossover,70,2.7,Petrol,yes,2008,Land Cruiser Prado
+Audi,33500,hatch,35,1.8,Petrol,yes,2014,A5
+Volkswagen,10000,sedan,66,1.6,Petrol,yes,2011,Polo
+Mitsubishi,NA,crossover,205,2.5,Diesel,yes,2006,Pajero Sport
+Mitsubishi,2199,hatch,400,1.8,Other,yes,1991,Galant
+Audi,16800,hatch,47,1.2,Petrol,yes,2013,A3
+Mercedes-Benz,25500,crossover,200,4,Diesel,yes,2007,GL 420
+Audi,2099,sedan,314,1.9,Diesel,no,1996,A4
+Toyota,20000,crossover,250,4.2,Diesel,yes,2000,Land Cruiser 100
+Volkswagen,7400,van,380,2.5,Diesel,yes,2000,T4 (Transporter)
+Volkswagen,12000,other,209,NA,Diesel,yes,2006,T5 (Transporter) 
+Mercedes-Benz,NA,van,273,2.9,Diesel,yes,2001,Sprinter 310
+BMW,16500,sedan,150,2.5,Petrol,yes,2005,325
+Audi,18500,sedan,137,NA,Petrol,yes,2009,A6
+Mercedes-Benz,43500,other,51,3,Diesel,yes,2014,E-Class
+Renault,7450,other,190,NA,Diesel,yes,2004,Grand Scenic
+Volkswagen,3100,hatch,118,1.8,Gas,yes,1993,Golf III
+Mercedes-Benz,12100,sedan,310,2.2,Diesel,yes,2005,E-Class
+Mercedes-Benz,12000,van,230,2.2,Diesel,yes,2008,Vito
+Mitsubishi,33000,other,0,2.4,Diesel,yes,2016,L 200
+Renault,15672.15,van,0,1.5,Diesel,yes,2016,Dokker
+Renault,6600,van,134,1.5,Diesel,yes,2011,Kangoo
+Renault,12800,vagon,127,1.5,Diesel,yes,2011,Megane
+Mercedes-Benz,300000,sedan,68,6,Petrol,yes,2011,S 600
+Renault,7200,van,73,1.5,Diesel,yes,2012,Kangoo
+BMW,21000,hatch,79,1.6,Petrol,yes,2013,118
+Mitsubishi,5900,hatch,92,1.6,Petrol,yes,2003,Carisma
+Volkswagen,9500,van,205,2,Petrol,yes,2006,Passat B6
+Renault,2100,van,270,1.9,Diesel,no,2002,Scenic
+Audi,4000,sedan,260,NA,Diesel,no,2000,A6
+Toyota,8100,hatch,91,1.6,Petrol,yes,2007,Auris
+Mercedes-Benz,60000,crossover,140,5.5,Gas,yes,2008,G 55 AMG
+BMW,2899,sedan,260,2,Diesel,no,2000,320
+BMW,73900,sedan,57,4.4,Petrol,yes,2013,M5
+BMW,7998,sedan,253,NA,Petrol,yes,1998,323
+BMW,3400,vagon,191,2,Diesel,no,2002,320
+BMW,26500,sedan,70,3,Petrol,yes,2008,330
+Mitsubishi,5800,sedan,186,1.6,Petrol,yes,2006,Lancer
+BMW,9999,sedan,200,1.8,Petrol,yes,2004,318
+Volkswagen,12999,vagon,95,1.6,Diesel,yes,2010,Passat B6
+Mercedes-Benz,75500,van,14,2.2,Diesel,yes,2015,V 250
+BMW,2499,sedan,210,1.8,Petrol,no,2000,318
+Renault,2900,hatch,280,NA,Diesel,no,2005,Scenic
+Volkswagen,3500,hatch,201,1.9,Diesel,no,2002,Golf IV
+Audi,NA,sedan,177,1.8,Petrol,yes,1998,A4
+Volkswagen,10000,van,283,2.5,Diesel,yes,2005,T5 (Transporter)
+Mitsubishi,7000,sedan,133,1.6,Petrol,yes,2008,Lancer
+Mercedes-Benz,169000,van,1,3,Diesel,yes,2014,Sprinter 319
+Volkswagen,35999,crossover,84,3,Diesel,yes,2011,Touareg
+Mitsubishi,2900,sedan,350,1.6,Petrol,yes,1994,Lancer
+Volkswagen,32400,crossover,50,2,Diesel,yes,2014,Tiguan
+Mitsubishi,15000,crossover,95,2.5,Diesel,yes,2008,Pajero Sport
+Mitsubishi,7300,sedan,145,1.6,Petrol,yes,2008,Lancer
+Toyota,30500,sedan,7,2.5,Gas,yes,2015,Camry
+BMW,6300,sedan,270,3.5,Petrol,yes,1998,735
+Mitsubishi,6200,sedan,170,NA,Other,yes,2005,Lancer
+BMW,23500,crossover,180,4.8,Petrol,yes,2008,X5
+Volkswagen,NA,sedan,60,2,Diesel,yes,2013,Passat CC
+Volkswagen,10400,sedan,87,1.6,Petrol,yes,2011,Polo
+Volkswagen,22600,crossover,187,3,Diesel,yes,2008,Touareg
+Volkswagen,8900,hatch,128,1.4,Petrol,yes,2010,Polo
+Audi,8400,sedan,260,1.8,Gas,yes,2003,A4
+Toyota,13300,sedan,158,2,Gas,yes,2009,Avensis
+Mercedes-Benz,16500,van,152,2.2,Diesel,yes,2012,Vito
+Renault,4200,vagon,180,1.4,Petrol,yes,2000,Kangoo
+Volkswagen,NA,van,320,1.6,Diesel,yes,1985,T3 (Transporter)
+Volkswagen,18999,sedan,122,1.8,Petrol,yes,2012,Passat CC
+Mitsubishi,12400,sedan,52,1.6,Petrol,yes,2013,Lancer X
+Mitsubishi,3950,other,280,2.5,Diesel,no,1994,Pajero
+Toyota,13850,hatch,24,1.6,Other,yes,2010,Auris
+Mitsubishi,5999,hatch,108,1.3,Petrol,yes,2006,Colt
+Audi,2650,sedan,300,2,Petrol,yes,1987,80
+BMW,5400,sedan,404,2.5,Petrol,yes,1992,525
+Mercedes-Benz,28500,hatch,15,1.8,Diesel,yes,2014,A 180
+Mercedes-Benz,46900,other,10,2.2,Diesel,yes,2012,E-Class
+Renault,12000,sedan,81,2,Petrol,yes,2012,Fluence
+Volkswagen,29040,sedan,0,1.4,Petrol,yes,2015,Jetta
+Mercedes-Benz,NA,van,261,2.2,Diesel,yes,2007,Vito
+Audi,9800,hatch,165,1.6,Gas,yes,2006,A3
+BMW,7000,sedan,400,2.8,Petrol,yes,1998,528
+Volkswagen,12800,van,230,2,Diesel,yes,2011,T5 (Transporter) 
+BMW,8500,sedan,365,3,Diesel,yes,2001,530
+Toyota,3650,hatch,260,1.3,Petrol,yes,1996,Corolla
+Volkswagen,11500,other,51,1.6,Petrol,yes,2013,Polo
+Volkswagen,9800,van,130,1.6,Diesel,yes,2012,Caddy
+Renault,2350,van,380,1.9,Diesel,no,2003,Espace
+Audi,21800,vagon,172,2,Diesel,yes,2011,A4
+Volkswagen,20300,crossover,139,3,Diesel,yes,2008,Touareg
+Mercedes-Benz,11800,sedan,167,3.2,Diesel,yes,2003,E-Class
+Renault,8400,van,248,2.5,Diesel,yes,2005,Trafic
+BMW,NA,sedan,240,2.8,Gas,yes,1997,528
+Mercedes-Benz,81500,sedan,1,3,Diesel,yes,2016,GLE-Class
+Mitsubishi,20300,other,30,NA,Diesel,yes,2014,L 200
+Toyota,14900,other,175,3.5,Gas,yes,2008,Camry
+Mitsubishi,7500,sedan,79,1.6,Gas,yes,2008,Lancer
+Volkswagen,NA,other,440,1.4,Petrol,yes,2008,Eos
+Volkswagen,9400,hatch,187,1.6,Petrol,yes,2008,Golf V
+Renault,8700,hatch,21,1.6,Petrol,yes,2011,Sandero
+Volkswagen,13700,sedan,134,2,Diesel,yes,2012,Passat B6
+Audi,4000,sedan,263,1.9,Diesel,yes,1996,A4
+Audi,2850,sedan,260,NA,Other,no,1999,A6
+Volkswagen,8950,van,206,1.6,Diesel,yes,2012,Caddy
+Audi,4299,sedan,307,2.8,Gas,yes,1991,100
+Toyota,36500,crossover,2,3,Diesel,yes,2014,Hilux
+Mercedes-Benz,7600,van,315,2.9,Diesel,yes,1996,Sprinter 312
+Mitsubishi,9200,crossover,110,2.4,Petrol,yes,2006,Outlander
+Mercedes-Benz,13000,sedan,191,1.8,Petrol,yes,2005,E-Class
+Toyota,24000,crossover,132,4,Petrol,yes,2008,Land Cruiser Prado
+Mercedes-Benz,8500,van,630,2.9,Diesel,yes,1998,Sprinter 312
+Mercedes-Benz,20900,other,250,3.2,Diesel,yes,2007,ML 320
+BMW,18500,sedan,79,3,Diesel,no,2012,530
+Mercedes-Benz,2000,sedan,270,2.9,Diesel,no,1996,E-Class
+Audi,2400,sedan,310,2,Diesel,yes,1989,100
+Volkswagen,10800,sedan,63,20,Petrol,yes,2007,Passat B6
+BMW,NA,sedan,145,2,Diesel,yes,2007,320
+Renault,12399,van,177,2,Diesel,yes,2009,Trafic
+Toyota,15200,crossover,208,2,Petrol,yes,2006,Rav 4
+Audi,23800,crossover,205,4.2,Gas,yes,2007,Q7
+Volkswagen,3000,vagon,275,1.9,Diesel,no,2001,Passat B5
+Mercedes-Benz,4000,sedan,300,1.8,Petrol,yes,1996,C-Class
+Renault,7000,vagon,231,1.6,Petrol,yes,2008,Scenic
+Volkswagen,17900,van,92,1.4,Petrol,yes,2012,Sharan
+Audi,3400,vagon,223,2.5,Diesel,no,2001,A6
+Volkswagen,2200,hatch,33,1.9,Diesel,yes,1985,Golf II
+BMW,7000,sedan,227,3,Diesel,no,2004,730
+Audi,98000,other,12,5.2,Petrol,yes,2012,R8
+Volkswagen,12650,van,167,2,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,39040,sedan,0,2,Diesel,yes,2014,Passat CC
+BMW,25500,crossover,187,4.8,Gas,yes,2007,X5
+Volkswagen,9000,sedan,130,1.6,Petrol,yes,2012,Polo
+BMW,47300,crossover,10,2,Diesel,yes,2016,X3
+Renault,11500,van,177,2,Diesel,yes,2008,Trafic
+Mitsubishi,19000,crossover,47,2,Petrol,yes,2012,ASX
+Mercedes-Benz,18500,crossover,1,3.5,Gas,yes,2006,ML 350
+Volkswagen,7170,van,176,2,Diesel,yes,2005,Caddy
+Mercedes-Benz,28000,other,250,3,Diesel,yes,2008,GL 320
+Audi,28500,sedan,74,1.8,Petrol,yes,2012,A4
+Volkswagen,25500,other,52,2,Petrol,yes,2012,Scirocco
+Mitsubishi,10999,other,281,3,Gas,yes,2003,Pajero Wagon
+Mercedes-Benz,42500,sedan,100,5.5,Petrol,yes,2011,S 500
+Toyota,16800,crossover,165,2.7,Petrol,yes,2005,Land Cruiser Prado
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLS 350
+Volkswagen,17900,vagon,115,1.8,Petrol,yes,2012,Passat B7
+BMW,19900,other,218,3,Petrol,yes,2007,335
+Volkswagen,8750,van,200,2.5,Diesel,yes,2006,T5 (Transporter)
+Mercedes-Benz,111999,crossover,0,3,Diesel,yes,2016,GLS 350
+BMW,9500,sedan,217,2.2,Petrol,yes,2002,320
+Toyota,7500,hatch,98,1.3,Other,yes,2006,Yaris
+Mercedes-Benz,32500,crossover,254,3.2,Diesel,yes,2008,GL 320
+BMW,45000,sedan,110,NA,Other,yes,2012,535
+Audi,3700,vagon,273,2.5,Diesel,no,2003,A6
+Mitsubishi,8500,other,135,1.5,Gas,yes,2008,Lancer X
+Mercedes-Benz,109900,sedan,40,3,Diesel,yes,2014,S 350
+Mercedes-Benz,39900,other,88,6.2,Petrol,yes,2008,CL 63 AMG
+Mercedes-Benz,4700,sedan,260,2.7,Diesel,no,2002,E-Class
+Audi,13200,vagon,182,2.7,Gas,yes,2003,A6 Allroad
+Renault,6700,van,216,1.5,Diesel,yes,2010,Kangoo
+Volkswagen,23000,sedan,80,2,Other,yes,2011,Passat CC
+Volkswagen,6400,van,181,2,Diesel,yes,2008,Caddy
+Mercedes-Benz,97555,van,0,2.5,Diesel,yes,2016,V 250
+Audi,7900,sedan,242,2,Petrol,yes,2002,A4
+Mercedes-Benz,5300,sedan,380,3,Diesel,yes,1995,E-Class
+Toyota,24171.42,sedan,1,2.5,Petrol,yes,2016,Camry
+Audi,23400,sedan,112,2,Diesel,yes,2012,A4
+Toyota,10500,sedan,185,1.6,Petrol,yes,2008,Corolla
+Mercedes-Benz,4900,van,262,2.3,Diesel,yes,1998,Vito
+BMW,16300,crossover,258,4.4,Petrol,yes,2005,X5
+Renault,1300,van,265,1.9,Diesel,no,1999,Kangoo
+Mercedes-Benz,6700,van,1,2.2,Diesel,yes,2004,Vito
+Renault,2850,hatch,315,2.2,Diesel,no,2003,Laguna
+Mercedes-Benz,109999,crossover,0,2.98,Diesel,yes,2016,GLS 350
+Audi,3650,sedan,240,2.5,Diesel,no,2000,A6
+Volkswagen,4100,vagon,350,1.9,Diesel,yes,2003,Passat B5
+Mercedes-Benz,9300,van,159,2.2,Diesel,yes,2010,Vito
+BMW,18500,crossover,250,4.4,Gas,yes,2005,X5
+Mercedes-Benz,7600,sedan,384,2.8,Petrol,yes,2000,E-Class
+Mitsubishi,7300,sedan,112,1.6,Petrol,yes,2008,Lancer
+Renault,6700,van,220,2,Other,yes,2007,Trafic
+Toyota,14400,sedan,80,2.4,Petrol,yes,2008,Camry
+Toyota,10600,sedan,204,1.8,Other,yes,2008,Avensis
+BMW,139000,crossover,0,4.4,Petrol,yes,2016,X6 M
+Renault,10000,vagon,234,2,Diesel,yes,2008,Laguna
+Toyota,12999,sedan,160,3.5,Gas,yes,2007,Avalon
+Renault,6800,van,168,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,15850,crossover,98,2,Diesel,yes,2008,Tiguan
+Renault,6000,van,162,1.4,Petrol,yes,2007,Kangoo
+Volkswagen,44700,van,120,3.5,Petrol,yes,2013,Sharan
+Renault,5600,vagon,117,1.5,Diesel,yes,2008,Kangoo
+Renault,6100,vagon,130,NA,Diesel,yes,2009,Kangoo
+Mercedes-Benz,21500,sedan,159,1.8,Gas,yes,2012,E-Class
+Renault,7600,van,183,1.9,Diesel,yes,2006,Trafic
+Mercedes-Benz,1950,sedan,1,2,Diesel,yes,1979,200
+Volkswagen,9000,hatch,120,1.2,Diesel,yes,2012,Polo
+BMW,35500,sedan,235,3,Diesel,yes,2010,730
+Mitsubishi,5500,sedan,165,1.6,Petrol,yes,2006,Lancer
+Mercedes-Benz,32500,crossover,168,3.2,Diesel,yes,2009,GL 320
+BMW,18000,sedan,245,2.5,Petrol,yes,2007,523
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,18000,van,147,2.2,Diesel,yes,2013,Vito
+Renault,10999,vagon,216,1.5,Diesel,yes,2012,Megane
+Renault,7500,van,240,74,Diesel,yes,2005,Grand Scenic
+Mercedes-Benz,22700,sedan,140,5,Petrol,yes,2007,S 500
+Toyota,23500,sedan,75,2.5,Gas,yes,2011,Camry
+Mercedes-Benz,10000,other,275,2.15,Diesel,yes,2006,Sprinter 313
+Volkswagen,7700,van,157,1.9,Diesel,yes,2006,Caddy
+Volkswagen,10900,van,185,1.9,Diesel,yes,2009,Caddy
+Audi,12999,vagon,225,2.5,Diesel,yes,2004,A6 Allroad
+BMW,67500,crossover,1,2,Diesel,yes,2016,X5
+Volkswagen,6800,van,325,2.5,Diesel,yes,2001,T4 (Transporter)
+Mercedes-Benz,137000,crossover,82,5.5,Petrol,yes,2014,G 63 AMG
+Mitsubishi,4950,sedan,256,2,Petrol,yes,1997,Galant
+Renault,6999,sedan,65,1.4,Petrol,yes,2012,Symbol
+Mercedes-Benz,NA,crossover,1,3,Diesel,yes,2016,GLS 350
+Volkswagen,29800,hatch,0,2,Diesel,yes,2015,Golf VII
+Mercedes-Benz,16500,van,386,2.2,Diesel,yes,2006,Sprinter 313
+BMW,6500,sedan,320,3,Gas,yes,2000,530
+Renault,9999,vagon,100,1.5,Diesel,yes,2010,Kangoo
+Volkswagen,8500,vagon,200,1.9,Diesel,yes,2003,Passat B5
+Toyota,5750,hatch,160,1,Petrol,yes,2006,Yaris
+BMW,4950,sedan,1,3,Petrol,yes,1996,730
+BMW,19900,other,129,2,Petrol,yes,2008,320
+Mitsubishi,15900,crossover,180,3,Gas,yes,2008,Pajero Wagon
+Toyota,13500,crossover,131,2.4,Gas,yes,2006,Rav 4
+Volkswagen,18950,crossover,149,3,Diesel,yes,2008,Touareg
+BMW,75555,sedan,30,4.4,Petrol,yes,2014,550
+Volkswagen,3650,sedan,197,2,Petrol,yes,1996,Passat B4
+Volkswagen,16000,crossover,90,2,Petrol,yes,2011,Tiguan
+Toyota,21600,sedan,66,2.5,Petrol,yes,2011,Camry
+Toyota,9500,hatch,103,1.6,Petrol,yes,2008,Auris
+Audi,51500,crossover,28,3,Diesel,yes,2014,Q7
+Volkswagen,5000,van,400,2.4,Diesel,yes,1993,T4 (Transporter)
+Volkswagen,27000,other,90,2,Diesel,yes,2014,Tiguan
+Renault,10900,other,189,2,Diesel,yes,2012,Trafic
+BMW,37700,crossover,108,3,Petrol,yes,2011,X3
+Renault,6299,van,66,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,39900,sedan,29,6.3,Petrol,yes,2007,S 63 AMG
+BMW,26900,crossover,140,4.8,Petrol,yes,2009,X5
+Audi,8999,vagon,190,2.7,Gas,yes,2004,A6 Allroad
+Mercedes-Benz,20400,sedan,190,1.8,Gas,yes,2011,E-Class
+Volkswagen,16200,sedan,116,2,Diesel,yes,2011,Passat B7
+Mitsubishi,5700,other,230,2,Petrol,yes,2003,Galant
+BMW,9500,sedan,315,2.5,Diesel,yes,2001,525
+BMW,25999,sedan,80,2,Petrol,yes,2012,320
+Audi,28950,crossover,60,2,Petrol,yes,2012,Q5
+Audi,6499,sedan,270,4.2,Petrol,yes,2000,S8
+Audi,3500,sedan,35,1.8,Gas,yes,1990,80
+Renault,7450,hatch,68,1.2,Petrol,yes,2009,Modus
+Mercedes-Benz,10500,sedan,266,5,Gas,yes,2004,S 500
+BMW,31111,sedan,117,4.4,Petrol,yes,2010,750
+Toyota,27000,other,192,3,Diesel,yes,2007,Hilux
+Toyota,26250,sedan,17,2.5,Petrol,yes,2015,Camry
+Mercedes-Benz,7300,van,245,2.2,Diesel,yes,2002,Vito
+Renault,11400,van,122,2,Diesel,yes,2013,Trafic
+Volkswagen,2200,sedan,279,1.9,Diesel,no,2000,Passat B5
+Toyota,10500,van,200,2.7,Gas,yes,2005,Tacoma
+Toyota,10500,crossover,200,4.5,Gas,yes,1996,Land Cruiser 80
+Volkswagen,10974.21,sedan,1,1.6,Petrol,yes,2015,Polo
+Volkswagen,11500,sedan,119,2,Diesel,yes,2007,Passat B6
+BMW,42000,sedan,59,4.4,Petrol,yes,2009,750
+Mercedes-Benz,17990,crossover,230,3,Diesel,yes,2007,R 320
+Audi,2500,vagon,300,2.5,Diesel,no,2002,A6
+Audi,16500,sedan,136,3.2,Petrol,yes,2007,A6
+Audi,47000,crossover,64,3,Diesel,yes,2013,Q7
+Mitsubishi,5300,hatch,240,1.8,Petrol,yes,2000,Carisma
+Toyota,41900,van,0,2.5,Diesel,yes,2016,Hiace
+BMW,7000,other,226,2.5,Petrol,yes,1999,525
+Mercedes-Benz,26600,van,122,3,Diesel,yes,2012,Vito
+Mitsubishi,6500,sedan,133,1.6,Petrol,yes,2007,Lancer
+Volkswagen,9600,sedan,185,1.6,Gas,yes,2008,Jetta
+Mitsubishi,3200,sedan,106,2,Gas,yes,1997,Galant
+Mercedes-Benz,11200,other,198,6,Petrol,yes,2004,S 600
+Mitsubishi,7900,crossover,143,2,Gas,yes,2007,Outlander
+Audi,29602.95,sedan,1,1.4,Petrol,yes,2016,A4
+Mercedes-Benz,8950,sedan,154,3.5,Gas,no,2005,CLS 350
+Renault,8700,vagon,123,1.5,Diesel,yes,2009,Megane
+Toyota,12900,sedan,177,2.4,Gas,yes,2009,Camry
+Renault,10600,vagon,113,1.5,Diesel,yes,2011,Megane
+Toyota,8500,hatch,93,1.6,Petrol,yes,2007,Auris
+BMW,NA,sedan,99,4.4,Petrol,yes,2013,Alpina
+Toyota,14900,hatch,100,1.8,Other,yes,2010,Prius
+Mitsubishi,2500,other,350,1.8,Petrol,yes,1990,Galant
+Toyota,14400,crossover,119,NA,Gas,yes,2008,Rav 4
+Audi,34000,crossover,118,2,Diesel,yes,2013,Q5
+Mercedes-Benz,39000,crossover,195,5,Gas,yes,2002,G 500
+Mercedes-Benz,10500,sedan,285,1.8,Gas,yes,2003,E-Class
+Mitsubishi,2600,vagon,93,2.5,Petrol,no,1997,Galant
+BMW,4800,sedan,111,2.5,Gas,yes,1991,525
+Volkswagen,7800,van,352,2.5,Diesel,yes,2001,T4 (Transporter)
+Mercedes-Benz,99999,crossover,0,3,Petrol,yes,2016,GLE-Class
+Mercedes-Benz,NA,crossover,27,3,Diesel,yes,2015,G 350
+Toyota,15300,crossover,130,2,Petrol,yes,2008,Rav 4
+Toyota,8500,hatch,55,1,Petrol,yes,2011,Aygo
+Renault,3000,van,217,1.4,Petrol,yes,2004,Kangoo
+BMW,5000,sedan,400,2,Petrol,yes,1991,520
+Renault,11403.99,sedan,0,1.2,Petrol,yes,2016,Logan
+BMW,59900,crossover,30,2,Diesel,yes,2016,X5
+Volkswagen,5400,hatch,320,1.4,Petrol,yes,2002,Golf IV
+Volkswagen,17500,vagon,160,1.4,Petrol,yes,2011,Passat B7
+Renault,6300,sedan,167,1.6,Petrol,yes,2005,Megane
+Volkswagen,1900,hatch,300,1.9,Diesel,no,1999,Golf IV
+BMW,NA,crossover,5,3,Diesel,yes,2016,X6
+Toyota,12400,sedan,135,3.5,Gas,yes,2007,Camry
+Volkswagen,8500,hatch,141,2,Petrol,yes,2000,Beetle
+Mercedes-Benz,47600,van,0,2.2,Diesel,yes,2016,Vito
+Mercedes-Benz,39900,crossover,126,5.5,Petrol,yes,2010,GL 500
+Volkswagen,3350,hatch,122,1.4,Petrol,yes,1993,Golf III
+Mercedes-Benz,222000,crossover,0,6.3,Petrol,yes,2016,S 63 AMG
+Renault,8200,vagon,180,1.6,Gas,yes,2010,Kangoo
+Volkswagen,22200,hatch,51,2,Petrol,yes,2013,New Beetle
+Renault,7999,van,141,1.5,Diesel,yes,2006,Scenic
+Volkswagen,2499,sedan,220,1.9,Diesel,no,2004,Passat B5
+BMW,66200,crossover,70,3,Diesel,yes,2014,X5
+Audi,21000,hatch,34,1.4,Petrol,yes,2013,A3
+Volkswagen,7200,van,270,1.9,Diesel,yes,2008,T5 (Transporter) 
+BMW,34900,crossover,65,2,Petrol,yes,2013,X3
+Audi,17500,sedan,94,1.8,Petrol,yes,2008,A4
+Volkswagen,9600,van,148,1.6,Diesel,yes,2011,Caddy
+Audi,NA,vagon,0,3,Diesel,yes,2016,A6 Allroad
+Toyota,2200,vagon,300,2,Petrol,yes,1989,Camry
+BMW,32000,crossover,108,3,Petrol,yes,2008,X6
+Volkswagen,4700,van,342,1.9,Diesel,yes,1996,T4 (Transporter) 
+Mitsubishi,1180,sedan,280,1.8,Diesel,yes,1986,Galant
+Volkswagen,11000,van,282,2.5,Diesel,yes,2006,T5 (Transporter)
+Volkswagen,24000,sedan,105,2,Diesel,yes,2012,Passat CC
+Renault,4100,other,270,1.2,Gas,yes,1999,Kangoo
+BMW,5800,sedan,270,4.4,Petrol,yes,1996,540
+Audi,8600,sedan,260,2.5,Diesel,yes,2002,A6
+Audi,3500,other,360,2.2,Gas,yes,1990,100
+Mercedes-Benz,7777,sedan,270,2.8,Gas,yes,1996,S 140
+Mercedes-Benz,129999,crossover,0,4.67,Petrol,yes,2016,GLS 500
+Volkswagen,19500,crossover,112,2.5,Diesel,yes,2008,Touareg
+BMW,13300,vagon,157,2,Diesel,yes,2007,520
+Mitsubishi,15900,crossover,180,3,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,28500,sedan,90,5.5,Petrol,yes,2007,S 500
+BMW,27300,sedan,155,2,Diesel,yes,2012,520
+Renault,4800,sedan,100,1.4,Petrol,yes,2006,Clio
+Mercedes-Benz,11499,other,157,2.2,Diesel,yes,2012,Vito
+BMW,8500,sedan,284,1.9,Gas,yes,2000,316
+Mercedes-Benz,3800,van,462,2.2,Diesel,no,2001,Sprinter
+Mercedes-Benz,47600,van,0,2.2,Diesel,yes,2016,Vito
+Toyota,17900,crossover,133,2.7,Gas,yes,2007,Land Cruiser Prado
+Audi,11800,vagon,278,2.5,Diesel,yes,2005,A6
+Audi,11115,hatch,150,1.6,Petrol,yes,2008,A3
+Volkswagen,12999,van,160,2,Diesel,yes,2011,T6 (Transporter)
+Toyota,24842.03,sedan,0,1.8,Petrol,yes,2016,Avensis
+Audi,7200,sedan,300,NA,Gas,yes,1999,A6
+Mitsubishi,8400,sedan,250,2,Petrol,yes,2007,Lancer X
+Toyota,15500,sedan,129,3.5,Other,yes,2008,Camry
+BMW,13999,sedan,265,3,Diesel,yes,2005,730
+Mitsubishi,10200,sedan,104,NA,Gas,yes,2008,Lancer X
+Toyota,42900,crossover,51,3,Diesel,yes,2013,Land Cruiser Prado
+Volkswagen,47777,sedan,91,3,Diesel,yes,2012,Phaeton
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,7550,hatch,155,2,Petrol,yes,2007,Laguna
+BMW,68500,sedan,43,4.4,Petrol,yes,2013,750
+Volkswagen,8400,van,300,2.5,Diesel,yes,2004,T5 (Transporter)
+Renault,24193.65,van,1,1.6,Diesel,yes,2015,Trafic
+Renault,14700,crossover,61,1.5,Diesel,yes,2013,Duster
+Renault,8300,van,127,1.5,Diesel,yes,2011,Kangoo
+Volkswagen,19000,vagon,185,2,Diesel,yes,2011,Passat B7
+BMW,3700,sedan,305,1.9,Petrol,no,2000,318
+BMW,4500,sedan,307,2.5,Petrol,yes,1994,525
+Toyota,9400,sedan,140,1.6,Petrol,yes,2008,Corolla
+Toyota,25000,crossover,115,5.7,Petrol,yes,2007,Tundra
+Volkswagen,18000,sedan,110,2,Diesel,yes,2013,Passat B7
+Toyota,12300,sedan,135,2.4,Petrol,yes,2006,Camry
+Mercedes-Benz,20500,sedan,235,2.2,Diesel,yes,2007,E-Class
+Renault,11900,other,162,2.3,Other,yes,2011,Master
+BMW,3990,sedan,350,2.5,Petrol,yes,1995,525
+BMW,2200,sedan,290,1.6,Petrol,yes,1985,316
+Mercedes-Benz,13600,van,190,2.2,Diesel,yes,2009,Vito
+BMW,17300,sedan,180,3,Diesel,yes,2007,730
+Mercedes-Benz,12500,van,386,2.2,Diesel,yes,2008,Sprinter 313
+BMW,13900,crossover,184,3,Gas,yes,2004,X5
+BMW,39600,crossover,98,3,Petrol,yes,2011,X6
+Volkswagen,11700,van,185,1.4,Petrol,yes,2008,Cross Touran
+Audi,7200,sedan,250,2.4,Gas,yes,1998,A6
+Audi,10999,sedan,200,1.8,Petrol,yes,2006,A4
+Renault,7900,vagon,105,1.5,Diesel,yes,2010,Kangoo
+Mercedes-Benz,13900,other,168,5,Petrol,yes,2002,CL 500
+Volkswagen,4500,vagon,390,1.9,Diesel,yes,1996,Passat B4
+Toyota,9000,crossover,180,1.8,Gas,yes,2003,Matrix
+Renault,10500,vagon,233,1.5,Diesel,yes,2009,Laguna
+Mercedes-Benz,13000,sedan,220,5,Petrol,yes,2003,S 500
+Renault,7300,van,186,1.5,Diesel,yes,2010,Kangoo
+Toyota,21999,sedan,80,2.5,Other,yes,2012,Camry
+Mitsubishi,10500,sedan,57,1.5,Gas,yes,2011,Lancer X
+BMW,NA,crossover,8,2,Diesel,yes,2016,X5
+Renault,8300,van,137,1.5,Diesel,yes,2014,Kangoo
+Mitsubishi,12300,other,175,2.5,Diesel,yes,2008,L 200
+Renault,14999,van,54,66,Other,yes,2012,Kangoo
+Mercedes-Benz,2900,sedan,282,2.2,Diesel,no,2000,210
+Mercedes-Benz,12950,sedan,313,3.2,Diesel,yes,2003,S 320
+BMW,23500,crossover,140,4.8,Petrol,yes,2008,X5
+Audi,33300,crossover,71,2,Petrol,yes,2011,Q5
+Mitsubishi,8200,crossover,210,2.4,Gas,yes,2007,Outlander
+Audi,1900,sedan,450,2.5,Diesel,no,1996,A6
+Volkswagen,7800,sedan,220,1.8,Gas,yes,2002,Passat B5
+Volkswagen,15900,sedan,103,NA,Petrol,yes,2012,Jetta
+Toyota,21800,crossover,170,4,Gas,yes,2006,Land Cruiser Prado
+Mercedes-Benz,22900,sedan,74,2.2,Diesel,yes,2011,C-Class
+Audi,2350,vagon,250,NA,Diesel,no,2001,A6
+Volkswagen,8500,other,200,1.6,Diesel,yes,2011,Caddy
+Audi,16700,vagon,131,2,Diesel,yes,2009,A4
+Toyota,10100,sedan,142,2.4,Petrol,yes,2007,Camry
+Renault,7700,van,125,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,10800,crossover,190,3,Gas,yes,2006,Pajero Sport
+Mercedes-Benz,4700,sedan,1,1.8,Petrol,yes,1996,C-Class
+Audi,5999,vagon,248,2.5,Diesel,yes,2000,A6
+Renault,5800,van,143,1.5,Diesel,yes,2007,Kangoo
+Mitsubishi,5400,sedan,200,1.6,Gas,yes,2005,Lancer
+Audi,3000,vagon,270,2.5,Diesel,yes,1998,A4
+Mitsubishi,3200,sedan,330,1.3,Gas,yes,1996,Lancer
+Mercedes-Benz,43999,sedan,132,3,Diesel,yes,2010,S 350
+Audi,3600,vagon,250,2.5,Other,yes,1999,A6
+Mercedes-Benz,5500,van,400,2.3,Diesel,yes,1996,Vito
+Volkswagen,10200,sedan,129,2,Petrol,yes,2006,Passat B6
+Mercedes-Benz,2990,sedan,300,2.8,Gas,yes,1979,S 280
+Mercedes-Benz,31500,sedan,123,2.2,Diesel,yes,2011,E-Class
+Toyota,11500,sedan,110,1.6,Petrol,yes,2011,Corolla
+Mitsubishi,10888,sedan,63,2,Petrol,yes,2012,Lancer X
+Toyota,38500,crossover,20,5.7,Gas,yes,2014,Tundra
+Audi,4500,vagon,206,2.5,Diesel,no,2003,A6
+Volkswagen,55555,van,63,2,Diesel,yes,2013,Multivan
+Volkswagen,13999,crossover,150,2.5,Diesel,yes,2005,Touareg
+Mercedes-Benz,177777,crossover,0,5.5,Petrol,yes,2016,G 63 AMG
+BMW,15000,other,205,NA,Petrol,yes,2004,530
+Volkswagen,6700,van,300,2.5,Diesel,yes,1997,T4 (Transporter)
+Renault,7100,van,135,1.5,Diesel,yes,2013,Kangoo
+Audi,81500,sedan,0,3,Petrol,yes,2016,A7
+Renault,6200,van,122,1.5,Diesel,yes,2007,Kangoo
+Mercedes-Benz,42500,sedan,110,3,Diesel,yes,2011,S 350
+BMW,75000,crossover,16,3,Diesel,yes,2015,X6
+BMW,48550,other,147,4,Diesel,yes,2012,X5
+Mercedes-Benz,11000,crossover,148,3.2,Petrol,yes,2001,ML 320
+BMW,7500,sedan,275,2,Gas,yes,1998,520
+Toyota,2999,vagon,5,1.3,Petrol,yes,1989,Corolla
+BMW,22500,other,100,3,Petrol,yes,2007,630
+Volkswagen,12800,other,298,NA,Diesel,yes,2005,T5 (Transporter)
+Volkswagen,11900,van,273,2.5,Diesel,yes,2003,T4 (Transporter)
+Volkswagen,17900,crossover,167,3,Diesel,yes,2007,Touareg
+Renault,6700,van,118,1.5,Diesel,yes,2012,Kangoo
+BMW,22900,other,25,3,Petrol,yes,2008,325
+Mercedes-Benz,6800,sedan,286,NA,Other,yes,2000,E-Class
+Toyota,9300,sedan,170,1.8,Gas,yes,2006,Avensis
+Volkswagen,20350,vagon,111,2,Diesel,yes,2012,Passat B7
+Volkswagen,72000,crossover,0,3,Diesel,yes,2016,Touareg
+BMW,16800,crossover,250,3,Diesel,yes,2004,X5
+Toyota,8900,sedan,115,1.6,Petrol,yes,2008,Corolla
+Mitsubishi,22800,crossover,43,2.4,Petrol,yes,2013,Outlander
+Mercedes-Benz,8500,sedan,500,3.2,Diesel,yes,2000,E-Class
+Volkswagen,8800,sedan,86,1.6,Petrol,yes,2012,Polo
+Volkswagen,14100,vagon,115,2,Diesel,yes,2011,Caddy
+Mitsubishi,9200,sedan,84,2,Petrol,yes,2009,Lancer X
+Toyota,75500,crossover,50,4.6,Petrol,yes,2015,Land Cruiser 200
+Volkswagen,2900,sedan,2,1.8,Petrol,yes,1989,Passat B3
+Audi,6100,sedan,305,1.6,Other,yes,1996,A4
+Volkswagen,NA,van,239,2,Diesel,yes,2012,T5 (Transporter)
+Renault,7245,sedan,87,1.4,Petrol,yes,2008,Megane
+Renault,2500,sedan,170,1.9,Diesel,no,2003,Scenic
+Renault,11200,vagon,165,1.5,Diesel,yes,2012,Megane
+Audi,NA,crossover,0,3,Diesel,yes,2015,Q7
+Renault,13500,van,198,1.5,Diesel,yes,2012,Grand Scenic
+Audi,13700,sedan,300,2,Diesel,yes,2005,A6
+Renault,6100,van,280,90,Diesel,yes,2008,Kangoo
+Audi,4300,sedan,340,2.4,Gas,yes,1998,A6
+Volkswagen,9999,van,310,2.5,Diesel,yes,2007,T5 (Transporter)
+Mercedes-Benz,6500,sedan,435,3.2,Petrol,yes,1994,S 140
+Mercedes-Benz,11400,van,174,2.2,Diesel,yes,2012,Vito
+Toyota,10500,sedan,180,3.5,Petrol,yes,2007,Camry
+Renault,2999,van,245,1.5,Diesel,yes,2003,Kangoo
+Mercedes-Benz,29500,crossover,180,3.2,Diesel,yes,2008,GL 320
+Audi,27000,other,222,NA,Diesel,yes,2007,Q7
+Mercedes-Benz,8000,sedan,300,2,Petrol,yes,2000,E-Class
+Mercedes-Benz,NA,sedan,0,2.2,Diesel,yes,2016,V 250
+Mitsubishi,10500,sedan,130,2,Petrol,yes,2008,Lancer X
+Mercedes-Benz,11400,sedan,220,5,Petrol,yes,2004,S 500
+Volkswagen,8300,hatch,147,1.6,Gas,yes,2006,Golf Plus
+Audi,6900,sedan,250,2.4,Gas,yes,1999,A6
+Mercedes-Benz,8500,other,210,4.3,Gas,yes,2000,ML 430
+Volkswagen,17300,crossover,148,3,Diesel,yes,2007,Touareg
+Mercedes-Benz,NA,crossover,1,3,Diesel,yes,2016,GLS 350
+Volkswagen,3500,vagon,215,2,Petrol,yes,1993,Passat B3
+Mercedes-Benz,249999,other,3,5.5,Petrol,yes,2016,S 63 AMG
+Volkswagen,5000,hatch,220,1.4,Petrol,yes,2003,Polo
+Mitsubishi,6900,sedan,118,1.6,Petrol,yes,2007,Lancer
+Toyota,5500,crossover,188,2,Petrol,yes,1999,Rav 4
+BMW,17900,other,98,3,Petrol,yes,2007,Z4
+Volkswagen,11500,van,210,2,Diesel,yes,2012,Caddy
+BMW,7200,sedan,448,3,Petrol,yes,1996,730
+Volkswagen,12800,vagon,167,1.6,Diesel,yes,2011,Golf Variant
+Volkswagen,7000,van,350,1,Other,yes,2004,T5 (Transporter) 
+BMW,8400,sedan,220,2.2,Gas,yes,2001,5 Series
+Audi,3000,van,11,1.9,Diesel,no,1999,A6
+Renault,5200,sedan,160,1.4,Gas,yes,2008,Symbol
+Mitsubishi,5700,vagon,120,1.3,Petrol,yes,2005,Colt
+Mitsubishi,6500,crossover,285,2.5,Diesel,yes,1991,Pajero
+Volkswagen,2300,vagon,200,1.9,Diesel,no,1999,Passat B5
+Volkswagen,7700,van,228,1.9,Diesel,yes,2006,Caddy
+Volkswagen,10999,van,110,1.6,Diesel,yes,2012,Caddy
+BMW,8700,other,212,3,Diesel,no,2004,730
+Mercedes-Benz,7200,sedan,312,3.2,Petrol,yes,2000,E-Class
+BMW,39000,crossover,38,3,Petrol,yes,2009,X6
+Mitsubishi,11999,crossover,235,3,Gas,yes,2007,Pajero Wagon
+Mercedes-Benz,129999,crossover,1,2.99,Diesel,yes,2016,GLS 350
+Volkswagen,18100,other,195,2,Diesel,yes,2011,T5 (Transporter)
+Mitsubishi,1600,vagon,185,1.3,Petrol,no,2000,Space Star
+Mercedes-Benz,138000,crossover,0,3,Diesel,yes,2016,G 350
+Mercedes-Benz,NA,sedan,34,3.5,Diesel,yes,2014,S 350
+Renault,2650,vagon,1,1,Diesel,no,2006,Laguna
+Volkswagen,23300,sedan,115,1.8,Petrol,yes,2012,Passat CC
+BMW,25500,crossover,210,3,Gas,yes,2008,X5
+Volkswagen,20900,sedan,122,2,Petrol,yes,2012,Passat B7
+Renault,3700,hatch,310,2,Petrol,yes,1996,Laguna
+Toyota,9500,sedan,125,1.6,Petrol,yes,2009,Corolla
+Mercedes-Benz,61500,crossover,121,3,Diesel,yes,2013,GL 350
+Toyota,NA,crossover,43,2.2,Diesel,yes,2013,Rav 4
+Volkswagen,8800,other,217,1.9,Diesel,yes,2005,Passat B5
+BMW,34900,vagon,136,2,Diesel,yes,2012,520
+Audi,3600,vagon,200,2.5,Diesel,yes,1999,A6
+Mercedes-Benz,28000,other,12,1.8,Petrol,yes,2013,C-Class
+Mercedes-Benz,140000,crossover,88,5.46,Petrol,yes,2013,G 63 AMG
+BMW,25800,sedan,203,2,Diesel,yes,2010,520
+Mitsubishi,25999,crossover,86,3.2,Diesel,yes,2011,Pajero Wagon
+Volkswagen,29600,sedan,11,1.4,Petrol,yes,2016,Passat B8
+BMW,3200,sedan,178,1.9,Petrol,no,2001,318
+Mercedes-Benz,6900,van,200,2.3,Diesel,yes,1999,Vito
+Mercedes-Benz,3550,sedan,274,3.2,Diesel,no,2002,E-Class
+BMW,2900,vagon,230,2,Diesel,no,2001,320
+Mercedes-Benz,11500,van,307,2.2,Diesel,yes,2007,Vito
+Volkswagen,11800,van,218,1.9,Diesel,yes,2008,T5 (Transporter)
+Mercedes-Benz,69900,sedan,76,6,Petrol,yes,2010,S 65 AMG
+Audi,23000,other,115,1.8,Petrol,yes,2008,S4
+Renault,6500,van,186,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,6000,vagon,284,1.9,Other,yes,2001,Golf IV
+Toyota,17200,sedan,50,1.6,Petrol,yes,2013,Corolla
+Toyota,76500,crossover,29,4.5,Diesel,yes,2014,Land Cruiser 200
+Volkswagen,6800,vagon,160,2.8,Petrol,yes,2001,Sharan
+Mercedes-Benz,250000,other,6,5.5,Petrol,yes,2016,S 63 AMG
+Volkswagen,3750,sedan,262,1.8,Petrol,yes,1994,Vento
+Volkswagen,8000,other,300,2.5,Diesel,yes,2007,T5 (Transporter)
+Mercedes-Benz,58500,crossover,128,3,Diesel,yes,2013,ML 350
+Renault,4999,sedan,114,1.4,Other,yes,2006,Clio
+BMW,NA,crossover,6,4.4,Petrol,yes,2016,X5 M
+Volkswagen,15800,sedan,56,1.4,Other,yes,2013,Jetta
+Volkswagen,9250,sedan,200,2,Diesel,yes,2007,Passat B6
+Mitsubishi,11300,crossover,128,2.4,Gas,yes,2008,Outlander
+Audi,1400,sedan,250,1.8,Petrol,no,1993,80
+Toyota,80000,crossover,23,4.5,Diesel,yes,2014,Land Cruiser 200
+BMW,11900,sedan,1,2.8,Petrol,yes,2002,528
+Volkswagen,9500,hatch,29,1.4,Petrol,yes,2014,Polo
+BMW,10999,other,162,2.2,Petrol,yes,2002,320
+Audi,23500,crossover,119,4.2,Gas,yes,2007,Q7
+BMW,24500,crossover,170,4.8,Gas,yes,2007,X5
+Mitsubishi,6000,sedan,150,1.8,Gas,yes,2004,Virage
+Mitsubishi,17500,crossover,194,3.2,Diesel,yes,2008,Pajero Wagon
+Toyota,9300,sedan,107,1.8,Gas,yes,2007,Avensis
+BMW,3500,vagon,280,3,Diesel,no,2001,330
+BMW,41500,vagon,42,2,Petrol,yes,2012,X3
+Volkswagen,10300,other,215,1.9,Other,yes,2007,T5 (Transporter)
+Mercedes-Benz,NA,crossover,1,3,Petrol,yes,2015,GLE-Class
+Audi,2200,vagon,280,2.5,Diesel,no,2002,A6 Allroad
+Renault,19692.08,crossover,0,1.5,Diesel,yes,2016,Duster
+Renault,7850,van,187,1.5,Diesel,yes,2010,Kangoo
+Mercedes-Benz,NA,sedan,42,3,Diesel,yes,2014,S 350
+Volkswagen,3950,hatch,280,NA,Gas,yes,1996,Golf III
+Toyota,20000,crossover,94,4.5,Petrol,yes,2005,Land Cruiser 105
+Audi,3800,sedan,253,2.5,Diesel,no,1998,A6
+BMW,13700,sedan,254,3,Diesel,yes,2003,530
+Toyota,31000,crossover,86,2.2,Diesel,yes,2014,Rav 4
+Volkswagen,14400,van,237,2,Diesel,yes,2010,T5 (Transporter)
+BMW,4700,sedan,200,NA,Petrol,yes,1996,316
+Toyota,17500,hatch,45,1.6,Petrol,yes,2014,Auris
+Renault,10396.23,vagon,1,1.2,Petrol,yes,2015,Logan
+BMW,27800,crossover,140,3,Diesel,yes,2009,X5
+Volkswagen,5800,sedan,200,1.4,Petrol,yes,2000,Golf IV
+Renault,5800,vagon,200,1.46,Diesel,yes,2008,Grand Scenic
+Audi,21500,vagon,165,2,Diesel,yes,2010,A6
+Volkswagen,3600,sedan,330,2.8,Gas,yes,1994,Passat B4
+BMW,6500,sedan,388,3.5,Petrol,yes,1998,735
+Volkswagen,10700,vagon,171,1.9,Diesel,yes,2010,Caddy
+BMW,3100,sedan,280,2,Diesel,no,1999,320
+Mercedes-Benz,38800,hatch,8,NA,Other,yes,2015,B-Class Electric Drive
+Mercedes-Benz,1350,vagon,240,2.5,Diesel,no,1992,E-Class
+Renault,NA,van,150,2.5,Diesel,yes,2007,Trafic
+Mercedes-Benz,4500,other,525,2.3,Diesel,yes,1995,Sprinter 208
+Mercedes-Benz,99000,sedan,0,3,Petrol,yes,2016,CLS 400
+Mercedes-Benz,5600,vagon,355,2.5,Diesel,yes,1996,250
+Mercedes-Benz,8500,sedan,388,4.3,Gas,yes,2000,S 430
+BMW,24000,crossover,108,2,Diesel,yes,2012,X1
+Mitsubishi,9300,sedan,95,2,Petrol,yes,2008,Lancer X
+Toyota,15700,sedan,125,3.5,Petrol,yes,2008,Camry
+Volkswagen,9600,sedan,106,1.6,Petrol,yes,2011,Polo
+Volkswagen,21000,other,93,2,Diesel,yes,2012,Passat B7
+Mercedes-Benz,3500,sedan,303,2.2,Diesel,no,2000,C-Class
+Toyota,45500,crossover,65,4,Gas,yes,2010,FJ Cruiser
+Audi,20900,crossover,185,4.2,Petrol,yes,2006,Q7
+Mercedes-Benz,6999,other,168,2.3,Petrol,yes,2001,CLK 230
+Mitsubishi,7999,sedan,112,2,Petrol,yes,2008,Lancer X
+BMW,6500,sedan,1,3,Petrol,yes,1995,730
+BMW,29500,sedan,230,3,Diesel,yes,2011,530
+Renault,10300,van,138,2,Diesel,yes,2011,Trafic
+Volkswagen,6000,other,1,2.5,Diesel,yes,1999,LT
+Volkswagen,3300,vagon,193,1.9,Diesel,no,2003,Passat B5
+Volkswagen,2400,vagon,320,1.9,Diesel,no,2000,Passat B5
+Audi,23900,sedan,180,3,Diesel,yes,2009,A6
+Volkswagen,9300,sedan,148,2,Petrol,yes,2006,Passat B6
+Mitsubishi,1699,hatch,294,1.6,Gas,yes,1992,Lancer
+Volkswagen,34000,crossover,92,3,Diesel,yes,2012,Touareg
+Toyota,14500,other,82,2,Gas,yes,2007,Rav 4
+Volkswagen,10400,sedan,78,1.6,Petrol,yes,2012,Polo
+BMW,6100,sedan,266,3.5,Petrol,no,1997,535
+Volkswagen,7200,other,177,1.9,Diesel,yes,2010,Caddy
+Mercedes-Benz,14000,other,144,2.2,Diesel,yes,2009,Vito
+Mercedes-Benz,8900,other,213,5,Other,yes,1999,CL 500
+BMW,22100,hatch,42,1.6,Petrol,yes,2013,116
+Mercedes-Benz,33500,van,0,NA,Diesel,yes,2015,Sprinter 316
+Mercedes-Benz,NA,sedan,20,3,Diesel,yes,2015,S 350
+Toyota,33950,other,65,5.7,Petrol,yes,2013,Tundra
+BMW,3499,sedan,101,2.5,Petrol,yes,1990,525
+Audi,28500,crossover,74,2,Petrol,yes,2010,Q5
+BMW,8800,crossover,284,3,Diesel,no,2004,X5
+Volkswagen,17200,other,15,1.2,Petrol,yes,2014,Golf VII
+Audi,3000,sedan,278,2.5,Diesel,no,1999,A6
+Toyota,25800,sedan,58,2.5,Petrol,yes,2015,Camry
+Mercedes-Benz,18500,sedan,143,5.5,Petrol,yes,2007,S 550
+Volkswagen,28793.04,van,0,1.6,Diesel,yes,2016,Caddy
+BMW,42900,sedan,2,2,Petrol,yes,2016,520
+Toyota,11800,sedan,90,1.8,Petrol,yes,2008,Avensis
+Renault,7100,van,171,1.5,Diesel,yes,2013,Kangoo
+Volkswagen,22000,sedan,26,1.8,Petrol,yes,2011,Passat CC
+Volkswagen,2700,hatch,400,1.8,Gas,yes,1989,Golf II
+Toyota,7000,other,220,1.8,Petrol,yes,2001,Celica
+BMW,6499,other,230,3,Diesel,no,2002,X5
+Volkswagen,15700,sedan,110,1.8,Petrol,yes,2011,Passat B7
+Mitsubishi,8100,sedan,113,2,Petrol,yes,2007,Lancer X
+Mercedes-Benz,12200,other,287,3.2,Petrol,yes,2000,ML 320
+Renault,5400,sedan,96,1.4,Petrol,yes,2007,Symbol
+Mercedes-Benz,5400,sedan,285,2.2,Diesel,no,2002,220
+Volkswagen,7700,vagon,287,1.9,Diesel,yes,2002,Sharan
+BMW,34000,sedan,115,5,Petrol,yes,2008,750
+Audi,3900,sedan,302,1.9,Diesel,no,2002,A6
+Mercedes-Benz,15000,sedan,240,5,Petrol,yes,2004,S 500
+Volkswagen,8999,sedan,120,2,Petrol,yes,2008,Passat B6
+Volkswagen,2400,sedan,268,99.99,Petrol,yes,1989,Passat B3
+Mitsubishi,6300,sedan,116,1.5,Petrol,yes,2008,Lancer X
+Volkswagen,7999,van,210,1.6,Gas,yes,2004,Touran
+Toyota,25200,crossover,128,4,Gas,yes,2007,Land Cruiser Prado
+BMW,26999,sedan,131,2,Diesel,yes,2011,520
+Mercedes-Benz,4000,sedan,460,2,Petrol,yes,1991,E-Class
+Audi,16000,sedan,180,3,Diesel,yes,2006,A6
+Mitsubishi,9200,sedan,115,2,Petrol,yes,2008,Lancer X
+Volkswagen,7000,van,250,2,Diesel,yes,2004,Caddy
+Toyota,85000,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,3200,sedan,320,1.8,Gas,yes,1990,Passat B3
+Renault,11000,hatch,127,1.5,Diesel,yes,2012,Megane
+Mercedes-Benz,13900,other,225,NA,Other,yes,2008,Vito
+Mitsubishi,10500,crossover,187,2.4,Gas,yes,2008,Outlander
+BMW,17500,sedan,102,2,Petrol,yes,2011,320
+Renault,7200,vagon,224,1.5,Diesel,yes,2004,Megane
+Toyota,8400,sedan,195,2.4,Gas,yes,2004,Camry
+Volkswagen,8350,van,1,1.6,Diesel,yes,2011,Caddy
+Audi,3200,vagon,244,2,Other,no,2006,A4
+Toyota,11999,sedan,147,2.4,Petrol,yes,2010,Camry
+Toyota,29900,crossover,144,4,Gas,yes,2008,Land Cruiser 200
+BMW,11500,sedan,290,3,Diesel,yes,2001,730
+Audi,NA,vagon,156,3,Diesel,yes,2009,A6
+Mitsubishi,13900,vagon,93,3,Gas,yes,2007,Outlander XL
+Mercedes-Benz,NA,sedan,300,2.6,Petrol,yes,2002,E-Class
+Renault,5000,van,245,1.5,Diesel,yes,2003,Kangoo
+Toyota,2350,hatch,211,1.3,Petrol,yes,1987,Corolla
+Volkswagen,12900,van,127,1.6,Diesel,yes,2011,Caddy
+Mercedes-Benz,8650,van,344,2.2,Diesel,yes,2006,Vito
+Toyota,22900,crossover,132,2.7,Gas,yes,2012,Venza
+Mercedes-Benz,13500,van,290,2.2,Diesel,yes,2007,Vito
+Audi,39999,crossover,45,2,Diesel,yes,2014,Q5
+Audi,2850,sedan,350,NA,Gas,yes,1988,80
+Volkswagen,3300,sedan,250,1.9,Diesel,no,2000,Passat B5
+Audi,32500,crossover,108,2,Diesel,yes,2012,Q5
+Mitsubishi,13300,crossover,135,3,Petrol,yes,2009,Outlander XL
+Volkswagen,2900,vagon,257,1.9,Diesel,no,2003,Passat B5
+Volkswagen,2900,sedan,315,1.6,Gas,yes,1990,Passat B3
+Mercedes-Benz,21000,crossover,208,3.5,Petrol,yes,2007,ML 350
+Mercedes-Benz,NA,crossover,1,3,Petrol,yes,2016,GLE-Class
+Toyota,89000,crossover,61,4.5,Diesel,yes,2011,Land Cruiser 200
+Volkswagen,23500,crossover,48,2,Diesel,yes,2012,Tiguan
+Mitsubishi,4200,van,350,1.8,Gas,yes,1992,Space Wagon
+Mercedes-Benz,70500,crossover,99,5.5,Petrol,yes,2009,G 55 AMG
+Mercedes-Benz,124000,crossover,0,4,Petrol,yes,2016,GLS 400
+Audi,39999,crossover,45,2,Diesel,yes,2014,Q3
+Mercedes-Benz,1999,sedan,400,2.2,Diesel,no,2000,200
+Mercedes-Benz,14300,other,175,2.2,Diesel,yes,2013,Sprinter 313
+Toyota,13500,sedan,139,2.4,Gas,yes,2008,Camry
+Toyota,13300,sedan,104,2.4,Petrol,yes,2007,Camry
+Toyota,8200,hatch,82,1.3,Petrol,yes,2006,Yaris
+Renault,12000,van,137,1.5,Diesel,yes,2012,Grand Scenic
+Renault,2150,vagon,199,1.9,Diesel,no,2003,Laguna
+Toyota,29000,vagon,60,2.2,Diesel,yes,2013,Rav 4
+Mercedes-Benz,9500,van,280,2.2,Diesel,yes,2005,Vito
+Volkswagen,NA,van,146,2.5,Diesel,yes,2009,T5 (Transporter)
+Volkswagen,10600,van,120,2,Diesel,yes,2007,Touran
+Mercedes-Benz,12500,van,238,2.2,Diesel,yes,2008,Vito
+Mitsubishi,10200,crossover,269,3.2,Diesel,yes,2002,Pajero Wagon
+Toyota,NA,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,50250,sedan,0,2.2,Diesel,yes,2015,E-Class
+Volkswagen,8900,van,100,1.4,Gas,yes,2009,Caddy
+Mercedes-Benz,5500,other,220,2.2,Diesel,yes,2002,Vito
+BMW,45500,sedan,42,2,Diesel,yes,2014,520
+Renault,10499,other,146,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,99999,crossover,0,2.99,Diesel,yes,2016,GLE-Class
+Renault,9100,van,166,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,24500,sedan,180,3.2,Diesel,yes,2008,S 320
+BMW,63500,crossover,1,2,Diesel,yes,2016,X5
+BMW,3600,sedan,221,3,Diesel,no,2002,530
+Renault,12900,crossover,89,2.5,Petrol,yes,2010,Koleos
+Renault,6400,sedan,51,1.6,Petrol,yes,2011,Logan
+BMW,8800,sedan,287,3,Petrol,yes,2001,530
+Renault,7199,sedan,104,1.5,Diesel,yes,2010,Symbol
+Renault,3300,van,250,1.5,Diesel,yes,2005,Kangoo
+Audi,2400,sedan,305,2.3,Gas,yes,1989,100
+Volkswagen,8400,hatch,60,1.39,Petrol,yes,2007,Polo
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mitsubishi,7600,sedan,180,2,Gas,yes,2008,Lancer X
+Renault,3800,van,196,1.9,Diesel,yes,1998,Kangoo
+Mercedes-Benz,13100,sedan,160,3.7,Petrol,yes,2003,S 350
+Toyota,21950,crossover,141,2.7,Gas,yes,2010,Venza
+Renault,8800,van,205,1.9,Diesel,yes,2006,Trafic
+Toyota,10500,hatch,17,1,Petrol,yes,2016,Yaris
+Mitsubishi,24000,crossover,20,2.5,Diesel,yes,2013,Pajero Sport
+Toyota,11800,sedan,189,2.4,Gas,yes,2007,Camry
+Mercedes-Benz,20300,sedan,198,1.8,Gas,yes,2011,E-Class
+Toyota,11600,hatch,24,1.6,Petrol,yes,2009,Auris
+Mercedes-Benz,22000,crossover,225,3.5,Gas,yes,2007,ML 350
+Mercedes-Benz,15800,sedan,231,3.5,Gas,yes,2005,E-Class
+Mercedes-Benz,13500,van,97,2.2,Diesel,yes,2013,Vito
+Mitsubishi,16500,crossover,150,3.5,Gas,yes,2008,Outlander XL
+Mercedes-Benz,25000,van,156,NA,Other,yes,2012,Viano
+Volkswagen,40000,other,170,2,Diesel,yes,2011,Multivan
+Mitsubishi,9500,crossover,150,2,Gas,yes,2007,Outlander
+Renault,6300,van,149,1.5,Other,yes,2011,Kangoo
+BMW,4999,sedan,256,2.5,Diesel,no,2001,525
+Toyota,14990,crossover,280,4.2,Diesel,yes,2000,Land Cruiser 100
+Audi,16400,vagon,152,2,Diesel,yes,2011,A4
+Mercedes-Benz,21300,crossover,190,6.3,Petrol,yes,2007,ML 63 AMG
+Audi,24996,sedan,98,3.2,Petrol,yes,2009,A5
+Volkswagen,22900,sedan,45,2,Petrol,yes,2013,Jetta
+Toyota,8300,hatch,136,1.6,Petrol,yes,2007,Corolla
+Audi,12900,vagon,255,2.5,Diesel,yes,2005,A6 Allroad
+Volkswagen,12199,vagon,149,2,Diesel,yes,2008,Passat B6
+Audi,9999,vagon,198,2.7,Petrol,yes,2003,A6 Allroad
+Mercedes-Benz,35500,crossover,99,5,Gas,yes,2005,G 63 AMG
+Mitsubishi,11500,crossover,170,3,Gas,yes,2005,Pajero Sport
+Mercedes-Benz,38500,sedan,134,3,Diesel,yes,2010,S 350
+Mercedes-Benz,5500,other,250,2.2,Diesel,yes,2002,Vito
+Volkswagen,9400,van,124,2,Gas,yes,2008,Caddy
+Volkswagen,8400,van,143,1.6,Diesel,yes,2011,Caddy
+Audi,2650,vagon,268,2.5,Diesel,no,2000,A6
+Toyota,12000,sedan,133,2.4,Gas,yes,2008,Camry
+Mercedes-Benz,7500,sedan,187,2,Gas,yes,2001,C-Class
+Volkswagen,25900,hatch,35,2,Diesel,yes,2015,Golf VII
+Volkswagen,7500,sedan,280,1.8,Other,yes,2005,Passat B5
+Mercedes-Benz,35000,other,147,6,Petrol,yes,2001,S 600
+Audi,6200,vagon,316,2.6,Gas,yes,1997,A6
+Volkswagen,1550,sedan,161,1.8,Petrol,no,1994,Vento
+Mercedes-Benz,6500,van,450,2.3,Diesel,yes,1996,Vito
+Volkswagen,8999,vagon,161,2,Diesel,yes,2008,Passat B6
+Mitsubishi,21000,crossover,29,2,Gas,yes,2014,ASX
+Mercedes-Benz,8800,other,575,2.2,Diesel,yes,2006,Sprinter 313
+Volkswagen,13500,hatch,48,1.4,Petrol,yes,2012,Polo
+Toyota,3300,van,345,2.4,Diesel,yes,1992,Hiace
+Mercedes-Benz,5990,sedan,400,2,Gas,yes,1998,E-Class
+Mercedes-Benz,14800,crossover,242,2.7,Diesel,yes,2005,ML 270
+Mercedes-Benz,NA,crossover,1,3,Diesel,yes,2016,GL 350
+Mercedes-Benz,47000,sedan,0,1.6,Petrol,yes,2016,C-Class
+Volkswagen,7990,sedan,180,1.8,Gas,yes,2005,Passat B5
+Volkswagen,8999,sedan,120,1.9,Diesel,yes,2007,Passat B6
+Volkswagen,23999,sedan,0,1.6,Diesel,yes,2016,Jetta
+Audi,6500,vagon,421,2.5,Diesel,yes,1996,A6
+Toyota,14000,sedan,1,1.8,Petrol,yes,2008,Avensis
+Audi,23500,crossover,116,4.2,Petrol,yes,2008,Q7
+Renault,9500,sedan,89,1.6,Petrol,yes,2012,Fluence
+Mercedes-Benz,125000,sedan,18,3,Diesel,yes,2015,S 350
+Volkswagen,9000,van,181,1.6,Diesel,yes,2011,Caddy
+Mitsubishi,2300,sedan,523,1.8,Diesel,yes,1989,Lancer
+Volkswagen,10650,sedan,65,1.6,Petrol,yes,2013,Polo
+Audi,6399,vagon,230,2.5,Diesel,yes,1999,A6
+Mitsubishi,7500,sedan,140,1.5,Petrol,yes,2007,Lancer X
+Volkswagen,6450,van,220,NA,Other,yes,2004,Caddy
+Volkswagen,10200,van,105,1.6,Diesel,yes,2014,Caddy
+Mercedes-Benz,3300,sedan,200,2.7,Diesel,no,2000,E-Class
+Mercedes-Benz,8300,sedan,175,2,Petrol,yes,2001,E-Class
+Volkswagen,7400,van,168,1.9,Diesel,yes,2005,Caddy
+Toyota,28600,crossover,66,2.2,Diesel,yes,2013,Rav 4
+Audi,2800,sedan,230,2.8,Petrol,yes,1997,A8
+BMW,38000,crossover,223,3,Diesel,yes,2011,535
+Toyota,11000,hatch,74,1.4,Other,yes,2010,Prius
+Audi,11800,sedan,240,4.2,Gas,yes,2001,S8
+Renault,9500,van,275,2,Diesel,yes,2008,Trafic
+BMW,23700,crossover,117,2,Diesel,yes,2010,X1
+Mercedes-Benz,35000,other,135,5.5,Petrol,yes,2008,CL 550
+Volkswagen,11000,vagon,171,1.9,Diesel,yes,2009,Golf Variant
+BMW,25800,crossover,205,3,Diesel,yes,2008,X5
+Renault,13200,van,230,2,Diesel,yes,2009,Trafic
+Toyota,4650,hatch,170,1,Petrol,yes,2002,Yaris
+Mercedes-Benz,8300,van,337,2.2,Diesel,yes,2002,Vito
+Mitsubishi,18900,crossover,106,3.8,Gas,yes,2007,Pajero Wagon
+Mitsubishi,12750,sedan,59,2.4,Petrol,yes,2009,Galant
+Mercedes-Benz,3800,other,107,0.6,Petrol,yes,2000,Smart
+Volkswagen,11500,other,189,2,Diesel,yes,2011,Caddy
+Mercedes-Benz,4600,van,200,2.2,Diesel,yes,2000,Vito
+BMW,1550,sedan,300,NA,Gas,no,1994,316
+Renault,7899,van,129,1.5,Other,yes,2014,Kangoo
+Mercedes-Benz,70999,crossover,0,2.2,Diesel,yes,2016,GLE-Class
+Toyota,8800,sedan,300,2.4,Petrol,yes,2004,Camry
+Renault,8250,sedan,16,1.2,Petrol,yes,2014,Logan
+Toyota,21500,crossover,89,2,Petrol,yes,2012,Rav 4
+Toyota,195000,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,2400,hatch,1,2,Petrol,no,2002,Laguna
+Volkswagen,12500,other,160,2.5,Other,yes,2006,T5 (Transporter)
+Mercedes-Benz,2499,hatch,201,1.7,Diesel,no,2001,A 170
+BMW,14990,sedan,135,2.5,Petrol,yes,2005,525
+Audi,NA,sedan,166,3,Gas,yes,2005,A6
+Mitsubishi,8260,sedan,65,1.5,Petrol,yes,2011,Lancer
+BMW,6800,sedan,230,2,Petrol,yes,1998,520
+Mercedes-Benz,NA,crossover,131,5,Petrol,yes,2008,G 500
+Mitsubishi,1999,van,168,1.8,Petrol,no,2001,Space Star
+BMW,8500,other,265,2.8,Gas,yes,1998,328
+Toyota,24800,crossover,90,4,Petrol,yes,2008,FJ Cruiser
+Mitsubishi,11800,crossover,120,3,Petrol,yes,2008,Outlander XL
+Mercedes-Benz,35500,sedan,100,3.5,Petrol,yes,2010,S 350
+Mercedes-Benz,9400,van,250,2.2,Diesel,yes,2005,Vito
+Volkswagen,14900,sedan,73,NA,Petrol,yes,2012,Jetta
+Volkswagen,NA,other,28,2,Diesel,yes,2015,Amarok
+Mercedes-Benz,14899,other,209,3,Diesel,yes,2006,E-Class
+Volkswagen,15500,sedan,178,2,Diesel,no,2013,Passat B7
+Renault,2200,van,290,2.2,Diesel,no,1999,Espace
+Volkswagen,8950,sedan,133,1.6,Petrol,yes,2012,Polo
+BMW,17800,vagon,159,3,Diesel,yes,2008,530
+Audi,85555,sedan,40,3,Diesel,yes,2015,A8
+Volkswagen,2000,sedan,300,1.3,Petrol,yes,1988,Jetta
+Mercedes-Benz,20500,sedan,56,5,Other,yes,2003,SL 500 (550)
+Toyota,30990,crossover,68,2.7,Gas,yes,2012,Land Cruiser Prado
+Toyota,39900,crossover,11,4,Petrol,yes,2013,Tacoma
+BMW,21900,other,25,2.5,Petrol,yes,2007,325
+Mercedes-Benz,NA,sedan,16,3,Diesel,yes,2014,S 350
+Mercedes-Benz,185555,other,0,4.7,Petrol,yes,2016,S 500
+Toyota,12400,hatch,52,1.4,Other,yes,2012,Prius
+Mercedes-Benz,5000,other,200,2,Petrol,yes,1998,CLK 200
+Volkswagen,3200,sedan,367,2,Petrol,yes,1991,Passat B3
+BMW,6500,sedan,300,2.8,Gas,yes,1997,528
+Mercedes-Benz,13600,hatch,147,2,Diesel,yes,2008,B 200
+BMW,19990,other,164,NA,Gas,yes,2004,645
+Audi,23500,crossover,245,3,Diesel,yes,2007,Q7
+Audi,15499,sedan,177,2,Diesel,yes,2007,A6
+Mercedes-Benz,12950,sedan,197,2.7,Diesel,yes,2002,E-Class
+Volkswagen,1800,sedan,250,NA,Petrol,yes,1991,Jetta
+Toyota,23500,sedan,80,2.5,Gas,yes,2014,Camry
+BMW,NA,hatch,107,2,Petrol,yes,2006,118
+Renault,5950,hatch,200,1.5,Diesel,yes,2005,Megane
+Mitsubishi,2900,hatch,300,1.8,Petrol,yes,1989,Galant
+Mercedes-Benz,5000,van,400,2.2,Diesel,yes,1999,Vito
+Mercedes-Benz,1200,sedan,240,2.4,Petrol,no,1999,E-Class
+Volkswagen,6500,sedan,190,1.9,Diesel,yes,2006,Caddy
+Volkswagen,51900,crossover,30,3,Diesel,yes,2015,Touareg
+BMW,104999,crossover,2,3,Diesel,yes,2016,X5
+Renault,4650,van,165,1.5,Diesel,yes,2003,Kangoo
+Mitsubishi,16000,crossover,59,NA,Petrol,yes,2012,ASX
+Audi,12500,sedan,165,2.4,Petrol,yes,2006,A6
+Volkswagen,10250,sedan,144,2,Diesel,yes,2008,Passat B6
+Audi,23500,other,83,2,Petrol,yes,2009,A5
+Volkswagen,17777,van,250,2,Diesel,yes,2010,T6 (Transporter)
+Mercedes-Benz,8300,sedan,233,3.2,Diesel,yes,2001,320
+Mercedes-Benz,39900,sedan,84,5.5,Petrol,yes,2011,S 500
+Mercedes-Benz,5200,van,400,2.9,Diesel,yes,1999,Sprinter 210
+Audi,31500,sedan,140,2,Diesel,yes,2012,A6
+BMW,4800,sedan,11,2.5,Diesel,yes,1994,325
+Volkswagen,7700,other,290,NA,Diesel,yes,2000,T4 (Transporter)
+Volkswagen,21550,crossover,58,1.4,Petrol,yes,2014,Tiguan
+Audi,42900,crossover,140,3,Diesel,yes,2013,Q7
+Audi,2600,sedan,160,2.5,Diesel,no,2002,A6
+Volkswagen,31310,van,0,2,Diesel,yes,2014,Touran
+Volkswagen,11800,van,230,1.9,Diesel,yes,2009,T5 (Transporter)
+Mercedes-Benz,107000,van,0,2.2,Diesel,yes,2016,V 250
+Toyota,14800,crossover,150,NA,Gas,yes,2006,Land Cruiser Prado
+BMW,9800,sedan,264,3,Diesel,yes,2001,530
+Volkswagen,7950,van,144,2,Gas,yes,2012,Caddy
+Mercedes-Benz,9000,crossover,220,3.2,Gas,yes,1999,ML 320
+BMW,7400,sedan,279,3.5,Gas,yes,1998,735
+Toyota,24000,sedan,60,2.5,Gas,yes,2014,Camry
+Mercedes-Benz,4800,sedan,199,3.2,Diesel,yes,1999,E-Class
+Toyota,22000,crossover,155,2.7,Gas,yes,2008,Land Cruiser Prado
+BMW,42500,crossover,29,3,Petrol,yes,2011,X3
+Toyota,17200,crossover,149,4.7,Petrol,yes,2008,Land Cruiser 200
+Renault,8100,crossover,90,1.9,Diesel,yes,2005,Kangoo
+Volkswagen,37900,crossover,73,3,Diesel,yes,2012,Touareg
+Volkswagen,30000,sedan,67,2,Diesel,yes,2013,Passat B7
+Volkswagen,7700,van,247,1.9,Diesel,yes,2002,T4 (Transporter)
+BMW,32500,sedan,148,3,Petrol,yes,2011,535
+BMW,18700,crossover,260,3,Diesel,yes,2006,X5
+Renault,8100,van,219,2.5,Diesel,yes,2010,Master
+Toyota,5050,hatch,138,1,Petrol,yes,2001,Yaris
+Mercedes-Benz,11800,van,165,2.2,Diesel,yes,2012,Vito
+Mercedes-Benz,68700,crossover,980,3,Diesel,yes,2013,GL 350
+Audi,2000,sedan,360,1.6,Other,no,1996,A4
+Mercedes-Benz,NA,other,24,4.7,Petrol,yes,2014,S 500
+Mercedes-Benz,19200,sedan,173,5,Petrol,yes,2007,S 500
+BMW,16400,sedan,187,4.8,Gas,yes,2007,750
+BMW,6300,sedan,330,2.5,Petrol,yes,1999,525
+Mitsubishi,15000,crossover,295,2.8,Diesel,yes,2004,Pajero Wagon
+Toyota,23000,other,211,4,Gas,yes,2007,Land Cruiser Prado
+Mercedes-Benz,23700,sedan,86,1.8,Gas,yes,2013,E-Class
+BMW,11900,sedan,160,2.5,Petrol,yes,2005,525
+Audi,7900,sedan,274,1.8,Gas,yes,2003,A6
+Mercedes-Benz,4400,sedan,308,3.2,Gas,no,2000,S 320
+Volkswagen,9900,other,181,1.6,Diesel,yes,2012,Caddy
+Volkswagen,6900,hatch,113,1.4,Petrol,yes,2003,Polo
+Mercedes-Benz,NA,crossover,63,5.5,Petrol,yes,2011,GL 550
+Mercedes-Benz,71999,crossover,0,2.2,Diesel,yes,2016,GLE-Class
+BMW,11800,crossover,210,3,Diesel,no,2006,X5
+Renault,10999,van,207,2,Diesel,yes,2012,Trafic
+Mitsubishi,18900,crossover,93,3.8,Gas,yes,2008,Pajero Wagon
+Toyota,28900,crossover,120,3.5,Petrol,yes,2011,Highlander
+Toyota,15500,crossover,130,2,Petrol,yes,2008,Rav 4
+BMW,63000,crossover,0,2,Diesel,yes,2015,X5
+Toyota,11700,sedan,145,2.4,Petrol,yes,2007,Camry
+BMW,4999,sedan,327,3,Diesel,no,2001,730
+Toyota,13300,crossover,140,2.4,Petrol,yes,2007,Rav 4
+Mercedes-Benz,8200,sedan,260,3,Diesel,yes,1997,S 300
+Renault,8900,van,150,2,Diesel,yes,2009,Trafic
+Renault,NA,vagon,117,1.5,Diesel,yes,2007,Grand Scenic
+BMW,60500,sedan,110,3,Diesel,yes,2011,740
+BMW,8900,sedan,294,2.2,Gas,yes,2000,520
+BMW,69500,sedan,62,4.4,Petrol,yes,2013,750
+Volkswagen,9000,hatch,95,1.6,Gas,yes,2005,Golf V
+BMW,17800,crossover,265,3,Diesel,yes,2005,X5
+Mercedes-Benz,2100,other,350,2.4,Diesel,yes,1988,MB
+Toyota,10700,sedan,57,1.3,Petrol,yes,2010,Corolla
+Volkswagen,17777,van,250,2,Diesel,yes,2010,Caravelle
+Audi,73500,sedan,53,6.3,Petrol,yes,2013,A8
+Mitsubishi,9800,crossover,92,2,Petrol,yes,2008,Outlander
+Volkswagen,35900,van,132,2,Diesel,yes,2011,Multivan
+Toyota,7500,hatch,135,1,Petrol,yes,2008,Yaris
+Renault,2500,sedan,105,1.4,Petrol,yes,1990,19
+Audi,39999,crossover,100,3,Diesel,yes,2011,Q7
+Volkswagen,22000,crossover,123,3,Diesel,yes,2009,Touareg
+Toyota,9800,sedan,65,1.6,Petrol,yes,2009,Auris
+BMW,29999,other,73,2,Petrol,yes,2012,320
+Mercedes-Benz,7500,sedan,350,2.2,Diesel,yes,2003,Sprinter 208
+Toyota,12500,sedan,138,3.5,Petrol,yes,2007,Camry
+BMW,39500,crossover,115,4.4,Petrol,yes,2010,X5
+Volkswagen,10600,van,240,2.5,Diesel,yes,2009,T5 (Transporter)
+Mitsubishi,18200,crossover,147,3.2,Diesel,yes,2008,Pajero Wagon
+Mercedes-Benz,189999,other,0,4.7,Petrol,yes,2016,S 500
+Audi,4800,other,207,2,Petrol,yes,2002,A4
+BMW,3999,sedan,238,1.8,Petrol,yes,1996,318
+Toyota,35500,crossover,165,4,Gas,yes,2011,Land Cruiser Prado
+Toyota,13500,sedan,158,2.4,Gas,yes,2008,Camry
+Mercedes-Benz,17500,sedan,130,3,Petrol,yes,2007,C-Class
+Mercedes-Benz,59999,sedan,11,4.7,Petrol,yes,2012,CLS 500
+Mercedes-Benz,7999,van,255,2.2,Diesel,yes,2004,Vito
+Toyota,16000,sedan,89,1.8,Other,yes,2011,Avensis
+BMW,5850,sedan,250,4.5,Gas,yes,2001,745
+Renault,7999,van,154,1.5,Diesel,yes,2008,Scenic
+Renault,8900,van,181,1.9,Diesel,yes,2006,Trafic
+BMW,43719,crossover,0,2,Petrol,yes,2016,X3
+Volkswagen,8800,van,193,2.5,Diesel,yes,2005,T5 (Transporter)
+Volkswagen,9990,van,194,1.6,Diesel,yes,2011,Caddy
+Volkswagen,17999,sedan,80,1.8,Petrol,yes,2012,Passat B7
+Mercedes-Benz,37500,other,94,5.5,Petrol,yes,2009,CL 55 AMG
+Audi,20500,sedan,155,3,Petrol,yes,2010,A6
+Audi,4999,crossover,242,2.5,Diesel,no,2003,A6 Allroad
+Mercedes-Benz,4100,crossover,1,3.5,Petrol,yes,2007,ML 350
+Mercedes-Benz,17499,sedan,145,1.8,Petrol,yes,2008,C-Class
+Audi,8200,other,326,2.5,Diesel,yes,2002,A6
+Volkswagen,7000,sedan,185,2.8,Gas,yes,2004,Passat B5
+Volkswagen,9200,vagon,107,1.6,Petrol,yes,2010,Golf Variant
+Volkswagen,4000,hatch,105,2,Other,yes,2013,Golf VII
+Renault,17500,crossover,20,1.5,Diesel,yes,2016,Duster
+BMW,1950,sedan,162,1.6,Petrol,no,1995,316
+Audi,11000,other,120,1.8,Petrol,yes,2000,TT
+Audi,7750,sedan,259,3,Diesel,no,2005,A6
+Mercedes-Benz,NA,sedan,6,3,Diesel,yes,2014,S 350
+Volkswagen,24300,sedan,1,1.6,Diesel,yes,2016,Jetta
+Renault,4100,van,190,1.9,Diesel,yes,2002,Kangoo
+Mercedes-Benz,69900,crossover,69,3,Diesel,yes,2014,GL 350
+Volkswagen,29999,other,129,2,Diesel,yes,2014,Caravelle
+Renault,11999,vagon,188,1.5,Other,yes,2012,Megane
+Volkswagen,13900,vagon,122,1.4,Gas,yes,2013,Touran
+BMW,8900,vagon,274,2,Diesel,no,2007,320
+Audi,26000,sedan,105,2,Petrol,yes,2012,A6
+BMW,2700,other,287,2.5,Diesel,no,1998,525
+Mitsubishi,9700,crossover,45,NA,Other,yes,2001,Pajero Sport
+Toyota,12500,sedan,140,3.5,Gas,yes,2007,Camry
+BMW,4500,sedan,28,1.9,Petrol,yes,1995,318
+Toyota,88000,crossover,3,4.5,Diesel,yes,2016,Land Cruiser 200
+Mitsubishi,8700,sedan,143,2,Gas,yes,2007,Lancer X
+Volkswagen,NA,van,124,1.6,Diesel,yes,2013,Caddy
+Volkswagen,17200,van,230,3.2,Gas,yes,2008,Multivan
+Toyota,30600,crossover,39,2.2,Diesel,yes,2014,Rav 4
+Mercedes-Benz,15300,sedan,240,2.2,Diesel,yes,2006,E-Class
+Toyota,73900,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,12500,sedan,84,2,Petrol,yes,2007,Passat B6
+Mercedes-Benz,30500,crossover,150,5.5,Petrol,yes,2002,G 500
+Audi,NA,sedan,280,4.2,Petrol,yes,2000,A8
+BMW,64900,crossover,73,3,Diesel,yes,2013,X6 M
+Toyota,11200,sedan,179,2.4,Petrol,yes,2006,Camry
+BMW,9900,other,93,2.5,Petrol,yes,2003,Z4
+Mercedes-Benz,55000,sedan,34,2.2,Diesel,yes,2011,S 250
+Volkswagen,3000,vagon,250,1.9,Diesel,yes,1992,Passat B3
+BMW,33000,sedan,147,3,Diesel,yes,2009,730
+Renault,13000,hatch,75,2,Petrol,yes,2013,Megane
+Mercedes-Benz,33900,other,102,5,Petrol,yes,2008,CL 55 AMG
+Mercedes-Benz,NA,vagon,100,3,Diesel,yes,2014,CLS 350
+BMW,17500,crossover,210,3,Diesel,yes,2005,X5
+Audi,33000,crossover,60,2,Petrol,yes,2011,Q5
+Volkswagen,11999,sedan,240,1.6,Petrol,yes,2010,Jetta
+Toyota,14700,sedan,141,3.5,Petrol,yes,2008,Camry
+Audi,2600,vagon,253,1.8,Gas,no,1997,A4
+BMW,10900,sedan,375,4.4,Gas,yes,2001,740
+Toyota,99999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Toyota,3900,hatch,160,1,Petrol,yes,1999,Yaris
+Renault,5700,van,275,1.9,Diesel,yes,2002,Trafic
+Audi,90999,sedan,4,3,Diesel,yes,2015,A8
+Mercedes-Benz,25500,sedan,137,2.2,Diesel,yes,2012,E-Class
+BMW,3300,sedan,360,2.5,Petrol,yes,1992,525
+Toyota,28700,crossover,80,3.5,Gas,yes,2012,Highlander
+Volkswagen,9300,van,200,1.6,Diesel,yes,2011,Caddy
+Toyota,9400,sedan,148,1.8,Gas,yes,2008,Avensis
+Audi,23000,crossover,180,3.6,Gas,yes,2007,Q7
+BMW,4500,sedan,370,4,Petrol,yes,1993,540
+Mitsubishi,23900,crossover,1,1.8,Petrol,yes,2015,ASX
+Mercedes-Benz,107999,crossover,0,3,Diesel,yes,2016,GLS 350
+Volkswagen,12900,sedan,157,1.8,Gas,yes,2008,Passat B6
+Mercedes-Benz,14900,van,211,2.2,Other,yes,2012,Vito
+Volkswagen,15900,other,160,1.8,Petrol,yes,2010,Passat B6
+Volkswagen,9300,van,154,2,Gas,yes,2010,Caddy
+Renault,1900,vagon,200,2,Gas,yes,1987,25
+Mercedes-Benz,7000,other,260,2.2,Diesel,yes,2000,Vito
+Mitsubishi,14700,crossover,240,3.2,Diesel,yes,2006,Pajero Wagon
+Mercedes-Benz,160000,other,8,4.6,Petrol,yes,2015,S 500
+Toyota,21800,crossover,63,2,Petrol,yes,2013,Rav 4
+Audi,68000,crossover,20,3,Petrol,yes,2014,Q5
+Mercedes-Benz,7000,sedan,370,2.2,Petrol,yes,1995,E-Class
+Mercedes-Benz,70000,other,21,6,Petrol,yes,2008,S 600
+Volkswagen,22000,sedan,65,2,Petrol,yes,2012,Passat B7
+Mercedes-Benz,18900,other,50,3.5,Petrol,yes,2005,SLK 350
+BMW,9700,sedan,267,2,Diesel,yes,2002,320
+Volkswagen,4850,vagon,300,1.9,Diesel,yes,1998,Golf III
+Mercedes-Benz,8700,other,260,2.2,Diesel,yes,2003,Sprinter 213
+Toyota,14700,sedan,139,3.5,Petrol,yes,2008,Camry
+Volkswagen,23999,sedan,1,1.6,Diesel,yes,2016,Jetta
+Mitsubishi,24500,crossover,11,2.5,Diesel,yes,2012,Pajero Sport
+Mercedes-Benz,23500,van,161,2.2,Diesel,yes,2011,Vito
+Volkswagen,10000,van,160,1.6,Diesel,yes,2012,Caddy
+Volkswagen,9700,van,255,2.5,Diesel,yes,2002,T4 (Transporter)
+Renault,7000,vagon,280,1.5,Diesel,yes,2005,Megane
+Renault,1550,other,245,2,Petrol,no,1998,Megane
+Audi,15000,sedan,38,NA,Diesel,no,2015,A5
+Mercedes-Benz,47600,van,0,2.2,Diesel,yes,2016,Vito
+Toyota,27000,other,105,2.5,Diesel,yes,2013,Hilux
+BMW,65000,crossover,0,2.5,Diesel,yes,2016,X5
+BMW,29999,sedan,130,NA,Petrol,yes,2009,750
+Renault,20007,sedan,2,1.5,Diesel,yes,2015,Fluence
+Volkswagen,15500,crossover,167,3.2,Petrol,yes,2006,Touareg
+Volkswagen,3999,hatch,152,1,Petrol,yes,2000,Lupo
+BMW,19990,sedan,1,2,Diesel,yes,2008,320
+Mercedes-Benz,38000,sedan,42,1.6,Petrol,yes,2014,C-Class
+Mercedes-Benz,NA,crossover,0,3,Petrol,yes,2016,GLE-Class
+Volkswagen,20477.54,van,1,1.6,Diesel,yes,2015,Caddy
+Volkswagen,9650,vagon,200,1.4,Gas,yes,2009,Passat B6
+BMW,37500,crossover,115,3,Diesel,yes,2008,X5 M
+Mercedes-Benz,12700,van,193,2.2,Diesel,yes,2011,Vito
+BMW,NA,sedan,259,3,Diesel,yes,2001,330
+Toyota,4500,hatch,207,1.3,Petrol,yes,1995,Corolla
+Mercedes-Benz,2400,sedan,3,3,Diesel,yes,1998,E-Class
+Mercedes-Benz,6999,sedan,330,2,Petrol,yes,1997,200
+Volkswagen,8900,van,169,1.9,Diesel,yes,2008,Caddy
+Mitsubishi,7999,sedan,78,1.5,Gas,yes,2011,Lancer X
+Volkswagen,13500,van,124,2,Diesel,yes,2013,T6 (Transporter) 
+Audi,1999,sedan,400,1.6,Diesel,yes,1986,80
+Mercedes-Benz,109999,crossover,1,3,Diesel,yes,2016,GLS 350
+Mercedes-Benz,14100,van,138,2.2,Diesel,yes,2009,Vito
+Volkswagen,4600,other,290,1.9,Diesel,yes,1995,T4 (Transporter) 
+Mercedes-Benz,3250,sedan,250,4,Diesel,no,2002,S 400
+Mercedes-Benz,19500,sedan,157,3.5,Petrol,yes,2006,S 350
+Audi,2300,vagon,25,2.5,Diesel,no,1999,A6
+Toyota,11700,sedan,131,2.4,Petrol,yes,2007,Camry
+Audi,7000,vagon,210,3,Diesel,no,2006,A6
+BMW,6950,sedan,320,4.4,Gas,yes,1997,740
+Mitsubishi,4500,sedan,300,1.6,Gas,yes,2000,Carisma
+Renault,12500,vagon,150,1.5,Diesel,yes,2011,Kangoo
+BMW,2200,sedan,290,2.5,Gas,yes,1997,525
+Renault,10750,hatch,136,1.5,Diesel,yes,2011,Megane
+Mitsubishi,8400,sedan,91,1.5,Petrol,yes,2009,Lancer X
+Volkswagen,28500,sedan,50,2,Petrol,yes,2013,Passat B7
+BMW,44800,sedan,63,4.4,Petrol,yes,2012,750
+Toyota,11707.8,hatch,1,1,Petrol,yes,2016,Yaris
+Toyota,NA,sedan,21,2.5,Petrol,yes,2015,Camry
+BMW,32500,sedan,131,2,Diesel,yes,2010,520
+Renault,10500,vagon,165,1.5,Diesel,yes,2012,Megane
+Mercedes-Benz,11999,other,215,2.2,Diesel,yes,2007,Vito
+Mercedes-Benz,20300,sedan,198,1.8,Gas,yes,2011,E-Class
+BMW,6100,sedan,205,1.8,Petrol,yes,2001,316
+Volkswagen,12300,other,248,1.9,Diesel,yes,2010,T5 (Transporter)
+Mercedes-Benz,2400,sedan,237,3.8,Petrol,yes,1983,S 350
+Toyota,11750,sedan,69,1.6,Petrol,yes,2013,Corolla
+BMW,52300,sedan,55,3,Diesel,yes,2013,530
+Audi,30900,crossover,70,2,Petrol,yes,2012,Q3
+Volkswagen,5200,van,235,2,Diesel,yes,2005,Caddy
+BMW,26800,sedan,47,2,Petrol,yes,2013,320
+Volkswagen,74000,crossover,1,3,Diesel,yes,2015,Touareg
+Renault,10700,van,150,1.9,Diesel,yes,2006,Trafic
+Mercedes-Benz,17500,vagon,113,2.2,Diesel,yes,2010,C-Class
+Toyota,30800,crossover,56,2.7,Gas,yes,2011,Land Cruiser Prado
+Volkswagen,2800,hatch,140,1.4,Petrol,no,2002,Golf IV
+Mercedes-Benz,12600,sedan,123,3.2,Petrol,yes,2000,S 320
+Volkswagen,17500,sedan,144,1.4,Petrol,yes,2012,Passat CC
+Toyota,68000,crossover,67,4.5,Diesel,yes,2012,Land Cruiser 200
+Toyota,34000,crossover,44,3.5,Petrol,yes,2012,Highlander
+Toyota,28900,crossover,20,2.5,Petrol,yes,2013,Rav 4
+Volkswagen,9900,van,230,1.9,Diesel,yes,2008,T5 (Transporter) 
+Toyota,46500,other,4,3,Diesel,yes,2016,Hilux
+BMW,24700,other,85,4.4,Petrol,yes,2004,645
+Volkswagen,3400,van,175,1.4,Petrol,yes,2003,Caddy
+Mitsubishi,7700,sedan,139,1.5,Petrol,yes,2008,Lancer X
+Toyota,19000,crossover,263,4.2,Diesel,yes,2001,Land Cruiser 100
+Mercedes-Benz,NA,sedan,62,3,Diesel,yes,2013,S 350
+BMW,3500,sedan,185,2,Diesel,no,2002,320
+Volkswagen,14800,other,177,2,Diesel,yes,2012,T5 (Transporter) 
+Mitsubishi,36000,crossover,0,2.4,Petrol,yes,2016,Outlander
+Volkswagen,13995,van,147,1.6,Diesel,yes,2013,Touran
+Toyota,9900,sedan,130,2.3,Gas,yes,2006,Camry
+Mitsubishi,7500,hatch,138,1.3,Petrol,yes,2008,Colt
+Volkswagen,24800,sedan,2,1.6,Diesel,yes,2016,Jetta
+Renault,7100,hatch,127,2,Gas,yes,2006,Laguna
+Toyota,45000,crossover,80,4,Petrol,yes,2013,Land Cruiser Prado
+Mercedes-Benz,2499,sedan,300,3,Diesel,no,1999,E-Class
+Mitsubishi,9500,sedan,136,2,Petrol,yes,2008,Lancer X
+BMW,21000,sedan,186,3,Petrol,yes,2007,530
+Volkswagen,11450,van,140,2,Diesel,yes,2010,Caddy
+Volkswagen,66346,crossover,0,3,Diesel,yes,2016,Touareg
+Volkswagen,8200,hatch,164,1.4,Petrol,yes,2008,Polo
+BMW,11700,sedan,316,NA,Diesel,yes,2001,730
+Renault,4599,van,190,1.5,Diesel,yes,2003,Kangoo
+Volkswagen,8900,sedan,62,1.6,Petrol,yes,2012,Polo
+Toyota,13900,hatch,79,1.6,Gas,yes,2012,Auris
+BMW,5500,sedan,200,3.5,Gas,yes,1998,535
+Mercedes-Benz,5500,sedan,360,2,Gas,yes,1994,E-Class
+BMW,14500,sedan,215,NA,Petrol,yes,2006,525
+Audi,3300,sedan,300,1.8,Gas,yes,1987,80
+Volkswagen,5950,van,213,1.6,Gas,yes,2008,Caddy
+Mitsubishi,18999,crossover,173,3.8,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,124000,crossover,0,3,Petrol,yes,2016,GLS 400
+Volkswagen,11500,sedan,130,2,Diesel,yes,2008,Passat B6
+Mercedes-Benz,4500,sedan,240,3.2,Other,no,2005,C-Class
+BMW,34900,crossover,35,3,Petrol,yes,2011,X3
+Mitsubishi,12999,crossover,84,2.4,Petrol,yes,2009,Outlander XL
+Toyota,20900,crossover,180,4.7,Gas,yes,2004,Land Cruiser 100
+Audi,NA,sedan,370,2.6,Other,yes,1997,A6
+Mercedes-Benz,26900,sedan,150,5.5,Petrol,yes,2008,S 550
+Volkswagen,49999,van,59,2,Diesel,yes,2014,Multivan
+Mercedes-Benz,4000,crossover,287,4,Diesel,no,2003,ML 400
+Volkswagen,52300,crossover,19,3,Diesel,yes,2014,Touareg
+Mercedes-Benz,29500,sedan,37,1.8,Petrol,yes,2012,E-Class
+Volkswagen,19100,other,120,1.4,Petrol,yes,2012,Scirocco
+BMW,45500,other,26,2,Diesel,yes,2015,520
+Toyota,4000,sedan,243,2,Gas,yes,1987,Mark II
+BMW,7200,sedan,255,2.8,Petrol,yes,1998,328
+Volkswagen,1900,vagon,504,1.6,Gas,yes,1988,Passat B2
+BMW,24700,vagon,142,2,Diesel,yes,2012,520
+Toyota,21000,crossover,74,2,Petrol,yes,2011,Rav 4
+Mitsubishi,14500,crossover,90,3,Petrol,yes,2008,Pajero Wagon
+Mercedes-Benz,13800,sedan,222,3,Gas,yes,2007,E-Class
+Volkswagen,25999,sedan,60,1.8,Petrol,yes,2014,Passat B7
+Volkswagen,9555,sedan,174,2,Petrol,yes,2007,Passat B6
+Renault,9700,van,152,2,Diesel,yes,2010,Trafic
+Renault,11700,van,150,2.3,Diesel,yes,2012,Master
+Volkswagen,5300,vagon,259,1.6,Gas,yes,1995,Passat B4
+Toyota,8700,hatch,140,NA,Petrol,yes,2008,Auris
+Audi,22500,sedan,159,5.2,Petrol,yes,2008,S8
+Renault,8300,van,221,1.5,Diesel,yes,2009,Kangoo
+Volkswagen,4900,van,198,1.6,Petrol,no,2004,Touran
+Volkswagen,2850,vagon,450,1.6,Diesel,yes,1988,Passat B2
+Volkswagen,4999,van,225,NA,Other,no,2003,Caravelle
+BMW,8100,sedan,245,2,Diesel,no,2007,320
+Volkswagen,4400,sedan,290,1.8,Gas,yes,1997,Vento
+BMW,9500,crossover,110,3,Diesel,no,2008,X6
+Audi,9500,vagon,160,2.7,Gas,yes,2003,A6 Allroad
+Toyota,4350,sedan,35,2.2,Gas,yes,1999,Camry
+Volkswagen,10000,van,224,2.5,Diesel,yes,2006,T5 (Transporter)
+BMW,65000,crossover,58,4.4,Petrol,yes,2014,X5
+Toyota,1750,hatch,270,1.6,Petrol,yes,1985,Corolla
+Renault,8200,hatch,79,1.6,Petrol,yes,2008,Laguna
+Volkswagen,22000,crossover,98,2,Diesel,yes,2013,Tiguan
+Volkswagen,13300.95,sedan,0,1.6,Petrol,yes,2016,Polo
+Volkswagen,9300,vagon,150,2,Petrol,yes,2008,Passat B6
+Audi,12000,sedan,9,NA,Petrol,no,2015,A4
+Volkswagen,6700,van,300,2.4,Diesel,yes,2000,T4 (Transporter)
+Mercedes-Benz,3900,sedan,385,1.8,Petrol,yes,1995,C-Class
+Mercedes-Benz,2150,sedan,360,2.9,Diesel,no,1997,E-Class
+Renault,11111,vagon,190,1.5,Diesel,yes,2011,Laguna
+Renault,7500,other,160,2,Gas,yes,2006,Scenic
+BMW,99999,other,0,3,Petrol,yes,2016,640
+Volkswagen,20500,hatch,56,2,Diesel,yes,2014,Golf VII
+Mercedes-Benz,119000,sedan,27,3,Diesel,yes,2014,S 350
+Mitsubishi,40800,crossover,0,2.4,Diesel,yes,2016,Pajero Sport
+Renault,10700,van,265,2,Diesel,yes,2007,Trafic
+Volkswagen,69990,van,1,2,Diesel,yes,2015,Multivan
+Mercedes-Benz,NA,other,24,4.7,Petrol,yes,2014,S 500
+Volkswagen,12500,sedan,128,2,Petrol,yes,2007,Passat B6
+Audi,15000,vagon,154,2,Diesel,yes,2008,A6
+Mitsubishi,6300,sedan,126,1.6,Petrol,yes,2006,Lancer
+Renault,13500,hatch,10,1.6,Petrol,yes,2014,Megane
+Toyota,9700,sedan,130,NA,Petrol,yes,2008,Corolla
+Renault,NA,vagon,177,1.5,Diesel,yes,2007,Megane
+Volkswagen,1900,sedan,297,1.6,Petrol,yes,1987,Jetta
+Volkswagen,17800,crossover,210,3,Diesel,yes,2005,Touareg
+Volkswagen,10000,van,71,1.2,Petrol,yes,2013,Caddy
+Volkswagen,10800,other,146,1.6,Diesel,yes,2012,Caddy
+Mercedes-Benz,10900,sedan,360,5,Gas,yes,2001,S 500
+Volkswagen,15000,vagon,69,1.4,Petrol,yes,2012,Touran
+Volkswagen,9850,sedan,110,2,Petrol,yes,2006,Passat B6
+Mitsubishi,8500,crossover,320,3,Gas,yes,2000,Pajero Sport
+Volkswagen,14900,vagon,170,1.6,Diesel,yes,2011,Passat B7
+Mitsubishi,11500,sedan,72,2.4,Petrol,yes,2006,Lancer Evolution
+Audi,8500,sedan,247,2.5,Diesel,yes,2001,A6
+Mitsubishi,12900,sedan,40,2,Petrol,yes,2013,Lancer Evolution
+Mitsubishi,30500,sedan,72,2,Petrol,yes,2006,Lancer Evolution
+Audi,13900,sedan,150,NA,Petrol,yes,2006,A8
+Mercedes-Benz,43500,other,51,3,Diesel,yes,2014,E-Class
+BMW,30500,sedan,73,2,Petrol,yes,2013,520
+Mercedes-Benz,1650,sedan,257,1.8,Gas,no,1995,CL 180
+Renault,9300,vagon,184,1.5,Diesel,yes,2011,Megane
+Mercedes-Benz,30500,sedan,89,6.3,Petrol,yes,2008,CLS 63 AMG
+Mercedes-Benz,16300,van,175,2.2,Diesel,yes,2010,Vito
+Renault,5750,crossover,225,2,Gas,yes,2003,Scenic
+Mercedes-Benz,54500,sedan,29,3,Diesel,yes,2012,CLS 350
+Mercedes-Benz,11500,van,209,2.2,Diesel,yes,2008,Vito
+Mercedes-Benz,53500,crossover,46,5.5,Petrol,yes,2008,G 500
+Renault,11300,van,183,2,Diesel,yes,2011,Trafic
+Volkswagen,13900,crossover,156,2,Diesel,yes,2012,T6 (Transporter) 
+Audi,14300,sedan,175,3,Petrol,yes,2006,A6
+Volkswagen,7500,van,280,1.6,Diesel,yes,2011,Caddy
+Volkswagen,55555,van,63,2,Diesel,yes,2013,Multivan
+Volkswagen,6999,hatch,195,1.4,Diesel,yes,2004,Golf V
+Mitsubishi,11499,van,98,2.4,Petrol,yes,2008,Grandis
+BMW,2850,sedan,214,1.9,Petrol,no,2000,318
+Renault,7150,van,118,1.5,Diesel,yes,2008,Kangoo
+Audi,25900,sedan,104,2.8,Petrol,yes,2010,A6
+Toyota,12500,hatch,75,1.4,Diesel,yes,2012,Yaris
+Mercedes-Benz,6200,van,196,2.2,Diesel,yes,2002,Vito
+Toyota,28700,crossover,92,3.5,Gas,yes,2012,Highlander
+Volkswagen,6000,van,419,2.4,Diesel,yes,1995,T4 (Transporter)
+Audi,44800,crossover,100,3,Diesel,yes,2014,Q7
+BMW,37000,crossover,186,5,Petrol,yes,2008,X6
+Audi,8000,sedan,234,2.4,Petrol,yes,1998,A6
+Volkswagen,21500,sedan,1,1.4,Petrol,yes,2015,Jetta
+BMW,4700,sedan,200,2.5,Petrol,yes,1995,525
+Mitsubishi,15500,crossover,102,3,Petrol,yes,2009,Outlander XL
+Renault,7900,van,149,1.5,Diesel,yes,2013,Kangoo
+Toyota,32000,sedan,22,2.5,Petrol,yes,2015,Camry
+BMW,26999,crossover,180,4.8,Gas,yes,2008,X5
+Toyota,7500,vagon,196,1.59,Petrol,yes,2003,Corolla Verso
+Mercedes-Benz,10999,van,191,2.2,Diesel,yes,2007,Vito
+Mitsubishi,7300,sedan,121,1.6,Gas,yes,2008,Lancer
+Mercedes-Benz,8000,van,280,2.2,Diesel,yes,2005,Vito
+Mitsubishi,17555,crossover,115,3,Gas,yes,2008,Pajero Wagon
+Volkswagen,38000,crossover,189,3,Diesel,yes,2012,Touareg
+BMW,15400,vagon,179,2,Diesel,yes,2007,520
+Volkswagen,10200,van,137,1.9,Diesel,yes,2008,Caddy
+Mercedes-Benz,7900,van,234,2.2,Diesel,yes,2002,Vito
+Mitsubishi,6700,sedan,161,1.6,Gas,yes,2006,Lancer
+Mercedes-Benz,30000,other,176,5.5,Petrol,yes,2003,E-Class
+Volkswagen,11000,vagon,210,1.9,Diesel,yes,2008,Touran
+Mercedes-Benz,38500,sedan,56,2.2,Diesel,yes,2014,CLA-Class
+Mercedes-Benz,7600,van,280,2.2,Diesel,yes,2001,Vito
+Volkswagen,8900,hatch,90,1.9,Diesel,yes,2009,Golf V
+Toyota,23400,crossover,120,4,Petrol,yes,2007,Land Cruiser Prado
+Audi,37000,sedan,37,2.8,Petrol,yes,2012,A6
+Renault,5600,vagon,151,1.5,Diesel,yes,2006,Kangoo
+Volkswagen,18900,sedan,75,1.8,Petrol,yes,2011,Passat B7
+Mercedes-Benz,15900,vagon,219,3,Diesel,yes,2007,E-Class
+Mercedes-Benz,15200,sedan,170,2.8,Gas,yes,2007,E-Class
+Volkswagen,20700,vagon,74,2,Diesel,yes,2013,Passat B7
+BMW,8200,sedan,210,3.5,Petrol,yes,1999,735
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLS 350
+Mitsubishi,5400,van,300,1.6,Petrol,yes,2002,Space Star
+BMW,NA,crossover,0,3,Diesel,yes,2016,X5
+Mitsubishi,11700,sedan,82,1.5,Gas,yes,2010,Lancer X
+Mitsubishi,12000,sedan,121,2.4,Gas,yes,2009,Galant
+Volkswagen,8100,sedan,198,1.6,Petrol,yes,2007,Jetta
+BMW,19999,sedan,290,4.8,Petrol,yes,2006,750
+Audi,32000,sedan,103,2,Diesel,yes,2013,A6
+Volkswagen,6200,van,340,1.8,Petrol,yes,1999,Sharan
+BMW,1500,sedan,270,2.5,Diesel,no,1992,525
+Volkswagen,17900,other,119,2,Diesel,yes,2012,T6 (Transporter)
+Mercedes-Benz,42500,sedan,140,5.5,Petrol,yes,2011,S 500
+BMW,4000,sedan,335,2,Diesel,no,1999,320
+Mitsubishi,13200,crossover,124,3,Gas,yes,2008,Pajero Sport
+Mercedes-Benz,24990,sedan,85,5.5,Petrol,yes,2008,S 500
+BMW,45200,crossover,68,3,Diesel,yes,2012,X5
+Toyota,18400,crossover,160,4.7,Gas,yes,2003,Land Cruiser 100
+Volkswagen,9500,van,212,1.9,Diesel,yes,2006,T5 (Transporter)
+Audi,25500,sedan,140,2.8,Petrol,yes,2012,A6
+Volkswagen,14999,crossover,200,4.2,Petrol,yes,2007,Touareg
+Volkswagen,10200,van,153,2.5,Diesel,yes,2007,T5 (Transporter)
+Mitsubishi,7900,sedan,238,2,Gas,yes,2008,Lancer X
+BMW,52222,hatch,3,2,Diesel,yes,2016,4 Series Gran Coupe
+Mitsubishi,9000,sedan,110,2,Petrol,yes,2008,Lancer X
+Audi,4200,vagon,215,2.5,Diesel,no,2003,A6
+Toyota,39500,other,100,4.5,Diesel,yes,2008,Land Cruiser 200
+BMW,9000,sedan,245,3,Diesel,yes,2001,730
+Mercedes-Benz,NA,crossover,35,3,Diesel,yes,2015,G 350
+Mercedes-Benz,12000,van,177,NA,Diesel,yes,2013,Vito
+Mercedes-Benz,23900,sedan,96,3.5,Petrol,yes,2009,S 350
+Mercedes-Benz,55000,crossover,128,3,Diesel,yes,2013,ML 350
+Mercedes-Benz,NA,crossover,0,3,Petrol,yes,2016,GLS 400
+Mercedes-Benz,11700,sedan,246,5,Petrol,yes,2004,S 500
+Mitsubishi,40990,crossover,0,2.4,Diesel,yes,2016,Pajero Sport
+BMW,42000,sedan,71,2.99,Petrol,yes,2011,535
+Renault,12100,crossover,138,2.5,Gas,yes,2008,Koleos
+Toyota,11400,sedan,118,1.4,Petrol,yes,2010,Corolla
+Audi,7000,vagon,320,1.9,Diesel,yes,1996,A4
+BMW,3500,hatch,1,2,Petrol,yes,2006,120
+Mitsubishi,23800,crossover,45,2.5,Diesel,yes,2015,Pajero Sport
+Mercedes-Benz,10550,other,128,NA,Other,yes,2011,Vito
+Mercedes-Benz,9500,other,220,2.2,Diesel,yes,2005,Vito
+BMW,37500,other,55,2,Petrol,yes,2014,428
+Renault,6000,van,1,1.9,Diesel,yes,2005,Trafic
+Mercedes-Benz,71999,crossover,0,2.2,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,10150,other,190,2.2,Diesel,yes,2008,Vito
+Audi,8300,sedan,186,2.5,Diesel,yes,1996,A6
+BMW,2600,sedan,270,2,Diesel,no,1998,320
+Mercedes-Benz,3500,sedan,250,2.7,Diesel,no,2001,E-Class
+Mercedes-Benz,29900,crossover,86,3,Gas,yes,2010,GLK 300
+Volkswagen,19999,sedan,107,1.8,Petrol,yes,2011,Passat B7
+Volkswagen,6000,van,180,2,Diesel,yes,2008,Caddy
+Mercedes-Benz,10900,van,360,2.2,Other,yes,2006,Vito
+Volkswagen,9999,van,165,1.6,Diesel,yes,2012,Caddy
+BMW,3700,other,110,2.5,Petrol,yes,1983,323
+Volkswagen,12400,other,145,3.2,Petrol,yes,2007,Passat B6
+Audi,3500,sedan,354,2.5,Diesel,no,2000,A6
+BMW,20900,crossover,230,3,Diesel,yes,2006,X5
+Audi,4150,sedan,220,2.5,Diesel,no,1999,A8
+Volkswagen,4100,van,345,1.9,Diesel,no,2002,Sharan
+Volkswagen,11500,sedan,70,NA,Other,yes,2014,Polo
+Mercedes-Benz,28500,sedan,86,5.5,Petrol,yes,2007,S 500
+Toyota,18900,crossover,90,2,Petrol,yes,2010,Rav 4
+Mercedes-Benz,12500,van,167,2.2,Diesel,yes,2013,Vito
+Mercedes-Benz,5500,sedan,199,NA,Petrol,yes,2000,E-Class
+Renault,2700,other,160,2,Petrol,no,2001,Megane
+Audi,10200,sedan,120,2,Petrol,yes,2006,A4
+BMW,6900,sedan,238,3.5,Petrol,yes,1999,535
+Mitsubishi,13200,crossover,79,2.4,Gas,yes,2009,Outlander
+Mercedes-Benz,124999,crossover,0,3,Diesel,yes,2016,GLS 350
+BMW,4100,sedan,200,2.7,Petrol,yes,1988,525
+Mercedes-Benz,3200,sedan,229,2.7,Diesel,no,2001,E-Class
+Toyota,13699,crossover,160,2,Petrol,yes,2007,Rav 4
+Mitsubishi,5700,sedan,176,1.6,Petrol,yes,2004,Lancer
+Mercedes-Benz,6500,other,215,2.2,Diesel,yes,2000,Vito
+Volkswagen,44000,crossover,150,4.2,Diesel,yes,2011,Touareg
+BMW,37000,sedan,130,3,Petrol,yes,2011,535
+BMW,29500,crossover,189,3,Diesel,yes,2009,X5
+Audi,NA,hatch,243,2,Gas,yes,1987,100
+Mercedes-Benz,99000,van,5,1.6,Diesel,yes,2013,Viano
+Audi,2999,vagon,320,NA,Diesel,no,1999,A6
+Volkswagen,4900,sedan,350,2,Gas,yes,2000,Bora
+Renault,5950,sedan,277,1.5,Diesel,yes,2005,Megane
+Mercedes-Benz,18300,sedan,155,3,Petrol,yes,2007,C-Class
+Mercedes-Benz,4700,sedan,280,2.8,Petrol,yes,1993,E-Class
+Toyota,37500,crossover,115,5.7,Petrol,yes,2008,Land Cruiser 200
+Volkswagen,15000,van,230,2.5,Diesel,yes,2007,T5 (Transporter)
+Audi,2800,sedan,330,NA,Other,yes,1989,80
+Mercedes-Benz,6900,sedan,350,3,Diesel,yes,1994,E-Class
+Mercedes-Benz,8300,van,230,2.2,Diesel,yes,2004,Vito
+Renault,3600,van,199,1.9,Diesel,yes,2000,Kangoo
+Audi,2500,vagon,280,2.5,Other,no,1999,A6
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,40500,crossover,37,3,Diesel,yes,2012,Touareg
+Mercedes-Benz,85000,van,3,2.2,Diesel,yes,2016,V 250
+Audi,4800,sedan,341,2.8,Petrol,yes,1992,100
+Volkswagen,2500,sedan,440,1.6,Diesel,yes,1991,Jetta
+Mercedes-Benz,9500,vagon,180,3.2,Diesel,yes,2002,E-Class
+Toyota,12700,sedan,160,2.4,Gas,yes,2005,Camry
+Volkswagen,10799,van,128,1.9,Diesel,yes,2010,Caddy
+Audi,27300,other,150,4.2,Petrol,yes,2007,Q7
+Volkswagen,20500,hatch,800,2,Diesel,yes,2013,Golf VII
+Toyota,21999,other,65,2.5,Petrol,yes,2012,Camry
+Mercedes-Benz,14500,van,300,2.2,Diesel,yes,2005,Sprinter 313
+Audi,12500,sedan,146,1.8,Petrol,yes,2007,A4
+Renault,7200,van,123,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,8200,sedan,160,2,Gas,yes,2007,Lancer X
+BMW,NA,crossover,5,4.4,Petrol,yes,2015,X5 M
+Mercedes-Benz,23999,other,150,NA,Diesel,yes,2010,Vito
+Volkswagen,13200,van,150,1.4,Gas,yes,2012,Touran
+Audi,22500,sedan,124,3,Diesel,yes,2008,A8
+BMW,4000,sedan,230,2,Diesel,no,2003,320
+Mitsubishi,9200,other,0,NA,Other,yes,2007,Lancer X
+Toyota,11300,sedan,260,3.5,Gas,yes,2007,Camry
+Toyota,9300,sedan,185,1.6,Gas,yes,2008,Corolla
+Mitsubishi,20000,crossover,135,2.5,Diesel,yes,2011,Pajero Sport
+Volkswagen,17000,other,184,3.6,Petrol,yes,2008,Passat B6
+BMW,56500,crossover,45,3,Diesel,yes,2014,X6
+BMW,8700,sedan,355,3,Diesel,yes,2000,730
+BMW,4600,sedan,340,2,Gas,yes,1995,520
+BMW,5500,sedan,249,NA,Other,yes,1995,530
+Mercedes-Benz,35000,crossover,154,3.2,Diesel,yes,2008,GL 320
+Volkswagen,6900,sedan,256,1.8,Petrol,yes,2002,Passat B5
+Volkswagen,4200,van,210,2,Diesel,no,2004,Touran
+Renault,5650,hatch,175,99.99,Other,yes,2002,Laguna
+Mitsubishi,22500,other,57,2.5,Other,yes,2013,L 200
+Volkswagen,4500,van,380,2,Diesel,yes,2004,Caddy
+Volkswagen,4800,hatch,90,1.9,Diesel,yes,1992,Golf III
+Volkswagen,10900,sedan,136,1.8,Petrol,yes,2008,Passat B6
+Volkswagen,9900,other,178,2.5,Diesel,yes,2007,T5 (Transporter)
+Mercedes-Benz,NA,other,0,4.7,Petrol,yes,2016,S 500
+Mercedes-Benz,11900,sedan,110,2.57,Petrol,yes,2004,C-Class
+Mercedes-Benz,5200,sedan,370,2,Petrol,yes,1997,E-Class
+Volkswagen,17400,sedan,73,1.8,Petrol,yes,2012,Passat B7
+Renault,6799,van,140,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,NA,van,138,2.4,Petrol,yes,2007,Grandis
+Mercedes-Benz,2300,sedan,300,2,Petrol,yes,1991,E-Class
+Mercedes-Benz,32800,crossover,109,3,Diesel,yes,2011,ML 350
+Volkswagen,4999,sedan,145,2,Petrol,yes,2000,Jetta
+Volkswagen,10900,vagon,185,1.4,Petrol,yes,2011,Golf VI
+Mercedes-Benz,34999,other,85,5.5,Petrol,yes,2008,CL 55 AMG
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLE-Class
+Volkswagen,4100,hatch,160,1.8,Petrol,yes,2006,Pointer
+Toyota,9000,sedan,232,2.4,Petrol,yes,2005,Camry
+Toyota,18900,crossover,161,2.7,Gas,yes,2005,Land Cruiser Prado
+Volkswagen,16500,sedan,123,1.8,Petrol,yes,2010,Passat B6
+Toyota,14474,crossover,115,2,Petrol,yes,2007,Rav 4
+BMW,3999,sedan,250,3,Diesel,no,2000,530
+Audi,5000,sedan,370,2.3,Petrol,yes,1991,100
+Toyota,9200,sedan,250,2.4,Gas,yes,2003,Camry
+Renault,NA,van,195,1.5,Diesel,yes,2013,Grand Scenic
+Volkswagen,6000,vagon,250,5,Diesel,yes,1995,Passat B4
+Volkswagen,13300,sedan,133,1.8,Petrol,yes,2008,Passat B6
+Renault,17639.47,crossover,0,1.2,Petrol,yes,2016,Captur
+BMW,27500,sedan,100,2,Petrol,yes,2012,520
+BMW,13200,sedan,150,2.2,Petrol,yes,2004,520
+Audi,35500,crossover,130,3,Diesel,yes,2010,Q7
+Toyota,41900,crossover,89,4.5,Diesel,yes,2012,Land Cruiser 200
+Renault,10500,crossover,93,1.5,Diesel,yes,2011,Duster
+Mercedes-Benz,9999,sedan,250,2.2,Diesel,yes,2000,E-Class
+Mercedes-Benz,24800,other,160,3,Diesel,yes,2007,S 320
+Mercedes-Benz,3300,sedan,330,2,Gas,yes,1988,190
+Mitsubishi,6900,sedan,122,1.6,Petrol,yes,2008,Lancer
+Renault,7300,van,212,1.5,Diesel,yes,2009,Kangoo
+Renault,NA,van,192,99.99,Diesel,yes,2011,Kangoo
+Mitsubishi,11500,crossover,142,2.4,Petrol,yes,2006,Outlander
+Toyota,36800,crossover,77,3,Diesel,yes,2012,Land Cruiser Prado
+Mercedes-Benz,19600,van,127,1.8,Diesel,yes,2013,B 180
+Mitsubishi,8300,crossover,161,2,Petrol,yes,2005,Outlander
+Mercedes-Benz,13200,van,260,2.2,Diesel,yes,2008,Sprinter 213
+Mercedes-Benz,28900,other,78,3.5,Petrol,yes,2010,E-Class
+BMW,6900,sedan,280,2,Gas,yes,2000,320
+Renault,7600,sedan,97,1.6,Petrol,yes,2008,Megane
+BMW,27500,sedan,170,2,Diesel,yes,2011,520
+Mercedes-Benz,295000,sedan,29,6,Petrol,yes,2011,S-Guard
+BMW,30000,sedan,78,3.8,Petrol,yes,1993,M5
+Volkswagen,7700,sedan,180,1.8,Petrol,yes,2004,Passat B5
+Mercedes-Benz,135555,sedan,54,4.66,Petrol,yes,2015,S 500
+Mercedes-Benz,11300,other,220,3.2,Diesel,yes,2003,E-Class
+Toyota,12200,sedan,157,2.4,Gas,yes,2009,Camry
+Mercedes-Benz,50900,crossover,85,3,Diesel,yes,2013,ML 350
+Mercedes-Benz,33900,other,100,5.5,Petrol,yes,2007,CL 500
+Renault,11900,van,143,2,Diesel,yes,2009,Trafic
+Mitsubishi,7400,sedan,189,1.6,Petrol,yes,2006,Lancer
+BMW,10800,sedan,288,4.4,Gas,yes,2002,745
+Toyota,22700,crossover,128,4,Gas,yes,2007,Land Cruiser Prado
+Mercedes-Benz,15000,sedan,67,2.8,Other,yes,1995,E-Class
+Renault,8300,hatch,168,1.6,Petrol,yes,2009,Laguna
+Renault,8499,van,120,1.5,Diesel,yes,2014,Kangoo
+Volkswagen,8950,sedan,140,1.6,Petrol,yes,2012,Polo
+Volkswagen,7500,sedan,272,2.8,Gas,yes,2002,Passat B5
+Volkswagen,11350,van,190,75,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,6650,van,223,1.9,Diesel,yes,2007,Caddy
+Toyota,12800,sedan,203,NA,Petrol,yes,2005,Camry
+Audi,6500,sedan,365,4.2,Gas,yes,1995,A8
+Volkswagen,8700,van,158,2.5,Diesel,yes,2003,T5 (Transporter)
+Volkswagen,12000,sedan,152,1.8,Petrol,yes,2008,Passat B6
+Audi,16800,sedan,47,1.2,Petrol,yes,2013,A3
+Volkswagen,NA,vagon,160,1.9,Diesel,yes,2003,Passat B5
+Toyota,24000,sedan,16,2.5,Petrol,yes,2013,Camry
+Volkswagen,20900,sedan,82,2,Diesel,yes,2012,Passat B7
+Volkswagen,17500,sedan,76,1.8,Petrol,yes,2012,Passat B7
+Mitsubishi,8000,sedan,119,NA,Petrol,yes,2007,Lancer
+Mercedes-Benz,38200,sedan,39,1.6,Petrol,yes,2015,C-Class
+Mercedes-Benz,5400,other,300,3.2,Gas,yes,1997,E-Class
+Volkswagen,17800,sedan,180,3,Diesel,yes,2007,Touareg
+BMW,13000,sedan,90,5,Petrol,no,2008,M5
+Volkswagen,10950,van,177,2,Diesel,yes,2011,Caddy
+Mercedes-Benz,3800,crossover,250,3.2,Gas,no,1998,ML 320
+Mitsubishi,7300,sedan,137,1.6,Gas,yes,2006,Lancer
+Mercedes-Benz,19900,other,52,1.8,Petrol,yes,2007,SLK 200
+Mercedes-Benz,7300,vagon,380,2.9,Diesel,yes,1997,E-Class
+Audi,9800,vagon,253,1.9,Diesel,yes,2002,A6
+Renault,6450,van,186,1.5,Diesel,yes,2011,Kangoo
+Volkswagen,28500,other,38,2,Diesel,yes,2015,Tiguan
+Renault,10600,vagon,220,2.5,Other,yes,2006,Trafic
+Volkswagen,11900,sedan,144,1.6,Gas,yes,2009,Jetta
+Toyota,7900,sedan,170,3,Petrol,yes,2004,Camry
+Mercedes-Benz,43900,crossover,89,3,Diesel,yes,2012,ML 350
+Volkswagen,8800,van,145,2,Gas,yes,2011,Caddy
+Volkswagen,1899,hatch,300,1.6,Gas,yes,1984,Golf II
+Volkswagen,8400,sedan,230,1.8,Gas,yes,2003,Passat B5
+Volkswagen,25200,crossover,78,2,Diesel,yes,2012,Tiguan
+Renault,12499,van,78,2,Diesel,yes,2012,Trafic
+Volkswagen,5300,van,505,2.4,Diesel,yes,1995,T4 (Transporter)
+Volkswagen,8900,van,167,2,Gas,yes,2011,Caddy
+Mercedes-Benz,2500,vagon,122,2.3,Petrol,yes,1987,230
+Mercedes-Benz,NA,sedan,20,3,Diesel,yes,2015,S 350
+Volkswagen,15900,vagon,1,2,Diesel,yes,2012,T6 (Transporter) 
+BMW,20000,sedan,230,6,Petrol,yes,2008,760
+BMW,69999,crossover,0,2.5,Diesel,yes,2016,X5
+Volkswagen,10000,sedan,98,1.6,Petrol,yes,2013,Polo
+Mercedes-Benz,9500,other,650,2.2,Diesel,yes,2005,Sprinter 313
+Renault,12179,vagon,95,1.5,Diesel,yes,2012,Megane
+Toyota,20900,crossover,69,2.7,Petrol,yes,2008,Land Cruiser Prado
+BMW,7400,sedan,277,3.5,Gas,yes,1998,535
+Audi,18300,sedan,125,2,Diesel,yes,2009,A6
+Mercedes-Benz,2650,sedan,30,2.3,Gas,yes,1985,230
+BMW,2500,sedan,242,3,Diesel,no,2001,530
+Audi,52999,crossover,112,4.2,Diesel,yes,2008,Q7
+Audi,1750,vagon,330,1.9,Diesel,no,1996,A4
+Volkswagen,2200,hatch,260,1.8,Gas,yes,1987,Golf II
+Renault,6000,vagon,225,1.5,Diesel,yes,2004,Megane
+BMW,40000,crossover,48,4.4,Petrol,yes,2009,X6
+Mercedes-Benz,103333,crossover,0,3,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,14800,sedan,218,3.2,Diesel,yes,2004,S 320
+BMW,28000,crossover,116,NA,Petrol,yes,2008,X5
+Mercedes-Benz,12900,hatch,130,2,Petrol,yes,2007,B 200
+Volkswagen,21900,crossover,136,3,Diesel,yes,2008,Touareg
+BMW,5300,sedan,204,1.8,Petrol,yes,1993,318
+Mitsubishi,1599,sedan,156,1.8,Petrol,yes,1989,Galant
+BMW,2300,sedan,189,1.8,Petrol,no,2002,318
+Renault,7200,sedan,69,1.4,Petrol,yes,2012,Symbol
+BMW,104999,crossover,2,3,Diesel,yes,2016,X5
+Mercedes-Benz,199999,crossover,0,5.5,Petrol,yes,2016,GLS 63
+Mitsubishi,11300,crossover,209,3.5,Petrol,yes,1999,Pajero Wagon
+Toyota,12800,sedan,123,3.5,Petrol,yes,2007,Camry
+Audi,46000,sedan,170,4.2,Diesel,yes,2011,A8
+BMW,2500,sedan,290,2,Diesel,no,1999,320
+Mercedes-Benz,93555,crossover,0,NA,Other,yes,2016,GLS 350
+Volkswagen,22500,other,200,2,Petrol,yes,2011,Scirocco
+Toyota,36700,crossover,43,3,Diesel,yes,2012,Land Cruiser Prado
+BMW,25500,crossover,130,3,Gas,yes,2007,X5
+Volkswagen,9300,van,220,2.5,Diesel,yes,2003,T4 (Transporter)
+Mercedes-Benz,11299,van,1,2.2,Other,yes,2006,Vito
+Toyota,12900,hatch,37,1.4,Petrol,yes,2013,Auris
+Toyota,23600,sedan,105,2.5,Gas,yes,2012,Camry
+Toyota,21900,crossover,110,4,Gas,yes,2006,FJ Cruiser
+Renault,4350,van,198,1.5,Diesel,yes,2004,Kangoo
+Audi,3100,vagon,210,2.5,Diesel,no,2000,A6
+BMW,7400,sedan,290,3,Petrol,yes,2000,330
+Volkswagen,10999,van,95,1.6,Diesel,yes,2012,Caddy
+Mitsubishi,18700,crossover,52,2,Petrol,yes,2013,Outlander XL
+Mercedes-Benz,18200,sedan,103,2,Gas,yes,2007,C-Class
+Mercedes-Benz,12500,sedan,230,5,Petrol,yes,2002,E-Class
+Mercedes-Benz,12900,hatch,150,2,Diesel,yes,2008,B 180
+Volkswagen,12500,sedan,161,2,Petrol,yes,2006,Passat B6
+Toyota,25500,sedan,58,2.5,Petrol,yes,2015,Camry
+Volkswagen,55555,van,50,2,Diesel,yes,2013,Multivan
+BMW,12799,sedan,181,2.2,Gas,yes,2004,520
+Volkswagen,6300,crossover,213,2.5,Diesel,no,2004,Touareg
+Volkswagen,8800,hatch,168,1.2,Diesel,yes,2013,Polo
+Volkswagen,30000,van,135,2,Diesel,yes,2011,Multivan
+Renault,5000,vagon,150,NA,Other,yes,2006,Kangoo
+Volkswagen,15880,vagon,139,1.4,Gas,yes,2011,Passat B7
+Audi,8150,sedan,162,2,Petrol,yes,2004,A4
+Renault,3400,hatch,77,1.8,Other,yes,1994,Laguna
+Volkswagen,6500,van,185,NA,Diesel,yes,2006,Caddy
+Toyota,13000,hatch,42,1.3,Petrol,yes,2013,Auris
+Volkswagen,51900,crossover,30,3,Diesel,yes,2015,Touareg
+Toyota,37700,crossover,62,3,Diesel,yes,2013,Land Cruiser Prado
+Audi,6700,sedan,470,2,Diesel,yes,1997,A6
+BMW,11700,sedan,270,3,Diesel,yes,2003,530
+Mercedes-Benz,13000,other,580,2.2,Diesel,yes,2005,Sprinter 313
+Mercedes-Benz,62999,crossover,0,2.2,Diesel,yes,2016,GLC-Class
+Renault,13321.33,crossover,0,1.5,Diesel,yes,2016,Sandero StepWay
+BMW,115800,crossover,4,3,Diesel,yes,2016,X5 M
+Mercedes-Benz,27300,crossover,185,3,Diesel,yes,2008,GL 320
+Toyota,64000,crossover,128,4.5,Diesel,yes,2013,Land Cruiser 200
+Volkswagen,10000,sedan,90,9,Petrol,no,2010,Passat B6
+BMW,129222,sedan,2,5,Petrol,yes,2016,750
+Mitsubishi,11900,crossover,129,2.5,Diesel,yes,2007,Pajero Sport
+BMW,47500,sedan,0,2,Diesel,yes,2016,320
+BMW,12800,sedan,140,2.2,Petrol,yes,2005,520
+Mercedes-Benz,3900,sedan,375,2,Petrol,yes,1990,190
+Volkswagen,12650,hatch,11,1,Petrol,yes,2014,Up
+Volkswagen,5600,vagon,235,1.4,Petrol,yes,2000,Golf IV
+Mercedes-Benz,19600,vagon,127,1.8,Diesel,yes,2013,B 180
+Toyota,28900,sedan,15,2.5,Petrol,yes,2015,Camry
+Volkswagen,10900,sedan,273,2,Petrol,yes,2006,Passat B6
+Volkswagen,11500,sedan,169,1.8,Petrol,yes,2008,Passat B6
+Mitsubishi,9950,crossover,275,3.5,Gas,yes,2000,Pajero Wagon
+Mercedes-Benz,23800,sedan,195,2.2,Diesel,yes,2010,E-Class
+Renault,8750,vagon,46,1.6,Petrol,yes,2012,Logan
+Toyota,19900,sedan,28,1.6,Petrol,yes,2014,Corolla
+Audi,6000,sedan,226,1.8,Petrol,yes,1995,A4
+Volkswagen,12900,sedan,136,1.6,Diesel,yes,2010,Passat B6
+BMW,8999,sedan,221,4.5,Petrol,yes,2002,745
+Volkswagen,8700,van,244,2.5,Diesel,yes,2001,T4 (Transporter)
+Toyota,19800,crossover,44,2,Petrol,yes,2013,Rav 4
+Volkswagen,9000,sedan,51,1.6,Petrol,yes,2013,Polo
+Volkswagen,21000,vagon,58,2,Diesel,yes,2013,Passat B7
+Audi,2100,vagon,275,2.5,Diesel,no,1998,A4
+Mercedes-Benz,5000,van,400,2.3,Diesel,yes,1998,Vito
+Toyota,9500,other,90,1.6,Other,yes,2008,Corolla
+Volkswagen,34500,van,167,2,Diesel,yes,2013,Multivan
+Mercedes-Benz,NA,other,22,3,Diesel,yes,2015,S 350
+Toyota,9800,hatch,98,1.6,Gas,yes,2008,Auris
+Audi,10399,sedan,293,1.8,Gas,yes,2004,A4
+Mercedes-Benz,42000,sedan,38,2.2,Diesel,yes,2014,CLA 220
+Audi,14500,hatch,60,1.2,Petrol,yes,2011,A1
+Toyota,13621.43,sedan,0,1.3,Petrol,yes,2015,Corolla
+BMW,3300,sedan,250,3,Diesel,no,2000,530
+Volkswagen,34899.14,vagon,0,2,Petrol,yes,2016,Passat B8
+Mercedes-Benz,4500,van,180,2.3,Diesel,yes,1998,Vito
+Volkswagen,1800,vagon,280,1.9,Diesel,yes,1996,Golf III
+Mitsubishi,12999,crossover,140,2.4,Gas,yes,2007,Outlander XL
+Volkswagen,8800,van,240,1.9,Diesel,yes,2003,T5 (Transporter)
+Audi,22000,crossover,152,4.2,Petrol,yes,2007,Q7
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,159999,other,0,3,Petrol,yes,2016,S 400
+Toyota,35555,crossover,40,3.5,Petrol,yes,2013,Highlander
+Volkswagen,2600,sedan,330,1.9,Petrol,no,1999,Passat B5
+Volkswagen,11818.95,sedan,1,1.6,Petrol,yes,2014,Polo
+Renault,8000,van,207,1.9,Diesel,yes,2003,Trafic
+Audi,52055.25,crossover,0,2,Diesel,yes,2016,Q5
+Audi,5300,other,182,2.5,Diesel,no,2002,A4
+Renault,1800,van,123,1.9,Diesel,yes,2004,Kangoo
+Volkswagen,8500,van,216,1.9,Diesel,yes,2005,Caddy
+Mercedes-Benz,99999,crossover,30,5,Petrol,yes,2013,G 500
+Toyota,23000,crossover,233,4,Gas,yes,2006,Land Cruiser Prado
+Audi,11900,sedan,131,2,Petrol,yes,2007,A4
+Mercedes-Benz,6999,sedan,200,2.6,Gas,yes,2001,C-Class
+Toyota,9999,sedan,345,3,Gas,yes,2005,Camry
+Audi,8999,vagon,200,2.7,Petrol,yes,2003,A6 Allroad
+Renault,10800,other,206,2,Diesel,yes,2008,Trafic
+Mercedes-Benz,7777,other,230,2.8,Petrol,yes,1980,E-Class
+Mercedes-Benz,11500,van,200,2.2,Diesel,yes,2010,Vito
+Mitsubishi,2300,sedan,420,1.8,Petrol,yes,1991,Galant
+Audi,2000,vagon,310,2.5,Other,no,1998,A6
+Volkswagen,28900,crossover,1,2,Petrol,yes,2016,Tiguan
+Mercedes-Benz,7700,sedan,285,3,Diesel,yes,1999,E-Class
+Renault,20996.24,crossover,0,1.5,Diesel,yes,2016,Captur
+Toyota,8600,sedan,126,1.6,Petrol,yes,2008,Corolla
+Toyota,11000,sedan,156,1.8,Petrol,yes,2008,Avensis
+Volkswagen,11200,sedan,260,3.2,Petrol,yes,2006,Passat B6
+BMW,5000,sedan,270,3,Diesel,yes,2000,530
+BMW,14900,vagon,221,2,Diesel,yes,2008,520
+Mercedes-Benz,9500,other,280,2.9,Diesel,yes,1998,Sprinter 312
+Volkswagen,7700,sedan,220,2.8,Petrol,yes,2004,Passat B5
+Toyota,14699,sedan,155,NA,Petrol,yes,2006,Camry
+Toyota,21900,sedan,88,2.5,Petrol,yes,2012,Camry
+BMW,77500,crossover,1,3,Petrol,yes,2016,X6
+Mercedes-Benz,8000,other,245,2.2,Diesel,yes,2004,Vito
+Mercedes-Benz,2199,van,396,2.2,Diesel,no,2000,Vito
+Mercedes-Benz,41900,sedan,95,6.3,Petrol,yes,2011,S 63 AMG
+Mitsubishi,9000,sedan,158,NA,Other,yes,2007,Lancer
+Mercedes-Benz,20300,sedan,198,1.8,Gas,yes,2011,E-Class
+Toyota,14300,sedan,71,2.4,Petrol,yes,2008,Camry
+Mercedes-Benz,83000,crossover,70,3,Diesel,yes,2013,GL 350
+BMW,6500,sedan,311,2,Gas,yes,1998,520
+Mercedes-Benz,8500,van,281,2.2,Diesel,yes,2004,Vito
+Renault,5400,vagon,181,1.5,Diesel,no,2010,Megane
+Toyota,16800,crossover,238,4,Gas,yes,2004,Land Cruiser Prado
+Toyota,22900,crossover,34,2,Petrol,yes,2012,Rav 4
+Mercedes-Benz,21300,crossover,143,3.2,Diesel,yes,2007,ML 320
+BMW,16999,other,90,2.5,Petrol,yes,2008,325
+Volkswagen,15700,sedan,110,1.8,Petrol,yes,2011,Passat B7
+Mercedes-Benz,5300,van,244,2.2,Diesel,yes,2000,Vito
+Mercedes-Benz,16500,sedan,280,3.7,Petrol,yes,2004,S 350
+BMW,8999,sedan,250,1.9,Petrol,yes,2001,316
+Toyota,14850,hatch,51,1.6,Petrol,yes,2013,Auris
+BMW,24100,vagon,112,3,Petrol,yes,2011,X1
+BMW,66500,crossover,1,2,Diesel,yes,2016,X5
+BMW,24500,crossover,195,4.8,Petrol,yes,2007,X5
+Toyota,23499,crossover,174,4,Gas,yes,2008,Land Cruiser Prado
+Volkswagen,14500,vagon,229,2,Diesel,yes,2009,Passat B6
+Renault,13300.95,vagon,0,1.5,Diesel,yes,2016,Logan
+Audi,1852.5,sedan,150,1.8,Petrol,yes,1985,80
+Mitsubishi,8777,sedan,180,20,Petrol,yes,2007,Lancer X
+BMW,8500,sedan,245,2.2,Petrol,yes,2002,523
+BMW,NA,crossover,4,4.4,Petrol,yes,2016,X5 M
+Mercedes-Benz,21500,sedan,146,1.8,Gas,yes,2012,E-Class
+Volkswagen,10000,sedan,174,2,Petrol,yes,2005,Passat B6
+Mercedes-Benz,4500,other,356,2.3,Petrol,yes,1991,230
+Volkswagen,21900,van,166,2,Diesel,yes,2011,T6 (Transporter)
+Mercedes-Benz,102800,van,0,2.2,Diesel,yes,2016,V 250
+Renault,7750,van,70,1.5,Diesel,yes,2014,Kangoo
+Volkswagen,9750,van,253,1.9,Diesel,yes,2010,Caddy
+Toyota,10500,sedan,82,NA,Petrol,yes,2010,Corolla
+Renault,3100,vagon,150,1.9,Diesel,no,2003,Laguna
+BMW,1900,sedan,200,1.8,Gas,no,1994,318
+BMW,14300,sedan,217,3,Diesel,yes,2005,530
+Renault,4300,van,1,1.5,Diesel,yes,2003,Kangoo
+Audi,17000,crossover,127,3,Diesel,no,2013,Q7
+Toyota,17999,crossover,35,2,Petrol,yes,2009,Rav 4
+Mercedes-Benz,99000,crossover,0,3,Diesel,yes,2016,GLS 350
+Audi,3100,hatch,114,1.6,Petrol,no,2001,A3
+Mitsubishi,6999,sedan,113,1.5,Petrol,yes,2007,Lancer X
+Toyota,13900,sedan,102,3.5,Gas,yes,2008,Camry
+Mercedes-Benz,68500,crossover,68,3,Diesel,yes,2014,GL 350
+Mercedes-Benz,11800,van,148,2.2,Diesel,yes,2010,Vito
+Mercedes-Benz,115555,crossover,39,3,Diesel,yes,2015,G 350
+Audi,21000,sedan,210,4.2,Petrol,yes,2009,A8
+Mercedes-Benz,3150,vagon,220,1.7,Other,no,2001,A 170
+Volkswagen,NA,van,158,1.6,Diesel,yes,2012,Caddy
+Audi,1950,vagon,300,1.8,Petrol,no,1999,A4
+Mitsubishi,12500,crossover,120,2.5,Diesel,yes,2007,Pajero Sport
+Mitsubishi,5999,van,258,NA,Diesel,yes,1999,L 400
+Volkswagen,3100,sedan,232,1.9,Diesel,no,2001,Passat B5
+Toyota,19800,crossover,252,4.2,Diesel,yes,2002,Land Cruiser 100
+Audi,4200,sedan,236,2.5,Diesel,no,2003,A6
+Audi,2100,vagon,230,1.9,Diesel,no,2000,A4
+BMW,32500,vagon,162,3,Diesel,yes,2012,530
+Audi,15500,vagon,180,2,Diesel,yes,2007,A6
+Mercedes-Benz,11500,sedan,224,2.6,Gas,yes,2003,E-Class
+Audi,3000,sedan,265,1.8,Petrol,yes,1988,80
+BMW,4550,sedan,200,2.8,Petrol,no,1997,528
+BMW,18777,sedan,134,4.4,Petrol,yes,2005,545
+Audi,19700,crossover,172,3.6,Petrol,yes,2007,Q7
+Renault,10950,vagon,180,1.5,Diesel,yes,2011,Megane
+Volkswagen,9600,other,128,1.9,Diesel,yes,2010,Caddy
+Renault,6700,hatch,96,1.4,Petrol,yes,2010,Sandero
+Audi,2411,sedan,240,1.6,Other,yes,1987,80
+BMW,23500,crossover,74,2,Petrol,yes,2010,X1
+Volkswagen,9000,sedan,59,2,Petrol,no,2010,Passat B6
+Volkswagen,38764,sedan,0,2,Diesel,yes,2014,Passat CC
+Volkswagen,5500,van,380,2.4,Diesel,yes,1999,T4 (Transporter) 
+Mercedes-Benz,11800,sedan,150,2.2,Diesel,yes,2004,E-Class
+Toyota,52500,crossover,43,3,Other,yes,2014,Land Cruiser Prado
+Audi,31500,sedan,80,2,Petrol,yes,2013,A6
+Volkswagen,22800,other,161,2,Diesel,yes,2011,Amarok
+BMW,21000,other,130,2.5,Petrol,yes,2006,325
+Mercedes-Benz,9200,sedan,172,2.8,Petrol,yes,1997,E-Class
+Mercedes-Benz,9900,sedan,320,2.7,Diesel,yes,2001,E-Class
+Volkswagen,1500,sedan,265,1.8,Petrol,yes,1988,Jetta
+BMW,2850,sedan,386,5,Gas,yes,1988,750
+Volkswagen,19999,other,48,2,Diesel,yes,2012,Eos
+BMW,7000,sedan,338,NA,Petrol,yes,1997,728
+Volkswagen,11250,other,159,1.4,Petrol,yes,2010,Golf VI
+Mitsubishi,46000,crossover,0,2.4,Diesel,yes,2016,Pajero Sport
+Volkswagen,18300,van,1,2.2,Diesel,yes,2013,T5 (Transporter) 
+BMW,42900,sedan,2,2,Petrol,yes,2016,520
+BMW,18400,crossover,256,3,Diesel,yes,2006,X5
+BMW,12900,sedan,121,2,Petrol,yes,2007,318
+Toyota,37600,crossover,126,4.7,Petrol,yes,2008,Land Cruiser 200
+BMW,46000,sedan,5,2,Petrol,yes,2016,520
+Volkswagen,3200,other,650,1.9,Diesel,yes,1999,Caddy
+BMW,6500,vagon,377,2,Gas,yes,1998,520
+Volkswagen,55555,van,53,2,Diesel,yes,2013,Multivan
+Mitsubishi,6900,sedan,190,1.6,Gas,yes,2007,Lancer
+Mercedes-Benz,104000,crossover,16,5.5,Petrol,yes,2015,ML 63 AMG
+Mitsubishi,5200,van,200,1.6,Petrol,yes,2003,Space Star
+Mitsubishi,8400,sedan,147,2,Petrol,yes,2007,Lancer
+Volkswagen,1450,hatch,200,1.8,Gas,no,1992,Golf III
+Volkswagen,13700,other,103,2,Diesel,yes,2013,T5 (Transporter) 
+Toyota,18950,sedan,45,1.8,Petrol,yes,2014,Avensis
+Mercedes-Benz,13000,other,247,2.2,Diesel,yes,2012,Sprinter 313
+Mitsubishi,19800,crossover,67,2,Gas,yes,2013,Outlander
+Mercedes-Benz,22500,sedan,213,2.2,Diesel,yes,2012,E-Class
+BMW,19500,sedan,190,6,Petrol,yes,2004,760
+Volkswagen,NA,other,67,1.8,Other,yes,2008,Passat B6
+Mercedes-Benz,6100,sedan,300,3.2,Diesel,no,2004,E-Class
+Volkswagen,19000,sedan,109,1.8,Petrol,yes,2012,Passat B7
+Mercedes-Benz,3799,van,350,2.2,Diesel,yes,2000,Vito
+BMW,8900,sedan,250,2.5,Diesel,yes,2001,525
+Volkswagen,8000,van,350,1.9,Diesel,yes,2004,T5 (Transporter)
+BMW,4500,sedan,300,3,Diesel,yes,2000,330
+Volkswagen,2600,van,10,2.4,Diesel,yes,1988,LT
+Mercedes-Benz,31669,hatch,2,NA,Other,yes,2015,B-Class Electric Drive
+BMW,4300,sedan,240,2.5,Diesel,no,2002,525
+Mercedes-Benz,82555,crossover,69,3,Diesel,yes,2014,GL 350
+BMW,31000,vagon,15,NA,Other,yes,2015,I3
+Renault,10000,other,214,2.5,Diesel,yes,2008,Master
+Audi,56000,crossover,44,3,Diesel,yes,2014,Q7
+BMW,21700,sedan,259,3,Diesel,yes,2007,730
+BMW,89999,crossover,17,4.4,Petrol,yes,2015,X6 M
+Mitsubishi,9200,sedan,92,2,Petrol,yes,2010,Lancer X
+Audi,21700,sedan,167,3,Petrol,yes,2009,A6
+Mercedes-Benz,27300,crossover,149,5.46,Petrol,yes,2009,ML 550
+Volkswagen,15200,van,132,2.5,Diesel,yes,2009,T5 (Transporter)
+Volkswagen,9800,van,86,1.2,Petrol,yes,2013,Caddy
+Mercedes-Benz,14500,vagon,178,3,Diesel,yes,2006,E-Class
+Renault,7800,vagon,120,1.5,Diesel,yes,2008,Megane
+Mercedes-Benz,15250,sedan,210,2.2,Diesel,yes,2006,E-Class
+Audi,13500,sedan,196,2.4,Petrol,yes,2005,A6
+Audi,23333,other,150,4.2,Petrol,yes,2007,Q7
+Mercedes-Benz,18800,other,90,1.8,Petrol,yes,2008,CLC 180
+Mitsubishi,6700,sedan,193,1.6,Petrol,yes,2005,Lancer
+Toyota,8200,crossover,212,2,Gas,yes,2003,Rav 4
+Volkswagen,1650,sedan,210,1.8,Petrol,no,1998,Passat B4
+Audi,49500,crossover,44,3,Diesel,yes,2013,Q7
+Volkswagen,22900,sedan,48,2,Petrol,yes,2013,Jetta
+Volkswagen,10300,hatch,110,1.9,Diesel,yes,2008,Caddy
+Toyota,12500,sedan,175,2.4,Petrol,yes,2007,Camry
+Mitsubishi,6800,sedan,170,1.6,Petrol,yes,2004,Lancer
+BMW,33500,sedan,1,1.5,Petrol,yes,2015,318
+Toyota,20711.25,vagon,0,1.6,Petrol,yes,2016,Auris
+Volkswagen,1499,sedan,220,2,Gas,no,1995,Passat B4
+Volkswagen,18900,hatch,56,2,Petrol,yes,2012,Golf GTI
+BMW,67500,crossover,1,2,Diesel,yes,2016,X5
+BMW,52000,crossover,7,2,Diesel,yes,2015,X1
+Volkswagen,17777,van,250,2,Diesel,yes,2010,T5 (Transporter)
+Toyota,33333,vagon,73,4,Gas,yes,2009,FJ Cruiser
+Mercedes-Benz,3200,sedan,390,2.3,Gas,yes,1985,E-Class
+Audi,88888,crossover,0,3,Diesel,yes,2015,Q7
+Renault,7450,other,208,1.9,Other,yes,2004,Trafic
+Mercedes-Benz,31990,crossover,95,4.7,Petrol,yes,2010,GL 450
+BMW,14200,sedan,263,3,Petrol,yes,2004,530
+Renault,6300,vagon,202,1.5,Diesel,yes,2005,Scenic
+Renault,4350,van,188,1.5,Diesel,yes,2004,Kangoo
+Renault,11100,vagon,155,1.5,Diesel,yes,2012,Megane
+Volkswagen,6300,vagon,132,2.8,Gas,yes,1995,Passat B4
+Renault,3300,van,256,1.5,Diesel,yes,2005,Kangoo
+Mercedes-Benz,7450,sedan,300,2,Gas,yes,2000,C-Class
+Mitsubishi,1850,sedan,145,1.6,Petrol,no,1997,Carisma
+Mercedes-Benz,14000,other,360,2.2,Diesel,yes,2005,Sprinter 313
+Renault,12850,other,226,2.3,Diesel,yes,2012,Master
+Toyota,18300,hatch,30,1.6,Petrol,yes,2014,Auris
+Volkswagen,14700,sedan,97,1.4,Gas,yes,2012,Passat B7
+Volkswagen,3000,sedan,250,2.8,Gas,no,2002,Passat B5
+Mercedes-Benz,12999,sedan,247,4,Diesel,yes,2003,S 400
+Mercedes-Benz,11850,van,177,2.2,Diesel,yes,2011,Vito
+Volkswagen,19500,sedan,128,1.8,Petrol,yes,2013,Passat B7
+Mercedes-Benz,20200,sedan,69,2,Petrol,yes,2010,E-Class
+Renault,5899,vagon,189,NA,Diesel,no,2012,Megane
+Volkswagen,12000,sedan,186,3.2,Petrol,yes,2006,Passat B6
+Mercedes-Benz,17800,van,162,1.8,Diesel,yes,2012,B 180
+Toyota,16800,crossover,154,2.7,Petrol,yes,2005,Land Cruiser Prado
+Mercedes-Benz,10600,van,250,2.2,Diesel,yes,2006,Sprinter 313
+Mercedes-Benz,114999,crossover,0,3,Diesel,yes,2016,GLS 350
+Renault,4700,hatch,124,1.5,Diesel,yes,2003,Kangoo
+Mercedes-Benz,20000,crossover,251,5,Gas,yes,2002,ML 500
+Mercedes-Benz,104999,crossover,1,3,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,9000,sedan,198,4,Diesel,yes,2002,S 400
+BMW,36300,crossover,83,3.5,Petrol,yes,2010,X6
+Toyota,19400,crossover,88,2,Petrol,yes,2011,Rav 4
+Mercedes-Benz,7700,sedan,230,2,Petrol,yes,2001,C-Class
+Mitsubishi,17500,other,69,2.5,Diesel,yes,2011,L 200
+BMW,31000,sedan,100,2,Petrol,yes,2012,520
+Toyota,33715.5,crossover,1,2.7,Petrol,yes,2015,Land Cruiser Prado
+BMW,13500,sedan,233,3,Diesel,yes,2003,530
+Renault,4900,van,150,2,Diesel,yes,2007,Trafic
+Mercedes-Benz,7700,sedan,340,2.2,Diesel,yes,2002,Vito
+Renault,10900,vagon,175,1.5,Diesel,yes,2012,Megane
+BMW,2200,sedan,303,2.5,Diesel,no,1999,525
+BMW,7200,sedan,270,3,Diesel,no,2004,530
+BMW,20500,other,114,NA,Other,yes,2008,325
+Toyota,9150,hatch,87,1.6,Petrol,yes,2008,Auris
+BMW,5900,sedan,10,2.8,Petrol,yes,1989,328
+Mitsubishi,4800,hatch,206,1.6,Gas,yes,2001,Carisma
+Volkswagen,1500,vagon,200,1.8,Gas,yes,1996,Passat B4
+Volkswagen,9500,hatch,62,1.2,Diesel,yes,2011,Polo
+Mercedes-Benz,3500,van,395,2.3,Diesel,no,1997,Vito
+Volkswagen,3650,sedan,300,1.8,Petrol,yes,1996,Vento
+Mercedes-Benz,63000,crossover,160,3,Diesel,yes,2013,GL 350
+Volkswagen,6600,van,240,1.9,Diesel,yes,2008,Caddy
+Toyota,36000,crossover,940,NA,Other,yes,2009,Land Cruiser 200
+Toyota,10200,sedan,185,1.8,Petrol,yes,2007,Avensis
+Volkswagen,19995,hatch,68,1.4,Petrol,yes,2013,Scirocco
+Mitsubishi,9800,sedan,56,1.5,Petrol,yes,2011,Lancer X
+Mercedes-Benz,11200,sedan,198,6,Petrol,yes,2004,S 600
+Volkswagen,18000,crossover,117,2.5,Diesel,yes,2006,Touareg
+Mitsubishi,28500,crossover,0,2,Petrol,yes,2016,Outlander
+Mitsubishi,9800,sedan,81,2,Petrol,yes,2009,Lancer X
+BMW,33000,sedan,60,NA,Petrol,yes,2012,328
+Audi,3900,sedan,247,2.5,Diesel,no,2003,A6
+Mitsubishi,22711.65,crossover,1,2,Petrol,yes,2016,Outlander
+BMW,6500,sedan,298,2.5,Petrol,yes,1998,523
+Volkswagen,4999,hatch,300,1.9,Diesel,yes,1999,Golf IV
+Renault,9100,vagon,176,1.5,Diesel,yes,2011,Scenic
+Mercedes-Benz,11000,sedan,319,2.2,Diesel,yes,2003,220
+Volkswagen,7200,hatch,159,1.6,Petrol,yes,2007,Golf V
+Mercedes-Benz,10900,van,191,2.2,Diesel,yes,2007,Vito
+Volkswagen,35000,sedan,74,4.2,Gas,yes,2011,Phaeton
+Mitsubishi,11500,sedan,134,2.4,Petrol,yes,2009,Galant
+Mercedes-Benz,9999,van,154,2.2,Diesel,yes,2007,Vito
+Audi,NA,sedan,300,2.5,Diesel,no,2001,A8
+Renault,4400,other,220,1.5,Diesel,yes,2004,Kangoo
+Mitsubishi,4000,hatch,367,1.6,Gas,yes,1997,Carisma
+Audi,29999,other,90,3,Petrol,yes,2011,A5
+Volkswagen,14800,van,65,1.6,Diesel,yes,2012,Caddy
+Mitsubishi,5500,crossover,30,2.5,Diesel,yes,2014,Pajero Sport
+BMW,39333,sedan,6,2,Petrol,yes,2016,520
+Mitsubishi,17000,crossover,215,3.8,Petrol,yes,2006,Pajero Wagon
+Toyota,20900,sedan,18,2.5,Other,yes,2013,Camry
+BMW,15000,sedan,137,2.2,Petrol,yes,2004,520
+Volkswagen,1850,other,200,1.6,Petrol,no,1999,Golf IV
+Renault,6900,hatch,47,1.4,Petrol,yes,2012,Sandero
+Volkswagen,11990,sedan,245,2,Diesel,yes,2007,Passat B6
+Mercedes-Benz,41000,van,55,3,Diesel,yes,2011,Viano
+BMW,42000,sedan,5,2,Petrol,yes,2016,520
+Renault,7700,van,128,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,7750,van,265,1.9,Diesel,yes,2002,T4 (Transporter)
+Toyota,10100,other,83,1.6,Other,yes,2008,Corolla
+Renault,7500,van,128,1.5,Diesel,yes,2009,Kangoo
+BMW,2700,sedan,220,2.5,Petrol,no,2002,525
+Mercedes-Benz,74900,other,32,6.3,Petrol,yes,2007,CL 63 AMG
+Renault,800,van,200,1.9,Diesel,no,1997,Scenic
+Mitsubishi,17900,crossover,89,3.8,Gas,yes,2008,Pajero Wagon
+Audi,29000,vagon,213,3,Diesel,yes,2010,A6 Allroad
+BMW,17100,crossover,188,3,Diesel,yes,2004,X5
+Volkswagen,33500,crossover,98,NA,Petrol,yes,2013,Touareg
+Mercedes-Benz,1600,sedan,338,3,Diesel,no,1991,E-Class
+Mercedes-Benz,11777,other,300,4.3,Gas,yes,2001,S 430
+BMW,6699,sedan,299,2.2,Petrol,yes,2001,523
+BMW,3900,vagon,240,3,Diesel,no,2001,530
+Volkswagen,6500,sedan,212,1.6,Petrol,yes,2003,Bora
+BMW,51500,hatch,77,3,Petrol,yes,2012,535
+Renault,6500,vagon,186,1.5,Diesel,yes,2004,Megane
+BMW,10800,other,191,2.2,Petrol,yes,2002,320
+Audi,2450,vagon,240,2.5,Diesel,no,2000,A4
+Volkswagen,NA,van,207,2,Diesel,yes,2012,T5 (Transporter) 
+Toyota,28800,crossover,29,2.2,Diesel,yes,2015,Rav 4
+Volkswagen,6900,crossover,258,2.5,Diesel,no,2004,Touareg
+Toyota,47700,crossover,810,4.7,Gas,yes,2010,Land Cruiser 200
+Volkswagen,12700,van,75,1.2,Petrol,yes,2012,Caddy
+Toyota,7900,hatch,56,1,Gas,yes,2011,Aygo
+BMW,18000,sedan,180,2,Other,no,2011,520
+BMW,8899,sedan,170,2.2,Petrol,yes,2001,520
+Mercedes-Benz,57000,other,0,1.6,Petrol,yes,2016,C-Class
+Mercedes-Benz,2100,sedan,535,2.2,Diesel,yes,1995,210
+Audi,10900,sedan,199,3,Petrol,yes,2004,A8
+Toyota,42500,crossover,50,3,Diesel,yes,2012,Land Cruiser Prado
+BMW,51900,crossover,106,3,Diesel,yes,2012,X6
+Renault,3300,van,345,1.9,Diesel,yes,2001,Kangoo
+Mercedes-Benz,187555,other,0,4.7,Petrol,yes,2016,S 500
+Mercedes-Benz,16555,sedan,159,3.5,Gas,yes,2006,CLS 350
+Mercedes-Benz,7000,van,197,2.2,Diesel,yes,2002,Vito
+Mercedes-Benz,9300,sedan,342,2.6,Petrol,yes,2003,E-Class
+Audi,11500,sedan,85,1.8,Petrol,yes,2007,A4
+Mercedes-Benz,8000,crossover,314,99.99,Gas,yes,2000,ML 320
+Toyota,16672.5,hatch,1,1.6,Petrol,yes,2016,Auris
+Volkswagen,37990,crossover,140,3,Diesel,yes,2012,Touareg
+Mercedes-Benz,2400,sedan,1,2,Petrol,no,2001,C-Class
+Mercedes-Benz,219900,crossover,33,5.5,Petrol,yes,2013,G 63 AMG
+Renault,7150,van,142,1.5,Diesel,yes,2012,Kangoo
+Toyota,5000,crossover,289,2,Diesel,no,2003,Rav 4
+Mercedes-Benz,7700,sedan,390,3,Diesel,no,2007,E-Class
+Mercedes-Benz,28500,crossover,190,3.2,Diesel,yes,2008,ML 320
+Toyota,61000,crossover,100,4.5,Diesel,yes,2013,Land Cruiser 200
+Toyota,14700,vagon,98,2,Gas,yes,2008,Rav 4
+Toyota,NA,hatch,28,1.6,Petrol,yes,2014,Auris
+Volkswagen,21900,sedan,107,3,Diesel,yes,2009,Phaeton
+Audi,53700,crossover,67,3,Petrol,yes,2013,A6 Allroad
+Volkswagen,19200,crossover,155,2.5,Diesel,yes,2008,Touareg
+Audi,18700,sedan,136,2,Diesel,yes,2009,A6
+Audi,9600,vagon,230,2.5,Diesel,yes,2004,A6
+Volkswagen,10500,van,312,2.5,Diesel,yes,1998,Multivan
+BMW,77777,sedan,8,4.4,Petrol,yes,2014,750
+Volkswagen,18200,crossover,179,2.5,Diesel,yes,2007,Touareg
+Audi,52000,other,124,3,Diesel,yes,2012,A8
+BMW,13700,sedan,122,2,Petrol,yes,2007,318
+BMW,15000,vagon,245,3,Gas,yes,2006,530
+Mitsubishi,6400,sedan,127,1.6,Petrol,yes,2006,Lancer
+Toyota,54000,crossover,147,4.5,Diesel,yes,2011,Land Cruiser 200
+Toyota,23700,sedan,100,2.5,Gas,yes,2014,Camry
+Volkswagen,13950,hatch,104,1.2,Petrol,yes,2012,Golf Plus
+Toyota,23200,crossover,160,4,Petrol,yes,2008,Land Cruiser Prado
+Audi,2750,sedan,450,2.5,Diesel,no,1999,A6
+Audi,89999,crossover,0,3,Diesel,yes,2015,Q7
+BMW,NA,sedan,65,2,Petrol,yes,2012,320
+BMW,21700,crossover,125,3,Petrol,yes,2007,X5
+Mercedes-Benz,NA,van,12,2.2,Diesel,yes,2015,V 250
+Toyota,16500,crossover,328,4.2,Diesel,yes,1993,Land Cruiser 80
+BMW,15000,crossover,85,4.8,Petrol,no,2010,X5
+Mercedes-Benz,61500,crossover,16,3,Diesel,yes,2013,ML 350
+BMW,1700,sedan,200,2,Diesel,no,2003,320
+BMW,8500,sedan,352,2.17,Petrol,yes,2001,520
+Toyota,9999,sedan,250,2.4,Petrol,yes,2003,Camry
+Audi,3300,vagon,300,2.5,Diesel,no,1999,A6
+Volkswagen,19900,other,112,2,Petrol,yes,2012,Passat CC
+Renault,11300,vagon,160,1.5,Diesel,yes,2011,Laguna
+Toyota,14900,sedan,149,3.5,Gas,yes,2008,Camry
+Toyota,39000,crossover,88,4.5,Diesel,yes,2009,Land Cruiser 200
+Mercedes-Benz,69900,sedan,76,6,Petrol,yes,2010,S 500
+BMW,72900,crossover,16,3,Diesel,yes,2015,X6
+Renault,10500,vagon,185,1.5,Diesel,yes,2011,Megane
+Mercedes-Benz,NA,sedan,23,5.5,Petrol,yes,2011,S 550
+Renault,13900,vagon,89,1.5,Diesel,yes,2013,Megane
+Renault,6800,van,131,1.5,Diesel,yes,2013,Kangoo
+Mercedes-Benz,8500,van,270,2.2,Diesel,yes,2004,Vito
+Volkswagen,7600,van,158,2,Diesel,yes,2009,Caddy
+Toyota,30950,other,45,4.6,Petrol,yes,2013,Tundra
+Audi,3990,sedan,413,2.6,Gas,yes,1993,100
+Audi,68500,sedan,44,6.3,Petrol,yes,2012,A8
+Audi,33500,sedan,42,2,Petrol,yes,2013,A5
+BMW,11500,sedan,193,4.4,Petrol,yes,2000,740
+Toyota,24999,crossover,153,4,Petrol,yes,2007,Land Cruiser Prado
+Audi,1850,sedan,250,2.5,Diesel,no,1999,A4
+Mercedes-Benz,29500,crossover,55,2.2,Diesel,no,2013,ML 250
+Mercedes-Benz,57000,sedan,43,4.7,Petrol,yes,2012,CLS 500
+Audi,30900,hatch,195,3,Diesel,yes,2010,A5
+Toyota,7000,crossover,150,2,Petrol,yes,2000,Rav 4
+Volkswagen,8950,van,170,1.6,Diesel,yes,2013,Caddy
+Mitsubishi,2500,crossover,1,2.5,Diesel,yes,2007,L 200
+Renault,8350,other,141,1.5,Other,yes,2012,Megane
+BMW,11400,sedan,206,3,Petrol,yes,2003,530
+Renault,6200,van,322,NA,Diesel,yes,2001,Trafic
+Volkswagen,2600,sedan,280,2.5,Diesel,no,2000,Passat B5
+Mercedes-Benz,7450,sedan,219,2,Petrol,yes,2000,C-Class
+Audi,9499,sedan,255,2.5,Diesel,yes,2002,A6
+Toyota,14900,sedan,106,2.4,Petrol,yes,2008,Camry
+Volkswagen,8200,van,265,2.5,Diesel,no,2007,Caravelle
+Mercedes-Benz,12500,sedan,208,3.2,Diesel,yes,2004,S 320
+Toyota,14500,sedan,127,2.4,Gas,yes,2008,Camry
+BMW,41000,crossover,80,3,Petrol,yes,2012,X6
+Volkswagen,12000,sedan,73,1.6,Gas,yes,2011,Polo
+Mercedes-Benz,5600,sedan,230,2.7,Diesel,no,2005,E-Class
+Mercedes-Benz,5200,other,220,3.2,Petrol,no,2004,CLK 320
+BMW,2999,sedan,299,NA,Diesel,no,2000,530
+Mitsubishi,9200,sedan,48,1.5,Petrol,yes,2008,Lancer X
+Toyota,25999,other,108,4,Petrol,yes,2008,Land Cruiser Prado
+BMW,6300,sedan,241,2.8,Gas,yes,1995,328
+Mercedes-Benz,116500,sedan,25,3,Diesel,yes,2015,S 350
+BMW,15500,sedan,216,2,Diesel,yes,2008,320
+Toyota,17500,crossover,168,4,Gas,yes,2004,Land Cruiser Prado
+Volkswagen,9999,sedan,182,1.6,Petrol,yes,2006,Jetta
+Toyota,NA,sedan,15,2.5,Petrol,yes,2015,Camry
+BMW,34600,crossover,163,5,Petrol,yes,2011,X5 M
+Renault,1185.6,hatch,160,2,Petrol,yes,1987,25
+Mercedes-Benz,20500,sedan,222,5.5,Petrol,yes,2006,S 500
+Renault,10900,van,138,2,Diesel,yes,2012,Trafic
+Volkswagen,7800,hatch,230,1.6,Petrol,yes,2004,Golf V
+Audi,3900,vagon,232,2.5,Diesel,no,2003,A6
+Toyota,15500,crossover,120,2,Petrol,yes,2008,Rav 4
+Audi,48000,crossover,126,3,Diesel,yes,2013,Q7
+Mercedes-Benz,12800,van,193,2.2,Other,yes,2011,Vito
+Toyota,14900,crossover,288,4.7,Gas,yes,2000,Land Cruiser 100
+Mercedes-Benz,1990,hatch,230,1.7,Other,no,2001,A 170
+Mitsubishi,9800,sedan,148,2,Gas,yes,2008,Lancer X
+BMW,2200,sedan,460,2.4,Diesel,yes,1988,324
+Mercedes-Benz,8700,sedan,290,2.7,Diesel,yes,1999,E-Class
+Renault,3450,van,196,1.9,Diesel,yes,2001,Kangoo
+Mercedes-Benz,22000,van,236,2.2,Diesel,yes,2012,Sprinter 313
+Mercedes-Benz,6500,other,315,2,Petrol,yes,1996,E-Class
+Mitsubishi,12999,crossover,111,3,Gas,yes,2008,Outlander XL
+Volkswagen,21500,sedan,2,1.6,Diesel,yes,2016,Jetta
+BMW,2750,vagon,254,2.8,Gas,no,1997,528
+Mitsubishi,23000,crossover,125,3,Gas,yes,2008,Pajero Wagon
+Volkswagen,NA,van,165,2,Diesel,yes,2013,T5 (Transporter) 
+BMW,8200,sedan,368,3,Diesel,yes,2000,730
+BMW,32900,crossover,90,3,Petrol,yes,2008,X6
+Mercedes-Benz,13600,sedan,115,NA,Petrol,yes,2007,C-Class
+Toyota,22000,sedan,73,3.5,Petrol,yes,2010,Camry
+Mitsubishi,2800,other,290,2,Petrol,yes,1990,Galant
+Mercedes-Benz,12999,van,130,2.2,Diesel,yes,2012,Vito
+Toyota,32000,sedan,22,2.5,Petrol,yes,2015,Camry
+Toyota,99999,crossover,0,4.5,Petrol,yes,2016,Land Cruiser 200
+Renault,6950,van,310,2.5,Diesel,yes,2005,Master
+Renault,10500,hatch,148,1.5,Diesel,yes,2012,Megane
+BMW,6899,vagon,320,2.8,Gas,yes,1997,528
+Mercedes-Benz,6200,sedan,450,4.2,Gas,yes,1997,S 420
+Toyota,65000,other,60,4.5,Diesel,yes,2013,Land Cruiser 200
+Mercedes-Benz,13900,other,272,2.2,Diesel,yes,2005,Sprinter 313
+Renault,5800,van,217,2,Gas,yes,2003,Scenic
+BMW,28700,other,160,3,Petrol,yes,2011,528
+BMW,16700,crossover,260,4.4,Petrol,yes,2003,X5
+Volkswagen,18500,sedan,217,3,Diesel,yes,2006,Phaeton
+Renault,3999,other,227,1.4,Gas,yes,2005,Kangoo
+Renault,8800,van,200,1.9,Diesel,yes,2005,Trafic
+Mercedes-Benz,14500,van,205,2.2,Diesel,yes,2012,Sprinter 313
+BMW,8900,sedan,240,2,Diesel,yes,2001,320
+Volkswagen,1300,other,10,1.6,Diesel,yes,1984,T2 (Transporter)
+BMW,NA,crossover,36,3,Diesel,yes,2013,X5
+Renault,8550,van,160,1.5,Diesel,yes,2014,Kangoo
+Mercedes-Benz,10900,sedan,160,3,Diesel,yes,1996,S 140
+BMW,48535.5,crossover,0,2,Diesel,yes,2016,X3
+BMW,25000,crossover,130,3,Diesel,yes,2012,X5
+BMW,NA,crossover,1,3,Diesel,yes,2016,X6
+Mitsubishi,7000,sedan,99,1.6,Petrol,yes,2005,Lancer
+BMW,5500,sedan,402,2,Petrol,yes,1988,520
+Mercedes-Benz,2800,other,220,2.3,Petrol,no,2000,CLK 230
+Mercedes-Benz,86000,crossover,3,3,Diesel,yes,2016,GLS 350
+Volkswagen,9555,van,152,1.6,Diesel,yes,2012,Caddy
+Renault,7200,van,1,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,7700,van,300,2.2,Other,yes,2004,Vito
+BMW,75000,crossover,20,3,Diesel,yes,2014,X5
+Volkswagen,8650,hatch,150,1.4,Petrol,yes,2008,Golf V
+Toyota,10700,sedan,109,1.8,Petrol,yes,2007,Avensis
+Volkswagen,6300,van,1,2.5,Diesel,yes,2000,T4 (Transporter)
+Toyota,13900,sedan,102,3.5,Gas,yes,2008,Camry
+Audi,3150,vagon,310,2.5,Diesel,no,1999,A6
+Volkswagen,7500,other,360,2.5,Diesel,yes,2001,T4 (Transporter)
+Volkswagen,6550,sedan,150,1.6,Petrol,yes,2001,Bora
+BMW,7100,sedan,313,2,Gas,yes,1997,520
+Volkswagen,3700,sedan,370,2,Gas,yes,1992,Passat B3
+Toyota,20999,sedan,77,2.5,Gas,yes,2012,Camry
+Renault,6750,vagon,1,1.5,Diesel,yes,2004,Scenic
+Mercedes-Benz,8400,other,457,5,Gas,yes,1996,S 140
+Toyota,103999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,5200,sedan,432,3,Diesel,yes,1992,E-Class
+BMW,51000,sedan,97,3,Diesel,yes,2011,730
+Toyota,27400,crossover,52,3.5,Gas,yes,2012,Venza
+Toyota,12500,crossover,201,2,Petrol,yes,2006,Rav 4
+Mercedes-Benz,24800,vagon,118,3,Diesel,yes,2010,E-Class
+Volkswagen,10600,sedan,118,1.8,Petrol,yes,2008,Passat B6
+Mitsubishi,8900,other,150,3,Petrol,yes,2000,Eclipse
+Renault,7200,van,101,1.5,Diesel,yes,2012,Kangoo
+BMW,10000,sedan,274,4.4,Gas,yes,1998,540
+Toyota,11700,crossover,126,2.4,Gas,yes,2008,Rav 4
+BMW,64999,sedan,15,3,Petrol,yes,2012,640
+Toyota,28900,crossover,83,3.5,Gas,yes,2012,Highlander
+BMW,60500,crossover,1,2,Diesel,yes,2016,X5
+Renault,5500,sedan,153,1.6,Other,yes,2006,Megane
+BMW,85900,crossover,19,3,Diesel,yes,2015,X6
+BMW,49000,vagon,56,5,Petrol,yes,2007,M5
+Audi,22999,crossover,125,4.2,Petrol,yes,2008,Q7
+Mercedes-Benz,22900,sedan,197,2.2,Diesel,yes,2010,E-Class
+BMW,85555,sedan,10,3,Other,yes,2015,6 Series Gran Coupe
+Mercedes-Benz,30000,crossover,97,2.14,Diesel,yes,2011,ML 250
+BMW,2600,sedan,250,1.6,Petrol,yes,1996,316
+Volkswagen,12500,crossover,230,3.2,Gas,yes,2003,Touareg
+BMW,6800,sedan,4,2.8,Gas,yes,1995,528
+Mercedes-Benz,8700,other,280,NA,Diesel,yes,2001,Vito
+Audi,4000,sedan,315,2.3,Gas,yes,1991,100
+Audi,36500,crossover,159,3,Diesel,yes,2010,Q7
+Volkswagen,10000,van,231,1.9,Diesel,yes,2005,T5 (Transporter)
+Volkswagen,4700,hatch,160,2,Diesel,no,2006,Golf VI
+BMW,27900,crossover,140,3,Diesel,yes,2009,X5
+Renault,7440,other,148,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,17999,van,260,2.2,Diesel,yes,2013,Sprinter 316
+Volkswagen,7750,van,235,1.9,Diesel,yes,2004,T5 (Transporter)
+Mercedes-Benz,7200,van,285,2.2,Diesel,yes,2001,Vito
+Volkswagen,3700,hatch,121,1.9,Diesel,yes,1993,Golf III
+Volkswagen,12000,vagon,187,2,Diesel,yes,2008,Passat B6
+Audi,48000,crossover,75,3,Diesel,yes,2013,Q7
+BMW,49000,sedan,58,4.4,Other,yes,2011,750
+Mercedes-Benz,69500,crossover,84,3,Diesel,yes,2014,GL 350
+BMW,19999,crossover,227,3,Diesel,yes,2006,X5
+Volkswagen,30900,sedan,30,2,Diesel,yes,2014,Passat B7
+Mercedes-Benz,44000,other,39,5.5,Petrol,yes,2009,SL 55 AMG
+Mercedes-Benz,10200,sedan,174,2.6,Gas,yes,2001,E-Class
+Mercedes-Benz,22000,crossover,160,4,Diesel,yes,2008,ML 400
+Mercedes-Benz,46700,sedan,115,5.5,Petrol,yes,2011,S 500
+Mercedes-Benz,9999,sedan,322,2.7,Diesel,yes,2000,E-Class
+Volkswagen,4400,other,145,2.4,Diesel,yes,1993,T4 (Transporter)
+Mercedes-Benz,45000,sedan,1,1.6,Petrol,yes,2015,C-Class
+Toyota,63333,other,33,4.5,Diesel,yes,2013,Land Cruiser 200
+Volkswagen,12999,van,237,2.5,Diesel,yes,2005,T5 (Transporter)
+Mercedes-Benz,15000,other,115,2.2,Diesel,yes,2011,Vito
+BMW,24500,sedan,114,NA,Petrol,yes,2005,M5
+Volkswagen,1950,hatch,94,1.1,Petrol,yes,1982,Golf II
+BMW,58500,crossover,80,3,Diesel,yes,2013,X5
+Renault,6250,van,205,1.6,Petrol,yes,2007,Scenic
+Volkswagen,8900,sedan,160,1.6,Petrol,yes,2006,Jetta
+Mercedes-Benz,65000,other,0,1.6,Petrol,yes,2016,C-Class
+Volkswagen,15200,sedan,185,1.4,Petrol,yes,2011,Golf VI
+Mercedes-Benz,121500,sedan,22,3,Diesel,yes,2014,S 350
+Audi,30999,sedan,108,2,Diesel,yes,2013,A6
+BMW,22000,other,138,4.8,Petrol,yes,2007,650
+Mercedes-Benz,10999,other,185,NA,Diesel,yes,2005,Vito
+Mitsubishi,10500,crossover,95,3.2,Diesel,no,2008,Pajero Wagon
+BMW,3900,sedan,380,3,Gas,yes,1989,530
+Mitsubishi,9900,sedan,55,2,Other,yes,2008,Lancer X
+Mercedes-Benz,3799,sedan,210,2,Petrol,yes,1995,E-Class
+BMW,5000,sedan,305,2,Petrol,yes,1995,520
+Audi,5300,other,370,1.8,Petrol,yes,1997,A6
+Renault,6450,vagon,100,1.5,Diesel,yes,2009,Kangoo
+Toyota,44700,crossover,120,3.5,Petrol,yes,2013,Venza
+Toyota,17800,crossover,220,3,Diesel,yes,2004,Land Cruiser Prado
+BMW,26900,sedan,158,5,Petrol,yes,2007,M5
+Volkswagen,14999,vagon,128,1.4,Gas,yes,2011,Passat B7
+Mercedes-Benz,7650,sedan,340,5,Gas,yes,1997,S 140
+Volkswagen,12750,vagon,130,1.4,Gas,yes,2010,Passat B6
+Volkswagen,16000,hatch,26,1.2,Petrol,yes,2014,Polo
+Toyota,65000,crossover,90,4.5,Diesel,yes,2012,Land Cruiser 200
+Renault,11999,hatch,65,1.5,Diesel,yes,2014,Megane
+BMW,3000,sedan,294,2,Diesel,no,1999,320
+Volkswagen,17900,crossover,70,1.4,Petrol,yes,2012,Tiguan
+Volkswagen,8200,sedan,180,NA,Petrol,yes,2007,Jetta
+Mercedes-Benz,109000,crossover,0,3.5,Diesel,yes,2016,GLS 350
+Mercedes-Benz,4500,sedan,409,2.6,Petrol,yes,1991,E-Class
+Audi,19300,sedan,113,1.8,Petrol,yes,2009,A4
+Volkswagen,11300,van,250,2.5,Diesel,yes,2007,T5 (Transporter)
+Mitsubishi,18800,crossover,47,3,Petrol,yes,2008,Pajero Wagon
+Toyota,28900,crossover,83,NA,Petrol,yes,2012,Highlander
+Mercedes-Benz,4150,other,277,2.2,Diesel,yes,2000,Vito
+Volkswagen,14000,crossover,194,3.2,Gas,yes,2004,Touareg
+Mercedes-Benz,13995,van,126,2.2,Diesel,yes,2013,Vito
+Toyota,NA,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,6400,other,209,1.5,Diesel,yes,2005,Scenic
+Renault,4900,sedan,90,1.4,Gas,yes,2008,Symbol
+Audi,6400,sedan,261,1.8,Gas,yes,2000,A6
+Renault,9400,van,270,2,Diesel,yes,2007,Trafic
+Toyota,15400,sedan,152,3.5,Gas,yes,2007,Avalon
+Volkswagen,2900,sedan,238,2,Gas,no,2002,Passat B5
+Renault,9500,vagon,78,1.5,Diesel,yes,2014,Kangoo
+Volkswagen,23000,sedan,75,2,Petrol,yes,2013,Passat CC
+Toyota,10600,crossover,185,2,Gas,yes,2005,Rav 4
+Renault,NA,other,179,2.5,Diesel,yes,2011,Trafic
+Audi,2550,sedan,304,2.5,Diesel,no,1998,A6
+Volkswagen,8500,sedan,87,1.6,Petrol,yes,2011,Polo
+Audi,10500,vagon,243,2.5,Diesel,yes,2003,A6
+Mercedes-Benz,18000,sedan,99,1.8,Petrol,yes,2008,C-Class
+Mercedes-Benz,6100,sedan,280,NA,Gas,yes,1999,E-Class
+Mitsubishi,1550,hatch,1,1.8,Diesel,yes,1990,Galant
+BMW,3800,sedan,250,3,Diesel,no,2002,330
+BMW,42000,crossover,128,4.4,Petrol,yes,2011,X5 M
+Mitsubishi,8500,sedan,110,2,Petrol,yes,2008,Lancer X
+Toyota,24900,sedan,30,2.5,Petrol,yes,2014,Camry
+Volkswagen,10200,vagon,186,2,Diesel,yes,2005,Passat B6
+BMW,14200,sedan,135,3,Gas,yes,2004,530
+Volkswagen,22500,crossover,151,3,Diesel,yes,2009,Touareg
+Toyota,47500,crossover,45,3,Diesel,yes,2014,Land Cruiser Prado
+BMW,4500,vagon,360,2,Petrol,yes,1993,520
+BMW,8300,sedan,197,3,Petrol,yes,2001,530
+Toyota,14300,hatch,60,1.6,Petrol,yes,2013,Auris
+Renault,18456.16,crossover,1,1.5,Diesel,yes,2016,Duster
+Renault,6999,van,173,1.5,Diesel,yes,2006,Scenic
+Mitsubishi,7500,sedan,94,1.6,Petrol,yes,2007,Lancer
+Volkswagen,2850,sedan,350,2,Petrol,yes,1990,Passat B3
+Mercedes-Benz,5800,van,282,2.2,Diesel,yes,2001,Vito
+Toyota,3700,hatch,282,1.4,Petrol,yes,1995,Corolla
+Renault,6555,vagon,180,1.5,Diesel,yes,2005,Megane
+Volkswagen,23999,sedan,80,1.8,Petrol,yes,2012,Passat CC
+Mitsubishi,21500,crossover,47,2.4,Petrol,yes,2012,Outlander XL
+Volkswagen,5700,sedan,300,1.8,Petrol,yes,2001,Passat B5
+Mercedes-Benz,200000,van,19,3.5,Petrol,yes,2013,Sprinter 324
+Mercedes-Benz,NA,vagon,200,2.2,Diesel,yes,2006,Vito
+Mercedes-Benz,16800,van,116,NA,Other,yes,2013,Vito
+Volkswagen,4500,vagon,220,2,Diesel,no,2005,Touran
+BMW,6800,sedan,297,2.5,Petrol,yes,2000,525
+Toyota,22900,sedan,84,2.5,Petrol,yes,2012,Camry
+Volkswagen,8500,van,210,2.5,Diesel,yes,2003,T4 (Transporter)
+Renault,8700,van,183,1.9,Diesel,yes,2004,Trafic
+BMW,12200,sedan,239,3,Diesel,yes,2001,730
+BMW,23900,crossover,235,3,Diesel,yes,2007,X5
+Mitsubishi,16000,vagon,174,3.2,Diesel,yes,2006,Pajero Wagon
+Mitsubishi,7000,sedan,158,1.6,Petrol,yes,2007,Lancer
+Renault,9450,vagon,215,1.5,Diesel,yes,2008,Laguna
+Mercedes-Benz,41900,sedan,95,6.3,Other,yes,2011,S 550
+Mitsubishi,7400,sedan,148,1.6,Petrol,yes,2008,Lancer
+Renault,2350,vagon,245,1.9,Diesel,no,2003,Laguna
+Audi,3390,sedan,289,2.5,Diesel,no,2002,A6
+BMW,40755,sedan,0,2,Petrol,yes,2016,520
+Volkswagen,3300,vagon,430,1.8,Petrol,yes,1991,Passat B3
+Toyota,1100,van,140,2,Gas,yes,1985,Lite Ace
+Volkswagen,49500,crossover,80,3,Diesel,yes,2014,Touareg
+Renault,8700,hatch,100,1.5,Diesel,yes,2010,Megane
+Mercedes-Benz,20400,sedan,190,1.8,Gas,yes,2011,E-Class
+BMW,17700,sedan,98,2,Petrol,yes,2011,320
+BMW,17900,sedan,69,2,Petrol,yes,2008,320
+Mercedes-Benz,5500,sedan,269,2.2,Petrol,yes,1994,E-Class
+Mercedes-Benz,6200,sedan,221,1.8,Gas,yes,2004,E-Class
+Mercedes-Benz,3500,van,257,2.2,Diesel,no,2002,C-Class
+BMW,8100,vagon,180,2,Diesel,no,2011,118
+Toyota,2499,vagon,254,2,Diesel,no,2000,Avensis
+Audi,3150,sedan,10,2.8,Petrol,yes,1991,100
+Audi,4000,sedan,380,2.8,Petrol,yes,1997,A6
+Audi,4000,vagon,234,2.5,Diesel,no,2001,A6
+BMW,2050,vagon,300,2.5,Diesel,no,1997,525
+Mercedes-Benz,2600,hatch,178,1.7,Diesel,no,1999,A 170
+Mercedes-Benz,24500,sedan,222,5.5,Petrol,yes,2005,S 550
+BMW,14600,sedan,116,2.5,Diesel,yes,2004,525
+Mercedes-Benz,16900,van,180,3,Diesel,yes,2008,Vito
+Volkswagen,14500,crossover,173,2.5,Diesel,yes,2005,Touareg
+Volkswagen,38365,sedan,0,2,Diesel,yes,2016,Passat B8
+Mercedes-Benz,17800,van,162,1.8,Diesel,yes,2012,B 180
+Renault,5000,sedan,200,1.4,Petrol,yes,2007,Clio
+Toyota,42000,crossover,97,4,Petrol,yes,2010,4Runner
+BMW,35500,sedan,67,3,Petrol,yes,2011,535
+BMW,3000,sedan,190,2,Petrol,no,2000,520
+BMW,22600,crossover,69,3,Petrol,no,2013,X6
+Volkswagen,12900,other,142,2,Petrol,yes,2007,Eos
+Mercedes-Benz,7600,van,240,2.2,Diesel,yes,2005,Vito
+Toyota,37700,crossover,47,5.7,Gas,yes,2009,Sequoia
+Toyota,8300,sedan,168,2,Petrol,yes,2004,Avensis
+BMW,65500,crossover,1,2,Diesel,yes,2016,X5
+Audi,34500,sedan,71,2.8,Petrol,yes,2012,A6
+Toyota,7500,sedan,185,3,Gas,yes,2004,Camry
+Volkswagen,13900,vagon,123,1.4,Gas,yes,2013,Touran
+Renault,4000,van,194,1.9,Diesel,yes,2000,Kangoo
+Audi,2300,vagon,250,2.5,Diesel,no,2001,A6
+Renault,5650,van,140,1.4,Petrol,yes,2009,Kangoo
+Volkswagen,11800,other,200,1.9,Diesel,yes,2007,T5 (Transporter)
+Audi,44000,hatch,1,1.4,Petrol,yes,2016,A5
+Audi,23400,crossover,105,4.2,Gas,yes,2007,Q7
+BMW,2666,sedan,360,2.5,Diesel,no,1996,525
+BMW,23900,crossover,60,2,Petrol,yes,2013,X1
+Toyota,14000,sedan,25,2.5,Petrol,yes,2014,Camry
+Mercedes-Benz,NA,sedan,9,3,Diesel,yes,2014,S 350
+BMW,69999,crossover,1,2,Petrol,yes,2016,X5
+Mercedes-Benz,58999,crossover,0,2.2,Diesel,yes,2016,GLC-Class
+Audi,22500,other,71,3.6,Petrol,yes,2007,Q7
+Toyota,10300,sedan,108,1.6,Petrol,yes,2008,Corolla
+Renault,9300,van,200,1.5,Diesel,yes,2009,Grand Scenic
+Audi,37800,other,126,3,Diesel,yes,2010,Q7
+Toyota,10400,sedan,144,1.3,Petrol,yes,2012,Corolla
+Renault,6700,van,160,1.5,Diesel,yes,2012,Kangoo
+Renault,7400,van,156,1.5,Diesel,yes,2012,Kangoo
+Renault,11200,van,157,1.5,Diesel,yes,2012,Grand Scenic
+Toyota,25900,crossover,97,4,Petrol,yes,2009,Land Cruiser Prado
+Mercedes-Benz,11500,van,381,2.2,Diesel,yes,2006,Vito
+BMW,3950,sedan,300,2,Petrol,yes,1986,320
+Renault,11500,vagon,230,1.5,Diesel,yes,2012,Megane
+Toyota,2600,sedan,218,2.2,Petrol,yes,1994,Camry
+Mercedes-Benz,55000,crossover,40,4.6,Petrol,yes,2013,ML 500
+Renault,6650,hatch,274,1.5,Diesel,yes,2006,Scenic
+Mercedes-Benz,11900,van,215,2.2,Diesel,yes,2011,Vito
+Renault,8600,van,100,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,6700,van,310,2.2,Diesel,yes,2001,Vito
+Volkswagen,21500,crossover,178,3,Diesel,yes,2008,Touareg
+Toyota,17777,other,120,2.7,Gas,yes,2006,Land Cruiser Prado
+BMW,29000,sedan,170,2,Diesel,yes,2011,520
+Volkswagen,7300,sedan,125,1.6,Petrol,yes,2005,Golf Plus
+BMW,58000,crossover,64,2,Diesel,yes,2015,X5
+Toyota,43999,other,1,2.8,Diesel,yes,2016,Hilux
+Volkswagen,16450,van,143,2,Diesel,yes,2012,T5 (Transporter) 
+Toyota,26900,crossover,118,2.7,Petrol,yes,2011,Land Cruiser Prado
+Renault,8250,sedan,148,NA,Other,yes,2011,Kangoo
+BMW,13900,sedan,262,4,Diesel,yes,2003,740
+Mercedes-Benz,5500,sedan,250,2.7,Diesel,no,2003,E-Class
+Volkswagen,3350,other,223,1.9,Diesel,yes,1997,Caddy
+Volkswagen,9800,hatch,98,1.6,Gas,yes,2010,Golf V
+Mercedes-Benz,13899,hatch,232,3,Diesel,yes,2007,Vito
+Mercedes-Benz,15200,sedan,195,3,Diesel,yes,2007,E-Class
+Toyota,8900,sedan,87,1.6,Petrol,yes,2008,Corolla
+Mitsubishi,17800,crossover,111,2.4,Petrol,yes,2011,Outlander XL
+Audi,2650,vagon,270,2.5,Diesel,no,2001,A6
+Mitsubishi,10200,sedan,102,2,Gas,yes,2008,Lancer X
+Toyota,12500,sedan,150,2.4,Petrol,yes,2007,Camry
+Audi,2500,hatch,200,1.6,Petrol,no,1999,A3
+Toyota,16200,crossover,219,2.7,Petrol,yes,2006,Land Cruiser Prado
+BMW,28000,sedan,127,2.5,Petrol,yes,2010,523
+Mercedes-Benz,5000,sedan,240,2.2,Diesel,no,2004,E-Class
+Toyota,8900,sedan,154,1.6,Gas,yes,2008,Corolla
+BMW,29900,crossover,182,3,Petrol,yes,2008,X6
+Mercedes-Benz,12950,other,199,2.2,Diesel,yes,2012,Vito
+Volkswagen,19999,van,152,2,Diesel,yes,2012,Sharan
+Audi,10000,sedan,131,1.8,Petrol,yes,2005,A4
+BMW,39500,vagon,93,2,Diesel,yes,2012,525
+Volkswagen,7900,other,123,1.4,Petrol,yes,2008,Polo
+Volkswagen,6000,van,460,1.9,Diesel,yes,1997,T4 (Transporter)
+Mercedes-Benz,219900,crossover,30,6.3,Petrol,yes,2013,G 63 AMG
+Mercedes-Benz,5600,van,480,2.2,Diesel,yes,1999,Vito
+Mercedes-Benz,15900,sedan,216,5,Petrol,yes,2007,E-Class
+Mitsubishi,6700,sedan,155,1.6,Gas,yes,2007,Lancer
+Renault,9500,van,189,2,Diesel,yes,2010,Trafic
+Audi,3600,sedan,374,2.5,Diesel,no,2001,A8
+Renault,9150,van,220,2,Diesel,yes,2007,Trafic
+Mercedes-Benz,41500,other,26,NA,Diesel,yes,2013,E-Class
+Mercedes-Benz,12900,van,185,2.2,Diesel,yes,2011,Vito
+Renault,11500,vagon,175,1.5,Diesel,yes,2011,Megane
+Toyota,39500,other,1,2.8,Diesel,yes,2016,Hilux
+Renault,4200,sedan,310,1.4,Petrol,yes,2003,Symbol
+BMW,4100,sedan,26,3,Diesel,no,2001,530
+Volkswagen,9200,van,248,1.9,Diesel,yes,2007,T5 (Transporter) 
+Audi,11400,crossover,250,2.5,Diesel,yes,2005,A6 Allroad
+Volkswagen,24000,sedan,3,1.6,Diesel,yes,2016,Jetta
+Renault,1800,hatch,1,1.4,Gas,yes,1988,11
+Mercedes-Benz,3799,sedan,210,2,Petrol,yes,1995,200
+Mercedes-Benz,NA,sedan,0,3,Petrol,yes,2016,S 400
+Mercedes-Benz,110000,crossover,0,3,Diesel,yes,2016,GLS 350
+BMW,16999,vagon,213,3,Diesel,yes,2007,530
+Audi,6700,sedan,340,2.6,Gas,yes,1997,A6
+Toyota,2200,hatch,50,2,Petrol,yes,1991,Carina
+Renault,NA,sedan,100,1.4,Petrol,yes,2009,Symbol
+Toyota,4500,sedan,238,1.8,Petrol,yes,1996,Carina
+BMW,10999,sedan,225,3,Diesel,yes,2003,530
+Mitsubishi,NA,sedan,90,2,Petrol,yes,2007,Lancer Evolution
+BMW,4000,sedan,380,2,Petrol,yes,1991,320
+Volkswagen,5400,vagon,250,1.6,Petrol,yes,2003,Golf IV
+Mercedes-Benz,1700,sedan,300,2,Gas,no,1998,E-Class
+Audi,4000,sedan,320,2.8,Petrol,yes,1992,100
+Mercedes-Benz,55000,crossover,127,5.5,Petrol,yes,2008,G 55 AMG
+BMW,NA,crossover,0,2,Diesel,yes,2016,X5
+BMW,2500,sedan,44,2.5,Diesel,no,1997,525
+Mitsubishi,15999,crossover,149,3,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,7800,hatch,125,2.3,Petrol,yes,2001,C-Class
+Volkswagen,12114.35,sedan,0,1.6,Petrol,yes,2016,Polo
+Volkswagen,12500,sedan,118,1.6,Diesel,yes,2011,Golf VI
+Renault,11500,van,160,1.5,Diesel,yes,2010,Grand Scenic
+Volkswagen,10999,other,240,2.5,Diesel,yes,2005,LT
+Mercedes-Benz,15300,other,234,2.2,Diesel,yes,2008,Sprinter 315
+Mercedes-Benz,12300,sedan,300,2.2,Diesel,yes,2003,E-Class
+Volkswagen,12400,other,248,2.5,Diesel,yes,2007,T5 (Transporter)
+Mercedes-Benz,50800,crossover,30,3.5,Diesel,yes,2013,ML 350
+BMW,77500,crossover,1,3,Petrol,yes,2016,X6
+Renault,6800,van,119,1.5,Diesel,yes,2012,Kangoo
+BMW,6399,sedan,282,2,Petrol,yes,1995,520
+Audi,3550,sedan,196,2.8,Petrol,no,1997,A8
+BMW,31800,sedan,115,3,Diesel,yes,2010,5 Series GT
+Volkswagen,1700,hatch,247,1.4,Petrol,no,1999,Golf IV
+BMW,14300,sedan,198,NA,Petrol,yes,2005,523
+Mercedes-Benz,14900,sedan,171,3.2,Gas,yes,2006,E-Class
+Mercedes-Benz,5100,sedan,295,3,Diesel,yes,1994,E-Class
+Renault,12800,hatch,125,1.1,Diesel,yes,2012,Laguna
+BMW,5650,sedan,284,2,Petrol,yes,1998,520
+Audi,5200,sedan,250,1.8,Other,yes,1996,A4
+Renault,10100,vagon,190,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,295000,sedan,29,6,Petrol,yes,2011,S 600
+Mercedes-Benz,14750,sedan,220,3,Diesel,yes,2005,E-Class
+Volkswagen,4500,sedan,230,2,Gas,yes,1996,Passat B4
+Audi,19500,crossover,186,4.2,Petrol,yes,2006,Q7
+BMW,5400,crossover,250,3,Petrol,no,2003,X5
+Mercedes-Benz,12900,sedan,255,2.7,Diesel,yes,2003,E-Class
+Mitsubishi,15900,crossover,138,3,Gas,yes,2008,Pajero Wagon
+Renault,11800,other,179,1.5,Diesel,yes,2012,Megane
+BMW,34500,sedan,45,2,Petrol,yes,2013,320
+Renault,6200,van,228,2.5,Diesel,yes,2005,Trafic
+Mercedes-Benz,16500,other,107,2.2,Diesel,yes,2010,Vito
+Volkswagen,55555,van,53,2,Diesel,yes,2013,Multivan
+Toyota,22500,sedan,12,2.5,Petrol,yes,2014,Camry
+Audi,8700,sedan,202,3.7,Gas,yes,1998,A8
+Mercedes-Benz,NA,van,30,2.2,Diesel,yes,2014,Viano
+Audi,35900,sedan,105,2,Diesel,yes,2014,A6
+Mercedes-Benz,6800,van,242,2.2,Diesel,yes,1999,Vito
+Mitsubishi,18500,crossover,135,3,Gas,yes,2009,Pajero Wagon
+BMW,12300,sedan,140,2,Petrol,yes,2005,318
+Toyota,18800,vagon,89,1.8,Petrol,yes,2012,Avensis
+Mercedes-Benz,11700,crossover,284,4,Diesel,yes,2004,ML 400
+Audi,14900,crossover,183,3.6,Petrol,no,2007,Q7
+Volkswagen,8900,sedan,144,1.6,Petrol,yes,2013,Polo
+BMW,NA,sedan,1,3,Petrol,yes,2010,535
+Volkswagen,9000,sedan,223,2.5,Diesel,yes,2007,T5 (Transporter)
+BMW,7250,sedan,300,2.8,Petrol,yes,1999,528
+Mercedes-Benz,35900,crossover,254,5,Gas,yes,2004,G 500
+Toyota,14900,sedan,110,3.5,Petrol,yes,2008,Camry
+Volkswagen,14900,van,170,1.6,Diesel,yes,2012,Touran
+Mercedes-Benz,46500,crossover,127,5,Petrol,yes,2008,G 500
+Mercedes-Benz,7900,other,206,2.2,Diesel,yes,2004,Vito
+Toyota,35999,crossover,102,4,Petrol,yes,2011,Land Cruiser Prado
+Volkswagen,6800,sedan,178,NA,Gas,yes,2004,Passat B5
+Volkswagen,7300,van,240,1.9,Diesel,yes,2005,Caddy
+BMW,12600,sedan,219,3.6,Petrol,yes,2002,735
+BMW,9000,sedan,225,2.2,Gas,yes,2002,320
+BMW,37200,other,115,3,Diesel,yes,2011,X5
+Audi,63200,sedan,0,2,Petrol,yes,2016,A6
+BMW,86999,crossover,0,3,Diesel,yes,2016,X6
+Volkswagen,NA,van,270,2.4,Diesel,yes,1993,T4 (Transporter)
+Mercedes-Benz,4100,sedan,410,2.2,Diesel,yes,1998,E-Class
+Mercedes-Benz,11800,sedan,215,3.2,Petrol,yes,2003,E-Class
+Audi,4000,vagon,300,2.5,Diesel,no,2001,A6
+Mercedes-Benz,35500,sedan,160,6.5,Petrol,yes,2008,S 65 AMG
+Volkswagen,9900,van,149,1.6,Diesel,yes,2013,Caddy
+BMW,9999,sedan,271,3,Diesel,yes,2001,530
+Volkswagen,11500,sedan,118,2,Petrol,yes,2007,Passat B6
+BMW,4850,sedan,280,3,Diesel,no,1999,730
+Mitsubishi,5850,hatch,130,1.3,Petrol,yes,2005,Colt
+Toyota,103999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,NA,sedan,25,3,Diesel,yes,2014,S 350
+BMW,32000,crossover,138,3,Petrol,yes,2009,X6
+Toyota,61990,crossover,68,4.5,Diesel,yes,2012,Land Cruiser 200
+Toyota,8888,sedan,172,1.8,Gas,yes,2005,Avensis
+Mitsubishi,12700,crossover,110,2.4,Gas,yes,2008,Outlander XL
+Toyota,28500,sedan,16,2.5,Petrol,yes,2015,Camry
+BMW,47777,crossover,142,3,Other,yes,2011,X6
+BMW,NA,crossover,16,NA,Other,yes,2016,X5 M
+BMW,16700,sedan,150,2,Diesel,yes,2008,520
+Volkswagen,8757,sedan,32,NA,Other,no,2015,Jetta
+Mercedes-Benz,2500,sedan,200,2.3,Gas,yes,1986,190
+Toyota,12900,sedan,134,2.4,Gas,yes,2007,Camry
+Volkswagen,9900,van,300,2.5,Diesel,yes,2007,T5 (Transporter)
+Toyota,51500,crossover,70,3,Diesel,yes,2015,Land Cruiser Prado
+Volkswagen,6200,van,180,2,Diesel,yes,2005,Caddy
+Audi,13333,other,178,2,Diesel,yes,2008,A6
+Mercedes-Benz,43500,other,51,3,Diesel,yes,2014,E-Class
+BMW,23000,other,100,NA,Other,yes,2005,630
+Volkswagen,7777,other,214,2,Other,yes,2007,Caddy
+Volkswagen,3600,vagon,470,1.8,Petrol,yes,1994,Passat B4
+Volkswagen,20600,sedan,107,1.8,Petrol,yes,2012,Passat CC
+Volkswagen,9500,van,188,1.9,Diesel,yes,2006,T5 (Transporter)
+Mercedes-Benz,17500,van,220,3,Diesel,yes,2012,Vito
+Volkswagen,9000,sedan,178,1.9,Diesel,yes,2009,Jetta
+Volkswagen,10500,van,246,2.5,Diesel,yes,2006,T5 (Transporter)
+Mercedes-Benz,11300,sedan,363,3.2,Gas,yes,2004,E-Class
+Renault,9200,hatch,12,1.2,Petrol,yes,2013,Sandero
+BMW,18500,sedan,114,2,Gas,yes,2008,520
+Mercedes-Benz,NA,crossover,0,4.7,Petrol,yes,2016,GLS 500
+Volkswagen,28713.75,hatch,0,2,Diesel,yes,2015,Golf VII
+BMW,31500,crossover,130,3,Diesel,yes,2010,X5
+Renault,2950,vagon,280,19,Diesel,yes,2000,Kangoo
+Toyota,11999,sedan,139,3.5,Petrol,yes,2008,Camry
+BMW,67431,crossover,0,2,Diesel,yes,2016,X5
+Volkswagen,3500,hatch,220,2,Petrol,yes,1994,Golf III
+Renault,9300,van,94,2,Diesel,yes,2012,Trafic
+BMW,62000,crossover,1,2,Diesel,yes,2016,X5
+Volkswagen,35500,crossover,112,NA,Diesel,yes,2011,Touareg
+BMW,57000,crossover,47,3,Diesel,yes,2014,X6
+Volkswagen,7900,van,229,1.9,Diesel,yes,2001,T4 (Transporter)
+BMW,4400,sedan,300,2,Petrol,yes,1995,520
+BMW,62900,crossover,1,2,Diesel,yes,2016,X5
+Mercedes-Benz,23900,sedan,124,1.8,Petrol,yes,2012,E-Class
+Audi,21900,other,114,3.2,Petrol,yes,2009,A5
+BMW,15250,other,144,3,Petrol,yes,2004,530
+Mercedes-Benz,4300,sedan,280,1.8,Petrol,yes,1993,C-Class
+Mercedes-Benz,14600,van,217,2.2,Diesel,yes,2012,Vito
+Toyota,15000,crossover,184,2.7,Gas,yes,2005,Land Cruiser Prado
+Mercedes-Benz,5300,van,212,2.2,Diesel,yes,2000,Vito
+Mercedes-Benz,7000,sedan,190,2.6,Petrol,yes,2001,210
+BMW,13900,other,80,3,Petrol,yes,2002,330
+Toyota,29000,crossover,120,4,Gas,yes,2008,Land Cruiser Prado
+Volkswagen,20900,sedan,25,2,Diesel,yes,2015,Jetta
+Mercedes-Benz,20500,sedan,173,1.8,Gas,yes,2011,E-Class
+Toyota,22999,crossover,152,4,Gas,yes,2008,Land Cruiser Prado
+Audi,95000,other,30,5.2,Petrol,yes,2010,R8
+Volkswagen,9599,sedan,88,1.6,Petrol,yes,2014,Polo
+Toyota,NA,crossover,1,2.2,Diesel,yes,2016,Rav 4
+Volkswagen,10500,van,299,2.5,Diesel,yes,2002,Syncro
+Mercedes-Benz,23999,sedan,204,3.5,Petrol,yes,2008,S 350
+Renault,10200,van,149,1.5,Diesel,yes,2013,Kangoo
+Toyota,9950,sedan,142,1.8,Petrol,yes,2008,Avensis
+Toyota,21999,crossover,69,3.5,Petrol,yes,2008,Highlander
+Mercedes-Benz,18500,crossover,180,3.5,Petrol,yes,2008,ML 350
+Mercedes-Benz,40500,crossover,54,5.5,Petrol,yes,2011,GL 500
+Volkswagen,3100,sedan,390,1.8,Petrol,yes,1990,Passat B3
+BMW,8100,crossover,175,3,Gas,no,2005,X5
+Audi,8100,vagon,302,2.5,Diesel,yes,2000,A6
+Volkswagen,2600,vagon,288,1.8,Petrol,yes,1990,Passat B3
+Renault,7299,sedan,160,2,Petrol,yes,2007,Megane
+Volkswagen,9600,sedan,107,1.6,Gas,yes,2011,Polo
+Renault,10500,vagon,250,1.5,Diesel,yes,2011,Megane
+Mercedes-Benz,26800,vagon,160,2.2,Diesel,yes,2014,E-Class
+Audi,31999,sedan,107,3,Diesel,yes,2009,A8
+Volkswagen,12900,van,176,2.5,Diesel,yes,2008,T5 (Transporter)
+Audi,NA,crossover,65,2.99,Diesel,yes,2013,A6 Allroad
+BMW,16500,other,122,2.2,Petrol,yes,2005,520
+Mitsubishi,6000,hatch,118,1.3,Petrol,yes,2006,Colt
+Toyota,NA,hatch,76,1,Petrol,yes,2007,Aygo
+Toyota,9950,sedan,140,1.4,Petrol,yes,2012,Corolla
+Mercedes-Benz,NA,crossover,27,3,Diesel,yes,2015,G 350
+Volkswagen,5700,vagon,199,1.8,Petrol,yes,1999,Sharan
+Audi,48000,crossover,90,3,Diesel,yes,2013,Q7
+Volkswagen,1296.75,sedan,1,1.6,Petrol,yes,1980,Jetta
+Mitsubishi,8000,crossover,220,2.4,Gas,yes,2004,Outlander
+Mitsubishi,12500,other,48,1.6,Petrol,yes,2012,Lancer X
+Mercedes-Benz,57000,sedan,61,4.6,Petrol,yes,2012,S 500
+Mercedes-Benz,85000,sedan,36,5.5,Petrol,yes,2014,CLS 63 AMG
+Renault,4200,sedan,180,1.4,Petrol,yes,2003,Symbol
+BMW,10600,sedan,285,2,Diesel,yes,2007,520
+Audi,55000,crossover,65,2.99,Diesel,yes,2013,A6 Allroad
+Mercedes-Benz,31500,van,95,2.2,Diesel,yes,2013,Vito
+Audi,9300,sedan,186,2.8,Petrol,yes,2000,A6
+Renault,4299,vagon,300,1.5,Diesel,yes,2003,Kangoo
+Audi,48900,other,82,4.2,Petrol,yes,2007,R8
+Toyota,13300,other,131,2.4,Gas,yes,2008,Camry
+Audi,13300,sedan,207,4.2,Petrol,yes,2004,A8
+BMW,49900,other,82,3,Petrol,yes,2012,640
+BMW,13700,sedan,276,3,Diesel,yes,2004,530
+Volkswagen,5999,van,300,1.8,Gas,yes,1999,Sharan
+Toyota,8700,hatch,38,1.6,Petrol,yes,2008,Auris
+Volkswagen,18000,crossover,155,2,Diesel,yes,2011,Tiguan
+BMW,55500,crossover,90,3,Diesel,yes,2013,X5 M
+BMW,3500,sedan,280,NA,Petrol,no,1996,730
+BMW,3100,vagon,1,2,Diesel,no,2002,320
+Toyota,30900,sedan,15,2.5,Petrol,yes,2015,Camry
+Mercedes-Benz,23500,sedan,200,5.5,Petrol,yes,2008,S 550
+Volkswagen,9200,other,156,1.9,Diesel,yes,2009,Golf Variant
+Volkswagen,13000,sedan,140,2,Petrol,yes,2006,Jetta
+BMW,NA,crossover,0,3,Diesel,yes,2015,X6
+Mercedes-Benz,55000,crossover,120,5.5,Petrol,yes,2008,G 55 AMG
+Audi,27500,sedan,74,2,Diesel,yes,2011,A5
+BMW,5900,vagon,220,2.8,Petrol,yes,1995,520
+Mercedes-Benz,126000,sedan,14,3,Diesel,yes,2015,S 350
+Mercedes-Benz,27500,crossover,152,6.3,Petrol,yes,2007,ML 63 AMG
+Renault,7200,van,285,1.9,Diesel,yes,2002,Trafic
+Renault,6750,van,140,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,21000,crossover,122,3.2,Diesel,yes,2009,Pajero Wagon
+Volkswagen,15900,sedan,77,1.4,Petrol,yes,2012,Jetta
+Volkswagen,10000,sedan,56,1.8,Petrol,no,2015,Passat B7
+Renault,4500,other,1,1.5,Diesel,yes,2007,Kangoo
+Mercedes-Benz,29000,crossover,139,3.5,Gas,yes,2009,ML 350
+Volkswagen,NA,other,197,2,Diesel,no,2011,T5 (Transporter)
+Mercedes-Benz,109999,crossover,0,2.98,Diesel,yes,2016,GLS 350
+Mercedes-Benz,9999,sedan,80,2.3,Petrol,yes,2002,C-Class
+BMW,32500,crossover,152,3,Diesel,yes,2008,X5
+Mercedes-Benz,111999,crossover,0,3,Diesel,yes,2016,GL 350
+Mercedes-Benz,29500,crossover,253,5,Petrol,yes,2003,G 500
+Audi,2850,vagon,280,2.5,Diesel,no,1999,A6
+Mitsubishi,7200,sedan,83,1.6,Other,yes,2007,Lancer
+Volkswagen,6250,hatch,170,1.4,Petrol,yes,2001,Golf IV
+Volkswagen,6500,sedan,74,2,Petrol,no,2011,Jetta
+Volkswagen,6000,sedan,271,1.8,Petrol,yes,2000,Passat B5
+Mitsubishi,9500,crossover,140,2.4,Gas,yes,2007,Outlander
+Mercedes-Benz,17200,sedan,180,3,Diesel,yes,2006,320
+Volkswagen,18200,hatch,23,1.4,Petrol,yes,2013,Golf VI
+Mercedes-Benz,6900,vagon,235,2.9,Diesel,yes,1997,E-Class
+Toyota,10200,other,105,1.5,Gas,yes,2005,Scion
+Toyota,17394.09,hatch,0,1.33,Petrol,yes,2016,Yaris
+Audi,22500,sedan,99,2,Other,yes,2010,A5
+Mercedes-Benz,28950,sedan,40,1.8,Petrol,yes,2014,C-Class
+BMW,15500,sedan,89,2,Diesel,no,2012,520
+Renault,13600,vagon,164,1.5,Diesel,yes,2013,Grand Scenic
+Mitsubishi,3250,sedan,380,1.8,Petrol,yes,1993,Galant
+BMW,17000,vagon,158,2,Diesel,yes,2010,320
+Renault,12350,van,159,2,Diesel,yes,2010,Trafic
+Mercedes-Benz,24500,sedan,163,5.5,Gas,yes,2007,S 550
+BMW,11800,sedan,218,2,Petrol,yes,2006,320
+Toyota,30000,crossover,85,2.7,Gas,yes,2010,Land Cruiser Prado
+Audi,4850,sedan,270,2.6,Petrol,yes,1995,A6
+Volkswagen,11900,vagon,132,1.4,Petrol,yes,2011,Golf VI
+Renault,6500,van,127,1.5,Diesel,yes,2011,Kangoo
+Mercedes-Benz,12000,sedan,298,5,Petrol,yes,2004,S 500
+BMW,4300,sedan,200,2,Diesel,no,2002,320
+BMW,NA,crossover,1,3,Diesel,yes,2014,X6
+Renault,5600,vagon,112,1.5,Diesel,yes,2006,Kangoo
+Mercedes-Benz,24500,other,77,1.8,Petrol,yes,2011,C-Class
+BMW,26900,crossover,170,3,Diesel,yes,2008,X5
+Mercedes-Benz,11500,van,180,2.2,Diesel,yes,2011,Vito
+Mitsubishi,9700,van,247,2.4,Gas,yes,2008,Grandis
+Renault,10500,vagon,185,1.5,Diesel,yes,2011,Megane
+Renault,10900,vagon,180,1.5,Diesel,yes,2012,Megane
+Renault,7500,other,440,2,Diesel,yes,2008,Trafic
+Mercedes-Benz,25500,crossover,77,3.5,Petrol,yes,2008,ML 350
+Volkswagen,15500,sedan,80,1.4,Petrol,yes,2013,Jetta
+Volkswagen,9750,van,159,1.9,Diesel,yes,2009,Caddy
+BMW,16100,crossover,194,3,Diesel,yes,2004,X5
+Mercedes-Benz,300000,other,37,5,Petrol,yes,2012,G 500
+Mercedes-Benz,5500,sedan,440,2,Petrol,yes,1978,E-Class
+Volkswagen,9200,vagon,171,1.6,Petrol,yes,2008,Caddy
+Volkswagen,5150,van,240,2,Diesel,yes,2005,Caddy
+Toyota,100000,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,8999,other,126,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,16800,sedan,125,1.8,Petrol,yes,2008,C-Class
+Mercedes-Benz,8200,sedan,280,2.4,Gas,yes,1997,E-Class
+Mercedes-Benz,24950,other,60,1.8,Petrol,yes,2013,C-Class
+Audi,80999,crossover,0,3,Diesel,yes,2016,Q7
+Mercedes-Benz,7300,van,207,2.2,Diesel,yes,2003,Vito
+Volkswagen,2200,hatch,400,1.3,Other,yes,1987,Golf II
+BMW,21335,other,105,3,Petrol,yes,2008,335
+BMW,45000,crossover,80,3,Petrol,yes,2011,X6
+Renault,6750,van,155,1.5,Diesel,yes,2012,Kangoo
+Renault,7000,van,210,1.5,Diesel,yes,2005,Kangoo
+BMW,12090,hatch,145,1.6,Petrol,yes,2010,116
+BMW,27900,sedan,38,2,Petrol,yes,2013,320
+Renault,2100,vagon,237,1.9,Diesel,no,2001,Laguna
+Renault,6800,sedan,152,1.6,Petrol,yes,2007,Megane
+Volkswagen,11500,van,163,2.5,Diesel,yes,2008,T5 (Transporter)
+Toyota,17900,sedan,35,1.6,Petrol,yes,2014,Corolla
+Mercedes-Benz,125000,sedan,9,3,Diesel,yes,2014,S 350
+BMW,6500,sedan,1,3.5,Petrol,yes,1999,535
+BMW,8000,sedan,194,2,Petrol,yes,1985,520
+Toyota,14200,sedan,31,NA,Petrol,yes,2014,Corolla
+Volkswagen,13500,van,124,2,Diesel,yes,2013,T5 (Transporter) 
diff --git a/Udemy DataScience Workbook/Data_Files/data_preprocessing_homework.pdf b/Udemy DataScience Workbook/Data_Files/data_preprocessing_homework.pdf
new file mode 100644
index 0000000..b863e32
Binary files /dev/null and b/Udemy DataScience Workbook/Data_Files/data_preprocessing_homework.pdf differ
diff --git a/Udemy DataScience Workbook/Data_Files/df_preprocessed.csv b/Udemy DataScience Workbook/Data_Files/df_preprocessed.csv
new file mode 100644
index 0000000..ae09c79
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/df_preprocessed.csv	
@@ -0,0 +1,701 @@
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diff --git a/Udemy DataScience Workbook/Data_Files/iris_dataset.csv b/Udemy DataScience Workbook/Data_Files/iris_dataset.csv
new file mode 100644
index 0000000..433fd1e
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/iris_dataset.csv	
@@ -0,0 +1,151 @@
+sepal_length,sepal_width,petal_length,petal_width
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diff --git a/Udemy DataScience Workbook/Data_Files/iris_with_answers.csv b/Udemy DataScience Workbook/Data_Files/iris_with_answers.csv
new file mode 100644
index 0000000..6464198
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/iris_with_answers.csv	
@@ -0,0 +1,151 @@
+sepal_length,sepal_width,petal_length,petal_width,species
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diff --git a/Udemy DataScience Workbook/Data_Files/output.csv b/Udemy DataScience Workbook/Data_Files/output.csv
new file mode 100644
index 0000000..14a3d75
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/output.csv	
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/Udemy DataScience Workbook/Data_Files/passwords_random.csv b/Udemy DataScience Workbook/Data_Files/passwords_random.csv
new file mode 100644
index 0000000..4448484
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/passwords_random.csv	
@@ -0,0 +1 @@
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2547 326 8075 8503 2487 8015 6312 4669 8855 2530 2735 9914 5695 4227 4004 2840 1628 4105 5641 2298 5881 9377 4770 3957 7003 9493 9896 1263 1631 5644 940 5415 9464 8019 9121 4491 3806 4632 2729 5748 7297 3972 7329 7050 2397 3196 7813 3794 7342 2395 3372 3216 1833 9837 8663 3964 5671 6951 6070 2453 9910 2132 2455 1352 8042 2094 6087 6818 8808 8800 7029 6754 9113 3286 2762 2400 5134 8906 1139 4688 7204 8889 1346 6539 6826 385 3782 1390 4917 1150 9646 5129 3995 360 9805 6093 7236 7576 8410 4326 7919 8016 7148 609 7617 5652 2138 5307 1639 8240 1536 4974 2161 4888 7075 4399 2357 5127 7389 102 3035 9264 2250 6477 1764 6877 8159 1028 901 1457 2256 7927 3758 5959 7999 2377 8198 7619 4920 6149 5302 440 5753 2383 4957 7226 7468 6181 8158 6544 569 1872 7739 8536 1189 3774 6857 9149 2716 8561 2619 132 5267 7012 8823 4378 3342 789 7287 1625 8423 3434 1876 9271 6731 4785 3518 9187 9530 148 3214 3207 3475 7922 9148 3856 7636 1361 7480 13 1669 9495 5079 423 152 9515 8055 531 210 8828 5338 4611 8707 7208 5303 2516 694 9087 8288 9393 8656 4936 6075 6119 5275 9737 7747 5385 1366 3271 6345 5594 5403 4691 801 4492 1143 3563 6972 4008 9314 5100 7240 6852 5880 2472 9957 692 422 4182 1551 4199 3233 5254 5988 2525 355 4929 4450 6623 8526 1873 4986 7920 8703 8997 6115 3567 7870 1592 8248 4204 5840 5973 1965 9832 1035 622 8453 8319 6372 1142 8751 46 1463 570 6207 2162 1394 1031 3412 6701 3000 3876 7280 9015 2236 5755 8067 2126 6859 1004 187 5614 2541 857 8960 3320 3785 7082 8655 6025 897 1315 833 8545 3128 3246 7128 3036 585 1955 7323 1456 5392 1609 9994 1079 266 1765 3746 9634 2190 6633 8045 9522 5894 4026 5200 9447 4201 8915 8228 4406 9833 2702 9237 7547 1370 4374 1224 4525 8491 541 8501 5575 1408 7254 1228 1946 6158 6655 3267 3291 61 1020 9887 5209 7413 4036 9763 976 4139 8542 480 5440 1351 8393 8029 3388 4397 1417 5664 4662 3033 1973 8268 8079 8795 8263 1423 9025 9675 2931 1708 1407 246 9526 2229 6178 3799 5839 3522 303 8238 1488 2978 3606 401 1557 9251 8190 9918 2315 8468 6350 2335 4773 9254 4267 4116 5843 6358 2945 6405 5556 1835 864 8031 248 6930 7318 6676 9202 9369 3138 793 5022 7988 3550 8212 9108 8907 3135 6462 2650 9381 538 8696 6238 3576 2036 6582 5770 961 8730 55 7956 3977 6982 5183 9989 4621 4659 2016 5918 6601 3454 8123 5555 6902 3438 5627 1490 2597 3837 703 2747 5948 1285 3759 8137 1223 9870 9262 1863 7994 2780 5624 7276 9513 7017 5816 4859 1739 7052 5533 5198 9089 1934 3754 0 7526 1403 7423 5000 3631 3471 3014 9456 4409 2286 9630 7094 4846 7696 7550 6751 7434 3483 2081 3647 2211 5885 5846 6722 3893 768 5720 3419 3365 7774 9492 5878 1221 6917 7288 8229 6189 2255 7382 1789 508 5639 1558 8309 5727 799 6828 6954 1745 8727 1599 9299 9324 8669 381 4981 2957 9489 7447 283 5442 2925 9550 2010 4758 6876 4931 4412 5229 5166 9755 8975 9676 2420 5905 959 6590 8891 2096 8282 6646 1728 7370 1034 8340 7682 1487 9077 5329 4056 4192 3752 3971 1344 3195 1323 6427 256 3841 1278 1968 9738 3116 1090 4779 3265 5625 951 2198 7338 5146 4018 2528 874 6668 9509 8220 6210 1273 5898 179 5833 4339 1817 9656 8903 1023 4181 1236 8940 704 3642 6966 4070 205 8699 6661 7273 1953 6728 4673 2121 1610 2116 3302 1309 5506 7102 8114 1759 6579 1521 9053 9378 2549 8674 2850 830 863 8214 6788 3748 4364 2365 9147 5411 1929 6510 3037 9083 2195 1730 138 1048 5393 119 9370 529 6317 4117 9067 765 7304 1158 4855 6529 8814 1554 9955 8773 2763 2544 4368 3761 8064 5249 6850 9575 3194 1392 4519 7176 2340 3041 2216 7115 2110 4382 9179 1130 8643 7810 376 3109 8261 3142 9722 3613 5962 743 9699 7504 988 4789 2042 8547 7350 9029 699 8959 2942 4013 9974 9433 421 3363 3950 4133 1803 3910 175 5923 3750 9736 6384 4222 6031 1317 7653 1050 3169 5656 3548 8739 8201 1155 2328 1287 9401 9008 7875 5986 5078 5263 6486 1731 5970 6304 267 8853 2672 5580 1255 7650 3743 7492 6339 9190 7846 5781 7950 9500 5653 7010 3829 5357 3215 5853 7580 9168 8189 4251 4019 8969 418 8944 330 2129 5617 5536 2318 9081 7449 9328 859 9956 1606 1933 866 6783 2127 4733 909 2095 8681 9663 4544 757 3863 2444 1741 3854 6421 744 7083 1498 2347 8359 1447 6139 481 5754 5775 3324 6706 8283 3444 9981 8200 8188 6079 9508 7952 5718 7203 9342 7071 6551 274 5583 3143 8582 5334 3441 6018 5049 6081 3517 7822 6946 6816 1239 5286 6371 9651 7716 8156 293 8543 5790 5041 4569 3045 7110 6073 8164 7876 3698 8998 8259 4973 9742 9936 8919 5186 7066 4298 3161 5707 8092 7363 2985 6051 9175 8534 1030 5591 6726 665 3532 6438 2416 9648 1621 9973 7030 2306 4207 8378 5636 4124 9665 3315 4193 6957 8316 639 1503 4198 6038 2969 6526 489 689 4437 6444 2551 3670 2443 5979 8558 6969 9354 214 4371 6999 4289 916 4798 5018 8520 1650 726 5320 5847 9021 5143 6651 9713 5640 7865 1643 9078 271 4850 6296 3802 8792 8083 1117 6200 549 8802 5495 2040 5769 2209 8858 8659 5891 8701 8607 8131 3667 598 756 5780 257 997 4380 1791 3241 502 1801 6725 8917 7501 6183 4343 4786 7213 690 7227 6987 5220 7871 126 9094 4878 1680 3784 7884 825 9608 9780 6146 6157 835 7416 7845 2035 2645 3836 6615 2621 8779 6743 5551 1217 4975 3030 5696 9636 7565 5912 4574 5017 741 9758 8849 3923 8813 3553 2749 9407 6314 5308 1996 4445 8276 7494 5978 5526 3068 4713 5068 7488 4090 3148 7574 1910 334 4916 2268 5065 1191 5383 2319 2417 3183 7332 1128 4504 7228 6509 1706 4417 5822 5682 9364 8269 6530 1141 6989 7679 5390 4886 6095 6965 522 2362 3542 5367 9678 1209 4353 7451 3729 4581 3489 8564 8040 7589 2903 2665 3538 3001 7582 8579 5255 2080 291 846 9996 7279 5924 3814 2107 2940 1480 8732 5919 6786 6224 568 4096 396 5318 8507 4142 7123 2579 6399 6716 5067 1083 5150 8 6611 9142 5985 1210 3817 8968 4583 7892 724 5658 9050 4890 2794 2441 3310 3284 9146 1869 1840 3847 1595 2902 760 2791 3318 3989 5268 7084 3600 3184 7170 1861 7581 7252 128 5548 8983 3119 1160 4696 4425 8143 971 6067 7978 4356 9253 626 7109 681 3607 5214 6996 2425 2909 7797 6473 4948 1555 8842 3009 2316 2681 4932 2570 6803 2502 9417 5289 1563 1025 7444 1334 8044 9017 3332 7910 6552 6419 7995 6431 5476 7695 7105 9587 4945 7527 5829 8839 7719 922 6002 6753 1687 8039 3108 1633 3501 3503 8506 9325 4684 240 4348 2206 2753 9655 4635 8455 2797 675 7011 2540 1174 3204 8051 3066 8317 9859 8931 7366 4667 858 7035 5351 4315 2654 523 720 1474 2366 8822 5248 9128 4367 641 6931 4323 8781 7126 6520 1663 842 1773 2926 5253 2108 5561 7077 8241 4328 7499 9205 939 72 78 3333 8087 4015 7286 2409 6362 5994 2693 8145 9100 8886 7359 165 1758 8964 5090 4521 6682 8652 6474 7092 5216 5875 4296 1293 7702 8683 8470 8662 7945 8846 1262 4985 8193 6133 7116 8602 6027 2805 2403 8912 6618 4512 8952 6913 6065 8460 4925 9123 7538 1275 8909 54 5380 6746 5278 1825 367 8152 2270 1171 2233 8048 7223 7883 2796 1743 9897 8465 5512 7354 9440 3715 5013 5673 5043 6423 818 5284 7198 3914 1602 1952 6039 7162 550 7430 8119 3635 4912 4052 7614 9004 1879 6563 2622 3155 7749 3309 5036 7514 8777 7431 5511 3879 482 7402 628 7608 2118 1054 8239 5514 1330 3637 3706 6418 6324 5066 6525 9983 2846 469 4331 7406 1911 4119 2872 1489 6046 6153 6814 6173 4272 5815 5886 3089 7913 7220 5441 2573 3582 6500 7754 707 9923 3065 4214 9561 5142 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\ No newline at end of file
diff --git a/Udemy DataScience Workbook/Data_Files/real_estate_price_size.csv b/Udemy DataScience Workbook/Data_Files/real_estate_price_size.csv
new file mode 100644
index 0000000..6f51299
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/real_estate_price_size.csv	
@@ -0,0 +1,101 @@
+price,size
+234314.144,643.09
+228581.528,656.22
+281626.336,487.29
+401255.608,1504.75
+458674.256,1275.46
+245050.28,575.19
+265129.064,570.89
+175716.48,620.82
+331101.344,682.26
+218630.608,694.52
+279555.096,1060.36
+494778.992,1842.51
+215472.104,694.52
+418753.008,1009.25
+444192.008,1300.96
+440201.616,1379.72
+248337.6,690.54
+234178.16,623.94
+225451.984,681.07
+299416.976,1027.76
+268125.08,620.71
+171795.24,549.69
+412569.472,1207.45
+183459.488,518.38
+168047.264,525.81
+362519.72,1103.3
+271793.312,570.89
+406852.304,1334.1
+297760.44,681.07
+368988.432,1496.36
+301635.728,1010.33
+225452.32,681.07
+207742.248,597.9
+191486.896,525.81
+285223.176,857.54
+302000.92,622.97
+269225.92,823.21
+233493.208,570.25
+292965.216,685.48
+245747.2,698.29
+310045.712,1021.95
+217468.224,682.26
+287350,823.21
+414682.648,1334.1
+293044.496,1060.36
+300061.48,698.29
+204302.976,633.19
+201778.048,698.29
+257828.416,633.19
+262423.504,617.05
+225656.12,647.5
+393069.76,1021.95
+258637.008,1021.95
+269523.056,727.88
+255629.16,647.5
+500681.128,1508.84
+320345.52,1032.06
+395242.096,1283.85
+330677.128,827.84
+251332.592,569.17
+251188.824,828.16
+263311.696,698.5
+359674.44,1205.62
+334938.872,1021.95
+302393.384,685.48
+304587.272,827.09
+355251.2,1009.25
+271726.752,549.8
+294582.944,685.48
+454512.76,1496.36
+276875.632,1021.95
+181587.576,643.41
+298926.496,656.22
+211724.096,549.8
+228313.024,685.48
+286161.6,685.48
+382120.152,1183.46
+365863.936,1334.1
+251560.04,682.26
+342988.456,1188.62
+180307.216,681.07
+408637.816,1122.34
+190909.056,681.07
+282683.544,643.09
+303597.216,685.48
+376253.808,1009.25
+154282.128,479.75
+327252.112,1028.41
+211904.536,601.66
+354512.112,1236.93
+251140.656,694.52
+338078.168,1071.55
+298170.88,694.52
+266684.248,698.29
+262477.856,698.29
+252460.4,549.8
+310522.592,1037.44
+383635.568,1504.75
+225145.248,648.29
+274922.856,705.29
diff --git a/Udemy DataScience Workbook/Data_Files/real_estate_price_size_year.csv b/Udemy DataScience Workbook/Data_Files/real_estate_price_size_year.csv
new file mode 100644
index 0000000..32952f3
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/real_estate_price_size_year.csv	
@@ -0,0 +1,101 @@
+price,size,year
+234314.144,643.09,2015
+228581.528,656.22,2009
+281626.336,487.29,2018
+401255.608,1504.75,2015
+458674.256,1275.46,2009
+245050.28,575.19,2006
+265129.064,570.89,2015
+175716.48,620.82,2006
+331101.344,682.26,2018
+218630.608,694.52,2009
+279555.096,1060.36,2009
+494778.992,1842.51,2009
+215472.104,694.52,2015
+418753.008,1009.25,2018
+444192.008,1300.96,2006
+440201.616,1379.72,2006
+248337.6,690.54,2018
+234178.16,623.94,2006
+225451.984,681.07,2006
+299416.976,1027.76,2018
+268125.08,620.71,2015
+171795.24,549.69,2015
+412569.472,1207.45,2015
+183459.488,518.38,2015
+168047.264,525.81,2009
+362519.72,1103.3,2018
+271793.312,570.89,2018
+406852.304,1334.1,2015
+297760.44,681.07,2015
+368988.432,1496.36,2015
+301635.728,1010.33,2006
+225452.32,681.07,2006
+207742.248,597.9,2009
+191486.896,525.81,2015
+285223.176,857.54,2018
+302000.92,622.97,2018
+269225.92,823.21,2006
+233493.208,570.25,2006
+292965.216,685.48,2018
+245747.2,698.29,2015
+310045.712,1021.95,2015
+217468.224,682.26,2018
+287350,823.21,2009
+414682.648,1334.1,2009
+293044.496,1060.36,2018
+300061.48,698.29,2015
+204302.976,633.19,2009
+201778.048,698.29,2006
+257828.416,633.19,2015
+262423.504,617.05,2015
+225656.12,647.5,2015
+393069.76,1021.95,2015
+258637.008,1021.95,2009
+269523.056,727.88,2006
+255629.16,647.5,2015
+500681.128,1508.84,2018
+320345.52,1032.06,2018
+395242.096,1283.85,2015
+330677.128,827.84,2006
+251332.592,569.17,2015
+251188.824,828.16,2018
+263311.696,698.5,2015
+359674.44,1205.62,2015
+334938.872,1021.95,2009
+302393.384,685.48,2015
+304587.272,827.09,2015
+355251.2,1009.25,2006
+271726.752,549.8,2015
+294582.944,685.48,2018
+454512.76,1496.36,2006
+276875.632,1021.95,2009
+181587.576,643.41,2006
+298926.496,656.22,2015
+211724.096,549.8,2015
+228313.024,685.48,2018
+286161.6,685.48,2018
+382120.152,1183.46,2018
+365863.936,1334.1,2006
+251560.04,682.26,2009
+342988.456,1188.62,2006
+180307.216,681.07,2006
+408637.816,1122.34,2015
+190909.056,681.07,2006
+282683.544,643.09,2018
+303597.216,685.48,2018
+376253.808,1009.25,2018
+154282.128,479.75,2006
+327252.112,1028.41,2009
+211904.536,601.66,2018
+354512.112,1236.93,2018
+251140.656,694.52,2018
+338078.168,1071.55,2015
+298170.88,694.52,2015
+266684.248,698.29,2018
+262477.856,698.29,2006
+252460.4,549.8,2009
+310522.592,1037.44,2009
+383635.568,1504.75,2006
+225145.248,648.29,2015
+274922.856,705.29,2006
diff --git a/Udemy DataScience Workbook/Data_Files/real_estate_price_size_year_view.csv b/Udemy DataScience Workbook/Data_Files/real_estate_price_size_year_view.csv
new file mode 100644
index 0000000..6090bab
--- /dev/null
+++ b/Udemy DataScience Workbook/Data_Files/real_estate_price_size_year_view.csv	
@@ -0,0 +1,101 @@
+price,size,year,view
+234314.144,643.09,2015,No sea view
+228581.528,656.22,2009,No sea view
+281626.336,487.29,2018,Sea view
+401255.608,1504.75,2015,No sea view
+458674.256,1275.46,2009,Sea view
+245050.28,575.19,2006,Sea view
+265129.064,570.89,2015,Sea view
+175716.48,620.82,2006,No sea view
+331101.344,682.26,2018,Sea view
+218630.608,694.52,2009,No sea view
+279555.096,1060.36,2009,No sea view
+494778.992,1842.51,2009,Sea view
+215472.104,694.52,2015,No sea view
+418753.008,1009.25,2018,No sea view
+444192.008,1300.96,2006,Sea view
+440201.616,1379.72,2006,Sea view
+248337.6,690.54,2018,No sea view
+234178.16,623.94,2006,Sea view
+225451.984,681.07,2006,No sea view
+299416.976,1027.76,2018,No sea view
+268125.08,620.71,2015,Sea view
+171795.24,549.69,2015,No sea view
+412569.472,1207.45,2015,Sea view
+183459.488,518.38,2015,No sea view
+168047.264,525.81,2009,No sea view
+362519.72,1103.3,2018,Sea view
+271793.312,570.89,2018,Sea view
+406852.304,1334.1,2015,Sea view
+297760.44,681.07,2015,Sea view
+368988.432,1496.36,2015,No sea view
+301635.728,1010.33,2006,No sea view
+225452.32,681.07,2006,No sea view
+207742.248,597.9,2009,No sea view
+191486.896,525.81,2015,No sea view
+285223.176,857.54,2018,No sea view
+302000.92,622.97,2018,Sea view
+269225.92,823.21,2006,No sea view
+233493.208,570.25,2006,Sea view
+292965.216,685.48,2018,Sea view
+245747.2,698.29,2015,No sea view
+310045.712,1021.95,2015,No sea view
+217468.224,682.26,2018,No sea view
+287350,823.21,2009,Sea view
+414682.648,1334.1,2009,No sea view
+293044.496,1060.36,2018,No sea view
+300061.48,698.29,2015,Sea view
+204302.976,633.19,2009,No sea view
+201778.048,698.29,2006,No sea view
+257828.416,633.19,2015,Sea view
+262423.504,617.05,2015,Sea view
+225656.12,647.5,2015,No sea view
+393069.76,1021.95,2015,Sea view
+258637.008,1021.95,2009,No sea view
+269523.056,727.88,2006,Sea view
+255629.16,647.5,2015,Sea view
+500681.128,1508.84,2018,Sea view
+320345.52,1032.06,2018,No sea view
+395242.096,1283.85,2015,Sea view
+330677.128,827.84,2006,Sea view
+251332.592,569.17,2015,Sea view
+251188.824,828.16,2018,No sea view
+263311.696,698.5,2015,No sea view
+359674.44,1205.62,2015,No sea view
+334938.872,1021.95,2009,Sea view
+302393.384,685.48,2015,Sea view
+304587.272,827.09,2015,No sea view
+355251.2,1009.25,2006,Sea view
+271726.752,549.8,2015,Sea view
+294582.944,685.48,2018,Sea view
+454512.76,1496.36,2006,Sea view
+276875.632,1021.95,2009,No sea view
+181587.576,643.41,2006,No sea view
+298926.496,656.22,2015,Sea view
+211724.096,549.8,2015,No sea view
+228313.024,685.48,2018,No sea view
+286161.6,685.48,2018,Sea view
+382120.152,1183.46,2018,Sea view
+365863.936,1334.1,2006,No sea view
+251560.04,682.26,2009,Sea view
+342988.456,1188.62,2006,Sea view
+180307.216,681.07,2006,No sea view
+408637.816,1122.34,2015,Sea view
+190909.056,681.07,2006,No sea view
+282683.544,643.09,2018,Sea view
+303597.216,685.48,2018,Sea view
+376253.808,1009.25,2018,Sea view
+154282.128,479.75,2006,No sea view
+327252.112,1028.41,2009,Sea view
+211904.536,601.66,2018,No sea view
+354512.112,1236.93,2018,No sea view
+251140.656,694.52,2018,No sea view
+338078.168,1071.55,2015,No sea view
+298170.88,694.52,2015,Sea view
+266684.248,698.29,2018,No sea view
+262477.856,698.29,2006,Sea view
+252460.4,549.8,2009,Sea view
+310522.592,1037.44,2009,No sea view
+383635.568,1504.75,2006,No sea view
+225145.248,648.29,2015,No sea view
+274922.856,705.29,2006,Sea view
diff --git a/Udemy DataScience Workbook/Defining+a+Function+in+Python+-+Lecture_Py3.ipynb b/Udemy DataScience Workbook/Defining+a+Function+in+Python+-+Lecture_Py3.ipynb
new file mode 100644
index 0000000..2b8f23b
--- /dev/null
+++ b/Udemy DataScience Workbook/Defining+a+Function+in+Python+-+Lecture_Py3.ipynb	
@@ -0,0 +1,52 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simple():\n",
+    "    print (\"My first function\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My first function\n"
+     ]
+    }
+   ],
+   "source": [
+    "simple()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Errors+when+adding+scalars,+vectors,+and+matrices+in+Python.ipynb b/Udemy DataScience Workbook/Errors+when+adding+scalars,+vectors,+and+matrices+in+Python.ipynb
new file mode 100644
index 0000000..e66d31f
--- /dev/null
+++ b/Udemy DataScience Workbook/Errors+when+adding+scalars,+vectors,+and+matrices+in+Python.ipynb	
@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Addition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Addition of scalars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "5 + 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "10 - 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Addition of matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 = np.array([[5,12,6],[-3,0,14]])\n",
+    "m1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m3 = np.array([[5,3],[-2,4]])\n",
+    "m3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 + m3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Addition of vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "v1 = np.array([1,2,3,4,5])\n",
+    "v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v3 = np.array([1,2,3])\n",
+    "v3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1+v3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exceptions (addition with a scalar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1 + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 + np.array([1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1 + np.array([1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Example_bank_data.csv b/Udemy DataScience Workbook/Example_bank_data.csv
new file mode 100644
index 0000000..dd45fff
--- /dev/null
+++ b/Udemy DataScience Workbook/Example_bank_data.csv	
@@ -0,0 +1,519 @@
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diff --git a/Udemy DataScience Workbook/Final_Exercise_Audioboooks_Machine_Learning.ipynb b/Udemy DataScience Workbook/Final_Exercise_Audioboooks_Machine_Learning.ipynb
new file mode 100644
index 0000000..a26da77
--- /dev/null
+++ b/Udemy DataScience Workbook/Final_Exercise_Audioboooks_Machine_Learning.ipynb	
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problem\n",
+    "\n",
+    "You are given data from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If we can focus our efforts SOLELY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) ,Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (0 or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import Liberaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a class that will batch the data\n",
+    "\n",
+    "Whenever you want to batch the data you need to have appropriate methods. There are some batching methods integrated in TensorFlow and sklearn, but some problems may need specific coding. \n",
+    "\n",
+    "Here we show how these methods look like. You can use them for any machine learning framework you need (directly or after little fine-tuning)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz' .format(dataset))\n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float64), npz['targets'].astype(np.int64)\n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "        \n",
+    "        # A method which loads the next batch\n",
+    "        def _next_(self):\n",
+    "            if self.curr_batch >= self.batch_count:\n",
+    "                self.curr_batch = 0\n",
+    "                raise StopIteration()\n",
+    "            # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "            batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "            inputs_batch = self.inputs[batch_slice]\n",
+    "            targets_batch = self.targets[batch_slice]\n",
+    "            self.curr_batch += 1\n",
+    "            \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already however, here is the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        \n",
+    "            classes_num = 2\n",
+    "            targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "            targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "            \n",
+    "            # The function will return the inputs batch and the one-hot encoded\n",
+    "            \n",
+    "            return inputs_batch, targets_one_hot\n",
+    "        \n",
+    "         # A method needed for iterating over the batches, as we will put them in a loop\n",
+    "         # An iterator in Python is a class with a method __next__ that defines exactly how to iterate through its objects\n",
+    "        def _iter_(self):\n",
+    "            return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n",
+    "We will create an algorithm which is essentially copy-pasting the MNIST code and we will simply adjust where needed.\n",
+    "Once more, I will put the whole code in one piece as we can simply rerun the cell and train a new model. That's because the whole algorithm is contained in the cell and we have the tf.reset_default_graph() function.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 10\n",
+    "# Input size depends on the number of input variables. We have 10 of them\n",
+    "output_size = 2\n",
+    "# Output size is 2 as we one-hot encoded the targets\n",
+    "# Choose hidden layer zsize\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Reset the default graph, so you can fiddle with the hyperparameters and then rerun the code.\n",
+    "tf.reset_default_graph()\n",
+    "\n",
+    "# Create the placeholders\n",
+    "\n",
+    "inputs = tf.placeholder(tf.float64, [None, input_size])\n",
+    "targets = tf.placeholder(tf.int64, [None, output_size])\n",
+    "\n",
+    "# Outline the model. We will create a net with 2 hidden layers\n",
+    "weight_1 = tf.get_variable(\"weight_\", [input_size, hidden_layer_size])\n",
+    "biases_1 = tf.get_variable(\"baises_1\", [hidden_layer_size])\n",
+    "outputs_1 = tf.nn.relu(tf.matmul(inputs, weight_1) + biases_1)\n",
+    "\n",
+    "weight_2 = tf.get_variable(\"weight_2\", [hidden_layer_size, hidden_layer_size])\n",
+    "biases_2 = tf.get_variable(\"baises_2\", [hidden_layer_size])\n",
+    "output_2 = tf.nn.sigmoid(tf.matmul(outputs_1, weight_2)+ biases_2)\n",
+    "\n",
+    "weight_3 = tf.get_variable(\"weight_3\", [hidden_layer_size, output_size])\n",
+    "baises_3 = tf.get_variable(\"biases_3\", [output_size])\n",
+    "# We will incorporate the softmax activation into the loss\n",
+    "outputs = tf.matmul(output_2, weight_3) + baises_3\n",
+    "# Use the softmax cross entropy loss with logits\n",
+    "\n",
+    "loss = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels = targets) \n",
+    "mean_loss = tf.reduce_mean(loss)\n",
+    "# Get a 0 or 1 for every input indicating whether it output the correct answer\n",
+    "out_equals_target = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))\n",
+    "accuracy = tf.reuduce_mean(tf.cast(out_equals_target, tf.float64))\n",
+    "\n",
+    "# Optimize with ADAM\n",
+    "optimize = tf.trian.AdamOptimizer(learning_rate = 0.0001).minimize(mean_loss)\n",
+    "\n",
+    "# Create a session\n",
+    "sess = tf.interactiveSession()\n",
+    "\n",
+    "# initialize the variable\n",
+    "initializer = tf.global_variable_initializer()\n",
+    "sess.run(initializer)\n",
+    "         \n",
+    "# choose the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "prev_validation_loss = 9999999\n",
+    "\n",
+    "# Load the first batch of training and validation, using the class we created. \n",
+    "\n",
+    "train_data = Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data = Audiobooks_Data_Reader('validation')\n",
+    "\n",
+    "# Create the loop for epochs\n",
+    "for epoch_counter in range(max_epochs):\n",
+    "    # set the epoch loss to 0 and make it a float\n",
+    "    curr_epoch_loss = 0\n",
+    "    \n",
+    " # Iterate over the training data \n",
+    "    # Since train_data is an instance of the Audiobooks_Data_Reader class,\n",
+    "    # we can iterate through it by implicitly using the __next__ method\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        batch_loss = sess.run([optimize, mean_loss],\n",
+    "            feed_dict={inputs: input_batch, targets: target_batch})\n",
+    "    \n",
+    "        # Record the batch loss into the current epoch loss\n",
+    "        curr_epoch_loss += batch_loss\n",
+    "    \n",
+    "     # Find the mean curr_epoch_loss\n",
+    "        #batch_count is a variable, defined in the Audiobooks_data_reader class\n",
+    "    curr_epoch_loss /= train_data.batch_count\n",
+    "    \n",
+    "        # Set validation loss and accuracy for the epoch to 0\n",
+    "    \n",
+    "    validation_loss = 0\n",
+    "    validation_accuracy = 0\n",
+    "    \n",
+    "    # forward propagate the validation set using the same logic of the code\n",
+    "    for input_batch, target_batch in validation_data:\n",
+    "        validation_loss, validation_accuracy = sess.run([mean_loss, accuracy],\n",
+    "                feed_dict={inputs: input_batch, targets: target_batch})\n",
+    "    # print statistics for the epoch\n",
+    "    print('Epoch'+str(epoch_counter+1)+\n",
+    "          '. Trianing loss: '+'{0:.3f}'.format(curr_epoch_loss)+\n",
+    "          '. Validation loss: '+'{0:.3f}'.format(validation_loss)+\n",
+    "          '. Validation accuracy: '+'{0:.2f}'.format(validation_accuracy * 100.)+'%')  \n",
+    "    #Trigger early stopping if validation loss begins increasing\n",
+    "    if validation_loss > prev_validation_loss:\n",
+    "        break\n",
+    "    \n",
+    "    # Store this epoch's validation loss to be used as previous in the next iteration\n",
+    "    prev_validation_loss = validation_loss\n",
+    "    \n",
+    "    print('End of training.')\n",
+    "      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/For Loops.ipynb b/Udemy DataScience Workbook/For Loops.ipynb
new file mode 100644
index 0000000..f1cb21e
--- /dev/null
+++ b/Udemy DataScience Workbook/For Loops.ipynb	
@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "71b995d9",
+   "metadata": {},
+   "source": [
+    "# For Loops - Exercise #1\n",
+    "Create a For loop that prints every digit on a new line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "528e5bb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = [0,1,2,3,4,5,6,7,8,9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6811ad2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "for n in digits:\n",
+    "    print (n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "096bf234",
+   "metadata": {},
+   "source": [
+    "Adjust the code, so the digits are all printed on the same line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "28282536",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 5 6 7 8 9 "
+     ]
+    }
+   ],
+   "source": [
+    "for n in digits:\n",
+    "    print (n, end = \" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc553117",
+   "metadata": {},
+   "source": [
+    "# While Loops and Incrementing - Exercise #1\n",
+    "Create a while loop that will print all odd numbers from 0 to 30 on the same row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9b9441dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 "
+     ]
+    }
+   ],
+   "source": [
+    "x = 1\n",
+    "while x<=30:\n",
+    "    print(x, end = \" \")\n",
+    "    x = x+2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47dcfad3",
+   "metadata": {},
+   "source": [
+    "# Lists with the range() Function - Exercise #1\n",
+    "Use the range() function to create a list with all numbers from 1 to 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "42862846",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_list = range(1, 11, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "09b582e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = list(range(1, 11, 1))\n",
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1ed60059",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "range(1, 11)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "672df6f9",
+   "metadata": {},
+   "source": [
+    "Use the range() function to create a list with all even numbers from 0 to 30 included."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d2f3ef8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = list(range(0, 31, 2))\n",
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c45b70d8",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements and Loops - Exercise #1\n",
+    "Create a For loop that will print all the variables from a given list multiplied by 2. Let the list contain all numbers from 1 to 10. Create it with the help of the range() function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ef0d3fd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 4 6 8 10 12 14 16 18 20 "
+     ]
+    }
+   ],
+   "source": [
+    "my_list = list(range(1,11))\n",
+    "for n in my_list:\n",
+    "    n = n * 2\n",
+    "    print(n,end = \" \")\n",
+    "    n = n+1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "579cab8b",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements and Loops - Exercise #2\n",
+    "Create a little program that runs a loop over all values from 1 to 30. Let it print all odd numbers, and in the place of the even numbers, it should print \"Even\". Help yourself with the range() function to solve this exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "8289bfe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_list = list(range(1,31))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "481458ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] "
+     ]
+    }
+   ],
+   "source": [
+    "print(my_list, end = \" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0f69b56d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 even 3 even 5 even 7 even 9 even 11 even 13 even 15 even 17 even 19 even 21 even 23 even 25 even 27 even 29 even "
+     ]
+    }
+   ],
+   "source": [
+    "for n in range(1,31):\n",
+    "    if n % 2 == 1:\n",
+    "        print(n, end = \" \")\n",
+    "    else:\n",
+    "        print(\"even\", end = \" \")\n",
+    "        n = n + 1\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f39ef12",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements and Loops - Exercise #3\n",
+    "You have the following list of numbers. Iterate over this list, printing out each list value multiplied by 10. Find two solutions of this problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "fd73c62c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_num = [1,2,3,4,5,6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f47cb115",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 20 30 40 50 60 "
+     ]
+    }
+   ],
+   "source": [
+    "for n in my_num:\n",
+    "    n = n * 10\n",
+    "    print(n, end = \" \")\n",
+    "    n = n+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "03b6f2d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 20 30 40 50 60 "
+     ]
+    }
+   ],
+   "source": [
+    "for n in range(1,7):\n",
+    "    n = n * 10\n",
+    "    print(n, end = \" \")\n",
+    "    n = n+1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc811bb7",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements, Functions, and Loops - Exercise #1\n",
+    "You are provided with the nums list. Complete the code in the cell that follows. Use a while loop to count the number of values lower than 20."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c721ca91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1,35,12,24,31,51,70,100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "df16eff6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count(nums):\n",
+    "    Total = 0\n",
+    "    index = 0\n",
+    "    while index < len(nums):\n",
+    "        if nums[index] > 20:\n",
+    "            Total += 1\n",
+    "        index +=1\n",
+    "    return Total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4c4d3152",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1,35,12,24,31,51,70,100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d5842c7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "875189e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The number of values lower than 20 is: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "def count_values_below_20(nums):\n",
+    "    count = 0\n",
+    "    index = 0\n",
+    "\n",
+    "    while index < len(nums):\n",
+    "        if nums[index] < 20:\n",
+    "            count += 1\n",
+    "        index += 1\n",
+    "\n",
+    "    return count\n",
+    "\n",
+    "nums = [15, 25, 10, 5, 30, 18, 9, 24, 22]\n",
+    "\n",
+    "result = count_values_below_20(nums)\n",
+    "print(\"The number of values lower than 20 is:\", result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bd1aec5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1,35,12,24,31,51,70,100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1ce2a4a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count(nums):\n",
+    "    Total = 0\n",
+    "    for X in nums:\n",
+    "        if X <= 19:\n",
+    "            Total += 1\n",
+    "    return Total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "cb9cec5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1,35,12,24,31,51,70,100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "dfbdd79b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51820dc1",
+   "metadata": {},
+   "source": [
+    "# How to Iterate over Dictionaries - Exercise #1\n",
+    "In this exercise you will use the same dictionaries as the ones we used in the lesson - prices and quantity. This time, don't just calculate all the money Jan spent. Calculate how much she spent on products with a price of 5 dollars or more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "279f5a11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prices = {\n",
+    "    \"box_of_spaghetti\" : 4,\n",
+    "    \"lasagna\"  : 5,\n",
+    "    \"hamburger\" : 2\n",
+    "   }\n",
+    "quantity = {\n",
+    "    \"box_of_spaghetti\" : 6,\n",
+    "    \"lasagna\"  : 10,\n",
+    "    \"hamburger\" : 0\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6c91dcd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "74\n"
+     ]
+    }
+   ],
+   "source": [
+    "money_spent = 0\n",
+    "for i in prices:\n",
+    "    money_spent = money_spent + (prices[i] * quantity[i])\n",
+    "print(money_spent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c21193e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50\n"
+     ]
+    }
+   ],
+   "source": [
+    "money_spent = 0\n",
+    "for i in prices:\n",
+    "    if prices[i] >= 5:\n",
+    "        money_spent = money_spent + (prices[i] * quantity[i])\n",
+    "print(money_spent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f39da66",
+   "metadata": {},
+   "source": [
+    "# How to Iterate over Dictionaries - Exercise #2\n",
+    "How much did Jan spent on products that cost less than 5 dollars?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "61f05c54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24\n"
+     ]
+    }
+   ],
+   "source": [
+    "money_spent = 0\n",
+    "for i in prices:\n",
+    "    if prices[i] < 5:\n",
+    "        money_spent = money_spent + (prices[i] * quantity[i])\n",
+    "print(money_spent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9278cfef",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/Functions.ipynb b/Udemy DataScience Workbook/Functions.ipynb
new file mode 100644
index 0000000..e309580
--- /dev/null
+++ b/Udemy DataScience Workbook/Functions.ipynb	
@@ -0,0 +1,647 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6bb5e767",
+   "metadata": {},
+   "source": [
+    "How to Create a Function with a Parameter - Exercise #1\n",
+    "Define a function called multiplication_by_2(x) that returns a value equal to its argument multiplied by 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2572d938",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def multiplication_by(a):\n",
+    "    return a * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b7009699",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multiplication_by(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0bb0dba",
+   "metadata": {},
+   "source": [
+    "# How to Create a Function with a Parameter - Exercise #2\n",
+    "Define a function called division_by_2(x) that returns a float value equal to its argument divided by 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fd5b1125",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def division_by(x):\n",
+    "    return x/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3bfbf419",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "division_by(8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "378d0926",
+   "metadata": {},
+   "source": [
+    "# Defining a Function in Python - Exercise #1\n",
+    "Define a function called exponentiation_exp_2(x) that states the value of the argument accompanied by the phrase \"Raised to the power of 2:\" and returns a value equal to its argument raised to the power of 2. This time, use a new variable, called result, in the body of the Function. Call the function with some argument to verify it works properly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c032fe28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def exponentiation_exp(a):\n",
+    "    result = a ** 2\n",
+    "    print(\"result = \", result)\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8aae8fbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "result =  16\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exponentiation_exp(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff56fcc9",
+   "metadata": {},
+   "source": [
+    "# How to Use a Function within a Function - Exercise #1\n",
+    "Define a function called plus_five() that adds 5 to its argument. Then, define another function named m_by_3() that multiplies the argument (the result obtained from plus_five()) by 3. Verify your code was correct by calling the second function with an argument of 5. Was your output equal to 30?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "df8a4325",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plus_five(x):\n",
+    "    return x + 5\n",
+    "def m_by_3(x):\n",
+    "    return plus_five(x) * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6e97cd4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m_by_3(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b900d918",
+   "metadata": {},
+   "source": [
+    "# Conditional Statements and Functions - Exercise #1\n",
+    "Define a function, called compare_the_two(), with two arguments. If the first one is greater than the second one, let it print \"Greater\". If the second one is greater, it should print \"Less\". Let it print \"Equal\" if the two values are the same number.\n",
+    "\n",
+    "This topic is covered in"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e58757ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_the_two(a,b):\n",
+    "    if a>b:\n",
+    "        print(\"Greater\")\n",
+    "    elif a<b:\n",
+    "        print(\"Less\")\n",
+    "    else:\n",
+    "        print(\"Equal\")\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d3a11c5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Greater\n"
+     ]
+    }
+   ],
+   "source": [
+    "compare_the_two(9,7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6836e757",
+   "metadata": {},
+   "source": [
+    "# Obtain the maximum number\n",
+    "among the values 25, 65, 890, and 15."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f2b9b6b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "890"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(25,65,890,15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef702643",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Obtain the minimum number among the values 25, 65, 890, and 15.\n",
+    "min(25,65,890,15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d8bedb72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find the absolute value of -100.\n",
+    "abs(-100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "43ef1a12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "56"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Round the value of 55.5.\n",
+    "round(55.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "25a59d93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35.568"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Round 35.56789 to the third digit.\n",
+    "round(35.56789,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d5cc9e63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Find the sum of all elements in the provided list, called \"Numbers\".\n",
+    "Numbers = [1, 5, 64, 24.5]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a5e3d54a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Built-in Functions in Python - Exercise #7\n",
+    "#Use a built-in function to raise 10 to the power of 3.\n",
+    "pow(10,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "633daf1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#In one line of code, find how many characters there are in the word \"Elephant\"?\n",
+    "len(\"Elephant\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8043cca3",
+   "metadata": {},
+   "source": [
+    "# Built-in Functions in Python - Exercise #9\n",
+    "Create a function, called distance_from_zero(), that returns the absolute value of a provided single argument and prints a statement \"Not Possible\" if the argument provided is not a number. To solve the task, use the type() function in the body of distance_from_zero().\n",
+    "\n",
+    "Call the function with the values of -10 and \"cat\" to verify it works correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "58900c38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def distance_from_zero(a):\n",
+    "    if isinstance (a, (int,float)):\n",
+    "        return abs(a)\n",
+    "    else:\n",
+    "        print(\" not Possible\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "606f23f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " not Possible\n"
+     ]
+    }
+   ],
+   "source": [
+    "distance_from_zero('cat')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d535464e",
+   "metadata": {},
+   "source": [
+    "# Lists - Exercise #1\n",
+    "Create a list, called Numbers. Let it contain the numbers 10, 25, 40, and 50.\n",
+    "\n",
+    "Print the element at index 2 from the list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e284269c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50\n"
+     ]
+    }
+   ],
+   "source": [
+    "Numbers = [10,40,50]\n",
+    "print(Numbers[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "923ef244",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers[0] = 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "95d27567",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[15, 40, 50]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85d585d5",
+   "metadata": {},
+   "source": [
+    "# Append the number 100 to the Numbers list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "215f8f5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers.insert(4, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "caefbc57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[15, 40, 50, 100]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b95cca35",
+   "metadata": {},
+   "source": [
+    "# With the help of the .extend() method, add the numbers 115 an 140 to the Numbers list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4d295948",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers.extend([115,140])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "507672bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[15, 40, 50, 100, 115, 140]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d6cb62c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The fourth element of the Number list is 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The fourth element of the Number list is\", Numbers[3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "4b896a48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 6 numbers in the Numbers list\n"
+     ]
+    }
+   ],
+   "source": [
+    "len(Numbers)\n",
+    "print(\"There are\", len(Numbers), \"numbers in the Numbers list\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "090df15a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/GD-function-example.xlsx b/Udemy DataScience Workbook/GD-function-example.xlsx
new file mode 100644
index 0000000..1e9ec49
Binary files /dev/null and b/Udemy DataScience Workbook/GD-function-example.xlsx differ
diff --git a/Udemy DataScience Workbook/Heatmaps_with_comments.ipynb b/Udemy DataScience Workbook/Heatmaps_with_comments.ipynb
new file mode 100644
index 0000000..3a737ce
--- /dev/null
+++ b/Udemy DataScience Workbook/Heatmaps_with_comments.ipynb	
@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cluster analysis\n",
+    "In this notebook we explore heatmaps and dendrograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "# We don't need matplotlib this time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the standardized data\n",
+    "# index_col is an argument we can set to one of the columns\n",
+    "# this will cause one of the Series to become the index\n",
+    "data = pd.read_csv('3.01.+Country+clusters.csv', index_col='Country')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new data frame for the inputs, so we can clean it\n",
+    "x_scaled = data.copy()\n",
+    "# Drop the variables that are unnecessary for this solution\n",
+    "x_scaled = x_scaled.drop(['Language'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>USA</th>\n",
+       "      <td>44.97</td>\n",
+       "      <td>-103.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>62.40</td>\n",
+       "      <td>-96.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>France</th>\n",
+       "      <td>46.75</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UK</th>\n",
+       "      <td>54.01</td>\n",
+       "      <td>-2.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Germany</th>\n",
+       "      <td>51.15</td>\n",
+       "      <td>10.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Australia</th>\n",
+       "      <td>-25.45</td>\n",
+       "      <td>133.11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Latitude  Longitude\n",
+       "Country                       \n",
+       "USA           44.97    -103.77\n",
+       "Canada        62.40     -96.80\n",
+       "France        46.75       2.40\n",
+       "UK            54.01      -2.53\n",
+       "Germany       51.15      10.40\n",
+       "Australia    -25.45     133.11"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check what's inside\n",
+    "x_scaled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.matrix.ClusterGrid at 0x2df9a364d0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Using the Seaborn method 'clustermap' we can get a heatmap and dendrograms for both the observations and the features\n",
+    "# The cmap 'mako' is the coolest if you ask me\n",
+    "sns.clustermap(x_scaled, cmap='mako')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/How+to+Choose+the+Number+of+Clusters+-+Exercise.ipynb b/Udemy DataScience Workbook/How+to+Choose+the+Number+of+Clusters+-+Exercise.ipynb
new file mode 100644
index 0000000..281d8ca
--- /dev/null
+++ b/Udemy DataScience Workbook/How+to+Choose+the+Number+of+Clusters+-+Exercise.ipynb	
@@ -0,0 +1,858 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to Choose the Number of Clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same code as in the previous exercise, find the WCSS for clustering solutions with 1 to 10 clusters (you can try with more if you wish).\n",
+    "\n",
+    "Find the most suitable solutions, run them and compare the results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'Countries_exercise.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude\n",
+       "0           Aruba  -69.982677  12.520880\n",
+       "1     Afghanistan   66.004734  33.835231\n",
+       "2          Angola   17.537368 -12.293361\n",
+       "3        Anguilla  -63.064989  18.223959\n",
+       "4         Albania   20.049834  41.142450\n",
+       "..            ...         ...        ...\n",
+       "236         Samoa -172.164851 -13.753243\n",
+       "237         Yemen   47.586762  15.909280\n",
+       "238  South Africa   25.083901 -29.000341\n",
+       "239        Zambia   27.774759 -13.458242\n",
+       "240      Zimbabwe   29.851441 -19.004204\n",
+       "\n",
+       "[241 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Countries-exercise.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remove the duplicate index column from the dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the <i>'Longtitude'</i> and <i>'Latitude'</i> columns. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the <i>'Longtitude'</i> and <i>'Latitude'</i> columns. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8644\\2111637734.py:1: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
+      "  plt.scatter(raw_data['Longitude'], raw_data['Latitude'],cmap='rainbow')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(raw_data['Longitude'], raw_data['Latitude'],cmap='rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.xlabel('Lattitude')\n",
+    "plt.ylim(-90,90)\n",
+    "plt.ylabel('Longitude')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure to select the appropriate features since we are no longer using the categorical variable for our clustering but rather <i>'Longitude'</i> and <i>'Laditude'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = raw_data.iloc[:,1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 4 clusters initially."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=4)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(4)\n",
+    "kmeans.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clustering results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 1, 3, 3, 3, 0, 1, 0, 1, 0, 2, 0, 1, 2, 3, 0, 0, 3, 3, 3,\n",
+       "       2, 3, 0, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 2, 2, 0, 0, 3, 1, 2, 3, 0,\n",
+       "       0, 0, 1, 1, 0, 3, 1, 1, 1, 1, 3, 3, 3, 3, 0, 1, 3, 1, 3, 1, 0, 0,\n",
+       "       3, 3, 0, 3, 2, 1, 3, 3, 2, 0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 1, 3, 1,\n",
+       "       2, 1, 2, 0, 1, 3, 1, 3, 2, 3, 0, 2, 0, 3, 0, 0, 3, 0, 3, 1, 3, 0,\n",
+       "       2, 0, 0, 0, 0, 2, 1, 1, 2, 3, 0, 2, 0, 3, 3, 1, 3, 0, 0, 3, 3, 3,\n",
+       "       2, 1, 3, 3, 3, 0, 0, 1, 2, 3, 3, 3, 2, 3, 2, 2, 0, 3, 1, 0, 0, 2,\n",
+       "       0, 2, 3, 2, 3, 1, 1, 3, 3, 0, 2, 2, 0, 0, 1, 1, 1, 2, 2, 2, 3, 1,\n",
+       "       2, 3, 1, 0, 1, 0, 3, 2, 0, 3, 0, 0, 0, 3, 2, 1, 3, 2, 3, 1, 3, 0,\n",
+       "       0, 1, 3, 0, 1, 3, 3, 3, 0, 1, 0, 0, 1, 0, 3, 2, 0, 0, 2, 1, 1, 3,\n",
+       "       3, 2, 0, 0, 3, 1, 1, 0, 3, 1, 1, 1, 1, 2, 2, 1, 1, 0, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "identified_clusters = kmeans.fit_predict(X)\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Clusters</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude  Clusters\n",
+       "0           Aruba  -69.982677  12.520880         1\n",
+       "1     Afghanistan   66.004734  33.835231         0\n",
+       "2          Angola   17.537368 -12.293361         0\n",
+       "3        Anguilla  -63.064989  18.223959         1\n",
+       "4         Albania   20.049834  41.142450         3\n",
+       "..            ...         ...        ...       ...\n",
+       "236         Samoa -172.164851 -13.753243         1\n",
+       "237         Yemen   47.586762  15.909280         0\n",
+       "238  South Africa   25.083901 -29.000341         0\n",
+       "239        Zambia   27.774759 -13.458242         0\n",
+       "240      Zimbabwe   29.851441 -19.004204         0\n",
+       "\n",
+       "[241 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a copy of the mapped data\n",
+    "data_with_clusters = raw_data.copy()\n",
+    "# Create a new Series, containing the identified cluster for each observation\n",
+    "data_with_clusters['Clusters'] = identified_clusters\n",
+    "data_with_clusters\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the data once again but separate the data by the clusters we defined.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data using the longitude and the latitude\n",
+    "# c (color) is an argument which could be coded with a variable \n",
+    "# The variable in this case has values 0,1,2, indicating to plt.scatter, that there are three colors (0,1,2)\n",
+    "# All points in cluster 0 will be the same colour, all points in cluster 1 - another one, etc.\n",
+    "plt.scatter(data_with_clusters['Longitude'], data_with_clusters['Latitude'], c=data_with_clusters['Clusters'], cmap= 'rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting the number of clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### WCSS - Within Cluster sum of squares"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the ingerated <i>sklearn</i> method <i> 'inertia_' </i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "256573.086759343"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the WCSS for the current solution\n",
+    "kmeans.inertia_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a loop that calculates and saves the WCSS for any number of clusters from 1 up to 10 (or more if you wish)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create an empty list\n",
+    "wcss = []\n",
+    "\n",
+    "# Create all possible cluster solutions with a loop\n",
+    "for i in range(1,11):\n",
+    "    kmeans = KMeans(i) # Cluster solution with i clusters\n",
+    "    kmeans.fit(X)  # Fit the data\n",
+    "    # Find WCSS for the current iteration\n",
+    "    wcss_iter = kmeans.inertia_\n",
+    "    # Append the value to the WCSS list\n",
+    "    wcss.append(wcss_iter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1413859.6798344273,\n",
+       " 657012.0797586325,\n",
+       " 325235.52237055893,\n",
+       " 256573.086759343,\n",
+       " 192217.1197012072,\n",
+       " 151734.45550708374,\n",
+       " 128007.1788207854,\n",
+       " 102932.14383201556,\n",
+       " 84927.05022223276,\n",
+       " 76439.49644818819]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a variable containing the numbers from 1 to 10, so we can use it as X axis of the future plot\n",
+    "number_clusters = range(1,11)\n",
+    "# Plot the number of clusters vs WCSS\n",
+    "plt.plot(number_clusters, wcss)\n",
+    "plt.title('The Elbow Method')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('With-Cluster Sum of squares')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the Elbow Curve, plot several graphs with the appropriate amounts of clusters you believe would best fit the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare the scatter plots to determine which one to use in our further analysis. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<i>Hint: we already created the scatter plot for 4 clusters, so we only have to slightly alter our code.</i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=10)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=10)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(10)\n",
+    "kmeans.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "identified_clusters = kmeans.fit_predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_clusters = raw_data.copy()\n",
+    "data_with_clusters['Cluster'] = identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data_with_clusters['Longitude'], data_with_clusters['Latitude'], c=data_with_clusters['Cluster'], cmap= 'rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/How+to+Choose+the+Number+of+Clusters+-+Solution.ipynb b/Udemy DataScience Workbook/How+to+Choose+the+Number+of+Clusters+-+Solution.ipynb
new file mode 100644
index 0000000..1b3e4c1
--- /dev/null
+++ b/Udemy DataScience Workbook/How+to+Choose+the+Number+of+Clusters+-+Solution.ipynb	
@@ -0,0 +1,1015 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to Choose the Number of Clusters "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same code as in the previous exercise, find the WCSS for clustering solutions with 1 to 10 clusters (you can try with more if you wish).\n",
+    "\n",
+    "Find the most suitable solutions, run them and compare the results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'Countries_exercise.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude\n",
+       "0           Aruba  -69.982677  12.520880\n",
+       "1     Afghanistan   66.004734  33.835231\n",
+       "2          Angola   17.537368 -12.293361\n",
+       "3        Anguilla  -63.064989  18.223959\n",
+       "4         Albania   20.049834  41.142450\n",
+       "..            ...         ...        ...\n",
+       "236         Samoa -172.164851 -13.753243\n",
+       "237         Yemen   47.586762  15.909280\n",
+       "238  South Africa   25.083901 -29.000341\n",
+       "239        Zambia   27.774759 -13.458242\n",
+       "240      Zimbabwe   29.851441 -19.004204\n",
+       "\n",
+       "[241 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data\n",
+    "data = pd.read_csv('Countries-exercise.csv')\n",
+    "# Check the data\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the <i>'Longtitude'</i> and <i>'Latitude'</i> columns. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data['Longitude'], data['Latitude'])\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90, 90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure to select the appropriate features since we are no longer using the categorical variable for our clustering but rather 'Longitude' and 'Laditude'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Longitude   Latitude\n",
+       "0    -69.982677  12.520880\n",
+       "1     66.004734  33.835231\n",
+       "2     17.537368 -12.293361\n",
+       "3    -63.064989  18.223959\n",
+       "4     20.049834  41.142450\n",
+       "..          ...        ...\n",
+       "236 -172.164851 -13.753243\n",
+       "237   47.586762  15.909280\n",
+       "238   25.083901 -29.000341\n",
+       "239   27.774759 -13.458242\n",
+       "240   29.851441 -19.004204\n",
+       "\n",
+       "[241 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = data.iloc[:,1:3]\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 4 clusters initially. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=4)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clustering Resutls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 1, 0, 2, 2, 2, 1, 0, 2, 0, 1, 3, 1, 0, 3, 2, 1, 1, 2, 2, 2,\n",
+       "       3, 2, 1, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 3, 3, 1, 1, 2, 0, 3, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 2, 0, 0, 0, 0, 2, 2, 2, 2, 1, 0, 2, 0, 2, 0, 2, 1,\n",
+       "       2, 2, 1, 2, 3, 0, 2, 2, 3, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0,\n",
+       "       3, 0, 3, 1, 0, 2, 0, 2, 3, 2, 1, 3, 1, 2, 1, 1, 2, 2, 2, 0, 2, 2,\n",
+       "       3, 1, 1, 1, 1, 3, 0, 0, 3, 2, 1, 3, 2, 2, 2, 0, 2, 1, 1, 2, 2, 2,\n",
+       "       3, 0, 2, 2, 2, 1, 1, 0, 3, 2, 2, 2, 3, 2, 3, 3, 1, 2, 0, 1, 1, 3,\n",
+       "       1, 3, 2, 3, 2, 0, 0, 2, 2, 1, 3, 3, 1, 1, 0, 0, 0, 3, 3, 3, 2, 0,\n",
+       "       3, 2, 0, 2, 0, 1, 2, 3, 1, 2, 1, 1, 1, 2, 3, 0, 2, 3, 2, 0, 2, 1,\n",
+       "       1, 0, 2, 2, 0, 2, 2, 2, 1, 0, 1, 2, 0, 2, 2, 3, 1, 1, 3, 0, 0, 2,\n",
+       "       2, 3, 1, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 3, 3, 0, 0, 1, 1, 1, 1])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "identified_clusters = kmeans.fit_predict(x)\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude  Cluster\n",
+       "0           Aruba  -69.982677  12.520880        0\n",
+       "1     Afghanistan   66.004734  33.835231        1\n",
+       "2          Angola   17.537368 -12.293361        1\n",
+       "3        Anguilla  -63.064989  18.223959        0\n",
+       "4         Albania   20.049834  41.142450        2\n",
+       "..            ...         ...        ...      ...\n",
+       "236         Samoa -172.164851 -13.753243        0\n",
+       "237         Yemen   47.586762  15.909280        1\n",
+       "238  South Africa   25.083901 -29.000341        1\n",
+       "239        Zambia   27.774759 -13.458242        1\n",
+       "240      Zimbabwe   29.851441 -19.004204        1\n",
+       "\n",
+       "[241 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_clusters = data.copy()\n",
+    "data_with_clusters['Cluster'] = identified_clusters\n",
+    "data_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the data once again. This time use the <i> c </i> parameter to separate the data by the clusters we defined.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data['Longitude'], data['Latitude'], c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90, 90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting the number of clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### WCSS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the ingerated <i>sklearn</i> method <i> 'inertia_' </i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "256590.585754061"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.inertia_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a loop that calculates and saves the WCSS for any number of clusters from 1 up to 10 (or more if you wish)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "wcss = []\n",
+    "# 'cl_num' is a that keeps track the highest number of clusters we want to use the WCSS method for.\n",
+    "# Note that 'range' doesn't include the upper boundery\n",
+    "cl_num = 11\n",
+    "for i in range (1,cl_num):\n",
+    "    kmeans= KMeans(i)\n",
+    "    kmeans.fit(x)\n",
+    "    wcss_iter = kmeans.inertia_\n",
+    "    wcss.append(wcss_iter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1413859.6798344273,\n",
+       " 657012.0797586325,\n",
+       " 325235.52237055893,\n",
+       " 256573.086759343,\n",
+       " 192217.1197012072,\n",
+       " 151734.45550708374,\n",
+       " 126758.35414179866,\n",
+       " 103719.81382400187,\n",
+       " 85096.33351772628,\n",
+       " 76984.64203063425]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Within-cluster Sum of Squares')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "number_clusters = range(1,cl_num)\n",
+    "plt.plot(number_clusters, wcss)\n",
+    "plt.title('The Elbow Method')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('Within-cluster Sum of Squares')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the Elbow Curve, plot several graphs with the appropriate amount of clusters you believe would best fit the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this this specific case, that would be 2 or 3.\n",
+    "Compare the scatter plots to determine which one to use in any further analysis. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<i>Hint: we already created the scatter plot for 4 clusters, so we only have to slightly alter our code.</i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(2)\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "identified_clusters = kmeans.fit_predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_clusters = data.copy()\n",
+    "data_with_clusters['Cluster'] = identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data['Longitude'], data['Latitude'], c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90, 90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=3)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans = KMeans(3)\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "identified_clusters = kmeans.fit_predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_clusters = data.copy()\n",
+    "data_with_clusters['Cluster'] = identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data['Longitude'], data['Latitude'], c=data_with_clusters['Cluster'], cmap = 'rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90, 90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Think about the geological developments of Earth and how it split from one big continent (Pangea) into smaller multiple smaller ones. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Improved_model.html.html b/Udemy DataScience Workbook/Improved_model.html.html
new file mode 100644
index 0000000..1ffaacc
--- /dev/null
+++ b/Udemy DataScience Workbook/Improved_model.html.html	
@@ -0,0 +1,32665 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+
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+
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+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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+  </style>
+
+
+
+<style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+ * Mozilla scrollbar styling
+ */
+
+/* use standard opaque scrollbars for most nodes */
+[data-jp-theme-scrollbars='true'] {
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+    var(--jp-scrollbar-background-color);
+}
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+/* for code nodes, use a transparent style of scrollbar. These selectors
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+  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
+}
+
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+
+.jp-scrollbar-tiny {
+  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
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+}
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+ */
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+}
+
+[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-thumb {
+  background: rgb(var(--jp-scrollbar-thumb-color));
+  border: var(--jp-scrollbar-thumb-margin) solid transparent;
+  background-clip: content-box;
+  border-radius: var(--jp-scrollbar-thumb-radius);
+}
+
+[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-track:horizontal {
+  border-left: var(--jp-scrollbar-endpad) solid
+    var(--jp-scrollbar-background-color);
+  border-right: var(--jp-scrollbar-endpad) solid
+    var(--jp-scrollbar-background-color);
+}
+
+[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-track:vertical {
+  border-top: var(--jp-scrollbar-endpad) solid
+    var(--jp-scrollbar-background-color);
+  border-bottom: var(--jp-scrollbar-endpad) solid
+    var(--jp-scrollbar-background-color);
+}
+
+/* for code nodes, use a transparent style of scrollbar */
+
+[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar::-webkit-scrollbar,
+[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar::-webkit-scrollbar,
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-hscrollbar::-webkit-scrollbar-corner,
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-vscrollbar::-webkit-scrollbar-corner {
+  background-color: transparent;
+}
+
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-hscrollbar::-webkit-scrollbar-thumb,
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-vscrollbar::-webkit-scrollbar-thumb {
+  background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
+  border: var(--jp-scrollbar-thumb-margin) solid transparent;
+  background-clip: content-box;
+  border-radius: var(--jp-scrollbar-thumb-radius);
+}
+
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-hscrollbar::-webkit-scrollbar-track:horizontal {
+  border-left: var(--jp-scrollbar-endpad) solid transparent;
+  border-right: var(--jp-scrollbar-endpad) solid transparent;
+}
+
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-vscrollbar::-webkit-scrollbar-track:vertical {
+  border-top: var(--jp-scrollbar-endpad) solid transparent;
+  border-bottom: var(--jp-scrollbar-endpad) solid transparent;
+}
+
+/* tiny scrollbar */
+
+.jp-scrollbar-tiny::-webkit-scrollbar,
+.jp-scrollbar-tiny::-webkit-scrollbar-corner {
+  background-color: transparent;
+  height: 4px;
+  width: 4px;
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-thumb {
+  background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-track:horizontal {
+  border-left: 0px solid transparent;
+  border-right: 0px solid transparent;
+}
+
+.jp-scrollbar-tiny::-webkit-scrollbar-track:vertical {
+  border-top: 0px solid transparent;
+  border-bottom: 0px solid transparent;
+}
+
+/*
+ * Phosphor
+ */
+
+.lm-ScrollBar[data-orientation='horizontal'] {
+  min-height: 16px;
+  max-height: 16px;
+  min-width: 45px;
+  border-top: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] {
+  min-width: 16px;
+  max-width: 16px;
+  min-height: 45px;
+  border-left: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar-button {
+  background-color: #f0f0f0;
+  background-position: center center;
+  min-height: 15px;
+  max-height: 15px;
+  min-width: 15px;
+  max-width: 15px;
+}
+
+.lm-ScrollBar-button:hover {
+  background-color: #dadada;
+}
+
+.lm-ScrollBar-button.lm-mod-active {
+  background-color: #cdcdcd;
+}
+
+.lm-ScrollBar-track {
+  background: #f0f0f0;
+}
+
+.lm-ScrollBar-thumb {
+  background: #cdcdcd;
+}
+
+.lm-ScrollBar-thumb:hover {
+  background: #bababa;
+}
+
+.lm-ScrollBar-thumb.lm-mod-active {
+  background: #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='horizontal'] .lm-ScrollBar-thumb {
+  height: 100%;
+  min-width: 15px;
+  border-left: 1px solid #a0a0a0;
+  border-right: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] .lm-ScrollBar-thumb {
+  width: 100%;
+  min-height: 15px;
+  border-top: 1px solid #a0a0a0;
+  border-bottom: 1px solid #a0a0a0;
+}
+
+.lm-ScrollBar[data-orientation='horizontal']
+  .lm-ScrollBar-button[data-action='decrement'] {
+  background-image: var(--jp-icon-caret-left);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='horizontal']
+  .lm-ScrollBar-button[data-action='increment'] {
+  background-image: var(--jp-icon-caret-right);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='vertical']
+  .lm-ScrollBar-button[data-action='decrement'] {
+  background-image: var(--jp-icon-caret-up);
+  background-size: 17px;
+}
+
+.lm-ScrollBar[data-orientation='vertical']
+  .lm-ScrollBar-button[data-action='increment'] {
+  background-image: var(--jp-icon-caret-down);
+  background-size: 17px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-Widget, /* </DEPRECATED> */
+.lm-Widget {
+  box-sizing: border-box;
+  position: relative;
+  overflow: hidden;
+  cursor: default;
+}
+
+
+/* <DEPRECATED> */ .p-Widget.p-mod-hidden, /* </DEPRECATED> */
+.lm-Widget.lm-mod-hidden {
+  display: none !important;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-CommandPalette, /* </DEPRECATED> */
+.lm-CommandPalette {
+  display: flex;
+  flex-direction: column;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */ .p-CommandPalette-search, /* </DEPRECATED> */
+.lm-CommandPalette-search {
+  flex: 0 0 auto;
+}
+
+
+/* <DEPRECATED> */ .p-CommandPalette-content, /* </DEPRECATED> */
+.lm-CommandPalette-content {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  min-height: 0;
+  overflow: auto;
+  list-style-type: none;
+}
+
+
+/* <DEPRECATED> */ .p-CommandPalette-header, /* </DEPRECATED> */
+.lm-CommandPalette-header {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+
+/* <DEPRECATED> */ .p-CommandPalette-item, /* </DEPRECATED> */
+.lm-CommandPalette-item {
+  display: flex;
+  flex-direction: row;
+}
+
+
+/* <DEPRECATED> */ .p-CommandPalette-itemIcon, /* </DEPRECATED> */
+.lm-CommandPalette-itemIcon {
+  flex: 0 0 auto;
+}
+
+
+/* <DEPRECATED> */ .p-CommandPalette-itemContent, /* </DEPRECATED> */
+.lm-CommandPalette-itemContent {
+  flex: 1 1 auto;
+  overflow: hidden;
+}
+
+
+/* <DEPRECATED> */ .p-CommandPalette-itemShortcut, /* </DEPRECATED> */
+.lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
+}
+
+
+/* <DEPRECATED> */ .p-CommandPalette-itemLabel, /* </DEPRECATED> */
+.lm-CommandPalette-itemLabel {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.lm-close-icon {
+	border:1px solid transparent;
+  background-color: transparent;
+  position: absolute;
+	z-index:1;
+	right:3%;
+	top: 0;
+	bottom: 0;
+	margin: auto;
+	padding: 7px 0;
+	display: none;
+	vertical-align: middle;
+  outline: 0;
+  cursor: pointer;
+}
+.lm-close-icon:after {
+	content: "X";
+	display: block;
+	width: 15px;
+	height: 15px;
+	text-align: center;
+	color:#000;
+	font-weight: normal;
+	font-size: 12px;
+	cursor: pointer;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-DockPanel, /* </DEPRECATED> */
+.lm-DockPanel {
+  z-index: 0;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-widget, /* </DEPRECATED> */
+.lm-DockPanel-widget {
+  z-index: 0;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-tabBar, /* </DEPRECATED> */
+.lm-DockPanel-tabBar {
+  z-index: 1;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle, /* </DEPRECATED> */
+.lm-DockPanel-handle {
+  z-index: 2;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle.p-mod-hidden, /* </DEPRECATED> */
+.lm-DockPanel-handle.lm-mod-hidden {
+  display: none !important;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle:after, /* </DEPRECATED> */
+.lm-DockPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
+}
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='horizontal'],
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='horizontal'] {
+  cursor: ew-resize;
+}
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='vertical'],
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='vertical'] {
+  cursor: ns-resize;
+}
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='horizontal']:after,
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='horizontal']:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
+}
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='vertical']:after,
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='vertical']:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-overlay, /* </DEPRECATED> */
+.lm-DockPanel-overlay {
+  z-index: 3;
+  box-sizing: border-box;
+  pointer-events: none;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-overlay.p-mod-hidden, /* </DEPRECATED> */
+.lm-DockPanel-overlay.lm-mod-hidden {
+  display: none !important;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-Menu, /* </DEPRECATED> */
+.lm-Menu {
+  z-index: 10000;
+  position: absolute;
+  white-space: nowrap;
+  overflow-x: hidden;
+  overflow-y: auto;
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */ .p-Menu-content, /* </DEPRECATED> */
+.lm-Menu-content {
+  margin: 0;
+  padding: 0;
+  display: table;
+  list-style-type: none;
+}
+
+
+/* <DEPRECATED> */ .p-Menu-item, /* </DEPRECATED> */
+.lm-Menu-item {
+  display: table-row;
+}
+
+
+/* <DEPRECATED> */
+.p-Menu-item.p-mod-hidden,
+.p-Menu-item.p-mod-collapsed,
+/* </DEPRECATED> */
+.lm-Menu-item.lm-mod-hidden,
+.lm-Menu-item.lm-mod-collapsed {
+  display: none !important;
+}
+
+
+/* <DEPRECATED> */
+.p-Menu-itemIcon,
+.p-Menu-itemSubmenuIcon,
+/* </DEPRECATED> */
+.lm-Menu-itemIcon,
+.lm-Menu-itemSubmenuIcon {
+  display: table-cell;
+  text-align: center;
+}
+
+
+/* <DEPRECATED> */ .p-Menu-itemLabel, /* </DEPRECATED> */
+.lm-Menu-itemLabel {
+  display: table-cell;
+  text-align: left;
+}
+
+
+/* <DEPRECATED> */ .p-Menu-itemShortcut, /* </DEPRECATED> */
+.lm-Menu-itemShortcut {
+  display: table-cell;
+  text-align: right;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-MenuBar, /* </DEPRECATED> */
+.lm-MenuBar {
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */ .p-MenuBar-content, /* </DEPRECATED> */
+.lm-MenuBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: row;
+  list-style-type: none;
+}
+
+
+/* <DEPRECATED> */ .p--MenuBar-item, /* </DEPRECATED> */
+.lm-MenuBar-item {
+  box-sizing: border-box;
+}
+
+
+/* <DEPRECATED> */
+.p-MenuBar-itemIcon,
+.p-MenuBar-itemLabel,
+/* </DEPRECATED> */
+.lm-MenuBar-itemIcon,
+.lm-MenuBar-itemLabel {
+  display: inline-block;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-ScrollBar, /* </DEPRECATED> */
+.lm-ScrollBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */
+.p-ScrollBar[data-orientation='horizontal'],
+/* </DEPRECATED> */
+.lm-ScrollBar[data-orientation='horizontal'] {
+  flex-direction: row;
+}
+
+
+/* <DEPRECATED> */
+.p-ScrollBar[data-orientation='vertical'],
+/* </DEPRECATED> */
+.lm-ScrollBar[data-orientation='vertical'] {
+  flex-direction: column;
+}
+
+
+/* <DEPRECATED> */ .p-ScrollBar-button, /* </DEPRECATED> */
+.lm-ScrollBar-button {
+  box-sizing: border-box;
+  flex: 0 0 auto;
+}
+
+
+/* <DEPRECATED> */ .p-ScrollBar-track, /* </DEPRECATED> */
+.lm-ScrollBar-track {
+  box-sizing: border-box;
+  position: relative;
+  overflow: hidden;
+  flex: 1 1 auto;
+}
+
+
+/* <DEPRECATED> */ .p-ScrollBar-thumb, /* </DEPRECATED> */
+.lm-ScrollBar-thumb {
+  box-sizing: border-box;
+  position: absolute;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-SplitPanel-child, /* </DEPRECATED> */
+.lm-SplitPanel-child {
+  z-index: 0;
+}
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle, /* </DEPRECATED> */
+.lm-SplitPanel-handle {
+  z-index: 1;
+}
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle.p-mod-hidden, /* </DEPRECATED> */
+.lm-SplitPanel-handle.lm-mod-hidden {
+  display: none !important;
+}
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle:after, /* </DEPRECATED> */
+.lm-SplitPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
+}
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
+  cursor: ew-resize;
+}
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
+  cursor: ns-resize;
+}
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle:after,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
+}
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle:after,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-TabBar, /* </DEPRECATED> */
+.lm-TabBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar[data-orientation='horizontal'], /* </DEPRECATED> */
+.lm-TabBar[data-orientation='horizontal'] {
+  flex-direction: row;
+  align-items: flex-end;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar[data-orientation='vertical'], /* </DEPRECATED> */
+.lm-TabBar[data-orientation='vertical'] {
+  flex-direction: column;
+  align-items: flex-end;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar-content, /* </DEPRECATED> */
+.lm-TabBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex: 1 1 auto;
+  list-style-type: none;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar[data-orientation='horizontal'] > .p-TabBar-content,
+/* </DEPRECATED> */
+.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
+  flex-direction: row;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar[data-orientation='vertical'] > .p-TabBar-content,
+/* </DEPRECATED> */
+.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
+  flex-direction: column;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar-tab, /* </DEPRECATED> */
+.lm-TabBar-tab {
+  display: flex;
+  flex-direction: row;
+  box-sizing: border-box;
+  overflow: hidden;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar-tabIcon,
+.p-TabBar-tabCloseIcon,
+/* </DEPRECATED> */
+.lm-TabBar-tabIcon,
+.lm-TabBar-tabCloseIcon {
+  flex: 0 0 auto;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar-tabLabel, /* </DEPRECATED> */
+.lm-TabBar-tabLabel {
+  flex: 1 1 auto;
+  overflow: hidden;
+  white-space: nowrap;
+}
+
+
+.lm-TabBar-tabInput {
+  user-select: all;
+  width: 100%;
+  box-sizing : border-box;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar-tab.p-mod-hidden, /* </DEPRECATED> */
+.lm-TabBar-tab.lm-mod-hidden {
+  display: none !important;
+}
+
+
+.lm-TabBar-addButton.lm-mod-hidden {
+  display: none !important;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar.p-mod-dragging .p-TabBar-tab, /* </DEPRECATED> */
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
+  position: relative;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab,
+/* </DEPRECATED> */
+.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
+  left: 0;
+  transition: left 150ms ease;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab,
+/* </DEPRECATED> */
+.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
+  top: 0;
+  transition: top 150ms ease;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging,
+/* </DEPRECATED> */
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
+  transition: none;
+}
+
+.lm-TabBar-tabLabel .lm-TabBar-tabInput {
+  user-select: all;
+  width: 100%;
+  box-sizing : border-box;
+  background: inherit;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-TabPanel-tabBar, /* </DEPRECATED> */
+.lm-TabPanel-tabBar {
+  z-index: 1;
+}
+
+
+/* <DEPRECATED> */ .p-TabPanel-stackedPanel, /* </DEPRECATED> */
+.lm-TabPanel-stackedPanel {
+  z-index: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+@charset "UTF-8";
+html{
+  -webkit-box-sizing:border-box;
+          box-sizing:border-box; }
+
+*,
+*::before,
+*::after{
+  -webkit-box-sizing:inherit;
+          box-sizing:inherit; }
+
+body{
+  font-size:14px;
+  font-weight:400;
+  letter-spacing:0;
+  line-height:1.28581;
+  text-transform:none;
+  color:#182026;
+  font-family:-apple-system, "BlinkMacSystemFont", "Segoe UI", "Roboto", "Oxygen", "Ubuntu", "Cantarell", "Open Sans", "Helvetica Neue", "Icons16", sans-serif; }
+
+p{
+  margin-bottom:10px;
+  margin-top:0; }
+
+small{
+  font-size:12px; }
+
+strong{
+  font-weight:600; }
+
+::-moz-selection{
+  background:rgba(125, 188, 255, 0.6); }
+
+::selection{
+  background:rgba(125, 188, 255, 0.6); }
+.bp3-heading{
+  color:#182026;
+  font-weight:600;
+  margin:0 0 10px;
+  padding:0; }
+  .bp3-dark .bp3-heading{
+    color:#f5f8fa; }
+
+h1.bp3-heading, .bp3-running-text h1{
+  font-size:36px;
+  line-height:40px; }
+
+h2.bp3-heading, .bp3-running-text h2{
+  font-size:28px;
+  line-height:32px; }
+
+h3.bp3-heading, .bp3-running-text h3{
+  font-size:22px;
+  line-height:25px; }
+
+h4.bp3-heading, .bp3-running-text h4{
+  font-size:18px;
+  line-height:21px; }
+
+h5.bp3-heading, .bp3-running-text h5{
+  font-size:16px;
+  line-height:19px; }
+
+h6.bp3-heading, .bp3-running-text h6{
+  font-size:14px;
+  line-height:16px; }
+.bp3-ui-text{
+  font-size:14px;
+  font-weight:400;
+  letter-spacing:0;
+  line-height:1.28581;
+  text-transform:none; }
+
+.bp3-monospace-text{
+  font-family:monospace;
+  text-transform:none; }
+
+.bp3-text-muted{
+  color:#5c7080; }
+  .bp3-dark .bp3-text-muted{
+    color:#a7b6c2; }
+
+.bp3-text-disabled{
+  color:rgba(92, 112, 128, 0.6); }
+  .bp3-dark .bp3-text-disabled{
+    color:rgba(167, 182, 194, 0.6); }
+
+.bp3-text-overflow-ellipsis{
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal; }
+.bp3-running-text{
+  font-size:14px;
+  line-height:1.5; }
+  .bp3-running-text h1{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h1{
+      color:#f5f8fa; }
+  .bp3-running-text h2{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h2{
+      color:#f5f8fa; }
+  .bp3-running-text h3{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h3{
+      color:#f5f8fa; }
+  .bp3-running-text h4{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h4{
+      color:#f5f8fa; }
+  .bp3-running-text h5{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h5{
+      color:#f5f8fa; }
+  .bp3-running-text h6{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h6{
+      color:#f5f8fa; }
+  .bp3-running-text hr{
+    border:none;
+    border-bottom:1px solid rgba(16, 22, 26, 0.15);
+    margin:20px 0; }
+    .bp3-dark .bp3-running-text hr{
+      border-color:rgba(255, 255, 255, 0.15); }
+  .bp3-running-text p{
+    margin:0 0 10px;
+    padding:0; }
+
+.bp3-text-large{
+  font-size:16px; }
+
+.bp3-text-small{
+  font-size:12px; }
+a{
+  color:#106ba3;
+  text-decoration:none; }
+  a:hover{
+    color:#106ba3;
+    cursor:pointer;
+    text-decoration:underline; }
+  a .bp3-icon, a .bp3-icon-standard, a .bp3-icon-large{
+    color:inherit; }
+  a code,
+  .bp3-dark a code{
+    color:inherit; }
+  .bp3-dark a,
+  .bp3-dark a:hover{
+    color:#48aff0; }
+    .bp3-dark a .bp3-icon, .bp3-dark a .bp3-icon-standard, .bp3-dark a .bp3-icon-large,
+    .bp3-dark a:hover .bp3-icon,
+    .bp3-dark a:hover .bp3-icon-standard,
+    .bp3-dark a:hover .bp3-icon-large{
+      color:inherit; }
+.bp3-running-text code, .bp3-code{
+  font-family:monospace;
+  text-transform:none;
+  background:rgba(255, 255, 255, 0.7);
+  border-radius:3px;
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);
+  color:#5c7080;
+  font-size:smaller;
+  padding:2px 5px; }
+  .bp3-dark .bp3-running-text code, .bp3-running-text .bp3-dark code, .bp3-dark .bp3-code{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+    color:#a7b6c2; }
+  .bp3-running-text a > code, a > .bp3-code{
+    color:#137cbd; }
+    .bp3-dark .bp3-running-text a > code, .bp3-running-text .bp3-dark a > code, .bp3-dark a > .bp3-code{
+      color:inherit; }
+
+.bp3-running-text pre, .bp3-code-block{
+  font-family:monospace;
+  text-transform:none;
+  background:rgba(255, 255, 255, 0.7);
+  border-radius:3px;
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+  color:#182026;
+  display:block;
+  font-size:13px;
+  line-height:1.4;
+  margin:10px 0;
+  padding:13px 15px 12px;
+  word-break:break-all;
+  word-wrap:break-word; }
+  .bp3-dark .bp3-running-text pre, .bp3-running-text .bp3-dark pre, .bp3-dark .bp3-code-block{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+  .bp3-running-text pre > code, .bp3-code-block > code{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:inherit;
+    font-size:inherit;
+    padding:0; }
+
+.bp3-running-text kbd, .bp3-key{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background:#ffffff;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  color:#5c7080;
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  font-family:inherit;
+  font-size:12px;
+  height:24px;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  line-height:24px;
+  min-width:24px;
+  padding:3px 6px;
+  vertical-align:middle; }
+  .bp3-running-text kbd .bp3-icon, .bp3-key .bp3-icon, .bp3-running-text kbd .bp3-icon-standard, .bp3-key .bp3-icon-standard, .bp3-running-text kbd .bp3-icon-large, .bp3-key .bp3-icon-large{
+    margin-right:5px; }
+  .bp3-dark .bp3-running-text kbd, .bp3-running-text .bp3-dark kbd, .bp3-dark .bp3-key{
+    background:#394b59;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+    color:#a7b6c2; }
+.bp3-running-text blockquote, .bp3-blockquote{
+  border-left:solid 4px rgba(167, 182, 194, 0.5);
+  margin:0 0 10px;
+  padding:0 20px; }
+  .bp3-dark .bp3-running-text blockquote, .bp3-running-text .bp3-dark blockquote, .bp3-dark .bp3-blockquote{
+    border-color:rgba(115, 134, 148, 0.5); }
+.bp3-running-text ul,
+.bp3-running-text ol, .bp3-list{
+  margin:10px 0;
+  padding-left:30px; }
+  .bp3-running-text ul li:not(:last-child), .bp3-running-text ol li:not(:last-child), .bp3-list li:not(:last-child){
+    margin-bottom:5px; }
+  .bp3-running-text ul ol, .bp3-running-text ol ol, .bp3-list ol,
+  .bp3-running-text ul ul,
+  .bp3-running-text ol ul,
+  .bp3-list ul{
+    margin-top:5px; }
+
+.bp3-list-unstyled{
+  list-style:none;
+  margin:0;
+  padding:0; }
+  .bp3-list-unstyled li{
+    padding:0; }
+.bp3-rtl{
+  text-align:right; }
+
+.bp3-dark{
+  color:#f5f8fa; }
+
+:focus{
+  outline:rgba(19, 124, 189, 0.6) auto 2px;
+  outline-offset:2px;
+  -moz-outline-radius:6px; }
+
+.bp3-focus-disabled :focus{
+  outline:none !important; }
+  .bp3-focus-disabled :focus ~ .bp3-control-indicator{
+    outline:none !important; }
+
+.bp3-alert{
+  max-width:400px;
+  padding:20px; }
+
+.bp3-alert-body{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex; }
+  .bp3-alert-body .bp3-icon{
+    font-size:40px;
+    margin-right:20px;
+    margin-top:0; }
+
+.bp3-alert-contents{
+  word-break:break-word; }
+
+.bp3-alert-footer{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:reverse;
+      -ms-flex-direction:row-reverse;
+          flex-direction:row-reverse;
+  margin-top:10px; }
+  .bp3-alert-footer .bp3-button{
+    margin-left:10px; }
+.bp3-breadcrumbs{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  cursor:default;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-wrap:wrap;
+      flex-wrap:wrap;
+  height:30px;
+  list-style:none;
+  margin:0;
+  padding:0; }
+  .bp3-breadcrumbs > li{
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center;
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex; }
+    .bp3-breadcrumbs > li::after{
+      background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.71 7.29l-4-4a1.003 1.003 0 00-1.42 1.42L8.59 8 5.3 11.29c-.19.18-.3.43-.3.71a1.003 1.003 0 001.71.71l4-4c.18-.18.29-.43.29-.71 0-.28-.11-.53-.29-.71z' fill='%235C7080'/%3e%3c/svg%3e");
+      content:"";
+      display:block;
+      height:16px;
+      margin:0 5px;
+      width:16px; }
+    .bp3-breadcrumbs > li:last-of-type::after{
+      display:none; }
+
+.bp3-breadcrumb,
+.bp3-breadcrumb-current,
+.bp3-breadcrumbs-collapsed{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  font-size:16px; }
+
+.bp3-breadcrumb,
+.bp3-breadcrumbs-collapsed{
+  color:#5c7080; }
+
+.bp3-breadcrumb:hover{
+  text-decoration:none; }
+
+.bp3-breadcrumb.bp3-disabled{
+  color:rgba(92, 112, 128, 0.6);
+  cursor:not-allowed; }
+
+.bp3-breadcrumb .bp3-icon{
+  margin-right:5px; }
+
+.bp3-breadcrumb-current{
+  color:inherit;
+  font-weight:600; }
+  .bp3-breadcrumb-current .bp3-input{
+    font-size:inherit;
+    font-weight:inherit;
+    vertical-align:baseline; }
+
+.bp3-breadcrumbs-collapsed{
+  background:#ced9e0;
+  border:none;
+  border-radius:3px;
+  cursor:pointer;
+  margin-right:2px;
+  padding:1px 5px;
+  vertical-align:text-bottom; }
+  .bp3-breadcrumbs-collapsed::before{
+    background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cg fill='%235C7080'%3e%3ccircle cx='2' cy='8.03' r='2'/%3e%3ccircle cx='14' cy='8.03' r='2'/%3e%3ccircle cx='8' cy='8.03' r='2'/%3e%3c/g%3e%3c/svg%3e") center no-repeat;
+    content:"";
+    display:block;
+    height:16px;
+    width:16px; }
+  .bp3-breadcrumbs-collapsed:hover{
+    background:#bfccd6;
+    color:#182026;
+    text-decoration:none; }
+
+.bp3-dark .bp3-breadcrumb,
+.bp3-dark .bp3-breadcrumbs-collapsed{
+  color:#a7b6c2; }
+
+.bp3-dark .bp3-breadcrumbs > li::after{
+  color:#a7b6c2; }
+
+.bp3-dark .bp3-breadcrumb.bp3-disabled{
+  color:rgba(167, 182, 194, 0.6); }
+
+.bp3-dark .bp3-breadcrumb-current{
+  color:#f5f8fa; }
+
+.bp3-dark .bp3-breadcrumbs-collapsed{
+  background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-breadcrumbs-collapsed:hover{
+    background:rgba(16, 22, 26, 0.6);
+    color:#f5f8fa; }
+.bp3-button{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  border:none;
+  border-radius:3px;
+  cursor:pointer;
+  font-size:14px;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  padding:5px 10px;
+  text-align:left;
+  vertical-align:middle;
+  min-height:30px;
+  min-width:30px; }
+  .bp3-button > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-button > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-button::before,
+  .bp3-button > *{
+    margin-right:7px; }
+  .bp3-button:empty::before,
+  .bp3-button > :last-child{
+    margin-right:0; }
+  .bp3-button:empty{
+    padding:0 !important; }
+  .bp3-button:disabled, .bp3-button.bp3-disabled{
+    cursor:not-allowed; }
+  .bp3-button.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-button.bp3-align-right,
+  .bp3-align-right .bp3-button{
+    text-align:right; }
+  .bp3-button.bp3-align-left,
+  .bp3-align-left .bp3-button{
+    text-align:left; }
+  .bp3-button:not([class*="bp3-intent-"]){
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    color:#182026; }
+    .bp3-button:not([class*="bp3-intent-"]):hover{
+      background-clip:padding-box;
+      background-color:#ebf1f5;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+    .bp3-button:not([class*="bp3-intent-"]):active, .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      background-color:#d8e1e8;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
+      background-color:rgba(206, 217, 224, 0.5);
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed;
+      outline:none; }
+      .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active:hover, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active:hover{
+        background:rgba(206, 217, 224, 0.7); }
+  .bp3-button.bp3-intent-primary{
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+    .bp3-button.bp3-intent-primary:hover, .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-primary:hover{
+      background-color:#106ba3;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
+      background-color:#0e5a8a;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-primary:disabled, .bp3-button.bp3-intent-primary.bp3-disabled{
+      background-color:rgba(19, 124, 189, 0.5);
+      background-image:none;
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-success{
+    background-color:#0f9960;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+    .bp3-button.bp3-intent-success:hover, .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-success:hover{
+      background-color:#0d8050;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
+      background-color:#0a6640;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-success:disabled, .bp3-button.bp3-intent-success.bp3-disabled{
+      background-color:rgba(15, 153, 96, 0.5);
+      background-image:none;
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-warning{
+    background-color:#d9822b;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+    .bp3-button.bp3-intent-warning:hover, .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-warning:hover{
+      background-color:#bf7326;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
+      background-color:#a66321;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-warning:disabled, .bp3-button.bp3-intent-warning.bp3-disabled{
+      background-color:rgba(217, 130, 43, 0.5);
+      background-image:none;
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-danger{
+    background-color:#db3737;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+    .bp3-button.bp3-intent-danger:hover, .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-danger:hover{
+      background-color:#c23030;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
+      background-color:#a82a2a;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-danger:disabled, .bp3-button.bp3-intent-danger.bp3-disabled{
+      background-color:rgba(219, 55, 55, 0.5);
+      background-image:none;
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{
+    stroke:#ffffff; }
+  .bp3-button.bp3-large,
+  .bp3-large .bp3-button{
+    min-height:40px;
+    min-width:40px;
+    font-size:16px;
+    padding:5px 15px; }
+    .bp3-button.bp3-large::before,
+    .bp3-button.bp3-large > *,
+    .bp3-large .bp3-button::before,
+    .bp3-large .bp3-button > *{
+      margin-right:10px; }
+    .bp3-button.bp3-large:empty::before,
+    .bp3-button.bp3-large > :last-child,
+    .bp3-large .bp3-button:empty::before,
+    .bp3-large .bp3-button > :last-child{
+      margin-right:0; }
+  .bp3-button.bp3-small,
+  .bp3-small .bp3-button{
+    min-height:24px;
+    min-width:24px;
+    padding:0 7px; }
+  .bp3-button.bp3-loading{
+    position:relative; }
+    .bp3-button.bp3-loading[class*="bp3-icon-"]::before{
+      visibility:hidden; }
+    .bp3-button.bp3-loading .bp3-button-spinner{
+      margin:0;
+      position:absolute; }
+    .bp3-button.bp3-loading > :not(.bp3-button-spinner){
+      visibility:hidden; }
+  .bp3-button[class*="bp3-icon-"]::before{
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-style:normal;
+    font-weight:400;
+    line-height:1;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    color:#5c7080; }
+  .bp3-button .bp3-icon, .bp3-button .bp3-icon-standard, .bp3-button .bp3-icon-large{
+    color:#5c7080; }
+    .bp3-button .bp3-icon.bp3-align-right, .bp3-button .bp3-icon-standard.bp3-align-right, .bp3-button .bp3-icon-large.bp3-align-right{
+      margin-left:7px; }
+  .bp3-button .bp3-icon:first-child:last-child,
+  .bp3-button .bp3-spinner + .bp3-icon:last-child{
+    margin:0 -7px; }
+  .bp3-dark .bp3-button:not([class*="bp3-intent-"]){
+    background-color:#394b59;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover, .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      color:#f5f8fa; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover{
+      background-color:#30404d;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      background-color:#202b33;
+      background-image:none;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
+      background-color:rgba(57, 75, 89, 0.5);
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+      .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active{
+        background:rgba(57, 75, 89, 0.7); }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-button-spinner .bp3-spinner-head{
+      background:rgba(16, 22, 26, 0.5);
+      stroke:#8a9ba8; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"])[class*="bp3-icon-"]::before{
+      color:#a7b6c2; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-large{
+      color:#a7b6c2; }
+  .bp3-dark .bp3-button[class*="bp3-intent-"]{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:hover{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:active, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-active{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:disabled, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-disabled{
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.3); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{
+      stroke:#8a9ba8; }
+  .bp3-button:disabled::before,
+  .bp3-button:disabled .bp3-icon, .bp3-button:disabled .bp3-icon-standard, .bp3-button:disabled .bp3-icon-large, .bp3-button.bp3-disabled::before,
+  .bp3-button.bp3-disabled .bp3-icon, .bp3-button.bp3-disabled .bp3-icon-standard, .bp3-button.bp3-disabled .bp3-icon-large, .bp3-button[class*="bp3-intent-"]::before,
+  .bp3-button[class*="bp3-intent-"] .bp3-icon, .bp3-button[class*="bp3-intent-"] .bp3-icon-standard, .bp3-button[class*="bp3-intent-"] .bp3-icon-large{
+    color:inherit !important; }
+  .bp3-button.bp3-minimal{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+    .bp3-button.bp3-minimal:hover{
+      background:rgba(167, 182, 194, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026;
+      text-decoration:none; }
+    .bp3-button.bp3-minimal:active, .bp3-button.bp3-minimal.bp3-active{
+      background:rgba(115, 134, 148, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026; }
+    .bp3-button.bp3-minimal:disabled, .bp3-button.bp3-minimal:disabled:hover, .bp3-button.bp3-minimal.bp3-disabled, .bp3-button.bp3-minimal.bp3-disabled:hover{
+      background:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed; }
+      .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{
+        background:rgba(115, 134, 148, 0.3); }
+    .bp3-dark .bp3-button.bp3-minimal{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:inherit; }
+      .bp3-dark .bp3-button.bp3-minimal:hover, .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+      .bp3-dark .bp3-button.bp3-minimal:hover{
+        background:rgba(138, 155, 168, 0.15); }
+      .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{
+        background:rgba(138, 155, 168, 0.3);
+        color:#f5f8fa; }
+      .bp3-dark .bp3-button.bp3-minimal:disabled, .bp3-dark .bp3-button.bp3-minimal:disabled:hover, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover{
+        background:none;
+        color:rgba(167, 182, 194, 0.6);
+        cursor:not-allowed; }
+        .bp3-dark .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{
+          background:rgba(138, 155, 168, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-primary{
+      color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:hover, .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.15);
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(16, 107, 163, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+        stroke:#106ba3; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary{
+        color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:hover{
+          background:rgba(19, 124, 189, 0.2);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+          background:rgba(19, 124, 189, 0.3);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
+          background:none;
+          color:rgba(72, 175, 240, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
+            background:rgba(19, 124, 189, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-success{
+      color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:hover, .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.15);
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(13, 128, 80, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+        stroke:#0d8050; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success{
+        color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:hover{
+          background:rgba(15, 153, 96, 0.2);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+          background:rgba(15, 153, 96, 0.3);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
+          background:none;
+          color:rgba(61, 204, 145, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
+            background:rgba(15, 153, 96, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-warning{
+      color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:hover, .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.15);
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(191, 115, 38, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+        stroke:#bf7326; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning{
+        color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:hover{
+          background:rgba(217, 130, 43, 0.2);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+          background:rgba(217, 130, 43, 0.3);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
+          background:none;
+          color:rgba(255, 179, 102, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
+            background:rgba(217, 130, 43, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-danger{
+      color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:hover, .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.15);
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(194, 48, 48, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+        stroke:#c23030; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger{
+        color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:hover{
+          background:rgba(219, 55, 55, 0.2);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+          background:rgba(219, 55, 55, 0.3);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
+          background:none;
+          color:rgba(255, 115, 115, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
+            background:rgba(219, 55, 55, 0.3); }
+  .bp3-button.bp3-outlined{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    border:1px solid rgba(24, 32, 38, 0.2);
+    -webkit-box-sizing:border-box;
+            box-sizing:border-box; }
+    .bp3-button.bp3-outlined:hover{
+      background:rgba(167, 182, 194, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026;
+      text-decoration:none; }
+    .bp3-button.bp3-outlined:active, .bp3-button.bp3-outlined.bp3-active{
+      background:rgba(115, 134, 148, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026; }
+    .bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined.bp3-disabled:hover{
+      background:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed; }
+      .bp3-button.bp3-outlined:disabled.bp3-active, .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{
+        background:rgba(115, 134, 148, 0.3); }
+    .bp3-dark .bp3-button.bp3-outlined{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:inherit; }
+      .bp3-dark .bp3-button.bp3-outlined:hover, .bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+      .bp3-dark .bp3-button.bp3-outlined:hover{
+        background:rgba(138, 155, 168, 0.15); }
+      .bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{
+        background:rgba(138, 155, 168, 0.3);
+        color:#f5f8fa; }
+      .bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{
+        background:none;
+        color:rgba(167, 182, 194, 0.6);
+        cursor:not-allowed; }
+        .bp3-dark .bp3-button.bp3-outlined:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{
+          background:rgba(138, 155, 168, 0.3); }
+    .bp3-button.bp3-outlined.bp3-intent-primary{
+      color:#106ba3; }
+      .bp3-button.bp3-outlined.bp3-intent-primary:hover, .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#106ba3; }
+      .bp3-button.bp3-outlined.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.15);
+        color:#106ba3; }
+      .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#106ba3; }
+      .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(16, 107, 163, 0.5); }
+        .bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+      .bp3-button.bp3-outlined.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+        stroke:#106ba3; }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{
+        color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:hover{
+          background:rgba(19, 124, 189, 0.2);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
+          background:rgba(19, 124, 189, 0.3);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
+          background:none;
+          color:rgba(72, 175, 240, 0.5); }
+          .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{
+            background:rgba(19, 124, 189, 0.3); }
+    .bp3-button.bp3-outlined.bp3-intent-success{
+      color:#0d8050; }
+      .bp3-button.bp3-outlined.bp3-intent-success:hover, .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#0d8050; }
+      .bp3-button.bp3-outlined.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.15);
+        color:#0d8050; }
+      .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#0d8050; }
+      .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(13, 128, 80, 0.5); }
+        .bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+      .bp3-button.bp3-outlined.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+        stroke:#0d8050; }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{
+        color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:hover{
+          background:rgba(15, 153, 96, 0.2);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
+          background:rgba(15, 153, 96, 0.3);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
+          background:none;
+          color:rgba(61, 204, 145, 0.5); }
+          .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{
+            background:rgba(15, 153, 96, 0.3); }
+    .bp3-button.bp3-outlined.bp3-intent-warning{
+      color:#bf7326; }
+      .bp3-button.bp3-outlined.bp3-intent-warning:hover, .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#bf7326; }
+      .bp3-button.bp3-outlined.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.15);
+        color:#bf7326; }
+      .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#bf7326; }
+      .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(191, 115, 38, 0.5); }
+        .bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+      .bp3-button.bp3-outlined.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+        stroke:#bf7326; }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{
+        color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:hover{
+          background:rgba(217, 130, 43, 0.2);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
+          background:rgba(217, 130, 43, 0.3);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
+          background:none;
+          color:rgba(255, 179, 102, 0.5); }
+          .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{
+            background:rgba(217, 130, 43, 0.3); }
+    .bp3-button.bp3-outlined.bp3-intent-danger{
+      color:#c23030; }
+      .bp3-button.bp3-outlined.bp3-intent-danger:hover, .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#c23030; }
+      .bp3-button.bp3-outlined.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.15);
+        color:#c23030; }
+      .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#c23030; }
+      .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(194, 48, 48, 0.5); }
+        .bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+      .bp3-button.bp3-outlined.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+        stroke:#c23030; }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{
+        color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:hover{
+          background:rgba(219, 55, 55, 0.2);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
+          background:rgba(219, 55, 55, 0.3);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
+          background:none;
+          color:rgba(255, 115, 115, 0.5); }
+          .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{
+            background:rgba(219, 55, 55, 0.3); }
+    .bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled:hover{
+      border-color:rgba(92, 112, 128, 0.1); }
+    .bp3-dark .bp3-button.bp3-outlined{
+      border-color:rgba(255, 255, 255, 0.4); }
+      .bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{
+        border-color:rgba(255, 255, 255, 0.2); }
+    .bp3-button.bp3-outlined.bp3-intent-primary{
+      border-color:rgba(16, 107, 163, 0.6); }
+      .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
+        border-color:rgba(16, 107, 163, 0.2); }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{
+        border-color:rgba(72, 175, 240, 0.6); }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
+          border-color:rgba(72, 175, 240, 0.2); }
+    .bp3-button.bp3-outlined.bp3-intent-success{
+      border-color:rgba(13, 128, 80, 0.6); }
+      .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
+        border-color:rgba(13, 128, 80, 0.2); }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{
+        border-color:rgba(61, 204, 145, 0.6); }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
+          border-color:rgba(61, 204, 145, 0.2); }
+    .bp3-button.bp3-outlined.bp3-intent-warning{
+      border-color:rgba(191, 115, 38, 0.6); }
+      .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
+        border-color:rgba(191, 115, 38, 0.2); }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{
+        border-color:rgba(255, 179, 102, 0.6); }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
+          border-color:rgba(255, 179, 102, 0.2); }
+    .bp3-button.bp3-outlined.bp3-intent-danger{
+      border-color:rgba(194, 48, 48, 0.6); }
+      .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
+        border-color:rgba(194, 48, 48, 0.2); }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{
+        border-color:rgba(255, 115, 115, 0.6); }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
+          border-color:rgba(255, 115, 115, 0.2); }
+
+a.bp3-button{
+  text-align:center;
+  text-decoration:none;
+  -webkit-transition:none;
+  transition:none; }
+  a.bp3-button, a.bp3-button:hover, a.bp3-button:active{
+    color:#182026; }
+  a.bp3-button.bp3-disabled{
+    color:rgba(92, 112, 128, 0.6); }
+
+.bp3-button-text{
+  -webkit-box-flex:0;
+      -ms-flex:0 1 auto;
+          flex:0 1 auto; }
+
+.bp3-button.bp3-align-left .bp3-button-text, .bp3-button.bp3-align-right .bp3-button-text,
+.bp3-button-group.bp3-align-left .bp3-button-text,
+.bp3-button-group.bp3-align-right .bp3-button-text{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto; }
+.bp3-button-group{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex; }
+  .bp3-button-group .bp3-button{
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    position:relative;
+    z-index:4; }
+    .bp3-button-group .bp3-button:focus{
+      z-index:5; }
+    .bp3-button-group .bp3-button:hover{
+      z-index:6; }
+    .bp3-button-group .bp3-button:active, .bp3-button-group .bp3-button.bp3-active{
+      z-index:7; }
+    .bp3-button-group .bp3-button:disabled, .bp3-button-group .bp3-button.bp3-disabled{
+      z-index:3; }
+    .bp3-button-group .bp3-button[class*="bp3-intent-"]{
+      z-index:9; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:focus{
+        z-index:10; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:hover{
+        z-index:11; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:active, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-active{
+        z-index:12; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:disabled, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-disabled{
+        z-index:8; }
+  .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:first-child) .bp3-button,
+  .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:first-child){
+    border-bottom-left-radius:0;
+    border-top-left-radius:0; }
+  .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
+    border-bottom-right-radius:0;
+    border-top-right-radius:0;
+    margin-right:-1px; }
+  .bp3-button-group.bp3-minimal .bp3-button{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+    .bp3-button-group.bp3-minimal .bp3-button:hover{
+      background:rgba(167, 182, 194, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026;
+      text-decoration:none; }
+    .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+      background:rgba(115, 134, 148, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026; }
+    .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{
+      background:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed; }
+      .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{
+        background:rgba(115, 134, 148, 0.3); }
+    .bp3-dark .bp3-button-group.bp3-minimal .bp3-button{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:inherit; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover{
+        background:rgba(138, 155, 168, 0.15); }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+        background:rgba(138, 155, 168, 0.3);
+        color:#f5f8fa; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{
+        background:none;
+        color:rgba(167, 182, 194, 0.6);
+        cursor:not-allowed; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{
+          background:rgba(138, 155, 168, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
+      color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.15);
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(16, 107, 163, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+        stroke:#106ba3; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
+        color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
+          background:rgba(19, 124, 189, 0.2);
+          color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+          background:rgba(19, 124, 189, 0.3);
+          color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
+          background:none;
+          color:rgba(72, 175, 240, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
+            background:rgba(19, 124, 189, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
+      color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.15);
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(13, 128, 80, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+        stroke:#0d8050; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
+        color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
+          background:rgba(15, 153, 96, 0.2);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+          background:rgba(15, 153, 96, 0.3);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
+          background:none;
+          color:rgba(61, 204, 145, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
+            background:rgba(15, 153, 96, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
+      color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.15);
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(191, 115, 38, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+        stroke:#bf7326; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
+        color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
+          background:rgba(217, 130, 43, 0.2);
+          color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+          background:rgba(217, 130, 43, 0.3);
+          color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
+          background:none;
+          color:rgba(255, 179, 102, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
+            background:rgba(217, 130, 43, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
+      color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.15);
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(194, 48, 48, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+        stroke:#c23030; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
+        color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
+          background:rgba(219, 55, 55, 0.2);
+          color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+          background:rgba(219, 55, 55, 0.3);
+          color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
+          background:none;
+          color:rgba(255, 115, 115, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
+            background:rgba(219, 55, 55, 0.3); }
+  .bp3-button-group .bp3-popover-wrapper,
+  .bp3-button-group .bp3-popover-target{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-button-group.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-button-group .bp3-button.bp3-fill,
+  .bp3-button-group.bp3-fill .bp3-button:not(.bp3-fixed){
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-button-group.bp3-vertical{
+    -webkit-box-align:stretch;
+        -ms-flex-align:stretch;
+            align-items:stretch;
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column;
+    vertical-align:top; }
+    .bp3-button-group.bp3-vertical.bp3-fill{
+      height:100%;
+      width:unset; }
+    .bp3-button-group.bp3-vertical .bp3-button{
+      margin-right:0 !important;
+      width:100%; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:first-child .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:first-child{
+      border-radius:3px 3px 0 0; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:last-child .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:last-child{
+      border-radius:0 0 3px 3px; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:not(:last-child){
+      margin-bottom:-1px; }
+  .bp3-button-group.bp3-align-left .bp3-button{
+    text-align:left; }
+  .bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
+    margin-right:1px; }
+  .bp3-dark .bp3-button-group.bp3-vertical > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-dark .bp3-button-group.bp3-vertical > .bp3-button:not(:last-child){
+    margin-bottom:1px; }
+.bp3-callout{
+  font-size:14px;
+  line-height:1.5;
+  background-color:rgba(138, 155, 168, 0.15);
+  border-radius:3px;
+  padding:10px 12px 9px;
+  position:relative;
+  width:100%; }
+  .bp3-callout[class*="bp3-icon-"]{
+    padding-left:40px; }
+    .bp3-callout[class*="bp3-icon-"]::before{
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-style:normal;
+      font-weight:400;
+      line-height:1;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased;
+      color:#5c7080;
+      left:10px;
+      position:absolute;
+      top:10px; }
+  .bp3-callout.bp3-callout-icon{
+    padding-left:40px; }
+    .bp3-callout.bp3-callout-icon > .bp3-icon:first-child{
+      color:#5c7080;
+      left:10px;
+      position:absolute;
+      top:10px; }
+  .bp3-callout .bp3-heading{
+    line-height:20px;
+    margin-bottom:5px;
+    margin-top:0; }
+    .bp3-callout .bp3-heading:last-child{
+      margin-bottom:0; }
+  .bp3-dark .bp3-callout{
+    background-color:rgba(138, 155, 168, 0.2); }
+    .bp3-dark .bp3-callout[class*="bp3-icon-"]::before{
+      color:#a7b6c2; }
+  .bp3-callout.bp3-intent-primary{
+    background-color:rgba(19, 124, 189, 0.15); }
+    .bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-primary .bp3-heading{
+      color:#106ba3; }
+    .bp3-dark .bp3-callout.bp3-intent-primary{
+      background-color:rgba(19, 124, 189, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-primary .bp3-heading{
+        color:#48aff0; }
+  .bp3-callout.bp3-intent-success{
+    background-color:rgba(15, 153, 96, 0.15); }
+    .bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-success > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-success .bp3-heading{
+      color:#0d8050; }
+    .bp3-dark .bp3-callout.bp3-intent-success{
+      background-color:rgba(15, 153, 96, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-success > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-success .bp3-heading{
+        color:#3dcc91; }
+  .bp3-callout.bp3-intent-warning{
+    background-color:rgba(217, 130, 43, 0.15); }
+    .bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-warning .bp3-heading{
+      color:#bf7326; }
+    .bp3-dark .bp3-callout.bp3-intent-warning{
+      background-color:rgba(217, 130, 43, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-warning .bp3-heading{
+        color:#ffb366; }
+  .bp3-callout.bp3-intent-danger{
+    background-color:rgba(219, 55, 55, 0.15); }
+    .bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-danger .bp3-heading{
+      color:#c23030; }
+    .bp3-dark .bp3-callout.bp3-intent-danger{
+      background-color:rgba(219, 55, 55, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-danger .bp3-heading{
+        color:#ff7373; }
+  .bp3-running-text .bp3-callout{
+    margin:20px 0; }
+.bp3-card{
+  background-color:#ffffff;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+  padding:20px;
+  -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-card.bp3-dark,
+  .bp3-dark .bp3-card{
+    background-color:#30404d;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
+
+.bp3-elevation-0{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
+  .bp3-elevation-0.bp3-dark,
+  .bp3-dark .bp3-elevation-0{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
+
+.bp3-elevation-1{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-1.bp3-dark,
+  .bp3-dark .bp3-elevation-1{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-2{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-2.bp3-dark,
+  .bp3-dark .bp3-elevation-2{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-3{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-3.bp3-dark,
+  .bp3-dark .bp3-elevation-3{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-4{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-4.bp3-dark,
+  .bp3-dark .bp3-elevation-4{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }
+
+.bp3-card.bp3-interactive:hover{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  cursor:pointer; }
+  .bp3-card.bp3-interactive:hover.bp3-dark,
+  .bp3-dark .bp3-card.bp3-interactive:hover{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+
+.bp3-card.bp3-interactive:active{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  opacity:0.9;
+  -webkit-transition-duration:0;
+          transition-duration:0; }
+  .bp3-card.bp3-interactive:active.bp3-dark,
+  .bp3-dark .bp3-card.bp3-interactive:active{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-collapse{
+  height:0;
+  overflow-y:hidden;
+  -webkit-transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-collapse .bp3-collapse-body{
+    -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-collapse .bp3-collapse-body[aria-hidden="true"]{
+      display:none; }
+
+.bp3-context-menu .bp3-popover-target{
+  display:block; }
+
+.bp3-context-menu-popover-target{
+  position:fixed; }
+
+.bp3-divider{
+  border-bottom:1px solid rgba(16, 22, 26, 0.15);
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  margin:5px; }
+  .bp3-dark .bp3-divider{
+    border-color:rgba(16, 22, 26, 0.4); }
+.bp3-dialog-container{
+  opacity:1;
+  -webkit-transform:scale(1);
+          transform:scale(1);
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  min-height:100%;
+  pointer-events:none;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none;
+  width:100%; }
+  .bp3-dialog-container.bp3-overlay-enter > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear > .bp3-dialog{
+    opacity:0;
+    -webkit-transform:scale(0.5);
+            transform:scale(0.5); }
+  .bp3-dialog-container.bp3-overlay-enter-active > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear-active > .bp3-dialog{
+    opacity:1;
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:opacity, -webkit-transform;
+    transition-property:opacity, -webkit-transform;
+    transition-property:opacity, transform;
+    transition-property:opacity, transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-dialog-container.bp3-overlay-exit > .bp3-dialog{
+    opacity:1;
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-dialog-container.bp3-overlay-exit-active > .bp3-dialog{
+    opacity:0;
+    -webkit-transform:scale(0.5);
+            transform:scale(0.5);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:opacity, -webkit-transform;
+    transition-property:opacity, -webkit-transform;
+    transition-property:opacity, transform;
+    transition-property:opacity, transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+
+.bp3-dialog{
+  background:#ebf1f5;
+  border-radius:6px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:30px 0;
+  padding-bottom:20px;
+  pointer-events:all;
+  -webkit-user-select:text;
+     -moz-user-select:text;
+      -ms-user-select:text;
+          user-select:text;
+  width:500px; }
+  .bp3-dialog:focus{
+    outline:0; }
+  .bp3-dialog.bp3-dark,
+  .bp3-dark .bp3-dialog{
+    background:#293742;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+
+.bp3-dialog-header{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background:#ffffff;
+  border-radius:6px 6px 0 0;
+  -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  min-height:40px;
+  padding-left:20px;
+  padding-right:5px;
+  z-index:30; }
+  .bp3-dialog-header .bp3-icon-large,
+  .bp3-dialog-header .bp3-icon{
+    color:#5c7080;
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    margin-right:10px; }
+  .bp3-dialog-header .bp3-heading{
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    line-height:inherit;
+    margin:0; }
+    .bp3-dialog-header .bp3-heading:last-child{
+      margin-right:20px; }
+  .bp3-dark .bp3-dialog-header{
+    background:#30404d;
+    -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-dialog-header .bp3-icon-large,
+    .bp3-dark .bp3-dialog-header .bp3-icon{
+      color:#a7b6c2; }
+
+.bp3-dialog-body{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  line-height:18px;
+  margin:20px; }
+
+.bp3-dialog-footer{
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  margin:0 20px; }
+
+.bp3-dialog-footer-actions{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:end;
+      -ms-flex-pack:end;
+          justify-content:flex-end; }
+  .bp3-dialog-footer-actions .bp3-button{
+    margin-left:10px; }
+.bp3-multistep-dialog-panels{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex; }
+
+.bp3-multistep-dialog-left-panel{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:1;
+      -ms-flex:1;
+          flex:1;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column; }
+  .bp3-dark .bp3-multistep-dialog-left-panel{
+    background:#202b33; }
+
+.bp3-multistep-dialog-right-panel{
+  background-color:#f5f8fa;
+  border-left:1px solid rgba(16, 22, 26, 0.15);
+  border-radius:0 0 6px 0;
+  -webkit-box-flex:3;
+      -ms-flex:3;
+          flex:3;
+  min-width:0; }
+  .bp3-dark .bp3-multistep-dialog-right-panel{
+    background-color:#293742;
+    border-left:1px solid rgba(16, 22, 26, 0.4); }
+
+.bp3-multistep-dialog-footer{
+  background-color:#ffffff;
+  border-radius:0 0 6px 0;
+  border-top:1px solid rgba(16, 22, 26, 0.15);
+  padding:10px; }
+  .bp3-dark .bp3-multistep-dialog-footer{
+    background:#30404d;
+    border-top:1px solid rgba(16, 22, 26, 0.4); }
+
+.bp3-dialog-step-container{
+  background-color:#f5f8fa;
+  border-bottom:1px solid rgba(16, 22, 26, 0.15); }
+  .bp3-dark .bp3-dialog-step-container{
+    background:#293742;
+    border-bottom:1px solid rgba(16, 22, 26, 0.4); }
+  .bp3-dialog-step-container.bp3-dialog-step-viewed{
+    background-color:#ffffff; }
+    .bp3-dark .bp3-dialog-step-container.bp3-dialog-step-viewed{
+      background:#30404d; }
+
+.bp3-dialog-step{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background-color:#f5f8fa;
+  border-radius:6px;
+  cursor:not-allowed;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  margin:4px;
+  padding:6px 14px; }
+  .bp3-dark .bp3-dialog-step{
+    background:#293742; }
+  .bp3-dialog-step-viewed .bp3-dialog-step{
+    background-color:#ffffff;
+    cursor:pointer; }
+    .bp3-dark .bp3-dialog-step-viewed .bp3-dialog-step{
+      background:#30404d; }
+  .bp3-dialog-step:hover{
+    background-color:#f5f8fa; }
+    .bp3-dark .bp3-dialog-step:hover{
+      background:#293742; }
+
+.bp3-dialog-step-icon{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background-color:rgba(92, 112, 128, 0.6);
+  border-radius:50%;
+  color:#ffffff;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  height:25px;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  width:25px; }
+  .bp3-dark .bp3-dialog-step-icon{
+    background-color:rgba(167, 182, 194, 0.6); }
+  .bp3-active.bp3-dialog-step-viewed .bp3-dialog-step-icon{
+    background-color:#2b95d6; }
+  .bp3-dialog-step-viewed .bp3-dialog-step-icon{
+    background-color:#8a9ba8; }
+
+.bp3-dialog-step-title{
+  color:rgba(92, 112, 128, 0.6);
+  -webkit-box-flex:1;
+      -ms-flex:1;
+          flex:1;
+  padding-left:10px; }
+  .bp3-dark .bp3-dialog-step-title{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-active.bp3-dialog-step-viewed .bp3-dialog-step-title{
+    color:#2b95d6; }
+  .bp3-dialog-step-viewed:not(.bp3-active) .bp3-dialog-step-title{
+    color:#182026; }
+    .bp3-dark .bp3-dialog-step-viewed:not(.bp3-active) .bp3-dialog-step-title{
+      color:#f5f8fa; }
+.bp3-drawer{
+  background:#ffffff;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:0;
+  padding:0; }
+  .bp3-drawer:focus{
+    outline:0; }
+  .bp3-drawer.bp3-position-top{
+    height:50%;
+    left:0;
+    right:0;
+    top:0; }
+    .bp3-drawer.bp3-position-top.bp3-overlay-enter, .bp3-drawer.bp3-position-top.bp3-overlay-appear{
+      -webkit-transform:translateY(-100%);
+              transform:translateY(-100%); }
+    .bp3-drawer.bp3-position-top.bp3-overlay-enter-active, .bp3-drawer.bp3-position-top.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer.bp3-position-top.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer.bp3-position-top.bp3-overlay-exit-active{
+      -webkit-transform:translateY(-100%);
+              transform:translateY(-100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer.bp3-position-bottom{
+    bottom:0;
+    height:50%;
+    left:0;
+    right:0; }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-enter, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%); }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-enter-active, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-exit-active{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer.bp3-position-left{
+    bottom:0;
+    left:0;
+    top:0;
+    width:50%; }
+    .bp3-drawer.bp3-position-left.bp3-overlay-enter, .bp3-drawer.bp3-position-left.bp3-overlay-appear{
+      -webkit-transform:translateX(-100%);
+              transform:translateX(-100%); }
+    .bp3-drawer.bp3-position-left.bp3-overlay-enter-active, .bp3-drawer.bp3-position-left.bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer.bp3-position-left.bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer.bp3-position-left.bp3-overlay-exit-active{
+      -webkit-transform:translateX(-100%);
+              transform:translateX(-100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer.bp3-position-right{
+    bottom:0;
+    right:0;
+    top:0;
+    width:50%; }
+    .bp3-drawer.bp3-position-right.bp3-overlay-enter, .bp3-drawer.bp3-position-right.bp3-overlay-appear{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%); }
+    .bp3-drawer.bp3-position-right.bp3-overlay-enter-active, .bp3-drawer.bp3-position-right.bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer.bp3-position-right.bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer.bp3-position-right.bp3-overlay-exit-active{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+  .bp3-position-right):not(.bp3-vertical){
+    bottom:0;
+    right:0;
+    top:0;
+    width:50%; }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-appear{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-exit-active{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+  .bp3-position-right).bp3-vertical{
+    bottom:0;
+    height:50%;
+    left:0;
+    right:0; }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-appear{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-exit-active{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer.bp3-dark,
+  .bp3-dark .bp3-drawer{
+    background:#30404d;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+
+.bp3-drawer-header{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  border-radius:0;
+  -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  min-height:40px;
+  padding:5px;
+  padding-left:20px;
+  position:relative; }
+  .bp3-drawer-header .bp3-icon-large,
+  .bp3-drawer-header .bp3-icon{
+    color:#5c7080;
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    margin-right:10px; }
+  .bp3-drawer-header .bp3-heading{
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    line-height:inherit;
+    margin:0; }
+    .bp3-drawer-header .bp3-heading:last-child{
+      margin-right:20px; }
+  .bp3-dark .bp3-drawer-header{
+    -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-drawer-header .bp3-icon-large,
+    .bp3-dark .bp3-drawer-header .bp3-icon{
+      color:#a7b6c2; }
+
+.bp3-drawer-body{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  line-height:18px;
+  overflow:auto; }
+
+.bp3-drawer-footer{
+  -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  padding:10px 20px;
+  position:relative; }
+  .bp3-dark .bp3-drawer-footer{
+    -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4); }
+.bp3-editable-text{
+  cursor:text;
+  display:inline-block;
+  max-width:100%;
+  position:relative;
+  vertical-align:top;
+  white-space:nowrap; }
+  .bp3-editable-text::before{
+    bottom:-3px;
+    left:-3px;
+    position:absolute;
+    right:-3px;
+    top:-3px;
+    border-radius:3px;
+    content:"";
+    -webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-editable-text:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-editable-text.bp3-editable-text-editing::before{
+    background-color:#ffffff;
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-disabled::before{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-editable-text.bp3-intent-primary .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
+    color:#137cbd; }
+  .bp3-editable-text.bp3-intent-primary:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4); }
+  .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-success .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
+    color:#0f9960; }
+  .bp3-editable-text.bp3-intent-success:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4); }
+  .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-warning .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
+    color:#d9822b; }
+  .bp3-editable-text.bp3-intent-warning:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4); }
+  .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-danger .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
+    color:#db3737; }
+  .bp3-editable-text.bp3-intent-danger:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4); }
+  .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-editable-text:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15); }
+  .bp3-dark .bp3-editable-text.bp3-editable-text-editing::before{
+    background-color:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-disabled::before{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
+    color:#48aff0; }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4);
+            box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
+    color:#3dcc91; }
+  .bp3-dark .bp3-editable-text.bp3-intent-success:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4);
+            box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
+    color:#ffb366; }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4);
+            box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
+    color:#ff7373; }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4);
+            box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-editable-text-input,
+.bp3-editable-text-content{
+  color:inherit;
+  display:inherit;
+  font:inherit;
+  letter-spacing:inherit;
+  max-width:inherit;
+  min-width:inherit;
+  position:relative;
+  resize:none;
+  text-transform:inherit;
+  vertical-align:top; }
+
+.bp3-editable-text-input{
+  background:none;
+  border:none;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  padding:0;
+  white-space:pre-wrap;
+  width:100%; }
+  .bp3-editable-text-input::-webkit-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input::-moz-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input:-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input::-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input::placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input:focus{
+    outline:none; }
+  .bp3-editable-text-input::-ms-clear{
+    display:none; }
+
+.bp3-editable-text-content{
+  overflow:hidden;
+  padding-right:2px;
+  text-overflow:ellipsis;
+  white-space:pre; }
+  .bp3-editable-text-editing > .bp3-editable-text-content{
+    left:0;
+    position:absolute;
+    visibility:hidden; }
+  .bp3-editable-text-placeholder > .bp3-editable-text-content{
+    color:rgba(92, 112, 128, 0.6); }
+    .bp3-dark .bp3-editable-text-placeholder > .bp3-editable-text-content{
+      color:rgba(167, 182, 194, 0.6); }
+
+.bp3-editable-text.bp3-multiline{
+  display:block; }
+  .bp3-editable-text.bp3-multiline .bp3-editable-text-content{
+    overflow:auto;
+    white-space:pre-wrap;
+    word-wrap:break-word; }
+.bp3-divider{
+  border-bottom:1px solid rgba(16, 22, 26, 0.15);
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  margin:5px; }
+  .bp3-dark .bp3-divider{
+    border-color:rgba(16, 22, 26, 0.4); }
+.bp3-control-group{
+  -webkit-transform:translateZ(0);
+          transform:translateZ(0);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:stretch;
+      -ms-flex-align:stretch;
+          align-items:stretch; }
+  .bp3-control-group > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-control-group > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-control-group .bp3-button,
+  .bp3-control-group .bp3-html-select,
+  .bp3-control-group .bp3-input,
+  .bp3-control-group .bp3-select{
+    position:relative; }
+  .bp3-control-group .bp3-input{
+    border-radius:inherit;
+    z-index:2; }
+    .bp3-control-group .bp3-input:focus{
+      border-radius:3px;
+      z-index:14; }
+    .bp3-control-group .bp3-input[class*="bp3-intent"]{
+      z-index:13; }
+      .bp3-control-group .bp3-input[class*="bp3-intent"]:focus{
+        z-index:15; }
+    .bp3-control-group .bp3-input[readonly], .bp3-control-group .bp3-input:disabled, .bp3-control-group .bp3-input.bp3-disabled{
+      z-index:1; }
+  .bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input{
+    z-index:13; }
+    .bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input:focus{
+      z-index:15; }
+  .bp3-control-group .bp3-button,
+  .bp3-control-group .bp3-html-select select,
+  .bp3-control-group .bp3-select select{
+    -webkit-transform:translateZ(0);
+            transform:translateZ(0);
+    border-radius:inherit;
+    z-index:4; }
+    .bp3-control-group .bp3-button:focus,
+    .bp3-control-group .bp3-html-select select:focus,
+    .bp3-control-group .bp3-select select:focus{
+      z-index:5; }
+    .bp3-control-group .bp3-button:hover,
+    .bp3-control-group .bp3-html-select select:hover,
+    .bp3-control-group .bp3-select select:hover{
+      z-index:6; }
+    .bp3-control-group .bp3-button:active,
+    .bp3-control-group .bp3-html-select select:active,
+    .bp3-control-group .bp3-select select:active{
+      z-index:7; }
+    .bp3-control-group .bp3-button[readonly], .bp3-control-group .bp3-button:disabled, .bp3-control-group .bp3-button.bp3-disabled,
+    .bp3-control-group .bp3-html-select select[readonly],
+    .bp3-control-group .bp3-html-select select:disabled,
+    .bp3-control-group .bp3-html-select select.bp3-disabled,
+    .bp3-control-group .bp3-select select[readonly],
+    .bp3-control-group .bp3-select select:disabled,
+    .bp3-control-group .bp3-select select.bp3-disabled{
+      z-index:3; }
+    .bp3-control-group .bp3-button[class*="bp3-intent"],
+    .bp3-control-group .bp3-html-select select[class*="bp3-intent"],
+    .bp3-control-group .bp3-select select[class*="bp3-intent"]{
+      z-index:9; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:focus,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:focus,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:focus{
+        z-index:10; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:hover,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:hover,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:hover{
+        z-index:11; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:active,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:active,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:active{
+        z-index:12; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"][readonly], .bp3-control-group .bp3-button[class*="bp3-intent"]:disabled, .bp3-control-group .bp3-button[class*="bp3-intent"].bp3-disabled,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"][readonly],
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:disabled,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"].bp3-disabled,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"][readonly],
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:disabled,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"].bp3-disabled{
+        z-index:8; }
+  .bp3-control-group .bp3-input-group > .bp3-icon,
+  .bp3-control-group .bp3-input-group > .bp3-button,
+  .bp3-control-group .bp3-input-group > .bp3-input-left-container,
+  .bp3-control-group .bp3-input-group > .bp3-input-action{
+    z-index:16; }
+  .bp3-control-group .bp3-select::after,
+  .bp3-control-group .bp3-html-select::after,
+  .bp3-control-group .bp3-select > .bp3-icon,
+  .bp3-control-group .bp3-html-select > .bp3-icon{
+    z-index:17; }
+  .bp3-control-group .bp3-select:focus-within{
+    z-index:5; }
+  .bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){
+    margin-right:-1px; }
+  .bp3-control-group:not(.bp3-vertical) > .bp3-divider:not(:first-child){
+    margin-left:6px; }
+  .bp3-dark .bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){
+    margin-right:0; }
+  .bp3-dark .bp3-control-group:not(.bp3-vertical) > .bp3-button + .bp3-button{
+    margin-left:1px; }
+  .bp3-control-group .bp3-popover-wrapper,
+  .bp3-control-group .bp3-popover-target{
+    border-radius:inherit; }
+  .bp3-control-group > :first-child{
+    border-radius:3px 0 0 3px; }
+  .bp3-control-group > :last-child{
+    border-radius:0 3px 3px 0;
+    margin-right:0; }
+  .bp3-control-group > :only-child{
+    border-radius:3px;
+    margin-right:0; }
+  .bp3-control-group .bp3-input-group .bp3-button{
+    border-radius:3px; }
+  .bp3-control-group .bp3-numeric-input:not(:first-child) .bp3-input-group{
+    border-bottom-left-radius:0;
+    border-top-left-radius:0; }
+  .bp3-control-group.bp3-fill{
+    width:100%; }
+  .bp3-control-group > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-control-group.bp3-fill > *:not(.bp3-fixed){
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-control-group.bp3-vertical{
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column; }
+    .bp3-control-group.bp3-vertical > *{
+      margin-top:-1px; }
+    .bp3-control-group.bp3-vertical > :first-child{
+      border-radius:3px 3px 0 0;
+      margin-top:0; }
+    .bp3-control-group.bp3-vertical > :last-child{
+      border-radius:0 0 3px 3px; }
+.bp3-control{
+  cursor:pointer;
+  display:block;
+  margin-bottom:10px;
+  position:relative;
+  text-transform:none; }
+  .bp3-control input:checked ~ .bp3-control-indicator{
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+  .bp3-control:hover input:checked ~ .bp3-control-indicator{
+    background-color:#106ba3;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+  .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
+    background:#0e5a8a;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-control input:disabled:checked ~ .bp3-control-indicator{
+    background:rgba(19, 124, 189, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-dark .bp3-control input:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control:hover input:checked ~ .bp3-control-indicator{
+    background-color:#106ba3;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
+    background-color:#0e5a8a;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-control input:disabled:checked ~ .bp3-control-indicator{
+    background:rgba(14, 90, 138, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-control:not(.bp3-align-right){
+    padding-left:26px; }
+    .bp3-control:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-26px; }
+  .bp3-control.bp3-align-right{
+    padding-right:26px; }
+    .bp3-control.bp3-align-right .bp3-control-indicator{
+      margin-right:-26px; }
+  .bp3-control.bp3-disabled{
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed; }
+  .bp3-control.bp3-inline{
+    display:inline-block;
+    margin-right:20px; }
+  .bp3-control input{
+    left:0;
+    opacity:0;
+    position:absolute;
+    top:0;
+    z-index:-1; }
+  .bp3-control .bp3-control-indicator{
+    background-clip:padding-box;
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    border:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    cursor:pointer;
+    display:inline-block;
+    font-size:16px;
+    height:1em;
+    margin-right:10px;
+    margin-top:-3px;
+    position:relative;
+    -webkit-user-select:none;
+       -moz-user-select:none;
+        -ms-user-select:none;
+            user-select:none;
+    vertical-align:middle;
+    width:1em; }
+    .bp3-control .bp3-control-indicator::before{
+      content:"";
+      display:block;
+      height:1em;
+      width:1em; }
+  .bp3-control:hover .bp3-control-indicator{
+    background-color:#ebf1f5; }
+  .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
+    background:#d8e1e8;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-control input:disabled ~ .bp3-control-indicator{
+    background:rgba(206, 217, 224, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    cursor:not-allowed; }
+  .bp3-control input:focus ~ .bp3-control-indicator{
+    outline:rgba(19, 124, 189, 0.6) auto 2px;
+    outline-offset:2px;
+    -moz-outline-radius:6px; }
+  .bp3-control.bp3-align-right .bp3-control-indicator{
+    float:right;
+    margin-left:10px;
+    margin-top:1px; }
+  .bp3-control.bp3-large{
+    font-size:16px; }
+    .bp3-control.bp3-large:not(.bp3-align-right){
+      padding-left:30px; }
+      .bp3-control.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
+        margin-left:-30px; }
+    .bp3-control.bp3-large.bp3-align-right{
+      padding-right:30px; }
+      .bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
+        margin-right:-30px; }
+    .bp3-control.bp3-large .bp3-control-indicator{
+      font-size:20px; }
+    .bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
+      margin-top:0; }
+  .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+  .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{
+    background-color:#106ba3;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+  .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
+    background:#0e5a8a;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+    background:rgba(19, 124, 189, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-dark .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{
+    background-color:#106ba3;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
+    background-color:#0e5a8a;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+    background:rgba(14, 90, 138, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-control.bp3-checkbox .bp3-control-indicator{
+    border-radius:3px; }
+  .bp3-control.bp3-checkbox input:checked ~ .bp3-control-indicator::before{
+    background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M12 5c-.28 0-.53.11-.71.29L7 9.59l-2.29-2.3a1.003 1.003 0 00-1.42 1.42l3 3c.18.18.43.29.71.29s.53-.11.71-.29l5-5A1.003 1.003 0 0012 5z' fill='white'/%3e%3c/svg%3e"); }
+  .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator::before{
+    background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 7H5c-.55 0-1 .45-1 1s.45 1 1 1h6c.55 0 1-.45 1-1s-.45-1-1-1z' fill='white'/%3e%3c/svg%3e"); }
+  .bp3-control.bp3-radio .bp3-control-indicator{
+    border-radius:50%; }
+  .bp3-control.bp3-radio input:checked ~ .bp3-control-indicator::before{
+    background-image:radial-gradient(#ffffff, #ffffff 28%, transparent 32%); }
+  .bp3-control.bp3-radio input:checked:disabled ~ .bp3-control-indicator::before{
+    opacity:0.5; }
+  .bp3-control.bp3-radio input:focus ~ .bp3-control-indicator{
+    -moz-outline-radius:16px; }
+  .bp3-control.bp3-switch input ~ .bp3-control-indicator{
+    background:rgba(167, 182, 194, 0.5); }
+  .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
+    background:rgba(115, 134, 148, 0.5); }
+  .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
+    background:rgba(92, 112, 128, 0.5); }
+  .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
+    background:rgba(206, 217, 224, 0.5); }
+    .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
+      background:rgba(255, 255, 255, 0.8); }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
+    background:#137cbd; }
+  .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
+    background:#106ba3; }
+  .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
+    background:#0e5a8a; }
+  .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
+    background:rgba(19, 124, 189, 0.5); }
+    .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
+      background:rgba(255, 255, 255, 0.8); }
+  .bp3-control.bp3-switch:not(.bp3-align-right){
+    padding-left:38px; }
+    .bp3-control.bp3-switch:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-38px; }
+  .bp3-control.bp3-switch.bp3-align-right{
+    padding-right:38px; }
+    .bp3-control.bp3-switch.bp3-align-right .bp3-control-indicator{
+      margin-right:-38px; }
+  .bp3-control.bp3-switch .bp3-control-indicator{
+    border:none;
+    border-radius:1.75em;
+    -webkit-box-shadow:none !important;
+            box-shadow:none !important;
+    min-width:1.75em;
+    -webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    width:auto; }
+    .bp3-control.bp3-switch .bp3-control-indicator::before{
+      background:#ffffff;
+      border-radius:50%;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+      height:calc(1em - 4px);
+      left:0;
+      margin:2px;
+      position:absolute;
+      -webkit-transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+      transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+      width:calc(1em - 4px); }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
+    left:calc(100% - 1em); }
+  .bp3-control.bp3-switch.bp3-large:not(.bp3-align-right){
+    padding-left:45px; }
+    .bp3-control.bp3-switch.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-45px; }
+  .bp3-control.bp3-switch.bp3-large.bp3-align-right{
+    padding-right:45px; }
+    .bp3-control.bp3-switch.bp3-large.bp3-align-right .bp3-control-indicator{
+      margin-right:-45px; }
+  .bp3-dark .bp3-control.bp3-switch input ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.5); }
+  .bp3-dark .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.7); }
+  .bp3-dark .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.9); }
+  .bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
+    background:rgba(57, 75, 89, 0.5); }
+    .bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
+      background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
+    background:#137cbd; }
+  .bp3-dark .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
+    background:#106ba3; }
+  .bp3-dark .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
+    background:#0e5a8a; }
+  .bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
+    background:rgba(14, 90, 138, 0.5); }
+    .bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
+      background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-switch .bp3-control-indicator::before{
+    background:#394b59;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-control.bp3-switch .bp3-switch-inner-text{
+    font-size:0.7em;
+    text-align:center; }
+  .bp3-control.bp3-switch .bp3-control-indicator-child:first-child{
+    line-height:0;
+    margin-left:0.5em;
+    margin-right:1.2em;
+    visibility:hidden; }
+  .bp3-control.bp3-switch .bp3-control-indicator-child:last-child{
+    line-height:1em;
+    margin-left:1.2em;
+    margin-right:0.5em;
+    visibility:visible; }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:first-child{
+    line-height:1em;
+    visibility:visible; }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:last-child{
+    line-height:0;
+    visibility:hidden; }
+  .bp3-dark .bp3-control{
+    color:#f5f8fa; }
+    .bp3-dark .bp3-control.bp3-disabled{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-control .bp3-control-indicator{
+      background-color:#394b59;
+      background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+      background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-control:hover .bp3-control-indicator{
+      background-color:#30404d; }
+    .bp3-dark .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
+      background:#202b33;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-control input:disabled ~ .bp3-control-indicator{
+      background:rgba(57, 75, 89, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      cursor:not-allowed; }
+    .bp3-dark .bp3-control.bp3-checkbox input:disabled:checked ~ .bp3-control-indicator, .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+      color:rgba(167, 182, 194, 0.6); }
+.bp3-file-input{
+  cursor:pointer;
+  display:inline-block;
+  height:30px;
+  position:relative; }
+  .bp3-file-input input{
+    margin:0;
+    min-width:200px;
+    opacity:0; }
+    .bp3-file-input input:disabled + .bp3-file-upload-input,
+    .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
+      background:rgba(206, 217, 224, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed;
+      resize:none; }
+      .bp3-file-input input:disabled + .bp3-file-upload-input::after,
+      .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
+        background-color:rgba(206, 217, 224, 0.5);
+        background-image:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:rgba(92, 112, 128, 0.6);
+        cursor:not-allowed;
+        outline:none; }
+        .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active:hover,
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active,
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active:hover{
+          background:rgba(206, 217, 224, 0.7); }
+      .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input, .bp3-dark
+      .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
+        background:rgba(57, 75, 89, 0.5);
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:rgba(167, 182, 194, 0.6); }
+        .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after, .bp3-dark
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
+          background-color:rgba(57, 75, 89, 0.5);
+          background-image:none;
+          -webkit-box-shadow:none;
+                  box-shadow:none;
+          color:rgba(167, 182, 194, 0.6); }
+          .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-dark
+          .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active{
+            background:rgba(57, 75, 89, 0.7); }
+  .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
+    color:#182026; }
+  .bp3-dark .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
+    color:#f5f8fa; }
+  .bp3-file-input.bp3-fill{
+    width:100%; }
+  .bp3-file-input.bp3-large,
+  .bp3-large .bp3-file-input{
+    height:40px; }
+  .bp3-file-input .bp3-file-upload-input-custom-text::after{
+    content:attr(bp3-button-text); }
+
+.bp3-file-upload-input{
+  -webkit-appearance:none;
+     -moz-appearance:none;
+          appearance:none;
+  background:#ffffff;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+  color:#182026;
+  font-size:14px;
+  font-weight:400;
+  height:30px;
+  line-height:30px;
+  outline:none;
+  padding:0 10px;
+  -webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  vertical-align:middle;
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  color:rgba(92, 112, 128, 0.6);
+  left:0;
+  padding-right:80px;
+  position:absolute;
+  right:0;
+  top:0;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-file-upload-input::-webkit-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input::-moz-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input:-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input::-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input::placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input:focus, .bp3-file-upload-input.bp3-active{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-file-upload-input[type="search"], .bp3-file-upload-input.bp3-round{
+    border-radius:30px;
+    -webkit-box-sizing:border-box;
+            box-sizing:border-box;
+    padding-left:10px; }
+  .bp3-file-upload-input[readonly]{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-file-upload-input:disabled, .bp3-file-upload-input.bp3-disabled{
+    background:rgba(206, 217, 224, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed;
+    resize:none; }
+  .bp3-file-upload-input::after{
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    color:#182026;
+    min-height:24px;
+    min-width:24px;
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    border-radius:3px;
+    content:"Browse";
+    line-height:24px;
+    margin:3px;
+    position:absolute;
+    right:0;
+    text-align:center;
+    top:0;
+    width:70px; }
+    .bp3-file-upload-input::after:hover{
+      background-clip:padding-box;
+      background-color:#ebf1f5;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+    .bp3-file-upload-input::after:active, .bp3-file-upload-input::after.bp3-active{
+      background-color:#d8e1e8;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-file-upload-input::after:disabled, .bp3-file-upload-input::after.bp3-disabled{
+      background-color:rgba(206, 217, 224, 0.5);
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed;
+      outline:none; }
+      .bp3-file-upload-input::after:disabled.bp3-active, .bp3-file-upload-input::after:disabled.bp3-active:hover, .bp3-file-upload-input::after.bp3-disabled.bp3-active, .bp3-file-upload-input::after.bp3-disabled.bp3-active:hover{
+        background:rgba(206, 217, 224, 0.7); }
+  .bp3-file-upload-input:hover::after{
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+  .bp3-file-upload-input:active::after{
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-large .bp3-file-upload-input{
+    font-size:16px;
+    height:40px;
+    line-height:40px;
+    padding-right:95px; }
+    .bp3-large .bp3-file-upload-input[type="search"], .bp3-large .bp3-file-upload-input.bp3-round{
+      padding:0 15px; }
+    .bp3-large .bp3-file-upload-input::after{
+      min-height:30px;
+      min-width:30px;
+      line-height:30px;
+      margin:5px;
+      width:85px; }
+  .bp3-dark .bp3-file-upload-input{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa;
+    color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-file-upload-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-file-upload-input:disabled, .bp3-dark .bp3-file-upload-input.bp3-disabled{
+      background:rgba(57, 75, 89, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::after{
+      background-color:#394b59;
+      background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+      background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+      color:#f5f8fa; }
+      .bp3-dark .bp3-file-upload-input::after:hover, .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
+        color:#f5f8fa; }
+      .bp3-dark .bp3-file-upload-input::after:hover{
+        background-color:#30404d;
+        -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
+        background-color:#202b33;
+        background-image:none;
+        -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+                box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+      .bp3-dark .bp3-file-upload-input::after:disabled, .bp3-dark .bp3-file-upload-input::after.bp3-disabled{
+        background-color:rgba(57, 75, 89, 0.5);
+        background-image:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:rgba(167, 182, 194, 0.6); }
+        .bp3-dark .bp3-file-upload-input::after:disabled.bp3-active, .bp3-dark .bp3-file-upload-input::after.bp3-disabled.bp3-active{
+          background:rgba(57, 75, 89, 0.7); }
+      .bp3-dark .bp3-file-upload-input::after .bp3-button-spinner .bp3-spinner-head{
+        background:rgba(16, 22, 26, 0.5);
+        stroke:#8a9ba8; }
+    .bp3-dark .bp3-file-upload-input:hover::after{
+      background-color:#30404d;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-file-upload-input:active::after{
+      background-color:#202b33;
+      background-image:none;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+.bp3-file-upload-input::after{
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+.bp3-form-group{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:0 0 15px; }
+  .bp3-form-group label.bp3-label{
+    margin-bottom:5px; }
+  .bp3-form-group .bp3-control{
+    margin-top:7px; }
+  .bp3-form-group .bp3-form-helper-text{
+    color:#5c7080;
+    font-size:12px;
+    margin-top:5px; }
+  .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
+    color:#106ba3; }
+  .bp3-form-group.bp3-intent-success .bp3-form-helper-text{
+    color:#0d8050; }
+  .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
+    color:#bf7326; }
+  .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
+    color:#c23030; }
+  .bp3-form-group.bp3-inline{
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start;
+    -webkit-box-orient:horizontal;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:row;
+            flex-direction:row; }
+    .bp3-form-group.bp3-inline.bp3-large label.bp3-label{
+      line-height:40px;
+      margin:0 10px 0 0; }
+    .bp3-form-group.bp3-inline label.bp3-label{
+      line-height:30px;
+      margin:0 10px 0 0; }
+  .bp3-form-group.bp3-disabled .bp3-label,
+  .bp3-form-group.bp3-disabled .bp3-text-muted,
+  .bp3-form-group.bp3-disabled .bp3-form-helper-text{
+    color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-dark .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
+    color:#48aff0; }
+  .bp3-dark .bp3-form-group.bp3-intent-success .bp3-form-helper-text{
+    color:#3dcc91; }
+  .bp3-dark .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
+    color:#ffb366; }
+  .bp3-dark .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
+    color:#ff7373; }
+  .bp3-dark .bp3-form-group .bp3-form-helper-text{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-label,
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-text-muted,
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-form-helper-text{
+    color:rgba(167, 182, 194, 0.6) !important; }
+.bp3-input-group{
+  display:block;
+  position:relative; }
+  .bp3-input-group .bp3-input{
+    position:relative;
+    width:100%; }
+    .bp3-input-group .bp3-input:not(:first-child){
+      padding-left:30px; }
+    .bp3-input-group .bp3-input:not(:last-child){
+      padding-right:30px; }
+  .bp3-input-group .bp3-input-action,
+  .bp3-input-group > .bp3-input-left-container,
+  .bp3-input-group > .bp3-button,
+  .bp3-input-group > .bp3-icon{
+    position:absolute;
+    top:0; }
+    .bp3-input-group .bp3-input-action:first-child,
+    .bp3-input-group > .bp3-input-left-container:first-child,
+    .bp3-input-group > .bp3-button:first-child,
+    .bp3-input-group > .bp3-icon:first-child{
+      left:0; }
+    .bp3-input-group .bp3-input-action:last-child,
+    .bp3-input-group > .bp3-input-left-container:last-child,
+    .bp3-input-group > .bp3-button:last-child,
+    .bp3-input-group > .bp3-icon:last-child{
+      right:0; }
+  .bp3-input-group .bp3-button{
+    min-height:24px;
+    min-width:24px;
+    margin:3px;
+    padding:0 7px; }
+    .bp3-input-group .bp3-button:empty{
+      padding:0; }
+  .bp3-input-group > .bp3-input-left-container,
+  .bp3-input-group > .bp3-icon{
+    z-index:1; }
+  .bp3-input-group > .bp3-input-left-container > .bp3-icon,
+  .bp3-input-group > .bp3-icon{
+    color:#5c7080; }
+    .bp3-input-group > .bp3-input-left-container > .bp3-icon:empty,
+    .bp3-input-group > .bp3-icon:empty{
+      font-family:"Icons16", sans-serif;
+      font-size:16px;
+      font-style:normal;
+      font-weight:400;
+      line-height:1;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased; }
+  .bp3-input-group > .bp3-input-left-container > .bp3-icon,
+  .bp3-input-group > .bp3-icon,
+  .bp3-input-group .bp3-input-action > .bp3-spinner{
+    margin:7px; }
+  .bp3-input-group .bp3-tag{
+    margin:5px; }
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus),
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
+    color:#5c7080; }
+    .bp3-dark .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus), .bp3-dark
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
+      color:#a7b6c2; }
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large{
+      color:#5c7080; }
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled,
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled{
+    color:rgba(92, 112, 128, 0.6) !important; }
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-large,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-standard,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-large{
+      color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-input-group.bp3-disabled{
+    cursor:not-allowed; }
+    .bp3-input-group.bp3-disabled .bp3-icon{
+      color:rgba(92, 112, 128, 0.6); }
+  .bp3-input-group.bp3-large .bp3-button{
+    min-height:30px;
+    min-width:30px;
+    margin:5px; }
+  .bp3-input-group.bp3-large > .bp3-input-left-container > .bp3-icon,
+  .bp3-input-group.bp3-large > .bp3-icon,
+  .bp3-input-group.bp3-large .bp3-input-action > .bp3-spinner{
+    margin:12px; }
+  .bp3-input-group.bp3-large .bp3-input{
+    font-size:16px;
+    height:40px;
+    line-height:40px; }
+    .bp3-input-group.bp3-large .bp3-input[type="search"], .bp3-input-group.bp3-large .bp3-input.bp3-round{
+      padding:0 15px; }
+    .bp3-input-group.bp3-large .bp3-input:not(:first-child){
+      padding-left:40px; }
+    .bp3-input-group.bp3-large .bp3-input:not(:last-child){
+      padding-right:40px; }
+  .bp3-input-group.bp3-small .bp3-button{
+    min-height:20px;
+    min-width:20px;
+    margin:2px; }
+  .bp3-input-group.bp3-small .bp3-tag{
+    min-height:20px;
+    min-width:20px;
+    margin:2px; }
+  .bp3-input-group.bp3-small > .bp3-input-left-container > .bp3-icon,
+  .bp3-input-group.bp3-small > .bp3-icon,
+  .bp3-input-group.bp3-small .bp3-input-action > .bp3-spinner{
+    margin:4px; }
+  .bp3-input-group.bp3-small .bp3-input{
+    font-size:12px;
+    height:24px;
+    line-height:24px;
+    padding-left:8px;
+    padding-right:8px; }
+    .bp3-input-group.bp3-small .bp3-input[type="search"], .bp3-input-group.bp3-small .bp3-input.bp3-round{
+      padding:0 12px; }
+    .bp3-input-group.bp3-small .bp3-input:not(:first-child){
+      padding-left:24px; }
+    .bp3-input-group.bp3-small .bp3-input:not(:last-child){
+      padding-right:24px; }
+  .bp3-input-group.bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    width:100%; }
+  .bp3-input-group.bp3-round .bp3-button,
+  .bp3-input-group.bp3-round .bp3-input,
+  .bp3-input-group.bp3-round .bp3-tag{
+    border-radius:30px; }
+  .bp3-dark .bp3-input-group .bp3-icon{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-input-group.bp3-disabled .bp3-icon{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-input-group.bp3-intent-primary .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-primary .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-primary .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+              box-shadow:inset 0 0 0 1px #137cbd; }
+    .bp3-input-group.bp3-intent-primary .bp3-input:disabled, .bp3-input-group.bp3-intent-primary .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-primary > .bp3-icon{
+    color:#106ba3; }
+    .bp3-dark .bp3-input-group.bp3-intent-primary > .bp3-icon{
+      color:#48aff0; }
+  .bp3-input-group.bp3-intent-success .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-success .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-success .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+              box-shadow:inset 0 0 0 1px #0f9960; }
+    .bp3-input-group.bp3-intent-success .bp3-input:disabled, .bp3-input-group.bp3-intent-success .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-success > .bp3-icon{
+    color:#0d8050; }
+    .bp3-dark .bp3-input-group.bp3-intent-success > .bp3-icon{
+      color:#3dcc91; }
+  .bp3-input-group.bp3-intent-warning .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-warning .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-warning .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+              box-shadow:inset 0 0 0 1px #d9822b; }
+    .bp3-input-group.bp3-intent-warning .bp3-input:disabled, .bp3-input-group.bp3-intent-warning .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-warning > .bp3-icon{
+    color:#bf7326; }
+    .bp3-dark .bp3-input-group.bp3-intent-warning > .bp3-icon{
+      color:#ffb366; }
+  .bp3-input-group.bp3-intent-danger .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-danger .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-danger .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #db3737;
+              box-shadow:inset 0 0 0 1px #db3737; }
+    .bp3-input-group.bp3-intent-danger .bp3-input:disabled, .bp3-input-group.bp3-intent-danger .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-danger > .bp3-icon{
+    color:#c23030; }
+    .bp3-dark .bp3-input-group.bp3-intent-danger > .bp3-icon{
+      color:#ff7373; }
+.bp3-input{
+  -webkit-appearance:none;
+     -moz-appearance:none;
+          appearance:none;
+  background:#ffffff;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+  color:#182026;
+  font-size:14px;
+  font-weight:400;
+  height:30px;
+  line-height:30px;
+  outline:none;
+  padding:0 10px;
+  -webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  vertical-align:middle; }
+  .bp3-input::-webkit-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input::-moz-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input:-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input::-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input::placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input:focus, .bp3-input.bp3-active{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-input[type="search"], .bp3-input.bp3-round{
+    border-radius:30px;
+    -webkit-box-sizing:border-box;
+            box-sizing:border-box;
+    padding-left:10px; }
+  .bp3-input[readonly]{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-input:disabled, .bp3-input.bp3-disabled{
+    background:rgba(206, 217, 224, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed;
+    resize:none; }
+  .bp3-input.bp3-large{
+    font-size:16px;
+    height:40px;
+    line-height:40px; }
+    .bp3-input.bp3-large[type="search"], .bp3-input.bp3-large.bp3-round{
+      padding:0 15px; }
+  .bp3-input.bp3-small{
+    font-size:12px;
+    height:24px;
+    line-height:24px;
+    padding-left:8px;
+    padding-right:8px; }
+    .bp3-input.bp3-small[type="search"], .bp3-input.bp3-small.bp3-round{
+      padding:0 12px; }
+  .bp3-input.bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    width:100%; }
+  .bp3-dark .bp3-input{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+    .bp3-dark .bp3-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-input:disabled, .bp3-dark .bp3-input.bp3-disabled{
+      background:rgba(57, 75, 89, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-input.bp3-intent-primary{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-primary:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-primary[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+              box-shadow:inset 0 0 0 1px #137cbd; }
+    .bp3-input.bp3-intent-primary:disabled, .bp3-input.bp3-intent-primary.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-primary:focus{
+        -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-primary[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+                box-shadow:inset 0 0 0 1px #137cbd; }
+      .bp3-dark .bp3-input.bp3-intent-primary:disabled, .bp3-dark .bp3-input.bp3-intent-primary.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-success{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-success:focus{
+      -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-success[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+              box-shadow:inset 0 0 0 1px #0f9960; }
+    .bp3-input.bp3-intent-success:disabled, .bp3-input.bp3-intent-success.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-success:focus{
+        -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-success[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+                box-shadow:inset 0 0 0 1px #0f9960; }
+      .bp3-dark .bp3-input.bp3-intent-success:disabled, .bp3-dark .bp3-input.bp3-intent-success.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-warning{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-warning:focus{
+      -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-warning[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+              box-shadow:inset 0 0 0 1px #d9822b; }
+    .bp3-input.bp3-intent-warning:disabled, .bp3-input.bp3-intent-warning.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-warning:focus{
+        -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-warning[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+                box-shadow:inset 0 0 0 1px #d9822b; }
+      .bp3-dark .bp3-input.bp3-intent-warning:disabled, .bp3-dark .bp3-input.bp3-intent-warning.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-danger{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-danger:focus{
+      -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-danger[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #db3737;
+              box-shadow:inset 0 0 0 1px #db3737; }
+    .bp3-input.bp3-intent-danger:disabled, .bp3-input.bp3-intent-danger.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-danger:focus{
+        -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-danger[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #db3737;
+                box-shadow:inset 0 0 0 1px #db3737; }
+      .bp3-dark .bp3-input.bp3-intent-danger:disabled, .bp3-dark .bp3-input.bp3-intent-danger.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input::-ms-clear{
+    display:none; }
+textarea.bp3-input{
+  max-width:100%;
+  padding:10px; }
+  textarea.bp3-input, textarea.bp3-input.bp3-large, textarea.bp3-input.bp3-small{
+    height:auto;
+    line-height:inherit; }
+  textarea.bp3-input.bp3-small{
+    padding:8px; }
+  .bp3-dark textarea.bp3-input{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+    .bp3-dark textarea.bp3-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark textarea.bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark textarea.bp3-input:disabled, .bp3-dark textarea.bp3-input.bp3-disabled{
+      background:rgba(57, 75, 89, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+label.bp3-label{
+  display:block;
+  margin-bottom:15px;
+  margin-top:0; }
+  label.bp3-label .bp3-html-select,
+  label.bp3-label .bp3-input,
+  label.bp3-label .bp3-select,
+  label.bp3-label .bp3-slider,
+  label.bp3-label .bp3-popover-wrapper{
+    display:block;
+    margin-top:5px;
+    text-transform:none; }
+  label.bp3-label .bp3-button-group{
+    margin-top:5px; }
+  label.bp3-label .bp3-select select,
+  label.bp3-label .bp3-html-select select{
+    font-weight:400;
+    vertical-align:top;
+    width:100%; }
+  label.bp3-label.bp3-disabled,
+  label.bp3-label.bp3-disabled .bp3-text-muted{
+    color:rgba(92, 112, 128, 0.6); }
+  label.bp3-label.bp3-inline{
+    line-height:30px; }
+    label.bp3-label.bp3-inline .bp3-html-select,
+    label.bp3-label.bp3-inline .bp3-input,
+    label.bp3-label.bp3-inline .bp3-input-group,
+    label.bp3-label.bp3-inline .bp3-select,
+    label.bp3-label.bp3-inline .bp3-popover-wrapper{
+      display:inline-block;
+      margin:0 0 0 5px;
+      vertical-align:top; }
+    label.bp3-label.bp3-inline .bp3-button-group{
+      margin:0 0 0 5px; }
+    label.bp3-label.bp3-inline .bp3-input-group .bp3-input{
+      margin-left:0; }
+    label.bp3-label.bp3-inline.bp3-large{
+      line-height:40px; }
+  label.bp3-label:not(.bp3-inline) .bp3-popover-target{
+    display:block; }
+  .bp3-dark label.bp3-label{
+    color:#f5f8fa; }
+    .bp3-dark label.bp3-label.bp3-disabled,
+    .bp3-dark label.bp3-label.bp3-disabled .bp3-text-muted{
+      color:rgba(167, 182, 194, 0.6); }
+.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 14px;
+          flex:1 1 14px;
+  min-height:0;
+  padding:0;
+  width:30px; }
+  .bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:first-child{
+    border-radius:0 3px 0 0; }
+  .bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:last-child{
+    border-radius:0 0 3px 0; }
+
+.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:first-child{
+  border-radius:3px 0 0 0; }
+
+.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:last-child{
+  border-radius:0 0 0 3px; }
+
+.bp3-numeric-input.bp3-large .bp3-button-group.bp3-vertical > .bp3-button{
+  width:40px; }
+
+form{
+  display:block; }
+.bp3-html-select select,
+.bp3-select select{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  border:none;
+  border-radius:3px;
+  cursor:pointer;
+  font-size:14px;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  padding:5px 10px;
+  text-align:left;
+  vertical-align:middle;
+  background-color:#f5f8fa;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+  color:#182026;
+  -moz-appearance:none;
+  -webkit-appearance:none;
+  border-radius:3px;
+  height:30px;
+  padding:0 25px 0 10px;
+  width:100%; }
+  .bp3-html-select select > *, .bp3-select select > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-html-select select > .bp3-fill, .bp3-select select > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-html-select select::before,
+  .bp3-select select::before, .bp3-html-select select > *, .bp3-select select > *{
+    margin-right:7px; }
+  .bp3-html-select select:empty::before,
+  .bp3-select select:empty::before,
+  .bp3-html-select select > :last-child,
+  .bp3-select select > :last-child{
+    margin-right:0; }
+  .bp3-html-select select:hover,
+  .bp3-select select:hover{
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+  .bp3-html-select select:active,
+  .bp3-select select:active, .bp3-html-select select.bp3-active,
+  .bp3-select select.bp3-active{
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-html-select select:disabled,
+  .bp3-select select:disabled, .bp3-html-select select.bp3-disabled,
+  .bp3-select select.bp3-disabled{
+    background-color:rgba(206, 217, 224, 0.5);
+    background-image:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed;
+    outline:none; }
+    .bp3-html-select select:disabled.bp3-active,
+    .bp3-select select:disabled.bp3-active, .bp3-html-select select:disabled.bp3-active:hover,
+    .bp3-select select:disabled.bp3-active:hover, .bp3-html-select select.bp3-disabled.bp3-active,
+    .bp3-select select.bp3-disabled.bp3-active, .bp3-html-select select.bp3-disabled.bp3-active:hover,
+    .bp3-select select.bp3-disabled.bp3-active:hover{
+      background:rgba(206, 217, 224, 0.7); }
+
+.bp3-html-select.bp3-minimal select,
+.bp3-select.bp3-minimal select{
+  background:none;
+  -webkit-box-shadow:none;
+          box-shadow:none; }
+  .bp3-html-select.bp3-minimal select:hover,
+  .bp3-select.bp3-minimal select:hover{
+    background:rgba(167, 182, 194, 0.3);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:#182026;
+    text-decoration:none; }
+  .bp3-html-select.bp3-minimal select:active,
+  .bp3-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal select.bp3-active,
+  .bp3-select.bp3-minimal select.bp3-active{
+    background:rgba(115, 134, 148, 0.3);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:#182026; }
+  .bp3-html-select.bp3-minimal select:disabled,
+  .bp3-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal select:disabled:hover,
+  .bp3-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal select.bp3-disabled,
+  .bp3-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal select.bp3-disabled:hover,
+  .bp3-select.bp3-minimal select.bp3-disabled:hover{
+    background:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed; }
+    .bp3-html-select.bp3-minimal select:disabled.bp3-active,
+    .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active,
+    .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active{
+      background:rgba(115, 134, 148, 0.3); }
+  .bp3-dark .bp3-html-select.bp3-minimal select, .bp3-html-select.bp3-minimal .bp3-dark select,
+  .bp3-dark .bp3-select.bp3-minimal select, .bp3-select.bp3-minimal .bp3-dark select{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:inherit; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover, .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active,
+    .bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover{
+      background:rgba(138, 155, 168, 0.15); }
+    .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active,
+    .bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{
+      background:rgba(138, 155, 168, 0.3);
+      color:#f5f8fa; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal .bp3-dark select:disabled,
+    .bp3-dark .bp3-select.bp3-minimal select:disabled, .bp3-select.bp3-minimal .bp3-dark select:disabled, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:disabled:hover, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover{
+      background:none;
+      color:rgba(167, 182, 194, 0.6);
+      cursor:not-allowed; }
+      .bp3-dark .bp3-html-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active{
+        background:rgba(138, 155, 168, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-primary,
+  .bp3-select.bp3-minimal select.bp3-intent-primary{
+    color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal select.bp3-intent-primary:active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:hover{
+      background:rgba(19, 124, 189, 0.15);
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{
+      background:rgba(19, 124, 189, 0.3);
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled{
+      background:none;
+      color:rgba(16, 107, 163, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active{
+        background:rgba(19, 124, 189, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+      stroke:#106ba3; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary{
+      color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.2);
+        color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(72, 175, 240, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-success,
+  .bp3-select.bp3-minimal select.bp3-intent-success{
+    color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal select.bp3-intent-success:active,
+    .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-success:hover{
+      background:rgba(15, 153, 96, 0.15);
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:active,
+    .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{
+      background:rgba(15, 153, 96, 0.3);
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled{
+      background:none;
+      color:rgba(13, 128, 80, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active{
+        background:rgba(15, 153, 96, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+      stroke:#0d8050; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success{
+      color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.2);
+        color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(61, 204, 145, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-warning,
+  .bp3-select.bp3-minimal select.bp3-intent-warning{
+    color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal select.bp3-intent-warning:active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:hover{
+      background:rgba(217, 130, 43, 0.15);
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{
+      background:rgba(217, 130, 43, 0.3);
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled{
+      background:none;
+      color:rgba(191, 115, 38, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active{
+        background:rgba(217, 130, 43, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+      stroke:#bf7326; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning{
+      color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.2);
+        color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(255, 179, 102, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-danger,
+  .bp3-select.bp3-minimal select.bp3-intent-danger{
+    color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal select.bp3-intent-danger:active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:hover{
+      background:rgba(219, 55, 55, 0.15);
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{
+      background:rgba(219, 55, 55, 0.3);
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled{
+      background:none;
+      color:rgba(194, 48, 48, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active{
+        background:rgba(219, 55, 55, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+      stroke:#c23030; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger{
+      color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.2);
+        color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(255, 115, 115, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+
+.bp3-html-select.bp3-large select,
+.bp3-select.bp3-large select{
+  font-size:16px;
+  height:40px;
+  padding-right:35px; }
+
+.bp3-dark .bp3-html-select select, .bp3-dark .bp3-select select{
+  background-color:#394b59;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+  color:#f5f8fa; }
+  .bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover, .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover{
+    background-color:#30404d;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{
+    background-color:#202b33;
+    background-image:none;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-html-select select:disabled, .bp3-dark .bp3-select select:disabled, .bp3-dark .bp3-html-select select.bp3-disabled, .bp3-dark .bp3-select select.bp3-disabled{
+    background-color:rgba(57, 75, 89, 0.5);
+    background-image:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-html-select select:disabled.bp3-active, .bp3-dark .bp3-select select:disabled.bp3-active, .bp3-dark .bp3-html-select select.bp3-disabled.bp3-active, .bp3-dark .bp3-select select.bp3-disabled.bp3-active{
+      background:rgba(57, 75, 89, 0.7); }
+  .bp3-dark .bp3-html-select select .bp3-button-spinner .bp3-spinner-head, .bp3-dark .bp3-select select .bp3-button-spinner .bp3-spinner-head{
+    background:rgba(16, 22, 26, 0.5);
+    stroke:#8a9ba8; }
+
+.bp3-html-select select:disabled,
+.bp3-select select:disabled{
+  background-color:rgba(206, 217, 224, 0.5);
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  color:rgba(92, 112, 128, 0.6);
+  cursor:not-allowed; }
+
+.bp3-html-select .bp3-icon,
+.bp3-select .bp3-icon, .bp3-select::after{
+  color:#5c7080;
+  pointer-events:none;
+  position:absolute;
+  right:7px;
+  top:7px; }
+  .bp3-html-select .bp3-disabled.bp3-icon,
+  .bp3-select .bp3-disabled.bp3-icon, .bp3-disabled.bp3-select::after{
+    color:rgba(92, 112, 128, 0.6); }
+.bp3-html-select,
+.bp3-select{
+  display:inline-block;
+  letter-spacing:normal;
+  position:relative;
+  vertical-align:middle; }
+  .bp3-html-select select::-ms-expand,
+  .bp3-select select::-ms-expand{
+    display:none; }
+  .bp3-html-select .bp3-icon,
+  .bp3-select .bp3-icon{
+    color:#5c7080; }
+    .bp3-html-select .bp3-icon:hover,
+    .bp3-select .bp3-icon:hover{
+      color:#182026; }
+    .bp3-dark .bp3-html-select .bp3-icon, .bp3-dark
+    .bp3-select .bp3-icon{
+      color:#a7b6c2; }
+      .bp3-dark .bp3-html-select .bp3-icon:hover, .bp3-dark
+      .bp3-select .bp3-icon:hover{
+        color:#f5f8fa; }
+  .bp3-html-select.bp3-large::after,
+  .bp3-html-select.bp3-large .bp3-icon,
+  .bp3-select.bp3-large::after,
+  .bp3-select.bp3-large .bp3-icon{
+    right:12px;
+    top:12px; }
+  .bp3-html-select.bp3-fill,
+  .bp3-html-select.bp3-fill select,
+  .bp3-select.bp3-fill,
+  .bp3-select.bp3-fill select{
+    width:100%; }
+  .bp3-dark .bp3-html-select option, .bp3-dark
+  .bp3-select option{
+    background-color:#30404d;
+    color:#f5f8fa; }
+  .bp3-dark .bp3-html-select option:disabled, .bp3-dark
+  .bp3-select option:disabled{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-dark .bp3-html-select::after, .bp3-dark
+  .bp3-select::after{
+    color:#a7b6c2; }
+
+.bp3-select::after{
+  font-family:"Icons16", sans-serif;
+  font-size:16px;
+  font-style:normal;
+  font-weight:400;
+  line-height:1;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  content:""; }
+.bp3-running-text table, table.bp3-html-table{
+  border-spacing:0;
+  font-size:14px; }
+  .bp3-running-text table th, table.bp3-html-table th,
+  .bp3-running-text table td,
+  table.bp3-html-table td{
+    padding:11px;
+    text-align:left;
+    vertical-align:top; }
+  .bp3-running-text table th, table.bp3-html-table th{
+    color:#182026;
+    font-weight:600; }
+  
+  .bp3-running-text table td,
+  table.bp3-html-table td{
+    color:#182026; }
+  .bp3-running-text table tbody tr:first-child th, table.bp3-html-table tbody tr:first-child th,
+  .bp3-running-text table tbody tr:first-child td,
+  table.bp3-html-table tbody tr:first-child td,
+  .bp3-running-text table tfoot tr:first-child th,
+  table.bp3-html-table tfoot tr:first-child th,
+  .bp3-running-text table tfoot tr:first-child td,
+  table.bp3-html-table tfoot tr:first-child td{
+    -webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
+  .bp3-dark .bp3-running-text table th, .bp3-running-text .bp3-dark table th, .bp3-dark table.bp3-html-table th{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-running-text table td, .bp3-running-text .bp3-dark table td, .bp3-dark table.bp3-html-table td{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-running-text table tbody tr:first-child th, .bp3-running-text .bp3-dark table tbody tr:first-child th, .bp3-dark table.bp3-html-table tbody tr:first-child th,
+  .bp3-dark .bp3-running-text table tbody tr:first-child td,
+  .bp3-running-text .bp3-dark table tbody tr:first-child td,
+  .bp3-dark table.bp3-html-table tbody tr:first-child td,
+  .bp3-dark .bp3-running-text table tfoot tr:first-child th,
+  .bp3-running-text .bp3-dark table tfoot tr:first-child th,
+  .bp3-dark table.bp3-html-table tfoot tr:first-child th,
+  .bp3-dark .bp3-running-text table tfoot tr:first-child td,
+  .bp3-running-text .bp3-dark table tfoot tr:first-child td,
+  .bp3-dark table.bp3-html-table tfoot tr:first-child td{
+    -webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }
+
+table.bp3-html-table.bp3-html-table-condensed th,
+table.bp3-html-table.bp3-html-table-condensed td, table.bp3-html-table.bp3-small th,
+table.bp3-html-table.bp3-small td{
+  padding-bottom:6px;
+  padding-top:6px; }
+
+table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
+  background:rgba(191, 204, 214, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
+  -webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered tbody tr td,
+table.bp3-html-table.bp3-html-table-bordered tfoot tr td{
+  -webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
+  table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child),
+  table.bp3-html-table.bp3-html-table-bordered tfoot tr td:not(:first-child){
+    -webkit-box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
+  -webkit-box-shadow:none;
+          box-shadow:none; }
+  table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:not(:first-child){
+    -webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-interactive tbody tr:hover td{
+  background-color:rgba(191, 204, 214, 0.3);
+  cursor:pointer; }
+
+table.bp3-html-table.bp3-interactive tbody tr:active td{
+  background-color:rgba(191, 204, 214, 0.4); }
+
+.bp3-dark table.bp3-html-table{ }
+  .bp3-dark table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
+    background:rgba(92, 112, 128, 0.15); }
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
+    -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td,
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered tfoot tr td{
+    -webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }
+    .bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child),
+    .bp3-dark table.bp3-html-table.bp3-html-table-bordered tfoot tr td:not(:first-child){
+      -webkit-box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15);
+              box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); }
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
+    -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }
+    .bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:first-child{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-dark table.bp3-html-table.bp3-interactive tbody tr:hover td{
+    background-color:rgba(92, 112, 128, 0.3);
+    cursor:pointer; }
+  .bp3-dark table.bp3-html-table.bp3-interactive tbody tr:active td{
+    background-color:rgba(92, 112, 128, 0.4); }
+
+.bp3-key-combo{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center; }
+  .bp3-key-combo > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-key-combo > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-key-combo::before,
+  .bp3-key-combo > *{
+    margin-right:5px; }
+  .bp3-key-combo:empty::before,
+  .bp3-key-combo > :last-child{
+    margin-right:0; }
+
+.bp3-hotkey-dialog{
+  padding-bottom:0;
+  top:40px; }
+  .bp3-hotkey-dialog .bp3-dialog-body{
+    margin:0;
+    padding:0; }
+  .bp3-hotkey-dialog .bp3-hotkey-label{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1; }
+
+.bp3-hotkey-column{
+  margin:auto;
+  max-height:80vh;
+  overflow-y:auto;
+  padding:30px; }
+  .bp3-hotkey-column .bp3-heading{
+    margin-bottom:20px; }
+    .bp3-hotkey-column .bp3-heading:not(:first-child){
+      margin-top:40px; }
+
+.bp3-hotkey{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:justify;
+      -ms-flex-pack:justify;
+          justify-content:space-between;
+  margin-left:0;
+  margin-right:0; }
+  .bp3-hotkey:not(:last-child){
+    margin-bottom:10px; }
+.bp3-icon{
+  display:inline-block;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  vertical-align:text-bottom; }
+  .bp3-icon:not(:empty)::before{
+    content:"" !important;
+    content:unset !important; }
+  .bp3-icon > svg{
+    display:block; }
+    .bp3-icon > svg:not([fill]){
+      fill:currentColor; }
+
+.bp3-icon.bp3-intent-primary, .bp3-icon-standard.bp3-intent-primary, .bp3-icon-large.bp3-intent-primary{
+  color:#106ba3; }
+  .bp3-dark .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-icon-large.bp3-intent-primary{
+    color:#48aff0; }
+
+.bp3-icon.bp3-intent-success, .bp3-icon-standard.bp3-intent-success, .bp3-icon-large.bp3-intent-success{
+  color:#0d8050; }
+  .bp3-dark .bp3-icon.bp3-intent-success, .bp3-dark .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-icon-large.bp3-intent-success{
+    color:#3dcc91; }
+
+.bp3-icon.bp3-intent-warning, .bp3-icon-standard.bp3-intent-warning, .bp3-icon-large.bp3-intent-warning{
+  color:#bf7326; }
+  .bp3-dark .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-icon-large.bp3-intent-warning{
+    color:#ffb366; }
+
+.bp3-icon.bp3-intent-danger, .bp3-icon-standard.bp3-intent-danger, .bp3-icon-large.bp3-intent-danger{
+  color:#c23030; }
+  .bp3-dark .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-icon-large.bp3-intent-danger{
+    color:#ff7373; }
+
+span.bp3-icon-standard{
+  font-family:"Icons16", sans-serif;
+  font-size:16px;
+  font-style:normal;
+  font-weight:400;
+  line-height:1;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  display:inline-block; }
+
+span.bp3-icon-large{
+  font-family:"Icons20", sans-serif;
+  font-size:20px;
+  font-style:normal;
+  font-weight:400;
+  line-height:1;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  display:inline-block; }
+
+span.bp3-icon:empty{
+  font-family:"Icons20";
+  font-size:inherit;
+  font-style:normal;
+  font-weight:400;
+  line-height:1; }
+  span.bp3-icon:empty::before{
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased; }
+
+.bp3-icon-add::before{
+  content:""; }
+
+.bp3-icon-add-column-left::before{
+  content:""; }
+
+.bp3-icon-add-column-right::before{
+  content:""; }
+
+.bp3-icon-add-row-bottom::before{
+  content:""; }
+
+.bp3-icon-add-row-top::before{
+  content:""; }
+
+.bp3-icon-add-to-artifact::before{
+  content:""; }
+
+.bp3-icon-add-to-folder::before{
+  content:""; }
+
+.bp3-icon-airplane::before{
+  content:""; }
+
+.bp3-icon-align-center::before{
+  content:""; }
+
+.bp3-icon-align-justify::before{
+  content:""; }
+
+.bp3-icon-align-left::before{
+  content:""; }
+
+.bp3-icon-align-right::before{
+  content:""; }
+
+.bp3-icon-alignment-bottom::before{
+  content:""; }
+
+.bp3-icon-alignment-horizontal-center::before{
+  content:""; }
+
+.bp3-icon-alignment-left::before{
+  content:""; }
+
+.bp3-icon-alignment-right::before{
+  content:""; }
+
+.bp3-icon-alignment-top::before{
+  content:""; }
+
+.bp3-icon-alignment-vertical-center::before{
+  content:""; }
+
+.bp3-icon-annotation::before{
+  content:""; }
+
+.bp3-icon-application::before{
+  content:""; }
+
+.bp3-icon-applications::before{
+  content:""; }
+
+.bp3-icon-archive::before{
+  content:""; }
+
+.bp3-icon-arrow-bottom-left::before{
+  content:"↙"; }
+
+.bp3-icon-arrow-bottom-right::before{
+  content:"↘"; }
+
+.bp3-icon-arrow-down::before{
+  content:"↓"; }
+
+.bp3-icon-arrow-left::before{
+  content:"←"; }
+
+.bp3-icon-arrow-right::before{
+  content:"→"; }
+
+.bp3-icon-arrow-top-left::before{
+  content:"↖"; }
+
+.bp3-icon-arrow-top-right::before{
+  content:"↗"; }
+
+.bp3-icon-arrow-up::before{
+  content:"↑"; }
+
+.bp3-icon-arrows-horizontal::before{
+  content:"↔"; }
+
+.bp3-icon-arrows-vertical::before{
+  content:"↕"; }
+
+.bp3-icon-asterisk::before{
+  content:"*"; }
+
+.bp3-icon-automatic-updates::before{
+  content:""; }
+
+.bp3-icon-badge::before{
+  content:""; }
+
+.bp3-icon-ban-circle::before{
+  content:""; }
+
+.bp3-icon-bank-account::before{
+  content:""; }
+
+.bp3-icon-barcode::before{
+  content:""; }
+
+.bp3-icon-blank::before{
+  content:""; }
+
+.bp3-icon-blocked-person::before{
+  content:""; }
+
+.bp3-icon-bold::before{
+  content:""; }
+
+.bp3-icon-book::before{
+  content:""; }
+
+.bp3-icon-bookmark::before{
+  content:""; }
+
+.bp3-icon-box::before{
+  content:""; }
+
+.bp3-icon-briefcase::before{
+  content:""; }
+
+.bp3-icon-bring-data::before{
+  content:""; }
+
+.bp3-icon-build::before{
+  content:""; }
+
+.bp3-icon-calculator::before{
+  content:""; }
+
+.bp3-icon-calendar::before{
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+.bp3-icon-projects::before{
+  content:""; }
+
+.bp3-icon-properties::before{
+  content:""; }
+
+.bp3-icon-property::before{
+  content:""; }
+
+.bp3-icon-publish-function::before{
+  content:""; }
+
+.bp3-icon-pulse::before{
+  content:""; }
+
+.bp3-icon-random::before{
+  content:""; }
+
+.bp3-icon-record::before{
+  content:""; }
+
+.bp3-icon-redo::before{
+  content:""; }
+
+.bp3-icon-refresh::before{
+  content:""; }
+
+.bp3-icon-regression-chart::before{
+  content:""; }
+
+.bp3-icon-remove::before{
+  content:""; }
+
+.bp3-icon-remove-column::before{
+  content:""; }
+
+.bp3-icon-remove-column-left::before{
+  content:""; }
+
+.bp3-icon-remove-column-right::before{
+  content:""; }
+
+.bp3-icon-remove-row-bottom::before{
+  content:""; }
+
+.bp3-icon-remove-row-top::before{
+  content:""; }
+
+.bp3-icon-repeat::before{
+  content:""; }
+
+.bp3-icon-reset::before{
+  content:""; }
+
+.bp3-icon-resolve::before{
+  content:""; }
+
+.bp3-icon-rig::before{
+  content:""; }
+
+.bp3-icon-right-join::before{
+  content:""; }
+
+.bp3-icon-ring::before{
+  content:""; }
+
+.bp3-icon-rotate-document::before{
+  content:""; }
+
+.bp3-icon-rotate-page::before{
+  content:""; }
+
+.bp3-icon-satellite::before{
+  content:""; }
+
+.bp3-icon-saved::before{
+  content:""; }
+
+.bp3-icon-scatter-plot::before{
+  content:""; }
+
+.bp3-icon-search::before{
+  content:""; }
+
+.bp3-icon-search-around::before{
+  content:""; }
+
+.bp3-icon-search-template::before{
+  content:""; }
+
+.bp3-icon-search-text::before{
+  content:""; }
+
+.bp3-icon-segmented-control::before{
+  content:""; }
+
+.bp3-icon-select::before{
+  content:""; }
+
+.bp3-icon-selection::before{
+  content:"⦿"; }
+
+.bp3-icon-send-to::before{
+  content:""; }
+
+.bp3-icon-send-to-graph::before{
+  content:""; }
+
+.bp3-icon-send-to-map::before{
+  content:""; }
+
+.bp3-icon-series-add::before{
+  content:""; }
+
+.bp3-icon-series-configuration::before{
+  content:""; }
+
+.bp3-icon-series-derived::before{
+  content:""; }
+
+.bp3-icon-series-filtered::before{
+  content:""; }
+
+.bp3-icon-series-search::before{
+  content:""; }
+
+.bp3-icon-settings::before{
+  content:""; }
+
+.bp3-icon-share::before{
+  content:""; }
+
+.bp3-icon-shield::before{
+  content:""; }
+
+.bp3-icon-shop::before{
+  content:""; }
+
+.bp3-icon-shopping-cart::before{
+  content:""; }
+
+.bp3-icon-signal-search::before{
+  content:""; }
+
+.bp3-icon-sim-card::before{
+  content:""; }
+
+.bp3-icon-slash::before{
+  content:""; }
+
+.bp3-icon-small-cross::before{
+  content:""; }
+
+.bp3-icon-small-minus::before{
+  content:""; }
+
+.bp3-icon-small-plus::before{
+  content:""; }
+
+.bp3-icon-small-tick::before{
+  content:""; }
+
+.bp3-icon-snowflake::before{
+  content:""; }
+
+.bp3-icon-social-media::before{
+  content:""; }
+
+.bp3-icon-sort::before{
+  content:""; }
+
+.bp3-icon-sort-alphabetical::before{
+  content:""; }
+
+.bp3-icon-sort-alphabetical-desc::before{
+  content:""; }
+
+.bp3-icon-sort-asc::before{
+  content:""; }
+
+.bp3-icon-sort-desc::before{
+  content:""; }
+
+.bp3-icon-sort-numerical::before{
+  content:""; }
+
+.bp3-icon-sort-numerical-desc::before{
+  content:""; }
+
+.bp3-icon-split-columns::before{
+  content:""; }
+
+.bp3-icon-square::before{
+  content:""; }
+
+.bp3-icon-stacked-chart::before{
+  content:""; }
+
+.bp3-icon-star::before{
+  content:"★"; }
+
+.bp3-icon-star-empty::before{
+  content:"☆"; }
+
+.bp3-icon-step-backward::before{
+  content:""; }
+
+.bp3-icon-step-chart::before{
+  content:""; }
+
+.bp3-icon-step-forward::before{
+  content:""; }
+
+.bp3-icon-stop::before{
+  content:""; }
+
+.bp3-icon-stopwatch::before{
+  content:""; }
+
+.bp3-icon-strikethrough::before{
+  content:""; }
+
+.bp3-icon-style::before{
+  content:""; }
+
+.bp3-icon-swap-horizontal::before{
+  content:""; }
+
+.bp3-icon-swap-vertical::before{
+  content:""; }
+
+.bp3-icon-symbol-circle::before{
+  content:""; }
+
+.bp3-icon-symbol-cross::before{
+  content:""; }
+
+.bp3-icon-symbol-diamond::before{
+  content:""; }
+
+.bp3-icon-symbol-square::before{
+  content:""; }
+
+.bp3-icon-symbol-triangle-down::before{
+  content:""; }
+
+.bp3-icon-symbol-triangle-up::before{
+  content:""; }
+
+.bp3-icon-tag::before{
+  content:""; }
+
+.bp3-icon-take-action::before{
+  content:""; }
+
+.bp3-icon-taxi::before{
+  content:""; }
+
+.bp3-icon-text-highlight::before{
+  content:""; }
+
+.bp3-icon-th::before{
+  content:""; }
+
+.bp3-icon-th-derived::before{
+  content:""; }
+
+.bp3-icon-th-disconnect::before{
+  content:""; }
+
+.bp3-icon-th-filtered::before{
+  content:""; }
+
+.bp3-icon-th-list::before{
+  content:""; }
+
+.bp3-icon-thumbs-down::before{
+  content:""; }
+
+.bp3-icon-thumbs-up::before{
+  content:""; }
+
+.bp3-icon-tick::before{
+  content:"✓"; }
+
+.bp3-icon-tick-circle::before{
+  content:""; }
+
+.bp3-icon-time::before{
+  content:"⏲"; }
+
+.bp3-icon-timeline-area-chart::before{
+  content:""; }
+
+.bp3-icon-timeline-bar-chart::before{
+  content:""; }
+
+.bp3-icon-timeline-events::before{
+  content:""; }
+
+.bp3-icon-timeline-line-chart::before{
+  content:""; }
+
+.bp3-icon-tint::before{
+  content:""; }
+
+.bp3-icon-torch::before{
+  content:""; }
+
+.bp3-icon-tractor::before{
+  content:""; }
+
+.bp3-icon-train::before{
+  content:""; }
+
+.bp3-icon-translate::before{
+  content:""; }
+
+.bp3-icon-trash::before{
+  content:""; }
+
+.bp3-icon-tree::before{
+  content:""; }
+
+.bp3-icon-trending-down::before{
+  content:""; }
+
+.bp3-icon-trending-up::before{
+  content:""; }
+
+.bp3-icon-truck::before{
+  content:""; }
+
+.bp3-icon-two-columns::before{
+  content:""; }
+
+.bp3-icon-unarchive::before{
+  content:""; }
+
+.bp3-icon-underline::before{
+  content:"⎁"; }
+
+.bp3-icon-undo::before{
+  content:"⎌"; }
+
+.bp3-icon-ungroup-objects::before{
+  content:""; }
+
+.bp3-icon-unknown-vehicle::before{
+  content:""; }
+
+.bp3-icon-unlock::before{
+  content:""; }
+
+.bp3-icon-unpin::before{
+  content:""; }
+
+.bp3-icon-unresolve::before{
+  content:""; }
+
+.bp3-icon-updated::before{
+  content:""; }
+
+.bp3-icon-upload::before{
+  content:""; }
+
+.bp3-icon-user::before{
+  content:""; }
+
+.bp3-icon-variable::before{
+  content:""; }
+
+.bp3-icon-vertical-bar-chart-asc::before{
+  content:""; }
+
+.bp3-icon-vertical-bar-chart-desc::before{
+  content:""; }
+
+.bp3-icon-vertical-distribution::before{
+  content:""; }
+
+.bp3-icon-video::before{
+  content:""; }
+
+.bp3-icon-volume-down::before{
+  content:""; }
+
+.bp3-icon-volume-off::before{
+  content:""; }
+
+.bp3-icon-volume-up::before{
+  content:""; }
+
+.bp3-icon-walk::before{
+  content:""; }
+
+.bp3-icon-warning-sign::before{
+  content:""; }
+
+.bp3-icon-waterfall-chart::before{
+  content:""; }
+
+.bp3-icon-widget::before{
+  content:""; }
+
+.bp3-icon-widget-button::before{
+  content:""; }
+
+.bp3-icon-widget-footer::before{
+  content:""; }
+
+.bp3-icon-widget-header::before{
+  content:""; }
+
+.bp3-icon-wrench::before{
+  content:""; }
+
+.bp3-icon-zoom-in::before{
+  content:""; }
+
+.bp3-icon-zoom-out::before{
+  content:""; }
+
+.bp3-icon-zoom-to-fit::before{
+  content:""; }
+.bp3-submenu > .bp3-popover-wrapper{
+  display:block; }
+
+.bp3-submenu .bp3-popover-target{
+  display:block; }
+  .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{ }
+
+.bp3-submenu.bp3-popover{
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  padding:0 5px; }
+  .bp3-submenu.bp3-popover > .bp3-popover-content{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-submenu.bp3-popover, .bp3-submenu.bp3-popover.bp3-dark{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+    .bp3-dark .bp3-submenu.bp3-popover > .bp3-popover-content, .bp3-submenu.bp3-popover.bp3-dark > .bp3-popover-content{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+.bp3-menu{
+  background:#ffffff;
+  border-radius:3px;
+  color:#182026;
+  list-style:none;
+  margin:0;
+  min-width:180px;
+  padding:5px;
+  text-align:left; }
+
+.bp3-menu-divider{
+  border-top:1px solid rgba(16, 22, 26, 0.15);
+  display:block;
+  margin:5px; }
+  .bp3-dark .bp3-menu-divider{
+    border-top-color:rgba(255, 255, 255, 0.15); }
+
+.bp3-menu-item{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  border-radius:2px;
+  color:inherit;
+  line-height:20px;
+  padding:5px 7px;
+  text-decoration:none;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-menu-item > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-menu-item > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-menu-item::before,
+  .bp3-menu-item > *{
+    margin-right:7px; }
+  .bp3-menu-item:empty::before,
+  .bp3-menu-item > :last-child{
+    margin-right:0; }
+  .bp3-menu-item > .bp3-fill{
+    word-break:break-word; }
+  .bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+    background-color:rgba(167, 182, 194, 0.3);
+    cursor:pointer;
+    text-decoration:none; }
+  .bp3-menu-item.bp3-disabled{
+    background-color:inherit;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed; }
+  .bp3-dark .bp3-menu-item{
+    color:inherit; }
+    .bp3-dark .bp3-menu-item:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+      background-color:rgba(138, 155, 168, 0.15);
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-disabled{
+      background-color:inherit;
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-menu-item.bp3-intent-primary{
+    color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-primary::before, .bp3-menu-item.bp3-intent-primary::after,
+    .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
+      color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary.bp3-active{
+      background-color:#137cbd; }
+    .bp3-menu-item.bp3-intent-primary:active{
+      background-color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary:active, .bp3-menu-item.bp3-intent-primary:active::before, .bp3-menu-item.bp3-intent-primary:active::after,
+    .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-menu-item.bp3-intent-primary.bp3-active::after,
+    .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-success{
+    color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-success::before, .bp3-menu-item.bp3-intent-success::after,
+    .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
+      color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success.bp3-active{
+      background-color:#0f9960; }
+    .bp3-menu-item.bp3-intent-success:active{
+      background-color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-menu-item.bp3-intent-success:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success:active, .bp3-menu-item.bp3-intent-success:active::before, .bp3-menu-item.bp3-intent-success:active::after,
+    .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-menu-item.bp3-intent-success.bp3-active::after,
+    .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-warning{
+    color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-warning::before, .bp3-menu-item.bp3-intent-warning::after,
+    .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
+      color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning.bp3-active{
+      background-color:#d9822b; }
+    .bp3-menu-item.bp3-intent-warning:active{
+      background-color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning:active, .bp3-menu-item.bp3-intent-warning:active::before, .bp3-menu-item.bp3-intent-warning:active::after,
+    .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-menu-item.bp3-intent-warning.bp3-active::after,
+    .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-danger{
+    color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-danger::before, .bp3-menu-item.bp3-intent-danger::after,
+    .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
+      color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger.bp3-active{
+      background-color:#db3737; }
+    .bp3-menu-item.bp3-intent-danger:active{
+      background-color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger:active, .bp3-menu-item.bp3-intent-danger:active::before, .bp3-menu-item.bp3-intent-danger:active::after,
+    .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-menu-item.bp3-intent-danger.bp3-active::after,
+    .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item::before{
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-style:normal;
+    font-weight:400;
+    line-height:1;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    margin-right:7px; }
+  .bp3-menu-item::before,
+  .bp3-menu-item > .bp3-icon{
+    color:#5c7080;
+    margin-top:2px; }
+  .bp3-menu-item .bp3-menu-item-label{
+    color:#5c7080; }
+  .bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+    color:inherit; }
+  .bp3-menu-item.bp3-active, .bp3-menu-item:active{
+    background-color:rgba(115, 134, 148, 0.3); }
+  .bp3-menu-item.bp3-disabled{
+    background-color:inherit !important;
+    color:rgba(92, 112, 128, 0.6) !important;
+    cursor:not-allowed !important;
+    outline:none !important; }
+    .bp3-menu-item.bp3-disabled::before,
+    .bp3-menu-item.bp3-disabled > .bp3-icon,
+    .bp3-menu-item.bp3-disabled .bp3-menu-item-label{
+      color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-large .bp3-menu-item{
+    font-size:16px;
+    line-height:22px;
+    padding:9px 7px; }
+    .bp3-large .bp3-menu-item .bp3-icon{
+      margin-top:3px; }
+    .bp3-large .bp3-menu-item::before{
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-style:normal;
+      font-weight:400;
+      line-height:1;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased;
+      margin-right:10px;
+      margin-top:1px; }
+
+button.bp3-menu-item{
+  background:none;
+  border:none;
+  text-align:left;
+  width:100%; }
+.bp3-menu-header{
+  border-top:1px solid rgba(16, 22, 26, 0.15);
+  display:block;
+  margin:5px;
+  cursor:default;
+  padding-left:2px; }
+  .bp3-dark .bp3-menu-header{
+    border-top-color:rgba(255, 255, 255, 0.15); }
+  .bp3-menu-header:first-of-type{
+    border-top:none; }
+  .bp3-menu-header > h6{
+    color:#182026;
+    font-weight:600;
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    line-height:17px;
+    margin:0;
+    padding:10px 7px 0 1px; }
+    .bp3-dark .bp3-menu-header > h6{
+      color:#f5f8fa; }
+  .bp3-menu-header:first-of-type > h6{
+    padding-top:0; }
+  .bp3-large .bp3-menu-header > h6{
+    font-size:18px;
+    padding-bottom:5px;
+    padding-top:15px; }
+  .bp3-large .bp3-menu-header:first-of-type > h6{
+    padding-top:0; }
+
+.bp3-dark .bp3-menu{
+  background:#30404d;
+  color:#f5f8fa; }
+
+.bp3-dark .bp3-menu-item{ }
+  .bp3-dark .bp3-menu-item.bp3-intent-primary{
+    color:#48aff0; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-icon{
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary::before, .bp3-dark .bp3-menu-item.bp3-intent-primary::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
+      color:#48aff0; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active{
+      background-color:#137cbd; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:active{
+      background-color:#106ba3; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
+    .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label,
+    .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary:active, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-dark .bp3-menu-item.bp3-intent-success{
+    color:#3dcc91; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-icon{
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success::before, .bp3-dark .bp3-menu-item.bp3-intent-success::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
+      color:#3dcc91; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active{
+      background-color:#0f9960; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success:active{
+      background-color:#0d8050; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
+    .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label,
+    .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success:active, .bp3-dark .bp3-menu-item.bp3-intent-success:active::before, .bp3-dark .bp3-menu-item.bp3-intent-success:active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-dark .bp3-menu-item.bp3-intent-warning{
+    color:#ffb366; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-icon{
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning::before, .bp3-dark .bp3-menu-item.bp3-intent-warning::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
+      color:#ffb366; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active{
+      background-color:#d9822b; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:active{
+      background-color:#bf7326; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
+    .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label,
+    .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning:active, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-dark .bp3-menu-item.bp3-intent-danger{
+    color:#ff7373; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-icon{
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger::before, .bp3-dark .bp3-menu-item.bp3-intent-danger::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
+      color:#ff7373; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active{
+      background-color:#db3737; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:active{
+      background-color:#c23030; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
+    .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label,
+    .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger:active, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-dark .bp3-menu-item::before,
+  .bp3-dark .bp3-menu-item > .bp3-icon{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-menu-item .bp3-menu-item-label{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-menu-item.bp3-active, .bp3-dark .bp3-menu-item:active{
+    background-color:rgba(138, 155, 168, 0.3); }
+  .bp3-dark .bp3-menu-item.bp3-disabled{
+    color:rgba(167, 182, 194, 0.6) !important; }
+    .bp3-dark .bp3-menu-item.bp3-disabled::before,
+    .bp3-dark .bp3-menu-item.bp3-disabled > .bp3-icon,
+    .bp3-dark .bp3-menu-item.bp3-disabled .bp3-menu-item-label{
+      color:rgba(167, 182, 194, 0.6) !important; }
+
+.bp3-dark .bp3-menu-divider,
+.bp3-dark .bp3-menu-header{
+  border-color:rgba(255, 255, 255, 0.15); }
+
+.bp3-dark .bp3-menu-header > h6{
+  color:#f5f8fa; }
+
+.bp3-label .bp3-menu{
+  margin-top:5px; }
+.bp3-navbar{
+  background-color:#ffffff;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  height:50px;
+  padding:0 15px;
+  position:relative;
+  width:100%;
+  z-index:10; }
+  .bp3-navbar.bp3-dark,
+  .bp3-dark .bp3-navbar{
+    background-color:#394b59; }
+  .bp3-navbar.bp3-dark{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-navbar{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-navbar.bp3-fixed-top{
+    left:0;
+    position:fixed;
+    right:0;
+    top:0; }
+
+.bp3-navbar-heading{
+  font-size:16px;
+  margin-right:15px; }
+
+.bp3-navbar-group{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  height:50px; }
+  .bp3-navbar-group.bp3-align-left{
+    float:left; }
+  .bp3-navbar-group.bp3-align-right{
+    float:right; }
+
+.bp3-navbar-divider{
+  border-left:1px solid rgba(16, 22, 26, 0.15);
+  height:20px;
+  margin:0 10px; }
+  .bp3-dark .bp3-navbar-divider{
+    border-left-color:rgba(255, 255, 255, 0.15); }
+.bp3-non-ideal-state{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  height:100%;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  text-align:center;
+  width:100%; }
+  .bp3-non-ideal-state > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-non-ideal-state > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-non-ideal-state::before,
+  .bp3-non-ideal-state > *{
+    margin-bottom:20px; }
+  .bp3-non-ideal-state:empty::before,
+  .bp3-non-ideal-state > :last-child{
+    margin-bottom:0; }
+  .bp3-non-ideal-state > *{
+    max-width:400px; }
+
+.bp3-non-ideal-state-visual{
+  color:rgba(92, 112, 128, 0.6);
+  font-size:60px; }
+  .bp3-dark .bp3-non-ideal-state-visual{
+    color:rgba(167, 182, 194, 0.6); }
+
+.bp3-overflow-list{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-wrap:nowrap;
+      flex-wrap:nowrap;
+  min-width:0; }
+
+.bp3-overflow-list-spacer{
+  -ms-flex-negative:1;
+      flex-shrink:1;
+  width:1px; }
+
+body.bp3-overlay-open{
+  overflow:hidden; }
+
+.bp3-overlay{
+  bottom:0;
+  left:0;
+  position:static;
+  right:0;
+  top:0;
+  z-index:20; }
+  .bp3-overlay:not(.bp3-overlay-open){
+    pointer-events:none; }
+  .bp3-overlay.bp3-overlay-container{
+    overflow:hidden;
+    position:fixed; }
+    .bp3-overlay.bp3-overlay-container.bp3-overlay-inline{
+      position:absolute; }
+  .bp3-overlay.bp3-overlay-scroll-container{
+    overflow:auto;
+    position:fixed; }
+    .bp3-overlay.bp3-overlay-scroll-container.bp3-overlay-inline{
+      position:absolute; }
+  .bp3-overlay.bp3-overlay-inline{
+    display:inline;
+    overflow:visible; }
+
+.bp3-overlay-content{
+  position:fixed;
+  z-index:20; }
+  .bp3-overlay-inline .bp3-overlay-content,
+  .bp3-overlay-scroll-container .bp3-overlay-content{
+    position:absolute; }
+
+.bp3-overlay-backdrop{
+  bottom:0;
+  left:0;
+  position:fixed;
+  right:0;
+  top:0;
+  opacity:1;
+  background-color:rgba(16, 22, 26, 0.7);
+  overflow:auto;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none;
+  z-index:20; }
+  .bp3-overlay-backdrop.bp3-overlay-enter, .bp3-overlay-backdrop.bp3-overlay-appear{
+    opacity:0; }
+  .bp3-overlay-backdrop.bp3-overlay-enter-active, .bp3-overlay-backdrop.bp3-overlay-appear-active{
+    opacity:1;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-overlay-backdrop.bp3-overlay-exit{
+    opacity:1; }
+  .bp3-overlay-backdrop.bp3-overlay-exit-active{
+    opacity:0;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-overlay-backdrop:focus{
+    outline:none; }
+  .bp3-overlay-inline .bp3-overlay-backdrop{
+    position:absolute; }
+.bp3-panel-stack{
+  overflow:hidden;
+  position:relative; }
+
+.bp3-panel-stack-header{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-negative:0;
+      flex-shrink:0;
+  height:30px;
+  z-index:1; }
+  .bp3-dark .bp3-panel-stack-header{
+    -webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);
+            box-shadow:0 1px rgba(255, 255, 255, 0.15); }
+  .bp3-panel-stack-header > span{
+    -webkit-box-align:stretch;
+        -ms-flex-align:stretch;
+            align-items:stretch;
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-flex:1;
+        -ms-flex:1;
+            flex:1; }
+  .bp3-panel-stack-header .bp3-heading{
+    margin:0 5px; }
+
+.bp3-button.bp3-panel-stack-header-back{
+  margin-left:5px;
+  padding-left:0;
+  white-space:nowrap; }
+  .bp3-button.bp3-panel-stack-header-back .bp3-icon{
+    margin:0 2px; }
+
+.bp3-panel-stack-view{
+  bottom:0;
+  left:0;
+  position:absolute;
+  right:0;
+  top:0;
+  background-color:#ffffff;
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin-right:-1px;
+  overflow-y:auto;
+  z-index:1; }
+  .bp3-dark .bp3-panel-stack-view{
+    background-color:#30404d; }
+  .bp3-panel-stack-view:nth-last-child(n + 4){
+    display:none; }
+
+.bp3-panel-stack-push .bp3-panel-stack-enter, .bp3-panel-stack-push .bp3-panel-stack-appear{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0; }
+
+.bp3-panel-stack-push .bp3-panel-stack-enter-active, .bp3-panel-stack-push .bp3-panel-stack-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack-push .bp3-panel-stack-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack-push .bp3-panel-stack-exit-active{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-enter, .bp3-panel-stack-pop .bp3-panel-stack-appear{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-enter-active, .bp3-panel-stack-pop .bp3-panel-stack-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-exit-active{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+.bp3-panel-stack2{
+  overflow:hidden;
+  position:relative; }
+
+.bp3-panel-stack2-header{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-negative:0;
+      flex-shrink:0;
+  height:30px;
+  z-index:1; }
+  .bp3-dark .bp3-panel-stack2-header{
+    -webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);
+            box-shadow:0 1px rgba(255, 255, 255, 0.15); }
+  .bp3-panel-stack2-header > span{
+    -webkit-box-align:stretch;
+        -ms-flex-align:stretch;
+            align-items:stretch;
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-flex:1;
+        -ms-flex:1;
+            flex:1; }
+  .bp3-panel-stack2-header .bp3-heading{
+    margin:0 5px; }
+
+.bp3-button.bp3-panel-stack2-header-back{
+  margin-left:5px;
+  padding-left:0;
+  white-space:nowrap; }
+  .bp3-button.bp3-panel-stack2-header-back .bp3-icon{
+    margin:0 2px; }
+
+.bp3-panel-stack2-view{
+  bottom:0;
+  left:0;
+  position:absolute;
+  right:0;
+  top:0;
+  background-color:#ffffff;
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin-right:-1px;
+  overflow-y:auto;
+  z-index:1; }
+  .bp3-dark .bp3-panel-stack2-view{
+    background-color:#30404d; }
+  .bp3-panel-stack2-view:nth-last-child(n + 4){
+    display:none; }
+
+.bp3-panel-stack2-push .bp3-panel-stack2-enter, .bp3-panel-stack2-push .bp3-panel-stack2-appear{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0; }
+
+.bp3-panel-stack2-push .bp3-panel-stack2-enter-active, .bp3-panel-stack2-push .bp3-panel-stack2-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack2-push .bp3-panel-stack2-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack2-push .bp3-panel-stack2-exit-active{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack2-pop .bp3-panel-stack2-enter, .bp3-panel-stack2-pop .bp3-panel-stack2-appear{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0; }
+
+.bp3-panel-stack2-pop .bp3-panel-stack2-enter-active, .bp3-panel-stack2-pop .bp3-panel-stack2-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack2-pop .bp3-panel-stack2-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack2-pop .bp3-panel-stack2-exit-active{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+.bp3-popover{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  -webkit-transform:scale(1);
+          transform:scale(1);
+  border-radius:3px;
+  display:inline-block;
+  z-index:20; }
+  .bp3-popover .bp3-popover-arrow{
+    height:30px;
+    position:absolute;
+    width:30px; }
+    .bp3-popover .bp3-popover-arrow::before{
+      height:20px;
+      margin:5px;
+      width:20px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover{
+    margin-bottom:17px;
+    margin-top:-17px; }
+    .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
+      bottom:-11px; }
+      .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(-90deg);
+                transform:rotate(-90deg); }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover{
+    margin-left:17px; }
+    .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
+      left:-11px; }
+      .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(0);
+                transform:rotate(0); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover{
+    margin-top:17px; }
+    .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
+      top:-11px; }
+      .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(90deg);
+                transform:rotate(90deg); }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover{
+    margin-left:-17px;
+    margin-right:17px; }
+    .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
+      right:-11px; }
+      .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(180deg);
+                transform:rotate(180deg); }
+  .bp3-tether-element-attached-middle > .bp3-popover > .bp3-popover-arrow{
+    top:50%;
+    -webkit-transform:translateY(-50%);
+            transform:translateY(-50%); }
+  .bp3-tether-element-attached-center > .bp3-popover > .bp3-popover-arrow{
+    right:50%;
+    -webkit-transform:translateX(50%);
+            transform:translateX(50%); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
+    top:-0.3934px; }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
+    right:-0.3934px; }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
+    left:-0.3934px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
+    bottom:-0.3934px; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:top left;
+            transform-origin:top left; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:top center;
+            transform-origin:top center; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:top right;
+            transform-origin:top right; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:center left;
+            transform-origin:center left; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:center center;
+            transform-origin:center center; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:center right;
+            transform-origin:center right; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:bottom left;
+            transform-origin:bottom left; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:bottom center;
+            transform-origin:bottom center; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:bottom right;
+            transform-origin:bottom right; }
+  .bp3-popover .bp3-popover-content{
+    background:#ffffff;
+    color:inherit; }
+  .bp3-popover .bp3-popover-arrow::before{
+    -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
+            box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-popover .bp3-popover-arrow-border{
+    fill:#10161a;
+    fill-opacity:0.1; }
+  .bp3-popover .bp3-popover-arrow-fill{
+    fill:#ffffff; }
+  .bp3-popover-enter > .bp3-popover, .bp3-popover-appear > .bp3-popover{
+    -webkit-transform:scale(0.3);
+            transform:scale(0.3); }
+  .bp3-popover-enter-active > .bp3-popover, .bp3-popover-appear-active > .bp3-popover{
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-popover-exit > .bp3-popover{
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-popover-exit-active > .bp3-popover{
+    -webkit-transform:scale(0.3);
+            transform:scale(0.3);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-popover .bp3-popover-content{
+    border-radius:3px;
+    position:relative; }
+  .bp3-popover.bp3-popover-content-sizing .bp3-popover-content{
+    max-width:350px;
+    padding:20px; }
+  .bp3-popover-target + .bp3-overlay .bp3-popover.bp3-popover-content-sizing{
+    width:350px; }
+  .bp3-popover.bp3-minimal{
+    margin:0 !important; }
+    .bp3-popover.bp3-minimal .bp3-popover-arrow{
+      display:none; }
+    .bp3-popover.bp3-minimal.bp3-popover{
+      -webkit-transform:scale(1);
+              transform:scale(1); }
+      .bp3-popover-enter > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1); }
+      .bp3-popover-enter-active > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear-active > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1);
+        -webkit-transition-delay:0;
+                transition-delay:0;
+        -webkit-transition-duration:100ms;
+                transition-duration:100ms;
+        -webkit-transition-property:-webkit-transform;
+        transition-property:-webkit-transform;
+        transition-property:transform;
+        transition-property:transform, -webkit-transform;
+        -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+                transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+      .bp3-popover-exit > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1); }
+      .bp3-popover-exit-active > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1);
+        -webkit-transition-delay:0;
+                transition-delay:0;
+        -webkit-transition-duration:100ms;
+                transition-duration:100ms;
+        -webkit-transition-property:-webkit-transform;
+        transition-property:-webkit-transform;
+        transition-property:transform;
+        transition-property:transform, -webkit-transform;
+        -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+                transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-popover.bp3-dark,
+  .bp3-dark .bp3-popover{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+    .bp3-popover.bp3-dark .bp3-popover-content,
+    .bp3-dark .bp3-popover .bp3-popover-content{
+      background:#30404d;
+      color:inherit; }
+    .bp3-popover.bp3-dark .bp3-popover-arrow::before,
+    .bp3-dark .bp3-popover .bp3-popover-arrow::before{
+      -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
+              box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
+    .bp3-popover.bp3-dark .bp3-popover-arrow-border,
+    .bp3-dark .bp3-popover .bp3-popover-arrow-border{
+      fill:#10161a;
+      fill-opacity:0.2; }
+    .bp3-popover.bp3-dark .bp3-popover-arrow-fill,
+    .bp3-dark .bp3-popover .bp3-popover-arrow-fill{
+      fill:#30404d; }
+
+.bp3-popover-arrow::before{
+  border-radius:2px;
+  content:"";
+  display:block;
+  position:absolute;
+  -webkit-transform:rotate(45deg);
+          transform:rotate(45deg); }
+
+.bp3-tether-pinned .bp3-popover-arrow{
+  display:none; }
+
+.bp3-popover-backdrop{
+  background:rgba(255, 255, 255, 0); }
+
+.bp3-transition-container{
+  opacity:1;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  z-index:20; }
+  .bp3-transition-container.bp3-popover-enter, .bp3-transition-container.bp3-popover-appear{
+    opacity:0; }
+  .bp3-transition-container.bp3-popover-enter-active, .bp3-transition-container.bp3-popover-appear-active{
+    opacity:1;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-transition-container.bp3-popover-exit{
+    opacity:1; }
+  .bp3-transition-container.bp3-popover-exit-active{
+    opacity:0;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-transition-container:focus{
+    outline:none; }
+  .bp3-transition-container.bp3-popover-leave .bp3-popover-content{
+    pointer-events:none; }
+  .bp3-transition-container[data-x-out-of-boundaries]{
+    display:none; }
+
+span.bp3-popover-target{
+  display:inline-block; }
+
+.bp3-popover-wrapper.bp3-fill{
+  width:100%; }
+
+.bp3-portal{
+  left:0;
+  position:absolute;
+  right:0;
+  top:0; }
+@-webkit-keyframes linear-progress-bar-stripes{
+  from{
+    background-position:0 0; }
+  to{
+    background-position:30px 0; } }
+@keyframes linear-progress-bar-stripes{
+  from{
+    background-position:0 0; }
+  to{
+    background-position:30px 0; } }
+
+.bp3-progress-bar{
+  background:rgba(92, 112, 128, 0.2);
+  border-radius:40px;
+  display:block;
+  height:8px;
+  overflow:hidden;
+  position:relative;
+  width:100%; }
+  .bp3-progress-bar .bp3-progress-meter{
+    background:linear-gradient(-45deg, rgba(255, 255, 255, 0.2) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.2) 50%, rgba(255, 255, 255, 0.2) 75%, transparent 75%);
+    background-color:rgba(92, 112, 128, 0.8);
+    background-size:30px 30px;
+    border-radius:40px;
+    height:100%;
+    position:absolute;
+    -webkit-transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    width:100%; }
+  .bp3-progress-bar:not(.bp3-no-animation):not(.bp3-no-stripes) .bp3-progress-meter{
+    animation:linear-progress-bar-stripes 300ms linear infinite reverse; }
+  .bp3-progress-bar.bp3-no-stripes .bp3-progress-meter{
+    background-image:none; }
+
+.bp3-dark .bp3-progress-bar{
+  background:rgba(16, 22, 26, 0.5); }
+  .bp3-dark .bp3-progress-bar .bp3-progress-meter{
+    background-color:#8a9ba8; }
+
+.bp3-progress-bar.bp3-intent-primary .bp3-progress-meter{
+  background-color:#137cbd; }
+
+.bp3-progress-bar.bp3-intent-success .bp3-progress-meter{
+  background-color:#0f9960; }
+
+.bp3-progress-bar.bp3-intent-warning .bp3-progress-meter{
+  background-color:#d9822b; }
+
+.bp3-progress-bar.bp3-intent-danger .bp3-progress-meter{
+  background-color:#db3737; }
+@-webkit-keyframes skeleton-glow{
+  from{
+    background:rgba(206, 217, 224, 0.2);
+    border-color:rgba(206, 217, 224, 0.2); }
+  to{
+    background:rgba(92, 112, 128, 0.2);
+    border-color:rgba(92, 112, 128, 0.2); } }
+@keyframes skeleton-glow{
+  from{
+    background:rgba(206, 217, 224, 0.2);
+    border-color:rgba(206, 217, 224, 0.2); }
+  to{
+    background:rgba(92, 112, 128, 0.2);
+    border-color:rgba(92, 112, 128, 0.2); } }
+.bp3-skeleton{
+  -webkit-animation:1000ms linear infinite alternate skeleton-glow;
+          animation:1000ms linear infinite alternate skeleton-glow;
+  background:rgba(206, 217, 224, 0.2);
+  background-clip:padding-box !important;
+  border-color:rgba(206, 217, 224, 0.2) !important;
+  border-radius:2px;
+  -webkit-box-shadow:none !important;
+          box-shadow:none !important;
+  color:transparent !important;
+  cursor:default;
+  pointer-events:none;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-skeleton::before, .bp3-skeleton::after,
+  .bp3-skeleton *{
+    visibility:hidden !important; }
+.bp3-slider{
+  height:40px;
+  min-width:150px;
+  width:100%;
+  cursor:default;
+  outline:none;
+  position:relative;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-slider:hover{
+    cursor:pointer; }
+  .bp3-slider:active{
+    cursor:-webkit-grabbing;
+    cursor:grabbing; }
+  .bp3-slider.bp3-disabled{
+    cursor:not-allowed;
+    opacity:0.5; }
+  .bp3-slider.bp3-slider-unlabeled{
+    height:16px; }
+
+.bp3-slider-track,
+.bp3-slider-progress{
+  height:6px;
+  left:0;
+  right:0;
+  top:5px;
+  position:absolute; }
+
+.bp3-slider-track{
+  border-radius:3px;
+  overflow:hidden; }
+
+.bp3-slider-progress{
+  background:rgba(92, 112, 128, 0.2); }
+  .bp3-dark .bp3-slider-progress{
+    background:rgba(16, 22, 26, 0.5); }
+  .bp3-slider-progress.bp3-intent-primary{
+    background-color:#137cbd; }
+  .bp3-slider-progress.bp3-intent-success{
+    background-color:#0f9960; }
+  .bp3-slider-progress.bp3-intent-warning{
+    background-color:#d9822b; }
+  .bp3-slider-progress.bp3-intent-danger{
+    background-color:#db3737; }
+
+.bp3-slider-handle{
+  background-color:#f5f8fa;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+  color:#182026;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+  cursor:pointer;
+  height:16px;
+  left:0;
+  position:absolute;
+  top:0;
+  width:16px; }
+  .bp3-slider-handle:hover{
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+  .bp3-slider-handle:active, .bp3-slider-handle.bp3-active{
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-slider-handle:disabled, .bp3-slider-handle.bp3-disabled{
+    background-color:rgba(206, 217, 224, 0.5);
+    background-image:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed;
+    outline:none; }
+    .bp3-slider-handle:disabled.bp3-active, .bp3-slider-handle:disabled.bp3-active:hover, .bp3-slider-handle.bp3-disabled.bp3-active, .bp3-slider-handle.bp3-disabled.bp3-active:hover{
+      background:rgba(206, 217, 224, 0.7); }
+  .bp3-slider-handle:focus{
+    z-index:1; }
+  .bp3-slider-handle:hover{
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+    cursor:-webkit-grab;
+    cursor:grab;
+    z-index:2; }
+  .bp3-slider-handle.bp3-active{
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);
+    cursor:-webkit-grabbing;
+    cursor:grabbing; }
+  .bp3-disabled .bp3-slider-handle{
+    background:#bfccd6;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    pointer-events:none; }
+  .bp3-dark .bp3-slider-handle{
+    background-color:#394b59;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+    .bp3-dark .bp3-slider-handle:hover, .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{
+      color:#f5f8fa; }
+    .bp3-dark .bp3-slider-handle:hover{
+      background-color:#30404d;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{
+      background-color:#202b33;
+      background-image:none;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-slider-handle:disabled, .bp3-dark .bp3-slider-handle.bp3-disabled{
+      background-color:rgba(57, 75, 89, 0.5);
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+      .bp3-dark .bp3-slider-handle:disabled.bp3-active, .bp3-dark .bp3-slider-handle.bp3-disabled.bp3-active{
+        background:rgba(57, 75, 89, 0.7); }
+    .bp3-dark .bp3-slider-handle .bp3-button-spinner .bp3-spinner-head{
+      background:rgba(16, 22, 26, 0.5);
+      stroke:#8a9ba8; }
+    .bp3-dark .bp3-slider-handle, .bp3-dark .bp3-slider-handle:hover{
+      background-color:#394b59; }
+    .bp3-dark .bp3-slider-handle.bp3-active{
+      background-color:#293742; }
+  .bp3-dark .bp3-disabled .bp3-slider-handle{
+    background:#5c7080;
+    border-color:#5c7080;
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-slider-handle .bp3-slider-label{
+    background:#394b59;
+    border-radius:3px;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+    color:#f5f8fa;
+    margin-left:8px; }
+    .bp3-dark .bp3-slider-handle .bp3-slider-label{
+      background:#e1e8ed;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+      color:#394b59; }
+    .bp3-disabled .bp3-slider-handle .bp3-slider-label{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-slider-handle.bp3-start, .bp3-slider-handle.bp3-end{
+    width:8px; }
+  .bp3-slider-handle.bp3-start{
+    border-bottom-right-radius:0;
+    border-top-right-radius:0; }
+  .bp3-slider-handle.bp3-end{
+    border-bottom-left-radius:0;
+    border-top-left-radius:0;
+    margin-left:8px; }
+    .bp3-slider-handle.bp3-end .bp3-slider-label{
+      margin-left:0; }
+
+.bp3-slider-label{
+  -webkit-transform:translate(-50%, 20px);
+          transform:translate(-50%, 20px);
+  display:inline-block;
+  font-size:12px;
+  line-height:1;
+  padding:2px 5px;
+  position:absolute;
+  vertical-align:top; }
+
+.bp3-slider.bp3-vertical{
+  height:150px;
+  min-width:40px;
+  width:40px; }
+  .bp3-slider.bp3-vertical .bp3-slider-track,
+  .bp3-slider.bp3-vertical .bp3-slider-progress{
+    bottom:0;
+    height:auto;
+    left:5px;
+    top:0;
+    width:6px; }
+  .bp3-slider.bp3-vertical .bp3-slider-progress{
+    top:auto; }
+  .bp3-slider.bp3-vertical .bp3-slider-label{
+    -webkit-transform:translate(20px, 50%);
+            transform:translate(20px, 50%); }
+  .bp3-slider.bp3-vertical .bp3-slider-handle{
+    top:auto; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle .bp3-slider-label{
+      margin-left:0;
+      margin-top:-8px; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end, .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
+      height:8px;
+      margin-left:0;
+      width:16px; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
+      border-bottom-right-radius:3px;
+      border-top-left-radius:0; }
+      .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start .bp3-slider-label{
+        -webkit-transform:translate(20px);
+                transform:translate(20px); }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end{
+      border-bottom-left-radius:0;
+      border-bottom-right-radius:0;
+      border-top-left-radius:3px;
+      margin-bottom:8px; }
+
+@-webkit-keyframes pt-spinner-animation{
+  from{
+    -webkit-transform:rotate(0deg);
+            transform:rotate(0deg); }
+  to{
+    -webkit-transform:rotate(360deg);
+            transform:rotate(360deg); } }
+
+@keyframes pt-spinner-animation{
+  from{
+    -webkit-transform:rotate(0deg);
+            transform:rotate(0deg); }
+  to{
+    -webkit-transform:rotate(360deg);
+            transform:rotate(360deg); } }
+
+.bp3-spinner{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  overflow:visible;
+  vertical-align:middle; }
+  .bp3-spinner svg{
+    display:block; }
+  .bp3-spinner path{
+    fill-opacity:0; }
+  .bp3-spinner .bp3-spinner-head{
+    stroke:rgba(92, 112, 128, 0.8);
+    stroke-linecap:round;
+    -webkit-transform-origin:center;
+            transform-origin:center;
+    -webkit-transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-spinner .bp3-spinner-track{
+    stroke:rgba(92, 112, 128, 0.2); }
+
+.bp3-spinner-animation{
+  -webkit-animation:pt-spinner-animation 500ms linear infinite;
+          animation:pt-spinner-animation 500ms linear infinite; }
+  .bp3-no-spin > .bp3-spinner-animation{
+    -webkit-animation:none;
+            animation:none; }
+
+.bp3-dark .bp3-spinner .bp3-spinner-head{
+  stroke:#8a9ba8; }
+
+.bp3-dark .bp3-spinner .bp3-spinner-track{
+  stroke:rgba(16, 22, 26, 0.5); }
+
+.bp3-spinner.bp3-intent-primary .bp3-spinner-head{
+  stroke:#137cbd; }
+
+.bp3-spinner.bp3-intent-success .bp3-spinner-head{
+  stroke:#0f9960; }
+
+.bp3-spinner.bp3-intent-warning .bp3-spinner-head{
+  stroke:#d9822b; }
+
+.bp3-spinner.bp3-intent-danger .bp3-spinner-head{
+  stroke:#db3737; }
+.bp3-tabs.bp3-vertical{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex; }
+  .bp3-tabs.bp3-vertical > .bp3-tab-list{
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start;
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column; }
+    .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab{
+      border-radius:3px;
+      padding:0 10px;
+      width:100%; }
+      .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab[aria-selected="true"]{
+        background-color:rgba(19, 124, 189, 0.2);
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+    .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab-indicator-wrapper .bp3-tab-indicator{
+      background-color:rgba(19, 124, 189, 0.2);
+      border-radius:3px;
+      bottom:0;
+      height:auto;
+      left:0;
+      right:0;
+      top:0; }
+  .bp3-tabs.bp3-vertical > .bp3-tab-panel{
+    margin-top:0;
+    padding-left:20px; }
+
+.bp3-tab-list{
+  -webkit-box-align:end;
+      -ms-flex-align:end;
+          align-items:flex-end;
+  border:none;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  list-style:none;
+  margin:0;
+  padding:0;
+  position:relative; }
+  .bp3-tab-list > *:not(:last-child){
+    margin-right:20px; }
+
+.bp3-tab{
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  color:#182026;
+  cursor:pointer;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  font-size:14px;
+  line-height:30px;
+  max-width:100%;
+  position:relative;
+  vertical-align:top; }
+  .bp3-tab a{
+    color:inherit;
+    display:block;
+    text-decoration:none; }
+  .bp3-tab-indicator-wrapper ~ .bp3-tab{
+    background-color:transparent !important;
+    -webkit-box-shadow:none !important;
+            box-shadow:none !important; }
+  .bp3-tab[aria-disabled="true"]{
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed; }
+  .bp3-tab[aria-selected="true"]{
+    border-radius:0;
+    -webkit-box-shadow:inset 0 -3px 0 #106ba3;
+            box-shadow:inset 0 -3px 0 #106ba3; }
+  .bp3-tab[aria-selected="true"], .bp3-tab:not([aria-disabled="true"]):hover{
+    color:#106ba3; }
+  .bp3-tab:focus{
+    -moz-outline-radius:0; }
+  .bp3-large > .bp3-tab{
+    font-size:16px;
+    line-height:40px; }
+
+.bp3-tab-panel{
+  margin-top:20px; }
+  .bp3-tab-panel[aria-hidden="true"]{
+    display:none; }
+
+.bp3-tab-indicator-wrapper{
+  left:0;
+  pointer-events:none;
+  position:absolute;
+  top:0;
+  -webkit-transform:translateX(0), translateY(0);
+          transform:translateX(0), translateY(0);
+  -webkit-transition:height, width, -webkit-transform;
+  transition:height, width, -webkit-transform;
+  transition:height, transform, width;
+  transition:height, transform, width, -webkit-transform;
+  -webkit-transition-duration:200ms;
+          transition-duration:200ms;
+  -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+          transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-tab-indicator-wrapper .bp3-tab-indicator{
+    background-color:#106ba3;
+    bottom:0;
+    height:3px;
+    left:0;
+    position:absolute;
+    right:0; }
+  .bp3-tab-indicator-wrapper.bp3-no-animation{
+    -webkit-transition:none;
+    transition:none; }
+
+.bp3-dark .bp3-tab{
+  color:#f5f8fa; }
+  .bp3-dark .bp3-tab[aria-disabled="true"]{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-dark .bp3-tab[aria-selected="true"]{
+    -webkit-box-shadow:inset 0 -3px 0 #48aff0;
+            box-shadow:inset 0 -3px 0 #48aff0; }
+  .bp3-dark .bp3-tab[aria-selected="true"], .bp3-dark .bp3-tab:not([aria-disabled="true"]):hover{
+    color:#48aff0; }
+
+.bp3-dark .bp3-tab-indicator{
+  background-color:#48aff0; }
+
+.bp3-flex-expander{
+  -webkit-box-flex:1;
+      -ms-flex:1 1;
+          flex:1 1; }
+.bp3-tag{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background-color:#5c7080;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  color:#f5f8fa;
+  font-size:12px;
+  line-height:16px;
+  max-width:100%;
+  min-height:20px;
+  min-width:20px;
+  padding:2px 6px;
+  position:relative; }
+  .bp3-tag.bp3-interactive{
+    cursor:pointer; }
+    .bp3-tag.bp3-interactive:hover{
+      background-color:rgba(92, 112, 128, 0.85); }
+    .bp3-tag.bp3-interactive.bp3-active, .bp3-tag.bp3-interactive:active{
+      background-color:rgba(92, 112, 128, 0.7); }
+  .bp3-tag > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-tag > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-tag::before,
+  .bp3-tag > *{
+    margin-right:4px; }
+  .bp3-tag:empty::before,
+  .bp3-tag > :last-child{
+    margin-right:0; }
+  .bp3-tag:focus{
+    outline:rgba(19, 124, 189, 0.6) auto 2px;
+    outline-offset:0;
+    -moz-outline-radius:6px; }
+  .bp3-tag.bp3-round{
+    border-radius:30px;
+    padding-left:8px;
+    padding-right:8px; }
+  .bp3-dark .bp3-tag{
+    background-color:#bfccd6;
+    color:#182026; }
+    .bp3-dark .bp3-tag.bp3-interactive{
+      cursor:pointer; }
+      .bp3-dark .bp3-tag.bp3-interactive:hover{
+        background-color:rgba(191, 204, 214, 0.85); }
+      .bp3-dark .bp3-tag.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-interactive:active{
+        background-color:rgba(191, 204, 214, 0.7); }
+    .bp3-dark .bp3-tag > .bp3-icon, .bp3-dark .bp3-tag .bp3-icon-standard, .bp3-dark .bp3-tag .bp3-icon-large{
+      fill:currentColor; }
+  .bp3-tag > .bp3-icon, .bp3-tag .bp3-icon-standard, .bp3-tag .bp3-icon-large{
+    fill:#ffffff; }
+  .bp3-tag.bp3-large,
+  .bp3-large .bp3-tag{
+    font-size:14px;
+    line-height:20px;
+    min-height:30px;
+    min-width:30px;
+    padding:5px 10px; }
+    .bp3-tag.bp3-large::before,
+    .bp3-tag.bp3-large > *,
+    .bp3-large .bp3-tag::before,
+    .bp3-large .bp3-tag > *{
+      margin-right:7px; }
+    .bp3-tag.bp3-large:empty::before,
+    .bp3-tag.bp3-large > :last-child,
+    .bp3-large .bp3-tag:empty::before,
+    .bp3-large .bp3-tag > :last-child{
+      margin-right:0; }
+    .bp3-tag.bp3-large.bp3-round,
+    .bp3-large .bp3-tag.bp3-round{
+      padding-left:12px;
+      padding-right:12px; }
+  .bp3-tag.bp3-intent-primary{
+    background:#137cbd;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-primary.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-primary.bp3-interactive:hover{
+        background-color:rgba(19, 124, 189, 0.85); }
+      .bp3-tag.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-primary.bp3-interactive:active{
+        background-color:rgba(19, 124, 189, 0.7); }
+  .bp3-tag.bp3-intent-success{
+    background:#0f9960;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-success.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-success.bp3-interactive:hover{
+        background-color:rgba(15, 153, 96, 0.85); }
+      .bp3-tag.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-success.bp3-interactive:active{
+        background-color:rgba(15, 153, 96, 0.7); }
+  .bp3-tag.bp3-intent-warning{
+    background:#d9822b;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-warning.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-warning.bp3-interactive:hover{
+        background-color:rgba(217, 130, 43, 0.85); }
+      .bp3-tag.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-warning.bp3-interactive:active{
+        background-color:rgba(217, 130, 43, 0.7); }
+  .bp3-tag.bp3-intent-danger{
+    background:#db3737;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-danger.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-danger.bp3-interactive:hover{
+        background-color:rgba(219, 55, 55, 0.85); }
+      .bp3-tag.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-danger.bp3-interactive:active{
+        background-color:rgba(219, 55, 55, 0.7); }
+  .bp3-tag.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-tag.bp3-minimal > .bp3-icon, .bp3-tag.bp3-minimal .bp3-icon-standard, .bp3-tag.bp3-minimal .bp3-icon-large{
+    fill:#5c7080; }
+  .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
+    background-color:rgba(138, 155, 168, 0.2);
+    color:#182026; }
+    .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
+        background-color:rgba(92, 112, 128, 0.3); }
+      .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
+        background-color:rgba(92, 112, 128, 0.4); }
+    .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
+      color:#f5f8fa; }
+      .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
+          background-color:rgba(191, 204, 214, 0.3); }
+        .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
+          background-color:rgba(191, 204, 214, 0.4); }
+      .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) > .bp3-icon, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-large{
+        fill:#a7b6c2; }
+  .bp3-tag.bp3-minimal.bp3-intent-primary{
+    background-color:rgba(19, 124, 189, 0.15);
+    color:#106ba3; }
+    .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
+        background-color:rgba(19, 124, 189, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
+        background-color:rgba(19, 124, 189, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-primary > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-large{
+      fill:#137cbd; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary{
+      background-color:rgba(19, 124, 189, 0.25);
+      color:#48aff0; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
+          background-color:rgba(19, 124, 189, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
+          background-color:rgba(19, 124, 189, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-success{
+    background-color:rgba(15, 153, 96, 0.15);
+    color:#0d8050; }
+    .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
+        background-color:rgba(15, 153, 96, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
+        background-color:rgba(15, 153, 96, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-success > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-large{
+      fill:#0f9960; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success{
+      background-color:rgba(15, 153, 96, 0.25);
+      color:#3dcc91; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
+          background-color:rgba(15, 153, 96, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
+          background-color:rgba(15, 153, 96, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-warning{
+    background-color:rgba(217, 130, 43, 0.15);
+    color:#bf7326; }
+    .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
+        background-color:rgba(217, 130, 43, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
+        background-color:rgba(217, 130, 43, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-warning > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-large{
+      fill:#d9822b; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning{
+      background-color:rgba(217, 130, 43, 0.25);
+      color:#ffb366; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
+          background-color:rgba(217, 130, 43, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
+          background-color:rgba(217, 130, 43, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-danger{
+    background-color:rgba(219, 55, 55, 0.15);
+    color:#c23030; }
+    .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
+        background-color:rgba(219, 55, 55, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
+        background-color:rgba(219, 55, 55, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-danger > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-large{
+      fill:#db3737; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger{
+      background-color:rgba(219, 55, 55, 0.25);
+      color:#ff7373; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
+          background-color:rgba(219, 55, 55, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
+          background-color:rgba(219, 55, 55, 0.45); }
+
+.bp3-tag-remove{
+  background:none;
+  border:none;
+  color:inherit;
+  cursor:pointer;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  margin-bottom:-2px;
+  margin-right:-6px !important;
+  margin-top:-2px;
+  opacity:0.5;
+  padding:2px;
+  padding-left:0; }
+  .bp3-tag-remove:hover{
+    background:none;
+    opacity:0.8;
+    text-decoration:none; }
+  .bp3-tag-remove:active{
+    opacity:1; }
+  .bp3-tag-remove:empty::before{
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-style:normal;
+    font-weight:400;
+    line-height:1;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    content:""; }
+  .bp3-large .bp3-tag-remove{
+    margin-right:-10px !important;
+    padding:0 5px 0 0; }
+    .bp3-large .bp3-tag-remove:empty::before{
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-style:normal;
+      font-weight:400;
+      line-height:1; }
+.bp3-tag-input{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  cursor:text;
+  height:auto;
+  line-height:inherit;
+  min-height:30px;
+  padding-left:5px;
+  padding-right:0; }
+  .bp3-tag-input > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-tag-input > .bp3-tag-input-values{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-tag-input .bp3-tag-input-icon{
+    color:#5c7080;
+    margin-left:2px;
+    margin-right:7px;
+    margin-top:7px; }
+  .bp3-tag-input .bp3-tag-input-values{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-orient:horizontal;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:row;
+            flex-direction:row;
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center;
+    -ms-flex-item-align:stretch;
+        align-self:stretch;
+    -ms-flex-wrap:wrap;
+        flex-wrap:wrap;
+    margin-right:7px;
+    margin-top:5px;
+    min-width:0; }
+    .bp3-tag-input .bp3-tag-input-values > *{
+      -webkit-box-flex:0;
+          -ms-flex-positive:0;
+              flex-grow:0;
+      -ms-flex-negative:0;
+          flex-shrink:0; }
+    .bp3-tag-input .bp3-tag-input-values > .bp3-fill{
+      -webkit-box-flex:1;
+          -ms-flex-positive:1;
+              flex-grow:1;
+      -ms-flex-negative:1;
+          flex-shrink:1; }
+    .bp3-tag-input .bp3-tag-input-values::before,
+    .bp3-tag-input .bp3-tag-input-values > *{
+      margin-right:5px; }
+    .bp3-tag-input .bp3-tag-input-values:empty::before,
+    .bp3-tag-input .bp3-tag-input-values > :last-child{
+      margin-right:0; }
+    .bp3-tag-input .bp3-tag-input-values:first-child .bp3-input-ghost:first-child{
+      padding-left:5px; }
+    .bp3-tag-input .bp3-tag-input-values > *{
+      margin-bottom:5px; }
+  .bp3-tag-input .bp3-tag{
+    overflow-wrap:break-word; }
+    .bp3-tag-input .bp3-tag.bp3-active{
+      outline:rgba(19, 124, 189, 0.6) auto 2px;
+      outline-offset:0;
+      -moz-outline-radius:6px; }
+  .bp3-tag-input .bp3-input-ghost{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    line-height:20px;
+    width:80px; }
+    .bp3-tag-input .bp3-input-ghost:disabled, .bp3-tag-input .bp3-input-ghost.bp3-disabled{
+      cursor:not-allowed; }
+  .bp3-tag-input .bp3-button,
+  .bp3-tag-input .bp3-spinner{
+    margin:3px;
+    margin-left:0; }
+  .bp3-tag-input .bp3-button{
+    min-height:24px;
+    min-width:24px;
+    padding:0 7px; }
+  .bp3-tag-input.bp3-large{
+    height:auto;
+    min-height:40px; }
+    .bp3-tag-input.bp3-large::before,
+    .bp3-tag-input.bp3-large > *{
+      margin-right:10px; }
+    .bp3-tag-input.bp3-large:empty::before,
+    .bp3-tag-input.bp3-large > :last-child{
+      margin-right:0; }
+    .bp3-tag-input.bp3-large .bp3-tag-input-icon{
+      margin-left:5px;
+      margin-top:10px; }
+    .bp3-tag-input.bp3-large .bp3-input-ghost{
+      line-height:30px; }
+    .bp3-tag-input.bp3-large .bp3-button{
+      min-height:30px;
+      min-width:30px;
+      padding:5px 10px;
+      margin:5px;
+      margin-left:0; }
+    .bp3-tag-input.bp3-large .bp3-spinner{
+      margin:8px;
+      margin-left:0; }
+  .bp3-tag-input.bp3-active{
+    background-color:#ffffff;
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-tag-input .bp3-tag-input-icon, .bp3-tag-input.bp3-dark .bp3-tag-input-icon{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-tag-input .bp3-input-ghost, .bp3-tag-input.bp3-dark .bp3-input-ghost{
+    color:#f5f8fa; }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-webkit-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-moz-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost:-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-dark .bp3-tag-input.bp3-active, .bp3-tag-input.bp3-dark.bp3-active{
+    background-color:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-primary, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-success, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-warning, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-danger, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-input-ghost{
+  background:none;
+  border:none;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  padding:0; }
+  .bp3-input-ghost::-webkit-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost::-moz-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost:-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost::-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost::placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost:focus{
+    outline:none !important; }
+.bp3-toast{
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  background-color:#ffffff;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  margin:20px 0 0;
+  max-width:500px;
+  min-width:300px;
+  pointer-events:all;
+  position:relative !important; }
+  .bp3-toast.bp3-toast-enter, .bp3-toast.bp3-toast-appear{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px); }
+  .bp3-toast.bp3-toast-enter-active, .bp3-toast.bp3-toast-appear-active{
+    -webkit-transform:translateY(0);
+            transform:translateY(0);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-toast.bp3-toast-enter ~ .bp3-toast, .bp3-toast.bp3-toast-appear ~ .bp3-toast{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px); }
+  .bp3-toast.bp3-toast-enter-active ~ .bp3-toast, .bp3-toast.bp3-toast-appear-active ~ .bp3-toast{
+    -webkit-transform:translateY(0);
+            transform:translateY(0);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-toast.bp3-toast-exit{
+    opacity:1;
+    -webkit-filter:blur(0);
+            filter:blur(0); }
+  .bp3-toast.bp3-toast-exit-active{
+    opacity:0;
+    -webkit-filter:blur(10px);
+            filter:blur(10px);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:opacity, filter;
+    transition-property:opacity, filter, -webkit-filter;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-toast.bp3-toast-exit ~ .bp3-toast{
+    -webkit-transform:translateY(0);
+            transform:translateY(0); }
+  .bp3-toast.bp3-toast-exit-active ~ .bp3-toast{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px);
+    -webkit-transition-delay:50ms;
+            transition-delay:50ms;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-toast .bp3-button-group{
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    padding:5px;
+    padding-left:0; }
+  .bp3-toast > .bp3-icon{
+    color:#5c7080;
+    margin:12px;
+    margin-right:0; }
+  .bp3-toast.bp3-dark,
+  .bp3-dark .bp3-toast{
+    background-color:#394b59;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+    .bp3-toast.bp3-dark > .bp3-icon,
+    .bp3-dark .bp3-toast > .bp3-icon{
+      color:#a7b6c2; }
+  .bp3-toast[class*="bp3-intent-"] a{
+    color:rgba(255, 255, 255, 0.7); }
+    .bp3-toast[class*="bp3-intent-"] a:hover{
+      color:#ffffff; }
+  .bp3-toast[class*="bp3-intent-"] > .bp3-icon{
+    color:#ffffff; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button, .bp3-toast[class*="bp3-intent-"] .bp3-button::before,
+  .bp3-toast[class*="bp3-intent-"] .bp3-button .bp3-icon, .bp3-toast[class*="bp3-intent-"] .bp3-button:active{
+    color:rgba(255, 255, 255, 0.7) !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:focus{
+    outline-color:rgba(255, 255, 255, 0.5); }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:hover{
+    background-color:rgba(255, 255, 255, 0.15) !important;
+    color:#ffffff !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:active{
+    background-color:rgba(255, 255, 255, 0.3) !important;
+    color:#ffffff !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button::after{
+    background:rgba(255, 255, 255, 0.3) !important; }
+  .bp3-toast.bp3-intent-primary{
+    background-color:#137cbd;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-success{
+    background-color:#0f9960;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-warning{
+    background-color:#d9822b;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-danger{
+    background-color:#db3737;
+    color:#ffffff; }
+
+.bp3-toast-message{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  padding:11px;
+  word-break:break-word; }
+
+.bp3-toast-container{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box !important;
+  display:-ms-flexbox !important;
+  display:flex !important;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  left:0;
+  overflow:hidden;
+  padding:0 20px 20px;
+  pointer-events:none;
+  right:0;
+  z-index:40; }
+  .bp3-toast-container.bp3-toast-container-in-portal{
+    position:fixed; }
+  .bp3-toast-container.bp3-toast-container-inline{
+    position:absolute; }
+  .bp3-toast-container.bp3-toast-container-top{
+    top:0; }
+  .bp3-toast-container.bp3-toast-container-bottom{
+    bottom:0;
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:reverse;
+        -ms-flex-direction:column-reverse;
+            flex-direction:column-reverse;
+    top:auto; }
+  .bp3-toast-container.bp3-toast-container-left{
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start; }
+  .bp3-toast-container.bp3-toast-container-right{
+    -webkit-box-align:end;
+        -ms-flex-align:end;
+            align-items:flex-end; }
+
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active),
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active) ~ .bp3-toast, .bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active),
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active) ~ .bp3-toast,
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-exit-active ~ .bp3-toast,
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-leave-active ~ .bp3-toast{
+  -webkit-transform:translateY(60px);
+          transform:translateY(60px); }
+.bp3-tooltip{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  -webkit-transform:scale(1);
+          transform:scale(1); }
+  .bp3-tooltip .bp3-popover-arrow{
+    height:22px;
+    position:absolute;
+    width:22px; }
+    .bp3-tooltip .bp3-popover-arrow::before{
+      height:14px;
+      margin:4px;
+      width:14px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip{
+    margin-bottom:11px;
+    margin-top:-11px; }
+    .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
+      bottom:-8px; }
+      .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(-90deg);
+                transform:rotate(-90deg); }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip{
+    margin-left:11px; }
+    .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
+      left:-8px; }
+      .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(0);
+                transform:rotate(0); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip{
+    margin-top:11px; }
+    .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
+      top:-8px; }
+      .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(90deg);
+                transform:rotate(90deg); }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip{
+    margin-left:-11px;
+    margin-right:11px; }
+    .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
+      right:-8px; }
+      .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(180deg);
+                transform:rotate(180deg); }
+  .bp3-tether-element-attached-middle > .bp3-tooltip > .bp3-popover-arrow{
+    top:50%;
+    -webkit-transform:translateY(-50%);
+            transform:translateY(-50%); }
+  .bp3-tether-element-attached-center > .bp3-tooltip > .bp3-popover-arrow{
+    right:50%;
+    -webkit-transform:translateX(50%);
+            transform:translateX(50%); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
+    top:-0.22183px; }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
+    right:-0.22183px; }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
+    left:-0.22183px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
+    bottom:-0.22183px; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:top left;
+            transform-origin:top left; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:top center;
+            transform-origin:top center; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:top right;
+            transform-origin:top right; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:center left;
+            transform-origin:center left; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:center center;
+            transform-origin:center center; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:center right;
+            transform-origin:center right; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:bottom left;
+            transform-origin:bottom left; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:bottom center;
+            transform-origin:bottom center; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:bottom right;
+            transform-origin:bottom right; }
+  .bp3-tooltip .bp3-popover-content{
+    background:#394b59;
+    color:#f5f8fa; }
+  .bp3-tooltip .bp3-popover-arrow::before{
+    -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
+            box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-tooltip .bp3-popover-arrow-border{
+    fill:#10161a;
+    fill-opacity:0.1; }
+  .bp3-tooltip .bp3-popover-arrow-fill{
+    fill:#394b59; }
+  .bp3-popover-enter > .bp3-tooltip, .bp3-popover-appear > .bp3-tooltip{
+    -webkit-transform:scale(0.8);
+            transform:scale(0.8); }
+  .bp3-popover-enter-active > .bp3-tooltip, .bp3-popover-appear-active > .bp3-tooltip{
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-popover-exit > .bp3-tooltip{
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-popover-exit-active > .bp3-tooltip{
+    -webkit-transform:scale(0.8);
+            transform:scale(0.8);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-tooltip .bp3-popover-content{
+    padding:10px 12px; }
+  .bp3-tooltip.bp3-dark,
+  .bp3-dark .bp3-tooltip{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+    .bp3-tooltip.bp3-dark .bp3-popover-content,
+    .bp3-dark .bp3-tooltip .bp3-popover-content{
+      background:#e1e8ed;
+      color:#394b59; }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow::before,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow::before{
+      -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
+              box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow-border,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow-border{
+      fill:#10161a;
+      fill-opacity:0.2; }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow-fill,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow-fill{
+      fill:#e1e8ed; }
+  .bp3-tooltip.bp3-intent-primary .bp3-popover-content{
+    background:#137cbd;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-primary .bp3-popover-arrow-fill{
+    fill:#137cbd; }
+  .bp3-tooltip.bp3-intent-success .bp3-popover-content{
+    background:#0f9960;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-success .bp3-popover-arrow-fill{
+    fill:#0f9960; }
+  .bp3-tooltip.bp3-intent-warning .bp3-popover-content{
+    background:#d9822b;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-warning .bp3-popover-arrow-fill{
+    fill:#d9822b; }
+  .bp3-tooltip.bp3-intent-danger .bp3-popover-content{
+    background:#db3737;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-danger .bp3-popover-arrow-fill{
+    fill:#db3737; }
+
+.bp3-tooltip-indicator{
+  border-bottom:dotted 1px;
+  cursor:help; }
+.bp3-tree .bp3-icon, .bp3-tree .bp3-icon-standard, .bp3-tree .bp3-icon-large{
+  color:#5c7080; }
+  .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-tree .bp3-icon-large.bp3-intent-primary{
+    color:#137cbd; }
+  .bp3-tree .bp3-icon.bp3-intent-success, .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-tree .bp3-icon-large.bp3-intent-success{
+    color:#0f9960; }
+  .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-tree .bp3-icon-large.bp3-intent-warning{
+    color:#d9822b; }
+  .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-tree .bp3-icon-large.bp3-intent-danger{
+    color:#db3737; }
+
+.bp3-tree-node-list{
+  list-style:none;
+  margin:0;
+  padding-left:0; }
+
+.bp3-tree-root{
+  background-color:transparent;
+  cursor:default;
+  padding-left:0;
+  position:relative; }
+
+.bp3-tree-node-content-0{
+  padding-left:0px; }
+
+.bp3-tree-node-content-1{
+  padding-left:23px; }
+
+.bp3-tree-node-content-2{
+  padding-left:46px; }
+
+.bp3-tree-node-content-3{
+  padding-left:69px; }
+
+.bp3-tree-node-content-4{
+  padding-left:92px; }
+
+.bp3-tree-node-content-5{
+  padding-left:115px; }
+
+.bp3-tree-node-content-6{
+  padding-left:138px; }
+
+.bp3-tree-node-content-7{
+  padding-left:161px; }
+
+.bp3-tree-node-content-8{
+  padding-left:184px; }
+
+.bp3-tree-node-content-9{
+  padding-left:207px; }
+
+.bp3-tree-node-content-10{
+  padding-left:230px; }
+
+.bp3-tree-node-content-11{
+  padding-left:253px; }
+
+.bp3-tree-node-content-12{
+  padding-left:276px; }
+
+.bp3-tree-node-content-13{
+  padding-left:299px; }
+
+.bp3-tree-node-content-14{
+  padding-left:322px; }
+
+.bp3-tree-node-content-15{
+  padding-left:345px; }
+
+.bp3-tree-node-content-16{
+  padding-left:368px; }
+
+.bp3-tree-node-content-17{
+  padding-left:391px; }
+
+.bp3-tree-node-content-18{
+  padding-left:414px; }
+
+.bp3-tree-node-content-19{
+  padding-left:437px; }
+
+.bp3-tree-node-content-20{
+  padding-left:460px; }
+
+.bp3-tree-node-content{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  height:30px;
+  padding-right:5px;
+  width:100%; }
+  .bp3-tree-node-content:hover{
+    background-color:rgba(191, 204, 214, 0.4); }
+
+.bp3-tree-node-caret,
+.bp3-tree-node-caret-none{
+  min-width:30px; }
+
+.bp3-tree-node-caret{
+  color:#5c7080;
+  cursor:pointer;
+  padding:7px;
+  -webkit-transform:rotate(0deg);
+          transform:rotate(0deg);
+  -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-tree-node-caret:hover{
+    color:#182026; }
+  .bp3-dark .bp3-tree-node-caret{
+    color:#a7b6c2; }
+    .bp3-dark .bp3-tree-node-caret:hover{
+      color:#f5f8fa; }
+  .bp3-tree-node-caret.bp3-tree-node-caret-open{
+    -webkit-transform:rotate(90deg);
+            transform:rotate(90deg); }
+  .bp3-tree-node-caret.bp3-icon-standard::before{
+    content:""; }
+
+.bp3-tree-node-icon{
+  margin-right:7px;
+  position:relative; }
+
+.bp3-tree-node-label{
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  position:relative;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-tree-node-label span{
+    display:inline; }
+
+.bp3-tree-node-secondary-label{
+  padding:0 5px;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-tree-node-secondary-label .bp3-popover-wrapper,
+  .bp3-tree-node-secondary-label .bp3-popover-target{
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center;
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex; }
+
+.bp3-tree-node.bp3-disabled .bp3-tree-node-content{
+  background-color:inherit;
+  color:rgba(92, 112, 128, 0.6);
+  cursor:not-allowed; }
+
+.bp3-tree-node.bp3-disabled .bp3-tree-node-caret,
+.bp3-tree-node.bp3-disabled .bp3-tree-node-icon{
+  color:rgba(92, 112, 128, 0.6);
+  cursor:not-allowed; }
+
+.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
+  background-color:#137cbd; }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content,
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-standard, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-large{
+    color:#ffffff; }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret::before{
+    color:rgba(255, 255, 255, 0.7); }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret:hover::before{
+    color:#ffffff; }
+
+.bp3-dark .bp3-tree-node-content:hover{
+  background-color:rgba(92, 112, 128, 0.3); }
+
+.bp3-dark .bp3-tree .bp3-icon, .bp3-dark .bp3-tree .bp3-icon-standard, .bp3-dark .bp3-tree .bp3-icon-large{
+  color:#a7b6c2; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-primary{
+    color:#137cbd; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-success{
+    color:#0f9960; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-warning{
+    color:#d9822b; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-danger{
+    color:#db3737; }
+
+.bp3-dark .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
+  background-color:#137cbd; }
+.bp3-omnibar{
+  -webkit-filter:blur(0);
+          filter:blur(0);
+  opacity:1;
+  background-color:#ffffff;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  left:calc(50% - 250px);
+  top:20vh;
+  width:500px;
+  z-index:21; }
+  .bp3-omnibar.bp3-overlay-enter, .bp3-omnibar.bp3-overlay-appear{
+    -webkit-filter:blur(20px);
+            filter:blur(20px);
+    opacity:0.2; }
+  .bp3-omnibar.bp3-overlay-enter-active, .bp3-omnibar.bp3-overlay-appear-active{
+    -webkit-filter:blur(0);
+            filter:blur(0);
+    opacity:1;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:filter, opacity;
+    transition-property:filter, opacity, -webkit-filter;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-omnibar.bp3-overlay-exit{
+    -webkit-filter:blur(0);
+            filter:blur(0);
+    opacity:1; }
+  .bp3-omnibar.bp3-overlay-exit-active{
+    -webkit-filter:blur(20px);
+            filter:blur(20px);
+    opacity:0.2;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:filter, opacity;
+    transition-property:filter, opacity, -webkit-filter;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-omnibar .bp3-input{
+    background-color:transparent;
+    border-radius:0; }
+    .bp3-omnibar .bp3-input, .bp3-omnibar .bp3-input:focus{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-omnibar .bp3-menu{
+    background-color:transparent;
+    border-radius:0;
+    -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+    max-height:calc(60vh - 40px);
+    overflow:auto; }
+    .bp3-omnibar .bp3-menu:empty{
+      display:none; }
+  .bp3-dark .bp3-omnibar, .bp3-omnibar.bp3-dark{
+    background-color:#30404d;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }
+
+.bp3-omnibar-overlay .bp3-overlay-backdrop{
+  background-color:rgba(16, 22, 26, 0.2); }
+
+.bp3-select-popover .bp3-popover-content{
+  padding:5px; }
+
+.bp3-select-popover .bp3-input-group{
+  margin-bottom:0; }
+
+.bp3-select-popover .bp3-menu{
+  max-height:300px;
+  max-width:400px;
+  overflow:auto;
+  padding:0; }
+  .bp3-select-popover .bp3-menu:not(:first-child){
+    padding-top:5px; }
+
+.bp3-multi-select{
+  min-width:150px; }
+
+.bp3-multi-select-popover .bp3-menu{
+  max-height:300px;
+  max-width:400px;
+  overflow:auto; }
+
+.bp3-select-popover .bp3-popover-content{
+  padding:5px; }
+
+.bp3-select-popover .bp3-input-group{
+  margin-bottom:0; }
+
+.bp3-select-popover .bp3-menu{
+  max-height:300px;
+  max-width:400px;
+  overflow:auto;
+  padding:0; }
+  .bp3-select-popover .bp3-menu:not(:first-child){
+    padding-top:5px; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+/* Icons urls */
+
+:root {
+  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-bug: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0yMCA4aC0yLjgxYy0uNDUtLjc4LTEuMDctMS40NS0xLjgyLTEuOTZMMTcgNC40MSAxNS41OSAzbC0yLjE3IDIuMTdDMTIuOTYgNS4wNiAxMi40OSA1IDEyIDVjLS40OSAwLS45Ni4wNi0xLjQxLjE3TDguNDEgMyA3IDQuNDFsMS42MiAxLjYzQzcuODggNi41NSA3LjI2IDcuMjIgNi44MSA4SDR2MmgyLjA5Yy0uMDUuMzMtLjA5LjY2LS4wOSAxdjFINHYyaDJ2MWMwIC4zNC4wNC42Ny4wOSAxSDR2MmgyLjgxYzEuMDQgMS43OSAyLjk3IDMgNS4xOSAzczQuMTUtMS4yMSA1LjE5LTNIMjB2LTJoLTIuMDljLjA1LS4zMy4wOS0uNjYuMDktMXYtMWgydi0yaC0ydi0xYzAtLjM0LS4wNC0uNjctLjA5LTFIMjBWOHptLTYgOGgtNHYtMmg0djJ6bTAtNGgtNHYtMmg0djJ6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-build: url(data:image/svg+xml;base64,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);
+  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-right: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik03LjIsNS4yTDEwLjksOWwtMy44LDMuOFY1LjJINy4yeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-up-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iMTUuNCwxMy4zIDkuOSw3LjcgNC40LDEzLjIgMy42LDEyLjUgOS45LDYuMyAxNi4xLDEyLjYgIi8+Cgk8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-up: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik01LjIsMTAuNUw5LDYuOGwzLjgsMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-case-sensitive: url(data:image/svg+xml;base64,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);
+  --jp-icon-check: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik05IDE2LjE3TDQuODMgMTJsLTEuNDIgMS40MUw5IDE5IDIxIDdsLTEuNDEtMS40MXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-circle-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDJDNi40NyAyIDIgNi40NyAyIDEyczQuNDcgMTAgMTAgMTAgMTAtNC40NyAxMC0xMFMxNy41MyAyIDEyIDJ6bTAgMThjLTQuNDEgMC04LTMuNTktOC04czMuNTktOCA4LTggOCAzLjU5IDggOC0zLjU5IDgtOCA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-circle: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iOSIgY3k9IjkiIHI9IjgiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-clear: url(data:image/svg+xml;base64,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);
+  --jp-icon-close: url(data:image/svg+xml;base64,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);
+  --jp-icon-code: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTExLjQgMTguNkw2LjggMTRMMTEuNCA5LjRMMTAgOEw0IDE0TDEwIDIwTDExLjQgMTguNlpNMTYuNiAxOC42TDIxLjIgMTRMMTYuNiA5LjRMMTggOEwyNCAxNEwxOCAyMEwxNi42IDE4LjZWMTguNloiLz4KCTwvZz4KPC9zdmc+Cg==);
+  --jp-icon-console: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwMCAyMDAiPgogIDxnIGNsYXNzPSJqcC1pY29uLWJyYW5kMSBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiMwMjg4RDEiPgogICAgPHBhdGggZD0iTTIwIDE5LjhoMTYwdjE1OS45SDIweiIvPgogIDwvZz4KICA8ZyBjbGFzcz0ianAtaWNvbi1zZWxlY3RhYmxlLWludmVyc2UiIGZpbGw9IiNmZmYiPgogICAgPHBhdGggZD0iTTEwNSAxMjcuM2g0MHYxMi44aC00MHpNNTEuMSA3N0w3NCA5OS45bC0yMy4zIDIzLjMgMTAuNSAxMC41IDIzLjMtMjMuM0w5NSA5OS45IDg0LjUgODkuNCA2MS42IDY2LjV6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-copy: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTExLjksMUgzLjJDMi40LDEsMS43LDEuNywxLjcsMi41djEwLjJoMS41VjIuNWg4LjdWMXogTTE0LjEsMy45aC04Yy0wLjgsMC0xLjUsMC43LTEuNSwxLjV2MTAuMmMwLDAuOCwwLjcsMS41LDEuNSwxLjVoOCBjMC44LDAsMS41LTAuNywxLjUtMS41VjUuNEMxNS41LDQuNiwxNC45LDMuOSwxNC4xLDMuOXogTTE0LjEsMTUuNWgtOFY1LjRoOFYxNS41eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-copyright: url(data:image/svg+xml;base64,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);
+  --jp-icon-cut: url(data:image/svg+xml;base64,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);
+  --jp-icon-download: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDloLTRWM0g5djZINWw3IDcgNy03ek01IDE4djJoMTR2LTJINXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-edit: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMgMTcuMjVWMjFoMy43NUwxNy44MSA5Ljk0bC0zLjc1LTMuNzVMMyAxNy4yNXpNMjAuNzEgNy4wNGMuMzktLjM5LjM5LTEuMDIgMC0xLjQxbC0yLjM0LTIuMzRjLS4zOS0uMzktMS4wMi0uMzktMS40MSAwbC0xLjgzIDEuODMgMy43NSAzLjc1IDEuODMtMS44M3oiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-ellipses: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iNSIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxMiIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxOSIgY3k9IjEyIiByPSIyIi8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-extension: url(data:image/svg+xml;base64,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);
+  --jp-icon-fast-forward: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTQgMThsOC41LTZMNCA2djEyem05LTEydjEybDguNS02TDEzIDZ6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-file-upload: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTkgMTZoNnYtNmg0bC03LTctNyA3aDR6bS00IDJoMTR2Mkg1eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-file: url(data:image/svg+xml;base64,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);
+  --jp-icon-filter-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEwIDE4aDR2LTJoLTR2MnpNMyA2djJoMThWNkgzem0zIDdoMTJ2LTJINnYyeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY4YzAtMS4xLS45LTItMi0yaC04bC0yLTJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-html5: url(data:image/svg+xml;base64,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);
+  --jp-icon-image: url(data:image/svg+xml;base64,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);
+  --jp-icon-inspector: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY2YzAtMS4xLS45LTItMi0yem0tNSAxNEg0di00aDExdjR6bTAtNUg0VjloMTF2NHptNSA1aC00VjloNHY5eiIvPgo8L3N2Zz4K);
+  --jp-icon-json: url(data:image/svg+xml;base64,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);
+  --jp-icon-julia: url(data:image/svg+xml;base64,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);
+  --jp-icon-jupyter-favicon: url(data:image/svg+xml;base64,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);
+  --jp-icon-jupyter: url(data:image/svg+xml;base64,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);
+  --jp-icon-jupyterlab-wordmark: url(data:image/svg+xml;base64,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+  --jp-icon-kernel: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzYxNjE2MSIgZD0iTTE1IDlIOXY2aDZWOXptLTIgNGgtMnYtMmgydjJ6bTgtMlY5aC0yVjdjMC0xLjEtLjktMi0yLTJoLTJWM2gtMnYyaC0yVjNIOXYySDdjLTEuMSAwLTIgLjktMiAydjJIM3YyaDJ2MkgzdjJoMnYyYzAgMS4xLjkgMiAyIDJoMnYyaDJ2LTJoMnYyaDJ2LTJoMmMxLjEgMCAyLS45IDItMnYtMmgydi0yaC0ydi0yaDJ6bS00IDZIN1Y3aDEwdjEweiIvPgo8L3N2Zz4K);
+  --jp-icon-keyboard: url(data:image/svg+xml;base64,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);
+  --jp-icon-launcher: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTkgMTlINVY1aDdWM0g1YTIgMiAwIDAwLTIgMnYxNGEyIDIgMCAwMDIgMmgxNGMxLjEgMCAyLS45IDItMnYtN2gtMnY3ek0xNCAzdjJoMy41OWwtOS44MyA5LjgzIDEuNDEgMS40MUwxOSA2LjQxVjEwaDJWM2gtN3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-line-form: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGZpbGw9IndoaXRlIiBkPSJNNS44OCA0LjEyTDEzLjc2IDEybC03Ljg4IDcuODhMOCAyMmwxMC0xMEw4IDJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-link: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMuOSAxMmMwLTEuNzEgMS4zOS0zLjEgMy4xLTMuMWg0VjdIN2MtMi43NiAwLTUgMi4yNC01IDVzMi4yNCA1IDUgNWg0di0xLjlIN2MtMS43MSAwLTMuMS0xLjM5LTMuMS0zLjF6TTggMTNoOHYtMkg4djJ6bTktNmgtNHYxLjloNGMxLjcxIDAgMy4xIDEuMzkgMy4xIDMuMXMtMS4zOSAzLjEtMy4xIDMuMWgtNFYxN2g0YzIuNzYgMCA1LTIuMjQgNS01cy0yLjI0LTUtNS01eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xOSA1djE0SDVWNWgxNG0xLjEtMkgzLjljLS41IDAtLjkuNC0uOS45djE2LjJjMCAuNC40LjkuOS45aDE2LjJjLjQgMCAuOS0uNS45LS45VjMuOWMwLS41LS41LS45LS45LS45ek0xMSA3aDZ2MmgtNlY3em0wIDRoNnYyaC02di0yem0wIDRoNnYyaC02ek03IDdoMnYySDd6bTAgNGgydjJIN3ptMCA0aDJ2Mkg3eiIvPgo8L3N2Zz4=);
+  --jp-icon-listings-info: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA1MC45NzggNTAuOTc4IiBzdHlsZT0iZW5hYmxlLWJhY2tncm91bmQ6bmV3IDAgMCA1MC45NzggNTAuOTc4OyIgeG1sOnNwYWNlPSJwcmVzZXJ2ZSI+Cgk8Zz4KCQk8cGF0aCBzdHlsZT0iZmlsbDojMDEwMDAyOyIgZD0iTTQzLjUyLDcuNDU4QzM4LjcxMSwyLjY0OCwzMi4zMDcsMCwyNS40ODksMEMxOC42NywwLDEyLjI2NiwyLjY0OCw3LjQ1OCw3LjQ1OAoJCQljLTkuOTQzLDkuOTQxLTkuOTQzLDI2LjExOSwwLDM2LjA2MmM0LjgwOSw0LjgwOSwxMS4yMTIsNy40NTYsMTguMDMxLDcuNDU4YzAsMCwwLjAwMSwwLDAuMDAyLDAKCQkJYzYuODE2LDAsMTMuMjIxLTIuNjQ4LDE4LjAyOS03LjQ1OGM0LjgwOS00LjgwOSw3LjQ1Ny0xMS4yMTIsNy40NTctMTguMDNDNTAuOTc3LDE4LjY3LDQ4LjMyOCwxMi4yNjYsNDMuNTIsNy40NTh6CgkJCSBNNDIuMTA2LDQyLjEwNWMtNC40MzIsNC40MzEtMTAuMzMyLDYuODcyLTE2LjYxNSw2Ljg3MmgtMC4wMDJjLTYuMjg1LTAuMDAxLTEyLjE4Ny0yLjQ0MS0xNi42MTctNi44NzIKCQkJYy05LjE2Mi05LjE2My05LjE2Mi0yNC4wNzEsMC0zMy4yMzNDMTMuMzAzLDQuNDQsMTkuMjA0LDIsMjUuNDg5LDJjNi4yODQsMCwxMi4xODYsMi40NCwxNi42MTcsNi44NzIKCQkJYzQuNDMxLDQuNDMxLDYuODcxLDEwLjMzMiw2Ljg3MSwxNi42MTdDNDguOTc3LDMxLjc3Miw0Ni41MzYsMzcuNjc1LDQyLjEwNiw0Mi4xMDV6Ii8+CgkJPHBhdGggc3R5bGU9ImZpbGw6IzAxMDAwMjsiIGQ9Ik0yMy41NzgsMzIuMjE4Yy0wLjAyMy0xLjczNCwwLjE0My0zLjA1OSwwLjQ5Ni0zLjk3MmMwLjM1My0wLjkxMywxLjExLTEuOTk3LDIuMjcyLTMuMjUzCgkJCWMwLjQ2OC0wLjUzNiwwLjkyMy0xLjA2MiwxLjM2Ny0xLjU3NWMwLjYyNi0wLjc1MywxLjEwNC0xLjQ3OCwxLjQzNi0yLjE3NWMwLjMzMS0wLjcwNywwLjQ5NS0xLjU0MSwwLjQ5NS0yLjUKCQkJYzAtMS4wOTYtMC4yNi0yLjA4OC0wLjc3OS0yLjk3OWMtMC41NjUtMC44NzktMS41MDEtMS4zMzYtMi44MDYtMS4zNjljLTEuODAyLDAuMDU3LTIuOTg1LDAuNjY3LTMuNTUsMS44MzIKCQkJYy0wLjMwMSwwLjUzNS0wLjUwMywxLjE0MS0wLjYwNywxLjgxNGMtMC4xMzksMC43MDctMC4yMDcsMS40MzItMC4yMDcsMi4xNzRoLTIuOTM3Yy0wLjA5MS0yLjIwOCwwLjQwNy00LjExNCwxLjQ5My01LjcxOQoJCQljMS4wNjItMS42NCwyLjg1NS0yLjQ4MSw1LjM3OC0yLjUyN2MyLjE2LDAuMDIzLDMuODc0LDAuNjA4LDUuMTQxLDEuNzU4YzEuMjc4LDEuMTYsMS45MjksMi43NjQsMS45NSw0LjgxMQoJCQljMCwxLjE0Mi0wLjEzNywyLjExMS0wLjQxLDIuOTExYy0wLjMwOSwwLjg0NS0wLjczMSwxLjU5My0xLjI2OCwyLjI0M2MtMC40OTIsMC42NS0xLjA2OCwxLjMxOC0xLjczLDIuMDAyCgkJCWMtMC42NSwwLjY5Ny0xLjMxMywxLjQ3OS0xLjk4NywyLjM0NmMtMC4yMzksMC4zNzctMC40MjksMC43NzctMC41NjUsMS4xOTljLTAuMTYsMC45NTktMC4yMTcsMS45NTEtMC4xNzEsMi45NzkKCQkJQzI2LjU4OSwzMi4yMTgsMjMuNTc4LDMyLjIxOCwyMy41NzgsMzIuMjE4eiBNMjMuNTc4LDM4LjIydi0zLjQ4NGgzLjA3NnYzLjQ4NEgyMy41Nzh6Ii8+Cgk8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-markdown: url(data:image/svg+xml;base64,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);
+  --jp-icon-new-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwIDZoLThsLTItMkg0Yy0xLjExIDAtMS45OS44OS0xLjk5IDJMMiAxOGMwIDEuMTEuODkgMiAyIDJoMTZjMS4xMSAwIDItLjg5IDItMlY4YzAtMS4xMS0uODktMi0yLTJ6bS0xIDhoLTN2M2gtMnYtM2gtM3YtMmgzVjloMnYzaDN2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-not-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-notebook: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbi13YXJuMCBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-numbering: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTQgMTlINlYxOS41SDVWMjAuNUg2VjIxSDRWMjJIN1YxOEg0VjE5Wk01IDEwSDZWNkg0VjdINVYxMFpNNCAxM0g1LjhMNCAxNS4xVjE2SDdWMTVINS4yTDcgMTIuOVYxMkg0VjEzWk05IDdWOUgyM1Y3SDlaTTkgMjFIMjNWMTlIOVYyMVpNOSAxNUgyM1YxM0g5VjE1WiIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-offline-bolt: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDIuMDJjLTUuNTEgMC05Ljk4IDQuNDctOS45OCA5Ljk4czQuNDcgOS45OCA5Ljk4IDkuOTggOS45OC00LjQ3IDkuOTgtOS45OFMxNy41MSAyLjAyIDEyIDIuMDJ6TTExLjQ4IDIwdi02LjI2SDhMMTMgNHY2LjI2aDMuMzVMMTEuNDggMjB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-palette: url(data:image/svg+xml;base64,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);
+  --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-pdf: url(data:image/svg+xml;base64,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);
+  --jp-icon-python: url(data:image/svg+xml;base64,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);
+  --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
+  --jp-icon-react: url(data:image/svg+xml;base64,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);
+  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-regex: url(data:image/svg+xml;base64,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);
+  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-search: url(data:image/svg+xml;base64,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);
+  --jp-icon-settings: url(data:image/svg+xml;base64,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);
+  --jp-icon-spreadsheet: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNENBRjUwIiBkPSJNMi4yIDIuMnYxNy42aDE3LjZWMi4ySDIuMnptMTUuNCA3LjdoLTUuNVY0LjRoNS41djUuNXpNOS45IDQuNHY1LjVINC40VjQuNGg1LjV6bS01LjUgNy43aDUuNXY1LjVINC40di01LjV6bTcuNyA1LjV2LTUuNWg1LjV2NS41aC01LjV6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-stop: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik02IDZoMTJ2MTJINnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4=);
+  --jp-icon-tag: url(data:image/svg+xml;base64,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);
+  --jp-icon-terminal: url(data:image/svg+xml;base64,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);
+  --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTUgMTVIM3YyaDEydi0yem0wLThIM3YyaDEyVjd6TTMgMTNoMTh2LTJIM3Yyem0wIDhoMTh2LTJIM3Yyek0zIDN2MmgxOFYzSDN6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-toc: url(data:image/svg+xml;base64,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);
+  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4=);
+  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
+}
+
+/* Icon CSS class declarations */
+
+.jp-AddIcon {
+  background-image: var(--jp-icon-add);
+}
+.jp-BugIcon {
+  background-image: var(--jp-icon-bug);
+}
+.jp-BuildIcon {
+  background-image: var(--jp-icon-build);
+}
+.jp-CaretDownEmptyIcon {
+  background-image: var(--jp-icon-caret-down-empty);
+}
+.jp-CaretDownEmptyThinIcon {
+  background-image: var(--jp-icon-caret-down-empty-thin);
+}
+.jp-CaretDownIcon {
+  background-image: var(--jp-icon-caret-down);
+}
+.jp-CaretLeftIcon {
+  background-image: var(--jp-icon-caret-left);
+}
+.jp-CaretRightIcon {
+  background-image: var(--jp-icon-caret-right);
+}
+.jp-CaretUpEmptyThinIcon {
+  background-image: var(--jp-icon-caret-up-empty-thin);
+}
+.jp-CaretUpIcon {
+  background-image: var(--jp-icon-caret-up);
+}
+.jp-CaseSensitiveIcon {
+  background-image: var(--jp-icon-case-sensitive);
+}
+.jp-CheckIcon {
+  background-image: var(--jp-icon-check);
+}
+.jp-CircleEmptyIcon {
+  background-image: var(--jp-icon-circle-empty);
+}
+.jp-CircleIcon {
+  background-image: var(--jp-icon-circle);
+}
+.jp-ClearIcon {
+  background-image: var(--jp-icon-clear);
+}
+.jp-CloseIcon {
+  background-image: var(--jp-icon-close);
+}
+.jp-CodeIcon {
+  background-image: var(--jp-icon-code);
+}
+.jp-ConsoleIcon {
+  background-image: var(--jp-icon-console);
+}
+.jp-CopyIcon {
+  background-image: var(--jp-icon-copy);
+}
+.jp-CopyrightIcon {
+  background-image: var(--jp-icon-copyright);
+}
+.jp-CutIcon {
+  background-image: var(--jp-icon-cut);
+}
+.jp-DownloadIcon {
+  background-image: var(--jp-icon-download);
+}
+.jp-EditIcon {
+  background-image: var(--jp-icon-edit);
+}
+.jp-EllipsesIcon {
+  background-image: var(--jp-icon-ellipses);
+}
+.jp-ExtensionIcon {
+  background-image: var(--jp-icon-extension);
+}
+.jp-FastForwardIcon {
+  background-image: var(--jp-icon-fast-forward);
+}
+.jp-FileIcon {
+  background-image: var(--jp-icon-file);
+}
+.jp-FileUploadIcon {
+  background-image: var(--jp-icon-file-upload);
+}
+.jp-FilterListIcon {
+  background-image: var(--jp-icon-filter-list);
+}
+.jp-FolderIcon {
+  background-image: var(--jp-icon-folder);
+}
+.jp-Html5Icon {
+  background-image: var(--jp-icon-html5);
+}
+.jp-ImageIcon {
+  background-image: var(--jp-icon-image);
+}
+.jp-InspectorIcon {
+  background-image: var(--jp-icon-inspector);
+}
+.jp-JsonIcon {
+  background-image: var(--jp-icon-json);
+}
+.jp-JuliaIcon {
+  background-image: var(--jp-icon-julia);
+}
+.jp-JupyterFaviconIcon {
+  background-image: var(--jp-icon-jupyter-favicon);
+}
+.jp-JupyterIcon {
+  background-image: var(--jp-icon-jupyter);
+}
+.jp-JupyterlabWordmarkIcon {
+  background-image: var(--jp-icon-jupyterlab-wordmark);
+}
+.jp-KernelIcon {
+  background-image: var(--jp-icon-kernel);
+}
+.jp-KeyboardIcon {
+  background-image: var(--jp-icon-keyboard);
+}
+.jp-LauncherIcon {
+  background-image: var(--jp-icon-launcher);
+}
+.jp-LineFormIcon {
+  background-image: var(--jp-icon-line-form);
+}
+.jp-LinkIcon {
+  background-image: var(--jp-icon-link);
+}
+.jp-ListIcon {
+  background-image: var(--jp-icon-list);
+}
+.jp-ListingsInfoIcon {
+  background-image: var(--jp-icon-listings-info);
+}
+.jp-MarkdownIcon {
+  background-image: var(--jp-icon-markdown);
+}
+.jp-NewFolderIcon {
+  background-image: var(--jp-icon-new-folder);
+}
+.jp-NotTrustedIcon {
+  background-image: var(--jp-icon-not-trusted);
+}
+.jp-NotebookIcon {
+  background-image: var(--jp-icon-notebook);
+}
+.jp-NumberingIcon {
+  background-image: var(--jp-icon-numbering);
+}
+.jp-OfflineBoltIcon {
+  background-image: var(--jp-icon-offline-bolt);
+}
+.jp-PaletteIcon {
+  background-image: var(--jp-icon-palette);
+}
+.jp-PasteIcon {
+  background-image: var(--jp-icon-paste);
+}
+.jp-PdfIcon {
+  background-image: var(--jp-icon-pdf);
+}
+.jp-PythonIcon {
+  background-image: var(--jp-icon-python);
+}
+.jp-RKernelIcon {
+  background-image: var(--jp-icon-r-kernel);
+}
+.jp-ReactIcon {
+  background-image: var(--jp-icon-react);
+}
+.jp-RedoIcon {
+  background-image: var(--jp-icon-redo);
+}
+.jp-RefreshIcon {
+  background-image: var(--jp-icon-refresh);
+}
+.jp-RegexIcon {
+  background-image: var(--jp-icon-regex);
+}
+.jp-RunIcon {
+  background-image: var(--jp-icon-run);
+}
+.jp-RunningIcon {
+  background-image: var(--jp-icon-running);
+}
+.jp-SaveIcon {
+  background-image: var(--jp-icon-save);
+}
+.jp-SearchIcon {
+  background-image: var(--jp-icon-search);
+}
+.jp-SettingsIcon {
+  background-image: var(--jp-icon-settings);
+}
+.jp-SpreadsheetIcon {
+  background-image: var(--jp-icon-spreadsheet);
+}
+.jp-StopIcon {
+  background-image: var(--jp-icon-stop);
+}
+.jp-TabIcon {
+  background-image: var(--jp-icon-tab);
+}
+.jp-TableRowsIcon {
+  background-image: var(--jp-icon-table-rows);
+}
+.jp-TagIcon {
+  background-image: var(--jp-icon-tag);
+}
+.jp-TerminalIcon {
+  background-image: var(--jp-icon-terminal);
+}
+.jp-TextEditorIcon {
+  background-image: var(--jp-icon-text-editor);
+}
+.jp-TocIcon {
+  background-image: var(--jp-icon-toc);
+}
+.jp-TreeViewIcon {
+  background-image: var(--jp-icon-tree-view);
+}
+.jp-TrustedIcon {
+  background-image: var(--jp-icon-trusted);
+}
+.jp-UndoIcon {
+  background-image: var(--jp-icon-undo);
+}
+.jp-VegaIcon {
+  background-image: var(--jp-icon-vega);
+}
+.jp-YamlIcon {
+  background-image: var(--jp-icon-yaml);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+.jp-Icon,
+.jp-MaterialIcon {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
+
+.jp-Icon-cover {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: cover;
+}
+
+/**
+ * (DEPRECATED) Support for specific CSS icon sizes
+ */
+
+.jp-Icon-16 {
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
+
+.jp-Icon-18 {
+  background-size: 18px;
+  min-width: 18px;
+  min-height: 18px;
+}
+
+.jp-Icon-20 {
+  background-size: 20px;
+  min-width: 20px;
+  min-height: 20px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for icons as inline SVG HTMLElements
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+.jp-icon1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+.jp-icon2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+.jp-icon3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+.jp-icon4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+.jp-icon1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+.jp-icon2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+.jp-icon3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+.jp-icon4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+/* recolor the accent elements of an icon */
+.jp-icon-accent0[fill] {
+  fill: var(--jp-layout-color0);
+}
+.jp-icon-accent1[fill] {
+  fill: var(--jp-layout-color1);
+}
+.jp-icon-accent2[fill] {
+  fill: var(--jp-layout-color2);
+}
+.jp-icon-accent3[fill] {
+  fill: var(--jp-layout-color3);
+}
+.jp-icon-accent4[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-accent0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+.jp-icon-accent1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+.jp-icon-accent2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+.jp-icon-accent3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+.jp-icon-accent4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+/* set the color of an icon to transparent */
+.jp-icon-none[fill] {
+  fill: none;
+}
+
+.jp-icon-none[stroke] {
+  stroke: none;
+}
+/* brand icon colors. Same for light and dark */
+.jp-icon-brand0[fill] {
+  fill: var(--jp-brand-color0);
+}
+.jp-icon-brand1[fill] {
+  fill: var(--jp-brand-color1);
+}
+.jp-icon-brand2[fill] {
+  fill: var(--jp-brand-color2);
+}
+.jp-icon-brand3[fill] {
+  fill: var(--jp-brand-color3);
+}
+.jp-icon-brand4[fill] {
+  fill: var(--jp-brand-color4);
+}
+
+.jp-icon-brand0[stroke] {
+  stroke: var(--jp-brand-color0);
+}
+.jp-icon-brand1[stroke] {
+  stroke: var(--jp-brand-color1);
+}
+.jp-icon-brand2[stroke] {
+  stroke: var(--jp-brand-color2);
+}
+.jp-icon-brand3[stroke] {
+  stroke: var(--jp-brand-color3);
+}
+.jp-icon-brand4[stroke] {
+  stroke: var(--jp-brand-color4);
+}
+/* warn icon colors. Same for light and dark */
+.jp-icon-warn0[fill] {
+  fill: var(--jp-warn-color0);
+}
+.jp-icon-warn1[fill] {
+  fill: var(--jp-warn-color1);
+}
+.jp-icon-warn2[fill] {
+  fill: var(--jp-warn-color2);
+}
+.jp-icon-warn3[fill] {
+  fill: var(--jp-warn-color3);
+}
+
+.jp-icon-warn0[stroke] {
+  stroke: var(--jp-warn-color0);
+}
+.jp-icon-warn1[stroke] {
+  stroke: var(--jp-warn-color1);
+}
+.jp-icon-warn2[stroke] {
+  stroke: var(--jp-warn-color2);
+}
+.jp-icon-warn3[stroke] {
+  stroke: var(--jp-warn-color3);
+}
+/* icon colors that contrast well with each other and most backgrounds */
+.jp-icon-contrast0[fill] {
+  fill: var(--jp-icon-contrast-color0);
+}
+.jp-icon-contrast1[fill] {
+  fill: var(--jp-icon-contrast-color1);
+}
+.jp-icon-contrast2[fill] {
+  fill: var(--jp-icon-contrast-color2);
+}
+.jp-icon-contrast3[fill] {
+  fill: var(--jp-icon-contrast-color3);
+}
+
+.jp-icon-contrast0[stroke] {
+  stroke: var(--jp-icon-contrast-color0);
+}
+.jp-icon-contrast1[stroke] {
+  stroke: var(--jp-icon-contrast-color1);
+}
+.jp-icon-contrast2[stroke] {
+  stroke: var(--jp-icon-contrast-color2);
+}
+.jp-icon-contrast3[stroke] {
+  stroke: var(--jp-icon-contrast-color3);
+}
+
+/* CSS for icons in selected items in the settings editor */
+#setting-editor .jp-PluginList .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+#setting-editor
+  .jp-PluginList
+  .jp-mod-selected
+  .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+/* CSS for icons in selected filebrowser listing items */
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+/* CSS for icons in selected tabs in the sidebar tab manager */
+#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+
+#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+#tab-manager
+  .lm-TabBar-tab.jp-mod-active
+  .jp-icon-hover
+  :hover
+  .jp-icon-selectable[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+#tab-manager
+  .lm-TabBar-tab.jp-mod-active
+  .jp-icon-hover
+  :hover
+  .jp-icon-selectable-inverse[fill] {
+  fill: #fff;
+}
+
+/**
+ * TODO: come up with non css-hack solution for showing the busy icon on top
+ *  of the close icon
+ * CSS for complex behavior of close icon of tabs in the sidebar tab manager
+ */
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon3[fill] {
+  fill: none;
+}
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty.jp-mod-active
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: #fff;
+}
+
+/**
+* TODO: come up with non css-hack solution for showing the busy icon on top
+*  of the close icon
+* CSS for complex behavior of close icon of tabs in the main area tabbar
+*/
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon3[fill] {
+  fill: none;
+}
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+/* CSS for icons in status bar */
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+/* special handling for splash icon CSS. While the theme CSS reloads during
+   splash, the splash icon can loose theming. To prevent that, we set a
+   default for its color variable */
+:root {
+  --jp-warn-color0: var(--md-orange-700);
+}
+
+/* not sure what to do with this one, used in filebrowser listing */
+.jp-DragIcon {
+  margin-right: 4px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for alt colors for icons as inline SVG HTMLElements
+ */
+
+/* alt recolor the primary elements of an icon */
+.jp-icon-alt .jp-icon0[fill] {
+  fill: var(--jp-layout-color0);
+}
+.jp-icon-alt .jp-icon1[fill] {
+  fill: var(--jp-layout-color1);
+}
+.jp-icon-alt .jp-icon2[fill] {
+  fill: var(--jp-layout-color2);
+}
+.jp-icon-alt .jp-icon3[fill] {
+  fill: var(--jp-layout-color3);
+}
+.jp-icon-alt .jp-icon4[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-alt .jp-icon0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+.jp-icon-alt .jp-icon1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+.jp-icon-alt .jp-icon2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+.jp-icon-alt .jp-icon3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+.jp-icon-alt .jp-icon4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* alt recolor the accent elements of an icon */
+.jp-icon-alt .jp-icon-accent0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+.jp-icon-alt .jp-icon-accent1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+.jp-icon-alt .jp-icon-accent2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+.jp-icon-alt .jp-icon-accent3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+.jp-icon-alt .jp-icon-accent4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-alt .jp-icon-accent0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+.jp-icon-alt .jp-icon-accent1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+.jp-icon-alt .jp-icon-accent2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+.jp-icon-alt .jp-icon-accent3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+.jp-icon-alt .jp-icon-accent4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-icon-hoverShow:not(:hover) svg {
+  display: none !important;
+}
+
+/**
+ * Support for hover colors for icons as inline SVG HTMLElements
+ */
+
+/**
+ * regular colors
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon-hover :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+.jp-icon-hover :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+.jp-icon-hover :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+.jp-icon-hover :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+.jp-icon-hover :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+.jp-icon-hover :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+.jp-icon-hover :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+.jp-icon-hover :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+.jp-icon-hover :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/* recolor the accent elements of an icon */
+.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
+.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
+.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
+.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
+.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* set the color of an icon to transparent */
+.jp-icon-hover :hover .jp-icon-none-hover[fill] {
+  fill: none;
+}
+
+.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
+  stroke: none;
+}
+
+/**
+ * inverse colors
+ */
+
+/* inverse recolor the primary elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* inverse recolor the accent elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-switch {
+  display: flex;
+  align-items: center;
+  padding-left: 4px;
+  padding-right: 4px;
+  font-size: var(--jp-ui-font-size1);
+  background-color: transparent;
+  color: var(--jp-ui-font-color1);
+  border: none;
+  height: 20px;
+}
+
+.jp-switch:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-switch-label {
+  margin-right: 5px;
+}
+
+.jp-switch-track {
+  cursor: pointer;
+  background-color: var(--jp-border-color1);
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 34px;
+  height: 16px;
+  width: 35px;
+  position: relative;
+}
+
+.jp-switch-track::before {
+  content: '';
+  position: absolute;
+  height: 10px;
+  width: 10px;
+  margin: 3px;
+  left: 0px;
+  background-color: var(--jp-ui-inverse-font-color1);
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 50%;
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track {
+  background-color: var(--jp-warn-color0);
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track::before {
+  /* track width (35) - margins (3 + 3) - thumb width (10) */
+  left: 19px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* Sibling imports */
+
+/* Override Blueprint's _reset.scss styles */
+html {
+  box-sizing: unset;
+}
+
+*,
+*::before,
+*::after {
+  box-sizing: unset;
+}
+
+body {
+  color: unset;
+  font-family: var(--jp-ui-font-family);
+}
+
+p {
+  margin-top: unset;
+  margin-bottom: unset;
+}
+
+small {
+  font-size: unset;
+}
+
+strong {
+  font-weight: unset;
+}
+
+/* Override Blueprint's _typography.scss styles */
+a {
+  text-decoration: unset;
+  color: unset;
+}
+a:hover {
+  text-decoration: unset;
+  color: unset;
+}
+
+/* Override Blueprint's _accessibility.scss styles */
+:focus {
+  outline: unset;
+  outline-offset: unset;
+  -moz-outline-radius: unset;
+}
+
+/* Styles for ui-components */
+.jp-Button {
+  border-radius: var(--jp-border-radius);
+  padding: 0px 12px;
+  font-size: var(--jp-ui-font-size1);
+}
+
+/* Use our own theme for hover styles */
+button.jp-Button.bp3-button.bp3-minimal:hover {
+  background-color: var(--jp-layout-color2);
+}
+.jp-Button.minimal {
+  color: unset !important;
+}
+
+.jp-Button.jp-ToolbarButtonComponent {
+  text-transform: none;
+}
+
+.jp-InputGroup input {
+  box-sizing: border-box;
+  border-radius: 0;
+  background-color: transparent;
+  color: var(--jp-ui-font-color0);
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+}
+
+.jp-InputGroup input:focus {
+  box-shadow: inset 0 0 0 var(--jp-border-width)
+      var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-InputGroup input::placeholder,
+input::placeholder {
+  color: var(--jp-ui-font-color3);
+}
+
+.jp-BPIcon {
+  display: inline-block;
+  vertical-align: middle;
+  margin: auto;
+}
+
+/* Stop blueprint futzing with our icon fills */
+.bp3-icon.jp-BPIcon > svg:not([fill]) {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-InputGroupAction {
+  padding: 6px;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select {
+  background-color: initial;
+  border: none;
+  border-radius: 0;
+  box-shadow: none;
+  color: var(--jp-ui-font-color0);
+  display: block;
+  font-size: var(--jp-ui-font-size1);
+  height: 24px;
+  line-height: 14px;
+  padding: 0 25px 0 10px;
+  text-align: left;
+  -moz-appearance: none;
+  -webkit-appearance: none;
+}
+
+/* Use our own theme for hover and option styles */
+.jp-HTMLSelect.jp-DefaultStyle select:hover,
+.jp-HTMLSelect.jp-DefaultStyle select > option {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color0);
+}
+select {
+  box-sizing: border-box;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapse {
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-top: 1px solid var(--jp-border-color2);
+  border-bottom: 1px solid var(--jp-border-color2);
+}
+
+.jp-Collapse-header {
+  padding: 1px 12px;
+  color: var(--jp-ui-font-color1);
+  background-color: var(--jp-layout-color1);
+  font-size: var(--jp-ui-font-size2);
+}
+
+.jp-Collapse-header:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-Collapse-contents {
+  padding: 0px 12px 0px 12px;
+  background-color: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  overflow: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-commandpalette-search-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Overall styles
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+  padding-bottom: 0px;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Modal variant
+|----------------------------------------------------------------------------*/
+
+.jp-ModalCommandPalette {
+  position: absolute;
+  z-index: 10000;
+  top: 38px;
+  left: 30%;
+  margin: 0;
+  padding: 4px;
+  width: 40%;
+  box-shadow: var(--jp-elevation-z4);
+  border-radius: 4px;
+  background: var(--jp-layout-color0);
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette {
+  max-height: 40vh;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
+  display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
+  display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
+  margin-left: 4px;
+  margin-right: 4px;
+}
+
+.jp-ModalCommandPalette
+  .lm-CommandPalette
+  .lm-CommandPalette-item.lm-mod-disabled {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Search
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-search {
+  padding: 4px;
+  background-color: var(--jp-layout-color1);
+  z-index: 2;
+}
+
+.lm-CommandPalette-wrapper {
+  overflow: overlay;
+  padding: 0px 9px;
+  background-color: var(--jp-input-active-background);
+  height: 30px;
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+}
+
+.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
+  box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-SearchIconGroup {
+  color: white;
+  background-color: var(--jp-brand-color1);
+  position: absolute;
+  top: 4px;
+  right: 4px;
+  padding: 5px 5px 1px 5px;
+}
+
+.jp-SearchIconGroup svg {
+  height: 20px;
+  width: 20px;
+}
+
+.jp-SearchIconGroup .jp-icon3[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-input {
+  background: transparent;
+  width: calc(100% - 18px);
+  float: left;
+  border: none;
+  outline: none;
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  line-height: var(--jp-private-commandpalette-search-height);
+}
+
+.lm-CommandPalette-input::-webkit-input-placeholder,
+.lm-CommandPalette-input::-moz-placeholder,
+.lm-CommandPalette-input:-ms-input-placeholder {
+  color: var(--jp-ui-font-color2);
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Results
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-header:first-child {
+  margin-top: 0px;
+}
+
+.lm-CommandPalette-header {
+  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+  color: var(--jp-ui-font-color1);
+  cursor: pointer;
+  display: flex;
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  letter-spacing: 1px;
+  margin-top: 8px;
+  padding: 8px 0 8px 12px;
+  text-transform: uppercase;
+}
+
+.lm-CommandPalette-header.lm-mod-active {
+  background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-header > mark {
+  background-color: transparent;
+  font-weight: bold;
+  color: var(--jp-ui-font-color1);
+}
+
+.lm-CommandPalette-item {
+  padding: 4px 12px 4px 4px;
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  font-weight: 400;
+  display: flex;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled {
+  color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item.lm-mod-active {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
+  background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-itemContent {
+  overflow: hidden;
+}
+
+.lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled mark {
+  color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
+  margin: 0 4px 0 0;
+  position: relative;
+  width: 16px;
+  top: 2px;
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
+  opacity: 0.6;
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemCaption {
+  display: none;
+}
+
+.lm-CommandPalette-content {
+  background-color: var(--jp-layout-color1);
+}
+
+.lm-CommandPalette-content:empty:after {
+  content: 'No results';
+  margin: auto;
+  margin-top: 20px;
+  width: 100px;
+  display: block;
+  font-size: var(--jp-ui-font-size2);
+  font-family: var(--jp-ui-font-family);
+  font-weight: lighter;
+}
+
+.lm-CommandPalette-emptyMessage {
+  text-align: center;
+  margin-top: 24px;
+  line-height: 1.32;
+  padding: 0px 8px;
+  color: var(--jp-content-font-color3);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Dialog {
+  position: absolute;
+  z-index: 10000;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  top: 0px;
+  left: 0px;
+  margin: 0;
+  padding: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-dialog-background);
+}
+
+.jp-Dialog-content {
+  display: flex;
+  flex-direction: column;
+  margin-left: auto;
+  margin-right: auto;
+  background: var(--jp-layout-color1);
+  padding: 24px;
+  padding-bottom: 12px;
+  min-width: 300px;
+  min-height: 150px;
+  max-width: 1000px;
+  max-height: 500px;
+  box-sizing: border-box;
+  box-shadow: var(--jp-elevation-z20);
+  word-wrap: break-word;
+  border-radius: var(--jp-border-radius);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color1);
+  resize: both;
+}
+
+.jp-Dialog-button {
+  overflow: visible;
+}
+
+button.jp-Dialog-button:focus {
+  outline: 1px solid var(--jp-brand-color1);
+  outline-offset: 4px;
+  -moz-outline-radius: 0px;
+}
+
+button.jp-Dialog-button:focus::-moz-focus-inner {
+  border: 0;
+}
+
+button.jp-Dialog-close-button {
+  padding: 0;
+  height: 100%;
+  min-width: unset;
+  min-height: unset;
+}
+
+.jp-Dialog-header {
+  display: flex;
+  justify-content: space-between;
+  flex: 0 0 auto;
+  padding-bottom: 12px;
+  font-size: var(--jp-ui-font-size3);
+  font-weight: 400;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-Dialog-body {
+  display: flex;
+  flex-direction: column;
+  flex: 1 1 auto;
+  font-size: var(--jp-ui-font-size1);
+  background: var(--jp-layout-color1);
+  overflow: auto;
+}
+
+.jp-Dialog-footer {
+  display: flex;
+  flex-direction: row;
+  justify-content: flex-end;
+  flex: 0 0 auto;
+  margin-left: -12px;
+  margin-right: -12px;
+  padding: 12px;
+}
+
+.jp-Dialog-title {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.jp-Dialog-body > .jp-select-wrapper {
+  width: 100%;
+}
+
+.jp-Dialog-body > button {
+  padding: 0px 16px;
+}
+
+.jp-Dialog-body > label {
+  line-height: 1.4;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-Dialog-button.jp-mod-styled:not(:last-child) {
+  margin-right: 12px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-HoverBox {
+  position: fixed;
+}
+
+.jp-HoverBox.jp-mod-outofview {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-IFrame {
+  width: 100%;
+  height: 100%;
+}
+
+.jp-IFrame > iframe {
+  border: none;
+}
+
+/*
+When drag events occur, `p-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-IFrame {
+  position: relative;
+}
+
+body.lm-mod-override-cursor .jp-IFrame:before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
+
+.jp-Input-Boolean-Dialog {
+  flex-direction: row-reverse;
+  align-items: end;
+  width: 100%;
+}
+
+.jp-Input-Boolean-Dialog > label {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MainAreaWidget > :focus {
+  outline: none;
+}
+
+/**
+ * google-material-color v1.2.6
+ * https://github.com/danlevan/google-material-color
+ */
+:root {
+  --md-red-50: #ffebee;
+  --md-red-100: #ffcdd2;
+  --md-red-200: #ef9a9a;
+  --md-red-300: #e57373;
+  --md-red-400: #ef5350;
+  --md-red-500: #f44336;
+  --md-red-600: #e53935;
+  --md-red-700: #d32f2f;
+  --md-red-800: #c62828;
+  --md-red-900: #b71c1c;
+  --md-red-A100: #ff8a80;
+  --md-red-A200: #ff5252;
+  --md-red-A400: #ff1744;
+  --md-red-A700: #d50000;
+
+  --md-pink-50: #fce4ec;
+  --md-pink-100: #f8bbd0;
+  --md-pink-200: #f48fb1;
+  --md-pink-300: #f06292;
+  --md-pink-400: #ec407a;
+  --md-pink-500: #e91e63;
+  --md-pink-600: #d81b60;
+  --md-pink-700: #c2185b;
+  --md-pink-800: #ad1457;
+  --md-pink-900: #880e4f;
+  --md-pink-A100: #ff80ab;
+  --md-pink-A200: #ff4081;
+  --md-pink-A400: #f50057;
+  --md-pink-A700: #c51162;
+
+  --md-purple-50: #f3e5f5;
+  --md-purple-100: #e1bee7;
+  --md-purple-200: #ce93d8;
+  --md-purple-300: #ba68c8;
+  --md-purple-400: #ab47bc;
+  --md-purple-500: #9c27b0;
+  --md-purple-600: #8e24aa;
+  --md-purple-700: #7b1fa2;
+  --md-purple-800: #6a1b9a;
+  --md-purple-900: #4a148c;
+  --md-purple-A100: #ea80fc;
+  --md-purple-A200: #e040fb;
+  --md-purple-A400: #d500f9;
+  --md-purple-A700: #aa00ff;
+
+  --md-deep-purple-50: #ede7f6;
+  --md-deep-purple-100: #d1c4e9;
+  --md-deep-purple-200: #b39ddb;
+  --md-deep-purple-300: #9575cd;
+  --md-deep-purple-400: #7e57c2;
+  --md-deep-purple-500: #673ab7;
+  --md-deep-purple-600: #5e35b1;
+  --md-deep-purple-700: #512da8;
+  --md-deep-purple-800: #4527a0;
+  --md-deep-purple-900: #311b92;
+  --md-deep-purple-A100: #b388ff;
+  --md-deep-purple-A200: #7c4dff;
+  --md-deep-purple-A400: #651fff;
+  --md-deep-purple-A700: #6200ea;
+
+  --md-indigo-50: #e8eaf6;
+  --md-indigo-100: #c5cae9;
+  --md-indigo-200: #9fa8da;
+  --md-indigo-300: #7986cb;
+  --md-indigo-400: #5c6bc0;
+  --md-indigo-500: #3f51b5;
+  --md-indigo-600: #3949ab;
+  --md-indigo-700: #303f9f;
+  --md-indigo-800: #283593;
+  --md-indigo-900: #1a237e;
+  --md-indigo-A100: #8c9eff;
+  --md-indigo-A200: #536dfe;
+  --md-indigo-A400: #3d5afe;
+  --md-indigo-A700: #304ffe;
+
+  --md-blue-50: #e3f2fd;
+  --md-blue-100: #bbdefb;
+  --md-blue-200: #90caf9;
+  --md-blue-300: #64b5f6;
+  --md-blue-400: #42a5f5;
+  --md-blue-500: #2196f3;
+  --md-blue-600: #1e88e5;
+  --md-blue-700: #1976d2;
+  --md-blue-800: #1565c0;
+  --md-blue-900: #0d47a1;
+  --md-blue-A100: #82b1ff;
+  --md-blue-A200: #448aff;
+  --md-blue-A400: #2979ff;
+  --md-blue-A700: #2962ff;
+
+  --md-light-blue-50: #e1f5fe;
+  --md-light-blue-100: #b3e5fc;
+  --md-light-blue-200: #81d4fa;
+  --md-light-blue-300: #4fc3f7;
+  --md-light-blue-400: #29b6f6;
+  --md-light-blue-500: #03a9f4;
+  --md-light-blue-600: #039be5;
+  --md-light-blue-700: #0288d1;
+  --md-light-blue-800: #0277bd;
+  --md-light-blue-900: #01579b;
+  --md-light-blue-A100: #80d8ff;
+  --md-light-blue-A200: #40c4ff;
+  --md-light-blue-A400: #00b0ff;
+  --md-light-blue-A700: #0091ea;
+
+  --md-cyan-50: #e0f7fa;
+  --md-cyan-100: #b2ebf2;
+  --md-cyan-200: #80deea;
+  --md-cyan-300: #4dd0e1;
+  --md-cyan-400: #26c6da;
+  --md-cyan-500: #00bcd4;
+  --md-cyan-600: #00acc1;
+  --md-cyan-700: #0097a7;
+  --md-cyan-800: #00838f;
+  --md-cyan-900: #006064;
+  --md-cyan-A100: #84ffff;
+  --md-cyan-A200: #18ffff;
+  --md-cyan-A400: #00e5ff;
+  --md-cyan-A700: #00b8d4;
+
+  --md-teal-50: #e0f2f1;
+  --md-teal-100: #b2dfdb;
+  --md-teal-200: #80cbc4;
+  --md-teal-300: #4db6ac;
+  --md-teal-400: #26a69a;
+  --md-teal-500: #009688;
+  --md-teal-600: #00897b;
+  --md-teal-700: #00796b;
+  --md-teal-800: #00695c;
+  --md-teal-900: #004d40;
+  --md-teal-A100: #a7ffeb;
+  --md-teal-A200: #64ffda;
+  --md-teal-A400: #1de9b6;
+  --md-teal-A700: #00bfa5;
+
+  --md-green-50: #e8f5e9;
+  --md-green-100: #c8e6c9;
+  --md-green-200: #a5d6a7;
+  --md-green-300: #81c784;
+  --md-green-400: #66bb6a;
+  --md-green-500: #4caf50;
+  --md-green-600: #43a047;
+  --md-green-700: #388e3c;
+  --md-green-800: #2e7d32;
+  --md-green-900: #1b5e20;
+  --md-green-A100: #b9f6ca;
+  --md-green-A200: #69f0ae;
+  --md-green-A400: #00e676;
+  --md-green-A700: #00c853;
+
+  --md-light-green-50: #f1f8e9;
+  --md-light-green-100: #dcedc8;
+  --md-light-green-200: #c5e1a5;
+  --md-light-green-300: #aed581;
+  --md-light-green-400: #9ccc65;
+  --md-light-green-500: #8bc34a;
+  --md-light-green-600: #7cb342;
+  --md-light-green-700: #689f38;
+  --md-light-green-800: #558b2f;
+  --md-light-green-900: #33691e;
+  --md-light-green-A100: #ccff90;
+  --md-light-green-A200: #b2ff59;
+  --md-light-green-A400: #76ff03;
+  --md-light-green-A700: #64dd17;
+
+  --md-lime-50: #f9fbe7;
+  --md-lime-100: #f0f4c3;
+  --md-lime-200: #e6ee9c;
+  --md-lime-300: #dce775;
+  --md-lime-400: #d4e157;
+  --md-lime-500: #cddc39;
+  --md-lime-600: #c0ca33;
+  --md-lime-700: #afb42b;
+  --md-lime-800: #9e9d24;
+  --md-lime-900: #827717;
+  --md-lime-A100: #f4ff81;
+  --md-lime-A200: #eeff41;
+  --md-lime-A400: #c6ff00;
+  --md-lime-A700: #aeea00;
+
+  --md-yellow-50: #fffde7;
+  --md-yellow-100: #fff9c4;
+  --md-yellow-200: #fff59d;
+  --md-yellow-300: #fff176;
+  --md-yellow-400: #ffee58;
+  --md-yellow-500: #ffeb3b;
+  --md-yellow-600: #fdd835;
+  --md-yellow-700: #fbc02d;
+  --md-yellow-800: #f9a825;
+  --md-yellow-900: #f57f17;
+  --md-yellow-A100: #ffff8d;
+  --md-yellow-A200: #ffff00;
+  --md-yellow-A400: #ffea00;
+  --md-yellow-A700: #ffd600;
+
+  --md-amber-50: #fff8e1;
+  --md-amber-100: #ffecb3;
+  --md-amber-200: #ffe082;
+  --md-amber-300: #ffd54f;
+  --md-amber-400: #ffca28;
+  --md-amber-500: #ffc107;
+  --md-amber-600: #ffb300;
+  --md-amber-700: #ffa000;
+  --md-amber-800: #ff8f00;
+  --md-amber-900: #ff6f00;
+  --md-amber-A100: #ffe57f;
+  --md-amber-A200: #ffd740;
+  --md-amber-A400: #ffc400;
+  --md-amber-A700: #ffab00;
+
+  --md-orange-50: #fff3e0;
+  --md-orange-100: #ffe0b2;
+  --md-orange-200: #ffcc80;
+  --md-orange-300: #ffb74d;
+  --md-orange-400: #ffa726;
+  --md-orange-500: #ff9800;
+  --md-orange-600: #fb8c00;
+  --md-orange-700: #f57c00;
+  --md-orange-800: #ef6c00;
+  --md-orange-900: #e65100;
+  --md-orange-A100: #ffd180;
+  --md-orange-A200: #ffab40;
+  --md-orange-A400: #ff9100;
+  --md-orange-A700: #ff6d00;
+
+  --md-deep-orange-50: #fbe9e7;
+  --md-deep-orange-100: #ffccbc;
+  --md-deep-orange-200: #ffab91;
+  --md-deep-orange-300: #ff8a65;
+  --md-deep-orange-400: #ff7043;
+  --md-deep-orange-500: #ff5722;
+  --md-deep-orange-600: #f4511e;
+  --md-deep-orange-700: #e64a19;
+  --md-deep-orange-800: #d84315;
+  --md-deep-orange-900: #bf360c;
+  --md-deep-orange-A100: #ff9e80;
+  --md-deep-orange-A200: #ff6e40;
+  --md-deep-orange-A400: #ff3d00;
+  --md-deep-orange-A700: #dd2c00;
+
+  --md-brown-50: #efebe9;
+  --md-brown-100: #d7ccc8;
+  --md-brown-200: #bcaaa4;
+  --md-brown-300: #a1887f;
+  --md-brown-400: #8d6e63;
+  --md-brown-500: #795548;
+  --md-brown-600: #6d4c41;
+  --md-brown-700: #5d4037;
+  --md-brown-800: #4e342e;
+  --md-brown-900: #3e2723;
+
+  --md-grey-50: #fafafa;
+  --md-grey-100: #f5f5f5;
+  --md-grey-200: #eeeeee;
+  --md-grey-300: #e0e0e0;
+  --md-grey-400: #bdbdbd;
+  --md-grey-500: #9e9e9e;
+  --md-grey-600: #757575;
+  --md-grey-700: #616161;
+  --md-grey-800: #424242;
+  --md-grey-900: #212121;
+
+  --md-blue-grey-50: #eceff1;
+  --md-blue-grey-100: #cfd8dc;
+  --md-blue-grey-200: #b0bec5;
+  --md-blue-grey-300: #90a4ae;
+  --md-blue-grey-400: #78909c;
+  --md-blue-grey-500: #607d8b;
+  --md-blue-grey-600: #546e7a;
+  --md-blue-grey-700: #455a64;
+  --md-blue-grey-800: #37474f;
+  --md-blue-grey-900: #263238;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Spinner {
+  position: absolute;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  z-index: 10;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-layout-color0);
+  outline: none;
+}
+
+.jp-SpinnerContent {
+  font-size: 10px;
+  margin: 50px auto;
+  text-indent: -9999em;
+  width: 3em;
+  height: 3em;
+  border-radius: 50%;
+  background: var(--jp-brand-color3);
+  background: linear-gradient(
+    to right,
+    #f37626 10%,
+    rgba(255, 255, 255, 0) 42%
+  );
+  position: relative;
+  animation: load3 1s infinite linear, fadeIn 1s;
+}
+
+.jp-SpinnerContent:before {
+  width: 50%;
+  height: 50%;
+  background: #f37626;
+  border-radius: 100% 0 0 0;
+  position: absolute;
+  top: 0;
+  left: 0;
+  content: '';
+}
+
+.jp-SpinnerContent:after {
+  background: var(--jp-layout-color0);
+  width: 75%;
+  height: 75%;
+  border-radius: 50%;
+  content: '';
+  margin: auto;
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  right: 0;
+}
+
+@keyframes fadeIn {
+  0% {
+    opacity: 0;
+  }
+  100% {
+    opacity: 1;
+  }
+}
+
+@keyframes load3 {
+  0% {
+    transform: rotate(0deg);
+  }
+  100% {
+    transform: rotate(360deg);
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+button.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: none;
+  box-sizing: border-box;
+  text-align: center;
+  line-height: 32px;
+  height: 32px;
+  padding: 0px 12px;
+  letter-spacing: 0.8px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+input.jp-mod-styled {
+  background: var(--jp-input-background);
+  height: 28px;
+  box-sizing: border-box;
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+  padding-left: 7px;
+  padding-right: 7px;
+  font-size: var(--jp-ui-font-size2);
+  color: var(--jp-ui-font-color0);
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+input[type='checkbox'].jp-mod-styled {
+  appearance: checkbox;
+  -webkit-appearance: checkbox;
+  -moz-appearance: checkbox;
+  height: auto;
+}
+
+input.jp-mod-styled:focus {
+  border: var(--jp-border-width) solid var(--md-blue-500);
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-FileDialog-Checkbox {
+  margin-top: 35px;
+  display: flex;
+  flex-direction: row;
+  align-items: end;
+  width: 100%;
+}
+
+.jp-FileDialog-Checkbox > label {
+  flex: 1 1 auto;
+}
+
+.jp-select-wrapper {
+  display: flex;
+  position: relative;
+  flex-direction: column;
+  padding: 1px;
+  background-color: var(--jp-layout-color1);
+  height: 28px;
+  box-sizing: border-box;
+  margin-bottom: 12px;
+}
+
+.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-input-active-background);
+}
+
+select.jp-mod-styled:hover {
+  background-color: var(--jp-layout-color1);
+  cursor: pointer;
+  color: var(--jp-ui-font-color0);
+  background-color: var(--jp-input-hover-background);
+  box-shadow: inset 0 0px 1px rgba(0, 0, 0, 0.5);
+}
+
+select.jp-mod-styled {
+  flex: 1 1 auto;
+  height: 32px;
+  width: 100%;
+  font-size: var(--jp-ui-font-size2);
+  background: var(--jp-input-background);
+  color: var(--jp-ui-font-color0);
+  padding: 0 25px 0 8px;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-toolbar-height: calc(
+    28px + var(--jp-border-width)
+  ); /* leave 28px for content */
+}
+
+.jp-Toolbar {
+  color: var(--jp-ui-font-color1);
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  background: var(--jp-toolbar-background);
+  min-height: var(--jp-toolbar-micro-height);
+  padding: 2px;
+  z-index: 1;
+  overflow-x: auto;
+}
+
+/* Toolbar items */
+
+.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
+  flex-grow: 1;
+  flex-shrink: 1;
+}
+
+.jp-Toolbar-item.jp-Toolbar-kernelStatus {
+  display: inline-block;
+  width: 32px;
+  background-repeat: no-repeat;
+  background-position: center;
+  background-size: 16px;
+}
+
+.jp-Toolbar > .jp-Toolbar-item {
+  flex: 0 0 auto;
+  display: flex;
+  padding-left: 1px;
+  padding-right: 1px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: var(--jp-private-toolbar-height);
+  height: 100%;
+}
+
+/* Toolbar buttons */
+
+/* This is the div we use to wrap the react component into a Widget */
+div.jp-ToolbarButton {
+  color: transparent;
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0px;
+  margin: 0px;
+}
+
+button.jp-ToolbarButtonComponent {
+  background: var(--jp-layout-color1);
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0px 6px;
+  margin: 0px;
+  height: 24px;
+  border-radius: var(--jp-border-radius);
+  display: flex;
+  align-items: center;
+  text-align: center;
+  font-size: 14px;
+  min-width: unset;
+  min-height: unset;
+}
+
+button.jp-ToolbarButtonComponent:disabled {
+  opacity: 0.4;
+}
+
+button.jp-ToolbarButtonComponent span {
+  padding: 0px;
+  flex: 0 0 auto;
+}
+
+button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
+  font-size: var(--jp-ui-font-size1);
+  line-height: 100%;
+  padding-left: 2px;
+  color: var(--jp-ui-font-color1);
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar.jp-Toolbar-micro {
+  padding: 0;
+  min-height: 0;
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar {
+  border: none;
+  box-shadow: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ body.p-mod-override-cursor *, /* </DEPRECATED> */
+body.lm-mod-override-cursor * {
+  cursor: inherit !important;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-JSONEditor {
+  display: flex;
+  flex-direction: column;
+  width: 100%;
+}
+
+.jp-JSONEditor-host {
+  flex: 1 1 auto;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0px;
+  background: var(--jp-layout-color0);
+  min-height: 50px;
+  padding: 1px;
+}
+
+.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
+  border-color: red;
+  outline-color: red;
+}
+
+.jp-JSONEditor-header {
+  display: flex;
+  flex: 1 0 auto;
+  padding: 0 0 0 12px;
+}
+
+.jp-JSONEditor-header label {
+  flex: 0 0 auto;
+}
+
+.jp-JSONEditor-commitButton {
+  height: 16px;
+  width: 16px;
+  background-size: 18px;
+  background-repeat: no-repeat;
+  background-position: center;
+}
+
+.jp-JSONEditor-host.jp-mod-focused {
+  background-color: var(--jp-input-active-background);
+  border: 1px solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+}
+
+.jp-Editor.jp-mod-dropTarget {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+}
+
+/* BASICS */
+
+.CodeMirror {
+  /* Set height, width, borders, and global font properties here */
+  font-family: monospace;
+  height: 300px;
+  color: black;
+  direction: ltr;
+}
+
+/* PADDING */
+
+.CodeMirror-lines {
+  padding: 4px 0; /* Vertical padding around content */
+}
+.CodeMirror pre.CodeMirror-line,
+.CodeMirror pre.CodeMirror-line-like {
+  padding: 0 4px; /* Horizontal padding of content */
+}
+
+.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
+  background-color: white; /* The little square between H and V scrollbars */
+}
+
+/* GUTTER */
+
+.CodeMirror-gutters {
+  border-right: 1px solid #ddd;
+  background-color: #f7f7f7;
+  white-space: nowrap;
+}
+.CodeMirror-linenumbers {}
+.CodeMirror-linenumber {
+  padding: 0 3px 0 5px;
+  min-width: 20px;
+  text-align: right;
+  color: #999;
+  white-space: nowrap;
+}
+
+.CodeMirror-guttermarker { color: black; }
+.CodeMirror-guttermarker-subtle { color: #999; }
+
+/* CURSOR */
+
+.CodeMirror-cursor {
+  border-left: 1px solid black;
+  border-right: none;
+  width: 0;
+}
+/* Shown when moving in bi-directional text */
+.CodeMirror div.CodeMirror-secondarycursor {
+  border-left: 1px solid silver;
+}
+.cm-fat-cursor .CodeMirror-cursor {
+  width: auto;
+  border: 0 !important;
+  background: #7e7;
+}
+.cm-fat-cursor div.CodeMirror-cursors {
+  z-index: 1;
+}
+.cm-fat-cursor-mark {
+  background-color: rgba(20, 255, 20, 0.5);
+  -webkit-animation: blink 1.06s steps(1) infinite;
+  -moz-animation: blink 1.06s steps(1) infinite;
+  animation: blink 1.06s steps(1) infinite;
+}
+.cm-animate-fat-cursor {
+  width: auto;
+  border: 0;
+  -webkit-animation: blink 1.06s steps(1) infinite;
+  -moz-animation: blink 1.06s steps(1) infinite;
+  animation: blink 1.06s steps(1) infinite;
+  background-color: #7e7;
+}
+@-moz-keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
+}
+@-webkit-keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
+}
+@keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
+}
+
+/* Can style cursor different in overwrite (non-insert) mode */
+.CodeMirror-overwrite .CodeMirror-cursor {}
+
+.cm-tab { display: inline-block; text-decoration: inherit; }
+
+.CodeMirror-rulers {
+  position: absolute;
+  left: 0; right: 0; top: -50px; bottom: 0;
+  overflow: hidden;
+}
+.CodeMirror-ruler {
+  border-left: 1px solid #ccc;
+  top: 0; bottom: 0;
+  position: absolute;
+}
+
+/* DEFAULT THEME */
+
+.cm-s-default .cm-header {color: blue;}
+.cm-s-default .cm-quote {color: #090;}
+.cm-negative {color: #d44;}
+.cm-positive {color: #292;}
+.cm-header, .cm-strong {font-weight: bold;}
+.cm-em {font-style: italic;}
+.cm-link {text-decoration: underline;}
+.cm-strikethrough {text-decoration: line-through;}
+
+.cm-s-default .cm-keyword {color: #708;}
+.cm-s-default .cm-atom {color: #219;}
+.cm-s-default .cm-number {color: #164;}
+.cm-s-default .cm-def {color: #00f;}
+.cm-s-default .cm-variable,
+.cm-s-default .cm-punctuation,
+.cm-s-default .cm-property,
+.cm-s-default .cm-operator {}
+.cm-s-default .cm-variable-2 {color: #05a;}
+.cm-s-default .cm-variable-3, .cm-s-default .cm-type {color: #085;}
+.cm-s-default .cm-comment {color: #a50;}
+.cm-s-default .cm-string {color: #a11;}
+.cm-s-default .cm-string-2 {color: #f50;}
+.cm-s-default .cm-meta {color: #555;}
+.cm-s-default .cm-qualifier {color: #555;}
+.cm-s-default .cm-builtin {color: #30a;}
+.cm-s-default .cm-bracket {color: #997;}
+.cm-s-default .cm-tag {color: #170;}
+.cm-s-default .cm-attribute {color: #00c;}
+.cm-s-default .cm-hr {color: #999;}
+.cm-s-default .cm-link {color: #00c;}
+
+.cm-s-default .cm-error {color: #f00;}
+.cm-invalidchar {color: #f00;}
+
+.CodeMirror-composing { border-bottom: 2px solid; }
+
+/* Default styles for common addons */
+
+div.CodeMirror span.CodeMirror-matchingbracket {color: #0b0;}
+div.CodeMirror span.CodeMirror-nonmatchingbracket {color: #a22;}
+.CodeMirror-matchingtag { background: rgba(255, 150, 0, .3); }
+.CodeMirror-activeline-background {background: #e8f2ff;}
+
+/* STOP */
+
+/* The rest of this file contains styles related to the mechanics of
+   the editor. You probably shouldn't touch them. */
+
+.CodeMirror {
+  position: relative;
+  overflow: hidden;
+  background: white;
+}
+
+.CodeMirror-scroll {
+  overflow: scroll !important; /* Things will break if this is overridden */
+  /* 50px is the magic margin used to hide the element's real scrollbars */
+  /* See overflow: hidden in .CodeMirror */
+  margin-bottom: -50px; margin-right: -50px;
+  padding-bottom: 50px;
+  height: 100%;
+  outline: none; /* Prevent dragging from highlighting the element */
+  position: relative;
+}
+.CodeMirror-sizer {
+  position: relative;
+  border-right: 50px solid transparent;
+}
+
+/* The fake, visible scrollbars. Used to force redraw during scrolling
+   before actual scrolling happens, thus preventing shaking and
+   flickering artifacts. */
+.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
+  position: absolute;
+  z-index: 6;
+  display: none;
+  outline: none;
+}
+.CodeMirror-vscrollbar {
+  right: 0; top: 0;
+  overflow-x: hidden;
+  overflow-y: scroll;
+}
+.CodeMirror-hscrollbar {
+  bottom: 0; left: 0;
+  overflow-y: hidden;
+  overflow-x: scroll;
+}
+.CodeMirror-scrollbar-filler {
+  right: 0; bottom: 0;
+}
+.CodeMirror-gutter-filler {
+  left: 0; bottom: 0;
+}
+
+.CodeMirror-gutters {
+  position: absolute; left: 0; top: 0;
+  min-height: 100%;
+  z-index: 3;
+}
+.CodeMirror-gutter {
+  white-space: normal;
+  height: 100%;
+  display: inline-block;
+  vertical-align: top;
+  margin-bottom: -50px;
+}
+.CodeMirror-gutter-wrapper {
+  position: absolute;
+  z-index: 4;
+  background: none !important;
+  border: none !important;
+}
+.CodeMirror-gutter-background {
+  position: absolute;
+  top: 0; bottom: 0;
+  z-index: 4;
+}
+.CodeMirror-gutter-elt {
+  position: absolute;
+  cursor: default;
+  z-index: 4;
+}
+.CodeMirror-gutter-wrapper ::selection { background-color: transparent }
+.CodeMirror-gutter-wrapper ::-moz-selection { background-color: transparent }
+
+.CodeMirror-lines {
+  cursor: text;
+  min-height: 1px; /* prevents collapsing before first draw */
+}
+.CodeMirror pre.CodeMirror-line,
+.CodeMirror pre.CodeMirror-line-like {
+  /* Reset some styles that the rest of the page might have set */
+  -moz-border-radius: 0; -webkit-border-radius: 0; border-radius: 0;
+  border-width: 0;
+  background: transparent;
+  font-family: inherit;
+  font-size: inherit;
+  margin: 0;
+  white-space: pre;
+  word-wrap: normal;
+  line-height: inherit;
+  color: inherit;
+  z-index: 2;
+  position: relative;
+  overflow: visible;
+  -webkit-tap-highlight-color: transparent;
+  -webkit-font-variant-ligatures: contextual;
+  font-variant-ligatures: contextual;
+}
+.CodeMirror-wrap pre.CodeMirror-line,
+.CodeMirror-wrap pre.CodeMirror-line-like {
+  word-wrap: break-word;
+  white-space: pre-wrap;
+  word-break: normal;
+}
+
+.CodeMirror-linebackground {
+  position: absolute;
+  left: 0; right: 0; top: 0; bottom: 0;
+  z-index: 0;
+}
+
+.CodeMirror-linewidget {
+  position: relative;
+  z-index: 2;
+  padding: 0.1px; /* Force widget margins to stay inside of the container */
+}
+
+.CodeMirror-widget {}
+
+.CodeMirror-rtl pre { direction: rtl; }
+
+.CodeMirror-code {
+  outline: none;
+}
+
+/* Force content-box sizing for the elements where we expect it */
+.CodeMirror-scroll,
+.CodeMirror-sizer,
+.CodeMirror-gutter,
+.CodeMirror-gutters,
+.CodeMirror-linenumber {
+  -moz-box-sizing: content-box;
+  box-sizing: content-box;
+}
+
+.CodeMirror-measure {
+  position: absolute;
+  width: 100%;
+  height: 0;
+  overflow: hidden;
+  visibility: hidden;
+}
+
+.CodeMirror-cursor {
+  position: absolute;
+  pointer-events: none;
+}
+.CodeMirror-measure pre { position: static; }
+
+div.CodeMirror-cursors {
+  visibility: hidden;
+  position: relative;
+  z-index: 3;
+}
+div.CodeMirror-dragcursors {
+  visibility: visible;
+}
+
+.CodeMirror-focused div.CodeMirror-cursors {
+  visibility: visible;
+}
+
+.CodeMirror-selected { background: #d9d9d9; }
+.CodeMirror-focused .CodeMirror-selected { background: #d7d4f0; }
+.CodeMirror-crosshair { cursor: crosshair; }
+.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirror-line > span > span::selection { background: #d7d4f0; }
+.CodeMirror-line::-moz-selection, .CodeMirror-line > span::-moz-selection, .CodeMirror-line > span > span::-moz-selection { background: #d7d4f0; }
+
+.cm-searching {
+  background-color: #ffa;
+  background-color: rgba(255, 255, 0, .4);
+}
+
+/* Used to force a border model for a node */
+.cm-force-border { padding-right: .1px; }
+
+@media print {
+  /* Hide the cursor when printing */
+  .CodeMirror div.CodeMirror-cursors {
+    visibility: hidden;
+  }
+}
+
+/* See issue #2901 */
+.cm-tab-wrap-hack:after { content: ''; }
+
+/* Help users use markselection to safely style text background */
+span.CodeMirror-selectedtext { background: none; }
+
+.CodeMirror-dialog {
+  position: absolute;
+  left: 0; right: 0;
+  background: inherit;
+  z-index: 15;
+  padding: .1em .8em;
+  overflow: hidden;
+  color: inherit;
+}
+
+.CodeMirror-dialog-top {
+  border-bottom: 1px solid #eee;
+  top: 0;
+}
+
+.CodeMirror-dialog-bottom {
+  border-top: 1px solid #eee;
+  bottom: 0;
+}
+
+.CodeMirror-dialog input {
+  border: none;
+  outline: none;
+  background: transparent;
+  width: 20em;
+  color: inherit;
+  font-family: monospace;
+}
+
+.CodeMirror-dialog button {
+  font-size: 70%;
+}
+
+.CodeMirror-foldmarker {
+  color: blue;
+  text-shadow: #b9f 1px 1px 2px, #b9f -1px -1px 2px, #b9f 1px -1px 2px, #b9f -1px 1px 2px;
+  font-family: arial;
+  line-height: .3;
+  cursor: pointer;
+}
+.CodeMirror-foldgutter {
+  width: .7em;
+}
+.CodeMirror-foldgutter-open,
+.CodeMirror-foldgutter-folded {
+  cursor: pointer;
+}
+.CodeMirror-foldgutter-open:after {
+  content: "\25BE";
+}
+.CodeMirror-foldgutter-folded:after {
+  content: "\25B8";
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.CodeMirror {
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  border: 0;
+  border-radius: 0;
+  height: auto;
+  /* Changed to auto to autogrow */
+}
+
+.CodeMirror pre {
+  padding: 0 var(--jp-code-padding);
+}
+
+.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-dialog {
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+}
+
+/* This causes https://github.com/jupyter/jupyterlab/issues/522 */
+/* May not cause it not because we changed it! */
+.CodeMirror-lines {
+  padding: var(--jp-code-padding) 0;
+}
+
+.CodeMirror-linenumber {
+  padding: 0 8px;
+}
+
+.jp-CodeMirrorEditor {
+  cursor: text;
+}
+
+.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+  border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
+}
+
+/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
+@media screen and (min-width: 2138px) and (max-width: 4319px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+    border-left: var(--jp-code-cursor-width1) solid
+      var(--jp-editor-cursor-color);
+  }
+}
+
+/* When zoomed out less than 33% */
+@media screen and (min-width: 4320px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+    border-left: var(--jp-code-cursor-width2) solid
+      var(--jp-editor-cursor-color);
+  }
+}
+
+.CodeMirror.jp-mod-readOnly .CodeMirror-cursor {
+  display: none;
+}
+
+.CodeMirror-gutters {
+  border-right: 1px solid var(--jp-border-color2);
+  background-color: var(--jp-layout-color0);
+}
+
+.jp-CollaboratorCursor {
+  border-left: 5px solid transparent;
+  border-right: 5px solid transparent;
+  border-top: none;
+  border-bottom: 3px solid;
+  background-clip: content-box;
+  margin-left: -5px;
+  margin-right: -5px;
+}
+
+.CodeMirror-selectedtext.cm-searching {
+  background-color: var(--jp-search-selected-match-background-color) !important;
+  color: var(--jp-search-selected-match-color) !important;
+}
+
+.cm-searching {
+  background-color: var(
+    --jp-search-unselected-match-background-color
+  ) !important;
+  color: var(--jp-search-unselected-match-color) !important;
+}
+
+.CodeMirror-focused .CodeMirror-selected {
+  background-color: var(--jp-editor-selected-focused-background);
+}
+
+.CodeMirror-selected {
+  background-color: var(--jp-editor-selected-background);
+}
+
+.jp-CollaboratorCursor-hover {
+  position: absolute;
+  z-index: 1;
+  transform: translateX(-50%);
+  color: white;
+  border-radius: 3px;
+  padding-left: 4px;
+  padding-right: 4px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+  text-align: center;
+  font-size: var(--jp-ui-font-size1);
+  white-space: nowrap;
+}
+
+.jp-CodeMirror-ruler {
+  border-left: 1px dashed var(--jp-border-color2);
+}
+
+/**
+ * Here is our jupyter theme for CodeMirror syntax highlighting
+ * This is used in our marked.js syntax highlighting and CodeMirror itself
+ * The string "jupyter" is set in ../codemirror/widget.DEFAULT_CODEMIRROR_THEME
+ * This came from the classic notebook, which came form highlight.js/GitHub
+ */
+
+/**
+ * CodeMirror themes are handling the background/color in this way. This works
+ * fine for CodeMirror editors outside the notebook, but the notebook styles
+ * these things differently.
+ */
+.CodeMirror.cm-s-jupyter {
+  background: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+}
+
+/* In the notebook, we want this styling to be handled by its container */
+.jp-CodeConsole .CodeMirror.cm-s-jupyter,
+.jp-Notebook .CodeMirror.cm-s-jupyter {
+  background: transparent;
+}
+
+.cm-s-jupyter .CodeMirror-cursor {
+  border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
+}
+.cm-s-jupyter span.cm-keyword {
+  color: var(--jp-mirror-editor-keyword-color);
+  font-weight: bold;
+}
+.cm-s-jupyter span.cm-atom {
+  color: var(--jp-mirror-editor-atom-color);
+}
+.cm-s-jupyter span.cm-number {
+  color: var(--jp-mirror-editor-number-color);
+}
+.cm-s-jupyter span.cm-def {
+  color: var(--jp-mirror-editor-def-color);
+}
+.cm-s-jupyter span.cm-variable {
+  color: var(--jp-mirror-editor-variable-color);
+}
+.cm-s-jupyter span.cm-variable-2 {
+  color: var(--jp-mirror-editor-variable-2-color);
+}
+.cm-s-jupyter span.cm-variable-3 {
+  color: var(--jp-mirror-editor-variable-3-color);
+}
+.cm-s-jupyter span.cm-punctuation {
+  color: var(--jp-mirror-editor-punctuation-color);
+}
+.cm-s-jupyter span.cm-property {
+  color: var(--jp-mirror-editor-property-color);
+}
+.cm-s-jupyter span.cm-operator {
+  color: var(--jp-mirror-editor-operator-color);
+  font-weight: bold;
+}
+.cm-s-jupyter span.cm-comment {
+  color: var(--jp-mirror-editor-comment-color);
+  font-style: italic;
+}
+.cm-s-jupyter span.cm-string {
+  color: var(--jp-mirror-editor-string-color);
+}
+.cm-s-jupyter span.cm-string-2 {
+  color: var(--jp-mirror-editor-string-2-color);
+}
+.cm-s-jupyter span.cm-meta {
+  color: var(--jp-mirror-editor-meta-color);
+}
+.cm-s-jupyter span.cm-qualifier {
+  color: var(--jp-mirror-editor-qualifier-color);
+}
+.cm-s-jupyter span.cm-builtin {
+  color: var(--jp-mirror-editor-builtin-color);
+}
+.cm-s-jupyter span.cm-bracket {
+  color: var(--jp-mirror-editor-bracket-color);
+}
+.cm-s-jupyter span.cm-tag {
+  color: var(--jp-mirror-editor-tag-color);
+}
+.cm-s-jupyter span.cm-attribute {
+  color: var(--jp-mirror-editor-attribute-color);
+}
+.cm-s-jupyter span.cm-header {
+  color: var(--jp-mirror-editor-header-color);
+}
+.cm-s-jupyter span.cm-quote {
+  color: var(--jp-mirror-editor-quote-color);
+}
+.cm-s-jupyter span.cm-link {
+  color: var(--jp-mirror-editor-link-color);
+}
+.cm-s-jupyter span.cm-error {
+  color: var(--jp-mirror-editor-error-color);
+}
+.cm-s-jupyter span.cm-hr {
+  color: #999;
+}
+
+.cm-s-jupyter span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+
+.cm-s-jupyter .CodeMirror-activeline-background,
+.cm-s-jupyter .CodeMirror-gutter {
+  background-color: var(--jp-layout-color2);
+}
+
+/* Styles for shared cursors (remote cursor locations and selected ranges) */
+.jp-CodeMirrorEditor .remote-caret {
+  position: relative;
+  border-left: 2px solid black;
+  margin-left: -1px;
+  margin-right: -1px;
+  box-sizing: border-box;
+}
+
+.jp-CodeMirrorEditor .remote-caret > div {
+  white-space: nowrap;
+  position: absolute;
+  top: -1.15em;
+  padding-bottom: 0.05em;
+  left: -2px;
+  font-size: 0.95em;
+  background-color: rgb(250, 129, 0);
+  font-family: var(--jp-ui-font-family);
+  font-weight: bold;
+  line-height: normal;
+  user-select: none;
+  color: white;
+  padding-left: 2px;
+  padding-right: 2px;
+  z-index: 3;
+  transition: opacity 0.3s ease-in-out;
+}
+
+.jp-CodeMirrorEditor .remote-caret.hide-name > div {
+  transition-delay: 0.7s;
+  opacity: 0;
+}
+
+.jp-CodeMirrorEditor .remote-caret:hover > div {
+  opacity: 1;
+  transition-delay: 0s;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| RenderedText
+|----------------------------------------------------------------------------*/
+
+:root {
+  /* This is the padding value to fill the gaps between lines containing spans with background color. */
+  --jp-private-code-span-padding: calc(
+    (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
+  );
+}
+
+.jp-RenderedText {
+  text-align: left;
+  padding-left: var(--jp-code-padding);
+  line-height: var(--jp-code-line-height);
+  font-family: var(--jp-code-font-family);
+}
+
+.jp-RenderedText pre,
+.jp-RenderedJavaScript pre,
+.jp-RenderedHTMLCommon pre {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-code-font-size);
+  border: none;
+  margin: 0px;
+  padding: 0px;
+}
+
+.jp-RenderedText pre a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+.jp-RenderedText pre a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
+}
+.jp-RenderedText pre a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+/* console foregrounds and backgrounds */
+.jp-RenderedText pre .ansi-black-fg {
+  color: #3e424d;
+}
+.jp-RenderedText pre .ansi-red-fg {
+  color: #e75c58;
+}
+.jp-RenderedText pre .ansi-green-fg {
+  color: #00a250;
+}
+.jp-RenderedText pre .ansi-yellow-fg {
+  color: #ddb62b;
+}
+.jp-RenderedText pre .ansi-blue-fg {
+  color: #208ffb;
+}
+.jp-RenderedText pre .ansi-magenta-fg {
+  color: #d160c4;
+}
+.jp-RenderedText pre .ansi-cyan-fg {
+  color: #60c6c8;
+}
+.jp-RenderedText pre .ansi-white-fg {
+  color: #c5c1b4;
+}
+
+.jp-RenderedText pre .ansi-black-bg {
+  background-color: #3e424d;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-red-bg {
+  background-color: #e75c58;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-green-bg {
+  background-color: #00a250;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-yellow-bg {
+  background-color: #ddb62b;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-blue-bg {
+  background-color: #208ffb;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-magenta-bg {
+  background-color: #d160c4;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-cyan-bg {
+  background-color: #60c6c8;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-white-bg {
+  background-color: #c5c1b4;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-black-intense-fg {
+  color: #282c36;
+}
+.jp-RenderedText pre .ansi-red-intense-fg {
+  color: #b22b31;
+}
+.jp-RenderedText pre .ansi-green-intense-fg {
+  color: #007427;
+}
+.jp-RenderedText pre .ansi-yellow-intense-fg {
+  color: #b27d12;
+}
+.jp-RenderedText pre .ansi-blue-intense-fg {
+  color: #0065ca;
+}
+.jp-RenderedText pre .ansi-magenta-intense-fg {
+  color: #a03196;
+}
+.jp-RenderedText pre .ansi-cyan-intense-fg {
+  color: #258f8f;
+}
+.jp-RenderedText pre .ansi-white-intense-fg {
+  color: #a1a6b2;
+}
+
+.jp-RenderedText pre .ansi-black-intense-bg {
+  background-color: #282c36;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-red-intense-bg {
+  background-color: #b22b31;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-green-intense-bg {
+  background-color: #007427;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-yellow-intense-bg {
+  background-color: #b27d12;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-blue-intense-bg {
+  background-color: #0065ca;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-magenta-intense-bg {
+  background-color: #a03196;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-cyan-intense-bg {
+  background-color: #258f8f;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-white-intense-bg {
+  background-color: #a1a6b2;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-default-inverse-fg {
+  color: var(--jp-ui-inverse-font-color0);
+}
+.jp-RenderedText pre .ansi-default-inverse-bg {
+  background-color: var(--jp-inverse-layout-color0);
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-bold {
+  font-weight: bold;
+}
+.jp-RenderedText pre .ansi-underline {
+  text-decoration: underline;
+}
+
+.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
+  background: var(--jp-rendermime-error-background);
+  padding-top: var(--jp-code-padding);
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedLatex
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedLatex {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
+}
+
+/* Left-justify outputs.*/
+.jp-OutputArea-output.jp-RenderedLatex {
+  padding: var(--jp-code-padding);
+  text-align: left;
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedHTML
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedHTMLCommon {
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-content-font-family);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
+  /* Give a bit more R padding on Markdown text to keep line lengths reasonable */
+  padding-right: 20px;
+}
+
+.jp-RenderedHTMLCommon em {
+  font-style: italic;
+}
+
+.jp-RenderedHTMLCommon strong {
+  font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon u {
+  text-decoration: underline;
+}
+
+.jp-RenderedHTMLCommon a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+/* Headings */
+
+.jp-RenderedHTMLCommon h1,
+.jp-RenderedHTMLCommon h2,
+.jp-RenderedHTMLCommon h3,
+.jp-RenderedHTMLCommon h4,
+.jp-RenderedHTMLCommon h5,
+.jp-RenderedHTMLCommon h6 {
+  line-height: var(--jp-content-heading-line-height);
+  font-weight: var(--jp-content-heading-font-weight);
+  font-style: normal;
+  margin: var(--jp-content-heading-margin-top) 0
+    var(--jp-content-heading-margin-bottom) 0;
+}
+
+.jp-RenderedHTMLCommon h1:first-child,
+.jp-RenderedHTMLCommon h2:first-child,
+.jp-RenderedHTMLCommon h3:first-child,
+.jp-RenderedHTMLCommon h4:first-child,
+.jp-RenderedHTMLCommon h5:first-child,
+.jp-RenderedHTMLCommon h6:first-child {
+  margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
+}
+
+.jp-RenderedHTMLCommon h1:last-child,
+.jp-RenderedHTMLCommon h2:last-child,
+.jp-RenderedHTMLCommon h3:last-child,
+.jp-RenderedHTMLCommon h4:last-child,
+.jp-RenderedHTMLCommon h5:last-child,
+.jp-RenderedHTMLCommon h6:last-child {
+  margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
+}
+
+.jp-RenderedHTMLCommon h1 {
+  font-size: var(--jp-content-font-size5);
+}
+
+.jp-RenderedHTMLCommon h2 {
+  font-size: var(--jp-content-font-size4);
+}
+
+.jp-RenderedHTMLCommon h3 {
+  font-size: var(--jp-content-font-size3);
+}
+
+.jp-RenderedHTMLCommon h4 {
+  font-size: var(--jp-content-font-size2);
+}
+
+.jp-RenderedHTMLCommon h5 {
+  font-size: var(--jp-content-font-size1);
+}
+
+.jp-RenderedHTMLCommon h6 {
+  font-size: var(--jp-content-font-size0);
+}
+
+/* Lists */
+
+.jp-RenderedHTMLCommon ul:not(.list-inline),
+.jp-RenderedHTMLCommon ol:not(.list-inline) {
+  padding-left: 2em;
+}
+
+.jp-RenderedHTMLCommon ul {
+  list-style: disc;
+}
+
+.jp-RenderedHTMLCommon ul ul {
+  list-style: square;
+}
+
+.jp-RenderedHTMLCommon ul ul ul {
+  list-style: circle;
+}
+
+.jp-RenderedHTMLCommon ol {
+  list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol ol {
+  list-style: upper-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol {
+  list-style: lower-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol {
+  list-style: lower-roman;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol ol {
+  list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol,
+.jp-RenderedHTMLCommon ul {
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon ul ul,
+.jp-RenderedHTMLCommon ul ol,
+.jp-RenderedHTMLCommon ol ul,
+.jp-RenderedHTMLCommon ol ol {
+  margin-bottom: 0em;
+}
+
+.jp-RenderedHTMLCommon hr {
+  color: var(--jp-border-color2);
+  background-color: var(--jp-border-color1);
+  margin-top: 1em;
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon > pre {
+  margin: 1.5em 2em;
+}
+
+.jp-RenderedHTMLCommon pre,
+.jp-RenderedHTMLCommon code {
+  border: 0;
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  line-height: var(--jp-code-line-height);
+  padding: 0;
+  white-space: pre-wrap;
+}
+
+.jp-RenderedHTMLCommon :not(pre) > code {
+  background-color: var(--jp-layout-color2);
+  padding: 1px 5px;
+}
+
+/* Tables */
+
+.jp-RenderedHTMLCommon table {
+  border-collapse: collapse;
+  border-spacing: 0;
+  border: none;
+  color: var(--jp-ui-font-color1);
+  font-size: 12px;
+  table-layout: fixed;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.jp-RenderedHTMLCommon thead {
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  vertical-align: bottom;
+}
+
+.jp-RenderedHTMLCommon td,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon tr {
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
+  max-width: none;
+}
+
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
+  text-align: right;
+}
+
+.jp-RenderedHTMLCommon th {
+  font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
+  background: var(--jp-layout-color0);
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
+  background: var(--jp-rendermime-table-row-background);
+}
+
+.jp-RenderedHTMLCommon tbody tr:hover {
+  background: var(--jp-rendermime-table-row-hover-background);
+}
+
+.jp-RenderedHTMLCommon table {
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon p {
+  text-align: left;
+  margin: 0px;
+}
+
+.jp-RenderedHTMLCommon p {
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon img {
+  -moz-force-broken-image-icon: 1;
+}
+
+/* Restrict to direct children as other images could be nested in other content. */
+.jp-RenderedHTMLCommon > img {
+  display: block;
+  margin-left: 0;
+  margin-right: 0;
+  margin-bottom: 1em;
+}
+
+/* Change color behind transparent images if they need it... */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
+  background-color: var(--jp-inverse-layout-color1);
+}
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
+  background-color: var(--jp-inverse-layout-color1);
+}
+/* ...or leave it untouched if they don't */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-dark-background {
+}
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-light-background {
+}
+
+.jp-RenderedHTMLCommon img,
+.jp-RenderedImage img,
+.jp-RenderedHTMLCommon svg,
+.jp-RenderedSVG svg {
+  max-width: 100%;
+  height: auto;
+}
+
+.jp-RenderedHTMLCommon img.jp-mod-unconfined,
+.jp-RenderedImage img.jp-mod-unconfined,
+.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
+.jp-RenderedSVG svg.jp-mod-unconfined {
+  max-width: none;
+}
+
+.jp-RenderedHTMLCommon .alert {
+  padding: var(--jp-notebook-padding);
+  border: var(--jp-border-width) solid transparent;
+  border-radius: var(--jp-border-radius);
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon .alert-info {
+  color: var(--jp-info-color0);
+  background-color: var(--jp-info-color3);
+  border-color: var(--jp-info-color2);
+}
+.jp-RenderedHTMLCommon .alert-info hr {
+  border-color: var(--jp-info-color3);
+}
+.jp-RenderedHTMLCommon .alert-info > p:last-child,
+.jp-RenderedHTMLCommon .alert-info > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-warning {
+  color: var(--jp-warn-color0);
+  background-color: var(--jp-warn-color3);
+  border-color: var(--jp-warn-color2);
+}
+.jp-RenderedHTMLCommon .alert-warning hr {
+  border-color: var(--jp-warn-color3);
+}
+.jp-RenderedHTMLCommon .alert-warning > p:last-child,
+.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-success {
+  color: var(--jp-success-color0);
+  background-color: var(--jp-success-color3);
+  border-color: var(--jp-success-color2);
+}
+.jp-RenderedHTMLCommon .alert-success hr {
+  border-color: var(--jp-success-color3);
+}
+.jp-RenderedHTMLCommon .alert-success > p:last-child,
+.jp-RenderedHTMLCommon .alert-success > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-danger {
+  color: var(--jp-error-color0);
+  background-color: var(--jp-error-color3);
+  border-color: var(--jp-error-color2);
+}
+.jp-RenderedHTMLCommon .alert-danger hr {
+  border-color: var(--jp-error-color3);
+}
+.jp-RenderedHTMLCommon .alert-danger > p:last-child,
+.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon blockquote {
+  margin: 1em 2em;
+  padding: 0 1em;
+  border-left: 5px solid var(--jp-border-color2);
+}
+
+a.jp-InternalAnchorLink {
+  visibility: hidden;
+  margin-left: 8px;
+  color: var(--md-blue-800);
+}
+
+h1:hover .jp-InternalAnchorLink,
+h2:hover .jp-InternalAnchorLink,
+h3:hover .jp-InternalAnchorLink,
+h4:hover .jp-InternalAnchorLink,
+h5:hover .jp-InternalAnchorLink,
+h6:hover .jp-InternalAnchorLink {
+  visibility: visible;
+}
+
+.jp-RenderedHTMLCommon kbd {
+  background-color: var(--jp-rendermime-table-row-background);
+  border: 1px solid var(--jp-border-color0);
+  border-bottom-color: var(--jp-border-color2);
+  border-radius: 3px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+  display: inline-block;
+  font-size: 0.8em;
+  line-height: 1em;
+  padding: 0.2em 0.5em;
+}
+
+/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
+ * At the bottom of cells this is a bit too much as there is also spacing
+ * between cells. Going all the way to 0 gets too tight between markdown and
+ * code cells.
+ */
+.jp-RenderedHTMLCommon > *:last-child {
+  margin-bottom: 0.5em;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MimeDocument {
+  outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-filebrowser-button-height: 28px;
+  --jp-private-filebrowser-button-width: 48px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser {
+  display: flex;
+  flex-direction: column;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  border-bottom: none;
+  height: auto;
+  margin: var(--jp-toolbar-header-margin);
+  box-shadow: none;
+}
+
+.jp-BreadCrumbs {
+  flex: 0 0 auto;
+  margin: 8px 12px 8px 12px;
+}
+
+.jp-BreadCrumbs-item {
+  margin: 0px 2px;
+  padding: 0px 2px;
+  border-radius: var(--jp-border-radius);
+  cursor: pointer;
+}
+
+.jp-BreadCrumbs-item:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-BreadCrumbs-item:first-child {
+  margin-left: 0px;
+}
+
+.jp-BreadCrumbs-item.jp-mod-dropTarget {
+  background-color: var(--jp-brand-color2);
+  opacity: 0.7;
+}
+
+/*-----------------------------------------------------------------------------
+| Buttons
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  padding: 0px;
+  margin: 8px 12px 0px 12px;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  justify-content: flex-start;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar .jp-Toolbar-item {
+  flex: 0 0 auto;
+  padding-left: 0px;
+  padding-right: 2px;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar .jp-ToolbarButtonComponent {
+  width: 40px;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar
+  .jp-Toolbar-item:first-child
+  .jp-ToolbarButtonComponent {
+  width: 72px;
+  background: var(--jp-brand-color1);
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar
+  .jp-Toolbar-item:first-child
+  .jp-ToolbarButtonComponent:focus-visible {
+  background-color: var(--jp-brand-color0);
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar
+  .jp-Toolbar-item:first-child
+  .jp-ToolbarButtonComponent
+  .jp-icon3 {
+  fill: white;
+}
+
+/*-----------------------------------------------------------------------------
+| Other styles
+|----------------------------------------------------------------------------*/
+
+.jp-FileDialog.jp-mod-conflict input {
+  color: var(--jp-error-color1);
+}
+
+.jp-FileDialog .jp-new-name-title {
+  margin-top: 12px;
+}
+
+.jp-LastModified-hidden {
+  display: none;
+}
+
+.jp-FileBrowser-filterBox {
+  padding: 0px;
+  flex: 0 0 auto;
+  margin: 8px 12px 0px 12px;
+}
+
+/*-----------------------------------------------------------------------------
+| DirListing
+|----------------------------------------------------------------------------*/
+
+.jp-DirListing {
+  flex: 1 1 auto;
+  display: flex;
+  flex-direction: column;
+  outline: 0;
+}
+
+.jp-DirListing:focus-visible {
+  border: 1px solid var(--jp-brand-color1);
+}
+
+.jp-DirListing-header {
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  overflow: hidden;
+  border-top: var(--jp-border-width) solid var(--jp-border-color2);
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  z-index: 2;
+}
+
+.jp-DirListing-headerItem {
+  padding: 4px 12px 2px 12px;
+  font-weight: 500;
+}
+
+.jp-DirListing-headerItem:hover {
+  background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-headerItem.jp-id-name {
+  flex: 1 0 84px;
+}
+
+.jp-DirListing-headerItem.jp-id-modified {
+  flex: 0 0 112px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+}
+
+.jp-id-narrow {
+  display: none;
+  flex: 0 0 5px;
+  padding: 4px 4px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+  color: var(--jp-border-color2);
+}
+
+.jp-DirListing-narrow .jp-id-narrow {
+  display: block;
+}
+
+.jp-DirListing-narrow .jp-id-modified,
+.jp-DirListing-narrow .jp-DirListing-itemModified {
+  display: none;
+}
+
+.jp-DirListing-headerItem.jp-mod-selected {
+  font-weight: 600;
+}
+
+/* increase specificity to override bundled default */
+.jp-DirListing-content {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+  overflow: auto;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-content mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
+}
+
+.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+/* Style the directory listing content when a user drops a file to upload */
+.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
+  outline: 5px dashed rgba(128, 128, 128, 0.5);
+  outline-offset: -10px;
+  cursor: copy;
+}
+
+.jp-DirListing-item {
+  display: flex;
+  flex-direction: row;
+  padding: 4px 12px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-DirListing-item[data-is-dot] {
+  opacity: 75%;
+}
+
+.jp-DirListing-item.jp-mod-selected {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.jp-DirListing-item.jp-mod-dropTarget {
+  background: var(--jp-brand-color3);
+}
+
+.jp-DirListing-item:hover:not(.jp-mod-selected) {
+  background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-itemIcon {
+  flex: 0 0 20px;
+  margin-right: 4px;
+}
+
+.jp-DirListing-itemText {
+  flex: 1 0 64px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  user-select: none;
+}
+
+.jp-DirListing-itemModified {
+  flex: 0 0 125px;
+  text-align: right;
+}
+
+.jp-DirListing-editor {
+  flex: 1 0 64px;
+  outline: none;
+  border: none;
+}
+
+.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon:before {
+  color: var(--jp-success-color1);
+  content: '\25CF';
+  font-size: 8px;
+  position: absolute;
+  left: -8px;
+}
+
+.jp-DirListing-item.jp-mod-running.jp-mod-selected
+  .jp-DirListing-itemIcon:before {
+  color: var(--jp-ui-inverse-font-color1);
+}
+
+.jp-DirListing-item.lm-mod-drag-image,
+.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
+  font-size: var(--jp-ui-font-size1);
+  padding-left: 4px;
+  margin-left: 4px;
+  width: 160px;
+  background-color: var(--jp-ui-inverse-font-color2);
+  box-shadow: var(--jp-elevation-z2);
+  border-radius: 0px;
+  color: var(--jp-ui-font-color1);
+  transform: translateX(-40%) translateY(-58%);
+}
+
+.jp-DirListing-deadSpace {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+  overflow: auto;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-Document {
+  min-width: 120px;
+  min-height: 120px;
+  outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+}
+
+/*-----------------------------------------------------------------------------
+| Main OutputArea
+| OutputArea has a list of Outputs
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea {
+  overflow-y: auto;
+}
+
+.jp-OutputArea-child {
+  display: flex;
+  flex-direction: row;
+}
+
+body[data-format='mobile'] .jp-OutputArea-child {
+  flex-direction: column;
+}
+
+.jp-OutputPrompt {
+  flex: 0 0 var(--jp-cell-prompt-width);
+  color: var(--jp-cell-outprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+  opacity: var(--jp-cell-prompt-opacity);
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+body[data-format='mobile'] .jp-OutputPrompt {
+  flex: 0 0 auto;
+  text-align: left;
+}
+
+.jp-OutputArea-output {
+  height: auto;
+  overflow: auto;
+  user-select: text;
+  -moz-user-select: text;
+  -webkit-user-select: text;
+  -ms-user-select: text;
+}
+
+.jp-OutputArea-child .jp-OutputArea-output {
+  flex-grow: 1;
+  flex-shrink: 1;
+}
+
+body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
+  margin-left: var(--jp-notebook-padding);
+}
+
+/**
+ * Isolated output.
+ */
+.jp-OutputArea-output.jp-mod-isolated {
+  width: 100%;
+  display: block;
+}
+
+/*
+When drag events occur, `p-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
+  position: relative;
+}
+
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
+
+/* pre */
+
+.jp-OutputArea-output pre {
+  border: none;
+  margin: 0px;
+  padding: 0px;
+  overflow-x: auto;
+  overflow-y: auto;
+  word-break: break-all;
+  word-wrap: break-word;
+  white-space: pre-wrap;
+}
+
+/* tables */
+
+.jp-OutputArea-output.jp-RenderedHTMLCommon table {
+  margin-left: 0;
+  margin-right: 0;
+}
+
+/* description lists */
+
+.jp-OutputArea-output dl,
+.jp-OutputArea-output dt,
+.jp-OutputArea-output dd {
+  display: block;
+}
+
+.jp-OutputArea-output dl {
+  width: 100%;
+  overflow: hidden;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-OutputArea-output dt {
+  font-weight: bold;
+  float: left;
+  width: 20%;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-OutputArea-output dd {
+  float: left;
+  width: 80%;
+  padding: 0;
+  margin: 0;
+}
+
+/* Hide the gutter in case of
+ *  - nested output areas (e.g. in the case of output widgets)
+ *  - mirrored output areas
+ */
+.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| executeResult is added to any Output-result for the display of the object
+| returned by a cell
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea-output.jp-OutputArea-executeResult {
+  margin-left: 0px;
+  flex: 1 1 auto;
+}
+
+/* Text output with the Out[] prompt needs a top padding to match the
+ * alignment of the Out[] prompt itself.
+ */
+.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
+  padding-top: var(--jp-code-padding);
+  border-top: var(--jp-border-width) solid transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| The Stdin output
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea-stdin {
+  line-height: var(--jp-code-line-height);
+  padding-top: var(--jp-code-padding);
+  display: flex;
+}
+
+.jp-Stdin-prompt {
+  color: var(--jp-content-font-color0);
+  padding-right: var(--jp-code-padding);
+  vertical-align: baseline;
+  flex: 0 0 auto;
+}
+
+.jp-Stdin-input {
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  color: inherit;
+  background-color: inherit;
+  width: 42%;
+  min-width: 200px;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+  flex: 0 0 70%;
+}
+
+.jp-Stdin-input:focus {
+  box-shadow: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Output Area View
+|----------------------------------------------------------------------------*/
+
+.jp-LinkedOutputView .jp-OutputArea {
+  height: 100%;
+  display: block;
+}
+
+.jp-LinkedOutputView .jp-OutputArea-output:only-child {
+  height: 100%;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapser {
+  flex: 0 0 var(--jp-cell-collapser-width);
+  padding: 0px;
+  margin: 0px;
+  border: none;
+  outline: none;
+  background: transparent;
+  border-radius: var(--jp-border-radius);
+  opacity: 1;
+}
+
+.jp-Collapser-child {
+  display: block;
+  width: 100%;
+  box-sizing: border-box;
+  /* height: 100% doesn't work because the height of its parent is computed from content */
+  position: absolute;
+  top: 0px;
+  bottom: 0px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Header/Footer
+|----------------------------------------------------------------------------*/
+
+/* Hidden by zero height by default */
+.jp-CellHeader,
+.jp-CellFooter {
+  height: 0px;
+  width: 100%;
+  padding: 0px;
+  margin: 0px;
+  border: none;
+  outline: none;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Input
+|----------------------------------------------------------------------------*/
+
+/* All input areas */
+.jp-InputArea {
+  display: flex;
+  flex-direction: row;
+  overflow: hidden;
+}
+
+body[data-format='mobile'] .jp-InputArea {
+  flex-direction: column;
+}
+
+.jp-InputArea-editor {
+  flex: 1 1 auto;
+  overflow: hidden;
+}
+
+.jp-InputArea-editor {
+  /* This is the non-active, default styling */
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  border-radius: 0px;
+  background: var(--jp-cell-editor-background);
+}
+
+body[data-format='mobile'] .jp-InputArea-editor {
+  margin-left: var(--jp-notebook-padding);
+}
+
+.jp-InputPrompt {
+  flex: 0 0 var(--jp-cell-prompt-width);
+  color: var(--jp-cell-inprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  opacity: var(--jp-cell-prompt-opacity);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+  opacity: var(--jp-cell-prompt-opacity);
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+body[data-format='mobile'] .jp-InputPrompt {
+  flex: 0 0 auto;
+  text-align: left;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Placeholder {
+  display: flex;
+  flex-direction: row;
+  flex: 1 1 auto;
+}
+
+.jp-Placeholder-prompt {
+  box-sizing: border-box;
+}
+
+.jp-Placeholder-content {
+  flex: 1 1 auto;
+  border: none;
+  background: transparent;
+  height: 20px;
+  box-sizing: border-box;
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon {
+  width: 32px;
+  height: 16px;
+  border: 1px solid transparent;
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon:hover {
+  border: 1px solid var(--jp-border-color1);
+  box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.25);
+  background-color: var(--jp-layout-color0);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-cell-scrolling-output-offset: 5px;
+}
+
+/*-----------------------------------------------------------------------------
+| Cell
+|----------------------------------------------------------------------------*/
+
+.jp-Cell {
+  padding: var(--jp-cell-padding);
+  margin: 0px;
+  border: none;
+  outline: none;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Common input/output
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-inputWrapper,
+.jp-Cell-outputWrapper {
+  display: flex;
+  flex-direction: row;
+  padding: 0px;
+  margin: 0px;
+  /* Added to reveal the box-shadow on the input and output collapsers. */
+  overflow: visible;
+}
+
+/* Only input/output areas inside cells */
+.jp-Cell-inputArea,
+.jp-Cell-outputArea {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Collapser
+|----------------------------------------------------------------------------*/
+
+/* Make the output collapser disappear when there is not output, but do so
+ * in a manner that leaves it in the layout and preserves its width.
+ */
+.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
+  border: none !important;
+  background: transparent !important;
+}
+
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
+  min-height: var(--jp-cell-collapser-min-height);
+}
+
+/*-----------------------------------------------------------------------------
+| Output
+|----------------------------------------------------------------------------*/
+
+/* Put a space between input and output when there IS output */
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
+  margin-top: 5px;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
+  overflow-y: auto;
+  max-height: 200px;
+  box-shadow: inset 0 0 6px 2px rgba(0, 0, 0, 0.3);
+  margin-left: var(--jp-private-cell-scrolling-output-offset);
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+  flex: 0 0
+    calc(
+      var(--jp-cell-prompt-width) -
+        var(--jp-private-cell-scrolling-output-offset)
+    );
+}
+
+/*-----------------------------------------------------------------------------
+| CodeCell
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| MarkdownCell
+|----------------------------------------------------------------------------*/
+
+.jp-MarkdownOutput {
+  flex: 1 1 auto;
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-left: var(--jp-code-padding);
+}
+
+.jp-MarkdownOutput.jp-RenderedHTMLCommon {
+  overflow: auto;
+}
+
+.jp-showHiddenCellsButton {
+  margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
+  margin-top: var(--jp-code-padding);
+  border: 1px solid var(--jp-border-color2);
+  background-color: var(--jp-border-color3) !important;
+  color: var(--jp-content-font-color0) !important;
+}
+
+.jp-showHiddenCellsButton:hover {
+  background-color: var(--jp-border-color2) !important;
+}
+
+.jp-collapseHeadingButton {
+  display: none;
+}
+
+.jp-MarkdownCell:hover .jp-collapseHeadingButton {
+  display: flex;
+  min-height: var(--jp-cell-collapser-min-height);
+  position: absolute;
+  right: 0;
+  top: 0;
+  bottom: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel-toolbar {
+  padding: 2px;
+}
+
+.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
+  border: none;
+  box-shadow: none;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown select {
+  height: 24px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: 14px;
+  border-radius: 0;
+  display: block;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown span {
+  top: 5px !important;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-notebook-dragImage-width: 304px;
+  --jp-private-notebook-dragImage-height: 36px;
+  --jp-private-notebook-selected-color: var(--md-blue-400);
+  --jp-private-notebook-active-color: var(--md-green-400);
+}
+
+/*-----------------------------------------------------------------------------
+| Imports
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Notebook
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel {
+  display: block;
+  height: 100%;
+}
+
+.jp-NotebookPanel.jp-Document {
+  min-width: 240px;
+  min-height: 120px;
+}
+
+.jp-Notebook {
+  padding: var(--jp-notebook-padding);
+  outline: none;
+  overflow: auto;
+  background: var(--jp-layout-color0);
+}
+
+.jp-Notebook.jp-mod-scrollPastEnd::after {
+  display: block;
+  content: '';
+  min-height: var(--jp-notebook-scroll-padding);
+}
+
+.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
+  contain: strict;
+}
+
+.jp-Notebook-render * {
+  contain: none !important;
+}
+
+.jp-Notebook .jp-Cell {
+  overflow: visible;
+}
+
+.jp-Notebook .jp-Cell .jp-InputPrompt {
+  cursor: move;
+  float: left;
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook state related styling
+|
+| The notebook and cells each have states, here are the possibilities:
+|
+| - Notebook
+|   - Command
+|   - Edit
+| - Cell
+|   - None
+|   - Active (only one can be active)
+|   - Selected (the cells actions are applied to)
+|   - Multiselected (when multiple selected, the cursor)
+|   - No outputs
+|----------------------------------------------------------------------------*/
+
+/* Command or edit modes */
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+/* cell is active */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
+  background: var(--jp-brand-color1);
+}
+
+/* cell is dirty */
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
+  color: var(--jp-warn-color1);
+}
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt:before {
+  color: var(--jp-warn-color1);
+  content: '•';
+}
+
+.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
+  background: var(--jp-warn-color1);
+}
+
+/* collapser is hovered */
+.jp-Notebook .jp-Cell .jp-Collapser:hover {
+  box-shadow: var(--jp-elevation-z2);
+  background: var(--jp-brand-color1);
+  opacity: var(--jp-cell-collapser-not-active-hover-opacity);
+}
+
+/* cell is active and collapser is hovered */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
+  background: var(--jp-brand-color0);
+  opacity: 1;
+}
+
+/* Command mode */
+
+.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
+  background: var(--jp-notebook-multiselected-color);
+}
+
+.jp-Notebook.jp-mod-commandMode
+  .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
+  background: transparent;
+}
+
+/* Edit mode */
+
+.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
+  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-cell-editor-active-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook drag and drop
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-cell.jp-mod-dropSource {
+  opacity: 0.5;
+}
+
+.jp-Notebook-cell.jp-mod-dropTarget,
+.jp-Notebook.jp-mod-commandMode
+  .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
+  border-top-color: var(--jp-private-notebook-selected-color);
+  border-top-style: solid;
+  border-top-width: 2px;
+}
+
+.jp-dragImage {
+  display: block;
+  flex-direction: row;
+  width: var(--jp-private-notebook-dragImage-width);
+  height: var(--jp-private-notebook-dragImage-height);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background);
+  overflow: visible;
+}
+
+.jp-dragImage-singlePrompt {
+  box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
+}
+
+.jp-dragImage .jp-dragImage-content {
+  flex: 1 1 auto;
+  z-index: 2;
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  line-height: var(--jp-code-line-height);
+  padding: var(--jp-code-padding);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background-color);
+  color: var(--jp-content-font-color3);
+  text-align: left;
+  margin: 4px 4px 4px 0px;
+}
+
+.jp-dragImage .jp-dragImage-prompt {
+  flex: 0 0 auto;
+  min-width: 36px;
+  color: var(--jp-cell-inprompt-font-color);
+  padding: var(--jp-code-padding);
+  padding-left: 12px;
+  font-family: var(--jp-cell-prompt-font-family);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: 1.9;
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+}
+
+.jp-dragImage-multipleBack {
+  z-index: -1;
+  position: absolute;
+  height: 32px;
+  width: 300px;
+  top: 8px;
+  left: 8px;
+  background: var(--jp-layout-color2);
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
+}
+
+/*-----------------------------------------------------------------------------
+| Cell toolbar
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookTools {
+  display: block;
+  min-width: var(--jp-sidebar-min-width);
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+    * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  overflow: auto;
+}
+
+.jp-NotebookTools-tool {
+  padding: 0px 12px 0 12px;
+}
+
+.jp-ActiveCellTool {
+  padding: 12px;
+  background-color: var(--jp-layout-color1);
+  border-top: none !important;
+}
+
+.jp-ActiveCellTool .jp-InputArea-prompt {
+  flex: 0 0 auto;
+  padding-left: 0px;
+}
+
+.jp-ActiveCellTool .jp-InputArea-editor {
+  flex: 1 1 auto;
+  background: var(--jp-cell-editor-background);
+  border-color: var(--jp-cell-editor-border-color);
+}
+
+.jp-ActiveCellTool .jp-InputArea-editor .CodeMirror {
+  background: transparent;
+}
+
+.jp-MetadataEditorTool {
+  flex-direction: column;
+  padding: 12px 0px 12px 0px;
+}
+
+.jp-RankedPanel > :not(:first-child) {
+  margin-top: 12px;
+}
+
+.jp-KeySelector select.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+}
+
+.jp-KeySelector label,
+.jp-MetadataEditorTool label {
+  line-height: 1.4;
+}
+
+.jp-NotebookTools .jp-select-wrapper {
+  margin-top: 4px;
+  margin-bottom: 0px;
+}
+
+.jp-NotebookTools .jp-Collapse {
+  margin-top: 16px;
+}
+
+/*-----------------------------------------------------------------------------
+| Presentation Mode (.jp-mod-presentationMode)
+|----------------------------------------------------------------------------*/
+
+.jp-mod-presentationMode .jp-Notebook {
+  --jp-content-font-size1: var(--jp-content-presentation-font-size1);
+  --jp-code-font-size: var(--jp-code-presentation-font-size);
+}
+
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
+  flex: 0 0 110px;
+}
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-Placeholder {
+  padding-left: 55px;
+}
+
+.jp-Cell-Placeholder-wrapper {
+  background: #fff;
+  border: 1px solid;
+  border-color: #e5e6e9 #dfe0e4 #d0d1d5;
+  border-radius: 4px;
+  -webkit-border-radius: 4px;
+  margin: 10px 15px;
+}
+
+.jp-Cell-Placeholder-wrapper-inner {
+  padding: 15px;
+  position: relative;
+}
+
+.jp-Cell-Placeholder-wrapper-body {
+  background-repeat: repeat;
+  background-size: 50% auto;
+}
+
+.jp-Cell-Placeholder-wrapper-body div {
+  background: #f6f7f8;
+  background-image: -webkit-linear-gradient(
+    left,
+    #f6f7f8 0%,
+    #edeef1 20%,
+    #f6f7f8 40%,
+    #f6f7f8 100%
+  );
+  background-repeat: no-repeat;
+  background-size: 800px 104px;
+  height: 104px;
+  position: relative;
+}
+
+.jp-Cell-Placeholder-wrapper-body div {
+  position: absolute;
+  right: 15px;
+  left: 15px;
+  top: 15px;
+}
+
+div.jp-Cell-Placeholder-h1 {
+  top: 20px;
+  height: 20px;
+  left: 15px;
+  width: 150px;
+}
+
+div.jp-Cell-Placeholder-h2 {
+  left: 15px;
+  top: 50px;
+  height: 10px;
+  width: 100px;
+}
+
+div.jp-Cell-Placeholder-content-1,
+div.jp-Cell-Placeholder-content-2,
+div.jp-Cell-Placeholder-content-3 {
+  left: 15px;
+  right: 15px;
+  height: 10px;
+}
+
+div.jp-Cell-Placeholder-content-1 {
+  top: 100px;
+}
+
+div.jp-Cell-Placeholder-content-2 {
+  top: 120px;
+}
+
+div.jp-Cell-Placeholder-content-3 {
+  top: 140px;
+}
+
+</style>
+
+    <style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+  /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+  --jp-shadow-base-lightness: 0;
+  --jp-shadow-umbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.2
+  );
+  --jp-shadow-penumbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.14
+  );
+  --jp-shadow-ambient-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.12
+  );
+  --jp-elevation-z0: none;
+  --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+    0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 3px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+    0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 5px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+    0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 10px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+    0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 18px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+    0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0px 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+    0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0px 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+    0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0px 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+    0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0px 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+    0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+  /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+  --jp-border-width: 1px;
+  --jp-border-color0: var(--md-grey-400);
+  --jp-border-color1: var(--md-grey-400);
+  --jp-border-color2: var(--md-grey-300);
+  --jp-border-color3: var(--md-grey-200);
+  --jp-border-radius: 2px;
+
+  /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-ui-font-scale-factor: 1.2;
+  --jp-ui-font-size0: 0.83333em;
+  --jp-ui-font-size1: 13px; /* Base font size */
+  --jp-ui-font-size2: 1.2em;
+  --jp-ui-font-size3: 1.44em;
+
+  --jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica,
+    Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
+
+  /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+  /* Defaults use Material Design specification */
+  --jp-ui-font-color0: rgba(0, 0, 0, 1);
+  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+  /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+  /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-content-line-height: 1.6;
+  --jp-content-font-scale-factor: 1.2;
+  --jp-content-font-size0: 0.83333em;
+  --jp-content-font-size1: 14px; /* Base font size */
+  --jp-content-font-size2: 1.2em;
+  --jp-content-font-size3: 1.44em;
+  --jp-content-font-size4: 1.728em;
+  --jp-content-font-size5: 2.0736em;
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-content-presentation-font-size1: 17px;
+
+  --jp-content-heading-line-height: 1;
+  --jp-content-heading-margin-top: 1.2em;
+  --jp-content-heading-margin-bottom: 0.8em;
+  --jp-content-heading-font-weight: 500;
+
+  /* Defaults use Material Design specification */
+  --jp-content-font-color0: rgba(0, 0, 0, 1);
+  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+  --jp-content-link-color: var(--md-blue-700);
+
+  --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI',
+    Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+    'Segoe UI Symbol';
+
+  /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+  --jp-code-font-size: 13px;
+  --jp-code-line-height: 1.3077; /* 17px for 13px base */
+  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+  --jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace;
+  --jp-code-font-family: var(--jp-code-font-family-default);
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-code-presentation-font-size: 16px;
+
+  /* may need to tweak cursor width if you change font size */
+  --jp-code-cursor-width0: 1.4px;
+  --jp-code-cursor-width1: 2px;
+  --jp-code-cursor-width2: 4px;
+
+  /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+  --jp-layout-color0: white;
+  --jp-layout-color1: white;
+  --jp-layout-color2: var(--md-grey-200);
+  --jp-layout-color3: var(--md-grey-400);
+  --jp-layout-color4: var(--md-grey-600);
+
+  /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+  --jp-inverse-layout-color0: #111111;
+  --jp-inverse-layout-color1: var(--md-grey-900);
+  --jp-inverse-layout-color2: var(--md-grey-800);
+  --jp-inverse-layout-color3: var(--md-grey-700);
+  --jp-inverse-layout-color4: var(--md-grey-600);
+
+  /* Brand/accent */
+
+  --jp-brand-color0: var(--md-blue-900);
+  --jp-brand-color1: var(--md-blue-700);
+  --jp-brand-color2: var(--md-blue-300);
+  --jp-brand-color3: var(--md-blue-100);
+  --jp-brand-color4: var(--md-blue-50);
+
+  --jp-accent-color0: var(--md-green-900);
+  --jp-accent-color1: var(--md-green-700);
+  --jp-accent-color2: var(--md-green-300);
+  --jp-accent-color3: var(--md-green-100);
+
+  /* State colors (warn, error, success, info) */
+
+  --jp-warn-color0: var(--md-orange-900);
+  --jp-warn-color1: var(--md-orange-700);
+  --jp-warn-color2: var(--md-orange-300);
+  --jp-warn-color3: var(--md-orange-100);
+
+  --jp-error-color0: var(--md-red-900);
+  --jp-error-color1: var(--md-red-700);
+  --jp-error-color2: var(--md-red-300);
+  --jp-error-color3: var(--md-red-100);
+
+  --jp-success-color0: var(--md-green-900);
+  --jp-success-color1: var(--md-green-700);
+  --jp-success-color2: var(--md-green-300);
+  --jp-success-color3: var(--md-green-100);
+
+  --jp-info-color0: var(--md-cyan-900);
+  --jp-info-color1: var(--md-cyan-700);
+  --jp-info-color2: var(--md-cyan-300);
+  --jp-info-color3: var(--md-cyan-100);
+
+  /* Cell specific styles */
+
+  --jp-cell-padding: 5px;
+
+  --jp-cell-collapser-width: 8px;
+  --jp-cell-collapser-min-height: 20px;
+  --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+  --jp-cell-editor-background: var(--md-grey-100);
+  --jp-cell-editor-border-color: var(--md-grey-300);
+  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-cell-editor-active-background: var(--jp-layout-color0);
+  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+  --jp-cell-prompt-width: 64px;
+  --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+  --jp-cell-prompt-letter-spacing: 0px;
+  --jp-cell-prompt-opacity: 1;
+  --jp-cell-prompt-not-active-opacity: 0.5;
+  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+  /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+  --jp-cell-inprompt-font-color: #307fc1;
+  /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+  --jp-cell-outprompt-font-color: #bf5b3d;
+
+  /* Notebook specific styles */
+
+  --jp-notebook-padding: 10px;
+  --jp-notebook-select-background: var(--jp-layout-color1);
+  --jp-notebook-multiselected-color: var(--md-blue-50);
+
+  /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+  --jp-notebook-scroll-padding: calc(
+    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+  );
+
+  /* Rendermime styles */
+
+  --jp-rendermime-error-background: #fdd;
+  --jp-rendermime-table-row-background: var(--md-grey-100);
+  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+  /* Dialog specific styles */
+
+  --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+  /* Console specific styles */
+
+  --jp-console-padding: 10px;
+
+  /* Toolbar specific styles */
+
+  --jp-toolbar-border-color: var(--jp-border-color1);
+  --jp-toolbar-micro-height: 8px;
+  --jp-toolbar-background: var(--jp-layout-color1);
+  --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+  --jp-toolbar-header-margin: 4px 4px 0px 4px;
+  --jp-toolbar-active-background: var(--md-grey-300);
+
+  /* Statusbar specific styles */
+
+  --jp-statusbar-height: 24px;
+
+  /* Input field styles */
+
+  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-input-active-background: var(--jp-layout-color1);
+  --jp-input-hover-background: var(--jp-layout-color1);
+  --jp-input-background: var(--md-grey-100);
+  --jp-input-border-color: var(--jp-border-color1);
+  --jp-input-active-border-color: var(--jp-brand-color1);
+  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+  /* General editor styles */
+
+  --jp-editor-selected-background: #d9d9d9;
+  --jp-editor-selected-focused-background: #d7d4f0;
+  --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+  /* Code mirror specific styles */
+
+  --jp-mirror-editor-keyword-color: #008000;
+  --jp-mirror-editor-atom-color: #88f;
+  --jp-mirror-editor-number-color: #080;
+  --jp-mirror-editor-def-color: #00f;
+  --jp-mirror-editor-variable-color: var(--md-grey-900);
+  --jp-mirror-editor-variable-2-color: #05a;
+  --jp-mirror-editor-variable-3-color: #085;
+  --jp-mirror-editor-punctuation-color: #05a;
+  --jp-mirror-editor-property-color: #05a;
+  --jp-mirror-editor-operator-color: #aa22ff;
+  --jp-mirror-editor-comment-color: #408080;
+  --jp-mirror-editor-string-color: #ba2121;
+  --jp-mirror-editor-string-2-color: #708;
+  --jp-mirror-editor-meta-color: #aa22ff;
+  --jp-mirror-editor-qualifier-color: #555;
+  --jp-mirror-editor-builtin-color: #008000;
+  --jp-mirror-editor-bracket-color: #997;
+  --jp-mirror-editor-tag-color: #170;
+  --jp-mirror-editor-attribute-color: #00c;
+  --jp-mirror-editor-header-color: blue;
+  --jp-mirror-editor-quote-color: #090;
+  --jp-mirror-editor-link-color: #00c;
+  --jp-mirror-editor-error-color: #f00;
+  --jp-mirror-editor-hr-color: #999;
+
+  /* Vega extension styles */
+
+  --jp-vega-background: white;
+
+  /* Sidebar-related styles */
+
+  --jp-sidebar-min-width: 250px;
+
+  /* Search-related styles */
+
+  --jp-search-toggle-off-opacity: 0.5;
+  --jp-search-toggle-hover-opacity: 0.8;
+  --jp-search-toggle-on-opacity: 1;
+  --jp-search-selected-match-background-color: rgb(245, 200, 0);
+  --jp-search-selected-match-color: black;
+  --jp-search-unselected-match-background-color: var(
+    --jp-inverse-layout-color0
+  );
+  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+  /* Icon colors that work well with light or dark backgrounds */
+  --jp-icon-contrast-color0: var(--md-purple-600);
+  --jp-icon-contrast-color1: var(--md-green-600);
+  --jp-icon-contrast-color2: var(--md-pink-600);
+  --jp-icon-contrast-color3: var(--md-blue-600);
+}
+</style>
+
+<style type="text/css">
+/* Force rendering true colors when outputing to pdf */
+* {
+  -webkit-print-color-adjust: exact;
+}
+
+/* Misc */
+a.anchor-link {
+  display: none;
+}
+
+.highlight  {
+  margin: 0.4em;
+}
+
+/* Input area styling */
+.jp-InputArea {
+  overflow: hidden;
+}
+
+.jp-InputArea-editor {
+  overflow: hidden;
+}
+
+.CodeMirror pre {
+  margin: 0;
+  padding: 0;
+}
+
+/* Using table instead of flexbox so that we can use break-inside property */
+/* CSS rules under this comment should not be required anymore after we move to the JupyterLab 4.0 CSS */
+
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+  min-width: calc(
+    var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
+  );
+}
+
+.jp-OutputArea-child {
+  display: table;
+  width: 100%;
+}
+
+.jp-OutputPrompt {
+  display: table-cell;
+  vertical-align: top;
+  min-width: var(--jp-cell-prompt-width);
+}
+
+body[data-format='mobile'] .jp-OutputPrompt {
+  display: table-row;
+}
+
+.jp-OutputArea-output {
+  display: table-cell;
+  width: 100%;
+}
+
+body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
+  display: table-row;
+}
+
+.jp-OutputArea-output.jp-OutputArea-executeResult {
+  width: 100%;
+}
+
+/* Hiding the collapser by default */
+.jp-Collapser {
+  display: none;
+}
+
+@media print {
+  .jp-Cell-inputWrapper,
+  .jp-Cell-outputWrapper {
+    display: block;
+  }
+
+  .jp-OutputArea-child {
+    break-inside: avoid-page;
+  }
+}
+</style>
+
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: {
+                    automatic: true
+                    }
+                }
+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+        }
+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
+
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Improving-the-model-that-predicts-the-price-of-a-fairly-used-car">Improving the model that predicts the price of a fairly used car<a class="anchor-link" href="#Improving-the-model-that-predicts-the-price-of-a-fairly-used-car">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Import-libraries">Import libraries<a class="anchor-link" href="#Import-libraries">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sum</span>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">'display.max_columns'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Load-raw-data">Load raw data<a class="anchor-link" href="#Load-raw-data">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">raw_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'Real-life+example.csv'</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">raw_data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Brand</th>
+      <th>Price</th>
+      <th>Body</th>
+      <th>Mileage</th>
+      <th>EngineV</th>
+      <th>Engine Type</th>
+      <th>Registration</th>
+      <th>Year</th>
+      <th>Model</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>BMW</td>
+      <td>4200.0</td>
+      <td>sedan</td>
+      <td>277</td>
+      <td>2.0</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>1991</td>
+      <td>320</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Mercedes-Benz</td>
+      <td>7900.0</td>
+      <td>van</td>
+      <td>427</td>
+      <td>2.9</td>
+      <td>Diesel</td>
+      <td>yes</td>
+      <td>1999</td>
+      <td>Sprinter 212</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Mercedes-Benz</td>
+      <td>13300.0</td>
+      <td>sedan</td>
+      <td>358</td>
+      <td>5.0</td>
+      <td>Gas</td>
+      <td>yes</td>
+      <td>2003</td>
+      <td>S 500</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Audi</td>
+      <td>23000.0</td>
+      <td>crossover</td>
+      <td>240</td>
+      <td>4.2</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>2007</td>
+      <td>Q7</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Toyota</td>
+      <td>18300.0</td>
+      <td>crossover</td>
+      <td>120</td>
+      <td>2.0</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>2011</td>
+      <td>Rav 4</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Preprocessing-the-data">Preprocessing the data<a class="anchor-link" href="#Preprocessing-the-data">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Exploring-the-descriptive-statistics-of-the-variables">Exploring the descriptive statistics of the variables<a class="anchor-link" href="#Exploring-the-descriptive-statistics-of-the-variables">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">raw_data</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="s1">'all'</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Brand</th>
+      <th>Price</th>
+      <th>Body</th>
+      <th>Mileage</th>
+      <th>EngineV</th>
+      <th>Engine Type</th>
+      <th>Registration</th>
+      <th>Year</th>
+      <th>Model</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>4345</td>
+      <td>4173.000000</td>
+      <td>4345</td>
+      <td>4345.000000</td>
+      <td>4195.000000</td>
+      <td>4345</td>
+      <td>4345</td>
+      <td>4345.000000</td>
+      <td>4345</td>
+    </tr>
+    <tr>
+      <th>unique</th>
+      <td>7</td>
+      <td>NaN</td>
+      <td>6</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>4</td>
+      <td>2</td>
+      <td>NaN</td>
+      <td>312</td>
+    </tr>
+    <tr>
+      <th>top</th>
+      <td>Volkswagen</td>
+      <td>NaN</td>
+      <td>sedan</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>Diesel</td>
+      <td>yes</td>
+      <td>NaN</td>
+      <td>E-Class</td>
+    </tr>
+    <tr>
+      <th>freq</th>
+      <td>936</td>
+      <td>NaN</td>
+      <td>1649</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2019</td>
+      <td>3947</td>
+      <td>NaN</td>
+      <td>199</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>NaN</td>
+      <td>19418.746935</td>
+      <td>NaN</td>
+      <td>161.237284</td>
+      <td>2.790734</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2006.550058</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>NaN</td>
+      <td>25584.242620</td>
+      <td>NaN</td>
+      <td>105.705797</td>
+      <td>5.066437</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>6.719097</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>NaN</td>
+      <td>600.000000</td>
+      <td>NaN</td>
+      <td>0.000000</td>
+      <td>0.600000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1969.000000</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>NaN</td>
+      <td>6999.000000</td>
+      <td>NaN</td>
+      <td>86.000000</td>
+      <td>1.800000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2003.000000</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>NaN</td>
+      <td>11500.000000</td>
+      <td>NaN</td>
+      <td>155.000000</td>
+      <td>2.200000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2008.000000</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>NaN</td>
+      <td>21700.000000</td>
+      <td>NaN</td>
+      <td>230.000000</td>
+      <td>3.000000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2012.000000</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>NaN</td>
+      <td>300000.000000</td>
+      <td>NaN</td>
+      <td>980.000000</td>
+      <td>99.990000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2016.000000</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Dealing-with-missing-values">Dealing with missing values<a class="anchor-link" href="#Dealing-with-missing-values">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">raw_data</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Brand             0
+Price           172
+Body              0
+Mileage           0
+EngineV         150
+Engine Type       0
+Registration      0
+Year              0
+Model             0
+dtype: int64</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">raw_data</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="s1">'all'</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Brand</th>
+      <th>Price</th>
+      <th>Body</th>
+      <th>Mileage</th>
+      <th>EngineV</th>
+      <th>Engine Type</th>
+      <th>Registration</th>
+      <th>Year</th>
+      <th>Model</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>4025</td>
+      <td>4025.000000</td>
+      <td>4025</td>
+      <td>4025.000000</td>
+      <td>4025.000000</td>
+      <td>4025</td>
+      <td>4025</td>
+      <td>4025.000000</td>
+      <td>4025</td>
+    </tr>
+    <tr>
+      <th>unique</th>
+      <td>7</td>
+      <td>NaN</td>
+      <td>6</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>4</td>
+      <td>2</td>
+      <td>NaN</td>
+      <td>306</td>
+    </tr>
+    <tr>
+      <th>top</th>
+      <td>Volkswagen</td>
+      <td>NaN</td>
+      <td>sedan</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>Diesel</td>
+      <td>yes</td>
+      <td>NaN</td>
+      <td>E-Class</td>
+    </tr>
+    <tr>
+      <th>freq</th>
+      <td>880</td>
+      <td>NaN</td>
+      <td>1534</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1861</td>
+      <td>3654</td>
+      <td>NaN</td>
+      <td>188</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>NaN</td>
+      <td>19552.308065</td>
+      <td>NaN</td>
+      <td>163.572174</td>
+      <td>2.764586</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2006.379627</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>NaN</td>
+      <td>25815.734988</td>
+      <td>NaN</td>
+      <td>103.394703</td>
+      <td>4.935941</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>6.695595</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>NaN</td>
+      <td>600.000000</td>
+      <td>NaN</td>
+      <td>0.000000</td>
+      <td>0.600000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1969.000000</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>NaN</td>
+      <td>6999.000000</td>
+      <td>NaN</td>
+      <td>90.000000</td>
+      <td>1.800000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2003.000000</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>NaN</td>
+      <td>11500.000000</td>
+      <td>NaN</td>
+      <td>158.000000</td>
+      <td>2.200000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2007.000000</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>NaN</td>
+      <td>21900.000000</td>
+      <td>NaN</td>
+      <td>230.000000</td>
+      <td>3.000000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2012.000000</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>NaN</td>
+      <td>300000.000000</td>
+      <td>NaN</td>
+      <td>980.000000</td>
+      <td>99.990000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2016.000000</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Exploring-PDFs">Exploring PDFs<a class="anchor-link" href="#Exploring-PDFs">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'Price'</span><span class="p">])</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>C:\Users\Chizy\AppData\Local\Temp\ipykernel_2108\4196895882.py:1: UserWarning: 
+
+`distplot` is a deprecated function and will be removed in seaborn v0.14.0.
+
+Please adapt your code to use either `displot` (a figure-level function with
+similar flexibility) or `histplot` (an axes-level function for histograms).
+
+For a guide to updating your code to use the new functions, please see
+https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
+
+  sns.distplot(data[&#39;Price&#39;])
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&lt;Axes: xlabel=&#39;Price&#39;, ylabel=&#39;Density&#39;&gt;</pre>
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Outliers-are-not-removed">Outliers are not removed<a class="anchor-link" href="#Outliers-are-not-removed">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Checking-the-OLS-assumptions">Checking the OLS assumptions<a class="anchor-link" href="#Checking-the-OLS-assumptions">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we decided to use some matplotlib code, without explaining it</span>
+<span class="c1"># You can simply use plt.scatter() for each of them (with your current knowledge)</span>
+<span class="c1"># But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)</span>
+<span class="n">f</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">,</span> <span class="n">ax3</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">'Price'</span><span class="p">])</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Year and Price'</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'EngineV'</span><span class="p">],</span><span class="n">data</span><span class="p">[</span><span class="s1">'Price'</span><span class="p">])</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Engine and Price'</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'Mileage'</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">'Price'</span><span class="p">])</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Mileage and Price'</span><span class="p">)</span>
+<span class="c1">#ax4.scatter(data['Brand'], data['Price'])</span>
+<span class="c1">#ax4.set_title('Brand and Price')</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Let's check the three scatters once again</span>
+<span class="n">f</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">,</span> <span class="n">ax3</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+
+<span class="c1"># Scatter plot for Year and log_price with regression line</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'Price'</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'s'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">line_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">})</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Year and Price'</span><span class="p">)</span>
+
+<span class="c1"># Scatter plot for EngineV and log_price with regression line</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'EngineV'</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'Price'</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'s'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">line_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">})</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'EngineV and Price'</span><span class="p">)</span>
+
+<span class="c1"># Scatter plot for Mileage and log_price with regression line</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'Mileage'</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'Price'</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax3</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'s'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">line_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">})</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Mileage and Price'</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Relaxing-the-assumptions">Relaxing the assumptions<a class="anchor-link" href="#Relaxing-the-assumptions">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Transform the 'Price' log transformation</span>
+<span class="n">log_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'Price'</span><span class="p">])</span>
+<span class="c1">#Add log_price to the dataframe</span>
+
+<span class="n">data</span><span class="p">[</span><span class="s1">'log_price'</span><span class="p">]</span> <span class="o">=</span> <span class="n">log_price</span>
+<span class="n">data</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>C:\Users\Chizy\AppData\Local\Temp\ipykernel_2108\1987317337.py:5: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+  data[&#39;log_price&#39;] = log_price
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Brand</th>
+      <th>Price</th>
+      <th>Body</th>
+      <th>Mileage</th>
+      <th>EngineV</th>
+      <th>Engine Type</th>
+      <th>Registration</th>
+      <th>Year</th>
+      <th>Model</th>
+      <th>log_price</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>BMW</td>
+      <td>4200.0</td>
+      <td>sedan</td>
+      <td>277</td>
+      <td>2.0</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>1991</td>
+      <td>320</td>
+      <td>8.342840</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Mercedes-Benz</td>
+      <td>7900.0</td>
+      <td>van</td>
+      <td>427</td>
+      <td>2.9</td>
+      <td>Diesel</td>
+      <td>yes</td>
+      <td>1999</td>
+      <td>Sprinter 212</td>
+      <td>8.974618</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Mercedes-Benz</td>
+      <td>13300.0</td>
+      <td>sedan</td>
+      <td>358</td>
+      <td>5.0</td>
+      <td>Gas</td>
+      <td>yes</td>
+      <td>2003</td>
+      <td>S 500</td>
+      <td>9.495519</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Audi</td>
+      <td>23000.0</td>
+      <td>crossover</td>
+      <td>240</td>
+      <td>4.2</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>2007</td>
+      <td>Q7</td>
+      <td>10.043249</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Toyota</td>
+      <td>18300.0</td>
+      <td>crossover</td>
+      <td>120</td>
+      <td>2.0</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>2011</td>
+      <td>Rav 4</td>
+      <td>9.814656</td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+    </tr>
+    <tr>
+      <th>4339</th>
+      <td>Toyota</td>
+      <td>17900.0</td>
+      <td>sedan</td>
+      <td>35</td>
+      <td>1.6</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>2014</td>
+      <td>Corolla</td>
+      <td>9.792556</td>
+    </tr>
+    <tr>
+      <th>4340</th>
+      <td>Mercedes-Benz</td>
+      <td>125000.0</td>
+      <td>sedan</td>
+      <td>9</td>
+      <td>3.0</td>
+      <td>Diesel</td>
+      <td>yes</td>
+      <td>2014</td>
+      <td>S 350</td>
+      <td>11.736069</td>
+    </tr>
+    <tr>
+      <th>4341</th>
+      <td>BMW</td>
+      <td>6500.0</td>
+      <td>sedan</td>
+      <td>1</td>
+      <td>3.5</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>1999</td>
+      <td>535</td>
+      <td>8.779557</td>
+    </tr>
+    <tr>
+      <th>4342</th>
+      <td>BMW</td>
+      <td>8000.0</td>
+      <td>sedan</td>
+      <td>194</td>
+      <td>2.0</td>
+      <td>Petrol</td>
+      <td>yes</td>
+      <td>1985</td>
+      <td>520</td>
+      <td>8.987197</td>
+    </tr>
+    <tr>
+      <th>4344</th>
+      <td>Volkswagen</td>
+      <td>13500.0</td>
+      <td>van</td>
+      <td>124</td>
+      <td>2.0</td>
+      <td>Diesel</td>
+      <td>yes</td>
+      <td>2013</td>
+      <td>T5 (Transporter)</td>
+      <td>9.510445</td>
+    </tr>
+  </tbody>
+</table>
+<p>4025 rows × 10 columns</p>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Let's check the three scatters once again</span>
+<span class="n">f</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span> <span class="p">,</span> <span class="n">ax2</span><span class="p">,</span> <span class="n">ax3</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">],</span><span class="n">data</span><span class="p">[</span><span class="s1">'log_price'</span><span class="p">])</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Year and log_price'</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'EngineV'</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">'log_price'</span><span class="p">])</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'EngineV and log_price'</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'Mileage'</span><span class="p">],</span><span class="n">data</span><span class="p">[</span><span class="s1">'log_price'</span><span class="p">])</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Mileage and log_price'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Let's check the three scatters once again</span>
+<span class="n">f</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">,</span> <span class="n">ax3</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+
+<span class="c1"># Scatter plot for Year and log_price with regression line</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'log_price'</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'s'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">line_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">})</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Year and log_price'</span><span class="p">)</span>
+
+<span class="c1"># Scatter plot for EngineV and log_price with regression line</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'EngineV'</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'log_price'</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'s'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">line_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">})</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'EngineV and log_price'</span><span class="p">)</span>
+
+<span class="c1"># Scatter plot for Mileage and log_price with regression line</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'Mileage'</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">'log_price'</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax3</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'s'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">line_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">})</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Mileage and log_price'</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_cleaned</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">'Price'</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_cleaned</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="s1">'all'</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Brand</th>
+      <th>Body</th>
+      <th>Mileage</th>
+      <th>EngineV</th>
+      <th>Engine Type</th>
+      <th>Registration</th>
+      <th>Year</th>
+      <th>Model</th>
+      <th>log_price</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>4025</td>
+      <td>4025</td>
+      <td>4025.000000</td>
+      <td>4025.000000</td>
+      <td>4025</td>
+      <td>4025</td>
+      <td>4025.000000</td>
+      <td>4025</td>
+      <td>4025.000000</td>
+    </tr>
+    <tr>
+      <th>unique</th>
+      <td>7</td>
+      <td>6</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>4</td>
+      <td>2</td>
+      <td>NaN</td>
+      <td>306</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>top</th>
+      <td>Volkswagen</td>
+      <td>sedan</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>Diesel</td>
+      <td>yes</td>
+      <td>NaN</td>
+      <td>E-Class</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>freq</th>
+      <td>880</td>
+      <td>1534</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1861</td>
+      <td>3654</td>
+      <td>NaN</td>
+      <td>188</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>163.572174</td>
+      <td>2.764586</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2006.379627</td>
+      <td>NaN</td>
+      <td>9.409665</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>103.394703</td>
+      <td>4.935941</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>6.695595</td>
+      <td>NaN</td>
+      <td>0.933136</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>0.000000</td>
+      <td>0.600000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1969.000000</td>
+      <td>NaN</td>
+      <td>6.396930</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>90.000000</td>
+      <td>1.800000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2003.000000</td>
+      <td>NaN</td>
+      <td>8.853523</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>158.000000</td>
+      <td>2.200000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2007.000000</td>
+      <td>NaN</td>
+      <td>9.350102</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>230.000000</td>
+      <td>3.000000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2012.000000</td>
+      <td>NaN</td>
+      <td>9.994242</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>980.000000</td>
+      <td>99.990000</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>2016.000000</td>
+      <td>NaN</td>
+      <td>12.611538</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Multicollinearity">Multicollinearity<a class="anchor-link" href="#Multicollinearity">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_cleaned</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[17]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([&#39;Brand&#39;, &#39;Body&#39;, &#39;Mileage&#39;, &#39;EngineV&#39;, &#39;Engine Type&#39;,
+       &#39;Registration&#39;, &#39;Year&#39;, &#39;Model&#39;, &#39;log_price&#39;], dtype=object)</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># sklearn does not have a built-in way to check for multicollinearity</span>
+<span class="c1"># one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones</span>
+<span class="c1"># surely it is an issue nonetheless, thus we will try to deal with it</span>
+
+<span class="c1"># Here's the relevant module</span>
+<span class="c1"># full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor</span>
+<span class="kn">from</span> <span class="nn">statsmodels.stats.outliers_influence</span> <span class="kn">import</span> <span class="n">variance_inflation_factor</span>
+
+<span class="c1"># To make this as easy as possible to use, we declare a variable where we put</span>
+<span class="c1"># all features where we want to check for multicollinearity</span>
+<span class="c1"># since our categorical data is not yet preprocessed, we will only take the numerical ones</span>
+<span class="n">variables</span> <span class="o">=</span> <span class="n">data_cleaned</span><span class="p">[[</span><span class="s1">'Mileage'</span><span class="p">,</span> <span class="s1">'Year'</span><span class="p">,</span> <span class="s1">'EngineV'</span><span class="p">]]</span>
+
+<span class="c1"># we create a new data frame which will include all the VIFs</span>
+<span class="c1"># note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)</span>
+<span class="n">vif</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
+<span class="c1"># here we make use of the variance_inflation_factor, which will basically output the respective VIFs </span>
+<span class="n">vif</span><span class="p">[</span><span class="s1">'VIF'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">variance_inflation_factor</span><span class="p">(</span><span class="n">variables</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="n">variables</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+<span class="c1"># Finally, I like to include names so it is easier to explore the result</span>
+<span class="n">vif</span><span class="p">[</span><span class="s1">'Features'</span><span class="p">]</span> <span class="o">=</span> <span class="n">variables</span><span class="o">.</span><span class="n">columns</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vif</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>VIF</th>
+      <th>Features</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>3.481307</td>
+      <td>Mileage</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>3.748134</td>
+      <td>Year</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1.314535</td>
+      <td>EngineV</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>With all vifs less than 5, It means that the correlation between your independent variables (Mileage, Year, and EngineV) is not excessively high.
+So, it looks like your dataset is behaving well in terms of multicollinearity. You can proceed with your regression analysis without major concerns about the variables playing too nicely with each other.</p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Create-dummy-variables">Create dummy variables<a class="anchor-link" href="#Create-dummy-variables">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># To include the categorical data in the regression, let's create dummies</span>
+<span class="c1"># There is a very convenient method called: 'get_dummies' which does that seemlessly</span>
+<span class="c1"># It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity</span>
+<span class="n">data_with_dummies</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">data_cleaned</span><span class="p">,</span><span class="n">drop_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_with_dummies</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[21]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Mileage</th>
+      <th>EngineV</th>
+      <th>Year</th>
+      <th>log_price</th>
+      <th>Brand_BMW</th>
+      <th>Brand_Mercedes-Benz</th>
+      <th>Brand_Mitsubishi</th>
+      <th>Brand_Renault</th>
+      <th>Brand_Toyota</th>
+      <th>Brand_Volkswagen</th>
+      <th>Body_hatch</th>
+      <th>Body_other</th>
+      <th>Body_sedan</th>
+      <th>Body_vagon</th>
+      <th>Body_van</th>
+      <th>Engine Type_Gas</th>
+      <th>Engine Type_Other</th>
+      <th>Engine Type_Petrol</th>
+      <th>Registration_yes</th>
+      <th>Model_100</th>
+      <th>Model_11</th>
+      <th>Model_116</th>
+      <th>Model_118</th>
+      <th>Model_120</th>
+      <th>Model_19</th>
+      <th>Model_190</th>
+      <th>Model_200</th>
+      <th>Model_21</th>
+      <th>Model_210</th>
+      <th>Model_220</th>
+      <th>Model_230</th>
+      <th>Model_25</th>
+      <th>Model_250</th>
+      <th>Model_300</th>
+      <th>Model_316</th>
+      <th>Model_318</th>
+      <th>Model_320</th>
+      <th>Model_323</th>
+      <th>Model_324</th>
+      <th>Model_325</th>
+      <th>Model_328</th>
+      <th>Model_330</th>
+      <th>Model_335</th>
+      <th>Model_4 Series Gran Coupe</th>
+      <th>Model_428</th>
+      <th>Model_4Runner</th>
+      <th>Model_5</th>
+      <th>Model_5 Series</th>
+      <th>Model_5 Series GT</th>
+      <th>Model_520</th>
+      <th>Model_523</th>
+      <th>Model_524</th>
+      <th>Model_525</th>
+      <th>Model_528</th>
+      <th>Model_530</th>
+      <th>Model_535</th>
+      <th>Model_540</th>
+      <th>Model_545</th>
+      <th>Model_550</th>
+      <th>Model_6 Series Gran Coupe</th>
+      <th>Model_630</th>
+      <th>Model_640</th>
+      <th>Model_645</th>
+      <th>Model_650</th>
+      <th>Model_730</th>
+      <th>Model_735</th>
+      <th>Model_740</th>
+      <th>Model_745</th>
+      <th>Model_750</th>
+      <th>Model_760</th>
+      <th>Model_80</th>
+      <th>Model_9</th>
+      <th>Model_90</th>
+      <th>Model_A 140</th>
+      <th>Model_A 150</th>
+      <th>Model_A 170</th>
+      <th>Model_A 180</th>
+      <th>Model_A1</th>
+      <th>Model_A3</th>
+      <th>Model_A4</th>
+      <th>Model_A4 Allroad</th>
+      <th>Model_A5</th>
+      <th>Model_A6</th>
+      <th>Model_A6 Allroad</th>
+      <th>Model_A7</th>
+      <th>Model_A8</th>
+      <th>Model_ASX</th>
+      <th>Model_Amarok</th>
+      <th>Model_Auris</th>
+      <th>Model_Avalon</th>
+      <th>Model_Avensis</th>
+      <th>Model_Aygo</th>
+      <th>Model_B 170</th>
+      <th>Model_B 180</th>
+      <th>Model_B 200</th>
+      <th>Model_Beetle</th>
+      <th>Model_Bora</th>
+      <th>Model_C-Class</th>
+      <th>Model_CL 180</th>
+      <th>Model_CL 500</th>
+      <th>Model_CL 55 AMG</th>
+      <th>Model_CL 550</th>
+      <th>Model_CL 63 AMG</th>
+      <th>Model_CLA 200</th>
+      <th>Model_CLA 220</th>
+      <th>Model_CLA-Class</th>
+      <th>Model_CLC 180</th>
+      <th>Model_CLC 200</th>
+      <th>Model_CLK 200</th>
+      <th>Model_CLK 220</th>
+      <th>Model_CLK 230</th>
+      <th>Model_CLK 240</th>
+      <th>Model_CLK 280</th>
+      <th>Model_CLK 320</th>
+      <th>Model_CLK 430</th>
+      <th>Model_CLS 350</th>
+      <th>Model_CLS 400</th>
+      <th>Model_CLS 500</th>
+      <th>Model_CLS 63 AMG</th>
+      <th>Model_Caddy</th>
+      <th>Model_Camry</th>
+      <th>Model_Captur</th>
+      <th>Model_Caravelle</th>
+      <th>Model_Carina</th>
+      <th>Model_Carisma</th>
+      <th>Model_Celica</th>
+      <th>Model_Clio</th>
+      <th>Model_Colt</th>
+      <th>Model_Corolla</th>
+      <th>Model_Corolla Verso</th>
+      <th>Model_Cross Touran</th>
+      <th>Model_Dokker</th>
+      <th>Model_Duster</th>
+      <th>Model_E-Class</th>
+      <th>Model_Eclipse</th>
+      <th>Model_Eos</th>
+      <th>Model_Espace</th>
+      <th>Model_FJ Cruiser</th>
+      <th>Model_Fluence</th>
+      <th>Model_Fortuner</th>
+      <th>Model_G 320</th>
+      <th>Model_G 350</th>
+      <th>Model_G 500</th>
+      <th>Model_G 55 AMG</th>
+      <th>Model_G 63 AMG</th>
+      <th>Model_GL 320</th>
+      <th>Model_GL 350</th>
+      <th>Model_GL 420</th>
+      <th>Model_GL 450</th>
+      <th>Model_GL 500</th>
+      <th>Model_GL 550</th>
+      <th>Model_GLC-Class</th>
+      <th>Model_GLE-Class</th>
+      <th>Model_GLK 220</th>
+      <th>Model_GLK 300</th>
+      <th>Model_GLS 350</th>
+      <th>Model_GLS 400</th>
+      <th>Model_GLS 500</th>
+      <th>Model_GLS 63</th>
+      <th>Model_Galant</th>
+      <th>Model_Golf GTI</th>
+      <th>Model_Golf II</th>
+      <th>Model_Golf III</th>
+      <th>Model_Golf IV</th>
+      <th>Model_Golf Plus</th>
+      <th>Model_Golf V</th>
+      <th>Model_Golf VI</th>
+      <th>Model_Golf VII</th>
+      <th>Model_Golf Variant</th>
+      <th>Model_Grand Scenic</th>
+      <th>Model_Grandis</th>
+      <th>Model_Hiace</th>
+      <th>Model_Highlander</th>
+      <th>Model_Hilux</th>
+      <th>Model_I3</th>
+      <th>Model_IQ</th>
+      <th>Model_Jetta</th>
+      <th>Model_Kangoo</th>
+      <th>Model_Koleos</th>
+      <th>Model_L 200</th>
+      <th>Model_LT</th>
+      <th>Model_Laguna</th>
+      <th>Model_Lancer</th>
+      <th>Model_Lancer Evolution</th>
+      <th>Model_Lancer X</th>
+      <th>Model_Lancer X Sportback</th>
+      <th>Model_Land Cruiser 100</th>
+      <th>Model_Land Cruiser 105</th>
+      <th>Model_Land Cruiser 200</th>
+      <th>Model_Land Cruiser 76</th>
+      <th>Model_Land Cruiser 80</th>
+      <th>Model_Land Cruiser Prado</th>
+      <th>Model_Latitude</th>
+      <th>Model_Lite Ace</th>
+      <th>Model_Logan</th>
+      <th>Model_Lupo</th>
+      <th>Model_M5</th>
+      <th>Model_M6</th>
+      <th>Model_MB</th>
+      <th>Model_ML 250</th>
+      <th>Model_ML 270</th>
+      <th>Model_ML 280</th>
+      <th>Model_ML 320</th>
+      <th>Model_ML 350</th>
+      <th>Model_ML 400</th>
+      <th>Model_ML 430</th>
+      <th>Model_ML 500</th>
+      <th>Model_ML 550</th>
+      <th>Model_ML 63 AMG</th>
+      <th>Model_Mark II</th>
+      <th>Model_Master</th>
+      <th>Model_Matrix</th>
+      <th>Model_Megane</th>
+      <th>Model_Modus</th>
+      <th>Model_Multivan</th>
+      <th>Model_New Beetle</th>
+      <th>Model_Outlander</th>
+      <th>Model_Outlander XL</th>
+      <th>Model_Pajero</th>
+      <th>Model_Pajero Pinin</th>
+      <th>Model_Pajero Sport</th>
+      <th>Model_Pajero Wagon</th>
+      <th>Model_Passat B2</th>
+      <th>Model_Passat B3</th>
+      <th>Model_Passat B4</th>
+      <th>Model_Passat B5</th>
+      <th>Model_Passat B6</th>
+      <th>Model_Passat B7</th>
+      <th>Model_Passat B8</th>
+      <th>Model_Passat CC</th>
+      <th>Model_Phaeton</th>
+      <th>Model_Pointer</th>
+      <th>Model_Polo</th>
+      <th>Model_Previa</th>
+      <th>Model_Prius</th>
+      <th>Model_Q3</th>
+      <th>Model_Q5</th>
+      <th>Model_Q7</th>
+      <th>Model_R 320</th>
+      <th>Model_R8</th>
+      <th>Model_Rav 4</th>
+      <th>Model_S 140</th>
+      <th>Model_S 250</th>
+      <th>Model_S 280</th>
+      <th>Model_S 300</th>
+      <th>Model_S 320</th>
+      <th>Model_S 350</th>
+      <th>Model_S 400</th>
+      <th>Model_S 420</th>
+      <th>Model_S 430</th>
+      <th>Model_S 500</th>
+      <th>Model_S 55</th>
+      <th>Model_S 550</th>
+      <th>Model_S 600</th>
+      <th>Model_S 63 AMG</th>
+      <th>Model_S 65 AMG</th>
+      <th>Model_S-Guard</th>
+      <th>Model_S4</th>
+      <th>Model_S5</th>
+      <th>Model_S8</th>
+      <th>Model_SL 500 (550)</th>
+      <th>Model_SL 55 AMG</th>
+      <th>Model_SLK 200</th>
+      <th>Model_SLK 350</th>
+      <th>Model_Sandero</th>
+      <th>Model_Sandero StepWay</th>
+      <th>Model_Scenic</th>
+      <th>Model_Scion</th>
+      <th>Model_Scirocco</th>
+      <th>Model_Sequoia</th>
+      <th>Model_Sharan</th>
+      <th>Model_Sienna</th>
+      <th>Model_Smart</th>
+      <th>Model_Space Star</th>
+      <th>Model_Space Wagon</th>
+      <th>Model_Sprinter</th>
+      <th>Model_Sprinter 208</th>
+      <th>Model_Sprinter 210</th>
+      <th>Model_Sprinter 211</th>
+      <th>Model_Sprinter 212</th>
+      <th>Model_Sprinter 213</th>
+      <th>Model_Sprinter 311</th>
+      <th>Model_Sprinter 312</th>
+      <th>Model_Sprinter 313</th>
+      <th>Model_Sprinter 315</th>
+      <th>Model_Sprinter 316</th>
+      <th>Model_Sprinter 318</th>
+      <th>Model_Sprinter 319</th>
+      <th>Model_Sprinter 324</th>
+      <th>Model_Symbol</th>
+      <th>Model_Syncro</th>
+      <th>Model_T2 (Transporter)</th>
+      <th>Model_T3 (Transporter)</th>
+      <th>Model_T4 (Transporter)</th>
+      <th>Model_T4 (Transporter)</th>
+      <th>Model_T5 (Transporter)</th>
+      <th>Model_T5 (Transporter)</th>
+      <th>Model_T6 (Transporter)</th>
+      <th>Model_T6 (Transporter)</th>
+      <th>Model_TT</th>
+      <th>Model_Tacoma</th>
+      <th>Model_Tiguan</th>
+      <th>Model_Touareg</th>
+      <th>Model_Touran</th>
+      <th>Model_Trafic</th>
+      <th>Model_Tundra</th>
+      <th>Model_Up</th>
+      <th>Model_V 250</th>
+      <th>Model_Vaneo</th>
+      <th>Model_Vento</th>
+      <th>Model_Venza</th>
+      <th>Model_Viano</th>
+      <th>Model_Virage</th>
+      <th>Model_Vista</th>
+      <th>Model_Vito</th>
+      <th>Model_X1</th>
+      <th>Model_X3</th>
+      <th>Model_X5</th>
+      <th>Model_X5 M</th>
+      <th>Model_X6</th>
+      <th>Model_X6 M</th>
+      <th>Model_Yaris</th>
+      <th>Model_Z3</th>
+      <th>Model_Z4</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>277</td>
+      <td>2.0</td>
+      <td>1991</td>
+      <td>8.342840</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
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+      <td>False</td>
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+    </tr>
+    <tr>
+      <th>1</th>
+      <td>427</td>
+      <td>2.9</td>
+      <td>1999</td>
+      <td>8.974618</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
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+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>120</td>
+      <td>2.0</td>
+      <td>2011</td>
+      <td>9.814656</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Rearrange-to-have-the-dependent-variable-on-first-column">Rearrange to have the dependent variable on first column<a class="anchor-link" href="#Rearrange-to-have-the-dependent-variable-on-first-column">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df</span>
+<span class="c1"># Since each problem is different, that must be done manually</span>
+<span class="c1"># We can display all possible features and then choose the desired order</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">options</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">max_columns</span> <span class="o">=</span> <span class="mi">999</span>
+<span class="n">data_with_dummies</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([&#39;Mileage&#39;, &#39;EngineV&#39;, &#39;Year&#39;, &#39;log_price&#39;, &#39;Brand_BMW&#39;,
+       &#39;Brand_Mercedes-Benz&#39;, &#39;Brand_Mitsubishi&#39;, &#39;Brand_Renault&#39;,
+       &#39;Brand_Toyota&#39;, &#39;Brand_Volkswagen&#39;, &#39;Body_hatch&#39;, &#39;Body_other&#39;,
+       &#39;Body_sedan&#39;, &#39;Body_vagon&#39;, &#39;Body_van&#39;, &#39;Engine Type_Gas&#39;,
+       &#39;Engine Type_Other&#39;, &#39;Engine Type_Petrol&#39;, &#39;Registration_yes&#39;,
+       &#39;Model_100&#39;, &#39;Model_11&#39;, &#39;Model_116&#39;, &#39;Model_118&#39;, &#39;Model_120&#39;,
+       &#39;Model_19&#39;, &#39;Model_190&#39;, &#39;Model_200&#39;, &#39;Model_21&#39;, &#39;Model_210&#39;,
+       &#39;Model_220&#39;, &#39;Model_230&#39;, &#39;Model_25&#39;, &#39;Model_250&#39;, &#39;Model_300&#39;,
+       &#39;Model_316&#39;, &#39;Model_318&#39;, &#39;Model_320&#39;, &#39;Model_323&#39;, &#39;Model_324&#39;,
+       &#39;Model_325&#39;, &#39;Model_328&#39;, &#39;Model_330&#39;, &#39;Model_335&#39;,
+       &#39;Model_4 Series Gran Coupe&#39;, &#39;Model_428&#39;, &#39;Model_4Runner&#39;,
+       &#39;Model_5&#39;, &#39;Model_5 Series&#39;, &#39;Model_5 Series GT&#39;, &#39;Model_520&#39;,
+       &#39;Model_523&#39;, &#39;Model_524&#39;, &#39;Model_525&#39;, &#39;Model_528&#39;, &#39;Model_530&#39;,
+       &#39;Model_535&#39;, &#39;Model_540&#39;, &#39;Model_545&#39;, &#39;Model_550&#39;,
+       &#39;Model_6 Series Gran Coupe&#39;, &#39;Model_630&#39;, &#39;Model_640&#39;, &#39;Model_645&#39;,
+       &#39;Model_650&#39;, &#39;Model_730&#39;, &#39;Model_735&#39;, &#39;Model_740&#39;, &#39;Model_745&#39;,
+       &#39;Model_750&#39;, &#39;Model_760&#39;, &#39;Model_80&#39;, &#39;Model_9&#39;, &#39;Model_90&#39;,
+       &#39;Model_A 140&#39;, &#39;Model_A 150&#39;, &#39;Model_A 170&#39;, &#39;Model_A 180&#39;,
+       &#39;Model_A1&#39;, &#39;Model_A3&#39;, &#39;Model_A4&#39;, &#39;Model_A4 Allroad&#39;, &#39;Model_A5&#39;,
+       &#39;Model_A6&#39;, &#39;Model_A6 Allroad&#39;, &#39;Model_A7&#39;, &#39;Model_A8&#39;,
+       &#39;Model_ASX&#39;, &#39;Model_Amarok&#39;, &#39;Model_Auris&#39;, &#39;Model_Avalon&#39;,
+       &#39;Model_Avensis&#39;, &#39;Model_Aygo&#39;, &#39;Model_B 170&#39;, &#39;Model_B 180&#39;,
+       &#39;Model_B 200&#39;, &#39;Model_Beetle&#39;, &#39;Model_Bora&#39;, &#39;Model_C-Class&#39;,
+       &#39;Model_CL 180&#39;, &#39;Model_CL 500&#39;, &#39;Model_CL 55 AMG&#39;, &#39;Model_CL 550&#39;,
+       &#39;Model_CL 63 AMG&#39;, &#39;Model_CLA 200&#39;, &#39;Model_CLA 220&#39;,
+       &#39;Model_CLA-Class&#39;, &#39;Model_CLC 180&#39;, &#39;Model_CLC 200&#39;,
+       &#39;Model_CLK 200&#39;, &#39;Model_CLK 220&#39;, &#39;Model_CLK 230&#39;, &#39;Model_CLK 240&#39;,
+       &#39;Model_CLK 280&#39;, &#39;Model_CLK 320&#39;, &#39;Model_CLK 430&#39;, &#39;Model_CLS 350&#39;,
+       &#39;Model_CLS 400&#39;, &#39;Model_CLS 500&#39;, &#39;Model_CLS 63 AMG&#39;,
+       &#39;Model_Caddy&#39;, &#39;Model_Camry&#39;, &#39;Model_Captur&#39;, &#39;Model_Caravelle&#39;,
+       &#39;Model_Carina&#39;, &#39;Model_Carisma&#39;, &#39;Model_Celica&#39;, &#39;Model_Clio&#39;,
+       &#39;Model_Colt&#39;, &#39;Model_Corolla&#39;, &#39;Model_Corolla Verso&#39;,
+       &#39;Model_Cross Touran&#39;, &#39;Model_Dokker&#39;, &#39;Model_Duster&#39;,
+       &#39;Model_E-Class&#39;, &#39;Model_Eclipse&#39;, &#39;Model_Eos&#39;, &#39;Model_Espace&#39;,
+       &#39;Model_FJ Cruiser&#39;, &#39;Model_Fluence&#39;, &#39;Model_Fortuner&#39;,
+       &#39;Model_G 320&#39;, &#39;Model_G 350&#39;, &#39;Model_G 500&#39;, &#39;Model_G 55 AMG&#39;,
+       &#39;Model_G 63 AMG&#39;, &#39;Model_GL 320&#39;, &#39;Model_GL 350&#39;, &#39;Model_GL 420&#39;,
+       &#39;Model_GL 450&#39;, &#39;Model_GL 500&#39;, &#39;Model_GL 550&#39;, &#39;Model_GLC-Class&#39;,
+       &#39;Model_GLE-Class&#39;, &#39;Model_GLK 220&#39;, &#39;Model_GLK 300&#39;,
+       &#39;Model_GLS 350&#39;, &#39;Model_GLS 400&#39;, &#39;Model_GLS 500&#39;, &#39;Model_GLS 63&#39;,
+       &#39;Model_Galant&#39;, &#39;Model_Golf GTI&#39;, &#39;Model_Golf II&#39;,
+       &#39;Model_Golf III&#39;, &#39;Model_Golf IV&#39;, &#39;Model_Golf Plus&#39;,
+       &#39;Model_Golf V&#39;, &#39;Model_Golf VI&#39;, &#39;Model_Golf VII&#39;,
+       &#39;Model_Golf Variant&#39;, &#39;Model_Grand Scenic&#39;, &#39;Model_Grandis&#39;,
+       &#39;Model_Hiace&#39;, &#39;Model_Highlander&#39;, &#39;Model_Hilux&#39;, &#39;Model_I3&#39;,
+       &#39;Model_IQ&#39;, &#39;Model_Jetta&#39;, &#39;Model_Kangoo&#39;, &#39;Model_Koleos&#39;,
+       &#39;Model_L 200&#39;, &#39;Model_LT&#39;, &#39;Model_Laguna&#39;, &#39;Model_Lancer&#39;,
+       &#39;Model_Lancer Evolution&#39;, &#39;Model_Lancer X&#39;,
+       &#39;Model_Lancer X Sportback&#39;, &#39;Model_Land Cruiser 100&#39;,
+       &#39;Model_Land Cruiser 105&#39;, &#39;Model_Land Cruiser 200&#39;,
+       &#39;Model_Land Cruiser 76&#39;, &#39;Model_Land Cruiser 80&#39;,
+       &#39;Model_Land Cruiser Prado&#39;, &#39;Model_Latitude&#39;, &#39;Model_Lite Ace&#39;,
+       &#39;Model_Logan&#39;, &#39;Model_Lupo&#39;, &#39;Model_M5&#39;, &#39;Model_M6&#39;, &#39;Model_MB&#39;,
+       &#39;Model_ML 250&#39;, &#39;Model_ML 270&#39;, &#39;Model_ML 280&#39;, &#39;Model_ML 320&#39;,
+       &#39;Model_ML 350&#39;, &#39;Model_ML 400&#39;, &#39;Model_ML 430&#39;, &#39;Model_ML 500&#39;,
+       &#39;Model_ML 550&#39;, &#39;Model_ML 63 AMG&#39;, &#39;Model_Mark II&#39;, &#39;Model_Master&#39;,
+       &#39;Model_Matrix&#39;, &#39;Model_Megane&#39;, &#39;Model_Modus&#39;, &#39;Model_Multivan&#39;,
+       &#39;Model_New Beetle&#39;, &#39;Model_Outlander&#39;, &#39;Model_Outlander XL&#39;,
+       &#39;Model_Pajero&#39;, &#39;Model_Pajero Pinin&#39;, &#39;Model_Pajero Sport&#39;,
+       &#39;Model_Pajero Wagon&#39;, &#39;Model_Passat B2&#39;, &#39;Model_Passat B3&#39;,
+       &#39;Model_Passat B4&#39;, &#39;Model_Passat B5&#39;, &#39;Model_Passat B6&#39;,
+       &#39;Model_Passat B7&#39;, &#39;Model_Passat B8&#39;, &#39;Model_Passat CC&#39;,
+       &#39;Model_Phaeton&#39;, &#39;Model_Pointer&#39;, &#39;Model_Polo&#39;, &#39;Model_Previa&#39;,
+       &#39;Model_Prius&#39;, &#39;Model_Q3&#39;, &#39;Model_Q5&#39;, &#39;Model_Q7&#39;, &#39;Model_R 320&#39;,
+       &#39;Model_R8&#39;, &#39;Model_Rav 4&#39;, &#39;Model_S 140&#39;, &#39;Model_S 250&#39;,
+       &#39;Model_S 280&#39;, &#39;Model_S 300&#39;, &#39;Model_S 320&#39;, &#39;Model_S 350&#39;,
+       &#39;Model_S 400&#39;, &#39;Model_S 420&#39;, &#39;Model_S 430&#39;, &#39;Model_S 500&#39;,
+       &#39;Model_S 55&#39;, &#39;Model_S 550&#39;, &#39;Model_S 600&#39;, &#39;Model_S 63 AMG&#39;,
+       &#39;Model_S 65 AMG&#39;, &#39;Model_S-Guard&#39;, &#39;Model_S4&#39;, &#39;Model_S5&#39;,
+       &#39;Model_S8&#39;, &#39;Model_SL 500 (550)&#39;, &#39;Model_SL 55 AMG&#39;,
+       &#39;Model_SLK 200&#39;, &#39;Model_SLK 350&#39;, &#39;Model_Sandero&#39;,
+       &#39;Model_Sandero StepWay&#39;, &#39;Model_Scenic&#39;, &#39;Model_Scion&#39;,
+       &#39;Model_Scirocco&#39;, &#39;Model_Sequoia&#39;, &#39;Model_Sharan&#39;, &#39;Model_Sienna&#39;,
+       &#39;Model_Smart&#39;, &#39;Model_Space Star&#39;, &#39;Model_Space Wagon&#39;,
+       &#39;Model_Sprinter&#39;, &#39;Model_Sprinter 208&#39;, &#39;Model_Sprinter 210&#39;,
+       &#39;Model_Sprinter 211&#39;, &#39;Model_Sprinter 212&#39;, &#39;Model_Sprinter 213&#39;,
+       &#39;Model_Sprinter 311&#39;, &#39;Model_Sprinter 312&#39;, &#39;Model_Sprinter 313&#39;,
+       &#39;Model_Sprinter 315&#39;, &#39;Model_Sprinter 316&#39;, &#39;Model_Sprinter 318&#39;,
+       &#39;Model_Sprinter 319&#39;, &#39;Model_Sprinter 324&#39;, &#39;Model_Symbol&#39;,
+       &#39;Model_Syncro&#39;, &#39;Model_T2 (Transporter)&#39;, &#39;Model_T3 (Transporter)&#39;,
+       &#39;Model_T4 (Transporter)&#39;, &#39;Model_T4 (Transporter) &#39;,
+       &#39;Model_T5 (Transporter)&#39;, &#39;Model_T5 (Transporter) &#39;,
+       &#39;Model_T6 (Transporter)&#39;, &#39;Model_T6 (Transporter) &#39;, &#39;Model_TT&#39;,
+       &#39;Model_Tacoma&#39;, &#39;Model_Tiguan&#39;, &#39;Model_Touareg&#39;, &#39;Model_Touran&#39;,
+       &#39;Model_Trafic&#39;, &#39;Model_Tundra&#39;, &#39;Model_Up&#39;, &#39;Model_V 250&#39;,
+       &#39;Model_Vaneo&#39;, &#39;Model_Vento&#39;, &#39;Model_Venza&#39;, &#39;Model_Viano&#39;,
+       &#39;Model_Virage&#39;, &#39;Model_Vista&#39;, &#39;Model_Vito&#39;, &#39;Model_X1&#39;,
+       &#39;Model_X3&#39;, &#39;Model_X5&#39;, &#39;Model_X5 M&#39;, &#39;Model_X6&#39;, &#39;Model_X6 M&#39;,
+       &#39;Model_Yaris&#39;, &#39;Model_Z3&#39;, &#39;Model_Z4&#39;], dtype=object)</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a list of column names with 'log_price' at the beginning</span>
+<span class="n">new_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'log_price'</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">data_with_dummies</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">col</span> <span class="o">!=</span> <span class="s1">'log_price'</span><span class="p">]</span>
+
+<span class="c1"># Reorder the columns in the DataFrame</span>
+<span class="n">data_with_dummies</span> <span class="o">=</span> <span class="n">data_with_dummies</span><span class="p">[</span><span class="n">new_order</span><span class="p">]</span>
+
+<span class="c1"># Now, when you print the column names, 'log_price' should be the first column</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">data_with_dummies</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>[&#39;log_price&#39; &#39;Mileage&#39; &#39;EngineV&#39; &#39;Year&#39; &#39;Brand_BMW&#39; &#39;Brand_Mercedes-Benz&#39;
+ &#39;Brand_Mitsubishi&#39; &#39;Brand_Renault&#39; &#39;Brand_Toyota&#39; &#39;Brand_Volkswagen&#39;
+ &#39;Body_hatch&#39; &#39;Body_other&#39; &#39;Body_sedan&#39; &#39;Body_vagon&#39; &#39;Body_van&#39;
+ &#39;Engine Type_Gas&#39; &#39;Engine Type_Other&#39; &#39;Engine Type_Petrol&#39;
+ &#39;Registration_yes&#39; &#39;Model_100&#39; &#39;Model_11&#39; &#39;Model_116&#39; &#39;Model_118&#39;
+ &#39;Model_120&#39; &#39;Model_19&#39; &#39;Model_190&#39; &#39;Model_200&#39; &#39;Model_21&#39; &#39;Model_210&#39;
+ &#39;Model_220&#39; &#39;Model_230&#39; &#39;Model_25&#39; &#39;Model_250&#39; &#39;Model_300&#39; &#39;Model_316&#39;
+ &#39;Model_318&#39; &#39;Model_320&#39; &#39;Model_323&#39; &#39;Model_324&#39; &#39;Model_325&#39; &#39;Model_328&#39;
+ &#39;Model_330&#39; &#39;Model_335&#39; &#39;Model_4 Series Gran Coupe&#39; &#39;Model_428&#39;
+ &#39;Model_4Runner&#39; &#39;Model_5&#39; &#39;Model_5 Series&#39; &#39;Model_5 Series GT&#39;
+ &#39;Model_520&#39; &#39;Model_523&#39; &#39;Model_524&#39; &#39;Model_525&#39; &#39;Model_528&#39; &#39;Model_530&#39;
+ &#39;Model_535&#39; &#39;Model_540&#39; &#39;Model_545&#39; &#39;Model_550&#39;
+ &#39;Model_6 Series Gran Coupe&#39; &#39;Model_630&#39; &#39;Model_640&#39; &#39;Model_645&#39;
+ &#39;Model_650&#39; &#39;Model_730&#39; &#39;Model_735&#39; &#39;Model_740&#39; &#39;Model_745&#39; &#39;Model_750&#39;
+ &#39;Model_760&#39; &#39;Model_80&#39; &#39;Model_9&#39; &#39;Model_90&#39; &#39;Model_A 140&#39; &#39;Model_A 150&#39;
+ &#39;Model_A 170&#39; &#39;Model_A 180&#39; &#39;Model_A1&#39; &#39;Model_A3&#39; &#39;Model_A4&#39;
+ &#39;Model_A4 Allroad&#39; &#39;Model_A5&#39; &#39;Model_A6&#39; &#39;Model_A6 Allroad&#39; &#39;Model_A7&#39;
+ &#39;Model_A8&#39; &#39;Model_ASX&#39; &#39;Model_Amarok&#39; &#39;Model_Auris&#39; &#39;Model_Avalon&#39;
+ &#39;Model_Avensis&#39; &#39;Model_Aygo&#39; &#39;Model_B 170&#39; &#39;Model_B 180&#39; &#39;Model_B 200&#39;
+ &#39;Model_Beetle&#39; &#39;Model_Bora&#39; &#39;Model_C-Class&#39; &#39;Model_CL 180&#39; &#39;Model_CL 500&#39;
+ &#39;Model_CL 55 AMG&#39; &#39;Model_CL 550&#39; &#39;Model_CL 63 AMG&#39; &#39;Model_CLA 200&#39;
+ &#39;Model_CLA 220&#39; &#39;Model_CLA-Class&#39; &#39;Model_CLC 180&#39; &#39;Model_CLC 200&#39;
+ &#39;Model_CLK 200&#39; &#39;Model_CLK 220&#39; &#39;Model_CLK 230&#39; &#39;Model_CLK 240&#39;
+ &#39;Model_CLK 280&#39; &#39;Model_CLK 320&#39; &#39;Model_CLK 430&#39; &#39;Model_CLS 350&#39;
+ &#39;Model_CLS 400&#39; &#39;Model_CLS 500&#39; &#39;Model_CLS 63 AMG&#39; &#39;Model_Caddy&#39;
+ &#39;Model_Camry&#39; &#39;Model_Captur&#39; &#39;Model_Caravelle&#39; &#39;Model_Carina&#39;
+ &#39;Model_Carisma&#39; &#39;Model_Celica&#39; &#39;Model_Clio&#39; &#39;Model_Colt&#39; &#39;Model_Corolla&#39;
+ &#39;Model_Corolla Verso&#39; &#39;Model_Cross Touran&#39; &#39;Model_Dokker&#39; &#39;Model_Duster&#39;
+ &#39;Model_E-Class&#39; &#39;Model_Eclipse&#39; &#39;Model_Eos&#39; &#39;Model_Espace&#39;
+ &#39;Model_FJ Cruiser&#39; &#39;Model_Fluence&#39; &#39;Model_Fortuner&#39; &#39;Model_G 320&#39;
+ &#39;Model_G 350&#39; &#39;Model_G 500&#39; &#39;Model_G 55 AMG&#39; &#39;Model_G 63 AMG&#39;
+ &#39;Model_GL 320&#39; &#39;Model_GL 350&#39; &#39;Model_GL 420&#39; &#39;Model_GL 450&#39;
+ &#39;Model_GL 500&#39; &#39;Model_GL 550&#39; &#39;Model_GLC-Class&#39; &#39;Model_GLE-Class&#39;
+ &#39;Model_GLK 220&#39; &#39;Model_GLK 300&#39; &#39;Model_GLS 350&#39; &#39;Model_GLS 400&#39;
+ &#39;Model_GLS 500&#39; &#39;Model_GLS 63&#39; &#39;Model_Galant&#39; &#39;Model_Golf GTI&#39;
+ &#39;Model_Golf II&#39; &#39;Model_Golf III&#39; &#39;Model_Golf IV&#39; &#39;Model_Golf Plus&#39;
+ &#39;Model_Golf V&#39; &#39;Model_Golf VI&#39; &#39;Model_Golf VII&#39; &#39;Model_Golf Variant&#39;
+ &#39;Model_Grand Scenic&#39; &#39;Model_Grandis&#39; &#39;Model_Hiace&#39; &#39;Model_Highlander&#39;
+ &#39;Model_Hilux&#39; &#39;Model_I3&#39; &#39;Model_IQ&#39; &#39;Model_Jetta&#39; &#39;Model_Kangoo&#39;
+ &#39;Model_Koleos&#39; &#39;Model_L 200&#39; &#39;Model_LT&#39; &#39;Model_Laguna&#39; &#39;Model_Lancer&#39;
+ &#39;Model_Lancer Evolution&#39; &#39;Model_Lancer X&#39; &#39;Model_Lancer X Sportback&#39;
+ &#39;Model_Land Cruiser 100&#39; &#39;Model_Land Cruiser 105&#39;
+ &#39;Model_Land Cruiser 200&#39; &#39;Model_Land Cruiser 76&#39; &#39;Model_Land Cruiser 80&#39;
+ &#39;Model_Land Cruiser Prado&#39; &#39;Model_Latitude&#39; &#39;Model_Lite Ace&#39;
+ &#39;Model_Logan&#39; &#39;Model_Lupo&#39; &#39;Model_M5&#39; &#39;Model_M6&#39; &#39;Model_MB&#39;
+ &#39;Model_ML 250&#39; &#39;Model_ML 270&#39; &#39;Model_ML 280&#39; &#39;Model_ML 320&#39;
+ &#39;Model_ML 350&#39; &#39;Model_ML 400&#39; &#39;Model_ML 430&#39; &#39;Model_ML 500&#39;
+ &#39;Model_ML 550&#39; &#39;Model_ML 63 AMG&#39; &#39;Model_Mark II&#39; &#39;Model_Master&#39;
+ &#39;Model_Matrix&#39; &#39;Model_Megane&#39; &#39;Model_Modus&#39; &#39;Model_Multivan&#39;
+ &#39;Model_New Beetle&#39; &#39;Model_Outlander&#39; &#39;Model_Outlander XL&#39; &#39;Model_Pajero&#39;
+ &#39;Model_Pajero Pinin&#39; &#39;Model_Pajero Sport&#39; &#39;Model_Pajero Wagon&#39;
+ &#39;Model_Passat B2&#39; &#39;Model_Passat B3&#39; &#39;Model_Passat B4&#39; &#39;Model_Passat B5&#39;
+ &#39;Model_Passat B6&#39; &#39;Model_Passat B7&#39; &#39;Model_Passat B8&#39; &#39;Model_Passat CC&#39;
+ &#39;Model_Phaeton&#39; &#39;Model_Pointer&#39; &#39;Model_Polo&#39; &#39;Model_Previa&#39; &#39;Model_Prius&#39;
+ &#39;Model_Q3&#39; &#39;Model_Q5&#39; &#39;Model_Q7&#39; &#39;Model_R 320&#39; &#39;Model_R8&#39; &#39;Model_Rav 4&#39;
+ &#39;Model_S 140&#39; &#39;Model_S 250&#39; &#39;Model_S 280&#39; &#39;Model_S 300&#39; &#39;Model_S 320&#39;
+ &#39;Model_S 350&#39; &#39;Model_S 400&#39; &#39;Model_S 420&#39; &#39;Model_S 430&#39; &#39;Model_S 500&#39;
+ &#39;Model_S 55&#39; &#39;Model_S 550&#39; &#39;Model_S 600&#39; &#39;Model_S 63 AMG&#39;
+ &#39;Model_S 65 AMG&#39; &#39;Model_S-Guard&#39; &#39;Model_S4&#39; &#39;Model_S5&#39; &#39;Model_S8&#39;
+ &#39;Model_SL 500 (550)&#39; &#39;Model_SL 55 AMG&#39; &#39;Model_SLK 200&#39; &#39;Model_SLK 350&#39;
+ &#39;Model_Sandero&#39; &#39;Model_Sandero StepWay&#39; &#39;Model_Scenic&#39; &#39;Model_Scion&#39;
+ &#39;Model_Scirocco&#39; &#39;Model_Sequoia&#39; &#39;Model_Sharan&#39; &#39;Model_Sienna&#39;
+ &#39;Model_Smart&#39; &#39;Model_Space Star&#39; &#39;Model_Space Wagon&#39; &#39;Model_Sprinter&#39;
+ &#39;Model_Sprinter 208&#39; &#39;Model_Sprinter 210&#39; &#39;Model_Sprinter 211&#39;
+ &#39;Model_Sprinter 212&#39; &#39;Model_Sprinter 213&#39; &#39;Model_Sprinter 311&#39;
+ &#39;Model_Sprinter 312&#39; &#39;Model_Sprinter 313&#39; &#39;Model_Sprinter 315&#39;
+ &#39;Model_Sprinter 316&#39; &#39;Model_Sprinter 318&#39; &#39;Model_Sprinter 319&#39;
+ &#39;Model_Sprinter 324&#39; &#39;Model_Symbol&#39; &#39;Model_Syncro&#39;
+ &#39;Model_T2 (Transporter)&#39; &#39;Model_T3 (Transporter)&#39;
+ &#39;Model_T4 (Transporter)&#39; &#39;Model_T4 (Transporter) &#39;
+ &#39;Model_T5 (Transporter)&#39; &#39;Model_T5 (Transporter) &#39;
+ &#39;Model_T6 (Transporter)&#39; &#39;Model_T6 (Transporter) &#39; &#39;Model_TT&#39;
+ &#39;Model_Tacoma&#39; &#39;Model_Tiguan&#39; &#39;Model_Touareg&#39; &#39;Model_Touran&#39;
+ &#39;Model_Trafic&#39; &#39;Model_Tundra&#39; &#39;Model_Up&#39; &#39;Model_V 250&#39; &#39;Model_Vaneo&#39;
+ &#39;Model_Vento&#39; &#39;Model_Venza&#39; &#39;Model_Viano&#39; &#39;Model_Virage&#39; &#39;Model_Vista&#39;
+ &#39;Model_Vito&#39; &#39;Model_X1&#39; &#39;Model_X3&#39; &#39;Model_X5&#39; &#39;Model_X5 M&#39; &#39;Model_X6&#39;
+ &#39;Model_X6 M&#39; &#39;Model_Yaris&#39; &#39;Model_Z3&#39; &#39;Model_Z4&#39;]
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_preprocessed</span> <span class="o">=</span> <span class="n">data_with_dummies</span>
+<span class="n">data_preprocessed</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[24]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>log_price</th>
+      <th>Mileage</th>
+      <th>EngineV</th>
+      <th>Year</th>
+      <th>Brand_BMW</th>
+      <th>Brand_Mercedes-Benz</th>
+      <th>Brand_Mitsubishi</th>
+      <th>Brand_Renault</th>
+      <th>Brand_Toyota</th>
+      <th>Brand_Volkswagen</th>
+      <th>Body_hatch</th>
+      <th>Body_other</th>
+      <th>Body_sedan</th>
+      <th>Body_vagon</th>
+      <th>Body_van</th>
+      <th>Engine Type_Gas</th>
+      <th>Engine Type_Other</th>
+      <th>Engine Type_Petrol</th>
+      <th>Registration_yes</th>
+      <th>Model_100</th>
+      <th>Model_11</th>
+      <th>Model_116</th>
+      <th>Model_118</th>
+      <th>Model_120</th>
+      <th>Model_19</th>
+      <th>Model_190</th>
+      <th>Model_200</th>
+      <th>Model_21</th>
+      <th>Model_210</th>
+      <th>Model_220</th>
+      <th>Model_230</th>
+      <th>Model_25</th>
+      <th>Model_250</th>
+      <th>Model_300</th>
+      <th>Model_316</th>
+      <th>Model_318</th>
+      <th>Model_320</th>
+      <th>Model_323</th>
+      <th>Model_324</th>
+      <th>Model_325</th>
+      <th>Model_328</th>
+      <th>Model_330</th>
+      <th>Model_335</th>
+      <th>Model_4 Series Gran Coupe</th>
+      <th>Model_428</th>
+      <th>Model_4Runner</th>
+      <th>Model_5</th>
+      <th>Model_5 Series</th>
+      <th>Model_5 Series GT</th>
+      <th>Model_520</th>
+      <th>Model_523</th>
+      <th>Model_524</th>
+      <th>Model_525</th>
+      <th>Model_528</th>
+      <th>Model_530</th>
+      <th>Model_535</th>
+      <th>Model_540</th>
+      <th>Model_545</th>
+      <th>Model_550</th>
+      <th>Model_6 Series Gran Coupe</th>
+      <th>Model_630</th>
+      <th>Model_640</th>
+      <th>Model_645</th>
+      <th>Model_650</th>
+      <th>Model_730</th>
+      <th>Model_735</th>
+      <th>Model_740</th>
+      <th>Model_745</th>
+      <th>Model_750</th>
+      <th>Model_760</th>
+      <th>Model_80</th>
+      <th>Model_9</th>
+      <th>Model_90</th>
+      <th>Model_A 140</th>
+      <th>Model_A 150</th>
+      <th>Model_A 170</th>
+      <th>Model_A 180</th>
+      <th>Model_A1</th>
+      <th>Model_A3</th>
+      <th>Model_A4</th>
+      <th>Model_A4 Allroad</th>
+      <th>Model_A5</th>
+      <th>Model_A6</th>
+      <th>Model_A6 Allroad</th>
+      <th>Model_A7</th>
+      <th>Model_A8</th>
+      <th>Model_ASX</th>
+      <th>Model_Amarok</th>
+      <th>Model_Auris</th>
+      <th>Model_Avalon</th>
+      <th>Model_Avensis</th>
+      <th>Model_Aygo</th>
+      <th>Model_B 170</th>
+      <th>Model_B 180</th>
+      <th>Model_B 200</th>
+      <th>Model_Beetle</th>
+      <th>Model_Bora</th>
+      <th>Model_C-Class</th>
+      <th>Model_CL 180</th>
+      <th>Model_CL 500</th>
+      <th>Model_CL 55 AMG</th>
+      <th>Model_CL 550</th>
+      <th>Model_CL 63 AMG</th>
+      <th>Model_CLA 200</th>
+      <th>Model_CLA 220</th>
+      <th>Model_CLA-Class</th>
+      <th>Model_CLC 180</th>
+      <th>Model_CLC 200</th>
+      <th>Model_CLK 200</th>
+      <th>Model_CLK 220</th>
+      <th>Model_CLK 230</th>
+      <th>Model_CLK 240</th>
+      <th>Model_CLK 280</th>
+      <th>Model_CLK 320</th>
+      <th>Model_CLK 430</th>
+      <th>Model_CLS 350</th>
+      <th>Model_CLS 400</th>
+      <th>Model_CLS 500</th>
+      <th>Model_CLS 63 AMG</th>
+      <th>Model_Caddy</th>
+      <th>Model_Camry</th>
+      <th>Model_Captur</th>
+      <th>Model_Caravelle</th>
+      <th>Model_Carina</th>
+      <th>Model_Carisma</th>
+      <th>Model_Celica</th>
+      <th>Model_Clio</th>
+      <th>Model_Colt</th>
+      <th>Model_Corolla</th>
+      <th>Model_Corolla Verso</th>
+      <th>Model_Cross Touran</th>
+      <th>Model_Dokker</th>
+      <th>Model_Duster</th>
+      <th>Model_E-Class</th>
+      <th>Model_Eclipse</th>
+      <th>Model_Eos</th>
+      <th>Model_Espace</th>
+      <th>Model_FJ Cruiser</th>
+      <th>Model_Fluence</th>
+      <th>Model_Fortuner</th>
+      <th>Model_G 320</th>
+      <th>Model_G 350</th>
+      <th>Model_G 500</th>
+      <th>Model_G 55 AMG</th>
+      <th>Model_G 63 AMG</th>
+      <th>Model_GL 320</th>
+      <th>Model_GL 350</th>
+      <th>Model_GL 420</th>
+      <th>Model_GL 450</th>
+      <th>Model_GL 500</th>
+      <th>Model_GL 550</th>
+      <th>Model_GLC-Class</th>
+      <th>Model_GLE-Class</th>
+      <th>Model_GLK 220</th>
+      <th>Model_GLK 300</th>
+      <th>Model_GLS 350</th>
+      <th>Model_GLS 400</th>
+      <th>Model_GLS 500</th>
+      <th>Model_GLS 63</th>
+      <th>Model_Galant</th>
+      <th>Model_Golf GTI</th>
+      <th>Model_Golf II</th>
+      <th>Model_Golf III</th>
+      <th>Model_Golf IV</th>
+      <th>Model_Golf Plus</th>
+      <th>Model_Golf V</th>
+      <th>Model_Golf VI</th>
+      <th>Model_Golf VII</th>
+      <th>Model_Golf Variant</th>
+      <th>Model_Grand Scenic</th>
+      <th>Model_Grandis</th>
+      <th>Model_Hiace</th>
+      <th>Model_Highlander</th>
+      <th>Model_Hilux</th>
+      <th>Model_I3</th>
+      <th>Model_IQ</th>
+      <th>Model_Jetta</th>
+      <th>Model_Kangoo</th>
+      <th>Model_Koleos</th>
+      <th>Model_L 200</th>
+      <th>Model_LT</th>
+      <th>Model_Laguna</th>
+      <th>Model_Lancer</th>
+      <th>Model_Lancer Evolution</th>
+      <th>Model_Lancer X</th>
+      <th>Model_Lancer X Sportback</th>
+      <th>Model_Land Cruiser 100</th>
+      <th>Model_Land Cruiser 105</th>
+      <th>Model_Land Cruiser 200</th>
+      <th>Model_Land Cruiser 76</th>
+      <th>Model_Land Cruiser 80</th>
+      <th>Model_Land Cruiser Prado</th>
+      <th>Model_Latitude</th>
+      <th>Model_Lite Ace</th>
+      <th>Model_Logan</th>
+      <th>Model_Lupo</th>
+      <th>Model_M5</th>
+      <th>Model_M6</th>
+      <th>Model_MB</th>
+      <th>Model_ML 250</th>
+      <th>Model_ML 270</th>
+      <th>Model_ML 280</th>
+      <th>Model_ML 320</th>
+      <th>Model_ML 350</th>
+      <th>Model_ML 400</th>
+      <th>Model_ML 430</th>
+      <th>Model_ML 500</th>
+      <th>Model_ML 550</th>
+      <th>Model_ML 63 AMG</th>
+      <th>Model_Mark II</th>
+      <th>Model_Master</th>
+      <th>Model_Matrix</th>
+      <th>Model_Megane</th>
+      <th>Model_Modus</th>
+      <th>Model_Multivan</th>
+      <th>Model_New Beetle</th>
+      <th>Model_Outlander</th>
+      <th>Model_Outlander XL</th>
+      <th>Model_Pajero</th>
+      <th>Model_Pajero Pinin</th>
+      <th>Model_Pajero Sport</th>
+      <th>Model_Pajero Wagon</th>
+      <th>Model_Passat B2</th>
+      <th>Model_Passat B3</th>
+      <th>Model_Passat B4</th>
+      <th>Model_Passat B5</th>
+      <th>Model_Passat B6</th>
+      <th>Model_Passat B7</th>
+      <th>Model_Passat B8</th>
+      <th>Model_Passat CC</th>
+      <th>Model_Phaeton</th>
+      <th>Model_Pointer</th>
+      <th>Model_Polo</th>
+      <th>Model_Previa</th>
+      <th>Model_Prius</th>
+      <th>Model_Q3</th>
+      <th>Model_Q5</th>
+      <th>Model_Q7</th>
+      <th>Model_R 320</th>
+      <th>Model_R8</th>
+      <th>Model_Rav 4</th>
+      <th>Model_S 140</th>
+      <th>Model_S 250</th>
+      <th>Model_S 280</th>
+      <th>Model_S 300</th>
+      <th>Model_S 320</th>
+      <th>Model_S 350</th>
+      <th>Model_S 400</th>
+      <th>Model_S 420</th>
+      <th>Model_S 430</th>
+      <th>Model_S 500</th>
+      <th>Model_S 55</th>
+      <th>Model_S 550</th>
+      <th>Model_S 600</th>
+      <th>Model_S 63 AMG</th>
+      <th>Model_S 65 AMG</th>
+      <th>Model_S-Guard</th>
+      <th>Model_S4</th>
+      <th>Model_S5</th>
+      <th>Model_S8</th>
+      <th>Model_SL 500 (550)</th>
+      <th>Model_SL 55 AMG</th>
+      <th>Model_SLK 200</th>
+      <th>Model_SLK 350</th>
+      <th>Model_Sandero</th>
+      <th>Model_Sandero StepWay</th>
+      <th>Model_Scenic</th>
+      <th>Model_Scion</th>
+      <th>Model_Scirocco</th>
+      <th>Model_Sequoia</th>
+      <th>Model_Sharan</th>
+      <th>Model_Sienna</th>
+      <th>Model_Smart</th>
+      <th>Model_Space Star</th>
+      <th>Model_Space Wagon</th>
+      <th>Model_Sprinter</th>
+      <th>Model_Sprinter 208</th>
+      <th>Model_Sprinter 210</th>
+      <th>Model_Sprinter 211</th>
+      <th>Model_Sprinter 212</th>
+      <th>Model_Sprinter 213</th>
+      <th>Model_Sprinter 311</th>
+      <th>Model_Sprinter 312</th>
+      <th>Model_Sprinter 313</th>
+      <th>Model_Sprinter 315</th>
+      <th>Model_Sprinter 316</th>
+      <th>Model_Sprinter 318</th>
+      <th>Model_Sprinter 319</th>
+      <th>Model_Sprinter 324</th>
+      <th>Model_Symbol</th>
+      <th>Model_Syncro</th>
+      <th>Model_T2 (Transporter)</th>
+      <th>Model_T3 (Transporter)</th>
+      <th>Model_T4 (Transporter)</th>
+      <th>Model_T4 (Transporter)</th>
+      <th>Model_T5 (Transporter)</th>
+      <th>Model_T5 (Transporter)</th>
+      <th>Model_T6 (Transporter)</th>
+      <th>Model_T6 (Transporter)</th>
+      <th>Model_TT</th>
+      <th>Model_Tacoma</th>
+      <th>Model_Tiguan</th>
+      <th>Model_Touareg</th>
+      <th>Model_Touran</th>
+      <th>Model_Trafic</th>
+      <th>Model_Tundra</th>
+      <th>Model_Up</th>
+      <th>Model_V 250</th>
+      <th>Model_Vaneo</th>
+      <th>Model_Vento</th>
+      <th>Model_Venza</th>
+      <th>Model_Viano</th>
+      <th>Model_Virage</th>
+      <th>Model_Vista</th>
+      <th>Model_Vito</th>
+      <th>Model_X1</th>
+      <th>Model_X3</th>
+      <th>Model_X5</th>
+      <th>Model_X5 M</th>
+      <th>Model_X6</th>
+      <th>Model_X6 M</th>
+      <th>Model_Yaris</th>
+      <th>Model_Z3</th>
+      <th>Model_Z4</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>8.342840</td>
+      <td>277</td>
+      <td>2.0</td>
+      <td>1991</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
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+    </tr>
+    <tr>
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+      <td>False</td>
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+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>9.814656</td>
+      <td>120</td>
+      <td>2.0</td>
+      <td>2011</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Linear-regression-model">Linear regression model<a class="anchor-link" href="#Linear-regression-model">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Declare-the-inputs-and-the-targets">Declare the inputs and the targets<a class="anchor-link" href="#Declare-the-inputs-and-the-targets">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">target</span> <span class="o">=</span> <span class="n">data_preprocessed</span><span class="p">[</span><span class="s1">'log_price'</span><span class="p">]</span> <span class="c1">#  Dependent variable</span>
+<span class="c1">#Input are everything but the dependent variable, so i'll drop 'log_price'</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">data_preprocessed</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">'log_price'</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[26]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">inputs</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[26]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Mileage</th>
+      <th>EngineV</th>
+      <th>Year</th>
+      <th>Brand_BMW</th>
+      <th>Brand_Mercedes-Benz</th>
+      <th>Brand_Mitsubishi</th>
+      <th>Brand_Renault</th>
+      <th>Brand_Toyota</th>
+      <th>Brand_Volkswagen</th>
+      <th>Body_hatch</th>
+      <th>Body_other</th>
+      <th>Body_sedan</th>
+      <th>Body_vagon</th>
+      <th>Body_van</th>
+      <th>Engine Type_Gas</th>
+      <th>Engine Type_Other</th>
+      <th>Engine Type_Petrol</th>
+      <th>Registration_yes</th>
+      <th>Model_100</th>
+      <th>Model_11</th>
+      <th>Model_116</th>
+      <th>Model_118</th>
+      <th>Model_120</th>
+      <th>Model_19</th>
+      <th>Model_190</th>
+      <th>Model_200</th>
+      <th>Model_21</th>
+      <th>Model_210</th>
+      <th>Model_220</th>
+      <th>Model_230</th>
+      <th>Model_25</th>
+      <th>Model_250</th>
+      <th>Model_300</th>
+      <th>Model_316</th>
+      <th>Model_318</th>
+      <th>Model_320</th>
+      <th>Model_323</th>
+      <th>Model_324</th>
+      <th>Model_325</th>
+      <th>Model_328</th>
+      <th>Model_330</th>
+      <th>Model_335</th>
+      <th>Model_4 Series Gran Coupe</th>
+      <th>Model_428</th>
+      <th>Model_4Runner</th>
+      <th>Model_5</th>
+      <th>Model_5 Series</th>
+      <th>Model_5 Series GT</th>
+      <th>Model_520</th>
+      <th>Model_523</th>
+      <th>Model_524</th>
+      <th>Model_525</th>
+      <th>Model_528</th>
+      <th>Model_530</th>
+      <th>Model_535</th>
+      <th>Model_540</th>
+      <th>Model_545</th>
+      <th>Model_550</th>
+      <th>Model_6 Series Gran Coupe</th>
+      <th>Model_630</th>
+      <th>Model_640</th>
+      <th>Model_645</th>
+      <th>Model_650</th>
+      <th>Model_730</th>
+      <th>Model_735</th>
+      <th>Model_740</th>
+      <th>Model_745</th>
+      <th>Model_750</th>
+      <th>Model_760</th>
+      <th>Model_80</th>
+      <th>Model_9</th>
+      <th>Model_90</th>
+      <th>Model_A 140</th>
+      <th>Model_A 150</th>
+      <th>Model_A 170</th>
+      <th>Model_A 180</th>
+      <th>Model_A1</th>
+      <th>Model_A3</th>
+      <th>Model_A4</th>
+      <th>Model_A4 Allroad</th>
+      <th>Model_A5</th>
+      <th>Model_A6</th>
+      <th>Model_A6 Allroad</th>
+      <th>Model_A7</th>
+      <th>Model_A8</th>
+      <th>Model_ASX</th>
+      <th>Model_Amarok</th>
+      <th>Model_Auris</th>
+      <th>Model_Avalon</th>
+      <th>Model_Avensis</th>
+      <th>Model_Aygo</th>
+      <th>Model_B 170</th>
+      <th>Model_B 180</th>
+      <th>Model_B 200</th>
+      <th>Model_Beetle</th>
+      <th>Model_Bora</th>
+      <th>Model_C-Class</th>
+      <th>Model_CL 180</th>
+      <th>Model_CL 500</th>
+      <th>Model_CL 55 AMG</th>
+      <th>Model_CL 550</th>
+      <th>Model_CL 63 AMG</th>
+      <th>Model_CLA 200</th>
+      <th>Model_CLA 220</th>
+      <th>Model_CLA-Class</th>
+      <th>Model_CLC 180</th>
+      <th>Model_CLC 200</th>
+      <th>Model_CLK 200</th>
+      <th>Model_CLK 220</th>
+      <th>Model_CLK 230</th>
+      <th>Model_CLK 240</th>
+      <th>Model_CLK 280</th>
+      <th>Model_CLK 320</th>
+      <th>Model_CLK 430</th>
+      <th>Model_CLS 350</th>
+      <th>Model_CLS 400</th>
+      <th>Model_CLS 500</th>
+      <th>Model_CLS 63 AMG</th>
+      <th>Model_Caddy</th>
+      <th>Model_Camry</th>
+      <th>Model_Captur</th>
+      <th>Model_Caravelle</th>
+      <th>Model_Carina</th>
+      <th>Model_Carisma</th>
+      <th>Model_Celica</th>
+      <th>Model_Clio</th>
+      <th>Model_Colt</th>
+      <th>Model_Corolla</th>
+      <th>Model_Corolla Verso</th>
+      <th>Model_Cross Touran</th>
+      <th>Model_Dokker</th>
+      <th>Model_Duster</th>
+      <th>Model_E-Class</th>
+      <th>Model_Eclipse</th>
+      <th>Model_Eos</th>
+      <th>Model_Espace</th>
+      <th>Model_FJ Cruiser</th>
+      <th>Model_Fluence</th>
+      <th>Model_Fortuner</th>
+      <th>Model_G 320</th>
+      <th>Model_G 350</th>
+      <th>Model_G 500</th>
+      <th>Model_G 55 AMG</th>
+      <th>Model_G 63 AMG</th>
+      <th>Model_GL 320</th>
+      <th>Model_GL 350</th>
+      <th>Model_GL 420</th>
+      <th>Model_GL 450</th>
+      <th>Model_GL 500</th>
+      <th>Model_GL 550</th>
+      <th>Model_GLC-Class</th>
+      <th>Model_GLE-Class</th>
+      <th>Model_GLK 220</th>
+      <th>Model_GLK 300</th>
+      <th>Model_GLS 350</th>
+      <th>Model_GLS 400</th>
+      <th>Model_GLS 500</th>
+      <th>Model_GLS 63</th>
+      <th>Model_Galant</th>
+      <th>Model_Golf GTI</th>
+      <th>Model_Golf II</th>
+      <th>Model_Golf III</th>
+      <th>Model_Golf IV</th>
+      <th>Model_Golf Plus</th>
+      <th>Model_Golf V</th>
+      <th>Model_Golf VI</th>
+      <th>Model_Golf VII</th>
+      <th>Model_Golf Variant</th>
+      <th>Model_Grand Scenic</th>
+      <th>Model_Grandis</th>
+      <th>Model_Hiace</th>
+      <th>Model_Highlander</th>
+      <th>Model_Hilux</th>
+      <th>Model_I3</th>
+      <th>Model_IQ</th>
+      <th>Model_Jetta</th>
+      <th>Model_Kangoo</th>
+      <th>Model_Koleos</th>
+      <th>Model_L 200</th>
+      <th>Model_LT</th>
+      <th>Model_Laguna</th>
+      <th>Model_Lancer</th>
+      <th>Model_Lancer Evolution</th>
+      <th>Model_Lancer X</th>
+      <th>Model_Lancer X Sportback</th>
+      <th>Model_Land Cruiser 100</th>
+      <th>Model_Land Cruiser 105</th>
+      <th>Model_Land Cruiser 200</th>
+      <th>Model_Land Cruiser 76</th>
+      <th>Model_Land Cruiser 80</th>
+      <th>Model_Land Cruiser Prado</th>
+      <th>Model_Latitude</th>
+      <th>Model_Lite Ace</th>
+      <th>Model_Logan</th>
+      <th>Model_Lupo</th>
+      <th>Model_M5</th>
+      <th>Model_M6</th>
+      <th>Model_MB</th>
+      <th>Model_ML 250</th>
+      <th>Model_ML 270</th>
+      <th>Model_ML 280</th>
+      <th>Model_ML 320</th>
+      <th>Model_ML 350</th>
+      <th>Model_ML 400</th>
+      <th>Model_ML 430</th>
+      <th>Model_ML 500</th>
+      <th>Model_ML 550</th>
+      <th>Model_ML 63 AMG</th>
+      <th>Model_Mark II</th>
+      <th>Model_Master</th>
+      <th>Model_Matrix</th>
+      <th>Model_Megane</th>
+      <th>Model_Modus</th>
+      <th>Model_Multivan</th>
+      <th>Model_New Beetle</th>
+      <th>Model_Outlander</th>
+      <th>Model_Outlander XL</th>
+      <th>Model_Pajero</th>
+      <th>Model_Pajero Pinin</th>
+      <th>Model_Pajero Sport</th>
+      <th>Model_Pajero Wagon</th>
+      <th>Model_Passat B2</th>
+      <th>Model_Passat B3</th>
+      <th>Model_Passat B4</th>
+      <th>Model_Passat B5</th>
+      <th>Model_Passat B6</th>
+      <th>Model_Passat B7</th>
+      <th>Model_Passat B8</th>
+      <th>Model_Passat CC</th>
+      <th>Model_Phaeton</th>
+      <th>Model_Pointer</th>
+      <th>Model_Polo</th>
+      <th>Model_Previa</th>
+      <th>Model_Prius</th>
+      <th>Model_Q3</th>
+      <th>Model_Q5</th>
+      <th>Model_Q7</th>
+      <th>Model_R 320</th>
+      <th>Model_R8</th>
+      <th>Model_Rav 4</th>
+      <th>Model_S 140</th>
+      <th>Model_S 250</th>
+      <th>Model_S 280</th>
+      <th>Model_S 300</th>
+      <th>Model_S 320</th>
+      <th>Model_S 350</th>
+      <th>Model_S 400</th>
+      <th>Model_S 420</th>
+      <th>Model_S 430</th>
+      <th>Model_S 500</th>
+      <th>Model_S 55</th>
+      <th>Model_S 550</th>
+      <th>Model_S 600</th>
+      <th>Model_S 63 AMG</th>
+      <th>Model_S 65 AMG</th>
+      <th>Model_S-Guard</th>
+      <th>Model_S4</th>
+      <th>Model_S5</th>
+      <th>Model_S8</th>
+      <th>Model_SL 500 (550)</th>
+      <th>Model_SL 55 AMG</th>
+      <th>Model_SLK 200</th>
+      <th>Model_SLK 350</th>
+      <th>Model_Sandero</th>
+      <th>Model_Sandero StepWay</th>
+      <th>Model_Scenic</th>
+      <th>Model_Scion</th>
+      <th>Model_Scirocco</th>
+      <th>Model_Sequoia</th>
+      <th>Model_Sharan</th>
+      <th>Model_Sienna</th>
+      <th>Model_Smart</th>
+      <th>Model_Space Star</th>
+      <th>Model_Space Wagon</th>
+      <th>Model_Sprinter</th>
+      <th>Model_Sprinter 208</th>
+      <th>Model_Sprinter 210</th>
+      <th>Model_Sprinter 211</th>
+      <th>Model_Sprinter 212</th>
+      <th>Model_Sprinter 213</th>
+      <th>Model_Sprinter 311</th>
+      <th>Model_Sprinter 312</th>
+      <th>Model_Sprinter 313</th>
+      <th>Model_Sprinter 315</th>
+      <th>Model_Sprinter 316</th>
+      <th>Model_Sprinter 318</th>
+      <th>Model_Sprinter 319</th>
+      <th>Model_Sprinter 324</th>
+      <th>Model_Symbol</th>
+      <th>Model_Syncro</th>
+      <th>Model_T2 (Transporter)</th>
+      <th>Model_T3 (Transporter)</th>
+      <th>Model_T4 (Transporter)</th>
+      <th>Model_T4 (Transporter)</th>
+      <th>Model_T5 (Transporter)</th>
+      <th>Model_T5 (Transporter)</th>
+      <th>Model_T6 (Transporter)</th>
+      <th>Model_T6 (Transporter)</th>
+      <th>Model_TT</th>
+      <th>Model_Tacoma</th>
+      <th>Model_Tiguan</th>
+      <th>Model_Touareg</th>
+      <th>Model_Touran</th>
+      <th>Model_Trafic</th>
+      <th>Model_Tundra</th>
+      <th>Model_Up</th>
+      <th>Model_V 250</th>
+      <th>Model_Vaneo</th>
+      <th>Model_Vento</th>
+      <th>Model_Venza</th>
+      <th>Model_Viano</th>
+      <th>Model_Virage</th>
+      <th>Model_Vista</th>
+      <th>Model_Vito</th>
+      <th>Model_X1</th>
+      <th>Model_X3</th>
+      <th>Model_X5</th>
+      <th>Model_X5 M</th>
+      <th>Model_X6</th>
+      <th>Model_X6 M</th>
+      <th>Model_Yaris</th>
+      <th>Model_Z3</th>
+      <th>Model_Z4</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>277</td>
+      <td>2.0</td>
+      <td>1991</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
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+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
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+      <td>False</td>
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+      <td>False</td>
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+      <td>False</td>
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+    </tr>
+    <tr>
+      <th>1</th>
+      <td>427</td>
+      <td>2.9</td>
+      <td>1999</td>
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+      <td>True</td>
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+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>True</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+<p>4025 rows × 323 columns</p>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Train-Test-Spilt">Train Test Spilt<a class="anchor-link" href="#Train-Test-Spilt">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import the module for the split</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="c1"># split the variables with an 80 - 20 split and some random state for example use random_state = 365</span>
+
+<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">365</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Create-the-regression">Create the regression<a class="anchor-link" href="#Create-the-regression">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the regression object</span>
+<span class="n">reg</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
+<span class="c1"># fit the the regression with the trained inputs and the target</span>
+<span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[28]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">LinearRegression</label><div class="sk-toggleable__content"><pre>LinearRegression()</pre></div></div></div></div></div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[29]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Let's check the outputs of the regression</span>
+<span class="c1"># I'll store them in y_hat as this is the 'theoretical' name of the predictions</span>
+<span class="n">y_hat</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[30]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot</span>
+<span class="c1"># The closer the points to the 45-degree line, the better the prediction</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span> <span class="mf">0.2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Target(y_train)'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span> <span class="mi">18</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Prediction(Y_hat)'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span> <span class="mi">18</span><span class="p">)</span>
+<span class="c1"># Sometimes the plot will have different scales of the x-axis and the y-axis</span>
+<span class="c1"># This is an issue as we won't be able to interpret the '45-degree line'</span>
+<span class="c1"># We want the x-axis and the y-axis to be the same</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">13</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">13</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Scatter plot for Year and log_price with regression line</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_hat</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'s'</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">line_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Target(y_train)'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span> <span class="mi">18</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Prediction(Y_hat)'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span> <span class="mi">18</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">13</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">13</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[31]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>(6.0, 13.0)</pre>
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Check-for-residue">Check for residue<a class="anchor-link" href="#Check-for-residue">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Another useful check of our model is a residual plot</span>
+<span class="c1"># We can plot the PDF of the residuals and check for anomalies</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">y_train</span><span class="o">-</span><span class="n">y_hat</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Residuals PDF"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
+
+<span class="c1"># In the best case scenario this plot should be normally distributed</span>
+<span class="c1">#And our graph happened to be nornally distributed with the midpoint at zero, meaning that our prediction fits our target</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>C:\Users\Chizy\AppData\Local\Temp\ipykernel_2108\2934438678.py:3: UserWarning: 
+
+`distplot` is a deprecated function and will be removed in seaborn v0.14.0.
+
+Please adapt your code to use either `displot` (a figure-level function with
+similar flexibility) or `histplot` (an axes-level function for histograms).
+
+For a guide to updating your code to use the new functions, please see
+https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
+
+  sns.distplot(y_train-y_hat)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[32]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Text(0.5, 1.0, &#39;Residuals PDF&#39;)</pre>
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="R-Squared-of-our-model">R-Squared of our model<a class="anchor-link" href="#R-Squared-of-our-model">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="c1"># Note that this is NOT the adjusted R-squared</span>
+<span class="c1"># in other words... find the Adjusted R-squared to have the appropriate measure :)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[33]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>0.9410857085028603</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Adjusted-R2">Adjusted R2<a class="anchor-link" href="#Adjusted-R2">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="Formula-for-Adjusted-R%5E2">Formula for Adjusted R^2<a class="anchor-link" href="#Formula-for-Adjusted-R%5E2">&#182;</a></h3><p>$R^2_{adj.} = 1 - (1-R^2)*\frac{n-1}{n-p-1}$</p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[34]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Get the shape of x, to facilitate the creation of the Adjusted R^2 metric</span>
+<span class="n">x_train</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[34]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>(3220, 323)</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[35]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If we want to find the Adjusted R-squared we can do so by knowing the r2, the # observations, the # features</span>
+<span class="n">r2</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="c1"># Number of observations is the shape along axis 0</span>
+<span class="n">n</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="c1"># Number of features (predictors, p) is the shape along axis 1</span>
+<span class="n">p</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c1"># We find the Adjusted R-squared using the formula</span>
+<span class="n">Adj_r2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">r2</span><span class="p">)</span><span class="o">*</span> <span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="n">p</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">Adj_r2</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[35]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>0.9345148120409901</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Feature-Selection">Feature Selection<a class="anchor-link" href="#Feature-Selection">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[36]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import the feature selection module from sklearn</span>
+<span class="c1"># This module allows us to select the most appopriate features for our regression</span>
+<span class="c1"># There exist many different approaches to feature selection, however, we will use one of the simplest</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <span class="n">f_regression</span>
+
+<span class="c1"># f_regression finds the F-statistics for the *simple* regressions created with each of the independent variables</span>
+<span class="c1"># In our case, this would mean running a simple linear regression on GPA where SAT is the independent variable</span>
+<span class="c1"># and a simple linear regression on GPA where Rand 1,2,3 is the indepdent variable</span>
+<span class="c1"># The limitation of this approach is that it does not take into account the mutual effect of the two features</span>
+<span class="n">f_regression</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
+
+<span class="c1">#There are two output arrays</span>
+<span class="c1"># The first one contains the F-statistics for each of the regressions</span>
+<span class="c1"># The second one contains the p-values of these F-statistics</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[36]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
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+        6.01376927e-001, 6.85024613e-001, 7.95535047e-002, 1.75529365e-004,
+        1.39028045e-014, 6.81975855e-001, 2.28028853e-004, 1.26486512e-001,
+        3.73764587e-001, 1.09905717e-001, 1.33851176e-001, 6.70212905e-001,
+        4.49327808e-001, 6.73575171e-011, 3.52668313e-002, 4.69537352e-001,
+        9.67432151e-001, 6.31620457e-011, 1.00000000e+000, 4.65496452e-002,
+        1.21390210e-004, 2.02025339e-008, 2.57302704e-001, 7.04803460e-004,
+        8.91981750e-001, 3.40248085e-001, 6.66724461e-001, 5.83221602e-001,
+        1.00000000e+000, 6.03919769e-001, 6.43900200e-001, 2.95109444e-001,
+        9.28112666e-001, 2.15910154e-004, 1.00000000e+000, 3.75678769e-001,
+        2.31400817e-001, 1.07557544e-001, 6.69175228e-002, 2.13495231e-001,
+        1.77244755e-002, 2.55315670e-001, 1.00000000e+000, 2.62329963e-001,
+        3.62648525e-001, 6.98283335e-001, 8.67631905e-001, 8.41694589e-001,
+        5.82654840e-001, 2.25752785e-001, 9.54668885e-001, 8.12262775e-001,
+        7.45977702e-001, 3.11392793e-001, 2.77867310e-002, 2.94550329e-003,
+        5.15234482e-002, 8.70599844e-001, 1.70456776e-002, 2.32418760e-002,
+        2.66085948e-003, 3.25914134e-002, 3.87907274e-001, 7.92210128e-001,
+        7.40340068e-001, 8.90623082e-001, 2.67587449e-001, 6.90853933e-001,
+        1.00754811e-002, 6.89055296e-008, 4.32934445e-001, 2.04960304e-002,
+        4.43239721e-002, 6.51183356e-001, 8.06907592e-008, 5.31985601e-001,
+        6.63600657e-004, 2.22523086e-002, 4.31816253e-002, 1.00000000e+000,
+        6.98283335e-001, 6.94986205e-002, 4.39863254e-003, 4.00579913e-004,
+        6.26179589e-019, 2.36500444e-007, 3.39282802e-015, 4.84506304e-003,
+        3.37943093e-002, 3.41070801e-001, 1.03219301e-001]))</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[37]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Since we are more interested in the latter (p-values), we can just take the second array</span>
+<span class="n">p_values</span> <span class="o">=</span> <span class="n">f_regression</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">p_values</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[37]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([2.56639281e-309, 3.69407883e-002, 0.00000000e+000, 2.13304455e-005,
+       4.51213014e-022, 1.29007360e-005, 1.07285031e-029, 1.94649116e-014,
+       6.22095784e-014, 1.71728648e-017, 4.86784025e-003, 3.11596603e-018,
+       4.08318404e-023, 1.08134002e-010, 4.58256964e-008, 1.41634169e-002,
+       1.84953305e-004, 2.09323244e-146, 3.16474261e-006, 1.00000000e+000,
+       9.26246814e-001, 9.87330889e-001, 1.83021249e-001, 9.11127887e-002,
+       1.19813629e-005, 2.53345414e-002, 1.90000585e-002, 2.08261811e-002,
+       4.86672603e-001, 5.69318689e-002, 1.00770104e-003, 4.05692136e-001,
+       1.07911231e-001, 4.77880471e-003, 4.91650256e-006, 7.55819283e-005,
+       4.90532752e-001, 6.79087191e-002, 8.26756792e-001, 3.98106900e-001,
+       2.31688947e-001, 4.33166919e-001, 1.22718673e-001, 1.00000000e+000,
+       1.89630151e-001, 1.00000000e+000, 6.01247666e-001, 3.75883636e-002,
+       4.13817142e-001, 5.91417515e-001, 1.25528377e-001, 8.14801678e-012,
+       1.68556062e-001, 2.42028095e-001, 4.33383053e-001, 1.39233795e-001,
+       6.48887602e-001, 1.63039120e-003, 3.85561860e-002, 5.17179158e-001,
+       4.41821584e-004, 3.71343697e-001, 5.32761253e-001, 7.20978206e-001,
+       4.09527800e-001, 8.64753022e-001, 5.63591273e-001, 1.42773118e-003,
+       4.71586242e-001, 5.30703625e-009, 1.34748018e-002, 1.83021249e-001,
+       2.16459985e-002, 7.34813292e-001, 9.89122411e-003, 3.68574535e-001,
+       6.66441099e-001, 5.51175993e-001, 4.43305980e-004, 2.86342637e-001,
+       3.35086125e-003, 8.80657206e-008, 7.35425272e-001, 2.50471868e-002,
+       3.80894623e-002, 4.01958798e-001, 3.85931106e-001, 8.29302428e-001,
+       8.27233532e-001, 6.71061839e-001, 5.46268783e-001, 9.68555856e-001,
+       2.97385141e-001, 6.83120185e-001, 2.24054490e-001, 7.98829944e-002,
+       7.74621412e-001, 1.00000000e+000, 4.64693438e-001, 1.07626058e-001,
+       1.00000000e+000, 6.47070340e-003, 1.44611890e-001, 1.89630151e-001,
+       1.00000000e+000, 1.00000000e+000, 6.81585635e-001, 7.89753766e-002,
+       5.52414839e-001, 1.26031599e-001, 8.12262775e-001, 5.32761253e-001,
+       6.20087988e-001, 2.44760711e-001, 9.28392404e-002, 1.00000000e+000,
+       9.07988454e-002, 3.19204353e-002, 3.41680389e-004, 5.19563591e-002,
+       1.00000000e+000, 4.42762754e-001, 4.06122042e-003, 3.99665611e-003,
+       5.57008646e-001, 6.74717576e-002, 9.78408658e-003, 2.77099111e-003,
+       6.02532109e-001, 1.00000000e+000, 7.11148676e-001, 5.01945642e-001,
+       9.27493882e-004, 6.72904741e-001, 1.00000000e+000, 6.02532109e-001,
+       3.24910587e-002, 8.26249124e-001, 8.34188757e-001, 1.00000000e+000,
+       4.40746655e-004, 2.99839421e-006, 5.56621416e-004, 3.76191257e-010,
+       1.01276166e-002, 1.68808863e-009, 4.34900697e-001, 1.31882699e-001,
+       7.35283039e-002, 2.08717435e-001, 9.42660004e-002, 4.58042029e-009,
+       2.91378789e-001, 3.42031569e-001, 1.58497540e-018, 4.88624552e-004,
+       2.23462920e-004, 4.10323481e-011, 7.55702579e-009, 6.43900200e-001,
+       9.50372855e-009, 6.07613924e-009, 2.96823255e-010, 5.09345354e-001,
+       1.78926525e-001, 9.06121568e-001, 4.51607438e-001, 7.24165492e-001,
+       5.91496727e-001, 9.04286267e-001, 3.01764465e-001, 4.36726460e-003,
+       1.45529256e-003, 2.18784422e-001, 1.00000000e+000, 5.99223423e-002,
+       9.74787951e-016, 9.07064650e-001, 7.03783041e-001, 2.06076823e-001,
+       8.72593903e-008, 6.03304420e-006, 5.22102438e-001, 5.54278957e-002,
+       7.34813292e-001, 7.61703682e-002, 1.00000000e+000, 6.12517681e-028,
+       8.13086341e-002, 9.09954117e-001, 1.01974973e-009, 9.95704967e-001,
+       1.03579279e-002, 8.84971529e-002, 3.29403271e-002, 2.66984999e-004,
+       5.03783861e-002, 6.07751197e-002, 6.69335455e-002, 7.86322149e-001,
+       6.08838773e-001, 7.57552813e-001, 1.82214428e-004, 7.23171120e-001,
+       1.00000000e+000, 1.33597912e-001, 3.93444560e-001, 1.68935551e-002,
+       1.00000000e+000, 3.60467380e-001, 7.43784280e-001, 1.68285534e-003,
+       1.00000000e+000, 1.00901157e-005, 5.26457888e-001, 8.00001965e-001,
+       4.62580458e-001, 1.11882697e-001, 6.41575085e-001, 1.88970570e-001,
+       3.44770075e-002, 1.69454213e-002, 7.59920516e-007, 8.47328813e-006,
+       1.34741492e-013, 3.35770762e-001, 3.63941304e-003, 1.96044222e-002,
+       6.37819043e-003, 4.76202033e-002, 2.44760711e-001, 2.65676768e-002,
+       6.01376927e-001, 6.85024613e-001, 7.95535047e-002, 1.75529365e-004,
+       1.39028045e-014, 6.81975855e-001, 2.28028853e-004, 1.26486512e-001,
+       3.73764587e-001, 1.09905717e-001, 1.33851176e-001, 6.70212905e-001,
+       4.49327808e-001, 6.73575171e-011, 3.52668313e-002, 4.69537352e-001,
+       9.67432151e-001, 6.31620457e-011, 1.00000000e+000, 4.65496452e-002,
+       1.21390210e-004, 2.02025339e-008, 2.57302704e-001, 7.04803460e-004,
+       8.91981750e-001, 3.40248085e-001, 6.66724461e-001, 5.83221602e-001,
+       1.00000000e+000, 6.03919769e-001, 6.43900200e-001, 2.95109444e-001,
+       9.28112666e-001, 2.15910154e-004, 1.00000000e+000, 3.75678769e-001,
+       2.31400817e-001, 1.07557544e-001, 6.69175228e-002, 2.13495231e-001,
+       1.77244755e-002, 2.55315670e-001, 1.00000000e+000, 2.62329963e-001,
+       3.62648525e-001, 6.98283335e-001, 8.67631905e-001, 8.41694589e-001,
+       5.82654840e-001, 2.25752785e-001, 9.54668885e-001, 8.12262775e-001,
+       7.45977702e-001, 3.11392793e-001, 2.77867310e-002, 2.94550329e-003,
+       5.15234482e-002, 8.70599844e-001, 1.70456776e-002, 2.32418760e-002,
+       2.66085948e-003, 3.25914134e-002, 3.87907274e-001, 7.92210128e-001,
+       7.40340068e-001, 8.90623082e-001, 2.67587449e-001, 6.90853933e-001,
+       1.00754811e-002, 6.89055296e-008, 4.32934445e-001, 2.04960304e-002,
+       4.43239721e-002, 6.51183356e-001, 8.06907592e-008, 5.31985601e-001,
+       6.63600657e-004, 2.22523086e-002, 4.31816253e-002, 1.00000000e+000,
+       6.98283335e-001, 6.94986205e-002, 4.39863254e-003, 4.00579913e-004,
+       6.26179589e-019, 2.36500444e-007, 3.39282802e-015, 4.84506304e-003,
+       3.37943093e-002, 3.41070801e-001, 1.03219301e-001])</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[38]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p_values</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[38]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([0.   , 0.037, 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,
+       0.   , 0.005, 0.   , 0.   , 0.   , 0.   , 0.014, 0.   , 0.   ,
+       0.   , 1.   , 0.926, 0.987, 0.183, 0.091, 0.   , 0.025, 0.019,
+       0.021, 0.487, 0.057, 0.001, 0.406, 0.108, 0.005, 0.   , 0.   ,
+       0.491, 0.068, 0.827, 0.398, 0.232, 0.433, 0.123, 1.   , 0.19 ,
+       1.   , 0.601, 0.038, 0.414, 0.591, 0.126, 0.   , 0.169, 0.242,
+       0.433, 0.139, 0.649, 0.002, 0.039, 0.517, 0.   , 0.371, 0.533,
+       0.721, 0.41 , 0.865, 0.564, 0.001, 0.472, 0.   , 0.013, 0.183,
+       0.022, 0.735, 0.01 , 0.369, 0.666, 0.551, 0.   , 0.286, 0.003,
+       0.   , 0.735, 0.025, 0.038, 0.402, 0.386, 0.829, 0.827, 0.671,
+       0.546, 0.969, 0.297, 0.683, 0.224, 0.08 , 0.775, 1.   , 0.465,
+       0.108, 1.   , 0.006, 0.145, 0.19 , 1.   , 1.   , 0.682, 0.079,
+       0.552, 0.126, 0.812, 0.533, 0.62 , 0.245, 0.093, 1.   , 0.091,
+       0.032, 0.   , 0.052, 1.   , 0.443, 0.004, 0.004, 0.557, 0.067,
+       0.01 , 0.003, 0.603, 1.   , 0.711, 0.502, 0.001, 0.673, 1.   ,
+       0.603, 0.032, 0.826, 0.834, 1.   , 0.   , 0.   , 0.001, 0.   ,
+       0.01 , 0.   , 0.435, 0.132, 0.074, 0.209, 0.094, 0.   , 0.291,
+       0.342, 0.   , 0.   , 0.   , 0.   , 0.   , 0.644, 0.   , 0.   ,
+       0.   , 0.509, 0.179, 0.906, 0.452, 0.724, 0.591, 0.904, 0.302,
+       0.004, 0.001, 0.219, 1.   , 0.06 , 0.   , 0.907, 0.704, 0.206,
+       0.   , 0.   , 0.522, 0.055, 0.735, 0.076, 1.   , 0.   , 0.081,
+       0.91 , 0.   , 0.996, 0.01 , 0.088, 0.033, 0.   , 0.05 , 0.061,
+       0.067, 0.786, 0.609, 0.758, 0.   , 0.723, 1.   , 0.134, 0.393,
+       0.017, 1.   , 0.36 , 0.744, 0.002, 1.   , 0.   , 0.526, 0.8  ,
+       0.463, 0.112, 0.642, 0.189, 0.034, 0.017, 0.   , 0.   , 0.   ,
+       0.336, 0.004, 0.02 , 0.006, 0.048, 0.245, 0.027, 0.601, 0.685,
+       0.08 , 0.   , 0.   , 0.682, 0.   , 0.126, 0.374, 0.11 , 0.134,
+       0.67 , 0.449, 0.   , 0.035, 0.47 , 0.967, 0.   , 1.   , 0.047,
+       0.   , 0.   , 0.257, 0.001, 0.892, 0.34 , 0.667, 0.583, 1.   ,
+       0.604, 0.644, 0.295, 0.928, 0.   , 1.   , 0.376, 0.231, 0.108,
+       0.067, 0.213, 0.018, 0.255, 1.   , 0.262, 0.363, 0.698, 0.868,
+       0.842, 0.583, 0.226, 0.955, 0.812, 0.746, 0.311, 0.028, 0.003,
+       0.052, 0.871, 0.017, 0.023, 0.003, 0.033, 0.388, 0.792, 0.74 ,
+       0.891, 0.268, 0.691, 0.01 , 0.   , 0.433, 0.02 , 0.044, 0.651,
+       0.   , 0.532, 0.001, 0.022, 0.043, 1.   , 0.698, 0.069, 0.004,
+       0.   , 0.   , 0.   , 0.   , 0.005, 0.034, 0.341, 0.103])</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Finding-weight-and-bias">Finding weight and bias<a class="anchor-link" href="#Finding-weight-and-bias">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[39]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Obtain the bias (intercept) of the regression</span>
+<span class="n">reg</span><span class="o">.</span><span class="n">intercept_</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[39]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>-271886118.8957569</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[40]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Obtain the weights (coefficients) of the regression</span>
+<span class="n">reg</span><span class="o">.</span><span class="n">coef_</span>
+<span class="c1"># Note that they are barely interpretable if at all</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[40]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([-7.11168307e-04, -2.35976356e-03,  8.68792315e-02,  2.45087768e+07,
+        2.45087768e+07,  3.35460065e+08,  2.30199764e+09,  1.45542358e+09,
+        4.62245051e+08,  9.66255861e-03,  1.23547420e-02, -1.06618585e-01,
+       -1.31590684e-01, -5.92526948e-02, -9.57940713e-02, -4.74364368e-02,
+       -6.14403758e-02,  8.89392304e-01,  2.71885954e+08, -7.18996649e+09,
+        2.47377176e+08,  2.47377177e+08,  2.47377175e+08, -2.03011169e+09,
+        2.47377177e+08,  2.47377177e+08, -2.03011169e+09,  2.47377176e+08,
+        2.47377177e+08,  2.47377177e+08, -2.03011169e+09,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.59254546e+09,
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+        2.47377178e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.71885953e+08, -2.03011169e+09,  2.71885954e+08,
+        2.47377176e+08,  2.47377176e+08,  2.47377176e+08,  2.47377176e+08,
+        2.71885953e+08,  2.71885953e+08,  2.71885953e+08,  2.71885954e+08,
+        2.71885954e+08,  2.71885954e+08,  2.71885954e+08,  2.71885954e+08,
+        2.71885954e+08, -6.35741121e+07, -1.90359097e+08, -1.18353763e+09,
+       -1.18353763e+09, -1.18353763e+09, -1.18353763e+09,  2.47377176e+08,
+        2.47377176e+08,  2.47377176e+08, -1.90359097e+08, -1.90359098e+08,
+        2.47377177e+08, -8.93153042e+08,  2.47377177e+08,  2.47377177e+08,
+       -7.09605288e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,
+       -1.08592764e+09, -7.86250753e+08,  2.47377177e+08,  2.47377176e+08,
+        2.47377177e+08,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  1.82078318e+09,
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+       -1.46384430e+09, -1.90359097e+08, -1.18353763e+09, -6.35741122e+07,
+       -1.18353763e+09, -2.03011169e+09, -6.35741125e+07, -1.18353763e+09,
+       -1.18353763e+09, -1.58640664e+08, -2.03011169e+09, -2.03011169e+09,
+        2.47377177e+08, -6.35741116e+07,  1.06343391e+09, -2.03011169e+09,
+       -1.18353763e+09, -2.03011169e+09, -1.18353763e+09,  5.60770216e+07,
+        2.47377178e+08,  2.47377178e+08,  2.47377178e+08,  2.47377178e+08,
+        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  2.47377178e+08,  2.47377178e+08,
+        2.47377178e+08,  2.47377178e+08, -6.35741121e+07, -1.90359098e+08,
+       -1.90359097e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,
+       -1.90359098e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,
+       -2.03011169e+09, -6.35741119e+07, -1.18353763e+09, -1.18353763e+09,
+       -1.18353763e+09,  2.47377177e+08, -2.56326823e+08, -1.90359098e+08,
+       -2.03011169e+09, -2.03011169e+09, -6.35741122e+07, -1.90359098e+08,
+       -2.03011169e+09, -6.35741124e+07, -6.35741114e+07, -6.35741123e+07,
+       -6.35741125e+07, -1.18353763e+09, -4.21205487e+03, -1.18353762e+09,
+       -1.18353763e+09, -1.18353763e+09, -1.18353763e+09, -2.03011169e+09,
+       -1.18353763e+09, -2.03011169e+09, -1.90359098e+08,  2.47377178e+08,
+        2.47377178e+08,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,
+       -1.69340410e-07,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,
+       -2.77046898e-07, -2.03011169e+09, -1.18353763e+09, -2.03011169e+09,
+        1.42292599e-06, -1.90359097e+08, -1.90359098e+08, -6.35741121e+07,
+       -6.35741119e+07, -6.35741112e+07, -6.35741120e+07, -6.35741119e+07,
+       -6.35741117e+07, -1.90359097e+08, -1.90359097e+08, -1.90359097e+08,
+       -1.90359098e+08, -1.90359098e+08, -1.90359098e+08, -1.90359097e+08,
+       -1.90359097e+08, -1.90359097e+08, -1.90359099e+08, -1.90359098e+08,
+       -1.18353763e+09, -1.18353763e+09,  2.71885954e+08,  2.71885954e+08,
+        2.71885954e+08,  2.47377177e+08,  2.71885955e+08, -1.18353763e+09,
+        2.47377177e+08,  2.47377178e+08,  2.47377178e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377177e+08,  6.17630946e-14,  2.47377177e+08,
+        2.47377178e+08,  2.47377178e+08,  2.47377178e+08,  2.47377179e+08,
+        2.71885954e+08,  2.71885954e+08,  2.71885954e+08,  2.47377177e+08,
+       -1.00867231e-13,  2.47377177e+08,  2.47377177e+08, -2.03011169e+09,
+       -2.03011169e+09, -2.03011169e+09, -1.57485136e-13, -1.90359098e+08,
+       -1.18353763e+09, -1.90359097e+08, -1.18353763e+09,  2.47377176e+08,
+       -6.35741123e+07, -6.35741115e+07,  2.27283470e-14,  2.47377177e+08,
+        2.47377176e+08,  2.47377176e+08,  2.47377177e+08,  2.47377176e+08,
+        2.47377176e+08,  2.47377177e+08,  2.47377176e+08,  2.47377177e+08,
+        2.47377176e+08,  2.47377177e+08,  2.47377177e+08,  2.47377179e+08,
+       -2.03011169e+09, -1.90359097e+08, -1.90359098e+08, -1.90359098e+08,
+       -1.90359097e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,
+       -1.90359098e+08, -1.90359098e+08,  2.71885954e+08, -1.18353763e+09,
+       -1.90359098e+08, -1.90359097e+08, -1.90359098e+08, -2.03011169e+09,
+       -1.18353763e+09, -1.90359099e+08,  2.47377178e+08,  2.47377176e+08,
+       -1.90359098e+08, -1.18353763e+09,  2.47377177e+08,  1.11022302e-15,
+       -1.18353763e+09,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,
+        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377178e+08,
+       -1.18353763e+09,  2.47377177e+08,  2.47377177e+08])</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[41]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a regression summary where we can compare them with one another</span>
+<span class="n">reg_summary</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">inputs</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'Features'</span><span class="p">])</span>
+
+<span class="n">reg_summary</span><span class="p">[</span><span class="s1">'Weights'</span><span class="p">]</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">coef_</span>
+<span class="n">reg_summary</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[41]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Features</th>
+      <th>Weights</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>Mileage</td>
+      <td>-7.111683e-04</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>EngineV</td>
+      <td>-2.359764e-03</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Year</td>
+      <td>8.687923e-02</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Brand_BMW</td>
+      <td>2.450878e+07</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Brand_Mercedes-Benz</td>
+      <td>2.450878e+07</td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td>...</td>
+      <td>...</td>
+    </tr>
+    <tr>
+      <th>318</th>
+      <td>Model_X6</td>
+      <td>2.473772e+08</td>
+    </tr>
+    <tr>
+      <th>319</th>
+      <td>Model_X6 M</td>
+      <td>2.473772e+08</td>
+    </tr>
+    <tr>
+      <th>320</th>
+      <td>Model_Yaris</td>
+      <td>-1.183538e+09</td>
+    </tr>
+    <tr>
+      <th>321</th>
+      <td>Model_Z3</td>
+      <td>2.473772e+08</td>
+    </tr>
+    <tr>
+      <th>322</th>
+      <td>Model_Z4</td>
+      <td>2.473772e+08</td>
+    </tr>
+  </tbody>
+</table>
+<p>323 rows × 2 columns</p>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[42]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reg_summary</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[42]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Features</th>
+      <th>Weights</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>Mileage</td>
+      <td>-7.111683e-04</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>EngineV</td>
+      <td>-2.359764e-03</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Year</td>
+      <td>8.687923e-02</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Brand_BMW</td>
+      <td>2.450878e+07</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Brand_Mercedes-Benz</td>
+      <td>2.450878e+07</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>Brand_Mitsubishi</td>
+      <td>3.354601e+08</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>Brand_Renault</td>
+      <td>2.301998e+09</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>Brand_Toyota</td>
+      <td>1.455424e+09</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>Brand_Volkswagen</td>
+      <td>4.622451e+08</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>Body_hatch</td>
+      <td>9.662559e-03</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>Body_other</td>
+      <td>1.235474e-02</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>Body_sedan</td>
+      <td>-1.066186e-01</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>Body_vagon</td>
+      <td>-1.315907e-01</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>Body_van</td>
+      <td>-5.925269e-02</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>Engine Type_Gas</td>
+      <td>-9.579407e-02</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>Engine Type_Other</td>
+      <td>-4.743644e-02</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>Engine Type_Petrol</td>
+      <td>-6.144038e-02</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>Registration_yes</td>
+      <td>8.893923e-01</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>Model_100</td>
+      <td>2.718860e+08</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>Model_11</td>
+      <td>-7.189966e+09</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[43]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check the different categories in the 'Brand' variable</span>
+<span class="n">data</span><span class="p">[</span><span class="s1">'Brand'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+<span class="c1"># In this way we can see which 'Brand' is actually the benchmark</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[43]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([&#39;BMW&#39;, &#39;Mercedes-Benz&#39;, &#39;Audi&#39;, &#39;Toyota&#39;, &#39;Renault&#39;, &#39;Volkswagen&#39;,
+       &#39;Mitsubishi&#39;], dtype=object)</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[55]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">brands</span><span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'Brand'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+<span class="n">bodies</span><span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'Body'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+<span class="n">engine_types</span><span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'Engine Type'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+<span class="n">registrations</span><span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'Registration'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+<span class="n">dummy_values</span><span class="o">=</span><span class="p">[</span><span class="n">brands</span><span class="p">,</span><span class="n">bodies</span><span class="p">,</span><span class="n">engine_types</span><span class="p">,</span><span class="n">registrations</span><span class="p">]</span>
+
+<span class="n">dummy_strings</span><span class="o">=</span><span class="p">[</span><span class="s1">'Brand'</span><span class="p">,</span><span class="s1">'Body'</span><span class="p">,</span><span class="s1">'Engine Type'</span><span class="p">,</span><span class="s1">'Registration'</span><span class="p">]</span>
+
+<span class="n">dummy_cols</span><span class="o">=</span> <span class="n">data_preprocessed</span><span class="o">.</span><span class="n">columns</span>
+
+
+
+<span class="k">for</span> <span class="n">vals</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">dummy_values</span><span class="p">,</span><span class="n">dummy_strings</span><span class="p">):</span>
+
+    <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">vals</span><span class="p">:</span>
+
+        <span class="n">found</span><span class="o">=</span><span class="kc">False</span>
+
+        <span class="k">for</span> <span class="n">dummy_col</span> <span class="ow">in</span> <span class="n">dummy_cols</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">dummy_col</span><span class="o">==</span><span class="n">label</span> <span class="o">+</span> <span class="s1">'_'</span> <span class="o">+</span> <span class="n">val</span><span class="p">:</span>
+
+                <span class="n">found</span><span class="o">=</span><span class="kc">True</span>
+                <span class="k">break</span>  <span class="c1"># exit the inner loop when a match is found</span>
+
+        <span class="k">if</span> <span class="n">found</span><span class="o">==</span><span class="kc">False</span><span class="p">:</span>
+
+            <span class="nb">print</span><span class="p">(</span><span class="n">label</span> <span class="o">+</span><span class="s1">' missing:'</span><span class="p">,</span><span class="n">val</span><span class="p">)</span>
+            
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Brand missing: Audi
+Body missing: crossover
+Engine Type missing: Diesel
+Registration missing: no
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Testing">Testing<a class="anchor-link" href="#Testing">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[45]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Once we have trained and fine-tuned our model, we can proceed to testing it</span>
+<span class="c1"># Testing is done on a dataset that the algorithm has never seen</span>
+<span class="c1"># Luckily we have prepared such a dataset</span>
+<span class="c1"># Our test inputs are 'x_test', while the outputs: 'y_test' </span>
+<span class="c1"># We SHOULD NOT TRAIN THE MODEL ON THEM, we just feed them and find the predictions</span>
+<span class="c1"># If the predictions are far off, we will know that our model overfitted</span>
+<span class="n">y_hat_test</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[46]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a scatter plot with the test targets and the test predictions</span>
+<span class="c1"># You can include the argument 'alpha' which will introduce opacity to the graph</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_hat_test</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Target(y_test)'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span> <span class="mi">18</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Prediction(y_hat_test'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span> <span class="mi">18</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">13</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mf">6.13</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[47]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Finally, let's manually check these predictions</span>
+<span class="c1"># To obtain the actual prices, we take the exponential of the log_price</span>
+<span class="n">df_pf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">y_hat_test</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'Prediction'</span><span class="p">])</span>
+<span class="n">df_pf</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>C:\Users\Chizy\AppData\Local\Temp\ipykernel_2108\2906630971.py:3: RuntimeWarning: overflow encountered in exp
+  df_pf = pd.DataFrame(np.exp(y_hat_test), columns=[&#39;Prediction&#39;])
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[47]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Prediction</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>30228.855333</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>9383.112639</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>6003.067434</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>8074.467696</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>11280.346946</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[48]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># We can also include the test targets in that data frame (so we can manually compare them)</span>
+<span class="n">df_pf</span><span class="p">[</span><span class="s1">'Target'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span>
+<span class="n">df_pf</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+<span class="c1"># Note that we have a lot of missing values</span>
+<span class="c1"># There is no reason to have ANY missing values, though</span>
+<span class="c1"># This suggests that something is wrong with the data frame / indexing</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[48]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Prediction</th>
+      <th>Target</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>30228.855333</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>9383.112639</td>
+      <td>7900.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>6003.067434</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>8074.467696</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>11280.346946</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[49]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># After displaying y_test, we find what the issue is</span>
+<span class="c1"># The old indexes are preserved</span>
+<span class="c1"># Therefore, to get a proper result, we must reset the index and drop the old indexing</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">y_test</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[49]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>0    10.257659
+1     9.093807
+2     8.787220
+3     8.985946
+4     9.323669
+Name: log_price, dtype: float64</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[50]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Let's overwrite the 'Target' column with the appropriate values</span>
+<span class="c1"># Again, we need the exponential of the test log price</span>
+<span class="n">df_pf</span><span class="p">[</span><span class="s1">'Target'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span>
+<span class="n">df_pf</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[50]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Prediction</th>
+      <th>Target</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>30228.855333</td>
+      <td>28500.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>9383.112639</td>
+      <td>8900.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>6003.067434</td>
+      <td>6550.0</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>8074.467696</td>
+      <td>7990.0</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>11280.346946</td>
+      <td>11200.0</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[51]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Additionally, we can calculate the difference between the targets and the predictions</span>
+<span class="c1"># Note that this is actually the residual (we already plotted the residuals)</span>
+<span class="n">df_pf</span><span class="p">[</span><span class="s1">'Residual'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pf</span><span class="p">[</span><span class="s1">'Target'</span><span class="p">]</span> <span class="o">-</span> <span class="n">df_pf</span><span class="p">[</span><span class="s1">'Prediction'</span><span class="p">]</span>
+<span class="c1"># Since OLS is basically an algorithm which minimizes the total sum of squared errors (residuals),</span>
+<span class="c1"># this comparison makes a lot of sense</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[52]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Finally, it makes sense to see how far off we are from the result percentage-wise</span>
+<span class="c1"># Here, we take the absolute difference in %, so we can easily order the data frame</span>
+<span class="n">df_pf</span><span class="p">[</span><span class="s1">'Difference%'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">absolute</span><span class="p">(</span><span class="n">df_pf</span><span class="p">[</span><span class="s1">'Residual'</span><span class="p">]</span><span class="o">/</span><span class="n">df_pf</span><span class="p">[</span><span class="s1">'Target'</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">df_pf</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[52]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Prediction</th>
+      <th>Target</th>
+      <th>Residual</th>
+      <th>Difference%</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>30228.855333</td>
+      <td>28500.0</td>
+      <td>-1728.855333</td>
+      <td>6.066159</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>9383.112639</td>
+      <td>8900.0</td>
+      <td>-483.112639</td>
+      <td>5.428232</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>6003.067434</td>
+      <td>6550.0</td>
+      <td>546.932566</td>
+      <td>8.350116</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>8074.467696</td>
+      <td>7990.0</td>
+      <td>-84.467696</td>
+      <td>1.057168</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>11280.346946</td>
+      <td>11200.0</td>
+      <td>-80.346946</td>
+      <td>0.717383</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>4280.485476</td>
+      <td>4600.0</td>
+      <td>319.514524</td>
+      <td>6.945968</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>11427.087287</td>
+      <td>8350.0</td>
+      <td>-3077.087287</td>
+      <td>36.851345</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>2895.962467</td>
+      <td>3300.0</td>
+      <td>404.037533</td>
+      <td>12.243562</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>39228.722376</td>
+      <td>43500.0</td>
+      <td>4271.277624</td>
+      <td>9.819029</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>22173.636762</td>
+      <td>25500.0</td>
+      <td>3326.363238</td>
+      <td>13.044562</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>4899.198732</td>
+      <td>5200.0</td>
+      <td>300.801268</td>
+      <td>5.784640</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>13110.051878</td>
+      <td>15300.0</td>
+      <td>2189.948122</td>
+      <td>14.313386</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>20551.654463</td>
+      <td>22500.0</td>
+      <td>1948.345537</td>
+      <td>8.659313</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>13015.274654</td>
+      <td>13500.0</td>
+      <td>484.725346</td>
+      <td>3.590558</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>2718.022542</td>
+      <td>2600.0</td>
+      <td>-118.022542</td>
+      <td>4.539329</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>3900.793694</td>
+      <td>2900.0</td>
+      <td>-1000.793694</td>
+      <td>34.510127</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>10857.592502</td>
+      <td>9900.0</td>
+      <td>-957.592502</td>
+      <td>9.672652</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>4330.566126</td>
+      <td>4800.0</td>
+      <td>469.433874</td>
+      <td>9.779872</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>10710.004512</td>
+      <td>13100.0</td>
+      <td>2389.995488</td>
+      <td>18.244240</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>9723.514589</td>
+      <td>12650.0</td>
+      <td>2926.485411</td>
+      <td>23.134272</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[53]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_pf</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>c:\Users\Chizy\Desktop\MyContent\Udemy DataScience Workbook\venv\Lib\site-packages\pandas\core\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract
+  sqr = _ensure_numeric((avg - values) ** 2)
+c:\Users\Chizy\Desktop\MyContent\Udemy DataScience Workbook\venv\Lib\site-packages\pandas\core\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract
+  sqr = _ensure_numeric((avg - values) ** 2)
+c:\Users\Chizy\Desktop\MyContent\Udemy DataScience Workbook\venv\Lib\site-packages\pandas\core\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract
+  sqr = _ensure_numeric((avg - values) ** 2)
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[53]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Prediction</th>
+      <th>Target</th>
+      <th>Residual</th>
+      <th>Difference%</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>8.050000e+02</td>
+      <td>805.000000</td>
+      <td>8.050000e+02</td>
+      <td>805.000000</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>inf</td>
+      <td>18297.659888</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>NaN</td>
+      <td>20081.200958</td>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>0.000000e+00</td>
+      <td>1150.000000</td>
+      <td>-inf</td>
+      <td>0.072466</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>6.702651e+03</td>
+      <td>6999.000000</td>
+      <td>-1.574397e+03</td>
+      <td>5.661504</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>1.194286e+04</td>
+      <td>11450.000000</td>
+      <td>-1.598741e+01</td>
+      <td>13.313279</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>2.270802e+04</td>
+      <td>22500.000000</td>
+      <td>1.351802e+03</td>
+      <td>24.983670</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>inf</td>
+      <td>199999.000000</td>
+      <td>7.291851e+04</td>
+      <td>inf</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[54]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Sometimes it is useful to check these outputs manually</span>
+<span class="c1"># To see all rows, we use the relevant pandas syntax</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">options</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">max_rows</span> <span class="o">=</span> <span class="mi">999</span>
+<span class="c1"># Moreover, to make the dataset clear, we can display the result with only 2 digits after the dot </span>
+
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">'display.float_format'</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'</span><span class="si">%.2f</span><span class="s1">'</span> <span class="o">%</span><span class="k">x</span>)
+<span class="c1"># Finally, we sort by difference in % and manually check the model</span>
+<span class="n">df_pf</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">'Difference%'</span><span class="p">])</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[54]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Prediction</th>
+      <th>Target</th>
+      <th>Residual</th>
+      <th>Difference%</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>681</th>
+      <td>15211.01</td>
+      <td>15200.00</td>
+      <td>-11.01</td>
+      <td>0.07</td>
+    </tr>
+    <tr>
+      <th>700</th>
+      <td>6305.83</td>
+      <td>6300.00</td>
+      <td>-5.83</td>
+      <td>0.09</td>
+    </tr>
+    <tr>
+      <th>540</th>
+      <td>6706.65</td>
+      <td>6700.00</td>
+      <td>-6.65</td>
+      <td>0.10</td>
+    </tr>
+    <tr>
+      <th>399</th>
+      <td>12486.63</td>
+      <td>12500.00</td>
+      <td>13.37</td>
+      <td>0.11</td>
+    </tr>
+    <tr>
+      <th>374</th>
+      <td>8715.33</td>
+      <td>8700.00</td>
+      <td>-15.33</td>
+      <td>0.18</td>
+    </tr>
+    <tr>
+      <th>74</th>
+      <td>3206.26</td>
+      <td>3200.00</td>
+      <td>-6.26</td>
+      <td>0.20</td>
+    </tr>
+    <tr>
+      <th>49</th>
+      <td>6513.15</td>
+      <td>6500.00</td>
+      <td>-13.15</td>
+      <td>0.20</td>
+    </tr>
+    <tr>
+      <th>397</th>
+      <td>17863.45</td>
+      <td>17900.00</td>
+      <td>36.55</td>
+      <td>0.20</td>
+    </tr>
+    <tr>
+      <th>216</th>
+      <td>6715.99</td>
+      <td>6700.00</td>
+      <td>-15.99</td>
+      <td>0.24</td>
+    </tr>
+    <tr>
+      <th>725</th>
+      <td>8826.34</td>
+      <td>8800.00</td>
+      <td>-26.34</td>
+      <td>0.30</td>
+    </tr>
+    <tr>
+      <th>26</th>
+      <td>51340.32</td>
+      <td>51500.00</td>
+      <td>159.68</td>
+      <td>0.31</td>
+    </tr>
+    <tr>
+      <th>615</th>
+      <td>14051.69</td>
+      <td>14100.00</td>
+      <td>48.31</td>
+      <td>0.34</td>
+    </tr>
+    <tr>
+      <th>536</th>
+      <td>2107.62</td>
+      <td>2100.00</td>
+      <td>-7.62</td>
+      <td>0.36</td>
+    </tr>
+    <tr>
+      <th>413</th>
+      <td>5828.76</td>
+      <td>5850.00</td>
+      <td>21.24</td>
+      <td>0.36</td>
+    </tr>
+    <tr>
+      <th>27</th>
+      <td>12653.40</td>
+      <td>12700.00</td>
+      <td>46.60</td>
+      <td>0.37</td>
+    </tr>
+    <tr>
+      <th>714</th>
+      <td>12447.09</td>
+      <td>12500.00</td>
+      <td>52.91</td>
+      <td>0.42</td>
+    </tr>
+    <tr>
+      <th>193</th>
+      <td>2837.86</td>
+      <td>2850.00</td>
+      <td>12.14</td>
+      <td>0.43</td>
+    </tr>
+    <tr>
+      <th>53</th>
+      <td>13541.54</td>
+      <td>13600.00</td>
+      <td>58.46</td>
+      <td>0.43</td>
+    </tr>
+    <tr>
+      <th>757</th>
+      <td>7734.58</td>
+      <td>7700.00</td>
+      <td>-34.58</td>
+      <td>0.45</td>
+    </tr>
+    <tr>
+      <th>706</th>
+      <td>12836.82</td>
+      <td>12900.00</td>
+      <td>63.18</td>
+      <td>0.49</td>
+    </tr>
+    <tr>
+      <th>618</th>
+      <td>30492.98</td>
+      <td>30650.00</td>
+      <td>157.02</td>
+      <td>0.51</td>
+    </tr>
+    <tr>
+      <th>259</th>
+      <td>9551.54</td>
+      <td>9499.00</td>
+      <td>-52.54</td>
+      <td>0.55</td>
+    </tr>
+    <tr>
+      <th>417</th>
+      <td>14318.96</td>
+      <td>14400.00</td>
+      <td>81.04</td>
+      <td>0.56</td>
+    </tr>
+    <tr>
+      <th>781</th>
+      <td>7543.03</td>
+      <td>7500.00</td>
+      <td>-43.03</td>
+      <td>0.57</td>
+    </tr>
+    <tr>
+      <th>466</th>
+      <td>25045.23</td>
+      <td>24900.00</td>
+      <td>-145.23</td>
+      <td>0.58</td>
+    </tr>
+    <tr>
+      <th>250</th>
+      <td>6538.30</td>
+      <td>6500.00</td>
+      <td>-38.30</td>
+      <td>0.59</td>
+    </tr>
+    <tr>
+      <th>448</th>
+      <td>109346.97</td>
+      <td>110000.00</td>
+      <td>653.03</td>
+      <td>0.59</td>
+    </tr>
+    <tr>
+      <th>461</th>
+      <td>5633.89</td>
+      <td>5600.00</td>
+      <td>-33.89</td>
+      <td>0.61</td>
+    </tr>
+    <tr>
+      <th>525</th>
+      <td>7352.50</td>
+      <td>7400.00</td>
+      <td>47.50</td>
+      <td>0.64</td>
+    </tr>
+    <tr>
+      <th>96</th>
+      <td>15607.46</td>
+      <td>15499.00</td>
+      <td>-108.46</td>
+      <td>0.70</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>11280.35</td>
+      <td>11200.00</td>
+      <td>-80.35</td>
+      <td>0.72</td>
+    </tr>
+    <tr>
+      <th>463</th>
+      <td>11077.66</td>
+      <td>10990.00</td>
+      <td>-87.66</td>
+      <td>0.80</td>
+    </tr>
+    <tr>
+      <th>801</th>
+      <td>39657.24</td>
+      <td>39999.00</td>
+      <td>341.76</td>
+      <td>0.85</td>
+    </tr>
+    <tr>
+      <th>298</th>
+      <td>23106.56</td>
+      <td>22900.00</td>
+      <td>-206.56</td>
+      <td>0.90</td>
+    </tr>
+    <tr>
+      <th>256</th>
+      <td>2219.87</td>
+      <td>2200.00</td>
+      <td>-19.87</td>
+      <td>0.90</td>
+    </tr>
+    <tr>
+      <th>344</th>
+      <td>25027.42</td>
+      <td>24800.00</td>
+      <td>-227.42</td>
+      <td>0.92</td>
+    </tr>
+    <tr>
+      <th>79</th>
+      <td>27755.46</td>
+      <td>27500.00</td>
+      <td>-255.46</td>
+      <td>0.93</td>
+    </tr>
+    <tr>
+      <th>471</th>
+      <td>20195.56</td>
+      <td>20000.00</td>
+      <td>-195.56</td>
+      <td>0.98</td>
+    </tr>
+    <tr>
+      <th>292</th>
+      <td>7978.67</td>
+      <td>7900.00</td>
+      <td>-78.67</td>
+      <td>1.00</td>
+    </tr>
+    <tr>
+      <th>733</th>
+      <td>9897.28</td>
+      <td>9999.00</td>
+      <td>101.72</td>
+      <td>1.02</td>
+    </tr>
+    <tr>
+      <th>21</th>
+      <td>23557.83</td>
+      <td>23800.00</td>
+      <td>242.17</td>
+      <td>1.02</td>
+    </tr>
+    <tr>
+      <th>521</th>
+      <td>8182.71</td>
+      <td>8100.00</td>
+      <td>-82.71</td>
+      <td>1.02</td>
+    </tr>
+    <tr>
+      <th>98</th>
+      <td>10094.92</td>
+      <td>10200.00</td>
+      <td>105.08</td>
+      <td>1.03</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>8074.47</td>
+      <td>7990.00</td>
+      <td>-84.47</td>
+      <td>1.06</td>
+    </tr>
+    <tr>
+      <th>546</th>
+      <td>16675.21</td>
+      <td>16500.00</td>
+      <td>-175.21</td>
+      <td>1.06</td>
+    </tr>
+    <tr>
+      <th>224</th>
+      <td>7883.21</td>
+      <td>7800.00</td>
+      <td>-83.21</td>
+      <td>1.07</td>
+    </tr>
+    <tr>
+      <th>59</th>
+      <td>9394.45</td>
+      <td>9500.00</td>
+      <td>105.55</td>
+      <td>1.11</td>
+    </tr>
+    <tr>
+      <th>324</th>
+      <td>17194.49</td>
+      <td>16999.00</td>
+      <td>-195.49</td>
+      <td>1.15</td>
+    </tr>
+    <tr>
+      <th>416</th>
+      <td>7407.42</td>
+      <td>7500.00</td>
+      <td>92.58</td>
+      <td>1.23</td>
+    </tr>
+    <tr>
+      <th>768</th>
+      <td>10863.14</td>
+      <td>10999.00</td>
+      <td>135.86</td>
+      <td>1.24</td>
+    </tr>
+    <tr>
+      <th>638</th>
+      <td>2227.46</td>
+      <td>2200.00</td>
+      <td>-27.46</td>
+      <td>1.25</td>
+    </tr>
+    <tr>
+      <th>231</th>
+      <td>109352.13</td>
+      <td>107999.00</td>
+      <td>-1353.13</td>
+      <td>1.25</td>
+    </tr>
+    <tr>
+      <th>673</th>
+      <td>9014.15</td>
+      <td>8900.00</td>
+      <td>-114.15</td>
+      <td>1.28</td>
+    </tr>
+    <tr>
+      <th>544</th>
+      <td>64649.72</td>
+      <td>65500.00</td>
+      <td>850.28</td>
+      <td>1.30</td>
+    </tr>
+    <tr>
+      <th>326</th>
+      <td>6807.32</td>
+      <td>6900.00</td>
+      <td>92.68</td>
+      <td>1.34</td>
+    </tr>
+    <tr>
+      <th>418</th>
+      <td>11436.89</td>
+      <td>11600.00</td>
+      <td>163.11</td>
+      <td>1.41</td>
+    </tr>
+    <tr>
+      <th>688</th>
+      <td>2464.71</td>
+      <td>2500.00</td>
+      <td>35.29</td>
+      <td>1.41</td>
+    </tr>
+    <tr>
+      <th>720</th>
+      <td>9947.38</td>
+      <td>9800.00</td>
+      <td>-147.38</td>
+      <td>1.50</td>
+    </tr>
+    <tr>
+      <th>797</th>
+      <td>33474.09</td>
+      <td>34000.00</td>
+      <td>525.91</td>
+      <td>1.55</td>
+    </tr>
+    <tr>
+      <th>360</th>
+      <td>21559.42</td>
+      <td>21900.00</td>
+      <td>340.58</td>
+      <td>1.56</td>
+    </tr>
+    <tr>
+      <th>562</th>
+      <td>9104.53</td>
+      <td>9250.00</td>
+      <td>145.47</td>
+      <td>1.57</td>
+    </tr>
+    <tr>
+      <th>503</th>
+      <td>12087.91</td>
+      <td>11900.00</td>
+      <td>-187.91</td>
+      <td>1.58</td>
+    </tr>
+    <tr>
+      <th>384</th>
+      <td>8939.11</td>
+      <td>8800.00</td>
+      <td>-139.11</td>
+      <td>1.58</td>
+    </tr>
+    <tr>
+      <th>713</th>
+      <td>35453.02</td>
+      <td>34899.14</td>
+      <td>-553.88</td>
+      <td>1.59</td>
+    </tr>
+    <tr>
+      <th>257</th>
+      <td>34436.11</td>
+      <td>34999.00</td>
+      <td>562.89</td>
+      <td>1.61</td>
+    </tr>
+    <tr>
+      <th>272</th>
+      <td>2897.71</td>
+      <td>2850.00</td>
+      <td>-47.71</td>
+      <td>1.67</td>
+    </tr>
+    <tr>
+      <th>671</th>
+      <td>3440.49</td>
+      <td>3500.00</td>
+      <td>59.51</td>
+      <td>1.70</td>
+    </tr>
+    <tr>
+      <th>735</th>
+      <td>33320.06</td>
+      <td>33900.00</td>
+      <td>579.94</td>
+      <td>1.71</td>
+    </tr>
+    <tr>
+      <th>784</th>
+      <td>18817.25</td>
+      <td>18500.00</td>
+      <td>-317.25</td>
+      <td>1.71</td>
+    </tr>
+    <tr>
+      <th>697</th>
+      <td>13857.09</td>
+      <td>14100.00</td>
+      <td>242.91</td>
+      <td>1.72</td>
+    </tr>
+    <tr>
+      <th>381</th>
+      <td>20040.20</td>
+      <td>19700.00</td>
+      <td>-340.20</td>
+      <td>1.73</td>
+    </tr>
+    <tr>
+      <th>565</th>
+      <td>35262.29</td>
+      <td>35900.00</td>
+      <td>637.71</td>
+      <td>1.78</td>
+    </tr>
+    <tr>
+      <th>278</th>
+      <td>24059.43</td>
+      <td>24500.00</td>
+      <td>440.57</td>
+      <td>1.80</td>
+    </tr>
+    <tr>
+      <th>576</th>
+      <td>64649.72</td>
+      <td>63500.00</td>
+      <td>-1149.72</td>
+      <td>1.81</td>
+    </tr>
+    <tr>
+      <th>363</th>
+      <td>19627.25</td>
+      <td>19990.00</td>
+      <td>362.75</td>
+      <td>1.81</td>
+    </tr>
+    <tr>
+      <th>484</th>
+      <td>26993.16</td>
+      <td>27500.00</td>
+      <td>506.84</td>
+      <td>1.84</td>
+    </tr>
+    <tr>
+      <th>213</th>
+      <td>32584.87</td>
+      <td>31990.00</td>
+      <td>-594.87</td>
+      <td>1.86</td>
+    </tr>
+    <tr>
+      <th>632</th>
+      <td>3056.57</td>
+      <td>3000.00</td>
+      <td>-56.57</td>
+      <td>1.89</td>
+    </tr>
+    <tr>
+      <th>509</th>
+      <td>98085.79</td>
+      <td>99999.00</td>
+      <td>1913.21</td>
+      <td>1.91</td>
+    </tr>
+    <tr>
+      <th>336</th>
+      <td>19170.78</td>
+      <td>18800.00</td>
+      <td>-370.78</td>
+      <td>1.97</td>
+    </tr>
+    <tr>
+      <th>166</th>
+      <td>10782.03</td>
+      <td>10999.00</td>
+      <td>216.97</td>
+      <td>1.97</td>
+    </tr>
+    <tr>
+      <th>480</th>
+      <td>17047.74</td>
+      <td>17394.09</td>
+      <td>346.35</td>
+      <td>1.99</td>
+    </tr>
+    <tr>
+      <th>165</th>
+      <td>1958.31</td>
+      <td>1999.00</td>
+      <td>40.69</td>
+      <td>2.04</td>
+    </tr>
+    <tr>
+      <th>660</th>
+      <td>29500.27</td>
+      <td>28900.00</td>
+      <td>-600.27</td>
+      <td>2.08</td>
+    </tr>
+    <tr>
+      <th>712</th>
+      <td>23990.17</td>
+      <td>24500.00</td>
+      <td>509.83</td>
+      <td>2.08</td>
+    </tr>
+    <tr>
+      <th>422</th>
+      <td>30909.33</td>
+      <td>31600.00</td>
+      <td>690.67</td>
+      <td>2.19</td>
+    </tr>
+    <tr>
+      <th>455</th>
+      <td>11959.28</td>
+      <td>11700.00</td>
+      <td>-259.28</td>
+      <td>2.22</td>
+    </tr>
+    <tr>
+      <th>653</th>
+      <td>11042.29</td>
+      <td>10799.00</td>
+      <td>-243.29</td>
+      <td>2.25</td>
+    </tr>
+    <tr>
+      <th>800</th>
+      <td>56673.17</td>
+      <td>58000.00</td>
+      <td>1326.83</td>
+      <td>2.29</td>
+    </tr>
+    <tr>
+      <th>212</th>
+      <td>43942.54</td>
+      <td>45000.00</td>
+      <td>1057.46</td>
+      <td>2.35</td>
+    </tr>
+    <tr>
+      <th>58</th>
+      <td>11224.40</td>
+      <td>11500.00</td>
+      <td>275.60</td>
+      <td>2.40</td>
+    </tr>
+    <tr>
+      <th>230</th>
+      <td>12978.30</td>
+      <td>13300.00</td>
+      <td>321.70</td>
+      <td>2.42</td>
+    </tr>
+    <tr>
+      <th>528</th>
+      <td>9166.68</td>
+      <td>9400.00</td>
+      <td>233.32</td>
+      <td>2.48</td>
+    </tr>
+    <tr>
+      <th>260</th>
+      <td>8478.83</td>
+      <td>8700.00</td>
+      <td>221.17</td>
+      <td>2.54</td>
+    </tr>
+    <tr>
+      <th>232</th>
+      <td>11184.55</td>
+      <td>10900.00</td>
+      <td>-284.55</td>
+      <td>2.61</td>
+    </tr>
+    <tr>
+      <th>130</th>
+      <td>8524.29</td>
+      <td>8300.00</td>
+      <td>-224.29</td>
+      <td>2.70</td>
+    </tr>
+    <tr>
+      <th>684</th>
+      <td>14494.88</td>
+      <td>14900.00</td>
+      <td>405.12</td>
+      <td>2.72</td>
+    </tr>
+    <tr>
+      <th>582</th>
+      <td>37938.31</td>
+      <td>39000.00</td>
+      <td>1061.69</td>
+      <td>2.72</td>
+    </tr>
+    <tr>
+      <th>663</th>
+      <td>18384.89</td>
+      <td>18900.00</td>
+      <td>515.11</td>
+      <td>2.73</td>
+    </tr>
+    <tr>
+      <th>287</th>
+      <td>23828.27</td>
+      <td>24500.00</td>
+      <td>671.73</td>
+      <td>2.74</td>
+    </tr>
+    <tr>
+      <th>376</th>
+      <td>11570.15</td>
+      <td>11900.00</td>
+      <td>329.85</td>
+      <td>2.77</td>
+    </tr>
+    <tr>
+      <th>532</th>
+      <td>25592.54</td>
+      <td>24900.00</td>
+      <td>-692.54</td>
+      <td>2.78</td>
+    </tr>
+    <tr>
+      <th>67</th>
+      <td>2004.72</td>
+      <td>1950.00</td>
+      <td>-54.72</td>
+      <td>2.81</td>
+    </tr>
+    <tr>
+      <th>211</th>
+      <td>6374.72</td>
+      <td>6200.00</td>
+      <td>-174.72</td>
+      <td>2.82</td>
+    </tr>
+    <tr>
+      <th>449</th>
+      <td>194242.37</td>
+      <td>199999.00</td>
+      <td>5756.63</td>
+      <td>2.88</td>
+    </tr>
+    <tr>
+      <th>76</th>
+      <td>58649.23</td>
+      <td>57000.00</td>
+      <td>-1649.23</td>
+      <td>2.89</td>
+    </tr>
+    <tr>
+      <th>274</th>
+      <td>3398.26</td>
+      <td>3500.00</td>
+      <td>101.74</td>
+      <td>2.91</td>
+    </tr>
+    <tr>
+      <th>711</th>
+      <td>12864.69</td>
+      <td>12500.00</td>
+      <td>-364.69</td>
+      <td>2.92</td>
+    </tr>
+    <tr>
+      <th>267</th>
+      <td>64695.71</td>
+      <td>66666.00</td>
+      <td>1970.29</td>
+      <td>2.96</td>
+    </tr>
+    <tr>
+      <th>284</th>
+      <td>29563.28</td>
+      <td>28700.00</td>
+      <td>-863.28</td>
+      <td>3.01</td>
+    </tr>
+    <tr>
+      <th>726</th>
+      <td>6787.74</td>
+      <td>6999.00</td>
+      <td>211.26</td>
+      <td>3.02</td>
+    </tr>
+    <tr>
+      <th>308</th>
+      <td>5769.23</td>
+      <td>5600.00</td>
+      <td>-169.23</td>
+      <td>3.02</td>
+    </tr>
+    <tr>
+      <th>762</th>
+      <td>23184.61</td>
+      <td>22500.00</td>
+      <td>-684.61</td>
+      <td>3.04</td>
+    </tr>
+    <tr>
+      <th>769</th>
+      <td>10824.58</td>
+      <td>10500.00</td>
+      <td>-324.58</td>
+      <td>3.09</td>
+    </tr>
+    <tr>
+      <th>566</th>
+      <td>22372.19</td>
+      <td>21700.00</td>
+      <td>-672.19</td>
+      <td>3.10</td>
+    </tr>
+    <tr>
+      <th>575</th>
+      <td>15019.12</td>
+      <td>15500.00</td>
+      <td>480.88</td>
+      <td>3.10</td>
+    </tr>
+    <tr>
+      <th>354</th>
+      <td>10058.12</td>
+      <td>9750.00</td>
+      <td>-308.12</td>
+      <td>3.16</td>
+    </tr>
+    <tr>
+      <th>36</th>
+      <td>8769.20</td>
+      <td>8500.00</td>
+      <td>-269.20</td>
+      <td>3.17</td>
+    </tr>
+    <tr>
+      <th>708</th>
+      <td>10844.83</td>
+      <td>11200.00</td>
+      <td>355.17</td>
+      <td>3.17</td>
+    </tr>
+    <tr>
+      <th>742</th>
+      <td>20522.18</td>
+      <td>21200.00</td>
+      <td>677.82</td>
+      <td>3.20</td>
+    </tr>
+    <tr>
+      <th>157</th>
+      <td>23223.72</td>
+      <td>22500.00</td>
+      <td>-723.72</td>
+      <td>3.22</td>
+    </tr>
+    <tr>
+      <th>537</th>
+      <td>1934.64</td>
+      <td>1999.00</td>
+      <td>64.36</td>
+      <td>3.22</td>
+    </tr>
+    <tr>
+      <th>61</th>
+      <td>11770.33</td>
+      <td>11400.00</td>
+      <td>-370.33</td>
+      <td>3.25</td>
+    </tr>
+    <tr>
+      <th>190</th>
+      <td>43881.06</td>
+      <td>42500.00</td>
+      <td>-1381.06</td>
+      <td>3.25</td>
+    </tr>
+    <tr>
+      <th>489</th>
+      <td>17450.09</td>
+      <td>16900.00</td>
+      <td>-550.09</td>
+      <td>3.25</td>
+    </tr>
+    <tr>
+      <th>285</th>
+      <td>23341.27</td>
+      <td>22600.00</td>
+      <td>-741.27</td>
+      <td>3.28</td>
+    </tr>
+    <tr>
+      <th>514</th>
+      <td>10919.19</td>
+      <td>11300.00</td>
+      <td>380.81</td>
+      <td>3.37</td>
+    </tr>
+    <tr>
+      <th>659</th>
+      <td>21192.26</td>
+      <td>20500.00</td>
+      <td>-692.26</td>
+      <td>3.38</td>
+    </tr>
+    <tr>
+      <th>592</th>
+      <td>12749.66</td>
+      <td>13200.00</td>
+      <td>450.34</td>
+      <td>3.41</td>
+    </tr>
+    <tr>
+      <th>262</th>
+      <td>20283.08</td>
+      <td>21000.00</td>
+      <td>716.92</td>
+      <td>3.41</td>
+    </tr>
+    <tr>
+      <th>419</th>
+      <td>16201.55</td>
+      <td>16800.00</td>
+      <td>598.45</td>
+      <td>3.56</td>
+    </tr>
+    <tr>
+      <th>555</th>
+      <td>7974.91</td>
+      <td>7699.00</td>
+      <td>-275.91</td>
+      <td>3.58</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>13015.27</td>
+      <td>13500.00</td>
+      <td>484.73</td>
+      <td>3.59</td>
+    </tr>
+    <tr>
+      <th>247</th>
+      <td>6890.35</td>
+      <td>7150.00</td>
+      <td>259.65</td>
+      <td>3.63</td>
+    </tr>
+    <tr>
+      <th>557</th>
+      <td>5483.10</td>
+      <td>5700.00</td>
+      <td>216.90</td>
+      <td>3.81</td>
+    </tr>
+    <tr>
+      <th>585</th>
+      <td>20200.76</td>
+      <td>21000.00</td>
+      <td>799.24</td>
+      <td>3.81</td>
+    </tr>
+    <tr>
+      <th>332</th>
+      <td>13917.01</td>
+      <td>14474.00</td>
+      <td>556.99</td>
+      <td>3.85</td>
+    </tr>
+    <tr>
+      <th>60</th>
+      <td>11016.60</td>
+      <td>10600.00</td>
+      <td>-416.60</td>
+      <td>3.93</td>
+    </tr>
+    <tr>
+      <th>487</th>
+      <td>3840.88</td>
+      <td>3999.00</td>
+      <td>158.12</td>
+      <td>3.95</td>
+    </tr>
+    <tr>
+      <th>134</th>
+      <td>8524.71</td>
+      <td>8200.00</td>
+      <td>-324.71</td>
+      <td>3.96</td>
+    </tr>
+    <tr>
+      <th>745</th>
+      <td>11551.56</td>
+      <td>11111.00</td>
+      <td>-440.56</td>
+      <td>3.97</td>
+    </tr>
+    <tr>
+      <th>759</th>
+      <td>17687.48</td>
+      <td>16999.00</td>
+      <td>-688.48</td>
+      <td>4.05</td>
+    </tr>
+    <tr>
+      <th>482</th>
+      <td>33095.36</td>
+      <td>34500.00</td>
+      <td>1404.64</td>
+      <td>4.07</td>
+    </tr>
+    <tr>
+      <th>655</th>
+      <td>26864.09</td>
+      <td>25800.00</td>
+      <td>-1064.09</td>
+      <td>4.12</td>
+    </tr>
+    <tr>
+      <th>385</th>
+      <td>27698.29</td>
+      <td>28900.00</td>
+      <td>1201.71</td>
+      <td>4.16</td>
+    </tr>
+    <tr>
+      <th>590</th>
+      <td>31149.15</td>
+      <td>29900.00</td>
+      <td>-1249.15</td>
+      <td>4.18</td>
+    </tr>
+    <tr>
+      <th>752</th>
+      <td>12454.14</td>
+      <td>13000.00</td>
+      <td>545.86</td>
+      <td>4.20</td>
+    </tr>
+    <tr>
+      <th>158</th>
+      <td>35396.30</td>
+      <td>33950.00</td>
+      <td>-1446.30</td>
+      <td>4.26</td>
+    </tr>
+    <tr>
+      <th>32</th>
+      <td>20103.15</td>
+      <td>21000.00</td>
+      <td>896.85</td>
+      <td>4.27</td>
+    </tr>
+    <tr>
+      <th>473</th>
+      <td>4170.89</td>
+      <td>4000.00</td>
+      <td>-170.89</td>
+      <td>4.27</td>
+    </tr>
+    <tr>
+      <th>239</th>
+      <td>64649.72</td>
+      <td>62000.00</td>
+      <td>-2649.72</td>
+      <td>4.27</td>
+    </tr>
+    <tr>
+      <th>276</th>
+      <td>11964.00</td>
+      <td>12500.00</td>
+      <td>536.00</td>
+      <td>4.29</td>
+    </tr>
+    <tr>
+      <th>196</th>
+      <td>25045.23</td>
+      <td>23999.00</td>
+      <td>-1046.23</td>
+      <td>4.36</td>
+    </tr>
+    <tr>
+      <th>798</th>
+      <td>3442.95</td>
+      <td>3600.00</td>
+      <td>157.05</td>
+      <td>4.36</td>
+    </tr>
+    <tr>
+      <th>411</th>
+      <td>29647.28</td>
+      <td>31000.00</td>
+      <td>1352.72</td>
+      <td>4.36</td>
+    </tr>
+    <tr>
+      <th>382</th>
+      <td>15012.39</td>
+      <td>15700.00</td>
+      <td>687.61</td>
+      <td>4.38</td>
+    </tr>
+    <tr>
+      <th>669</th>
+      <td>8143.20</td>
+      <td>7800.00</td>
+      <td>-343.20</td>
+      <td>4.40</td>
+    </tr>
+    <tr>
+      <th>787</th>
+      <td>25063.05</td>
+      <td>23999.00</td>
+      <td>-1064.05</td>
+      <td>4.43</td>
+    </tr>
+    <tr>
+      <th>462</th>
+      <td>12010.10</td>
+      <td>11500.00</td>
+      <td>-510.10</td>
+      <td>4.44</td>
+    </tr>
+    <tr>
+      <th>352</th>
+      <td>11176.69</td>
+      <td>10700.00</td>
+      <td>-476.69</td>
+      <td>4.46</td>
+    </tr>
+    <tr>
+      <th>440</th>
+      <td>17286.75</td>
+      <td>18100.00</td>
+      <td>813.25</td>
+      <td>4.49</td>
+    </tr>
+    <tr>
+      <th>564</th>
+      <td>34276.07</td>
+      <td>35900.00</td>
+      <td>1623.93</td>
+      <td>4.52</td>
+    </tr>
+    <tr>
+      <th>483</th>
+      <td>25088.99</td>
+      <td>24000.00</td>
+      <td>-1088.99</td>
+      <td>4.54</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>2718.02</td>
+      <td>2600.00</td>
+      <td>-118.02</td>
+      <td>4.54</td>
+    </tr>
+    <tr>
+      <th>389</th>
+      <td>3770.21</td>
+      <td>3950.00</td>
+      <td>179.79</td>
+      <td>4.55</td>
+    </tr>
+    <tr>
+      <th>95</th>
+      <td>53853.04</td>
+      <td>51500.00</td>
+      <td>-2353.04</td>
+      <td>4.57</td>
+    </tr>
+    <tr>
+      <th>476</th>
+      <td>10463.07</td>
+      <td>9999.00</td>
+      <td>-464.07</td>
+      <td>4.64</td>
+    </tr>
+    <tr>
+      <th>743</th>
+      <td>8008.22</td>
+      <td>8400.00</td>
+      <td>391.78</td>
+      <td>4.66</td>
+    </tr>
+    <tr>
+      <th>549</th>
+      <td>13401.92</td>
+      <td>12800.00</td>
+      <td>-601.92</td>
+      <td>4.70</td>
+    </tr>
+    <tr>
+      <th>90</th>
+      <td>13616.65</td>
+      <td>13000.00</td>
+      <td>-616.65</td>
+      <td>4.74</td>
+    </tr>
+    <tr>
+      <th>705</th>
+      <td>19805.25</td>
+      <td>20800.00</td>
+      <td>994.75</td>
+      <td>4.78</td>
+    </tr>
+    <tr>
+      <th>426</th>
+      <td>11005.68</td>
+      <td>10500.00</td>
+      <td>-505.68</td>
+      <td>4.82</td>
+    </tr>
+    <tr>
+      <th>732</th>
+      <td>7517.14</td>
+      <td>7900.00</td>
+      <td>382.86</td>
+      <td>4.85</td>
+    </tr>
+    <tr>
+      <th>573</th>
+      <td>17220.82</td>
+      <td>18100.00</td>
+      <td>879.18</td>
+      <td>4.86</td>
+    </tr>
+    <tr>
+      <th>372</th>
+      <td>2307.20</td>
+      <td>2200.00</td>
+      <td>-107.20</td>
+      <td>4.87</td>
+    </tr>
+    <tr>
+      <th>118</th>
+      <td>9130.01</td>
+      <td>9600.00</td>
+      <td>469.99</td>
+      <td>4.90</td>
+    </tr>
+    <tr>
+      <th>680</th>
+      <td>109346.97</td>
+      <td>114999.00</td>
+      <td>5652.03</td>
+      <td>4.91</td>
+    </tr>
+    <tr>
+      <th>583</th>
+      <td>4748.92</td>
+      <td>4999.00</td>
+      <td>250.08</td>
+      <td>5.00</td>
+    </tr>
+    <tr>
+      <th>755</th>
+      <td>2677.67</td>
+      <td>2550.00</td>
+      <td>-127.67</td>
+      <td>5.01</td>
+    </tr>
+    <tr>
+      <th>185</th>
+      <td>25642.46</td>
+      <td>26999.00</td>
+      <td>1356.54</td>
+      <td>5.02</td>
+    </tr>
+    <tr>
+      <th>624</th>
+      <td>11203.43</td>
+      <td>11800.00</td>
+      <td>596.57</td>
+      <td>5.06</td>
+    </tr>
+    <tr>
+      <th>424</th>
+      <td>3987.44</td>
+      <td>4200.00</td>
+      <td>212.56</td>
+      <td>5.06</td>
+    </tr>
+    <tr>
+      <th>526</th>
+      <td>6834.18</td>
+      <td>6500.00</td>
+      <td>-334.18</td>
+      <td>5.14</td>
+    </tr>
+    <tr>
+      <th>709</th>
+      <td>54169.36</td>
+      <td>51500.00</td>
+      <td>-2669.36</td>
+      <td>5.18</td>
+    </tr>
+    <tr>
+      <th>501</th>
+      <td>6540.19</td>
+      <td>6899.00</td>
+      <td>358.81</td>
+      <td>5.20</td>
+    </tr>
+    <tr>
+      <th>199</th>
+      <td>5996.99</td>
+      <td>5700.00</td>
+      <td>-296.99</td>
+      <td>5.21</td>
+    </tr>
+    <tr>
+      <th>65</th>
+      <td>19982.41</td>
+      <td>18990.00</td>
+      <td>-992.41</td>
+      <td>5.23</td>
+    </tr>
+    <tr>
+      <th>651</th>
+      <td>3001.45</td>
+      <td>2850.00</td>
+      <td>-151.45</td>
+      <td>5.31</td>
+    </tr>
+    <tr>
+      <th>227</th>
+      <td>8216.39</td>
+      <td>7800.00</td>
+      <td>-416.39</td>
+      <td>5.34</td>
+    </tr>
+    <tr>
+      <th>141</th>
+      <td>11638.91</td>
+      <td>12300.00</td>
+      <td>661.09</td>
+      <td>5.37</td>
+    </tr>
+    <tr>
+      <th>156</th>
+      <td>77117.34</td>
+      <td>81500.00</td>
+      <td>4382.66</td>
+      <td>5.38</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>9383.11</td>
+      <td>8900.00</td>
+      <td>-483.11</td>
+      <td>5.43</td>
+    </tr>
+    <tr>
+      <th>560</th>
+      <td>6961.07</td>
+      <td>6600.00</td>
+      <td>-361.07</td>
+      <td>5.47</td>
+    </tr>
+    <tr>
+      <th>229</th>
+      <td>27883.22</td>
+      <td>29500.00</td>
+      <td>1616.78</td>
+      <td>5.48</td>
+    </tr>
+    <tr>
+      <th>627</th>
+      <td>92087.91</td>
+      <td>97500.00</td>
+      <td>5412.09</td>
+      <td>5.55</td>
+    </tr>
+    <tr>
+      <th>117</th>
+      <td>14038.48</td>
+      <td>13300.00</td>
+      <td>-738.48</td>
+      <td>5.55</td>
+    </tr>
+    <tr>
+      <th>744</th>
+      <td>17416.25</td>
+      <td>16500.00</td>
+      <td>-916.25</td>
+      <td>5.55</td>
+    </tr>
+    <tr>
+      <th>802</th>
+      <td>12088.25</td>
+      <td>11450.00</td>
+      <td>-638.25</td>
+      <td>5.57</td>
+    </tr>
+    <tr>
+      <th>297</th>
+      <td>40127.52</td>
+      <td>42500.00</td>
+      <td>2372.48</td>
+      <td>5.58</td>
+    </tr>
+    <tr>
+      <th>664</th>
+      <td>16792.66</td>
+      <td>15900.00</td>
+      <td>-892.66</td>
+      <td>5.61</td>
+    </tr>
+    <tr>
+      <th>586</th>
+      <td>18167.73</td>
+      <td>17200.00</td>
+      <td>-967.73</td>
+      <td>5.63</td>
+    </tr>
+    <tr>
+      <th>68</th>
+      <td>27263.83</td>
+      <td>28900.00</td>
+      <td>1636.17</td>
+      <td>5.66</td>
+    </tr>
+    <tr>
+      <th>51</th>
+      <td>6555.59</td>
+      <td>6950.00</td>
+      <td>394.41</td>
+      <td>5.68</td>
+    </tr>
+    <tr>
+      <th>459</th>
+      <td>11415.57</td>
+      <td>10800.00</td>
+      <td>-615.57</td>
+      <td>5.70</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>4899.20</td>
+      <td>5200.00</td>
+      <td>300.80</td>
+      <td>5.78</td>
+    </tr>
+    <tr>
+      <th>630</th>
+      <td>11642.12</td>
+      <td>10999.00</td>
+      <td>-643.12</td>
+      <td>5.85</td>
+    </tr>
+    <tr>
+      <th>299</th>
+      <td>59312.24</td>
+      <td>63000.00</td>
+      <td>3687.76</td>
+      <td>5.85</td>
+    </tr>
+    <tr>
+      <th>654</th>
+      <td>5458.98</td>
+      <td>5800.00</td>
+      <td>341.02</td>
+      <td>5.88</td>
+    </tr>
+    <tr>
+      <th>445</th>
+      <td>10059.50</td>
+      <td>9500.00</td>
+      <td>-559.50</td>
+      <td>5.89</td>
+    </tr>
+    <tr>
+      <th>578</th>
+      <td>15882.42</td>
+      <td>14999.00</td>
+      <td>-883.42</td>
+      <td>5.89</td>
+    </tr>
+    <tr>
+      <th>609</th>
+      <td>6869.42</td>
+      <td>7300.00</td>
+      <td>430.58</td>
+      <td>5.90</td>
+    </tr>
+    <tr>
+      <th>465</th>
+      <td>9004.49</td>
+      <td>8500.00</td>
+      <td>-504.49</td>
+      <td>5.94</td>
+    </tr>
+    <tr>
+      <th>776</th>
+      <td>6112.31</td>
+      <td>6500.00</td>
+      <td>387.69</td>
+      <td>5.96</td>
+    </tr>
+    <tr>
+      <th>409</th>
+      <td>10057.03</td>
+      <td>10700.00</td>
+      <td>642.97</td>
+      <td>6.01</td>
+    </tr>
+    <tr>
+      <th>442</th>
+      <td>5450.83</td>
+      <td>5800.00</td>
+      <td>349.17</td>
+      <td>6.02</td>
+    </tr>
+    <tr>
+      <th>0</th>
+      <td>30228.86</td>
+      <td>28500.00</td>
+      <td>-1728.86</td>
+      <td>6.07</td>
+    </tr>
+    <tr>
+      <th>145</th>
+      <td>30536.34</td>
+      <td>28787.85</td>
+      <td>-1748.49</td>
+      <td>6.07</td>
+    </tr>
+    <tr>
+      <th>92</th>
+      <td>10987.90</td>
+      <td>11700.00</td>
+      <td>712.10</td>
+      <td>6.09</td>
+    </tr>
+    <tr>
+      <th>405</th>
+      <td>8262.86</td>
+      <td>8800.00</td>
+      <td>537.14</td>
+      <td>6.10</td>
+    </tr>
+    <tr>
+      <th>167</th>
+      <td>28542.74</td>
+      <td>26900.00</td>
+      <td>-1642.74</td>
+      <td>6.11</td>
+    </tr>
+    <tr>
+      <th>181</th>
+      <td>4886.77</td>
+      <td>4600.00</td>
+      <td>-286.77</td>
+      <td>6.23</td>
+    </tr>
+    <tr>
+      <th>541</th>
+      <td>10622.75</td>
+      <td>9999.00</td>
+      <td>-623.75</td>
+      <td>6.24</td>
+    </tr>
+    <tr>
+      <th>263</th>
+      <td>1967.13</td>
+      <td>2099.00</td>
+      <td>131.87</td>
+      <td>6.28</td>
+    </tr>
+    <tr>
+      <th>672</th>
+      <td>17804.56</td>
+      <td>19000.00</td>
+      <td>1195.44</td>
+      <td>6.29</td>
+    </tr>
+    <tr>
+      <th>152</th>
+      <td>5902.43</td>
+      <td>6300.00</td>
+      <td>397.57</td>
+      <td>6.31</td>
+    </tr>
+    <tr>
+      <th>225</th>
+      <td>58999.17</td>
+      <td>62999.00</td>
+      <td>3999.83</td>
+      <td>6.35</td>
+    </tr>
+    <tr>
+      <th>334</th>
+      <td>46824.03</td>
+      <td>43999.00</td>
+      <td>-2825.03</td>
+      <td>6.42</td>
+    </tr>
+    <tr>
+      <th>767</th>
+      <td>2432.69</td>
+      <td>2600.00</td>
+      <td>167.31</td>
+      <td>6.43</td>
+    </tr>
+    <tr>
+      <th>666</th>
+      <td>9305.11</td>
+      <td>9950.00</td>
+      <td>644.89</td>
+      <td>6.48</td>
+    </tr>
+    <tr>
+      <th>610</th>
+      <td>4067.90</td>
+      <td>4350.00</td>
+      <td>282.10</td>
+      <td>6.48</td>
+    </tr>
+    <tr>
+      <th>346</th>
+      <td>37165.88</td>
+      <td>34900.00</td>
+      <td>-2265.88</td>
+      <td>6.49</td>
+    </tr>
+    <tr>
+      <th>101</th>
+      <td>12248.10</td>
+      <td>11500.00</td>
+      <td>-748.10</td>
+      <td>6.51</td>
+    </tr>
+    <tr>
+      <th>174</th>
+      <td>7572.68</td>
+      <td>8100.00</td>
+      <td>527.32</td>
+      <td>6.51</td>
+    </tr>
+    <tr>
+      <th>393</th>
+      <td>21837.03</td>
+      <td>20500.00</td>
+      <td>-1337.03</td>
+      <td>6.52</td>
+    </tr>
+    <tr>
+      <th>289</th>
+      <td>9919.12</td>
+      <td>9300.00</td>
+      <td>-619.12</td>
+      <td>6.66</td>
+    </tr>
+    <tr>
+      <th>357</th>
+      <td>96042.20</td>
+      <td>89999.00</td>
+      <td>-6043.20</td>
+      <td>6.71</td>
+    </tr>
+    <tr>
+      <th>55</th>
+      <td>7739.78</td>
+      <td>8300.00</td>
+      <td>560.22</td>
+      <td>6.75</td>
+    </tr>
+    <tr>
+      <th>140</th>
+      <td>10247.89</td>
+      <td>10999.00</td>
+      <td>751.11</td>
+      <td>6.83</td>
+    </tr>
+    <tr>
+      <th>57</th>
+      <td>13573.72</td>
+      <td>12700.00</td>
+      <td>-873.72</td>
+      <td>6.88</td>
+    </tr>
+    <tr>
+      <th>123</th>
+      <td>8274.39</td>
+      <td>8888.00</td>
+      <td>613.61</td>
+      <td>6.90</td>
+    </tr>
+    <tr>
+      <th>464</th>
+      <td>2887.13</td>
+      <td>2700.00</td>
+      <td>-187.13</td>
+      <td>6.93</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>4280.49</td>
+      <td>4600.00</td>
+      <td>319.51</td>
+      <td>6.95</td>
+    </tr>
+    <tr>
+      <th>338</th>
+      <td>16367.58</td>
+      <td>15300.00</td>
+      <td>-1067.58</td>
+      <td>6.98</td>
+    </tr>
+    <tr>
+      <th>146</th>
+      <td>6741.83</td>
+      <td>6300.00</td>
+      <td>-441.83</td>
+      <td>7.01</td>
+    </tr>
+    <tr>
+      <th>163</th>
+      <td>10274.74</td>
+      <td>9600.00</td>
+      <td>-674.74</td>
+      <td>7.03</td>
+    </tr>
+    <tr>
+      <th>339</th>
+      <td>46581.86</td>
+      <td>43500.00</td>
+      <td>-3081.86</td>
+      <td>7.08</td>
+    </tr>
+    <tr>
+      <th>614</th>
+      <td>22293.90</td>
+      <td>24000.00</td>
+      <td>1706.10</td>
+      <td>7.11</td>
+    </tr>
+    <tr>
+      <th>398</th>
+      <td>8035.09</td>
+      <td>7500.00</td>
+      <td>-535.09</td>
+      <td>7.13</td>
+    </tr>
+    <tr>
+      <th>281</th>
+      <td>3712.23</td>
+      <td>4000.00</td>
+      <td>287.77</td>
+      <td>7.19</td>
+    </tr>
+    <tr>
+      <th>264</th>
+      <td>13456.31</td>
+      <td>14500.00</td>
+      <td>1043.69</td>
+      <td>7.20</td>
+    </tr>
+    <tr>
+      <th>588</th>
+      <td>6673.96</td>
+      <td>7199.00</td>
+      <td>525.04</td>
+      <td>7.29</td>
+    </tr>
+    <tr>
+      <th>408</th>
+      <td>17166.98</td>
+      <td>15999.00</td>
+      <td>-1167.98</td>
+      <td>7.30</td>
+    </tr>
+    <tr>
+      <th>100</th>
+      <td>20763.53</td>
+      <td>22400.00</td>
+      <td>1636.47</td>
+      <td>7.31</td>
+    </tr>
+    <tr>
+      <th>254</th>
+      <td>4634.43</td>
+      <td>5000.00</td>
+      <td>365.57</td>
+      <td>7.31</td>
+    </tr>
+    <tr>
+      <th>233</th>
+      <td>22540.34</td>
+      <td>21000.00</td>
+      <td>-1540.34</td>
+      <td>7.33</td>
+    </tr>
+    <tr>
+      <th>188</th>
+      <td>33345.57</td>
+      <td>36000.00</td>
+      <td>2654.43</td>
+      <td>7.37</td>
+    </tr>
+    <tr>
+      <th>106</th>
+      <td>6205.20</td>
+      <td>6700.00</td>
+      <td>494.80</td>
+      <td>7.39</td>
+    </tr>
+    <tr>
+      <th>631</th>
+      <td>13749.47</td>
+      <td>12800.00</td>
+      <td>-949.47</td>
+      <td>7.42</td>
+    </tr>
+    <tr>
+      <th>345</th>
+      <td>4906.11</td>
+      <td>5300.00</td>
+      <td>393.89</td>
+      <td>7.43</td>
+    </tr>
+    <tr>
+      <th>343</th>
+      <td>21489.76</td>
+      <td>19999.00</td>
+      <td>-1490.76</td>
+      <td>7.45</td>
+    </tr>
+    <tr>
+      <th>736</th>
+      <td>64619.43</td>
+      <td>69999.00</td>
+      <td>5379.57</td>
+      <td>7.69</td>
+    </tr>
+    <tr>
+      <th>657</th>
+      <td>20892.73</td>
+      <td>19400.00</td>
+      <td>-1492.73</td>
+      <td>7.69</td>
+    </tr>
+    <tr>
+      <th>589</th>
+      <td>8491.05</td>
+      <td>9200.00</td>
+      <td>708.95</td>
+      <td>7.71</td>
+    </tr>
+    <tr>
+      <th>467</th>
+      <td>3137.24</td>
+      <td>3400.00</td>
+      <td>262.76</td>
+      <td>7.73</td>
+    </tr>
+    <tr>
+      <th>722</th>
+      <td>61406.17</td>
+      <td>57000.00</td>
+      <td>-4406.17</td>
+      <td>7.73</td>
+    </tr>
+    <tr>
+      <th>103</th>
+      <td>7008.81</td>
+      <td>7600.00</td>
+      <td>591.19</td>
+      <td>7.78</td>
+    </tr>
+    <tr>
+      <th>107</th>
+      <td>17794.23</td>
+      <td>19300.00</td>
+      <td>1505.77</td>
+      <td>7.80</td>
+    </tr>
+    <tr>
+      <th>778</th>
+      <td>2913.09</td>
+      <td>2700.00</td>
+      <td>-213.09</td>
+      <td>7.89</td>
+    </tr>
+    <tr>
+      <th>38</th>
+      <td>13922.03</td>
+      <td>12900.00</td>
+      <td>-1022.03</td>
+      <td>7.92</td>
+    </tr>
+    <tr>
+      <th>488</th>
+      <td>25294.20</td>
+      <td>23400.00</td>
+      <td>-1894.20</td>
+      <td>8.09</td>
+    </tr>
+    <tr>
+      <th>291</th>
+      <td>59458.46</td>
+      <td>55000.00</td>
+      <td>-4458.46</td>
+      <td>8.11</td>
+    </tr>
+    <tr>
+      <th>554</th>
+      <td>20486.18</td>
+      <td>18950.00</td>
+      <td>-1536.18</td>
+      <td>8.11</td>
+    </tr>
+    <tr>
+      <th>667</th>
+      <td>13047.55</td>
+      <td>14200.00</td>
+      <td>1152.45</td>
+      <td>8.12</td>
+    </tr>
+    <tr>
+      <th>475</th>
+      <td>9352.13</td>
+      <td>8650.00</td>
+      <td>-702.13</td>
+      <td>8.12</td>
+    </tr>
+    <tr>
+      <th>792</th>
+      <td>32994.80</td>
+      <td>30500.00</td>
+      <td>-2494.80</td>
+      <td>8.18</td>
+    </tr>
+    <tr>
+      <th>793</th>
+      <td>6702.65</td>
+      <td>7300.00</td>
+      <td>597.35</td>
+      <td>8.18</td>
+    </tr>
+    <tr>
+      <th>178</th>
+      <td>23852.98</td>
+      <td>26000.00</td>
+      <td>2147.02</td>
+      <td>8.26</td>
+    </tr>
+    <tr>
+      <th>402</th>
+      <td>5963.16</td>
+      <td>6500.00</td>
+      <td>536.84</td>
+      <td>8.26</td>
+    </tr>
+    <tr>
+      <th>52</th>
+      <td>38422.39</td>
+      <td>41900.00</td>
+      <td>3477.61</td>
+      <td>8.30</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>6003.07</td>
+      <td>6550.00</td>
+      <td>546.93</td>
+      <td>8.35</td>
+    </tr>
+    <tr>
+      <th>665</th>
+      <td>6688.38</td>
+      <td>7300.00</td>
+      <td>611.62</td>
+      <td>8.38</td>
+    </tr>
+    <tr>
+      <th>779</th>
+      <td>19054.71</td>
+      <td>20800.00</td>
+      <td>1745.29</td>
+      <td>8.39</td>
+    </tr>
+    <tr>
+      <th>288</th>
+      <td>3360.19</td>
+      <td>3100.00</td>
+      <td>-260.19</td>
+      <td>8.39</td>
+    </tr>
+    <tr>
+      <th>323</th>
+      <td>14929.05</td>
+      <td>16300.00</td>
+      <td>1370.95</td>
+      <td>8.41</td>
+    </tr>
+    <tr>
+      <th>85</th>
+      <td>22027.55</td>
+      <td>20300.00</td>
+      <td>-1727.55</td>
+      <td>8.51</td>
+    </tr>
+    <tr>
+      <th>35</th>
+      <td>22027.55</td>
+      <td>20300.00</td>
+      <td>-1727.55</td>
+      <td>8.51</td>
+    </tr>
+    <tr>
+      <th>330</th>
+      <td>8357.21</td>
+      <td>7700.00</td>
+      <td>-657.21</td>
+      <td>8.54</td>
+    </tr>
+    <tr>
+      <th>353</th>
+      <td>22153.23</td>
+      <td>20400.00</td>
+      <td>-1753.23</td>
+      <td>8.59</td>
+    </tr>
+    <tr>
+      <th>749</th>
+      <td>17054.19</td>
+      <td>15700.00</td>
+      <td>-1354.19</td>
+      <td>8.63</td>
+    </tr>
+    <tr>
+      <th>512</th>
+      <td>10417.13</td>
+      <td>11403.99</td>
+      <td>986.86</td>
+      <td>8.65</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>20551.65</td>
+      <td>22500.00</td>
+      <td>1948.35</td>
+      <td>8.66</td>
+    </tr>
+    <tr>
+      <th>795</th>
+      <td>26009.78</td>
+      <td>28500.00</td>
+      <td>2490.22</td>
+      <td>8.74</td>
+    </tr>
+    <tr>
+      <th>761</th>
+      <td>12731.48</td>
+      <td>11700.00</td>
+      <td>-1031.48</td>
+      <td>8.82</td>
+    </tr>
+    <tr>
+      <th>486</th>
+      <td>18506.54</td>
+      <td>17000.00</td>
+      <td>-1506.54</td>
+      <td>8.86</td>
+    </tr>
+    <tr>
+      <th>472</th>
+      <td>3592.48</td>
+      <td>3300.00</td>
+      <td>-292.48</td>
+      <td>8.86</td>
+    </tr>
+    <tr>
+      <th>567</th>
+      <td>23222.71</td>
+      <td>25500.00</td>
+      <td>2277.29</td>
+      <td>8.93</td>
+    </tr>
+    <tr>
+      <th>626</th>
+      <td>6826.47</td>
+      <td>7500.00</td>
+      <td>673.53</td>
+      <td>8.98</td>
+    </tr>
+    <tr>
+      <th>244</th>
+      <td>9482.59</td>
+      <td>8700.00</td>
+      <td>-782.59</td>
+      <td>9.00</td>
+    </tr>
+    <tr>
+      <th>547</th>
+      <td>6975.75</td>
+      <td>6400.00</td>
+      <td>-575.75</td>
+      <td>9.00</td>
+    </tr>
+    <tr>
+      <th>553</th>
+      <td>18326.60</td>
+      <td>16800.00</td>
+      <td>-1526.60</td>
+      <td>9.09</td>
+    </tr>
+    <tr>
+      <th>430</th>
+      <td>27001.23</td>
+      <td>24750.00</td>
+      <td>-2251.23</td>
+      <td>9.10</td>
+    </tr>
+    <tr>
+      <th>435</th>
+      <td>24552.29</td>
+      <td>22500.00</td>
+      <td>-2052.29</td>
+      <td>9.12</td>
+    </tr>
+    <tr>
+      <th>195</th>
+      <td>25017.11</td>
+      <td>22900.00</td>
+      <td>-2117.11</td>
+      <td>9.25</td>
+    </tr>
+    <tr>
+      <th>115</th>
+      <td>14066.37</td>
+      <td>15500.00</td>
+      <td>1433.63</td>
+      <td>9.25</td>
+    </tr>
+    <tr>
+      <th>410</th>
+      <td>25215.17</td>
+      <td>27800.00</td>
+      <td>2584.83</td>
+      <td>9.30</td>
+    </tr>
+    <tr>
+      <th>182</th>
+      <td>52390.49</td>
+      <td>57777.00</td>
+      <td>5386.51</td>
+      <td>9.32</td>
+    </tr>
+    <tr>
+      <th>191</th>
+      <td>24614.93</td>
+      <td>22500.00</td>
+      <td>-2114.93</td>
+      <td>9.40</td>
+    </tr>
+    <tr>
+      <th>78</th>
+      <td>31509.05</td>
+      <td>28800.00</td>
+      <td>-2709.05</td>
+      <td>9.41</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>10857.59</td>
+      <td>9900.00</td>
+      <td>-957.59</td>
+      <td>9.67</td>
+    </tr>
+    <tr>
+      <th>268</th>
+      <td>4605.75</td>
+      <td>5100.00</td>
+      <td>494.25</td>
+      <td>9.69</td>
+    </tr>
+    <tr>
+      <th>510</th>
+      <td>7679.11</td>
+      <td>6999.00</td>
+      <td>-680.11</td>
+      <td>9.72</td>
+    </tr>
+    <tr>
+      <th>359</th>
+      <td>13449.96</td>
+      <td>14899.00</td>
+      <td>1449.04</td>
+      <td>9.73</td>
+    </tr>
+    <tr>
+      <th>645</th>
+      <td>19098.09</td>
+      <td>17400.00</td>
+      <td>-1698.09</td>
+      <td>9.76</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>4330.57</td>
+      <td>4800.00</td>
+      <td>469.43</td>
+      <td>9.78</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>39228.72</td>
+      <td>43500.00</td>
+      <td>4271.28</td>
+      <td>9.82</td>
+    </tr>
+    <tr>
+      <th>782</th>
+      <td>2911.16</td>
+      <td>2650.00</td>
+      <td>-261.16</td>
+      <td>9.85</td>
+    </tr>
+    <tr>
+      <th>740</th>
+      <td>13959.83</td>
+      <td>15500.00</td>
+      <td>1540.17</td>
+      <td>9.94</td>
+    </tr>
+    <tr>
+      <th>739</th>
+      <td>4184.64</td>
+      <td>3800.00</td>
+      <td>-384.64</td>
+      <td>10.12</td>
+    </tr>
+    <tr>
+      <th>125</th>
+      <td>12670.45</td>
+      <td>11500.00</td>
+      <td>-1170.45</td>
+      <td>10.18</td>
+    </tr>
+    <tr>
+      <th>29</th>
+      <td>5386.74</td>
+      <td>6000.00</td>
+      <td>613.26</td>
+      <td>10.22</td>
+    </tr>
+    <tr>
+      <th>518</th>
+      <td>39141.03</td>
+      <td>35500.00</td>
+      <td>-3641.03</td>
+      <td>10.26</td>
+    </tr>
+    <tr>
+      <th>246</th>
+      <td>10476.67</td>
+      <td>9500.00</td>
+      <td>-976.67</td>
+      <td>10.28</td>
+    </tr>
+    <tr>
+      <th>348</th>
+      <td>4483.97</td>
+      <td>5000.00</td>
+      <td>516.03</td>
+      <td>10.32</td>
+    </tr>
+    <tr>
+      <th>379</th>
+      <td>8696.33</td>
+      <td>9700.00</td>
+      <td>1003.67</td>
+      <td>10.35</td>
+    </tr>
+    <tr>
+      <th>391</th>
+      <td>8964.70</td>
+      <td>10000.00</td>
+      <td>1035.30</td>
+      <td>10.35</td>
+    </tr>
+    <tr>
+      <th>556</th>
+      <td>5739.40</td>
+      <td>5200.00</td>
+      <td>-539.40</td>
+      <td>10.37</td>
+    </tr>
+    <tr>
+      <th>469</th>
+      <td>8512.76</td>
+      <td>9500.00</td>
+      <td>987.24</td>
+      <td>10.39</td>
+    </tr>
+    <tr>
+      <th>84</th>
+      <td>4305.44</td>
+      <td>3900.00</td>
+      <td>-405.44</td>
+      <td>10.40</td>
+    </tr>
+    <tr>
+      <th>584</th>
+      <td>33929.47</td>
+      <td>37900.00</td>
+      <td>3970.53</td>
+      <td>10.48</td>
+    </tr>
+    <tr>
+      <th>563</th>
+      <td>5997.22</td>
+      <td>6700.00</td>
+      <td>702.78</td>
+      <td>10.49</td>
+    </tr>
+    <tr>
+      <th>364</th>
+      <td>27739.55</td>
+      <td>31000.00</td>
+      <td>3260.45</td>
+      <td>10.52</td>
+    </tr>
+    <tr>
+      <th>242</th>
+      <td>9395.10</td>
+      <td>8500.00</td>
+      <td>-895.10</td>
+      <td>10.53</td>
+    </tr>
+    <tr>
+      <th>197</th>
+      <td>23054.57</td>
+      <td>25800.00</td>
+      <td>2745.43</td>
+      <td>10.64</td>
+    </tr>
+    <tr>
+      <th>734</th>
+      <td>25323.00</td>
+      <td>28350.00</td>
+      <td>3027.00</td>
+      <td>10.68</td>
+    </tr>
+    <tr>
+      <th>377</th>
+      <td>12411.94</td>
+      <td>13900.00</td>
+      <td>1488.06</td>
+      <td>10.71</td>
+    </tr>
+    <tr>
+      <th>620</th>
+      <td>30347.45</td>
+      <td>34000.00</td>
+      <td>3652.55</td>
+      <td>10.74</td>
+    </tr>
+    <tr>
+      <th>550</th>
+      <td>17165.39</td>
+      <td>15500.00</td>
+      <td>-1665.39</td>
+      <td>10.74</td>
+    </tr>
+    <tr>
+      <th>639</th>
+      <td>3927.19</td>
+      <td>4400.00</td>
+      <td>472.81</td>
+      <td>10.75</td>
+    </tr>
+    <tr>
+      <th>754</th>
+      <td>2228.68</td>
+      <td>2499.00</td>
+      <td>270.32</td>
+      <td>10.82</td>
+    </tr>
+    <tr>
+      <th>201</th>
+      <td>2584.33</td>
+      <td>2900.00</td>
+      <td>315.67</td>
+      <td>10.89</td>
+    </tr>
+    <tr>
+      <th>571</th>
+      <td>9974.83</td>
+      <td>8990.00</td>
+      <td>-984.83</td>
+      <td>10.95</td>
+    </tr>
+    <tr>
+      <th>210</th>
+      <td>50737.64</td>
+      <td>57000.00</td>
+      <td>6262.36</td>
+      <td>10.99</td>
+    </tr>
+    <tr>
+      <th>194</th>
+      <td>4181.54</td>
+      <td>4700.00</td>
+      <td>518.46</td>
+      <td>11.03</td>
+    </tr>
+    <tr>
+      <th>451</th>
+      <td>11824.42</td>
+      <td>13300.00</td>
+      <td>1475.58</td>
+      <td>11.09</td>
+    </tr>
+    <tr>
+      <th>500</th>
+      <td>34352.54</td>
+      <td>30900.00</td>
+      <td>-3452.54</td>
+      <td>11.17</td>
+    </tr>
+    <tr>
+      <th>356</th>
+      <td>31072.01</td>
+      <td>35000.00</td>
+      <td>3927.99</td>
+      <td>11.22</td>
+    </tr>
+    <tr>
+      <th>608</th>
+      <td>9901.71</td>
+      <td>8900.00</td>
+      <td>-1001.71</td>
+      <td>11.26</td>
+    </tr>
+    <tr>
+      <th>62</th>
+      <td>8123.03</td>
+      <td>7300.00</td>
+      <td>-823.03</td>
+      <td>11.27</td>
+    </tr>
+    <tr>
+      <th>552</th>
+      <td>13300.81</td>
+      <td>11950.00</td>
+      <td>-1350.81</td>
+      <td>11.30</td>
+    </tr>
+    <tr>
+      <th>513</th>
+      <td>8867.20</td>
+      <td>9999.00</td>
+      <td>1131.80</td>
+      <td>11.32</td>
+    </tr>
+    <tr>
+      <th>155</th>
+      <td>13213.07</td>
+      <td>14900.00</td>
+      <td>1686.93</td>
+      <td>11.32</td>
+    </tr>
+    <tr>
+      <th>223</th>
+      <td>44560.45</td>
+      <td>39999.00</td>
+      <td>-4561.45</td>
+      <td>11.40</td>
+    </tr>
+    <tr>
+      <th>320</th>
+      <td>44560.45</td>
+      <td>39999.00</td>
+      <td>-4561.45</td>
+      <td>11.40</td>
+    </tr>
+    <tr>
+      <th>622</th>
+      <td>11484.51</td>
+      <td>10300.00</td>
+      <td>-1184.51</td>
+      <td>11.50</td>
+    </tr>
+    <tr>
+      <th>56</th>
+      <td>7804.78</td>
+      <td>6999.00</td>
+      <td>-805.78</td>
+      <td>11.51</td>
+    </tr>
+    <tr>
+      <th>750</th>
+      <td>22287.84</td>
+      <td>25200.00</td>
+      <td>2912.16</td>
+      <td>11.56</td>
+    </tr>
+    <tr>
+      <th>437</th>
+      <td>24321.68</td>
+      <td>21800.00</td>
+      <td>-2521.68</td>
+      <td>11.57</td>
+    </tr>
+    <tr>
+      <th>369</th>
+      <td>2343.59</td>
+      <td>2100.00</td>
+      <td>-243.59</td>
+      <td>11.60</td>
+    </tr>
+    <tr>
+      <th>248</th>
+      <td>11932.57</td>
+      <td>13500.00</td>
+      <td>1567.43</td>
+      <td>11.61</td>
+    </tr>
+    <tr>
+      <th>94</th>
+      <td>10054.06</td>
+      <td>9000.00</td>
+      <td>-1054.06</td>
+      <td>11.71</td>
+    </tr>
+    <tr>
+      <th>110</th>
+      <td>40781.07</td>
+      <td>36500.00</td>
+      <td>-4281.07</td>
+      <td>11.73</td>
+    </tr>
+    <tr>
+      <th>189</th>
+      <td>2779.76</td>
+      <td>3149.25</td>
+      <td>369.49</td>
+      <td>11.73</td>
+    </tr>
+    <tr>
+      <th>531</th>
+      <td>7056.08</td>
+      <td>8000.00</td>
+      <td>943.92</td>
+      <td>11.80</td>
+    </tr>
+    <tr>
+      <th>44</th>
+      <td>13196.07</td>
+      <td>11800.00</td>
+      <td>-1396.07</td>
+      <td>11.83</td>
+    </tr>
+    <tr>
+      <th>313</th>
+      <td>8365.18</td>
+      <td>9500.00</td>
+      <td>1134.82</td>
+      <td>11.95</td>
+    </tr>
+    <tr>
+      <th>202</th>
+      <td>2729.63</td>
+      <td>3100.00</td>
+      <td>370.37</td>
+      <td>11.95</td>
+    </tr>
+    <tr>
+      <th>641</th>
+      <td>7279.35</td>
+      <td>6500.00</td>
+      <td>-779.35</td>
+      <td>11.99</td>
+    </tr>
+    <tr>
+      <th>548</th>
+      <td>6600.31</td>
+      <td>7500.00</td>
+      <td>899.69</td>
+      <td>12.00</td>
+    </tr>
+    <tr>
+      <th>160</th>
+      <td>8577.61</td>
+      <td>9750.00</td>
+      <td>1172.39</td>
+      <td>12.02</td>
+    </tr>
+    <tr>
+      <th>373</th>
+      <td>13331.21</td>
+      <td>11900.00</td>
+      <td>-1431.21</td>
+      <td>12.03</td>
+    </tr>
+    <tr>
+      <th>689</th>
+      <td>3074.51</td>
+      <td>3500.00</td>
+      <td>425.49</td>
+      <td>12.16</td>
+    </tr>
+    <tr>
+      <th>647</th>
+      <td>2020.09</td>
+      <td>2300.00</td>
+      <td>279.91</td>
+      <td>12.17</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>2895.96</td>
+      <td>3300.00</td>
+      <td>404.04</td>
+      <td>12.24</td>
+    </tr>
+    <tr>
+      <th>150</th>
+      <td>7808.12</td>
+      <td>8900.00</td>
+      <td>1091.88</td>
+      <td>12.27</td>
+    </tr>
+    <tr>
+      <th>45</th>
+      <td>21139.89</td>
+      <td>24100.00</td>
+      <td>2960.11</td>
+      <td>12.28</td>
+    </tr>
+    <tr>
+      <th>280</th>
+      <td>24999.38</td>
+      <td>28500.00</td>
+      <td>3500.62</td>
+      <td>12.28</td>
+    </tr>
+    <tr>
+      <th>523</th>
+      <td>2718.83</td>
+      <td>3100.00</td>
+      <td>381.17</td>
+      <td>12.30</td>
+    </tr>
+    <tr>
+      <th>217</th>
+      <td>42942.54</td>
+      <td>38233.00</td>
+      <td>-4709.54</td>
+      <td>12.32</td>
+    </tr>
+    <tr>
+      <th>89</th>
+      <td>11131.10</td>
+      <td>12700.00</td>
+      <td>1568.90</td>
+      <td>12.35</td>
+    </tr>
+    <tr>
+      <th>619</th>
+      <td>7447.84</td>
+      <td>8500.00</td>
+      <td>1052.16</td>
+      <td>12.38</td>
+    </tr>
+    <tr>
+      <th>378</th>
+      <td>4248.90</td>
+      <td>4850.00</td>
+      <td>601.10</td>
+      <td>12.39</td>
+    </tr>
+    <tr>
+      <th>161</th>
+      <td>2802.47</td>
+      <td>3200.00</td>
+      <td>397.53</td>
+      <td>12.42</td>
+    </tr>
+    <tr>
+      <th>315</th>
+      <td>7351.03</td>
+      <td>8400.00</td>
+      <td>1048.97</td>
+      <td>12.49</td>
+    </tr>
+    <tr>
+      <th>300</th>
+      <td>14528.10</td>
+      <td>12900.00</td>
+      <td>-1628.10</td>
+      <td>12.62</td>
+    </tr>
+    <tr>
+      <th>395</th>
+      <td>22532.79</td>
+      <td>19999.00</td>
+      <td>-2533.79</td>
+      <td>12.67</td>
+    </tr>
+    <tr>
+      <th>205</th>
+      <td>3832.33</td>
+      <td>3400.00</td>
+      <td>-432.33</td>
+      <td>12.72</td>
+    </tr>
+    <tr>
+      <th>177</th>
+      <td>19197.99</td>
+      <td>22000.00</td>
+      <td>2802.01</td>
+      <td>12.74</td>
+    </tr>
+    <tr>
+      <th>295</th>
+      <td>18942.17</td>
+      <td>16800.00</td>
+      <td>-2142.17</td>
+      <td>12.75</td>
+    </tr>
+    <tr>
+      <th>412</th>
+      <td>19190.84</td>
+      <td>22000.00</td>
+      <td>2809.16</td>
+      <td>12.77</td>
+    </tr>
+    <tr>
+      <th>691</th>
+      <td>73324.12</td>
+      <td>65000.00</td>
+      <td>-8324.12</td>
+      <td>12.81</td>
+    </tr>
+    <tr>
+      <th>187</th>
+      <td>12141.67</td>
+      <td>10750.00</td>
+      <td>-1391.67</td>
+      <td>12.95</td>
+    </tr>
+    <tr>
+      <th>319</th>
+      <td>13444.25</td>
+      <td>11900.00</td>
+      <td>-1544.25</td>
+      <td>12.98</td>
+    </tr>
+    <tr>
+      <th>329</th>
+      <td>32212.96</td>
+      <td>28500.00</td>
+      <td>-3712.96</td>
+      <td>13.03</td>
+    </tr>
+    <tr>
+      <th>407</th>
+      <td>10173.73</td>
+      <td>9000.00</td>
+      <td>-1173.73</td>
+      <td>13.04</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>22173.64</td>
+      <td>25500.00</td>
+      <td>3326.36</td>
+      <td>13.04</td>
+    </tr>
+    <tr>
+      <th>675</th>
+      <td>3216.30</td>
+      <td>3700.00</td>
+      <td>483.70</td>
+      <td>13.07</td>
+    </tr>
+    <tr>
+      <th>270</th>
+      <td>13061.64</td>
+      <td>11550.00</td>
+      <td>-1511.64</td>
+      <td>13.09</td>
+    </tr>
+    <tr>
+      <th>22</th>
+      <td>7640.17</td>
+      <td>8800.00</td>
+      <td>1159.83</td>
+      <td>13.18</td>
+    </tr>
+    <tr>
+      <th>271</th>
+      <td>8941.66</td>
+      <td>7900.00</td>
+      <td>-1041.66</td>
+      <td>13.19</td>
+    </tr>
+    <tr>
+      <th>171</th>
+      <td>8848.20</td>
+      <td>10200.00</td>
+      <td>1351.80</td>
+      <td>13.25</td>
+    </tr>
+    <tr>
+      <th>454</th>
+      <td>37294.36</td>
+      <td>43000.00</td>
+      <td>5705.64</td>
+      <td>13.27</td>
+    </tr>
+    <tr>
+      <th>136</th>
+      <td>28900.44</td>
+      <td>33333.00</td>
+      <td>4432.56</td>
+      <td>13.30</td>
+    </tr>
+    <tr>
+      <th>116</th>
+      <td>10084.88</td>
+      <td>8900.00</td>
+      <td>-1184.88</td>
+      <td>13.31</td>
+    </tr>
+    <tr>
+      <th>170</th>
+      <td>10766.99</td>
+      <td>9500.00</td>
+      <td>-1266.99</td>
+      <td>13.34</td>
+    </tr>
+    <tr>
+      <th>517</th>
+      <td>19048.58</td>
+      <td>16800.00</td>
+      <td>-2248.58</td>
+      <td>13.38</td>
+    </tr>
+    <tr>
+      <th>606</th>
+      <td>23296.66</td>
+      <td>26900.00</td>
+      <td>3603.34</td>
+      <td>13.40</td>
+    </tr>
+    <tr>
+      <th>580</th>
+      <td>27706.06</td>
+      <td>32000.00</td>
+      <td>4293.94</td>
+      <td>13.42</td>
+    </tr>
+    <tr>
+      <th>717</th>
+      <td>2856.79</td>
+      <td>3300.00</td>
+      <td>443.21</td>
+      <td>13.43</td>
+    </tr>
+    <tr>
+      <th>607</th>
+      <td>8400.37</td>
+      <td>7400.00</td>
+      <td>-1000.37</td>
+      <td>13.52</td>
+    </tr>
+    <tr>
+      <th>173</th>
+      <td>14874.09</td>
+      <td>17200.00</td>
+      <td>2325.91</td>
+      <td>13.52</td>
+    </tr>
+    <tr>
+      <th>595</th>
+      <td>2853.47</td>
+      <td>3300.00</td>
+      <td>446.53</td>
+      <td>13.53</td>
+    </tr>
+    <tr>
+      <th>133</th>
+      <td>1929.25</td>
+      <td>1699.00</td>
+      <td>-230.25</td>
+      <td>13.55</td>
+    </tr>
+    <tr>
+      <th>240</th>
+      <td>6740.68</td>
+      <td>7800.00</td>
+      <td>1059.32</td>
+      <td>13.58</td>
+    </tr>
+    <tr>
+      <th>542</th>
+      <td>15113.32</td>
+      <td>13300.00</td>
+      <td>-1813.32</td>
+      <td>13.63</td>
+    </tr>
+    <tr>
+      <th>764</th>
+      <td>8146.17</td>
+      <td>9450.00</td>
+      <td>1303.83</td>
+      <td>13.80</td>
+    </tr>
+    <tr>
+      <th>432</th>
+      <td>16956.75</td>
+      <td>14900.00</td>
+      <td>-2056.75</td>
+      <td>13.80</td>
+    </tr>
+    <tr>
+      <th>138</th>
+      <td>8616.78</td>
+      <td>10000.00</td>
+      <td>1383.22</td>
+      <td>13.83</td>
+    </tr>
+    <tr>
+      <th>97</th>
+      <td>27560.75</td>
+      <td>32000.00</td>
+      <td>4439.25</td>
+      <td>13.87</td>
+    </tr>
+    <tr>
+      <th>568</th>
+      <td>11782.11</td>
+      <td>13700.00</td>
+      <td>1917.89</td>
+      <td>14.00</td>
+    </tr>
+    <tr>
+      <th>668</th>
+      <td>6047.87</td>
+      <td>5300.00</td>
+      <td>-747.87</td>
+      <td>14.11</td>
+    </tr>
+    <tr>
+      <th>198</th>
+      <td>3263.77</td>
+      <td>3800.00</td>
+      <td>536.23</td>
+      <td>14.11</td>
+    </tr>
+    <tr>
+      <th>368</th>
+      <td>25652.89</td>
+      <td>29900.00</td>
+      <td>4247.11</td>
+      <td>14.20</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>13110.05</td>
+      <td>15300.00</td>
+      <td>2189.95</td>
+      <td>14.31</td>
+    </tr>
+    <tr>
+      <th>269</th>
+      <td>6154.50</td>
+      <td>7200.00</td>
+      <td>1045.50</td>
+      <td>14.52</td>
+    </tr>
+    <tr>
+      <th>222</th>
+      <td>85955.91</td>
+      <td>74999.00</td>
+      <td>-10956.91</td>
+      <td>14.61</td>
+    </tr>
+    <tr>
+      <th>331</th>
+      <td>13772.47</td>
+      <td>11999.00</td>
+      <td>-1773.47</td>
+      <td>14.78</td>
+    </tr>
+    <tr>
+      <th>433</th>
+      <td>7923.76</td>
+      <td>9300.00</td>
+      <td>1376.24</td>
+      <td>14.80</td>
+    </tr>
+    <tr>
+      <th>127</th>
+      <td>32350.70</td>
+      <td>37990.00</td>
+      <td>5639.30</td>
+      <td>14.84</td>
+    </tr>
+    <tr>
+      <th>504</th>
+      <td>3043.80</td>
+      <td>2650.00</td>
+      <td>-393.80</td>
+      <td>14.86</td>
+    </tr>
+    <tr>
+      <th>508</th>
+      <td>9993.19</td>
+      <td>8700.00</td>
+      <td>-1293.19</td>
+      <td>14.86</td>
+    </tr>
+    <tr>
+      <th>375</th>
+      <td>24702.61</td>
+      <td>21500.00</td>
+      <td>-3202.61</td>
+      <td>14.90</td>
+    </tr>
+    <tr>
+      <th>317</th>
+      <td>63213.55</td>
+      <td>55000.00</td>
+      <td>-8213.55</td>
+      <td>14.93</td>
+    </tr>
+    <tr>
+      <th>495</th>
+      <td>12647.28</td>
+      <td>10999.00</td>
+      <td>-1648.28</td>
+      <td>14.99</td>
+    </tr>
+    <tr>
+      <th>243</th>
+      <td>11812.55</td>
+      <td>13899.00</td>
+      <td>2086.45</td>
+      <td>15.01</td>
+    </tr>
+    <tr>
+      <th>716</th>
+      <td>3398.91</td>
+      <td>4000.00</td>
+      <td>601.09</td>
+      <td>15.03</td>
+    </tr>
+    <tr>
+      <th>340</th>
+      <td>12482.76</td>
+      <td>10850.00</td>
+      <td>-1632.76</td>
+      <td>15.05</td>
+    </tr>
+    <tr>
+      <th>153</th>
+      <td>6099.48</td>
+      <td>5300.00</td>
+      <td>-799.48</td>
+      <td>15.08</td>
+    </tr>
+    <tr>
+      <th>347</th>
+      <td>5248.40</td>
+      <td>6200.00</td>
+      <td>951.60</td>
+      <td>15.35</td>
+    </tr>
+    <tr>
+      <th>104</th>
+      <td>6052.23</td>
+      <td>7150.00</td>
+      <td>1097.77</td>
+      <td>15.35</td>
+    </tr>
+    <tr>
+      <th>83</th>
+      <td>9809.02</td>
+      <td>8499.00</td>
+      <td>-1310.02</td>
+      <td>15.41</td>
+    </tr>
+    <tr>
+      <th>450</th>
+      <td>6174.68</td>
+      <td>7300.00</td>
+      <td>1125.32</td>
+      <td>15.42</td>
+    </tr>
+    <tr>
+      <th>593</th>
+      <td>3635.06</td>
+      <td>4300.00</td>
+      <td>664.94</td>
+      <td>15.46</td>
+    </tr>
+    <tr>
+      <th>135</th>
+      <td>3347.79</td>
+      <td>2899.00</td>
+      <td>-448.79</td>
+      <td>15.48</td>
+    </tr>
+    <tr>
+      <th>93</th>
+      <td>20557.05</td>
+      <td>17800.00</td>
+      <td>-2757.05</td>
+      <td>15.49</td>
+    </tr>
+    <tr>
+      <th>392</th>
+      <td>28877.80</td>
+      <td>25000.00</td>
+      <td>-3877.80</td>
+      <td>15.51</td>
+    </tr>
+    <tr>
+      <th>529</th>
+      <td>5082.76</td>
+      <td>4400.00</td>
+      <td>-682.76</td>
+      <td>15.52</td>
+    </tr>
+    <tr>
+      <th>159</th>
+      <td>17104.46</td>
+      <td>14800.00</td>
+      <td>-2304.46</td>
+      <td>15.57</td>
+    </tr>
+    <tr>
+      <th>579</th>
+      <td>14842.29</td>
+      <td>17595.00</td>
+      <td>2752.71</td>
+      <td>15.64</td>
+    </tr>
+    <tr>
+      <th>786</th>
+      <td>3817.05</td>
+      <td>3300.00</td>
+      <td>-517.05</td>
+      <td>15.67</td>
+    </tr>
+    <tr>
+      <th>42</th>
+      <td>9373.98</td>
+      <td>8100.00</td>
+      <td>-1273.98</td>
+      <td>15.73</td>
+    </tr>
+    <tr>
+      <th>702</th>
+      <td>6888.50</td>
+      <td>5950.00</td>
+      <td>-938.50</td>
+      <td>15.77</td>
+    </tr>
+    <tr>
+      <th>206</th>
+      <td>6903.35</td>
+      <td>8200.00</td>
+      <td>1296.65</td>
+      <td>15.81</td>
+    </tr>
+    <tr>
+      <th>43</th>
+      <td>34332.85</td>
+      <td>40800.00</td>
+      <td>6467.15</td>
+      <td>15.85</td>
+    </tr>
+    <tr>
+      <th>387</th>
+      <td>16799.16</td>
+      <td>14500.00</td>
+      <td>-2299.16</td>
+      <td>15.86</td>
+    </tr>
+    <tr>
+      <th>492</th>
+      <td>98480.67</td>
+      <td>85000.00</td>
+      <td>-13480.67</td>
+      <td>15.86</td>
+    </tr>
+    <tr>
+      <th>414</th>
+      <td>6139.72</td>
+      <td>7300.00</td>
+      <td>1160.28</td>
+      <td>15.89</td>
+    </tr>
+    <tr>
+      <th>325</th>
+      <td>15437.68</td>
+      <td>13300.00</td>
+      <td>-2137.68</td>
+      <td>16.07</td>
+    </tr>
+    <tr>
+      <th>322</th>
+      <td>4064.76</td>
+      <td>3500.00</td>
+      <td>-564.76</td>
+      <td>16.14</td>
+    </tr>
+    <tr>
+      <th>265</th>
+      <td>14087.55</td>
+      <td>16800.00</td>
+      <td>2712.45</td>
+      <td>16.15</td>
+    </tr>
+    <tr>
+      <th>208</th>
+      <td>26137.11</td>
+      <td>22500.00</td>
+      <td>-3637.11</td>
+      <td>16.16</td>
+    </tr>
+    <tr>
+      <th>72</th>
+      <td>27304.45</td>
+      <td>23500.00</td>
+      <td>-3804.45</td>
+      <td>16.19</td>
+    </tr>
+    <tr>
+      <th>383</th>
+      <td>9637.17</td>
+      <td>11500.00</td>
+      <td>1862.83</td>
+      <td>16.20</td>
+    </tr>
+    <tr>
+      <th>605</th>
+      <td>33099.94</td>
+      <td>39500.00</td>
+      <td>6400.06</td>
+      <td>16.20</td>
+    </tr>
+    <tr>
+      <th>218</th>
+      <td>36023.63</td>
+      <td>30990.00</td>
+      <td>-5033.63</td>
+      <td>16.24</td>
+    </tr>
+    <tr>
+      <th>429</th>
+      <td>37352.82</td>
+      <td>44600.00</td>
+      <td>7247.18</td>
+      <td>16.25</td>
+    </tr>
+    <tr>
+      <th>753</th>
+      <td>10462.54</td>
+      <td>8999.00</td>
+      <td>-1463.54</td>
+      <td>16.26</td>
+    </tr>
+    <tr>
+      <th>126</th>
+      <td>31976.86</td>
+      <td>27500.00</td>
+      <td>-4476.86</td>
+      <td>16.28</td>
+    </tr>
+    <tr>
+      <th>253</th>
+      <td>20915.39</td>
+      <td>24996.00</td>
+      <td>4080.61</td>
+      <td>16.33</td>
+    </tr>
+    <tr>
+      <th>587</th>
+      <td>1673.12</td>
+      <td>2000.00</td>
+      <td>326.88</td>
+      <td>16.34</td>
+    </tr>
+    <tr>
+      <th>570</th>
+      <td>14221.55</td>
+      <td>17000.00</td>
+      <td>2778.45</td>
+      <td>16.34</td>
+    </tr>
+    <tr>
+      <th>625</th>
+      <td>34297.36</td>
+      <td>41000.00</td>
+      <td>6702.64</td>
+      <td>16.35</td>
+    </tr>
+    <tr>
+      <th>707</th>
+      <td>19907.26</td>
+      <td>17100.00</td>
+      <td>-2807.26</td>
+      <td>16.42</td>
+    </tr>
+    <tr>
+      <th>686</th>
+      <td>10362.29</td>
+      <td>8900.00</td>
+      <td>-1462.29</td>
+      <td>16.43</td>
+    </tr>
+    <tr>
+      <th>219</th>
+      <td>23291.25</td>
+      <td>20000.00</td>
+      <td>-3291.25</td>
+      <td>16.46</td>
+    </tr>
+    <tr>
+      <th>747</th>
+      <td>2213.58</td>
+      <td>1900.00</td>
+      <td>-313.58</td>
+      <td>16.50</td>
+    </tr>
+    <tr>
+      <th>406</th>
+      <td>8738.60</td>
+      <td>7500.00</td>
+      <td>-1238.60</td>
+      <td>16.51</td>
+    </tr>
+    <tr>
+      <th>770</th>
+      <td>6877.84</td>
+      <td>5900.00</td>
+      <td>-977.84</td>
+      <td>16.57</td>
+    </tr>
+    <tr>
+      <th>427</th>
+      <td>10377.09</td>
+      <td>8900.00</td>
+      <td>-1477.09</td>
+      <td>16.60</td>
+    </tr>
+    <tr>
+      <th>380</th>
+      <td>21349.94</td>
+      <td>18300.00</td>
+      <td>-3049.94</td>
+      <td>16.67</td>
+    </tr>
+    <tr>
+      <th>386</th>
+      <td>2858.60</td>
+      <td>2450.00</td>
+      <td>-408.60</td>
+      <td>16.68</td>
+    </tr>
+    <tr>
+      <th>493</th>
+      <td>11676.82</td>
+      <td>9999.00</td>
+      <td>-1677.82</td>
+      <td>16.78</td>
+    </tr>
+    <tr>
+      <th>251</th>
+      <td>7892.51</td>
+      <td>9500.00</td>
+      <td>1607.49</td>
+      <td>16.92</td>
+    </tr>
+    <tr>
+      <th>303</th>
+      <td>5399.74</td>
+      <td>6500.00</td>
+      <td>1100.26</td>
+      <td>16.93</td>
+    </tr>
+    <tr>
+      <th>434</th>
+      <td>25408.55</td>
+      <td>21700.00</td>
+      <td>-3708.55</td>
+      <td>17.09</td>
+    </tr>
+    <tr>
+      <th>255</th>
+      <td>10609.93</td>
+      <td>12800.00</td>
+      <td>2190.07</td>
+      <td>17.11</td>
+    </tr>
+    <tr>
+      <th>648</th>
+      <td>28114.25</td>
+      <td>24000.00</td>
+      <td>-4114.25</td>
+      <td>17.14</td>
+    </tr>
+    <tr>
+      <th>47</th>
+      <td>39356.61</td>
+      <td>47500.00</td>
+      <td>8143.39</td>
+      <td>17.14</td>
+    </tr>
+    <tr>
+      <th>321</th>
+      <td>50516.19</td>
+      <td>61000.00</td>
+      <td>10483.81</td>
+      <td>17.19</td>
+    </tr>
+    <tr>
+      <th>316</th>
+      <td>9481.20</td>
+      <td>11450.00</td>
+      <td>1968.80</td>
+      <td>17.19</td>
+    </tr>
+    <tr>
+      <th>390</th>
+      <td>13594.21</td>
+      <td>16450.00</td>
+      <td>2855.79</td>
+      <td>17.36</td>
+    </tr>
+    <tr>
+      <th>349</th>
+      <td>12675.07</td>
+      <td>10800.00</td>
+      <td>-1875.07</td>
+      <td>17.36</td>
+    </tr>
+    <tr>
+      <th>789</th>
+      <td>11505.09</td>
+      <td>9800.00</td>
+      <td>-1705.09</td>
+      <td>17.40</td>
+    </tr>
+    <tr>
+      <th>602</th>
+      <td>9082.32</td>
+      <td>10999.00</td>
+      <td>1916.68</td>
+      <td>17.43</td>
+    </tr>
+    <tr>
+      <th>203</th>
+      <td>31495.17</td>
+      <td>26800.00</td>
+      <td>-4695.17</td>
+      <td>17.52</td>
+    </tr>
+    <tr>
+      <th>748</th>
+      <td>11282.67</td>
+      <td>9600.00</td>
+      <td>-1682.67</td>
+      <td>17.53</td>
+    </tr>
+    <tr>
+      <th>109</th>
+      <td>19281.74</td>
+      <td>16400.00</td>
+      <td>-2881.74</td>
+      <td>17.57</td>
+    </tr>
+    <tr>
+      <th>658</th>
+      <td>7992.08</td>
+      <td>9700.00</td>
+      <td>1707.92</td>
+      <td>17.61</td>
+    </tr>
+    <tr>
+      <th>758</th>
+      <td>5881.36</td>
+      <td>5000.00</td>
+      <td>-881.36</td>
+      <td>17.63</td>
+    </tr>
+    <tr>
+      <th>168</th>
+      <td>18117.61</td>
+      <td>22000.00</td>
+      <td>3882.39</td>
+      <td>17.65</td>
+    </tr>
+    <tr>
+      <th>746</th>
+      <td>11412.71</td>
+      <td>9700.00</td>
+      <td>-1712.71</td>
+      <td>17.66</td>
+    </tr>
+    <tr>
+      <th>119</th>
+      <td>15774.43</td>
+      <td>13400.00</td>
+      <td>-2374.43</td>
+      <td>17.72</td>
+    </tr>
+    <tr>
+      <th>496</th>
+      <td>4357.87</td>
+      <td>5300.00</td>
+      <td>942.13</td>
+      <td>17.78</td>
+    </tr>
+    <tr>
+      <th>415</th>
+      <td>8126.96</td>
+      <td>6900.00</td>
+      <td>-1226.96</td>
+      <td>17.78</td>
+    </tr>
+    <tr>
+      <th>309</th>
+      <td>11434.04</td>
+      <td>9700.00</td>
+      <td>-1734.04</td>
+      <td>17.88</td>
+    </tr>
+    <tr>
+      <th>421</th>
+      <td>74745.66</td>
+      <td>63333.00</td>
+      <td>-11412.66</td>
+      <td>18.02</td>
+    </tr>
+    <tr>
+      <th>796</th>
+      <td>18302.49</td>
+      <td>15500.00</td>
+      <td>-2802.49</td>
+      <td>18.08</td>
+    </tr>
+    <tr>
+      <th>695</th>
+      <td>3679.35</td>
+      <td>4500.00</td>
+      <td>820.65</td>
+      <td>18.24</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>10710.00</td>
+      <td>13100.00</td>
+      <td>2390.00</td>
+      <td>18.24</td>
+    </tr>
+    <tr>
+      <th>637</th>
+      <td>6293.06</td>
+      <td>7700.00</td>
+      <td>1406.94</td>
+      <td>18.27</td>
+    </tr>
+    <tr>
+      <th>154</th>
+      <td>13370.87</td>
+      <td>11300.00</td>
+      <td>-2070.87</td>
+      <td>18.33</td>
+    </tr>
+    <tr>
+      <th>474</th>
+      <td>9355.15</td>
+      <td>7900.00</td>
+      <td>-1455.15</td>
+      <td>18.42</td>
+    </tr>
+    <tr>
+      <th>142</th>
+      <td>26420.53</td>
+      <td>32400.00</td>
+      <td>5979.47</td>
+      <td>18.46</td>
+    </tr>
+    <tr>
+      <th>179</th>
+      <td>3790.91</td>
+      <td>4650.00</td>
+      <td>859.09</td>
+      <td>18.48</td>
+    </tr>
+    <tr>
+      <th>704</th>
+      <td>5459.66</td>
+      <td>6700.00</td>
+      <td>1240.34</td>
+      <td>18.51</td>
+    </tr>
+    <tr>
+      <th>485</th>
+      <td>4480.19</td>
+      <td>5500.00</td>
+      <td>1019.81</td>
+      <td>18.54</td>
+    </tr>
+    <tr>
+      <th>333</th>
+      <td>8536.82</td>
+      <td>7200.00</td>
+      <td>-1336.82</td>
+      <td>18.57</td>
+    </tr>
+    <tr>
+      <th>420</th>
+      <td>40274.34</td>
+      <td>49500.00</td>
+      <td>9225.66</td>
+      <td>18.64</td>
+    </tr>
+    <tr>
+      <th>674</th>
+      <td>89582.34</td>
+      <td>75500.00</td>
+      <td>-14082.34</td>
+      <td>18.65</td>
+    </tr>
+    <tr>
+      <th>366</th>
+      <td>6884.55</td>
+      <td>5800.00</td>
+      <td>-1084.55</td>
+      <td>18.70</td>
+    </tr>
+    <tr>
+      <th>719</th>
+      <td>51073.39</td>
+      <td>43000.00</td>
+      <td>-8073.39</td>
+      <td>18.78</td>
+    </tr>
+    <tr>
+      <th>490</th>
+      <td>6090.18</td>
+      <td>7500.00</td>
+      <td>1409.82</td>
+      <td>18.80</td>
+    </tr>
+    <tr>
+      <th>642</th>
+      <td>19364.44</td>
+      <td>16300.00</td>
+      <td>-3064.44</td>
+      <td>18.80</td>
+    </tr>
+    <tr>
+      <th>694</th>
+      <td>8280.51</td>
+      <td>10200.00</td>
+      <td>1919.49</td>
+      <td>18.82</td>
+    </tr>
+    <tr>
+      <th>252</th>
+      <td>5598.65</td>
+      <td>6900.00</td>
+      <td>1301.35</td>
+      <td>18.86</td>
+    </tr>
+    <tr>
+      <th>498</th>
+      <td>85858.88</td>
+      <td>105999.00</td>
+      <td>20140.12</td>
+      <td>19.00</td>
+    </tr>
+    <tr>
+      <th>596</th>
+      <td>5506.55</td>
+      <td>6800.00</td>
+      <td>1293.45</td>
+      <td>19.02</td>
+    </tr>
+    <tr>
+      <th>597</th>
+      <td>8817.95</td>
+      <td>10900.00</td>
+      <td>2082.05</td>
+      <td>19.10</td>
+    </tr>
+    <tr>
+      <th>192</th>
+      <td>11319.17</td>
+      <td>9500.00</td>
+      <td>-1819.17</td>
+      <td>19.15</td>
+    </tr>
+    <tr>
+      <th>180</th>
+      <td>11445.18</td>
+      <td>9599.00</td>
+      <td>-1846.18</td>
+      <td>19.23</td>
+    </tr>
+    <tr>
+      <th>699</th>
+      <td>5650.29</td>
+      <td>7000.00</td>
+      <td>1349.71</td>
+      <td>19.28</td>
+    </tr>
+    <tr>
+      <th>634</th>
+      <td>4433.53</td>
+      <td>5500.00</td>
+      <td>1066.47</td>
+      <td>19.39</td>
+    </tr>
+    <tr>
+      <th>545</th>
+      <td>12724.62</td>
+      <td>15800.00</td>
+      <td>3075.38</td>
+      <td>19.46</td>
+    </tr>
+    <tr>
+      <th>771</th>
+      <td>2092.87</td>
+      <td>2600.00</td>
+      <td>507.13</td>
+      <td>19.50</td>
+    </tr>
+    <tr>
+      <th>478</th>
+      <td>36593.81</td>
+      <td>45500.00</td>
+      <td>8906.19</td>
+      <td>19.57</td>
+    </tr>
+    <tr>
+      <th>337</th>
+      <td>60274.39</td>
+      <td>75000.00</td>
+      <td>14725.61</td>
+      <td>19.63</td>
+    </tr>
+    <tr>
+      <th>184</th>
+      <td>46983.85</td>
+      <td>58500.00</td>
+      <td>11516.15</td>
+      <td>19.69</td>
+    </tr>
+    <tr>
+      <th>724</th>
+      <td>8747.37</td>
+      <td>10900.00</td>
+      <td>2152.63</td>
+      <td>19.75</td>
+    </tr>
+    <tr>
+      <th>129</th>
+      <td>22708.02</td>
+      <td>18950.00</td>
+      <td>-3758.02</td>
+      <td>19.83</td>
+    </tr>
+    <tr>
+      <th>773</th>
+      <td>13185.60</td>
+      <td>11000.00</td>
+      <td>-2185.60</td>
+      <td>19.87</td>
+    </tr>
+    <tr>
+      <th>305</th>
+      <td>9594.67</td>
+      <td>8000.00</td>
+      <td>-1594.67</td>
+      <td>19.93</td>
+    </tr>
+    <tr>
+      <th>678</th>
+      <td>16676.56</td>
+      <td>13900.00</td>
+      <td>-2776.56</td>
+      <td>19.98</td>
+    </tr>
+    <tr>
+      <th>718</th>
+      <td>5399.84</td>
+      <td>4500.00</td>
+      <td>-899.84</td>
+      <td>20.00</td>
+    </tr>
+    <tr>
+      <th>569</th>
+      <td>6639.69</td>
+      <td>8300.00</td>
+      <td>1660.31</td>
+      <td>20.00</td>
+    </tr>
+    <tr>
+      <th>581</th>
+      <td>5676.99</td>
+      <td>7100.00</td>
+      <td>1423.01</td>
+      <td>20.04</td>
+    </tr>
+    <tr>
+      <th>591</th>
+      <td>34847.46</td>
+      <td>28950.00</td>
+      <td>-5897.46</td>
+      <td>20.37</td>
+    </tr>
+    <tr>
+      <th>572</th>
+      <td>3010.71</td>
+      <td>2499.00</td>
+      <td>-511.71</td>
+      <td>20.48</td>
+    </tr>
+    <tr>
+      <th>75</th>
+      <td>5315.27</td>
+      <td>6700.00</td>
+      <td>1384.73</td>
+      <td>20.67</td>
+    </tr>
+    <tr>
+      <th>404</th>
+      <td>6940.02</td>
+      <td>8750.00</td>
+      <td>1809.98</td>
+      <td>20.69</td>
+    </tr>
+    <tr>
+      <th>574</th>
+      <td>6056.80</td>
+      <td>7650.00</td>
+      <td>1593.20</td>
+      <td>20.83</td>
+    </tr>
+    <tr>
+      <th>599</th>
+      <td>8708.11</td>
+      <td>7200.00</td>
+      <td>-1508.11</td>
+      <td>20.95</td>
+    </tr>
+    <tr>
+      <th>715</th>
+      <td>16175.59</td>
+      <td>20477.54</td>
+      <td>4301.95</td>
+      <td>21.01</td>
+    </tr>
+    <tr>
+      <th>88</th>
+      <td>5290.04</td>
+      <td>6700.00</td>
+      <td>1409.96</td>
+      <td>21.04</td>
+    </tr>
+    <tr>
+      <th>656</th>
+      <td>1973.68</td>
+      <td>2500.00</td>
+      <td>526.32</td>
+      <td>21.05</td>
+    </tr>
+    <tr>
+      <th>277</th>
+      <td>4181.72</td>
+      <td>5300.00</td>
+      <td>1118.28</td>
+      <td>21.10</td>
+    </tr>
+    <tr>
+      <th>649</th>
+      <td>4732.08</td>
+      <td>6000.00</td>
+      <td>1267.92</td>
+      <td>21.13</td>
+    </tr>
+    <tr>
+      <th>468</th>
+      <td>12222.81</td>
+      <td>15500.00</td>
+      <td>3277.19</td>
+      <td>21.14</td>
+    </tr>
+    <tr>
+      <th>148</th>
+      <td>32724.25</td>
+      <td>41500.00</td>
+      <td>8775.75</td>
+      <td>21.15</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>13328.75</td>
+      <td>11000.00</td>
+      <td>-2328.75</td>
+      <td>21.17</td>
+    </tr>
+    <tr>
+      <th>64</th>
+      <td>36828.78</td>
+      <td>46800.00</td>
+      <td>9971.22</td>
+      <td>21.31</td>
+    </tr>
+    <tr>
+      <th>494</th>
+      <td>6920.60</td>
+      <td>8800.00</td>
+      <td>1879.40</td>
+      <td>21.36</td>
+    </tr>
+    <tr>
+      <th>149</th>
+      <td>19020.64</td>
+      <td>24193.65</td>
+      <td>5173.01</td>
+      <td>21.38</td>
+    </tr>
+    <tr>
+      <th>234</th>
+      <td>24740.92</td>
+      <td>31500.00</td>
+      <td>6759.08</td>
+      <td>21.46</td>
+    </tr>
+    <tr>
+      <th>128</th>
+      <td>18255.49</td>
+      <td>15000.00</td>
+      <td>-3255.49</td>
+      <td>21.70</td>
+    </tr>
+    <tr>
+      <th>775</th>
+      <td>11692.08</td>
+      <td>9600.00</td>
+      <td>-2092.08</td>
+      <td>21.79</td>
+    </tr>
+    <tr>
+      <th>765</th>
+      <td>17173.49</td>
+      <td>22000.00</td>
+      <td>4826.51</td>
+      <td>21.94</td>
+    </tr>
+    <tr>
+      <th>342</th>
+      <td>11700.38</td>
+      <td>15000.00</td>
+      <td>3299.62</td>
+      <td>22.00</td>
+    </tr>
+    <tr>
+      <th>799</th>
+      <td>2623.69</td>
+      <td>2150.00</td>
+      <td>-473.69</td>
+      <td>22.03</td>
+    </tr>
+    <tr>
+      <th>507</th>
+      <td>2026.96</td>
+      <td>2600.00</td>
+      <td>573.04</td>
+      <td>22.04</td>
+    </tr>
+    <tr>
+      <th>183</th>
+      <td>8434.23</td>
+      <td>6900.00</td>
+      <td>-1534.23</td>
+      <td>22.24</td>
+    </tr>
+    <tr>
+      <th>261</th>
+      <td>2717.11</td>
+      <td>3500.00</td>
+      <td>782.89</td>
+      <td>22.37</td>
+    </tr>
+    <tr>
+      <th>215</th>
+      <td>2713.87</td>
+      <td>3500.00</td>
+      <td>786.13</td>
+      <td>22.46</td>
+    </tr>
+    <tr>
+      <th>741</th>
+      <td>6200.75</td>
+      <td>8000.00</td>
+      <td>1799.25</td>
+      <td>22.49</td>
+    </tr>
+    <tr>
+      <th>371</th>
+      <td>10783.10</td>
+      <td>8800.00</td>
+      <td>-1983.10</td>
+      <td>22.54</td>
+    </tr>
+    <tr>
+      <th>511</th>
+      <td>22566.89</td>
+      <td>18400.00</td>
+      <td>-4166.89</td>
+      <td>22.65</td>
+    </tr>
+    <tr>
+      <th>558</th>
+      <td>18486.02</td>
+      <td>23900.00</td>
+      <td>5413.98</td>
+      <td>22.65</td>
+    </tr>
+    <tr>
+      <th>105</th>
+      <td>42831.62</td>
+      <td>55555.00</td>
+      <td>12723.38</td>
+      <td>22.90</td>
+    </tr>
+    <tr>
+      <th>91</th>
+      <td>18116.63</td>
+      <td>23500.00</td>
+      <td>5383.37</td>
+      <td>22.91</td>
+    </tr>
+    <tr>
+      <th>113</th>
+      <td>13535.73</td>
+      <td>11000.00</td>
+      <td>-2535.73</td>
+      <td>23.05</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>9723.51</td>
+      <td>12650.00</td>
+      <td>2926.49</td>
+      <td>23.13</td>
+    </tr>
+    <tr>
+      <th>479</th>
+      <td>14919.17</td>
+      <td>12114.35</td>
+      <td>-2804.82</td>
+      <td>23.15</td>
+    </tr>
+    <tr>
+      <th>447</th>
+      <td>17573.11</td>
+      <td>22900.00</td>
+      <td>5326.89</td>
+      <td>23.26</td>
+    </tr>
+    <tr>
+      <th>312</th>
+      <td>20558.01</td>
+      <td>16672.50</td>
+      <td>-3885.51</td>
+      <td>23.30</td>
+    </tr>
+    <tr>
+      <th>207</th>
+      <td>16030.55</td>
+      <td>12999.00</td>
+      <td>-3031.55</td>
+      <td>23.32</td>
+    </tr>
+    <tr>
+      <th>294</th>
+      <td>17535.66</td>
+      <td>22900.00</td>
+      <td>5364.34</td>
+      <td>23.43</td>
+    </tr>
+    <tr>
+      <th>533</th>
+      <td>2977.77</td>
+      <td>3900.00</td>
+      <td>922.23</td>
+      <td>23.65</td>
+    </tr>
+    <tr>
+      <th>139</th>
+      <td>22121.67</td>
+      <td>29000.00</td>
+      <td>6878.33</td>
+      <td>23.72</td>
+    </tr>
+    <tr>
+      <th>40</th>
+      <td>29567.87</td>
+      <td>38764.00</td>
+      <td>9196.13</td>
+      <td>23.72</td>
+    </tr>
+    <tr>
+      <th>358</th>
+      <td>22081.93</td>
+      <td>28999.00</td>
+      <td>6917.07</td>
+      <td>23.85</td>
+    </tr>
+    <tr>
+      <th>611</th>
+      <td>8174.40</td>
+      <td>6600.00</td>
+      <td>-1574.40</td>
+      <td>23.85</td>
+    </tr>
+    <tr>
+      <th>456</th>
+      <td>2813.36</td>
+      <td>3700.00</td>
+      <td>886.64</td>
+      <td>23.96</td>
+    </tr>
+    <tr>
+      <th>629</th>
+      <td>9728.36</td>
+      <td>12800.00</td>
+      <td>3071.64</td>
+      <td>24.00</td>
+    </tr>
+    <tr>
+      <th>169</th>
+      <td>23236.27</td>
+      <td>18700.00</td>
+      <td>-4536.27</td>
+      <td>24.26</td>
+    </tr>
+    <tr>
+      <th>696</th>
+      <td>14539.53</td>
+      <td>11700.00</td>
+      <td>-2839.53</td>
+      <td>24.27</td>
+    </tr>
+    <tr>
+      <th>236</th>
+      <td>4915.54</td>
+      <td>6500.00</td>
+      <td>1584.46</td>
+      <td>24.38</td>
+    </tr>
+    <tr>
+      <th>505</th>
+      <td>22276.96</td>
+      <td>17900.00</td>
+      <td>-4376.96</td>
+      <td>24.45</td>
+    </tr>
+    <tr>
+      <th>77</th>
+      <td>10082.85</td>
+      <td>8100.00</td>
+      <td>-1982.85</td>
+      <td>24.48</td>
+    </tr>
+    <tr>
+      <th>729</th>
+      <td>3113.25</td>
+      <td>2500.00</td>
+      <td>-613.25</td>
+      <td>24.53</td>
+    </tr>
+    <tr>
+      <th>760</th>
+      <td>30902.33</td>
+      <td>24800.00</td>
+      <td>-6102.33</td>
+      <td>24.61</td>
+    </tr>
+    <tr>
+      <th>258</th>
+      <td>12346.01</td>
+      <td>9900.00</td>
+      <td>-2446.01</td>
+      <td>24.71</td>
+    </tr>
+    <tr>
+      <th>453</th>
+      <td>6161.68</td>
+      <td>8200.00</td>
+      <td>2038.32</td>
+      <td>24.86</td>
+    </tr>
+    <tr>
+      <th>616</th>
+      <td>21017.12</td>
+      <td>28000.00</td>
+      <td>6982.88</td>
+      <td>24.94</td>
+    </tr>
+    <tr>
+      <th>241</th>
+      <td>3748.29</td>
+      <td>3000.00</td>
+      <td>-748.29</td>
+      <td>24.94</td>
+    </tr>
+    <tr>
+      <th>226</th>
+      <td>3061.17</td>
+      <td>2450.00</td>
+      <td>-611.17</td>
+      <td>24.95</td>
+    </tr>
+    <tr>
+      <th>111</th>
+      <td>9998.69</td>
+      <td>8000.00</td>
+      <td>-1998.69</td>
+      <td>24.98</td>
+    </tr>
+    <tr>
+      <th>530</th>
+      <td>3749.11</td>
+      <td>5000.00</td>
+      <td>1250.89</td>
+      <td>25.02</td>
+    </tr>
+    <tr>
+      <th>522</th>
+      <td>5390.58</td>
+      <td>7200.00</td>
+      <td>1809.42</td>
+      <td>25.13</td>
+    </tr>
+    <tr>
+      <th>621</th>
+      <td>9709.07</td>
+      <td>13000.00</td>
+      <td>3290.93</td>
+      <td>25.31</td>
+    </tr>
+    <tr>
+      <th>803</th>
+      <td>3760.79</td>
+      <td>3000.00</td>
+      <td>-760.79</td>
+      <td>25.36</td>
+    </tr>
+    <tr>
+      <th>439</th>
+      <td>4400.37</td>
+      <td>5900.00</td>
+      <td>1499.63</td>
+      <td>25.42</td>
+    </tr>
+    <tr>
+      <th>87</th>
+      <td>3203.55</td>
+      <td>4299.00</td>
+      <td>1095.45</td>
+      <td>25.48</td>
+    </tr>
+    <tr>
+      <th>436</th>
+      <td>18827.10</td>
+      <td>14999.00</td>
+      <td>-3828.10</td>
+      <td>25.52</td>
+    </tr>
+    <tr>
+      <th>635</th>
+      <td>9674.76</td>
+      <td>13000.00</td>
+      <td>3325.24</td>
+      <td>25.58</td>
+    </tr>
+    <tr>
+      <th>350</th>
+      <td>5205.98</td>
+      <td>7000.00</td>
+      <td>1794.02</td>
+      <td>25.63</td>
+    </tr>
+    <tr>
+      <th>751</th>
+      <td>18944.86</td>
+      <td>25500.00</td>
+      <td>6555.14</td>
+      <td>25.71</td>
+    </tr>
+    <tr>
+      <th>341</th>
+      <td>31179.60</td>
+      <td>42000.00</td>
+      <td>10820.40</td>
+      <td>25.76</td>
+    </tr>
+    <tr>
+      <th>804</th>
+      <td>26319.25</td>
+      <td>20900.00</td>
+      <td>-5419.25</td>
+      <td>25.93</td>
+    </tr>
+    <tr>
+      <th>172</th>
+      <td>2959.68</td>
+      <td>2350.00</td>
+      <td>-609.68</td>
+      <td>25.94</td>
+    </tr>
+    <tr>
+      <th>214</th>
+      <td>8863.85</td>
+      <td>12000.00</td>
+      <td>3136.15</td>
+      <td>26.13</td>
+    </tr>
+    <tr>
+      <th>652</th>
+      <td>38039.83</td>
+      <td>51500.00</td>
+      <td>13460.17</td>
+      <td>26.14</td>
+    </tr>
+    <tr>
+      <th>112</th>
+      <td>13761.17</td>
+      <td>10900.00</td>
+      <td>-2861.17</td>
+      <td>26.25</td>
+    </tr>
+    <tr>
+      <th>687</th>
+      <td>22356.85</td>
+      <td>17700.00</td>
+      <td>-4656.85</td>
+      <td>26.31</td>
+    </tr>
+    <tr>
+      <th>144</th>
+      <td>10767.98</td>
+      <td>8500.00</td>
+      <td>-2267.98</td>
+      <td>26.68</td>
+    </tr>
+    <tr>
+      <th>446</th>
+      <td>37385.04</td>
+      <td>29500.00</td>
+      <td>-7885.04</td>
+      <td>26.73</td>
+    </tr>
+    <tr>
+      <th>108</th>
+      <td>10013.18</td>
+      <td>7900.00</td>
+      <td>-2113.18</td>
+      <td>26.75</td>
+    </tr>
+    <tr>
+      <th>37</th>
+      <td>6589.41</td>
+      <td>8999.00</td>
+      <td>2409.59</td>
+      <td>26.78</td>
+    </tr>
+    <tr>
+      <th>311</th>
+      <td>24995.51</td>
+      <td>19700.00</td>
+      <td>-5295.51</td>
+      <td>26.88</td>
+    </tr>
+    <tr>
+      <th>677</th>
+      <td>7821.99</td>
+      <td>10700.00</td>
+      <td>2878.01</td>
+      <td>26.90</td>
+    </tr>
+    <tr>
+      <th>279</th>
+      <td>6736.55</td>
+      <td>5300.00</td>
+      <td>-1436.55</td>
+      <td>27.10</td>
+    </tr>
+    <tr>
+      <th>307</th>
+      <td>11445.85</td>
+      <td>9000.00</td>
+      <td>-2445.85</td>
+      <td>27.18</td>
+    </tr>
+    <tr>
+      <th>361</th>
+      <td>2909.80</td>
+      <td>4000.00</td>
+      <td>1090.20</td>
+      <td>27.26</td>
+    </tr>
+    <tr>
+      <th>175</th>
+      <td>24627.39</td>
+      <td>33900.00</td>
+      <td>9272.61</td>
+      <td>27.35</td>
+    </tr>
+    <tr>
+      <th>293</th>
+      <td>82760.24</td>
+      <td>64900.00</td>
+      <td>-17860.24</td>
+      <td>27.52</td>
+    </tr>
+    <tr>
+      <th>497</th>
+      <td>5787.88</td>
+      <td>8000.00</td>
+      <td>2212.12</td>
+      <td>27.65</td>
+    </tr>
+    <tr>
+      <th>527</th>
+      <td>19048.95</td>
+      <td>14900.00</td>
+      <td>-4148.95</td>
+      <td>27.85</td>
+    </tr>
+    <tr>
+      <th>730</th>
+      <td>4668.85</td>
+      <td>3650.00</td>
+      <td>-1018.85</td>
+      <td>27.91</td>
+    </tr>
+    <tr>
+      <th>561</th>
+      <td>7564.39</td>
+      <td>10500.00</td>
+      <td>2935.61</td>
+      <td>27.96</td>
+    </tr>
+    <tr>
+      <th>670</th>
+      <td>30477.38</td>
+      <td>23800.00</td>
+      <td>-6677.38</td>
+      <td>28.06</td>
+    </tr>
+    <tr>
+      <th>296</th>
+      <td>11154.62</td>
+      <td>8700.00</td>
+      <td>-2454.62</td>
+      <td>28.21</td>
+    </tr>
+    <tr>
+      <th>80</th>
+      <td>21802.49</td>
+      <td>30385.00</td>
+      <td>8582.51</td>
+      <td>28.25</td>
+    </tr>
+    <tr>
+      <th>151</th>
+      <td>65745.19</td>
+      <td>92222.00</td>
+      <td>26476.81</td>
+      <td>28.71</td>
+    </tr>
+    <tr>
+      <th>633</th>
+      <td>21739.14</td>
+      <td>30500.00</td>
+      <td>8760.86</td>
+      <td>28.72</td>
+    </tr>
+    <tr>
+      <th>162</th>
+      <td>25245.24</td>
+      <td>35500.00</td>
+      <td>10254.76</td>
+      <td>28.89</td>
+    </tr>
+    <tr>
+      <th>598</th>
+      <td>15218.48</td>
+      <td>11800.00</td>
+      <td>-3418.48</td>
+      <td>28.97</td>
+    </tr>
+    <tr>
+      <th>470</th>
+      <td>55229.59</td>
+      <td>77777.00</td>
+      <td>22547.41</td>
+      <td>28.99</td>
+    </tr>
+    <tr>
+      <th>283</th>
+      <td>55229.59</td>
+      <td>77777.00</td>
+      <td>22547.41</td>
+      <td>28.99</td>
+    </tr>
+    <tr>
+      <th>249</th>
+      <td>8665.27</td>
+      <td>6700.00</td>
+      <td>-1965.27</td>
+      <td>29.33</td>
+    </tr>
+    <tr>
+      <th>327</th>
+      <td>11514.96</td>
+      <td>8900.00</td>
+      <td>-2614.96</td>
+      <td>29.38</td>
+    </tr>
+    <tr>
+      <th>186</th>
+      <td>2951.62</td>
+      <td>4200.00</td>
+      <td>1248.38</td>
+      <td>29.72</td>
+    </tr>
+    <tr>
+      <th>355</th>
+      <td>5449.56</td>
+      <td>4200.00</td>
+      <td>-1249.56</td>
+      <td>29.75</td>
+    </tr>
+    <tr>
+      <th>70</th>
+      <td>39888.50</td>
+      <td>57000.00</td>
+      <td>17111.50</td>
+      <td>30.02</td>
+    </tr>
+    <tr>
+      <th>443</th>
+      <td>8386.38</td>
+      <td>6450.00</td>
+      <td>-1936.38</td>
+      <td>30.02</td>
+    </tr>
+    <tr>
+      <th>176</th>
+      <td>5242.13</td>
+      <td>7500.00</td>
+      <td>2257.87</td>
+      <td>30.10</td>
+    </tr>
+    <tr>
+      <th>693</th>
+      <td>17055.42</td>
+      <td>24500.00</td>
+      <td>7444.58</td>
+      <td>30.39</td>
+    </tr>
+    <tr>
+      <th>783</th>
+      <td>18140.29</td>
+      <td>13900.00</td>
+      <td>-4240.29</td>
+      <td>30.51</td>
+    </tr>
+    <tr>
+      <th>477</th>
+      <td>15633.25</td>
+      <td>22500.00</td>
+      <td>6866.75</td>
+      <td>30.52</td>
+    </tr>
+    <tr>
+      <th>640</th>
+      <td>10150.80</td>
+      <td>7777.00</td>
+      <td>-2373.80</td>
+      <td>30.52</td>
+    </tr>
+    <tr>
+      <th>774</th>
+      <td>2485.85</td>
+      <td>1900.00</td>
+      <td>-585.85</td>
+      <td>30.83</td>
+    </tr>
+    <tr>
+      <th>763</th>
+      <td>2073.96</td>
+      <td>2999.00</td>
+      <td>925.04</td>
+      <td>30.84</td>
+    </tr>
+    <tr>
+      <th>132</th>
+      <td>19812.54</td>
+      <td>28713.75</td>
+      <td>8901.21</td>
+      <td>31.00</td>
+    </tr>
+    <tr>
+      <th>365</th>
+      <td>12983.41</td>
+      <td>9900.00</td>
+      <td>-3083.41</td>
+      <td>31.15</td>
+    </tr>
+    <tr>
+      <th>46</th>
+      <td>7869.15</td>
+      <td>5999.00</td>
+      <td>-1870.15</td>
+      <td>31.17</td>
+    </tr>
+    <tr>
+      <th>710</th>
+      <td>12071.60</td>
+      <td>9200.00</td>
+      <td>-2871.60</td>
+      <td>31.21</td>
+    </tr>
+    <tr>
+      <th>534</th>
+      <td>8941.06</td>
+      <td>13000.00</td>
+      <td>4058.94</td>
+      <td>31.22</td>
+    </tr>
+    <tr>
+      <th>499</th>
+      <td>13336.31</td>
+      <td>10150.00</td>
+      <td>-3186.31</td>
+      <td>31.39</td>
+    </tr>
+    <tr>
+      <th>30</th>
+      <td>14200.19</td>
+      <td>10800.00</td>
+      <td>-3400.19</td>
+      <td>31.48</td>
+    </tr>
+    <tr>
+      <th>66</th>
+      <td>3105.70</td>
+      <td>4550.00</td>
+      <td>1444.30</td>
+      <td>31.74</td>
+    </tr>
+    <tr>
+      <th>682</th>
+      <td>4624.41</td>
+      <td>6800.00</td>
+      <td>2175.59</td>
+      <td>31.99</td>
+    </tr>
+    <tr>
+      <th>441</th>
+      <td>3511.87</td>
+      <td>5200.00</td>
+      <td>1688.13</td>
+      <td>32.46</td>
+    </tr>
+    <tr>
+      <th>520</th>
+      <td>4779.20</td>
+      <td>3600.00</td>
+      <td>-1179.20</td>
+      <td>32.76</td>
+    </tr>
+    <tr>
+      <th>644</th>
+      <td>11942.86</td>
+      <td>17777.00</td>
+      <td>5834.14</td>
+      <td>32.82</td>
+    </tr>
+    <tr>
+      <th>81</th>
+      <td>16542.07</td>
+      <td>12450.00</td>
+      <td>-4092.07</td>
+      <td>32.87</td>
+    </tr>
+    <tr>
+      <th>400</th>
+      <td>15827.38</td>
+      <td>11900.00</td>
+      <td>-3927.38</td>
+      <td>33.00</td>
+    </tr>
+    <tr>
+      <th>71</th>
+      <td>4216.67</td>
+      <td>6300.00</td>
+      <td>2083.33</td>
+      <td>33.07</td>
+    </tr>
+    <tr>
+      <th>147</th>
+      <td>9333.37</td>
+      <td>7000.00</td>
+      <td>-2333.37</td>
+      <td>33.33</td>
+    </tr>
+    <tr>
+      <th>559</th>
+      <td>7732.20</td>
+      <td>11600.00</td>
+      <td>3867.80</td>
+      <td>33.34</td>
+    </tr>
+    <tr>
+      <th>766</th>
+      <td>12012.45</td>
+      <td>9000.00</td>
+      <td>-3012.45</td>
+      <td>33.47</td>
+    </tr>
+    <tr>
+      <th>728</th>
+      <td>19812.54</td>
+      <td>29800.00</td>
+      <td>9987.46</td>
+      <td>33.51</td>
+    </tr>
+    <tr>
+      <th>245</th>
+      <td>5317.17</td>
+      <td>8000.00</td>
+      <td>2682.83</td>
+      <td>33.54</td>
+    </tr>
+    <tr>
+      <th>114</th>
+      <td>46006.01</td>
+      <td>34419.45</td>
+      <td>-11586.56</td>
+      <td>33.66</td>
+    </tr>
+    <tr>
+      <th>543</th>
+      <td>36381.57</td>
+      <td>55000.00</td>
+      <td>18618.43</td>
+      <td>33.85</td>
+    </tr>
+    <tr>
+      <th>703</th>
+      <td>23207.21</td>
+      <td>17300.00</td>
+      <td>-5907.21</td>
+      <td>34.15</td>
+    </tr>
+    <tr>
+      <th>438</th>
+      <td>12083.74</td>
+      <td>9000.00</td>
+      <td>-3083.74</td>
+      <td>34.26</td>
+    </tr>
+    <tr>
+      <th>646</th>
+      <td>1642.87</td>
+      <td>2500.00</td>
+      <td>857.13</td>
+      <td>34.29</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>3900.79</td>
+      <td>2900.00</td>
+      <td>-1000.79</td>
+      <td>34.51</td>
+    </tr>
+    <tr>
+      <th>431</th>
+      <td>67578.74</td>
+      <td>49900.00</td>
+      <td>-17678.74</td>
+      <td>35.43</td>
+    </tr>
+    <tr>
+      <th>273</th>
+      <td>5030.79</td>
+      <td>7800.00</td>
+      <td>2769.21</td>
+      <td>35.50</td>
+    </tr>
+    <tr>
+      <th>692</th>
+      <td>12682.44</td>
+      <td>9350.00</td>
+      <td>-3332.44</td>
+      <td>35.64</td>
+    </tr>
+    <tr>
+      <th>25</th>
+      <td>44889.92</td>
+      <td>69900.00</td>
+      <td>25010.08</td>
+      <td>35.78</td>
+    </tr>
+    <tr>
+      <th>335</th>
+      <td>5702.84</td>
+      <td>9000.00</td>
+      <td>3297.16</td>
+      <td>36.64</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>11427.09</td>
+      <td>8350.00</td>
+      <td>-3077.09</td>
+      <td>36.85</td>
+    </tr>
+    <tr>
+      <th>310</th>
+      <td>13828.77</td>
+      <td>21900.00</td>
+      <td>8071.23</td>
+      <td>36.85</td>
+    </tr>
+    <tr>
+      <th>600</th>
+      <td>5591.66</td>
+      <td>8900.00</td>
+      <td>3308.34</td>
+      <td>37.17</td>
+    </tr>
+    <tr>
+      <th>617</th>
+      <td>16260.94</td>
+      <td>11850.00</td>
+      <td>-4410.94</td>
+      <td>37.22</td>
+    </tr>
+    <tr>
+      <th>209</th>
+      <td>19627.05</td>
+      <td>14300.00</td>
+      <td>-5327.05</td>
+      <td>37.25</td>
+    </tr>
+    <tr>
+      <th>394</th>
+      <td>2005.13</td>
+      <td>3200.00</td>
+      <td>1194.87</td>
+      <td>37.34</td>
+    </tr>
+    <tr>
+      <th>727</th>
+      <td>46345.58</td>
+      <td>74000.00</td>
+      <td>27654.42</td>
+      <td>37.37</td>
+    </tr>
+    <tr>
+      <th>777</th>
+      <td>51526.76</td>
+      <td>37500.00</td>
+      <td>-14026.76</td>
+      <td>37.40</td>
+    </tr>
+    <tr>
+      <th>785</th>
+      <td>29732.92</td>
+      <td>47700.00</td>
+      <td>17967.08</td>
+      <td>37.67</td>
+    </tr>
+    <tr>
+      <th>515</th>
+      <td>46378.55</td>
+      <td>74500.00</td>
+      <td>28121.45</td>
+      <td>37.75</td>
+    </tr>
+    <tr>
+      <th>31</th>
+      <td>16572.98</td>
+      <td>11999.00</td>
+      <td>-4573.98</td>
+      <td>38.12</td>
+    </tr>
+    <tr>
+      <th>772</th>
+      <td>6644.61</td>
+      <td>4800.00</td>
+      <td>-1844.61</td>
+      <td>38.43</td>
+    </tr>
+    <tr>
+      <th>460</th>
+      <td>12197.10</td>
+      <td>8800.00</td>
+      <td>-3397.10</td>
+      <td>38.60</td>
+    </tr>
+    <tr>
+      <th>304</th>
+      <td>64451.49</td>
+      <td>104999.00</td>
+      <td>40547.51</td>
+      <td>38.62</td>
+    </tr>
+    <tr>
+      <th>367</th>
+      <td>39888.50</td>
+      <td>65000.00</td>
+      <td>25111.50</td>
+      <td>38.63</td>
+    </tr>
+    <tr>
+      <th>220</th>
+      <td>17656.32</td>
+      <td>28793.04</td>
+      <td>11136.72</td>
+      <td>38.68</td>
+    </tr>
+    <tr>
+      <th>50</th>
+      <td>15936.61</td>
+      <td>11400.00</td>
+      <td>-4536.61</td>
+      <td>39.79</td>
+    </tr>
+    <tr>
+      <th>388</th>
+      <td>71675.00</td>
+      <td>121500.00</td>
+      <td>49825.00</td>
+      <td>41.01</td>
+    </tr>
+    <tr>
+      <th>551</th>
+      <td>16226.28</td>
+      <td>11500.00</td>
+      <td>-4726.28</td>
+      <td>41.10</td>
+    </tr>
+    <tr>
+      <th>302</th>
+      <td>8889.98</td>
+      <td>6299.00</td>
+      <td>-2590.98</td>
+      <td>41.13</td>
+    </tr>
+    <tr>
+      <th>235</th>
+      <td>9669.09</td>
+      <td>16500.00</td>
+      <td>6830.91</td>
+      <td>41.40</td>
+    </tr>
+    <tr>
+      <th>535</th>
+      <td>14822.55</td>
+      <td>10450.00</td>
+      <td>-4372.55</td>
+      <td>41.84</td>
+    </tr>
+    <tr>
+      <th>601</th>
+      <td>16930.40</td>
+      <td>29500.00</td>
+      <td>12569.60</td>
+      <td>42.61</td>
+    </tr>
+    <tr>
+      <th>73</th>
+      <td>5700.21</td>
+      <td>10000.00</td>
+      <td>4299.79</td>
+      <td>43.00</td>
+    </tr>
+    <tr>
+      <th>701</th>
+      <td>32880.90</td>
+      <td>22900.00</td>
+      <td>-9980.90</td>
+      <td>43.58</td>
+    </tr>
+    <tr>
+      <th>137</th>
+      <td>6477.68</td>
+      <td>4500.00</td>
+      <td>-1977.68</td>
+      <td>43.95</td>
+    </tr>
+    <tr>
+      <th>756</th>
+      <td>10016.88</td>
+      <td>18000.00</td>
+      <td>7983.12</td>
+      <td>44.35</td>
+    </tr>
+    <tr>
+      <th>237</th>
+      <td>11415.61</td>
+      <td>7900.00</td>
+      <td>-3515.61</td>
+      <td>44.50</td>
+    </tr>
+    <tr>
+      <th>539</th>
+      <td>2050.79</td>
+      <td>3700.00</td>
+      <td>1649.21</td>
+      <td>44.57</td>
+    </tr>
+    <tr>
+      <th>328</th>
+      <td>13804.82</td>
+      <td>9500.00</td>
+      <td>-4304.82</td>
+      <td>45.31</td>
+    </tr>
+    <tr>
+      <th>676</th>
+      <td>24080.07</td>
+      <td>16555.00</td>
+      <td>-7525.07</td>
+      <td>45.45</td>
+    </tr>
+    <tr>
+      <th>403</th>
+      <td>8443.28</td>
+      <td>15500.00</td>
+      <td>7056.72</td>
+      <td>45.53</td>
+    </tr>
+    <tr>
+      <th>444</th>
+      <td>17048.89</td>
+      <td>11707.80</td>
+      <td>-5341.09</td>
+      <td>45.62</td>
+    </tr>
+    <tr>
+      <th>69</th>
+      <td>8419.88</td>
+      <td>15500.00</td>
+      <td>7080.12</td>
+      <td>45.68</td>
+    </tr>
+    <tr>
+      <th>221</th>
+      <td>31124.28</td>
+      <td>21300.00</td>
+      <td>-9824.28</td>
+      <td>46.12</td>
+    </tr>
+    <tr>
+      <th>428</th>
+      <td>13157.10</td>
+      <td>9000.00</td>
+      <td>-4157.10</td>
+      <td>46.19</td>
+    </tr>
+    <tr>
+      <th>643</th>
+      <td>1733.85</td>
+      <td>1185.60</td>
+      <td>-548.25</td>
+      <td>46.24</td>
+    </tr>
+    <tr>
+      <th>41</th>
+      <td>38632.53</td>
+      <td>73500.00</td>
+      <td>34867.47</td>
+      <td>47.44</td>
+    </tr>
+    <tr>
+      <th>612</th>
+      <td>62100.23</td>
+      <td>42000.00</td>
+      <td>-20100.23</td>
+      <td>47.86</td>
+    </tr>
+    <tr>
+      <th>628</th>
+      <td>1196.37</td>
+      <td>2300.00</td>
+      <td>1103.63</td>
+      <td>47.98</td>
+    </tr>
+    <tr>
+      <th>794</th>
+      <td>65898.02</td>
+      <td>129222.00</td>
+      <td>63323.98</td>
+      <td>49.00</td>
+    </tr>
+    <tr>
+      <th>738</th>
+      <td>2313.02</td>
+      <td>1550.00</td>
+      <td>-763.02</td>
+      <td>49.23</td>
+    </tr>
+    <tr>
+      <th>164</th>
+      <td>2972.03</td>
+      <td>1990.00</td>
+      <td>-982.03</td>
+      <td>49.35</td>
+    </tr>
+    <tr>
+      <th>228</th>
+      <td>1417.83</td>
+      <td>2800.00</td>
+      <td>1382.17</td>
+      <td>49.36</td>
+    </tr>
+    <tr>
+      <th>502</th>
+      <td>22471.17</td>
+      <td>44700.00</td>
+      <td>22228.83</td>
+      <td>49.73</td>
+    </tr>
+    <tr>
+      <th>721</th>
+      <td>71981.49</td>
+      <td>144900.00</td>
+      <td>72918.51</td>
+      <td>50.32</td>
+    </tr>
+    <tr>
+      <th>481</th>
+      <td>50745.07</td>
+      <td>33715.50</td>
+      <td>-17029.57</td>
+      <td>50.51</td>
+    </tr>
+    <tr>
+      <th>577</th>
+      <td>7343.52</td>
+      <td>14999.00</td>
+      <td>7655.48</td>
+      <td>51.04</td>
+    </tr>
+    <tr>
+      <th>519</th>
+      <td>26609.57</td>
+      <td>17600.00</td>
+      <td>-9009.57</td>
+      <td>51.19</td>
+    </tr>
+    <tr>
+      <th>275</th>
+      <td>17888.88</td>
+      <td>11800.00</td>
+      <td>-6088.88</td>
+      <td>51.60</td>
+    </tr>
+    <tr>
+      <th>457</th>
+      <td>20479.37</td>
+      <td>13500.00</td>
+      <td>-6979.37</td>
+      <td>51.70</td>
+    </tr>
+    <tr>
+      <th>603</th>
+      <td>12461.52</td>
+      <td>8200.00</td>
+      <td>-4261.52</td>
+      <td>51.97</td>
+    </tr>
+    <tr>
+      <th>401</th>
+      <td>20828.11</td>
+      <td>13700.00</td>
+      <td>-7128.11</td>
+      <td>52.03</td>
+    </tr>
+    <tr>
+      <th>362</th>
+      <td>18980.92</td>
+      <td>39900.00</td>
+      <td>20919.08</td>
+      <td>52.43</td>
+    </tr>
+    <tr>
+      <th>780</th>
+      <td>28362.55</td>
+      <td>60500.00</td>
+      <td>32137.45</td>
+      <td>53.12</td>
+    </tr>
+    <tr>
+      <th>99</th>
+      <td>3521.95</td>
+      <td>2300.00</td>
+      <td>-1221.95</td>
+      <td>53.13</td>
+    </tr>
+    <tr>
+      <th>102</th>
+      <td>9427.42</td>
+      <td>20500.00</td>
+      <td>11072.58</td>
+      <td>54.01</td>
+    </tr>
+    <tr>
+      <th>370</th>
+      <td>5408.75</td>
+      <td>3500.00</td>
+      <td>-1908.75</td>
+      <td>54.54</td>
+    </tr>
+    <tr>
+      <th>516</th>
+      <td>10854.85</td>
+      <td>24000.00</td>
+      <td>13145.15</td>
+      <td>54.77</td>
+    </tr>
+    <tr>
+      <th>48</th>
+      <td>65041.89</td>
+      <td>41900.00</td>
+      <td>-23141.89</td>
+      <td>55.23</td>
+    </tr>
+    <tr>
+      <th>491</th>
+      <td>7715.56</td>
+      <td>4900.00</td>
+      <td>-2815.56</td>
+      <td>57.46</td>
+    </tr>
+    <tr>
+      <th>318</th>
+      <td>30316.04</td>
+      <td>19200.00</td>
+      <td>-11116.04</td>
+      <td>57.90</td>
+    </tr>
+    <tr>
+      <th>34</th>
+      <td>20712.46</td>
+      <td>13000.00</td>
+      <td>-7712.46</td>
+      <td>59.33</td>
+    </tr>
+    <tr>
+      <th>122</th>
+      <td>2719.53</td>
+      <td>1700.00</td>
+      <td>-1019.53</td>
+      <td>59.97</td>
+    </tr>
+    <tr>
+      <th>39</th>
+      <td>3906.56</td>
+      <td>2400.00</td>
+      <td>-1506.56</td>
+      <td>62.77</td>
+    </tr>
+    <tr>
+      <th>790</th>
+      <td>5055.93</td>
+      <td>3099.00</td>
+      <td>-1956.93</td>
+      <td>63.15</td>
+    </tr>
+    <tr>
+      <th>200</th>
+      <td>4243.79</td>
+      <td>2600.00</td>
+      <td>-1643.79</td>
+      <td>63.22</td>
+    </tr>
+    <tr>
+      <th>788</th>
+      <td>10676.78</td>
+      <td>6499.00</td>
+      <td>-4177.78</td>
+      <td>64.28</td>
+    </tr>
+    <tr>
+      <th>86</th>
+      <td>16658.12</td>
+      <td>10100.00</td>
+      <td>-6558.12</td>
+      <td>64.93</td>
+    </tr>
+    <tr>
+      <th>662</th>
+      <td>6656.03</td>
+      <td>4000.00</td>
+      <td>-2656.03</td>
+      <td>66.40</td>
+    </tr>
+    <tr>
+      <th>425</th>
+      <td>2486.42</td>
+      <td>8000.00</td>
+      <td>5513.58</td>
+      <td>68.92</td>
+    </tr>
+    <tr>
+      <th>661</th>
+      <td>13706.76</td>
+      <td>8000.00</td>
+      <td>-5706.76</td>
+      <td>71.33</td>
+    </tr>
+    <tr>
+      <th>23</th>
+      <td>6524.07</td>
+      <td>3799.00</td>
+      <td>-2725.07</td>
+      <td>71.73</td>
+    </tr>
+    <tr>
+      <th>423</th>
+      <td>41367.65</td>
+      <td>23900.00</td>
+      <td>-17467.65</td>
+      <td>73.09</td>
+    </tr>
+    <tr>
+      <th>238</th>
+      <td>60487.88</td>
+      <td>34600.00</td>
+      <td>-25887.88</td>
+      <td>74.82</td>
+    </tr>
+    <tr>
+      <th>524</th>
+      <td>9215.35</td>
+      <td>5000.00</td>
+      <td>-4215.35</td>
+      <td>84.31</td>
+    </tr>
+    <tr>
+      <th>636</th>
+      <td>6870.33</td>
+      <td>3600.00</td>
+      <td>-3270.33</td>
+      <td>90.84</td>
+    </tr>
+    <tr>
+      <th>506</th>
+      <td>9213.19</td>
+      <td>4800.00</td>
+      <td>-4413.19</td>
+      <td>91.94</td>
+    </tr>
+    <tr>
+      <th>613</th>
+      <td>1444.13</td>
+      <td>34999.00</td>
+      <td>33554.87</td>
+      <td>95.87</td>
+    </tr>
+    <tr>
+      <th>791</th>
+      <td>5130.28</td>
+      <td>2600.00</td>
+      <td>-2530.28</td>
+      <td>97.32</td>
+    </tr>
+    <tr>
+      <th>623</th>
+      <td>0.00</td>
+      <td>1800.00</td>
+      <td>1800.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>306</th>
+      <td>0.00</td>
+      <td>44000.00</td>
+      <td>44000.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>286</th>
+      <td>0.00</td>
+      <td>35000.00</td>
+      <td>35000.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>121</th>
+      <td>0.00</td>
+      <td>3800.00</td>
+      <td>3800.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>396</th>
+      <td>0.00</td>
+      <td>38500.00</td>
+      <td>38500.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>124</th>
+      <td>0.00</td>
+      <td>4100.00</td>
+      <td>4100.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>690</th>
+      <td>0.00</td>
+      <td>6500.00</td>
+      <td>6500.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>24</th>
+      <td>0.00</td>
+      <td>32000.00</td>
+      <td>32000.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>301</th>
+      <td>0.00</td>
+      <td>8500.00</td>
+      <td>8500.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>351</th>
+      <td>0.00</td>
+      <td>18800.00</td>
+      <td>18800.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>723</th>
+      <td>0.00</td>
+      <td>1650.00</td>
+      <td>1650.00</td>
+      <td>100.00</td>
+    </tr>
+    <tr>
+      <th>54</th>
+      <td>29058.70</td>
+      <td>14250.00</td>
+      <td>-14808.70</td>
+      <td>103.92</td>
+    </tr>
+    <tr>
+      <th>266</th>
+      <td>20157.28</td>
+      <td>9000.00</td>
+      <td>-11157.28</td>
+      <td>123.97</td>
+    </tr>
+    <tr>
+      <th>594</th>
+      <td>9463.58</td>
+      <td>3650.00</td>
+      <td>-5813.58</td>
+      <td>159.28</td>
+    </tr>
+    <tr>
+      <th>143</th>
+      <td>4534.02</td>
+      <td>1700.00</td>
+      <td>-2834.02</td>
+      <td>166.71</td>
+    </tr>
+    <tr>
+      <th>131</th>
+      <td>3200.86</td>
+      <td>1200.00</td>
+      <td>-2000.86</td>
+      <td>166.74</td>
+    </tr>
+    <tr>
+      <th>452</th>
+      <td>3875.31</td>
+      <td>1450.00</td>
+      <td>-2425.31</td>
+      <td>167.26</td>
+    </tr>
+    <tr>
+      <th>685</th>
+      <td>35212.93</td>
+      <td>12900.00</td>
+      <td>-22312.93</td>
+      <td>172.97</td>
+    </tr>
+    <tr>
+      <th>604</th>
+      <td>14347.91</td>
+      <td>4200.00</td>
+      <td>-10147.91</td>
+      <td>241.62</td>
+    </tr>
+    <tr>
+      <th>737</th>
+      <td>8975.55</td>
+      <td>2400.00</td>
+      <td>-6575.55</td>
+      <td>273.98</td>
+    </tr>
+    <tr>
+      <th>28</th>
+      <td>48602.40</td>
+      <td>11200.00</td>
+      <td>-37402.40</td>
+      <td>333.95</td>
+    </tr>
+    <tr>
+      <th>538</th>
+      <td>28240.95</td>
+      <td>5500.00</td>
+      <td>-22740.95</td>
+      <td>413.47</td>
+    </tr>
+    <tr>
+      <th>290</th>
+      <td>inf</td>
+      <td>19999.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>731</th>
+      <td>inf</td>
+      <td>99000.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>282</th>
+      <td>inf</td>
+      <td>20996.24</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>679</th>
+      <td>inf</td>
+      <td>1150.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>120</th>
+      <td>inf</td>
+      <td>17639.47</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>204</th>
+      <td>inf</td>
+      <td>12900.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>683</th>
+      <td>inf</td>
+      <td>20000.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>650</th>
+      <td>inf</td>
+      <td>11300.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>458</th>
+      <td>inf</td>
+      <td>7450.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>314</th>
+      <td>inf</td>
+      <td>10200.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>63</th>
+      <td>inf</td>
+      <td>37500.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>698</th>
+      <td>inf</td>
+      <td>11700.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>33</th>
+      <td>inf</td>
+      <td>6000.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+    <tr>
+      <th>82</th>
+      <td>inf</td>
+      <td>4000.00</td>
+      <td>-inf</td>
+      <td>inf</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span> 
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div>
+</body>
+
+
+
+
+
+
+
+</html>
diff --git a/Udemy DataScience Workbook/Improved_model.html.ipynb b/Udemy DataScience Workbook/Improved_model.html.ipynb
new file mode 100644
index 0000000..297326c
--- /dev/null
+++ b/Udemy DataScience Workbook/Improved_model.html.ipynb	
@@ -0,0 +1,19919 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Improving the model that predicts the price of a fairly used car"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import statsmodels.api as sum\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "sns.set()\n",
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('Real-life+example.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preprocessing the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "Model             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4025         4025  4025.000000     4025  \n",
+       "unique           4            2          NaN      306  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          1861         3654          NaN      188  \n",
+       "mean           NaN          NaN  2006.379627      NaN  \n",
+       "std            NaN          NaN     6.695595      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2007.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_2108\\4196895882.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Outliers are not removed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we decided to use some matplotlib code, without explaining it\n",
+    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
+    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1,3, sharey=True, figsize = (15,3))\n",
+    "ax1.scatter(data['Year'], data['Price'])\n",
+    "ax1.set_title('Year and Price')\n",
+    "ax2.scatter(data['EngineV'],data['Price'])\n",
+    "ax2.set_title('Engine and Price')\n",
+    "ax3.scatter(data['Mileage'], data['Price'])\n",
+    "ax3.set_title('Mileage and Price')\n",
+    "#ax4.scatter(data['Brand'], data['Price'])\n",
+    "#ax4.set_title('Brand and Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 3))\n",
+    "\n",
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=data['Year'], y=data['Price'], ax=ax1, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax1.set_title('Year and Price')\n",
+    "\n",
+    "# Scatter plot for EngineV and log_price with regression line\n",
+    "sns.regplot(x=data['EngineV'], y=data['Price'], ax=ax2, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax2.set_title('EngineV and Price')\n",
+    "\n",
+    "# Scatter plot for Mileage and log_price with regression line\n",
+    "sns.regplot(x=data['Mileage'], y=data['Price'], ax=ax3, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax3.set_title('Mileage and Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_2108\\1987317337.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  data['log_price'] = log_price\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4339</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>Corolla</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4340</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>S 350</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4341</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>535</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4342</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>8000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>520</td>\n",
+       "      <td>8.987197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4344</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>T5 (Transporter)</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4025 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "4339         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "4340  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "4341            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "4342            BMW    8000.0      sedan      194      2.0      Petrol   \n",
+       "4344     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year              Model  log_price  \n",
+       "0             yes  1991                320   8.342840  \n",
+       "1             yes  1999       Sprinter 212   8.974618  \n",
+       "2             yes  2003              S 500   9.495519  \n",
+       "3             yes  2007                 Q7  10.043249  \n",
+       "4             yes  2011              Rav 4   9.814656  \n",
+       "...           ...   ...                ...        ...  \n",
+       "4339          yes  2014            Corolla   9.792556  \n",
+       "4340          yes  2014              S 350  11.736069  \n",
+       "4341          yes  1999                535   8.779557  \n",
+       "4342          yes  1985                520   8.987197  \n",
+       "4344          yes  2013  T5 (Transporter)    9.510445  \n",
+       "\n",
+       "[4025 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#Transform the 'Price' log transformation\n",
+    "log_price = np.log(data['Price'])\n",
+    "#Add log_price to the dataframe\n",
+    "\n",
+    "data['log_price'] = log_price\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1 , ax2, ax3) = plt.subplots(1,3, sharey=True, figsize= (15,3))\n",
+    "ax1.scatter(data['Year'],data['log_price'])\n",
+    "ax1.set_title('Year and log_price')\n",
+    "ax2.scatter(data['EngineV'], data['log_price'])\n",
+    "ax2.set_title('EngineV and log_price')\n",
+    "ax3.scatter(data['Mileage'],data['log_price'])\n",
+    "ax3.set_title('Mileage and log_price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 3))\n",
+    "\n",
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=data['Year'], y=data['log_price'], ax=ax1, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax1.set_title('Year and log_price')\n",
+    "\n",
+    "# Scatter plot for EngineV and log_price with regression line\n",
+    "sns.regplot(x=data['EngineV'], y=data['log_price'], ax=ax2, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax2.set_title('EngineV and log_price')\n",
+    "\n",
+    "# Scatter plot for Mileage and log_price with regression line\n",
+    "sns.regplot(x=data['Mileage'], y=data['log_price'], ax=ax3, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax3.set_title('Mileage and log_price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>306</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>188</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.409665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.933136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.396930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.853523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.994242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.611538</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand   Body      Mileage      EngineV Engine Type Registration  \\\n",
+       "count         4025   4025  4025.000000  4025.000000        4025         4025   \n",
+       "unique           7      6          NaN          NaN           4            2   \n",
+       "top     Volkswagen  sedan          NaN          NaN      Diesel          yes   \n",
+       "freq           880   1534          NaN          NaN        1861         3654   \n",
+       "mean           NaN    NaN   163.572174     2.764586         NaN          NaN   \n",
+       "std            NaN    NaN   103.394703     4.935941         NaN          NaN   \n",
+       "min            NaN    NaN     0.000000     0.600000         NaN          NaN   \n",
+       "25%            NaN    NaN    90.000000     1.800000         NaN          NaN   \n",
+       "50%            NaN    NaN   158.000000     2.200000         NaN          NaN   \n",
+       "75%            NaN    NaN   230.000000     3.000000         NaN          NaN   \n",
+       "max            NaN    NaN   980.000000    99.990000         NaN          NaN   \n",
+       "\n",
+       "               Year    Model    log_price  \n",
+       "count   4025.000000     4025  4025.000000  \n",
+       "unique          NaN      306          NaN  \n",
+       "top             NaN  E-Class          NaN  \n",
+       "freq            NaN      188          NaN  \n",
+       "mean    2006.379627      NaN     9.409665  \n",
+       "std        6.695595      NaN     0.933136  \n",
+       "min     1969.000000      NaN     6.396930  \n",
+       "25%     2003.000000      NaN     8.853523  \n",
+       "50%     2007.000000      NaN     9.350102  \n",
+       "75%     2012.000000      NaN     9.994242  \n",
+       "max     2016.000000      NaN    12.611538  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'Model', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn does not have a built-in way to check for multicollinearity\n",
+    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
+    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
+    "\n",
+    "# Here's the relevant module\n",
+    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# To make this as easy as possible to use, we declare a variable where we put\n",
+    "# all features where we want to check for multicollinearity\n",
+    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
+    "variables = data_cleaned[['Mileage', 'Year', 'EngineV']]\n",
+    "\n",
+    "# we create a new data frame which will include all the VIFs\n",
+    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
+    "vif = pd.DataFrame()\n",
+    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
+    "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range (variables.shape[1])]\n",
+    "# Finally, I like to include names so it is easier to explore the result\n",
+    "vif['Features'] = variables.columns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.481307</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.748134</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.314535</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        VIF Features\n",
+       "0  3.481307  Mileage\n",
+       "1  3.748134     Year\n",
+       "2  1.314535  EngineV"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With all vifs less than 5, It means that the correlation between your independent variables (Mileage, Year, and EngineV) is not excessively high.\n",
+    "So, it looks like your dataset is behaving well in terms of multicollinearity. You can proceed with your regression analysis without major concerns about the variables playing too nicely with each other. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To include the categorical data in the regression, let's create dummies\n",
+    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
+    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
+    "data_with_dummies = pd.get_dummies(data_cleaned,drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  Year  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0  1991   8.342840       True                False   \n",
+       "1      427      2.9  1999   8.974618      False                 True   \n",
+       "2      358      5.0  2003   9.495519      False                 True   \n",
+       "3      240      4.2  2007  10.043249      False                False   \n",
+       "4      120      2.0  2011   9.814656      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  Model_100  \\\n",
+       "0              False                True              True      False   \n",
+       "1              False               False              True      False   \n",
+       "2              False               False              True      False   \n",
+       "3              False                True              True      False   \n",
+       "4              False                True              True      False   \n",
+       "\n",
+       "   Model_11  Model_116  Model_118  Model_120  Model_19  Model_190  Model_200  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_21  Model_210  Model_220  Model_230  Model_25  Model_250  Model_300  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_316  Model_318  Model_320  Model_323  Model_324  Model_325  \\\n",
+       "0      False      False       True      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_328  Model_330  Model_335  Model_4 Series Gran Coupe  Model_428  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_4Runner  Model_5  Model_5 Series  Model_5 Series GT  Model_520  \\\n",
+       "0          False    False           False              False      False   \n",
+       "1          False    False           False              False      False   \n",
+       "2          False    False           False              False      False   \n",
+       "3          False    False           False              False      False   \n",
+       "4          False    False           False              False      False   \n",
+       "\n",
+       "   Model_523  Model_524  Model_525  Model_528  Model_530  Model_535  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_540  Model_545  Model_550  Model_6 Series Gran Coupe  Model_630  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_640  Model_645  Model_650  Model_730  Model_735  Model_740  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_745  Model_750  Model_760  Model_80  Model_9  Model_90  Model_A 140  \\\n",
+       "0      False      False      False     False    False     False        False   \n",
+       "1      False      False      False     False    False     False        False   \n",
+       "2      False      False      False     False    False     False        False   \n",
+       "3      False      False      False     False    False     False        False   \n",
+       "4      False      False      False     False    False     False        False   \n",
+       "\n",
+       "   Model_A 150  Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0        False        False        False     False     False     False   \n",
+       "1        False        False        False     False     False     False   \n",
+       "2        False        False        False     False     False     False   \n",
+       "3        False        False        False     False     False     False   \n",
+       "4        False        False        False     False     False     False   \n",
+       "\n",
+       "   Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  Model_A8  \\\n",
+       "0             False     False     False             False     False     False   \n",
+       "1             False     False     False             False     False     False   \n",
+       "2             False     False     False             False     False     False   \n",
+       "3             False     False     False             False     False     False   \n",
+       "4             False     False     False             False     False     False   \n",
+       "\n",
+       "   Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  Model_Avensis  \\\n",
+       "0      False         False        False         False          False   \n",
+       "1      False         False        False         False          False   \n",
+       "2      False         False        False         False          False   \n",
+       "3      False         False        False         False          False   \n",
+       "4      False         False        False         False          False   \n",
+       "\n",
+       "   Model_Aygo  Model_B 170  Model_B 180  Model_B 200  Model_Beetle  \\\n",
+       "0       False        False        False        False         False   \n",
+       "1       False        False        False        False         False   \n",
+       "2       False        False        False        False         False   \n",
+       "3       False        False        False        False         False   \n",
+       "4       False        False        False        False         False   \n",
+       "\n",
+       "   Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  Model_CL 55 AMG  \\\n",
+       "0       False          False         False         False            False   \n",
+       "1       False          False         False         False            False   \n",
+       "2       False          False         False         False            False   \n",
+       "3       False          False         False         False            False   \n",
+       "4       False          False         False         False            False   \n",
+       "\n",
+       "   Model_CL 550  Model_CL 63 AMG  Model_CLA 200  Model_CLA 220  \\\n",
+       "0         False            False          False          False   \n",
+       "1         False            False          False          False   \n",
+       "2         False            False          False          False   \n",
+       "3         False            False          False          False   \n",
+       "4         False            False          False          False   \n",
+       "\n",
+       "   Model_CLA-Class  Model_CLC 180  Model_CLC 200  Model_CLK 200  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 220  Model_CLK 230  Model_CLK 240  Model_CLK 280  Model_CLK 320  \\\n",
+       "0          False          False          False          False          False   \n",
+       "1          False          False          False          False          False   \n",
+       "2          False          False          False          False          False   \n",
+       "3          False          False          False          False          False   \n",
+       "4          False          False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 430  Model_CLS 350  Model_CLS 400  Model_CLS 500  \\\n",
+       "0          False          False          False          False   \n",
+       "1          False          False          False          False   \n",
+       "2          False          False          False          False   \n",
+       "3          False          False          False          False   \n",
+       "4          False          False          False          False   \n",
+       "\n",
+       "   Model_CLS 63 AMG  Model_Caddy  Model_Camry  Model_Captur  Model_Caravelle  \\\n",
+       "0             False        False        False         False            False   \n",
+       "1             False        False        False         False            False   \n",
+       "2             False        False        False         False            False   \n",
+       "3             False        False        False         False            False   \n",
+       "4             False        False        False         False            False   \n",
+       "\n",
+       "   Model_Carina  Model_Carisma  Model_Celica  Model_Clio  Model_Colt  \\\n",
+       "0         False          False         False       False       False   \n",
+       "1         False          False         False       False       False   \n",
+       "2         False          False         False       False       False   \n",
+       "3         False          False         False       False       False   \n",
+       "4         False          False         False       False       False   \n",
+       "\n",
+       "   Model_Corolla  Model_Corolla Verso  Model_Cross Touran  Model_Dokker  \\\n",
+       "0          False                False               False         False   \n",
+       "1          False                False               False         False   \n",
+       "2          False                False               False         False   \n",
+       "3          False                False               False         False   \n",
+       "4          False                False               False         False   \n",
+       "\n",
+       "   Model_Duster  Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  \\\n",
+       "0         False          False          False      False         False   \n",
+       "1         False          False          False      False         False   \n",
+       "2         False          False          False      False         False   \n",
+       "3         False          False          False      False         False   \n",
+       "4         False          False          False      False         False   \n",
+       "\n",
+       "   Model_FJ Cruiser  Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  \\\n",
+       "0             False          False           False        False        False   \n",
+       "1             False          False           False        False        False   \n",
+       "2             False          False           False        False        False   \n",
+       "3             False          False           False        False        False   \n",
+       "4             False          False           False        False        False   \n",
+       "\n",
+       "   Model_G 500  Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0        False           False           False         False         False   \n",
+       "1        False           False           False         False         False   \n",
+       "2        False           False           False         False         False   \n",
+       "3        False           False           False         False         False   \n",
+       "4        False           False           False         False         False   \n",
+       "\n",
+       "   Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  Model_Golf GTI  \\\n",
+       "0          False          False         False         False           False   \n",
+       "1          False          False         False         False           False   \n",
+       "2          False          False         False         False           False   \n",
+       "3          False          False         False         False           False   \n",
+       "4          False          False         False         False           False   \n",
+       "\n",
+       "   Model_Golf II  Model_Golf III  Model_Golf IV  Model_Golf Plus  \\\n",
+       "0          False           False          False            False   \n",
+       "1          False           False          False            False   \n",
+       "2          False           False          False            False   \n",
+       "3          False           False          False            False   \n",
+       "4          False           False          False            False   \n",
+       "\n",
+       "   Model_Golf V  Model_Golf VI  Model_Golf VII  Model_Golf Variant  \\\n",
+       "0         False          False           False               False   \n",
+       "1         False          False           False               False   \n",
+       "2         False          False           False               False   \n",
+       "3         False          False           False               False   \n",
+       "4         False          False           False               False   \n",
+       "\n",
+       "   Model_Grand Scenic  Model_Grandis  Model_Hiace  Model_Highlander  \\\n",
+       "0               False          False        False             False   \n",
+       "1               False          False        False             False   \n",
+       "2               False          False        False             False   \n",
+       "3               False          False        False             False   \n",
+       "4               False          False        False             False   \n",
+       "\n",
+       "   Model_Hilux  Model_I3  Model_IQ  Model_Jetta  Model_Kangoo  Model_Koleos  \\\n",
+       "0        False     False     False        False         False         False   \n",
+       "1        False     False     False        False         False         False   \n",
+       "2        False     False     False        False         False         False   \n",
+       "3        False     False     False        False         False         False   \n",
+       "4        False     False     False        False         False         False   \n",
+       "\n",
+       "   Model_L 200  Model_LT  Model_Laguna  Model_Lancer  Model_Lancer Evolution  \\\n",
+       "0        False     False         False         False                   False   \n",
+       "1        False     False         False         False                   False   \n",
+       "2        False     False         False         False                   False   \n",
+       "3        False     False         False         False                   False   \n",
+       "4        False     False         False         False                   False   \n",
+       "\n",
+       "   Model_Lancer X  Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0           False                     False                   False   \n",
+       "1           False                     False                   False   \n",
+       "2           False                     False                   False   \n",
+       "3           False                     False                   False   \n",
+       "4           False                     False                   False   \n",
+       "\n",
+       "   Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                   False                   False                  False   \n",
+       "1                   False                   False                  False   \n",
+       "2                   False                   False                  False   \n",
+       "3                   False                   False                  False   \n",
+       "4                   False                   False                  False   \n",
+       "\n",
+       "   Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                  False                     False           False   \n",
+       "1                  False                     False           False   \n",
+       "2                  False                     False           False   \n",
+       "3                  False                     False           False   \n",
+       "4                  False                     False           False   \n",
+       "\n",
+       "   Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0           False        False       False     False     False     False   \n",
+       "1           False        False       False     False     False     False   \n",
+       "2           False        False       False     False     False     False   \n",
+       "3           False        False       False     False     False     False   \n",
+       "4           False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0         False         False         False         False         False   \n",
+       "1         False         False         False         False         False   \n",
+       "2         False         False         False         False         False   \n",
+       "3         False         False         False         False         False   \n",
+       "4         False         False         False         False         False   \n",
+       "\n",
+       "   Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0          False         False         False         False        False   \n",
+       "1          False         False         False         False        False   \n",
+       "2          False         False         False         False        False   \n",
+       "3          False         False         False         False        False   \n",
+       "4          False         False         False         False        False   \n",
+       "\n",
+       "   Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0           False             False            False               False   \n",
+       "1           False             False            False               False   \n",
+       "2           False             False            False               False   \n",
+       "3           False             False            False               False   \n",
+       "4           False             False            False               False   \n",
+       "\n",
+       "   Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  Model_Pajero Wagon  \\\n",
+       "0         False               False               False               False   \n",
+       "1         False               False               False               False   \n",
+       "2         False               False               False               False   \n",
+       "3         False               False               False               False   \n",
+       "4         False               False               False               False   \n",
+       "\n",
+       "   Model_Passat B2  Model_Passat B3  Model_Passat B4  Model_Passat B5  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Passat B6  Model_Passat B7  Model_Passat B8  Model_Passat CC  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  Model_Prius  \\\n",
+       "0          False          False       False         False        False   \n",
+       "1          False          False       False         False        False   \n",
+       "2          False          False       False         False        False   \n",
+       "3          False          False       False         False        False   \n",
+       "4          False          False       False         False        False   \n",
+       "\n",
+       "   Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  Model_Rav 4  \\\n",
+       "0     False     False     False        False     False        False   \n",
+       "1     False     False     False        False     False        False   \n",
+       "2     False     False     False        False     False        False   \n",
+       "3     False     False      True        False     False        False   \n",
+       "4     False     False     False        False     False         True   \n",
+       "\n",
+       "   Model_S 140  Model_S 250  Model_S 280  Model_S 300  Model_S 320  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False        False   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 350  Model_S 400  Model_S 420  Model_S 430  Model_S 500  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False         True   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  Model_S 65 AMG  \\\n",
+       "0       False        False        False           False           False   \n",
+       "1       False        False        False           False           False   \n",
+       "2       False        False        False           False           False   \n",
+       "3       False        False        False           False           False   \n",
+       "4       False        False        False           False           False   \n",
+       "\n",
+       "   Model_S-Guard  Model_S4  Model_S5  Model_S8  Model_SL 500 (550)  \\\n",
+       "0          False     False     False     False               False   \n",
+       "1          False     False     False     False               False   \n",
+       "2          False     False     False     False               False   \n",
+       "3          False     False     False     False               False   \n",
+       "4          False     False     False     False               False   \n",
+       "\n",
+       "   Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  Model_Sandero  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_Sandero StepWay  Model_Scenic  Model_Scion  Model_Scirocco  \\\n",
+       "0                  False         False        False           False   \n",
+       "1                  False         False        False           False   \n",
+       "2                  False         False        False           False   \n",
+       "3                  False         False        False           False   \n",
+       "4                  False         False        False           False   \n",
+       "\n",
+       "   Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  Model_Space Star  \\\n",
+       "0          False         False         False        False             False   \n",
+       "1          False         False         False        False             False   \n",
+       "2          False         False         False        False             False   \n",
+       "3          False         False         False        False             False   \n",
+       "4          False         False         False        False             False   \n",
+       "\n",
+       "   Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  Model_Sprinter 210  \\\n",
+       "0              False           False               False               False   \n",
+       "1              False           False               False               False   \n",
+       "2              False           False               False               False   \n",
+       "3              False           False               False               False   \n",
+       "4              False           False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 211  Model_Sprinter 212  Model_Sprinter 213  \\\n",
+       "0               False               False               False   \n",
+       "1               False                True               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 311  Model_Sprinter 312  Model_Sprinter 313  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 315  Model_Sprinter 316  Model_Sprinter 318  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 319  Model_Sprinter 324  Model_Symbol  Model_Syncro  \\\n",
+       "0               False               False         False         False   \n",
+       "1               False               False         False         False   \n",
+       "2               False               False         False         False   \n",
+       "3               False               False         False         False   \n",
+       "4               False               False         False         False   \n",
+       "\n",
+       "   Model_T2 (Transporter)  Model_T3 (Transporter)  Model_T4 (Transporter)  \\\n",
+       "0                   False                   False                   False   \n",
+       "1                   False                   False                   False   \n",
+       "2                   False                   False                   False   \n",
+       "3                   False                   False                   False   \n",
+       "4                   False                   False                   False   \n",
+       "\n",
+       "   Model_T4 (Transporter)   Model_T5 (Transporter)  Model_T5 (Transporter)   \\\n",
+       "0                    False                   False                    False   \n",
+       "1                    False                   False                    False   \n",
+       "2                    False                   False                    False   \n",
+       "3                    False                   False                    False   \n",
+       "4                    False                   False                    False   \n",
+       "\n",
+       "   Model_T6 (Transporter)  Model_T6 (Transporter)   Model_TT  Model_Tacoma  \\\n",
+       "0                   False                    False     False         False   \n",
+       "1                   False                    False     False         False   \n",
+       "2                   False                    False     False         False   \n",
+       "3                   False                    False     False         False   \n",
+       "4                   False                    False     False         False   \n",
+       "\n",
+       "   Model_Tiguan  Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  \\\n",
+       "0         False          False         False         False         False   \n",
+       "1         False          False         False         False         False   \n",
+       "2         False          False         False         False         False   \n",
+       "3         False          False         False         False         False   \n",
+       "4         False          False         False         False         False   \n",
+       "\n",
+       "   Model_Up  Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0     False        False        False        False        False        False   \n",
+       "1     False        False        False        False        False        False   \n",
+       "2     False        False        False        False        False        False   \n",
+       "3     False        False        False        False        False        False   \n",
+       "4     False        False        False        False        False        False   \n",
+       "\n",
+       "   Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0         False        False       False     False     False     False   \n",
+       "1         False        False       False     False     False     False   \n",
+       "2         False        False       False     False     False     False   \n",
+       "3         False        False       False     False     False     False   \n",
+       "4         False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0       False     False       False        False     False     False  \n",
+       "1       False     False       False        False     False     False  \n",
+       "2       False     False       False        False     False     False  \n",
+       "3       False     False       False        False     False     False  \n",
+       "4       False     False       False        False     False     False  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Rearrange to have the dependent variable on first column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'Year', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes',\n",
+       "       'Model_100', 'Model_11', 'Model_116', 'Model_118', 'Model_120',\n",
+       "       'Model_19', 'Model_190', 'Model_200', 'Model_21', 'Model_210',\n",
+       "       'Model_220', 'Model_230', 'Model_25', 'Model_250', 'Model_300',\n",
+       "       'Model_316', 'Model_318', 'Model_320', 'Model_323', 'Model_324',\n",
+       "       'Model_325', 'Model_328', 'Model_330', 'Model_335',\n",
+       "       'Model_4 Series Gran Coupe', 'Model_428', 'Model_4Runner',\n",
+       "       'Model_5', 'Model_5 Series', 'Model_5 Series GT', 'Model_520',\n",
+       "       'Model_523', 'Model_524', 'Model_525', 'Model_528', 'Model_530',\n",
+       "       'Model_535', 'Model_540', 'Model_545', 'Model_550',\n",
+       "       'Model_6 Series Gran Coupe', 'Model_630', 'Model_640', 'Model_645',\n",
+       "       'Model_650', 'Model_730', 'Model_735', 'Model_740', 'Model_745',\n",
+       "       'Model_750', 'Model_760', 'Model_80', 'Model_9', 'Model_90',\n",
+       "       'Model_A 140', 'Model_A 150', 'Model_A 170', 'Model_A 180',\n",
+       "       'Model_A1', 'Model_A3', 'Model_A4', 'Model_A4 Allroad', 'Model_A5',\n",
+       "       'Model_A6', 'Model_A6 Allroad', 'Model_A7', 'Model_A8',\n",
+       "       'Model_ASX', 'Model_Amarok', 'Model_Auris', 'Model_Avalon',\n",
+       "       'Model_Avensis', 'Model_Aygo', 'Model_B 170', 'Model_B 180',\n",
+       "       'Model_B 200', 'Model_Beetle', 'Model_Bora', 'Model_C-Class',\n",
+       "       'Model_CL 180', 'Model_CL 500', 'Model_CL 55 AMG', 'Model_CL 550',\n",
+       "       'Model_CL 63 AMG', 'Model_CLA 200', 'Model_CLA 220',\n",
+       "       'Model_CLA-Class', 'Model_CLC 180', 'Model_CLC 200',\n",
+       "       'Model_CLK 200', 'Model_CLK 220', 'Model_CLK 230', 'Model_CLK 240',\n",
+       "       'Model_CLK 280', 'Model_CLK 320', 'Model_CLK 430', 'Model_CLS 350',\n",
+       "       'Model_CLS 400', 'Model_CLS 500', 'Model_CLS 63 AMG',\n",
+       "       'Model_Caddy', 'Model_Camry', 'Model_Captur', 'Model_Caravelle',\n",
+       "       'Model_Carina', 'Model_Carisma', 'Model_Celica', 'Model_Clio',\n",
+       "       'Model_Colt', 'Model_Corolla', 'Model_Corolla Verso',\n",
+       "       'Model_Cross Touran', 'Model_Dokker', 'Model_Duster',\n",
+       "       'Model_E-Class', 'Model_Eclipse', 'Model_Eos', 'Model_Espace',\n",
+       "       'Model_FJ Cruiser', 'Model_Fluence', 'Model_Fortuner',\n",
+       "       'Model_G 320', 'Model_G 350', 'Model_G 500', 'Model_G 55 AMG',\n",
+       "       'Model_G 63 AMG', 'Model_GL 320', 'Model_GL 350', 'Model_GL 420',\n",
+       "       'Model_GL 450', 'Model_GL 500', 'Model_GL 550', 'Model_GLC-Class',\n",
+       "       'Model_GLE-Class', 'Model_GLK 220', 'Model_GLK 300',\n",
+       "       'Model_GLS 350', 'Model_GLS 400', 'Model_GLS 500', 'Model_GLS 63',\n",
+       "       'Model_Galant', 'Model_Golf GTI', 'Model_Golf II',\n",
+       "       'Model_Golf III', 'Model_Golf IV', 'Model_Golf Plus',\n",
+       "       'Model_Golf V', 'Model_Golf VI', 'Model_Golf VII',\n",
+       "       'Model_Golf Variant', 'Model_Grand Scenic', 'Model_Grandis',\n",
+       "       'Model_Hiace', 'Model_Highlander', 'Model_Hilux', 'Model_I3',\n",
+       "       'Model_IQ', 'Model_Jetta', 'Model_Kangoo', 'Model_Koleos',\n",
+       "       'Model_L 200', 'Model_LT', 'Model_Laguna', 'Model_Lancer',\n",
+       "       'Model_Lancer Evolution', 'Model_Lancer X',\n",
+       "       'Model_Lancer X Sportback', 'Model_Land Cruiser 100',\n",
+       "       'Model_Land Cruiser 105', 'Model_Land Cruiser 200',\n",
+       "       'Model_Land Cruiser 76', 'Model_Land Cruiser 80',\n",
+       "       'Model_Land Cruiser Prado', 'Model_Latitude', 'Model_Lite Ace',\n",
+       "       'Model_Logan', 'Model_Lupo', 'Model_M5', 'Model_M6', 'Model_MB',\n",
+       "       'Model_ML 250', 'Model_ML 270', 'Model_ML 280', 'Model_ML 320',\n",
+       "       'Model_ML 350', 'Model_ML 400', 'Model_ML 430', 'Model_ML 500',\n",
+       "       'Model_ML 550', 'Model_ML 63 AMG', 'Model_Mark II', 'Model_Master',\n",
+       "       'Model_Matrix', 'Model_Megane', 'Model_Modus', 'Model_Multivan',\n",
+       "       'Model_New Beetle', 'Model_Outlander', 'Model_Outlander XL',\n",
+       "       'Model_Pajero', 'Model_Pajero Pinin', 'Model_Pajero Sport',\n",
+       "       'Model_Pajero Wagon', 'Model_Passat B2', 'Model_Passat B3',\n",
+       "       'Model_Passat B4', 'Model_Passat B5', 'Model_Passat B6',\n",
+       "       'Model_Passat B7', 'Model_Passat B8', 'Model_Passat CC',\n",
+       "       'Model_Phaeton', 'Model_Pointer', 'Model_Polo', 'Model_Previa',\n",
+       "       'Model_Prius', 'Model_Q3', 'Model_Q5', 'Model_Q7', 'Model_R 320',\n",
+       "       'Model_R8', 'Model_Rav 4', 'Model_S 140', 'Model_S 250',\n",
+       "       'Model_S 280', 'Model_S 300', 'Model_S 320', 'Model_S 350',\n",
+       "       'Model_S 400', 'Model_S 420', 'Model_S 430', 'Model_S 500',\n",
+       "       'Model_S 55', 'Model_S 550', 'Model_S 600', 'Model_S 63 AMG',\n",
+       "       'Model_S 65 AMG', 'Model_S-Guard', 'Model_S4', 'Model_S5',\n",
+       "       'Model_S8', 'Model_SL 500 (550)', 'Model_SL 55 AMG',\n",
+       "       'Model_SLK 200', 'Model_SLK 350', 'Model_Sandero',\n",
+       "       'Model_Sandero StepWay', 'Model_Scenic', 'Model_Scion',\n",
+       "       'Model_Scirocco', 'Model_Sequoia', 'Model_Sharan', 'Model_Sienna',\n",
+       "       'Model_Smart', 'Model_Space Star', 'Model_Space Wagon',\n",
+       "       'Model_Sprinter', 'Model_Sprinter 208', 'Model_Sprinter 210',\n",
+       "       'Model_Sprinter 211', 'Model_Sprinter 212', 'Model_Sprinter 213',\n",
+       "       'Model_Sprinter 311', 'Model_Sprinter 312', 'Model_Sprinter 313',\n",
+       "       'Model_Sprinter 315', 'Model_Sprinter 316', 'Model_Sprinter 318',\n",
+       "       'Model_Sprinter 319', 'Model_Sprinter 324', 'Model_Symbol',\n",
+       "       'Model_Syncro', 'Model_T2 (Transporter)', 'Model_T3 (Transporter)',\n",
+       "       'Model_T4 (Transporter)', 'Model_T4 (Transporter) ',\n",
+       "       'Model_T5 (Transporter)', 'Model_T5 (Transporter) ',\n",
+       "       'Model_T6 (Transporter)', 'Model_T6 (Transporter) ', 'Model_TT',\n",
+       "       'Model_Tacoma', 'Model_Tiguan', 'Model_Touareg', 'Model_Touran',\n",
+       "       'Model_Trafic', 'Model_Tundra', 'Model_Up', 'Model_V 250',\n",
+       "       'Model_Vaneo', 'Model_Vento', 'Model_Venza', 'Model_Viano',\n",
+       "       'Model_Virage', 'Model_Vista', 'Model_Vito', 'Model_X1',\n",
+       "       'Model_X3', 'Model_X5', 'Model_X5 M', 'Model_X6', 'Model_X6 M',\n",
+       "       'Model_Yaris', 'Model_Z3', 'Model_Z4'], dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df\n",
+    "# Since each problem is different, that must be done manually\n",
+    "# We can display all possible features and then choose the desired order\n",
+    "pd.options.display.max_columns = 999\n",
+    "data_with_dummies.columns.values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['log_price' 'Mileage' 'EngineV' 'Year' 'Brand_BMW' 'Brand_Mercedes-Benz'\n",
+      " 'Brand_Mitsubishi' 'Brand_Renault' 'Brand_Toyota' 'Brand_Volkswagen'\n",
+      " 'Body_hatch' 'Body_other' 'Body_sedan' 'Body_vagon' 'Body_van'\n",
+      " 'Engine Type_Gas' 'Engine Type_Other' 'Engine Type_Petrol'\n",
+      " 'Registration_yes' 'Model_100' 'Model_11' 'Model_116' 'Model_118'\n",
+      " 'Model_120' 'Model_19' 'Model_190' 'Model_200' 'Model_21' 'Model_210'\n",
+      " 'Model_220' 'Model_230' 'Model_25' 'Model_250' 'Model_300' 'Model_316'\n",
+      " 'Model_318' 'Model_320' 'Model_323' 'Model_324' 'Model_325' 'Model_328'\n",
+      " 'Model_330' 'Model_335' 'Model_4 Series Gran Coupe' 'Model_428'\n",
+      " 'Model_4Runner' 'Model_5' 'Model_5 Series' 'Model_5 Series GT'\n",
+      " 'Model_520' 'Model_523' 'Model_524' 'Model_525' 'Model_528' 'Model_530'\n",
+      " 'Model_535' 'Model_540' 'Model_545' 'Model_550'\n",
+      " 'Model_6 Series Gran Coupe' 'Model_630' 'Model_640' 'Model_645'\n",
+      " 'Model_650' 'Model_730' 'Model_735' 'Model_740' 'Model_745' 'Model_750'\n",
+      " 'Model_760' 'Model_80' 'Model_9' 'Model_90' 'Model_A 140' 'Model_A 150'\n",
+      " 'Model_A 170' 'Model_A 180' 'Model_A1' 'Model_A3' 'Model_A4'\n",
+      " 'Model_A4 Allroad' 'Model_A5' 'Model_A6' 'Model_A6 Allroad' 'Model_A7'\n",
+      " 'Model_A8' 'Model_ASX' 'Model_Amarok' 'Model_Auris' 'Model_Avalon'\n",
+      " 'Model_Avensis' 'Model_Aygo' 'Model_B 170' 'Model_B 180' 'Model_B 200'\n",
+      " 'Model_Beetle' 'Model_Bora' 'Model_C-Class' 'Model_CL 180' 'Model_CL 500'\n",
+      " 'Model_CL 55 AMG' 'Model_CL 550' 'Model_CL 63 AMG' 'Model_CLA 200'\n",
+      " 'Model_CLA 220' 'Model_CLA-Class' 'Model_CLC 180' 'Model_CLC 200'\n",
+      " 'Model_CLK 200' 'Model_CLK 220' 'Model_CLK 230' 'Model_CLK 240'\n",
+      " 'Model_CLK 280' 'Model_CLK 320' 'Model_CLK 430' 'Model_CLS 350'\n",
+      " 'Model_CLS 400' 'Model_CLS 500' 'Model_CLS 63 AMG' 'Model_Caddy'\n",
+      " 'Model_Camry' 'Model_Captur' 'Model_Caravelle' 'Model_Carina'\n",
+      " 'Model_Carisma' 'Model_Celica' 'Model_Clio' 'Model_Colt' 'Model_Corolla'\n",
+      " 'Model_Corolla Verso' 'Model_Cross Touran' 'Model_Dokker' 'Model_Duster'\n",
+      " 'Model_E-Class' 'Model_Eclipse' 'Model_Eos' 'Model_Espace'\n",
+      " 'Model_FJ Cruiser' 'Model_Fluence' 'Model_Fortuner' 'Model_G 320'\n",
+      " 'Model_G 350' 'Model_G 500' 'Model_G 55 AMG' 'Model_G 63 AMG'\n",
+      " 'Model_GL 320' 'Model_GL 350' 'Model_GL 420' 'Model_GL 450'\n",
+      " 'Model_GL 500' 'Model_GL 550' 'Model_GLC-Class' 'Model_GLE-Class'\n",
+      " 'Model_GLK 220' 'Model_GLK 300' 'Model_GLS 350' 'Model_GLS 400'\n",
+      " 'Model_GLS 500' 'Model_GLS 63' 'Model_Galant' 'Model_Golf GTI'\n",
+      " 'Model_Golf II' 'Model_Golf III' 'Model_Golf IV' 'Model_Golf Plus'\n",
+      " 'Model_Golf V' 'Model_Golf VI' 'Model_Golf VII' 'Model_Golf Variant'\n",
+      " 'Model_Grand Scenic' 'Model_Grandis' 'Model_Hiace' 'Model_Highlander'\n",
+      " 'Model_Hilux' 'Model_I3' 'Model_IQ' 'Model_Jetta' 'Model_Kangoo'\n",
+      " 'Model_Koleos' 'Model_L 200' 'Model_LT' 'Model_Laguna' 'Model_Lancer'\n",
+      " 'Model_Lancer Evolution' 'Model_Lancer X' 'Model_Lancer X Sportback'\n",
+      " 'Model_Land Cruiser 100' 'Model_Land Cruiser 105'\n",
+      " 'Model_Land Cruiser 200' 'Model_Land Cruiser 76' 'Model_Land Cruiser 80'\n",
+      " 'Model_Land Cruiser Prado' 'Model_Latitude' 'Model_Lite Ace'\n",
+      " 'Model_Logan' 'Model_Lupo' 'Model_M5' 'Model_M6' 'Model_MB'\n",
+      " 'Model_ML 250' 'Model_ML 270' 'Model_ML 280' 'Model_ML 320'\n",
+      " 'Model_ML 350' 'Model_ML 400' 'Model_ML 430' 'Model_ML 500'\n",
+      " 'Model_ML 550' 'Model_ML 63 AMG' 'Model_Mark II' 'Model_Master'\n",
+      " 'Model_Matrix' 'Model_Megane' 'Model_Modus' 'Model_Multivan'\n",
+      " 'Model_New Beetle' 'Model_Outlander' 'Model_Outlander XL' 'Model_Pajero'\n",
+      " 'Model_Pajero Pinin' 'Model_Pajero Sport' 'Model_Pajero Wagon'\n",
+      " 'Model_Passat B2' 'Model_Passat B3' 'Model_Passat B4' 'Model_Passat B5'\n",
+      " 'Model_Passat B6' 'Model_Passat B7' 'Model_Passat B8' 'Model_Passat CC'\n",
+      " 'Model_Phaeton' 'Model_Pointer' 'Model_Polo' 'Model_Previa' 'Model_Prius'\n",
+      " 'Model_Q3' 'Model_Q5' 'Model_Q7' 'Model_R 320' 'Model_R8' 'Model_Rav 4'\n",
+      " 'Model_S 140' 'Model_S 250' 'Model_S 280' 'Model_S 300' 'Model_S 320'\n",
+      " 'Model_S 350' 'Model_S 400' 'Model_S 420' 'Model_S 430' 'Model_S 500'\n",
+      " 'Model_S 55' 'Model_S 550' 'Model_S 600' 'Model_S 63 AMG'\n",
+      " 'Model_S 65 AMG' 'Model_S-Guard' 'Model_S4' 'Model_S5' 'Model_S8'\n",
+      " 'Model_SL 500 (550)' 'Model_SL 55 AMG' 'Model_SLK 200' 'Model_SLK 350'\n",
+      " 'Model_Sandero' 'Model_Sandero StepWay' 'Model_Scenic' 'Model_Scion'\n",
+      " 'Model_Scirocco' 'Model_Sequoia' 'Model_Sharan' 'Model_Sienna'\n",
+      " 'Model_Smart' 'Model_Space Star' 'Model_Space Wagon' 'Model_Sprinter'\n",
+      " 'Model_Sprinter 208' 'Model_Sprinter 210' 'Model_Sprinter 211'\n",
+      " 'Model_Sprinter 212' 'Model_Sprinter 213' 'Model_Sprinter 311'\n",
+      " 'Model_Sprinter 312' 'Model_Sprinter 313' 'Model_Sprinter 315'\n",
+      " 'Model_Sprinter 316' 'Model_Sprinter 318' 'Model_Sprinter 319'\n",
+      " 'Model_Sprinter 324' 'Model_Symbol' 'Model_Syncro'\n",
+      " 'Model_T2 (Transporter)' 'Model_T3 (Transporter)'\n",
+      " 'Model_T4 (Transporter)' 'Model_T4 (Transporter) '\n",
+      " 'Model_T5 (Transporter)' 'Model_T5 (Transporter) '\n",
+      " 'Model_T6 (Transporter)' 'Model_T6 (Transporter) ' 'Model_TT'\n",
+      " 'Model_Tacoma' 'Model_Tiguan' 'Model_Touareg' 'Model_Touran'\n",
+      " 'Model_Trafic' 'Model_Tundra' 'Model_Up' 'Model_V 250' 'Model_Vaneo'\n",
+      " 'Model_Vento' 'Model_Venza' 'Model_Viano' 'Model_Virage' 'Model_Vista'\n",
+      " 'Model_Vito' 'Model_X1' 'Model_X3' 'Model_X5' 'Model_X5 M' 'Model_X6'\n",
+      " 'Model_X6 M' 'Model_Yaris' 'Model_Z3' 'Model_Z4']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a list of column names with 'log_price' at the beginning\n",
+    "new_order = ['log_price'] + [col for col in data_with_dummies.columns if col != 'log_price']\n",
+    "\n",
+    "# Reorder the columns in the DataFrame\n",
+    "data_with_dummies = data_with_dummies[new_order]\n",
+    "\n",
+    "# Now, when you print the column names, 'log_price' should be the first column\n",
+    "print(data_with_dummies.columns.values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Year  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0  1991       True                False   \n",
+       "1   8.974618      427      2.9  1999      False                 True   \n",
+       "2   9.495519      358      5.0  2003      False                 True   \n",
+       "3  10.043249      240      4.2  2007      False                False   \n",
+       "4   9.814656      120      2.0  2011      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  Model_100  \\\n",
+       "0              False                True              True      False   \n",
+       "1              False               False              True      False   \n",
+       "2              False               False              True      False   \n",
+       "3              False                True              True      False   \n",
+       "4              False                True              True      False   \n",
+       "\n",
+       "   Model_11  Model_116  Model_118  Model_120  Model_19  Model_190  Model_200  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_21  Model_210  Model_220  Model_230  Model_25  Model_250  Model_300  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_316  Model_318  Model_320  Model_323  Model_324  Model_325  \\\n",
+       "0      False      False       True      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_328  Model_330  Model_335  Model_4 Series Gran Coupe  Model_428  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_4Runner  Model_5  Model_5 Series  Model_5 Series GT  Model_520  \\\n",
+       "0          False    False           False              False      False   \n",
+       "1          False    False           False              False      False   \n",
+       "2          False    False           False              False      False   \n",
+       "3          False    False           False              False      False   \n",
+       "4          False    False           False              False      False   \n",
+       "\n",
+       "   Model_523  Model_524  Model_525  Model_528  Model_530  Model_535  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_540  Model_545  Model_550  Model_6 Series Gran Coupe  Model_630  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_640  Model_645  Model_650  Model_730  Model_735  Model_740  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_745  Model_750  Model_760  Model_80  Model_9  Model_90  Model_A 140  \\\n",
+       "0      False      False      False     False    False     False        False   \n",
+       "1      False      False      False     False    False     False        False   \n",
+       "2      False      False      False     False    False     False        False   \n",
+       "3      False      False      False     False    False     False        False   \n",
+       "4      False      False      False     False    False     False        False   \n",
+       "\n",
+       "   Model_A 150  Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0        False        False        False     False     False     False   \n",
+       "1        False        False        False     False     False     False   \n",
+       "2        False        False        False     False     False     False   \n",
+       "3        False        False        False     False     False     False   \n",
+       "4        False        False        False     False     False     False   \n",
+       "\n",
+       "   Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  Model_A8  \\\n",
+       "0             False     False     False             False     False     False   \n",
+       "1             False     False     False             False     False     False   \n",
+       "2             False     False     False             False     False     False   \n",
+       "3             False     False     False             False     False     False   \n",
+       "4             False     False     False             False     False     False   \n",
+       "\n",
+       "   Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  Model_Avensis  \\\n",
+       "0      False         False        False         False          False   \n",
+       "1      False         False        False         False          False   \n",
+       "2      False         False        False         False          False   \n",
+       "3      False         False        False         False          False   \n",
+       "4      False         False        False         False          False   \n",
+       "\n",
+       "   Model_Aygo  Model_B 170  Model_B 180  Model_B 200  Model_Beetle  \\\n",
+       "0       False        False        False        False         False   \n",
+       "1       False        False        False        False         False   \n",
+       "2       False        False        False        False         False   \n",
+       "3       False        False        False        False         False   \n",
+       "4       False        False        False        False         False   \n",
+       "\n",
+       "   Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  Model_CL 55 AMG  \\\n",
+       "0       False          False         False         False            False   \n",
+       "1       False          False         False         False            False   \n",
+       "2       False          False         False         False            False   \n",
+       "3       False          False         False         False            False   \n",
+       "4       False          False         False         False            False   \n",
+       "\n",
+       "   Model_CL 550  Model_CL 63 AMG  Model_CLA 200  Model_CLA 220  \\\n",
+       "0         False            False          False          False   \n",
+       "1         False            False          False          False   \n",
+       "2         False            False          False          False   \n",
+       "3         False            False          False          False   \n",
+       "4         False            False          False          False   \n",
+       "\n",
+       "   Model_CLA-Class  Model_CLC 180  Model_CLC 200  Model_CLK 200  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 220  Model_CLK 230  Model_CLK 240  Model_CLK 280  Model_CLK 320  \\\n",
+       "0          False          False          False          False          False   \n",
+       "1          False          False          False          False          False   \n",
+       "2          False          False          False          False          False   \n",
+       "3          False          False          False          False          False   \n",
+       "4          False          False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 430  Model_CLS 350  Model_CLS 400  Model_CLS 500  \\\n",
+       "0          False          False          False          False   \n",
+       "1          False          False          False          False   \n",
+       "2          False          False          False          False   \n",
+       "3          False          False          False          False   \n",
+       "4          False          False          False          False   \n",
+       "\n",
+       "   Model_CLS 63 AMG  Model_Caddy  Model_Camry  Model_Captur  Model_Caravelle  \\\n",
+       "0             False        False        False         False            False   \n",
+       "1             False        False        False         False            False   \n",
+       "2             False        False        False         False            False   \n",
+       "3             False        False        False         False            False   \n",
+       "4             False        False        False         False            False   \n",
+       "\n",
+       "   Model_Carina  Model_Carisma  Model_Celica  Model_Clio  Model_Colt  \\\n",
+       "0         False          False         False       False       False   \n",
+       "1         False          False         False       False       False   \n",
+       "2         False          False         False       False       False   \n",
+       "3         False          False         False       False       False   \n",
+       "4         False          False         False       False       False   \n",
+       "\n",
+       "   Model_Corolla  Model_Corolla Verso  Model_Cross Touran  Model_Dokker  \\\n",
+       "0          False                False               False         False   \n",
+       "1          False                False               False         False   \n",
+       "2          False                False               False         False   \n",
+       "3          False                False               False         False   \n",
+       "4          False                False               False         False   \n",
+       "\n",
+       "   Model_Duster  Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  \\\n",
+       "0         False          False          False      False         False   \n",
+       "1         False          False          False      False         False   \n",
+       "2         False          False          False      False         False   \n",
+       "3         False          False          False      False         False   \n",
+       "4         False          False          False      False         False   \n",
+       "\n",
+       "   Model_FJ Cruiser  Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  \\\n",
+       "0             False          False           False        False        False   \n",
+       "1             False          False           False        False        False   \n",
+       "2             False          False           False        False        False   \n",
+       "3             False          False           False        False        False   \n",
+       "4             False          False           False        False        False   \n",
+       "\n",
+       "   Model_G 500  Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0        False           False           False         False         False   \n",
+       "1        False           False           False         False         False   \n",
+       "2        False           False           False         False         False   \n",
+       "3        False           False           False         False         False   \n",
+       "4        False           False           False         False         False   \n",
+       "\n",
+       "   Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  Model_Golf GTI  \\\n",
+       "0          False          False         False         False           False   \n",
+       "1          False          False         False         False           False   \n",
+       "2          False          False         False         False           False   \n",
+       "3          False          False         False         False           False   \n",
+       "4          False          False         False         False           False   \n",
+       "\n",
+       "   Model_Golf II  Model_Golf III  Model_Golf IV  Model_Golf Plus  \\\n",
+       "0          False           False          False            False   \n",
+       "1          False           False          False            False   \n",
+       "2          False           False          False            False   \n",
+       "3          False           False          False            False   \n",
+       "4          False           False          False            False   \n",
+       "\n",
+       "   Model_Golf V  Model_Golf VI  Model_Golf VII  Model_Golf Variant  \\\n",
+       "0         False          False           False               False   \n",
+       "1         False          False           False               False   \n",
+       "2         False          False           False               False   \n",
+       "3         False          False           False               False   \n",
+       "4         False          False           False               False   \n",
+       "\n",
+       "   Model_Grand Scenic  Model_Grandis  Model_Hiace  Model_Highlander  \\\n",
+       "0               False          False        False             False   \n",
+       "1               False          False        False             False   \n",
+       "2               False          False        False             False   \n",
+       "3               False          False        False             False   \n",
+       "4               False          False        False             False   \n",
+       "\n",
+       "   Model_Hilux  Model_I3  Model_IQ  Model_Jetta  Model_Kangoo  Model_Koleos  \\\n",
+       "0        False     False     False        False         False         False   \n",
+       "1        False     False     False        False         False         False   \n",
+       "2        False     False     False        False         False         False   \n",
+       "3        False     False     False        False         False         False   \n",
+       "4        False     False     False        False         False         False   \n",
+       "\n",
+       "   Model_L 200  Model_LT  Model_Laguna  Model_Lancer  Model_Lancer Evolution  \\\n",
+       "0        False     False         False         False                   False   \n",
+       "1        False     False         False         False                   False   \n",
+       "2        False     False         False         False                   False   \n",
+       "3        False     False         False         False                   False   \n",
+       "4        False     False         False         False                   False   \n",
+       "\n",
+       "   Model_Lancer X  Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0           False                     False                   False   \n",
+       "1           False                     False                   False   \n",
+       "2           False                     False                   False   \n",
+       "3           False                     False                   False   \n",
+       "4           False                     False                   False   \n",
+       "\n",
+       "   Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                   False                   False                  False   \n",
+       "1                   False                   False                  False   \n",
+       "2                   False                   False                  False   \n",
+       "3                   False                   False                  False   \n",
+       "4                   False                   False                  False   \n",
+       "\n",
+       "   Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                  False                     False           False   \n",
+       "1                  False                     False           False   \n",
+       "2                  False                     False           False   \n",
+       "3                  False                     False           False   \n",
+       "4                  False                     False           False   \n",
+       "\n",
+       "   Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0           False        False       False     False     False     False   \n",
+       "1           False        False       False     False     False     False   \n",
+       "2           False        False       False     False     False     False   \n",
+       "3           False        False       False     False     False     False   \n",
+       "4           False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0         False         False         False         False         False   \n",
+       "1         False         False         False         False         False   \n",
+       "2         False         False         False         False         False   \n",
+       "3         False         False         False         False         False   \n",
+       "4         False         False         False         False         False   \n",
+       "\n",
+       "   Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0          False         False         False         False        False   \n",
+       "1          False         False         False         False        False   \n",
+       "2          False         False         False         False        False   \n",
+       "3          False         False         False         False        False   \n",
+       "4          False         False         False         False        False   \n",
+       "\n",
+       "   Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0           False             False            False               False   \n",
+       "1           False             False            False               False   \n",
+       "2           False             False            False               False   \n",
+       "3           False             False            False               False   \n",
+       "4           False             False            False               False   \n",
+       "\n",
+       "   Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  Model_Pajero Wagon  \\\n",
+       "0         False               False               False               False   \n",
+       "1         False               False               False               False   \n",
+       "2         False               False               False               False   \n",
+       "3         False               False               False               False   \n",
+       "4         False               False               False               False   \n",
+       "\n",
+       "   Model_Passat B2  Model_Passat B3  Model_Passat B4  Model_Passat B5  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Passat B6  Model_Passat B7  Model_Passat B8  Model_Passat CC  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  Model_Prius  \\\n",
+       "0          False          False       False         False        False   \n",
+       "1          False          False       False         False        False   \n",
+       "2          False          False       False         False        False   \n",
+       "3          False          False       False         False        False   \n",
+       "4          False          False       False         False        False   \n",
+       "\n",
+       "   Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  Model_Rav 4  \\\n",
+       "0     False     False     False        False     False        False   \n",
+       "1     False     False     False        False     False        False   \n",
+       "2     False     False     False        False     False        False   \n",
+       "3     False     False      True        False     False        False   \n",
+       "4     False     False     False        False     False         True   \n",
+       "\n",
+       "   Model_S 140  Model_S 250  Model_S 280  Model_S 300  Model_S 320  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False        False   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 350  Model_S 400  Model_S 420  Model_S 430  Model_S 500  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False         True   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  Model_S 65 AMG  \\\n",
+       "0       False        False        False           False           False   \n",
+       "1       False        False        False           False           False   \n",
+       "2       False        False        False           False           False   \n",
+       "3       False        False        False           False           False   \n",
+       "4       False        False        False           False           False   \n",
+       "\n",
+       "   Model_S-Guard  Model_S4  Model_S5  Model_S8  Model_SL 500 (550)  \\\n",
+       "0          False     False     False     False               False   \n",
+       "1          False     False     False     False               False   \n",
+       "2          False     False     False     False               False   \n",
+       "3          False     False     False     False               False   \n",
+       "4          False     False     False     False               False   \n",
+       "\n",
+       "   Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  Model_Sandero  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_Sandero StepWay  Model_Scenic  Model_Scion  Model_Scirocco  \\\n",
+       "0                  False         False        False           False   \n",
+       "1                  False         False        False           False   \n",
+       "2                  False         False        False           False   \n",
+       "3                  False         False        False           False   \n",
+       "4                  False         False        False           False   \n",
+       "\n",
+       "   Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  Model_Space Star  \\\n",
+       "0          False         False         False        False             False   \n",
+       "1          False         False         False        False             False   \n",
+       "2          False         False         False        False             False   \n",
+       "3          False         False         False        False             False   \n",
+       "4          False         False         False        False             False   \n",
+       "\n",
+       "   Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  Model_Sprinter 210  \\\n",
+       "0              False           False               False               False   \n",
+       "1              False           False               False               False   \n",
+       "2              False           False               False               False   \n",
+       "3              False           False               False               False   \n",
+       "4              False           False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 211  Model_Sprinter 212  Model_Sprinter 213  \\\n",
+       "0               False               False               False   \n",
+       "1               False                True               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 311  Model_Sprinter 312  Model_Sprinter 313  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 315  Model_Sprinter 316  Model_Sprinter 318  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 319  Model_Sprinter 324  Model_Symbol  Model_Syncro  \\\n",
+       "0               False               False         False         False   \n",
+       "1               False               False         False         False   \n",
+       "2               False               False         False         False   \n",
+       "3               False               False         False         False   \n",
+       "4               False               False         False         False   \n",
+       "\n",
+       "   Model_T2 (Transporter)  Model_T3 (Transporter)  Model_T4 (Transporter)  \\\n",
+       "0                   False                   False                   False   \n",
+       "1                   False                   False                   False   \n",
+       "2                   False                   False                   False   \n",
+       "3                   False                   False                   False   \n",
+       "4                   False                   False                   False   \n",
+       "\n",
+       "   Model_T4 (Transporter)   Model_T5 (Transporter)  Model_T5 (Transporter)   \\\n",
+       "0                    False                   False                    False   \n",
+       "1                    False                   False                    False   \n",
+       "2                    False                   False                    False   \n",
+       "3                    False                   False                    False   \n",
+       "4                    False                   False                    False   \n",
+       "\n",
+       "   Model_T6 (Transporter)  Model_T6 (Transporter)   Model_TT  Model_Tacoma  \\\n",
+       "0                   False                    False     False         False   \n",
+       "1                   False                    False     False         False   \n",
+       "2                   False                    False     False         False   \n",
+       "3                   False                    False     False         False   \n",
+       "4                   False                    False     False         False   \n",
+       "\n",
+       "   Model_Tiguan  Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  \\\n",
+       "0         False          False         False         False         False   \n",
+       "1         False          False         False         False         False   \n",
+       "2         False          False         False         False         False   \n",
+       "3         False          False         False         False         False   \n",
+       "4         False          False         False         False         False   \n",
+       "\n",
+       "   Model_Up  Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0     False        False        False        False        False        False   \n",
+       "1     False        False        False        False        False        False   \n",
+       "2     False        False        False        False        False        False   \n",
+       "3     False        False        False        False        False        False   \n",
+       "4     False        False        False        False        False        False   \n",
+       "\n",
+       "   Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0         False        False       False     False     False     False   \n",
+       "1         False        False       False     False     False     False   \n",
+       "2         False        False       False     False     False     False   \n",
+       "3         False        False       False     False     False     False   \n",
+       "4         False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0       False     False       False        False     False     False  \n",
+       "1       False     False       False        False     False     False  \n",
+       "2       False     False       False        False     False     False  \n",
+       "3       False     False       False        False     False     False  \n",
+       "4       False     False       False        False     False     False  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed = data_with_dummies\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Declare the inputs and the targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = data_preprocessed['log_price'] #  Dependent variable\n",
+    "#Input are everything but the dependent variable, so i'll drop 'log_price'\n",
+    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4339</th>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4340</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4341</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4342</th>\n",
+       "      <td>194</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4344</th>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4025 rows × 323 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Mileage  EngineV  Year  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0         277      2.0  1991       True                False   \n",
+       "1         427      2.9  1999      False                 True   \n",
+       "2         358      5.0  2003      False                 True   \n",
+       "3         240      4.2  2007      False                False   \n",
+       "4         120      2.0  2011      False                False   \n",
+       "...       ...      ...   ...        ...                  ...   \n",
+       "4339       35      1.6  2014      False                False   \n",
+       "4340        9      3.0  2014      False                 True   \n",
+       "4341        1      3.5  1999       True                False   \n",
+       "4342      194      2.0  1985       True                False   \n",
+       "4344      124      2.0  2013      False                False   \n",
+       "\n",
+       "      Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                False          False         False             False   \n",
+       "1                False          False         False             False   \n",
+       "2                False          False         False             False   \n",
+       "3                False          False         False             False   \n",
+       "4                False          False          True             False   \n",
+       "...                ...            ...           ...               ...   \n",
+       "4339             False          False          True             False   \n",
+       "4340             False          False         False             False   \n",
+       "4341             False          False         False             False   \n",
+       "4342             False          False         False             False   \n",
+       "4344             False          False         False              True   \n",
+       "\n",
+       "      Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  \\\n",
+       "0          False       False        True       False     False   \n",
+       "1          False       False       False       False      True   \n",
+       "2          False       False        True       False     False   \n",
+       "3          False       False       False       False     False   \n",
+       "4          False       False       False       False     False   \n",
+       "...          ...         ...         ...         ...       ...   \n",
+       "4339       False       False        True       False     False   \n",
+       "4340       False       False        True       False     False   \n",
+       "4341       False       False        True       False     False   \n",
+       "4342       False       False        True       False     False   \n",
+       "4344       False       False       False       False      True   \n",
+       "\n",
+       "      Engine Type_Gas  Engine Type_Other  Engine Type_Petrol  \\\n",
+       "0               False              False                True   \n",
+       "1               False              False               False   \n",
+       "2                True              False               False   \n",
+       "3               False              False                True   \n",
+       "4               False              False                True   \n",
+       "...               ...                ...                 ...   \n",
+       "4339            False              False                True   \n",
+       "4340            False              False               False   \n",
+       "4341            False              False                True   \n",
+       "4342            False              False                True   \n",
+       "4344            False              False               False   \n",
+       "\n",
+       "      Registration_yes  Model_100  Model_11  Model_116  Model_118  Model_120  \\\n",
+       "0                 True      False     False      False      False      False   \n",
+       "1                 True      False     False      False      False      False   \n",
+       "2                 True      False     False      False      False      False   \n",
+       "3                 True      False     False      False      False      False   \n",
+       "4                 True      False     False      False      False      False   \n",
+       "...                ...        ...       ...        ...        ...        ...   \n",
+       "4339              True      False     False      False      False      False   \n",
+       "4340              True      False     False      False      False      False   \n",
+       "4341              True      False     False      False      False      False   \n",
+       "4342              True      False     False      False      False      False   \n",
+       "4344              True      False     False      False      False      False   \n",
+       "\n",
+       "      Model_19  Model_190  Model_200  Model_21  Model_210  Model_220  \\\n",
+       "0        False      False      False     False      False      False   \n",
+       "1        False      False      False     False      False      False   \n",
+       "2        False      False      False     False      False      False   \n",
+       "3        False      False      False     False      False      False   \n",
+       "4        False      False      False     False      False      False   \n",
+       "...        ...        ...        ...       ...        ...        ...   \n",
+       "4339     False      False      False     False      False      False   \n",
+       "4340     False      False      False     False      False      False   \n",
+       "4341     False      False      False     False      False      False   \n",
+       "4342     False      False      False     False      False      False   \n",
+       "4344     False      False      False     False      False      False   \n",
+       "\n",
+       "      Model_230  Model_25  Model_250  Model_300  Model_316  Model_318  \\\n",
+       "0         False     False      False      False      False      False   \n",
+       "1         False     False      False      False      False      False   \n",
+       "2         False     False      False      False      False      False   \n",
+       "3         False     False      False      False      False      False   \n",
+       "4         False     False      False      False      False      False   \n",
+       "...         ...       ...        ...        ...        ...        ...   \n",
+       "4339      False     False      False      False      False      False   \n",
+       "4340      False     False      False      False      False      False   \n",
+       "4341      False     False      False      False      False      False   \n",
+       "4342      False     False      False      False      False      False   \n",
+       "4344      False     False      False      False      False      False   \n",
+       "\n",
+       "      Model_320  Model_323  Model_324  Model_325  Model_328  Model_330  \\\n",
+       "0          True      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False      False      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_335  Model_4 Series Gran Coupe  Model_428  Model_4Runner  Model_5  \\\n",
+       "0         False                      False      False          False    False   \n",
+       "1         False                      False      False          False    False   \n",
+       "2         False                      False      False          False    False   \n",
+       "3         False                      False      False          False    False   \n",
+       "4         False                      False      False          False    False   \n",
+       "...         ...                        ...        ...            ...      ...   \n",
+       "4339      False                      False      False          False    False   \n",
+       "4340      False                      False      False          False    False   \n",
+       "4341      False                      False      False          False    False   \n",
+       "4342      False                      False      False          False    False   \n",
+       "4344      False                      False      False          False    False   \n",
+       "\n",
+       "      Model_5 Series  Model_5 Series GT  Model_520  Model_523  Model_524  \\\n",
+       "0              False              False      False      False      False   \n",
+       "1              False              False      False      False      False   \n",
+       "2              False              False      False      False      False   \n",
+       "3              False              False      False      False      False   \n",
+       "4              False              False      False      False      False   \n",
+       "...              ...                ...        ...        ...        ...   \n",
+       "4339           False              False      False      False      False   \n",
+       "4340           False              False      False      False      False   \n",
+       "4341           False              False      False      False      False   \n",
+       "4342           False              False       True      False      False   \n",
+       "4344           False              False      False      False      False   \n",
+       "\n",
+       "      Model_525  Model_528  Model_530  Model_535  Model_540  Model_545  \\\n",
+       "0         False      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False       True      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_550  Model_6 Series Gran Coupe  Model_630  Model_640  Model_645  \\\n",
+       "0         False                      False      False      False      False   \n",
+       "1         False                      False      False      False      False   \n",
+       "2         False                      False      False      False      False   \n",
+       "3         False                      False      False      False      False   \n",
+       "4         False                      False      False      False      False   \n",
+       "...         ...                        ...        ...        ...        ...   \n",
+       "4339      False                      False      False      False      False   \n",
+       "4340      False                      False      False      False      False   \n",
+       "4341      False                      False      False      False      False   \n",
+       "4342      False                      False      False      False      False   \n",
+       "4344      False                      False      False      False      False   \n",
+       "\n",
+       "      Model_650  Model_730  Model_735  Model_740  Model_745  Model_750  \\\n",
+       "0         False      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False      False      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_760  Model_80  Model_9  Model_90  Model_A 140  Model_A 150  \\\n",
+       "0         False     False    False     False        False        False   \n",
+       "1         False     False    False     False        False        False   \n",
+       "2         False     False    False     False        False        False   \n",
+       "3         False     False    False     False        False        False   \n",
+       "4         False     False    False     False        False        False   \n",
+       "...         ...       ...      ...       ...          ...          ...   \n",
+       "4339      False     False    False     False        False        False   \n",
+       "4340      False     False    False     False        False        False   \n",
+       "4341      False     False    False     False        False        False   \n",
+       "4342      False     False    False     False        False        False   \n",
+       "4344      False     False    False     False        False        False   \n",
+       "\n",
+       "      Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0           False        False     False     False     False   \n",
+       "1           False        False     False     False     False   \n",
+       "2           False        False     False     False     False   \n",
+       "3           False        False     False     False     False   \n",
+       "4           False        False     False     False     False   \n",
+       "...           ...          ...       ...       ...       ...   \n",
+       "4339        False        False     False     False     False   \n",
+       "4340        False        False     False     False     False   \n",
+       "4341        False        False     False     False     False   \n",
+       "4342        False        False     False     False     False   \n",
+       "4344        False        False     False     False     False   \n",
+       "\n",
+       "      Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  \\\n",
+       "0                False     False     False             False     False   \n",
+       "1                False     False     False             False     False   \n",
+       "2                False     False     False             False     False   \n",
+       "3                False     False     False             False     False   \n",
+       "4                False     False     False             False     False   \n",
+       "...                ...       ...       ...               ...       ...   \n",
+       "4339             False     False     False             False     False   \n",
+       "4340             False     False     False             False     False   \n",
+       "4341             False     False     False             False     False   \n",
+       "4342             False     False     False             False     False   \n",
+       "4344             False     False     False             False     False   \n",
+       "\n",
+       "      Model_A8  Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  \\\n",
+       "0        False      False         False        False         False   \n",
+       "1        False      False         False        False         False   \n",
+       "2        False      False         False        False         False   \n",
+       "3        False      False         False        False         False   \n",
+       "4        False      False         False        False         False   \n",
+       "...        ...        ...           ...          ...           ...   \n",
+       "4339     False      False         False        False         False   \n",
+       "4340     False      False         False        False         False   \n",
+       "4341     False      False         False        False         False   \n",
+       "4342     False      False         False        False         False   \n",
+       "4344     False      False         False        False         False   \n",
+       "\n",
+       "      Model_Avensis  Model_Aygo  Model_B 170  Model_B 180  Model_B 200  \\\n",
+       "0             False       False        False        False        False   \n",
+       "1             False       False        False        False        False   \n",
+       "2             False       False        False        False        False   \n",
+       "3             False       False        False        False        False   \n",
+       "4             False       False        False        False        False   \n",
+       "...             ...         ...          ...          ...          ...   \n",
+       "4339          False       False        False        False        False   \n",
+       "4340          False       False        False        False        False   \n",
+       "4341          False       False        False        False        False   \n",
+       "4342          False       False        False        False        False   \n",
+       "4344          False       False        False        False        False   \n",
+       "\n",
+       "      Model_Beetle  Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  \\\n",
+       "0            False       False          False         False         False   \n",
+       "1            False       False          False         False         False   \n",
+       "2            False       False          False         False         False   \n",
+       "3            False       False          False         False         False   \n",
+       "4            False       False          False         False         False   \n",
+       "...            ...         ...            ...           ...           ...   \n",
+       "4339         False       False          False         False         False   \n",
+       "4340         False       False          False         False         False   \n",
+       "4341         False       False          False         False         False   \n",
+       "4342         False       False          False         False         False   \n",
+       "4344         False       False          False         False         False   \n",
+       "\n",
+       "      Model_CL 55 AMG  Model_CL 550  Model_CL 63 AMG  Model_CLA 200  \\\n",
+       "0               False         False            False          False   \n",
+       "1               False         False            False          False   \n",
+       "2               False         False            False          False   \n",
+       "3               False         False            False          False   \n",
+       "4               False         False            False          False   \n",
+       "...               ...           ...              ...            ...   \n",
+       "4339            False         False            False          False   \n",
+       "4340            False         False            False          False   \n",
+       "4341            False         False            False          False   \n",
+       "4342            False         False            False          False   \n",
+       "4344            False         False            False          False   \n",
+       "\n",
+       "      Model_CLA 220  Model_CLA-Class  Model_CLC 180  Model_CLC 200  \\\n",
+       "0             False            False          False          False   \n",
+       "1             False            False          False          False   \n",
+       "2             False            False          False          False   \n",
+       "3             False            False          False          False   \n",
+       "4             False            False          False          False   \n",
+       "...             ...              ...            ...            ...   \n",
+       "4339          False            False          False          False   \n",
+       "4340          False            False          False          False   \n",
+       "4341          False            False          False          False   \n",
+       "4342          False            False          False          False   \n",
+       "4344          False            False          False          False   \n",
+       "\n",
+       "      Model_CLK 200  Model_CLK 220  Model_CLK 230  Model_CLK 240  \\\n",
+       "0             False          False          False          False   \n",
+       "1             False          False          False          False   \n",
+       "2             False          False          False          False   \n",
+       "3             False          False          False          False   \n",
+       "4             False          False          False          False   \n",
+       "...             ...            ...            ...            ...   \n",
+       "4339          False          False          False          False   \n",
+       "4340          False          False          False          False   \n",
+       "4341          False          False          False          False   \n",
+       "4342          False          False          False          False   \n",
+       "4344          False          False          False          False   \n",
+       "\n",
+       "      Model_CLK 280  Model_CLK 320  Model_CLK 430  Model_CLS 350  \\\n",
+       "0             False          False          False          False   \n",
+       "1             False          False          False          False   \n",
+       "2             False          False          False          False   \n",
+       "3             False          False          False          False   \n",
+       "4             False          False          False          False   \n",
+       "...             ...            ...            ...            ...   \n",
+       "4339          False          False          False          False   \n",
+       "4340          False          False          False          False   \n",
+       "4341          False          False          False          False   \n",
+       "4342          False          False          False          False   \n",
+       "4344          False          False          False          False   \n",
+       "\n",
+       "      Model_CLS 400  Model_CLS 500  Model_CLS 63 AMG  Model_Caddy  \\\n",
+       "0             False          False             False        False   \n",
+       "1             False          False             False        False   \n",
+       "2             False          False             False        False   \n",
+       "3             False          False             False        False   \n",
+       "4             False          False             False        False   \n",
+       "...             ...            ...               ...          ...   \n",
+       "4339          False          False             False        False   \n",
+       "4340          False          False             False        False   \n",
+       "4341          False          False             False        False   \n",
+       "4342          False          False             False        False   \n",
+       "4344          False          False             False        False   \n",
+       "\n",
+       "      Model_Camry  Model_Captur  Model_Caravelle  Model_Carina  Model_Carisma  \\\n",
+       "0           False         False            False         False          False   \n",
+       "1           False         False            False         False          False   \n",
+       "2           False         False            False         False          False   \n",
+       "3           False         False            False         False          False   \n",
+       "4           False         False            False         False          False   \n",
+       "...           ...           ...              ...           ...            ...   \n",
+       "4339        False         False            False         False          False   \n",
+       "4340        False         False            False         False          False   \n",
+       "4341        False         False            False         False          False   \n",
+       "4342        False         False            False         False          False   \n",
+       "4344        False         False            False         False          False   \n",
+       "\n",
+       "      Model_Celica  Model_Clio  Model_Colt  Model_Corolla  \\\n",
+       "0            False       False       False          False   \n",
+       "1            False       False       False          False   \n",
+       "2            False       False       False          False   \n",
+       "3            False       False       False          False   \n",
+       "4            False       False       False          False   \n",
+       "...            ...         ...         ...            ...   \n",
+       "4339         False       False       False           True   \n",
+       "4340         False       False       False          False   \n",
+       "4341         False       False       False          False   \n",
+       "4342         False       False       False          False   \n",
+       "4344         False       False       False          False   \n",
+       "\n",
+       "      Model_Corolla Verso  Model_Cross Touran  Model_Dokker  Model_Duster  \\\n",
+       "0                   False               False         False         False   \n",
+       "1                   False               False         False         False   \n",
+       "2                   False               False         False         False   \n",
+       "3                   False               False         False         False   \n",
+       "4                   False               False         False         False   \n",
+       "...                   ...                 ...           ...           ...   \n",
+       "4339                False               False         False         False   \n",
+       "4340                False               False         False         False   \n",
+       "4341                False               False         False         False   \n",
+       "4342                False               False         False         False   \n",
+       "4344                False               False         False         False   \n",
+       "\n",
+       "      Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  Model_FJ Cruiser  \\\n",
+       "0             False          False      False         False             False   \n",
+       "1             False          False      False         False             False   \n",
+       "2             False          False      False         False             False   \n",
+       "3             False          False      False         False             False   \n",
+       "4             False          False      False         False             False   \n",
+       "...             ...            ...        ...           ...               ...   \n",
+       "4339          False          False      False         False             False   \n",
+       "4340          False          False      False         False             False   \n",
+       "4341          False          False      False         False             False   \n",
+       "4342          False          False      False         False             False   \n",
+       "4344          False          False      False         False             False   \n",
+       "\n",
+       "      Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  Model_G 500  \\\n",
+       "0             False           False        False        False        False   \n",
+       "1             False           False        False        False        False   \n",
+       "2             False           False        False        False        False   \n",
+       "3             False           False        False        False        False   \n",
+       "4             False           False        False        False        False   \n",
+       "...             ...             ...          ...          ...          ...   \n",
+       "4339          False           False        False        False        False   \n",
+       "4340          False           False        False        False        False   \n",
+       "4341          False           False        False        False        False   \n",
+       "4342          False           False        False        False        False   \n",
+       "4344          False           False        False        False        False   \n",
+       "\n",
+       "      Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0              False           False         False         False   \n",
+       "1              False           False         False         False   \n",
+       "2              False           False         False         False   \n",
+       "3              False           False         False         False   \n",
+       "4              False           False         False         False   \n",
+       "...              ...             ...           ...           ...   \n",
+       "4339           False           False         False         False   \n",
+       "4340           False           False         False         False   \n",
+       "4341           False           False         False         False   \n",
+       "4342           False           False         False         False   \n",
+       "4344           False           False         False         False   \n",
+       "\n",
+       "      Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0            False         False         False         False            False   \n",
+       "1            False         False         False         False            False   \n",
+       "2            False         False         False         False            False   \n",
+       "3            False         False         False         False            False   \n",
+       "4            False         False         False         False            False   \n",
+       "...            ...           ...           ...           ...              ...   \n",
+       "4339         False         False         False         False            False   \n",
+       "4340         False         False         False         False            False   \n",
+       "4341         False         False         False         False            False   \n",
+       "4342         False         False         False         False            False   \n",
+       "4344         False         False         False         False            False   \n",
+       "\n",
+       "      Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0               False          False          False          False   \n",
+       "1               False          False          False          False   \n",
+       "2               False          False          False          False   \n",
+       "3               False          False          False          False   \n",
+       "4               False          False          False          False   \n",
+       "...               ...            ...            ...            ...   \n",
+       "4339            False          False          False          False   \n",
+       "4340            False          False          False          False   \n",
+       "4341            False          False          False          False   \n",
+       "4342            False          False          False          False   \n",
+       "4344            False          False          False          False   \n",
+       "\n",
+       "      Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  \\\n",
+       "0             False          False         False         False   \n",
+       "1             False          False         False         False   \n",
+       "2             False          False         False         False   \n",
+       "3             False          False         False         False   \n",
+       "4             False          False         False         False   \n",
+       "...             ...            ...           ...           ...   \n",
+       "4339          False          False         False         False   \n",
+       "4340          False          False         False         False   \n",
+       "4341          False          False         False         False   \n",
+       "4342          False          False         False         False   \n",
+       "4344          False          False         False         False   \n",
+       "\n",
+       "      Model_Golf GTI  Model_Golf II  Model_Golf III  Model_Golf IV  \\\n",
+       "0              False          False           False          False   \n",
+       "1              False          False           False          False   \n",
+       "2              False          False           False          False   \n",
+       "3              False          False           False          False   \n",
+       "4              False          False           False          False   \n",
+       "...              ...            ...             ...            ...   \n",
+       "4339           False          False           False          False   \n",
+       "4340           False          False           False          False   \n",
+       "4341           False          False           False          False   \n",
+       "4342           False          False           False          False   \n",
+       "4344           False          False           False          False   \n",
+       "\n",
+       "      Model_Golf Plus  Model_Golf V  Model_Golf VI  Model_Golf VII  \\\n",
+       "0               False         False          False           False   \n",
+       "1               False         False          False           False   \n",
+       "2               False         False          False           False   \n",
+       "3               False         False          False           False   \n",
+       "4               False         False          False           False   \n",
+       "...               ...           ...            ...             ...   \n",
+       "4339            False         False          False           False   \n",
+       "4340            False         False          False           False   \n",
+       "4341            False         False          False           False   \n",
+       "4342            False         False          False           False   \n",
+       "4344            False         False          False           False   \n",
+       "\n",
+       "      Model_Golf Variant  Model_Grand Scenic  Model_Grandis  Model_Hiace  \\\n",
+       "0                  False               False          False        False   \n",
+       "1                  False               False          False        False   \n",
+       "2                  False               False          False        False   \n",
+       "3                  False               False          False        False   \n",
+       "4                  False               False          False        False   \n",
+       "...                  ...                 ...            ...          ...   \n",
+       "4339               False               False          False        False   \n",
+       "4340               False               False          False        False   \n",
+       "4341               False               False          False        False   \n",
+       "4342               False               False          False        False   \n",
+       "4344               False               False          False        False   \n",
+       "\n",
+       "      Model_Highlander  Model_Hilux  Model_I3  Model_IQ  Model_Jetta  \\\n",
+       "0                False        False     False     False        False   \n",
+       "1                False        False     False     False        False   \n",
+       "2                False        False     False     False        False   \n",
+       "3                False        False     False     False        False   \n",
+       "4                False        False     False     False        False   \n",
+       "...                ...          ...       ...       ...          ...   \n",
+       "4339             False        False     False     False        False   \n",
+       "4340             False        False     False     False        False   \n",
+       "4341             False        False     False     False        False   \n",
+       "4342             False        False     False     False        False   \n",
+       "4344             False        False     False     False        False   \n",
+       "\n",
+       "      Model_Kangoo  Model_Koleos  Model_L 200  Model_LT  Model_Laguna  \\\n",
+       "0            False         False        False     False         False   \n",
+       "1            False         False        False     False         False   \n",
+       "2            False         False        False     False         False   \n",
+       "3            False         False        False     False         False   \n",
+       "4            False         False        False     False         False   \n",
+       "...            ...           ...          ...       ...           ...   \n",
+       "4339         False         False        False     False         False   \n",
+       "4340         False         False        False     False         False   \n",
+       "4341         False         False        False     False         False   \n",
+       "4342         False         False        False     False         False   \n",
+       "4344         False         False        False     False         False   \n",
+       "\n",
+       "      Model_Lancer  Model_Lancer Evolution  Model_Lancer X  \\\n",
+       "0            False                   False           False   \n",
+       "1            False                   False           False   \n",
+       "2            False                   False           False   \n",
+       "3            False                   False           False   \n",
+       "4            False                   False           False   \n",
+       "...            ...                     ...             ...   \n",
+       "4339         False                   False           False   \n",
+       "4340         False                   False           False   \n",
+       "4341         False                   False           False   \n",
+       "4342         False                   False           False   \n",
+       "4344         False                   False           False   \n",
+       "\n",
+       "      Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0                        False                   False   \n",
+       "1                        False                   False   \n",
+       "2                        False                   False   \n",
+       "3                        False                   False   \n",
+       "4                        False                   False   \n",
+       "...                        ...                     ...   \n",
+       "4339                     False                   False   \n",
+       "4340                     False                   False   \n",
+       "4341                     False                   False   \n",
+       "4342                     False                   False   \n",
+       "4344                     False                   False   \n",
+       "\n",
+       "      Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                      False                   False                  False   \n",
+       "1                      False                   False                  False   \n",
+       "2                      False                   False                  False   \n",
+       "3                      False                   False                  False   \n",
+       "4                      False                   False                  False   \n",
+       "...                      ...                     ...                    ...   \n",
+       "4339                   False                   False                  False   \n",
+       "4340                   False                   False                  False   \n",
+       "4341                   False                   False                  False   \n",
+       "4342                   False                   False                  False   \n",
+       "4344                   False                   False                  False   \n",
+       "\n",
+       "      Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                     False                     False           False   \n",
+       "1                     False                     False           False   \n",
+       "2                     False                     False           False   \n",
+       "3                     False                     False           False   \n",
+       "4                     False                     False           False   \n",
+       "...                     ...                       ...             ...   \n",
+       "4339                  False                     False           False   \n",
+       "4340                  False                     False           False   \n",
+       "4341                  False                     False           False   \n",
+       "4342                  False                     False           False   \n",
+       "4344                  False                     False           False   \n",
+       "\n",
+       "      Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0              False        False       False     False     False     False   \n",
+       "1              False        False       False     False     False     False   \n",
+       "2              False        False       False     False     False     False   \n",
+       "3              False        False       False     False     False     False   \n",
+       "4              False        False       False     False     False     False   \n",
+       "...              ...          ...         ...       ...       ...       ...   \n",
+       "4339           False        False       False     False     False     False   \n",
+       "4340           False        False       False     False     False     False   \n",
+       "4341           False        False       False     False     False     False   \n",
+       "4342           False        False       False     False     False     False   \n",
+       "4344           False        False       False     False     False     False   \n",
+       "\n",
+       "      Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0            False         False         False         False         False   \n",
+       "1            False         False         False         False         False   \n",
+       "2            False         False         False         False         False   \n",
+       "3            False         False         False         False         False   \n",
+       "4            False         False         False         False         False   \n",
+       "...            ...           ...           ...           ...           ...   \n",
+       "4339         False         False         False         False         False   \n",
+       "4340         False         False         False         False         False   \n",
+       "4341         False         False         False         False         False   \n",
+       "4342         False         False         False         False         False   \n",
+       "4344         False         False         False         False         False   \n",
+       "\n",
+       "      Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0            False         False         False         False            False   \n",
+       "1            False         False         False         False            False   \n",
+       "2            False         False         False         False            False   \n",
+       "3            False         False         False         False            False   \n",
+       "4            False         False         False         False            False   \n",
+       "...            ...           ...           ...           ...              ...   \n",
+       "4339         False         False         False         False            False   \n",
+       "4340         False         False         False         False            False   \n",
+       "4341         False         False         False         False            False   \n",
+       "4342         False         False         False         False            False   \n",
+       "4344         False         False         False         False            False   \n",
+       "\n",
+       "      Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0             False         False         False         False        False   \n",
+       "1             False         False         False         False        False   \n",
+       "2             False         False         False         False        False   \n",
+       "3             False         False         False         False        False   \n",
+       "4             False         False         False         False        False   \n",
+       "...             ...           ...           ...           ...          ...   \n",
+       "4339          False         False         False         False        False   \n",
+       "4340          False         False         False         False        False   \n",
+       "4341          False         False         False         False        False   \n",
+       "4342          False         False         False         False        False   \n",
+       "4344          False         False         False         False        False   \n",
+       "\n",
+       "      Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0              False             False            False               False   \n",
+       "1              False             False            False               False   \n",
+       "2              False             False            False               False   \n",
+       "3              False             False            False               False   \n",
+       "4              False             False            False               False   \n",
+       "...              ...               ...              ...                 ...   \n",
+       "4339           False             False            False               False   \n",
+       "4340           False             False            False               False   \n",
+       "4341           False             False            False               False   \n",
+       "4342           False             False            False               False   \n",
+       "4344           False             False            False               False   \n",
+       "\n",
+       "      Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  \\\n",
+       "0            False               False               False   \n",
+       "1            False               False               False   \n",
+       "2            False               False               False   \n",
+       "3            False               False               False   \n",
+       "4            False               False               False   \n",
+       "...            ...                 ...                 ...   \n",
+       "4339         False               False               False   \n",
+       "4340         False               False               False   \n",
+       "4341         False               False               False   \n",
+       "4342         False               False               False   \n",
+       "4344         False               False               False   \n",
+       "\n",
+       "      Model_Pajero Wagon  Model_Passat B2  Model_Passat B3  Model_Passat B4  \\\n",
+       "0                  False            False            False            False   \n",
+       "1                  False            False            False            False   \n",
+       "2                  False            False            False            False   \n",
+       "3                  False            False            False            False   \n",
+       "4                  False            False            False            False   \n",
+       "...                  ...              ...              ...              ...   \n",
+       "4339               False            False            False            False   \n",
+       "4340               False            False            False            False   \n",
+       "4341               False            False            False            False   \n",
+       "4342               False            False            False            False   \n",
+       "4344               False            False            False            False   \n",
+       "\n",
+       "      Model_Passat B5  Model_Passat B6  Model_Passat B7  Model_Passat B8  \\\n",
+       "0               False            False            False            False   \n",
+       "1               False            False            False            False   \n",
+       "2               False            False            False            False   \n",
+       "3               False            False            False            False   \n",
+       "4               False            False            False            False   \n",
+       "...               ...              ...              ...              ...   \n",
+       "4339            False            False            False            False   \n",
+       "4340            False            False            False            False   \n",
+       "4341            False            False            False            False   \n",
+       "4342            False            False            False            False   \n",
+       "4344            False            False            False            False   \n",
+       "\n",
+       "      Model_Passat CC  Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  \\\n",
+       "0               False          False          False       False         False   \n",
+       "1               False          False          False       False         False   \n",
+       "2               False          False          False       False         False   \n",
+       "3               False          False          False       False         False   \n",
+       "4               False          False          False       False         False   \n",
+       "...               ...            ...            ...         ...           ...   \n",
+       "4339            False          False          False       False         False   \n",
+       "4340            False          False          False       False         False   \n",
+       "4341            False          False          False       False         False   \n",
+       "4342            False          False          False       False         False   \n",
+       "4344            False          False          False       False         False   \n",
+       "\n",
+       "      Model_Prius  Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  \\\n",
+       "0           False     False     False     False        False     False   \n",
+       "1           False     False     False     False        False     False   \n",
+       "2           False     False     False     False        False     False   \n",
+       "3           False     False     False      True        False     False   \n",
+       "4           False     False     False     False        False     False   \n",
+       "...           ...       ...       ...       ...          ...       ...   \n",
+       "4339        False     False     False     False        False     False   \n",
+       "4340        False     False     False     False        False     False   \n",
+       "4341        False     False     False     False        False     False   \n",
+       "4342        False     False     False     False        False     False   \n",
+       "4344        False     False     False     False        False     False   \n",
+       "\n",
+       "      Model_Rav 4  Model_S 140  Model_S 250  Model_S 280  Model_S 300  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4            True        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False        False        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_S 320  Model_S 350  Model_S 400  Model_S 420  Model_S 430  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4           False        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False         True        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_S 500  Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  \\\n",
+       "0           False       False        False        False           False   \n",
+       "1           False       False        False        False           False   \n",
+       "2            True       False        False        False           False   \n",
+       "3           False       False        False        False           False   \n",
+       "4           False       False        False        False           False   \n",
+       "...           ...         ...          ...          ...             ...   \n",
+       "4339        False       False        False        False           False   \n",
+       "4340        False       False        False        False           False   \n",
+       "4341        False       False        False        False           False   \n",
+       "4342        False       False        False        False           False   \n",
+       "4344        False       False        False        False           False   \n",
+       "\n",
+       "      Model_S 65 AMG  Model_S-Guard  Model_S4  Model_S5  Model_S8  \\\n",
+       "0              False          False     False     False     False   \n",
+       "1              False          False     False     False     False   \n",
+       "2              False          False     False     False     False   \n",
+       "3              False          False     False     False     False   \n",
+       "4              False          False     False     False     False   \n",
+       "...              ...            ...       ...       ...       ...   \n",
+       "4339           False          False     False     False     False   \n",
+       "4340           False          False     False     False     False   \n",
+       "4341           False          False     False     False     False   \n",
+       "4342           False          False     False     False     False   \n",
+       "4344           False          False     False     False     False   \n",
+       "\n",
+       "      Model_SL 500 (550)  Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  \\\n",
+       "0                  False            False          False          False   \n",
+       "1                  False            False          False          False   \n",
+       "2                  False            False          False          False   \n",
+       "3                  False            False          False          False   \n",
+       "4                  False            False          False          False   \n",
+       "...                  ...              ...            ...            ...   \n",
+       "4339               False            False          False          False   \n",
+       "4340               False            False          False          False   \n",
+       "4341               False            False          False          False   \n",
+       "4342               False            False          False          False   \n",
+       "4344               False            False          False          False   \n",
+       "\n",
+       "      Model_Sandero  Model_Sandero StepWay  Model_Scenic  Model_Scion  \\\n",
+       "0             False                  False         False        False   \n",
+       "1             False                  False         False        False   \n",
+       "2             False                  False         False        False   \n",
+       "3             False                  False         False        False   \n",
+       "4             False                  False         False        False   \n",
+       "...             ...                    ...           ...          ...   \n",
+       "4339          False                  False         False        False   \n",
+       "4340          False                  False         False        False   \n",
+       "4341          False                  False         False        False   \n",
+       "4342          False                  False         False        False   \n",
+       "4344          False                  False         False        False   \n",
+       "\n",
+       "      Model_Scirocco  Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  \\\n",
+       "0              False          False         False         False        False   \n",
+       "1              False          False         False         False        False   \n",
+       "2              False          False         False         False        False   \n",
+       "3              False          False         False         False        False   \n",
+       "4              False          False         False         False        False   \n",
+       "...              ...            ...           ...           ...          ...   \n",
+       "4339           False          False         False         False        False   \n",
+       "4340           False          False         False         False        False   \n",
+       "4341           False          False         False         False        False   \n",
+       "4342           False          False         False         False        False   \n",
+       "4344           False          False         False         False        False   \n",
+       "\n",
+       "      Model_Space Star  Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  \\\n",
+       "0                False              False           False               False   \n",
+       "1                False              False           False               False   \n",
+       "2                False              False           False               False   \n",
+       "3                False              False           False               False   \n",
+       "4                False              False           False               False   \n",
+       "...                ...                ...             ...                 ...   \n",
+       "4339             False              False           False               False   \n",
+       "4340             False              False           False               False   \n",
+       "4341             False              False           False               False   \n",
+       "4342             False              False           False               False   \n",
+       "4344             False              False           False               False   \n",
+       "\n",
+       "      Model_Sprinter 210  Model_Sprinter 211  Model_Sprinter 212  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False                True   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 213  Model_Sprinter 311  Model_Sprinter 312  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 313  Model_Sprinter 315  Model_Sprinter 316  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 318  Model_Sprinter 319  Model_Sprinter 324  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Symbol  Model_Syncro  Model_T2 (Transporter)  \\\n",
+       "0            False         False                   False   \n",
+       "1            False         False                   False   \n",
+       "2            False         False                   False   \n",
+       "3            False         False                   False   \n",
+       "4            False         False                   False   \n",
+       "...            ...           ...                     ...   \n",
+       "4339         False         False                   False   \n",
+       "4340         False         False                   False   \n",
+       "4341         False         False                   False   \n",
+       "4342         False         False                   False   \n",
+       "4344         False         False                   False   \n",
+       "\n",
+       "      Model_T3 (Transporter)  Model_T4 (Transporter)  Model_T4 (Transporter)   \\\n",
+       "0                      False                   False                    False   \n",
+       "1                      False                   False                    False   \n",
+       "2                      False                   False                    False   \n",
+       "3                      False                   False                    False   \n",
+       "4                      False                   False                    False   \n",
+       "...                      ...                     ...                      ...   \n",
+       "4339                   False                   False                    False   \n",
+       "4340                   False                   False                    False   \n",
+       "4341                   False                   False                    False   \n",
+       "4342                   False                   False                    False   \n",
+       "4344                   False                   False                    False   \n",
+       "\n",
+       "      Model_T5 (Transporter)  Model_T5 (Transporter)   Model_T6 (Transporter)  \\\n",
+       "0                      False                    False                   False   \n",
+       "1                      False                    False                   False   \n",
+       "2                      False                    False                   False   \n",
+       "3                      False                    False                   False   \n",
+       "4                      False                    False                   False   \n",
+       "...                      ...                      ...                     ...   \n",
+       "4339                   False                    False                   False   \n",
+       "4340                   False                    False                   False   \n",
+       "4341                   False                    False                   False   \n",
+       "4342                   False                    False                   False   \n",
+       "4344                   False                     True                   False   \n",
+       "\n",
+       "      Model_T6 (Transporter)   Model_TT  Model_Tacoma  Model_Tiguan  \\\n",
+       "0                       False     False         False         False   \n",
+       "1                       False     False         False         False   \n",
+       "2                       False     False         False         False   \n",
+       "3                       False     False         False         False   \n",
+       "4                       False     False         False         False   \n",
+       "...                       ...       ...           ...           ...   \n",
+       "4339                    False     False         False         False   \n",
+       "4340                    False     False         False         False   \n",
+       "4341                    False     False         False         False   \n",
+       "4342                    False     False         False         False   \n",
+       "4344                    False     False         False         False   \n",
+       "\n",
+       "      Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  Model_Up  \\\n",
+       "0             False         False         False         False     False   \n",
+       "1             False         False         False         False     False   \n",
+       "2             False         False         False         False     False   \n",
+       "3             False         False         False         False     False   \n",
+       "4             False         False         False         False     False   \n",
+       "...             ...           ...           ...           ...       ...   \n",
+       "4339          False         False         False         False     False   \n",
+       "4340          False         False         False         False     False   \n",
+       "4341          False         False         False         False     False   \n",
+       "4342          False         False         False         False     False   \n",
+       "4344          False         False         False         False     False   \n",
+       "\n",
+       "      Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4           False        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False        False        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0            False        False       False     False     False     False   \n",
+       "1            False        False       False     False     False     False   \n",
+       "2            False        False       False     False     False     False   \n",
+       "3            False        False       False     False     False     False   \n",
+       "4            False        False       False     False     False     False   \n",
+       "...            ...          ...         ...       ...       ...       ...   \n",
+       "4339         False        False       False     False     False     False   \n",
+       "4340         False        False       False     False     False     False   \n",
+       "4341         False        False       False     False     False     False   \n",
+       "4342         False        False       False     False     False     False   \n",
+       "4344         False        False       False     False     False     False   \n",
+       "\n",
+       "      Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0          False     False       False        False     False     False  \n",
+       "1          False     False       False        False     False     False  \n",
+       "2          False     False       False        False     False     False  \n",
+       "3          False     False       False        False     False     False  \n",
+       "4          False     False       False        False     False     False  \n",
+       "...          ...       ...         ...          ...       ...       ...  \n",
+       "4339       False     False       False        False     False     False  \n",
+       "4340       False     False       False        False     False     False  \n",
+       "4341       False     False       False        False     False     False  \n",
+       "4342       False     False       False        False     False     False  \n",
+       "4344       False     False       False        False     False     False  \n",
+       "\n",
+       "[4025 rows x 323 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Train Test Spilt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the module for the split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# split the variables with an 80 - 20 split and some random state for example use random_state = 365\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(inputs, target, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create the regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create the regression object\n",
+    "reg = LinearRegression()\n",
+    "# fit the the regression with the trained inputs and the target\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's check the outputs of the regression\n",
+    "# I'll store them in y_hat as this is the 'theoretical' name of the predictions\n",
+    "y_hat = reg.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot\n",
+    "# The closer the points to the 45-degree line, the better the prediction\n",
+    "plt.scatter(y_train, y_hat, alpha= 0.2)\n",
+    "plt.xlabel('Target(y_train)', size= 18)\n",
+    "plt.ylabel('Prediction(Y_hat)', size= 18)\n",
+    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
+    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
+    "# We want the x-axis and the y-axis to be the same\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6.0, 13.0)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=y_train, y=y_hat, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "plt.xlabel('Target(y_train)', size= 18)\n",
+    "plt.ylabel('Prediction(Y_hat)', size= 18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check for residue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_2108\\2934438678.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(y_train-y_hat)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Residuals PDF')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Another useful check of our model is a residual plot\n",
+    "# We can plot the PDF of the residuals and check for anomalies\n",
+    "sns.distplot(y_train-y_hat)\n",
+    "plt.title(\"Residuals PDF\", size=18)\n",
+    "\n",
+    "# In the best case scenario this plot should be normally distributed\n",
+    "#And our graph happened to be nornally distributed with the midpoint at zero, meaning that our prediction fits our target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# R-Squared of our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9410857085028603"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_train, y_train)\n",
+    "# Note that this is NOT the adjusted R-squared\n",
+    "# in other words... find the Adjusted R-squared to have the appropriate measure :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adjusted R2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3220, 323)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the shape of x, to facilitate the creation of the Adjusted R^2 metric\n",
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9345148120409901"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If we want to find the Adjusted R-squared we can do so by knowing the r2, the # observations, the # features\n",
+    "r2 = reg.score(x_train, y_train)\n",
+    "# Number of observations is the shape along axis 0\n",
+    "n = x_train.shape[0]\n",
+    "# Number of features (predictors, p) is the shape along axis 1\n",
+    "p = x_train.shape[1]\n",
+    "\n",
+    "# We find the Adjusted R-squared using the formula\n",
+    "Adj_r2 = 1-(1-r2)* (n-1)/(n-p-1)\n",
+    "Adj_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.77503789e+03, 4.35676244e+00, 4.13082009e+03, 1.81202877e+01,\n",
+       "        9.46723457e+01, 1.90844651e+01, 1.30693718e+02, 5.91312636e+01,\n",
+       "        5.68044564e+01, 7.32783831e+01, 7.93890307e+00, 7.67267201e+01,\n",
+       "        9.95728691e+01, 4.19460543e+01, 3.00296891e+01, 6.02422631e+00,\n",
+       "        1.40106743e+01, 7.37501879e+02, 2.17908132e+01, 0.00000000e+00,\n",
+       "        8.57016665e-03, 2.52183205e-04, 1.77368305e+00, 2.85631239e+00,\n",
+       "        1.92264268e+01, 5.00553865e+00, 5.50707175e+00, 5.34654600e+00,\n",
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+       "        7.40340068e-001, 8.90623082e-001, 2.67587449e-001, 6.90853933e-001,\n",
+       "        1.00754811e-002, 6.89055296e-008, 4.32934445e-001, 2.04960304e-002,\n",
+       "        4.43239721e-002, 6.51183356e-001, 8.06907592e-008, 5.31985601e-001,\n",
+       "        6.63600657e-004, 2.22523086e-002, 4.31816253e-002, 1.00000000e+000,\n",
+       "        6.98283335e-001, 6.94986205e-002, 4.39863254e-003, 4.00579913e-004,\n",
+       "        6.26179589e-019, 2.36500444e-007, 3.39282802e-015, 4.84506304e-003,\n",
+       "        3.37943093e-002, 3.41070801e-001, 1.03219301e-001]))"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import the feature selection module from sklearn\n",
+    "# This module allows us to select the most appopriate features for our regression\n",
+    "# There exist many different approaches to feature selection, however, we will use one of the simplest\n",
+    "\n",
+    "from sklearn.feature_selection import f_regression\n",
+    "\n",
+    "# f_regression finds the F-statistics for the *simple* regressions created with each of the independent variables\n",
+    "# In our case, this would mean running a simple linear regression on GPA where SAT is the independent variable\n",
+    "# and a simple linear regression on GPA where Rand 1,2,3 is the indepdent variable\n",
+    "# The limitation of this approach is that it does not take into account the mutual effect of the two features\n",
+    "f_regression(x_train,y_train)\n",
+    "\n",
+    "#There are two output arrays\n",
+    "# The first one contains the F-statistics for each of the regressions\n",
+    "# The second one contains the p-values of these F-statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       4.13817142e-001, 5.91417515e-001, 1.25528377e-001, 8.14801678e-012,\n",
+       "       1.68556062e-001, 2.42028095e-001, 4.33383053e-001, 1.39233795e-001,\n",
+       "       6.48887602e-001, 1.63039120e-003, 3.85561860e-002, 5.17179158e-001,\n",
+       "       4.41821584e-004, 3.71343697e-001, 5.32761253e-001, 7.20978206e-001,\n",
+       "       4.09527800e-001, 8.64753022e-001, 5.63591273e-001, 1.42773118e-003,\n",
+       "       4.71586242e-001, 5.30703625e-009, 1.34748018e-002, 1.83021249e-001,\n",
+       "       2.16459985e-002, 7.34813292e-001, 9.89122411e-003, 3.68574535e-001,\n",
+       "       6.66441099e-001, 5.51175993e-001, 4.43305980e-004, 2.86342637e-001,\n",
+       "       3.35086125e-003, 8.80657206e-008, 7.35425272e-001, 2.50471868e-002,\n",
+       "       3.80894623e-002, 4.01958798e-001, 3.85931106e-001, 8.29302428e-001,\n",
+       "       8.27233532e-001, 6.71061839e-001, 5.46268783e-001, 9.68555856e-001,\n",
+       "       2.97385141e-001, 6.83120185e-001, 2.24054490e-001, 7.98829944e-002,\n",
+       "       7.74621412e-001, 1.00000000e+000, 4.64693438e-001, 1.07626058e-001,\n",
+       "       1.00000000e+000, 6.47070340e-003, 1.44611890e-001, 1.89630151e-001,\n",
+       "       1.00000000e+000, 1.00000000e+000, 6.81585635e-001, 7.89753766e-002,\n",
+       "       5.52414839e-001, 1.26031599e-001, 8.12262775e-001, 5.32761253e-001,\n",
+       "       6.20087988e-001, 2.44760711e-001, 9.28392404e-002, 1.00000000e+000,\n",
+       "       9.07988454e-002, 3.19204353e-002, 3.41680389e-004, 5.19563591e-002,\n",
+       "       1.00000000e+000, 4.42762754e-001, 4.06122042e-003, 3.99665611e-003,\n",
+       "       5.57008646e-001, 6.74717576e-002, 9.78408658e-003, 2.77099111e-003,\n",
+       "       6.02532109e-001, 1.00000000e+000, 7.11148676e-001, 5.01945642e-001,\n",
+       "       9.27493882e-004, 6.72904741e-001, 1.00000000e+000, 6.02532109e-001,\n",
+       "       3.24910587e-002, 8.26249124e-001, 8.34188757e-001, 1.00000000e+000,\n",
+       "       4.40746655e-004, 2.99839421e-006, 5.56621416e-004, 3.76191257e-010,\n",
+       "       1.01276166e-002, 1.68808863e-009, 4.34900697e-001, 1.31882699e-001,\n",
+       "       7.35283039e-002, 2.08717435e-001, 9.42660004e-002, 4.58042029e-009,\n",
+       "       2.91378789e-001, 3.42031569e-001, 1.58497540e-018, 4.88624552e-004,\n",
+       "       2.23462920e-004, 4.10323481e-011, 7.55702579e-009, 6.43900200e-001,\n",
+       "       9.50372855e-009, 6.07613924e-009, 2.96823255e-010, 5.09345354e-001,\n",
+       "       1.78926525e-001, 9.06121568e-001, 4.51607438e-001, 7.24165492e-001,\n",
+       "       5.91496727e-001, 9.04286267e-001, 3.01764465e-001, 4.36726460e-003,\n",
+       "       1.45529256e-003, 2.18784422e-001, 1.00000000e+000, 5.99223423e-002,\n",
+       "       9.74787951e-016, 9.07064650e-001, 7.03783041e-001, 2.06076823e-001,\n",
+       "       8.72593903e-008, 6.03304420e-006, 5.22102438e-001, 5.54278957e-002,\n",
+       "       7.34813292e-001, 7.61703682e-002, 1.00000000e+000, 6.12517681e-028,\n",
+       "       8.13086341e-002, 9.09954117e-001, 1.01974973e-009, 9.95704967e-001,\n",
+       "       1.03579279e-002, 8.84971529e-002, 3.29403271e-002, 2.66984999e-004,\n",
+       "       5.03783861e-002, 6.07751197e-002, 6.69335455e-002, 7.86322149e-001,\n",
+       "       6.08838773e-001, 7.57552813e-001, 1.82214428e-004, 7.23171120e-001,\n",
+       "       1.00000000e+000, 1.33597912e-001, 3.93444560e-001, 1.68935551e-002,\n",
+       "       1.00000000e+000, 3.60467380e-001, 7.43784280e-001, 1.68285534e-003,\n",
+       "       1.00000000e+000, 1.00901157e-005, 5.26457888e-001, 8.00001965e-001,\n",
+       "       4.62580458e-001, 1.11882697e-001, 6.41575085e-001, 1.88970570e-001,\n",
+       "       3.44770075e-002, 1.69454213e-002, 7.59920516e-007, 8.47328813e-006,\n",
+       "       1.34741492e-013, 3.35770762e-001, 3.63941304e-003, 1.96044222e-002,\n",
+       "       6.37819043e-003, 4.76202033e-002, 2.44760711e-001, 2.65676768e-002,\n",
+       "       6.01376927e-001, 6.85024613e-001, 7.95535047e-002, 1.75529365e-004,\n",
+       "       1.39028045e-014, 6.81975855e-001, 2.28028853e-004, 1.26486512e-001,\n",
+       "       3.73764587e-001, 1.09905717e-001, 1.33851176e-001, 6.70212905e-001,\n",
+       "       4.49327808e-001, 6.73575171e-011, 3.52668313e-002, 4.69537352e-001,\n",
+       "       9.67432151e-001, 6.31620457e-011, 1.00000000e+000, 4.65496452e-002,\n",
+       "       1.21390210e-004, 2.02025339e-008, 2.57302704e-001, 7.04803460e-004,\n",
+       "       8.91981750e-001, 3.40248085e-001, 6.66724461e-001, 5.83221602e-001,\n",
+       "       1.00000000e+000, 6.03919769e-001, 6.43900200e-001, 2.95109444e-001,\n",
+       "       9.28112666e-001, 2.15910154e-004, 1.00000000e+000, 3.75678769e-001,\n",
+       "       2.31400817e-001, 1.07557544e-001, 6.69175228e-002, 2.13495231e-001,\n",
+       "       1.77244755e-002, 2.55315670e-001, 1.00000000e+000, 2.62329963e-001,\n",
+       "       3.62648525e-001, 6.98283335e-001, 8.67631905e-001, 8.41694589e-001,\n",
+       "       5.82654840e-001, 2.25752785e-001, 9.54668885e-001, 8.12262775e-001,\n",
+       "       7.45977702e-001, 3.11392793e-001, 2.77867310e-002, 2.94550329e-003,\n",
+       "       5.15234482e-002, 8.70599844e-001, 1.70456776e-002, 2.32418760e-002,\n",
+       "       2.66085948e-003, 3.25914134e-002, 3.87907274e-001, 7.92210128e-001,\n",
+       "       7.40340068e-001, 8.90623082e-001, 2.67587449e-001, 6.90853933e-001,\n",
+       "       1.00754811e-002, 6.89055296e-008, 4.32934445e-001, 2.04960304e-002,\n",
+       "       4.43239721e-002, 6.51183356e-001, 8.06907592e-008, 5.31985601e-001,\n",
+       "       6.63600657e-004, 2.22523086e-002, 4.31816253e-002, 1.00000000e+000,\n",
+       "       6.98283335e-001, 6.94986205e-002, 4.39863254e-003, 4.00579913e-004,\n",
+       "       6.26179589e-019, 2.36500444e-007, 3.39282802e-015, 4.84506304e-003,\n",
+       "       3.37943093e-002, 3.41070801e-001, 1.03219301e-001])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Since we are more interested in the latter (p-values), we can just take the second array\n",
+    "p_values = f_regression(x_train,y_train)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.037, 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,\n",
+       "       0.   , 0.005, 0.   , 0.   , 0.   , 0.   , 0.014, 0.   , 0.   ,\n",
+       "       0.   , 1.   , 0.926, 0.987, 0.183, 0.091, 0.   , 0.025, 0.019,\n",
+       "       0.021, 0.487, 0.057, 0.001, 0.406, 0.108, 0.005, 0.   , 0.   ,\n",
+       "       0.491, 0.068, 0.827, 0.398, 0.232, 0.433, 0.123, 1.   , 0.19 ,\n",
+       "       1.   , 0.601, 0.038, 0.414, 0.591, 0.126, 0.   , 0.169, 0.242,\n",
+       "       0.433, 0.139, 0.649, 0.002, 0.039, 0.517, 0.   , 0.371, 0.533,\n",
+       "       0.721, 0.41 , 0.865, 0.564, 0.001, 0.472, 0.   , 0.013, 0.183,\n",
+       "       0.022, 0.735, 0.01 , 0.369, 0.666, 0.551, 0.   , 0.286, 0.003,\n",
+       "       0.   , 0.735, 0.025, 0.038, 0.402, 0.386, 0.829, 0.827, 0.671,\n",
+       "       0.546, 0.969, 0.297, 0.683, 0.224, 0.08 , 0.775, 1.   , 0.465,\n",
+       "       0.108, 1.   , 0.006, 0.145, 0.19 , 1.   , 1.   , 0.682, 0.079,\n",
+       "       0.552, 0.126, 0.812, 0.533, 0.62 , 0.245, 0.093, 1.   , 0.091,\n",
+       "       0.032, 0.   , 0.052, 1.   , 0.443, 0.004, 0.004, 0.557, 0.067,\n",
+       "       0.01 , 0.003, 0.603, 1.   , 0.711, 0.502, 0.001, 0.673, 1.   ,\n",
+       "       0.603, 0.032, 0.826, 0.834, 1.   , 0.   , 0.   , 0.001, 0.   ,\n",
+       "       0.01 , 0.   , 0.435, 0.132, 0.074, 0.209, 0.094, 0.   , 0.291,\n",
+       "       0.342, 0.   , 0.   , 0.   , 0.   , 0.   , 0.644, 0.   , 0.   ,\n",
+       "       0.   , 0.509, 0.179, 0.906, 0.452, 0.724, 0.591, 0.904, 0.302,\n",
+       "       0.004, 0.001, 0.219, 1.   , 0.06 , 0.   , 0.907, 0.704, 0.206,\n",
+       "       0.   , 0.   , 0.522, 0.055, 0.735, 0.076, 1.   , 0.   , 0.081,\n",
+       "       0.91 , 0.   , 0.996, 0.01 , 0.088, 0.033, 0.   , 0.05 , 0.061,\n",
+       "       0.067, 0.786, 0.609, 0.758, 0.   , 0.723, 1.   , 0.134, 0.393,\n",
+       "       0.017, 1.   , 0.36 , 0.744, 0.002, 1.   , 0.   , 0.526, 0.8  ,\n",
+       "       0.463, 0.112, 0.642, 0.189, 0.034, 0.017, 0.   , 0.   , 0.   ,\n",
+       "       0.336, 0.004, 0.02 , 0.006, 0.048, 0.245, 0.027, 0.601, 0.685,\n",
+       "       0.08 , 0.   , 0.   , 0.682, 0.   , 0.126, 0.374, 0.11 , 0.134,\n",
+       "       0.67 , 0.449, 0.   , 0.035, 0.47 , 0.967, 0.   , 1.   , 0.047,\n",
+       "       0.   , 0.   , 0.257, 0.001, 0.892, 0.34 , 0.667, 0.583, 1.   ,\n",
+       "       0.604, 0.644, 0.295, 0.928, 0.   , 1.   , 0.376, 0.231, 0.108,\n",
+       "       0.067, 0.213, 0.018, 0.255, 1.   , 0.262, 0.363, 0.698, 0.868,\n",
+       "       0.842, 0.583, 0.226, 0.955, 0.812, 0.746, 0.311, 0.028, 0.003,\n",
+       "       0.052, 0.871, 0.017, 0.023, 0.003, 0.033, 0.388, 0.792, 0.74 ,\n",
+       "       0.891, 0.268, 0.691, 0.01 , 0.   , 0.433, 0.02 , 0.044, 0.651,\n",
+       "       0.   , 0.532, 0.001, 0.022, 0.043, 1.   , 0.698, 0.069, 0.004,\n",
+       "       0.   , 0.   , 0.   , 0.   , 0.005, 0.034, 0.341, 0.103])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Finding weight and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-271886118.8957569"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the bias (intercept) of the regression\n",
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-7.11168307e-04, -2.35976356e-03,  8.68792315e-02,  2.45087768e+07,\n",
+       "        2.45087768e+07,  3.35460065e+08,  2.30199764e+09,  1.45542358e+09,\n",
+       "        4.62245051e+08,  9.66255861e-03,  1.23547420e-02, -1.06618585e-01,\n",
+       "       -1.31590684e-01, -5.92526948e-02, -9.57940713e-02, -4.74364368e-02,\n",
+       "       -6.14403758e-02,  8.89392304e-01,  2.71885954e+08, -7.18996649e+09,\n",
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+       "        2.47377177e+08,  2.47377177e+08, -2.03011169e+09,  2.47377176e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08, -2.03011169e+09,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.59254546e+09,\n",
+       "       -1.18353763e+09, -2.65819554e+07,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377178e+08,  2.47377177e+08,\n",
+       "        2.47377178e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.71885953e+08, -2.03011169e+09,  2.71885954e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08,  2.47377176e+08,  2.47377176e+08,\n",
+       "        2.71885953e+08,  2.71885953e+08,  2.71885953e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08,  2.71885954e+08,  2.71885954e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08, -6.35741121e+07, -1.90359097e+08, -1.18353763e+09,\n",
+       "       -1.18353763e+09, -1.18353763e+09, -1.18353763e+09,  2.47377176e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08, -1.90359097e+08, -1.90359098e+08,\n",
+       "        2.47377177e+08, -8.93153042e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -7.09605288e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -1.08592764e+09, -7.86250753e+08,  2.47377177e+08,  2.47377176e+08,\n",
+       "        2.47377177e+08,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  1.82078318e+09,\n",
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+       "       -1.46384430e+09, -1.90359097e+08, -1.18353763e+09, -6.35741122e+07,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -6.35741125e+07, -1.18353763e+09,\n",
+       "       -1.18353763e+09, -1.58640664e+08, -2.03011169e+09, -2.03011169e+09,\n",
+       "        2.47377177e+08, -6.35741116e+07,  1.06343391e+09, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -1.18353763e+09,  5.60770216e+07,\n",
+       "        2.47377178e+08,  2.47377178e+08,  2.47377178e+08,  2.47377178e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377178e+08,  2.47377178e+08,\n",
+       "        2.47377178e+08,  2.47377178e+08, -6.35741121e+07, -1.90359098e+08,\n",
+       "       -1.90359097e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -6.35741119e+07, -1.18353763e+09, -1.18353763e+09,\n",
+       "       -1.18353763e+09,  2.47377177e+08, -2.56326823e+08, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -2.03011169e+09, -6.35741122e+07, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -6.35741124e+07, -6.35741114e+07, -6.35741123e+07,\n",
+       "       -6.35741125e+07, -1.18353763e+09, -4.21205487e+03, -1.18353762e+09,\n",
+       "       -1.18353763e+09, -1.18353763e+09, -1.18353763e+09, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -1.90359098e+08,  2.47377178e+08,\n",
+       "        2.47377178e+08,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -1.69340410e-07,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -2.77046898e-07, -2.03011169e+09, -1.18353763e+09, -2.03011169e+09,\n",
+       "        1.42292599e-06, -1.90359097e+08, -1.90359098e+08, -6.35741121e+07,\n",
+       "       -6.35741119e+07, -6.35741112e+07, -6.35741120e+07, -6.35741119e+07,\n",
+       "       -6.35741117e+07, -1.90359097e+08, -1.90359097e+08, -1.90359097e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08, -1.90359098e+08, -1.90359097e+08,\n",
+       "       -1.90359097e+08, -1.90359097e+08, -1.90359099e+08, -1.90359098e+08,\n",
+       "       -1.18353763e+09, -1.18353763e+09,  2.71885954e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08,  2.47377177e+08,  2.71885955e+08, -1.18353763e+09,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377178e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  6.17630946e-14,  2.47377177e+08,\n",
+       "        2.47377178e+08,  2.47377178e+08,  2.47377178e+08,  2.47377179e+08,\n",
+       "        2.71885954e+08,  2.71885954e+08,  2.71885954e+08,  2.47377177e+08,\n",
+       "       -1.00867231e-13,  2.47377177e+08,  2.47377177e+08, -2.03011169e+09,\n",
+       "       -2.03011169e+09, -2.03011169e+09, -1.57485136e-13, -1.90359098e+08,\n",
+       "       -1.18353763e+09, -1.90359097e+08, -1.18353763e+09,  2.47377176e+08,\n",
+       "       -6.35741123e+07, -6.35741115e+07,  2.27283470e-14,  2.47377177e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08,  2.47377177e+08,  2.47377176e+08,\n",
+       "        2.47377176e+08,  2.47377177e+08,  2.47377176e+08,  2.47377177e+08,\n",
+       "        2.47377176e+08,  2.47377177e+08,  2.47377177e+08,  2.47377179e+08,\n",
+       "       -2.03011169e+09, -1.90359097e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359097e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08,  2.71885954e+08, -1.18353763e+09,\n",
+       "       -1.90359098e+08, -1.90359097e+08, -1.90359098e+08, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -1.90359099e+08,  2.47377178e+08,  2.47377176e+08,\n",
+       "       -1.90359098e+08, -1.18353763e+09,  2.47377177e+08,  1.11022302e-15,\n",
+       "       -1.18353763e+09,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377178e+08,\n",
+       "       -1.18353763e+09,  2.47377177e+08,  2.47377177e+08])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the weights (coefficients) of the regression\n",
+    "reg.coef_\n",
+    "# Note that they are barely interpretable if at all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-7.111683e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>-2.359764e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Year</td>\n",
+       "      <td>8.687923e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>Model_X6</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>Model_X6 M</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>Model_Yaris</td>\n",
+       "      <td>-1.183538e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>Model_Z3</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>Model_Z4</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>323 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Features       Weights\n",
+       "0                Mileage -7.111683e-04\n",
+       "1                EngineV -2.359764e-03\n",
+       "2                   Year  8.687923e-02\n",
+       "3              Brand_BMW  2.450878e+07\n",
+       "4    Brand_Mercedes-Benz  2.450878e+07\n",
+       "..                   ...           ...\n",
+       "318             Model_X6  2.473772e+08\n",
+       "319           Model_X6 M  2.473772e+08\n",
+       "320          Model_Yaris -1.183538e+09\n",
+       "321             Model_Z3  2.473772e+08\n",
+       "322             Model_Z4  2.473772e+08\n",
+       "\n",
+       "[323 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a regression summary where we can compare them with one another\n",
+    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
+    "\n",
+    "reg_summary['Weights'] = reg.coef_\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-7.111683e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>-2.359764e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Year</td>\n",
+       "      <td>8.687923e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>3.354601e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>2.301998e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>1.455424e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>4.622451e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>9.662559e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>1.235474e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-1.066186e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-1.315907e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-5.925269e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-9.579407e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-4.743644e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-6.144038e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>8.893923e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Model_100</td>\n",
+       "      <td>2.718860e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Model_11</td>\n",
+       "      <td>-7.189966e+09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features       Weights\n",
+       "0               Mileage -7.111683e-04\n",
+       "1               EngineV -2.359764e-03\n",
+       "2                  Year  8.687923e-02\n",
+       "3             Brand_BMW  2.450878e+07\n",
+       "4   Brand_Mercedes-Benz  2.450878e+07\n",
+       "5      Brand_Mitsubishi  3.354601e+08\n",
+       "6         Brand_Renault  2.301998e+09\n",
+       "7          Brand_Toyota  1.455424e+09\n",
+       "8      Brand_Volkswagen  4.622451e+08\n",
+       "9            Body_hatch  9.662559e-03\n",
+       "10           Body_other  1.235474e-02\n",
+       "11           Body_sedan -1.066186e-01\n",
+       "12           Body_vagon -1.315907e-01\n",
+       "13             Body_van -5.925269e-02\n",
+       "14      Engine Type_Gas -9.579407e-02\n",
+       "15    Engine Type_Other -4.743644e-02\n",
+       "16   Engine Type_Petrol -6.144038e-02\n",
+       "17     Registration_yes  8.893923e-01\n",
+       "18            Model_100  2.718860e+08\n",
+       "19             Model_11 -7.189966e+09"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the different categories in the 'Brand' variable\n",
+    "data['Brand'].unique()\n",
+    "# In this way we can see which 'Brand' is actually the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brand missing: Audi\n",
+      "Body missing: crossover\n",
+      "Engine Type missing: Diesel\n",
+      "Registration missing: no\n"
+     ]
+    }
+   ],
+   "source": [
+    "brands= data['Brand'].unique()\n",
+    "\n",
+    "bodies= data['Body'].unique()\n",
+    "\n",
+    "engine_types= data['Engine Type'].unique()\n",
+    "\n",
+    "registrations= data['Registration'].unique()\n",
+    "\n",
+    "dummy_values=[brands,bodies,engine_types,registrations]\n",
+    "\n",
+    "dummy_strings=['Brand','Body','Engine Type','Registration']\n",
+    "\n",
+    "dummy_cols= data_preprocessed.columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "for vals, label in zip(dummy_values,dummy_strings):\n",
+    "\n",
+    "    for val in vals:\n",
+    "\n",
+    "        found=False\n",
+    "\n",
+    "        for dummy_col in dummy_cols:\n",
+    "\n",
+    "            if dummy_col==label + '_' + val:\n",
+    "\n",
+    "                found=True\n",
+    "                break  # exit the inner loop when a match is found\n",
+    "\n",
+    "        if found==False:\n",
+    "\n",
+    "            print(label +' missing:',val)\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Once we have trained and fine-tuned our model, we can proceed to testing it\n",
+    "# Testing is done on a dataset that the algorithm has never seen\n",
+    "# Luckily we have prepared such a dataset\n",
+    "# Our test inputs are 'x_test', while the outputs: 'y_test' \n",
+    "# We SHOULD NOT TRAIN THE MODEL ON THEM, we just feed them and find the predictions\n",
+    "# If the predictions are far off, we will know that our model overfitted\n",
+    "y_hat_test = reg.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ILkF6ZhHSMwuRnlmEk2dKYLW7Ln4y1SmyokJWFGg1vl9WNJIIWVEgK7yyRES1S1g+SPf06dPo06cPNm3ahKeffhoNGjTw7k9JScH69etx8803w+1241//+ldQj//EE09g7969mDt3LtasWYN27dph/PjxOH78eKVjXS4Xxo8fDwBYuXIlnnvuOXz00Ud48803Q3uS5FOZ3YVjmYUoKLHDoBMRF6WDQSfCUuJABgNTvSOJAiRRhMut+Nzvlj2TvSWRVx2JqHYJOSx99dVX0Gg0mD17NvR63xM3JUnCzJkzodPp8OOPP1b5sU+ePInvv/8ezz33HDp37oyWLVvimWeeQXJyMtavX1/p+C+//BKnT5/GK6+8grS0NNx888144okn8O9//xtOp/OSnyNVpqoqcgtssDrcSIgxcH4KwaCTEGPSoszm9rm/zOZGjEkLg06q5sqIiEITcljKzMzElVdeicTExIDHJSQkoGXLlsjLy6vyY8fHx+Ptt99Ghw4dvNsEQYAgCCguLq50/O7du3HVVVchNjbWu61bt24oLS3FwYMHqzwuXZxnfooTMSadz/2cn1L/CIKA5Hgj9DoJlmIHnC4ZiqLC6ZJhKXZAr5OQHG/kfDYiqnVCnuAtCALsdnuVjlUUBTqd7x+uvsTExKB3794Vtn355Zc4efIkpk2bVun4M2fOoFGjRhW2JScnAwCys7PRsWPHKo99IUkK2xsH6wTBJUAFoNWIcIuVe6MXBNidTgiiAI2fOSx1WfnXS337uomJ0qNVMxG5BTYUW52wO92QJAEN4o1IjjfCbNDW295cDPviH3vjH3vjXzh/Lws5LKWkpCA9PR15eXlISkrye1x2djaOHTuGtm3bXvJYP//8M6ZOnYoBAwagT58+lfbb7XbExMRU2FZ+a9DhcFzyuAAQE2MM6fy6Rm90IbfIAZdbgdlc+farwyVDkCQkJpjr9UKE9fHrJh5A00axFd4hadRrKl1Rqo+9qQr2xT/2xj/25vIKOSzdfPPNOHjwIGbOnInXX3/d5yV2p9OJ6dOnQ1VV9OvX75LG2bJlC5566il06tQJc+bM8XmMwWCoNDepPCSZTKZLGrdccbENsux74mp9pKoqJAEotjph0nje5XS+gmI7EmIMsFsdcNjq33wxSRIRE2Pk1w0AN1Dha4C98Y198Y+98Y+98S821gjRx52PSxFyWLrnnnuwZs0abNmyBXfddRduvfVW5OfnAwC2b9+O9PR0fPLJJzh58iSSkpIwZsyYoMf44IMPMHv2bNxyyy14+eWX/d7Ka9SoUaWlCXJzcwF4PsMuFLKswO3nXT71VYNYPfJLXcjLL4VRJ0EjiXDLCspsbuh1EhKj9ZBlFUD9neTNrxv/2Bvf2Bf/2Bv/2JvKwvn+opDDUlRUFN555x088MAD+N///of9+/d79/3tb38D4LkKkZSUhEWLFlWYfF0VH374IWbNmoUxY8Zg+vTpASeH3nDDDfjss89QWlqKqKgoAMDOnTthNptDuv1HvpkNWsTFmSE73bCUOCArbkiiiPhoPT8HjIiI6gxBDdN7u202G1atWoUtW7YgPT0dpaWlMBqNaNmyJfr27YtRo0ZVmk90MSdOnMCgQYPQp08fzJgxo8I+g8EAvV6PoqIixMbGQqfTweFw4Pbbb0dKSgqeeuopZGZmYvr06RgzZgweeuihkJ6fxVLG1H4BjUZEfLwZBQWlKLW6uIL3ecp7w6+bytgb39gX/9gb/9gb/xISzGGb+B62sHQ5LF68GPPmzfO5b/DgwRg8eDDGjh2L5cuXo2vXrgA8azM9//zz2L17N2JjYzFs2DA8/PDDId+35BdiZfwm9Y+98Y+98Y198Y+98Y+98a9GhaWpU6eiZcuWmDRp0kWPnTlzJtLT0/H++++HMmRE8AuxMn6T+sfe+Mfe+Ma++Mfe+Mfe+BfOsBTyo3z66afYvn17lY7dvXs39u3bF+qQRERERNUmqAneGRkZ+Pzzzyttz87OxoIFC/yep6oqTp8+jSNHjiA+Pj74KomIiIgiJKiw1Lx5c2zevBkZGRnebYIgIDs7G4sXLw54bvndvv79+wdfJREREVGEBBWWJEnC9OnTsWTJEu+2n376CdHR0QHfmi+KIkwmE9q3b4+JEydeerVERERE1SzodZZ69OiBHj16eP/etm1bpKWl1cpJ20REREQXE/KilC+++CISExMv+fzvvvsO+fn5uPPOO0MthYiIiCjsQg5LgwcPDun8RYsWYe/evQxLREREVCOFZwECIiIiojqKYYmIiIgoAIYlIiIiogAYloiIiIgCYFgiIiIiCoBhiYiIiCgAhiUiIiKiABiWiIiIiAJgWCIiIiIKgGGJiIiIKACGJSIiIqIAGJaIiIiIAoh4WFJVNdIlEBEREfmlCfUBpk6dipYtW2LSpEkXPXbmzJlIT0/H+++/79327LPPoqSkJNQyiIiIiC6LkK8sffrpp9i+fXuVjt29ezf27dtXYVu7du3QpUuXUMsgIiIiuiyCurKUkZGBzz//vNL27OxsLFiwwO95qqri9OnTOHLkCOLj44OvkoiIiChCggpLzZs3x+bNm5GRkeHdJggCsrOzsXjx4oDnls9N6t+/f/BVEhEREUVIUGFJkiRMnz4dS5Ys8W776aefEB0djbZt2/o9TxRFmEwmtG/fHhMnTrz0aomIiIiqWdATvHv06IEePXp4/962bVukpaVVmLRNREREVFeE/G64F198EYmJieGohYiIiKjGCTksDR48OBx1EBEREdVIIYelcseOHcPhw4dht9uhKEqFfbIsw2azIScnB9u3b8fGjRvDNSwRERHRZRVyWFIUBU8//TS++OKLix6rqioEQQh1SCIiIqJqE3JYWrNmDdavXw8A0Gq1iI2NxdmzZxEbGwu9Xg+LxQKXywVBENCuXTuMGTMm5KKJiIiIqkvIK3h/8cUXEAQBY8eOxS+//IIvv/wSGo0GvXv3xrfffos9e/Zg5syZ0Ol0yM3NRZ8+fcJQNhEREVH1CDksHT58GEajEU888QQkSYLZbEZaWhp+/PFHAIBOp8Pw4cMxefJk5Ofnc4kBIiIiqlVCDkulpaVo1qwZDAaDd1vr1q2Rm5uLgoIC77bhw4fDZDLhm2++CXVIIiIiomoTclgyGo0QxYoP06JFCwDA8ePHvdt0Oh1SUlJw6tSpUIckIiIiqjYhh6UmTZrg1KlTcDgc3m3NmjWDqqo4fPhwhWOdTiecTmeoQxIRERFVm5DDUpcuXWC1WvHKK69411cq/5y4L774wvsBuseOHUNGRgYaNWoU6pBERERE1SbksDRmzBhotVp8+OGH6NOnD5xOJ9q2bYsWLVrgl19+wfjx4/Hyyy/j3nvvhaIouO6668JRNxEREVG1CDkstWjRAnPnzkV0dDTKysqg0+kAANOnT4cgCPjhhx+wbNky5OXlwWAw4O9//3vIRRMRERFVl7B83MnNN9+MLl26YPfu3d5tvXv3xtKlS/Huu+8iMzMTV1xxBf7+9797J38TERER1QZh+2y4mJgY9OvXr8K2bt26oVu3buEagoiIiKjahXwbjoiIiKguC9uVpePHj2PPnj0oKCiA3W4PeOyjjz4armGJiIiILquwhKXp06dj7dq1Fz1OVVUIgsCwRERERLVGyGFp5cqVWLNmDQAgKioKKSkpMJlMIRdGREREVBOEHJZWr14NQRAwcuRITJkyBXq9Phx1ERER1TiqqsLulCErKiRRgEEnQRCESJdFl1nIYenEiROIjY3FtGnTIElSOGoiIiKqcax2F3ItNhRbXZAVBZIoIsakRXK8ESaDNtLl0WUUclhSVRVNmzZlUCIiojrLanch40wJHE4ZZqMGWo0GLrcCS4kDVocbqY2iGZjqsJCXDkhLS0NmZiZkWQ5HPURERDWKqqrItdjgcMqIj9FDp/XcetNpJcTH6OFwysi12LyfhUp1T8hh6a9//SuKiorwwQcfhKMeIiKiGsXulFFsdcFs9H0zxmzUoNjqgt3JiwZ1VVC34U6dOlVp2/XXX48bbrgBr7zyCo4fP44BAwagYcOGASd6N2/ePPhKiYiIIkBWVMiKAq3G949MjSRCVtyQFV5ZqquCCksDBgwIuH/VqlVYtWpVwGMEQcCBAweCGZaIiChiJFGAJIpwuRXotJXn57plz2RvSeS74uqqoMJSOO7H8p4uERHVJgadhBiTFpYSh8+wVGZzIz5aD4OOb3Sqq4IKS1u3br1cdRAREdVIgiAgOd4Iq8MNS7EDZqMGGkmEW1ZQZnNDr5OQHG/kekt1WFBhqWnTpmEvYO3atTh9+jQeeuihsD82ERFROJgMWqQ2ij5vnSU3JFFEfLSe6yzVAyG/Gy5Uq1evxptvvlmlY5csWYIxY8YEPGbdunVo06ZNpT+ZmZnhKJeIiOopk0GLlEbRuLJZLK5sFocrm8Uihesr1Qth+SDd6rBixQrMnz8fnTt3Dnjc4cOH0aVLF8ydO7fC9oSEhMtZHhER1QOCIMCorzU/OilMavy/eE5ODmbMmIEff/wRqampFz3+yJEjaNOmDZKSki5/cURERFTnRfw23MXs378fWq0W69atQ8eOHS96/OHDh9GqVatqqIyIiIjqgxp/Zalfv37o169flY4tKipCTk4Odu/ejQ8//BAWiwXXXHMNJk+ejJYtW4ZUhyTV+FxZ7cp7wt5Uxt74x974xr74x974x974F843J9b4sBSM9PR0AJ61nF588UXY7XYsWrQII0eOxPr169GgQYNLfuyYGGO4yqxz2Bv/2Bv/2Bvf2Bf/2Bv/2JvLq06Fpc6dO+OHH35AfHy8d72LhQsXok+fPli7di0mTZp0yY9dXGyDLCvhKrVOkCQRMTFG9sYH9sY/9sY39sU/9sY/9sa/2FgjRDE8V9zqVFgCKr/rzWg0olmzZsjJyQnpcWVZgdvNL0Rf2Bv/2Bv/2Bvf2Bf/2Bv/2JvKwvmBIXXqJufHH3+Mrl27wmq1ereVlpYiIyMDrVu3jmBlREREVFvV6rAkyzLy8vJgt9sBAL169YKiKJgyZQrS09Px22+/4eGHH0ZCQgKGDBkS4WqJiIioNqrVYSk7Oxs9evTAxo0bAQCNGzfGsmXLYLVaMWLECNx7772Ijo7G8uXLodfrI1wtERER1Ua1as7SSy+9VOHvzZo1w+HDhytsu+qqq7B06dLqLIuIiIjqsFp9ZYmIiIjocot4WFLDOV2diIiIKMxCDku33XYbli5dirNnz17S+W+88Qa2bNkSahlEREREl0XIYenYsWN49dVX0adPHzzwwAPYsmUL3G53lc9v0KABmjZtGmoZRERERJdFyGHplVdeQffu3aEoCrZt24aHH34YPXv2xIsvvohDhw6Fo0YiIiKiiBHUME0aysnJweeff47PP/8cx44d8zy4IKBdu3YYOnQobr/9dsTGxoZjqIiwWMq4OuoFNBoR8fFm9sYH9sY/9sY39sU/9sY/9sa/hARz2D5gOGxh6Xy//fYbPv30U2zcuBGFhYUAAJ1Oh379+mHw4MHo1auX97Pbagt+IVbGb1L/2Bv/2Bvf2Bf/2Bv/2Bv/anxYKifLMn766Sds3boVa9asgc1mAwAkJSVh6NChGDFiBJKTky/X8GHFL8TK+E3qH3vjH3vjG/viH3vjH3vjXzjD0mVdOuDXX3/F9u3bsW3bNthsNqiqClEUkZubi0WLFqF///54++23L2cJRERERCEJ+wreGRkZ+Pzzz7Fu3TqcPn0agGctpTZt2mDIkCG44447kJeXh1WrVmHlypWYN28eRFHEhAkTwl0KERERUcjCEpYKCwuxYcMGrFu3Dvv27QPgCUjR0dG47bbbMGzYMFx99dXe4+Pj4/F///d/aNmyJWbNmoUVK1YwLBEREVGNFHJYevDBB/Hdd9/B7XZDVVUIgoCuXbti6NChGDBgQMAPsO3bty9mzZqFgoKCUMsgIiIiuixCDkv/+c9/AABNmjTB4MGDMWTIkCovMllUVITU1FR06tQp1DKIiIiILouQw9Jtt92GoUOHonv37kEvB9CuXTts3rw51BKIiIiILpuQw9Jrr70WjjqIiIiIaqSglg5YtGgRnE5nWAuw2+1YuHBhWB+TiIiIKFyCDkt//vOfsX79+pAHdrvd+OSTT9C/f3+88847IT8eERER0eUQVFhatWoVTCYTpkyZgltuuQVLly5FXl5eUANmZGTg1VdfRe/evfHss88iKSkJa9asCeoxiIiIiKpL0B934na78d5772HJkiWw2WwQBAFt27bF9ddfj7S0NLRo0QIxMTEwGAwoKSmBxWLBmTNnsHfvXuzZsweZmZneNZjGjRuHiRMnQqMJ+9qYYcel5CvjMvv+sTf+sTe+sS/+sTf+sTf+hfPjToJOKRqNBvfffz/uuusuvPvuu1izZg0OHDiAAwcOBHw3XHkmi42NxZgxY3DPPfcgOjr60isnIiIiqgaXfEknISEBU6ZMwaOPPoodO3bghx9+wI8//ojMzEzvB+YCgFarRUpKCjp16oRevXqhV69e0Ol0YSmeiIiI6HIL+f6XXq/HTTfdhJtuusm7zWazobi4GDqdDrGxsRDFy/p5vURERESXzWWZLGQ0GmE0Gi/HQxMRhURVVdidMmRFhSQKMOikoBfUrY1jE9Glq/kzq4mIwsRqdyHXYkOx1QVZUSCJImJMWiTHG2EyaOvs2EQUmrCFpa1bt+Lnn39GSUmJ90N1fREEAS+88EK4hiUiqhKr3YWMMyVwOGWYjRpoNRq43AosJQ5YHW6kNoq+bKGlKmPHRPn/0HEiiqyQw5LVasWECROwd+9e7zZfQUkQBKiqyrBERNVOVVXkWmxwOGXEx/wRSnRaCTqtBEuxA7kWG1IaacJ+W6yqY0eb+cYXopoq5LD03nvv4eeffwYApKWloVWrVjAYDCEXRkQULnanjGKrC2aj75c8s1GDYqsLdqcMoz68sxOCGZuIaqaQXxU2bdoEQRDw7LPPYsSIEeGoiYgorGRFhawo0PpZAFcjiZAVN2QlqDV6a/zYRBQeIb+nPzMzE40aNWJQIqIaSxIFSKIIl58Vjt2yZ8K1JIb/nWmRHJuIwiPksGQ0GhEbGxuOWoiILguDTkKMSYsym9vn/jKbGzEmLQw6qU6NTUThEXJY6tixIzIyMlBaWhqOeoiIwk4QBCTHG6HXeSZUO10yFEWF0yXDUuyAXichOd54WdY8iuTYRBQeIYelCRMmwOFw4KWXXgpHPUTkg6qqsDncKLW5YHP4X5qjrgulDyaDFqmNohEfrYfdqaCozAm7U0F8tP6yLhtw/thxUToUl7mQY7GiuMyFuCjdZR+biEIX8gTvpKQk3HvvvVi2bBn279+P3r17o2HDhtBq/X/zDxs2LNRhieoNLmboEY4+mAxapDTSRH4VbZVXkYhqk5DD0p///GfvGkqHDh3CoUOHLnoOwxJR1URyIcWaJJx9EAQh7MsDXMz59ceYtdBqPBO+C0udsDllLkpJVMOF/IrRpEmTcNRBRBeI5EKKNUlt7wMXpSSq/UIOS//5z3/CUQcRXSCSCynWJLW9D1yUkqj2C3mCNxFdHn8sZuj729SzmKFS5xczrO19qO31E1EYP0gXAE6fPo1t27bhxIkTKCsrg9lsRmpqKnr27ImUlJRwDkVU552/mKFOW3kNnvqymGFt70Ntr5+IwhSWZFnGyy+/jA8//BCy7LmUXP6huYBnQuVf//pXTJ06FTod78sTVUX5YoaWEofPH7JlNjfio/V1fjHD2t6H2l4/EYUpLD311FPYvHkzVFVFw4YN0b59e0RFRaG4uBgHDhxAXl4eVq5cicLCQsybNy8cQxLVeeWLGVodbliKHTAbNdBIItyygjKbu94sZljb+1Db6yeiMISlr7/+Gps2bYLZbMasWbNw6623Vtivqiq++OILzJgxA5s3b8Ydd9yBvn37hjosUb1QvpjhH+sLuSGJIuKj9fVqnaXa3ofaXj9RfRdyWFq1ahUEQcALL7yAgQMHVtovCAIGDRoEnU6HRx99FKtXr2ZYIgpCjVlIMcJqex9qe/1E9VnIYel///sfkpOTfQal8w0cOBDJycn43//+F+qQRPVOJBZSrIlqex9qe/1E9VXISweUlJSgYcOGVTq2UaNGKCgoCHVIIiIiomoTcliKi4vDqVOnLnqcqqo4deoUYmNjQx2SiIiIqNqEHJauu+46FBYWYuXKlQGP++ijj2CxWHDdddeFOiQRERFRtQk5LI0cORKqquKf//wn3nnnHZSVlVXYX1ZWhrfffhsvvPACBEHAyJEjQx2SiIiIqNqEPNOwe/fuGDVqFFasWIG5c+diwYIFSE1NRVRUFEpLS5GRkQFZlqGqKkaOHInu3buHo24iIiKiahGWt2U888wzaNq0KRYvXozi4mIcPXq0wv7Y2FhMmjQJ48ePD8dwRERERNUmbO9hHTduHEaPHo3du3fj+PHjKC0thdlsxhVXXIHrr78eBoMhXEMRERERVZuwLvih0+lw44034sYbbwznwxIRERFFTMgTvImIiIjqsqCuLPXp0weCIGD58uVo3ry5d1swBEHAtm3bgjqHiIiIKFKCCktnzpyBIAhwu90VtgWDn4NEREREtUlQYenFF18EACQlJVXaRkRERFQXBRWWBg8eXKVtRERERHVFyBO8Fy5ciLVr11bp2MWLF2PKlCmXPNaSJUswZsyYgMdYLBY8+eSTuOGGG9ClSxc8//zzsNlslzwmERER1W9hCUtr1qyp0rFffvklvv7660saZ8WKFZg/f/5Fj3vkkUdw8uRJLFu2DAsWLMD27dvx3HPPXdKYREREREHdhsvKysIPP/xQafvZs2exevVqv+epqorTp08jPT0dJpMpqAJzcnIwY8YM/Pjjj0hNTQ147N69e7Fr1y5s3LgRrVq1AgDMnDkTEyZMwBNPPIGGDRsGNTYRERFRUGEpMTERb7zxBnJzc73bBEHA77//jmeeeeai56uqGvRnw+3fvx9arRbr1q3Dm2++iaysLL/H7t69G0lJSd6gBABdunSBIAjYs2cPbr311qDGJiIiIgoqLBkMBjz11FOYN2+ed9vp06eh0+nQoEEDv+eJogiTyYT27dsHPWepX79+6NevX5WOzcnJQePGjSts0+l0iIuLQ3Z2dlDjXkiSuH7nhcp7wt5Uxt74x974xr74x974x974F86VioL+uJNBgwZh0KBB3r+3bdsWHTp0wIoVK8JX1SWy2WzQ6XSVtuv1ejgcjpAeOybGGNL5dRl74x974x974xv74h974x97c3mF/NlwDz30UKWrOZFiMBjgdDorbXc4HEHPlbpQcbENsqyE9Bh1jSSJiIkxsjc+sDf+sTe+sS/+sTf+sTf+xcYaIYrhueIWlrAEACUlJdi0aROGDx9eYf+//vUvlJWV4e677w54qy4cGjVqhC1btlTY5nQ6UVhYiOTk5JAeW5YVuN38QvSFvfGPvfGPvfGNffGPvfGPvalMVcP3WGGJXN999x369OmDGTNmICcnp8K+b7/9Fm+++SZuu+027NixIxzD+XXDDTfgzJkzOHnypHfbrl27AADXX3/9ZR2biIiI6qaQw9L+/fvxwAMPoKysDKmpqZVugw0cOBDt27dHUVERHn744QpBJlSyLCMvLw92ux0A0LFjR3Tq1AmPP/449u3bh507d+LZZ5/FnXfeyWUDiIiI6JKEHJbeffdduN1ujBw5Ehs3bkTz5s0r7L/77ruxevVqjBo1CjabDW+//XaoQ3plZ2ejR48e2LhxIwDPMgYLFy5Es2bNcM899+Cxxx5Dr169uCglUT2gqipsDjdKbS7YHG6o4bwGT0T1mqCG+IrSs2dPOJ1OfPfddz7fiVbO4XDgxhtvRHR0NL755ptQhowIi6WM94MvoNGIiI83szc+sDf+XY7eWO0u5FpsKLa6ICsKJFFEjEmL5HgjTAZtWMa43Pg14x974x97419CgjlsSyqE/CgWiwXNmzcPGJQAz9v3U1JScPbs2VCHJCLystpdyDhTAkuJAwadiLgoHQw6EZYSBzLOlMBqd0W6RCKq5UIOS4mJiZUmdftjsVgQFRUV6pBERAA8t95yLTY4nDLiY/TQaSUIggCdVkJ8jB4Op4xci4235IgoJCGHpfbt2+Ps2bPYtGlTwOO2bduG7OxsXHXVVaEOSUQEALA7ZRRbXTAbfa+CYjZqUGx1we6Uq7kyIqpLQg5Ld911F1RVxbRp0/Dxxx9Xejec0+nE2rVrMWXKFAiCgLvuuivUIYmIAACyokJWFGg1vl/KNJIIWVEgK7yyRESXLuRFKfv164c77rgD69atw3PPPYcXXngBKSkpMJlMKCsrw++//w6n0wlVVXHrrbfilltuCUfdRESQRAGSKMLlVqDTSpX2u2XPZG9JDOOHRBFRvRNyWAKAl156Ca1bt8Y777yDkpISHDlypMJ+k8mEe++9F3//+9/DMRwREQDAoJMQY9LCUuLwGZbKbG7ER+th0FXeR0RUVWEJS6IoYtKkSbjvvvuwe/dunDx5EoWFhTAajUhNTUXnzp1hNpvDMRQRkZcgCEiON8LqcMNS7IDZqIFGEuGWFZTZ3NDrJCTHGyGE8+PHiajeCUtYKqfVatG9e3d07949nA9LROSXyaBFaqPo89ZZckMSRcRH62vVOktEVHOFNSwREUWCyaBFSiMN7E4ZsqJCEgUYdBKvKBFRWAQVlkaNGgVBEDBnzhw0atTIuy0YgiDggw8+COocIqKLEQQBRj1//yOi8AvqlWXPnj0QBAE2m63CtmDwNz0iIiKqTYIKS3//+98hCALi4+O92x566KGwF0VERERUUwQVlh5++OFK2xiWiIiIqC4Lz8fxEhEREdVRQV1ZUhQlLIOKIjMaERER1Q5BhaVwfAiuIAg4cOBAyI9DREREVB2CCkuqGvqHUYbjMYiIiIiqS1Bhafny5T63L1y4ELt27UK3bt1w9913o127doiJiYHD4UB6ejpWrVqFr7/+GgMGDMC0adPCUjgRERFRdQgqLHXp0qXStg0bNuCnn37CmDFjMH369Er7GzVqhJ49e2LBggVYvHgxunXrhpEjR156xURERETVKOSZ1suWLUN0dDQmT54c8Li///3viI2NxUcffRTqkERERETVJuSwlJ6ejpSUFOh0uoDHaTQaNG/eHL///nuoQxIRERFVm5DDUlRUFLKzsy96nMvlwu+//15h9W8iIiKimi7ksHT11VcjPz8fy5YtC3jcggULUFRUhM6dO4c6JBEREVG1Cfkjuu+77z5s374dr7zyCtLT0zF48GBceeWVMJlMKC0txcGDB/HRRx9hy5Yt0Ol0mDBhQjjqJiIiIqoWIYelrl274vHHH8fcuXOxdu1arF27ttIxqqpCq9Vi9uzZaNu2bahDEhEREVWbsHzuyKRJk7BixQr06NEDWq0Wqqp6/+h0OgwYMABr167FoEGDwjEcERERUbUJ+cpSueuvvx7vvPMO7HY7srKyUFxcjLi4ODRp0gR6vT5cwxARERFVq7CFpXIGgwGtWrUK98MSERERRURYw9J3332Hbdu24fjx4ygpKcGaNWtQXFyM5cuXY+TIkUhISAjncERERESXXVjCUn5+Ph577DHs3r0bgGdCtyAIAIDTp09j4cKFeP/99/H222+jY8eO4RiSiIiIqFqEPMHb6XRi/Pjx+Omnn2A2m9G/f380bNjwjwFEEbGxsSgqKsJ9992HrKysUIckIiIiqjYhh6UVK1bg0KFDuPbaa/HVV1/h9ddfR9OmTb3709LSsHXrVlx33XWw2Wz417/+FeqQRERERNUm5LC0YcMGiKKIV1991e+cpKioKMyZMweSJOG7774LdUgiIiKiahNyWDp+/DhatWqF5s2bBzyuadOmSE1NrdLnyBERERHVFCGHJUVRqnysVquFJEmhDklERERUbUIOS02bNkVGRgZKS0sDHmexWJCenl5hPhMRERFRTRdyWOrduzdcLhdeffXVgMf985//hCzL6NmzZ6hDEhEREVWbkNdZGj9+PNasWYNVq1YhPz8fgwYNQklJCQDg2LFjOHLkCFasWIE9e/bAbDbj3nvvDXVIIiIiomoTclhKTEzEW2+9hQcffBBbtmzB1q1bvftuv/12AJ5FKk0mE+bOnVthDSYiIiKimi7k23CA50N0161bh7Fjx6Jx48ZQVdX7JzExEcOGDcNnn32GXr16hWM4IiIiomojqKqqhvIAJ06cQGpqqvfjTQDAarWipKQEJpMJ0dHRIRdZE1gsZXC7q/7Ov/pAoxERH29mb3xgb/xjb3xjX/xjb/xjb/xLSDBDksJyTSj0K0uPPPIIbrrpJlgsFu82k8mEhg0b1pmgRERERPVXyGHp1KlT0Ov1iI+PD0c9RERERDVKyGHJbDYHtTAlERERUW0SclgaOXIkTp48iaVLl4ajHiIiIqIaJeSlAzp06IBOnTrh1VdfxYoVK3DdddchKSkJBoPB7zmPPvpoqMMSERERVYuQw9KkSZMgCAJUVUVWVhZOnz7t91hVVSEIAsMSERER1Rohh6UbbrghHHUQEdVqqqrC7pQhKyokUYBBJ1VYUuVix0dJ2mqsloiCEXJYev/998NRBxFRrWW1u5BrsaHY6oKsKJBEETEmLZLjjTAZKocgX8fHR+uhM+ojUD0RXcwlh6UDBw7gl19+QVlZGRo3bowbb7wRCQkJ4ayNiKjGs9pdyDhTAodThtmogVajgcutwFLigNXhRmqj6AqByd/xBSV2HMssRGKUFnqNFMFnREQXCjosnTp1Ck8//TT27t1bYbtWq8V9992HRx55BJLEb3QiqvtUVUWuxQaHU0Z8zB9XhXRaCTqtBEuxA7kWG1IaabxzO/0dbzRoYXW4IbvcaNbAHPAWHhFVr6CWDigtLcU999yDvXv3Vvj8N1VV4XQ68fbbb+P555+/XLUSEdUodqeMYqsLZqPv3zvNRg2KrS7YnXKVjo8x6VBc5vQeT0Q1Q1BhacWKFTh9+jTMZjOeffZZfPfdd/jll1/w2Wef4S9/+QtUVcUnn3yCY8eOXa56iYhqDFlRISsKtBrfL6UaSYSsKJAVtUrHayURsqx6jyeimiGo23DffPMNBEHAokWLKrwLrm3btnj55ZdhMBiwatUqbN26Fa1atQp7sURENYkkCpBEES63Ap228vQDt+yZvC2JQpWOd8kKJEnwHk9ENUNQV5YyMjLQpEkTv8sF3H333VBVFYcPHw5LcURENZlBJyHGpEWZze1zf5nNjRiTFgadVKXji61OxJh13uOJqGYIes5SYmKi3/1XXHEFAKCwsDCkooiIagNBEJAcb4Re55nM7XTJUBQVTpcMS7EDep2E5Hijd7J2oOMLiu0w6TUVjieimiGosORyuaDV+l84Ta/3vLvD4XCEVtV5FEXB66+/jp49e+Laa6/FxIkTcerUKb/Hr1u3Dm3atKn0JzMzM2w1ERGVMxm0SG0UjfhoPexOBUVlTtidCuKj9ZWWDQh0fEKMAa2axcHsY10mIoqskBel9EVVwzc58a233sKHH36Il156CY0aNcKrr76KCRMmYP369dDpdJWOP3z4MLp06YK5c+dW2M41oIjocjEZtEhppKnyCt6+jo8yaRFl1MJid1Zz9UR0MZclLIWL0+nE0qVL8dRTT6FPnz4AgHnz5qFnz5746quvcPvtt1c658iRI2jTpg2SkpKquVoiqs8EQYBRX/WX1AuP5603oporqNtw1e3QoUMoKytD9+7dvdtiYmLQvn17/PTTTz7POXz4MN+JR0RERGET9JWlkpISv0GlqsdU9cN3z5w5AwBo3Lhxhe3JycnefecrKipCTk4Odu/ejQ8//BAWiwXXXHMNJk+ejJYtW1ZpTH8kqUbnyogo7wl7Uxl74x974xv74h974x974184L9YGHZbS09MxduxYv/sFQQh4jCAIOHDgQJXGstlsAFBpbpJer0dRUZHP2gDPnKkXX3wRdrsdixYtwsiRI7F+/Xo0aNCgSuP6EhNjvORz6zr2xj/2xj/2xjf2xT/2xj/25vIKOiyFc/L2xRgMBgCeuUvl/x/wvNvOaKz8hdG5c2f88MMPiI+P997/X7hwIfr06YO1a9di0qRJl1xLcbENsqxc8vl1kSSJiIkxsjc+sDf+sTe+sS/+sTf+sTf+xcYaIYrhueIWVFjaunVrWAatqvLbb7m5uWjRooV3e25uLtq0aePznAvf9WY0GtGsWTPk5OSEVIssK3C7+YXoC3vjH3vjH3vjG/viH3vjH3tTWTiv7QQVlpo2bRq+kaugbdu2iIqKwo8//ugNS8XFxThw4ABGjx5d6fiPP/4Yc+fOxbZt22AymQB4FtLMyMjAsGHDqrV2IiIiqhtq9IwwnU6H0aNHY86cOdi6dSsOHTqExx9/HI0aNcKAAQMgyzLy8vJgt9sBAL169YKiKJgyZQrS09Px22+/4eGHH0ZCQgKGDBkS4WdDREREtVGNDksA8Mgjj2DYsGH4v//7P4wYMQKSJOG9996DVqtFdnY2evTogY0bNwLw3LZbtmwZrFYrRowYgXvvvRfR0dFYvny5d3VxIiIiomAIanXO2K7FLJYy3g++gEYjIj7ezN74wN74x974xr74x974x974l5BgDtuSCjX+yhIRERFRJDEsEREREQXAsEREREQUAMMSERERUQAMS0REREQBMCwRERERBcCwRERERBQAwxIRERFRAAxLRERERAEwLBEREREFwLBEREREFADDEhEREVEADEtEREREATAsEREREQXAsEREREQUAMMSERERUQAMS0REREQBMCwRERERBcCwRERERBQAwxIRERFRAAxLRERERAEwLBEREREFwLBEREREFADDEhEREVEADEtEREREATAsEREREQXAsEREREQUAMMSERERUQAMS0REREQBMCwRERERBcCwRERERBQAwxIRERFRAAxLRERERAEwLBEREREFwLBEREREFADDEhEREVEADEtEREREATAsEREREQXAsEREREQUAMMSERERUQAMS0REREQBMCwRERERBcCwRERERBQAwxIRERFRAAxLRERERAEwLBEREREFwLBEREREFADDEhEREVEAmkgXQEQ1j6qqsDtlyIoKSRRg0EkQBCHSZRERRQTDEhFVYLW7kGuxodjqgqwokEQRMSYtkuONMBm0kS6PiKjaMSwRkZfV7kLGmRI4nDLMRg20Gg1cbgWWEgesDjdSG0UzMBFRvcM5S0QEwHPrLddig8MpIz5GD53Wc+tNp5UQH6OHwykj12KDqqqRLpWIqFoxLBERAMDulFFsdcFs9H3B2WzUoNjqgt0pV3NlwVNVFTaHG6U2F2wONwMeEYWEt+GICAAgKypkRYFW4/tlQSOJkBU3ZKVmBw/OuSKicGNYIiIAgCQKkEQRLrcCnVaqtN8te4KHJNbcd8VxzhURXQ68DUdEAACDTkKMSYsym9vn/jKbGzEmLQy6ykGqJuCcKyK6XHhlicKmfJ6I1eH5YWvSa2DUa6q0Ps+F6/rotSIcLsXnOj+yLCO30A67U4ZBJyE5zgBJ+uMHuKIoKCpzwuGS4XbJKCxzwiUriDPr0STRgIJS97l5LJ7HV1QBZr0IQRDgdKuAqkIjCVBUwOF0IrvADqdLRrRBhKyoKLKrMOtFNEs0wWTSQxBEGLQCLCUO5BfbIcsKtFoRifFm5J4tg14rQqeVIImA06WgqMwFrUaAKEpIMIv4/awdDocbEATER2lgdwNmnQiNRgOjQYKiCBAg43S+HaqqIj5KC7NRC4fL02+tJEKSBEgiUGZ3weUGoowSRFGErACCKqOwzAVZAcwGCUYtkJnvhKoCjeL1aBBrQJHNMw9JVdw4llWM4sNOSJKIaJMWcdE6yG4ZhWVuqLKMjT84ICtuuNwCYgwABAkKBEjAuZ4CCgARAjQaEZIECFBRXOZCUZkTbkVFtEGHaLOIKKMBiuLG6bNW5BXZ4XYDZh3QrGEskuP1+D3XhoIiO9yKCxpJB6NeQny0Aa2bmAFJhzKbE9l5xThjcUBWVUCWoREBlypAVhQIggYarYhGcVokxhhQUKpAhIwymwwZKrSSgCijBJNBD5NOglargazC8+8DGWeLXHC5VYiCDJdLgd0NxEbp0LqxEZZSBZl5pSi1u2HUiIiP1cNw7nxBBaLMOug1AmxOGTaXimiTFglRGhSVOnAi2wqnLMOgFRFn1kMVVLjcKvR6DfLyS+F0KzDqdWjeQA8FAn7PtaLU6oCsAvFmLeJjjIiL0sNk1CE+SgdFUZFX7IDTpUCnUXA6z4ozBVYoCpAcq0NsjBF6nRaSCEiCABkCTAYJCdFGmPUCTuaU4XR+KcpsMox6ASaDHokxOiTGmhFr1sHhlGGxugBVgU4SYLW7UWaXIYie7xe3DKhQoRFFaDUidBoJJqMOZoMGBp0Ih0uFoqow6T3fx24FkKCg2OqG3aXApNegcYIe+SUuFFudEAE4XW6U2twQBKBpAzNcKmCxlKGkzIGCEidkWYaqAka9BFHSIDFGB7NBC6dbgUtWodOIiDZqUFjqQHa+DXaXJ+ybDVpoNBJ0GhF6rQQVAsovlioqKrz+uGXPH40kQiOJPrdLogBVVWF1uOFwugEI0OskmA2eXyzKX8cuHKP8l45A65ldzvXOuJZacASVv2ZVicVSBrdbiXQZNYpGIyI+3gyLpQzFpQ6cPFPi/eEBAGaDFi2SzWjRMPCtjwvnmLjcKtxuGVqteO7F6I85J/lFdvx8JA85FhvcsgyNJKFhvBGd0pLQvGE0zhbacORUIU7nl+FUTgnOFNjgdLuhlSRoNQIkSYBJr4XTJaPU6oIMQKcRoSoKBFGEVvK8kCuKCofTE/xk2fPD/3yiAGgkID7KgNhoHcqsLpTZ3XC43HC5VagKIIiAIAoQoXpehFQV8rnvNlXw/EB1+ZgrLQAQRUASAa1GhKyqcDtVqAAEAfD8H0AjAgo8jwt4/qOWvyArnvoEAC43vOeeP92o/KE0ImDSS5AVFXanAkX1bC8/pvw4vlDUPBI8X2fquX+gYKbeayRAEgC32/95Rh1g0GkgCgIUVYDD6fL8QnGOr5dEAYBGAxi1EkwGLTQaEUa9BlqNCEVWoddKECUgv8gBp1uGTqOBKABuRYFWkuB0uVFU5oDDpUAQBWhEAVpJRFy0ARBUlJa5YHO54XIpEARAI4gwGbSIMmsRZdQgxmxAlFEDRQUsRTbkFdthtbvhdCtQFRUmgwbJcUbEmHUwGz0hWoUAASqiTTpIkgCXS4GiqiizueFwydBrRZiMOkiC57XB870uQ4AKWVVQanOjpMx17hdFFTEmPRrE6REXpUeUSQdZVlFidUKFgBiTFiaDFjqNAFUFXLLqc25dVefenf86XNWfUfVlXl9CghmSFJ4baAxLVcSwVFn5N2lmdiH2H8/HqdxSaEQRUUYNIAAlVhcURUWz5Ci0bRHv85vwwjkmsqLgVG4ZikudiDHr0DzZ88VeZnOjuMyJ9KxC2B0yEmL0MBkkWO0yCoodiDJp0enKBsg6a0VRqQN5RTZkZBfBLavQaiQIUGF3uOGUVWg0AjSSCBEC3LInmImSBFX1/KZq0GtgtbnhquI/t1YAlHOJQsW50BLg+PLwEQ4iKgc5onAT4QlAsgxv4K/KOSIAnV6ESa+BRiNAEERP2LC5oJFE6LQiJBGwOtywORSIgufB3TIgK+eNq5z7vlI94V4VPNugen4JkCRA0ojQiBIaxhmQHG9ErsWO7PwyKFARpdfAJStwuhSoUKHTSkiKM8Fs1ECEgOQEI6IMOoii540OpVYXnG4FcecCVandc1X0XHmIjdJBqxWRlVsGS4kDDs+lNZj0ErRaCS6XDLcMmIwSmiSaYdRroCieq28mvQZRJi3O5FuhAmjZOBrRJh1cbgVlNjf0OgnJcUbkFtrOm3snVth//ty7YMNS5Xl9/h+7tgtnWOKcJQqJqqrIKbAi12KDQatBfIweWq0ErUZCQowBOp2EPIsNOQXWSnNFLpxjotWIsJQ4oagqmiSZoaqApdQJrUZCrFmLgycLkF9oR/NkM6KMWoiCiCijFi0aRqG41IFvfz2NEqsDeq2I3AIbVIhIjDUiyqiFzaHAraieWwIOFU6njGiTFpJGgCwAoqhCFETIAGw2t8/fmC+kOXfnz6X+8cJ9/lP09811KUHJ38VxBiWqDgoAp9sTlKr6Q0M9d56gqnC4FNgcMswGCQ6n5yqP0aBBrFmHUpsMh0tBlEkDpwuwuwBRAvQ6EYLo+d6SRM9VUQXn/qsCGkmAJAEazbmrwbIKraSizO5CZl4pzhZbIWkEaCXPrTBAQGy0HlqNBk6XglKrEwJw7vaZjNgoLQpLnSi2umAyeN4YAMFTBwA4XTIUqHCce3FwOj234lyyfC60qdDrJEQbNVAgwK3IkEQRORYrLCUOxMfokBBjgMMtIzvfCq1WgkGrQanNc+uufG6d3eHGkcxC2B3usM+947y+S8crS1Uky/yx5IsgCLA73ZDlc/fkL/ipXv7boCQK0GnFSvfjXW7Fs+3c5Rb3uT4L512p0Uie30ZtTjcEwXNZ/sJxFEWF261Aq5WgKCpcsuK5hSR45hMoiuo9p/wrXjw316B8W/lD8huCKHyEc/8jQDh3K9jzHSYInrlC8rn7voIg/LGv/H8CXKU9/yWg/BixfGKQ6rmKI3gGhqp6boUL+OPKr3DueEkUoALQSiIUVfUeW04SBe9yGeXzewDPY6oqKpxT/ppz/nOEqkIUPVezz/3VO9/JeytcEv94fTr3+qWVRAi+3nmqesbWav54PZUksUo/oyq95lbhsWszURTC9jw4wbuKwnUpr65RFM+LhCgCoq9vwHMvJoIoQBTFP17Mys8VPS8kAjy/LQqK8MfjnHeuJ4d5XlwFwfOiW2EYwfNHFAQowh8vVML5r7q48P+XbxGggmmJqLoI3m+2c/8VfH3TVfGG9XmHVTzjj9eJ8nBUfpBw3obyX8z+ePny/K/nl6k/XkcEAArUc/O3PK97KlQIKs5NEhe8cwOF8x5JFYRzr5Ge/6re10wBAoQ/Xh/L5xtCgKCo514vKz9d9dyVtQtfT6vyM+rC19yqPjbxyhIRERFRQLxcQkRERBQAwxIRERFRAAxLRERERAEwLBEREREFwLBEREREFADDEhEREVEADEtEREREATAsEREREQXAsEREREQUAMMSERERUQAMS0REREQBMCwRERERBcCwFMBnn32GW2+9FR06dMBtt92GTZs2RbqkiPvxxx/Rpk0bn39uuummSJcXcW63GwsWLEDfvn1x3XXXYdSoUfjll18iXVaNUFpaihkzZqBHjx7o0qULnnrqKeTn50e6rIhbsmQJxowZU2HbwYMHMXr0aFx77bXo168fli9fHqHqIstXbwDg5MmTuPbaa5GZmRmBqiLPV1/+85//YOjQobjuuuvQr18/vPzyy7Db7RGqMHJ89Wbjxo0YNGgQrrnmGtx888145513oKpqcA+skk+fffaZ2r59e/WDDz5QT548qb711ltq27Zt1Z9//jnSpUWUw+FQc3NzK/z56quv1DZt2qirV6+OdHkR9/rrr6t/+tOf1O+++07NyMhQp0+frl5//fVqTk5OpEuLuHHjxqm9e/dWv/nmG/XIkSPqgw8+qN56662qw+GIdGkR88EHH6ht27ZVR48e7d1WUFCgdu3aVZ06dap69OhRdfXq1WqHDh3q3feXr96oqqoePXpU7devn5qWlqaeOnUqQtVFjq++/PTTT2q7du3URYsWqSdOnFC/+eYbtVevXuo//vGPCFZa/Xz15ttvv1XbtWunLl++XP3999/VL7/8Ur322mvVZcuWBfXYvLLkg6qqWLBgAcaOHYtRo0ahRYsWeOCBB3DjjTdi165dkS4vonQ6HZKSkrx/zGYzXnzxRQwePBhDhw6NdHkRt2XLFtx+++3o0aMHUlJS8I9//AMlJSX1/urSwYMHsWPHDsycORO9e/fGlVdeiVdeeQW5ubnYsGFDpMurdjk5Ofjb3/6GOXPmIDU1tcK+VatWQavVYubMmWjVqhWGDh2Ke++9F2+//XZkiq1mgXqzZMkSDBs2DLGxsZEpLoIC9WXlypXo2rUr/va3vyE1NRW9e/fG448/jvXr18PpdEam4GoUqDd5eXmYNGkSxowZg+bNm2PAgAG48cYb8f333wc1BsOSDydOnEBWVhYGDRpUYft7772H+++/P0JV1UyLFy+GzWbD008/HelSaoTExERs27YNmZmZkGUZH3/8MXQ6Hdq2bRvp0iIqIyMDANC5c2fvNrPZjJSUlHr5C8j+/fuh1Wqxbt06dOzYscK+3bt3o0uXLtBoNN5t3bp1Q0ZGBs6ePVvdpVa7QL3ZsmULXnzxxXr5ehOoL+PGjavUE1EU4XK5UFpaWp1lRkSg3gwZMgSPPfYYAEBRFPz3v//FTz/9hD/96U9BjaG5+CH1z4kTJwAAVqsV48ePx4EDB9CsWTM88MAD6NevX4SrqzkKCgqwbNkyPPnkk4iLi4t0OTXC9OnT8eijj+Kmm26CJEkQRRFvvPEGWrRoEenSIio5ORkAkJ2djVatWgEAZFnGmTNnkJiYGMnSIqJfv35+X0vOnDmDtLS0CtvO71+DBg0ue32RFKg3n3zyCQDP3Mn6JlBf2rdvX+HvLpcLy5Ytw9VXX42EhITqKC+iAvWm3OnTp9G/f3+43W706NEDI0aMCGoMXlnyoTyJP/3007j99tuxdOlS/OlPf8KDDz6IH374IcLV1RwffvghoqOj8de//jXSpdQYR48eRXR0NN588018/PHHGDJkCJ566ikcPHgw0qVFVIcOHXDFFVdgxowZyMnJgd1ux2uvvQaLxQKXyxXp8moUu90OnU5XYZterwcAOByOSJREtYjb7caUKVOQnp6OGTNmRLqcGiMmJgaffPIJ5s+fj0OHDmHKlClBnc8rSz5otVoAwPjx4zF48GAAQLt27XDgwAH861//Qvfu3SNZXo3x2Wef4c4774TBYIh0KTVCdnY2nnzySSxbtsx7u6lDhw44evQo3njjDbz11lsRrjBydDodFi5ciClTpqBXr17QarUYNGgQ+vbtC1Hk72znMxgMleaZlIckk8kUiZKoligtLcVjjz2GXbt2YeHChbjmmmsiXVKNERUVhfbt26N9+/aQZRlPPvkkJk+ejKZNm1bpfIYlHxo2bAgAlS6Ft27dGt98800EKqp5Dh06hFOnTlWa11Wf/frrr3C5XOjQoUOF7R07dsS3334boapqjlatWmHNmjUoLCyERqNBVFQUhg0bhm7dukW6tBqlUaNGyM3NrbCt/O/lr01EF8rNzcXEiRORlZWF9957DzfccEOkS6oRdu/eDZ1OVyE4tmnTBoCnZ1UNS/yVzoerrroKZrMZv/76a4XtR44cqfdzT8rt3r0biYmJ9X7i8vkaNWoEADh8+HCF7UeOHKn0Do36prS0FKNHj8ahQ4cQFxeHqKgoZGZm4sCBA0FPtKzrbrjhBuzZsweyLHu37dy5Ey1btqyX87vo4oqKinDPPfegoKAAK1asYFA6z/Lly/HCCy9U2Pbrr79Co9EE9brMsOSDwWDAhAkT8Oabb+KLL77A77//jkWLFuH777/HfffdF+nyaoQDBw540zl5XHPNNbj++uvx9NNPY+fOncjIyMD8+fPxww8/YNKkSZEuL6KioqKgqipmz56N9PR0/Pbbb3jggQfQrVs33ta+wNChQ1FaWorp06fj6NGjWLt2LZYtW8Z34pJfL774Ik6dOoVXX30VCQkJyMvL8/45P3TXR/feey/27duHefPm4eTJk9i0aRNeffVVjB07FvHx8VV+HN6G8+PBBx+E0WjEvHnzkJOTg1atWuGNN95A165dI11ajZCXl8d3wF1AFEUsWrQI8+fPx9SpU1FUVIS0tDQsW7as0ttZ66O5c+di1qxZGDFiBHQ6HQYMGIDJkydHuqwaJzExEe+++y5mz56NwYMHIykpCVOmTPHOnyQ6nyzL2LhxI1wuF+65555K+7du3YpmzZpFoLKaoVOnTliyZAnmz5+PZcuWISEhAePGjcPEiRODehxBVYNd85uIiIio/uBtOCIiIqIAGJaIiIiIAmBYIiIiIgqAYYmIiIgoAIYlIiIiogAYloiIiIgCYFgiIiIiCoBhiYiIiCgAhiWiOmbt2rVo06ZN0H/GjBkT6dLDJj09PeD+Q4cOoUOHDnjzzTerqaLKVFXFsWPHIjZ+uWPHjuHCtYmnTZuGrl27VvpAX6L6iit4E9Ux27dvx+LFiyttz87ORnZ2NnQ6Ha6++upK+9u3b49nnnmmOkq8bE6cOIF//vOfsFqt+Oijj3we43K5MGTIEJSWlmLz5s3Q6/XVXCWwb98+zJo1CykpKZgzZ061jw94Ptx47ty5+Pjjj70fLFru7NmzGDBgAK677jq89957EamPqCbhZ8MR1TG9e/dG7969K21/4403sHDhQiQlJfkNErXdF198gR07dqBTp05+j1m6dCmOHDmCl19+OSJBCQA+/PBD7Nu3DykpKREZHwD279+PFStW+NzXoEEDTJw4EfPnz8cXX3yB22+/vZqrI6pZeBuOiOqNgoICLF68GCkpKbjjjjsiXU6NNmbMGERHR2POnDlwOp2RLocoohiWiKjeePfdd2G1WnHXXXdBFPnyF0hUVBQGDRqE7OxsrF69OtLlEEUU5ywR1RPlt+GaNm2K//znP5X2FxcXY+XKldi+fTuOHj2K0tJSGI1GtGjRAn379sXYsWMRGxtb4Zw2bdoAAL7//nu89NJL2Lp1K0RRxFVXXYWlS5d658Fs3rwZK1asQHp6Oux2u3dC+bXXXoubbrrJb01btmzBqlWr8Ntvv6GkpATx8fHo0qULxo0bh6uuusp7XGZmJm666aZK55//uHa7Hb169UJxcTG2b9+Ohg0beo974oknsGHDBvTt29fnfC8AWLduHSZPnowrrrgCmzZtuli7ffrxxx8xduzYStu7dOmC999/3/t3p9OJjz76CBs3bsTRo0fhcrnQuHFj9OnTB+PHj0dycnKlxygqKsLSpUvxn//8BydPnoQgCEhOTkaXLl0wduxY778VAPTr1w9ZWVmVHmPr1q1o1qyZ9++//vorhg8fjlatWmHjxo2X9JyJ6gLOWSIiZGRk4N5770V2djY0Gg1atGiBpk2bIisrC/v378f+/fuxYcMGrFmzBmazudL5Dz/8MPbu3Yu0tDQUFBQgKSkJGo0GiqJgxowZWLVqFQCgcePGaNKkCQ4ePIgnn3wSAwYM8FmP2+3GP/7xD6xfvx4AkJiYiDZt2iAzMxNffPEFNm3ahGnTpmH06NEAAL1ej06dOnknsUdFRSEtLQ1JSUnex9yxYweKiorQvn37CkEJAIYOHYoNGzZgx44dKCgoQEJCQqWaPvvsMwDAkCFDgm/wOdHR0ejUqRNOnjyJ/Px8JCQkIDU1FWlpad5jcnNzMWnSJBw8eBCCIKBJkyaIi4vD0aNHsWzZMnz22Wd46623cP3113vPKSwsxPDhw3Hy5EnodDq0aNECWq0WJ0+exOrVq/H555/jrbfeQq9evQAAV199NcxmM44cOQIA3jleF87h6tChA+Li4nDs2DEcOnQIbdu2veTnTlSrqURUL7z++utqWlqa2rdv30r7Ro8eraalpanDhw9Xc3JyvNsVRVE//fRTtW3btmpaWpr6wQcfVDgvLS1NTUtLU6+++mp1165dqqqqqizLqsViUVVVVVeuXKmmpaWp11xzjbphwwbveYWFherDDz/sPf/CmubMmaOmpaWpvXr1Ur/99lvvdrfbrS5fvlxt37692qZNG3XHjh0+n+Pdd99d6TnOnDlTTUtLU2fNmlVpnyzLap8+fdS0tDT1/fffr7T/zJkzatu2bdV27dpV6M+levrpp9W0tDT1ySefrLBdURT1r3/9q5qWlqaOGDFCPXbsmHdfcXGxOnXqVDUtLU3t2rWrmpub69336quvep93fn5+hXMeeughNS0tTR04cGCFsXbu3Ontv8vl8lvr/fffr6alpanvvvtuqE+bqNbiTXuieu7s2bPedYlmzZpV4RaPIAi488470aVLFwDA4cOHfT7Gn//8Z9xwww0AAFEUERcXB0VR8MYbbwAApkyZgltvvdV7fGxsLF577bUKt4bOr2fZsmUAgLfeegs9e/b07pMkCWPGjMG9994LVVUxf/78Kj/PH3/8EQBw5ZVXVtoniiIGDx4MAPj8888r7f/888+hKAp69uzp8xZYuGzduhV79+5FcnIy3n33XVxxxRXefdHR0Zg9ezY6duwIi8Xi7RHgWTcKAAYOHFjhqlh0dDT+7//+DzfeeCNuuOEG2O32oGsqv+q1c+fOS3xWRLUfwxJRPdegQQPs3LkTv/76a4XbQeVkWUZUVBQA+P1he/4toXK//PIL8vLyoNPpMHTo0Er7tVotRo0aVWn7t99+C6fTidatW1eYl3S+v/zlLwA86xXl5+f7f3LnyczMBAC0aNHC5/4hQ4ZAEATs27cPx48fr7AvHLfgqmLLli0AgJtvvhkmk6nSfkEQvO/i27Ztm3d7amoqAM8E9nXr1qGkpMS7r2HDhvjXv/6FWbNmwWAwBF1Ty5YtAQCnTp0K+lyiuoJzlogIAGAwGJCVlYXffvsNv//+O06dOoVjx47h4MGDsFqtAABFUXyee/7coHLlV6tSU1P9/pD2tThm+XlnzpzBiBEjfJ6nnve+lOPHjyMxMTHAMwOsVitsNhsAICYmxucxzZo1Q9euXbFz5058/vnnePzxxwF4AtmxY8cQFxeHvn37BhwnVOVziLZt2+a9WnSh4uJiAJ55ZqqqQhAEjB8/Hps3b0ZeXh4mT54MjUaDDh064MYbb0SvXr3QsWNHCIJwSTVFR0cD8Cy7QFRfMSwREY4fP44ZM2Zg165dFbZHRUWhc+fOyM3N9fvDG4DPMGSxWADA5xWS8x//QuVXRUpLS/Hzzz9ftPby8FDVYwJdXRk6dCh27tyJ9evX47HHHoMgCN6rSoMGDYJOp7voWKEoLS0F8Mdq64HIsoyysjJERUWhcePG+Pzzz7FkyRJs3rwZOTk52Lt3L/bu3Ys333wTTZs2xbRp03DzzTcHXVP5v19V+kxUVzEsEdVz+fn5GD16NPLz89GkSRMMHz4c7du3xxVXXIFmzZpBEAQ8+eSTAcOSL0ajEcAfAcCXsrIyv+cNHDgQr7/+elBj+nN+yDn/FtWFBgwYgJkzZyIrKwt79uzBtddeiw0bNgCAz1uJ4Vb+3J955hnvO/2qKjExEdOmTcO0adNw+PBh7Nq1Czt37sSOHTuQlZWFRx55BCtXrsQ111wT1OMWFRUBqPxOOaL6hGGJqJ5bs2YN8vPzERcXhzVr1vh823xOTk7Qj1s+/+nkyZOw2+0+r+j4CmDlc2QCfRiuzWbDb7/95l2KQJKkgLXExsZCq9XC5XJ5r3j5YjAYcNttt2HlypX4+uuv4XK5UFhYiHbt2qFdu3YBxwiHli1b4tChQwGfe3Z2NnJyctC4cWPvEgg5OTk4ceIErr32WhgMhgofjnz27FkMHz4cWVlZ+OKLL4IOS+X9utitTqK6jBO8ieq58onPTZo08RmUjh49il9++QWA59ZPVV1//fVISEiAy+Xy+Q4zVVW96y+dr3fv3pAkCcePH8f333/v87GXLVuGMWPG4C9/+Yt3LhIA77wc9YK1diVJ8n4O25kzZwLWPWzYMADA119/ja1btwII/8Ruf3WWz4nauHGj34nr06ZNw1//+lc8+eSTADxrUt15552455578M0331Q6vkGDBt7gev6cs/NXML+wjvOVB+Xz35lHVN8wLBHVc+U/BA8dOoQvv/zSu11VVXz77beYMGECXC4XAFQIJhej0+nwt7/9DQC8q3uXs1qtePbZZ7F3795K5zVt2hR33XUXAM/K2uev7K0oCj755BMsXLgQADBq1KgK857KF8zMzc2F2+2u8LjlCy/6GvN8HTp0QJs2bZCVlYU1a9ZAq9Vi0KBBVX7eVVFe5+nTpytsv/XWW5GWlobi4mKMHz++whWm0tJSPPfcc/jvf/8LQRAwadIkAIBGo8Ftt90GAJg9ezb27dtX4TG/+uor7NixAwC8i1ICFeeSXVjH+crnjfl6xyNRfcHbcET13LBhw/Dhhx/i5MmTeOSRR9C0aVPEx8cjOzsb+fn50Gq16NKlC3bt2hX07bgxY8bg119/xYYNG/Dggw+iSZMmSExMxLFjx2C1WnHNNddg3759lW6jTZs2DTk5Odi2bRseeOABJCcno2HDhsjKyvK+K2vgwIF47LHHKpxXfqssKysLAwYMQHJyMj766CMIgoBevXph1apV2LNnz0XrHjJkCF588UVYrVYMHDgQ8fHxQT3viymv8+eff8Ytt9yC1q1bY+HChdBqtXjrrbcwYcIEHDx4ELfffjtatmwJo9GIjIwM77sSp06dWiH4PP7449izZw8OHDiAu+66y/tvmJubi9zcXADAiBEjKpyTmpoKk8kEq9WK4cOHo1mzZpg9e3aFVbpdLpc3fPXu3TusPSCqTXhliaiei4qKwurVqzFp0iRceeWVKCgoQHp6OqKiojB06FCsWbMGL7zwAgDP1adAVyEuJIoiXnvtNbzwwgu47rrrUFRUhKNHj6JNmzZ44403cM899wCo/A41vV6PRYsWYd68eejZsydcLhcOHjwIWZbRtWtXvPzyy5g/f36lkNWtWzdMmTIFTZs2RW5uLjIzM3H27FkAnh/2cXFxyMzMrLSO0oXuuOMO72NfjrWV7rzzTkyYMAFJSUnIzMzEwYMHvbfImjdvjk8//RRTpkxBx44dkZeXhyNHjsBsNmPgwIH44IMPvH0rZzab8f777+ORRx7BVVddhcLCQhw6dAiqquKmm27CkiVL8Nxzz1U6Z8GCBWjbti2sVisyMzO9t2TL7dq1C3a7HVdeeSXat28f9j4Q1Rb8IF0iipgVK1Zg5syZ+NOf/oSlS5de9vEWLlyIN954A+PGjcPTTz/t97jDhw/jjjvuQFJSErZv337RCeR11aOPPorNmzfj5Zdfxp133hnpcogihleWiOiycDgc6NGjB0aOHOn39t327dsBoNquWowdOxbR0dH47LPP4HQ6/R73ySefAPAsF1Bfg1JBQQG2bt2KlJSUsM/ZIqptGJaI6LLQ6/VISkrCnj178NJLL1VY1NDhcGDhwoXYvn07DAZDtaxhBHhW7x43bhwKCgq8i02WO3DgALKysrBixQp89NFH0Ol0GDlyZLXUVRP9+9//hsvlwkMPPVRvAyNROd6GI6LLZvfu3Zg4cSKsVisMBgNatGgBURRx6tQplJWVQa/X44UXXsDtt99ebTW5XC4MHz4ceXl5+Oqrr7zvChs4cCAyMjK8xz3++OPed/OdLy8vD4888sgljT106FDv0gQ12ZkzZzBw4EB0794dixcvjnQ5RBHHd8MR0WXTuXNnbNq0Cf/+97/x/fffIzs7G263G8nJyfjLX/6CUaNGoXXr1tVak1arxSuvvIIhQ4bg3Xff9QafTp064fTp00hISMCoUaO8b82/kMPhqNLHsPhy4403XnLd1WnevHkwGo345z//GelSiGoEXlkiIiIiCoBzloiIiIgCYFgiIiIiCoBhiYiIiCgAhiUiIiKiABiWiIiIiAJgWCIiIiIKgGGJiIiIKACGJSIiIqIA/h9IFAWs84ianAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot with the test targets and the test predictions\n",
+    "# You can include the argument 'alpha' which will introduce opacity to the graph\n",
+    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
+    "plt.xlabel('Target(y_test)', size= 18)\n",
+    "plt.ylabel('Prediction(y_hat_test', size= 18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6.13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_2108\\2906630971.py:3: RuntimeWarning: overflow encountered in exp\n",
+      "  df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction\n",
+       "0  30228.855333\n",
+       "1   9383.112639\n",
+       "2   6003.067434\n",
+       "3   8074.467696\n",
+       "4  11280.346946"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, let's manually check these predictions\n",
+    "# To obtain the actual prices, we take the exponential of the log_price\n",
+    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>7900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction  Target\n",
+       "0  30228.855333     NaN\n",
+       "1   9383.112639  7900.0\n",
+       "2   6003.067434     NaN\n",
+       "3   8074.467696     NaN\n",
+       "4  11280.346946     NaN"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can also include the test targets in that data frame (so we can manually compare them)\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf.head()\n",
+    "# Note that we have a lot of missing values\n",
+    "# There is no reason to have ANY missing values, though\n",
+    "# This suggests that something is wrong with the data frame / indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    10.257659\n",
+       "1     9.093807\n",
+       "2     8.787220\n",
+       "3     8.985946\n",
+       "4     9.323669\n",
+       "Name: log_price, dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# After displaying y_test, we find what the issue is\n",
+    "# The old indexes are preserved\n",
+    "# Therefore, to get a proper result, we must reset the index and drop the old indexing\n",
+    "y_test = y_test.reset_index(drop=True)\n",
+    "y_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>28500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>8900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>6550.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>7990.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>11200.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction   Target\n",
+       "0  30228.855333  28500.0\n",
+       "1   9383.112639   8900.0\n",
+       "2   6003.067434   6550.0\n",
+       "3   8074.467696   7990.0\n",
+       "4  11280.346946  11200.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's overwrite the 'Target' column with the appropriate values\n",
+    "# Again, we need the exponential of the test log price\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Additionally, we can calculate the difference between the targets and the predictions\n",
+    "# Note that this is actually the residual (we already plotted the residuals)\n",
+    "df_pf['Residual'] = df_pf['Target'] - df_pf['Prediction']\n",
+    "# Since OLS is basically an algorithm which minimizes the total sum of squared errors (residuals),\n",
+    "# this comparison makes a lot of sense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "        vertical-align: top;\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>28500.0</td>\n",
+       "      <td>-1728.855333</td>\n",
+       "      <td>6.066159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>8900.0</td>\n",
+       "      <td>-483.112639</td>\n",
+       "      <td>5.428232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>6550.0</td>\n",
+       "      <td>546.932566</td>\n",
+       "      <td>8.350116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>7990.0</td>\n",
+       "      <td>-84.467696</td>\n",
+       "      <td>1.057168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>11200.0</td>\n",
+       "      <td>-80.346946</td>\n",
+       "      <td>0.717383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4280.485476</td>\n",
+       "      <td>4600.0</td>\n",
+       "      <td>319.514524</td>\n",
+       "      <td>6.945968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11427.087287</td>\n",
+       "      <td>8350.0</td>\n",
+       "      <td>-3077.087287</td>\n",
+       "      <td>36.851345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2895.962467</td>\n",
+       "      <td>3300.0</td>\n",
+       "      <td>404.037533</td>\n",
+       "      <td>12.243562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>39228.722376</td>\n",
+       "      <td>43500.0</td>\n",
+       "      <td>4271.277624</td>\n",
+       "      <td>9.819029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22173.636762</td>\n",
+       "      <td>25500.0</td>\n",
+       "      <td>3326.363238</td>\n",
+       "      <td>13.044562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4899.198732</td>\n",
+       "      <td>5200.0</td>\n",
+       "      <td>300.801268</td>\n",
+       "      <td>5.784640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13110.051878</td>\n",
+       "      <td>15300.0</td>\n",
+       "      <td>2189.948122</td>\n",
+       "      <td>14.313386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20551.654463</td>\n",
+       "      <td>22500.0</td>\n",
+       "      <td>1948.345537</td>\n",
+       "      <td>8.659313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13015.274654</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>484.725346</td>\n",
+       "      <td>3.590558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2718.022542</td>\n",
+       "      <td>2600.0</td>\n",
+       "      <td>-118.022542</td>\n",
+       "      <td>4.539329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3900.793694</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>-1000.793694</td>\n",
+       "      <td>34.510127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10857.592502</td>\n",
+       "      <td>9900.0</td>\n",
+       "      <td>-957.592502</td>\n",
+       "      <td>9.672652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>4330.566126</td>\n",
+       "      <td>4800.0</td>\n",
+       "      <td>469.433874</td>\n",
+       "      <td>9.779872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10710.004512</td>\n",
+       "      <td>13100.0</td>\n",
+       "      <td>2389.995488</td>\n",
+       "      <td>18.244240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9723.514589</td>\n",
+       "      <td>12650.0</td>\n",
+       "      <td>2926.485411</td>\n",
+       "      <td>23.134272</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Prediction   Target     Residual  Difference%\n",
+       "0   30228.855333  28500.0 -1728.855333     6.066159\n",
+       "1    9383.112639   8900.0  -483.112639     5.428232\n",
+       "2    6003.067434   6550.0   546.932566     8.350116\n",
+       "3    8074.467696   7990.0   -84.467696     1.057168\n",
+       "4   11280.346946  11200.0   -80.346946     0.717383\n",
+       "5    4280.485476   4600.0   319.514524     6.945968\n",
+       "6   11427.087287   8350.0 -3077.087287    36.851345\n",
+       "7    2895.962467   3300.0   404.037533    12.243562\n",
+       "8   39228.722376  43500.0  4271.277624     9.819029\n",
+       "9   22173.636762  25500.0  3326.363238    13.044562\n",
+       "10   4899.198732   5200.0   300.801268     5.784640\n",
+       "11  13110.051878  15300.0  2189.948122    14.313386\n",
+       "12  20551.654463  22500.0  1948.345537     8.659313\n",
+       "13  13015.274654  13500.0   484.725346     3.590558\n",
+       "14   2718.022542   2600.0  -118.022542     4.539329\n",
+       "15   3900.793694   2900.0 -1000.793694    34.510127\n",
+       "16  10857.592502   9900.0  -957.592502     9.672652\n",
+       "17   4330.566126   4800.0   469.433874     9.779872\n",
+       "18  10710.004512  13100.0  2389.995488    18.244240\n",
+       "19   9723.514589  12650.0  2926.485411    23.134272"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, it makes sense to see how far off we are from the result percentage-wise\n",
+    "# Here, we take the absolute difference in %, so we can easily order the data frame\n",
+    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
+    "df_pf.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>8.050000e+02</td>\n",
+       "      <td>805.000000</td>\n",
+       "      <td>8.050000e+02</td>\n",
+       "      <td>805.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>18297.659888</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20081.200958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1150.000000</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>0.072466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>6.702651e+03</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>-1.574397e+03</td>\n",
+       "      <td>5.661504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.194286e+04</td>\n",
+       "      <td>11450.000000</td>\n",
+       "      <td>-1.598741e+01</td>\n",
+       "      <td>13.313279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.270802e+04</td>\n",
+       "      <td>22500.000000</td>\n",
+       "      <td>1.351802e+03</td>\n",
+       "      <td>24.983670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>199999.000000</td>\n",
+       "      <td>7.291851e+04</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Prediction         Target      Residual  Difference%\n",
+       "count  8.050000e+02     805.000000  8.050000e+02   805.000000\n",
+       "mean            inf   18297.659888          -inf          inf\n",
+       "std             NaN   20081.200958           NaN          NaN\n",
+       "min    0.000000e+00    1150.000000          -inf     0.072466\n",
+       "25%    6.702651e+03    6999.000000 -1.574397e+03     5.661504\n",
+       "50%    1.194286e+04   11450.000000 -1.598741e+01    13.313279\n",
+       "75%    2.270802e+04   22500.000000  1.351802e+03    24.983670\n",
+       "max             inf  199999.000000  7.291851e+04          inf"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>15211.01</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>-11.01</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>6305.83</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>-5.83</td>\n",
+       "      <td>0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>6706.65</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-6.65</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>12486.63</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>13.37</td>\n",
+       "      <td>0.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>8715.33</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-15.33</td>\n",
+       "      <td>0.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>3206.26</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-6.26</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>6513.15</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-13.15</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>17863.45</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>36.55</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>6715.99</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-15.99</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>8826.34</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-26.34</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>51340.32</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>159.68</td>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>14051.69</td>\n",
+       "      <td>14100.00</td>\n",
+       "      <td>48.31</td>\n",
+       "      <td>0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>2107.62</td>\n",
+       "      <td>2100.00</td>\n",
+       "      <td>-7.62</td>\n",
+       "      <td>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>5828.76</td>\n",
+       "      <td>5850.00</td>\n",
+       "      <td>21.24</td>\n",
+       "      <td>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>12653.40</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>46.60</td>\n",
+       "      <td>0.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>12447.09</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>52.91</td>\n",
+       "      <td>0.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>2837.86</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>12.14</td>\n",
+       "      <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>13541.54</td>\n",
+       "      <td>13600.00</td>\n",
+       "      <td>58.46</td>\n",
+       "      <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>7734.58</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-34.58</td>\n",
+       "      <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>12836.82</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>63.18</td>\n",
+       "      <td>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>30492.98</td>\n",
+       "      <td>30650.00</td>\n",
+       "      <td>157.02</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>9551.54</td>\n",
+       "      <td>9499.00</td>\n",
+       "      <td>-52.54</td>\n",
+       "      <td>0.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>14318.96</td>\n",
+       "      <td>14400.00</td>\n",
+       "      <td>81.04</td>\n",
+       "      <td>0.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>781</th>\n",
+       "      <td>7543.03</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-43.03</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25045.23</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>-145.23</td>\n",
+       "      <td>0.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>6538.30</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-38.30</td>\n",
+       "      <td>0.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>109346.97</td>\n",
+       "      <td>110000.00</td>\n",
+       "      <td>653.03</td>\n",
+       "      <td>0.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>5633.89</td>\n",
+       "      <td>5600.00</td>\n",
+       "      <td>-33.89</td>\n",
+       "      <td>0.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>7352.50</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>47.50</td>\n",
+       "      <td>0.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>15607.46</td>\n",
+       "      <td>15499.00</td>\n",
+       "      <td>-108.46</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.35</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-80.35</td>\n",
+       "      <td>0.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>11077.66</td>\n",
+       "      <td>10990.00</td>\n",
+       "      <td>-87.66</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>801</th>\n",
+       "      <td>39657.24</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>341.76</td>\n",
+       "      <td>0.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>23106.56</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-206.56</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>2219.87</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-19.87</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>25027.42</td>\n",
+       "      <td>24800.00</td>\n",
+       "      <td>-227.42</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>27755.46</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>-255.46</td>\n",
+       "      <td>0.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>20195.56</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-195.56</td>\n",
+       "      <td>0.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>7978.67</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-78.67</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>9897.28</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>101.72</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>23557.83</td>\n",
+       "      <td>23800.00</td>\n",
+       "      <td>242.17</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>8182.71</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-82.71</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>10094.92</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>105.08</td>\n",
+       "      <td>1.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.47</td>\n",
+       "      <td>7990.00</td>\n",
+       "      <td>-84.47</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>16675.21</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>-175.21</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>7883.21</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-83.21</td>\n",
+       "      <td>1.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>9394.45</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>105.55</td>\n",
+       "      <td>1.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>17194.49</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-195.49</td>\n",
+       "      <td>1.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>7407.42</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>92.58</td>\n",
+       "      <td>1.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>10863.14</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>135.86</td>\n",
+       "      <td>1.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>2227.46</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-27.46</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>109352.13</td>\n",
+       "      <td>107999.00</td>\n",
+       "      <td>-1353.13</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>9014.15</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-114.15</td>\n",
+       "      <td>1.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>65500.00</td>\n",
+       "      <td>850.28</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>6807.32</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>92.68</td>\n",
+       "      <td>1.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11436.89</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>163.11</td>\n",
+       "      <td>1.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>688</th>\n",
+       "      <td>2464.71</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>35.29</td>\n",
+       "      <td>1.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>9947.38</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-147.38</td>\n",
+       "      <td>1.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>33474.09</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>525.91</td>\n",
+       "      <td>1.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>21559.42</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>340.58</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9104.53</td>\n",
+       "      <td>9250.00</td>\n",
+       "      <td>145.47</td>\n",
+       "      <td>1.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>12087.91</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-187.91</td>\n",
+       "      <td>1.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>8939.11</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-139.11</td>\n",
+       "      <td>1.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>35453.02</td>\n",
+       "      <td>34899.14</td>\n",
+       "      <td>-553.88</td>\n",
+       "      <td>1.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>34436.11</td>\n",
+       "      <td>34999.00</td>\n",
+       "      <td>562.89</td>\n",
+       "      <td>1.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>2897.71</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>-47.71</td>\n",
+       "      <td>1.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>3440.49</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>59.51</td>\n",
+       "      <td>1.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>33320.06</td>\n",
+       "      <td>33900.00</td>\n",
+       "      <td>579.94</td>\n",
+       "      <td>1.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>784</th>\n",
+       "      <td>18817.25</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>-317.25</td>\n",
+       "      <td>1.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>13857.09</td>\n",
+       "      <td>14100.00</td>\n",
+       "      <td>242.91</td>\n",
+       "      <td>1.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>20040.20</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>-340.20</td>\n",
+       "      <td>1.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>35262.29</td>\n",
+       "      <td>35900.00</td>\n",
+       "      <td>637.71</td>\n",
+       "      <td>1.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>24059.43</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>440.57</td>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>63500.00</td>\n",
+       "      <td>-1149.72</td>\n",
+       "      <td>1.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>19627.25</td>\n",
+       "      <td>19990.00</td>\n",
+       "      <td>362.75</td>\n",
+       "      <td>1.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>26993.16</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>506.84</td>\n",
+       "      <td>1.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>32584.87</td>\n",
+       "      <td>31990.00</td>\n",
+       "      <td>-594.87</td>\n",
+       "      <td>1.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>3056.57</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-56.57</td>\n",
+       "      <td>1.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>98085.79</td>\n",
+       "      <td>99999.00</td>\n",
+       "      <td>1913.21</td>\n",
+       "      <td>1.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>19170.78</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>-370.78</td>\n",
+       "      <td>1.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>10782.03</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>216.97</td>\n",
+       "      <td>1.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>17047.74</td>\n",
+       "      <td>17394.09</td>\n",
+       "      <td>346.35</td>\n",
+       "      <td>1.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1958.31</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>40.69</td>\n",
+       "      <td>2.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>29500.27</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>-600.27</td>\n",
+       "      <td>2.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>23990.17</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>509.83</td>\n",
+       "      <td>2.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>30909.33</td>\n",
+       "      <td>31600.00</td>\n",
+       "      <td>690.67</td>\n",
+       "      <td>2.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>11959.28</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-259.28</td>\n",
+       "      <td>2.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>11042.29</td>\n",
+       "      <td>10799.00</td>\n",
+       "      <td>-243.29</td>\n",
+       "      <td>2.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>56673.17</td>\n",
+       "      <td>58000.00</td>\n",
+       "      <td>1326.83</td>\n",
+       "      <td>2.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>43942.54</td>\n",
+       "      <td>45000.00</td>\n",
+       "      <td>1057.46</td>\n",
+       "      <td>2.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>11224.40</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>275.60</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>12978.30</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>321.70</td>\n",
+       "      <td>2.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>9166.68</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>233.32</td>\n",
+       "      <td>2.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>8478.83</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>221.17</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>11184.55</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>-284.55</td>\n",
+       "      <td>2.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>8524.29</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-224.29</td>\n",
+       "      <td>2.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>14494.88</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>405.12</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>37938.31</td>\n",
+       "      <td>39000.00</td>\n",
+       "      <td>1061.69</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>18384.89</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>515.11</td>\n",
+       "      <td>2.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>23828.27</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>671.73</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>11570.15</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>329.85</td>\n",
+       "      <td>2.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>25592.54</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>-692.54</td>\n",
+       "      <td>2.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>2004.72</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>-54.72</td>\n",
+       "      <td>2.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6374.72</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>-174.72</td>\n",
+       "      <td>2.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>194242.37</td>\n",
+       "      <td>199999.00</td>\n",
+       "      <td>5756.63</td>\n",
+       "      <td>2.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>58649.23</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>-1649.23</td>\n",
+       "      <td>2.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>3398.26</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>101.74</td>\n",
+       "      <td>2.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>12864.69</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-364.69</td>\n",
+       "      <td>2.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>64695.71</td>\n",
+       "      <td>66666.00</td>\n",
+       "      <td>1970.29</td>\n",
+       "      <td>2.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>29563.28</td>\n",
+       "      <td>28700.00</td>\n",
+       "      <td>-863.28</td>\n",
+       "      <td>3.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>6787.74</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>211.26</td>\n",
+       "      <td>3.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>5769.23</td>\n",
+       "      <td>5600.00</td>\n",
+       "      <td>-169.23</td>\n",
+       "      <td>3.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>23184.61</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-684.61</td>\n",
+       "      <td>3.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>10824.58</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-324.58</td>\n",
+       "      <td>3.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>22372.19</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>-672.19</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>15019.12</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>480.88</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>10058.12</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>-308.12</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>8769.20</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-269.20</td>\n",
+       "      <td>3.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>10844.83</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>355.17</td>\n",
+       "      <td>3.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>20522.18</td>\n",
+       "      <td>21200.00</td>\n",
+       "      <td>677.82</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>23223.72</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-723.72</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>1934.64</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>64.36</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>11770.33</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-370.33</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>43881.06</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>-1381.06</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>17450.09</td>\n",
+       "      <td>16900.00</td>\n",
+       "      <td>-550.09</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>23341.27</td>\n",
+       "      <td>22600.00</td>\n",
+       "      <td>-741.27</td>\n",
+       "      <td>3.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>10919.19</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>380.81</td>\n",
+       "      <td>3.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>21192.26</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-692.26</td>\n",
+       "      <td>3.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>12749.66</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>450.34</td>\n",
+       "      <td>3.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>20283.08</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>716.92</td>\n",
+       "      <td>3.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>16201.55</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>598.45</td>\n",
+       "      <td>3.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>7974.91</td>\n",
+       "      <td>7699.00</td>\n",
+       "      <td>-275.91</td>\n",
+       "      <td>3.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13015.27</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>484.73</td>\n",
+       "      <td>3.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>6890.35</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>259.65</td>\n",
+       "      <td>3.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>5483.10</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>216.90</td>\n",
+       "      <td>3.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>20200.76</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>799.24</td>\n",
+       "      <td>3.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>13917.01</td>\n",
+       "      <td>14474.00</td>\n",
+       "      <td>556.99</td>\n",
+       "      <td>3.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>11016.60</td>\n",
+       "      <td>10600.00</td>\n",
+       "      <td>-416.60</td>\n",
+       "      <td>3.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>3840.88</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>158.12</td>\n",
+       "      <td>3.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>8524.71</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-324.71</td>\n",
+       "      <td>3.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>11551.56</td>\n",
+       "      <td>11111.00</td>\n",
+       "      <td>-440.56</td>\n",
+       "      <td>3.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>17687.48</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-688.48</td>\n",
+       "      <td>4.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>33095.36</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>1404.64</td>\n",
+       "      <td>4.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>26864.09</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>-1064.09</td>\n",
+       "      <td>4.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>27698.29</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>1201.71</td>\n",
+       "      <td>4.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>31149.15</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>-1249.15</td>\n",
+       "      <td>4.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>12454.14</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>545.86</td>\n",
+       "      <td>4.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>35396.30</td>\n",
+       "      <td>33950.00</td>\n",
+       "      <td>-1446.30</td>\n",
+       "      <td>4.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>20103.15</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>896.85</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>4170.89</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-170.89</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>62000.00</td>\n",
+       "      <td>-2649.72</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>11964.00</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>536.00</td>\n",
+       "      <td>4.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>25045.23</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-1046.23</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>3442.95</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>157.05</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>29647.28</td>\n",
+       "      <td>31000.00</td>\n",
+       "      <td>1352.72</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>15012.39</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>687.61</td>\n",
+       "      <td>4.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>8143.20</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-343.20</td>\n",
+       "      <td>4.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>25063.05</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-1064.05</td>\n",
+       "      <td>4.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>12010.10</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-510.10</td>\n",
+       "      <td>4.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>11176.69</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-476.69</td>\n",
+       "      <td>4.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>17286.75</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>813.25</td>\n",
+       "      <td>4.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>34276.07</td>\n",
+       "      <td>35900.00</td>\n",
+       "      <td>1623.93</td>\n",
+       "      <td>4.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>25088.99</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-1088.99</td>\n",
+       "      <td>4.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2718.02</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-118.02</td>\n",
+       "      <td>4.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>3770.21</td>\n",
+       "      <td>3950.00</td>\n",
+       "      <td>179.79</td>\n",
+       "      <td>4.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>53853.04</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>-2353.04</td>\n",
+       "      <td>4.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>10463.07</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-464.07</td>\n",
+       "      <td>4.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>8008.22</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>391.78</td>\n",
+       "      <td>4.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>13401.92</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>-601.92</td>\n",
+       "      <td>4.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>13616.65</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>-616.65</td>\n",
+       "      <td>4.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>19805.25</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>994.75</td>\n",
+       "      <td>4.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>11005.68</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-505.68</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>7517.14</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>382.86</td>\n",
+       "      <td>4.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>17220.82</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>879.18</td>\n",
+       "      <td>4.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>2307.20</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-107.20</td>\n",
+       "      <td>4.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>9130.01</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>469.99</td>\n",
+       "      <td>4.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>109346.97</td>\n",
+       "      <td>114999.00</td>\n",
+       "      <td>5652.03</td>\n",
+       "      <td>4.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>4748.92</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>250.08</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>2677.67</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>-127.67</td>\n",
+       "      <td>5.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>25642.46</td>\n",
+       "      <td>26999.00</td>\n",
+       "      <td>1356.54</td>\n",
+       "      <td>5.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>11203.43</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>596.57</td>\n",
+       "      <td>5.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>3987.44</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>212.56</td>\n",
+       "      <td>5.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>6834.18</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-334.18</td>\n",
+       "      <td>5.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>54169.36</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>-2669.36</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>6540.19</td>\n",
+       "      <td>6899.00</td>\n",
+       "      <td>358.81</td>\n",
+       "      <td>5.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>5996.99</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>-296.99</td>\n",
+       "      <td>5.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19982.41</td>\n",
+       "      <td>18990.00</td>\n",
+       "      <td>-992.41</td>\n",
+       "      <td>5.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3001.45</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>-151.45</td>\n",
+       "      <td>5.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>8216.39</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-416.39</td>\n",
+       "      <td>5.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>11638.91</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>661.09</td>\n",
+       "      <td>5.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>77117.34</td>\n",
+       "      <td>81500.00</td>\n",
+       "      <td>4382.66</td>\n",
+       "      <td>5.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.11</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-483.11</td>\n",
+       "      <td>5.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>6961.07</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-361.07</td>\n",
+       "      <td>5.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>27883.22</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>1616.78</td>\n",
+       "      <td>5.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>92087.91</td>\n",
+       "      <td>97500.00</td>\n",
+       "      <td>5412.09</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>14038.48</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-738.48</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>17416.25</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>-916.25</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>802</th>\n",
+       "      <td>12088.25</td>\n",
+       "      <td>11450.00</td>\n",
+       "      <td>-638.25</td>\n",
+       "      <td>5.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>40127.52</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>2372.48</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>16792.66</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-892.66</td>\n",
+       "      <td>5.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>18167.73</td>\n",
+       "      <td>17200.00</td>\n",
+       "      <td>-967.73</td>\n",
+       "      <td>5.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>27263.83</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>1636.17</td>\n",
+       "      <td>5.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>6555.59</td>\n",
+       "      <td>6950.00</td>\n",
+       "      <td>394.41</td>\n",
+       "      <td>5.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>11415.57</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-615.57</td>\n",
+       "      <td>5.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4899.20</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>300.80</td>\n",
+       "      <td>5.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>11642.12</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>-643.12</td>\n",
+       "      <td>5.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>59312.24</td>\n",
+       "      <td>63000.00</td>\n",
+       "      <td>3687.76</td>\n",
+       "      <td>5.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>5458.98</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>341.02</td>\n",
+       "      <td>5.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>10059.50</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-559.50</td>\n",
+       "      <td>5.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>15882.42</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-883.42</td>\n",
+       "      <td>5.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>6869.42</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>430.58</td>\n",
+       "      <td>5.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>9004.49</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-504.49</td>\n",
+       "      <td>5.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>776</th>\n",
+       "      <td>6112.31</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>387.69</td>\n",
+       "      <td>5.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>10057.03</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>642.97</td>\n",
+       "      <td>6.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>5450.83</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>349.17</td>\n",
+       "      <td>6.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.86</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>-1728.86</td>\n",
+       "      <td>6.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>30536.34</td>\n",
+       "      <td>28787.85</td>\n",
+       "      <td>-1748.49</td>\n",
+       "      <td>6.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>10987.90</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>712.10</td>\n",
+       "      <td>6.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>8262.86</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>537.14</td>\n",
+       "      <td>6.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>28542.74</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>-1642.74</td>\n",
+       "      <td>6.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>4886.77</td>\n",
+       "      <td>4600.00</td>\n",
+       "      <td>-286.77</td>\n",
+       "      <td>6.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>10622.75</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-623.75</td>\n",
+       "      <td>6.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>1967.13</td>\n",
+       "      <td>2099.00</td>\n",
+       "      <td>131.87</td>\n",
+       "      <td>6.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>672</th>\n",
+       "      <td>17804.56</td>\n",
+       "      <td>19000.00</td>\n",
+       "      <td>1195.44</td>\n",
+       "      <td>6.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>5902.43</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>397.57</td>\n",
+       "      <td>6.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>58999.17</td>\n",
+       "      <td>62999.00</td>\n",
+       "      <td>3999.83</td>\n",
+       "      <td>6.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>46824.03</td>\n",
+       "      <td>43999.00</td>\n",
+       "      <td>-2825.03</td>\n",
+       "      <td>6.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>2432.69</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>167.31</td>\n",
+       "      <td>6.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>9305.11</td>\n",
+       "      <td>9950.00</td>\n",
+       "      <td>644.89</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>4067.90</td>\n",
+       "      <td>4350.00</td>\n",
+       "      <td>282.10</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>37165.88</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>-2265.88</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>12248.10</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-748.10</td>\n",
+       "      <td>6.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7572.68</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>527.32</td>\n",
+       "      <td>6.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>21837.03</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-1337.03</td>\n",
+       "      <td>6.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>9919.12</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>-619.12</td>\n",
+       "      <td>6.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>96042.20</td>\n",
+       "      <td>89999.00</td>\n",
+       "      <td>-6043.20</td>\n",
+       "      <td>6.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>7739.78</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>560.22</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10247.89</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>751.11</td>\n",
+       "      <td>6.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>13573.72</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>-873.72</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>8274.39</td>\n",
+       "      <td>8888.00</td>\n",
+       "      <td>613.61</td>\n",
+       "      <td>6.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>2887.13</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-187.13</td>\n",
+       "      <td>6.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4280.49</td>\n",
+       "      <td>4600.00</td>\n",
+       "      <td>319.51</td>\n",
+       "      <td>6.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>16367.58</td>\n",
+       "      <td>15300.00</td>\n",
+       "      <td>-1067.58</td>\n",
+       "      <td>6.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6741.83</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>-441.83</td>\n",
+       "      <td>7.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>10274.74</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-674.74</td>\n",
+       "      <td>7.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>46581.86</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>-3081.86</td>\n",
+       "      <td>7.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>22293.90</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>1706.10</td>\n",
+       "      <td>7.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8035.09</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-535.09</td>\n",
+       "      <td>7.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>3712.23</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>287.77</td>\n",
+       "      <td>7.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>13456.31</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>1043.69</td>\n",
+       "      <td>7.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>6673.96</td>\n",
+       "      <td>7199.00</td>\n",
+       "      <td>525.04</td>\n",
+       "      <td>7.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>17166.98</td>\n",
+       "      <td>15999.00</td>\n",
+       "      <td>-1167.98</td>\n",
+       "      <td>7.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>20763.53</td>\n",
+       "      <td>22400.00</td>\n",
+       "      <td>1636.47</td>\n",
+       "      <td>7.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>4634.43</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>365.57</td>\n",
+       "      <td>7.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>22540.34</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>-1540.34</td>\n",
+       "      <td>7.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>33345.57</td>\n",
+       "      <td>36000.00</td>\n",
+       "      <td>2654.43</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>6205.20</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>494.80</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>13749.47</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>-949.47</td>\n",
+       "      <td>7.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>4906.11</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>393.89</td>\n",
+       "      <td>7.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>21489.76</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-1490.76</td>\n",
+       "      <td>7.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>64619.43</td>\n",
+       "      <td>69999.00</td>\n",
+       "      <td>5379.57</td>\n",
+       "      <td>7.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>20892.73</td>\n",
+       "      <td>19400.00</td>\n",
+       "      <td>-1492.73</td>\n",
+       "      <td>7.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>8491.05</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>708.95</td>\n",
+       "      <td>7.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>3137.24</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>262.76</td>\n",
+       "      <td>7.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>61406.17</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>-4406.17</td>\n",
+       "      <td>7.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>7008.81</td>\n",
+       "      <td>7600.00</td>\n",
+       "      <td>591.19</td>\n",
+       "      <td>7.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>17794.23</td>\n",
+       "      <td>19300.00</td>\n",
+       "      <td>1505.77</td>\n",
+       "      <td>7.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>778</th>\n",
+       "      <td>2913.09</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-213.09</td>\n",
+       "      <td>7.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>13922.03</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-1022.03</td>\n",
+       "      <td>7.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>25294.20</td>\n",
+       "      <td>23400.00</td>\n",
+       "      <td>-1894.20</td>\n",
+       "      <td>8.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>59458.46</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-4458.46</td>\n",
+       "      <td>8.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>20486.18</td>\n",
+       "      <td>18950.00</td>\n",
+       "      <td>-1536.18</td>\n",
+       "      <td>8.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>13047.55</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>1152.45</td>\n",
+       "      <td>8.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>9352.13</td>\n",
+       "      <td>8650.00</td>\n",
+       "      <td>-702.13</td>\n",
+       "      <td>8.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>792</th>\n",
+       "      <td>32994.80</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>-2494.80</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>793</th>\n",
+       "      <td>6702.65</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>597.35</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>23852.98</td>\n",
+       "      <td>26000.00</td>\n",
+       "      <td>2147.02</td>\n",
+       "      <td>8.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>5963.16</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>536.84</td>\n",
+       "      <td>8.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>38422.39</td>\n",
+       "      <td>41900.00</td>\n",
+       "      <td>3477.61</td>\n",
+       "      <td>8.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.07</td>\n",
+       "      <td>6550.00</td>\n",
+       "      <td>546.93</td>\n",
+       "      <td>8.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>665</th>\n",
+       "      <td>6688.38</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>611.62</td>\n",
+       "      <td>8.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>779</th>\n",
+       "      <td>19054.71</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>1745.29</td>\n",
+       "      <td>8.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>3360.19</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>-260.19</td>\n",
+       "      <td>8.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>14929.05</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>1370.95</td>\n",
+       "      <td>8.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>22027.55</td>\n",
+       "      <td>20300.00</td>\n",
+       "      <td>-1727.55</td>\n",
+       "      <td>8.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>22027.55</td>\n",
+       "      <td>20300.00</td>\n",
+       "      <td>-1727.55</td>\n",
+       "      <td>8.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>8357.21</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-657.21</td>\n",
+       "      <td>8.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>22153.23</td>\n",
+       "      <td>20400.00</td>\n",
+       "      <td>-1753.23</td>\n",
+       "      <td>8.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>17054.19</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-1354.19</td>\n",
+       "      <td>8.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>10417.13</td>\n",
+       "      <td>11403.99</td>\n",
+       "      <td>986.86</td>\n",
+       "      <td>8.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20551.65</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>1948.35</td>\n",
+       "      <td>8.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>26009.78</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>2490.22</td>\n",
+       "      <td>8.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>12731.48</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-1031.48</td>\n",
+       "      <td>8.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>18506.54</td>\n",
+       "      <td>17000.00</td>\n",
+       "      <td>-1506.54</td>\n",
+       "      <td>8.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>3592.48</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-292.48</td>\n",
+       "      <td>8.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>23222.71</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>2277.29</td>\n",
+       "      <td>8.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>6826.47</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>673.53</td>\n",
+       "      <td>8.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>9482.59</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-782.59</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>6975.75</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-575.75</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>18326.60</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-1526.60</td>\n",
+       "      <td>9.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>27001.23</td>\n",
+       "      <td>24750.00</td>\n",
+       "      <td>-2251.23</td>\n",
+       "      <td>9.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>24552.29</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-2052.29</td>\n",
+       "      <td>9.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>25017.11</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-2117.11</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>14066.37</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>1433.63</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>25215.17</td>\n",
+       "      <td>27800.00</td>\n",
+       "      <td>2584.83</td>\n",
+       "      <td>9.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>52390.49</td>\n",
+       "      <td>57777.00</td>\n",
+       "      <td>5386.51</td>\n",
+       "      <td>9.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>24614.93</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-2114.93</td>\n",
+       "      <td>9.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>31509.05</td>\n",
+       "      <td>28800.00</td>\n",
+       "      <td>-2709.05</td>\n",
+       "      <td>9.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10857.59</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-957.59</td>\n",
+       "      <td>9.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>4605.75</td>\n",
+       "      <td>5100.00</td>\n",
+       "      <td>494.25</td>\n",
+       "      <td>9.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>7679.11</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-680.11</td>\n",
+       "      <td>9.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>13449.96</td>\n",
+       "      <td>14899.00</td>\n",
+       "      <td>1449.04</td>\n",
+       "      <td>9.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>19098.09</td>\n",
+       "      <td>17400.00</td>\n",
+       "      <td>-1698.09</td>\n",
+       "      <td>9.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>4330.57</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>469.43</td>\n",
+       "      <td>9.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>39228.72</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>4271.28</td>\n",
+       "      <td>9.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>782</th>\n",
+       "      <td>2911.16</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>-261.16</td>\n",
+       "      <td>9.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>13959.83</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>1540.17</td>\n",
+       "      <td>9.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>4184.64</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-384.64</td>\n",
+       "      <td>10.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>12670.45</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-1170.45</td>\n",
+       "      <td>10.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5386.74</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>613.26</td>\n",
+       "      <td>10.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>39141.03</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>-3641.03</td>\n",
+       "      <td>10.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>10476.67</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-976.67</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>4483.97</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>516.03</td>\n",
+       "      <td>10.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>8696.33</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>1003.67</td>\n",
+       "      <td>10.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>8964.70</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1035.30</td>\n",
+       "      <td>10.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>5739.40</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>-539.40</td>\n",
+       "      <td>10.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>8512.76</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>987.24</td>\n",
+       "      <td>10.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4305.44</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>-405.44</td>\n",
+       "      <td>10.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>33929.47</td>\n",
+       "      <td>37900.00</td>\n",
+       "      <td>3970.53</td>\n",
+       "      <td>10.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>5997.22</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>702.78</td>\n",
+       "      <td>10.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>27739.55</td>\n",
+       "      <td>31000.00</td>\n",
+       "      <td>3260.45</td>\n",
+       "      <td>10.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>9395.10</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-895.10</td>\n",
+       "      <td>10.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>23054.57</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>2745.43</td>\n",
+       "      <td>10.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>25323.00</td>\n",
+       "      <td>28350.00</td>\n",
+       "      <td>3027.00</td>\n",
+       "      <td>10.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>12411.94</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>1488.06</td>\n",
+       "      <td>10.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>30347.45</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>3652.55</td>\n",
+       "      <td>10.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>17165.39</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-1665.39</td>\n",
+       "      <td>10.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>3927.19</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>472.81</td>\n",
+       "      <td>10.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>2228.68</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>270.32</td>\n",
+       "      <td>10.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>2584.33</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>315.67</td>\n",
+       "      <td>10.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>9974.83</td>\n",
+       "      <td>8990.00</td>\n",
+       "      <td>-984.83</td>\n",
+       "      <td>10.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>50737.64</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>6262.36</td>\n",
+       "      <td>10.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>4181.54</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>518.46</td>\n",
+       "      <td>11.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>11824.42</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>1475.58</td>\n",
+       "      <td>11.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>34352.54</td>\n",
+       "      <td>30900.00</td>\n",
+       "      <td>-3452.54</td>\n",
+       "      <td>11.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>31072.01</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>3927.99</td>\n",
+       "      <td>11.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>9901.71</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1001.71</td>\n",
+       "      <td>11.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>8123.03</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-823.03</td>\n",
+       "      <td>11.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>13300.81</td>\n",
+       "      <td>11950.00</td>\n",
+       "      <td>-1350.81</td>\n",
+       "      <td>11.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>8867.20</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>1131.80</td>\n",
+       "      <td>11.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>13213.07</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>1686.93</td>\n",
+       "      <td>11.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>44560.45</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>-4561.45</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>44560.45</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>-4561.45</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>11484.51</td>\n",
+       "      <td>10300.00</td>\n",
+       "      <td>-1184.51</td>\n",
+       "      <td>11.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>7804.78</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-805.78</td>\n",
+       "      <td>11.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>22287.84</td>\n",
+       "      <td>25200.00</td>\n",
+       "      <td>2912.16</td>\n",
+       "      <td>11.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>24321.68</td>\n",
+       "      <td>21800.00</td>\n",
+       "      <td>-2521.68</td>\n",
+       "      <td>11.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>2343.59</td>\n",
+       "      <td>2100.00</td>\n",
+       "      <td>-243.59</td>\n",
+       "      <td>11.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>11932.57</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>1567.43</td>\n",
+       "      <td>11.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>10054.06</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-1054.06</td>\n",
+       "      <td>11.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>40781.07</td>\n",
+       "      <td>36500.00</td>\n",
+       "      <td>-4281.07</td>\n",
+       "      <td>11.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>2779.76</td>\n",
+       "      <td>3149.25</td>\n",
+       "      <td>369.49</td>\n",
+       "      <td>11.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>7056.08</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>943.92</td>\n",
+       "      <td>11.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>13196.07</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-1396.07</td>\n",
+       "      <td>11.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>8365.18</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1134.82</td>\n",
+       "      <td>11.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>2729.63</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>370.37</td>\n",
+       "      <td>11.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>7279.35</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-779.35</td>\n",
+       "      <td>11.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>6600.31</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>899.69</td>\n",
+       "      <td>12.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>8577.61</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>1172.39</td>\n",
+       "      <td>12.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>13331.21</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1431.21</td>\n",
+       "      <td>12.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>3074.51</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>425.49</td>\n",
+       "      <td>12.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2020.09</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>279.91</td>\n",
+       "      <td>12.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2895.96</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>404.04</td>\n",
+       "      <td>12.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>7808.12</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>1091.88</td>\n",
+       "      <td>12.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>21139.89</td>\n",
+       "      <td>24100.00</td>\n",
+       "      <td>2960.11</td>\n",
+       "      <td>12.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>24999.38</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>3500.62</td>\n",
+       "      <td>12.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>2718.83</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>381.17</td>\n",
+       "      <td>12.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>42942.54</td>\n",
+       "      <td>38233.00</td>\n",
+       "      <td>-4709.54</td>\n",
+       "      <td>12.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11131.10</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>1568.90</td>\n",
+       "      <td>12.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>7447.84</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1052.16</td>\n",
+       "      <td>12.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>4248.90</td>\n",
+       "      <td>4850.00</td>\n",
+       "      <td>601.10</td>\n",
+       "      <td>12.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>2802.47</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>397.53</td>\n",
+       "      <td>12.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>7351.03</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1048.97</td>\n",
+       "      <td>12.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>14528.10</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-1628.10</td>\n",
+       "      <td>12.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>22532.79</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-2533.79</td>\n",
+       "      <td>12.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>3832.33</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>-432.33</td>\n",
+       "      <td>12.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>19197.99</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>2802.01</td>\n",
+       "      <td>12.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>18942.17</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-2142.17</td>\n",
+       "      <td>12.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>19190.84</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>2809.16</td>\n",
+       "      <td>12.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>73324.12</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>-8324.12</td>\n",
+       "      <td>12.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>12141.67</td>\n",
+       "      <td>10750.00</td>\n",
+       "      <td>-1391.67</td>\n",
+       "      <td>12.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>13444.25</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1544.25</td>\n",
+       "      <td>12.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>32212.96</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>-3712.96</td>\n",
+       "      <td>13.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>10173.73</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-1173.73</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22173.64</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>3326.36</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>3216.30</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>483.70</td>\n",
+       "      <td>13.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>13061.64</td>\n",
+       "      <td>11550.00</td>\n",
+       "      <td>-1511.64</td>\n",
+       "      <td>13.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>7640.17</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1159.83</td>\n",
+       "      <td>13.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>8941.66</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-1041.66</td>\n",
+       "      <td>13.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8848.20</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1351.80</td>\n",
+       "      <td>13.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>37294.36</td>\n",
+       "      <td>43000.00</td>\n",
+       "      <td>5705.64</td>\n",
+       "      <td>13.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>28900.44</td>\n",
+       "      <td>33333.00</td>\n",
+       "      <td>4432.56</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>10084.88</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1184.88</td>\n",
+       "      <td>13.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>10766.99</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-1266.99</td>\n",
+       "      <td>13.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>19048.58</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-2248.58</td>\n",
+       "      <td>13.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>23296.66</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>3603.34</td>\n",
+       "      <td>13.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>27706.06</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>4293.94</td>\n",
+       "      <td>13.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>2856.79</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>443.21</td>\n",
+       "      <td>13.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>8400.37</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>-1000.37</td>\n",
+       "      <td>13.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>14874.09</td>\n",
+       "      <td>17200.00</td>\n",
+       "      <td>2325.91</td>\n",
+       "      <td>13.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>2853.47</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>446.53</td>\n",
+       "      <td>13.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>1929.25</td>\n",
+       "      <td>1699.00</td>\n",
+       "      <td>-230.25</td>\n",
+       "      <td>13.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>6740.68</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>1059.32</td>\n",
+       "      <td>13.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>15113.32</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-1813.32</td>\n",
+       "      <td>13.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>8146.17</td>\n",
+       "      <td>9450.00</td>\n",
+       "      <td>1303.83</td>\n",
+       "      <td>13.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>16956.75</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>-2056.75</td>\n",
+       "      <td>13.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>8616.78</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1383.22</td>\n",
+       "      <td>13.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>27560.75</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>4439.25</td>\n",
+       "      <td>13.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>11782.11</td>\n",
+       "      <td>13700.00</td>\n",
+       "      <td>1917.89</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>6047.87</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-747.87</td>\n",
+       "      <td>14.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>3263.77</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>536.23</td>\n",
+       "      <td>14.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>25652.89</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>4247.11</td>\n",
+       "      <td>14.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13110.05</td>\n",
+       "      <td>15300.00</td>\n",
+       "      <td>2189.95</td>\n",
+       "      <td>14.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>6154.50</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1045.50</td>\n",
+       "      <td>14.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>85955.91</td>\n",
+       "      <td>74999.00</td>\n",
+       "      <td>-10956.91</td>\n",
+       "      <td>14.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>13772.47</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>-1773.47</td>\n",
+       "      <td>14.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>7923.76</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>1376.24</td>\n",
+       "      <td>14.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>32350.70</td>\n",
+       "      <td>37990.00</td>\n",
+       "      <td>5639.30</td>\n",
+       "      <td>14.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>3043.80</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>-393.80</td>\n",
+       "      <td>14.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>9993.19</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1293.19</td>\n",
+       "      <td>14.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>24702.61</td>\n",
+       "      <td>21500.00</td>\n",
+       "      <td>-3202.61</td>\n",
+       "      <td>14.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>63213.55</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-8213.55</td>\n",
+       "      <td>14.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>12647.28</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>-1648.28</td>\n",
+       "      <td>14.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>11812.55</td>\n",
+       "      <td>13899.00</td>\n",
+       "      <td>2086.45</td>\n",
+       "      <td>15.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>3398.91</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>601.09</td>\n",
+       "      <td>15.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>12482.76</td>\n",
+       "      <td>10850.00</td>\n",
+       "      <td>-1632.76</td>\n",
+       "      <td>15.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>6099.48</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-799.48</td>\n",
+       "      <td>15.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>5248.40</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>951.60</td>\n",
+       "      <td>15.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>6052.23</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>1097.77</td>\n",
+       "      <td>15.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>9809.02</td>\n",
+       "      <td>8499.00</td>\n",
+       "      <td>-1310.02</td>\n",
+       "      <td>15.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>6174.68</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>1125.32</td>\n",
+       "      <td>15.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>3635.06</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>664.94</td>\n",
+       "      <td>15.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>3347.79</td>\n",
+       "      <td>2899.00</td>\n",
+       "      <td>-448.79</td>\n",
+       "      <td>15.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>20557.05</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>-2757.05</td>\n",
+       "      <td>15.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>28877.80</td>\n",
+       "      <td>25000.00</td>\n",
+       "      <td>-3877.80</td>\n",
+       "      <td>15.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>5082.76</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-682.76</td>\n",
+       "      <td>15.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>17104.46</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-2304.46</td>\n",
+       "      <td>15.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>14842.29</td>\n",
+       "      <td>17595.00</td>\n",
+       "      <td>2752.71</td>\n",
+       "      <td>15.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>786</th>\n",
+       "      <td>3817.05</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-517.05</td>\n",
+       "      <td>15.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>9373.98</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-1273.98</td>\n",
+       "      <td>15.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>6888.50</td>\n",
+       "      <td>5950.00</td>\n",
+       "      <td>-938.50</td>\n",
+       "      <td>15.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>6903.35</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>1296.65</td>\n",
+       "      <td>15.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>34332.85</td>\n",
+       "      <td>40800.00</td>\n",
+       "      <td>6467.15</td>\n",
+       "      <td>15.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>16799.16</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>-2299.16</td>\n",
+       "      <td>15.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>98480.67</td>\n",
+       "      <td>85000.00</td>\n",
+       "      <td>-13480.67</td>\n",
+       "      <td>15.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>6139.72</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>1160.28</td>\n",
+       "      <td>15.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>15437.68</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-2137.68</td>\n",
+       "      <td>16.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>4064.76</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-564.76</td>\n",
+       "      <td>16.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>14087.55</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>2712.45</td>\n",
+       "      <td>16.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>26137.11</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-3637.11</td>\n",
+       "      <td>16.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>27304.45</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-3804.45</td>\n",
+       "      <td>16.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>9637.17</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>1862.83</td>\n",
+       "      <td>16.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>33099.94</td>\n",
+       "      <td>39500.00</td>\n",
+       "      <td>6400.06</td>\n",
+       "      <td>16.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>36023.63</td>\n",
+       "      <td>30990.00</td>\n",
+       "      <td>-5033.63</td>\n",
+       "      <td>16.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>37352.82</td>\n",
+       "      <td>44600.00</td>\n",
+       "      <td>7247.18</td>\n",
+       "      <td>16.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>10462.54</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-1463.54</td>\n",
+       "      <td>16.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>31976.86</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>-4476.86</td>\n",
+       "      <td>16.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>20915.39</td>\n",
+       "      <td>24996.00</td>\n",
+       "      <td>4080.61</td>\n",
+       "      <td>16.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>1673.12</td>\n",
+       "      <td>2000.00</td>\n",
+       "      <td>326.88</td>\n",
+       "      <td>16.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>14221.55</td>\n",
+       "      <td>17000.00</td>\n",
+       "      <td>2778.45</td>\n",
+       "      <td>16.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>34297.36</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>6702.64</td>\n",
+       "      <td>16.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>19907.26</td>\n",
+       "      <td>17100.00</td>\n",
+       "      <td>-2807.26</td>\n",
+       "      <td>16.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>10362.29</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1462.29</td>\n",
+       "      <td>16.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>23291.25</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-3291.25</td>\n",
+       "      <td>16.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>2213.58</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-313.58</td>\n",
+       "      <td>16.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>8738.60</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-1238.60</td>\n",
+       "      <td>16.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>6877.84</td>\n",
+       "      <td>5900.00</td>\n",
+       "      <td>-977.84</td>\n",
+       "      <td>16.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>10377.09</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1477.09</td>\n",
+       "      <td>16.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>21349.94</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>-3049.94</td>\n",
+       "      <td>16.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>2858.60</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-408.60</td>\n",
+       "      <td>16.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>11676.82</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-1677.82</td>\n",
+       "      <td>16.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>7892.51</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1607.49</td>\n",
+       "      <td>16.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>5399.74</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1100.26</td>\n",
+       "      <td>16.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>25408.55</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>-3708.55</td>\n",
+       "      <td>17.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>10609.93</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>2190.07</td>\n",
+       "      <td>17.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>28114.25</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-4114.25</td>\n",
+       "      <td>17.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>39356.61</td>\n",
+       "      <td>47500.00</td>\n",
+       "      <td>8143.39</td>\n",
+       "      <td>17.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>50516.19</td>\n",
+       "      <td>61000.00</td>\n",
+       "      <td>10483.81</td>\n",
+       "      <td>17.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>9481.20</td>\n",
+       "      <td>11450.00</td>\n",
+       "      <td>1968.80</td>\n",
+       "      <td>17.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>13594.21</td>\n",
+       "      <td>16450.00</td>\n",
+       "      <td>2855.79</td>\n",
+       "      <td>17.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>12675.07</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1875.07</td>\n",
+       "      <td>17.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>11505.09</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-1705.09</td>\n",
+       "      <td>17.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>9082.32</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>1916.68</td>\n",
+       "      <td>17.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>31495.17</td>\n",
+       "      <td>26800.00</td>\n",
+       "      <td>-4695.17</td>\n",
+       "      <td>17.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>11282.67</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1682.67</td>\n",
+       "      <td>17.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>19281.74</td>\n",
+       "      <td>16400.00</td>\n",
+       "      <td>-2881.74</td>\n",
+       "      <td>17.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>7992.08</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>1707.92</td>\n",
+       "      <td>17.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>5881.36</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-881.36</td>\n",
+       "      <td>17.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>18117.61</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>3882.39</td>\n",
+       "      <td>17.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>11412.71</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-1712.71</td>\n",
+       "      <td>17.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>15774.43</td>\n",
+       "      <td>13400.00</td>\n",
+       "      <td>-2374.43</td>\n",
+       "      <td>17.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>4357.87</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>942.13</td>\n",
+       "      <td>17.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8126.96</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1226.96</td>\n",
+       "      <td>17.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>11434.04</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-1734.04</td>\n",
+       "      <td>17.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>74745.66</td>\n",
+       "      <td>63333.00</td>\n",
+       "      <td>-11412.66</td>\n",
+       "      <td>18.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>18302.49</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-2802.49</td>\n",
+       "      <td>18.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>3679.35</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>820.65</td>\n",
+       "      <td>18.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10710.00</td>\n",
+       "      <td>13100.00</td>\n",
+       "      <td>2390.00</td>\n",
+       "      <td>18.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>6293.06</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>1406.94</td>\n",
+       "      <td>18.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>13370.87</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>-2070.87</td>\n",
+       "      <td>18.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>9355.15</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-1455.15</td>\n",
+       "      <td>18.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>26420.53</td>\n",
+       "      <td>32400.00</td>\n",
+       "      <td>5979.47</td>\n",
+       "      <td>18.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>3790.91</td>\n",
+       "      <td>4650.00</td>\n",
+       "      <td>859.09</td>\n",
+       "      <td>18.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>5459.66</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1240.34</td>\n",
+       "      <td>18.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>4480.19</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1019.81</td>\n",
+       "      <td>18.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>8536.82</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1336.82</td>\n",
+       "      <td>18.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>40274.34</td>\n",
+       "      <td>49500.00</td>\n",
+       "      <td>9225.66</td>\n",
+       "      <td>18.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>89582.34</td>\n",
+       "      <td>75500.00</td>\n",
+       "      <td>-14082.34</td>\n",
+       "      <td>18.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>6884.55</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>-1084.55</td>\n",
+       "      <td>18.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>51073.39</td>\n",
+       "      <td>43000.00</td>\n",
+       "      <td>-8073.39</td>\n",
+       "      <td>18.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>6090.18</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>1409.82</td>\n",
+       "      <td>18.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>19364.44</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>-3064.44</td>\n",
+       "      <td>18.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>8280.51</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1919.49</td>\n",
+       "      <td>18.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>5598.65</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>1301.35</td>\n",
+       "      <td>18.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>85858.88</td>\n",
+       "      <td>105999.00</td>\n",
+       "      <td>20140.12</td>\n",
+       "      <td>19.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>5506.55</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>1293.45</td>\n",
+       "      <td>19.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>8817.95</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2082.05</td>\n",
+       "      <td>19.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>11319.17</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-1819.17</td>\n",
+       "      <td>19.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>11445.18</td>\n",
+       "      <td>9599.00</td>\n",
+       "      <td>-1846.18</td>\n",
+       "      <td>19.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>5650.29</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1349.71</td>\n",
+       "      <td>19.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>4433.53</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1066.47</td>\n",
+       "      <td>19.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>12724.62</td>\n",
+       "      <td>15800.00</td>\n",
+       "      <td>3075.38</td>\n",
+       "      <td>19.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>2092.87</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>507.13</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36593.81</td>\n",
+       "      <td>45500.00</td>\n",
+       "      <td>8906.19</td>\n",
+       "      <td>19.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>60274.39</td>\n",
+       "      <td>75000.00</td>\n",
+       "      <td>14725.61</td>\n",
+       "      <td>19.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>46983.85</td>\n",
+       "      <td>58500.00</td>\n",
+       "      <td>11516.15</td>\n",
+       "      <td>19.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>8747.37</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2152.63</td>\n",
+       "      <td>19.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>22708.02</td>\n",
+       "      <td>18950.00</td>\n",
+       "      <td>-3758.02</td>\n",
+       "      <td>19.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13185.60</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2185.60</td>\n",
+       "      <td>19.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>9594.67</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1594.67</td>\n",
+       "      <td>19.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>16676.56</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-2776.56</td>\n",
+       "      <td>19.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>5399.84</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-899.84</td>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>6639.69</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>1660.31</td>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>5676.99</td>\n",
+       "      <td>7100.00</td>\n",
+       "      <td>1423.01</td>\n",
+       "      <td>20.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>34847.46</td>\n",
+       "      <td>28950.00</td>\n",
+       "      <td>-5897.46</td>\n",
+       "      <td>20.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>3010.71</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-511.71</td>\n",
+       "      <td>20.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>5315.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1384.73</td>\n",
+       "      <td>20.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>6940.02</td>\n",
+       "      <td>8750.00</td>\n",
+       "      <td>1809.98</td>\n",
+       "      <td>20.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>6056.80</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>1593.20</td>\n",
+       "      <td>20.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>8708.11</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1508.11</td>\n",
+       "      <td>20.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>16175.59</td>\n",
+       "      <td>20477.54</td>\n",
+       "      <td>4301.95</td>\n",
+       "      <td>21.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>5290.04</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1409.96</td>\n",
+       "      <td>21.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>1973.68</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>526.32</td>\n",
+       "      <td>21.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>4181.72</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>1118.28</td>\n",
+       "      <td>21.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>4732.08</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>1267.92</td>\n",
+       "      <td>21.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12222.81</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>3277.19</td>\n",
+       "      <td>21.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>32724.25</td>\n",
+       "      <td>41500.00</td>\n",
+       "      <td>8775.75</td>\n",
+       "      <td>21.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>13328.75</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2328.75</td>\n",
+       "      <td>21.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>36828.78</td>\n",
+       "      <td>46800.00</td>\n",
+       "      <td>9971.22</td>\n",
+       "      <td>21.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>6920.60</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1879.40</td>\n",
+       "      <td>21.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>19020.64</td>\n",
+       "      <td>24193.65</td>\n",
+       "      <td>5173.01</td>\n",
+       "      <td>21.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>24740.92</td>\n",
+       "      <td>31500.00</td>\n",
+       "      <td>6759.08</td>\n",
+       "      <td>21.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>18255.49</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-3255.49</td>\n",
+       "      <td>21.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>775</th>\n",
+       "      <td>11692.08</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-2092.08</td>\n",
+       "      <td>21.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>17173.49</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>4826.51</td>\n",
+       "      <td>21.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>11700.38</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>3299.62</td>\n",
+       "      <td>22.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>2623.69</td>\n",
+       "      <td>2150.00</td>\n",
+       "      <td>-473.69</td>\n",
+       "      <td>22.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>2026.96</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>573.04</td>\n",
+       "      <td>22.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>8434.23</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1534.23</td>\n",
+       "      <td>22.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>2717.11</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>782.89</td>\n",
+       "      <td>22.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>2713.87</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>786.13</td>\n",
+       "      <td>22.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>6200.75</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>1799.25</td>\n",
+       "      <td>22.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>10783.10</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-1983.10</td>\n",
+       "      <td>22.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>22566.89</td>\n",
+       "      <td>18400.00</td>\n",
+       "      <td>-4166.89</td>\n",
+       "      <td>22.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>18486.02</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>5413.98</td>\n",
+       "      <td>22.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>42831.62</td>\n",
+       "      <td>55555.00</td>\n",
+       "      <td>12723.38</td>\n",
+       "      <td>22.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>18116.63</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>5383.37</td>\n",
+       "      <td>22.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>13535.73</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2535.73</td>\n",
+       "      <td>23.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9723.51</td>\n",
+       "      <td>12650.00</td>\n",
+       "      <td>2926.49</td>\n",
+       "      <td>23.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>14919.17</td>\n",
+       "      <td>12114.35</td>\n",
+       "      <td>-2804.82</td>\n",
+       "      <td>23.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>17573.11</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>5326.89</td>\n",
+       "      <td>23.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>20558.01</td>\n",
+       "      <td>16672.50</td>\n",
+       "      <td>-3885.51</td>\n",
+       "      <td>23.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>16030.55</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-3031.55</td>\n",
+       "      <td>23.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>17535.66</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>5364.34</td>\n",
+       "      <td>23.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>2977.77</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>922.23</td>\n",
+       "      <td>23.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>22121.67</td>\n",
+       "      <td>29000.00</td>\n",
+       "      <td>6878.33</td>\n",
+       "      <td>23.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>29567.87</td>\n",
+       "      <td>38764.00</td>\n",
+       "      <td>9196.13</td>\n",
+       "      <td>23.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>22081.93</td>\n",
+       "      <td>28999.00</td>\n",
+       "      <td>6917.07</td>\n",
+       "      <td>23.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>8174.40</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-1574.40</td>\n",
+       "      <td>23.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>2813.36</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>886.64</td>\n",
+       "      <td>23.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>9728.36</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>3071.64</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>23236.27</td>\n",
+       "      <td>18700.00</td>\n",
+       "      <td>-4536.27</td>\n",
+       "      <td>24.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>14539.53</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-2839.53</td>\n",
+       "      <td>24.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>4915.54</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1584.46</td>\n",
+       "      <td>24.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>22276.96</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-4376.96</td>\n",
+       "      <td>24.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>10082.85</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-1982.85</td>\n",
+       "      <td>24.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>3113.25</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-613.25</td>\n",
+       "      <td>24.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>30902.33</td>\n",
+       "      <td>24800.00</td>\n",
+       "      <td>-6102.33</td>\n",
+       "      <td>24.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>12346.01</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-2446.01</td>\n",
+       "      <td>24.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>6161.68</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>2038.32</td>\n",
+       "      <td>24.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>21017.12</td>\n",
+       "      <td>28000.00</td>\n",
+       "      <td>6982.88</td>\n",
+       "      <td>24.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>3748.29</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-748.29</td>\n",
+       "      <td>24.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>3061.17</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-611.17</td>\n",
+       "      <td>24.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>9998.69</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1998.69</td>\n",
+       "      <td>24.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>3749.11</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>1250.89</td>\n",
+       "      <td>25.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>5390.58</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1809.42</td>\n",
+       "      <td>25.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>9709.07</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>3290.93</td>\n",
+       "      <td>25.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>803</th>\n",
+       "      <td>3760.79</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-760.79</td>\n",
+       "      <td>25.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>4400.37</td>\n",
+       "      <td>5900.00</td>\n",
+       "      <td>1499.63</td>\n",
+       "      <td>25.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>3203.55</td>\n",
+       "      <td>4299.00</td>\n",
+       "      <td>1095.45</td>\n",
+       "      <td>25.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>18827.10</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-3828.10</td>\n",
+       "      <td>25.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>9674.76</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>3325.24</td>\n",
+       "      <td>25.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>5205.98</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1794.02</td>\n",
+       "      <td>25.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>18944.86</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>6555.14</td>\n",
+       "      <td>25.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>31179.60</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>10820.40</td>\n",
+       "      <td>25.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>804</th>\n",
+       "      <td>26319.25</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-5419.25</td>\n",
+       "      <td>25.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>2959.68</td>\n",
+       "      <td>2350.00</td>\n",
+       "      <td>-609.68</td>\n",
+       "      <td>25.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>8863.85</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>3136.15</td>\n",
+       "      <td>26.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>38039.83</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>13460.17</td>\n",
+       "      <td>26.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>13761.17</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>-2861.17</td>\n",
+       "      <td>26.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>22356.85</td>\n",
+       "      <td>17700.00</td>\n",
+       "      <td>-4656.85</td>\n",
+       "      <td>26.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>10767.98</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-2267.98</td>\n",
+       "      <td>26.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>37385.04</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>-7885.04</td>\n",
+       "      <td>26.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>10013.18</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-2113.18</td>\n",
+       "      <td>26.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>6589.41</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>2409.59</td>\n",
+       "      <td>26.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>24995.51</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>-5295.51</td>\n",
+       "      <td>26.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>7821.99</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>2878.01</td>\n",
+       "      <td>26.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>6736.55</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-1436.55</td>\n",
+       "      <td>27.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>11445.85</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-2445.85</td>\n",
+       "      <td>27.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>2909.80</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>1090.20</td>\n",
+       "      <td>27.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>24627.39</td>\n",
+       "      <td>33900.00</td>\n",
+       "      <td>9272.61</td>\n",
+       "      <td>27.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>82760.24</td>\n",
+       "      <td>64900.00</td>\n",
+       "      <td>-17860.24</td>\n",
+       "      <td>27.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>5787.88</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>2212.12</td>\n",
+       "      <td>27.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>19048.95</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>-4148.95</td>\n",
+       "      <td>27.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>4668.85</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-1018.85</td>\n",
+       "      <td>27.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>7564.39</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>2935.61</td>\n",
+       "      <td>27.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>30477.38</td>\n",
+       "      <td>23800.00</td>\n",
+       "      <td>-6677.38</td>\n",
+       "      <td>28.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>11154.62</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-2454.62</td>\n",
+       "      <td>28.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>21802.49</td>\n",
+       "      <td>30385.00</td>\n",
+       "      <td>8582.51</td>\n",
+       "      <td>28.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>65745.19</td>\n",
+       "      <td>92222.00</td>\n",
+       "      <td>26476.81</td>\n",
+       "      <td>28.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>21739.14</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>8760.86</td>\n",
+       "      <td>28.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>25245.24</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>10254.76</td>\n",
+       "      <td>28.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>15218.48</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-3418.48</td>\n",
+       "      <td>28.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>55229.59</td>\n",
+       "      <td>77777.00</td>\n",
+       "      <td>22547.41</td>\n",
+       "      <td>28.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>55229.59</td>\n",
+       "      <td>77777.00</td>\n",
+       "      <td>22547.41</td>\n",
+       "      <td>28.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>8665.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-1965.27</td>\n",
+       "      <td>29.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>11514.96</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-2614.96</td>\n",
+       "      <td>29.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>2951.62</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>1248.38</td>\n",
+       "      <td>29.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>5449.56</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-1249.56</td>\n",
+       "      <td>29.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>39888.50</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>17111.50</td>\n",
+       "      <td>30.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>8386.38</td>\n",
+       "      <td>6450.00</td>\n",
+       "      <td>-1936.38</td>\n",
+       "      <td>30.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>5242.13</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>2257.87</td>\n",
+       "      <td>30.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>17055.42</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>7444.58</td>\n",
+       "      <td>30.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>783</th>\n",
+       "      <td>18140.29</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-4240.29</td>\n",
+       "      <td>30.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15633.25</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>6866.75</td>\n",
+       "      <td>30.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>10150.80</td>\n",
+       "      <td>7777.00</td>\n",
+       "      <td>-2373.80</td>\n",
+       "      <td>30.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774</th>\n",
+       "      <td>2485.85</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-585.85</td>\n",
+       "      <td>30.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>2073.96</td>\n",
+       "      <td>2999.00</td>\n",
+       "      <td>925.04</td>\n",
+       "      <td>30.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>19812.54</td>\n",
+       "      <td>28713.75</td>\n",
+       "      <td>8901.21</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>12983.41</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-3083.41</td>\n",
+       "      <td>31.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>7869.15</td>\n",
+       "      <td>5999.00</td>\n",
+       "      <td>-1870.15</td>\n",
+       "      <td>31.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>12071.60</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>-2871.60</td>\n",
+       "      <td>31.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>8941.06</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>4058.94</td>\n",
+       "      <td>31.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>13336.31</td>\n",
+       "      <td>10150.00</td>\n",
+       "      <td>-3186.31</td>\n",
+       "      <td>31.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>14200.19</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-3400.19</td>\n",
+       "      <td>31.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3105.70</td>\n",
+       "      <td>4550.00</td>\n",
+       "      <td>1444.30</td>\n",
+       "      <td>31.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>682</th>\n",
+       "      <td>4624.41</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>2175.59</td>\n",
+       "      <td>31.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>3511.87</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>1688.13</td>\n",
+       "      <td>32.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>4779.20</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-1179.20</td>\n",
+       "      <td>32.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>11942.86</td>\n",
+       "      <td>17777.00</td>\n",
+       "      <td>5834.14</td>\n",
+       "      <td>32.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>16542.07</td>\n",
+       "      <td>12450.00</td>\n",
+       "      <td>-4092.07</td>\n",
+       "      <td>32.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>15827.38</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-3927.38</td>\n",
+       "      <td>33.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>4216.67</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>2083.33</td>\n",
+       "      <td>33.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>9333.37</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-2333.37</td>\n",
+       "      <td>33.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>7732.20</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>3867.80</td>\n",
+       "      <td>33.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>12012.45</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-3012.45</td>\n",
+       "      <td>33.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>19812.54</td>\n",
+       "      <td>29800.00</td>\n",
+       "      <td>9987.46</td>\n",
+       "      <td>33.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>5317.17</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>2682.83</td>\n",
+       "      <td>33.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>46006.01</td>\n",
+       "      <td>34419.45</td>\n",
+       "      <td>-11586.56</td>\n",
+       "      <td>33.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>36381.57</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>18618.43</td>\n",
+       "      <td>33.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>23207.21</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>-5907.21</td>\n",
+       "      <td>34.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>12083.74</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-3083.74</td>\n",
+       "      <td>34.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>1642.87</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>857.13</td>\n",
+       "      <td>34.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3900.79</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>-1000.79</td>\n",
+       "      <td>34.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>67578.74</td>\n",
+       "      <td>49900.00</td>\n",
+       "      <td>-17678.74</td>\n",
+       "      <td>35.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>5030.79</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>2769.21</td>\n",
+       "      <td>35.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>12682.44</td>\n",
+       "      <td>9350.00</td>\n",
+       "      <td>-3332.44</td>\n",
+       "      <td>35.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>44889.92</td>\n",
+       "      <td>69900.00</td>\n",
+       "      <td>25010.08</td>\n",
+       "      <td>35.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>5702.84</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>3297.16</td>\n",
+       "      <td>36.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11427.09</td>\n",
+       "      <td>8350.00</td>\n",
+       "      <td>-3077.09</td>\n",
+       "      <td>36.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>13828.77</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>8071.23</td>\n",
+       "      <td>36.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>5591.66</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>3308.34</td>\n",
+       "      <td>37.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>16260.94</td>\n",
+       "      <td>11850.00</td>\n",
+       "      <td>-4410.94</td>\n",
+       "      <td>37.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>19627.05</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-5327.05</td>\n",
+       "      <td>37.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>2005.13</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>1194.87</td>\n",
+       "      <td>37.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>46345.58</td>\n",
+       "      <td>74000.00</td>\n",
+       "      <td>27654.42</td>\n",
+       "      <td>37.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>777</th>\n",
+       "      <td>51526.76</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-14026.76</td>\n",
+       "      <td>37.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>785</th>\n",
+       "      <td>29732.92</td>\n",
+       "      <td>47700.00</td>\n",
+       "      <td>17967.08</td>\n",
+       "      <td>37.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>46378.55</td>\n",
+       "      <td>74500.00</td>\n",
+       "      <td>28121.45</td>\n",
+       "      <td>37.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>16572.98</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>-4573.98</td>\n",
+       "      <td>38.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>6644.61</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1844.61</td>\n",
+       "      <td>38.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>12197.10</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-3397.10</td>\n",
+       "      <td>38.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>64451.49</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>40547.51</td>\n",
+       "      <td>38.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>39888.50</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>25111.50</td>\n",
+       "      <td>38.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>17656.32</td>\n",
+       "      <td>28793.04</td>\n",
+       "      <td>11136.72</td>\n",
+       "      <td>38.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>15936.61</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-4536.61</td>\n",
+       "      <td>39.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>71675.00</td>\n",
+       "      <td>121500.00</td>\n",
+       "      <td>49825.00</td>\n",
+       "      <td>41.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>16226.28</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-4726.28</td>\n",
+       "      <td>41.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>8889.98</td>\n",
+       "      <td>6299.00</td>\n",
+       "      <td>-2590.98</td>\n",
+       "      <td>41.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>9669.09</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>6830.91</td>\n",
+       "      <td>41.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>14822.55</td>\n",
+       "      <td>10450.00</td>\n",
+       "      <td>-4372.55</td>\n",
+       "      <td>41.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>16930.40</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>12569.60</td>\n",
+       "      <td>42.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>5700.21</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>4299.79</td>\n",
+       "      <td>43.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>32880.90</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-9980.90</td>\n",
+       "      <td>43.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>6477.68</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-1977.68</td>\n",
+       "      <td>43.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>10016.88</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>7983.12</td>\n",
+       "      <td>44.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>11415.61</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-3515.61</td>\n",
+       "      <td>44.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>2050.79</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>1649.21</td>\n",
+       "      <td>44.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>13804.82</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-4304.82</td>\n",
+       "      <td>45.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>24080.07</td>\n",
+       "      <td>16555.00</td>\n",
+       "      <td>-7525.07</td>\n",
+       "      <td>45.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>8443.28</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>7056.72</td>\n",
+       "      <td>45.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>17048.89</td>\n",
+       "      <td>11707.80</td>\n",
+       "      <td>-5341.09</td>\n",
+       "      <td>45.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>8419.88</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>7080.12</td>\n",
+       "      <td>45.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>31124.28</td>\n",
+       "      <td>21300.00</td>\n",
+       "      <td>-9824.28</td>\n",
+       "      <td>46.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>13157.10</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-4157.10</td>\n",
+       "      <td>46.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>1733.85</td>\n",
+       "      <td>1185.60</td>\n",
+       "      <td>-548.25</td>\n",
+       "      <td>46.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>38632.53</td>\n",
+       "      <td>73500.00</td>\n",
+       "      <td>34867.47</td>\n",
+       "      <td>47.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>62100.23</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>-20100.23</td>\n",
+       "      <td>47.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>1196.37</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>1103.63</td>\n",
+       "      <td>47.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>794</th>\n",
+       "      <td>65898.02</td>\n",
+       "      <td>129222.00</td>\n",
+       "      <td>63323.98</td>\n",
+       "      <td>49.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>2313.02</td>\n",
+       "      <td>1550.00</td>\n",
+       "      <td>-763.02</td>\n",
+       "      <td>49.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>2972.03</td>\n",
+       "      <td>1990.00</td>\n",
+       "      <td>-982.03</td>\n",
+       "      <td>49.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>1417.83</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>1382.17</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>22471.17</td>\n",
+       "      <td>44700.00</td>\n",
+       "      <td>22228.83</td>\n",
+       "      <td>49.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>71981.49</td>\n",
+       "      <td>144900.00</td>\n",
+       "      <td>72918.51</td>\n",
+       "      <td>50.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>50745.07</td>\n",
+       "      <td>33715.50</td>\n",
+       "      <td>-17029.57</td>\n",
+       "      <td>50.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>7343.52</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>7655.48</td>\n",
+       "      <td>51.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>26609.57</td>\n",
+       "      <td>17600.00</td>\n",
+       "      <td>-9009.57</td>\n",
+       "      <td>51.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>17888.88</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-6088.88</td>\n",
+       "      <td>51.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>20479.37</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-6979.37</td>\n",
+       "      <td>51.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>12461.52</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-4261.52</td>\n",
+       "      <td>51.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>20828.11</td>\n",
+       "      <td>13700.00</td>\n",
+       "      <td>-7128.11</td>\n",
+       "      <td>52.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>18980.92</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>20919.08</td>\n",
+       "      <td>52.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>780</th>\n",
+       "      <td>28362.55</td>\n",
+       "      <td>60500.00</td>\n",
+       "      <td>32137.45</td>\n",
+       "      <td>53.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>3521.95</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1221.95</td>\n",
+       "      <td>53.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>9427.42</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>11072.58</td>\n",
+       "      <td>54.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>5408.75</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-1908.75</td>\n",
+       "      <td>54.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>10854.85</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>13145.15</td>\n",
+       "      <td>54.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>65041.89</td>\n",
+       "      <td>41900.00</td>\n",
+       "      <td>-23141.89</td>\n",
+       "      <td>55.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>7715.56</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-2815.56</td>\n",
+       "      <td>57.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>30316.04</td>\n",
+       "      <td>19200.00</td>\n",
+       "      <td>-11116.04</td>\n",
+       "      <td>57.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>20712.46</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>-7712.46</td>\n",
+       "      <td>59.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>2719.53</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-1019.53</td>\n",
+       "      <td>59.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>3906.56</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-1506.56</td>\n",
+       "      <td>62.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>5055.93</td>\n",
+       "      <td>3099.00</td>\n",
+       "      <td>-1956.93</td>\n",
+       "      <td>63.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>4243.79</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-1643.79</td>\n",
+       "      <td>63.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>10676.78</td>\n",
+       "      <td>6499.00</td>\n",
+       "      <td>-4177.78</td>\n",
+       "      <td>64.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>16658.12</td>\n",
+       "      <td>10100.00</td>\n",
+       "      <td>-6558.12</td>\n",
+       "      <td>64.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>6656.03</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-2656.03</td>\n",
+       "      <td>66.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>2486.42</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>5513.58</td>\n",
+       "      <td>68.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>13706.76</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-5706.76</td>\n",
+       "      <td>71.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>6524.07</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-2725.07</td>\n",
+       "      <td>71.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>41367.65</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>-17467.65</td>\n",
+       "      <td>73.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>60487.88</td>\n",
+       "      <td>34600.00</td>\n",
+       "      <td>-25887.88</td>\n",
+       "      <td>74.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>9215.35</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-4215.35</td>\n",
+       "      <td>84.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>6870.33</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-3270.33</td>\n",
+       "      <td>90.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>9213.19</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-4413.19</td>\n",
+       "      <td>91.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>1444.13</td>\n",
+       "      <td>34999.00</td>\n",
+       "      <td>33554.87</td>\n",
+       "      <td>95.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>5130.28</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-2530.28</td>\n",
+       "      <td>97.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>44000.00</td>\n",
+       "      <td>44000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>29058.70</td>\n",
+       "      <td>14250.00</td>\n",
+       "      <td>-14808.70</td>\n",
+       "      <td>103.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>20157.28</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-11157.28</td>\n",
+       "      <td>123.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>9463.58</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-5813.58</td>\n",
+       "      <td>159.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>4534.02</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-2834.02</td>\n",
+       "      <td>166.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>3200.86</td>\n",
+       "      <td>1200.00</td>\n",
+       "      <td>-2000.86</td>\n",
+       "      <td>166.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>3875.31</td>\n",
+       "      <td>1450.00</td>\n",
+       "      <td>-2425.31</td>\n",
+       "      <td>167.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>35212.93</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-22312.93</td>\n",
+       "      <td>172.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>14347.91</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-10147.91</td>\n",
+       "      <td>241.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>8975.55</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-6575.55</td>\n",
+       "      <td>273.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>48602.40</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-37402.40</td>\n",
+       "      <td>333.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>28240.95</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-22740.95</td>\n",
+       "      <td>413.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>99000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>20996.24</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>1150.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>17639.47</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>683</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction    Target  Residual  Difference%\n",
+       "681    15211.01  15200.00    -11.01         0.07\n",
+       "700     6305.83   6300.00     -5.83         0.09\n",
+       "540     6706.65   6700.00     -6.65         0.10\n",
+       "399    12486.63  12500.00     13.37         0.11\n",
+       "374     8715.33   8700.00    -15.33         0.18\n",
+       "74      3206.26   3200.00     -6.26         0.20\n",
+       "49      6513.15   6500.00    -13.15         0.20\n",
+       "397    17863.45  17900.00     36.55         0.20\n",
+       "216     6715.99   6700.00    -15.99         0.24\n",
+       "725     8826.34   8800.00    -26.34         0.30\n",
+       "26     51340.32  51500.00    159.68         0.31\n",
+       "615    14051.69  14100.00     48.31         0.34\n",
+       "536     2107.62   2100.00     -7.62         0.36\n",
+       "413     5828.76   5850.00     21.24         0.36\n",
+       "27     12653.40  12700.00     46.60         0.37\n",
+       "714    12447.09  12500.00     52.91         0.42\n",
+       "193     2837.86   2850.00     12.14         0.43\n",
+       "53     13541.54  13600.00     58.46         0.43\n",
+       "757     7734.58   7700.00    -34.58         0.45\n",
+       "706    12836.82  12900.00     63.18         0.49\n",
+       "618    30492.98  30650.00    157.02         0.51\n",
+       "259     9551.54   9499.00    -52.54         0.55\n",
+       "417    14318.96  14400.00     81.04         0.56\n",
+       "781     7543.03   7500.00    -43.03         0.57\n",
+       "466    25045.23  24900.00   -145.23         0.58\n",
+       "250     6538.30   6500.00    -38.30         0.59\n",
+       "448   109346.97 110000.00    653.03         0.59\n",
+       "461     5633.89   5600.00    -33.89         0.61\n",
+       "525     7352.50   7400.00     47.50         0.64\n",
+       "96     15607.46  15499.00   -108.46         0.70\n",
+       "4      11280.35  11200.00    -80.35         0.72\n",
+       "463    11077.66  10990.00    -87.66         0.80\n",
+       "801    39657.24  39999.00    341.76         0.85\n",
+       "298    23106.56  22900.00   -206.56         0.90\n",
+       "256     2219.87   2200.00    -19.87         0.90\n",
+       "344    25027.42  24800.00   -227.42         0.92\n",
+       "79     27755.46  27500.00   -255.46         0.93\n",
+       "471    20195.56  20000.00   -195.56         0.98\n",
+       "292     7978.67   7900.00    -78.67         1.00\n",
+       "733     9897.28   9999.00    101.72         1.02\n",
+       "21     23557.83  23800.00    242.17         1.02\n",
+       "521     8182.71   8100.00    -82.71         1.02\n",
+       "98     10094.92  10200.00    105.08         1.03\n",
+       "3       8074.47   7990.00    -84.47         1.06\n",
+       "546    16675.21  16500.00   -175.21         1.06\n",
+       "224     7883.21   7800.00    -83.21         1.07\n",
+       "59      9394.45   9500.00    105.55         1.11\n",
+       "324    17194.49  16999.00   -195.49         1.15\n",
+       "416     7407.42   7500.00     92.58         1.23\n",
+       "768    10863.14  10999.00    135.86         1.24\n",
+       "638     2227.46   2200.00    -27.46         1.25\n",
+       "231   109352.13 107999.00  -1353.13         1.25\n",
+       "673     9014.15   8900.00   -114.15         1.28\n",
+       "544    64649.72  65500.00    850.28         1.30\n",
+       "326     6807.32   6900.00     92.68         1.34\n",
+       "418    11436.89  11600.00    163.11         1.41\n",
+       "688     2464.71   2500.00     35.29         1.41\n",
+       "720     9947.38   9800.00   -147.38         1.50\n",
+       "797    33474.09  34000.00    525.91         1.55\n",
+       "360    21559.42  21900.00    340.58         1.56\n",
+       "562     9104.53   9250.00    145.47         1.57\n",
+       "503    12087.91  11900.00   -187.91         1.58\n",
+       "384     8939.11   8800.00   -139.11         1.58\n",
+       "713    35453.02  34899.14   -553.88         1.59\n",
+       "257    34436.11  34999.00    562.89         1.61\n",
+       "272     2897.71   2850.00    -47.71         1.67\n",
+       "671     3440.49   3500.00     59.51         1.70\n",
+       "735    33320.06  33900.00    579.94         1.71\n",
+       "784    18817.25  18500.00   -317.25         1.71\n",
+       "697    13857.09  14100.00    242.91         1.72\n",
+       "381    20040.20  19700.00   -340.20         1.73\n",
+       "565    35262.29  35900.00    637.71         1.78\n",
+       "278    24059.43  24500.00    440.57         1.80\n",
+       "576    64649.72  63500.00  -1149.72         1.81\n",
+       "363    19627.25  19990.00    362.75         1.81\n",
+       "484    26993.16  27500.00    506.84         1.84\n",
+       "213    32584.87  31990.00   -594.87         1.86\n",
+       "632     3056.57   3000.00    -56.57         1.89\n",
+       "509    98085.79  99999.00   1913.21         1.91\n",
+       "336    19170.78  18800.00   -370.78         1.97\n",
+       "166    10782.03  10999.00    216.97         1.97\n",
+       "480    17047.74  17394.09    346.35         1.99\n",
+       "165     1958.31   1999.00     40.69         2.04\n",
+       "660    29500.27  28900.00   -600.27         2.08\n",
+       "712    23990.17  24500.00    509.83         2.08\n",
+       "422    30909.33  31600.00    690.67         2.19\n",
+       "455    11959.28  11700.00   -259.28         2.22\n",
+       "653    11042.29  10799.00   -243.29         2.25\n",
+       "800    56673.17  58000.00   1326.83         2.29\n",
+       "212    43942.54  45000.00   1057.46         2.35\n",
+       "58     11224.40  11500.00    275.60         2.40\n",
+       "230    12978.30  13300.00    321.70         2.42\n",
+       "528     9166.68   9400.00    233.32         2.48\n",
+       "260     8478.83   8700.00    221.17         2.54\n",
+       "232    11184.55  10900.00   -284.55         2.61\n",
+       "130     8524.29   8300.00   -224.29         2.70\n",
+       "684    14494.88  14900.00    405.12         2.72\n",
+       "582    37938.31  39000.00   1061.69         2.72\n",
+       "663    18384.89  18900.00    515.11         2.73\n",
+       "287    23828.27  24500.00    671.73         2.74\n",
+       "376    11570.15  11900.00    329.85         2.77\n",
+       "532    25592.54  24900.00   -692.54         2.78\n",
+       "67      2004.72   1950.00    -54.72         2.81\n",
+       "211     6374.72   6200.00   -174.72         2.82\n",
+       "449   194242.37 199999.00   5756.63         2.88\n",
+       "76     58649.23  57000.00  -1649.23         2.89\n",
+       "274     3398.26   3500.00    101.74         2.91\n",
+       "711    12864.69  12500.00   -364.69         2.92\n",
+       "267    64695.71  66666.00   1970.29         2.96\n",
+       "284    29563.28  28700.00   -863.28         3.01\n",
+       "726     6787.74   6999.00    211.26         3.02\n",
+       "308     5769.23   5600.00   -169.23         3.02\n",
+       "762    23184.61  22500.00   -684.61         3.04\n",
+       "769    10824.58  10500.00   -324.58         3.09\n",
+       "566    22372.19  21700.00   -672.19         3.10\n",
+       "575    15019.12  15500.00    480.88         3.10\n",
+       "354    10058.12   9750.00   -308.12         3.16\n",
+       "36      8769.20   8500.00   -269.20         3.17\n",
+       "708    10844.83  11200.00    355.17         3.17\n",
+       "742    20522.18  21200.00    677.82         3.20\n",
+       "157    23223.72  22500.00   -723.72         3.22\n",
+       "537     1934.64   1999.00     64.36         3.22\n",
+       "61     11770.33  11400.00   -370.33         3.25\n",
+       "190    43881.06  42500.00  -1381.06         3.25\n",
+       "489    17450.09  16900.00   -550.09         3.25\n",
+       "285    23341.27  22600.00   -741.27         3.28\n",
+       "514    10919.19  11300.00    380.81         3.37\n",
+       "659    21192.26  20500.00   -692.26         3.38\n",
+       "592    12749.66  13200.00    450.34         3.41\n",
+       "262    20283.08  21000.00    716.92         3.41\n",
+       "419    16201.55  16800.00    598.45         3.56\n",
+       "555     7974.91   7699.00   -275.91         3.58\n",
+       "13     13015.27  13500.00    484.73         3.59\n",
+       "247     6890.35   7150.00    259.65         3.63\n",
+       "557     5483.10   5700.00    216.90         3.81\n",
+       "585    20200.76  21000.00    799.24         3.81\n",
+       "332    13917.01  14474.00    556.99         3.85\n",
+       "60     11016.60  10600.00   -416.60         3.93\n",
+       "487     3840.88   3999.00    158.12         3.95\n",
+       "134     8524.71   8200.00   -324.71         3.96\n",
+       "745    11551.56  11111.00   -440.56         3.97\n",
+       "759    17687.48  16999.00   -688.48         4.05\n",
+       "482    33095.36  34500.00   1404.64         4.07\n",
+       "655    26864.09  25800.00  -1064.09         4.12\n",
+       "385    27698.29  28900.00   1201.71         4.16\n",
+       "590    31149.15  29900.00  -1249.15         4.18\n",
+       "752    12454.14  13000.00    545.86         4.20\n",
+       "158    35396.30  33950.00  -1446.30         4.26\n",
+       "32     20103.15  21000.00    896.85         4.27\n",
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+       "407    10173.73   9000.00  -1173.73        13.04\n",
+       "9      22173.64  25500.00   3326.36        13.04\n",
+       "675     3216.30   3700.00    483.70        13.07\n",
+       "270    13061.64  11550.00  -1511.64        13.09\n",
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+       "517    19048.58  16800.00  -2248.58        13.38\n",
+       "606    23296.66  26900.00   3603.34        13.40\n",
+       "580    27706.06  32000.00   4293.94        13.42\n",
+       "717     2856.79   3300.00    443.21        13.43\n",
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+       "173    14874.09  17200.00   2325.91        13.52\n",
+       "595     2853.47   3300.00    446.53        13.53\n",
+       "133     1929.25   1699.00   -230.25        13.55\n",
+       "240     6740.68   7800.00   1059.32        13.58\n",
+       "542    15113.32  13300.00  -1813.32        13.63\n",
+       "764     8146.17   9450.00   1303.83        13.80\n",
+       "432    16956.75  14900.00  -2056.75        13.80\n",
+       "138     8616.78  10000.00   1383.22        13.83\n",
+       "97     27560.75  32000.00   4439.25        13.87\n",
+       "568    11782.11  13700.00   1917.89        14.00\n",
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+       "198     3263.77   3800.00    536.23        14.11\n",
+       "368    25652.89  29900.00   4247.11        14.20\n",
+       "11     13110.05  15300.00   2189.95        14.31\n",
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+       "127    32350.70  37990.00   5639.30        14.84\n",
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+       "508     9993.19   8700.00  -1293.19        14.86\n",
+       "375    24702.61  21500.00  -3202.61        14.90\n",
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+       "716     3398.91   4000.00    601.09        15.03\n",
+       "340    12482.76  10850.00  -1632.76        15.05\n",
+       "153     6099.48   5300.00   -799.48        15.08\n",
+       "347     5248.40   6200.00    951.60        15.35\n",
+       "104     6052.23   7150.00   1097.77        15.35\n",
+       "83      9809.02   8499.00  -1310.02        15.41\n",
+       "450     6174.68   7300.00   1125.32        15.42\n",
+       "593     3635.06   4300.00    664.94        15.46\n",
+       "135     3347.79   2899.00   -448.79        15.48\n",
+       "93     20557.05  17800.00  -2757.05        15.49\n",
+       "392    28877.80  25000.00  -3877.80        15.51\n",
+       "529     5082.76   4400.00   -682.76        15.52\n",
+       "159    17104.46  14800.00  -2304.46        15.57\n",
+       "579    14842.29  17595.00   2752.71        15.64\n",
+       "786     3817.05   3300.00   -517.05        15.67\n",
+       "42      9373.98   8100.00  -1273.98        15.73\n",
+       "702     6888.50   5950.00   -938.50        15.77\n",
+       "206     6903.35   8200.00   1296.65        15.81\n",
+       "43     34332.85  40800.00   6467.15        15.85\n",
+       "387    16799.16  14500.00  -2299.16        15.86\n",
+       "492    98480.67  85000.00 -13480.67        15.86\n",
+       "414     6139.72   7300.00   1160.28        15.89\n",
+       "325    15437.68  13300.00  -2137.68        16.07\n",
+       "322     4064.76   3500.00   -564.76        16.14\n",
+       "265    14087.55  16800.00   2712.45        16.15\n",
+       "208    26137.11  22500.00  -3637.11        16.16\n",
+       "72     27304.45  23500.00  -3804.45        16.19\n",
+       "383     9637.17  11500.00   1862.83        16.20\n",
+       "605    33099.94  39500.00   6400.06        16.20\n",
+       "218    36023.63  30990.00  -5033.63        16.24\n",
+       "429    37352.82  44600.00   7247.18        16.25\n",
+       "753    10462.54   8999.00  -1463.54        16.26\n",
+       "126    31976.86  27500.00  -4476.86        16.28\n",
+       "253    20915.39  24996.00   4080.61        16.33\n",
+       "587     1673.12   2000.00    326.88        16.34\n",
+       "570    14221.55  17000.00   2778.45        16.34\n",
+       "625    34297.36  41000.00   6702.64        16.35\n",
+       "707    19907.26  17100.00  -2807.26        16.42\n",
+       "686    10362.29   8900.00  -1462.29        16.43\n",
+       "219    23291.25  20000.00  -3291.25        16.46\n",
+       "747     2213.58   1900.00   -313.58        16.50\n",
+       "406     8738.60   7500.00  -1238.60        16.51\n",
+       "770     6877.84   5900.00   -977.84        16.57\n",
+       "427    10377.09   8900.00  -1477.09        16.60\n",
+       "380    21349.94  18300.00  -3049.94        16.67\n",
+       "386     2858.60   2450.00   -408.60        16.68\n",
+       "493    11676.82   9999.00  -1677.82        16.78\n",
+       "251     7892.51   9500.00   1607.49        16.92\n",
+       "303     5399.74   6500.00   1100.26        16.93\n",
+       "434    25408.55  21700.00  -3708.55        17.09\n",
+       "255    10609.93  12800.00   2190.07        17.11\n",
+       "648    28114.25  24000.00  -4114.25        17.14\n",
+       "47     39356.61  47500.00   8143.39        17.14\n",
+       "321    50516.19  61000.00  10483.81        17.19\n",
+       "316     9481.20  11450.00   1968.80        17.19\n",
+       "390    13594.21  16450.00   2855.79        17.36\n",
+       "349    12675.07  10800.00  -1875.07        17.36\n",
+       "789    11505.09   9800.00  -1705.09        17.40\n",
+       "602     9082.32  10999.00   1916.68        17.43\n",
+       "203    31495.17  26800.00  -4695.17        17.52\n",
+       "748    11282.67   9600.00  -1682.67        17.53\n",
+       "109    19281.74  16400.00  -2881.74        17.57\n",
+       "658     7992.08   9700.00   1707.92        17.61\n",
+       "758     5881.36   5000.00   -881.36        17.63\n",
+       "168    18117.61  22000.00   3882.39        17.65\n",
+       "746    11412.71   9700.00  -1712.71        17.66\n",
+       "119    15774.43  13400.00  -2374.43        17.72\n",
+       "496     4357.87   5300.00    942.13        17.78\n",
+       "415     8126.96   6900.00  -1226.96        17.78\n",
+       "309    11434.04   9700.00  -1734.04        17.88\n",
+       "421    74745.66  63333.00 -11412.66        18.02\n",
+       "796    18302.49  15500.00  -2802.49        18.08\n",
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+       "18     10710.00  13100.00   2390.00        18.24\n",
+       "637     6293.06   7700.00   1406.94        18.27\n",
+       "154    13370.87  11300.00  -2070.87        18.33\n",
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+       "704     5459.66   6700.00   1240.34        18.51\n",
+       "485     4480.19   5500.00   1019.81        18.54\n",
+       "333     8536.82   7200.00  -1336.82        18.57\n",
+       "420    40274.34  49500.00   9225.66        18.64\n",
+       "674    89582.34  75500.00 -14082.34        18.65\n",
+       "366     6884.55   5800.00  -1084.55        18.70\n",
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+       "490     6090.18   7500.00   1409.82        18.80\n",
+       "642    19364.44  16300.00  -3064.44        18.80\n",
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+       "192    11319.17   9500.00  -1819.17        19.15\n",
+       "180    11445.18   9599.00  -1846.18        19.23\n",
+       "699     5650.29   7000.00   1349.71        19.28\n",
+       "634     4433.53   5500.00   1066.47        19.39\n",
+       "545    12724.62  15800.00   3075.38        19.46\n",
+       "771     2092.87   2600.00    507.13        19.50\n",
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+       "337    60274.39  75000.00  14725.61        19.63\n",
+       "184    46983.85  58500.00  11516.15        19.69\n",
+       "724     8747.37  10900.00   2152.63        19.75\n",
+       "129    22708.02  18950.00  -3758.02        19.83\n",
+       "773    13185.60  11000.00  -2185.60        19.87\n",
+       "305     9594.67   8000.00  -1594.67        19.93\n",
+       "678    16676.56  13900.00  -2776.56        19.98\n",
+       "718     5399.84   4500.00   -899.84        20.00\n",
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+       "591    34847.46  28950.00  -5897.46        20.37\n",
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+       "404     6940.02   8750.00   1809.98        20.69\n",
+       "574     6056.80   7650.00   1593.20        20.83\n",
+       "599     8708.11   7200.00  -1508.11        20.95\n",
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+       "656     1973.68   2500.00    526.32        21.05\n",
+       "277     4181.72   5300.00   1118.28        21.10\n",
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+       "20     13328.75  11000.00  -2328.75        21.17\n",
+       "64     36828.78  46800.00   9971.22        21.31\n",
+       "494     6920.60   8800.00   1879.40        21.36\n",
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+       "775    11692.08   9600.00  -2092.08        21.79\n",
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+       "342    11700.38  15000.00   3299.62        22.00\n",
+       "799     2623.69   2150.00   -473.69        22.03\n",
+       "507     2026.96   2600.00    573.04        22.04\n",
+       "183     8434.23   6900.00  -1534.23        22.24\n",
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+       "741     6200.75   8000.00   1799.25        22.49\n",
+       "371    10783.10   8800.00  -1983.10        22.54\n",
+       "511    22566.89  18400.00  -4166.89        22.65\n",
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+       "113    13535.73  11000.00  -2535.73        23.05\n",
+       "19      9723.51  12650.00   2926.49        23.13\n",
+       "479    14919.17  12114.35  -2804.82        23.15\n",
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+       "312    20558.01  16672.50  -3885.51        23.30\n",
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+       "533     2977.77   3900.00    922.23        23.65\n",
+       "139    22121.67  29000.00   6878.33        23.72\n",
+       "40     29567.87  38764.00   9196.13        23.72\n",
+       "358    22081.93  28999.00   6917.07        23.85\n",
+       "611     8174.40   6600.00  -1574.40        23.85\n",
+       "456     2813.36   3700.00    886.64        23.96\n",
+       "629     9728.36  12800.00   3071.64        24.00\n",
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+       "696    14539.53  11700.00  -2839.53        24.27\n",
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+       "505    22276.96  17900.00  -4376.96        24.45\n",
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+       "258    12346.01   9900.00  -2446.01        24.71\n",
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+       "616    21017.12  28000.00   6982.88        24.94\n",
+       "241     3748.29   3000.00   -748.29        24.94\n",
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+       "111     9998.69   8000.00  -1998.69        24.98\n",
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+       "803     3760.79   3000.00   -760.79        25.36\n",
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+       "436    18827.10  14999.00  -3828.10        25.52\n",
+       "635     9674.76  13000.00   3325.24        25.58\n",
+       "350     5205.98   7000.00   1794.02        25.63\n",
+       "751    18944.86  25500.00   6555.14        25.71\n",
+       "341    31179.60  42000.00  10820.40        25.76\n",
+       "804    26319.25  20900.00  -5419.25        25.93\n",
+       "172     2959.68   2350.00   -609.68        25.94\n",
+       "214     8863.85  12000.00   3136.15        26.13\n",
+       "652    38039.83  51500.00  13460.17        26.14\n",
+       "112    13761.17  10900.00  -2861.17        26.25\n",
+       "687    22356.85  17700.00  -4656.85        26.31\n",
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+       "108    10013.18   7900.00  -2113.18        26.75\n",
+       "37      6589.41   8999.00   2409.59        26.78\n",
+       "311    24995.51  19700.00  -5295.51        26.88\n",
+       "677     7821.99  10700.00   2878.01        26.90\n",
+       "279     6736.55   5300.00  -1436.55        27.10\n",
+       "307    11445.85   9000.00  -2445.85        27.18\n",
+       "361     2909.80   4000.00   1090.20        27.26\n",
+       "175    24627.39  33900.00   9272.61        27.35\n",
+       "293    82760.24  64900.00 -17860.24        27.52\n",
+       "497     5787.88   8000.00   2212.12        27.65\n",
+       "527    19048.95  14900.00  -4148.95        27.85\n",
+       "730     4668.85   3650.00  -1018.85        27.91\n",
+       "561     7564.39  10500.00   2935.61        27.96\n",
+       "670    30477.38  23800.00  -6677.38        28.06\n",
+       "296    11154.62   8700.00  -2454.62        28.21\n",
+       "80     21802.49  30385.00   8582.51        28.25\n",
+       "151    65745.19  92222.00  26476.81        28.71\n",
+       "633    21739.14  30500.00   8760.86        28.72\n",
+       "162    25245.24  35500.00  10254.76        28.89\n",
+       "598    15218.48  11800.00  -3418.48        28.97\n",
+       "470    55229.59  77777.00  22547.41        28.99\n",
+       "283    55229.59  77777.00  22547.41        28.99\n",
+       "249     8665.27   6700.00  -1965.27        29.33\n",
+       "327    11514.96   8900.00  -2614.96        29.38\n",
+       "186     2951.62   4200.00   1248.38        29.72\n",
+       "355     5449.56   4200.00  -1249.56        29.75\n",
+       "70     39888.50  57000.00  17111.50        30.02\n",
+       "443     8386.38   6450.00  -1936.38        30.02\n",
+       "176     5242.13   7500.00   2257.87        30.10\n",
+       "693    17055.42  24500.00   7444.58        30.39\n",
+       "783    18140.29  13900.00  -4240.29        30.51\n",
+       "477    15633.25  22500.00   6866.75        30.52\n",
+       "640    10150.80   7777.00  -2373.80        30.52\n",
+       "774     2485.85   1900.00   -585.85        30.83\n",
+       "763     2073.96   2999.00    925.04        30.84\n",
+       "132    19812.54  28713.75   8901.21        31.00\n",
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+       "46      7869.15   5999.00  -1870.15        31.17\n",
+       "710    12071.60   9200.00  -2871.60        31.21\n",
+       "534     8941.06  13000.00   4058.94        31.22\n",
+       "499    13336.31  10150.00  -3186.31        31.39\n",
+       "30     14200.19  10800.00  -3400.19        31.48\n",
+       "66      3105.70   4550.00   1444.30        31.74\n",
+       "682     4624.41   6800.00   2175.59        31.99\n",
+       "441     3511.87   5200.00   1688.13        32.46\n",
+       "520     4779.20   3600.00  -1179.20        32.76\n",
+       "644    11942.86  17777.00   5834.14        32.82\n",
+       "81     16542.07  12450.00  -4092.07        32.87\n",
+       "400    15827.38  11900.00  -3927.38        33.00\n",
+       "71      4216.67   6300.00   2083.33        33.07\n",
+       "147     9333.37   7000.00  -2333.37        33.33\n",
+       "559     7732.20  11600.00   3867.80        33.34\n",
+       "766    12012.45   9000.00  -3012.45        33.47\n",
+       "728    19812.54  29800.00   9987.46        33.51\n",
+       "245     5317.17   8000.00   2682.83        33.54\n",
+       "114    46006.01  34419.45 -11586.56        33.66\n",
+       "543    36381.57  55000.00  18618.43        33.85\n",
+       "703    23207.21  17300.00  -5907.21        34.15\n",
+       "438    12083.74   9000.00  -3083.74        34.26\n",
+       "646     1642.87   2500.00    857.13        34.29\n",
+       "15      3900.79   2900.00  -1000.79        34.51\n",
+       "431    67578.74  49900.00 -17678.74        35.43\n",
+       "273     5030.79   7800.00   2769.21        35.50\n",
+       "692    12682.44   9350.00  -3332.44        35.64\n",
+       "25     44889.92  69900.00  25010.08        35.78\n",
+       "335     5702.84   9000.00   3297.16        36.64\n",
+       "6      11427.09   8350.00  -3077.09        36.85\n",
+       "310    13828.77  21900.00   8071.23        36.85\n",
+       "600     5591.66   8900.00   3308.34        37.17\n",
+       "617    16260.94  11850.00  -4410.94        37.22\n",
+       "209    19627.05  14300.00  -5327.05        37.25\n",
+       "394     2005.13   3200.00   1194.87        37.34\n",
+       "727    46345.58  74000.00  27654.42        37.37\n",
+       "777    51526.76  37500.00 -14026.76        37.40\n",
+       "785    29732.92  47700.00  17967.08        37.67\n",
+       "515    46378.55  74500.00  28121.45        37.75\n",
+       "31     16572.98  11999.00  -4573.98        38.12\n",
+       "772     6644.61   4800.00  -1844.61        38.43\n",
+       "460    12197.10   8800.00  -3397.10        38.60\n",
+       "304    64451.49 104999.00  40547.51        38.62\n",
+       "367    39888.50  65000.00  25111.50        38.63\n",
+       "220    17656.32  28793.04  11136.72        38.68\n",
+       "50     15936.61  11400.00  -4536.61        39.79\n",
+       "388    71675.00 121500.00  49825.00        41.01\n",
+       "551    16226.28  11500.00  -4726.28        41.10\n",
+       "302     8889.98   6299.00  -2590.98        41.13\n",
+       "235     9669.09  16500.00   6830.91        41.40\n",
+       "535    14822.55  10450.00  -4372.55        41.84\n",
+       "601    16930.40  29500.00  12569.60        42.61\n",
+       "73      5700.21  10000.00   4299.79        43.00\n",
+       "701    32880.90  22900.00  -9980.90        43.58\n",
+       "137     6477.68   4500.00  -1977.68        43.95\n",
+       "756    10016.88  18000.00   7983.12        44.35\n",
+       "237    11415.61   7900.00  -3515.61        44.50\n",
+       "539     2050.79   3700.00   1649.21        44.57\n",
+       "328    13804.82   9500.00  -4304.82        45.31\n",
+       "676    24080.07  16555.00  -7525.07        45.45\n",
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+       "444    17048.89  11707.80  -5341.09        45.62\n",
+       "69      8419.88  15500.00   7080.12        45.68\n",
+       "221    31124.28  21300.00  -9824.28        46.12\n",
+       "428    13157.10   9000.00  -4157.10        46.19\n",
+       "643     1733.85   1185.60   -548.25        46.24\n",
+       "41     38632.53  73500.00  34867.47        47.44\n",
+       "612    62100.23  42000.00 -20100.23        47.86\n",
+       "628     1196.37   2300.00   1103.63        47.98\n",
+       "794    65898.02 129222.00  63323.98        49.00\n",
+       "738     2313.02   1550.00   -763.02        49.23\n",
+       "164     2972.03   1990.00   -982.03        49.35\n",
+       "228     1417.83   2800.00   1382.17        49.36\n",
+       "502    22471.17  44700.00  22228.83        49.73\n",
+       "721    71981.49 144900.00  72918.51        50.32\n",
+       "481    50745.07  33715.50 -17029.57        50.51\n",
+       "577     7343.52  14999.00   7655.48        51.04\n",
+       "519    26609.57  17600.00  -9009.57        51.19\n",
+       "275    17888.88  11800.00  -6088.88        51.60\n",
+       "457    20479.37  13500.00  -6979.37        51.70\n",
+       "603    12461.52   8200.00  -4261.52        51.97\n",
+       "401    20828.11  13700.00  -7128.11        52.03\n",
+       "362    18980.92  39900.00  20919.08        52.43\n",
+       "780    28362.55  60500.00  32137.45        53.12\n",
+       "99      3521.95   2300.00  -1221.95        53.13\n",
+       "102     9427.42  20500.00  11072.58        54.01\n",
+       "370     5408.75   3500.00  -1908.75        54.54\n",
+       "516    10854.85  24000.00  13145.15        54.77\n",
+       "48     65041.89  41900.00 -23141.89        55.23\n",
+       "491     7715.56   4900.00  -2815.56        57.46\n",
+       "318    30316.04  19200.00 -11116.04        57.90\n",
+       "34     20712.46  13000.00  -7712.46        59.33\n",
+       "122     2719.53   1700.00  -1019.53        59.97\n",
+       "39      3906.56   2400.00  -1506.56        62.77\n",
+       "790     5055.93   3099.00  -1956.93        63.15\n",
+       "200     4243.79   2600.00  -1643.79        63.22\n",
+       "788    10676.78   6499.00  -4177.78        64.28\n",
+       "86     16658.12  10100.00  -6558.12        64.93\n",
+       "662     6656.03   4000.00  -2656.03        66.40\n",
+       "425     2486.42   8000.00   5513.58        68.92\n",
+       "661    13706.76   8000.00  -5706.76        71.33\n",
+       "23      6524.07   3799.00  -2725.07        71.73\n",
+       "423    41367.65  23900.00 -17467.65        73.09\n",
+       "238    60487.88  34600.00 -25887.88        74.82\n",
+       "524     9215.35   5000.00  -4215.35        84.31\n",
+       "636     6870.33   3600.00  -3270.33        90.84\n",
+       "506     9213.19   4800.00  -4413.19        91.94\n",
+       "613     1444.13  34999.00  33554.87        95.87\n",
+       "791     5130.28   2600.00  -2530.28        97.32\n",
+       "623        0.00   1800.00   1800.00       100.00\n",
+       "306        0.00  44000.00  44000.00       100.00\n",
+       "286        0.00  35000.00  35000.00       100.00\n",
+       "121        0.00   3800.00   3800.00       100.00\n",
+       "396        0.00  38500.00  38500.00       100.00\n",
+       "124        0.00   4100.00   4100.00       100.00\n",
+       "690        0.00   6500.00   6500.00       100.00\n",
+       "24         0.00  32000.00  32000.00       100.00\n",
+       "301        0.00   8500.00   8500.00       100.00\n",
+       "351        0.00  18800.00  18800.00       100.00\n",
+       "723        0.00   1650.00   1650.00       100.00\n",
+       "54     29058.70  14250.00 -14808.70       103.92\n",
+       "266    20157.28   9000.00 -11157.28       123.97\n",
+       "594     9463.58   3650.00  -5813.58       159.28\n",
+       "143     4534.02   1700.00  -2834.02       166.71\n",
+       "131     3200.86   1200.00  -2000.86       166.74\n",
+       "452     3875.31   1450.00  -2425.31       167.26\n",
+       "685    35212.93  12900.00 -22312.93       172.97\n",
+       "604    14347.91   4200.00 -10147.91       241.62\n",
+       "737     8975.55   2400.00  -6575.55       273.98\n",
+       "28     48602.40  11200.00 -37402.40       333.95\n",
+       "538    28240.95   5500.00 -22740.95       413.47\n",
+       "290         inf  19999.00      -inf          inf\n",
+       "731         inf  99000.00      -inf          inf\n",
+       "282         inf  20996.24      -inf          inf\n",
+       "679         inf   1150.00      -inf          inf\n",
+       "120         inf  17639.47      -inf          inf\n",
+       "204         inf  12900.00      -inf          inf\n",
+       "683         inf  20000.00      -inf          inf\n",
+       "650         inf  11300.00      -inf          inf\n",
+       "458         inf   7450.00      -inf          inf\n",
+       "314         inf  10200.00      -inf          inf\n",
+       "63          inf  37500.00      -inf          inf\n",
+       "698         inf  11700.00      -inf          inf\n",
+       "33          inf   6000.00      -inf          inf\n",
+       "82          inf   4000.00      -inf          inf"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sometimes it is useful to check these outputs manually\n",
+    "# To see all rows, we use the relevant pandas syntax\n",
+    "pd.options.display.max_rows = 999\n",
+    "# Moreover, to make the dataset clear, we can display the result with only 2 digits after the dot \n",
+    "\n",
+    "pd.set_option('display.float_format', lambda x: '%.2f' %x)\n",
+    "# Finally, we sort by difference in % and manually check the model\n",
+    "df_pf.sort_values(by=['Difference%'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Improved_model.html.pdf b/Udemy DataScience Workbook/Improved_model.html.pdf
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index 0000000..79fbd1b
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diff --git a/Udemy DataScience Workbook/LICT_file.ipynb b/Udemy DataScience Workbook/LICT_file.ipynb
new file mode 100644
index 0000000..fd1e71d
--- /dev/null
+++ b/Udemy DataScience Workbook/LICT_file.ipynb	
@@ -0,0 +1,10824 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "Unable to import required dependencies:\npytz: No module named 'pytz'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\DataScience_Projects\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\__init__.py:29\u001b[0m\n\u001b[0;32m     26\u001b[0m         _missing_dependencies\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_dependency\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_e\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _missing_dependencies:  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[1;32m---> 29\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m     30\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnable to import required dependencies:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(_missing_dependencies)\n\u001b[0;32m     31\u001b[0m     )\n\u001b[0;32m     32\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m _hard_dependencies, _dependency, _missing_dependencies\n\u001b[0;32m     34\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     35\u001b[0m     \u001b[38;5;66;03m# numpy compat\u001b[39;00m\n",
+      "\u001b[1;31mImportError\u001b[0m: Unable to import required dependencies:\npytz: No module named 'pytz'"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read the input file\n",
+    "with open('passwords_random.txt', 'r') as file:\n",
+    "    data = file.read()\n",
+    "\n",
+    "# Replace spaces with commas\n",
+    "data = data.replace(' ', ',')\n",
+    "\n",
+    "# Write the data to a new file\n",
+    "with open('output.csv', 'w') as file:\n",
+    "   file.write(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6568,\n",
+      "5877,\n",
+      "9559,\n",
+      "7219,\n",
+      "4537,\n",
+      "2002,\n",
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+      "707,\n",
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+      "3065,\n",
+      "4214,\n",
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+      "5831,\n",
+      "6909,\n",
+      "1466,\n",
+      "936,\n",
+      "2481,\n",
+      "9566,\n",
+      "4546,\n",
+      "219,\n",
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+      "1921,\n",
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+      "3421,\n",
+      "1440,\n",
+      "9650,\n",
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+      "5791,\n",
+      "1120,\n",
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+      "3684,\n",
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+      "2842,\n",
+      "563,\n",
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+      "1972,\n",
+      "3692,\n",
+      "4710,\n",
+      "4590,\n",
+      "7535,\n",
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+      "1431,\n",
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+      "361,\n",
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+      "2503,\n",
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+      "2717,\n",
+      "2905,\n",
+      "425,\n",
+      "4123,\n",
+      "1914,\n",
+      "503,\n",
+      "6,\n",
+      "9813,\n",
+      "4858,\n",
+      "82,\n",
+      "4712,\n",
+      "2433,\n",
+      "9230,\n",
+      "6821,\n",
+      "9483,\n",
+      "3502,\n",
+      "2440,\n",
+      "3338,\n",
+      "514,\n",
+      "7330,\n",
+      "7528,\n",
+      "935,\n",
+      "6480,\n",
+      "1404,\n",
+      "8247,\n",
+      "6556,\n",
+      "8170,\n",
+      "3062,\n",
+      "4398,\n",
+      "2471,\n",
+      "8991,\n",
+      "3544,\n",
+      "1700,\n",
+      "2767,\n",
+      "4711,\n",
+      "7668,\n",
+      "783,\n",
+      "7044,\n",
+      "390,\n",
+      "5354,\n",
+      "7831,\n",
+      "2200,\n",
+      "7564,\n",
+      "605,\n",
+      "6230,\n",
+      "3769,\n",
+      "6832,\n",
+      "6594,\n",
+      "7779,\n",
+      "7672,\n",
+      "234,\n",
+      "3870,\n",
+      "9683,\n",
+      "5299,\n",
+      "6932,\n",
+      "2519,\n",
+      "1359,\n",
+      "9463,\n",
+      "7972,\n",
+      "7072,\n",
+      "967,\n",
+      "2170,\n",
+      "9525,\n",
+      "7016,\n",
+      "9263,\n",
+      "8715,\n",
+      "5698,\n",
+      "8735,\n",
+      "1340,\n",
+      "2890,\n",
+      "1247,\n",
+      "3980,\n",
+      "7179,\n",
+      "3134,\n",
+      "4930,\n",
+      "7483,\n",
+      "2020,\n",
+      "5145,\n",
+      "7847,\n",
+      "3695,\n",
+      "3173,\n",
+      "7930,\n",
+      "9988,\n",
+      "3699,\n",
+      "5124,\n",
+      "5560,\n",
+      "1850,\n",
+      "4954,\n",
+      "4255,\n",
+      "6262,\n",
+      "6492,\n",
+      "4144,\n",
+      "5384\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Replace all commas followed by a newline character\n",
+    "modified_data = data.replace(',', ',\\n')\n",
+    "\n",
+    "print(modified_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6568, 5877, 9559, 7219, 4537, 2002, 6059, 8199, 5804, 1389, 4847, 6528, 8518, 9541, 606, 8488, 3643, 7665, 8390, 1339, 9434, 6792, 6690, 6944, 7984, 3803, 5458, 6503, 4880, 9844, 3303, 2795, 3973, 4753, 6534, 6903, 1041, 5328, 8467, 2626, 6817, 5204, 5489, 5868, 5576, 9770, 9178, 162, 9857, 8347, 9745, 8666, 9592, 9510, 53, 1013, 4080, 7982, 9389, 7520, 348, 3276, 4570, 8245, 7962, 3589, 9435, 462, 2350, 6665, 5185, 142, 5760, 5765, 6060, 8990, 1436, 2180, 6147, 3076, 6448, 7616, 9125, 6168, 831, 8007, 1029, 5528, 5598, 7607, 5606, 9689, 5950, 7481, 8794, 6308, 4556, 2488, 8505, 7470, 3191, 5541, 7438, 5825, 2863, 5025, 4461, 9873, 9236, 2327, 8482, 3890, 908, 2060, 4062, 5897, 6237, 9958, 6127, 3710, 9544, 2339, 723, 4386, 5432, 2046, 9485, 2734, 9885, 224, 6470, 7104, 4254, 6254, 6306, 5034, 1710, 1108, 7823, 7687, 7107, 1864, 6336, 7360, 9072, 1802, 1549, 1375, 9382, 3838, 4877, 4508, 5279, 1571, 2386, 4248, 9521, 6834, 9035, 4189, 5131, 7194, 4391, 1981, 1349, 1225, 1848, 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109, 9701, 2374, 3097, 9871, 3075, 9336, 5350, 6493, 1405, 4076, 540, 8704, 635, 6352, 3498, 3885, 3140, 8872, 1858, 244, 8661, 7080, 2542, 8392, 840, 4903, 7928, 3369, 3959, 7601, 8371, 6577, 9309, 1755, 6424, 7549, 5642, 6553, 9964, 4120, 2468, 2054, 1591, 7735, 3370, 9291, 1788, 8292, 8407, 5799, 7898, 3266, 1024, 1963, 4175, 1397, 745, 2401, 394, 4996, 1288, 3492, 4717, 1319, 7250, 5187, 4671, 7946, 8556, 1427, 682, 4086, 4497, 656, 383, 3396, 6167, 8697, 2527, 6740, 5314, 7807, 7932, 5189, 7705, 5420, 4321, 8530, 296, 9247, 9171, 3289, 2916, 3132, 2822, 9311, 1575, 5920, 3125, 3159, 89, 6084, 2070, 5428, 4173, 1445, 6161, 4025, 2605, 2197, 9877, 1722, 3961, 3279, 9999, 9909, 7426, 564, 4247, 2426, 8788, 992, 6670, 8521, 4282, 8769, 4720, 8593, 5175, 4392, 4156, 1000, 2510, 2421, 1021, 3916, 7643, 386, 4287, 8632, 1712, 3482, 7140, 1576, 2586, 8383, 1524, 2834, 3811, 8413, 7455, 7753, 8642, 5934, 8192, 9815, 4502, 1590, 375, 4471, 6634, 5111, 7796, 2011, 2007, 4075, 7241, 1066, 7541, 4258, 1726, 6675, 4266, 2100, 1635, 7555, 4533, 1151, 918, 8452, 9151, 333, 1793, 986, 7685, 7654, 493, 3978, 409, 1565, 9109, 1185, 7212, 5459, 2479, 1064, 1300, 7645, 7068, 7422, 8868, 5588, 3264, 8946, 1097, 8449, 6386, 8176, 8796, 7196, 1201, 7902, 7887, 2713, 6947, 1768, 3800, 642, 2862, 9672, 5592, 4395, 6220, 696, 4054, 6280, 8525, 4043, 1134, 7530, 6420, 3623, 8324, 3090, 8570, 7292, 6609, 8364, 6401, 5865, 6375, 4704, 5014, 7800, 488, 4588, 1807, 8852, 3243, 1903, 6693, 284, 6201, 8880, 7606, 6752, 7987, 2352, 6928, 3508, 3677, 1353, 6735, 6867, 7397, 3493, 1245, 3924, 4104, 1506, 1279, 2836, 5938, 991, 2214, 4976, 1711, 2086, 9056, 4787, 9576, 2764, 6516, 7639, 7802, 7421, 4693, 1118, 1111, 584, 9969, 9710, 5313, 4966, 9414, 7433, 4024, 3557, 1645, 2415, 3727, 1938, 1699, 5737, 2546, 9399, 9159, 8180, 9222, 1543, 8342, 6734, 7834, 2500, 2914, 5297, 6259, 4947, 6325, 4042, 580, 5771, 1203, 6397, 4220, 9642, 3474, 698, 2581, 1220, 7572, 4421, 2358, 4422, 7699, 1810, 599, 1925, 9439, 6394, 7337, 6497, 5159, 5325, 2393, 2996, 3225, 6490, 5888, 444, 159, 8435, 6941, 5932, 4283, 6478, 7367, 6767, 5691, 250, 6182, 4401, 3735, 3136, 6908, 4780, 2023, 9927, 2066, 3111, 4177, 6562, 9861, 9448, 7244, 1470, 5326, 7577, 4915, 7553, 5579, 6035, 4707, 6513, 6714, 1685, 3581, 6937, 7936, 6871, 2858, 471, 3998, 8183, 2668, 5882, 4853, 6415, 7327, 6793, 9203, 5492, 7788, 1671, 3821, 3399, 990, 559, 3975, 197, 6227, 3141, 9460, 1321, 5051, 9000, 9479, 7428, 1707, 4893, 9572, 1261, 9898, 4101, 9410, 5856, 3455, 740, 3433, 6125, 1235, 4297, 5681, 5262, 2637, 6298, 2231, 3945, 2439, 3373, 3127, 2578, 1913, 996, 8037, 1208, 7038, 6274, 6234, 8825, 8376, 8233, 1147, 8353, 4473, 1792, 2375, 69, 4881, 110, 4513, 7015, 8327, 9366, 472, 3067, 9396, 1684, 8014, 4507, 1379, 8178, 8381, 1132, 6742, 6769, 1849, 1893, 1943, 4434, 7134, 5666, 3886, 1241, 532, 4600, 6275, 6781, 4223, 9598, 7372, 1478, 9998, 2223, 8770, 1345, 4094, 1264, 3951, 3153, 9777, 7960, 7197, 9107, 11, 2465, 8191, 9507, 3833, 9322, 8494, 6469, 3152, 2294, 913, 2456, 534, 8873, 3999, 7392, 9360, 2632, 5485, 1281, 1561, 7627, 604, 3376, 9906, 3969, 7503, 2920, 1324, 5300, 9210, 3936, 9892, 5733, 9040, 4833, 7744, 7624, 5883, 8420, 8523, 5484, 6276, 8337, 9472, 3524, 1306, 9992, 1166, 9319, 545, 5467, 6839, 6581, 8489, 2517, 2448, 3921, 5232, 198, 7658, 2230, 4841, 9249, 519, 6894, 9895, 6257, 9854, 5359, 1336, 1763, 5332, 4183, 1854, 7857, 7018, 9662, 4194, 2188, 5669, 5378, 7181, 2688, 3239, 7638, 2013, 2342, 7777, 962, 9352, 932, 8772, 6395, 7724, 7493, 9781, 6342, 292, 2272, 6823, 5179, 7733, 1200, 449, 954, 9099, 4860, 3187, 4250, 2689, 5369, 728, 8277, 1546, 6442, 4596, 2910, 3470, 5629, 4459, 9038, 2758, 308, 8597, 2663, 4167, 1145, 1740, 4012, 3114, 787, 2785, 6744, 8154, 5803, 6614, 5686, 477, 6755, 889, 5776, 9196, 8006, 8845, 1105, 4876, 714, 9136, 3405, 4178, 8149, 1679, 6465, 8111, 120, 995, 602, 8982, 9852, 5706, 5321, 8712, 1579, 3354, 6300, 459, 695, 9647, 4988, 9274, 48, 9084, 2219, 6678, 3430, 3010, 511, 6378, 8531, 3693, 1299, 9539, 3162, 5501, 3868, 924, 999, 1081, 7911, 9617, 4405, 143, 461, 7205, 6260, 7958, 5519, 4828, 9951, 6621, 6341, 4744, 4300, 5344, 9220, 8022, 7264, 6135, 3539, 6912, 1246, 8121, 5242, 7804, 1226, 3020, 8677, 5499, 6920, 8251, 9103, 3381, 2259, 4134, 3334, 6689, 9667, 4887, 2431, 6134, 9946, 2962, 1411, 3467, 4788, 3996, 968, 5261, 4114, 8609, 5683, 177, 5447, 7578, 3516, 748, 8790, 1717, 4935, 5382, 5388, 7373, 3586, 6844, 4046, 1977, 3221, 2631, 2320, 8941, 6249, 2886, 7106, 3662, 2155, 2261, 359, 553, 62, 9054, 8020, 5368, 2119, 9817, 845, 7755, 3420, 403, 5972, 3590, 4336, 9398, 2056, 3792, 3004, 5281, 4212, 2884, 2145, 8402, 4883, 4603, 6297, 3570, 470, 313, 6403, 9061, 3976, 2987, 3675, 5477, 36, 687, 285, 1270, 5498, 6774, 2372, 9499, 8720, 3597, 6659, 855, 6968, 2073, 6105, 3281, 9762, 60, 6344, 3449, 9450, 3156, 4830, 2300, 8631, 5619, 6253, 2576, 6180, 876, 3340, 3828, 6044, 9828, 6319, 944, 2590, 666, 4446, 5097, 9052, 2005, 8115, 3174, 3882, 2220, 4447, 5532, 5670, 1586, 6302, 8789, 1062, 5006, 294, 6546, 1416, 6686, 5974, 6791, 3240, 7655, 3535, 4951, 3002, 4362, 2588, 8463, 7590, 9574, 3726, 7214, 7519, 1995, 7189, 972, 3021, 8036, 4146, 7418, 4271, 1468, 5480, 6616, 1179, 2709, 6559, 1733, 3974, 9137, 2971, 3245, 7752, 9975, 3462, 4407, 1325, 3632, 7027, 2248, 9907, 9177, 8078, 1289, 5294, 7310, 387, 3851, 9819, 4208, 4148, 9940, 2224, 9278, 3451, 9119, 8861, 325, 2166, 3708, 9681, 2617, 8218, 9338, 9413, 1331, 8837, 658, 1027, 7374, 441, 5963, 7763, 4041, 207, 6589, 2781, 638, 8782, 1127, 640, 9794, 2163, 6266, 3742, 4653, 3588, 3531, 2896, 2633, 8012, 5260, 7925, 3072, 343, 2445, 4626, 3468, 3018, 377, 4523, 3051, 2123, 1818, 8450, 9838, 766, 4737, 3877, 4856, 288, 436, 5144, 9743, 749, 8140, 5019, 2677, 9392, 2676, 5373, 3304, 955, 3262, 9593, 290, 6033, 9295, 427, 2575, 4518, 2435, 9102, 9297, 9019, 9562, 3367, 2823, 8972, 8603, 4360, 5577, 5568, 7132, 1310, 5454, 9332, 9553, 2606, 2563, 1057, 7375, 7486, 4701, 5092, 3352, 9259, 7341, 6811, 5867, 6732, 4429, 2055, 2146, 8351, 2437, 4209, 388, 7559, 8925, 4675, 7243, 9416, 4803, 8308, 8911, 6789, 4387, 9386, 6042, 3545, 4269, 5080, 7491, 9628, 2341, 8953, 9948, 4337, 5687, 6652, 2871, 4657, 9674, 5125, 6327, 872, 1123, 6329, 2196, 3941, 8271, 9286, 1819, 9715, 1058, 3115, 7790, 798, 4344, 7404, 7837, 9960, 9932, 4599, 7063, 8943, 6295, 7400, 1451, 1703, 7511, 9455, 7516, 6247, 2703, 5031, 7953, 5887, 2928, 3121, 8090, 9734, 4462, 5983, 5256, 1296, 8768, 1036, 3579, 2696, 9901, 9643, 5224, 1172, 9938, 9188, 1626, 7760, 5, 7411, 6624, 7669, 3593, 3560, 47, 4069, 5778, 6484, 4870, 3795, 8665, 4642, 896, 5819, 5460, 7230, 4500, 3526, 5896, 7285, 1573, 5989, 6953, 3213, 242, 6518, 9186, 679, 4084, 7100, 6845, 1049, 2109, 3569, 4517, 9826, 7343, 6393, 4351, 337, 8671, 3113, 2807, 1116, 3679, 1787, 7395, 5358, 5637, 3327, 9812, 3928, 5311, 7684, 1751, 4373, 2391, 4531, 1469, 8380, 4306, 6396, 20, 8440, 7248, 1932, 9379, 6468, 2868, 1860, 1971, 1799, 2594, 6120, 2561, 931, 7103, 8676, 9182, 7393, 3086, 9615, 2124, 2748, 1072, 4999, 8640, 4730, 8928, 8112, 7299, 1425, 9584, 1006, 3927, 8153, 5689, 4825, 3268, 746, 1884, 5597, 5654, 1622, 8244, 3392, 7242, 5448, 1253, 3952, 7766, 7611, 6131, 2652, 8851, 2428, 5773, 9919, 1267, 9501, 9947, 31, 5987, 9174, 7652, 3922, 5793, 7584, 5857, 6410, 8601, 8700, 8476, 8330, 3371, 4334, 2361, 1617, 9987, 9007, 3269, 4679, 7563, 9347, 5096, 90, 9346, 3038, 9372, 6316, 8767, 1931, 2028, 2574, 9573, 2609, 5650, 9560, 8877, 8021, 8306, 4747, 5802, 9191, 1290, 2857, 5120, 437, 5782, 8714, 1419, 9285, 1009, 8113, 79, 8046, 1822, 3486, 4001, 6558, 8895, 3908, 5317, 3397, 8809, 7407, 6289, 6282, 8382, 1446, 5361, 812, 1537, 1559, 23, 370, 3920, 5180, 7153, 7990, 1011, 8227, 3696, 6174, 8580, 4016, 3831, 7233, 151, 7817, 5604, 7757, 4210, 8047, 3940, 4235, 3719, 3380, 9851, 763, 4560, 5233, 861, 7859, 208, 6768, 7560, 3546, 3137, 8458, 9409, 4268, 5910, 6121, 1133, 2876, 800, 318, 1244, 7880, 9935, 4804, 804, 5044, 8448, 4027, 9330, 218, 2378, 6417, 8497, 1256, 6144, 3823, 1146, 9978, 5747, 839, 6058, 4172, 5264, 6244, 3139, 2685, 1093, 8901, 6334, 5259, 2895, 4863, 2267, 7300, 2305, 7612, 9702, 5172, 6152, 5884, 5335, 1237, 4854, 6571, 8106, 74, 3452, 4725, 9790, 6389, 6797, 2566, 8146, 6082, 8003, 4760, 5702, 9288, 9403, 543, 9729, 3898, 5968, 1103, 3319, 1619, 5027, 7573, 5054, 7957, 4499, 6896, 6036, 3107, 5520, 6565, 5542, 6338, 7302, 8065, 77, 9839, 1355, 8513, 2496, 247, 4646, 4969, 6620, 2638, 8434, 6718, 4467, 2253, 4490, 2789, 3985, 1173, 9353, 2447, 2460, 6778, 2768, 1838, 1053, 4253, 336, 4372, 2906, 4379, 3147, 9864, 3164, 9334, 6660, 2776, 7625, 105, 6799, 8089, 3292, 2771, 7780, 4196, 5531, 3144, 5937, 904, 413, 5739, 6683, 4053, 354, 5611, 6758, 3883, 616, 9668, 6239, 3681, 6114, 6408, 9965, 6402, 4630, 9939, 8548, 3377, 6022, 4628, 9481, 7, 6554, 4618, 50, 7457, 5895, 7998, 5112, 1110, 2647, 7391, 8328, 3364, 5132, 8980, 2360, 9368, 4842, 2820, 3120, 2160, 5914, 44, 960, 5207, 7137, 581, 4563, 2311, 4137, 9760, 1074, 1820, 9774, 1046, 6519, 6984, 5122, 6506, 6390, 521, 2240, 7731, 2337, 2122, 9090, 363, 3595, 3044, 5578, 9520, 9804, 8053, 4819, 5012, 4553, 9764, 1956, 577, 796, 4689, 7026, 7597, 673, 5925, 5086, 127, 5601, 4423, 6647, 6429, 6905, 6942, 3633, 6904, 4702, 4430, 8354, 9112, 1851, 9673, 8346, 8854, 1183, 4844, 9605, 7477, 3091, 65, 7193, 465, 1776, 5408, 1682, 583, 304, 6458, 3915, 6057, 850, 9719, 8296, 5469, 2731, 769, 2877, 9597, 3884, 9305, 2625, 1900, 2469, 3172, 7864, 7587, 8594, 9778, 7005, 4489, 8649, 937, 7814, 7828, 9126, 6872, 2998, 1043, 5538, 1240, 6208, 3779, 9400, 335, 6092, 4031, 3641, 1501, 1519, 974, 7868, 1070, 3527, 8902, 3070, 7144, 9333, 802, 8116, 8298, 670, 3783, 2983, 5599, 930, 648, 4230, 9586, 5304, 6895, 9432, 9462, 5424, 6878, 1794, 777, 705, 948, 1376, 5638, 6404, 832, 4705, 977, 9704, 5421, 1387, 6910, 7854, 6439, 57, 6922, 2380, 6110, 596, 1459, 6019, 3314, 1001, 8206, 5677, 2698, 5095, 6875, 6487, 2761, 7278, 4403, 736, 1385, 270, 5543, 9944, 6043, 5353, 6766, 8744, 644, 7848, 8103, 9161, 719, 3808, 4229, 941, 6924, 5423, 5038, 3812, 320, 6578, 6387, 2152, 4060, 820, 146, 4225, 3864, 3725, 3857, 3092, 4911, 8281, 613, 6809, 7593, 7801, 6597, 9397, 4219, 6628, 6056, 442, 4724, 4312, 7143, 9284, 3891, 1998, 2, 4213, 8693, 6600, 9616, 9920, 4660, 8290, 1560, 7704, 5221, 4739, 4233, 8043, 9388, 4493, 9799, 7592, 8496, 3630, 408, 3763, 7173, 7659, 6991, 6426, 7507, 3437, 9111, 6542, 5208, 8321, 9950, 8892, 7446, 1099, 314, 9442, 3658, 3481, 7113, 5907, 9363, 5559, 6015, 2889, 5685, 2783, 6992, 8406, 1135, 2860, 1990, 556, 8614, 3056, 6606, 6407, 5917, 4905, 1615, 3298, 7164, 1348, 4284, 1088, 7495, 2228, 6664, 3236, 5021, 9184, 4440, 245, 5218, 567, 9680, 9827, 327, 186, 1012, 4165, 781, 3408, 9301, 1620, 4582, 4768, 9169, 5119, 1198, 2683, 2548, 6745, 7379, 7245, 9073, 1507, 1588, 7764, 5273, 7313, 9660, 7794, 6772, 1277, 2891, 4048, 1527, 7305, 8667, 662, 4790, 4716, 7355, 5468, 1061, 9787, 1332, 3627, 9621, 3168, 5508, 9114, 2601, 9532, 9216, 9176, 3176, 950, 3208, 821, 2227, 3906, 4997, 1520, 8443, 871, 715, 9856, 7085, 9937, 7322, 5059, 3839, 3620, 2147, 882, 3301, 8386, 3671, 8914, 6172, 1654, 5648, 7294, 4567, 8918, 4482, 4960, 8576, 7289, 3789, 8472, 140, 2821, 2721, 3619, 4549, 7478, 6163, 4047, 3200, 4633, 7209, 8881, 4566, 9751, 1144, 4115, 2520, 1790, 6673, 4572, 8650, 5083, 8325, 3494, 2063, 3888, 1542, 249, 6918, 1388, 7177, 6576, 1989, 9912, 2376, 1284, 2466, 3963, 3151, 9534, 8077, 6870, 3871, 42, 4510, 2003, 5126, 5324, 4914, 2817, 379, 6412, 4191, 546, 8126, 7405, 2804, 1905, 6836, 5435, 1689, 7924, 3724, 6184, 9766, 9049, 1169, 231, 4775, 17, 911, 8811, 5005, 9607, 1067, 7353, 3622, 1566, 9779, 7180, 7977, 9215, 7352, 9144, 5436, 9170, 7882, 6482, 6523, 8692, 2427, 4895, 8034, 2292, 9949, 4237, 8225, 1657, 1494, 1059, 7131, 2001, 5632, 8538, 5057, 7059, 7793, 623, 1294, 2450, 5892, 9785, 8499, 5375, 2649, 3344, 8165, 2314, 701, 6566, 2953, 2859, 9157, 8270, 6078, 1122, 1660, 2565, 739, 5287, 478, 4281, 3739, 9419, 3407, 4174, 1413, 209, 1747, 1069, 3512, 8628, 7937, 4655, 467, 3702, 2128, 1541, 7224, 2344, 4261, 8136, 5348, 2719, 6854, 4595, 1540, 2326, 5832, 1705, 1153, 5584, 9133, 1357, 5163, 3846, 8295, 8041, 9320, 7836, 2499, 9879, 9536, 8262, 6107, 9115, 8318, 9496, 9307, 3273, 4305, 1552, 8026, 3610, 7537, 3747, 3443, 9834, 3387, 4557, 1714, 2875, 1640, 6228, 530, 9189, 6488, 7750, 495, 8634, 860, 7133, 7055, 2602, 7467, 7748, 111, 8132, 2312, 2049, 3644, 562, 8080, 778, 8124, 8361, 3081, 6409, 1647, 2892, 5449, 9782, 4011, 8424, 7873, 898, 7939, 7938, 9609, 2752, 8025, 4869, 8750, 8847, 526, 8816, 3323, 9548, 7856, 542, 5169, 4978, 4150, 2031, 392, 1958, 533, 6790, 5679, 8110, 5272, 9801, 1280, 3993, 3612, 4007, 2707, 2356, 5759, 5634, 9733, 3294, 4832, 9922, 7437, 9180, 7087, 1777, 9200, 5715, 8722, 7331, 9459, 8332, 3257, 5834, 1692, 3414, 6866, 9390, 2992, 3701, 3628, 2658, 6406, 2436, 2508, 2949, 3718, 5596, 8208, 7722, 6575, 3723, 6028, 4772, 8285, 727, 9091, 1737, 5400, 8539, 9420, 5015, 9831, 4641, 468, 8694, 9158, 3205, 9002, 5995, 8375, 7145, 8606, 6515, 6998, 4778, 5170, 1564, 7076, 2911, 6934, 758, 4109, 1629, 9313, 7257, 4335, 2090, 239, 4394, 8857, 7551, 4681, 5270, 7311, 4818, 5824, 7349, 7586, 659, 8562, 6481, 7172, 7225, 8242, 647, 9443, 9356, 9757, 8032, 2779, 4840, 827, 4820, 1486, 8698, 1770, 66, 196, 236, 3193, 6143, 6695, 7989, 3351, 8377, 9533, 6012, 1100, 994, 1337, 3861, 7710, 4539, 5661, 1509, 9074, 5040, 1936, 7532, 6141, 4472, 9929, 7954, 3094, 8323, 7424, 2657, 6950, 7271, 6340, 9567, 1168, 8841, 5094, 3199, 5104, 9193, 1839, 4051, 7489, 4835, 6430, 7390, 5033, 1902, 5570, 6960, 6933, 2656, 222, 5414, 9373, 2743, 6522, 5965, 2105, 117, 6422, 4926, 307, 970, 5798, 2083, 3832, 6132, 2363, 430, 1082, 8414, 2324, 6382, 424, 4327, 9518, 4127, 3171, 9129, 28, 6032, 6459, 4750, 5947, 667, 3219, 4457, 9716, 4652, 8196, 194, 1433, 1304, 8999, 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5384]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split the modified text by newlines first to get rows, and then by commas to get individual values\n",
+    "rows = modified_data.split('\\n')\n",
+    "int_list = []\n",
+    "\n",
+    "for row in rows:\n",
+    "    # Split each row by commas to get individual values\n",
+    "    values = row.split(',')\n",
+    "    \n",
+    "    for value in values:\n",
+    "        # Check if the value is not an empty string\n",
+    "        if value.strip():\n",
+    "            # Convert each value to an integer and append to the int_list\n",
+    "            int_list.append(int(value.strip()))\n",
+    "\n",
+    "print(int_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          \n",
+      "0     6568\n",
+      "1     5877\n",
+      "2     9559\n",
+      "3     7219\n",
+      "4     4537\n",
+      "...    ...\n",
+      "9995  4255\n",
+      "9996  6262\n",
+      "9997  6492\n",
+      "9998  4144\n",
+      "9999  5384\n",
+      "\n",
+      "[10000 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(int_list, columns=[\"\"])\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([''], dtype='object')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = ['Numbers']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Numbers'], dtype='object')"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Numbers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Numbers\n",
+       "0        6568\n",
+       "1        5877\n",
+       "2        9559\n",
+       "3        7219\n",
+       "4        4537\n",
+       "...       ...\n",
+       "9995     4255\n",
+       "9996     6262\n",
+       "9997     6492\n",
+       "9998     4144\n",
+       "9999     5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Numbers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Numbers\n",
+       "0        6568\n",
+       "1        5877\n",
+       "2        9559\n",
+       "3        7219\n",
+       "4        4537\n",
+       "...       ...\n",
+       "9995     4255\n",
+       "9996     6262\n",
+       "9997     6492\n",
+       "9998     4144\n",
+       "9999     5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Numbers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Numbers\n",
+       "0        6568\n",
+       "1        5877\n",
+       "2        9559\n",
+       "3        7219\n",
+       "4        4537\n",
+       "...       ...\n",
+       "9995     4255\n",
+       "9996     6262\n",
+       "9997     6492\n",
+       "9998     4144\n",
+       "9999     5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Numbers'], dtype='object')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Numbers\"] = df[\"Numbers\"].astype(str).str.zfill(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Numbers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Numbers\n",
+       "0       6568\n",
+       "1       5877\n",
+       "2       9559\n",
+       "3       7219\n",
+       "4       4537\n",
+       "...      ...\n",
+       "9995    4255\n",
+       "9996    6262\n",
+       "9997    6492\n",
+       "9998    4144\n",
+       "9999    5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the column labels\n",
+    "df.columns = [\"\" for _ in df.columns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>4255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>6262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>6492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>4144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>5384</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          \n",
+       "0     6568\n",
+       "1     5877\n",
+       "2     9559\n",
+       "3     7219\n",
+       "4     4537\n",
+       "...    ...\n",
+       "9995  4255\n",
+       "9996  6262\n",
+       "9997  6492\n",
+       "9998  4144\n",
+       "9999  5384\n",
+       "\n",
+       "[10000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = [None] * len(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Save the DataFrame to an Excel file\n",
+    "df.to_csv('Pasword_Numbers.txt', index=False)  # 'numbers.xlsx' is the file name you can change\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/LICT_output.csv b/Udemy DataScience Workbook/LICT_output.csv
new file mode 100644
index 0000000..827c0be
--- /dev/null
+++ b/Udemy DataScience Workbook/LICT_output.csv	
@@ -0,0 +1,602 @@
+Jan/08/2024,23:45:17,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.237:63824->67.215.6.194:22,,len,52
+Jan/08/2024,23:45:17,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.237:63824->67.215.6.194:22,,len,52
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+Jan/11/2024,03:42:26,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.149:49198->92.204.185.129:22,,len,52
+Jan/11/2024,03:42:28,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.149:49198->92.204.185.129:22,,len,52
+Jan/11/2024,03:42:28,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.149:49198->92.204.185.129:22,,len,52
+Jan/11/2024,03:42:32,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.149:49198->92.204.185.129:22,,len,52
+Jan/11/2024,03:42:32,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.149:49198->92.204.185.129:22,,len,52
+Jan/11/2024,03:42:40,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.149:49198->92.204.185.129:22,,len,52
+Jan/11/2024,03:42:40,firewall,info,forward:,in:HighRise,out:Uplink-1,,src-mac,4c:5e:0c:46:67:88,,proto,TCP,(SYN),,10.101.40.149:49198->92.204.185.129:22,,len,52
+Jan/11/2024,06:26:48,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:26:48,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:26:49,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:26:49,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:26:51,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:26:51,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:26:55,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:26:55,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:27:03,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,06:27:03,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.1.192:58277->119.94.9.228:21,,len,52
+Jan/11/2024,09:55:03,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:50600->192.30.255.112:22,,len,52
+Jan/11/2024,09:55:03,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:50600->192.30.255.112:22,,len,52
+Jan/11/2024,10:24:30,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:51912->140.82.121.4:22,,len,52
+Jan/11/2024,10:24:30,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:51912->140.82.121.4:22,,len,52
+Jan/11/2024,11:16:18,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:54539->192.30.255.112:22,,len,52
+Jan/11/2024,11:16:18,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:54539->192.30.255.112:22,,len,52
+Jan/11/2024,12:57:58,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:57955->140.82.121.3:22,,len,52
+Jan/11/2024,12:57:58,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:57955->140.82.121.3:22,,len,52
+Jan/11/2024,13:37:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50181->87.98.185.184:22,,len,52
+Jan/11/2024,13:37:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50181->87.98.185.184:22,,len,52
+Jan/11/2024,13:37:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50181->87.98.185.184:22,,len,52
+Jan/11/2024,13:37:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50181->87.98.185.184:22,,len,52
+Jan/11/2024,13:37:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50181->87.98.185.184:22,,len,48
+Jan/11/2024,13:37:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50181->87.98.185.184:22,,len,48
+Jan/11/2024,13:45:10,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:58492->140.82.121.3:22,,len,52
+Jan/11/2024,13:45:10,firewall,info,forward:,in:Backbone-8,out:Uplink-1,,src-mac,d4:ca:6d:01:86:c5,,proto,TCP,(SYN),,10.101.7.190:58492->140.82.121.3:22,,len,52
+Jan/11/2024,13:52:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50258->87.98.185.184:22,,len,52
+Jan/11/2024,13:52:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50258->87.98.185.184:22,,len,52
+Jan/11/2024,13:52:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50258->87.98.185.184:22,,len,52
+Jan/11/2024,13:52:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50258->87.98.185.184:22,,len,52
+Jan/11/2024,13:52:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50258->87.98.185.184:22,,len,48
+Jan/11/2024,13:52:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50258->87.98.185.184:22,,len,48
+Jan/11/2024,14:07:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50331->87.98.185.184:22,,len,52
+Jan/11/2024,14:07:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50331->87.98.185.184:22,,len,52
+Jan/11/2024,14:07:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50331->87.98.185.184:22,,len,52
+Jan/11/2024,14:07:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50331->87.98.185.184:22,,len,52
+Jan/11/2024,14:07:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50331->87.98.185.184:22,,len,48
+Jan/11/2024,14:07:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50331->87.98.185.184:22,,len,48
+Jan/11/2024,14:22:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50387->87.98.185.184:22,,len,52
+Jan/11/2024,14:22:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50387->87.98.185.184:22,,len,52
+Jan/11/2024,14:22:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50387->87.98.185.184:22,,len,52
+Jan/11/2024,14:22:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50387->87.98.185.184:22,,len,52
+Jan/11/2024,14:22:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50387->87.98.185.184:22,,len,48
+Jan/11/2024,14:22:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50387->87.98.185.184:22,,len,48
+Jan/11/2024,15:22:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50529->87.98.185.184:22,,len,52
+Jan/11/2024,15:22:12,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50529->87.98.185.184:22,,len,52
+Jan/11/2024,15:22:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50529->87.98.185.184:22,,len,52
+Jan/11/2024,15:22:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50529->87.98.185.184:22,,len,52
+Jan/11/2024,15:22:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50529->87.98.185.184:22,,len,48
+Jan/11/2024,15:22:13,firewall,info,forward:,in:NewICT,out:Uplink-1,,src-mac,64:d1:54:74:0f:db,,proto,TCP,(SYN),,10.101.55.103:50529->87.98.185.184:22,,len,48
diff --git a/Udemy DataScience Workbook/Lict_Raw.txt b/Udemy DataScience Workbook/Lict_Raw.txt
new file mode 100644
index 0000000..dd97b4a
--- /dev/null
+++ b/Udemy DataScience Workbook/Lict_Raw.txt	
@@ -0,0 +1,602 @@
+Jan/08/2024 23:45:17 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:63824->67.215.6.194:22, len 52
+Jan/08/2024 23:45:17 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:63824->67.215.6.194:22, len 52
+Jan/08/2024 23:45:18 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:63824->67.215.6.194:22, len 52
+Jan/08/2024 23:45:18 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:63824->67.215.6.194:22, len 52
+Jan/08/2024 23:45:18 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:63824->67.215.6.194:22, len 52
+Jan/08/2024 23:45:18 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:63824->67.215.6.194:22, len 52
+Jan/08/2024 23:45:19 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:63824->67.215.6.194:22, len 52
+Jan/08/2024 23:45:19 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:63824->67.215.6.194:22, len 52
+Jan/08/2024 23:45:49 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:49 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:50 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:50 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:51 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:51 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:51 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:51 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:52 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:45:52 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64037->62.138.16.31:22, len 52
+Jan/08/2024 23:46:22 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:22 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:22 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:22 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:23 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:23 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:24 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:24 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:25 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:46:25 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64238->85.25.213.232:22, len 52
+Jan/08/2024 23:47:03 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:03 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:04 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:04 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:08 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:08 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:47:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64394->85.93.93.11:22, len 52
+Jan/08/2024 23:48:07 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:07 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:07 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:07 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:08 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:08 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:08 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:08 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:64806->188.138.57.52:22, len 52
+Jan/08/2024 23:48:40 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:40 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:40 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:40 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:41 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:41 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:42 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:42 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:42 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:48:42 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65030->207.38.88.145:22, len 52
+Jan/08/2024 23:49:14 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:14 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:15 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:15 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:16 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:16 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:16 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:16 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:17 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:17 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65240->85.93.89.85:22, len 52
+Jan/08/2024 23:49:47 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:47 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:48 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:48 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:48 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:48 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:49 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:49 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:49 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:49:49 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:65479->209.126.120.246:22, len 52
+Jan/08/2024 23:50:19 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:19 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:20 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:20 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:20 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:20 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:21 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:21 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:21 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:21 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49291->67.43.228.250:22, len 52
+Jan/08/2024 23:50:51 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
+Jan/08/2024 23:50:51 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
+Jan/08/2024 23:50:52 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
+Jan/08/2024 23:50:52 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
+Jan/08/2024 23:50:52 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
+Jan/08/2024 23:50:52 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
+Jan/08/2024 23:50:53 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
+Jan/08/2024 23:50:53 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
+Jan/08/2024 23:50:53 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49486->92.204.185.129:22, len 52
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+Jan/08/2024 23:51:24 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49684->207.38.88.203:22, len 52
+Jan/08/2024 23:51:24 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49684->207.38.88.203:22, len 52
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+Jan/08/2024 23:51:25 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49684->207.38.88.203:22, len 52
+Jan/08/2024 23:51:25 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49684->207.38.88.203:22, len 52
+Jan/08/2024 23:51:25 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49684->207.38.88.203:22, len 52
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+Jan/08/2024 23:51:55 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
+Jan/08/2024 23:51:56 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
+Jan/08/2024 23:51:56 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
+Jan/08/2024 23:51:56 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
+Jan/08/2024 23:51:56 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
+Jan/08/2024 23:51:57 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
+Jan/08/2024 23:51:57 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
+Jan/08/2024 23:51:58 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
+Jan/08/2024 23:51:58 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49893->209.126.120.242:22, len 52
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+Jan/08/2024 23:52:28 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50104->62.138.0.98:22, len 52
+Jan/08/2024 23:52:28 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50104->62.138.0.98:22, len 52
+Jan/08/2024 23:52:28 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50104->62.138.0.98:22, len 52
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+Jan/08/2024 23:52:29 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50104->62.138.0.98:22, len 52
+Jan/08/2024 23:52:29 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50104->62.138.0.98:22, len 52
+Jan/08/2024 23:52:29 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50104->62.138.0.98:22, len 52
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+Jan/08/2024 23:52:30 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50104->62.138.0.98:22, len 52
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+Jan/08/2024 23:53:01 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50346->62.138.16.86:22, len 52
+Jan/08/2024 23:53:01 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50346->62.138.16.86:22, len 52
+Jan/08/2024 23:53:01 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50346->62.138.16.86:22, len 52
+Jan/08/2024 23:53:02 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50346->62.138.16.86:22, len 52
+Jan/08/2024 23:53:02 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50346->62.138.16.86:22, len 52
+Jan/08/2024 23:53:32 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:32 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:32 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:32 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:33 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:33 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:33 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:33 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:34 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:53:34 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50526->134.119.179.91:22, len 52
+Jan/08/2024 23:54:04 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:04 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:05 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:05 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:05 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:05 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:06 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:06 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:06 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:06 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50723->209.126.120.93:22, len 52
+Jan/08/2024 23:54:36 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:36 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:37 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:37 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:37 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:37 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:38 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:38 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:38 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:54:38 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:50917->62.138.24.84:22, len 52
+Jan/08/2024 23:55:08 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:08 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:09 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:10 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:10 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:10 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:10 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51116->134.119.222.23:22, len 52
+Jan/08/2024 23:55:40 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:40 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:41 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:41 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:41 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:41 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:42 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:42 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:43 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:55:43 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51325->209.126.122.31:22, len 52
+Jan/08/2024 23:56:13 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:13 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:13 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:13 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:14 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:14 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:15 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:15 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:15 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:15 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51511->148.72.152.18:22, len 52
+Jan/08/2024 23:56:46 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:46 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:46 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:46 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:47 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:47 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:47 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:47 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:48 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:56:48 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:51862->62.138.16.226:22, len 52
+Jan/08/2024 23:57:18 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:52081->67.43.228.130:22, len 52
+Jan/08/2024 23:57:18 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:52081->67.43.228.130:22, len 52
+Jan/09/2024 01:25:28 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:28 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:29 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:29 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:31 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:31 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:35 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:35 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:43 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 01:25:43 firewall,info forward: in:HighRise out:Uplink-1, src-mac 4c:5e:0c:46:67:88, proto TCP (SYN), 10.101.40.237:49327->151.106.18.253:22, len 52
+Jan/09/2024 02:35:40 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54270->119.94.5.228:21, len 52
+Jan/09/2024 02:35:40 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54270->119.94.5.228:21, len 52
+Jan/09/2024 02:35:41 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54270->119.94.5.228:21, len 52
+Jan/09/2024 02:35:41 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54270->119.94.5.228:21, len 52
+Jan/09/2024 02:35:43 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54270->119.94.5.228:21, len 52
+Jan/09/2024 02:35:43 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54270->119.94.5.228:21, len 52
+Jan/09/2024 02:35:47 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54270->119.94.5.228:21, len 52
+Jan/09/2024 02:35:47 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54270->119.94.5.228:21, len 52
+Jan/09/2024 02:37:55 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
+Jan/09/2024 02:37:55 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
+Jan/09/2024 02:37:56 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
+Jan/09/2024 02:37:56 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
+Jan/09/2024 02:37:58 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
+Jan/09/2024 02:37:58 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
+Jan/09/2024 02:38:02 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
+Jan/09/2024 02:38:02 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
+Jan/09/2024 02:38:10 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:54804->119.94.5.228:21, len 52
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+Jan/09/2024 02:40:14 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:14 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:15 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:15 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:16 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:16 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:21 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:21 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:28 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:40:28 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55212->119.94.5.228:21, len 52
+Jan/09/2024 02:43:32 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55692->119.94.5.228:21, len 52
+Jan/09/2024 02:43:32 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55692->119.94.5.228:21, len 52
+Jan/09/2024 02:43:33 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55692->119.94.5.228:21, len 52
+Jan/09/2024 02:43:33 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55692->119.94.5.228:21, len 52
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+Jan/09/2024 02:43:39 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55692->119.94.5.228:21, len 52
+Jan/09/2024 02:43:39 firewall,info forward: in:NewICT out:Uplink-1, src-mac 64:d1:54:74:0f:db, proto TCP (SYN), 10.101.1.192:55692->119.94.5.228:21, len 52
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diff --git a/Udemy DataScience Workbook/Line+Continuation+-+Exercise_Py3.ipynb b/Udemy DataScience Workbook/Line+Continuation+-+Exercise_Py3.ipynb
new file mode 100644
index 0000000..9c5b64e
--- /dev/null
+++ b/Udemy DataScience Workbook/Line+Continuation+-+Exercise_Py3.ipynb	
@@ -0,0 +1,68 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Line Continuation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add a backslash in the code below, so it is a one-line code. Observe the change in the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-26"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "15 + 31\n",
+    "- 26"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/List slicing.ipynb b/Udemy DataScience Workbook/List slicing.ipynb
new file mode 100644
index 0000000..da869a4
--- /dev/null
+++ b/Udemy DataScience Workbook/List slicing.ipynb	
@@ -0,0 +1,626 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dd95aaaa",
+   "metadata": {},
+   "source": [
+    "# List Slicing - Exercise #1\n",
+    "Using list slicing, obtain the numbers 100 and 115."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7fb379fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers = [15, 40, 50, 100, 115, 140]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "798db088",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 115]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers[3:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d9baad6",
+   "metadata": {},
+   "source": [
+    "Using slicing, extract the first four elements from the list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "62daa532",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[15, 40, 50, 100]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers[:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d44f8bec",
+   "metadata": {},
+   "source": [
+    "Using slicing, extract all the elements from the list from the 3rd position onwards."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2eb17564",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 100, 115, 140]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers[2:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3555455f",
+   "metadata": {},
+   "source": [
+    "Using slicing, extract the last 4 elements from the list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0fbbb2c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 100, 115, 140]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers[2:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed6377d",
+   "metadata": {},
+   "source": [
+    "Which is the position of the value 15?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4fd07d6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The value 15 is in the 0 th position\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The value 15 is in the\", Numbers.index(15),'th', \"position\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "436e55ac",
+   "metadata": {},
+   "source": [
+    "# Create a list, called Two_Numbers. Let its elements be the values 1 and 2. Then, create a new one, named All_Numbers, that will contain both the Two_Numbers and the Numbers lists in the given order.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e731dfd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers = [15, 40, 50, 100, 115, 140]\n",
+    "Two_Numbers = [1,2]\n",
+    "All_Numbers = [Numbers,Two_Numbers]\n",
+    "All_Numbers.sort(reverse = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "426696db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2], [15, 40, 50, 100, 115, 140]]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "All_Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfa9a0a1",
+   "metadata": {},
+   "source": [
+    "Sort all the numbers in the Numbers list from the largest to the smallest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "188f233d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Numbers = [15, 40, 50, 100, 115, 140]\n",
+    "Numbers.sort(reverse = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "9222b65e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[140, 115, 100, 50, 40, 15]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89052050",
+   "metadata": {},
+   "source": [
+    "# Tuples - Exercise #1\n",
+    "Create a tuple, called Cars, with elements \"BMW\", \"Dodge\", and \"Ford\" in the given order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "eb761ab5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('BMW', 'Dodge', 'Ford')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Cars = ('BMW','Dodge','Ford')\n",
+    "Cars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba6c67b3",
+   "metadata": {},
+   "source": [
+    "Access the second element of the Cars tuple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "842d3eb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Dodge'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Cars[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e499028d",
+   "metadata": {},
+   "source": [
+    "Call a method that would allow you to extract the provided name and age separately. Then print the name and age values to see if you worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "abe29e40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Peter\n",
+      "24\n"
+     ]
+    }
+   ],
+   "source": [
+    "(name, age) = 'Peter,24'.split(\",\")\n",
+    "print(name)\n",
+    "print(age)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53083d2e",
+   "metadata": {},
+   "source": [
+    "Create a function called rectangle_info() that takes the length and width of a rectangle as arguments. The function should then return the area and the perimeter of the rectangle. Call the function with arguments 2 and 10 to verify it worked correctly.\n",
+    "P=2(l+w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c4283c30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rectangle_info(l, w):\n",
+    "    Perimeter = 2 * ( l + w )\n",
+    "    Area = l * w\n",
+    "    print(\"The Area of the rectangle and it's perimeter is\", Area, \"and\", Perimeter, \"respectively\" )\n",
+    "    return Area, Perimeter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "22a9709a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Area of the rectangle and it's perimeter is 48 and 32 respectively\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(48, 32)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rectangle_info(12, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bf981a5",
+   "metadata": {},
+   "source": [
+    "This is the menu of a close-by restaurant:\n",
+    "\n",
+    "Menu = {'meal_1':'Spaghetti', 'meal_2':'Fries', 'meal_3':'Hamburger', 'meal_4':'Lasagna'}\n",
+    "\n",
+    "What is the second meal in the list?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5d2cd8a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fries\n"
+     ]
+    }
+   ],
+   "source": [
+    "Menu = {'meal_1':'Spaghetti', 'meal_2':'Fries', 'meal_3':'Hamburger', 'meal_4':'Lasagna'}\n",
+    "print (Menu[\"meal_2\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7f42833",
+   "metadata": {},
+   "source": [
+    "Add a new meal - \"Soup\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dcdfc507",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Menu['meal_5'] = \"Soup\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "da376bac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'meal_1': 'Spaghetti',\n",
+       " 'meal_2': 'Fries',\n",
+       " 'meal_3': 'Hamburger',\n",
+       " 'meal_4': 'Lasagna',\n",
+       " 'meal_5': 'Soup'}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Menu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86fb8830",
+   "metadata": {},
+   "source": [
+    "Replace the Hamburger with a Cheeseburger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5ba1f05e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'meal_1': 'Spaghetti',\n",
+       " 'meal_2': 'Fries',\n",
+       " 'meal_3': 'Cheeseburger',\n",
+       " 'meal_4': 'Lasagna',\n",
+       " 'meal_5': 'Soup'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Menu['meal_3'] = \"Cheeseburger\"\n",
+    "Menu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33b08c37",
+   "metadata": {},
+   "source": [
+    "Attach the Desserts list in the form of a sixth meal.\n",
+    "Dessert = ['Pancakes', 'Ice-cream', 'Tiramisu']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f456b7a0",
+   "metadata": {},
+   "source": [
+    "Dessert = ['Pancakes', 'Ice-cream', 'Tiramisu']\n",
+    "Create a new dictionary called Price_list that contains the first five meals of the Menu dictionary as keys and assign the following five values as prices (assumed in dollars): 10, 5, 8, 12, 5. Start by Price_list = {}.Menu['meal_6'] = Dessert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b1adf8d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'meal_1': 'Spaghetti',\n",
+       " 'meal_2': 'Fries',\n",
+       " 'meal_3': 'Cheeseburger',\n",
+       " 'meal_4': 'Lasagna',\n",
+       " 'meal_5': 'Soup',\n",
+       " 'meal_6': ['Pancakes', 'Ice-cream', 'Tiramisu']}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Menu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01a18bf4",
+   "metadata": {},
+   "source": [
+    "Create a new dictionary called Price_list that contains the first five meals of the Menu dictionary as keys and assign the following five values as prices (assumed in dollars): 10, 5, 8, 12, 5. Start by Price_list = {}."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "bdf92bcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Price_list = {'Spaghetti': 10, 'Fries': 5, 'Cheeseburger': 8, 'Lasagna': 12, 'Soup': 5}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "73ed4196",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Spaghetti': 10, 'Fries': 5, 'Cheeseburger': 8, 'Lasagna': 12, 'Soup': 5}"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Price_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8599f53d",
+   "metadata": {},
+   "source": [
+    "Use the print() function and the .get() method to check the price of the Spaghetti in the Price_list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5404a212",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Price_list.get('Spaghetti'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a00f89e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/Logistic_Regression Exercise_Done.ipynb b/Udemy DataScience Workbook/Logistic_Regression Exercise_Done.ipynb
new file mode 100644
index 0000000..c9c8bfe
--- /dev/null
+++ b/Udemy DataScience Workbook/Logistic_Regression Exercise_Done.ipynb	
@@ -0,0 +1,465 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "sns.set() # To convert all matplotlib visualization to a more beautiful one from seaborn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load raw dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1363</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1954</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1653</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1593</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>1722</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1555</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>1524</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>1461</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>168 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      SAT Admitted\n",
+       "0    1363       No\n",
+       "1    1792      Yes\n",
+       "2    1954      Yes\n",
+       "3    1653       No\n",
+       "4    1593       No\n",
+       "..    ...      ...\n",
+       "163  1722      Yes\n",
+       "164  1750      Yes\n",
+       "165  1555       No\n",
+       "166  1524       No\n",
+       "167  1461       No\n",
+       "\n",
+       "[168 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data  = pd.read_csv('2.01.+Admittance.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1363</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1954</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1653</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1593</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>1722</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>1750</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1555</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>1524</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>1461</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>168 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      SAT  Admitted\n",
+       "0    1363         0\n",
+       "1    1792         1\n",
+       "2    1954         1\n",
+       "3    1653         0\n",
+       "4    1593         0\n",
+       "..    ...       ...\n",
+       "163  1722         1\n",
+       "164  1750         1\n",
+       "165  1555         0\n",
+       "166  1524         0\n",
+       "167  1461         0\n",
+       "\n",
+       "[168 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make a copy of the dataset and replace all yes and no with 1 and 0 respectively\n",
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Variable declaration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Independent and dependent\n",
+    "y = data['Admitted']\n",
+    "x1 = data['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot a scatter plot of the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x1,y)#, color= 'C0')\n",
+    "plt.xlabel('SAT', fontsize= 20)\n",
+    "plt.ylabel('Admitted', fontsize= 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot with a regression line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4916\\2997539726.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  y_hat = x1* results_lin.params[1]+results_lin.params[0]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_lin = sm.OLS(y,x)\n",
+    "results_lin = reg_lin.fit()\n",
+    "\n",
+    "#create a scatter plot\n",
+    "plt.scatter(x1,y, color= 'C0')\n",
+    "# Plot the regression line. The coefficients are coming from results_lin.params\n",
+    "y_hat = x1* results_lin.params[1]+results_lin.params[0]\n",
+    "plt.plot(x1, y_hat, lw=2.5, color='C8')\n",
+    "plt.xlabel('SAT', fontsize= 20)\n",
+    "plt.ylabel('Admitted', fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot a logistic regression curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.137766\n",
+      "         Iterations 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4916\\2732497953.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  f_sorted = np.sort(f(x1,results_log.params[0], results_log.params[1]))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reg_log =sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "\n",
+    "def f(x,b0,b1):\n",
+    "    return np.array(np.exp(b0+x*b1) / (1 + np.exp(b0 + x * b1)))\n",
+    "f_sorted = np.sort(f(x1,results_log.params[0], results_log.params[1]))\n",
+    "x_sorted = np.sort(np.array(x1))\n",
+    "\n",
+    "plt.scatter(x1,y,color = 'C0')# Plots the points\n",
+    "plt.xlabel('SAT', fontsize= 20)\n",
+    "plt.ylabel('Admitted', fontsize= 20)\n",
+    "plt.plot(x_sorted,f_sorted, color='C8')#Draws the curve\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Making+predictions.ipynb b/Udemy DataScience Workbook/Making+predictions.ipynb
new file mode 100644
index 0000000..c5d7f9f
--- /dev/null
+++ b/Udemy DataScience Workbook/Making+predictions.ipynb	
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dummy variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('1.03. Dummies.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Map the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['Attendance'] = data['Attendance'].map({'Yes':1,'No': 0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['GPA']\n",
+    "x1 = data[['SAT','Attendance']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(y,x).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the regression line(s) on the scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(data['SAT'],y)\n",
+    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
+    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
+    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837')\n",
+    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the regression line(s) on the scatter plot and colour the data points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(data['SAT'],y,c=data['Attendance'],cmap='RdYlGn_r')\n",
+    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
+    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
+    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837')\n",
+    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add the original regression line for comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(data['SAT'],data['GPA'], c=data['Attendance'],cmap='RdYlGn_r')\n",
+    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
+    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
+    "yhat = 0.0017*data['SAT'] + 0.275\n",
+    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837', label ='regression line1')\n",
+    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026', label ='regression line2')\n",
+    "fig = plt.plot(data['SAT'],yhat, lw=3, c='#4C72B0', label ='regression line')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to make predictions based on the regressions we create"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_data = pd.DataFrame({'const': 1,'SAT': [1700, 1670], 'Attendance': [0, 1]})\n",
+    "new_data = new_data[['const','SAT','Attendance']]\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_data.rename(index={0: 'Bob',1:'Alice'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = results.predict(new_data)\n",
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictionsdf = pd.DataFrame({'Predictions':predictions})\n",
+    "joined = new_data.join(predictionsdf)\n",
+    "joined.rename(index={0: 'Bob',1:'Alice'})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Making+predictions_with_comments.ipynb b/Udemy DataScience Workbook/Making+predictions_with_comments.ipynb
new file mode 100644
index 0000000..07822ad
--- /dev/null
+++ b/Udemy DataScience Workbook/Making+predictions_with_comments.ipynb	
@@ -0,0 +1,327 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dummy variables or how to deal with categorical predictors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "# We can override the default matplotlib styles with those of Seaborn\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the data from a .csv in the same folder. Since we will do some preprocessing, the variable is not called 'data' just yet!\n",
+    "raw_data = pd.read_csv('1.03. Dummies.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Let's check what's inside this data frame\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Map the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Map all 'No' entries with 0, and all 'Yes' entries with 1. Put that in a new variable called 'data'\n",
+    "# Note data is a copy of raw_data, because of how pointers in Python work\n",
+    "data = raw_data.copy()\n",
+    "data['Attendance'] = data['Attendance'].map({'Yes': 1, 'No': 0})\n",
+    "\n",
+    "# Let's see what's inside\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# This method gives us very nice descriptive statistics.\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Following the regression equation, our dependent variable (y) is the GPA\n",
+    "y = data ['GPA']\n",
+    "# Similarly, our independent variable (x) is the SAT score\n",
+    "x1 = data [['SAT','Attendance']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add a constant. Esentially, we are adding a new column (equal in lenght to x), which consists only of 1s\n",
+    "x = sm.add_constant(x1)\n",
+    "# Fit the model, according to the OLS (ordinary least squares) method with a dependent variable y and an idependent x\n",
+    "results = sm.OLS(y,x).fit()\n",
+    "# Print a nice summary of the regression.\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the regression line(s) on the scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a scatter plot of SAT and GPA\n",
+    "plt.scatter(data['SAT'],y)\n",
+    "# Define the two regression equations, depending on whether they attended (yes), or didn't (no)\n",
+    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
+    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
+    "# Plot the two regression lines\n",
+    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837')\n",
+    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026')\n",
+    "# Name your axes :)\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the regression line(s) on the scatter plot and color the data points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In this code I want to colour the points depending on attendance\n",
+    "# Note: This code would have been very easy in Seaborn\n",
+    "\n",
+    "# Create one scatter plot which contains all observations\n",
+    "# Use the series 'Attendance' as color, and choose a colour map of your choice\n",
+    "# The colour map we've chosen is completely arbitrary\n",
+    "plt.scatter(data['SAT'],data['GPA'], c=data['Attendance'],cmap='RdYlGn_r')\n",
+    "\n",
+    "# Define the two regression equations (one with a dummy = 1, the other with dummy = 0)\n",
+    "# We have those above already, but for the sake of consistency, we will also include them here\n",
+    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
+    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
+    "\n",
+    "# Plot the two regression lines\n",
+    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837')\n",
+    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add the original regression line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Same as above, this time we are including the regression line WITHOUT the dummies.\n",
+    "\n",
+    "# In this code I want to color the points depending on attendance\n",
+    "# Note: This code would have been very easy in Seaborn\n",
+    "\n",
+    "# Create one scatter plot which contains all observations\n",
+    "# Use the series 'Attendance' as color, and choose a colour map of your choice\n",
+    "# The colour map we've chosen is completely arbitrary\n",
+    "plt.scatter(data['SAT'],data['GPA'], c=data['Attendance'],cmap='RdYlGn_r')\n",
+    "\n",
+    "# Define the two regression equations (one with a dummy = 1, the other with dummy = 0)\n",
+    "# We have those above already, but for the sake of consistency, we will also include them here\n",
+    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
+    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
+    "# Original regression line\n",
+    "yhat = 0.0017*data['SAT'] + 0.275\n",
+    "\n",
+    "# Plot the two regression lines\n",
+    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837', label ='regression line1')\n",
+    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026', label ='regression line2')\n",
+    "# Plot the original regression line\n",
+    "fig = plt.plot(data['SAT'],yhat, lw=3, c='#4C72B0', label ='regression line')\n",
+    "\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to make predictions based on the regressions we create"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's see what's inside the independent variable. \n",
+    "# The first column comes from the 'add_constant' method. It's only 1s, told ya! \n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new data frame, identical in organization to X.\n",
+    "# The constant is always 1, while each of the lines corresponds to an observation (student)\n",
+    "new_data = pd.DataFrame({'const': 1,'SAT': [1700, 1670], 'Attendance': [0, 1]})\n",
+    "# By default, when you create a df (not load, but create), the columns are sorted alphabetically\n",
+    "# So if we don't reorder them, they would be 'Attendance', 'const', 'SAT'\n",
+    "# If you feed them in the wrong order, you will get wrong results!\n",
+    "new_data = new_data[['const','SAT','Attendance']]\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I am renaming the indices for the purposes of this example.\n",
+    "# That's by not really a good practice => I won't overwrite the variable.\n",
+    "# If I want to use NumPy, sklearn, etc. methods on a df with renamed indices, they will simply be lost\n",
+    "# and returned to 0,1,2,3, etc.\n",
+    "new_data.rename(index={0: 'Bob',1:'Alice'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the predict method on the regression with the new data as a single argument\n",
+    "predictions = results.predict(new_data)\n",
+    "# The result\n",
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we want we can create a data frame, including everything\n",
+    "predictionsdf = pd.DataFrame({'Predictions':predictions})\n",
+    "# Join the two data frames\n",
+    "joined = new_data.join(predictionsdf)\n",
+    "# Rename the indices as before (not a good practice in general) \n",
+    "joined.rename(index={0: 'Bob',1:'Alice'})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Market+segmentation+example_Part2_with_comments.ipynb b/Udemy DataScience Workbook/Market+segmentation+example_Part2_with_comments.ipynb
new file mode 100644
index 0000000..670a870
--- /dev/null
+++ b/Udemy DataScience Workbook/Market+segmentation+example_Part2_with_comments.ipynb	
@@ -0,0 +1,1257 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Market segmentation example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "# Set the styles to Seaborn\n",
+    "sns.set()\n",
+    "# Import the KMeans module so we can perform k-means clustering with sklearn\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the data\n",
+    "data = pd.read_csv ('3.12.+Example.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Satisfaction</th>\n",
+       "      <th>Loyalty</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>-0.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10</td>\n",
+       "      <td>-0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>-0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-1.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-0.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.73</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Satisfaction  Loyalty\n",
+       "0              4    -1.33\n",
+       "1              6    -0.28\n",
+       "2              5    -0.99\n",
+       "3              7    -0.29\n",
+       "4              4     1.06\n",
+       "5              1    -1.66\n",
+       "6             10    -0.97\n",
+       "7              8    -0.32\n",
+       "8              8     1.02\n",
+       "9              8     0.68\n",
+       "10            10    -0.34\n",
+       "11             5     0.39\n",
+       "12             5    -1.69\n",
+       "13             2     0.67\n",
+       "14             7     0.27\n",
+       "15             9     1.36\n",
+       "16             8     1.38\n",
+       "17             7     1.36\n",
+       "18             7    -0.34\n",
+       "19             9     0.67\n",
+       "20            10     1.18\n",
+       "21             3    -1.69\n",
+       "22             4     1.04\n",
+       "23             3    -0.96\n",
+       "24             6     1.03\n",
+       "25             9    -0.99\n",
+       "26            10     0.37\n",
+       "27             9     0.03\n",
+       "28             3    -1.36\n",
+       "29             5     0.73"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check what's inside\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a preliminary plot to see if you can spot something"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Loyalty')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We are creating a scatter plot of the two variables\n",
+    "plt.scatter(data['Satisfaction'],data['Loyalty'])\n",
+    "# Name your axes \n",
+    "plt.xlabel('Satisfaction')\n",
+    "plt.ylabel('Loyalty')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select both features by creating a copy of the data variable\n",
+    "x = data.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an object (which we would call kmeans)\n",
+    "# The number in the brackets is K, or the number of clusters we are aiming for\n",
+    "kmeans = KMeans(2)\n",
+    "# Fit the data\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a copy of the input data\n",
+    "clusters = x.copy()\n",
+    "# Take note of the predicted clusters \n",
+    "clusters['cluster_pred']=kmeans.fit_predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Loyalty')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data using the longitude and the latitude\n",
+    "# c (color) is an argument which could be coded with a variable \n",
+    "# The variable in this case has values 0,1, indicating to plt.scatter, that there are two colors (0,1)\n",
+    "# All points in cluster 0 will be the same colour, all points in cluster 1 - another one, etc.\n",
+    "# cmap is the color map. Rainbow is a nice one, but you can check others here: https://matplotlib.org/users/colormaps.html\n",
+    "plt.scatter(clusters['Satisfaction'],clusters['Loyalty'],c=clusters['cluster_pred'],cmap='rainbow')\n",
+    "plt.xlabel('Satisfaction')\n",
+    "plt.ylabel('Loyalty')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Standardize the variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's standardize and check the new result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.93138063, -1.3318111 ],\n",
+       "       [-0.15523011, -0.28117124],\n",
+       "       [-0.54330537, -0.99160391],\n",
+       "       [ 0.23284516, -0.29117733],\n",
+       "       [-0.93138063,  1.05964534],\n",
+       "       [-2.09560642, -1.6620122 ],\n",
+       "       [ 1.39707095, -0.97159172],\n",
+       "       [ 0.62092042, -0.32119561],\n",
+       "       [ 0.62092042,  1.01962097],\n",
+       "       [ 0.62092042,  0.67941378],\n",
+       "       [ 1.39707095, -0.3412078 ],\n",
+       "       [-0.54330537,  0.38923705],\n",
+       "       [-0.54330537, -1.69203048],\n",
+       "       [-1.70753116,  0.66940768],\n",
+       "       [ 0.23284516,  0.26916393],\n",
+       "       [ 1.00899568,  1.35982816],\n",
+       "       [ 0.62092042,  1.37984035],\n",
+       "       [ 0.23284516,  1.35982816],\n",
+       "       [ 0.23284516, -0.3412078 ],\n",
+       "       [ 1.00899568,  0.66940768],\n",
+       "       [ 1.39707095,  1.17971847],\n",
+       "       [-1.31945589, -1.69203048],\n",
+       "       [-0.93138063,  1.03963316],\n",
+       "       [-1.31945589, -0.96158562],\n",
+       "       [-0.15523011,  1.02962706],\n",
+       "       [ 1.00899568, -0.99160391],\n",
+       "       [ 1.39707095,  0.36922486],\n",
+       "       [ 1.00899568,  0.02901767],\n",
+       "       [-1.31945589, -1.36182938],\n",
+       "       [-0.54330537,  0.72944425]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import a library which can do that easily\n",
+    "from sklearn import preprocessing\n",
+    "# Scale the inputs\n",
+    "# preprocessing.scale scales each variable (column in x) with respect to itself\n",
+    "# The new result is an array\n",
+    "x_scaled = preprocessing.scale(x)\n",
+    "x_scaled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Take advantage of the Elbow method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[59.999999999999986,\n",
+       " 30.35236058808774,\n",
+       " 17.913349527387968,\n",
+       " 10.247181805928422,\n",
+       " 7.792695153937187,\n",
+       " 6.549836791599331,\n",
+       " 5.326631124753925,\n",
+       " 4.352037765292863,\n",
+       " 3.8193164232069607]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Createa an empty list\n",
+    "wcss =[]\n",
+    "\n",
+    "# Create all possible cluster solutions with a loop\n",
+    "# We have chosen to get solutions from 1 to 9 clusters; you can ammend that if you wish\n",
+    "for i in range(1,10):\n",
+    "    # Clsuter solution with i clusters\n",
+    "    kmeans = KMeans(i)\n",
+    "    # Fit the STANDARDIZED data\n",
+    "    kmeans.fit(x_scaled)\n",
+    "    # Append the WCSS for the iteration\n",
+    "    wcss.append(kmeans.inertia_)\n",
+    "    \n",
+    "# Check the result\n",
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'WCSS')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the number of clusters vs WCSS\n",
+    "plt.plot(range(1,10),wcss)\n",
+    "# Name your axes\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore clustering solutions and select the number of clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fiddle with K (the number of clusters)\n",
+    "kmeans_new = KMeans(9)\n",
+    "# Fit the data\n",
+    "kmeans_new.fit(x_scaled)\n",
+    "# Create a new data frame with the predicted clusters\n",
+    "clusters_new = x.copy()\n",
+    "clusters_new['cluster_pred'] = kmeans_new.fit_predict(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Satisfaction</th>\n",
+       "      <th>Loyalty</th>\n",
+       "      <th>cluster_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>-0.28</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-0.99</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.29</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.66</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10</td>\n",
+       "      <td>-0.97</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.32</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.02</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>-0.34</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.69</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.36</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.38</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.36</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.34</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.18</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-1.69</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-0.96</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.99</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-1.36</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.73</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Satisfaction  Loyalty  cluster_pred\n",
+       "0              4    -1.33             1\n",
+       "1              6    -0.28             3\n",
+       "2              5    -0.99             1\n",
+       "3              7    -0.29             3\n",
+       "4              4     1.06             7\n",
+       "5              1    -1.66             5\n",
+       "6             10    -0.97             8\n",
+       "7              8    -0.32             3\n",
+       "8              8     1.02             4\n",
+       "9              8     0.68             0\n",
+       "10            10    -0.34             6\n",
+       "11             5     0.39             2\n",
+       "12             5    -1.69             1\n",
+       "13             2     0.67             7\n",
+       "14             7     0.27             3\n",
+       "15             9     1.36             4\n",
+       "16             8     1.38             4\n",
+       "17             7     1.36             4\n",
+       "18             7    -0.34             3\n",
+       "19             9     0.67             0\n",
+       "20            10     1.18             4\n",
+       "21             3    -1.69             5\n",
+       "22             4     1.04             7\n",
+       "23             3    -0.96             5\n",
+       "24             6     1.03             2\n",
+       "25             9    -0.99             8\n",
+       "26            10     0.37             6\n",
+       "27             9     0.03             6\n",
+       "28             3    -1.36             5\n",
+       "29             5     0.73             2"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check if everything seems right\n",
+    "clusters_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Loyalty')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.scatter(clusters_new['Satisfaction'],clusters_new['Loyalty'],c=clusters_new['cluster_pred'],cmap='rainbow')\n",
+    "plt.xlabel('Satisfaction')\n",
+    "plt.ylabel('Loyalty')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Satisfaction</th>\n",
+       "      <th>Loyalty</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>-0.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10</td>\n",
+       "      <td>-0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>-0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-1.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-0.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.73</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Satisfaction  Loyalty\n",
+       "0              4    -1.33\n",
+       "1              6    -0.28\n",
+       "2              5    -0.99\n",
+       "3              7    -0.29\n",
+       "4              4     1.06\n",
+       "5              1    -1.66\n",
+       "6             10    -0.97\n",
+       "7              8    -0.32\n",
+       "8              8     1.02\n",
+       "9              8     0.68\n",
+       "10            10    -0.34\n",
+       "11             5     0.39\n",
+       "12             5    -1.69\n",
+       "13             2     0.67\n",
+       "14             7     0.27\n",
+       "15             9     1.36\n",
+       "16             8     1.38\n",
+       "17             7     1.36\n",
+       "18             7    -0.34\n",
+       "19             9     0.67\n",
+       "20            10     1.18\n",
+       "21             3    -1.69\n",
+       "22             4     1.04\n",
+       "23             3    -0.96\n",
+       "24             6     1.03\n",
+       "25             9    -0.99\n",
+       "26            10     0.37\n",
+       "27             9     0.03\n",
+       "28             3    -1.36\n",
+       "29             5     0.73"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Heatmap_data = x.copy()\n",
+    "Heatmap_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.matrix.ClusterGrid at 0x61a7834210>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.clustermap(x, cmap='mako')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Market+segmentation+example_with_comments.ipynb b/Udemy DataScience Workbook/Market+segmentation+example_with_comments.ipynb
new file mode 100644
index 0000000..6cb23a6
--- /dev/null
+++ b/Udemy DataScience Workbook/Market+segmentation+example_with_comments.ipynb	
@@ -0,0 +1,664 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Market segmentation example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we explore a bit more sophisticated example of clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "# Set the styles to Seaborn\n",
+    "sns.set()\n",
+    "# Import the KMeans module so we can perform k-means clustering with sklearn\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the data\n",
+    "data = pd.read_csv ('3.12.+Example.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Satisfaction</th>\n",
+       "      <th>Loyalty</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>-0.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10</td>\n",
+       "      <td>-0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>-0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-1.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-0.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>9</td>\n",
+       "      <td>-0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-1.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.73</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Satisfaction  Loyalty\n",
+       "0              4    -1.33\n",
+       "1              6    -0.28\n",
+       "2              5    -0.99\n",
+       "3              7    -0.29\n",
+       "4              4     1.06\n",
+       "5              1    -1.66\n",
+       "6             10    -0.97\n",
+       "7              8    -0.32\n",
+       "8              8     1.02\n",
+       "9              8     0.68\n",
+       "10            10    -0.34\n",
+       "11             5     0.39\n",
+       "12             5    -1.69\n",
+       "13             2     0.67\n",
+       "14             7     0.27\n",
+       "15             9     1.36\n",
+       "16             8     1.38\n",
+       "17             7     1.36\n",
+       "18             7    -0.34\n",
+       "19             9     0.67\n",
+       "20            10     1.18\n",
+       "21             3    -1.69\n",
+       "22             4     1.04\n",
+       "23             3    -0.96\n",
+       "24             6     1.03\n",
+       "25             9    -0.99\n",
+       "26            10     0.37\n",
+       "27             9     0.03\n",
+       "28             3    -1.36\n",
+       "29             5     0.73"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check what's inside\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a preliminary plot to see if you can spot something"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Loyalty')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We are creating a scatter plot of the two variables\n",
+    "plt.scatter(data['Satisfaction'],data['Loyalty'])\n",
+    "# Name your axes \n",
+    "plt.xlabel('Satisfaction')\n",
+    "plt.ylabel('Loyalty')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select both features by creating a copy of the data variable\n",
+    "x = data.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an object (which we would call kmeans)\n",
+    "# The number in the brackets is K, or the number of clusters we are aiming for\n",
+    "kmeans = KMeans(2)\n",
+    "# Fit the data\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a copy of the input data\n",
+    "clusters = x.copy()\n",
+    "# Take note of the predicted clusters \n",
+    "clusters['cluster_pred']=kmeans.fit_predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Loyalty')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data using the longitude and the latitude\n",
+    "# c (color) is an argument which could be coded with a variable \n",
+    "# The variable in this case has values 0,1, indicating to plt.scatter, that there are two colors (0,1)\n",
+    "# All points in cluster 0 will be the same colour, all points in cluster 1 - another one, etc.\n",
+    "# cmap is the color map. Rainbow is a nice one, but you can check others here: https://matplotlib.org/users/colormaps.html\n",
+    "plt.scatter(clusters['Satisfaction'],clusters['Loyalty'],c=clusters['cluster_pred'],cmap='rainbow')\n",
+    "plt.xlabel('Satisfaction')\n",
+    "plt.ylabel('Loyalty')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Standardize the variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's standardize and check the new result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.93138063, -1.3318111 ],\n",
+       "       [-0.15523011, -0.28117124],\n",
+       "       [-0.54330537, -0.99160391],\n",
+       "       [ 0.23284516, -0.29117733],\n",
+       "       [-0.93138063,  1.05964534],\n",
+       "       [-2.09560642, -1.6620122 ],\n",
+       "       [ 1.39707095, -0.97159172],\n",
+       "       [ 0.62092042, -0.32119561],\n",
+       "       [ 0.62092042,  1.01962097],\n",
+       "       [ 0.62092042,  0.67941378],\n",
+       "       [ 1.39707095, -0.3412078 ],\n",
+       "       [-0.54330537,  0.38923705],\n",
+       "       [-0.54330537, -1.69203048],\n",
+       "       [-1.70753116,  0.66940768],\n",
+       "       [ 0.23284516,  0.26916393],\n",
+       "       [ 1.00899568,  1.35982816],\n",
+       "       [ 0.62092042,  1.37984035],\n",
+       "       [ 0.23284516,  1.35982816],\n",
+       "       [ 0.23284516, -0.3412078 ],\n",
+       "       [ 1.00899568,  0.66940768],\n",
+       "       [ 1.39707095,  1.17971847],\n",
+       "       [-1.31945589, -1.69203048],\n",
+       "       [-0.93138063,  1.03963316],\n",
+       "       [-1.31945589, -0.96158562],\n",
+       "       [-0.15523011,  1.02962706],\n",
+       "       [ 1.00899568, -0.99160391],\n",
+       "       [ 1.39707095,  0.36922486],\n",
+       "       [ 1.00899568,  0.02901767],\n",
+       "       [-1.31945589, -1.36182938],\n",
+       "       [-0.54330537,  0.72944425]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import a library which can do that easily\n",
+    "from sklearn import preprocessing\n",
+    "# Scale the inputs\n",
+    "# preprocessing.scale scales each variable (column in x) with respect to itself\n",
+    "# The new result is an array\n",
+    "x_scaled = preprocessing.scale(x)\n",
+    "x_scaled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Take advantage of the Elbow method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[59.999999999999986,\n",
+       " 29.818973034723143,\n",
+       " 17.913349527387968,\n",
+       " 10.247181805928422,\n",
+       " 7.792695153937187,\n",
+       " 6.569489487091783,\n",
+       " 5.366082587179451,\n",
+       " 4.477519065139349,\n",
+       " 3.687975963852466]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Createa an empty list\n",
+    "wcss =[]\n",
+    "\n",
+    "# Create all possible cluster solutions with a loop\n",
+    "# We have chosen to get solutions from 1 to 9 clusters; you can ammend that if you wish\n",
+    "for i in range(1,10):\n",
+    "    # Clsuter solution with i clusters\n",
+    "    kmeans = KMeans(i)\n",
+    "    # Fit the STANDARDIZED data\n",
+    "    kmeans.fit(x_scaled)\n",
+    "    # Append the WCSS for the iteration\n",
+    "    wcss.append(kmeans.inertia_)\n",
+    "    \n",
+    "# Check the result\n",
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'WCSS')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the number of clusters vs WCSS\n",
+    "plt.plot(range(1,10),wcss)\n",
+    "# Name your axes\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_All_Exercises.ipynb b/Udemy DataScience Workbook/Minimal_example_All_Exercises.ipynb
new file mode 100644
index 0000000..172d688
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_All_Exercises.ipynb	
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    1. Change the number of observations to 100,000 and see what happens.\n",
+    "    2. Change the number of observations to 1,000,000 and see what happens.\n",
+    "    3. Play around with the learning rate. Values like 0.0001, 0.001, 0.1, 1 are all interesting to observe. \n",
+    "    4. Change the loss function. L2-norm loss (without dividing by 2) is a good way to start. \n",
+    "    5. Тry with the L1-norm loss, given by the sum of the ABSOLUTE value of yj - tj. The L1-norm loss is given by:\n",
+    "## $$ \\Sigma_i = |y_i-t_i| $$\n",
+    "    6. Create a function f(x,z) = 13*xs + 7*zs - 12. Does the algorithm work in the same way?\n",
+    "    \n",
+    "    \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "You can either use this file for all the exercises, or check the solutions of EACH ONE of them in the separate files we have provided. All other files are solutions of each problem. If you feel confident enough, you can simply change values in this file. Please note that it will be nice, if you return the file to starting position after you have solved a problem, so you can use the lecture as a basis for comparison."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,1)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.04157342]\n",
+      " [ 0.0147821 ]]\n",
+      "[-0.0950374]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "237.9375214891913\n",
+      "31.4800482021761\n",
+      "13.964111079749713\n",
+      "12.000581469441467\n",
+      "11.403132854519125\n",
+      "10.948876832364975\n",
+      "10.524447122520742\n",
+      "10.117945442536769\n",
+      "9.727516123042948\n",
+      "9.35239729033499\n",
+      "8.99197331800431\n",
+      "8.645666653912972\n",
+      "8.312924101254117\n",
+      "7.993214350903906\n",
+      "7.686026932294549\n",
+      "7.390871372550447\n",
+      "7.1072764100243635\n",
+      "6.8347892414188935\n",
+      "6.572974798758853\n",
+      "6.321415054730407\n",
+      "6.079708355234708\n",
+      "5.847468778084804\n",
+      "5.624325516821062\n",
+      "5.409922288661207\n",
+      "5.203916765639678\n",
+      "5.005980028028001\n",
+      "4.815796039163488\n",
+      "4.633061140847767\n",
+      "4.4574835685093985\n",
+      "4.288782985356572\n",
+      "4.12669003477598\n",
+      "3.970945910263261\n",
+      "3.8213019421983203\n",
+      "3.6775192008057793\n",
+      "3.539368114666553\n",
+      "3.406628104171544\n",
+      "3.2790872293321054\n",
+      "3.1565418513850334\n",
+      "3.0387963076517286\n",
+      "2.925662599132429\n",
+      "2.8169600903366825\n",
+      "2.7125152208707997\n",
+      "2.6121612283218028\n",
+      "2.5157378819954075\n",
+      "2.423091227082896\n",
+      "2.334073338848407\n",
+      "2.2485420864441985\n",
+      "2.1663609059767195\n",
+      "2.0873985824612014\n",
+      "2.011529040316614\n",
+      "1.9386311420664715\n",
+      "1.8685884949240754\n",
+      "1.8012892649534196\n",
+      "1.736625998508966\n",
+      "1.6744954506692766\n",
+      "1.6147984203904842\n",
+      "1.5574395921164736\n",
+      "1.502327383592824\n",
+      "1.4493737996415557\n",
+      "1.3984942916631948\n",
+      "1.3496076226418432\n",
+      "1.3026357374376938\n",
+      "1.2575036381599094\n",
+      "1.2141392644208733\n",
+      "1.1724733782806203\n",
+      "1.1324394536977374\n",
+      "1.0939735703102351\n",
+      "1.057014311376785\n",
+      "1.021502665715382\n",
+      "0.9873819334828523\n",
+      "0.954597635644767\n",
+      "0.9230974269912247\n",
+      "0.8928310125596193\n",
+      "0.8637500673309364\n",
+      "0.8358081590713827\n",
+      "0.8089606741961197\n",
+      "0.7831647465367783\n",
+      "0.7583791888989695\n",
+      "0.7345644273005566\n",
+      "0.7116824377856588\n",
+      "0.6896966857135168\n",
+      "0.6685720674252766\n",
+      "0.648274854195565\n",
+      "0.6287726383793519\n",
+      "0.6100342816681262\n",
+      "0.5920298653727621\n",
+      "0.5747306426536968\n",
+      "0.5581089926221506\n",
+      "0.5421383762390956\n",
+      "0.5267932939415738\n",
+      "0.5120492449286902\n",
+      "0.4978826880422906\n",
+      "0.484271004179858\n",
+      "0.4711924601796094\n",
+      "0.45862617412013645\n",
+      "0.44655208197918217\n",
+      "0.4349509055983266\n",
+      "0.4238041219024145\n",
+      "0.4130939333246\n",
+      "0.40280323938977297\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.99729385]\n",
+      " [-2.99025311]] [4.30403862]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_1_Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_1_Solution.ipynb
new file mode 100644
index 0000000..8569a4f
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_1_Solution.ipynb	
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    1. Change the number of observations to 100,000 and see what happens.\n",
+    "\n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the variable \"observations\" and change it to 100000, instead of 1000.\n",
+    "\n",
+    "Here are some takeaways:\n",
+    "1. It takes the algorithm much more time to solve the problem.\n",
+    "2. The result is the same as we SCALED the deltas, so the number of observations doesn't matter.\n",
+    "3. matplotlib cannot plot the data, as there are too many points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 100000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,1)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.00686548]\n",
+      " [0.03361849]]\n",
+      "[-0.0103003]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "233.600496884892\n",
+      "36.241231859788456\n",
+      "14.355324237975136\n",
+      "11.568630931135218\n",
+      "10.875120564784945\n",
+      "10.424718557762137\n",
+      "10.015606148991033\n",
+      "9.625245489731178\n",
+      "9.250620531999013\n",
+      "8.890860859410031\n",
+      "8.545350910337753\n",
+      "8.213523471473374\n",
+      "7.894836398736943\n",
+      "7.588769300558956\n",
+      "7.294822423674476\n",
+      "7.012515808346054\n",
+      "6.741388501550289\n",
+      "6.480997803988643\n",
+      "6.230918547206692\n",
+      "5.990742399370911\n",
+      "5.760077198540075\n",
+      "5.538546312339373\n",
+      "5.325788022991434\n",
+      "5.121454936700017\n",
+      "4.925213416422069\n",
+      "4.736743037101917\n",
+      "4.5557360624781404\n",
+      "4.38189694260882\n",
+      "4.214941831294725\n",
+      "4.054598122612496\n",
+      "3.9006040058010387\n",
+      "3.7527080377743993\n",
+      "3.610668732563032\n",
+      "3.474254167013191\n",
+      "3.34324160210054\n",
+      "3.217417119239723\n",
+      "3.096575270996026\n",
+      "2.9805187456288156\n",
+      "2.869058044919012\n",
+      "2.76201117475457\n",
+      "2.65920334796872\n",
+      "2.560466698945792\n",
+      "2.4656400095286126\n",
+      "2.374568445779938\n",
+      "2.2871033051681\n",
+      "2.2031017737640637\n",
+      "2.122426693053475\n",
+      "2.044946335982895\n",
+      "1.9705341918746064\n",
+      "1.8990687598587346\n",
+      "1.8304333504854629\n",
+      "1.7645158951933693\n",
+      "1.7012087633227837\n",
+      "1.640408586375415\n",
+      "1.5820160892332573\n",
+      "1.5259359280612184\n",
+      "1.4720765346287812\n",
+      "1.4203499667965147\n",
+      "1.370671764923287\n",
+      "1.3229608139597553\n",
+      "1.2771392110029178\n",
+      "1.2331321380955091\n",
+      "1.1908677400625076\n",
+      "1.150277007185306\n",
+      "1.1112936625219876\n",
+      "1.0738540536896803\n",
+      "1.037897048932333\n",
+      "1.0033639373041963\n",
+      "0.9701983328060155\n",
+      "0.9383460823174301\n",
+      "0.9077551771752276\n",
+      "0.8783756682531009\n",
+      "0.8501595844042281\n",
+      "0.8230608541335173\n",
+      "0.7970352303716265\n",
+      "0.7720402182279189\n",
+      "0.7480350056043907\n",
+      "0.7249803965572883\n",
+      "0.702838747297586\n",
+      "0.6815739047258436\n",
+      "0.6611511474010775\n",
+      "0.6415371288472556\n",
+      "0.6226998231048445\n",
+      "0.604608472438515\n",
+      "0.5872335371156145\n",
+      "0.570546647173404\n",
+      "0.5545205560963091\n",
+      "0.5391290963275446\n",
+      "0.5243471365424732\n",
+      "0.5101505406139365\n",
+      "0.496516128202555\n",
+      "0.48342163690763906\n",
+      "0.47084568591693254\n",
+      "0.4587677410958118\n",
+      "0.4471680814589494\n",
+      "0.4360277669697036\n",
+      "0.42532860761463903\n",
+      "0.4150531337026977\n",
+      "0.4051845673405179\n",
+      "0.39570679503732875\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.00103878]\n",
+      " [-3.00002025]] [4.33586272]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_2_Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_2_Solution.ipynb
new file mode 100644
index 0000000..f1d0702
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_2_Solution.ipynb	
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    2. Change the number of observations to 1,000,000 and see what happens.\n",
+    "    \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "    \n",
+    "## Solution\n",
+    "\n",
+    "Find the variable \"observations\" and change it to 1000000, instead of 1000.\n",
+    "\n",
+    "Here are some takeaways:\n",
+    "1. It takes the algorithm much more time to solve the problem.\n",
+    "2. The result is the same as we SCALED the deltas, so the number of observations doesn't matter.\n",
+    "3. matplotlib cannot plot the data, as there are too many points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "OverflowError",
+     "evalue": "Exceeded cell block limit in Agg.  Please set the value of rcParams['agg.path.chunksize'], (currently 0) to be greater than 100 or increase the path simplification threshold(rcParams['path.simplify_threshold'] = 0.111111111111 by default and path.simplify_threshold = 0.111111111111 on the input).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    338\u001b[0m     \u001b[39mpass\u001b[39;00m\n\u001b[0;32m    339\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 340\u001b[0m     \u001b[39mreturn\u001b[39;00m printer(obj)\n\u001b[0;32m    341\u001b[0m \u001b[39m# Finally look for special method names\u001b[39;00m\n\u001b[0;32m    342\u001b[0m method \u001b[39m=\u001b[39m get_real_method(obj, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_method)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbackend_bases\u001b[39;00m \u001b[39mimport\u001b[39;00m FigureCanvasBase\n\u001b[0;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[1;32m--> 152\u001b[0m fig\u001b[39m.\u001b[39;49mcanvas\u001b[39m.\u001b[39;49mprint_figure(bytes_io, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkw)\n\u001b[0;32m    153\u001b[0m data \u001b[39m=\u001b[39m bytes_io\u001b[39m.\u001b[39mgetvalue()\n\u001b[0;32m    154\u001b[0m \u001b[39mif\u001b[39;00m fmt \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39msvg\u001b[39m\u001b[39m'\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\backend_bases.py:2187\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2183\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   2184\u001b[0m     \u001b[39m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m   2185\u001b[0m     \u001b[39m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m   2186\u001b[0m     \u001b[39mwith\u001b[39;00m cbook\u001b[39m.\u001b[39m_setattr_cm(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, dpi\u001b[39m=\u001b[39mdpi):\n\u001b[1;32m-> 2187\u001b[0m         result \u001b[39m=\u001b[39m print_method(\n\u001b[0;32m   2188\u001b[0m             filename,\n\u001b[0;32m   2189\u001b[0m             facecolor\u001b[39m=\u001b[39;49mfacecolor,\n\u001b[0;32m   2190\u001b[0m             edgecolor\u001b[39m=\u001b[39;49medgecolor,\n\u001b[0;32m   2191\u001b[0m             orientation\u001b[39m=\u001b[39;49morientation,\n\u001b[0;32m   2192\u001b[0m             bbox_inches_restore\u001b[39m=\u001b[39;49m_bbox_inches_restore,\n\u001b[0;32m   2193\u001b[0m             \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m   2194\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m   2195\u001b[0m     \u001b[39mif\u001b[39;00m bbox_inches \u001b[39mand\u001b[39;00m restore_bbox:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2039\u001b[0m     optional_kws \u001b[39m=\u001b[39m {  \u001b[39m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[0;32m   2040\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mdpi\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mfacecolor\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39medgecolor\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39morientation\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   2041\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mbbox_inches_restore\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m   2042\u001b[0m     skip \u001b[39m=\u001b[39m optional_kws \u001b[39m-\u001b[39m {\u001b[39m*\u001b[39minspect\u001b[39m.\u001b[39msignature(meth)\u001b[39m.\u001b[39mparameters}\n\u001b[1;32m-> 2043\u001b[0m     print_method \u001b[39m=\u001b[39m functools\u001b[39m.\u001b[39mwraps(meth)(\u001b[39mlambda\u001b[39;00m \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: meth(\n\u001b[0;32m   2044\u001b[0m         \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m{k: v \u001b[39mfor\u001b[39;49;00m k, v \u001b[39min\u001b[39;49;00m kwargs\u001b[39m.\u001b[39;49mitems() \u001b[39mif\u001b[39;49;00m k \u001b[39mnot\u001b[39;49;00m \u001b[39min\u001b[39;49;00m skip}))\n\u001b[0;32m   2045\u001b[0m \u001b[39melse\u001b[39;00m:  \u001b[39m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[0;32m   2046\u001b[0m     print_method \u001b[39m=\u001b[39m meth\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:497\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mprint_png\u001b[39m(\u001b[39mself\u001b[39m, filename_or_obj, \u001b[39m*\u001b[39m, metadata\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, pil_kwargs\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    451\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    452\u001b[0m \u001b[39m    Write the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m    453\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    495\u001b[0m \u001b[39m        *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m    496\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 497\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_print_pil(filename_or_obj, \u001b[39m\"\u001b[39;49m\u001b[39mpng\u001b[39;49m\u001b[39m\"\u001b[39;49m, pil_kwargs, metadata)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:445\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[1;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[0;32m    440\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_print_pil\u001b[39m(\u001b[39mself\u001b[39m, filename_or_obj, fmt, pil_kwargs, metadata\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    441\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    442\u001b[0m \u001b[39m    Draw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[0;32m    443\u001b[0m \u001b[39m    *pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[0;32m    444\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 445\u001b[0m     FigureCanvasAgg\u001b[39m.\u001b[39;49mdraw(\u001b[39mself\u001b[39;49m)\n\u001b[0;32m    446\u001b[0m     mpl\u001b[39m.\u001b[39mimage\u001b[39m.\u001b[39mimsave(\n\u001b[0;32m    447\u001b[0m         filename_or_obj, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuffer_rgba(), \u001b[39mformat\u001b[39m\u001b[39m=\u001b[39mfmt, origin\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mupper\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m    448\u001b[0m         dpi\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mdpi, metadata\u001b[39m=\u001b[39mmetadata, pil_kwargs\u001b[39m=\u001b[39mpil_kwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[39m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[0;32m    386\u001b[0m \u001b[39mwith\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtoolbar\u001b[39m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtoolbar\n\u001b[0;32m    387\u001b[0m       \u001b[39melse\u001b[39;00m nullcontext()):\n\u001b[1;32m--> 388\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfigure\u001b[39m.\u001b[39;49mdraw(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrenderer)\n\u001b[0;32m    389\u001b[0m     \u001b[39m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[0;32m    390\u001b[0m     \u001b[39m# don't forget to call the superclass.\u001b[39;00m\n\u001b[0;32m    391\u001b[0m     \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mdraw()\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[39m@wraps\u001b[39m(draw)\n\u001b[0;32m     94\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdraw_wrapper\u001b[39m(artist, renderer, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m---> 95\u001b[0m     result \u001b[39m=\u001b[39m draw(artist, renderer, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m     96\u001b[0m     \u001b[39mif\u001b[39;00m renderer\u001b[39m.\u001b[39m_rasterizing:\n\u001b[0;32m     97\u001b[0m         renderer\u001b[39m.\u001b[39mstop_rasterizing()\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3151\u001b[0m         \u001b[39m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[0;32m   3153\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[1;32m-> 3154\u001b[0m mimage\u001b[39m.\u001b[39;49m_draw_list_compositing_images(\n\u001b[0;32m   3155\u001b[0m     renderer, \u001b[39mself\u001b[39;49m, artists, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msuppressComposite)\n\u001b[0;32m   3157\u001b[0m \u001b[39mfor\u001b[39;00m sfig \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msubfigs:\n\u001b[0;32m   3158\u001b[0m     sfig\u001b[39m.\u001b[39mdraw(renderer)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[39mif\u001b[39;00m not_composite \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         a\u001b[39m.\u001b[39;49mdraw(renderer)\n\u001b[0;32m    133\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[39m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[39m=\u001b[39m []\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py:497\u001b[0m, in \u001b[0;36mAxes3D.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    494\u001b[0m         axis\u001b[39m.\u001b[39mdraw(renderer)\n\u001b[0;32m    496\u001b[0m \u001b[39m# Then rest\u001b[39;00m\n\u001b[1;32m--> 497\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mdraw(renderer)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3067\u001b[0m \u001b[39mif\u001b[39;00m artists_rasterized:\n\u001b[0;32m   3068\u001b[0m     _draw_rasterized(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[1;32m-> 3070\u001b[0m mimage\u001b[39m.\u001b[39;49m_draw_list_compositing_images(\n\u001b[0;32m   3071\u001b[0m     renderer, \u001b[39mself\u001b[39;49m, artists, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfigure\u001b[39m.\u001b[39;49msuppressComposite)\n\u001b[0;32m   3073\u001b[0m renderer\u001b[39m.\u001b[39mclose_group(\u001b[39m'\u001b[39m\u001b[39maxes\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m   3074\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstale \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[39mif\u001b[39;00m not_composite \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         a\u001b[39m.\u001b[39;49mdraw(renderer)\n\u001b[0;32m    133\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[39m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[39m=\u001b[39m []\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\mpl_toolkits\\mplot3d\\art3d.py:272\u001b[0m, in \u001b[0;36mLine3D.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    270\u001b[0m xs, ys, zs \u001b[39m=\u001b[39m proj3d\u001b[39m.\u001b[39mproj_transform(xs3d, ys3d, zs3d, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39mM)\n\u001b[0;32m    271\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_data(xs, ys)\n\u001b[1;32m--> 272\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mdraw(renderer)\n\u001b[0;32m    273\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstale \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[39m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[39mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[0;32m     73\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[39mif\u001b[39;00m artist\u001b[39m.\u001b[39mget_agg_filter() \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\lines.py:801\u001b[0m, in \u001b[0;36mLine2D.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    798\u001b[0m         gc\u001b[39m.\u001b[39mset_foreground(lc_rgba, isRGBA\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m    800\u001b[0m         gc\u001b[39m.\u001b[39mset_dashes(\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dash_pattern)\n\u001b[1;32m--> 801\u001b[0m         renderer\u001b[39m.\u001b[39;49mdraw_path(gc, tpath, affine\u001b[39m.\u001b[39;49mfrozen())\n\u001b[0;32m    802\u001b[0m         gc\u001b[39m.\u001b[39mrestore()\n\u001b[0;32m    804\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_marker \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_markersize \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:173\u001b[0m, in \u001b[0;36mRendererAgg.draw_path\u001b[1;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[0;32m    165\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    166\u001b[0m         msg \u001b[39m=\u001b[39m (\n\u001b[0;32m    167\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mExceeded cell block limit in Agg.  Please set \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    168\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mthe value of rcParams[\u001b[39m\u001b[39m'\u001b[39m\u001b[39magg.path.chunksize\u001b[39m\u001b[39m'\u001b[39m\u001b[39m], \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    169\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(currently \u001b[39m\u001b[39m{\u001b[39;00mnmax\u001b[39m}\u001b[39;00m\u001b[39m) to be greater than 100 \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    170\u001b[0m             \u001b[39m+\u001b[39m inc_threshold\n\u001b[0;32m    171\u001b[0m         )\n\u001b[1;32m--> 173\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mOverflowError\u001b[39;00m(msg) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
+      "\u001b[1;31mOverflowError\u001b[0m: Exceeded cell block limit in Agg.  Please set the value of rcParams['agg.path.chunksize'], (currently 0) to be greater than 100 or increase the path simplification threshold(rcParams['path.simplify_threshold'] = 0.111111111111 by default and path.simplify_threshold = 0.111111111111 on the input)."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,1)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.09878249]\n",
+      " [-0.0526282 ]]\n",
+      "[ 0.0526253]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "217.729872167\n",
+      "34.6368661276\n",
+      "13.9643011021\n",
+      "11.281326477\n",
+      "10.6047460805\n",
+      "10.1652328307\n",
+      "9.766394056\n",
+      "9.38592442333\n",
+      "9.02080634085\n",
+      "8.67017844201\n",
+      "8.33343886826\n",
+      "8.01003453897\n",
+      "7.69943703608\n",
+      "7.40113917233\n",
+      "7.11465387887\n",
+      "6.83951337846\n",
+      "6.57526841822\n",
+      "6.32148753569\n",
+      "6.07775635429\n",
+      "5.84367690673\n",
+      "5.61886698516\n",
+      "5.40295951717\n",
+      "5.19560196635\n",
+      "4.99645575672\n",
+      "4.80519571986\n",
+      "4.62150956403\n",
+      "4.44509736418\n",
+      "4.27567107228\n",
+      "4.11295404697\n",
+      "3.95668060188\n",
+      "3.80659557177\n",
+      "3.66245389596\n",
+      "3.52402021813\n",
+      "3.39106850203\n",
+      "3.26338166246\n",
+      "3.14075121076\n",
+      "3.02297691441\n",
+      "2.90986647007\n",
+      "2.8012351896\n",
+      "2.6969056985\n",
+      "2.59670764628\n",
+      "2.50047742832\n",
+      "2.40805791871\n",
+      "2.31929821374\n",
+      "2.23405338546\n",
+      "2.15218424504\n",
+      "2.07355711556\n",
+      "1.99804361364\n",
+      "1.9255204399\n",
+      "1.85586917761\n",
+      "1.78897609932\n",
+      "1.72473198118\n",
+      "1.6630319246\n",
+      "1.60377518495\n",
+      "1.5468650071\n",
+      "1.4922084674\n",
+      "1.43971632197\n",
+      "1.38930286099\n",
+      "1.34088576873\n",
+      "1.29438598916\n",
+      "1.24972759687\n",
+      "1.20683767307\n",
+      "1.16564618657\n",
+      "1.12608587939\n",
+      "1.08809215697\n",
+      "1.0516029827\n",
+      "1.01655877659\n",
+      "0.982902318034\n",
+      "0.950578652341\n",
+      "0.919535001031\n",
+      "0.889720675644\n",
+      "0.861086994979\n",
+      "0.833587205607\n",
+      "0.807176405531\n",
+      "0.781811470867\n",
+      "0.757450985435\n",
+      "0.734055173133\n",
+      "0.711585832986\n",
+      "0.690006276778\n",
+      "0.66928126914\n",
+      "0.649376970024\n",
+      "0.630260879441\n",
+      "0.611901784402\n",
+      "0.594269707949\n",
+      "0.577335860207\n",
+      "0.561072591381\n",
+      "0.545453346602\n",
+      "0.530452622573\n",
+      "0.516045925927\n",
+      "0.502209733229\n",
+      "0.488921452572\n",
+      "0.476159386688\n",
+      "0.463902697515\n",
+      "0.45213137218\n",
+      "0.440826190317\n",
+      "0.429968692683\n",
+      "0.419541151022\n",
+      "0.409526539115\n",
+      "0.399908504978\n",
+      "0.390671344167\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.99987799]\n",
+      " [-3.00022491]] [ 4.3435049]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "OverflowError",
+     "evalue": "In draw_path: Exceeded cell block limit",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    330\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 237\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    238\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    122\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2198\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2199\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2200\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2201\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2202\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    546\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    463\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 464\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    465\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    466\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1143\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1144\u001b[1;33m                 renderer, self, dsu, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1146\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2424\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2426\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2428\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    820\u001b[0m                     \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_sketch_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_sketch_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 822\u001b[1;33m                 \u001b[0mdrawFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maffine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    823\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m_draw_lines\u001b[1;34m(self, renderer, gc, path, trans)\u001b[0m\n\u001b[0;32m   1265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1266\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1267\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lineFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1269\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_steps_pre\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m_draw_solid\u001b[1;34m(self, renderer, gc, path, trans)\u001b[0m\n\u001b[0;32m   1291\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'solid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1292\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_dashes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dashOffset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dashSeq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1293\u001b[1;33m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_dashed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw_path\u001b[1;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[0;32m    164\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_mathtext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mOverflowError\u001b[0m: In draw_path: Exceeded cell block limit"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2230e5a6ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_3.a.+Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_3.a.+Solution.ipynb
new file mode 100644
index 0000000..d26226c
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_3.a.+Solution.ipynb	
@@ -0,0 +1,512 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    3. Play around with the learning rate. Values like 0.0001, 0.001, 0.1, 1 are all interesting to observe. \n",
+    "       \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the variable called \"learning_rate\" and change it to 0.0001.\n",
+    "\n",
+    "Here are some takeaways:\n",
+    "1. It takes the algorithm the same time to finish working.\n",
+    "2. The loss is not minimized.\n",
+    "3. The weights and biases are far from what we want them to be.\n",
+    "4. More iterations are needed for this learning rate to solve the problem.\n",
+    "5. The problem IS NOT SOLVED.\n",
+    "6. The final graph looks like a 45-degree line, but look at the axes -> it is not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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gh0uKmF+cSl5qPHmpWhLjQgvkyZD1543dbufSSy/l6quv5pJLLgEgNzeXjo4O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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x25245ab3c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.06858163]\n",
+      " [ 0.07337634]]\n",
+      "[ 0.05779229]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.0001"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "226.15990119\n",
+      "224.844157907\n",
+      "223.536489342\n",
+      "222.236845814\n",
+      "220.945177948\n",
+      "219.66143667\n",
+      "218.385573213\n",
+      "217.117539106\n",
+      "215.85728618\n",
+      "214.60476656\n",
+      "213.359932666\n",
+      "212.122737214\n",
+      "210.893133208\n",
+      "209.671073942\n",
+      "208.456513\n",
+      "207.249404248\n",
+      "206.04970184\n",
+      "204.857360209\n",
+      "203.67233407\n",
+      "202.494578418\n",
+      "201.324048524\n",
+      "200.160699934\n",
+      "199.004488469\n",
+      "197.855370221\n",
+      "196.713301553\n",
+      "195.578239096\n",
+      "194.450139748\n",
+      "193.328960675\n",
+      "192.214659303\n",
+      "191.107193323\n",
+      "190.006520684\n",
+      "188.912599597\n",
+      "187.825388528\n",
+      "186.744846199\n",
+      "185.670931588\n",
+      "184.603603923\n",
+      "183.542822685\n",
+      "182.488547603\n",
+      "181.440738655\n",
+      "180.399356066\n",
+      "179.364360304\n",
+      "178.335712082\n",
+      "177.313372355\n",
+      "176.297302317\n",
+      "175.287463402\n",
+      "174.283817282\n",
+      "173.286325863\n",
+      "172.294951287\n",
+      "171.30965593\n",
+      "170.330402397\n",
+      "169.357153526\n",
+      "168.389872382\n",
+      "167.428522259\n",
+      "166.473066674\n",
+      "165.523469372\n",
+      "164.579694319\n",
+      "163.641705705\n",
+      "162.709467938\n",
+      "161.782945646\n",
+      "160.862103677\n",
+      "159.946907091\n",
+      "159.037321167\n",
+      "158.133311396\n",
+      "157.234843482\n",
+      "156.34188334\n",
+      "155.454397095\n",
+      "154.572351079\n",
+      "153.695711834\n",
+      "152.824446106\n",
+      "151.958520846\n",
+      "151.09790321\n",
+      "150.242560554\n",
+      "149.392460436\n",
+      "148.547570614\n",
+      "147.707859043\n",
+      "146.873293878\n",
+      "146.043843467\n",
+      "145.219476356\n",
+      "144.400161281\n",
+      "143.585867174\n",
+      "142.776563155\n",
+      "141.972218537\n",
+      "141.172802821\n",
+      "140.378285693\n",
+      "139.588637031\n",
+      "138.803826893\n",
+      "138.023825525\n",
+      "137.248603355\n",
+      "136.478130992\n",
+      "135.712379227\n",
+      "134.951319031\n",
+      "134.194921553\n",
+      "133.44315812\n",
+      "132.696000236\n",
+      "131.953419578\n",
+      "131.215388001\n",
+      "130.48187753\n",
+      "129.752860365\n",
+      "129.028308874\n",
+      "128.308195598\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.46775749]\n",
+      " [-0.75662704]] [ 0.11158401]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EpKCIg5n9xMwWmlmJmRXu9dogM1tmZh+bWc+wasw0M7vXzNaY2QfRP33CrimT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\nRERiUlCIiEhM/x89sR9+DI1vOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x25245f34518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_3.b.+Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_3.b.+Solution.ipynb
new file mode 100644
index 0000000..2fdacc9
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_3.b.+Solution.ipynb	
@@ -0,0 +1,512 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    3. Play around with the learning rate. Values like 0.0001, 0.001, 0.1, 1 are all interesting to observe. \n",
+    "       \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the variable called \"learning_rate\" and change it to 0.001.\n",
+    "\n",
+    "Here are some takeaways:\n",
+    "1. It takes the algorithm the same time to finish working.\n",
+    "2. The loss is not minimized (completely).\n",
+    "3. The weights and biases are somewhat far from what we want them to be.\n",
+    "4. More iterations are needed for this learning rate to solve the problem.\n",
+    "5. The problem IS NOT SOLVED.\n",
+    "6. The final graph looks like a 45-degree line, as the weights are almost correct."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RUcGcOXMoLy/3GePvQ27ps/DDdQdo8XJN/HhDmWJXEFmMgRMS49FAbZeZn/2rnKLx8fzo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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2af12918ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.05136018]\n",
+      " [ 0.05856613]]\n",
+      "[ 0.06193808]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.001"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "237.702537072\n",
+      "222.623080676\n",
+      "208.552302441\n",
+      "195.422454992\n",
+      "183.170352508\n",
+      "171.737062872\n",
+      "161.067620641\n",
+      "151.110759424\n",
+      "141.818662364\n",
+      "133.14672948\n",
+      "125.05336074\n",
+      "117.499753792\n",
+      "110.449715372\n",
+      "103.86948544\n",
+      "97.7275732094\n",
+      "91.9946042458\n",
+      "86.64317789\n",
+      "81.6477343125\n",
+      "76.9844305409\n",
+      "72.6310248558\n",
+      "68.5667689898\n",
+      "64.7723076001\n",
+      "61.229584524\n",
+      "57.9217553582\n",
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+      "51.9489762922\n",
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+      "42.1975144771\n",
+      "40.148277398\n",
+      "38.234085386\n",
+      "36.4459189689\n",
+      "34.7753628771\n",
+      "33.2145654645\n",
+      "31.7562008615\n",
+      "30.3934336755\n",
+      "29.1198860678\n",
+      "27.9296070451\n",
+      "26.8170438176\n",
+      "25.7770150838\n",
+      "24.8046861115\n",
+      "23.8955454958\n",
+      "23.0453834781\n",
+      "22.2502717239\n",
+      "21.5065444591\n",
+      "20.8107808739\n",
+      "20.1597887096\n",
+      "19.5505889468\n",
+      "18.9804015232\n",
+      "18.4466320097\n",
+      "17.9468591815\n",
+      "17.4788234232\n",
+      "17.0404159124\n",
+      "16.6296685289\n",
+      "16.244744441\n",
+      "15.8839293234\n",
+      "15.545623164\n",
+      "15.2283326199\n",
+      "14.9306638863\n",
+      "14.6513160431\n",
+      "14.3890748472\n",
+      "14.1428069418\n",
+      "13.911454452\n",
+      "13.6940299441\n",
+      "13.4896117211\n",
+      "13.2973394342\n",
+      "13.1164099872\n",
+      "12.9460737162\n",
+      "12.7856308235\n",
+      "12.6344280515\n",
+      "12.491855578\n",
+      "12.3573441199\n",
+      "12.2303622302\n",
+      "12.1104137757\n",
+      "11.9970355838\n",
+      "11.8897952457\n",
+      "11.788289067\n",
+      "11.6921401553\n",
+      "11.6009966352\n",
+      "11.5145299825\n",
+      "11.4324334698\n",
+      "11.3544207161\n",
+      "11.2802243325\n",
+      "11.2095946588\n",
+      "11.1422985845\n",
+      "11.0781184479\n",
+      "11.0168510093\n",
+      "10.9583064918\n",
+      "10.9023076872\n",
+      "10.8486891201\n",
+      "10.7972962692\n",
+      "10.747984839\n",
+      "10.7006200814\n",
+      "10.6550761612\n",
+      "10.6112355651\n",
+      "10.5689885487\n",
+      "10.5282326217\n",
+      "10.4888720662\n",
+      "10.4508174878\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.93848578]\n",
+      " [-2.8597321 ]] [ 0.50347943]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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di5CsF2YKjw8AzKwxiYvvUqEpsLHE/U3AeSULzGwgMBAgLy8vRW1JtjpwuIjT75gcun7D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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2af12da3550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_3.c.+Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_3.c.+Solution.ipynb
new file mode 100644
index 0000000..e0bb404
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_3.c.+Solution.ipynb	
@@ -0,0 +1,512 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    3. Play around with the learning rate. Values like 0.0001, 0.001, 0.1, 1 are all interesting to observe. \n",
+    "       \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the variable called \"learning_rate\" and change it to 0.1.\n",
+    "\n",
+    "Here are some takeaways:\n",
+    "1. It takes the algorithm the same time to finish working.\n",
+    "2. The loss DIVERGES TO INFINITY.\n",
+    "3. The weights and biases are completely random and extremely big.\n",
+    "4. More iterations would not solve the issue, as the loss is not converging.\n",
+    "5. The problem IS NOT SOLVED.\n",
+    "6. The final graph looks like a 45-degree line, but look at the axes -> it is not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0Rj328U+aqGgz4ZEkHru8JGh9XfcgFzy+iyv/396YK9KNFOPVZTFa+4s17M1sNg/b7dqX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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x226321dcb00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.02218571]\n",
+      " [ 0.0845565 ]]\n",
+      "[ 0.01944659]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "252.284463128\n",
+      "1524.43927105\n",
+      "9586.15478096\n",
+      "60656.6002447\n",
+      "384385.984529\n",
+      "2437656.82137\n",
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+      "98167676.6508\n",
+      "623251214.366\n",
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+      "25135922346.2\n",
+      "159658899168.0\n",
+      "1.01421127411e+12\n",
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+      "2.60060457954e+14\n",
+      "1.65228558065e+15\n",
+      "1.04979517746e+16\n",
+      "6.67007196184e+16\n",
+      "4.23800355213e+17\n",
+      "2.69274818284e+18\n",
+      "1.71093251641e+19\n",
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+      "1.90058432185e+31\n",
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+      "7.67321533618e+32\n",
+      "4.87553784939e+33\n",
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+      "3.20843734226e+38\n",
+      "2.03863157293e+39\n",
+      "1.2953404603e+40\n",
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+      "5.2296702285e+41\n",
+      "3.32291698474e+42\n",
+      "2.1113716193e+43\n",
+      "1.3415592802e+44\n",
+      "8.52422797994e+44\n",
+      "5.41626924411e+45\n",
+      "3.4414814577e+46\n",
+      "2.18670713968e+47\n",
+      "1.38942724938e+48\n",
+      "8.82837965157e+48\n",
+      "5.60952632206e+49\n",
+      "3.56427643602e+50\n",
+      "2.26473070696e+51\n",
+      "1.43900319386e+52\n",
+      "9.14338373943e+52\n",
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+      "9.46962744739e+56\n",
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+      "2.51590598121e+63\n",
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+      "1.089523693e+73\n",
+      "6.92280132646e+73\n",
+      "4.39872749105e+74\n",
+      "2.7949384401e+75\n",
+      "1.77589561978e+76\n",
+      "1.12839882521e+77\n",
+      "7.16981276691e+77\n",
+      "4.55567782986e+78\n",
+      "2.89466422125e+79\n",
+      "1.83926108622e+80\n",
+      "1.16866105521e+81\n",
+      "7.42563778569e+81\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ -3.44684207e+40]\n",
+      " [  3.86285091e+40]] [ -1.96060233e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Zo6aV6j0L7ziWGlX0VyAiqU0/peJg7JxVDHthTszL/+m0rpye1TKBiURE4kdF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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22632653518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_3.d.+Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_3.d.+Solution.ipynb
new file mode 100644
index 0000000..994f645
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_3.d.+Solution.ipynb	
@@ -0,0 +1,512 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    3. Play around with the learning rate. Values like 0.0001, 0.001, 0.1, 1 are all interesting to observe. \n",
+    "       \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the variable called \"learning_rate\" and change it to 1.\n",
+    "\n",
+    "Here are some takeaways:\n",
+    "1. It takes the algorithm the same time to finish working.\n",
+    "2. The loss DIVERGES TO INFINITY.\n",
+    "3. The weights and biases are completely random and extremely big.\n",
+    "4. More iterations would not solve the issue, as the loss is not converging.\n",
+    "5. The problem IS NOT SOLVED.\n",
+    "6. The final graph looks like a 45-degree line, but look at the axes -> it is not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZcRxs0TFz9jyiw1WujtX+ulG797yz2WxYrVbMZjOHDx92NeP1bnLgT9wD/Su7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\nI/CCzMPDw3ONwAsyDw8PzzXC/wc7nbA5H68oPwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2cb4a8e2be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.00035649]\n",
+      " [-0.04930032]]\n",
+      "[ 0.01260633]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "206.639985309\n",
+      "188542.493431\n",
+      "184709393.476\n",
+      "182304957310.0\n",
+      "1.81265150854e+14\n",
+      "1.81537824132e+17\n",
+      "1.83081795981e+20\n",
+      "1.8586620324e+23\n",
+      "1.89869944235e+26\n",
+      "1.95081209374e+29\n",
+      "2.014970927e+32\n",
+      "2.09123278986e+35\n",
+      "2.17973801687e+38\n",
+      "2.28070867726e+41\n",
+      "2.39444745673e+44\n",
+      "2.52133714445e+47\n",
+      "2.66184070186e+50\n",
+      "2.81650189465e+53\n",
+      "2.98594647372e+56\n",
+      "3.17088389566e+59\n",
+      "3.37210957695e+62\n",
+      "3.59050768042e+65\n",
+      "3.82705443597e+68\n",
+      "4.08282200161e+71\n",
+      "4.35898287411e+74\n",
+      "4.65681486252e+77\n",
+      "4.97770664094e+80\n",
+      "5.32316390084e+83\n",
+      "5.69481612654e+86\n",
+      "6.09442402122e+89\n",
+      "6.52388761438e+92\n",
+      "6.98525508568e+95\n",
+      "7.48073234369e+98\n",
+      "8.01269340253e+101\n",
+      "8.58369160312e+104\n",
+      "9.19647173065e+107\n",
+      "9.85398308417e+110\n",
+      "1.05593935593e+114\n",
+      "1.13161048098e+117\n",
+      "1.21277685602e+120\n",
+      "1.29983041458e+123\n",
+      "1.39319173641e+126\n",
+      "1.49331207275e+129\n",
+      "1.60067552132e+132\n",
+      "1.71580136163e+135\n",
+      "1.8392465616e+138\n",
+      "1.97160846774e+141\n",
+      "2.11352769166e+144\n",
+      "2.26569120693e+147\n",
+      "2.42883567124e+150\n",
+      "2.60375098973e+153\n",
+      "2.79128413694e+156\n",
+      "2.9923432556e+159\n",
+      "3.20790205224e+162\n",
+      "3.43900451081e+165\n",
+      "3.68676994707e+168\n",
+      "3.95239842834e+171\n",
+      "4.23717658461e+174\n",
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+      "1.48266097086e+229\n",
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+      "2.41320988847e+250\n",
+      "2.58712293195e+253\n",
+      "2.77356945507e+256\n",
+      "2.97345270756e+259\n",
+      "3.18774103429e+262\n",
+      "3.41747256647e+265\n",
+      "3.6637602509e+268\n",
+      "3.92779724171e+271\n",
+      "4.21086268065e+274\n",
+      "4.51432789397e+277\n",
+      "4.83966303586e+280\n",
+      "5.18844421072e+283\n",
+      "5.56236110867e+286\n",
+      "5.96322519134e+289\n",
+      "6.39297846758e+292\n",
+      "6.8537029017e+295\n",
+      "7.34763049955e+298\n",
+      "7.87715412172e+301\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  2.10133604e+151]\n",
+      " [  6.75539128e+151]] [  6.14913033e+149]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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lRrbE55BbWkV/Pyce/WU/m+Math5evKwfzyw91OD4G3MG8Ngv+wEorS17fjijiP8sPYiv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\ncnrstDaI52yNAOJUVU0AUBT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hxf4MHvxpH1ZaDdY6LdnFlWaL0cvRipNF5nv9rmJsuDub\n43Jade1d40Ox0Cp8siEBN3sdb84ZyPheXaM1LLqnzl7C4wPACvi7dlXpdlVV56uqekhRlJ+AWGq6\npBZ05RlPTbGy0PLFzcPYn1rIjP4+aDQ1K2u9nayw1CgUV+opbmYKLMCQQGcyCisoLK+uV2Sw5vka\nKvXnt6OeJInWaW2S+ObWESxce5w9SfnMGODDy5dH4Wzb9Gw4IToTc816aryWds25l4GXOzAcswhy\ns2uwicyymAxKq1rOizufmoynY0231P7UAi79YEu98+ebJETbumt8KPO/3YNWo7Bw7iAuHegrZTdE\nl9IZZj2JWs19dnx4/RDu/i4agBGvrO2giERb+GRjAqPDaroYfWXsQXRBMjG7kyip1PPDzmS8HK1M\nx+qW6jiVJM6Fs62s3m1rvmdMNAh0tWXRvOGmYo+n6Cw0PDOzL9/eNlKShOiyJFF0EvZWFqx9eCJ2\nutPJoblSHXVZaBTGRbjz4mX9Gj1fUFZ91vG8MWfAWd/TE9jW7veQfsbiybUPT2BYsAvP/H7QdKyv\njyPL7x3LbWNDTONQQnRFkig6CYNR5YbPd5CQ07CY38AAZ+6bHNHkvXqjypBAFyy1p/9z/nr36POK\n57Ff9p/X/d1V3YkDN14QxLUjahZ7JuaU8s+RLMqra87/a2IYvy8YQy8vB7PEKURbkjGKTkKjwNQo\nb+x0WkaEuDHA3wmNovCfpQf5cVcKXg5WvDF7AP9eepCq2sFqa0sNdjoLckurWLj2eL3nPbh4nzne\nRo8wLsKdf8/oS29vB1LyyvhhZzK3LNpFRmE5Aa42vH31IIYHu5o7TCHajCSKTkJRFB6fGtng+KtX\n9qe3twMvLo8lOa+MN2YP4IHaJFBRbWT+hCCsLbW8tvJIvfuScss6JO6eJMTdjqen92FyH0/TrKXy\nagNONpakFZRzzbAAnpnVt9ky8EJ0RbJndhex8Vg2C76PxlKr4bFLevPErwfMHVKP4WBtwf2TI7hp\nVLCpMJ/RqLJo6wleX3UEBysLXr2yPxf38zZzpEKcndYuuJMxii5ifC8Pfl8wBmcbS55ZepBbx4S0\n+2s+Nb1hC6cn0Shw3chA1j8ykdvHhZqSRHpBOTd8sYMXl8cyLtydVQ+MlyQhujVJFF1ImIc9v909\nhgtC3fhyS2KD8z/ccQHPNVFI8M1WzmJyq9074+6JYbzy55EWru6+Roe5seK+cbxyRX/c7E9PWV66\nL41L3t3IvpQCXruyP5/fPAwPB6tmniRE1yddT11QRbWByGdWtcuz+/s5kZhTSmmVnm7wv8ZZC3Kz\n5anpfbi4r1e91dMFZVU8s/QQy2LSGRLozNtXDyLY3a6ZJwnR+XX2Wk/iPGxsZqOj83UgrbDdnt3Z\nPTktknljgrGy0NY7vul4No/+vJ+ckkoeubgX8yeEYaGVxrjoOSRRdEGTIj35875x9PFxYNeJfK7+\n5Pw2AHS0tqC82kC1oQc2IYDeXg58e/vIBl1IFdUGXlt5hK+2niDc057PbhpGf38nM0UphPnI16Iu\nyFKroa+vI4qi4OdyfmUhnGws2fefi/nxzlFtFN3ZCT3L7hs7nbbli5phY6nF5YySJvHZJbz/z3Fy\nSk5XzD2QWsiM9zbx1dYT3DImmOX3jpUkIXosaVF0YXmlVdz4+Y7zesbGRyfx464UnvrNPNNtG1uJ\n3pzWVNdtSqi7HQk5pbjZ63jliv5MjfImu6SShWuO892OZJbsSeXO8WFoFFi49jju9lZ8e9tIxka4\nn/NrCtEdyGB2F7buSBa3fLXrrO5xtrU8p9pPXZ2lVkGn1bDgwnBuHROCtWX9lkl8dglvrjrKqkOZ\nAMwa6MtLl0XhJAUVRTcmg9k9gL+LzVl98P/vuiF4O1kx+6PzG9PoKjQKGNWa8u2XD/Lj0Ut6m/bx\nOFOYhz0f3ziUvcn5FJRVMynSs4OjFaLzkkTRRaXml3HDFzsoKKtGo4CDtSUBrjYcSi9qclrr88sO\nkWXG7VU7mlGF4cEu/Gdmv1aPLwwOdGnnqIToeiRRdFHrj2ZTUFbNjRcEcce4UALdbAHYm5xPfHYp\nj/wc0+CeM5PElD5ebI3PabCVandgoVF4d+4gZvT3kd3khDhPkii6qOtHBnLN8IB6pcUB+vo6csWH\nW1u8//1rBzNroC/BT6xorxDbhYVGQW+s32T618QwPlofX+/YwecvaTAOIYQ4N5IouihFUbDU1v+m\nbDSq9P5361ZsP78slpiUgvYIrd2Mi3Bn0/Ec0+9XDvGjr48jH29IqHfdneNDJUkI0YZkHUU3YTSq\nhD71Z4vXnVqHkFNSyeebG9aL6sxOJYkB/k7877ohHM0s5qUVhwlys2XJv0aZdp+b0MvDnGEK0e1I\nougGVLVhkmisUN3jUyP5bcEYAl1tOyq0Jk0+x1lF390+En8XGxZ8H01eaRUL5w7il/mjGBrkyvBg\nV/zRa60AAAuzSURBVGwstQwLlgFpIdqSdD11A3UHo28fG8Ijl/Rm5cEMnvz1AAEuthzPKgHgnTXH\nMBiNJOc1vanRPw9P4LllsU3WkwrzsCM+++wWyTVm3zl0e/k523DLV7vQKPDglF7cOT4UmzortZ+e\n0Ye0gvIGtZqEEOdHFtx1E6WVenQWmnqD2/mlVUxbuInMogozRta4ESGu7EzMa/TcivvGMuO9zY2e\nGxTgzEc3DMHH6fxKlwghZOOiHsfOyqLBDKjPNiV0yiTx6pX9m0wSAM//EYuNpZa+Po4EuNZPCJf0\n85YkIUQHk66nbuz7ncktXnP9yECm9/fh7u+iKSzvmNIeTzayjeuUPl68f+1g/orN5P4fa/YEj80o\nwtvRmneuGchlA/0oqqjGTvajFqLDSYuiG7tjXGiDYw7Wpz9oQ9zt+PeMvowJd+fxqU1ve9reg9/X\njgjk4xuGoKISXzueArBgUhj/PDKBKwb7o9EoONvqGrSahBDtT76edWPejtbMGujLzaOCuPWrXZRV\nGZgz1J9FW04A0MfHARudloNphU1Wjx0a5MKepPx2i/Ghi3pxz6Rwft+XxuurjnCyqJJZA315Ylok\nfs7SxSREZyCJohubPdSf2UP9AXhgSi9sdVr+b3uS6fzfsSc5lF5Yb+bQmdozSQBYaBWu/Ggr+1IK\nTOsjhgW7tutrCiHOjrTje4hbx4YQ5mnPofQixkW4E/3MRTjb6nj4pxj2nKhJBpN6e/Di5VHomune\nuX5kYJvEo9Uo+DpZ88aqo6QXlPPWVQP5/e4xkiSE6ISkRdGDZBZW4O9iw1tXDcTVTsdrV/bntq93\nE59TwvYnJ+NkY8nM9zfhbGvJ8BBXVuzPqHf/ZYN8Gd/Lg+92ND9IPm90MF9tPdHk+Z/uGsWIkJqE\nkF9aha2VVtY+CNGJSYuiB5k10JeNj07Cq3ZPhsl9vLh6mD+fbUwgraCcF5bHkpBTyhtzBrA9Ppcp\nfbz46a5R9PVxBGDpvnTu+r89zb7GwP9v796DrKzrOI6/P1wWxDWXADVZEEG8kIEBgpeUMmPAUVAT\nS5tylUmdJDMlR+OPsGnKBkutNHUwHQwvWTg4WeElb1NQIRHXUKQlWbxhGggILHz74zzQQQ4Pu8s5\n5zm7+3nNnDnP7ffM9zd7zn7Pc/v++tQwYXjtXtdPPWfQriQB0P3AKicJswqX6QN3kq4DbgF6RcQ6\n5epB3w6cBWwC6iJiwb724wfuWm7DB9sYc9uLfLBtO+9s3MoVo/pz49jjiIhd5bk3bW3kW48u4onF\nrxfcx8EHdGbckMN5YN5qjjn0IF55awM7CnysulV1ZNl3x5SyO2bWDBX/wJ2kPsBoIP88xlhgYPK6\nHPh5BqG1Kwd17cy0CYN5Z+NWBtcezHWfOwZgtzEculV14kcXDtnrPq76zADOH9obgBVvFk4SAIu+\nM7p4gZtZ2WR5jeJW4Hpgdt6y8cCMyB3mzJNUI+ljEVH4p6wVxSkDevLI5Sdx1CHVVHXa87dDRHDS\nD57Za/s3129hSG0NPau7sO79wiPoLZo6mk5+BsKsVcrkmytpPNAQER8ehq038Fre/JpkmZXYyP49\n6FG9Z8VZgDufe3W3cbmvOL0/nTr8/4jjgbmrWfPu5j0qwu68e+qxr53CR7p2LkHUZlYOJTuikPQ0\ncFiBVVOAb5M77bQ/+7+c3Okp+vYtzi2btqcnl77BtDkrds0/MHEEpw3sxZWjBrD8jfXc/fwqnn/5\nbaY9uYILh9fyyPxcnp96ziDqTj0yq7DNrIjKfjFb0ieAZ8hdrAaoBdYCI4CbgOci4qFk2xXAp/d1\n6skXs0tj2dr1XHDXn9m0dTsfPbCK++pOZEifmt222bZ9B9/77TIa3tvM9EtO5M31H7BxSyP9e1Vn\nFLWZNVVTL2aX/RpFRCwGdp2jkFQPDE/uenocmCTpYWAk8F9fn8jG2xu28NUZ89m0dTu9aw5gxsQR\nDCjwz79zxw7cNP74XfM7b701s7aj0h64+x25W2NXkjviuDTbcNqnLY3bufKXL9Hw3maOPrSaGZeN\n5LCDnQDM2qvME0VE9MubDuCq7KKxiODGWYt5afW7DDuiO/deMpyablVZh2VmGco8UVhlmb1wLbMW\nNHDGsYdwx8VDUwsGmln74ERhu+nboxuTRx/NFaMGeOwHMwOcKOxDhvbtztC+3bMOw8wqiH8ymplZ\nKicKMzNL5URhZmapnCjMzCyVE4WZmaVyojAzs1ROFGZmlsqJwszMUmU6ZnaxSHobWJ11HEXWE1iX\ndRAl1h76CO5nW9LW+nhERPTa10ZtIlG0RZLmN6VOfGvWHvoI7mdb0h76WIhPPZmZWSonCjMzS+VE\nUbnuyTqAMmgPfQT3sy1pD33cg69RmJlZKh9RmJlZKieKCiVpmqR/Slok6TFJNVnHVAqSJkhaKmmH\npDZ3N4mkMZJWSFop6Yas4ykFSb+Q9JakJVnHUiqS+kh6VtKy5PP6jaxjKicnisr1FHB8RAwGXgZu\nzDieUlkCnA+8kHUgxSapI3AHMBYYBFwkaVC2UZXE/cCYrIMosUbguogYBJwEXNVG/5YFOVFUqIh4\nMiIak9l5QG2W8ZRKRCyPiBVZx1EiI4CVEbEqIrYCDwPjM46p6CLiBeA/WcdRShHxekQsSKY3AMuB\n3tlGVT5OFK3DZcDvsw7Cmq038Fre/Bra0T+XtkpSP+CTwF+yjaR8PGZ2hiQ9DRxWYNWUiJidbDOF\n3GHvzHLGVkxN6adZayCpGvgNcE1ErM86nnJxoshQRJyZtl5SHXA28Nloxfcx76ufbVgD0CdvvjZZ\nZq2QpM7kksTMiJiVdTzl5FNPFUrSGOB6YFxEbMo6HmuRvwEDJR0pqQr4IvB4xjFZC0gScC+wPCJ+\nnHU85eZEUbl+BhwEPCVpoaS7sg6oFCSdJ2kNcDLwhKQ5WcdULMnNCJOAOeQufv4qIpZmG1XxSXoI\nmAscI2mNpIlZx1QCpwJfBs5Ivo8LJZ2VdVDl4iezzcwslY8ozMwslROFmZmlcqIwM7NUThRmZpbK\nicLMLGPNKawo6XRJCyQ1SrrgQ+u2592V9Xje8klJYcqQ1LO58TlRmBWBpDpJh+9H+36SLi5mTNaq\n3E/TCyv+G6gDHiywbnNEnJC8xuUt/xNwJrC6JcE5UZgVRx3Q4kQB9AOcKNqpQoUVJQ2Q9AdJL0l6\nUdKxybb1EbEI2NGM/f89IupbGp8ThdleSLpW0pLkdU3yq39J3vrJkqYmh//DgZnJIf8Bkuol/VDS\nX5PXUUmb+/NPF0h6P5m8GTgtaf9NSR9P2i1MxiQZWM6+W0W4B/h6RAwDJgN3NqFNV0nzJc2TdG6x\nAnGtJ7MCJA0DLgVGAiJXKfT5QttGxK8lTQImR8T8pD3A+ogYIekrwG3k6nbtzQ1J+7OT9j8Fbo+I\nmUn5j47F6Zm1BknxwVOAR5PPEkCXJjQ9IiIaJPUH/ihpcUS8ur/xOFGYFfYp4LGI2AggaRZwWjP3\n8VDe+63NbDsXmCKpFpgVEa80s721bh2A9yLihOY0ioiG5H2VpOfIlUPf70ThU09mTVfD7t+ZrvvY\nPgpMN+7ch6QOQFXBhhEPAuOAzcAcSWe0JGBrnZIS5v+SNAFyRQklDUlrI6m7pC7JdE9y9amWFSMe\nJwqzwl4EzpXUTdKBwHnkBo86RFKP5AuZfyppA7kijvm+kPc+N5muB4Yl0+OAzoXaJ6cOVkXET8hV\nnB1cjE5ZZdpLYcUvARMl/QNYSjI6oqQTk0KaE4C7Je0sNHkcMD/Z/lng5ohYlrS5OmlTCyySNL1Z\n8bkooFlhkq4lN7ogwPSIuE3S1cDVwCpgLVAfEVMlfR74PrkjgJPJVYu9DziL3A+yiyJipaRDgdnJ\nsmfIXaysTsY6mAP0IHerZBdy1Uq3AW8AF0dEmx5u1CqXE4VZCUiqB4ZHxLqsYzHbXz71ZGZmqXxE\nYWZmqXxEYWZmqZwozMwslROFmZmlcqIwM7NUThRmZpbKicLMzFL9D1HsO6vairsYAAAAAElFTkSu\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2cb4ad654e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_4_Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_4_Solution.ipynb
new file mode 100644
index 0000000..4834ceb
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_4_Solution.ipynb	
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    4. Change the loss function. L2-norm loss (without dividing by 2) is a good way to start.    \n",
+    "    \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the variable \"loss\", where we defined the loss function. Change it from:\n",
+    "\n",
+    "\"loss = np.sum(deltas ** 2) / 2 / observations\"\n",
+    "\n",
+    "to \n",
+    "\n",
+    "\"loss = np.sum(deltas ** 2) / observations\"\n",
+    "    \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "Here are some takeaways:\n",
+    "1. It takes the algorithm the same time to finish working.\n",
+    "2. The loss is as minimized as the one in the lecture.\n",
+    "3. The weights and biases are almost the same as the ones in the lecture.\n",
+    "4. The problem is as solved as that in the lecture.\n",
+    "5. More iterations would yield a better result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x25caee10f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.08943219]\n",
+      " [0.03083914]]\n",
+      "[0.08571966]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "466.21369340916686\n",
+      "81.10026314012572\n",
+      "29.047476239627184\n",
+      "20.428435711243583\n",
+      "17.920226932157586\n",
+      "16.399807834407294\n",
+      "15.118513337010912\n",
+      "13.956353915204112\n",
+      "12.888242863907701\n",
+      "11.9043083625953\n",
+      "10.997551485301578\n",
+      "10.161859264351788\n",
+      "9.391652507019923\n",
+      "8.68179813604728\n",
+      "8.027566757318063\n",
+      "7.4245998040479035\n",
+      "6.868880278558593\n",
+      "6.356705954518614\n",
+      "5.884664706586982\n",
+      "5.44961177771825\n",
+      "5.048648828507671\n",
+      "4.679104628315602\n",
+      "4.338517259421487\n",
+      "4.024617715632323\n",
+      "3.7353147860757034\n",
+      "3.4686811234723045\n",
+      "3.222940404073917\n",
+      "2.9964554937261174\n",
+      "2.7877175412173485\n",
+      "2.595335926253938\n",
+      "2.4180289950940432\n",
+      "2.2546155221209685\n",
+      "2.1040068404725307\n",
+      "1.9651995893004224\n",
+      "1.8372690293415184\n",
+      "1.7193628822692297\n",
+      "1.610695652782405\n",
+      "1.510543395605329\n",
+      "1.4182388925363554\n",
+      "1.3331672074145144\n",
+      "1.2547615893910684\n",
+      "1.1824996972135\n",
+      "1.1159001193679314\n",
+      "1.0545191668970415\n",
+      "0.9979479175271122\n",
+      "0.9458094914120632\n",
+      "0.8977565403453817\n",
+      "0.8534689337129571\n",
+      "0.8126516257705871\n",
+      "0.7750326900378429\n",
+      "0.7403615077133655\n",
+      "0.7084070980427336\n",
+      "0.6789565795157776\n",
+      "0.6518137516417345\n",
+      "0.6267977878540137\n",
+      "0.6037420308366318\n",
+      "0.5824928822467434\n",
+      "0.5629087794365535\n",
+      "0.544859252357498\n",
+      "0.5282240543637422\n",
+      "0.5128923611243904\n",
+      "0.4987620323075157\n",
+      "0.4857389311173285\n",
+      "0.4737362971512081\n",
+      "0.46267416839855524\n",
+      "0.4524788485307945\n",
+      "0.4430824159335841\n",
+      "0.4344222712104041\n",
+      "0.42644072014296136\n",
+      "0.4190845893300792\n",
+      "0.4123048719444505\n",
+      "0.40605640124725906\n",
+      "0.40029754968562575\n",
+      "0.3949899515682446\n",
+      "0.3900982474716662\n",
+      "0.3855898486744544\n",
+      "0.38143472004986845\n",
+      "0.3776051799706874\n",
+      "0.3740757158931427\n",
+      "0.37082281439136705\n",
+      "0.36782480451004324\n",
+      "0.36506171339166577\n",
+      "0.362515133216591\n",
+      "0.3601680985694338\n",
+      "0.358004973414816\n",
+      "0.35601134692949415\n",
+      "0.3541739374968995\n",
+      "0.35248050422449717\n",
+      "0.3509197653944854\n",
+      "0.3494813233045587\n",
+      "0.34815559499801224\n",
+      "0.3469337484217152\n",
+      "0.3458076435866317\n",
+      "0.34476977833889966\n",
+      "0.3438132383801941\n",
+      "0.34293165120440244\n",
+      "0.34211914364374313\n",
+      "0.34137030274149754\n",
+      "0.34068013969068073\n",
+      "0.3400440565984189\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    # Note that we multiply by 2, as the derivative of the loss changes!\n",
+    "    weights = weights - learning_rate * 2 * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * 2 * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.00190744]\n",
+      " [-3.00196552]] [4.92952188]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x25cb0e9f710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_5_Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_5_Solution.ipynb
new file mode 100644
index 0000000..25642b9
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_5_Solution.ipynb	
@@ -0,0 +1,522 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    5. Тry with the L1-norm loss, given by the sum of the ABSOLUTE value of yj - tj. The L1-norm loss is given by:\n",
+    "## $$ \\Sigma_i = |y_i-t_i| $$\n",
+    "    \n",
+    "    \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the line where we define the L2-norm loss, and change it from:\n",
+    "\n",
+    "loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "\n",
+    "to\n",
+    "\n",
+    "loss = np.sum(np.absolute(deltas)) / 2 / observations\n",
+    "\n",
+    "You can keep the rescaling, so it is easier to compare the two methods.\n",
+    "\n",
+    "Some takeaways:\n",
+    "1. Any function that has the property to be lower for better results and higher for worse results can be a loss function.\n",
+    "This includes the L1-norm loss.\n",
+    "2. Almost everything seems identical.\n",
+    "3. The values of the loss we generally lower (as we don't elevate to the second power).\n",
+    "4. For our problem, both L1-norm and L2-norm work equally well. \n",
+    "5. Generally, L1-norm has more limitations than L2-norm. You can read more about that online. Overall, L2-norm loss is much more stable and computationally efficient. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6WZEuQsTDr3JjsSgnBnNlEqj624N2nkAF2WAwIDzc/xoqe/fuxYYNGzBz5kzM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\ni8AZZA4ODo5bBM4gc3BwcNwi/H8LPb6/6kVxmgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a70571eb00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.01361905]\n",
+      " [-0.08449486]]\n",
+      "[-0.0133745]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.67125126097\n",
+      "3.5288961583\n",
+      "2.41114806505\n",
+      "2.34869254721\n",
+      "2.3016373751\n",
+      "2.25557657907\n",
+      "2.21045537361\n",
+      "2.16624293135\n",
+      "2.1229169253\n",
+      "2.08045819426\n",
+      "2.03884889837\n",
+      "1.99807187829\n",
+      "1.9581104296\n",
+      "1.9189482205\n",
+      "1.8805692592\n",
+      "1.8429578783\n",
+      "1.80609872537\n",
+      "1.76997675555\n",
+      "1.73457722508\n",
+      "1.69988568515\n",
+      "1.66588797593\n",
+      "1.63257022081\n",
+      "1.5999188207\n",
+      "1.56792044851\n",
+      "1.53656204368\n",
+      "1.50583080686\n",
+      "1.4757141947\n",
+      "1.4461999147\n",
+      "1.41727592023\n",
+      "1.38893040556\n",
+      "1.36115180112\n",
+      "1.33392876869\n",
+      "1.30725019684\n",
+      "1.28110519635\n",
+      "1.25548309581\n",
+      "1.23037343721\n",
+      "1.20576597171\n",
+      "1.18165065546\n",
+      "1.15801764547\n",
+      "1.13485729562\n",
+      "1.1121601527\n",
+      "1.08991695258\n",
+      "1.06811861641\n",
+      "1.0467562469\n",
+      "1.02582112473\n",
+      "1.00530470494\n",
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+      "0.43332313315\n",
+      "0.425762885481\n",
+      "0.418537343294\n",
+      "0.411618239914\n",
+      "0.404903356352\n",
+      "0.398391827753\n",
+      "0.392125560068\n",
+      "0.386095796405\n",
+      "0.380281873162\n",
+      "0.374679785896\n",
+      "0.369305285742\n",
+      "0.364111144769\n",
+      "0.359147391212\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(np.absolute(deltas)) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.99504448]\n",
+      " [-3.0013878 ]] [ 4.31558026]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7L450g0V2QSMKkWqYWTdSV9kfQ+p+DJcA1d6kJ/ilPxM4z92PcPevglUb3L07\nqava78rwkUOAacHr7wQuBe529yOAUlLTxogkQkEhUr0TgH+4+5fu/gXwd+DELN/jL1X+Pi7L174O\nDDOzG4BvVQkfkdgpKETCa8p//59pkKHeq3lcseM9zKwWqV1LO7/Q/XHgdFK7sp43s96707BITVBQ\niFRvGnCmme1lZg2B75C6WVMLM2tmZvVJTbm+w0Zg76+9xw+q/P168Hgp0C14fDpQt7rXm1k7oNzd\n7yE1Y+thNbFRIrtDs8eKVMPd3woOPO+YMv0hd59hZiNJ3emtHFhU5SWPAA+Y2Y6D2QD1zewNUl/I\nzgmW/Q4Yb2Zvkrrn9JfB8rnAdjObE7xXfWCQmW0jdSe5kTW+kSIh6awnkQiY2VKg1N0/SboXkT2l\nXU8iIpKWRhQiIpKWRhQiIpKWgkJERNJSUIiISFoKChERSUtBISIiaSkoREQkrf8HEg6cve1dSPcA\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a705baf160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_6.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_6.ipynb
new file mode 100644
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+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_6.ipynb	
@@ -0,0 +1,574 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    6. Create a function f(x,z) = 13*xs + 7*zs - 12. Does the algorithm work in the same way?\n",
+    "    \n",
+    "    \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the line of code, where we declare the targets, and change it from:\n",
+    "\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "to \n",
+    "\n",
+    "targets = 13*xs + 7*zs - 12 + noise\n",
+    "\n",
+    "Some takeaways:\n",
+    "\n",
+    "1. The solution is almost found, judging by the values of the loss and those of the weights and biases.\n",
+    "2. I would say this algorithm is AS effective as that in the lecture. Still needs more iterations.\n",
+    "3. All else equal, the values of the loss are higher, as the values of the targets are higher.\n",
+    "4. The same algorithm and methodology solved a completely different problem. That's machine learning for you. \n",
+    "\n",
+    "See you in the next section!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 13*xs + 7*zs - 12 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "OverflowError",
+     "evalue": "In draw_path: Exceeded cell block limit",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    330\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 237\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    238\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    122\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2198\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2199\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2200\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2201\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2202\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    546\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    463\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 464\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    465\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    466\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1143\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1144\u001b[1;33m                 renderer, self, dsu, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1146\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    291\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m         \u001b[1;31m# Then rest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 293\u001b[1;33m         \u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_axis_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2424\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2426\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2428\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\mpl_toolkits\\mplot3d\\art3d.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    129\u001b[0m         \u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mproj3d\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproj_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs3d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys3d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzs3d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    820\u001b[0m                     \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_sketch_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_sketch_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 822\u001b[1;33m                 \u001b[0mdrawFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maffine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    823\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m_draw_lines\u001b[1;34m(self, renderer, gc, path, trans)\u001b[0m\n\u001b[0;32m   1265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1266\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1267\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lineFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1269\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_steps_pre\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m_draw_solid\u001b[1;34m(self, renderer, gc, path, trans)\u001b[0m\n\u001b[0;32m   1291\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'solid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1292\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_dashes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dashOffset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dashSeq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1293\u001b[1;33m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_dashed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw_path\u001b[1;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[0;32m    164\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_mathtext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mOverflowError\u001b[0m: In draw_path: Exceeded cell block limit"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e4c13e0b00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.00563075]\n",
+      " [-0.09377213]]\n",
+      "[ 0.08205837]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3723.93403368\n",
+      "476.774336811\n",
+      "112.761705731\n",
+      "69.8729136597\n",
+      "62.8334726567\n",
+      "59.8754001669\n",
+      "57.4578782722\n",
+      "55.1832362254\n",
+      "53.0039196264\n",
+      "50.911488489\n",
+      "48.9019826951\n",
+      "46.9720605607\n",
+      "45.1185641331\n",
+      "43.3384662376\n",
+      "41.6288602133\n",
+      "39.9869545737\n",
+      "38.4100683832\n",
+      "36.8956268721\n",
+      "35.4411572316\n",
+      "34.0442845761\n",
+      "32.7027280657\n",
+      "31.4142971813\n",
+      "30.1768881487\n",
+      "28.9884805029\n",
+      "27.8471337896\n",
+      "26.7509843961\n",
+      "25.6982425091\n",
+      "24.6871891916\n",
+      "23.7161735766\n",
+      "22.7836101715\n",
+      "21.887976269\n",
+      "21.0278094613\n",
+      "20.2017052517\n",
+      "19.4083147616\n",
+      "18.6463425279\n",
+      "17.914544388\n",
+      "17.2117254481\n",
+      "16.5367381321\n",
+      "15.8884803079\n",
+      "15.2658934878\n",
+      "14.6679611004\n",
+      "14.0937068303\n",
+      "13.5421930243\n",
+      "13.0125191602\n",
+      "12.5038203765\n",
+      "12.0152660602\n",
+      "11.5460584905\n",
+      "11.0954315365\n",
+      "10.6626494059\n",
+      "10.2470054439\n",
+      "9.84782097923\n",
+      "9.46444421584\n",
+      "9.09624916893\n",
+      "8.74263464266\n",
+      "8.40302324854\n",
+      "8.07686046266\n",
+      "7.76361372025\n",
+      "7.46277154611\n",
+      "7.17384271943\n",
+      "6.89635547177\n",
+      "6.62985671669\n",
+      "6.37391130999\n",
+      "6.12810133915\n",
+      "5.89202544101\n",
+      "5.66529814638\n",
+      "5.44754925063\n",
+      "5.23842320925\n",
+      "5.03757855729\n",
+      "4.84468735179\n",
+      "4.65943463634\n",
+      "4.4815179268\n",
+      "4.31064671741\n",
+      "4.14654200642\n",
+      "3.98893584055\n",
+      "3.83757087747\n",
+      "3.69219996561\n",
+      "3.55258574059\n",
+      "3.41850023765\n",
+      "3.28972451947\n",
+      "3.1660483186\n",
+      "3.0472696942\n",
+      "2.93319470229\n",
+      "2.82363707907\n",
+      "2.71841793676\n",
+      "2.61736547158\n",
+      "2.52031468313\n",
+      "2.42710710506\n",
+      "2.33759054626\n",
+      "2.25161884241\n",
+      "2.16905161728\n",
+      "2.08975405355\n",
+      "2.01359667265\n",
+      "1.94045512336\n",
+      "1.87020997879\n",
+      "1.80274654133\n",
+      "1.73795465541\n",
+      "1.6757285276\n",
+      "1.61596655391\n",
+      "1.55857115385\n",
+      "1.50344861114\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 13.00003998]\n",
+      " [  6.99994096]] [-10.39789187]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "OverflowError",
+     "evalue": "In draw_path: Exceeded cell block limit",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    330\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 237\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    238\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    122\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2198\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2199\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2200\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2201\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2202\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    546\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    463\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 464\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    465\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    466\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1143\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1144\u001b[1;33m                 renderer, self, dsu, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1146\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2424\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2426\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2428\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    820\u001b[0m                     \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_sketch_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_sketch_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 822\u001b[1;33m                 \u001b[0mdrawFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maffine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    823\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m_draw_lines\u001b[1;34m(self, renderer, gc, path, trans)\u001b[0m\n\u001b[0;32m   1265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1266\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1267\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lineFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1269\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_steps_pre\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m_draw_solid\u001b[1;34m(self, renderer, gc, path, trans)\u001b[0m\n\u001b[0;32m   1291\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'solid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1292\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_dashes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dashOffset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dashSeq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1293\u001b[1;33m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_dashed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw_path\u001b[1;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[0;32m    164\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_mathtext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mOverflowError\u001b[0m: In draw_path: Exceeded cell block limit"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e4bc0b25c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Exercise_6_Solution.ipynb b/Udemy DataScience Workbook/Minimal_example_Exercise_6_Solution.ipynb
new file mode 100644
index 0000000..1d488d2
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Exercise_6_Solution.ipynb	
@@ -0,0 +1,549 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    6. Create a function f(x,z) = 13*xs + 7*zs - 12. Does the algorithm work in the same way?\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the line of code, where we declare the targets, and change it from:\n",
+    "\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "to \n",
+    "\n",
+    "targets = 13*xs + 7*zs - 12 + noise\n",
+    "\n",
+    "Some takeaways:\n",
+    "\n",
+    "1. The solution is almost found, judging by the values of the loss and those of the weights and biases.\n",
+    "2. I would say this algorithm is AS effective as that in the lecture. Still needs more iterations.\n",
+    "3. All else equal, the values of the loss are higher, as the values of the targets are higher.\n",
+    "4. The same algorithm and methodology solved a completely different problem. That's machine learning for you. \n",
+    "\n",
+    "See you in the next section!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 13*xs + 7*zs - 12 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "plt.rcParams['agg.path.chunksize'] = 200\n",
+    "\n",
+    "targets = targets.reshape(observations,1)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.00563075]\n",
+      " [-0.09377213]]\n",
+      "[ 0.08205837]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3723.93403368\n",
+      "476.774336811\n",
+      "112.761705731\n",
+      "69.8729136597\n",
+      "62.8334726567\n",
+      "59.8754001669\n",
+      "57.4578782722\n",
+      "55.1832362254\n",
+      "53.0039196264\n",
+      "50.911488489\n",
+      "48.9019826951\n",
+      "46.9720605607\n",
+      "45.1185641331\n",
+      "43.3384662376\n",
+      "41.6288602133\n",
+      "39.9869545737\n",
+      "38.4100683832\n",
+      "36.8956268721\n",
+      "35.4411572316\n",
+      "34.0442845761\n",
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+      "2.71841793676\n",
+      "2.61736547158\n",
+      "2.52031468313\n",
+      "2.42710710506\n",
+      "2.33759054626\n",
+      "2.25161884241\n",
+      "2.16905161728\n",
+      "2.08975405355\n",
+      "2.01359667265\n",
+      "1.94045512336\n",
+      "1.87020997879\n",
+      "1.80274654133\n",
+      "1.73795465541\n",
+      "1.6757285276\n",
+      "1.61596655391\n",
+      "1.55857115385\n",
+      "1.50344861114\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 13.00003998]\n",
+      " [  6.99994096]] [-10.39789187]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "OverflowError",
+     "evalue": "In draw_path: Exceeded cell block limit",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    330\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 237\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    238\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    122\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2198\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2199\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2200\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2201\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2202\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    546\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    463\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 464\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    465\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    466\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1143\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1144\u001b[1;33m                 renderer, self, dsu, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1146\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2424\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2426\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2428\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    820\u001b[0m                     \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_sketch_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_sketch_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 822\u001b[1;33m                 \u001b[0mdrawFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maffine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    823\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m_draw_lines\u001b[1;34m(self, renderer, gc, path, trans)\u001b[0m\n\u001b[0;32m   1265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1266\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1267\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lineFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1269\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_steps_pre\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m_draw_solid\u001b[1;34m(self, renderer, gc, path, trans)\u001b[0m\n\u001b[0;32m   1291\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'solid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1292\u001b[0m         \u001b[0mgc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_dashes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dashOffset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dashSeq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1293\u001b[1;33m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw_dashed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda2\\envs\\ipykernel_py3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw_path\u001b[1;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[0;32m    164\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_mathtext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mOverflowError\u001b[0m: In draw_path: Exceeded cell block limit"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e4bc0b25c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Part_1.ipynb b/Udemy DataScience Workbook/Minimal_example_Part_1.ipynb
new file mode 100644
index 0000000..781c489
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Part_1.ipynb	
@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Part_2.ipynb b/Udemy DataScience Workbook/Minimal_example_Part_2.ipynb
new file mode 100644
index 0000000..a2fa7c6
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Part_2.ipynb	
@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case. It should be n x m, where m is the number of output variables, so 1000 x 1.\n",
+    "print (targets.shape)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Part_3.ipynb b/Udemy DataScience Workbook/Minimal_example_Part_3.ipynb
new file mode 100644
index 0000000..4c64fee
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Part_3.ipynb	
@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations x 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions.сх of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two matrices (vectors) into one.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We add a random small noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to our f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# Check the shape of the targets just in case.\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Minimal_example_Part_4_Complete.ipynb b/Udemy DataScience Workbook/Minimal_example_Part_4_Complete.ipynb
new file mode 100644
index 0000000..51bb06f
--- /dev/null
+++ b/Udemy DataScience Workbook/Minimal_example_Part_4_Complete.ipynb	
@@ -0,0 +1,488 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Linear Regression. Minimal example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy is a must for this example.\n",
+    "import numpy as np\n",
+    "\n",
+    "# matplotlib and mpl_toolkits are not necessary. We employ them for the sole purpose of visualizing the results.  \n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate random input data to train on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations by 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two vectors into a matrix. Alternatives are stack, dstack, hstack, etc.\n",
+    "inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# Check if the dimensions of the inputs are the same as the ones we defined in the linear model lectures. \n",
+    "# They should be n x k, where n is the number of observations, and k is the number of variables, so 1000 x 2.\n",
+    "print (inputs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the targets we will aim at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We want to \"make up\" a function, use the ML methodology, and see if the algorithm has learned it.\n",
+    "# We add a small random noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to the f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "\n",
+    "print (targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the training data\n",
+    "The point is to see that there is a strong trend that our model should learn to reproduce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In order to use the 3D plot, the objects should have a certain shape, so we reshape the targets.\n",
+    "# The proper method to use is reshape and takes as arguments the dimensions in which we want to fit the object.\n",
+    "targets = targets.reshape(observations,1)\n",
+    "\n",
+    "# Plotting according to the conventional matplotlib.pyplot syntax\n",
+    "\n",
+    "# Declare the figure\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "# A method allowing us to create the 3D plot\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Choose the axes.\n",
+    "ax.plot(xs, zs, targets)\n",
+    "\n",
+    "# Set labels\n",
+    "ax.set_xlabel('xs')\n",
+    "ax.set_ylabel('zs')\n",
+    "ax.set_zlabel('Targets')\n",
+    "\n",
+    "# You can fiddle with the azim parameter to plot the data from different angles. Just change the value of azim=100\n",
+    "# to azim = 0 ; azim = 200, or whatever. Check and see what happens.\n",
+    "ax.view_init(azim=100)\n",
+    "\n",
+    "# So far we were just describing the plot. This method actually shows the plot. \n",
+    "plt.show()\n",
+    "\n",
+    "# We reshape the targets back to the shape that they were in before plotting.\n",
+    "# This reshaping is a side-effect of the 3D plot. Sorry for that.\n",
+    "targets = targets.reshape(observations,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.0034714 ]\n",
+      " [-0.02130822]]\n",
+      "[-0.04346321]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We will initialize the weights and biases randomly in some small initial range.\n",
+    "# init_range is the variable that will measure that.\n",
+    "# You can play around with the initial range, but we don't really encourage you to do so.\n",
+    "# High initial ranges may prevent the machine learning algorithm from learning.\n",
+    "init_range = 0.1\n",
+    "\n",
+    "# Weights are of size k x m, where k is the number of input variables and m is the number of output variables\n",
+    "# In our case, the weights matrix is 2x1 since there are 2 inputs (x and z) and one output (y)\n",
+    "weights = np.random.uniform(low=-init_range, high=init_range, size=(2, 1))\n",
+    "\n",
+    "# Biases are of size 1 since there is only 1 output. The bias is a scalar.\n",
+    "biases = np.random.uniform(low=-init_range, high=init_range, size=1)\n",
+    "\n",
+    "#Print the weights to get a sense of how they were initialized.\n",
+    "print (weights)\n",
+    "print (biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set a learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set some small learning rate (denoted eta in the lecture). \n",
+    "# 0.02 is going to work quite well for our example. Once again, you can play around with it.\n",
+    "# It is HIGHLY recommended that you play around with it.\n",
+    "learning_rate = 0.02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "225.83328794708112\n",
+      "36.37216400583828\n",
+      "14.676120806512827\n",
+      "11.804659695818094\n",
+      "11.08128375697556\n",
+      "10.618830376616938\n",
+      "10.201384786748891\n",
+      "9.803551872907297\n",
+      "9.421828238120135\n",
+      "9.055259826065702\n",
+      "8.703209799887969\n",
+      "8.365099041333865\n",
+      "8.040375151939545\n",
+      "7.72850804732064\n",
+      "7.428988682546895\n",
+      "7.141328173354717\n",
+      "6.865056992563697\n",
+      "6.599724203043648\n",
+      "6.344896721605794\n",
+      "6.1001586121039075\n",
+      "5.865110406531272\n",
+      "5.639368452998323\n",
+      "5.4225642895258925\n",
+      "5.214344042631818\n",
+      "5.01436784972931\n",
+      "4.822309304394266\n",
+      "4.637854923596062\n",
+      "4.460703636022289\n",
+      "4.290566290662202\n",
+      "4.127165184846794\n",
+      "3.970233610975173\n",
+      "3.819515421187364\n",
+      "3.674764609273019\n",
+      "3.5357449091335633\n",
+      "3.4022294091424454\n",
+      "3.274000181773983\n",
+      "3.1508479278962893\n",
+      "3.032571635147711\n",
+      "2.9189782498391526\n",
+      "2.8098823618467175\n",
+      "2.705105901980429\n",
+      "2.60447785133499\n",
+      "2.507833962148211\n",
+      "2.4150164897114608\n",
+      "2.3258739348946023\n",
+      "2.2402607968651087\n",
+      "2.1580373355977485\n",
+      "2.07906934378724\n",
+      "2.0032279277915372\n",
+      "1.9303892972482177\n",
+      "1.8604345630205943\n",
+      "1.7932495431437192\n",
+      "1.7287245764535601\n",
+      "1.6667543435951606\n",
+      "1.6072376951176062\n",
+      "1.5500774863752114\n",
+      "1.4951804189654723\n",
+      "1.4424568884449436\n",
+      "1.391820838074501\n",
+      "1.3431896183552585\n",
+      "1.2964838521258735\n",
+      "1.2516273050010487\n",
+      "1.2085467609397518\n",
+      "1.1671719027400607\n",
+      "1.1274351972655732\n",
+      "1.089271785216029\n",
+      "1.0526193752622568\n",
+      "1.0174181423726132\n",
+      "0.9836106301649923\n",
+      "0.9511416571249957\n",
+      "0.9199582265372134\n",
+      "0.8900094399825754\n",
+      "0.8612464142606209\n",
+      "0.8336222016010341\n",
+      "0.8070917130342646\n",
+      "0.7816116447961117\n",
+      "0.7571404076461812\n",
+      "0.7336380589848293\n",
+      "0.711066237657793\n",
+      "0.689388101342103\n",
+      "0.6685682664110715\n",
+      "0.6485727501802013\n",
+      "0.629368915439751\n",
+      "0.6109254171834176\n",
+      "0.593212151446191\n",
+      "0.5762002061678686\n",
+      "0.5598618140020228\n",
+      "0.544170306993407\n",
+      "0.5291000730498153\n",
+      "0.5146265141373498\n",
+      "0.500726006130858\n",
+      "0.48737586025401064\n",
+      "0.474554286046084\n",
+      "0.4622403557949956\n",
+      "0.45041397037853637\n",
+      "0.43905582645805374\n",
+      "0.4281473849710341\n",
+      "0.417670840871153\n",
+      "0.40760909406640855\n",
+      "0.39794572150790214\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We iterate over our training dataset 100 times. That works well with a learning rate of 0.02.\n",
+    "# The proper number of iterations is something we will talk about later on, but generally\n",
+    "# a lower learning rate would need more iterations, while a higher learning rate would need less iterations\n",
+    "# keep in mind that a high learning rate may cause the loss to diverge to infinity, instead of converge to 0.\n",
+    "for i in range (100):\n",
+    "    \n",
+    "    # This is the linear model: y = xw + b equation\n",
+    "    outputs = np.dot(inputs,weights) + biases\n",
+    "    # The deltas are the differences between the outputs and the targets\n",
+    "    # Note that deltas here is a vector 1000 x 1\n",
+    "    deltas = outputs - targets\n",
+    "        \n",
+    "    # We are considering the L2-norm loss, but divided by 2, so it is consistent with the lectures.\n",
+    "    # Moreover, we further divide it by the number of observations.\n",
+    "    # This is simple rescaling by a constant. We explained that this doesn't change the optimization logic,\n",
+    "    # as any function holding the basic property of being lower for better results, and higher for worse results\n",
+    "    # can be a loss function.\n",
+    "    loss = np.sum(deltas ** 2) / 2 / observations\n",
+    "    \n",
+    "    # We print the loss function value at each step so we can observe whether it is decreasing as desired.\n",
+    "    print (loss)\n",
+    "    \n",
+    "    # Another small trick is to scale the deltas the same way as the loss function\n",
+    "    # In this way our learning rate is independent of the number of samples (observations).\n",
+    "    # Again, this doesn't change anything in principle, it simply makes it easier to pick a single learning rate\n",
+    "    # that can remain the same if we change the number of training samples (observations).\n",
+    "    # You can try solving the problem without rescaling to see how that works for you.\n",
+    "    deltas_scaled = deltas / observations\n",
+    "    \n",
+    "    # Finally, we must apply the gradient descent update rules from the relevant lecture.\n",
+    "    # The weights are 2x1, learning rate is 1x1 (scalar), inputs are 1000x2, and deltas_scaled are 1000x1\n",
+    "    # We must transpose the inputs so that we get an allowed operation.\n",
+    "    weights = weights - learning_rate * np.dot(inputs.T,deltas_scaled)\n",
+    "    biases = biases - learning_rate * np.sum(deltas_scaled)\n",
+    "    \n",
+    "    # The weights are updated in a linear algebraic way (a matrix minus another matrix)\n",
+    "    # The biases, however, are just a single number here, so we must transform the deltas into a scalar.\n",
+    "    # The two lines are both consistent with the gradient descent methodology. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print weights and biases and see if we have worked correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.0034714 ]\n",
+      " [-0.02130822]] [-0.04346321]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the weights and the biases, so we can see if they have converged to what we wanted.\n",
+    "# When declared the targets, following the f(x,z), we knew the weights should be 2 and -3, while the bias: 5.\n",
+    "print (weights, biases)\n",
+    "\n",
+    "# Note that they may be convergING. So more iterations are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot last outputs vs targets\n",
+    "Since they are the last ones at the end of the training, they represent the final model accuracy. <br/>\n",
+    "The closer this plot is to a 45 degree line, the closer target and output values are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We print the outputs and the targets in order to see if they have a linear relationship.\n",
+    "# Again, that's not needed. Moreover, in later lectures, that would not even be possible.\n",
+    "plt.plot(outputs,targets)\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Udemy DataScience Workbook/Modules & Packages.ipynb b/Udemy DataScience Workbook/Modules & Packages.ipynb
new file mode 100644
index 0000000..bc613e5
--- /dev/null
+++ b/Udemy DataScience Workbook/Modules & Packages.ipynb	
@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "73f69a9f",
+   "metadata": {},
+   "source": [
+    "# 4 Different ways of importing modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9b15f871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8c3596bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt(16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6bcbd6c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "65e3614a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f327aad2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Most loved method by experts:\n",
+    "from math import sqrt as s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "46da31ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s(49)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e5e8893f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math as m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6456eda7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.sqrt(64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "52e356b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Also this, but unprofessional \n",
+    "from math import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2e551af7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "899fc852",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in module math:\n",
+      "\n",
+      "NAME\n",
+      "    math\n",
+      "\n",
+      "DESCRIPTION\n",
+      "    This module provides access to the mathematical functions\n",
+      "    defined by the C standard.\n",
+      "\n",
+      "FUNCTIONS\n",
+      "    acos(x, /)\n",
+      "        Return the arc cosine (measured in radians) of x.\n",
+      "        \n",
+      "        The result is between 0 and pi.\n",
+      "    \n",
+      "    acosh(x, /)\n",
+      "        Return the inverse hyperbolic cosine of x.\n",
+      "    \n",
+      "    asin(x, /)\n",
+      "        Return the arc sine (measured in radians) of x.\n",
+      "        \n",
+      "        The result is between -pi/2 and pi/2.\n",
+      "    \n",
+      "    asinh(x, /)\n",
+      "        Return the inverse hyperbolic sine of x.\n",
+      "    \n",
+      "    atan(x, /)\n",
+      "        Return the arc tangent (measured in radians) of x.\n",
+      "        \n",
+      "        The result is between -pi/2 and pi/2.\n",
+      "    \n",
+      "    atan2(y, x, /)\n",
+      "        Return the arc tangent (measured in radians) of y/x.\n",
+      "        \n",
+      "        Unlike atan(y/x), the signs of both x and y are considered.\n",
+      "    \n",
+      "    atanh(x, /)\n",
+      "        Return the inverse hyperbolic tangent of x.\n",
+      "    \n",
+      "    ceil(x, /)\n",
+      "        Return the ceiling of x as an Integral.\n",
+      "        \n",
+      "        This is the smallest integer >= x.\n",
+      "    \n",
+      "    comb(n, k, /)\n",
+      "        Number of ways to choose k items from n items without repetition and without order.\n",
+      "        \n",
+      "        Evaluates to n! / (k! * (n - k)!) when k <= n and evaluates\n",
+      "        to zero when k > n.\n",
+      "        \n",
+      "        Also called the binomial coefficient because it is equivalent\n",
+      "        to the coefficient of k-th term in polynomial expansion of the\n",
+      "        expression (1 + x)**n.\n",
+      "        \n",
+      "        Raises TypeError if either of the arguments are not integers.\n",
+      "        Raises ValueError if either of the arguments are negative.\n",
+      "    \n",
+      "    copysign(x, y, /)\n",
+      "        Return a float with the magnitude (absolute value) of x but the sign of y.\n",
+      "        \n",
+      "        On platforms that support signed zeros, copysign(1.0, -0.0)\n",
+      "        returns -1.0.\n",
+      "    \n",
+      "    cos(x, /)\n",
+      "        Return the cosine of x (measured in radians).\n",
+      "    \n",
+      "    cosh(x, /)\n",
+      "        Return the hyperbolic cosine of x.\n",
+      "    \n",
+      "    degrees(x, /)\n",
+      "        Convert angle x from radians to degrees.\n",
+      "    \n",
+      "    dist(p, q, /)\n",
+      "        Return the Euclidean distance between two points p and q.\n",
+      "        \n",
+      "        The points should be specified as sequences (or iterables) of\n",
+      "        coordinates.  Both inputs must have the same dimension.\n",
+      "        \n",
+      "        Roughly equivalent to:\n",
+      "            sqrt(sum((px - qx) ** 2.0 for px, qx in zip(p, q)))\n",
+      "    \n",
+      "    erf(x, /)\n",
+      "        Error function at x.\n",
+      "    \n",
+      "    erfc(x, /)\n",
+      "        Complementary error function at x.\n",
+      "    \n",
+      "    exp(x, /)\n",
+      "        Return e raised to the power of x.\n",
+      "    \n",
+      "    expm1(x, /)\n",
+      "        Return exp(x)-1.\n",
+      "        \n",
+      "        This function avoids the loss of precision involved in the direct evaluation of exp(x)-1 for small x.\n",
+      "    \n",
+      "    fabs(x, /)\n",
+      "        Return the absolute value of the float x.\n",
+      "    \n",
+      "    factorial(x, /)\n",
+      "        Find x!.\n",
+      "        \n",
+      "        Raise a ValueError if x is negative or non-integral.\n",
+      "    \n",
+      "    floor(x, /)\n",
+      "        Return the floor of x as an Integral.\n",
+      "        \n",
+      "        This is the largest integer <= x.\n",
+      "    \n",
+      "    fmod(x, y, /)\n",
+      "        Return fmod(x, y), according to platform C.\n",
+      "        \n",
+      "        x % y may differ.\n",
+      "    \n",
+      "    frexp(x, /)\n",
+      "        Return the mantissa and exponent of x, as pair (m, e).\n",
+      "        \n",
+      "        m is a float and e is an int, such that x = m * 2.**e.\n",
+      "        If x is 0, m and e are both 0.  Else 0.5 <= abs(m) < 1.0.\n",
+      "    \n",
+      "    fsum(seq, /)\n",
+      "        Return an accurate floating point sum of values in the iterable seq.\n",
+      "        \n",
+      "        Assumes IEEE-754 floating point arithmetic.\n",
+      "    \n",
+      "    gamma(x, /)\n",
+      "        Gamma function at x.\n",
+      "    \n",
+      "    gcd(*integers)\n",
+      "        Greatest Common Divisor.\n",
+      "    \n",
+      "    hypot(...)\n",
+      "        hypot(*coordinates) -> value\n",
+      "        \n",
+      "        Multidimensional Euclidean distance from the origin to a point.\n",
+      "        \n",
+      "        Roughly equivalent to:\n",
+      "            sqrt(sum(x**2 for x in coordinates))\n",
+      "        \n",
+      "        For a two dimensional point (x, y), gives the hypotenuse\n",
+      "        using the Pythagorean theorem:  sqrt(x*x + y*y).\n",
+      "        \n",
+      "        For example, the hypotenuse of a 3/4/5 right triangle is:\n",
+      "        \n",
+      "            >>> hypot(3.0, 4.0)\n",
+      "            5.0\n",
+      "    \n",
+      "    isclose(a, b, *, rel_tol=1e-09, abs_tol=0.0)\n",
+      "        Determine whether two floating point numbers are close in value.\n",
+      "        \n",
+      "          rel_tol\n",
+      "            maximum difference for being considered \"close\", relative to the\n",
+      "            magnitude of the input values\n",
+      "          abs_tol\n",
+      "            maximum difference for being considered \"close\", regardless of the\n",
+      "            magnitude of the input values\n",
+      "        \n",
+      "        Return True if a is close in value to b, and False otherwise.\n",
+      "        \n",
+      "        For the values to be considered close, the difference between them\n",
+      "        must be smaller than at least one of the tolerances.\n",
+      "        \n",
+      "        -inf, inf and NaN behave similarly to the IEEE 754 Standard.  That\n",
+      "        is, NaN is not close to anything, even itself.  inf and -inf are\n",
+      "        only close to themselves.\n",
+      "    \n",
+      "    isfinite(x, /)\n",
+      "        Return True if x is neither an infinity nor a NaN, and False otherwise.\n",
+      "    \n",
+      "    isinf(x, /)\n",
+      "        Return True if x is a positive or negative infinity, and False otherwise.\n",
+      "    \n",
+      "    isnan(x, /)\n",
+      "        Return True if x is a NaN (not a number), and False otherwise.\n",
+      "    \n",
+      "    isqrt(n, /)\n",
+      "        Return the integer part of the square root of the input.\n",
+      "    \n",
+      "    lcm(*integers)\n",
+      "        Least Common Multiple.\n",
+      "    \n",
+      "    ldexp(x, i, /)\n",
+      "        Return x * (2**i).\n",
+      "        \n",
+      "        This is essentially the inverse of frexp().\n",
+      "    \n",
+      "    lgamma(x, /)\n",
+      "        Natural logarithm of absolute value of Gamma function at x.\n",
+      "    \n",
+      "    log(...)\n",
+      "        log(x, [base=math.e])\n",
+      "        Return the logarithm of x to the given base.\n",
+      "        \n",
+      "        If the base not specified, returns the natural logarithm (base e) of x.\n",
+      "    \n",
+      "    log10(x, /)\n",
+      "        Return the base 10 logarithm of x.\n",
+      "    \n",
+      "    log1p(x, /)\n",
+      "        Return the natural logarithm of 1+x (base e).\n",
+      "        \n",
+      "        The result is computed in a way which is accurate for x near zero.\n",
+      "    \n",
+      "    log2(x, /)\n",
+      "        Return the base 2 logarithm of x.\n",
+      "    \n",
+      "    modf(x, /)\n",
+      "        Return the fractional and integer parts of x.\n",
+      "        \n",
+      "        Both results carry the sign of x and are floats.\n",
+      "    \n",
+      "    nextafter(x, y, /)\n",
+      "        Return the next floating-point value after x towards y.\n",
+      "    \n",
+      "    perm(n, k=None, /)\n",
+      "        Number of ways to choose k items from n items without repetition and with order.\n",
+      "        \n",
+      "        Evaluates to n! / (n - k)! when k <= n and evaluates\n",
+      "        to zero when k > n.\n",
+      "        \n",
+      "        If k is not specified or is None, then k defaults to n\n",
+      "        and the function returns n!.\n",
+      "        \n",
+      "        Raises TypeError if either of the arguments are not integers.\n",
+      "        Raises ValueError if either of the arguments are negative.\n",
+      "    \n",
+      "    pow(x, y, /)\n",
+      "        Return x**y (x to the power of y).\n",
+      "    \n",
+      "    prod(iterable, /, *, start=1)\n",
+      "        Calculate the product of all the elements in the input iterable.\n",
+      "        \n",
+      "        The default start value for the product is 1.\n",
+      "        \n",
+      "        When the iterable is empty, return the start value.  This function is\n",
+      "        intended specifically for use with numeric values and may reject\n",
+      "        non-numeric types.\n",
+      "    \n",
+      "    radians(x, /)\n",
+      "        Convert angle x from degrees to radians.\n",
+      "    \n",
+      "    remainder(x, y, /)\n",
+      "        Difference between x and the closest integer multiple of y.\n",
+      "        \n",
+      "        Return x - n*y where n*y is the closest integer multiple of y.\n",
+      "        In the case where x is exactly halfway between two multiples of\n",
+      "        y, the nearest even value of n is used. The result is always exact.\n",
+      "    \n",
+      "    sin(x, /)\n",
+      "        Return the sine of x (measured in radians).\n",
+      "    \n",
+      "    sinh(x, /)\n",
+      "        Return the hyperbolic sine of x.\n",
+      "    \n",
+      "    sqrt(x, /)\n",
+      "        Return the square root of x.\n",
+      "    \n",
+      "    tan(x, /)\n",
+      "        Return the tangent of x (measured in radians).\n",
+      "    \n",
+      "    tanh(x, /)\n",
+      "        Return the hyperbolic tangent of x.\n",
+      "    \n",
+      "    trunc(x, /)\n",
+      "        Truncates the Real x to the nearest Integral toward 0.\n",
+      "        \n",
+      "        Uses the __trunc__ magic method.\n",
+      "    \n",
+      "    ulp(x, /)\n",
+      "        Return the value of the least significant bit of the float x.\n",
+      "\n",
+      "DATA\n",
+      "    e = 2.718281828459045\n",
+      "    inf = inf\n",
+      "    nan = nan\n",
+      "    pi = 3.141592653589793\n",
+      "    tau = 6.283185307179586\n",
+      "\n",
+      "FILE\n",
+      "    (built-in)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(math)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25284062",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise - Copy.ipynb b/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise - Copy.ipynb
new file mode 100644
index 0000000..f1b8cdb
--- /dev/null
+++ b/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise - Copy.ipynb	
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Linear Regression - Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size' and 'year'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scipy\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size_year.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>252460.400</td>\n",
+       "      <td>549.80</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>310522.592</td>\n",
+       "      <td>1037.44</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>383635.568</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>225145.248</td>\n",
+       "      <td>648.29</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>274922.856</td>\n",
+       "      <td>705.29</td>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         price     size  year\n",
+       "0   234314.144   643.09  2015\n",
+       "1   228581.528   656.22  2009\n",
+       "2   281626.336   487.29  2018\n",
+       "3   401255.608  1504.75  2015\n",
+       "4   458674.256  1275.46  2009\n",
+       "..         ...      ...   ...\n",
+       "95  252460.400   549.80  2009\n",
+       "96  310522.592  1037.44  2009\n",
+       "97  383635.568  1504.75  2006\n",
+       "98  225145.248   648.29  2015\n",
+       "99  274922.856   705.29  2006\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "      <td>2012.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "      <td>4.729021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "      <td>2006.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size         year\n",
+       "count     100.000000   100.000000   100.000000\n",
+       "mean   292289.470160   853.024200  2012.600000\n",
+       "std     77051.727525   297.941951     4.729021\n",
+       "min    154282.128000   479.750000  2006.000000\n",
+       "25%    234280.148000   643.330000  2009.000000\n",
+       "50%    280590.716000   696.405000  2015.000000\n",
+       "75%    335723.696000  1029.322500  2018.000000\n",
+       "max    500681.128000  1842.510000  2018.000000"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = data['price']#dependent\n",
+    "x1 = data[['size', 'year']]# independent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.776</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.772</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   168.5</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Tue, 31 Oct 2023</td> <th>  Prob (F-statistic):</th> <td>2.77e-32</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>13:08:31</td>     <th>  Log-Likelihood:    </th> <td> -1191.7</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   2389.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    97</td>      <th>  BIC:               </th> <td>   2397.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>-5.772e+06</td> <td> 1.58e+06</td> <td>   -3.647</td> <td> 0.000</td> <td>-8.91e+06</td> <td>-2.63e+06</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>size</th>  <td>  227.7009</td> <td>   12.474</td> <td>   18.254</td> <td> 0.000</td> <td>  202.943</td> <td>  252.458</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>year</th>  <td> 2916.7853</td> <td>  785.896</td> <td>    3.711</td> <td> 0.000</td> <td> 1357.000</td> <td> 4476.571</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>10.083</td> <th>  Durbin-Watson:     </th> <td>   2.250</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.006</td> <th>  Jarque-Bera (JB):  </th> <td>   3.678</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.095</td> <th>  Prob(JB):          </th> <td>   0.159</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.080</td> <th>  Cond. No.          </th> <td>9.41e+05</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.41e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.776   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.772   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     168.5   \\\\\n",
+       "\\textbf{Date:}             & Tue, 31 Oct 2023 & \\textbf{  Prob (F-statistic):} &  2.77e-32   \\\\\n",
+       "\\textbf{Time:}             &     13:08:31     & \\textbf{  Log-Likelihood:    } &   -1191.7   \\\\\n",
+       "\\textbf{No. Observations:} &         100      & \\textbf{  AIC:               } &     2389.   \\\\\n",
+       "\\textbf{Df Residuals:}     &          97      & \\textbf{  BIC:               } &     2397.   \\\\\n",
+       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &   -5.772e+06  &     1.58e+06     &    -3.647  &         0.000        &    -8.91e+06    &    -2.63e+06     \\\\\n",
+       "\\textbf{size}  &     227.7009  &       12.474     &    18.254  &         0.000        &      202.943    &      252.458     \\\\\n",
+       "\\textbf{year}  &    2916.7853  &      785.896     &     3.711  &         0.000        &     1357.000    &     4476.571     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 10.083 & \\textbf{  Durbin-Watson:     } &    2.250  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.006 & \\textbf{  Jarque-Bera (JB):  } &    3.678  \\\\\n",
+       "\\textbf{Skew:}          &  0.095 & \\textbf{  Prob(JB):          } &    0.159  \\\\\n",
+       "\\textbf{Kurtosis:}      &  2.080 & \\textbf{  Cond. No.          } & 9.41e+05  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 9.41e+05. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.776\n",
+       "Model:                            OLS   Adj. R-squared:                  0.772\n",
+       "Method:                 Least Squares   F-statistic:                     168.5\n",
+       "Date:                Tue, 31 Oct 2023   Prob (F-statistic):           2.77e-32\n",
+       "Time:                        13:08:31   Log-Likelihood:                -1191.7\n",
+       "No. Observations:                 100   AIC:                             2389.\n",
+       "Df Residuals:                      97   BIC:                             2397.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const      -5.772e+06   1.58e+06     -3.647      0.000   -8.91e+06   -2.63e+06\n",
+       "size         227.7009     12.474     18.254      0.000     202.943     252.458\n",
+       "year        2916.7853    785.896      3.711      0.000    1357.000    4476.571\n",
+       "==============================================================================\n",
+       "Omnibus:                       10.083   Durbin-Watson:                   2.250\n",
+       "Prob(Omnibus):                  0.006   Jarque-Bera (JB):                3.678\n",
+       "Skew:                           0.095   Prob(JB):                        0.159\n",
+       "Kurtosis:                       2.080   Cond. No.                     9.41e+05\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 9.41e+05. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(Y, x).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise+Solution.ipynb b/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise+Solution.ipynb
new file mode 100644
index 0000000..9c7f831
--- /dev/null
+++ b/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise+Solution.ipynb	
@@ -0,0 +1,425 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Linear Regression - Exercise Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size' and 'year'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size_year.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size  year\n",
+       "0  234314.144   643.09  2015\n",
+       "1  228581.528   656.22  2009\n",
+       "2  281626.336   487.29  2018\n",
+       "3  401255.608  1504.75  2015\n",
+       "4  458674.256  1275.46  2009"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "      <td>2012.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "      <td>4.729021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "      <td>2006.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size         year\n",
+       "count     100.000000   100.000000   100.000000\n",
+       "mean   292289.470160   853.024200  2012.600000\n",
+       "std     77051.727525   297.941951     4.729021\n",
+       "min    154282.128000   479.750000  2006.000000\n",
+       "25%    234280.148000   643.330000  2009.000000\n",
+       "50%    280590.716000   696.405000  2015.000000\n",
+       "75%    335723.696000  1029.322500  2018.000000\n",
+       "max    500681.128000  1842.510000  2018.000000"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['price']\n",
+    "x1 = data[['size','year']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.776</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.772</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   168.5</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Tue, 26 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>2.77e-32</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>10:58:43</td>     <th>  Log-Likelihood:    </th> <td> -1191.7</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   2389.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    97</td>      <th>  BIC:               </th> <td>   2397.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>-5.772e+06</td> <td> 1.58e+06</td> <td>   -3.647</td> <td> 0.000</td> <td>-8.91e+06</td> <td>-2.63e+06</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>size</th>  <td>  227.7009</td> <td>   12.474</td> <td>   18.254</td> <td> 0.000</td> <td>  202.943</td> <td>  252.458</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>year</th>  <td> 2916.7853</td> <td>  785.896</td> <td>    3.711</td> <td> 0.000</td> <td> 1357.000</td> <td> 4476.571</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>10.083</td> <th>  Durbin-Watson:     </th> <td>   2.250</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.006</td> <th>  Jarque-Bera (JB):  </th> <td>   3.678</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.095</td> <th>  Prob(JB):          </th> <td>   0.159</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.080</td> <th>  Cond. No.          </th> <td>9.41e+05</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.776\n",
+       "Model:                            OLS   Adj. R-squared:                  0.772\n",
+       "Method:                 Least Squares   F-statistic:                     168.5\n",
+       "Date:                Tue, 26 Jun 2018   Prob (F-statistic):           2.77e-32\n",
+       "Time:                        10:58:43   Log-Likelihood:                -1191.7\n",
+       "No. Observations:                 100   AIC:                             2389.\n",
+       "Df Residuals:                      97   BIC:                             2397.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const      -5.772e+06   1.58e+06     -3.647      0.000   -8.91e+06   -2.63e+06\n",
+       "size         227.7009     12.474     18.254      0.000     202.943     252.458\n",
+       "year        2916.7853    785.896      3.711      0.000    1357.000    4476.571\n",
+       "==============================================================================\n",
+       "Omnibus:                       10.083   Durbin-Watson:                   2.250\n",
+       "Prob(Omnibus):                  0.006   Jarque-Bera (JB):                3.678\n",
+       "Skew:                           0.095   Prob(JB):                        0.159\n",
+       "Kurtosis:                       2.080   Cond. No.                     9.41e+05\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 9.41e+05. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(y,x).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise.ipynb b/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise.ipynb
new file mode 100644
index 0000000..d79d5cf
--- /dev/null
+++ b/Udemy DataScience Workbook/Multiple+Linear+Regression+Exercise.ipynb	
@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Linear Regression - Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size' and 'year'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Multiple+Linear+Regression+with+Dummies+Exercise+Solution.ipynb b/Udemy DataScience Workbook/Multiple+Linear+Regression+with+Dummies+Exercise+Solution.ipynb
new file mode 100644
index 0000000..13748d0
--- /dev/null
+++ b/Udemy DataScience Workbook/Multiple+Linear+Regression+with+Dummies+Exercise+Solution.ipynb	
@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Linear Regression with Dummies - Exercise Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year_view.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size', 'year', and 'view'.\n",
+    "\n",
+    "#### Regarding the 'view' variable:\n",
+    "There are two options: 'Sea view' and 'No sea view'. You are expected to create a dummy variable for view and include it in the regression\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('real_estate_price_size_year_view.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "      <th>view</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>Sea view</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size  year         view\n",
+       "0  234314.144   643.09  2015  No sea view\n",
+       "1  228581.528   656.22  2009  No sea view\n",
+       "2  281626.336   487.29  2018     Sea view\n",
+       "3  401255.608  1504.75  2015  No sea view\n",
+       "4  458674.256  1275.46  2009     Sea view"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "      <th>view</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "      <td>2012.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "      <td>4.729021</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "      <td>2006.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "      <td>2018.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "      <td>2018.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                price         size         year         view\n",
+       "count      100.000000   100.000000   100.000000          100\n",
+       "unique            NaN          NaN          NaN            2\n",
+       "top               NaN          NaN          NaN  No sea view\n",
+       "freq              NaN          NaN          NaN           51\n",
+       "mean    292289.470160   853.024200  2012.600000          NaN\n",
+       "std      77051.727525   297.941951     4.729021          NaN\n",
+       "min     154282.128000   479.750000  2006.000000          NaN\n",
+       "25%     234280.148000   643.330000  2009.000000          NaN\n",
+       "50%     280590.716000   696.405000  2015.000000          NaN\n",
+       "75%     335723.696000  1029.322500  2018.000000          NaN\n",
+       "max     500681.128000  1842.510000  2018.000000          NaN"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a dummy variable for 'view'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data['view'] = data['view'].map({'Sea view': 1, 'No sea view': 0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "      <th>view</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size  year  view\n",
+       "0  234314.144   643.09  2015     0\n",
+       "1  228581.528   656.22  2009     0\n",
+       "2  281626.336   487.29  2018     1\n",
+       "3  401255.608  1504.75  2015     0\n",
+       "4  458674.256  1275.46  2009     1"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['price']\n",
+    "x1 = data[['size','year','view']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.913</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.910</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   335.2</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Tue, 26 Jun 2018</td> <th>  Prob (F-statistic):</th> <td>1.02e-50</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:20:46</td>     <th>  Log-Likelihood:    </th> <td> -1144.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   2297.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    96</td>      <th>  BIC:               </th> <td>   2308.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>-5.398e+06</td> <td> 9.94e+05</td> <td>   -5.431</td> <td> 0.000</td> <td>-7.37e+06</td> <td>-3.43e+06</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>size</th>  <td>  223.0316</td> <td>    7.838</td> <td>   28.455</td> <td> 0.000</td> <td>  207.473</td> <td>  238.590</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>year</th>  <td> 2718.9489</td> <td>  493.502</td> <td>    5.510</td> <td> 0.000</td> <td> 1739.356</td> <td> 3698.542</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>view</th>  <td> 5.673e+04</td> <td> 4627.695</td> <td>   12.258</td> <td> 0.000</td> <td> 4.75e+04</td> <td> 6.59e+04</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>29.224</td> <th>  Durbin-Watson:     </th> <td>   1.965</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  64.957</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 1.088</td> <th>  Prob(JB):          </th> <td>7.85e-15</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 6.295</td> <th>  Cond. No.          </th> <td>9.42e+05</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.913\n",
+       "Model:                            OLS   Adj. R-squared:                  0.910\n",
+       "Method:                 Least Squares   F-statistic:                     335.2\n",
+       "Date:                Tue, 26 Jun 2018   Prob (F-statistic):           1.02e-50\n",
+       "Time:                        11:20:46   Log-Likelihood:                -1144.6\n",
+       "No. Observations:                 100   AIC:                             2297.\n",
+       "Df Residuals:                      96   BIC:                             2308.\n",
+       "Df Model:                           3                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const      -5.398e+06   9.94e+05     -5.431      0.000   -7.37e+06   -3.43e+06\n",
+       "size         223.0316      7.838     28.455      0.000     207.473     238.590\n",
+       "year        2718.9489    493.502      5.510      0.000    1739.356    3698.542\n",
+       "view        5.673e+04   4627.695     12.258      0.000    4.75e+04    6.59e+04\n",
+       "==============================================================================\n",
+       "Omnibus:                       29.224   Durbin-Watson:                   1.965\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               64.957\n",
+       "Skew:                           1.088   Prob(JB):                     7.85e-15\n",
+       "Kurtosis:                       6.295   Cond. No.                     9.42e+05\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 9.42e+05. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(y,x).fit()\n",
+    "results.summary()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Multiple+Linear+Regression+with+Dummies+Exercise.ipynb b/Udemy DataScience Workbook/Multiple+Linear+Regression+with+Dummies+Exercise.ipynb
new file mode 100644
index 0000000..0fc4b15
--- /dev/null
+++ b/Udemy DataScience Workbook/Multiple+Linear+Regression+with+Dummies+Exercise.ipynb	
@@ -0,0 +1,703 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Linear Regression with Dummies - Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year_view.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size', 'year', and 'view'.\n",
+    "\n",
+    "#### Regarding the 'view' variable:\n",
+    "There are two options: 'Sea view' and 'No sea view'. You are expected to create a dummy variable for view and include it in the regression\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('real_estate_price_size_year_view.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "      <th>view</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>Sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>252460.400</td>\n",
+       "      <td>549.80</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>Sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>310522.592</td>\n",
+       "      <td>1037.44</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>383635.568</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>225145.248</td>\n",
+       "      <td>648.29</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>No sea view</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>274922.856</td>\n",
+       "      <td>705.29</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>Sea view</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         price     size  year         view\n",
+       "0   234314.144   643.09  2015  No sea view\n",
+       "1   228581.528   656.22  2009  No sea view\n",
+       "2   281626.336   487.29  2018     Sea view\n",
+       "3   401255.608  1504.75  2015  No sea view\n",
+       "4   458674.256  1275.46  2009     Sea view\n",
+       "..         ...      ...   ...          ...\n",
+       "95  252460.400   549.80  2009     Sea view\n",
+       "96  310522.592  1037.44  2009  No sea view\n",
+       "97  383635.568  1504.75  2006  No sea view\n",
+       "98  225145.248   648.29  2015  No sea view\n",
+       "99  274922.856   705.29  2006     Sea view\n",
+       "\n",
+       "[100 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a dummy variable for 'view'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['view'] = data['view'].map({'Sea view': 1, 'No sea view':0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "      <th>view</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>252460.400</td>\n",
+       "      <td>549.80</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>310522.592</td>\n",
+       "      <td>1037.44</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>383635.568</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>225145.248</td>\n",
+       "      <td>648.29</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>274922.856</td>\n",
+       "      <td>705.29</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         price     size  year  view\n",
+       "0   234314.144   643.09  2015     0\n",
+       "1   228581.528   656.22  2009     0\n",
+       "2   281626.336   487.29  2018     1\n",
+       "3   401255.608  1504.75  2015     0\n",
+       "4   458674.256  1275.46  2009     1\n",
+       "..         ...      ...   ...   ...\n",
+       "95  252460.400   549.80  2009     1\n",
+       "96  310522.592  1037.44  2009     0\n",
+       "97  383635.568  1504.75  2006     0\n",
+       "98  225145.248   648.29  2015     0\n",
+       "99  274922.856   705.29  2006     1\n",
+       "\n",
+       "[100 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "      <th>view</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "      <td>2012.600000</td>\n",
+       "      <td>0.490000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "      <td>4.729021</td>\n",
+       "      <td>0.502418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "      <td>2006.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "      <td>2018.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "      <td>2018.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size         year        view\n",
+       "count     100.000000   100.000000   100.000000  100.000000\n",
+       "mean   292289.470160   853.024200  2012.600000    0.490000\n",
+       "std     77051.727525   297.941951     4.729021    0.502418\n",
+       "min    154282.128000   479.750000  2006.000000    0.000000\n",
+       "25%    234280.148000   643.330000  2009.000000    0.000000\n",
+       "50%    280590.716000   696.405000  2015.000000    0.000000\n",
+       "75%    335723.696000  1029.322500  2018.000000    1.000000\n",
+       "max    500681.128000  1842.510000  2018.000000    1.000000"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = data['price']# dependent variable\n",
+    "x1 = data[['size', 'year', 'view']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.913</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.910</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   335.2</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Tue, 31 Oct 2023</td> <th>  Prob (F-statistic):</th> <td>1.02e-50</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>15:13:57</td>     <th>  Log-Likelihood:    </th> <td> -1144.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   2297.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    96</td>      <th>  BIC:               </th> <td>   2308.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>-5.398e+06</td> <td> 9.94e+05</td> <td>   -5.431</td> <td> 0.000</td> <td>-7.37e+06</td> <td>-3.43e+06</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>size</th>  <td>  223.0316</td> <td>    7.838</td> <td>   28.455</td> <td> 0.000</td> <td>  207.473</td> <td>  238.590</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>year</th>  <td> 2718.9489</td> <td>  493.502</td> <td>    5.510</td> <td> 0.000</td> <td> 1739.356</td> <td> 3698.542</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>view</th>  <td> 5.673e+04</td> <td> 4627.695</td> <td>   12.258</td> <td> 0.000</td> <td> 4.75e+04</td> <td> 6.59e+04</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>29.224</td> <th>  Durbin-Watson:     </th> <td>   1.965</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  64.957</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 1.088</td> <th>  Prob(JB):          </th> <td>7.85e-15</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 6.295</td> <th>  Cond. No.          </th> <td>9.42e+05</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.42e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.913   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.910   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     335.2   \\\\\n",
+       "\\textbf{Date:}             & Tue, 31 Oct 2023 & \\textbf{  Prob (F-statistic):} &  1.02e-50   \\\\\n",
+       "\\textbf{Time:}             &     15:13:57     & \\textbf{  Log-Likelihood:    } &   -1144.6   \\\\\n",
+       "\\textbf{No. Observations:} &         100      & \\textbf{  AIC:               } &     2297.   \\\\\n",
+       "\\textbf{Df Residuals:}     &          96      & \\textbf{  BIC:               } &     2308.   \\\\\n",
+       "\\textbf{Df Model:}         &           3      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &   -5.398e+06  &     9.94e+05     &    -5.431  &         0.000        &    -7.37e+06    &    -3.43e+06     \\\\\n",
+       "\\textbf{size}  &     223.0316  &        7.838     &    28.455  &         0.000        &      207.473    &      238.590     \\\\\n",
+       "\\textbf{year}  &    2718.9489  &      493.502     &     5.510  &         0.000        &     1739.356    &     3698.542     \\\\\n",
+       "\\textbf{view}  &    5.673e+04  &     4627.695     &    12.258  &         0.000        &     4.75e+04    &     6.59e+04     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 29.224 & \\textbf{  Durbin-Watson:     } &    1.965  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   64.957  \\\\\n",
+       "\\textbf{Skew:}          &  1.088 & \\textbf{  Prob(JB):          } & 7.85e-15  \\\\\n",
+       "\\textbf{Kurtosis:}      &  6.295 & \\textbf{  Cond. No.          } & 9.42e+05  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 9.42e+05. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.913\n",
+       "Model:                            OLS   Adj. R-squared:                  0.910\n",
+       "Method:                 Least Squares   F-statistic:                     335.2\n",
+       "Date:                Tue, 31 Oct 2023   Prob (F-statistic):           1.02e-50\n",
+       "Time:                        15:13:57   Log-Likelihood:                -1144.6\n",
+       "No. Observations:                 100   AIC:                             2297.\n",
+       "Df Residuals:                      96   BIC:                             2308.\n",
+       "Df Model:                           3                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const      -5.398e+06   9.94e+05     -5.431      0.000   -7.37e+06   -3.43e+06\n",
+       "size         223.0316      7.838     28.455      0.000     207.473     238.590\n",
+       "year        2718.9489    493.502      5.510      0.000    1739.356    3698.542\n",
+       "view        5.673e+04   4627.695     12.258      0.000    4.75e+04    6.59e+04\n",
+       "==============================================================================\n",
+       "Omnibus:                       29.224   Durbin-Watson:                   1.965\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               64.957\n",
+       "Skew:                           1.088   Prob(JB):                     7.85e-15\n",
+       "Kurtosis:                       6.295   Cond. No.                     9.42e+05\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 9.42e+05. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(Y, x).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/My_MNIST_part2.ipynb b/Udemy DataScience Workbook/My_MNIST_part2.ipynb
new file mode 100644
index 0000000..2d93f48
--- /dev/null
+++ b/Udemy DataScience Workbook/My_MNIST_part2.ipynb	
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Loading the data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the traininng and testing dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load dataset\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "\n",
+    "mnist_trian, mnist_test = mnist_dataset['train'],mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "#defining the number of validation samples as a % of the train samples\n",
+    "\n",
+    "num_validation_samples = 0.1*mnist_info.splits['train'].num_examples\n",
+    "\n",
+    "# Cast this number to integer\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64) \n",
+    "\n",
+    "#store the number of test samples in a dedicated variable\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "# Scaling the data to have a more numerically stable data\n",
+    "\n",
+    "def scale(image, label):\n",
+    "    # Ensuring that the value is float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255\n",
+    "\n",
+    "    return image, label\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "\n",
+    "scaled_train_and_validation_data = mnist_trian.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "#Shuffling the data\n",
+    "\n",
+    "buffer_size = 10000\n",
+    "\n",
+    "\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(buffer_size)\n",
+    "\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "#the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine batch size\n",
+    "batch_size = 100\n",
+    "train_data = train_data.batch(batch_size)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "540/540 - 13s - loss: 0.4082 - accuracy: 0.8836 - val_loss: 0.2059 - val_accuracy: 0.9407 - 13s/epoch - 23ms/step\n",
+      "Epoch 2/50\n",
+      "540/540 - 6s - loss: 0.1803 - accuracy: 0.9474 - val_loss: 0.1585 - val_accuracy: 0.9538 - 6s/epoch - 11ms/step\n",
+      "Epoch 3/50\n",
+      "540/540 - 6s - loss: 0.1370 - accuracy: 0.9597 - val_loss: 0.1217 - val_accuracy: 0.9640 - 6s/epoch - 12ms/step\n",
+      "Epoch 4/50\n",
+      "540/540 - 6s - loss: 0.1116 - accuracy: 0.9676 - val_loss: 0.1033 - val_accuracy: 0.9690 - 6s/epoch - 11ms/step\n",
+      "Epoch 5/50\n",
+      "540/540 - 6s - loss: 0.0925 - accuracy: 0.9722 - val_loss: 0.0900 - val_accuracy: 0.9728 - 6s/epoch - 11ms/step\n",
+      "Epoch 6/50\n",
+      "540/540 - 6s - loss: 0.0816 - accuracy: 0.9746 - val_loss: 0.0849 - val_accuracy: 0.9738 - 6s/epoch - 11ms/step\n",
+      "Epoch 7/50\n",
+      "540/540 - 3s - loss: 0.0692 - accuracy: 0.9787 - val_loss: 0.0737 - val_accuracy: 0.9775 - 3s/epoch - 6ms/step\n",
+      "Epoch 8/50\n",
+      "540/540 - 4s - loss: 0.0652 - accuracy: 0.9800 - val_loss: 0.0695 - val_accuracy: 0.9788 - 4s/epoch - 7ms/step\n",
+      "Epoch 9/50\n",
+      "540/540 - 6s - loss: 0.0566 - accuracy: 0.9826 - val_loss: 0.0629 - val_accuracy: 0.9807 - 6s/epoch - 11ms/step\n",
+      "Epoch 10/50\n",
+      "540/540 - 3s - loss: 0.0499 - accuracy: 0.9844 - val_loss: 0.0569 - val_accuracy: 0.9833 - 3s/epoch - 6ms/step\n",
+      "Epoch 11/50\n",
+      "540/540 - 3s - loss: 0.0476 - accuracy: 0.9854 - val_loss: 0.0607 - val_accuracy: 0.9825 - 3s/epoch - 6ms/step\n",
+      "Epoch 12/50\n",
+      "540/540 - 3s - loss: 0.0409 - accuracy: 0.9878 - val_loss: 0.0526 - val_accuracy: 0.9857 - 3s/epoch - 6ms/step\n",
+      "Epoch 13/50\n",
+      "540/540 - 6s - loss: 0.0390 - accuracy: 0.9876 - val_loss: 0.0458 - val_accuracy: 0.9858 - 6s/epoch - 11ms/step\n",
+      "Epoch 14/50\n",
+      "540/540 - 6s - loss: 0.0358 - accuracy: 0.9886 - val_loss: 0.0458 - val_accuracy: 0.9875 - 6s/epoch - 12ms/step\n",
+      "Epoch 15/50\n",
+      "540/540 - 6s - loss: 0.0310 - accuracy: 0.9904 - val_loss: 0.0407 - val_accuracy: 0.9868 - 6s/epoch - 12ms/step\n",
+      "Epoch 16/50\n",
+      "540/540 - 6s - loss: 0.0288 - accuracy: 0.9910 - val_loss: 0.0472 - val_accuracy: 0.9847 - 6s/epoch - 11ms/step\n",
+      "Epoch 17/50\n",
+      "540/540 - 6s - loss: 0.0267 - accuracy: 0.9921 - val_loss: 0.0334 - val_accuracy: 0.9910 - 6s/epoch - 11ms/step\n",
+      "Epoch 18/50\n",
+      "540/540 - 6s - loss: 0.0233 - accuracy: 0.9927 - val_loss: 0.0349 - val_accuracy: 0.9907 - 6s/epoch - 11ms/step\n",
+      "Epoch 19/50\n",
+      "540/540 - 6s - loss: 0.0210 - accuracy: 0.9940 - val_loss: 0.0300 - val_accuracy: 0.9917 - 6s/epoch - 11ms/step\n",
+      "Epoch 20/50\n",
+      "540/540 - 6s - loss: 0.0209 - accuracy: 0.9933 - val_loss: 0.0358 - val_accuracy: 0.9898 - 6s/epoch - 11ms/step\n",
+      "Epoch 21/50\n",
+      "540/540 - 6s - loss: 0.0192 - accuracy: 0.9943 - val_loss: 0.0231 - val_accuracy: 0.9933 - 6s/epoch - 11ms/step\n",
+      "Epoch 22/50\n",
+      "540/540 - 6s - loss: 0.0171 - accuracy: 0.9947 - val_loss: 0.0269 - val_accuracy: 0.9927 - 6s/epoch - 11ms/step\n",
+      "Epoch 23/50\n",
+      "540/540 - 6s - loss: 0.0158 - accuracy: 0.9951 - val_loss: 0.0324 - val_accuracy: 0.9898 - 6s/epoch - 11ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0xa95c01b1d0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Define the model\n",
+    "\n",
+    "model = tf.keras.Sequential([\n",
+    "    # the first layer(input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "     # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape= (28,28,1)), # input layer\n",
+    "     # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    \n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # first hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "\n",
+    "    # tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'), # 3rd hidden layer    \n",
+    "    # tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'),# 4th hidden layer\n",
+    "    # tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'), # 5th hidden layer\n",
+    "    tf.keras.layers.Dense(output_size,activation= 'softmax') # output layer  # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    ])\n",
+    "# Choose the optimer and the loss function\n",
+    "# Define the optimizer to be used, \n",
+    "#loss function\n",
+    "# and the metrics we are interested in at each iteration\n",
+    "model.compile(optimizer= 'adam', loss='sparse_categorical_crossentropy', metrics= ['accuracy'])\n",
+    "# ADAM = Adaptive Moment estimation\n",
+    "\n",
+    "#Training the model\n",
+    "# Determine the max. number of epchs\n",
+    "num_epochs = 50\n",
+    "# fit the model, \n",
+    "# specify the training data, \n",
+    "# the total number of epochs \n",
+    "# and the validation data created(inputs , targets)\n",
+    "\n",
+    "# set an early stopping mechanism\n",
+    "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n",
+    "\n",
+    "model.fit(train_data, epochs= num_epochs,callbacks= [early_stopping], validation_data= (validation_inputs, validation_targets), verbose=2)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_loss, test_accuraccy = model.evaluate(test_inputs, test_target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise Solution\n",
+    "1 It took longer time (about 11s)\n",
+    "Max accuracy of 98.7%\n",
+    "\n",
+    "2 It took longer about 13s\n",
+    "Max accuracy of 98.8%\n",
+    "\n",
+    "3.Additional hidden layers(depth) of 5 with a width of 100 ran for about 11s but with lower accuracy of 98.3%\n",
+    "\n",
+    "4.It took longer time (about 8s)\n",
+    "Max accuracy of 95.8%\n",
+    "\n",
+    "5.It took longer time (about 12s)\n",
+    "Max accuracy of 95.0%  However, interchanging the activation functions (tanh for the first layer and relu for the 2nd layer) gave an improved accuracy of 97.6%\n",
+    "\n",
+    "6.Required time reduced to 10s as  well as the max accuracy of 83.0%\n",
+    "\n",
+    "7.It took too long(about 259s) however the code section took about 21mins to run\n",
+    "Max accuracy of 96.3%\n",
+    "8."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Notes.txt b/Udemy DataScience Workbook/Notes.txt
new file mode 100644
index 0000000..e271701
--- /dev/null
+++ b/Udemy DataScience Workbook/Notes.txt	
@@ -0,0 +1,3 @@
+Create project on git repository and clone to local machine     Done
+Understand the abscenteism case
+Complete the abscenteism project 
\ No newline at end of file
diff --git a/Udemy DataScience Workbook/Pasword_Numbers.txt b/Udemy DataScience Workbook/Pasword_Numbers.txt
new file mode 100644
index 0000000..e390869
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diff --git a/Udemy DataScience Workbook/Pasword_Numbers.xlsx b/Udemy DataScience Workbook/Pasword_Numbers.xlsx
new file mode 100644
index 0000000..0e44db6
--- /dev/null
+++ b/Udemy DataScience Workbook/Pasword_Numbers.xlsx	
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diff --git a/Udemy DataScience Workbook/Python & Git.txt b/Udemy DataScience Workbook/Python & Git.txt
new file mode 100644
index 0000000..1703bee
--- /dev/null
+++ b/Udemy DataScience Workbook/Python & Git.txt	
@@ -0,0 +1,23 @@
+################## Python  ############################
+## To create a virtual enviroment for a project         
+   # First, open command terminal into the project root folder
+	type cmd in the file explorer
+   # Secondly, setup virtual enviroment       ***(Once per project)***
+	python -m venv venv
+
+## To activate virtual enviroment for a project  *** Each time you want to edit project files***
+	venv\scripts\activate
+
+## To install a missing/required package unto a virtual enviroment
+	# First, Ensure you have activated virtual enviroment using instruction above
+	# Secondly, Install the package
+		pip install [package-name]
+## To view installed packages on virtual environment
+	pip3 list
+
+## To launch VS code to work on a project 
+	code .
+
+## When you are done working on a virtual enviroment/VSCode
+   # Deactivate virtual enviroment
+	venv\scripts\deactivate
\ No newline at end of file
diff --git a/Udemy DataScience Workbook/Python basics.ipynb b/Udemy DataScience Workbook/Python basics.ipynb
new file mode 100644
index 0000000..bb1e674
--- /dev/null
+++ b/Udemy DataScience Workbook/Python basics.ipynb	
@@ -0,0 +1,506 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9bc6a2b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99.0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = 99\n",
+    "float(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "60bafe0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "531a5223",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = 0.99\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "87b50f4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6ae1d262",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cbe5dd26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 days\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(str(m) + \" days\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "17b342b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "str(m)  days\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"str(m)  days\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d6493f80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'Big Houses'\n",
+      "Big  Houses\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"'Big\" + \" Houses'\")\n",
+    "print(\"Big\", \" Houses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7f78d10f",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "unterminated string literal (detected at line 1) (4111028140.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[12], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    print('Aren't you hungry?')\u001b[0m\n\u001b[1;37m                             ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated string literal (detected at line 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Aren't you hungry?')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e7d3fe98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 22/4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d72f4be5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9128f7db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "22%4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e52d6a4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "True and not False and True or not False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "386f85c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "True or False and False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fa5af17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "False and True or False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bbda09fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 != 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "24bc95d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "50 ==50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cf1fe8bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The condition has been satisfied\n"
+     ]
+    }
+   ],
+   "source": [
+    "if 5> 2:\n",
+    "    print('The condition has been satisfied')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a45e352",
+   "metadata": {},
+   "source": [
+    "Assign 10 to the variable x and 25 to the variable y. In the same cell, create 2 conditional statements. Let the first one print \"Both conditions are correct\" if x is greater then 3 and y is greater than 13. Let the second one print \"At least one of the conditions is false\" if x is less than or equal to 3 or y is less than or equal to 13. Change the values assigned to x and y and re-run the cell to verify your code still works.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0e595a9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Both conditions are correct\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 10\n",
+    "y = 25\n",
+    "if x>3 and y>13:\n",
+    "    print('Both conditions are correct')\n",
+    "if x<=3 or y<=13:\n",
+    "    print('At least one of the conditions is false')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8761411c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 10\n",
+    "y = 25\n",
+    "if x<=3 or y<=13:\n",
+    "    print('At least one of the conditions is false')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85cd4a58",
+   "metadata": {},
+   "source": [
+    "Let x represent the number of orders received during a certain day. Create a program that prints \"A busy day\" if x is greater than 100, and \"A calm day\" otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6513794e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A busy day\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 103\n",
+    "if x > 100:\n",
+    "    print ('A busy day')\n",
+    "else:\n",
+    "    print('A calm day')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca8d3610",
+   "metadata": {},
+   "source": [
+    "Create the following piece of code: If x > 200, print out \"Big\"; If x > 100 and x <= 200, print out \"Average\"; and If x <= 100, print out \"Small\". Use the if, elif, and else keywords in your code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0bddb03a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "samll\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 95\n",
+    "if x > 200:\n",
+    "    print(\"Big\")\n",
+    "elif 100 < x <= 200:\n",
+    "    print(\"Average\")\n",
+    "else:\n",
+    "    print(\"samll\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b95b1bb4",
+   "metadata": {},
+   "source": [
+    "Add a new elif statement, so that, eventually, the program prints \"Small\" if x >= 0 and x <= 100, and \"Negative\" if x < 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4b8b51bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Negative\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = -95\n",
+    "if x > 200:\n",
+    "    print(\"Big\")\n",
+    "elif 100 < x <= 200:\n",
+    "    print(\"Average\")\n",
+    "elif x >=0 and x<= 100:\n",
+    "    print(\"Small\")\n",
+    "else:\n",
+    "    print(\"Negative\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/Python_Venv Installation.txt b/Udemy DataScience Workbook/Python_Venv Installation.txt
new file mode 100644
index 0000000..a3fb9da
--- /dev/null
+++ b/Udemy DataScience Workbook/Python_Venv Installation.txt	
@@ -0,0 +1,48 @@
+Download and Install python
+
+Confirm python installation: python -v  From cmd
+
+Install virtual env: python -m venv venv
+
+Activate venv: venv\script\activate
+
+
+Note:
+Warning: PowerShell detected that you might be using a screen reader and has disabled PSReadLine for compatibility purposes. 
+If you want to re-enable it, run 'Import-Module PSReadLine'.
+Solution:
+	Run powershell as administrator and ...
+	type Get-ExecutionPolicy
+	PS C:\Windows\system32> Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy Bypass -Force
+	PS C:\Windows\system32> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+
+Package installation
+
+Note: To upgrade:- pip install --upgrade name
+eg. python.exe -m pip install --upgrade pip
+
+To install multiple packages at once:
+create a file with name requirements.txt in a directory.
+enter packages like this in it:
+
+matplotlib==1.3.1 numpy==1.18.4
+
+cd to the directory where requirements.txt is located.
+activate your virtualenv.
+run: pip install -r requirements.txt in your shell. each time you update this file you can install them from file with command in step 5
+and do not repeat entering them in shell.
+
+To install single packages:
+in venv environment type
+	pip install packageName (pandas, numpy,)
+
+pandas==2.0
+numpy==1.18.4
+BeautifulSoup4==4.6.0
+SciPy==1.11.4
+seaborn==0.13.1
+scikit-learn==1.3
+statsmodels==0.14
+Tensorflow==2.12
+tensorflow_datasets==4.9.4
+matplotlib==3.8.2
\ No newline at end of file
diff --git a/Udemy DataScience Workbook/Real-life+example.csv b/Udemy DataScience Workbook/Real-life+example.csv
new file mode 100644
index 0000000..7dbe08c
--- /dev/null
+++ b/Udemy DataScience Workbook/Real-life+example.csv	
@@ -0,0 +1,4346 @@
+Brand,Price,Body,Mileage,EngineV,Engine Type,Registration,Year,Model
+BMW,4200,sedan,277,2,Petrol,yes,1991,320
+Mercedes-Benz,7900,van,427,2.9,Diesel,yes,1999,Sprinter 212
+Mercedes-Benz,13300,sedan,358,5,Gas,yes,2003,S 500
+Audi,23000,crossover,240,4.2,Petrol,yes,2007,Q7
+Toyota,18300,crossover,120,2,Petrol,yes,2011,Rav 4
+Mercedes-Benz,199999,crossover,0,5.5,Petrol,yes,2016,GLS 63
+BMW,6100,sedan,438,2,Gas,yes,1997,320
+Audi,14200,vagon,200,2.7,Diesel,yes,2006,A6
+Renault,10799,vagon,193,1.5,Diesel,yes,2012,Megane
+Volkswagen,1400,other,212,1.8,Gas,no,1999,Golf IV
+Renault,11950,vagon,177,1.5,Diesel,yes,2011,Megane
+Renault,2500,sedan,260,1.79,Petrol,yes,1994,19
+Audi,9500,vagon,165,2.7,Gas,yes,2003,A6 Allroad
+Volkswagen,10500,sedan,100,1.8,Petrol,yes,2008,Passat B6
+Toyota,16000,crossover,250,4.7,Gas,yes,2001,Land Cruiser 100
+Renault,8600,hatch,84,1.5,Diesel,yes,2012,Clio
+BMW,2990,other,203,2,Petrol,no,2001,318
+Mercedes-Benz,NA,van,240,2.2,Diesel,yes,2007,Vito
+Toyota,26500,crossover,21,2,Petrol,yes,2013,Rav 4
+Audi,3500,vagon,250,2.5,Diesel,no,1998,A6
+Toyota,38233,other,0,2.4,Diesel,yes,2016,Hilux
+Volkswagen,7500,hatch,132,1.4,Diesel,yes,2006,Polo
+Audi,6800,sedan,225,2.4,Gas,yes,1998,A6
+Mitsubishi,10500,crossover,130,2.4,Gas,yes,2006,Outlander
+Audi,24900,sedan,163,4.2,Diesel,yes,2008,A8
+Volkswagen,20800,crossover,151,3,Diesel,yes,2008,Touareg
+Audi,6500,sedan,330,2.4,Petrol,yes,1999,A6
+Mercedes-Benz,13566,other,171,2.2,Other,no,2011,Vito
+Mitsubishi,8500,hatch,65,1.3,Petrol,yes,2010,Colt
+Mercedes-Benz,NA,crossover,0,4.6,Petrol,yes,2016,GLS 500
+Audi,2900,sedan,1,2.3,Gas,yes,1989,100
+BMW,21500,other,72,3,Petrol,yes,2007,Z4
+Mitsubishi,17900,crossover,87,3.8,Gas,yes,2008,Pajero Wagon
+BMW,28500,crossover,160,4.8,Gas,yes,2008,X5
+Volkswagen,9800,van,160,1.9,Diesel,yes,2009,Caddy
+Toyota,14500,sedan,147,2.4,Gas,yes,2006,Camry
+BMW,6000,sedan,300,2.8,Petrol,yes,1996,528
+BMW,67500,crossover,1,2.5,Diesel,yes,2016,X5
+Audi,33900,other,14,1.8,Petrol,yes,2014,TT
+Audi,2500,sedan,180,NA,Petrol,no,2000,A4
+Mercedes-Benz,9800,van,230,2.15,Diesel,yes,2004,Vito
+Mercedes-Benz,63000,crossover,86,5.5,Petrol,yes,2009,G 55 AMG
+BMW,7700,sedan,326,2.8,Petrol,yes,1998,528
+Mercedes-Benz,9600,van,30,2.2,Diesel,yes,2006,Vito
+Toyota,12800,sedan,181,2.4,Petrol,yes,2008,Camry
+BMW,41000,crossover,80,3,Petrol,yes,2012,X6
+Mitsubishi,2500,sedan,220,2.4,Petrol,yes,1987,Galant
+BMW,3700,sedan,231,2.5,Petrol,yes,1990,525
+Mercedes-Benz,29500,sedan,134,5,Petrol,yes,2008,S 500
+BMW,6500,sedan,335,2.8,Petrol,yes,1996,528
+Mercedes-Benz,NA,sedan,145,3.2,Diesel,yes,2007,E-Class
+Renault,8250,van,66,1.5,Diesel,yes,2014,Kangoo
+Mercedes-Benz,19500,crossover,172,3.5,Other,yes,2008,ML 350
+Mercedes-Benz,13500,sedan,317,5,Petrol,yes,2001,S 500
+Mercedes-Benz,9200,van,266,2.2,Diesel,yes,2008,Vito
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLS 350
+Toyota,10900,sedan,156,NA,Gas,yes,2005,Camry
+Volkswagen,5000,vagon,265,2,Diesel,no,2006,Passat B6
+BMW,23900,crossover,162,4.8,Petrol,yes,2007,X5
+BMW,36900,sedan,81,3,Diesel,yes,2010,730
+BMW,8900,other,253,1.8,Petrol,yes,1995,318
+Renault,7800,van,200,1.9,Diesel,yes,2002,Trafic
+BMW,133000,crossover,7,4.4,Petrol,yes,2015,X5 M
+BMW,1650,sedan,270,1.8,Petrol,no,1995,318
+Mercedes-Benz,50000,sedan,240,3.5,Petrol,yes,2006,S 350
+Renault,9700,van,245,2,Diesel,yes,2008,Trafic
+Mercedes-Benz,9999,van,127,2.2,Diesel,yes,2005,Vito
+Audi,69999,crossover,21,3,Petrol,yes,2015,A6 Allroad
+Mitsubishi,6500,sedan,152,1.6,Petrol,yes,2007,Lancer
+Audi,4300,sedan,267,1.9,Diesel,no,2003,A6
+Audi,3000,sedan,267,2.5,Diesel,no,1999,A6
+Mercedes-Benz,8700,sedan,240,2.2,Diesel,yes,2000,E-Class
+Renault,9000,vagon,178,2,Petrol,yes,2008,Scenic
+Mercedes-Benz,20400,sedan,190,1.8,Gas,yes,2011,E-Class
+BMW,9000,sedan,175,3,Petrol,no,2006,330
+Mercedes-Benz,13400,sedan,105,1.8,Petrol,yes,2006,E-Class
+Volkswagen,3700,sedan,195,1.9,Diesel,no,2001,Passat B5
+Volkswagen,9850,van,126,2,Diesel,yes,2012,Caddy
+Audi,3700,hatch,420,2,Diesel,no,2004,A3
+Toyota,15700,crossover,195,4,Gas,yes,2003,Land Cruiser Prado
+Mercedes-Benz,24500,sedan,180,2.2,Diesel,yes,2012,E-Class
+Mitsubishi,7500,sedan,195,1.6,Gas,yes,2007,Lancer
+Volkswagen,11990,van,231,2.5,Diesel,yes,2002,Caravelle
+Volkswagen,13708.5,hatch,51,1.4,Petrol,yes,2014,Polo
+BMW,8200,sedan,287,2.5,Gas,yes,1999,525
+Renault,6999,van,222,1.5,Diesel,yes,2009,Kangoo
+Renault,6750,vagon,214,1.5,Diesel,yes,2004,Megane
+Audi,18500,sedan,245,3,Diesel,yes,2006,A8
+Toyota,14299,sedan,83,2,Petrol,yes,2009,Avensis
+Mercedes-Benz,28500,crossover,210,3.2,Diesel,yes,2007,GL 320
+Mitsubishi,17000,crossover,89,3.8,Petrol,yes,2008,Pajero Wagon
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GL 350
+Mercedes-Benz,25700,crossover,130,4.7,Gas,yes,2008,GL 450
+Audi,2650,vagon,333,2.5,Diesel,no,2000,A6
+Mitsubishi,8500,sedan,116,1.5,Petrol,yes,2008,Lancer X
+Mercedes-Benz,4000,sedan,405,2,Diesel,yes,1992,200
+Renault,11000,other,131,1.5,Diesel,yes,2011,Kangoo
+Mercedes-Benz,14500,sedan,138,2,Petrol,yes,2007,E-Class
+BMW,4099,sedan,50,2.5,Petrol,yes,1988,520
+Volkswagen,31500,crossover,1,2,Petrol,yes,2014,Tiguan
+Renault,4500,van,207,1.5,Diesel,yes,2003,Kangoo
+Volkswagen,6700,other,207,1.9,Diesel,yes,2004,Caddy
+Renault,NA,van,85,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,20800,crossover,31,2.4,Petrol,yes,2011,Outlander XL
+Mercedes-Benz,107999,crossover,0,2.98,Diesel,yes,2016,GLS 350
+BMW,3500,sedan,334,2,Diesel,no,1999,320
+Audi,39000,sedan,18,1.8,Other,yes,2015,A5
+Toyota,13500,crossover,150,4.7,Gas,yes,2002,Land Cruiser 100
+Mitsubishi,6150,sedan,175,1.6,Petrol,yes,2007,Lancer
+BMW,45500,crossover,76,4.4,Petrol,yes,2010,X6 M
+BMW,4900,sedan,295,2,Petrol,yes,1994,520
+BMW,47000,other,5,2,Petrol,yes,2014,Z4
+Toyota,19200,crossover,190,2.7,Gas,yes,2005,Land Cruiser Prado
+Renault,NA,vagon,180,1.5,Diesel,yes,2011,Megane
+Mitsubishi,11100,crossover,119,2.4,Gas,yes,2008,Outlander
+Mitsubishi,8200,sedan,139,1.5,Other,yes,2012,Lancer X
+Volkswagen,10300,sedan,235,2,Petrol,yes,2007,Passat B6
+Renault,11990,van,210,1.1,Diesel,yes,2010,Scenic
+Mitsubishi,800,sedan,340,NA,Other,yes,1986,Galant
+BMW,46800,sedan,33,2,Diesel,yes,2014,520
+Volkswagen,1500,other,260,1.8,Gas,no,1994,Golf III
+BMW,6350,crossover,156,4.4,Gas,no,2002,X5
+Mercedes-Benz,NA,sedan,16,3,Diesel,yes,2014,S 350
+Mercedes-Benz,8900,hatch,155,1.5,Petrol,yes,2005,A 150
+Mercedes-Benz,12700,van,160,3,Diesel,yes,2007,Vito
+Mitsubishi,8000,sedan,207,1.6,Gas,yes,2007,Lancer
+Toyota,29500,crossover,63,4,Petrol,yes,2008,FJ Cruiser
+Volkswagen,NA,hatch,100,2,Petrol,yes,2013,Golf GTI
+Renault,7100,van,208,1.5,Diesel,yes,2008,Kangoo
+Renault,10850,crossover,103,2.5,Gas,yes,2010,Koleos
+Volkswagen,NA,van,277,2.5,Diesel,yes,2001,T4 (Transporter)
+Toyota,195000,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,27000,sedan,57,2,Diesel,yes,2013,Passat B7
+Volkswagen,18100,other,64,2,Petrol,yes,2011,Scirocco
+BMW,27000,sedan,137,5,Other,yes,2005,M5
+BMW,8990,other,300,2.2,Petrol,yes,2002,320
+Toyota,32900,crossover,82,2.7,Petrol,yes,2013,Venza
+BMW,25500,other,163,4.8,Petrol,yes,2008,X5
+Toyota,8900,crossover,153,2,Petrol,yes,2003,Rav 4
+Audi,11950,sedan,190,3,Petrol,yes,2006,A6
+Mercedes-Benz,22700,sedan,125,2.2,Diesel,yes,2010,E-Class
+Audi,600,sedan,112,1.6,Petrol,yes,1980,80
+BMW,13000,sedan,165,2.2,Gas,yes,2003,520
+Audi,25000,other,60,2,Petrol,yes,2011,A5
+Toyota,26900,crossover,119,4,Gas,yes,2008,Land Cruiser Prado
+Mercedes-Benz,87777,van,0,2.2,Diesel,yes,2016,V 250
+Volkswagen,6000,van,200,2,Diesel,yes,2008,Caddy
+Audi,9800,vagon,257,2.5,Diesel,yes,2003,A6
+Audi,13900,other,197,2,Diesel,yes,2006,A6
+Renault,12200,van,164,2.5,Diesel,yes,2009,Trafic
+Mercedes-Benz,13500,crossover,205,2.7,Diesel,yes,2005,ML 270
+BMW,18100,crossover,200,4.8,Petrol,yes,2005,X5
+BMW,33000,crossover,200,3,Diesel,yes,2010,X5
+Volkswagen,16900,crossover,145,1.4,Petrol,yes,2011,Tiguan
+Mercedes-Benz,22000,sedan,110,1.8,Petrol,yes,2011,C-Class
+Volkswagen,10700,sedan,195,3.2,Petrol,yes,2007,Passat B6
+Mercedes-Benz,7800,sedan,226,2.2,Diesel,yes,2000,E-Class
+Mercedes-Benz,17300,sedan,184,1.8,Gas,yes,2008,E-Class
+BMW,70000,sedan,16,3,Diesel,yes,2014,730
+Volkswagen,2200,hatch,118,1.4,Petrol,no,2001,Lupo
+BMW,8900,sedan,256,2.2,Petrol,yes,2002,5 Series
+Mercedes-Benz,31600,sedan,33,1.6,Petrol,yes,2013,CLA 200
+Volkswagen,7900,other,168,1.9,Diesel,yes,2006,T5 (Transporter)
+Toyota,28500,crossover,97,3.5,Petrol,yes,2012,Highlander
+Renault,6900,van,140,1.5,Diesel,yes,2012,Kangoo
+BMW,8850,vagon,280,2.5,Diesel,yes,2000,525
+Renault,3100,van,164,1.4,Petrol,yes,1999,Kangoo
+BMW,8900,sedan,250,2.5,Petrol,yes,2000,525
+Renault,6100,sedan,130,1.4,Petrol,yes,2005,Megane
+BMW,1400,sedan,260,1.8,Petrol,no,1996,318
+Volkswagen,8800,van,93,1.6,Diesel,yes,2012,Caddy
+Renault,10299,other,176,2,Diesel,yes,2013,Trafic
+Renault,6500,sedan,170,1.6,Petrol,yes,2004,Megane
+Mercedes-Benz,169000,other,1,4.7,Petrol,yes,2015,S 500
+Toyota,9100,sedan,151,1.6,Gas,yes,2007,Corolla
+Volkswagen,9950,hatch,95,1.4,Petrol,yes,2010,Polo
+Toyota,24500,crossover,260,4.7,Gas,yes,2003,Land Cruiser 100
+Audi,51500,crossover,27,3,Diesel,yes,2013,Q7
+Volkswagen,12900,van,290,1.9,Diesel,yes,2008,T5 (Transporter)
+Mercedes-Benz,8000,van,250,2.2,Diesel,yes,2006,Vito
+Mitsubishi,8100,sedan,112,2.4,Petrol,yes,2007,Galant
+Renault,10100,van,164,2,Diesel,yes,2012,Trafic
+BMW,1900,sedan,300,2.5,Diesel,no,1997,5 Series
+Renault,10800,van,187,2,Diesel,yes,2012,Trafic
+Audi,8000,sedan,234,4.2,Gas,no,2004,A8
+Mercedes-Benz,15300,other,120,2.6,Petrol,yes,2004,CLK 240
+Volkswagen,2350,hatch,182,1.9,Diesel,no,1998,Golf IV
+Volkswagen,23999,sedan,88,NA,Other,yes,2012,Passat CC
+BMW,38888,vagon,5,0.65,Other,yes,2015,I3
+Mitsubishi,7300,hatch,126,NA,Other,yes,2004,Colt
+Toyota,99999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,17650,sedan,110,1.8,Petrol,yes,2012,Passat B7
+Volkswagen,12999,van,237,2.5,Diesel,yes,2005,Multivan
+Mercedes-Benz,18500,other,90,1.8,Petrol,yes,2008,C-Class
+Volkswagen,9200,sedan,195,2,Petrol,yes,2006,Passat B6
+Volkswagen,14500,sedan,1,1.6,Other,yes,2016,Polo
+Volkswagen,18000,vagon,18,1.6,Diesel,yes,2011,Passat B7
+Mercedes-Benz,6800,van,250,1.7,Diesel,yes,2003,Vaneo
+BMW,20500,crossover,73,2,Petrol,yes,2011,X1
+BMW,28000,other,100,5,Petrol,yes,2005,M5
+Volkswagen,10650,van,130,2,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,20300,other,30,1.4,Petrol,yes,2013,Scirocco
+Volkswagen,3000,vagon,267,1.9,Diesel,no,1999,Passat B5
+Toyota,25250,crossover,112,4.5,Gas,yes,2007,Land Cruiser 100
+Mitsubishi,1389,hatch,245,1.3,Petrol,no,1996,Colt
+Mercedes-Benz,17600,sedan,174,4,Diesel,yes,2005,S 400
+Mercedes-Benz,14200,van,180,2.2,Diesel,yes,2007,Vito
+BMW,13700,crossover,270,4.6,Gas,yes,2004,X5
+Mercedes-Benz,23900,sedan,187,5.5,Petrol,yes,2007,S 550
+Volkswagen,3700,crossover,267,NA,Diesel,no,2000,Golf IV
+Volkswagen,32000,sedan,144,3.6,Petrol,yes,2013,Passat CC
+BMW,4599,sedan,267,2,Gas,yes,1991,520
+Volkswagen,2350,sedan,300,1.9,Diesel,no,1998,Passat B5
+Audi,19999,vagon,160,2,Petrol,yes,2010,A4 Allroad
+BMW,28900,sedan,78,2,Petrol,yes,2012,528
+Volkswagen,3800,sedan,300,2.8,Gas,yes,1996,Passat B4
+Audi,4649,vagon,350,NA,Diesel,yes,2000,A6
+Volkswagen,1800,other,300,1.3,Petrol,yes,1989,Golf II
+Toyota,135000,crossover,1,4.5,Diesel,yes,2011,Land Cruiser 200
+Volkswagen,9700,sedan,201,2,Petrol,yes,2007,Passat B6
+Mitsubishi,25342.2,other,1,2.5,Diesel,yes,2016,L 200
+Volkswagen,24900,sedan,1,1.6,Diesel,yes,2016,Jetta
+Renault,4999,sedan,140,1.4,Petrol,yes,2011,Logan
+Volkswagen,NA,other,142,2.5,Diesel,yes,2007,Multivan
+BMW,4700,sedan,390,2.5,Petrol,yes,1991,525
+Mercedes-Benz,4999,sedan,1,2.8,Petrol,yes,1997,E-Class
+Mercedes-Benz,9299,sedan,350,2.7,Diesel,yes,2000,E-Class
+Mercedes-Benz,25000,sedan,130,5.5,Petrol,yes,2007,S 550
+Mitsubishi,12900,crossover,148,3,Gas,yes,2008,Pajero Sport
+Audi,9700,vagon,189,2.5,Diesel,yes,2003,A6
+Renault,10100,van,64,2,Diesel,yes,2014,Trafic
+BMW,2800,sedan,296,3,Diesel,no,2001,730
+Mercedes-Benz,13500,van,350,2.9,Diesel,yes,2000,Sprinter 312
+Mitsubishi,8900,hatch,125,1.8,Petrol,yes,2009,Lancer X Sportback
+Mercedes-Benz,109999,crossover,0,3,Diesel,yes,2016,GLS 350
+Mercedes-Benz,11500,sedan,180,3.2,Gas,yes,2004,E-Class
+BMW,36900,crossover,82,3,Petrol,yes,2010,X6
+Toyota,33200,crossover,150,4,Gas,yes,2010,Land Cruiser Prado
+Volkswagen,10200,van,253,2.5,Diesel,yes,2008,T5 (Transporter)
+Volkswagen,16999,sedan,0,1.6,Petrol,yes,2016,Jetta
+Toyota,10800,sedan,179,3,Gas,yes,2005,Camry
+Mercedes-Benz,35500,other,99,2.99,Diesel,yes,2011,E-Class
+Volkswagen,9900,hatch,156,1.6,Petrol,yes,2007,Golf Plus
+Renault,7900,vagon,91,1.5,Diesel,yes,2008,Scenic
+Mercedes-Benz,13900,sedan,216,2.2,Diesel,yes,2007,E-Class
+Volkswagen,20000,sedan,102,1.8,Petrol,yes,2011,Passat CC
+Toyota,28500,crossover,38,2.2,Diesel,yes,2015,Rav 4
+Mitsubishi,7200,sedan,178,1.6,Petrol,yes,2007,Lancer
+Mitsubishi,9000,crossover,200,2,Gas,yes,2006,Outlander
+Audi,36500,crossover,115,3,Diesel,yes,2010,Q7
+Volkswagen,8000,hatch,41,1,Petrol,yes,2012,Up
+Audi,43000,other,11,1.8,Petrol,yes,2015,A6
+Toyota,13700,sedan,48,1.4,Diesel,yes,2013,Corolla
+Toyota,7899,sedan,165,1.8,Gas,yes,2006,Avensis
+Renault,13999,van,180,2,Diesel,yes,2012,Trafic
+Volkswagen,10500,van,176,1.6,Diesel,yes,2012,Caddy
+Mercedes-Benz,5500,sedan,100,99.99,Petrol,yes,2007,S 550
+Volkswagen,20800,other,160,2,Diesel,yes,2012,Amarok
+Volkswagen,2500,sedan,488,2,Petrol,yes,1991,Passat B3
+Mercedes-Benz,32000,crossover,176,3.2,Gas,yes,2003,G 320
+Mercedes-Benz,14500,sedan,220,5,Petrol,yes,2004,S 500
+Toyota,16900,hatch,20,1.6,Petrol,yes,2014,Auris
+Mercedes-Benz,13600,van,172,2.2,Diesel,yes,2013,Vito
+Audi,2600,vagon,273,2.5,Diesel,no,1999,A6
+Mercedes-Benz,NA,van,0,2.2,Diesel,yes,2016,V 250
+BMW,12000,sedan,237,3,Diesel,yes,2003,530
+Volkswagen,NA,sedan,118,1.6,Gas,yes,2008,Jetta
+Volkswagen,18800,vagon,106,1.6,Diesel,yes,2013,Passat B7
+Toyota,35500,crossover,85,3,Diesel,yes,2012,Land Cruiser Prado
+Mercedes-Benz,29900,other,71,5.5,Petrol,yes,2007,CL 500
+Renault,6750,other,110,1.5,Diesel,yes,2012,Kangoo
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+BMW,34900,crossover,117,3,Petrol,yes,2011,X5
+Volkswagen,7700,van,177,1.9,Diesel,yes,2008,T5 (Transporter) 
+Renault,9250,vagon,220,1.5,Diesel,yes,2010,Megane
+Renault,8400,hatch,84,1.5,Diesel,yes,2011,Kangoo
+Toyota,11750,sedan,93,1.6,Petrol,yes,2013,Corolla
+Mercedes-Benz,5800,van,368,2.2,Diesel,yes,2000,Vito
+Toyota,5900,crossover,180,3,Diesel,no,2003,Land Cruiser Prado
+BMW,44999,crossover,87,3,Diesel,yes,2011,X6
+Volkswagen,10500,van,170,1.6,Diesel,yes,2013,Caddy
+Mercedes-Benz,17900,sedan,190,3,Diesel,yes,2007,E-Class
+Audi,9999,sedan,190,3,Petrol,yes,2007,A6
+Audi,9999,sedan,190,2.5,Diesel,yes,2003,A6
+Renault,4900,van,472,2.5,Diesel,yes,2000,Master
+Volkswagen,61000,crossover,2,3,Diesel,yes,2016,Touareg
+Toyota,38300,crossover,130,5.7,Gas,yes,2009,Land Cruiser 200
+Toyota,17700,sedan,30,1.6,Petrol,yes,2014,Corolla
+Toyota,600,sedan,10,1.5,Petrol,yes,1979,Corolla
+Volkswagen,55555,van,50,2,Diesel,yes,2013,Multivan
+BMW,18000,crossover,280,3,Diesel,yes,2005,X5
+Renault,7600,other,125,1.5,Diesel,yes,2013,Kangoo
+Renault,6900,sedan,68,1.5,Diesel,yes,2013,Logan
+Renault,15635.1,vagon,0,1.5,Diesel,yes,2016,Dokker
+Renault,10550,van,195,2,Diesel,yes,2011,Trafic
+Volkswagen,12500,van,190,1.9,Diesel,yes,2008,T5 (Transporter)
+Audi,13000,sedan,157,3,Petrol,yes,2006,A6
+BMW,7400,sedan,285,4.4,Gas,yes,1997,540
+Audi,64400,other,41,NA,Other,yes,2014,A8
+Renault,9500,hatch,100,1.5,Diesel,yes,2009,Megane
+Audi,18800,crossover,197,4.2,Petrol,yes,2007,Q7
+Toyota,8000,sedan,176,3,Petrol,yes,2002,Camry
+Volkswagen,8700,vagon,250,1.9,Diesel,yes,2002,Passat B5
+Mercedes-Benz,9999,crossover,300,3.2,Petrol,yes,1998,ML 320
+Volkswagen,16700,sedan,175,2,Petrol,yes,2011,Passat CC
+Toyota,13900,hatch,90,1.6,Petrol,yes,2012,Auris
+Volkswagen,18700,other,145,2,Diesel,yes,2011,T5 (Transporter) 
+Mitsubishi,6200,sedan,152,1.6,Petrol,yes,2006,Lancer
+Mercedes-Benz,NA,van,450,2.2,Diesel,yes,2000,Vito
+Renault,3600,van,22,1.9,Diesel,yes,2001,Kangoo
+Renault,7500,vagon,98,1.4,Petrol,yes,2007,Kangoo
+Renault,2300,van,200,1.9,Diesel,yes,2002,Kangoo
+Volkswagen,16900,crossover,133,2.5,Diesel,yes,2007,Touareg
+Mitsubishi,13499,crossover,116,2.4,Petrol,yes,2008,Outlander XL
+Renault,5700,van,180,1.5,Diesel,yes,2007,Kangoo
+Renault,7250,van,83,NA,Other,yes,2013,Kangoo
+Mitsubishi,17900,crossover,140,3.8,Gas,yes,2007,Pajero Wagon
+Volkswagen,7800,vagon,289,NA,Diesel,yes,2003,Passat B5
+Volkswagen,13900,other,170,1.9,Diesel,yes,2006,Multivan
+Audi,7950,vagon,167,2.5,Diesel,yes,2000,A6
+BMW,9700,sedan,250,2,Petrol,yes,2003,318
+Volkswagen,7500,sedan,240,1.8,Petrol,yes,2002,Passat B5
+Audi,19999,crossover,117,4.2,Petrol,yes,2007,Q7
+BMW,9100,sedan,230,2,Gas,yes,2004,318
+Renault,8100,van,135,NA,Diesel,yes,2008,Kangoo
+Volkswagen,13300,hatch,73,1.4,Petrol,yes,2011,Golf VI
+Volkswagen,21000,hatch,22,1.2,Other,yes,2014,Golf VII
+Audi,6600,sedan,360,1.8,Petrol,yes,1998,A6
+Audi,7600,vagon,226,3,Diesel,no,2006,A6
+Mercedes-Benz,3500,hatch,258,1.7,Diesel,no,2002,A 170
+Volkswagen,3500,sedan,3,1.8,Petrol,yes,1991,Passat B3
+Mercedes-Benz,17900,van,167,2.2,Diesel,yes,2012,Vito
+Renault,1950,vagon,286,1.9,Diesel,no,2001,Laguna
+Volkswagen,7990,sedan,90,1.4,Petrol,yes,2003,New Beetle
+Volkswagen,65000,crossover,24,NA,Other,yes,2015,Touareg
+Volkswagen,6500,sedan,320,1.9,Diesel,yes,2001,Passat B5
+Mitsubishi,3500,sedan,300,2,Diesel,yes,1996,Galant
+Toyota,14100,sedan,113,2.4,Gas,yes,2009,Camry
+Toyota,2999,other,300,1.6,Petrol,yes,1991,Corolla
+BMW,66900,crossover,70,3,Diesel,yes,2014,X5
+Toyota,13100,sedan,200,3.5,Gas,yes,2006,Camry
+Volkswagen,6900,sedan,300,1.8,Petrol,yes,1998,Passat B5
+Volkswagen,23999,sedan,80,2,Diesel,yes,2014,Passat B7
+Mitsubishi,6990,crossover,225,2.5,Petrol,yes,1997,Pajero
+Toyota,13999,sedan,198,2.4,Gas,yes,2009,Camry
+Renault,7600,van,108,1,Diesel,yes,2013,Kangoo
+Renault,11100,vagon,180,1.5,Diesel,yes,2012,Megane
+Mercedes-Benz,16500,other,260,2.2,Diesel,yes,2011,Sprinter 319
+Mercedes-Benz,199999,crossover,0,5.5,Petrol,yes,2016,GLS 63
+Mercedes-Benz,44000,other,82,5.5,Petrol,yes,2010,S 500
+Audi,39900,other,12,1.8,Petrol,yes,2014,A5
+Mercedes-Benz,20500,sedan,173,1.8,Gas,yes,2011,E-Class
+BMW,8000,crossover,214,4.4,Petrol,no,2000,X5
+BMW,11600,sedan,142,2,Petrol,yes,2005,320
+Audi,3500,sedan,265,2,Diesel,yes,1986,100
+Renault,11500,vagon,170,1.5,Diesel,yes,2012,Megane
+Volkswagen,13700,other,103,2,Diesel,yes,2011,Passat B6
+Mercedes-Benz,13700,van,198,3.2,Gas,yes,2006,Viano
+Renault,7700,vagon,200,1.5,Diesel,yes,2007,Megane
+Mercedes-Benz,5800,sedan,231,2.2,Petrol,yes,1994,E-Class
+Volkswagen,9500,sedan,95,2,Petrol,yes,2007,Passat B6
+Mercedes-Benz,24900,sedan,140,5.5,Petrol,yes,2009,S 550
+Volkswagen,55500,crossover,47,3,Diesel,yes,2016,Touareg
+Volkswagen,6500,van,186,2.8,Petrol,yes,2000,Sharan
+Mercedes-Benz,8300,sedan,175,3.2,Diesel,no,2006,E-Class
+Mitsubishi,12000,crossover,130,3,Gas,yes,2007,Pajero Sport
+Mercedes-Benz,15000,crossover,185,3.7,Gas,yes,2005,ML 350
+BMW,8100,sedan,246,3,Diesel,yes,2000,530
+Volkswagen,11200,van,178,NA,Diesel,yes,2008,T5 (Transporter)
+Renault,8000,sedan,175,2,Gas,yes,2005,Megane
+Mercedes-Benz,5500,van,285,2.3,Diesel,yes,1999,Vito
+Mercedes-Benz,NA,van,0,2.2,Diesel,yes,2016,V 250
+Renault,9600,vagon,184,1.5,Diesel,yes,2011,Megane
+Volkswagen,12500,vagon,96,NA,Petrol,yes,2010,Golf VI
+BMW,10800,sedan,180,2,Petrol,yes,2002,318
+Mercedes-Benz,10999,van,263,2.2,Diesel,yes,2005,Vito
+BMW,44000,crossover,80,3,Diesel,yes,2012,X5
+Toyota,15700,sedan,108,NA,Petrol,yes,2008,Camry
+Toyota,19200,hatch,54,1.4,Other,yes,2012,Prius
+Mercedes-Benz,19600,van,127,1.8,Diesel,yes,2013,B 180
+Volkswagen,9000,sedan,135,1.6,Gas,yes,2011,Polo
+BMW,10900,sedan,245,3,Diesel,yes,2003,530
+Volkswagen,12900,van,180,2,Diesel,yes,2012,T5 (Transporter) 
+BMW,6600,sedan,285,2.5,Petrol,yes,1999,525
+Renault,15820.35,sedan,0,1.5,Diesel,yes,2016,Fluence
+Toyota,10200,sedan,180,2.4,Petrol,yes,2003,Camry
+Mercedes-Benz,6000,sedan,366,3.2,Gas,yes,1994,E-Class
+Audi,3500,sedan,1,2,Other,yes,1988,90
+Volkswagen,7800,van,380,2.5,Diesel,yes,1999,T4 (Transporter)
+Toyota,35900,crossover,84,3,Diesel,yes,2013,Land Cruiser Prado
+Mercedes-Benz,NA,crossover,0,4.6,Petrol,yes,2016,GLS 500
+Renault,11700,crossover,90,1.5,Diesel,yes,2011,Duster
+Volkswagen,15200,crossover,58,2,Petrol,yes,2008,Tiguan
+Mercedes-Benz,2500,sedan,111,2.3,Gas,yes,1992,E-Class
+Volkswagen,16800,other,164,2,Diesel,yes,2010,T5 (Transporter)
+BMW,50499,sedan,128,3,Diesel,yes,2013,730
+Mercedes-Benz,2650,sedan,470,2,Gas,yes,1985,190
+BMW,2250,sedan,300,2.5,Petrol,no,1997,523
+Mercedes-Benz,9300,sedan,270,2.7,Diesel,yes,2001,E-Class
+Toyota,11500,van,161,2.5,Diesel,yes,2002,Hiace
+BMW,13000,sedan,304,4.4,Other,yes,2002,745
+Mercedes-Benz,7500,sedan,319,3.2,Gas,yes,1997,S 320
+Mercedes-Benz,17999,other,38,2,Petrol,yes,2009,CLC 200
+Renault,7300,vagon,162,1.5,Diesel,yes,2006,Megane
+Toyota,29900,crossover,117,3,Diesel,yes,2008,Land Cruiser Prado
+Renault,4950,vagon,170,1.5,Diesel,yes,2004,Kangoo
+Audi,36000,vagon,40,3,Diesel,yes,2010,Q7
+Audi,1700,sedan,177,1.6,Petrol,no,1996,A4
+Audi,12450,vagon,1,2,Diesel,yes,2008,A4
+Volkswagen,13893.75,sedan,0,1.6,Petrol,yes,2016,Polo
+Volkswagen,44600,crossover,60,3,Diesel,yes,2013,Touareg
+Toyota,35138.29,crossover,0,2,Petrol,yes,2016,Rav 4
+BMW,3500,sedan,313,2.5,Diesel,yes,1994,325
+Mercedes-Benz,12800,other,260,2.7,Diesel,yes,2005,Sprinter 316
+Volkswagen,27000,sedan,85,2,Diesel,yes,2013,Passat CC
+Volkswagen,14800,sedan,143,2,Diesel,yes,2010,Passat B6
+Renault,2200,other,155,1.6,Petrol,no,2004,Scenic
+Volkswagen,2900,van,433,2.4,Diesel,no,1995,T4 (Transporter)
+Audi,12000,sedan,200,6,Petrol,yes,2004,A8
+Volkswagen,41000,crossover,180,3,Diesel,yes,2013,Touareg
+Audi,16300,vagon,180,3.2,Petrol,yes,2006,A6 Allroad
+Renault,5300,van,145,1.5,Other,yes,2010,Kangoo
+Mercedes-Benz,40500,sedan,39,2.2,Diesel,yes,2015,E-Class
+Mercedes-Benz,4300,van,300,2.2,Diesel,no,2005,Vito
+Volkswagen,4000,crossover,137,2.5,Diesel,no,2004,Touareg
+Volkswagen,9600,hatch,132,1.6,Petrol,yes,2007,Golf V
+Mitsubishi,18000,crossover,112,3,Gas,yes,2008,Pajero Wagon
+Renault,11300,vagon,184,1.5,Diesel,yes,2011,Megane
+Volkswagen,4999,hatch,131,1.4,Petrol,yes,2002,Polo
+Mercedes-Benz,24999,sedan,112,2.2,Diesel,yes,2012,C-Class
+Toyota,23300,crossover,93,2.7,Other,yes,2011,Venza
+Toyota,14900,crossover,124,4,Petrol,yes,2006,Fortuner
+Audi,22800,sedan,184,2.7,Diesel,yes,2010,A6
+Mercedes-Benz,35500,sedan,137,3,Diesel,yes,2011,E-Class
+Volkswagen,15000,other,280,2.8,Diesel,yes,2006,LT
+Toyota,8650,sedan,132,1.6,Petrol,yes,2008,Corolla
+Mitsubishi,22400,crossover,96,2.5,Diesel,yes,2011,Pajero Sport
+Mitsubishi,10400,sedan,123,2,Petrol,yes,2009,Lancer X
+Mercedes-Benz,195000,crossover,2,5.5,Petrol,yes,2016,GLS 63
+Audi,10500,vagon,198,4.2,Gas,yes,2005,A6 Allroad
+Renault,11900,crossover,68,1.5,Diesel,yes,2011,Duster
+BMW,36900,sedan,17,2,Petrol,yes,2013,520
+BMW,24500,crossover,170,3,Diesel,yes,2006,X5
+Volkswagen,4700,van,400,2.4,Diesel,yes,1999,T4 (Transporter)
+Toyota,18100,hatch,30,1.6,Petrol,yes,2013,Auris
+Mercedes-Benz,115000,sedan,47,4.6,Petrol,yes,2013,S 500
+Mercedes-Benz,10900,sedan,217,3.2,Diesel,yes,2002,E-Class
+BMW,7000,sedan,280,3,Petrol,yes,2001,530
+Mercedes-Benz,NA,sedan,20,3,Diesel,yes,2015,S 350
+Volkswagen,9999,vagon,161,2,Diesel,yes,2008,Passat B6
+Mitsubishi,12300,other,160,2.5,Diesel,yes,2008,L 200
+Volkswagen,67622.55,crossover,0,3,Diesel,yes,2016,Touareg
+Volkswagen,18900,other,45,2,Diesel,yes,2013,Passat B7
+Volkswagen,6999,sedan,248,1.8,Petrol,yes,2002,Passat B5
+Mercedes-Benz,10250,sedan,261,1.8,Petrol,yes,2003,E-Class
+Toyota,3500,sedan,286,1.6,Petrol,yes,2000,Corolla
+BMW,13500,crossover,250,3,Diesel,no,2006,X5
+BMW,2450,sedan,328,2.5,Diesel,no,1996,525
+Volkswagen,7500,van,180,2.5,Diesel,yes,2004,T5 (Transporter) 
+Volkswagen,4900,vagon,263,2,Diesel,no,2007,Passat B6
+Audi,4600,vagon,242,NA,Diesel,no,2004,A6
+Mercedes-Benz,5000,vagon,248,2,Gas,yes,1996,E-Class
+BMW,29900,crossover,235,3,Diesel,yes,2009,X5
+Renault,7000,sedan,110,1.4,Petrol,yes,2012,Symbol
+Mitsubishi,11800,sedan,1,1.5,Gas,yes,2010,Lancer X
+Mercedes-Benz,16500,van,223,2.2,Diesel,yes,2009,Vito
+BMW,5300,sedan,147,2.2,Gas,no,2001,320
+Mercedes-Benz,7200,van,200,2.2,Diesel,yes,2004,Vito
+Volkswagen,8500,van,230,1.9,Diesel,yes,2008,Caddy
+BMW,39000,crossover,180,3,Diesel,yes,2011,X5
+Volkswagen,1850,vagon,270,1.9,Diesel,yes,2000,Passat B5
+Audi,36100,sedan,75,3,Diesel,yes,2011,A7
+Mitsubishi,3149.25,hatch,330,2,Gas,yes,1993,Galant
+Renault,5800,van,159,1.5,Diesel,yes,2007,Kangoo
+BMW,6700,sedan,370,2,Petrol,yes,1997,320
+Renault,18988.13,crossover,0,1.5,Diesel,yes,2016,Duster
+Mitsubishi,6300,sedan,128,NA,Petrol,yes,2007,Colt
+BMW,7500,sedan,425,NA,Gas,yes,1996,535
+Volkswagen,28660,van,0,1.6,Diesel,yes,2016,Caddy
+BMW,2999,sedan,210,2,Other,no,1999,320
+BMW,64500,crossover,1,2,Diesel,yes,2016,X5
+Toyota,21500,crossover,135,2,Gas,yes,2007,Land Cruiser Prado
+BMW,85555,sedan,10,3,Diesel,yes,2015,640
+Volkswagen,8650,van,108,1.6,Diesel,yes,2013,Caddy
+BMW,10600,sedan,1,3,Diesel,yes,2000,530
+Toyota,6900,hatch,109,1.3,Gas,yes,2007,Yaris
+Mercedes-Benz,2900,other,248,2,Petrol,no,1999,CLK 200
+Mercedes-Benz,12250,other,185,2.2,Diesel,yes,2012,Vito
+BMW,21700,crossover,101,2.5,Gas,yes,2007,X3
+Mercedes-Benz,2000,sedan,320,2.2,Diesel,no,1998,E-Class
+Audi,9900,vagon,284,2.5,Diesel,yes,2004,A6
+Mercedes-Benz,5850,van,240,2.2,Diesel,yes,2001,Vito
+Toyota,8200,vagon,204,1.4,Diesel,yes,2005,Corolla
+Volkswagen,NA,sedan,381,2.5,Diesel,yes,2000,T4 (Transporter) 
+Renault,7300,vagon,200,1.5,Diesel,yes,2008,Megane
+Mitsubishi,8000,sedan,145,2,Gas,yes,2007,Lancer
+Renault,6600,van,184,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,22500,sedan,164,1.8,Gas,yes,2012,E-Class
+Mercedes-Benz,13200,other,196,2.2,Other,yes,2012,Sprinter 313
+Volkswagen,1200,other,300,1.4,Gas,yes,2000,Caddy
+Volkswagen,8900,van,264,2.5,Diesel,yes,2003,T4 (Transporter)
+Mitsubishi,9200,sedan,163,2,Petrol,yes,2008,Lancer X
+Mercedes-Benz,10200,sedan,154,2.5,Petrol,yes,2006,C-Class
+Mercedes-Benz,2700,sedan,121,2.5,Diesel,yes,1986,E-Class
+Mercedes-Benz,9500,sedan,246,6,Petrol,yes,1997,S 600
+Mitsubishi,26850,crossover,84,3.2,Diesel,yes,2012,Pajero Wagon
+Audi,21900,crossover,183,4.2,Petrol,yes,2007,Q7
+Volkswagen,1700,hatch,250,1.4,Petrol,no,1998,Polo
+Mercedes-Benz,NA,crossover,0,2.2,Diesel,yes,2016,GLC-Class
+Toyota,NA,crossover,49,4.5,Diesel,yes,2014,Land Cruiser 200
+Volkswagen,6500,hatch,225,1.4,Petrol,yes,2000,Golf IV
+Mitsubishi,10600,crossover,85,NA,Gas,yes,2006,Outlander
+Renault,5000,vagon,34,1.5,Diesel,yes,2013,Logan
+BMW,38000,sedan,74,2,Diesel,yes,2014,520
+Volkswagen,9599,sedan,140,1.8,Petrol,yes,2008,Passat B6
+Mercedes-Benz,10800,other,230,1.8,Petrol,yes,2006,C-Class
+Toyota,18800,crossover,105,2,Gas,yes,2011,Rav 4
+Mitsubishi,7650,sedan,133,1.5,Gas,yes,2008,Lancer
+Renault,6300,vagon,125,2,Petrol,yes,2003,Laguna
+BMW,5000,other,75,1.9,Petrol,no,1998,Z3
+Toyota,28500,crossover,107,3.5,Gas,yes,2011,Highlander
+BMW,13000,sedan,193,3.6,Petrol,yes,2003,735
+Toyota,16999,crossover,157,2.7,Gas,yes,2007,Land Cruiser Prado
+BMW,34500,other,160,3,Diesel,yes,2011,X5
+Volkswagen,10900,vagon,204,2,Diesel,yes,2007,Passat B6
+Mitsubishi,18999,crossover,121,3.8,Gas,yes,2008,Pajero Wagon
+BMW,9800,sedan,350,3,Diesel,yes,2001,530
+Volkswagen,4300,vagon,298,1.8,Petrol,yes,1998,Golf III
+Audi,3900,vagon,260,2.5,Diesel,no,2003,A6
+Volkswagen,13499,van,154,2,Diesel,yes,2013,T5 (Transporter) 
+Audi,19990,sedan,127,2.8,Petrol,yes,2010,A6
+Renault,3400,van,260,2.2,Diesel,no,2005,Espace
+Toyota,27800,sedan,157,3.5,Petrol,yes,2011,Camry
+Volkswagen,2300,hatch,320,1.6,Gas,yes,1989,Golf II
+BMW,30500,sedan,630,NA,Petrol,yes,2010,523
+Renault,7650,van,151,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,10000,van,220,1.9,Diesel,yes,2008,Caddy
+Volkswagen,9700,vagon,130,1.6,Gas,yes,2008,Golf Variant
+Audi,1950,sedan,320,19,Other,no,1996,A4
+Audi,3800,sedan,270,2.5,Diesel,no,2000,A6
+Toyota,26900,crossover,115,4,Gas,yes,2008,Land Cruiser Prado
+BMW,NA,other,35,3,Petrol,yes,2008,630
+Toyota,12600,sedan,205,2.4,Gas,yes,2006,Camry
+Volkswagen,14899,van,158,2,Diesel,yes,2012,T5 (Transporter) 
+Renault,4999,vagon,252,1.5,Diesel,yes,2007,Kangoo
+BMW,57777,other,40,4,Diesel,yes,2012,X6
+Volkswagen,12350,van,167,1.4,Petrol,yes,2011,Touran
+Audi,7999,sedan,177,2.7,Gas,yes,2003,A6
+BMW,21600,crossover,200,4.8,Other,yes,2007,X5
+Mitsubishi,8000,sedan,67,1.5,Petrol,yes,2009,Lancer X
+Volkswagen,6799,hatch,1,1.4,Gas,yes,1998,Golf IV
+Toyota,25749.75,sedan,0,2.5,Petrol,yes,2016,Camry
+Volkswagen,8899,vagon,198,1.9,Other,yes,2004,Passat B5
+Mercedes-Benz,26800,sedan,162,5.5,Petrol,yes,2007,S 500
+Toyota,25555,crossover,128,4.7,Gas,yes,2004,Land Cruiser 100
+Audi,25000,crossover,112,3.2,Petrol,yes,2010,Q5
+Volkswagen,15800,van,194,2,Diesel,yes,2012,T5 (Transporter) 
+BMW,3150,sedan,200,2,Diesel,no,1999,320
+Toyota,12300,sedan,157,2.4,Gas,yes,2007,Camry
+Toyota,45500,crossover,45,3.5,Petrol,yes,2015,Highlander
+Renault,6500,other,406,2.5,Diesel,yes,2004,Master
+Toyota,18999,crossover,95,2,Petrol,yes,2011,Rav 4
+BMW,8500,sedan,260,2.2,Gas,yes,2001,520
+Mercedes-Benz,8500,van,350,2.2,Diesel,yes,2004,Sprinter 211
+Toyota,13500,crossover,126,4,Petrol,no,2011,Land Cruiser Prado
+Volkswagen,2200,sedan,180,1.9,Diesel,no,2003,Passat B5
+Volkswagen,18500,vagon,125,2,Diesel,yes,2011,Passat B7
+Toyota,8420,other,150,2.7,Gas,yes,2005,Tacoma
+Audi,17500,vagon,144,2,Diesel,yes,2010,A4
+Audi,9000,sedan,158,2,Gas,yes,2003,A4
+Volkswagen,4400,hatch,238,1.6,Petrol,yes,1994,Golf III
+BMW,NA,crossover,0,3,Diesel,yes,2016,X5
+Renault,7500,van,200,2.2,Diesel,yes,2002,Espace
+Toyota,17950,sedan,87,2.4,Petrol,yes,2010,Camry
+BMW,51500,crossover,23,3,Petrol,yes,2013,X6
+Mitsubishi,10800,crossover,165,3,Gas,yes,2007,Pajero Sport
+Volkswagen,33700,van,11,1.4,Petrol,yes,2016,Touran
+Volkswagen,17000,crossover,158,2.5,Diesel,yes,2007,Touareg
+Volkswagen,11200,crossover,1,4.2,Petrol,yes,2004,Touareg
+BMW,4500,sedan,140,3,Diesel,no,2000,530
+Toyota,43999,other,0,2.8,Diesel,yes,2016,Hilux
+Audi,39800,sedan,180,4.2,Other,yes,2012,A8
+BMW,6500,sedan,272,2.8,Petrol,yes,1996,328
+Volkswagen,18700,vagon,196,2,Diesel,yes,2011,Passat B7
+Audi,38500,vagon,75,2,Diesel,yes,2013,Q3
+BMW,8200,sedan,241,2,Petrol,yes,1998,320
+Audi,7500,vagon,240,2,Petrol,yes,2002,A4
+Toyota,11550,sedan,95,2.4,Petrol,yes,2007,Camry
+Mercedes-Benz,39500,sedan,30,1.6,Petrol,yes,2014,C-Class
+Volkswagen,6200,sedan,161,4.2,Gas,no,2004,Phaeton
+BMW,17200,crossover,308,4.6,Gas,yes,2003,X5
+BMW,2900,sedan,280,2.5,Diesel,no,1997,525
+Renault,14600,other,169,2.3,Diesel,yes,2013,Master
+BMW,34900,crossover,110,4.4,Petrol,yes,2010,X6
+Toyota,164500,crossover,0,4.5,Diesel,yes,2015,Land Cruiser 200
+Audi,NA,crossover,1,3,Diesel,yes,2016,A6 Allroad
+BMW,15299,other,136,NA,Petrol,yes,2008,523
+Mercedes-Benz,7000,other,170,2.2,Petrol,no,2001,CLK 220
+Mercedes-Benz,32000,van,255,3,Diesel,yes,2008,Sprinter 318
+Renault,7500,vagon,168,1.5,Diesel,yes,2005,Grand Scenic
+Volkswagen,9000,other,217,2.5,Diesel,yes,2005,T5 (Transporter)
+Mitsubishi,7000,sedan,180,1.6,Gas,yes,2004,Lancer
+Mercedes-Benz,199999,crossover,0,5.5,Petrol,yes,2016,GLS 63
+Audi,2700,sedan,234,2.5,Other,no,2000,A6
+Volkswagen,10950,van,165,2,Diesel,yes,2012,Caddy
+Toyota,15500,sedan,120,3.5,Petrol,yes,2008,Camry
+Mercedes-Benz,8700,sedan,210,5,Petrol,yes,1997,S 500
+Mercedes-Benz,6000,van,200,2.2,Diesel,yes,2000,Vito
+Mitsubishi,16950,crossover,135,3,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,15000,crossover,400,3,Diesel,yes,1997,Sprinter 212
+Toyota,26500,crossover,102,2.5,Diesel,yes,2013,Hilux
+Mercedes-Benz,3100,vagon,400,2.2,Diesel,no,2002,C-Class
+Toyota,59900,crossover,90,4.5,Diesel,yes,2012,Land Cruiser 200
+Toyota,63000,crossover,3,4.2,Diesel,yes,2013,Land Cruiser 76
+Mercedes-Benz,19800,crossover,218,3,Diesel,yes,2006,ML 280
+Volkswagen,13000,van,110,1.9,Diesel,yes,2009,T5 (Transporter)
+Audi,12200,sedan,130,2.4,Petrol,yes,2006,A6
+Mitsubishi,5700,other,170,1.3,Petrol,yes,2006,Colt
+Audi,2550,sedan,370,2.5,Diesel,no,1998,A6
+Toyota,13500,sedan,144,3.5,Gas,yes,2007,Camry
+Renault,10200,van,183,2,Diesel,yes,2012,Trafic
+Audi,13000,other,161,1.8,Gas,yes,2001,TT
+Volkswagen,10500,sedan,210,5,Diesel,yes,2004,Phaeton
+BMW,3100,sedan,315,2.5,Diesel,no,1998,525
+BMW,9400,sedan,221,4.4,Gas,yes,2004,745
+Audi,10000,sedan,206,2.5,Diesel,yes,2003,A6
+Mitsubishi,4200,sedan,270,2,Gas,yes,1998,Galant
+Renault,7500,sedan,131,1.6,Petrol,yes,2009,Megane
+Mercedes-Benz,38600,other,12,1.8,Petrol,yes,2012,E-Class
+Toyota,8900,hatch,140,1.6,Petrol,yes,2008,Auris
+Renault,4800,van,113,15,Diesel,yes,2006,Kangoo
+Toyota,24000,crossover,270,4.2,Diesel,yes,1994,Land Cruiser 80
+Mercedes-Benz,51500,other,75,6.3,Petrol,yes,2009,CL 63 AMG
+Mercedes-Benz,5100,sedan,270,3.2,Diesel,no,2001,S 320
+Mitsubishi,4999,sedan,60,1.6,Petrol,yes,2007,Lancer
+Mitsubishi,6666,sedan,162,NA,Petrol,yes,2007,Lancer
+Mitsubishi,9450,sedan,125,2,Gas,yes,2008,Lancer X
+Audi,3850,vagon,215,2.5,Diesel,no,2002,A6
+Renault,7800,vagon,170,1.5,Diesel,no,2013,Megane
+Audi,49700,crossover,80,3,Diesel,yes,2013,Q7
+Mitsubishi,9000,sedan,78,2.4,Gas,yes,2006,Galant
+Toyota,18990,crossover,95,2,Petrol,yes,2011,Rav 4
+Mitsubishi,7200,sedan,102,1.6,Petrol,yes,2007,Lancer
+BMW,117000,crossover,1,NA,Other,yes,2016,X6 M
+Volkswagen,11900,other,112,2,Diesel,yes,2011,T6 (Transporter) 
+Toyota,2800,sedan,287,1.3,Petrol,yes,1995,Corolla
+Volkswagen,2200,sedan,272,1.9,Diesel,no,1998,Passat B5
+Mitsubishi,17500,crossover,170,3.8,Gas,yes,2008,Pajero Wagon
+Audi,49900,other,79,4.2,Petrol,yes,2007,R8
+Volkswagen,8650,van,238,2.5,Diesel,yes,2002,T4 (Transporter)
+Audi,8800,sedan,205,1.8,Petrol,yes,2003,A4
+Mercedes-Benz,69999,crossover,0,2.2,Diesel,yes,2016,GLE-Class
+Volkswagen,8910.53,hatch,103,1.4,Petrol,yes,2012,Polo
+Toyota,15500,crossover,165,2.7,Gas,yes,2005,Land Cruiser Prado
+Audi,19500,crossover,175,3.6,Petrol,yes,2007,Q7
+Toyota,27700,sedan,21,2.5,Petrol,yes,2015,Camry
+Volkswagen,9600,van,255,1.9,Diesel,yes,2004,T5 (Transporter)
+BMW,12500,crossover,176,3,Gas,yes,2002,X5
+Audi,4799,vagon,260,2.5,Diesel,no,2002,A6 Allroad
+Toyota,20900,sedan,70,2.5,Petrol,yes,2012,Camry
+Audi,14900,sedan,203,2.8,Petrol,yes,2008,A6
+Toyota,12750,hatch,164,1.4,Diesel,yes,2012,Auris
+BMW,5900,hatch,243,2,Petrol,no,2006,116
+Mercedes-Benz,2700,sedan,400,2.9,Diesel,yes,1997,E-Class
+Audi,4700,sedan,400,2.6,Petrol,yes,1996,A6
+Audi,9500,other,0,NA,Other,yes,2003,A6
+BMW,77000,other,64,4.4,Petrol,yes,2012,M6
+Renault,5900,van,250,1.9,Diesel,yes,2005,Trafic
+Volkswagen,24000,sedan,67,1.8,Petrol,yes,2012,Passat CC
+Audi,18400,vagon,151,2,Diesel,yes,2010,A4
+Mercedes-Benz,56800,sedan,74,6.3,Petrol,yes,2010,S 63 AMG
+Renault,8000,hatch,50,1.6,Petrol,yes,2012,Sandero
+Mercedes-Benz,5000,sedan,382,2,Petrol,yes,1994,E-Class
+Mitsubishi,9900,sedan,98,2,Petrol,yes,2009,Lancer X
+Mercedes-Benz,18900,other,111,2.5,Petrol,yes,2009,C-Class
+Toyota,20500,crossover,139,4,Gas,yes,2005,Land Cruiser Prado
+Volkswagen,2500,vagon,232,1.6,Petrol,no,2001,Golf IV
+Renault,9000,vagon,179,1.5,Diesel,yes,2010,Megane
+Renault,7700,sedan,100,1.6,Gas,yes,2008,Megane
+Audi,32300,hatch,54,2.8,Petrol,yes,2011,A6
+Mercedes-Benz,72700,van,42,2.2,Diesel,yes,2014,V 250
+Renault,6200,sedan,246,1.4,Petrol,yes,2005,Megane
+Audi,4000,sedan,400,2,Petrol,yes,1992,100
+Toyota,9003.15,sedan,43,1.3,Petrol,yes,2014,Corolla
+Mercedes-Benz,14900,other,195,3.2,Gas,yes,1999,CLK 320
+Mercedes-Benz,NA,van,0,2.2,Diesel,yes,2016,V 250
+Audi,2200,sedan,250,NA,Diesel,no,2001,A6
+Volkswagen,NA,van,1,NA,Diesel,yes,2005,Caddy
+BMW,65393.25,crossover,0,2,Diesel,yes,2016,X5
+Renault,9000,van,158,2,Diesel,yes,2012,Trafic
+Audi,NA,sedan,60,6.3,Petrol,yes,2013,A8
+Audi,8500,sedan,253,2.5,Diesel,yes,2001,A6
+Renault,5900,hatch,171,1.5,Other,yes,2005,Megane
+Mitsubishi,5100,hatch,250,1.3,Gas,yes,2006,Colt
+Mercedes-Benz,40500,sedan,108,5.5,Petrol,yes,2010,S 500
+Audi,8600,sedan,154,1.8,Petrol,yes,2001,A6
+Mitsubishi,5900,sedan,142,1.6,Gas,yes,2005,Lancer
+Volkswagen,17900,other,123,1.8,Petrol,yes,2010,Passat CC
+BMW,34400,crossover,37,2,Diesel,yes,2013,X1
+BMW,13500,sedan,138,2.5,Petrol,yes,2004,525
+Renault,9600,van,175,2,Diesel,yes,2007,Trafic
+Mercedes-Benz,15000,other,82,1.8,Petrol,yes,2004,SLK 200
+Mercedes-Benz,22000,other,53,3,Petrol,yes,2007,CLK 280
+Volkswagen,13700,other,200,2.5,Diesel,yes,2005,Touareg
+BMW,38000,sedan,61,NA,Petrol,yes,2013,535
+Toyota,18800,sedan,47,2.4,Petrol,yes,2009,Camry
+Volkswagen,7600,hatch,127,1.4,Petrol,yes,2007,Polo
+Toyota,11950,hatch,120,1.4,Petrol,yes,2013,Auris
+Audi,11300,sedan,202,2.7,Diesel,no,2008,A6
+Volkswagen,13500,van,43,2,Diesel,yes,2014,T5 (Transporter) 
+Toyota,11990,sedan,129,2.4,Petrol,yes,2007,Camry
+Toyota,16500,other,130,2.7,Gas,yes,2004,Land Cruiser Prado
+BMW,19000,crossover,215,3,Diesel,yes,2006,X5
+Renault,5500,van,160,1.5,Diesel,yes,2005,Kangoo
+BMW,39999,sedan,45,3,Petrol,yes,2011,535
+Toyota,15500,sedan,140,2.4,Gas,yes,2007,Camry
+Mitsubishi,11700,crossover,197,3,Gas,yes,2006,Pajero Sport
+Volkswagen,2750,vagon,1,1.9,Diesel,no,2001,Passat B5
+Renault,7150,vagon,1,1.5,Diesel,yes,2005,Megane
+BMW,10500,hatch,220,2,Petrol,yes,2005,118
+Mercedes-Benz,24900,sedan,97,1.8,Petrol,yes,2012,E-Class
+Toyota,NA,sedan,24,2.5,Petrol,yes,2015,Camry
+Mercedes-Benz,26000,other,22,1.8,Petrol,yes,2014,C-Class
+Mercedes-Benz,21500,crossover,148,6.2,Petrol,yes,2008,ML 63 AMG
+BMW,2222,sedan,268,1.8,Petrol,no,1996,318
+BMW,2900,vagon,236,2,Diesel,no,2001,320
+Volkswagen,4600,hatch,1,1.8,Gas,yes,1996,Golf III
+Mercedes-Benz,3200,sedan,198,2,Gas,yes,1992,190
+Mitsubishi,19400,crossover,78,2.4,Petrol,yes,2012,Outlander XL
+Renault,4000,sedan,215,1.9,Diesel,no,2005,Laguna
+BMW,65099,crossover,0,2,Diesel,yes,2016,X5
+BMW,53999,sedan,80,2.99,Diesel,yes,2012,550
+Renault,9200,vagon,155,1.5,Diesel,yes,2010,Clio
+Mitsubishi,19400,crossover,78,NA,Petrol,yes,2012,Outlander XL
+Mercedes-Benz,109999,van,0,2.2,Diesel,yes,2016,V 250
+Mercedes-Benz,23500,van,170,2.2,Diesel,yes,2011,Vito
+Audi,4800,sedan,390,2.6,Petrol,yes,1993,100
+Toyota,13500,sedan,100,2.4,Petrol,yes,2007,Camry
+Toyota,8700,sedan,131,1.6,Gas,yes,2007,Corolla
+Mercedes-Benz,12300,sedan,423,5,Gas,yes,1997,S 500
+Mitsubishi,7900,other,148,1.8,Petrol,yes,2003,Pajero Pinin
+Volkswagen,10700,van,153,2,Diesel,yes,2010,Caddy
+Renault,6800,van,185,1.6,Other,yes,2006,Kangoo
+Renault,8500,van,157,1.5,Diesel,no,2013,Scenic
+Mitsubishi,18700,crossover,97,3.2,Diesel,yes,2009,Pajero Wagon
+BMW,1900,vagon,285,2.5,Diesel,no,1997,525
+Mercedes-Benz,15500,van,75,2,Diesel,yes,2011,Vito
+Volkswagen,8700,hatch,148,1.4,Other,yes,2008,Polo
+Mercedes-Benz,47300,sedan,121,3,Diesel,yes,2012,S 350
+BMW,5000,sedan,400,2.5,Petrol,yes,1991,525
+Toyota,24000,crossover,73,4,Petrol,yes,2008,Land Cruiser Prado
+Volkswagen,14800,sedan,85,1.4,Petrol,yes,2013,Jetta
+Mitsubishi,5500,crossover,1,2,Petrol,yes,1986,Pajero
+Mercedes-Benz,6400,van,290,2.2,Diesel,yes,1999,Vito
+Mitsubishi,19000,crossover,139,2.4,Gas,yes,2011,Outlander XL
+Mercedes-Benz,11700,van,211,2.2,Diesel,yes,2007,Vito
+Volkswagen,6500,vagon,101,2,Diesel,yes,2011,Passat B6
+Volkswagen,4999,sedan,145,2,Petrol,yes,2000,Bora
+Volkswagen,1900,hatch,100,1.6,Petrol,yes,1989,Golf II
+Renault,8999,vagon,100,1.5,Diesel,yes,2009,Kangoo
+Volkswagen,12700,van,164,1.6,Diesel,yes,2012,Touran
+Mercedes-Benz,2200,sedan,168,2,Petrol,no,1994,E-Class
+BMW,32500,vagon,65,2,Petrol,yes,2014,328
+Volkswagen,16800,vagon,149,1.4,Gas,yes,2013,Passat B7
+Mercedes-Benz,8999,van,360,2.2,Diesel,yes,2006,Vito
+Audi,2500,sedan,407,2.8,Gas,no,1999,A6
+BMW,37000,sedan,95,4.4,Petrol,yes,2011,750
+Volkswagen,19500,sedan,82,1.8,Petrol,yes,2011,Passat B7
+Volkswagen,10900,van,240,2.5,Diesel,yes,2007,T5 (Transporter)
+BMW,36500,sedan,33,2,Diesel,yes,2014,328
+Renault,7300,van,154,1.5,Diesel,yes,2012,Kangoo
+BMW,17500,vagon,125,3,Diesel,yes,2006,530
+Audi,24500,sedan,122,4,Diesel,yes,2008,A8
+Toyota,15500,crossover,146,2.4,Gas,yes,2008,Rav 4
+Mercedes-Benz,4050,sedan,200,NA,Diesel,yes,2001,S 320
+Mitsubishi,25000,crossover,0,2,Petrol,yes,2016,Outlander
+Mercedes-Benz,11900,sedan,167,1.8,Petrol,yes,2005,C-Class
+Mercedes-Benz,8000,van,40,2.9,Diesel,yes,1999,Sprinter 212
+Mitsubishi,16900,crossover,126,3,Gas,yes,2008,Pajero Wagon
+Toyota,8800,crossover,205,2,Petrol,yes,2002,Rav 4
+BMW,13000,sedan,220,2.5,Petrol,yes,2005,523
+Renault,7500,van,43,1.5,Diesel,yes,2013,Kangoo
+Toyota,8300,sedan,140,3,Petrol,yes,2002,Camry
+Toyota,18700,crossover,250,4.7,Gas,yes,2004,Land Cruiser 100
+Toyota,28787.85,sedan,0,2.5,Petrol,yes,2016,Camry
+Mercedes-Benz,16500,sedan,190,2.2,Diesel,yes,2007,C-Class
+Mitsubishi,13900,crossover,127,3,Petrol,yes,2008,Outlander XL
+Audi,1350,vagon,400,1.9,Diesel,no,1994,80
+Toyota,20859.15,crossover,1,2,Petrol,yes,2016,Rav 4
+Mitsubishi,28800,crossover,100,3.2,Diesel,yes,2013,Pajero Wagon
+Mercedes-Benz,34900,crossover,64,2.2,Diesel,yes,2013,GLK 220
+BMW,37500,other,45,2,Petrol,yes,2013,520
+Mercedes-Benz,19700,sedan,220,2.2,Diesel,yes,2009,E-Class
+Mercedes-Benz,12800,other,116,2.2,Diesel,yes,2013,Vito
+Mercedes-Benz,8000,van,293,NA,Diesel,yes,1999,Vito
+Renault,4600,van,265,1.5,Diesel,yes,2008,Kangoo
+Mercedes-Benz,17977,van,322,3,Diesel,yes,2007,Sprinter 318
+Volkswagen,11962,sedan,0,1.6,Petrol,yes,2015,Polo
+Volkswagen,12500,vagon,195,1.6,Diesel,yes,2013,Golf VI
+Volkswagen,9200,van,268,2.5,Diesel,yes,2003,T4 (Transporter)
+Mercedes-Benz,28500,sedan,148,2.2,Diesel,yes,2011,E-Class
+Volkswagen,16950,sedan,38,2,Diesel,yes,2014,Jetta
+Mercedes-Benz,24750,sedan,212,5.5,Petrol,yes,2006,S 500
+Mercedes-Benz,57000,crossover,3,2.2,Diesel,yes,2015,ML 250
+Toyota,11300,hatch,87,1,Petrol,yes,2009,IQ
+Mercedes-Benz,51000,sedan,119,4.7,Petrol,yes,2012,S 500
+Audi,NA,sedan,2,3,Diesel,yes,2015,A8
+Audi,3000,vagon,270,1.9,Diesel,no,2001,A4
+Toyota,9800,other,250,1.8,Petrol,yes,2000,Celica
+Volkswagen,12850,crossover,200,2.5,Diesel,yes,2006,Touareg
+Mercedes-Benz,34999,sedan,150,2.8,Petrol,yes,1969,S 280
+BMW,52300,sedan,55,3,Diesel,yes,2013,5 Series GT
+BMW,3650,sedan,300,2,Petrol,yes,1990,520
+BMW,39900,crossover,82,3,Petrol,yes,2011,X6
+Renault,7400,vagon,200,1.5,Diesel,yes,2007,Megane
+Volkswagen,13999,van,175,2,Diesel,yes,2012,T5 (Transporter) 
+Mercedes-Benz,15500,sedan,270,3.2,Diesel,no,2007,S 320
+Renault,8500,vagon,130,1.5,Diesel,yes,2012,Clio
+BMW,32500,other,86,3,Petrol,yes,2010,5 Series GT
+Toyota,14700,sedan,131,3.5,Gas,yes,2007,Camry
+Volkswagen,6500,van,310,1.9,Diesel,yes,1997,T4 (Transporter)
+Renault,7800,van,139,1.5,Diesel,yes,2013,Kangoo
+Mercedes-Benz,8700,van,290,2.2,Diesel,yes,2005,Vito
+Renault,9000,hatch,56,1.6,Petrol,yes,2011,Megane
+Mercedes-Benz,14800,van,139,3,Diesel,yes,2008,Vito
+Toyota,13400,sedan,125,2.4,Petrol,yes,2008,Camry
+Volkswagen,2650,hatch,280,1.8,Petrol,yes,1987,Golf II
+BMW,7777,other,375,4,Diesel,yes,2000,740
+Toyota,6900,hatch,92,1.3,Petrol,yes,2006,Yaris
+Mercedes-Benz,1850,sedan,281,2,Diesel,yes,1985,190
+Volkswagen,10500,other,270,2.5,Diesel,yes,2007,T5 (Transporter)
+Toyota,NA,crossover,140,4.2,Diesel,yes,1993,Land Cruiser 80
+Mercedes-Benz,NA,crossover,154,3,Diesel,yes,2009,ML 320
+Audi,7800,sedan,90,2.2,Gas,yes,1994,S4
+Mercedes-Benz,4100,sedan,215,4.3,Petrol,no,2001,CLK 430
+BMW,4400,sedan,260,2,Gas,yes,1990,520
+BMW,41500,crossover,176,3,Diesel,yes,2012,X5
+Mercedes-Benz,13999,van,98,2.2,Diesel,yes,2011,Vito
+BMW,NA,crossover,0,3,Diesel,yes,2016,X5
+Mercedes-Benz,16800,van,116,2.2,Diesel,yes,2013,Vito
+BMW,58500,crossover,80,3,Diesel,yes,2013,X5 M
+Volkswagen,10200,sedan,280,1.8,Petrol,yes,2006,Passat B5
+Mitsubishi,13300,crossover,110,2.4,Gas,yes,2008,Outlander XL
+Renault,6300,van,180,1.5,Diesel,yes,2012,Kangoo
+Audi,16999,hatch,69,1.4,Petrol,yes,2012,A1
+Toyota,20100,crossover,48,2,Petrol,yes,2012,Rav 4
+Volkswagen,10800,sedan,100,1.8,Petrol,yes,2008,Passat B6
+Mercedes-Benz,129999,sedan,37,4.7,Petrol,yes,2014,S 500
+Mercedes-Benz,14200,sedan,245,2.2,Diesel,yes,2007,E-Class
+BMW,4700,sedan,333,3,Petrol,yes,1994,730
+BMW,5700,sedan,252,2.5,Diesel,yes,1997,525
+BMW,11900,sedan,300,2.5,Gas,yes,2004,525
+Mercedes-Benz,3750,vagon,330,2.2,Gas,yes,1995,E-Class
+Mercedes-Benz,2700,sedan,350,3,Diesel,yes,1988,300
+Volkswagen,2999,vagon,239,1.9,Diesel,no,2003,Passat B5
+Mercedes-Benz,8600,sedan,240,3.2,Diesel,yes,2001,E-Class
+Toyota,6950,sedan,192,2,Petrol,yes,2003,Avensis
+Mitsubishi,22900,crossover,69,2.4,Petrol,yes,2014,Outlander XL
+Volkswagen,8900,sedan,185,1.6,Petrol,yes,2007,Jetta
+Volkswagen,7500,van,317,1.9,Diesel,yes,2008,T5 (Transporter) 
+BMW,28700,sedan,160,3,Petrol,yes,2011,528
+Mercedes-Benz,12000,other,218,2.2,Diesel,yes,2007,Vito
+BMW,10999,sedan,150,2,Gas,yes,2003,320
+Audi,23000,crossover,167,3.6,Petrol,yes,2008,Q7
+Volkswagen,16500,sedan,87,NA,Other,yes,2012,Passat B7
+Audi,2550,vagon,1,2.5,Diesel,no,1998,A6
+Toyota,8800,crossover,388,4.2,Diesel,yes,1997,Land Cruiser 80
+Renault,10000,van,220,2,Diesel,yes,2007,Trafic
+Renault,6300,van,73,1.5,Diesel,yes,2008,Kangoo
+Toyota,13800,other,160,3.5,Gas,yes,2006,Camry
+Volkswagen,10750,van,176,2,Diesel,yes,2012,T5 (Transporter) 
+Volkswagen,12500,van,207,1.9,Diesel,yes,2007,Sharan
+Mercedes-Benz,5500,sedan,250,2.2,Diesel,no,2003,E-Class
+Volkswagen,13300,van,250,2.5,Diesel,yes,2008,T5 (Transporter)
+Audi,NA,crossover,65,3,Diesel,yes,2013,A6
+Volkswagen,20000,crossover,115,2.5,Diesel,yes,2008,Touareg
+Volkswagen,2500,sedan,324,1.6,Diesel,yes,1984,Jetta
+Renault,3500,van,30,1.4,Gas,yes,2006,Kangoo
+Renault,9300,sedan,94,1.5,Diesel,yes,2011,Fluence
+Audi,30500,sedan,110,3,Petrol,yes,2011,A6
+Audi,18300,vagon,190,2,Diesel,yes,2011,A6
+Audi,41000,crossover,88,4.2,Diesel,yes,2010,Q7
+BMW,4100,hatch,160,1.6,Petrol,yes,2004,1 Series
+Volkswagen,22500,van,62,2,Diesel,yes,2011,Caravelle
+Volkswagen,8900,van,272,1.9,Diesel,yes,2005,Caddy
+BMW,13950,crossover,278,3,Diesel,yes,2003,X5
+Volkswagen,8999,van,125,1.6,Diesel,yes,2011,Caddy
+Toyota,14100,hatch,67,1.6,Petrol,yes,2012,Auris
+Mercedes-Benz,NA,sedan,240,2.2,Diesel,yes,2001,E-Class
+Volkswagen,6950,sedan,132,1.6,Other,yes,2004,Bora
+Volkswagen,7350,hatch,180,1.4,Petrol,yes,2003,Golf IV
+Mercedes-Benz,17500,other,180,3,Diesel,yes,2008,Vito
+Mercedes-Benz,20500,sedan,222,5.5,Petrol,yes,2006,S 500
+Renault,6700,van,1,NA,Diesel,yes,2012,Kangoo
+Mercedes-Benz,115000,sedan,49,4.7,Petrol,yes,2014,S 500
+Mitsubishi,11100,vagon,132,2.4,Gas,yes,2006,Grandis
+BMW,29900,sedan,175,2,Diesel,yes,2011,520
+Toyota,17595,sedan,131,2,Petrol,yes,2011,Avensis
+Volkswagen,6800,van,170,2,Gas,no,2011,Caddy
+Volkswagen,6500,other,547,1.9,Diesel,yes,1998,T4 (Transporter)
+Audi,4150,vagon,249,2.5,Diesel,no,2004,A6
+Toyota,32950,other,50,5.7,Petrol,yes,2013,Tundra
+Mercedes-Benz,7500,other,406,2.2,Diesel,yes,2003,Vito
+BMW,33700,crossover,134,4.4,Petrol,yes,2009,X6
+Mitsubishi,8350,sedan,98,1.5,Petrol,yes,2010,Lancer X
+BMW,4100,sedan,350,2.4,Diesel,yes,1992,525
+BMW,4700,sedan,230,2.5,Petrol,yes,1990,525
+Mercedes-Benz,14500,van,207,2.2,Diesel,yes,2008,Vito
+Mercedes-Benz,9600,van,184,2.2,Diesel,yes,2012,Vito
+Mercedes-Benz,17800,van,162,1.8,Diesel,yes,2012,B 180
+Mercedes-Benz,40000,crossover,152,5,Gas,yes,2006,G 500
+Toyota,24000,crossover,152,4,Gas,yes,2006,Land Cruiser Prado
+Volkswagen,8300,other,151,2,Petrol,yes,2005,T5 (Transporter)
+BMW,3800,sedan,278,2.9,Diesel,no,2001,530
+Audi,27500,sedan,150,2,Petrol,yes,2013,A6
+Volkswagen,5200,vagon,248,2,Diesel,no,2005,Passat B6
+Volkswagen,NA,vagon,217,2,Diesel,no,2008,Passat B6
+Mercedes-Benz,NA,sedan,245,2.2,Diesel,yes,2002,E-Class
+Mercedes-Benz,14200,crossover,210,NA,Gas,yes,2004,ML 350
+Volkswagen,8500,sedan,220,2,Other,yes,2005,Passat B5
+Mercedes-Benz,43500,sedan,59,4.6,Petrol,yes,2011,S 500
+BMW,14000,crossover,240,4.4,Gas,yes,2002,X5
+Volkswagen,5200,van,442,2.4,Diesel,yes,1992,T4 (Transporter)
+Mercedes-Benz,10000,van,450,NA,Diesel,yes,1998,Sprinter 312
+Mitsubishi,11500,crossover,191,3,Gas,yes,2006,Pajero Sport
+BMW,41000,sedan,97,4.4,Petrol,yes,2011,750
+Mercedes-Benz,62000,crossover,150,3,Diesel,yes,2013,GL 350
+Audi,35900,crossover,143,3,Diesel,yes,2010,Q7
+Mercedes-Benz,8200,other,199,2.2,Diesel,yes,2001,Vito
+Mercedes-Benz,17300,sedan,107,1.8,Petrol,yes,2008,C-Class
+BMW,6800,crossover,242,99.99,Diesel,no,2005,X3
+Audi,30999,sedan,2,1.4,Petrol,yes,2016,A3
+BMW,3099,sedan,230,3,Diesel,no,2003,530
+Audi,5700,vagon,250,2,Diesel,no,2006,A4
+Volkswagen,7400,sedan,178,1.8,Gas,yes,2001,Passat B5
+Toyota,14500,other,180,2.5,Diesel,yes,2007,Hilux
+Volkswagen,7300,van,214,1.9,Diesel,yes,2001,T4 (Transporter)
+Mercedes-Benz,33800,crossover,120,3,Diesel,yes,2010,ML 350
+BMW,3900,sedan,330,3,Diesel,no,2000,530
+Mercedes-Benz,NA,sedan,322,2.7,Diesel,no,2000,E-Class
+Volkswagen,10700,van,274,2.5,Diesel,yes,2001,T4 (Transporter)
+Volkswagen,NA,hatch,128,1.6,Petrol,yes,2008,Golf V
+Audi,17800,sedan,177,4.2,Petrol,yes,2008,A8
+Mitsubishi,14500,crossover,167,3,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,29900,sedan,83,2.2,Diesel,yes,2012,E-Class
+Mercedes-Benz,8100,van,330,2.2,Diesel,yes,2001,Vito
+Mercedes-Benz,29500,sedan,64,1.6,Petrol,yes,2013,CLA 200
+Mercedes-Benz,12700,other,198,2.2,Diesel,yes,2011,Vito
+Volkswagen,1199,hatch,250,1.9,Diesel,no,1994,Golf III
+Volkswagen,12999,van,250,1.9,Diesel,yes,2009,T5 (Transporter)
+Audi,34419.45,sedan,0,1.4,Petrol,yes,2016,Q3
+Mitsubishi,8600,sedan,145,2,Petrol,yes,2009,Lancer X
+Renault,10450,van,106,2,Diesel,yes,2013,Trafic
+Mercedes-Benz,17800,van,145,2.2,Diesel,yes,2010,Vito
+Audi,3550,sedan,215,2.5,Diesel,no,2001,A6
+Mercedes-Benz,114999,crossover,0,2.99,Diesel,yes,2016,GLS 350
+Renault,13400,vagon,190,1.5,Diesel,yes,2013,Laguna
+BMW,2250,sedan,350,2.5,Diesel,yes,1998,525
+Mitsubishi,14999,crossover,115,3,Petrol,yes,2008,Outlander XL
+Volkswagen,10600,van,140,2,Diesel,yes,2011,T6 (Transporter) 
+Audi,34000,crossover,119,2,Diesel,yes,2013,Q5
+BMW,33500,sedan,65,2,Petrol,yes,2013,520
+Audi,22800,sedan,122,2,Petrol,yes,2011,A6
+BMW,66666,crossover,0,2,Diesel,yes,2016,X5
+Audi,18500,crossover,185,3.6,Petrol,yes,2008,Q7
+Volkswagen,17900,sedan,192,2.5,Diesel,yes,2007,Multivan
+Mercedes-Benz,6950,other,190,2.2,Diesel,yes,1999,Vito
+Mercedes-Benz,105000,crossover,1,3,Diesel,yes,2016,GLS 350
+Renault,11999,van,107,2,Diesel,yes,2013,Trafic
+Renault,8000,van,189,1.9,Diesel,yes,2005,Trafic
+BMW,4600,sedan,280,3,Other,no,1999,730
+Renault,7400,vagon,242,1.5,Diesel,yes,2007,Megane
+Audi,27500,crossover,147,3,Diesel,yes,2008,Q7
+Toyota,NA,crossover,33,2.7,Gas,yes,2012,Venza
+Toyota,27500,crossover,85,4,Petrol,yes,2007,Land Cruiser Prado
+Volkswagen,9800,van,196,1.6,Diesel,yes,2011,Caddy
+Volkswagen,21000,crossover,173,3,Diesel,yes,2008,Touareg
+Audi,9000,vagon,199,2.7,Petrol,yes,2003,A6 Allroad
+Audi,55000,crossover,100,6,Diesel,yes,2009,Q7
+Mercedes-Benz,38900,crossover,65,4.5,Petrol,yes,2010,GL 450
+Renault,4000,sedan,180,1.4,Gas,yes,2006,Logan
+Toyota,13300,sedan,152,2,Petrol,yes,2009,Avensis
+Volkswagen,34500,crossover,108,3,Diesel,yes,2012,Touareg
+BMW,24999,vagon,24,2,Petrol,yes,2012,X1
+Volkswagen,9750,hatch,130,1.4,Petrol,yes,2005,Golf V
+BMW,5900,sedan,390,1.8,Gas,yes,1996,318
+Volkswagen,6500,van,290,1.9,Diesel,yes,2004,T5 (Transporter) 
+Volkswagen,11000,vagon,127,1.6,Diesel,yes,2012,Golf VI
+Mercedes-Benz,18800,sedan,320,5,Petrol,yes,1998,S 500
+Volkswagen,13700,van,222,2,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,8500,van,184,2,Diesel,yes,2007,Caddy
+Volkswagen,19100,sedan,4,1.6,Other,yes,2016,Jetta
+Renault,10200,hatch,70,1.5,Diesel,yes,2013,Sandero
+Mercedes-Benz,173333,other,0,3,Petrol,yes,2016,S 400
+BMW,7800,sedan,310,2,Gas,yes,2000,520
+Mercedes-Benz,9150,sedan,273,2.7,Diesel,yes,2000,E-Class
+Renault,7900,van,268,1.5,Diesel,yes,2008,Grand Scenic
+Toyota,72000,crossover,40,4.5,Diesel,yes,2014,Land Cruiser 200
+Mercedes-Benz,49500,crossover,52,3,Diesel,yes,2013,ML 350
+Volkswagen,45000,sedan,90,3,Diesel,yes,2011,Phaeton
+Renault,8300,van,141,2,Diesel,yes,2011,Trafic
+Renault,5100,vagon,156,1.5,Diesel,yes,2006,Kangoo
+Toyota,15300,other,116,2.4,Petrol,yes,2009,Camry
+Volkswagen,7300,van,142,2,Diesel,yes,2006,Caddy
+Toyota,14700,hatch,98,1.6,Gas,yes,2013,Auris
+Mercedes-Benz,7900,other,197,2.2,Diesel,yes,2003,Vito
+Volkswagen,2900,vagon,243,1.9,Diesel,no,1999,Passat B5
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLE-Class
+BMW,3200,sedan,237,2.5,Petrol,yes,1990,525
+Mercedes-Benz,4400,sedan,160,2.7,Diesel,no,2000,E-Class
+Toyota,20800,crossover,107,4,Petrol,yes,2006,Land Cruiser Prado
+Toyota,27800,crossover,98,2.2,Diesel,yes,2013,Rav 4
+Audi,16500,vagon,171,2,Diesel,yes,2008,A6
+Mercedes-Benz,NA,van,190,2.2,Diesel,yes,2007,Vito
+Renault,10500,van,67,1.6,Petrol,yes,2012,Scenic
+Mitsubishi,16999,crossover,107,3,Gas,yes,2008,Pajero Wagon
+Audi,35000,sedan,92,2.8,Petrol,yes,2013,A6
+Mercedes-Benz,21500,crossover,142,3.5,Petrol,yes,2008,ML 350
+Mercedes-Benz,15800,other,264,2.2,Other,yes,2009,Vito
+BMW,75555,sedan,30,4.4,Petrol,yes,2014,550
+Renault,NA,van,159,1.5,Diesel,yes,2011,Kangoo
+Toyota,8900,other,180,2,Petrol,yes,2003,Rav 4
+Renault,6300,vagon,189,1.5,Diesel,yes,2008,Kangoo
+Mitsubishi,20500,crossover,109,3.2,Diesel,yes,2009,Pajero Wagon
+Volkswagen,11300,van,165,1.9,Diesel,yes,2010,Caddy
+Mercedes-Benz,2300,sedan,261,2.3,Gas,yes,1978,E-Class
+Volkswagen,42500,crossover,63,3,Diesel,yes,2014,Touareg
+Toyota,28000,crossover,95,3.5,Gas,yes,2011,Highlander
+Mitsubishi,17500,crossover,38,1.8,Petrol,yes,2012,ASX
+Volkswagen,7800,van,282,2.5,Diesel,yes,2000,T4 (Transporter)
+Mercedes-Benz,74999,crossover,1,2.2,Diesel,yes,2016,GLE-Class
+Renault,10200,other,39,1.5,Diesel,yes,2014,Kangoo
+BMW,22500,sedan,179,3,Diesel,yes,2007,730
+Mercedes-Benz,52777,sedan,60,3,Diesel,yes,2012,CLS 350
+Toyota,14250,van,127,3.5,Other,yes,2007,Sienna
+Toyota,26200,crossover,156,4,Gas,yes,2007,Land Cruiser Prado
+Volkswagen,9800,van,299,2.5,Diesel,yes,2002,T4 (Transporter)
+Mercedes-Benz,47600,van,0,2.2,Diesel,yes,2016,Vito
+Volkswagen,8700,other,357,2.5,Diesel,yes,2003,T4 (Transporter) 
+BMW,30500,crossover,147,3,Diesel,yes,2008,X5
+Renault,11700,vagon,177,1.5,Diesel,yes,2012,Laguna
+BMW,29500,sedan,850,NA,Petrol,yes,2011,523
+Mercedes-Benz,6400,hatch,177,2,Diesel,no,2008,B 200
+BMW,36400,crossover,80,3,Petrol,yes,2011,X5
+Audi,75000,crossover,0,3,Diesel,yes,2015,Q7
+Volkswagen,6000,hatch,195,1.2,Gas,yes,2004,Polo
+Mercedes-Benz,4900,van,248,2.2,Diesel,yes,2003,Vito
+Mercedes-Benz,6999,sedan,288,3.2,Gas,yes,2001,E-Class
+Volkswagen,9900,other,171,1.6,Diesel,yes,2011,Caddy
+Volkswagen,3500,sedan,189,1.8,Petrol,yes,1993,Vento
+Volkswagen,NA,van,162,2,Diesel,yes,2011,T5 (Transporter) 
+BMW,1750,vagon,200,2,Petrol,no,2000,520
+Renault,4600,sedan,230,1.5,Diesel,yes,2007,Kangoo
+Mercedes-Benz,NA,other,0,3,Other,yes,2016,S 400
+Volkswagen,9800,van,220,1.9,Diesel,yes,2008,Caddy
+Renault,5950,van,135,1.5,Diesel,yes,2012,Kangoo
+Renault,7400,van,120,1.5,Diesel,yes,2012,Kangoo
+Audi,12800,sedan,217,1.8,Petrol,yes,2007,A4
+BMW,7300,sedan,350,NA,Petrol,yes,1997,520
+Volkswagen,9000,sedan,59,1.6,Gas,yes,2013,Polo
+Volkswagen,1550,hatch,130,1.3,Petrol,yes,1986,Golf II
+Mitsubishi,18600,crossover,52,2,Petrol,yes,2013,Outlander
+BMW,3900,sedan,340,2.5,Diesel,no,2000,525
+Audi,92222,crossover,0,3,Diesel,yes,2016,Q7
+Volkswagen,5400,other,314,2.4,Diesel,yes,1993,T4 (Transporter)
+Audi,16000,sedan,250,2,Diesel,yes,2008,A6
+Volkswagen,3500,other,304,1.9,Diesel,no,2006,Touran
+Mercedes-Benz,6500,sedan,270,2.8,Gas,yes,1999,E-Class
+Mercedes-Benz,7000,sedan,300,4.3,Gas,yes,2000,E-Class
+Renault,10499,vagon,121,1.5,Diesel,yes,2013,Megane
+Renault,13900,crossover,111,2.5,Gas,yes,2010,Koleos
+BMW,6400,other,220,2.5,Petrol,yes,1997,323
+BMW,10800,sedan,246,3,Diesel,yes,2003,530
+BMW,12700,hatch,130,2,Petrol,yes,2007,118
+BMW,38500,sedan,140,2,Diesel,yes,2012,520
+Mitsubishi,5999,sedan,150,1.6,Petrol,yes,2005,Lancer
+Renault,9500,hatch,77,1.6,Petrol,yes,2011,Megane
+Volkswagen,12000,van,196,2,Diesel,yes,2011,T5 (Transporter) 
+Mercedes-Benz,38700,sedan,117,5.5,Petrol,yes,2010,S 550
+Mercedes-Benz,40000,other,119,6.3,Petrol,yes,2008,ML 63 AMG
+Mercedes-Benz,6900,other,311,2.2,Diesel,yes,1999,Vito
+Audi,24300,sedan,43,1.8,Petrol,yes,2012,A4
+Mitsubishi,17700,crossover,165,3,Gas,yes,2007,Pajero Wagon
+Toyota,18999,crossover,153,2.7,Gas,yes,2006,Land Cruiser Prado
+BMW,8300,sedan,200,4.4,Petrol,yes,2001,740
+Toyota,25000,crossover,80,4,Gas,yes,2008,FJ Cruiser
+Volkswagen,NA,vagon,161,1.4,Petrol,yes,2011,Passat B7
+Mitsubishi,12499,crossover,142,2.4,Gas,yes,2008,Outlander XL
+BMW,2899,sedan,340,2.4,Diesel,yes,1988,524
+BMW,19900,other,95,4.5,Petrol,yes,2005,645
+Audi,51000,crossover,44,3,Diesel,yes,2014,Q7
+Audi,81999,crossover,0,2.98,Diesel,yes,2016,Q7
+BMW,9500,sedan,400,3.5,Gas,yes,1999,735
+Mitsubishi,10200,crossover,99,2,Gas,yes,2008,Outlander
+Mercedes-Benz,5500,van,260,2.2,Diesel,no,2004,Vito
+Volkswagen,18500,sedan,74,1.8,Petrol,yes,2011,Passat CC
+Volkswagen,11200,van,200,2.5,Diesel,yes,2003,T4 (Transporter)
+BMW,3099,sedan,320,2,Diesel,no,2000,320
+Audi,27300,other,65,2,Petrol,yes,2011,TT
+Volkswagen,1450,other,200,1.8,Gas,yes,1987,Jetta
+Mitsubishi,9700,sedan,120,2.4,Gas,yes,2006,Galant
+Mitsubishi,18900,crossover,127,3,Gas,yes,2008,Pajero Wagon
+Audi,33500,crossover,67,2,Petrol,yes,2011,Q5
+Volkswagen,5500,van,400,1.9,Diesel,yes,1995,T4 (Transporter)
+Volkswagen,5300,sedan,270,1.8,Gas,yes,1997,Passat B5
+Toyota,25500,crossover,200,4,Petrol,yes,2007,Land Cruiser Prado
+Toyota,23660,crossover,125,3.5,Gas,yes,2013,Highlander
+Volkswagen,3100,hatch,311,1.6,Gas,yes,1986,Golf II
+Mitsubishi,5900,sedan,170,1.6,Petrol,yes,2007,Lancer
+Volkswagen,15900,sedan,150,2,Diesel,yes,2012,Passat B7
+Mercedes-Benz,105999,crossover,0,2.98,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,18500,van,170,2.2,Diesel,yes,2010,Vito
+BMW,86999,crossover,0,3,Diesel,yes,2016,X6
+BMW,27800,sedan,170,2,Diesel,yes,2011,520
+Volkswagen,9000,van,275,2.5,Diesel,yes,2002,T4 (Transporter)
+Toyota,12200,sedan,185,3.5,Petrol,yes,2008,Camry
+Toyota,5800,sedan,200,2.2,Petrol,yes,1999,Camry
+Volkswagen,14499,van,164,2,Diesel,yes,2013,T5 (Transporter) 
+Audi,37000,other,170,3,Diesel,yes,2010,Q7
+Volkswagen,4500,hatch,199,1.8,Petrol,yes,1996,Golf III
+Audi,6557.85,sedan,327,4.2,Gas,yes,2000,A8
+Renault,6800,sedan,219,1.6,Gas,yes,2007,Megane
+BMW,37900,crossover,97,3,Diesel,yes,2010,X6
+Audi,14200,sedan,176,3,Diesel,yes,2005,A6
+Mitsubishi,9350,sedan,94,1.5,Gas,yes,2010,Lancer X
+Volkswagen,2200,vagon,150,1.6,Petrol,yes,1986,Passat B2
+Toyota,3100,hatch,283,1.3,Petrol,yes,1994,Corolla
+Mitsubishi,8400,crossover,100,3.2,Diesel,yes,2011,Pajero Wagon
+Audi,30650,vagon,35,2,Petrol,yes,2013,A4 Allroad
+Audi,8800,sedan,298,2.4,Gas,yes,1999,A6
+Mercedes-Benz,NA,other,25,4.7,Petrol,yes,2014,S 500
+Mitsubishi,7699,crossover,254,3,Petrol,yes,2002,Pajero Sport
+Renault,6400,van,136,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,16200,hatch,44,1.4,Petrol,yes,2012,Golf VI
+Volkswagen,4100,sedan,88,1.8,Gas,yes,1993,Vento
+Mercedes-Benz,6800,sedan,235,2.2,Diesel,yes,1999,C-Class
+Volkswagen,10600,sedan,75,1.6,Petrol,yes,2012,Polo
+Toyota,13000,crossover,149,2,Petrol,yes,2006,Rav 4
+Toyota,6500,sedan,248,2,Petrol,yes,2001,Avensis
+Audi,25500,vagon,181,3,Petrol,yes,2009,A6 Allroad
+Volkswagen,28000,crossover,132,2,Diesel,yes,2011,Amarok
+Toyota,9800,sedan,123,NA,Petrol,yes,2008,Corolla
+Audi,3500,sedan,200,2.5,Diesel,no,2001,A8
+Volkswagen,14899,van,165,2,Diesel,yes,2013,Caddy
+BMW,3500,sedan,300,3,Diesel,no,1999,530
+BMW,3500,sedan,272,2,Diesel,yes,2000,320
+Volkswagen,71138,crossover,0,3,Diesel,yes,2016,Touareg
+Renault,9658.94,sedan,1,1.2,Petrol,yes,2016,Logan
+Volkswagen,13000,van,178,2.5,Diesel,yes,2007,T6 (Transporter)
+Toyota,8000,hatch,115,1.6,Petrol,yes,2007,Corolla
+BMW,56500,crossover,1,2,Diesel,yes,2016,X5
+Renault,9700,vagon,114,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,7300,hatch,176,1.4,Petrol,yes,2004,A 140
+Renault,9000,vagon,190,1.5,Diesel,yes,2009,Megane
+Volkswagen,17900,sedan,96,2.5,Petrol,yes,2013,Passat B7
+Volkswagen,10700,sedan,147,2,Petrol,yes,2007,Passat B6
+Mercedes-Benz,11600,sedan,186,3.2,Petrol,yes,2003,E-Class
+Volkswagen,15000,van,275,2.5,Diesel,yes,2008,T5 (Transporter)
+Volkswagen,8350,hatch,200,1.4,Petrol,yes,2005,Golf V
+Mercedes-Benz,7700,sedan,199,5,Petrol,yes,1996,S 500
+BMW,47000,other,5,2,Petrol,yes,2014,Z4
+Audi,72000,sedan,55,6.3,Petrol,yes,2012,A8
+Mitsubishi,17499,crossover,149,3,Gas,yes,2008,Pajero Wagon
+Renault,11300,vagon,141,1.5,Diesel,yes,2013,Megane
+Toyota,8500,sedan,168,1.6,Petrol,yes,2008,Corolla
+Mercedes-Benz,14490,vagon,212,2.2,Diesel,yes,2008,E-Class
+Volkswagen,11999,van,176,2,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,4300,hatch,217,1.6,Gas,yes,1997,Golf III
+BMW,35500,crossover,33,2,Diesel,yes,2014,X1
+Toyota,2800,other,421,1.3,Gas,yes,1988,Corolla
+BMW,37976.25,sedan,0,2,Petrol,yes,2016,520
+Renault,10500,van,183,2.5,Diesel,yes,2006,Trafic
+Toyota,74999,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,7300,van,125,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,12200,crossover,140,2.4,Petrol,yes,2007,Outlander XL
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,13600,van,199,2.2,Diesel,yes,2009,Vito
+Audi,5000,sedan,280,2.3,Gas,yes,1992,100
+Renault,12800,other,188,2,Diesel,yes,2008,Trafic
+Volkswagen,12499,other,184,2,Diesel,yes,2012,T6 (Transporter) 
+Mitsubishi,2800,sedan,150,2,Gas,yes,1996,Galant
+Renault,7300,van,200,1.5,Diesel,yes,2007,Megane
+Mitsubishi,6800,sedan,82,2,Petrol,yes,2007,Lancer
+Mitsubishi,9500,crossover,225,2.4,Gas,yes,2004,Outlander
+Audi,15900,hatch,160,1.4,Petrol,yes,2011,A3
+Volkswagen,8900,other,180,1.9,Diesel,yes,2010,Caddy
+Toyota,16030,sedan,90,2.5,Gas,yes,2012,Camry
+BMW,28000,sedan,170,2,Diesel,yes,2011,520
+Audi,10700,vagon,222,2.5,Diesel,yes,2005,A6
+BMW,25000,crossover,150,30,Petrol,yes,2007,X5
+Mercedes-Benz,7000,sedan,230,2,Gas,yes,2000,E-Class
+Volkswagen,8800,van,260,2.5,Diesel,yes,2008,T5 (Transporter) 
+Volkswagen,35000,crossover,20,2,Diesel,yes,2014,Tiguan
+Audi,8000,vagon,200,2.7,Diesel,yes,2006,A6
+Mercedes-Benz,15400,sedan,260,2.2,Diesel,yes,2008,E-Class
+Volkswagen,7650,sedan,325,1.8,Gas,yes,2003,Passat B5
+Mitsubishi,10600,crossover,52,2,Petrol,yes,2008,Outlander
+Renault,8200,vagon,192,1.5,Diesel,yes,2010,Logan
+Volkswagen,8700,other,520,2.5,Diesel,yes,1999,T4 (Transporter)
+BMW,45000,sedan,29,2,Diesel,yes,2016,520
+Volkswagen,5699,van,258,2.5,Diesel,yes,2001,T4 (Transporter) 
+Mercedes-Benz,13200,van,217,3.7,Gas,yes,2005,Viano
+Volkswagen,11500,van,70,2,Diesel,yes,2013,Caddy
+BMW,3300,sedan,233,28,Petrol,no,1997,528
+Volkswagen,12700,van,213,2,Diesel,yes,2012,T5 (Transporter) 
+Mercedes-Benz,NA,other,176,3,Diesel,yes,2008,E-Class
+Volkswagen,12300,hatch,62,1.4,Petrol,yes,2012,Polo
+Mercedes-Benz,35500,sedan,100,3.5,Petrol,yes,2010,S 350
+Audi,15500,sedan,210,2,Diesel,yes,2008,A4
+Mercedes-Benz,33000,crossover,109,2.2,Diesel,yes,2012,GLK 220
+Mercedes-Benz,NA,van,173,2.2,Diesel,yes,2011,Vito
+BMW,6500,sedan,280,2,Petrol,yes,1997,320
+Volkswagen,2350,sedan,11,1.9,Other,no,2000,Passat B5
+Volkswagen,17900,crossover,130,3.6,Gas,yes,2007,Touareg
+BMW,34500,other,160,3,Diesel,yes,2010,X5
+Renault,12300,sedan,99,2.5,Petrol,yes,2011,Latitude
+Mercedes-Benz,20500,sedan,193,3.5,Other,yes,2006,CLS 350
+BMW,75555,sedan,30,4.4,Petrol,yes,2014,M5
+Toyota,12800,sedan,145,2.4,Petrol,yes,2008,Camry
+Mercedes-Benz,12800,other,360,2.2,Diesel,yes,2008,Vito
+Audi,6900,sedan,327,3.7,Petrol,yes,1997,A8
+Volkswagen,17200,crossover,220,2.5,Diesel,yes,2006,Touareg
+Renault,6800,sedan,87,1.4,Petrol,yes,2013,Symbol
+Audi,14300,other,80,4.2,Petrol,yes,2004,A8
+Volkswagen,11600,sedan,169,1.4,Gas,no,2013,Passat B7
+Volkswagen,11990,van,231,2.5,Diesel,yes,2002,T4 (Transporter)
+BMW,77777,sedan,8,4.4,Petrol,yes,2014,750
+Mercedes-Benz,6800,sedan,186,NA,Petrol,yes,1998,C-Class
+BMW,2800,sedan,300,1.8,Other,yes,1987,318
+Mercedes-Benz,28500,other,65,1.8,Petrol,yes,2011,E-Class
+Audi,42700,crossover,75,3,Diesel,yes,2013,Q7
+Volkswagen,6950,other,217,1.9,Diesel,yes,2006,Caddy
+Volkswagen,7300,hatch,117,1.4,Petrol,yes,2007,Polo
+Mercedes-Benz,NA,sedan,25,3,Diesel,yes,2014,S 350
+Mitsubishi,27565.2,crossover,1,2.5,Diesel,yes,2016,Pajero Sport
+Volkswagen,12900,vagon,145,1.6,Diesel,yes,2011,Golf Variant
+Volkswagen,5900,sedan,217,2.3,Petrol,yes,1999,Bora
+Mercedes-Benz,2600,hatch,111,1.4,Petrol,no,1999,A 140
+Audi,2799,vagon,390,1.9,Diesel,no,1998,A6
+Mitsubishi,21000,crossover,98,2.5,Diesel,yes,2011,Pajero Sport
+Audi,4999,sedan,231,1.9,Diesel,no,2002,A6
+Volkswagen,6900,sedan,160,1.6,Petrol,yes,2003,Bora
+Mercedes-Benz,5800,sedan,366,3.2,Gas,yes,1991,S 320
+Mitsubishi,15700,other,68,1.8,Petrol,yes,2011,ASX
+Volkswagen,74500,crossover,0,3,Diesel,yes,2015,Touareg
+Mercedes-Benz,22900,sedan,131,5.5,Gas,yes,2008,S 500
+Mercedes-Benz,32000,crossover,96,3.5,Petrol,yes,2010,ML 350
+Mercedes-Benz,35300,sedan,3,1.6,Petrol,yes,2014,CLA 200
+Volkswagen,6900,sedan,320,1.8,Gas,yes,2002,Passat B5
+BMW,7000,sedan,286,2.8,Petrol,yes,2001,520
+Volkswagen,35800,crossover,92,3,Diesel,yes,2011,Touareg
+Volkswagen,10700,hatch,60,1.4,Petrol,yes,2010,Polo
+BMW,23999,crossover,194,3,Petrol,yes,2007,X5
+Volkswagen,2700,hatch,300,1.9,Diesel,no,2000,Golf IV
+Mercedes-Benz,10000,sedan,80,NA,Diesel,yes,2010,S 350
+Toyota,18200,crossover,146,2.7,Gas,yes,2006,Land Cruiser Prado
+Audi,2450,vagon,325,2.5,Diesel,no,2000,A6
+Audi,15800,hatch,123,1.4,Petrol,yes,2010,A3
+Volkswagen,9400,vagon,176,1.9,Diesel,yes,2005,Passat B5
+Mitsubishi,5800,vagon,230,1.6,Petrol,yes,2005,Space Star
+Volkswagen,13499,sedan,162,1.4,Petrol,yes,2012,Passat B7
+Volkswagen,39615,sedan,0,2,Diesel,yes,2016,Passat B8
+Toyota,23300,crossover,40,2,Petrol,yes,2014,Rav 4
+Audi,2100,sedan,217,1.9,Diesel,no,1996,A4
+BMW,3000,sedan,350,99.99,Other,yes,1992,520
+Mercedes-Benz,2700,sedan,337,2.3,Gas,yes,1988,230
+Renault,NA,van,153,2,Diesel,yes,2012,Trafic
+Volkswagen,6700,van,16,2,Diesel,yes,2005,Caddy
+BMW,43163.25,crossover,0,2,Diesel,yes,2016,X1
+Renault,2750,hatch,186,22,Diesel,no,2005,Laguna
+Toyota,28350,crossover,92,2.2,Diesel,yes,2013,Rav 4
+Toyota,29500,crossover,90,2.7,Gas,yes,2010,Land Cruiser Prado
+BMW,NA,crossover,15,4.39,Petrol,yes,2015,X5 M
+Volkswagen,8200,sedan,258,1.9,Diesel,yes,2004,Passat B5
+Mercedes-Benz,10999,other,269,2.2,Diesel,yes,2006,Sprinter 313
+Mercedes-Benz,NA,crossover,1,3,Diesel,yes,2016,GLS 350
+Renault,8299,sedan,19,1.2,Petrol,yes,2013,Logan
+Toyota,20500,sedan,105,2.5,Petrol,yes,2013,Camry
+Volkswagen,14500,van,168,2,Diesel,yes,2012,T5 (Transporter) 
+Toyota,17300,other,135,3.5,Gas,yes,2009,Camry
+Mercedes-Benz,18700,sedan,105,5.5,Petrol,yes,2006,S 500
+Toyota,13200,sedan,38,1.33,Petrol,yes,2014,Corolla
+Volkswagen,2450,sedan,200,1.3,Gas,yes,1991,Jetta
+Audi,55000,hatch,24,2.8,Other,yes,2014,A7
+Toyota,20000,van,135,2.4,Gas,yes,2006,Previa
+Volkswagen,4000,hatch,266,1.9,Diesel,yes,1993,Golf III
+Mitsubishi,16900,other,120,3,Petrol,yes,2008,Pajero Wagon
+Mitsubishi,11450,sedan,60,1.6,Gas,yes,2012,Lancer X
+Mercedes-Benz,24500,crossover,236,5,Petrol,yes,2000,G 500
+Renault,12350,sedan,64,2,Gas,yes,2011,Fluence
+Mercedes-Benz,6900,sedan,239,2.3,Petrol,yes,1996,E-Class
+Toyota,30500,crossover,40,2.7,Petrol,yes,2011,Land Cruiser Prado
+Mitsubishi,8800,sedan,105,2,Gas,yes,2008,Lancer X
+Audi,9700,sedan,249,2.5,Diesel,yes,2004,A6
+BMW,63500,crossover,33,3,Petrol,yes,2015,X5
+Mitsubishi,8900,sedan,96,2,Petrol,yes,2008,Lancer X
+Audi,1950,vagon,210,1.8,Gas,no,1996,A4
+Audi,4400,sedan,190,2.5,Diesel,no,2003,A6
+Toyota,12450,sedan,162,3.5,Gas,yes,2007,Camry
+Audi,3200,sedan,295,2.5,Diesel,no,2000,A6
+Audi,12600,sedan,294,3,Gas,yes,2004,A8
+Renault,NA,vagon,137,1.9,Diesel,yes,2008,Kangoo
+Mercedes-Benz,4400,sedan,225,2,Gas,yes,1995,E-Class
+Mercedes-Benz,3900,sedan,111,2.5,Diesel,yes,1989,E-Class
+Mercedes-Benz,154999,crossover,0,4.6,Petrol,yes,2016,GLS 500
+Volkswagen,8499,sedan,101,1.6,Petrol,yes,2012,Polo
+Mercedes-Benz,8000,other,230,2.2,Diesel,yes,2001,Vito
+Volkswagen,3500,hatch,250,1.9,Diesel,no,2000,Beetle
+Audi,11900,sedan,195,2.4,Gas,yes,2005,A6
+Toyota,24800,sedan,43,2.5,Gas,yes,2015,Camry
+Audi,3800,vagon,190,2.5,Diesel,no,2002,A6
+Volkswagen,4800,sedan,190,99.99,Petrol,yes,1998,Passat B5
+Volkswagen,7500,van,230,1.9,Diesel,yes,2006,Caddy
+BMW,8999,sedan,270,2.8,Petrol,yes,1998,528
+Volkswagen,5000,van,326,2.4,Diesel,yes,1995,T4 (Transporter)
+Mercedes-Benz,21000,crossover,135,3.5,Petrol,yes,2007,ML 350
+Renault,4200,van,142,1.9,Diesel,yes,2007,Kangoo
+Mitsubishi,18000,crossover,160,3.2,Diesel,yes,2007,Pajero Wagon
+Volkswagen,8500,van,220,2.5,Diesel,yes,2003,T4 (Transporter)
+Renault,3750,hatch,232,1.8,Petrol,yes,1997,Laguna
+Volkswagen,9000,other,212,2,Gas,yes,2007,Caddy
+Mitsubishi,6300,crossover,235,3.2,Diesel,no,2001,Pajero
+BMW,8400,sedan,297,2.2,Petrol,yes,2000,320
+Volkswagen,5000,hatch,190,NA,Petrol,yes,1998,Golf IV
+Volkswagen,17000,van,157,2,Diesel,yes,2010,T5 (Transporter)
+Volkswagen,3000,sedan,240,1.8,Petrol,yes,1991,Jetta
+Mercedes-Benz,21300,crossover,114,4.7,Petrol,yes,2007,GL 450
+BMW,16500,crossover,230,3,Gas,yes,2004,X5
+Mercedes-Benz,7300,other,250,2.2,Diesel,yes,2005,Sprinter 311
+Mercedes-Benz,NA,van,300,2.3,Diesel,yes,1996,Vito
+Mercedes-Benz,2300,sedan,300,3,Diesel,yes,1999,E-Class
+BMW,8200,sedan,250,4.4,Petrol,yes,1997,540
+Audi,35000,sedan,61,2.8,Petrol,yes,2012,A6
+Volkswagen,7800,vagon,193,1.9,Diesel,yes,2002,Passat B5
+Volkswagen,8200,hatch,157,1.6,Petrol,yes,2008,Golf Plus
+Audi,39999,crossover,45,2,Diesel,yes,2014,Q5
+Renault,11900,other,173,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,3600,sedan,276,2.7,Diesel,no,2001,E-Class
+Volkswagen,3399,sedan,201,1.8,Petrol,yes,1991,Passat B3
+BMW,2650,vagon,335,2,Petrol,no,2000,320
+Mercedes-Benz,11700,hatch,103,1.5,Petrol,yes,2007,A 150
+Mercedes-Benz,1600,hatch,180,NA,Petrol,no,2000,A 160
+BMW,2550,vagon,312,2.5,Diesel,no,1999,525
+Renault,NA,van,220,2,Diesel,yes,2008,Trafic
+Mercedes-Benz,2550,vagon,367,3.2,Diesel,no,2000,E-Class
+Audi,31700,sedan,32,1.8,Petrol,yes,2014,A4
+Mercedes-Benz,125000,sedan,18,3,Diesel,yes,2015,S 350
+BMW,20500,crossover,285,4.8,Petrol,yes,2006,X5
+Mitsubishi,2600,other,200,2.4,Petrol,no,2002,Lancer
+BMW,2500,vagon,250,2.5,Diesel,no,2000,525
+Mercedes-Benz,13200,van,182,2.2,Diesel,yes,2011,Vito
+Mercedes-Benz,31000,crossover,220,3.2,Diesel,yes,2008,GL 320
+Audi,30900,vagon,118,2,Petrol,yes,2013,A4 Allroad
+Mitsubishi,4750,sedan,167,1.6,Petrol,yes,2003,Carisma
+Volkswagen,NA,van,160,2,Diesel,yes,2011,T6 (Transporter) 
+Audi,NA,sedan,62,3,Petrol,yes,2013,A8
+Toyota,8950,hatch,118,1.6,Petrol,yes,2008,Auris
+Renault,8300,van,177,1.5,Diesel,yes,2007,Grand Scenic
+Audi,6900,sedan,360,2.5,Diesel,yes,1997,A6
+Volkswagen,5000,sedan,81,NA,Other,no,2013,Passat B7
+BMW,69999,crossover,71,4.4,Petrol,yes,2011,X5 M
+Volkswagen,3250,sedan,255,1.9,Diesel,no,2003,Passat B5
+Volkswagen,11900,hatch,91,1.4,Petrol,yes,2010,Golf VI
+Mitsubishi,9600,other,114,2,Petrol,yes,1999,Eclipse
+Mercedes-Benz,21200,vagon,206,2.1,Diesel,yes,2011,C-Class
+Toyota,102999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,11900,van,277,2.2,Diesel,yes,2007,Vito
+Renault,9999,van,185,1.5,Diesel,yes,2008,Kangoo
+Renault,8500,sedan,57,1.2,Gas,yes,2013,Logan
+Volkswagen,6500,other,385,2.4,Diesel,yes,1997,T4 (Transporter)
+Mitsubishi,8700,sedan,80,1.5,Petrol,yes,2008,Lancer X
+Volkswagen,8599,van,169,1.6,Diesel,yes,2011,Caddy
+Volkswagen,12700,hatch,156,1.6,Diesel,yes,2011,Golf VI
+Mitsubishi,7700,sedan,135,2,Gas,yes,2007,Lancer
+Mitsubishi,2020,other,90,NA,Other,no,2008,L 200
+Volkswagen,NA,van,123,1.2,Petrol,yes,2013,Caddy
+Mitsubishi,8700,sedan,150,2.4,Gas,yes,2007,Galant
+Mitsubishi,9300,sedan,95,2,Petrol,yes,2007,Lancer X
+Mercedes-Benz,3550,sedan,238,2.2,Diesel,no,2001,E-Class
+Audi,29800,crossover,151,3,Diesel,yes,2007,Q7
+Renault,3900,van,202,1.9,Diesel,yes,2001,Kangoo
+BMW,NA,crossover,200,3,Diesel,yes,2009,X5
+Volkswagen,7000,van,345,1.9,Diesel,yes,2006,T5 (Transporter) 
+Audi,17700,other,70,2,Petrol,yes,2009,TT
+Toyota,9999,sedan,225,3,Gas,yes,2004,Camry
+Audi,25300,hatch,180,2,Diesel,yes,2011,A5
+Audi,22500,vagon,175,2,Gas,yes,2010,A4 Allroad
+Volkswagen,18600,sedan,2,1.6,Petrol,yes,2015,Jetta
+Mitsubishi,6999,sedan,160,2,Petrol,yes,2007,Lancer
+Toyota,16200,crossover,281,4.2,Diesel,yes,1999,Land Cruiser 100
+Mercedes-Benz,29999,hatch,9,NA,Other,yes,2015,B-Class Electric Drive
+Toyota,18000,crossover,320,4.2,Diesel,yes,1999,Land Cruiser 100
+Mercedes-Benz,27300,sedan,144,2.14,Diesel,yes,2011,E-Class
+Toyota,2700,sedan,550,1.6,Gas,yes,1989,Carina
+BMW,12000,crossover,218,3,Diesel,no,2006,X5
+Mercedes-Benz,39900,hatch,2,NA,Other,yes,2015,B-Class Electric Drive
+Volkswagen,1800,other,280,1.9,Diesel,no,1997,Passat B5
+Audi,20500,sedan,106,1.8,Petrol,yes,2011,A4
+Volkswagen,10493,other,1,1.4,Petrol,yes,2014,Polo
+BMW,16800,sedan,154,3,Petrol,yes,2006,330
+Volkswagen,12500,sedan,177,NA,Petrol,yes,2008,Passat B6
+BMW,8200,sedan,238,3,Diesel,no,2004,730
+BMW,8000,sedan,330,NA,Diesel,yes,1997,525
+Mercedes-Benz,14500,van,171,2.2,Diesel,yes,2011,Vito
+Volkswagen,24500,crossover,62,2,Diesel,yes,2012,Passat CC
+Toyota,13250,sedan,96,2.4,Gas,yes,2008,Rav 4
+Toyota,6400,sedan,250,3,Gas,yes,1998,Camry
+Mercedes-Benz,29500,sedan,135,5.5,Petrol,yes,2008,S 550
+Volkswagen,69990,van,1,5,Diesel,yes,2015,Multivan
+Audi,2600,vagon,110,2.5,Diesel,no,2002,A6
+Volkswagen,4900,sedan,500,1.9,Diesel,yes,1995,Passat B4
+Renault,10500,vagon,185,1.5,Diesel,yes,2011,Megane
+Volkswagen,NA,crossover,90,3.6,Petrol,yes,2008,Touareg
+Volkswagen,27200,crossover,1,2,Diesel,yes,2015,Tiguan
+Mercedes-Benz,1350,sedan,360,2.5,Diesel,no,1989,E-Class
+Mercedes-Benz,NA,sedan,24,3,Diesel,yes,2015,S 350
+Toyota,13900,sedan,139,2.4,Other,yes,2008,Camry
+BMW,12800,sedan,194,4.4,Gas,yes,2002,745
+Toyota,21200,crossover,135,4,Gas,yes,2007,Land Cruiser Prado
+Mercedes-Benz,8700,sedan,196,2,Gas,yes,2000,E-Class
+Toyota,8800,sedan,165,1.8,Petrol,yes,2007,Avensis
+Toyota,12900,sedan,129,1.8,Petrol,yes,2009,Avensis
+BMW,9500,sedan,339,3,Diesel,yes,2001,530
+BMW,10700,sedan,243,3,Diesel,yes,2002,530
+Volkswagen,10800,van,156,1.6,Diesel,yes,2012,Caddy
+Mitsubishi,2400,hatch,350,2,Gas,yes,1989,Galant
+Renault,13300,other,132,1.5,Diesel,yes,2012,Megane
+BMW,34500,crossover,85,3,Petrol,yes,2010,X5
+BMW,57000,other,20,3.5,Petrol,yes,2013,X6
+Renault,7200,van,146,1.5,Diesel,yes,2012,Kangoo
+Toyota,23400,sedan,69,2.5,Gas,yes,2014,Camry
+Mercedes-Benz,6500,sedan,215,55,Petrol,no,2001,S 55
+Toyota,48000,crossover,59,3,Diesel,yes,2014,Land Cruiser Prado
+BMW,6800,sedan,325,NA,Other,no,2005,525
+Volkswagen,4200,sedan,1,1.4,Petrol,yes,2012,Golf VI
+Mercedes-Benz,6200,other,340,2.4,Gas,yes,1998,E-Class
+BMW,137000,crossover,2,4.4,Petrol,yes,2016,X5 M
+Audi,1300,sedan,250,NA,Gas,no,1994,80
+BMW,2900,sedan,225,2.5,Petrol,no,1997,523
+Mercedes-Benz,12700,other,199,2.2,Diesel,yes,2011,Vito
+Audi,16999,vagon,140,4.2,Petrol,yes,2007,A6 Allroad
+Volkswagen,13972.07,sedan,0,1.6,Petrol,yes,2016,Polo
+Mercedes-Benz,41900,sedan,95,6.2,Petrol,yes,2011,S 500
+Renault,6650,vagon,227,1.5,Diesel,yes,2005,Megane
+Mercedes-Benz,9200,other,280,2.4,Petrol,yes,2003,E-Class
+BMW,15300,sedan,250,2.5,Petrol,yes,2006,525
+Volkswagen,1450,van,100,1.6,Diesel,yes,1988,T3 (Transporter)
+Volkswagen,5900,van,278,1.9,Diesel,yes,2005,Caddy
+Mercedes-Benz,4100,sedan,400,2,Diesel,yes,1987,190
+Mercedes-Benz,4900,sedan,282,3.2,Gas,yes,1993,E-Class
+BMW,12900,vagon,260,3,Diesel,yes,2004,330
+Toyota,33333,vagon,73,4,Gas,yes,2009,FJ Cruiser
+Volkswagen,7200,hatch,127,1.39,Petrol,yes,2007,Polo
+BMW,77777,crossover,1,3,Diesel,yes,2014,X6
+Mercedes-Benz,2300,hatch,175,1.4,Petrol,no,2000,A 140
+Volkswagen,13000,crossover,235,3.2,Gas,yes,2005,Touareg
+Volkswagen,3000,van,318,1.9,Diesel,yes,1993,T4 (Transporter) 
+Renault,7999,van,92,1.5,Diesel,yes,2013,Kangoo
+Mercedes-Benz,11000,van,250,2.2,Diesel,yes,2005,Vito
+BMW,81000,sedan,23,4.4,Petrol,yes,2014,M5
+BMW,3800,sedan,304,1.9,Gas,no,2000,318
+BMW,4300,other,280,2.5,Petrol,yes,1985,325
+Audi,4400,sedan,340,2.8,Gas,yes,1992,100
+Audi,3150,vagon,320,2.5,Diesel,no,2002,A6
+Volkswagen,10900,other,287,2.5,Other,yes,2004,T5 (Transporter)
+Volkswagen,6400,van,305,NA,Diesel,yes,2004,Caddy
+Audi,3050,vagon,250,2.5,Other,no,2001,A6
+Volkswagen,30385,vagon,0,2,Diesel,yes,2014,Touran
+BMW,20999,crossover,170,4.8,Petrol,yes,2008,X5
+Volkswagen,1500,sedan,255,1.9,Diesel,no,1998,Polo
+Mitsubishi,NA,crossover,120,2,Gas,yes,2008,Outlander
+Toyota,8500,vagon,200,2,Petrol,yes,1999,Vista
+Toyota,9200,sedan,115,1.6,Gas,yes,2008,Corolla
+Toyota,20800,sedan,49,3.5,Petrol,yes,2011,Camry
+Renault,8100,van,206,1.9,Diesel,yes,2005,Trafic
+Volkswagen,13500,sedan,104,1.8,Petrol,yes,2010,Passat B6
+Renault,4900,van,218,1.5,Diesel,yes,2006,Kangoo
+Volkswagen,6600,hatch,136,1.4,Gas,yes,2001,Golf IV
+Mitsubishi,1450,crossover,245,2.8,Diesel,no,1996,Pajero
+Volkswagen,3600,sedan,320,1.8,Gas,yes,1989,Passat B3
+Toyota,24000,sedan,51,2.5,Gas,yes,2014,Camry
+Toyota,12700,sedan,130,2.4,Petrol,yes,2008,Camry
+Mercedes-Benz,14500,other,315,5,Gas,yes,2004,CL 500
+BMW,2299,sedan,200,1.8,Petrol,no,1999,318
+BMW,8900,sedan,234,3.5,Gas,yes,1999,735
+Audi,30500,vagon,152,3,Diesel,yes,2012,A6
+Mercedes-Benz,4000,sedan,370,3.2,Diesel,no,2002,E-Class
+Toyota,12900,sedan,137,2.4,Petrol,yes,2008,Camry
+Volkswagen,3300,sedan,200,1.8,Petrol,yes,1989,Passat B3
+Toyota,9800,sedan,178,1.8,Petrol,yes,2007,Avensis
+Renault,15200,other,173,2.3,Diesel,yes,2013,Master
+Volkswagen,4950,vagon,290,1.8,Petrol,yes,1996,Passat B4
+Volkswagen,55500,crossover,36,3,Diesel,yes,2015,Touareg
+Volkswagen,17900,van,188,2.5,Diesel,yes,2008,Multivan
+Mercedes-Benz,5900,crossover,271,2.7,Diesel,no,2003,ML 270
+Toyota,20500,crossover,225,4.7,Gas,yes,2003,Land Cruiser 100
+BMW,5000,other,300,1.8,Petrol,yes,1994,318
+Volkswagen,11500,van,100,2,Diesel,yes,2010,T5 (Transporter) 
+Mercedes-Benz,115000,other,35,NA,Diesel,yes,2015,G 350
+Volkswagen,2900,hatch,230,1.6,Petrol,yes,2000,Golf IV
+Mitsubishi,4800,sedan,247,2,Gas,yes,1998,Galant
+Renault,1550,vagon,235,2,Petrol,no,1998,Laguna
+Mitsubishi,16000,crossover,147,2.5,Diesel,yes,2008,Pajero Sport
+Volkswagen,6400,van,312,2.5,Diesel,yes,2000,T4 (Transporter) 
+BMW,5800,other,400,2,Petrol,yes,1998,520
+Mercedes-Benz,11000,sedan,263,3.2,Diesel,yes,2001,E-Class
+Renault,4500,sedan,305,1.5,Diesel,yes,2004,Megane
+Renault,14200,crossover,83,2,Diesel,yes,2012,Koleos
+BMW,71500,crossover,19,2.5,Diesel,yes,2015,X5
+Audi,6200,sedan,200,1.8,Petrol,yes,2003,A4
+Volkswagen,10500,van,130,1.9,Diesel,yes,2010,Caddy
+Toyota,12700,sedan,136,3.5,Petrol,yes,2007,Camry
+Audi,7000,other,126,2.6,Petrol,yes,1998,80
+BMW,4500,sedan,230,4,Gas,yes,1994,540
+BMW,4800,vagon,400,2.5,Diesel,yes,1994,525
+Renault,11500,van,170,1.5,Diesel,yes,2012,Grand Scenic
+BMW,42000,crossover,33,2,Petrol,yes,2014,X3
+Audi,2500,sedan,371,2.2,Gas,yes,1987,100
+Mercedes-Benz,22600,crossover,165,3,Diesel,yes,2007,ML 320
+Volkswagen,9900,sedan,80,1.6,Petrol,yes,2011,Polo
+Mercedes-Benz,13500,van,259,3,Diesel,yes,2007,Vito
+Volkswagen,10600,other,140,2,Other,yes,2007,Passat B6
+Mercedes-Benz,12500,van,242,2.2,Diesel,yes,2008,Vito
+Renault,7499,van,123,1.5,Diesel,yes,2009,Kangoo
+Volkswagen,9999,hatch,118,1.2,Diesel,yes,2011,Polo
+Toyota,18600,hatch,73,1.6,Petrol,yes,2013,Auris
+Volkswagen,7600,hatch,225,1.4,Petrol,yes,2004,Golf V
+Volkswagen,NA,other,201,1.9,Diesel,yes,2007,T5 (Transporter)
+Volkswagen,9200,hatch,127,1.6,Petrol,yes,2007,Golf V
+Audi,3700,vagon,290,1.9,Diesel,no,2002,A4
+BMW,26000,crossover,270,4.8,Petrol,yes,2008,X5
+BMW,12950,sedan,268,4,Diesel,yes,2004,740
+Mitsubishi,15200,crossover,193,3,Gas,yes,2008,Pajero Wagon
+Audi,62000,vagon,24,3,Diesel,yes,2015,A6 Allroad
+Toyota,68500,van,1,3.5,Petrol,yes,2016,Sienna
+Volkswagen,2500,hatch,111,1.6,Gas,yes,1999,Golf IV
+Volkswagen,2600,other,296,1.9,Diesel,no,2000,Golf IV
+Mercedes-Benz,12250,van,300,2.2,Diesel,yes,2007,Vito
+Toyota,39900,crossover,94,4,Gas,yes,2010,Land Cruiser Prado
+Volkswagen,1500,other,160,1.4,Petrol,no,1999,Golf IV
+Mercedes-Benz,3700,sedan,270,3.5,Petrol,no,2004,S 350
+BMW,8999,other,160,1.8,Petrol,yes,1997,318
+Mitsubishi,9900,other,200,2.5,Diesel,yes,2007,L 200
+Volkswagen,16800,crossover,249,5,Diesel,yes,2005,Touareg
+Volkswagen,23999,sedan,0,1.6,Diesel,yes,2016,Jetta
+Mercedes-Benz,36000,crossover,162,3,Diesel,yes,2009,GL 320
+Mitsubishi,15800,other,146,2.5,Diesel,yes,2013,L 200
+Volkswagen,19300,sedan,136,2,Diesel,yes,2011,Passat B7
+Renault,6450,van,148,1.5,Diesel,yes,2012,Kangoo
+Renault,4200,van,220,1.5,Diesel,yes,2004,Kangoo
+Mercedes-Benz,NA,sedan,29,6,Petrol,yes,2011,S-Guard
+BMW,32000,crossover,52,3,Petrol,yes,2012,X5
+Toyota,12500,sedan,110,2.4,Gas,yes,2007,Camry
+BMW,47900,crossover,178,4,Diesel,yes,2012,X5
+Mercedes-Benz,21999,sedan,200,3,Diesel,yes,2008,E-Class
+Audi,15000,sedan,168,NA,Other,yes,2007,A6
+Toyota,7500,hatch,74,1.3,Petrol,yes,2008,Yaris
+Mercedes-Benz,125000,sedan,17,3,Diesel,yes,2015,S 350
+BMW,30500,hatch,110,4.4,Petrol,yes,2008,X6
+Volkswagen,17191.2,sedan,0,1.6,Petrol,yes,2016,Jetta
+Mercedes-Benz,45555,sedan,42,3,Diesel,yes,2011,S 350
+BMW,15200,sedan,227,2.5,Petrol,yes,2008,525
+Volkswagen,8700,sedan,270,NA,Diesel,yes,2009,Caddy
+Volkswagen,7800,van,226,1.9,Diesel,yes,2005,T5 (Transporter) 
+Volkswagen,22000,hatch,40,1.4,Petrol,yes,2013,Golf VII
+Mitsubishi,9300,crossover,156,2.4,Gas,yes,2006,Outlander
+Mercedes-Benz,31900,sedan,97,2.2,Diesel,yes,2011,E-Class
+Mercedes-Benz,9350,van,160,2.2,Diesel,yes,2008,Vito
+BMW,32500,sedan,134,2,Diesel,yes,2010,520
+Renault,10200,vagon,151,1.5,Diesel,yes,2011,Megane
+Toyota,8200,crossover,200,2,Gas,yes,2002,Rav 4
+Mercedes-Benz,7400,sedan,190,2,Petrol,yes,2002,C-Class
+Mitsubishi,14200,crossover,155,3,Gas,yes,2007,Outlander XL
+Volkswagen,24900,sedan,1,1.6,Diesel,yes,2016,Jetta
+Mercedes-Benz,24300,sedan,150,3.5,Gas,yes,2008,S 350
+Volkswagen,9300,van,176,1.9,Diesel,yes,2005,T5 (Transporter)
+Volkswagen,21000,crossover,130,2.5,Diesel,yes,2008,Touareg
+Audi,36000,crossover,145,3,Petrol,yes,2010,Q7
+Mitsubishi,22900,crossover,69,2.4,Petrol,yes,2014,Outlander
+Mercedes-Benz,9900,sedan,275,2.2,Other,yes,2005,E-Class
+Volkswagen,3950,hatch,288,1.8,Gas,yes,1996,Golf III
+Toyota,28700,crossover,100,3.5,Petrol,yes,2012,Highlander
+Volkswagen,16900,van,121,2,Diesel,yes,2014,T5 (Transporter) 
+Mercedes-Benz,7300,sedan,245,2.8,Gas,yes,1998,E-Class
+Mercedes-Benz,2200,sedan,400,2,Petrol,yes,1988,190
+Toyota,25500,other,103,3.5,Petrol,yes,2010,Venza
+Volkswagen,7650,other,260,2,Diesel,yes,2005,Caddy
+Toyota,22800,other,55,2.5,Petrol,yes,2013,Camry
+Renault,8900,van,170,1.9,Diesel,yes,2005,Trafic
+Renault,10990,sedan,118,2,Petrol,yes,2012,Fluence
+Mercedes-Benz,107777,crossover,0,3,Diesel,yes,2016,GLS 350
+Toyota,17000,sedan,95,2,Gas,yes,2011,Avensis
+Renault,999,sedan,430,2,Gas,yes,1988,25
+Audi,12800,other,0,NA,Other,yes,2005,A6
+BMW,66500,crossover,0,2,Other,yes,2016,X5
+Volkswagen,4300,vagon,289,2,Petrol,yes,1994,Passat B4
+BMW,3350,vagon,295,2,Diesel,no,2002,320
+Toyota,10400,sedan,87,1.6,Petrol,yes,2008,Corolla
+BMW,22700,crossover,216,4.8,Petrol,yes,2007,X5
+Renault,6700,van,102,1.5,Diesel,yes,2012,Kangoo
+Audi,3250,sedan,285,NA,Gas,no,1996,A8
+Audi,3800,sedan,257,2,Petrol,yes,1993,80
+Volkswagen,10900,vagon,185,1.4,Petrol,yes,2011,Golf VII
+Mercedes-Benz,11000,van,300,2.2,Diesel,yes,2007,Vito
+Volkswagen,51900,crossover,30,3,Diesel,yes,2015,Touareg
+Mercedes-Benz,18900,sedan,232,3,Diesel,yes,2007,E-Class
+BMW,25500,sedan,148,3,Diesel,yes,2007,535
+Renault,14500,sedan,98,1.5,Diesel,yes,2014,Fluence
+BMW,7900,sedan,260,3.5,Petrol,yes,1997,535
+Audi,44800,crossover,95,3,Diesel,yes,2014,Q7
+Toyota,26200,other,73,2.5,Diesel,yes,2012,Hilux
+BMW,14000,crossover,164,3,Diesel,no,2006,X5
+Renault,7900,van,300,1.9,Diesel,yes,2006,Trafic
+Renault,1350,sedan,100,1.7,Petrol,yes,1986,21
+Volkswagen,10777,van,157,1.6,Diesel,yes,2012,Caddy
+Renault,8300,van,217,1.9,Diesel,yes,2004,Trafic
+Volkswagen,40000,sedan,1,1.4,Petrol,yes,2015,Passat B8
+Mercedes-Benz,16800,other,266,3,Diesel,yes,2007,E-Class
+Audi,43000,crossover,1,2,Petrol,yes,2016,Q3
+Toyota,22900,sedan,60,2.5,Petrol,yes,2012,Camry
+Audi,10900,sedan,225,2.4,Petrol,yes,2005,A6
+BMW,2550,sedan,280,1.6,Petrol,no,2000,316
+Mercedes-Benz,18700,other,142,2.2,Diesel,yes,2007,Viano
+Toyota,3800,sedan,220,1.6,Petrol,yes,1994,Carina
+Mercedes-Benz,7700,van,290,2.2,Diesel,yes,2004,Vito
+BMW,112000,sedan,16,3,Diesel,yes,2015,730
+Mercedes-Benz,2600,van,280,2.9,Diesel,no,1997,Sprinter 312
+Volkswagen,14900,vagon,165,1.4,Petrol,yes,2013,Passat B7
+Mitsubishi,11960,other,140,2.5,Diesel,yes,2007,L 200
+Mitsubishi,19800,crossover,130,2.5,Diesel,yes,2011,Pajero Sport
+Mercedes-Benz,12800,van,116,2.2,Diesel,yes,2012,Vito
+Mercedes-Benz,34000,sedan,69,2,Petrol,yes,2013,E-Class
+Audi,29999,sedan,97,3,Petrol,yes,2011,S5
+Renault,10499,other,160,NA,Diesel,yes,2013,Trafic
+Mitsubishi,4900,vagon,250,2.5,Petrol,yes,2001,Galant
+BMW,12900,sedan,200,2.5,Petrol,yes,2006,523
+Mercedes-Benz,12600,sedan,300,2.7,Diesel,yes,2003,E-Class
+Volkswagen,9800,other,117,1.6,Diesel,yes,2014,Caddy
+Audi,3650,sedan,270,1.9,Diesel,yes,2002,A6
+Renault,1150,hatch,250,1.4,Petrol,yes,1987,5
+Volkswagen,37900,crossover,73,3,Diesel,yes,2012,Touareg
+Volkswagen,17900,crossover,145,3,Diesel,yes,2007,Touareg
+BMW,37500,crossover,110,4.4,Petrol,yes,2011,X6
+BMW,27900,sedan,170,3,Petrol,yes,2011,530
+Mercedes-Benz,177000,crossover,0,5.5,Petrol,yes,2016,GLS 63
+Volkswagen,10600,van,153,1.6,Diesel,yes,2012,Caddy
+Mercedes-Benz,39900,crossover,127,5.5,Petrol,yes,2010,GL 550
+Volkswagen,5200,van,260,1.9,Diesel,no,2005,Touran
+Volkswagen,16999,sedan,105,1.8,Petrol,yes,2011,Passat B7
+Toyota,10200,sedan,122,7.2,Petrol,yes,2011,Corolla
+Renault,8400,van,260,1.9,Diesel,yes,2005,Trafic
+Audi,30900,crossover,27,2,Petrol,yes,2011,Q5
+Mercedes-Benz,9500,van,248,2.2,Diesel,yes,2005,Vito
+Mitsubishi,12800,crossover,153,1.6,Gas,yes,2011,ASX
+BMW,48900,crossover,60,3,Diesel,yes,2011,X3
+Renault,11600,vagon,165,1.5,Diesel,yes,2012,Megane
+Volkswagen,20200,hatch,22,1.2,Petrol,yes,2014,Golf VII
+Mercedes-Benz,12700,hatch,140,1.7,Gas,yes,2007,B 170
+Mercedes-Benz,144900,sedan,16,3,Diesel,yes,2014,S 350
+Toyota,17835,crossover,240,2.7,Gas,yes,2007,Land Cruiser Prado
+Toyota,NA,vagon,160,2.4,Gas,yes,2007,Rav 4
+Mercedes-Benz,30900,sedan,71,2.2,Diesel,yes,2012,E-Class
+Renault,9500,sedan,88,1.6,Gas,yes,2012,Fluence
+Volkswagen,11500,van,192,1.9,Diesel,yes,2008,Caddy
+Renault,18932.55,hatch,0,1.5,Diesel,yes,2016,Megane
+Mercedes-Benz,33000,vagon,91,NA,Other,yes,2013,E-Class
+Renault,11700,vagon,155,1.5,Diesel,yes,2012,Megane
+Renault,9900,crossover,201,1.9,Diesel,yes,2006,Kangoo
+Mercedes-Benz,19500,sedan,180,3,Diesel,yes,2008,E-Class
+Mercedes-Benz,6000,sedan,350,2,Diesel,yes,1985,200
+Audi,3400,vagon,261,2.5,Diesel,no,2004,A6
+Volkswagen,8200,other,102,1.6,Diesel,yes,2012,Caddy
+Toyota,15600,sedan,165,3.5,Gas,yes,2008,Camry
+BMW,NA,sedan,43,3,Petrol,yes,2011,535
+Toyota,47500,crossover,45,3,Diesel,yes,2014,Land Cruiser Prado
+Audi,33900,sedan,58,2,Petrol,yes,2014,A5
+Toyota,97500,crossover,2,4.6,Petrol,yes,2016,Land Cruiser 200
+Renault,15000,sedan,21,1.5,Diesel,yes,2014,Fluence
+Toyota,28999,sedan,86,3.5,Petrol,yes,2013,Camry
+Audi,15000,hatch,66,1.2,Petrol,yes,2011,A1
+Toyota,36000,crossover,66,3,Diesel,yes,2010,Land Cruiser Prado
+Toyota,19500,crossover,150,4,Petrol,yes,2005,Land Cruiser Prado
+Mercedes-Benz,5000,sedan,350,1.8,Other,yes,1995,C-Class
+Mercedes-Benz,17800,sedan,73,3,Petrol,yes,2008,C-Class
+Mercedes-Benz,5000,sedan,215,2.6,Gas,yes,1992,E-Class
+Renault,3100,hatch,275,1.2,Petrol,yes,1991,Clio
+Renault,1200,sedan,240,1.7,Gas,yes,1988,9
+BMW,3800,sedan,100,1.8,Petrol,yes,1992,318
+Renault,1999,vagon,260,1.9,Diesel,no,2001,Laguna
+Mercedes-Benz,42900,hatch,3,NA,Other,yes,2016,B-Class Electric Drive
+Mercedes-Benz,8800,sedan,330,5,Petrol,yes,2000,S 500
+Renault,8800,van,250,1.9,Diesel,yes,2006,Trafic
+Mercedes-Benz,15300,other,168,3.2,Petrol,yes,2003,CLK 320
+BMW,39900,sedan,5,2,Petrol,yes,2016,520
+Renault,7400,van,181,2,Diesel,yes,2007,Trafic
+Volkswagen,28000,van,163,2,Diesel,yes,2010,Multivan
+Volkswagen,12300,van,182,2,Diesel,yes,2011,T6 (Transporter) 
+Renault,3600,sedan,101,2,Petrol,yes,1997,Megane
+Toyota,8450,sedan,193,1.6,Gas,yes,2007,Corolla
+Volkswagen,2500,hatch,297,1.6,Petrol,yes,1995,Golf III
+Toyota,9950,sedan,106,1.6,Gas,yes,2008,Corolla
+Mercedes-Benz,23200,sedan,58,1.8,Petrol,yes,2010,C-Class
+Toyota,10500,sedan,124,1.8,Petrol,yes,2008,Avensis
+Mercedes-Benz,11200,sedan,280,2.2,Diesel,yes,2005,E-Class
+Volkswagen,9750,van,1,1.9,Diesel,yes,2004,T5 (Transporter)
+Toyota,20800,crossover,70,2.7,Petrol,yes,2008,Land Cruiser Prado
+Audi,33500,hatch,35,1.8,Petrol,yes,2014,A5
+Volkswagen,10000,sedan,66,1.6,Petrol,yes,2011,Polo
+Mitsubishi,NA,crossover,205,2.5,Diesel,yes,2006,Pajero Sport
+Mitsubishi,2199,hatch,400,1.8,Other,yes,1991,Galant
+Audi,16800,hatch,47,1.2,Petrol,yes,2013,A3
+Mercedes-Benz,25500,crossover,200,4,Diesel,yes,2007,GL 420
+Audi,2099,sedan,314,1.9,Diesel,no,1996,A4
+Toyota,20000,crossover,250,4.2,Diesel,yes,2000,Land Cruiser 100
+Volkswagen,7400,van,380,2.5,Diesel,yes,2000,T4 (Transporter)
+Volkswagen,12000,other,209,NA,Diesel,yes,2006,T5 (Transporter) 
+Mercedes-Benz,NA,van,273,2.9,Diesel,yes,2001,Sprinter 310
+BMW,16500,sedan,150,2.5,Petrol,yes,2005,325
+Audi,18500,sedan,137,NA,Petrol,yes,2009,A6
+Mercedes-Benz,43500,other,51,3,Diesel,yes,2014,E-Class
+Renault,7450,other,190,NA,Diesel,yes,2004,Grand Scenic
+Volkswagen,3100,hatch,118,1.8,Gas,yes,1993,Golf III
+Mercedes-Benz,12100,sedan,310,2.2,Diesel,yes,2005,E-Class
+Mercedes-Benz,12000,van,230,2.2,Diesel,yes,2008,Vito
+Mitsubishi,33000,other,0,2.4,Diesel,yes,2016,L 200
+Renault,15672.15,van,0,1.5,Diesel,yes,2016,Dokker
+Renault,6600,van,134,1.5,Diesel,yes,2011,Kangoo
+Renault,12800,vagon,127,1.5,Diesel,yes,2011,Megane
+Mercedes-Benz,300000,sedan,68,6,Petrol,yes,2011,S 600
+Renault,7200,van,73,1.5,Diesel,yes,2012,Kangoo
+BMW,21000,hatch,79,1.6,Petrol,yes,2013,118
+Mitsubishi,5900,hatch,92,1.6,Petrol,yes,2003,Carisma
+Volkswagen,9500,van,205,2,Petrol,yes,2006,Passat B6
+Renault,2100,van,270,1.9,Diesel,no,2002,Scenic
+Audi,4000,sedan,260,NA,Diesel,no,2000,A6
+Toyota,8100,hatch,91,1.6,Petrol,yes,2007,Auris
+Mercedes-Benz,60000,crossover,140,5.5,Gas,yes,2008,G 55 AMG
+BMW,2899,sedan,260,2,Diesel,no,2000,320
+BMW,73900,sedan,57,4.4,Petrol,yes,2013,M5
+BMW,7998,sedan,253,NA,Petrol,yes,1998,323
+BMW,3400,vagon,191,2,Diesel,no,2002,320
+BMW,26500,sedan,70,3,Petrol,yes,2008,330
+Mitsubishi,5800,sedan,186,1.6,Petrol,yes,2006,Lancer
+BMW,9999,sedan,200,1.8,Petrol,yes,2004,318
+Volkswagen,12999,vagon,95,1.6,Diesel,yes,2010,Passat B6
+Mercedes-Benz,75500,van,14,2.2,Diesel,yes,2015,V 250
+BMW,2499,sedan,210,1.8,Petrol,no,2000,318
+Renault,2900,hatch,280,NA,Diesel,no,2005,Scenic
+Volkswagen,3500,hatch,201,1.9,Diesel,no,2002,Golf IV
+Audi,NA,sedan,177,1.8,Petrol,yes,1998,A4
+Volkswagen,10000,van,283,2.5,Diesel,yes,2005,T5 (Transporter)
+Mitsubishi,7000,sedan,133,1.6,Petrol,yes,2008,Lancer
+Mercedes-Benz,169000,van,1,3,Diesel,yes,2014,Sprinter 319
+Volkswagen,35999,crossover,84,3,Diesel,yes,2011,Touareg
+Mitsubishi,2900,sedan,350,1.6,Petrol,yes,1994,Lancer
+Volkswagen,32400,crossover,50,2,Diesel,yes,2014,Tiguan
+Mitsubishi,15000,crossover,95,2.5,Diesel,yes,2008,Pajero Sport
+Mitsubishi,7300,sedan,145,1.6,Petrol,yes,2008,Lancer
+Toyota,30500,sedan,7,2.5,Gas,yes,2015,Camry
+BMW,6300,sedan,270,3.5,Petrol,yes,1998,735
+Mitsubishi,6200,sedan,170,NA,Other,yes,2005,Lancer
+BMW,23500,crossover,180,4.8,Petrol,yes,2008,X5
+Volkswagen,NA,sedan,60,2,Diesel,yes,2013,Passat CC
+Volkswagen,10400,sedan,87,1.6,Petrol,yes,2011,Polo
+Volkswagen,22600,crossover,187,3,Diesel,yes,2008,Touareg
+Volkswagen,8900,hatch,128,1.4,Petrol,yes,2010,Polo
+Audi,8400,sedan,260,1.8,Gas,yes,2003,A4
+Toyota,13300,sedan,158,2,Gas,yes,2009,Avensis
+Mercedes-Benz,16500,van,152,2.2,Diesel,yes,2012,Vito
+Renault,4200,vagon,180,1.4,Petrol,yes,2000,Kangoo
+Volkswagen,NA,van,320,1.6,Diesel,yes,1985,T3 (Transporter)
+Volkswagen,18999,sedan,122,1.8,Petrol,yes,2012,Passat CC
+Mitsubishi,12400,sedan,52,1.6,Petrol,yes,2013,Lancer X
+Mitsubishi,3950,other,280,2.5,Diesel,no,1994,Pajero
+Toyota,13850,hatch,24,1.6,Other,yes,2010,Auris
+Mitsubishi,5999,hatch,108,1.3,Petrol,yes,2006,Colt
+Audi,2650,sedan,300,2,Petrol,yes,1987,80
+BMW,5400,sedan,404,2.5,Petrol,yes,1992,525
+Mercedes-Benz,28500,hatch,15,1.8,Diesel,yes,2014,A 180
+Mercedes-Benz,46900,other,10,2.2,Diesel,yes,2012,E-Class
+Renault,12000,sedan,81,2,Petrol,yes,2012,Fluence
+Volkswagen,29040,sedan,0,1.4,Petrol,yes,2015,Jetta
+Mercedes-Benz,NA,van,261,2.2,Diesel,yes,2007,Vito
+Audi,9800,hatch,165,1.6,Gas,yes,2006,A3
+BMW,7000,sedan,400,2.8,Petrol,yes,1998,528
+Volkswagen,12800,van,230,2,Diesel,yes,2011,T5 (Transporter) 
+BMW,8500,sedan,365,3,Diesel,yes,2001,530
+Toyota,3650,hatch,260,1.3,Petrol,yes,1996,Corolla
+Volkswagen,11500,other,51,1.6,Petrol,yes,2013,Polo
+Volkswagen,9800,van,130,1.6,Diesel,yes,2012,Caddy
+Renault,2350,van,380,1.9,Diesel,no,2003,Espace
+Audi,21800,vagon,172,2,Diesel,yes,2011,A4
+Volkswagen,20300,crossover,139,3,Diesel,yes,2008,Touareg
+Mercedes-Benz,11800,sedan,167,3.2,Diesel,yes,2003,E-Class
+Renault,8400,van,248,2.5,Diesel,yes,2005,Trafic
+BMW,NA,sedan,240,2.8,Gas,yes,1997,528
+Mercedes-Benz,81500,sedan,1,3,Diesel,yes,2016,GLE-Class
+Mitsubishi,20300,other,30,NA,Diesel,yes,2014,L 200
+Toyota,14900,other,175,3.5,Gas,yes,2008,Camry
+Mitsubishi,7500,sedan,79,1.6,Gas,yes,2008,Lancer
+Volkswagen,NA,other,440,1.4,Petrol,yes,2008,Eos
+Volkswagen,9400,hatch,187,1.6,Petrol,yes,2008,Golf V
+Renault,8700,hatch,21,1.6,Petrol,yes,2011,Sandero
+Volkswagen,13700,sedan,134,2,Diesel,yes,2012,Passat B6
+Audi,4000,sedan,263,1.9,Diesel,yes,1996,A4
+Audi,2850,sedan,260,NA,Other,no,1999,A6
+Volkswagen,8950,van,206,1.6,Diesel,yes,2012,Caddy
+Audi,4299,sedan,307,2.8,Gas,yes,1991,100
+Toyota,36500,crossover,2,3,Diesel,yes,2014,Hilux
+Mercedes-Benz,7600,van,315,2.9,Diesel,yes,1996,Sprinter 312
+Mitsubishi,9200,crossover,110,2.4,Petrol,yes,2006,Outlander
+Mercedes-Benz,13000,sedan,191,1.8,Petrol,yes,2005,E-Class
+Toyota,24000,crossover,132,4,Petrol,yes,2008,Land Cruiser Prado
+Mercedes-Benz,8500,van,630,2.9,Diesel,yes,1998,Sprinter 312
+Mercedes-Benz,20900,other,250,3.2,Diesel,yes,2007,ML 320
+BMW,18500,sedan,79,3,Diesel,no,2012,530
+Mercedes-Benz,2000,sedan,270,2.9,Diesel,no,1996,E-Class
+Audi,2400,sedan,310,2,Diesel,yes,1989,100
+Volkswagen,10800,sedan,63,20,Petrol,yes,2007,Passat B6
+BMW,NA,sedan,145,2,Diesel,yes,2007,320
+Renault,12399,van,177,2,Diesel,yes,2009,Trafic
+Toyota,15200,crossover,208,2,Petrol,yes,2006,Rav 4
+Audi,23800,crossover,205,4.2,Gas,yes,2007,Q7
+Volkswagen,3000,vagon,275,1.9,Diesel,no,2001,Passat B5
+Mercedes-Benz,4000,sedan,300,1.8,Petrol,yes,1996,C-Class
+Renault,7000,vagon,231,1.6,Petrol,yes,2008,Scenic
+Volkswagen,17900,van,92,1.4,Petrol,yes,2012,Sharan
+Audi,3400,vagon,223,2.5,Diesel,no,2001,A6
+Volkswagen,2200,hatch,33,1.9,Diesel,yes,1985,Golf II
+BMW,7000,sedan,227,3,Diesel,no,2004,730
+Audi,98000,other,12,5.2,Petrol,yes,2012,R8
+Volkswagen,12650,van,167,2,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,39040,sedan,0,2,Diesel,yes,2014,Passat CC
+BMW,25500,crossover,187,4.8,Gas,yes,2007,X5
+Volkswagen,9000,sedan,130,1.6,Petrol,yes,2012,Polo
+BMW,47300,crossover,10,2,Diesel,yes,2016,X3
+Renault,11500,van,177,2,Diesel,yes,2008,Trafic
+Mitsubishi,19000,crossover,47,2,Petrol,yes,2012,ASX
+Mercedes-Benz,18500,crossover,1,3.5,Gas,yes,2006,ML 350
+Volkswagen,7170,van,176,2,Diesel,yes,2005,Caddy
+Mercedes-Benz,28000,other,250,3,Diesel,yes,2008,GL 320
+Audi,28500,sedan,74,1.8,Petrol,yes,2012,A4
+Volkswagen,25500,other,52,2,Petrol,yes,2012,Scirocco
+Mitsubishi,10999,other,281,3,Gas,yes,2003,Pajero Wagon
+Mercedes-Benz,42500,sedan,100,5.5,Petrol,yes,2011,S 500
+Toyota,16800,crossover,165,2.7,Petrol,yes,2005,Land Cruiser Prado
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLS 350
+Volkswagen,17900,vagon,115,1.8,Petrol,yes,2012,Passat B7
+BMW,19900,other,218,3,Petrol,yes,2007,335
+Volkswagen,8750,van,200,2.5,Diesel,yes,2006,T5 (Transporter)
+Mercedes-Benz,111999,crossover,0,3,Diesel,yes,2016,GLS 350
+BMW,9500,sedan,217,2.2,Petrol,yes,2002,320
+Toyota,7500,hatch,98,1.3,Other,yes,2006,Yaris
+Mercedes-Benz,32500,crossover,254,3.2,Diesel,yes,2008,GL 320
+BMW,45000,sedan,110,NA,Other,yes,2012,535
+Audi,3700,vagon,273,2.5,Diesel,no,2003,A6
+Mitsubishi,8500,other,135,1.5,Gas,yes,2008,Lancer X
+Mercedes-Benz,109900,sedan,40,3,Diesel,yes,2014,S 350
+Mercedes-Benz,39900,other,88,6.2,Petrol,yes,2008,CL 63 AMG
+Mercedes-Benz,4700,sedan,260,2.7,Diesel,no,2002,E-Class
+Audi,13200,vagon,182,2.7,Gas,yes,2003,A6 Allroad
+Renault,6700,van,216,1.5,Diesel,yes,2010,Kangoo
+Volkswagen,23000,sedan,80,2,Other,yes,2011,Passat CC
+Volkswagen,6400,van,181,2,Diesel,yes,2008,Caddy
+Mercedes-Benz,97555,van,0,2.5,Diesel,yes,2016,V 250
+Audi,7900,sedan,242,2,Petrol,yes,2002,A4
+Mercedes-Benz,5300,sedan,380,3,Diesel,yes,1995,E-Class
+Toyota,24171.42,sedan,1,2.5,Petrol,yes,2016,Camry
+Audi,23400,sedan,112,2,Diesel,yes,2012,A4
+Toyota,10500,sedan,185,1.6,Petrol,yes,2008,Corolla
+Mercedes-Benz,4900,van,262,2.3,Diesel,yes,1998,Vito
+BMW,16300,crossover,258,4.4,Petrol,yes,2005,X5
+Renault,1300,van,265,1.9,Diesel,no,1999,Kangoo
+Mercedes-Benz,6700,van,1,2.2,Diesel,yes,2004,Vito
+Renault,2850,hatch,315,2.2,Diesel,no,2003,Laguna
+Mercedes-Benz,109999,crossover,0,2.98,Diesel,yes,2016,GLS 350
+Audi,3650,sedan,240,2.5,Diesel,no,2000,A6
+Volkswagen,4100,vagon,350,1.9,Diesel,yes,2003,Passat B5
+Mercedes-Benz,9300,van,159,2.2,Diesel,yes,2010,Vito
+BMW,18500,crossover,250,4.4,Gas,yes,2005,X5
+Mercedes-Benz,7600,sedan,384,2.8,Petrol,yes,2000,E-Class
+Mitsubishi,7300,sedan,112,1.6,Petrol,yes,2008,Lancer
+Renault,6700,van,220,2,Other,yes,2007,Trafic
+Toyota,14400,sedan,80,2.4,Petrol,yes,2008,Camry
+Toyota,10600,sedan,204,1.8,Other,yes,2008,Avensis
+BMW,139000,crossover,0,4.4,Petrol,yes,2016,X6 M
+Renault,10000,vagon,234,2,Diesel,yes,2008,Laguna
+Toyota,12999,sedan,160,3.5,Gas,yes,2007,Avalon
+Renault,6800,van,168,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,15850,crossover,98,2,Diesel,yes,2008,Tiguan
+Renault,6000,van,162,1.4,Petrol,yes,2007,Kangoo
+Volkswagen,44700,van,120,3.5,Petrol,yes,2013,Sharan
+Renault,5600,vagon,117,1.5,Diesel,yes,2008,Kangoo
+Renault,6100,vagon,130,NA,Diesel,yes,2009,Kangoo
+Mercedes-Benz,21500,sedan,159,1.8,Gas,yes,2012,E-Class
+Renault,7600,van,183,1.9,Diesel,yes,2006,Trafic
+Mercedes-Benz,1950,sedan,1,2,Diesel,yes,1979,200
+Volkswagen,9000,hatch,120,1.2,Diesel,yes,2012,Polo
+BMW,35500,sedan,235,3,Diesel,yes,2010,730
+Mitsubishi,5500,sedan,165,1.6,Petrol,yes,2006,Lancer
+Mercedes-Benz,32500,crossover,168,3.2,Diesel,yes,2009,GL 320
+BMW,18000,sedan,245,2.5,Petrol,yes,2007,523
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,18000,van,147,2.2,Diesel,yes,2013,Vito
+Renault,10999,vagon,216,1.5,Diesel,yes,2012,Megane
+Renault,7500,van,240,74,Diesel,yes,2005,Grand Scenic
+Mercedes-Benz,22700,sedan,140,5,Petrol,yes,2007,S 500
+Toyota,23500,sedan,75,2.5,Gas,yes,2011,Camry
+Mercedes-Benz,10000,other,275,2.15,Diesel,yes,2006,Sprinter 313
+Volkswagen,7700,van,157,1.9,Diesel,yes,2006,Caddy
+Volkswagen,10900,van,185,1.9,Diesel,yes,2009,Caddy
+Audi,12999,vagon,225,2.5,Diesel,yes,2004,A6 Allroad
+BMW,67500,crossover,1,2,Diesel,yes,2016,X5
+Volkswagen,6800,van,325,2.5,Diesel,yes,2001,T4 (Transporter)
+Mercedes-Benz,137000,crossover,82,5.5,Petrol,yes,2014,G 63 AMG
+Mitsubishi,4950,sedan,256,2,Petrol,yes,1997,Galant
+Renault,6999,sedan,65,1.4,Petrol,yes,2012,Symbol
+Mercedes-Benz,NA,crossover,1,3,Diesel,yes,2016,GLS 350
+Volkswagen,29800,hatch,0,2,Diesel,yes,2015,Golf VII
+Mercedes-Benz,16500,van,386,2.2,Diesel,yes,2006,Sprinter 313
+BMW,6500,sedan,320,3,Gas,yes,2000,530
+Renault,9999,vagon,100,1.5,Diesel,yes,2010,Kangoo
+Volkswagen,8500,vagon,200,1.9,Diesel,yes,2003,Passat B5
+Toyota,5750,hatch,160,1,Petrol,yes,2006,Yaris
+BMW,4950,sedan,1,3,Petrol,yes,1996,730
+BMW,19900,other,129,2,Petrol,yes,2008,320
+Mitsubishi,15900,crossover,180,3,Gas,yes,2008,Pajero Wagon
+Toyota,13500,crossover,131,2.4,Gas,yes,2006,Rav 4
+Volkswagen,18950,crossover,149,3,Diesel,yes,2008,Touareg
+BMW,75555,sedan,30,4.4,Petrol,yes,2014,550
+Volkswagen,3650,sedan,197,2,Petrol,yes,1996,Passat B4
+Volkswagen,16000,crossover,90,2,Petrol,yes,2011,Tiguan
+Toyota,21600,sedan,66,2.5,Petrol,yes,2011,Camry
+Toyota,9500,hatch,103,1.6,Petrol,yes,2008,Auris
+Audi,51500,crossover,28,3,Diesel,yes,2014,Q7
+Volkswagen,5000,van,400,2.4,Diesel,yes,1993,T4 (Transporter)
+Volkswagen,27000,other,90,2,Diesel,yes,2014,Tiguan
+Renault,10900,other,189,2,Diesel,yes,2012,Trafic
+BMW,37700,crossover,108,3,Petrol,yes,2011,X3
+Renault,6299,van,66,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,39900,sedan,29,6.3,Petrol,yes,2007,S 63 AMG
+BMW,26900,crossover,140,4.8,Petrol,yes,2009,X5
+Audi,8999,vagon,190,2.7,Gas,yes,2004,A6 Allroad
+Mercedes-Benz,20400,sedan,190,1.8,Gas,yes,2011,E-Class
+Volkswagen,16200,sedan,116,2,Diesel,yes,2011,Passat B7
+Mitsubishi,5700,other,230,2,Petrol,yes,2003,Galant
+BMW,9500,sedan,315,2.5,Diesel,yes,2001,525
+BMW,25999,sedan,80,2,Petrol,yes,2012,320
+Audi,28950,crossover,60,2,Petrol,yes,2012,Q5
+Audi,6499,sedan,270,4.2,Petrol,yes,2000,S8
+Audi,3500,sedan,35,1.8,Gas,yes,1990,80
+Renault,7450,hatch,68,1.2,Petrol,yes,2009,Modus
+Mercedes-Benz,10500,sedan,266,5,Gas,yes,2004,S 500
+BMW,31111,sedan,117,4.4,Petrol,yes,2010,750
+Toyota,27000,other,192,3,Diesel,yes,2007,Hilux
+Toyota,26250,sedan,17,2.5,Petrol,yes,2015,Camry
+Mercedes-Benz,7300,van,245,2.2,Diesel,yes,2002,Vito
+Renault,11400,van,122,2,Diesel,yes,2013,Trafic
+Volkswagen,2200,sedan,279,1.9,Diesel,no,2000,Passat B5
+Toyota,10500,van,200,2.7,Gas,yes,2005,Tacoma
+Toyota,10500,crossover,200,4.5,Gas,yes,1996,Land Cruiser 80
+Volkswagen,10974.21,sedan,1,1.6,Petrol,yes,2015,Polo
+Volkswagen,11500,sedan,119,2,Diesel,yes,2007,Passat B6
+BMW,42000,sedan,59,4.4,Petrol,yes,2009,750
+Mercedes-Benz,17990,crossover,230,3,Diesel,yes,2007,R 320
+Audi,2500,vagon,300,2.5,Diesel,no,2002,A6
+Audi,16500,sedan,136,3.2,Petrol,yes,2007,A6
+Audi,47000,crossover,64,3,Diesel,yes,2013,Q7
+Mitsubishi,5300,hatch,240,1.8,Petrol,yes,2000,Carisma
+Toyota,41900,van,0,2.5,Diesel,yes,2016,Hiace
+BMW,7000,other,226,2.5,Petrol,yes,1999,525
+Mercedes-Benz,26600,van,122,3,Diesel,yes,2012,Vito
+Mitsubishi,6500,sedan,133,1.6,Petrol,yes,2007,Lancer
+Volkswagen,9600,sedan,185,1.6,Gas,yes,2008,Jetta
+Mitsubishi,3200,sedan,106,2,Gas,yes,1997,Galant
+Mercedes-Benz,11200,other,198,6,Petrol,yes,2004,S 600
+Mitsubishi,7900,crossover,143,2,Gas,yes,2007,Outlander
+Audi,29602.95,sedan,1,1.4,Petrol,yes,2016,A4
+Mercedes-Benz,8950,sedan,154,3.5,Gas,no,2005,CLS 350
+Renault,8700,vagon,123,1.5,Diesel,yes,2009,Megane
+Toyota,12900,sedan,177,2.4,Gas,yes,2009,Camry
+Renault,10600,vagon,113,1.5,Diesel,yes,2011,Megane
+Toyota,8500,hatch,93,1.6,Petrol,yes,2007,Auris
+BMW,NA,sedan,99,4.4,Petrol,yes,2013,Alpina
+Toyota,14900,hatch,100,1.8,Other,yes,2010,Prius
+Mitsubishi,2500,other,350,1.8,Petrol,yes,1990,Galant
+Toyota,14400,crossover,119,NA,Gas,yes,2008,Rav 4
+Audi,34000,crossover,118,2,Diesel,yes,2013,Q5
+Mercedes-Benz,39000,crossover,195,5,Gas,yes,2002,G 500
+Mercedes-Benz,10500,sedan,285,1.8,Gas,yes,2003,E-Class
+Mitsubishi,2600,vagon,93,2.5,Petrol,no,1997,Galant
+BMW,4800,sedan,111,2.5,Gas,yes,1991,525
+Volkswagen,7800,van,352,2.5,Diesel,yes,2001,T4 (Transporter)
+Mercedes-Benz,99999,crossover,0,3,Petrol,yes,2016,GLE-Class
+Mercedes-Benz,NA,crossover,27,3,Diesel,yes,2015,G 350
+Toyota,15300,crossover,130,2,Petrol,yes,2008,Rav 4
+Toyota,8500,hatch,55,1,Petrol,yes,2011,Aygo
+Renault,3000,van,217,1.4,Petrol,yes,2004,Kangoo
+BMW,5000,sedan,400,2,Petrol,yes,1991,520
+Renault,11403.99,sedan,0,1.2,Petrol,yes,2016,Logan
+BMW,59900,crossover,30,2,Diesel,yes,2016,X5
+Volkswagen,5400,hatch,320,1.4,Petrol,yes,2002,Golf IV
+Volkswagen,17500,vagon,160,1.4,Petrol,yes,2011,Passat B7
+Renault,6300,sedan,167,1.6,Petrol,yes,2005,Megane
+Volkswagen,1900,hatch,300,1.9,Diesel,no,1999,Golf IV
+BMW,NA,crossover,5,3,Diesel,yes,2016,X6
+Toyota,12400,sedan,135,3.5,Gas,yes,2007,Camry
+Volkswagen,8500,hatch,141,2,Petrol,yes,2000,Beetle
+Mercedes-Benz,47600,van,0,2.2,Diesel,yes,2016,Vito
+Mercedes-Benz,39900,crossover,126,5.5,Petrol,yes,2010,GL 500
+Volkswagen,3350,hatch,122,1.4,Petrol,yes,1993,Golf III
+Mercedes-Benz,222000,crossover,0,6.3,Petrol,yes,2016,S 63 AMG
+Renault,8200,vagon,180,1.6,Gas,yes,2010,Kangoo
+Volkswagen,22200,hatch,51,2,Petrol,yes,2013,New Beetle
+Renault,7999,van,141,1.5,Diesel,yes,2006,Scenic
+Volkswagen,2499,sedan,220,1.9,Diesel,no,2004,Passat B5
+BMW,66200,crossover,70,3,Diesel,yes,2014,X5
+Audi,21000,hatch,34,1.4,Petrol,yes,2013,A3
+Volkswagen,7200,van,270,1.9,Diesel,yes,2008,T5 (Transporter) 
+BMW,34900,crossover,65,2,Petrol,yes,2013,X3
+Audi,17500,sedan,94,1.8,Petrol,yes,2008,A4
+Volkswagen,9600,van,148,1.6,Diesel,yes,2011,Caddy
+Audi,NA,vagon,0,3,Diesel,yes,2016,A6 Allroad
+Toyota,2200,vagon,300,2,Petrol,yes,1989,Camry
+BMW,32000,crossover,108,3,Petrol,yes,2008,X6
+Volkswagen,4700,van,342,1.9,Diesel,yes,1996,T4 (Transporter) 
+Mitsubishi,1180,sedan,280,1.8,Diesel,yes,1986,Galant
+Volkswagen,11000,van,282,2.5,Diesel,yes,2006,T5 (Transporter)
+Volkswagen,24000,sedan,105,2,Diesel,yes,2012,Passat CC
+Renault,4100,other,270,1.2,Gas,yes,1999,Kangoo
+BMW,5800,sedan,270,4.4,Petrol,yes,1996,540
+Audi,8600,sedan,260,2.5,Diesel,yes,2002,A6
+Audi,3500,other,360,2.2,Gas,yes,1990,100
+Mercedes-Benz,7777,sedan,270,2.8,Gas,yes,1996,S 140
+Mercedes-Benz,129999,crossover,0,4.67,Petrol,yes,2016,GLS 500
+Volkswagen,19500,crossover,112,2.5,Diesel,yes,2008,Touareg
+BMW,13300,vagon,157,2,Diesel,yes,2007,520
+Mitsubishi,15900,crossover,180,3,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,28500,sedan,90,5.5,Petrol,yes,2007,S 500
+BMW,27300,sedan,155,2,Diesel,yes,2012,520
+Renault,4800,sedan,100,1.4,Petrol,yes,2006,Clio
+Mercedes-Benz,11499,other,157,2.2,Diesel,yes,2012,Vito
+BMW,8500,sedan,284,1.9,Gas,yes,2000,316
+Mercedes-Benz,3800,van,462,2.2,Diesel,no,2001,Sprinter
+Mercedes-Benz,47600,van,0,2.2,Diesel,yes,2016,Vito
+Toyota,17900,crossover,133,2.7,Gas,yes,2007,Land Cruiser Prado
+Audi,11800,vagon,278,2.5,Diesel,yes,2005,A6
+Audi,11115,hatch,150,1.6,Petrol,yes,2008,A3
+Volkswagen,12999,van,160,2,Diesel,yes,2011,T6 (Transporter)
+Toyota,24842.03,sedan,0,1.8,Petrol,yes,2016,Avensis
+Audi,7200,sedan,300,NA,Gas,yes,1999,A6
+Mitsubishi,8400,sedan,250,2,Petrol,yes,2007,Lancer X
+Toyota,15500,sedan,129,3.5,Other,yes,2008,Camry
+BMW,13999,sedan,265,3,Diesel,yes,2005,730
+Mitsubishi,10200,sedan,104,NA,Gas,yes,2008,Lancer X
+Toyota,42900,crossover,51,3,Diesel,yes,2013,Land Cruiser Prado
+Volkswagen,47777,sedan,91,3,Diesel,yes,2012,Phaeton
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,7550,hatch,155,2,Petrol,yes,2007,Laguna
+BMW,68500,sedan,43,4.4,Petrol,yes,2013,750
+Volkswagen,8400,van,300,2.5,Diesel,yes,2004,T5 (Transporter)
+Renault,24193.65,van,1,1.6,Diesel,yes,2015,Trafic
+Renault,14700,crossover,61,1.5,Diesel,yes,2013,Duster
+Renault,8300,van,127,1.5,Diesel,yes,2011,Kangoo
+Volkswagen,19000,vagon,185,2,Diesel,yes,2011,Passat B7
+BMW,3700,sedan,305,1.9,Petrol,no,2000,318
+BMW,4500,sedan,307,2.5,Petrol,yes,1994,525
+Toyota,9400,sedan,140,1.6,Petrol,yes,2008,Corolla
+Toyota,25000,crossover,115,5.7,Petrol,yes,2007,Tundra
+Volkswagen,18000,sedan,110,2,Diesel,yes,2013,Passat B7
+Toyota,12300,sedan,135,2.4,Petrol,yes,2006,Camry
+Mercedes-Benz,20500,sedan,235,2.2,Diesel,yes,2007,E-Class
+Renault,11900,other,162,2.3,Other,yes,2011,Master
+BMW,3990,sedan,350,2.5,Petrol,yes,1995,525
+BMW,2200,sedan,290,1.6,Petrol,yes,1985,316
+Mercedes-Benz,13600,van,190,2.2,Diesel,yes,2009,Vito
+BMW,17300,sedan,180,3,Diesel,yes,2007,730
+Mercedes-Benz,12500,van,386,2.2,Diesel,yes,2008,Sprinter 313
+BMW,13900,crossover,184,3,Gas,yes,2004,X5
+BMW,39600,crossover,98,3,Petrol,yes,2011,X6
+Volkswagen,11700,van,185,1.4,Petrol,yes,2008,Cross Touran
+Audi,7200,sedan,250,2.4,Gas,yes,1998,A6
+Audi,10999,sedan,200,1.8,Petrol,yes,2006,A4
+Renault,7900,vagon,105,1.5,Diesel,yes,2010,Kangoo
+Mercedes-Benz,13900,other,168,5,Petrol,yes,2002,CL 500
+Volkswagen,4500,vagon,390,1.9,Diesel,yes,1996,Passat B4
+Toyota,9000,crossover,180,1.8,Gas,yes,2003,Matrix
+Renault,10500,vagon,233,1.5,Diesel,yes,2009,Laguna
+Mercedes-Benz,13000,sedan,220,5,Petrol,yes,2003,S 500
+Renault,7300,van,186,1.5,Diesel,yes,2010,Kangoo
+Toyota,21999,sedan,80,2.5,Other,yes,2012,Camry
+Mitsubishi,10500,sedan,57,1.5,Gas,yes,2011,Lancer X
+BMW,NA,crossover,8,2,Diesel,yes,2016,X5
+Renault,8300,van,137,1.5,Diesel,yes,2014,Kangoo
+Mitsubishi,12300,other,175,2.5,Diesel,yes,2008,L 200
+Renault,14999,van,54,66,Other,yes,2012,Kangoo
+Mercedes-Benz,2900,sedan,282,2.2,Diesel,no,2000,210
+Mercedes-Benz,12950,sedan,313,3.2,Diesel,yes,2003,S 320
+BMW,23500,crossover,140,4.8,Petrol,yes,2008,X5
+Audi,33300,crossover,71,2,Petrol,yes,2011,Q5
+Mitsubishi,8200,crossover,210,2.4,Gas,yes,2007,Outlander
+Audi,1900,sedan,450,2.5,Diesel,no,1996,A6
+Volkswagen,7800,sedan,220,1.8,Gas,yes,2002,Passat B5
+Volkswagen,15900,sedan,103,NA,Petrol,yes,2012,Jetta
+Toyota,21800,crossover,170,4,Gas,yes,2006,Land Cruiser Prado
+Mercedes-Benz,22900,sedan,74,2.2,Diesel,yes,2011,C-Class
+Audi,2350,vagon,250,NA,Diesel,no,2001,A6
+Volkswagen,8500,other,200,1.6,Diesel,yes,2011,Caddy
+Audi,16700,vagon,131,2,Diesel,yes,2009,A4
+Toyota,10100,sedan,142,2.4,Petrol,yes,2007,Camry
+Renault,7700,van,125,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,10800,crossover,190,3,Gas,yes,2006,Pajero Sport
+Mercedes-Benz,4700,sedan,1,1.8,Petrol,yes,1996,C-Class
+Audi,5999,vagon,248,2.5,Diesel,yes,2000,A6
+Renault,5800,van,143,1.5,Diesel,yes,2007,Kangoo
+Mitsubishi,5400,sedan,200,1.6,Gas,yes,2005,Lancer
+Audi,3000,vagon,270,2.5,Diesel,yes,1998,A4
+Mitsubishi,3200,sedan,330,1.3,Gas,yes,1996,Lancer
+Mercedes-Benz,43999,sedan,132,3,Diesel,yes,2010,S 350
+Audi,3600,vagon,250,2.5,Other,yes,1999,A6
+Mercedes-Benz,5500,van,400,2.3,Diesel,yes,1996,Vito
+Volkswagen,10200,sedan,129,2,Petrol,yes,2006,Passat B6
+Mercedes-Benz,2990,sedan,300,2.8,Gas,yes,1979,S 280
+Mercedes-Benz,31500,sedan,123,2.2,Diesel,yes,2011,E-Class
+Toyota,11500,sedan,110,1.6,Petrol,yes,2011,Corolla
+Mitsubishi,10888,sedan,63,2,Petrol,yes,2012,Lancer X
+Toyota,38500,crossover,20,5.7,Gas,yes,2014,Tundra
+Audi,4500,vagon,206,2.5,Diesel,no,2003,A6
+Volkswagen,55555,van,63,2,Diesel,yes,2013,Multivan
+Volkswagen,13999,crossover,150,2.5,Diesel,yes,2005,Touareg
+Mercedes-Benz,177777,crossover,0,5.5,Petrol,yes,2016,G 63 AMG
+BMW,15000,other,205,NA,Petrol,yes,2004,530
+Volkswagen,6700,van,300,2.5,Diesel,yes,1997,T4 (Transporter)
+Renault,7100,van,135,1.5,Diesel,yes,2013,Kangoo
+Audi,81500,sedan,0,3,Petrol,yes,2016,A7
+Renault,6200,van,122,1.5,Diesel,yes,2007,Kangoo
+Mercedes-Benz,42500,sedan,110,3,Diesel,yes,2011,S 350
+BMW,75000,crossover,16,3,Diesel,yes,2015,X6
+BMW,48550,other,147,4,Diesel,yes,2012,X5
+Mercedes-Benz,11000,crossover,148,3.2,Petrol,yes,2001,ML 320
+BMW,7500,sedan,275,2,Gas,yes,1998,520
+Toyota,2999,vagon,5,1.3,Petrol,yes,1989,Corolla
+BMW,22500,other,100,3,Petrol,yes,2007,630
+Volkswagen,12800,other,298,NA,Diesel,yes,2005,T5 (Transporter)
+Volkswagen,11900,van,273,2.5,Diesel,yes,2003,T4 (Transporter)
+Volkswagen,17900,crossover,167,3,Diesel,yes,2007,Touareg
+Renault,6700,van,118,1.5,Diesel,yes,2012,Kangoo
+BMW,22900,other,25,3,Petrol,yes,2008,325
+Mercedes-Benz,6800,sedan,286,NA,Other,yes,2000,E-Class
+Toyota,9300,sedan,170,1.8,Gas,yes,2006,Avensis
+Volkswagen,20350,vagon,111,2,Diesel,yes,2012,Passat B7
+Volkswagen,72000,crossover,0,3,Diesel,yes,2016,Touareg
+BMW,16800,crossover,250,3,Diesel,yes,2004,X5
+Toyota,8900,sedan,115,1.6,Petrol,yes,2008,Corolla
+Mitsubishi,22800,crossover,43,2.4,Petrol,yes,2013,Outlander
+Mercedes-Benz,8500,sedan,500,3.2,Diesel,yes,2000,E-Class
+Volkswagen,8800,sedan,86,1.6,Petrol,yes,2012,Polo
+Volkswagen,14100,vagon,115,2,Diesel,yes,2011,Caddy
+Mitsubishi,9200,sedan,84,2,Petrol,yes,2009,Lancer X
+Toyota,75500,crossover,50,4.6,Petrol,yes,2015,Land Cruiser 200
+Volkswagen,2900,sedan,2,1.8,Petrol,yes,1989,Passat B3
+Audi,6100,sedan,305,1.6,Other,yes,1996,A4
+Volkswagen,NA,van,239,2,Diesel,yes,2012,T5 (Transporter)
+Renault,7245,sedan,87,1.4,Petrol,yes,2008,Megane
+Renault,2500,sedan,170,1.9,Diesel,no,2003,Scenic
+Renault,11200,vagon,165,1.5,Diesel,yes,2012,Megane
+Audi,NA,crossover,0,3,Diesel,yes,2015,Q7
+Renault,13500,van,198,1.5,Diesel,yes,2012,Grand Scenic
+Audi,13700,sedan,300,2,Diesel,yes,2005,A6
+Renault,6100,van,280,90,Diesel,yes,2008,Kangoo
+Audi,4300,sedan,340,2.4,Gas,yes,1998,A6
+Volkswagen,9999,van,310,2.5,Diesel,yes,2007,T5 (Transporter)
+Mercedes-Benz,6500,sedan,435,3.2,Petrol,yes,1994,S 140
+Mercedes-Benz,11400,van,174,2.2,Diesel,yes,2012,Vito
+Toyota,10500,sedan,180,3.5,Petrol,yes,2007,Camry
+Renault,2999,van,245,1.5,Diesel,yes,2003,Kangoo
+Mercedes-Benz,29500,crossover,180,3.2,Diesel,yes,2008,GL 320
+Audi,27000,other,222,NA,Diesel,yes,2007,Q7
+Mercedes-Benz,8000,sedan,300,2,Petrol,yes,2000,E-Class
+Mercedes-Benz,NA,sedan,0,2.2,Diesel,yes,2016,V 250
+Mitsubishi,10500,sedan,130,2,Petrol,yes,2008,Lancer X
+Mercedes-Benz,11400,sedan,220,5,Petrol,yes,2004,S 500
+Volkswagen,8300,hatch,147,1.6,Gas,yes,2006,Golf Plus
+Audi,6900,sedan,250,2.4,Gas,yes,1999,A6
+Mercedes-Benz,8500,other,210,4.3,Gas,yes,2000,ML 430
+Volkswagen,17300,crossover,148,3,Diesel,yes,2007,Touareg
+Mercedes-Benz,NA,crossover,1,3,Diesel,yes,2016,GLS 350
+Volkswagen,3500,vagon,215,2,Petrol,yes,1993,Passat B3
+Mercedes-Benz,249999,other,3,5.5,Petrol,yes,2016,S 63 AMG
+Volkswagen,5000,hatch,220,1.4,Petrol,yes,2003,Polo
+Mitsubishi,6900,sedan,118,1.6,Petrol,yes,2007,Lancer
+Toyota,5500,crossover,188,2,Petrol,yes,1999,Rav 4
+BMW,17900,other,98,3,Petrol,yes,2007,Z4
+Volkswagen,11500,van,210,2,Diesel,yes,2012,Caddy
+BMW,7200,sedan,448,3,Petrol,yes,1996,730
+Volkswagen,12800,vagon,167,1.6,Diesel,yes,2011,Golf Variant
+Volkswagen,7000,van,350,1,Other,yes,2004,T5 (Transporter) 
+BMW,8400,sedan,220,2.2,Gas,yes,2001,5 Series
+Audi,3000,van,11,1.9,Diesel,no,1999,A6
+Renault,5200,sedan,160,1.4,Gas,yes,2008,Symbol
+Mitsubishi,5700,vagon,120,1.3,Petrol,yes,2005,Colt
+Mitsubishi,6500,crossover,285,2.5,Diesel,yes,1991,Pajero
+Volkswagen,2300,vagon,200,1.9,Diesel,no,1999,Passat B5
+Volkswagen,7700,van,228,1.9,Diesel,yes,2006,Caddy
+Volkswagen,10999,van,110,1.6,Diesel,yes,2012,Caddy
+BMW,8700,other,212,3,Diesel,no,2004,730
+Mercedes-Benz,7200,sedan,312,3.2,Petrol,yes,2000,E-Class
+BMW,39000,crossover,38,3,Petrol,yes,2009,X6
+Mitsubishi,11999,crossover,235,3,Gas,yes,2007,Pajero Wagon
+Mercedes-Benz,129999,crossover,1,2.99,Diesel,yes,2016,GLS 350
+Volkswagen,18100,other,195,2,Diesel,yes,2011,T5 (Transporter)
+Mitsubishi,1600,vagon,185,1.3,Petrol,no,2000,Space Star
+Mercedes-Benz,138000,crossover,0,3,Diesel,yes,2016,G 350
+Mercedes-Benz,NA,sedan,34,3.5,Diesel,yes,2014,S 350
+Renault,2650,vagon,1,1,Diesel,no,2006,Laguna
+Volkswagen,23300,sedan,115,1.8,Petrol,yes,2012,Passat CC
+BMW,25500,crossover,210,3,Gas,yes,2008,X5
+Volkswagen,20900,sedan,122,2,Petrol,yes,2012,Passat B7
+Renault,3700,hatch,310,2,Petrol,yes,1996,Laguna
+Toyota,9500,sedan,125,1.6,Petrol,yes,2009,Corolla
+Mercedes-Benz,61500,crossover,121,3,Diesel,yes,2013,GL 350
+Toyota,NA,crossover,43,2.2,Diesel,yes,2013,Rav 4
+Volkswagen,8800,other,217,1.9,Diesel,yes,2005,Passat B5
+BMW,34900,vagon,136,2,Diesel,yes,2012,520
+Audi,3600,vagon,200,2.5,Diesel,yes,1999,A6
+Mercedes-Benz,28000,other,12,1.8,Petrol,yes,2013,C-Class
+Mercedes-Benz,140000,crossover,88,5.46,Petrol,yes,2013,G 63 AMG
+BMW,25800,sedan,203,2,Diesel,yes,2010,520
+Mitsubishi,25999,crossover,86,3.2,Diesel,yes,2011,Pajero Wagon
+Volkswagen,29600,sedan,11,1.4,Petrol,yes,2016,Passat B8
+BMW,3200,sedan,178,1.9,Petrol,no,2001,318
+Mercedes-Benz,6900,van,200,2.3,Diesel,yes,1999,Vito
+Mercedes-Benz,3550,sedan,274,3.2,Diesel,no,2002,E-Class
+BMW,2900,vagon,230,2,Diesel,no,2001,320
+Mercedes-Benz,11500,van,307,2.2,Diesel,yes,2007,Vito
+Volkswagen,11800,van,218,1.9,Diesel,yes,2008,T5 (Transporter)
+Mercedes-Benz,69900,sedan,76,6,Petrol,yes,2010,S 65 AMG
+Audi,23000,other,115,1.8,Petrol,yes,2008,S4
+Renault,6500,van,186,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,6000,vagon,284,1.9,Other,yes,2001,Golf IV
+Toyota,17200,sedan,50,1.6,Petrol,yes,2013,Corolla
+Toyota,76500,crossover,29,4.5,Diesel,yes,2014,Land Cruiser 200
+Volkswagen,6800,vagon,160,2.8,Petrol,yes,2001,Sharan
+Mercedes-Benz,250000,other,6,5.5,Petrol,yes,2016,S 63 AMG
+Volkswagen,3750,sedan,262,1.8,Petrol,yes,1994,Vento
+Volkswagen,8000,other,300,2.5,Diesel,yes,2007,T5 (Transporter)
+Mercedes-Benz,58500,crossover,128,3,Diesel,yes,2013,ML 350
+Renault,4999,sedan,114,1.4,Other,yes,2006,Clio
+BMW,NA,crossover,6,4.4,Petrol,yes,2016,X5 M
+Volkswagen,15800,sedan,56,1.4,Other,yes,2013,Jetta
+Volkswagen,9250,sedan,200,2,Diesel,yes,2007,Passat B6
+Mitsubishi,11300,crossover,128,2.4,Gas,yes,2008,Outlander
+Audi,1400,sedan,250,1.8,Petrol,no,1993,80
+Toyota,80000,crossover,23,4.5,Diesel,yes,2014,Land Cruiser 200
+BMW,11900,sedan,1,2.8,Petrol,yes,2002,528
+Volkswagen,9500,hatch,29,1.4,Petrol,yes,2014,Polo
+BMW,10999,other,162,2.2,Petrol,yes,2002,320
+Audi,23500,crossover,119,4.2,Gas,yes,2007,Q7
+BMW,24500,crossover,170,4.8,Gas,yes,2007,X5
+Mitsubishi,6000,sedan,150,1.8,Gas,yes,2004,Virage
+Mitsubishi,17500,crossover,194,3.2,Diesel,yes,2008,Pajero Wagon
+Toyota,9300,sedan,107,1.8,Gas,yes,2007,Avensis
+BMW,3500,vagon,280,3,Diesel,no,2001,330
+BMW,41500,vagon,42,2,Petrol,yes,2012,X3
+Volkswagen,10300,other,215,1.9,Other,yes,2007,T5 (Transporter)
+Mercedes-Benz,NA,crossover,1,3,Petrol,yes,2015,GLE-Class
+Audi,2200,vagon,280,2.5,Diesel,no,2002,A6 Allroad
+Renault,19692.08,crossover,0,1.5,Diesel,yes,2016,Duster
+Renault,7850,van,187,1.5,Diesel,yes,2010,Kangoo
+Mercedes-Benz,NA,sedan,42,3,Diesel,yes,2014,S 350
+Volkswagen,3950,hatch,280,NA,Gas,yes,1996,Golf III
+Toyota,20000,crossover,94,4.5,Petrol,yes,2005,Land Cruiser 105
+Audi,3800,sedan,253,2.5,Diesel,no,1998,A6
+BMW,13700,sedan,254,3,Diesel,yes,2003,530
+Toyota,31000,crossover,86,2.2,Diesel,yes,2014,Rav 4
+Volkswagen,14400,van,237,2,Diesel,yes,2010,T5 (Transporter)
+BMW,4700,sedan,200,NA,Petrol,yes,1996,316
+Toyota,17500,hatch,45,1.6,Petrol,yes,2014,Auris
+Renault,10396.23,vagon,1,1.2,Petrol,yes,2015,Logan
+BMW,27800,crossover,140,3,Diesel,yes,2009,X5
+Volkswagen,5800,sedan,200,1.4,Petrol,yes,2000,Golf IV
+Renault,5800,vagon,200,1.46,Diesel,yes,2008,Grand Scenic
+Audi,21500,vagon,165,2,Diesel,yes,2010,A6
+Volkswagen,3600,sedan,330,2.8,Gas,yes,1994,Passat B4
+BMW,6500,sedan,388,3.5,Petrol,yes,1998,735
+Volkswagen,10700,vagon,171,1.9,Diesel,yes,2010,Caddy
+BMW,3100,sedan,280,2,Diesel,no,1999,320
+Mercedes-Benz,38800,hatch,8,NA,Other,yes,2015,B-Class Electric Drive
+Mercedes-Benz,1350,vagon,240,2.5,Diesel,no,1992,E-Class
+Renault,NA,van,150,2.5,Diesel,yes,2007,Trafic
+Mercedes-Benz,4500,other,525,2.3,Diesel,yes,1995,Sprinter 208
+Mercedes-Benz,99000,sedan,0,3,Petrol,yes,2016,CLS 400
+Mercedes-Benz,5600,vagon,355,2.5,Diesel,yes,1996,250
+Mercedes-Benz,8500,sedan,388,4.3,Gas,yes,2000,S 430
+BMW,24000,crossover,108,2,Diesel,yes,2012,X1
+Mitsubishi,9300,sedan,95,2,Petrol,yes,2008,Lancer X
+Toyota,15700,sedan,125,3.5,Petrol,yes,2008,Camry
+Volkswagen,9600,sedan,106,1.6,Petrol,yes,2011,Polo
+Volkswagen,21000,other,93,2,Diesel,yes,2012,Passat B7
+Mercedes-Benz,3500,sedan,303,2.2,Diesel,no,2000,C-Class
+Toyota,45500,crossover,65,4,Gas,yes,2010,FJ Cruiser
+Audi,20900,crossover,185,4.2,Petrol,yes,2006,Q7
+Mercedes-Benz,6999,other,168,2.3,Petrol,yes,2001,CLK 230
+Mitsubishi,7999,sedan,112,2,Petrol,yes,2008,Lancer X
+BMW,6500,sedan,1,3,Petrol,yes,1995,730
+BMW,29500,sedan,230,3,Diesel,yes,2011,530
+Renault,10300,van,138,2,Diesel,yes,2011,Trafic
+Volkswagen,6000,other,1,2.5,Diesel,yes,1999,LT
+Volkswagen,3300,vagon,193,1.9,Diesel,no,2003,Passat B5
+Volkswagen,2400,vagon,320,1.9,Diesel,no,2000,Passat B5
+Audi,23900,sedan,180,3,Diesel,yes,2009,A6
+Volkswagen,9300,sedan,148,2,Petrol,yes,2006,Passat B6
+Mitsubishi,1699,hatch,294,1.6,Gas,yes,1992,Lancer
+Volkswagen,34000,crossover,92,3,Diesel,yes,2012,Touareg
+Toyota,14500,other,82,2,Gas,yes,2007,Rav 4
+Volkswagen,10400,sedan,78,1.6,Petrol,yes,2012,Polo
+BMW,6100,sedan,266,3.5,Petrol,no,1997,535
+Volkswagen,7200,other,177,1.9,Diesel,yes,2010,Caddy
+Mercedes-Benz,14000,other,144,2.2,Diesel,yes,2009,Vito
+Mercedes-Benz,8900,other,213,5,Other,yes,1999,CL 500
+BMW,22100,hatch,42,1.6,Petrol,yes,2013,116
+Mercedes-Benz,33500,van,0,NA,Diesel,yes,2015,Sprinter 316
+Mercedes-Benz,NA,sedan,20,3,Diesel,yes,2015,S 350
+Toyota,33950,other,65,5.7,Petrol,yes,2013,Tundra
+BMW,3499,sedan,101,2.5,Petrol,yes,1990,525
+Audi,28500,crossover,74,2,Petrol,yes,2010,Q5
+BMW,8800,crossover,284,3,Diesel,no,2004,X5
+Volkswagen,17200,other,15,1.2,Petrol,yes,2014,Golf VII
+Audi,3000,sedan,278,2.5,Diesel,no,1999,A6
+Toyota,25800,sedan,58,2.5,Petrol,yes,2015,Camry
+Mercedes-Benz,18500,sedan,143,5.5,Petrol,yes,2007,S 550
+Volkswagen,28793.04,van,0,1.6,Diesel,yes,2016,Caddy
+BMW,42900,sedan,2,2,Petrol,yes,2016,520
+Toyota,11800,sedan,90,1.8,Petrol,yes,2008,Avensis
+Renault,7100,van,171,1.5,Diesel,yes,2013,Kangoo
+Volkswagen,22000,sedan,26,1.8,Petrol,yes,2011,Passat CC
+Volkswagen,2700,hatch,400,1.8,Gas,yes,1989,Golf II
+Toyota,7000,other,220,1.8,Petrol,yes,2001,Celica
+BMW,6499,other,230,3,Diesel,no,2002,X5
+Volkswagen,15700,sedan,110,1.8,Petrol,yes,2011,Passat B7
+Mitsubishi,8100,sedan,113,2,Petrol,yes,2007,Lancer X
+Mercedes-Benz,12200,other,287,3.2,Petrol,yes,2000,ML 320
+Renault,5400,sedan,96,1.4,Petrol,yes,2007,Symbol
+Mercedes-Benz,5400,sedan,285,2.2,Diesel,no,2002,220
+Volkswagen,7700,vagon,287,1.9,Diesel,yes,2002,Sharan
+BMW,34000,sedan,115,5,Petrol,yes,2008,750
+Audi,3900,sedan,302,1.9,Diesel,no,2002,A6
+Mercedes-Benz,15000,sedan,240,5,Petrol,yes,2004,S 500
+Volkswagen,8999,sedan,120,2,Petrol,yes,2008,Passat B6
+Volkswagen,2400,sedan,268,99.99,Petrol,yes,1989,Passat B3
+Mitsubishi,6300,sedan,116,1.5,Petrol,yes,2008,Lancer X
+Volkswagen,7999,van,210,1.6,Gas,yes,2004,Touran
+Toyota,25200,crossover,128,4,Gas,yes,2007,Land Cruiser Prado
+BMW,26999,sedan,131,2,Diesel,yes,2011,520
+Mercedes-Benz,4000,sedan,460,2,Petrol,yes,1991,E-Class
+Audi,16000,sedan,180,3,Diesel,yes,2006,A6
+Mitsubishi,9200,sedan,115,2,Petrol,yes,2008,Lancer X
+Volkswagen,7000,van,250,2,Diesel,yes,2004,Caddy
+Toyota,85000,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,3200,sedan,320,1.8,Gas,yes,1990,Passat B3
+Renault,11000,hatch,127,1.5,Diesel,yes,2012,Megane
+Mercedes-Benz,13900,other,225,NA,Other,yes,2008,Vito
+Mitsubishi,10500,crossover,187,2.4,Gas,yes,2008,Outlander
+BMW,17500,sedan,102,2,Petrol,yes,2011,320
+Renault,7200,vagon,224,1.5,Diesel,yes,2004,Megane
+Toyota,8400,sedan,195,2.4,Gas,yes,2004,Camry
+Volkswagen,8350,van,1,1.6,Diesel,yes,2011,Caddy
+Audi,3200,vagon,244,2,Other,no,2006,A4
+Toyota,11999,sedan,147,2.4,Petrol,yes,2010,Camry
+Toyota,29900,crossover,144,4,Gas,yes,2008,Land Cruiser 200
+BMW,11500,sedan,290,3,Diesel,yes,2001,730
+Audi,NA,vagon,156,3,Diesel,yes,2009,A6
+Mitsubishi,13900,vagon,93,3,Gas,yes,2007,Outlander XL
+Mercedes-Benz,NA,sedan,300,2.6,Petrol,yes,2002,E-Class
+Renault,5000,van,245,1.5,Diesel,yes,2003,Kangoo
+Toyota,2350,hatch,211,1.3,Petrol,yes,1987,Corolla
+Volkswagen,12900,van,127,1.6,Diesel,yes,2011,Caddy
+Mercedes-Benz,8650,van,344,2.2,Diesel,yes,2006,Vito
+Toyota,22900,crossover,132,2.7,Gas,yes,2012,Venza
+Mercedes-Benz,13500,van,290,2.2,Diesel,yes,2007,Vito
+Audi,39999,crossover,45,2,Diesel,yes,2014,Q5
+Audi,2850,sedan,350,NA,Gas,yes,1988,80
+Volkswagen,3300,sedan,250,1.9,Diesel,no,2000,Passat B5
+Audi,32500,crossover,108,2,Diesel,yes,2012,Q5
+Mitsubishi,13300,crossover,135,3,Petrol,yes,2009,Outlander XL
+Volkswagen,2900,vagon,257,1.9,Diesel,no,2003,Passat B5
+Volkswagen,2900,sedan,315,1.6,Gas,yes,1990,Passat B3
+Mercedes-Benz,21000,crossover,208,3.5,Petrol,yes,2007,ML 350
+Mercedes-Benz,NA,crossover,1,3,Petrol,yes,2016,GLE-Class
+Toyota,89000,crossover,61,4.5,Diesel,yes,2011,Land Cruiser 200
+Volkswagen,23500,crossover,48,2,Diesel,yes,2012,Tiguan
+Mitsubishi,4200,van,350,1.8,Gas,yes,1992,Space Wagon
+Mercedes-Benz,70500,crossover,99,5.5,Petrol,yes,2009,G 55 AMG
+Mercedes-Benz,124000,crossover,0,4,Petrol,yes,2016,GLS 400
+Audi,39999,crossover,45,2,Diesel,yes,2014,Q3
+Mercedes-Benz,1999,sedan,400,2.2,Diesel,no,2000,200
+Mercedes-Benz,14300,other,175,2.2,Diesel,yes,2013,Sprinter 313
+Toyota,13500,sedan,139,2.4,Gas,yes,2008,Camry
+Toyota,13300,sedan,104,2.4,Petrol,yes,2007,Camry
+Toyota,8200,hatch,82,1.3,Petrol,yes,2006,Yaris
+Renault,12000,van,137,1.5,Diesel,yes,2012,Grand Scenic
+Renault,2150,vagon,199,1.9,Diesel,no,2003,Laguna
+Toyota,29000,vagon,60,2.2,Diesel,yes,2013,Rav 4
+Mercedes-Benz,9500,van,280,2.2,Diesel,yes,2005,Vito
+Volkswagen,NA,van,146,2.5,Diesel,yes,2009,T5 (Transporter)
+Volkswagen,10600,van,120,2,Diesel,yes,2007,Touran
+Mercedes-Benz,12500,van,238,2.2,Diesel,yes,2008,Vito
+Mitsubishi,10200,crossover,269,3.2,Diesel,yes,2002,Pajero Wagon
+Toyota,NA,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,50250,sedan,0,2.2,Diesel,yes,2015,E-Class
+Volkswagen,8900,van,100,1.4,Gas,yes,2009,Caddy
+Mercedes-Benz,5500,other,220,2.2,Diesel,yes,2002,Vito
+BMW,45500,sedan,42,2,Diesel,yes,2014,520
+Renault,10499,other,146,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,99999,crossover,0,2.99,Diesel,yes,2016,GLE-Class
+Renault,9100,van,166,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,24500,sedan,180,3.2,Diesel,yes,2008,S 320
+BMW,63500,crossover,1,2,Diesel,yes,2016,X5
+BMW,3600,sedan,221,3,Diesel,no,2002,530
+Renault,12900,crossover,89,2.5,Petrol,yes,2010,Koleos
+Renault,6400,sedan,51,1.6,Petrol,yes,2011,Logan
+BMW,8800,sedan,287,3,Petrol,yes,2001,530
+Renault,7199,sedan,104,1.5,Diesel,yes,2010,Symbol
+Renault,3300,van,250,1.5,Diesel,yes,2005,Kangoo
+Audi,2400,sedan,305,2.3,Gas,yes,1989,100
+Volkswagen,8400,hatch,60,1.39,Petrol,yes,2007,Polo
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mitsubishi,7600,sedan,180,2,Gas,yes,2008,Lancer X
+Renault,3800,van,196,1.9,Diesel,yes,1998,Kangoo
+Mercedes-Benz,13100,sedan,160,3.7,Petrol,yes,2003,S 350
+Toyota,21950,crossover,141,2.7,Gas,yes,2010,Venza
+Renault,8800,van,205,1.9,Diesel,yes,2006,Trafic
+Toyota,10500,hatch,17,1,Petrol,yes,2016,Yaris
+Mitsubishi,24000,crossover,20,2.5,Diesel,yes,2013,Pajero Sport
+Toyota,11800,sedan,189,2.4,Gas,yes,2007,Camry
+Mercedes-Benz,20300,sedan,198,1.8,Gas,yes,2011,E-Class
+Toyota,11600,hatch,24,1.6,Petrol,yes,2009,Auris
+Mercedes-Benz,22000,crossover,225,3.5,Gas,yes,2007,ML 350
+Mercedes-Benz,15800,sedan,231,3.5,Gas,yes,2005,E-Class
+Mercedes-Benz,13500,van,97,2.2,Diesel,yes,2013,Vito
+Mitsubishi,16500,crossover,150,3.5,Gas,yes,2008,Outlander XL
+Mercedes-Benz,25000,van,156,NA,Other,yes,2012,Viano
+Volkswagen,40000,other,170,2,Diesel,yes,2011,Multivan
+Mitsubishi,9500,crossover,150,2,Gas,yes,2007,Outlander
+Renault,6300,van,149,1.5,Other,yes,2011,Kangoo
+BMW,4999,sedan,256,2.5,Diesel,no,2001,525
+Toyota,14990,crossover,280,4.2,Diesel,yes,2000,Land Cruiser 100
+Audi,16400,vagon,152,2,Diesel,yes,2011,A4
+Mercedes-Benz,21300,crossover,190,6.3,Petrol,yes,2007,ML 63 AMG
+Audi,24996,sedan,98,3.2,Petrol,yes,2009,A5
+Volkswagen,22900,sedan,45,2,Petrol,yes,2013,Jetta
+Toyota,8300,hatch,136,1.6,Petrol,yes,2007,Corolla
+Audi,12900,vagon,255,2.5,Diesel,yes,2005,A6 Allroad
+Volkswagen,12199,vagon,149,2,Diesel,yes,2008,Passat B6
+Audi,9999,vagon,198,2.7,Petrol,yes,2003,A6 Allroad
+Mercedes-Benz,35500,crossover,99,5,Gas,yes,2005,G 63 AMG
+Mitsubishi,11500,crossover,170,3,Gas,yes,2005,Pajero Sport
+Mercedes-Benz,38500,sedan,134,3,Diesel,yes,2010,S 350
+Mercedes-Benz,5500,other,250,2.2,Diesel,yes,2002,Vito
+Volkswagen,9400,van,124,2,Gas,yes,2008,Caddy
+Volkswagen,8400,van,143,1.6,Diesel,yes,2011,Caddy
+Audi,2650,vagon,268,2.5,Diesel,no,2000,A6
+Toyota,12000,sedan,133,2.4,Gas,yes,2008,Camry
+Mercedes-Benz,7500,sedan,187,2,Gas,yes,2001,C-Class
+Volkswagen,25900,hatch,35,2,Diesel,yes,2015,Golf VII
+Volkswagen,7500,sedan,280,1.8,Other,yes,2005,Passat B5
+Mercedes-Benz,35000,other,147,6,Petrol,yes,2001,S 600
+Audi,6200,vagon,316,2.6,Gas,yes,1997,A6
+Volkswagen,1550,sedan,161,1.8,Petrol,no,1994,Vento
+Mercedes-Benz,6500,van,450,2.3,Diesel,yes,1996,Vito
+Volkswagen,8999,vagon,161,2,Diesel,yes,2008,Passat B6
+Mitsubishi,21000,crossover,29,2,Gas,yes,2014,ASX
+Mercedes-Benz,8800,other,575,2.2,Diesel,yes,2006,Sprinter 313
+Volkswagen,13500,hatch,48,1.4,Petrol,yes,2012,Polo
+Toyota,3300,van,345,2.4,Diesel,yes,1992,Hiace
+Mercedes-Benz,5990,sedan,400,2,Gas,yes,1998,E-Class
+Mercedes-Benz,14800,crossover,242,2.7,Diesel,yes,2005,ML 270
+Mercedes-Benz,NA,crossover,1,3,Diesel,yes,2016,GL 350
+Mercedes-Benz,47000,sedan,0,1.6,Petrol,yes,2016,C-Class
+Volkswagen,7990,sedan,180,1.8,Gas,yes,2005,Passat B5
+Volkswagen,8999,sedan,120,1.9,Diesel,yes,2007,Passat B6
+Volkswagen,23999,sedan,0,1.6,Diesel,yes,2016,Jetta
+Audi,6500,vagon,421,2.5,Diesel,yes,1996,A6
+Toyota,14000,sedan,1,1.8,Petrol,yes,2008,Avensis
+Audi,23500,crossover,116,4.2,Petrol,yes,2008,Q7
+Renault,9500,sedan,89,1.6,Petrol,yes,2012,Fluence
+Mercedes-Benz,125000,sedan,18,3,Diesel,yes,2015,S 350
+Volkswagen,9000,van,181,1.6,Diesel,yes,2011,Caddy
+Mitsubishi,2300,sedan,523,1.8,Diesel,yes,1989,Lancer
+Volkswagen,10650,sedan,65,1.6,Petrol,yes,2013,Polo
+Audi,6399,vagon,230,2.5,Diesel,yes,1999,A6
+Mitsubishi,7500,sedan,140,1.5,Petrol,yes,2007,Lancer X
+Volkswagen,6450,van,220,NA,Other,yes,2004,Caddy
+Volkswagen,10200,van,105,1.6,Diesel,yes,2014,Caddy
+Mercedes-Benz,3300,sedan,200,2.7,Diesel,no,2000,E-Class
+Mercedes-Benz,8300,sedan,175,2,Petrol,yes,2001,E-Class
+Volkswagen,7400,van,168,1.9,Diesel,yes,2005,Caddy
+Toyota,28600,crossover,66,2.2,Diesel,yes,2013,Rav 4
+Audi,2800,sedan,230,2.8,Petrol,yes,1997,A8
+BMW,38000,crossover,223,3,Diesel,yes,2011,535
+Toyota,11000,hatch,74,1.4,Other,yes,2010,Prius
+Audi,11800,sedan,240,4.2,Gas,yes,2001,S8
+Renault,9500,van,275,2,Diesel,yes,2008,Trafic
+BMW,23700,crossover,117,2,Diesel,yes,2010,X1
+Mercedes-Benz,35000,other,135,5.5,Petrol,yes,2008,CL 550
+Volkswagen,11000,vagon,171,1.9,Diesel,yes,2009,Golf Variant
+BMW,25800,crossover,205,3,Diesel,yes,2008,X5
+Renault,13200,van,230,2,Diesel,yes,2009,Trafic
+Toyota,4650,hatch,170,1,Petrol,yes,2002,Yaris
+Mercedes-Benz,8300,van,337,2.2,Diesel,yes,2002,Vito
+Mitsubishi,18900,crossover,106,3.8,Gas,yes,2007,Pajero Wagon
+Mitsubishi,12750,sedan,59,2.4,Petrol,yes,2009,Galant
+Mercedes-Benz,3800,other,107,0.6,Petrol,yes,2000,Smart
+Volkswagen,11500,other,189,2,Diesel,yes,2011,Caddy
+Mercedes-Benz,4600,van,200,2.2,Diesel,yes,2000,Vito
+BMW,1550,sedan,300,NA,Gas,no,1994,316
+Renault,7899,van,129,1.5,Other,yes,2014,Kangoo
+Mercedes-Benz,70999,crossover,0,2.2,Diesel,yes,2016,GLE-Class
+Toyota,8800,sedan,300,2.4,Petrol,yes,2004,Camry
+Renault,8250,sedan,16,1.2,Petrol,yes,2014,Logan
+Toyota,21500,crossover,89,2,Petrol,yes,2012,Rav 4
+Toyota,195000,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,2400,hatch,1,2,Petrol,no,2002,Laguna
+Volkswagen,12500,other,160,2.5,Other,yes,2006,T5 (Transporter)
+Mercedes-Benz,2499,hatch,201,1.7,Diesel,no,2001,A 170
+BMW,14990,sedan,135,2.5,Petrol,yes,2005,525
+Audi,NA,sedan,166,3,Gas,yes,2005,A6
+Mitsubishi,8260,sedan,65,1.5,Petrol,yes,2011,Lancer
+BMW,6800,sedan,230,2,Petrol,yes,1998,520
+Mercedes-Benz,NA,crossover,131,5,Petrol,yes,2008,G 500
+Mitsubishi,1999,van,168,1.8,Petrol,no,2001,Space Star
+BMW,8500,other,265,2.8,Gas,yes,1998,328
+Toyota,24800,crossover,90,4,Petrol,yes,2008,FJ Cruiser
+Mitsubishi,11800,crossover,120,3,Petrol,yes,2008,Outlander XL
+Mercedes-Benz,35500,sedan,100,3.5,Petrol,yes,2010,S 350
+Mercedes-Benz,9400,van,250,2.2,Diesel,yes,2005,Vito
+Volkswagen,14900,sedan,73,NA,Petrol,yes,2012,Jetta
+Volkswagen,NA,other,28,2,Diesel,yes,2015,Amarok
+Mercedes-Benz,14899,other,209,3,Diesel,yes,2006,E-Class
+Volkswagen,15500,sedan,178,2,Diesel,no,2013,Passat B7
+Renault,2200,van,290,2.2,Diesel,no,1999,Espace
+Volkswagen,8950,sedan,133,1.6,Petrol,yes,2012,Polo
+BMW,17800,vagon,159,3,Diesel,yes,2008,530
+Audi,85555,sedan,40,3,Diesel,yes,2015,A8
+Volkswagen,2000,sedan,300,1.3,Petrol,yes,1988,Jetta
+Mercedes-Benz,20500,sedan,56,5,Other,yes,2003,SL 500 (550)
+Toyota,30990,crossover,68,2.7,Gas,yes,2012,Land Cruiser Prado
+Toyota,39900,crossover,11,4,Petrol,yes,2013,Tacoma
+BMW,21900,other,25,2.5,Petrol,yes,2007,325
+Mercedes-Benz,NA,sedan,16,3,Diesel,yes,2014,S 350
+Mercedes-Benz,185555,other,0,4.7,Petrol,yes,2016,S 500
+Toyota,12400,hatch,52,1.4,Other,yes,2012,Prius
+Mercedes-Benz,5000,other,200,2,Petrol,yes,1998,CLK 200
+Volkswagen,3200,sedan,367,2,Petrol,yes,1991,Passat B3
+BMW,6500,sedan,300,2.8,Gas,yes,1997,528
+Mercedes-Benz,13600,hatch,147,2,Diesel,yes,2008,B 200
+BMW,19990,other,164,NA,Gas,yes,2004,645
+Audi,23500,crossover,245,3,Diesel,yes,2007,Q7
+Audi,15499,sedan,177,2,Diesel,yes,2007,A6
+Mercedes-Benz,12950,sedan,197,2.7,Diesel,yes,2002,E-Class
+Volkswagen,1800,sedan,250,NA,Petrol,yes,1991,Jetta
+Toyota,23500,sedan,80,2.5,Gas,yes,2014,Camry
+BMW,NA,hatch,107,2,Petrol,yes,2006,118
+Renault,5950,hatch,200,1.5,Diesel,yes,2005,Megane
+Mitsubishi,2900,hatch,300,1.8,Petrol,yes,1989,Galant
+Mercedes-Benz,5000,van,400,2.2,Diesel,yes,1999,Vito
+Mercedes-Benz,1200,sedan,240,2.4,Petrol,no,1999,E-Class
+Volkswagen,6500,sedan,190,1.9,Diesel,yes,2006,Caddy
+Volkswagen,51900,crossover,30,3,Diesel,yes,2015,Touareg
+BMW,104999,crossover,2,3,Diesel,yes,2016,X5
+Renault,4650,van,165,1.5,Diesel,yes,2003,Kangoo
+Mitsubishi,16000,crossover,59,NA,Petrol,yes,2012,ASX
+Audi,12500,sedan,165,2.4,Petrol,yes,2006,A6
+Volkswagen,10250,sedan,144,2,Diesel,yes,2008,Passat B6
+Audi,23500,other,83,2,Petrol,yes,2009,A5
+Volkswagen,17777,van,250,2,Diesel,yes,2010,T6 (Transporter)
+Mercedes-Benz,8300,sedan,233,3.2,Diesel,yes,2001,320
+Mercedes-Benz,39900,sedan,84,5.5,Petrol,yes,2011,S 500
+Mercedes-Benz,5200,van,400,2.9,Diesel,yes,1999,Sprinter 210
+Audi,31500,sedan,140,2,Diesel,yes,2012,A6
+BMW,4800,sedan,11,2.5,Diesel,yes,1994,325
+Volkswagen,7700,other,290,NA,Diesel,yes,2000,T4 (Transporter)
+Volkswagen,21550,crossover,58,1.4,Petrol,yes,2014,Tiguan
+Audi,42900,crossover,140,3,Diesel,yes,2013,Q7
+Audi,2600,sedan,160,2.5,Diesel,no,2002,A6
+Volkswagen,31310,van,0,2,Diesel,yes,2014,Touran
+Volkswagen,11800,van,230,1.9,Diesel,yes,2009,T5 (Transporter)
+Mercedes-Benz,107000,van,0,2.2,Diesel,yes,2016,V 250
+Toyota,14800,crossover,150,NA,Gas,yes,2006,Land Cruiser Prado
+BMW,9800,sedan,264,3,Diesel,yes,2001,530
+Volkswagen,7950,van,144,2,Gas,yes,2012,Caddy
+Mercedes-Benz,9000,crossover,220,3.2,Gas,yes,1999,ML 320
+BMW,7400,sedan,279,3.5,Gas,yes,1998,735
+Toyota,24000,sedan,60,2.5,Gas,yes,2014,Camry
+Mercedes-Benz,4800,sedan,199,3.2,Diesel,yes,1999,E-Class
+Toyota,22000,crossover,155,2.7,Gas,yes,2008,Land Cruiser Prado
+BMW,42500,crossover,29,3,Petrol,yes,2011,X3
+Toyota,17200,crossover,149,4.7,Petrol,yes,2008,Land Cruiser 200
+Renault,8100,crossover,90,1.9,Diesel,yes,2005,Kangoo
+Volkswagen,37900,crossover,73,3,Diesel,yes,2012,Touareg
+Volkswagen,30000,sedan,67,2,Diesel,yes,2013,Passat B7
+Volkswagen,7700,van,247,1.9,Diesel,yes,2002,T4 (Transporter)
+BMW,32500,sedan,148,3,Petrol,yes,2011,535
+BMW,18700,crossover,260,3,Diesel,yes,2006,X5
+Renault,8100,van,219,2.5,Diesel,yes,2010,Master
+Toyota,5050,hatch,138,1,Petrol,yes,2001,Yaris
+Mercedes-Benz,11800,van,165,2.2,Diesel,yes,2012,Vito
+Mercedes-Benz,68700,crossover,980,3,Diesel,yes,2013,GL 350
+Audi,2000,sedan,360,1.6,Other,no,1996,A4
+Mercedes-Benz,NA,other,24,4.7,Petrol,yes,2014,S 500
+Mercedes-Benz,19200,sedan,173,5,Petrol,yes,2007,S 500
+BMW,16400,sedan,187,4.8,Gas,yes,2007,750
+BMW,6300,sedan,330,2.5,Petrol,yes,1999,525
+Mitsubishi,15000,crossover,295,2.8,Diesel,yes,2004,Pajero Wagon
+Toyota,23000,other,211,4,Gas,yes,2007,Land Cruiser Prado
+Mercedes-Benz,23700,sedan,86,1.8,Gas,yes,2013,E-Class
+BMW,11900,sedan,160,2.5,Petrol,yes,2005,525
+Audi,7900,sedan,274,1.8,Gas,yes,2003,A6
+Mercedes-Benz,4400,sedan,308,3.2,Gas,no,2000,S 320
+Volkswagen,9900,other,181,1.6,Diesel,yes,2012,Caddy
+Volkswagen,6900,hatch,113,1.4,Petrol,yes,2003,Polo
+Mercedes-Benz,NA,crossover,63,5.5,Petrol,yes,2011,GL 550
+Mercedes-Benz,71999,crossover,0,2.2,Diesel,yes,2016,GLE-Class
+BMW,11800,crossover,210,3,Diesel,no,2006,X5
+Renault,10999,van,207,2,Diesel,yes,2012,Trafic
+Mitsubishi,18900,crossover,93,3.8,Gas,yes,2008,Pajero Wagon
+Toyota,28900,crossover,120,3.5,Petrol,yes,2011,Highlander
+Toyota,15500,crossover,130,2,Petrol,yes,2008,Rav 4
+BMW,63000,crossover,0,2,Diesel,yes,2015,X5
+Toyota,11700,sedan,145,2.4,Petrol,yes,2007,Camry
+BMW,4999,sedan,327,3,Diesel,no,2001,730
+Toyota,13300,crossover,140,2.4,Petrol,yes,2007,Rav 4
+Mercedes-Benz,8200,sedan,260,3,Diesel,yes,1997,S 300
+Renault,8900,van,150,2,Diesel,yes,2009,Trafic
+Renault,NA,vagon,117,1.5,Diesel,yes,2007,Grand Scenic
+BMW,60500,sedan,110,3,Diesel,yes,2011,740
+BMW,8900,sedan,294,2.2,Gas,yes,2000,520
+BMW,69500,sedan,62,4.4,Petrol,yes,2013,750
+Volkswagen,9000,hatch,95,1.6,Gas,yes,2005,Golf V
+BMW,17800,crossover,265,3,Diesel,yes,2005,X5
+Mercedes-Benz,2100,other,350,2.4,Diesel,yes,1988,MB
+Toyota,10700,sedan,57,1.3,Petrol,yes,2010,Corolla
+Volkswagen,17777,van,250,2,Diesel,yes,2010,Caravelle
+Audi,73500,sedan,53,6.3,Petrol,yes,2013,A8
+Mitsubishi,9800,crossover,92,2,Petrol,yes,2008,Outlander
+Volkswagen,35900,van,132,2,Diesel,yes,2011,Multivan
+Toyota,7500,hatch,135,1,Petrol,yes,2008,Yaris
+Renault,2500,sedan,105,1.4,Petrol,yes,1990,19
+Audi,39999,crossover,100,3,Diesel,yes,2011,Q7
+Volkswagen,22000,crossover,123,3,Diesel,yes,2009,Touareg
+Toyota,9800,sedan,65,1.6,Petrol,yes,2009,Auris
+BMW,29999,other,73,2,Petrol,yes,2012,320
+Mercedes-Benz,7500,sedan,350,2.2,Diesel,yes,2003,Sprinter 208
+Toyota,12500,sedan,138,3.5,Petrol,yes,2007,Camry
+BMW,39500,crossover,115,4.4,Petrol,yes,2010,X5
+Volkswagen,10600,van,240,2.5,Diesel,yes,2009,T5 (Transporter)
+Mitsubishi,18200,crossover,147,3.2,Diesel,yes,2008,Pajero Wagon
+Mercedes-Benz,189999,other,0,4.7,Petrol,yes,2016,S 500
+Audi,4800,other,207,2,Petrol,yes,2002,A4
+BMW,3999,sedan,238,1.8,Petrol,yes,1996,318
+Toyota,35500,crossover,165,4,Gas,yes,2011,Land Cruiser Prado
+Toyota,13500,sedan,158,2.4,Gas,yes,2008,Camry
+Mercedes-Benz,17500,sedan,130,3,Petrol,yes,2007,C-Class
+Mercedes-Benz,59999,sedan,11,4.7,Petrol,yes,2012,CLS 500
+Mercedes-Benz,7999,van,255,2.2,Diesel,yes,2004,Vito
+Toyota,16000,sedan,89,1.8,Other,yes,2011,Avensis
+BMW,5850,sedan,250,4.5,Gas,yes,2001,745
+Renault,7999,van,154,1.5,Diesel,yes,2008,Scenic
+Renault,8900,van,181,1.9,Diesel,yes,2006,Trafic
+BMW,43719,crossover,0,2,Petrol,yes,2016,X3
+Volkswagen,8800,van,193,2.5,Diesel,yes,2005,T5 (Transporter)
+Volkswagen,9990,van,194,1.6,Diesel,yes,2011,Caddy
+Volkswagen,17999,sedan,80,1.8,Petrol,yes,2012,Passat B7
+Mercedes-Benz,37500,other,94,5.5,Petrol,yes,2009,CL 55 AMG
+Audi,20500,sedan,155,3,Petrol,yes,2010,A6
+Audi,4999,crossover,242,2.5,Diesel,no,2003,A6 Allroad
+Mercedes-Benz,4100,crossover,1,3.5,Petrol,yes,2007,ML 350
+Mercedes-Benz,17499,sedan,145,1.8,Petrol,yes,2008,C-Class
+Audi,8200,other,326,2.5,Diesel,yes,2002,A6
+Volkswagen,7000,sedan,185,2.8,Gas,yes,2004,Passat B5
+Volkswagen,9200,vagon,107,1.6,Petrol,yes,2010,Golf Variant
+Volkswagen,4000,hatch,105,2,Other,yes,2013,Golf VII
+Renault,17500,crossover,20,1.5,Diesel,yes,2016,Duster
+BMW,1950,sedan,162,1.6,Petrol,no,1995,316
+Audi,11000,other,120,1.8,Petrol,yes,2000,TT
+Audi,7750,sedan,259,3,Diesel,no,2005,A6
+Mercedes-Benz,NA,sedan,6,3,Diesel,yes,2014,S 350
+Volkswagen,24300,sedan,1,1.6,Diesel,yes,2016,Jetta
+Renault,4100,van,190,1.9,Diesel,yes,2002,Kangoo
+Mercedes-Benz,69900,crossover,69,3,Diesel,yes,2014,GL 350
+Volkswagen,29999,other,129,2,Diesel,yes,2014,Caravelle
+Renault,11999,vagon,188,1.5,Other,yes,2012,Megane
+Volkswagen,13900,vagon,122,1.4,Gas,yes,2013,Touran
+BMW,8900,vagon,274,2,Diesel,no,2007,320
+Audi,26000,sedan,105,2,Petrol,yes,2012,A6
+BMW,2700,other,287,2.5,Diesel,no,1998,525
+Mitsubishi,9700,crossover,45,NA,Other,yes,2001,Pajero Sport
+Toyota,12500,sedan,140,3.5,Gas,yes,2007,Camry
+BMW,4500,sedan,28,1.9,Petrol,yes,1995,318
+Toyota,88000,crossover,3,4.5,Diesel,yes,2016,Land Cruiser 200
+Mitsubishi,8700,sedan,143,2,Gas,yes,2007,Lancer X
+Volkswagen,NA,van,124,1.6,Diesel,yes,2013,Caddy
+Volkswagen,17200,van,230,3.2,Gas,yes,2008,Multivan
+Toyota,30600,crossover,39,2.2,Diesel,yes,2014,Rav 4
+Mercedes-Benz,15300,sedan,240,2.2,Diesel,yes,2006,E-Class
+Toyota,73900,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,12500,sedan,84,2,Petrol,yes,2007,Passat B6
+Mercedes-Benz,30500,crossover,150,5.5,Petrol,yes,2002,G 500
+Audi,NA,sedan,280,4.2,Petrol,yes,2000,A8
+BMW,64900,crossover,73,3,Diesel,yes,2013,X6 M
+Toyota,11200,sedan,179,2.4,Petrol,yes,2006,Camry
+BMW,9900,other,93,2.5,Petrol,yes,2003,Z4
+Mercedes-Benz,55000,sedan,34,2.2,Diesel,yes,2011,S 250
+Volkswagen,3000,vagon,250,1.9,Diesel,yes,1992,Passat B3
+BMW,33000,sedan,147,3,Diesel,yes,2009,730
+Renault,13000,hatch,75,2,Petrol,yes,2013,Megane
+Mercedes-Benz,33900,other,102,5,Petrol,yes,2008,CL 55 AMG
+Mercedes-Benz,NA,vagon,100,3,Diesel,yes,2014,CLS 350
+BMW,17500,crossover,210,3,Diesel,yes,2005,X5
+Audi,33000,crossover,60,2,Petrol,yes,2011,Q5
+Volkswagen,11999,sedan,240,1.6,Petrol,yes,2010,Jetta
+Toyota,14700,sedan,141,3.5,Petrol,yes,2008,Camry
+Audi,2600,vagon,253,1.8,Gas,no,1997,A4
+BMW,10900,sedan,375,4.4,Gas,yes,2001,740
+Toyota,99999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Toyota,3900,hatch,160,1,Petrol,yes,1999,Yaris
+Renault,5700,van,275,1.9,Diesel,yes,2002,Trafic
+Audi,90999,sedan,4,3,Diesel,yes,2015,A8
+Mercedes-Benz,25500,sedan,137,2.2,Diesel,yes,2012,E-Class
+BMW,3300,sedan,360,2.5,Petrol,yes,1992,525
+Toyota,28700,crossover,80,3.5,Gas,yes,2012,Highlander
+Volkswagen,9300,van,200,1.6,Diesel,yes,2011,Caddy
+Toyota,9400,sedan,148,1.8,Gas,yes,2008,Avensis
+Audi,23000,crossover,180,3.6,Gas,yes,2007,Q7
+BMW,4500,sedan,370,4,Petrol,yes,1993,540
+Mitsubishi,23900,crossover,1,1.8,Petrol,yes,2015,ASX
+Mercedes-Benz,107999,crossover,0,3,Diesel,yes,2016,GLS 350
+Volkswagen,12900,sedan,157,1.8,Gas,yes,2008,Passat B6
+Mercedes-Benz,14900,van,211,2.2,Other,yes,2012,Vito
+Volkswagen,15900,other,160,1.8,Petrol,yes,2010,Passat B6
+Volkswagen,9300,van,154,2,Gas,yes,2010,Caddy
+Renault,1900,vagon,200,2,Gas,yes,1987,25
+Mercedes-Benz,7000,other,260,2.2,Diesel,yes,2000,Vito
+Mitsubishi,14700,crossover,240,3.2,Diesel,yes,2006,Pajero Wagon
+Mercedes-Benz,160000,other,8,4.6,Petrol,yes,2015,S 500
+Toyota,21800,crossover,63,2,Petrol,yes,2013,Rav 4
+Audi,68000,crossover,20,3,Petrol,yes,2014,Q5
+Mercedes-Benz,7000,sedan,370,2.2,Petrol,yes,1995,E-Class
+Mercedes-Benz,70000,other,21,6,Petrol,yes,2008,S 600
+Volkswagen,22000,sedan,65,2,Petrol,yes,2012,Passat B7
+Mercedes-Benz,18900,other,50,3.5,Petrol,yes,2005,SLK 350
+BMW,9700,sedan,267,2,Diesel,yes,2002,320
+Volkswagen,4850,vagon,300,1.9,Diesel,yes,1998,Golf III
+Mercedes-Benz,8700,other,260,2.2,Diesel,yes,2003,Sprinter 213
+Toyota,14700,sedan,139,3.5,Petrol,yes,2008,Camry
+Volkswagen,23999,sedan,1,1.6,Diesel,yes,2016,Jetta
+Mitsubishi,24500,crossover,11,2.5,Diesel,yes,2012,Pajero Sport
+Mercedes-Benz,23500,van,161,2.2,Diesel,yes,2011,Vito
+Volkswagen,10000,van,160,1.6,Diesel,yes,2012,Caddy
+Volkswagen,9700,van,255,2.5,Diesel,yes,2002,T4 (Transporter)
+Renault,7000,vagon,280,1.5,Diesel,yes,2005,Megane
+Renault,1550,other,245,2,Petrol,no,1998,Megane
+Audi,15000,sedan,38,NA,Diesel,no,2015,A5
+Mercedes-Benz,47600,van,0,2.2,Diesel,yes,2016,Vito
+Toyota,27000,other,105,2.5,Diesel,yes,2013,Hilux
+BMW,65000,crossover,0,2.5,Diesel,yes,2016,X5
+BMW,29999,sedan,130,NA,Petrol,yes,2009,750
+Renault,20007,sedan,2,1.5,Diesel,yes,2015,Fluence
+Volkswagen,15500,crossover,167,3.2,Petrol,yes,2006,Touareg
+Volkswagen,3999,hatch,152,1,Petrol,yes,2000,Lupo
+BMW,19990,sedan,1,2,Diesel,yes,2008,320
+Mercedes-Benz,38000,sedan,42,1.6,Petrol,yes,2014,C-Class
+Mercedes-Benz,NA,crossover,0,3,Petrol,yes,2016,GLE-Class
+Volkswagen,20477.54,van,1,1.6,Diesel,yes,2015,Caddy
+Volkswagen,9650,vagon,200,1.4,Gas,yes,2009,Passat B6
+BMW,37500,crossover,115,3,Diesel,yes,2008,X5 M
+Mercedes-Benz,12700,van,193,2.2,Diesel,yes,2011,Vito
+BMW,NA,sedan,259,3,Diesel,yes,2001,330
+Toyota,4500,hatch,207,1.3,Petrol,yes,1995,Corolla
+Mercedes-Benz,2400,sedan,3,3,Diesel,yes,1998,E-Class
+Mercedes-Benz,6999,sedan,330,2,Petrol,yes,1997,200
+Volkswagen,8900,van,169,1.9,Diesel,yes,2008,Caddy
+Mitsubishi,7999,sedan,78,1.5,Gas,yes,2011,Lancer X
+Volkswagen,13500,van,124,2,Diesel,yes,2013,T6 (Transporter) 
+Audi,1999,sedan,400,1.6,Diesel,yes,1986,80
+Mercedes-Benz,109999,crossover,1,3,Diesel,yes,2016,GLS 350
+Mercedes-Benz,14100,van,138,2.2,Diesel,yes,2009,Vito
+Volkswagen,4600,other,290,1.9,Diesel,yes,1995,T4 (Transporter) 
+Mercedes-Benz,3250,sedan,250,4,Diesel,no,2002,S 400
+Mercedes-Benz,19500,sedan,157,3.5,Petrol,yes,2006,S 350
+Audi,2300,vagon,25,2.5,Diesel,no,1999,A6
+Toyota,11700,sedan,131,2.4,Petrol,yes,2007,Camry
+Audi,7000,vagon,210,3,Diesel,no,2006,A6
+BMW,6950,sedan,320,4.4,Gas,yes,1997,740
+Mitsubishi,4500,sedan,300,1.6,Gas,yes,2000,Carisma
+Renault,12500,vagon,150,1.5,Diesel,yes,2011,Kangoo
+BMW,2200,sedan,290,2.5,Gas,yes,1997,525
+Renault,10750,hatch,136,1.5,Diesel,yes,2011,Megane
+Mitsubishi,8400,sedan,91,1.5,Petrol,yes,2009,Lancer X
+Volkswagen,28500,sedan,50,2,Petrol,yes,2013,Passat B7
+BMW,44800,sedan,63,4.4,Petrol,yes,2012,750
+Toyota,11707.8,hatch,1,1,Petrol,yes,2016,Yaris
+Toyota,NA,sedan,21,2.5,Petrol,yes,2015,Camry
+BMW,32500,sedan,131,2,Diesel,yes,2010,520
+Renault,10500,vagon,165,1.5,Diesel,yes,2012,Megane
+Mercedes-Benz,11999,other,215,2.2,Diesel,yes,2007,Vito
+Mercedes-Benz,20300,sedan,198,1.8,Gas,yes,2011,E-Class
+BMW,6100,sedan,205,1.8,Petrol,yes,2001,316
+Volkswagen,12300,other,248,1.9,Diesel,yes,2010,T5 (Transporter)
+Mercedes-Benz,2400,sedan,237,3.8,Petrol,yes,1983,S 350
+Toyota,11750,sedan,69,1.6,Petrol,yes,2013,Corolla
+BMW,52300,sedan,55,3,Diesel,yes,2013,530
+Audi,30900,crossover,70,2,Petrol,yes,2012,Q3
+Volkswagen,5200,van,235,2,Diesel,yes,2005,Caddy
+BMW,26800,sedan,47,2,Petrol,yes,2013,320
+Volkswagen,74000,crossover,1,3,Diesel,yes,2015,Touareg
+Renault,10700,van,150,1.9,Diesel,yes,2006,Trafic
+Mercedes-Benz,17500,vagon,113,2.2,Diesel,yes,2010,C-Class
+Toyota,30800,crossover,56,2.7,Gas,yes,2011,Land Cruiser Prado
+Volkswagen,2800,hatch,140,1.4,Petrol,no,2002,Golf IV
+Mercedes-Benz,12600,sedan,123,3.2,Petrol,yes,2000,S 320
+Volkswagen,17500,sedan,144,1.4,Petrol,yes,2012,Passat CC
+Toyota,68000,crossover,67,4.5,Diesel,yes,2012,Land Cruiser 200
+Toyota,34000,crossover,44,3.5,Petrol,yes,2012,Highlander
+Toyota,28900,crossover,20,2.5,Petrol,yes,2013,Rav 4
+Volkswagen,9900,van,230,1.9,Diesel,yes,2008,T5 (Transporter) 
+Toyota,46500,other,4,3,Diesel,yes,2016,Hilux
+BMW,24700,other,85,4.4,Petrol,yes,2004,645
+Volkswagen,3400,van,175,1.4,Petrol,yes,2003,Caddy
+Mitsubishi,7700,sedan,139,1.5,Petrol,yes,2008,Lancer X
+Toyota,19000,crossover,263,4.2,Diesel,yes,2001,Land Cruiser 100
+Mercedes-Benz,NA,sedan,62,3,Diesel,yes,2013,S 350
+BMW,3500,sedan,185,2,Diesel,no,2002,320
+Volkswagen,14800,other,177,2,Diesel,yes,2012,T5 (Transporter) 
+Mitsubishi,36000,crossover,0,2.4,Petrol,yes,2016,Outlander
+Volkswagen,13995,van,147,1.6,Diesel,yes,2013,Touran
+Toyota,9900,sedan,130,2.3,Gas,yes,2006,Camry
+Mitsubishi,7500,hatch,138,1.3,Petrol,yes,2008,Colt
+Volkswagen,24800,sedan,2,1.6,Diesel,yes,2016,Jetta
+Renault,7100,hatch,127,2,Gas,yes,2006,Laguna
+Toyota,45000,crossover,80,4,Petrol,yes,2013,Land Cruiser Prado
+Mercedes-Benz,2499,sedan,300,3,Diesel,no,1999,E-Class
+Mitsubishi,9500,sedan,136,2,Petrol,yes,2008,Lancer X
+BMW,21000,sedan,186,3,Petrol,yes,2007,530
+Volkswagen,11450,van,140,2,Diesel,yes,2010,Caddy
+Volkswagen,66346,crossover,0,3,Diesel,yes,2016,Touareg
+Volkswagen,8200,hatch,164,1.4,Petrol,yes,2008,Polo
+BMW,11700,sedan,316,NA,Diesel,yes,2001,730
+Renault,4599,van,190,1.5,Diesel,yes,2003,Kangoo
+Volkswagen,8900,sedan,62,1.6,Petrol,yes,2012,Polo
+Toyota,13900,hatch,79,1.6,Gas,yes,2012,Auris
+BMW,5500,sedan,200,3.5,Gas,yes,1998,535
+Mercedes-Benz,5500,sedan,360,2,Gas,yes,1994,E-Class
+BMW,14500,sedan,215,NA,Petrol,yes,2006,525
+Audi,3300,sedan,300,1.8,Gas,yes,1987,80
+Volkswagen,5950,van,213,1.6,Gas,yes,2008,Caddy
+Mitsubishi,18999,crossover,173,3.8,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,124000,crossover,0,3,Petrol,yes,2016,GLS 400
+Volkswagen,11500,sedan,130,2,Diesel,yes,2008,Passat B6
+Mercedes-Benz,4500,sedan,240,3.2,Other,no,2005,C-Class
+BMW,34900,crossover,35,3,Petrol,yes,2011,X3
+Mitsubishi,12999,crossover,84,2.4,Petrol,yes,2009,Outlander XL
+Toyota,20900,crossover,180,4.7,Gas,yes,2004,Land Cruiser 100
+Audi,NA,sedan,370,2.6,Other,yes,1997,A6
+Mercedes-Benz,26900,sedan,150,5.5,Petrol,yes,2008,S 550
+Volkswagen,49999,van,59,2,Diesel,yes,2014,Multivan
+Mercedes-Benz,4000,crossover,287,4,Diesel,no,2003,ML 400
+Volkswagen,52300,crossover,19,3,Diesel,yes,2014,Touareg
+Mercedes-Benz,29500,sedan,37,1.8,Petrol,yes,2012,E-Class
+Volkswagen,19100,other,120,1.4,Petrol,yes,2012,Scirocco
+BMW,45500,other,26,2,Diesel,yes,2015,520
+Toyota,4000,sedan,243,2,Gas,yes,1987,Mark II
+BMW,7200,sedan,255,2.8,Petrol,yes,1998,328
+Volkswagen,1900,vagon,504,1.6,Gas,yes,1988,Passat B2
+BMW,24700,vagon,142,2,Diesel,yes,2012,520
+Toyota,21000,crossover,74,2,Petrol,yes,2011,Rav 4
+Mitsubishi,14500,crossover,90,3,Petrol,yes,2008,Pajero Wagon
+Mercedes-Benz,13800,sedan,222,3,Gas,yes,2007,E-Class
+Volkswagen,25999,sedan,60,1.8,Petrol,yes,2014,Passat B7
+Volkswagen,9555,sedan,174,2,Petrol,yes,2007,Passat B6
+Renault,9700,van,152,2,Diesel,yes,2010,Trafic
+Renault,11700,van,150,2.3,Diesel,yes,2012,Master
+Volkswagen,5300,vagon,259,1.6,Gas,yes,1995,Passat B4
+Toyota,8700,hatch,140,NA,Petrol,yes,2008,Auris
+Audi,22500,sedan,159,5.2,Petrol,yes,2008,S8
+Renault,8300,van,221,1.5,Diesel,yes,2009,Kangoo
+Volkswagen,4900,van,198,1.6,Petrol,no,2004,Touran
+Volkswagen,2850,vagon,450,1.6,Diesel,yes,1988,Passat B2
+Volkswagen,4999,van,225,NA,Other,no,2003,Caravelle
+BMW,8100,sedan,245,2,Diesel,no,2007,320
+Volkswagen,4400,sedan,290,1.8,Gas,yes,1997,Vento
+BMW,9500,crossover,110,3,Diesel,no,2008,X6
+Audi,9500,vagon,160,2.7,Gas,yes,2003,A6 Allroad
+Toyota,4350,sedan,35,2.2,Gas,yes,1999,Camry
+Volkswagen,10000,van,224,2.5,Diesel,yes,2006,T5 (Transporter)
+BMW,65000,crossover,58,4.4,Petrol,yes,2014,X5
+Toyota,1750,hatch,270,1.6,Petrol,yes,1985,Corolla
+Renault,8200,hatch,79,1.6,Petrol,yes,2008,Laguna
+Volkswagen,22000,crossover,98,2,Diesel,yes,2013,Tiguan
+Volkswagen,13300.95,sedan,0,1.6,Petrol,yes,2016,Polo
+Volkswagen,9300,vagon,150,2,Petrol,yes,2008,Passat B6
+Audi,12000,sedan,9,NA,Petrol,no,2015,A4
+Volkswagen,6700,van,300,2.4,Diesel,yes,2000,T4 (Transporter)
+Mercedes-Benz,3900,sedan,385,1.8,Petrol,yes,1995,C-Class
+Mercedes-Benz,2150,sedan,360,2.9,Diesel,no,1997,E-Class
+Renault,11111,vagon,190,1.5,Diesel,yes,2011,Laguna
+Renault,7500,other,160,2,Gas,yes,2006,Scenic
+BMW,99999,other,0,3,Petrol,yes,2016,640
+Volkswagen,20500,hatch,56,2,Diesel,yes,2014,Golf VII
+Mercedes-Benz,119000,sedan,27,3,Diesel,yes,2014,S 350
+Mitsubishi,40800,crossover,0,2.4,Diesel,yes,2016,Pajero Sport
+Renault,10700,van,265,2,Diesel,yes,2007,Trafic
+Volkswagen,69990,van,1,2,Diesel,yes,2015,Multivan
+Mercedes-Benz,NA,other,24,4.7,Petrol,yes,2014,S 500
+Volkswagen,12500,sedan,128,2,Petrol,yes,2007,Passat B6
+Audi,15000,vagon,154,2,Diesel,yes,2008,A6
+Mitsubishi,6300,sedan,126,1.6,Petrol,yes,2006,Lancer
+Renault,13500,hatch,10,1.6,Petrol,yes,2014,Megane
+Toyota,9700,sedan,130,NA,Petrol,yes,2008,Corolla
+Renault,NA,vagon,177,1.5,Diesel,yes,2007,Megane
+Volkswagen,1900,sedan,297,1.6,Petrol,yes,1987,Jetta
+Volkswagen,17800,crossover,210,3,Diesel,yes,2005,Touareg
+Volkswagen,10000,van,71,1.2,Petrol,yes,2013,Caddy
+Volkswagen,10800,other,146,1.6,Diesel,yes,2012,Caddy
+Mercedes-Benz,10900,sedan,360,5,Gas,yes,2001,S 500
+Volkswagen,15000,vagon,69,1.4,Petrol,yes,2012,Touran
+Volkswagen,9850,sedan,110,2,Petrol,yes,2006,Passat B6
+Mitsubishi,8500,crossover,320,3,Gas,yes,2000,Pajero Sport
+Volkswagen,14900,vagon,170,1.6,Diesel,yes,2011,Passat B7
+Mitsubishi,11500,sedan,72,2.4,Petrol,yes,2006,Lancer Evolution
+Audi,8500,sedan,247,2.5,Diesel,yes,2001,A6
+Mitsubishi,12900,sedan,40,2,Petrol,yes,2013,Lancer Evolution
+Mitsubishi,30500,sedan,72,2,Petrol,yes,2006,Lancer Evolution
+Audi,13900,sedan,150,NA,Petrol,yes,2006,A8
+Mercedes-Benz,43500,other,51,3,Diesel,yes,2014,E-Class
+BMW,30500,sedan,73,2,Petrol,yes,2013,520
+Mercedes-Benz,1650,sedan,257,1.8,Gas,no,1995,CL 180
+Renault,9300,vagon,184,1.5,Diesel,yes,2011,Megane
+Mercedes-Benz,30500,sedan,89,6.3,Petrol,yes,2008,CLS 63 AMG
+Mercedes-Benz,16300,van,175,2.2,Diesel,yes,2010,Vito
+Renault,5750,crossover,225,2,Gas,yes,2003,Scenic
+Mercedes-Benz,54500,sedan,29,3,Diesel,yes,2012,CLS 350
+Mercedes-Benz,11500,van,209,2.2,Diesel,yes,2008,Vito
+Mercedes-Benz,53500,crossover,46,5.5,Petrol,yes,2008,G 500
+Renault,11300,van,183,2,Diesel,yes,2011,Trafic
+Volkswagen,13900,crossover,156,2,Diesel,yes,2012,T6 (Transporter) 
+Audi,14300,sedan,175,3,Petrol,yes,2006,A6
+Volkswagen,7500,van,280,1.6,Diesel,yes,2011,Caddy
+Volkswagen,55555,van,63,2,Diesel,yes,2013,Multivan
+Volkswagen,6999,hatch,195,1.4,Diesel,yes,2004,Golf V
+Mitsubishi,11499,van,98,2.4,Petrol,yes,2008,Grandis
+BMW,2850,sedan,214,1.9,Petrol,no,2000,318
+Renault,7150,van,118,1.5,Diesel,yes,2008,Kangoo
+Audi,25900,sedan,104,2.8,Petrol,yes,2010,A6
+Toyota,12500,hatch,75,1.4,Diesel,yes,2012,Yaris
+Mercedes-Benz,6200,van,196,2.2,Diesel,yes,2002,Vito
+Toyota,28700,crossover,92,3.5,Gas,yes,2012,Highlander
+Volkswagen,6000,van,419,2.4,Diesel,yes,1995,T4 (Transporter)
+Audi,44800,crossover,100,3,Diesel,yes,2014,Q7
+BMW,37000,crossover,186,5,Petrol,yes,2008,X6
+Audi,8000,sedan,234,2.4,Petrol,yes,1998,A6
+Volkswagen,21500,sedan,1,1.4,Petrol,yes,2015,Jetta
+BMW,4700,sedan,200,2.5,Petrol,yes,1995,525
+Mitsubishi,15500,crossover,102,3,Petrol,yes,2009,Outlander XL
+Renault,7900,van,149,1.5,Diesel,yes,2013,Kangoo
+Toyota,32000,sedan,22,2.5,Petrol,yes,2015,Camry
+BMW,26999,crossover,180,4.8,Gas,yes,2008,X5
+Toyota,7500,vagon,196,1.59,Petrol,yes,2003,Corolla Verso
+Mercedes-Benz,10999,van,191,2.2,Diesel,yes,2007,Vito
+Mitsubishi,7300,sedan,121,1.6,Gas,yes,2008,Lancer
+Mercedes-Benz,8000,van,280,2.2,Diesel,yes,2005,Vito
+Mitsubishi,17555,crossover,115,3,Gas,yes,2008,Pajero Wagon
+Volkswagen,38000,crossover,189,3,Diesel,yes,2012,Touareg
+BMW,15400,vagon,179,2,Diesel,yes,2007,520
+Volkswagen,10200,van,137,1.9,Diesel,yes,2008,Caddy
+Mercedes-Benz,7900,van,234,2.2,Diesel,yes,2002,Vito
+Mitsubishi,6700,sedan,161,1.6,Gas,yes,2006,Lancer
+Mercedes-Benz,30000,other,176,5.5,Petrol,yes,2003,E-Class
+Volkswagen,11000,vagon,210,1.9,Diesel,yes,2008,Touran
+Mercedes-Benz,38500,sedan,56,2.2,Diesel,yes,2014,CLA-Class
+Mercedes-Benz,7600,van,280,2.2,Diesel,yes,2001,Vito
+Volkswagen,8900,hatch,90,1.9,Diesel,yes,2009,Golf V
+Toyota,23400,crossover,120,4,Petrol,yes,2007,Land Cruiser Prado
+Audi,37000,sedan,37,2.8,Petrol,yes,2012,A6
+Renault,5600,vagon,151,1.5,Diesel,yes,2006,Kangoo
+Volkswagen,18900,sedan,75,1.8,Petrol,yes,2011,Passat B7
+Mercedes-Benz,15900,vagon,219,3,Diesel,yes,2007,E-Class
+Mercedes-Benz,15200,sedan,170,2.8,Gas,yes,2007,E-Class
+Volkswagen,20700,vagon,74,2,Diesel,yes,2013,Passat B7
+BMW,8200,sedan,210,3.5,Petrol,yes,1999,735
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLS 350
+Mitsubishi,5400,van,300,1.6,Petrol,yes,2002,Space Star
+BMW,NA,crossover,0,3,Diesel,yes,2016,X5
+Mitsubishi,11700,sedan,82,1.5,Gas,yes,2010,Lancer X
+Mitsubishi,12000,sedan,121,2.4,Gas,yes,2009,Galant
+Volkswagen,8100,sedan,198,1.6,Petrol,yes,2007,Jetta
+BMW,19999,sedan,290,4.8,Petrol,yes,2006,750
+Audi,32000,sedan,103,2,Diesel,yes,2013,A6
+Volkswagen,6200,van,340,1.8,Petrol,yes,1999,Sharan
+BMW,1500,sedan,270,2.5,Diesel,no,1992,525
+Volkswagen,17900,other,119,2,Diesel,yes,2012,T6 (Transporter)
+Mercedes-Benz,42500,sedan,140,5.5,Petrol,yes,2011,S 500
+BMW,4000,sedan,335,2,Diesel,no,1999,320
+Mitsubishi,13200,crossover,124,3,Gas,yes,2008,Pajero Sport
+Mercedes-Benz,24990,sedan,85,5.5,Petrol,yes,2008,S 500
+BMW,45200,crossover,68,3,Diesel,yes,2012,X5
+Toyota,18400,crossover,160,4.7,Gas,yes,2003,Land Cruiser 100
+Volkswagen,9500,van,212,1.9,Diesel,yes,2006,T5 (Transporter)
+Audi,25500,sedan,140,2.8,Petrol,yes,2012,A6
+Volkswagen,14999,crossover,200,4.2,Petrol,yes,2007,Touareg
+Volkswagen,10200,van,153,2.5,Diesel,yes,2007,T5 (Transporter)
+Mitsubishi,7900,sedan,238,2,Gas,yes,2008,Lancer X
+BMW,52222,hatch,3,2,Diesel,yes,2016,4 Series Gran Coupe
+Mitsubishi,9000,sedan,110,2,Petrol,yes,2008,Lancer X
+Audi,4200,vagon,215,2.5,Diesel,no,2003,A6
+Toyota,39500,other,100,4.5,Diesel,yes,2008,Land Cruiser 200
+BMW,9000,sedan,245,3,Diesel,yes,2001,730
+Mercedes-Benz,NA,crossover,35,3,Diesel,yes,2015,G 350
+Mercedes-Benz,12000,van,177,NA,Diesel,yes,2013,Vito
+Mercedes-Benz,23900,sedan,96,3.5,Petrol,yes,2009,S 350
+Mercedes-Benz,55000,crossover,128,3,Diesel,yes,2013,ML 350
+Mercedes-Benz,NA,crossover,0,3,Petrol,yes,2016,GLS 400
+Mercedes-Benz,11700,sedan,246,5,Petrol,yes,2004,S 500
+Mitsubishi,40990,crossover,0,2.4,Diesel,yes,2016,Pajero Sport
+BMW,42000,sedan,71,2.99,Petrol,yes,2011,535
+Renault,12100,crossover,138,2.5,Gas,yes,2008,Koleos
+Toyota,11400,sedan,118,1.4,Petrol,yes,2010,Corolla
+Audi,7000,vagon,320,1.9,Diesel,yes,1996,A4
+BMW,3500,hatch,1,2,Petrol,yes,2006,120
+Mitsubishi,23800,crossover,45,2.5,Diesel,yes,2015,Pajero Sport
+Mercedes-Benz,10550,other,128,NA,Other,yes,2011,Vito
+Mercedes-Benz,9500,other,220,2.2,Diesel,yes,2005,Vito
+BMW,37500,other,55,2,Petrol,yes,2014,428
+Renault,6000,van,1,1.9,Diesel,yes,2005,Trafic
+Mercedes-Benz,71999,crossover,0,2.2,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,10150,other,190,2.2,Diesel,yes,2008,Vito
+Audi,8300,sedan,186,2.5,Diesel,yes,1996,A6
+BMW,2600,sedan,270,2,Diesel,no,1998,320
+Mercedes-Benz,3500,sedan,250,2.7,Diesel,no,2001,E-Class
+Mercedes-Benz,29900,crossover,86,3,Gas,yes,2010,GLK 300
+Volkswagen,19999,sedan,107,1.8,Petrol,yes,2011,Passat B7
+Volkswagen,6000,van,180,2,Diesel,yes,2008,Caddy
+Mercedes-Benz,10900,van,360,2.2,Other,yes,2006,Vito
+Volkswagen,9999,van,165,1.6,Diesel,yes,2012,Caddy
+BMW,3700,other,110,2.5,Petrol,yes,1983,323
+Volkswagen,12400,other,145,3.2,Petrol,yes,2007,Passat B6
+Audi,3500,sedan,354,2.5,Diesel,no,2000,A6
+BMW,20900,crossover,230,3,Diesel,yes,2006,X5
+Audi,4150,sedan,220,2.5,Diesel,no,1999,A8
+Volkswagen,4100,van,345,1.9,Diesel,no,2002,Sharan
+Volkswagen,11500,sedan,70,NA,Other,yes,2014,Polo
+Mercedes-Benz,28500,sedan,86,5.5,Petrol,yes,2007,S 500
+Toyota,18900,crossover,90,2,Petrol,yes,2010,Rav 4
+Mercedes-Benz,12500,van,167,2.2,Diesel,yes,2013,Vito
+Mercedes-Benz,5500,sedan,199,NA,Petrol,yes,2000,E-Class
+Renault,2700,other,160,2,Petrol,no,2001,Megane
+Audi,10200,sedan,120,2,Petrol,yes,2006,A4
+BMW,6900,sedan,238,3.5,Petrol,yes,1999,535
+Mitsubishi,13200,crossover,79,2.4,Gas,yes,2009,Outlander
+Mercedes-Benz,124999,crossover,0,3,Diesel,yes,2016,GLS 350
+BMW,4100,sedan,200,2.7,Petrol,yes,1988,525
+Mercedes-Benz,3200,sedan,229,2.7,Diesel,no,2001,E-Class
+Toyota,13699,crossover,160,2,Petrol,yes,2007,Rav 4
+Mitsubishi,5700,sedan,176,1.6,Petrol,yes,2004,Lancer
+Mercedes-Benz,6500,other,215,2.2,Diesel,yes,2000,Vito
+Volkswagen,44000,crossover,150,4.2,Diesel,yes,2011,Touareg
+BMW,37000,sedan,130,3,Petrol,yes,2011,535
+BMW,29500,crossover,189,3,Diesel,yes,2009,X5
+Audi,NA,hatch,243,2,Gas,yes,1987,100
+Mercedes-Benz,99000,van,5,1.6,Diesel,yes,2013,Viano
+Audi,2999,vagon,320,NA,Diesel,no,1999,A6
+Volkswagen,4900,sedan,350,2,Gas,yes,2000,Bora
+Renault,5950,sedan,277,1.5,Diesel,yes,2005,Megane
+Mercedes-Benz,18300,sedan,155,3,Petrol,yes,2007,C-Class
+Mercedes-Benz,4700,sedan,280,2.8,Petrol,yes,1993,E-Class
+Toyota,37500,crossover,115,5.7,Petrol,yes,2008,Land Cruiser 200
+Volkswagen,15000,van,230,2.5,Diesel,yes,2007,T5 (Transporter)
+Audi,2800,sedan,330,NA,Other,yes,1989,80
+Mercedes-Benz,6900,sedan,350,3,Diesel,yes,1994,E-Class
+Mercedes-Benz,8300,van,230,2.2,Diesel,yes,2004,Vito
+Renault,3600,van,199,1.9,Diesel,yes,2000,Kangoo
+Audi,2500,vagon,280,2.5,Other,no,1999,A6
+Toyota,NA,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Volkswagen,40500,crossover,37,3,Diesel,yes,2012,Touareg
+Mercedes-Benz,85000,van,3,2.2,Diesel,yes,2016,V 250
+Audi,4800,sedan,341,2.8,Petrol,yes,1992,100
+Volkswagen,2500,sedan,440,1.6,Diesel,yes,1991,Jetta
+Mercedes-Benz,9500,vagon,180,3.2,Diesel,yes,2002,E-Class
+Toyota,12700,sedan,160,2.4,Gas,yes,2005,Camry
+Volkswagen,10799,van,128,1.9,Diesel,yes,2010,Caddy
+Audi,27300,other,150,4.2,Petrol,yes,2007,Q7
+Volkswagen,20500,hatch,800,2,Diesel,yes,2013,Golf VII
+Toyota,21999,other,65,2.5,Petrol,yes,2012,Camry
+Mercedes-Benz,14500,van,300,2.2,Diesel,yes,2005,Sprinter 313
+Audi,12500,sedan,146,1.8,Petrol,yes,2007,A4
+Renault,7200,van,123,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,8200,sedan,160,2,Gas,yes,2007,Lancer X
+BMW,NA,crossover,5,4.4,Petrol,yes,2015,X5 M
+Mercedes-Benz,23999,other,150,NA,Diesel,yes,2010,Vito
+Volkswagen,13200,van,150,1.4,Gas,yes,2012,Touran
+Audi,22500,sedan,124,3,Diesel,yes,2008,A8
+BMW,4000,sedan,230,2,Diesel,no,2003,320
+Mitsubishi,9200,other,0,NA,Other,yes,2007,Lancer X
+Toyota,11300,sedan,260,3.5,Gas,yes,2007,Camry
+Toyota,9300,sedan,185,1.6,Gas,yes,2008,Corolla
+Mitsubishi,20000,crossover,135,2.5,Diesel,yes,2011,Pajero Sport
+Volkswagen,17000,other,184,3.6,Petrol,yes,2008,Passat B6
+BMW,56500,crossover,45,3,Diesel,yes,2014,X6
+BMW,8700,sedan,355,3,Diesel,yes,2000,730
+BMW,4600,sedan,340,2,Gas,yes,1995,520
+BMW,5500,sedan,249,NA,Other,yes,1995,530
+Mercedes-Benz,35000,crossover,154,3.2,Diesel,yes,2008,GL 320
+Volkswagen,6900,sedan,256,1.8,Petrol,yes,2002,Passat B5
+Volkswagen,4200,van,210,2,Diesel,no,2004,Touran
+Renault,5650,hatch,175,99.99,Other,yes,2002,Laguna
+Mitsubishi,22500,other,57,2.5,Other,yes,2013,L 200
+Volkswagen,4500,van,380,2,Diesel,yes,2004,Caddy
+Volkswagen,4800,hatch,90,1.9,Diesel,yes,1992,Golf III
+Volkswagen,10900,sedan,136,1.8,Petrol,yes,2008,Passat B6
+Volkswagen,9900,other,178,2.5,Diesel,yes,2007,T5 (Transporter)
+Mercedes-Benz,NA,other,0,4.7,Petrol,yes,2016,S 500
+Mercedes-Benz,11900,sedan,110,2.57,Petrol,yes,2004,C-Class
+Mercedes-Benz,5200,sedan,370,2,Petrol,yes,1997,E-Class
+Volkswagen,17400,sedan,73,1.8,Petrol,yes,2012,Passat B7
+Renault,6799,van,140,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,NA,van,138,2.4,Petrol,yes,2007,Grandis
+Mercedes-Benz,2300,sedan,300,2,Petrol,yes,1991,E-Class
+Mercedes-Benz,32800,crossover,109,3,Diesel,yes,2011,ML 350
+Volkswagen,4999,sedan,145,2,Petrol,yes,2000,Jetta
+Volkswagen,10900,vagon,185,1.4,Petrol,yes,2011,Golf VI
+Mercedes-Benz,34999,other,85,5.5,Petrol,yes,2008,CL 55 AMG
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLE-Class
+Volkswagen,4100,hatch,160,1.8,Petrol,yes,2006,Pointer
+Toyota,9000,sedan,232,2.4,Petrol,yes,2005,Camry
+Toyota,18900,crossover,161,2.7,Gas,yes,2005,Land Cruiser Prado
+Volkswagen,16500,sedan,123,1.8,Petrol,yes,2010,Passat B6
+Toyota,14474,crossover,115,2,Petrol,yes,2007,Rav 4
+BMW,3999,sedan,250,3,Diesel,no,2000,530
+Audi,5000,sedan,370,2.3,Petrol,yes,1991,100
+Toyota,9200,sedan,250,2.4,Gas,yes,2003,Camry
+Renault,NA,van,195,1.5,Diesel,yes,2013,Grand Scenic
+Volkswagen,6000,vagon,250,5,Diesel,yes,1995,Passat B4
+Volkswagen,13300,sedan,133,1.8,Petrol,yes,2008,Passat B6
+Renault,17639.47,crossover,0,1.2,Petrol,yes,2016,Captur
+BMW,27500,sedan,100,2,Petrol,yes,2012,520
+BMW,13200,sedan,150,2.2,Petrol,yes,2004,520
+Audi,35500,crossover,130,3,Diesel,yes,2010,Q7
+Toyota,41900,crossover,89,4.5,Diesel,yes,2012,Land Cruiser 200
+Renault,10500,crossover,93,1.5,Diesel,yes,2011,Duster
+Mercedes-Benz,9999,sedan,250,2.2,Diesel,yes,2000,E-Class
+Mercedes-Benz,24800,other,160,3,Diesel,yes,2007,S 320
+Mercedes-Benz,3300,sedan,330,2,Gas,yes,1988,190
+Mitsubishi,6900,sedan,122,1.6,Petrol,yes,2008,Lancer
+Renault,7300,van,212,1.5,Diesel,yes,2009,Kangoo
+Renault,NA,van,192,99.99,Diesel,yes,2011,Kangoo
+Mitsubishi,11500,crossover,142,2.4,Petrol,yes,2006,Outlander
+Toyota,36800,crossover,77,3,Diesel,yes,2012,Land Cruiser Prado
+Mercedes-Benz,19600,van,127,1.8,Diesel,yes,2013,B 180
+Mitsubishi,8300,crossover,161,2,Petrol,yes,2005,Outlander
+Mercedes-Benz,13200,van,260,2.2,Diesel,yes,2008,Sprinter 213
+Mercedes-Benz,28900,other,78,3.5,Petrol,yes,2010,E-Class
+BMW,6900,sedan,280,2,Gas,yes,2000,320
+Renault,7600,sedan,97,1.6,Petrol,yes,2008,Megane
+BMW,27500,sedan,170,2,Diesel,yes,2011,520
+Mercedes-Benz,295000,sedan,29,6,Petrol,yes,2011,S-Guard
+BMW,30000,sedan,78,3.8,Petrol,yes,1993,M5
+Volkswagen,7700,sedan,180,1.8,Petrol,yes,2004,Passat B5
+Mercedes-Benz,135555,sedan,54,4.66,Petrol,yes,2015,S 500
+Mercedes-Benz,11300,other,220,3.2,Diesel,yes,2003,E-Class
+Toyota,12200,sedan,157,2.4,Gas,yes,2009,Camry
+Mercedes-Benz,50900,crossover,85,3,Diesel,yes,2013,ML 350
+Mercedes-Benz,33900,other,100,5.5,Petrol,yes,2007,CL 500
+Renault,11900,van,143,2,Diesel,yes,2009,Trafic
+Mitsubishi,7400,sedan,189,1.6,Petrol,yes,2006,Lancer
+BMW,10800,sedan,288,4.4,Gas,yes,2002,745
+Toyota,22700,crossover,128,4,Gas,yes,2007,Land Cruiser Prado
+Mercedes-Benz,15000,sedan,67,2.8,Other,yes,1995,E-Class
+Renault,8300,hatch,168,1.6,Petrol,yes,2009,Laguna
+Renault,8499,van,120,1.5,Diesel,yes,2014,Kangoo
+Volkswagen,8950,sedan,140,1.6,Petrol,yes,2012,Polo
+Volkswagen,7500,sedan,272,2.8,Gas,yes,2002,Passat B5
+Volkswagen,11350,van,190,75,Diesel,yes,2011,T5 (Transporter) 
+Volkswagen,6650,van,223,1.9,Diesel,yes,2007,Caddy
+Toyota,12800,sedan,203,NA,Petrol,yes,2005,Camry
+Audi,6500,sedan,365,4.2,Gas,yes,1995,A8
+Volkswagen,8700,van,158,2.5,Diesel,yes,2003,T5 (Transporter)
+Volkswagen,12000,sedan,152,1.8,Petrol,yes,2008,Passat B6
+Audi,16800,sedan,47,1.2,Petrol,yes,2013,A3
+Volkswagen,NA,vagon,160,1.9,Diesel,yes,2003,Passat B5
+Toyota,24000,sedan,16,2.5,Petrol,yes,2013,Camry
+Volkswagen,20900,sedan,82,2,Diesel,yes,2012,Passat B7
+Volkswagen,17500,sedan,76,1.8,Petrol,yes,2012,Passat B7
+Mitsubishi,8000,sedan,119,NA,Petrol,yes,2007,Lancer
+Mercedes-Benz,38200,sedan,39,1.6,Petrol,yes,2015,C-Class
+Mercedes-Benz,5400,other,300,3.2,Gas,yes,1997,E-Class
+Volkswagen,17800,sedan,180,3,Diesel,yes,2007,Touareg
+BMW,13000,sedan,90,5,Petrol,no,2008,M5
+Volkswagen,10950,van,177,2,Diesel,yes,2011,Caddy
+Mercedes-Benz,3800,crossover,250,3.2,Gas,no,1998,ML 320
+Mitsubishi,7300,sedan,137,1.6,Gas,yes,2006,Lancer
+Mercedes-Benz,19900,other,52,1.8,Petrol,yes,2007,SLK 200
+Mercedes-Benz,7300,vagon,380,2.9,Diesel,yes,1997,E-Class
+Audi,9800,vagon,253,1.9,Diesel,yes,2002,A6
+Renault,6450,van,186,1.5,Diesel,yes,2011,Kangoo
+Volkswagen,28500,other,38,2,Diesel,yes,2015,Tiguan
+Renault,10600,vagon,220,2.5,Other,yes,2006,Trafic
+Volkswagen,11900,sedan,144,1.6,Gas,yes,2009,Jetta
+Toyota,7900,sedan,170,3,Petrol,yes,2004,Camry
+Mercedes-Benz,43900,crossover,89,3,Diesel,yes,2012,ML 350
+Volkswagen,8800,van,145,2,Gas,yes,2011,Caddy
+Volkswagen,1899,hatch,300,1.6,Gas,yes,1984,Golf II
+Volkswagen,8400,sedan,230,1.8,Gas,yes,2003,Passat B5
+Volkswagen,25200,crossover,78,2,Diesel,yes,2012,Tiguan
+Renault,12499,van,78,2,Diesel,yes,2012,Trafic
+Volkswagen,5300,van,505,2.4,Diesel,yes,1995,T4 (Transporter)
+Volkswagen,8900,van,167,2,Gas,yes,2011,Caddy
+Mercedes-Benz,2500,vagon,122,2.3,Petrol,yes,1987,230
+Mercedes-Benz,NA,sedan,20,3,Diesel,yes,2015,S 350
+Volkswagen,15900,vagon,1,2,Diesel,yes,2012,T6 (Transporter) 
+BMW,20000,sedan,230,6,Petrol,yes,2008,760
+BMW,69999,crossover,0,2.5,Diesel,yes,2016,X5
+Volkswagen,10000,sedan,98,1.6,Petrol,yes,2013,Polo
+Mercedes-Benz,9500,other,650,2.2,Diesel,yes,2005,Sprinter 313
+Renault,12179,vagon,95,1.5,Diesel,yes,2012,Megane
+Toyota,20900,crossover,69,2.7,Petrol,yes,2008,Land Cruiser Prado
+BMW,7400,sedan,277,3.5,Gas,yes,1998,535
+Audi,18300,sedan,125,2,Diesel,yes,2009,A6
+Mercedes-Benz,2650,sedan,30,2.3,Gas,yes,1985,230
+BMW,2500,sedan,242,3,Diesel,no,2001,530
+Audi,52999,crossover,112,4.2,Diesel,yes,2008,Q7
+Audi,1750,vagon,330,1.9,Diesel,no,1996,A4
+Volkswagen,2200,hatch,260,1.8,Gas,yes,1987,Golf II
+Renault,6000,vagon,225,1.5,Diesel,yes,2004,Megane
+BMW,40000,crossover,48,4.4,Petrol,yes,2009,X6
+Mercedes-Benz,103333,crossover,0,3,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,14800,sedan,218,3.2,Diesel,yes,2004,S 320
+BMW,28000,crossover,116,NA,Petrol,yes,2008,X5
+Mercedes-Benz,12900,hatch,130,2,Petrol,yes,2007,B 200
+Volkswagen,21900,crossover,136,3,Diesel,yes,2008,Touareg
+BMW,5300,sedan,204,1.8,Petrol,yes,1993,318
+Mitsubishi,1599,sedan,156,1.8,Petrol,yes,1989,Galant
+BMW,2300,sedan,189,1.8,Petrol,no,2002,318
+Renault,7200,sedan,69,1.4,Petrol,yes,2012,Symbol
+BMW,104999,crossover,2,3,Diesel,yes,2016,X5
+Mercedes-Benz,199999,crossover,0,5.5,Petrol,yes,2016,GLS 63
+Mitsubishi,11300,crossover,209,3.5,Petrol,yes,1999,Pajero Wagon
+Toyota,12800,sedan,123,3.5,Petrol,yes,2007,Camry
+Audi,46000,sedan,170,4.2,Diesel,yes,2011,A8
+BMW,2500,sedan,290,2,Diesel,no,1999,320
+Mercedes-Benz,93555,crossover,0,NA,Other,yes,2016,GLS 350
+Volkswagen,22500,other,200,2,Petrol,yes,2011,Scirocco
+Toyota,36700,crossover,43,3,Diesel,yes,2012,Land Cruiser Prado
+BMW,25500,crossover,130,3,Gas,yes,2007,X5
+Volkswagen,9300,van,220,2.5,Diesel,yes,2003,T4 (Transporter)
+Mercedes-Benz,11299,van,1,2.2,Other,yes,2006,Vito
+Toyota,12900,hatch,37,1.4,Petrol,yes,2013,Auris
+Toyota,23600,sedan,105,2.5,Gas,yes,2012,Camry
+Toyota,21900,crossover,110,4,Gas,yes,2006,FJ Cruiser
+Renault,4350,van,198,1.5,Diesel,yes,2004,Kangoo
+Audi,3100,vagon,210,2.5,Diesel,no,2000,A6
+BMW,7400,sedan,290,3,Petrol,yes,2000,330
+Volkswagen,10999,van,95,1.6,Diesel,yes,2012,Caddy
+Mitsubishi,18700,crossover,52,2,Petrol,yes,2013,Outlander XL
+Mercedes-Benz,18200,sedan,103,2,Gas,yes,2007,C-Class
+Mercedes-Benz,12500,sedan,230,5,Petrol,yes,2002,E-Class
+Mercedes-Benz,12900,hatch,150,2,Diesel,yes,2008,B 180
+Volkswagen,12500,sedan,161,2,Petrol,yes,2006,Passat B6
+Toyota,25500,sedan,58,2.5,Petrol,yes,2015,Camry
+Volkswagen,55555,van,50,2,Diesel,yes,2013,Multivan
+BMW,12799,sedan,181,2.2,Gas,yes,2004,520
+Volkswagen,6300,crossover,213,2.5,Diesel,no,2004,Touareg
+Volkswagen,8800,hatch,168,1.2,Diesel,yes,2013,Polo
+Volkswagen,30000,van,135,2,Diesel,yes,2011,Multivan
+Renault,5000,vagon,150,NA,Other,yes,2006,Kangoo
+Volkswagen,15880,vagon,139,1.4,Gas,yes,2011,Passat B7
+Audi,8150,sedan,162,2,Petrol,yes,2004,A4
+Renault,3400,hatch,77,1.8,Other,yes,1994,Laguna
+Volkswagen,6500,van,185,NA,Diesel,yes,2006,Caddy
+Toyota,13000,hatch,42,1.3,Petrol,yes,2013,Auris
+Volkswagen,51900,crossover,30,3,Diesel,yes,2015,Touareg
+Toyota,37700,crossover,62,3,Diesel,yes,2013,Land Cruiser Prado
+Audi,6700,sedan,470,2,Diesel,yes,1997,A6
+BMW,11700,sedan,270,3,Diesel,yes,2003,530
+Mercedes-Benz,13000,other,580,2.2,Diesel,yes,2005,Sprinter 313
+Mercedes-Benz,62999,crossover,0,2.2,Diesel,yes,2016,GLC-Class
+Renault,13321.33,crossover,0,1.5,Diesel,yes,2016,Sandero StepWay
+BMW,115800,crossover,4,3,Diesel,yes,2016,X5 M
+Mercedes-Benz,27300,crossover,185,3,Diesel,yes,2008,GL 320
+Toyota,64000,crossover,128,4.5,Diesel,yes,2013,Land Cruiser 200
+Volkswagen,10000,sedan,90,9,Petrol,no,2010,Passat B6
+BMW,129222,sedan,2,5,Petrol,yes,2016,750
+Mitsubishi,11900,crossover,129,2.5,Diesel,yes,2007,Pajero Sport
+BMW,47500,sedan,0,2,Diesel,yes,2016,320
+BMW,12800,sedan,140,2.2,Petrol,yes,2005,520
+Mercedes-Benz,3900,sedan,375,2,Petrol,yes,1990,190
+Volkswagen,12650,hatch,11,1,Petrol,yes,2014,Up
+Volkswagen,5600,vagon,235,1.4,Petrol,yes,2000,Golf IV
+Mercedes-Benz,19600,vagon,127,1.8,Diesel,yes,2013,B 180
+Toyota,28900,sedan,15,2.5,Petrol,yes,2015,Camry
+Volkswagen,10900,sedan,273,2,Petrol,yes,2006,Passat B6
+Volkswagen,11500,sedan,169,1.8,Petrol,yes,2008,Passat B6
+Mitsubishi,9950,crossover,275,3.5,Gas,yes,2000,Pajero Wagon
+Mercedes-Benz,23800,sedan,195,2.2,Diesel,yes,2010,E-Class
+Renault,8750,vagon,46,1.6,Petrol,yes,2012,Logan
+Toyota,19900,sedan,28,1.6,Petrol,yes,2014,Corolla
+Audi,6000,sedan,226,1.8,Petrol,yes,1995,A4
+Volkswagen,12900,sedan,136,1.6,Diesel,yes,2010,Passat B6
+BMW,8999,sedan,221,4.5,Petrol,yes,2002,745
+Volkswagen,8700,van,244,2.5,Diesel,yes,2001,T4 (Transporter)
+Toyota,19800,crossover,44,2,Petrol,yes,2013,Rav 4
+Volkswagen,9000,sedan,51,1.6,Petrol,yes,2013,Polo
+Volkswagen,21000,vagon,58,2,Diesel,yes,2013,Passat B7
+Audi,2100,vagon,275,2.5,Diesel,no,1998,A4
+Mercedes-Benz,5000,van,400,2.3,Diesel,yes,1998,Vito
+Toyota,9500,other,90,1.6,Other,yes,2008,Corolla
+Volkswagen,34500,van,167,2,Diesel,yes,2013,Multivan
+Mercedes-Benz,NA,other,22,3,Diesel,yes,2015,S 350
+Toyota,9800,hatch,98,1.6,Gas,yes,2008,Auris
+Audi,10399,sedan,293,1.8,Gas,yes,2004,A4
+Mercedes-Benz,42000,sedan,38,2.2,Diesel,yes,2014,CLA 220
+Audi,14500,hatch,60,1.2,Petrol,yes,2011,A1
+Toyota,13621.43,sedan,0,1.3,Petrol,yes,2015,Corolla
+BMW,3300,sedan,250,3,Diesel,no,2000,530
+Volkswagen,34899.14,vagon,0,2,Petrol,yes,2016,Passat B8
+Mercedes-Benz,4500,van,180,2.3,Diesel,yes,1998,Vito
+Volkswagen,1800,vagon,280,1.9,Diesel,yes,1996,Golf III
+Mitsubishi,12999,crossover,140,2.4,Gas,yes,2007,Outlander XL
+Volkswagen,8800,van,240,1.9,Diesel,yes,2003,T5 (Transporter)
+Audi,22000,crossover,152,4.2,Petrol,yes,2007,Q7
+Mercedes-Benz,NA,crossover,0,3,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,159999,other,0,3,Petrol,yes,2016,S 400
+Toyota,35555,crossover,40,3.5,Petrol,yes,2013,Highlander
+Volkswagen,2600,sedan,330,1.9,Petrol,no,1999,Passat B5
+Volkswagen,11818.95,sedan,1,1.6,Petrol,yes,2014,Polo
+Renault,8000,van,207,1.9,Diesel,yes,2003,Trafic
+Audi,52055.25,crossover,0,2,Diesel,yes,2016,Q5
+Audi,5300,other,182,2.5,Diesel,no,2002,A4
+Renault,1800,van,123,1.9,Diesel,yes,2004,Kangoo
+Volkswagen,8500,van,216,1.9,Diesel,yes,2005,Caddy
+Mercedes-Benz,99999,crossover,30,5,Petrol,yes,2013,G 500
+Toyota,23000,crossover,233,4,Gas,yes,2006,Land Cruiser Prado
+Audi,11900,sedan,131,2,Petrol,yes,2007,A4
+Mercedes-Benz,6999,sedan,200,2.6,Gas,yes,2001,C-Class
+Toyota,9999,sedan,345,3,Gas,yes,2005,Camry
+Audi,8999,vagon,200,2.7,Petrol,yes,2003,A6 Allroad
+Renault,10800,other,206,2,Diesel,yes,2008,Trafic
+Mercedes-Benz,7777,other,230,2.8,Petrol,yes,1980,E-Class
+Mercedes-Benz,11500,van,200,2.2,Diesel,yes,2010,Vito
+Mitsubishi,2300,sedan,420,1.8,Petrol,yes,1991,Galant
+Audi,2000,vagon,310,2.5,Other,no,1998,A6
+Volkswagen,28900,crossover,1,2,Petrol,yes,2016,Tiguan
+Mercedes-Benz,7700,sedan,285,3,Diesel,yes,1999,E-Class
+Renault,20996.24,crossover,0,1.5,Diesel,yes,2016,Captur
+Toyota,8600,sedan,126,1.6,Petrol,yes,2008,Corolla
+Toyota,11000,sedan,156,1.8,Petrol,yes,2008,Avensis
+Volkswagen,11200,sedan,260,3.2,Petrol,yes,2006,Passat B6
+BMW,5000,sedan,270,3,Diesel,yes,2000,530
+BMW,14900,vagon,221,2,Diesel,yes,2008,520
+Mercedes-Benz,9500,other,280,2.9,Diesel,yes,1998,Sprinter 312
+Volkswagen,7700,sedan,220,2.8,Petrol,yes,2004,Passat B5
+Toyota,14699,sedan,155,NA,Petrol,yes,2006,Camry
+Toyota,21900,sedan,88,2.5,Petrol,yes,2012,Camry
+BMW,77500,crossover,1,3,Petrol,yes,2016,X6
+Mercedes-Benz,8000,other,245,2.2,Diesel,yes,2004,Vito
+Mercedes-Benz,2199,van,396,2.2,Diesel,no,2000,Vito
+Mercedes-Benz,41900,sedan,95,6.3,Petrol,yes,2011,S 63 AMG
+Mitsubishi,9000,sedan,158,NA,Other,yes,2007,Lancer
+Mercedes-Benz,20300,sedan,198,1.8,Gas,yes,2011,E-Class
+Toyota,14300,sedan,71,2.4,Petrol,yes,2008,Camry
+Mercedes-Benz,83000,crossover,70,3,Diesel,yes,2013,GL 350
+BMW,6500,sedan,311,2,Gas,yes,1998,520
+Mercedes-Benz,8500,van,281,2.2,Diesel,yes,2004,Vito
+Renault,5400,vagon,181,1.5,Diesel,no,2010,Megane
+Toyota,16800,crossover,238,4,Gas,yes,2004,Land Cruiser Prado
+Toyota,22900,crossover,34,2,Petrol,yes,2012,Rav 4
+Mercedes-Benz,21300,crossover,143,3.2,Diesel,yes,2007,ML 320
+BMW,16999,other,90,2.5,Petrol,yes,2008,325
+Volkswagen,15700,sedan,110,1.8,Petrol,yes,2011,Passat B7
+Mercedes-Benz,5300,van,244,2.2,Diesel,yes,2000,Vito
+Mercedes-Benz,16500,sedan,280,3.7,Petrol,yes,2004,S 350
+BMW,8999,sedan,250,1.9,Petrol,yes,2001,316
+Toyota,14850,hatch,51,1.6,Petrol,yes,2013,Auris
+BMW,24100,vagon,112,3,Petrol,yes,2011,X1
+BMW,66500,crossover,1,2,Diesel,yes,2016,X5
+BMW,24500,crossover,195,4.8,Petrol,yes,2007,X5
+Toyota,23499,crossover,174,4,Gas,yes,2008,Land Cruiser Prado
+Volkswagen,14500,vagon,229,2,Diesel,yes,2009,Passat B6
+Renault,13300.95,vagon,0,1.5,Diesel,yes,2016,Logan
+Audi,1852.5,sedan,150,1.8,Petrol,yes,1985,80
+Mitsubishi,8777,sedan,180,20,Petrol,yes,2007,Lancer X
+BMW,8500,sedan,245,2.2,Petrol,yes,2002,523
+BMW,NA,crossover,4,4.4,Petrol,yes,2016,X5 M
+Mercedes-Benz,21500,sedan,146,1.8,Gas,yes,2012,E-Class
+Volkswagen,10000,sedan,174,2,Petrol,yes,2005,Passat B6
+Mercedes-Benz,4500,other,356,2.3,Petrol,yes,1991,230
+Volkswagen,21900,van,166,2,Diesel,yes,2011,T6 (Transporter)
+Mercedes-Benz,102800,van,0,2.2,Diesel,yes,2016,V 250
+Renault,7750,van,70,1.5,Diesel,yes,2014,Kangoo
+Volkswagen,9750,van,253,1.9,Diesel,yes,2010,Caddy
+Toyota,10500,sedan,82,NA,Petrol,yes,2010,Corolla
+Renault,3100,vagon,150,1.9,Diesel,no,2003,Laguna
+BMW,1900,sedan,200,1.8,Gas,no,1994,318
+BMW,14300,sedan,217,3,Diesel,yes,2005,530
+Renault,4300,van,1,1.5,Diesel,yes,2003,Kangoo
+Audi,17000,crossover,127,3,Diesel,no,2013,Q7
+Toyota,17999,crossover,35,2,Petrol,yes,2009,Rav 4
+Mercedes-Benz,99000,crossover,0,3,Diesel,yes,2016,GLS 350
+Audi,3100,hatch,114,1.6,Petrol,no,2001,A3
+Mitsubishi,6999,sedan,113,1.5,Petrol,yes,2007,Lancer X
+Toyota,13900,sedan,102,3.5,Gas,yes,2008,Camry
+Mercedes-Benz,68500,crossover,68,3,Diesel,yes,2014,GL 350
+Mercedes-Benz,11800,van,148,2.2,Diesel,yes,2010,Vito
+Mercedes-Benz,115555,crossover,39,3,Diesel,yes,2015,G 350
+Audi,21000,sedan,210,4.2,Petrol,yes,2009,A8
+Mercedes-Benz,3150,vagon,220,1.7,Other,no,2001,A 170
+Volkswagen,NA,van,158,1.6,Diesel,yes,2012,Caddy
+Audi,1950,vagon,300,1.8,Petrol,no,1999,A4
+Mitsubishi,12500,crossover,120,2.5,Diesel,yes,2007,Pajero Sport
+Mitsubishi,5999,van,258,NA,Diesel,yes,1999,L 400
+Volkswagen,3100,sedan,232,1.9,Diesel,no,2001,Passat B5
+Toyota,19800,crossover,252,4.2,Diesel,yes,2002,Land Cruiser 100
+Audi,4200,sedan,236,2.5,Diesel,no,2003,A6
+Audi,2100,vagon,230,1.9,Diesel,no,2000,A4
+BMW,32500,vagon,162,3,Diesel,yes,2012,530
+Audi,15500,vagon,180,2,Diesel,yes,2007,A6
+Mercedes-Benz,11500,sedan,224,2.6,Gas,yes,2003,E-Class
+Audi,3000,sedan,265,1.8,Petrol,yes,1988,80
+BMW,4550,sedan,200,2.8,Petrol,no,1997,528
+BMW,18777,sedan,134,4.4,Petrol,yes,2005,545
+Audi,19700,crossover,172,3.6,Petrol,yes,2007,Q7
+Renault,10950,vagon,180,1.5,Diesel,yes,2011,Megane
+Volkswagen,9600,other,128,1.9,Diesel,yes,2010,Caddy
+Renault,6700,hatch,96,1.4,Petrol,yes,2010,Sandero
+Audi,2411,sedan,240,1.6,Other,yes,1987,80
+BMW,23500,crossover,74,2,Petrol,yes,2010,X1
+Volkswagen,9000,sedan,59,2,Petrol,no,2010,Passat B6
+Volkswagen,38764,sedan,0,2,Diesel,yes,2014,Passat CC
+Volkswagen,5500,van,380,2.4,Diesel,yes,1999,T4 (Transporter) 
+Mercedes-Benz,11800,sedan,150,2.2,Diesel,yes,2004,E-Class
+Toyota,52500,crossover,43,3,Other,yes,2014,Land Cruiser Prado
+Audi,31500,sedan,80,2,Petrol,yes,2013,A6
+Volkswagen,22800,other,161,2,Diesel,yes,2011,Amarok
+BMW,21000,other,130,2.5,Petrol,yes,2006,325
+Mercedes-Benz,9200,sedan,172,2.8,Petrol,yes,1997,E-Class
+Mercedes-Benz,9900,sedan,320,2.7,Diesel,yes,2001,E-Class
+Volkswagen,1500,sedan,265,1.8,Petrol,yes,1988,Jetta
+BMW,2850,sedan,386,5,Gas,yes,1988,750
+Volkswagen,19999,other,48,2,Diesel,yes,2012,Eos
+BMW,7000,sedan,338,NA,Petrol,yes,1997,728
+Volkswagen,11250,other,159,1.4,Petrol,yes,2010,Golf VI
+Mitsubishi,46000,crossover,0,2.4,Diesel,yes,2016,Pajero Sport
+Volkswagen,18300,van,1,2.2,Diesel,yes,2013,T5 (Transporter) 
+BMW,42900,sedan,2,2,Petrol,yes,2016,520
+BMW,18400,crossover,256,3,Diesel,yes,2006,X5
+BMW,12900,sedan,121,2,Petrol,yes,2007,318
+Toyota,37600,crossover,126,4.7,Petrol,yes,2008,Land Cruiser 200
+BMW,46000,sedan,5,2,Petrol,yes,2016,520
+Volkswagen,3200,other,650,1.9,Diesel,yes,1999,Caddy
+BMW,6500,vagon,377,2,Gas,yes,1998,520
+Volkswagen,55555,van,53,2,Diesel,yes,2013,Multivan
+Mitsubishi,6900,sedan,190,1.6,Gas,yes,2007,Lancer
+Mercedes-Benz,104000,crossover,16,5.5,Petrol,yes,2015,ML 63 AMG
+Mitsubishi,5200,van,200,1.6,Petrol,yes,2003,Space Star
+Mitsubishi,8400,sedan,147,2,Petrol,yes,2007,Lancer
+Volkswagen,1450,hatch,200,1.8,Gas,no,1992,Golf III
+Volkswagen,13700,other,103,2,Diesel,yes,2013,T5 (Transporter) 
+Toyota,18950,sedan,45,1.8,Petrol,yes,2014,Avensis
+Mercedes-Benz,13000,other,247,2.2,Diesel,yes,2012,Sprinter 313
+Mitsubishi,19800,crossover,67,2,Gas,yes,2013,Outlander
+Mercedes-Benz,22500,sedan,213,2.2,Diesel,yes,2012,E-Class
+BMW,19500,sedan,190,6,Petrol,yes,2004,760
+Volkswagen,NA,other,67,1.8,Other,yes,2008,Passat B6
+Mercedes-Benz,6100,sedan,300,3.2,Diesel,no,2004,E-Class
+Volkswagen,19000,sedan,109,1.8,Petrol,yes,2012,Passat B7
+Mercedes-Benz,3799,van,350,2.2,Diesel,yes,2000,Vito
+BMW,8900,sedan,250,2.5,Diesel,yes,2001,525
+Volkswagen,8000,van,350,1.9,Diesel,yes,2004,T5 (Transporter)
+BMW,4500,sedan,300,3,Diesel,yes,2000,330
+Volkswagen,2600,van,10,2.4,Diesel,yes,1988,LT
+Mercedes-Benz,31669,hatch,2,NA,Other,yes,2015,B-Class Electric Drive
+BMW,4300,sedan,240,2.5,Diesel,no,2002,525
+Mercedes-Benz,82555,crossover,69,3,Diesel,yes,2014,GL 350
+BMW,31000,vagon,15,NA,Other,yes,2015,I3
+Renault,10000,other,214,2.5,Diesel,yes,2008,Master
+Audi,56000,crossover,44,3,Diesel,yes,2014,Q7
+BMW,21700,sedan,259,3,Diesel,yes,2007,730
+BMW,89999,crossover,17,4.4,Petrol,yes,2015,X6 M
+Mitsubishi,9200,sedan,92,2,Petrol,yes,2010,Lancer X
+Audi,21700,sedan,167,3,Petrol,yes,2009,A6
+Mercedes-Benz,27300,crossover,149,5.46,Petrol,yes,2009,ML 550
+Volkswagen,15200,van,132,2.5,Diesel,yes,2009,T5 (Transporter)
+Volkswagen,9800,van,86,1.2,Petrol,yes,2013,Caddy
+Mercedes-Benz,14500,vagon,178,3,Diesel,yes,2006,E-Class
+Renault,7800,vagon,120,1.5,Diesel,yes,2008,Megane
+Mercedes-Benz,15250,sedan,210,2.2,Diesel,yes,2006,E-Class
+Audi,13500,sedan,196,2.4,Petrol,yes,2005,A6
+Audi,23333,other,150,4.2,Petrol,yes,2007,Q7
+Mercedes-Benz,18800,other,90,1.8,Petrol,yes,2008,CLC 180
+Mitsubishi,6700,sedan,193,1.6,Petrol,yes,2005,Lancer
+Toyota,8200,crossover,212,2,Gas,yes,2003,Rav 4
+Volkswagen,1650,sedan,210,1.8,Petrol,no,1998,Passat B4
+Audi,49500,crossover,44,3,Diesel,yes,2013,Q7
+Volkswagen,22900,sedan,48,2,Petrol,yes,2013,Jetta
+Volkswagen,10300,hatch,110,1.9,Diesel,yes,2008,Caddy
+Toyota,12500,sedan,175,2.4,Petrol,yes,2007,Camry
+Mitsubishi,6800,sedan,170,1.6,Petrol,yes,2004,Lancer
+BMW,33500,sedan,1,1.5,Petrol,yes,2015,318
+Toyota,20711.25,vagon,0,1.6,Petrol,yes,2016,Auris
+Volkswagen,1499,sedan,220,2,Gas,no,1995,Passat B4
+Volkswagen,18900,hatch,56,2,Petrol,yes,2012,Golf GTI
+BMW,67500,crossover,1,2,Diesel,yes,2016,X5
+BMW,52000,crossover,7,2,Diesel,yes,2015,X1
+Volkswagen,17777,van,250,2,Diesel,yes,2010,T5 (Transporter)
+Toyota,33333,vagon,73,4,Gas,yes,2009,FJ Cruiser
+Mercedes-Benz,3200,sedan,390,2.3,Gas,yes,1985,E-Class
+Audi,88888,crossover,0,3,Diesel,yes,2015,Q7
+Renault,7450,other,208,1.9,Other,yes,2004,Trafic
+Mercedes-Benz,31990,crossover,95,4.7,Petrol,yes,2010,GL 450
+BMW,14200,sedan,263,3,Petrol,yes,2004,530
+Renault,6300,vagon,202,1.5,Diesel,yes,2005,Scenic
+Renault,4350,van,188,1.5,Diesel,yes,2004,Kangoo
+Renault,11100,vagon,155,1.5,Diesel,yes,2012,Megane
+Volkswagen,6300,vagon,132,2.8,Gas,yes,1995,Passat B4
+Renault,3300,van,256,1.5,Diesel,yes,2005,Kangoo
+Mercedes-Benz,7450,sedan,300,2,Gas,yes,2000,C-Class
+Mitsubishi,1850,sedan,145,1.6,Petrol,no,1997,Carisma
+Mercedes-Benz,14000,other,360,2.2,Diesel,yes,2005,Sprinter 313
+Renault,12850,other,226,2.3,Diesel,yes,2012,Master
+Toyota,18300,hatch,30,1.6,Petrol,yes,2014,Auris
+Volkswagen,14700,sedan,97,1.4,Gas,yes,2012,Passat B7
+Volkswagen,3000,sedan,250,2.8,Gas,no,2002,Passat B5
+Mercedes-Benz,12999,sedan,247,4,Diesel,yes,2003,S 400
+Mercedes-Benz,11850,van,177,2.2,Diesel,yes,2011,Vito
+Volkswagen,19500,sedan,128,1.8,Petrol,yes,2013,Passat B7
+Mercedes-Benz,20200,sedan,69,2,Petrol,yes,2010,E-Class
+Renault,5899,vagon,189,NA,Diesel,no,2012,Megane
+Volkswagen,12000,sedan,186,3.2,Petrol,yes,2006,Passat B6
+Mercedes-Benz,17800,van,162,1.8,Diesel,yes,2012,B 180
+Toyota,16800,crossover,154,2.7,Petrol,yes,2005,Land Cruiser Prado
+Mercedes-Benz,10600,van,250,2.2,Diesel,yes,2006,Sprinter 313
+Mercedes-Benz,114999,crossover,0,3,Diesel,yes,2016,GLS 350
+Renault,4700,hatch,124,1.5,Diesel,yes,2003,Kangoo
+Mercedes-Benz,20000,crossover,251,5,Gas,yes,2002,ML 500
+Mercedes-Benz,104999,crossover,1,3,Diesel,yes,2016,GLE-Class
+Mercedes-Benz,9000,sedan,198,4,Diesel,yes,2002,S 400
+BMW,36300,crossover,83,3.5,Petrol,yes,2010,X6
+Toyota,19400,crossover,88,2,Petrol,yes,2011,Rav 4
+Mercedes-Benz,7700,sedan,230,2,Petrol,yes,2001,C-Class
+Mitsubishi,17500,other,69,2.5,Diesel,yes,2011,L 200
+BMW,31000,sedan,100,2,Petrol,yes,2012,520
+Toyota,33715.5,crossover,1,2.7,Petrol,yes,2015,Land Cruiser Prado
+BMW,13500,sedan,233,3,Diesel,yes,2003,530
+Renault,4900,van,150,2,Diesel,yes,2007,Trafic
+Mercedes-Benz,7700,sedan,340,2.2,Diesel,yes,2002,Vito
+Renault,10900,vagon,175,1.5,Diesel,yes,2012,Megane
+BMW,2200,sedan,303,2.5,Diesel,no,1999,525
+BMW,7200,sedan,270,3,Diesel,no,2004,530
+BMW,20500,other,114,NA,Other,yes,2008,325
+Toyota,9150,hatch,87,1.6,Petrol,yes,2008,Auris
+BMW,5900,sedan,10,2.8,Petrol,yes,1989,328
+Mitsubishi,4800,hatch,206,1.6,Gas,yes,2001,Carisma
+Volkswagen,1500,vagon,200,1.8,Gas,yes,1996,Passat B4
+Volkswagen,9500,hatch,62,1.2,Diesel,yes,2011,Polo
+Mercedes-Benz,3500,van,395,2.3,Diesel,no,1997,Vito
+Volkswagen,3650,sedan,300,1.8,Petrol,yes,1996,Vento
+Mercedes-Benz,63000,crossover,160,3,Diesel,yes,2013,GL 350
+Volkswagen,6600,van,240,1.9,Diesel,yes,2008,Caddy
+Toyota,36000,crossover,940,NA,Other,yes,2009,Land Cruiser 200
+Toyota,10200,sedan,185,1.8,Petrol,yes,2007,Avensis
+Volkswagen,19995,hatch,68,1.4,Petrol,yes,2013,Scirocco
+Mitsubishi,9800,sedan,56,1.5,Petrol,yes,2011,Lancer X
+Mercedes-Benz,11200,sedan,198,6,Petrol,yes,2004,S 600
+Volkswagen,18000,crossover,117,2.5,Diesel,yes,2006,Touareg
+Mitsubishi,28500,crossover,0,2,Petrol,yes,2016,Outlander
+Mitsubishi,9800,sedan,81,2,Petrol,yes,2009,Lancer X
+BMW,33000,sedan,60,NA,Petrol,yes,2012,328
+Audi,3900,sedan,247,2.5,Diesel,no,2003,A6
+Mitsubishi,22711.65,crossover,1,2,Petrol,yes,2016,Outlander
+BMW,6500,sedan,298,2.5,Petrol,yes,1998,523
+Volkswagen,4999,hatch,300,1.9,Diesel,yes,1999,Golf IV
+Renault,9100,vagon,176,1.5,Diesel,yes,2011,Scenic
+Mercedes-Benz,11000,sedan,319,2.2,Diesel,yes,2003,220
+Volkswagen,7200,hatch,159,1.6,Petrol,yes,2007,Golf V
+Mercedes-Benz,10900,van,191,2.2,Diesel,yes,2007,Vito
+Volkswagen,35000,sedan,74,4.2,Gas,yes,2011,Phaeton
+Mitsubishi,11500,sedan,134,2.4,Petrol,yes,2009,Galant
+Mercedes-Benz,9999,van,154,2.2,Diesel,yes,2007,Vito
+Audi,NA,sedan,300,2.5,Diesel,no,2001,A8
+Renault,4400,other,220,1.5,Diesel,yes,2004,Kangoo
+Mitsubishi,4000,hatch,367,1.6,Gas,yes,1997,Carisma
+Audi,29999,other,90,3,Petrol,yes,2011,A5
+Volkswagen,14800,van,65,1.6,Diesel,yes,2012,Caddy
+Mitsubishi,5500,crossover,30,2.5,Diesel,yes,2014,Pajero Sport
+BMW,39333,sedan,6,2,Petrol,yes,2016,520
+Mitsubishi,17000,crossover,215,3.8,Petrol,yes,2006,Pajero Wagon
+Toyota,20900,sedan,18,2.5,Other,yes,2013,Camry
+BMW,15000,sedan,137,2.2,Petrol,yes,2004,520
+Volkswagen,1850,other,200,1.6,Petrol,no,1999,Golf IV
+Renault,6900,hatch,47,1.4,Petrol,yes,2012,Sandero
+Volkswagen,11990,sedan,245,2,Diesel,yes,2007,Passat B6
+Mercedes-Benz,41000,van,55,3,Diesel,yes,2011,Viano
+BMW,42000,sedan,5,2,Petrol,yes,2016,520
+Renault,7700,van,128,1.5,Diesel,yes,2012,Kangoo
+Volkswagen,7750,van,265,1.9,Diesel,yes,2002,T4 (Transporter)
+Toyota,10100,other,83,1.6,Other,yes,2008,Corolla
+Renault,7500,van,128,1.5,Diesel,yes,2009,Kangoo
+BMW,2700,sedan,220,2.5,Petrol,no,2002,525
+Mercedes-Benz,74900,other,32,6.3,Petrol,yes,2007,CL 63 AMG
+Renault,800,van,200,1.9,Diesel,no,1997,Scenic
+Mitsubishi,17900,crossover,89,3.8,Gas,yes,2008,Pajero Wagon
+Audi,29000,vagon,213,3,Diesel,yes,2010,A6 Allroad
+BMW,17100,crossover,188,3,Diesel,yes,2004,X5
+Volkswagen,33500,crossover,98,NA,Petrol,yes,2013,Touareg
+Mercedes-Benz,1600,sedan,338,3,Diesel,no,1991,E-Class
+Mercedes-Benz,11777,other,300,4.3,Gas,yes,2001,S 430
+BMW,6699,sedan,299,2.2,Petrol,yes,2001,523
+BMW,3900,vagon,240,3,Diesel,no,2001,530
+Volkswagen,6500,sedan,212,1.6,Petrol,yes,2003,Bora
+BMW,51500,hatch,77,3,Petrol,yes,2012,535
+Renault,6500,vagon,186,1.5,Diesel,yes,2004,Megane
+BMW,10800,other,191,2.2,Petrol,yes,2002,320
+Audi,2450,vagon,240,2.5,Diesel,no,2000,A4
+Volkswagen,NA,van,207,2,Diesel,yes,2012,T5 (Transporter) 
+Toyota,28800,crossover,29,2.2,Diesel,yes,2015,Rav 4
+Volkswagen,6900,crossover,258,2.5,Diesel,no,2004,Touareg
+Toyota,47700,crossover,810,4.7,Gas,yes,2010,Land Cruiser 200
+Volkswagen,12700,van,75,1.2,Petrol,yes,2012,Caddy
+Toyota,7900,hatch,56,1,Gas,yes,2011,Aygo
+BMW,18000,sedan,180,2,Other,no,2011,520
+BMW,8899,sedan,170,2.2,Petrol,yes,2001,520
+Mercedes-Benz,57000,other,0,1.6,Petrol,yes,2016,C-Class
+Mercedes-Benz,2100,sedan,535,2.2,Diesel,yes,1995,210
+Audi,10900,sedan,199,3,Petrol,yes,2004,A8
+Toyota,42500,crossover,50,3,Diesel,yes,2012,Land Cruiser Prado
+BMW,51900,crossover,106,3,Diesel,yes,2012,X6
+Renault,3300,van,345,1.9,Diesel,yes,2001,Kangoo
+Mercedes-Benz,187555,other,0,4.7,Petrol,yes,2016,S 500
+Mercedes-Benz,16555,sedan,159,3.5,Gas,yes,2006,CLS 350
+Mercedes-Benz,7000,van,197,2.2,Diesel,yes,2002,Vito
+Mercedes-Benz,9300,sedan,342,2.6,Petrol,yes,2003,E-Class
+Audi,11500,sedan,85,1.8,Petrol,yes,2007,A4
+Mercedes-Benz,8000,crossover,314,99.99,Gas,yes,2000,ML 320
+Toyota,16672.5,hatch,1,1.6,Petrol,yes,2016,Auris
+Volkswagen,37990,crossover,140,3,Diesel,yes,2012,Touareg
+Mercedes-Benz,2400,sedan,1,2,Petrol,no,2001,C-Class
+Mercedes-Benz,219900,crossover,33,5.5,Petrol,yes,2013,G 63 AMG
+Renault,7150,van,142,1.5,Diesel,yes,2012,Kangoo
+Toyota,5000,crossover,289,2,Diesel,no,2003,Rav 4
+Mercedes-Benz,7700,sedan,390,3,Diesel,no,2007,E-Class
+Mercedes-Benz,28500,crossover,190,3.2,Diesel,yes,2008,ML 320
+Toyota,61000,crossover,100,4.5,Diesel,yes,2013,Land Cruiser 200
+Toyota,14700,vagon,98,2,Gas,yes,2008,Rav 4
+Toyota,NA,hatch,28,1.6,Petrol,yes,2014,Auris
+Volkswagen,21900,sedan,107,3,Diesel,yes,2009,Phaeton
+Audi,53700,crossover,67,3,Petrol,yes,2013,A6 Allroad
+Volkswagen,19200,crossover,155,2.5,Diesel,yes,2008,Touareg
+Audi,18700,sedan,136,2,Diesel,yes,2009,A6
+Audi,9600,vagon,230,2.5,Diesel,yes,2004,A6
+Volkswagen,10500,van,312,2.5,Diesel,yes,1998,Multivan
+BMW,77777,sedan,8,4.4,Petrol,yes,2014,750
+Volkswagen,18200,crossover,179,2.5,Diesel,yes,2007,Touareg
+Audi,52000,other,124,3,Diesel,yes,2012,A8
+BMW,13700,sedan,122,2,Petrol,yes,2007,318
+BMW,15000,vagon,245,3,Gas,yes,2006,530
+Mitsubishi,6400,sedan,127,1.6,Petrol,yes,2006,Lancer
+Toyota,54000,crossover,147,4.5,Diesel,yes,2011,Land Cruiser 200
+Toyota,23700,sedan,100,2.5,Gas,yes,2014,Camry
+Volkswagen,13950,hatch,104,1.2,Petrol,yes,2012,Golf Plus
+Toyota,23200,crossover,160,4,Petrol,yes,2008,Land Cruiser Prado
+Audi,2750,sedan,450,2.5,Diesel,no,1999,A6
+Audi,89999,crossover,0,3,Diesel,yes,2015,Q7
+BMW,NA,sedan,65,2,Petrol,yes,2012,320
+BMW,21700,crossover,125,3,Petrol,yes,2007,X5
+Mercedes-Benz,NA,van,12,2.2,Diesel,yes,2015,V 250
+Toyota,16500,crossover,328,4.2,Diesel,yes,1993,Land Cruiser 80
+BMW,15000,crossover,85,4.8,Petrol,no,2010,X5
+Mercedes-Benz,61500,crossover,16,3,Diesel,yes,2013,ML 350
+BMW,1700,sedan,200,2,Diesel,no,2003,320
+BMW,8500,sedan,352,2.17,Petrol,yes,2001,520
+Toyota,9999,sedan,250,2.4,Petrol,yes,2003,Camry
+Audi,3300,vagon,300,2.5,Diesel,no,1999,A6
+Volkswagen,19900,other,112,2,Petrol,yes,2012,Passat CC
+Renault,11300,vagon,160,1.5,Diesel,yes,2011,Laguna
+Toyota,14900,sedan,149,3.5,Gas,yes,2008,Camry
+Toyota,39000,crossover,88,4.5,Diesel,yes,2009,Land Cruiser 200
+Mercedes-Benz,69900,sedan,76,6,Petrol,yes,2010,S 500
+BMW,72900,crossover,16,3,Diesel,yes,2015,X6
+Renault,10500,vagon,185,1.5,Diesel,yes,2011,Megane
+Mercedes-Benz,NA,sedan,23,5.5,Petrol,yes,2011,S 550
+Renault,13900,vagon,89,1.5,Diesel,yes,2013,Megane
+Renault,6800,van,131,1.5,Diesel,yes,2013,Kangoo
+Mercedes-Benz,8500,van,270,2.2,Diesel,yes,2004,Vito
+Volkswagen,7600,van,158,2,Diesel,yes,2009,Caddy
+Toyota,30950,other,45,4.6,Petrol,yes,2013,Tundra
+Audi,3990,sedan,413,2.6,Gas,yes,1993,100
+Audi,68500,sedan,44,6.3,Petrol,yes,2012,A8
+Audi,33500,sedan,42,2,Petrol,yes,2013,A5
+BMW,11500,sedan,193,4.4,Petrol,yes,2000,740
+Toyota,24999,crossover,153,4,Petrol,yes,2007,Land Cruiser Prado
+Audi,1850,sedan,250,2.5,Diesel,no,1999,A4
+Mercedes-Benz,29500,crossover,55,2.2,Diesel,no,2013,ML 250
+Mercedes-Benz,57000,sedan,43,4.7,Petrol,yes,2012,CLS 500
+Audi,30900,hatch,195,3,Diesel,yes,2010,A5
+Toyota,7000,crossover,150,2,Petrol,yes,2000,Rav 4
+Volkswagen,8950,van,170,1.6,Diesel,yes,2013,Caddy
+Mitsubishi,2500,crossover,1,2.5,Diesel,yes,2007,L 200
+Renault,8350,other,141,1.5,Other,yes,2012,Megane
+BMW,11400,sedan,206,3,Petrol,yes,2003,530
+Renault,6200,van,322,NA,Diesel,yes,2001,Trafic
+Volkswagen,2600,sedan,280,2.5,Diesel,no,2000,Passat B5
+Mercedes-Benz,7450,sedan,219,2,Petrol,yes,2000,C-Class
+Audi,9499,sedan,255,2.5,Diesel,yes,2002,A6
+Toyota,14900,sedan,106,2.4,Petrol,yes,2008,Camry
+Volkswagen,8200,van,265,2.5,Diesel,no,2007,Caravelle
+Mercedes-Benz,12500,sedan,208,3.2,Diesel,yes,2004,S 320
+Toyota,14500,sedan,127,2.4,Gas,yes,2008,Camry
+BMW,41000,crossover,80,3,Petrol,yes,2012,X6
+Volkswagen,12000,sedan,73,1.6,Gas,yes,2011,Polo
+Mercedes-Benz,5600,sedan,230,2.7,Diesel,no,2005,E-Class
+Mercedes-Benz,5200,other,220,3.2,Petrol,no,2004,CLK 320
+BMW,2999,sedan,299,NA,Diesel,no,2000,530
+Mitsubishi,9200,sedan,48,1.5,Petrol,yes,2008,Lancer X
+Toyota,25999,other,108,4,Petrol,yes,2008,Land Cruiser Prado
+BMW,6300,sedan,241,2.8,Gas,yes,1995,328
+Mercedes-Benz,116500,sedan,25,3,Diesel,yes,2015,S 350
+BMW,15500,sedan,216,2,Diesel,yes,2008,320
+Toyota,17500,crossover,168,4,Gas,yes,2004,Land Cruiser Prado
+Volkswagen,9999,sedan,182,1.6,Petrol,yes,2006,Jetta
+Toyota,NA,sedan,15,2.5,Petrol,yes,2015,Camry
+BMW,34600,crossover,163,5,Petrol,yes,2011,X5 M
+Renault,1185.6,hatch,160,2,Petrol,yes,1987,25
+Mercedes-Benz,20500,sedan,222,5.5,Petrol,yes,2006,S 500
+Renault,10900,van,138,2,Diesel,yes,2012,Trafic
+Volkswagen,7800,hatch,230,1.6,Petrol,yes,2004,Golf V
+Audi,3900,vagon,232,2.5,Diesel,no,2003,A6
+Toyota,15500,crossover,120,2,Petrol,yes,2008,Rav 4
+Audi,48000,crossover,126,3,Diesel,yes,2013,Q7
+Mercedes-Benz,12800,van,193,2.2,Other,yes,2011,Vito
+Toyota,14900,crossover,288,4.7,Gas,yes,2000,Land Cruiser 100
+Mercedes-Benz,1990,hatch,230,1.7,Other,no,2001,A 170
+Mitsubishi,9800,sedan,148,2,Gas,yes,2008,Lancer X
+BMW,2200,sedan,460,2.4,Diesel,yes,1988,324
+Mercedes-Benz,8700,sedan,290,2.7,Diesel,yes,1999,E-Class
+Renault,3450,van,196,1.9,Diesel,yes,2001,Kangoo
+Mercedes-Benz,22000,van,236,2.2,Diesel,yes,2012,Sprinter 313
+Mercedes-Benz,6500,other,315,2,Petrol,yes,1996,E-Class
+Mitsubishi,12999,crossover,111,3,Gas,yes,2008,Outlander XL
+Volkswagen,21500,sedan,2,1.6,Diesel,yes,2016,Jetta
+BMW,2750,vagon,254,2.8,Gas,no,1997,528
+Mitsubishi,23000,crossover,125,3,Gas,yes,2008,Pajero Wagon
+Volkswagen,NA,van,165,2,Diesel,yes,2013,T5 (Transporter) 
+BMW,8200,sedan,368,3,Diesel,yes,2000,730
+BMW,32900,crossover,90,3,Petrol,yes,2008,X6
+Mercedes-Benz,13600,sedan,115,NA,Petrol,yes,2007,C-Class
+Toyota,22000,sedan,73,3.5,Petrol,yes,2010,Camry
+Mitsubishi,2800,other,290,2,Petrol,yes,1990,Galant
+Mercedes-Benz,12999,van,130,2.2,Diesel,yes,2012,Vito
+Toyota,32000,sedan,22,2.5,Petrol,yes,2015,Camry
+Toyota,99999,crossover,0,4.5,Petrol,yes,2016,Land Cruiser 200
+Renault,6950,van,310,2.5,Diesel,yes,2005,Master
+Renault,10500,hatch,148,1.5,Diesel,yes,2012,Megane
+BMW,6899,vagon,320,2.8,Gas,yes,1997,528
+Mercedes-Benz,6200,sedan,450,4.2,Gas,yes,1997,S 420
+Toyota,65000,other,60,4.5,Diesel,yes,2013,Land Cruiser 200
+Mercedes-Benz,13900,other,272,2.2,Diesel,yes,2005,Sprinter 313
+Renault,5800,van,217,2,Gas,yes,2003,Scenic
+BMW,28700,other,160,3,Petrol,yes,2011,528
+BMW,16700,crossover,260,4.4,Petrol,yes,2003,X5
+Volkswagen,18500,sedan,217,3,Diesel,yes,2006,Phaeton
+Renault,3999,other,227,1.4,Gas,yes,2005,Kangoo
+Renault,8800,van,200,1.9,Diesel,yes,2005,Trafic
+Mercedes-Benz,14500,van,205,2.2,Diesel,yes,2012,Sprinter 313
+BMW,8900,sedan,240,2,Diesel,yes,2001,320
+Volkswagen,1300,other,10,1.6,Diesel,yes,1984,T2 (Transporter)
+BMW,NA,crossover,36,3,Diesel,yes,2013,X5
+Renault,8550,van,160,1.5,Diesel,yes,2014,Kangoo
+Mercedes-Benz,10900,sedan,160,3,Diesel,yes,1996,S 140
+BMW,48535.5,crossover,0,2,Diesel,yes,2016,X3
+BMW,25000,crossover,130,3,Diesel,yes,2012,X5
+BMW,NA,crossover,1,3,Diesel,yes,2016,X6
+Mitsubishi,7000,sedan,99,1.6,Petrol,yes,2005,Lancer
+BMW,5500,sedan,402,2,Petrol,yes,1988,520
+Mercedes-Benz,2800,other,220,2.3,Petrol,no,2000,CLK 230
+Mercedes-Benz,86000,crossover,3,3,Diesel,yes,2016,GLS 350
+Volkswagen,9555,van,152,1.6,Diesel,yes,2012,Caddy
+Renault,7200,van,1,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,7700,van,300,2.2,Other,yes,2004,Vito
+BMW,75000,crossover,20,3,Diesel,yes,2014,X5
+Volkswagen,8650,hatch,150,1.4,Petrol,yes,2008,Golf V
+Toyota,10700,sedan,109,1.8,Petrol,yes,2007,Avensis
+Volkswagen,6300,van,1,2.5,Diesel,yes,2000,T4 (Transporter)
+Toyota,13900,sedan,102,3.5,Gas,yes,2008,Camry
+Audi,3150,vagon,310,2.5,Diesel,no,1999,A6
+Volkswagen,7500,other,360,2.5,Diesel,yes,2001,T4 (Transporter)
+Volkswagen,6550,sedan,150,1.6,Petrol,yes,2001,Bora
+BMW,7100,sedan,313,2,Gas,yes,1997,520
+Volkswagen,3700,sedan,370,2,Gas,yes,1992,Passat B3
+Toyota,20999,sedan,77,2.5,Gas,yes,2012,Camry
+Renault,6750,vagon,1,1.5,Diesel,yes,2004,Scenic
+Mercedes-Benz,8400,other,457,5,Gas,yes,1996,S 140
+Toyota,103999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,5200,sedan,432,3,Diesel,yes,1992,E-Class
+BMW,51000,sedan,97,3,Diesel,yes,2011,730
+Toyota,27400,crossover,52,3.5,Gas,yes,2012,Venza
+Toyota,12500,crossover,201,2,Petrol,yes,2006,Rav 4
+Mercedes-Benz,24800,vagon,118,3,Diesel,yes,2010,E-Class
+Volkswagen,10600,sedan,118,1.8,Petrol,yes,2008,Passat B6
+Mitsubishi,8900,other,150,3,Petrol,yes,2000,Eclipse
+Renault,7200,van,101,1.5,Diesel,yes,2012,Kangoo
+BMW,10000,sedan,274,4.4,Gas,yes,1998,540
+Toyota,11700,crossover,126,2.4,Gas,yes,2008,Rav 4
+BMW,64999,sedan,15,3,Petrol,yes,2012,640
+Toyota,28900,crossover,83,3.5,Gas,yes,2012,Highlander
+BMW,60500,crossover,1,2,Diesel,yes,2016,X5
+Renault,5500,sedan,153,1.6,Other,yes,2006,Megane
+BMW,85900,crossover,19,3,Diesel,yes,2015,X6
+BMW,49000,vagon,56,5,Petrol,yes,2007,M5
+Audi,22999,crossover,125,4.2,Petrol,yes,2008,Q7
+Mercedes-Benz,22900,sedan,197,2.2,Diesel,yes,2010,E-Class
+BMW,85555,sedan,10,3,Other,yes,2015,6 Series Gran Coupe
+Mercedes-Benz,30000,crossover,97,2.14,Diesel,yes,2011,ML 250
+BMW,2600,sedan,250,1.6,Petrol,yes,1996,316
+Volkswagen,12500,crossover,230,3.2,Gas,yes,2003,Touareg
+BMW,6800,sedan,4,2.8,Gas,yes,1995,528
+Mercedes-Benz,8700,other,280,NA,Diesel,yes,2001,Vito
+Audi,4000,sedan,315,2.3,Gas,yes,1991,100
+Audi,36500,crossover,159,3,Diesel,yes,2010,Q7
+Volkswagen,10000,van,231,1.9,Diesel,yes,2005,T5 (Transporter)
+Volkswagen,4700,hatch,160,2,Diesel,no,2006,Golf VI
+BMW,27900,crossover,140,3,Diesel,yes,2009,X5
+Renault,7440,other,148,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,17999,van,260,2.2,Diesel,yes,2013,Sprinter 316
+Volkswagen,7750,van,235,1.9,Diesel,yes,2004,T5 (Transporter)
+Mercedes-Benz,7200,van,285,2.2,Diesel,yes,2001,Vito
+Volkswagen,3700,hatch,121,1.9,Diesel,yes,1993,Golf III
+Volkswagen,12000,vagon,187,2,Diesel,yes,2008,Passat B6
+Audi,48000,crossover,75,3,Diesel,yes,2013,Q7
+BMW,49000,sedan,58,4.4,Other,yes,2011,750
+Mercedes-Benz,69500,crossover,84,3,Diesel,yes,2014,GL 350
+BMW,19999,crossover,227,3,Diesel,yes,2006,X5
+Volkswagen,30900,sedan,30,2,Diesel,yes,2014,Passat B7
+Mercedes-Benz,44000,other,39,5.5,Petrol,yes,2009,SL 55 AMG
+Mercedes-Benz,10200,sedan,174,2.6,Gas,yes,2001,E-Class
+Mercedes-Benz,22000,crossover,160,4,Diesel,yes,2008,ML 400
+Mercedes-Benz,46700,sedan,115,5.5,Petrol,yes,2011,S 500
+Mercedes-Benz,9999,sedan,322,2.7,Diesel,yes,2000,E-Class
+Volkswagen,4400,other,145,2.4,Diesel,yes,1993,T4 (Transporter)
+Mercedes-Benz,45000,sedan,1,1.6,Petrol,yes,2015,C-Class
+Toyota,63333,other,33,4.5,Diesel,yes,2013,Land Cruiser 200
+Volkswagen,12999,van,237,2.5,Diesel,yes,2005,T5 (Transporter)
+Mercedes-Benz,15000,other,115,2.2,Diesel,yes,2011,Vito
+BMW,24500,sedan,114,NA,Petrol,yes,2005,M5
+Volkswagen,1950,hatch,94,1.1,Petrol,yes,1982,Golf II
+BMW,58500,crossover,80,3,Diesel,yes,2013,X5
+Renault,6250,van,205,1.6,Petrol,yes,2007,Scenic
+Volkswagen,8900,sedan,160,1.6,Petrol,yes,2006,Jetta
+Mercedes-Benz,65000,other,0,1.6,Petrol,yes,2016,C-Class
+Volkswagen,15200,sedan,185,1.4,Petrol,yes,2011,Golf VI
+Mercedes-Benz,121500,sedan,22,3,Diesel,yes,2014,S 350
+Audi,30999,sedan,108,2,Diesel,yes,2013,A6
+BMW,22000,other,138,4.8,Petrol,yes,2007,650
+Mercedes-Benz,10999,other,185,NA,Diesel,yes,2005,Vito
+Mitsubishi,10500,crossover,95,3.2,Diesel,no,2008,Pajero Wagon
+BMW,3900,sedan,380,3,Gas,yes,1989,530
+Mitsubishi,9900,sedan,55,2,Other,yes,2008,Lancer X
+Mercedes-Benz,3799,sedan,210,2,Petrol,yes,1995,E-Class
+BMW,5000,sedan,305,2,Petrol,yes,1995,520
+Audi,5300,other,370,1.8,Petrol,yes,1997,A6
+Renault,6450,vagon,100,1.5,Diesel,yes,2009,Kangoo
+Toyota,44700,crossover,120,3.5,Petrol,yes,2013,Venza
+Toyota,17800,crossover,220,3,Diesel,yes,2004,Land Cruiser Prado
+BMW,26900,sedan,158,5,Petrol,yes,2007,M5
+Volkswagen,14999,vagon,128,1.4,Gas,yes,2011,Passat B7
+Mercedes-Benz,7650,sedan,340,5,Gas,yes,1997,S 140
+Volkswagen,12750,vagon,130,1.4,Gas,yes,2010,Passat B6
+Volkswagen,16000,hatch,26,1.2,Petrol,yes,2014,Polo
+Toyota,65000,crossover,90,4.5,Diesel,yes,2012,Land Cruiser 200
+Renault,11999,hatch,65,1.5,Diesel,yes,2014,Megane
+BMW,3000,sedan,294,2,Diesel,no,1999,320
+Volkswagen,17900,crossover,70,1.4,Petrol,yes,2012,Tiguan
+Volkswagen,8200,sedan,180,NA,Petrol,yes,2007,Jetta
+Mercedes-Benz,109000,crossover,0,3.5,Diesel,yes,2016,GLS 350
+Mercedes-Benz,4500,sedan,409,2.6,Petrol,yes,1991,E-Class
+Audi,19300,sedan,113,1.8,Petrol,yes,2009,A4
+Volkswagen,11300,van,250,2.5,Diesel,yes,2007,T5 (Transporter)
+Mitsubishi,18800,crossover,47,3,Petrol,yes,2008,Pajero Wagon
+Toyota,28900,crossover,83,NA,Petrol,yes,2012,Highlander
+Mercedes-Benz,4150,other,277,2.2,Diesel,yes,2000,Vito
+Volkswagen,14000,crossover,194,3.2,Gas,yes,2004,Touareg
+Mercedes-Benz,13995,van,126,2.2,Diesel,yes,2013,Vito
+Toyota,NA,crossover,1,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,6400,other,209,1.5,Diesel,yes,2005,Scenic
+Renault,4900,sedan,90,1.4,Gas,yes,2008,Symbol
+Audi,6400,sedan,261,1.8,Gas,yes,2000,A6
+Renault,9400,van,270,2,Diesel,yes,2007,Trafic
+Toyota,15400,sedan,152,3.5,Gas,yes,2007,Avalon
+Volkswagen,2900,sedan,238,2,Gas,no,2002,Passat B5
+Renault,9500,vagon,78,1.5,Diesel,yes,2014,Kangoo
+Volkswagen,23000,sedan,75,2,Petrol,yes,2013,Passat CC
+Toyota,10600,crossover,185,2,Gas,yes,2005,Rav 4
+Renault,NA,other,179,2.5,Diesel,yes,2011,Trafic
+Audi,2550,sedan,304,2.5,Diesel,no,1998,A6
+Volkswagen,8500,sedan,87,1.6,Petrol,yes,2011,Polo
+Audi,10500,vagon,243,2.5,Diesel,yes,2003,A6
+Mercedes-Benz,18000,sedan,99,1.8,Petrol,yes,2008,C-Class
+Mercedes-Benz,6100,sedan,280,NA,Gas,yes,1999,E-Class
+Mitsubishi,1550,hatch,1,1.8,Diesel,yes,1990,Galant
+BMW,3800,sedan,250,3,Diesel,no,2002,330
+BMW,42000,crossover,128,4.4,Petrol,yes,2011,X5 M
+Mitsubishi,8500,sedan,110,2,Petrol,yes,2008,Lancer X
+Toyota,24900,sedan,30,2.5,Petrol,yes,2014,Camry
+Volkswagen,10200,vagon,186,2,Diesel,yes,2005,Passat B6
+BMW,14200,sedan,135,3,Gas,yes,2004,530
+Volkswagen,22500,crossover,151,3,Diesel,yes,2009,Touareg
+Toyota,47500,crossover,45,3,Diesel,yes,2014,Land Cruiser Prado
+BMW,4500,vagon,360,2,Petrol,yes,1993,520
+BMW,8300,sedan,197,3,Petrol,yes,2001,530
+Toyota,14300,hatch,60,1.6,Petrol,yes,2013,Auris
+Renault,18456.16,crossover,1,1.5,Diesel,yes,2016,Duster
+Renault,6999,van,173,1.5,Diesel,yes,2006,Scenic
+Mitsubishi,7500,sedan,94,1.6,Petrol,yes,2007,Lancer
+Volkswagen,2850,sedan,350,2,Petrol,yes,1990,Passat B3
+Mercedes-Benz,5800,van,282,2.2,Diesel,yes,2001,Vito
+Toyota,3700,hatch,282,1.4,Petrol,yes,1995,Corolla
+Renault,6555,vagon,180,1.5,Diesel,yes,2005,Megane
+Volkswagen,23999,sedan,80,1.8,Petrol,yes,2012,Passat CC
+Mitsubishi,21500,crossover,47,2.4,Petrol,yes,2012,Outlander XL
+Volkswagen,5700,sedan,300,1.8,Petrol,yes,2001,Passat B5
+Mercedes-Benz,200000,van,19,3.5,Petrol,yes,2013,Sprinter 324
+Mercedes-Benz,NA,vagon,200,2.2,Diesel,yes,2006,Vito
+Mercedes-Benz,16800,van,116,NA,Other,yes,2013,Vito
+Volkswagen,4500,vagon,220,2,Diesel,no,2005,Touran
+BMW,6800,sedan,297,2.5,Petrol,yes,2000,525
+Toyota,22900,sedan,84,2.5,Petrol,yes,2012,Camry
+Volkswagen,8500,van,210,2.5,Diesel,yes,2003,T4 (Transporter)
+Renault,8700,van,183,1.9,Diesel,yes,2004,Trafic
+BMW,12200,sedan,239,3,Diesel,yes,2001,730
+BMW,23900,crossover,235,3,Diesel,yes,2007,X5
+Mitsubishi,16000,vagon,174,3.2,Diesel,yes,2006,Pajero Wagon
+Mitsubishi,7000,sedan,158,1.6,Petrol,yes,2007,Lancer
+Renault,9450,vagon,215,1.5,Diesel,yes,2008,Laguna
+Mercedes-Benz,41900,sedan,95,6.3,Other,yes,2011,S 550
+Mitsubishi,7400,sedan,148,1.6,Petrol,yes,2008,Lancer
+Renault,2350,vagon,245,1.9,Diesel,no,2003,Laguna
+Audi,3390,sedan,289,2.5,Diesel,no,2002,A6
+BMW,40755,sedan,0,2,Petrol,yes,2016,520
+Volkswagen,3300,vagon,430,1.8,Petrol,yes,1991,Passat B3
+Toyota,1100,van,140,2,Gas,yes,1985,Lite Ace
+Volkswagen,49500,crossover,80,3,Diesel,yes,2014,Touareg
+Renault,8700,hatch,100,1.5,Diesel,yes,2010,Megane
+Mercedes-Benz,20400,sedan,190,1.8,Gas,yes,2011,E-Class
+BMW,17700,sedan,98,2,Petrol,yes,2011,320
+BMW,17900,sedan,69,2,Petrol,yes,2008,320
+Mercedes-Benz,5500,sedan,269,2.2,Petrol,yes,1994,E-Class
+Mercedes-Benz,6200,sedan,221,1.8,Gas,yes,2004,E-Class
+Mercedes-Benz,3500,van,257,2.2,Diesel,no,2002,C-Class
+BMW,8100,vagon,180,2,Diesel,no,2011,118
+Toyota,2499,vagon,254,2,Diesel,no,2000,Avensis
+Audi,3150,sedan,10,2.8,Petrol,yes,1991,100
+Audi,4000,sedan,380,2.8,Petrol,yes,1997,A6
+Audi,4000,vagon,234,2.5,Diesel,no,2001,A6
+BMW,2050,vagon,300,2.5,Diesel,no,1997,525
+Mercedes-Benz,2600,hatch,178,1.7,Diesel,no,1999,A 170
+Mercedes-Benz,24500,sedan,222,5.5,Petrol,yes,2005,S 550
+BMW,14600,sedan,116,2.5,Diesel,yes,2004,525
+Mercedes-Benz,16900,van,180,3,Diesel,yes,2008,Vito
+Volkswagen,14500,crossover,173,2.5,Diesel,yes,2005,Touareg
+Volkswagen,38365,sedan,0,2,Diesel,yes,2016,Passat B8
+Mercedes-Benz,17800,van,162,1.8,Diesel,yes,2012,B 180
+Renault,5000,sedan,200,1.4,Petrol,yes,2007,Clio
+Toyota,42000,crossover,97,4,Petrol,yes,2010,4Runner
+BMW,35500,sedan,67,3,Petrol,yes,2011,535
+BMW,3000,sedan,190,2,Petrol,no,2000,520
+BMW,22600,crossover,69,3,Petrol,no,2013,X6
+Volkswagen,12900,other,142,2,Petrol,yes,2007,Eos
+Mercedes-Benz,7600,van,240,2.2,Diesel,yes,2005,Vito
+Toyota,37700,crossover,47,5.7,Gas,yes,2009,Sequoia
+Toyota,8300,sedan,168,2,Petrol,yes,2004,Avensis
+BMW,65500,crossover,1,2,Diesel,yes,2016,X5
+Audi,34500,sedan,71,2.8,Petrol,yes,2012,A6
+Toyota,7500,sedan,185,3,Gas,yes,2004,Camry
+Volkswagen,13900,vagon,123,1.4,Gas,yes,2013,Touran
+Renault,4000,van,194,1.9,Diesel,yes,2000,Kangoo
+Audi,2300,vagon,250,2.5,Diesel,no,2001,A6
+Renault,5650,van,140,1.4,Petrol,yes,2009,Kangoo
+Volkswagen,11800,other,200,1.9,Diesel,yes,2007,T5 (Transporter)
+Audi,44000,hatch,1,1.4,Petrol,yes,2016,A5
+Audi,23400,crossover,105,4.2,Gas,yes,2007,Q7
+BMW,2666,sedan,360,2.5,Diesel,no,1996,525
+BMW,23900,crossover,60,2,Petrol,yes,2013,X1
+Toyota,14000,sedan,25,2.5,Petrol,yes,2014,Camry
+Mercedes-Benz,NA,sedan,9,3,Diesel,yes,2014,S 350
+BMW,69999,crossover,1,2,Petrol,yes,2016,X5
+Mercedes-Benz,58999,crossover,0,2.2,Diesel,yes,2016,GLC-Class
+Audi,22500,other,71,3.6,Petrol,yes,2007,Q7
+Toyota,10300,sedan,108,1.6,Petrol,yes,2008,Corolla
+Renault,9300,van,200,1.5,Diesel,yes,2009,Grand Scenic
+Audi,37800,other,126,3,Diesel,yes,2010,Q7
+Toyota,10400,sedan,144,1.3,Petrol,yes,2012,Corolla
+Renault,6700,van,160,1.5,Diesel,yes,2012,Kangoo
+Renault,7400,van,156,1.5,Diesel,yes,2012,Kangoo
+Renault,11200,van,157,1.5,Diesel,yes,2012,Grand Scenic
+Toyota,25900,crossover,97,4,Petrol,yes,2009,Land Cruiser Prado
+Mercedes-Benz,11500,van,381,2.2,Diesel,yes,2006,Vito
+BMW,3950,sedan,300,2,Petrol,yes,1986,320
+Renault,11500,vagon,230,1.5,Diesel,yes,2012,Megane
+Toyota,2600,sedan,218,2.2,Petrol,yes,1994,Camry
+Mercedes-Benz,55000,crossover,40,4.6,Petrol,yes,2013,ML 500
+Renault,6650,hatch,274,1.5,Diesel,yes,2006,Scenic
+Mercedes-Benz,11900,van,215,2.2,Diesel,yes,2011,Vito
+Renault,8600,van,100,1.5,Diesel,yes,2012,Kangoo
+Mercedes-Benz,6700,van,310,2.2,Diesel,yes,2001,Vito
+Volkswagen,21500,crossover,178,3,Diesel,yes,2008,Touareg
+Toyota,17777,other,120,2.7,Gas,yes,2006,Land Cruiser Prado
+BMW,29000,sedan,170,2,Diesel,yes,2011,520
+Volkswagen,7300,sedan,125,1.6,Petrol,yes,2005,Golf Plus
+BMW,58000,crossover,64,2,Diesel,yes,2015,X5
+Toyota,43999,other,1,2.8,Diesel,yes,2016,Hilux
+Volkswagen,16450,van,143,2,Diesel,yes,2012,T5 (Transporter) 
+Toyota,26900,crossover,118,2.7,Petrol,yes,2011,Land Cruiser Prado
+Renault,8250,sedan,148,NA,Other,yes,2011,Kangoo
+BMW,13900,sedan,262,4,Diesel,yes,2003,740
+Mercedes-Benz,5500,sedan,250,2.7,Diesel,no,2003,E-Class
+Volkswagen,3350,other,223,1.9,Diesel,yes,1997,Caddy
+Volkswagen,9800,hatch,98,1.6,Gas,yes,2010,Golf V
+Mercedes-Benz,13899,hatch,232,3,Diesel,yes,2007,Vito
+Mercedes-Benz,15200,sedan,195,3,Diesel,yes,2007,E-Class
+Toyota,8900,sedan,87,1.6,Petrol,yes,2008,Corolla
+Mitsubishi,17800,crossover,111,2.4,Petrol,yes,2011,Outlander XL
+Audi,2650,vagon,270,2.5,Diesel,no,2001,A6
+Mitsubishi,10200,sedan,102,2,Gas,yes,2008,Lancer X
+Toyota,12500,sedan,150,2.4,Petrol,yes,2007,Camry
+Audi,2500,hatch,200,1.6,Petrol,no,1999,A3
+Toyota,16200,crossover,219,2.7,Petrol,yes,2006,Land Cruiser Prado
+BMW,28000,sedan,127,2.5,Petrol,yes,2010,523
+Mercedes-Benz,5000,sedan,240,2.2,Diesel,no,2004,E-Class
+Toyota,8900,sedan,154,1.6,Gas,yes,2008,Corolla
+BMW,29900,crossover,182,3,Petrol,yes,2008,X6
+Mercedes-Benz,12950,other,199,2.2,Diesel,yes,2012,Vito
+Volkswagen,19999,van,152,2,Diesel,yes,2012,Sharan
+Audi,10000,sedan,131,1.8,Petrol,yes,2005,A4
+BMW,39500,vagon,93,2,Diesel,yes,2012,525
+Volkswagen,7900,other,123,1.4,Petrol,yes,2008,Polo
+Volkswagen,6000,van,460,1.9,Diesel,yes,1997,T4 (Transporter)
+Mercedes-Benz,219900,crossover,30,6.3,Petrol,yes,2013,G 63 AMG
+Mercedes-Benz,5600,van,480,2.2,Diesel,yes,1999,Vito
+Mercedes-Benz,15900,sedan,216,5,Petrol,yes,2007,E-Class
+Mitsubishi,6700,sedan,155,1.6,Gas,yes,2007,Lancer
+Renault,9500,van,189,2,Diesel,yes,2010,Trafic
+Audi,3600,sedan,374,2.5,Diesel,no,2001,A8
+Renault,9150,van,220,2,Diesel,yes,2007,Trafic
+Mercedes-Benz,41500,other,26,NA,Diesel,yes,2013,E-Class
+Mercedes-Benz,12900,van,185,2.2,Diesel,yes,2011,Vito
+Renault,11500,vagon,175,1.5,Diesel,yes,2011,Megane
+Toyota,39500,other,1,2.8,Diesel,yes,2016,Hilux
+Renault,4200,sedan,310,1.4,Petrol,yes,2003,Symbol
+BMW,4100,sedan,26,3,Diesel,no,2001,530
+Volkswagen,9200,van,248,1.9,Diesel,yes,2007,T5 (Transporter) 
+Audi,11400,crossover,250,2.5,Diesel,yes,2005,A6 Allroad
+Volkswagen,24000,sedan,3,1.6,Diesel,yes,2016,Jetta
+Renault,1800,hatch,1,1.4,Gas,yes,1988,11
+Mercedes-Benz,3799,sedan,210,2,Petrol,yes,1995,200
+Mercedes-Benz,NA,sedan,0,3,Petrol,yes,2016,S 400
+Mercedes-Benz,110000,crossover,0,3,Diesel,yes,2016,GLS 350
+BMW,16999,vagon,213,3,Diesel,yes,2007,530
+Audi,6700,sedan,340,2.6,Gas,yes,1997,A6
+Toyota,2200,hatch,50,2,Petrol,yes,1991,Carina
+Renault,NA,sedan,100,1.4,Petrol,yes,2009,Symbol
+Toyota,4500,sedan,238,1.8,Petrol,yes,1996,Carina
+BMW,10999,sedan,225,3,Diesel,yes,2003,530
+Mitsubishi,NA,sedan,90,2,Petrol,yes,2007,Lancer Evolution
+BMW,4000,sedan,380,2,Petrol,yes,1991,320
+Volkswagen,5400,vagon,250,1.6,Petrol,yes,2003,Golf IV
+Mercedes-Benz,1700,sedan,300,2,Gas,no,1998,E-Class
+Audi,4000,sedan,320,2.8,Petrol,yes,1992,100
+Mercedes-Benz,55000,crossover,127,5.5,Petrol,yes,2008,G 55 AMG
+BMW,NA,crossover,0,2,Diesel,yes,2016,X5
+BMW,2500,sedan,44,2.5,Diesel,no,1997,525
+Mitsubishi,15999,crossover,149,3,Gas,yes,2008,Pajero Wagon
+Mercedes-Benz,7800,hatch,125,2.3,Petrol,yes,2001,C-Class
+Volkswagen,12114.35,sedan,0,1.6,Petrol,yes,2016,Polo
+Volkswagen,12500,sedan,118,1.6,Diesel,yes,2011,Golf VI
+Renault,11500,van,160,1.5,Diesel,yes,2010,Grand Scenic
+Volkswagen,10999,other,240,2.5,Diesel,yes,2005,LT
+Mercedes-Benz,15300,other,234,2.2,Diesel,yes,2008,Sprinter 315
+Mercedes-Benz,12300,sedan,300,2.2,Diesel,yes,2003,E-Class
+Volkswagen,12400,other,248,2.5,Diesel,yes,2007,T5 (Transporter)
+Mercedes-Benz,50800,crossover,30,3.5,Diesel,yes,2013,ML 350
+BMW,77500,crossover,1,3,Petrol,yes,2016,X6
+Renault,6800,van,119,1.5,Diesel,yes,2012,Kangoo
+BMW,6399,sedan,282,2,Petrol,yes,1995,520
+Audi,3550,sedan,196,2.8,Petrol,no,1997,A8
+BMW,31800,sedan,115,3,Diesel,yes,2010,5 Series GT
+Volkswagen,1700,hatch,247,1.4,Petrol,no,1999,Golf IV
+BMW,14300,sedan,198,NA,Petrol,yes,2005,523
+Mercedes-Benz,14900,sedan,171,3.2,Gas,yes,2006,E-Class
+Mercedes-Benz,5100,sedan,295,3,Diesel,yes,1994,E-Class
+Renault,12800,hatch,125,1.1,Diesel,yes,2012,Laguna
+BMW,5650,sedan,284,2,Petrol,yes,1998,520
+Audi,5200,sedan,250,1.8,Other,yes,1996,A4
+Renault,10100,vagon,190,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,295000,sedan,29,6,Petrol,yes,2011,S 600
+Mercedes-Benz,14750,sedan,220,3,Diesel,yes,2005,E-Class
+Volkswagen,4500,sedan,230,2,Gas,yes,1996,Passat B4
+Audi,19500,crossover,186,4.2,Petrol,yes,2006,Q7
+BMW,5400,crossover,250,3,Petrol,no,2003,X5
+Mercedes-Benz,12900,sedan,255,2.7,Diesel,yes,2003,E-Class
+Mitsubishi,15900,crossover,138,3,Gas,yes,2008,Pajero Wagon
+Renault,11800,other,179,1.5,Diesel,yes,2012,Megane
+BMW,34500,sedan,45,2,Petrol,yes,2013,320
+Renault,6200,van,228,2.5,Diesel,yes,2005,Trafic
+Mercedes-Benz,16500,other,107,2.2,Diesel,yes,2010,Vito
+Volkswagen,55555,van,53,2,Diesel,yes,2013,Multivan
+Toyota,22500,sedan,12,2.5,Petrol,yes,2014,Camry
+Audi,8700,sedan,202,3.7,Gas,yes,1998,A8
+Mercedes-Benz,NA,van,30,2.2,Diesel,yes,2014,Viano
+Audi,35900,sedan,105,2,Diesel,yes,2014,A6
+Mercedes-Benz,6800,van,242,2.2,Diesel,yes,1999,Vito
+Mitsubishi,18500,crossover,135,3,Gas,yes,2009,Pajero Wagon
+BMW,12300,sedan,140,2,Petrol,yes,2005,318
+Toyota,18800,vagon,89,1.8,Petrol,yes,2012,Avensis
+Mercedes-Benz,11700,crossover,284,4,Diesel,yes,2004,ML 400
+Audi,14900,crossover,183,3.6,Petrol,no,2007,Q7
+Volkswagen,8900,sedan,144,1.6,Petrol,yes,2013,Polo
+BMW,NA,sedan,1,3,Petrol,yes,2010,535
+Volkswagen,9000,sedan,223,2.5,Diesel,yes,2007,T5 (Transporter)
+BMW,7250,sedan,300,2.8,Petrol,yes,1999,528
+Mercedes-Benz,35900,crossover,254,5,Gas,yes,2004,G 500
+Toyota,14900,sedan,110,3.5,Petrol,yes,2008,Camry
+Volkswagen,14900,van,170,1.6,Diesel,yes,2012,Touran
+Mercedes-Benz,46500,crossover,127,5,Petrol,yes,2008,G 500
+Mercedes-Benz,7900,other,206,2.2,Diesel,yes,2004,Vito
+Toyota,35999,crossover,102,4,Petrol,yes,2011,Land Cruiser Prado
+Volkswagen,6800,sedan,178,NA,Gas,yes,2004,Passat B5
+Volkswagen,7300,van,240,1.9,Diesel,yes,2005,Caddy
+BMW,12600,sedan,219,3.6,Petrol,yes,2002,735
+BMW,9000,sedan,225,2.2,Gas,yes,2002,320
+BMW,37200,other,115,3,Diesel,yes,2011,X5
+Audi,63200,sedan,0,2,Petrol,yes,2016,A6
+BMW,86999,crossover,0,3,Diesel,yes,2016,X6
+Volkswagen,NA,van,270,2.4,Diesel,yes,1993,T4 (Transporter)
+Mercedes-Benz,4100,sedan,410,2.2,Diesel,yes,1998,E-Class
+Mercedes-Benz,11800,sedan,215,3.2,Petrol,yes,2003,E-Class
+Audi,4000,vagon,300,2.5,Diesel,no,2001,A6
+Mercedes-Benz,35500,sedan,160,6.5,Petrol,yes,2008,S 65 AMG
+Volkswagen,9900,van,149,1.6,Diesel,yes,2013,Caddy
+BMW,9999,sedan,271,3,Diesel,yes,2001,530
+Volkswagen,11500,sedan,118,2,Petrol,yes,2007,Passat B6
+BMW,4850,sedan,280,3,Diesel,no,1999,730
+Mitsubishi,5850,hatch,130,1.3,Petrol,yes,2005,Colt
+Toyota,103999,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Mercedes-Benz,NA,sedan,25,3,Diesel,yes,2014,S 350
+BMW,32000,crossover,138,3,Petrol,yes,2009,X6
+Toyota,61990,crossover,68,4.5,Diesel,yes,2012,Land Cruiser 200
+Toyota,8888,sedan,172,1.8,Gas,yes,2005,Avensis
+Mitsubishi,12700,crossover,110,2.4,Gas,yes,2008,Outlander XL
+Toyota,28500,sedan,16,2.5,Petrol,yes,2015,Camry
+BMW,47777,crossover,142,3,Other,yes,2011,X6
+BMW,NA,crossover,16,NA,Other,yes,2016,X5 M
+BMW,16700,sedan,150,2,Diesel,yes,2008,520
+Volkswagen,8757,sedan,32,NA,Other,no,2015,Jetta
+Mercedes-Benz,2500,sedan,200,2.3,Gas,yes,1986,190
+Toyota,12900,sedan,134,2.4,Gas,yes,2007,Camry
+Volkswagen,9900,van,300,2.5,Diesel,yes,2007,T5 (Transporter)
+Toyota,51500,crossover,70,3,Diesel,yes,2015,Land Cruiser Prado
+Volkswagen,6200,van,180,2,Diesel,yes,2005,Caddy
+Audi,13333,other,178,2,Diesel,yes,2008,A6
+Mercedes-Benz,43500,other,51,3,Diesel,yes,2014,E-Class
+BMW,23000,other,100,NA,Other,yes,2005,630
+Volkswagen,7777,other,214,2,Other,yes,2007,Caddy
+Volkswagen,3600,vagon,470,1.8,Petrol,yes,1994,Passat B4
+Volkswagen,20600,sedan,107,1.8,Petrol,yes,2012,Passat CC
+Volkswagen,9500,van,188,1.9,Diesel,yes,2006,T5 (Transporter)
+Mercedes-Benz,17500,van,220,3,Diesel,yes,2012,Vito
+Volkswagen,9000,sedan,178,1.9,Diesel,yes,2009,Jetta
+Volkswagen,10500,van,246,2.5,Diesel,yes,2006,T5 (Transporter)
+Mercedes-Benz,11300,sedan,363,3.2,Gas,yes,2004,E-Class
+Renault,9200,hatch,12,1.2,Petrol,yes,2013,Sandero
+BMW,18500,sedan,114,2,Gas,yes,2008,520
+Mercedes-Benz,NA,crossover,0,4.7,Petrol,yes,2016,GLS 500
+Volkswagen,28713.75,hatch,0,2,Diesel,yes,2015,Golf VII
+BMW,31500,crossover,130,3,Diesel,yes,2010,X5
+Renault,2950,vagon,280,19,Diesel,yes,2000,Kangoo
+Toyota,11999,sedan,139,3.5,Petrol,yes,2008,Camry
+BMW,67431,crossover,0,2,Diesel,yes,2016,X5
+Volkswagen,3500,hatch,220,2,Petrol,yes,1994,Golf III
+Renault,9300,van,94,2,Diesel,yes,2012,Trafic
+BMW,62000,crossover,1,2,Diesel,yes,2016,X5
+Volkswagen,35500,crossover,112,NA,Diesel,yes,2011,Touareg
+BMW,57000,crossover,47,3,Diesel,yes,2014,X6
+Volkswagen,7900,van,229,1.9,Diesel,yes,2001,T4 (Transporter)
+BMW,4400,sedan,300,2,Petrol,yes,1995,520
+BMW,62900,crossover,1,2,Diesel,yes,2016,X5
+Mercedes-Benz,23900,sedan,124,1.8,Petrol,yes,2012,E-Class
+Audi,21900,other,114,3.2,Petrol,yes,2009,A5
+BMW,15250,other,144,3,Petrol,yes,2004,530
+Mercedes-Benz,4300,sedan,280,1.8,Petrol,yes,1993,C-Class
+Mercedes-Benz,14600,van,217,2.2,Diesel,yes,2012,Vito
+Toyota,15000,crossover,184,2.7,Gas,yes,2005,Land Cruiser Prado
+Mercedes-Benz,5300,van,212,2.2,Diesel,yes,2000,Vito
+Mercedes-Benz,7000,sedan,190,2.6,Petrol,yes,2001,210
+BMW,13900,other,80,3,Petrol,yes,2002,330
+Toyota,29000,crossover,120,4,Gas,yes,2008,Land Cruiser Prado
+Volkswagen,20900,sedan,25,2,Diesel,yes,2015,Jetta
+Mercedes-Benz,20500,sedan,173,1.8,Gas,yes,2011,E-Class
+Toyota,22999,crossover,152,4,Gas,yes,2008,Land Cruiser Prado
+Audi,95000,other,30,5.2,Petrol,yes,2010,R8
+Volkswagen,9599,sedan,88,1.6,Petrol,yes,2014,Polo
+Toyota,NA,crossover,1,2.2,Diesel,yes,2016,Rav 4
+Volkswagen,10500,van,299,2.5,Diesel,yes,2002,Syncro
+Mercedes-Benz,23999,sedan,204,3.5,Petrol,yes,2008,S 350
+Renault,10200,van,149,1.5,Diesel,yes,2013,Kangoo
+Toyota,9950,sedan,142,1.8,Petrol,yes,2008,Avensis
+Toyota,21999,crossover,69,3.5,Petrol,yes,2008,Highlander
+Mercedes-Benz,18500,crossover,180,3.5,Petrol,yes,2008,ML 350
+Mercedes-Benz,40500,crossover,54,5.5,Petrol,yes,2011,GL 500
+Volkswagen,3100,sedan,390,1.8,Petrol,yes,1990,Passat B3
+BMW,8100,crossover,175,3,Gas,no,2005,X5
+Audi,8100,vagon,302,2.5,Diesel,yes,2000,A6
+Volkswagen,2600,vagon,288,1.8,Petrol,yes,1990,Passat B3
+Renault,7299,sedan,160,2,Petrol,yes,2007,Megane
+Volkswagen,9600,sedan,107,1.6,Gas,yes,2011,Polo
+Renault,10500,vagon,250,1.5,Diesel,yes,2011,Megane
+Mercedes-Benz,26800,vagon,160,2.2,Diesel,yes,2014,E-Class
+Audi,31999,sedan,107,3,Diesel,yes,2009,A8
+Volkswagen,12900,van,176,2.5,Diesel,yes,2008,T5 (Transporter)
+Audi,NA,crossover,65,2.99,Diesel,yes,2013,A6 Allroad
+BMW,16500,other,122,2.2,Petrol,yes,2005,520
+Mitsubishi,6000,hatch,118,1.3,Petrol,yes,2006,Colt
+Toyota,NA,hatch,76,1,Petrol,yes,2007,Aygo
+Toyota,9950,sedan,140,1.4,Petrol,yes,2012,Corolla
+Mercedes-Benz,NA,crossover,27,3,Diesel,yes,2015,G 350
+Volkswagen,5700,vagon,199,1.8,Petrol,yes,1999,Sharan
+Audi,48000,crossover,90,3,Diesel,yes,2013,Q7
+Volkswagen,1296.75,sedan,1,1.6,Petrol,yes,1980,Jetta
+Mitsubishi,8000,crossover,220,2.4,Gas,yes,2004,Outlander
+Mitsubishi,12500,other,48,1.6,Petrol,yes,2012,Lancer X
+Mercedes-Benz,57000,sedan,61,4.6,Petrol,yes,2012,S 500
+Mercedes-Benz,85000,sedan,36,5.5,Petrol,yes,2014,CLS 63 AMG
+Renault,4200,sedan,180,1.4,Petrol,yes,2003,Symbol
+BMW,10600,sedan,285,2,Diesel,yes,2007,520
+Audi,55000,crossover,65,2.99,Diesel,yes,2013,A6 Allroad
+Mercedes-Benz,31500,van,95,2.2,Diesel,yes,2013,Vito
+Audi,9300,sedan,186,2.8,Petrol,yes,2000,A6
+Renault,4299,vagon,300,1.5,Diesel,yes,2003,Kangoo
+Audi,48900,other,82,4.2,Petrol,yes,2007,R8
+Toyota,13300,other,131,2.4,Gas,yes,2008,Camry
+Audi,13300,sedan,207,4.2,Petrol,yes,2004,A8
+BMW,49900,other,82,3,Petrol,yes,2012,640
+BMW,13700,sedan,276,3,Diesel,yes,2004,530
+Volkswagen,5999,van,300,1.8,Gas,yes,1999,Sharan
+Toyota,8700,hatch,38,1.6,Petrol,yes,2008,Auris
+Volkswagen,18000,crossover,155,2,Diesel,yes,2011,Tiguan
+BMW,55500,crossover,90,3,Diesel,yes,2013,X5 M
+BMW,3500,sedan,280,NA,Petrol,no,1996,730
+BMW,3100,vagon,1,2,Diesel,no,2002,320
+Toyota,30900,sedan,15,2.5,Petrol,yes,2015,Camry
+Mercedes-Benz,23500,sedan,200,5.5,Petrol,yes,2008,S 550
+Volkswagen,9200,other,156,1.9,Diesel,yes,2009,Golf Variant
+Volkswagen,13000,sedan,140,2,Petrol,yes,2006,Jetta
+BMW,NA,crossover,0,3,Diesel,yes,2015,X6
+Mercedes-Benz,55000,crossover,120,5.5,Petrol,yes,2008,G 55 AMG
+Audi,27500,sedan,74,2,Diesel,yes,2011,A5
+BMW,5900,vagon,220,2.8,Petrol,yes,1995,520
+Mercedes-Benz,126000,sedan,14,3,Diesel,yes,2015,S 350
+Mercedes-Benz,27500,crossover,152,6.3,Petrol,yes,2007,ML 63 AMG
+Renault,7200,van,285,1.9,Diesel,yes,2002,Trafic
+Renault,6750,van,140,1.5,Diesel,yes,2012,Kangoo
+Mitsubishi,21000,crossover,122,3.2,Diesel,yes,2009,Pajero Wagon
+Volkswagen,15900,sedan,77,1.4,Petrol,yes,2012,Jetta
+Volkswagen,10000,sedan,56,1.8,Petrol,no,2015,Passat B7
+Renault,4500,other,1,1.5,Diesel,yes,2007,Kangoo
+Mercedes-Benz,29000,crossover,139,3.5,Gas,yes,2009,ML 350
+Volkswagen,NA,other,197,2,Diesel,no,2011,T5 (Transporter)
+Mercedes-Benz,109999,crossover,0,2.98,Diesel,yes,2016,GLS 350
+Mercedes-Benz,9999,sedan,80,2.3,Petrol,yes,2002,C-Class
+BMW,32500,crossover,152,3,Diesel,yes,2008,X5
+Mercedes-Benz,111999,crossover,0,3,Diesel,yes,2016,GL 350
+Mercedes-Benz,29500,crossover,253,5,Petrol,yes,2003,G 500
+Audi,2850,vagon,280,2.5,Diesel,no,1999,A6
+Mitsubishi,7200,sedan,83,1.6,Other,yes,2007,Lancer
+Volkswagen,6250,hatch,170,1.4,Petrol,yes,2001,Golf IV
+Volkswagen,6500,sedan,74,2,Petrol,no,2011,Jetta
+Volkswagen,6000,sedan,271,1.8,Petrol,yes,2000,Passat B5
+Mitsubishi,9500,crossover,140,2.4,Gas,yes,2007,Outlander
+Mercedes-Benz,17200,sedan,180,3,Diesel,yes,2006,320
+Volkswagen,18200,hatch,23,1.4,Petrol,yes,2013,Golf VI
+Mercedes-Benz,6900,vagon,235,2.9,Diesel,yes,1997,E-Class
+Toyota,10200,other,105,1.5,Gas,yes,2005,Scion
+Toyota,17394.09,hatch,0,1.33,Petrol,yes,2016,Yaris
+Audi,22500,sedan,99,2,Other,yes,2010,A5
+Mercedes-Benz,28950,sedan,40,1.8,Petrol,yes,2014,C-Class
+BMW,15500,sedan,89,2,Diesel,no,2012,520
+Renault,13600,vagon,164,1.5,Diesel,yes,2013,Grand Scenic
+Mitsubishi,3250,sedan,380,1.8,Petrol,yes,1993,Galant
+BMW,17000,vagon,158,2,Diesel,yes,2010,320
+Renault,12350,van,159,2,Diesel,yes,2010,Trafic
+Mercedes-Benz,24500,sedan,163,5.5,Gas,yes,2007,S 550
+BMW,11800,sedan,218,2,Petrol,yes,2006,320
+Toyota,30000,crossover,85,2.7,Gas,yes,2010,Land Cruiser Prado
+Audi,4850,sedan,270,2.6,Petrol,yes,1995,A6
+Volkswagen,11900,vagon,132,1.4,Petrol,yes,2011,Golf VI
+Renault,6500,van,127,1.5,Diesel,yes,2011,Kangoo
+Mercedes-Benz,12000,sedan,298,5,Petrol,yes,2004,S 500
+BMW,4300,sedan,200,2,Diesel,no,2002,320
+BMW,NA,crossover,1,3,Diesel,yes,2014,X6
+Renault,5600,vagon,112,1.5,Diesel,yes,2006,Kangoo
+Mercedes-Benz,24500,other,77,1.8,Petrol,yes,2011,C-Class
+BMW,26900,crossover,170,3,Diesel,yes,2008,X5
+Mercedes-Benz,11500,van,180,2.2,Diesel,yes,2011,Vito
+Mitsubishi,9700,van,247,2.4,Gas,yes,2008,Grandis
+Renault,10500,vagon,185,1.5,Diesel,yes,2011,Megane
+Renault,10900,vagon,180,1.5,Diesel,yes,2012,Megane
+Renault,7500,other,440,2,Diesel,yes,2008,Trafic
+Mercedes-Benz,25500,crossover,77,3.5,Petrol,yes,2008,ML 350
+Volkswagen,15500,sedan,80,1.4,Petrol,yes,2013,Jetta
+Volkswagen,9750,van,159,1.9,Diesel,yes,2009,Caddy
+BMW,16100,crossover,194,3,Diesel,yes,2004,X5
+Mercedes-Benz,300000,other,37,5,Petrol,yes,2012,G 500
+Mercedes-Benz,5500,sedan,440,2,Petrol,yes,1978,E-Class
+Volkswagen,9200,vagon,171,1.6,Petrol,yes,2008,Caddy
+Volkswagen,5150,van,240,2,Diesel,yes,2005,Caddy
+Toyota,100000,crossover,0,4.5,Diesel,yes,2016,Land Cruiser 200
+Renault,8999,other,126,2,Diesel,yes,2012,Trafic
+Mercedes-Benz,16800,sedan,125,1.8,Petrol,yes,2008,C-Class
+Mercedes-Benz,8200,sedan,280,2.4,Gas,yes,1997,E-Class
+Mercedes-Benz,24950,other,60,1.8,Petrol,yes,2013,C-Class
+Audi,80999,crossover,0,3,Diesel,yes,2016,Q7
+Mercedes-Benz,7300,van,207,2.2,Diesel,yes,2003,Vito
+Volkswagen,2200,hatch,400,1.3,Other,yes,1987,Golf II
+BMW,21335,other,105,3,Petrol,yes,2008,335
+BMW,45000,crossover,80,3,Petrol,yes,2011,X6
+Renault,6750,van,155,1.5,Diesel,yes,2012,Kangoo
+Renault,7000,van,210,1.5,Diesel,yes,2005,Kangoo
+BMW,12090,hatch,145,1.6,Petrol,yes,2010,116
+BMW,27900,sedan,38,2,Petrol,yes,2013,320
+Renault,2100,vagon,237,1.9,Diesel,no,2001,Laguna
+Renault,6800,sedan,152,1.6,Petrol,yes,2007,Megane
+Volkswagen,11500,van,163,2.5,Diesel,yes,2008,T5 (Transporter)
+Toyota,17900,sedan,35,1.6,Petrol,yes,2014,Corolla
+Mercedes-Benz,125000,sedan,9,3,Diesel,yes,2014,S 350
+BMW,6500,sedan,1,3.5,Petrol,yes,1999,535
+BMW,8000,sedan,194,2,Petrol,yes,1985,520
+Toyota,14200,sedan,31,NA,Petrol,yes,2014,Corolla
+Volkswagen,13500,van,124,2,Diesel,yes,2013,T5 (Transporter) 
diff --git a/Udemy DataScience Workbook/Requirement.txt b/Udemy DataScience Workbook/Requirement.txt
new file mode 100644
index 0000000..e69de29
diff --git a/Udemy DataScience Workbook/Scalars,+Vectors,+and+Matrices.ipynb b/Udemy DataScience Workbook/Scalars,+Vectors,+and+Matrices.ipynb
new file mode 100644
index 0000000..63981e4
--- /dev/null
+++ b/Udemy DataScience Workbook/Scalars,+Vectors,+and+Matrices.ipynb	
@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Declaring scalars, vectors, and matrices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scalars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = np.array([5,-2,4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = np.array([[5,12,6],[-3,0,14]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "type(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s_array = np.array([5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(s_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data shapes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "s.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s_array.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a column vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v.reshape(1,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v.reshape(3,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m+s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m+s_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v+s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v+s_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m+v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Selecting+the+number+of+clusters_with_comments.ipynb b/Udemy DataScience Workbook/Selecting+the+number+of+clusters_with_comments.ipynb
new file mode 100644
index 0000000..443c9db
--- /dev/null
+++ b/Udemy DataScience Workbook/Selecting+the+number+of+clusters_with_comments.ipynb	
@@ -0,0 +1,449 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basics of cluster analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we explore the issue of selecting the right number of clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "# Set the styles to Seaborn\n",
+    "sns.set()\n",
+    "# Import the KMeans module so we can perform k-means clustering with sklearn\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the country clusters data\n",
+    "data = pd.read_csv('Countries-exercise.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Latitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aruba</td>\n",
+       "      <td>-69.982677</td>\n",
+       "      <td>12.520880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>66.004734</td>\n",
+       "      <td>33.835231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Angola</td>\n",
+       "      <td>17.537368</td>\n",
+       "      <td>-12.293361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Anguilla</td>\n",
+       "      <td>-63.064989</td>\n",
+       "      <td>18.223959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>20.049834</td>\n",
+       "      <td>41.142450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Samoa</td>\n",
+       "      <td>-172.164851</td>\n",
+       "      <td>-13.753243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Yemen</td>\n",
+       "      <td>47.586762</td>\n",
+       "      <td>15.909280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>25.083901</td>\n",
+       "      <td>-29.000341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>27.774759</td>\n",
+       "      <td>-13.458242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>29.851441</td>\n",
+       "      <td>-19.004204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>241 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             name   Longitude   Latitude\n",
+       "0           Aruba  -69.982677  12.520880\n",
+       "1     Afghanistan   66.004734  33.835231\n",
+       "2          Angola   17.537368 -12.293361\n",
+       "3        Anguilla  -63.064989  18.223959\n",
+       "4         Albania   20.049834  41.142450\n",
+       "..            ...         ...        ...\n",
+       "236         Samoa -172.164851 -13.753243\n",
+       "237         Yemen   47.586762  15.909280\n",
+       "238  South Africa   25.083901 -29.000341\n",
+       "239        Zambia   27.774759 -13.458242\n",
+       "240      Zimbabwe   29.851441 -19.004204\n",
+       "\n",
+       "[241 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check out the data manually \n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Map the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Language'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3790\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3789\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3790\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m   3791\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Language'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\Selecting+the+number+of+clusters_with_comments.ipynb Cell 9\u001b[0m line \u001b[0;36m4\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Selecting%2Bthe%2Bnumber%2Bof%2Bclusters_with_comments.ipynb#X11sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m data_mapped \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mcopy()\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Selecting%2Bthe%2Bnumber%2Bof%2Bclusters_with_comments.ipynb#X11sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# Map languages with 0, 1, and 2. Note that this is not the best way to do that, but for now we will use it \u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Selecting%2Bthe%2Bnumber%2Bof%2Bclusters_with_comments.ipynb#X11sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m data_mapped[\u001b[39m'\u001b[39m\u001b[39mLanguage\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m=\u001b[39mdata_mapped[\u001b[39m'\u001b[39;49m\u001b[39mLanguage\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39mmap({\u001b[39m'\u001b[39m\u001b[39mEnglish\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m0\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mFrench\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m1\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mGerman\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m2\u001b[39m})\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Selecting%2Bthe%2Bnumber%2Bof%2Bclusters_with_comments.ipynb#X11sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39m# Check if we did it correctly\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Selecting%2Bthe%2Bnumber%2Bof%2Bclusters_with_comments.ipynb#X11sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m data_mapped\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:3893\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3891\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m   3892\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3893\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[0;32m   3894\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3895\u001b[0m     indexer \u001b[39m=\u001b[39m [indexer]\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3797\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3792\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(casted_key, \u001b[39mslice\u001b[39m) \u001b[39mor\u001b[39;00m (\n\u001b[0;32m   3793\u001b[0m         \u001b[39misinstance\u001b[39m(casted_key, abc\u001b[39m.\u001b[39mIterable)\n\u001b[0;32m   3794\u001b[0m         \u001b[39mand\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(x, \u001b[39mslice\u001b[39m) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m casted_key)\n\u001b[0;32m   3795\u001b[0m     ):\n\u001b[0;32m   3796\u001b[0m         \u001b[39mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3797\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m   3798\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m   3799\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3800\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3801\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3802\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Language'"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a copy of the original dataset\n",
+    "data_mapped = data.copy()\n",
+    "# Map languages with 0, 1, and 2. Note that this is not the best way to do that, but for now we will use it \n",
+    "data_mapped['Language']=data_mapped['Language'].map({'English':0,'French':1,'German':2})\n",
+    "# Check if we did it correctly\n",
+    "data_mapped"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select the features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iloc is a method used to 'slice' data \n",
+    "# 'slice' is not technically correct as there are methods 'slice' which are a bit different\n",
+    "# The term used by pandas is 'selection by position'\n",
+    "# The first argument of identifies the rows we want to keep\n",
+    "# The second - the columns\n",
+    "# When choosing the columns, e.g. a:b, we will keep columns a,a+1,a+2,...,b-1 ; so column b is excluded\n",
+    "x = data_mapped.iloc[:,1:4]\n",
+    "# for this particular case, we are choosing columns 1 and 2\n",
+    "# Note column indices in Python start from 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Check if we worked correctly\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an object (which we would call kmeans)\n",
+    "# The number in the brackets is K, or the number of clusters we are aiming for\n",
+    "kmeans = KMeans(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fit the input data, i.e. cluster the data in X in K clusters\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a variable which will contain the predicted clusters for each observation\n",
+    "identified_clusters = kmeans.fit_predict(x)\n",
+    "# Check the result\n",
+    "identified_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a copy of the mapped data\n",
+    "data_with_clusters = data_mapped.copy()\n",
+    "# Create a new Series, containing the identified cluster for each observation\n",
+    "data_with_clusters['Cluster'] = identified_clusters\n",
+    "# Check the result\n",
+    "data_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the data using the longitude and the latitude\n",
+    "# c (color) is an argument which could be coded with a variable \n",
+    "# The variable in this case has values 0,1,2, indicating to plt.scatter, that there are three colors (0,1,2)\n",
+    "# All points in cluster 0 will be the same colour, all points in cluster 1 - another one, etc.\n",
+    "# cmap is the color map. Rainbow is a nice one, but you can check others here: https://matplotlib.org/users/colormaps.html\n",
+    "plt.scatter(data_with_clusters['Longitude'],data_with_clusters['Latitude'],c=data_with_clusters['Cluster'],cmap='rainbow')\n",
+    "plt.xlim(-180,180)\n",
+    "plt.ylim(-90,90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting the number of clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### WCSS (within-cluster sum of squares)\n",
+    "WCSS is a measure developed within the ANOVA framework. It gives a very good idea about the different distance between different clusters and within clusters, thus providing us a rule for deciding the appropriate number of clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the WCSS for the current solution\n",
+    "kmeans.inertia_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an empty list\n",
+    "wcss=[]\n",
+    "\n",
+    "# Create all possible cluster solutions with a loop\n",
+    "for i in range(1,7):\n",
+    "    # Cluster solution with i clusters\n",
+    "    kmeans = KMeans(i)\n",
+    "    # Fit the data\n",
+    "    kmeans.fit(x)\n",
+    "    # Find WCSS for the current iteration\n",
+    "    wcss_iter = kmeans.inertia_\n",
+    "    # Append the value to the WCSS list\n",
+    "    wcss.append(wcss_iter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's see what we got\n",
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a variable containing the numbers from 1 to 6, so we can use it as X axis of the future plot\n",
+    "number_clusters = range(1,7)\n",
+    "# Plot the number of clusters vs WCSS\n",
+    "plt.plot(number_clusters,wcss)\n",
+    "# Name your graph\n",
+    "plt.title('The Elbow Method')\n",
+    "# Name the x-axis\n",
+    "plt.xlabel('Number of clusters')\n",
+    "# Name the y-axis\n",
+    "plt.ylabel('Within-cluster Sum of Squares')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Shortcuts-for-Jupyter.pdf b/Udemy DataScience Workbook/Shortcuts-for-Jupyter.pdf
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diff --git a/Udemy DataScience Workbook/Simple+Linear+Regression+Exercise - Copy2.ipynb b/Udemy DataScience Workbook/Simple+Linear+Regression+Exercise - Copy2.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression - exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size.csv'. \n",
+    "\n",
+    "You are expected to create a simple linear regression (similar to the one in the lecture), using the new data.\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables is 'size'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scipy\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>252460.400</td>\n",
+       "      <td>549.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>310522.592</td>\n",
+       "      <td>1037.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>383635.568</td>\n",
+       "      <td>1504.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>225145.248</td>\n",
+       "      <td>648.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>274922.856</td>\n",
+       "      <td>705.29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         price     size\n",
+       "0   234314.144   643.09\n",
+       "1   228581.528   656.22\n",
+       "2   281626.336   487.29\n",
+       "3   401255.608  1504.75\n",
+       "4   458674.256  1275.46\n",
+       "..         ...      ...\n",
+       "95  252460.400   549.80\n",
+       "96  310522.592  1037.44\n",
+       "97  383635.568  1504.75\n",
+       "98  225145.248   648.29\n",
+       "99  274922.856   705.29\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size\n",
+       "count     100.000000   100.000000\n",
+       "mean   292289.470160   853.024200\n",
+       "std     77051.727525   297.941951\n",
+       "min    154282.128000   479.750000\n",
+       "25%    234280.148000   643.330000\n",
+       "50%    280590.716000   696.405000\n",
+       "75%    335723.696000  1029.322500\n",
+       "max    500681.128000  1842.510000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1 = data['size']#Independent\n",
+    "Y = data['price']#dependent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x1, Y)\n",
+    "plt.xlabel('size', fontsize = 20)\n",
+    "plt.ylabel('price', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.745</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.742</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   285.9</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Tue, 31 Oct 2023</td> <th>  Prob (F-statistic):</th> <td>8.13e-31</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>10:54:42</td>     <th>  Log-Likelihood:    </th> <td> -1198.3</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   2401.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    98</td>      <th>  BIC:               </th> <td>   2406.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td> 1.019e+05</td> <td> 1.19e+04</td> <td>    8.550</td> <td> 0.000</td> <td> 7.83e+04</td> <td> 1.26e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>size</th>  <td>  223.1787</td> <td>   13.199</td> <td>   16.909</td> <td> 0.000</td> <td>  196.986</td> <td>  249.371</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 6.262</td> <th>  Durbin-Watson:     </th> <td>   2.267</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.044</td> <th>  Jarque-Bera (JB):  </th> <td>   2.938</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.117</td> <th>  Prob(JB):          </th> <td>   0.230</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.194</td> <th>  Cond. No.          </th> <td>2.75e+03</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.75e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.745   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.742   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     285.9   \\\\\n",
+       "\\textbf{Date:}             & Tue, 31 Oct 2023 & \\textbf{  Prob (F-statistic):} &  8.13e-31   \\\\\n",
+       "\\textbf{Time:}             &     10:54:42     & \\textbf{  Log-Likelihood:    } &   -1198.3   \\\\\n",
+       "\\textbf{No. Observations:} &         100      & \\textbf{  AIC:               } &     2401.   \\\\\n",
+       "\\textbf{Df Residuals:}     &          98      & \\textbf{  BIC:               } &     2406.   \\\\\n",
+       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &    1.019e+05  &     1.19e+04     &     8.550  &         0.000        &     7.83e+04    &     1.26e+05     \\\\\n",
+       "\\textbf{size}  &     223.1787  &       13.199     &    16.909  &         0.000        &      196.986    &      249.371     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       &  6.262 & \\textbf{  Durbin-Watson:     } &    2.267  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.044 & \\textbf{  Jarque-Bera (JB):  } &    2.938  \\\\\n",
+       "\\textbf{Skew:}          &  0.117 & \\textbf{  Prob(JB):          } &    0.230  \\\\\n",
+       "\\textbf{Kurtosis:}      &  2.194 & \\textbf{  Cond. No.          } & 2.75e+03  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 2.75e+03. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.745\n",
+       "Model:                            OLS   Adj. R-squared:                  0.742\n",
+       "Method:                 Least Squares   F-statistic:                     285.9\n",
+       "Date:                Tue, 31 Oct 2023   Prob (F-statistic):           8.13e-31\n",
+       "Time:                        10:54:42   Log-Likelihood:                -1198.3\n",
+       "No. Observations:                 100   AIC:                             2401.\n",
+       "Df Residuals:                      98   BIC:                             2406.\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const       1.019e+05   1.19e+04      8.550      0.000    7.83e+04    1.26e+05\n",
+       "size         223.1787     13.199     16.909      0.000     196.986     249.371\n",
+       "==============================================================================\n",
+       "Omnibus:                        6.262   Durbin-Watson:                   2.267\n",
+       "Prob(Omnibus):                  0.044   Jarque-Bera (JB):                2.938\n",
+       "Skew:                           0.117   Prob(JB):                        0.230\n",
+       "Kurtosis:                       2.194   Cond. No.                     2.75e+03\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 2.75e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(Y, x).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the regression line on the initial scatter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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86623kJiYiAceeKDRsezsbCiVSkRFRWHlypW4dOkSFi5ciJycHKxbtw4VFRUAAH9/f5PzAgICUFxcDACorKw0exwAqqqq7NKHLXx9ZZU728zHR2nyt7diHPQYh1sYCz1XxKHfnTFQ+iixYU8WCust5g9XBWDC8HgkJ4hcV1dTInobIwAo670JNdEPwMfMMb4f9OQcB9kkY7t27cKJEyewe/dus8dnzJiB8ePHQ61WAwDi4uIQGRmJRx55BGfOnEFgYCAA/doxw78BfYIUFBQEAAgMDDQuxK9/HABatGhhlz6spVQqoFYHW32+nKlUQa6+BLfAOOgxDre4KhZ1OgEZF26gsKQS4apAdL0jAj4unMp0dhyGD+iIIf1utz4GZ+cDp+eKa9tqADD0e4QozaVhpvizoSfHOMgmGdu+fTtu3LhhXDhv8Oabb+K///0vVq9ebUzEDGJj9cPJBQUFxqlFjUaD9u3bG9toNBrEx8cDAGJiYqDRmBbsM3wdHR2N2tpam/uwlk4noKTkZvMNPYiPjxIqVRBKSipQV6dz9eW4DOOgxzjc4spYpGVqGo8KhQZgwggJo0J24ur3RNuIILSN0P/iLylu/vNZeTMXLQ+K35ao5O5jqFN1A4orLbZzdRzchTvGQaUKEjVSJ5tkbPHixaisNH1DDh8+HM8++yz++Mc/Yvbs2dBoNFi7dq3x+JkzZwAAnTt3Rrt27RASEoLjx48bE6mSkhJkZGRg4sSJAIDk5GRs3rwZdXV18PHR/y/k2LFj6NixIyIiIhAaGmpzH7aorXWPN5ez1dXpvPa118c46DEOtzg7Fobq8w0VllZh+bbTLit/4fbvCUGA6vREBGjMz+w0dLPddJQnLNJ/IeF1uX0cnESOcZDNxGp0dDQ6dOhg8gcAIiIiEB0djREjRuDo0aP46KOPcOnSJRw8eBCvvfYaRo8ejU6dOsHf3x8TJ07E4sWLceDAAWRmZuL5559HTEwMhg8fDgAYO3YsysrKMGfOHJw/fx47duzA2rVrMX36dACwSx9ERHLkiurznsBP+wMi97cUnYhdv/fXW4kYeQ3ZjIw1Z8iQIXj//fexatUqfPbZZwgNDcUDDzyA5557ztjm2WefRW1tLebOnYvKykokJyfj888/h5+fHwB9Yrd69WrMnz8fqampiIyMxOzZs5GammrXPoiI5EZK9fmEDmqL7cTS6QT51jvTVSH8cE9R2xgB+v0kuY2R91IIgsD/xshAXZ0OhYXlrr4Mp/L1VUKtDoZWWy67IWd7Yhz0GIdbXBGLYxkFWPXvjGbbTftjV/TvGtNsu+akZ2mwcX+OSQKoDg3A+KGxxqlQd31PBF5eJ3obo7rAtii8+6RN2xi5axyczR3jEB4e7FlrxoiIyHUcXX2+vqbWpmlLq7Bi51m33ZpJUaURvY0RABT1+Ro1avEV98lzMRkjIqJmGarPW5qqtKr6fANi16Ylxjbe9s6VIvepRLetinoQJT2+ALiNEf0f2SzgJyIi13FE9XlzpKxNcwctf0qVlIjduOc0SnquZyJGJpiMERGRKEnxUZiZ2q3R/ozhoQF2mzosKhe3U4nYdg5TV4HIfSr43zggqnl55ze4nyQ1idOUREQkWlJ8FBJjIx12l6Mz16ZZS8pImAAlbtx3CYKv+HPI+zAZIyIiSZRKhd3KVzTkrLVp1vDXfIWWp8aJbl/ccyOqo0Y78IrIUzAZIyIit2FYm2bubkoDe6xNk0rKaBgAXBuqBRTN7ydJBHDNGBERuRlnrE0TK+zHoZISsbLYv+HasBImYiQJR8aIiMjtOHptWrMEHSL3h0k65dqwEsdcC3k8JmNEROSWHLk2zRKpU5Llnd/AzY6zHHQ15A2YjBEREQHwKc9G+JE+ks7haBjZA5MxIiLyelJHwwrvSkddsOUiuERicQE/ERF5rRbn35F+p+SwEiZiZFccGSMiIq8kvVxFEaDgGAbZH5MxIiLyKlKTsJqw/ihK3uugqyFiMkZERF5CUVuCVt+1lXQOF+iTMzAZIyIijyd1NIxbGZEzMRkjIiKPJXU/SYCjYeR8TMaISFZ0OgGZeVrXVGUnWZE6Gnb9vssQfKWdQ2QPTMaISDaOnP4dn+44jcLSKuNj6tAAjB8a69T9Csm9hf04FH7FP0o6h6Nh5EpMxohIFtIyNVi+7XSjx7WlVVix86zTN5AmN8T9JEmmWDCFiNyeTidgw54si2027c+BTic46YrI3UTuU0lKxMo7vsREjNwGR8aIyO1l5xeZTE2aU1hahez8IpdsLE1N0+kEZOcXOW6NX3Em1F93kXQKkzByN0zGiMjtFZVbTsSktiPnSM/SYOP+HGgdtMZP/XWIpPaFd51AXXCczc9LZG+cpiQitxcWHGDXdp6gTifgXG4hjmUUIDNP63ZTtOlZGqzYedYkEQNurfFLz9JY3bf1+0kyESP3xJExInJ7ce3CEB4aYHGqMjxUPwXmDdIyNdi4Lxs3iiuNj7nTXaU6nYCN+3Msttm0PweJsZGSpyy5nyR5Ir5DicjtKZUKTBgRb7HNuKGxXlFvLD1Lf1dp/UQMsM+Ik71k5xc1GhFryLDGT6zIfSpJiVhNy2T92jAmYiQDfJcSkSwkJ0Th1ceSER5qOhUZHhrgFmUtDMVoHTltKHbEydVTlvZc46eoLbFqSrKo7wFJ5xC5EqcpiUg27upxG+LbqJBxsdCtKvA7eqG6gZQRJ1feVWqvNX7cT5K8BZMxIpIVpVLhVuUrDAvVG3JEMVq53FUa1y4M6tAAi4mjpTV+AVc2Q3V2mqTnZLkKkjNOUxIRWcnZ04ZyuatUqVRg/NBYi22aWuMXuU8lLRF7uBjaP5RJvUQit8JkjIjISo5YqG6JYcTJErneVRp+ME7ytKT2D2WAHzf2JvljMkZEZCVnTxvaMuLkTJJGDAUdIvep4FNdILr/a8NKOC1JHoXJGBGRlVwxbZgUH4Vn/tQDES0DTR53l7tKAfEjhtEHWkrcT3IWkzDySFzAT0RkJVsXqlsrOSEKQ/rdjuOnLuNGSaXb3FVq0NxI4O1BuVje9TlJfbprEubwvTfJK8h2ZOzixYtITEzEjh07jI+dO3cOEydORK9evZCSkoIvvvjC5BydTocPP/wQAwcORK9evTB16lTk5+ebtHFGH0TkGVw5beijVKDL7eHo3zUGCR3UbpUAWBoJ3J00RlIiVtj/iNsmYulZGrz0yREs3HQSq/6dgYWbTuKlT464ReFdkhdZJmM1NTWYNWsWbt68aXxMq9ViypQpaN++PbZv346ZM2di8eLF2L59u7HNxx9/jI0bN+Kdd97B5s2bodPp8OSTT6K6utqpfRCR50iKj8LM1G6NFta707Shs5m70WBm+4+xO2mMpH6uDStBXWg3O16Z/Thy703yPrKcply+fDlCQkJMHtu6dSv8/Pwwb948+Pr6olOnTsjLy8OqVaswduxYVFdXY82aNZg1axYGDx4MAFi2bBkGDhyIvXv3YvTo0U7pg4g8T1J8FBJjIzld9X8MI4aG+muSkzA330/SkXtvkndy33d7E9LS0rBlyxYsWLDA5PETJ06gb9++8PW9lV/2798fubm5uH79OjIzM1FeXo4BAwYYj6tUKnTt2hVpaWlO64OIPJOhGK07Thu6QlJ8FHYnjZGUiOn8o2Sxn6SzS5qQ55PVyFhJSQlmz56NuXPnonXr1ibHCgoKEBcXZ/JYVJR+euDKlSsoKNDfNt3wvKioKOMxZ/TRqlUrCa/YlK+ve39A2ZuPj9Lkb2/FOOgxDre4eywUVVcR9m0nSecYCrdK+aXkqjiUVtSIbueMz213fz84i5zjIKtk7K233kJiYiIeeOCBRscqKyvh7+9v8lhAgH7NQlVVFSoqKgDAbJvi4mKn9WEtpVIBtTrY6vPlTKUKcvUluAXGQY9xuMXRsajTCci4cAOFJZUIVwWi6x0R8GluxG+jxBHBAV8AHSfBlg2unP2eaNe6peh2zvzc5s+GnhzjIJtkbNeuXThx4gR2795t9nhgYKBxEb2BIflp0aIFAgP1NXmqq6uN/za0CQoKclof1tLpBJSU3Gy+oQfx8VFCpQpCSUkF6up0rr4cl2Ec9BiHW5wRi7RMDTbsyUJhvem48NAATBgRj+SExjclBGa/jaBfF0l6DuM2Rtpyq67RVe+J29SBCA8NMIlNQ+GqANymDoTWytcmBX829NwxDipVkKiROtkkY9u3b8eNGzeMC+cN3nzzTfz3v/9FTEwMNBrTu1cMX0dHR6O2ttb4WPv27U3axMfHA4BT+rBFba17vLmcra5O57WvvT7GQc9cHLy11pOj3hNNbX5eWFqF5dtON7pLVOo2RtcHX4LgFwbY6dpd8bMxrt4NCmaPD4mFTifYbV9SMfgZoSfHOMgmGVu8eDEqKytNHhs+fDieffZZ/PGPf8S//vUvbN68GXV1dfDx8QEAHDt2DB07dkRERARCQ0MREhKC48ePGxOpkpISZGRkYOLEiQCA5ORkh/dBRPaVnqXBxv05Jguq1aEBGD801ivLSthKyp2C0QfETdfV5641w6QylDRp+N4LDw3AOL73SCLZJGNNjSpFREQgOjoaY8eOxerVqzFnzhw8+eSTOH36NNauXYu3334bgH6d18SJE7F48WKEh4ejTZs2WLRoEWJiYjB8+HAAcEofRGQ/TY3gGGo9eWudL1uIuVNQW1ohORHzlCSsPpY0IXuRTTLWnIiICKxevRrz589HamoqIiMjMXv2bKSmphrbPPvss6itrcXcuXNRWVmJ5ORkfP755/Dz83NqH0RkO9Z6cozmtjKSWjOsLuh2FN5z2oYrcm+GkiZEtlAIguC8CW2yWl2dDoWFjl8I6k58fZVQq4Oh1ZbLbv7fnhgHvYZxyMzTYuGmk82eN3tcosf9snTke6KpuPYIPY35cW9I6svRo2H82dBjHPTcMQ7h4cGetYCfiKi+5kZwpLYjPXObn0sdDStK2o2a8HvtfGVEnovJGBHJkqXNqK1pR3r1tzL6ssdf0NJP2uiWJ64NI3I0JmNEJEvmRnAaCg/VL6gmaQxbGUlxbagWUPg45oKIPJz89gwgIsKtERxLxg2N5eJ9iSL3qSTXDdPvJ8lEjMhaTMaISLYMtZ7UoaZTkeGhASxrIZGyqsCqJMzbpyV1OgGZeVocyyhAZp7WqUVeyXNwmpKIZM1Taz05c1cBqUlYecdZuNlZ2p2VnogFh8lemIwRkex5Wq0nZ/2SD856GS0ufSLpHG8fCTNgwWGyJ05TEhG5EcMv+YY3Jhh+yadnaZo4U5rIfSpJidiNgVlMxP6P2ILDnLIksZiMERG5CWf8krd2gb4usLXVz+lpxGwZVVhahez8IudcEMkepymJiNyE2F/yWZe0uCsiRFrnQh0i90ubyuVImHksOEz2xmSMiMhNiP4lX1YtqV+pI2GCTzCup1yRdI43YcFhsjcmY0QkK4ZSAp5056SB6F/yIf6i2vlr/ouWp/4s6Ro4GtY8Fhwme2MyRkSyceT07/h0x2kUemgpAbG/5OPbNz/dKHU0rChxO2paDZN0jreqv2VUU1hwmKTgAn4ikoW0TA3eW5dmkogB9r/L0JXssauAtQv0mYhJw4LDZE8cGSMit6fTCdiwJ8tim037c5AYGyn70QjDL/mGdcbCQwMwrpkRQMlJGPeTtImnFhwm52MyRkRuLzu/qNGIWEOGUgKeUPxV6i95qUkYwLVh9uJpBYfJNZiMEZHb88ZSAmJ+ySsqf0fkd3GS+mUSRuR+mIwRkdtjKQEzNioQJqF5ZevxKO220lFX43WcuXcoeT4mY0Tk9uLahSE8NMDiVKW3lBJQ/TQWATf2STqHo2H2xQ3Cyd54NyURuT2lUoEJI+IttvGGUgKR+1SSErEbA88xEbMzZ+0dSt6FyRgRyUJyQhRefSwZ4V5YSsD6/STbOOiKxDMU6T2WUYDMPK2sN8/mBuHkKJymJCLZuKvHbYhvo0LGxULvWKsj8/0kPW06T8oG4bzDkqRgMkZEsuItpQTkXq7CMJ3XkGE6T46jmd54Vy85B5MxIiIJHH0XXeBv6xGaMVPSOe6UhAHip/PkVqSXd/WSozAZIyISydHTblJHw8p7fIab0Y/a/Lz25qnTedwgnByFC/iJiERw5F101izQx3gB1W3GWf2cjuSp03n22DuUyBwmY0REzXDkXXSS75IcUogbI0px5vx1HD3rnncoevJ0HjcIJ0fgNCURUTMcMe1m7QL99CwNNu3PMSmA6253KHr6dB43CCd748gYEVEz7DntpqzItapmmCERW7HzbKOdCNyt4Kg3TOcZ7urt3zUGCR3Usn4t5HoOS8bKyspQUFDgqO6JiJzGXtNukftUiDjcQ/Tz1rTsY7xTUm4FRzmdRySeXacp9+/fj61bt+LkyZMoKyuDQqFARkYGAODxxx9H+/btMXPmTERGRtrzaYnIixgqujtzesjWabeWaSPhX3RE0nM2LFchxzsUOZ1HJI5dkrHy8nK88MILOHToEABAEBr/zywrKwtHjx7Fvn378Omnn6Jbt272eGoi8iJHTv+OT3ecdvp6KcO0m7kipgZNTbtJnZK8cffP0LW4o9Hj1k6VOrouWnO8pUgvkS3skow9++yz+OGHHwAAvXv3Rq9evbBmzRqTNgkJCfjhhx9w48YNzJgxA//9738RGhpqj6cnIi+QlqnB8m2nGz3urIruhmm3hnXGwkMDMM5MMmjvCvrWTJV62nZERJ7K5mTsq6++wg8//AB/f38sWbIEw4YNw82bNxslY59//jn27duHWbNm4fr161i/fj2efvppW5+eiLyATidgw54si22cUdFd1LSbg/aTlDpV6onbERF5KpsX8O/cuRMKhQJTp07FsGHDLLYdNmwYpk2bBkEQcODAAVufmoi8RHZ+UaM7CBsyrJdyNEt30UXuUzlsY28pdyjKbbE/kbezORn75ZdfAAD333+/qPajRo0CAOTm5kp+rhs3buCll15C//79kZiYiGnTpuHXX381Hp87dy7i4+NN/qSkpBiP63Q6fPjhhxg4cCB69eqFqVOnIj8/3+Q5zp07h4kTJ6JXr15ISUnBF198YXLcHn0QkTTuXtE9MP9zq8tVSGGYKg1v5g5FKYv9icj1bJ6mLCsrAwDRd0iq1fr/NdbU1Eh+rpkzZ0Kn02HVqlUIDg7GBx98gMmTJ2Pv3r0ICgpCVlYWnnrqKUycONF4jo+Pj/HfH3/8MTZu3IgFCxYgJiYGixYtwpNPPondu3fD398fWq0WU6ZMQUpKCt5++238/PPPePvttxEcHIyxY8farQ8iksadK7pLTcJKuyxHZdvHrH6+pPgoJHeJxu/aSuRfKUZokF+jqVJ3T16JyJTNI2NhYWEAgMuXL4tqbxjJMiRlYhUXF6NNmzb429/+hh49eqBTp054+umnodFokJOTA0EQcP78eXTr1g2RkZHGP+Hh4QCA6upqrFmzBs8++ywGDx6MhIQELFu2DAUFBdi7dy8AYOvWrfDz88O8efPQqVMnjB07FpMnT8aqVavs1gcRSRfXLqzRaFBDzq7obs1+kteGldiUiBkolQp079wKA7qZLzjqzskrETVmczKWmJgIANi8ebOo9mvWrIFCoUDPnj0lPU/Lli2xZMkSxMXFAQAKCwuxdu1axMTEoHPnzrh06RJu3ryJO+5ofEs4AGRmZqK8vBwDBgwwPqZSqdC1a1ekpaUBAE6cOIG+ffvC1/fWgGH//v2Rm5uL69ev26UPIpJOqVRgwoh4i22cWdHdmv0kpU5J2sKw2N8SOW9HRORpbJ6mHDt2LPbu3YutW7fijjvuwGOPmf9fX1VVFRYtWoQDBw5AoVBgzJgxVj/n66+/jq1bt8Lf3x+ffPIJWrRogezsbADA+vXrcejQISiVSgwaNAjPP/88QkNDjbsBtG7d2qSvqKgo47GCggJjslf/OABcuXLFLn20atXK6tft6+tdu1f5+ChN/vZWjINe/ztjEBIcgE93nkZhSb3SEqoATBgej+QEx98ZqP46RPI52j+U2X0TYDHviYkj4s2WAjGYMCIe/v4+TR6XA/5s6DEOenKOg82fEffeey9GjRqF//73v1iwYAHWr1+Prl27Go8vXboUv/32G3744QcUFxcbz6m/sF6qxx57DI8++ig2bNiAmTNnYuPGjcjOzoZSqURUVBRWrlyJS5cuYeHChcjJycG6detQUVEBAPD39zfpKyAgwHhdlZWVZo8D+mTSHn1YS6lUQK0Otvp8OVOpglx9CW6BcQDu6hGEft1aI+PCDRSWVCJcFYiud0TAx9EjYiU5wH/imm9X33j9nYqOLHdq6T0xfEBHhAQHYNWuM7hRXGl8vFVYEKY+2A139bjNgVfmXPzZ0GMc9OQYB7v8h23BggXw8fHB7t27cfnyZfz2229QKPQfjp999hmAW1X5Bw8ejCVLltj0fJ07dwYAzJ8/H6dOncKXX36J+fPnY/z48ca1aHFxcYiMjMQjjzyCM2fOIDAwEIB+3Zfh34A+QQoK0n/jAgMDUV1dbfJchgSqRYsWdunDWjqdgJKSm1afL0c+PkqoVEEoKalAXZ3O1ZfjMoyDniEO5WWVaBsRhLYR+p+5kmLH/lxIHQ2rC0lAycATgLbcQVck/j3RpV1LLJl5N7IuaVFUVo2wEH/Et9evMdM68PqchT8beoyDnjvGQaUKEjVSZ5dkzN/fH4sWLcJDDz2EzZs3Iy0tDYWFhcbjISEhSEpKwsMPP4yhQ4da9RyFhYU4evQoRowYYVyPpVQq0blzZ2g0GiiVykY3BcTG6mvyFBQUGKcWNRoN2rdvb2yj0WgQH69fixITEwONRmPSh+Hr6Oho1NbW2tyHLWpr3ePN5Wx1dTqvfe31MQ56zopD+OGe8Km4KOkc47owO16fue2MDIPvYmMR2zbMpD9Pqy/Gnw09xkFPjnGw61KGAQMGGBe3l5eXo6ysDEFBQVCppG8L0tD169fxwgsvYPXq1Rg4cCAAfXmMjIwMpKSkYPbs2dBoNFi7dq3xnDNnzgDQj6S1a9cOISEhOH78uDGRKikpQUZGhrEURnJyMjZv3oy6ujpjSYxjx46hY8eOiIiIQGhoqM19EJH7k7pAv/CudNQFWy7Iao2mtjOaOCIewwd0tPvzEZFr2HWV25UrV3D2rH77jeDgYERHR0OlUuHMmTP47LPPRJe/MCcuLg6DBg3C3/72N6SlpSE7OxuvvPIKSkpKMHnyZIwYMQJHjx7FRx99hEuXLuHgwYN47bXXMHr0aHTq1An+/v6YOHEiFi9ejAMHDiAzMxPPP/88YmJiMHz4cAD6mxHKysowZ84cnD9/Hjt27MDatWsxffp0ALBLH0TkvqwtV+GoRGzFzrONirdqS6uwfNtpHDn9u92fk4hcQyEYFnPZoLy8HPPmzcN//vMfDBo0CJ988onJ8bVr1+Lvf/87lEolxo8fj1deecWkGKtYpaWlWLJkCfbv34/S0lL06dMHr7zyinE68uuvv8aqVatw4cIFhIaG4oEHHsBzzz1nXEBfV1eHpUuXYseOHaisrERycjLeeOMNtG3b1vgcp0+fxvz585GRkYHIyEg8/vjjJkVk7dGHNerqdCgslP8aDyl8fZVQq4Oh1ZbLbsjZnhgHPYfGQVeLyAPhkk5xZKkKnU7AS58csVhFv1VYEBY/fZfHTTlKwZ8NPcZBzx3jEB4eLGrNmM3JWF1dHSZNmoSTJ09CEAR07NgRX3/9tUmb1atX4/3330dtbS0UCgVGjBiB999/35an9TpMxtzjB8sVGAc9R8VB6kgY4NhEDAAy87RYuOlks+1endjbZD2Yt+HPhh7joOeOcRCbjNk8Tbl161b89NNPAIBJkyaZrNkyePLJJ3Hs2DFjDbI9e/Zgz549tj41EZHVgnI/cMp+ktYQvZ1RWXXzjYjI7dmcjO3evRsKhQKTJk3CnDlzmrxjMCQkBK+++ioefvhhCIKALVu22PrURERWidynQkjO66Lbl8XOd2oFfdHbGYX4N9+IiNyezXdTGirf//nPfxbVfvz48di6dSt++eUXW5+aiAiA+fIP5rZGsmZK8uqQYqdts2Rg2M6ouTVj8e3VXr1mjMhT2JyM1dTUAIBxQ+7mxMTEAABu3vSuAqZE5BhNlX8YPzQWSfG3tkiSmog9mL4dOvhgdlwREjo4so5+Y0qlAuOHxmLFzrNNtpn6YDcolQomY0QewOZpSkNylZOTI6p9bm4uAPHJGxFRUyyVf1ix8yzSszRWlat4IH0XdNDf8S12/Za9JcVHYWZqt0YbfoeHBuCZP/XwqO2MiLydzSNjSUlJyMvLw8cff4w1a9YYt0FqyqpVq6BQKJCUlGTrUxORF9PpBGzc3/R/AtsE/IaRl8ZI6vOB9F2NHhO7fssRkuKjkBgbaaYCv7w3+CYiUzaPjBnWih07dgx//etf8dtvv5ltp9FoMGvWLHz77bcA9GvHiNyBTicgM0+LYxkFyMzTym7aR+7Xb63s/KIm11TtThqDld1miu6rvK6F2UQsPFSf/LiSUqlAQgc1+neNQUIHtdPXrxGR49k8MtajRw9MnToVn332Gb799lt899136Ny5M26//XYEBQWhoqIC+fn5yM7Ohk6nr/sxYcIE9OnTx+aLJ7KV2PVG7kru128Lc9OHn945A7cFXpHUj7kkzGDc0FgmP0TkcHbZm/KFF15ASEgIPv74Y1RVVSE7O9tkDZmhrqyvry+mT5+Ov/71r/Z4WiKbGNYbNWRYbzQztZtbJzRyv35bNZw+3J00RtL5hQOOoS6kK2a2b5zQhocGYJwXJLRE5B7skowpFApMnz4dqamp2LdvH44dO4arV6+iuLgYQUFBiImJQe/evfHggw82WYeMyJmaW28EAJv25yAxNtItR0bkfv32YCj/8EXcHySfW79mWFPrsjw1bkTkfuySjBlERUVhwoQJmDBhgj27JbI7S+uNDApLq5Cd7/yyBmLI/frtQYk6yYlYU4VbDeuyiIhcwa7JGJFciN5uxkVlDZoj9+u3lTvuJ0lEZC1Jydjvv/+uP8nXF1FRUSaPWeO221gnh1xD9HYzLixrYIncr99qaX+FOmeFpFMeSN+F8NAALBwicOqRiNySpGRsyJAhAID27dsbN/o2PCaVQqFARkaGVecS2UrMdjPuUNagKXK/fmuovw6R1H7j749i05VxADx/ypaI5E1SnTFBEIx3RjZ8zJo/RK5i2G7GEncuayD365fC2gr6hkTMwFOnbIlI/iSNjL333nsAgJCQkEaPEcmNYbsZuZY1kPv1iyE1CRuTvg11TXysedyULRF5DEnJWGpqaqPHRo8eDT8/P7tdEJEzyb2sgdyvvynWLNC3VLzV06Zsiciz2Hw35YwZM6BQKPD0008jMTHRHtdE5FRyL2sg9+uvz7fkZ6iPD5J0zrVhJUjP0gDpjQvgGvTtEiX7BJWIPJfNe1OePXsWhw8fRnV1tT2uh4i8VOQ+lVWJGKAfIRzZt12T7b75MV+fsBERuSGbR8Zu3rwJAIiNtbyYmIjIHHvUDNPpBBw/ZznZ8vQdCYhIvmweGevSpQsAID093eaLISLvIjkRG3YE2j+UNXpYyo4ERETuxuZk7M0330RISAhee+01bN++HWVljT8oiYjqs6ZchfYPZUDkALPHvH1HAiKSN5unKfft24dBgwbhq6++wty5c/H666+jbdu2CA8Ph7+/f5PnKRQKrFu3ztanJyI50dUi8kC4pFMMU5KWPqy8dkcCIvIINidjn3zyCRQK/RoMQzHXS5cu4dKlS2bbKxQKCIJgPIeIvIMj95P0xh0JiMhz2JyMJScn2+M6iMhDhWTOQlD+KknnSN3U27AjwYqdTZe38JQdCYjI89icjK1fv94e10FEHkjqaFjFbZNQdqe0jcANmtqRIDjQF8P6tEVibKRV/RIROZrNyZg52dnZKCgoQFlZGdRqNdq0aYP27ds74qmIyA05ckrSHJ1OQHZ+EWrqdHhiVBdkXy7CgfTLKK+sRXllLXYdzsXBU1cw3kO2iSIiz2K3ZKy4uBgff/wxvvrqK9y4caPR8Q4dOuChhx7ClClTuH0SkQeTmohdG3IdUDZ9s09z0rM0jUbDzNGWVmHFzrOYmdqNCRkRuRWbS1sAwOnTpzF8+HB88cUXuH79unEhf/0/ubm5WLZsGR588EEUFBTY42mJyI1YU67i2rASmxOxFTvPNpuI1bdpfw50OsHq5yQisjebR8auX7+OadOmobi4GL6+vnjggQdw3333oUOHDggKCsLNmzdx4cIFHDhwAF9//TUuXryI6dOnY+vWrQgI4G3mRHJn7X6SttLpBGzcnyP5PEPxV0/Zz5OI5M/mZOwf//gHioqKoFKpsGrVKvTq1atRm4SEBIwaNQqPPvoonnrqKWRnZ2Pbtm2YMGGCrU9PRC7k7LVh9Ymput8UFn8lIndi8zTlwYMHoVAo8MILL5hNxOrr27cv/t//+38QBAG7du2y9amJCPoRosw8LY5lFCAzT+uUKThrpyTtlYgBtiVULP5KRO7E5pGxy5cvAwCGDh0qqv3IkSPx3nvv4eLFi7Y+NZHdGO7GKyqvQliwvjioHGpSmVu8rg4NcOhdg5K3MUreh9qwfna/DmsTKhZ/JSJ3Y3MyFhISgqqqKlRViftfqlKpH4zz9XVIVQ3yEvZMnlyR0NiDYfF6Q466a9CVU5IN6XQCdDoBwYG+KK+slXQui78SkbuxOSPq168f/vvf/2Lnzp3461//2mz7b7/9FoB+ypLIGvZMnpyd0NiLmMXrm/bnIDE20vbEw4b9JB0hLVODL/dkSV4vFh4agHFunmATkXeyec3YM888gxYtWmDlypX497//bbHtL7/8giVLlsDf319U4tbQjRs38NJLL6F///5ITEzEtGnT8OuvvxqPnzt3DhMnTkSvXr2QkpKCL774wuR8nU6HDz/8EAMHDkSvXr0wdepU5Ofnm7RxRh9kvaZKGRiSp/Qsjei+dDoBa7/OtNhmzVfncOSXK05biyWWmMXrhrsGbRG5T+VWidiR079j+bbTzb728NAAzBhzJ2aPS8S0P3bF7HGJWDjjLiZiROSWbB4ZEwQBb775Jt588028/PLL2LZtG+6//37ExcVBpVKhqqoK+fn5OHToEP7973+jtrYWI0eOREZGBjIyMsz2OWbMGLOPz5w5EzqdDqtWrUJwcDA++OADTJ48GXv37kVlZSWmTJmClJQUvP322/j555/x9ttvIzg4GGPHjgUAfPzxx9i4cSMWLFiAmJgYLFq0CE8++SR2794Nf39/aLVap/RB1rF2NKipKc3MS9pmp7gqquuwevc5AO41dSl28bq1i9ydsZ+kVDqdgFW7zlhsExzoi/sHdIAqxB+hQf6yWftHRN7N5mRs1KhRJl+npaUhLS3NbFtBEKBQKPDNN9/gm2++MdtGoVCYTcaKi4vRpk0bTJ8+HXFxcQCAp59+Gg8++CBycnJw9OhR+Pn5Yd68efD19UWnTp2Ql5eHVatWYezYsaiursaaNWswa9YsDB48GACwbNkyDBw4EHv37sXo0aOxdetWh/dB1pMyGmSoIWVpSjOvoFTS87vT1KXYxevWLHKXvp/kRJTd+bHk55Eq65IWN4orLbYpr6zF1u9ujZa7UwJNRNQUm6cpzVXbb+qP2PbmtGzZEkuWLDEmYoWFhVi7di1iYmLQuXNnnDhxAn379jW5MaB///7Izc3F9evXkZmZifLycgwYMMB4XKVSoWvXrsbk0Rl9kPWkjgY1N6V55Ua5VdfhDhXc49qFQR1qOdGSetegteUqnJGIAUBRWbXkc6yZviYicjabR8YOHDhgj+uQ5PXXX8fWrVvh7++PTz75BC1atEBBQYExUTOIitL/b/jKlSvGLZhat27dqI3hmDP6aNWqlXUvGoCvr112r5INHx+lyd8RqkBR50WoAqFUKrCpmSnNX3ILrbquwtIq/Pp7MbrcLm0tlbUaxsFg4oh4LN92usnzJoyIh7+/j6jnUH8dIumatCMKAaW//Ta3FSG8pbjvvzmbDuQguUu0x0xZNvWe8DaMgx7joCfnONj8WdqmTRt7XIckjz32GB599FFs2LABM2fOxMaNG1FZWQl/f9M97gzbLVVVVaGiogIAzLYpLi4GAKf0YS2lUgG1Otjq8+VMpQoCAPRr2QIRuzMsTlW1CgtCv55tkXHhBgqbmdKsrNZZfU01gvO/H4Y4GAwf0BEhwQFYteuMSUxahQVh6oPdcFeP25rvdKMVycl4Aa7YSCi5ZQtEtAxsdqrSnMKSKvyurUT3ztb/Z8gdNXxPeCvGQY9x0JNjHGRZ7Ktz584AgPnz5+PUqVP48ssvERgYiOpq02kMQ/LTokULBAbq/1ddXV1t/LehTVCQ/hvnjD6spdMJKCm5afX5cuTjo4RKFYSSkgrU1ekTp/HD4iyOBo0bGouS4pvIv1Ls0GvzUwjQaq2b5pTKXBwMurRriSUz70bWJS2KyqoRFuKP+PZqKJUKi9fnU/wzVEfukXQd2j+U/d8/nPO6G/LxUWLamO54b535NanNyb9SjLYR8vuQNsfSe8KbMA56jIOeO8ZBpQoSNVInm2SssLAQR48exYgRI4zrsZRKJTp37gyNRoOYmBhoNKbrQgxfR0dHo7a21vhY+/btTdrEx8cDgFP6sEVtrXu8uZytrk5nfO2JnVthZmq3RovyDTWkEju3Qm2tDqFBfg67nvDQAHS6raXTvx/149BQbNsw478NBVGbYnXxVjd4/93V4zY886ceVtUZCw3yM8ZPrjsuNGTpPeFNGAc9xkFPjnGQTTJ2/fp1vPDCC1i9ejUGDhwIAKipqUFGRgZSUlLQqlUrbN68GXV1dfDx0a+TOXbsGDp27IiIiAiEhoYiJCQEx48fNyZSJSUlyMjIwMSJEwEAycnJDu+DbJcUH4XE2EiLv0wNC9yt3UjaErlWcHenCvq2SE6IQs87Iozff1ULf6z+T4bFBf71b2aQ644LROS5ZLPKLS4uDoMGDcLf/vY3pKWlITs7G6+88gpKSkowefJkjB07FmVlZZgzZw7Onz+PHTt2YO3atZg+fToA/TqviRMnYvHixThw4AAyMzPx/PPPIyYmBsOHDwcAp/RB9qFUKpDQQY3+XWOQ0EHdKDlSKhUYPzTWYh+BIhe3GygUwIwxd8ryF7Y1+0m6YyJmUP/73/X2cEwYFmexvSGBtmfRYCIie1EITdWScEOlpaVYsmQJ9u/fj9LSUvTp0wevvPIKYmP1v3RPnz6N+fPnIyMjA5GRkXj88ceNI1YAUFdXh6VLl2LHjh2orKxEcnIy3njjDbRt29bYxhl9WKOuTofCQtes1XEVX18l1OpgaLXlVg85mxsFMUxpZuUXYf+Jy6L7mjGmG5ITnJ+I2RIHTxkNA5qPg6XvdVJ8FHQ6AS99csTiaGl4aAAWzrjL7Uc+7fGz4QkYBz3GQc8d4xAeHixqzZiskjFvxmTM+h+sptYH7f3xEjZ/e77Z81sE+GLKqASXjYhZFQc320/SHsTEwdJasMw8LRZuOtns88wel2gsGuyu3PGXjiswDnqMg547xkFsMiabNWNE1jJMaTUUEixukf+fh3V226lJc8lH9IGWkvtx90RMrKa+14Djt5AiIrIWkzHyWuEh4oqItgp1z3IIDaflnm7/CaIj90jqw1OSMDEcuYUUEZEtmIyR1xJzx6XULYWcxbAQ3WB30hhJ52tC/wih73r53MFjB3L+fhORZ2MyRl5LqVSgX5cofPNjfpNt+naJcrvF3DqdgI3/t9WT1CQMAB5I3wUAUKcf8apyDoY7bOsnsQ3JtWwJEcmbN/3HmLyATicgM0+LYxkFyMzTWix+qtMJOH7OcimDH89pXL4peEPZ+UXQllZJTsRSf/qnMREDvLOcQ1J8FGamdmu0yXp4aABmpnbzmsSUiNwLR8bIY6qRSy3maUhqLCksrUJ2fpFb3V03MLsDBiZJO6d+EtbQpv05SIyNlOX33BpiigYTETkTkzEv5ynVyBuuoTIwjP6YG/WQ2911PkVpUB+9T9I538flNVvOwR0TTkezdNclEZGzcZrSi3lKNfL6a6iasml/TqPpRlndXbdRAZXEROzqkGLZJZzOImU6m4jI0Tgy5qXEJjBymL6ydrpRDnfXWVNB/4H0XfqRQKVCXgmnk3jKaDAReQ6OjHkpKQmMu7N29EfM/pWuvLtOaiL2WtY7eCz7a5MpWUPCaYmrE05n8pTRYCLyLBwZ81KeNH1ly+iP4e46S3saOps1o2G722TjD3GNF6J7SzkHMTeheNJoMBF5FiZjXsqTpq9snW5s7u46p91tqqtB5IEISacYKuj3t9DGHRNOexI77SjXu2eJyPMxGfNSclgvJZY9Rn+aurvOWeuLrBkNk7KVkaeWc5ByF60njQYTkWfhmjEv5e7rpaRyRDFPZ6wvCj3zpORE7NqwEqv2lDQknP27xiChg1o239umSL2L1pNGg4nIs3BkzIs5avrK0rSeI6f87Dn644z1RVKTsJrIESjq9U+rnssTSZ129KTRYCLyLEzGvJy9p68sTesBcPiUn72KeTpyfZE1U5IYL6BMWw7U6qSf66GkTjt6y80MRCQ/TMbIbglMc+t3zLFUId+VHLW+SPKU5JBr8PUPApeTN2bNtKOn38xARPLEZIzsQsy0niXuVlJA7C96VQt/ZOZpmx1VdNQCfU/ZV9Qa1k47eurNDEQkX0zGyC7ETOtZImXKzxkJiJhf9CFBflj9nwwUlVUbH2s47epbnAb1j0MkPbfYxfneXknelmlH7k1JRO6EyRjZhT3KAYjpw1kJiFKpwJ+HxOKTXU3/oi+rqGn0WP1p15GXOkt+XimJmNSN0T0Rpx2JyBMwGSO7sEc5gOb6cGYCkp6lweYD5qdd1aEBqK6pQ3llrdnju5PGAJekPZ+UUhWsJG+K045EJHesM0Z2IWYPREuaKykgtaaULZqqL2YwqEdry4mYBEVJuyXXDPOkfUXtxdNqqBGRd2EyRnYhpoisJc2VFHBWAiIm6duffrnRY7uTxkhOxK4NK0FN+L2SzgFYSZ6IyNMwGSO7sVQFf2Tfdjb17awEREzSV39UzAe1ViVh1lTQN2AleSIiz8I1Y2RX5tbvdG7TEi9/etTiec2tcXJWAiI2mQsO9MXmO0dL7t+WJMyAleSJiDwLkzGyu4ZlAzLztDZXsy81c+diQ/ZIQFQt/Jtt88Lty3BfxEFJ/dojCTNgJXkiIs/CaUpyOFunGHU6ock7G+t7dEhnmxMQXZ3lGwB2J42RlIhVRQyzayJm4IiN0YmIyDU4MkYOZ+sUY9al5kfWACBfU4bQIH+byhpk/1Zk9nGp68IA+46GmcOSDrd4804ERCR/TMbI4Wxd41S/wr0l/zmSh/8cybOtCKyZgTHJC/SHXAOUzlk8z0ry3ImAiOSP05TkcGLKXlha4xQW0vw6rvoMRWDTszSSzgNgkthYW67CWYkYNV0Tzpb3ABGRszEZI6ewZY1TfHu1VQVlv/gmC0fPFiAzTyu6GGxCezUS1TlOL1dB0jmzEDARkSNxmpKcxto1TmLuHjSntKIGn/0nA4D4aavoAy0x7w5JT8MkzEWkFAL29qlcInJvHBkjp7J225qmRtbEam7aKnKfCpH7VJL65GiYa3EnAiLyFBwZI7tz1J1t9UfWMnIL8Z+jeZL7MFdcVmoSVpS026ptjMi+uBMBEXkKJmNkV83d2WZromYYWYtrF4YfzhaIKnlRX/1pK6lJGOC+U5LeWNqBOxEQkaeQVTJWVFSEpUuX4n//+x/KysoQHx+PF198EX369AEATJkyBUeOHDE5p2/fvli/fj0AoKqqCgsWLMA333yDyspKpKSkYM6cOQgPDze2P3r0KBYtWoRff/0VrVu3xjPPPIP777/feNwefXgqw51tDRmmCEf2bYfj5zR2KUFg7ToyACgqu4nIfR0knfNN+/NuWybBW0s7cCcCIvIUsloz9sILL+DkyZNYunQptm/fji5duuCJJ57AhQsXAABZWVl46623cPjwYeOf5cuXG883HFu+fDnWrVuHCxcu4NlnnzUe//XXXzF9+nQMHDgQO3bswMMPP4zZs2fj6NGjdu3DE4m5s+2bH/PtWoLAmnVku5PG4IHfu0h6ngfSd7ltmQRvL+3AnQiIyBPIZmQsLy8PP/zwAzZu3IikpCQAwOuvv47vv/8eu3fvxsSJE3Hjxg307NkTkZGRjc6/evUqdu3ahZUrVxpH0pYuXYqRI0fi5MmTSExMxLp16xAfH4/nn38eANCpUydkZGRg9erVGDBggF368FRi7myzpLmNwptSfx1ZYVkltuw/b3Yfy4dj/om/tNkgqe8H0nfZ5RodRWxpB3e6ZkfgTgREJHeyGRlTq9VYtWoVunfvbnxMoVBAoVCgpKQEWVlZUCgU6Nixo9nz09PTAQD9+/c3PtaxY0dER0cjLS0NAHDixIlGCVP//v2Rnp4OQRDs0oensvWONcNaLmsY1pHddWdr/GVkfKPju5PGSErEDhYObJSI2XqNjiCltIOns/YuXSIidyCbkTGVSoV77zW9g23Pnj3Iy8vDa6+9huzsbISGhmLevHn44Ycf0KJFC4wcORJPP/00/P39cfXqVajVagQEmE5nREVFoaCgAABQUFCAmJiYRscrKiqg1Wrt0kf9tWVS+fq6b+4coQq0uY/SihqT1+jjozT5W4x+d8ZA6aPEhj1ZWBf3B8nXYC4Js3SNztBUHMyNAJrjimt2BGveD56KsdBjHPQYBz05x0E2yVhDP/30E1599VUMHz4cgwcPxmuvvYaqqir06NEDU6ZMwblz57Bw4UL8/vvvWLhwISoqKuDv33hbnYCAAFRV6UcXKisrG7UxfF1dXW2XPqylVCqgVgdbfb6j9WvZAhG7M3CjuNLqPoorasy+RpUqSFI/wwd0xPCL0iq3pv70T9QKfs22a+oanaFhHNq1binqvHatW7r1e0cqqe8HT8ZY6DEOeoyDnhzjIMtkbP/+/Zg1axZ69+6NxYsXAwDmzZuHl19+GS1b6n9BxcXFwc/PD88//zxmz56NwMBAs8lQVVUVgoL037iAgIBGbQxfBwUF2aUPa+l0AkpKblp9vjOMHxaH5dtOW33+xj1ZiAgNQHKCftG1j48SKlUQSkoqUFenE9WH+usQyc/b3GiYpWt0hqbicJs6EOGhASi0VNpBFYDb1IHQasudcakOZc37wVMxFnqMgx7joOeOcVCpgkSN1MkuGfvyyy8xf/58jBw5En//+9+No06+vr7GRMwgNla/ObVh6rCoqAjV1dUmI1cajQbR0dEAgNatW0OjMb37TKPRoEWLFggNDbVLH7aorXWPN1dTEju3wszUbo3KLISHBqBvlyh882N+s31s2JOFnndEmKz5qavTNfvalTd/RcQPiZKu99qwEqRnaRCakSV6yq+pa3QGc3EY11xphyGx0OkEj9qfUcz7wVswFnqMgx7joCfHOMgqGdu4cSPeeecdTJo0CXPmzIFCceuX4aRJk9C2bVu89957xsfOnDkDPz8/3H777YiMjIROp0N6erpxgf3Fixdx9epVJCcnAwD69OmDH3/80eQ5jx07ht69e0OpVCIpKcnmPjydpTvbAv19sevwRYvnW7OXoC3FW5Pio9CzUyu8sOIHlIlMyNxpv0NDaQdzCfA4D68zRkTkKWSTjF28eBHvvvsuhg0bhunTp+P69evGY4GBgRgxYgTeffdd9OjRA/fccw/OnDmDhQsX4oknnkBISAhCQkJw//33Y+7cuXj33XcRFBSEN998E3379kWvXr0A6BO61NRULF68GKmpqTh48CC++eYbrF69GgAQHR1tcx/ewHBnW0NR4eKmacXemRl2fDD8Sn6SdG3mKuj7+irx2Mh4SQVk3Wm/Q5Z2ICKSN4Ugk3oLK1euxLJly8weS01NxYIFC7BhwwZs2LAB+fn5iIyMxCOPPIJp06YZR6Ru3ryJd999F3v27AEADBo0CHPnzoVafStxOHToEBYtWoTc3Fy0bdsWzzzzDEaNGmU8bo8+rFFXp0NhobzX/WTmabFw08lm280el4iEDmr4+iqhVgdDqy1vNOQsdTRM2/cAalsmW2yTnqXBum+yRI2QGa7RGSzFwZswDrcwFnqMgx7joOeOcQgPDxa1Zkw2yZi384RkTKcT8NInR5rdS3DhjLugVCrM/mA5ej/J2lodXlzxg8U1ZPWv0Rnc8QPGFRiHWxgLPcZBj3HQc8c4iE3GPH8RE7kNw16CljS5l6Cgk5yIXRtWInljb19fpdnCsfVxv0MiIrInJmPkVNbsJaj+OgSR+8MkPY/UJMzWayQiIrKWbBbwk+cQu+Dcr+DfwNfjJfVtSxJmzTUSERHZiskYOZVOJ5gkOH0Tos0mOFKnJEtj56Py9mca9W9LAtXUXaFERET2xGSMnCY9S9OoHpY6NADj69XDsmaB/gPpu6DODkC/Ljk4fk5jsX8iIiJ3wzVj5BTpWRqs2Hm20Z2U2tIqrNh5FulZGsmJ2IPp241bGWlLq/DNj/kW+yciInJHHBkjh9PpBGzcn9Pk8d1JY4BL0vqUsp8kAGzan4PE2Eiu+SIiIrfDZIwcLju/yGxtMbVvIb7o+bikvqQmYQbutIURERFRfUzGyOHMbR20O2mMpD6qIu/HXv9lQHqG1ddx4v+mKs0t6rfnwn8iIiIpmIyRw4UF36rXNem29Xik9XZJ52v/UIbaWh3C8rQ2Xce3P/2Gb3/6rdGifjE3FhARETkKF/CTw8W1C4M6NAC7k8ZISsSKB2cA42/t1mXox1b1F/WLubGAiIjIkZiMkcNFH2iJL+L+IOmca8NKoAtqb/KYmO2UpNi4L9vijQWAfuG/TsftW4mIyHGYjJHjOGA/SUtbFY3s207SyJm2rNripuXArYX/REREjsI1Y+QQ1hRvFbuVkaWtigQAe37Ml/zclpi7AYGIiMhemIyRXSmrChBxKE7SOdbsJ2luq6LaWh32ptk3EQNMb0AgIiKyNyZjZDdSR8O0yftQG9bPbs//7U+XIdh5eVeLAF/EtQuzb6dERET1MBkjmwUU7IDqzGRJ51gzGtYcTVGF3fu84zYV640REZFDMRkjm0heoD9UCyh8HHItUWFBotoF+fugd1wr/HD2arNtI8MCodMJTMiIiMhheDclWaXlTw9ZdaekoxIxAEjp3RaKZnImhQJY9td78NjILs22BYDvTv6Olz45wnpjRETkMEzGSBpdFSL3qeB/Y7/oU5orV2Evvr5KjEhuZ7HNiOR28Pf3EdXWgAVgiYjIkZiMkWjhh3sg8kCk6Pba5P1OScLqeyQlFiP7tms06qVQACP7tsMjKbeKxnZq01JS3ywAS0REjsA1Y9QsZeUVRHwfL+kcZydh9T2SEouHBnXCtz9dhqaoAlFhQUjp3Ra+vrf+76HTCc1W32/IUAC2YUkNIiIiWzAZI4sivm0DZV2p6PbXUq4APsEOvCJxfH2VGN63fZPHs/OLmq2+bw4LwBIRkb1xmpLM8is8hMh9KtGJWLX6Hv1omB0TMZ1OwJnz13H0bAEy87R2nSK0NqlSBfnb7RqIiIgAjoxRQ4KAyP3S1lI1nJLU6QSzWxVJkZ6lwab9OSisN3qlDg3A+KGxSIqPktSXOVZX1WeFCyIisjMmY2TU4uJiBJ+fJ7p9Ue9dqIlIMXksPUuDjftzTKYApSZR6VkarNh5ttHjhrsaZ6Z2szkhi2sXBnVogOSpypKb1TY9LxERUUOcpiQoaksRuU8lOhGrCU3EtWElZhOxFTvPNkpwpJSGELOw3h53NSqVCowfGtt8wwa4TyUREdkbkzEvF5T3EVp910Z0++uD81HU/2Cjx+2VRIlZWG+4q9FWSfFRmJnaDepQcQlWeGgA96kkIiK74zSll1LUaNHqfx1Ety+/41Xc7PRqk8elJFGWSkOczLkm6nrsdVdjUnwUEmMjkZ1fhJM517DvxOUm244bGsttkYiIyO6YjHkjXTVUp/8iuvm1ocVobu8gscmRpXbpWRqLyVB99pwuVCoVSOigRkIHNeLahTVa8xYeGoBxdrpxgIiIqCEmY17I/8Z38C9sPNXYkLkF+k0Rmxw11U5KEVZHThfWHymz5W5QIiIisZiMeSFl1W8WjwsKf1wfel1Sn2LuTrSUREkpwtpwutAepTTqM4yUEREROQOTMS9UHTEEgsIfCqFxmYbrg3Mh+IVL7tNwd6K5khQGltZciZ3mHNanrcl0oT1KaRAREbkS76b0QrqgDihO/CdqQ7pD56tCXUBrFPf4EteGlViViBk0dXdieGhAs7XBxE5zJsbe2qjcHqU0LNHpBGTmaXEsw/47ABARERnIamSsqKgIS5cuxf/+9z+UlZUhPj4eL774Ivr06QMAOHr0KBYtWoRff/0VrVu3xjPPPIP777/feH5VVRUWLFiAb775BpWVlUhJScGcOXMQHn4rAXFGH+6gJuI+aAf8AAhCs4vzpbB2zZXUaU6xpTQSYyOtmrLkiBsRETmLrEbGXnjhBZw8eRJLly7F9u3b0aVLFzzxxBO4cOECfv31V0yfPh0DBw7Ejh078PDDD2P27Nk4evSo8fy33noLhw8fxvLly7Fu3TpcuHABzz77rPG4s/pwK3ZMxAwMa676d41BQge1qGRITBHW+tOcjqxH5ugRNyIiovpkMzKWl5eHH374ARs3bkRSUhIA4PXXX8f333+P3bt348aNG4iPj8fzzz8PAOjUqRMyMjKwevVqDBgwAFevXsWuXbuwcuVK40ja0qVLMXLkSJw8eRKJiYlYt26dw/ugphmmORuVllAFYNwQ0xEpe5TSMMfRI25EREQNyWZkTK1WY9WqVejevbvxMYVCAYVCgZKSEpw4caJRstO/f3+kp6dDEASkp6cbHzPo2LEjoqOjkZaWBgBO6YMsS4qPwqIZd2H2uETMGNMN7864G0v/ek+jqUFbS2k0xZk7ABAREQEySsZUKhXuvfde+Pv7Gx/bs2cP8vLyMHDgQBQUFCAmJsbknKioKFRUVECr1eLq1atQq9UICAho1KagoAAAnNIHNc8wzTmgWwy6d25ldgTKsMbMEmvqkTlqxI2IiKgpspmmbOinn37Cq6++iuHDh2Pw4MGorKw0SdQAGL+urq5GRUVFo+MAEBAQgKoq/S9WZ/RhC19f2eTOduHjozT5u6GJI+KxfNvpJs+fMCIe/v4+kp4zQhUoup2zvh/NxcFbMA63MBZ6jIMe46An5zjIMhnbv38/Zs2ahd69e2Px4sUA9AlRw2TH8HVQUBACAwPNJkNVVVUICgpyWh/WUioVUKuDrT5fzlQq83EbPqAjQoIDsGrXGdworjQ+3iosCFMf7Ia7etwm+bn6tWyBiN0ZJv011CosCP16toWPk9eMNRUHb8M43MJY6DEOeoyDnhzjILtk7Msvv8T8+fMxcuRI/P3vfzeOOrVu3RoajeldbhqNBi1atEBoaChiYmJQVFSE6upqk5ErjUaD6Ohop/VhLZ1OQEnJTavPlyMfHyVUqiCUlFSgrk5ntk2Xdi2xZObdyLqkRVFZNcJC/BHfXn8Hp1ZbbtXzjh8WZ3HEbdzQWJQUO+97ISYO3oBxuIWx0GMc9BgHPXeMg0oVJGqkTlbJ2MaNG/HOO+9g0qRJmDNnDhT1yjL06dMHP/74o0n7Y8eOoXfv3lAqlUhKSoJOp0N6erpxgf3Fixdx9epVJCcnO60PW9TWuseby9nq6nTNvvbYtmHGf+t0gk0FWhM7tzJ/V+f/bRie2LmVS74XYuLgDRiHWxgLPcZBj3HQk2McZJOMXbx4Ee+++y6GDRuG6dOn4/r1W3snBgYGYtKkSUhNTcXixYuRmpqKgwcP4ptvvsHq1asBANHR0bj//vsxd+5cvPvuuwgKCsKbb76Jvn37olevXgDglD48hb33g3Qlc6+FG4YTEZGzKASZ1FtYuXIlli1bZvZYamoqFixYgEOHDmHRokXIzc1F27Zt8cwzz2DUqFHGdjdv3sS7776LPXv2AAAGDRqEuXPnQq2+tSm0M/qwRl2dDoWF1k272ZuzqtP7+iqhVgdDqy132P9y5FBp3xlxkAPG4RbGQo9x0GMc9NwxDuHhwaKmKWWTjHk7d0nGDNXpm9LcHpRSOPoHy5mvxRbu+AHjCozDLYyFHuOgxzjouWMcxCZj8rv/k1xGbHV6OWyo7UmvhYiI5I3JGInmSdXpPem1EBGRvDEZI9E8qTq9J70WIiKSNyZjJJqj9oN0BU96LUREJG9Mxki0zm1aIjTIz2Iba/aDdAVH7W1JREQkFZMxEiU9S4OXPz2K0ooai+3GDY2VRS0upVKB8UNjLbaRy2shIiJ5YzJGzTKUgLC04D08NMAtSkHodAIy87Q4llGAzDytxbshk+KjMDO1W6MRMnd5LURE5B1kU4GfXENMCYjQID8smD4Avr6uze2tKeCaFB+Fnp1a4dufLkNTVIGosCCk9G7r8tdCRETeg8kYWSSmBERpRQ3O/1aMhA5qi+0cqakCrtrSKqzYebbJkS5zCdyetHy3qsBPRESejf/9J4vkUALC2gKuTU2/GhK49CyN3a+ViIioISZjZJEcSkBYU8CVFfiJiMhdcJqSLDKUgGhu8b4zS0DodAKy84tQVF6FsGDL11Zf/dE7KQmcK6dfiYjI8zEZI4sMJSAsbajtzBIQ5tZ4hTRT+8yg/uidHKZfiYjIO3CakprlLiUgmlrjVdZM7TOg8eidHKZfiYjIO3BkjERJio9CYmykyfRgXLswp42IiVnjZUnD0Tt3nH4lIiLvxGSMRFMqFaLWTzVc02WPpE3MGi9AX/Os/i4B4aEBGGemTIW7Tb8SEZH3YjJGdmVN4VUxxK7denRoZ4SHBIpKBA3Trw2vt6kEjoiIyBGYjJHdWFt4VQyxa7fCQwIl3f3o6ulXIiIiJmNkF2LrdiXGRlqV6DhyjZfY6VciIiJH4N2UZJWGG3Jn5mklF16VwrDGyxKu8SIiIjniyBhJZm5dWHCguLeSLXW7uMaLiIg8EZMxkqSpdWHllbWizre1bhfXeBERkadhMkai2Vrry151u7jGi4iIPAnXjJFoYmt9NYVruoiIiBrjyBiJJna9V3Cgr8m0Jdd0ERERNY3JGIkmdr3XjDHdoFQouKaLiIhIBCZjJJrYWl8J7dVMvoiIiETimjESjbW+iIiI7I/JGEliqPWlDjWdsgwPDbBpuyMiIiJvxWlKkoy1voiIiOyHyRhZhbW+iIiI7IPJmJfS6QSObBEREbkBJmNeyNzekurQAIxnLTAiIiKn4wJ+L2PYW7JheQptaRVW7DyL9CyNi66MiIjIOzEZ8yJi9pbctD8HOp3gpCsiIiIi2SZjn376KSZNmmTy2Ny5cxEfH2/yJyUlxXhcp9Phww8/xMCBA9GrVy9MnToV+fn5Jn2cO3cOEydORK9evZCSkoIvvvjC5Lg9+nAVMXtLFpZWITu/yDkXRERERPJMxjZs2ID333+/0eNZWVl46qmncPjwYeOfbdu2GY9//PHH2LhxI9555x1s3rwZOp0OTz75JKqrqwEAWq0WU6ZMQfv27bF9+3bMnDkTixcvxvbt2+3ah6uI3VtSbDsiIiKynawW8F+9ehVvvvkmjh8/jttvv93kmCAIOH/+PKZNm4bIyMhG51ZXV2PNmjWYNWsWBg8eDABYtmwZBg4ciL1792L06NHYunUr/Pz8MG/ePPj6+qJTp07Iy8vDqlWrMHbsWLv04Upi95YU246IiIhsJ6uRsV9++QV+fn7497//jZ49e5ocu3TpEm7evIk77rjD7LmZmZkoLy/HgAEDjI+pVCp07doVaWlpAIATJ06gb9++8PW9laP2798fubm5uH79ul36cCXD3pKWhIfqy1wQERGRc8hqZCwlJcVkDVh92dnZAID169fj0KFDUCqVGDRoEJ5//nmEhoaioKAAANC6dWuT86KioozHCgoKEBcX1+g4AFy5csUufbRq1Urai67H19f23HniiHgs33a6yeMTRsTD39/H5uexBx8fpcnf3opx0GMcbmEs9BgHPcZBT85xkFUyZkl2djaUSiWioqKwcuVKXLp0CQsXLkROTg7WrVuHiooKAIC/v7/JeQEBASguLgYAVFZWmj0OAFVVVXbpw1pKpQJqdbDV5xsMH9ARIcEBWLXrDG4UVxofbxUWhKkPdsNdPW6z+TnsTaUKcvUluAXGQY9xuIWx0GMc9BgHPTnGwWOSsRkzZmD8+PFQq/Vb9MTFxSEyMhKPPPIIzpw5g8DAQAD6tWOGfwP6BCkoSP+NCwwMNC7Er38cAFq0aGGXPqyl0wkoKblp9fn1dWnXEktm3o2sS1oUlVUjLMQf8e3VUCoV0GrL7fIc9uDjo4RKFYSSkgrU1elcfTkuwzjoMQ63MBZ6jIMe46DnjnFQqYJEjdR5TDKmVCqNiZhBbGwsAP3UoWFqUaPRoH379sY2Go0G8fHxAICYmBhoNKZFTw1fR0dHo7a21uY+bFFba983V2zbMOO/dTrBbeuL1dXp7P7a5Yhx0GMcbmEs9BgHPcZBT45xkN/EahNmz56NyZMnmzx25swZAEDnzp2RkJCAkJAQHD9+3Hi8pKQEGRkZSE5OBgAkJycjPT0ddXV1xjbHjh1Dx44dERERYZc+iIiIiOrzmGRsxIgROHr0KD766CNcunQJBw8exGuvvYbRo0ejU6dO8Pf3x8SJE7F48WIcOHAAmZmZeP755xETE4Phw4cDAMaOHYuysjLMmTMH58+fx44dO7B27VpMnz4dAOzSBxEREVF9HjNNOWTIELz//vtYtWoVPvvsM4SGhuKBBx7Ac889Z2zz7LPPora2FnPnzkVlZSWSk5Px+eefw8/PDwAQERGB1atXY/78+UhNTUVkZCRmz56N1NRUu/ZBREREZKAQBME9FwqRibo6HQoL3WdxvTP4+iqhVgdDqy2X3fy/PTEOeozDLYyFHuOgxzjouWMcwsODRS3g95hpSiIiIiI5YjJGRERE5EJMxoiIiIhciMkYERERkQsxGSMiIiJyId5NKROC4L4V8h3Jx0fpNttauBLjoMc43MJY6DEOeoyDnrvFQalUQKFQNNuOyRgRERGRC3GakoiIiMiFmIwRERERuRCTMSIiIiIXYjJGRERE5EJMxoiIiIhciMkYERERkQsxGSMiIiJyISZjRERERC7EZIyIiIjIhZiMEREREbkQkzEiIiIiF2IyRkRERORCTMaIiIiIXIjJGLnErl27MGrUKHTv3h33338/vv76a+Oxy5cvY/r06ejduzfuuecevP/++6irqzM5f8OGDRgyZAh69OiB8ePHIyMjw9kvwWa1tbX44IMPcN999yExMRETJkzAzz//bDx+7tw5TJw4Eb169UJKSgq++OILk/N1Oh0+/PBDDBw4EL169cLUqVORn5/v5Fdhm08//RSTJk0yecwer7u5PtyNuTh8++23GDt2LBITE5GSkoK///3vqKysNB6vqqrC22+/jQEDBiAxMREvvvgiCgsLTfo4evQoHnroIfTs2RMjR47EV1995ZTXYy1zcahv7ty5SElJMXnME98PgPlYaDQavPDCC+jTpw/69etn9nve3GejmM9Xd2IuDr/88gsmTZqExMREDB48GIsXL0Z1dbXxuCzfEwKRk+3atUvo2rWr8OWXXwp5eXnCxx9/LCQkJAg//fSTUF1dLQwfPlyYNm2akJWVJezbt0/o27ev8MEHHxjP37Fjh9CjRw/hX//6l5CTkyO89NJLQt++fYUbN2648FVJ9+GHHwp333238P333wu5ubnCnDlzhKSkJOHq1atCYWGh0K9fP+HVV18Vzp8/L2zbtk3o3r27sG3bNuP5y5cvF/r16yd89913wrlz54THH39cGD58uFBVVeXCVyXel19+KSQkJAgTJ040PmaP1y2mD3diLg5paWlCly5dhE8++US4ePGi8L///U8YNGiQ8MorrxjbvPLKK8LQoUOFtLQ04dSpU8KYMWOECRMmGI+fP39e6N69u7B06VLh/PnzwurVq4WuXbsKR44ccerrE8tcHOrbt2+fEBcXJ9x3330mj3va+0EQzMeiqqpKuP/++4VHH31U+OWXX4Sff/5ZGDVqlPDkk08a2zT32Sjm89WdNPUZ0bdvX+GNN94QcnNzhUOHDgkDBgwQ/v73vxvbyPE9wWSMnEqn0wn33XefsGDBApPHH3/8cWHlypXC7t27hW7duglFRUXGY5s3bxZ69+5t/EEaPny4sHDhQuPxmpoa4d577xVWrlzpnBdhJ3/84x+F9957z/h1aWmpEBcXJ+zZs0dYuXKlcM899wg1NTXG40uWLBGGDx8uCIL+gzkxMVHYsGGD8XhxcbHQo0cPYffu3c57EVYoKCgQpk+fLvTq1UsYOXKkyQetPV53c324C0txePHFF4XJkyebtN+5c6dw5513ClVVVUJBQYGQkJAg/O9//zMev3DhghAXFyf89NNPgiAIwuuvvy786U9/MunjhRdeEB5//HEHvirpLMXB4OrVq0L//v2FiRMnmiRjnvR+EATLsdi+fbvQq1cv4dq1a8bHDh06JAwZMkQoLS0VBKH5z0Yxn6/uwFIcDEm54TULgiC8++67wujRowVBkO97gtOU5FQXL17Eb7/9hgceeMDk8c8//xzTp0/HiRMncOedd6Jly5bGY/3790dZWRnOnTuHGzduIDc3FwMGDDAe9/X1RZ8+fZCWlua012EPERER+O6773D58mXU1dVhy5Yt8Pf3R0JCAk6cOIG+ffvC19fX2L5///7Izc3F9evXkZmZifLycpM4qFQqdO3a1e3j8Msvv8DPzw///ve/0bNnT5Nj9njdzfXhLizF4fHHH8fLL79s8phSqURNTQ3KysqQnp4OQP+6DDp27Ijo6GiTONSPk6F9eno6BEFwxEuyiqU4AIAgCHjllVfw4IMPom/fvibHPOn9AFiOxeHDh9G/f3+0atXK+NjAgQOxf/9+hISEiPpsbO7z1V1YikN4eDgAYNOmTairq8Ply5dx8OBBYzu5vid8m29CZD8XL14EANy8eRNPPPEEMjIy0LZtW8yYMQMpKSkoKChATEyMyTlRUVEAgCtXrhh/eFq3bt2oTWZmphNegf3MmTMH/+///T8MGTIEPj4+UCqVWL58Odq3b4+CggLExcWZtK8fh4KCAgDm42A45q5SUlIarfsxsMfrbq6P+r/MXMlSHLp27WrydU1NDdauXYtu3bohPDwcV69ehVqtRkBAgEm7hnEw97NUUVEBrVZr/KXmapbiAABr167FtWvXsHLlSnz66acmxzzp/QBYjsXFixfRp08frFixArt27UJtbS3uuecevPTSS1CpVBZjYfhsbO7z1Vwy7AqW4tC7d2/MmDEDH3zwAZYtW4a6ujr0798fb7zxBgD5vic4MkZOVVZWBgB4+eWXMXr0aKxZswZ33303nn76aRw9ehSVlZXw9/c3OcfwC6eqqgoVFRUAYLZNVVWVE16B/Zw/fx6hoaFYsWIFtmzZgoceegizZs3CuXPnvCoO9dnjdTfXh9zU1tZi9uzZyMnJwZtvvgkAqKioaPQagebjYPi6/mJnd5aZmYmPPvoIixYtMvt6ven9UFZWhl27diErKwtLlizBvHnzkJ6ejqeffhqCIHhNLMrKynDhwgVMmDAB//znP/HBBx8gNzcXr7/+OgD5vic4MkZO5efnBwB44oknkJqaCgDo0qULMjIy8I9//AOBgYGNflEYfjhatGiBwMBAAI1/mVRVVSEoKMjRl283V65cwYsvvoi1a9eiT58+AIDu3bvj/PnzWL58uaQ4GP5taCOnODRkj9fdXB9yUlZWhueeew4//vgjPvroI/To0QOA+dcImMYhICCgURvD13J4j1RVVWHWrFmYMWMGEhISzLbxpveDr68vWrRogSVLlhg/R1u2bImHH34YZ86cEfXZ6AmxWLRoEYqLi/Hhhx8CgHHadfLkyZg8ebJs3xMcGSOnio6OBoBGQ8SdO3fG5cuXERMTA41GY3LM8HV0dLRx6NlcG0PfcnDq1CnU1NSge/fuJo/37NkTeXl5XhOHhuzxupvrQy40Go2x3Mnnn3+Oe++913gsJiYGRUVFjX6h1I9D69atzcahRYsWCA0NdfwLsNGpU6eQk5ODjz76CImJiUhMTMSnn36K33//HYmJiThx4oRXvR9iYmLQsWNHYyIGALGxsQD05Sq8JRbp6elmPzcBIDc3V7ZxYDJGTnXnnXciODgYp06dMnk8Ozsb7du3R3JyMjIyMozTmQBw7NgxBAcHIyEhAREREejYsSOOHz9uPF5bW4sTJ04gOTnZaa/DVoZ1G1lZWSaPZ2dn4/bbb0dycjLS09NN6v8cO3YMHTt2REREBBISEhASEmISh5KSEmRkZMgqDg3Z43U314ccFBcX47HHHkNhYSE2bNjQ6HualJQEnU5nXMgP6NcUXb161di2T58++PHHH03OO3bsGHr37g2l0v0/+nv06IG9e/fiX//6F3bt2oVdu3bhz3/+M6KiorBr1y5069bNa94PgP51ZGZmmtSay87OBgB06NBB1Gdjc5+vchAdHd3oc9PwdceOHeX7nnDZfZzktVasWCEkJiYKu3fvNqkzduzYMaGyslIYOnSo8MQTTwjnzp0z1sFZvny58fwtW7YIPXr0EHbs2GGspdOvXz9Z1Rmrq6sTxo0bJ4wcOVI4evSocPHiRWHZsmVCly5dhJ9//lm4fv26kJycLLz88stCTk6OsH37dqF79+7Cjh07jH0sXbpU6Nu3r7B//36TWjrV1dUufGXSvPzyyya3rdvjdYvpw900jMPLL78s3HnnncLRo0cFjUZj8qe2tlYQBH2ZipSUFOHYsWPGOmP1+8jOzhbuvPNOYdGiRcL58+eFzz//3K3rjAlC4zg09OGHHzaqM+aJ7wdBaByLGzduCHfddZfw9NNPC9nZ2cKJEyeE0aNHC5MmTTK2ae6zUcznq7tpGIdDhw4J8fHxwrJly4S8vDzhyJEjwogRI4Rp06YZ28jxPcFkjFxizZo1QkpKinDnnXcKf/zjH4V9+/YZj+Xm5gpTpkwRunfvLtxzzz3C+++/L9TV1Zmcv3r1amHQoEFCjx49hPHjxwsZGRnOfgk2KyoqEt566y1h8ODBQmJiovDoo48Kx48fNx4/deqU8MgjjwjdunUT7rvvPmH9+vUm59fW1goLFy4U+vfvL/Tq1UuYOnWqkJ+f7+yXYRNzv3zt8bqb68Pd1I9DbW2t0L17dyEuLs7sH8NrLS8vF+bMmSP06dNH6NOnj/DCCy8IhYWFJv0ePHhQGD16tNCtWzdh5MiRwldffeX01yaFNcmYJ74fBMF8LC5evChMmzZN6Nmzp5CcnCy8+uqrQnFxsUmb5j4bxXy+uhNzcfjf//4nPPzww0KvXr2E++67T3j33XeF8vJy43E5vicUguBGBWeIiIiIvIz7LxwgIiIi8mBMxoiIiIhciMkYERERkQsxGSMiIiJyISZjRERERC7EZIyIiIjIhZiMEREREbkQkzEiIgc5fvw44uPjER8fb7I9CxFRfUzGiIiIiFzI19UXQETkqQIDA9G+fXvjv4mIzOF2SEREREQuxGlKIiIiIhfiNCURkUiCIODrr7/Grl27cPbsWRQXFyMkJAQdO3bEfffdh/HjxyM0NNTY/vjx4/jLX/4CAPjiiy/Qr18/AEBKSgp+++03Uc/Zpk0bfPvtt40eP3nyJDZs2IATJ07gxo0bCAoKQlxcHO6//3786U9/gp+fnx1eMRE5A5MxIiKRXnrpJezevdvksaKiIpw8edKYHH3xxRe4/fbb7facDdea6XQ6LFy4EP/4xz9MHq+urkZaWhrS0tKwdetWrFy5EtHR0Xa7DiJyHCZjREQi/Oc//zEmYo899hjGjh2LqKgoFBcX45tvvsHy5ctx9epVzJs3D2vWrLHY11dffQWdTtfk8VdffRV79uyBr68v3nrrLZNjH374oTERGz58OCZPnoxOnTqhtLQU+/fvx0cffYSMjAxMnz4dW7ZsQUBAgG0vnIgcjskYEZEIe/fuBQAMGDAAr732mvFxtVqNp556CrW1tVi+fDmOHDkCrVYLtVrdZF9BQUFNHlu1ahX27NkDAHjttdfQt29f47Hc3Fx8+umnAIBJkyZh7ty5xmNhYWGYMmUKkpKS8Oc//xnnzp3Dxo0bMWXKFOteMBE5DRfwExGJUF1dDUA/LVlbW9vo+Pjx47Fq1Sp89dVXJuvGpPj222+xbNkyAMDDDz+MCRMmmBzfsmULdDodgoKC8Pzzz5vto0ePHhg1ahQAYOvWrVZdBxE5F5MxIiIRkpOTAQDnzp3DI488go0bN+Ly5cvG4+Hh4bj33nvRqVMn+PpKn3TIycnBrFmzoNPpkJiYiDfeeKNRmx9//BEAcMcddwAAysvLzf7p2bMnAODChQvQarWSr4WInIvTlEREIkyYMAF79uzBqVOn8Msvv+CXX34BAHTs2BH33HMPhgwZgn79+kGplP5/XK1Wi6eeegrl5eWIjo7G8uXL4e/v36idIfn75Zdf0Lt3b1F9FxQUWJwyJSLX48gYEZEIgYGB+PLLLzF79mzjyBQAXLx4EevXr8fkyZMxdOhQHDp0SFK/NTU1ePbZZ3H58mUEBATgo48+QmRkpNm2ZWVlkq/bmnOIyLk4MkZEJJK/vz+eeOIJPPHEE8jNzcUPP/yAI0eO4OjRoygvL8dvv/2Gp59+Glu2bMGdd94pqs933nnHOP04b9489OjRo8m2gYGBKCsrw6hRo4xry4hI/jgyRkRkhdtvvx0TJkzAihUrcOzYMbz22mtQKBSoqanBpk2bRPXx5ZdfYsuWLQD05TLGjBljsf1tt90GAM0WjOUud0TywmSMiKgZFRUVeOKJJzBo0CBs2LCh0XF/f3889thjiIuLAwBcvXq12T6PHj2K9957D4C+XMbLL7/c7Dl9+vQBoF8zVlBQ0GS7N954A/369cPYsWM5TUkkA0zGiIiaERQUhKtXr+Lq1avYsmULqqqqGrUpLi7G77//DgBo3769xf7y8vLw3HPPoba2Fh06dMD7778PHx+fZq/jkUceAQDU1tbi7bffRl1dXaM2p06dws6dO1FUVISwsDCEhISIeYlE5EJMxoiIRHjiiScAAFlZWZgyZQq+//57Y4J28OBBPP744ygtLYWPjw8effTRJvspLS3FU089haKiIrRs2RKffvopwsLCUFlZ2WSpCkNdsy5dumD8+PEA9DXJ/vKXv+Dw4cMoLCzEpUuXsH79ekydOhU1NTUICAjASy+95PjAEJHNuICfiEiE1NRUnDp1Cps2bUJ6ejqefPLJRm38/PzwzjvvGKcrzdm/fz8uXLgAALh58yZSU1NRWVlpcZ3Xe++9h4ceegiAvip/dXU1tm3bhhMnThiTxPqCg4OxdOlSJCQkSH2ZROQCTMaIiER66623cN9992Hbtm04ffo0bty4AT8/P0RHR+Puu+/GpEmTmt0kvH7SVVNTg5qaGknX4Ofnh/nz52PMmDHYvHkzTp48iWvXrkGpVKJdu3YYOHAgHnvsMcTExFjzEonIBRQCb7shIiIichmuGSMiIiJyISZjRERERC7EZIyIiIjIhZiMEREREbkQkzEiIiIiF2IyRkRERORCTMaIiIiIXIjJGBEREZELMRkjIiIiciEmY0REREQuxGSMiIiIyIWYjBERERG5EJMxIiIiIhdiMkZERETkQkzGiIiIiFzo/wO96/p7qTcZ4QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# yhat = b0 + b1x1\n",
+    "plt.scatter(x1, Y)\n",
+    "yhat = 223.1787\t*x1 + 1.019e+05\t\n",
+    "fig = plt.plot(x1,yhat, lw=4, c ='orange', label ='Regression line')\n",
+    "plt.xlabel('size', fontsize = 20)\n",
+    "plt.ylabel('price', fontsize = 20)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Simple+Linear+Regression+Exercise+Solution.ipynb b/Udemy DataScience Workbook/Simple+Linear+Regression+Exercise+Solution.ipynb
new file mode 100644
index 0000000..d8721ce
--- /dev/null
+++ b/Udemy DataScience Workbook/Simple+Linear+Regression+Exercise+Solution.ipynb	
@@ -0,0 +1,491 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression - Exercise Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size.csv'. \n",
+    "\n",
+    "You are expected to create a simple linear regression (similar to the one in the lecture), using the new data.\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variable is 'size'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size\n",
+       "0  234314.144   643.09\n",
+       "1  228581.528   656.22\n",
+       "2  281626.336   487.29\n",
+       "3  401255.608  1504.75\n",
+       "4  458674.256  1275.46"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size\n",
+       "count     100.000000   100.000000\n",
+       "mean   292289.470160   853.024200\n",
+       "std     77051.727525   297.941951\n",
+       "min    154282.128000   479.750000\n",
+       "25%    234280.148000   643.330000\n",
+       "50%    280590.716000   696.405000\n",
+       "75%    335723.696000  1029.322500\n",
+       "max    500681.128000  1842.510000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['price']\n",
+    "x1 = data['size']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x1,y)\n",
+    "plt.xlabel('Size',fontsize=20)\n",
+    "plt.ylabel('Price',fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.745</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.742</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   285.9</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Tue, 31 Oct 2023</td> <th>  Prob (F-statistic):</th> <td>8.13e-31</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:07:41</td>     <th>  Log-Likelihood:    </th> <td> -1198.3</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   2401.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    98</td>      <th>  BIC:               </th> <td>   2406.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td> 1.019e+05</td> <td> 1.19e+04</td> <td>    8.550</td> <td> 0.000</td> <td> 7.83e+04</td> <td> 1.26e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>size</th>  <td>  223.1787</td> <td>   13.199</td> <td>   16.909</td> <td> 0.000</td> <td>  196.986</td> <td>  249.371</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 6.262</td> <th>  Durbin-Watson:     </th> <td>   2.267</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.044</td> <th>  Jarque-Bera (JB):  </th> <td>   2.938</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.117</td> <th>  Prob(JB):          </th> <td>   0.230</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.194</td> <th>  Cond. No.          </th> <td>2.75e+03</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.75e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.745   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.742   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     285.9   \\\\\n",
+       "\\textbf{Date:}             & Tue, 31 Oct 2023 & \\textbf{  Prob (F-statistic):} &  8.13e-31   \\\\\n",
+       "\\textbf{Time:}             &     11:07:41     & \\textbf{  Log-Likelihood:    } &   -1198.3   \\\\\n",
+       "\\textbf{No. Observations:} &         100      & \\textbf{  AIC:               } &     2401.   \\\\\n",
+       "\\textbf{Df Residuals:}     &          98      & \\textbf{  BIC:               } &     2406.   \\\\\n",
+       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &    1.019e+05  &     1.19e+04     &     8.550  &         0.000        &     7.83e+04    &     1.26e+05     \\\\\n",
+       "\\textbf{size}  &     223.1787  &       13.199     &    16.909  &         0.000        &      196.986    &      249.371     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       &  6.262 & \\textbf{  Durbin-Watson:     } &    2.267  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.044 & \\textbf{  Jarque-Bera (JB):  } &    2.938  \\\\\n",
+       "\\textbf{Skew:}          &  0.117 & \\textbf{  Prob(JB):          } &    0.230  \\\\\n",
+       "\\textbf{Kurtosis:}      &  2.194 & \\textbf{  Cond. No.          } & 2.75e+03  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 2.75e+03. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.745\n",
+       "Model:                            OLS   Adj. R-squared:                  0.742\n",
+       "Method:                 Least Squares   F-statistic:                     285.9\n",
+       "Date:                Tue, 31 Oct 2023   Prob (F-statistic):           8.13e-31\n",
+       "Time:                        11:07:41   Log-Likelihood:                -1198.3\n",
+       "No. Observations:                 100   AIC:                             2401.\n",
+       "Df Residuals:                      98   BIC:                             2406.\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const       1.019e+05   1.19e+04      8.550      0.000    7.83e+04    1.26e+05\n",
+       "size         223.1787     13.199     16.909      0.000     196.986     249.371\n",
+       "==============================================================================\n",
+       "Omnibus:                        6.262   Durbin-Watson:                   2.267\n",
+       "Prob(Omnibus):                  0.044   Jarque-Bera (JB):                2.938\n",
+       "Skew:                           0.117   Prob(JB):                        0.230\n",
+       "Kurtosis:                       2.194   Cond. No.                     2.75e+03\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 2.75e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(y,x).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x1,y)\n",
+    "yhat = x1*223.1787+101900\n",
+    "fig = plt.plot(x1,yhat, lw=4, c='orange', label ='regression line')\n",
+    "plt.xlabel('Size', fontsize = 20)\n",
+    "plt.ylabel('Price', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Simple+Linear+Regression+Exercise.ipynb b/Udemy DataScience Workbook/Simple+Linear+Regression+Exercise.ipynb
new file mode 100644
index 0000000..570b51c
--- /dev/null
+++ b/Udemy DataScience Workbook/Simple+Linear+Regression+Exercise.ipynb	
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression - exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size.csv'. \n",
+    "\n",
+    "You are expected to create a simple linear regression (similar to the one in the lecture), using the new data.\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables is 'size'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the regression line on the initial scatter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Simple+Linear+Regression+with+sklearn+-+Exercise+Solution.ipynb b/Udemy DataScience Workbook/Simple+Linear+Regression+with+sklearn+-+Exercise+Solution.ipynb
new file mode 100644
index 0000000..eb9c617
--- /dev/null
+++ b/Udemy DataScience Workbook/Simple+Linear+Regression+with+sklearn+-+Exercise+Solution.ipynb	
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression - Exercise Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size.csv'. \n",
+    "\n",
+    "You are expected to create a simple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "Apart from that, please:\n",
+    "-  Create a scatter plot (with or without a regression line)\n",
+    "-  Calculate the R-squared\n",
+    "-  Display the intercept and coefficient(s)\n",
+    "-  Using the model make a prediction about an apartment with size 750 sq.ft.\n",
+    "\n",
+    "Note: In this exercise, the dependent variable is 'price', while the independent variable is 'size'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size\n",
+       "0  234314.144   643.09\n",
+       "1  228581.528   656.22\n",
+       "2  281626.336   487.29\n",
+       "3  401255.608  1504.75\n",
+       "4  458674.256  1275.46"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size\n",
+       "count     100.000000   100.000000\n",
+       "mean   292289.470160   853.024200\n",
+       "std     77051.727525   297.941951\n",
+       "min    154282.128000   479.750000\n",
+       "25%    234280.148000   643.330000\n",
+       "50%    280590.716000   696.405000\n",
+       "75%    335723.696000  1029.322500\n",
+       "max    500681.128000  1842.510000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data['size']\n",
+    "y = data['price']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x17017efaf98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x,y)\n",
+    "plt.xlabel('Size',fontsize=20)\n",
+    "plt.ylabel('Price',fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transform the inputs into a matrix (2D object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_matrix = x.values.reshape(-1,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x_matrix,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7447391865847586"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_matrix,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101912.60180122912"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([223.17874259])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making predictions\n",
+    "\n",
+    "You find an apartment online with a size of 750 sq.ft.\n",
+    "\n",
+    "All else equal what should be its price according to the model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([269296.65874718])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.predict(750)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Simple+Linear+Regression+with+sklearn+-+Exercise.ipynb b/Udemy DataScience Workbook/Simple+Linear+Regression+with+sklearn+-+Exercise.ipynb
new file mode 100644
index 0000000..c1c4de7
--- /dev/null
+++ b/Udemy DataScience Workbook/Simple+Linear+Regression+with+sklearn+-+Exercise.ipynb	
@@ -0,0 +1,687 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression - Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size.csv'. \n",
+    "\n",
+    "You are expected to create a simple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "Apart from that, please:\n",
+    "-  Create a scatter plot (with or without a regression line)\n",
+    "-  Calculate the R-squared\n",
+    "-  Display the intercept and coefficient(s)\n",
+    "-  Using the model make a prediction about an apartment with size 750 sq.ft.\n",
+    "\n",
+    "Note: In this exercise, the dependent variable is 'price', while the independent variable is 'size'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size\n",
+       "0  234314.144   643.09\n",
+       "1  228581.528   656.22\n",
+       "2  281626.336   487.29\n",
+       "3  401255.608  1504.75\n",
+       "4  458674.256  1275.46"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data['size'] # INDEPENDENT\n",
+    "y = data['price']#dependent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100,)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transform the inputs into a matrix (2D object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_matrix = x.values.reshape(-1, 1)\n",
+    "y_matrix = y.values.reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 643.09],\n",
+       "       [ 656.22],\n",
+       "       [ 487.29],\n",
+       "       [1504.75],\n",
+       "       [1275.46],\n",
+       "       [ 575.19],\n",
+       "       [ 570.89],\n",
+       "       [ 620.82],\n",
+       "       [ 682.26],\n",
+       "       [ 694.52],\n",
+       "       [1060.36],\n",
+       "       [1842.51],\n",
+       "       [ 694.52],\n",
+       "       [1009.25],\n",
+       "       [1300.96],\n",
+       "       [1379.72],\n",
+       "       [ 690.54],\n",
+       "       [ 623.94],\n",
+       "       [ 681.07],\n",
+       "       [1027.76],\n",
+       "       [ 620.71],\n",
+       "       [ 549.69],\n",
+       "       [1207.45],\n",
+       "       [ 518.38],\n",
+       "       [ 525.81],\n",
+       "       [1103.3 ],\n",
+       "       [ 570.89],\n",
+       "       [1334.1 ],\n",
+       "       [ 681.07],\n",
+       "       [1496.36],\n",
+       "       [1010.33],\n",
+       "       [ 681.07],\n",
+       "       [ 597.9 ],\n",
+       "       [ 525.81],\n",
+       "       [ 857.54],\n",
+       "       [ 622.97],\n",
+       "       [ 823.21],\n",
+       "       [ 570.25],\n",
+       "       [ 685.48],\n",
+       "       [ 698.29],\n",
+       "       [1021.95],\n",
+       "       [ 682.26],\n",
+       "       [ 823.21],\n",
+       "       [1334.1 ],\n",
+       "       [1060.36],\n",
+       "       [ 698.29],\n",
+       "       [ 633.19],\n",
+       "       [ 698.29],\n",
+       "       [ 633.19],\n",
+       "       [ 617.05],\n",
+       "       [ 647.5 ],\n",
+       "       [1021.95],\n",
+       "       [1021.95],\n",
+       "       [ 727.88],\n",
+       "       [ 647.5 ],\n",
+       "       [1508.84],\n",
+       "       [1032.06],\n",
+       "       [1283.85],\n",
+       "       [ 827.84],\n",
+       "       [ 569.17],\n",
+       "       [ 828.16],\n",
+       "       [ 698.5 ],\n",
+       "       [1205.62],\n",
+       "       [1021.95],\n",
+       "       [ 685.48],\n",
+       "       [ 827.09],\n",
+       "       [1009.25],\n",
+       "       [ 549.8 ],\n",
+       "       [ 685.48],\n",
+       "       [1496.36],\n",
+       "       [1021.95],\n",
+       "       [ 643.41],\n",
+       "       [ 656.22],\n",
+       "       [ 549.8 ],\n",
+       "       [ 685.48],\n",
+       "       [ 685.48],\n",
+       "       [1183.46],\n",
+       "       [1334.1 ],\n",
+       "       [ 682.26],\n",
+       "       [1188.62],\n",
+       "       [ 681.07],\n",
+       "       [1122.34],\n",
+       "       [ 681.07],\n",
+       "       [ 643.09],\n",
+       "       [ 685.48],\n",
+       "       [1009.25],\n",
+       "       [ 479.75],\n",
+       "       [1028.41],\n",
+       "       [ 601.66],\n",
+       "       [1236.93],\n",
+       "       [ 694.52],\n",
+       "       [1071.55],\n",
+       "       [ 694.52],\n",
+       "       [ 698.29],\n",
+       "       [ 698.29],\n",
+       "       [ 549.8 ],\n",
+       "       [1037.44],\n",
+       "       [1504.75],\n",
+       "       [ 648.29],\n",
+       "       [ 705.29]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.fit(x_matrix,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7447391865847587"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_matrix, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101912.60180122912"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([223.17874259])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making predictions\n",
+    "\n",
+    "You find an apartment online with a size of 750 sq.ft.\n",
+    "\n",
+    "All else equal what should be its price according to the model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   size\n",
+       "0   750"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data = pd.DataFrame(data = [750, ], columns = ['size'])\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\base.py:458: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([269296.65874718])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\base.py:458: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>size</th>\n",
+       "      <th>predicted_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750</td>\n",
+       "      <td>269296.658747</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   size  predicted_price\n",
+       "0   750    269296.658747"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data['predicted_price'] = reg.predict(new_data)\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# yhat = b0 + b1x1\n",
+    "plt.scatter(x, y)\n",
+    "yhat = reg.coef_*x_matrix + reg.intercept_\t\n",
+    "fig = plt.plot(x,yhat, lw=4, c ='orange', label ='Regression line')\n",
+    "plt.xlabel('size', fontsize = 20)\n",
+    "plt.ylabel('price', fontsize = 20)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Simple+linear+regression+tutorial.ipynb b/Udemy DataScience Workbook/Simple+linear+regression+tutorial.ipynb
new file mode 100644
index 0000000..ed0318e
--- /dev/null
+++ b/Udemy DataScience Workbook/Simple+linear+regression+tutorial.ipynb	
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('1.01. Simple linear regression.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create your first regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data ['GPA']\n",
+    "x1 = data ['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(x1,y)\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(y,x).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(x1,y)\n",
+    "yhat = 0.0017*x1 + 0.275\n",
+    "fig = plt.plot(x1,yhat, lw=4, c='orange', label ='regression line')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(x1,y)\n",
+    "yhat = 0.0017*x1 + 0\n",
+    "fig = plt.plot(x1,yhat, lw=4, c='green', label ='regression line')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.xlim(0)\n",
+    "plt.ylim(0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(x1,y)\n",
+    "yhat = 0*x1 + 0.275\n",
+    "fig = plt.plot(x1,yhat, lw=4, c='red', label ='regression line')\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Simple+linear+regression_with_comments.ipynb b/Udemy DataScience Workbook/Simple+linear+regression_with_comments.ipynb
new file mode 100644
index 0000000..22dfaed
--- /dev/null
+++ b/Udemy DataScience Workbook/Simple+linear+regression_with_comments.ipynb	
@@ -0,0 +1,522 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "# We can override the default matplotlib styles with those of Seaborn\n",
+    "#import seaborn as sns\n",
+    "#sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "data = pd.read_csv('1.01.+Simple+linear+regression.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1714</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1664</td>\n",
+       "      <td>2.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1760</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1685</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1693</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>1936</td>\n",
+       "      <td>3.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>1810</td>\n",
+       "      <td>3.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>3.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>3.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>2050</td>\n",
+       "      <td>3.81</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>84 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SAT   GPA\n",
+       "0   1714  2.40\n",
+       "1   1664  2.52\n",
+       "2   1760  2.54\n",
+       "3   1685  2.74\n",
+       "4   1693  2.83\n",
+       "..   ...   ...\n",
+       "79  1936  3.71\n",
+       "80  1810  3.71\n",
+       "81  1987  3.73\n",
+       "82  1962  3.76\n",
+       "83  2050  3.81\n",
+       "\n",
+       "[84 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's check what's inside this data frame\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1845.273810</td>\n",
+       "      <td>3.330238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>104.530661</td>\n",
+       "      <td>0.271617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1634.000000</td>\n",
+       "      <td>2.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1772.000000</td>\n",
+       "      <td>3.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1846.000000</td>\n",
+       "      <td>3.380000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1934.000000</td>\n",
+       "      <td>3.502500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2050.000000</td>\n",
+       "      <td>3.810000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               SAT        GPA\n",
+       "count    84.000000  84.000000\n",
+       "mean   1845.273810   3.330238\n",
+       "std     104.530661   0.271617\n",
+       "min    1634.000000   2.400000\n",
+       "25%    1772.000000   3.190000\n",
+       "50%    1846.000000   3.380000\n",
+       "75%    1934.000000   3.502500\n",
+       "max    2050.000000   3.810000"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This method gives us very nice descriptive statistics. We don't need this as of now, but will later on!\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create your first regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Following the regression equation, our dependent variable (y) is the GPA\n",
+    "y = data ['GPA']\n",
+    "# Similarly, our independent variable (x) is the SAT score\n",
+    "x1 = data ['SAT']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot a scatter plot (first we put the horizontal axis, then the vertical axis)\n",
+    "plt.scatter(x1,y)\n",
+    "# Name the axes\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>           <td>GPA</td>       <th>  R-squared:         </th> <td>   0.406</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.399</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   56.05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Tue, 31 Oct 2023</td> <th>  Prob (F-statistic):</th> <td>7.20e-11</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:03:19</td>     <th>  Log-Likelihood:    </th> <td>  12.672</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>    84</td>      <th>  AIC:               </th> <td>  -21.34</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    82</td>      <th>  BIC:               </th> <td>  -16.48</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>    0.2750</td> <td>    0.409</td> <td>    0.673</td> <td> 0.503</td> <td>   -0.538</td> <td>    1.088</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>SAT</th>   <td>    0.0017</td> <td>    0.000</td> <td>    7.487</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>12.839</td> <th>  Durbin-Watson:     </th> <td>   0.950</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  16.155</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.722</td> <th>  Prob(JB):          </th> <td>0.000310</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 4.590</td> <th>  Cond. No.          </th> <td>3.29e+04</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.29e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &       GPA        & \\textbf{  R-squared:         } &     0.406   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.399   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     56.05   \\\\\n",
+       "\\textbf{Date:}             & Tue, 31 Oct 2023 & \\textbf{  Prob (F-statistic):} &  7.20e-11   \\\\\n",
+       "\\textbf{Time:}             &     11:03:19     & \\textbf{  Log-Likelihood:    } &    12.672   \\\\\n",
+       "\\textbf{No. Observations:} &          84      & \\textbf{  AIC:               } &    -21.34   \\\\\n",
+       "\\textbf{Df Residuals:}     &          82      & \\textbf{  BIC:               } &    -16.48   \\\\\n",
+       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const} &       0.2750  &        0.409     &     0.673  &         0.503        &       -0.538    &        1.088     \\\\\n",
+       "\\textbf{SAT}   &       0.0017  &        0.000     &     7.487  &         0.000        &        0.001    &        0.002     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 12.839 & \\textbf{  Durbin-Watson:     } &    0.950  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &  0.002 & \\textbf{  Jarque-Bera (JB):  } &   16.155  \\\\\n",
+       "\\textbf{Skew:}          & -0.722 & \\textbf{  Prob(JB):          } & 0.000310  \\\\\n",
+       "\\textbf{Kurtosis:}      &  4.590 & \\textbf{  Cond. No.          } & 3.29e+04  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
+       " [2] The condition number is large, 3.29e+04. This might indicate that there are \\newline\n",
+       " strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                    GPA   R-squared:                       0.406\n",
+       "Model:                            OLS   Adj. R-squared:                  0.399\n",
+       "Method:                 Least Squares   F-statistic:                     56.05\n",
+       "Date:                Tue, 31 Oct 2023   Prob (F-statistic):           7.20e-11\n",
+       "Time:                        11:03:19   Log-Likelihood:                 12.672\n",
+       "No. Observations:                  84   AIC:                            -21.34\n",
+       "Df Residuals:                      82   BIC:                            -16.48\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const          0.2750      0.409      0.673      0.503      -0.538       1.088\n",
+       "SAT            0.0017      0.000      7.487      0.000       0.001       0.002\n",
+       "==============================================================================\n",
+       "Omnibus:                       12.839   Durbin-Watson:                   0.950\n",
+       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               16.155\n",
+       "Skew:                          -0.722   Prob(JB):                     0.000310\n",
+       "Kurtosis:                       4.590   Cond. No.                     3.29e+04\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 3.29e+04. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add a constant. Essentially, we are adding a new column (equal in lenght to x), which consists only of 1s\n",
+    "x = sm.add_constant(x1)\n",
+    "# Fit the model, according to the OLS (ordinary least squares) method with a dependent variable y and an idependent x\n",
+    "results = sm.OLS(y,x).fit()\n",
+    "# Print a nice summary of the regression. That's one of the strong points of statsmodels -> the summaries\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a scatter plot\n",
+    "plt.scatter(x1,y)\n",
+    "# Define the regression equation, so we can plot it later\n",
+    "yhat = 0.0017*x1 + 0.275\n",
+    "# Plot the regression line against the independent variable (SAT)\n",
+    "fig = plt.plot(x1,yhat, lw=4, c='orange', label ='regression line')\n",
+    "# Label the axes\n",
+    "plt.xlabel('SAT', fontsize = 20)\n",
+    "plt.ylabel('GPA', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+1-+Exercise.ipynb b/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+1-+Exercise.ipynb
new file mode 100644
index 0000000..256c9c0
--- /dev/null
+++ b/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+1-+Exercise.ipynb	
@@ -0,0 +1,2330 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Species Segmentation with Cluster Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Iris flower dataset is one of the most popular ones for machine learning. You can read a lot about it online and have probably already heard of it: https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
+    "\n",
+    "We didn't want to use it in the lectures, but believe that it would be very interesting for you to try it out (and maybe read about it on your own).\n",
+    "\n",
+    "There are 4 features: sepal length, sepal width, petal length, and petal width.\n",
+    "\n",
+    "Start by creating 2 clusters. Then standardize the data and try again. Does it make a difference?\n",
+    "\n",
+    "Use the Elbow rule to determine how many clusters are there.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have already solved the first exercise, so you can start from there (you've done taken advantage of the Elbow Method).\n",
+    "\n",
+    "Plot the data with 2, 3 and 5 clusters. What do you think that means?\n",
+    "\n",
+    "Finally, import the CSV with the correct answers (iris_with_answers.csv) and check if the clustering worked as expected. Note that this is not how we usually go about clustering problems. If we have the answers prior to that, we would go for classification (e.g. a logistic regression)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.cluster import KMeans\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'iris_dataset.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width\n",
+       "0             5.1          3.5           1.4          0.2\n",
+       "1             4.9          3.0           1.4          0.2\n",
+       "2             4.7          3.2           1.3          0.2\n",
+       "3             4.6          3.1           1.5          0.2\n",
+       "4             5.0          3.6           1.4          0.2\n",
+       "..            ...          ...           ...          ...\n",
+       "145           6.7          3.0           5.2          2.3\n",
+       "146           6.3          2.5           5.0          1.9\n",
+       "147           6.5          3.0           5.2          2.0\n",
+       "148           6.2          3.4           5.4          2.3\n",
+       "149           5.9          3.0           5.1          1.8\n",
+       "\n",
+       "[150 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('iris_dataset.csv')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this exercise, try to cluster the iris flowers by the shape of their sepal. \n",
+    "\n",
+    "<i> Use the 'sepal_length' and 'sepal_width' variables.</i> "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data['sepal_length'], data['sepal_width'],)\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Sepal width')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clustering (unscaled data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Separate the original data into 2 clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "X = data.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Standardize the variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import and use the <i> method </i> function from sklearn to standardize the data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       [ 1.28034050e+00,  1.06445364e-01,  7.62758643e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
+       "         9.22063763e-01],\n",
+       "       [ 1.15917263e+00,  3.37848329e-01,  1.21768427e+00,\n",
+       "         1.44795564e+00],\n",
+       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
+       "         1.71090158e+00],\n",
+       "       [ 1.03800476e+00, -1.24957601e-01,  8.19624347e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [ 5.53333275e-01, -1.28197243e+00,  7.05892939e-01,\n",
+       "         9.22063763e-01],\n",
+       "       [ 7.95669016e-01, -1.24957601e-01,  8.19624347e-01,\n",
+       "         1.05353673e+00],\n",
+       "       [ 4.32165405e-01,  8.00654259e-01,  9.33355755e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [ 6.86617933e-02, -1.24957601e-01,  7.62758643e-01,\n",
+       "         7.90590793e-01]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import a library which can do that easily\n",
+    "from sklearn import preprocessing\n",
+    "# Scale the inputs\n",
+    "# preprocessing.scale scales each variable (column in x) with respect to itself\n",
+    "# The new result is an array\n",
+    "X_scaled = preprocessing.scale(X)\n",
+    "X_scaled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clustering (scaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a k-means object with 2 clusters\n",
+    "kmeans_scaled = KMeans(2)\n",
+    "kmeans_scaled.fit(X_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create a copy of data, so we can see the clusters next to the original data\n",
+    "Clusters_scaled = data.copy()\n",
+    "Clusters_scaled['Cluster_predict']= kmeans.fit_predict(X_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>Cluster_predict</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width  Cluster_predict\n",
+       "0             5.1          3.5           1.4          0.2                1\n",
+       "1             4.9          3.0           1.4          0.2                1\n",
+       "2             4.7          3.2           1.3          0.2                1\n",
+       "3             4.6          3.1           1.5          0.2                1\n",
+       "4             5.0          3.6           1.4          0.2                1\n",
+       "..            ...          ...           ...          ...              ...\n",
+       "145           6.7          3.0           5.2          2.3                0\n",
+       "146           6.3          2.5           5.0          1.9                0\n",
+       "147           6.5          3.0           5.2          2.0                0\n",
+       "148           6.2          3.4           5.4          2.3                0\n",
+       "149           5.9          3.0           5.1          1.8                0\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Clusters_scaled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xae3fe67e90>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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n76U+sjrHr41oSHIiUkbG/XeT8fQTQGWRPVwuMl58DvsH75O//BcwjKT1L5XoW36jxdmnoe/cAUqhAfq2rdg//wTPGWMoemkGWOTwFqKxW/qojaX/tKNZFMof/GT8caPOiudtnPFvFx2PDdS7jeXTrHx3twPNUKhAsI0Dm3RWvmjjtNdddD65/m1ES75eidSwZUv1xKTKz8buXWRdND7h3UpJSpE9eSL67l1oZYkJgBYIfoDY5s8j46nHktc/IURc/PaJwdJ/2gEqEhMAFdAIeGD+RU48hfVrY/tCg+/udlT83ZA2fPDRpU5K90SqMNYwJDkRKaHFny4HiDgbq+3LzxLWn1RmXbIIy5rVFcnIoTSlcL40DbzeBPdMCBFPy6fZ0Iyab6soU8NXAuvesdarjRUvWMO2gdIwvbBmZv3aiIUkJyIlGGt+jvh7DdBME/LzE9OhFGb9bgGqlttben4+xqaNCeqRECLelIKdi42Qqxk12fFd/W51b19gidiGMmHHgsTfTpfkRKSI5Ay6apSUvFdCiDipw8dJMj5xJDkRKSHQp2/E35cX2SM3NzEdSmG+4SPC3tIpZ+bkEujeI0E9EkLEm6ZB+6EBND1yatB+eP0Gq3Y8zh/+tg6g6dBxhAyIFWnqwPPTgfAZugZ4R56csP6kMt/wEfh7Hxn21o7SNFxXXgM2W4J7JoSIpwHXeFFmzbdcNF1hzYDe4331a+NqX/jbOppCt8KRk+rXRiwkORGp4fDDKf2/vwA1V/8NtGlL0az/JLpXqUnTKHz935ht2qI0reI9Kk9WvKPPoPTPNyavf0KIuOg6OsCgGz0AaJbKT0bNUBh2OP1NF/bs+rXR8bgAw+92V/zdkDasMHqGi4y2ib+xIxMhiJRR+vd/4Dv2OJrf/Gf07dsBUA4H7okXU/ywPBpbldmlKwXfLMYx603s772NfuAA/m49gpOwnXaGTMImRBMx5G9eDhsZYNV0K3t+NDDsiq6nBSdha94pPklD3nU+OgwPsOoVG7u+N9Ctii6nBuh7qZfsLskZ46Yp1bhG1wUCJvn5JcnuRkwsFp2cnEwKCkrw+81kdyeh0jX2dI0bJPZ0jD1d44b0jb2ucefmZmIYdf/SJF+vhBBCCJFSJDkRQgghREqR5EQIIYQQKUUGxIqUou/eheO16dg/nAsuF/6j83BPuQrfsGPj1oa2bx/ON2dg++B9tJISAn374brsCnzHnxicXOBQSmFd8A2OGS9jWbUSlZmJd8w4XBddimrdOm79EkIIESTJiUgZlmXfk33BWLRSF5oZnPTH2LYVx5z3Kb3+r5TceU/NyUMUjFUraXHuWWhFhWCaaICxdSv2D+fimnIlxQ89GtqGUjS77Wac019EGQZaIIACLL+swfncMxS+Pw9/vwH16pMQQohQcltHpIaSErInnh+SmABofj8AGc88gX3u7Pq14fWSPfE8tINFaGWJCYAWCLbhnP4SjllvhKxif2smzukvli0X7Fd5nR+t+CBZE86TAntCCBFnkpyIlOCY/R+0AwUhiUlVStdxPvdMvdqwz5+HsXtX2KnflabhfO7pyto1SpHx3DPBafNroAUCGHt2Y//vB/XqlxBCiFCSnIiUYP1uAUSotKuZJtaffqjXVQrrdwtQlvB3MjWlsGxYj3agIPj/iwqxrPsFLcJUQMpiwbpwQcx9EkIIUZ0kJyI1JGIuwEY13aAQQqQvSU5ESvAdexxEqLSrdB3f0QPrVczOd+yIijEsNbahafh7HIFqkRP8/1nZ+I/oFfa2DgTHxPiOHRFzn4QQQlQnyYlICe5zzkdlt0CFqQmjmSauP/1fvdrwnDGGQJu2Yav5akrh+tP1lU/raBqu624Ie1tH6QZm69Z4zjy7Xv0SQggRSpITkRoyMymc9S4qIyMkeVBGcIxI6XV/xjP23Pq1YbNR9O//oJo1D0mCyttzXXo57osmh6zinnARrsuuCFkOgldyVLNmFM76D9jt9euXEEKIEDLPiUgZ/sFDKVi4rGISNs3txtf/aNxTrsQ34vj4tNFvAPkLl+F8cwb2sknY/EceFZyEbeTJ1edR0TSKH34Mz2ln4pzxMpbVq1CZmXjKJ2Fr2zYu/RJCCFFJqhInULpWrYT0jT1d4waJPR1jT9e4IX1jl6rEQgghhEgLkpwIIYQQIqVIciKEEEKIlCIDYlORz4f9vx/geGMGxtbfMVu3xn3hRbjPGw8ZGcnuXd2ZJrZPP8bx2itYNqyHVi2xjzufwPiJqOwWye6dEKIRKd2jseZNK5vmWvCVarTqF6DfZT46jAjUtx6oSEGSnKSa0lKyJ56H7bsFKN1AMwPov2+h2bJlOF98jgOz56Nat052L2vn99P86ik45s2pqObLlt9w/vgj9n89zYEPPsLs0jXZvRRCNAJ7VujMPTcDXzEoM5iJHNyq8es8K/2v8jLiPo8kKE2M3NZJMc3+cRfWxd8BVBTB05RCQ2Fs2kjz665MZvfqLOPZJysK4lUttKcphb5nN9mXTEjMlPVCiEbN74YPJzpDEhMAFQj+vPJFG+veke/ZTY0kJylEKzyAY+braGbNj2NpgQD2r77A2LghwT2Lks+H84Xnws6sqgUCWNauCRb7E0KICDbNs+Daq4ckJiF0xfLnYi9rIVKTJCcpxLJiOZrHE3EZBVgXLUxMh2JkbP4Vff++iMsoi0WSEyFErXYsMtAsEa6ymhr5vxh4ixPXJ9HwJDlJJU3lNkdTiUMIkXzycZKWJDlJIf6j81C1VN3VAN+wYxPToRgFunXHzG0ZcRnN78c3XKr5CiEiaz8sgPJHGO2qK3J7B7A1S1yfRMOT5CSFqOwWuCdeHLYyrzIMvCeeROCIngnuWZSsVlxX/QkVZvi8Mgz8vfrErV6OEKLp6nG2H2crE80IcwnF1Dj6Wm9iOyUanCQnKab4ngfwDRkGUJGkKE1DAYGu3Sj610tJ7F3dld4wFe/pZwGHVPPVdMzWbSh8bVb1IntCCHEIiwPOmOXCmgGaXpmglCcr/S730mu8P1ndEw1Enr9KNRkZFL43D/vc2dUnYTv/QsjMTHYP68ZioeiV17H9bz6O16Zj2bQBo2UurnHnU3rhJFSLnGT3UAjRSLQ52mTidyX8/EZwEjZ/qUbLowIcdZmPTifIJGxNkVQlTqB0rVoJ6Rt7usYNEns6xp6ucUP6xi5ViYUQQgiRFiQ5EUIIIURKkeRECCGEECkl6uRk//793HzzzQwbNoy8vDyuuuoqNm3aFHb5uXPn0qtXr2r/bdu2rV4dF42A30/GQ/eT26cbLdq0gMxMMieNR9+6JewqxuqVZJ99Gq0Oa02rDrnk5h2J45UXw7ehFNYvPiXr4gvJGTyAFqOOw/nMk2j5++MfTxS0gnyczz5F85EjoHt3MidegO3zT2SCOiGEqIOoB8ReeOGFmKbJnXfeSWZmJk899RQ//fQTn3zyCU6ns9ry/+///T9WrlzJ448/HvJ6bm4uRpVHTOtKBsQ2Em43uUOPxti5A0Vw8jgom+xR1znw3jz8h8xzYn9zBs2n3gCHLK8BvoGDODD/M6g6B4xp0uwv1+F8a2ZF5ePyv69yczkwez6BXr0bMMiaGRvW02Ls6Wj794FpokFF/9wXTODg09NC42ii0mp/P0S6xp6ucUP6xp4SA2ILCwvp2LEj999/P/3796d79+5ce+217Nmzhw0bai5Gt379enr16kXr1q1D/oslMRGNR/bF49F37gAqE42Kn02TFuPPgaoFDvfupfmNf655ecD64zIy/357SBvOV17A8dbM4HJllY81QDNNtIICsiedD1UqIidEIED2pPPR8vcH+1H2cnn/7O/8G+dL0xLbJyGEaGSiSk6ys7N57LHH6NkzOENpfn4+M2bMoF27dvTo0aPGddatW0f37t3r31PReJSWYv3mK8JNPaABmtcTcpJufu+doFTYdRTgeHNG5QumiXPas2G7oAUCGL9vCd5KSSDbl59h/La5IhmpiXPas6GJmRBCiBAxT8J211138c4772Cz2Zg2bRoZGRnVliksLGT37t0sW7aMWbNmUVBQQP/+/bn55pvp2rVr7J22NM5L4uWXtKK5tNUYWRZ9i1bL3UIF2P83H9911wNgXfJdxOU1QCstxeIuhWbN0LbvxNi2NXIbFgv2RQsxTz8jmu7Xi33xdyiLBc1f84yVGmDs2I5t9w7MwzonrF/JkC77e03SNfZ0jRvSN/aGijvm5GTy5MmMHz+emTNnct111zFr1iz69u0bskz5rR6lFA899BBut5tp06YxceJE5s2bR6tWraJuV9c1cnIaySypYWRlVR+b06RkRC5eCMGTtNVqVG7LOk7xmJOTCc0yobj291DTNBwOK45E7i92S51iyc7OgEa+H9dVk9/fI0jX2NM1bkjf2OMdd71niDVNkzPPPJMBAwbw0EMPVft9fn4+OTk5aGUf2C6Xi5EjR3L55Zdz1VVXRd1eIGBSVOSqT5eTxjB0srKcFBW5CASa8GX90lJaHNa21qsnpfc9iOe64ADYjGuuwPbOWxFv6+DM4MD2PcEXTJOso/uib9sadh2A4lnv4Bt9erQRxMz68Uc0m3B+2N8rQHXoSOGKNdDEx12lzf5eg3SNPV3jhvSNva5xZ2U5o7q6EtWVk/z8fBYtWsQf//hHLJbgqrqu06NHD/bs2VPjOrm5uSH/3+l00qlTJ3bv3h1N0yEa+0joQMBs9DFEZHPgO+4ErN9+XWPioABsNkquvBbK3oeiux+g1Ttvo6h53IkGlE66OOR9c11zHZl33lpjF5RhYHboiOukP1S0kQj+kafg7Hw4+vZtNY870TRKr74Ov9IS2q9kavL7ewTpGnu6xg3pG3u8447qJtG+ffuYOnUqixYtqnjN5/OxZs2aGge9vv322wwdOpTS0tKK14qLi/ntt9/CDqAVTUPhzHcx27UHypKRMhWPEs98N/Rx2tatOfjokzUvD/jyjqHk/kdC2nBdcQ2eCyYElyu7CqEIVnNWLVpQOPPdxF+dMAwKZ/0HlZMb7EfZy+X985x7Aa6rr01sn4QQopGJKjnp2bMnJ5xwAvfffz9Lly5l/fr13HrrrRQVFXHppZcSCATYu3cvbrcbgBNOOAHTNLnlllvYsGEDq1at4vrrryc3N5dzzjmnQQISKcLhIP/Hnym5/q+YubnBk7PTie+Pp5G/ZDn+E0+qtornkss48MnX+IYOR9lsKIsFs30Hiu97iAMff1l9bhBd5+Azz1M46128J/+BQOfD8fc9ipLb/07+gmUEevdJULChAj17kb/ge0puv5tA36OgSxd8o/5A4cx3OPivF9NijhMhhKiPqMecHDx4kMcee4zPPvuMgwcPMmjQIG699VaOOOIItm3bxqhRo3jooYcqko+ff/6Zxx57jJUrV6KUYsSIEdx22220b98+pg7LJGyNU7rGnq5xg8SejrGna9yQvrE31CRs9R4Qm2iSnDRO6Rp7usYNEns6xp6ucUP6xp4SM8QKIYQQQjQ0SU6EEEIIkVIkOUlFPh/22f8h+5wzyR3cnxanj8Lx+qtQ5amnevN6ybj7Tlr2OpxWHXJp2aUdza+YjL5rZ/zaiKEqsb57Fxn/fJCcE4eRM2QAza+6FOviCLPHJqIqsWnifO5pcgf0Cr5Xh7Uh+7wx6Gt/iV8bTcjeVTpf/MXOa4Oc/KsPfHuXjcLf6jbJXl3t/0Xn65vtzByWwaxjM/j2DjsFG8O3EfDA2rcszB7j5M3Bmcw+28natywEPHHtlhAiTmTMSQLV6d5caSnZE8/D9t0ClG6gmQGUpoGCQM+eHJg9H9W6df06UlxM7uB+GPv3V1T9rdgJLBYOfPgp/rxj6tdGDFWJLcu+J/uCsWilLjQzOEdI+VTwpdf/lZI77wmdfTURVYlNk5yTjsXyy5rq7xUaB59/Gc854SddS7f70CtfsrLgDgeaoVCB4LbSDIVmwOjpLrqcWv9CjGtmWvlqqh1NJ6QNgD8876bH2aGlAzyFMPfcDPauNEBXYGpoukKZGq2PDjDmP6XYs+rdrRDptt3LpWvckL6xy5iTNNHsH3dVXCkoP0FrSqGhMDZtpPl1V9a7jRbnn42+P3hloaJqbvnPfj/Z55xZ7zairkpcUkL2xPNDEhOgokZNxjNPYJ87O6SNRFQlbvbX67H8siYkjor3CkXza6+E4uJ6tdFU7FxisOAOB1CZNJT/bPrg4ylOinfW7wrKvlU6X021g9KqtaEC8Ok1Dgp/DW3j65sd7Pu57KPOLNtyZf/uW6XzzS2OevVJCBF/kpykEK3wAI6Zr6OFqVirBQLYv/oCY+OG2BspyMfyw9KIFYP1khLs774VexsxVCV2zP4P2oGCkMSkKqXrOJ97pvKFRFQlNk0c771NuEuL5YlQ5gP3xN5GE7LiRWvFFYxqlIbphzVvWOvVxsqXrWhhP7WCe9zqGZW1nYp3amyaawlJZEK6FdDY+IGFkt3xve0khKgfSU5SiGXFcjRP5JvgCrAuWhhzG/b/fRixFk15G/a5c2Juw7rgmzpVJbZ99GHlOt8tiDibq2aaWH/6AbxeAPRdwarEkWJRFgvWhQui6XoIfftWNK83chuA7ZuvYm6jKdn+bfgkAIJXK7YvqN+Mvdu+qaWNgMa2byrb2LXUqLhKEmmdXd837TpHQjQ2kpykkkQM/zET0UYM91ujjb1xDZVKD4nYJNG2IbuJEI2SJCcpxH90Hspmi7iMBviGHRtzG57Rp9f6ea0BnrPGxNyG74SRwUG8tbTh/WNltWDfscdFHB+idB3f0QOh7P0x23cg0LFTxFg0vz/4d2NkdjwMZbVFbgPwHndizG00JR2P94e/rQNouqLjiPqNAep0QiByG4ai0/GVbbQbHEDTI+/xmqFoN7j+A3WFEPEjyUkKUdktcE+8GBWm9ooyDLwnnkTgiJ6xN9KyFf6Bx4Q94SrAzMjEc8HE2NvIyMB33AkR21A2W0gBPPc556OyW4SNXTNNXH/6v8oXdD30/x/ahmEQOKwz3lNOjSGAyjbc554f9raOApSmUXLH3bG30YT0v8oX/paLptAscOTFvnq10e9KLyrshbngHtf3Mm/FK806KLqdGT5p0gxF9zF+MtvJJRYhUokkJymm+J4H8A0ZBlBxolaahgICXbtR9K+X6t3GgXc+wMzJDf7tstdU+c+GQeF7c+vdRtRViTMzKZz1Liojo6KCL4AyLACUXvdnPGPPDWkjEVWJi5/8F/6yAoI1VUs++OwLkBXn51AbqQ7DAoy4L1j0s2oyoBkK3QKjX3HRrEP9koDW/UxGPuYJJjuHtKEZcMo0Ny26hbZx4qNuWvYxARV8lBjK/lW06mty4j/d9eqTECL+ZJ6TBKrzc/A+H/a5s3G8MQNj6++YrVvjvvAi3OdfCJmZ8emM203m/ffgeGcW2sGDKJsN76hTKbn/YcwOHePTht9PxkP34Zj5GnphIZrNhveEkRy8/xHMw7vUuIq+cweO16Zj/3AumtuNr//RuKdcie+QOVEqKIXt809wvPoKlnW/YDZvjmfcebgnTUa1bBmfOEwT57NP4nxpGvq+/WAx8A0eysH7Hsbse1TEVdNx7oM9y3VWTbexa7GBxa5z2Mk++k72kN0tfh81+37WWT3dyrYFFjRNcdjIAEdN8ZHbs+b32O+GDe9b+GWWlZKdOpkdTPpM9HHEOD+WBniSOB23O6Rv3JC+sUvhvzJpkZw0Qekae7rGDRJ7OsaernFD+sYuk7AJIYQQIi1IciKEEEKIlCLJiRBCCCFSiiXZHRBNmGli+/RjHK+9gmXDemjVEvu48wmMn4jKblHjKvruXRUDYnG58B+dh3vKVfWa20UIbxEsvNvOxrkW/C4Ni1PRY4yfEfd6sDWih638blj8oI11b1nxFmsYNuhyqp/j7nOT0TbZvRMifmRAbAKl1YApv5/mV0/BMW9ORcVgCD4WbbZrz4EPPsLs0jVklairEjcCabXND5EqsRdthbeOa4bfVV5TuvJfi1Mx4btimsfpAbVyDRG7+wDMHJqJp0AnNA7QLXDu/0pp3T+5+1iqbPNkSNfYZUCsaFQynn0S+38/ACorBkOwwrK+ZzfZl0wInYI+hqrEQtTFnDGZ+F3l/08L+dfvgtlnxenx/AY277wMPAWh/S+vkW364YNznEnqmRDxJ8mJiD+fD+cLz4Ut/qcFAljWrgkW+ysTdVViIepgz3Kd4u3BE3jNNIq3aexdndofhaW7Ye9KnUhxeIt0Nv1XChiKpiG1j0jRKBmbf0Xfvy/iMspiCUlOoq1KLERdrH/fQvgTejmN9e+m9vC7X+dbqT0OxYb3rYnojhANTpITEX+xDGNqXEOfRGNhNq4xSuHI4SHSjSQnIu4C3bpj5kaeOl7z+/ENH1Hx/6OtSixEXRxxrg9qrcOt6HmuPxHdiVn3M+oSh0aPsfUrrChEqpDkRMSf1Yrrqj+hwjxZowwDf68+IfVyoq5KLEQdtM0zyexQUdayBopmnVTSn3KpTUZbaNWvrHhhjRTWZiY9xoRP8IVoTCQ5EQ2i9IapeE8/CyC0yrCmY7ZuQ+Frs0IfC46hKrEQdTH2gxIMB1SpvV3xs8UZ/H1jMOa9Uuwtqva//F+FZoGz33eFWVOIxkeSE9EwLBaKXnmdwhmz8J5wEoHOh0NeHq5776fgm8WY3bpXW8U/eCgFC5dR+ucb8ffqTeDwLnhOP5MDsz+k5O77Gt0cJyI1ZB8Ok1cW0/tCH9ZmoBkKazPoPcHHJSuKyTos2T2sG0cLmLyyhH5XeLFlKzRDYcmA7mP8XPxjMW2OTu2rP0JEQyZhS6B0naQH0jf2dI0bJPZ0jD1d44b0jV0mYRNCCCFEWpDkRAghhBApRZITIYQQQqSU1J4WMRV5PNg/eB/HzNcxdmzHbNce18SL8Yw7DxyO+LTh82H/7wc43piBsfV3zNatcV94Ee7zxkNGRo2rWL5bQLN778Ly8yowFYHDOlN609/wnH9hfPoUixiqEovU4iuGde9YWfuOFfd+jewuJkde4qPr6X70Jj5T+pbPdRbf56BgQ/A7XHY3k2G3e+h6Ws2P6wZ8sGmuhTVvWineppPVEXpeYKHHOC+WJJa9ObhNY/UMK799bCHg1Wg/OMBRU7y0HVjz+ABlwub/WVjzupUDm3TsOYqe5/noM8GHrXnNbRTv1FjzmpVf51tRPmh9tJ2+l3ppPzR+jzaX7tFY86aVTXMt+Eo1WvUL0O8yHx1GBGSsfBMkA2KjoB0sIvu8s7H+9ANK19FMMzgvh2ni7zeAwvfmolrkhF2/TgOHSkvJnngetu8WoHQDzQwE5wtREOjZkwOz56Natw5ZJeP/PUTG/3so2Mey18prlnpOO4Oi1/5d/+CjFUNV4qaoMQ+SK96pMefsDIq2lO1VSkMzFCqgcdjJfk5/zYVhD79+Y47929vtrHq5fCr4qkcV9Jnk46QnPCHL+0pg3ngnu763oOkKZWrB69KmIrePydnvu3C2TPxH7davDeZf5MT0gwoE4yjfhsPv8pB3fWg5iIAPPr7cwW//s1Yshxbsd7OOirFzSsnqHBrHziUG/73Qid9dvY1jpnoYemv9S07sWaEz99wMfMUE39sqbfS/ysuI+zxJT1Aa8/5eHzIgNgU0u/UmLCuXA8FJwcr/1QDLmtU0u/HP9W/jH3dhXfxd2d8OntA1pdBQGJs20vy6K0OWN1avrJaYVP3Z/tGHOF55sd79ilbUVYlFyvnkKgcHt2qgyv6j8uSz9SuDJQ9HyEwasd+/1GtITCp//mWmlY1zQy8bLfy7nd3Lgq+Vnzwxg+sUrNf54s+Jf69c+zU+usRJwFu53aDy50X32dn6VWgcPzxh47ePLSHLlW//kp0a/7vMGXLYeovhw0lOfK6a2/jhcTu/fli/C/R+N3w40RmSmFRtY+WLNta9IzcBmhpJTupI27MH+/vvhpxoQ34fCGD/7wfo27fF3kbhARwzX69IfGps46svMDZuqHit2V23BX8X5m8qIOPJR2PuU0xiqEosUsu+VTq7llhCTjghTI2fZ1jxNc6n+iNafF/57dmaYg++9n2VxMxdAGvfsoacOKtSAY0tn1oo/C2xX+3XzrLi91CRWB5KMxQrXqgsBxHwwqpXbGGXVwGNfasMdi2tPG2sf9eKt4iwNYw0XbF8Wv2KEW6aZ8G1Vw/7/qIrlj8nZS2aGklO6si67PuwiUk5TSms3y+OuQ3LiuVoHk/EZRRgXbSwcp1VKyLWKtUAffeumPsUi1iqEovUsn2RAXrkK1u+Eo39a5reR0jBep3IFYA1CjdXxr3nJwPTV0vioTR2Lk7sIJ3t3xllV29qpgIa2xdW9qlgvY6nIHIcmqHY8V3lVYodi4yIZxFlauxaamDWY+jJjkUGmiXCvmhq5P9i4C2OvQ2RepreJ0tDScQtiJiq+ca/G/Umt2savzTehNGGnqq7e6p+ZEXfSALaEClHkpM68g0aElLzpSZK0/ANGRZzG/6j81C1VN3VAN+wYyvX6dc/4rGrALNt25j7FItYqhKL1NJheCDspfpy1kxFyyOb3sC/nCMiFdgDUGR1qYy7bV4A3Vr7GTSeT67URcdjAxE/4TVDBZcpk3OEWaV2T81UQAvuG2U6DAtEfKs0XdFuUKBeT3a1HxZA+SPsi7oit3cAW7PY2xCpR5KTOlJt2+IZe27YBEUZBp4zxmB27BR7G9ktcE+8OGxlXmUYeE88icARPSteK/7Hg8HfhfmbGlB6/dSY+xSTGKoSi9TSur9J28F+NCPMuCFd0XeyD2tmgjuWAMPudJf9FP6sW/UJFEcu9LrAhxbmNphmKDqf4ie7a2IvAfSZ6MNip+Jpm0OpgEb/qyvjMOzQ73Jv2OU1Q9Gyb4B2QyqTk14X+LA1I2zsytQYcI0v9iCAHmf7cbYyw+6LmBpHX1v/J4JEapHkJArFjzyG/6j+ABUJhNJ1FODvfSTFjz9d/zbueaDi6ktFG5qGAgJdu1H0r5dClg/0P5rSG28JLlfl9fKfPX88HfdVf6p3v6IVdVVikXL++JKb5p1U8GRVdsIqPwl1OiHA0Nsij49qrA4fZXLUZeUn1EOPKkXvC330ONsfss5x93loM7Ds6bryE7UWXKdFD5NRT7tJNGcrxegZLgwbISf28vEbQ+/w0Pmk0Ks5x0z10uXUYGwV65Rt/8x2itNmuEIOW1tzOH2mC8NxSBtlPw/8i4fuZ4W+V9GyOOCMWS6sGaFJUHkb/S730mt8/doQqUfmOYmWx4N99n9wznwNfccOzHbtcE+8BPc554Mz8kxLdX4O3ufDPnd29UnYzr8QMmv+qmr59mua/ePvWNasBqUwOx1GydRb8Fw4qT7R1o9pYvvffByvTceyaQNGy1xKx51P6YWTIs4H05Q09rkPvMWw7m0ra98OTsKWdXhwErbuZ/rRa3l6s7HHvvkTgyX32zmwMfgFJLurydDbPHQ/M8wkbF7Y+EGVSdg66PQa76H7OV6sNc+dmBBFvwefrNr8sQXTq9GubBK2doNq3iZmADZ/VDYJ26869haKXuf56D3Bhz275jaKd2j8/JqVzfOtmF6d1kf76XupN+QWUH2V7tb4+Y3gJGz+Uo2WRwU46jIfnU5IjUnYGvv+HquGmudEkpMEStedF9I39nSNGyT2dIw9XeOG9I1dJmETQgghRFqQ5EQIIYQQKUWSEyGEEEKkFClIkIpiqEoctUAA2/z/4nz9VYzffsVs2RL3+RPwjJ+AalZz6VHr/z6k+d9uRN+5AwCVkYHriqspvfPe+PRJiBRSsFFj9XQbW78yUAo6HR+g3xQfub2TN57AWwyfXO1g65cWlB/QoXU/k1NfLCU7TnU0A95gXaUtn1owfYAGub1NTn2plNyeta4uRFzIgNgEaqiqxFHzesmaPAH7559WVAwun5PE7Hx4sGJwh44hqzS75a84ZrwCVK987D+8CwVLV0ZsUgaLpVfc0Lhj3zDbwmfXBmvshFTaNeHkp9z0vjDyo6sNEXvxbngjr1nZhGTlR1/lx/eZb7nofHL9no5xF8KMvs0wvTW38YeXXBxxdvg2GvM2r690jV0GxKaJaKsSxyLznw9i+/Lz4N8OVGlDKfRtW8m64pKQ5Y1VK6slJlV/tmz5jebX1r9fQqSCA5s0PrvWgQrUUGlXwRd/drDv58R/dL57UmbwaglQefRpFT9/OCnyVAZ18c5JmZgV85lVb+PTq5zUUmJMiLiQ5CSFxFKVOGouF47pL0Vsw7psKZYVP1W8llWWEEWqfGyf817sfRIihax+tbyERM1ViTUdVk+vX6XdaBVuBte+yiShOg0V0Fj+fOx36l35ULwtchsojcX3SwVg0fAkOUkhsVQljrqNX35GLz4YuQ1dx/pdZRvGr5siLq8B+GWGRtE0bPvGCLlicigV0Nj6dWKH662eYSNypWQAxbq3Y08c1s6y1qmNzR/KUEXR8CQ5SSVNpoyoEI1XKh4iqdgnIRqSJCcpJJaqxFG30acvZrPI5Ts108Q3vLKNQNduEZdXAIZ8mxJNQ6cTAuGLzBEcGNvp+MReKex3mZfIlZIBNHqeF3sBvN7jfXVqo+tpcpVUNDxJTlJILFWJo5aRgfvSKyK24TtmEP6jB1a8VvTMC8HfhfmTGuAZMzb2PgmRQo66rPwEH67SLvS7vH6VdqOV3RUcLVXYPoFC0xV518WeODhbQ7OOkdtAUwz7u1QAFg1PkpMUE21V4liU/O0OvCeeVNaGUdmGpmF27ETRy6+HLB84Og/3JZcFl6vyevnP/sM6c/CF6fXulxCpIKeHYtSzbjSjhkq7muKkJ9y0Oirxj4pe8FUJWsUFSlXl3+DPp7/piksbesXF2+pt/OF5F1WKjAvRYCQ5STUZGRS+N4+iaS/jG3YsgcM6488bSPE/n6Dg029QbdrUvw27naKZ71L00gx8I44PtjHgaIofeISCLxdiduxUbZXiR5+iaPobmO3aVXxUKaeTkuv+TMEPq+vfJyFSSM9z/Yz/upS+l/jI7hogq6tJn4k+xn9ZSp+Jybmt0awtXL6+mMNO8qMZAAp0aNXPZMKiYg4/pf7P+DqyYcqGYrqM9pUlQgo0yOltcsFXxRwxTp4jFokhk7AlULpO0gPpG3u6xg0SezrGnq5xQ/rGLpOwCSGEECItSHIihBBCiJQiyYkQQgghUkrUk1Ps37+fhx9+mG+//RaPx8PgwYP529/+Rvfu3WtcvqCggPvvv59vvvkGTdM444wzuOWWW3A6618HIh6MdWtxTn8R6zdfgabhO/EkXFOuCv+4rseD/YP3ccx8HWPHdsx27XFNvBjPuPPA4Uho3+vF6yXjgX/gfOsNtIMHUTYb3lP+SMn9D2O2a1/jKvr2bThem479o/+C14v/mMG4plyJf9CQmtswTWyffozjtVewbFgPrVpiH3c+gfETUdktGi62OFMKti8wWD3Dyr5VBtZMRfcxfo68yEdG6/gN2Vo13cpPz9oo2aWhG9AmL8Bx93to3b/m+7ieIlj7byvr37PiKdBo0cOk72QvXU4NoMXpa0fpblhwp4PfPrUQ8IKtuaL3hT6G3uHFEmZKnr2rdFa9YmXnYgtWGxw2ysaRkz1kd6n5vXLth4V/d7B5vgW/B6yZip7n+Rl2lwdbnIpwJ4K7ED653Mn2hQYqAJoObQfaOfUVF81qPqQo3Kyx+lUbWz43UH7ocGyw8nGrfjVvc78bFj9oY91bVrzFGoYNupzq57j73GS0rbmNg9s0Vs+w8tvHFgJejfaDAxw1xUvbgckbF6EU/P65wc8zrOSvM7BlK44Y56PPJB+OFjWvU7pHY82bVjbNteAr1WjVL0C/y3x0GBFAq21i2xTiKYRfZgWPW2+hRs4RJn0v9XL4KeGP211LdVa/amPXDwaGTdHtdD99J/to1qFRDRmNStQDYi+88EJM0+TOO+8kMzOTp556ip9++olPPvmkxoTj4osvxuVyce+991JUVMQdd9zB4MGDeeSRR2LqcDwHxNrfmknzv1wHmlZRAE8ZFkBx8NkX8Jx7Qcjy2sEiss87G+tPP6B0Hc00g4/7mib+fgMofG8uqkVO2PZSZsBUcTG5g/th7N9fve6oxcKBDz/Fn3dMyCrWBd+QPel88Hor3yuLBc3vp+TWOymdektoG34/za+egmPenIrKxxB8ZNls1z5Y+bhLnGq8NyCl4Nvb7KyebgtWpQ0E3y1NB1tzGPN+Ka3DnEigbtvcNGH2mU52Lwvue6F1n+HER930vST0CZGiLRqzz86gZKdWtphW0b9uZ/g49SU3ej3nxduzXOe90zMOKTYX7J8j1+Si70uwZYWus/IlKwvucFR5r4KP4GoGjJ7uosupoU97FKzXePvkQ4vNBduwNjeZtLiEjHoW4U6Ewi0wc2gzMKtW84Xyp13O+bCEdoNCP2o3/8/g48udKLNK5WOLQgXg+Ac91eZScR+AmUMz8RToVdoI/k3dAuf+r7RaIrv1a4P5Fzkx/YdUVw5oDL/LQ9718Z2zpE77ewA+/z8HG96zhhxTaJDRWjH2g1JadA99r/as0Jl7bga+YlBmaBz9r/Iy4j5P0hOUusRe+KvG7LEZlO4u66yqPG57nO3jlOfd6Ic8qr30URtL/2kP7hv+ytgNG5zxbxcdj03uE1QpMSC2sLCQjh07cv/999O/f3+6d+/Otddey549e9iwoXoxup9++onvv/+eRx55hL59+zJ8+HD+8Y9/8MEHH7B79+5omo4745c1wcTENCtOnABawA+BAM3/72qMDetD1ml2601YVi4PLldWOE8zTTTAsmY1zW78c6K6Xy8tzj8bff9+oIa6o34/2eecGbK8dqCArIsvBI8n9L0qq6eT+fD92D7/JGSdjGefxP7fD4LLVV1HKfQ9u8m+ZEKjmJN77VsWVk8PXiKorLeioUwNbzF8OMFJoJ6f74vutZUlJsG/XSn489c3OSitcrgoBR9NdgY/4FRlobby/v0638KPT9W/ONsH55QnJlWLwQX/dedrzD0/9LLGziUGC+5whPSl/GfTBx9PcVK8M/QMMvvsjLLEpHobvoMac8Y1jksn747KhIrP5UO2oYI5Z2eGLF+8Q+PjK0KTBiB48lEa397mYNfS0I/needl4CkIfY/K3zfTDx+cE/rl0LVf46NLgvtnterKwKL77Gz9KvGTlqx8wcqG9y0hfSkvKujar/HhpAxUlXOc3w0fTnSGJCZV1135oo1176T+DNVKwfyLnbj2lh23KvS43TjXwvJ/hR63v31isPSf9uBy/tDYAx6Yf5ETT2GCAkiwqJKT7OxsHnvsMXr2DN7yyM/PZ8aMGbRr144ePXpUW37ZsmW0bt065JbPkCFD0DSNH374oZ5drx/n9JdA08PUHQ3+r+PVygnPtD17sL//bsiJNmSdQAD7fz9A376tIbobPwX5WH5YGqnuKHpJCfZ336p4zfHWTLTSkrCVjJVh4Jz2bOULPh/OF55DC5N8aIEAlrVrsH63IMYgEkMpWP6cDbQwM4UGNEr36Pz63/p9MP78uo3I8+/CwnsrbxnuXGKwf02E4nRKY+VL1nolTRtmW/AVR65Qu+cnndK9la+seNEaftp3FTyBrnmjsprv71/puPfrEds4sF6ncEsMASTQnuXgLYr8Xpk+WPdeZSLw8+vWYOKnal5HMxQrX6o8UZXuhr0rI79X3iKdTf+tbGPtLCt+T+Q2VryQ2ArDZoBgm+F2k4BG4a96SNK0aZ4F1149JDEJoavgcZriti8wKNgQ6biFFS9YMatcJF0+zRb2mFKmhq8E1r2T2ArZiRLzp+pdd93FO++8g81mY9q0aWRkVP+Gs3v3btq3D73ZarPZaNGiBTt37oy1aSyW+t9Qt339RfAqSRhawI/9m6/wlLVl/Wlp2MSkYh2lsC9bgu/wzjX+vvySVjSXtuLN+slHdag7Co55cwhMmAiArZYqyFoggHXRwortom/ajL5/X+Q2LBbsixeiTjyxrl1POE8hFKyL/M1Ssyh2LrLQ54KaE7fatrnfDf6S2q9H71piVLy/uxZbQm6b1MSdr1P8m0HLI2O7OvXrXCuhtydqovH7xzaOujR4HG3/1hK5mq+psWOhBYsluPym2XU5oWj8+oGdwVMTO118NFa/UpeKwRq/vG6j7/hg1fHtCyzhT7YET9LbvrVUbPPf/mepQxuKTbOt9Bob3OY7FhlVrubU3MaO74y4fJ6Wq21/L9ymUbIzcnu6RbFriYVupwbj2LXYEnJLoxpTI/8XA9OlY2see9/rq7bYa40DDddejZKtBjlHKJSCnYsjV8gG2LnIwsA/Je/WTkOd12JOTiZPnsz48eOZOXMm1113HbNmzaJv374hy7hcLmw1FLKz2+14PJ6Y2tV1jZyczNoXrE0d3kjD0CvbyrTX6c82a+aAWvqXlZXEwcDO2rNsDbDZLNjK47DWfulXg8r3qg7xaYDTacMZj23ZQNx1uIetoWGzW8nJify+htvm3tK69EQL2e8dDtC08NdaKtrMziAn/BCoiCx1/DKWkWknJyd4bNTlnr/FYlTEUUuNywrODBs5Oan7zdhax/fKYrGQkxP8yLXU4W6KrlVu8xq++9VAw2qr3Betdfp0j9Pn6SHCfsYV1GVtDYejcpvbbIeMiwujRU4m9iQmJ+XCxe5w1C2O8uO2bne9NWy2yv0qmeJ9Xos5ovLbOA888AArVqzgzTff5KGHHgpZxuFw4PVWv7bs8XhqvNJSF6apKCqq0yd6RBnHnYDtt9/CXg1RhoHnuBNwFQQH32p9BpBdZWBnjetoGoV981AFNQ/YNQydrCwnRUUuAoEkDYg98RRaUPt3sOLRZ+Iri8M+eDjOefPCrqMMA//wYykuj7tVB7JbtqwY11Ijv5+DeYPxh3mvUoFSkNPTScEGLeylcdMPrQe5KSioeb+oyza3ZGbgL4HwW0XRdrCfgoLgsZSbp2P6I38QOHIVeutSCup0Mqju8NMM1s2p7ekzRbuRlW10PM7Orx8ZYb8Zarqi3TAfBQXBqyBdztRZ8VptH2iKTn+MPY5E6DMZVr2ZQW1HVa+JlftJ+2OtbF9iDXv1RDMUHY8LUFAQ/BLX/mSA2tvocqaLgoLgftZmiJWNn1jLBunW3EaHYyvbiIfa9nczCzLbOSnZFf42mOmH3LzK96rVMRbMVyJ8OdQVLXuZlPrdlCZxP6kt9roct87WJlquq2J/7zDMwY5FEW5poWg9yEtBQfIqRdf1vJaV5Wy4AbH5+fl8+OGH+P2Vb4Su6/To0YM9e/ZUW75du3bVXvd6vRw4cIA29agR4/eb9f6v5LKrwDRrzGIVgFKUTr6iYnlfy9Z4xp6LClP1ShkGnjPG4GvXIWyb5RsuEKh//2P+LzsX/8BjItUdxczIxHXehRXrlF4wAZwZYSsZa4EApVdfV9mGZuC68k+oMF+llWHg79UH97Djkvc+1OG/QMDk6Gs94e/Z6wpna5Mup/nqtc2PvNhLpMQE4Ni73RXLtx3iJ7d3IPz4Dk3R7wovSo899h7jfFgzI1eobX20iS2ncp1+V3rDX7LWFJoFek/yVizf8UQ/9lwzYhstjjBpdljy94VI/7UaYGJtHvm90q2KI86t3E96X+QN1seJMJ7pqCs8FcvbW5pljxeHb8PazKTrGf6KdXpd6MVij9xG/6u8cX0vatvfTWXS/2pv2N1dMxTZXU06nlD5XnU904uzlRl+fzc1BvwpvnE0ROztj/XTokfk47b/VT5MKtfpf7UnfAKrK6wZcMT5yY29rue1aEWVnOzbt4+pU6eyaNGiitd8Ph9r1qypcZ6TwYMHs2vXLrZsqRzR9v333wNwzDHHVFs+kQJH9uXgU8+BppU9PhykDAN0nYPPPE+gZ6+QdYofeQz/Uf2Dy5VXDNZ1FODvfSTFjz+dsP7Xx4F3PsDMyQVqqDtqGBS+NzdkeZWTS+Ebb4HNFpKcKUvwfSu55Xa8fxgdsk7pDVPxnn5WcLmq62g6Zus2FL42q273AZKs9wQ/fS8LXrEIqVCrK6zN4IxZLoy63fEL69h7vLQZWJ7wV6/7fMIjnpA5LDQNTnvdRUYbVXbiUSH96zraz8A/1/8R0THvl1YWfwvZU8Ceozj7P6FXMDsMCzDiPndIX8p/1i0w+hVXtXkZxs5xoVtrbsPaDMbOqf9V0kS44LOSKp+m1bfh2e+HXiFs3lHxx5eDj41Wq3wMHPegm/ZDQj/Qx7xXir1F6HtU/r5pFjj7/dCqxM5WitEzXBi2Q9qwBH8eeoeHziclfqzCgGt89Bgb3N8r+6VAVzhyFae/6QqZ78PiCB5n1ozgcVeufN1+l3vpNT55Vw7qStODlaOdrYKxHnrcdjvTT951ocdt19EBBt0YvLJVvt3K1zHswb9nz05M/xMt6nlOrrzySn7//Xfuv/9+srOzeeGFF/j222+ZM2cObdu2JT8/n+bNm+NwOFBKMXHiRDweD/fccw+lpaXcfvvtDB06tNotoLqKd+E/45c1OKe/hPWbLwEN38iySdh69a55BY8H++z/4Jz5GvqOHZjt2uGeeAnuc86HWiaWS5l5TgDcbjLvvwfHO7MqJ2EbdWpwErYOHWtcRd/6O87XpmP76L9oXi++gYNwXX41/iFDa27DNLH9bz6O16Zj2bQBo2UupePOp/TCSRHng0k1SsG2r4OTsO1fbWDJVPQon4StbeTDJ5ptvuJFK8ufs1G6R0PTg5OwjfiHh7Z5YSZhqzKZk+eARotuJn0n++h6mj9uk7AV74KFdzjY8llwEjZrM0WvC/wMv8uDJcxdnz3LdVZNt7FrsYHFrnPYyT76TvaQ3a3m96p0Lyy8y8Hm/1kIlE3CdsQ5fob/3YOtWXziSARXPnx8hZOdi4KTsKFrtM3zc+pLLppXL/QNwIFfNVZPt/H75wamX6PDsX76Xe4LO/Ge3w2L/mFn3bvBp6kMOxx+ip8R97tp1q7mNop+1/h5hpXNH1swvRrtyiZhazco/p9Bdd3flQlbPjVYPcNGwQYde5biiHF++kzy4siteZ3S3Ro/vxGchM1fqtHyqABHXeaj0wmpMQlbXWN3FwSP2w3vWfEUaeT0CB63Xf4Y/rjducRg1XQre340MOyKrqcFJ2Fr3in50zE01DwnUScnBw8e5LHHHuOzzz7j4MGDDBo0iFtvvZUjjjiCbdu2MWrUKB566CHOOeccIDij7L333su3336L3W5n9OjR3HbbbdjtsX3dlKrEjVO6xp6ucYPEno6xp2vckL6xp0xykmySnDRO6Rp7usYNEns6xp6ucUP6xp4SM8QKIYQQQjQ0SU6EEEIIkVKSP3NLY9NUqhKLlLNzscGqV63s+cnA4qysPJrZruY7r4moShyLaKsS+0phw3tWfnnLgmuPTvNOJkde7KPbWX6MMBOc7f9FZ/V0K9u+NdB0OOykAEdd5iWnR81tBDzBKfl/mWWlZKdOZgeTPhN8HDHOH/Zpq4KNwcGqW78yUAo6HR+sGJzbO36X7KOtSizqLmQg+88G1maKHmP9HDnJF3xiRqQ0GXMShSZTlTgJ0jX2usStFCx50MaPT9lDq/nqCosTznqnlHaDQ9dNRFXiWERbldi1T2POOCcF6/Sy6TM1NF2hTI32w/yc+W8X1kMmMF0z08pXU+1oemilXYA/PO+mx9mhj5V6CmHuuRnsXWkEH+E0K9tofXSAMf8pxX5IdeUNsy18dm1oEUPNUCgTTn7KTe8LIz+6WpftHm1V4sYgVY5zpeDrW+ysea2GauLZcPb7pbTqG9/+pUrsiSZjTlJAU6lKLFLLr/+18ONTZZVHq1aPNTX8LvhwYrBUfMXrCapKHK1YqhJ/fr2DAxvLCtqVV2ktm3Rq1/cGC+8Jvayxb5XOV1PtoLRqbagAfHqNg8JfQ9v4+mYH+34u+6gzQ9vYt0rnm1tCr3ge2KTx2bUOVKCGar4Kvvhzlb8Xo1iqEou6W/OGlTWvhakmXhSschxofLlfWpG9v46aTFVikXKWT7OGTC5VlTI1PIWw/v3K+xuJqEoci2irEh/4VeP3z8MXC1Smxtp/W3EfqHxt5cvWCLesgn9n9YzKxKx4p8amuRHaCGhs/MBCye7K369+tXz9mmuWazqsnl6/SrDRViUWdVeXauIlO/WyYooiVUlyUkfWZd/XqSqx9fvFCeqRaApMP+xeFrlCrabDju8qZ9nd8Z0RPgko487XObApsYd3XaoSb19QGcfOxbVXvzO9Gnt+qlxu2ze1tBHQ2PZN5fK7lhoR39vydXZ9X7WNyJVgVUBj69f1O7Ft+zZyv4JVietQHVBU496vUfirHjbxg2Dl46rHlEg9kpzUVeMamiMaiZh2q1TdFaPsl4pw8ohXG7G8Vwk51OvSRqpu5xQnH9VNgyQndeQbNCRs0b9yStPwDRmWoB6JpsCwQpuBgbC3dSA41XeH4ZVX7doPD0T8Zg/gyDFp0T2xg/I6Hu+PeEVH0xUdR1TG0WFY7fVQdKuizdGV63Q6IULhNIK3QzodX7l8u8GR39vyddoNjraN+tVy6XR85H5phqLj8Ymve9MUOFsFiweGu60DYPo12g+X9zeVSXJSR6pt2zpVJTY7himiIUQYR1/rDX+JX1fYsqDnuZWj9zoMD9RelfhKH0aChyz0v8oXPmkqq0p85MWVcbTorjjspPAJjaYrek/w4ajyAFy/K72osDlX8O+UF2oEaNZB0e3MCG0Yiu5j/CGPax9VsX64cUDU+0maIy/x1VqVuP+VCR401ERoWvCYijSeJ6OdSdfTUr9YYDqT5CQKTaUqsUgt3c/yk3d9zZVHrU44Y6YLa5UCeImqShytWKoSj3rWTXY3E1AVJ+ryKwrtBgcYca8nZPnW/UxGPuYJJjuHtKEZcMo0Ny0OKTB44qNuWvYpa6P8akVZVdhWfU1O/Kc7ZPmcHopRz7rRaqoYrClOesJNq6Pqd1UqlqrEou6OvMTHkRcfWk1coekKW3M4c5Yr7Bw6IjXIPCfRaipViRMsXWOPJu7t3xmsnm5l7woDw6Hodoafvpf4qp3QyyWiKnEsoq1K7CuB9f+xsvYtC6VVJmHrPsYf9urPvp/LJmFbYEHTFIeNDHDUFB+5PcNX893w/iGTsE0MTsIWrrpy/nqd1a9Yg5OwodHpuGDF4JZH1r7/1nW7R1uVONWl0nGuFGz90mD1q1b2/2JgzSybhO0iHxmt43/aS6XYE0kK/5VJenJSD+m680L6xp6ucYPEno6xp2vckL6xyyRsQgghhEgLkpwIIYQQIqVIciKEEEKIlCLz9wqRIqKtSnxwOyy43cHWrywEfGDPVhx5sY8ht3jDFv3b/UPZYNVlBoZN0XV0sI3mnZI39My1Hxb+3cHm+Rb8HrBmKnqe52fYXR5sGTWvk4iqxFs+11l8n4OCDcHvcNndTIbd7qHraTXPjxHwwaa5Fta8aaV4m05WR+h5gYUe47xYIo+Vb1AHt2msnmHlt48tBLwa7QcHOGqKl7YD02dcRF2582HNmzY2zrHgPaiR2yfAUZf6OOykAFoMcwbGy66lOqtftbHrh+BxW/7ZEG6wfFMgA2ITKF0HTEH6xt5QVYl3fKfzwTkZVeb8CFZdBY2MdiYXfV9S7SmUH560seRBe7Dyrb+y0q5uDT6u3CnOk37VJfaC9Rpvn5yJWfHkc2Uc1uYmkxaXkNE6dJ1EVCX+9nY7q14uf9a0/KwUbKPPJB8nPRH6iLOvBOaNd7Lre0vF30YHTEVuH5Oz33fhbJn4j9qtXxvMvyi0wGD5Pjb8Lg9518f3kfPGfJznr9WZM86Jp0ArO64qK333meRl5GOeiE/BNVTsSx+1sfSf1Y9bwwZn/NtFx2OTO5mcDIgVoomKtiqxacK88RkVH6CVJ8/gv6W7ND6cGPpV/fcvDJY8WNaGP7TSbsAL8y924i6Id2S1m312RlliUj0O30GNOeNCL50koirx71/qNSQmlT//MtPKxrmhkzEu/Lud3cuMkL9N2fYpWK/zxZ/DXJ5pQK79Gh9d4iTgraG6MrDoPjtbv5L6MhCscfXhJCeeA1rZ9gut9P3LTCurX038xCi/fWKw9J9hjlsPzL/Iiacw4d1KCElOhEiyaKsSr37VSsBT9WR+KI3tCw28VRKaFS/Yws8oW5YErf13Yj98f/9Kx71fJ1IcB9brFG6pfCURVYkX31eerNRclRjg+4crkw13Aax9yxp2ll8V0NjyqYXC3xJ7X2DtLCt+DxFnSl3xglQ+BtjyqYWDW/UIMxzD8mm2hNftWT4t/HGrTA1fCax7p2nOJifJiRBJFEtV4s3zLdRaFU5pbP26SiXjhZEr7aJg+3eJHYK28f26fKhqbJxduVwiqhIXrI+UMAX7VLi58qNzz08Gpq+WxENpdarCHE/bvzPKrt7UTAWCSawIHl+6JcIxpTQO/q5TujtxCaZSwXFotdXRaqrVlSU5ESKJmlRV4iilbFXiaJdP0e2Rqv1KRfJWpR5JToRIoliqEncZ7SfyN3tAUxx2Ymgl40iVdtGgw/DEFkI74py6DMZU9Di7ssheIqoS5xxRVocnQp+yulRekmibF0C31n56az80sQMXOx4biPgJrxkq6YMpU0WHYQFMf4RjSlM0P8wM1rNKEE0L7jO17b9NtbqyJCdCJFm0VYn7XeHDsFUW/KtO0eHYALYqxQIHXOONWDHY4oA+E+pXaTdanU8ysedGSgQULY4wye5a+UoiqhIPu7O8EGD4k8LQWyvbcORCrwt8YU8imqHofIqf7K6J/X7eZ6IPi53IlY+vlsrHAF3+6KdZJzN84quCx2mia1YNuCb8Z4OmK6wZ0Ht8Yo/bRJHkRIgki7Yqsa7DGW+Xln1QVk1Sgv9mtFGcPtMV0sbhowIMubXmNgx7sMqxIzfekdVu7BwXuhVqisPaDMbOKQ1ZPhFViQ8fZXLUZeUf+FX/VnCd3hf6qj2ufNx9HtoMDH6DrUhStOA6LXqYjHo6tI1EcLZSjJ7hwrAdUvm4bPsPvcND55Oa5rfuaOmW4HFmz1Zl2y+00nfvC/1V9onE6To6wKAbwx+3p7/pwp6d8G4lhMxzkkCNeQ6A+krX2BuyKnHR1rJJ2L62YPrAlqU4cpKPobeHn4StYjKnpQZ6lUnYsjonr0pr6V5YeJeDzf+zECibhO2Ic/wM/7sn5OpPVYmoSrz5E4Ml99s5sFFHAdldTYbe5qH7mWEmYfPCxg+qTMLWQafXeA/dz/FiDTOZXCIU/a7x8wwrmz+2YHo12pVNwtZuUPyPw8Z+nLv2aayZaWXjbAveYo2WvQP0vcxH55Nrn4StIWPfucRg1XQre340MOyKrqclf/LEclKVuIwkJ41TusaernGDxJ6Osadr3JC+scskbEIIIYRIC5KcCCGEECKlSHIihBBCiJQiVYlFo6YUbP3SYPWrVvLXGtiyFEeM9dNnkjcpT5/UR7RViWPx+fU21v/Hhiob02nJhBH3uul7SfzmONm7SmfVK1Z2LrZgtcFho2wcOdlDdpfkDW+LpSpxwUaN1dNtbP3KQCnodHyAflN85PZOn/EEQiSLDIhNoHQdMAUNE7sy4Yu/OFj3lrVKNV8FOjhzFWfPdpHbK7nvc0NVJY7FKz0z8RzQKa/6W9Y6AIf/wc8ZM+v/uOvKl6wsuMMRGkfZY76jp7vocmriH12NpSrxhtkWPrs2+BhPSDVfE05+yk3vCyMnc+l6rKdr3JC+scuAWCEOseoVK+veCl78q5xgTANTw12g8eEkJ2YjmMYh2qrEsfjgPEdZYgI1Vdrd8qmFbd/Ur42dSwwW3BF6Qi//2fTBx1OcFO9MbPE7iL4q8YFNGp9d60AFaqjmq+CLP1f5e0KIBiFHmGiUlAkrpoWvqKoCwUJdv3+e+kWxoq1KHIvt30QqFhg8AX96TWa92ljxojXCDJsaph/WvJHYCqqxVCVe/Wr5flVzVWJNh9XTm2YlWCFShSQnolEq2aVxcFvk6rG6RbF9YWoPq4qlKnHsIl+1cO2v31WN7d/WUjHY1Ni+ILHJYixVibd9E7kSrApobP06tfcrIRo7SU5Eo9S4RkqF11TiAFKztGssVYlTMQ4h0owkJ6JRatZe0axj5Oqxpl+j47GJrbQbrViqEscu8lnX0bJ+Z+WOx4cvsAfBAb4dRyR2EFAsVYnrVvk4tfcrIRo7SU5Eo6TpMOBP4SuqakawxHnnU1J/RGy0VYlj0WGEn/C3dYIn4lOeq99TcP2v8kWsfKxZ4MiLE1s8LZaqxEdVVDUONw4I+l3eNCvBCpEqJDkRjVb/K3z0vCD4Dbby5BOsKmpvoThjpgs99cfDRl2VOBZjZ7uxZZc/5ndopV3ofLKfziPr10aHYQFG3Bd8HPnQisG6BUa/4gpbxLAhRVuVOKeHYtSzbjSjehxoipOecNPqqPR5VFSIZJB5ThIoXZ+Dh4aLXSn4/fOySdjWGdiaK44Y56fPJB/Oet6miIeGrEoci0//ZGfjHGvlJGwZMOwuN/0vj99tij3LdVZNt7FrsYHFrnPYyT76TvaQ3S152yOWqsT563VWv2INTsKGRqfj/PS73EfLI2vff9P1WE/XuCF9Y5eqxGUkOWmc0jX2dI0bJPZ0jD1d44b0jV0mYRNCCCFEWpDkRAghhBApRZITIYQQQqQUmeZQiAbgLoBfZlrZOMeKvxha9LRz5GQvnU8OoMWpvIynCNb+28r696x4CjRa9DDpO9lLl1MDaGG+duz+oWyw6jIDw6boOjpY+bh5p+QNPfOVwob3rPzylgXXHp3mnUyOvNhHt7P8GDJLvBDsWqqz+lUbu34IHrflFcuT8fRbosiA2ARK1wFTkF6xF2zQmTPWGZwO3gTQKqr09rzAx6in3WGTh7oq2qIx++wMSnZqZU8DV7bR7Qwfp77kRj/kq8cPT9pY8qAdzaJQ/spKu7o1+Lhyp+PjOydMXba5a5/GnHFOCtbpwWlYVGXF4PbD/Jz5bxfW+pX8SYp02t+rSte4oeFiX/qojaX/rH7cGjY4498uOh6b3LmcZECsEI2AGYAPJzlx52tlFXDLquCWTU62/h0LK1+q3+UApeCjyU5Kd2ugqrfx63wLPz4VWhTx9y8MljxYVvnYH1ppN+CF+Rc7cRfUq1sx+fx6Bwc2ltVIUqEVg3d9b7DwHnviOyVEivjtE4Ol/wxz3Hpg/kVOPIXJ6l3DkuREiDja+qVB0W96xMJxK6bZUPX4YrVzicH+NRGK0ymNlS9ZCVSZQHfFC7bwU7KbGn5X8BZRIh34VeP3zyNUDDY11v7bivtAQrslRMpYPi38catMDV8JrHunad77lOREiDja8Z2Bbol0p1SjeIdO8fbYB57s+M6IWPsFwJ2vc2BT5eG9Y2HkSrso2P5dYoeg7Vxc+/S9pldjz0+NYJpfIeJMqeAxEvG4JV4Vy1OPJCdCxJFScRrtGrGRhm8iERLyXgkhGiVJToSIow7D/Zj+SCddRWZ7k8x6jLJvPzxQ67cpR45Ji+6V947aD49caRct2PdE6jCs9vZ0q6LN0alfvFGIeNM0aD+09qra7eNSsTz1SHIiRBx1HhWgeWczfCKgwYBrvPUqSNhheIDc3hGSDU3R70ofRpUxsQOu8UasGGxxQJ8Jia2026K74rCTIlQM1hW9J/hw5CS0W0KkjAHXhK9YrukKawb0Ht80K2RLciJEHOkGnDHLhSNHlX3jCZ54y0/APc/1M+Dq+n2YaBqc9rqLjDbBKrmHttF1tJ+Bf/aGrHP4qABDbq258rFhD/49R269uhWTUc+6ye5WVjFYK4uj7Jtiu8EBRtzrSXynhEgRXUcHGHRj+OP29Ddd2LOT1buGJfOcJJDMAZA+sbvzYc2bNjbOtuAvMWjR08+Rk70cfkocJ2ErhF9mlU3CdkCjRTeTvpN9dD3NH3YelYrJnJYa6FUmYcvqHP+Pgbpuc18JrP+PlbVvWSitMglb9zH+kKs/jUm67e/l0jVuaNjYdy4xWDXdyp4fDQy7outpyZ88sZxUJS4jyUnjlK6xp2vcILGnY+zpGjekb+wyCZsQQggh0oIkJ0IIIYRIKZKcCCGEECKlRD0l5IEDB3j88cf56quvKC4uplevXtx4440MGjSoxuWnTZvGk08+We31devWRd1ZkVwHt2v8/JqVzR9ZCHg12h0T4KgpXtoNit/91ZLdwTZ+/dCC36XR5ugAR03x0WFY43qWP5aqxDsXG6x61cqenwwszsrKo5ntah4WFktV4nTlLYKFd9vZODe4X1mcih5j/Iy414MtK9m9qzvTH6ydtOZ1K0VbdJytTHpd4KfX+T6szZLdOyHiJ+oBsVOmTGHv3r3cfffdtGzZkjfeeIP33nuP2bNn061bt2rL/+Uvf8Fms3HzzTeHvN66deuYOiwDYpNj+wKDDyc5CXgrC8yVV8kccquHQVO9EdevS+y7lunMuyADf2ll8bfyNvKu9zDsTm/cnnRpSNFWJVYKljxo48en7BXLQfCRWosTznqnlHaDQ9+zWKoSJ1qq7O9FW+Gt45rhd2kE36zKfy1OxYTvimneMb5tNkTsfjd8dLGTrV9bKio3lz9+nd1VMXZOadhENlFSZZsnQ7rGnhIDYrds2cLChQu55557GDRoEF27duWuu+6iTZs2zJs3r8Z11q9fz5FHHknr1q1D/hONh/tAsGqt30PIRF7lVTK/f9jOls/rV9/BVwIfTgxNTKq28dMzdjbNTfLZtg5iqUr8638t/PiUPWQ5CL4PflfwffEVVy4fS1XidDZnTCZ+V/n/00L+9btg9lmZyehW1JY8aGfbt8HjrOIYUcF9oGiLxqdXO5LYOyHiK6rkJCcnhxdffJF+/fpVvKZpGpqmUVRUVG15r9fLb7/9VuMVFdF4rHvLiq+UspNtdZqhWDGtfifDDbOteA4QcTbE5c+l/gk3lqrEy6dZw05RrUwNTyGsf78yoYmlKnG62rO8vMhiuO2hUbxNY+/q1L4P5iuGn1+3hj0+VEBjxyIL+9ekdhxC1FVUX0WzsrI48cQTQ177+OOP2bJlC7fffnu15Tdu3EggEODjjz/mgQcewOPxMHjwYG6++WbatGkTe6ctjfMALL+kFc2lrVSwY1Hk3ST4wWhE3C61xb5zsQXNABVmaIkyg9VpNVNP6Ym5di22oFtUhPo6GsU7NFy7DbIOU5h+2L0s8vur6bBzkYUBUwIVbVS9/VMTd75O8W8GLY9M3mX+VNjfN862Ej4xKaex8T0r7Y+O3zTg8Y59z1odf2ktcWiKXYsttO2f2BpJVaXCNk+WdI29oeKu13XyH3/8kdtuu41TTz2VkSNHVvv9+vXrAXA6nTz11FPs37+fxx9/nEsuuYQ5c+bgcER/GVLXNXJyGsdl2HCyspzJ7kJUrNbal4G6bZdwsdvq1Abk5GSmdHJit1P7uRDIzs6gRQ4E6nI+1DRsNgs5OcHD1eEITmFfW9qRlZ1BTgrUpUnm/m631205h9NGTk78d6x4xX6weV2W0nBm2MnJqWPQDaixfcbFU7rGHu+4Y05OPvvsM2666SYGDhzIo48+WuMyY8eO5YQTTiA3t7JoxxFHHMEJJ5zAF198wemnnx51u6apKCoqjbXbSWUYOllZToqKXAQCjWfAVJvBFtbPsxHurKsZig7DTQoK3GH/Rm2xtx5kQc2M0IauaD3ApKjEDSk8HrrlQAPTFynpVjTroAhkuigoCL7S9hgHe37SI1yyV7Qe7KWgIPiNODdPx/RH/iBw5Cr01qUVbSRDKuzvh5+hs+RJB5EzRsXhZ7gpKIhfH+Mdu/1wsDgzygb1hqEgN6+UgoLkXi1L9jZPlnSNva5xZ2U5o7q6ElNy8uabb/LAAw8wevRoHnnkEWy28N84qiYmAG3atKFFixbs2rUrlqYBGv1I6EDAbFQx9LzAy+KHbPjdqsZxJyqg0f9qT51iChd797FeFt5jw3tQ1XiSVqbGgGu8Kf++dRxp0ryzjeLtWs23XTTof7UXU5mYZVffB/zJyydXhEk2dIWtGfQY68VftnzbISa5vQMUbAgztkVT9LvCi9LNinWSKZn7e6sBJpkdFCU7oOYERdGskyK3r79B3qt4xa47oO8lPla+VPO4E81QtBsUoEWvQNpv82RL19jjHXfUN4lmzZrFfffdx6RJk3j88ccjJiZPPPEEf/zjH6n6tPK2bdsoKCigR48esfVYJJwjB05/w4VhI6S8fXmVzMG3eOjyh/rNQ2LNhDNmlWLJOKSNsp+Pvs5Lj7Ep8Klbi1iqEnc/y0/e9TVXHrU64YyZrpA5LGKpSpzOxn5QguGA4PtU/v4Gf7Y4g79vDIbe4aHjccHjrGIAtRbcB5ofpvjDi+GvXArR2EQ1z8nmzZs566yzGDlyJHfffXfI7xwOB3a7ncLCQrKzs7HZbKxevZoLL7yQc889l0svvZR9+/bx4IMP4nA4mDVrFloMk1bIPCfJc3CrxuqySdhMr0abgQH6Xe6l/ZDaY6lr7MU7Nda8ZmXThxYCbo3W/YOTsHUc0cgmYYuhKvH27wxWT7eyd4WB4VB0O8NP30t8NOsQZhK2GKoSJ1Iq7e/uA/Dd3+1s+q8VvwssTuh+lo9j7/XgaBH/9hoqdtMPm+ZZWPOGlaLfdZwtFb0u8NFrvA9bCkzClkrbPNHSNfaUqEr8/PPP88QTT9T4u3HjxjFu3DguueQSXn/9dYYOHQrAokWLeOqpp1i3bh02m41Ro0bxt7/9jezs7Dp3sipJThqndI09XeMGiT0dY0/XuCF9Y0+J5CQVSHLSOKVr7OkaN0js6Rh7usYN6Rt7SswQK4QQQgjR0CQ5EUIIIURKSf1iJSJlJKIqcVNRuBm+vcPB9gUWlB/sOU6OmuLlmL/60OUrgRBCRCTJiaiTmqoSH9yqsf4/1jpVJU4nWz4LvldVp3B17dVY+oiDtW/ZmLSoJOkVg4UQIpXJdzhRq0RUJW4q/F6Yf0l5YlK14FxZQrdF4+PLpXqsEEJEIsmJqFUiqhI3FT89ZS1L2sLP4fPbx5aK2WGFEEJUJ8mJqNX27yJfFSmvSixgy+cWIpfk01Cmxu4f5dATQohw5BNS1K5RzYSTZCr6WY+FEEKEkuRE1KrDsZGnjtcMRfthjWt6+YZy2Mk+aquAq+mK1kfLE05CCBGOJCeiVr0v9GFxAnrNl1BUIFgxWMAxf/WVFeALf7np8D/4scgQHSGECEuSE1GrRFQlbiosNhj9mqvs4smhVXCheWfF6FeleqwQQkQisy2IOul0fICJC0tirkqcTrqeGmDid8V8e4eTHQsNzAA4Wij6Xuph0E0yCZsQQtRGkhNRZ80PUwy/08vwO+UWTm1adIez3nJVKYrlSqtiYEIIUR/yHU4IIYQQKUWSEyGEEEKkFElOhBBCCJFSZMxJAhibNuB49WXsX34OGmQcexwll15J4Mi+ye5ag1ImbPnUYPVrNg5s0MlsBT3GWeg53os9O9m9a1juAvhlppWNc6z4i6FFTztHTvbS+eQAmszTlnABD2yYbeGXWVZKdupkdjDpM8HHEeP8GPZk904IcShNKdWo5v8MBEzy80uS3Y06s82bQ9ZVlwGgBYKP2yrDAmaA4seexn3R5GR2r8GYfvjkage/zrOiGaqyYKCmyGynGPtBKdldGtWuV2cFG3TmjHXi2q+BCaBVvAc9L/Ax6mk3Whpcs6wcDFyS1MHAnkKYe24Ge1cawbl6TA1NVyhTo/XRAcb8pxR7VnzbTJXYEy1d44b0jb2ucefmZmIYdf/gS4OPyOTRf9tM1tVTwDQrEhMALeBHU4pmN96AZeXy5HWwAf30rI1f/xu8MFe1kjFKo3SPxkeXOGlcaXHdmAH4cJITd75WVigxGHv5e7D+HQsrX7ImsYfp5+ubHez7ueyjrqx4pSr7d98qnW9ukSrRQqQaSU4akHPGK6AUWrizsG7gfPmFxHYqAQI+WPGCNWydGRXQyF9rsKOWgoKN0dYvDYp+00MTskOsmGZDpc8Xq6Qq3qmxaa4l7PZQAY2NH1go2S332oRIJZKcNCDrt1+FXDE5lBbwY/36y8R1KEEKN+u490fetTSLapLJyY7vDHRL5KrExTt0irfLyTARdi01Kq6ShKMCGru+b3r7ohCNmSQnDclsgvct6iJNwwZQUpU4taTxvihEYybJSQPynTASZYT/RqYMC74TRiauQwmS3c3EkRv5voXya3QY3vTq8XQY7sf0R65KnNneJLODnDUTod3gAFqYgpXlNEPRbnDT2xeFaMwkOWlArksvB7TwX97MAK4rrk5gjxLDsEL/q3yg1Ry5ZihyegXoMKLpnRA6jwrQvLMZUiAxhAYDrvGiy12EhGjWQdHtTH/Y7aEZiu5j/GS2k2RRiFQiyUkDMrt24+DzL4NhhFxBURYLStMo/n9P4h+Ql8QeNpyBN3jpdrofCK1kjKZwtlac/pqrSc73oRtwxiwXjhxV9o09GHv5e9DzXD8DrvYlsYfp58RH3bTsYwIq+CgxlP2raNXX5MR/SpVoIVKNzHOSAMaG9ThefQn7l59joPCMOD44CdtR/ZLdtQalTNj8Pws/v2alcJNORkudHuM89LzQi6NFsnvXsNz5sOZNGxtnW/CXGLTo6efIyV4OPyV9JmFLpXkf/G7Y8P4hk7BNDE7CZmmAJ4lTKfZESte4IX1jb6h5TiQ5SaB03XkhfWNP17hBYk/H2NM1bkjf2GUSNiGEEEKkBUlOhBBCCJFSJDkRQgghREqRqsRCiLha/76Fpf+0cfB3HU2Hlkc6GHa3m04j4ncffv8vOqunW9n2rYGmw2EnBTjqMi85PRrVEDohRBiSnAgh4uZ/lzn49UMrwUeog48l7VmuM3dcBsfc6GHo3+r/GPWamVa+mmpH0ysLKhZuDiYrf3jeTY+z/fVuQwiRXHJbRwgRF6tftZYlJlCemFT9+YfH7OxZXr+PnH2rdL6aagelhRTzUwENFYBPr3FQ+GuaPKstRBMmyYkQIi5+eNJG+GI2wYRh4d32erWx8mUrWthPrWAbq2fY6tWGECL5JDkRQsRFyS6N0Csmh9LYt7p+HznbvrGEXDE5lApobPtGagMI0dhJciKEiI9EjEWV8a5CpAVJToQQcZHRrrKWUM2CtWzqo9MJgfBFFQnWMOp0fNMrKClEupHkRAgRFwP/7CX8bZ1gQnHsvZ56tdHvSi8qbH4TbKPvZd56tSGESD5JToQQcdH/ch9dTyt/VLjq1Y3gz8f81UvbvPpdOWndz2TkYx7QVMgVFM1QaAacMs1Ni25y70eIxk7mORFCxM1pr7lZ+46fZY/aObgNNE2jZR+T4Xe56XRifCZhO/IiH23yAsFJ2BZY0DTFYSMDHDXFR27P9Cm4JkRTJsmJECKuel/gp/cF/irVSt1xr9Laqm/ZFRTqd5tICJGa5LaOEEIIIVKKJCdCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVKKJCdCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVKKJCdCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVKKJCdCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVJK1MnJgQMH+Pvf/84JJ5zAwIEDmTBhAsuWLQu7/LZt27j66qsZOHAgxx13HE8++SSBQKBenRaNgzLht48N/jvRyYw8Jy8Ogp+es+ApTHbPhBBCpDJLtCtMnTqVvXv38vjjj9OyZUveeOMNLr/8cmbPnk23bt1ClvX5fFx++eV06dKFt956i99//5077rgDXde54YYb4haESD2mHz652sGv86xohkIFNIq2wM4fbfz4LytjPyglu4tKdjeFEEKkoKiunGzZsoWFCxdyzz33MGjQILp27cpdd91FmzZtmDdvXrXlP/74Y3bs2ME///lPevbsySmnnMLUqVN57bXX8Hq9cQtCpJ6fnrXx63+Dua8KaJW/UBqlezQ+usSJktxECCFEDaJKTnJycnjxxRfp169fxWuapqFpGkVFRdWWX7ZsGX379iU7O7vitWHDhlFcXMwvv/xSj26LVBbwwYoXrKC0Gn+vAhr5aw12fGckuGdCCCEag6hu62RlZXHiiSeGvPbxxx+zZcsWbr/99mrL79q1i3bt2oW81qZNGwB27tzJgAEDou0vABZL4xzHaxh6yL9NVdEmDff+yDFqFsWuxRYOP7FpXz5Jl21eE4k9/WJP17ghfWNvqLijHnNS1Y8//shtt93GqaeeysiRI6v93u12k5WVFfKa3W4HwOPxxNSmrmvk5GTGtG6qyMpyJrsLDcqfVfsyGhoOp42cHFvDdygFNPVtHonEnn7SNW5I39jjHXfMyclnn33GTTfdxMCBA3n00UdrXMbhcFQbW1KelGRkZMTUrmkqiopKY1o32QxDJyvLSVGRi0DATHZ3GozWChwtM3Dvr/m2DgQHzObmuSgoaLrvA6TPNq+JxJ5+sadr3JC+sdc17qwsZ1RXV2JKTt58800eeOABRo8ezSOPPILNVvO333bt2rF+/fqQ1/bs2QNA27ZtY2kaAL+/cW/4QMBs9DFEpEH/K718/4itxnEnmqFo0cOk3TA/fn8S+pcETX6bRyCxp1/s6Ro3pG/s8Y476ptEs2bN4r777mPSpEk8/vjjYRMTgMGDB7NmzRqKi4srXlu8eDGZmZn07t07th6LRmHgDV66nR7MPDSjyrgSTeFsrTj9NRda+AsrQggh0lhUycnmzZt58MEH+cMf/sDVV1/Nvn372Lt3L3v37uXgwYN4vV727t1bcSvnlFNOoXXr1vzlL39h7dq1fPbZZzz++ONMmTIlYlIjGj/dAn98xc3oGS46nRAgq7NJuzw47l4vF35TQna3pj0QVgghROyiuq3z8ccf4/P5+PTTT/n0009Dfjdu3DjGjRvHJZdcwuuvv87QoUOx2+28/PLL3HvvvVxwwQVkZ2czceJErr322rgGIVKTpkO30/10O92PxaKTk5NJQUH63MoRQggRG02pxjUVViBgkp9fkuxuxKTyBF2Sdvck0zX2dI0bJPZ0jD1d44b0jb2ucefmZkY1IDa9HsgWQgghRMqT5EQIIYQQKUWSEyGEEEKkFElOhBBCCJFSJDkRQgghREqR5EQIIYQQKUWSEyGEEEKkFElOhBBCCJFSJDkRQgghREppdDPEKqUwzUbV5RCGoadVOe2q0jX2dI0bJPZ0jD1d44b0jb0uceu6hhZFtddGl5wIIYQQommT2zpCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVKKJCdCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVKKJCdCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVKKJCcNZPPmzeTl5fH++++HXWbu3Ln06tWr2n/btm1LYE/jY/fu3TXGEi7+goICbrzxRgYPHsyQIUO49957cblcCe51/UUbd1Pa5gBz5szh9NNPp1+/fpxxxhl89NFHYZf1eDzce++9DB8+nLy8PG688Uby8/MT2Nv4iSbuH374ocZtvmTJkgT2uP6WLFlSYxy9evVi1KhRNa7TVLZ5LLE3le3u9/t56qmnOOmkk8jLy2PSpEksX7487PLx+my31KPPIgyfz8dNN91EaWlpxOXWrVvHkCFDePzxx0Nez83NbcjuNYi1a9dit9v57LPPQspiN2/evMblb7jhBlwuFzNmzKCoqIg77riD0tJSHnnkkUR1OS6ijbspbfMPPviAO+64g9tvv53jjz+eDz/8kKlTp9KuXTvy8vKqLX/PPfewbNkynnnmGWw2G3fffTc33HADb775ZhJ6H7to4163bh2dO3dm1qxZIa9nZ2cnqstxkZeXx4IFC0JeW758Oddffz3XXnttjes0lW0eS+xNZbtPmzaNd999l4cffpjDDjuMl156iSuuuIL58+fTpk2basvH7bNdibh77LHH1CWXXKJ69uyp3nvvvbDLXXHFFeq+++5LYM8azosvvqjOOuusOi37448/qp49e6qNGzdWvPbtt9+qXr16qV27djVUFxtENHEr1XS2uWma6qSTTlIPP/xwyOtTpkxRzz//fLXld+3apXr37q2++uqritd+/fVX1bNnT/Xjjz82eH/jJdq4lVLq7rvvVtdcc00iupdQJSUl6qSTTlK33nprjb9vKtu8JrXFrlTT2e5jxoxRDz30UMX/P3jwoOrZs6f6+OOPqy0bz892ua0TZ0uXLuXtt9/m4YcfrnXZdevW0b179wT0quFFE8uyZcto3bp1yPJDhgxB0zR++OGHhupig4h2GzaVbb5582a2b9/OWWedFfL6K6+8wtVXX11t+fLtOmzYsIrXunbtStu2bVm6dGnDdjaOoo0bms42P9Tzzz+Py+Xib3/7W42/byrbvCa1xQ5NZ7u3bNmSL7/8km3bthEIBHj77bex2Wz07t272rLx/GyX5CSOioqKuOWWW7jzzjtp3759xGULCwvZvXs3y5Yt46yzzuK4447j2muvZfPmzQnqbXytX7+e/Px8Jk2axLHHHsuECRP45ptvalx29+7d1d4fm81GixYt2LlzZyK6GzfRxN2Utnl5n0tLS7n88ssZPnw4559/Pl988UWNy+/evZucnBzsdnvI623atGHXrl0N3t94iTZugA0bNvDrr79yzjnnMGLECC677DJWrlyZqC43iPz8fGbMmME111xDixYtalymqWzzQ9Uldmg62/2OO+7AarUyatQo+vXrxxNPPMHTTz9N586dqy0bz892SU7i6J577iEvL6/at6qabNiwAQClFA899BBPPvkkHo+HiRMnsm/fvobualz5/X5+/fVXCgsLuf7663nxxRc5+uijueqqq1i0aFG15V0uFzabrdrrdrsdj8eTiC7HRbRxN6VtXlxcDMDf/vY3zjzzTKZPn86IESO49tprm/Q2jzbunTt3cvDgQUpLS7nzzjt57rnnaNWqFRdddBEbN25MdPfjZtasWTRv3pzx48eHXaapbPND1SX2prTdN27cSPPmzfnXv/7F22+/zTnnnMNNN93EL7/8Um3ZeG5zGRAbJ3PmzGHZsmXMmzevTssPGjSIRYsWkZOTUzGQ8tlnn2XkyJG8//77XHXVVQ3Z3biyWCwsWbIEwzBwOBwAHHXUUWzYsIFXXnmF4cOHhyzvcDjwer3V/o7H4yEjIyMhfY6HaONuStvcarUCcPnllzNu3DgA+vTpw5o1a3j11Vej2uZOp7PhOxwn0cbdvn17li5ditPprFi3X79+rFmzhjfeeIN77703sQHEyZw5cxg7dmzFfl+TprLND1WX2JvKdt+5cyc33ngjM2bMYNCgQUAwjo0bN/LMM8/w3HPPhSwfz892uXISJ++99x779+9n5MiR5OXlVYzav/vuu7niiitqXCc3NzfkCQ+n00mnTp3YvXt3QvocT5mZmdUO1iOOOKLGWNq1a8eePXtCXvN6vRw4cKDG0d+pLJq4oels87Zt2wLQs2fPkNd79OhR42PR7dq148CBA9U+uPbs2VPxtxqDaOMGyMrKqjhBAei6Tvfu3RvdNi+3du1atm7dWusV4qayzauqa+zQNLb7ihUr8Pl89OvXL+T1AQMGsGXLlmrLx/OzXZKTOHn00UeZP38+c+bMqfgPgo9VPfDAA9WWf/vttxk6dGjI48bFxcX89ttv9OjRI1HdjosNGzYwcODAas/vr169usZYBg8ezK5du0J27u+//x6AY445pmE7G0fRxt2Utnnfvn3JzMxkxYoVIa+vX7++xnvRxxxzDKZphgyK27x5M7t372bw4MEN3t94iTbub775hry8PLZu3Vrxmt/vZ+3atY1um5dbtmwZLVu2rHFAZFVNZZtXVdfYm8p2b9euHRAc3FvV+vXr6dKlS7Xl4/rZHuvjRaJ2VR8l9vv9as+ePcrlcimllNqxY4caNGiQuu6669T69evVypUr1aWXXqpOOeUU5Xa7k9ntqAUCAXXuueeq008/XS1dulRt3LhRPfjgg+qoo45S69atqxa7aZrqwgsvVOPGjVMrVqxQixYtqvWxvFQUbdxNaZsrpdS//vUvlZeXp+bNm6e2bNminnvuOdW7d2+1ePFipZRSe/bsUcXFxRXLT506VZ188slq8eLFasWKFWrs2LHqoosuSlb3YxZN3AcPHlQnnXSSmjBhglq1apVau3atmjp1qho8eLDau3dvMsOI2W233aYuvfTSGn/XVLd5ubrG3lS2eyAQUBMmTFCjR49WixYtUps3b1ZPPPGE6tOnj1q+fHmDfrZLctKAqiYnW7durTbvyerVq9Vll12mjjnmGDVw4EB1/fXXqx07diSru/Wyd+9edeutt6oRI0aofv36qfHjx6ulS5cqpWqOfd++fer6669XRx99tBo6dKi6++67G+UJOtq4m9I2V0qp6dOnq5NPPln17dtXjRkzRn366acVv+vZs6d6+umnK/5/SUmJuuOOO9SgQYPUoEGD1NSpU1V+fn4yul1v0cS9ZcsWdf3116shQ4aoAQMGqClTpqh169Ylo9txccUVV6i//OUvNf6uKW9zpaKLvals9wMHDqh77rlHjRw5UuXl5anx48erJUuWKKUa9rNdU0qp6K61CCGEEEI0HBlzIoQQQoiUIsmJEEIIIVKKJCdCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVKKJCdCCCGESCmSnAghhBAipUhyIoQQQoiUIsmJEEIIIVKKJCdCCCGESCmSnAghhBAipfx/Qxj7o/Hu2DgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(Clusters_scaled['sepal_length'], Clusters_scaled['sepal_width'], c=Clusters_scaled['Cluster_predict'], cmap='rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Take Advantage of the Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### WCSS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[600.0,\n",
+       " 223.73200573676345,\n",
+       " 141.1541781338865,\n",
+       " 114.68221609937967,\n",
+       " 91.19197002146461,\n",
+       " 80.2497223557713,\n",
+       " 72.65464969103242,\n",
+       " 63.29740897957997,\n",
+       " 54.61531716218526]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wcss = []\n",
+    "# 'cl_num' is a that keeps track the highest number of clusters we want to use the WCSS method for. \n",
+    "# We have it set at 10 right now, but it is completely arbitrary.\n",
+    "cl_num = 10\n",
+    "for i in range (1,cl_num):\n",
+    "    kmeans = KMeans(i)\n",
+    "    kmeans.fit(X_scaled)\n",
+    "    wcss_iter = kmeans.inertia_\n",
+    "    wcss.append(wcss_iter)\n",
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Within_Cluster Sum of Squares')"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "number_clusters = range(1, cl_num)\n",
+    "plt.plot(number_clusters, wcss)\n",
+    "plt.title('The Elbow Method')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('Within_Cluster Sum of Squares')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many clusters are there?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#There are 3 clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Understanding the Elbow Curve\n",
+    "\n",
+    "Construct and compare the scatter plots to determine which number of clusters is appropriate for further use in our analysis. Based on the Elbow Curve, 2, 3 or 5 seem the most likely."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>Cluster_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width  Cluster_pred\n",
+       "0             5.1          3.5           1.4          0.2             0\n",
+       "1             4.9          3.0           1.4          0.2             0\n",
+       "2             4.7          3.2           1.3          0.2             0\n",
+       "3             4.6          3.1           1.5          0.2             0\n",
+       "4             5.0          3.6           1.4          0.2             0\n",
+       "..            ...          ...           ...          ...           ...\n",
+       "145           6.7          3.0           5.2          2.3             1\n",
+       "146           6.3          2.5           5.0          1.9             1\n",
+       "147           6.5          3.0           5.2          2.0             1\n",
+       "148           6.2          3.4           5.4          2.3             1\n",
+       "149           5.9          3.0           5.1          1.8             1\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fiddle with K (the number of clusters)\n",
+    "kmeans_new = KMeans(2)\n",
+    "kmeans_new.fit(X_scaled)\n",
+    "cluster_new = X.copy()\n",
+    "cluster_new['Cluster_pred'] = kmeans_new.fit_predict(X_scaled)\n",
+    "cluster_new\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xae3da29990>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cluster_new['sepal_length'],cluster_new['sepal_width'], c = cluster_new['Cluster_pred'], cmap= 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3 Clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>Cluster_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width  Cluster_pred\n",
+       "0             5.1          3.5           1.4          0.2             0\n",
+       "1             4.9          3.0           1.4          0.2             0\n",
+       "2             4.7          3.2           1.3          0.2             0\n",
+       "3             4.6          3.1           1.5          0.2             0\n",
+       "4             5.0          3.6           1.4          0.2             0\n",
+       "..            ...          ...           ...          ...           ...\n",
+       "145           6.7          3.0           5.2          2.3             2\n",
+       "146           6.3          2.5           5.0          1.9             1\n",
+       "147           6.5          3.0           5.2          2.0             2\n",
+       "148           6.2          3.4           5.4          2.3             2\n",
+       "149           5.9          3.0           5.1          1.8             1\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans_new = KMeans(3)\n",
+    "kmeans_new.fit(X_scaled)\n",
+    "cluster_new = X.copy()\n",
+    "cluster_new['Cluster_pred'] = kmeans_new.fit_predict(X_scaled)\n",
+    "cluster_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xae3d776910>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cluster_new['sepal_length'],cluster_new['sepal_width'], c = cluster_new['Cluster_pred'], cmap= 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5 Clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>Cluster_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width  Cluster_pred\n",
+       "0             5.1          3.5           1.4          0.2             3\n",
+       "1             4.9          3.0           1.4          0.2             0\n",
+       "2             4.7          3.2           1.3          0.2             0\n",
+       "3             4.6          3.1           1.5          0.2             0\n",
+       "4             5.0          3.6           1.4          0.2             3\n",
+       "..            ...          ...           ...          ...           ...\n",
+       "145           6.7          3.0           5.2          2.3             1\n",
+       "146           6.3          2.5           5.0          1.9             4\n",
+       "147           6.5          3.0           5.2          2.0             4\n",
+       "148           6.2          3.4           5.4          2.3             1\n",
+       "149           5.9          3.0           5.1          1.8             4\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans_new = KMeans(5)\n",
+    "kmeans_new.fit(X_scaled)\n",
+    "cluster_new = X.copy()\n",
+    "cluster_new['Cluster_pred'] = kmeans_new.fit_predict(X_scaled)\n",
+    "cluster_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xae2597ae10>"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cluster_new['sepal_length'],cluster_new['sepal_width'], c = cluster_new['Cluster_pred'], cmap= 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>Cluster_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width  Cluster_pred\n",
+       "0             5.1          3.5           1.4          0.2             3\n",
+       "1             4.9          3.0           1.4          0.2             2\n",
+       "2             4.7          3.2           1.3          0.2             2\n",
+       "3             4.6          3.1           1.5          0.2             2\n",
+       "4             5.0          3.6           1.4          0.2             3\n",
+       "..            ...          ...           ...          ...           ...\n",
+       "145           6.7          3.0           5.2          2.3             1\n",
+       "146           6.3          2.5           5.0          1.9             0\n",
+       "147           6.5          3.0           5.2          2.0             1\n",
+       "148           6.2          3.4           5.4          2.3             1\n",
+       "149           5.9          3.0           5.1          1.8             0\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans_new = KMeans(4)\n",
+    "kmeans_new.fit(X_scaled)\n",
+    "cluster_new = X.copy()\n",
+    "cluster_new['Cluster_pred'] = kmeans_new.fit_predict(X_scaled)\n",
+    "cluster_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xae402dbe90>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cluster_new['sepal_length'],cluster_new['sepal_width'], c = cluster_new['Cluster_pred'], cmap= 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
+       "0             5.1          3.5           1.4          0.2     setosa\n",
+       "1             4.9          3.0           1.4          0.2     setosa\n",
+       "2             4.7          3.2           1.3          0.2     setosa\n",
+       "3             4.6          3.1           1.5          0.2     setosa\n",
+       "4             5.0          3.6           1.4          0.2     setosa\n",
+       "..            ...          ...           ...          ...        ...\n",
+       "145           6.7          3.0           5.2          2.3  virginica\n",
+       "146           6.3          2.5           5.0          1.9  virginica\n",
+       "147           6.5          3.0           5.2          2.0  virginica\n",
+       "148           6.2          3.4           5.4          2.3  virginica\n",
+       "149           5.9          3.0           5.1          1.8  virginica\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Checked_data = pd.read_csv('iris_with_answers.csv')\n",
+    "Checked_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2], dtype=int64)"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Checked_data['species'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width  species\n",
+       "0             5.1          3.5           1.4          0.2        0\n",
+       "1             4.9          3.0           1.4          0.2        0\n",
+       "2             4.7          3.2           1.3          0.2        0\n",
+       "3             4.6          3.1           1.5          0.2        0\n",
+       "4             5.0          3.6           1.4          0.2        0\n",
+       "..            ...          ...           ...          ...      ...\n",
+       "145           6.7          3.0           5.2          2.3        2\n",
+       "146           6.3          2.5           5.0          1.9        2\n",
+       "147           6.5          3.0           5.2          2.0        2\n",
+       "148           6.2          3.4           5.4          2.3        2\n",
+       "149           5.9          3.0           5.1          1.8        2\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Checked_data['species'] = Checked_data['species'].map({'setosa': 0, 'versicolor': 1,'virginica': 2 })\n",
+    "Checked_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(Checked_data['sepal_length'], Checked_data['sepal_width'], c=Checked_data['species'],cmap='rainbow')\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Sepal width')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examining the other scatter plot (petal length vs petal width), we see that in fact the features which actually make the species different are petals and NOT sepals!\n",
+    "\n",
+    "Note that 'real data' is the data observed in the real world (biological data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'petal width')"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(Checked_data['petal_length'], Checked_data['petal_width'], c=Checked_data['species'],cmap='rainbow')\n",
+    "plt.xlabel('petal length')\n",
+    "plt.ylabel('petal width')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Our clustering solution data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems that our solution takes into account mainly the sepal features Instead of the petals..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Further clarifications"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original dataset has 3 sub-species of the Iris flower. Therefore, the number of clusters is 3.\n",
+    "\n",
+    "This shows us that:\n",
+    "<li> the Eblow method is imperfect (we might have opted for 2 or even 4) </li>\n",
+    "<li> k-means is very useful in moments where we already know the number of clusters - in this case: 3 </li>\n",
+    "<li> biology cannot be always quantified (or better).. quantified with k-means! Other methods are much better at that </li>\n",
+    "\n",
+    "Finally, you can try to classify them (instead of cluster them, now that you have all the data)! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+1-+Solution.ipynb b/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+1-+Solution.ipynb
new file mode 100644
index 0000000..4621f1e
--- /dev/null
+++ b/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+1-+Solution.ipynb	
@@ -0,0 +1,312 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Species Segmentation with Cluster Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Iris flower dataset is one of the most popular ones for machine learning. You can read a lot about it online and have probably already heard of it: https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
+    "\n",
+    "We didn't want to use it in the lectures, but believe that it would be very interesting for you to try it out (and maybe read about it on your own).\n",
+    "\n",
+    "There are 4 features: sepal length, sepal width, petal length, and petal width.\n",
+    "\n",
+    "Start by creating 2 clusters. Then standardize the data and try again. Does it make a difference?\n",
+    "\n",
+    "Use the Elbow rule to determine how many clusters are there.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'iris_dataset.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load the data\n",
+    "data = pd.read_csv('iris_dataset.csv')\n",
+    "# Check the data\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this exercise, try to cluster the iris flowers by the shape of their sepal. \n",
+    "\n",
+    "<i> Use the 'sepal_length' and 'sepal_width' variables.</i> "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(data['sepal_length'],data['sepal_width'])\n",
+    "# name your axes\n",
+    "plt.xlabel('Lenght of sepal')\n",
+    "plt.ylabel('Width of sepal')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering (unscaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a variable which will contain the data for the clustering\n",
+    "x = data.copy()\n",
+    "# create a k-means object with 2 clusters\n",
+    "kmeans = KMeans(2)\n",
+    "# fit the data\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a copy of data, so we can see the clusters next to the original data\n",
+    "clusters = data.copy()\n",
+    "# predict the cluster for each observation\n",
+    "clusters['cluster_pred']=kmeans.fit_predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(clusters['sepal_length'], clusters['sepal_width'], c= clusters ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Standardize the variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import and use the <i> scale </i> method from sklearn to standardize the data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import some preprocessing module\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "# scale the data for better results\n",
+    "x_scaled = preprocessing.scale(data)\n",
+    "x_scaled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering (scaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# create a k-means object with 2 clusters\n",
+    "kmeans_scaled = KMeans(2)\n",
+    "# fit the data\n",
+    "kmeans_scaled.fit(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a copy of data, so we can see the clusters next to the original data\n",
+    "clusters_scaled = data.copy()\n",
+    "# predict the cluster for each observation\n",
+    "clusters_scaled['cluster_pred']=kmeans_scaled.fit_predict(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(clusters_scaled['sepal_length'], clusters_scaled['sepal_width'], c= clusters_scaled ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like the two solutions are identical. That is because the original features have very similar scales to start with!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Take Advantage of the Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### WCSS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wcss = []\n",
+    "# 'cl_num' is a that keeps track the highest number of clusters we want to use the WCSS method for. \n",
+    "# We have it set at 10 right now, but it is completely arbitrary.\n",
+    "cl_num = 10\n",
+    "for i in range (1,cl_num):\n",
+    "    kmeans= KMeans(i)\n",
+    "    kmeans.fit(x_scaled)\n",
+    "    wcss_iter = kmeans.inertia_\n",
+    "    wcss.append(wcss_iter)\n",
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "number_clusters = range(1,cl_num)\n",
+    "plt.plot(number_clusters, wcss)\n",
+    "plt.title('The Elbow Method')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('Within-cluster Sum of Squares')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems like 2 or 3-cluster solutions are the best. \n",
+    "\n",
+    "To be continued... "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+2+-+Exercise.ipynb b/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+2+-+Exercise.ipynb
new file mode 100644
index 0000000..b135286
--- /dev/null
+++ b/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+2+-+Exercise.ipynb	
@@ -0,0 +1,1084 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Species Segmentation with Cluster Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Iris flower dataset is one of the most popular ones for machine learning. You can read a lot about it online and have probably already heard of it: https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
+    "\n",
+    "We didn't want to use it in the lectures, but believe that it would be very interesting for you to try it out (and maybe read about it on your own).\n",
+    "\n",
+    "There are 4 features: sepal length, sepal width, petal length, and petal width.\n",
+    "\n",
+    "***\n",
+    "\n",
+    "You have already solved the first exercise, so you can start from there (you've done taken advantage of the Elbow Method).\n",
+    "\n",
+    "Plot the data with 2, 3 and 5 clusters. What do you think that means?\n",
+    "\n",
+    "Finally, import the CSV with the correct answers (iris_with_answers.csv) and check if the clustering worked as expected. Note that this is not how we usually go about clustering problems. If we have the answers prior to that, we would go for classification (e.g. a logistic regression)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'iris_dataset.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width\n",
+       "0             5.1          3.5           1.4          0.2\n",
+       "1             4.9          3.0           1.4          0.2\n",
+       "2             4.7          3.2           1.3          0.2\n",
+       "3             4.6          3.1           1.5          0.2\n",
+       "4             5.0          3.6           1.4          0.2\n",
+       "..            ...          ...           ...          ...\n",
+       "145           6.7          3.0           5.2          2.3\n",
+       "146           6.3          2.5           5.0          1.9\n",
+       "147           6.5          3.0           5.2          2.0\n",
+       "148           6.2          3.4           5.4          2.3\n",
+       "149           5.9          3.0           5.1          1.8\n",
+       "\n",
+       "[150 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data\n",
+    "data = pd.read_csv('iris_dataset.csv')\n",
+    "# Check the data\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this exercise, try to cluster the iris flowers by the shape of their sepal. \n",
+    "\n",
+    "<i> Use the 'sepal_length' and 'sepal_width' variables.</i> "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(data['sepal_length'],data['sepal_width'])\n",
+    "# name your axes\n",
+    "plt.xlabel('Lenght of sepal')\n",
+    "plt.ylabel('Width of sepal')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering (unscaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a variable which will contain the data for the clustering\n",
+    "x = data.copy()\n",
+    "# create a k-means object with 2 clusters\n",
+    "kmeans = KMeans(2)\n",
+    "# fit the data\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create a copy of data, so we can see the clusters next to the original data\n",
+    "clusters = data.copy()\n",
+    "# predict the cluster for each observation\n",
+    "clusters['cluster_pred']=kmeans.fit_predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xb1300865d0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(clusters['sepal_length'], clusters['sepal_width'], c= clusters ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Standardize the variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import and use the <i> scale </i> method from sklearn to standardize the data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-9.00681170e-01,  1.03205722e+00, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.14301691e+00, -1.24957601e-01, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.38535265e+00,  3.37848329e-01, -1.39813811e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.50652052e+00,  1.06445364e-01, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00,  1.26346019e+00, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-5.37177559e-01,  1.95766909e+00, -1.17067529e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-1.50652052e+00,  8.00654259e-01, -1.34127240e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-1.02184904e+00,  8.00654259e-01, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.74885626e+00, -3.56360566e-01, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.14301691e+00,  1.06445364e-01, -1.28440670e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-5.37177559e-01,  1.49486315e+00, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.26418478e+00,  8.00654259e-01, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.26418478e+00, -1.24957601e-01, -1.34127240e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-1.87002413e+00, -1.24957601e-01, -1.51186952e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-5.25060772e-02,  2.18907205e+00, -1.45500381e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.73673948e-01,  3.11468391e+00, -1.28440670e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-5.37177559e-01,  1.95766909e+00, -1.39813811e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-9.00681170e-01,  1.03205722e+00, -1.34127240e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-1.73673948e-01,  1.72626612e+00, -1.17067529e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-9.00681170e-01,  1.72626612e+00, -1.28440670e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-5.37177559e-01,  8.00654259e-01, -1.17067529e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-9.00681170e-01,  1.49486315e+00, -1.28440670e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-1.50652052e+00,  1.26346019e+00, -1.56873522e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-9.00681170e-01,  5.69251294e-01, -1.17067529e+00,\n",
+       "        -9.18557817e-01],\n",
+       "       [-1.26418478e+00,  8.00654259e-01, -1.05694388e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00, -1.24957601e-01, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00,  8.00654259e-01, -1.22754100e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-7.79513300e-01,  1.03205722e+00, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-7.79513300e-01,  8.00654259e-01, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.38535265e+00,  3.37848329e-01, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.26418478e+00,  1.06445364e-01, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-5.37177559e-01,  8.00654259e-01, -1.28440670e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-7.79513300e-01,  2.42047502e+00, -1.28440670e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-4.16009689e-01,  2.65187798e+00, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.14301691e+00,  1.06445364e-01, -1.28440670e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-1.02184904e+00,  3.37848329e-01, -1.45500381e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-4.16009689e-01,  1.03205722e+00, -1.39813811e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.14301691e+00,  1.06445364e-01, -1.28440670e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-1.74885626e+00, -1.24957601e-01, -1.39813811e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-9.00681170e-01,  8.00654259e-01, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00,  1.03205722e+00, -1.39813811e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-1.62768839e+00, -1.74477836e+00, -1.39813811e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-1.74885626e+00,  3.37848329e-01, -1.39813811e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00,  1.03205722e+00, -1.22754100e+00,\n",
+       "        -7.87084847e-01],\n",
+       "       [-9.00681170e-01,  1.72626612e+00, -1.05694388e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-1.26418478e+00, -1.24957601e-01, -1.34127240e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-9.00681170e-01,  1.72626612e+00, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.50652052e+00,  3.37848329e-01, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-6.58345429e-01,  1.49486315e+00, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
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+       "         7.90590793e-01],\n",
+       "       [ 1.28034050e+00,  1.06445364e-01,  9.33355755e-01,\n",
+       "         1.18500970e+00],\n",
+       "       [ 1.03800476e+00,  1.06445364e-01,  1.04708716e+00,\n",
+       "         1.57942861e+00],\n",
+       "       [ 1.28034050e+00,  1.06445364e-01,  7.62758643e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
+       "         9.22063763e-01],\n",
+       "       [ 1.15917263e+00,  3.37848329e-01,  1.21768427e+00,\n",
+       "         1.44795564e+00],\n",
+       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
+       "         1.71090158e+00],\n",
+       "       [ 1.03800476e+00, -1.24957601e-01,  8.19624347e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [ 5.53333275e-01, -1.28197243e+00,  7.05892939e-01,\n",
+       "         9.22063763e-01],\n",
+       "       [ 7.95669016e-01, -1.24957601e-01,  8.19624347e-01,\n",
+       "         1.05353673e+00],\n",
+       "       [ 4.32165405e-01,  8.00654259e-01,  9.33355755e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [ 6.86617933e-02, -1.24957601e-01,  7.62758643e-01,\n",
+       "         7.90590793e-01]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import some preprocessing module\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "# scale the data for better results\n",
+    "x_scaled = preprocessing.scale(data)\n",
+    "x_scaled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering (scaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a k-means object with 2 clusters\n",
+    "kmeans_scaled = KMeans(2)\n",
+    "# fit the data\n",
+    "kmeans_scaled.fit(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create a copy of data, so we can see the clusters next to the original data\n",
+    "clusters_scaled = data.copy()\n",
+    "# predict the cluster for each observation\n",
+    "clusters_scaled['cluster_pred']=kmeans_scaled.fit_predict(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xb12ffa9610>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(clusters_scaled['sepal_length'], clusters_scaled['sepal_width'], c= clusters_scaled ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like the two solutions are identical. That is because the original features have very similar scales to start with!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Take Advantage of the Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### WCSS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[600.0,\n",
+       " 223.73200573676345,\n",
+       " 140.96581663074699,\n",
+       " 114.61550694959806,\n",
+       " 91.02916928379733,\n",
+       " 81.6477918401683,\n",
+       " 71.9471341262925,\n",
+       " 62.452683728324764,\n",
+       " 55.37967417784802]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wcss = []\n",
+    "# 'cl_num' is a that keeps track the highest number of clusters we want to use the WCSS method for. \n",
+    "# We have it set at 10 right now, but it is completely arbitrary.\n",
+    "cl_num = 10\n",
+    "for i in range (1,cl_num):\n",
+    "    kmeans= KMeans(i)\n",
+    "    kmeans.fit(x_scaled)\n",
+    "    wcss_iter = kmeans.inertia_\n",
+    "    wcss.append(wcss_iter)\n",
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Within-cluster Sum of Squares')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "number_clusters = range(1,cl_num)\n",
+    "plt.plot(number_clusters, wcss)\n",
+    "plt.title('The Elbow Method')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('Within-cluster Sum of Squares')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Understanding the Elbow Curve\n",
+    "\n",
+    "Construct and compare the scatter plots to determine which number of clusters is appropriate for further use in our analysis. Based on the Elbow Curve, 2, 3 or 5 seem the most likely."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 clusters\n",
+    "\n",
+    "Start by separating the standardized data into 2 clusters (you've already done that!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct a scatter plot of the original data using the standardized clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3 clusters\n",
+    "Redo the same for 3 and 5 clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5 clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare your solutions to the original iris dataset\n",
+    "\n",
+    "The original (full) iris data is located in <i>iris_with_answers.csv</i>. Load the csv, plot the data and compare it with your solution. \n",
+    "\n",
+    "Obviously there are only 3 types, because that's the original (truthful) iris dataset.\n",
+    "\n",
+    "The 2-cluster solution seemed good, but in real life the iris dataset has 3 SPECIES (a 3-cluster solution). Therefore, clustering cannot be trusted at all times. Sometimes it seems like x clusters are a good solution, but in real life, there are more (or less)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+2+-+Solution.ipynb b/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+2+-+Solution.ipynb
new file mode 100644
index 0000000..1569cfb
--- /dev/null
+++ b/Udemy DataScience Workbook/Species+Segmentation+with+Cluster+Analysis+Part+2+-+Solution.ipynb	
@@ -0,0 +1,1791 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Species Segmentation with Cluster Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Iris flower dataset is one of the most popular ones for machine learning. You can read a lot about it online and have probably already heard of it: https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
+    "\n",
+    "We didn't want to use it in the lectures, but believe that it would be very interesting for you to try it out (and maybe read about it on your own).\n",
+    "\n",
+    "There are 4 features: sepal length, sepal width, petal length, and petal width.\n",
+    "\n",
+    "***\n",
+    "\n",
+    "You have already solved the first exercise, so you can start from there (you've done taken advantage of the Elbow Method).\n",
+    "\n",
+    "Plot the data with 2, 3 and 5 clusters. What do you think that means?\n",
+    "\n",
+    "Finally, import the CSV with the correct answers (iris_with_answers.csv) and check if the clustering worked as expected. Note that this is not how we usually go about clustering problems. If we have the answers prior to that, we would go for classification (e.g. a logistic regression)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data from the csv file: <i> 'iris_dataset.csv'</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width\n",
+       "0             5.1          3.5           1.4          0.2\n",
+       "1             4.9          3.0           1.4          0.2\n",
+       "2             4.7          3.2           1.3          0.2\n",
+       "3             4.6          3.1           1.5          0.2\n",
+       "4             5.0          3.6           1.4          0.2\n",
+       "..            ...          ...           ...          ...\n",
+       "145           6.7          3.0           5.2          2.3\n",
+       "146           6.3          2.5           5.0          1.9\n",
+       "147           6.5          3.0           5.2          2.0\n",
+       "148           6.2          3.4           5.4          2.3\n",
+       "149           5.9          3.0           5.1          1.8\n",
+       "\n",
+       "[150 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data\n",
+    "data = pd.read_csv('iris_dataset.csv')\n",
+    "# Check the data\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this exercise, try to cluster the iris flowers by the shape of their sepal. \n",
+    "\n",
+    "<i> Hint: Use the 'sepal_length' and 'sepal_width' variables.</i> "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(data['sepal_length'],data['sepal_width'])\n",
+    "# Name your axes\n",
+    "plt.xlabel('Lenght of sepal')\n",
+    "plt.ylabel('Width of sepal')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering (unscaled data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Separate the original data into 2 clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a variable which will contain the data for the clustering\n",
+    "x = data.copy()\n",
+    "# create a k-means object with 2 clusters\n",
+    "kmeans = KMeans(2)\n",
+    "# fit the data\n",
+    "kmeans.fit(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create a copy of data, so we can see the clusters next to the original data\n",
+    "clusters = data.copy()\n",
+    "# predict the cluster for each observation\n",
+    "clusters['cluster_pred']=kmeans.fit_predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x674be63e50>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(clusters['sepal_length'], clusters['sepal_width'], c= clusters ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Standardize the variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import and use the <i> scale </i> function from sklearn to standardize the data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-9.00681170e-01,  1.03205722e+00, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.14301691e+00, -1.24957601e-01, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.38535265e+00,  3.37848329e-01, -1.39813811e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.50652052e+00,  1.06445364e-01, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00,  1.26346019e+00, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-5.37177559e-01,  1.95766909e+00, -1.17067529e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-1.50652052e+00,  8.00654259e-01, -1.34127240e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-1.02184904e+00,  8.00654259e-01, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.74885626e+00, -3.56360566e-01, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.14301691e+00,  1.06445364e-01, -1.28440670e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-5.37177559e-01,  1.49486315e+00, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.26418478e+00,  8.00654259e-01, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.26418478e+00, -1.24957601e-01, -1.34127240e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-1.87002413e+00, -1.24957601e-01, -1.51186952e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-5.25060772e-02,  2.18907205e+00, -1.45500381e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.73673948e-01,  3.11468391e+00, -1.28440670e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-5.37177559e-01,  1.95766909e+00, -1.39813811e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-9.00681170e-01,  1.03205722e+00, -1.34127240e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-1.73673948e-01,  1.72626612e+00, -1.17067529e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-9.00681170e-01,  1.72626612e+00, -1.28440670e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-5.37177559e-01,  8.00654259e-01, -1.17067529e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-9.00681170e-01,  1.49486315e+00, -1.28440670e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-1.50652052e+00,  1.26346019e+00, -1.56873522e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-9.00681170e-01,  5.69251294e-01, -1.17067529e+00,\n",
+       "        -9.18557817e-01],\n",
+       "       [-1.26418478e+00,  8.00654259e-01, -1.05694388e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00, -1.24957601e-01, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00,  8.00654259e-01, -1.22754100e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-7.79513300e-01,  1.03205722e+00, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-7.79513300e-01,  8.00654259e-01, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.38535265e+00,  3.37848329e-01, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.26418478e+00,  1.06445364e-01, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-5.37177559e-01,  8.00654259e-01, -1.28440670e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-7.79513300e-01,  2.42047502e+00, -1.28440670e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-4.16009689e-01,  2.65187798e+00, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.14301691e+00,  1.06445364e-01, -1.28440670e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-1.02184904e+00,  3.37848329e-01, -1.45500381e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-4.16009689e-01,  1.03205722e+00, -1.39813811e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.14301691e+00,  1.06445364e-01, -1.28440670e+00,\n",
+       "        -1.44444970e+00],\n",
+       "       [-1.74885626e+00, -1.24957601e-01, -1.39813811e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-9.00681170e-01,  8.00654259e-01, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00,  1.03205722e+00, -1.39813811e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-1.62768839e+00, -1.74477836e+00, -1.39813811e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-1.74885626e+00,  3.37848329e-01, -1.39813811e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.02184904e+00,  1.03205722e+00, -1.22754100e+00,\n",
+       "        -7.87084847e-01],\n",
+       "       [-9.00681170e-01,  1.72626612e+00, -1.05694388e+00,\n",
+       "        -1.05003079e+00],\n",
+       "       [-1.26418478e+00, -1.24957601e-01, -1.34127240e+00,\n",
+       "        -1.18150376e+00],\n",
+       "       [-9.00681170e-01,  1.72626612e+00, -1.22754100e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-1.50652052e+00,  3.37848329e-01, -1.34127240e+00,\n",
+       "        -1.31297673e+00],\n",
+       "       [-6.58345429e-01,  1.49486315e+00, -1.28440670e+00,\n",
+       "        -1.31297673e+00],\n",
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+       "         1.18500970e+00],\n",
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+       "         1.57942861e+00],\n",
+       "       [ 1.28034050e+00,  1.06445364e-01,  7.62758643e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
+       "         9.22063763e-01],\n",
+       "       [ 1.15917263e+00,  3.37848329e-01,  1.21768427e+00,\n",
+       "         1.44795564e+00],\n",
+       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
+       "         1.71090158e+00],\n",
+       "       [ 1.03800476e+00, -1.24957601e-01,  8.19624347e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [ 5.53333275e-01, -1.28197243e+00,  7.05892939e-01,\n",
+       "         9.22063763e-01],\n",
+       "       [ 7.95669016e-01, -1.24957601e-01,  8.19624347e-01,\n",
+       "         1.05353673e+00],\n",
+       "       [ 4.32165405e-01,  8.00654259e-01,  9.33355755e-01,\n",
+       "         1.44795564e+00],\n",
+       "       [ 6.86617933e-02, -1.24957601e-01,  7.62758643e-01,\n",
+       "         7.90590793e-01]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import some preprocessing module\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "# scale the data for better results\n",
+    "x_scaled = preprocessing.scale(data)\n",
+    "x_scaled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering (scaled data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a k-means object with 2 clusters\n",
+    "kmeans_scaled = KMeans(2)\n",
+    "# fit the data\n",
+    "kmeans_scaled.fit(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create a copy of data, so we can see the clusters next to the original data\n",
+    "clusters_scaled = data.copy()\n",
+    "# predict the cluster for each observation\n",
+    "clusters_scaled['cluster_pred']=kmeans_scaled.fit_predict(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x674bed11d0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create a scatter plot based on two corresponding features (sepal_length and sepal_width; OR petal_length and petal_width)\n",
+    "plt.scatter(clusters_scaled['sepal_length'], clusters_scaled['sepal_width'], c= clusters_scaled ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Take Advantage of the Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### WCSS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[600.0,\n",
+       " 223.73200573676345,\n",
+       " 140.96581663074699,\n",
+       " 114.55117670798518,\n",
+       " 91.06677122728534,\n",
+       " 80.23060704506955,\n",
+       " 70.88546993521874,\n",
+       " 63.7819896909509,\n",
+       " 54.551650224692146]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wcss = []\n",
+    "# 'cl_num' is a that keeps track the highest number of clusters we want to use the WCSS method for. We have it set at 10 right now, but it is completely arbitrary.\n",
+    "cl_num = 10\n",
+    "for i in range (1,cl_num):\n",
+    "    kmeans= KMeans(i)\n",
+    "    kmeans.fit(x_scaled)\n",
+    "    wcss_iter = kmeans.inertia_\n",
+    "    wcss.append(wcss_iter)\n",
+    "wcss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Within-cluster Sum of Squares')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "number_clusters = range(1,cl_num)\n",
+    "plt.plot(number_clusters, wcss)\n",
+    "plt.title('The Elbow Method')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('Within-cluster Sum of Squares')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the Elbow Curve, plot several graphs with the appropriate amounts of clusters you believe would best fit the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Understanding the Elbow Curve"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct and compare the scatter plots to determine which number of clusters is appropriate for further use in our analysis. Based on the Elbow Curve, 2, 3 or 5 seem the most likely."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start by seperating the standardized data into 2 clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans_2 = KMeans(2)\n",
+    "kmeans_2.fit(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct a scatter plot of the original data using the standartized clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Remember that we are plotting the non-standardized values of the sepal length and width. \n",
+    "clusters_2 = x.copy()\n",
+    "clusters_2['cluster_pred']=kmeans_2.fit_predict(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x67501e7d90>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(clusters_2['sepal_length'], clusters_2['sepal_width'], c= clusters_2 ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3 Clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Redo the same for 3 and 5 clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=3)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans_3 = KMeans(3)\n",
+    "kmeans_3.fit(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "clusters_3 = x.copy()\n",
+    "clusters_3['cluster_pred']=kmeans_3.fit_predict(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x6750235ed0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(clusters_3['sepal_length'], clusters_3['sepal_width'], c= clusters_3 ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5 Clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=5)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=5)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans_5 = KMeans(5)\n",
+    "kmeans_5.fit(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "clusters_5 = x.copy()\n",
+    "clusters_5['cluster_pred']=kmeans_5.fit_predict(x_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x675032a810>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(clusters_5['sepal_length'], clusters_5['sepal_width'], c= clusters_5 ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare your solutions to the original iris dataset\n",
+    "\n",
+    "The original (full) iris data is located in <i>iris_with_answers.csv</i>. Load the csv, plot the data and compare it with your solution. \n",
+    "\n",
+    "Obviously there are only 3 species of Iris, because that's the original (truthful) iris dataset.\n",
+    "\n",
+    "The 2-cluster solution seemed good, but in real life the iris dataset has 3 SPECIES (a 3-cluster solution). Therefore, clustering cannot be trusted at all times. Sometimes it seems like x clusters are a good solution, but in real life, there are more (or less)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "real_data = pd.read_csv('iris_with_answers.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "real_data['species'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# We use the map function to change any 'yes' values to 1 and 'no'values to 0. \n",
+    "real_data['species'] = real_data['species'].map({'setosa':0, 'versicolor':1 , 'virginica':2})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width  species\n",
+       "0           5.1          3.5           1.4          0.2        0\n",
+       "1           4.9          3.0           1.4          0.2        0\n",
+       "2           4.7          3.2           1.3          0.2        0\n",
+       "3           4.6          3.1           1.5          0.2        0\n",
+       "4           5.0          3.6           1.4          0.2        0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "real_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scatter plots (which we will use for comparison)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 'Real data'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the first graph it seems like the clustering solution is much more intertwined than what we imagined (and what we found before)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x67504c2390>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(real_data['sepal_length'], real_data['sepal_width'], c= real_data ['species'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examining the other scatter plot (petal length vs petal width), we see that in fact the features which actually make the species different are petals and NOT sepals!\n",
+    "\n",
+    "Note that 'real data' is the data observed in the real world (biological data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x675054a390>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(real_data['petal_length'], real_data['petal_width'], c= real_data ['species'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Our clustering solution data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems that our solution takes into account mainly the sepal features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x67516f0550>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(clusters_3['sepal_length'], clusters_3['sepal_width'], c= clusters_3 ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of the petals..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x6751755d50>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(clusters_3['petal_length'], clusters_3['petal_width'], c= clusters_3 ['cluster_pred'], cmap = 'rainbow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Further clarifications"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In fact, if you read about it, the original dataset has 3 sub-species of the Iris flower. Therefore, the number of clusters is 3.\n",
+    "\n",
+    "This shows us that:\n",
+    "<li> the Eblow method is imperfect (we might have opted for 2 or even 4) </li>\n",
+    "<li> k-means is very useful in moments where we already know the number of clusters - in this case: 3 </li>\n",
+    "<li> biology cannot be always quantified (or better).. quantified with k-means! Other methods are much better at that </li>\n",
+    "\n",
+    "Finally, you can try to classify them (instead of cluster them, now that you have all the data)! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Create dendograms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width\n",
+       "0             5.1          3.5           1.4          0.2\n",
+       "1             4.9          3.0           1.4          0.2\n",
+       "2             4.7          3.2           1.3          0.2\n",
+       "3             4.6          3.1           1.5          0.2\n",
+       "4             5.0          3.6           1.4          0.2\n",
+       "..            ...          ...           ...          ...\n",
+       "145           6.7          3.0           5.2          2.3\n",
+       "146           6.3          2.5           5.0          1.9\n",
+       "147           6.5          3.0           5.2          2.0\n",
+       "148           6.2          3.4           5.4          2.3\n",
+       "149           5.9          3.0           5.1          1.8\n",
+       "\n",
+       "[150 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Heatmap_data = x.copy()\n",
+    "Heatmap_data    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.matrix.ClusterGrid at 0x674bda9bd0>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.clustermap(x, cmap= 'mako')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TF_intro.npz b/Udemy DataScience Workbook/TF_intro.npz
new file mode 100644
index 0000000..034052e
Binary files /dev/null and b/Udemy DataScience Workbook/TF_intro.npz differ
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_Learning_Part3_with_comments (1).ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_Learning_Part3_with_comments (1).ipynb
new file mode 100644
index 0000000..d2a27c5
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_Learning_Part3_with_comments (1).ipynb	
@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example. Audiobooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem\n",
+    "\n",
+    "You are given data from an Audiobook app. Logically, it relates only to the audio versions of books. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertizing to him/her. If we can focus our efforts ONLY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, Book length in mins_avg (average of all purchases), Book length in minutes_sum (sum of all purchases), Price Paid_avg (average of all purchases), Price paid_sum (sum of all purchases), Review (a Boolean variable), Review (out of 10), Total minutes listened, Completion (from 0 to 1), Support requests (number), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "So these are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (so 0, or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we must import the libraries once again since we haven't imported them in this file\n",
+    "import numpy as np\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's create a temporary variable npz, where we will store each of the three Audiobooks datasets\n",
+    "npz = np.load('Audiobooks_data_train.npz')\n",
+    "\n",
+    "# we extract the inputs using the keyword under which we saved them\n",
+    "# to ensure that they are all floats, let's also take care of that\n",
+    "train_inputs = npz['inputs'].astype(np.float)\n",
+    "# targets must be int because of sparse_categorical_crossentropy (we want to be able to smoothly one-hot encode them)\n",
+    "train_targets = npz['targets'].astype(np.int)\n",
+    "\n",
+    "# we load the validation data in the temporary variable\n",
+    "npz = np.load('Audiobooks_data_validation.npz')\n",
+    "# we can load the inputs and the targets in the same line\n",
+    "validation_inputs, validation_targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n",
+    "\n",
+    "# we load the test data in the temporary variable\n",
+    "npz = np.load('Audiobooks_data_test.npz')\n",
+    "# we create 2 variables that will contain the test inputs and the test targets\n",
+    "test_inputs, test_targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model\n",
+    "Outline, optimizers, loss, early stopping and training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 3579 samples, validate on 447 samples\n",
+      "Epoch 1/100\n",
+      "3579/3579 - 0s - loss: 0.5406 - accuracy: 0.7868 - val_loss: 0.4206 - val_accuracy: 0.8479\n",
+      "Epoch 2/100\n",
+      "3579/3579 - 0s - loss: 0.3729 - accuracy: 0.8720 - val_loss: 0.3309 - val_accuracy: 0.8680\n",
+      "Epoch 3/100\n",
+      "3579/3579 - 0s - loss: 0.3252 - accuracy: 0.8824 - val_loss: 0.3018 - val_accuracy: 0.8904\n",
+      "Epoch 4/100\n",
+      "3579/3579 - 0s - loss: 0.3036 - accuracy: 0.8863 - val_loss: 0.2882 - val_accuracy: 0.8881\n",
+      "Epoch 5/100\n",
+      "3579/3579 - 0s - loss: 0.2902 - accuracy: 0.8908 - val_loss: 0.2800 - val_accuracy: 0.8993\n",
+      "Epoch 6/100\n",
+      "3579/3579 - 0s - loss: 0.2813 - accuracy: 0.8961 - val_loss: 0.2663 - val_accuracy: 0.9016\n",
+      "Epoch 7/100\n",
+      "3579/3579 - 0s - loss: 0.2706 - accuracy: 0.9019 - val_loss: 0.2619 - val_accuracy: 0.8971\n",
+      "Epoch 8/100\n",
+      "3579/3579 - 0s - loss: 0.2652 - accuracy: 0.9008 - val_loss: 0.2587 - val_accuracy: 0.9060\n",
+      "Epoch 9/100\n",
+      "3579/3579 - 0s - loss: 0.2612 - accuracy: 0.9014 - val_loss: 0.2567 - val_accuracy: 0.9016\n",
+      "Epoch 10/100\n",
+      "3579/3579 - 0s - loss: 0.2562 - accuracy: 0.9036 - val_loss: 0.2577 - val_accuracy: 0.9038\n",
+      "Epoch 11/100\n",
+      "3579/3579 - 0s - loss: 0.2525 - accuracy: 0.9056 - val_loss: 0.2507 - val_accuracy: 0.9083\n",
+      "Epoch 12/100\n",
+      "3579/3579 - 0s - loss: 0.2481 - accuracy: 0.9044 - val_loss: 0.2494 - val_accuracy: 0.9105\n",
+      "Epoch 13/100\n",
+      "3579/3579 - 0s - loss: 0.2462 - accuracy: 0.9098 - val_loss: 0.2610 - val_accuracy: 0.8949\n",
+      "Epoch 14/100\n",
+      "3579/3579 - 0s - loss: 0.2453 - accuracy: 0.9072 - val_loss: 0.2480 - val_accuracy: 0.9016\n",
+      "Epoch 15/100\n",
+      "3579/3579 - 0s - loss: 0.2477 - accuracy: 0.9089 - val_loss: 0.2455 - val_accuracy: 0.9038\n",
+      "Epoch 16/100\n",
+      "3579/3579 - 0s - loss: 0.2423 - accuracy: 0.9092 - val_loss: 0.2459 - val_accuracy: 0.9172\n",
+      "Epoch 17/100\n",
+      "3579/3579 - 0s - loss: 0.2399 - accuracy: 0.9098 - val_loss: 0.2403 - val_accuracy: 0.9150\n",
+      "Epoch 18/100\n",
+      "3579/3579 - 0s - loss: 0.2378 - accuracy: 0.9114 - val_loss: 0.2472 - val_accuracy: 0.9016\n",
+      "Epoch 19/100\n",
+      "3579/3579 - 0s - loss: 0.2366 - accuracy: 0.9120 - val_loss: 0.2437 - val_accuracy: 0.9083\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1b9933850b8>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set the input and output sizes\n",
+    "input_size = 10\n",
+    "output_size = 2\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])\n",
+    "\n",
+    "\n",
+    "### Choose the optimizer and the loss function\n",
+    "\n",
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "### Training\n",
+    "# That's where we train the model we have built.\n",
+    "\n",
+    "# set the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# set a maximum number of training epochs\n",
+    "max_epochs = 100\n",
+    "\n",
+    "# set an early stopping mechanism\n",
+    "# let's set patience=2, to be a bit tolerant against random validation loss increases\n",
+    "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n",
+    "\n",
+    "# fit the model\n",
+    "# note that this time the train, validation and test data are not iterable\n",
+    "model.fit(train_inputs, # train inputs\n",
+    "          train_targets, # train targets\n",
+    "          batch_size=batch_size, # batch size\n",
+    "          epochs=max_epochs, # epochs that we will train for (assuming early stopping doesn't kick in)\n",
+    "          # callbacks are functions called by a task when a task is completed\n",
+    "          # task here is to check if val_loss is increasing\n",
+    "          callbacks=[early_stopping], # early stopping\n",
+    "          validation_data=(validation_inputs, validation_targets), # validation data\n",
+    "          verbose = 2 # making sure we get enough information about the training process\n",
+    "          )  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_Learning_Part3_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_Learning_Part3_with_comments.ipynb
new file mode 100644
index 0000000..d2a27c5
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_Learning_Part3_with_comments.ipynb	
@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example. Audiobooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem\n",
+    "\n",
+    "You are given data from an Audiobook app. Logically, it relates only to the audio versions of books. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertizing to him/her. If we can focus our efforts ONLY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, Book length in mins_avg (average of all purchases), Book length in minutes_sum (sum of all purchases), Price Paid_avg (average of all purchases), Price paid_sum (sum of all purchases), Review (a Boolean variable), Review (out of 10), Total minutes listened, Completion (from 0 to 1), Support requests (number), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "So these are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (so 0, or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we must import the libraries once again since we haven't imported them in this file\n",
+    "import numpy as np\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's create a temporary variable npz, where we will store each of the three Audiobooks datasets\n",
+    "npz = np.load('Audiobooks_data_train.npz')\n",
+    "\n",
+    "# we extract the inputs using the keyword under which we saved them\n",
+    "# to ensure that they are all floats, let's also take care of that\n",
+    "train_inputs = npz['inputs'].astype(np.float)\n",
+    "# targets must be int because of sparse_categorical_crossentropy (we want to be able to smoothly one-hot encode them)\n",
+    "train_targets = npz['targets'].astype(np.int)\n",
+    "\n",
+    "# we load the validation data in the temporary variable\n",
+    "npz = np.load('Audiobooks_data_validation.npz')\n",
+    "# we can load the inputs and the targets in the same line\n",
+    "validation_inputs, validation_targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n",
+    "\n",
+    "# we load the test data in the temporary variable\n",
+    "npz = np.load('Audiobooks_data_test.npz')\n",
+    "# we create 2 variables that will contain the test inputs and the test targets\n",
+    "test_inputs, test_targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model\n",
+    "Outline, optimizers, loss, early stopping and training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 3579 samples, validate on 447 samples\n",
+      "Epoch 1/100\n",
+      "3579/3579 - 0s - loss: 0.5406 - accuracy: 0.7868 - val_loss: 0.4206 - val_accuracy: 0.8479\n",
+      "Epoch 2/100\n",
+      "3579/3579 - 0s - loss: 0.3729 - accuracy: 0.8720 - val_loss: 0.3309 - val_accuracy: 0.8680\n",
+      "Epoch 3/100\n",
+      "3579/3579 - 0s - loss: 0.3252 - accuracy: 0.8824 - val_loss: 0.3018 - val_accuracy: 0.8904\n",
+      "Epoch 4/100\n",
+      "3579/3579 - 0s - loss: 0.3036 - accuracy: 0.8863 - val_loss: 0.2882 - val_accuracy: 0.8881\n",
+      "Epoch 5/100\n",
+      "3579/3579 - 0s - loss: 0.2902 - accuracy: 0.8908 - val_loss: 0.2800 - val_accuracy: 0.8993\n",
+      "Epoch 6/100\n",
+      "3579/3579 - 0s - loss: 0.2813 - accuracy: 0.8961 - val_loss: 0.2663 - val_accuracy: 0.9016\n",
+      "Epoch 7/100\n",
+      "3579/3579 - 0s - loss: 0.2706 - accuracy: 0.9019 - val_loss: 0.2619 - val_accuracy: 0.8971\n",
+      "Epoch 8/100\n",
+      "3579/3579 - 0s - loss: 0.2652 - accuracy: 0.9008 - val_loss: 0.2587 - val_accuracy: 0.9060\n",
+      "Epoch 9/100\n",
+      "3579/3579 - 0s - loss: 0.2612 - accuracy: 0.9014 - val_loss: 0.2567 - val_accuracy: 0.9016\n",
+      "Epoch 10/100\n",
+      "3579/3579 - 0s - loss: 0.2562 - accuracy: 0.9036 - val_loss: 0.2577 - val_accuracy: 0.9038\n",
+      "Epoch 11/100\n",
+      "3579/3579 - 0s - loss: 0.2525 - accuracy: 0.9056 - val_loss: 0.2507 - val_accuracy: 0.9083\n",
+      "Epoch 12/100\n",
+      "3579/3579 - 0s - loss: 0.2481 - accuracy: 0.9044 - val_loss: 0.2494 - val_accuracy: 0.9105\n",
+      "Epoch 13/100\n",
+      "3579/3579 - 0s - loss: 0.2462 - accuracy: 0.9098 - val_loss: 0.2610 - val_accuracy: 0.8949\n",
+      "Epoch 14/100\n",
+      "3579/3579 - 0s - loss: 0.2453 - accuracy: 0.9072 - val_loss: 0.2480 - val_accuracy: 0.9016\n",
+      "Epoch 15/100\n",
+      "3579/3579 - 0s - loss: 0.2477 - accuracy: 0.9089 - val_loss: 0.2455 - val_accuracy: 0.9038\n",
+      "Epoch 16/100\n",
+      "3579/3579 - 0s - loss: 0.2423 - accuracy: 0.9092 - val_loss: 0.2459 - val_accuracy: 0.9172\n",
+      "Epoch 17/100\n",
+      "3579/3579 - 0s - loss: 0.2399 - accuracy: 0.9098 - val_loss: 0.2403 - val_accuracy: 0.9150\n",
+      "Epoch 18/100\n",
+      "3579/3579 - 0s - loss: 0.2378 - accuracy: 0.9114 - val_loss: 0.2472 - val_accuracy: 0.9016\n",
+      "Epoch 19/100\n",
+      "3579/3579 - 0s - loss: 0.2366 - accuracy: 0.9120 - val_loss: 0.2437 - val_accuracy: 0.9083\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1b9933850b8>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set the input and output sizes\n",
+    "input_size = 10\n",
+    "output_size = 2\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])\n",
+    "\n",
+    "\n",
+    "### Choose the optimizer and the loss function\n",
+    "\n",
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "### Training\n",
+    "# That's where we train the model we have built.\n",
+    "\n",
+    "# set the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# set a maximum number of training epochs\n",
+    "max_epochs = 100\n",
+    "\n",
+    "# set an early stopping mechanism\n",
+    "# let's set patience=2, to be a bit tolerant against random validation loss increases\n",
+    "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n",
+    "\n",
+    "# fit the model\n",
+    "# note that this time the train, validation and test data are not iterable\n",
+    "model.fit(train_inputs, # train inputs\n",
+    "          train_targets, # train targets\n",
+    "          batch_size=batch_size, # batch size\n",
+    "          epochs=max_epochs, # epochs that we will train for (assuming early stopping doesn't kick in)\n",
+    "          # callbacks are functions called by a task when a task is completed\n",
+    "          # task here is to check if val_loss is increasing\n",
+    "          callbacks=[early_stopping], # early stopping\n",
+    "          validation_data=(validation_inputs, validation_targets), # validation data\n",
+    "          verbose = 2 # making sure we get enough information about the training process\n",
+    "          )  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example. Audiobooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem\n",
+    "\n",
+    "You are given data from an Audiobook app. Logically, it relates only to the audio versions of books. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertizing to him/her. If we can focus our efforts ONLY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, Book length in mins_avg (average of all purchases), Book length in minutes_sum (sum of all purchases), Price Paid_avg (average of all purchases), Price paid_sum (sum of all purchases), Review (a Boolean variable), Review (out of 10), Total minutes listened, Completion (from 0 to 1), Support requests (number), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "So these are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (so 0, or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we must import the libraries once again since we haven't imported them in this file\n",
+    "import numpy as np\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's create a temporary variable npz, where we will store each of the three Audiobooks datasets\n",
+    "npz = np.load('Audiobooks_data_train.npz')\n",
+    "\n",
+    "# we extract the inputs using the keyword under which we saved them\n",
+    "# to ensure that they are all floats, let's also take care of that\n",
+    "train_inputs = npz['inputs'].astype(np.float)\n",
+    "# targets must be int because of sparse_categorical_crossentropy (we want to be able to smoothly one-hot encode them)\n",
+    "train_targets = npz['targets'].astype(np.int)\n",
+    "\n",
+    "# we load the validation data in the temporary variable\n",
+    "npz = np.load('Audiobooks_data_validation.npz')\n",
+    "# we can load the inputs and the targets in the same line\n",
+    "validation_inputs, validation_targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n",
+    "\n",
+    "# we load the test data in the temporary variable\n",
+    "npz = np.load('Audiobooks_data_test.npz')\n",
+    "# we create 2 variables that will contain the test inputs and the test targets\n",
+    "test_inputs, test_targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model\n",
+    "Outline, optimizers, loss, early stopping and training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 3579 samples, validate on 447 samples\n",
+      "Epoch 1/100\n",
+      "3579/3579 - 0s - loss: 0.5406 - accuracy: 0.7868 - val_loss: 0.4206 - val_accuracy: 0.8479\n",
+      "Epoch 2/100\n",
+      "3579/3579 - 0s - loss: 0.3729 - accuracy: 0.8720 - val_loss: 0.3309 - val_accuracy: 0.8680\n",
+      "Epoch 3/100\n",
+      "3579/3579 - 0s - loss: 0.3252 - accuracy: 0.8824 - val_loss: 0.3018 - val_accuracy: 0.8904\n",
+      "Epoch 4/100\n",
+      "3579/3579 - 0s - loss: 0.3036 - accuracy: 0.8863 - val_loss: 0.2882 - val_accuracy: 0.8881\n",
+      "Epoch 5/100\n",
+      "3579/3579 - 0s - loss: 0.2902 - accuracy: 0.8908 - val_loss: 0.2800 - val_accuracy: 0.8993\n",
+      "Epoch 6/100\n",
+      "3579/3579 - 0s - loss: 0.2813 - accuracy: 0.8961 - val_loss: 0.2663 - val_accuracy: 0.9016\n",
+      "Epoch 7/100\n",
+      "3579/3579 - 0s - loss: 0.2706 - accuracy: 0.9019 - val_loss: 0.2619 - val_accuracy: 0.8971\n",
+      "Epoch 8/100\n",
+      "3579/3579 - 0s - loss: 0.2652 - accuracy: 0.9008 - val_loss: 0.2587 - val_accuracy: 0.9060\n",
+      "Epoch 9/100\n",
+      "3579/3579 - 0s - loss: 0.2612 - accuracy: 0.9014 - val_loss: 0.2567 - val_accuracy: 0.9016\n",
+      "Epoch 10/100\n",
+      "3579/3579 - 0s - loss: 0.2562 - accuracy: 0.9036 - val_loss: 0.2577 - val_accuracy: 0.9038\n",
+      "Epoch 11/100\n",
+      "3579/3579 - 0s - loss: 0.2525 - accuracy: 0.9056 - val_loss: 0.2507 - val_accuracy: 0.9083\n",
+      "Epoch 12/100\n",
+      "3579/3579 - 0s - loss: 0.2481 - accuracy: 0.9044 - val_loss: 0.2494 - val_accuracy: 0.9105\n",
+      "Epoch 13/100\n",
+      "3579/3579 - 0s - loss: 0.2462 - accuracy: 0.9098 - val_loss: 0.2610 - val_accuracy: 0.8949\n",
+      "Epoch 14/100\n",
+      "3579/3579 - 0s - loss: 0.2453 - accuracy: 0.9072 - val_loss: 0.2480 - val_accuracy: 0.9016\n",
+      "Epoch 15/100\n",
+      "3579/3579 - 0s - loss: 0.2477 - accuracy: 0.9089 - val_loss: 0.2455 - val_accuracy: 0.9038\n",
+      "Epoch 16/100\n",
+      "3579/3579 - 0s - loss: 0.2423 - accuracy: 0.9092 - val_loss: 0.2459 - val_accuracy: 0.9172\n",
+      "Epoch 17/100\n",
+      "3579/3579 - 0s - loss: 0.2399 - accuracy: 0.9098 - val_loss: 0.2403 - val_accuracy: 0.9150\n",
+      "Epoch 18/100\n",
+      "3579/3579 - 0s - loss: 0.2378 - accuracy: 0.9114 - val_loss: 0.2472 - val_accuracy: 0.9016\n",
+      "Epoch 19/100\n",
+      "3579/3579 - 0s - loss: 0.2366 - accuracy: 0.9120 - val_loss: 0.2437 - val_accuracy: 0.9083\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1b9933850b8>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set the input and output sizes\n",
+    "input_size = 10\n",
+    "output_size = 2\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])\n",
+    "\n",
+    "\n",
+    "### Choose the optimizer and the loss function\n",
+    "\n",
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "### Training\n",
+    "# That's where we train the model we have built.\n",
+    "\n",
+    "# set the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# set a maximum number of training epochs\n",
+    "max_epochs = 100\n",
+    "\n",
+    "# set an early stopping mechanism\n",
+    "# let's set patience=2, to be a bit tolerant against random validation loss increases\n",
+    "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n",
+    "\n",
+    "# fit the model\n",
+    "# note that this time the train, validation and test data are not iterable\n",
+    "model.fit(train_inputs, # train inputs\n",
+    "          train_targets, # train targets\n",
+    "          batch_size=batch_size, # batch size\n",
+    "          epochs=max_epochs, # epochs that we will train for (assuming early stopping doesn't kick in)\n",
+    "          # callbacks are functions called by a task when a task is completed\n",
+    "          # task here is to check if val_loss is increasing\n",
+    "          callbacks=[early_stopping], # early stopping\n",
+    "          validation_data=(validation_inputs, validation_targets), # validation data\n",
+    "          verbose = 2 # making sure we get enough information about the training process\n",
+    "          )  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "448/448 [==============================] - 0s 41us/sample - loss: 0.2391 - accuracy: 0.9107\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_inputs, test_targets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Test loss: 0.24. Test accuracy: 91.07%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('\\nTest loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the initial model and hyperparameters given in this notebook, the final test accuracy should be roughly around 91%.\n",
+    "\n",
+    "Note that each time the code is rerun, we get a different accuracy because each training is different. \n",
+    "\n",
+    "We have intentionally reached a suboptimal solution, so you can have space to build on it!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_learning_Homework (1).ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_learning_Homework (1).ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final exercise\n",
+    "\n",
+    "Based on everything you have seen in the course (including the preprocessing exercise in this section), try to achieve a better accuracy. Obviously, there is no 'correct' answer to this exercise. It depends solely on your own motivation and willingness to become better at machine learning. \n",
+    "\n",
+    "Don't be shy to post your result in the Q&A section, where you can discuss your results with other students.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problem\n",
+    "\n",
+    "You are given data from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If we can focus our efforts SOLELY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) ,Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (0 or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Create a class that will batch the data\n",
+    "\n",
+    "Whenever you want to batch the data you need to have appropriate methods. There are some batching methods integrated in TensorFlow and sklearn, but some problems may need specific coding. \n",
+    "\n",
+    "Here we show how these methods look like. You can use them for any machine learning framework you need (directly or after little fine-tuning)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Create a class that will do the batching for the algorithm\n",
+    "# This code is extremely reusable. You should just change Audiobooks_data everywhere in the code\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    # Dataset is a mandatory arugment, while the batch_size is optional\n",
+    "    # If you don't input batch_size, it will automatically take the value: None\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "    \n",
+    "        # The dataset that loads is one of \"train\", \"validation\", \"test\".\n",
+    "        # e.g. if I call this class with x('train',5), it will load 'Audiobooks_data_train.npz' with a batch size of 5.\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        \n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n",
+    "        \n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "    \n",
+    "    # A method which loads the next batch\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "            \n",
+    "        # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "        \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already but we're giving you the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "        \n",
+    "        # The function will return the inputs batch and the one-hot encoded targets\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "    \n",
+    "        \n",
+    "    # A method needed for iterating over the batches, as we will put them in a loop\n",
+    "    # This tells Python that the class we're defining is iterable, i.e. that we can use it like:\n",
+    "    # for input, output in data: \n",
+    "        # do things\n",
+    "    # An iterator in Python is a class with a method __next__ that defines exactly how to iterate through its objects\n",
+    "    def __iter__(self):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n",
+    "We will create an algorithm which is essentially copy-pasting the MNIST code and we will simply adjust where needed.\n",
+    "Once more, I will put the whole code in one piece as we can simply rerun the cell and train a new model. That's because the whole algorithm is contained in the cell and we have the tf.reset_default_graph() function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We need to import TF (we didn't need it so far)\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Input size depends on the number of input variables. We have 10 of them\n",
+    "input_size = 10\n",
+    "# Output size is 2, as we one-hot encoded the targets.\n",
+    "output_size = 2\n",
+    "# Choose a hidden_layer_size\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Reset the default graph, so you can fiddle with the hyperparameters and then rerun the code.\n",
+    "tf.reset_default_graph()\n",
+    "\n",
+    "# Create the placeholders\n",
+    "inputs = tf.placeholder(tf.float32, [None, input_size])\n",
+    "targets = tf.placeholder(tf.int32, [None, output_size])\n",
+    "\n",
+    "# Outline the model. We will create a net with 2 hidden layers\n",
+    "weights_1 = tf.get_variable(\"weights_1\", [input_size, hidden_layer_size])\n",
+    "biases_1 = tf.get_variable(\"biases_1\", [hidden_layer_size])\n",
+    "outputs_1 = tf.nn.relu(tf.matmul(inputs, weights_1) + biases_1)\n",
+    "\n",
+    "weights_2 = tf.get_variable(\"weights_2\", [hidden_layer_size, hidden_layer_size])\n",
+    "biases_2 = tf.get_variable(\"biases_2\", [hidden_layer_size])\n",
+    "outputs_2 = tf.nn.sigmoid(tf.matmul(outputs_1, weights_2) + biases_2)\n",
+    "\n",
+    "weights_3 = tf.get_variable(\"weights_3\", [hidden_layer_size, output_size])\n",
+    "biases_3 = tf.get_variable(\"biases_3\", [output_size])\n",
+    "# We will incorporate the softmax activation into the loss, as in the previous example\n",
+    "outputs = tf.matmul(outputs_2, weights_3) + biases_3\n",
+    "\n",
+    "# Use the softmax cross entropy loss with logits\n",
+    "loss = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)\n",
+    "mean_loss = tf.reduce_mean(loss)\n",
+    "\n",
+    "# Get a 0 or 1 for every input indicating whether it output the correct answer\n",
+    "out_equals_target = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(out_equals_target, tf.float32))\n",
+    "\n",
+    "# Optimize with Adam\n",
+    "optimize = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(mean_loss)\n",
+    "\n",
+    "# Create a session\n",
+    "sess = tf.InteractiveSession()\n",
+    "\n",
+    "# Initialize the variables\n",
+    "initializer = tf.global_variables_initializer()\n",
+    "sess.run(initializer)\n",
+    "\n",
+    "# Choose the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# Set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "prev_validation_loss = 9999999.\n",
+    "\n",
+    "# Load the first batch of training and validation, using the class we created. \n",
+    "# Arguments are ending of 'Audiobooks_Data_<...>', where for <...> we input 'train', 'validation', or 'test'\n",
+    "# depending on what we want to load\n",
+    "train_data = Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data = Audiobooks_Data_Reader('validation')\n",
+    "\n",
+    "# Create the loop for epochs \n",
+    "for epoch_counter in range(max_epochs):\n",
+    "    \n",
+    "    # Set the epoch loss to 0, and make it a float\n",
+    "    curr_epoch_loss = 0.\n",
+    "    \n",
+    "    # Iterate over the training data \n",
+    "    # Since train_data is an instance of the Audiobooks_Data_Reader class,\n",
+    "    # we can iterate through it by implicitly using the __next__ method we defined above.\n",
+    "    # As a reminder, it batches samples together, one-hot encodes the targets, and returns\n",
+    "    # inputs and targets batch by batch\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        _, batch_loss = sess.run([optimize, mean_loss], \n",
+    "            feed_dict={inputs: input_batch, targets: target_batch})\n",
+    "        \n",
+    "        #Record the batch loss into the current epoch loss\n",
+    "        curr_epoch_loss += batch_loss\n",
+    "    \n",
+    "    # Find the mean curr_epoch_loss\n",
+    "    # batch_count is a variable, defined in the Audiobooks_Data_Reader class\n",
+    "    curr_epoch_loss /= train_data.batch_count\n",
+    "    \n",
+    "    # Set validation loss and accuracy for the epoch to zero\n",
+    "    validation_loss = 0.\n",
+    "    validation_accuracy = 0.\n",
+    "    \n",
+    "    # Use the same logic of the code to forward propagate the validation set\n",
+    "    # There will be a single batch, as the class was created in this way\n",
+    "    for input_batch, target_batch in validation_data:\n",
+    "        validation_loss, validation_accuracy = sess.run([mean_loss, accuracy],\n",
+    "            feed_dict={inputs: input_batch, targets: target_batch})\n",
+    "    \n",
+    "    # Print statistics for the current epoch\n",
+    "    print('Epoch '+str(epoch_counter+1)+\n",
+    "          '. Training loss: '+'{0:.3f}'.format(curr_epoch_loss)+\n",
+    "          '. Validation loss: '+'{0:.3f}'.format(validation_loss)+\n",
+    "          '. Validation accuracy: '+'{0:.2f}'.format(validation_accuracy * 100.)+'%')\n",
+    "    \n",
+    "    # Trigger early stopping if validation loss begins increasing.\n",
+    "    if validation_loss > prev_validation_loss:\n",
+    "        break\n",
+    "        \n",
+    "    # Store this epoch's validation loss to be used as previous in the next iteration.\n",
+    "    prev_validation_loss = validation_loss\n",
+    "    \n",
+    "print('End of training.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load the test data, following the same logic as we did for the train_data and validation data\n",
+    "test_data = Audiobooks_Data_Reader('test')\n",
+    "\n",
+    "# Forward propagate through the training set. This time we only need the accuracy\n",
+    "for inputs_batch, targets_batch in test_data:\n",
+    "    test_accuracy = sess.run([accuracy],\n",
+    "                     feed_dict={inputs: inputs_batch, targets: targets_batch})\n",
+    "\n",
+    "# Get the test accuracy in percentages\n",
+    "# When sess.run is has a single output, we get a list (that's how it was coded by Google), rather than a float.\n",
+    "# Therefore, we must take the first value from the list (the value at position 0)\n",
+    "test_accuracy_percent = test_accuracy[0] * 100.\n",
+    "\n",
+    "# Print the test accuracy\n",
+    "print('Test accuracy: '+'{0:.2f}'.format(test_accuracy_percent)+'%')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_learning_Homework.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_learning_Homework.ipynb
new file mode 100644
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+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Machine_learning_Homework.ipynb	
@@ -0,0 +1,335 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final exercise\n",
+    "\n",
+    "Based on everything you have seen in the course (including the preprocessing exercise in this section), try to achieve a better accuracy. Obviously, there is no 'correct' answer to this exercise. It depends solely on your own motivation and willingness to become better at machine learning. \n",
+    "\n",
+    "Don't be shy to post your result in the Q&A section, where you can discuss your results with other students.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problem\n",
+    "\n",
+    "You are given data from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If we can focus our efforts SOLELY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) ,Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (0 or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Create a class that will batch the data\n",
+    "\n",
+    "Whenever you want to batch the data you need to have appropriate methods. There are some batching methods integrated in TensorFlow and sklearn, but some problems may need specific coding. \n",
+    "\n",
+    "Here we show how these methods look like. You can use them for any machine learning framework you need (directly or after little fine-tuning)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Create a class that will do the batching for the algorithm\n",
+    "# This code is extremely reusable. You should just change Audiobooks_data everywhere in the code\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    # Dataset is a mandatory arugment, while the batch_size is optional\n",
+    "    # If you don't input batch_size, it will automatically take the value: None\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "    \n",
+    "        # The dataset that loads is one of \"train\", \"validation\", \"test\".\n",
+    "        # e.g. if I call this class with x('train',5), it will load 'Audiobooks_data_train.npz' with a batch size of 5.\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        \n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n",
+    "        \n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "    \n",
+    "    # A method which loads the next batch\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "            \n",
+    "        # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "        \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already but we're giving you the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "        \n",
+    "        # The function will return the inputs batch and the one-hot encoded targets\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "    \n",
+    "        \n",
+    "    # A method needed for iterating over the batches, as we will put them in a loop\n",
+    "    # This tells Python that the class we're defining is iterable, i.e. that we can use it like:\n",
+    "    # for input, output in data: \n",
+    "        # do things\n",
+    "    # An iterator in Python is a class with a method __next__ that defines exactly how to iterate through its objects\n",
+    "    def __iter__(self):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n",
+    "We will create an algorithm which is essentially copy-pasting the MNIST code and we will simply adjust where needed.\n",
+    "Once more, I will put the whole code in one piece as we can simply rerun the cell and train a new model. That's because the whole algorithm is contained in the cell and we have the tf.reset_default_graph() function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "`tape` is required when a `Tensor` loss is passed. Received: loss=KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='tf.math.reduce_mean_8/Mean:0', description=\"created by layer 'tf.math.reduce_mean_8'\"), tape=None.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\TensorFlow_Audiobooks_Machine_learning_Homework.ipynb Cell 7\u001b[0m line \u001b[0;36m6\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_Machine_learning_Homework.ipynb#W6sZmlsZQ%3D%3D?line=61'>62</a>\u001b[0m \u001b[39m# Optimize with Adam\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_Machine_learning_Homework.ipynb#W6sZmlsZQ%3D%3D?line=62'>63</a>\u001b[0m optimizer \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39moptimizers\u001b[39m.\u001b[39mAdam(learning_rate\u001b[39m=\u001b[39m\u001b[39m0.0001\u001b[39m)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_Machine_learning_Homework.ipynb#W6sZmlsZQ%3D%3D?line=63'>64</a>\u001b[0m optimize \u001b[39m=\u001b[39m optimizer\u001b[39m.\u001b[39;49mminimize(mean_loss, var_list\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_Machine_learning_Homework.ipynb#W6sZmlsZQ%3D%3D?line=68'>69</a>\u001b[0m \u001b[39m# Create a session\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_Machine_learning_Homework.ipynb#W6sZmlsZQ%3D%3D?line=69'>70</a>\u001b[0m sess \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mInteractiveSession()\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\optimizers\\optimizer.py:543\u001b[0m, in \u001b[0;36m_BaseOptimizer.minimize\u001b[1;34m(self, loss, var_list, tape)\u001b[0m\n\u001b[0;32m    522\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mminimize\u001b[39m(\u001b[39mself\u001b[39m, loss, var_list, tape\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m    523\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Minimize `loss` by updating `var_list`.\u001b[39;00m\n\u001b[0;32m    524\u001b[0m \n\u001b[0;32m    525\u001b[0m \u001b[39m    This method simply computes gradient using `tf.GradientTape` and calls\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    541\u001b[0m \u001b[39m      None\u001b[39;00m\n\u001b[0;32m    542\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 543\u001b[0m     grads_and_vars \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcompute_gradients(loss, var_list, tape)\n\u001b[0;32m    544\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_gradients(grads_and_vars)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\optimizers\\optimizer.py:262\u001b[0m, in \u001b[0;36m_BaseOptimizer.compute_gradients\u001b[1;34m(self, loss, var_list, tape)\u001b[0m\n\u001b[0;32m    244\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Compute gradients of loss on trainable variables.\u001b[39;00m\n\u001b[0;32m    245\u001b[0m \n\u001b[0;32m    246\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[39m  gradient can be `None`.\u001b[39;00m\n\u001b[0;32m    260\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    261\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mcallable\u001b[39m(loss) \u001b[39mand\u001b[39;00m tape \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 262\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    263\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m`tape` is required when a `Tensor` loss is passed. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    264\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mReceived: loss=\u001b[39m\u001b[39m{\u001b[39;00mloss\u001b[39m}\u001b[39;00m\u001b[39m, tape=\u001b[39m\u001b[39m{\u001b[39;00mtape\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    265\u001b[0m     )\n\u001b[0;32m    266\u001b[0m \u001b[39mif\u001b[39;00m tape \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    267\u001b[0m     tape \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mGradientTape()\n",
+      "\u001b[1;31mValueError\u001b[0m: `tape` is required when a `Tensor` loss is passed. Received: loss=KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='tf.math.reduce_mean_8/Mean:0', description=\"created by layer 'tf.math.reduce_mean_8'\"), tape=None."
+     ]
+    }
+   ],
+   "source": [
+    "# We need to import TF (we didn't need it so far)\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Input size depends on the number of input variables. We have 10 of them\n",
+    "input_size = 10\n",
+    "# Output size is 2, as we one-hot encoded the targets.\n",
+    "output_size = 2\n",
+    "# Choose a hidden_layer_size\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Reset the default graph, so you can fiddle with the hyperparameters and then rerun the code.\n",
+    "tf.compat.v1.reset_default_graph()\n",
+    "\n",
+    "\n",
+    "# Create the placeholders\n",
+    "# Create the placeholders\n",
+    "inputs = tf.keras.Input(shape=(input_size,), dtype=tf.float32)\n",
+    "targets = tf.keras.Input(shape=(output_size,), dtype=tf.int32)\n",
+    "\n",
+    "\n",
+    "# Outline the model. We will create a net with 2 hidden layers\n",
+    "weights_1 = tf.Variable(tf.random.normal([input_size, hidden_layer_size]), name=\"weights_1\")\n",
+    "weight_1 = tf.Variable()\n",
+    "biases_1 = tf.Variable(tf.zeros([hidden_layer_size]), name=\"biases_1\")\n",
+    "\n",
+    "weights_2 = tf.Variable(tf.random.normal([hidden_layer_size, hidden_layer_size]), name=\"weights_2\")\n",
+    "biases_2 = tf.Variable(tf.zeros([hidden_layer_size]), name=\"biases_2\")\n",
+    "\n",
+    "weights_3 = tf.Variable(tf.random.normal([hidden_layer_size, output_size]), name=\"weights_3\")\n",
+    "biases_3 = tf.Variable(tf.zeros([output_size]), name=\"biases_3\")\n",
+    "\n",
+    "# Define the model using the Keras API\n",
+    "model = tf.keras.Sequential([\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu', input_shape=(input_size,)),\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'),\n",
+    "    tf.keras.layers.Dense(output_size)\n",
+    "])\n",
+    "\n",
+    "outputs = model(inputs)\n",
+    "\n",
+    "\n",
+    "# Use the softmax cross entropy loss with logits\n",
+    "loss = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)\n",
+    "mean_loss = tf.reduce_mean(loss)\n",
+    "\n",
+    "# Get a 0 or 1 for every input indicating whether it output the correct answer\n",
+    "out_equals_target = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(out_equals_target, tf.float32))\n",
+    "\n",
+    "# Optimize with Adam\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
+    "optimize = optimizer.minimize(mean_loss, var_list=None)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a session\n",
+    "sess = tf.InteractiveSession()\n",
+    "\n",
+    "# Initialize the variables\n",
+    "initializer = tf.global_variables_initializer()\n",
+    "sess.run(initializer)\n",
+    "\n",
+    "# Choose the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# Set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "prev_validation_loss = 9999999.\n",
+    "\n",
+    "# Load the first batch of training and validation, using the class we created. \n",
+    "# Arguments are ending of 'Audiobooks_Data_<...>', where for <...> we input 'train', 'validation', or 'test'\n",
+    "# depending on what we want to load\n",
+    "train_data = Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data = Audiobooks_Data_Reader('validation')\n",
+    "\n",
+    "# Create the loop for epochs \n",
+    "for epoch_counter in range(max_epochs):\n",
+    "    \n",
+    "    # Set the epoch loss to 0, and make it a float\n",
+    "    curr_epoch_loss = 0.\n",
+    "    \n",
+    "    # Iterate over the training data \n",
+    "    # Since train_data is an instance of the Audiobooks_Data_Reader class,\n",
+    "    # we can iterate through it by implicitly using the __next__ method we defined above.\n",
+    "    # As a reminder, it batches samples together, one-hot encodes the targets, and returns\n",
+    "    # inputs and targets batch by batch\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        _, batch_loss = sess.run([optimize, mean_loss]),\n",
+    "        feed_dict={inputs: input_batch, targets: target_batch}\n",
+    "\n",
+    "        \n",
+    "        #Record the batch loss into the current epoch loss\n",
+    "        curr_epoch_loss += batch_loss\n",
+    "    \n",
+    "    # Find the mean curr_epoch_loss\n",
+    "    # batch_count is a variable, defined in the Audiobooks_Data_Reader class\n",
+    "    curr_epoch_loss /= train_data.batch_count\n",
+    "    \n",
+    "    # Set validation loss and accuracy for the epoch to zero\n",
+    "    validation_loss = 0.\n",
+    "    validation_accuracy = 0.\n",
+    "    \n",
+    "    # Use the same logic of the code to forward propagate the validation set\n",
+    "    # There will be a single batch, as the class was created in this way\n",
+    "    for input_batch, target_batch in validation_data:\n",
+    "        validation_loss, validation_accuracy = sess.run([mean_loss, accuracy]),\n",
+    "        feed_dict={inputs: input_batch, targets: target_batch}\n",
+    "    \n",
+    "    # Print statistics for the current epoch\n",
+    "    print('Epoch '+str(epoch_counter+1)+\n",
+    "          '. Training loss: '+'{0:.3f}'.format(curr_epoch_loss)+\n",
+    "          '. Validation loss: '+'{0:.3f}'.format(validation_loss)+\n",
+    "          '. Validation accuracy: '+'{0:.2f}'.format(validation_accuracy * 100.)+'%')\n",
+    "    \n",
+    "    # Trigger early stopping if validation loss begins increasing.\n",
+    "    if validation_loss > prev_validation_loss:\n",
+    "        break\n",
+    "        \n",
+    "    # Store this epoch's validation loss to be used as previous in the next iteration.\n",
+    "    prev_validation_loss = validation_loss\n",
+    "    \n",
+    "print('End of training.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load the test data, following the same logic as we did for the train_data and validation data\n",
+    "test_data = Audiobooks_Data_Reader('test')\n",
+    "\n",
+    "# Forward propagate through the training set. This time we only need the accuracy\n",
+    "for inputs_batch, targets_batch in test_data:\n",
+    "    test_accuracy = sess.run([accuracy],\n",
+    "                     feed_dict={inputs: inputs_batch, targets: targets_batch})\n",
+    "\n",
+    "# Get the test accuracy in percentages\n",
+    "# When sess.run is has a single output, we get a list (that's how it was coded by Google), rather than a float.\n",
+    "# Therefore, we must take the first value from the list (the value at position 0)\n",
+    "test_accuracy_percent = test_accuracy[0] * 100.\n",
+    "\n",
+    "# Print the test accuracy\n",
+    "print('Test accuracy: '+'{0:.2f}'.format(test_accuracy_percent)+'%')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Outlining_the_model_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Outlining_the_model_with_comments.ipynb
new file mode 100644
index 0000000..a6d9a07
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Outlining_the_model_with_comments.ipynb	
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problem\n",
+    "\n",
+    "You are given data from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If we can focus our efforts SOLELY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) ,Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (0 or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Create a class that will batch the data\n",
+    "\n",
+    "Whenever you want to batch the data you need to have appropriate methods. There are some batching methods integrated in TensorFlow and sklearn, but some problems may need specific coding. \n",
+    "\n",
+    "Here we show how these methods look like. You can use them for any machine learning framework you need (directly or after little fine-tuning)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Create a class that will do the batching for the algorithm\n",
+    "# This code is extremely reusable. You should just change Audiobooks_data everywhere in the code\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    # Dataset is a mandatory arugment, while the batch_size is optional\n",
+    "    # If you don't input batch_size, it will automatically take the value: None\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "    \n",
+    "        # The dataset that loads is one of \"train\", \"validation\", \"test\".\n",
+    "        # e.g. if I call this class with x('train',5), it will load 'Audiobooks_data_train.npz' with a batch size of 5.\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        \n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n",
+    "        \n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "    \n",
+    "    # A method which loads the next batch\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "            \n",
+    "        # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "        \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already but we're giving you the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "        \n",
+    "        # The function will return the inputs batch and the one-hot encoded targets\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "    \n",
+    "        \n",
+    "    # A method needed for iterating over the batches, as we will put them in a loop\n",
+    "    # This tells Python that the class we're defining is iterable, i.e. that we can use it like:\n",
+    "    # for input, output in data: \n",
+    "        # do things\n",
+    "    # An iterator in Python is a class with a method __next__ that defines exactly how to iterate through its objects\n",
+    "    def __iter__(self):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n",
+    "We will create an algorithm which is essentially copy-pasting the MNIST code and we will simply adjust where needed.\n",
+    "Once more, I will put the whole code in one piece as we can simply rerun the cell and train a new model. That's because the whole algorithm is contained in the cell and we have the tf.reset_default_graph() function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# We need to import TF (we didn't need it so far)\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Input size depends on the number of input variables. We have 10 of them\n",
+    "input_size = 10\n",
+    "# Output size is 2, as we one-hot encoded the targets.\n",
+    "output_size = 2\n",
+    "# Choose a hidden_layer_size\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Reset the default graph, so you can fiddle with the hyperparameters and then rerun the code.\n",
+    "tf.reset_default_graph()\n",
+    "\n",
+    "# Create the placeholders\n",
+    "inputs = tf.placeholder(tf.float32, [None, input_size])\n",
+    "targets = tf.placeholder(tf.int32, [None, output_size])\n",
+    "\n",
+    "# Outline the model. We will create a net with 2 hidden layers\n",
+    "weights_1 = tf.get_variable(\"weights_1\", [input_size, hidden_layer_size])\n",
+    "biases_1 = tf.get_variable(\"biases_1\", [hidden_layer_size])\n",
+    "outputs_1 = tf.nn.relu(tf.matmul(inputs, weights_1) + biases_1)\n",
+    "\n",
+    "weights_2 = tf.get_variable(\"weights_2\", [hidden_layer_size, hidden_layer_size])\n",
+    "biases_2 = tf.get_variable(\"biases_2\", [hidden_layer_size])\n",
+    "outputs_2 = tf.nn.sigmoid(tf.matmul(outputs_1, weights_2) + biases_2)\n",
+    "\n",
+    "weights_3 = tf.get_variable(\"weights_3\", [hidden_layer_size, output_size])\n",
+    "biases_3 = tf.get_variable(\"biases_3\", [output_size])\n",
+    "# We will incorporate the softmax activation into the loss, as in the previous example\n",
+    "outputs = tf.matmul(outputs_2, weights_3) + biases_3\n",
+    "\n",
+    "# Use the softmax cross entropy loss with logits\n",
+    "loss = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)\n",
+    "mean_loss = tf.reduce_mean(loss)\n",
+    "\n",
+    "# Get a 0 or 1 for every input indicating whether it output the correct answer\n",
+    "out_equals_target = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(out_equals_target, tf.float32))\n",
+    "\n",
+    "# Optimize with Adam\n",
+    "optimize = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(mean_loss)\n",
+    "\n",
+    "# Create a session\n",
+    "sess = tf.InteractiveSession()\n",
+    "\n",
+    "# Initialize the variables\n",
+    "initializer = tf.global_variables_initializer()\n",
+    "sess.run(initializer)\n",
+    "\n",
+    "# Choose the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# Set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "prev_validation_loss = 9999999.\n",
+    "\n",
+    "# Load the first batch of training and validation, using the class we created. \n",
+    "# Arguments are ending of 'Audiobooks_Data_<...>', where for <...> we input 'train', 'validation', or 'test'\n",
+    "# depending on what we want to load\n",
+    "train_data = Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data = Audiobooks_Data_Reader('validation')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing.ipynb
new file mode 100644
index 0000000..3ba48a8
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing.ipynb	
@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocess the data. Balance the dataset. Create 3 datasets: training, validation, and test. Save the newly created sets in a tensor friendly format (e.g. *.npz)\n",
+    "\n",
+    "Since we are dealing with real life data, we will need to preprocess it a bit. This is the relevant code, which is not that hard, but is crucial to creating a good model.\n",
+    "\n",
+    "If you want to know how to do that, go through the code. In any case, this should do the trick for most datasets organized in the way: many inputs, and then 1 cell containing the targets (supervised learning datasets). Keep in mind that a specific problem may require additional preprocessing.\n",
+    "\n",
+    "Note that we have removed the header row, which contains the names of the categories. We simply want the data.\n",
+    "\n",
+    "This code does not include comments - it is the same as the one in the lesson. Please refer to the other file if you want the code with comments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the data from the csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "raw_csv_data = np.loadtxt('Audiobooks_data.csv', delimiter = ',')\n",
+    "\n",
+    "unscaled_inputs_all = raw_csv_data[:,1:-1]\n",
+    "targets_all = raw_csv_data[:,-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Balance the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "num_one_targets = int(np.sum(targets_all))\n",
+    "zero_targets_counter = 0\n",
+    "indices_to_remove = []\n",
+    "\n",
+    "for i in range(targets_all.shape[0]):\n",
+    "    if targets_all[i] ==0:\n",
+    "        zero_targets_counter += 1\n",
+    "        if zero_targets_counter > num_one_targets:\n",
+    "            indices_to_remove.append(i)\n",
+    "            \n",
+    "unscaled_inputs_equal_priors = np.delete(unscaled_inputs_all, indices_to_remove, axis = 0)\n",
+    "targets_equal_priors = np.delete (targets_all, indices_to_remove, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Standardize the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "scaled_inputs = preprocessing.scale(unscaled_inputs_equal_priors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shuffle the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "shuffled_indices = np.arange(scaled_inputs.shape[0])\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "\n",
+    "shuffled_inputs = scaled_inputs[shuffled_indices]\n",
+    "shuffled_targets = targets_equal_priors[shuffled_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split the dataset into train, validation, and test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "samples_count = shuffled_inputs.shape[0]\n",
+    "\n",
+    "train_samples_count = int(0.8*samples_count)\n",
+    "validation_samples_count = int(0.1*samples_count)\n",
+    "test_samples_count = samples_count - train_samples_count - validation_samples_count\n",
+    "\n",
+    "train_inputs = shuffled_inputs[:train_samples_count]\n",
+    "train_targets = shuffled_targets[:train_samples_count]\n",
+    "\n",
+    "validation_inputs = shuffled_inputs[train_samples_count:train_samples_count+validation_samples_count]\n",
+    "validation_targets = shuffled_targets[train_samples_count:train_samples_count+validation_samples_count]\n",
+    "\n",
+    "test_inputs = shuffled_inputs[train_samples_count+validation_samples_count:]\n",
+    "test_targets = shuffled_targets[train_samples_count+validation_samples_count:]\n",
+    "\n",
+    "print(np.sum(train_targets), train_samples_count, np.sum(train_targets) / train_samples_count)\n",
+    "print(np.sum(validation_targets), validation_samples_count, np.sum(validation_targets) / validation_samples_count)\n",
+    "print(np.sum(test_targets), test_samples_count, np.sum(test_targets) / test_samples_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save the three datasets in *.npz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "np.savez('Audiobooks_data_train', inputs=train_inputs, targets=train_targets)\n",
+    "np.savez('Audiobooks_data_validation', inputs=validation_inputs, targets=validation_targets)\n",
+    "np.savez('Audiobooks_data_test', inputs=test_inputs, targets=test_targets)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_Exercise.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_Exercise.ipynb
new file mode 100644
index 0000000..7b6cb96
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_Exercise.ipynb	
@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing exercise\n",
+    "\n",
+    "It makes sense to shuffle the indices prior to balancing the dataset. \n",
+    "\n",
+    "Using the code from the lesson (below), shuffle the indices and then balance the dataset.\n",
+    "\n",
+    "At the end of the course, you will have an exercise to create the same machine learning algorithm, with preprocessing done in this way.\n",
+    "\n",
+    "Note: This is more of a programming exercise rather than a machine learning one. Being able to complete it successfully will ensure you understand the preprocessing. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the data from the csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# We will use the sklearn preprocessing library, as it will be easier to standardize the data.\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "# Load the data\n",
+    "raw_csv_data = np.loadtxt('Audiobooks_data.csv',delimiter=',')\n",
+    "\n",
+    "# The inputs are all columns in the csv, except for the first one [:,0]\n",
+    "# (which is just the arbitrary customer IDs that bear no useful information),\n",
+    "# and the last one [:,-1] (which is our targets)\n",
+    "\n",
+    "unscaled_inputs_all = raw_csv_data[:,1:-1]\n",
+    "\n",
+    "# The targets are in the last column. That's how datasets are conventionally organized.\n",
+    "targets_all = raw_csv_data[:,-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Balance the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Count how many targets are 1 (meaning that the customer did convert)\n",
+    "num_one_targets = int(np.sum(targets_all))\n",
+    "\n",
+    "# Set a counter for targets that are 0 (meaning that the customer did not convert)\n",
+    "zero_targets_counter = 0\n",
+    "\n",
+    "# We want to create a \"balanced\" dataset, so we will have to remove some input/target pairs.\n",
+    "# Declare a variable that will do that:\n",
+    "indices_to_remove = []\n",
+    "\n",
+    "# Count the number of targets that are 0. \n",
+    "# Once there are as many 0s as 1s, mark entries where the target is 0.\n",
+    "for i in range(targets_all.shape[0]):\n",
+    "    if targets_all[i] == 0:\n",
+    "        zero_targets_counter += 1\n",
+    "        if zero_targets_counter > num_one_targets:\n",
+    "            indices_to_remove.append(i)\n",
+    "\n",
+    "# Create two new variables, one that will contain the inputs, and one that will contain the targets.\n",
+    "# We delete all indices that we marked \"to remove\" in the loop above.\n",
+    "unscaled_inputs_equal_priors = np.delete(unscaled_inputs_all, indices_to_remove, axis=0)\n",
+    "targets_equal_priors = np.delete(targets_all, indices_to_remove, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Standardize the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# That's the only place we use sklearn functionality. We will take advantage of its preprocessing capabilities\n",
+    "# It's a simple line of code, which standardizes the inputs, as we explained in one of the lectures.\n",
+    "# At the end of the business case, you can try to run the algorithm WITHOUT this line of code. \n",
+    "# The result will be interesting.\n",
+    "scaled_inputs = preprocessing.scale(unscaled_inputs_equal_priors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shuffle the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# When the data was collected it was actually arranged by date\n",
+    "# Shuffle the indices of the data, so the data is not arranged in any way when we feed it.\n",
+    "# Since we will be batching, we want the data to be as randomly spread out as possible\n",
+    "shuffled_indices = np.arange(scaled_inputs.shape[0])\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "\n",
+    "# Use the shuffled indices to shuffle the inputs and targets.\n",
+    "shuffled_inputs = scaled_inputs[shuffled_indices]\n",
+    "shuffled_targets = targets_equal_priors[shuffled_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split the dataset into train, validation, and test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Count the total number of samples\n",
+    "samples_count = shuffled_inputs.shape[0]\n",
+    "\n",
+    "# Count the samples in each subset, assuming we want 80-10-10 distribution of training, validation, and test.\n",
+    "# Naturally, the numbers are integers.\n",
+    "train_samples_count = int(0.8 * samples_count)\n",
+    "validation_samples_count = int(0.1 * samples_count)\n",
+    "\n",
+    "# The 'test' dataset contains all remaining data.\n",
+    "test_samples_count = samples_count - train_samples_count - validation_samples_count\n",
+    "\n",
+    "# Create variables that record the inputs and targets for training\n",
+    "# In our shuffled dataset, they are the first \"train_samples_count\" observations\n",
+    "train_inputs = shuffled_inputs[:train_samples_count]\n",
+    "train_targets = shuffled_targets[:train_samples_count]\n",
+    "\n",
+    "# Create variables that record the inputs and targets for validation.\n",
+    "# They are the next \"validation_samples_count\" observations, folllowing the \"train_samples_count\" we already assigned\n",
+    "validation_inputs = shuffled_inputs[train_samples_count:train_samples_count+validation_samples_count]\n",
+    "validation_targets = shuffled_targets[train_samples_count:train_samples_count+validation_samples_count]\n",
+    "\n",
+    "# Create variables that record the inputs and targets for test.\n",
+    "# They are everything that is remaining.\n",
+    "test_inputs = shuffled_inputs[train_samples_count+validation_samples_count:]\n",
+    "test_targets = shuffled_targets[train_samples_count+validation_samples_count:]\n",
+    "\n",
+    "# We balanced our dataset to be 50-50 (for targets 0 and 1), but the training, validation, and test were \n",
+    "# taken from a shuffled dataset. Check if they are balanced, too. Note that each time you rerun this code, \n",
+    "# you will get different values, as each time they are shuffled randomly.\n",
+    "# Normally you preprocess ONCE, so you need not rerun this code once it is done.\n",
+    "# If you rerun this whole sheet, the npzs will be overwritten with your newly preprocessed data.\n",
+    "\n",
+    "# Print the number of targets that are 1s, the total number of samples, and the proportion for training, validation, and test.\n",
+    "print(np.sum(train_targets), train_samples_count, np.sum(train_targets) / train_samples_count)\n",
+    "print(np.sum(validation_targets), validation_samples_count, np.sum(validation_targets) / validation_samples_count)\n",
+    "print(np.sum(test_targets), test_samples_count, np.sum(test_targets) / test_samples_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save the three datasets in *.npz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Save the three datasets in *.npz.\n",
+    "# In the next lesson, you will see that it is extremely valuable to name them in such a coherent way!\n",
+    "\n",
+    "np.savez('Audiobooks_data_train', inputs=train_inputs, targets=train_targets)\n",
+    "np.savez('Audiobooks_data_validation', inputs=validation_inputs, targets=validation_targets)\n",
+    "np.savez('Audiobooks_data_test', inputs=test_inputs, targets=test_targets)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_Exercise_Solution.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_Exercise_Solution.ipynb
new file mode 100644
index 0000000..4a372e6
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_Exercise_Solution.ipynb	
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing exercise\n",
+    "\n",
+    "It makes sense to shuffle the indices prior to balancing the dataset. \n",
+    "\n",
+    "Using the code from the lesson (below), shuffle the indices and then balance the dataset.\n",
+    "\n",
+    "At the end of the course, you will have an exercise to create the same machine learning algorithm, with preprocessing done in this way.\n",
+    "\n",
+    "Note: This is more of a programming exercise rather than a machine learning one. Being able to complete it successfully will ensure you understand the preprocessing. \n",
+    "\n",
+    "Good luck!\n",
+    "\n",
+    "**Solution:**\n",
+    "\n",
+    "Scroll down to the 'Exercise Solution' section"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the data from the csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# We will use the sklearn preprocessing library, as it will be easier to standardize the data.\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "# Load the data\n",
+    "raw_csv_data = np.loadtxt('Audiobooks_data.csv',delimiter=',')\n",
+    "\n",
+    "# The inputs are all columns in the csv, except for the first one [:,0]\n",
+    "# (which is just the arbitrary customer IDs that bear no useful information),\n",
+    "# and the last one [:,-1] (which is our targets)\n",
+    "\n",
+    "unscaled_inputs_all = raw_csv_data[:,1:-1]\n",
+    "\n",
+    "# The targets are in the last column. That's how datasets are conventionally organized.\n",
+    "targets_all = raw_csv_data[:,-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EXERCISE SOLUTION\n",
+    "\n",
+    "We shuffle the indices before balancing (to remove any day effects, etc.)\n",
+    "\n",
+    "However, we still have to shuffle them AFTER we balance the dataset as otherwise, all targets that are 1s will be contained in the train_targets.\n",
+    "\n",
+    "This code is suboptimal, but is the easiest way to complete the exercise. Still, as we do the preprocessing only once, speed in not something we are aiming for.\n",
+    "\n",
+    "We record the variables in themselves, so we don't amend the code that follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When the data was collected it was actually arranged by date\n",
+    "# Shuffle the indices of the data, so the data is not arranged in any way when we feed it.\n",
+    "# Since we will be batching, we want the data to be as randomly spread out as possible\n",
+    "shuffled_indices = np.arange(unscaled_inputs_all.shape[0])\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "\n",
+    "# Use the shuffled indices to shuffle the inputs and targets.\n",
+    "unscaled_inputs_all = unscaled_inputs_all[shuffled_indices]\n",
+    "targets_all = targets_all[shuffled_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Balance the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count how many targets are 1 (meaning that the customer did convert)\n",
+    "num_one_targets = int(np.sum(targets_all))\n",
+    "\n",
+    "# Set a counter for targets that are 0 (meaning that the customer did not convert)\n",
+    "zero_targets_counter = 0\n",
+    "\n",
+    "# We want to create a \"balanced\" dataset, so we will have to remove some input/target pairs.\n",
+    "# Declare a variable that will do that:\n",
+    "indices_to_remove = []\n",
+    "\n",
+    "# Count the number of targets that are 0. \n",
+    "# Once there are as many 0s as 1s, mark entries where the target is 0.\n",
+    "for i in range(targets_all.shape[0]):\n",
+    "    if targets_all[i] == 0:\n",
+    "        zero_targets_counter += 1\n",
+    "        if zero_targets_counter > num_one_targets:\n",
+    "            indices_to_remove.append(i)\n",
+    "\n",
+    "# Create two new variables, one that will contain the inputs, and one that will contain the targets.\n",
+    "# We delete all indices that we marked \"to remove\" in the loop above.\n",
+    "unscaled_inputs_equal_priors = np.delete(unscaled_inputs_all, indices_to_remove, axis=0)\n",
+    "targets_equal_priors = np.delete(targets_all, indices_to_remove, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Standardize the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# That's the only place we use sklearn functionality. We will take advantage of its preprocessing capabilities\n",
+    "# It's a simple line of code, which standardizes the inputs, as we explained in one of the lectures.\n",
+    "# At the end of the business case, you can try to run the algorithm WITHOUT this line of code. \n",
+    "# The result will be interesting.\n",
+    "scaled_inputs = preprocessing.scale(unscaled_inputs_equal_priors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shuffle the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When the data was collected it was actually arranged by date\n",
+    "# Shuffle the indices of the data, so the data is not arranged in any way when we feed it.\n",
+    "# Since we will be batching, we want the data to be as randomly spread out as possible\n",
+    "shuffled_indices = np.arange(scaled_inputs.shape[0])\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "\n",
+    "# Use the shuffled indices to shuffle the inputs and targets.\n",
+    "shuffled_inputs = scaled_inputs[shuffled_indices]\n",
+    "shuffled_targets = targets_equal_priors[shuffled_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split the dataset into train, validation, and test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1817.0 3579 0.5076837105336687\n",
+      "200.0 447 0.44742729306487694\n",
+      "220.0 448 0.49107142857142855\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count the total number of samples\n",
+    "samples_count = shuffled_inputs.shape[0]\n",
+    "\n",
+    "# Count the samples in each subset, assuming we want 80-10-10 distribution of training, validation, and test.\n",
+    "# Naturally, the numbers are integers.\n",
+    "train_samples_count = int(0.8 * samples_count)\n",
+    "validation_samples_count = int(0.1 * samples_count)\n",
+    "\n",
+    "# The 'test' dataset contains all remaining data.\n",
+    "test_samples_count = samples_count - train_samples_count - validation_samples_count\n",
+    "\n",
+    "# Create variables that record the inputs and targets for training\n",
+    "# In our shuffled dataset, they are the first \"train_samples_count\" observations\n",
+    "train_inputs = shuffled_inputs[:train_samples_count]\n",
+    "train_targets = shuffled_targets[:train_samples_count]\n",
+    "\n",
+    "# Create variables that record the inputs and targets for validation.\n",
+    "# They are the next \"validation_samples_count\" observations, folllowing the \"train_samples_count\" we already assigned\n",
+    "validation_inputs = shuffled_inputs[train_samples_count:train_samples_count+validation_samples_count]\n",
+    "validation_targets = shuffled_targets[train_samples_count:train_samples_count+validation_samples_count]\n",
+    "\n",
+    "# Create variables that record the inputs and targets for test.\n",
+    "# They are everything that is remaining.\n",
+    "test_inputs = shuffled_inputs[train_samples_count+validation_samples_count:]\n",
+    "test_targets = shuffled_targets[train_samples_count+validation_samples_count:]\n",
+    "\n",
+    "# We balanced our dataset to be 50-50 (for targets 0 and 1), but the training, validation, and test were \n",
+    "# taken from a shuffled dataset. Check if they are balanced, too. Note that each time you rerun this code, \n",
+    "# you will get different values, as each time they are shuffled randomly.\n",
+    "# Normally you preprocess ONCE, so you need not rerun this code once it is done.\n",
+    "# If you rerun this whole sheet, the npzs will be overwritten with your newly preprocessed data.\n",
+    "\n",
+    "# Print the number of targets that are 1s, the total number of samples, and the proportion for training, validation, and test.\n",
+    "print(np.sum(train_targets), train_samples_count, np.sum(train_targets) / train_samples_count)\n",
+    "print(np.sum(validation_targets), validation_samples_count, np.sum(validation_targets) / validation_samples_count)\n",
+    "print(np.sum(test_targets), test_samples_count, np.sum(test_targets) / test_samples_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save the three datasets in *.npz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the three datasets in *.npz.\n",
+    "# In the next lesson, you will see that it is extremely valuable to name them in such a coherent way!\n",
+    "\n",
+    "np.savez('Audiobooks_data_train', inputs=train_inputs, targets=train_targets)\n",
+    "np.savez('Audiobooks_data_validation', inputs=validation_inputs, targets=validation_targets)\n",
+    "np.savez('Audiobooks_data_test', inputs=test_inputs, targets=test_targets)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb
new file mode 100644
index 0000000..ac76b1e
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb	
@@ -0,0 +1,274 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problem\n",
+    "\n",
+    "You are given data from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If we can focus our efforts SOLELY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) ,Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (0 or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocess the data. Balance the dataset. Create 3 datasets: training, validation, and test. Save the newly created sets in a tensor friendly format (e.g. *.npz)\n",
+    "\n",
+    "Since we are dealing with real life data, we will need to preprocess it a bit. This is the relevant code, which is not that hard, but is crucial to creating a good model.\n",
+    "\n",
+    "If you want to know how to do that, go through the code with comments. In any case, this should do the trick for most datasets organized in the way: many inputs, and then 1 cell containing the targets (supersized learning datasets). Keep in mind that a specific problem may require additional preprocessing.\n",
+    "\n",
+    "Note that we have removed the header row, which contains the names of the categories. We simply want the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the data from the csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# We will use the sklearn preprocessing library, as it will be easier to standardize the data.\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "# Load the data\n",
+    "raw_csv_data = np.loadtxt('Audiobooks_data.csv',delimiter=',')\n",
+    "\n",
+    "# The inputs are all columns in the csv, except for the first one [:,0]\n",
+    "# (which is just the arbitrary customer IDs that bear no useful information),\n",
+    "# and the last one [:,-1] (which is our targets)\n",
+    "\n",
+    "unscaled_inputs_all = raw_csv_data[:,1:-1]\n",
+    "\n",
+    "# The targets are in the last column. That's how datasets are conventionally organized.\n",
+    "targets_all = raw_csv_data[:,-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Balance the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Count how many targets are 1 (meaning that the customer did convert)\n",
+    "num_one_targets = int(np.sum(targets_all))\n",
+    "\n",
+    "# Set a counter for targets that are 0 (meaning that the customer did not convert)\n",
+    "zero_targets_counter = 0\n",
+    "\n",
+    "# We want to create a \"balanced\" dataset, so we will have to remove some input/target pairs.\n",
+    "# Declare a variable that will do that:\n",
+    "indices_to_remove = []\n",
+    "\n",
+    "# Count the number of targets that are 0. \n",
+    "# Once there are as many 0s as 1s, mark entries where the target is 0.\n",
+    "for i in range(targets_all.shape[0]):\n",
+    "    if targets_all[i] == 0:\n",
+    "        zero_targets_counter += 1\n",
+    "        if zero_targets_counter > num_one_targets:\n",
+    "            indices_to_remove.append(i)\n",
+    "\n",
+    "# Create two new variables, one that will contain the inputs, and one that will contain the targets.\n",
+    "# We delete all indices that we marked \"to remove\" in the loop above.\n",
+    "unscaled_inputs_equal_priors = np.delete(unscaled_inputs_all, indices_to_remove, axis=0)\n",
+    "targets_equal_priors = np.delete(targets_all, indices_to_remove, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Standardize the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# That's the only place we use sklearn functionality. We will take advantage of its preprocessing capabilities\n",
+    "# It's a simple line of code, which standardizes the inputs, as we explained in one of the lectures.\n",
+    "# At the end of the business case, you can try to run the algorithm WITHOUT this line of code. \n",
+    "# The result will be interesting.\n",
+    "scaled_inputs = preprocessing.scale(unscaled_inputs_equal_priors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shuffle the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# When the data was collected it was actually arranged by date\n",
+    "# Shuffle the indices of the data, so the data is not arranged in any way when we feed it.\n",
+    "# Since we will be batching, we want the data to be as randomly spread out as possible\n",
+    "shuffled_indices = np.arange(scaled_inputs.shape[0])\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "\n",
+    "# Use the shuffled indices to shuffle the inputs and targets.\n",
+    "shuffled_inputs = scaled_inputs[shuffled_indices]\n",
+    "shuffled_targets = targets_equal_priors[shuffled_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split the dataset into train, validation, and test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1791.0 3579 0.5004191114836547\n",
+      "231.0 447 0.5167785234899329\n",
+      "215.0 448 0.4799107142857143\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count the total number of samples\n",
+    "samples_count = shuffled_inputs.shape[0]\n",
+    "\n",
+    "# Count the samples in each subset, assuming we want 80-10-10 distribution of training, validation, and test.\n",
+    "# Naturally, the numbers are integers.\n",
+    "train_samples_count = int(0.8 * samples_count)\n",
+    "validation_samples_count = int(0.1 * samples_count)\n",
+    "\n",
+    "# The 'test' dataset contains all remaining data.\n",
+    "test_samples_count = samples_count - train_samples_count - validation_samples_count\n",
+    "\n",
+    "# Create variables that record the inputs and targets for training\n",
+    "# In our shuffled dataset, they are the first \"train_samples_count\" observations\n",
+    "train_inputs = shuffled_inputs[:train_samples_count]\n",
+    "train_targets = shuffled_targets[:train_samples_count]\n",
+    "\n",
+    "# Create variables that record the inputs and targets for validation.\n",
+    "# They are the next \"validation_samples_count\" observations, folllowing the \"train_samples_count\" we already assigned\n",
+    "validation_inputs = shuffled_inputs[train_samples_count:train_samples_count+validation_samples_count]\n",
+    "validation_targets = shuffled_targets[train_samples_count:train_samples_count+validation_samples_count]\n",
+    "\n",
+    "# Create variables that record the inputs and targets for test.\n",
+    "# They are everything that is remaining.\n",
+    "test_inputs = shuffled_inputs[train_samples_count+validation_samples_count:]\n",
+    "test_targets = shuffled_targets[train_samples_count+validation_samples_count:]\n",
+    "\n",
+    "# We balanced our dataset to be 50-50 (for targets 0 and 1), but the training, validation, and test were \n",
+    "# taken from a shuffled dataset. Check if they are balanced, too. Note that each time you rerun this code, \n",
+    "# you will get different values, as each time they are shuffled randomly.\n",
+    "# Normally you preprocess ONCE, so you need not rerun this code once it is done.\n",
+    "# If you rerun this whole sheet, the npzs will be overwritten with your newly preprocessed data.\n",
+    "\n",
+    "# Print the number of targets that are 1s, the total number of samples, and the proportion for training, validation, and test.\n",
+    "print(np.sum(train_targets), train_samples_count, np.sum(train_targets) / train_samples_count)\n",
+    "print(np.sum(validation_targets), validation_samples_count, np.sum(validation_targets) / validation_samples_count)\n",
+    "print(np.sum(test_targets), test_samples_count, np.sum(test_targets) / test_samples_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save the three datasets in *.npz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Save the three datasets in *.npz.\n",
+    "# In the next lesson, you will see that it is extremely valuable to name them in such a coherent way!\n",
+    "\n",
+    "np.savez('Audiobooks_data_train', inputs=train_inputs, targets=train_targets)\n",
+    "np.savez('Audiobooks_data_validation', inputs=validation_inputs, targets=validation_targets)\n",
+    "np.savez('Audiobooks_data_test', inputs=test_inputs, targets=test_targets)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments (1).ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments (1).ipynb
new file mode 100644
index 0000000..29cf182
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments (1).ipynb	
@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problem\n",
+    "\n",
+    "You are given data from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If we can focus our efforts SOLELY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) ,Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (0 or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Create a class that will batch the data\n",
+    "\n",
+    "Whenever you want to batch the data you need to have appropriate methods. There are some batching methods integrated in TensorFlow and sklearn, but some problems may need specific coding. \n",
+    "\n",
+    "Here we show how these methods look like. You can use them for any machine learning framework you need (directly or after little fine-tuning)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Create a class that will do the batching for the algorithm\n",
+    "# This code is extremely reusable. You should just change Audiobooks_data everywhere in the code\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    # Dataset is a mandatory arugment, while the batch_size is optional\n",
+    "    # If you don't input batch_size, it will automatically take the value: None\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "    \n",
+    "        # The dataset that loads is one of \"train\", \"validation\", \"test\".\n",
+    "        # e.g. if I call this class with x('train',5), it will load 'Audiobooks_data_train.npz' with a batch size of 5.\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        \n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n",
+    "        \n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "    \n",
+    "    # A method which loads the next batch\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "            \n",
+    "        # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "        \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already but we're giving you the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "        \n",
+    "        # The function will return the inputs batch and the one-hot encoded targets\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "    \n",
+    "        \n",
+    "    # A method needed for iterating over the batches, as we will put them in a loop\n",
+    "    # This tells Python that the class we're defining is iterable, i.e. that we can use it like:\n",
+    "    # for input, output in data: \n",
+    "        # do things\n",
+    "    # An iterator in Python is a class with a method __next__ that defines exactly how to iterate through its objects\n",
+    "    def __iter__(self):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n",
+    "We will create an algorithm which is essentially copy-pasting the MNIST code and we will simply adjust where needed.\n",
+    "Once more, I will put the whole code in one piece as we can simply rerun the cell and train a new model. That's because the whole algorithm is contained in the cell and we have the tf.reset_default_graph() function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments (1).ipynb Cell 6\u001b[0m line \u001b[0;36m6\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=63'>64</a>\u001b[0m prev_validation_loss \u001b[39m=\u001b[39m \u001b[39m9999999.\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=65'>66</a>\u001b[0m \u001b[39m# Load the first batch of training and validation, using the class we created. \u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=66'>67</a>\u001b[0m \u001b[39m# Arguments are ending of 'Audiobooks_Data_<...>', where for <...> we input 'train', 'validation', or 'test'\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=67'>68</a>\u001b[0m \u001b[39m# depending on what we want to load\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=68'>69</a>\u001b[0m train_data \u001b[39m=\u001b[39m Audiobooks_Data_Reader(\u001b[39m'\u001b[39;49m\u001b[39mtrain\u001b[39;49m\u001b[39m'\u001b[39;49m, batch_size)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=69'>70</a>\u001b[0m validation_data \u001b[39m=\u001b[39m Audiobooks_Data_Reader(\u001b[39m'\u001b[39m\u001b[39mvalidation\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=71'>72</a>\u001b[0m \u001b[39m# Create the loop for epochs \u001b[39;00m\n",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments (1).ipynb Cell 6\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m npz \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mload(\u001b[39m'\u001b[39m\u001b[39mAudiobooks_data_\u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m.npz\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(dataset))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39m# Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minputs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtargets \u001b[39m=\u001b[39m npz[\u001b[39m'\u001b[39m\u001b[39minputs\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mastype(np\u001b[39m.\u001b[39;49mfloat), npz[\u001b[39m'\u001b[39m\u001b[39mtargets\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mastype(np\u001b[39m.\u001b[39mint)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39m# Counts the batch number, given the size you feed it later\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m \u001b[39m# If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments%20%281%29.ipynb#W5sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m \u001b[39mif\u001b[39;00m batch_size \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\numpy\\__init__.py:338\u001b[0m, in \u001b[0;36m__getattr__\u001b[1;34m(attr)\u001b[0m\n\u001b[0;32m    333\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m    334\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIn the future `np.\u001b[39m\u001b[39m{\u001b[39;00mattr\u001b[39m}\u001b[39;00m\u001b[39m` will be defined as the \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    335\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mcorresponding NumPy scalar.\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFutureWarning\u001b[39;00m, stacklevel\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n\u001b[0;32m    337\u001b[0m \u001b[39mif\u001b[39;00m attr \u001b[39min\u001b[39;00m __former_attrs__:\n\u001b[1;32m--> 338\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(__former_attrs__[attr])\n\u001b[0;32m    340\u001b[0m \u001b[39mif\u001b[39;00m attr \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m    341\u001b[0m     \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtesting\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mtesting\u001b[39;00m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations"
+     ]
+    }
+   ],
+   "source": [
+    "# We need to import TF (we didn't need it so far)\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Input size depends on the number of input variables. We have 10 of them\n",
+    "input_size = 10\n",
+    "# Output size is 2, as we one-hot encoded the targets.\n",
+    "output_size = 2\n",
+    "# Choose a hidden_layer_size\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Reset the default graph, so you can fiddle with the hyperparameters and then rerun the code.\n",
+    "tf.compat.v1.reset_default_graph()\n",
+    "# Create the placeholders\n",
+    "inputs = tf.keras.Input(shape=(input_size,), dtype=tf.float32)\n",
+    "targets = tf.keras.Input(shape=(output_size,), dtype=tf.int32)\n",
+    "\n",
+    "\n",
+    "# Outline the model. We will create a net with 2 hidden layers\n",
+    "weights_1 = tf.Variable(tf.random.normal([input_size, hidden_layer_size]), name=\"weights_1\")\n",
+    "biases_1 = tf.Variable(tf.zeros([hidden_layer_size]), name=\"biases_1\")\n",
+    "\n",
+    "weights_2 = tf.Variable(tf.random.normal([hidden_layer_size, hidden_layer_size]), name=\"weights_2\")\n",
+    "biases_2 = tf.Variable(tf.zeros([hidden_layer_size]), name=\"biases_2\")\n",
+    "\n",
+    "weights_3 = tf.Variable(tf.random.normal([hidden_layer_size, output_size]), name=\"weights_3\")\n",
+    "biases_3 = tf.Variable(tf.zeros([output_size]), name=\"biases_3\")\n",
+    "\n",
+    "\n",
+    "# Define the model using the Keras API\n",
+    "model = tf.keras.Sequential([\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu', input_shape=(input_size,)),\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'),\n",
+    "    tf.keras.layers.Dense(output_size)\n",
+    "])\n",
+    "\n",
+    "outputs = model(inputs)\n",
+    "# Use the softmax cross entropy loss with logits\n",
+    "loss = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)\n",
+    "mean_loss = tf.reduce_mean(loss)\n",
+    "\n",
+    "# Get a 0 or 1 for every input indicating whether it output the correct answer\n",
+    "out_equals_target = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(out_equals_target, tf.float64))\n",
+    "\n",
+    "# Optimize with Adam\n",
+    "model.compile(optimizer=tf.optimizers.Adam(), loss=\"sparse_categorical_crossentropy\")\n",
+    "#optimize = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(mean_loss)\n",
+    "\n",
+    "# Create a session\n",
+    "#sess=tf.compat.v1.InteractiveSession()\n",
+    "sess = tf.compat.v1.InteractiveSession()\n",
+    "with tf.compat.v1.get_default_graph().as_default():\n",
+    "\n",
+    "# Initialize the variables\n",
+    "    initializer = tf.compat.v1.global_variables_initializer()\n",
+    "    tf.compat.v1.disable_eager_execution()\n",
+    "    sess.run(initializer)\n",
+    "\n",
+    "# Choose the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# Set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "prev_validation_loss = 9999999.\n",
+    "\n",
+    "# Load the first batch of training and validation, using the class we created. \n",
+    "# Arguments are ending of 'Audiobooks_Data_<...>', where for <...> we input 'train', 'validation', or 'test'\n",
+    "# depending on what we want to load\n",
+    "train_data = Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data = Audiobooks_Data_Reader('validation')\n",
+    "\n",
+    "# Create the loop for epochs \n",
+    "for epoch_counter in range(max_epochs):\n",
+    "    \n",
+    "    # Set the epoch loss to 0, and make it a float\n",
+    "    curr_epoch_loss = 0.\n",
+    "    \n",
+    "    # Iterate over the training data \n",
+    "    # Since train_data is an instance of the Audiobooks_Data_Reader class,\n",
+    "    # we can iterate through it by implicitly using the __next__ method we defined above.\n",
+    "    # As a reminder, it batches samples together, one-hot encodes the targets, and returns\n",
+    "    # inputs and targets batch by batch\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        _, batch_loss = sess.run([optimize, mean_loss], \n",
+    "            feed_dict={inputs: input_batch, targets: target_batch})\n",
+    "        \n",
+    "        #Record the batch loss into the current epoch loss\n",
+    "        curr_epoch_loss += batch_loss\n",
+    "    \n",
+    "    # Find the mean curr_epoch_loss\n",
+    "    # batch_count is a variable, defined in the Audiobooks_Data_Reader class\n",
+    "    curr_epoch_loss /= train_data.batch_count\n",
+    "    \n",
+    "    # Set validation loss and accuracy for the epoch to zero\n",
+    "    validation_loss = 0.\n",
+    "    validation_accuracy = 0.\n",
+    "    \n",
+    "    # Use the same logic of the code to forward propagate the validation set\n",
+    "    # There will be a single batch, as the class was created in this way\n",
+    "    for input_batch, target_batch in validation_data:\n",
+    "        validation_loss, validation_accuracy = sess.run([mean_loss, accuracy],\n",
+    "            feed_dict={inputs: input_batch, targets: target_batch})\n",
+    "    \n",
+    "    # Print statistics for the current epoch\n",
+    "    print('Epoch '+str(epoch_counter+1)+\n",
+    "          '. Training loss: '+'{0:.3f}'.format(curr_epoch_loss)+\n",
+    "          '. Validation loss: '+'{0:.3f}'.format(validation_loss)+\n",
+    "          '. Validation accuracy: '+'{0:.2f}'.format(validation_accuracy * 100.)+'%')\n",
+    "    \n",
+    "    # Trigger early stopping if validation loss begins increasing.\n",
+    "    if validation_loss > prev_validation_loss:\n",
+    "        break\n",
+    "        \n",
+    "    # Store this epoch's validation loss to be used as previous in the next iteration.\n",
+    "    prev_validation_loss = validation_loss\n",
+    "    \n",
+    "print('End of training.')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb
new file mode 100644
index 0000000..e683b50
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb	
@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audiobooks business case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Problem\n",
+    "\n",
+    "You are given data from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertising to him/her. If we can focus our efforts SOLELY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, ), Book length overall (sum of the minute length of all purchases), Book length avg (average length in minutes of all purchases), Price paid_overall (sum of all purchases) ,Price Paid avg (average of all purchases), Review (a Boolean variable whether the customer left a review), Review out of 10 (if the customer left a review, his/her review out of 10, Total minutes listened, Completion (from 0 to 1), Support requests (number of support requests; everything from forgotten password to assistance for using the App), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "These are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (0 or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Create a class that will batch the data\n",
+    "\n",
+    "Whenever you want to batch the data you need to have appropriate methods. There are some batching methods integrated in TensorFlow and sklearn, but some problems may need specific coding. \n",
+    "\n",
+    "Here we show how these methods look like. You can use them for any machine learning framework you need (directly or after little fine-tuning)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Create a class that will do the batching for the algorithm\n",
+    "# This code is extremely reusable. You should just change Audiobooks_data everywhere in the code\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    # Dataset is a mandatory arugment, while the batch_size is optional\n",
+    "    # If you don't input batch_size, it will automatically take the value: None\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "    \n",
+    "        # The dataset that loads is one of \"train\", \"validation\", \"test\".\n",
+    "        # e.g. if I call this class with x('train',5), it will load 'Audiobooks_data_train.npz' with a batch size of 5.\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        \n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float64), npz['targets'].astype(np.int64)\n",
+    "        \n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "    \n",
+    "    # A method which loads the next batch\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "            \n",
+    "        # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "        \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already but we're giving you the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "        \n",
+    "        # The function will return the inputs batch and the one-hot encoded targets\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "    \n",
+    "        \n",
+    "    # A method needed for iterating over the batches, as we will put them in a loop\n",
+    "    # This tells Python that the class we're defining is iterable, i.e. that we can use it like:\n",
+    "    # for input, output in data: \n",
+    "        # do things\n",
+    "    # An iterator in Python is a class with a method __next__ that defines exactly how to iterate through its objects\n",
+    "    def __iter__(self):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n",
+    "We will create an algorithm which is essentially copy-pasting the MNIST code and we will simply adjust where needed.\n",
+    "Once more, I will put the whole code in one piece as we can simply rerun the cell and train a new model. That's because the whole algorithm is contained in the cell and we have the tf.reset_default_graph() function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Shapes (100, 2, 2) and (100, 2) are incompatible",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb#W5sZmlsZQ%3D%3D?line=47'>48</a>\u001b[0m     \u001b[39m# Assuming target_batch is one-hot encoded, if not, switch back to SparseCategoricalCrossentropy\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb#W5sZmlsZQ%3D%3D?line=48'>49</a>\u001b[0m     target_batch_one_hot \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mone_hot(target_batch, depth\u001b[39m=\u001b[39moutput_size)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb#W5sZmlsZQ%3D%3D?line=49'>50</a>\u001b[0m     loss \u001b[39m=\u001b[39m loss_object(target_batch_one_hot, predictions)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb#W5sZmlsZQ%3D%3D?line=51'>52</a>\u001b[0m gradients \u001b[39m=\u001b[39m tape\u001b[39m.\u001b[39mgradient(loss, model\u001b[39m.\u001b[39mtrainable_variables)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb#W5sZmlsZQ%3D%3D?line=52'>53</a>\u001b[0m optimizer\u001b[39m.\u001b[39mapply_gradients(\u001b[39mzip\u001b[39m(gradients, model\u001b[39m.\u001b[39mtrainable_variables))\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\losses.py:143\u001b[0m, in \u001b[0;36mLoss.__call__\u001b[1;34m(self, y_true, y_pred, sample_weight)\u001b[0m\n\u001b[0;32m    138\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    139\u001b[0m     call_fn \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39m__internal__\u001b[39m.\u001b[39mautograph\u001b[39m.\u001b[39mtf_convert(\n\u001b[0;32m    140\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcall, tf\u001b[39m.\u001b[39m__internal__\u001b[39m.\u001b[39mautograph\u001b[39m.\u001b[39mcontrol_status_ctx()\n\u001b[0;32m    141\u001b[0m     )\n\u001b[1;32m--> 143\u001b[0m losses \u001b[39m=\u001b[39m call_fn(y_true, y_pred)\n\u001b[0;32m    145\u001b[0m in_mask \u001b[39m=\u001b[39m losses_utils\u001b[39m.\u001b[39mget_mask(y_pred)\n\u001b[0;32m    146\u001b[0m out_mask \u001b[39m=\u001b[39m losses_utils\u001b[39m.\u001b[39mget_mask(losses)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\losses.py:270\u001b[0m, in \u001b[0;36mLossFunctionWrapper.call\u001b[1;34m(self, y_true, y_pred)\u001b[0m\n\u001b[0;32m    263\u001b[0m     y_pred, y_true \u001b[39m=\u001b[39m losses_utils\u001b[39m.\u001b[39msqueeze_or_expand_dimensions(\n\u001b[0;32m    264\u001b[0m         y_pred, y_true\n\u001b[0;32m    265\u001b[0m     )\n\u001b[0;32m    267\u001b[0m ag_fn \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39m__internal__\u001b[39m.\u001b[39mautograph\u001b[39m.\u001b[39mtf_convert(\n\u001b[0;32m    268\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfn, tf\u001b[39m.\u001b[39m__internal__\u001b[39m.\u001b[39mautograph\u001b[39m.\u001b[39mcontrol_status_ctx()\n\u001b[0;32m    269\u001b[0m )\n\u001b[1;32m--> 270\u001b[0m \u001b[39mreturn\u001b[39;00m ag_fn(y_true, y_pred, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fn_kwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:153\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[1;32m--> 153\u001b[0m   \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    154\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m    155\u001b[0m   \u001b[39mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2221\u001b[0m, in \u001b[0;36mcategorical_crossentropy\u001b[1;34m(y_true, y_pred, from_logits, label_smoothing, axis)\u001b[0m\n\u001b[0;32m   2213\u001b[0m     \u001b[39mreturn\u001b[39;00m y_true \u001b[39m*\u001b[39m (\u001b[39m1.0\u001b[39m \u001b[39m-\u001b[39m label_smoothing) \u001b[39m+\u001b[39m (\n\u001b[0;32m   2214\u001b[0m         label_smoothing \u001b[39m/\u001b[39m num_classes\n\u001b[0;32m   2215\u001b[0m     )\n\u001b[0;32m   2217\u001b[0m y_true \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39m__internal__\u001b[39m.\u001b[39msmart_cond\u001b[39m.\u001b[39msmart_cond(\n\u001b[0;32m   2218\u001b[0m     label_smoothing, _smooth_labels, \u001b[39mlambda\u001b[39;00m: y_true\n\u001b[0;32m   2219\u001b[0m )\n\u001b[1;32m-> 2221\u001b[0m \u001b[39mreturn\u001b[39;00m backend\u001b[39m.\u001b[39;49mcategorical_crossentropy(\n\u001b[0;32m   2222\u001b[0m     y_true, y_pred, from_logits\u001b[39m=\u001b[39;49mfrom_logits, axis\u001b[39m=\u001b[39;49maxis\n\u001b[0;32m   2223\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\backend.py:5573\u001b[0m, in \u001b[0;36mcategorical_crossentropy\u001b[1;34m(target, output, from_logits, axis)\u001b[0m\n\u001b[0;32m   5571\u001b[0m target \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mconvert_to_tensor(target)\n\u001b[0;32m   5572\u001b[0m output \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mconvert_to_tensor(output)\n\u001b[1;32m-> 5573\u001b[0m target\u001b[39m.\u001b[39;49mshape\u001b[39m.\u001b[39;49massert_is_compatible_with(output\u001b[39m.\u001b[39;49mshape)\n\u001b[0;32m   5575\u001b[0m output, from_logits \u001b[39m=\u001b[39m _get_logits(\n\u001b[0;32m   5576\u001b[0m     output, from_logits, \u001b[39m\"\u001b[39m\u001b[39mSoftmax\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcategorical_crossentropy\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   5577\u001b[0m )\n\u001b[0;32m   5578\u001b[0m \u001b[39mif\u001b[39;00m from_logits:\n",
+      "\u001b[1;31mValueError\u001b[0m: Shapes (100, 2, 2) and (100, 2) are incompatible"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "# Input size depends on the number of input variables. We have 10 of them\n",
+    "input_size = 10\n",
+    "# Output size is 2, as we one-hot encoded the targets.\n",
+    "output_size = 2\n",
+    "# Choose a hidden_layer_size\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Create the model using the Sequential API\n",
+    "model = tf.keras.Sequential([\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu', input_shape=(input_size,)),\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'),\n",
+    "    tf.keras.layers.Dense(output_size)\n",
+    "])\n",
+    "\n",
+    "# Use the softmax cross entropy loss with logits\n",
+    "loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)\n",
+    "\n",
+    "\n",
+    "# Define the optimizer\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
+    "\n",
+    "# Define metrics\n",
+    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
+    "train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
+    "\n",
+    "# Choose the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# Set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "prev_validation_loss = 9999999.\n",
+    "\n",
+    "# Load the first batch of training and validation\n",
+    "# Replace this with your actual data loading logic\n",
+    "train_data =  Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data =  Audiobooks_Data_Reader('validation')\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch_counter in range(max_epochs):\n",
+    "    train_loss.reset_states()\n",
+    "    train_accuracy.reset_states()\n",
+    "\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        with tf.GradientTape() as tape:\n",
+    "            predictions = model(input_batch)\n",
+    "            # Assuming target_batch is one-hot encoded, if not, switch back to SparseCategoricalCrossentropy\n",
+    "            target_batch_one_hot = tf.one_hot(target_batch, depth=output_size)\n",
+    "            loss = loss_object(target_batch_one_hot, predictions)\n",
+    "\n",
+    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
+    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
+    "\n",
+    "        train_loss(loss)\n",
+    "        train_accuracy(target_batch, predictions)\n",
+    "\n",
+    "    # Validation\n",
+    "    validation_loss = 0.\n",
+    "    validation_accuracy = 0.\n",
+    "\n",
+    "    for input_batch, target_batch in validation_data:\n",
+    "        predictions = model(input_batch)\n",
+    "        loss = loss_object(target_batch, predictions)\n",
+    "\n",
+    "        validation_loss += loss\n",
+    "        validation_accuracy += tf.reduce_sum(tf.cast(tf.equal(tf.argmax(predictions, 1), tf.cast(target_batch, tf.int64)), tf.float64))\n",
+    "\n",
+    "    validation_loss /= len(validation_data)\n",
+    "    validation_accuracy /= len(validation_data)\n",
+    "\n",
+    "    # Print statistics for the current epoch\n",
+    "    print('Epoch ' + str(epoch_counter + 1) +\n",
+    "          '. Training loss: ' + '{:.3f}'.format(train_loss.result()) +\n",
+    "          '. Training accuracy: ' + '{:.2%}'.format(train_accuracy.result()) +\n",
+    "          '. Validation loss: ' + '{:.3f}'.format(validation_loss) +\n",
+    "          '. Validation accuracy: ' + '{:.2%}'.format(validation_accuracy))\n",
+    "\n",
+    "    # Trigger early stopping if validation loss begins increasing.\n",
+    "    if validation_loss > prev_validation_loss:\n",
+    "        break\n",
+    "\n",
+    "    prev_validation_loss = validation_loss\n",
+    "\n",
+    "print('End of training.')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_MNIST_All_Exercises.ipynb b/Udemy DataScience Workbook/TensorFlow_MNIST_All_Exercises.ipynb
new file mode 100644
index 0000000..65922f5
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_MNIST_All_Exercises.ipynb	
@@ -0,0 +1,398 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises\n",
+    "\n",
+    "There are several main adjustments you may try.\n",
+    "\n",
+    "Please pay attention to the time it takes for each epoch to conclude.\n",
+    "\n",
+    "Using the code from the lecture as the basis, fiddle with the hyperparameters of the algorithm.\n",
+    "\n",
+    "1. The *width* (the hidden layer size) of the algorithm. Try a hidden layer size of 200. How does the validation accuracy of the model change? What about the time it took the algorithm to train? Can you find a hidden layer size that does better?\n",
+    "\n",
+    "2. The *depth* of the algorithm. Add another hidden layer to the algorithm. This is an extremely important exercise! How does the validation accuracy change? What about the time it took the algorithm to train? Hint: Be careful with the shapes of the weights and the biases.\n",
+    "\n",
+    "3. The *width and depth* of the algorithm. Add as many additional layers as you need to reach 5 hidden layers. Moreover, adjust the width of the algorithm as you find suitable. How does the validation accuracy change? What about the time it took the algorithm to train?\n",
+    "\n",
+    "4. Fiddle with the activation functions. Try applying sigmoid transformation to both layers. The sigmoid activation is given by the string 'sigmoid'.\n",
+    "\n",
+    "5. Fiddle with the activation functions. Try applying a ReLu to the first hidden layer and tanh to the second one. The tanh activation is given by the string 'tanh'.\n",
+    "\n",
+    "6. Adjust the batch size. Try a batch size of 10000. How does the required time change? What about the accuracy?\n",
+    "\n",
+    "7. Adjust the batch size. Try a batch size of 1. That's the SGD. How do the time and accuracy change? Is the result coherent with the theory?\n",
+    "\n",
+    "8. Adjust the learning rate. Try a value of 0.0001. Does it make a difference?\n",
+    "\n",
+    "9. Adjust the learning rate. Try a value of 0.02. Does it make a difference?\n",
+    "\n",
+    "10. Combine all the methods above and try to reach a validation accuracy of 98.5+ percent.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "540/540 - 6s - loss: 0.3206 - accuracy: 0.9105 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "540/540 - 5s - loss: 0.1344 - accuracy: 0.9604 - val_loss: 0.1131 - val_accuracy: 0.9680\n",
+      "Epoch 3/5\n",
+      "540/540 - 5s - loss: 0.0969 - accuracy: 0.9710 - val_loss: 0.0972 - val_accuracy: 0.9745\n",
+      "Epoch 4/5\n",
+      "540/540 - 5s - loss: 0.0752 - accuracy: 0.9776 - val_loss: 0.0841 - val_accuracy: 0.9745\n",
+      "Epoch 5/5\n",
+      "540/540 - 5s - loss: 0.0609 - accuracy: 0.9812 - val_loss: 0.0681 - val_accuracy: 0.9790\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x229f22494a8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 1s 1s/step - loss: 0.0897 - accuracy: 0.97 - 1s 1s/step - loss: 0.0897 - accuracy: 0.9728"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.09. Test accuracy: 97.28%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the initial model and hyperparameters given in this notebook, the final test accuracy should be roughly around 97%.\n",
+    "\n",
+    "Each time the code is rerun, we get a different accuracy as the batches are shuffled, the weights are initialized in a different way, etc.\n",
+    "\n",
+    "Finally, we have intentionally reached a suboptimal solution, so you can have space to build on it."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_MNIST_Part2_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_MNIST_Part2_with_comments.ipynb
new file mode 100644
index 0000000..75301f5
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_MNIST_Part2_with_comments.ipynb	
@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_MNIST_Part3_with_comments (1).ipynb b/Udemy DataScience Workbook/TensorFlow_MNIST_Part3_with_comments (1).ipynb
new file mode 100644
index 0000000..6c555fe
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_MNIST_Part3_with_comments (1).ipynb	
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_MNIST_Part4_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_MNIST_Part4_with_comments.ipynb
new file mode 100644
index 0000000..f8a016e
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_MNIST_Part4_with_comments.ipynb	
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_MNIST_Part5_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_MNIST_Part5_with_comments.ipynb
new file mode 100644
index 0000000..2d14382
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_MNIST_Part5_with_comments.ipynb	
@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_MNIST_Part6_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_MNIST_Part6_with_comments.ipynb
new file mode 100644
index 0000000..f9ee5bd
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_MNIST_Part6_with_comments.ipynb	
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "540/540 - 6s - loss: 0.3206 - accuracy: 0.9105 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "540/540 - 5s - loss: 0.1344 - accuracy: 0.9604 - val_loss: 0.1131 - val_accuracy: 0.9680\n",
+      "Epoch 3/5\n",
+      "540/540 - 5s - loss: 0.0969 - accuracy: 0.9710 - val_loss: 0.0972 - val_accuracy: 0.9745\n",
+      "Epoch 4/5\n",
+      "540/540 - 5s - loss: 0.0752 - accuracy: 0.9776 - val_loss: 0.0841 - val_accuracy: 0.9745\n",
+      "Epoch 5/5\n",
+      "540/540 - 5s - loss: 0.0609 - accuracy: 0.9812 - val_loss: 0.0681 - val_accuracy: 0.9790\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x229f22494a8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_MNIST_complete_with_comments.ipynb b/Udemy DataScience Workbook/TensorFlow_MNIST_complete_with_comments.ipynb
new file mode 100644
index 0000000..5996f35
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_MNIST_complete_with_comments.ipynb	
@@ -0,0 +1,363 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Neural Network for MNIST Classification\n",
+    "\n",
+    "We'll apply all the knowledge from the lectures in this section to write a deep neural network. The problem we've chosen is referred to as the \"Hello World\" of deep learning because for most students it is the first deep learning algorithm they see.\n",
+    "\n",
+    "The dataset is called MNIST and refers to handwritten digit recognition. You can find more about it on Yann LeCun's website (Director of AI Research, Facebook). He is one of the pioneers of what we've been talking about and of more complex approaches that are widely used today, such as covolutional neural networks (CNNs). \n",
+    "\n",
+    "The dataset provides 70,000 images (28x28 pixels) of handwritten digits (1 digit per image). \n",
+    "\n",
+    "The goal is to write an algorithm that detects which digit is written. Since there are only 10 digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9), this is a classification problem with 10 classes. \n",
+    "\n",
+    "Our goal would be to build a neural network with 2 hidden layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# TensorFLow includes a data provider for MNIST that we'll use.\n",
+    "# It comes with the tensorflow-datasets module, therefore, if you haven't please install the package using\n",
+    "# pip install tensorflow-datasets \n",
+    "# or\n",
+    "# conda install tensorflow-datasets\n",
+    "\n",
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "That's where we load and preprocess our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember the comment from above\n",
+    "# these datasets will be stored in C:\\Users\\*USERNAME*\\tensorflow_datasets\\...\n",
+    "# the first time you download a dataset, it is stored in the respective folder \n",
+    "# every other time, it is automatically loading the copy on your computer \n",
+    "\n",
+    "# tfds.load actually loads a dataset (or downloads and then loads if that's the first time you use it) \n",
+    "# in our case, we are interesteed in the MNIST; the name of the dataset is the only mandatory argument\n",
+    "# there are other arguments we can specify, which we can find useful\n",
+    "# mnist_dataset = tfds.load(name='mnist', as_supervised=True)\n",
+    "mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n",
+    "# with_info=True will also provide us with a tuple containing information about the version, features, number of samples\n",
+    "# we will use this information a bit below and we will store it in mnist_info\n",
+    "\n",
+    "# as_supervised=True will load the dataset in a 2-tuple structure (input, target) \n",
+    "# alternatively, as_supervised=False, would return a dictionary\n",
+    "# obviously we prefer to have our inputs and targets separated \n",
+    "\n",
+    "# once we have loaded the dataset, we can easily extract the training and testing dataset with the built references\n",
+    "mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']\n",
+    "\n",
+    "# by default, TF has training and testing datasets, but no validation sets\n",
+    "# thus we must split it on our own\n",
+    "\n",
+    "# we start by defining the number of validation samples as a % of the train samples\n",
+    "# this is also where we make use of mnist_info (we don't have to count the observations)\n",
+    "num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples\n",
+    "# let's cast this number to an integer, as a float may cause an error along the way\n",
+    "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n",
+    "\n",
+    "# let's also store the number of test samples in a dedicated variable (instead of using the mnist_info one)\n",
+    "num_test_samples = mnist_info.splits['test'].num_examples\n",
+    "# once more, we'd prefer an integer (rather than the default float)\n",
+    "num_test_samples = tf.cast(num_test_samples, tf.int64)\n",
+    "\n",
+    "\n",
+    "# normally, we would like to scale our data in some way to make the result more numerically stable\n",
+    "# in this case we will simply prefer to have inputs between 0 and 1\n",
+    "# let's define a function called: scale, that will take an MNIST image and its label\n",
+    "def scale(image, label):\n",
+    "    # we make sure the value is a float\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "    # since the possible values for the inputs are 0 to 255 (256 different shades of grey)\n",
+    "    # if we divide each element by 255, we would get the desired result -> all elements will be between 0 and 1 \n",
+    "    image /= 255.\n",
+    "\n",
+    "    return image, label\n",
+    "\n",
+    "\n",
+    "# the method .map() allows us to apply a custom transformation to a given dataset\n",
+    "# we have already decided that we will get the validation data from mnist_train, so \n",
+    "scaled_train_and_validation_data = mnist_train.map(scale)\n",
+    "\n",
+    "# finally, we scale and batch the test data\n",
+    "# we scale it so it has the same magnitude as the train and validation\n",
+    "# there is no need to shuffle it, because we won't be training on the test data\n",
+    "# there would be a single batch, equal to the size of the test data\n",
+    "test_data = mnist_test.map(scale)\n",
+    "\n",
+    "\n",
+    "# let's also shuffle the data\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "# this BUFFER_SIZE parameter is here for cases when we're dealing with enormous datasets\n",
+    "# then we can't shuffle the whole dataset in one go because we can't fit it all in memory\n",
+    "# so instead TF only stores BUFFER_SIZE samples in memory at a time and shuffles them\n",
+    "# if BUFFER_SIZE=1 => no shuffling will actually happen\n",
+    "# if BUFFER_SIZE >= num samples => shuffling is uniform\n",
+    "# BUFFER_SIZE in between - a computational optimization to approximate uniform shuffling\n",
+    "\n",
+    "# luckily for us, there is a shuffle method readily available and we just need to specify the buffer size\n",
+    "shuffled_train_and_validation_data = scaled_train_and_validation_data.shuffle(BUFFER_SIZE)\n",
+    "\n",
+    "# once we have scaled and shuffled the data, we can proceed to actually extracting the train and validation\n",
+    "# our validation data would be equal to 10% of the training set, which we've already calculated\n",
+    "# we use the .take() method to take that many samples\n",
+    "# finally, we create a batch with a batch size equal to the total number of validation samples\n",
+    "validation_data = shuffled_train_and_validation_data.take(num_validation_samples)\n",
+    "\n",
+    "# similarly, the train_data is everything else, so we skip as many samples as there are in the validation dataset\n",
+    "train_data = shuffled_train_and_validation_data.skip(num_validation_samples)\n",
+    "\n",
+    "# determine the batch size\n",
+    "BATCH_SIZE = 100\n",
+    "\n",
+    "# we can also take advantage of the occasion to batch the train data\n",
+    "# this would be very helpful when we train, as we would be able to iterate over the different batches\n",
+    "train_data = train_data.batch(BATCH_SIZE)\n",
+    "\n",
+    "validation_data = validation_data.batch(num_validation_samples)\n",
+    "\n",
+    "# batch the test data\n",
+    "test_data = test_data.batch(num_test_samples)\n",
+    "\n",
+    "\n",
+    "# takes next batch (it is the only batch)\n",
+    "# because as_supervized=True, we've got a 2-tuple structure\n",
+    "validation_inputs, validation_targets = next(iter(validation_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outline the model\n",
+    "When thinking about a deep learning algorithm, we mostly imagine building the model. So, let's do it :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_size = 784\n",
+    "output_size = 10\n",
+    "# Use same hidden layer size for both hidden layers. Not a necessity.\n",
+    "hidden_layer_size = 50\n",
+    "    \n",
+    "# define how the model will look like\n",
+    "model = tf.keras.Sequential([\n",
+    "    \n",
+    "    # the first layer (the input layer)\n",
+    "    # each observation is 28x28x1 pixels, therefore it is a tensor of rank 3\n",
+    "    # since we don't know CNNs yet, we don't know how to feed such input into our net, so we must flatten the images\n",
+    "    # there is a convenient method 'Flatten' that simply takes our 28x28x1 tensor and orders it into a (None,) \n",
+    "    # or (28x28x1,) = (784,) vector\n",
+    "    # this allows us to actually create a feed forward neural network\n",
+    "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)), # input layer\n",
+    "    \n",
+    "    # tf.keras.layers.Dense is basically implementing: output = activation(dot(input, weight) + bias)\n",
+    "    # it takes several arguments, but the most important ones for us are the hidden_layer_size and the activation function\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer\n",
+    "    \n",
+    "    # the final layer is no different, we just make sure to activate it with softmax\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax') # output layer\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose the optimizer and the loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define the optimizer we'd like to use, \n",
+    "# the loss function, \n",
+    "# and the metrics we are interested in obtaining at each iteration\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training\n",
+    "That's where we train the model we have built."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "540/540 - 6s - loss: 0.3206 - accuracy: 0.9105 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/5\n",
+      "540/540 - 5s - loss: 0.1344 - accuracy: 0.9604 - val_loss: 0.1131 - val_accuracy: 0.9680\n",
+      "Epoch 3/5\n",
+      "540/540 - 5s - loss: 0.0969 - accuracy: 0.9710 - val_loss: 0.0972 - val_accuracy: 0.9745\n",
+      "Epoch 4/5\n",
+      "540/540 - 5s - loss: 0.0752 - accuracy: 0.9776 - val_loss: 0.0841 - val_accuracy: 0.9745\n",
+      "Epoch 5/5\n",
+      "540/540 - 5s - loss: 0.0609 - accuracy: 0.9812 - val_loss: 0.0681 - val_accuracy: 0.9790\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x229f22494a8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum number of epochs\n",
+    "NUM_EPOCHS = 5\n",
+    "\n",
+    "# we fit the model, specifying the\n",
+    "# training data\n",
+    "# the total number of epochs\n",
+    "# and the validation data we just created ourselves in the format: (inputs,targets)\n",
+    "model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose =2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model\n",
+    "\n",
+    "As we discussed in the lectures, after training on the training data and validating on the validation data, we test the final prediction power of our model by running it on the test dataset that the algorithm has NEVER seen before.\n",
+    "\n",
+    "It is very important to realize that fiddling with the hyperparameters overfits the validation dataset. \n",
+    "\n",
+    "The test is the absolute final instance. You should not test before you are completely done with adjusting your model.\n",
+    "\n",
+    "If you adjust your model after testing, you will start overfitting the test dataset, which will defeat its purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1/Unknown - 1s 1s/step - loss: 0.0897 - accuracy: 0.97 - 1s 1s/step - loss: 0.0897 - accuracy: 0.9728"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss: 0.09. Test accuracy: 97.28%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can apply some nice formatting if we want to\n",
+    "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the initial model and hyperparameters given in this notebook, the final test accuracy should be roughly around 97%.\n",
+    "\n",
+    "Each time the code is rerun, we get a different accuracy as the batches are shuffled, the weights are initialized in a different way, etc.\n",
+    "\n",
+    "Finally, we have intentionally reached a suboptimal solution, so you can have space to build on it."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:py3-TF2.0]",
+   "language": "python",
+   "name": "conda-env-py3-TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Minimal_example_All_exercises.ipynb b/Udemy DataScience Workbook/TensorFlow_Minimal_example_All_exercises.ipynb
new file mode 100644
index 0000000..e7a9621
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Minimal_example_All_exercises.ipynb	
@@ -0,0 +1,2557 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    1. Change the number of observations to 100,000 and see what happens.\n",
+    "    2. Play around with the learning rate. Values like 0.0001, 0.001, 0.1, 1 are all interesting to observe. \n",
+    "    3. Change the loss function. An alternative loss for regressions is the Huber loss. \n",
+    "    The Huber loss is more appropriate than the L2-norm when we have outliers, as it is less sensitive to them (in our example we don't have outliers, but you will surely stumble upon a dataset with outliers in the future). The L2-norm loss puts all differences *to the square*, so outliers have a lot of influence on the outcome. \n",
+    "    The proper syntax of the Huber loss is 'huber_loss'\n",
+    "    \n",
+    "    \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "You can either use this file for all the exercises, or check the solutions of EACH ONE of them in the separate files we have provided. All other files are solutions of each problem. If you feel confident enough, you can simply change values in this file. Please note that it will be nice, if you return the file to starting position after you have solved a problem, so you can use the lecture as a basis for comparison."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy and TensorFlow are required for this example.\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data generation\n",
+    "\n",
+    "We generate data using the exact same logic and code as the example from the previous notebook. The only difference now is that we save it to an npz file. Npz is numpy's file type which allows you to save numpy arrays into a single .npz file. We introduce this change because in machine learning most often: \n",
+    "\n",
+    "* you are given some data (csv, database, etc.)\n",
+    "* you preprocess it into a desired format (later on we will see methods for preprocesing)\n",
+    "* you save it into npz files (if you're working in Python) to access later\n",
+    "\n",
+    "Nothing to worry about - this is literally saving your NumPy arrays into a file that you can later access, nothing more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 1000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations x 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two matrices (vectors) into one.\n",
+    "generated_inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# We add a random small noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to our f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "generated_targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# save into an npz file called \"TF_intro\"\n",
+    "np.savez('TF_intro', inputs=generated_inputs, targets=generated_targets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving with TensorFlow\n",
+    "\n",
+    "<i/>Note: This intro is just the basics of TensorFlow which has way more capabilities and depth than that.<i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the training data from the NPZ\n",
+    "training_data = np.load('TF_intro.npz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1000/1000 - 0s - loss: 24.5755\n",
+      "Epoch 2/100\n",
+      "1000/1000 - 0s - loss: 1.1773\n",
+      "Epoch 3/100\n",
+      "1000/1000 - 0s - loss: 0.4253\n",
+      "Epoch 4/100\n",
+      "1000/1000 - 0s - loss: 0.3853\n",
+      "Epoch 5/100\n",
+      "1000/1000 - 0s - loss: 0.3727\n",
+      "Epoch 6/100\n",
+      "1000/1000 - 0s - loss: 0.3932\n",
+      "Epoch 7/100\n",
+      "1000/1000 - 0s - loss: 0.3817\n",
+      "Epoch 8/100\n",
+      "1000/1000 - 0s - loss: 0.3877\n",
+      "Epoch 9/100\n",
+      "1000/1000 - 0s - loss: 0.3729\n",
+      "Epoch 10/100\n",
+      "1000/1000 - 0s - loss: 0.3982\n",
+      "Epoch 11/100\n",
+      "1000/1000 - 0s - loss: 0.3809\n",
+      "Epoch 12/100\n",
+      "1000/1000 - 0s - loss: 0.3788\n",
+      "Epoch 13/100\n",
+      "1000/1000 - 0s - loss: 0.3714\n",
+      "Epoch 14/100\n",
+      "1000/1000 - 0s - loss: 0.3608\n",
+      "Epoch 15/100\n",
+      "1000/1000 - 0s - loss: 0.3507\n",
+      "Epoch 16/100\n",
+      "1000/1000 - 0s - loss: 0.3918\n",
+      "Epoch 17/100\n",
+      "1000/1000 - 0s - loss: 0.3697\n",
+      "Epoch 18/100\n",
+      "1000/1000 - 0s - loss: 0.3811\n",
+      "Epoch 19/100\n",
+      "1000/1000 - 0s - loss: 0.3781\n",
+      "Epoch 20/100\n",
+      "1000/1000 - 0s - loss: 0.3974\n",
+      "Epoch 21/100\n",
+      "1000/1000 - 0s - loss: 0.3974\n",
+      "Epoch 22/100\n",
+      "1000/1000 - 0s - loss: 0.3724\n",
+      "Epoch 23/100\n",
+      "1000/1000 - 0s - loss: 0.3561\n",
+      "Epoch 24/100\n",
+      "1000/1000 - 0s - loss: 0.3691\n",
+      "Epoch 25/100\n",
+      "1000/1000 - 0s - loss: 0.3650\n",
+      "Epoch 26/100\n",
+      "1000/1000 - 0s - loss: 0.3569\n",
+      "Epoch 27/100\n",
+      "1000/1000 - 0s - loss: 0.3707\n",
+      "Epoch 28/100\n",
+      "1000/1000 - 0s - loss: 0.4100\n",
+      "Epoch 29/100\n",
+      "1000/1000 - 0s - loss: 0.3703\n",
+      "Epoch 30/100\n",
+      "1000/1000 - 0s - loss: 0.3598\n",
+      "Epoch 31/100\n",
+      "1000/1000 - 0s - loss: 0.3775\n",
+      "Epoch 32/100\n",
+      "1000/1000 - 0s - loss: 0.3936\n",
+      "Epoch 33/100\n",
+      "1000/1000 - 0s - loss: 0.3968\n",
+      "Epoch 34/100\n",
+      "1000/1000 - 0s - loss: 0.3614\n",
+      "Epoch 35/100\n",
+      "1000/1000 - 0s - loss: 0.3588\n",
+      "Epoch 36/100\n",
+      "1000/1000 - 0s - loss: 0.3777\n",
+      "Epoch 37/100\n",
+      "1000/1000 - 0s - loss: 0.3637\n",
+      "Epoch 38/100\n",
+      "1000/1000 - 0s - loss: 0.3662\n",
+      "Epoch 39/100\n",
+      "1000/1000 - 0s - loss: 0.3655\n",
+      "Epoch 40/100\n",
+      "1000/1000 - 0s - loss: 0.3582\n",
+      "Epoch 41/100\n",
+      "1000/1000 - 0s - loss: 0.3759\n",
+      "Epoch 42/100\n",
+      "1000/1000 - 0s - loss: 0.4468\n",
+      "Epoch 43/100\n",
+      "1000/1000 - 0s - loss: 0.3613\n",
+      "Epoch 44/100\n",
+      "1000/1000 - 0s - loss: 0.3905\n",
+      "Epoch 45/100\n",
+      "1000/1000 - 0s - loss: 0.3825\n",
+      "Epoch 46/100\n",
+      "1000/1000 - 0s - loss: 0.3810\n",
+      "Epoch 47/100\n",
+      "1000/1000 - 0s - loss: 0.3546\n",
+      "Epoch 48/100\n",
+      "1000/1000 - 0s - loss: 0.3520\n",
+      "Epoch 49/100\n",
+      "1000/1000 - 0s - loss: 0.3878\n",
+      "Epoch 50/100\n",
+      "1000/1000 - 0s - loss: 0.3748\n",
+      "Epoch 51/100\n",
+      "1000/1000 - 0s - loss: 0.3978\n",
+      "Epoch 52/100\n",
+      "1000/1000 - 0s - loss: 0.3669\n",
+      "Epoch 53/100\n",
+      "1000/1000 - 0s - loss: 0.3650\n",
+      "Epoch 54/100\n",
+      "1000/1000 - 0s - loss: 0.3869\n",
+      "Epoch 55/100\n",
+      "1000/1000 - 0s - loss: 0.3952\n",
+      "Epoch 56/100\n",
+      "1000/1000 - 0s - loss: 0.3897\n",
+      "Epoch 57/100\n",
+      "1000/1000 - 0s - loss: 0.3698\n",
+      "Epoch 58/100\n",
+      "1000/1000 - 0s - loss: 0.3655\n",
+      "Epoch 59/100\n",
+      "1000/1000 - 0s - loss: 0.3717\n",
+      "Epoch 60/100\n",
+      "1000/1000 - 0s - loss: 0.3942\n",
+      "Epoch 61/100\n",
+      "1000/1000 - 0s - loss: 0.4334\n",
+      "Epoch 62/100\n",
+      "1000/1000 - 0s - loss: 0.3836\n",
+      "Epoch 63/100\n",
+      "1000/1000 - 0s - loss: 0.3631\n",
+      "Epoch 64/100\n",
+      "1000/1000 - 0s - loss: 0.3804\n",
+      "Epoch 65/100\n",
+      "1000/1000 - 0s - loss: 0.3671\n",
+      "Epoch 66/100\n",
+      "1000/1000 - 0s - loss: 0.3801\n",
+      "Epoch 67/100\n",
+      "1000/1000 - 0s - loss: 0.4032\n",
+      "Epoch 68/100\n",
+      "1000/1000 - 0s - loss: 0.3764\n",
+      "Epoch 69/100\n",
+      "1000/1000 - 0s - loss: 0.3549\n",
+      "Epoch 70/100\n",
+      "1000/1000 - 0s - loss: 0.3585\n",
+      "Epoch 71/100\n",
+      "1000/1000 - 0s - loss: 0.3747\n",
+      "Epoch 72/100\n",
+      "1000/1000 - 0s - loss: 0.3633\n",
+      "Epoch 73/100\n",
+      "1000/1000 - 0s - loss: 0.3493\n",
+      "Epoch 74/100\n",
+      "1000/1000 - 0s - loss: 0.3924\n",
+      "Epoch 75/100\n",
+      "1000/1000 - 0s - loss: 0.4246\n",
+      "Epoch 76/100\n",
+      "1000/1000 - 0s - loss: 0.3701\n",
+      "Epoch 77/100\n",
+      "1000/1000 - 0s - loss: 0.3959\n",
+      "Epoch 78/100\n",
+      "1000/1000 - 0s - loss: 0.3923\n",
+      "Epoch 79/100\n",
+      "1000/1000 - 0s - loss: 0.3587\n",
+      "Epoch 80/100\n",
+      "1000/1000 - 0s - loss: 0.3729\n",
+      "Epoch 81/100\n",
+      "1000/1000 - 0s - loss: 0.3649\n",
+      "Epoch 82/100\n",
+      "1000/1000 - 0s - loss: 0.3611\n",
+      "Epoch 83/100\n",
+      "1000/1000 - 0s - loss: 0.3701\n",
+      "Epoch 84/100\n",
+      "1000/1000 - 0s - loss: 0.3699\n",
+      "Epoch 85/100\n",
+      "1000/1000 - 0s - loss: 0.3494\n",
+      "Epoch 86/100\n",
+      "1000/1000 - 0s - loss: 0.3613\n",
+      "Epoch 87/100\n",
+      "1000/1000 - 0s - loss: 0.3933\n",
+      "Epoch 88/100\n",
+      "1000/1000 - 0s - loss: 0.4031\n",
+      "Epoch 89/100\n",
+      "1000/1000 - 0s - loss: 0.3814\n",
+      "Epoch 90/100\n",
+      "1000/1000 - 0s - loss: 0.3481\n",
+      "Epoch 91/100\n",
+      "1000/1000 - 0s - loss: 0.3664\n",
+      "Epoch 92/100\n",
+      "1000/1000 - 0s - loss: 0.3691\n",
+      "Epoch 93/100\n",
+      "1000/1000 - 0s - loss: 0.3599\n",
+      "Epoch 94/100\n",
+      "1000/1000 - 0s - loss: 0.3817\n",
+      "Epoch 95/100\n",
+      "1000/1000 - 0s - loss: 0.3572\n",
+      "Epoch 96/100\n",
+      "1000/1000 - 0s - loss: 0.3699\n",
+      "Epoch 97/100\n",
+      "1000/1000 - 0s - loss: 0.3666\n",
+      "Epoch 98/100\n",
+      "1000/1000 - 0s - loss: 0.3667\n",
+      "Epoch 99/100\n",
+      "1000/1000 - 0s - loss: 0.4198\n",
+      "Epoch 100/100\n",
+      "1000/1000 - 0s - loss: 0.3667\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x212be2c1550>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Declare a variable where we will store the input size of our model\n",
+    "# It should be equal to the number of variables you have\n",
+    "input_size = 2\n",
+    "# Declare the output size of the model\n",
+    "# It should be equal to the number of outputs you've got (for regressions that's usually 1)\n",
+    "output_size = 1\n",
+    "\n",
+    "# Outline the model\n",
+    "# We lay out the model in 'Sequential'\n",
+    "# Note that there are no calculations involved - we are just describing our network\n",
+    "model = tf.keras.Sequential([\n",
+    "                            # Each 'layer' is listed here\n",
+    "                            # The method 'Dense' indicates, our mathematical operation to be (xw + b)\n",
+    "                            tf.keras.layers.Dense(output_size,\n",
+    "                                                 # there are extra arguments you can include to customize your model\n",
+    "                                                 # in our case we are just trying to create a solution that is \n",
+    "                                                 # as close as possible to our NumPy model\n",
+    "                                                 kernel_initializer=tf.random_uniform_initializer(minval=-0.1, maxval=0.1),\n",
+    "                                                 bias_initializer=tf.random_uniform_initializer(minval=-0.1, maxval=0.1)\n",
+    "                                                 )\n",
+    "                            ])\n",
+    "\n",
+    "# We can also define a custom optimizer, where we can specify the learning rate\n",
+    "custom_optimizer = tf.keras.optimizers.SGD(learning_rate=0.02)\n",
+    "# Note that sometimes you may also need a custom loss function \n",
+    "# That's much harder to implement and won't be covered in this course though\n",
+    "\n",
+    "# 'compile' is the place where you select and indicate the optimizers and the loss\n",
+    "model.compile(optimizer=custom_optimizer, loss='mean_squared_error')\n",
+    "\n",
+    "# finally we fit the model, indicating the inputs and targets\n",
+    "# if they are not otherwise specified the number of epochs will be 1 (a single epoch of training), \n",
+    "# so the number of epochs is 'kind of' mandatory, too\n",
+    "# we can play around with verbose; we prefer verbose=2\n",
+    "model.fit(training_data['inputs'], training_data['targets'], epochs=100, verbose=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the weights and bias\n",
+    "Extracting the weight(s) and bias(es) of a model is not an essential step for the machine learning process. In fact, usually they would not tell us much in a deep learning context. However, this simple example was set up in a way, which allows us to verify if the answers we get are correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[ 1.9776261],\n",
+       "        [-2.9764102]], dtype=float32), array([4.975083], dtype=float32)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Extracting the weights and biases is achieved quite easily\n",
+    "model.layers[0].get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.9776261],\n",
+       "       [-2.9764102]], dtype=float32)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can save the weights and biases in separate variables for easier examination\n",
+    "# Note that there can be hundreds or thousands of them!\n",
+    "weights = model.layers[0].get_weights()[0]\n",
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.975083], dtype=float32)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can save the weights and biases in separate variables for easier examination\n",
+    "# Note that there can be hundreds or thousands of them!\n",
+    "bias = model.layers[0].get_weights()[1]\n",
+    "bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the outputs (make predictions)\n",
+    "Once more, this is not an essential step, however, we usually want to be able to make predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "       [  1.1],\n",
+       "       [ 39.5]], dtype=float32)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can predict new values in order to actually make use of the model\n",
+    "# Sometimes it is useful to round the values to be able to read the output\n",
+    "# Usually we use this method on NEW DATA, rather than our original training data\n",
+    "model.predict_on_batch(training_data['inputs']).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ -9.8],\n",
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+       "       [-12.2],\n",
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+       "       [-11.4],\n",
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+       "       [  9.7],\n",
+       "       [ 25.2],\n",
+       "       [  2.8],\n",
+       "       [ 13.8],\n",
+       "       [-24.5],\n",
+       "       [ -4.7],\n",
+       "       [-29.6],\n",
+       "       [ 29.8],\n",
+       "       [ 29.5],\n",
+       "       [-25.3],\n",
+       "       [  1.1],\n",
+       "       [  9.2],\n",
+       "       [ -8.2],\n",
+       "       [ 15.8],\n",
+       "       [  6.9],\n",
+       "       [ -1.2],\n",
+       "       [ 17.2],\n",
+       "       [ -2.3],\n",
+       "       [-13.7],\n",
+       "       [ 22.4],\n",
+       "       [ 10.2],\n",
+       "       [ 21.1],\n",
+       "       [ 22.2],\n",
+       "       [ -4.3],\n",
+       "       [  7.9],\n",
+       "       [ 22.8],\n",
+       "       [  4.9],\n",
+       "       [ 28.7],\n",
+       "       [-18.8],\n",
+       "       [  1.6],\n",
+       "       [ 39.9]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If we display our targets (actual observed values), we can manually compare the outputs and the targets\n",
+    "training_data['targets'].round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The model is optimized, so the outputs are calculated based on the last form of the model\n",
+    "\n",
+    "# We have to np.squeeze the arrays in order to fit them to what the plot function expects.\n",
+    "# Doesn't change anything as we cut dimensions of size 1 - just a technicality.\n",
+    "plt.plot(np.squeeze(model.predict_on_batch(training_data['inputs'])), np.squeeze(training_data['targets']))\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()\n",
+    "\n",
+    "# Voila - what you see should be exactly the same as in the previous notebook!\n",
+    "# You probably don't see the point of TensorFlow now - it took us the same number of lines of code\n",
+    "# to achieve this simple result. However, once we go deeper in the next chapter,\n",
+    "# TensorFlow will save us hundreds of lines of code."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:python3.7_TF2.0]",
+   "language": "python",
+   "name": "conda-env-python3.7_TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Minimal_example_Exercise_1_Solution.ipynb b/Udemy DataScience Workbook/TensorFlow_Minimal_example_Exercise_1_Solution.ipynb
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@@ -0,0 +1,576 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    1. Change the number of observations to 100,000 and see what happens.\n",
+    "\n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "\n",
+    "If you don't do that, your algorithm may keep the OLD values of all parameters.\n",
+    "\n",
+    "## Solution\n",
+    "\n",
+    "Find the variable \"observations\" and change it to 100000, instead of 1000.\n",
+    "\n",
+    "Here are some takeaways:\n",
+    "1. It takes the algorithm more time to solve the problem.\n",
+    "2. No further adjustments are needed, as all the code is written irrespeective of the number of observations\n",
+    "3. The result is the same as we the loss was scaled (the mean_squared_loss, so the average L2-norm).\n",
+    "4. The loss starts oscillating at some point.\n",
+    "4. matplotlib may not be able to plot the data, as there are too many points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We must always import the relevant libraries for our problem at hand. NumPy and TensorFlow are required for this example.\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data generation\n",
+    "\n",
+    "We generate data using the exact same logic and code as the example from the previous notebook. The only difference now is that we save it to an npz file. Npz is numpy's file type which allows you to save numpy arrays into a single .npz file. We introduce this change because in machine learning most often: \n",
+    "\n",
+    "* you are given some data (csv, database, etc.)\n",
+    "* you preprocess it into a desired format (later on we will see methods for preprocesing)\n",
+    "* you save it into npz files (if you're working in Python) to access later\n",
+    "\n",
+    "Nothing to worry about - this is literally saving your NumPy arrays into a file that you can later access, nothing more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, we should declare a variable containing the size of the training set we want to generate.\n",
+    "observations = 100000\n",
+    "\n",
+    "# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.\n",
+    "# We have picked x and z, since it is easier to differentiate them.\n",
+    "# We generate them randomly, drawing from an uniform distribution. There are 3 arguments of this method (low, high, size).\n",
+    "# The size of xs and zs is observations x 1. In this case: 1000 x 1.\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "# Combine the two dimensions of the input into one input matrix. \n",
+    "# This is the X matrix from the linear model y = x*w + b.\n",
+    "# column_stack is a Numpy method, which combines two matrices (vectors) into one.\n",
+    "generated_inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "# We add a random small noise to the function i.e. f(x,z) = 2x - 3z + 5 + <small noise>\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "# Produce the targets according to our f(x,z) = 2x - 3z + 5 + noise definition.\n",
+    "# In this way, we are basically saying: the weights should be 2 and -3, while the bias is 5.\n",
+    "generated_targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "# save into an npz file called \"TF_intro\"\n",
+    "np.savez('TF_intro', inputs=generated_inputs, targets=generated_targets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving with TensorFlow\n",
+    "\n",
+    "<i/>Note: This intro is just the basics of TensorFlow which has way more capabilities and depth than that.<i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the training data from the NPZ\n",
+    "training_data = np.load('TF_intro.npz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "100000/100000 - 2s - loss: 0.6400\n",
+      "Epoch 2/100\n",
+      "100000/100000 - 2s - loss: 0.3813\n",
+      "Epoch 3/100\n",
+      "100000/100000 - 3s - loss: 0.3848\n",
+      "Epoch 4/100\n",
+      "100000/100000 - 3s - loss: 0.3793\n",
+      "Epoch 5/100\n",
+      "100000/100000 - 2s - loss: 0.3819\n",
+      "Epoch 6/100\n",
+      "100000/100000 - 2s - loss: 0.3837\n",
+      "Epoch 7/100\n",
+      "100000/100000 - 2s - loss: 0.3841\n",
+      "Epoch 8/100\n",
+      "100000/100000 - 3s - loss: 0.3835\n",
+      "Epoch 9/100\n",
+      "100000/100000 - 2s - loss: 0.3801\n",
+      "Epoch 10/100\n",
+      "100000/100000 - 2s - loss: 0.3807\n",
+      "Epoch 11/100\n",
+      "100000/100000 - 2s - loss: 0.3807\n",
+      "Epoch 12/100\n",
+      "100000/100000 - 2s - loss: 0.3810\n",
+      "Epoch 13/100\n",
+      "100000/100000 - 2s - loss: 0.3809\n",
+      "Epoch 14/100\n",
+      "100000/100000 - 2s - loss: 0.3805\n",
+      "Epoch 15/100\n",
+      "100000/100000 - 2s - loss: 0.3839\n",
+      "Epoch 16/100\n",
+      "100000/100000 - 2s - loss: 0.3802\n",
+      "Epoch 17/100\n",
+      "100000/100000 - 2s - loss: 0.3799\n",
+      "Epoch 18/100\n",
+      "100000/100000 - 2s - loss: 0.3817\n",
+      "Epoch 19/100\n",
+      "100000/100000 - 2s - loss: 0.3807\n",
+      "Epoch 20/100\n",
+      "100000/100000 - 2s - loss: 0.3778\n",
+      "Epoch 21/100\n",
+      "100000/100000 - 2s - loss: 0.3830\n",
+      "Epoch 22/100\n",
+      "100000/100000 - 3s - loss: 0.3827\n",
+      "Epoch 23/100\n",
+      "100000/100000 - 2s - loss: 0.3782\n",
+      "Epoch 24/100\n",
+      "100000/100000 - 2s - loss: 0.3841\n",
+      "Epoch 25/100\n",
+      "100000/100000 - 2s - loss: 0.3837\n",
+      "Epoch 26/100\n",
+      "100000/100000 - 2s - loss: 0.3823\n",
+      "Epoch 27/100\n",
+      "100000/100000 - 2s - loss: 0.3859\n",
+      "Epoch 28/100\n",
+      "100000/100000 - 2s - loss: 0.3818\n",
+      "Epoch 29/100\n",
+      "100000/100000 - 2s - loss: 0.3817\n",
+      "Epoch 30/100\n",
+      "100000/100000 - 2s - loss: 0.3826\n",
+      "Epoch 31/100\n",
+      "100000/100000 - 2s - loss: 0.3817\n",
+      "Epoch 32/100\n",
+      "100000/100000 - 2s - loss: 0.3819\n",
+      "Epoch 33/100\n",
+      "100000/100000 - 2s - loss: 0.3797\n",
+      "Epoch 34/100\n",
+      "100000/100000 - 2s - loss: 0.3825\n",
+      "Epoch 35/100\n",
+      "100000/100000 - 2s - loss: 0.3816\n",
+      "Epoch 36/100\n",
+      "100000/100000 - 2s - loss: 0.3798\n",
+      "Epoch 37/100\n",
+      "100000/100000 - 2s - loss: 0.3817\n",
+      "Epoch 38/100\n",
+      "100000/100000 - 2s - loss: 0.3828\n",
+      "Epoch 39/100\n",
+      "100000/100000 - 3s - loss: 0.3843\n",
+      "Epoch 40/100\n",
+      "100000/100000 - 2s - loss: 0.3794\n",
+      "Epoch 41/100\n",
+      "100000/100000 - 2s - loss: 0.3814\n",
+      "Epoch 42/100\n",
+      "100000/100000 - 2s - loss: 0.3820\n",
+      "Epoch 43/100\n",
+      "100000/100000 - 2s - loss: 0.3810\n",
+      "Epoch 44/100\n",
+      "100000/100000 - 2s - loss: 0.3817\n",
+      "Epoch 45/100\n",
+      "100000/100000 - 2s - loss: 0.3841\n",
+      "Epoch 46/100\n",
+      "100000/100000 - 2s - loss: 0.3834\n",
+      "Epoch 47/100\n",
+      "100000/100000 - 2s - loss: 0.3809\n",
+      "Epoch 48/100\n",
+      "100000/100000 - 2s - loss: 0.3825\n",
+      "Epoch 49/100\n",
+      "100000/100000 - 2s - loss: 0.3818\n",
+      "Epoch 50/100\n",
+      "100000/100000 - 2s - loss: 0.3809\n",
+      "Epoch 51/100\n",
+      "100000/100000 - 2s - loss: 0.3819\n",
+      "Epoch 52/100\n",
+      "100000/100000 - 2s - loss: 0.3808\n",
+      "Epoch 53/100\n",
+      "100000/100000 - 2s - loss: 0.3790\n",
+      "Epoch 54/100\n",
+      "100000/100000 - 2s - loss: 0.3845\n",
+      "Epoch 55/100\n",
+      "100000/100000 - 2s - loss: 0.3815\n",
+      "Epoch 56/100\n",
+      "100000/100000 - 2s - loss: 0.3791\n",
+      "Epoch 57/100\n",
+      "100000/100000 - 2s - loss: 0.3817\n",
+      "Epoch 58/100\n",
+      "100000/100000 - 2s - loss: 0.3818\n",
+      "Epoch 59/100\n",
+      "100000/100000 - 2s - loss: 0.3826\n",
+      "Epoch 60/100\n",
+      "100000/100000 - 2s - loss: 0.3814\n",
+      "Epoch 61/100\n",
+      "100000/100000 - 2s - loss: 0.3816\n",
+      "Epoch 62/100\n",
+      "100000/100000 - 2s - loss: 0.3825\n",
+      "Epoch 63/100\n",
+      "100000/100000 - 2s - loss: 0.3801\n",
+      "Epoch 64/100\n",
+      "100000/100000 - 2s - loss: 0.3814\n",
+      "Epoch 65/100\n",
+      "100000/100000 - 2s - loss: 0.3815\n",
+      "Epoch 66/100\n",
+      "100000/100000 - 2s - loss: 0.3827\n",
+      "Epoch 67/100\n",
+      "100000/100000 - 2s - loss: 0.3801\n",
+      "Epoch 68/100\n",
+      "100000/100000 - 3s - loss: 0.3850\n",
+      "Epoch 69/100\n",
+      "100000/100000 - 2s - loss: 0.3801\n",
+      "Epoch 70/100\n",
+      "100000/100000 - 2s - loss: 0.3815\n",
+      "Epoch 71/100\n",
+      "100000/100000 - 2s - loss: 0.3837\n",
+      "Epoch 72/100\n",
+      "100000/100000 - 2s - loss: 0.3826\n",
+      "Epoch 73/100\n",
+      "100000/100000 - 2s - loss: 0.3817\n",
+      "Epoch 74/100\n",
+      "100000/100000 - 2s - loss: 0.3820\n",
+      "Epoch 75/100\n",
+      "100000/100000 - 2s - loss: 0.3803\n",
+      "Epoch 76/100\n",
+      "100000/100000 - 2s - loss: 0.3820\n",
+      "Epoch 77/100\n",
+      "100000/100000 - 2s - loss: 0.3807\n",
+      "Epoch 78/100\n",
+      "100000/100000 - 2s - loss: 0.3836\n",
+      "Epoch 79/100\n",
+      "100000/100000 - 2s - loss: 0.3807\n",
+      "Epoch 80/100\n",
+      "100000/100000 - 2s - loss: 0.3856\n",
+      "Epoch 81/100\n",
+      "100000/100000 - 2s - loss: 0.3818\n",
+      "Epoch 82/100\n",
+      "100000/100000 - 2s - loss: 0.3826\n",
+      "Epoch 83/100\n",
+      "100000/100000 - 2s - loss: 0.3818\n",
+      "Epoch 84/100\n",
+      "100000/100000 - 2s - loss: 0.3810\n",
+      "Epoch 85/100\n",
+      "100000/100000 - 2s - loss: 0.3841\n",
+      "Epoch 86/100\n",
+      "100000/100000 - 2s - loss: 0.3798\n",
+      "Epoch 87/100\n",
+      "100000/100000 - 2s - loss: 0.3800\n",
+      "Epoch 88/100\n",
+      "100000/100000 - 2s - loss: 0.3822\n",
+      "Epoch 89/100\n",
+      "100000/100000 - 2s - loss: 0.3810\n",
+      "Epoch 90/100\n",
+      "100000/100000 - 2s - loss: 0.3839\n",
+      "Epoch 91/100\n",
+      "100000/100000 - 2s - loss: 0.3821\n",
+      "Epoch 92/100\n",
+      "100000/100000 - 2s - loss: 0.3827\n",
+      "Epoch 93/100\n",
+      "100000/100000 - 2s - loss: 0.3785\n",
+      "Epoch 94/100\n",
+      "100000/100000 - 2s - loss: 0.3823\n",
+      "Epoch 95/100\n",
+      "100000/100000 - 2s - loss: 0.3782\n",
+      "Epoch 96/100\n",
+      "100000/100000 - 2s - loss: 0.3816\n",
+      "Epoch 97/100\n",
+      "100000/100000 - 2s - loss: 0.3810\n",
+      "Epoch 98/100\n",
+      "100000/100000 - 2s - loss: 0.3831\n",
+      "Epoch 99/100\n",
+      "100000/100000 - 2s - loss: 0.3827\n",
+      "Epoch 100/100\n",
+      "100000/100000 - 2s - loss: 0.3788\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1f66449e208>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Declare a variable where we will store the input size of our model\n",
+    "# It should be equal to the number of variables you have\n",
+    "input_size = 2\n",
+    "# Declare the output size of the model\n",
+    "# It should be equal to the number of outputs you've got (for regressions that's usually 1)\n",
+    "output_size = 1\n",
+    "\n",
+    "# Outline the model\n",
+    "# We lay out the model in 'Sequential'\n",
+    "# Note that there are no calculations involved - we are just describing our network\n",
+    "model = tf.keras.Sequential([\n",
+    "                            # Each 'layer' is listed here\n",
+    "                            # The method 'Dense' indicates, our mathematical operation to be (xw + b)\n",
+    "                            tf.keras.layers.Dense(output_size,\n",
+    "                                                 # there are extra arguments you can include to customize your model\n",
+    "                                                 # in our case we are just trying to create a solution that is \n",
+    "                                                 # as close as possible to our NumPy model\n",
+    "                                                 kernel_initializer=tf.random_uniform_initializer(minval=-0.1, maxval=0.1),\n",
+    "                                                 bias_initializer=tf.random_uniform_initializer(minval=-0.1, maxval=0.1)\n",
+    "                                                 )\n",
+    "                            ])\n",
+    "\n",
+    "# We can also define a custom optimizer, where we can specify the learning rate\n",
+    "custom_optimizer = tf.keras.optimizers.SGD(learning_rate=0.02)\n",
+    "# Note that sometimes you may also need a custom loss function \n",
+    "# That's much harder to implement and won't be covered in this course though\n",
+    "\n",
+    "# 'compile' is the place where you select and indicate the optimizers and the loss\n",
+    "model.compile(optimizer=custom_optimizer, loss='mean_squared_error')\n",
+    "\n",
+    "# finally we fit the model, indicating the inputs and targets\n",
+    "# if they are not otherwise specified the number of epochs will be 1 (a single epoch of training), \n",
+    "# so the number of epochs is 'kind of' mandatory, too\n",
+    "# we can play around with verbose; we prefer verbose=2\n",
+    "model.fit(training_data['inputs'], training_data['targets'], epochs=100, verbose=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the weights and bias\n",
+    "Extracting the weight(s) and bias(es) of a model is not an essential step for the machine learning process. In fact, usually they would not tell us much in a deep learning context. However, this simple example was set up in a way, which allows us to verify if the answers we get are correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[ 1.9821367],\n",
+       "        [-2.9964035]], dtype=float32), array([5.0029154], dtype=float32)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Extracting the weights and biases is achieved quite easily\n",
+    "model.layers[0].get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.9821367],\n",
+       "       [-2.9964035]], dtype=float32)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can save the weights and biases in separate variables for easier examination\n",
+    "# Note that there can be hundreds or thousands of them!\n",
+    "weights = model.layers[0].get_weights()[0]\n",
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5.0029154], dtype=float32)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can save the weights and biases in separate variables for easier examination\n",
+    "# Note that there can be hundreds or thousands of them!\n",
+    "bias = model.layers[0].get_weights()[1]\n",
+    "bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the outputs (make predictions)\n",
+    "Once more, this is not an essential step, however, we usually want to be able to make predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1.4],\n",
+       "       [  7.5],\n",
+       "       [ 31.6],\n",
+       "       ...,\n",
+       "       [-19.2],\n",
+       "       [ 34.1],\n",
+       "       [ 21.4]], dtype=float32)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can predict new values in order to actually make use of the model\n",
+    "# Sometimes it is useful to round the values to be able to read the output\n",
+    "# Usually we use this method on NEW DATA, rather than our original training data\n",
+    "model.predict_on_batch(training_data['inputs']).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  0.7],\n",
+       "       [  7.1],\n",
+       "       [ 32.6],\n",
+       "       ...,\n",
+       "       [-19.4],\n",
+       "       [ 34.2],\n",
+       "       [ 22.1]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If we display our targets (actual observed values), we can manually compare the outputs and the targets\n",
+    "training_data['targets'].round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sRlJZBoUq1+od5+wipM5iUKhSrXQRF3xsL6ZN2bzCaiQNh0GhStz9yDNMP+Pa0uPtIqTOZVBoxLXSRdz1hRmMXW90hdVIWlcGhUbMfl+5hnsee670eLsIqTsYFBoRrXQRBoTUXQwKrRNPnJN6n0GhYfHEOal/GBRqWStdxOnveR0HT5tUYTWSqjaqjg+NiC9HxJ0RcWtE/DQixg96bnZELIyIuyLigDrqU3MvrFjZ8r4IQ0LqfnV1FFcAszNzRUR8CZgNHBcROwMHA68FtgOujIhXZebKmupUoZWA+O3nD2DcWJtVqVfU0lFk5uWZuaJYvA6YUDw+CDgvM1/IzMXAQmBaHTWq4bcPPtVyF2FISL2lE36iPwycXzzenkZwrLakWKcaeMirJKgwKCLiSmCbJk+dmJkXFmNOBFYA565+WZPxuYb3nwXMApg0yXnwkXT0eTfzs/kPlR5vSEi9rbKgyMy3DfV8RBwOvBPYLzNXh8ESYOKgYROApr+xMnMOMAdgYGCgaZiodXYRkl6qlqmniJgBHAe8JTOXDXrqIuAHEfFVGjuzpwI31FBi32klIKbv/HLO+quBCquR1Enq2kfxj8BY4IqIALguMz+embdHxAXAHTSmpI7yiKdqeeKcpLWpJSgyc8chnvsi8MU2ltO3WukifjN7X7bdbMMKq5HUqTrhqCe12apVyQ4n2EVIKseg6DOtdBGLT5tJMTUoqY/VcsKd2m/pU39o+YgmQ0IS2FH0BQ95lbQuDIoedtrFC/j2tYtKjzckJDVjUPQouwhJI8Wg6DGtBMS/HLkHb5q6ZYXVSOoFBkUPsYuQVAWDogd4yKukKhkUXczLb0hqB4OiSznNJKldPOGuyzzw+2WGhKS2sqPoIgaEpDoYFF3gmPPn85ObHyw19tj9X8Un9p1acUWS+olB0eHsIiTVzaDoUK0ExIKTZ7DhmNEVViOpnxkUHcguQlInMSg6iCfOSepEHh7bAVauSu8VIalj2VHUzGkmSZ3OjqImrZw4t91mGxgSkmpjR1EDuwhJ3cSgaKP/85t7OenC20uNvfhv/oKdt9u02oIkqQSDok3sIiR1K4OiYq0ExMIvvp31RrvbSFJnMSgq4r0iJPUKg6ICTjNJ6iXOc4ygzNZPnJOkTmdHMUIMCEm9yqBYR88vX8lOJ11aaqyHvErqRrUGRUQcC3wZ2CozH4/GBYzOBGYCy4AjMvOmOmscil2EpH5QW1BExERgOnD/oNVvB6YW/+0BfKv42lH+457HOfSs60uNXXTqTEaN8gJ+krpXnR3FGcCngQsHrTsI+H5mJnBdRIyPiG0zc2ktFTZhFyGp39QSFBFxIPBgZt7ykstlbw88MGh5SbGu9qC45q5HOeK7N5Yaa0BI6iWVBUVEXAls0+SpE4ETgP2bvazJulzD+88CZgFMmjRpmFWWYxchqZ9VFhSZ+bZm6yPidcAUYHU3MQG4KSKm0eggJg4aPgF4aA3vPweYAzAwMNA0TNbVVy6/i2/8YmGpsQaEpF7V9qmnzLwN2Hr1ckTcCwwURz1dBHwiIs6jsRP7qTr2T7Ry+Y1vHPJ63rXrdhVXJEn16bTzKC6mcWjsQhqHx36o3QV89PtzueKOR0qNtYuQ1A9qD4rMnDzocQJH1VHH8pWrmHriJaXG/vbzBzBubO3/6ySpLfxtB9z58NPM+NqvSo21i5DUbwwKKBUSi0+byUsO5ZWkvmBQAOPGrsezL6xo+tyZB+/GQbtt3+aKJKlz9H1Q3P3IM2sMCaeZJKnPg+LRp59n+hnX/sn6eX//NrYYN7aGiiSp8/R1UDwzqJM4fK9XcMz+r2azDdevsSJJ6jx9HRQTXrYhH3vzDrxjl23ZZcL4usuRpI7U10Exdr3RzJ75mrrLkKSO5j2zJUlDMigkSUMyKCRJQzIoJElDMigkSUMyKCRJQzIoJElDMigkSUOKxr2CultEPAbcV8NHbwk8XsPn1qkftxn6c7vd5t73iszcam2DeiIo6hIRczNzoO462qkftxn6c7vdZq3m1JMkaUgGhSRpSAbFuplTdwE16Mdthv7cbrdZgPsoJElrYUchSRqSQbEOIuLYiMiI2LJYjoj4ekQsjIhbI2L3umscKRHx5Yi4s9iun0bE+EHPzS62+a6IOKDOOkdaRMwotmthRBxfdz1ViIiJEXF1RCyIiNsj4lPF+s0j4oqIuLv4+rK6a61CRIyOiJsj4ufF8pSIuL7Y7vMjYkzdNdbNoBimiJgITAfuH7T67cDU4r9ZwLdqKK0qVwB/lpm7AP8PmA0QETsDBwOvBWYA/xQRo2urcgQV2/FNGt/XnYFDiu3tNSuAv8vM1wB7AkcV23k8cFVmTgWuKpZ70aeABYOWvwScUWz3E8CRtVTVQQyK4TsD+DQweCfPQcD3s+E6YHxEbFtLdSMsMy/PzNU3Gb8OmFA8Pgg4LzNfyMzFwEJgWh01VmAasDAzF2Xmi8B5NLa3p2Tm0sy8qXj8DI1fmtvT2NZzimHnAO+up8LqRMQE4B3Ad4rlAPYFflQM6cntbpVBMQwRcSDwYGbe8pKntgceGLS8pFjXaz4MXFI87uVt7uVtayoiJgOvB64HXp6ZS6ERJsDW9VVWma/R+INvVbG8BfDkoD+Kev57XkZf3zN7KBFxJbBNk6dOBE4A9m/2sibruuawsqG2OTMvLMacSGOq4tzVL2syvmu2eS16edv+RESMA34MHJ2ZTzf+uO5dEfFO4NHMnBcR+6xe3WRoz37PyzIo1iAz39ZsfUS8DpgC3FL8IE0AboqIaTT++pg4aPgE4KGKSx0xa9rm1SLicOCdwH75X8dVd/U2r0Uvb9sfiYj1aYTEuZn5k2L1IxGxbWYuLaZQH62vwkrsDRwYETOBDYBNaXQY4yNivaKr6NnveSucempRZt6WmVtn5uTMnEzjl8numfkwcBHwV8XRT3sCT61u3btdRMwAjgMOzMxlg566CDg4IsZGxBQaO/JvqKPGCtwITC2OghlDY6f9RTXXNOKKefmzgQWZ+dVBT10EHF48Phy4sN21VSkzZ2fmhOLn+GDgF5n5QeBq4H3FsJ7b7uGwoxhZFwMzaezQXQZ8qN5yRtQ/AmOBK4pO6rrM/Hhm3h4RFwB30JiSOiozV9ZY54jJzBUR8QngMmA08M+ZeXvNZVVhb+Aw4LaImF+sOwE4HbggIo6kcXTf+2uqr92OA86LiC8AN9MI0b7mmdmSpCE59SRJGpJBIUkakkEhSRqSQSFJGpJBIUkakkEhjYCIOCIitluH10+OiENHsiZppBgU0sg4Ahh2UACTAYNCHcnzKKQ1iIhjaFwAERpXF/0Z8PPM/LPi+WOBccBvge8BDwJ/APaicQXW84G3Fq8/NDMXRsT3ivf4UfEez2bmuIi4DngNsJjGFUsvB74LjKHxB917M/PuSjdYWgM7CqmJiHgDjTPr96Bxj4aPAk1v3FP80p8LfDAzd8vMPxRPPZ2Z02ic1f61tXzk8cCvitefAXwcODMzdwMGaFwqRqqFQSE19ybgp5n5XGY+C/wE+IsW3+OHg77u1eJrfwOcEBHHAa8YFD5S2xkUUnPNLjc9nj/+mdlgLe+RTR6vWP0excX4mt5mMzN/ABxIYyrrsojYt0TNUiUMCqm5a4F3R8RGEbEx8N9o3Kxp64jYIiLG0rjk+mrPAJu85D3++6Cvvyke3wu8oXh8ELB+s9dHxA7Aosz8Oo2ruO4yEhslDYdXj5WayMybih3Pqy+Z/p3MvDEiTqZx97fFwJ2DXvI94H9HxOqd2QBjI+J6Gn+QHVKsOwu4MCJuoHEf6ueK9bcCKyLiluK9NgD+MiKWAw8DJ4/4RkoledSTVIGIuBcYyMzH665FWldOPUmShmRHIUkakh2FJGlIBoUkaUgGhSRpSAaFJGlIBoUkaUgGhSRpSP8JkuTkB01Ajo8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The model is optimized, so the outputs are calculated based on the last form of the model\n",
+    "\n",
+    "# We have to np.squeeze the arrays in order to fit them to what the plot function expects.\n",
+    "# Doesn't change anything as we cut dimensions of size 1 - just a technicality.\n",
+    "plt.plot(np.squeeze(model.predict_on_batch(training_data['inputs'])), np.squeeze(training_data['targets']))\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()\n",
+    "\n",
+    "# Voila - what you see should be exactly the same as in the previous notebook!\n",
+    "# You probably don't see the point of TensorFlow now - it took us the same number of lines of code\n",
+    "# to achieve this simple result. However, once we go deeper in the next chapter,\n",
+    "# TensorFlow will save us hundreds of lines of code."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:python3.7_TF2.0]",
+   "language": "python",
+   "name": "conda-env-python3.7_TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part1.ipynb b/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part1.ipynb
new file mode 100644
index 0000000..004aa13
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part1.ipynb	
@@ -0,0 +1,78 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Minimal example with TensorFlow 2.0\n",
+    "In this notebook we will recreate our machine learning algorithm using TF 2.0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = 1000\n",
+    "\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "generated_inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "generated_targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "np.savez('TF_intro', inputs=generated_inputs, targets=generated_targets)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:python3.7_TF2.0]",
+   "language": "python",
+   "name": "conda-env-python3.7_TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part2.ipynb b/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part2.ipynb
new file mode 100644
index 0000000..00d9a97
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part2.ipynb	
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Minimal example with TensorFlow 2.0\n",
+    "In this notebook we will recreate our machine learning algorithm using TF 2.0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = 1000\n",
+    "\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "generated_inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "generated_targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "np.savez('TF_intro', inputs=generated_inputs, targets=generated_targets)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving with TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_data = np.load('TF_intro.npz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1000/1000 - 0s - loss: 24.5755\n",
+      "Epoch 2/100\n",
+      "1000/1000 - 0s - loss: 1.1773\n",
+      "Epoch 3/100\n",
+      "1000/1000 - 0s - loss: 0.4253\n",
+      "Epoch 4/100\n",
+      "1000/1000 - 0s - loss: 0.3853\n",
+      "Epoch 5/100\n",
+      "1000/1000 - 0s - loss: 0.3727\n",
+      "Epoch 6/100\n",
+      "1000/1000 - 0s - loss: 0.3932\n",
+      "Epoch 7/100\n",
+      "1000/1000 - 0s - loss: 0.3817\n",
+      "Epoch 8/100\n",
+      "1000/1000 - 0s - loss: 0.3877\n",
+      "Epoch 9/100\n",
+      "1000/1000 - 0s - loss: 0.3729\n",
+      "Epoch 10/100\n",
+      "1000/1000 - 0s - loss: 0.3982\n",
+      "Epoch 11/100\n",
+      "1000/1000 - 0s - loss: 0.3809\n",
+      "Epoch 12/100\n",
+      "1000/1000 - 0s - loss: 0.3788\n",
+      "Epoch 13/100\n",
+      "1000/1000 - 0s - loss: 0.3714\n",
+      "Epoch 14/100\n",
+      "1000/1000 - 0s - loss: 0.3608\n",
+      "Epoch 15/100\n",
+      "1000/1000 - 0s - loss: 0.3507\n",
+      "Epoch 16/100\n",
+      "1000/1000 - 0s - loss: 0.3918\n",
+      "Epoch 17/100\n",
+      "1000/1000 - 0s - loss: 0.3697\n",
+      "Epoch 18/100\n",
+      "1000/1000 - 0s - loss: 0.3811\n",
+      "Epoch 19/100\n",
+      "1000/1000 - 0s - loss: 0.3781\n",
+      "Epoch 20/100\n",
+      "1000/1000 - 0s - loss: 0.3974\n",
+      "Epoch 21/100\n",
+      "1000/1000 - 0s - loss: 0.3974\n",
+      "Epoch 22/100\n",
+      "1000/1000 - 0s - loss: 0.3724\n",
+      "Epoch 23/100\n",
+      "1000/1000 - 0s - loss: 0.3561\n",
+      "Epoch 24/100\n",
+      "1000/1000 - 0s - loss: 0.3691\n",
+      "Epoch 25/100\n",
+      "1000/1000 - 0s - loss: 0.3650\n",
+      "Epoch 26/100\n",
+      "1000/1000 - 0s - loss: 0.3569\n",
+      "Epoch 27/100\n",
+      "1000/1000 - 0s - loss: 0.3707\n",
+      "Epoch 28/100\n",
+      "1000/1000 - 0s - loss: 0.4100\n",
+      "Epoch 29/100\n",
+      "1000/1000 - 0s - loss: 0.3703\n",
+      "Epoch 30/100\n",
+      "1000/1000 - 0s - loss: 0.3598\n",
+      "Epoch 31/100\n",
+      "1000/1000 - 0s - loss: 0.3775\n",
+      "Epoch 32/100\n",
+      "1000/1000 - 0s - loss: 0.3936\n",
+      "Epoch 33/100\n",
+      "1000/1000 - 0s - loss: 0.3968\n",
+      "Epoch 34/100\n",
+      "1000/1000 - 0s - loss: 0.3614\n",
+      "Epoch 35/100\n",
+      "1000/1000 - 0s - loss: 0.3588\n",
+      "Epoch 36/100\n",
+      "1000/1000 - 0s - loss: 0.3777\n",
+      "Epoch 37/100\n",
+      "1000/1000 - 0s - loss: 0.3637\n",
+      "Epoch 38/100\n",
+      "1000/1000 - 0s - loss: 0.3662\n",
+      "Epoch 39/100\n",
+      "1000/1000 - 0s - loss: 0.3655\n",
+      "Epoch 40/100\n",
+      "1000/1000 - 0s - loss: 0.3582\n",
+      "Epoch 41/100\n",
+      "1000/1000 - 0s - loss: 0.3759\n",
+      "Epoch 42/100\n",
+      "1000/1000 - 0s - loss: 0.4468\n",
+      "Epoch 43/100\n",
+      "1000/1000 - 0s - loss: 0.3613\n",
+      "Epoch 44/100\n",
+      "1000/1000 - 0s - loss: 0.3905\n",
+      "Epoch 45/100\n",
+      "1000/1000 - 0s - loss: 0.3825\n",
+      "Epoch 46/100\n",
+      "1000/1000 - 0s - loss: 0.3810\n",
+      "Epoch 47/100\n",
+      "1000/1000 - 0s - loss: 0.3546\n",
+      "Epoch 48/100\n",
+      "1000/1000 - 0s - loss: 0.3520\n",
+      "Epoch 49/100\n",
+      "1000/1000 - 0s - loss: 0.3878\n",
+      "Epoch 50/100\n",
+      "1000/1000 - 0s - loss: 0.3748\n",
+      "Epoch 51/100\n",
+      "1000/1000 - 0s - loss: 0.3978\n",
+      "Epoch 52/100\n",
+      "1000/1000 - 0s - loss: 0.3669\n",
+      "Epoch 53/100\n",
+      "1000/1000 - 0s - loss: 0.3650\n",
+      "Epoch 54/100\n",
+      "1000/1000 - 0s - loss: 0.3869\n",
+      "Epoch 55/100\n",
+      "1000/1000 - 0s - loss: 0.3952\n",
+      "Epoch 56/100\n",
+      "1000/1000 - 0s - loss: 0.3897\n",
+      "Epoch 57/100\n",
+      "1000/1000 - 0s - loss: 0.3698\n",
+      "Epoch 58/100\n",
+      "1000/1000 - 0s - loss: 0.3655\n",
+      "Epoch 59/100\n",
+      "1000/1000 - 0s - loss: 0.3717\n",
+      "Epoch 60/100\n",
+      "1000/1000 - 0s - loss: 0.3942\n",
+      "Epoch 61/100\n",
+      "1000/1000 - 0s - loss: 0.4334\n",
+      "Epoch 62/100\n",
+      "1000/1000 - 0s - loss: 0.3836\n",
+      "Epoch 63/100\n",
+      "1000/1000 - 0s - loss: 0.3631\n",
+      "Epoch 64/100\n",
+      "1000/1000 - 0s - loss: 0.3804\n",
+      "Epoch 65/100\n",
+      "1000/1000 - 0s - loss: 0.3671\n",
+      "Epoch 66/100\n",
+      "1000/1000 - 0s - loss: 0.3801\n",
+      "Epoch 67/100\n",
+      "1000/1000 - 0s - loss: 0.4032\n",
+      "Epoch 68/100\n",
+      "1000/1000 - 0s - loss: 0.3764\n",
+      "Epoch 69/100\n",
+      "1000/1000 - 0s - loss: 0.3549\n",
+      "Epoch 70/100\n",
+      "1000/1000 - 0s - loss: 0.3585\n",
+      "Epoch 71/100\n",
+      "1000/1000 - 0s - loss: 0.3747\n",
+      "Epoch 72/100\n",
+      "1000/1000 - 0s - loss: 0.3633\n",
+      "Epoch 73/100\n",
+      "1000/1000 - 0s - loss: 0.3493\n",
+      "Epoch 74/100\n",
+      "1000/1000 - 0s - loss: 0.3924\n",
+      "Epoch 75/100\n",
+      "1000/1000 - 0s - loss: 0.4246\n",
+      "Epoch 76/100\n",
+      "1000/1000 - 0s - loss: 0.3701\n",
+      "Epoch 77/100\n",
+      "1000/1000 - 0s - loss: 0.3959\n",
+      "Epoch 78/100\n",
+      "1000/1000 - 0s - loss: 0.3923\n",
+      "Epoch 79/100\n",
+      "1000/1000 - 0s - loss: 0.3587\n",
+      "Epoch 80/100\n",
+      "1000/1000 - 0s - loss: 0.3729\n",
+      "Epoch 81/100\n",
+      "1000/1000 - 0s - loss: 0.3649\n",
+      "Epoch 82/100\n",
+      "1000/1000 - 0s - loss: 0.3611\n",
+      "Epoch 83/100\n",
+      "1000/1000 - 0s - loss: 0.3701\n",
+      "Epoch 84/100\n",
+      "1000/1000 - 0s - loss: 0.3699\n",
+      "Epoch 85/100\n",
+      "1000/1000 - 0s - loss: 0.3494\n",
+      "Epoch 86/100\n",
+      "1000/1000 - 0s - loss: 0.3613\n",
+      "Epoch 87/100\n",
+      "1000/1000 - 0s - loss: 0.3933\n",
+      "Epoch 88/100\n",
+      "1000/1000 - 0s - loss: 0.4031\n",
+      "Epoch 89/100\n",
+      "1000/1000 - 0s - loss: 0.3814\n",
+      "Epoch 90/100\n",
+      "1000/1000 - 0s - loss: 0.3481\n",
+      "Epoch 91/100\n",
+      "1000/1000 - 0s - loss: 0.3664\n",
+      "Epoch 92/100\n",
+      "1000/1000 - 0s - loss: 0.3691\n",
+      "Epoch 93/100\n",
+      "1000/1000 - 0s - loss: 0.3599\n",
+      "Epoch 94/100\n",
+      "1000/1000 - 0s - loss: 0.3817\n",
+      "Epoch 95/100\n",
+      "1000/1000 - 0s - loss: 0.3572\n",
+      "Epoch 96/100\n",
+      "1000/1000 - 0s - loss: 0.3699\n",
+      "Epoch 97/100\n",
+      "1000/1000 - 0s - loss: 0.3666\n",
+      "Epoch 98/100\n",
+      "1000/1000 - 0s - loss: 0.3667\n",
+      "Epoch 99/100\n",
+      "1000/1000 - 0s - loss: 0.4198\n",
+      "Epoch 100/100\n",
+      "1000/1000 - 0s - loss: 0.3667\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x212be2c1550>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_size = 2\n",
+    "output_size = 1\n",
+    "\n",
+    "model = tf.keras.Sequential([\n",
+    "                            tf.keras.layers.Dense(output_size)\n",
+    "                            ])\n",
+    "\n",
+    "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
+    "\n",
+    "model.fit(training_data['inputs'], training_data['targets'], epochs=100, verbose=0)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:python3.7_TF2.0]",
+   "language": "python",
+   "name": "conda-env-python3.7_TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part3.ipynb b/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part3.ipynb
new file mode 100644
index 0000000..d3d6608
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Minimal_example_Part3.ipynb	
@@ -0,0 +1,2477 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Minimal example with TensorFlow 2.0\n",
+    "In this notebook we will recreate our machine learning algorithm using TF 2.0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = 1000\n",
+    "\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "generated_inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "generated_targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "np.savez('TF_intro', inputs=generated_inputs, targets=generated_targets)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving with TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_data = np.load('TF_intro.npz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1000/1000 - 0s - loss: 61.5810\n",
+      "Epoch 2/100\n",
+      "1000/1000 - 0s - loss: 21.7226\n",
+      "Epoch 3/100\n",
+      "1000/1000 - 0s - loss: 18.9159\n",
+      "Epoch 4/100\n",
+      "1000/1000 - 0s - loss: 16.6079\n",
+      "Epoch 5/100\n",
+      "1000/1000 - 0s - loss: 14.6734\n",
+      "Epoch 6/100\n",
+      "1000/1000 - 0s - loss: 12.9462\n",
+      "Epoch 7/100\n",
+      "1000/1000 - 0s - loss: 11.4140\n",
+      "Epoch 8/100\n",
+      "1000/1000 - 0s - loss: 10.0861\n",
+      "Epoch 9/100\n",
+      "1000/1000 - 0s - loss: 8.9095\n",
+      "Epoch 10/100\n",
+      "1000/1000 - 0s - loss: 7.8870\n",
+      "Epoch 11/100\n",
+      "1000/1000 - 0s - loss: 6.9847\n",
+      "Epoch 12/100\n",
+      "1000/1000 - 0s - loss: 6.1800\n",
+      "Epoch 13/100\n",
+      "1000/1000 - 0s - loss: 5.4785\n",
+      "Epoch 14/100\n",
+      "1000/1000 - 0s - loss: 4.8543\n",
+      "Epoch 15/100\n",
+      "1000/1000 - 0s - loss: 4.3176\n",
+      "Epoch 16/100\n",
+      "1000/1000 - 0s - loss: 3.8369\n",
+      "Epoch 17/100\n",
+      "1000/1000 - 0s - loss: 3.4152\n",
+      "Epoch 18/100\n",
+      "1000/1000 - 0s - loss: 3.0486\n",
+      "Epoch 19/100\n",
+      "1000/1000 - 0s - loss: 2.7196\n",
+      "Epoch 20/100\n",
+      "1000/1000 - 0s - loss: 2.4316\n",
+      "Epoch 21/100\n",
+      "1000/1000 - 0s - loss: 2.1813\n",
+      "Epoch 22/100\n",
+      "1000/1000 - 0s - loss: 1.9618\n",
+      "Epoch 23/100\n",
+      "1000/1000 - 0s - loss: 1.7645\n",
+      "Epoch 24/100\n",
+      "1000/1000 - 0s - loss: 1.5917\n",
+      "Epoch 25/100\n",
+      "1000/1000 - 0s - loss: 1.4423\n",
+      "Epoch 26/100\n",
+      "1000/1000 - 0s - loss: 1.3093\n",
+      "Epoch 27/100\n",
+      "1000/1000 - 0s - loss: 1.1921\n",
+      "Epoch 28/100\n",
+      "1000/1000 - 0s - loss: 1.0884\n",
+      "Epoch 29/100\n",
+      "1000/1000 - 0s - loss: 0.9985\n",
+      "Epoch 30/100\n",
+      "1000/1000 - 0s - loss: 0.9191\n",
+      "Epoch 31/100\n",
+      "1000/1000 - 0s - loss: 0.8493\n",
+      "Epoch 32/100\n",
+      "1000/1000 - 0s - loss: 0.7873\n",
+      "Epoch 33/100\n",
+      "1000/1000 - 0s - loss: 0.7331\n",
+      "Epoch 34/100\n",
+      "1000/1000 - 0s - loss: 0.6847\n",
+      "Epoch 35/100\n",
+      "1000/1000 - 0s - loss: 0.6428\n",
+      "Epoch 36/100\n",
+      "1000/1000 - 0s - loss: 0.6063\n",
+      "Epoch 37/100\n",
+      "1000/1000 - 0s - loss: 0.5728\n",
+      "Epoch 38/100\n",
+      "1000/1000 - 0s - loss: 0.5454\n",
+      "Epoch 39/100\n",
+      "1000/1000 - 0s - loss: 0.5193\n",
+      "Epoch 40/100\n",
+      "1000/1000 - 0s - loss: 0.4981\n",
+      "Epoch 41/100\n",
+      "1000/1000 - 0s - loss: 0.4778\n",
+      "Epoch 42/100\n",
+      "1000/1000 - 0s - loss: 0.4616\n",
+      "Epoch 43/100\n",
+      "1000/1000 - 0s - loss: 0.4469\n",
+      "Epoch 44/100\n",
+      "1000/1000 - 0s - loss: 0.4336\n",
+      "Epoch 45/100\n",
+      "1000/1000 - 0s - loss: 0.4230\n",
+      "Epoch 46/100\n",
+      "1000/1000 - 0s - loss: 0.4119\n",
+      "Epoch 47/100\n",
+      "1000/1000 - 0s - loss: 0.4026\n",
+      "Epoch 48/100\n",
+      "1000/1000 - 0s - loss: 0.3942\n",
+      "Epoch 49/100\n",
+      "1000/1000 - 0s - loss: 0.3882\n",
+      "Epoch 50/100\n",
+      "1000/1000 - 0s - loss: 0.3821\n",
+      "Epoch 51/100\n",
+      "1000/1000 - 0s - loss: 0.3767\n",
+      "Epoch 52/100\n",
+      "1000/1000 - 0s - loss: 0.3724\n",
+      "Epoch 53/100\n",
+      "1000/1000 - 0s - loss: 0.3675\n",
+      "Epoch 54/100\n",
+      "1000/1000 - 0s - loss: 0.3643\n",
+      "Epoch 55/100\n",
+      "1000/1000 - 0s - loss: 0.3613\n",
+      "Epoch 56/100\n",
+      "1000/1000 - 0s - loss: 0.3582\n",
+      "Epoch 57/100\n",
+      "1000/1000 - 0s - loss: 0.3558\n",
+      "Epoch 58/100\n",
+      "1000/1000 - 0s - loss: 0.3532\n",
+      "Epoch 59/100\n",
+      "1000/1000 - 0s - loss: 0.3525\n",
+      "Epoch 60/100\n",
+      "1000/1000 - 0s - loss: 0.3503\n",
+      "Epoch 61/100\n",
+      "1000/1000 - 0s - loss: 0.3483\n",
+      "Epoch 62/100\n",
+      "1000/1000 - 0s - loss: 0.3478\n",
+      "Epoch 63/100\n",
+      "1000/1000 - 0s - loss: 0.3463\n",
+      "Epoch 64/100\n",
+      "1000/1000 - 0s - loss: 0.3448\n",
+      "Epoch 65/100\n",
+      "1000/1000 - 0s - loss: 0.3443\n",
+      "Epoch 66/100\n",
+      "1000/1000 - 0s - loss: 0.3435\n",
+      "Epoch 67/100\n",
+      "1000/1000 - 0s - loss: 0.3427\n",
+      "Epoch 68/100\n",
+      "1000/1000 - 0s - loss: 0.3419\n",
+      "Epoch 69/100\n",
+      "1000/1000 - 0s - loss: 0.3418\n",
+      "Epoch 70/100\n",
+      "1000/1000 - 0s - loss: 0.3418\n",
+      "Epoch 71/100\n",
+      "1000/1000 - 0s - loss: 0.3407\n",
+      "Epoch 72/100\n",
+      "1000/1000 - 0s - loss: 0.3403\n",
+      "Epoch 73/100\n",
+      "1000/1000 - 0s - loss: 0.3401\n",
+      "Epoch 74/100\n",
+      "1000/1000 - 0s - loss: 0.3399\n",
+      "Epoch 75/100\n",
+      "1000/1000 - 0s - loss: 0.3397\n",
+      "Epoch 76/100\n",
+      "1000/1000 - 0s - loss: 0.3392\n",
+      "Epoch 77/100\n",
+      "1000/1000 - 0s - loss: 0.3392\n",
+      "Epoch 78/100\n",
+      "1000/1000 - 0s - loss: 0.3394\n",
+      "Epoch 79/100\n",
+      "1000/1000 - 0s - loss: 0.3394\n",
+      "Epoch 80/100\n",
+      "1000/1000 - 0s - loss: 0.3390\n",
+      "Epoch 81/100\n",
+      "1000/1000 - 0s - loss: 0.3386\n",
+      "Epoch 82/100\n",
+      "1000/1000 - 0s - loss: 0.3390\n",
+      "Epoch 83/100\n",
+      "1000/1000 - 0s - loss: 0.3383\n",
+      "Epoch 84/100\n",
+      "1000/1000 - 0s - loss: 0.3386\n",
+      "Epoch 85/100\n",
+      "1000/1000 - 0s - loss: 0.3387\n",
+      "Epoch 86/100\n",
+      "1000/1000 - 0s - loss: 0.3382\n",
+      "Epoch 87/100\n",
+      "1000/1000 - 0s - loss: 0.3381\n",
+      "Epoch 88/100\n",
+      "1000/1000 - 0s - loss: 0.3389\n",
+      "Epoch 89/100\n",
+      "1000/1000 - 0s - loss: 0.3382\n",
+      "Epoch 90/100\n",
+      "1000/1000 - 0s - loss: 0.3382\n",
+      "Epoch 91/100\n",
+      "1000/1000 - 0s - loss: 0.3383\n",
+      "Epoch 92/100\n",
+      "1000/1000 - 0s - loss: 0.3380\n",
+      "Epoch 93/100\n",
+      "1000/1000 - 0s - loss: 0.3380\n",
+      "Epoch 94/100\n",
+      "1000/1000 - 0s - loss: 0.3378\n",
+      "Epoch 95/100\n",
+      "1000/1000 - 0s - loss: 0.3384\n",
+      "Epoch 96/100\n",
+      "1000/1000 - 0s - loss: 0.3378\n",
+      "Epoch 97/100\n",
+      "1000/1000 - 0s - loss: 0.3380\n",
+      "Epoch 98/100\n",
+      "1000/1000 - 0s - loss: 0.3381\n",
+      "Epoch 99/100\n",
+      "1000/1000 - 0s - loss: 0.3381\n",
+      "Epoch 100/100\n",
+      "1000/1000 - 0s - loss: 0.3377\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1a4ef3d79e8>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_size = 2\n",
+    "output_size = 1\n",
+    "\n",
+    "model = tf.keras.Sequential([\n",
+    "                            tf.keras.layers.Dense(output_size)\n",
+    "                            ])\n",
+    "\n",
+    "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
+    "\n",
+    "model.fit(training_data['inputs'], training_data['targets'], epochs=100, verbose=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[ 1.9980499],\n",
+       "        [-3.004382 ]], dtype=float32), array([5.0288405], dtype=float32)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.layers[0].get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.9980499],\n",
+       "       [-3.004382 ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights = model.layers[0].get_weights()[0]\n",
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5.0288405], dtype=float32)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bias = model.layers[0].get_weights()[1]\n",
+    "bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the outputs (make predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ -1.1],\n",
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+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict_on_batch(training_data['inputs']).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "       [-14. ],\n",
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+       "       [ 16.8],\n",
+       "       [ 34.5],\n",
+       "       [  2. ],\n",
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+       "       [ 35.3],\n",
+       "       [-18.4],\n",
+       "       [-18.7],\n",
+       "       [-11. ],\n",
+       "       [-22. ],\n",
+       "       [ -4. ],\n",
+       "       [ 32.2],\n",
+       "       [ -7.2],\n",
+       "       [-14.8],\n",
+       "       [ 23.5],\n",
+       "       [-32.4],\n",
+       "       [-21.3],\n",
+       "       [-34.4],\n",
+       "       [  9.7],\n",
+       "       [  7.8],\n",
+       "       [ 30.2],\n",
+       "       [-35.3],\n",
+       "       [  9.5],\n",
+       "       [-27.4],\n",
+       "       [ -3.8],\n",
+       "       [-24.7],\n",
+       "       [ 20.3],\n",
+       "       [ -1. ],\n",
+       "       [-20.7]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_data['targets'].round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.squeeze(model.predict_on_batch(training_data['inputs'])), np.squeeze(training_data['targets']))\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:python3.7_TF2.0]",
+   "language": "python",
+   "name": "conda-env-python3.7_TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/TensorFlow_Minimal_example_complete.ipynb b/Udemy DataScience Workbook/TensorFlow_Minimal_example_complete.ipynb
new file mode 100644
index 0000000..00f5062
--- /dev/null
+++ b/Udemy DataScience Workbook/TensorFlow_Minimal_example_complete.ipynb	
@@ -0,0 +1,2482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Minimal example with TensorFlow 2.0\n",
+    "In this notebook we will recreate our machine learning algorithm using TF 2.0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = 1000\n",
+    "\n",
+    "xs = np.random.uniform(low=-10, high=10, size=(observations,1))\n",
+    "zs = np.random.uniform(-10, 10, (observations,1))\n",
+    "\n",
+    "generated_inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "noise = np.random.uniform(-1, 1, (observations,1))\n",
+    "\n",
+    "generated_targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "np.savez('TF_intro', inputs=generated_inputs, targets=generated_targets)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving with TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_data = np.load('TF_intro.npz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1000/1000 - 0s - loss: 24.5755\n",
+      "Epoch 2/100\n",
+      "1000/1000 - 0s - loss: 1.1773\n",
+      "Epoch 3/100\n",
+      "1000/1000 - 0s - loss: 0.4253\n",
+      "Epoch 4/100\n",
+      "1000/1000 - 0s - loss: 0.3853\n",
+      "Epoch 5/100\n",
+      "1000/1000 - 0s - loss: 0.3727\n",
+      "Epoch 6/100\n",
+      "1000/1000 - 0s - loss: 0.3932\n",
+      "Epoch 7/100\n",
+      "1000/1000 - 0s - loss: 0.3817\n",
+      "Epoch 8/100\n",
+      "1000/1000 - 0s - loss: 0.3877\n",
+      "Epoch 9/100\n",
+      "1000/1000 - 0s - loss: 0.3729\n",
+      "Epoch 10/100\n",
+      "1000/1000 - 0s - loss: 0.3982\n",
+      "Epoch 11/100\n",
+      "1000/1000 - 0s - loss: 0.3809\n",
+      "Epoch 12/100\n",
+      "1000/1000 - 0s - loss: 0.3788\n",
+      "Epoch 13/100\n",
+      "1000/1000 - 0s - loss: 0.3714\n",
+      "Epoch 14/100\n",
+      "1000/1000 - 0s - loss: 0.3608\n",
+      "Epoch 15/100\n",
+      "1000/1000 - 0s - loss: 0.3507\n",
+      "Epoch 16/100\n",
+      "1000/1000 - 0s - loss: 0.3918\n",
+      "Epoch 17/100\n",
+      "1000/1000 - 0s - loss: 0.3697\n",
+      "Epoch 18/100\n",
+      "1000/1000 - 0s - loss: 0.3811\n",
+      "Epoch 19/100\n",
+      "1000/1000 - 0s - loss: 0.3781\n",
+      "Epoch 20/100\n",
+      "1000/1000 - 0s - loss: 0.3974\n",
+      "Epoch 21/100\n",
+      "1000/1000 - 0s - loss: 0.3974\n",
+      "Epoch 22/100\n",
+      "1000/1000 - 0s - loss: 0.3724\n",
+      "Epoch 23/100\n",
+      "1000/1000 - 0s - loss: 0.3561\n",
+      "Epoch 24/100\n",
+      "1000/1000 - 0s - loss: 0.3691\n",
+      "Epoch 25/100\n",
+      "1000/1000 - 0s - loss: 0.3650\n",
+      "Epoch 26/100\n",
+      "1000/1000 - 0s - loss: 0.3569\n",
+      "Epoch 27/100\n",
+      "1000/1000 - 0s - loss: 0.3707\n",
+      "Epoch 28/100\n",
+      "1000/1000 - 0s - loss: 0.4100\n",
+      "Epoch 29/100\n",
+      "1000/1000 - 0s - loss: 0.3703\n",
+      "Epoch 30/100\n",
+      "1000/1000 - 0s - loss: 0.3598\n",
+      "Epoch 31/100\n",
+      "1000/1000 - 0s - loss: 0.3775\n",
+      "Epoch 32/100\n",
+      "1000/1000 - 0s - loss: 0.3936\n",
+      "Epoch 33/100\n",
+      "1000/1000 - 0s - loss: 0.3968\n",
+      "Epoch 34/100\n",
+      "1000/1000 - 0s - loss: 0.3614\n",
+      "Epoch 35/100\n",
+      "1000/1000 - 0s - loss: 0.3588\n",
+      "Epoch 36/100\n",
+      "1000/1000 - 0s - loss: 0.3777\n",
+      "Epoch 37/100\n",
+      "1000/1000 - 0s - loss: 0.3637\n",
+      "Epoch 38/100\n",
+      "1000/1000 - 0s - loss: 0.3662\n",
+      "Epoch 39/100\n",
+      "1000/1000 - 0s - loss: 0.3655\n",
+      "Epoch 40/100\n",
+      "1000/1000 - 0s - loss: 0.3582\n",
+      "Epoch 41/100\n",
+      "1000/1000 - 0s - loss: 0.3759\n",
+      "Epoch 42/100\n",
+      "1000/1000 - 0s - loss: 0.4468\n",
+      "Epoch 43/100\n",
+      "1000/1000 - 0s - loss: 0.3613\n",
+      "Epoch 44/100\n",
+      "1000/1000 - 0s - loss: 0.3905\n",
+      "Epoch 45/100\n",
+      "1000/1000 - 0s - loss: 0.3825\n",
+      "Epoch 46/100\n",
+      "1000/1000 - 0s - loss: 0.3810\n",
+      "Epoch 47/100\n",
+      "1000/1000 - 0s - loss: 0.3546\n",
+      "Epoch 48/100\n",
+      "1000/1000 - 0s - loss: 0.3520\n",
+      "Epoch 49/100\n",
+      "1000/1000 - 0s - loss: 0.3878\n",
+      "Epoch 50/100\n",
+      "1000/1000 - 0s - loss: 0.3748\n",
+      "Epoch 51/100\n",
+      "1000/1000 - 0s - loss: 0.3978\n",
+      "Epoch 52/100\n",
+      "1000/1000 - 0s - loss: 0.3669\n",
+      "Epoch 53/100\n",
+      "1000/1000 - 0s - loss: 0.3650\n",
+      "Epoch 54/100\n",
+      "1000/1000 - 0s - loss: 0.3869\n",
+      "Epoch 55/100\n",
+      "1000/1000 - 0s - loss: 0.3952\n",
+      "Epoch 56/100\n",
+      "1000/1000 - 0s - loss: 0.3897\n",
+      "Epoch 57/100\n",
+      "1000/1000 - 0s - loss: 0.3698\n",
+      "Epoch 58/100\n",
+      "1000/1000 - 0s - loss: 0.3655\n",
+      "Epoch 59/100\n",
+      "1000/1000 - 0s - loss: 0.3717\n",
+      "Epoch 60/100\n",
+      "1000/1000 - 0s - loss: 0.3942\n",
+      "Epoch 61/100\n",
+      "1000/1000 - 0s - loss: 0.4334\n",
+      "Epoch 62/100\n",
+      "1000/1000 - 0s - loss: 0.3836\n",
+      "Epoch 63/100\n",
+      "1000/1000 - 0s - loss: 0.3631\n",
+      "Epoch 64/100\n",
+      "1000/1000 - 0s - loss: 0.3804\n",
+      "Epoch 65/100\n",
+      "1000/1000 - 0s - loss: 0.3671\n",
+      "Epoch 66/100\n",
+      "1000/1000 - 0s - loss: 0.3801\n",
+      "Epoch 67/100\n",
+      "1000/1000 - 0s - loss: 0.4032\n",
+      "Epoch 68/100\n",
+      "1000/1000 - 0s - loss: 0.3764\n",
+      "Epoch 69/100\n",
+      "1000/1000 - 0s - loss: 0.3549\n",
+      "Epoch 70/100\n",
+      "1000/1000 - 0s - loss: 0.3585\n",
+      "Epoch 71/100\n",
+      "1000/1000 - 0s - loss: 0.3747\n",
+      "Epoch 72/100\n",
+      "1000/1000 - 0s - loss: 0.3633\n",
+      "Epoch 73/100\n",
+      "1000/1000 - 0s - loss: 0.3493\n",
+      "Epoch 74/100\n",
+      "1000/1000 - 0s - loss: 0.3924\n",
+      "Epoch 75/100\n",
+      "1000/1000 - 0s - loss: 0.4246\n",
+      "Epoch 76/100\n",
+      "1000/1000 - 0s - loss: 0.3701\n",
+      "Epoch 77/100\n",
+      "1000/1000 - 0s - loss: 0.3959\n",
+      "Epoch 78/100\n",
+      "1000/1000 - 0s - loss: 0.3923\n",
+      "Epoch 79/100\n",
+      "1000/1000 - 0s - loss: 0.3587\n",
+      "Epoch 80/100\n",
+      "1000/1000 - 0s - loss: 0.3729\n",
+      "Epoch 81/100\n",
+      "1000/1000 - 0s - loss: 0.3649\n",
+      "Epoch 82/100\n",
+      "1000/1000 - 0s - loss: 0.3611\n",
+      "Epoch 83/100\n",
+      "1000/1000 - 0s - loss: 0.3701\n",
+      "Epoch 84/100\n",
+      "1000/1000 - 0s - loss: 0.3699\n",
+      "Epoch 85/100\n",
+      "1000/1000 - 0s - loss: 0.3494\n",
+      "Epoch 86/100\n",
+      "1000/1000 - 0s - loss: 0.3613\n",
+      "Epoch 87/100\n",
+      "1000/1000 - 0s - loss: 0.3933\n",
+      "Epoch 88/100\n",
+      "1000/1000 - 0s - loss: 0.4031\n",
+      "Epoch 89/100\n",
+      "1000/1000 - 0s - loss: 0.3814\n",
+      "Epoch 90/100\n",
+      "1000/1000 - 0s - loss: 0.3481\n",
+      "Epoch 91/100\n",
+      "1000/1000 - 0s - loss: 0.3664\n",
+      "Epoch 92/100\n",
+      "1000/1000 - 0s - loss: 0.3691\n",
+      "Epoch 93/100\n",
+      "1000/1000 - 0s - loss: 0.3599\n",
+      "Epoch 94/100\n",
+      "1000/1000 - 0s - loss: 0.3817\n",
+      "Epoch 95/100\n",
+      "1000/1000 - 0s - loss: 0.3572\n",
+      "Epoch 96/100\n",
+      "1000/1000 - 0s - loss: 0.3699\n",
+      "Epoch 97/100\n",
+      "1000/1000 - 0s - loss: 0.3666\n",
+      "Epoch 98/100\n",
+      "1000/1000 - 0s - loss: 0.3667\n",
+      "Epoch 99/100\n",
+      "1000/1000 - 0s - loss: 0.4198\n",
+      "Epoch 100/100\n",
+      "1000/1000 - 0s - loss: 0.3667\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x212be2c1550>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_size = 2\n",
+    "output_size = 1\n",
+    "\n",
+    "model = tf.keras.Sequential([\n",
+    "                            tf.keras.layers.Dense(output_size,\n",
+    "                                                 kernel_initializer=tf.random_uniform_initializer(minval=-0.1, maxval=0.1),\n",
+    "                                                 bias_initializer=tf.random_uniform_initializer(minval=-0.1, maxval=0.1)\n",
+    "                                                 )\n",
+    "                            ])\n",
+    "\n",
+    "custom_optimizer = tf.keras.optimizers.SGD(learning_rate=0.02)\n",
+    "\n",
+    "model.compile(optimizer=custom_optimizer, loss='mean_squared_error')\n",
+    "\n",
+    "model.fit(training_data['inputs'], training_data['targets'], epochs=100, verbose=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[ 1.9776261],\n",
+       "        [-2.9764102]], dtype=float32), array([4.975083], dtype=float32)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.layers[0].get_weights()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.9776261],\n",
+       "       [-2.9764102]], dtype=float32)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights = model.layers[0].get_weights()[0]\n",
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.975083], dtype=float32)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bias = model.layers[0].get_weights()[1]\n",
+    "bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the outputs (make predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ -9.8],\n",
+       "       [  9.9],\n",
+       "       [ -6. ],\n",
+       "       [ 17.9],\n",
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+       "       [ 22.8],\n",
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+       "       [-10. ],\n",
+       "       [ 34.2],\n",
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+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
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+    "model.predict_on_batch(training_data['inputs']).round(1)"
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+  },
+  {
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+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
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+       "       [ 13.8],\n",
+       "       [-24.5],\n",
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+       "       [ 29.5],\n",
+       "       [-25.3],\n",
+       "       [  1.1],\n",
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+       "       [ -8.2],\n",
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+       "       [ -1.2],\n",
+       "       [ 17.2],\n",
+       "       [ -2.3],\n",
+       "       [-13.7],\n",
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+       "       [ 10.2],\n",
+       "       [ 21.1],\n",
+       "       [ 22.2],\n",
+       "       [ -4.3],\n",
+       "       [  7.9],\n",
+       "       [ 22.8],\n",
+       "       [  4.9],\n",
+       "       [ 28.7],\n",
+       "       [-18.8],\n",
+       "       [  1.6],\n",
+       "       [ 39.9]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_data['targets'].round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.squeeze(model.predict_on_batch(training_data['inputs'])), np.squeeze(training_data['targets']))\n",
+    "plt.xlabel('outputs')\n",
+    "plt.ylabel('targets')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:python3.7_TF2.0]",
+   "language": "python",
+   "name": "conda-env-python3.7_TF2.0-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Tensor_minimal_exercise (2).ipynb b/Udemy DataScience Workbook/Tensor_minimal_exercise (2).ipynb
new file mode 100644
index 0000000..91fd6a2
--- /dev/null
+++ b/Udemy DataScience Workbook/Tensor_minimal_exercise (2).ipynb	
@@ -0,0 +1,516 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using the same code as before, please solve the following exercises\n",
+    "    1. Change the number of observations to 100,000 and see what happens.\n",
+    "    2. Play around with the learning rate. Values like 0.0001, 0.001, 0.1, 1 are all interesting to observe. \n",
+    "    3. Change the loss function. An alternative loss for regressions is the Huber loss. \n",
+    "    The Huber loss is more appropriate than the L2-norm when we have outliers, as it is less sensitive to them (in our example we don't have outliers, but you will surely stumble upon a dataset with outliers in the future). The L2-norm loss puts all differences *to the square*, so outliers have a lot of influence on the outcome. \n",
+    "    The proper syntax of the Huber loss is 'huber_loss'\n",
+    "    \n",
+    "    \n",
+    "Useful tip: When you change something, don't forget to RERUN all cells. This can be done easily by clicking:\n",
+    "Kernel -> Restart & Run All\n",
+    "If you don't do that, your algorithm will keep the OLD values of all parameters.\n",
+    "\n",
+    "You can either use this file for all the exercises, or check the solutions of EACH ONE of them in the separate files we have provided. All other files are solutions of each problem. If you feel confident enough, you can simply change values in this file. Please note that it will be nice, if you return the file to starting position after you have solved a problem, so you can use the lecture as a basis for comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DAta generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = 10000\n",
+    "\n",
+    "xs = np.random.uniform(low=-10,high=10,size=(observations,1))\n",
+    "zs = np.random.uniform(-10,10,(observations,1))\n",
+    "\n",
+    "generated_inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "noise = np.random.uniform(-1,1,(observations,1))\n",
+    "\n",
+    "generated_targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "np.savez('TF_intro', inputs = generated_inputs, targets = generated_targets)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SOlving with TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_data = np.load('TF_intro.npz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "313/313 - 3s - loss: 4.1913 - 3s/epoch - 10ms/step\n",
+      "Epoch 2/100\n",
+      "313/313 - 1s - loss: 0.3420 - 1s/epoch - 4ms/step\n",
+      "Epoch 3/100\n",
+      "313/313 - 1s - loss: 0.3423 - 1s/epoch - 4ms/step\n",
+      "Epoch 4/100\n",
+      "313/313 - 1s - loss: 0.3447 - 1s/epoch - 4ms/step\n",
+      "Epoch 5/100\n",
+      "313/313 - 1s - loss: 0.3443 - 1s/epoch - 4ms/step\n",
+      "Epoch 6/100\n",
+      "313/313 - 2s - loss: 0.3437 - 2s/epoch - 5ms/step\n",
+      "Epoch 7/100\n",
+      "313/313 - 1s - loss: 0.3451 - 1s/epoch - 4ms/step\n",
+      "Epoch 8/100\n",
+      "313/313 - 1s - loss: 0.3437 - 1s/epoch - 3ms/step\n",
+      "Epoch 9/100\n",
+      "313/313 - 1s - loss: 0.3418 - 855ms/epoch - 3ms/step\n",
+      "Epoch 10/100\n",
+      "313/313 - 1s - loss: 0.3450 - 898ms/epoch - 3ms/step\n",
+      "Epoch 11/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 4ms/step\n",
+      "Epoch 12/100\n",
+      "313/313 - 1s - loss: 0.3435 - 1s/epoch - 4ms/step\n",
+      "Epoch 13/100\n",
+      "313/313 - 1s - loss: 0.3413 - 1s/epoch - 4ms/step\n",
+      "Epoch 14/100\n",
+      "313/313 - 1s - loss: 0.3438 - 1s/epoch - 4ms/step\n",
+      "Epoch 15/100\n",
+      "313/313 - 1s - loss: 0.3448 - 1s/epoch - 4ms/step\n",
+      "Epoch 16/100\n",
+      "313/313 - 1s - loss: 0.3430 - 1s/epoch - 4ms/step\n",
+      "Epoch 17/100\n",
+      "313/313 - 1s - loss: 0.3457 - 1s/epoch - 4ms/step\n",
+      "Epoch 18/100\n",
+      "313/313 - 1s - loss: 0.3459 - 1s/epoch - 4ms/step\n",
+      "Epoch 19/100\n",
+      "313/313 - 2s - loss: 0.3429 - 2s/epoch - 5ms/step\n",
+      "Epoch 20/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 4ms/step\n",
+      "Epoch 21/100\n",
+      "313/313 - 1s - loss: 0.3442 - 1s/epoch - 4ms/step\n",
+      "Epoch 22/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 4ms/step\n",
+      "Epoch 23/100\n",
+      "313/313 - 1s - loss: 0.3443 - 1s/epoch - 4ms/step\n",
+      "Epoch 24/100\n",
+      "313/313 - 1s - loss: 0.3450 - 1s/epoch - 4ms/step\n",
+      "Epoch 25/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 4ms/step\n",
+      "Epoch 26/100\n",
+      "313/313 - 1s - loss: 0.3453 - 1s/epoch - 5ms/step\n",
+      "Epoch 27/100\n",
+      "313/313 - 1s - loss: 0.3415 - 1s/epoch - 4ms/step\n",
+      "Epoch 28/100\n",
+      "313/313 - 1s - loss: 0.3448 - 1s/epoch - 4ms/step\n",
+      "Epoch 29/100\n",
+      "313/313 - 1s - loss: 0.3436 - 1s/epoch - 4ms/step\n",
+      "Epoch 30/100\n",
+      "313/313 - 1s - loss: 0.3448 - 1s/epoch - 4ms/step\n",
+      "Epoch 31/100\n",
+      "313/313 - 1s - loss: 0.3433 - 1s/epoch - 5ms/step\n",
+      "Epoch 32/100\n",
+      "313/313 - 1s - loss: 0.3435 - 1s/epoch - 4ms/step\n",
+      "Epoch 33/100\n",
+      "313/313 - 1s - loss: 0.3435 - 1s/epoch - 4ms/step\n",
+      "Epoch 34/100\n",
+      "313/313 - 1s - loss: 0.3435 - 1s/epoch - 5ms/step\n",
+      "Epoch 35/100\n",
+      "313/313 - 1s - loss: 0.3437 - 1s/epoch - 5ms/step\n",
+      "Epoch 36/100\n",
+      "313/313 - 2s - loss: 0.3451 - 2s/epoch - 6ms/step\n",
+      "Epoch 37/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 4ms/step\n",
+      "Epoch 38/100\n",
+      "313/313 - 1s - loss: 0.3465 - 1s/epoch - 4ms/step\n",
+      "Epoch 39/100\n",
+      "313/313 - 1s - loss: 0.3436 - 1s/epoch - 4ms/step\n",
+      "Epoch 40/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 4ms/step\n",
+      "Epoch 41/100\n",
+      "313/313 - 1s - loss: 0.3427 - 1s/epoch - 4ms/step\n",
+      "Epoch 42/100\n",
+      "313/313 - 1s - loss: 0.3439 - 1s/epoch - 4ms/step\n",
+      "Epoch 43/100\n",
+      "313/313 - 1s - loss: 0.3437 - 1s/epoch - 4ms/step\n",
+      "Epoch 44/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 4ms/step\n",
+      "Epoch 45/100\n",
+      "313/313 - 1s - loss: 0.3423 - 1s/epoch - 4ms/step\n",
+      "Epoch 46/100\n",
+      "313/313 - 1s - loss: 0.3416 - 1s/epoch - 4ms/step\n",
+      "Epoch 47/100\n",
+      "313/313 - 1s - loss: 0.3413 - 1s/epoch - 4ms/step\n",
+      "Epoch 48/100\n",
+      "313/313 - 1s - loss: 0.3446 - 1s/epoch - 4ms/step\n",
+      "Epoch 49/100\n",
+      "313/313 - 1s - loss: 0.3449 - 1s/epoch - 4ms/step\n",
+      "Epoch 50/100\n",
+      "313/313 - 1s - loss: 0.3444 - 1s/epoch - 4ms/step\n",
+      "Epoch 51/100\n",
+      "313/313 - 1s - loss: 0.3446 - 1s/epoch - 4ms/step\n",
+      "Epoch 52/100\n",
+      "313/313 - 1s - loss: 0.3420 - 1s/epoch - 3ms/step\n",
+      "Epoch 53/100\n",
+      "313/313 - 1s - loss: 0.3450 - 1s/epoch - 4ms/step\n",
+      "Epoch 54/100\n",
+      "313/313 - 1s - loss: 0.3412 - 1s/epoch - 4ms/step\n",
+      "Epoch 55/100\n",
+      "313/313 - 1s - loss: 0.3456 - 1s/epoch - 4ms/step\n",
+      "Epoch 56/100\n",
+      "313/313 - 1s - loss: 0.3448 - 1s/epoch - 3ms/step\n",
+      "Epoch 57/100\n",
+      "313/313 - 1s - loss: 0.3428 - 1s/epoch - 4ms/step\n",
+      "Epoch 58/100\n",
+      "313/313 - 1s - loss: 0.3441 - 1s/epoch - 4ms/step\n",
+      "Epoch 59/100\n",
+      "313/313 - 1s - loss: 0.3435 - 1s/epoch - 4ms/step\n",
+      "Epoch 60/100\n",
+      "313/313 - 1s - loss: 0.3441 - 1s/epoch - 4ms/step\n",
+      "Epoch 61/100\n",
+      "313/313 - 1s - loss: 0.3425 - 1s/epoch - 3ms/step\n",
+      "Epoch 62/100\n",
+      "313/313 - 1s - loss: 0.3444 - 1s/epoch - 4ms/step\n",
+      "Epoch 63/100\n",
+      "313/313 - 1s - loss: 0.3446 - 1s/epoch - 3ms/step\n",
+      "Epoch 64/100\n",
+      "313/313 - 1s - loss: 0.3437 - 1s/epoch - 3ms/step\n",
+      "Epoch 65/100\n",
+      "313/313 - 1s - loss: 0.3426 - 1s/epoch - 4ms/step\n",
+      "Epoch 66/100\n",
+      "313/313 - 1s - loss: 0.3433 - 1s/epoch - 4ms/step\n",
+      "Epoch 67/100\n",
+      "313/313 - 1s - loss: 0.3454 - 1s/epoch - 4ms/step\n",
+      "Epoch 68/100\n",
+      "313/313 - 1s - loss: 0.3435 - 1s/epoch - 4ms/step\n",
+      "Epoch 69/100\n",
+      "313/313 - 1s - loss: 0.3429 - 1s/epoch - 4ms/step\n",
+      "Epoch 70/100\n",
+      "313/313 - 1s - loss: 0.3459 - 1s/epoch - 3ms/step\n",
+      "Epoch 71/100\n",
+      "313/313 - 1s - loss: 0.3422 - 1s/epoch - 3ms/step\n",
+      "Epoch 72/100\n",
+      "313/313 - 1s - loss: 0.3427 - 1s/epoch - 4ms/step\n",
+      "Epoch 73/100\n",
+      "313/313 - 1s - loss: 0.3439 - 1s/epoch - 4ms/step\n",
+      "Epoch 74/100\n",
+      "313/313 - 1s - loss: 0.3460 - 1s/epoch - 3ms/step\n",
+      "Epoch 75/100\n",
+      "313/313 - 1s - loss: 0.3424 - 1s/epoch - 3ms/step\n",
+      "Epoch 76/100\n",
+      "313/313 - 1s - loss: 0.3442 - 1s/epoch - 4ms/step\n",
+      "Epoch 77/100\n",
+      "313/313 - 1s - loss: 0.3438 - 1s/epoch - 3ms/step\n",
+      "Epoch 78/100\n",
+      "313/313 - 1s - loss: 0.3417 - 1s/epoch - 4ms/step\n",
+      "Epoch 79/100\n",
+      "313/313 - 1s - loss: 0.3445 - 1s/epoch - 4ms/step\n",
+      "Epoch 80/100\n",
+      "313/313 - 1s - loss: 0.3428 - 1s/epoch - 4ms/step\n",
+      "Epoch 81/100\n",
+      "313/313 - 1s - loss: 0.3429 - 1s/epoch - 4ms/step\n",
+      "Epoch 82/100\n",
+      "313/313 - 1s - loss: 0.3434 - 1s/epoch - 3ms/step\n",
+      "Epoch 83/100\n",
+      "313/313 - 1s - loss: 0.3432 - 1s/epoch - 3ms/step\n",
+      "Epoch 84/100\n",
+      "313/313 - 1s - loss: 0.3432 - 1s/epoch - 4ms/step\n",
+      "Epoch 85/100\n",
+      "313/313 - 1s - loss: 0.3428 - 1s/epoch - 3ms/step\n",
+      "Epoch 86/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 3ms/step\n",
+      "Epoch 87/100\n",
+      "313/313 - 1s - loss: 0.3433 - 1s/epoch - 4ms/step\n",
+      "Epoch 88/100\n",
+      "313/313 - 1s - loss: 0.3421 - 1s/epoch - 4ms/step\n",
+      "Epoch 89/100\n",
+      "313/313 - 1s - loss: 0.3422 - 1s/epoch - 4ms/step\n",
+      "Epoch 90/100\n",
+      "313/313 - 1s - loss: 0.3439 - 1s/epoch - 4ms/step\n",
+      "Epoch 91/100\n",
+      "313/313 - 1s - loss: 0.3465 - 1s/epoch - 3ms/step\n",
+      "Epoch 92/100\n",
+      "313/313 - 1s - loss: 0.3444 - 1s/epoch - 3ms/step\n",
+      "Epoch 93/100\n",
+      "313/313 - 1s - loss: 0.3451 - 1s/epoch - 4ms/step\n",
+      "Epoch 94/100\n",
+      "313/313 - 1s - loss: 0.3432 - 1s/epoch - 4ms/step\n",
+      "Epoch 95/100\n",
+      "313/313 - 1s - loss: 0.3448 - 1s/epoch - 4ms/step\n",
+      "Epoch 96/100\n",
+      "313/313 - 1s - loss: 0.3431 - 1s/epoch - 3ms/step\n",
+      "Epoch 97/100\n",
+      "313/313 - 1s - loss: 0.3435 - 1s/epoch - 3ms/step\n",
+      "Epoch 98/100\n",
+      "313/313 - 1s - loss: 0.3425 - 1s/epoch - 4ms/step\n",
+      "Epoch 99/100\n",
+      "313/313 - 1s - loss: 0.3436 - 1s/epoch - 3ms/step\n",
+      "Epoch 100/100\n",
+      "313/313 - 1s - loss: 0.3454 - 1s/epoch - 3ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x4893694090>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_size = 2 # xs & zs\n",
+    "output_size = 1 # y\n",
+    "\n",
+    "model = tf.keras.Sequential([\n",
+    "                            tf.keras.layers.Dense(output_size, \n",
+    "                                                  kernel_initializer=tf.random_uniform_initializer(minval=-0.1),\n",
+    "                                                  bias_initializer=tf.random_uniform_initializer(minval=-0.1,maxval=0.1)\n",
+    "                                                 )\n",
+    "        ])\n",
+    "custom_optimizer = tf.keras.optimizers.SGD(learning_rate= 0.01)\n",
+    "model.compile(optimizer = custom_optimizer, loss = 'mean_squared_error')\n",
+    "\n",
+    "model.fit(training_data['inputs'], training_data['targets'], epochs = 100, verbose= 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[ 1.9987564],\n",
+       "        [-3.0218806]], dtype=float32),\n",
+       " array([4.997692], dtype=float32)]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.layers[0].get_weights()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.9987564],\n",
+       "       [-3.0218806]], dtype=float32)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights = model.layers[0].get_weights()[0]\n",
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.997692], dtype=float32)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bias = model.layers[0].get_weights()[1]\n",
+    "bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract the output (make predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-21.4],\n",
+       "       [ 31.1],\n",
+       "       [  4.3],\n",
+       "       ...,\n",
+       "       [-15.7],\n",
+       "       [-32.2],\n",
+       "       [ 10.9]], dtype=float32)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict_on_batch(training_data['inputs']).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-20.9],\n",
+       "       [ 30.9],\n",
+       "       [  5.2],\n",
+       "       ...,\n",
+       "       [-15.2],\n",
+       "       [-32.8],\n",
+       "       [ 10.2]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_data['targets'].round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.squeeze(model.predict_on_batch(training_data['inputs'])), np.squeeze(training_data['targets']))\n",
+    "plt.xlabel('Outputs')\n",
+    "plt.ylabel('Targets')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Tensorflow_Example.ipynb b/Udemy DataScience Workbook/Tensorflow_Example.ipynb
new file mode 100644
index 0000000..12913e2
--- /dev/null
+++ b/Udemy DataScience Workbook/Tensorflow_Example.ipynb	
@@ -0,0 +1,2493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Minimal example with TensorFlow 2.0\n",
+    "In this notebook we will recreate our machine learning algorithm using TF 2.0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DAta generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = 1000\n",
+    "\n",
+    "xs = np.random.uniform(low=-10,high=10,size=(observations,1))\n",
+    "zs = np.random.uniform(-10,10,(observations,1))\n",
+    "\n",
+    "generated_inputs = np.column_stack((xs,zs))\n",
+    "\n",
+    "noise = np.random.uniform(-1,1,(observations,1))\n",
+    "\n",
+    "generated_targets = 2*xs - 3*zs + 5 + noise\n",
+    "\n",
+    "np.savez('TF_intro', inputs = generated_inputs, targets = generated_targets)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SOlving with TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_data = np.load('TF_intro.npz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n",
+      "Epoch 1/100\n",
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
+      "\n",
+      "32/32 - 2s - loss: 28.8627 - 2s/epoch - 61ms/step\n",
+      "Epoch 2/100\n",
+      "32/32 - 0s - loss: 1.3434 - 143ms/epoch - 4ms/step\n",
+      "Epoch 3/100\n",
+      "32/32 - 0s - loss: 0.4131 - 144ms/epoch - 5ms/step\n",
+      "Epoch 4/100\n",
+      "32/32 - 0s - loss: 0.3479 - 156ms/epoch - 5ms/step\n",
+      "Epoch 5/100\n",
+      "32/32 - 0s - loss: 0.3982 - 144ms/epoch - 5ms/step\n",
+      "Epoch 6/100\n",
+      "32/32 - 0s - loss: 0.3530 - 139ms/epoch - 4ms/step\n",
+      "Epoch 7/100\n",
+      "32/32 - 0s - loss: 0.3771 - 137ms/epoch - 4ms/step\n",
+      "Epoch 8/100\n",
+      "32/32 - 0s - loss: 0.3455 - 148ms/epoch - 5ms/step\n",
+      "Epoch 9/100\n",
+      "32/32 - 0s - loss: 0.4081 - 141ms/epoch - 4ms/step\n",
+      "Epoch 10/100\n",
+      "32/32 - 0s - loss: 0.3581 - 140ms/epoch - 4ms/step\n",
+      "Epoch 11/100\n",
+      "32/32 - 0s - loss: 0.3622 - 138ms/epoch - 4ms/step\n",
+      "Epoch 12/100\n",
+      "32/32 - 0s - loss: 0.3711 - 142ms/epoch - 4ms/step\n",
+      "Epoch 13/100\n",
+      "32/32 - 0s - loss: 0.3598 - 143ms/epoch - 4ms/step\n",
+      "Epoch 14/100\n",
+      "32/32 - 0s - loss: 0.3555 - 135ms/epoch - 4ms/step\n",
+      "Epoch 15/100\n",
+      "32/32 - 0s - loss: 0.3531 - 125ms/epoch - 4ms/step\n",
+      "Epoch 16/100\n",
+      "32/32 - 0s - loss: 0.4019 - 116ms/epoch - 4ms/step\n",
+      "Epoch 17/100\n",
+      "32/32 - 0s - loss: 0.3401 - 120ms/epoch - 4ms/step\n",
+      "Epoch 18/100\n",
+      "32/32 - 0s - loss: 0.3524 - 127ms/epoch - 4ms/step\n",
+      "Epoch 19/100\n",
+      "32/32 - 0s - loss: 0.3912 - 128ms/epoch - 4ms/step\n",
+      "Epoch 20/100\n",
+      "32/32 - 0s - loss: 0.3589 - 119ms/epoch - 4ms/step\n",
+      "Epoch 21/100\n",
+      "32/32 - 0s - loss: 0.3602 - 116ms/epoch - 4ms/step\n",
+      "Epoch 22/100\n",
+      "32/32 - 0s - loss: 0.3720 - 121ms/epoch - 4ms/step\n",
+      "Epoch 23/100\n",
+      "32/32 - 0s - loss: 0.3622 - 123ms/epoch - 4ms/step\n",
+      "Epoch 24/100\n",
+      "32/32 - 0s - loss: 0.3762 - 119ms/epoch - 4ms/step\n",
+      "Epoch 25/100\n",
+      "32/32 - 0s - loss: 0.3529 - 116ms/epoch - 4ms/step\n",
+      "Epoch 26/100\n",
+      "32/32 - 0s - loss: 0.3712 - 119ms/epoch - 4ms/step\n",
+      "Epoch 27/100\n",
+      "32/32 - 0s - loss: 0.3772 - 130ms/epoch - 4ms/step\n",
+      "Epoch 28/100\n",
+      "32/32 - 0s - loss: 0.3819 - 116ms/epoch - 4ms/step\n",
+      "Epoch 29/100\n",
+      "32/32 - 0s - loss: 0.3671 - 117ms/epoch - 4ms/step\n",
+      "Epoch 30/100\n",
+      "32/32 - 0s - loss: 0.3586 - 116ms/epoch - 4ms/step\n",
+      "Epoch 31/100\n",
+      "32/32 - 0s - loss: 0.4958 - 123ms/epoch - 4ms/step\n",
+      "Epoch 32/100\n",
+      "32/32 - 0s - loss: 0.3600 - 120ms/epoch - 4ms/step\n",
+      "Epoch 33/100\n",
+      "32/32 - 0s - loss: 0.3455 - 119ms/epoch - 4ms/step\n",
+      "Epoch 34/100\n",
+      "32/32 - 0s - loss: 0.3714 - 118ms/epoch - 4ms/step\n",
+      "Epoch 35/100\n",
+      "32/32 - 0s - loss: 0.3690 - 125ms/epoch - 4ms/step\n",
+      "Epoch 36/100\n",
+      "32/32 - 0s - loss: 0.3728 - 123ms/epoch - 4ms/step\n",
+      "Epoch 37/100\n",
+      "32/32 - 0s - loss: 0.3565 - 118ms/epoch - 4ms/step\n",
+      "Epoch 38/100\n",
+      "32/32 - 0s - loss: 0.3847 - 154ms/epoch - 5ms/step\n",
+      "Epoch 39/100\n",
+      "32/32 - 0s - loss: 0.3520 - 118ms/epoch - 4ms/step\n",
+      "Epoch 40/100\n",
+      "32/32 - 0s - loss: 0.3790 - 121ms/epoch - 4ms/step\n",
+      "Epoch 41/100\n",
+      "32/32 - 0s - loss: 0.3397 - 121ms/epoch - 4ms/step\n",
+      "Epoch 42/100\n",
+      "32/32 - 0s - loss: 0.3714 - 118ms/epoch - 4ms/step\n",
+      "Epoch 43/100\n",
+      "32/32 - 0s - loss: 0.4155 - 125ms/epoch - 4ms/step\n",
+      "Epoch 44/100\n",
+      "32/32 - 0s - loss: 0.3393 - 119ms/epoch - 4ms/step\n",
+      "Epoch 45/100\n",
+      "32/32 - 0s - loss: 0.3573 - 118ms/epoch - 4ms/step\n",
+      "Epoch 46/100\n",
+      "32/32 - 0s - loss: 0.3730 - 118ms/epoch - 4ms/step\n",
+      "Epoch 47/100\n",
+      "32/32 - 0s - loss: 0.4103 - 122ms/epoch - 4ms/step\n",
+      "Epoch 48/100\n",
+      "32/32 - 0s - loss: 0.3756 - 117ms/epoch - 4ms/step\n",
+      "Epoch 49/100\n",
+      "32/32 - 0s - loss: 0.3569 - 121ms/epoch - 4ms/step\n",
+      "Epoch 50/100\n",
+      "32/32 - 0s - loss: 0.3407 - 122ms/epoch - 4ms/step\n",
+      "Epoch 51/100\n",
+      "32/32 - 0s - loss: 0.3694 - 120ms/epoch - 4ms/step\n",
+      "Epoch 52/100\n",
+      "32/32 - 0s - loss: 0.3503 - 119ms/epoch - 4ms/step\n",
+      "Epoch 53/100\n",
+      "32/32 - 0s - loss: 0.3501 - 116ms/epoch - 4ms/step\n",
+      "Epoch 54/100\n",
+      "32/32 - 0s - loss: 0.3439 - 129ms/epoch - 4ms/step\n",
+      "Epoch 55/100\n",
+      "32/32 - 0s - loss: 0.3721 - 135ms/epoch - 4ms/step\n",
+      "Epoch 56/100\n",
+      "32/32 - 0s - loss: 0.3974 - 116ms/epoch - 4ms/step\n",
+      "Epoch 57/100\n",
+      "32/32 - 0s - loss: 0.3445 - 117ms/epoch - 4ms/step\n",
+      "Epoch 58/100\n",
+      "32/32 - 0s - loss: 0.3683 - 123ms/epoch - 4ms/step\n",
+      "Epoch 59/100\n",
+      "32/32 - 0s - loss: 0.3809 - 130ms/epoch - 4ms/step\n",
+      "Epoch 60/100\n",
+      "32/32 - 0s - loss: 0.3599 - 124ms/epoch - 4ms/step\n",
+      "Epoch 61/100\n",
+      "32/32 - 0s - loss: 0.3589 - 118ms/epoch - 4ms/step\n",
+      "Epoch 62/100\n",
+      "32/32 - 0s - loss: 0.3511 - 121ms/epoch - 4ms/step\n",
+      "Epoch 63/100\n",
+      "32/32 - 0s - loss: 0.3908 - 131ms/epoch - 4ms/step\n",
+      "Epoch 64/100\n",
+      "32/32 - 0s - loss: 0.3651 - 147ms/epoch - 5ms/step\n",
+      "Epoch 65/100\n",
+      "32/32 - 0s - loss: 0.3804 - 125ms/epoch - 4ms/step\n",
+      "Epoch 66/100\n",
+      "32/32 - 0s - loss: 0.3834 - 127ms/epoch - 4ms/step\n",
+      "Epoch 67/100\n",
+      "32/32 - 0s - loss: 0.3584 - 117ms/epoch - 4ms/step\n",
+      "Epoch 68/100\n",
+      "32/32 - 0s - loss: 0.3554 - 120ms/epoch - 4ms/step\n",
+      "Epoch 69/100\n",
+      "32/32 - 0s - loss: 0.3552 - 138ms/epoch - 4ms/step\n",
+      "Epoch 70/100\n",
+      "32/32 - 0s - loss: 0.3786 - 121ms/epoch - 4ms/step\n",
+      "Epoch 71/100\n",
+      "32/32 - 0s - loss: 0.4031 - 122ms/epoch - 4ms/step\n",
+      "Epoch 72/100\n",
+      "32/32 - 0s - loss: 0.3732 - 119ms/epoch - 4ms/step\n",
+      "Epoch 73/100\n",
+      "32/32 - 0s - loss: 0.3535 - 117ms/epoch - 4ms/step\n",
+      "Epoch 74/100\n",
+      "32/32 - 0s - loss: 0.3883 - 121ms/epoch - 4ms/step\n",
+      "Epoch 75/100\n",
+      "32/32 - 0s - loss: 0.3614 - 120ms/epoch - 4ms/step\n",
+      "Epoch 76/100\n",
+      "32/32 - 0s - loss: 0.3553 - 119ms/epoch - 4ms/step\n",
+      "Epoch 77/100\n",
+      "32/32 - 0s - loss: 0.3729 - 118ms/epoch - 4ms/step\n",
+      "Epoch 78/100\n",
+      "32/32 - 0s - loss: 0.3530 - 139ms/epoch - 4ms/step\n",
+      "Epoch 79/100\n",
+      "32/32 - 0s - loss: 0.3534 - 135ms/epoch - 4ms/step\n",
+      "Epoch 80/100\n",
+      "32/32 - 0s - loss: 0.3512 - 136ms/epoch - 4ms/step\n",
+      "Epoch 81/100\n",
+      "32/32 - 0s - loss: 0.3938 - 141ms/epoch - 4ms/step\n",
+      "Epoch 82/100\n",
+      "32/32 - 0s - loss: 0.3547 - 123ms/epoch - 4ms/step\n",
+      "Epoch 83/100\n",
+      "32/32 - 0s - loss: 0.3618 - 119ms/epoch - 4ms/step\n",
+      "Epoch 84/100\n",
+      "32/32 - 0s - loss: 0.3786 - 118ms/epoch - 4ms/step\n",
+      "Epoch 85/100\n",
+      "32/32 - 0s - loss: 0.3670 - 121ms/epoch - 4ms/step\n",
+      "Epoch 86/100\n",
+      "32/32 - 0s - loss: 0.3489 - 117ms/epoch - 4ms/step\n",
+      "Epoch 87/100\n",
+      "32/32 - 0s - loss: 0.3524 - 116ms/epoch - 4ms/step\n",
+      "Epoch 88/100\n",
+      "32/32 - 0s - loss: 0.3596 - 116ms/epoch - 4ms/step\n",
+      "Epoch 89/100\n",
+      "32/32 - 0s - loss: 0.3561 - 116ms/epoch - 4ms/step\n",
+      "Epoch 90/100\n",
+      "32/32 - 0s - loss: 0.3572 - 129ms/epoch - 4ms/step\n",
+      "Epoch 91/100\n",
+      "32/32 - 0s - loss: 0.3757 - 114ms/epoch - 4ms/step\n",
+      "Epoch 92/100\n",
+      "32/32 - 0s - loss: 0.3729 - 112ms/epoch - 4ms/step\n",
+      "Epoch 93/100\n",
+      "32/32 - 0s - loss: 0.3614 - 119ms/epoch - 4ms/step\n",
+      "Epoch 94/100\n",
+      "32/32 - 0s - loss: 0.3689 - 118ms/epoch - 4ms/step\n",
+      "Epoch 95/100\n",
+      "32/32 - 0s - loss: 0.3639 - 115ms/epoch - 4ms/step\n",
+      "Epoch 96/100\n",
+      "32/32 - 0s - loss: 0.3617 - 115ms/epoch - 4ms/step\n",
+      "Epoch 97/100\n",
+      "32/32 - 0s - loss: 0.3381 - 117ms/epoch - 4ms/step\n",
+      "Epoch 98/100\n",
+      "32/32 - 0s - loss: 0.3502 - 125ms/epoch - 4ms/step\n",
+      "Epoch 99/100\n",
+      "32/32 - 0s - loss: 0.3712 - 116ms/epoch - 4ms/step\n",
+      "Epoch 100/100\n",
+      "32/32 - 0s - loss: 0.3537 - 117ms/epoch - 4ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x5f8f8f23d0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_size = 2 # xs & zs\n",
+    "output_size = 1 # y\n",
+    "\n",
+    "model = tf.keras.Sequential([\n",
+    "                            tf.keras.layers.Dense(output_size, \n",
+    "                                                  kernel_initializer=tf.random_uniform_initializer(minval=-0.1),\n",
+    "                                                  bias_initializer=tf.random_uniform_initializer(minval=-0.1,maxval=0.1)\n",
+    "                                                 )\n",
+    "        ])\n",
+    "custom_optimizer = tf.keras.optimizers.SGD(learning_rate= 0.02)\n",
+    "model.compile(optimizer = custom_optimizer, loss = 'mean_squared_error')\n",
+    "\n",
+    "model.fit(training_data['inputs'], training_data['targets'], epochs = 100, verbose= 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[ 2.0555673],\n",
+       "        [-2.9967723]], dtype=float32),\n",
+       " array([4.999736], dtype=float32)]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.layers[0].get_weights()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.0555673],\n",
+       "       [-2.9967723]], dtype=float32)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights = model.layers[0].get_weights()[0]\n",
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.999736], dtype=float32)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bias = model.layers[0].get_weights()[1]\n",
+    "bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract the output (make predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  6.2],\n",
+       "       [ -1.9],\n",
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+       "       [ -8.4],\n",
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+       "       [  5.9],\n",
+       "       [ 11.1],\n",
+       "       [ 40.7],\n",
+       "       [ 15.8],\n",
+       "       [-12.7],\n",
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+       "       [ -7. ],\n",
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+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
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+    "model.predict_on_batch(training_data['inputs']).round(1)"
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+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
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+       "       [-13.4],\n",
+       "       [ 29.5],\n",
+       "       [-20.7],\n",
+       "       [ 10.3],\n",
+       "       [-24.4],\n",
+       "       [-14.1],\n",
+       "       [ 34.5],\n",
+       "       [ 32.3],\n",
+       "       [ 27. ],\n",
+       "       [  3.4],\n",
+       "       [ -3. ],\n",
+       "       [  5.5]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_data['targets'].round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.squeeze(model.predict_on_batch(training_data['inputs'])), np.squeeze(training_data['targets']))\n",
+    "plt.xlabel('Outputs')\n",
+    "plt.ylabel('Targets')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Tensorflow_doc_example.ipynb b/Udemy DataScience Workbook/Tensorflow_doc_example.ipynb
new file mode 100644
index 0000000..489b235
--- /dev/null
+++ b/Udemy DataScience Workbook/Tensorflow_doc_example.ipynb	
@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Create a class that will do the batching for the algorithm\n",
+    "# This code is extremely reusable. You should just change Audiobooks_data everywhere in the code\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    # Dataset is a mandatory arugment, while the batch_size is optional\n",
+    "    # If you don't input batch_size, it will automatically take the value: None\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "    \n",
+    "        # The dataset that loads is one of \"train\", \"validation\", \"test\".\n",
+    "        # e.g. if I call this class with x('train',5), it will load 'Audiobooks_data_train.npz' with a batch size of 5.\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        \n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float64), npz['targets'].astype(np.int64)\n",
+    "        \n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "    \n",
+    "    # A method which loads the next batch\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "            \n",
+    "        # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "        \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already but we're giving you the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "        \n",
+    "        # The function will return the inputs batch and the one-hot encoded targets\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "    def __iter__(self):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an optimizer.\n",
+    "opt = tf.keras.optimizers.experimental.SGD(learning_rate=0.1)\n",
+    "var1, var2 = tf.Variable(1.0), tf.Variable(2.0)\n",
+    "\n",
+    "# Compute the gradients for a list of variables.\n",
+    "with tf.GradientTape() as tape:\n",
+    "  loss = 3 * var1 * var1 + 2 * var2 * var2\n",
+    "grads = tape.gradient(loss, [var1, var2])\n",
+    "\n",
+    "# Process the gradients.\n",
+    "grads[0] = grads[0] + 1\n",
+    "\n",
+    "# Ask the optimizer to apply the gradients on variables.\n",
+    "opt.apply_gradients(zip(grads, [var1, var2]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Tensorflow_test.ipynb b/Udemy DataScience Workbook/Tensorflow_test.ipynb
new file mode 100644
index 0000000..343d9f9
--- /dev/null
+++ b/Udemy DataScience Workbook/Tensorflow_test.ipynb	
@@ -0,0 +1,86 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import libraries\n",
+    "import tensorflow as tf\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "\n",
+    "# Load data\n",
+    "# Assume the data has the following columns:\n",
+    "# customer_id: a unique identifier for each customer\n",
+    "# purchase: a binary variable indicating whether the customer purchased an item in the last month (1 = yes, 0 = no)\n",
+    "# duration: the total duration of items viewed by the customer in the last month (in minutes)\n",
+    "# category: the most frequent category of items viewed by the customer in the last month (categorical variable)\n",
+    "# rating: the average rating given by the customer to the items viewed in the last month (numeric variable from 1 to 5)\n",
+    "# return: the target variable indicating whether the customer returned to the shop and purchased another item in the next month (1 = yes, 0 = no)\n",
+    "data = pd.read_csv(\"customer_data.csv\")\n",
+    "\n",
+    "# Explore data\n",
+    "print(data.head())\n",
+    "print(data.describe())\n",
+    "print(data.info())\n",
+    "\n",
+    "# Preprocess data\n",
+    "# Convert category to numeric values\n",
+    "le = LabelEncoder()\n",
+    "data[\"category\"] = le.fit_transform(data[\"category\"])\n",
+    "\n",
+    "# Split data into features (X) and target (y)\n",
+    "X = data.drop([\"customer_id\", \"return\"], axis=1)\n",
+    "y = data[\"return\"]\n",
+    "\n",
+    "# Split data into train and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Scale data to range [0, 1]\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train = scaler.fit_transform(X_train)\n",
+    "X_test = scaler.transform(X_test)\n",
+    "\n",
+    "# Define the DNN architecture\n",
+    "model = tf.keras.Sequential([\n",
+    "  tf.keras.layers.Dense(16, activation=\"relu\", input_shape=(4,)), # input layer for 4 features\n",
+    "  tf.keras.layers.Dense(8, activation=\"relu\"), # hidden layer\n",
+    "  tf.keras.layers.Dense(1, activation=\"sigmoid\") # output layer for binary classification\n",
+    "])\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.1)\n",
+    "\n",
+    "# Test the model\n",
+    "y_pred = model.predict(X_test)\n",
+    "y_pred = (y_pred > 0.5).astype(int) # convert probabilities to labels\n",
+    "\n",
+    "# Evaluate the model performance\n",
+    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "print(\"Confusion matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
+    "print(\"Classification report:\\n\", classification_report(y_test, y_pred))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Tensors.ipynb b/Udemy DataScience Workbook/Tensors.ipynb
new file mode 100644
index 0000000..a22e815
--- /dev/null
+++ b/Udemy DataScience Workbook/Tensors.ipynb	
@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a tensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 = np.array([[5,12,6],[-3,0,14]])\n",
+    "m1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m2 = np.array([[9,8,7],[1,3,-5]])\n",
+    "m2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "t = np.array([m1,m2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking its shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Manually creating a tensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_manual = np.array([[[ 5, 12,  6], [-3,  0, 14]], [[ 9,  8,  7], [ 1,  3, -5]]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_manual"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Testing+the+Model+-+Exercise..ipynb b/Udemy DataScience Workbook/Testing+the+Model+-+Exercise..ipynb
new file mode 100644
index 0000000..7196869
--- /dev/null
+++ b/Udemy DataScience Workbook/Testing+the+Model+-+Exercise..ipynb	
@@ -0,0 +1,1496 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using your solution so far, test the model on new data.\n",
+    "\n",
+    "The new data is located in the ‘Bank_data_testing.csv’.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>513</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>514</td>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>515</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>516</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>517</td>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  interest_rate  credit  march  may  previous  duration    y\n",
+       "0             0          1.334     0.0    1.0  0.0       0.0     117.0   no\n",
+       "1             1          0.767     0.0    0.0  2.0       1.0     274.0  yes\n",
+       "2             2          4.858     0.0    1.0  0.0       0.0     167.0   no\n",
+       "3             3          4.120     0.0    0.0  0.0       0.0     686.0  yes\n",
+       "4             4          4.856     0.0    1.0  0.0       0.0     157.0   no\n",
+       "..          ...            ...     ...    ...  ...       ...       ...  ...\n",
+       "513         513          1.334     0.0    1.0  0.0       0.0     204.0   no\n",
+       "514         514          0.861     0.0    0.0  2.0       1.0     806.0  yes\n",
+       "515         515          0.879     0.0    0.0  0.0       0.0     290.0   no\n",
+       "516         516          0.877     0.0    0.0  5.0       1.0     473.0  yes\n",
+       "517         517          4.965     0.0    0.0  0.0       0.0     142.0   no\n",
+       "\n",
+       "[518 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Bank_data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration  y\n",
+       "0            1.334     0.0    1.0  0.0       0.0     117.0  0\n",
+       "1            0.767     0.0    0.0  2.0       1.0     274.0  1\n",
+       "2            4.858     0.0    1.0  0.0       0.0     167.0  0\n",
+       "3            4.120     0.0    0.0  0.0       0.0     686.0  1\n",
+       "4            4.856     0.0    1.0  0.0       0.0     157.0  0\n",
+       "..             ...     ...    ...  ...       ...       ... ..\n",
+       "513          1.334     0.0    1.0  0.0       0.0     204.0  0\n",
+       "514          0.861     0.0    0.0  2.0       1.0     806.0  1\n",
+       "515          0.879     0.0    0.0  0.0       0.0     290.0  0\n",
+       "516          0.877     0.0    0.0  5.0       1.0     473.0  1\n",
+       "517          4.965     0.0    0.0  0.0       0.0     142.0  0\n",
+       "\n",
+       "[518 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Unnamed: 0'],axis=1)\n",
+    "data['y'] = data['y'].map({'yes':1, 'no': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.835776</td>\n",
+       "      <td>0.034749</td>\n",
+       "      <td>0.266409</td>\n",
+       "      <td>0.388031</td>\n",
+       "      <td>0.127413</td>\n",
+       "      <td>382.177606</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.876903</td>\n",
+       "      <td>0.183321</td>\n",
+       "      <td>0.442508</td>\n",
+       "      <td>0.814527</td>\n",
+       "      <td>0.333758</td>\n",
+       "      <td>344.295990</td>\n",
+       "      <td>0.500483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.635000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.042750</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.466000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.956500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>482.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.970000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2653.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       interest_rate      credit       march         may    previous  \\\n",
+       "count     518.000000  518.000000  518.000000  518.000000  518.000000   \n",
+       "mean        2.835776    0.034749    0.266409    0.388031    0.127413   \n",
+       "std         1.876903    0.183321    0.442508    0.814527    0.333758   \n",
+       "min         0.635000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%         1.042750    0.000000    0.000000    0.000000    0.000000   \n",
+       "50%         1.466000    0.000000    0.000000    0.000000    0.000000   \n",
+       "75%         4.956500    0.000000    1.000000    0.000000    0.000000   \n",
+       "max         4.970000    1.000000    1.000000    5.000000    1.000000   \n",
+       "\n",
+       "          duration           y  \n",
+       "count   518.000000  518.000000  \n",
+       "mean    382.177606    0.500000  \n",
+       "std     344.295990    0.500483  \n",
+       "min       9.000000    0.000000  \n",
+       "25%     155.000000    0.000000  \n",
+       "50%     266.500000    0.500000  \n",
+       "75%     482.750000    1.000000  \n",
+       "max    2653.000000    1.000000  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 'duration' as the independet variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression and graph the scatter plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.546118\n",
+      "         Iterations 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Mon, 13 Nov 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>11:07:22</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &      518    \\\\\n",
+       "\\textbf{Model:}           &      Logit       & \\textbf{  Df Residuals:      } &      516    \\\\\n",
+       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        1    \\\\\n",
+       "\\textbf{Date:}            & Mon, 13 Nov 2023 & \\textbf{  Pseudo R-squ.:     } &   0.2121    \\\\\n",
+       "\\textbf{Time:}            &     11:07:22     & \\textbf{  Log-Likelihood:    } &   -282.89   \\\\\n",
+       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -359.05   \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 5.387e-35   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}    &      -1.7001  &        0.192     &    -8.863  &         0.000        &       -2.076    &       -1.324     \\\\\n",
+       "\\textbf{duration} &       0.0051  &        0.001     &     9.159  &         0.000        &        0.004    &        0.006     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Logit Regression Results}\n",
+       "\\end{center}"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   No. Observations:                  518\n",
+       "Model:                          Logit   Df Residuals:                      516\n",
+       "Method:                           MLE   Df Model:                            1\n",
+       "Date:                Mon, 13 Nov 2023   Pseudo R-squ.:                  0.2121\n",
+       "Time:                        11:07:22   Log-Likelihood:                -282.89\n",
+       "converged:                       True   LL-Null:                       -359.05\n",
+       "Covariance Type:            nonrobust   LLR p-value:                 5.387e-35\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
+       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
+       "==============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Expand the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can be omitting many causal factors in our simple logistic model, so we instead switch to a multivariate logistic regression model. Add the ‘interest_rate’, ‘march’, ‘credit’ and ‘previous’ estimators to our model and run the regression again. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the independent variable(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimators = ['interest_rate','march', 'credit', 'previous','duration']\n",
+    "X1_all = data[estimators]\n",
+    "y = data['y']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.336664\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td>Model:</td>              <td>Logit</td>           <td>Method:</td>          <td>MLE</td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <td>Dependent Variable:</td>         <td>y</td>        <td>Pseudo R-squared:</td>    <td>0.514</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "         <td>Date:</td>        <td>2023-11-13 11:33</td>       <td>AIC:</td>         <td>360.7836</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "   <td>No. Observations:</td>         <td>518</td>             <td>BIC:</td>         <td>386.2834</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "       <td>Df Model:</td>              <td>5</td>         <td>Log-Likelihood:</td>    <td>-174.39</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "     <td>Df Residuals:</td>           <td>512</td>           <td>LL-Null:</td>        <td>-359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "      <td>Converged:</td>           <td>1.0000</td>        <td>LLR p-value:</td>    <td>1.2114e-77</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "    <td>No. Iterations:</td>        <td>7.0000</td>           <td>Scale:</td>         <td>1.0000</td>  \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td></td>         <th>Coef.</th>  <th>Std.Err.</th>    <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>         <td>-0.0211</td>  <td>0.3113</td>  <td>-0.0677</td> <td>0.9460</td> <td>-0.6313</td> <td>0.5891</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>interest_rate</th> <td>-0.8001</td>  <td>0.0895</td>  <td>-8.9434</td> <td>0.0000</td> <td>-0.9755</td> <td>-0.6248</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>march</th>         <td>-1.8322</td>  <td>0.3297</td>  <td>-5.5563</td> <td>0.0000</td> <td>-2.4785</td> <td>-1.1859</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>credit</th>        <td>2.3585</td>   <td>1.0875</td>  <td>2.1688</td>  <td>0.0301</td> <td>0.2271</td>  <td>4.4900</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>previous</th>      <td>1.5363</td>   <td>0.5010</td>  <td>3.0666</td>  <td>0.0022</td> <td>0.5544</td>  <td>2.5182</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th>      <td>0.0070</td>   <td>0.0007</td>  <td>9.3810</td>  <td>0.0000</td> <td>0.0055</td>  <td>0.0084</td> \n",
+       "</tr>\n",
+       "</table><br/>\n"
+      ],
+      "text/latex": [
+       "\\begin{table}\n",
+       "\\caption{Results: Logit}\n",
+       "\\label{}\n",
+       "\\begin{center}\n",
+       "\\begin{tabular}{llll}\n",
+       "\\hline\n",
+       "Model:              & Logit            & Method:           & MLE         \\\\\n",
+       "Dependent Variable: & y                & Pseudo R-squared: & 0.514       \\\\\n",
+       "Date:               & 2023-11-13 11:33 & AIC:              & 360.7836    \\\\\n",
+       "No. Observations:   & 518              & BIC:              & 386.2834    \\\\\n",
+       "Df Model:           & 5                & Log-Likelihood:   & -174.39     \\\\\n",
+       "Df Residuals:       & 512              & LL-Null:          & -359.05     \\\\\n",
+       "Converged:          & 1.0000           & LLR p-value:      & 1.2114e-77  \\\\\n",
+       "No. Iterations:     & 7.0000           & Scale:            & 1.0000      \\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n",
+       "\\end{center}\n",
+       "\n",
+       "\\begin{center}\n",
+       "\\begin{tabular}{lrrrrrr}\n",
+       "\\hline\n",
+       "               &   Coef. & Std.Err. &       z & P$> |$z$|$ &  [0.025 &  0.975]  \\\\\n",
+       "\\hline\n",
+       "const          & -0.0211 &   0.3113 & -0.0677 &      0.9460 & -0.6313 &  0.5891  \\\\\n",
+       "interest\\_rate & -0.8001 &   0.0895 & -8.9434 &      0.0000 & -0.9755 & -0.6248  \\\\\n",
+       "march          & -1.8322 &   0.3297 & -5.5563 &      0.0000 & -2.4785 & -1.1859  \\\\\n",
+       "credit         &  2.3585 &   1.0875 &  2.1688 &      0.0301 &  0.2271 &  4.4900  \\\\\n",
+       "previous       &  1.5363 &   0.5010 &  3.0666 &      0.0022 &  0.5544 &  2.5182  \\\\\n",
+       "duration       &  0.0070 &   0.0007 &  9.3810 &      0.0000 &  0.0055 &  0.0084  \\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n",
+       "\\end{center}\n",
+       "\\end{table}\n",
+       "\\bigskip\n"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary2.Summary'>\n",
+       "\"\"\"\n",
+       "                         Results: Logit\n",
+       "=================================================================\n",
+       "Model:              Logit            Method:           MLE       \n",
+       "Dependent Variable: y                Pseudo R-squared: 0.514     \n",
+       "Date:               2023-11-13 11:33 AIC:              360.7836  \n",
+       "No. Observations:   518              BIC:              386.2834  \n",
+       "Df Model:           5                Log-Likelihood:   -174.39   \n",
+       "Df Residuals:       512              LL-Null:          -359.05   \n",
+       "Converged:          1.0000           LLR p-value:      1.2114e-77\n",
+       "No. Iterations:     7.0000           Scale:            1.0000    \n",
+       "-----------------------------------------------------------------\n",
+       "                   Coef.  Std.Err.    z    P>|z|   [0.025  0.975]\n",
+       "-----------------------------------------------------------------\n",
+       "const             -0.0211   0.3113 -0.0677 0.9460 -0.6313  0.5891\n",
+       "interest_rate     -0.8001   0.0895 -8.9434 0.0000 -0.9755 -0.6248\n",
+       "march             -1.8322   0.3297 -5.5563 0.0000 -2.4785 -1.1859\n",
+       "credit             2.3585   1.0875  2.1688 0.0301  0.2271  4.4900\n",
+       "previous           1.5363   0.5010  3.0666 0.0022  0.5544  2.5182\n",
+       "duration           0.0070   0.0007  9.3810 0.0000  0.0055  0.0084\n",
+       "=================================================================\n",
+       "\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X1_all = sm.add_constant(X1_all)\n",
+    "reg_Logit = sm.Logit(y,X1_all)\n",
+    "results_logit = reg_Logit.fit()\n",
+    "results_logit.summary2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the confusion matrix of the model and estimate its accuracy. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<i> For convenience we have already provided you with a function that finds the confusion matrix and the model accuracy.</i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def confusion_matrix(data,actual_values,model):\n",
+    "        \n",
+    "        # Confusion matrix \n",
+    "        \n",
+    "        # Parameters\n",
+    "        # ----------\n",
+    "        # data: data frame or array\n",
+    "            # data is a data frame formatted in the same way as your input data (without the actual values)\n",
+    "            # e.g. const, var1, var2, etc. Order is very important!\n",
+    "        # actual_values: data frame or array\n",
+    "            # These are the actual values from the test_data\n",
+    "            # In the case of a logistic regression, it should be a single column with 0s and 1s\n",
+    "            \n",
+    "        # model: a LogitResults object\n",
+    "            # this is the variable where you have the fitted model \n",
+    "            # e.g. results_log in this course\n",
+    "        # ----------\n",
+    "        \n",
+    "        #Predict the values using the Logit model\n",
+    "        pred_values = model.predict(data)\n",
+    "        # Specify the bins \n",
+    "        bins=np.array([0,0.5,1])\n",
+    "        # Create a histogram, where if values are between 0 and 0.5 tell will be considered 0\n",
+    "        # if they are between 0.5 and 1, they will be considered 1\n",
+    "        cm = np.histogram2d(actual_values, pred_values, bins=bins)[0]\n",
+    "        # Calculate the accuracy\n",
+    "        accuracy = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "        # Return the confusion matrix and \n",
+    "        return cm, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[218.,  41.],\n",
+       "        [ 30., 229.]]),\n",
+       " 0.862934362934363)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(X1_all,y,results_logit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the test data from the ‘Bank_data_testing.csv’ file provided. (Remember to convert the outcome variable ‘y’ into Boolean). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load new data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>487.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1468.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>217</td>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>458.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>218</td>\n",
+       "      <td>1.264</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>397.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>219</td>\n",
+       "      <td>1.281</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>220</td>\n",
+       "      <td>0.739</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>233.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>221</td>\n",
+       "      <td>1.046</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>238.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>222 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  interest_rate  credit  march  may  previous  duration    y\n",
+       "0             0          1.313     0.0    1.0  0.0       0.0     487.0   no\n",
+       "1             1          4.961     0.0    0.0  0.0       0.0     132.0   no\n",
+       "2             2          4.856     0.0    1.0  0.0       0.0      92.0   no\n",
+       "3             3          4.120     0.0    0.0  0.0       0.0    1468.0  yes\n",
+       "4             4          4.963     0.0    0.0  0.0       0.0      36.0   no\n",
+       "..          ...            ...     ...    ...  ...       ...       ...  ...\n",
+       "217         217          4.963     0.0    0.0  0.0       0.0     458.0  yes\n",
+       "218         218          1.264     0.0    1.0  1.0       0.0     397.0  yes\n",
+       "219         219          1.281     0.0    1.0  0.0       0.0      34.0   no\n",
+       "220         220          0.739     0.0    0.0  2.0       0.0     233.0   no\n",
+       "221         221          1.046     0.0    0.0  0.0       0.0     238.0  yes\n",
+       "\n",
+       "[222 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data2 = pd.read_csv('Bank_data_testing.csv')\n",
+    "new_data = raw_data2.copy()\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.313</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>487.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1468.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>458.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>1.264</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>397.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>1.281</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>0.739</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>233.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>1.046</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>238.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>222 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration  y\n",
+       "0            1.313     0.0    1.0  0.0       0.0     487.0  0\n",
+       "1            4.961     0.0    0.0  0.0       0.0     132.0  0\n",
+       "2            4.856     0.0    1.0  0.0       0.0      92.0  0\n",
+       "3            4.120     0.0    0.0  0.0       0.0    1468.0  1\n",
+       "4            4.963     0.0    0.0  0.0       0.0      36.0  0\n",
+       "..             ...     ...    ...  ...       ...       ... ..\n",
+       "217          4.963     0.0    0.0  0.0       0.0     458.0  1\n",
+       "218          1.264     0.0    1.0  1.0       0.0     397.0  1\n",
+       "219          1.281     0.0    1.0  0.0       0.0      34.0  0\n",
+       "220          0.739     0.0    0.0  2.0       0.0     233.0  0\n",
+       "221          1.046     0.0    0.0  0.0       0.0     238.0  1\n",
+       "\n",
+       "[222 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data = new_data.drop(['Unnamed: 0'],axis=1)\n",
+    "test_data['y'] = test_data['y'].map({'yes':1, 'no': 0})\n",
+    "test_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = test_data['y']\n",
+    "X1_test = test_data[estimators]\n",
+    "X_test = sm.add_constant(X1_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Determine the test confusion matrix and the test accuracy and compare them with the train confusion matrix and the train accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[93., 18.],\n",
+       "        [13., 98.]]),\n",
+       " 0.8603603603603603)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Determine the Confusion Matrix and the accuracy of the model with the new data. Note that the model itself stays the same (results_logit).\n",
+    "# test accuracy\n",
+    "confusion_matrix(X_test, y_test, results_logit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "Determine the test confusion matrix and the test accuracy and compare them with the train confusion matrix and the train accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[218.,  41.],\n",
+       "        [ 30., 229.]]),\n",
+       " 0.862934362934363)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(X1_all,y, results_logit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the test acccuracy we see a number which is a tiny but lower: 86.04%, compared to 86.29% for train accuracy. \n",
+    "\n",
+    "In general, we always expect the test accuracy to be lower than the train one. If the test accuracy is higher, this is just due to luck."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Testing+the+Model+-+Solution.ipynb b/Udemy DataScience Workbook/Testing+the+Model+-+Solution.ipynb
new file mode 100644
index 0000000..94f327e
--- /dev/null
+++ b/Udemy DataScience Workbook/Testing+the+Model+-+Solution.ipynb	
@@ -0,0 +1,1859 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using your solution so far, test the model on new data.\n",
+    "\n",
+    "The new data is located in the ‘Bank_data_testing.csv’.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'Bank-data.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\Testing+the+Model+-+Solution.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Testing%2Bthe%2BModel%2B-%2BSolution.ipynb#W6sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m raw_data \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(\u001b[39m'\u001b[39;49m\u001b[39mBank-data.csv\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/Testing%2Bthe%2BModel%2B-%2BSolution.ipynb#W6sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m raw_data\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:948\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    944\u001b[0m     dtype_backend\u001b[39m=\u001b[39mdtype_backend,\n\u001b[0;32m    945\u001b[0m )\n\u001b[0;32m    946\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 948\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:611\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    608\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[0;32m    610\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 611\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m    613\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[0;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1448\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1445\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m   1447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m-> 1448\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1705\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1703\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[0;32m   1704\u001b[0m         mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m-> 1705\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[0;32m   1706\u001b[0m     f,\n\u001b[0;32m   1707\u001b[0m     mode,\n\u001b[0;32m   1708\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1709\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1710\u001b[0m     memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[0;32m   1711\u001b[0m     is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[0;32m   1712\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[0;32m   1713\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1714\u001b[0m )\n\u001b[0;32m   1715\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m   1716\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\common.py:863\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    858\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[0;32m    859\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    860\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    861\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[0;32m    862\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[1;32m--> 863\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[0;32m    864\u001b[0m             handle,\n\u001b[0;32m    865\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[0;32m    866\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[0;32m    867\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[0;32m    868\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    869\u001b[0m         )\n\u001b[0;32m    870\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    871\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m    872\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Bank-data.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Bank-data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that <i> interest rate</i> indicates the 3-month interest rate between banks and <i> duration </i> indicates the time since the last contact was made with a given consumer. The <i> previous </i> variable shows whether the last marketing campaign was successful with this customer. The <i>march</i> and <i> may </i> are Boolean variables that account for when the call was made to the specific customer and <i> credit </i> shows if the customer has enough credit to avoid defaulting."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to know whether the bank marketing strategy was successful, so we need to transform the outcome variable into Boolean values in order to run regressions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.899</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.962</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.962</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>704.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4.865</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.365</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>374.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.773</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.714</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>169.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>4.864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>4.966</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>215.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.904</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.849</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.811</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.264</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>337.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>4.076</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.262</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>663.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.695</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4.960</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>255.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1.354</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1.244</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.748</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>272.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.644</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>398.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>4.968</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>0.885</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>608.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>4.153</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>0.735</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>716.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>4.859</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>619.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>4.864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>406.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>4.153</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>0.646</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>1.266</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>326.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>1.405</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>470.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>4.076</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>1.050</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>1.029</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>0.748</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>171.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>4.961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>498.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>1.268</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>4.959</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>4.021</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>796.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>279.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1.327</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>476.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>479.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>1.266</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration  y\n",
+       "0            1.334     0.0    1.0  0.0       0.0     117.0  0\n",
+       "1            0.767     0.0    0.0  2.0       1.0     274.0  1\n",
+       "2            4.858     0.0    1.0  0.0       0.0     167.0  0\n",
+       "3            4.120     0.0    0.0  0.0       0.0     686.0  1\n",
+       "4            4.856     0.0    1.0  0.0       0.0     157.0  0\n",
+       "5            0.899     0.0    0.0  1.0       0.0     126.0  0\n",
+       "6            4.962     0.0    0.0  0.0       0.0      84.0  0\n",
+       "7            4.858     0.0    1.0  0.0       0.0      17.0  0\n",
+       "8            4.962     0.0    0.0  0.0       0.0     704.0  1\n",
+       "9            4.865     0.0    0.0  0.0       0.0     185.0  0\n",
+       "10           1.365     0.0    0.0  1.0       1.0     374.0  0\n",
+       "11           0.773     0.0    0.0  0.0       0.0      91.0  1\n",
+       "12           0.714     0.0    0.0  2.0       1.0     169.0  1\n",
+       "13           4.864     0.0    0.0  0.0       0.0     249.0  0\n",
+       "14           4.966     0.0    0.0  0.0       0.0     215.0  0\n",
+       "15           0.904     0.0    0.0  0.0       0.0     324.0  1\n",
+       "16           0.849     0.0    0.0  2.0       1.0     159.0  1\n",
+       "17           1.811     1.0    0.0  0.0       0.0     120.0  1\n",
+       "18           1.264     0.0    1.0  0.0       0.0     337.0  1\n",
+       "19           4.076     0.0    0.0  0.0       0.0     640.0  0\n",
+       "20           1.262     0.0    0.0  0.0       0.0     663.0  1\n",
+       "21           0.695     0.0    1.0  2.0       1.0     403.0  1\n",
+       "22           4.960     0.0    0.0  0.0       0.0     300.0  0\n",
+       "23           4.963     0.0    0.0  0.0       0.0     255.0  1\n",
+       "24           1.354     0.0    1.0  1.0       1.0     293.0  1\n",
+       "25           1.244     0.0    1.0  0.0       0.0      68.0  0\n",
+       "26           0.748     0.0    0.0  1.0       0.0     266.0  1\n",
+       "27           0.878     0.0    0.0  0.0       0.0     272.0  1\n",
+       "28           0.644     0.0    0.0  0.0       0.0     398.0  1\n",
+       "29           4.968     0.0    0.0  0.0       0.0     126.0  0\n",
+       "..             ...     ...    ...  ...       ...       ... ..\n",
+       "488          0.885     0.0    0.0  2.0       1.0     608.0  1\n",
+       "489          4.153     0.0    0.0  0.0       0.0     250.0  0\n",
+       "490          0.735     0.0    0.0  0.0       0.0     107.0  1\n",
+       "491          4.965     0.0    0.0  0.0       0.0     716.0  1\n",
+       "492          4.859     0.0    1.0  0.0       0.0     619.0  1\n",
+       "493          4.864     0.0    0.0  0.0       0.0     406.0  0\n",
+       "494          4.963     0.0    0.0  0.0       0.0      73.0  0\n",
+       "495          1.334     0.0    1.0  0.0       0.0     127.0  0\n",
+       "496          4.153     0.0    0.0  0.0       0.0     187.0  0\n",
+       "497          0.646     1.0    0.0  1.0       1.0     180.0  1\n",
+       "498          1.266     0.0    1.0  0.0       0.0     326.0  1\n",
+       "499          1.405     0.0    0.0  1.0       0.0     109.0  1\n",
+       "500          0.900     0.0    0.0  2.0       1.0     470.0  1\n",
+       "501          4.076     0.0    0.0  0.0       0.0      73.0  0\n",
+       "502          1.050     0.0    0.0  0.0       0.0     141.0  0\n",
+       "503          1.029     0.0    0.0  1.0       0.0     115.0  0\n",
+       "504          0.748     0.0    0.0  1.0       1.0     171.0  0\n",
+       "505          4.961     0.0    0.0  0.0       0.0     498.0  0\n",
+       "506          1.268     0.0    0.0  0.0       0.0     365.0  1\n",
+       "507          4.959     0.0    0.0  0.0       0.0      10.0  0\n",
+       "508          4.021     0.0    0.0  1.0       0.0     796.0  1\n",
+       "509          0.877     0.0    0.0  2.0       1.0     279.0  1\n",
+       "510          1.327     0.0    1.0  0.0       0.0     476.0  1\n",
+       "511          4.965     0.0    0.0  0.0       0.0     479.0  1\n",
+       "512          1.266     0.0    1.0  1.0       0.0     225.0  0\n",
+       "513          1.334     0.0    1.0  0.0       0.0     204.0  0\n",
+       "514          0.861     0.0    0.0  2.0       1.0     806.0  1\n",
+       "515          0.879     0.0    0.0  0.0       0.0     290.0  0\n",
+       "516          0.877     0.0    0.0  5.0       1.0     473.0  1\n",
+       "517          4.965     0.0    0.0  0.0       0.0     142.0  0\n",
+       "\n",
+       "[518 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We make sure to create a copy of the data before we start altering it. Note that we don't change the original data we loaded.\n",
+    "data = raw_data.copy()\n",
+    "# Removes the index column thata comes with the data\n",
+    "data = data.drop(['Unnamed: 0'], axis = 1)\n",
+    "# We use the map function to change any 'yes' values to 1 and 'no'values to 0. \n",
+    "data['y'] = data['y'].map({'yes':1, 'no':0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.835776</td>\n",
+       "      <td>0.034749</td>\n",
+       "      <td>0.266409</td>\n",
+       "      <td>0.388031</td>\n",
+       "      <td>0.127413</td>\n",
+       "      <td>382.177606</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.876903</td>\n",
+       "      <td>0.183321</td>\n",
+       "      <td>0.442508</td>\n",
+       "      <td>0.814527</td>\n",
+       "      <td>0.333758</td>\n",
+       "      <td>344.295990</td>\n",
+       "      <td>0.500483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.635000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.042750</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.466000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.956500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>482.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.970000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2653.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       interest_rate      credit       march         may    previous  \\\n",
+       "count     518.000000  518.000000  518.000000  518.000000  518.000000   \n",
+       "mean        2.835776    0.034749    0.266409    0.388031    0.127413   \n",
+       "std         1.876903    0.183321    0.442508    0.814527    0.333758   \n",
+       "min         0.635000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%         1.042750    0.000000    0.000000    0.000000    0.000000   \n",
+       "50%         1.466000    0.000000    0.000000    0.000000    0.000000   \n",
+       "75%         4.956500    0.000000    1.000000    0.000000    0.000000   \n",
+       "max         4.970000    1.000000    1.000000    5.000000    1.000000   \n",
+       "\n",
+       "          duration           y  \n",
+       "count   518.000000  518.000000  \n",
+       "mean    382.177606    0.500000  \n",
+       "std     344.295990    0.500483  \n",
+       "min       9.000000    0.000000  \n",
+       "25%     155.000000    0.000000  \n",
+       "50%     266.500000    0.500000  \n",
+       "75%     482.750000    1.000000  \n",
+       "max    2653.000000    1.000000  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 'duration' as the independet variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression and graph the scatter plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.546118\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>          <td>Wed, 30 Jan 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>              <td>17:12:17</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   No. Observations:                  518\n",
+       "Model:                          Logit   Df Residuals:                      516\n",
+       "Method:                           MLE   Df Model:                            1\n",
+       "Date:                Wed, 30 Jan 2019   Pseudo R-squ.:                  0.2121\n",
+       "Time:                        17:12:17   Log-Likelihood:                -282.89\n",
+       "converged:                       True   LL-Null:                       -359.05\n",
+       "                                        LLR p-value:                 5.387e-35\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
+       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
+       "==============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "# Get the regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1b794010cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot of x1 (Duration, no constant) and y (Subscribed)\n",
+    "plt.scatter(x1,y,color = 'C0')\n",
+    "# Don't forget to label your axes!\n",
+    "plt.xlabel('Duration', fontsize = 20)\n",
+    "plt.ylabel('Subscription', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Expand the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can be omitting many causal factors in our simple logistic model, so we instead switch to a multivariate logistic regression model. Add the ‘interest_rate’, ‘march’, ‘credit’ and ‘previous’ estimators to our model and run the regression again. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the independent variable(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To avoid writing them out every time, we save the names of the estimators of our model in a list. \n",
+    "estimators=['interest_rate','credit','march','previous','duration']\n",
+    "\n",
+    "X1_all = data[estimators]\n",
+    "y = data['y']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.336664\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.514</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <td>Dependent Variable:</td>         <td>y</td>              <td>AIC:</td>         <td>360.7836</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "         <td>Date:</td>        <td>2019-01-30 17:13</td>       <td>BIC:</td>         <td>386.2834</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "   <td>No. Observations:</td>         <td>518</td>        <td>Log-Likelihood:</td>    <td>-174.39</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "       <td>Df Model:</td>              <td>5</td>            <td>LL-Null:</td>        <td>-359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "     <td>Df Residuals:</td>           <td>512</td>         <td>LLR p-value:</td>    <td>1.2114e-77</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>         <td>1.0000</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "    <td>No. Iterations:</td>        <td>7.0000</td>              <td></td>               <td></td>     \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td></td>         <th>Coef.</th>  <th>Std.Err.</th>    <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>         <td>-0.0211</td>  <td>0.3113</td>  <td>-0.0677</td> <td>0.9460</td> <td>-0.6313</td> <td>0.5891</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>interest_rate</th> <td>-0.8001</td>  <td>0.0895</td>  <td>-8.9434</td> <td>0.0000</td> <td>-0.9755</td> <td>-0.6248</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>credit</th>        <td>2.3585</td>   <td>1.0875</td>  <td>2.1688</td>  <td>0.0301</td> <td>0.2271</td>  <td>4.4900</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>march</th>         <td>-1.8322</td>  <td>0.3297</td>  <td>-5.5563</td> <td>0.0000</td> <td>-2.4785</td> <td>-1.1859</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>previous</th>      <td>1.5363</td>   <td>0.5010</td>  <td>3.0666</td>  <td>0.0022</td> <td>0.5544</td>  <td>2.5182</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th>      <td>0.0070</td>   <td>0.0007</td>  <td>9.3810</td>  <td>0.0000</td> <td>0.0055</td>  <td>0.0084</td> \n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary2.Summary'>\n",
+       "\"\"\"\n",
+       "                         Results: Logit\n",
+       "=================================================================\n",
+       "Model:              Logit            Pseudo R-squared: 0.514     \n",
+       "Dependent Variable: y                AIC:              360.7836  \n",
+       "Date:               2019-01-30 17:13 BIC:              386.2834  \n",
+       "No. Observations:   518              Log-Likelihood:   -174.39   \n",
+       "Df Model:           5                LL-Null:          -359.05   \n",
+       "Df Residuals:       512              LLR p-value:      1.2114e-77\n",
+       "Converged:          1.0000           Scale:            1.0000    \n",
+       "No. Iterations:     7.0000                                       \n",
+       "-----------------------------------------------------------------\n",
+       "                   Coef.  Std.Err.    z    P>|z|   [0.025  0.975]\n",
+       "-----------------------------------------------------------------\n",
+       "const             -0.0211   0.3113 -0.0677 0.9460 -0.6313  0.5891\n",
+       "interest_rate     -0.8001   0.0895 -8.9434 0.0000 -0.9755 -0.6248\n",
+       "credit             2.3585   1.0875  2.1688 0.0301  0.2271  4.4900\n",
+       "march             -1.8322   0.3297 -5.5563 0.0000 -2.4785 -1.1859\n",
+       "previous           1.5363   0.5010  3.0666 0.0022  0.5544  2.5182\n",
+       "duration           0.0070   0.0007  9.3810 0.0000  0.0055  0.0084\n",
+       "=================================================================\n",
+       "\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_all = sm.add_constant(X1_all)\n",
+    "reg_logit = sm.Logit(y,X_all)\n",
+    "results_logit = reg_logit.fit()\n",
+    "results_logit.summary2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the confusion matrix of the model and estimate its accuracy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def confusion_matrix(data,actual_values,model):\n",
+    "        \n",
+    "        # Confusion matrix \n",
+    "        \n",
+    "        # Parameters\n",
+    "        # ----------\n",
+    "        # data: data frame or array\n",
+    "            # data is a data frame formatted in the same way as your input data (without the actual values)\n",
+    "            # e.g. const, var1, var2, etc. Order is very important!\n",
+    "        # actual_values: data frame or array\n",
+    "            # These are the actual values from the test_data\n",
+    "            # In the case of a logistic regression, it should be a single column with 0s and 1s\n",
+    "            \n",
+    "        # model: a LogitResults object\n",
+    "            # this is the variable where you have the fitted model \n",
+    "            # e.g. results_log in this course\n",
+    "        # ----------\n",
+    "        \n",
+    "        #Predict the values using the Logit model\n",
+    "        pred_values = model.predict(data)\n",
+    "        # Specify the bins \n",
+    "        bins=np.array([0,0.5,1])\n",
+    "        # Create a histogram, where if values are between 0 and 0.5 tell will be considered 0\n",
+    "        # if they are between 0.5 and 1, they will be considered 1\n",
+    "        cm = np.histogram2d(actual_values, pred_values, bins=bins)[0]\n",
+    "        # Calculate the accuracy\n",
+    "        accuracy = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "        # Return the confusion matrix and \n",
+    "        return cm, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[218.,  41.],\n",
+       "        [ 30., 229.]]), 0.862934362934363)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(X_all,y,results_logit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the test data from the 'Bank_data_testing.csv' file provided. (Remember to convert the outcome variable ‘y’ into Boolean). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load new data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>487.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1468.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>217</td>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>458.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>218</td>\n",
+       "      <td>1.264</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>397.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>219</td>\n",
+       "      <td>1.281</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>220</td>\n",
+       "      <td>0.739</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>233.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>221</td>\n",
+       "      <td>1.046</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>238.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>222 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  interest_rate  credit  march  may  previous  duration    y\n",
+       "0             0          1.313     0.0    1.0  0.0       0.0     487.0   no\n",
+       "1             1          4.961     0.0    0.0  0.0       0.0     132.0   no\n",
+       "2             2          4.856     0.0    1.0  0.0       0.0      92.0   no\n",
+       "3             3          4.120     0.0    0.0  0.0       0.0    1468.0  yes\n",
+       "4             4          4.963     0.0    0.0  0.0       0.0      36.0   no\n",
+       "..          ...            ...     ...    ...  ...       ...       ...  ...\n",
+       "217         217          4.963     0.0    0.0  0.0       0.0     458.0  yes\n",
+       "218         218          1.264     0.0    1.0  1.0       0.0     397.0  yes\n",
+       "219         219          1.281     0.0    1.0  0.0       0.0      34.0   no\n",
+       "220         220          0.739     0.0    0.0  2.0       0.0     233.0   no\n",
+       "221         221          1.046     0.0    0.0  0.0       0.0     238.0  yes\n",
+       "\n",
+       "[222 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We have to load data our model has never seen before.\n",
+    "raw_data2 = pd.read_csv('Bank_data_testing.csv')\n",
+    "data_test = raw_data2.copy()\n",
+    "data_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.313</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>487.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1468.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>4.963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>458.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>1.264</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>397.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>1.281</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>0.739</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>233.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>1.046</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>238.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>222 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration  y\n",
+       "0            1.313     0.0    1.0  0.0       0.0     487.0  0\n",
+       "1            4.961     0.0    0.0  0.0       0.0     132.0  0\n",
+       "2            4.856     0.0    1.0  0.0       0.0      92.0  0\n",
+       "3            4.120     0.0    0.0  0.0       0.0    1468.0  1\n",
+       "4            4.963     0.0    0.0  0.0       0.0      36.0  0\n",
+       "..             ...     ...    ...  ...       ...       ... ..\n",
+       "217          4.963     0.0    0.0  0.0       0.0     458.0  1\n",
+       "218          1.264     0.0    1.0  1.0       0.0     397.0  1\n",
+       "219          1.281     0.0    1.0  0.0       0.0      34.0  0\n",
+       "220          0.739     0.0    0.0  2.0       0.0     233.0  0\n",
+       "221          1.046     0.0    0.0  0.0       0.0     238.0  1\n",
+       "\n",
+       "[222 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Removes the index column thata comes with the data\n",
+    "data_test = data_test.drop(['Unnamed: 0'], axis = 1)\n",
+    "# Coverting the outcome variable into 1s and 0s again. \n",
+    "data_test['y'] = data_test['y'].map({'yes':1, 'no':0})\n",
+    "data_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = data_test['y']\n",
+    "# We already declared a list called 'estimators' that holds all relevant estimators for our model. \n",
+    "X1_test = data_test[estimators]\n",
+    "X_test = sm.add_constant(X1_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Determine the test confusion matrix and the test accuracy and compare them with the train confusion matrix and the train accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[93., 18.],\n",
+       "        [13., 98.]]), 0.8603603603603603)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Determine the Confusion Matrix and the accuracy of the model with the new data. Note that the model itself stays the same (results_logit).\n",
+    "# test accuracy\n",
+    "confusion_matrix(X_test, y_test, results_logit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[218.,  41.],\n",
+       "        [ 30., 229.]]), 0.862934362934363)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compare these values to the Confusion Matrix and the accuracy of the model with the old data.\n",
+    "# train accuracy\n",
+    "confusion_matrix(X_all,y, results_logit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the test acccuracy we see a number which is a tiny but lower: 86.04%, compared to 86.29% for train accuracy. \n",
+    "\n",
+    "In general, we always expect the test accuracy to be lower than the train one. If the test accuracy is higher, this is just due to luck.\n",
+    "\n",
+    "Note that when you run the regression, you may get different numbers than us!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Testing+the+model.ipynb b/Udemy DataScience Workbook/Testing+the+model.ipynb
new file mode 100644
index 0000000..545850e
--- /dev/null
+++ b/Udemy DataScience Workbook/Testing+the+model.ipynb	
@@ -0,0 +1,291 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "#Apply a fix to the statsmodels library\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('2.02. Binary predictors.csv')\n",
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "data['Gender'] = data['Gender'].map({'Female': 1, 'Male': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data[['SAT','Gender']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "# Get the regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "np.exp(1.94)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)})\n",
+    "#np.set_printoptions(formatter=None)\n",
+    "results_log.predict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.array(data['Admitted'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "results_log.pred_table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cm_df = pd.DataFrame(results_log.pred_table())\n",
+    "cm_df.columns = ['Predicted 0','Predicted 1']\n",
+    "cm_df = cm_df.rename(index={0: 'Actual 0',1:'Actual 1'})\n",
+    "cm_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "cm = np.array(cm_df)\n",
+    "accuracy_train = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "accuracy_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing the model and assessing its accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "test = pd.read_csv('2.03. Test dataset.csv')\n",
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "test['Admitted'] = test['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "test['Gender'] = test['Gender'].map({'Female': 1, 'Male': 0})\n",
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "test_actual = test['Admitted']\n",
+    "test_data = test.drop(['Admitted'],axis=1)\n",
+    "test_data = sm.add_constant(test_data)\n",
+    "#test_data = test_data[x.columns.values]\n",
+    "test_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def confusion_matrix(data,actual_values,model):\n",
+    "    \n",
+    "        pred_values = model.predict(data)\n",
+    "        bins=np.array([0,0.5,1])\n",
+    "        cm = np.histogram2d(actual_values, pred_values, bins=bins)[0]\n",
+    "        accuracy = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "        return cm, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cm = confusion_matrix(test_data,test_actual,results_log)\n",
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cm_df = pd.DataFrame(cm[0])\n",
+    "cm_df.columns = ['Predicted 0','Predicted 1']\n",
+    "cm_df = cm_df.rename(index={0: 'Actual 0',1:'Actual 1'})\n",
+    "cm_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print ('Missclassification rate: '+str((1+1)/19))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Testing+the+model_with_comments.ipynb b/Udemy DataScience Workbook/Testing+the+model_with_comments.ipynb
new file mode 100644
index 0000000..bda0ef7
--- /dev/null
+++ b/Udemy DataScience Workbook/Testing+the+model_with_comments.ipynb	
@@ -0,0 +1,1562 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "#Apply a fix to the statsmodels library\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1323</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1725</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1762</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1777</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1665</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1556</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1731</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1809</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1930</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1708</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2039</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1681</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1444</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1726</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1876</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1485</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1900</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SAT  Admitted  Gender\n",
+       "0   1323         0       0\n",
+       "1   1725         1       1\n",
+       "2   1762         1       1\n",
+       "3   1777         1       0\n",
+       "4   1665         0       0\n",
+       "5   1556         1       1\n",
+       "6   1731         1       1\n",
+       "7   1809         1       1\n",
+       "8   1930         1       1\n",
+       "9   1708         1       0\n",
+       "10  1970         1       1\n",
+       "11  2039         1       1\n",
+       "12  1681         1       1\n",
+       "13  1444         0       0\n",
+       "14  1726         0       0\n",
+       "15  1876         1       1\n",
+       "16  1485         0       0\n",
+       "17  1500         0       1\n",
+       "18  1900         1       0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('2.03.+Test+dataset.csv')\n",
+    "data = raw_data.copy()\n",
+    "data['Admitted'] = data['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "data['Gender'] = data['Gender'].map({'Female': 1, 'Male': 0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['Admitted']\n",
+    "x1 = data[['SAT','Gender']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.126762\n",
+      "         Iterations 11\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>       <td>Admitted</td>     <th>  No. Observations:  </th>  <td>    19</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>    16</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Mon, 13 Nov 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.7967</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>10:13:38</td>     <th>  Log-Likelihood:    </th> <td> -2.4085</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -11.849</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>7.940e-05</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>  <td> -107.9452</td> <td>   84.751</td> <td>   -1.274</td> <td> 0.203</td> <td> -274.055</td> <td>   58.164</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>SAT</th>    <td>    0.0629</td> <td>    0.049</td> <td>    1.273</td> <td> 0.203</td> <td>   -0.034</td> <td>    0.160</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Gender</th> <td>   11.9067</td> <td>    9.605</td> <td>    1.240</td> <td> 0.215</td> <td>   -6.918</td> <td>   30.732</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Possibly complete quasi-separation: A fraction 0.68 of observations can be<br/>perfectly predicted. This might indicate that there is complete<br/>quasi-separation. In this case some parameters will not be identified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &     Admitted     & \\textbf{  No. Observations:  } &       19    \\\\\n",
+       "\\textbf{Model:}           &      Logit       & \\textbf{  Df Residuals:      } &       16    \\\\\n",
+       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        2    \\\\\n",
+       "\\textbf{Date:}            & Mon, 13 Nov 2023 & \\textbf{  Pseudo R-squ.:     } &   0.7967    \\\\\n",
+       "\\textbf{Time:}            &     10:13:38     & \\textbf{  Log-Likelihood:    } &   -2.4085   \\\\\n",
+       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -11.849   \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 7.940e-05   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}  &    -107.9452  &       84.751     &    -1.274  &         0.203        &     -274.055    &       58.164     \\\\\n",
+       "\\textbf{SAT}    &       0.0629  &        0.049     &     1.273  &         0.203        &       -0.034    &        0.160     \\\\\n",
+       "\\textbf{Gender} &      11.9067  &        9.605     &     1.240  &         0.215        &       -6.918    &       30.732     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Logit Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Possibly complete quasi-separation: A fraction 0.68 of observations can be \\newline\n",
+       " perfectly predicted. This might indicate that there is complete \\newline\n",
+       " quasi-separation. In this case some parameters will not be identified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:               Admitted   No. Observations:                   19\n",
+       "Model:                          Logit   Df Residuals:                       16\n",
+       "Method:                           MLE   Df Model:                            2\n",
+       "Date:                Mon, 13 Nov 2023   Pseudo R-squ.:                  0.7967\n",
+       "Time:                        10:13:38   Log-Likelihood:                -2.4085\n",
+       "converged:                       True   LL-Null:                       -11.849\n",
+       "Covariance Type:            nonrobust   LLR p-value:                 7.940e-05\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const       -107.9452     84.751     -1.274      0.203    -274.055      58.164\n",
+       "SAT            0.0629      0.049      1.273      0.203      -0.034       0.160\n",
+       "Gender        11.9067      9.605      1.240      0.215      -6.918      30.732\n",
+       "==============================================================================\n",
+       "\n",
+       "Possibly complete quasi-separation: A fraction 0.68 of observations can be\n",
+       "perfectly predicted. This might indicate that there is complete\n",
+       "quasi-separation. In this case some parameters will not be identified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "# Get the regression summary\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.958750970637272"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(1.94)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.00, 1.00, 1.00, 0.98, 0.04, 0.85, 1.00, 1.00, 1.00, 0.36, 1.00,\n",
+       "       1.00, 1.00, 0.00, 0.63, 1.00, 0.00, 0.15, 1.00])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)})\n",
+    "#np.set_printoptions(formatter=None)\n",
+    "results_log.predict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1],\n",
+       "      dtype=int64)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array(data['Admitted'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5.00, 1.00],\n",
+       "       [1.00, 12.00]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_log.pred_table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predicted 0</th>\n",
+       "      <th>Predicted 1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Actual 0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Actual 1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Predicted 0  Predicted 1\n",
+       "Actual 0          5.0          1.0\n",
+       "Actual 1          1.0         12.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm_df = pd.DataFrame(results_log.pred_table())\n",
+    "cm_df.columns = ['Predicted 0','Predicted 1']\n",
+    "cm_df = cm_df.rename(index={0: 'Actual 0',1:'Actual 1'})\n",
+    "cm_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8947368421052632"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm = np.array(cm_df)\n",
+    "accuracy_train = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "accuracy_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing the model and assessing its accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1323</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1725</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1762</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1777</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1665</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1556</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1731</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1809</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1930</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1708</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1970</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2039</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1681</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1444</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1726</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1876</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1485</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1500</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1900</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Male</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SAT Admitted  Gender\n",
+       "0   1323       No    Male\n",
+       "1   1725      Yes  Female\n",
+       "2   1762      Yes  Female\n",
+       "3   1777      Yes    Male\n",
+       "4   1665       No    Male\n",
+       "5   1556      Yes  Female\n",
+       "6   1731      Yes  Female\n",
+       "7   1809      Yes  Female\n",
+       "8   1930      Yes  Female\n",
+       "9   1708      Yes    Male\n",
+       "10  1970      Yes  Female\n",
+       "11  2039      Yes  Female\n",
+       "12  1681      Yes  Female\n",
+       "13  1444       No    Male\n",
+       "14  1726       No    Male\n",
+       "15  1876      Yes  Female\n",
+       "16  1485       No    Male\n",
+       "17  1500       No  Female\n",
+       "18  1900      Yes    Male"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the test dataset\n",
+    "test = pd.read_csv('2.03.+Test+dataset.csv')\n",
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Admitted</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1323</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1725</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1762</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1777</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1665</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1556</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1731</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1809</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1930</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1708</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1970</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2039</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1681</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1444</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1726</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1876</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1485</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1900</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SAT  Admitted  Gender\n",
+       "0   1323         0       0\n",
+       "1   1725         1       1\n",
+       "2   1762         1       1\n",
+       "3   1777         1       0\n",
+       "4   1665         0       0\n",
+       "5   1556         1       1\n",
+       "6   1731         1       1\n",
+       "7   1809         1       1\n",
+       "8   1930         1       1\n",
+       "9   1708         1       0\n",
+       "10  1970         1       1\n",
+       "11  2039         1       1\n",
+       "12  1681         1       1\n",
+       "13  1444         0       0\n",
+       "14  1726         0       0\n",
+       "15  1876         1       1\n",
+       "16  1485         0       0\n",
+       "17  1500         0       1\n",
+       "18  1900         1       0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Map the test data as you did with the train data\n",
+    "test['Admitted'] = test['Admitted'].map({'Yes': 1, 'No': 0})\n",
+    "test['Gender'] = test['Gender'].map({'Female': 1, 'Male': 0})\n",
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>const</th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1323</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1725</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1777</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1665</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1556</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1731</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1809</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1708</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1970</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2039</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1681</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1444</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1726</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1876</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1485</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    const   SAT  Gender\n",
+       "0     1.0  1323       0\n",
+       "1     1.0  1725       1\n",
+       "2     1.0  1762       1\n",
+       "3     1.0  1777       0\n",
+       "4     1.0  1665       0\n",
+       "5     1.0  1556       1\n",
+       "6     1.0  1731       1\n",
+       "7     1.0  1809       1\n",
+       "8     1.0  1930       1\n",
+       "9     1.0  1708       0\n",
+       "10    1.0  1970       1\n",
+       "11    1.0  2039       1\n",
+       "12    1.0  1681       1\n",
+       "13    1.0  1444       0\n",
+       "14    1.0  1726       0\n",
+       "15    1.0  1876       1\n",
+       "16    1.0  1485       0\n",
+       "17    1.0  1500       1\n",
+       "18    1.0  1900       0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check how the inputs should look like\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>const</th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1323</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1725</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1777</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1665</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1556</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1731</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1809</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1708</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1970</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2039</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1681</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1444</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1726</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1876</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1485</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    const   SAT  Gender\n",
+       "0     1.0  1323       0\n",
+       "1     1.0  1725       1\n",
+       "2     1.0  1762       1\n",
+       "3     1.0  1777       0\n",
+       "4     1.0  1665       0\n",
+       "5     1.0  1556       1\n",
+       "6     1.0  1731       1\n",
+       "7     1.0  1809       1\n",
+       "8     1.0  1930       1\n",
+       "9     1.0  1708       0\n",
+       "10    1.0  1970       1\n",
+       "11    1.0  2039       1\n",
+       "12    1.0  1681       1\n",
+       "13    1.0  1444       0\n",
+       "14    1.0  1726       0\n",
+       "15    1.0  1876       1\n",
+       "16    1.0  1485       0\n",
+       "17    1.0  1500       1\n",
+       "18    1.0  1900       0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the actual values (true valies ; targets)\n",
+    "test_actual = test['Admitted']\n",
+    "# Prepare the test data to be predicted\n",
+    "test_data = test.drop(['Admitted'],axis=1)\n",
+    "test_data = sm.add_constant(test_data)\n",
+    "test_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unexpected exception formatting exception. Falling back to standard exception\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3548, in run_code\n",
+      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
+      "  File \"C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8492\\1300604357.py\", line 1, in <module>\n",
+      "    results_log.pred_table(test_data)\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py\", line 5216, in pred_table\n",
+      "    pred = np.array(self.predict() > threshold, dtype=float)\n",
+      "                    ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\generic.py\", line 2102, in __array_ufunc__\n",
+      "    return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\arraylike.py\", line 273, in array_ufunc\n",
+      "    result = maybe_dispatch_ufunc_to_dunder_op(self, ufunc, method, *inputs, **kwargs)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"ops_dispatch.pyx\", line 113, in pandas._libs.ops_dispatch.maybe_dispatch_ufunc_to_dunder_op\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\ops\\common.py\", line 76, in new_method\n",
+      "    return method(self, other)\n",
+      "           ^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\arraylike.py\", line 48, in __lt__\n",
+      "    return self._cmp_method(other, operator.lt)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\frame.py\", line 7628, in _cmp_method\n",
+      "    self, other = self._align_for_op(other, axis, flex=False, level=None)\n",
+      "                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\frame.py\", line 7872, in _align_for_op\n",
+      "    right = to_series(right)\n",
+      "            ^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\frame.py\", line 7864, in to_series\n",
+      "    raise ValueError(\n",
+      "ValueError: Unable to coerce to Series, length must be 3: given 19\n",
+      "\n",
+      "During handling of the above exception, another exception occurred:\n",
+      "\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2142, in showtraceback\n",
+      "    stb = self.InteractiveTB.structured_traceback(\n",
+      "          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\ultratb.py\", line 1435, in structured_traceback\n",
+      "    return FormattedTB.structured_traceback(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\ultratb.py\", line 1326, in structured_traceback\n",
+      "    return VerboseTB.structured_traceback(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\ultratb.py\", line 1173, in structured_traceback\n",
+      "    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
+      "                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\ultratb.py\", line 1063, in format_exception_as_a_whole\n",
+      "    self.get_records(etb, number_of_lines_of_context, tb_offset) if etb else []\n",
+      "    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\ultratb.py\", line 1155, in get_records\n",
+      "    FrameInfo(\n",
+      "  File \"c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\IPython\\core\\ultratb.py\", line 780, in __init__\n",
+      "    ix = inspect.getsourcelines(frame)\n",
+      "         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\Chizy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\inspect.py\", line 1244, in getsourcelines\n",
+      "    lines, lnum = findsource(object)\n",
+      "                  ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\Chizy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\inspect.py\", line 1073, in findsource\n",
+      "    raise OSError('source code not available')\n",
+      "OSError: source code not available\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_log.pred_table(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def confusion_matrix(data,actual_values,model):\n",
+    "        \n",
+    "        # Confusion matrix \n",
+    "        \n",
+    "        # Parameters\n",
+    "        # ----------\n",
+    "        # data: data frame or array\n",
+    "            # data is a data frame formatted in the same way as your input data (without the actual values)\n",
+    "            # e.g. const, var1, var2, etc. Order is very important!\n",
+    "        # actual_values: data frame or array\n",
+    "            # These are the actual values from the test_data\n",
+    "            # In the case of a logistic regression, it should be a single column with 0s and 1s\n",
+    "            \n",
+    "        # model: a LogitResults object\n",
+    "            # this is the variable where you have the fitted model \n",
+    "            # e.g. results_log in this course\n",
+    "        # ----------\n",
+    "        \n",
+    "        #Predict the values using the Logit model\n",
+    "        pred_values = model.predict(data)\n",
+    "        # Specify the bins \n",
+    "        bins=np.array([0,0.5,1])\n",
+    "        # Create a histogram, where if values are between 0 and 0.5 tell will be considered 0\n",
+    "        # if they are between 0.5 and 1, they will be considered 1\n",
+    "        cm = np.histogram2d(actual_values, pred_values, bins=bins)[0]\n",
+    "        # Calculate the accuracy\n",
+    "        accuracy = (cm[0,0]+cm[1,1])/cm.sum()\n",
+    "        # Return the confusion matrix and the accuracy\n",
+    "        return cm, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[5.00, 1.00],\n",
+       "        [1.00, 12.00]]),\n",
+       " 0.8947368421052632)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a confusion matrix with the test data\n",
+    "cm = confusion_matrix(test_data,test_actual,results_log)\n",
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predicted 0</th>\n",
+       "      <th>Predicted 1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Actual 0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Actual 1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Predicted 0  Predicted 1\n",
+       "Actual 0          5.0          1.0\n",
+       "Actual 1          1.0         12.0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Format for easier understanding (not needed later on)\n",
+    "cm_df = pd.DataFrame(cm[0])\n",
+    "cm_df.columns = ['Predicted 0','Predicted 1']\n",
+    "cm_df = cm_df.rename(index={0: 'Actual 0',1:'Actual 1'})\n",
+    "cm_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missclassification rate: 0.10526315789473684\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the missclassification rate\n",
+    "# Note that Accuracy + Missclassification rate = 1 = 100%\n",
+    "print ('Missclassification rate: '+str((1+1)/19))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Understanding+Logistic+Regression+Tables+-+Exercise.ipynb b/Udemy DataScience Workbook/Understanding+Logistic+Regression+Tables+-+Exercise.ipynb
new file mode 100644
index 0000000..981b7d1
--- /dev/null
+++ b/Udemy DataScience Workbook/Understanding+Logistic+Regression+Tables+-+Exercise.ipynb	
@@ -0,0 +1,960 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Logistic Regression Tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same code as in the previous exercise, try to interpret the summary table.\n",
+    "\n",
+    "### More information about the dataset: \n",
+    "Note that <i> interest rate</i> indicates the 3-month interest rate between banks and <i> duration </i> indicates the time since the last contact was made with a given consumer. The <i> previous </i> variable shows whether the last marketing campaign was successful with this customer. The <i>March</i> and <i> May </i> are Boolean variables that account for when the call was made to the specific customer and <i> credit </i> shows if the customer has enough credit to avoid defaulting.\n",
+    "\n",
+    "<i> Notes: \n",
+    "    <li> the first column of the dataset is an index one; </li>\n",
+    "    <li> you don't need the graph for this exercise; </li>\n",
+    "    <li> the dataset used is much bigger </li>\n",
+    "</i>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>513</td>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>514</td>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>515</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>516</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>517</td>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  interest_rate  credit  march  may  previous  duration    y\n",
+       "0             0          1.334     0.0    1.0  0.0       0.0     117.0   no\n",
+       "1             1          0.767     0.0    0.0  2.0       1.0     274.0  yes\n",
+       "2             2          4.858     0.0    1.0  0.0       0.0     167.0   no\n",
+       "3             3          4.120     0.0    0.0  0.0       0.0     686.0  yes\n",
+       "4             4          4.856     0.0    1.0  0.0       0.0     157.0   no\n",
+       "..          ...            ...     ...    ...  ...       ...       ...  ...\n",
+       "513         513          1.334     0.0    1.0  0.0       0.0     204.0   no\n",
+       "514         514          0.861     0.0    0.0  2.0       1.0     806.0  yes\n",
+       "515         515          0.879     0.0    0.0  0.0       0.0     290.0   no\n",
+       "516         516          0.877     0.0    0.0  5.0       1.0     473.0  yes\n",
+       "517         517          4.965     0.0    0.0  0.0       0.0     142.0   no\n",
+       "\n",
+       "[518 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Bank_data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>yes</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration    y\n",
+       "0            1.334     0.0    1.0  0.0       0.0     117.0   no\n",
+       "1            0.767     0.0    0.0  2.0       1.0     274.0  yes\n",
+       "2            4.858     0.0    1.0  0.0       0.0     167.0   no\n",
+       "3            4.120     0.0    0.0  0.0       0.0     686.0  yes\n",
+       "4            4.856     0.0    1.0  0.0       0.0     157.0   no\n",
+       "..             ...     ...    ...  ...       ...       ...  ...\n",
+       "513          1.334     0.0    1.0  0.0       0.0     204.0   no\n",
+       "514          0.861     0.0    0.0  2.0       1.0     806.0  yes\n",
+       "515          0.879     0.0    0.0  0.0       0.0     290.0   no\n",
+       "516          0.877     0.0    0.0  5.0       1.0     473.0  yes\n",
+       "517          4.965     0.0    0.0  0.0       0.0     142.0   no\n",
+       "\n",
+       "[518 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.copy()\n",
+    "data = data.drop(['Unnamed: 0'], axis=1)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['y']=data['y'].map({'yes':1, 'no': 0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.858</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.120</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.856</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>1.334</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>806.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>0.877</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>4.965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>518 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     interest_rate  credit  march  may  previous  duration  y\n",
+       "0            1.334     0.0    1.0  0.0       0.0     117.0  0\n",
+       "1            0.767     0.0    0.0  2.0       1.0     274.0  1\n",
+       "2            4.858     0.0    1.0  0.0       0.0     167.0  0\n",
+       "3            4.120     0.0    0.0  0.0       0.0     686.0  1\n",
+       "4            4.856     0.0    1.0  0.0       0.0     157.0  0\n",
+       "..             ...     ...    ...  ...       ...       ... ..\n",
+       "513          1.334     0.0    1.0  0.0       0.0     204.0  0\n",
+       "514          0.861     0.0    0.0  2.0       1.0     806.0  1\n",
+       "515          0.879     0.0    0.0  0.0       0.0     290.0  0\n",
+       "516          0.877     0.0    0.0  5.0       1.0     473.0  1\n",
+       "517          4.965     0.0    0.0  0.0       0.0     142.0  0\n",
+       "\n",
+       "[518 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>interest_rate</th>\n",
+       "      <th>credit</th>\n",
+       "      <th>march</th>\n",
+       "      <th>may</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "      <td>518.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.835776</td>\n",
+       "      <td>0.034749</td>\n",
+       "      <td>0.266409</td>\n",
+       "      <td>0.388031</td>\n",
+       "      <td>0.127413</td>\n",
+       "      <td>382.177606</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.876903</td>\n",
+       "      <td>0.183321</td>\n",
+       "      <td>0.442508</td>\n",
+       "      <td>0.814527</td>\n",
+       "      <td>0.333758</td>\n",
+       "      <td>344.295990</td>\n",
+       "      <td>0.500483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.635000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.042750</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.466000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.956500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>482.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.970000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2653.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       interest_rate      credit       march         may    previous  \\\n",
+       "count     518.000000  518.000000  518.000000  518.000000  518.000000   \n",
+       "mean        2.835776    0.034749    0.266409    0.388031    0.127413   \n",
+       "std         1.876903    0.183321    0.442508    0.814527    0.333758   \n",
+       "min         0.635000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%         1.042750    0.000000    0.000000    0.000000    0.000000   \n",
+       "50%         1.466000    0.000000    0.000000    0.000000    0.000000   \n",
+       "75%         4.956500    0.000000    1.000000    0.000000    0.000000   \n",
+       "max         4.970000    1.000000    1.000000    5.000000    1.000000   \n",
+       "\n",
+       "          duration           y  \n",
+       "count   518.000000  518.000000  \n",
+       "mean    382.177606    0.500000  \n",
+       "std     344.295990    0.500483  \n",
+       "min       9.000000    0.000000  \n",
+       "25%     155.000000    0.000000  \n",
+       "50%     266.500000    0.500000  \n",
+       "75%     482.750000    1.000000  \n",
+       "max    2653.000000    1.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 'duration' as the independent variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.546118\n",
+      "         Iterations 7\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Logit Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>            <td>Fri, 10 Nov 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                <td>10:55:33</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}   &        y         & \\textbf{  No. Observations:  } &      518    \\\\\n",
+       "\\textbf{Model:}           &      Logit       & \\textbf{  Df Residuals:      } &      516    \\\\\n",
+       "\\textbf{Method:}          &       MLE        & \\textbf{  Df Model:          } &        1    \\\\\n",
+       "\\textbf{Date:}            & Fri, 10 Nov 2023 & \\textbf{  Pseudo R-squ.:     } &   0.2121    \\\\\n",
+       "\\textbf{Time:}            &     10:55:33     & \\textbf{  Log-Likelihood:    } &   -282.89   \\\\\n",
+       "\\textbf{converged:}       &       True       & \\textbf{  LL-Null:           } &   -359.05   \\\\\n",
+       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{  LLR p-value:       } & 5.387e-35   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}    &      -1.7001  &        0.192     &    -8.863  &         0.000        &       -2.076    &       -1.324     \\\\\n",
+       "\\textbf{duration} &       0.0051  &        0.001     &     9.159  &         0.000        &        0.004    &        0.006     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{Logit Regression Results}\n",
+       "\\end{center}"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                           Logit Regression Results                           \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   No. Observations:                  518\n",
+       "Model:                          Logit   Df Residuals:                      516\n",
+       "Method:                           MLE   Df Model:                            1\n",
+       "Date:                Fri, 10 Nov 2023   Pseudo R-squ.:                  0.2121\n",
+       "Time:                        10:55:33   Log-Likelihood:                -282.89\n",
+       "converged:                       True   LL-Null:                       -359.05\n",
+       "Covariance Type:            nonrobust   LLR p-value:                 5.387e-35\n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
+       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
+       "==============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()\n",
+    "\n",
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dependent variable is 'y'. The model used is a Logit regression(logistic regression), while the method used is Maximum Likelihood Estimation(MLE). I has clearly converged after classsifying 518 observations.\n",
+    "\n",
+    "The Pseudo R-squared is 0.21 which is within the 'acceptable region'\n",
+    "The independent variable(duration) is significant and its coefficientis 0.0051\n",
+    "\n",
+    "The constant is also significant and = -1.70"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Understanding+Logistic+Regression+Tables+-+Solution.ipynb b/Udemy DataScience Workbook/Understanding+Logistic+Regression+Tables+-+Solution.ipynb
new file mode 100644
index 0000000..3cddaec
--- /dev/null
+++ b/Udemy DataScience Workbook/Understanding+Logistic+Regression+Tables+-+Solution.ipynb	
@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Logistic Regression Tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same code as in the previous exercise, try to interpret the summary table.\n",
+    "\n",
+    "### More information about the dataset: \n",
+    "Note that <i> interest rate</i> indicates the 3-month interest rate between banks and <i> duration </i> indicates the time since the last contact was made with a given consumer. The <i> previous </i> variable shows whether the last marketing campaign was successful with this customer. The <i>March</i> and <i> May </i> are Boolean variables that account for when the call was made to the specific customer and <i> credit </i> shows if the customer has enough credit to avoid defaulting.\n",
+    "\n",
+    "<i> Notes: \n",
+    "    <li> the first column of the dataset is an index one; </li>\n",
+    "    <li> you don't need the graph for this exercise; </li>\n",
+    "    <li> the dataset used is much bigger </li>\n",
+    "</i>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from scipy import stats\n",
+    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the ‘Bank_data.csv’ dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('Bank_data.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# We make sure to create a copy of the data before we start altering it. Note that we don't change the original data we loaded.\n",
+    "data = raw_data.copy()\n",
+    "\n",
+    "# Removes the index column thata comes with the data\n",
+    "data = data.drop(['Unnamed: 0'], axis = 1)\n",
+    "\n",
+    "# We use the map function to change any 'yes' values to 1 and 'no'values to 0. \n",
+    "data['y'] = data['y'].map({'yes':1, 'no':0})\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use 'duration' as the independet variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['y']\n",
+    "x1 = data['duration']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "reg_log = sm.Logit(y,x)\n",
+    "results_log = reg_log.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpretation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "results_log.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dependent variable is 'duration'. The model used is a Logit regression (logistic in common lingo), while the method - Maximum Likelihood Estimation (MLE). It has clearly converged after classifyin 518 observations. \n",
+    "\n",
+    "The Pseudo R-squared is 0.21 which is within the 'acceptable region'. \n",
+    "\n",
+    "The duration variable is significant and its coefficient is 0.0051.\n",
+    "\n",
+    "The constant is also significant and equals: -1.70"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/Untitled1.ipynb b/Udemy DataScience Workbook/Untitled1.ipynb
new file mode 100644
index 0000000..c1074b6
--- /dev/null
+++ b/Udemy DataScience Workbook/Untitled1.ipynb	
@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cb485126",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scipy\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3584335f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e1f3d6c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size\n",
+       "count     100.000000   100.000000\n",
+       "mean   292289.470160   853.024200\n",
+       "std     77051.727525   297.941951\n",
+       "min    154282.128000   479.750000\n",
+       "25%    234280.148000   643.330000\n",
+       "50%    280590.716000   696.405000\n",
+       "75%    335723.696000  1029.322500\n",
+       "max    500681.128000  1842.510000"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5637e75d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1 = data['size']\n",
+    "Y = data['price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "57243eb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x1, Y)\n",
+    "plt.xlabel('size', fontsize = 20)\n",
+    "plt.ylabel('price', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7f8d6315",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared (uncentered):</th>      <td>   0.971</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.971</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   3352.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Tue, 31 Oct 2023</td> <th>  Prob (F-statistic):</th>          <td>3.68e-78</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>10:15:33</td>     <th>  Log-Likelihood:    </th>          <td> -1226.2</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th>          <td>   2454.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    99</td>      <th>  BIC:               </th>          <td>   2457.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>size</th> <td>  329.7765</td> <td>    5.696</td> <td>   57.896</td> <td> 0.000</td> <td>  318.474</td> <td>  341.079</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 1.877</td> <th>  Durbin-Watson:     </th> <td>   1.997</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.391</td> <th>  Jarque-Bera (JB):  </th> <td>   1.730</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.320</td> <th>  Prob(JB):          </th> <td>   0.421</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.922</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                                 OLS Regression Results                                \n",
+       "=======================================================================================\n",
+       "Dep. Variable:                  price   R-squared (uncentered):                   0.971\n",
+       "Model:                            OLS   Adj. R-squared (uncentered):              0.971\n",
+       "Method:                 Least Squares   F-statistic:                              3352.\n",
+       "Date:                Tue, 31 Oct 2023   Prob (F-statistic):                    3.68e-78\n",
+       "Time:                        10:15:33   Log-Likelihood:                         -1226.2\n",
+       "No. Observations:                 100   AIC:                                      2454.\n",
+       "Df Residuals:                      99   BIC:                                      2457.\n",
+       "Df Model:                           1                                                  \n",
+       "Covariance Type:            nonrobust                                                  \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "size         329.7765      5.696     57.896      0.000     318.474     341.079\n",
+       "==============================================================================\n",
+       "Omnibus:                        1.877   Durbin-Watson:                   1.997\n",
+       "Prob(Omnibus):                  0.391   Jarque-Bera (JB):                1.730\n",
+       "Skew:                          -0.320   Prob(JB):                        0.421\n",
+       "Kurtosis:                       2.922   Cond. No.                         1.00\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
+       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "results = sm.OLS(Y, x1).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "157c323b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# yhat = b0 + b1x1\n",
+    "plt.scatter(x1, Y)\n",
+    "yhat = 0.0017*x1 + 0.275\n",
+    "fig = plt.plot(x1,yhat, lw = 4, c = 'orange', label = 'Regression line')\n",
+    "plt.xlabel('size', fontsize = 20)\n",
+    "plt.ylabel('price', fontsize = 20)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "26c007c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XBiMRV6latar9901ExOXOpsDy2uba3pkBFS6/F1B5plDkJBaLhfDwcEJDQ8nNzS3rcsRDVahQQSNEIlJ6fpsLP99ffLvIe+D6/7i+HhdTKHIyX19ffWmJiIh7MwxYHQMZicW37fY91Ozi+ppKgUKRiIiI/M+Zw/B5PXNt7zwFFSq7tp5SpKvPREREpMD+WeYC0dUjCq4u86BABBopEhEREcMGK6+B0weKb3vTBgjt4PqayoBCkYiIiDc7fQBWXG2u7V1nwc9zb/St6TMRERFv9ctb5gLRtWMKpss8OBCBRopERES8jy0flteCrD+Kb9vjZ6jezvU1lQMKRSIiIt4kcy+samyu7cAs8A1wbT3liKbPREREvMXul80FoiZPFUyXeVEgAo0UiYiIeD5bHiypBrmZxbftuQ1CWrq+pnJIoUhERMSTnUyE1c2Kb2fxLbi6zNff9TWVU5o+ExER8VS7ppoLRE0nw+A8rw5EoJEiERERz2PLhf9WBCOv+La9d0HVpq6vyQ0oFImIiHiSEzvgSxNrgvwqwx0nwEdRoJCmz0RERDzFjonmAlHzl+CuUwpEf6OzISIi4u7ys+G/geba9v0Fghu5th43pVAkIiLizv7aDF+Z2HE6MBQGHAUfX9fX5KY0fSYiIuKutj5uLhC1ehNu+0OBqBgaKRIREXE3eefgk4rm2vb7FaqYuOmraKRIRETErRzbaC4QVaoPg/MViBygUCQiIuIuNj0M39xQfLu270P/A2DR17wjNH0mIiJS3uWdgU8qm2vb/yBUqufScjyVIqSIiEh59sdac4HIGgWDbQpEV0ChSEREpLzaeC9816X4du3nQJ/dYLG4viYPpukzERGR8ib3FHwabK7tgN+hYi3X1uMlNFIkIiJSnqR+bS4QhbQpmC5TIHIajRSJiIiUFxvugCNLim8X+x+of4/r6/EyCkUiIiJlLeckfHaVuba3pkJQmEvL8VaaPhMRESlLKavMBaLQTgXTZQpELqORIhERkbKytg8cXV18uw6fQt07XF+Pl1MoEhERKW3Zf8GS6uba3nYMAk22lSui6TMREZHSdHiJuUAUfjMMMRSISpFGikRERErLd13hjzXFt+u0HGr3d3k5UpRCkYiIiKtlpcPSmuba3nEc/E1eiSZOpekzERERVzq4yFwgqj2gYLpMgajMaKRIRETEFQwDvo6Fv34uvu2NX0JET9fXJJelUCQiIuJsZ4/CcpO337jjJPhbXVqOmKPpMxEREWc68JG5QFRvyP9NlykQlRduGYpmzpxJTEwMwcHBBAcHExsby5dffnnZPuvWraN169YEBgbSoEEDZs2aVUrVioiIVzAM+KIZ/HRf8W27fgc3LHB9TeIQtwxFtWvX5uWXX2bLli1s2bKFrl270r9/f3bv3n3R9snJyfTu3ZuOHTuyfft2Jk2axNixY1myxMRN90RERIpz5jAs8oGMxOLb3nkKwrq6viZxmMUwDKOsi3CGkJAQXnvtNR544IELXpswYQIrVqxgz5499udGjhzJzp07iY+PN3X8zMxMrFYrGRkZBAcHO61uERFxc/s/gM0ji2939QPQ/l+ur0eKcOT72+0XWufn5/Ppp59y5swZYmNjL9omPj6eHj16FHnu5ptvZs6cOeTm5lKhQoUL+mRnZ5OdnW1/nJmZ6dzCRUTEvRk2WHkNnD5QfNub1kNoR9fXJFfELafPAHbt2kXlypUJCAhg5MiRLFu2jKioqIu2TUtLo2bNontE1KxZk7y8PP7888+L9pk+fTpWq9X+U6dOHad/BhERcVOnD8AiX3OB6K6zCkRuwm1DUaNGjdixYwc//fQTo0aNYtiwYSQlJV2yvcViKfK4cNbw788XmjhxIhkZGfafI0eOOK94ERFxX3vfgRVXF9/u2tEFV5f5Bbm+JnEKt50+8/f3p2HDhgC0adOGzZs38/bbb/PBBx9c0DYsLIy0tLQiz6Wnp+Pn50e1atUuevyAgAACAgKcX7iIiLgnwwbLa8O51OLb9vgJqrd3fU3iVG4biv7OMIwia4DOFxsby8qVK4s89/XXX9OmTZuLricSEREpInMfrGpkru3Ac+Ab6Np6xCXccvps0qRJbNiwgYMHD7Jr1y6eeeYZ1q5dy9133w0UTH3de++99vYjR47k0KFDjB8/nj179vDvf/+bOXPm8MQTT5TVRxAREXeR9Kq5QNTkyYLpMgUit+WWI0V//PEHQ4cOJTU1FavVSkxMDHFxcXTv3h2A1NRUDh8+bG9fv359Vq9ezbhx43jvvfeIiIjgnXfe4fbbby+rjyAiIuWdLQ+WVIfcjOLb9twKIa1cX5O4lMfsU+Rq2qdIRMSLnNwNq5uaazswG3z9XVuPlJgj399uOX0mIiLiMrteMBeImj73f9NlCkSewi2nz0RERJzOlgv/rQhGXvFteydA1Waur0lKlUKRiIjIiZ3wZYvi2/lVhjtOgI++Pj2Rps9ERMS77ZhkLhA1fxHuOqVA5MH0X1ZERLxTfjb81+Tl8332gLWxa+uRMqdQJCIi3uevLfBV2+LbBdSAW1PBx9f1NUmZ0/SZiIh4l63jzAWilm/A7ekKRF5EI0UiIuId8s7BJxXNte23H6o0dG09Uu5opEhERDzfsY3mAlHFujA4X4HISykUiYiIZ9s0Er65ofh2bWbAgENg0Vejt9L0mYiIeKa8M/BJZXNtb0mGypEuLUfKP4UiERHxCPk2g03Jx0k/lUVDYyvRe/oX3ym4MfRJAovF9QVKuadQJCIibm/VjqNMXL6LU1l5vF77n0SHfFd8p3azoeGDri9O3IZCkYiIuLUR8zbzTVI6Vt9THIwZbK7TgCNQsbZrCxO3o1AkIiJuKSfPxr1zfuKn5BPMjnyB7sGbiu8U0hpu3qzpMrkohSIREXE701cn8eH6ZAzgYExfc51i/wP173FpXeLedN2hiIi4lemrk/hgfTKxlXeYDkRx125TIJJiaaRIRETcRk6ejdkbkk2HoS1nmnDHb6+yqH1dF1cmnkChSERE3MZ/4g9yoJm5QPRq6r28f+wuggP9aFc/xMWViSdQKBIREfew910eODLWVNOb981gb1YkAC8NaIavjxZWS/EUikREpPxbaD7URCassv+5e1QofVtEuKIi8UAKRSIiUr6VIBBZgAc71ueZPlEuKko8kUKRiIiUTwlTIHGqqabP/v4I84/3BuC6BiHMu789/n66wFoco1AkIiLlTwlGh3wsMKJjfSb21uiQlIxCkYiIlB+GAYvMj/DMqfMb91Y6S72QigyNjdTokFwRhSIRESkfNj8C+2eaa3v9IogcxAOurUi8jEKRiIiUPQemyxhiuK4O8WoaZxQRkbJjy1cgknJDoUhERMrGhjtgsckJixu/VCASl9P0mYiIlD6NDkk5pJEiEREpPfk5CkRSbmmkSERESsc3HeHYD+badv8Ralzv2npE/kahSEREXE+jQ+IGNH0mIiKuk3dGgUjchkaKRETENVZcDacPmGvbaydcFePaekSKoVAkIiLOp9EhcUOaPhMREefJPq5AJG5LI0UiIuIciwPBlm2u7S2/QeUGrq1HxEEKRSIicuU0OiQeQNNnIiJScmePKhCJx9BIkYiIlIwjYejWoxAU7rpaRJxAoUhERByn0SHxQJo+ExER8079Zj4Q+VZUIBK3opEiERG5rHybwabk48T+XN18pzuOg/9VritKxAU0UiQiIpcUl5hKh1e+dywQDTEUiMQtKRSJiMhFxSWmMmvJIuLr3WSuQ+WrNV0mbk3TZyIicoF8m0HPhAh6XmOyw12nwa+SS2sScTWFIhERuYDvYgcmEjQ6JB5C02ciIvI/qV+bvrrs59PRfB71u4sLEik9GikSEZECDuw9dM2uZeQaFVjULdCFBYmULoUiERFxKBBFJqzCAoRbA2lXP8R1NYmUMk2fiYh4s8Ofmg5EW880tgcigMn9ovD1cWBna5FyTiNFIiLeyoHRoQYJn2PDF4AwayCT+0XRs6nuZSaeRaFIRMQbORCI8gfZWND+OOmnsgitUjBlphEi8UQKRSIiXuBcTj4vrU6iwcn53Of/qrlOdW6DjkvwBWKvrubS+kTKA4UiEREPN2LeZr5JSudgTF/wN9lpsA0sGg0S76JQJCLiwYoEIrO0GaN4KV19JiLioc7l5DPo7KPmA9E1jygQiVfTSJGIiIcK+syPbsEmGysMiWikSETEIzlwddnQcz+5sBAR96FQJCLiSVbHmA5Eb6TdTWTCKiKrVXRxUSLuwS1D0fTp02nbti1VqlQhNDSUAQMGsHfv3sv2Wbt2LRaL5YKfX375pZSqFhFxsYUWOLnLVNPIhFW8mz4YgEm9o1xZlYjbcMs1RevWrePRRx+lbdu25OXl8cwzz9CjRw+SkpKoVKnSZfvu3buX4OD/TbLXqFHD1eWKiLhEvs1gU/Jx0jPP0X9PHdP9IhNW2f/cPSqUIH9fV5Qn4nbcMhTFxcUVeTx37lxCQ0PZunUrnTp1umzf0NBQqlat6sLqRERcLy4xlakrk/imdj8q+54z1WfBXz15JmW0/XH3qFBm39vWVSWKuB23DEV/l5GRAUBISPF3a27ZsiVZWVlERUXx7LPP0qVLF1eXJyLiVHGJqYyav41kB/YeOndHHr+sTqJjxbNEVqvIpN5RGiES+RuLYRhufR2mYRj079+fEydOsGHDhku227t3L+vXr6d169ZkZ2fzn//8h1mzZrF27dqLji5lZ2eTnZ1tf5yZmUmdOnXIyMgoMv0mIlKa8m0GnV/5hh/q3Wy+ky63Fy+WmZmJ1Wo19f3t9qHo0Ucf5YsvvuCHH36gdu3aDvXt168fFouFFStWXPDalClTmDp16gXPKxSJSJly4FJ7mr0AzZ5zXS0ibsCRUOSWV58VGjNmDCtWrGDNmjUOByKA6667jv3791/0tYkTJ5KRkWH/OXLkyJWWKyJSIvk2g/jf/nIoEH0e9bsCkYiD3HJNkWEYjBkzhmXLlrF27Vrq169fouNs376d8PDwi74WEBBAQEDAlZQpInLF4hJTeXnVdtbW7WO6T2TCKha1D3RhVSKeyS1D0aOPPsrChQv5/PPPqVKlCmlpaQBYrVaCgoKAgpGelJQU5s2bB8Bbb71FZGQk0dHR5OTkMH/+fJYsWcKSJUvK7HOIiFxKvs1gxve/8lj6tfSsa67P07+P5r/HexJuDaRd/eIvPBGRotwyFM2cOROAG2+8scjzc+fOZfjw4QCkpqZy+PBh+2s5OTk88cQTpKSkEBQURHR0NF988QW9e/curbJFREyJS0xlyord/BTZ3XSfyIRVFE6uTe4Xha+PA2uPRATwgIXWpcWRhVoiIiUVl5jKxEVr2R49xHSfws0Yw62BTO4XRc+mF18WIOKNHPn+dsuRIhERT5RvM+iZEEHPaHPtRxx8lkat7+HtQZUJrVIwZaYRIpGSc3ooyszM5LPPPiM+Pp60tDTOnj3Lv//9b+rVq2dvc/ToUU6ePElgYCANGjRwdgkiIm7Jd7H5C4ILR4fub1id2KuruaokEa/i1FD03nvv8cwzz3Dq1Cmg4Coxi8XCmTNnirRbt24dd999N4GBgfz++++mdqIWEfFYZw7D5/WKb/d/CtcPhWlBtYhTOW2foilTpjB27FgyMzPx9/endevWl2w7cOBAwsPDyc7O1tVfIuLdFlpMB6IhB/5R5GauWlAt4lxOCUXbt29n2rRpANxzzz2kpaWxadOmS7+pjw933nknhmHwzTffOKMEERH348BmjJEJq9h4ugVQsKB65j2ttKBaxMmcEoreffddDMMgNjaWefPmYbVai+0TGxsLwK5du5xRgoiI+zi5y+FAVGjcTdfww4SuCkQiLuCUNUXr1q3DYrEwevRo030iIyMBSElJcUYJIiLlVr7NYFPycdJPZdE/yfwtifrse5vdWVcDutxepDQ4JRSlpqYC0KhRI9N9Cm+hcf6d6EVEPE1cYipTVyaRmpHFwZi+pvvlD7Lx7P8FKV1uL1I6nBKK/P39yc7OJjc313SfwiBVtWpVZ5QgIlLuxCWmMmr+NtpXSiA+ZpL5jkMMfEGX2ouUMqeEotq1a7Nnzx52795Nu3btTPX5+uuvAWjYsKEzShARKVfybQZTVyaR7MDoELccgMolu8G1iFw5pyy07tq1K4ZhMHfuXFPtDxw4wJw5c7BYLHTvbv7ePiIi7mJT8nHi691kvsMQQ4FIpIw5JRSNHj0aPz8/fvzxR6ZMmXLZtlu2bKFHjx6cPn2agIAAHn74YWeUICJSfhxcSOzP1c23H6JbUIqUB04JRddeey3PPfcchmEwbdo02rdvz6uvvmp/PS4ujldeeYVu3brRvn17kpOTsVgsvPzyy4SH60oKEfEgCy2w8W5TTVvtXkB8+z9dXJCImGUxDMNp/0R5/vnneemll7DZbFgsF79KovDWH88//zyTJ0921lu7nCN32RURL+XA3kP1E1YRZg3khwlddVWZiAs58v3ttNt8ALzwwgv89NNP3HbbbQQFBWEYRpGfChUq0KtXLzZs2OBWgUhE5GLybQY//vona5dPcDgQgW7TIVLeOHWk6Hx5eXkkJSWRnp5Ofn4+1apVIzo6mqCgIFe8nctppEhEzheXmMrTS3exo+HNpvtEJ37CGVtFbcQoUooc+f52yiX5Fz2wnx8xMTGuOryISJmJS0xl5PxtDm3GGN/+T16K0kaMIuWZy0KRiIgnyrcZ+Gy8m4Mxa8x3GmIQ67qSRMRJnBKKsrKy+OSTTwDo1asXNWrUuGz7Y8eO8eWXXwIwZMgQ/PyUzUSk/Pr7vct6VDbX76e2qVx3TZhrixMRp3FKGlm9ejXDhw+nVq1aDBkypNj2V111Fc888wxHjx4lJCSEvn0d2PFVRKQUrU5I5dnPEzl+Jseh6bLIhFW8HZXvwspExNmccvXZp59+CsDAgQNNjfr4+fkxePBgDMOwjzCJiJQ301cn8cjCbSyq9aDDgQggtEqgq0oTERdwSijatWsXFouFTp06me7TsWNHAHbu3OmMEkREnGp1wlE+WJ/MwZi+NAo8bKpP/YQV9kAUFhxAu/ohrixRRJzMKdNnv//+OwB16tQx3ad27doApKSkOKMEERGnybcZPPt5YolGhwpNuSVaV5iJuBmnhKK8vDwAsrOzTffJyckB4OzZs84oQUTEaXwX+7DtavPtzw9EVStW4OXbmmkPIhE35JRQVLNmTQ4ePEhiYiLXXXedqT67du0CKPZKNRGRUuXAztSRCat4pncTRlfLBQxiG1TnuquraYRIxE05JRRdf/31JCcnM3v2bB588EFTfT744AMsFovpECUi4nIOBqJqlfy5v0N9hSARD+GUhdaFl+Fv2bKFxx57jMvdOcQwDB577DG2bt1apK+ISJlZaHE4EAFM699UgUjEgzjt3mc33XQT33//PRaLhVatWjF27Fg6depEeHjBvHpqairr16/n3XfftQeiTp06sWaNA7vCliHd+0zEQ5UgDAE83Kk+E3tHuaIiEXEiR76/nRaKjh8/zo033khiYiIWy+X/kjEMg2bNmrFmzRpCQtzjklWFIhEPYxiwyPxgeWEgCqlUgX/0b0rvmAhXVSYiTlQmN4QNCQnh559/ZtKkScyePfuSV5VVqlSJhx9+mGnTphEUFOSstxcRMc+B0SEjoDo/tfiFt3UzVxGP57SRovOdPHmSNWvWsH37dv78808AqlevTqtWrejSpQtWq9XZb+lyGikS8RAOBCKGOP2vRxEpZWUyfebpFIpE3JwtDxZXMN9egUjEI5TJ9JmISLnlyOhQjY7Qfb3rahGRckuhSEQ8m6bLRMQkh0LR+vX/+9fT+Td/Pf/5knDkRrIiIpeTbzPYlHycvzL+ou8vjcx3VCAS8XoOrSny8fHBYrFgsVjs9zs7//kSFfC3Y5VXWlMkUv7FJaYydWUS8fVuMt+pwf1w3RzXFSUiZcqla4oulaG0XltEylJcYiqj5m8j2YE722t0SETO51AoutTu0+6yK7WIeKZ8m8GM1RtIjhlovpMCkYj8jS7JN0nTZyLlmCOLqZu9AM2ec10tIlKu6JJ8EfFohYup009l0T+ptul+n0f9Tv9mtVxYmYi4M/M3/rncQXx88PPz49VXX3XG4URELikuMZUOr3zPs/M+dSgQRSasIrRKoAsrExF355SRIn9/f3Jzc+nYsaMzDiciclElWUz9/46MY+mJboRbC+5bJiJyKU4JRRERERw6dAhfX19nHE5E5AL5NoOpK5McCkSRCasoXG00uV+UbuQqIpfllOmzws0Xt23b5ozDiYhc4Jcdnzu0/1BkwioAwqyBzLynFT2bhruqNBHxEE65+mzr1q3ExsZSt25dtm3b5pFXZ+nqM5Ey5MDVZRtrf4ylVh/ST2URWqVgykwjRCLey5Hvb6eMFLVu3Zp3332XQ4cO0blzZzZu3OiMw4qIOBSIIhNWYanVh9irq9G/RS1ir66mQCQipjllTdH9998PQKNGjdi5cycdO3akTp06xMTEcNVVV112rZHFYmHOHG2xLyJ/k7wA4u8x3bx+wiotphaRK+KU6bO/3/us8JDF3Q/NMAwsFgv5+flXWoLLafpMxPUK9x+K/bm66T63/vo6O842BtDaIRG5QKlv3li3bt0S3xBWRARKdjPXwsXU4dZAJveLUiASkSvilFB08OBBZxxGRLxUXGIqe7/5f8TXW2S6T3z7P3k7SoupRcR5dJsPESkzOXk2Pt6YzIjfG9KzpslO/fZDlYbEurQyEfFGLg1FeXl5nDhxAoCrrroKPz9lMBEpMH11ErM3JHOgmfnNGHVnexFxJadckn++3bt3M2bMGJo0aUJgYCBhYWGEhYURGBhIkyZNGDNmDImJic5+WxFxI9NXJxGV/IhDgejzqN9dWJGIiJOuPgOw2WyMHz+e9957D5vNxqUOa7FY8PHxYfTo0bzxxhv4+Dg9l7mErj4TcY6cPBv+n5i/JVDL3Qs4kW9l0YjriL26mgsrExFPVOpXnwEMGjSIJUuW2MNQdHQ07dq1o2bNmhiGQXp6Ops3byYxMZH8/Hzeeecdjh49yn//+19nlSAibsCRQFR47zLtPyQipcEpoWjhwoV89tlnWCwWmjdvzocffkjbtm0v2nbLli08/PDDbN++nc8++4zFixczaNAgZ5QhIuVM4b5D6aeyuOlgLyqd3WW6r27mKiKlzSnTZ126dGHdunU0atSILVu2UKlSpcu2P3PmDG3atGHv3r107tyZNWvWXGkJLqfpMxHHFO47lJqRxUEH7mzfeNdnZBmBgPYfEpErV+rTZwkJCVgsFiZMmFBsIAKoVKkSEyZM4P7772fnzp3OKEFEypG4xFRGzd+GAQ4FosLNGC3AvPvbcX3D6hohEpFS45RQlJOTA0BMTIzpPoVtc3NznVGCiJQT53LyGfffnfzWrB8+FvMD0YWBCOChTvXpeG0NV5QnInJJTglF9erVY8+ePWRkZJjuk5mZae8rIu4v42wuPf65lj9O5Tg0OtQg4XNsFCy+9rHAiI71mdg7ylVliohcklNC0e233860adNYsmQJXbp0MdWncGH2rbfe6owSRKQMdX7tew79dQ5wbLos5658nqlzkEPHz1IvpCJDYyPx93OPbTpExPM4ZaF1RkYGrVu35tChQyxYsIC77rrrsu0/++wzBg8eTL169di6dStWq/VKS3A5LbQWubjCQORIGIKCe5dp3yERcTVHvr+d8k8yq9XKt99+S6tWrRg8eDADBgxg+fLlpKSkkJubS15eHikpKSxfvpxbb72VgQMH0qpVK7777rsSBaLp06fTtm1bqlSpQmhoKAMGDGDv3r3F9lu3bh2tW7cmMDCQBg0aMGvWrJJ8XBGhYO3QU5/scDgQRSasosWvX2nfIREpd5wyUuTr+7/N2AzDwGK5/NUiZtpYLBby8vIu+lrPnj0ZNGgQbdu2JS8vj2eeeYZdu3aRlJR0yavfkpOTadq0KSNGjODhhx/mxx9/5JFHHmHRokXcfvvtxXxCjRSJnG/EvM18k5QOlOzqsnE3XctjN13jktpERM7nyPe3U0KRK27VYbFYyM/PN9X22LFjhIaGsm7dOjp16nTRNhMmTGDFihXs2bPH/tzIkSPZuXMn8fHxxb6HQpFIgcJA5Oh0WWEgqlqxAluf7a5L7UWkVJT6PkWTJ092xmFKrPCqt5CQSw/Hx8fH06NHjyLP3XzzzcyZM4fc3FwqVKhQ5LXs7Gyys7PtjwuvlhPxZudy8h0OROdfag/w8m3NFIhEpFxy+1BkGAbjx4+nQ4cONG3a9JLt0tLSqFmzZpHnatasSV5eHn/++Sfh4UV3zJ0+fTpTp051Sc0i7uqlL3aXOBBV8vfljbuaa3dqESm3nHZD2LIyevRoEhIS+OGHH4pt+/d1TIUzhxdb3zRx4kTGjx9vf5yZmUmdOnWusFoRN7bQwjQHmp8fiPrGhPP2oJYaIRKRcs2tQ9GYMWNYsWIF69evp3bt2pdtGxYWRlpaWpHn0tPT8fPzo1q1Cy8LDggIICAgwKn1irithebDzPlh6KqKfvw8qbv2HhIRt+CWocgwDMaMGcOyZctYu3Yt9evXL7ZPbGwsK1euLPLc119/TZs2bS5YTyQi/8eWC4v9TTcvDEQ+wJZnuxNS2XxfEZGy5pb/fHv00UeZP38+CxcupEqVKqSlpZGWlsa5c+fsbSZOnMi9995rfzxy5EgOHTrE+PHj2bNnD//+97+ZM2cOTzzxRFl8BJHyb6GlRIEI4P17WikQiYjbccuRopkzZwJw4403Fnl+7ty5DB8+HIDU1FQOHz5sf61+/fqsXr2acePG8d577xEREcE777xjao8iEa9TwumyAD8f3h7UQoupRcQtOWWfIm+gfYrEK+ScgM/M7zT9bFACO38/SXBgBR7q2IAO19bQYmoRKVdKfZ8iEfEADowO4VMBBuXwD9dVIyJS6hSKRMSxQDREg8si4pnccqG1iDjJ6YMKRCIi/0cjRSLeypEwVP166PGj62oRESkHFIpEvJFGh0RELqDpMxFv8ucmBSIRkUvQSJGIt3AkDDUcCe1muq4WEZFySKFIxBtodEhEpFiaPhPxZIc/UyASETFJI0UinsqRMNTqTWg8znW1iIi4AYUiEU+k0SEREYdp+kzEkyS9qkAkIlJCGikS8RSOhKGOS6HOra6rRUTEDSkUiXgCjQ6JiFwxTZ+JuLOfHlAgEhFxEo0UibgrR8JQzy0Q0tp1tYiIeACFIhF3pNEhERGnUygScSdxbeH4FvPtFYhERExTKBJxF46MDvU/BJXquq4WEREPpFAk4g40XSYi4nIKRSLlmSNhCBSIRESugEKRSHnlSCC64yT4W11WioiIN1AoEimPNF0mIlLqFIpEyhNNl4mIlBmFIpHywpFANCgXfPS/r4iIM+lvVZHyQNNlIiJlTqFIpCxpukxEpNxQKBIpZfk2g03Jx4n9ubr5ToNtYHEwQImIiEN8yroAEW8Sl5hKh1e+dywQDTEUiERESoFGikRKSVxiKj0TIuhZz4FOmi4TESk1CkUipSDfZtAzIcJ8B4UhEZFSp+kzEVczDHwXO/C/mgKRiEiZ0EiRiCs5eHXZ51G/099FpYiIyOUpFIm4igOBKDJhFQCL2ge6qhoRESmGQpGIk5zLyeel1Ukc/iuTj4M6mO4XmbAKCxBmDaRd/RDXFSgiIpelUCTiBA98tJnvfknnYExfCDLfrzAQAUzuF4Wvjy69FxEpKwpFIleo82vfc+ivcwWByKTC6TIoGCGa3C+Knk3DXVGeiIiYpFAkcgWmrtzF8RN/cjBmoOk++YNsLGp/nPRTWYRWKZgy0wiRiEjZsxiGoet/TcjMzMRqtZKRkUFwcHBZlyPlQE6eDf9PfB3rpMvtRURKlSPf39qnSKSEHAlEQ8/9pEAkIlLOKRSJOOr0QYcvt4+sVtF19YiIiFNoTZGIIxwIQ79l1abbvlkATOod5aqKRETESRSKRMwqwWaMAN0ahxLk7+DaIxERKXWaPhMpzrGNJQ5E9aoFMWd4W1dUJSIiTqaRIpHLcSAMfZfZlgcOTrY/vu+Gukzu18wVVYmIiAsoFIlcigOBKOeufA7GH+Te8LPUC6nI0NhI/P00ECsi4k4UikT+7uBi2DjYfPshBv7AAx0buKwkERFxPYUikfM5MDpE1NPQYrrrahERkVKlUCRSyJFApI0YRUQ8jhY9iCT+Q4FIREQ0UiRezpEwdN1H0GCYy0oREZGypVAk3kujQyIich5Nn4n3+WGgApGIiFxAI0XiXRwJQ93WQs3OrqpERETKGYUi8R4aHRIRkctQKBLPt+JqOH3AfHsFIhERr6RQJJ7NkdGhvnsh+FrX1SIiIuWaQpF4Lk2XiYiIAxSKxPM4EoZAgUhERACFIvEQ+TaDTcnHif25uvlOt/8FASGuK0pERNyK9ikStxeXmEqHV753LBANMRSIRESkCLcMRevXr6dfv35ERERgsVhYvnz5ZduvXbsWi8Vywc8vv/xSOgWLy8QlptIzIYL4ejeZ76TpMhERuQi3nD47c+YMzZs357777uP222833W/v3r0EBwfbH9eoUcMV5UkpybcZ9EyIMN9hYDb4+ruuIBERcWtuGYp69epFr169HO4XGhpK1apVnV+QlAnfxQ4MdGp0SEREiuGW02cl1bJlS8LDw+nWrRtr1qwp63LEQfk2g/jf/iq4usyBK8w+j/rdhVWJiIincMuRIkeFh4fz4Ycf0rp1a7Kzs/nPf/5Dt27dWLt2LZ06dbpon+zsbLKzs+2PMzMzS6tcuYi4xFSmrkxyaO1QZMJKwMKi9oGuK0xERDyGV4SiRo0a0ahRI/vj2NhYjhw5wuuvv37JUDR9+nSmTp1aWiXKJRw/nUOfd9aTmpnNwZi+pvtFJqzCAoRZA2lXX1eZiYhI8bxq+ux81113Hfv377/k6xMnTiQjI8P+c+TIkVKsTgBav/A1rf7xDfGR3UsUiAAm94vC18fBzRxFRMQrecVI0cVs376d8PDwS74eEBBAQEBAKVYk52s46QvybDgchgqFWQOZ3C+Knk0v/d9YRETkfG4Zik6fPs2vv/5qf5ycnMyOHTsICQmhbt26TJw4kZSUFObNmwfAW2+9RWRkJNHR0eTk5DB//nyWLFnCkiVLyuojyCUcP51Dm398gw3HAtGyJr+zqH0g6aeyCK1SMGWmESIREXGEW4aiLVu20KVLF/vj8ePHAzBs2DA++ugjUlNTOXz4sP31nJwcnnjiCVJSUggKCiI6OpovvviC3r17l3rtcmktp3zFiaw8h8IQFIwQLWofSOzV1VxUmYiIeAOLYRjawMWEzMxMrFYrGRkZRTaAFOeIfPoLwPHpssLF1D9M6KqRIRERuYAj399eu9Bayo9rn/kCH/JLvH5Ii6lFRMQZ3HL6TDxH2sks9kU7Pl0GUDM4gKm3RGsxtYiIOIVCkZSpsNVBptuePzoUHODLxqe7aYRIREScRtNnUjbyzjp0q47zA5GfDyRM7alAJCIiTqWRIikVOXk2/hN/kEPHz/LCmWYO9T0/ENUK9ufHSd2dXZ6IiIhCkbje9NVJzN6QjM0o+WaMAP+8szm3tq7t7PJEREQAhSJxsRe/2M3sDQep7neCLVFDTff7eyD67aXemi4TERGXUigSl1m2LYXZGw46NDq0JrM19x383414/X1h34t9XFGeiIhIEQpF4hL3fbSJNb8ccygQpfU+x5PvrsffN4/gQD9WjelEWNVAF1YpIiLyPwpF4nSdXv2OCmf2czBmlPlOQwzCgC3P9XBZXSIiIpejUCROk5Nno8/b61hf+ybTff51rD+WVm/ygAvrEhERMUOhSJxi+uokPlif7PDVZT4W+CU20nWFiYiImKRQJFck32bw2OLtHNy/gYMxj5vuV3h12YiO9fH30x6iIiJS9hSKpMRWbEvhySU72BvdF64x1+el1Pv48NjtAIzoGMnE3lEurFBERMQ8hSJxWL7NoPubaznw59kSb8b4z7tacGurWq4oT0REpEQ0byEOiUtMJer5OGqe+6nEgahL4xoKRCIiUu5opEhMi0tMZdT8bSQ7EIbuOTCNH063tD+uGxLI3OHtXFGeiIjIFVEoElPybQZTVyY5FIj+fquObo1rMEeBSEREyimFIrmsfJvBpuTjpO9eSHy9sab7nR+ImoRXYemoGwjy93VFiSIiIk6hUCSXtDohlWc/T2Tb1eZ3mb5p7/v8ml0XAB9LwSX3usJMRETcgUKRXFRJN2MsdGvzCF65s7n2IBIREbehbyy5wOqEoxzf9UGJA1FM7WD+ObilApGIiLgVjRRJEfk2g96Jtehdx1z7VrsXcDzfan98U5NQ/jWsrYuqExERcR2FIinCd7H50Z3C0aEAPx+uaxDCrHvaaDG1iIi4LYUiL1d4dVnVA6/Q5NhrpvtFJqxidJeruaFhDdrVD8HXx+LCKkVERFxPochL5eTZmLQ0gdW70khq0st0vya7PuOcEUi1Sv6M695IYUhERDyGQpEXKryyDCjxYupp/ZsqEImIiEfR5UFepjAQTa/1TokD0cOd6tM7JtwV5YmIiJQZjRR5kZw8m8N7D12d8Dn5FCyeDqlUgX/0b0rvmAhXlSgiIlJmFIq8yNwfDzgUiJY1PsL89kGkn8oitEqgFlSLiIhHUyjyFon/4OGU50w3j0xYxaL2QcReXc2FRYmIiJQfCkUe7HRWHuP+u53ZFczfmT4yYSVgIdxaMDIkIiLiLRSKPNQtMzaQ8HtmiRdTT+4XpakyERHxKrr6zAPdMmMDIRnfmw5E32a2LRKIZgxqQc+murpMRES8i0aKPMzprDxWhHQCkzNf54chgBEd69O3RS0XVCYiIlK+KRR5mMpLK5hue34g8rEUBKKJvaNcUZaIiEi5p1DkKQ59Aj8ONNX0X8f680n+4zzXpy6Hjp+lXkhFhsZG4u+n2VQREfFeCkVuLCfPxn/iD/LAkatN9ykcHerepBIPdGzgqtJERETcjkKRG8q3GTy2eDtfJBwlOaaf6X7nT5f9c2BLV5QmIiLithSK3ExcYirjP9lJ+4CfSI6ZaqrPbb++xrazTeyPY2oHUzlQ/+lFRETOp29GNxKXmMrI+dtKvPcQFASiFaM7Ors0ERERt6dQ5CbybQZTV+52KBCdvi2X7rnbOXziHHWvCuKfA1tqhEhEROQS9A3pJpIS4oiv19tU29773qFNyy68EOjH7GFtXVyZiIiIZ1Aocgdx7Wh2fLOppoXTZbeHVHRlRSIiIh5HG9OUZ4YNFlrARCDaeDrGHoh8LDA0NtLFxYmIiHgWjRSVV3+sge+6mmp6/Z5/czQ31P54RMf62ohRRETEQQpF5US+zWBT8nHST2XR40AngrIOmOp3/tVlFgs8pFt1iIiIlIhCUTkQl5jK1JVJpGec4beY/qb6TE0Zwdy/CtpWrOBL72ZhvHRbjEaIRERESkihqIytTjjKIwu3E1spgfiYSab65N92gh4p+bQ4lUVolUDa1Q/B18fi4kpFREQ8m0JRGVqdkMroRdsZVeNTJoR/bK7TEANfINb87c5ERETEBIWiMhKXmMojC7cR33gY4f5/Fd+h3YfQcITrCxMREfFSWoBSBgp2p05iVr0XTQWiVY33KhCJiIi4mEJRGdiUfJzUjCx6WuOLbRuZsIpq1mqlUJWIiIh3UygqA+mnsoptM/bwk0QmrCLcWrCQWkRERFxLoagMhFYJBOC99DsveO2cLYCGCctZcbIzAJP7RenKMhERkVKgUFQG2tUPIdwayOtpw5iS8pD9+ccP/z+aJC4hDz98LPD+kJb0bBpehpWKiIh4D4thGEZZF+EOMjMzsVqtZGRkEBwcfMXHi0tMZdT8bQBc7D/A+0Na0TtGgUhERORKOPL9rZGiMtKzaTgz72lFmDWwyPPh1kBm3aNAJCIiUtq0T1EZ6tk0nO5RYfZ7nml3ahERkbKjUFTGfH0sxF6tS+5FRETKmqbPRERERFAoEhEREQHcNBStX7+efv36ERERgcViYfny5cX2WbduHa1btyYwMJAGDRowa9Ys1xcqIiIibsMtQ9GZM2do3rw5M2bMMNU+OTmZ3r1707FjR7Zv386kSZMYO3YsS5YscXGlIiIi4i7ccqF1r1696NWrl+n2s2bNom7durz11lsANGnShC1btvD6669z++23u6hKERERcSduOVLkqPj4eHr06FHkuZtvvpktW7aQm5t70T7Z2dlkZmYW+RERERHP5RWhKC0tjZo1axZ5rmbNmuTl5fHnn39etM/06dOxWq32nzp16pRGqSIiIlJGvCIUAVgsRTdELLy7yd+fLzRx4kQyMjLsP0eOHHF5jSIiIlJ23HJNkaPCwsJIS0sr8lx6ejp+fn5Uq3bxjRMDAgIICAgojfJERESkHPCKUBQbG8vKlSuLPPf111/Tpk0bKlSoYOoYhSNLWlskIiLiPgq/twu/xy/HLUPR6dOn+fXXX+2Pk5OT2bFjByEhIdStW5eJEyeSkpLCvHnzABg5ciQzZsxg/PjxjBgxgvj4eObMmcOiRYtMv+epU6cAtLZIRETEDZ06dQqr1XrZNhbDTHQqZ9auXUuXLl0ueH7YsGF89NFHDB8+nIMHD7J27Vr7a+vWrWPcuHHs3r2biIgIJkyYwMiRI02/p81m4+jRo1SpUuWS65A8VWZmJnXq1OHIkSMEBweXdTllRuehgM5DAZ2HAjoP/6NzUaC8nQfDMDh16hQRERH4+Fx+KbVbhiIpXZmZmVitVjIyMsrFL3hZ0XkooPNQQOehgM7D/+hcFHDn8+A1V5+JiIiIXI5CkYiIiAgKRWJCQEAAkydP9votCnQeCug8FNB5KKDz8D86FwXc+TxoTZGIiIgIGikSERERARSKRERERACFIhERERFAochrpaSkcM8991CtWjUqVqxIixYt2Lp1q/11wzCYMmUKERERBAUFceONN7J79+4ix8jOzmbMmDFUr16dSpUqccstt/D777+X9ke5Inl5eTz77LPUr1+foKAgGjRowAsvvIDNZrO38cRzsX79evr160dERAQWi4Xly5cXed1Zn/nEiRMMHToUq9WK1Wpl6NChnDx50sWfzrzLnYfc3FwmTJhAs2bNqFSpEhEREdx7770cPXq0yDE8/Tz83cMPP4zFYuGtt94q8ry3nIc9e/Zwyy23YLVaqVKlCtdddx2HDx+2v+4J5wGKPxenT59m9OjR1K5dm6CgIJo0acLMmTOLtHHHc6FQ5IVOnDjBDTfcQIUKFfjyyy9JSkrijTfeoGrVqvY2r776Km+++SYzZsxg8+bNhIWF0b17d/vtTgAef/xxli1bxuLFi/nhhx84ffo0ffv2JT8/vww+Vcm88sorzJo1ixkzZrBnzx5effVVXnvtNd599117G088F2fOnKF58+bMmDHjoq876zMPGTKEHTt2EBcXR1xcHDt27GDo0KEu/3xmXe48nD17lm3btvHcc8+xbds2li5dyr59+7jllluKtPP083C+5cuX8/PPPxMREXHBa95wHn777Tc6dOhA48aNWbt2LTt37uS5554jMDDQ3sYTzgMUfy7GjRtHXFwc8+fPZ8+ePYwbN44xY8bw+eef29u45bkwxOtMmDDB6NChwyVft9lsRlhYmPHyyy/bn8vKyjKsVqsxa9YswzAM4+TJk0aFChWMxYsX29ukpKQYPj4+RlxcnOuKd7I+ffoY999/f5HnbrvtNuOee+4xDMM7zgVgLFu2zP7YWZ85KSnJAIyffvrJ3iY+Pt4AjF9++cXFn8pxfz8PF7Np0yYDMA4dOmQYhnedh99//92oVauWkZiYaNSrV8/45z//aX/NW87DwIED7X83XIwnngfDuPi5iI6ONl544YUiz7Vq1cp49tlnDcNw33OhkSIvtGLFCtq0acOdd95JaGgoLVu2ZPbs2fbXk5OTSUtLo0ePHvbnAgIC6Ny5Mxs3bgRg69at5ObmFmkTERFB06ZN7W3cQYcOHfjuu+/Yt28fADt37uSHH36gd+/egHedi0LO+szx8fFYrVbat29vb3PddddhtVrd8rwAZGRkYLFY7KOq3nIebDYbQ4cO5cknnyQ6OvqC173hPNhsNr744guuvfZabr75ZkJDQ2nfvn2RaSVvOA+FOnTowIoVK0hJScEwDNasWcO+ffu4+eabAfc9FwpFXujAgQPMnDmTa665hq+++oqRI0cyduxY5s2bB0BaWhoANWvWLNKvZs2a9tfS0tLw9/fnqquuumQbdzBhwgQGDx5M48aNqVChAi1btuTxxx9n8ODBgHedi0LO+sxpaWmEhoZecPzQ0FC3PC9ZWVk8/fTTDBkyxH4/J285D6+88gp+fn6MHTv2oq97w3lIT0/n9OnTvPzyy/Ts2ZOvv/6aW2+9ldtuu41169YB3nEeCr3zzjtERUVRu3Zt/P396dmzJ++//z4dOnQA3Pdc+JXJu0qZstlstGnThpdeegmAli1bsnv3bmbOnMm9995rb2exWIr0Mwzjguf+zkyb8uS///0v8+fPZ+HChURHR7Njxw4ef/xxIiIiGDZsmL2dN5yLv3PGZ75Ye3c8L7m5uQwaNAibzcb7779fbHtPOg9bt27l7bffZtu2bQ7X60nnofDii/79+zNu3DgAWrRowcaNG5k1axadO3e+ZF9POg+F3nnnHX766SdWrFhBvXr1WL9+PY888gjh4eHcdNNNl+xX3s+FRoq8UHh4OFFRUUWea9Kkif0KirCwMIALknp6erp99CAsLIycnBxOnDhxyTbu4Mknn+Tpp59m0KBBNGvWjKFDhzJu3DimT58OeNe5KOSszxwWFsYff/xxwfGPHTvmVuclNzeXu+66i+TkZL755psid/32hvOwYcMG0tPTqVu3Ln5+fvj5+XHo0CH+3//7f0RGRgLecR6qV6+On59fsX93evp5ADh37hyTJk3izTffpF+/fsTExDB69GgGDhzI66+/DrjvuVAo8kI33HADe/fuLfLcvn37qFevHgD169cnLCyMb775xv56Tk4O69at4/rrrwegdevWVKhQoUib1NRUEhMT7W3cwdmzZ/HxKfq/ga+vr/1fhd50Lgo56zPHxsaSkZHBpk2b7G1+/vlnMjIy3Oa8FAai/fv38+2331KtWrUir3vDeRg6dCgJCQns2LHD/hMREcGTTz7JV199BXjHefD396dt27aX/bvTG84DFPx/kZube9m/O932XJT+2m4pa5s2bTL8/PyMF1980di/f7+xYMECo2LFisb8+fPtbV5++WXDarUaS5cuNXbt2mUMHjzYCA8PNzIzM+1tRo4cadSuXdv49ttvjW3bthldu3Y1mjdvbuTl5ZXFxyqRYcOGGbVq1TJWrVplJCcnG0uXLjWqV69uPPXUU/Y2nnguTp06ZWzfvt3Yvn27ARhvvvmmsX37dvtVVc76zD179jRiYmKM+Ph4Iz4+3mjWrJnRt2/fUv+8l3K585Cbm2vccsstRu3atY0dO3YYqamp9p/s7Gz7MTz9PFzM368+MwzvOA9Lly41KlSoYHz44YfG/v37jXfffdfw9fU1NmzYYD+GJ5wHwyj+XHTu3NmIjo421qxZYxw4cMCYO3euERgYaLz//vv2Y7jjuVAo8lIrV640mjZtagQEBBiNGzc2PvzwwyKv22w2Y/LkyUZYWJgREBBgdOrUydi1a1eRNufOnTNGjx5thISEGEFBQUbfvn2Nw4cPl+bHuGKZmZnGY489ZtStW9cIDAw0GjRoYDzzzDNFvvQ88VysWbPGAC74GTZsmGEYzvvMf/31l3H33XcbVapUMapUqWLcfffdxokTJ0rpUxbvcuchOTn5oq8Bxpo1a+zH8PTzcDEXC0Xech7mzJljNGzY0AgMDDSaN29uLF++vMgxPOE8GEbx5yI1NdUYPny4ERERYQQGBhqNGjUy3njjDcNms9mP4Y7nwmIYhuHasSgRERGR8k9rikRERERQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSEQ8XGRkJBaLheHDh5d1KSJSzikUiYiIiKBQJCIiIgKA7n0mIiIigkaKRERERACFIhERERFAoUhE3MjRo0d5+umnadWqFVarFX9/f8LCwmjWrBmDBw/mo48+IjMzs0ifS119dvDgQSwWi+mfG2+88ZJ1bdq0iREjRnDttddSuXJlKlWqROPGjXn00UfZv3+/C86EiLiCX1kXICJixoYNG+jbt+8FoeePP/7gjz/+IDExkcWLF1O9enX69u1bKjXl5eUxduxYZs6cecFre/fuZe/evcyePZv33nuPESNGlEpNIlJyCkUiUu5lZ2czaNAgMjMzqVKlCqNGjaJLly6EhoaSm5vLoUOHiI+PZ8mSJaaPWatWLXbt2nXZNp988gnTpk0DoF69ehe8/sADDzBv3jwAevXqxd133821116LxWJhx44dvPXWW+zevZuHHnqIsLAw+vXr58CnFpHSpqvPRKTc+/777+nWrRsAK1euvORIUF5eHmfPniU4ONj+XGRkJIcOHWLYsGF89NFHpt9zy5YtdOrUiXPnztGkSRN++umnIsddsmQJd9xxBwCzZ8/mwQcfvOAYWVlZ9OnTh++//57IyEj279+Pn5/+LSpSXmlNkYiUe2lpafY/d+rU6ZLt/Pz8igSXkjp69CgDBgzg3LlzhISEsGLFiguOO336dABuvfXWiwYigMDAQGbMmAEUrGFau3btFdcmIq6jUCQi5V54eLj9z3PnznXpe507d44BAwaQkpKCn58fn376KQ0bNizSJiUlha1btwJw1113XfZ4TZo0oXr16gDEx8e7pmgRcQqFIhEp9zp06ECDBg0AePzxx2nXrh3Tp09n48aN5OTkOPW97r//fjZv3gzAO++8Q9euXS9os2XLFvufBw8eXOyVa3/++SdQdMRLRMofhSIRKfcqVKjAypUradKkCQCbN29m0qRJ3HDDDVStWpVevXqxcOFC8vPzr+h9pk2bxuLFiwF45JFHGDVq1EXbpaenl+j4Z8+eLXFtIuJ6WvEnIm4hKiqKXbt2sXLlSlauXMm6dev47bffOHfuHHFxccTFxfHmm2+yevVqQkNDHT7+kiVLmDx5MgDdunXj7bffvmTb88PXggULiImJMfUeV111lcN1iUjpUSgSEbfh6+vLgAEDGDBgAACpqal8+eWXvP/++2zdupWtW7fy8MMPs2zZMoeOu337du69914Mw6Bhw4Z88sknl71KrFq1avY/WywWmjZtWqLPIyLli6bPRMRthYeHc//99xMfH0+rVq0AWLVqFefOnTN9jLS0NPr378/Zs2exWq2sXLmSkJCQy/Zp2bKl/c9ff/11yYoXkXJHoUhE3F6FChXo3LkzULBX0cmTJ031y8rKYsCAARw5cgRfX18WL15M48aNi+3XsGFDoqKiAFi8eDGHDx8uce0iUn4oFIlIubdhwwZ+/fXXS76ek5PDunXrAKhcuTI1atQwddwHH3yQn3/+GYDXXnuNnj17mq7p2WefBQqC1W233caxY8cu2TY7O5v333+frKws08cXkdKnNUUiUu599913TJs2jY4dO9KnTx9iYmKoUaMG586dY9++fcyaNYtt27YBBUHHzK7R//73v1mwYAEAXbt2pXv37iQmJl6yfaVKlahfv7798eDBg/nqq6/4+OOP2bp1K1FRUTz88MN07tyZGjVqcObMGX777Tc2bNjA0qVLOX78OPfee+8VngkRcSWFIhFxCzabjXXr1tlHhC7mtttus+80XZzzp7y+//57mjVrdtn2nTt3vmBH6jlz5lCzZk3eeOMN/vzzT1588UVefPHFi/avVKkSvr6+pmoTkbKhUCQi5d5TTz1F+/bt+eabb4iPj+fo0aP2vYLCwsJo37499957L7179y7Vunx9fXnllVd44IEH+PDDD/n+++85ePAgmZmZVKxYkbp169KiRQt69OjBrbfeSlBQUKnWJyKO0Q1hRURERNBCaxERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERAeD/A1vgamnLbtM1AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(x1)\n",
+    "Y = 0.0017 * x1 + 0.275# Simulated Y values\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "plt.scatter(x1, Y)\n",
+    "\n",
+    "# Define the regression line\n",
+    "yhat = 0.0017 * x1 + 0.275\n",
+    "\n",
+    "# Plot the regression line\n",
+    "plt.plot(x1, yhat, lw=4, c='orange', label='Regression line')\n",
+    "\n",
+    "# Set labels and show the plot\n",
+    "plt.xlabel('size', fontsize=20)\n",
+    "plt.ylabel('price', fontsize=20)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5055466",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/Ustacky_MyProject_step2.ipynb b/Udemy DataScience Workbook/Ustacky_MyProject_step2.ipynb
new file mode 100644
index 0000000..503b7da
--- /dev/null
+++ b/Udemy DataScience Workbook/Ustacky_MyProject_step2.ipynb	
@@ -0,0 +1,6196 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "01a24aab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import glob\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import datetime as dt\n",
+    "os.chdir(r\"C:\\Users\\Chizy\\Desktop\\MyContent\\Ustacky\\My project\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d5bf8d2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extension = 'csv'\n",
+    "file_1 =  [i for i in glob.glob('*.{}'.format(extension))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "735f6be0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja_Branch.csv',\n",
+       " 'Lagos_Branch.csv',\n",
+       " 'Market_data.csv',\n",
+       " 'Port_Harcourt_Branch.csv']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f6ac03d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read each dataframe\n",
+    "df_A = pd.read_csv('Abuja_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "ab893353",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>552-44-5977</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>22320.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8928.00</td>\n",
+       "      <td>187488.00</td>\n",
+       "      <td>1/3/2019</td>\n",
+       "      <td>19:08</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>178560.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8928.00</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>430-53-4718</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>27133.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10853.28</td>\n",
+       "      <td>227918.88</td>\n",
+       "      <td>1/28/2019</td>\n",
+       "      <td>15:46</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>217065.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10853.28</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>602-16-6955</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>27576.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13788.00</td>\n",
+       "      <td>289548.00</td>\n",
+       "      <td>1/24/2019</td>\n",
+       "      <td>18:10</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>275760.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13788.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>690-01-6631</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>6296.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3148.20</td>\n",
+       "      <td>66112.20</td>\n",
+       "      <td>2/22/2019</td>\n",
+       "      <td>18:35</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>62964.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>3148.20</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>303-96-2227</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>35056.8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>17528.40</td>\n",
+       "      <td>368096.40</td>\n",
+       "      <td>3/2/2019</td>\n",
+       "      <td>17:16</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>350568.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>17528.40</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>332 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0    692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "1    351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "2    529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "3    299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "4    319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "..           ...    ...    ...           ...     ...                     ...   \n",
+       "327  552-44-5977      B  Abuja        Member    Male       Health and beauty   \n",
+       "328  430-53-4718      B  Abuja        Member    Male       Health and beauty   \n",
+       "329  602-16-6955      B  Abuja        Normal  Female       Sports and travel   \n",
+       "330  690-01-6631      B  Abuja        Normal    Male     Fashion accessories   \n",
+       "331  303-96-2227      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0       19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
+       "1        5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
+       "2        9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
+       "3       33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
+       "4       14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
+       "..          ...       ...       ...        ...        ...    ...     ...   \n",
+       "327     22320.0         8   8928.00  187488.00   1/3/2019  19:08    Card   \n",
+       "328     27133.2         8  10853.28  227918.88  1/28/2019  15:46    Card   \n",
+       "329     27576.0        10  13788.00  289548.00  1/24/2019  18:10    Epay   \n",
+       "330      6296.4        10   3148.20   66112.20  2/22/2019  18:35    Epay   \n",
+       "331     35056.8        10  17528.40  368096.40   3/2/2019  17:16    Epay   \n",
+       "\n",
+       "         cogs  gross margin percentage  gross income  Rating  \n",
+       "0     59227.2                 4.761905       2961.36     5.9  \n",
+       "1     20851.2                 4.761905       1042.56     4.5  \n",
+       "2     36734.4                 4.761905       1836.72     6.8  \n",
+       "3    202435.2                 4.761905      10121.76     4.5  \n",
+       "4     29016.0                 4.761905       1450.80     4.4  \n",
+       "..        ...                      ...           ...     ...  \n",
+       "327  178560.0                 4.761905       8928.00     6.2  \n",
+       "328  217065.6                 4.761905      10853.28     8.4  \n",
+       "329  275760.0                 4.761905      13788.00     6.0  \n",
+       "330   62964.0                 4.761905       3148.20     6.6  \n",
+       "331  350568.0                 4.761905      17528.40     4.4  \n",
+       "\n",
+       "[332 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aff26318",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_L = pd.read_csv('Lagos_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "f81b0249",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>886-18-2897</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>20361.6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5090.40</td>\n",
+       "      <td>106898.40</td>\n",
+       "      <td>3/22/2019</td>\n",
+       "      <td>19:06</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>101808.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5090.40</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>745-74-0715</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>20890.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2089.08</td>\n",
+       "      <td>43870.68</td>\n",
+       "      <td>3/10/2019</td>\n",
+       "      <td>20:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>41781.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2089.08</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>727-02-1313</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>11462.4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>573.12</td>\n",
+       "      <td>12035.52</td>\n",
+       "      <td>2/9/2019</td>\n",
+       "      <td>13:22</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>11462.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>573.12</td>\n",
+       "      <td>7.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>347-56-2442</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>23695.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1184.76</td>\n",
+       "      <td>24879.96</td>\n",
+       "      <td>2/22/2019</td>\n",
+       "      <td>15:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>23695.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1184.76</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>849-09-3807</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>31802.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11130.84</td>\n",
+       "      <td>233747.64</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>13:28</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>222616.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>11130.84</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>340 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0    750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1    631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2    123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3    373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4    355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "..           ...    ...    ...           ...     ...                     ...   \n",
+       "335  886-18-2897      A  Lagos        Normal  Female      Food and beverages   \n",
+       "336  745-74-0715      A  Lagos        Normal    Male  Electronic accessories   \n",
+       "337  727-02-1313      A  Lagos        Member    Male      Food and beverages   \n",
+       "338  347-56-2442      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "339  849-09-3807      A  Lagos        Member  Female     Fashion accessories   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0       26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1       16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2       20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3       31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4       24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "..          ...       ...       ...        ...        ...    ...     ...   \n",
+       "335     20361.6         5   5090.40  106898.40  3/22/2019  19:06    Card   \n",
+       "336     20890.8         2   2089.08   43870.68  3/10/2019  20:46    Epay   \n",
+       "337     11462.4         1    573.12   12035.52   2/9/2019  13:22    Cash   \n",
+       "338     23695.2         1   1184.76   24879.96  2/22/2019  15:33    Cash   \n",
+       "339     31802.4         7  11130.84  233747.64  2/18/2019  13:28    Cash   \n",
+       "\n",
+       "         cogs  gross margin percentage  gross income  Rating  \n",
+       "0    188218.8                 4.761905       9410.94     9.1  \n",
+       "1    116751.6                 4.761905       5837.58     7.4  \n",
+       "2    167673.6                 4.761905       8383.68     8.4  \n",
+       "3    217501.2                 4.761905      10875.06     5.3  \n",
+       "4    148694.4                 4.761905       7434.72     5.8  \n",
+       "..        ...                      ...           ...     ...  \n",
+       "335  101808.0                 4.761905       5090.40     4.5  \n",
+       "336   41781.6                 4.761905       2089.08     8.8  \n",
+       "337   11462.4                 4.761905        573.12     7.7  \n",
+       "338   23695.2                 4.761905       1184.76     4.1  \n",
+       "339  222616.8                 4.761905      11130.84     6.6  \n",
+       "\n",
+       "[340 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4e90494b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_p = pd.read_csv('Port_Harcourt_Branch.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "7bd0e3d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>226-31-3081</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>5500.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>28879.20</td>\n",
+       "      <td>3/8/2019</td>\n",
+       "      <td>10:29</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>27504.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1375.20</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699-14-3026</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30740.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>225941.94</td>\n",
+       "      <td>3/25/2019</td>\n",
+       "      <td>18:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>215182.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10759.14</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>315-22-5665</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>26481.6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>278056.80</td>\n",
+       "      <td>2/24/2019</td>\n",
+       "      <td>11:38</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>264816.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>13240.80</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>300-71-4605</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>30974.4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>162615.60</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>11:24</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>154872.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7743.60</td>\n",
+       "      <td>4.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>183-56-6882</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>35791.2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>150323.04</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>10:42</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>143164.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7158.24</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>328 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    226-31-3081      C  Port Harcourt        Normal  Female   \n",
+       "1    699-14-3026      C  Port Harcourt        Normal    Male   \n",
+       "2    315-22-5665      C  Port Harcourt        Normal  Female   \n",
+       "3    300-71-4605      C  Port Harcourt        Member    Male   \n",
+       "4    183-56-6882      C  Port Harcourt        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0    Electronic accessories      5500.8         5   1375.20   28879.20   \n",
+       "1    Electronic accessories     30740.4         7  10759.14  225941.94   \n",
+       "2        Home and lifestyle     26481.6        10  13240.80  278056.80   \n",
+       "3    Electronic accessories     30974.4         5   7743.60  162615.60   \n",
+       "4        Food and beverages     35791.2         4   7158.24  150323.04   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     3/8/2019  10:29    Cash   27504.0                 4.761905   \n",
+       "1    3/25/2019  18:30    Epay  215182.8                 4.761905   \n",
+       "2    2/24/2019  11:38    Epay  264816.0                 4.761905   \n",
+       "3    2/25/2019  11:24    Epay  154872.0                 4.761905   \n",
+       "4     2/6/2019  10:42    Epay  143164.8                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         1375.20     9.6  \n",
+       "1        10759.14     4.1  \n",
+       "2        13240.80     8.0  \n",
+       "3         7743.60     4.8  \n",
+       "4         7158.24     7.5  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[328 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e3802334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a list of the dataframes\n",
+    "pd_list = [df_L, df_A, df_p]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0db90e96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine all the df\n",
+    "df_ABC = pd.concat(pd_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1b43e9eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "323  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "324  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "325  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "326  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "327  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "323       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "324  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "325  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "326  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "327       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "323  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "324   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "325  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "326  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "327  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "323      12594.96     6.1  \n",
+       "324      12142.62     6.0  \n",
+       "325      14821.20     4.3  \n",
+       "326       1097.10     5.9  \n",
+       "327        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ABC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "09a3d601",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Export combined data as csv\n",
+    "df_ABC.to_csv('Market_data.csv', index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "dc2f293c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     1/5/2019  13:08    Epay  188218.8                 4.761905   \n",
+       "1     3/3/2019  13:23    Card  116751.6                 4.761905   \n",
+       "2    1/27/2019  20:33    Epay  167673.6                 4.761905   \n",
+       "3     2/8/2019  10:37    Epay  217501.2                 4.761905   \n",
+       "4    2/25/2019  14:36    Epay  148694.4                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         9410.94     9.1  \n",
+       "1         5837.58     7.4  \n",
+       "2         8383.68     8.4  \n",
+       "3        10875.06     5.3  \n",
+       "4         7434.72     5.8  \n",
+       "..            ...     ...  \n",
+       "995      12594.96     6.1  \n",
+       "996      12142.62     6.0  \n",
+       "997      14821.20     4.3  \n",
+       "998       1097.10     5.9  \n",
+       "999        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('Market_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "66c0ffff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>1/5/2019</td>\n",
+       "      <td>13:08</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>3/3/2019</td>\n",
+       "      <td>13:23</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>1/27/2019</td>\n",
+       "      <td>20:33</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2/8/2019</td>\n",
+       "      <td>10:37</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2/25/2019</td>\n",
+       "      <td>14:36</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     26888.4         7   9410.94  197629.74   1/5/2019  13:08    Epay   \n",
+       "1     16678.8         7   5837.58  122589.18   3/3/2019  13:23    Card   \n",
+       "2     20959.2         8   8383.68  176057.28  1/27/2019  20:33    Epay   \n",
+       "3     31071.6         7  10875.06  228376.26   2/8/2019  10:37    Epay   \n",
+       "4     24782.4         6   7434.72  156129.12  2/25/2019  14:36    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0  188218.8                 4.761905       9410.94     9.1  \n",
+       "1  116751.6                 4.761905       5837.58     7.4  \n",
+       "2  167673.6                 4.761905       8383.68     8.4  \n",
+       "3  217501.2                 4.761905      10875.06     5.3  \n",
+       "4  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View the first 5rows of the dataset\n",
+    "df = pd.read_csv('Market_data.csv')\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "022eba6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 17)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To check no of rows and column\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6ff0da19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',\n",
+       "       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',\n",
+       "       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',\n",
+       "       'Rating'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To check various column name in the dataset\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401ddd35",
+   "metadata": {},
+   "source": [
+    "# Statistical summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6185861a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1.000000e+03</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>20041.966800</td>\n",
+       "      <td>5.510000</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>116268.029640</td>\n",
+       "      <td>110731.456800</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>6.97270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9538.066205</td>\n",
+       "      <td>2.923431</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>88518.720636</td>\n",
+       "      <td>84303.543463</td>\n",
+       "      <td>6.131498e-14</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>1.71858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3628.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>3844.260000</td>\n",
+       "      <td>3661.200000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>11835.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>44792.055000</td>\n",
+       "      <td>42659.100000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>5.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>19882.800000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>91385.280000</td>\n",
+       "      <td>87033.600000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>7.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>28056.600000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>169686.090000</td>\n",
+       "      <td>161605.800000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>8.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>35985.600000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>375354.000000</td>\n",
+       "      <td>357480.000000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>10.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unit price     Quantity        Tax 5%          Total           cogs  \\\n",
+       "count   1000.000000  1000.000000   1000.000000    1000.000000    1000.000000   \n",
+       "mean   20041.966800     5.510000   5536.572840  116268.029640  110731.456800   \n",
+       "std     9538.066205     2.923431   4215.177173   88518.720636   84303.543463   \n",
+       "min     3628.800000     1.000000    183.060000    3844.260000    3661.200000   \n",
+       "25%    11835.000000     3.000000   2132.955000   44792.055000   42659.100000   \n",
+       "50%    19882.800000     5.000000   4351.680000   91385.280000   87033.600000   \n",
+       "75%    28056.600000     8.000000   8080.290000  169686.090000  161605.800000   \n",
+       "max    35985.600000    10.000000  17874.000000  375354.000000  357480.000000   \n",
+       "\n",
+       "       gross margin percentage  gross income      Rating  \n",
+       "count             1.000000e+03   1000.000000  1000.00000  \n",
+       "mean              4.761905e+00   5536.572840     6.97270  \n",
+       "std               6.131498e-14   4215.177173     1.71858  \n",
+       "min               4.761905e+00    183.060000     4.00000  \n",
+       "25%               4.761905e+00   2132.955000     5.50000  \n",
+       "50%               4.761905e+00   4351.680000     7.00000  \n",
+       "75%               4.761905e+00   8080.290000     8.50000  \n",
+       "max               4.761905e+00  17874.000000    10.00000  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To get statistical surmary\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a5e9895",
+   "metadata": {},
+   "source": [
+    "# Deductions:\n",
+    "There are 1000 rows.\n",
+    "\n",
+    "The mean, std, min, max and various percentage of the total summation of values for each columns with numeric values were provided with a single command (Describe) which shows the Standard Deviation (STD) of the unit price and gross income to be 9538.066205 and 4215.177173 respectively. Also, the mean value of the unit price and gross income is 20041.966800 and 5536.572840 respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "eaf11085",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
+       "0         False   False  False          False   False         False   \n",
+       "1         False   False  False          False   False         False   \n",
+       "2         False   False  False          False   False         False   \n",
+       "3         False   False  False          False   False         False   \n",
+       "4         False   False  False          False   False         False   \n",
+       "..          ...     ...    ...            ...     ...           ...   \n",
+       "995       False   False  False          False   False         False   \n",
+       "996       False   False  False          False   False         False   \n",
+       "997       False   False  False          False   False         False   \n",
+       "998       False   False  False          False   False         False   \n",
+       "999       False   False  False          False   False         False   \n",
+       "\n",
+       "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
+       "0         False     False   False  False  False  False    False  False   \n",
+       "1         False     False   False  False  False  False    False  False   \n",
+       "2         False     False   False  False  False  False    False  False   \n",
+       "3         False     False   False  False  False  False    False  False   \n",
+       "4         False     False   False  False  False  False    False  False   \n",
+       "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
+       "995       False     False   False  False  False  False    False  False   \n",
+       "996       False     False   False  False  False  False    False  False   \n",
+       "997       False     False   False  False  False  False    False  False   \n",
+       "998       False     False   False  False  False  False    False  False   \n",
+       "999       False     False   False  False  False  False    False  False   \n",
+       "\n",
+       "     gross margin percentage  gross income  Rating  \n",
+       "0                      False         False   False  \n",
+       "1                      False         False   False  \n",
+       "2                      False         False   False  \n",
+       "3                      False         False   False  \n",
+       "4                      False         False   False  \n",
+       "..                       ...           ...     ...  \n",
+       "995                    False         False   False  \n",
+       "996                    False         False   False  \n",
+       "997                    False         False   False  \n",
+       "998                    False         False   False  \n",
+       "999                    False         False   False  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Missing value:\n",
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "30d2d644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 0\n",
+       "Branch                     0\n",
+       "City                       0\n",
+       "Customer type              0\n",
+       "Gender                     0\n",
+       "Product line               0\n",
+       "Unit price                 0\n",
+       "Quantity                   0\n",
+       "Tax 5%                     0\n",
+       "Total                      0\n",
+       "Date                       0\n",
+       "Time                       0\n",
+       "Payment                    0\n",
+       "cogs                       0\n",
+       "gross margin percentage    0\n",
+       "gross income               0\n",
+       "Rating                     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "72bdce31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                     1000\n",
+       "City                       1000\n",
+       "Customer type              1000\n",
+       "Gender                     1000\n",
+       "Product line               1000\n",
+       "Unit price                 1000\n",
+       "Quantity                   1000\n",
+       "Tax 5%                     1000\n",
+       "Total                      1000\n",
+       "Date                       1000\n",
+       "Time                       1000\n",
+       "Payment                    1000\n",
+       "cogs                       1000\n",
+       "gross margin percentage    1000\n",
+       "gross income               1000\n",
+       "Rating                     1000\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.notnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26717e81",
+   "metadata": {},
+   "source": [
+    "# There are no missing values in the set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7810eb93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   Invoice ID               1000 non-null   object \n",
+      " 1   Branch                   1000 non-null   object \n",
+      " 2   City                     1000 non-null   object \n",
+      " 3   Customer type            1000 non-null   object \n",
+      " 4   Gender                   1000 non-null   object \n",
+      " 5   Product line             1000 non-null   object \n",
+      " 6   Unit price               1000 non-null   float64\n",
+      " 7   Quantity                 1000 non-null   int64  \n",
+      " 8   Tax 5%                   1000 non-null   float64\n",
+      " 9   Total                    1000 non-null   float64\n",
+      " 10  Date                     1000 non-null   object \n",
+      " 11  Time                     1000 non-null   object \n",
+      " 12  Payment                  1000 non-null   object \n",
+      " 13  cogs                     1000 non-null   float64\n",
+      " 14  gross margin percentage  1000 non-null   float64\n",
+      " 15  gross income             1000 non-null   float64\n",
+      " 16  Rating                   1000 non-null   float64\n",
+      "dtypes: float64(7), int64(1), object(9)\n",
+      "memory usage: 132.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the information of the dataframe\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f670b3ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Converting to datetime:\n",
+    "df['Date'] = pd.to_datetime(df['Date'])\n",
+    "df['Time'] = pd.to_datetime(df['Time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "15cdbef0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[ns]')"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Date'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "1dbc2015",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('<M8[ns]')"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Time'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f94d858f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>2019-01-05</td>\n",
+       "      <td>2023-07-17 13:08:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>2019-03-03</td>\n",
+       "      <td>2023-07-17 13:23:00</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>2019-01-27</td>\n",
+       "      <td>2023-07-17 20:33:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>2019-02-08</td>\n",
+       "      <td>2023-07-17 10:37:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>2019-02-25</td>\n",
+       "      <td>2023-07-17 14:36:00</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date                Time  \\\n",
+       "0     26888.4         7   9410.94  197629.74 2019-01-05 2023-07-17 13:08:00   \n",
+       "1     16678.8         7   5837.58  122589.18 2019-03-03 2023-07-17 13:23:00   \n",
+       "2     20959.2         8   8383.68  176057.28 2019-01-27 2023-07-17 20:33:00   \n",
+       "3     31071.6         7  10875.06  228376.26 2019-02-08 2023-07-17 10:37:00   \n",
+       "4     24782.4         6   7434.72  156129.12 2019-02-25 2023-07-17 14:36:00   \n",
+       "\n",
+       "  Payment      cogs  gross margin percentage  gross income  Rating  \n",
+       "0    Epay  188218.8                 4.761905       9410.94     9.1  \n",
+       "1    Card  116751.6                 4.761905       5837.58     7.4  \n",
+       "2    Epay  167673.6                 4.761905       8383.68     8.4  \n",
+       "3    Epay  217501.2                 4.761905      10875.06     5.3  \n",
+       "4    Epay  148694.4                 4.761905       7434.72     5.8  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1d8ff8ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extract features from date & time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "79154e07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['day'] = df['Date'].dt.day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1280e135",
+   "metadata": {},
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b0c8b6ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['month'] = df['Date'].dt.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "dc123e2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019     0   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019     0   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019     0   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019     0   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019     0   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "f1628da8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['year'] = df['Date'].dt.year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "f4cfc25c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019     0   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019     0   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019     0   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019     0   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019     0   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "458d83be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['hour'] = df['Time'].dt.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "c3db5ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019    13   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019    13   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019    20   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019    10   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019    14   \n",
+       "\n",
+       "   Minute  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "23ca4780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Minute'] = df['Time'].dt.minute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "0078b0cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  750-67-8428      A  Lagos        Member  Female       Health and beauty   \n",
+       "1  631-41-3108      A  Lagos        Normal    Male      Home and lifestyle   \n",
+       "2  123-19-1176      A  Lagos        Member    Male       Health and beauty   \n",
+       "3  373-73-7910      A  Lagos        Normal    Male       Sports and travel   \n",
+       "4  355-53-5943      A  Lagos        Member  Female  Electronic accessories   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "0     26888.4         7   9410.94  197629.74  ...    Epay  188218.8   \n",
+       "1     16678.8         7   5837.58  122589.18  ...    Card  116751.6   \n",
+       "2     20959.2         8   8383.68  176057.28  ...    Epay  167673.6   \n",
+       "3     31071.6         7  10875.06  228376.26  ...    Epay  217501.2   \n",
+       "4     24782.4         6   7434.72  156129.12  ...    Epay  148694.4   \n",
+       "\n",
+       "  gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "0                4.761905       9410.94     9.1    5      1  2019    13   \n",
+       "1                4.761905       5837.58     7.4    3      3  2019    13   \n",
+       "2                4.761905       8383.68     8.4   27      1  2019    20   \n",
+       "3                4.761905      10875.06     5.3    8      2  2019    10   \n",
+       "4                4.761905       7434.72     5.8   25      2  2019    14   \n",
+       "\n",
+       "   Minute  \n",
+       "0       8  \n",
+       "1      23  \n",
+       "2      33  \n",
+       "3      37  \n",
+       "4      36  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "d5ee1db5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "194e69de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_hour = pd.Series(df.nunique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "3f8951f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "5c9bd0e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Invoice ID                 1000\n",
+       "Branch                        3\n",
+       "City                          3\n",
+       "Customer type                 2\n",
+       "Gender                        2\n",
+       "Product line                  6\n",
+       "Unit price                  943\n",
+       "Quantity                     10\n",
+       "Tax 5%                      990\n",
+       "Total                       990\n",
+       "Date                         89\n",
+       "Time                        506\n",
+       "Payment                       3\n",
+       "cogs                        990\n",
+       "gross margin percentage       1\n",
+       "gross income                990\n",
+       "Rating                       61\n",
+       "day                          31\n",
+       "month                         3\n",
+       "year                          1\n",
+       "hour                         11\n",
+       "Minute                       60\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique(axis = 'columns' == 22)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "496b249f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      21\n",
+       "1      20\n",
+       "2      21\n",
+       "3      21\n",
+       "4      21\n",
+       "       ..\n",
+       "995    21\n",
+       "996    21\n",
+       "997    21\n",
+       "998    20\n",
+       "999    19\n",
+       "Length: 1000, dtype: int64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique(axis = 'columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "72fca3a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "       '2023-07-17T18:07:00.000000000', '2023-07-17T16:19:00.000000000',\n",
+       "       '2023-07-17T19:39:00.000000000', '2023-07-17T14:49:00.000000000',\n",
+       "       '2023-07-17T13:03:00.000000000', '2023-07-17T11:49:00.000000000',\n",
+       "       '2023-07-17T13:40:00.000000000', '2023-07-17T14:35:00.000000000',\n",
+       "       '2023-07-17T15:01:00.000000000', '2023-07-17T18:17:00.000000000',\n",
+       "       '2023-07-17T19:56:00.000000000', '2023-07-17T17:55:00.000000000',\n",
+       "       '2023-07-17T12:09:00.000000000', '2023-07-17T12:29:00.000000000',\n",
+       "       '2023-07-17T15:26:00.000000000', '2023-07-17T14:57:00.000000000',\n",
+       "       '2023-07-17T18:36:00.000000000', '2023-07-17T14:40:00.000000000',\n",
+       "       '2023-07-17T12:21:00.000000000', '2023-07-17T19:40:00.000000000',\n",
+       "       '2023-07-17T14:43:00.000000000', '2023-07-17T19:18:00.000000000',\n",
+       "       '2023-07-17T14:12:00.000000000', '2023-07-17T17:34:00.000000000',\n",
+       "       '2023-07-17T19:46:00.000000000', '2023-07-17T19:00:00.000000000',\n",
+       "       '2023-07-17T13:41:00.000000000', '2023-07-17T20:23:00.000000000',\n",
+       "       '2023-07-17T11:57:00.000000000', '2023-07-17T14:51:00.000000000',\n",
+       "       '2023-07-17T12:42:00.000000000', '2023-07-17T13:24:00.000000000',\n",
+       "       '2023-07-17T15:53:00.000000000', '2023-07-17T12:58:00.000000000',\n",
+       "       '2023-07-17T13:44:00.000000000', '2023-07-17T13:46:00.000000000',\n",
+       "       '2023-07-17T10:52:00.000000000', '2023-07-17T19:28:00.000000000',\n",
+       "       '2023-07-17T13:52:00.000000000', '2023-07-17T19:26:00.000000000',\n",
+       "       '2023-07-17T18:42:00.000000000', '2023-07-17T19:43:00.000000000',\n",
+       "       '2023-07-17T15:34:00.000000000', '2023-07-17T19:22:00.000000000',\n",
+       "       '2023-07-17T19:24:00.000000000', '2023-07-17T20:55:00.000000000',\n",
+       "       '2023-07-17T18:13:00.000000000', '2023-07-17T16:52:00.000000000',\n",
+       "       '2023-07-17T20:52:00.000000000', '2023-07-17T17:37:00.000000000',\n",
+       "       '2023-07-17T11:46:00.000000000', '2023-07-17T16:46:00.000000000',\n",
+       "       '2023-07-17T18:48:00.000000000', '2023-07-17T13:02:00.000000000',\n",
+       "       '2023-07-17T18:23:00.000000000', '2023-07-17T11:06:00.000000000',\n",
+       "       '2023-07-17T19:02:00.000000000', '2023-07-17T15:40:00.000000000',\n",
+       "       '2023-07-17T16:58:00.000000000', '2023-07-17T19:50:00.000000000',\n",
+       "       '2023-07-17T13:32:00.000000000', '2023-07-17T13:19:00.000000000',\n",
+       "       '2023-07-17T10:26:00.000000000', '2023-07-17T16:28:00.000000000',\n",
+       "       '2023-07-17T13:00:00.000000000', '2023-07-17T14:29:00.000000000',\n",
+       "       '2023-07-17T17:13:00.000000000', '2023-07-17T17:58:00.000000000',\n",
+       "       '2023-07-17T11:02:00.000000000', '2023-07-17T16:59:00.000000000',\n",
+       "       '2023-07-17T14:15:00.000000000', '2023-07-17T12:25:00.000000000',\n",
+       "       '2023-07-17T15:25:00.000000000', '2023-07-17T11:48:00.000000000',\n",
+       "       '2023-07-17T17:53:00.000000000', '2023-07-17T10:48:00.000000000',\n",
+       "       '2023-07-17T14:28:00.000000000', '2023-07-17T10:34:00.000000000',\n",
+       "       '2023-07-17T20:03:00.000000000', '2023-07-17T14:55:00.000000000',\n",
+       "       '2023-07-17T20:40:00.000000000', '2023-07-17T17:45:00.000000000',\n",
+       "       '2023-07-17T10:49:00.000000000', '2023-07-17T12:34:00.000000000',\n",
+       "       '2023-07-17T13:45:00.000000000', '2023-07-17T12:32:00.000000000',\n",
+       "       '2023-07-17T19:55:00.000000000', '2023-07-17T19:49:00.000000000',\n",
+       "       '2023-07-17T12:15:00.000000000', '2023-07-17T15:58:00.000000000',\n",
+       "       '2023-07-17T11:45:00.000000000', '2023-07-17T19:51:00.000000000',\n",
+       "       '2023-07-17T16:04:00.000000000', '2023-07-17T14:41:00.000000000',\n",
+       "       '2023-07-17T11:29:00.000000000', '2023-07-17T20:00:00.000000000',\n",
+       "       '2023-07-17T13:06:00.000000000', '2023-07-17T20:43:00.000000000',\n",
+       "       '2023-07-17T10:01:00.000000000', '2023-07-17T16:34:00.000000000',\n",
+       "       '2023-07-17T13:10:00.000000000', '2023-07-17T19:29:00.000000000',\n",
+       "       '2023-07-17T11:59:00.000000000', '2023-07-17T19:11:00.000000000',\n",
+       "       '2023-07-17T18:57:00.000000000', '2023-07-17T11:18:00.000000000',\n",
+       "       '2023-07-17T12:40:00.000000000', '2023-07-17T14:06:00.000000000',\n",
+       "       '2023-07-17T20:42:00.000000000', '2023-07-17T18:53:00.000000000',\n",
+       "       '2023-07-17T17:49:00.000000000', '2023-07-17T10:38:00.000000000',\n",
+       "       '2023-07-17T11:16:00.000000000', '2023-07-17T15:12:00.000000000',\n",
+       "       '2023-07-17T13:59:00.000000000', '2023-07-17T11:58:00.000000000',\n",
+       "       '2023-07-17T20:32:00.000000000', '2023-07-17T16:26:00.000000000',\n",
+       "       '2023-07-17T10:50:00.000000000', '2023-07-17T16:45:00.000000000',\n",
+       "       '2023-07-17T16:32:00.000000000', '2023-07-17T11:01:00.000000000',\n",
+       "       '2023-07-17T20:08:00.000000000', '2023-07-17T19:31:00.000000000',\n",
+       "       '2023-07-17T13:51:00.000000000', '2023-07-17T11:55:00.000000000',\n",
+       "       '2023-07-17T11:42:00.000000000', '2023-07-17T14:48:00.000000000',\n",
+       "       '2023-07-17T14:25:00.000000000', '2023-07-17T15:18:00.000000000',\n",
+       "       '2023-07-17T15:06:00.000000000', '2023-07-17T20:57:00.000000000',\n",
+       "       '2023-07-17T15:16:00.000000000', '2023-07-17T17:47:00.000000000',\n",
+       "       '2023-07-17T10:44:00.000000000', '2023-07-17T12:36:00.000000000',\n",
+       "       '2023-07-17T10:58:00.000000000', '2023-07-17T11:10:00.000000000',\n",
+       "       '2023-07-17T20:51:00.000000000', '2023-07-17T13:33:00.000000000',\n",
+       "       '2023-07-17T20:13:00.000000000', '2023-07-17T14:26:00.000000000',\n",
+       "       '2023-07-17T18:18:00.000000000', '2023-07-17T15:17:00.000000000',\n",
+       "       '2023-07-17T18:10:00.000000000', '2023-07-17T19:08:00.000000000',\n",
+       "       '2023-07-17T15:46:00.000000000', '2023-07-17T11:38:00.000000000',\n",
+       "       '2023-07-17T11:24:00.000000000', '2023-07-17T10:42:00.000000000',\n",
+       "       '2023-07-17T20:39:00.000000000', '2023-07-17T19:47:00.000000000',\n",
+       "       '2023-07-17T17:08:00.000000000', '2023-07-17T10:19:00.000000000',\n",
+       "       '2023-07-17T15:43:00.000000000', '2023-07-17T18:21:00.000000000',\n",
+       "       '2023-07-17T14:20:00.000000000', '2023-07-17T16:24:00.000000000',\n",
+       "       '2023-07-17T18:56:00.000000000', '2023-07-17T14:31:00.000000000',\n",
+       "       '2023-07-17T20:05:00.000000000', '2023-07-17T10:41:00.000000000',\n",
+       "       '2023-07-17T19:52:00.000000000', '2023-07-17T16:17:00.000000000',\n",
+       "       '2023-07-17T15:57:00.000000000', '2023-07-17T16:43:00.000000000',\n",
+       "       '2023-07-17T17:11:00.000000000', '2023-07-17T16:07:00.000000000',\n",
+       "       '2023-07-17T15:00:00.000000000', '2023-07-17T20:50:00.000000000',\n",
+       "       '2023-07-17T19:58:00.000000000', '2023-07-17T10:02:00.000000000',\n",
+       "       '2023-07-17T17:07:00.000000000', '2023-07-17T17:22:00.000000000',\n",
+       "       '2023-07-17T13:29:00.000000000', '2023-07-17T17:20:00.000000000',\n",
+       "       '2023-07-17T13:42:00.000000000', '2023-07-17T14:44:00.000000000',\n",
+       "       '2023-07-17T10:21:00.000000000', '2023-07-17T19:05:00.000000000',\n",
+       "       '2023-07-17T11:34:00.000000000', '2023-07-17T18:58:00.000000000',\n",
+       "       '2023-07-17T20:01:00.000000000', '2023-07-17T20:37:00.000000000',\n",
+       "       '2023-07-17T12:12:00.000000000', '2023-07-17T15:32:00.000000000',\n",
+       "       '2023-07-17T12:31:00.000000000', '2023-07-17T20:04:00.000000000',\n",
+       "       '2023-07-17T13:38:00.000000000', '2023-07-17T17:30:00.000000000',\n",
+       "       '2023-07-17T16:53:00.000000000', '2023-07-17T16:51:00.000000000',\n",
+       "       '2023-07-17T20:15:00.000000000', '2023-07-17T14:11:00.000000000',\n",
+       "       '2023-07-17T12:10:00.000000000', '2023-07-17T20:47:00.000000000',\n",
+       "       '2023-07-17T11:17:00.000000000', '2023-07-17T15:52:00.000000000',\n",
+       "       '2023-07-17T11:43:00.000000000', '2023-07-17T10:30:00.000000000',\n",
+       "       '2023-07-17T10:06:00.000000000', '2023-07-17T12:16:00.000000000',\n",
+       "       '2023-07-17T14:58:00.000000000', '2023-07-17T14:45:00.000000000',\n",
+       "       '2023-07-17T17:10:00.000000000', '2023-07-17T14:27:00.000000000',\n",
+       "       '2023-07-17T12:51:00.000000000', '2023-07-17T13:56:00.000000000',\n",
+       "       '2023-07-17T16:18:00.000000000', '2023-07-17T15:14:00.000000000',\n",
+       "       '2023-07-17T20:10:00.000000000', '2023-07-17T14:24:00.000000000',\n",
+       "       '2023-07-17T12:39:00.000000000', '2023-07-17T12:41:00.000000000',\n",
+       "       '2023-07-17T15:20:00.000000000', '2023-07-17T16:33:00.000000000',\n",
+       "       '2023-07-17T20:44:00.000000000', '2023-07-17T20:30:00.000000000',\n",
+       "       '2023-07-17T16:50:00.000000000', '2023-07-17T17:52:00.000000000',\n",
+       "       '2023-07-17T11:33:00.000000000', '2023-07-17T18:38:00.000000000',\n",
+       "       '2023-07-17T15:11:00.000000000', '2023-07-17T15:45:00.000000000',\n",
+       "       '2023-07-17T16:35:00.000000000', '2023-07-17T13:50:00.000000000',\n",
+       "       '2023-07-17T17:56:00.000000000', '2023-07-17T13:49:00.000000000',\n",
+       "       '2023-07-17T14:05:00.000000000', '2023-07-17T19:12:00.000000000'],\n",
+       "      dtype='datetime64[ns]')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(df['Time'], name = 'hour').unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d564b9cc",
+   "metadata": {},
+   "source": [
+    "# Examples on Unique Values in Columns eg Branch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "64651e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Invoice ID',\n",
+       " 'Branch',\n",
+       " 'City',\n",
+       " 'Customer type',\n",
+       " 'Gender',\n",
+       " 'Product line',\n",
+       " 'Payment']"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical_columns = [col for col in df.columns if df[col].dtype == \"object\" ]\n",
+    "categorical_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "df0c3d1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A', 'B', 'C']"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Branch'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "64a7a37a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Branch Column : 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Number of unique values in the Branch Column : {}\". format(len(df['Branch'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c991f6c",
+   "metadata": {},
+   "source": [
+    "# Unique Values in Columns - Payment, Product line, Gender and Customer type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "3c560e79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Epay', 'Card', 'Cash']"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "46c1d063",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Health and beauty',\n",
+       " 'Home and lifestyle',\n",
+       " 'Sports and travel',\n",
+       " 'Electronic accessories',\n",
+       " 'Food and beverages',\n",
+       " 'Fashion accessories']"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "02857072",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Product line Column : 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Number of unique values in the Product line Column : {}\". format(len(df['Product line'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "0fd80ec0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Female', 'Male']"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "0bbf9240",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Member', 'Normal']"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Customer type'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e358b2fb",
+   "metadata": {},
+   "source": [
+    "# Value_Count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "18d19007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fashion accessories       178\n",
+       "Food and beverages        174\n",
+       "Electronic accessories    170\n",
+       "Sports and travel         166\n",
+       "Home and lifestyle        160\n",
+       "Health and beauty         152\n",
+       "Name: Product line, dtype: int64"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "07448b7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "3448491c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Lagos            340\n",
+       "Abuja            332\n",
+       "Port Harcourt    328\n",
+       "Name: City, dtype: int64"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['City'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "3fcb5833",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Epay    345\n",
+       "Cash    344\n",
+       "Card    311\n",
+       "Name: Payment, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8a1b933",
+   "metadata": {},
+   "source": [
+    "# Groupby City column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "185c52ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "City = df.groupby('City')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "59eee3e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.groupby.generic.DataFrameGroupBy"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(City)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "7ee2f6e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Abuja': [340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, ...], 'Lagos': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], 'Port Harcourt': [672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, ...]}"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "5626b15d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            6652396.8\n",
+       "Lagos            6705176.4\n",
+       "Port Harcourt    6684393.6\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['Unit price'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "487b1c9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            20037.339759\n",
+       "Lagos            19721.107059\n",
+       "Port Harcourt    20379.248780\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['Unit price'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "2e398df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>18</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>16</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>15</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "340  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "341  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "342  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "343  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "344  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "     Unit price  Quantity    Tax 5%      Total  ... Payment      cogs  \\\n",
+       "340     19742.4         3   2961.36   62188.56  ...    Card   59227.2   \n",
+       "341      5212.8         4   1042.56   21893.76  ...    Epay   20851.2   \n",
+       "342      9183.6         4   1836.72   38571.12  ...    Cash   36734.4   \n",
+       "343     33739.2         6  10121.76  212556.96  ...    Cash  202435.2   \n",
+       "344     14508.0         2   1450.80   30466.80  ...    Epay   29016.0   \n",
+       "\n",
+       "    gross margin percentage  gross income  Rating  day  month  year  hour  \\\n",
+       "340                4.761905       2961.36     5.9   20      2  2019    13   \n",
+       "341                4.761905       1042.56     4.5    6      2  2019    18   \n",
+       "342                4.761905       1836.72     6.8    9      3  2019    17   \n",
+       "343                4.761905      10121.76     4.5   15      1  2019    16   \n",
+       "344                4.761905       1450.80     4.4   11      3  2019    15   \n",
+       "\n",
+       "     Minute  \n",
+       "340      27  \n",
+       "341       7  \n",
+       "342       3  \n",
+       "343      19  \n",
+       "344      30  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Abuja').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "c3775a60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1820531.52"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Abuja')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "1caaa895",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            1820531.52\n",
+       "Lagos            1820577.78\n",
+       "Port Harcourt    1895463.54\n",
+       "Name: gross income, dtype: float64"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "05fe8fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1820577.78"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Lagos')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "688e914b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1895463.54"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City.get_group('Port Harcourt')['gross income'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "cc493a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_Aggregate = City.agg({'gross income': 'sum'})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "5cb92d4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gross income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>City</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Abuja</th>\n",
+       "      <td>1820531.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lagos</th>\n",
+       "      <td>1820577.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Port Harcourt</th>\n",
+       "      <td>1895463.54</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               gross income\n",
+       "City                       \n",
+       "Abuja            1820531.52\n",
+       "Lagos            1820577.78\n",
+       "Port Harcourt    1895463.54"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_Aggregate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "29d2b82a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gross income    1895463.54\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_Aggregate.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "66b1220f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            17528.4\n",
+       "Lagos            17816.4\n",
+       "Port Harcourt    17874.0\n",
+       "Name: gross income, dtype: float64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "City['gross income'].max('City')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "fc4ee498",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_with_highest_income = city_Aggregate['gross income'].idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "ab710a2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Port Harcourt'"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_with_highest_income"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60754059",
+   "metadata": {},
+   "source": [
+    "# The city with the higehest gross income is Port Harcout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e53b88a7",
+   "metadata": {},
+   "source": [
+    "# Data Visualization using seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "ffc9432a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Data = df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "1bf2829b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>Minute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>750-67-8428</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>26888.4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>197629.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>188218.8</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>9410.94</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>631-41-3108</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>16678.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>122589.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>116751.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>5837.58</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>123-19-1176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>20959.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>176057.28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>167673.6</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>8383.68</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>20</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>373-73-7910</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>31071.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>228376.26</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>217501.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10875.06</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>355-53-5943</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>24782.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>156129.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>148694.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>7434.72</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>19</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>11</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>13</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    750-67-8428      A          Lagos        Member  Female   \n",
+       "1    631-41-3108      A          Lagos        Normal    Male   \n",
+       "2    123-19-1176      A          Lagos        Member    Male   \n",
+       "3    373-73-7910      A          Lagos        Normal    Male   \n",
+       "4    355-53-5943      A          Lagos        Member  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  ...  \\\n",
+       "0         Health and beauty     26888.4         7   9410.94  197629.74  ...   \n",
+       "1        Home and lifestyle     16678.8         7   5837.58  122589.18  ...   \n",
+       "2         Health and beauty     20959.2         8   8383.68  176057.28  ...   \n",
+       "3         Sports and travel     31071.6         7  10875.06  228376.26  ...   \n",
+       "4    Electronic accessories     24782.4         6   7434.72  156129.12  ...   \n",
+       "..                      ...         ...       ...       ...        ...  ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16  ...   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02  ...   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20  ...   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10  ...   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30  ...   \n",
+       "\n",
+       "    Payment      cogs gross margin percentage  gross income  Rating  day  \\\n",
+       "0      Epay  188218.8                4.761905       9410.94     9.1    5   \n",
+       "1      Card  116751.6                4.761905       5837.58     7.4    3   \n",
+       "2      Epay  167673.6                4.761905       8383.68     8.4   27   \n",
+       "3      Epay  217501.2                4.761905      10875.06     5.3    8   \n",
+       "4      Epay  148694.4                4.761905       7434.72     5.8   25   \n",
+       "..      ...       ...                     ...           ...     ...  ...   \n",
+       "995    Cash  251899.2                4.761905      12594.96     6.1   23   \n",
+       "996    Cash  242852.4                4.761905      12142.62     6.0    9   \n",
+       "997    Epay  296424.0                4.761905      14821.20     4.3   29   \n",
+       "998    Epay   21942.0                4.761905       1097.10     5.9   18   \n",
+       "999    Epay   14526.0                4.761905        726.30     6.2   29   \n",
+       "\n",
+       "     month  year  hour  Minute  \n",
+       "0        1  2019    13       8  \n",
+       "1        3  2019    13      23  \n",
+       "2        1  2019    20      33  \n",
+       "3        2  2019    10      37  \n",
+       "4        2  2019    14      36  \n",
+       "..     ...   ...   ...     ...  \n",
+       "995      1  2019    10      33  \n",
+       "996      1  2019    11      40  \n",
+       "997      3  2019    19      12  \n",
+       "998      2  2019    11      40  \n",
+       "999      1  2019    13      46  \n",
+       "\n",
+       "[1000 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "cd9e9763",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'City with max Gross income'}, xlabel='City', ylabel='gross income'>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "city_Aggregate.plot(kind='bar', ylabel = 'gross income', title = 'City with max Gross income', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "caa28f73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Customer_type_counts = Data['Customer type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "69c66158",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Member    501\n",
+       "Normal    499\n",
+       "Name: Customer type, dtype: int64"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Customer_type_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "68cf8924",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Customer_type_counts')"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(Customer_type_counts.values, labels=Customer_type_counts.index, autopct='%1.1f%%')\n",
+    "plt.title('Customer_type_counts')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "id": "96ab0009",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gender_counts = Data['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "38c9b442",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "dcf83ead",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Gender_counts.values')"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(gender_counts.values, labels=gender_counts.index, autopct='%1.1f%%')\n",
+    "plt.title('Gender_counts.values')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "c6911fd2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Hours spent')"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'City', y = 'hour', hue = 'Gender', aspect = 4)\n",
+    "plt.title('Hours spent')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75c59cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "a30ec3cd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_Sales Record')]"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, x = 'Branch').set(title = 'Highest_Sales Record')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aef1baed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "e817ede3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_Payment')]"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Payment').set(title = 'Highest_Payment')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb2b6f6f",
+   "metadata": {},
+   "source": [
+    "# Highest product line sold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "c19b150c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Highest_productline')]"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Product line', hue = 'City').set(title = 'Highest_productline')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "9150e7bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Most used_Payment_type')]"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Arq4u9u7dCwB5Dvn4VO7J8dIHrrzk/sY793E1atRA+/btERwcjHXr1uH06dNwcnJChw4d0KpVK1SqVKkwl1XqKlSogAEDBsDX1xcHDx7E1atX0bdvX3To0AHt2rXLN/nLPUTn0+FK+Xnz5o3we+62ze8hTpn9JUVFRQX169fHoEGDMH78eIUPigEBAdi6dStCQ0MVJiZSiobzATnD0bZs2YKUlBT4+/tj5MiRwr7k5GQcOnQIQP7Jn1ReQ74ACEMhq1Spku+9lZZ7//69zPb79+8LE9E9PDzy7GH7VO57/ql69erle6w0zk9jKYtMTEzQvXt3HD9+HL6+vpgzZ45M8uft7Q2JRAKRSIThw4fnW1dBn/fmzZtDRUUF2dnZMvO/EhMT8eTJEwA5X/7k11uWW373iMoHJjZEROVEz549UblyZcTHx8Pf3x/m5uZ4+fIlVFRUhCWhC5J7zktBw5pyr1716aT1LVu2wNXVFaGhocKqX0uXLoWamhqaN2+Ovn37YuTIkdDR0SnEFZaeP//8E8nJyThy5AieP3+ONWvWYM2aNRCJRGjUqBGcnZ3x7bffyrXZ27dvi3S+3Cs0FeaeGBoaFul8hZF7ArpIJIK2tjYMDAzy/NZbIpFg6tSpSg/jSU1NVbi9adOmaNasGa5fvw5vb2+ZxMbPz09os/zmZEjl9w299CE7vxW7cpfLvfobUDz3/FNFjaWsGjVqFI4fP47nz58jKCgIdnZ2AHJ6f6VzAtu3b69wGGBuBgYG+e7X1NSEjo4OEhMTZRLmot6jvD6bVH4wsSEiKic0NDTQv39/bNmyBbt27YK5uTmAnAnG1atXL3R9ilb4UpaxsTGOHz+OCxcu4PDhwwgODsa9e/fw8eNHhIWFISwsDKtWrcLOnTsVznUoa3R0dODt7Y3r16/D398fFy9exI0bN/Dx40fcvn0bt2/fxpo1a7Bx40aZFc1yP2geOHBA6cQjd09B7qF8Bd2TLzHsr7AT0L28vISkxsrKSpjfYmJiAm1tbWGo0fjx47F79+58r2HkyJG4fv06rly5gocPH6Ju3boA/jf3oWbNmgXOgyppue/5/Pnz5Va4y0teczy+Rt26dYOpqSliYmLg7e0tJDZnz57F8+fPARQ8TwpQ7r9Rij5Pue/Rd999B1dXV2VDp3KOiQ0RUTkyZMgQbNmyBZcvXxZW0VL23TWA7GT+2NjYfMvmXiQgr0UAOnbsKKyOlZCQgAsXLmDnzp0ICAjA69evMWLECFy7di3P+QOfK/cQl4JeYJnfN+ZSzZo1Q7NmzQDkvPckNDQUe/bswb59+5CUlIRvv/0W165dE3qzqlSpIhxb1FXeCnNPCtpfGnbs2AEgJ+k4ceJEnr0PCQkJBdY1cOBAzJs3DykpKfD29sZvv/2GBw8eICwsDEBOb83nJOTFIfc9z8jIKBMr+5U1qqqqGDZsGJYuXYqjR48iISEBYrFYmCelq6sLZ2fnAuspaGhYWlqaMDwv98Ieue9RSkoK79F/CBcPICIqR1q3bi3MH0hPT4e2trZSDwhSuf8PPjw8PN+yV69eVXhcXqRvQd+1a5ew8MDLly/l3qtTnA+muYe65ffgnJ2dLayopayKFSvC3t4eGzduxNy5cwHkJEe5l5XN/fJS6UtSCyt320ZERORbtqD9pUG68EGvXr3yTGokEoncqlWK6Orqom/fvgD+t5CD9GFYVVVV6ZX/SlL9+vVRoUIFAEW/58XpSyR6RTnHiBEjoKKigvT0dOzZswfx8fE4fvw4gJwEtqDhd0DBn/fr168LX2jk/ndUpUoVoRf73LlzBX7pQV8PJjZEROXMsGHDUKFCBVSoUAF9+/Yt1GR9Y2Nj1K9fHwBw+PBhJCYm5lnW09MTQE6viHTpZ2XlfhdE7vdsABB6bzIyMgpVpyLSlbsA2Yn8nwoICBDem1EUeV1Pu3bthG+HPT09i/QCTRMTE2Hy+OHDh/OcIJ6SkoIDBw4Uuv6SJl1FLr8esaNHj+LVq1dK1Tdq1CgAOT2Gx44dw549ewDkLO8rXVmtNGlpaaFz584AgLCwMGGFr9KSuze0OP5NFdc5atSoIbSTt7c3du/ejfT0dADKDUMDchbWkPZMK5J7Xpf0XFK9evUCkPM+G+m8Hvr6MbEhIipnfvzxR7x+/RqvX7/GunXrCn38d999ByCnh2PGjBkKx6hv27ZNWD7V0dFRePcHANy8ebPAb9/Pnj0r/P7py/ykw7hiY2PzXT1LWdKlWo8dO4Z///1Xbv+LFy/g5uaW5/GRkZFy75X51JkzZ4Tfc19PhQoVMH36dAA5q32NHDky32QxPT0dmzZtQlpamsz2b7/9Vqjjp59+UnjsL7/8UiaHokmXRQ4ICFCY2D19+hSzZs1Sur42bdqgQYMGAIBZs2YJw5GUWTTgS5k1a5YwDHLcuHF48OBBvuUDAwMVLnNdHHIv8vH06dMydQ5pknrz5k2sWLECQM5y28osTS81bdo0hcn+mTNnhBe2tm7dWhhCmvs4aa/QTz/9pPB9RLmFhoaWepJKn49zbIiI/mNGjx6Nffv2ITQ0FPv27cOLFy8wbtw4WFpa4t27d9i3b5/wDadYLMaSJUtkjr916xYmT56MZs2aoWfPnmjatCmMjY2RnZ2N58+fY+/evcJLGJs1a4aWLVvKHN+2bVsAOcPDfvzxR4wbN05mTLz0QVlZ48aNw7Fjx5CWlgYnJyfMnj0bzZo1Q2pqKkJDQ7Fu3TpIJBLUrl1bYeITHR0NJycn1K1bF46OjmjevDmqVasGFRUVvHz5EkePHhUeoKpXr44ePXrIHD9lyhRcuHABJ0+exIULF9C2bVu4urqiXbt2qFy5Mj58+IDHjx/j0qVLOHz4MBISEuSGVI0ZMwY7d+7EzZs34e3tjRcvXmDs2LGoVq0aXrx4ga1bt+L06dNo3rx5vj1TpWHo0KGYN28eXr58iW7dumH69Olo2LAh0tLScP78eaxfvx4ZGRlo2rSpUsPRgJxv9H/++WdhnpeBgYHSk/S/hFatWmHu3Ln47bffEBMTg86dO2P48OHo2rUrTExM8PHjR7x48QJXr17FoUOHEBkZiV27dqFx48bFHouZmZnwOVm9ejVMTU1Rp04dYdEGAwODz16dsEmTJtDU1ERaWhr++OMPqKurw8zMTEjuTExMFA4tc3BwQNWqVfH27VvhXirbWwNA+Lx36tQJ06dPh5WVFVJSUnD8+HFs3LgR2dnZ0NDQwNKlS+WONTMzw6pVqzBu3DgkJSXByckJAwYMgIODA2rUqIHs7Gy8evUK169fx5EjR3D37l38+eef6NChQxFbicoCJjZERP8xqqqq8PX1xfDhwxEcHIzQ0FCF32aamppi165dqFatmsJ6rl+/nu8wkYYNG8LLy0tufL6trS1at26NsLAw7N27V3gXj5Qyk8xz69y5M6ZMmYI1a9bg5cuX+P7772X26+vrw8fHB7///rvCxEbq4cOHePjwYZ77q1evDl9fX1SsWFFmu4qKCry9veHm5oYdO3bg1atXcHd3z7OeihUryr2UUE1NDbt374azszMePXqEs2fPyvR6AYCdnR2mTJmS78snS8OECRNw9uxZnDlzBv/++y+mTp0qs19LSwsbNmxAYGCg0onN0KFDsXDhQmHokouLC9TV1Ys99s/x448/Qk9PD3PnzkVqaio2b96MzZs3KyyroqIi97kp7lhmzJiBqKgoDBs2TGbf2rVrC3xfTEF0dHQwfvx4/P3337hx44bci00PHz4sLCKSm7q6OoYOHYrVq1cDyFkZTtml6QGge/fu6NGjB5YsWYJp06bJ7dfU1MTmzZvRtGlThccPGjQImpqamDp1KhISErB7926ZFxwruk4q35jYEBH9B4nFYhw5cgR+fn7Ys2cPrl+/jri4OFSsWFHouRg7dqzCh7FBgwbB3Nwc586dQ0hICGJiYhAbG4vMzEzo6+vDysoKTk5OGDp0qMKHURUVFfj5+eHvv/9GQEAAIiMjkZKS8llLGS9atAitWrXC5s2bcfPmTaSnp8PU1BQ9evTA1KlT80zOgJyhbCdPnkRQUBCCg4MRHR2N2NhYfPjwAWKxGA0bNkTPnj0xatSoPB9OK1SogL///hvfffcdduzYgeDgYDx//hzv379HxYoVUb16dVhZWcHOzg4ODg4Kv902MTHB+fPnsXbtWvj5+eHp06fQ0NBA3bp1MWTIELi6uiI4OLjIbVRS1NXVsWfPHmEZ8gcPHkAikcDExASdO3fGhAkTULduXZlFFwpSuXJldOvWTej5K0vD0HIbM2YMnJychKGbjx49QkJCAjQ0NGBkZIT69evD1tYWzs7O+X4GiyMOAwMDbNu2Dbdu3UJCQoIw96m4LFiwALVq1YKvry/u37+PpKQkpd6r4+LiIiQ2jo6OMquXKeOnn35C27Zt8c8//+DatWuIj4+HoaEhunTpgu+//x61atXK93gnJyd06tQJnp6eOHXqFO7fv4+4uDioqamhSpUqqF+/Ptq3bw8nJ6d8X+xK5YMoISGh5BfFJyIiIiqEli1b4vHjx2jTpg1OnDhR2uFQEe3Zswfjxo0DkPOiVek7bfIjXRBk9uzZmDNnTkmGR18ZLh5AREREZcrFixeF5bnLam8NKUf6niMzMzO5lcuIihsTGyIiIipTVq5cCQDQ09PDwIEDSzcYKrLw8HBhpbHRo0fLvFCXqCRwjg0RERGVquTkZMTGxiIpKQm+vr44deoUgJyFCbS1tUs5OiqMZ8+eISMjA/fv38cvv/wCICdBHTNmTClHRv8FTGyIiIjKsdjY2CK/3yb329pL06FDhzB58mSZbXXr1hXeEURFl5KSgqioqCIdW6NGjUKv5ubo6Ijo6GiZbX/88Ycwb4aoJDGxISIiKsc2b94MDw+PIh1b2KW1S5qKigpMTU3RrVs3/Pzzz+ytKQYRERFwcnIq0rF5LeOsDB0dHdSrVw/Tpk2Ds7NzkeogKiwmNkRERFSqhg8f/tnvWqGy4datW59dR1lLuKn84HLPRERERERU7nF5CiIiIiIiKveY2BARERERUbnHxIaIKB9paWl48uQJ0tLSSjuUMo9tpRy2k/LYVspjWymH7aS88thWTGyIiAqQlZVV2iGUG2wr5bCdlMe2Uh7bSjlsJ+WVt7ZiYkNEREREROUeExsiIiIiIir3+B4bov+4DlsjEJ/2sbTDKAfiSzuAcoRtpRy2k/LYVsor/ra6M6lNsddJVBLYY0NEREREROUeExsiIiIiIir3mNgQEREREVG5xzk2RERERFTmZWdnIykpCZmZmZ9Vh4aGBhITE5GcnFyM0X19SrKt1NXVoaurCxWV4u1jYWJDRERERGVaRkYGEhISoKenBz09PYhEoiLVk52djYyMDGhoaBT7Q/XXpqTaSiKRICMjA2/fvoVYLIaGhkax1c07SkRERERlWnJyMqpUqYIKFSoUOamhskEkEqFChQqoUqVKsfcEMbEhIiIiojItOzsbqqqqpR0GFSNVVVVkZ2cXa51MbIiIiIiIqNxjYkNEREREROUeExsiIiIiIir3mNgQEREREVG5x8SGiIiIiIjKPSY2RERERPSf8ezZM+jr60MsFgs/BgYGaNSoEcaOHYvbt2+XdohljlgshqOjY2mHUSC+oJOIiIiI/nMsLS0xePBgAEBKSgrCw8Oxb98+HD58GIcOHULbtm1LOUIqLCY2RERERPSfU7NmTcyZM0dm26JFi7B06VL8/vvvOHLkSClFRkXFoWhERERERADGjRsHALh27RpevnyJxYsXw97eHrVr14ahoSGsrKwwY8YMxMbGyhw3YcIEiMViREREKKx3/vz5EIvFOHz4MAAgKioKYrEYEydOxIMHD+Di4gJzc3PUqFEDY8aMwbt37wAA4eHh6Nu3L8zMzFCjRg1MmzYNKSkpCs8RHBwMFxcX1KxZE4aGhmjRogUWLVqEDx8+yJS7cOECxGIx3N3dcf36dfTv3x/Vq1eHubk5hg8fjqioKJk69fX1hd9zD9/buXNnEVq4ZDGxISIiIiICIBKJhN9DQkKwdu1aGBgYYMCAARg3bhwsLS2xZcsWdOvWDYmJiUJZV1dXAICnp6dcnZmZmdi1axeMjIzQq1cvmX1RUVHo3r070tPTMXLkSDRu3Bj79+/H8OHDcenSJTg5OUFLSwujRo2CpaUlduzYIdfLBABbt25F7969ceXKFfTo0QPjx4+HiYkJli5din79+iEjI0PumOvXr8PBwQFqamoYPXo0mjVrhqNHj6Jv375IS0sDAJiZmcHNzU34ffbs2cKPlZVVEVq4ZHEoGtF/XMCTDZCkJJd2GERE9JleTfoNZmZm0NTULO1Qyq1//vkHANC8eXPY2triwYMHqFSpkkwZX19fTJw4EZs2bcLMmTMBAG3btkXDhg3h5+eHxYsXo2LFikL5gIAAvHnzBt9//z3U1GQfvUNCQuDu7o6JEycCACQSCVxcXHDixAm4uLhg8+bNwqT9zMxMdO7cGb6+vpg7dy4MDQ0BAPfv34ebmxusrKxw8OBBVK5cWah/xYoVWLhwIf755x9MnTpV5tyBgYHYunUr+vfvL2wbP348du/ejaNHj6Jfv34wNzfHTz/9hD///BPm5uYKk6qyhD025ZSjoyPEYnGJH1PW5O66VcbXcM1ERERU/J48eQJ3d3e4u7tj7ty56NmzJ5YuXQpNTU3Mnz8fBgYGckkNAAwZMgS6uroICgqS2T5q1CgkJyfDz89PZruXlxdEIhFGjhwpV5eFhQXGjx8v/C0SiYREo0mTJjIrkamrq6NPnz7IzMzEgwcPhO3btm3Dx48f4eHhIZPUAMD06dNRtWpV7N+/X+7cNjY2MkkNAHzzzTcAkOeQurKOiU0+pA/RAwYMyLNMWFhYoR60S9LOnTvL7JhHKprc42CJiIio+Dx9+hQeHh7w8PDAP//8g+joaAwaNAinT59GmzZtAACHDh1C//79UatWLVSpUgVisRiVK1dGUlISXr16JVOfi4sLtLS04OXlJWyLiYnB6dOn0b59e9SsWVMuhsaNG0NFRfZx3NjYGAAUDvWS7nv58qWwLTw8HABw+vRpIVGT/nh4eEBdXR2PHj2Sq6tp06Zy26pVqwYAMsPsyhMORSMiIiKi/5yuXbsq7MmQWr16NebNm4eqVavCzs4OpqamwjC/9evXIz09Xaa8WCxG37594evri/v376N+/frYuXMnsrKyMGrUKIXn0NHRkdumqqpa4L7MzExhW3x8PABg6dKl+V2uHF1d3Tzrz8rKKlRdZQUTGyIiIiKiXD5+/Ii//voLJiYmuHDhAqpWrSrsk0gkWLVqlcLjXF1d4evrix07duCPP/7Azp07UblyZTg5OZVYrNIEKDo6WmEy9F/CoWglKDk5GYsXL0a7du1gbGwMc3NzDBgwAKGhoXJlr1+/jlmzZsHa2hrm5uYwNjaGjY0NVqxYIZOV52XixImYPHkyAGDy5Mkyy/F96uPHj/jzzz/RpEkTGBoaomXLlti8ebPS15WYmIiVK1fCwcEB9evXh4GBAerXr4/x48fj6dOncuXd3d0hFotx4cIF+Pv7w9bWFsbGxqhXrx7c3NyQmpoqd0xWVhZWrlyJ5s2bw8jICM2bN8fy5cshkUiUjjO3tLQ0zJ8/H40aNYKRkRFsbGxkuoo/dfToUTg7O6NGjRowMjKCtbU1Vq9eLfcNRmHbYuLEiRCLxTJLKUrlbifp39L/EHp4eMjc06ioqEIvLUlERETKeffuHZKSktCqVSuZpAbIWQpa0bMLALRp0wYNGzbE7t27cfLkSURGRmLw4MEluqBDq1atAPxvSFpJUFFRQXZ2donVX1zYY1NC4uPj4eDggHv37sHa2hp2dnZISkrCsWPH4OTkhO3bt6N3795CeU9PTwQEBMDGxgbdunVDamoqLl68iIULFyIiIiLfh3AgZ5J8YmIijh07BgcHh3yX4BszZgyuXr0Ke3t7qKqqwt/fHzNnzoS6unqeXaW5PXz4EIsXL0bHjh3Ru3dvaGtr4+HDh9i3bx9OnDiBc+fOwdzcXO64zZs349SpU3BwcECHDh1w+vRpbNy4EfHx8di0aZNM2enTp8Pb2xs1atTA2LFjkZ6ejrVr1+Ly5csFxqfIqFGjcOfOHWHS3YEDBzB16lTExsbixx9/lCn722+/Yfny5ahWrRqcnZ2ho6ODkJAQzJs3D+Hh4TJLORa1LZTRoUMHPHv2DL6+vmjfvj06dOgg7NPT04Orqyt27doFT09PtGjRQubY/JaWJCIiovwZGBhAS0sLN27cwIcPH6CtrQ0ASEhIEJY/zsvo0aPh5uaG6dOnA4DCRQOK05gxY+Dp6Qk3Nzf4+/ujevXqMvsTEhIQFRWlcE6NsipXrowXL158bqgljomNEqSrZigSExOjcLubmxvu3buHNWvWCCtMAMCbN29gZ2eH77//Hvb29kIG/8MPP2Dp0qXC2EYgp6tz6tSp8Pb2xqVLl9CuXbs8Y+zdu7eQ2Dg6OmL48OF5ln3x4gVCQkKEsZUTJkyAtbU11qxZo1RiU7duXTx48EBu5Y3z58+jb9++WLp0qcIu2rNnzyIoKAh16tQBAKSmpqJjx47Yt28ffvvtN5iYmADImTDv7e2Nxo0bIzAwUFgy8ccff0THjh0LjE+RqKgohIaGCl20M2fORKdOnbB48WL0798fFhYWQozLly9Ht27d4OnpKfyHTCKRYMaMGdi6dSsOHjyIPn36fFZbKEN6rb6+vujQoYPcEotFXVqSiIiI8qeiooIxY8ZgzZo16NChA3r27Ink5GScOnUKZmZmwjOLIi4uLliwYAFevnyJVq1aoVGjRiUaa8OGDbFs2TL8+OOPaN26Nbp16wZLS0skJycjMjISwcHBGDZsGFasWFHkc9ja2sLf3x8jR45EkyZNoKqqiu7du5f4tRUWh6IpIfeqGZ/+KOpJeffuHfz8/NCpUyeZpAYADA0NMXXqVLx9+1ZmmUBzc3OZpAbIWfJv7NixACC3pODnmD9/vsyEsTp16qBt27Z49OgRkpMLfp+Jnp6e3IM8kPOhr1+/fp6xTpgwQUhqAEBLSwsDBgyARCLB9evXhe27du0CkJMc5n5YNzU1xYQJEwqMT5EZM2bIjDs1MjLC5MmT8fHjR+zdu1fYvnHjRgA5675Lkxog5178+uuvEIlEMhMNi9oWxaUoS0sSERFRwX799VfMmzcPIpEIW7ZswdmzZ9G/f3/4+fnl+6Whnp4eHBwcAJR8b43UqFGjcPLkSTg4OCAsLAzr1q3DwYMH8e7dO0yaNOmzV+9dsmQJ+vXrh+DgYCxevBgLFy6UeXYrK/hVrhLyWzUjLCwM3bp1k9kWERGBrKwspKenK+zpefLkCQDg0aNH6NmzJwAgIyMDGzduhJ+fHx49eoT379/LzCf5dEnBz1HQ8n7KTDy7cOEC1q9fj6tXr+Ldu3f4+PGjsE9DQ6NI55W6ffs2gJz11T9lbW1dYGyKKDpOuu3WrVvCtvDwcFSsWDHPoX9aWlpySyYWpS2Ki/RbIS8vL4wYMQJAwUtLEhER/ZeZm5sjLi5ObpnlT6mrq2PGjBmYMWOG3L7czw6K3L59G5UqVZJ7T4xUjRo1kJCQoHBfx44d89w3fPjwPEfltGjRAlu2bMk3roLqzx1X7jk1RkZG2LZtW4F1lzYmNiVAuuzepUuXcOnSpTzLpaSkCL+PHDkSAQEBqF27Nvr16wcDAwOoqakhMTERGzZskFtS8HPo6enJbSvM8n4HDhyAq6srKlWqBDs7O5ibm0NLSwsikQg+Pj6Ijo5WeJyyywomJSVBRUUFVapUkSsvfctuYRkYGOS5LSkpSdgWHx8vvOQqL7nvW1HborgUZWlJIiIiKjknTpzA/fv3MWbMGIUv+KSSw8SmBEh7PKZMmYJFixYVWD4iIgIBAQHo2rUr9uzZIzMkLSwsDBs2bCixWItiyZIl0NTURFBQEGrVqiWz79MhUUWhq6uL7OxsvHv3Tm4lkjdv3hSpztjYWLnJdLGxscL5pHR0dCASiYRetYIUti2k3w4pSiBzJ1iFURpLSxIREZGsLVu24MWLF/D09ISWlhamTZtW2iH953COTQlo0aIFRCIRwsLClCovXRa4e/fucvNsFC0NnZcv9VKlp0+fom7dunIP8i9fvlS4xHFhNW7cGAAQEhIit68w7VHQcdJtuVeQa9WqFeLi4vD48WOl6i1sW0iX31a06MTNmzfltilzT0tjaUkiIiKStXLlSvz9998wMDDAtm3bUKNGjdIO6T+HiU0JMDIyQr9+/XD58mWsWrVK4btXwsPD8eHDBwCAmZkZAMgNW7t37x6WL1+u9Hmlk9jzWqmtuJiZmeHp06cyvSdpaWn48ccfZeaXFNWQIUMAAH/++afMsK+YmJgi914tW7ZMZmGEN2/eYO3atVBTU8OgQYOE7ePHjweQ09sWFxcnV8/r16/x4MED4e/CtkXz5s0BAD4+PjLbDx48iODgYLnyyt7T0aNH4927d19saUkiIiKSdevWLbx79w6XLl0S5lDTl8WhaCVk2bJlePToEebPn49du3ahTZs20NXVxYsXL3D9+nU8fvwYDx48gLa2Nlq2bImWLVvC398fr169QuvWrfH8+XMcP34c3bt3x8GDB5U6Z5s2baClpYX169cjOTlZGMb1ww8/FOu1jRs3Dm5ubrC1tYWzszOysrJw9uxZSCQSNG7cWJj8X1QdO3bE8OHDsXPnTtjY2KB3797IyMiAn58fWrVqhcDAwELXWaNGDVhbW8PZ2Vl4j01sbCzmzZsnLPUMAPb29pg1axb++usvNG/eHPb29jAzM0NcXByePHmC0NBQzJ07F/Xq1StSWzg6OqJGjRrw8fHBixcv0KRJEzx8+BDnz59H9+7dceLECZnydevWhYmJCfz8/KCtrQ1TU1OIRCJ8++23MnOlvvTSkkRERERlDXtsSkjlypVx4sQJ/Pbbb9DQ0MDevXuxadMmhIeHo379+tiwYYMwOV5VVRW7d+/GN998g8jISGzcuBH379/H77//joULFxbqnJ6enqhVqxa2bt2KhQsXFup4ZX333XdYsWIFKleujB07duDIkSNo3749Tpw4oXBhgqJYtWqVsLzypk2bcPLkSUyePBlLliwpUn3bt29Hv379cODAAXh6eqJq1apYtWqVwpVOfvnlFxw4cADW1tY4d+4c1q5di8DAQGRkZOCnn36S6eEpbFtoaWnh4MGDcHBwQEREBLZu3Yq0tDQcO3ZM6M3JTVVVFV5eXmjZsiV2796N3377DQsXLpRbzaQ0lpYkIiIiKktECQkJ8uOkiKjcadeuHZ4/f4779+8XahWW93PHQZJS8PuLiIiobHs16TeYmZl9lXMsY2NjFa5wWljZ2dnIyMiAhoZGgcs9/9d9ibYqrvsqxaFoRF+Bz1la8l1ndWRnqJdQZEREVFwsHXbkuS8tLQ0o4VcMEJV1TGyIyjEuLUlERESUg4kNUTm2cuVKxMTEoE6dOliwYAGXliQiIqL/LCY2ROXYrVu3SjsEIiIiojKBs6aIiIiIiKjcY2JDRERERETlHoeiEREREVG51mjdldIOIU93JrUp7RD+M5jYEBERERGVYVFRUWjatGm+ZRo3boyLFy9+oYjKJiY2RERERETlgKWlJQYPHqxwn5GR0ReOpuxhYkNEREREVA7UrFkTc+bMKe0wyiwuHkBERERE9BURi8VwdHTE8+fP4erqCktLS5iamsLR0RGXL1+WK//vv/9i/vz5sLW1haWlJYyMjNC6dWssWrQI79+/lynr4OCAqlWr4tWrVwrPPXr0aIjFYty4caNEri0/TGyIiIiIiL4yCQkJ6NmzJ549e4bRo0fDyckJYWFhcHJywoULF2TKHj58GF5eXrCwsMDQoUPh6uoKsViMNWvWoH///sjMzBTKurq64uPHj9i5c6fcOd+9e4djx46hWbNmBc4JKgkcikZEREREVA48efIE7u7uCve1bt0a9vb2wt937tyBi4sLNmzYAJFIBAAYMWIEnJycMH36dISHh0NFJaePw8XFBZMnT4aGhoZwfHZ2Ntzd3fHXX3/B399fmNvj7OyM2bNnw9vbGz/++KNQNwDs2rULGRkZGDlyZLFfuzKY2BARERERlQNPnz6Fh4eHwn0TJkyQSWxUVVUxb948mcSjQ4cO6N69OwIDA3H58mVYW1sDAExNTRXW+e233+Kvv/5CUFCQkNhUqFABQ4cOxdq1a3H+/Hl06tRJKO/t7Q1tbW0MHDjws6+1KDgUjYiIiIioHOjatSsSEhIU/ixZskSmrJmZGapXry5XhzSZuXXrlrBNIpHAy8sLvXr1goWFBfT19aGvr48GDRoAgNx8mtGjRwMAvLy8hG1hYWG4d+8e+vbtC11d3WK53sJijw0RERER0VfGwMAg3+1JSUnCNjc3N2zatAnVq1dHr169YGxsDHV1dWRlZWHZsmVIT0+XqaNOnTpo3749Dh8+jPj4eFSuXBk7duwAAIwaNaqErqhgTGyIiIiIiL4ysbGx+W6X9qrExsZi8+bNaNSoEU6ePAltbW0AOXNsnj9/jmXLlimsx9XVFcHBwdi9eze++eYb+Pv7o379+mjbtm0JXI1ymNgQ/cfVsF9b2iGUaWlpaYiOjoaZmRk0NTVLO5wyjW2lHLaT8thWREUXHR2N58+fyw1HCw0NBQBYWVkBACIjIyGRSNC5c2chqZG6dOlSnvU7OzujSpUq2LFjB7S1tfH+/XuMGDGimK+icDjHhoiIiIjoK5OVlYXff/8dEolE2Hbx4kWcOHECNWvWFHpWzMzMAABXrlxBdna2UPbFixf4448/8qxfQ0MDQ4cOxd27d+Hu7i78XZrYY0NEREREVA7kt9wzAMyZM0f4vVGjRggODoa9vT1sbW3x8uVL+Pn5QV1dHX///bew1LOxsTGcnZ1x6NAhdO7cGZ06dcKbN28QGBiI9u3bIyoqKs/zjR49GmvWrMHLly/Rv39/6OvrF9/FFgETGyIiIiIq1+5MaqNUuezsbGRkZEBDQ0N4sC9P8lvuGZBNbMRiMXbt2oV58+Zh27ZtSE9PR6tWrTB//ny0a9dO5rh169bB3Nwchw4dwsaNG1G9enVMmjQJEydOFHp0FKlduzbatGmDK1eulOqiAVJMbIiIiIiIyrAaNWogISGh0MeZmZlh+/btBZarVKkSFi1ahEWLFgnbpElgXFxcnklgWloaHj16BAsLC9ja2hY6vuJW/lJVIiIiIiIqdd7e3oiPj4erq6vMi0BLC3tsiIiIiIhIaStWrMDbt2+xfft2GBgYwNXVtbRDAsDEhoiIiIiICmHhwoXQ0NBA48aN4eHhIbwTp7QxsSEiIiIi+ooUZT5OWaq/qDjHhoiIiIiIyj0mNkREREREVO4xsSEiIiIionKPiQ0REREREZV7XDyA6D+uw9YIxKd9LO0wyoH40g6gHGFbKYftpDy2lfK+zrZaYl0FlhLtYqwxtRjrUk5jw4pf/Jz/NeyxISIiIiKico+JDRERERERlXtMbIiIiIiIqNxjYkNEREREREVmZWUFKyur0g6DiwcQERERUflWw31saYeQp6g5m4u1vuvXr2Pz5s0ICQnBq1evkJ2dDWNjY7Rp0wZDhw5Fly5divV85QkTGyIiIiKiMi47Oxtz587FunXroKamBltbW/Tq1Qvq6uqIjIzEiRMnsGfPHvz8889wc3Mr7XBLBRMbIiIiIqIybtGiRVi3bh2srKywY8cOWFpayuxPTU3Fpk2bEBcXV0oRlj7OsSEiIiIiKsOePHmCv//+G/r6+ti/f79cUgMAWlpamDZtGubMmQMA+PfffzF//nzY2trC0tISRkZGaNmyJRYsWID379/LHe/o6AixWIz09HT88ccfaNmyJapXr44lS5YIZY4ePYouXbrA2NgYderUwbRp05CQkFBi111Y7LEhIiIiIirDfHx8kJWVBVdXVxgaGuZbtkKFCgCAw4cPw8vLCx07dkSHDh2QnZ2N8PBwrFy5EsHBwTh27BjU1dXljh8xYgRu376NLl26QFdXFxYWFgAAX19fTJw4Ebq6unBxcYGenh4CAwPRp08fZGZmKqzrS2NiQ0RERERUhl26dAkAYGtrq/QxLi4umDx5MjQ0NGS2e3h4wN3dHf7+/hg8eLDccS9fvkRwcDD09PSQkZEBDQ0NJCUlYfbs2ahYsSLOnDmD2rVrAwDmzZuHPn364NWrVzAzM/uMKyweHIpGRERERFSGvXnzBgBgamqq9DGmpqZySQ0AjBs3DgAQFBSk8Lg5c+agcuXKMtuOHj2KpKQkDB8+XEhqAEBdXR3z5s1TOqaSxh4bIiIiIqKvjEQigbe3N3x8fHDv3j0kJSUhOztb2P/q1SuFx7Vs2VJu2+3btwEANjY2cvvatGkDNbWykVKUjSiIiIiIiEghQ0NDPHz4EDExMahTp45Sx7i5uWHTpk2oXr06evXqBWNjY6EHx8PDA+np6Xme61NJSUkAgKpVq8rtU1VVhb6+vrKXUqKY2BARERERlWHt2rXDxYsXcf78eXTq1KnA8rGxsdi8eTMaNWqEkydPQltbW9j3+vVreHh45HmsSCSS26arqwsAePv2rdy+rKwsxMXFwcTERJlLKVGcY0NEREREVIYNGzYMqqqq2L59u8LkIrf09HRERkZCIpGgc+fOMkkNAISGhhb6/I0bNwYAhISEyO27cuUKPn78WOg6SwITGyIiIiKiMqxmzZqYPn063r17h4EDByIyMlKuTFpaGtasWYMlS5YIK5RduXJFZl7NixcvsGDBgkKf38HBAbq6uti5cyf+/fdfYXtmZiYWLVpU6PpKCoeiEf3HBTzZAElKcmmHQURUbuis8C3tEOSkpaUhOjoaZmZm0NTULO1wil1sbCwMDCrmub8s/79YY8O84y6MuXPnIi0tDevWrUPr1q1ha2uLBg0aQF1dHVFRUQgKCkJcXBzmzp0LY2NjODs749ChQ+jcuTM6deqEN2/eIDAwELa2tgoTo/zo6elhyZIlmDRpEuzs7NC/f3/o6uoiMDAQmpqaMDY2LpZr/FxMbIiIiIiIyjgVFRUsXrwYgwYNwpYtWxASEoKQkBBkZ2fDyMgIdnZ2GD58OLp06QIAWLduHczNzXHo0CFs3LgR1atXx+TJk/H999/DwMCg0OcfNmwYdHV1sXTpUvj6+kJXVxe9evXCb7/9ho4dOxb35RaJKCEhQVLaQRBJubu7w8PDA4cPH1bqH4lYLEb79u1x9OhRpepYu3YtPD098ezZM6SlpWHt2rUYPnx4sV9HSbCysgIA3Lp1q1jrfT93HHtsiIgKgT02X15Oj03hH8Y/lZ2dLbx0UkWFMzLy8yXaqrjuqxTvqAJRUVEQi8UYMGBAnmXCwsIgFosxceLELxgZfY69e/fil19+gZaWFiZNmoTZs2cLyUJJkH6O+BkhIiIiKnkcikZfnXHjxmHAgAGoXr26zPaTJ08CAPbs2QMjI6PSCI2IiIiISggTG/rqVKlSBVWqVJHb/vLlSwBgUkNERET0FeJQtBIQHR2NKVOmoEGDBjAwMEDDhg0xZcoUPH/+XK6so6MjxGIx0tPT8dtvv6Fx48YwNjZGp06dEBQUBABITk6Gm5sbGjRoACMjI3Tr1g3Xrl1TeO7Y2FjMmTMHzZs3h6GhIWrWrIkRI0bg7t27Ssd//fp1zJo1C9bW1jA3N4exsTFsbGywYsUKZGZmypW3srKClZUVUlJS8PPPP6NBgwYwNDSEjY0NDh48qPAcz58/x5gxY2BhYYFq1arBwcEBwcHBSseYH3d3d4jFYly4cAEAsHPnTpm/xWIxxGKx3DC04OBguLi4oGbNmjA0NESLFi2waNEifPjwQe4cBw8ehIODA2rXrg0jIyM0atQIAwYMwJEjR4RzNm3aFADg6+srnFMax+LFiyEWi3HgwAGF17B582aIxWKsXbu2wOuVSCTw8vJCjx49YGZmBhMTE3Tu3BleXl5KtxkRERFReccem2L2+PFj9OzZE7GxsejZsycaNGiAe/fuwdvbG4GBgQgMDETNmjXljnN1dcXdu3fRq1cvpKamYu/evXBxcUFgYCB++OEHpKenw9nZGe/evYOfnx/69++PGzduCG+CBYCnT5+id+/eiImJgZ2dHRwdHREbG4vDhw/jzJkzOHjwIFq1alXgNXh6eiIgIAA2Njbo1q0bUlNTcfHiRSxcuBAREREKH5g/fvyI/v37Iz4+Hr1790Zqair8/PwwevRo7N+/H3Z2dkLZV69eoXv37oiJiUHXrl3RtGlTPHjwAP369SuRVTWsrKwwe/Zs+Pj4IDo6GrNnzwaQs3Sh1NatWzFjxgyIxWL07NkTVatWRUREBJYuXYoLFy7g8OHD0NDQAABs2bIFM2bMgLGxMXr37g19fX28evUKEREROHr0KHr37g0rKytMmDABGzZsQOPGjeHo6Cicy9zcHCNHjsSyZcvg6emJvn37ysW8Y8cOaGhoYOjQoflem0Qiwbhx47B3717Url0bAwcOhLq6OoKCgjB16lQ8ePCgTK0vT0RERFRSmNjk48mTJ3B3d1e4LyYmRuH2H374AbGxsVi5ciVGjx4tbN++fTu+//57/PDDDwp7MeLi4hAcHIyKFXPWOrezs8O3336LPn36oEuXLti8eTPU1HJul5WVFX799Vd4eXlh8uTJQh0TJkzA69ev4efnJyz1BwCzZs1Cly5dMG3aNIVvjFV0DUuXLoWqqqqwTSKRYOrUqfD29salS5fQrl07mWNevnyJ5s2byyQAgwYNQp8+fbB27VqZxGbhwoWIiYnB3LlzMXPmTLk2Km5NmjRBkyZNcPHiRURHR2POnDky++/fvw83NzdYWVnh4MGDqFy5srBvxYoVWLhwIf755x9MnToVwP+SjosXL6Jq1aoydcXFxQnn1NPTw4YNG2BlZSV3TgCwt7fHiRMnEBUVhRo1agjbb968iZs3b6J///7Q19fP99p27NiBvXv3YsSIEVixYoXwGcnIyMDIkSOxZs0aDBw4EM2aNVO+wYiIiIjKIQ5Fy8fTp0/h4eGh8EdRr8Xz589x/vx51K9fH6NGjZLZN2rUKNSrVw/nzp1TOCRt7ty5QlIDAH369IG6ujoSExPx+++/Cw+sAITV2m7fvi1su3HjBi5fvoyhQ4fKJDUAULt2bYwcORJ3795Vakiaubm5TFIDACKRCGPHjgUAYYjcpxYvXiwkNQDQqVMnmJmZISIiQtiWkZEBf39/GBgYYMqUKTLHjxw5ErVr1y4wvuK2bds2fPz4ER4eHjJJDQBMnz4dVatWxf79+2W2q6ury9wTqYISkdxGjx4NiUQCb29vme07duwAALnPkCIbN25ExYoV8ddff8nEo6GhgXnz5gEA9u3bp3RMREREROUVe2zy0bVrV7kHWqmwsDB069ZNZtvNmzcBAO3bt4dIJJLZJxKJYGNjgwcPHuD27dtyK3Y1adJE5m9VVVUYGBggJSUFZmZmMvukb3eVToYHgPDwcADAmzdvFPYyPXr0SPjfhg0bKr7g/5eRkYGNGzfCz88Pjx49wvv37yGR/O91R69evZI7Rk9PDxYWFnLbq1WrhitXrsjEkZaWBltbW7l19lVUVNCmTRv8+++/+cZX3KRtd/r0aYVJm7q6utB+ANCvXz8sWLAANjY26N+/Pzp06IB27dpBLBYX6rzdu3dHtWrV4OPjgzlz5kBFRQVpaWnYu3cvLCwsYGtrm+/xHz58wN27d2FiYoIVK1bI7f/48SMAyMRORERE9LViYlOMkpNzXnKY14uGDA0NAQBJSUly+3LPlZFSVVVVuF36zXzuifzx8fEAIMzjyUtKSkqe+6RGjhyJgIAA1K5dG/369YOBgQHU1NSQmJiIDRs2ID09Xan4pdeQnZ0t/C299k+HcElJ2+hLkrbd0qVLlSo/ffp06OvrY+vWrVi7di3WrFkDNTU1dOvWDe7u7goTPEVUVVXxzTffwMPDA6dOnUL37t1x8OBBJCYmYtq0aXLJ8acSEhIgkUgQExMDDw+PPMspc8+JiIiIyjsmNsVIR0cHQM7KZIpIt0vLlcS5//zzT4wbN67I9URERCAgIABdu3bFnj17ZIakhYWFYcOGDZ8VpzQBevv2rcL9b968+az6i0LadtHR0UrdG5FIhJEjR2LkyJGIi4tDSEgI9u/fD39/fzx58gQhISFyQ/nyMnLkSCxduhQ7duxA9+7dsWPHDqipqWHYsGFKx92sWbM8hwcSERER/Vdwjk0xki4fHBISIjN0C8iZfB8aGipTrjhJVzsLCwv7rHqePn0KIGeY1KcP59L4P0edOnWgqamJa9euIS0tTWZfdna2zLC1L0XadtIhaYWhr6+P3r17Y9u2bbC1tcWDBw/w5MkTABDaLysrK8/jq1WrBnt7ewQGBuLSpUsICQlBt27dYGJiUuC5dXR0UK9ePTx8+BAJCQmFjp2IiIjoa8LEphiZmZmhY8eOuHfvntziAl5eXrh37x5sbW3l5tcUh5YtW6JVq1bYt28f/Pz85PZnZ2fj4sWLBdYjnc9z6dIlme337t3D8uXLPztODQ0N9O3bF7GxsVizZo3Mvh07dnzx+TUAMGbMGKipqcHNzU3hwg4JCQm4ceOG8Pfp06eF+StSmZmZwpA26dwhsVgMkUiU5wp6Uq6ursjMzISrqyskEolSiwZIjR8/Hh8+fMD06dMVDjmLjIxEVFSU0vURERERlVccilbMli9fjp49e2L69OkICAhA/fr1cf/+fRw/fhxVq1YtluQgL5s3b4aTkxO+/fZbrF+/Hs2aNUOFChXw/PlzhIWF4e3bt3j9+nW+dbRs2RItW7aEv78/Xr16hdatW+P58+c4fvy4MAfkcy1YsADnz5/HokWLcOnSJTRp0gQPHjzAyZMnYWdnhzNnznz2OQqjYcOGWLZsGX788Ue0bt0a3bp1g6WlJZKTkxEZGYng4GAMGzZMmKDv6uoKbW1ttGvXDmZmZsjMzERQUBDu37+P/v37C8lhpUqV0KJFC4SEhGDSpEmoVasWVFRUMHDgQJkFIbp3747q1avj+fPnMDU1lVuUIj+urq4ICwuDr68vLl++jE6dOsHExARv3rzBo0ePEB4ejs2bN8ssJ01ERET0NWJiU8zq1KmDs2fPwsPDA6dPn8aJEydQtWpVDBs2DLNnz4a5uXmJndvCwgIXLlzAmjVrcOzYMXh7e0NVVRVGRkawsbGBs7NzgXWoqqpi9+7dWLBgAU6fPo1r166hZs2a+P3332Fvb18siY2xsTECAwPx66+/4vTp0wgJCUHTpk3h7++P8+fPf/HEBshZWtnKygpr165FSEgIjh8/Dl1dXVSvXh2TJk2SeVHmr7/+ilOnTuHq1asICAiAtrY2atasiZUrV+Kbb76Rqfeff/7Bzz//jKNHjyIpKQkSiQStWrWSSWxUVFQwePBgLF++HMOGDVN6fg6QM99n/fr16N69Ozw9PREYGIiUlBQYGBgI961z586f3T5EREREADBx4kT4+vrixo0bZe6LU1FCQoKk4GJEVJIGDRqEU6dO4fr161/8PxLv546DJCX5i56TiKg801nhW9ohyElLS0N0dDTMzMzkXqfwNYiNjc1z1VkAeHps5BeMpnAsHXYUa33Xr1/H5s2bERISglevXiE7OxvGxsZo06aNwvcZFlV2djYyMjKgoaEBFZX/zV4pzsSmoPtaWOyxISpl9+7dw6lTp2Bvb18q33y866yO7Az1L35eIqLy6m0ZfYhWA/Ayj1eXFffDNX152dnZmDt3LtatWwc1NTXY2tqiV69eUFdXR2RkJE6cOIE9e/bg559/hpubW2mHWyqY2BCVkr179+LRo0fYtWsXAGDWrFmlHBERERGVVYsWLcK6detgZWWFHTt2wNLSUmZ/amoqNm3ahLi4uFKKsPQxsSEqJdu3b0doaCjMzMywevVqtGnTprRDIiIiojLoyZMn+Pvvv6Gvr4/9+/crfKG5lpYWpk2bJrxI/d9//8WOHTsQFBSE6OhofPjwAdWrV4eTkxNmzpyJSpUqyRz/6tUrrFixAidPnkRMTAw0NTVhbGwMGxsbLFiwQOHL2Ddv3oyNGzciKioKBgYG+Oabb+Dm5iYzdO1LYmJDVEqOHj1a2iEQERFROeDj44OsrCy4uroqTGpyq1ChAgDg8OHD8PLyQseOHdGhQwdkZ2cjPDwcK1euRHBwMI4dOwZ19Zyh6B8+fECPHj3w7Nkz2NnZoXfv3khPT8fTp0/h6+uL6dOnyyU28+fPx8WLF9GjRw906dIFR48exZIlS5CZmYl58+aVTEMUgIkNEREREVEZJn2/oK2trdLHuLi4YPLkydDQ0JDZ7uHhAXd3d/j7+2Pw4MEAgHPnziEqKgqTJk3C4sWLAfxv8YD09HRoaWnJ1X/9+nUEBwfD2NgYAODm5oYWLVpg48aNmD17ttx5vwS+oJOIiIiIqAx78+YNAMDU1FTpY0xNTRUmF+PGjQMABAUFye1TlMDo6OgorGfWrFlCUgMAVapUgYODA5KTk/HoUR6rWJQw9tgQEREREX1lJBIJvL294ePjg3v37iEpKQnZ2dnC/levXgm/29jYwMjICMuXL8etW7fQvXt3tG3bFrVr186z/qZNm8ptq1atGgAgMTGxGK9EeUxsiIiIiIjKMENDQzx8+BAxMTGoU6eOUse4ublh06ZNqF69Onr16gVjY2Oh58XDw0NYZAAA9PT0cOLECbi7uyMgIAAnTpwAkNPr88MPP+C7776Tq1/RYgLSl4xnZWUV+hqLAxMbIiIiIqIyrF27drh48SLOnz+PTp06FVg+NjYWmzdvRqNGjXDy5Eloa2sL+16/fg0PDw+5Y2rUqIENGzYgKysLd+7cwZkzZ/DPP/9g1qxZqFy5MgYOHFis11QSOMeGiIiIiKgMGzZsGFRVVbF9+3a8ffs237Lp6emIjIyERCJB586dZZIaAAgNDc33eFVVVTRp0gTTpk3D+vXrAQDHjx//vAv4QpjYEBERERGVYTVr1sT06dPx7t07DBw4EJGRkXJl0tLSsGbNGixZsgRmZmYAgCtXrsjMq3nx4gUWLFggd+zdu3fx7Nkzue2xsbEAAE1NzeK5kBLGoWhERERERGXc3LlzkZaWhnXr1qF169awtbVFgwYNoK6ujqioKAQFBSEuLg5z586FsbExnJ2dcejQIXTu3BmdOnXCmzdvEBgYCFtbW7nEKCgoCHPnzkXbtm1Rt25d6Ovr4+nTpwgICICWlpbCOTZlERMbIiIiIirXLB12KFVO+m4WDQ0NqKiUr4FLKioqWLx4MQYNGoQtW7YgJCQEISEhyM7OhpGREezs7DB8+HB06dIFALBu3TqYm5vj0KFD2LhxI6pXr47Jkyfj+++/h4GBgUzdXbt2xbNnzxASEoLDhw8jJSUFJiYm6NOnD77//ns0aNCgNC650EQJCQmS0g6CiEpP1KnJyM5ILu0wiIioBCn74F9WxcbGyj2MF0V5Tmy+tC/RVsV1X6V4R4mIiIiIqNxjYkNEREREROUeExsiIiIiIir3uHgA0X9cDfu1pR1CmZaWlobo6GiYmZmVm+UuSwvbSjlsJ+WxrZTHtiJijw0REREREX0FmNgQEREREVG5x8SGiIiIiIjKPSY2RERERFTmSSR89eLXpCTuJxMbIiIiIirTNDU1kZaWVtphUDFKS0sr9oUumNgQERERUZlWsWJFvH//Hqmpqey5KeckEglSU1Px/v17VKxYsVjr5nLPRERERFSmqaiooEqVKkhJScHbt2+LXE92drbQU6Ciwu/381OSbaWpqYkqVaoUe71MbIiIiIiozFNRUYGOjg50dHSKXEdaWhqSkpJgZGTE9/0UoDy2FVNVIiIiIiIq95jYEBERERFRucfEhoiIiIiIyj0mNkREREREVO5x8QCi/7gOWyMQn/axtMMoB+JLO4ByhG2lHLaT8thWyvvybXVnUpsvfk4iRdhjQ0RERERE5d5n99jcv38fDx8+xIcPHzBkyJDiiImIiIiIiKhQitxjExERgQ4dOsDGxgajR4/GpEmThH3BwcEwMTHBsWPHiiVIIiIiIiKi/BQpsbl37x6cnZ3x7NkzTJo0Cd26dZPZb2NjgypVquDAgQPFESMREREREVG+ipTYuLu7AwCCgoKwaNEiNG/eXGa/SCRC69atce3atc+PkIiIiIiIqABFSmyCg4Ph5OSEmjVr5lmmevXqeP36dZEDIyIiIiIiUlaREpv379/DwMAg3zJpaWnIysoqUlBERERERESFUaTEplq1arh3716+ZW7cuAELC4uiVE9ERERERFQoRUpsevTogTNnzuDcuXMK9/v7+yMsLAyOjo6fFRwREREREZEyivQemxkzZuDQoUMYNGgQhg4dilevXgEANm/ejCtXrmD//v0wNzfH5MmTizVYIiIiIiIiRYqU2FStWhVHjx7F+PHjsWPHDmH7rFmzAACtWrXC5s2boaenVzxREhERERER5aNIiQ0AWFhYIDAwEDdv3kR4eDji4+Oho6ODVq1aoUWLFsUZIxERERERUb6KnNhINWnSBE2aNCmOWIiIiIiIiIqkSIsHEBERERERlSVF7rF5+/Ytdu7ciYiICCQmJip8Z41IJMKhQ4c+K0AiIiIiIqKCFCmxuX37NpydnZGQkACJRJJnOZFIVOTAiIiIiIiIlFWkxGbu3LmIj4/HzJkzMWLECFSrVg2qqqrFHRsRfQEBTzZAkpJc2mEQEVEZpbPCt7RDIFJKkRIb6cs3f/nll+KOh4iIiIiIqNCKtHiAuro6LC0tizsWomJ14cIFiMViuLu7l3YoX9zEiRMhFosRFRVV2qEQERERfRFFSmxsbW1x7dq14o7lq/DhwwcsW7YMtra2qFatGoyMjNCwYUP06tULCxcuxNOnT0s7RBk7d+6EWCzGzp07SzuUUmdlZQUrK6vSDoOIiIiIiqBIQ9F+++032NvbY/Xq1Zg6dWpxx1RuJScno2fPnrhz5w5q1qyJwYMHQ09PDy9evMC9e/ewYsUKWFpasreLiIiIiKiYFSmx+euvv1C/fn38+uuv2Lp1Kxo3bgxdXV25ciKRCGvWrPnsIMuL9evX486dOxgxYgRWrVoltypcZGQkMjIySik6IiIiIqKvV5GGovn4+CA4OBgSiQSRkZE4cuQIfHx8FP78l4SFhQEAvvvuO4VLXVtYWKBu3boy26TDnxISEjBt2jTUqVMHxsbGsLOzw7FjxxSe58OHD3B3d0fr1q1hZGQECwsLDB48GJcvX5Yr6+7uDrFYjAsXLsDHxwedOnWCiYkJHB0dMXHiREyePBkAMHnyZIjFYuFH6tWrV5g9ezZatGgBY2NjWFhYwMbGBjNmzEBSUlKBbfLy5UssXrwY9vb2qF27NgwNDWFlZYUZM2YgNjZWrnzuuSGbN29GmzZtYGRkhMaNG2PJkiXIzs6WOyY1NRULFixAo0aNYGRkBGtra3h6ehYYm1RUVBTEYjGio6MRHR0t0w7S+Tm55+tcuXIF/fv3h7m5uUxbeXl5YejQobCyshLuS//+/XH+/HmZ8wUHB0MsFmPKlCkK43nx4gX09fXh7Owssz05ORmLFy9Gu3btYGxsDHNzcwwYMAChoaFKXysRERHR16pIPTY3btwo7ji+CpUrVwYAPH36FE2aNFH6uMzMTPTp0wdpaWkYOnQoEhMT4efnh+HDh+Off/7B4MGDhbLp6eno06cPwsLC0LRpU0ycOBGxsbHw9/fHmTNnsHXrVrkHYgBYvXo1Lly4gF69eqFLly5QU1NDs2bNkJiYiGPHjsHBwUFufsmHDx/Qo0cPPHv2DHZ2dujduzcyMjIQGRkJHx8fTJs2TWFPXW4hISFYu3YtbG1t0bJlS6irq+PmzZvYsmULTp8+jXPnzkFPT0/uuPnz5+PixYvo0aMHunTpgqNHj2LJkiXIzMzEvHnzhHLZ2dkYOnQogoKC0LBhQwwcOBBxcXH4+eef0aFDB6XaX09PD7Nnz8b69esB5CRXUp/WceXKFSxfvhwdO3bE6NGj8fz5c2HfrFmz0LhxY3Tu3BlVq1ZFTEwMjh07hr59+8LLywuOjo4AABsbG5ibm+PQoUNYunQpNDU1Zc6xd+9eZGdnw8XFRdgWHx8PBwcH3Lt3D9bW1rCzs0NSUhKOHTsGJycnbN++Hb1791bqeomIiIi+RkVKbMzNzYs7jq9Cnz59sGfPHkydOhXXrl1Dly5d0LRpU5lv9RV59eoV6tati1OnTkFdXR1ATg9Kp06dMGvWLPTq1Qs6OjoAgL///hthYWEYPHgw/vnnH6FnaOLEiejatSumTZuGLl26COWlgoODcerUKTRq1EhmuzSxcXR0xPDhw2X2nTt3DlFRUZg0aRIWL14ssy85ORkVKlQosE1sbW3x4MEDVKpUSWa7r68vJk6ciE2bNmHmzJlyx12/fh3BwcEwNjYGALi5uaFFixbYuHEjZs+eDQ0NDaGeoKAg2NvbY/fu3cL7lCZMmIAuXboUGB8AiMVizJkzR+hhnDNnTp5lz549i9WrV2PEiBFy+y5dugQLCwuZba9evUKXLl0wf/58IbERiUQYPHgwli5dioCAAPTt21fmmD179kBLS0smQXVzc8O9e/ewZs0afPPNN8L2N2/ewM7ODt9//z3s7e3lkiQiIiKi/4oiDUUjxRwdHfHbb78hOzsbK1euRJ8+fWBhYYHmzZtj1qxZePz4cZ7H/vLLL0JSAwB169bFN998IyQeUj4+PlBXV8evv/4qM9ytcePGGDZsGBISEhQOYRs1apRcUqMsLS0tuW06OjpCcpEfAwMDuaQGAIYMGQJdXV0EBQUpPG7WrFlCUgMAVapUgYODA5KTk/Ho0SNh+65duwDkvDQ290tiGzVqJNPjUVyaNGmiMKkBIJfUAICxsTGcnJzw+PFjPHv2TNgujW337t0y5W/duoW7d+/CwcFBSE7fvXsHPz8/dOrUSSapAQBDQ0NMnToVb9++zbMtiYiIiP4LlOqxCQ4OBgC0bNkSmpqawt/KaN++fdEiK6emTZsGV1dXnD59GpcvX8b169cRHh6OTZs2wcvLC1u3boWDg4PMMerq6mjdurVcXdbW1ti0aRNu3boFFxcXJCUlITIyEvXq1UO1atXkynfo0AHbtm0TyufWsmXLQl+LjY0NjIyMsHz5cty6dQvdu3dHu3bt0KhRI4VziPJy6NAhbN++HTdu3EBCQgKysrKEfa9evVJ4TNOmTeW2Sa85MTFR2Hb79m1oa2ujWbNmcuWtra3h5eWldJzKyK8dIyMjsXz5cpw/fx4vX75Eenq6zP5Xr14JvZ116tRB8+bNcerUKcTHxwvDGKWJTu77FxERgaysLKSnpyt8J8+TJ08AAI8ePULPnj0/7wKJiIiIyimlEpvevXtDJBLhypUrqF27tvC3MuLi4j4rwPJIR0cHffv2FYYYJSYm4vfff8fmzZsxdepU2Nvby/R26OvrQ0VFvvPMwMAAAIRJ+snJyTLbP2VoaChTXlFdhaGnp4cTJ07A3d0dAQEBOHHiBICcBOOHH37A2LFjC6xj9erVmDdvHqpWrQo7OzuYmpoKw6XWr18v9/AvpWjujrRHJndilJSUpDDJA/7XHsUpr3Z88uQJ7OzskJycjI4dO6Jnz57Q0dGBiooKLl68iODgYLlrdXFxwbVr1+Dv749vv/0W2dnZ2L9/PwwMDGBnZyeUi4+PB5Az1O3SpUt5xpaSklIMV0hERERUPimV2Li5uUEkEqFKlSoyf5Ny9PT08NdffyEwMBDR0dG4e/euTA9DXFwcsrOz5ZIb6aph0od86dAkRauJ5d7+6fwaAEW+XzVq1MCGDRuQlZWFO3fu4OzZs/jnn38wc+ZMiMViDBw4MM9jP378iL/++gsmJia4cOECqlatKuyTSCRYtWpVkWLKTVdXF2/fvlW4782bN59d/6fyasd169YhISEBGzdulFnsAQB++OEHhb2cAwYMwNy5c7Fnzx58++23Qk/PhAkToKb2v3+a0vs5ZcoULFq0qBivhoiIiOjroVRi8+lk6vwmV5NiIpEI2traCvdlZmYiLCwMbdu2ldkuXcZXulqZrq4uLCws8OTJE8TExMDU1FSmvPTh+dPVzfKjqBckr3JNmjRBkyZN0Lp1azg4OOD48eP5Jjbv3r1DUlISOnXqJJPUAMC1a9eQmpqqdJx5ady4MS5cuIDr16/LDUcr7DLIqqqqyMzMLFIcT58+BQD06tVLZnt2drbCZbgBCD0zJ0+eRGRkpMJhaADQokULiEQiYTlxIiIiIpLHxQOK0bZt2xAREaFw36FDh/Dw4UPo6emhQYMGcvv/+OMPmYfqhw8fwtvbG7q6ujJzcoYOHYrMzEwsXLgQEolE2H737l3s3LkTurq6wupbypDO7YiJiZHbd/fuXZkJ71LSnqGCVuAyMDCAlpYWbty4gQ8fPgjbExIS4ObmpnSM+ZEmAYsWLZJJzu7cuSM3Mb8glStXxrt375CWllboOMzMzABAbqjYypUrcffu3TyPc3FxgUQigZeXF44cOYK6deuiefPmMmWMjIzQr18/XL58GatWrZK571Lh4eEybUxERET0X1Ok5Z5JsZMnT+KHH35AzZo10bZtW5iYmOD9+/e4desWQkNDoaKigmXLlsktk2xsbIzExER07NgR3bt3R1JSEvbv34+0tDT8/fffMkPLpk+fjhMnTmD37t14+PAhOnXqhLdv38Lf3x+ZmZnYsGGDwqFoeWnTpg20tLSwfv16JCcnCz0rP/zwA4KCgjB37ly0bdsWdevWhb6+PiIjI3H8+HFoaWnhu+++y7duFRUVjBkzBmvWrEGHDh3Qs2dPJCcn49SpUzAzM4OJiUkhWlexYcOGYd++fTh16hQ6duyIbt26IT4+Hvv370eXLl0QGBiodF22tra4du0ahgwZAmtra2hoaKBdu3awtrYu8FhXV1fs3LkTI0aMQL9+/aCvr4/w8HDcuHEDPXr0yDMOBwcH6OrqYtWqVcjMzMT333+vsNyyZcvw6NEjzJ8/H7t27UKbNm2gq6uLFy9e4Pr163j8+DEePHiQZ68gERER0ddOqcTGycmpSJWLRCIcOnSoSMeWRwsXLkS7du1w9uxZhISE4PXr1wAAExMTDB06FOPHj1e4epe6ujoOHDiAX3/9Fb6+vkhKSkLDhg0xa9YsuRXUNDU1cejQIaxcuRL+/v5Yt24dtLS0YGNjgx9//FGph/DcKleuDE9PTyxZsgRbt24Vhof98MMP6Nq1K549e4aQkBAcPnwYKSkpMDExQf/+/TF9+nTUq1evwPp//fVXVK5cGT4+PtiyZQsMDAzQv39/zJkzp9CxKqKiogIfHx94eHhg79692LBhAywtLfHHH3+gVq1ahUpsZs2ahYSEBAQGBuL8+fPIzs7G7NmzlYqzadOm8PPzwx9//IEjR45ARUUFbdu2RUBAAI4fP55nHFpaWnBycsLOnTshEokwaNAgheUqV66MEydOYNOmTfDz8xNe4mloaIjGjRtj1qxZwhw4IiIiov8iUUJCgvy4lk9IhysVunKR6D+5KlphSOfD3Lp1q5Qjof+qqFOTkZ2RXNphEBERFRtLhx0Kt6elpSE6OhpmZmZ8qXUBymNbKdVjI11uloiIiIiIqCzi4gFERERERFTuMbEhIiIiIqJyj6uilTLOrSEiIiIi+nzssSEiIiIionKPiQ0REREREZV7TGyIiIiIiKjcY2JDRERERETlXpESGw8PDwQHB+db5tKlS/Dw8ChSUERERERERIVRpMRmyZIluHjxYr5lmNgQEREREdGXUmJD0TIyMqCiwpFuRERERERU8oqceYhEojz3ZWRkIDQ0FIaGhkWtnoiIiIiISGlKv6CzadOmMn+vW7cOO3fulCuXlZWFuLg4pKWlYdSoUZ8fIRERERERUQGUTmyys7OFXhqRSASJRAKJRCJXTl1dHfXr14etrS1mzZpVfJESERERERHlQenE5tatW8LvlStXxqRJkzB79uwSCYqIiIiIiKgwlE5scrtx4wb09PSKOxYiIiIiIqIiKdLiAXp6eoiOjsaHDx8U7k9JScHt27eRlJT0WcEREREREREpo0g9Nn/++Se2b9+O+/fvK9yflZWFnj17YsyYMVi4cOFnBUhEJauG/drSDqFMS0tLQ3R0NMzMzKCpqVna4ZRpbCvlsJ2Ux7ZSHtuKqIg9NqdPn0aXLl2go6OjcL+uri7s7Oxw8uTJzwqOiIiIiIhIGUVKbJ4/f45atWrlW8bS0hLPnz8vUlBERERERESFUaTERiQSIT09Pd8y6enpyMrKKlJQREREREREhVGkxKZu3bo4ffq0wvfYADnvvDl16hRq1679WcEREREREREpo0iJzcCBA/Hvv/9i8uTJSExMlNmXmJiIyZMn48mTJxg8eHCxBElERERERJSfIq2KNnbsWBw+fBi+vr44duwYWrRoARMTE7x8+RIRERFITEyEjY0Nxo0bV9zxEhERERERySlSj426ujr8/f0xdepUZGdn4+zZs/Dx8cHZs2eRnZ2NadOmwc/PD+rq6sUdLxERERERkZwi9dgAQIUKFfDbb79hwYIFePjwIRITE6Gnp4c6depAVVW1OGMkIiIiIiLKV5ETGykVFRXUr1+/OGIhIiIiIiIqkiINRSMiIiIiIipLitRj07RpU6XKiUQiXL9+vSinICIiIiIiUlqREpvs7GyIRCK57UlJScLyz8bGxlw8gIiIiIiIvogiJTa3bt3Kc19UVBR++eUXxMbGwt/fv8iBEdGX0WFrBOLTPpZ2GOVAfGkHUI6wrZTDdlIe20p5xd9Wdya1KfY6iUpCsc+xqVGjBrZt24aEhAT8/vvvxV09ERERERGRnBJZPEBdXR2dO3fGgQMHSqJ6IiIiIiIiGSW2Klpqairi49l1TEREREREJa9EEpvg4GDs378ftWvXLonqiYiIiIiIZBRp8QAnJyeF2z9+/IiXL1/i2bNnkEgkmDVr1mcFR0REREREpIwiJTYXL15UuF0kEkEsFqNLly6YNGkSunbt+lnBERERERERKaNIiQ3nzhARERERUVlSYosHEBERERERfSlMbIiIiIiIqNxTaija5MmTi1S5SCTCmjVrinQsERERERGRspRKbHx8fBRuF4lEkEgkeW5nYkNERERERF+CUonNjRs3ZP7Ozs7GTz/9hPDwcEyYMAHW1tYwNDTEmzdvEBISgn/++QetW7eGu7t7iQRNRERERESUm1KJjbm5uczfK1aswNWrV3Hx4kUYGxsL2+vUqYP27dtj+PDhsLW1xcGDBzF9+vTijZiIiIiIiOgTRVo8wMvLC3379pVJanIzNTVFv3794Onp+VnBERERERERKaNIiU1MTAw0NTXzLVOhQgXExMQUKSgiIiIiIqLCKFJiY2pqiiNHjiAtLU3h/g8fPuDIkSMwNTX9rOCIiIiIiIiUodQcm0+NHDkSCxcuRI8ePeDm5gZra2vo6+sjLi4OISEh+PPPP/Hs2TP8+uuvxR0vERWzgCcbIElJLu0wiIi+GjorfL/4OdPS0hAdHQ0zM7MCR9UQfa2KlNhMmzYN//77L3bu3IkRI0YAAFRUVJCdnQ0AkEgkGD58OKZNm1Z8kRIREREREeWhSImNiooK1qxZgyFDhsDX1xd37txBUlISdHV10bhxYwwZMgQdOnQo7ljzZGVlBQC4devWFztnWSYWi9G+fXscPXq0tEMhJfDzS0RERPT5ipTYSHXo0KHYE5ioqCg0bdo03zKNGzfGxYsXi/W8heHo6Ijg4GAkJCSUWgxERERERPQ/n5XYlCRLS0sMHjxY4T4jI6MvHE35cuXKFWhpaZV2GKSkQ4cOlXYIREREROXeZyU2ly9fho+PD27duoWkpCTo6OjAysoKQ4cOhbW19WcFVrNmTcyZM+ez6vivqlu3bmmHQIVgaWlZ2iEQERERlXtFWu4ZAObOnYtevXphx44duHbtGp4+fYrr16/Dy8sLjo6O+Pnnn4szziKRSCTw8vJCjx49YGZmBhMTE3Tu3BleXl55lvfx8UGvXr1gbm4OExMTtGjRAj/88AOio6MB5MxfCQ4OFn6X/kycOBFAzlA66d8PHz7EN998g5o1a0IsFiMqKgoA8PHjR6xduxbt27eHsbExzM3N0bt3bwQGBsrFtHPnTojFYuzcuRPnzp1Dz549YWpqCktLS0yYMAFxcXFyx4jFYjg6Osptz8jIwPr162FnZ4fq1aujWrVqaNu2LX7++WelhtVdv34ds2bNgrW1NczNzWFsbAwbGxusWLECmZmZCo+JjY3F3Llz0apVKxgZGcHCwgL29vZYvXq1XNnbt29j3LhxaNiwIQwNDVGvXj0MGDAAx48flyt79OhRODs7o0aNGjAyMoK1tTVWr16NrKwsmXLZ2dnYsWMH7OzsYGFhARMTE1hZWWHYsGHCfZQ6ePAgHBwcULt2bRgZGaFRo0YYMGAAjhw5Inf+gIAA9O7dW2iHDh06YN26dXLnV+bzYGVlJcyzya0wn9+0tDSsXr0a7du3h7m5OapVq4ZmzZphzJgxuHPnjoI7Q0RERPR1KVKPja+vL9auXYu6deti9uzZ6NChAwwNDREbG4uLFy/Cw8MDGzZsEHpvSoNEIsG4ceOwd+9e1K5dGwMHDoS6ujqCgoIwdepUPHjwAIsWLZIpP2bMGPj5+cHU1BQDBw6Ejo4Onj17Bj8/P3Tt2hVmZmaYPXs2fHx8EB0djdmzZwvHf/pg+vTpU9jb26NBgwYYOnQo4uPjoaGhAYlEAldXVxw+fBi1a9fG2LFj8eHDB/j7+8PFxQVLlizBhAkT5K4nICAAgYGB6NmzJ7799luEhIRg165diIyMREBAQIHtkZaWhv79+yMkJAS1atXCsGHDUKFCBTx+/Bjbtm3DkCFDIBaL863D09MTAQEBsLGxQbdu3ZCamoqLFy9i4cKFiIiIkHvgfvz4MZycnBATEwNra2s4Ojriw4cPuHv3LpYtW4apU6cKZQ8fPoyxY8ciOzsbPXv2RJ06dRAbG4urV6/Cy8sLvXr1Esr+9ttvWL58OapVqwZnZ2fo6OggJCQE8+bNQ3h4ODw9PYWyCxcuxN9//w1LS0sMGjQIlSpVQkxMDEJDQ3H+/Hm0b98eALBlyxbMmDEDxsbG6N27N/T19fHq1StERETg6NGj6N27t1Dn+vXrMWfOHFSuXBkDBw6EtrY2AgIC8PPPPyM0NBQ7duyASCRS6vOQl8J+fidOnAh/f380atRIuLfPnz/HhQsXYGdnh0aNGuV7b4mIiIjKO1FCQoKksAd169YNL1++RGhoKHR0dOT2JyUlwcbGBsbGxjh16lSh6pYuHpDfHJvWrVvD3t5e+FvRqlKenp6YPn06RowYgRUrVkBNLSeHy8jIwMiRIxEQEICgoCA0a9YMALB582bMnDkTnTp1wq5du2TmqKSmpiItLQ2VK1cGkP/iAbkXP5g1axZ++eUXmf27du3ChAkT0L59e/j7+wsPty9evEDnzp2RkJCAsLAwWFhYAMjpsZk8eTLU1NRw5MgRtGvXDgCQlZWFPn364OLFizh58iRat24tnEPRqmjz58/HqlWr4OLignXr1kFVVVXYl5iYCFVVVVSqVElhe0s9e/YM1apVkzlWIpFg6tSp8Pb2RkBAgBAfAHTt2hVXr17F33//jVGjRsnU9eLFC1SrVg1ATq9O8+bNIZFIcOzYMbnFI3KXPXv2LPr164du3brB09MT2traQhwzZszA1q1b4enpiT59+gDIGealpaWF8PBwoay0fEJCgnBPO3XqhHv37uHu3buoWrWqzPnj4uKgr68PAIiMjESrVq1QuXJlnD17FtWrVweQ87nq168fgoOD8c8//8DFxQVAwZ8H4PM/v4mJibCwsECzZs1w6tQpmfuTlZWF5OTkfJPW93PH8T02RETFiO+xKbvYTsorj21VpKFo9+7dE74pV0RXVxdOTk64f/9+kQN7+vQpPDw8FP4okyxt3LgRFStWxF9//SU8FAKAhoYG5s2bBwDYt2+fsH3z5s1QVVXF8uXL5Sbea2lpCQ/AyjIyMsKsWbPktvv4+ADI6XXI/Y19tWrVMGnSJGRmZmLv3r1yxw0cOFAmaVBVVRV6wyIiIvKNJSsrC9u3b4euri48PDxkHnwBQE9Pr8CkBgDMzc3ljhWJRBg7diwAICgoSNgeERGBq1evwsbGRi6pASAkKkBOD+D79+8xefJkhSvi5S67ceNGAMCKFStkEhWRSIRff/0VIpEI+/fvlzleXV1d5jMgLf/pPVVUDoCQ1ADAnj178PHjR0yZMkVIaoCcz9WCBQsA/O8e55bX5yEvhfn8ikQiSCQSVKhQQe7+qKqqFtgTR0RERPQ1KPLiARJJ/h09nw7FKayuXbvKPaAqSzrcycTEBCtWrJDb//HjRwDAo0ePAAApKSm4f/8+atasiVq1ahU96FwaN26scKjRzZs3oaWlhZYtW8rtky6dreh9Jvk98CcmJuYby8OHD5GUlITOnTt/1kNuRkYGNm7cCD8/Pzx69Ajv37+X+Ry8evVK+P3q1asAADs7uwLrLUzZ8PBwVKxYMc95UlpaWsJ9BYB+/fph69atsLa2Rr9+/dChQwe0bt0aFStWlDmuX79+WLBgAWxsbNC/f3906NAB7dq1k2uvmzdvAoDCZc5bt24NLS0thfcvr8+DIoX9/Orq6sLe3h6nTp2Cra0t+vTpA2tra7Rq1UrpcxIRERGVd0VKbBo0aIDDhw/jl19+UfhNf3JyMg4dOoT69et/doBFkZCQAIlEgpiYGHh4eORZLiUlBcD/EgMTE5Nii8HAwEDh9uTkZJkeiNwMDQ0B5Azl+5Surq7cNum3859OWP9UcV2fdAhU7dq10a9fPxgYGEBNTQ2JiYnYsGED0tPTi3TOwpSNj4/Hx48flbqvAODh4QELCwv4+Phg6dKlWLp0KTQ1NdG3b1/88ccfqFKlCgBg+vTp0NfXx9atW7F27VqsWbMGampq6NatG9zd3YWhgcnJOUO28rq/VatWxcuXL+W251VekcJ+foGcoWvLly/Hvn378PvvvwMAdHR0MHz4cMyfP1+md4uIiIjoa1SkxMbV1RWTJ09G9+7dhcUDqlSpgnfv3gmLB8TExJTaymjSIXLNmjWTGR6VF2nSoOiBtKjy6rHS0dFBbGyswn3S7XkN8SsqPT09AJ93fREREQgICEDXrl2xZ88emSFPYWFh2LBhQ5HPmbtsjRo18i2ro6MDkUiEJ0+eKBW3uro6pk2bhmnTpuHly5cIDg7Gzp07sWvXLrx58wZ+fn4Acu7XyJEjMXLkSMTFxSEkJAT79++Hv78/njx5gpCQEKiqqgr3JjY2Fubm5nLne/v2rcL7V5gezMJ+fgGgYsWKmDdvHubNm4fIyEhcuHAB27Ztw4YNG5CWloaVK1cqfX4iIiKi8qhIc2yGDRuGCRMm4N69e3B1dUWdOnVQtWpV1KlTB66urrh37x6+++47DBs2rLjjVYqOjg7q1auHhw8fKrWMcaVKlVC/fn1ERUXh8ePHBZZXtqdEkSZNmiA1NVUYfpWbdPlhRUv/fo46depAV1cXERERSrWHIk+fPgUAdO/eXW4eR2hoqFx56VC7M2fOFFh3Ycq2atUKcXFxSt2nT5mYmGDgwIHYv38/atWqhaCgIKSmpsqV09fXR+/evbFt2zbY2triwYMHQiLVpEkTAMDFixfljrt69SpSU1M/+/4V9vP7KQsLC4wYMQJHjx5FpUqVFC6XTURERPS1KfJ7bNzd3XHs2DEMGzYMVlZWMDc3h5WVFYYPH46jR4/mO4TmSxg/fjw+fPiA6dOnywzZkYqMjBTeIwIAY8eORVZWFmbMmCH3sJuWlob4+Hjhb+mk8xcvXhQ6LumE/4ULF8q8+yUmJgZr166FmppanqvBFZWamhpGjx6NpKQkzJ49Wy4hS0xMxPv37/Otw8zMDABw6dIlme337t3D8uXL5cq3aNECLVu2REhIiMzyy1IxMTHC70OHDkWlSpWwdu1aYQ5LXmXHjx8PAJgyZYrCd/i8fv0aDx48AACkp6fj3LlzcvPBUlJS8P79e6irqwtJ2unTp4W5K1KZmZnCfZeuBjJo0CCoqalh7dq1Mr1RmZmZwuIBxZHQF+bz+/btW4WJckJCAtLT08vNSiZEREREn6NIQ9GCg4Oho6MDa2trWFtbF3dMAIAnT57A3d09z/1z5szJ93hXV1eEhYXB19cXly9fRqdOnWBiYoI3b97g0aNHCA8Px+bNm4WhT2PGjEFwcDD8/f3RsmVL9OrVCzo6Onj+/DlOnz6N1atXC+8ysbW1xcGDBzF69Gh069YNmpqaaNiwIXr06FHgdQ0ZMgSHDx/GsWPH0L59e/To0UN4j01cXBwWLVokzOcoTj///DPCwsKwe/duhIeHw97eHhUqVEBkZCROnz6N48ePC70RirRs2RItW7aEv78/Xr16hdatW+P58+c4fvw4unfvjoMHD8ods3HjRvTu3RvTp0/Hrl270KZNG6SlpeH+/fu4efOm0AtkYGCA9evXY8yYMejatSt69eqFOnXq4N27dwgPD4e5ubmw0pi9vT1mzZqFv/76C82bN4e9vT3MzMwQFxeHJ0+eIDQ0FHPnzkW9evWQmpqKPn36wMLCAq1atUL16tWRkpKCgIAAvH79Gt9//70wud7V1RXa2tpo164dzMzMkJmZiaCgINy/fx/9+/cXEjtLS0ssWLAAc+fORfv27dGvXz9oa2sjMDAQDx8+hIODg7DU8+cozOc3JiYGXbt2RYMGDdCkSROYmpoiLi4Ox44dQ2ZmJqZPn/7Z8RARERGVdUVKbJycnPDtt99i6dKlxR2PQLrcc14KSmxEIhHWr1+P7t27w9PTE4GBgUhJSYGBgQFq1qyJ33//HZ07d5Ypv3XrVnTp0gVeXl7YtWsXJBIJTExM0K9fP+F9NwAwatQoPHv2DPv378eyZcvw8eNHDB06VKnERiQSYceOHVi/fj18fX2xceNGaGhooEmTJpg8eTIcHBwKrKMoNDU1ceDAAWzcuBF79uzBjh07oKqqiurVq8PV1VXhfJHcVFVVsXv3bixYsACnT5/GtWvXhHa0t7dXmNjUqlUL586dw/LlyxEQEID169ejYsWKqFWrFmbOnClT1snJCadOncKKFSsQHByM48ePo0qVKrCyspJbLvqXX35B+/btsWHDBpw7dw6JiYnQ19dHjRo18NNPP2HQoEEAcuadLFy4EOfOnUNoaChiY2MhFotRp04dLFy4EP379xfq/PXXX3Hq1ClcvXoVAQEB0NbWRs2aNbFy5Up88803MuefMmUKatasibVr12LPnj3IyMhArVq1sGjRIkyYMOGzVwQECvf5NTc3x08//YTz58/j3LlziIuLQ5UqVdC0aVNMmjRJqdXmiIiIiMq7Ir2gs169eujfv3++PSpEVD5EnZqM7Ay+oJOIiIgKx9JhR2mHIKNIc2zs7OwQHBxc4LtsiIiIiIiIvoQiJTbz589HXFwcpk+fLjOpnoiIiIiIqDQUaY7NuHHjoKenB29vb+zZswc1atSAgYGB3NwCkUiEQ4cOFUugREREREREeSlSYpP7HR7p6el4+PAhHj58KFeuOCZRExERERERFaRIiQ2HnxERERERUVlS5Bd0EhERERERlRWFSmzCwsLg7OwMMzMzmJmZoU+fPggPDy+p2IiIiIiIiJSi9FC0O3fuwNnZGWlpacK28+fPw9nZGadPn0aDBg1KJEAiIiIiIqKCKN1js3LlSqSlpWHGjBl4+PAhHj16hBkzZiA1NRUrV64swRCJiIiIiIjyJ0pISFDqLZuNGzeGmZkZjh8/LrO9V69eiI6Oxu3bt0skQCIqWVGnJiM7I7m0wyAiIqJyxtJhR2mHIEPpHps3b96gdevWcttbtWqF2NjYYg2KiIiIiIioMJRObDIzM1GxYkW57RUrVkRmZmaxBkVERERERFQYXO6ZiIiIiIjKvUK9oHPPnj1yyzs/efIEADBo0CC58iKRCHv27PmM8IiIiIiIiApWqMTmyZMnQiLzqVOnTsltE4lERYuKiIiIiIioEJRObG7cuFGScRARERERERWZ0omNubl5ScZBRERERERUZEq/x4aI6L8oLS0N0dHRMDMzg6amZmmHU6axrZTDdlIe20p5bCvlsJ2UVx7biquiERERERFRucfEhoiIiIiIyj0mNkREREREVO4xsSEiIiIionKPiQ0REREREZV7TGyIiIiIiKjcY2JDRERERETlHhMbIiIiIiIq95jYEBERERFRucfEhoiIiIiIyj0mNkREREREVO6plXYARFS6OmyNQHzax9IOoxyIL+0AyhG2lXLYTspjWymvfLXVnUltSjsE+oqwx4aIiIiIiMo9JjZERERERFTuMbEhIiIiIqJyj4kNERERERGVe0xsiIiIiIio3GNiQ0RERERE5R4TGyIiIiIiKveY2BARERERUbnHxIaIiIiIiMo9JjZERERERFTuMbEhIiIiIqJyj4kNERERERGVe0xsiIiIiIio3GNiQ0RERERE5R4TGyIiIiIiKveY2BARERERUbnHxIaIiIiIiMo9tdIOgIhKV8CTDZCkJJd2GERE9B+U/MOXP6cxgMz//1FEZ4XvF4yGihN7bEqZo6MjxGJxaYdRoKioKIjFYkycOFGp8uXluoiIiIjo6/DV9thERUWhadOm+ZZJSEj4MsEQEREREVGJ+moTGylLS0sMHjy4tMMgIiIiIqIS9NUnNjVr1sScOXNKOwwiIiIiIipBnGPz/+7duwdXV1fUrl0bhoaGaNKkCebMmYP4+PhiKR8aGgoHBweYmprC0tISrq6ueP78eaFiTExMxMqVK+Hg4ID69evDwMAA9evXx/jx4/H06VO58u7u7hCLxbhw4QL8/f1ha2sLY2Nj1KtXD25ubkhNTZU7JisrCytXrkTz5s1hZGSE5s2bY/ny5ZBIJIWKVSotLQ3z589Ho0aNYGRkBBsbG3h5eeVZ/ujRo3B2dkaNGjVgZGQEa2trrF69GllZWUIZX19fiMVi/PnnnwrruHTpEsRiMaZMmSKzPTY2FnPmzEHz5s1haGiImjVrYsSIEbh7965cHVZWVrCyskJCQgLc3NzQqFEjVKlSBTt37gQAXL9+HbNmzYK1tTXMzc1hbGwMGxsbrFixApmZiqcjXrx4Eb169ZL7DOQ1H0kikcDLyws9evSAmZkZTExM0LlzZ4Xtl5aWhtWrV6N9+/YwNzdHtWrV0KxZM4wZMwZ37tzJs72JiIiIvhZffY+NMi5fvoz+/fsjPT0dffr0gbm5OcLCwrB+/XqcOHECJ0+ehL6+fpHLnzt3DgMHDoSKigr69esHExMTnDt3Dj179oSenp7ScT58+BCLFy9Gx44d0bt3b2hra+Phw4fYt28fTpw4gXPnzsHc3FzuuM2bN+PUqVNwcHBAhw4dcPr0aWzcuBHx8fHYtGmTTNnp06fD29sbNWrUwNixY5Geno61a9fi8uXLRWhZYNSoUbhz5w769OmDzMxMHDhwAFOnTkVsbCx+/PFHmbK//fYbli9fjmrVqsHZ2Rk6OjoICQnBvHnzEB4eDk9PTwCAk5MTZsyYgb1798LNzU3unHv27AEAuLi4CNuePn2K3r17IyYmBnZ2dnB0dERsbCwOHz6MM2fO4ODBg2jVqpVMPRkZGXB2dsb79+/Rs2dPqKurw9DQEADg6emJgIAA2NjYoFu3bkhNTcXFixexcOFCREREyCUfZ86cweDBg6GmpoZ+/frB2NgYFy9eRM+ePfNMasaNG4e9e/eidu3aGDhwINTV1REUFISpU6fiwYMHWLRokVB+4sSJ8Pf3R6NGjTBs2DBUqFABz58/x4ULF2BnZ4dGjRoV4q4RERERlT+ihISEon0VX8ZJFw/Ia46Nvb09WrdujezsbLRu3RqPHz/G/v370bVrV6GM9EF7xIgRWL16NQAUqXyLFi0QFRWFY8eOwdraGoDsgyug3EIGiYmJyM7ORuXKlWW2nz9/Hn379sU333yDVatWCdvd3d3h4eEBXV1dnD59GnXq1AEApKamomPHjnj8+DHu3r0LExMTAMCFCxfg5OSExo0bIzAwEBUrVgQAxMTEoGPHjnj37h2GDh2K9evXFxiro6MjgoODUb9+fZw8eRI6OjoAgNevX6NTp054+/YtwsPDYWFhAQA4e/Ys+vXrh27dusHT0xPa2tpCO82YMQNbt26Fp6cn+vTpAwAYN24c9uzZgzNnzqBFixbCeTMzM1GvXj1oa2vj1q1bEIlEAIAePXrg6tWr2Lt3L7p06SKU//fff9GlSxeYmZkhJCRE2G5lZYXo6GjY2dlh586d0NLSkrm+Z8+eoVq1alBVVRW2SSQSTJ06Fd7e3ggICEC7du0A5PSCNWvWDC9evMCJEydkEqhJkybBx8cHgOxnwNPTE9OnT8eIESOwYsUKqKnlfAeRkZGBkSNHIiAgAEFBQWjWrBkSExNhYWGBZs2a4dSpUzIxZWVlITk5Od8V6t7PHcflnomIiP4fl3vOkZaWhujoaJiZmUFTU7O0w1HKVz8U7enTp/Dw8JD7CQsLA5AzbOnx48fo1q2bTJICADNmzIC+vj727duHjIyMIpUPDQ1FZGQkevToISQ1ACASiTBv3jyZh9CC6OnpySU1AGBra4v69esjKChI4XETJkwQkhoA0NLSwoABAyCRSHD9+nVh+65duwAAbm5uQlIDAKamppgwYYLSceY2Y8YMIakBACMjI0yePBkfP34UkjoA2LhxIwBgxYoVQlID5LTTr7/+CpFIhP379wvbpcnq7t27Zc534sQJxMXFYdCgQUJSc+PGDVy+fBlDhw6VSWoAoHbt2hg5ciTu3r2rcEjab7/9JpfUAIC5ubncvROJRBg7diwAyNyL0NBQREdHw8HBQa5X6JdfflH4Gdi4cSMqVqyIv/76S0hqAEBDQwPz5s0DAOzbt084r0QiQYUKFeTqUlVV5bLbRERE9J/w1Q9F69q1q8wD8adu3rwJAOjQoYPcvooVK6J58+Y4ffo0/v33XzRs2LDQ5W/fvg0AsLGxkSsvnQvx7Nkzpa/nwoULWL9+Pa5evYp3797h48ePwj4NDQ2Fxyha9rpatWoAcnqBpPKLNXdSVhiKjpNuu3XrlrAtPDwcFStWzHP+jZaWFh49eiT83aVLFxgZGcHPzw+LFy8WHuiliU7uYWjh4eEAgDdv3sDd3V2ubmm9jx49QsOGDYXtmpqaeQ7hysjIwMaNG+Hn54dHjx7h/fv3MvOQXr16Jfwubde2bdvK1VOtWjVUr14dUVFRwrYPHz4IPWkrVqyQO0Z6z6Vx6+rqwt7eHqdOnYKtrS369OkDa2trtGrVKs/PBBEREdHX5qtPbAqSnJwzBMfAwEDhfumciqSkpCKVl/5v1apV8yyvbGJz4MABuLq6olKlSrCzs4O5uTm0tLQgEong4+OD6Ohohcfp6urKbZMmArkn5SclJUFFRQVVqlTJ87oKS1E7SbdJ2wYA4uPj8fHjR3h4eORZV0pKivC7qqoqBgwYgHXr1uHs2bOwt7dHYmIiTpw4gaZNm6J+/foydQNAYGAgAgMDlaofyLln0l6fT0mHg9WuXRv9+vWDgYEB1NTUkJiYiA0bNiA9PV0oK/3MKGpXIKdtcyc2CQkJkEgkiImJUbo9PD09sXz5cuzbtw+///47AEBHRwfDhw/H/PnzZXrBiIiIiL5G//nERjpMKjY2VuF+6XZpucKWlyYVb9++VVj+zZs3Sse6ZMkSaGpqIigoCLVq1ZLZ5+fnp3Q9edHV1UV2djbevXsnl4gVJs7cYmNjUb16dblt0vNJ6ejoQCQS4cmTJ0rX7eLignXr1mHPnj2wt7fHwYMHkZaWJtNbI60bAP7880+MGzdO6frzSmoiIiIQEBCArl27Ys+ePTLDv8LCwrBhwwaF53/37p3C+j5tW2n5Zs2a5Tm88FMVK1bEvHnzMG/ePERGRuLChQvYtm0bNmzYgLS0NKxcuVKpeoiIiIjKq69+jk1BmjRpAiBnKd5PffjwAdeuXYOWlpYwR6Ww5Rs3bgwAMhPTpZ49e4YXL14oHevTp09Rt25duaTm5cuXCpd7Lqz8Yg0NDS1SnYqOk26zsrIStrVq1QpxcXF4/Pix0nVLe2aOHj2KlJQU7N69G6qqqhg4cKBMOem8Fum8qs8lbevu3bvLzWlRdL3SdlW0styLFy/kPgM6OjqoV68eHj58qNSiEp+ysLDAiBEjcPToUVSqVAnHjx8vdB1ERERE5c1/PrFp164dLC0tcfLkSblvx5cvX453795hwIABwlyFwpa3trZGjRo1EBgYKPPQK5FI8Pvvv8sMBSuImZkZnj59KvMNf1paGn788UeZuTZFNWTIEAA5PRu5hznFxMTI9UIoa9myZcJQLCCnd2Lt2rVQU1PDoEGDhO3jx48HAEyZMgVxcXFy9bx+/RoPHjyQ2+7i4oKUlBRs2LABISEh6NKli9ywuZYtW6JVq1bYt2+fwp6t7OxshYlqXszMzADkLCSR271797B8+f+1d+fRNd37/8efkZiFGJIIMiqaEkPRn0TMkSLGGHpNaV33UmpujUUpmmpVqsZSKjSUFl9UGjWHJFWhRUuxBKVBo5EghJD8/rByruMkpKViO6/HWlkr2ft9zv6cd2I5r7P357NnWtT7+vpSqVIlIiMj2b9/v9m+adOm5fi769+/P9evX2fo0KEWl8gBnD592nT52qVLlyyeF+5e0nbz5k3DrGQiIiIi8iis/lK0AgUKMG/ePDp37kzXrl3p2LEjrq6uxMfHEx0djaenJ5MmTXqk+lmzZplqs+9jEx0dzcWLF6levXqeb6DYr18/Ro0aRePGjWnfvj137txhx44dZGVlUaNGDdMk9b+rUaNG9OzZk4iICPz8/Gjbti23bt1i7dq11KtX74HzU3Lj7u6Or68v7du3N93HJikpiQkTJpiWeoa7y2+PHDmSDz/8kDp16hAQEICrqyvJyckkJCQQFxfH+PHjqVatmtnzd+3alXfffZf333+frKwsi8vQsn322We0a9eOf//738yfP5/atWub7vWyb98+Ll26xMWLF/P0murWrUvdunVZt24dFy5coH79+pw7d45vv/2WwMBA1q9fb1Zva2tLWFgY3bt3JygoiODgYJydnYmJiSExMZEaNWpY/A306dOHffv2sXLlSvbu3UuTJk1wcXHhjz/+4MSJE8THx/PZZ5/h7u5OYmIiLVq0wNvbm5o1a1KhQgWSk5OJjIwkIyODoUOH5ul1iYiIiBiZ1QcbuPuJ+pYtW/jggw/Yvn07V65coXz58vTv359Ro0ZZTPr+q/VNmzZl/fr1TJ06lfXr11OkSBGaNGnC0qVL/9Iyyv/9738pWLAgCxcuZNmyZZQqVYrAwEAmTpzIa6+99jhawSeffMJzzz1HeHg4ixYtokKFCrzxxht06tTpbwWbpUuX8t5777FmzRouXbpE5cqVmTBhAiEhIRa1b7/9Ng0bNmTBggXs2rWL1NRUypQpg7u7O2PGjDE7w5OtUqVK+Pv7s3v3bkqUKEFQUFCO4/Dw8GD37t3MmTOHyMhIvvjiC2xtbXF2dsbPz4/27dvn+TXZ2tqyatUqJk2axLZt2/jxxx/x8vJiypQpprk+92vZsiVr164lNDSUdevWmf4GlixZQrdu3cyWxIa783vmz59PYGAg4eHhbN68mbS0NBwdHU3Hatq0KXB3db0xY8YQHR3Nrl27SE5OpmzZstSqVYuBAwfSvHnzPL82EREREaN6Zm/QKWIEV69epWrVqrzwwgts27YtX8agG3SKiIj8j27QeZcRb9CpMzYiT0BaWhqZmZlmZ2bu3LnDxIkTuXHjRq5nmp6EP5sWJPNWwXw7voiISE482yx77M9pxDfrkncKNiJPwMmTJ2ndujXNmzfHw8ODa9euERcXx6+//oq3t7dp8QQRERER+XsUbESegAoVKtChQwdiYmLYtm0bt2/fplKlSgwePJg333yT4sWL5/cQRURERAxNwUbkCShXrhzz5s3L72GIiIiIPLOs/j42IiIiIiJifAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieAo2IiIiIiJieHb5PQARyV/uAXPzewhPtfT0dM6ePYurqytFihTJ7+E81dSrvFGf8k69yjv1SkRnbERERERE5BmgYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoanYCMiIiIiIoZnl98DEJH85b/kAJfTb+f3MAzgcn4PwEDUq7xRn/JOvco79Spvnr4+/TLwpfweguHpjI2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBiego2IiIiIiBieXX4PQETyV1TCArLSrub3MERExArZh618osdLT0/n7NmzuLq6UqRIkSd6bPnn6YzNE+Tj44OPj0+e60NDQ3FwcGD37t3/4Kgkv/3VvwsRERERsWRVZ2zOnDlDrVq1HliTkpLyZAYjIiIiIiKPjVUFm2yenp5069Ytv4fxUP369aNz585UqlQpv4ci/6ANGzbk9xBEREREDM8qg42Xlxdjx47N72E8VNmyZSlbtmx+D0P+YZ6envk9BBERERHD0xybHERHR/PGG29Qr149KlasSMWKFWnatClLly7Nsf6nn34iJCSEGjVq4OTkRJUqVWjZsiVhYWE51qelpTFu3Di8vb1xcnLCz8+P9evXW9Q9aI5NVFQUbdu2xc3NjfLly+Pv78+8efO4c+eOWd2ZM2dwcHBgwIABnD59mpCQENzd3alQoQIdOnTg8OHDee7LTz/9xMiRI/H19TUd18/Pj7CwMDIyMnJ8TFJSEuPHj6devXo4Ozvj4eFBQEAAs2fPtqj9+eef6devHy+88AJOTk5Uq1aNzp078+2331rUbtq0ifbt2+Pu7o6zszO+vr7Mnj3b4vVnZmaybNkymjdvjoeHBy4uLvj4+NCjRw9iYmLMatevX0+bNm147rnncHZ2pnr16nTu3JlvvvnG4vh/p//Hjx+nV69eeHl54eDgwJkzZ4Dc59hkZWWxfPlyXn75ZVxdXXFxcaFp06YsX77cojY9PZ3Zs2fTsGFD3NzcqFixIrVr16Zv37788ssvOfxmRERERJ4tVnnG5mFmzZpFQkIC9evXp0KFCqSmprJ161aGDRvGiRMnmDZtmqn20KFDvPzyy9ja2tKmTRtcXV1JTU3l6NGjhIeHM3z4cLPnvn37NsHBwVy+fJm2bdty48YN1q5dy2uvvcaaNWto3rz5Q8c3f/58xo4dS+nSpenSpQvFihUjKiqKcePGERcXx7Jly7CxsTF7zG+//UaLFi2oVq0avXr14tSpU0RGRtKuXTt++OEHnJycHnrc8PBwoqKi8PPzo2XLlty4cYM9e/YwefJkDhw4YPGG++TJk7Rr147ExER8fX0JCgri+vXrHDlyhI8++ojBgwebajdu3Mh//vMfMjMzadWqFVWqVCEpKYn9+/ezfPlyWrdubap99913mTlzJhUrVqR9+/bY29sTGxvLhAkTiI+PJzw83FQ7efJkZs2ahaenJ127dqVEiRIkJiYSFxdHdHQ0DRs2BGDx4sW8+eablC9fnrZt21KmTBkuXLjAgQMH2LRpE23btn2k/p86dYqAgAC8vb3p3r07ly9fplChQrn2Oisri379+vHVV1/x3HPP0aVLFwoWLMjOnTsZPHgwx44dY+rUqab6AQMGsG7dOqpXr06PHj0oXLgw586dY/fu3TRv3pzq1as/9PcrIiIiYmRWGWwSEhIIDQ212B4QEED9+vX56KOP8PDwMNt3+/ZtunbtyoIFC3j99ddxdXUFYNWqVdy8eZMVK1bQpk0bs8ckJydbHOP8+fPUqVOHjRs3mt7Ydu3alQ4dOjB37tyHBpvTp08zYcIEHB0d2bFjh2n+zcSJE+nUqRMbN25k9erVvPLKK2aPi4mJYdKkSQwbNsy0berUqcyYMYOIiAiLAJaT4cOHM2PGDGxtbU3bsrKyGDx4MF988QXff/89DRo0MO3r168fiYmJzJo1i1dffdXsuX7//XfT90lJSQwYMAA7OzsiIyMtFni4t3bHjh3MnDmTli1bEh4eTrFixUzjePPNN1myZAnr16+nQ4cOACxbtowKFSoQExNjqs2uv3ehiGXLllGoUCH27NlDuXLlzI5/7+/x7/b/+++/Z+TIkbz99tsP6PD/LFu2jK+++orevXsTFhaGnd3df6q3bt0iJCSEOXPm0KVLF2rXrk1qair/93//R506ddi6davZ7+fOnTtcvaqlnEVEROTZZ5WXop06dYrp06dbfO3btw/AItQA2NnZ0adPH+7cuZPjpWFFixa12FamTJkcj//ee++ZfVrfpEkTXF1dOXDgwEPHvnr1am7fvs2gQYPMFhUoVKgQkyZNAmDFihUWj3N3d2fIkCFm23r37g2Qp+MCuLm5mb1pBrCxseE///kPADt37jRtP3DgAPv378fPz88i1ABUrFjR9P3KlSu5du0ab7zxRo6r1t1bu3DhQgDCwsLMgoqNjQ3vvPMONjY2rFmzxuzxBQsWNAWDe+tLly790Dow/z3+3f47OzszcuRIi+25WbhwIcWLF+fDDz80G1OhQoWYMGECAF9//bXptWRlZVG4cGGL34+trS0ODg55Pq6IiIiIUVnlGZsWLVpYvPm919WrV5k9ezabNm3i9OnTpKWlme2/cOGC6fsOHTowf/58evbsSceOHWnWrBkNGjQwndG5X6lSpXIMThUrVuSHH3546NgPHToEgL+/v8W++vXrU7Ro0RznzdSoUYMCBcxzbHZgSE1Nfehx4e7ZgoULF7J27VpOnDjBtWvXyMrKMu2/ty/79+8HyNOldX+lNj4+nuLFi+c4zwTuBswTJ06Yfu7UqRNLlizB19eXTp064e/vT/369SlevLjZ4zp16sSkSZPw8/MjODgYf39/GjRoYBEKHqX/D7r07F7Zl+u5uLjkOE/r9u3bAKbXWbJkSQICAti6dSuNGzemQ4cO+Pr6Uq9evTwfU0RERMTorDLYPMitW7do27YtBw8epGbNmrzyyiuUKVMGW1tbfvvtN1auXMnNmzdN9S+99BIbNmwgLCyMNWvWmD6tr127Nu+++y6NGzc2e/6SJUvmeFxbW1syMzMfOr7sy4ocHR1z3F+uXDnOnz9vsT2n42afCbh/wntuQkJCiIqK4rnnnqNTp044OjpiZ2dHamoqCxYsMOtLdlhycXF56PP+ldrLly9z+/Ztpk+fnmvNvUF0+vTpeHh4sGLFCmbMmMGMGTMoUqQIHTt2ZNq0aaZV54YOHUqZMmVYsmQJc+fOZc6cOdjZ2dGyZUtCQ0NNYfTv9j+3+pykpKSQlZVFYmJinl9neHg4M2fO5Ouvv2bKlCkA2Nvb07NnTyZOnGh2dktERETkWaRgc5/IyEgOHjxISEgIn3zyidm+NWvWsHLlSovH+Pv74+/vz40bN4iPjycqKorFixfzyiuvEBsb+1iX87W3twfuzktxc3Oz2H/p0iVTzeN04MABoqKiaNGiBatXrza75Gnfvn0sWLDArL5UqVIAOb7Jv9+9te7u7g+stbe3x8bGhoSEhDyNu2DBggwZMoQhQ4Zw/vx5YmJiiIiI4Msvv+SPP/5g7dq1wN3LuUJCQggJCSE5OZnY2FjWrFnDunXrSEhIIDY2Fltb27/d//sXE3jYa4S74fjey/sepHjx4kyYMIEJEyZw+vRpdu/ezeeff86CBQtIT0/n448/zvPxRURERIzIKufYPMipU6cAzFbhyhYXF/fAxxYtWpRGjRoxbdo0RowYwY0bN/L8xjSvatasCcCePXss9u3fv58bN27kuHTwo8ruS2BgoMU8jpz6UrduXQC2b9/+0Of+K7X16tUjOTmZkydPPrT2fi4uLnTp0oU1a9ZQuXJldu7cyY0bNyzqypQpQ9u2bfn8889p3Lgxx44dMwWpJ9F/e3t7qlWrxvHjx80WOMgrDw8PevfuzaZNmyhRokSOy2WLiIiIPGsUbO6TPTfm+++/N9u+Z88es2WEs8XGxnLlyhWL7UlJSQAUKVLksY6va9eu2NnZMXfuXLOzIRkZGabJ6z169Hisx4Tc+3L06FFmzpxpUf/iiy9St25dYmNjc+xbYmKi6fvu3btTokQJ5s6da5rDkltt//79ARg0aFCOq85dvHiRY8eOAXDz5k127dplNg8I7l7Cde3aNQoWLGgKadu2bTPNXcmWkZHB5cuXgf/9Hp9U//v378/169cZOnSoxRwvuLs6W/Z9cC5dumSap3SvlJQUbt68+dj/BkVERESeRroU7T6tWrXCzc2NWbNmcfToUby9vTlx4gSbN28mKCiIDRs2mNXPmTOHnTt30qhRI9zd3SlSpAgHDx5k165deHl5md3/5HHw9PRk0qRJjB8/noYNG9KpUyeKFSvG5s2bOX78OG3atLFYavhxqFu3LnXr1mXdunVcuHCB+vXrc+7cOb799lsCAwNzvMHowoULadu2LUOHDuXLL7/kpZdeIj09nV9//ZVDhw6ZzgI5Ojoyf/58+vbtS4sWLWjdujVVqlThzz//JD4+Hjc3N9PcpYCAAEaOHMmHH35InTp1CAgIwNXVleTkZBISEoiLi2P8+PFUq1aNGzdu0KFDBzw8PKhXrx6VKlUiLS2NqKgoLl68yLBhw0yT6/v06UOxYsVMCz9kZGSwc+dOfv31V4KDg03B7kn1v0+fPuzbt4+VK1eyd+9emjRpgouLC3/88QcnTpwgPj6ezz77DHd3dxITE2nRogXe3t7UrFmTChUqkJycTGRkJBkZGQwdOvSRxyMiIiLytFOwuU+JEiXYsGEDEydOJDY2lj179vD888+zaNEiHB0dLYJN3759KVmyJPv37yc2NpasrCwqVarEW2+9xcCBA/+R+S6DBg3Cy8uLuXPnsnr1am7dukXlypWZOnUqr7/++l+az5FXtra2rFq1ikmTJrFt2zZ+/PFHvLy8mDJlCgEBATkGm8qVK7Nr1y5mzpxJVFQU8+fPp3jx4lSuXJm33nrLrLZdu3Zs3bqVsLAwYmJi+Pbbbylbtiw+Pj4Wy0W//fbbNGzYkAULFrBr1y5SU1MpU6YM7u7ujBkzhq5duwJ3551MnjyZXbt2ERcXR1JSEg4ODlSpUoXJkycTHBxses533nmHrVu3sn//fqKioihWrBheXl58/PHH9OrVy+z4T6L/NjY2zJ8/n8DAQMLDw9m8eTNpaWk4Ojqa+t60aVPg7jLcY8aMITo6ml27dpGcnEzZsmWpVasWAwcOzNNqcyIiIiJGZ5OSkpL18DIReVZdG9+PrDTdxFNERJ48+zDLRZn+Senp6Zw9exZXV1ddqv0QRuyVztiIWLk/mxYk81bB/B6GiIhYoUuRIbnu82yz7AmORJ4FWjxAREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMT8FGREREREQMzy6/ByAi+cs9YG5+D+Gplp6eztmzZ3F1daVIkSL5PZynmnqVN+pT3qlXeadeieiMjYiIiIiIPAMUbERERERExPAUbERERERExPAUbEREHsLW1ja/h2AY6lXeqE95p17lnXqVN+pT3hmtVzYpKSlZ+T0IERERERGRR6EzNiIiIiIiYngKNiIiIiIiYngKNiIiIiIiYngKNiIiIiIiYngKNiIiIiIiYngKNiIiIiIiYngKNiIiIiIiYngKNiJW5MCBA3Tt2hV3d3cqVKhA8+bN+eqrr/J7WPlm1apVDBs2jKZNm+Lk5ISDgwMRERG51l+5coVx48ZRo0YNnJycqFGjBuPGjePKlStPcNRPXmJiIvPmzaNTp07UqFEDR0dHqlatSu/evYmPj8/xMdbYq5SUFEaNGkXLli2pWrUqTk5OeHt7065dO9avX09WluVt46yxT7mZNWsWDg4OODg4sG/fvhxrrLVfPj4+pt7c/zV8+HCLemvtU7aNGzfSsWNHPD09KV++PDVr1qRv376cO3fOrM5a+xQREZHr31P2V/v27c0eY5Re6QadIlZi9+7ddO7cmUKFChEcHEzJkiXZuHEjZ86cYcKECbz55pv5PcQnzsfHh7Nnz1K2bFmKFSvG2bNnmTt3Lj179rSoTUtLo1WrVhw+fJhmzZpRq1Ytfv75Z7Zu3YqPjw9RUVEUL148H17FP2/SpEl8/PHHeHp60rBhQxwdHTl58iSbNm0iKyuLxYsX06lTJ1O9tfYqISGBRo0aUa9ePby8vChdujRJSUlERUWRlJTEq6++yqxZs0z11tqnnBw7dozGjRtjZ2dHWloaW7ZsoX79+mY11twvHx8fUlNTGTBggMW+OnXq0KpVK9PP1tynrKwshg8fztKlS/H09KRFixaUKFGC8+fPExMTw6JFi/D19QWsu0+HDh1i06ZNOe7bsGEDR48eZfLkyQwdOhQwVq/s8nsAIvLPu337NkOGDMHGxoZNmzZRq1YtAEaPHk1gYCChoaF07NiRypUr5/NIn6zZs2fj5eWFm5sbYWFhTJ48OdfaWbNmcfjwYYYOHWpW99577/HBBx8wa9Ysxo0b9ySG/cS9+OKLREZG4ufnZ7Y9NjaWDh06MGLECNq0aUPhwoUB6+2Vu7s7Z86cwc7O/L/Wq1ev0rJlS8LDw3n99dfx9vYGrLdP97tz5w4DBgygRo0aVK5cmdWrV+dYZ+39KlWqFGPHjn1onTX36dNPP2Xp0qX897//5f3338fW1tZs/+3bt03fW3OfatasSc2aNS2237p1i0WLFmFnZ0f37t1N243UK52xEbEC27dvJzg4mJ49ezJ37lyzfWvXruXf//43I0aMYOLEifk0wvyXHWxyOmOTlZXFCy+8wNWrVzl27JjZJ1Pp6ek8//zzFCtWjF9++QUbG5snPfR8FRwczPbt29mxYwd16tRRr3Ixbtw45s2bR0REBEFBQerTPT766COmT5/Orl27+OSTT1i5cqXFGRtr75ePjw8Ahw8ffmCdNffpxo0bvPDCC5QqVYr4+HiLDxjuZc19epDs9wNBQUGmy7KN1ivNsRGxAnv27AGgefPmFvuyt8XExDzRMRnJyZMnOX/+PP/v//0/i9PtRYoUwc/Pj8TERBISEvJphPmnYMGCAKZPRtUrS+np6URHR2NjY8Pzzz8PqE/Zjhw5wvTp03nrrbdMZ7Jyon7d/TR9xYoVfPTRRyxevDjHkGPNfdqxYweXL18mKCiIO3fusGHDBsLCwliyZInF67XmPj3I8uXLAQgJCTFtM1qvdCmaiBU4efIkQI6Xmjk4OFC2bFlTjVjK7o2Xl1eO+7P7evLkSau6nO/s2bPs3LkTZ2dnqlevDqhXcHcRgfnz55OZmcmlS5fYsmUL586dY/To0WavH6y7T7dv32bgwIFUrVo1xwnw91K/4OLFiwwcONBsW0BAAJ9++illy5YFrLtPP/74IwB2dnb4+/tz4sQJ074CBQowcOBApk6dClh3n3Lz22+/sWvXLipUqEBAQIBpu9F6pWAjYgWyVy0pWbJkjvvt7e1JTEx8kkMylOz+lSpVKsf99vb2ZnXWICMjg/79+3Pz5k0mT55sOmOjXkFqairTp083/VywYEGmTJnCoEGDTNvUp7uXoGVPQM4+85cba+9Xr169aNiwId7e3hQqVIhjx44xffp0tmzZQvfu3dm8eTM2NjZW3adLly4BMGfOHGrVqsX27dupWrUqhw4dYtiwYcyZMwdPT0/69u1r1X3KTUREBJmZmfTo0cNsbpLReqVL0URE5C/JzMzkjTfeIDY2lldffZV//etf+T2kp4q7uzspKSn8+eefHDx4kHHjxjFlyhR69+5tNnnZmh0+fJgZM2YwePBgateund/DeeqNHj0af39/ypYti729PfXq1WPVqlX4+vryww8/8N133+X3EPNdZmYmAIUKFSIiIoIXX3yREiVK4OfnR3h4OAUKFGDOnDn5PMqnU2ZmJhEREdjY2NCrV6/8Hs4jUbARsQLZZ2py+0Tl6tWruZ7Nkf/1LzU1Ncf9V69eNat7lmVlZTFkyBBWr15Nt27dCAsLM9uvXv2Pra0t7u7uDB8+nPHjx/PNN98QHh4OqE8DBgzA09OTMWPG5Kne2vuVkwIFCtCjRw8A9u7dC1h3n7JfU+3atXFxcTHb5+3tjYeHB6dOnSIlJcWq+5STHTt2cO7cORo3boyHh4fZPqP1SsFGxArcf13/vbI/WX4aro19WmX3JrfJkQ+aw/QsyczMZNCgQXzxxRd06dKF+fPnU6CA+X8j6lXOmjVrBvxvIQ9r79PPP//M8ePHcXZ2Nrsp4MqVKwFo2bIlDg4OfPPNN4D6lZvsuTXXr18HrLtPVapUAXK/ZCp7e3p6ulX3KSc5LRqQzWi90hwbESvQsGFDZs6cyfbt2+ncubPZvu3bt5tqJGeVK1fGxcWFvXv3kpaWZrHcZWxsLC4uLrlOrnwWZGZmMnjwYCIiIggODubTTz+1uEcEqFe5uXDhAoBpCVpr71Pv3r1z3B4bG8vJkydp3bo15cqVw83NDVC/crN//34A9Qlo1KgRAMePH7fYl5GRQUJCAsWLF6dcuXI4OztbbZ/ul5ycTGRkJKVLl6Zt27YW+432N6UzNiJWoEmTJnh4ePD1119z6NAh0/arV6/y4YcfYmdnZ7qkQSzZ2NjQu3dvrl27xgcffGC2b+bMmaSkpNC7d++nYg3/f0L2mZqIiAg6duzIwoULcww1YN29OnToUI6Xa1y+fJl3330XwLTakDX3Ce7eHDenr5deegmAESNGMHv2bNNNBK25X7/++ispKSkW2+Pi4pg7dy6FCxemXbt2gHX3ydPTk+bNm5OQkMCyZcvM9oWFhZGamkpQUBB2dnZW3af7ffnll9y6dYtu3bqZbrJ8L6P1SjfoFLES0dHRdO7cmcKFC9O5c2fs7e3ZuHEjZ86cYfz48bz11lv5PcQnbtmyZcTFxQF376dx8OBBGjRogKenJwBBQUGmT7DS0tJo1aoVhw8fplmzZtSuXZuff/6ZLVu24OPjQ1RUlMUa/8+K0NBQpk+fTokSJXj99ddzDDVBQUGmN6HW2qsxY8awfPly/P39cXNzo1ixYpw9e5bvvvuOa9eu0b59e5YuXWq6fM9a+/QgAwYMyPEGnWC9/QoNDeWTTz6hcePGuLm5UbhwYY4ePcr27dspUKAAYWFhZpcQWWufAE6dOkVgYCBJSUm8/PLLVKlShUOHDhEdHY2rqytbt27F2dkZsO4+3cvPz48jR44QExNjWrb/fkbqlYKNiBXZv38/oaGh/PDDD2RkZPD8888zYMAAunXrlt9DyxfZb6JyM3r0aMaOHWv6OXsZ3w0bNnDx4kWcnZ1p3749o0ePzvW67mfBw/oEMHfuXHr27Gn62Rp7FRcXx/Lly4mPj+fChQtcv36d0qVLU6tWLf71r3/RuXNni081rbFPD/KgYAPW2a89e/awePFiDh48SFJSEunp6Tg5OdGgQQMGDhxI3bp1LR5jjX3Kdu7cOd577z22bdtGcnIyzs7OtG7dmlGjRuHo6GhWa819grvvCVq0aEHdunXZtm3bA2uN0isFGxERERERMTzNsREREREREcNTsBEREREREcNTsBEREREREcNTsBEREREREcNTsBEREREREcNTsBEREREREcNTsBEREREREcNTsBEREREREcNTsBEREREREcNTsBEREREREcNTsBEREZGnloODA0FBQfk9DBExAAUbERERERExPAUbERERERExPAUbERERKxEbG0vPnj2pUqUKTk5OVK9enV69ehEXF2equX79OqGhodSvXx9nZ2c8PDzo1q0be/futXi+AQMG4ODgwJkzZyz2hYaG4uDgwO7du03bdu/ejYODA6Ghofz0008EBwdTqVIl3Nzc6Nmzp9nzZNcCxMTE4ODgYPqKiIh4jF0RkWeFXX4PQERERP55ixYtYtSoURQtWpS2bdtSqVIlEhMT+f7771m/fj2+vr7cvHmTDh06sG/fPmrVqsWAAQNISkpi3bp1bN++nSVLltC+fftHHstPP/3E7Nmz8ff357XXXuPQoUNs2rSJI0eOEBcXR5EiRXBzc2P06NFMnz4dV1dXevToYXq8j4/PI49BRJ49CjYiIiLPuF9++YUxY8ZQvnx5oqKicHd3N+3LysriwoULAMyaNYt9+/bRrVs3Pv30U2xsbIC7Z2ZatGjBkCFDaNasGfb29o80ns2bN7NkyRKCg4NN2/r378+qVavYtGkTnTt3xt3dnbFjxzJ9+nTc3NwYO3bsIx1TRJ59uhRNRETkGff5559z584d3n77bbNQA2BjY4OLiwsAK1asoGDBgrzzzjumUANQo0YNevToQUpKCpGRkY88Hj8/P7NQA9CrVy8ADhw48MjPLyLWScFGRETkGbd//34AmjdvnmvNlStXOH36NF5eXlSsWNFiv7+/PwCHDx9+5PHUqlXLYlv2MVNTUx/5+UXEOinYiIiIPONSU1OxsbGhfPnyudZcvXoVAEdHxxz3Ozk5AXcD0KMqWbKkxTZbW1sA7ty588jPLyLWScFGRETkGVeqVCmzuTQ5yZ43k5SUlOP+7O33zq8pUODu24icwsjjCEAiIn+Fgo2IiMgzrm7dugBs374915qSJUvi4eFBQkICiYmJFvtjYmIA8xXJspdjzqn+0KFDjzJkkwIFCpCZmflYnktEnm0KNiIiIs+4Pn36YGtry7Rp0/jtt9/M9t17Jqd79+5kZGQwefJksrKyTDVHjhwhIiKCkiVLEhQUZNpep04d4O6iA/dav369KQg9qtKlS/P7778/lucSkWeblnsWERF5xlWvXp3Q0FBGjx6Nr68vQUFBuLq6cvHiRWJjYwkMDOT9999n6NChfPfdd6xatYrjx4/TpEkTLl26xLp168jIyGDBggVml6IFBQXh7u7OihUr+P3336lZsybHjx8nOjqawMBAvvvuu0cee+PGjVm3bh0hISHUrFkTW1tbAgMDqV69+iM/t4g8WxRsRERErEC/fv3w9vZmzpw5bNmyhbS0NBwdHalbty6dOnUCoEiRImzYsIGPP/6YdevWMW/ePIoWLYqfnx8jRozA19fX7DmLFi3K+vXrGTduHLt37yY+Pp569eoRGRlJVFTUYwk277//PgDR0dF88803ZGZm4uTkpGAjIhZsUlJSsh5eJiIiIiIi8vTSHBsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETE8BRsRERERETG8/w8wIaT2s2O13AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Product line', hue = 'Payment').set(title = 'Most used_Payment_type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "7726fd8a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Payment/Branch')]"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Payment', hue = 'Branch').set(title = 'Payment/Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35f6f49e",
+   "metadata": {},
+   "source": [
+    "# Branch with lowest rating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "480fddfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Branch_lowest rating')]"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(orient = 'v', x = 'City', y = 'Rating', data = Data).set(title = 'Branch_lowest rating')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "8f40e524",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'Gender/Branch')]"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data = Data, y = 'Gender', hue = 'Branch').set(title = 'Gender/Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c69c2742",
+   "metadata": {},
+   "source": [
+    "# Using Catplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "ac69c43b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc8074aca60>"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(data = Data, x = 'Product line', y = 'Quantity', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b909985",
+   "metadata": {},
+   "source": [
+    "# Using Catplot on total column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "2e377480",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc806fdbdc0>"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Total', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "b4a4684f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc8070a6800>"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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d7DTRp78UXX8zvgEAQOjYjqPP9lbq1bxSrSyqCskeo1I9eiwjXiNTPTLO1+ErRBpSQEKSokxD1yZ7dG2yR//RXyr32/qyqEqLT3Wa2HDUJzuCt1FWBBwt3O/Vwv1eScGxKEPbe6rHc/ROsiLy/yPQWLmKj0V2/xOR3R8A6oqQBAAAAIDGw1sha+1SWcs+k7llvQwn9Fd07YQk+a8dI//QG4KhhHq+SGt37iHvAz+pc0ii7PezJE90vdZwPk5yR/mSO8p33YTgOJPtucHARO4amfsKwlbHmVyH9ysqZ5aUM0uOaSmQnhUMTGRmy+7Yrd7/PwIAAJen0u/ob/nl+v2mUu044a/39zcN6bZuMZqSEa+s1ozoOp9Yy6VRHaI1qkPw82Sx19byg97q8Rxbiuv//5+LcbLK0ad7KvXpnmAwt12Mq7rLxPAUj7om8JUGcF4RGJ1Ygy/C+wNAHfGJAgAAAEDDZtsyt30ta9k8WWsXy6isCPmWjuVWoP+18g29QYHMbMlqQKdOYQxI1OKOUqDfAAX6DZAmPSzj2CGZeWuDozk2rZVRXhr2koyAX9bmdbI2r5P+9obspDYKZF4dHM3Rb4AU3yLsNQEAgKDjXlvvbC3TW1tKdaii/mc8xFuG7kuP1cN949UpvgF9XmtEkjwu3dQlRjd1iZEkFZUHtPSM0MSukkBE6ztUYeujggp9VBA8B+gcb2p4SrDTxLAUj9rHmhGtD2gwbFtm7mq5Doany+I5ud2R3R8A6ohPjgAAAAAaJOPgPrmXz5O14nO5jhSFZc9AWh/5ho6Tf9BovlyvA6dVO/mH3yj/8BvlDfjlKtgq61SXCVfh1rB0+vhnruIjci39h9xL/yHHcMnu3kf+010muvWSXFxIBwAg1HaX+PXaplJN3VGuMn/9fx5IjnHph33j9UB6nJI8DWu8RWOXHGtqYlqsJqYFx5ntLvFXByaWHPCqKARhl4uxpzSgqTvKNXVHuSSpd5KlYae6TAxr7+HPA5qfshK5l/5D7gWz5Dq8P9LVyE5sFekSAKBOCEkAAAAAaDjKSmStXiT3snkyd+aFZUu7VVv5B4+Vb8hYOaldwrJnk2RasntmqKpnhvS9f5VKimXlfRUczZG3Wq4Tx8NekuHYMndukrlzkzTzj3LiWsifMVCBrGwFMq6Wk9Q67DUBANCUbThSpVfzSjVrV4UCIchKpidampIRrzu6x8pjMl4rHLokWLo3wdK9veLkOI62n/Br8f5gYGLpQa9OVIU/FHumrcV+bS3263+3lMmQdEVrt0akeDQ81aNr2kUpzk1oAk2Ta89OuRfMkrVyvowqb6TLqWZ36RnpEgCgTghJAAAAAIisgF9m3trgOI31y2SEYYapExUt/8Bh8g8dp0CfK+kuEAoJSfJfO0b+a8dIti3X3vzgWI681XLtyJMRCH/rZqPspNxfLpT7y4WSpEDnHsGxHFnZsnv0kyxawwIAcLEcx1HON169kleqJQdC80Xd4OQoPZYZr7Edo+UyCEdEimEYSk9yKz3JrX/rG6+A7Sj3mE9LDni1+IBXK4uqVB6CziF15UjacNSnDUd9ejmvVG6XNLBtVPV4joFtoxRFuAaNmd8v66ulci+YKXP7xkhXc1Z2116RLgEA6oSQBAAAAICIcO0tkLXsM1krF8h14lhY9vT37i//0HHyDxwhxcSGZU9Icrlkd+kpu0tP+W6+W6ook7l5XXA0x8bVch0NzziVf2bu2Slzz05Fzf1ATnSMAn2vqh7N4bRNiUhNAAA0FlUBRzMKK/RqXok2H/fX+/u7DOnmLtF6LCNBA9pG1fv74/KZLkP920Spf5soPZaZoKqAo7WHq6pHc6w5XCVfBKdz+GxpZVGVVhZV6bkNJYq1DF3TLkojUoPjObJauWW6CE2g4TOKj8pa9IncX8yWq/hopMs5JycqWoEeGZEuAwDqhJAEAAAAgLAxTh6XtXKBrGXzZO7ZGZY97eQO8g0ZJ//g6/niu6GIiVNgwDAFBgyTHEfGgT3BwETuaplbv5bhqwp7SUZlhax1y2WtWy5JslM6VQcmAr37S1GesNcEAEBDdLLK1nvbyvT65lLtL6//b8BjTEN394zVI/3i1a0Fl68bkyjT0OD2Hg1u79ETV0plPltfHqoKjuc46NWGIz5FcjhHud/Rwv1eLdwf7HiSGGVoaPtgYGJEqkfpiZYMOpWgoXAcuXZuknvBTFlrFssI1H8Yrb75B18veaIjXQYA1EmD+5S5f/9+zZo1S/Pnz9eOHTtUVFSkli1batCgQXr88cc1cODAGus3btyoWbNmacOGDfr666919OhRDRkyRHPnzj3vPh9++KFef/11bd26VW63W9nZ2XryySd15ZVXnnV9fn6+nnnmGS1dulRlZWVKS0vT/fffrx/84AdyuWrPNfN6vXrppZc0ffp07du3T0lJSRo7dqyeeuopJScnX/oPCAAAAGhsfFUyN6yUe9k8mblfhmXMghMbJ3/2aPmGjguOUeBiZ8NlGHJSu8iX2kW+cbdLVV6Z274OjubIXS3XgT0RKct1YK+iDuyVPp8hxx2lQO8rFMgIjuZwUjrzZwoA0OzsLwvojc2l+tO2Mp301f9X3a09Lj3YJ04P9olT62hGoTUFcW6XRneI1ugOwS9Ni722lh30Vnea2Foc2S99T1Q5mrunUnP3VEqSkmNcGp7i0bCUYHCia0KD+/oEzUGVV9aqnOBIjd07Il3NRfGNuTXSJQBAnTW4v+XfeustvfTSS+rWrZtGjhyptm3bKj8/X3PnztXcuXP1zjvvaMKECdXr586dq9/97neKiopSjx49dPTohVsNvfDCC3rmmWfUsWNHPfDAAyorK9Pf//53jRs3TjNmzNCwYcNqrN+6davGjh2riooKTZgwQSkpKZo/f75+/vOfa9OmTXr55ZdrrLdtW3fddZdycnI0cOBA3XzzzSosLNS0adP0xRdfKCcnh6AEAAAAmjbHkatgi9zL5sn6cqGMspLQb2m4FMi8OjhO48oh3PnfWEV5gt0bMrNVJck4fEBm3hpZG1fL3LxORmV52EsyfFWyctfIyl0jz1/+ILt1sgKZ2cFOE/2ukmLiwl4TAADhsvm4T6/mleqjgvKQjE5ISzA1JSNBk3rEKNaqfTMamo4kj0vf7RKj73aJkSQVlQe09KA32GnigFe7S0Mfpj6fogpbHxZU6MOCCklSl3izusvEsPYeJccS3kHoGIcPyL3wY7kXfyqj7GSky7lo/oHDZXfuEekyAKDOjOLi4kh2uKpl9uzZatOmjQYPHlzj8RUrVujWW29VfHy8tm7dKo8neMFzy5Yt8nq96tevn44dO6b09PTzdpLIz8/XoEGD1LVrV+Xk5CgxMbH6fcaMGaPk5GStWbNGlvVtfuTGG2/UihUrNH36dI0dO1aS5PP5NHHiRC1evFizZ8/W8OHDq9dPnTpVU6ZM0W233aa33367ukXX6ccnTZqkN954o/5+aEAdxE8eWad1pe8tCmkdAFBfOK4BDZNx9JCsFfPlXv6ZXAf2hmXPQMdu8p8ep5HUOix7hgLHtTrw++Xauenb0RwN4M4qxzRl98iQP/NqBTKzgxcGz9JtEGhuOKYBjZvjOFp6sEqv5pZo/jfekOxxdVu3Hs1I0E2do2W6Gn6HpqQ/flOndcUPdAhxJU3XrhK/lhzwaukBrxYf8OpQRQhSOZehd5Kl4ae6TAxt71GSh898uEy2LXPTWrkXzJL59UoZToP6uq7OnLgElf+/PzXq83EAzU+D6yRxyy23nPXxwYMHa9iwYVq4cKE2b95cPRajT58+F/X+06ZNk9/v109+8pPqgMTp95k0aZLeffddLVmyRKNHj5Yk7dy5UytWrNCwYcOqAxKS5Ha79Ytf/EKLFy/W+++/XyMk8f7770uSfvWrX9WYYXbPPffo1Vdf1cyZM/Xb3/5WCQkJF1U7EBbeCskTE+kqAABAY+KtkLV2qaxln8ncsj4sF3bshCT5rx0j/9Abgl9KM/qgebAs2b2vUFXvK6TbH5RRfFRm3lqZuatl5a2RURr+O66MQCA4HmTb19JHb8tu0VKBjKsVyMqWv99AqUVS2GsCAOBS+W1Hs3dV6JW8Um046gvJHt/pFK3HMuN1TbuoGtdOga4JlromWLqvV5wcx9G2E/7qLhPLDnp1oiqyXyBvLfZra7Ffb20pk8uQrmjt1vD2wU4Tg9pFKc5NaAJ1VF4q97J5cufMkutg6G4usBMS5So5EbL3P817348ISABodBpcSOJ83G63JMk0L72t1bJlyySpOgRxptGjR+vdd9/V8uXLq58/3/oBAwYoMTFRy5cvr36ssrJSa9euVc+ePdW5c+darxk1apTeeOMNrV27VqNGjbrkfw+grlx7dsq9YFad18dNmSD/tdfJd9142mMBAIBzs22Z276WtWyerLWLZVRWhHxLx7QUuHKwfEPGKZA1SLIa1ekMQsBJah0crzJ0nLx2QK5dO4KBiY2r5crfLMMJ/92HrpPH5VrxudwrPpdjGLK7pgfHwGRmy+7eRzL5cwsAaHjKfLam7ijXHzaVak8IRh5EuaRJPWI1pV+8eiW56/390fQYhqHeSW71TnLrob7xCtiONh7zacmB4HiOlUVVqghELjRhO9L6Iz6tP+LTy3mlcrukq9tGVXeaGNg2SlEmISDU5NpXKHfOLFnL58nwVoZsH3/G1fJdP0GBrEGK+vh9Rc16L2R7ecffL/81Y0L2/gAQKo3m6szevXu1aNEiJScnq1+/fpf8Pvn5+YqPj1dycnKt57p371695sz1kpSWllZrvWEYSktL0/r161VeXq7Y2FgVFhbKtu2zrv/nPS4UkqisDN1fkmj6jBNHFf+X1+RZv/zCi898XVWl3Is/kXvxJ/JeOUSld/67nERSoAAanvg6ruPvU6B+uQ7tV/TK+fJ8mSPz6KGw7Onrmi7vtdfJO3CEnPgWwQf9/uCvJoTjWj1I7Rr8Ne4OGWUlcm9dr6hNa+Xe9JXM4qNhL8dwHJmFW2UWblXU7D/LjomTr8+Vquo3UL5+A2S3bBv2moBw4ZgGNA6HK2y9u6NSf9pRqeMhuEs/0W3o/p7R+n6vaLWLcUkKqLKy/kMYDQnHtdDpEy/16enWQz3d8gYcrT/q19Iin5YX+fTVUb98EZzO4bOlFUVVWlFUpd9sKFGMKV3T1q2h7d0amuxWRpLZKMbKIAQCAUV9vVLRi+YoatvXIdvGjo6Vd/D1qhzxXQXadwo+WOVT5Q2TFOMPKO6TqfW+Z9l371HFDf8icdzDJYiOjo50CWjmGkVIwufz6aGHHpLX69XTTz99WZ0kTp48qbZtz34h6vT4i5MnT9ZYL6nGaI5zvSY2Nvai1l/I/v37FQg07ZMGhEbSptXq9I9psirLL+t9POuXy9yyXnu/c7eK+2XXU3UAUD/a1HHd3r2ha1sINBdmZbmSNq9Vq40rFL8v/8IvqAdVCUk6lnmtjmVdK2+blOCDx08EfzVRHNdCoF234K+RExV9eL9a5OepRf4mxe3dIVcg/CEbV0WZPOuWybMu2LGwom0HnezeTyVp/VTauacciztr0XRwTAMatl3lhj7Yb2lukaUqp/6/uE3x2Loz1a9b2/sVa5bJe0Rq/P+1x9ZpFce18OkgaVJS8FdFQNpw0qW1xabWnHBpa6lLjiIXSqgISF8c9OmLg8GxNS0sR1clBnR1oq2rkwLqGuMwMbCJs8pOqvX6pWrz1WJFlRwP2T4VbVN1ZOAoHcu4RrYnWvJJ+ufjUP8RSnLH1Mt3BpLkj4799juDffsu+/3QPPXs2TPSJaCZa/AhCdu29cgjj2jFihWaPHmyJk2aFOmSwiY1NTXSJaCxcRzFfjJVsZ9Mq7e3tCrL1W3m/6rcV67y797DvG8AjU6nTp0iXQLQOAUCcm/+StGrchS1YYUMf2hmUp/JifLIe+UQea+9Tr70K2S5TLUL+a6ND8e1S9S5szTgGnkleSsr5N6+UVGb1ipq01qZhw9EpKSYw98o5vA3Sl71uZwoj3zpV6iq30BV9Rsoux3ng2geOKYB4bX6sE+vbanQvG98CsWggsyWpv69d4xu7hwlq8ndNV+3rlQc1yKnl6Q7Tv1zcZWtFUU+LSvya1mRT9tPRvZmxJN+Q4uOWlp06o9Ru2hDQ5Pd1b86x1/6jaFoWKzCbYpeNFuetUtCdh7tuFyq6j9YlSNvlq9XlmIMQx0u9KJOnXTi2pGX1H36TKe7TycktlbCJb8LAERegw5JOI6jxx57TNOnT9cdd9yhF1988bLfs0WLFufs4lBSUlK95sz1knTixNnvWDv9mtMdIuq6/sw9zoVWM7hYUTP/qKh6DEicKfaTaTItt3wT7g/J+wNAqPD3KXBxXHsLZC37TNbKBXKdOBaWPf29+8s/ZJz8V4+QYmJlSuIS4blxXKsH0dFS9ggFskeoQpJRtE9W7hqZuatlbl4voyr87WKNKq+iclcrKne1JMlulyp/ZrYCmdkK9OkvRdft7lWgseGYBoSe7Tiau6dSr+aWavXhqpDsMaaDR49lxGt4ikdGM7/BhuNaw9A+WvpeC+l7p25UPlge0NIDXi0+9WtvaWRDE4cqHf19d5X+vjv432TXBFPDUzzVv9rFcEbUqFR5Za3+Qu4FM2UWbgvZNnZCkvwjvyvfqFvktG538efO0R3k+9H/KLBnp9w5H8u9aE6dXuZERcs/+Hr5xtwqu3MPeS6leABoYBpsSMK2bT366KOaNm2aJk6cqNdff10ul+uy37d79+5avXq1ioqKlJycXOO5/Pz86jVnrpekgoKCWu/lOI4KCgqUkpKiuLg4SVK3bt3kcrnOuv5cewD1wVqZo6hZ74V0D8+sP8lJ6ST/NWNCug8A1Kcyn6049+V/hgCatJPFcq9aIGvZPJm7d4RlS7tdqnxDb5B/8PVy2qaEZU/gXJzkjvIld5TvugmSr0rm9txgYCJ3tcx9hRGpyXVov6JyZkk5s+SYlgLpWcHARGa27I7d6PAGALigSr+jv+aX6/d5pdp5sv7HTFmGdFtajB7NSFBGK0ZGoWFrH2vq9u6xur17MHi6q8SvJQe81b8OVdgRrW9XSUC7Ssr1/vbgKIQ+SVZ1YGJIe4+SPFzXaIiMo0VyL5wt9+JPZJSEbjRkoHsf+cZMkD97pOSOuuz3szv3kPeBn9Q5JFH2+1mShwAYgKalQYYkzgxIfO9739Obb74p06yf5OSQIUO0evVqLVy4UHfeeWeN5xYuXFi95rShQ4dWP/fjH/+4xvqvvvpKJ06c0PXXX1/9WHR0tAYMGKA1a9Zoz5496ty5c43XfPHFF/J4PBowYEC9/PsAkmQUH5Xnzy+FZS/P+y8p0Lu/nKTWYdkPAP5Z7jGf3t5SqnfquL7nXw/q9rQY/aBPvDK5cAd8y1clc8NKuZfNk5n7pYxA6O+kcmLi5M8eJd/QcbJ7ZvAlLxomd5QC/QYo0G+ANOlhGccOycxdIyt3tcxNX8koLw17SUbAL2vzOlmb10l/e0N2UhsFsrKDnSb6DZDiaHQLAPjWscqA3t5apv/dUqbDlfX/xW+C29DkXnH6Yd84dYxvkJeXgQvqmmCpa4Kl+3rFyXEcbS0OhiYWH/Bq2UGvTlaFYiBN3W0p9mtLsV9vbimTy5D6t3ZreIpHI1I8GpQcpViL0ETEOI7MLeuDXSPWLZfhhCZg41hu+QeNlu+6CbLTeodkjzojIAGgCWpwn2Jt29aUKVP0wQcfaPz48XrrrbfqLSAhSXfffbdeffVVvfDCC7rxxhuVmJgoSdqyZYv++te/qlu3bho+fHj1+h49emjw4MFaunSpPv/8c40dO1aS5PP59Oyzz0qS7rvvvhp7TJ48WWvWrNHTTz+tt99+u7rF3NSpU7Vt2zZNmjSpTuM2gLry/PllGWUlYdnLKCuR588vq/LR/w7LfgBw2sHygH62qlhzdgdboNc1JFHud/Te9nK9t71cN3eJ1m+vSVL7WNpWoplyHLkKtsha/rncq3LC8vnBMVwKZF4t/9Bx8l85RIqiMScaF6dVO/lH3CT/iJukgF+ugq3BwMTG1XLt2ibDCf8FdFfxEbmWfCr3kk/lGC7Z3ftUj+awu/WSXPw9BwDN0a4Sv/6wqVTTdpSr3F//fz+lxLr0w77xuj89TolRfEGLpsMwDPVp6Vaflm491DdeAdvR10d91V0mVhZVqSIQudCE7Ujrjvi07ohPL+WWyu2Srm4bpRGpwU4TA9pEKcokgB5yFeWyVnyuqAUz5dq/O2Tb2K3ayTf6VvlG3CS1SArZPgDQ3BnFxcWRjUT+k1//+td67rnnFB8frx/+8IdnDUjcdNNNysrKkiRt375dL774oiSpsrJSM2fOVLt27TRmTHAcQOvWravDDKc9//zzevbZZ9WxY0fdeuutKi8v14wZM1RRUaEZM2bUCElI0tatWzV27FhVVlZq/PjxSklJ0YIFC7Rp0ybdd999euWVV2qsDwQCuuOOO5STk6OBAwdq6NCh2rVrl2bPnq3U1FTl5OTUGvUBXCrX7h2K/eWDYd+3/Jm3ZXfuEfZ9ATRPMwrK9ZOVxSo+404O/6K76/Raa+S0Gr9PijL0wrVJui2N2e5oPoxjh2Qtny/38nlyHdgTlj0DHbvJP2RccJwGHajqJH7yyDqtK31vUUjrwEU4WSxr01qZG1fLzFsj18njka5ITnwL+TOuDo7myBjIf3+IGI5pQPisP1KlV3JL9fHuCtkhuNLbJ8nSlIx43Z4W26y/iE364zd1Wlf8QIcQV4Jw8wYcrT1cpcUHvFp6wKs1h6oUghzSJYuzDF2bHFU9niOzlVumq/n+t1rfjP275V74sdxLP5NRWR6yffx9r5LvugkK9L9WMsNzfzOf1wA0Z5cVkigqKtKcOXO0fft2VVRU6NVXX5UkHTlyRLt371bfvn0VExNzUe/58MMP6y9/+ct51/zhD3/Q3XcHvxhZunSpbr755nOu7dSpk3Jzc2s9Pn36dL3++uvaunWr3G63srOz9eSTT+qqq6466/vs3LlTzzzzjJYuXaqysjKlpaXp/vvv14MPPiiXq3Zy2uv16qWXXtLf/vY37du3T0lJSRo7dqyeeuoptW/f/rz/fsDF8Lz7vNyLPwn7vr6RN8v7wE/Cvi+A5sVxHP1mQ4me21D7bvdLDUmc9h/9E/RE/4Tqjk9Ak+OtkLV2qazl82RuXheWu92dhET5rr1O/iHjZHfpyTiNi8QFqkbOtuXamy9z42pZuavl2pkXljE2FxLo3EOBzGz5s7Jl98iQrAbXUBJNFMc0ILQcx9H8fV69kleiZQerQrLH0PZReiwjQdd39HDeJEIS+Fapz9aqoqrq8Rwbj/rUgDITSooyNOxUYGJ4ike9Ei3+G75YdiA4nnLBTFmbvgrZNk50jHxDxsk3ZrycDl1Dts+58HkNQHN2ySGJt99+W0899ZS8Xm/wjQxDx44dkxQcXTFkyBC9+OKLmjx5cv1VC6Amb4XipkyQUVUZ9q2dqGiV/X4W88gAhNSv1588a0BCuvyQhCQ90T9BT1zJCCw0IbYtc9vXspbNk7V2sYzKipBv6ZiWAlcOlm/IOAWyBvEF7GXgAlUTU14qc/P64GiO3NVyHS2KdEVyomMV6HuV/FnZCmRcLadtSqRLQhPGMQ0IjaqAow8LyvX7vFJtKfbX+/u7DOnWLjF6LDNeV7aJqvf3b8wISeBcjnttLTvo1ZL9wfEc207U/3+bl6N9jCsYmDg1nqNzPOds51R6Qu7Fn8q9cJZcR0L3+d1O6STfmAnyDR0nxcSFbJ8L4fMagObskv42/Mc//qGf/exnuvLKK/Xzn/9cCxYs0Lvvvlv9fJ8+fdSvXz/NnTuXkAQQQubOTREJSEiSUVUpc2eeAv0GRmR/AE3fRwXl5wxI1JffbChRz0SL0Rto9IyifXIv/1zW8s/lOnIwLHsG0vrIN3Sc/INGSfGJYdkTaFRi4xUYOEyBgcMkx5FxYE91YMLcukGGzxf2kozKclnrlslat0xS8OKsPzM7OJqjd38pyhP2mgAAdXOiytaftpXpjc2lOlBu1/v7x1qG7ukZq3/vF6+uCXyBClyMlh6Xbu4So5u7BLtqHygPaOkBb3Wnib2lke0udrDC1vSCCk0vCIbouyaYGnFGp4m2MbVHnjc3rl3bg10jVuXI8IWmO49jGAr0Hyzf9RMU6DuAzosAEGGX9In3lVdeUceOHTVnzhzFxcVpw4YNtdb07dtXK1euvNz6AJyHq3BbZPfftZ2QBICQOFge0E9XFodlr5+sLNaQ9h61j+WiABqZshJZqxfJvWyezJ15YdnSbtlG/sFj5Rs6Tk5ql7DsCTQJhiEntYt8qV3kG3e75K2Uue1rmbmrZeWukevAnoiU5TqwV1EH9kqfz5DjjlKg9xXB0RyZ2XJSOnPhFgAagG/KAnp9U6ne216mEl/9N/RvG+3Sv/WJ0/d7x6lVNOdEQH1IiTV1R/dY3dE9Vo7jaHdpQItPdZlYcsCrw5X1H3S6GLtKAtpVUq73tpdLkvomWRqW4tGIVI8GJ3uU5Kk9XrxJ8vtkrVks94KZMnduCtk2TlwL+UbcJN/oW+jkBgANyCWFJPLy8vQv//Ivios7dxug1NRUHT58+JILA3Bhrj07I7q/mbtGvutv444zAPXuZ6uKVVwVnomexVWOfraqWH8e3Tos+wGXJeCXmfeVrOWfyVq3LCx3ojtRHvkHDpd/6DgF+lwpubh4Dlw2T7QCWYMUyBqkKknG4QMy89bI2rha5uavwjIq558ZvipZuWtk5a6RR3+Q3Tq5OjAR6HdVRNsAA0BzlHfMp1fzSjSjoEL+EJwa9WhhaUpGvP6le6xiLEJxQKgYhqGuCZa6pluanB4nx3G0pdgf7DKx36vlB706GYIA1MXYXOzX5mK/3txSJpchXdnaXd1lYlBylGKtphWaMI4dlnvRHFmL5sh14njI9gl06SXfdRPkv2Y0188BoAG6pJCEbduyLjBr+MiRI4qKYm4dEEqu4mMR3d/asl5xP7xJdrfeCvTKDP7qmSHFt4hoXQAat41HqzRnd3hHCc3ZXancYz5ltnKHdV+grlx7C2QtnydrxXy5ToTn7/9A7yvkG3KD/FePkGIYSQOEktM2Rf5Rt8g/6hbJ75Nr56ZgYCJvjczdOyJSk+tokVyL5si9aI4c05TdIyMYmMjKlt2pu+RqWhfLAaAhcBxHSw549UpeqXK+8YZkj0HtovRoRrxu7BwtFx2DgLAzDEN9W7rVt6VbP+wbL7/taONRnxaf6jKxqqhKFYHIhSZsR/rqiE9fHfHpxdxSRbmkq9tFVY/nGNA2Sm5XIzx2OI5c276We8EsWV8tkWGHppuHY1ryZ4+U77oJsrv3pTMbADRglxSS6NGjh1atWnXO5/1+v5YvX66+fftecmEA6sAf/jnG/8wI+GXuzAu2+f70L5KkQIeusntlBUMT6VlyWidHuEoAjck7W8sis++WUr00pGVE9gbO6mSx3KsWyFo2L2xfktrtUuUbMk7+IWNpAwpEiuWW3bu/qnr3l+74NxnFR2XmrQ2O5shbI6P0ZNhLMgKB4HiQbV9LH/2v7BYtFci4WoGsbPn7DZRaJIW9JgBoSvy2o1m7KvRKbqk2Hqv/az2GpBs7R+uxjHgNSuZuZqAhsVyGrmobpavaRunHWQnyBhytOVwVHM2x36u1h6tC0k2mrqpsafnBKi0/WKX/t75EcZahwclRwU4TqR5ltnI37MCVt0LWivnBkRr7CkO2jZ3URr7Rt8g/8rtyEluFbB8AQP25pJDE7bffrl/84hf67W9/q5/97Gc1ngsEAnrqqae0a9cu/ehHP6qPGgGci9Uw73g2v9kl85tdcn8xW5Jkt2p3qtNEluxembI7dOXOMwBnVeaz9WFB+FuMS9L0ggr9v0GJTa6NJBoZX5XMDSvlXjZPZu6XMgKBkG/pxMTJnz1KvqHjZPfM4E4XoIFxklrLP3Sc/EPHyWsH5CrcHgxM5K6WK3+LDCf8M61dJ4/LteJzuVd8LscwZHdNDwYmMrNlp/WWzEu61AAAzU6pz9aft5frtc2l2lta/5/7PKZ0Z/dYPZIRr56JDfMaEoCaPKahoe09GtreoyevDB4nVhadCk0c8GrjUZ8iOZyjzO9o/jdezT/V7aalJ1jv6U4TPRMtGQ3gnNIo2if3gllyL/uHjPLQ3YwT6H2Fqq6boMCVQ6ULdF8HADQsl3TUfuihh/TZZ5/p17/+tf72t79Vj9W4//77tX79eu3Zs0ejR4/WvffeW6/FAqjJTmqlxjAV3HXskFyrcuRelSNJcmLjFeiZoUB6lgI9M2V3S5fcjOcBIK05XKXyCN0iUe53tPpQlUamRkdkfzRjjiNXwVZZy+fJvWqhjLLQ3yXuGC4FMgYGv3i9aijzUYHGwmXK7t5Hdvc+8o2fLJWelLl5nazc1TI3rpar+EjYSzIcR2bhVpmFWxX18fvBz/r9BgRHc2ReLadVu7DXBAANXVF5QG9uKdU7W8t0oqr+z39aegx9v3e8/q1PnNrFNIYrRwDOJd7t0vUdo3V9x+C1imOVAS07+G1oYvsJf0TrO+51NGd3ZfXY1JRYl4adCkyMSPGoU3wYgwO2LTN3tdwLZsra+GXItnGiouUfcr18YybI7pQWsn0AAKF1SX9Dud1u/f3vf9dzzz2nd999V8XFxZKkjz/+WAkJCfrRj36k//zP/2wQiUGgKbM795C+/CLSZVw0o7xU1terZH0dHNvjuN2yu/WpDk0EevaTYuMjXCWASFh/JLJjhBbsq9TQ9h5ZjXG+Jhod49ghWcvny718nlwH9oRlz0DHbvIPGSf/4OvlJLUOy54AQii+hQLZIxXIHhkMXO0rlJm7Ovhr20YZgfBfNDfKS2WtWSxrzWJJweNOIDM7+KtXJuFoAM3a9mKffr+pVH/dWa6qEDQC6hxv6pF+8bqnZ6zi3HTIA5qiVtGmbukao1u6xkiSDpQHqgMTi/d7ta8s9N0Iz+dAua3p+RWanh/sEtotwazuMjEsxaO2oQhulZXIvfQfci+YJdfh/fX//qfY7VLlGzNBvmE3SHEJIdsHABAeRnFx8WXFlR3H0Y4dO3T8+HElJCQoPT1dpklCGQgHc9Naxfx/P410GfXOMQzZndKCXSZOBSecVm0jXRaAMPjXRcf098K6jdvwL7q7TuuskdMuqoYol9Qj0VLvJLfSk4L/2yvRUvcWlqJMwhO4TN4KWWuXylo+T+bmdTKc0HdOcRIS5bvmOvmHjpPdpSfjNBqw+Mkj67Su9L1FIa0DTURlucytG2RuXC1r4+qQXjCuKycqWoE+/RXIzJY/K1tOcsdIl4QQ4pgGBDmOo1WHqvRKbqn+sbcyJHv0b+3WYxnxuqVrDIHvEEr64zd1Wlf8QIcQVwKcneM42lUSDE0sPhWcOFIZ/tFs59O3pVXdZWJwe48Soy490OXas1PuBbNkrZwvo8pbj1V+yzEMBbIGyXfdBAUyrm5yI6T5vAagObvsXkeGYahXr171UQuAixTo0U9OVLSMqtCcZEeK4Tgy9+TL3JMv5cySJNltU4JdJnplKpCeJSelM1/yAE3QwfLI3vEgSVW2tPm4X5uP17z71jSk7i0spSdZSk9yq3eSpV6JlnomuhVjcTzCedi2zG1fy1r+uaw1i2RU1i0IdDkc01LgysHyDRmnQFa2ZDGDGmh2omMV6D9Ygf6DVaXgXGZr46kuE1s2ROQcwqiqrO4o51Hwbjx/ZrYCWdkK9O4vRceGvSYACJWA7eiTPZX6fV6J1hwOTce86zt49Ghmgoa1j6KjLwAZhqFuLSx1a2FpcnqcHMfR5uP+6k4Tyw96ddIXmRGnp52+3vLG5jK5DOnK1m6NSA12mhjUznPh6yt+v6yvlsi9YKbM7bkhq9OJjZdv+I3yjb5VTjLBJwBoii4pJLF161YtWrRIEydOVJs2bWo9f/jwYc2YMUOjRo1Senr6ZRcJ4Bw8MfJfe53ciz+JdCUh5zp8QK7DB+Re8bkkyYlvEQxM9AyGJuwuPfkCCGjESny2Pt1TqU3HIztu43wCjrT9hF/bT/irZ21KkiGpa4JZHZxIT3IrPdFSryRL8bS4bdaMon1yL/9c1vLP5TpyMCx7BtL6yD9krHzXjJbiE8OyJ4DGwUnuKN/1HeW7/ntSlVfmjlyZuWuCoYl9hRGpyXVov6JyZkk5s+RY7uDn+1OjOeyO3QhFA2iUKvyOPthZpj/klaqgpP5D4G6XNDEtVo9mxKtvS66DADg3wzDUr5Vb/Vq59XC/ePltR18f9VV3mVhV5FVlBO9VsR3pqyM+fXXEp99tLFWUS8puF1XdaeKqtlFyn+qOYxQflfXFHLkXzZGr+GjIagp06i7fdRPkv/Y6yRMdsn0AAJF3SeM2HnroIS1ZskSbNm2S6yzthQKBgLKysjRixAi99tpr9VIogLNz7dmp2F/8INJlRJwT5VGge1/ZvTIV6JWlQPe+Ugx3ogENmTfgaMG+Sn1UUKHP9laqInBxH0lCNW6jPnWMM78NTiRZSk8M/nOSh/BEk1VeKmv1IrmXfSZzR15YtrRbtpF/8Fj5ho6Tk9olLHsiNGh1ikgxjh2SmbtGVu5qmZvWyigvi3RJspPaKJCVHew00W8Ac58bIY5paG6OVgb0v1vK9L9bynTUW//t7Vu4Dd2fHqcf9o1XahyjjiOB4xqaGm/A0epDVdWdJr46XCV/ZBtN1BBvSt+3CvX93Z+r9/blctmhSXQ4pin/wOHyXTdBds/MZhXU5bgGoDm7pE4SK1eu1PDhw88akJAk0zQ1fPhwrVix4rKKA3Bhduce8g8cLmvtkvDt2S5VTmy8XLt3ynAaxlw7o8ora8t6act6SZJjuGR37qFAenBEh90zU05S6whXCSBgO1p2sEofFZRr9u4KnahqQGffIbCvLKB9ZQEt+KbmbMz2Ma7q4ETvJLd6JVnqnWSpTTQXOxulgF9m3leyln8ma91yGb6qkG/pRHnkHzBM/qE3KND3SsnFnx0Al85p1U7+ETfJP+ImKeCXK3+LrLw1MjeulmvXNhlO+P++dhUfkWvJp3Iv+TT42b57X/mzshXIuFp2t14c9wA0GIUn/frDplJN21F+0cHvukiNdenhvvGanB6nFlGErQHUH49paFiKR8NSPPq/Cnb5XFVUpcX7g6GJ3GM+ReKqTXSgSpMOrdC/fzNfV5XuCtk+dmIr+UfdLN/Im+W0rN0xHQDQtF1SSOLQoUPq2LHjedekpqaqqKjokooCcHG89z4uc8t6GWUlId/LiUtQxf99NRg4qCiXmb9Z5vaNcm3PlZm/WUaV98JvEgaGY8vcvV3m7u3S5zMkSXZyh2CXiV7B4IST3LFZJYOBSHEcR+uO+PRRQblmFlboYEXDCFdF0sEKWwcrvFp8oOYxs7XHVR2cSE+yTv1yq32MixnDDZBrb4Gs5fNkrZgv14ljYdkz0PsK+YbcIP/Vw6WYuLDsCaCZMS3ZvTJV1StT+t6/SieLZW1aK3Pjapl5a+Q6eTzsJRmOLXNnnsydedLf35UT30L+jKuDozkyBhKGBhARXx2u0it5JZqzu1J2CL5F7NvS0mMZCfpetxhFmZwLAAi9BLdL13eM1vUdg2MmjlUGtPRglZYeCF6/2HHCH9L9u1Yc0g/3L9ADBxartb80ZPsEembIN2ZC8Lya8c0A0GxdUkgiNjZWhw8fPu+aw4cPy+PxXFJRAC6Ok9Ra3vt+pOjXnwn5Xt77fvTtRciYWAUyBiqQMTD4e79Prt07ZG7Plbl9o8ztuTJKT4a8prpyFX0jV9E3ci/9hyTJbtHy1HiOU90mOveQzEs6LAI4i23FPn1YUKEZBeUqDMEs3qboqNfWiqIqrSiq2YmgRZSh3ok1gxPpSZY6xplyEZ4Ir5PFcq9aIGvZPJm7d4RlS7tdqnxDxsk/ZKyctilh2RMAqrVIkv/a64JzmW1brj07q0dzuHbkyrDDH340Sk/KvSpH7lU5kqRAl54KZGbLn3m17B4ZksVnegChYTuOPt9XqVdyS2t9Zq8vI1I8eiwzXqNTPQSlAURUq2hTt3aN0a1dYyRJ+8sC1aM5lhzwal/Z5V/rMRxb1x/P08PfzNdNR9fLFaLeFY47Sv5rrwuO1OjSMyR7AAAal0u6ctC/f3/NnTtX//3f/62kpKRazxcXF+uTTz7RFVdccbn1Aagj/zVjVHVgj6JmvReyPbzj75f/mjHnXmC5ZXfvK7t7X/m+8y+S48g4sCcYmti2UeaOXLkOHwhZfRfLdfK4XGuXVI8qcTzRCvTop0CvrGB4onsfyRMT4SqBxmVvqV9/L6zQhwUVyjvmi3Q5TcbJKkerD1dp9eGaF2LjLEO9kiylJ545tsOtLvGmTBcXVOuNr0rm16vkXjZP5sZVMgKhD/04MXHyZ4+Sb+jYZjcTFUAD5nLJ7tpLdtde8t18t1ReKnPzelm5q2XmrpbraGS6SZq7d8jcvUNRn0yTEx2rQN+rqkdzEC4DUB+8AUfT88v1+7xSbQvBndSmIU3oFqMp/eLVv01Uvb8/ANSH1DhTk3rEalKPWDmOo8KSYGji9HiOo966h2db+Ms1+eASPfzNfPWqOBiymo8mtNO+wTer3Q3fVUKrliHbBwDQ+FxSSOIHP/iB7r77bt188836zW9+oyFDhlQ/t2zZMj3xxBMqLi7Wgw8+WG+FAriwqvH3y5Ehz6w/1ft7e8ffL9/4yRf3IsOQk9pF/tQu8o/8bvChY4eDYYnToYm9BRGZcXw2hrdS1qavZG36SpLkmKbsLr2qO00EemZKLZIiWyTQAB2pDGhWYYVmFFZoZYjupsLZlfkdrT/i0/ojPkkV1Y97TKlnolu9TwUoTneeSGthyU14om4cR66CrbKWz5N71UIZZaHvjOQYLgUyBso/dJz8Vw2VoujKBqCBi41XYOAwBQYOqw5IWxuDgQlz2wYZvvAHJo3Kclnrlslat0ySZKd0lj8zW4HMqxXo3Z9jK4CLUuy19cdtZXpzc2lIxgbGWYbu7RWrh/vGq0sCXXAANB6GYSitRfA6w/3pcbIdR1uO+7X4VJeJFQe9Oumrfc23X+lePbx/vu45uEzxdujGNs9rmaXXOlyvf7TuL9vrkjm7XFe28WlEikfDUzzKbudRjMX1EQBozozi4uJL+nbyl7/8pV599VUZhiGPx6N27drp0KFD8nq9chxHjz32mJ5++un6rhdAHVircuR5/yUZZSWX/V5OXIK89/3o/B0kLkd5qcwdm2TuCHabcBVuicjF1LqyUzqfCk1kKdArM3hnGnf3ohkq8dmau7tSHxWU64v9XgUikHXyL7q7TuuskdPqtC4twdS+soCqwt81PGwsQ+qReMbIjlMBih4tLEVzcUCSZBw7JGvFfLmXzZPrwJ6w7Bno0FX+oTfIf+11clq2CcueaJjiJ4+s07rS9xaFtA6gXngrZW77Wmbu6uBojgN7I12RHHeUAr2vODWaI1tOSmc+y4cQxzQ0ZntL/Xp9c6ne31auUn/9n+y0i3HpoT7x+tfecWrpcdX7+yM0OK4Bdee3HW046tOSA14t3VeudptX6MG9n2tk8ZaQ7XnCjNGf2o/QGx2u047Y83cT85hSdtsoDT8VmriqbVSzvKmE4xqA5uySQxKS9Pnnn+vtt9/WunXrdOLECSUmJmrAgAH6wQ9+oOuvv74+6wRwkYzio/L8+eXqURKXwj9wuLz3Pi4nqXU9VnYBviq5CrcFQxPbg7+M8tLw7X+R7KQ2CvTKDI7n6JUpu1Oa5DIjXRYQEt6Ao/n7KvVRQYU+21uhytBPHDiv+gxJ3NwlWn8e3Vp+29GuEr+2Fvu1rdivbcU+bS32a8cJvyoikQQJE5chdUswlZ50qvvEqQBFz0RLce5mcNHWWyFr7VJZy+fJ3LwuLB2OnIRE+a65Tv6h44LzUPmSDuICFZo24/CB6sCEuXmdjMqKC78oxOw2ycHAREa2Av2ukmLiIl1Sk8IxDY1R7jGfXs0t0YzCipAEwXsmWno0I153pMUSUm6EOK4BF8c4eVzW4rlyL/xYrmOHQ7ZPXmxHvdbhek1LHqoyK/qS3iPeMjS4/behiYxWbrmawXk6xzUAzdllhSQANHyuPTvlzvlY7kVz6rTeiYqWf/D18o25VXbnHiGurg5sW679u+Talvttt4ljhyJd1Tk5MXEK9OhX3W3CTutNS180agHb0dKDXn1UUKHZuyt0sip8HxtchmSfZ7v6CkkkRRlaNSFZ7WPPHXCyHUd7SgM1ghPbin3afsKvkrO0j2xKOseb3wYnkiylJ7rVK8lSYlQjD0/YtlzbN8q9bJ6sNYvC8mWdY1oKXDlYviHjFMjKlix3yPdE48IFKjQbfp9cOzd9O5pjz85IVxQctdcjIziaIytbdqfukquR/10XYRzT0Fg4jqNF+716Ja9UX+wPTev3a5Oj9GhGvG7oFN0svnRrqjiuAXXjyt8i94KZslZ/IcMfmo7Bfrn0cZsBeq3DWC1O6lPvNx608rg0LCUYmhiR4lH3FpaMJnj85rgGoDkjJAE0E3X+wPPWZ5Ln0hK34WIcLZK5baPM7bly7ciVua8w0iWdk2Nasrv1PhWayFSgZ4YU3yLSZQHn5TiOvjri04f55Zq1q0JFIZi9ey4d40x9r1uMJqbFaHuxXz9Ycvyca+srJPHOiJa6LS32ouo8zXEc7S+3awQnthX7tbXYp+IwBkoiITXWVR2c6J3kVq9ES72TLLWKbtjddIyifXIvny9r+Ty5jhwMy56Bbr3lHzpOvmtGS/GJYdkTjRMXqNBcGcVHZeatkblxtay8tTLKTka6JNmJLRXIyFYg82r5MwZKCUmRLqnR4ZiGhs5nO5pZWKFX80qVe6z+v8QzJH23S7Qey0jQ1e2i6v39EX4c14DzqPLKWv2F3AtmyizcFrJtDrlb6O2UUXordYz2RYev+3FqrEvDTgUmhqd41DHeCtveocRxDUBz1jSO5ADqTwMPSEiS0zpZ/sHXyz/41Fif0pMyd+bJ3BYcz+Eq3Coj4I9skacYAX+wtp150qd/kRScPW/3ygqGJtKz5LROjnCVQNCW4z7NKKjQR4Xl2lUSvlkarTwuTegWo9u6xeia5KjqO6uyWkdpx0m/nttQErK9n+ifcMkBCUkyDEMd4kx1iDM1usO3jzuOo8OVdq3gxLZivw5Xhi90Ekr7y23tL/fWutuubbSrOjiRnmSpV2JwhEe7GFfk7rooL5W1epHcyz6TuSMvLFvaLdsEOzMNvUFOapew7AkAjZWT1Fr+oTfIP/QGee2AXIXbq0dzuPK3yHDC/3en68RxuZbPk3v5PDmGIbtrugJZ2fJnZge7xZlcTgEaqxKfrfe3l+v1TaXaV1b/5z3RpnRXjzg90i9e3RM5VgBo2owjB+VeOFvuJXNllJwI2T5fJnTXHzqM1UftBqnKFf6ujPvLbf0tv0J/yw92oezewqwezTEsxaM2DfyGEQBAbXX6pN6yZUu5XC59+eWX6tGjh1q2bFmni9yGYejo0aOXXSQAnFd8CwX6D1ag/+Dg76u8chVslbk92G3C3LlJRkVZZGs8g/nNLpnf7JL7i9mSJLtVu2/Hc/TKlN2hK619ETa7S/z6e2GFPioo16bj4QsXxVmGbuoSrYndYjWqg0du19k/VzzRP0GGpN+EICjxRP8E/Uf/hHp/Xyn4GahdjKl2McGT5jMdqwxo2wl/jeDE9mK/vikPXzAllA5X2jp8sErLDlbVeDwpyqgOTnw7usNShzgzNOGJgF9m3leyln8ma91yGb6qC7/mMjlRHvkHDJN/6A0K9L1ScnGRBAAumsuU3b2P7O595Bs/WSo9KWvTV8GxHLlr5Co+EvaSDMeRWbhVZuFWRX38vpzYePn7DVQgK1uBjKvltGob9poAXLyD5QG9ublU72wrC8kYwVYel37QJ04P9o5T2xg+BwJowhxH5uZ1wa4R61eELNDquN3yDxqtqtHjFd2mp64+4FXFAa+WHPDqqDeyN6Dknwwo/2S5/ritXJLUr6WlEanB0MTgZI9aNPYRpQDQDNQpJDF48GAZhqGYmJgavweABinKI7v3FbJ7XyGfJNkBufYWBLtMbM+VuX2jXMUNJ8DlOnZIrlU5cq/KkSQ5sfEK9MxQID1LgZ6ZsrulS25ac6L+HK4IaNauCn1UUKEvD4X+i+PTolzSdR2jdXtajMZ1ilasdeETRsMw9MSVLdQz0dJPVhbXywiLpChDL1ybdFkdJC5Hq2hT10abuja5ZnjiRJWtHSe+DU6cHuGxp7RphCeKqxytOlSlVf/0Zy7BbahXYjA40fuMAEXnePOS5jW79hXIWjZP1or5cp04Vl/ln1cg/Qr5ho6T/+oRUkxcWPYEgGYjvoX8g0bJP2iU5Dhy7Ss8FZhYLXPbxoh0kDPKS+Ves0juNYskSYGO3RTIzA7+6pXJZ3eggdla7NPv80o1Pb9cVSH4Tq1rgqlH+sXr7p6xdTrHAYBGq6Jc7uXz5M6ZJdf+3SHbxm7VTr4xt8o3/CapRZIkqbuk7omWHugdJ9txtPm4X4tPBSZWHPSqxBfZkaebjvu16bhfr20qk2lIV7Vxn+o0Ea3sdlGKsfg+DQAaGqO4uLhpD8wGIIn5YjU4jozDB4JdJk6HJg7siXRV5+S43bK79anuNhHo0VeKC83d72i6TlbZmrunUh8VlGvRfq8CYfrb35A0PMWj29JidEuXGCV5Lv2i4cHygH62qlhzdldKkvyL7q7T66yR06r/+eYu0frtNUlqH9t47uwq8wXDE8HuE77qER6FJQHZTfhTXIxpqGeiVSM4kZ5kqVuCJeufO4+cLJZ71QJZyz6XuXt7WOqz26YGgxFDxsppmxKWPdH08XkNuEiV5TK3bAiO5ti4Wq7D+yNdkZyoaAX6XqlAZnA0h5Pc4cIvaqI4piGSHMfR8qIqvZpbonn7vBd+wSW4qo1bj2Uk6OYu0TLP0RkPTQvHNTRXxv7dci/8WO6ln8moLA/ZPv6+V8l33QQF+l97UaPN/Laj9Ud8WnIqNPHlIa8qG9D9Jh5Tym4bpRGp0RqeEqWr2kTVvq4RIXU9rhW9vVBxboKAAJqWSwpJLF++XAkJCcrKygpFTQBCgBO5CzhZLHNHbnVwwrV7u4xAA/o0fQbHMGR3Sgt2mTjVbYIWvzibSr+jz/dVakZhuebtrQzrCeKANm5NTIvVhG4x9R5IyD3m0ztbSvX2WxPqtL7FdR/ojrQYfb9PvDJbhX9uZahU+h3lnzwVnDgVoNhW7Ff+Sb98ke06GVJRLqlHC0v9EqSbj6/XsB2L1GHHGrns0P8Bd2Li5M8eKd/QcbJ7Zkp0VkM94/MacHmMon2yNp7qMrFlvYyq0HwxejHsdqnyZ2YHR3P0uVLyxES6pLDhmIZICNiO5uyu1Ct5JVp3xBeSPcZ19OjRzAQNSY6i024zw3ENzYodkLlhpdwLZsra9FXItnGiY+QbMk6+MePldOhaL+9Z6Xe0+nBVMDSx36uvjlSF7WahukhwGxqcHKVhKR6NSI1Wv5bWJXXSvBy5x3x6e0up3rmIa2u3p8XoB03s2hqA5u2SQhKtWrXSv/7rv+r5558PRU0AQoATuYvkrZCZv6V6PIe5c5MMb2Wkqzonu22KAj0zg90m0rPkpHTmy7tmym87WnrAq48KKzRnV4VOhrHdYHqipYlpMbotLVZpLeqe+L9UdT2uHXpnYbNqe+uzHRWe9Fd3nNh2IvjPO0/4GtSdFJfEcXR1SYHuObhUkw6tVGt/aei3NFwKZAyUf+g4+a8aKkV5Lvwi4BLxeQ2oR1XeYAj6dGjim12RrkiO5Q5+Xj8VmrA7dGvSn9k5piGcyv22pu0o1x82lWpXSf1/6HW7pDu6x+rRjHj1TuLLoeaK4xqahZJiuZd8KvfCj+U6UhSybeyUTvKNmSDf0HEhH1tZ4rO14mBVdaeJ3GOhCdFdqtYel4aleE6N54hS9xZWyEJ4zbVLKwCczSV9g9G2bVu53ZwQAGjCPDEK9L1Kgb5XySdJAb9ce/KDgYntuXJtz5Xr5PFIV1nNdfiAXIcPyL3ic0mSE98ieAG2ZzA0YXfpKVkct5sqx3G09rBPHxaUa9auCh2qCF8bgY5xZnUwIqNl6E7iLkdzCkhIkttlqFeSW72S3JK+vVs1YDvaUxrQ1lMdJ7aeClBsL/arzN+Abqk4iw6VR3V30XLdU7RUfcvD00o90KGr/ENvkP/a6+S0bBOWPQEA9SjKo0C/gQr0Gyjd+e8yjh4KjuXIWyNz01oZ5WVhL8nw+2RtXidr8zrpb2/IbtmmeixHoN8ARuoBl+BIZUBvbSnT21vKdMxb/+dBLaIMfT89Tv/WN14pfBkEoAlzFW4Ldo34MkeGLzQhAsdwKXDltcGRGn0HhC0smuB2aVynaI3rFC1JOloZ0LKDVVq8Pxia2HnSH5Y6zuWo19asXRWatatCktQh1tSwlNPjOTzqEFc/f//MKCjXT1YWq7jq8q4BzdldqaUHivTCtUm6LS22XmoDgEi4pJDE6NGjtXz5cjmO0yC/DAGAemdasruly+6WLt+42yXHkVH0TXVowtyeK1fRvkhXWc0oPSlr3XJZ65ZLkpwojwLd+8o+HZzo0U+K4UNsY7f5uE8zCsr1UUGFdpeGr0VAa49LE7rF6La0GA1qFxX2loC4NKbLULcWlrq1sPSdzt8+bjuOvikLfBucKPYH//mETycv88T5csQEvBp/ZK3uO7hEY45vkkuhr6UqLlHeQWNkjrghGC7jzzYANBlO63byj/yu/CO/GwxA52+RlXuqy0ThtojU5Dp+RK4ln8q95FM5hkt2977yZ2UrkJktu2svydW8gp7AxSg46dfv80r1wc6ykHRL6xhn6uF+8bqvV6wSmMEOoKny+2StXiT3gpky8zeHbBsnroV8I2+Sb9QtctqmhGyfumodberWrjG6tWvwxpJvygLVXSaW7Pfqm/LItuH8pjygv+ZX6K/5wdBE9xamRqQEAxPDUqLUOvriQhOO4+g3G0r03IaSequxuMrR9xcf1/YTfj3RP4HvCYFG4qabbtLy5ctVXFwc6VIahEsKSfzyl7/U9ddfr8cff1xPP/20WrZsWd91AUDDZhhy2neUv31H+YffGHyo+KhcO/K+7Taxe6cMJ3x39J+PUeWVtWW9tGW9pGBy2+7cQ4H04IgOu2emnKTWEa4SdbG7xK8ZhRX6qKBcm4+HL+kebxm6qUu0bk+L1YhUj9wuTn6aCpdhqFO8pU7xlq7rGF39uOM4Kqqwta3Yd2p0RzBEsf2EX0cqQ3NsMxxbw05s070Hl2ri4S+VEAj9mKMqw9Tc1lfq/fbD9Fmr/vLZlpJXuZS+9ajSkyz1TrLUK9Gt3kmW2kS7OPEHgKbAtGT3ylRVr0zptu/LOHlcZt5amblrZOatiUjHOMOxZe7Mk7kzT/r7u3ISEuXvNzA4miNjIJ/VgVPWHKrSK3kl+mR3ZUgitBmt3HosI14TusVwzgOgyTKOHZZ70RxZi+bIdSJ0n3sCXXrJd/0E+QeNbtCjKzvEmbqzR6zu7BErx3FUcDIYmlh8wKulB7w6GoJORRcj/2RA+SfL9O62YCe0jFZuDU+J0oiUaF2bHKUWUecP89V3QOJMz20okSHpiStbhOT9gcZg48aN+uMf/6gVK1Zo//79qqysVKtWrdS3b19df/31mjRpklq1ahXpMnEWRnFx8UWfU9x88806duyYtmzZoqioKHXp0kVt27atddHYMAzNnj273ooFcOmYmxgBFeUy8zefGs+xUWb+ZhlV3khXdU52cgcFemUFx3T0ypST3JG7qBuIQxUBzSqs0EcFFVp9uCps+0a5pLEdozUxLVbjOkUrxmpYfx44rkXOkcrAtx0nTnWf2H7CpwPll3bhoHv5Qd1TtEz3FC1Tt8rD9Vzt2a1JSNP77Yfpb+2u1TF33dqbt/K4qoMT6UlupScG/zcllvAE6gfHNaABsG259uwMjubYuFqunXky7MgHnwNdelaP5rB79JOsS7rnJaw4pqG+2I6jz/ZW6tW8Uq0sCs350KhUjx7LiNfIVA+f63BOHNfQqDmOXNu+lnvBLFlfLQnZ5xvHtOTPHinfdRNkd+/b6K8t2o6jTcf91Z0mVhz0qsTXcEaWmoZ0VRu3RqREa1iKR4PaRSn6jOt3HxWU6weLzx2E8S+6u077WCOnnff5d0a0ZPQGmh3btvXLX/5Sv//972VZlgYPHqy+ffsqNjZWhw8f1urVq7Vt2zYlJCRow4YNat068sF3OknUdEln1cuWLav+Z6/Xq+3bt2v79u211nFSAaBZi4lVIGOgAhkDg7/3++Xavf3UeI5gtwmj9GRkazyDq+gbuYq+kXvpPyRJdouWwfEcp37ZnXtIZsO/GNtUnKiy9cnuCs0oqNCiA17ZYTr/chnS8BSPJqbF6LudY5TkobUsamsTbapNe1ND2te8E6TYa2v7iW87T5zuQrGvrHaryhb+ct1+aJXuO7hUQ07W/hwZCt9EtdS05KH6c/uh2hLX8aJff8xra2VRVa2L8y3chtJPByeSLPVOcqtXoqVO8SbjaACgsXG5ZHftJbtrL/luvkcqL5W5eZ2s3DUyN34p17FDESnL3L1D5u4divpkmpzoWAX6XlU9msNp0z4iNQGhVul3NL2gXL/PK9X2E/XfRc80pNu6xWhKRryyWkfV+/sDQIPgrZC1Yn5wpMa+wpBtYye1kW/0LfKP/K6cxKZzx7TLMJTZyq3MVm490i9ePtvRhiM+LT4VmvjykFfeCE7nCDjSmsM+rTns0/MbS+QxpUHtPBqe4lFGS0s/XVkcljp+srJYQ9p71D724kaBAI3ZM888o9///ve68sor9e6776pbt2611qxbt06//OUvVVkZ+m65uHiX1EkCQOND2r0BchwZB/YEQxPbNsrckSvX4QORruqcHE+0Aj36KdArKxie6N5H8sREuqwmpdLvaN6+Ss0oKNe8fZVhPcm6uq1bt6XFakLXGCU3khMajmuNR6nP1o4Tfm075pWTu1a9Ni7QoD2rFW37Qr53uStKM9tcranthyqnZYZsI3zBn1jLUK9Eq0ZwoneSW10TTJm0b8ZZcFwDGrhTn9+tjatl5q6WuW2DDF/o/y67EDuls/yZ2QpkZSuQfkWDaWfNMQ2Xqthr652tZXpzS6kOVdT/nc7xlqH70mP1cN94dYrnRgDUHcc1NCbGwX1y58ySe9k/ZJSXhWyfQO8rVHXdBAWuHNooOl3Vt0q/oy8PVWnpAa8WH6jUuiM+BRrRN2711UlCkm7uEq0/j478nfJAOOTn5ys7O1utWrXSqlWrztslwnEc2bYt0/z2mnteXp5+97vfafny5Tp27JiSk5P1ne98R//5n/9ZYzTH7t27dcUVV+jOO+/Uf/zHf+iXv/ylFi9eLJ/Pp6uvvlrPPvusMjMza+25cuVKPfPMM9qwYYM8Ho9GjhypZ555Rg899NBZO0k4jqOpU6dq6tSp2rx5s/x+v9LT0/X9739f9957b421v/71r/Xcc89pzpw52rt3r958801t375dV111lebOnXuJP9HIaH5/awFAQ2EYclK7yJ/aRf6R3w0+dOxwMCxxqtuEa2+BDKdhfLI2vJWyNn0la9NXkiTHNGV36VXdaSLQM1NqkRTZIhshv+1oyQGvPiyo0NzdFToZxpZ9vZMs3Z4Wq9vSYtQ1gY8ECJ0WRbs0aNk8DVm5QK7io2HZc0lib73ffphmtM1WiRWZlo/lfkcbjvq04ahPUkX14x5T6tEiGJg4swNFWoKlKJPwBAA0WKc+v/tSu8h3w+2St1Lm1q9l5q2WlbtargN7I1KW68AeRR3YI33+kRx3lAK9r6gezeGkdG74ba69FYSvIUnaU+rXa5tK9eft5Srz1/95UXKMSz/sG68H0uPomAegabJtmRu/lHvBTFm5q0O2jRMVLf+Q6+UbM0F2p7SQ7dMYRFuGRqR6NCLVo6fUQiergh0oFx+o1JIDVco7FvlAbbjM2V2p3GM+ZbZyR7oUIOQ++OADBQIB3X///Rcco2EYRo2AxKeffqoHHnhApmnqO9/5jjp06KBt27bpf//3f7Vw4ULl5OQoKSmpxnvs2bNHY8aMUXp6uu655x4VFhbq008/1c0336zVq1erXbt21WsXL16siRMnyuVyacKECUpJSdHixYt1ww03KDExsVZ9juPo3/7t3/Thhx+qR48emjhxotxutxYtWqRHH31U27Zt07PPPlvrda+++qqWLl2q73znOxo1apSsRhiUq5eKy8rKVFJSooSEBMXFxV3We+3fv1+zZs3S/PnztWPHDhUVFally5YaNGiQHn/8cQ0cOLDWa06ePKnf/OY3mj17tg4dOqR27drplltu0RNPPKEWLVqcdZ8PP/xQr7/+urZu3Sq3263s7Gw9+eSTuvLKK8+6Pj8/X88884yWLl2qsrIypaWl6f7779cPfvADuVy1T6y8Xq9eeuklTZ8+Xfv27VNSUpLGjh2rp556SsnJyZf1MwLQdDmt2so/aLQ0aHTwgfJSmTs2ydyRK3N7rlwFmxvE3WqSZAQCMgu2yCzYIn02XVLwLrZgaCJLgV6ZctqmNPyLshHgOI5WH6rSR4UVmlVYocOV4Ztz3Sne1MRuMbotLVb9WlqMxkLonCyWe1WOrGXzZO4OzziN/Oh2mtp+qKYmD1NhTLsLvyBCvAFp03G/Nh2v2TbaMqTuLazq4ETvJEu9ktzq2cKqMVMUANBAeKIVuGKQAlcMUpUk4/ABmbnBwIS5eZ2MyooLvkV9M3xVsnLXyMpdI4/+ILtNcnVgItD3Kinm8q7Z1IVrz065F8yq8/q4KRPkv/Y6+a4bHxzxh2bn66NVejWvVDMLK0Jy9216oqUpGfG6o3usPARSATRFZSVyL/lU7pyP5Tq8P2Tb2Mkd5BszXr6hN0hxCSHbpzFrEeXSuE7RGtcpWpJ0pDKgZQeqtORUp4n8kxGczREG72wp1UtDWka6DCDkVq8OBtGGDRt2Ua87duyYfvjDH6pNmzb67LPP1KlTp+rnPvroI/3gBz/Q//zP/+i3v/1tjdctX75c//Vf/6Uf/ehH1Y89++yzev755zVt2jT9+Mc/liTZtq3HH39cfr9fn376qa699lpJNYMQ/+z999/Xhx9+qHvvvVcvvvhiddihqqpK9913n37/+99r4sSJ6t+/f62aFixYoH79+l3Uz6AhueRxG16vV6+++qr+8pe/qLDw21lW3bp101133aUpU6bI47n4Fo//9V//pZdeekndunXTkCFD1LZtW+Xn52vu3LlyHEfvvPOOJkyYUL2+rKxMN9xwg3JzczVq1ChdccUVysvL04IFC5SZmanPPvusVnDjhRde0DPPPKOOHTvq1ltvVVlZmf7+97+rsrJSM2bMqPWHeuvWrRo7dqwqKiqqUzfz58/X5s2bNXnyZL388ss11tu2rdtvv105OTkaOHCghg4dqsLCQs2ZM0epqanKyckhKIGwoyVgE+GrkmvXdpnbNwbHdGzPlVFeGumqzslOaqNAr8zgeI5emcF0uatxjHIIhU3H/n/27js+qjrt///rnDOTKamUkARIISR0kCYCQaRjBwR7w3vXXV3bdnXX/e3X1V3Xdd3bsq67a7nt7irYsKH0LqCgCSW9AKFD6iSTmXPO749JBmIChJCZSbmej0ceiWc+cz6fxHAyc877XJeHRQUuFhfWUFIVvDdFPe0q81IcLEh1MK5XWKcJRshxrR3yetC2b8S6binad5tQ9MD/nldZHCzqNZ7/63Uh66MHdMpglqpASoTmD040VJ5Ij7YQYZW7IDsTOa4J0Yl4PWi5WWiZW3ytOUryQr0iXyW49GG+wMTwcRiJ/aGZmz5aSyk7iu31p7FsXdPqfXjHTsZ9832YMVKqubMzTZMVpW6eyaxi9X53QOaYGBfGvcMjmNXXjtoJXyOK4JPXaqK9aQgmWjZ+iVIXmGOpqSjoIy7AM2Me+rDz2/S1Q1e0t8rLmv1u/0epK3g3TjWnLdttgK/taN718Tgt8nsiOrcLLriA7OxstmzZQnp6eqPHVq9ezYYNGxptmzJlChMmTOC5557jt7/9Lf/617+49tprm+z3oosuYu/eveTn5wMn2m0kJyezbdu2RjftNzx2xRVX8PrrrwO+4MJll13GxRdfzH/+859G+y4pKWHUqFHout6o3UZGRgZFRUXk5+djt9sbPWfHjh1kZGRw9913+6tJNLTb+MlPfsKf/vSns/zJtS+tqiRRVlbGlVdeSVZWFhaLhfT0dGJjYzl8+DCFhYX88Y9/5KOPPuLDDz9sUhLkTEaPHs2nn37KxIkTG23fsGEDc+bM4ec//zmXXnqpP4Dx9NNPk5mZyX333cfDDz/sH/+nP/2Jv/zlLzz99NP85je/8W/Pz8/nscceIy0tjeXLl/tLi/z4xz9m+vTp3HvvvWzZsqVRWZCf//znVFRU8M477zBr1iwAHnroIRYsWMCrr77K/PnzmTx5sn/8W2+9xfLly5k/fz4vvvii/2LUG2+8wd13383vf/97/vnPf57Vz0UIIQCwhmGkD8NIH4bnMsAwUEuLULMzfdUmsr9DPXYo1Kv0U8uOoG5eCZtXAmA6wtHThvqrTRipg9pNz+RAKar0srighkUFLnaVec/8hDYSaVW4PNkXjLgowYZFlZOCIkBME7UwG8u6z7FuWoFSXRH4KRUVfdhYvBmzYcwkrrKGcX6VTnaZl+wyD7vLfZ+zy7xUBrGFTaAYJhRU6hRU6nz2vUruiREag6JPBCcGxlgYEG2VEtJCCBFqFiv64FHog0fBNT9CKTuKlrUF7bvNWLK2BuXv5fcpuu5rD7L7W3j3BYzobujDfIEJ77AxEBnT6n1bNi3H9tpTKNWV57RGy9Y1aLu24b7lp3jHTz+nfYn2yWOYLC6o4dmsyiZVtdqCqvh6ot87LJIxsWFtvn8hhAg5rxfL12uwLnsfLSczYNOYzgg8ky/FM20OZlyfgM3T1fSNsHBDuoUb0sMxTZP8Ci9r9vvac6zdX8cxd2hDE+fK5fVVz53S237mwUJ0YOZpWqSvXbuWv/71r4222e12JkyYwNatWwHYunUrBQUFTZ7rdrs5evQoR48ebdTGY9iwYU26GvTp4zs2l5eX+7dlZWUBNLnGDpCUlESfPn0oKSnxb3O5XOzcuZOEhAT+93//t8lzvF7f6/Xc3Nwmj40ZM6bJto6mVSGJRx55hMzMTG688UYeeugh4uPj/Y8dOHCARx55hLfeestf6uNsXHnllc1unzhxIhdeeCErVqxg586djBo1CtM0ef3114mIiODXv/51o/E///nP+fe//80bb7zBgw8+6A8qvPnmm3i9Xn7xi1806r0yePBgrrvuOl5++WXWrFnDtGm+Uvd5eXls2LCBCy+80B+QALBarfzud79j9erVvPbaa41CEq+99hoAv//97xvdrXvTTTfx7LPP8v777/PEE08QGSklqYQQ50hVMfqmYvRNxTt9DgDK0YO+KhPZ36HmZqLtLTzDToJHqanGUl+CGMDULBj9BtWHJoajpw+DiObbJHUkh2p03i/0BSO2HA5eexSbBrP62lmQ6mRWXzsOKc8vAkg5dhjLxi+xrluKWloclDn1Pil4J12Md8IMzG49/dtVIDnSQnKkhVmJJ96Im6bJfpfhC07UByiyy73sLvNw3N3xwxMAe6p09lTpfLmv8V1DCU7VF5yItjAoxsqAGAuDYiz0sHfdaj5CCBFKZkwP39+wSRfjNnTUwmxfYCJzM2rBbhQz+CfE1fLjqOuXYl2/FFNRfK/Lh4/DO/x8X5hZa8EpI9Mk7INXCPvg1TZbl1Jdif35R6jbX0Ld3IWdskpUV1RRZ/BqTjX/3FHNPlfbVxtzaAo3pju5a2gE/aI6Xj9kIYQ4E6XsKJaVS7CuWoJadjRg8+iJ/fHM8LXCwiYXugNJURTSoq2kRVv5n0HhGKbJjuNeVpfWsna/m/UH6qjydrxzF9uPeCQkITq92NhYcnJyKC0tbVJJ4qGHHuKhhx4CfNek77rrLv9jx48fB+CFF1447f6rq6sbhSSioppeM2m42V8/qZJvRYUvjN+zZ88m4wF69erVKCRRVlaGaZqUlpby+OOPn3Y93xcbG3va76EjaNW7ho8//pgLLriAv//9700ei4+P57nnniM3N5clS5acdUjidKxWKwCa5ju5m5+fz/79+5k+fXqTlhp2u52JEyfy6aefUlBQQP/+/QFYt24dgD8EcbJp06bx8ssvs379ev/jpxs/ZswYoqOjWb9+vX9bbW0tW7duJT09naSkpCbPmTp1Kv/85z/ZunUrU6dOPeufgRBCnInZIw7vhDjfmxmAqgq0vKz64EQmauFuFD14FQ1OR9G9vrXlZcGnbwO+i6DGgBG+0MTAEZg9OkZ7ovI6gyXFNSwuqGH1fjdGkN7DqApclGBjQaqDy5MdRIfJ3eMigNy1WL5ei2XdUrSdX6OcJjXdVsyIKDwTZuDNmI2RcnbtNBRFoXe4Ru9wjakn3fhimiZHao0TwYkyX3Aiu9zLoZqOfddGg/0ug/0uN6tKG4cnetpVBsb4ghMDoy311SesxDnUTtOKRwgh2j1Vw+g/BKP/EDzzFkJVBZYdX/vacmRuDuiFj1NRTBOtYBdawS7CPnwV0xmBd+hY9BHj0Iedj9m9+RNgbR2QaLzvVzFRfD8j0WGVVuv8c2cVr2RXUxGACl89bCq3Dw7n9sHhEgYVQnQ+pomam4V12ftYtq4OWEtLU9Pwjp2MZ8Y8jPThElAMEVVRGN7dyvDuVu4eFonHMNl2pM5XaaK0ls2H63AHr3tvq313LHg3jAkRKhdccAHr169n7dq1XHTRRS1+XsPN8xs2bGDIkCFtvq6GMMWRI0eaffzQocZVyBvWM3LkSFatWnVWc3WG84itCklUVlaSkZFx2jGTJk3yl/VoC3v27GHVqlXExcUxdOhQAH9PltTU1Gaf0xCMyM/Pb/R1REQEcXFNL7qdPKbB6eZQFIXU1FS2bduGy+XC6XRSWFiIYRgtWtOZQhK1tbWnfVyIsxHRwnHye9cJWcJg0GjfB0CdG0tRDta8HVjzsrDk70StdYV2jSfR9hWh7SvCuvIjAPRusXjShuJNH4YnbSh6QnK76X9Y4zVZVlrH+8V1LC+tI5gV8cb2tDA3OYw5iTZiHfU/D6OOrvRPWI5rQWIYWPKysG9aRtjX64JyvDA1C3XDx+GeMJO6YWPB4gvK4m67HquRCpzfDc7vZsH3kth3l8Nxt0FuhU5OuU7OSZ9D3Se0rRypNThyoI71B+oabY+2KgyI1nwfURrpUb6v+zglPBFMclwToouyhMF5E3wfpom2r5CwHV9j3bEVa96OkAScFVcV1i2rsG5ZBYC3Twp1Q8fiGTIGT9pQsIYRtnllwAISDWwfvIK7Zzx1508J6Dyi7e0q8/L87lreL3bjCcDLqJQIlTsGObimnw2nRQE81NbKRRkRWPJaTQRNXS22zatwrFqCZU/+mce3khHVjdrJl1J74aUYMfV3LLfh+25x7kZEwYgoK3cPtFLjNdl6xMu6gx7WHfSw/ZgXvR0Wmthf7ZHjoGgRu73jVhy5/vrreeqpp3j11Ve58847G1V9OJ2xY8eyZMkStmzZEpCQxLBhwwBfCOPee+9t9FhJSQn79u1rtC0yMpKBAweSk5NDWVkZMTExbb6m9qxVIYlBgwaxd+/e047Zu3cvgwYNatWivs/j8fDjH/8Yt9vNww8/7K8k0VA25OS2GSdrSMA0jGv4+lQlQE41vqVzOJ3OVq3pVEpLSxuVSRHiXDRfXKepPXv2nHmQ6PgcMTA8w/dhGDgO7SV8Tx4Re3KJKMnFWlV+xl0Ei3b8MNqWVdBwgtbupLpvf6qS0qlOTMeVkIzZcAE1CLwmbC5T+eKwhVVHNar14F087O80mB3rZVasTh+7711Q7RHoqv9q5bgWWGHHDtE9cyPdv9uIrTw4d7RWJyRzbMREjg89H91Z35Zs/4GgzH2yBCDBBhfFAvUvG6u8UFyjUuBSKHSpFLp8X+93K5h0/BBBucdkyxEvW440vhDn1ExSHAb9nCapToMUh+9zgt1E6/jfdrsjxzUhhI8VhoyHIeNR62qJKMomKj+LqPwsbGXN3xEUaJZ9RVj2FcEXi9CtYVT37U/43sBdtDmZ841nKA7vgTcyJijzidYzTfi6XOX1fVY2HA9MVYdhkTo39/FyUQ8dTani6H4Ifu0V0VXJazURaGHHD9Pz61X02L4OSwBvUKjqm8bh86dSPmg0pmaBSpfvQ7R7KUBKd7ipu+88xbYKlS1lGlvLNHJd7eOmsqoatxwHRYt8v01FR5Kens5dd93Fs88+y4IFC3j55Zfp169fk3Hl5Y2vs9x444389a9/5ZFHHmHcuHEMHjy40eMul4sdO3Zw/vnnt2pdEyZMIDk5maVLl7Jx40YmTJgA+KrqPvLII81ec/7xj3/Mz3/+c+677z7+8Y9/NOncUFRUhKIoJCcnt2pN7VmrQhI/+9nPuP3227npppu48MILmzy+evVqPvzwQ15++eVzXqBhGNx1111s2LCBW2+9leuuu+6c99lR9O7dO9RLEF1QYmJiqJcgQiE5Gc7PQAfKTRP16EGsuVlY83ZgycvCcqD9vLC11LqIzsskOi8TANNixZsyEE/aUDzpw/CmDsZ0tvT+jpYxTF9S+71iN0tK6jjqDl5MOzFcZV6yjXnJYQyOkd66rSHHtZZTaqoJ27oG+6ZlWPN2BGVOPaYH7gum4x4/Hb13Mg7AEZSZz97gZra5vCb5FSdXnfCSU65TVGW0yzs6zpZLV9hZpbGzqvF2uwZpkVrj6hPRFlIiVKyqpCcCTY5rQnQx/dNh+uVUmibVh0oJ27GVsB1bsWZ/h+IJ/t2emqeOqMJdQZvPUutiwJoPqLzjd0GbU5wdr2Hy8Z46nt9dw7fHAnOzz+w+Vn4y2MG4nhapciXaPXmtJs6KYWDd9Q32VUsIy9wcsLaWpjUM97ip1Ey5Aj0pjUggMiAziWAaDNxQ//WRWoP1hzz8f19Xc7A2dCckIhw2EhN7hWx+IYLl//2//4fH4+Gf//wnY8eOJSMjg6FDh+J0Ojl8+DCZmZls27aNqKgof4eEnj178uKLL7Jw4UImTZrEjBkzSE9Px+12U1JSwoYNGxg3bhyLFy9u1ZpUVeXpp5/m6quvZu7cucybN4+EhATWrFnDwYMHGTp0KDt2ND7ne9ttt7FlyxbefvttvvrqKy666CISEhI4dOgQubm5bN26lRdffFFCEg2qqqq46KKLmDNnDtOmTWP8+PHExsZy+PBhNm7cyMqVK5k1axYVFRW8/fbbjZ57/fXXt3ge0zS59957eeedd7jmmmv43//930aPN/RW+X4Sp0FlZWWjcQ1fn6qKw6nGt2SOhgoRrVnTqXTkUjOi45LfOwFA3xTom4J36uV4ASrK0HIz0XIy0XIzUYtyAtYH8WwpXg/WvCyseVnw+X8xFQUjMRU9fTjGwBHo6cNP2UP5dEzTJOu4l8UFLhYV1LC3Onjfb6xdZW4/B1enOjg/NkxOAp4jOa6dgaGjZW3Fsm4plm/WoXjqzvycc2SG2fCOuRBvxmz0oaNB1bACwasJ03bsQPcIOP972Va3bpJf4SW7zMPuMi/ZZb6v8yq8ASk5HWy1OmSV6WSVNT42WlVIi7IwMMbKwBgLA6N9X6dFW7BJ6Yk2I8c1Ibqw5P6Q3J+6S6+lrs7te43+3Wa0zM1o+4pCvbqAsW1bj35oL0ZSWqiXIk5S7TF4I9fFP3ZUUVzV9u+XwlS4Ls3J3UMjGBDTEV8piq5KXquJFnFVYV33OdZlH6AePH3V7nNh9IzHM30unsmXQER0h33vLc6srx2ujYGlpTrvFdaEbB0J4VY5DoouQdM0/vznP3Pdddfx8ssvs2HDBr7++mvq6uro1q0bQ4YM4Y9//CPXXXddo3Ycs2fPZs2aNTzzzDOsWrWKlStX4nQ66d27NzfccAPXXnvtOa1rypQpfPjhhzz66KN8+OGH2O12LrroIl555RXuuOOOJuMVReH5559n1qxZvPrqqyxdupTq6mpiY2NJTU3lkUceYcqUKee0pvaqVSGJn/zkJyiKgmmaLF++nOXLlzcZs3TpUr744gv/f5umiaIoLQ5JGIbBPffcw5tvvsmCBQt4/vnnUb/Xg75///4AFBQUNLuP/Pz8RuMavt68eTMHDx4kLi6uReNPNYdpmhQUFJCQkOAvP9KvXz9UVT2rNQkhRLsXFYM+5kL0MfXVg9w1aAW7UbO/8wUn8rJQ3O2j15ximmgl+Wgl+bD8AwCM2AT09OHoA4ajDxyBmZAEpwgeFFV6WVRQw6ICF7vLgtf/OcqqcHmygwWpDiYn2LDIndgiwNS9BVjWf4Flw5eoZcEpUqwPPA/PpNl4z78IHOFnfkIHZtMUhnSzMqRb49NPHsOkqNLbKDixu8xLbrmH2vaRPTsnHsPXg3zX946fmgL9Ii0MjLEwKMYXnBgQbWFAjAWnpX2UBBVCiA4nzIY+dCz60LFw/U9Qjh5Cy9yMJXMz2s6vUVzVoV5hm7Iu/xD3bb8I9TIEcLhG51+7qnlpdxXHA1BlLzpM4YeDwvnR4AjinIFp2yGEEKGi7i3AuuwDLBu+COi5NO/w8/HMmIc+4gJQ5VjalQzvbg1pSGJEd4nhiK5l5MiRPPPMM2f1nPT0dJ599tkzjktOTqasrOyUj5/qsYkTJ/Lpp5822f7JJ5+ccl/z5s1j3rx5Z1zTgw8+yIMPPnjGcR1Bq0ISzz33XFuvo5GTAxJXXXUV//rXv9C0pn/I+/fvT0JCAl999RXV1dWN+qTU1tayYcMGEhISSE1N9W/PyMhg8+bNrFixoklgY8WKFf4xDSZNmuR/7Gc/+1mj8V9//TXl5eXMnDnTv81utzNmzBi2bNlCSUkJSUlJjZ6zcuVKbDYbY8aMOdsfixBCtB82B/rgUeiDR+EB0L2oJfloOb7QhJqTiVpxPNSr9FMP70c9vB/rBl94z4yI8gUm0n2hidLYVN7f42VxoYuthz1BW5dNg9l97SxIdTKrrx27RYIRIsAqyrBuWo5l3VK04pygTGnE9saTMQtvxizMXtJKzKoqpEdbSY+2csVJVep0w2RPtc7uMg/ZZQ0hCg85ZV6qvB2/b4duQl6Fl7wKL5+UnNiuAEkRmj84MfCkAEVUmIQnhBDibJg9euGdcjneKZf7Xp/n7/IFJjI3oxVmh3p558yy4UvcN9wFNrkzMVTyyj38PauKt/NduAMQ7kyM0PjJkAhuHuAkwiqvA4QQnYjuRftmPdZl72PZvT1g05iOcDwXXoxn+lzMeGn70lWN6hnakMLIEM8vhBAt1aqQxA033HDmQa1kGAZ33303b731FnPnzuXf//53swEJ8JUAufnmm/nLX/7CX/7yFx5++GH/Y3/7298oKyvjRz/6UaMy5TfeeCPPPvssTz75JJdeeinR0dEA7Nq1i//85z/069ePyZMn+8enpaUxceJE1q5dyxdffMGsWbMA8Hg8PProowDccsstjdZ16623smXLFh5++GFefPFF//xvvPEG2dnZXHfddS1qtyGEEB2GZsHoNxCj30A8s68G00Q5uM9XZaIhOBHA0oFnS6mqwPLNeizfrAegtxrG2Kg03NED6RY9gI1R6VRZHAGZW1NgSm8b8/s5uCzZQbRcBBSB5vWgbd+Edf3naN9uCkqrHNMRjnfcFDwZszEGDD9l5RZxgqYqpERaSIm0cPFJ57JM02RftU52+YnghC9E4aG8ruOHJ0yguEqnuEpn6V53o8f6OLX60ISFQTFWBtR/7maT42a1xyBcLh4JIU5Hs2AMGE7dgOEw/wcoFcfRsrb6AhOZW1Ary0K9wrOm1NWi5WX5KmeIoPrqoJtnsqr4tKSWQLz6GNHdyr3DI5ib4pCKekKITkWpOI5l1cdYV36EeuxwwObR+6TgmTEP78SZYHcGbB7RMZwfG4bTouAKwQ0XTovCuF5hQZ9XCCFaQykrK2tXZ1cfe+wxHn/8cSIiIrjjjjuaDUhcdtlljBgxAoDq6mouvvhiMjMzmTp1KiNHjiQrK4svv/yS4cOH8/nnnzeqMAHw17/+lUcffZS+ffsyZ84cXC4XixcvpqamhsWLFzcKSQDs3r2bWbNmUVtby9y5c0lISGDZsmXs2LGDW265pUkZFV3Xueaaa1i+fDljx45l0qRJFBUV8dFHH9G7d2+WL1/epNWHEIEWceuUFo2renVVQNchui6l7ChqbtaJ0ERxHopphHpZzdJR2B6RzLroQayPHsD66IEctMWc0z4v6BXGglQHc1McxDqkzGFbkOPaaZgmamE2lnWfY920AqW6IvBTKir6sDF4My7GO2YShNkCPmdXZpomh2qME8GJcq+/CsWR2vZ5bG0rvRwqA6N9gYmBMRYGxFgZFGMh1q42Ckd3JJnHPLy4q4qX/n3msoYAUTPe4upUBz8cHMFwKWUqhDhbhoFakudrzfHdZtS8LBSjY/ztcF/zIzyXBe7GHXGCYZp8WlLLs1lVfHWoLiBzTO9j495hEUxOsHXYv+Gia5H3oKKl1PydvqoRm1eheANTsdRUVfTRk3wtNQaNlJsTRCP3rT/OqzmuM47zrrqxRfuzTHmzReMWDnDyVEa3Fo0VQohQa3chiTvvvJO33377tGOee+45brzxxMG7vLycxx9/nI8++oiDBw8SFxfHlVdeyf333++vFPF977zzDs8//zy7d+/GarUybtw4fvOb3zB69Ohmx+fl5fHII4+wdu1aqqurSU1NZeHChdx+++2oatO7uNxuN0899RT//e9/2bt3LzExMcyaNYuHHnqI+Pj4s/iJCNE25I2caHdqXGj5O+vbc3yHlr8Tpc595ueFSK4jrlFoItcRf8Y3oEO6Wbg61clV/RwkR7aqeJM4DTmuNaUcO4xl45dY1y1FLS0Oypx6nxS8ky7GO2EGZreeQZlTnN7RWp3sMq+/4kR2uZecMg+lro5xAay1utkUX3AiunHrjt7O9hueOODS+dWmMpYU+3oRt+YE1RXJdp4YH0O89GwXQrSWqwpt5zdYvvO15lCPHQr1ik7JiE2g7pJrMZLTMRL7S+uNAKj1mvwn38Xfs6rIq/C2+f4tCsxPdXDPsEiGSdBPdDDyHlScVp0by+aVWJe9H9A2V0ZkDN4pl+OZeiVmj14Bm0d0bJnHPFz44Zlf07V1SGLtnF4S5BdCdBjtLiQhhAgMeSMn2j2vF7U4p75FRyZabiZKZXmoV3VKB61RrI8eyLrogayPHsj2iGR0VSM5QmNBqoP5qU6GdJM3BYEkx7V67losX6/Fsv4LtB1fB6VCixkRhWfCDLwZszFSBsgdKx1EeZ1BTtmJihPZZR52l3vZUxX4FiyhFGlV/IGJQdG+yhMDYywkRWioIfzdXVzg4hcbyyg7qW1Ka09QxYQpPDkhhvmpUlpXCHGOTBOltBhLfVsOLXs7iicwd8CeK1NRMRMS0ZPTMZLSMFIGoCelQYS0N22NY7U6L+2u5t+7qjkcgKpUkVaFWweEc8eQcPpGSIBcdEzyHlQ0RzlyAOuKj7Cu+SSg57H0/oPxTJ+Hd9wUsEo7A3FmN6846g/kn0pbhiSuSLbz+rQeLdqfEEK0B/KuRAghRPtgsWD0H4LRfwieS671naDdX3IiNJHzHerh/aFepV+cp4KrjmzhqiNbAHBb7FT3G0z4sPMwHCPQnYMBCUmIADEM1JxMrOuX+sp31p65hOK5MjUL+sgJeDJmoZ83Hizy+93RRIepnN8rjPO/1x+0ymOQV+71t+5o+FxUpWN0gjh1pcdk62EPWw83vsjn0BQGxFgYGONr3TEg2sKgGAspkZaA9kM3TZM/b6/k8e2VbbbPsjqTH6w+Tk65lwdGRrbbyhlCiA5AUTD7pODpk4Ln4mvAXYu2+1tfa47MzagH9oR6hX6KaaCUFvuqZ21c5t9u9IzDSEr3hSdS0jGS0n3VruTY2KyiSi//2FHFG7mugPQuT3Cq3DEkgoUDw4kOa1qJVQghOiTTRNv5ja9qxLYNAbtZwbRa8V4wDc/0eRipgwIyh+i8nhgfw9r9BxsF8wMlJkzhifExAZ9HCCHakoQkhBBCtE+Kgtk7GW/vZLxTLvdtOnYYLTcTtSE0sacAxWwfV/Bs3lpsudsgdxsApqZhJA9AHzDc95E+HKJiQrtI0eEpB/dhXf8Flg1fBC00pPcbiDdjNp7x0yAyJihziuCKsKqM7BnGyJ6NwxO1XpO8isbBiewyL/kVXgJwDSXoanSTb496+PaoB6jxbw9TIS3aF5wYGGNhYLTvc/8oC2HauV9ga+uAxMke316JAjwwSu6iFkK0EZsd/bwL0M+7gDrA8fAdaAW7Q72q01KPHEQ9chDLN+v824zIGF+LjuQ0jOR09OQBmL16QzPtU7uK7UfqeCarig+KagISihwcY+HuYRFcnepsk7+fQgjRLtS4sK5finX5BwFtcWl074Vn+hw8ky+Tc0mi1eKdGk9OiOEHq48HfK4nJ0gLSNFxVXsMthyuY9sRD5nHPBxw6XgME6uqEO/UGN7dyqieVs6PDSPc2nXfP3RGEpIQQgjRYZjdY/FeMI0Dwy/ivcIaPss+grNgJ5PKs8koz2ZcRT52s32UA1Z0Ha1gF1rBLvj8HQCMhKT60MQI9AHDMWMT5I42cWauKiybV2FdvxQtJzMoUxoxPfFOnIln0mzMPilBmVO0P3aLwrDu1ib9wj2GSUGFt1FwYneZh7wKL+5O0LmjzoCdx73sPN64D7umQP8oi791x8Bo39fp0VYclpYdyxcVuAIWkGjw5+2VpEdbpPWGECIgjNiEdh+SaI5aWYaatQWytvi3mXYnRlL/+nYd9VUneqeApfOeKjNNk2X73DyTWcnaA3UBmWNSfBj3DotkZl+bVDYSQnQaSmkx1uUfYF23NKCVHL1DRuOZMQ995ATQOu/fIxE881Od5JR7A/o+9IGRkfL+U3RImcc8vLirincLak5bUe29Qt+NNU6LwtWpDn44OILh3aXCbmfQqr+069evJykpicTExFOO2bdvH0VFRWRkZLR6cUIIIUSDMrfBR8U1LCqoYe1+N76XLVbocR5Le5wHQJjhYUxloT80MbE8h+7e6lAuuxF1fwnq/hKsqz8BfBei9QHDMeqrTRiJqaBK6loAho6WtRXLuqVYvlmH4gnMSeyTmWE2vGMuxJsxG33oaPldFKdkVRVfSCDGCjj823XDpLhKZ/dJwYnsMi855d6AlO8ONt2EnHLf93NyX1cFSInUGBhjZVCMpb5th5UBMRYiTrrD4IBL55cby4Ky1l9sLCMj3iZ38ggh2pyRlAZfrQz1MtqEUuvyt/ZrYFqsGH36nag4kTLA9xrd5jjNntq/Ot1kUYGLv2dVsbPMe+YnnCVVgTnJDu4dHsGo71WmEkKIDsvQ0bZvxLrsfSw7vg7YNKbdgSdjNp7pc+UmBREQD4yMRMEXqA/Evu8fGdnm+xUikA64dH61qazRuZ2WcHlNXs1x8WqOiyuS7TwxXiqodHStCklcccUV3H///dx///2nHPPuu+/yhz/8gWPHjrV6cUIIIbo2l9fg85Ja3i2oYdm+WjxnaPFYp1rZGD2AjdEDeIIrUEyDIdX7yCjP9gcnkt1Hg7P4FlDLjqBuXgmbfSeaTUc4etpQf4sOI3UwhNlCvEoRTOreQizrl2LZ8CVqWXB+V/WB5+HJmIV33BRwhAdlTtE5aapCapSF1CgLlyad2G6YJnur9UbBiYYKFBWejh+eMIHCSp3CSp3P9zR+rG+4xqD6yhPrDriD0gsWoKzO5Febynh9Wo+gzCeE6DqMfgNDvYSAUrwetOIctOIc/zZTUTDjE30VJ+o/9OR0iGj/rY3K6wxeya7mnzur2O86w5upVnBaFG5Md3LX0AhSIuWOZyFEJ1FZhnXNp1hXfIh65GDApjESEvFMn4dn0mx5Ly4CSlEUHhgVRXq0hV9sLGuT96UxYQpPToiRChKiw1lc4GqTfwdLimtZu/+g/Dvo4Fr1DsZsQf93wzCkrJ4QQoiz5jFMVuxzs7jAxScltVSfw93HpqKyIyKRHRGJ/LvPDAASa4+QUZ5NRnkOk8p3M7x6b1st/ZwpNdVYMjdjydwMgKlZMPoN9Lfn0NOHdYiTseIsVZZh3bQCy7rP0Ypyzjy+DRixvX3BiIxZvn7cQgSQqigkRVhIirAws6/dv900TQ7UGGSXefytO3yfvRxzt/2FnFDYW62zt1pn2T530OdeUlxL5jGPlIAUQrQpPW0oZpgdpe7s7rrqyBTTRKmvCMem5f7tRo84jOQ09KR0jJQBGMlpmN1i20U7vX3VOv/cWcUr2dVUBiCQGGtX+dHgcH4wKJzudrl7TgjROaiF2b6qEV8tR/EEppWrqajooyb4WmoMGdMu/maIrmN+qpOMeFur7qA/mdxBLzoi0zT58/bKNm09U1Zn8oPVx8kp9/oqtsgxvcMJWMw7Pz+fqCi5kCOEEOLMDNNk48E6FhfU8EFRTUAvju2x9+Q/9p78J87XDuo8Ww33hBUz25VDr+IdqIW7UfS2L0HbGoruRcvbgZa3Az59GwC9TwpGQ2hi4AjMHnEhXqVoFa8HbfsmrOs/R/t2E4quB3xK0+7EO24KnkkXYwwYLidjRMgpikKCUyPBqTHle1mdI7V6k+BETpmHAzWdIzwRLC/tquKpjG6hXoYQojOxOfBOmIF19cehXknIqUcPoh49iOWb9f5tZmS0LzSRnO4LUKQMwOzVB1T1NHtqO1nHPDybVcnighoC0ekqLcrC3cMiuLa/E4dFXksKIToBTx2WLauxLnsfLX9nwKYxw6PwTLkMz9QrMWMTAjaPEGcS79R4fVoPMo95eGlXFaxq2fOcFoVrUh38YHCEBPFFh9TWAYmTPb69EgV4YJRcE+9oWhySuOuuuxr99yeffEJJSUmTcbquU1payoYNG5g+ffq5r1AIIUSnZJom3x3zsKighvcKatjnCvxF4gZxDpV5/RxcnepkdE8ripIGTKcGoM6NWrAbLTcTLfs7tLwdKDXVQVvbmWj7itD2FWFd+REARvde9e05RmAMGI7RJyVoJ2HFWTJN1KJsLOuWYt20HKWqIvBTKir6sDF4My7GOzoDbPYzP0mIdqCnXWNSvMak+MYth8rcvsoT2eUnt+7wsrc6eH9DAsky5c023d87BTX86YJonBb5uyCEaDueGXMlJHEKSmU5lh1bYcdW/zbT7sBI7N+oXYfRJwUsbXOBwTRN1ux380xWFcsDVLnogl5h3DMsgkuT7KgStBVCdALKsUNYVy7Bsupj1IrjAZtHTx6AZ+Y8vBdMk3aqol0Z3t3qC9T/u2Xj866Pl/eVosNaVOAKWECiwZ+3V5IebZHWGx1Mi0MSb731lv9rRVHIzMwkMzOz2bGKojB69Ggee+yxc1+hEEKITiW/3MuiQheLCmrILQ9exYaoMIU5yQ4WpDqYFG9DU09xci/MhjHoPIxB5+G5AjB01L2FaNnfoeZmomVnopYdCdq6z0Q9dgh103Ks9aV/TWcEevow9IEj0NOH+/pGW8NCvMquTTl2GMvGL7Gs+wKttCgoc+q9U/BOmo134kzMbj2DMqcQwRBjU7kgzsYFcY1PMFZ6DHLLTgQndpf7Kk8UVeoE4EbaDsPlNdlwoI4ZfSUgJYRoO0ZSGt6xk7FsXRO0Ob2DR+EdPx21JA+tOBe1JL/DtPxQamvQcrPQcrP820zNgtG3ny8wkeSrOGEkpoK95SdVvYbJB0U1PJtVxbdH274svAJcmmTn3mERTf7uCiFEh2SaqNnfErbsfbSv16IYgalSZ2oWXxXHGfMw+g+RKo6iU5CAhOioDrh0frmxLChz/WJjGRnxNmlF00Jr167liiuu4P777+fBBx8MyRpaHJL49ttvAV9CfeTIkdx5553ccccdTcZpmkZMTAzh4eFtt0ohhBAdWmm1znuFLhYX1rDtSGD6OjbHrsElib5gxIy+dmxaK96YqhpGUhpGUhrMvApME+XIAV+ViZxMtNxM1NLitl98KymuKizfbsLy7SYATKsVo99gf7UJPW0IhEeGeJVdgLsWy9drsaz/Am3H1yhm4FsEmBFReCbMwJsxGyNlgJyIEV1KpFVldGwYo2Mbh8JqvCa55ScqTuyur0JRUOFF7yLpiau/PEpShEZKpIXkSN/nlAiN5EgLKZEa3W2q9M0UQpw19833oe3ahlId2DuyAMzwSNx3PIQZ0+PERkNHObDXF5io/9CK81CqA1+pqy0ouhetOBetONe/zVQUzPi+J9p1pKSjJ6dDRHSj51Z5DF7PcfGPnVXsqWr7Sko2Da7v7+SuYRGkR0s5bSFEJ+CuwbLhS19Ljb2FAZvG6NYTz9Qr8U65HDO6e8DmEUII0XK/2lRGWV1wTgCV1Zn8alMZr0/rcebBrVBcXMx55513+jWUlQVk7s6qxSGJpKQk/9fPPfccw4cPb7RNCCGEONlxt8FHRTUsKnCx7kBd0O7k1RSY1tvGgv5OLk2yE2lt46SzomDGJuCNTcA7abZvW0UZWl6WLziRm4lalIOit4/S74rHg5bzHVrOd8CbmIqCkZjqqzJRX23C7B4b6mV2DoaBmpOJdf1SLJtXodS6Aj6lqVnQzxuPZ9Js9PPGt1nZZiE6C4dFYUSPMEb0aByeqNNN8itOCk6Uecku95BX7qUu8JmmoDKB4iqd4iod9jd9PNKq+EMUKZEWkv1fayRFWLBLz3khRDPMmB64b/kp9ucfCfhc7lt+2jggAaBqmL2T8fZOhgkz6hdlohw7hFqUg1ac5wtPlOSiHjsc8DW2BcU0UfbvQd2/B75a4d9udO+FkZxORe/+fKT05W8VCexUurV5IDYmTOGHgyP40eBwejnk7jchRMenHNiLdfkHWNd9huIKXBtVfdB51M2Yhz5qElhafLlFCCFEgH13tI4lxcGtPrekuJbMYx6Gdw/cOdp+/fpxzTXXBGz/XUmr/mrfcMMNbb0OIYQQnUC1x+DzPbW8W1DD8n21eIJ4oWlCXBgLUh3MSXHQ0x7kk3pRMeijJ6GPnuT7b3cNWsFu1IZqE/k7UGprgrumU1BME60kH60kH5Z/AIARm4CePtxXbWLgCMyEJKlCcBaUg/uwbvgCy/ovUA83cwUyAPR+A/FmzMYzfhpExgRlTiE6kzBNYXA3K4O7WQGHf7vXMCmq9LK7vvJEdpmH3WVecsu91HTS0hOVHpMdx73sON58C6wEp+oPTyTXBylSIjWSIyzEO1XpTS9EF+YdP526/SWEffBqwOZwz12Id/z0lg1WFMweceg94tDHXHhie0VZfYuOkypOHNyLYnaM47p67BDqsUN037aehcBC4Iglgu2RKWyPSOabiH5sj0wm1xGPqZx9QDwpQuOuoRHclO4kvK0D5kIIEWyGgfbdV1iXvY8lc3PApjHD7HgzZuKZPs/XLkkIIUS789LuwAXkTjvvriqeyugWsP2npqaGrD1FZ9OikMT69esBGDNmDHa73f/fLZGRkdG6lQkhhOgQ6nSTFaW1LC6o4dOSWqq9wTvZOLy7latTHczr5yAxoh2l9W0O9MGj0AePwgOge1FL8tFyM9Gyv0PNzUQtPx7qVfqph/ejHt6PdcMXgK9tgz5guC84MXAERnJ6x65Q4K4Bm+PM486GqwrL5lVY1y9Fy8ls232fghHTE+/EmXgzZmH07ReUOYXoaiyqQlq0lbRoK5cnn9humCYlVXqj4ER2mYecci+Vno5xka219rsM9rvq2Hiw6WM2DZIjToQmGtp5JNeHKqLC5GKbEJ1d3dyFmCjYPnilzfftnrsQz9xbz31HUTHow89HH37+iW01LtQ9eScqThTnou4rQtGbD4y1Nz29Vcw4nsWM41n+bVWqje8iktgWkcL2yBS2RaSwI7wvHrX590kje1i5d1gEV6Y4sKgSeBNCdHBVFVjXfoZ1+Yeoh0sDNo0R1wfP9Ll4Jl0srUyFEKIdq/YYvFsQmpsW3ymo4U8XROO0hO6cSFZWFn/7299Yv349x44dIy4ujksuuYQHH3yQ7t1PtIRqaOFx/fXX89Of/pT/7//7/9i4cSOKojBjxgz+8pe/0KNHD7Zu3cqjjz7K119/jaqqzJkzh8cee4zw8HD/vurq6vi///s/li5dSnZ2NocPHyYqKorx48fzq1/96oytQk52+PBh/va3v/H555+zb98+IiIiyMjI4MEHH2TIkCFt9nNq0RWlyy+/HEVR2Lx5M2lpaf7/boljx46d0wKFEEK0P4ZpsuFgHYvyXXxYXMNxd/AuEKVGasxPdbIg1cHAmA5y4V6zYPQbiNFvIJ5ZC3ylgA/u81WZaAhOHNwb6lX6KVUVWL5Zj+UbXyjSDLOhpw72t+fQ04aCwxmy9akleViXfdDi8eF3z8M7YQaeGXMxktJaP7Gho+34Gsu6pVi+XoviqWv9vlrIDLPhHXMh3oxZ6EPHgCqlj4UIBVVR/O0oZifa/dtN06TUZTQKTjS08AhWz8tQcuuQU+4lp9wLuJs83sOm+oMTDUGKlEhfRYq+4ZpclBOiM1AUPPMWYiYkYnvtKZTqynPepRkeifuWn7a8gkRrOJwYA0ZgDBhxYpunDrW0GLUox19xQt2Th+IObone1oow3EysyGViRa5/W52isSO8L9sjfFUntkWm0CM9ndtH9+LC+LAWn9sTQoj2ynd+4H0sG5eh1DV9PdoWTEVBH3EBnhnz0IedD6oEgYUQor3bcrgOVxBv5jyZy2uy+VAdU3rbzzw4AD799FNuu+02NE3jkksuoU+fPmRnZ/PCCy+wYsUKli9fTkxMTKPnFBcXM2vWLEaNGsUtt9zCtm3bWLx4Mfv27eP//b//x7x585gyZQq33nor69at47XXXgPgmWee8e/j+PHjPPjgg0yYMIGZM2cSExNDUVERn332GcuWLePTTz9l9OjRZ1x/YWEhl19+OaWlpUybNo3LLruMw4cPs2TJElasWMGHH37I2LFj2+Rn1aKQxK9//WsURaFHjx6N/lsIIUTXYZom3x71sKighvcKXZS6gtdLI96hclWqgwX9nIzqae34f4MUBTO+L974vngnX+LbVH4MNSfTF5zI+Q61OA/FDGK/ktNQ6txYdm+H3dsBMBUVIykNfaCvRYeRPrxpn+hArKPsKLbXn8aydc3ZPa+uFuvqj7Gu/hjv2Mm4b77vrNar7i3Esn4plg1fopYdPdtlt4o+YASeSbPxjpsCjvAzjhdChIaiKPQJ1+gTrjGtz4ntpmlyuNZgd5mX320p59ujntAtMoSOug2Oug2+OdL0+9cU6Bt+UoAi0kJKREMlCo3uNrXj/70Xogvxjp+OPmhkq16rNdpPK16rtRlrGEZyuq+KWgND94Wbi05u15GLUlUR/PW1QpipM6qqmFFVxf5t5nYFc01f9OS0+u93AHpymrRwE0J0HF4vlq/XYF32fkArO5rOCDyTL8UzbQ5mXJ8zP0EIIUS7sa2Z8xDBtP2IJ2AhiYKCAh577LEm22fMmEH//v2544476NmzJ59//jmJiYn+xxctWsQPf/hD/vjHP/LEE080eu6GDRt47LHHuPPOOwHfea1rr72WL774gmuvvZYXX3yRyy67DACPx8OUKVN4++23eeihh+jVqxcAMTExZGVl0bt370b73rVrFzNnzuQPf/gDH3zwwRm/vzvuuIODBw/y3nvvMXXqVP/2X/3qV0ydOpV7772XDRs2tOyHdQYtCkl8v7eJ9DoRQoiuI6/cF4xYVFBDXkXwys9GhynMSXGwINVJRlwYWie/29SM7o5+/kXo51/k21DjQsvfiZaTiZrzHVr+zoDdFXG2FNNAK85BK86BLxYDvpKT+oARvjYdA4ZjxvWFNry4Zdm0vE3uTrRsXYO2a9uZ706sLMO6aQWWdZ+jFeWc05wtZcT2xpMxC2/GLMxevc/8BCFEu6UoCr0cGr0cGnNTHF02JHE6ugnFVTrFVTqr9zd9PNKq+Nt2NAQpUur/OynCgt3SuV8XCNERmTE9qL3nD767epd/iHXVkpY9L8yOd+JMPNPnnFvVr0BQNcyEJLwJSTCh/rWjaaIcO4xanINWnIva0LLj2KHQrrWFFNNEObAH9cAe+Gqlf7vRPRYjyRcS0VN8n83uvdr0Nb0QQpwLpewolpVLsK5aEtAbGPTE/nhm+CpSYgvNXcBCCCHOTeax0J6H+S6A8xcWFvL444832R4dHc3mzZupqKjgiSeeaBSQAFiwYAHPPvss7733XpOQREpKCj/+8Y/9/60oCldddRVffPEFI0aM8AckAKxWK3PmzOFPf/oT2dnZ/pCEzWZrEpAAGDx4MJMmTWLFihV4PB6s1lNXB//222/56quvuPnmmxsFJADS0tK45ZZbeO6559i5c2ebtN1oRw3chRBCtBf7qnXeK3SxuKCG7UG8sOPQFC5JsrMg1cH0PnZsWhc+Iedwog8biz6svnSU1+u7cy3nO3+bDqWyPLRrPIl6cB/qwX1Y134GgBHVDaM+MKEPGO474a214mWHaRL2wSuEffBqm61Vqa7E/vwj1O0voW7uwhMnfr0etO2bsK5fivbtpqD0pDbtTrzjpuCZdDHGgOFyElqITmhUzw7SGqqdqfSYZB3zkHWKEwu9nWqjEEXySUGKOIeKKsdTIULGSErDfdsvWhySqP77Bx3rIpSiYPbohd6jF/roSSe2V5b5WnQU11ecKMlFObAXxewY7ZfUY4dRjx2G7SfuyjLDo5pUnDDj+0oLOCFE8Jgmam6Wr6XG1tUouh6YaTQN79jJeGbMw0iX9+ZCCNHRHXAF5u9FSx2sCdz806dPZ/Hixc0+dttttwGwdetWCgoKmjzudrs5evQoR48e9XePABg2bBjq99pJxcfHAzB8+PAm+2l4bP/+xne9fPfddzzzzDNs2rSJgwcP4vE0Pqdz9OhR/3Obs3XrVgAOHTrUbLWM3Nxc/2cJSQghhGgzx2p1Piqu5d0CFxsO1BGsU3kWBab3sTE/1cmlSXYirNLbsVkWC0b/wRj9B+O55FrfHWz7S+rbc/g+1MOloV6ln1pxHHXrGn+5ZdNmR08bip4+HGPgCPT+g8HmOON+2jog0Xjfr2KioI8cj2XdUqyblgeldLKpqOjDxuDNmI139KSOdVFACHHWzo8Nw2lRQtYLs7MqdRmUuurYeLDpY3YNkiJOtPE4UY3C18ojUl5rCNG+dJbXQpExjUPOALUu1JJ8X8WJkjxq83Ow7y/CagSvQt+5UKorsOz8BnZ+499mhtkxkvqj17cmMZLTMfqkgDUsdAsVQnQ+7losm5b7WmqU5AVsGiO6O96pV+CZcgVmt54Bm0cIIURweYzQnoOp00Mz//HjxwF44YUXTjuuurq6UUgiMjKyyRhN08742MkhiK+++oorr7wSgKlTpzJnzhzCw8NRFIVPPvmErKws3O7TV8puWP/SpUtZunTpadffFlodkli1ahXPPfcc33zzDeXl5RhG077piqJw9GhwencLIYQ4e1Ueg89KallUWMPyvbUE8/rNxLgwrk51cmWKnR52uRPprCkKZu9kvL2T8U653Lfp2GG03Cxfe46cTNQ9+e3mzjXFXYtlx9dYdnwN+O7SMJIH+CtN6OnDISqm0XMsG5cHLCDRwPbBK/DBKwGdo4HeOwXvpNl4J8zA7B4blDmFEKEXblW5OtXBqzmuoM9tUQjq3/b2olaHnHIvOeVeoOkb8B421R+gONHGwxeg6BuuYenkLb6EEEFkd+JNH8ZnjnSe9lSyQa3D2t/L0Oq9jKwqYmRVMaMqizivqpgIo3201jsTpa4WLW8HWt4O/zZT0zD6pPjadaT4Kk4YiWngcIZwpUKIjkg5VIp1xYdY13x6zu02T0dPH+ZrqTF2Mlik8psQQnQ21hC/rw8LUYXshkDDhg0b2qTSwtl48skncbvdfP7554wfP77RY1u3biUrK+uM+2hY/1/+8hd+9KMfBWSdJ2tVSOLDDz/kf/7nfzAMg8TERAYMGOBPjQghhGjf6nST5ftqWVRQw2d7aoN6Z+t5Paws6OdgXj8HfSOkmFFbM7vH4r1gKlxQ36/LVeU7gdlQaaJgJ4ontP3YGii6jlawC61gF3z+DgBGQlJ9aGIERnxfbK8/FdpFtgEzIgrP+Ol4J83GSBkoJTuF6KJ+ODgiJCGJFVfEEu/UKK7UKar0UlTppbiq4WudUpdOiG+uCImjboOjboOvjzT9m6gpkBihkRxxcoCioRKFRjebiiLHciFEC7h1k3cLXPw9q4rdZScqR3hUC9sjU9gemeLfppgG6TUHGFlZzKiqIn+AItYTuAuEbUnRdbSSfLSSfFj3OQCmomD26uOrOJGSjpGUjp6c3iQYLYQQGAbajq2+qhHfbgrYzR6mNQzvhBm+lhrJ6QGZQwghRPsQ7wztNes4R2jmHzt2LEuWLGHLli1BD0kUFhbSrVu3JgEJl8vFt99+26J9jB3rq8q3ZcuW9huS+Mtf/oLD4eDNN9/koosuaus1CSGEaGO6YbLhYB2LClx8WFRDWV3wroj0j9JYkOpkQaqD9GhJ5weVMwJ9xAXoIy7w/benDrUopz408R1ablZA78w4W+r+EtT9JVhXfxLqpZwTU9PQz5uAZ9Js9PPGy10pQgiGd7dyRbKdJcW1QZvzimQ7I3r4Sp/3cmic36tpGfQ63WRv9YnQRHGll6KqE18H8/VCe6GbUFSpU1Sps3p/08cjrYqvAkV9cCI58kSAIjHcgt0iAQohuroyt8H/ZVfzr51VHKhpWnW1OaaikuPsTY6zN+/ETajfaNLXfYyRVUW+4ERlMSOrikh2d4yKrYppohzci3pwL2xe6d9udOtZ36ajvuJEcjpmjzgJEwvRFbmqsK77HOuyD3zHigAxYhPwTJuDZ/KlEBEVsHmEEEK0H8O7W3mvsCZk84/oHprzwTfeeCN//etfeeSRRxg3bhyDBw9u9LjL5WLHjh2cf/75bT53YmIieXl57Nq1yz+vruv87ne/48iRIy3ax5gxYxg7diyLFi3ikksu4aqrrmr0uGEYbNiwgUmTJrXJmlsVksjLy+Paa6+VgIQQQrRjpmmy/aiHRQU1vFfoYr+rZSfo2kKCU+Wqfk6uTnVwXg+r3HHZXljDMNKHYaQPw3PZ9WAYqKVFqPWVJrScTNSjzTR3Fy2ipwzAO+liPOOnQWRMqJcjhGhnnhgfw9r9B4MSPIgJU3hifMwZx4VpCqlRFlKjmn9bWOY2/NUnihuCFFW+ihQlVTqe4L20aDcqPSZZxzxkHWu+MlNvp0pyo+oTFn9rjziHiiqviYTotPZUeXl+ZxWvZbuoaotqfYrCXnsP9tp78HHPMfRyqPxocAS39/XS/UA+anEeanEuWnEuyoE97abN3pmox4+gHj8C2zf6t5nhUf7AhJHsqzhhxvcFVarWCtEZqXsLsC77AMuGL1DcgQsRe4efj2fGPN+NI3I8EUKILmVUz9DetDYyRPP37NmTF198kYULFzJp0iRmzJhBeno6brebkpISNmzYwLhx41i8eHGbz/2jH/2IFStWcPHFFzNv3jxsNhvr1q1j//79TJo0iXXr1rVoPy+++CJXXHEF//M//8Pzzz/PyJEjsdls7N27ly1btnDkyBEOHmybaxitCkn06NEDh8PRJgsQQgjRtnLKPCwqrGFxgYv8Cj1o88aEKcxJcbAg1cnEuDA06efd/qkqRt9UjL6peKfNAUA5evBEYCLnO7S9hSFeZPtmxPTEO3Em3oxZGH37hXo5Qoh2LN6p8eSEGH6w+njA53pyQkyblJaMsamMtIUxsmfTx3TDpNSlN2rfUVL/uajKy6EW3j3d2ZS6DEpddWxs5v26XYPkCF/1ieT6AEXySRUpIq1q8BcshDhnmcc8PJtZyXuFNQSik2F6tIV7hkVwTarTX61G7zEWfejYE4NqXah7CtCKc1GLc30Bin2FKN720WrvTJTqCiw7v4Gd3/i3mWF2jMRUf2jCSE73vd62Nq2MJIToAHQv2jfrsS57H8vu7QGbxnSE47nwYjzT52LGJwZsHiGEEO3b+bFhOC1KUFuNN3BaFMY1U80zWGbPns2aNWt45plnWLVqFStXrsTpdNK7d29uuOEGrr322oDMe/HFF/Pqq6/yt7/9jXfeeQeHw8HkyZN58803efzxx1u8n5SUFNauXcvf//53Pv30U9544w00TSMuLo6JEydy5ZVXttmalbKysrP+DfnNb37DihUrWLduHRaL9JQXoiOIuHVKi8ZVvboqoOsQgbG3ysv7hTW8W1DDd6e4szEQnBaFS5PszO/nYHofO2GaBCM6naoKtLysE8GJwuwOc7I1UExrGN4xF+KdNBt96Bi5I0UIcVYe21bB49sD1+rogZGRPDAq9GWEqz0GJfUBipODFMX1/x2KExXtXQ+bSspJ7Tt8FSl8X/cJ17BIAFV0MJ35PahpmqwqdfNMVhUrS90BmWNCXBj3DIvg4kR766rQeD2o+4p8gYkSX8UJtSQPpTZ0ZYfPlalpGL1TMJLT6tt1pGMk9QdHeKiXJrqIznxcCxSl4jiWVR9jXfkR6rHDAZtH75OCZ8Y8vBNngt0ZsHmE6GzkuCY6s/vWH+fVHFfQ5104wMlTGd2CPq84e60KSbhcLq666ipiY2P505/+RGKipDKFaO/kBU/nc7RW58OiWhYVuNhwsC5o81oUmN7XztWpDi5JtBMudz12LXVu1MJstJzvfMGJ3CyUmupQryoo9AEj8EyajXfcFDkRK4RoNdM0eXx7JX8OQFDigZGR3D8yst23uTJNk8O19a08KusDFCe19NhXrSMRisYsCvRtqDoRcVKQoj5E0c2mtvv/76Lr6YzvQT2GyfuFNTybVUVmAMLpCnB5sp17h0VyfiDuPjMMlEP7GlecKM5FrSxr+7mCyIjrg56UjpGS7m/ZYUbJiWnR9jrjcS0gTBO1YJevasTmVQG70cJUVfTRk3wtNQaNBHktJMRZk+Oa6Mwyj3m48MNDQZ937ZxeDO8e2nYfomVaVQZiwoQJeL1eNm/ezCeffEJ0dDRRUU3vVlIUhe3bt5/rGoUQQtSr8hh8WlLL4gIXy/e5A1LOtTkKkBEfxoJUJ1cm2+lulzvnu6wwG8bAERgDR+ABMHTUvYVo2d+h5maiZWeilh0J9SrbjBGbgDdjFp6M2Zi9eod6OUKITkBRFB4YFUV6tIVfbCyjrO7c/5jHhCk8OSGG+akd4645RVHo5dDo5dAY16vp43W6yZ4qneKq+vYdld5GX5e3wc+so/Ga1H//zbdSi7Iq9ZUnGleiSInUSAy3+Ev0CyFap9Jj8FqOi+d3VLG3uu1bGto1uCEtnLuGRtA/OoAVW1UVMz4Rb3wiXDDNt800UY4f9gcmfBUnclGPtE2f32BQD+5DPbgPtqzybzNievoqTqQM8AUoktMwe8bLRVQhAqnOjWXzSqzL3kcrzA7YNEZkDN4pl+OZeiVmj2ZeTAohhBDA8O5Wrki2s6S4NmhzXpFsl4BEB9Kqd16GYaBpGn379vVvM82mJ6qa2yaEEOLsuHWT5ftqWVRQw2cltdTowTu2juxhZUGqg6v6OekdLsEI0QxVw0hKw0hKg5lX+U6yHjngqzKR/R1abiZqaXGoV3lWTLsT77gpeCZdjJE+DFSpliKEaHvzU51kxNv41aayc3rDfkWynSfGxxDv7Dx/p8M0hf7RllNeKCxzG/42Hg3VJ3ztPLzsqdbxGEFecDtQ4THJPOZp9s52BUhwqvWhicYVKFIiLcQ5pAqFEKdywKXzr51VvJxdHZCAVnebyg8Hh3P7oHBiHSE6jisKZvde6N17oY+aiP8oUlWO1lBpoiQPrTgXZf8eFLNjHGTVsiO+8Pa3m/zbzPBI9KQ0f7UJPTkdMyFR2ucJcY6UIwewrvgI6+qPUaoqAjaP3n8wnunzfNUdraHr9S6EEKLjeGJ8DGv3H2yTG1TOJCZM4YnxMQGfR7SdVoUkMjMz23odQgghTqIbJusO1LG40MWHRTVBvWMyLcrCglQHC1IdpEVL6lGcJUXBjE3AW1+BAYDKMrTcrBOhiaIcFL3t78BrC0ZCEq6H/w02e6iXIoToAuKdGq9P60HmMQ8v7arilRb2ynRaFK5JdfCDwRFd8g6FGJvKSFsYI3s2fUw3TEpdvooLDdUnihvaelR5OVTTMS7utSUTKHUZlLrq2NhMiza7hj80kdQQpIjwVaJIjtSIlNZqogvKLvPwbFYV7+S7qAvAYSMlUuOuoRHcmO7EaWmn/8YiotGHjkEfOubENncN6p6CExUninNR9xYGrJR+W1OqK7Hs2ga7tvm3mWE2jMRUjOQB6Mm+ihNGn34QZgvhSoXoAEwTbec3vqoR2zYELEBlWq14L5iGZ/o8jNRBAZlDCCFE5xXv1HhyQgw/WH084HM9OaFz3cDSFQSwhp8QQoizYZom2454eLfAxfuFNRwI4kn83k6Vq/o5WZDq4LweVrmbULStyBj00ZPQR0/y/be7Fq1gF2pDtYn8HSi1NaFdYz3TGSEBCSFE0A3vbuWpjG4tDknkXR/ffi+qhZimKiRGWEiMsHAhTS9wVXsMSqoaKk/oTSpSBLNiV3tRq0N2uZfsci/gbvJ4T7vaqPqEr62H7+s+4RoWVV43is7BNE02HKzjmawqlu4JTEne0T2t3DsskiuS7Wgd8d+OzYGRNhQjbSjehm1eL2ppkS8wUZxX364jD6W2ZX/TQk2pc6Pl70LL30VD7NDUNIyEZF/FiZR0f7sOHOEhXasQ7UKNC+v6pViXfxDQqpFG9154ps/BM/kyiIoJ2DxCdHWWKW+2aFxZYJchREDNT3WSU+7l8e2VAZvjgZGRHaYFqjhBQhJCCBFi2WUeFhXUsLjARcEp+kwHQjebwtwUB/NTnUyMC0OVYIQIFpsdffAo9MGjfOV8dS9qST5abiZhH72OUlkeurVZu94d2UKIjkcCEq0XblUZ3E1lcLemx3vTNDlca/gDFMWVXorqAxXFlTr7qnW6XoQCjtQaHKk12Hq46Z3iFgX6RmiNqk80tPFIibQQE6ZI+Fa0e7ph8nFJLc9kVvL1kcBURJjd18Y9wyPJiAvrfP8mLJYT7fcurN9mGCiHSk9UmyjJRS3KRa0sC+VKW0zRdbS9BWh7C2D9Uv92o1fv+jYdA3wVJ5LTMaO7h3ClQgSPUlqMdfkHWNctDWgIyjtkNJ4Z89BHTgBNLl0IIYRoGw+MjEQB/hyAoMQDIyO5f2Rkm+9XBF6LX2ncddddp31cVVWioqIYOnQol19+OVFRUee8OCGE6Kz2VHl5r7CGRQU1zfaODpRwi8KlSXYWpDqZ2ttGmNbJTtCJjkmzYPQbiNFvIGreDqxfrQzZUgw5ySmEEF2Woij0cmj0cmiM69X0cbdusrfK17ajuLKhGoWvEkVhpZeKILZHay+8JvUVOZoP+kZZFX9woqH6REp9G4+kCAs2eS0qQsjlNXgr18VzO6ooDEBY3arCNf2d3DMsgkExXSyIq6qY8X3xxveFC6b6tpkmyvEjvsBEQ8WJ4lzUIwdCu9azoB4qRT1UimXLav82I6aHr+JEcnp9u450zJ7x0NnCMKJrMnS07RuxLnsfy46vAzaNaXfgyZiNZ/pczD4pAZtHCCFE16UoCg+MiiI92sIvNpZRdtL7d6dey4TyPMZUFnBeVTEJdWWEmV7qFAv7w2L4NiKZryNT2Ridhks7UYE4JkzhyQkxUkGiA2txSOKtt95q0ThFUXjwwQd59tlnufLKK1u9MCGE6GyO1up8UOQLRjTXDzpQrCrM6GNnQaqDixPthEtfadGOGUlpEMqQRHJ6yOYWQgjRvtk0hf7RFvpHN/82usxt+EMT/gBFfZiipErH2/UyFFR4TDKPeZoNBStAb6dGcmTjChTJ9ZUp4hxq57vjXrQLR2p1XthVzYu7qjnqbvsWh1FhCv8zMJwfD4kgQXoSn6AomN1j0bvHoo+ciP+oUFWBVpJX367DF6BQ95egmMFrP3ku1LKjqGVH4dtN/m2mMwI9KQ0jZYCvykZyOkZCotwVLzqOyjKsaz7FuuJD1CMHAzaNkZCIZ/o8PJNmSzsbIYQQQTE/1UlGvI1fbSqjeEc2d+77khsObiDcaNp6ssG1h32v86pVG2/FTeT5PjNJHjqQJ8bHEC+v9zu0Fr86X7JkyWkfNwyDY8eO8dVXX/HGG29w++23069fP4YPH37OixRCiI6q0mPwaUkti/JdrCx1B+3kuAJMig/j6v5Orkh20M0mwQjRMRj9BoZ2/pQBIZ1fCCFExxVjUxlpC2Nkz6aP6YZJqUuvr7rgC1IUn1SJ4lBNx7gY2JZMYJ9LZ59LZ0MzAWKHppAUofmqUERafEGKiBOVKCIk+CvOUkGFl+d2VPFmbjW1Aehy2Ddc444h4dw6MJxI+f1suYgo9CGj0YeMPrHNXYu6J/9ExYmSXNS9BSie4FVhPBeKqwrL7u2we7t/m2kNw0js36jihNG3H4TZQrZOIb5PLcz2VY34annA/r2Zioo+aoKvpcaQMVJ1RQghRNAl1JXx7o6nsWxdc1bPCzfc3L5/JbfvX4mXybhH34fp7BGgVYpgaHFIYtKkSS0aN3fuXG666SamTZvG3//+d/71r3+1enFCCNERuXWTL/fWsrighs/31FKjB++2wVE9rSxIdTIvxUHvcEkxio5HTxuKGWZHqasN+txmmB09bVjQ5xVCCNH5aapCYoSFxAgLFyY0vSBW7TH8FSj8rTyqdEoqvRRV6kF9Pdle1Ogm2eVessu9QNO7enra1UbVJ3zVKHxf9wnXsKhy0UX4bD1cxzOZlSwpriUQ/5KGdbdy77AI5vVzYJXfu7Zhs2OkDcVIG4q3YZvXi1paXN+uIxetOA+1JA+lpjqUK20xxVOHVrALrWAXDc1XTFXF6J2MkTwAIznNF55ISgNnREjXKroYTx2WLauxLnsfLX9nwKYxw6PwTLkMz9QrMWMTAjaPEEIIcTqWTcuxvfYUSnXlue1n6xq0Xdtw3/JTvOOnt9HqRLAFpM7b0KFDueSSS9iwYUOrnv/f//6XjRs3sn37dnbu3EldXR3PPfccN954Y7Pjt27dypNPPslXX31FVVUViYmJXHXVVfz85z/H4XA0+5x3332X559/nt27d2O1Whk3bhy/+c1vGDVqVLPj8/PzeeSRR1i7di3V1dWkpqaycOFCfvjDH6KqTe8QcLvdPPXUU7zzzjvs3buXmJgYZs2axUMPPURcXFyrfi5CiPZLN0zWHXDzbkENHxXXBLUndXq0hQWpDhb0c56y/LMQHYbNgXfCDKyrPw761N6JM8FmP/NAIYQQoo2FW1WGdFMZ0s3a5DHTNDlUY1Bc5fVXoiiq1Cmu8gUq9lXrAbnw294dqTU4Umuw9XDTO10tCiRGNG7f0VCBIiXSQkyYIq08OjnDNFm6p5ZnsqoC1upwSm8b9w2LYEpvm/w+BYPFgpHUHyOpP0y62LfNMFAOl56oONHwUXE8tGttIcUw0PYWou0thPVL/duN2N6+0ETKAIykdIzkNMwYuUtRtC3l2CGsK5dgWfVxQP/N6MkD8Mych/eCaVI5RQghROiYJmEfvELYB6+22S6V6krszz9C3f4S6uYulOpIHVDArqalpaXx2Wefteq5jz76KHv27KFHjx7ExcWxZ8+eU4796KOP+J//+R80TePKK6+kV69efPXVVzzxxBOsXbuWDz/8EJut8QuwJ598kkceeYS+ffty2223UV1dzXvvvcfs2bNZvHgxF154YaPxu3fvZtasWdTU1DBv3jwSEhL48ssv+fWvf82OHTt4+umnG403DIMbbriB5cuXM3bsWK644goKCwt58803WblyJcuXL5eghBCdgGmafH3Ew6ICF+8X1nAwiGWS+zg15qc6mJ/qYER3q5yUE52KZ8bckIQkPNPnBH1OIYQQ4kwURSHOqRHn1BjXq+njbt1kb5VOUZW3cSWKSt+2YIZ32wuvCYWVOoWVzfdUiApTSI6wNKpE4QtSaCRGWLBp8tq6o6r1mrxT4OLvWVXklHvP/ISzpCkwv5+Du4dFMKJHWJvvX5wlVcWM64se1xd93BTfNtNEKTvqD0xoJXm+rw/vD+lSz4Z6uBT1cGmjEtBGdHdfi46T2nWYsQlyMl6cHdNEzf6WsGXvo329FsUIzHksU7PgHTcFz4x5GP2HyO+pEEKIkGvrgETjfb+KiYJn3sKA7F8ETsBCErW1tYSFte4N47PPPktqaipJSUn87//+Lw8//HCz42pqavjZz36GoigsXbqUkSNHAr4Ll7/+9a954YUX+Mc//sHPfvYz/3Py8/N57LHHSEtLY/ny5URHRwPw4x//mOnTp3PvvfeyZcsWLJYTP5qf//znVFRU8M477zBr1iwAHnroIRYsWMCrr77K/PnzmTx5sn/8W2+9xfLly5k/fz4vvvii/+LlG2+8wd13383vf/97/vnPf7bqZyOECL3dZR4W5dewqNBF0SlOvAZCd5vK3BRfMGJCXBiqvMkUnZSRlIZ37OSz7gt3LrxjJ/tK2wohhBAdjE1T6B9tOWVFsTK30aj6hP/rSi8lVTrerpehoKLOJPOYh8xjTatQKEBvp+avOtHwOaW+pUecQ5WAcjtU5jZ4aXc1/9pVxaEAhNcjLAq3DHRy55AIEiOkel+7piiY3Xqid+uJPnIC/n/l1ZUnAhMNH6UlKGbwbnY4F2r5MdTvvoLvvvJvM53hGElp6MkD6gMUaRgJSaDJ76j4nloXlo3LfC019hYGbBqjW088U6/EO+VyzOjuAZtHCCGEOBuWjcsDFpBoYPvgFcyExE7VeuOxxx7j8ccfZ8mSJU2KC3QWAXvVvH79evr169eq506ZMqVF47766iuOHj3K3Llz/QEJ8N1p89vf/pYXXniBl19+mZ/+9Kf+kxhvvvkmXq+XX/ziF/6ABMDgwYO57rrrePnll1mzZg3Tpk0DIC8vjw0bNnDhhRf6AxIAVquV3/3ud6xevZrXXnutUUjitddeA+D3v/99o5MnN910E88++yzvv/8+TzzxBJGRkWf9sxFChEZJlZf3CmpYVFhDVjMnUwMl3KJwWZKdBalOpvaxSY9b0WW4b74Pbde2c+4P1xJmeCTum+8L+DxCCCFEKMTYVEbawhjZs+ljumGyz6X7q08Ufy9Icbi2Y1w8bEsmsM+ls8+ls6GZNg0OTSE5UiO5PjTRUIEiOcIXqIiwNm3HKU6v2mMQ3sqfW0mVl3/sqOL1HBfVAUj8xDlU7hgSwW0Dw4mxyf/bDi08En3wKPTBJ7XZddei7i3wVZworg9Q7M1H8QTvPf+5UFzVaLu/Rdv9rX+baQ3D6JuKkVJfcSIpHSMxVdocdFHKgb1Yl3+Add1nKK7qgM2jDzqPuhnz0EdNAouEdIQQQrQfStlRbK8/FZS5bK89hT5oZMDapBUXF3PeeecBkJCQQFZWFpqmNRm3Y8cOMjIyAEhPT2fLli0BWU9n0OavWgzD4C9/+Qvffvst999/f1vvvpFDhw4BkJyc3OSxmJgYYmJi2LNnD0VFRf7Axrp16wD8IYiTTZs2jZdffpn169f7Hz/d+DFjxhAdHc369ev922pra9m6dSvp6ekkJSU1ec7UqVP55z//ydatW5k6derZfstCiCA6UqvzQWENiwpq2HQoMH1sm2NVYWZfOwv6Obg4yY7TIifjRNdjxvTAfctPsT//SMDnct/yU+nxK4QQokvSVIWkCAtJERYuTGh6Aa3KY1BSpTeqPlFU5ftcXKlTo3e9MhQ1usnuMi+7y7yAu8njsXb1pOoTFpIiT7Ty6OPU0LpA6DnzmIcXd1XxUgvHp//nAFenOvjh4AiGd7e26DnfHq3j2awq3i+sIRC/hgOjLdw9LIJr+jul/UpnZrNj9B+C0X8I/uYsXi/q/pKT2nXkohbnodQE7gJzW1I8dWiFu9EKd9Pwr8lUVYyEJIyTKk7oSWkQLjdvdUqGgfbdV1iXvY8lc3PApjHD7HgzZuKZPs8XxBFCCCHaIdvrTwflJjwApboS2+tPU3vPHwI6j8ViYf/+/SxfvrzRzf0NXn/9dSwWC15v27cf7GxaHJK46667Tvu4YRgcO3aMbdu2ceTIEfr3789PfvKTc17g6fTs6bsVpri4uMlj5eXllJWVAb5qEA0hifz8fCIiIoiLi2vynP79+/vHNGj4OjW16Ys9RVFITU1l27ZtuFwunE4nhYWFGIbR7Pjvz3GmkERtbe1pHxfibES0cFxX/72r9Bh8ttfD+8Vu1hzwBOSEW3MUICPOwlXJNi5NDCMmrD4Y4a2jVv6Wia5qZAbG5Tfi/PjNgE1RfflN1IzMgC5+7BNCdCxd/fWaCB4LkOqAVIcCvSycfArBNE0O15oUV+kUVxmUVPs+F1fp7Kk2KHUZdL0IBRyuNThca7D1cNM70S0K9A1XfVUoIlQSw32fkyM0ksJVYsKUDt3K42CNwW+2VvPJXl/AvKUhCZfX5NUcF6/muLisbxh/GhtOnKNpUNw0TVYd8PCPXbWsPRiYO/3Hx1r4yWAHM3pbURUF0+OmtmMUFRBtKba372PsRb7/NgzUowewlORj2VP/UZKHWnE8tOtsIcUw0PYVoe0rgg1f+LfrPePxJqXhTezv+0jqjxkt4fHTac/n1pTqSuwbvsC+6mO0I/sDNo/eqzc1F12Be+JMTGf9T0RemwrR6cl7UBEIdrs9oPtXi3OD2s4ZwLJ1DWpJXkDbOo8bN46srCzeeOONJiGJuro63n33XWbOnMlnn30WsDV0Fi0OSbz11lstGhcWFsbVV1/NH//4x4C3kxg3bhxRUVF88sknfPvtt/4yIwB//OMf/V+Xl5f7v66oqCA2NrbZ/TWst6KiotF4oFFrjlM9x+l0ntX4MyktLUXX9TOOE6Ilmqmu26w9e/YEdB3tkduADcc0lh7WWHdcw20E78TkkAid2bE6M3vqxNp8p5ErD0Jwso1CdADnXUR8RQUJa5a0+a73T76CA+dNhi543BNCtFfOFo3qiq/XRPsVD8Rb4IJo4KS3wXUG7HcrlNYq7KtV2Vd74uu9tQrVescNA7SW14SiKoOiqubbmERoJn3sJr3tBn3sZv2HQW+7SYLNJKwdF5j74rDG4/lhVHjP7f/rJ3vrWHvAzf3965gV6zsf4jXgiyMab+y1kutq+x+CisnUHjo39/UyNNIAo4J9e9t8GtEZ9ErxfYzx9Zq2VJbhPLgHx4ESHAdKcB7Yg63scEiXeDa0IwfQjhzA9s06/zZPeBQ18Um44pN8n+MSqesWCx04wNWW2uO5NcfBPfTcspJuWV+hegNTBdVEoSJtGIfPn0Zl6hBQVDh63PchhOjg5D2oCJ309PSA7t+6/MOA7v9087pv+0XA9u9wOLjqqqt46623OHr0KD16nAi5fvbZZxw9epQbb7yxSUhi//79/N///R8rVqygqKiIiooK4uLimDVrFg888MApr503Jysri7/97W+sX7+eY8eOERcXxyWXXMKDDz5I9+7d2+x7DbQWhySWLDn9xQlVVYmKiiI9PR2bLTh97iIiInj00Ue59957mTVrFnPmzKFXr15s3ryZ7du3M2DAAHJycprtydIR9O7dO9RLEF1QYmJiqJcQFF7DZP1BD+8X1/HJ3joqPcG7zy09SmNechjzkm30i+yYxychgurGu6gYMJSIt/6O6qo6590Zzgiqbrgb6/lT6BpHPCFEx3G0RaO6yus10fH1P8V20zQpqzMpqTb8lSj2nFSJYm+1gbcLlqGo0hWyqxWyq5sGARSgt1Ml8aRKFEknVaKItYemCoVpmvw1q4Yns2vabJ8VXoXfZts4oNrpFqbwYq6bUlfzwZJz4dDg2lQbdwx0kCLvy0SrJMKQ4QDo+G52qHJVYdmTj+avOJGPdqAExWj73+FAsFZXYM3PIio/y7/NsDvRE/ufqDqR1B89Pgk66PnWYAj4azXdS9g363Gs+ghr3o6ATWM4I6jNmE3tRZdhxPYmBogJ2GxCiNCQ96Cik3LXYNm4LCRTWzZ8ifuGu8AWuEoZN910E6+88grvvPMOd955p3/7G2+8QWxsLBdffHGT52zYsIHnnnuOyZMnM2bMGKxWK9999x0vvfQSy5cvZ/Xq1acsAHCyTz/9lNtuuw1N07jkkkvo06cP2dnZvPDCC6xYsYLly5cTExPTlt9uwLQ4JDFp0qRArqPVbrnlFhISEnj66af59NNP0XWdkSNH8uGHH/LUU0+Rk5PTKEUTFRV1yioOlZWV/jEnj4fG1Siae05DhYiWjj95jlMJdKkZIZrTmX/vTNNk62EP7xa4+KCohkM1wTtJ0TdcY34/B/NTHQzvbu3QZXSFCIkLL6Zm+PnYXn/6nMqkecdOxn3zfagxPei8RzshRGfXmV+via7D4YCEaLigmce8hkmpS6eoUqe40ktxpU5RlZei+q8P13aMi41tyQT2uQz2uQw2HW7aj8+hKSRHaiRHWkiJqP8cqZESaSE5QiPcGpgyFI9tq+DJrLYLSJzs2V2BKevcw6Zy++Bwbh8cTg+7XOQVbcxuh+494bwL8AJegDo36p4C1JJctOJc1OI81D35KJ7A3PXf1tRaF2puJtbcTP8202rF6JuKkTwAPTkNIzkdI7E/hAXnxrn2LlCv1ZSyo1hWLsG6aglqWcsubLaGntQfz/R5eCfMAJudsIDNJIToKOQ9qOhotLwdKHWhaROj1NWi5WWhDx0bsDnGjh3L4MGDeeONN/whidLSUlasWMGdd96JxdL08v/kyZPJzs4mIqJxA7G3336bO++8kxdeeIFf/vKXp5332LFj3HHHHfTs2ZPPP/+8UYBq0aJF/PCHP+SPf/wjTzzxRBt8l4HX4pBEezZz5kxmzpzZZPuPf/xjVFVt1Iajf//+bN68mYMHDxIXF9dofH5+vn/MyeMBCgoKmuzfNE0KCgpISEggPDwcgH79+qGqarPjTzWHECKwdh33sKjAxaKCGoqrgtfCpodNZW4/BwtSHVzQKwxVghFCnBMzpge19/wBtSQP6/IPsa5qWQsOM8yOd+JMPNPnBLQfnBBCCCHahkVVSIqwkBRhgYSmF9yqPAbFlTrFVV6KKnVfeKLKF6goqvRS2wW7VtboJrvLvOwuaxqgAIi1q6T4QxQWkhsCFJEafZwamnr271UWFbh4fHvHaRTYL1Lj7mERXJ/mxGlpx71LROcTZsPoPxij/2D8/0J1L+r+El9gojgXtTgXrSQXxVUdypW2mOLxoBVmoxVmY63fZioqRkISRkq6LzSRnI6elAbhgW3H3OmZJmpuFtZl72PZuholQK2ZTU3DO3YynhnzMNKHS4sVIYQQHZpamB3a+YtyAhqSALjhhhv43e9+x/bt2xk5ciRvvfUWuq5z0003NTv+VO00rrvuOu6//35WrVp1xpDE22+/TUVFBU888USTCjMLFizg2Wef5b333pOQRKht2rSJkpISZs2a1ag8SEZGBps3b2bFihVcf/31jZ6zYsUK/5gGDRU0VqxYwc9+9rNG47/++mvKy8sbBTTsdjtjxoxhy5YtlJSUkJSU1Og5K1euxGazMWbMmLb5RoUQzSqu9PJeYQ3vFrjYebz5E4WBEGFRuCzZzoJUJ1N627C24mSjEOL0jKQ03Lf9osUhieq/fxDQ8mZCCCGECK4Iq8rQ7ipDu1ubPGaaJgdrDF9gokr3V59o+Fzq0umCnTw4XGtwuNZgy2FPk8esKiSGf7/6xImvY2xNAwUHXDq/3FgWhJWfu7GxVu4ZFsnlSfZWhUGECAjN4qvE0DcVMmb5tpkmyuH9vsBEcS5qSX2AIoAVA9qSYhpopUVopUWw4Uv/dqNnPEbKAPSkNH94wozp0bkvwrtrwOY4x33UYtm0HOuy99FK8tpmXc0worvjnXoFnilXYHbrGbB5hBBCiGBSA/i3s0XzF+cGfI7rrruOP/zhD7zxxhv+kMTYsWMZNGjQKZ/z0Ucf8corr/Dtt99SVlaGflL48sCBA2ecc+vWrf7PzRULcLvdHD16lKNHjzbq8tBedfiQREVFRZPWFfv37+fee+/FYrHwm9/8ptFjN954I88++yxPPvkkl156qT9AsWvXLv7zn//Qr18/Jk+e7B+flpbGxIkTWbt2LV988QWzZvneuHg8Hh599FHA1/LjZLfeeitbtmzh4Ycf5sUXX/SX1X/jjTfIzs7muuuua1G7DSHE2Tlco/NBUQ2LCmr46lDwylaGqTCzr52rU53MSrTJXUlCtDcSkBBCCCG6DEVRiHdqxDs1Lohr+rhbN9lTX4Hi5EoUDa09KjxdL0LhMaCgUqegsvm7k6PCFFJOCk0kR2q8k19DWV37/lldnGjn3mERTIgLk3aHomNQFMxevdF79UY//6ITm8uO+ipO+Nt15KIeKg3hQs+OeuQA6pEDjdomGlHdTlSbSE7HSE7DjO0Navs8n6KW5GFd9kGLx4ff7WtV4Zkx96yrGSqHSrGu+BDrmk9RqgNXrUdPH4Znxjy8YyeDpWnoUAghhOjI1LJjoZ2/PPDzx8bGMnPmTBYtWsQll1xCQUEB99xzzynHP/vss/zud7+jZ8+eTJs2jd69e/tb6Tz//PO43e4zznn8+HEAXnjhhdOOq66ulpBEa7322mts3LgRgJ07dwLw+uuvs27dOgAuu+wyLr/8cgD+9a9/8c477zB+/HhiY2PZu3cvn332GS6Xi2effZaRI0c22ndaWhoPPPAAjz76KBkZGcyZMweXy8XixYvxeDw8/fTTTXq1/O1vf2PWrFncdNNNzJ07l4SEBJYtW8aOHTu45ZZbGoUqAK6//nref/99Fi9eTHFxMZMmTaKoqIiPPvqIvn378vDDDwfixyZEl1RRZ/BxcQ2LC2tYVepGD9J5OlWBC+NtLEh1cEWyo9m7q4QQQgghhBDti01TSIu2khbdfBWKsjqzUfWJhlYeRZVe9lTpeNt3LiAgKupMvjvm4btjTatQtDdhKlzb38ndwyIYGCMX/UTnYMb0QI/pgX7eBfj/FbqqUEvy6kMT9RUnSotQDCOUS20xteI4auZmyNzs32banfWhifqKE0npGL2ToZme2sGilB3F9vrTjQIeLXpeXS3W1R9jXf0x3rGTcd98n696xqkYBtqOrb6qEd9uQjED88fGtIbVhzfmYSSnB2QOIYQQol3whvi9iyc489900018+umn3HPPPTgcDubPn9/sOK/XyxNPPEFCQgJr166lZ88T1aNM0+SZZ55p0XyRkb42ahs2bGDIkCHn/g2EWLsMSWzcuJG333670bZNmzaxadMmAJKSkvwhiXHjxrF+/Xo+//xzysrK6N69OzNnzuS+++7jvPPOa3b/v/zlL0lKSuL555/n5Zdfxmq1Mm7cOH7zm98wevToJuMHDRrEihUreOSRR1i2bBnV1dWkpqby+OOPc/vttzcZr2kab731Fk899RT//e9/+cc//kFMTAw33HADDz30EHFxzdzOIkSAWaa82aJxZYFdRpuo9Zp8sbeWRQUulu6txR3EnsNjY63M7+dkXj8H8U4teBMLIYQQQgghAkpRFLrZFLrZwhjVTMVxr2Gyr1r3hyaKTwpQFFXqHKntGBcnO6PoMIUfDgrnR4MjiJP3aaIrcEZgDBqJMWjkiW11btS9hSe168hFLclH8QSv0ua5UGpdaNnfomV/699mWq2+tiRJJypOGIn9g1It0LJpObbXnjrnag6WrWvQdm3DfctP8Y6f3vhBVxXWdZ9jXfYB6sG95zTP6RixCXimzcEz+VKIkOrGQgghuoBQV0myBmf+WbNmERcXR2lpKddcc80puxgcPXqUiooKLrrookYBCYBt27ZRU1PTovnGjh3LkiVL2LJli4QkAuX555/n+eefb9HYiy66iIsuuujMA7/nmmuu4Zprrmnx+LS0NF599dUWj7fZbNx///3cf//9Z702IURTXsNkzX43iwpq+Li4JqhlcAfFWFiQ6mR+Pwf9otrlYVMIIYQQQggRYBZVITnSQnKkhckJtiaPV3kMfwWKk4MUDa09aoMY7u4q+oZr/GRoBDcPcBJplep+oosLs2GkDsJIHYS3YZvuRd2/x1dpoiTPH6BQXFWhXGmLKR4PWmE2WmE2DZcaTEXFSEjyBSZOatlBeGTbTGqahH3wCmEftPw88Jko1ZXYn3+Euv0l1M1diLqvEOuyD7Bs+ALFXdtm83yfd/j5eGbMQx9xAagSIBNCCNF1GDHdCeVfPiO6e1DmsVgsvP3225SWlp6ycAD4WnM4HA6+/fZbXC4XTqcTgLKyMn7961+3eL4bb7yRv/71rzzyyCOMGzeOwYMHN3rc5XKxY8cOzj///NZ9Q0EmV/uEEO2WaZpsOVzHuwU1fFBYw+Eg3pnVN1xjQaqDBalOhnazSA9bIYQQQgghxGlFWFWGdlcZ2r35Vh4Ha4xGAYqiSt1XjaJSp9Sl0wU7ebTaiO5W7h0ewZwUB1ZV3qsJcUqaBaNvP4y+/SBjlm+baaIcOXCi4kRDy46yI6FdawsppoFWWoRWWgQbl/m3Gz3j/YEJIzkNIykds1tPOMvzOW0dkGi871fR1i1FO3IgIPsHMB3heC68GM/0uZjxiQGbRwghhGjPjKQ0+Gpl6OYPYlur0aNHN9sl4WSqqvKDH/yAv//970yaNImLL76YyspKli1bRmJiIgkJCS2aq2fPnrz44ossXLiQSZMmMWPGDNLT03G73ZSUlLBhwwbGjRvH4sWL2+JbCzgJSQgh2p2dxz0sKnCxqKCGkqrg3W7V064yL8XB/FQH43qFoUowQgghhBBCCNEGFEUh3qkR79QY30wHTrdusqfKF5woOqn6REOQIpiV9Nqz/lEaf5sQw+QEmwTZhWgtRcGMTUCPTUAfO/nE5vJjJwITxbloJbmoB/eFcKFnRz1yAPXIASxfr/VvM6K6+QMTDQEKs1dvUJuvPGPZuDxgAYkGgQpI6H1S8MyYh3fiTLA7AzKHEEII0VEY/QaGdv6UASGdvzm///3v6datG2+99RYvvfQSsbGxXHXVVTz44INMmDChxfuZPXs2a9as4ZlnnmHVqlWsXLkSp9NJ7969ueGGG7j22msD+F20LaWsrEzeaQvRBcT8X8ve2Jbd1ifAK2leUaWX9wprWJTvYmeZ98xPaCORVoXLkuxc3d/JRQk2LHIXkhAdRsStU1o0rurVVQFdhxBCtJX2/npNCBEapmlSVmfWhye8/pYeRfUVKfZW6Xjb4Zkd76obWzTOMuXNFu/zqn4OXp4SnNK1QgjAVYVaku8LTDQEKPYVohjBq/TZ1ky7EyMp7UTFieR0jN4pKFXlOH+zEKW6MtRLbDFTVdFHT/K11Bg08qyrZgghhLwHFZ2Wu4bwu+eh1AWurdWpmGF2qv/+AdjsQZ9bnB2pJCGECJlDNTrvF9awuKCGzYfrgjZvmAqz+vqCEbP62nFY5E2kEEIIIYQQon1SFIVuNoVutjBG9Qxr8rjXMNlXrfurTxR/rxLFkSC2LQy0gzXBqzQohACcERiDzsMYdFKP6zo36r5C1OK8E+069uSj1LlDt86zoNS60HK+Q8v5zr/NtFghzIbiqgrhylrOiIzBO+VyPFOvxOzRK9TLEUIIIdofmwPvhBlYV38c9Km9E2dKQKKDkJCEECKoyusMPi6uYVFBDav3uzGCdMeTqsBFCTbmpzq4PMlBjK350opCCCGEEEII0ZFYVIXkSAvJkRbA1uTxSo9ByfeqT5ScFKSo7UC5gzq9HZbMEKKrCbNh9BuE0W8Q/jqgho6yf8+J0ERJHlpRTocJHSheD3g9oV7GGen9B+OZPg/vuClgbRqaE0IIIcQJnhlzQxKS8EyfE/Q5RetISEII0Ui1xyDc2rYBghqvyRd7a1lU4OKLvbW4g3gS7vxYKwtSncxNcRDn1II3sRBCCCGEEEK0A5FWlaHdVYZ2tzZ5zDBNDtUY9a08TlSf8AUpdEpdOu0plhCmSRVAIdolVcPsk4K3TwpMnOnbZpooRw74Kk7423Xkoh4/EtKldjSm1Yr3gml4ps/DSB0U6uUIIYQQHYaRlIZ37GQsW9cEbU7v2MkYSWlBm0+cGwlJCNHJZR7z8OKulif30/9zgKtTHfxwcATDmzmJ1lJew2T1fjfv5rv4pKSWSk/wTq0NjrGwINXJ/FQHKZFymBNCCCGEEEKI5qiKQrxTI96pMT6u6eO1XpM91fUBikov/9pZTW6Ft+nAIIlzSPBdiA5DUTBjE9BjE9DHXnhic8XxE4GJhpYdB/eGcKHtk9EjDs+0K/FMvgyiYkK9HCGEEKJDct98H9qubSjVlQGfywyPxH3zfQGfR7QduXooRCd1wKXzq01lLCmuPavnubwmr+a4eDXHxRXJdp4YH0N8CyswmKbJ5kN1LCqo4f2imqD2vk2M0Lg61cH8fs5m75ASQgghhBBCCHF27BaF9Ggr6dG+91iVHpOHv64I2XpGyHs9ITo8M6ob+vBx6MPHndhYU41akn9Su45c1H1FKHoH6gfURrxDRuOZMQ995ATQ5NS9EEIIcS7MmB64b/kp9ucfCfhc7lt+ihnTI+DziLYjr7SE6IQWF7j4xcYyyurOrXrDkuJa1u4/yJMTYpif6mx2jGma7DjuZXGBi0WFNeypCt4b2J52lXn9HFyd6uD82DAURUqvCiGEEEIIIUSgjOoZ2pDCyBDPL4QIEEc4xsARGANHnNjmqUPdW1gfmqivOFGSj1J3djcDdQSm3YEnYzae6XMx+6SEejlCCCFEp+IdP526/SWEffBqwOZwz12Id/z0gO1fBIaEJIToREzT5M/bK3l8e9uVDiqrM/nB6uPklHt5YGSkP4hQVOllUUENiwtc7CoLXrnVSKvC5cm+YMTkBBsWVYIRQgghhBBCCBEM58eG4bQouLzBa6fYwGlRGNcrLOjzCiFCxBqG0W8gRr+BJ7YZOsqBvScqThTnohXnoVSHrsLNudKT06l58ClwhId6KUIIIUSnVTd3ISYKtg9eafN9u+cuxDP31jbfrwg8CUkI0Ym0dUDiZI9vr8TlMekTobGowMXWw56AzNMcmwaz+9qZn+pkVl87DosEI4QQQgghhBAi2MKtKlenOng1xxX0ua9JdeC0qEGfVwjRjqgaZu9kvL2TYcIM3zbTRDl60B+Y8LfrOHY4tGttKYdTAhJCCCFEoCkKnnkLMRMSsb32FEr1uV9HM8Mjcd/yU6kg0YFJSEKITmJRgStgAYkGz+6oCuj+T6YqMCXBxoJUB5clO4gOk5NhQgghhBBCCBFqPxwcEZKQxA8GRwR9TiFEB6AomD3j0XvGo4+58MT2irL6Fh0nKk6oB/aEbp2n4gneTUhCCCFEV+cdPx190Ehsrz+NZeua1u9n7GTcN9+HGdOjDVcngk1CEkJ0AgdcOr/cWBbqZbSJC3qFMb+fg7n9HPRyaKFejhBCCCGEEEKIkwzvbuWKZDtLimuDNucVyXaGd7cGbT4hRCcQFYM+/Hz04eef2FbjQt2Td6LiRHEuakkeIa1XapVjmxBCCBFMZkwPau/5A2pJHtblH2LZ8CVK3Znf25hhdrwTZ+KZPgcjKS0IKxWBJiEJITqBX20qo6wu+D1h28qQbhauTnVyVT8HyZFyWBJCCCGEEEKI9uyJ8TGs3X8wKO9DY8IUnhgfE/B5hBBdgMOJMWAExoAR/k22v/8e65bVIVuSEd09ZHMLIYQQXZmRlIb7tl/gvuEutLws1KIcX4Cy/Jiv0pPVihHdHSM5HSNlAHraMLDZQ71s0YbkaqQQHdx3R+uCegdPW0mK0Lg61cH8VCdDuklqXgghhBBCCCE6ininxpMTYvjB6uMBn+vJCTHEO6XKoBAiMIyUARDKkERyesjmFkIIIQRgs6MPHYs+dGyoVyKCTEISQnRwL+2uDvUSWqyXQ2VuioOrU52MjbWiKCEtaCiEEEIIIYQQopXmpzrJKffy+PbKgM3xwMhI5qc6A7Z/IYQw+g0M7fwpA0I6vxBCCCFEVyUhCSE6sGqPwbsFNaFexmlFWRWuSHGwoJ+DCxNsWFQJRgghhBBCCCFEZ/DAyEgU4M8BCEo8MDKS+0dGtvl+hRDiZHraUMwwe4t6kbc1M8zuK90thBBCCCGCTkISQnRgWw7X4fIGvgfs2bJrMDvRzoJUJzP72LFbJBghhBBCCCGEEJ2Noig8MCqK9GgLv9hYRlndub8/jQlTeHJCjFSQEEIEh82Bd8IMrKs/DvrU3okzpbe5EEIIIUSISEhCiA5s2xFPqJfgpykwtbeN+alOLkuyExWmhnpJQgghhBBCCCGCYH6qk4x4G7/aVMaS4tbfjX1Fsp0nxscQ79TacHVCCHF6nhlzQxKS8EyfE/Q5hRBCCCGEj4QkhOjAMo+FPiQxvlcYC1IdzElxEOuQE1lCCCGEEEII0RXFOzVen9aDzGMeXtpVBata9jynReGaVAc/GBzB8O7WgK5RCCGaYySl4R07GcvWNUGb0zt2MkZSWtDmE0IIIYQQjUlIQogO7IBLD+n8Y2OtfH5ZbEjXIIQQQgghhBCi/Rje3cpTGd3g3y0bn3d9PE6LVCIUQoSW++b70HZtQ6muDPhcZngk7pvvC/g8QgghhBDi1ORdqBAdmMc4936v50IJ6exCCCGEEEIIITo6CUgIIdoDM6YH7lt+GpS53Lf8FDOmR1DmEkIIIYQQzZN3okJ0YFY1tDGFME1iEkIIIYQQQgghhBCi4/OOn07d3FsDOod77kK846cHdA4hhBBCCHFmEpIQogOLd2ohnT/OEdr5hRBCCCGEEEIIIYRoK3VzF+KeuzAg+3bPXYgnwCEMIYQQQgjRMhKSEKIDG97dGtL5R4R4fiGEEEIIIYQQQggh2oyi4Jm3kNo7f4cZHtkmuzTDI6m983d45i0ERaqyCiGEEEK0B5ZQL0AI0XqjeoY2pDAyxPMLIYQQQgghhBBCCNHWvOOnow8aie31p7FsXdP6/YydjPvm+zBjerTh6oQQQgghxLmSkIQQHdj5sWE4LQourxn0uZ0WhXG9woI+rxBCCCGEEEIIIYQQgWbG9KD2nj+gluRhXf4h1lVLWva8MDveiTPxTJ+DkZQW4FUKIYQQQojWkHYbQnRg4VaVq1MdIZn7mlQHToscQoQQQgghhBBCCCFE52UkpeG+7RctHl/99w9w3/YLCUgIIYQQQrRjcoVTiA7uh4MjQjLvD0I0rxBCCCGEEEIIIYQQ7ZbNHuoVCCGEEEKIM5CQhBAd3PDuVq5IDu6bryuS7Qzvbg3qnEIIIYQQQgghhBBCCCGEEEIIca4kJCFEJ/DE+BhiwpSgzBUTpvDE+JigzCWEEEIIIYQQQgghhBBCCCGEEG1JQhJCdALxTo0nJ8QEZa4nJ8QQ79SCMpcQQgghhBBCCCGEEEIIIYQQQrQlS6gXIIRoG/NTneSUe3l8e2XA5nhgZCTzU50B278QQgghhBBCCCGEEEIIIQKv7LY+oV6CEEKETLusJPHf//6Xn/70p0yZMoVevXoRExPDm2++ecrx+fn5/OQnP2H06NHEx8czePBg5s6dy6effnrK57z77rtMmzaN3r17k5yczNVXX822bdtOO8fChQvp378/8fHxTJw4kX//+98YhtHseLfbzeOPP86YMWOIi4tj4MCB3HPPPRw8eLDlPwghztIDIyN5YGRkwPZ9f4D2LYQQQgghhBBCCCGEEEIIIYQQwdAuQxKPPvoor7zyCnv27CEuLu60Y7du3cqkSZN45513GDp0KHfccQfTpk3jm2++4YYbbuDPf/5zk+c8+eST3H777Rw6dIjbbruNefPm8dVXXzF79mzWrl3bZPzu3buZOnUqn3zyCdOnT+fHP/4xAL/+9a/52c9+1mS8YRjccMMNPPbYY3Tr1o0777yT8ePH8+abbzJ9+nQJSoiAURSFB0ZF8dJF3YgJU9pknzFhCi9d1I0HRkWhKG2zTyGEEEI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AAAAAAAAAZAoESQAAAAAAAAAAAAAAgEyBIAkAAAAAAAAAAAAAAJApECQBAAAAAAAAAAAAAAAyBYIkAAAAAAAAAAAAAABApkCQBAAAAAAAAAAAAAAAyBQIkgAAAAAAAAAAAAAAAJkCQRIAAAAAAAAAAAAAACBTIEgCAAAAAAAAAAAAAABkCgRJAAAAAAAAAAAAAACATIEgCQAAAAAAAAAAAAAAkCn8P7KvI8BxUm4vAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Unit price', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "f50c5fe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xc807e24e50>"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2140.38x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(kind = 'point', data = Data, x = 'Product line', y = 'Quantity', hue = 'Gender', aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b24cf5c",
+   "metadata": {},
+   "source": [
+    "# Summary of Insight\n",
+    " It was observed that the city with the highest gross income is 'Port Harcourt'.\n",
+    " \n",
+    " It can be observed that the most frequent customers are of the female gender and the most bought product is the 'Home and     lifestyle'. Also, the use of catplot with kind = 'point' is an easy to read representation.\n",
+    "\n",
+    " The most used payment method with respect to 'Product line' is Cash\n",
+    " \n",
+    " The branch with the lowest rating is \"B\" while 'C' has the highest\n",
+    " \n",
+    " It is observed that the most frequent customers are of the female gender and the most bought product is the 'Home and lifestyle'.\n",
+    " \n",
+    " It is observed that the most expensive 'product line' is the 'food and beverages' and it is mostly bought by the female gender."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dd96b18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8343c810",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Udemy DataScience Workbook/business_case - Copy.ipynb b/Udemy DataScience Workbook/business_case - Copy.ipynb
new file mode 100644
index 0000000..2eb5af9
--- /dev/null
+++ b/Udemy DataScience Workbook/business_case - Copy.ipynb	
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from sklearn import preprocessing\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = np.loadtxt('Audiobooks_data.csv', delimiter=',')\n",
+    "#The inputs are all columns in the csv, except for the first one [:,0]\n",
+    "# and the last one [:,-1] (which is our targets)\n",
+    "\n",
+    "unscaled_inputs_all = raw_data[:,1:-1]\n",
+    "# The targets are in the last column. That's how datasets are conventionally organized.\n",
+    "\n",
+    "targets_all = raw_data[:,-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/business_case.ipynb b/Udemy DataScience Workbook/business_case.ipynb
new file mode 100644
index 0000000..51082f0
--- /dev/null
+++ b/Udemy DataScience Workbook/business_case.ipynb	
@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from sklearn import preprocessing\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract data from csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = np.loadtxt('Audiobooks_data.csv', delimiter=',')\n",
+    "#The inputs are all columns in the csv, except for the first one [:,0]\n",
+    "# and the last one [:,-1] (which is our targets)\n",
+    "\n",
+    "unscaled_inputs_all = raw_data[:,1:-1]\n",
+    "# The targets are in the last column. That's how datasets are conventionally organized.\n",
+    "\n",
+    "targets_all = raw_data[:,-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Balance the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count how many targets are '1' (meaning that the customer did convert)\n",
+    "\n",
+    "num_1_targets = int(np.sum(targets_all))\n",
+    "# Count how many targets are '0' (meaning that the customer did not convert)\n",
+    "# Set counter for '0\n",
+    "zero_targets_counter = 0\n",
+    "# We want to create a \"balanced\" dataset, so we will have to remove some input/target pairs.\n",
+    "# Declare a variable that will do that:\n",
+    "indices_to_remove = []\n",
+    "# Count the number of targets that are 0. \n",
+    "# Once there are as many 0s as 1s, mark entries where the target is 0.\n",
+    "\n",
+    "for i in range(targets_all.shape[0]):\n",
+    "    if targets_all[i] == 0:\n",
+    "        zero_targets_counter += 1\n",
+    "        if zero_targets_counter >num_1_targets:\n",
+    "            indices_to_remove.append(i)\n",
+    "    \n",
+    "# Create two new variables, one that will contain the inputs, and one that will contain the targets.\n",
+    "# We delete all indices that we marked \"to remove\" in the loop above.\n",
+    "\n",
+    "unscaled_inputs_equal_priors = np.delete(unscaled_inputs_all, indices_to_remove, axis=0)\n",
+    "targets_equal_priors = np.delete(targets_all, indices_to_remove, axis=0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Standardize the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# With the  use of the sklearn functionality\n",
+    "scaled_inputs = preprocessing.scale(unscaled_inputs_equal_priors)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Shuffle the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When the data was collected it was actually arranged by date\n",
+    "# Shuffle the indices of the data, so the data is not arranged in any way when we feed it.\n",
+    "# Since we will be batching, we want the data to be as randomly spread out as possible\n",
+    "\n",
+    "shuffled_indices = np.arange(scaled_inputs.shape[0])\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "\n",
+    "# Use the shuffled indices to shuffle the inputs and targets\n",
+    "shuffle_inputs = scaled_inputs[shuffled_indices]\n",
+    "shuffled_targets = targets_equal_priors[shuffled_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Split dataset into Train, Validation and Test\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1784.0 3579 0.4984632578932663\n",
+      "231.0 447 0.5167785234899329\n",
+      "222.0 448 0.4955357142857143\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count the total no. of samples\n",
+    "\n",
+    "samples_count = shuffle_inputs.shape[0]\n",
+    "\n",
+    "# Count the samples in each subset, assuming we want 80-10-10 distribution of training, validation, and test.\n",
+    "train_samples_count = int(0.8 * samples_count)\n",
+    "validation_sample_count = int(0.1 * samples_count)\n",
+    "\n",
+    "# The 'test' dataset contains all remaining data.\n",
+    "test_sample_count = samples_count - train_samples_count - validation_sample_count\n",
+    "\n",
+    "# Create variables that record the inputs and targets for training\n",
+    "# they are the first \"train_samples_count\" observations in the shuffled dataset\n",
+    "train_inputs = shuffle_inputs[:train_samples_count]\n",
+    "train_targets = shuffled_targets[:train_samples_count]\n",
+    "\n",
+    "# Create variables that record the inputs and targets for validation.\n",
+    "# They are the next \"validation_samples_count\" observations, folllowing the \"train_samples_count\"\n",
+    "\n",
+    "validation_inputs = shuffle_inputs[train_samples_count:train_samples_count+validation_sample_count]\n",
+    "validation_targets = shuffled_targets[train_samples_count:train_samples_count+validation_sample_count]\n",
+    "\n",
+    "# Create variables that record the inputs and targets for test.\n",
+    "# They are everything that is remaining.\n",
+    "\n",
+    "test_inputs = shuffle_inputs[train_samples_count+validation_sample_count:]\n",
+    "test_targets = shuffled_targets[train_samples_count+validation_sample_count:]\n",
+    "\n",
+    "# Print the number of targets that are 1s, the total number of samples, and the proportion for training, validation, and test.\n",
+    "print(np.sum(train_targets), train_samples_count, np.sum(train_targets) / train_samples_count)\n",
+    "print(np.sum(validation_targets), validation_sample_count, np.sum(validation_targets) / validation_sample_count)\n",
+    "print(np.sum(test_targets), test_sample_count, np.sum(test_targets) / test_sample_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the three dataset in .npz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savez('Audiobooks_data_trian', inputs=train_inputs, targets=train_targets)\n",
+    "np.savez('Audiobooks_data_validation', inputs= validation_inputs, targets=validation_targets)\n",
+    "np.savez('Audiobooks_data_test', inputs= test_inputs, targets=test_targets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/data_preprocessing_homework.pdf b/Udemy DataScience Workbook/data_preprocessing_homework.pdf
new file mode 100644
index 0000000..b863e32
Binary files /dev/null and b/Udemy DataScience Workbook/data_preprocessing_homework.pdf differ
diff --git a/Udemy DataScience Workbook/df_preprocessed.csv b/Udemy DataScience Workbook/df_preprocessed.csv
new file mode 100644
index 0000000..ae09c79
--- /dev/null
+++ b/Udemy DataScience Workbook/df_preprocessed.csv	
@@ -0,0 +1,701 @@
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diff --git a/Udemy DataScience Workbook/iris_dataset.csv b/Udemy DataScience Workbook/iris_dataset.csv
new file mode 100644
index 0000000..433fd1e
--- /dev/null
+++ b/Udemy DataScience Workbook/iris_dataset.csv	
@@ -0,0 +1,151 @@
+sepal_length,sepal_width,petal_length,petal_width
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diff --git a/Udemy DataScience Workbook/iris_with_answers.csv b/Udemy DataScience Workbook/iris_with_answers.csv
new file mode 100644
index 0000000..6464198
--- /dev/null
+++ b/Udemy DataScience Workbook/iris_with_answers.csv	
@@ -0,0 +1,151 @@
+sepal_length,sepal_width,petal_length,petal_width,species
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+6.7,3,5.2,2.3,virginica
+6.3,2.5,5,1.9,virginica
+6.5,3,5.2,2,virginica
+6.2,3.4,5.4,2.3,virginica
+5.9,3,5.1,1.8,virginica
diff --git a/Udemy DataScience Workbook/lict_Numbers.txt b/Udemy DataScience Workbook/lict_Numbers.txt
new file mode 100644
index 0000000..62cba3a
--- /dev/null
+++ b/Udemy DataScience Workbook/lict_Numbers.txt	
@@ -0,0 +1,6 @@
+Numbers
+0nan
+52.0
+60.0
+48.0
+64.0
diff --git a/Udemy DataScience Workbook/machine learning algorithm.ipynb b/Udemy DataScience Workbook/machine learning algorithm.ipynb
new file mode 100644
index 0000000..f37c8ce
--- /dev/null
+++ b/Udemy DataScience Workbook/machine learning algorithm.ipynb	
@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem\n",
+    "\n",
+    "You are given data from an Audiobook app. Logically, it relates only to the audio versions of books. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.\n",
+    "\n",
+    "The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertizing to him/her. If we can focus our efforts ONLY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.\n",
+    "\n",
+    "You have a .csv summarizing the data. There are several variables: Customer ID, Book length in mins_avg (average of all purchases), Book length in minutes_sum (sum of all purchases), Price Paid_avg (average of all purchases), Price paid_sum (sum of all purchases), Review (a Boolean variable), Review (out of 10), Total minutes listened, Completion (from 0 to 1), Support requests (number), and Last visited minus purchase date (in days).\n",
+    "\n",
+    "So these are the inputs (excluding customer ID, as it is completely arbitrary. It's more like a name, than a number).\n",
+    "\n",
+    "The targets are a Boolean variable (so 0, or 1). We are taking a period of 2 years in our inputs, and the next 6 months as targets. So, in fact, we are predicting if: based on the last 2 years of activity and engagement, a customer will convert in the next 6 months. 6 months sounds like a reasonable time. If they don't convert after 6 months, chances are they've gone to a competitor or didn't like the Audiobook way of digesting information. \n",
+    "\n",
+    "The task is simple: create a machine learning algorithm, which is able to predict if a customer will buy again. \n",
+    "\n",
+    "This is a classification problem with two classes: won't buy and will buy, represented by 0s and 1s. \n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the machine learning algorithm\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import tensorflow as tf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's create a temporary variable npz, where we will store each of the three Audiobooks datasets\n",
+    "npz = np.load('Audiobooks_data_trian.npz')\n",
+    "\n",
+    "# we extract the inputs using the keyword under which we saved themm and also ensur that it is stored as float\n",
+    "train_inputs = npz['inputs'].astype(np.float64)\n",
+    "# targets must be int because of sparse_categorical_crossentropy (we want to be able to smoothly one-hot encode them)\n",
+    "train_targets = npz['targets'].astype(np.int64)\n",
+    "#Load the validation data in the temporary variable\n",
+    "npz = np.load('Audiobooks_data_validation.npz')\n",
+    "#Load the inputs and the targets in the same line\n",
+    "\n",
+    "validation_inputs, validation_targets = npz['inputs'].astype(np.float64), npz['targets'].astype(np.int64)\n",
+    "\n",
+    "# load the test data in the temporary variable\n",
+    "\n",
+    "npz = np.load('Audiobooks_data_test.npz')\n",
+    "\n",
+    "# creat 2 variables that will contain the test inputs and the test targets\n",
+    "\n",
+    "test_inputs, test_targets = npz['inputs'].astype(np.float64), npz['targets'].astype(np.int64)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# model\n",
+    "Outline, optimizers, loss, early stopping and training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n",
+      "Epoch 1/100\n",
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
+      "\n",
+      "36/36 - 5s - loss: 0.6341 - accuracy: 0.6323 - val_loss: 0.5363 - val_accuracy: 0.7248 - 5s/epoch - 143ms/step\n",
+      "Epoch 2/100\n",
+      "36/36 - 0s - loss: 0.4928 - accuracy: 0.7354 - val_loss: 0.4537 - val_accuracy: 0.7696 - 280ms/epoch - 8ms/step\n",
+      "Epoch 3/100\n",
+      "36/36 - 0s - loss: 0.4274 - accuracy: 0.7737 - val_loss: 0.4155 - val_accuracy: 0.7875 - 281ms/epoch - 8ms/step\n",
+      "Epoch 4/100\n",
+      "36/36 - 0s - loss: 0.3916 - accuracy: 0.7952 - val_loss: 0.3927 - val_accuracy: 0.7897 - 266ms/epoch - 7ms/step\n",
+      "Epoch 5/100\n",
+      "36/36 - 0s - loss: 0.3724 - accuracy: 0.8005 - val_loss: 0.3869 - val_accuracy: 0.7852 - 278ms/epoch - 8ms/step\n",
+      "Epoch 6/100\n",
+      "36/36 - 0s - loss: 0.3624 - accuracy: 0.8002 - val_loss: 0.3732 - val_accuracy: 0.8076 - 272ms/epoch - 8ms/step\n",
+      "Epoch 7/100\n",
+      "36/36 - 0s - loss: 0.3530 - accuracy: 0.8148 - val_loss: 0.3687 - val_accuracy: 0.7942 - 273ms/epoch - 8ms/step\n",
+      "Epoch 8/100\n",
+      "36/36 - 0s - loss: 0.3467 - accuracy: 0.8148 - val_loss: 0.3755 - val_accuracy: 0.7897 - 304ms/epoch - 8ms/step\n",
+      "Epoch 9/100\n",
+      "36/36 - 0s - loss: 0.3474 - accuracy: 0.8022 - val_loss: 0.3604 - val_accuracy: 0.8054 - 277ms/epoch - 8ms/step\n",
+      "Epoch 10/100\n",
+      "36/36 - 0s - loss: 0.3386 - accuracy: 0.8173 - val_loss: 0.3613 - val_accuracy: 0.8009 - 269ms/epoch - 7ms/step\n",
+      "Epoch 11/100\n",
+      "36/36 - 0s - loss: 0.3362 - accuracy: 0.8184 - val_loss: 0.3594 - val_accuracy: 0.8098 - 314ms/epoch - 9ms/step\n",
+      "Epoch 12/100\n",
+      "36/36 - 0s - loss: 0.3345 - accuracy: 0.8170 - val_loss: 0.3630 - val_accuracy: 0.8054 - 301ms/epoch - 8ms/step\n",
+      "Epoch 13/100\n",
+      "36/36 - 0s - loss: 0.3320 - accuracy: 0.8237 - val_loss: 0.3505 - val_accuracy: 0.8121 - 278ms/epoch - 8ms/step\n",
+      "Epoch 14/100\n",
+      "36/36 - 0s - loss: 0.3315 - accuracy: 0.8206 - val_loss: 0.3759 - val_accuracy: 0.7897 - 297ms/epoch - 8ms/step\n",
+      "Epoch 15/100\n",
+      "36/36 - 0s - loss: 0.3272 - accuracy: 0.8243 - val_loss: 0.3514 - val_accuracy: 0.7987 - 292ms/epoch - 8ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x9ef67fdc90>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set the input and output sizes\n",
+    "input_size = 10\n",
+    "output_size = 2\n",
+    "# Use same hidden layer size for both hidden layers.\n",
+    "hidden_layer = 50\n",
+    "\n",
+    "#Define the model\n",
+    "model = tf.keras.Sequential([\n",
+    "    tf.keras.layers.Dense(hidden_layer, activation='relu'),# first hidde \n",
+    "    tf.keras.layers.Dense(hidden_layer, activation='relu'),\n",
+    "    tf.keras.layers.Dense(output_size, activation='softmax')#output layer\n",
+    "                            ])\n",
+    "\n",
+    "\n",
+    "# Optimizer and the loss function\n",
+    "\n",
+    "model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "## Traning\n",
+    "#set_batch_size\n",
+    "\n",
+    "batch_size = 100\n",
+    "\n",
+    "# set max number of training epochs\n",
+    "max_epochs = 100\n",
+    "\n",
+    "# set an early stopping mechanism\n",
+    "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n",
+    "\n",
+    "# fit model\n",
+    "\n",
+    " # Recall that this time the train, validation and test data are not iterable\n",
+    "\n",
+    "model.fit(train_inputs,\n",
+    "          train_targets,\n",
+    "          batch_size = batch_size,\n",
+    "          epochs = max_epochs,\n",
+    "          callbacks = [early_stopping], \n",
+    "          validation_data = (validation_inputs, validation_targets),\n",
+    "          verbose = 2 # To make sure we get enough intormation \n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14/14 [==============================] - 0s 7ms/step - loss: 0.3328 - accuracy: 0.8281\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss, test_accuracy = model.evaluate(test_inputs, test_targets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Test loss: 0.33. Test accuracy: 82.81%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('\\nTest loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/output.csv b/Udemy DataScience Workbook/output.csv
new file mode 100644
index 0000000..14a3d75
--- /dev/null
+++ b/Udemy DataScience Workbook/output.csv	
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\ No newline at end of file
diff --git a/Udemy DataScience Workbook/passwords_random.csv b/Udemy DataScience Workbook/passwords_random.csv
new file mode 100644
index 0000000..4448484
--- /dev/null
+++ b/Udemy DataScience Workbook/passwords_random.csv	
@@ -0,0 +1 @@
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7208 5303 2516 694 9087 8288 9393 8656 4936 6075 6119 5275 9737 7747 5385 1366 3271 6345 5594 5403 4691 801 4492 1143 3563 6972 4008 9314 5100 7240 6852 5880 2472 9957 692 422 4182 1551 4199 3233 5254 5988 2525 355 4929 4450 6623 8526 1873 4986 7920 8703 8997 6115 3567 7870 1592 8248 4204 5840 5973 1965 9832 1035 622 8453 8319 6372 1142 8751 46 1463 570 6207 2162 1394 1031 3412 6701 3000 3876 7280 9015 2236 5755 8067 2126 6859 1004 187 5614 2541 857 8960 3320 3785 7082 8655 6025 897 1315 833 8545 3128 3246 7128 3036 585 1955 7323 1456 5392 1609 9994 1079 266 1765 3746 9634 2190 6633 8045 9522 5894 4026 5200 9447 4201 8915 8228 4406 9833 2702 9237 7547 1370 4374 1224 4525 8491 541 8501 5575 1408 7254 1228 1946 6158 6655 3267 3291 61 1020 9887 5209 7413 4036 9763 976 4139 8542 480 5440 1351 8393 8029 3388 4397 1417 5664 4662 3033 1973 8268 8079 8795 8263 1423 9025 9675 2931 1708 1407 246 9526 2229 6178 3799 5839 3522 303 8238 1488 2978 3606 401 1557 9251 8190 9918 2315 8468 6350 2335 4773 9254 4267 4116 5843 6358 2945 6405 5556 1835 864 8031 248 6930 7318 6676 9202 9369 3138 793 5022 7988 3550 8212 9108 8907 3135 6462 2650 9381 538 8696 6238 3576 2036 6582 5770 961 8730 55 7956 3977 6982 5183 9989 4621 4659 2016 5918 6601 3454 8123 5555 6902 3438 5627 1490 2597 3837 703 2747 5948 1285 3759 8137 1223 9870 9262 1863 7994 2780 5624 7276 9513 7017 5816 4859 1739 7052 5533 5198 9089 1934 3754 0 7526 1403 7423 5000 3631 3471 3014 9456 4409 2286 9630 7094 4846 7696 7550 6751 7434 3483 2081 3647 2211 5885 5846 6722 3893 768 5720 3419 3365 7774 9492 5878 1221 6917 7288 8229 6189 2255 7382 1789 508 5639 1558 8309 5727 799 6828 6954 1745 8727 1599 9299 9324 8669 381 4981 2957 9489 7447 283 5442 2925 9550 2010 4758 6876 4931 4412 5229 5166 9755 8975 9676 2420 5905 959 6590 8891 2096 8282 6646 1728 7370 1034 8340 7682 1487 9077 5329 4056 4192 3752 3971 1344 3195 1323 6427 256 3841 1278 1968 9738 3116 1090 4779 3265 5625 951 2198 7338 5146 4018 2528 874 6668 9509 8220 6210 1273 5898 179 5833 4339 1817 9656 8903 1023 4181 1236 8940 704 3642 6966 4070 205 8699 6661 7273 1953 6728 4673 2121 1610 2116 3302 1309 5506 7102 8114 1759 6579 1521 9053 9378 2549 8674 2850 830 863 8214 6788 3748 4364 2365 9147 5411 1929 6510 3037 9083 2195 1730 138 1048 5393 119 9370 529 6317 4117 9067 765 7304 1158 4855 6529 8814 1554 9955 8773 2763 2544 4368 3761 8064 5249 6850 9575 3194 1392 4519 7176 2340 3041 2216 7115 2110 4382 9179 1130 8643 7810 376 3109 8261 3142 9722 3613 5962 743 9699 7504 988 4789 2042 8547 7350 9029 699 8959 2942 4013 9974 9433 421 3363 3950 4133 1803 3910 175 5923 3750 9736 6384 4222 6031 1317 7653 1050 3169 5656 3548 8739 8201 1155 2328 1287 9401 9008 7875 5986 5078 5263 6486 1731 5970 6304 267 8853 2672 5580 1255 7650 3743 7492 6339 9190 7846 5781 7950 9500 5653 7010 3829 5357 3215 5853 7580 9168 8189 4251 4019 8969 418 8944 330 2129 5617 5536 2318 9081 7449 9328 859 9956 1606 1933 866 6783 2127 4733 909 2095 8681 9663 4544 757 3863 2444 1741 3854 6421 744 7083 1498 2347 8359 1447 6139 481 5754 5775 3324 6706 8283 3444 9981 8200 8188 6079 9508 7952 5718 7203 9342 7071 6551 274 5583 3143 8582 5334 3441 6018 5049 6081 3517 7822 6946 6816 1239 5286 6371 9651 7716 8156 293 8543 5790 5041 4569 3045 7110 6073 8164 7876 3698 8998 8259 4973 9742 9936 8919 5186 7066 4298 3161 5707 8092 7363 2985 6051 9175 8534 1030 5591 6726 665 3532 6438 2416 9648 1621 9973 7030 2306 4207 8378 5636 4124 9665 3315 4193 6957 8316 639 1503 4198 6038 2969 6526 489 689 4437 6444 2551 3670 2443 5979 8558 6969 9354 214 4371 6999 4289 916 4798 5018 8520 1650 726 5320 5847 9021 5143 6651 9713 5640 7865 1643 9078 271 4850 6296 3802 8792 8083 1117 6200 549 8802 5495 2040 5769 2209 8858 8659 5891 8701 8607 8131 3667 598 756 5780 257 997 4380 1791 3241 502 1801 6725 8917 7501 6183 4343 4786 7213 690 7227 6987 5220 7871 126 9094 4878 1680 3784 7884 825 9608 9780 6146 6157 835 7416 7845 2035 2645 3836 6615 2621 8779 6743 5551 1217 4975 3030 5696 9636 7565 5912 4574 5017 741 9758 8849 3923 8813 3553 2749 9407 6314 5308 1996 4445 8276 7494 5978 5526 3068 4713 5068 7488 4090 3148 7574 1910 334 4916 2268 5065 1191 5383 2319 2417 3183 7332 1128 4504 7228 6509 1706 4417 5822 5682 9364 8269 6530 1141 6989 7679 5390 4886 6095 6965 522 2362 3542 5367 9678 1209 4353 7451 3729 4581 3489 8564 8040 7589 2903 2665 3538 3001 7582 8579 5255 2080 291 846 9996 7279 5924 3814 2107 2940 1480 8732 5919 6786 6224 568 4096 396 5318 8507 4142 7123 2579 6399 6716 5067 1083 5150 8 6611 9142 5985 1210 3817 8968 4583 7892 724 5658 9050 4890 2794 2441 3310 3284 9146 1869 1840 3847 1595 2902 760 2791 3318 3989 5268 7084 3600 3184 7170 1861 7581 7252 128 5548 8983 3119 1160 4696 4425 8143 971 6067 7978 4356 9253 626 7109 681 3607 5214 6996 2425 2909 7797 6473 4948 1555 8842 3009 2316 2681 4932 2570 6803 2502 9417 5289 1563 1025 7444 1334 8044 9017 3332 7910 6552 6419 7995 6431 5476 7695 7105 9587 4945 7527 5829 8839 7719 922 6002 6753 1687 8039 3108 1633 3501 3503 8506 9325 4684 240 4348 2206 2753 9655 4635 8455 2797 675 7011 2540 1174 3204 8051 3066 8317 9859 8931 7366 4667 858 7035 5351 4315 2654 523 720 1474 2366 8822 5248 9128 4367 641 6931 4323 8781 7126 6520 1663 842 1773 2926 5253 2108 5561 7077 8241 4328 7499 9205 939 72 78 3333 8087 4015 7286 2409 6362 5994 2693 8145 9100 8886 7359 165 1758 8964 5090 4521 6682 8652 6474 7092 5216 5875 4296 1293 7702 8683 8470 8662 7945 8846 1262 4985 8193 6133 7116 8602 6027 2805 2403 8912 6618 4512 8952 6913 6065 8460 4925 9123 7538 1275 8909 54 5380 6746 5278 1825 367 8152 2270 1171 2233 8048 7223 7883 2796 1743 9897 8465 5512 7354 9440 3715 5013 5673 5043 6423 818 5284 7198 3914 1602 1952 6039 7162 550 7430 8119 3635 4912 4052 7614 9004 1879 6563 2622 3155 7749 3309 5036 7514 8777 7431 5511 3879 482 7402 628 7608 2118 1054 8239 5514 1330 3637 3706 6418 6324 5066 6525 9983 2846 469 4331 7406 1911 4119 2872 1489 6046 6153 6814 6173 4272 5815 5886 3089 7913 7220 5441 2573 3582 6500 7754 707 9923 3065 4214 9561 5142 8120 4068 4638 5831 6909 1466 936 2481 9566 4546 219 1251 2030 4410 8977 544 1921 5045 3421 1440 9650 6916 2793 5791 1120 4622 3684 7004 2842 563 8139 1972 3692 4710 4590 7535 7523 1431 2746 361 6085 4946 5312 6268 8223 1742 1604 3394 851 1207 8066 7691 5082 9847 4218 6700 7595 4206 9228 8741 7275 593 8786 7961 722 3552 633 5916 8567 536 2273 2608 8185 6198 5537 9118 3578 7709 6535 180 6986 3577 8950 3260 8819 8804 9005 5902 8498 9059 4307 5309 8670 6072 3017 3515 3697 4176 3027 9696 2503 6654 2717 2905 425 4123 1914 503 6 9813 4858 82 4712 2433 9230 6821 9483 3502 2440 3338 514 7330 7528 935 6480 1404 8247 6556 8170 3062 4398 2471 8991 3544 1700 2767 4711 7668 783 7044 390 5354 7831 2200 7564 605 6230 3769 6832 6594 7779 7672 234 3870 9683 5299 6932 2519 1359 9463 7972 7072 967 2170 9525 7016 9263 8715 5698 8735 1340 2890 1247 3980 7179 3134 4930 7483 2020 5145 7847 3695 3173 7930 9988 3699 5124 5560 1850 4954 4255 6262 6492 4144 5384
\ No newline at end of file
diff --git a/Udemy DataScience Workbook/passwords_random.txt b/Udemy DataScience Workbook/passwords_random.txt
new file mode 100644
index 0000000..4448484
--- /dev/null
+++ b/Udemy DataScience Workbook/passwords_random.txt	
@@ -0,0 +1 @@
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989 7852 7111 1991 677 649 6641 8246 4295 7118 3393 4416 5818 8326 6815 8428 3321 310 2844 2382 185 1690 6935 7396 6047 9173 9006 5952 8935 2865 5060 1992 5488 5990 2159 1510 5196 4867 1356 2596 358 3790 5647 2849 241 5820 6489 7907 9415 8367 2808 653 2480 4762 4577 9287 6863 297 9963 8127 94 3300 3834 838 5616 7881 4262 3931 5854 7850 3074 4428 2041 8278 5522 2446 7445 2866 2810 59 933 9767 4221 3192 3331 7648 1805 8765 9796 9219 7267 7138 7432 1396 8179 1870 9167 8862 5074 2582 2057 7701 3024 5801 6471 1268 6890 4072 8921 6349 3146 2882 856 5549 2801 7602 3926 6555 9156 6261 5812 1260 3514 4319 2024 1808 9930 709 6273 3157 8835 7187 7368 9358 4064 2848 4781 5860 9890 5371 372 8343 6622 9060 4639 8995 601 4989 223 130 8867 5710 104 9154 2053 1464 2006 3907 2293 3690 4200 3400 3445 7786 9846 56 7996 3308 7182 5029 5008 1450 6077 9276 5823 1691 8532 3061 6151 4536 5613 4808 7543 6288 594 702 2790 3459 5821 1196 3016 4687 9289 7761 2712 5713 772 2476 1499 9164 9580 1724 305 8710 7838 5457 5749 8955 7284 1338 8350 1964 8099 2484 8117 3932 1401 7805 7963 1598 1364 5996 1230 7675 4771 1098 3793 4186 7630 6004 3490 4388 2137 8529 7096 2133 4736 3006 1482 3668 8322 8746 1032 1675 2078 2994 8951 1978 262 4904 8345 3102 7878 3082 9104 466 5845 3385 7683 3737 7192 5251 5766 3801 7124 1530 4083 8689 2927 4249 574 9181 4438 5237 6243 4924 9293 1441 7877 5481 1847 8004 5545 280 3170 5783 8636 3457 5461 865 7346 8654 4751 38 3655 3043 3773 4923 3411 4277 7708 4349 3259 8641 1399 1454 4099 786 6889 917 2172 3058 8979 8559 8627 6573 7150 221 8174 645 2853 5402 1164 9351 9698 4171 1535 7622 9788 8252 4658 1161 5178 5767 9823 6142 4541 3293 1233 9226 1909 9391 2939 4505 5643 1316 171 1780 5327 4496 3744 1945 9677 2775 3609 2245 6213 5992 6869 8256 389 3190 9540 7474 8331 7893 9695 9818 7121 7895 8635 2523 886 6974 7117 4729 6807 7464 6162 450 8605 4761 149 3605 8528 3688 2143 7303 6948 7629 7891 8687 5527 8885 2349 1638 4686 844 6514 9883 7785 6441 6595 2615 2532 1481 5173 4807 2449 1887 5222 7562 204 12 2722 734 6236 1533 8589 6710 4777 8403 3049 8257 3983 8775 8546 6328 3439 1449 7497 2691 1302 5909 1618 1085 6843 3556 6645 8416 4216 7378 5939 5379 8673 6864 2938 7255 8660 3956 2659 4865 8194 1862 674 4576 4838 7335 6508 2618 6017 887 7646 6517 5515 7025 6938 7756 9820 8644 627 2512 2711 2452 8685 1962 7522 275 6392 9718 7175 4791 4961 6337 2816 9225 7482 4506 4619 5752 414 484 4594 1834 3435 9426 8167 8431 8429 735 2529 1205 6713 7425 1008 6656 8937 4432 5133 9250 6030 2967 189 6021 6498 721 8785 2847 2934 4826 9623 6359 1961 67 5217 5808 1386 2079 8888 1754 7098 7190 1832 631 9993 557 3830 3250 8728 9469 8304 4871 2800 2591 7676 3025 4257 6076 6798 4928 4475 9355 9588 836 650 5296 7154 6994 3840 9968 3158 8479 9916 5419 2900 7376 71 5908 6923 803 6272 7358 927 5108 6822 2348 6388 4941 183 8586 6476 1178 87 1432 1106 6541 3003 2497 40 1562 2089 8572 592 4211 9661 1242 2990 5828 1426 6218 2855 5602 1124 3992 3011 5731 9603 8293 166 7915 4236 5911 6194 5425 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7328 1889 9292 7729 3325 5674 455 1329 8756 6398 7151 6118 5721 2462 8933 5861 1060 7065 8315 347 9797 8551 3034 9470 1063 2092 4265 2186 966 2048 3771 2482 9497 451 3713 785 8301 3163 5212 3047 6252 2897 411 6103 5866 9759 1311 1852 9776 9967 3654 7509 2114 5331 2283 2550 1727 9395 18 4697 852 4135 2368 9080 7775 3809 8587 7234 4503 1589 6041 8776 485 4014 9436 1483 2185 5427 395 1014 3970 1003 9568 9051 2852 2980 4354 2085 9538 2589 1574 8129 8647 2176 8341 7840 2607 6995 4892 8511 6603 2641 7730 6800 5719 2661 4474 1382 8600 6351 2730 1948 2458 6354 4885 4534 1926 2026 329 7632 5490 8870 5609 817 8916 1525 8493 8771 2076 501 3389 5734 7045 8050 8175 6762 258 4851 9318 3252 6446 805 4545 7394 3894 2424 309 4040 5690 8905 5280 9739 1678 8803 637 7819 7261 286 1775 3849 5365 4381 8224 6186 1148 6170 1837 9682 630 3290 2946 7833 163 1646 8155 6919 7291 3547 7899 7803 8657 6117 9424 3766 7345 8001 6150 2473 6830 4470 7985 1600 2553 4035 3234 4795 9071 539 6080 512 1458 5628 2727 342 3402 6759 9977 3787 6679 6705 3858 4485 9475 7259 587 5513 5349 3005 136 5589 7835 7312 3816 2438 6662 873 2135 255 3875 8412 6219 9092 7825 260 516 8829 1827 272 1859 5858 8833 8973 5039 2285 8761 4246 118 4955 8173 6311 3594 1109 216 457 843 5626 1809 4897 8477 3540 1186 1485 5942 4203 7686 6640 750 2829 3316 4597 9241 9344 2572 5744 6102 6242 4889 7885 2684 5675 1611 5397 7641 3712 8213 7666 8581 5620 3088 3248 114 5062 8611 3987 5943 4063 2192 4543 6380 952 9257 5622 943 4852 1969 3186 8287 1184 3942 3685 6685 6221 338 7867 9962 3887 4480 9086 3409 6088 6545 1372 2478 8141 2699 7618 8226 9551 8349 8774 7081 3249 5663 3672 4332 6825 5465 5567 3069 3504 6277 8300 2666 2922 8512 206 4614 3646 2726 9619 7088 9235 9375 6246 3495 2526 7298 2061 3383 2655 6175 3565 287 5980 5722 9385 9649 7806 6164 4037 8840 1841 2827 6761 9270 7808 6880 1857 9670 2301 5717 322 9632 809 7385 9749 7506 5774 6364 1402 3073 1406 3477 3382 7983 9036 2838 6888 9905 6636 7388 2213 6100 8568 1693 43 1283 9984 712 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741 9758 8849 3923 8813 3553 2749 9407 6314 5308 1996 4445 8276 7494 5978 5526 3068 4713 5068 7488 4090 3148 7574 1910 334 4916 2268 5065 1191 5383 2319 2417 3183 7332 1128 4504 7228 6509 1706 4417 5822 5682 9364 8269 6530 1141 6989 7679 5390 4886 6095 6965 522 2362 3542 5367 9678 1209 4353 7451 3729 4581 3489 8564 8040 7589 2903 2665 3538 3001 7582 8579 5255 2080 291 846 9996 7279 5924 3814 2107 2940 1480 8732 5919 6786 6224 568 4096 396 5318 8507 4142 7123 2579 6399 6716 5067 1083 5150 8 6611 9142 5985 1210 3817 8968 4583 7892 724 5658 9050 4890 2794 2441 3310 3284 9146 1869 1840 3847 1595 2902 760 2791 3318 3989 5268 7084 3600 3184 7170 1861 7581 7252 128 5548 8983 3119 1160 4696 4425 8143 971 6067 7978 4356 9253 626 7109 681 3607 5214 6996 2425 2909 7797 6473 4948 1555 8842 3009 2316 2681 4932 2570 6803 2502 9417 5289 1563 1025 7444 1334 8044 9017 3332 7910 6552 6419 7995 6431 5476 7695 7105 9587 4945 7527 5829 8839 7719 922 6002 6753 1687 8039 3108 1633 3501 3503 8506 9325 4684 240 4348 2206 2753 9655 4635 8455 2797 675 7011 2540 1174 3204 8051 3066 8317 9859 8931 7366 4667 858 7035 5351 4315 2654 523 720 1474 2366 8822 5248 9128 4367 641 6931 4323 8781 7126 6520 1663 842 1773 2926 5253 2108 5561 7077 8241 4328 7499 9205 939 72 78 3333 8087 4015 7286 2409 6362 5994 2693 8145 9100 8886 7359 165 1758 8964 5090 4521 6682 8652 6474 7092 5216 5875 4296 1293 7702 8683 8470 8662 7945 8846 1262 4985 8193 6133 7116 8602 6027 2805 2403 8912 6618 4512 8952 6913 6065 8460 4925 9123 7538 1275 8909 54 5380 6746 5278 1825 367 8152 2270 1171 2233 8048 7223 7883 2796 1743 9897 8465 5512 7354 9440 3715 5013 5673 5043 6423 818 5284 7198 3914 1602 1952 6039 7162 550 7430 8119 3635 4912 4052 7614 9004 1879 6563 2622 3155 7749 3309 5036 7514 8777 7431 5511 3879 482 7402 628 7608 2118 1054 8239 5514 1330 3637 3706 6418 6324 5066 6525 9983 2846 469 4331 7406 1911 4119 2872 1489 6046 6153 6814 6173 4272 5815 5886 3089 7913 7220 5441 2573 3582 6500 7754 707 9923 3065 4214 9561 5142 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\ No newline at end of file
diff --git a/Udemy DataScience Workbook/pythonpackages.txt b/Udemy DataScience Workbook/pythonpackages.txt
new file mode 100644
index 0000000..f8533e9
--- /dev/null
+++ b/Udemy DataScience Workbook/pythonpackages.txt	
@@ -0,0 +1,1001 @@
+alabaster  ==  0.7.12
+anaconda-client  ==  1.11.1
+anaconda-navigator ==  2.4.0
+anaconda-project ==  0.11.1
+anyio ==  3.5.0
+appdirs ==  1.4.4
+argon2-cffi ==  21.3.0
+argon2-cffi-bindings ==  21.2.0
+arrow ==  1.2.3
+astroid ==  2.14.2
+astropy ==  5.1
+asttokens ==  2.0.5
+atomicwrites ==  1.4.0
+attrs ==  22.1.0
+automat ==  20.2.0
+autopep8 ==  1.6.0
+babel ==  2.11.0
+backcall ==  0.2.0
+backports ==  1.1
+backports.functools_lru_cache ==  1.6.4
+backports.tempfile ==  1
+backports.weakref ==  1.0.post1
+bcrypt ==  3.2.0
+beautifulsoup4 ==  4.11.1
+binaryornot ==  0.4.4
+black ==  22.6.0
+blas ==  1
+bleach ==  4.1.0
+blosc ==  1.21.3
+bokeh ==  2.4.3
+bottleneck ==  1.3.5
+brotli ==  1.0.9
+brotli-bin ==  1.0.9
+brotlipy ==  0.7.0
+bzip2 ==  1.0.8
+ca-certificates ==  2023.01.10
+certifi ==  2022.12.7
+cffi ==  1.15.1
+cfitsio ==  3.47
+chardet ==  4.0.0
+charls ==  2.2.0
+charset-normalizer ==  2.0.4
+click ==  8.0.4
+cloudpickle ==  2.0.0
+clyent ==  1.2.2
+colorama ==  0.4.6
+colorcet ==  3.0.1
+comm ==  0.1.2
+conda ==  23.1.0
+conda-build ==  3.23.3
+conda-content-trust ==  0.1.3
+conda-pack ==  0.6.0
+conda-package-handling ==  2.0.2
+conda-package-streaming ==  0.7.0
+conda-repo-cli ==  1.0.27
+conda-token ==  0.4.0
+conda-verify ==  3.4.2
+console_shortcut ==  0.1.1
+constantly ==  15.1.0
+contourpy ==  1.0.5
+cookiecutter ==  1.7.3
+cryptography ==  39.0.1
+cssselect ==  1.1.0
+curl ==  7.87.0
+cycler ==  0.11.0
+cytoolz ==  0.12.0
+daal4py ==  2023.0.2
+dal ==  2023.0.1
+dask ==  2022.7.0
+dask-core ==  2022.7.0
+datashader ==  0.14.4
+datashape ==  0.5.4
+debugpy ==  1.5.1
+decorator ==  5.1.1
+defusedxml ==  0.7.1
+diff-match-patch ==  20200713
+dill ==  0.3.6
+distributed ==  2022.7.0
+docstring-to-markdown ==  0.11
+docutils ==  0.18.1
+entrypoints ==  0.4
+et_xmlfile ==  1.1.0
+executing ==  0.8.3
+filelock ==  3.9.0
+flake8 ==  6.0.0
+flask ==  2.2.2
+flit-core ==  3.6.0
+fonttools ==  4.25.0
+freetype ==  2.12.1
+fsspec ==  2022.11.0
+future ==  0.18.3
+gensim ==  4.3.0
+giflib ==  5.2.1
+glib ==  2.69.1
+glob2 ==  0.7
+greenlet ==  2.0.1
+gst-plugins-base ==  1.18.5
+gstreamer ==  1.18.5
+h5py ==  3.7.0
+hdf5 ==  1.10.6
+heapdict ==  1.0.1
+holoviews ==  1.15.4
+huggingface_hub ==  0.10.1
+hvplot ==  0.8.2
+hyperlink ==  21.0.0
+icc_rt ==  2022.1.0
+icu ==  58.2
+idna ==  3.4
+imagecodecs ==  2021.8.26
+imageio ==  2.26.0
+imagesize ==  1.4.1
+imbalanced-learn ==  0.10.1
+importlib-metadata ==  4.11.3
+importlib_metadata ==  4.11.3
+incremental ==  21.3.0
+inflection ==  0.5.1
+iniconfig ==  1.1.1
+intake ==  0.6.7
+intel-openmp ==  2021.4.0
+intervaltree ==  3.1.0
+ipykernel ==  6.19.2
+ipython ==  8.10.0
+ipython_genutils ==  0.2.0
+ipywidgets ==  7.6.5
+isort ==  5.9.3
+itemadapter ==  0.3.0
+itemloaders ==  1.0.4
+itsdangerous ==  2.0.1
+jedi ==  0.18.1
+jellyfish ==  0.9.0
+jinja2 ==  3.1.2
+jinja2-time ==  0.2.0
+jmespath ==  0.10.0
+joblib ==  1.1.1
+jpeg ==  9e
+jq ==  1.6
+json5 ==  0.9.6
+jsonschema ==  4.17.3
+jupyter ==  1.0.0
+jupyter_client ==  7.3.4
+jupyter_console ==  6.6.2
+jupyter_core ==  5.2.0
+jupyter_server ==  1.23.4
+jupyterlab ==  3.5.3
+jupyterlab_pygments ==  0.1.2
+jupyterlab_server ==  2.19.0
+jupyterlab_widgets ==  1.0.0
+jxrlib ==  1.1
+keyring ==  23.4.0
+kiwisolver ==  1.4.4
+lazy-object-proxy ==  1.6.0
+lcms2 ==  2.12
+lerc ==  3
+libaec ==  1.0.4
+libarchive ==  3.6.2
+libbrotlicommon ==  1.0.9
+libbrotlidec ==  1.0.9
+libbrotlienc ==  1.0.9
+libclang ==  12.0.0
+libcurl ==  7.87.0
+libdeflate ==  1.17
+libffi ==  3.4.2
+libiconv ==  1.16
+liblief ==  0.12.3
+libogg ==  1.3.5
+libpng ==  1.6.39
+libsodium ==  1.0.18
+libspatialindex ==  1.9.3
+libssh2 ==  1.10.0
+libtiff ==  4.5.0
+libuv ==  1.44.2
+libvorbis ==  1.3.7
+libwebp ==  1.2.4
+libwebp-base ==  1.2.4
+libxml2 ==  2.9.14
+libxslt ==  1.1.35
+libzopfli ==  1.0.3
+llvmlite ==  0.39.1
+locket ==  1.0.0
+lxml ==  4.9.1
+lz4 ==  3.1.3
+lz4-c ==  1.9.4
+lzo ==  2.1
+m2-msys2-runtime ==  2.5.0.17080.65c939c
+m2-patch ==  2.7.5
+m2w64-libwinpthread-git ==  5.0.0.4634.697f757
+markdown ==  3.4.1
+markupsafe ==  2.1.1
+matplotlib ==  3.7.0
+matplotlib-base ==  3.7.0
+matplotlib-inline ==  0.1.6
+mccabe ==  0.7.0
+menuinst ==  1.4.19
+mistune ==  0.8.4
+mkl ==  2021.4.0
+mkl-service ==  2.4.0
+mkl_fft ==  1.3.1
+mkl_random ==  1.2.2
+mock ==  4.0.3
+mpmath ==  1.2.1
+msgpack-python ==  1.0.3
+msys2-conda-epoch ==  20160418
+multipledispatch ==  0.6.0
+munkres ==  1.1.4
+mypy_extensions ==  0.4.3
+navigator-updater ==  0.3.0
+nbclassic ==  0.5.2
+nbclient ==  0.5.13
+nbconvert ==  6.5.4
+nbformat ==  5.7.0
+nest-asyncio ==  1.5.6
+networkx ==  2.8.4
+ninja ==  1.10.2
+ninja-base ==  1.10.2
+nltk ==  3.7
+notebook ==  6.5.2
+notebook-shim ==  0.2.2
+numba ==  0.56.4
+numexpr ==  2.8.4
+numpy ==  1.23.5
+numpy-base ==  1.23.5
+numpydoc ==  1.5.0
+openjpeg ==  2.4.0
+openpyxl ==  3.0.10
+openssl ==  1.1.1t
+packaging ==  22
+pandas ==  1.5.3
+pandocfilters ==  1.5.0
+panel ==  0.14.3
+param ==  1.12.3
+paramiko ==  2.8.1
+parsel ==  1.6.0
+parso ==  0.8.3
+partd ==  1.2.0
+pathlib ==  1.0.1
+pathspec ==  0.10.3
+patsy ==  0.5.3
+pcre ==  8.45
+pep8 ==  1.7.1
+pexpect ==  4.8.0
+pickleshare ==  0.7.5
+pillow ==  9.4.0
+pip ==  22.3.1
+pkginfo ==  1.8.3
+platformdirs ==  2.5.2
+plotly ==  5.9.0
+pluggy ==  1.0.0
+ply ==  3.11
+pooch ==  1.4.0
+powershell_shortcut ==  0.0.1
+poyo ==  0.5.0
+prometheus_client ==  0.14.1
+prompt-toolkit ==  3.0.36
+prompt_toolkit ==  3.0.36
+protego ==  0.1.16
+psutil ==  5.9.0
+psycopg2 ==  2.9.6
+ptyprocess ==  0.7.0
+pure_eval ==  0.2.2
+py ==  1.11.0
+py-lief ==  0.12.3
+pyasn1 ==  0.4.8
+pyasn1-modules ==  0.2.8
+pycodestyle ==  2.10.0
+pycosat ==  0.6.4
+pycparser ==  2.21
+pyct ==  0.5.0
+pycurl ==  7.45.1
+pydispatcher ==  2.0.5
+pydocstyle ==  6.3.0
+pyerfa ==  2.0.0
+pyflakes ==  3.0.1
+pygments ==  2.11.2
+pyhamcrest ==  2.0.2
+pyjwt ==  2.4.0
+pylint ==  2.16.2
+pylint-venv ==  2.3.0
+pyls-spyder ==  0.4.0
+pymysql ==  1.0.3
+pynacl ==  1.5.0
+pyodbc ==  4.0.34
+pyopenssl ==  23.0.0
+pyparsing ==  3.0.9
+pyqt ==  5.15.7
+pyqt5-sip ==  12.11.0
+pyqtwebengine ==  5.15.7
+pyrsistent ==  0.18.0
+pysocks ==  1.7.1
+pytables ==  3.7.0
+pytest ==  7.1.2
+python ==  3.10.9
+python-dateutil ==  2.8.2
+python-fastjsonschema ==  2.16.2
+python-libarchive-c ==  2.9
+python-lsp-black ==  1.2.1
+python-lsp-jsonrpc ==  1.0.0
+python-lsp-server ==  1.7.1
+python-slugify ==  5.0.2
+python-snappy ==  0.6.1
+pytoolconfig ==  1.2.5
+pytorch ==  1.12.1
+pytz ==  2022.7
+pyviz_comms ==  2.0.2
+pywavelets ==  1.4.1
+pywin32 ==  305
+pywin32-ctypes ==  0.2.0
+pywinpty ==  2.0.10
+pyyaml ==  6
+pyzmq ==  23.2.0
+qdarkstyle ==  3.0.2
+qstylizer ==  0.2.2
+qt-main ==  5.15.2
+qt-webengine ==  5.15.9
+qtawesome ==  1.2.2
+qtconsole ==  5.4.0
+qtpy ==  2.2.0
+qtwebkit ==  5.212
+queuelib ==  1.5.0
+regex ==  2022.7.9
+requests ==  2.28.1
+requests-file ==  1.5.1
+rope ==  1.7.0
+rtree ==  1.0.1
+ruamel.yaml ==  0.17.21
+ruamel.yaml.clib ==  0.2.6
+ruamel_yaml ==  0.17.21
+scikit-image ==  0.19.3
+scikit-learn ==  1.2.1
+scikit-learn-intelex ==  2023.0.2
+scipy ==  1.10.0
+scrapy ==  2.8.0
+seaborn ==  0.12.2
+send2trash ==  1.8.0
+service_identity ==  18.1.0
+setuptools ==  65.6.3
+sip ==  6.6.2
+six ==  1.16.0
+smart_open ==  5.2.1
+snappy ==  1.1.9
+sniffio ==  1.2.0
+snowballstemmer ==  2.2.0
+sortedcontainers ==  2.4.0
+soupsieve ==  2.3.2.post1
+sphinx ==  5.0.2
+sphinxcontrib-applehelp ==  1.0.2
+sphinxcontrib-devhelp ==  1.0.2
+sphinxcontrib-htmlhelp ==  2.0.0
+sphinxcontrib-jsmath ==  1.0.1
+sphinxcontrib-qthelp ==  1.0.3
+sphinxcontrib-serializinghtml ==  1.1.5
+spyder ==  5.4.1
+spyder-kernels ==  2.4.1
+sqlalchemy ==  1.4.39
+sqlite ==  3.40.1
+stack_data ==  0.2.0
+statsmodels ==  0.13.5
+sympy ==  1.11.1
+tabulate ==  0.8.10
+tbb ==  2021.7.0
+tbb4py ==  2021.7.0
+tblib ==  1.7.0
+tenacity ==  8.0.1
+terminado ==  0.17.1
+text-unidecode ==  1.3
+textdistance ==  4.2.1
+threadpoolctl ==  2.2.0
+three-merge ==  0.1.1
+tifffile ==  2021.7.2
+tinycss2 ==  1.2.1
+tk ==  8.6.12
+tldextract ==  3.2.0
+tokenizers ==  0.11.4
+toml ==  0.10.2
+tomli ==  2.0.1
+tomlkit ==  0.11.1
+toolz ==  0.12.0
+tornado ==  6.1
+tqdm ==  4.64.1
+traitlets ==  5.7.1
+transformers ==  4.24.0
+twisted ==  22.2.0
+twisted-iocpsupport ==  1.0.2
+typing-extensions ==  4.4.0
+typing_extensions ==  4.4.0
+tzdata ==  2022g
+ujson ==  5.4.0
+unidecode ==  1.2.0
+urllib3 ==  1.26.14
+vc ==  14.2
+vs2015_runtime ==  14.27.29016
+w3lib ==  1.21.0
+watchdog ==  2.1.6
+wcwidth ==  0.2.5
+webencodings ==  0.5.1
+websocket-client ==  0.58.0
+werkzeug ==  2.2.2
+whatthepatch ==  1.0.2
+wheel ==  0.38.4
+widgetsnbextension ==  3.5.2
+win_inet_pton ==  1.1.0
+wincertstore ==  0.2
+winpty ==  0.4.3
+wrapt ==  1.14.1
+xarray ==  2022.11.0
+xlwings ==  0.29.1
+xz ==  5.2.10
+yaml ==  0.2.5
+yapf ==  0.31.0
+zeromq ==  4.3.4
+zfp ==  0.5.5
+zict ==  2.1.0
+zipp ==  3.11.0
+zlib ==  1.2.13
+zope ==  1
+zope.interface ==  5.4.0
+zstandard ==  0.19.0
+zstd ==  1.5.2
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diff --git a/Udemy DataScience Workbook/real_estate_price_size.csv b/Udemy DataScience Workbook/real_estate_price_size.csv
new file mode 100644
index 0000000..6f51299
--- /dev/null
+++ b/Udemy DataScience Workbook/real_estate_price_size.csv	
@@ -0,0 +1,101 @@
+price,size
+234314.144,643.09
+228581.528,656.22
+281626.336,487.29
+401255.608,1504.75
+458674.256,1275.46
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+168047.264,525.81
+362519.72,1103.3
+271793.312,570.89
+406852.304,1334.1
+297760.44,681.07
+368988.432,1496.36
+301635.728,1010.33
+225452.32,681.07
+207742.248,597.9
+191486.896,525.81
+285223.176,857.54
+302000.92,622.97
+269225.92,823.21
+233493.208,570.25
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+300061.48,698.29
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+304587.272,827.09
+355251.2,1009.25
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+294582.944,685.48
+454512.76,1496.36
+276875.632,1021.95
+181587.576,643.41
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+286161.6,685.48
+382120.152,1183.46
+365863.936,1334.1
+251560.04,682.26
+342988.456,1188.62
+180307.216,681.07
+408637.816,1122.34
+190909.056,681.07
+282683.544,643.09
+303597.216,685.48
+376253.808,1009.25
+154282.128,479.75
+327252.112,1028.41
+211904.536,601.66
+354512.112,1236.93
+251140.656,694.52
+338078.168,1071.55
+298170.88,694.52
+266684.248,698.29
+262477.856,698.29
+252460.4,549.8
+310522.592,1037.44
+383635.568,1504.75
+225145.248,648.29
+274922.856,705.29
diff --git a/Udemy DataScience Workbook/real_estate_price_size_year.csv b/Udemy DataScience Workbook/real_estate_price_size_year.csv
new file mode 100644
index 0000000..32952f3
--- /dev/null
+++ b/Udemy DataScience Workbook/real_estate_price_size_year.csv	
@@ -0,0 +1,101 @@
+price,size,year
+234314.144,643.09,2015
+228581.528,656.22,2009
+281626.336,487.29,2018
+401255.608,1504.75,2015
+458674.256,1275.46,2009
+245050.28,575.19,2006
+265129.064,570.89,2015
+175716.48,620.82,2006
+331101.344,682.26,2018
+218630.608,694.52,2009
+279555.096,1060.36,2009
+494778.992,1842.51,2009
+215472.104,694.52,2015
+418753.008,1009.25,2018
+444192.008,1300.96,2006
+440201.616,1379.72,2006
+248337.6,690.54,2018
+234178.16,623.94,2006
+225451.984,681.07,2006
+299416.976,1027.76,2018
+268125.08,620.71,2015
+171795.24,549.69,2015
+412569.472,1207.45,2015
+183459.488,518.38,2015
+168047.264,525.81,2009
+362519.72,1103.3,2018
+271793.312,570.89,2018
+406852.304,1334.1,2015
+297760.44,681.07,2015
+368988.432,1496.36,2015
+301635.728,1010.33,2006
+225452.32,681.07,2006
+207742.248,597.9,2009
+191486.896,525.81,2015
+285223.176,857.54,2018
+302000.92,622.97,2018
+269225.92,823.21,2006
+233493.208,570.25,2006
+292965.216,685.48,2018
+245747.2,698.29,2015
+310045.712,1021.95,2015
+217468.224,682.26,2018
+287350,823.21,2009
+414682.648,1334.1,2009
+293044.496,1060.36,2018
+300061.48,698.29,2015
+204302.976,633.19,2009
+201778.048,698.29,2006
+257828.416,633.19,2015
+262423.504,617.05,2015
+225656.12,647.5,2015
+393069.76,1021.95,2015
+258637.008,1021.95,2009
+269523.056,727.88,2006
+255629.16,647.5,2015
+500681.128,1508.84,2018
+320345.52,1032.06,2018
+395242.096,1283.85,2015
+330677.128,827.84,2006
+251332.592,569.17,2015
+251188.824,828.16,2018
+263311.696,698.5,2015
+359674.44,1205.62,2015
+334938.872,1021.95,2009
+302393.384,685.48,2015
+304587.272,827.09,2015
+355251.2,1009.25,2006
+271726.752,549.8,2015
+294582.944,685.48,2018
+454512.76,1496.36,2006
+276875.632,1021.95,2009
+181587.576,643.41,2006
+298926.496,656.22,2015
+211724.096,549.8,2015
+228313.024,685.48,2018
+286161.6,685.48,2018
+382120.152,1183.46,2018
+365863.936,1334.1,2006
+251560.04,682.26,2009
+342988.456,1188.62,2006
+180307.216,681.07,2006
+408637.816,1122.34,2015
+190909.056,681.07,2006
+282683.544,643.09,2018
+303597.216,685.48,2018
+376253.808,1009.25,2018
+154282.128,479.75,2006
+327252.112,1028.41,2009
+211904.536,601.66,2018
+354512.112,1236.93,2018
+251140.656,694.52,2018
+338078.168,1071.55,2015
+298170.88,694.52,2015
+266684.248,698.29,2018
+262477.856,698.29,2006
+252460.4,549.8,2009
+310522.592,1037.44,2009
+383635.568,1504.75,2006
+225145.248,648.29,2015
+274922.856,705.29,2006
diff --git a/Udemy DataScience Workbook/real_estate_price_size_year_view.csv b/Udemy DataScience Workbook/real_estate_price_size_year_view.csv
new file mode 100644
index 0000000..6090bab
--- /dev/null
+++ b/Udemy DataScience Workbook/real_estate_price_size_year_view.csv	
@@ -0,0 +1,101 @@
+price,size,year,view
+234314.144,643.09,2015,No sea view
+228581.528,656.22,2009,No sea view
+281626.336,487.29,2018,Sea view
+401255.608,1504.75,2015,No sea view
+458674.256,1275.46,2009,Sea view
+245050.28,575.19,2006,Sea view
+265129.064,570.89,2015,Sea view
+175716.48,620.82,2006,No sea view
+331101.344,682.26,2018,Sea view
+218630.608,694.52,2009,No sea view
+279555.096,1060.36,2009,No sea view
+494778.992,1842.51,2009,Sea view
+215472.104,694.52,2015,No sea view
+418753.008,1009.25,2018,No sea view
+444192.008,1300.96,2006,Sea view
+440201.616,1379.72,2006,Sea view
+248337.6,690.54,2018,No sea view
+234178.16,623.94,2006,Sea view
+225451.984,681.07,2006,No sea view
+299416.976,1027.76,2018,No sea view
+268125.08,620.71,2015,Sea view
+171795.24,549.69,2015,No sea view
+412569.472,1207.45,2015,Sea view
+183459.488,518.38,2015,No sea view
+168047.264,525.81,2009,No sea view
+362519.72,1103.3,2018,Sea view
+271793.312,570.89,2018,Sea view
+406852.304,1334.1,2015,Sea view
+297760.44,681.07,2015,Sea view
+368988.432,1496.36,2015,No sea view
+301635.728,1010.33,2006,No sea view
+225452.32,681.07,2006,No sea view
+207742.248,597.9,2009,No sea view
+191486.896,525.81,2015,No sea view
+285223.176,857.54,2018,No sea view
+302000.92,622.97,2018,Sea view
+269225.92,823.21,2006,No sea view
+233493.208,570.25,2006,Sea view
+292965.216,685.48,2018,Sea view
+245747.2,698.29,2015,No sea view
+310045.712,1021.95,2015,No sea view
+217468.224,682.26,2018,No sea view
+287350,823.21,2009,Sea view
+414682.648,1334.1,2009,No sea view
+293044.496,1060.36,2018,No sea view
+300061.48,698.29,2015,Sea view
+204302.976,633.19,2009,No sea view
+201778.048,698.29,2006,No sea view
+257828.416,633.19,2015,Sea view
+262423.504,617.05,2015,Sea view
+225656.12,647.5,2015,No sea view
+393069.76,1021.95,2015,Sea view
+258637.008,1021.95,2009,No sea view
+269523.056,727.88,2006,Sea view
+255629.16,647.5,2015,Sea view
+500681.128,1508.84,2018,Sea view
+320345.52,1032.06,2018,No sea view
+395242.096,1283.85,2015,Sea view
+330677.128,827.84,2006,Sea view
+251332.592,569.17,2015,Sea view
+251188.824,828.16,2018,No sea view
+263311.696,698.5,2015,No sea view
+359674.44,1205.62,2015,No sea view
+334938.872,1021.95,2009,Sea view
+302393.384,685.48,2015,Sea view
+304587.272,827.09,2015,No sea view
+355251.2,1009.25,2006,Sea view
+271726.752,549.8,2015,Sea view
+294582.944,685.48,2018,Sea view
+454512.76,1496.36,2006,Sea view
+276875.632,1021.95,2009,No sea view
+181587.576,643.41,2006,No sea view
+298926.496,656.22,2015,Sea view
+211724.096,549.8,2015,No sea view
+228313.024,685.48,2018,No sea view
+286161.6,685.48,2018,Sea view
+382120.152,1183.46,2018,Sea view
+365863.936,1334.1,2006,No sea view
+251560.04,682.26,2009,Sea view
+342988.456,1188.62,2006,Sea view
+180307.216,681.07,2006,No sea view
+408637.816,1122.34,2015,Sea view
+190909.056,681.07,2006,No sea view
+282683.544,643.09,2018,Sea view
+303597.216,685.48,2018,Sea view
+376253.808,1009.25,2018,Sea view
+154282.128,479.75,2006,No sea view
+327252.112,1028.41,2009,Sea view
+211904.536,601.66,2018,No sea view
+354512.112,1236.93,2018,No sea view
+251140.656,694.52,2018,No sea view
+338078.168,1071.55,2015,No sea view
+298170.88,694.52,2015,Sea view
+266684.248,698.29,2018,No sea view
+262477.856,698.29,2006,Sea view
+252460.4,549.8,2009,Sea view
+310522.592,1037.44,2009,No sea view
+383635.568,1504.75,2006,No sea view
+225145.248,648.29,2015,No sea view
+274922.856,705.29,2006,Sea view
diff --git a/Udemy DataScience Workbook/removal.txt b/Udemy DataScience Workbook/removal.txt
new file mode 100644
index 0000000..6a84442
--- /dev/null
+++ b/Udemy DataScience Workbook/removal.txt	
@@ -0,0 +1,10 @@
+pandas
+numpy
+BeautifulSoup4
+SciPy
+seaborn
+scikit-learn
+statsmodels
+Tensorflow
+tensorflow_datasets
+matplotlib
\ No newline at end of file
diff --git a/Udemy DataScience Workbook/residual_output.png b/Udemy DataScience Workbook/residual_output.png
new file mode 100644
index 0000000..3c415da
Binary files /dev/null and b/Udemy DataScience Workbook/residual_output.png differ
diff --git a/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-(DataCleaning).ipynb b/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-(DataCleaning).ipynb
new file mode 100644
index 0000000..aa01d89
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-(DataCleaning).ipynb	
@@ -0,0 +1,3265 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise - Dummies and VIF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "sns.set()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Real-life+example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preprocessing\n",
+    "\n",
+    "Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Price      Mileage      EngineV         Year\n",
+       "count    4173.000000  4345.000000  4195.000000  4345.000000\n",
+       "mean    19418.746935   161.237284     2.790734  2006.550058\n",
+       "std     25584.242620   105.705797     5.066437     6.719097\n",
+       "min       600.000000     0.000000     0.600000  1969.000000\n",
+       "25%      6999.000000    86.000000     1.800000  2003.000000\n",
+       "50%     11500.000000   155.000000     2.200000  2008.000000\n",
+       "75%     21700.000000   230.000000     3.000000  2012.000000\n",
+       "max    300000.000000   980.000000    99.990000  2016.000000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4340</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4341</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4342</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4343</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4344</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4345 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Brand  Price   Body  Mileage  EngineV  Engine Type  Registration   Year\n",
+       "0     False  False  False    False    False        False         False  False\n",
+       "1     False  False  False    False    False        False         False  False\n",
+       "2     False  False  False    False    False        False         False  False\n",
+       "3     False  False  False    False    False        False         False  False\n",
+       "4     False  False  False    False    False        False         False  False\n",
+       "...     ...    ...    ...      ...      ...          ...           ...    ...\n",
+       "4340  False  False  False    False    False        False         False  False\n",
+       "4341  False  False  False    False    False        False         False  False\n",
+       "4342  False  False  False    False    False        False         False  False\n",
+       "4343  False  False  False    False     True        False         False  False\n",
+       "4344  False  False  False    False    False        False         False  False\n",
+       "\n",
+       "[4345 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Price      Mileage      EngineV         Year\n",
+       "count    4025.000000  4025.000000  4025.000000  4025.000000\n",
+       "mean    19552.308065   163.572174     2.764586  2006.379627\n",
+       "std     25815.734988   103.394703     4.935941     6.695595\n",
+       "min       600.000000     0.000000     0.600000  1969.000000\n",
+       "25%      6999.000000    90.000000     1.800000  2003.000000\n",
+       "50%     11500.000000   158.000000     2.200000  2007.000000\n",
+       "75%     21900.000000   230.000000     3.000000  2012.000000\n",
+       "max    300000.000000   980.000000    99.990000  2016.000000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\216991009.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "data1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "data1.describe(include = 'all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\771842081.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data1['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\534165355.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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FFXOTDdX1VoiCgNw+Xk7gTInxOqhV3vWYKk+2KloLERH1LgYmCiu+5QSGZybAoFcrWosoCkhN5LAcEVE0YGCisHJqOE6Z5QTOlJbkXcDyYEWzwpUQEVFvYmCisOFweVB6vBEAMPaiUAlMvFKOiCgaMDBR2Cgtb4TLLSHZqEf/frFKlwMASE2MgSAAdc121DfblC6HiIh6CQMThQVBEPCdf7HKZIiiCEEQoOCqAgAArVqFAe3hbV97fUREFHkYmCjkeQC02l34tn39pazMBLTaXWi1u9DmcENStjxcNCABALD/GAMTEVGkYmCikCYIAuwON3bsq4G50QZBAJwuD/YersPew3U4cLwRbo8EAcp1NV3U3wgA2He0XrEaiIiodyl7XTZRN5XXtAAA+iXoAXhDEwBoNcpnfl9gOnrCArvTDbWofE1ERBRc/JedwkJl+w1uM5JDY7L36RLj9Ug26iBJMsqqLEqXQ0REvYCBiUKeJMs40R6Y+vczKFxNZ4IADB9oAgAcqmxqn4x+6kFEROGPgYlCXqW5FQ6XBxqViH4JMUqX04FKJUAURQxKjwcA7DvW6J+Q7nt4FK6RiIguHOcwUcgrLfcuVpmebIAohlaPjUoUYHO6/csblFU1Y8+hkxDbN2jUInKGJCFOr4Es8wa9REThij1MFPJ8q3tnJIfecJyPMVYLjVqEyy3B3GCF0+WB0+WBy630ogdERBQMDEwU0hxOD460T6QOldW9uyIKAtLbb5NibuSK30REkYaBiULagYpGeCQZcTEaxBs0SpdzTuntgc7cxMBERBRpGJgopH3ffruRASmxIX/FmW/Jg5PsYSIiijgMTBTSTgWmOIUrCSwt2QBBANrsbrTZXUqXQ0REQcTARCGrscWBqro2CAjN9ZfOpFWrkBSvA8B5TEREkYaBiULWvvab2Q5Mi4NeGx4rYKQmcuI3EVEkYmCikPV9e2DKHpyocCXdl5roXVjzJCd+ExFFFAYmCkmyLGPfMe/6S9mDwicwpbUHpkaLg2swERFFEAYmCkkn6q2wtDmhVYsYkmFUupxui43RIFavhgz2MhERRRIGJgpJB9pX9x6WmQCNOrw+phyWIyKKPOH1k4iiRunxJgDAyDAajvNJaQ9MnPhNRBQ5GJgo5Miy7O9hCqf5Sz6pplM9TJLEG+4SEUUCBiYKOSfqrWixuqBVixgaRvOXfEzxOmjUItweGQ0tdqXLISKiIGBgopBTWh6+85cA7414U0x6AEBtA4fliIgiQfj9NKKI5xuOC8f5Sz6+YbnaBqvClRARUTAwMFFIkWXZP+E7e5BJ0VouRFqSd8Xv6vo2yDLnMRERhTsGJgopJ+ra0GoL3/lLPv1MeqhEATaHBzX17GUiIgp3igcmSZKwevVqXHrppRg/fjzuvPNOVFRUnPX4xsZG3HvvvcjPz0dBQQFWrFgBm63jPJFt27bh6quvxtixYzFnzhxs3769w/7vv/8e8+bNw4QJE3DxxRdj2bJlaGlp6ZX3R+fH17s0LDMBapXiH88eU4mifz2mgxVNyhZDREQXTPGfSOvWrcPrr7+ORx99FG+88QYkScKCBQvgdDq7PH7RokUoLy/HK6+8ghdeeAGff/45li9f7t9fVFSEJUuW4MYbb8Q777yDwsJCLFy4EGVlZQCAuro6zJ8/HwMGDMDbb7+NdevWYdeuXXjggQf64u1SAJEwf8knPdk7LMfAREQU/hQNTE6nExs3bsSiRYswffp0ZGdnY9WqVaipqcFHH33U6fg9e/aguLgYTz31FHJzc1FYWIhHHnkEW7ZsQW1tLQDgpZdewsyZM3HrrbciKysLS5cuRW5uLl599VUAQFVVFaZOnYpHHnkEQ4cORV5eHn7605/iyy+/7NP3Tp1Fyvwln4z2eUyHKpu5HhMRUZhTNDCVlpaira0NhYWF/m1GoxE5OTnYuXNnp+NLSkqQkpKCrKws/7aCggIIgoBdu3ZBkiTs3r27w/kAYPLkyf7zjRs3Ds899xzUajUAoKysDFu2bMGUKVN64y3SeYiU+Us+SUY9NGoRNocbx2s55EtEFM7USr54TU0NACAjI6PD9tTUVP++09XW1nY6VqvVwmQyobq6GhaLBVarFenp6d0635VXXoljx45hwIABWLt27YW+HajDcM0glUoEJBmiKEAlCp33iwIEUYBaLUCWO+8PpoOVzQCA4QNN0Ou8H01BAIT22rqqTxQECIIAUQWoPAruF7z7RFEEIAHwtl3/frEor2nBgYpGZGUmdL8xopSqfd6aKoznr4UCtmNwsB2DJxLaUtHA5JusrdVqO2zX6XRobm7u8vgzj/Ud73A4YLfbz3o+h8PR6XkrV66EzWbDM888g1tvvRVbtmxBbGxsj96LKApITOzZc5Vma7BCp9NAVKk67dOoRcTotTCZDL1eR1m1BQCQl53WoS2dkhUxMVqoNVKn58To1VCrVYjRa6FWK7dfp9PA5Zag12s67B+cYUR5TQsOVllwc5h+PpRgNMYoXUJEYDsGB9sxeMK5LRUNTHq9dzVkp9Pp/xoAHA4HYmI6N6per+9yMrjD4YDBYIBOp/Of78z9XZ1vzJgxAIC1a9fisssuwz//+U/MmTOnR+9FkmRYLOF3+bivV8zhcMHucHfar9WoYLM70dQko7eWE3JLMlrtLnxzqA4AkJKgQ/mJJgDeIOrxyLDZnHC6PJ2eK8gS3G4PbHYnnE7l9jscLogqFex2FyTpVLBKTfB+rr8rq0PtSQu06s6hlE5RqUQYjTGwWGzweDoHVOoetmNwsB2DJ1Tb0miM6Xavl6KByTe8ZjabMWjQIP92s9mMkSNHdjo+PT0dH3/8cYdtTqcTTU1NSE1NhclkgsFggNls7nCM2WxGWloaAODIkSM4fvw4pk+f7t+flpYGk8nknzjeU2536HwIuss7kiRAkmR4upiY7JFkyJIMt1vulQUYBUFAm92F/3xzAq02F9QqAY0tDnx98CQAwKBXY3CGEZKELuuTZG9dkkfh/bIMEd5lMk4/LiFOC1OcFk2tTnxX1oCxWcnn0zxRy+ORwvL/p1DDdgwOtmPwhHNbKjqYmJ2djbi4OOzYscO/zWKxYN++fcjPz+90fH5+PmpqalBeXu7fVlxcDACYOHEiBEFAXl6ef5vPjh07MGnSJADAV199hUWLFsFisfj3Hz9+HI2NjR0mk1PfqqhtBQCkmGLg8Uhwujxwujxwh9BvIj0hCAJyh3pD0t7DdQpXQ0REPaVoYNJqtZg7dy5WrlyJTz75BKWlpVi8eDHS09Mxa9YseDwenDx50j83ady4ccjLy8PixYvxzTffoKioCMuWLcOcOXP8PUjz58/H+++/j5dffhllZWV4+umnsX//fsybNw8AMHv2bJhMJixZsgSHDh1CSUkJFi1ahLFjx+Lyyy9XrC2ine+ea75bikSS0RclAQD2ltXxNilERGFK8enqixYtwvXXX4+HH34YN910E1QqFTZs2ACNRoPq6mpMnToVW7duBeD9bX3t2rXIzMzEvHnzcPfdd2PatGkdFq6cOnUqHn/8cfztb3/Dj3/8YxQVFWH9+vX+3iOTyeRfk+mmm27Cr371K+Tk5GDDhg1QdTHpmfqGLzD5blobSUYMMkGrEdFgcaDC3Kp0OURE1AOKzmECAJVKhSVLlmDJkiWd9mVmZuLAgQMdtiUnJ2P16tXnPOecOXPOOXl76NCh+MMf/tCjein4Gix2tNndEAQgOUEf+AlhRqtWIXdIEvYcqsPXh+swKC1e6ZKIiOg8Kd7DRHS0fTmBpHgdNGG4llV3jB+eAoDzmIiIwlVk/nSisHLkhDcwpSRG3nCcz7j2q+OOVregsaXzmmBERBTaGJhIcUd9gSkC5y/5JMTpkDXAe7uXnaXmAEcTEVGoYWAiRdmdblSd9E6ETo3QHiZB8D4Kc7237Cn6vgZC+y1VBKF3bzdDRETBwcBEijpywgJJBuJiNIg947YikUClEiCKIlpsbuRelAxRFHCspgWHq5rRaneh1e5C5/XDiYgo1DAwkaIOt99wN1J7l1SiAJvTjf3HGlBW1YzMFO/95P7x1VHsPVyHfccaYHe42dNERBTiGJhIUYcqmwBE5oKVp3O5vauXD8nwzmM6XNkMh9MNV5jeIoCIKNowMJFiJElG2QlvD1NaUmT2MJ1pYEosNGoRbXY3ahttSpdDRETdxMBEijlR1wabwwOtRkRSfOQtWNkVlUrE4HTvwpWHKpoAnJoUfvpEcE4KJyIKLYqv9E3R61CVt3dpSLoRoiggWmY/jxxowuHKZpTXtMDucvsnhQNd32dOr1ODN+0hIlIWAxMp5nD7/KWL+huVLaSPJSfokWLS42STHaXHGjFhRCrKKprhdHdOjBq1iJwhSYjTa3jjXiIiBfVoSK62tjbYdVAUOtzewxRtgQkAsgcnAgC+P9oIt0fyTwo/88FJ4UREoaFHgenyyy/HggULsHXrVjidzmDXRFGgudWBk012CID/yrFoMjgtHjE6NWwON/Ye4v3liIhCXY8C0xNPPAFJknDfffdh6tSpWLFiBb799ttg10YR7FD7+ksDUuIQo4u+kWFRFDBykAkA8MXXJzjcRkQU4noUmP7rv/4LGzduxGeffYbbb78dRUVFuOGGGzB79mxs3LgRdXX8jZnOzTccNzwzQeFKlDNiYAJUooAKcyuq661Kl0NEROdwQcsKpKWl4ec//zm2bduGTZs2ITExEc888wymT5+OX//619i7d2+w6qQI4wtMw6I4MOm1av9cpm/K+EsGEVEou+B1mEpKSvDb3/4Wd9xxB3bt2oUpU6bggQcegM1mw0033YRXXnklCGVSJHG6PCivaQEADBtgUrYYhY0fngwBQIW5FY0tDqXLISKis+jR5JHy8nJs2bIF7777LqqqqjBgwADccsstuO6665CRkQEAmDt3Lu677z68+OKLuO2224JZM4W5YzUt8EgyEmK1SDHp0eZwK12SYhLidBiTlYxvyuqx72gDpozNULokIiLqQo8C05VXXgmdToeZM2fi0UcfRWFhYZfHXXTRRTh27NiF1EcRyHf/uGGZCVzJGsD0iZn4pqweR6stmDCiHwx6jdIlERHRGXoUmH7729/i2muvRXx8/DmP++Uvf4lf/vKXPSqMItfh9ivkhg2I3vlLpxucHo/0JANqGqzYX96IiSNTlS6JiIjO0KM5TB9++CHMZnOX+0pLS/GjH/3ogoqiyHP6fdFOXSFnAjuYvMYO6wcAOFjRDKcrSu4RQ0QURrrdw1RSUuJfK6a4uBg7d+5EQ0NDp+M+++wzVFRUBK9CCnseAHa7CwBQ02BFm90NjUpEcvv8Ja5lDQxKjUNCrBbNbU4crGzG6KFJSpdERESn6XZg+vvf/44tW7b4ewpWrFjR6RhfoJo9e3bwKqSwJggC7HYX9h1rgMst4cDxRgDe+6l9f7QBBr0agzOMEBDdXU2CICB3aBK++q4GpccaMWpwIlRidLcJEVEo6XZgevjhh/GTn/wEsixj3rx5WLZsGYYNG9bhGFEUYTQaMXz48KAXSuHNd6+06jrvAo39EvRwujzQai54ZYuIMbR/PPYcOgmrw43ympaovMceEVGo6nZgio+PR0FBAQDgz3/+M3JzcxEbG9trhVFkMjfZAAApiTEKVxJ6VKKIEQNN2Hu4HocqmhiYiIhCSLcD0+bNm3HZZZchMTERJ06cwIkTJ855/Jw5cy60NoowdqcbljbvzZpTTAxMXRmWmYBvDtejttGG5lYngyURUYjodmB64IEH8H//939ITEzEAw88cM5jBUFgYKJOTjbZAQAJsVrotSqFqwlNsXoNBqTEovJkGw5VNjEwERGFiG4Hpk8++QQpKSn+r4nOl7mxfTiOvUvnNGKgCZUn21BWZUFBTprS5RAREc4jMA0YMKDLr33cbjdaW1thMpmCUhhFnpOcv9Qt/VNiYdCrYbW7cay6hQtZEhGFgB5douR2u7F27Vq89957AIAdO3ZgypQpKCwsxLx589Dc3BzUIin8eSQZdc3eIblU9jCdkygI/lXQD7ffRoaIiJTVo8C0evVqvPjii7BYLACA3/3udzCZTHjwwQdx/PhxPPvss0EtksJffbMNkiRDp1HBGMt7pQXiu0Kuqq4NLVanwtUQEVGPAtP777+Pe+65BzfffDPKyspw6NAh/OIXv8Ctt96KxYsX49NPPw12nRTmaht885f0vOFuNxhjtUg26iDLwJ5DdUqXQ0QU9XoUmMxmM8aNGwcA+Ne//gVRFDFt2jQAQHp6OlpaWoJXIUWE2gbvgpWpnL/UbUMyvL1Mu0q7vm8jERH1nR4FptTUVFRWVgIAPv30U4waNQpJSd57X+3Zswfp6enBq5DCnizLqG30BiZeIdd9QzLiAQBHTlhQ3z7/i4iIlNGjwDR79mw88cQTuOOOO7Br1y785Cc/AQA89thjWLNmDX70ox8FtUgKb/XNdtgcHoiC9x5y1D2xeg3SkwwAgOL9tQpXQ0QU3XoUmO6++27cfvvtEAQB9957L372s58BAL799lvcfvvt+MUvfhHUIim8HTnhvTggyaiHWsV7x52PrAHeYTkGJiIiZXV7HabTCYKAu+66C3fddVeH7W+88UZQiqLI4gtMnL90/oZkGPHVtzU4VtOCBosdSUb20BERKaFHgQkAWlpaUFRUBKvVClmWO+3nrVHIp6zKuy4XA9P5i9GpMSTDiKPVFnx9uA4z8jKVLomIKCr1KDB98cUXWLRoEWw2W5f7eS858rFYnajhFXIXZGxWMo5WW7DnEAMTEZFSehSYnn32WVx00UV48MEHkZaWBlHkvBTq2qGKJgCAKU4HvbbHHZpRbUxWMrb85yhKyxthtbth0LMdiYj6Wo/+5S0rK8O6deswadKkYNdDEeZge2BKTzYoW0gYS0syID3JgJoGK747Wo+CUbwhLxFRX+tR11D//v3R2toa7FooAvkCUwYD0wWZMLwfAOBrrvpNRKSIHgWmu+66C7///e/9i1cSdcXmcKO81rvqu289IeqZCSNSAAB7y+rh9kgKV0NEFH16NCT33nvvoba2Fj/4wQ+QlJQEvb7jpc6CIODjjz8OSoEUvsqqmiHLQLJRj9gYDZwuj9Ilha2s/gmIi9Gg1ebCkRMWjBhoUrokIqKo0qPAlJ6eztufUEAHK5sAAFmZCcoWEgFEUUDu0CTs2FeL747WMzAREfWxHgWmJ554Ith1UAQ6eLwJADCsfbVqujCjfYHpSAOum5aldDlERFHlgq5PLisrw5dffgmz2YxbbrkFFRUVyM7ORlxcXLDqozBld7pR1r7C97BME07UtSlcUfjLHeq9wXV5TQtarE7EG7QKV0REFD16FJgkScKyZcuwadMmyLIMQRDwwx/+EOvWrcPx48fx2muvccguyh043gSPJCPFpEeKKYaBKQhMcTpkpsSh8mQrvj/WgItz+P8YEVFf6dFVcuvWrcN7772H3/3ud/jyyy/9t0ZZsmQJJEnCqlWrglokhZ/vjzUAAHKGJClcSWQZfZG3Pb8/0qBwJURE0aVHgWnTpk1YtGgRfvKTn8BkMvm3jxo1CosWLcKXX34ZrPooTO071ggAGD00WeFKIsvo9mG57441dHkPRyIi6h09Ckx1dXUYNWpUl/vS0tJgsVguqCgKb40tDpyoa4MgAKMGJypdTkQZnmmCViOiudWJypMc5iQi6is9CkyDBw/G559/3uW+4uJiDB48+IKKImXVW+w42dj1jZW74/uj3uGiIelGxMZoglUWAdCoRWQP8obQ747WK1wNEVH06NGk73nz5mHZsmVwuVy4/PLLIQgCysvLsWPHDmzcuBEPPPBAsOukPmB3erDrgBllVd4ewismZmJo//NfEmBf+/yl3KHsXeoNuUOT8E1ZPb470oAfTuYvJ0REfaFHgemGG25AQ0MDXnzxRbz++usAgHvuuQcajQYLFizATTfdFNQiqffZHG689+Ux2Bxu/7Yvv61G2nne0kSS5VOBiRO+e4VvHtOhyiY4nB7otCqFKyIiinw9XofpzjvvxI9+9CMUFxdDrVYjPj4e48aN6zAJnMLH0WoLbA43YvVqXDImHTv3m9HU6sTne6owOTftvM5jsbqg06qQNYArfPeG9CQDko161FvsOFDRiLFZ/ZQuiYgo4p13YPrHP/6BN954A3v37oXb7e2N0Ov1yMvLw0033YSZM2cGvUjqfRW1rQCA4QNNyEiOxbTx/fH+V+WoqmtD8b5azJw4sFvn2bnfDACYMKwf1KoeTZGjAARBwOiLkvD51yfw3ZEGBiYioj7Q7cDk8Xhw77334oMPPkBaWhquueYa9OvXD7Iso6amBsXFxfj1r3+N//qv/8KTTz7ZmzVTkLk9Eqrar7ga0C8WgHeRxHHDkrH7YB0+LqnAjLxMCAHOI8kydpZ6A1P+qNTeLDnqjR7aHpiOcj0mIqK+0O3A9Prrr+Ojjz7CQw89hLlz50IQOv749Hg8eOONN/D4449j0qRJuP7664NeLPWOwxVNcHkk6LUqJBl1/u0jBpnw3ZEG1DbYsPdwHcYPO3dPxuHKZjS2OBCjU3H9pV42anASREFATYMVdc029EuIUbokIqKI1u0xk82bN+PGG2/ELbfc0iksAYBKpcLNN9+Mn/70p3jnnXeCWiT1rm/L6gAA/fvFdvjeatUqjBrivdJt6/bygOfx9S5NGJ4CjZrDccEiCL6H4H/ExmiQ1X5T4++PNipcIRFR5Ov2T7WjR49i2rRpAY+79NJLcfDgwQsqivrWt2Xe9Xx8w3Gnyx2aBLVKwOGqZhyqbDrrOSRJRkl7YCrgcFzQqFQCRFFEi82NVrurw2PEQBMAYO/hOniULZOIKOJ1e0jOZrMhISHwVU+JiYloa+MKxOGiwWJH1clWCAAyughMBr0G+aPSsP27Grz75THc+9/juzxP6fFGNLc5EatX8/5xQaQSBdicbpRVNMPp7hiLRNHbG7i/vBFWuwvGGC1vl0JE1Eu63cMkyzJUqsDrvYiiyH+0w8g37b1LKYkx0J9lPZ8f5A+EShTw/dEG/xpLp5NlGe9+eQwAUDAqjVfH9QKXW4LT5enwSIjVQqMS4XB5UGVuVbpEIqKIxp9sUa68pgUAkJF89gUqU0wxuHzCAADA3/9VBumMQPzd0QYcrGiCWiXimkKuPN1XRFFAWpJ3sveBiiZliyEiinDntQ7T8uXLERcXd85jWlv5m244qa63AvAuI3AuP5oyFF98W43ymhaUlJpRMMq7mKUky9j0rzIAwIy8AUgy6nu3YOogPdmAypNtOHi8SelSiIgiWrd7mPLz8xEbGwtZls/5iI2NxaRJk7pdgCRJWL16NS699FKMHz8ed955JyoqKs56fGNjI+69917k5+ejoKAAK1asgM3W8Uax27Ztw9VXX42xY8dizpw52L59e4f9hw4dwsKFCzF58mQUFhZi0aJFOHHiRLdrjiQ1Dd0LTMZYLX44eRAA4K//PIiDFU2QZBkfFVfguLkVeq2KvUsKyEj2zjsrq2qG2yMpXA0RUeTqdg/TX/7yl14pYN26dXj99dfx5JNPIj09Hc888wwWLFiA9957D1qtttPxixYtgs1mwyuvvAKLxYKHHnoIVqsVTz31FACgqKgIS5Yswf33348pU6bgrbfewsKFC7F582ZkZWWhsbER8+fPR15eHv7yl7/A6XTiySefxIIFC/DOO+9Apzt3cIgkTpcH9c12AEBCXOe2PtOs/IHYdeAkKsyteOZve5DR3rsBAD+cPAjxhsDnoOAyxWmh16pgd3pQVtXsv3KOiIiCS9E5TE6nExs3bsSiRYswffp0ZGdnY9WqVaipqcFHH33U6fg9e/aguLgYTz31FHJzc1FYWIhHHnkEW7ZsQW1tLQDgpZdewsyZM3HrrbciKysLS5cuRW5uLl599VUAwMcffwyr1Yqnn34aI0aMwOjRo/HMM8+grKwMu3fv7tP3r7TaRm/PnEGvPuuE79PptWo8ODcPBaNS4ZFkVJ5sg06rwk8uuwjXFA7p5WqpK4IgoH/71Y37jnE9JiKi3qJoYCotLUVbWxsKCwv924xGI3JycrBz585Ox5eUlCAlJQVZWVn+bQUFBRAEAbt27YIkSdi9e3eH8wHA5MmT/ecrLCzEunXroNefmmsjit5msFgsQX1/oa62fTguPSm2y8VIu6LXqnHXtbmYd9VIXFkwEE8svBjXFA7xX+JOfc8XmPaX8zYpRES95bxvvhtMNTU1AICMjIwO21NTU/37TldbW9vpWK1WC5PJhOrqalgsFlitVqSnp5/1fJmZmcjMzOyw/49//CP0ej3y8/Mv6P2ow2x1a3OTt4cpPdkAURSg6iL0qEQBgihArRYgy6f2XzGp4814z5a3BAEQnN5zd3V+sX3lalEFqDxhvL+9AbzhWzr/5/dwPwAMTPVeiHHkhAVuSYJeq+j/1hdE1b4khYpLU1wQtmNwsB2DJxLaUtF/WX2Ttc+cq6TT6dDc3Nzl8V3Na9LpdHA4HLDb7Wc9n8Ph6LKGv/zlL3jttdfw8MMPIymp5wsuiqKAxMTOCz+GsoZWJwAgLckAnU4DsYt1tjRqETF6LUymsy870Gp1wmp3d7lPELy3zYmJ0UKt6TwpOUavhlqtQoxeC7U6fPfrdBq43BL0ek2fvj4AJBhF9DPFoK7JhqoGOya1X8EYzoxG3hsvGNiOwcF2DJ5wbktFA5NvWMzpdHYYInM4HIiJ6dyoer0eTqez03aHwwGDweCfsH3mMV2dT5ZlvPDCC3jxxRfxi1/8ArfccssFvRdJkmGxWC/oHH2tvNo7BJmWHAuHwwW7o3Po0WpUsNmdaGqS0dV6pIIANFtdKD3WAJe78w90g16NgelG2OxOOJ2db+AhyBLcbk/Y73c4XBBVKtjtLkiSdN7P7+l+wPs9GjEwAXVNNuz49gSy0s+99EcoU6lEGI0xsFhs8PCqvx5jOwYH2zF4QrUtjcaYbvd6KRqYfMNrZrMZgwYN8m83m80YOXJkp+PT09Px8ccfd9jmdDrR1NSE1NRUmEwmGAwGmM3mDseYzWakpZ36rdvlcuHBBx/EP/7xDzz44IO47bbbgvJ+3F0EhlBWU++9wi09yYDqky3wSJ0TkUeSIUsy3G65yxXcBUGALMmwO72rT59JpRIgyzIkD7o8v9S+HEXY75dliPAuk3H6cb39+oB3+4iBJnz1bQ2+P9oQdp/Drng8UkS8D6WxHYOD7Rg84dyWig4mZmdnIy4uDjt27PBvs1gs2LdvX5fzifLz81FTU4Py8nL/tuLiYgDAxIkTIQgC8vLy/Nt8duzY0WFtqPvvvx8ffPABnn322aCFpXDTYnWirX0YLTXp7MNtFB58ywlUmFthsXbuhSUiogujaGDSarWYO3cuVq5ciU8++QSlpaVYvHgx0tPTMWvWLHg8Hpw8edI/N2ncuHHIy8vD4sWL8c0336CoqAjLli3DnDlz/D1I8+fPx/vvv4+XX34ZZWVlePrpp7F//37MmzcPAPD2229j69atWLx4MQoKCnDy5En/w/c60aC2wTt/LNmog04TeEkBCm3xBi0yU7xDcaXlXF6AiCjYFJ+uvmjRIlx//fV4+OGHcdNNN0GlUmHDhg3QaDSorq7G1KlTsXXrVgDe4Z+1a9ciMzMT8+bNw913341p06Zh+fLl/vNNnToVjz/+OP72t7/hxz/+MYqKirB+/Xr/UgT/+Mc/AABPP/00pk6d2uHhe51o4FvhOz05vCaq09nlDEkEAOxnYCIiCjrFrz9WqVRYsmQJlixZ0mlfZmYmDhw40GFbcnIyVq9efc5zzpkzB3PmzOly38aNG3tcayQ5FZg4HBcpRg1Owkc7K7CfC1gSEQWd4j1MpAzfopUZnL8UMUYOMkEUBJibbKhrtgV+AhERdRsDU5Q62f4DNTUxfNfEoI5idGoMyYgHABysaFK2GCKiCMPAFKUaW7wLeSYZ9QGOpHDiu1ruwPEmResgIoo0DExRyOWW0GJ1AQCSjDqFq6FgGukLTOxhIiIKKgamKNTc6u1dUqtExMVoAhxN4WR4pgmCAJgbbf5eRCIiunAMTFGoof0HaWK8FsLZ7ppLYcmgV2NQKucxEREFGwNTFGpq72FKjONwXCQawWE5IqKgY2CKQg2W9sDECd8RaeQgEwDgwHGux0REFCwMTFGIPUyRzdfDVF1vhaWN95UjIgoGBqYodGoOEwNTJIqL0SAzxXvLG85jIiIKDgamKNTEwBTxOI+JiCi4GJiiUGOLHQADUyQbOch7I14uYElEFBwMTFFGkmU0tXrntTAwRS5fD1PVyVa02lzKFkNEFAEYmKJMS5sTHkmGIADGWK3S5VCQCILvIUAQBJjidMhINkAGcLiqWenyiIjCHgNTlGlsv0LOGKuFWsVvfyRQqQSIoogWmxutdpf/cVF/IwDg2yP18ChcIxFRuFMrXQD1rUYLlxSINCpRgM3pRllFM5zuU9FIo1YBAL47Ug+7w404vQayLCtVJhFRWGNgijK+HibOX4o8LrcEp+tUYOqX4P0e1zXZYWsPTERE1DMck4kyjVxSIGoY9BrEGzSQARw5YVG6HCKisMbAFGUYmKJLamIMAKCsqrnDpPAzH0REdG4ckosyDEzRJS3RgLIqC46caEaLzQ2g6zlMep0aqr4tjYgorDAwRZlTgan7N971XbIOdO6JYOdEaPP1MJXXtOC7I3XwSJ0Dk0YtImdIEieFExGdAwNTFJFl+bx7mE6/ZL2r3glRFCAFs0gKqniDBnqtCnanBzX1ViTEce0tIqKeYGCKInanB472q6hM3fzBebZL1n0MejUGZxghdNH7RMoTBAFpSQaU17SgttHGwERE1EOc9B1FLG3eW6LotCroteeXlX2XrJ/5cHvYvxTq0pO8w3LmRqvClRARhS8GpijS3B6YEgzsZYgm6ckGAEBtAwMTEVFPMTBFEV8Pk5HDMlElNdEAUQDa7G602XkjXiKinmBgiiLsYYpOGrWI/v1iAQAnm+wKV0NEFJ4YmKJIM3uYotbgDO+NeE822hSuhIgoPDEwRRFLm3dJAfYwRZ/B6fEAgJNNDExERD3BwBRFLG3e+SvsYYo+Q9p7mBosdnh4ZSMR0XljYIoizexhilpJRh1idGpIMlBv4TwmIqLzxcAURXiVXPQSBMF/mxRO/CYiOn8MTFFCluVTV8nFMjBFo7RE73pMnMdERHT+GJiihM3hhtvjvRccA1N0SvP3MNl4k10iovPEwBQlfL1LMTo1NGqVwtWQEvqZYiAKgM3hQZvNrXQ5RERhhYEpSvjnL7F3KWqpVSKSjHoAgJnDckRE54WBKUpw/hIBQIrp1LAcERF1HwNTlGhuZQ8TASkmbw9THQMTEdF5YWCKEhYre5joVA9TQ4sDLjcXsCQi6i4GpijBHiYCgNgYDQw6NWQuYElEdF4YmKIEe5jIxzcsx3lMRETdx8AUJdjDRD6nJn6zh4mIqLsYmKIEe5jIxxeY6riAJRFRtzEwRQFJlv3rMDEwUVKCDqIgwO70oNXmUrocIqKwwMAUBax2NzyStych3sDAFO1Uoogkow4A5zEREXUXA1MUaG51AABi9Wpo1PyWE+cxERGdL/70jAK8LQqdKSWRK34TEZ0PBqYowNui0JlSErxLCzRyAUsiom5hYIoC7GGiM52+gCV7mYiIAmNgigKneph0CldCocS3gKW5kYGJiCgQBqYocKqHSaNwJRRKfBO/zY1WhSshIgp9DExRgD1M1JVTgYkLWBIRBcLAFAWaOYeJunD6ApZ1zVxegIjoXBiYogBX+aaunL6A5bFqi8LVEBGFNgamCCdJsv8+cuxhojP5huWOMjAREZ0TA1OEa7W5IMuAACDewEnf1JHvSrmj1S0KV0JEFNoYmCKcb/5SbIwGahW/3dSRr4fpxMlWOJwehashIgpd/Aka4fzzl+I4HEedxcZoYNCrIckcliMiOhcGpgjX3Oa98S4nfNPZpLXfV+5wVbPClRARhS4GpghnaXMB4IRvOrvURAMABiYionNhYIpw7GGiQNKS2gNTZRMkLmBJRNQlBqYIxxvvUiD9EvTQaVRos7tRaW5VuhwiopDEwBThmrloJQUgigIu6m8EABw43qRsMUREIUrxwCRJElavXo1LL70U48ePx5133omKioqzHt/Y2Ih7770X+fn5KCgowIoVK2Czdbzb+rZt23D11Vdj7NixmDNnDrZv337W116wYAHWrFkT1PcUStjDRN0xLDMBAHCgoknZQoiIQpTigWndunV4/fXX8eijj+KNN97whxin09nl8YsWLUJ5eTleeeUVvPDCC/j888+xfPly//6ioiIsWbIEN954I9555x0UFhZi4cKFKCsr63Aep9OJ3/zmN/jiiy968+0pjjfepe4YnmkCABw43sh5TEREXVA0MDmdTmzcuBGLFi3C9OnTkZ2djVWrVqGmpgYfffRRp+P37NmD4uJiPPXUU8jNzUVhYSEeeeQRbNmyBbW1tQCAl156CTNnzsStt96KrKwsLF26FLm5uXj11Vf959m9ezeuu+46lJSUwGg09tn77WseSUKrlVfJUWCD0uKg1Yhos7tx4mSb0uUQEYUcRQNTaWkp2traUFhY6N9mNBqRk5ODnTt3djq+pKQEKSkpyMrK8m8rKCiAIAjYtWsXJEnC7t27O5wPACZPntzhfJ9//jkuvfRSbN68GfHx8b3wzkJDi9UFGYAgAPExvC0KnZ1KJWLYABMADssREXVFreSL19TUAAAyMjI6bE9NTfXvO11tbW2nY7VaLUwmE6qrq2GxWGC1WpGenn7O8y1evDhYbyGkNbd6h+PiDVqIoqBwNRTqsgeZsO9YAw4cb8QVEzOVLoeIKKQoGph8k7W12o7DRTqdDs3NnRfRs9lsnY71He9wOGC32896PofDEayyz0qtVnxKWAdtDu9wnClOe9baVCoRkGSIogBVF6FKFAQIggBRBag83H/W/YJ3nyiKAKQ+e/1gnEMlChBEATlDk/D2v4/gQEUTRNWp99SXVO33O1TxvocXhO0YHGzH4ImEtlQ0MOn13julO51O/9cA4HA4EBMT0+XxXU0GdzgcMBgM0Ol0/vOdub+r8wWTKApITIzt1dc4Xy6pHgCQnBBzztpsDVbodBqIKlWnfTF6NdRqFWL0WqjVEvefZb9Op4HLLUGv1/To+T3dH4xzaNQiYvRa5OUYodeq0GJ1ocnqRlb7RHAlGI29+/9rtGA7BgfbMXjCuS0VDUy+4TWz2YxBgwb5t5vNZowcObLT8enp6fj44487bHM6nWhqakJqaipMJhMMBgPMZnOHY8xmM9LS0nrhHZwiSTIsFmuvvsb5qj7ZAgAw6NRobOx6Iq+v58nhcMHucHfaL8gS3G4PbHYnnF3czZ77vfsdDhdElQp2uwuSJJ3383u6Pxjn0GpUsNmd0IoyRg1OxJ5DdfjPnkokxfb9vDeVSoTRGAOLxQaPp+uASIGxHYOD7Rg8odqWRmNMt3u9FA1M2dnZiIuLw44dO/yByWKxYN++fZg7d26n4/Pz87Fy5UqUl5dj8ODBAIDi4mIAwMSJEyEIAvLy8lBcXIwbbrjB/7wdO3Zg0qRJvf5+3O7Q+RAAQKPFOwwZb9CctTbvqIsASZLhkTpfTi7JMmRZhuQB959rvyxDhHdtr9OP6+3XD8Y5PJIMWZLhdssYPTQJew7VYe/hOlx98eAuX68veDxSyP3/FI7YjsHBdgyecG5LRQOTVqvF3LlzsXLlSiQlJWHAgAF45plnkJ6ejlmzZsHj8aChoQHx8fHQ6/UYN24c8vLysHjxYixfvhxWqxXLli3DnDlz/D1I8+fPx8KFC5GTk4Np06Zh06ZN2L9/Px577DEl36oimlq9gckUxzWYqHvGXJQMACirssBqd8Gg59WVRERACCxcuWjRIlx//fV4+OGHcdNNN0GlUmHDhg3QaDSorq7G1KlTsXXrVgCAIAhYu3YtMjMzMW/ePNx9992YNm1ah4Urp06discffxx/+9vf8OMf/xhFRUVYv359h6UIooUvMCXGMzBR9/QzxSAj2QBJlvH9sUalyyEiChmK9jABgEqlwpIlS7BkyZJO+zIzM3HgwIEO25KTk7F69epznnPOnDmYM2dOt17/008/7Xat4eZUDxMXraTuG3NRMqrrrfi2rB752alKl0NEFBIU72Gi3iHLMpra12HikBydD9+w3LdH6yHzNilERAAYmCKW1eGGq31iHXuY6HyMGGiCViOiudWJ47WtSpdDRBQSGJgiVGOLdzguVq+GRt15fSWis9GoRYwe6u1l2llqDnA0EVF0YGCKUP75S5zwTT1QMMo7d6l4fy2H5YiIwMAUsZpaOH+Jem7csH7QaVSoa7bjyAmL0uUQESmOgSlC+ZcUYGCibhAE38N73zm9Vo0Jw/sBAHbsr1W4OiIi5TEwRahTQ3Kc8E3nplIJEEURLTY3Wu0u/2Nce2Aq3meG6yyrjBMRRQvF12Gi3sElBai7VKIAm9ONsopmON2n7jXnkWToNCIsVie+LavDxBGpnM9ERFGLPUwRirdFofPlcktwujz+h8cjYVBaPACg6PsahasjIlIWA1OE8i0rwMBEF2LEQBMAYPfBOv9niogoGjEwRSBJltHsH5LjHCbqueQEPdKTDJAkGZ/urlS6HCIixTAwRaAWqwuSLEMAYIxlYKILk3tREgDgs92VcLg8AY4mIopMDEwRqKl96MQYq4VaxW8xXZjB6fFIMurQZndjO+cyEVGU4k/TCMQJ3xRMoiDgsgkDAAAfFB3336OQiCiaMDBFoFOBicNxFBxTRmcgIVYLc5MNn+ziXCYiij4MTBHIvwYT7yNHQaLTqnD99CwAwHtfHYXF6lS4IiKivsXAFIG4pAD1hkvGZGBwWjxsDg82f3FU6XKIiPoUA1ME4pAcBZsgeFcE/9kPhgMAPv+6Cgcqmvz3niMiinQMTBGIk74pmE6/11z/lDhcnJsGWQb+sOU71DZa0Wp3gYsNEFGkY2CKQPXNdgBAslGvcCUUCXz3mtt/rAF7D9dh5KBEJMRq0dTqxLp3vsX3R+thd7jZ00REEY2BKcLYHG602d0AvKs0EwWL715zsizj0nEZEAUBx2tbsWNfrdKlERH1OgamCFPX3rsUF6NBjE6tcDUUqZKMekzOSQUA7DlYh/98c0LhioiIehcDU4Spa7IBAPqxd4l62fCBJozNSgYA/N+nh1FSala4IiKi3sPAFGF8PUwMTNQXxg1LxohBJsgy8OLm71C0j7dOIaLIxMAUYU42+3qYYhSuhKKBIAiYOjYDBaNSIckyXnp3H77Yy+E5Ioo8DEwRxneFXD8Te5iob4iCgJuvHInpEwZABvDytlJs+rwMkiwrXRoRUdAwMEUYDsmREkRBwK1XjsQ1hYMBAO9vL8eLm7+Dw8UVmogoMjAwRRBZllHHITlSiCAI+MllWbjjmlFQiQJ2HTiJp/6627+QKhFROGNgiiBWhxs2h/c3eq7BRH1JEHwPAVPH9sf9P5uAuBgNjtW04NFXS3CsxqJ0iUREF4SBKYLUNXmH44wGDXQalcLVULQ4/dYprXYXWu0u9E+Jwz03jkdaUgwaWxx4/C+78eW31UqXSkTUY1zZMIL4h+NMHI6jvuO7dUpZRTOc7o5zlmYVDMK/95xAeW0LNry/H+U1LfjpjGFQq/i7GhGFF/6rFUE44ZuU5Lt1yukPAcDM/ExcNXkQAODjXZV49o2vYWlzKlssEdF5YmCKIL4hOU74plAiCAKuuWQIfv2TsdBrVThQ0YQVr+zE0WrOayKi8MHAFEFOXSHHHiYKPXkjUvDwrZOQnmRAY4sDT7y2G//55tS8JkEQIAi+r31/9z6IiJTGwBRBOCRHoa5/v1g8fOskjB/WD26PhI1b9+P97cfgAdBqd6HZ6oK5wYpmq8s/gbzV7gJXcyIipXHSd4TwrsHkW+WbQ3IUWnzLDgACYmM0+PX1Y/HOv4/gH18dw6bPj6DF5sJF/Y2QZSAmRgubzQmP5F0pXKMWkTMkCXF6DWSuHk5ECmFgihAtVpd/VeVko07haohOOX3ZAeBU4Lly8iCoVAK2fHEUHxVXYMLwfhg/vB/UGu/kcV9gIiIKBQxMEaKqrg0AkGLSQ6PmGkwUOs617ECKKQaXTeiPz/ecwJ5DddDrVMjLTleoUiKis2NgihBVJ1sBAAP6xSlcCVHXfMsOnCl3aBJ0GhU+Kq5A0Xe1SDTGII03jyaiEMNJ3xGi8qS3hykzNVbhSojO36zJg5A9KBEygH8WH+f954go5DAwRYiqOvYwUfgSBAFTxmQgPdkAl1vCp7uqOg3fEREpiYEpAsiyjKr2HqYBKexhovAkigKmj++PuBgNmtuc+OrbGl4VR0Qhg4EpAtRb7LA7PVCJAtKTDEqXQ9RjMTo1rrx4MERRwPHaVhw43qR0SUREABiYIoKvdyk92cCbmlLYS0+ORX52CgCgpPQk6tvXFyMiUhJ/ukaAyvYr5DJTOH+JIsOowYnITImFJMv4dFclHE7OZyIiZTEwRQDfGkwD+nH+EkUGQRBwyZgMGHRqNLc58X+fHVa6JCKKcgxMEYATvikS6bUqXDouAwKA4n21+Orb6oDPISLqLQxMYc7tkVBd374GE4fkKMKkJRkwYaR3PtOfPzyAmgarwhURUbRiYApz5kYb3B4ZOo0KyQlcHZkiz/jh/TAsMwEOlwe/f/tb2J1upUsioijEwBTmKszeCd/9+8VC9N4OniiiiIKA236YDVOcFlV1bdj4/n6uz0REfY6BKcwdON4IAMgaYFS4EqLekxCnwy9/PAYqUUDJgZP4x/ZypUsioijDwBTm9pV7A1PO4CSFKyHqXcMzTbj5ByMAAO/8+wg+/7pK4YqIKJowMIWx+mY7zI02iIKAkYNMSpdD1OumTxiAH148CADw5w8OoHh/rcIVEVG0YGAKY/vKGwAAQzPiEaNTK1wNUd+4/rIsTB/fHzKAP767D//aw54mIup9DExhbH/7cNyoIYkKV0LUdwRBwNxZIzF1TAYkWcafPzyANz89BI8kKV0aEUUwBqYwJcsy9h9rD0yDGJgouoiigPlXZ+PHlw4FAHxYXIHH/7IbVXVtEASh04OI6EIxMIWp6normtuc0KhFDMtMULocoj4nCAJ+NGUofv5fuYjRqXG02oLlG4vx2kcHUN3Qhla7y//gneiI6EJx4kuY8g3HDRuQAI1apXA1RMqZnJOOAalx+NN7+1Be04JPdlXi31+fQPZgE7IHJ6KfKQY5Q5IQp9dw/SYi6jEGpjC1s/3qoBzOXyKCKU6HmZMycbTagq8P1aHB4sC3Rxrw7ZEG9O8XC4fTg8LcdKhEDs8RUc8wMIWh8poWHKxshkoUcMnoDKXLIep1guB9AJ0Dj2+KkiAIyEyJw4B+sag62YYDFU2oOtmGE+2rg7/1rzJcMjodU0anYwDvu0hE54mBKQx9sqsSADBxZAoS43UKV0PUu1QqAaIoosXmBtB5SE0UBZx+fZwgCMhMjUNmahxabS4cOWFBWVUzLG1OfLDjOD7YcRxD0uMxZUwGJuekIS5G02fvhYjCFwNTmLFYnSja5x2OmzlpoMLVEPU+lSjA5nSjrKIZTnfn6dsGvRqDM4wQuuh9iovRYFJ2KuZfnY2yKgv+80019h6uw7GaFhyracGbnx7C+GH9cMmYDIy5KAkqkdfBEFHXGJjCzOdfn4DbI2FIejyy+vP+cRQ9XG4JTlfnwKTVnDvkqFQCNBo1hg9MxPCBJrRYndhVehI79tei0tyKkgMnUXLgJIyxWlyck4ZJ2am4qL+RN7Mmog4YmMJIfbMdHxUfBwD8YNJAri9D1A1d9VAlGnW4avIg1DfbUVbVjCMnLLC0OfHRzgp8tLMCCbFa5AxJQvZgE4ZnmpCaGMMARRTlFA9MkiRh7dq1+Pvf/46Wlhbk5+dj2bJlGDiw6+GmxsZG/O53v8O///1vCIKAa665Bvfffz9iYmL8x2zbtg1r1qxBZWUlLrroIixduhSFhYXndY5Q43JLWLf5W7TZ3RicHo/8UalKl0QUVrrqoYo3aFCQk4bbrxmFoycs2P59Db49Uo/mNie2f1+D7d/XAAD0WhUGpMShX4Ie/RL0SE7Qo58xBglxWsQbNIjVq7m8B1GEUzwwrVu3Dq+//jqefPJJpKen45lnnsGCBQvw3nvvQavVdjp+0aJFsNlseOWVV2CxWPDQQw/BarXiqaeeAgAUFRVhyZIluP/++zFlyhS89dZbWLhwITZv3oysrKxunSPUSLKM1z8+iKPVLYjVq/GrOaOhVnGuBVGwqNUiJoxIwfjhKXB7JBysaML+8kaUljeivLYFdqcHZVXNKKtqPus59FoV4mI0iIvRIN6gbf9T49/W4e8GLeJi1JwzRRRGFA1MTqcTGzduxH333Yfp06cDAFatWoVLL70UH330EWbPnt3h+D179qC4uBhbt271h59HHnkECxYswD333IO0tDS89NJLmDlzJm699VYAwNKlS7Fnzx68+uqreOSRR7p1jlBibrTilW2lKD3eBAHAwmtz0c8Uuj1hROGmq6vwBqXHY1B6PK6cPAgygKq6Nuw5YEZTqwOtVhdabC60Wl2wO92wOz2QZcDu9MDu9KCu2d7t1zbo1YiP0SDOoEF8jLY9TGm8207fbvCGMINe3eXQYF8syBloCgAXBaVIp2hgKi0tRVtbW4fhMqPRiJycHOzcubNTYCopKUFKSoo/6ABAQUEBBEHArl27cNVVV2H37t144IEHOjxv8uTJ+Oijj7p1jquvvro33mqPVJ1sxaN/LoHTJUGrEfGzmSMw5qJkpcsiiijdvQpvcJoR6UnuTvtjY9QYkBqP/Uca0GJz+oOTL0y53RLUahEtbS602V1otblgtXvPY7W7YbW7Udto61atggAYdGrotWrodSroNCrodWrE6tWI0aqh16oQo1PDoFcjOTEWktsNSZIhy4AMGZABjyR7hyfdElxuj/9rt3+bd7vva9+fogho1SpoNaL/zxid2v8wxmoR1/61QX9qu0GnhhhiC4a6PZJ/iNZ52p+nv1+HywOPJEGlVqO5xQab3Q1JBjQqsUMbaNQq6DQi1GoROo2q/eH9Wtv+91B7/+dDlmV4JBkej/dPtyTB4/F+piRZhizLkCRAkiVIkgxBEKASBYii90/f11qtCnqHGy63BFmS29dWC692UTQw1dR45wdkZHRcfDE1NdW/73S1tbWdjtVqtTCZTKiurobFYoHVakV6evpZzxfoHD0ligKSkmJ7/PyuxCfE4PdLZkCjFhFv0PbKKsWCAMgyMKNgMLr6BVEUvMMVmWlG7g+wX4AAGXKH43r79UOtDS50P4BO7RhK9Z1r//BBSefc73ZLHVaRkmXvDyMZgCgI3h88cvsPIum0r9t/KIVrB44MeNfJEtoXfvAtNNq+P7g/NDu2k+z/j+8P735BFKHVANo+6KyXAXiA9htBn9re4V0L6HJZjJ6/4hlfdfOz42sv75+y/2mCClBrvIHhQlb+a251AKIIiB1L8i1K628BoWP7+NouLkYT9Oko5xNmFQ1MNpv3t6oz5yrpdDo0N3eeK2Cz2bqc16TT6eBwOGC32896PofD0a1z9JQgCFCpghtoVCoR6cl98y0yBPgQBprQyv3K7g+FGrj/wr+HRBS6FJ1xqNfrAXjnMp3O4XB0ecWaXq/vdKzveIPBAJ1OF/B8gc5BREREdCZFA5NvaMxsNnfYbjabu5x8nZ6e3ulYp9OJpqYmpKamwmQywWAwnPN8gc5BREREdCZFA1N2djbi4uKwY8cO/zaLxYJ9+/YhPz+/0/H5+fmoqalBeXm5f1txcTEAYOLEiRAEAXl5ef5tPjt27MCkSZO6dQ4iIiKiMykamLRaLebOnYuVK1fik08+QWlpKRYvXoz09HTMmjULHo8HJ0+e9M9NGjduHPLy8rB48WJ88803KCoqwrJlyzBnzhx/D9L8+fPx/vvv4+WXX0ZZWRmefvpp7N+/H/Pmzev2OYiIiIhOJ8gKL57h8Xjw3HPP4e2334bdbvev9J2ZmYnKykpcccUVeOKJJ3DdddcBAOrr67FixQp88cUX0Ol0uOqqq/Dggw/65y8BwObNm7Fu3TrU1NRg2LBhWLJkSYelC7pzDiIiIiIfxQMTERERUajjuvxEREREATAwEREREQXAwEREREQUAAMTERERUQAMTEREREQBMDARERERBcDARERERBQAA1OUkiQJq1evxqWXXorx48fjzjvvREVFhdJlhZympiYsW7YM06ZNQ15eHm666SaUlJT492/fvh3XXXcdxo0bh6uuugrvv/9+h+c7HA6sWLEChYWFmDBhAu699140NDT09dsIKUePHsWECRPw9ttv+7ft378fc+fOxfjx4zFjxgz8+c9/7vAcfl5P2bx5M66++mqMGTMG11xzDbZt2+bfV1lZibvuugt5eXmYOnUqnn/+eXg8ng7P/+tf/4orrrgCY8eOxc9+9jPs27evr9+C4txuN1544QVcfvnlmDBhAm6++WZ8/fXX/v38PAb2hz/8AbfcckuHbcFot0DnUJRMUWnNmjXy5MmT5c8++0zev3+/fPvtt8uzZs2SHQ6H0qWFlPnz58uzZ8+Wd+7cKR85ckResWKFPHbsWLmsrEw+fPiwPGbMGPm5556TDx8+LP/pT3+Sc3Jy5K+++sr//AceeECeOXOmvHPnTnnv3r3ynDlz5JtvvlnBd6Qsp9MpX3fddfKIESPkTZs2ybIsyw0NDfLkyZPlBx98UD58+LD81ltvyWPGjJHfeust//P4efXavHmznJOTI7/22mtyeXm5vG7dOjk7O1vevXu37HQ65VmzZskLFy6UDxw4IP/zn/+UCwoK5BdeeMH//LffflseO3asvGXLFvnQoUPykiVL5IKCArm+vl7Bd9X3Vq9eLU+ZMkX+4osv5GPHjskPPfSQPHHiRLm2tpafx2547bXX5OzsbHnu3Ln+bcFot+6cQ0kMTFHI4XDIEyZMkP/617/6tzU3N8tjx46V33vvPQUrCy3Hjh2TR4wYIZeUlPi3SZIkz5w5U37++efl3/72t/L111/f4Tn33HOPfPvtt8uyLMs1NTVydna2/K9//cu//8iRI/KIESPk3bt3982bCDHPPvusfOutt3YITOvXr5enTp0qu1yuDsfNmjVLlmV+Xn0kSZIvv/xy+cknn+yw/fbbb5fXr18vv/fee/Lo0aPlpqYm/7433nhDzsvL8/9AmjVrlvz000/797tcLvmyyy6T169f3zdvIkRce+218hNPPOH/e0tLizxixAj5ww8/5OfxHGpqauS77rpLHj9+vHzVVVd1CEzBaLdA51Aah+SiUGlpKdra2jrcX89oNCInJwc7d+5UsLLQkpiYiD/+8Y8YM2aMf5sgCBAEARaLBSUlJR3aEAAuvvhi7Nq1C7IsY9euXf5tPkOHDkVaWlpUtvPOnTvx5ptv4sknn+ywvaSkBAUFBVCr1f5tF198MY4dO4a6ujp+XtsdPXoUVVVV+NGPftRh+4YNG3DXXXehpKQEubm5SEhI8O+7+OKL0draiv3796O+vh7Hjh3r0I5qtRqTJk2KqnYEgOTkZHz22WeorKyEx+PBm2++Ca1Wi+zsbH4ez+H777+HRqPBu+++i3HjxnXYF4x2C3QOpTEwRaGamhoAQEZGRoftqamp/n3k/Z/5sssug1ar9W/78MMPUV5ejksvvRQ1NTVIT0/v8JzU1FTYbDY0NjaitrYWiYmJnW7qHI3tbLFYcP/99+Phhx/u9Lk7WzsCQHV1NT+v7Y4ePQoAsFqtuOOOO1BYWIgbbrgBn376KQC24/l46KGHoNFocMUVV2DMmDFYtWoVVq9ejUGDBrEdz2HGjBlYs2YNBg4c2GlfMNot0DmUxsAUhWw2GwB0CAIAoNPp4HA4lCgpLOzevRsPPvggZs2ahenTp8Nut3dqQ9/fnU4nbDZbp/1AdLbz8uXLMWHChE69IwC6bEdfyHQ4HPy8tmttbQUALF26FLNnz8bGjRsxZcoU/PKXv8T27dvZjufh8OHDiI+Px+9//3u8+eabuO6663Dfffdh//79bMceCka7BTqH0tSBD6FIo9frAXh/qPu+BrwfyJiYGKXKCmkff/wx7rvvPuTl5WHlypUAvP8jO53ODsf5/h4TEwO9Xt9pPxB97bx582aUlJTgvffe63J/V+3k+8fRYDDw89pOo9EAAO644w78+Mc/BgCMGjUK+/btw8svv3xe7XjmMdHUjtXV1bj33nvxyiuvYNKkSQCAMWPG4PDhw1izZg0/jz0UjHYLdA6lsYcpCvm6RM1mc4ftZrMZaWlpSpQU0l577TX8+te/xuWXX47169f7f+PJyMjosg0NBgPi4+ORnp6OpqamTv8ARFs7b9q0CfX19Zg+fTomTJiACRMmAAD+93//FwsWLEB6enqX7QgAaWlp/Ly2873XESNGdNg+bNgwVFZWsh27ae/evXC5XB3mJgLAuHHjUF5eznbsoWC0W6BzKI2BKQplZ2cjLi4OO3bs8G+zWCzYt28f8vPzFaws9Lz++ut49NFHcfPNN+O5557r0F08adIkFBcXdzi+qKgIeXl5EEUREydOhCRJ/snfgHceSm1tbVS188qVK7F161Zs3rzZ/wCARYsW4bHHHkN+fj527drVYb2goqIiDB06FMnJyfy8tsvNzUVsbCz27t3bYfvBgwcxaNAg5OfnY9++ff6hO8DbjrGxscjOzkZycjKGDh3aoR3dbjdKSkqiqh19c2QOHDjQYfvBgwcxZMgQfh57KBjtFugcilP6Mj1SxnPPPScXFBTIH3/8cYf1MJxOp9KlhYwjR47Iubm58q9+9SvZbDZ3eFgsFvngwYNybm6u/Mwzz8iHDx+WN2zY0GkdpnvuuUeeMWOGXFRU5F+H6fRLcaPV6csK1NXVyfn5+fLSpUvlQ4cOyZs2bZLHjBkjv/322/7j+Xn1+v3vfy9PmDBBfu+99zqsw1RUVCTb7XZ55syZ8h133CHv37/fvw7TmjVr/M9/88035bFjx8pvv/22fx2myZMnR9U6TB6PR77pppvkq666St6+fbt89OhRedWqVfKoUaPkr7/+mp/Hblq6dGmHf8uC0W7dOYeSGJiilNvtlp9++mn54osvlsePHy/feeedckVFhdJlhZQXX3xRHjFiRJePpUuXyrIsy59//rk8e/ZsefTo0fJVV10lv//++x3O0dbWJj/00EPypEmT5EmTJsn33HOP3NDQoMTbCSmnByZZluW9e/fKP/3pT+XRo0fLl19+ufyXv/ylw/H8vJ6yceNGecaMGXJubq587bXXyv/85z/9+44dOybPnz9fHjNmjDx16lT5+eeflz0eT4fn/+lPf5KnTZsmjx07Vv7Zz34m79u3r6/fguKamprk5cuXy9OnT5cnTJgg//d//7e8Y8cO/35+HgM7MzDJcnDaLdA5lCTIsiwr3ctFREREFMo4h4mIiIgoAAYmIiIiogAYmIiIiIgCYGAiIiIiCoCBiYiIiCgABiYiIiKiABiYiIiIiAJgYCKisHPLLbdg5MiRuPHGG896zOLFizFy5Eg88MADAICRI0dizZo1AIC3334bI0eORGVlZZ/US0ThT610AUREPSGKIr7++mvU1NT47w/mY7Va8dlnn3XY9uabb3Y6joiou9jDRERhKScnBzqdDh988EGnfZ999hliYmI63OF8/PjxDExE1GMMTEQUlgwGAy677LIuA9PWrVtx5ZVXQq0+1Yl++pBcV0pKSjB37lyMGzcOBQUFWLp0KRoaGjocs3PnTtxxxx3Iz8/H6NGjMWPGDKxZswaSJPmPMZvNWLx4MQoKCpCfn49ly5Zh1apVmDFjRodz/f3vf8c111yD0aNHY/r06VizZk2Hu7QTUWhhYCKisHX11Vf7h+V8Wltb8e9//xuzZ8/u9nl27tyJ2267DXq9Hs8//zx+85vfoLi4GLfeeivsdjsAoLS0FLfddhtMJhNWrVqFF198EZMmTcLatWuxbds2AIDT6cS8efOwe/du/OY3v8ETTzyB0tJSbNy4scPr/eEPf8Bvf/tbFBYWYv369bj55pvx0ksv4be//W0QWoWIegPnMBFR2Jo+fTpiYmLwwQcf4LbbbgMA/POf/0RycjImTpzY7fM8++yzGDp0KP7whz9ApVIBAMaNG4drrrkGmzZtws0334zS0lJccskleOaZZyCK3t81p0yZgk8//RQ7duzANddcg3fffRdHjhzBpk2bMHr0aADAxRdfjJkzZ/pfq6WlBevWrcN///d/4+GHHwYATJ06FSaTCQ8//DDmz5+P4cOHB6N5iCiI2MNERGFLr9djxowZHYbl3n//ffzwhz+EIAjdOofNZsPevXtx2WWXQZZluN1uuN1uDBw4EFlZWfjyyy8BAHPmzMFLL70El8uF0tJSfPjhh1i9ejU8Hg9cLhcAoKioCAMHDvSHJQCIi4vD5Zdf7v/7nj17YLfbMWPGDP9rud1u/5Cd7/WIKLSwh4mIwtoPf/hD/L//9/9QU1MDnU6H7du34+677+728y0WCyRJwksvvYSXXnqp036dTgcAsNvtePTRR7Flyxa43W5kZmZiwoQJUKvVkGUZANDY2Ijk5ORO5zh9W1NTEwBg4cKFXdZjNpu7XTsR9R0GJiIKa9OmTUNsbCw++OADGAwGZGZmdujhCSQ2NhaCIOC2227DNddc02l/TEwMAOCxxx7Dhx9+iOeffx6XXHIJDAYDAKCwsNB/bFpaGo4dO9bpHPX19f6vjUYjAGDlypUYMmRIp2P79evX7dqJqO9wSI6IwppWq8XMmTPx4YcfYtu2bV2GnnOJi4tDTk4Ojhw5gjFjxvgfw4cPx5o1a7Bjxw4AwK5duzB58mTMnDnTH5a+++47NDQ0+K+SKygoQGVlJfbv3+8/v91uxxdffOH/+7hx46DRaFBbW9vh9dRqNZ577jkupkkUotjDRERh7+qrr8Zdd90FURT9E6nPxz333IOFCxfi3nvvxbXXXguPx4ONGzdi7969+OUvfwkAGDt2LLZt24a//e1vyMrKQmlpKV588UUIggCbzQYAmD17Nv74xz/iV7/6Ff7nf/4HRqMRL7/8Murr69G/f38AQGJiIhYsWIAXXngBra2tmDx5Mmpra/HCCy9AEARkZ2cHr2GIKGgYmIgo7F1yySUwGo3IyMhAVlbWeT9/6tSp2LBhA9auXYtFixZBo9EgNzcXL7/8MsaPHw8AeOCBB+ByufD888/D6XQiMzMTv/jFL3D48GF8+umn8Hg8UKvV2LBhAx577DEsX74carUa1157LUwmE44ePep/vbvvvhspKSl4/fXX8ac//QkJCQkoLCzEPffcg/j4+GA1CxEFkSD7ZisSEdEFOXToEI4cOYJZs2Z1uErv+uuvR3p6OtauXatgdUR0IdjDREQUJFarFf/zP/+Dn/3sZ/jBD34Aj8eDrVu34rvvvsN9992ndHlEdAHYw0REFEQffPABNmzYgLKyMsiyjJycHPziF7/A1KlTlS6NiC4AAxMRERFRAFxWgIiIiCgABiYiIiKiABiYiIiIiAJgYCIiIiIKgIGJiIiIKAAGJiIiIqIAGJiIiIiIAmBgIiIiIgqAgYmIiIgogP8PiYS77o9ysQwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data1['Mileage'].quantile(0.99)\n",
+    "data2 = data1[data1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\3426246741.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data2['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\823394628.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data3 = data2[data2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\3349117769.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot([data3['EngineV']])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot([data3['EngineV']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\534082538.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HHXcAAEaMGIGamhqsW7cO8+fPh0rVscTp8QhYLA0dem1vpVarYDIZYbFY4XYH/iFCbcO+DA72Y/D0hb6UJMBqc8JqdcDhdAduIzxwudyw2hxwOAK30WnVsNocqK4WEKJ9NfSFfuwpuqIvTSZjm840yRqGGi95lZWVITk52be9rKwMqampTdrHx8fj888/99vmcDhQXV2N2NhYVFZW4uTJkxg9erRfm3HjxuHll19GdXU1oqKiOlyvy6XMD7rb7VHssQcb+zI42I/B05v7UpIkCI+A+/wjEI8QEELA40azbdweAeERcLm8bTuiN/djTyNHX8p6kTMtLQ1hYWHYu3evb5vFYsGRI0eQkZHRpH1GRgZKSkpQUFDg25aZmQkAmDhxIiIiImA0GnHs2DG/1x07dgwmk6lTQYiIiIj6JlnPDOl0OixYsADr169HVFQUEhMTsW7dOsTHx2POnDlwu92orKxEeHg4DAYDxo4diwkTJuCRRx7BqlWr0NDQgJUrV2LevHmIi4sDACxcuBAvv/wy+vXrh4kTJyI7OxubN2/G/fffL+ehEhERUQ8laxgCvJMoulwuPPXUU7DZbMjIyMCrr74KrVaLoqIiXHnllXjuuecwf/58SJKEl156CatXr8aiRYug1+txzTXXYMWKFb79PfTQQ4iMjMTmzZtRXFyMpKQkLF++HLfddpuMR0lEREQ9lSQ6eoFUYdxuDyor6+Uuo1tpNCpERoaiqqqe18I7iX0ZHOzH4OkLfSlJEupsThzMrWh2AHVYiBYpSWbknKqC3ekK2EanVWPs0BiEGbTtHjPUF/qxp+iKvoyKCm3TAGpOjEBERESKxjBEREREisYwRERERIrGMERERESKxjBEREREisYwRERERIrGMERERESKxjBEREREiib7DNRERERKJElSi89zTuTuwzBERETUzdwAbDZni20Meg3U3VOO4jEMERERdSNJkmCzOXEkvxLOZpad0GpUSB8U1aElQqj9GIaIiIhk4HR5ml1TjboXB1ATERGRojEMERERkaIxDBEREZGiMQwRERGRojEMERERkaIxDBEREZGiMQwRERGRojEMERERkaIxDBEREZGidSgMlZaWBrsOIiKiLlVdZ8eZ8noub0FNdGg5jssvvxxTp07F/PnzMXv2bOh0umDXRUREFBSWege+OngGx09XQwAYGB+OqaPiodXw4gh5deiT8Nxzz8Hj8WDZsmWYPn06Vq9ejcOHDwe7NiIiok5psLnwwpYDOHY+CEkSUFBSix3fFqDO2vKq8aQcHTozdOONN+LGG29EaWkpPvjgA3z44Yd45513MHToUMyfPx833HADYmJigl0rERFRu2TllKHO6kREqA5TRsUDAHZ9dxY19Q5k5ZRh1vhEmSuknqBT5wjj4uLw61//Gp9++inef/99REZGYt26dZg1axYefPBBHDx4MFh1EhERtYul3oEjpyoBANPH9EdspBGxkUZcNSkJAHC6tA6VFpucJVIP0ekLpllZWXj66afxq1/9CtnZ2Zg2bRqeeOIJWK1W3H777Xj99deDUCYREVH7HDhRAY8ARgyKRP+YUN92c7gegxLCAQAHc8/JVR71IB26TFZQUIAPP/wQH330Ec6cOYPExET84he/wPz585GQkAAAWLBgAZYtW4aXX34Zv/zlL4NZMxERUYvO1dhQUFILALh26iBYah1+z49NiUZBcS0Ky+pQUW2Vo0TqQToUhq6++mro9XrMnj0ba9aswZQpUwK2GzJkCPLz8ztTHxERUbvlnqkBAAxNikD/mNAmYSgizHt26FRxLb47UYErJw2Qo0zqIToUhp5++mnccMMNCA8Pb7Hdb37zG/zmN7/pUGFEREQd4fEI31mh1GRzs+1GDYnGqeJanC6tRYPNiTCDtpsqpJ6mQ2OGPvvsM5SVlQV8LicnB9dff32niiIiIuqo0qoG2Bxu6LQqJMWGNdsuMlwPc5gOHgEczK0I2vtLktTKI2hvRUHS5jNDWVlZvlk7MzMzsW/fPlRWVjZp9+WXX6KwsDB4FRIREbVDfrH3rNDAuHCoVS0nj8EJJhw4UYGsnHJcNSm50+/tBmCztTx/kUolwdPpd6JganMYevfdd/Hhhx/6ku3q1aubtGkMS3Pnzg1ehURERG3k8QgUlHrDUOMdYy0ZlBCOAycqcKKoGjV1dphCO76igiRJsNmcOJJfCaer+bijVktwuQXyz1oAAAkxIVDxdJGs2hyGnnrqKdx8880QQmDRokVYuXIlhg4d6tdGpVLBZDJh2LBhQS+UiIioNcXn6uFwemDQqREXGdJq+/AQHWIjjSirsmJfThmunJjU6RqcLg8cTnfA5+ptTny2t9Bv9utB8eGYMTYBEgORbNochsLDwzF58mQAwJtvvomRI0ciNDS0lVcRERF1n4KSOgDe9cdUrVwiazSkvwllVVbsOVIalDDUHIfTjS+yilBndSI8RItQgxZlVQ3IL6lFeIgW44f367L3bkvQUvICtm0OQ9u2bcNll12GyMhInD17FmfPnm2x/bx58zpbGxERUZsJIXD2XD0AYEALA6cvNqS/CXt/KEXemRpU1doRGa4Pem0eIfDlgTOornMgxKDBkp+NRdk5K344dQ7/+b4Eh09WwhSqQ0piRNDfuy3jmADAoNdAHfR37x3aHIaeeOIJ/N///R8iIyPxxBNPtNhWkiSGISIi6laWegcabC6oVBJiI41tfl2IQYtBCSacKrbgYG5Fl6xXll9ci9JKKzRqCXOnDkSUyYCyc1YMTYpAbYMDh09WYv/xcgxKMLU66Ls92jqOSatRIX1QFMIMWkWeIWpzGPriiy/Qr18/39+JiIh6kuJzDQCA2EgjNOr2zRwzakgUThVb8F0XhCGPEDh0/tb90UOiEWP2D2pjhsYg90wNrHY3TpfUYnB/U1DfH2h5HBO1IwwlJiYG/Hsjl8uFuro6mM3moBRGRETUHmfPh6GE6NYHTl9s1JBobP8mH0fyq2B3uKHXBe+CUX6xBZYGJ3RaFdIGRjZ5Xq2SMHyAGQdzz+FoQVWXhCFqWYcmXXS5XHjppZewfft2AMDevXsxbdo0TJkyBYsWLUJNTU1QiyQiImqJxyNQej4M9Y9u/809CdEhiIkwwOX24Eh+0zn0OlPXofOLwY4cFAWtJvCv3eEDzFBJQEWNjWulyaBDYWjjxo14+eWXYbF450j43e9+B7PZjBUrVuD06dN44YUXglokERFRSypqbHC6PdBpVYg0tX8AtCRJGDcsBgDwXRBnoy4orYWlwQm9Vh3wrFAjo16DQQneM0I5p6uD9v7UNh0KQ5988gmWLl2KO+64A3l5eThx4gTuu+8+LFy4EI888gj+9a9/BbtOIiKiZhWfv4ssITq0wxMYjhvqHRd7MO8cPEEaRHyi0HulJDXZ3OxZoUaNYSm/2MLxPd2sQ2GorKwMY8eOBQD8+9//hkqlwsyZMwEA8fHxqK2tDV6FRERErSjuxHihRqnJZhj1aljqHb4lPTqjtsGBkkpvXUOTWr9lPibCAFOod620ovL6Tr8/tV2HwlBsbCyKiooAAP/6178wYsQIREVFAQAOHDiA+Pj44FVIRETUApfL4xtn05kwpFGrMHJwNIDgXCrLPWPx1RRm1LbpNUn9vOOdCkt5UqE7dSgMzZ07F8899xx+9atfITs7GzfffDMA4Nlnn8WLL77IVeuJiKjblFdb4RHecTdtDR3NGTfUG4Y6u4q9RwjkFXkvkQ1rw1mhRknnJ4ssLKuDx6O8+X7k0qEw9PDDD+Ouu+6CJEl49NFH8fOf/xwAcPjwYdx111247777glokERFRcxovRcVFGju9vteYlBhIkjeMVNR0/K6usxX1aLC7oNeqMSCu7bNhx5qN0GlUsDncyC+xdPj9qX3aPM/QhSRJwr333ot7773Xb/vWrVuDUhQREVFblVb+ONliZ4UZtRiaGIETRTU4mHuuw2uV5Z4/KzSkvwlqVdvPO6hUEvr3C0V+cS2+P1mJMUNiOvT+1D4dCkMAUFtbiz179qChoSHg1N1tXY7D4/HgpZdewrvvvova2lpkZGRg5cqVGDBgQMD2VVVV+N3vfoevvvoKkiThuuuuw2OPPQaj8ccvwaFDh7B27VocPnwYkZGRuPnmm/HAAw9A1Y4PJBER9Xwej0BplfcMTjDCEACMGxpzPgxVdCgMNdhdKCzzLhjbloHTF0vqF3Y+DJ1r92upYzoUhnbv3o0lS5bAag18CrE9a5Nt2rQJW7ZswfPPP4/4+HisW7cOixcvxvbt26HT6Zq0b3zf119/HRaLBU8++SQaGhqwdu1aAMCpU6ewcOFC3HDDDXj22Wdx7NgxrFixAkajEXfffXdHDpeIiHqoMxX1cLo80GpUMAdpgdVxw2Lw7r/zkHO6Cla7C0Z9+35V5hZVQwjv3WEdWfQ1MSYUkuS9Q66i2oroCEO790Ht06Ew9MILL2DIkCFYsWIF4uLiOnzGxeFw4LXXXsOyZcswa9YsAMCGDRswY8YM7Ny5E3PnzvVrf+DAAWRmZmLHjh1ISUkBADzzzDNYvHgxli5diri4OGzevBlDhw7F6tWrIUkSBg0ahGPHjmH//v0dqpGIiHquk2e8l6P6mY0dnl/oYvFRIYiNNKKsyooj+ZWYmBrb5tcKIXDs/KSJHTkrBAB6nRqxkUaUVlrxQ34lZo7t36H9UNt1KMXk5eXh4YcfxqRJkzBgwAAkJiY2ebRFTk4O6uvrMWXKFN82k8mE9PR07Nu3r0n7rKws9OvXzxeEAGDy5MmQJAnZ2dkAgK+//hpz5871G0S3ZMkSvPzyyx05VCIi6sHyznoHGQfrEhlwfjbqod6xOtnHytv12lNnLaipc0CjljAoIbzDNSScX1LkeGF1h/dBbdehM0P9+/dHXV1dp9+8pKQEAJCQkOC3PTY21vfchUpLS5u01el0MJvNKC4uRl1dHcrLyxEeHo7f/va3+Oqrr2AymTBv3jz86le/glrduYX3NK3MHtrXqM+v+qxu5+rP1BT7MjjYj8HTN/pSIO/8maGE6BCoVU3PDKkkCZIkQaUG1O7AZ47UKgmSSoJGI0EIb5spo+Kxc18h9h8vh9PtafZS2YX9KEkefPtDKQBgULwJRl3g17SlpsSYUHx3ogLHC6s79btHkgBJJUF9/tGcQH3Q3eT8THYoDN177734n//5H4wePRpJSR0baQ/AN+bo4rFBer0+4GKvVqs14DgivV4Pu93uC2hr167FwoUL8Ze//AVHjx7Fs88+i4aGBjz88MMdrlWlkhAZ2f7F//oCkyl4/49L6diXwcF+DJ7e3Jcl5+phqXdApZKQnBABTYBfokaDBhqNGkaDDhqNJ+B+tBoVjAYdzOYfJ2ycaA5BYr9QnCmvR05RDa6YlNxiLSaTEXUNDuw/XgYAGD0sBiEhgccLtaWmgUYdVJLkXXNNSIiN6vhkkg5PA4xGHTTawO8FBO4DucjxmexQGNq+fTtKS0tx1VVXISoqCgaD/+AuSZLw+eeft7qfxtc5HA6/fdjtdr+7wy5s73A4mmy32+0ICQmBRuM9nKlTp+KBBx4AAIwYMQKVlZX4n//5Hzz00EMdnoPC4xGwWBo69NreSq1WwWQywmKxwu1u/ktErWNfBgf7MXj6Ql9mfX8WANAvwgCH3Ymmvx0ASXjgcrlhtTngcARe70unVcNqc6C6WuDCm6MvSY/D33edxM49BRifEh3wtRf24/avT8Lh9CDKpEeEUYOGBnvA17S1pgFxYSgoqUXm92cxbXRCwHatkSTAanPCanW0uN5Zc33QnbriM2kyGdt0pqlDYSg+Pj4oS240XvIqKytDcvKPqbusrAypqakB3/fikOVwOFBdXY3Y2FhERkZCr9dj+PDhfm2GDRuGhoYGVFZWIjo68Ae6LVyu3vkDo7Pcbo9ijz3Y2JfBwX4Mnt7cl0cLqgEAcVEhcDczW7NHCAgh4HGj2TZuj4DwCLhcwm+qmMkjvGHoyKlKlFdZW7wzzOF04Z/7vMtUjRwcBY8AmksVba1paGIECkpqcTS/CpeMiGv2vVsiSRKER8B9/tGc5vpADnJ8JjsUhp577rmgvHlaWhrCwsKwd+9eXxiyWCw4cuQIFixY0KR9RkYG1q9fj4KCAgwcOBAAkJmZCQCYOHEi1Go1JkyYgIMHD/q97tixYzCZTDCbzUGpm4iI5HeiqBqANwx1hVizEUOTIpBbVIO9R0pxzSXNXyrLPlaOcxYbwoxapCRGBGUpjZTECHyRXcRB1N2gU6OU8vLy8Oabb2L9+vUoLS1FVlZWuwZW63Q6LFiwAOvXr8cXX3yBnJwcPPLII4iPj8ecOXPgdrtRXl4Om80GABg7diwmTJiARx55BIcOHcKePXuwcuVKzJs3D3Fx3tR83333Yffu3XjxxRdx+vRp7NixA3/+85+xaNGiTg+gJiKinsHS4PCtVB8X1XVjTKaO9F4F2XXwLNye5s9W7MwsBABMGx0fcOxSR6QkmiDBu9xITX2gi4AULB36F/N4PHjqqacwd+5c/P73v8err76KiooKbNq0CfPmzQt4J1hzlixZgltuuQVPPfUUbr/9dqjVarz66qvQarUoLi7G9OnTsWPHDgDe030vvfQSkpKSsGjRIjz88MOYOXMmVq1a5dvfJZdcgs2bN+PLL7/Etddei3Xr1uGee+7Bb37zm44cKhER9UCNy10kRIfA0MxdW8FwSXocwoxalFY24OtDxQHbHDxRjuOF1VCrJMwI4pxAIQatb+HWEzw71KU69AnatGkTtm/fjt/97neYNWsWpk2bBgBYvnw57r//fmzYsME3I3Rr1Go1li9fjuXLlzd5LikpCceOHfPbFh0djY0bN7a4zxkzZmDGjBltPBoiIuptGi+RDenfsYkN28qo1+D6qYPwzhcnsO3rU7g0PR563Y9XGRxON/7nXe/QjMvG9UdEmB5AbdDef/gAMwrL6nC8sBqT0to++SO1T4fODL3//vtYsmQJbr75Zr9xOCNGjMCSJUvwzTffBKs+IiKiJk6cPzOUkmjq8veaNT4RMREG1NQ5sDOr0O+5bbtPofhcPSLD9bj5sqFBf++U82HvFFew71IdCkMVFRUYMWJEwOfi4uJgsfAfjYiIuobd4UZBiffsS0pi154ZArxz8Nx8mXflg0/+k4+vDp6FxyPwzeFi7Pi2AACw8JpUhBiCf7lucH9v2CsoqYOrl06B0Bt0KAwNHDgQu3btCvhcZmam704vIiKiYDtZbIHbIxAZru/QQqgdkTEiFqOHRMPh8uD1T3Pw8Itf49VPjsIjBKaN7d+u9cvaIy7SiFCDBi63B0XlnV/5gQLrUIxdtGgRVq5cCafTicsvvxySJKGgoAB79+7Fa6+9hieeeCLYdRIREQH4cbzQ8CRzhyfSbS+VJOGhW8Zg575C/P2rPNRZnQjRa3Dd1IG49eoRaKizdcn7SpKEwQkmfH+qEqfOWjAovusvCypRh8LQT3/6U1RWVuLll1/Gli1bAABLly6FVqvF4sWLcfvttwe1SCIiokaNd1YNH2Du1vdVqSRcc0kyxg6NxvHCamSkxcIUpodeq0ZXrk/QGIZOFltweRe+j5J1+ALn3Xffjeuvvx6ZmZnQaDQIDw/H2LFjObEhERF1GbfHg9zzK9UPG9D144UCSYgO9a0q3x0axw2dKg7eXWrkr91h6OOPP8bWrVtx8OBBuFwuAN41wyZMmIDbb78ds2fPDnqRREREAFBUVg+7ww2jXoPEmDA0OFxyl9TlBid4w1BxRT2sdheM+q6bV0mp2tyjbrcbjz76KP7xj38gLi4O1113HWJiYiCEQElJCTIzM/Hggw/ixhtvxPPPP9+VNRMRkUI1Lk0xNDECKlX3jBeSW0SoDtEmA85ZbMgvqcWIgZFyl9TntDkMbdmyBTt37sSTTz6JBQsWNBm05na7sXXrVvz+97/HpEmTcMsttwS9WCIiUjbf4GmZLpHJZXB/E85ZbDhVbGEY6gJtvrV+27ZtuO222/CLX/wi4Oh9tVqNO+64Az/72c/wwQcfBLVIIiIiIYRvssVhSWZ5i+lmQ85fKjt5lvP4dYU2h6FTp05h5syZrbabMWMGjh8/3qmiiIiILlZWbUVNvQMatYTBCeFyl9PlJKnxIWGIbxC1BZIk+R4UHG2+TGa1WhER0fppycjISNTX13eqKCIiooudKPSeFRqUYIJWo26lde+mVktQqVSotboACMREGiFJQFWtHUUVdTCHeSebNOg16Ns90T3aHIaEEFCrW+9ylUoFIUSniiIiIrpY43ihYUl9f7yQWiXB6nAhr7AGDpcbABAZrkelxY5dB86cD4QqpA+KQphBy9+7ndSh5TiIiIi627HT1QC8M08rhdPlgcPphsPpRpTJAAAoqWyAw+mG08W1yoKlXZMVrFq1CmFhYS22qavj2ilERBRcFTVWlFVboZKkbp95uqeIiTAgt6gGFTVds/SHkrU5DGVkZABAq6fiQkNDMWnSpM5VRUREdIGcgmoAwOCEcMVOOhgT4T0zdK7GxstiQdbmT9Rbb73VlXUQERE1K+d0FQAgTcFz7JjD9FCrJDhdHljqHeinU2Yo7AocM0RERD2aEAJHCxiGVCoJ0efPDvFSWXAxDBERUY9WVmVFVa0dGrWEoYl9/06ylsQwDHUJhiEiIurRjp6/RJbSPwJ6rbJn1fGdGapmGAomhiEiIurRcs5fIuOaXEC/CCMAoKrWBpebt9YHC8MQERH1WB4hfGFIyeOFGoUaNTDo1PAIoNLCs0PBwjBEREQ9VmFpHSwNTuh1at/6XEomST8Ooi7npbKgYRgiIqIe61BeBQAgfWAkNGr+ygJ+HERdXmWVuZK+g58sIiLqsQ6dPAcAGJMSLXMlPYcvDFUzDAULwxAREfVIdVYnTp61AABGD2EYatR4maym3oEGm1PmavoGhiEiIuqRvj91DkIASf1CfYuUEmDQaRBm1AIATpdyPdBgYBgiIqIe6XCe9xIZzwo11XiprKCkVuZK+gaGISIi6nE8QuDwyUoAHC8USIyZYSiYGIaIiKjHOXXWgjqrE0a9GikKX4IjkMYzQ/klFq5gHwQMQ0RE1ONkHysH4L1Exlvqm4oyGSBJQG2DE5W1drnL6fX4CSMioh5FCIF9OWUAgEmpsTJX0zNp1CpEhXvPDp08WyNzNb0fwxAREfUo+SW1OGexQadVYTTHCzWrX6R3nbK8MxaZK+n9GIaIiKhHyTp/VmhsSoziV6lvSdz5MJR7hmeGOothiIiIegy/S2RpvETWktioEABAQYkFThdXsO8MhiEiIuoxTpfWoaLGBp1GhTGcX6hFphAtwoxauNwCBaW8xb4zGIaIiKjH2HukFAAwOiUaeh0vkbVEkiQMTjABAPJ4qaxTGIaIiKhHcLk9+M/3xQCAqSPjZa6mdxjc3xuGOG6ocxiGiIioRziYew6WBidMoTreRdZGjWeGcs/UcPLFTmAYIiKiHmH3obMAgGmj4jnRYhslx4dBrZJQU+fAOYtN7nJ6LX7aiIhIdlW1dhw+6V2YdfqYBJmr6T10GjWS48IB8FJZZzAMERGR7L45XAwhgGFJEUiIDpW7nF5l6Pm12/KKOPliRzEMERGRrNweD3Z9571ENmNMf5mr6X2GDzADAI4VVslbSC/GMERERLLKyinHOYsN4SFaTB7BiRbba3iyGQBQVF6P2gaHvMX0UgxDREQkGyEE/rH3NADgyglJ0HH5jXYzheiQGOO9tHi8sFreYnophiEiIpJNzulqFJTWQqdR4fIJiXKX02ulnj87lHO6WtY6eiuGISIiks1nmd6zQtPGJCA8RCdzNb1XWnIkAODYaY4b6gjZw5DH48HGjRsxY8YMjBs3DnfffTcKCwubbV9VVYVHH30UGRkZmDx5MlavXg2r1RqwrcPhwPXXX48nnniiq8onIqIOOlVswaG8c5AAzMkYIHc5vVrjIGqOG+oY2cPQpk2bsGXLFqxZswZbt26Fx+PB4sWL4XAE/sdcsmQJCgoK8Prrr+OPf/wjdu3ahVWrVgVs+1//9V84fvx4F1ZPREQd9f6uPADAlFHxiIsMkbma3s0UynFDnSFrGHI4HHjttdewZMkSzJo1C2lpadiwYQNKSkqwc+fOJu0PHDiAzMxMrF27FiNHjsSUKVPwzDPP4MMPP0Rpaalf2927d+PTTz/FsGHDuutwiIiojX7Ir8SR/CqoVRLmTR8sdzl9wnCOG+owWcNQTk4O6uvrMWXKFN82k8mE9PR07Nu3r0n7rKws9OvXDykpKb5tkydPhiRJyM7O9m2rrKzEihUrsGbNGkRGRnbtQRARUbsIIfD+v71nhS4fn4gYs1HmivqGxnFDORw31G4aOd+8pKQEAJCQ4D/1emxsrO+5C5WWljZpq9PpYDabUVxc7Nv25JNP4vLLL8cVV1yBv/71r0GrV6OR/apit1KfXxtIzTWCOo19GRzsx+CRsy//830J8ktqodeqMW/mkA7/bJUkQFJJUJ9/BKKSJEiSBJUaULsDt1GrJEgqCRqNBCECt2nOhf0oSZ5W62lrTR2pe9SQKEgAzpTXo9bqRGS4vk19FGhfcpDzMylrGGoc+KzT+d9BoNfrUVPTdI0Vq9XapG1je7vdDgDYunUr8vLy8MILLwS1VpVKQmSkMqeIN5n4/9qChX0ZHOzH4OnuvqyzOrH1ixMAgJ9eOQwDkzp39t7haYDRqING6wn4vNGggUajhtGgg0YTuI1Wo4LRoIPZ3PFxS4392Fo9ba2pI3VHRoZiWLIZx09XI6+4FlclR7W5pmD0QbDI8f2WNQwZDAYA3rFDjX8HALvdDqOxaWcYDIaAA6vtdjtCQkJw8uRJrFu3Dq+++ipCQoL7D+rxCFgsDUHdZ0+nVqtgMhlhsVjhdjf/JaLWsS+Dg/0YPHL15Zv/yEF1rR0J0SG4fFx/VFXVd3hfkgRYbU5YrQ44nO7AbYQHLpcbVpsDDkfgNjqtGlabA9XVAkK0r4YL+9Hj8bRaT1tr6mjd6QMjcfx0NfYcPotJw2Pa1EfN7au7dcVn0mQytulMk6xhqPGSV1lZGZKTk33by8rKkJqa2qR9fHw8Pv/8c79tDocD1dXViI2NxY4dO1BfX48777zT97zNZsP+/fvx2Wef4cCBA52q1+VS5g9ft9uj2GMPNvZlcLAfg6c7+/JUsQVfZBUBABZcNRwSWv65KkktX66RJEB4BNznH4F4hIAQAh43mm3j9ggIj4DL5W3bEW63B263aLWettbU0brTB0Vh2+5T+P5kJewOFzRqdZtqCkYfBIsc329Zw1BaWhrCwsKwd+9eXxiyWCw4cuQIFixY0KR9RkYG1q9fj4KCAgwcOBAAkJmZCQCYOHEipk6diuuvv97vNcuWLUN8fDyWLVvWxUdDRETNcTjdeOXjIxAALh0ZhxGDolps7wZgszlbbKNSSWAk9jckwYRQgwb1NhdOnrVg+ADeRNQWsoYhnU6HBQsWYP369YiKikJiYiLWrVuH+Ph4zJkzB263G5WVlQgPD4fBYMDYsWMxYcIEPPLII1i1ahUaGhqwcuVKzJs3D3FxcQAAs9ns9x4GgwGhoaG+8ERERN3v3S/zUHyuARGhOtx+ZctTnkiSBJvNiSP5lXC2cIYgxKDBwAQTJMgz4LcnUqkkjBwchcyjZTh88hzDUBvJfkvGkiVLcMstt+Cpp57C7bffDrVajVdffRVarRbFxcWYPn06duzYAcD7BXnppZeQlJSERYsW4eGHH8bMmTObnXSRiIjkdyjvHL7Y77089qvrRrR52Q2nywOH093sw8VxYwGNHhINADh8slLmSnoPWc8MAYBarcby5cuxfPnyJs8lJSXh2LFjftuio6OxcePGNu//rbfe6nSNRETUMedqbHj1kyMAgNkTkzDq/C9q6jqjBnsvQRaU1KKmzg61wqaF6Qj2EBERdQmH042XPjiM2gYnkmPDcMuslNZfRJ0WEabHoPhwAMB3uRUyV9M7MAwREVHQCSHw1mfHUFBSizCjFg/MHw2dVi13WZ0mnZ8M8cdH43aglZvfutWE4f0AANnHymWupHdgGCIioqD76Jt8fPN9CSQJ+PWNI/vEkhtuAHU2p9+jpsGJssoG1DQ4UW939Zi72yamesPQkfxKWO0umavp+WQfM0RERH3LlwfO4MOvTwHwzieU3spt9L1Bc3e4qVUSjEYdrFYH9Dp1j7m7LSE6FAnRISg+14AfTlVCy3FDLWLvEBFR0Hz7Qwne/sx748sN0wbh8glJMlcUXIHucGvc1tPubmu8VHaQ44ZaxTBERERBsfvgWbyy3Tux4qxx/XHj9MFyl6RoF14q62lBradhGCIiok4RQuCf+wrx109zvEFofCIWXJ3a6nIa1LUGxoUj2mSAw+nBmfKOrwGnBAxDRETUYS63B2/vPI53zq9EP3tiEn4xZzhUDEKykyTJd3bo5FmLzNX0bAxDRETUIVW1dvzhf7/DlwfOQALw08tTcPvsYW06I9T0FvWmt6tT500e4V2q6nRJLS+VtYB3kxERUbsdOFGOv+7IQZ3VCb1WjXuuT8f48wN2W9PaIqxcgDV4hvQ3IcqkR6XFjjPl9Rh4fjJG8scwREREbVZdZ8fWL04g82gZACA5Lgz33jASCdGhbXp9WxZh5QKswSNJEiYM74fPs4qQX1LLMNQMhiEiImqV0+XBF9lF2P6ffFjtLkgScHVGMm6aOaRDc9g03o4eiE7LERzB1BiGisrq4HR5OOdQAAxDRETULCEEso6V490vc1FRYwMADE4Ix8Kr05o9y9DSmCGOB+p+SbFhMIXqYKl3oKisDoP7m+QuqcdhGCIi6qFaG4gshOjS9887W4P//SIXuWdqAADmMB1umjkE00YlQKUKXBvHA/U8kiRhSH8TvjtRgVMltQxDATAMERH1QK2FCgAw6DXoiqVPz9XY8P6uPOw5UgrAe9nqJ5cMxDWTk6HXNf+OHA/U/X5cIDZwfzbm6cYwdLa8Hg6nu08smhtMDENERD1MW0KFVqNC+qAohBm0QTtD5BECX+4/g3e/zIXD5YEEYNroBNw0cwgiw/Vt3g/HA3UPtVqCSqVCrdUFIPBnoPFMXJTJgIgwHWrqHCgsq0NKYkS31trTMQwREfVQLYWKYKuus+OVj4/gSH4VAGD4ADNuv3IY7z7qwdQqCVaHC3mFNXC4An9OLjwTNzg+HN/lnsOp4lqGoYswDBERKdzp0lr88b1DqKq1Q6dR4WdXDMXl4xO5nEYv0dYzcYMSTPgu9xyKz9XD5nDD0MIlT6VhGCIi6uMChZrGTYfyKvDie4dhd7qREB2CB+aPbvOcQdS7mEJ1iAzXo6rWjtOltRg+wCx3ST0GwxARUR/W3EBsSSVhf24hXvy/g3C5BUYMjMRvbhqFUIO2+4ukbjM4IRxVtXbkFzMMXYhhiIioj2ppIHZpZQM+3VMAl1tgzNBoPDh/DNTN3C5PfcfA+HDsP16B0soGWO0uGPWMAQAXaiUi6vMax5Q0PsqrrPjH3tNwuQWS48Jw57UjoFHz14EShIfoEBNhgABQUFIrdzk9Bj/9REQK4nC68eWBM3C6PEiIDsVVGQMYhBRmUIL3DsF8hiEffgOIiBRCCIGvD5fAUu9AiEGDa6YMZBBSoMbpEsqqrKhvZWJPpeC3gIhIIY7mV6GorA4qlYQrJyQi5Pxg6cZZjCVJavFBfUOoQYvYSCMAoKCYZ4cADqAmIlKEqlo79h+vAABMTotFjNn7y1Ctan0W40ZdtfwHdb9B8eEoq7Iiv6QW6YOj5C5HdgxDRER9nNvtwdeHiuERAon9QjFswI+zD6vaMIsx0DXLf5B8BsaHY9/RMlTU2FDb4EB0hFHukmTFMERE1McdOFGBqlo79Fo1po6KD3jJqzuX/iD5GfUaxEYZUVppRVFZveLDEMcMERH1YcUV9TiY6708dunIOM4r04zWx03JXWHwDegXBgAoKq+TuRL58VtBRNRHeYTAO5+fgBDAgNgwJMeFdWp/jYEBCJwMemtgaM/q731JUmwYso6Vo7SyQfFnBRmGiIj6qF3fncGpYgu0ahUmj4jt1B1hfTkwtHf1977CFKqDKUQLS4MTReX1yBghd0XyYRgiIuqDahsceO/feQCAiWn9EGrs3JpjSggMbV39vS9Jig3DkfwqnC5V9i32ffNfl4hI4f7+1Uk02FxI6hca1FunL17a48KHy90bzwspW1Ks99JpYWkdPB7l3iXIMERE1Mfkl1jw1XdnAQC3XD4Uqt46mIe6XKzZCJ1WBbvTjVPFFrnLkQ3DEBFRH+IRAn/753EIAJeOjEdKYkSrryHlUqkkJMaEAgB+OFUpczXyYRgiIupD9vxQgrwzFui1avzs8hS5y6FeoP/5MHTsdJXMlciHYYiIqI+w2l1490vvoOnrpw1CZLhB5oqoN0iI9oahwtI61DUoc+FWhiEioj5i+zf5qKl3IC7SiKsmDZC7HOolQgwaRIbrIQAcKVDmpTKGISKiPqD4XD3+mVUIALh99nBoNfzxTm2X2E/Z44b4bSEi6uWEEHh753G4PQJjU6IxJiVa7pKol7lwELUSF+JlGCIi6uX2Hi3F0YIqaDUq/Pyq4XKXQ71QfHQoNGoJ5yw2lFZZ5S6n2zEMERH1Yg02F/73i1wAwNwpA9HPrOzVx6ljtBoVBieYACjzUhnDEBFRL/bB7jzfoOlrLhkodznUi6UNjAQAHMlXXhji2mRERL1UUXkdvsguAgD84upU6LRqv+c58TS1x/ABZgDA8cJqeIRQ1MzlDENERL2QSgX87xcnIAQwYXg/DEwwoc7mvKhN71xFnuQxIDYMeq0a9TYXzpTXY8D5dcuUgGGIiKgXOn66GvnFtdBqVEgdaMbB3IombXr7KvLUvdRqFYYmRuCH/EocL6xWVBjimCEiol6mwebCt9+XAAAmpsZCq1ZxFXkKitRkMwDlLc3BMERE1IsIIbDnhxLYnR4kxYZh5KAouUuiPqQxDB0vrFbUfEM9Igx5PB5s3LgRM2bMwLhx43D33XejsLCw2fZVVVV49NFHkZGRgcmTJ2P16tWwWq1++3vllVdw9dVXY9y4cbjuuuvw7rvvdsehEBF1qZNnLSgqr4dKJeG2q4ZBpeIlMAqewQkR0KhVsDQ4UVLZIHc53aZHhKFNmzZhy5YtWLNmDbZu3QqPx4PFixfD4XAEbL9kyRIUFBTg9ddfxx//+Efs2rULq1at8j2/efNmbN68GQ899BA++ugjLFy4EKtWrcK2bdu654CIiLpAndWJfUfLAAAZI2J9C2wSBYtWo0JKf+98Q8cKq+UtphvJHoYcDgdee+01LFmyBLNmzUJaWho2bNiAkpIS7Ny5s0n7AwcOIDMzE2vXrsXIkSMxZcoUPPPMM/jwww9RWloKAHjnnXdw11134dprr0VycjJuvfVW3HjjjTw7RES9lkcIfH2oGA6XBzERBowfFiN3SdRH+S6Vna6WtY7uJHsYysnJQX19PaZMmeLbZjKZkJ6ejn379jVpn5WVhX79+iElJcW3bfLkyZAkCdnZ2fB4PFi7di1uuukmv9epVCpYLJauOxAioi50OO8cyqqs0KpVmDE2gZfHqMs0zjd0TEHjhmS/tb6kxHtHREJCgt/22NhY33MXKi0tbdJWp9PBbDajuLgYKpXKL1gBwNmzZ/HJJ5/gtttu61StGoWtAq1Wq/z+pI5jXwaHUvpRkgBJJUF9/lFS2YBDuecAAFNGxcEcpodKkiBJElRqQO0OHIxaaqNSqc7/2fp+WtuXHG16Sk0/9qOqV9V9MbVKgqSSoNFISE2OhFoloarWjqo6O2IjQ5p9XTDJ+f2WPQw1DnzW6XR+2/V6PWpqagK2v7htY3u73d5ke0VFBe6++25ER0fjvvvu63CdKpWEyEhlXp83mbjWUbCwL4NDCf3o8DTAaNTB7rLj3wfOQgBITY7E6GGxAACjQQONRg2jQQeNJvAt9G1po9drW20TzPcLVpueVpPBoO2VdTfSalQwGnQwm73BZ9gAM3IKqlBYYUXqkH7Nvq4ryPH9lj0MGQwGAN6xQ41/BwC73Q6jsWmHGAyGgAOr7XY7QkL80+vJkydxzz33wO12480334TJZOpwnR6PgMWinJH1gDedm0xGWCxWuDlfSaewL4NDKf0oSYDV5kRdvQ2ffJMPq92FyHA9MtL6oaHB+3/6JOGBy+WG1eaAw+EOvJ8W2qhUKhgMWtjtzlb3E4z3C3abnlJTYz/abM5eVffFdFo1rDYHqqsFhABSEk3IKajC/pwSTBwW3ezrgqkrvt8mk7FNZ5pkD0ONl7zKysqQnJzs215WVobU1NQm7ePj4/H555/7bXM4HKiurkZsbKxvW3Z2Nu677z7ExcXhlVdeQVxcXKdrdbn67g/flrjdHsUee7CxL4Ojr/ejJEnwuD3YfbAY5dVWaDUqXDauP1QqCW6PdwyHRwgIIeBxw7ftYi238fafx9P6foLzfsFt03NqauxHDzxC1Yvq9uf2CAiPgMvlbT8s0QygADkFVd3+XZPj+y37hfe0tDSEhYVh7969vm0WiwVHjhxBRkZGk/YZGRkoKSlBQUGBb1tmZiYAYOLEiQCAQ4cOYfHixRg2bBj+9re/BSUIERF1py+yinDs/N08M8YkwBTadHgAUVcZlhQBSQLKq22otNjkLqfLyR6GdDodFixYgPXr1+OLL75ATk4OHnnkEcTHx2POnDlwu90oLy+Hzeb9xxg7diwmTJiARx55BIcOHcKePXuwcuVKzJs3D3FxcXC5XFi2bBmio6Px/PPPw263o7y8HOXl5aisrJT5aImIWpd5tBQffn0KAJCRFoskBa0RRT2DUa9Bclw4AO9s1H2d7JfJAO8kii6XC0899RRsNhsyMjLw6quvQqvVoqioCFdeeSWee+45zJ8/H5Ik4aWXXsLq1auxaNEi6PV6XHPNNVixYgUA71mhxrNGs2fP9nufxMRE/Otf/+r24yMiaqsTRdX4y/YjAICRg6MwYlCkzBWRUqUOMKOgpBbHC6tx6ch4ucvpUj0iDKnVaixfvhzLly9v8lxSUhKOHTvmty06OhobN24MuK8JEyY0aU9E1BuUVjbgxfcPw+X2YPSQaGSkx/bpsVHUs6UOMGPnvkJFzEQt+2UyIiICLPUObHj3IOqsTgyKD8eia9OgkjixIsln2PnJF4vPNcBSH3h5rL6CYYiISGZWuwsb/u8gyqqsiDYZ8PBPx0KvVctdFilcmFGLpH7e+fX6+rghhiEiIhk5nG5sfO8QCkprER6ixdJbxyIiTC93WUQAfjw7xDBERERdwu3x4E8f/oBjhdUw6NRY+rNxXImeepTUxjBUVC1rHV2NYYiISAYeIfDXHTn4LrcCWo0KD90yBgPjw+Uui8jPsCQzAKCwtA4NNpe8xXQhhiEiom4mhMDWL07gP9+XQCVJuO/GUUhN5i301PNEhusRazZCAMg903S90L6CYYiIqJtt/08+Ps8qAgD86roRGDcsRuaKiJo3bEAEgL49bohhiIioG/1rfxG27fbOLn377GGYMqpvT2ZHvd9wBYwbYhgiIuome34owd92HgcA3DBtEK6aNEDmioha1ziI+tRZCxxOt7zFdBGGISKibnAorwKvfnIUAsCVE5Jw4/TBcpdE1Cb9zEZEhOng9gicKrbIXU6XYBgiIupixwur8T8ffA+3R+DSkXH4+ZzhUKlUkCSpmYfcFRP9SJIk39mhY6erZa2lqzAMERF1odOltfjje4fgdHkwJiUai64dgQa7C3U2Z7OPersLXJGMepLGMJRzukreQrpIj1iolYioLyqtbMAf/u8grHYXhidF4P6bRsPh8uBIfiWcLSzAGmLQYGCCCRJ4ioh6hrSB3qkfcs9Y4HS5odX0reVieGaIiKgLVFRbsW7rAVjqHUiODcOSW8ZCd369MafLA4fT3ezD5eZ5IepZ4qNCEBGqg8vtQd6ZvjduiGGIiCjIqmrtWLf1ACotdiREh2DpreMQYuCJeOq9JEnynR3qi5fKGIaIiIKopt6Bde8cQHm1DbFmI5bdNh6mUJ3cZRF1WlqyGQCQU8AwREREzahtcGD91gMoqWxAtEmPZbePQ2Q4V6CnvqHxzFDeWQvsfWy+IYYhIqIgqG1w4IX//Q5nyusREabDstvHIybCKHdZREETazYiMlwPt0f0uXXKGIaIiDqpqtaOtVsO4HRpHcJDtFh+23jERYbIXRZRUEmShLTzCwr3tUtlDENERJ1QVm3Fc29n42xFPSLD9Xj85xPQPyZU7rKIukTaQDOAvheGeHsDEVEHnamox/qtB1BT5zg/WHocYsy8NEZ918hBUQCAk8UW1FmdCDNqZa4oOHhmiIioA04VW7D2b/tRU+dAYkwonlgwgUGI+rwokwGJ/UIhBPDDqUq5ywkahiEionbKPFqK5/+2H3VWJwYnhOPxOybAHOa9a6z59ca45hj1DaMHRwMADp88J3MlwcPLZEREbeTxCHz0zSl89E0+AGBMSjTuvWEkjHrvj1I3AJvN2ezrVSqJa45Rrzd6SBT+kXka3588B48QUPWBlM8wRERBJbXyg1EI0U2VBJel3oE/b/8BR/K9A0fnZAzAzy4fCpXKe7ySJMFmc7a47hjXHKO+YNgAM/Q6NSwNTpwurcWgeJPcJXUawxARBU1rZ0YAwKDXoLct8XjgRDne/Mcx1NQ7oNOqsPDqVEwdlRCwbeO6Y4HotByZQL2fRq1C+sBIHDhRgcMnKxmGiIgateXMiFajQvqgKIQZtL3iDFFNvQP/+68T2PNDKQCgf0wo7ps3Com8dZ4UbvSQ6PNh6ByunzpI7nI6jWGIiIKqpTMjvYXT5cY/s4rw8X/yYXO4IUnANZOTMW/GEN/K8xfrA8MmiNps1BDvLfZ5Z2pQ2+BAeEjvXn+PYYiI6Dyr3YV/HziDnfsKUVPvAAAMig/HHXOGY1D/CNjsLjjcgc96cXA0KUlMhBHJsWE4XVaHAycqMHNsf7lL6hSGISJSNI9H4ERRNb4+XIx9OWVwOL2RJtqkx00zh+DSkfFQq1So4+BoIj8ZI2JxuqwO+46WMgwREfUmHiFQcq4BeWdqcLywGgfzzqHO+uOg7/4xofjJJcm4JD0OGrX/gGcOjib60aS0WLy/6ySOFlT3+ktlDENE1Gu1dhu/2+NBeZUVReV1KCyrw6niWpw8W4N6m8uvXahBg/HD+2HmmP5ISTS1ul8iAuIiQ5AcF4bTpXXYf7wcl41LlLukDmMYIqJe6eLb+OttTpytqMfZ8nqcqajH2Yp6FJ+r9132upBOo8KgBBNSEk0YNTgaw5IimpwFIqLWZaTF4nRpHfbllDEMERF1F5fbg6LyOhw7XY1DuRWoqLGh0mJrcrankVajQv+YUAzoF4bkuDCkJEZgQGwYww9REGT4LpVVwdLggKmXXipjGCKiHsfjEaistaG82obyaqvvUVLZgLMV9XC5A89RFGbUwhyuR2S4Hv3MRkwdFY/B8Sbe9k7URWIjQzAwPhwFJbXY80Mp5mQMkLukDmEYIiJZOF1ulFRaUVbVgPJqG8ouCD3namxwe5qflNGgU6N/TCh0WjUiQrWIDNfDHKb3mwNIp1UjPjoEarWE1uZ3FEK0OE6IYYqoeTPHJOCtklp8ub8Isycl9cq1yhiGiKjL2RwulFVZUV3nQOaRUpRWNqCs2tpiSFGrJMSYjehnNqCf2YhYsxEJMaEYNSwWOsmDmnonDuZWNHt3l1otQaVSodbqAtD8G0kSoNNpYOcCq0QdcunIeLz77zyUVllxJL8So86vat+bMAwRUdDZHC6crahHaaUVZVVW3wSGFzPqNUiICkG/SCP6mY2+0NPPbIA5TO9bBLWRRqNCZGQIqqvrW61BrZJgdbiQV1gDh6v5GbEb5wdqqR3nECJqnlGvwbRRCfhifxH+lX2GYYiIlEkIgZLKBuw9Woo9P5SgrNLa5FxMRJgOCdEhGJ0Sg1hzCBKijTCF6gJenjLoNQjW8ObWlgdpnB+IcwgRddzlExLxxf4iHMyrQEWNFTERRrlLaheGISLqsNKqBuw9Uoq9R0pRfK7B77nIcD0SokMQFxWCfmYDDDoNwkK0SEkyI6+wBvkltQH3qdWoMHJw42Ku/s815qZeOCSBqE/rHxOKEQMjcbSgCl9kF+HWK4bJXVK7MAwRUbtUWmzIPFqGzKOlfoFGo5YwLMmMyHA94qNDEGbUNruPls7CtDTWR1JJcHgaYHe4OIaHqIe5evKA82HoDGZPHIDoCIPcJbUZwxARtcrS4EB2Thn2Hi3DicJqX0RRSRLSB0XikvQ4TBgeCw9Ei4Oa26KlsT5qlQSjUQdJeJAUF84xPEQ9yOgh0UgdYMaxwmp8sPskFs9Nl7ukNmMYIqKArHYX9h8vx96jpThyqgqeC65ZDU+KwCXpcZiYFuubZE2SJNS1cEdWewU6e6RWSdBoPZAEzwsR9TSSJOFnVwzFmjey8O33JZiTMQDJceFyl9UmDENE5BvEbHe4cTCvAnuPlOJQ3jm43D+GjoHx4bhkRBwmj4hFlKn3nP4mou4zOMGEySNikXm0DO98fgLLbx/f5K7QnohhiEjhbC439h8rx/7j5fg+7xwcrh8DUFyUERNTYzF1dAISo0Nb3A8HNRMRANx8WQq+y63AscJqbP9PPm6cPljuklrFMESkQC63B0fyq5B5tBT7j5fD5vjxclR4iBZD+pswJDECUeF6GPUaRJkMqLO1PHkhJyYkIgDoZzZi4dWpeOXjo/jo61MYmhSBkYOi5C6rRQxDRAphtbvw/alKfHeiHIfyzvktbBpq0GBgfDgGJYQj2mTwXTZzujzQ60S7Ji/koGYimjoqAccLa/DVwbPY/OEPWHbbuB49fkj2MOTxePDSSy/h3XffRW1tLTIyMrBy5UoMGBB4sbeqqir87ne/w1dffQVJknDdddfhscceg9H44wRPn376KV588UUUFRVhyJAhePzxxzFlypTuOiSiHsHjESgsq0PO6Sp8f6oSOQVVfut9mUJ1yEiLxeiUaNQ2OOB0tXxep62TFxIRAcDPZw9DQWktCkpqsXbLfjwwfwxGDIyUu6yAZA9DmzZtwpYtW/D8888jPj4e69atw+LFi7F9+3bodLom7ZcsWQKr1YrXX38dFosFTz75JBoaGrB27VoAwJ49e7B8+XI89thjmDZtGt577z3cc8892LZtG1JSUrr78KgPaWkhT8A7C7OcahscKCyrw+nSOhwvrMbxwmo02F1+beKiQjB+WAzGDY3B0MQIqNUq1Nm8a3wREQWTTqvG8tvGYeP7h3G8sBob/u87zJ06CD+5JBlajbr1HXQjWcOQw+HAa6+9hmXLlmHWrFkAgA0bNmDGjBnYuXMn5s6d69f+wIEDyMzMxI4dO3zB5plnnsHixYuxdOlSxMXF4S9/+Qtmz56NhQsXAgAef/xxHDhwAG+88QaeeeaZbj0+6jvcAGznbxt3uz2wOdyw2l2wOdywObx/ejwCEiSo1RLUKglqlcr3d71Ojeg6J2w2B9SS94eEXqOGVqtq0wrPbo8HdocbDXYXquscqLTYUGmxo9LiXe29sKwOVbX2Jq8z6NRIHWBG2sBIjB0ag4SLBkFz0DMRdaUQgxaP3joWf95+BNnHyrFt9yl8fagYP7l0IDLSYlucnLU7yRqGcnJyUF9f73cJy2QyIT09Hfv27WsShrKystCvXz+/MzyTJ0+GJEnIzs7GNddcg/379+OJJ57we90ll1yCnTt3du3B9EJCCO9wWAEIeH+Rn/9fq2dBehMhBFxuAbvTDYfTDXvjw+GG1eGGze6C9Xy4sdpdsNndsDpcP/63w416mwt1VgccTo/fpaZg0GpU0GlU0GnVkCT4LUHh9gjY7C6/O7xa0s9sRHJcGIb0j0BaciQS40LhdP742ovnAeKgZyLqalqNGr+ZNwp7j5bi3S/zUFFjw1ufHcM7nx/H8AFmDOkfgXFDYzA82SxbjbKGoZKSEgBAQkKC3/bY2FjfcxcqLS1t0lan08FsNqO4uBgWiwUNDQ2Ij49v0/7aQ6WSEBXV8q3F7WV3utFgc1209ceAEkiTzaKl/xS+P9ry61tc9KfbA5ScO786uMp/PIjU/F8C/FdLG5vd7Neg6aBc4RcaxEV/ERe1U6kBo06DYC8dKEnS+fDo/YwI4X+5LFBdnb2cplJ5zzapJAkqlfehUUlQq1V+Z3okSBAQcLk8zf77S5IEjVpCfExYk3XAfO8neVeLT4ozNdumre0620Zq/CxI3uU/Ovte3VV3T6ypPX3ZXTW1p01PqamxH73/h7L31H0xSfL+H7O2nKnuqGunh+En01J8Z9MvnMcMAMLCvfOXRUQYW6y1Pdo6x5GsYchqtQJAk7FBer0eNTU1AdsHGkek1+tht9ths9ma3Z/d3vQSQntIkvfyRzCFqFUIMfSMU4TUd7Xl2nyw2nT3+/XlullT762pt9bdXcI0OoSFNP+8StX9N2PIevuHweBNgQ6Hw2+73W73uzvswvYXt21sHxISAr1e3679EREREckahhoveZWVlfltLysrQ1xcXJP28fHxTdo6HA5UV1cjNjYWZrMZISEhbd4fERERkaxhKC0tDWFhYdi7d69vm8ViwZEjR5CRkdGkfUZGBkpKSlBQUODblpmZCQCYOHEiJEnChAkTfNsa7d27F5MmTeqioyAiIqLeTNYxQzqdDgsWLMD69esRFRWFxMRErFu3DvHx8ZgzZw7cbjcqKysRHh4Og8GAsWPHYsKECXjkkUewatUqNDQ0YOXKlZg3b57vzM+dd96Je+65B+np6Zg5cybef/99HD16FM8++6ych0pEREQ9lCRkninO7XbjD3/4A/7+97/DZrP5ZqBOSkpCUVERrrzySjz33HOYP38+AODcuXNYvXo1du/eDb1ej2uuuQYrVqzwjRcCgG3btmHTpk0oKSnB0KFDsXz5cs5ATURERAHJHoaIiIiI5MTFhIiIiEjRGIaIiIhI0RiGiIiISNEYhoiIiEjRGIaIiIhI0RiGiIiISNEYhoiIiEjRGIYUZvPmzfjFL37ht2337t24+eabMX78eFx//fX4+OOPfc/9/e9/R2pqasDHwoULfe2OHj2KBQsWYNy4cbjiiivw5ptvdtsxyaG9/QgA9fX1WL16NaZPn45Jkybh7rvvRl5enl+bb7/9FvPnz8fYsWNxzTXX4JNPPunyY5FbR/qyvLwcS5cuxaWXXoqpU6dizZo1aGho8Gvz6aef4tprr8WYMWMwb948fPvtt11+LN2turoaK1euxMyZMzFhwgTcfvvtyMrK8j3f2ufJbrdj9erVmDJlCsaPH49HH30UlZWVfm2U8pnsbF9eaOXKlXjiiSeabFdCX3a2H4uLi7F06VJMmzYNGRkZ+NWvfoUTJ074temS77YgxXj77bdFWlqaWLBggW9bVlaWSE1NFc8884zIzc0VH3/8sRg/frz44IMPhBBCWK1WUVZW5vd48803xYgRI8Q333wjhBCisrJSXHLJJWLFihUiNzdXvPfee2L06NHivffek+Mwu1xH+lEIIR5//HHxk5/8RGRnZ4vc3Fxx7733ilmzZgmbzSaEECI3N1eMHj1a/OEPfxC5ubnilVdeEenp6eI///lPdx9it+lIXzocDjF37lwxd+5csW/fPvH999+LW2+9VSxatMi3j2+//VaMHDlSvPHGGyI3N1c8//zzYtSoUSI3N7ebj7Br3Xnnnb5+OHnypFi9erUYM2aMyMvLa9Pn6YknnhCzZ88W+/btEwcPHhTz5s0Td9xxh+95JX0mO9uXQgjhdrvFCy+8IIYPHy4ef/xxv+eU0ped6Ue73S7mzp0rFixYIA4dOiSOHz8uHnzwQTFlyhRx7tw5IUTXfbcZhhSgpKRE3HvvvWLcuHHimmuu8fvFc99994mf/vSnfu03bdokLr/88oD7Ki4uFhMnThQvvviib9uf/vQnMX36dOF0On3bXnjhBTFnzpwgH4m8OtuPEydOFG+++abvv48ePSqGDx8uvv/+eyGEEE8//bS45ZZb/PaxdOlScdddd3XF4ciqM335z3/+UwwfPtzvh19xcbFITU0Ve/fuFUIIcdddd4mHHnrIbx+33nqrePrpp7voiLpffn6+GD58uMjKyvJt83g8Yvbs2eK///u/W/08lZSUiLS0NPHvf//b9/zJkyfF8OHDxf79+4UQyvlMdrYvhfCGnVtvvVVceumlYtasWU3CkBL6srP9+M0334jhw4eLkpIS3/M2m02MHTtWvPvuu0KIrvtu8zKZAvzwww/QarX46KOPMHbsWL/nCgoKMHHiRL9t6enpOHPmDM6ePdtkX+vWrUNsbCzuuece37asrCxMnjwZGs2P6/5eeumlyM/PR0VFRZCPRj6d7cfo6Gjs2LED586dg8PhwHvvvQez2Yzk5GQA3n68eA29Sy+9FNnZ2RB9bNWczvRlfn4+IiMjkZKS4ns+Pj4ekZGRyMzMhMfjwf79+5v05SWXXIJ9+/Z13UF1s8jISPz5z3/G6NGjfdskSYIkSbBYLK1+nrKzs33bGg0ePBhxcXG+flLKZ7KzfQkAe/bsQUpKCj7++GMkJSU1eQ8l9GVn+3HYsGH485//7Ft4HQBUKm9MsVgsXfrdZhhSgCuuuAIvvvgiBgwY0OS52NhYFBcX+20rKioC4F0U90LHjh3Dxx9/jKVLl0Kn0/m2l5SUID4+vsl+ATTZd2/W2X589tlnUVpaiqlTp2LcuHHYtm0b/vKXvyA8PBxA8/1otVpRVVXVFYckm870ZWxsLGpra1FXV+d7vq6uDjU1NaisrITFYkFDQ0PAviwpKemCo5GHyWTCZZdd5vdd/Oyzz1BQUIAZM2a0+nkqLS1FZGSk3yLXjW0a+0kpn8nO9iUA3HHHHXj22WcRHR0d8D2U0Jed7cd+/frhsssu83v+rbfegs1mw7Rp07r0u80wpHA33ngjdu7ciY8++ggulwtHjx7Fa6+9BgBwOp1+bV9//XWkpqbiyiuv9Ntus9n8PvwAfD9g7XZ7F1bfc7SlH48dO4YBAwbgr3/9K7Zs2YJLLrkEDzzwgO8Xf6B+bPxvh8PRjUcjr9b6cubMmQgPD8eTTz6J6upq1NXV4f/9v/8HSZLgdDphs9kAIOBnsi9/Hvfv348VK1Zgzpw5mDVrVqufJ6vV2uR5wL+flPqZbG9ftoUS+7Kz/fjPf/4TL7zwAn75y18iNTW1S7/bDEMKN2/ePNx///14+umnMXr0aNx///1YvHgxAPjOWADeL/I//vEP3HbbbZAkyW8fBoOhyQe58YMZEhLSxUfQM7TWj9999x3WrFmD5557zndm6L//+7+h0+l8v+j1en2Tfmz8b6PR2L0HJKPW+tJsNuPll19GTk4OLr30UkyfPh0JCQkYOXIkwsLCfEE80Geyr/bj559/jrvuugvjxo3D+vXrAbT+eQr0vQX8+0mJn8mO9GVbKK0vO9uP77zzDh566CFcf/31eOyxx3yvv/A1jYLx3WYYItx///3Yv38//v3vf+Pzzz9H//79oVar0b9/f1+bb775Bk6nEz/5yU+avD4+Ph5lZWV+2xr/+8Jrv31dS/2YnZ2N6Ohovz7VarVIT09HQUEBACAhISFgP4aEhPgFUyVo7TM5fvx4fPbZZ/jmm2+wZ88eLFu2DIWFhUhOTobZbEZISEjAvuyLn8e3334bDz74IC6//HL86U9/8v3CaO3zFB8fj+rq6ia/WC7sJ6V9Jjval22hpL7sbD+uW7cOq1atwsKFC/Hcc8/5xg115XebYUjh3n77baxZswZqtRpxcXFQqVT47LPPMH78eISGhvraZWVlIS0tDZGRkU32kZGRgezsbLjdbt+2PXv2YPDgwc1eP+9rWuvH+Ph4VFVV+X2JPR4PcnNzMWjQIADApEmTkJmZ6bffPXv2YMKECb4fBkrQWl8WFBTgtttuQ2VlJaKjo2EwGJCVlYXq6mpMnToVkiRhwoQJTfpy7969mDRpkkxH1TW2bNmCNWvW4I477sAf/vAHv8sHrX2eJk6cCI/H4xtIDQCnTp1CaWkpMjIy2rSPvqQzfdkWSunLzvbjunXr8Morr+Dxxx/HE0884Xcloku/2526F416nccff9zvNub//Oc/Ij09XXzwwQeisLBQbN68WYwcOdJ3i3KjhQsXiieffDLgPisqKkRGRoZ4/PHHxYkTJ8T7778vRo8eLf7+97936bHIqb39WF9fL+bMmSNuvfVW8d1334nc3Fzx29/+VowbN04UFhYKIYQ4fvy4GDlypFi3bp3Izc0Vr776ap+ch+Ri7e1Lp9MprrjiCvHwww+LU6dOiT179oiZM2eKp556yreP3bt3ixEjRojXXntN5ObmirVr14oxY8b0qXmGTp48KUaOHCnuv//+JnOBWSyWNn2eli5dKq644gqxZ88e3zxDF/5bKOUzGYy+vNCCBQua3FqvhL7sbD/u2bNHDB8+XKxZs6bJ6+vq6oQQXffdZhhSmIt/8QghxLvvviuuuuoqMWbMGDF//nzx1VdfNXndT37yE7Fu3bpm93vw4EHxs5/9TIwaNUpcfvnl4q233gp67T1JR/qxpKRELF26VEybNk1MmjRJ3HnnneLo0aN+bXbt2iXmzp0rRo0aJa655hrxySefdPmxyK0jfZmbmysWLVokxo0bJ2bOnCn+8Ic/+M1zJYQQH3zwgbjqqqvE6NGjxU033dSnfukIIcTLL78shg8fHvDR+Iu4tc9TfX29ePLJJ8WkSZPEpEmTxNKlS0VlZaVfGyV8JoPRlxcKFIbau4/eqLP9+NRTTzX7+o0bN/radcV3WxKij0xwQERERNQBfedCJREREVEHMAwRERGRojEMERERkaIxDBEREZGiMQwRERGRojEMERERkaIxDBEREZGiMQwRUa+2YsUKpKam4uuvvw74/O7du5GamupbLJKI6GKcdJGIejWLxYLrrrsOWq0WH3/8MUJCQnzP1dXV4frrr0d4eDjee+89v3WSiIga8cwQEfVqJpMJq1evxpkzZ7Bhwwa/51544QWUl5fjv/7rvxiEiKhZDENE1OtdccUVuP766/H222/j4MGDAIDs7Gy88847WLJkCdLS0nD27FksXboUkydPxtixY7Fo0SIcOXLEbz9FRUV47LHHMH36dIwcORJTpkzBY489hqqqKr/3+v3vf49FixZhzJgxePLJJ7v1WIko+HiZjIj6hOrqalx33XVISEjAli1bcPPNNyM0NBR/+9vfUFNTg3nz5sFoNOKBBx6A0WjEG2+8ge+//x7vvfceUlJSYLVacd111yEyMhK//vWvER4ejgMHDuCll17CzTffjGeeeQaANwyVlpbizjvvxKWXXorQ0FCMHz9e5qMnos7QyF0AEVEwmM1mrFq1Cg888ADuuusuFBUVYdu2bVCr1XjjjTdQXV2Nd955B4mJiQCAmTNn4tprr8Uf//hHbNy4Efn5+YiPj8fatWsxYMAAAMCll16KgwcPIjMz0++9+vfvj2XLlnX7MRJR12AYIqI+46qrrsK1116LHTt2YOXKlRg4cCAA4Ntvv8WIESMQFxcHl8sFAFCpVJg5cyY++ugjAMCIESOwZcsWeDwe5Ofno6CgALm5uTh58qTvNY1GjBjRvQdGRF2KYYiI+pQZM2Zgx44duOyyy3zbqqurUVBQgJEjRwZ8jdVqhdFoxF//+lf86U9/QnV1NWJiYjBq1CgYjUbU1tb6tb/wjjUi6v0YhoiozwsPD8fkyZPx2GOPBXxep9Nh+/bteP7557F8+XLMnz8fUVFRAICHHnoIhw8f7s5yiaibMQwRUZ83efJkbN++HYMHD0ZYWJhv++9+9zs4nU6sXr0a2dnZMJlMWLx4se/5+vp6ZGdnQ6Phj0qivoy31hNRn/fLX/4SHo8Hv/zlL7Fjxw58++23ePrpp/HWW29h8ODBAIAxY8bAYrHg+eefx969e7F9+3bccccdqKiogNVqlfkIiKgr8f/uEFGfFxcXh61bt+KFF17AqlWrYLfbMWjQIDz77LO45ZZbAAA33XQTioqK8P7772PLli2Ii4vDZZddhp///Od4+umnkZeXh5SUFJmPhIi6AucZIiIiIkXjZTIiIiJSNIYhIiIiUjSGISIiIlI0hiEiIiJSNIYhIiIiUjSGISIiIlI0hiEiIiJSNIYhIiIiUjSGISIiIlI0hiEiIiJSNIYhIiIiUjSGISIiIlK0/w8jf/lxRcAmhwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data3['Year'].quantile(0.01)\n",
+    "data4 = data3[data3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\2981342948.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data4['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize = (15,3))\n",
+    "ax1.scatter(data_cleaned['Year'], data_cleaned['Price'])\n",
+    "ax1.set_title('Price' and 'Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'], data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'], data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7484\\462586877.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_cleaned['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "...           ...   ...        ...  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_price  = np.log(data_cleaned['Price'])\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize = (15,3))\n",
+    "ax1.scatter(data_cleaned['Year'], data_cleaned['log_price'])\n",
+    "ax1.set_title('log_price' and 'Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'], data_cleaned['log_price'])\n",
+    "ax2.set_title('log_price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'], data_cleaned['log_price'])\n",
+    "ax3.set_title('log_price and Mileage')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_cleaned.drop(['Price'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "variables = data_cleaned[['Mileage', 'Year', 'EngineV']]\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns\n",
+    "\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand       Body  Mileage  EngineV Engine Type Registration  \\\n",
+       "0               BMW      sedan      277      2.0      Petrol          yes   \n",
+       "1     Mercedes-Benz        van      427      2.9      Diesel          yes   \n",
+       "2     Mercedes-Benz      sedan      358      5.0         Gas          yes   \n",
+       "3              Audi  crossover      240      4.2      Petrol          yes   \n",
+       "4            Toyota  crossover      120      2.0      Petrol          yes   \n",
+       "...             ...        ...      ...      ...         ...          ...   \n",
+       "3862     Volkswagen        van      163      2.5      Diesel          yes   \n",
+       "3863         Toyota      sedan       35      1.6      Petrol          yes   \n",
+       "3864  Mercedes-Benz      sedan        9      3.0      Diesel          yes   \n",
+       "3865            BMW      sedan        1      3.5      Petrol          yes   \n",
+       "3866     Volkswagen        van      124      2.0      Diesel          yes   \n",
+       "\n",
+       "      log_price  \n",
+       "0      8.342840  \n",
+       "1      8.974618  \n",
+       "2      9.495519  \n",
+       "3     10.043249  \n",
+       "4      9.814656  \n",
+       "...         ...  \n",
+       "3862   9.350102  \n",
+       "3863   9.792556  \n",
+       "3864  11.736069  \n",
+       "3865   8.779557  \n",
+       "3866   9.510445  \n",
+       "\n",
+       "[3867 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840       True                False   \n",
+       "1      427      2.9   8.974618      False                 True   \n",
+       "2      358      5.0   9.495519      False                 True   \n",
+       "3      240      4.2  10.043249      False                False   \n",
+       "4      120      2.0   9.814656      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['log_price','Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0       True                False   \n",
+       "1   8.974618      427      2.9      False                 True   \n",
+       "2   9.495519      358      5.0      False                 True   \n",
+       "3  10.043249      240      4.2      False                False   \n",
+       "4   9.814656      120      2.0      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preproccessed = data_with_dummies[cols]\n",
+    "data_preproccessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data_with_dummies_new' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\sklearn+-+Dummies+and+VIF+-(DataCleaning).ipynb Cell 57\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BDummies%2Band%2BVIF%2B-%28DataCleaning%29.ipynb#Y102sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m variables \u001b[39m=\u001b[39m data_with_dummies_new\u001b[39m.\u001b[39mdrop([\u001b[39m'\u001b[39m\u001b[39mlog_price\u001b[39m\u001b[39m'\u001b[39m], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'data_with_dummies_new' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "variables = data_with_dummies_new.drop(['log_price'], axis=1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-+Exercise+Solution.ipynb b/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-+Exercise+Solution.ipynb
new file mode 100644
index 0000000..d0913da
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-+Exercise+Solution.ipynb	
@@ -0,0 +1,3566 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise Solution - Dummies and VIF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please run all the cells below and find the exercise and the solution at the bottom of the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Real-life+example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\216991009.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\2589761964.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_1['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Ho+FI/jUZZYwekosMqwlCiDi/ChER0YlhiEkTkiTB6w2gYndDzJyVQ+G7idy+AIKKCgnxD/1kOcwAgHqXF/4A+16IiEhbDDFpJhBUowFDUdXo7dUOq6nH184Obz/gavP3+FpEREQ9xTkxaazVHRpaMhok2Czx314dkeUIhxh3gMNGRESkOYaYNNYSDjGZdnNC7iDKsJsgS4CqimgPDxERkVYYYtLYkRDT86EkAJAlCZn20LwYDikREZHWGGLSWIsnFDQybIkJMUBo5V+AIYaIiLSXUiGmqqoKZWVleO2117QuJS20thtOSpRIr47LzRBDRETaSpkQEwgEsHjxYrjdbq1LSRutnlCI6Z2emEAXzyQiIupdKRNiHnvsMWRkZGhdRlpxR2+vTtyd9JEQ08KeGCIi0lhKhJgNGzbg5Zdfxv333691KWkjEFThDy96Z7clMMSEh6Za3QEoKm+zJiIi7Wi+2J3L5cLNN9+M2267DSUlJQm5ptGYEtksYQzhJf4NhmO3S5IASZZgCD9a/KFeGJNRhs1shBzeS0k2AAal4+3Wxzvf/lyGzQijQUZQUeH2BpCdYYFBliDJEoxGCUL0/Fbu7rQ3nbC96Y3tTW9sr7Y0DzF33XUXysrKcP755yfkerIsISfHkZBrpRqn8/ibOPpVN2w2M4wmFXUuH4DQpF673QKb1Qij0QCb1QyjseMuSsc7H3vOiOxMC+qaPPAFBex2SygoWc3IzrYnrrHour3phu1Nb2xvemN7taFpiFm9ejU2btyINWvWJOyaqirgcqXX5GCDQYbTaYPL5YGidL6NoyQBHm8AHo8f/oCChubQ7tU2iwFutw+SUBEMKvB4/fD7O+57dLzzR5/LtBlR1wTUNrShKNsKs8kAj9ePpiaBRCzk2532phO2N72xvemN7U0Mp9MWV++OpiHm1VdfRX19PWbOnBlz/M4778Tbb7+NP//5z3FdNxhMz79IiqIes22SJEGoAkr4EVnozm4xQlEFVCEghICqoNO5LMc7f/S5yC3bTa3+6NcTqkAwKBK6HcHx2puO2N70xvamN7ZXG5qGmOXLl8Pr9cYcmzNnDm644QZccMEFGlWVHtq8oRDjSODt1RFOB9eKISIi7WkaYoqKijo9npeXd8xz1D29cXt1xJGtB7hWDBERaSc1phdTwkU2aLT3QoiJrBXj8QURSIHuRCIi6ps0vzvpaNu2bdO6BN0TQsAdGU6yJn44yWIywGyS4Q+oaPX4e2XIioiIqCvsiUlDvoCKoBKaYNsbw0nAkSGlyARiIiKiZGOISUORXhir2dBrCxJlhntfGGKIiEgrDDFpqDfnw0Rkcg8lIiLSGENMGmrrxfkwEeyJISIirTHEpCG3p/dur47IdDDEEBGRthhi0lCkJ8bei3cNZdpCw0ltHu5mTURE2mCISUO9udBdhM1igNEgQQBo5bwYIiLSAENMGmpLQoiRJAkZ4Z4eVxtDDBERJR9DTJppv9CdvRcn9gLtth/gvBgiItIAQ0ya8foVRKao2C29uyBzpp09MUREpB2GmDQTmQ9jNRsgy1Kvfq0jPTEMMURElHwMMWkmMpRk6+VeGOBIT0wLe2KIiEgDDDFpxu3r/dV6I6Ihxh2AKnibNRERJRdDTJqJDCcloyfGYTVBkgBFFWhu9fX61yMiImqPISbNREJMb0/qBQBZPnKbdW2Tt9e/HhERUXsMMWkmmT0xwJHJvXVNnqR8PSIiogiGmDTj9kXWiElWiAn1xNQ1syeGiIiSiyEmzbQlvScmMpzEnhgiIkouhpg0oqoCHl/y5sQA7YaT2BNDRERJxhCTRlo9AUTudLaaDUn5mtHhpCYPBG+zJiKiJGKISSPN4UXnbJbeX603IjN8d5LXr6DFwz2UiIgoeRhi0khkrZZkzYcBAINBju6WXdPAeTFERJQ8DDFpJNITk6z5MBFOR2heTE2TO6lfl4iI+jaGmDTS3BoZTkpuiMmMhJhG9sQQEVHyMMSkEVebNiEmy84QQ0REyccQk0aa20JzYpK10F1EtCeGa8UQEVESMcSkkchwUvLnxITuUKpp5JwYIiJKHoaYNNKs0XCSMzyc1OIORBfbIyIi6m1xhZjq6upE10E9pKgqWtzahBizydBuN2sOKRERUXLEFWJmzZqFBQsW4O2334bf7090TRQHV1totV4JgNWSnNV628vPtgLg5F4iIkqeuELMfffdB1VVsXjxYpx55plYtmwZvvnmm0TXRicgutCd1QhZSs5qve3lZ9kAcHIvERElT1zjDhdeeCEuvPBCVFdX4/XXX8cbb7yBv/71rxgxYgTmzZuHCy64APn5+YmulY6jSaNJvREF0Z4YTu4lIqLk6NHE3qKiIvziF7/AO++8g1dffRU5OTl44IEHMHPmTPzqV79CeXl5ouqkLkRur072fJiIaE8Mh5OIiChJenx30saNG3H77bdj/vz52LRpE8444wwsXboUHo8Hl1xyCVauXJmAMqkrWi10FxGZE8OJvURElCxxfeLt2bMHb7zxBt58800cOHAA/fv3x+WXX4558+ahpKQEAHDZZZdh8eLFWLFiBa688spE1kydaL+DtRYKskM9MQ0uHwJBFSYj794nIqLeFVeIOffcc2GxWHD22Wfj7rvvxrRp0zp93rBhw7B79+6e1EfdpHVPTIbNBKvZAK9fQV2zByV5Dk3qICKiviOuT7zbb78dF1xwATIzM4/7vOuuuw7XXXddXIXRidFqobsISZJQmGPD3upWVDcyxBARUe+Lq8//3XffRU1NTafntm7divPPP79HRdGJi/TEaHV3EgAUhoeUajm5l4iIkqDbn3gbN26EEAIAsH79emzYsAENDQ0dnvfRRx9h3759iauQukXrnhgAKMixA+AdSkRElBzd/sT7+9//jjfeeAOSJEGSJCxbtqzDcyIhZ+7cuYmrkLoUCCrRPYtsSd7Bur2iHC54R0REydPtT7zbbrsNP/jBDyCEwBVXXIE77rgDI0aMiHmOLMtwOp046aSTEl4oHVukF8ZokGA2yggEVU3qiNyhxBBDRETJ0O0Qk5mZialTpwIA/vKXv2DMmDFwODh5MxVEQkym3QxJgy0HIorCw0l1TR6oqoAsa1cLERGlv26HmNWrV2PGjBnIycnBwYMHcfDgweM+/6KLLuppbdRNrnYhRks5mRYYDRKCikCDy4v8cM8MERFRb+h2iFm6dCn+9re/IScnB0uXLj3ucyVJYohJokhPjNNu0rQOWZZQkG3DoXo3qps8DDFERNSruh1i/vWvf6GgoCD6e0od0Z4Yh7Y9MQCiIaa20QMM0boaIiJKZ90OMf379+/09xHBYBCtra3Izs5OSGHUfanSEwMcWSuGk3uJiKi3xbXYXTAYxOOPP441a9YAANatW4czzjgD06ZNwxVXXIHm5uaEFknHl0o9MYU53M2aiIiSI64Q8+ijj2LFihVwuVwAgHvuuQfZ2dm45ZZbsHfvXvzxj39MaJEUK7JWT+ThivbEpFKIcWtcCRERpbu4Qsxbb72FRYsW4dJLL0VlZSV27NiBa6+9Fj//+c/x61//Gh9++GGi66QwBUCrNxDzaGr1AQAyUiDEFOUeWbVXDS9+SERE1BviWt61pqYGEyZMAAB8/PHHkGUZ06dPBwAUFxejpaWl29eqr6/H/fffj08//RQ+nw9TpkzBkiVLMHz48HhKS2uSBHh9QVTsbohZ0K6pNbxvktUIjzeoVXkAgPwsKwyyBH9QRVOLD7lOq6b1EBFR+oqrJ6awsBD79+8HAHz44YcYNWoUcnNzAQBffvkliouLu32t66+/Hnv27MHTTz+NV155BVarFVdeeSU8Hs6pOJZAUIU/oMAfUOD2BqKBJjMFJvYaZDl6a/XhBg4pERFR74krxMydOxf33Xcf5s+fj02bNuEHP/gBAODee+/FY4891u1drJubm9G/f3/cc889GD9+PIYPH47rrrsONTU12LFjRzyl9TmRPZMMsgSL2aBxNSHF4Xkx1ZzcS0REvSiu4aQbb7wRdrsdGzZswE033YSf/exnAIBvvvkGV199Na699tpuXScrKytmEnBDQwNWrlyJ4uLiDvsyUee8PgVAaChJyy0H2ivKtQOV9ahmTwwREfWiuEKMJElYuHAhFi5cGHP8pZdeiruQ22+/HX/7299gNpuxYsUK2O32uK9lNMbVwZSyDAY5+qskSzCEHwDgC8SGGNkAGJSOYUYO38kUz/muXmuQJUiyBKNRghAS+uWH9tSqbvTE9V60b29fwPamN7Y3vbG92oorxABAS0sLPv/8c7jdbohO7kI50W0HrrjiCvzkJz/Biy++iOuvvx6rVq3CmDFjTrguWZaQk5OeG1NmZtrgCQjYbGYYTaF5MIoITaLOtJthNBpgs5phNHbcxdpmNcZ9vqvXmowybFYzsrNDwXPE4ND8qNomT4/eC6ezb21bwPamN7Y3vbG92ogrxHz66ae44YYbjjn5Np69kyLDR/feey/Ky8vxwgsv4L777jvh2lRVwOVKr2EMg0GG02lDS4sHHq8fHo8f/nAPTHP49mqLyYBgUIHH64ffr3S4hiTUuM939VqzyQCP14+mJgEhgAxzKKFXN7hRW9cC4wkm9kh7XS4PFKVjaEo3bG96Y3vTG9ubGE6nLa7enbhCzB//+EcMGzYMt9xyC4qKiiDL8XUrNTQ0YO3atTj33HNhNIZKkWUZI0aMQE1NTVzXBIBgMD3/IimKCqEKKOEHALjDt1TbLAYIIaAqiJ5rTxUi7vNdvVZRBYQqEAyGnpdhM8FskuEPqKiud0fXjomnven6XnaG7U1vbG96Y3u1EVf6qKysxI033ojJkydj4MCB6N+/f4dHd9TV1WHRokVYu3Zt9FggEEBFRQXXiekmrz8UYuyWuEcGE06WJBTlhIILb7MmIqLeEleI6devH1pbW3v8xUeOHInp06fjnnvuwYYNG7B9+3YsXboULpcLV155ZY+v3xdEbrG2WVMnxABHVu7lHUpERNRb4goxCxcuxBNPPBFd8K4nHnzwQUybNg2//vWv8aMf/QhNTU148cUX0a9fvx5fuy/wtLvFOpUUhdeKOcy1YoiIqJfE9cm3Zs0aVFdX45xzzkFubi6s1til5SVJwgcffNCta2VmZuKuu+7CXXfdFU8pfV4qDicBQDF7YoiIqJfF9clXXFx8QlsLUO8IBFUEldBE25TriYmEGO5mTUREvSSuT754bn2mxIv0whgNEkzG1NhyICLSE9Pg8sEXUGAxpVZ9RESkfz3673tlZSU+++wz1NTU4PLLL8e+fftQWlqKjIyMRNVHxxGZ1Gs1p1YvDABk2EzIsJnQ6gngcL0bg4sztS6JiIjSTFyffqqq4o477sCrr74KIQQkScJ3v/td/OlPf8LevXvxwgsvcLgpCSKTem2W1OzlKMmzY8f+Zhyqb2OIISKihIvr7qQ//elPWLNmDe655x589tln0W0HfvOb30BVVTz00EMJLZI6FxlOSsWeGAAoyQttOXCwnvNiiIgo8eIKMa+++ipuuOEG/OAHP0B2dnb0+KhRo3DDDTfgs88+S1R9dByp3hPTLy80L+ZQfZvGlRARUTqKK8TU1dVh1KhRnZ4rKiqCy+XqUVHUPSnfExPezfoQe2KIiKgXxBViBg8ejE8++aTTc+vXr8fgwYN7VBR1z5GemBQNMXlH1opRVO332CAiovQS16ffFVdcgTvuuAOBQACzZs2CJEnYs2cP1q1bh2effRZLly5NdJ3UieiWAyk6nJTrtEY3gqxp9ETnyBARESVCXCHmRz/6ERoaGrBixQqsWrUKALBo0SKYTCYsWLAAl1xySUKLpM55/eGemBQdTpIlCSW5DuypbsGhejdDDBERJVTcn37XXHMNzj//fKxfvx5GoxGZmZmYMGFCzERf6j1CiCPrxKRoTwwAlOTbwyGmDUCB1uUQEVEaOeEQ849//AMvvfQSysvLEQyGP0StVkyaNAmXXHIJzj777IQXSR0FFQFFDd3angoTeyUp9ACkmOP9IrdZ13FyLxERJVa3P/0URcFNN92Ef/7znygqKsL3v/995OfnQwiBw4cPY/369fjVr36FCy+8EPfff39v1kw4Mh8mtOVAXPOzE8ZgkCDLMlo8QQAi5lyOM7Q56EHeZk1ERAnW7RCzatUqvPfee7j11ltx2WWXQZJi/8etKApeeukl/O53v8PkyZPxwx/+MOHF0hEef2RSr/a9MAZZgscfROW+ZviDSsy5plYfAOAwb7MmIqIE6/Z/4VevXo2f/vSnuPzyyzsEGAAwGAy49NJL8eMf/xivv/56Qoukjrzh26tTYSgpIhBU4Q8oMQ+b2QBJAnwBBQ0tPq1LJCKiNNLtEFNVVYXp06d3+byzzjoL27dv71FR1LVUv706QpYlOB1mAMChOg4pERFR4nQ7xHg8HmRlZXX5vJycHLS18cOqt3n8qb3QXXs5GRYAwAGGGCIiSqBuhxghBAyGrv/XL8tydENI6j3eyO3V5tTuiQFCi94BwP6aVo0rISKidKLtbS0UtyPDSTroiXGGemL21TLEEBFR4pzQJ+Bdd92FjIyM4z6ntZUfVMkQWa1XTz0xB+vaoKoCstxxYjgREdGJ6nZPzJQpU+BwOCCEOO7D4XBg8uTJvVkzQV89MU67CWajjEBQRXUjb7UmIqLE6PYn4PPPP9+bddAJEEIcmdibQrdYH4skSSjOc2BvdQv217ZxDyUiIkoIzonRIX9QhRrZciDFb7GO6F8QCi6c3EtERInCEKNDkaEkk1GG0aCPtzCyh9J+Tu4lIqIE0ccnIMXweMPzYXQwqTeiJN8OgCGGiIgShyFGh9w6mtQb0S8/1BNT2+SFN7zvExERUU8wxOhQZDjJqqMQk2k3Iyu8/QBX7iUiokRgiNGh6O3VOhpOAoABhaE1hji5l4iIEoEhRoc8kR2sddQTAwADCiIhhj0xRETUcwwxOqSXHayPNqgoFGL21LRoXAkREaUDhhgdik7s1cFCd+0NKXYCAPZWt0TXuSEiIooXQ4wO6XFiLwAU59phMRngD6g41MDtB4iIqGcYYnRGFUK3w0myLGFgZEjpsEvjaoiISO8YYnTG4w1ChEdirDobTgKAIUWZAIDdhzkvhoiIeoYhRmdcbX4AgNkkwyBLGldz4gYXh0LMHoYYIiLqIYYYnXG5QyFGT6v1tjckHGL2Vrdyci8REfUIQ4zOtLgDAPR3Z1JESZ4DZpMMX0DBYU7uJSKiHmCI0ZnIcJJVZ5N6I2RZwqDC8JBSNYeUiIgofgwxOhMJMXrtiQE4L4aIiBKDIUZnWqJzYvTZEwMAg3mHEhERJQBDjM64InNidDqxFwCGlBzpiVFUVeNqiIhIrxhidCbSE6PHNWIi+uU7YLMY4Aso3AySiIjixhCjM9E5MToeTpIlCcP6ZQEAKg82a1wNERHpFUOMjqiqQKtH/8NJADC8X2gzyJ0HGGKIiCg+DDE60uL2R7ccsJj02xMDACMGhHtiGGKIiChODDE60tQamQ9jgKzDLQfaG1aSBQlAbZMXzeEhMiIiohPBEKMjR+bD6HsoCQDsViP6FTgAsDeGiIjiwxCjI82tPgCAPQ1CDACM6M8hJSIiih9DjI40RXpirOkRYoaH71Di5F4iIoqH5iGmqakJd9xxB6ZPn45JkybhkksuwcaNG7UuKyWl03AScGRy7+7DLQgqXPSOiIhOjOYhZtGiRfjyyy/x4IMP4tVXX8WoUaMwf/587Nq1S+vSUk5kYm+6hJiiHBsybCYEgiq3ICAiohOmaYjZs2cPPvvsM9x1112YPHkyhg4dittvvx2FhYVYs2aNlqWlJD0vdCdJkYcUfciyjJMHZQMAtu5p1LZAIiLSHU1DTE5ODp5++mmMGzcueizyAedyuTSsLDU16XRir8EQCiwtniBavYGYx9Dwondb9jLEEBHRidH009DpdGLGjBkxx959913s2bMH//d//xf3dY1GzUfJEspgCLWnOTyclGEzwXDUOjFypHfDABiUjmvI9OR8T69tMsjwBhTsOtCEQCB27kvk2Tv3NwMSYDTI0fZGfk13bG96Y3vTG9urrZT6L/0XX3yBW265BXPmzMHMmTPjuoYsS8jJcSS2sBQQVNTolgM5WTaYjLFDSjarEUajATarGUZjx0myPTmfqGsbjUYIxJ4vyHXAbjHC7QuixuXHmGF50XNOp+0435H0w/amN7Y3vbG92kiZEPPBBx9g8eLFmDRpEpYvXx73dVRVwOVyJ7Ay7RkMMgLh7QZkCYCqwu0OxjxHEiqCQQUerx9+v9LhGj0535vXBoDiPDt2HXRh/bcH0S/HCoNBhtNpg8vlgdIH7lpie9Mb25ve2N7EcDptcfXupESIeeGFF3DvvffivPPOw+9//3uYzeYeXS8YTL+/SI2toV6YDLsZqgAUVcScV4WAEAKq0vFcT8/35rUBoCQcYiqqGjB32pDocUVR0/K9PBa2N72xvemN7dWG5oNaq1atwt13341LL70UDz74YI8DTLqKTOp1OtLv+1OSHxr+23nAhUCwY08NERFRZzQNMVVVVfjd736Hc845BwsXLkRdXR1qa2tRW1uLlhauG9Jeo8sLAHDaTRpXknhZDjOcDjOCihqa4EtERNQNmg4nvfvuuwgEAnj//ffx/vvvx5y7+OKLcf/992tUWeqJ9MRk2tOvJ0aSJIwcmI2NW2uweXcjxo3I17okIiLSAU1DzC9+8Qv84he/0LIE3WhsSd/hJAAYPSQHG7fW4Jtd9fgpTtK6HCIi0gHN58RQ90SGkzLTcDgJAEoH50ACsK+mNRrYiIiIjochRifSeWIvEBomG1ISWr3368o6jashIiI9YIjRiUZXOMSk4ZyYiHHhhe6+rqzXuBIiItIDhhidSOeJvRHjh4dCzOZdDX1i0SgiIuoZhhgd8AcVtIW3HHA60nNODAAMLXEiw2aC2xfkrtZERNQlhhgdcIU3fjQaJNh0toP1iZBlCWOG5gIANm2t1rgaIiJKdQwxOtDcFgoxWQ4zJKnjLtHpZHx4Xsy6zYc1roSIiFIdQ4wOREKM02HRuJLeN35EHgyyhL2HW3C4Ib028iQiosRiiNGBpvC6KTmZ6TupN8JhNWHU4BwAwKatNRpXQ0REqYwhRgcii79lZ6Z/TwwAnFJaCADYuK1W40qIiCiVMcToQOT26pyMvhFiJo0sAABUHmjm6r1ERHRMDDE60Nd6YnIyLTg5PKT05Q72xhARUecYYnQg0hOT20dCDABMG1sCAPhiO0MMERF1jiFGB/pCT4wkRR4SJAmYNj4UYrbuaYLL7de4OiIiSkUMMSkuEFTR4g6t1puTpiHGYJAgyzJaPEG0egNodgdglGUMLMqAKgQ28C4lIiLqRPou/5ommttCvTBGg4QMmwnNbQGNK0o8gyzB4w+icl8z/EEFBlmCzWZG/3wH9lW34vPNh3H2KQMhhNC6VCIiSiHsiUlxTeEtB3Kd1rRfrTcQVOEPKPAHFASCKgYVZgAAKg+4UN/s1bg6IiJKNQwxKS6y0F2u06pxJcnnsJlQnGsHAKzfwr2UiIgoFkNMiovemZTV90IMAAzvnwUA+LyCIYaIiGIxxKS4yHBSXpZN40q0MbRfJmRZwt7qFhysa9O6HCIiSiEMMSmusQ8PJwGA1WzE6CGhhe8+++aQxtUQEVEqYYhJcdHhpD4aYgDgtDHFAID/fnsYiqpqXA0REaUKhpgUFwkxeX04xIwZmhu+vdyPb3c1aF0OERGlCIaYFBe9xbqPTuwFAKNBxrRwb8x/OKRERERhDDEpzOdX4PEFAfTt4SQAODO8DcFXO+rQwm0IiIgIDDEprSm8Wq/ZJMNu7duLKw8qysSgogwoqsDnm3m7NRERMcSktMhCdzmZ6b9ab3dMn9APAPDxVwe4BQERETHEpLLG8KTenAyzxpWkhmljimExGXCo3o3t+5q0LoeIiDTGEJPCmlpCcz+y03T36hNlsxhx6ugiAMBHXx7QuBoiItIaQ0wKi9xencMQEzWrrD8AYNO2WrjaOMGXiKgvY4hJYQ0tDDFHG1yciaElTiiqwKdfH9S6HCIi0hBDTAprdHkB9O2F7jozsyw8wffLg1zBl4ioD2OISWH1kRDThxe668ypo4qQYTOh3uXFl9vrtC6HiIg0whCTooKKiubIar19vCdGkiIPCZIkwWI2RufGvL9xn8bVERGRVhhiUlRTiw8CgNEgwenou7dYGwwSZFlGiyeIVm8g+jh1TBEMsoQd+5tRecildZlERKSBvr0MbAprP6lX7sML3RlkCR5/EJX7muEPKjHnhpY4sfNAM95btxfXXTyOC+AREfUx7IlJUfWc1BsjEFThDygxj5MHZQMANm2vjX6/iIio72CISVEN4Q/lnEyGmGPJy7KiJM8OVRV4b/1ercshIqIkY4hJUQ2u0HBSXhbXiDme8SPyAQCffHUQrZ6AxtUQEVEyMcSkqEhPTC57Yo5rQIED/Qsc8AUUfPjFfq3LISKiJGKISVH14Z6Yvn57dVckScI5UwYCAD7YuB8+v9LFK4iIKF0wxKSoxpZwT4yTw0ldmXhSAQqybWj1BPDxV9wYkoior2CISUFefxBt3iAA3p3UHQZZwtzThwAA3vl8D3wB9sYQEfUFDDEpKDKp12YxwmbhUj7dcfrYYuRnWeFyB/DRF+yNISLqCxhiUlB0Ui+HkrrNaJBxfqQ3Zt0ezo0hIuoDGGJSUGS1Xg4ldU9kb6XTx5WgINuGFncA//pif3SvJSIiSk8MMSmovjlyezV7YrrSfm8lb0DBuVNDdyq9tXYPqpvcaPUGwD4ZIqL0xBCTghqidyaxJ6Yrkb2VtuxuQPnOOpjNBuRkWuDxBfHiu9tQsbsBXl+QPTJERGmIISYFNUTXiGFPTHdF9lYKBlVMGhlaxXdzVWN0fhEREaWflAoxTz31FC6//HKty9BcPVfr7ZF++Q4U59qhCoGNW2u0LoeIiHpJyoSYF198EQ8//LDWZWhOVUV0TkxBtk3javRJkiSccnIBAKDygAuVB5o1roiIiHqD5iGmuroav/jFL7B8+XIMGTJE63I01+DyQlEFjAYJOZzYG7e8LCtGDMgCALzy0U6oqtC4IiIiSjTNQ8zmzZthMpnw5ptvYsKECVqXo7naJg8AID/LBlnmZNSemDQyH2aTjP21bdyOgIgoDWm+HOzs2bMxe/bshF7TaNQ8m8WtLjyptzDHFm2HwXDkV0mWYAg/2pPDa6LIBsCgdAw/PTnfm9fu7Lwsh9ory3KPru2wmjCltBCffXMYr31SiamjCpGVkXq9W+3f376A7U1vbG96S7X2ah5iEk2WJeTkOLQuI24uTwAAMKjY2aEdmZk2eAICNpsZRpMac85mNcJoNMBmNcNojD3X0/O9ee3jnbdaTT2+9imjirGvpg17q1vw0keVWPrzKR2ekyqczr41B4rtTW9sb3pLlfamXYhRVQGXy611GXHbd9gFAHDaTWhsbAMQSrxOpw0tLR54vH54PH74j9rkUBIqgkEFHq8f/k6W3O/J+d68dmfnZVmG1WqC1xvo8bXNJgMuOeckPPDil/is/CDeX1uFyaWFHZ6npcj763J5oCgdg1i6YXvTG9ub3nqrvU6nLa7enbQLMQAQDOr3L1J1Q2hOTJ7T0qEdiqJCqAJK+NGeKgSEEFAVdDjX0/O9ee3Oz4faraoqVCH36NqKKjCgIAPfPW0Q3lq7B//vna04aUAWHFZTh+dqTVFUXf/dPVFsb3pje9NbqrQ3NQa1CAAghEBNeGJvIW+vTqgLzxyKkjw7mtv8+Ms/t0EI3q1ERKR3DDEppM0bhMcXBADkM8QklMlowIK5o2GQJWzYWoP/fHNI65KIiKiHGGJSSOT26qwMMywmg8bVpJ+hJU5cdNZQAMCq93egukG/c6eIiCjFQsz999+P559/XusyNFPTyKGk3vbdUwejdFA2fAEFT7z+DXydTAYmIiJ9SKkQ09fVcj5Mr5NlCdecPwZOhxn7a9uw8p9bOT+GiEinGGJSSGRSL/dM6l05mRZcd9FYGGQJ6yqq8d6GfVqXREREcWCISSG14eGkghyGmN42cmA2fjx7BADgbx/txJc7ajWuiIiIThRDTAqpbeZwUm+QpMhDinmcM3kgpk/oByGAp97YjKpDLq1LJSKiE8AQkyICQRWN4X2TOJyUOAZDaC+mFk8Qrd5AzKPNF8TPzh2JscNy4Q+qePjv5ThU36Z1yURE1E0MMSmipskDAcBqNiDTnnqryeqVQZbg8QexZXcDynfWxTwqdjcgEFBx3UXjMKgoAy3uAJa/9FV0bhIREaU2hpgUcbAu1APQL98BSeq4WzP1TCCowh9QYh6BoApJAuxWI276aRn65TvQ2OLD8r9+iXqXl+8DEVGKY4hJEdEQk6ffHbj1pv1QkyxLuO7isSjItqKu2Yt7/7IRlQebwVVkiIhSF0NMijjQrieGkuPooabdh1tw9uSByM4wo6nVj+V//RI79jWxR4aIKEUxxKSIQwwxmmk/1GQyypgzdSBynRZ4/Qoe/ttXKN9Zp3WJRETUCYaYFBBUVBwO7+PTnyFGc1azEXOmDES/fAf8ARWPvFKO9zbs48q+REQphiEmBdQ0eqCoAhazAblOi9blEACzyYDzTh2EaWOLIQTw0r924Ok1FdxriYgohTDEpID2k3o5/yJ1yLKES84+CZecfVJ0i4J7/rIRe6tbtC6NiIjAEJMSjtxebde4EjqaLEs4d+og3PyzSchymHGgrg13/7+N+Md/d0Pl8BIRkaYYYlLAwfAqsf3zMzSuhNprfwt2vwIHllw2CeNH5EFRBV779y78duVG7OcKv0REmjFqXQC1v72aPTGpJHILduW+ZviDobkwU0oLkZtpxX+/PYS91S24+9kNuPCsoThn8kCYjPw/ARFRMvGnrsYUVcXh+tCdSVzoLjW1vwU7EFQxqCgDF5wxBAMKHAgoKl75uBJ3PLMOX1fWa10qEVGfwhCjseidSSYDcrOsWpdD3WS3mnDuqYPw8/NORpbDjOpGDx7+ezkefeVr1DZ5ojtlExFR7+FwksYO1IaGkkry7JD5oacrRqOMU0qLMHZYPv65bg8+/vIAvtpZh2+r6jFr0gDMmToQ2ZlWGLQulIgoTbEnRmN7wrfrDijkpF69icyZ2X3IhWH9nLh4+jD0z3cgqAi8v2Ef7vjzeny0aR94ExMRUe9giNFY1SEXAGBYP6fGlVC8InNmHFYjZp/SH7Mm9Uem3QSPL4hV7+/AspXrsW1vo9ZlEhGlHQ4naUgVAlWHQj0xw0oYYtKBJEkYWJiBfvkO7NzfjK8r67C3uhW/X/UlThlZgB/NHoHCbJvWZRIRpQWGGA1VN7jh8QVhNsrc+DHNGGQJ44bn4cKzhuKDDfvw0ZcHsGl7Lcor63DO5IGYe/oQ2Cz850dE1BMcTtJQZChpUHEmjAa+FenI6TDj5+eV4rfzT8WYIbkIKgLvrNuLW55ai3+XH4SqcsIMEVG8+MmpoaqDHEpKZ+1X/M3OtGDhRWOw8MIxKMyxweUOYOU7W7Fs5QZs3cP5MkRE8WB/toZ2hXtihjLEpKXOVvwFgO+fPgTb9zbii+112FfTit89vwnTvzmMedOHwmk3a1gxEZG+sCdGI4Ggin01oZ6YobwzKa21X/HXH1CgKCpGDcnFnVdNwayy/pAA/PurA1iyYi3eWrsbgaCqdclERLrAEKOR/bWtCCoCGTYTCrhSb5+UYTfhiu+W4rcLpmLUkFz4Agpe/WQX7nhmHb7d1aB1eUREKY8hRiO7Dh5ZH4bL0/c97efL5GTZcNPPJuHn3y1Fpt2E6kYPHvzbV3jkla9R0+TRulQiopTFOTEaqTzYDAAYUpypcSWkhfbzZRRVhc1mhs1swMXTh+GL7bWoqGpA+c46bK5qwPdOG4TvTRsCiyl2AwPBpYCJqI9jiNGAKgQqqkLDBaWDcjSuhrQUCKoIKgqMptC8GQCYNLIAo4fkoHxnPbbtbcKbn+3Gx18dxPdOG4zTxhbDIId67qwWI/dlIqI+jcNJGthX3QqXOwCL2YARA7K0LodSUH62DddcOAbnTh2EDJsJrjY/XvrXDtz553VY/ekubK6qh9cX5FAkEfVp7InRwDe76gEAowfncJE7OiZJkjCoKBP52RZs39uMryvr0dzmx7827sfXO+ugqAKnjS4GYwwR9VX8BNXAt+EQM3ZYnsaVkB4YZBmjhuTg4hlDMX54HowGCbVNXvx/b1bg9j+vw2ffHEJQ4W3ZRNT3sCcmydzeIHYeCN2ZNHZorsbVkJ6YjQZMPCkfJw/KxvZ9Tdi2twkH69rwzFtbsPrTXZh9ygCcMbYETgcXzCOivoEhJsm27GmAKgSKc+0o4G7GFAebxYgpo4pw6ZyTsb6iGu9t2Id6lw9//6gSr32yC5NGFmDGxH4oHZwDmXNmiCiNMcQk2TfhRczGDmMvDPWMzWLE96cNwdmnDMDnFdX45KuDqDrkwoatNdiwtQb5WVZMHVWEKaWFGFSUwUnARJR2GGKSSFVFdFLvOM6HoR6SpNDDYjZixsT+mDGxP/ZWt+CTrw5g7ebDqGv24u3P9+Dtz/egMMeGqaOKUHZSAYaUZEICjhtqurMGzbFez/VriChZGGKSaPPuBjS2+OCwGlE6KFvrckjH2q/4CxwJDblZVsybORwXzxyBL7bW4Mvttdhc1YCaRg/+8d/d+Md/dyPDZsKYYbkoHZSD0sE5yLCZOly/qzVoFABeb6DTc1qvX9PTcEZE+sEQk0Sflh8EAJw2phgmI5cpo/gda4dsALBbjRhc4oTNYsTkUYWYcFI+9la3oOqQCwdq29DqCWDd5mqs21wNAMjJtKA4z47iXDuK8+zIzrBg9JBcZFhNnX7oS5IErzeAit0NHTarNBnl4762tx0vXAHaBywiSiyGmCRxtfnx5Y46AMD0Cf00robSRWSH7PbMJrnDuYGFGRhYmAFVFWjxBNDiDuCrHXVocHnR2OJDY4sPW3Y3AgCcdhNGDcnF6CG5GFqSiX55Dshyx96Nzr62lo4XrgDtAxYRJR5DTJL899vDUFSBoSWZGFiYoXU51EfJsoT+BQ4MH5CN4f2y0NzmRU2jB9UNHlQ3utHo8sHlDmBdRTXWVYR6aqxmA4YUZ2JYvywM6+fE8P5ZMBpTd4mpVAtXRNR7GGKSQAiBT78ODSWdxV4YSiFWsxGDijIxqCi0Eak/qKCxxQ9VFdhb3YLdh1rg9SvYurcJW/c2RV+Xk2lBdoYZuU4r8rOsyMuycvVpIko6hpgk+LqyHofq3TCbZJw6qkjrcoiOyWw0YGBhBiaMyEeG1QRFUXGgrg27DjZj10EXdh1y4WBtW3QIqupQC4DQXVLZGRYU5drR0ubHsH5OlOQ5Op00TESUKAwxvSyoqPjbRzsBALMnDYDNwm856YcsS9H5NDMm9gcAeP0KtuxtxOffHsbhBjfqmj3w+JRosNm6pzH6+iyHGcW5dmSHe26yHBbkZllRkOuAx+OHBMBokCBJEgIBBQFFhT+gIhBU4Q0o8PkVeP1BeGN+VTr8ObrtggAUNTTfxWiQYDLKMBkNMBtlWC0G7D7kQmG2HTmZZuRkWlGQbUWmnSscE+kVP1F72SdfHcShejcy7SbMnTZE63KIuiWyBg062V7SbjVi5MBseHxBjAooEELA7Q2irjk0STioClQ3uFHf7EVzmx/Nbf6k1w8Avk5uUqoMb/nRnsNqjN6d1S/PER1eO3r7Bk4GJko9DDG9qM0bwBv/qQIAXHTWMNit/HZT6jvWGjQRsiyh/b0/kiTBYTPBYTPhpIHZ0aEotzeAQ/Vu1DS60dTqR3ObL/Rrqw++oIpAQEVQUaGoKlQ1dPeQySTDbDLAKEuwmAyhh9kAq9kAq8UIk9GARpcXsgyYDKFeFpNRhkGWYLUY0K8gA7v2u+APBBFUVPiDoV6dQFBFUFVht5jQ6gmg0eVFQ7jnqM0bROUBV4eA43SYMaDAgQGFGRhQkIHhA7JQlG3jVg5EKYSfqr1EVQWefWsLWj0B9M93YPqEEq1LIuqW461BAxxZh0bqpJemPZvFiGH9nBjWzxk9JkkS2vxB7KluhavFGx36Ofrax1v/ZmtVI3yBYIevl2E3oSjXjsZmH3yBjqvBmE2GaMCK9Kr4/AqqG9043BB67KluRdXBZjS1+uFq86OizY+K3UeGx6xmAwYVZWJwUSYGF2dgcFEmivPsMMh9e1Jz+wUGI79l1qNkYIjpJa98XIkvd9TBaJBx5XdL+/wPOdKfY92qHFmHJhHXPjrEdLbGTSK/7tHDZFaLEYOLnRhc7IQkAS2eAMp31qHNE0BTiw8NLV40uHxoavWhqcUPr1/B9n1N2L6v6UhdRhkDCzMwqCi0fEJRrh1FOTbkZFoSul9Vqq5EfPQCg5Iswa+64fEGYDEZulxckNtfUE9oHmJUVcXjjz+Ov//972hpacGUKVNwxx13YODAgVqXFhchBP65bi/+uX4vAODq75dieP8sjasiohMZJjMZZRTk2FCQE9pp3mY1YuywPBysdWNfTQv217SGf22DL6Cg8qALlQdjh6PMJhmF2XbkOi0oyLXDapKRaQ0Nu1lMBphNBljCw2cGWYLBEBoWiz7a/RmyhEBQhSx1/qGt1UrE7RcY9AdCk6x9AQWSLMPrDaAwxwYICf5gaO6UEKFeaiEEVAGoEAgEVEiyhFAzJUiyBIMkQZYBs8kIoxw6LstS6LwU+p6YjDIMRhkQAiZTaPK2OfyrySjDZjVxdeY+QPMQ86c//QmrVq3C/fffj+LiYjzwwANYsGAB1qxZA7NZX3cNtHkDeO7trfhiey0A4KIzh+K00cUaV0WUXMebFKzlEENPhskMsgR/UEVzqw9WswEjBmRhxIAsCCHgavOjqTW0tk5dkyd8x5YX/oCK/bWt2F/bClTWJ7QdFrMhOmfIajGgKMeOLIcZGTYTMu0mOO1mZNrNyHSYkGkzw2YxxN0rFAiqaHGHJmg3tfrQ3Br+tc2P5lY/Glq8qG/2wu0LIlU6P2RJgsNmhMNqiv5qt4b/bDXCbjUhw2ZEhi30PXPYTMgIPyeyOnVv9+Skas+a3mgaYvx+P5599lksXrwYM2fOBAA89NBDOOuss/Dee+9h7ty5WpbXbS63Hx99cQAffrEfLe4AjAYJP5o1AmefMkDr0oiSqju9HVr/fO7JMFlnr7VZjMjKsGDiSZH5NqGlFeqavahpdKPF44dfAQ7WtqLB5YXbG4Q/oISuFVTgD6hQVQEl+lChKKLDUFuEoobuBnN7j8wL2h1er+dYjAYpFGpsJtgsRphMMkyG8CRqgwRFEQgoKoLB0GRrt09Bi9uPFk8APv+JrX4cmYhtNMjIzjDDZjHCbDRAliVIEiAj9KskS1AUNXRHm6JCiNDfmEiPDSQg02aCLElQhYCqhntv1NDvA4oKr19BmzeAoKIiqISOA4AqBFrcoe01ToSEUK+bw2pCht2ETJspGoQybCbYLUaYTYbwpHIZRqMMo0GGw9GCllYvgsFwO4SAwJFaFVVE26CoAioAVVGj/14M4YnsNktoAntOpgUZFiOsFiMnkndB0xCzdetWtLW1Ydq0adFjTqcTo0ePxoYNG1IuxCiqilZPEE0tPlQ3urG/tg1b9zRi10EX1PBP5qIcGxZeOAZDip1dXI0o/XSnt2NIv/QbXu0svGXYTciwZ0GSJdhtZrS5/aioqu+wr5PVbMDooXlQFIH2wS/yYS4A+IIKvtlZD58/fNdVQIUvoMAfUCAA5DqtaHUH0OYNoNUdQIsn9GurJwBfQEFQEdF1fOIhyxKcdhOcDguyHGY4HWY4HSZkZViQYTejttENo0GCzWyEySjDbrdAKArGDs/v0K721wyqAt/srOs0VNqsRkwYUXBCr1eFgKIIyLKEwcVOtLoDcPsC4dAXgNsXCn8eXxCtngDqXV54faEhsEBQhQCiAbG2yRPX9yrRbBYD7JZQD5LDFulRate7FA5adqsRGVYTrObQHXtmkyG8TpKc1kFIEhr2W7333nv41a9+hfLyclit1ujx//3f/4XX68VTTz11wtcU4kgaT6TmNv9x92MxGmTYrUZYTIkfhZUkQJZlqKoKRRWhf2xHNVGWAKNR7vRcT8/35rU7Oy9JgAQJAiK0GFqatOtY54Aj7Y08Lx3adbzXBhVx5H/c6dSu8Adhh/PhuS3Hu/axXitJEoyGzl/bndcDoXApROhDvn04iryg/R1FUrueksiHn6J0v7bINSCFeoB6q13He318rw1/XyL/BmUJQKj3J/K5cuT7JsK9Rp1cHJ0Pm0a+JxHtP6ciH8Pte6MSTZKOGiiVYn5p97zOjsayWYywWQwJ7VUN9dSdeNjStCfG4wkl3aPnvlgsFjQ3N8d1TUmSYDAkPnXmOq1dP6mXybIcWh/DeOygdLxzPT3fm9fW8muzXVp87eOe1nG7UvPaPdbFf87S9f2i1Kfpfb+R3he/P3ZFT5/PB5vNpkVJREREpBOahpiSktACcDU1NTHHa2pqUFTEjRKJiIjo2DQNMaWlpcjIyMC6deuix1wuFyoqKjBlyhQNKyMiIqJUp+mcGLPZjMsuuwzLly9Hbm4u+vfvjwceeADFxcWYM2eOlqURERFRitN8sbsbbrgBwWAQt912G7xeL6ZMmYJnnnkGJpNJ69KIiIgohWl6izURERFRvLgrIREREekSQwwRERHpEkMMERER6RJDDBEREekSQwwRERHpEkMMERER6RJDDBEREekSQ0wKU1UVjz76KM466yxMnDgR11xzDfbt26d1WZ1qamrCHXfcgenTp2PSpEm45JJLsHHjxuj5tWvXYt68eZgwYQLOO+88vPXWWzGv9/l8WLZsGaZNm4aysjLcdNNNaGhoiHlOIq7RG6qqqlBWVobXXnstemzLli247LLLMHHiRMyePRt/+ctfYl7Tnfc2EddIpNWrV+N73/sexo0bh+9///t45513ouf279+PhQsXYtKkSTjzzDPx8MMPQ1GUmNe/+OKL+M53voPx48fjZz/7GSoqKmLOJ+IaiRIMBvHII49g1qxZKCsrw6WXXoqvvvoqej6d3t+nnnoKl19+ecJrS8Y1EtXeDz/8ED/4wQ9QVlaG2bNn4/e//z28Xm/0fLJ+PnV1jUS1t73bbrsNs2fPjjmmq/dXUMp67LHHxKmnnio++ugjsWXLFnH11VeLOXPmCJ/Pp3VpHVx11VVi7ty5YsOGDWLXrl1i2bJlYvz48aKyslLs3LlTjBs3Tjz44INi586d4s9//rMYPXq0+O9//xt9/dKlS8XZZ58tNmzYIMrLy8VFF10kLr300uj5RFyjN/j9fjFv3jwxcuRI8eqrrwohhGhoaBCnnnqquOWWW8TOnTvFK6+8IsaNGydeeeWV6Ou6em8TcY1EWr16tRg9erR44YUXxJ49e8Sf/vQnUVpaKr744gvh9/vFnDlzxP/8z/+Ibdu2iffff19MnTpVPPLII9HXv/baa2L8+PHijTfeEDt27BC/+c1vxNSpU0V9fX30+9jTayTSo48+Ks444wzx6aefit27d4tbb71VnHLKKaK6ujqt3t8XXnhBlJaWissuuyx6LFXa151rJKK9GzZsEKNGjRIrVqwQVVVV4uOPPxbTp08XS5cujT4nGT+funONRLS3vffff1+MHDlSzJo1K+a4nt5fhpgU5fP5RFlZmXjxxRejx5qbm8X48ePFmjVrNKyso927d4uRI0eKjRs3Ro+pqirOPvts8fDDD4vbb79d/PCHP4x5zaJFi8TVV18thBDi8OHDorS0VHz88cfR87t27RIjR44UX3zxhRBCJOQaveGPf/yj+PnPfx4TYp588klx5plnikAgEPO8OXPmCCG6994m4hqJoqqqmDVrlrj//vtjjl999dXiySefFGvWrBFjx44VTU1N0XMvvfSSmDRpUvQH1pw5c8Qf/vCH6PlAICBmzJghnnzySSGESMg1EumCCy4Q9913X/TPLS0tYuTIkeLdd99Ni/f38OHDYuHChWLixInivPPOi/mQS5X2dXWNRLX3pptuEldeeWXM819//XUxZswY4fP5kvbzqatrJKq9EdXV1eK0004Tl112WUyI0dv7y+GkFLV161a0tbVh2rRp0WNOpxOjR4/Ghg0bNKyso5ycHDz99NMYN25c9JgkSZAkCS6XCxs3boxpBwCcdtpp2LRpE4QQ2LRpU/RYxNChQ1FUVBRtayKukWgbNmzAyy+/jPvvvz/m+MaNGzF16lQYjUe2JjvttNOwe/du1NXVdeu9TcQ1EqWqqgoHDhzA+eefH3P8mWeewcKFC7Fx40aMGTMGWVlZMbW2trZiy5YtqK+vx+7du2NqNRqNmDx5ckx7e3qNRMrLy8NHH32E/fv3Q1EUvPzyyzCbzSgtLU2L93fz5s0wmUx48803MWHChJhzqdK+rq6RqPZeffXVWLJkScwxWZYRCATQ2tqatJ9PXV0jUe0FACEEli5digsvvBBTp06NOae395chJkUdPnwYAFBSUhJzvLCwMHouVTidTsyYMQNmszl67N1338WePXtw1lln4fDhwyguLo55TWFhITweDxobG1FdXY2cnBxYLJYOz4m0NRHXSCSXy4Wbb74Zt912W4f36Fi1AsChQ4e69d4m4hqJUlVVBQBwu92YP38+pk2bhh/96Ef48MMPE1ZrKrUXAG699VaYTCZ85zvfwbhx4/DQQw/h0UcfxaBBg9KivbNnz8Zjjz2GgQMHdjiXKu3r6hon4njtHT16NEpLS6N/DgQCWLlyJcaOHYvc3Nyk/Xzq6hqJai8ArFy5ErW1tVi0aFGHc3p7fxliUpTH4wGAmGAAABaLBT6fT4uSuu2LL77ALbfcgjlz5mDmzJnwer0d2hH5s9/vh8fj6XAeiG1rIq6RSHfddRfKyso69E4cq9bIDy+fz9et9zYR10iU1tZWAMCSJUswd+5cPPvsszjjjDNw3XXXYe3atWnXXgDYuXMnMjMz8cQTT+Dll1/GvHnzsHjxYmzZsiUt29teqrSvq2v0hmAwiJtvvhk7duzAnXfeCQBJ+/nU1TUSZevWrXj88cfxwAMPdFqT3t5fY9dPIS1YrVYAob+8kd8DoTfXZrNpVVaXPvjgAyxevBiTJk3C8uXLAYT+Yh79jzDyZ5vNBqvV2uk/0vZtTcQ1EmX16tXYuHEj1qxZ0+n5zmqJ/KO02+3dem8TcY1EMZlMAID58+fj4osvBgCMGjUKFRUVeO65506o1qOfE097j3WNRDl06BBuuukmrFy5EpMnTwYAjBs3Djt37sRjjz2Wdu/v0VKlfV1dI9FaW1tx4403Yv369Xj88ccxfvz4Y9ZxdK3J+BmXCD6fD4sXL8a1114b0/vUnt7eX/bEpKhIN1xNTU3M8ZqaGhQVFWlRUpdeeOEF/OpXv8KsWbPw5JNPRlN1SUlJp+2w2+3IzMxEcXExmpqaOvyFbt/WRFwjUV599VXU19dj5syZKCsrQ1lZGQDgzjvvxIIFC1BcXNxprQBQVFTUrfc2EddIlMj1Ro4cGXN8xIgR2L9/f9q1t7y8HIFAIGaOFwBMmDABe/bsSbv2Hi1V2tfVNRKppqYmehv9M888gxkzZkTPJevnU1fXSITy8nLs2LEDjz/+ePRn11NPPYWDBw+irKwMGzdu1N37yxCTokpLS5GRkYF169ZFj7lcLlRUVGDKlCkaVta5VatW4e6778all16KBx98MKabcPLkyVi/fn3M8z///HNMmjQJsizjlFNOgaqq0clvQGgeRnV1dbStibhGoixfvhxvv/02Vq9eHX0AwA033IB7770XU6ZMwaZNm2LWOPn8888xdOhQ5OXldeu9TcQ1EmXMmDFwOBwoLy+POb59+3YMGjQIU6ZMQUVFRXTYKVKrw+FAaWkp8vLyMHTo0Jhag8EgNm7cGNPenl4jUSLj9Nu2bevQ3iFDhqTd+3u0VGlfV9dIlObmZlxxxRVoaGjAiy++2OH7m6yfT11dIxHGjx+P9957D2+88Ub0Z9dPf/pTFBYWYvXq1Rg7dqz+3t8TupeJkurBBx8UU6dOFR988EHMffZ+v1/r0mLs2rVLjBkzRlx//fWipqYm5uFyucT27dvFmDFjxAMPPCB27twpnnnmmQ7rHyxatEjMnj1bfP7559E1FNrfFpiIa/Sm9rdY19XViSlTpoglS5aIHTt2iFdffVWMGzdOvPbaa9Hnd/XeJuIaifTEE0+IsrIysWbNmph1Yj7//HPh9XrF2WefLebPny+2bNkSXePlsccei77+5ZdfFuPHjxevvfZadI2XU089NbrGSyKukSiKoohLLrlEnHfeeWLt2rWiqqpKPPTQQ2LUqFHiq6++Srv3d8mSJTH/TlKlfd25RiLau2TJEjFmzBixdu3aDj+/gsGgECI5P5+6c41EtPdojz76aId1YvT0/jLEpLBgMCj+8Ic/iNNOO01MnDhRXHPNNWLfvn1al9XBihUrxMiRIzt9LFmyRAghxCeffCLmzp0rxo4dK8477zzx1ltvxVyjra1N3HrrrWLy5Mli8uTJYtGiRaKhoSHmOYm4Rm9pH2KEEKK8vFz8+Mc/FmPHjhWzZs0Szz//fMzzu/PeJuIaifTss8+K2bNnizFjxogLLrhAvP/++9Fzu3fvFldddZUYN26cOPPMM8XDDz8sFEWJef2f//xnMX36dDF+/Hjxs5/9TFRUVMScT8Q1EqWpqUncddddYubMmaKsrEz85Cc/EevWrYueT6f3t7MPuVRpX1fX6Gl7g8GgGDdu3DF/fkXqSdbPp66u0dP2dqazEKOn91cS4gRvQCciIiJKAZwTQ0RERLrEEENERES6xBBDREREusQQQ0RERLrEEENERES6xBBDREREusQQQ0RERLrEEENERES6xBBDRElx+eWX4+STT455jB07FjNnzsSyZcvQ3Nx8zNfu378fJ598Ml577bUkVkxEqc6odQFE1HeMHj0ad955Z/TPgUAAmzdvxoMPPogtW7bgr3/9KyRJ6vC6wsJCvPzyyxg0aFAyyyWiFMcQQ0RJk5GRgYkTJ8YcmzJlCtra2vDoo4+ivLy8w3kAMJvNnR4nor6Nw0lEpLmxY8cCAA4ePIjLL78cixcvxg033ICJEyfiqquu6nQ4adeuXfjlL3+JqVOnYsqUKVi4cCEqKyuj530+H/7whz9gxowZGDt2LM4//3y8/fbbSW8bEfUe9sQQkeaqqqoAAAMHDgQAvPPOO7jggguwYsUKqKra4fnV1dX4yU9+gqKiItx1112w2+147LHHcMUVV+Af//gHsrKycP311+OLL77ADTfcgOHDh+P999/Hr3/9a/j9flx00UXJbB4R9RKGGCJKGiEEgsFg9M/Nzc1Yv349VqxYgbKysmiPjMlkwrJly2A2mwGEJva2t3LlSvj9fjz33HMoKCgAAJSWluKSSy5BeXk5jEYjPv30Uzz00EP43ve+BwA466yz4PF4sHz5csydOxdGI3/8Eekd/xUTUdJs2LABY8aMiTkmyzJOP/10/Pa3v41O6h02bFg0wHRm06ZNmDhxYjTAAEBxcTE++ugjAMDy5cshSRJmzJgRE5pmz56NN998Ezt27MCoUaMS2TQi0gBDDBElzZgxY7Bs2TIAgCRJsFgsKCkpQUZGRszzHA7Hca/T1NSEAQMGHPe8EAKTJk3q9HxNTQ1DDFEaYIghoqRxOBwYN25cj6+TmZmJhoaGDsfXrl2LAQMGIDMzE3a7HX/5y186ff3gwYN7XAMRaY93JxGR7kyePBnl5eUxQaa+vh4LFizAJ598gqlTp8LtdkMIgXHjxkUf27dvxxNPPBEzxERE+sWeGCLSnSuvvBKrV6/GggULsHDhQphMJqxYsQLFxcU4//zzkZmZiSlTpuC6667Dddddh+HDh+Prr7/Go48+irPOOgu5ublaN4GIEoAhhoh0p6SkBKtWrcIDDzyApUuXwmw249RTT8VDDz2ErKwsAMDTTz+NRx55BE899RTq6+tRVFSEq666Ctdff73G1RNRokhCCKF1EUREREQninNiiIiISJcYYoiIiEiXGGKIiIhIlxhiiIiISJcYYoiIiEiXGGKIiIhIlxhiiIiISJcYYoiIiEiXGGKIiIhIlxhiiIiISJcYYoiIiEiX/n+xUqZjGOUoUQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\534165355.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\1950307955.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_2['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\823394628.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\1902427236.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_3['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\534082538.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\2748958191.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_4['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4548\\462586877.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_cleaned['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "...           ...   ...        ...  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840       True                False   \n",
+       "1      427      2.9   8.974618      False                 True   \n",
+       "2      358      5.0   9.495519      False                 True   \n",
+       "3      240      4.2  10.043249      False                False   \n",
+       "4      120      2.0   9.814656      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0       True                False   \n",
+       "1   8.974618      427      2.9      False                 True   \n",
+       "2   9.495519      358      5.0      False                 True   \n",
+       "3  10.043249      240      4.2      False                False   \n",
+       "4   9.814656      120      2.0      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed = data_with_dummies[cols]\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "### Part 1\n",
+    "Calculate the variance inflation factors for all variables contained in data_preprocessed. Anything strange?\n",
+    "\n",
+    "### Part 2\n",
+    "As mentioned in the lecture, your task is to calculate the variance inflation factor (VIF) of all variables including the dummies (but without the dependent variable).\n",
+    "\n",
+    "### Part 3\n",
+    "Now calculate the VIFs for a data frame where we include the dummies, without 'log_price', but DO NOT DROP THE FIRST DUMMY. Anything strange now?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part 1 - Solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variables = data_preprocessed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['log_price', 'Mileage', 'EngineV', 'Brand_BMW', 'Brand_Mercedes-Benz',\n",
+       "       'Brand_Mitsubishi', 'Brand_Renault', 'Brand_Toyota', 'Brand_Volkswagen',\n",
+       "       'Body_hatch', 'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+       "       'Engine Type_Gas', 'Engine Type_Other', 'Engine Type_Petrol',\n",
+       "       'Registration_yes'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert all boolean columns to int64\n",
+    "bool_columns = ['Brand_BMW', 'Brand_Mercedes-Benz',\n",
+    "       'Brand_Mitsubishi', 'Brand_Renault', 'Brand_Toyota', 'Brand_Volkswagen',\n",
+    "       'Body_hatch', 'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+    "       'Engine Type_Gas', 'Engine Type_Other', 'Engine Type_Petrol',\n",
+    "       'Registration_yes']\n",
+    "variables[bool_columns] = variables[bool_columns].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41.981260</td>\n",
+       "      <td>log_price</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.460434</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.445639</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.603990</td>\n",
+       "      <td>Brand_BMW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.084356</td>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.830297</td>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.281498</td>\n",
+       "      <td>Brand_Renault</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.406546</td>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3.312814</td>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.583516</td>\n",
+       "      <td>Body_hatch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.597487</td>\n",
+       "      <td>Body_other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>3.455354</td>\n",
+       "      <td>Body_sedan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.810633</td>\n",
+       "      <td>Body_vagon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.579105</td>\n",
+       "      <td>Body_van</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.711589</td>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.082223</td>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.506715</td>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>15.167906</td>\n",
+       "      <td>Registration_yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          VIF             features\n",
+       "0   41.981260            log_price\n",
+       "1    4.460434              Mileage\n",
+       "2   13.445639              EngineV\n",
+       "3    2.603990            Brand_BMW\n",
+       "4    3.084356  Brand_Mercedes-Benz\n",
+       "5    1.830297     Brand_Mitsubishi\n",
+       "6    2.281498        Brand_Renault\n",
+       "7    2.406546         Brand_Toyota\n",
+       "8    3.312814     Brand_Volkswagen\n",
+       "9    1.583516           Body_hatch\n",
+       "10   1.597487           Body_other\n",
+       "11   3.455354           Body_sedan\n",
+       "12   1.810633           Body_vagon\n",
+       "13   2.579105             Body_van\n",
+       "14   1.711589      Engine Type_Gas\n",
+       "15   1.082223    Engine Type_Other\n",
+       "16   2.506715   Engine Type_Petrol\n",
+       "17  15.167906     Registration_yes"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's simply use the data_preprocessed and the VIF code from above\n",
+    "\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Obviously, 'log_price' has a very high VIF. This implies it is most definitely **linearly correlated** with all the other variables. And this is no surprise! We are using a linear regression to determine 'log_price' given values of the independent variables! This is exactly what we expect - a linear relationship!\n",
+    "\n",
+    "However, to actually assess multicollinearity for the predictors, we have to drop 'log_price'. The multicollinearity assumption refers only to the idea that the **independent variables** shoud not be collinear."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part 2 - Solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.459662</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.841729</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.294007</td>\n",
+       "      <td>Brand_BMW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.868649</td>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.641712</td>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.086774</td>\n",
+       "      <td>Brand_Renault</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.162166</td>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.844515</td>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.464260</td>\n",
+       "      <td>Body_hatch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.534059</td>\n",
+       "      <td>Body_other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>3.120431</td>\n",
+       "      <td>Body_sedan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.581933</td>\n",
+       "      <td>Body_vagon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.470096</td>\n",
+       "      <td>Body_van</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.689146</td>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.082037</td>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2.498172</td>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>9.641446</td>\n",
+       "      <td>Registration_yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF             features\n",
+       "0   4.459662              Mileage\n",
+       "1   7.841729              EngineV\n",
+       "2   2.294007            Brand_BMW\n",
+       "3   2.868649  Brand_Mercedes-Benz\n",
+       "4   1.641712     Brand_Mitsubishi\n",
+       "5   2.086774        Brand_Renault\n",
+       "6   2.162166         Brand_Toyota\n",
+       "7   2.844515     Brand_Volkswagen\n",
+       "8   1.464260           Body_hatch\n",
+       "9   1.534059           Body_other\n",
+       "10  3.120431           Body_sedan\n",
+       "11  1.581933           Body_vagon\n",
+       "12  2.470096             Body_van\n",
+       "13  1.689146      Engine Type_Gas\n",
+       "14  1.082037    Engine Type_Other\n",
+       "15  2.498172   Engine Type_Petrol\n",
+       "16  9.641446     Registration_yes"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's simply drop log_price from data_preprocessed\n",
+    "variables = data_preprocessed.drop(['log_price'],axis=1)\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, all VIFs are pretty much acceptable. The ones that are particularly high are 'EngineV' and 'Registration_yes'. \n",
+    "\n",
+    "We already discussed 'EngineV' in the lecture.\n",
+    "\n",
+    "In the case of registration, the main issue is that **most values are 'yes'** so all types of problems come from there. One way this imbalance manifests is in multicollinearity. Remember that all independent variables are pretty good at determining 'log_price'? Well, if 'registration' is always 'yes', then if we predict 'log_price' we are predicting registration, too (it is going to be 'yes'). That is why, whenever a single category is so predominant, we may just drop the variable. \n",
+    "\n",
+    "Note that it will most probably be insignificant anyways."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part 3 - Solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_Audi</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Diesel</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_no</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_Audi  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840       False       True                False   \n",
+       "1      427      2.9   8.974618       False      False                 True   \n",
+       "2      358      5.0   9.495519       False      False                 True   \n",
+       "3      240      4.2  10.043249        True      False                False   \n",
+       "4      120      2.0   9.814656       False      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  ...  \\\n",
+       "0             False          False         False             False  ...   \n",
+       "1             False          False         False             False  ...   \n",
+       "2             False          False         False             False  ...   \n",
+       "3             False          False         False             False  ...   \n",
+       "4             False          False          True             False  ...   \n",
+       "\n",
+       "   Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Diesel  \\\n",
+       "0       False        True       False     False               False   \n",
+       "1       False       False       False      True                True   \n",
+       "2       False        True       False     False               False   \n",
+       "3       False       False       False     False               False   \n",
+       "4       False       False       False     False               False   \n",
+       "\n",
+       "   Engine Type_Gas  Engine Type_Other  Engine Type_Petrol  Registration_no  \\\n",
+       "0            False              False                True            False   \n",
+       "1            False              False               False            False   \n",
+       "2             True              False               False            False   \n",
+       "3            False              False                True            False   \n",
+       "4            False              False                True            False   \n",
+       "\n",
+       "   Registration_yes  \n",
+       "0              True  \n",
+       "1              True  \n",
+       "2              True  \n",
+       "3              True  \n",
+       "4              True  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To solve this one, we must create a new variable with dummies, without dropping the first one\n",
+    "data_with_dummies_new = pd.get_dummies(data_no_multicollinearity)#, drop_first=True)\n",
+    "data_with_dummies_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Mileage', 'EngineV', 'log_price', 'Brand_Audi', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_crossover', 'Body_hatch',\n",
+       "       'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+       "       'Engine Type_Diesel', 'Engine Type_Gas', 'Engine Type_Other',\n",
+       "       'Engine Type_Petrol', 'Registration_no', 'Registration_yes'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies_new.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert all boolean columns to int64\n",
+    "bool_columns = ['Brand_Audi', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_crossover', 'Body_hatch',\n",
+    "       'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+    "       'Engine Type_Diesel', 'Engine Type_Gas', 'Engine Type_Other',\n",
+    "       'Engine Type_Petrol', 'Registration_no', 'Registration_yes']\n",
+    "data_with_dummies_new[bool_columns] = data_with_dummies_new[bool_columns].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:198: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "  vif = 1. / (1. - r_squared_i)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.266554</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.575735</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Brand_Audi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Brand_BMW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Brand_Renault</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Body_crossover</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Body_hatch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Body_other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Body_sedan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Body_vagon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Body_van</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Engine Type_Diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Registration_no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>Registration_yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF             features\n",
+       "0   1.266554              Mileage\n",
+       "1   1.575735              EngineV\n",
+       "2        inf           Brand_Audi\n",
+       "3        inf            Brand_BMW\n",
+       "4        inf  Brand_Mercedes-Benz\n",
+       "5        inf     Brand_Mitsubishi\n",
+       "6        inf        Brand_Renault\n",
+       "7        inf         Brand_Toyota\n",
+       "8        inf     Brand_Volkswagen\n",
+       "9        inf       Body_crossover\n",
+       "10       inf           Body_hatch\n",
+       "11       inf           Body_other\n",
+       "12       inf           Body_sedan\n",
+       "13       inf           Body_vagon\n",
+       "14       inf             Body_van\n",
+       "15       inf   Engine Type_Diesel\n",
+       "16       inf      Engine Type_Gas\n",
+       "17       inf    Engine Type_Other\n",
+       "18       inf   Engine Type_Petrol\n",
+       "19       inf      Registration_no\n",
+       "20       inf     Registration_yes"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's simply drop 'log_price' from this new variable\n",
+    "variables = data_with_dummies_new.drop(['log_price'],axis=1)\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result that we get is that very interesting indeed. Most VIFs are equal to **inf**, or plus infinity. \n",
+    "\n",
+    "We even got an warning: *RuntimeWarning: divide by zero encountered in double_scalars, vif = 1. / (1. - r_squared_i)*\n",
+    "\n",
+    "The main reason is what we've discussed before. When a car is an 'Audi' all other brand dummies are 0. When a car is not 'Audi', at least one of them will be 1. By including all dummies have introduced multicollinearity (**perfect multicollinearity**)!!! \n",
+    "\n",
+    "If we run a regression including all these dummies, the coefficients would be inflated and completely off-mark. \n",
+    "\n",
+    "Now you see why we need to drop one of the dummy variables for each feature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned['Brand'].unique()# and find which ones are missing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-+Exercise.ipynb b/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-+Exercise.ipynb
new file mode 100644
index 0000000..02d2f78
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Dummies+and+VIF+-+Exercise.ipynb	
@@ -0,0 +1,4204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise - Dummies and VIF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please run all the cells below and find the exercise at the bottom of the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Real-life+example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\216991009.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\2589761964.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_1['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\534165355.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\1950307955.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_2['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\823394628.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\1902427236.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_3['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\534082538.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\2748958191.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_4['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8048\\462586877.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_cleaned['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "...           ...   ...        ...  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840       True                False   \n",
+       "1      427      2.9   8.974618      False                 True   \n",
+       "2      358      5.0   9.495519      False                 True   \n",
+       "3      240      4.2  10.043249      False                False   \n",
+       "4      120      2.0   9.814656      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0       True                False   \n",
+       "1   8.974618      427      2.9      False                 True   \n",
+       "2   9.495519      358      5.0      False                 True   \n",
+       "3  10.043249      240      4.2      False                False   \n",
+       "4   9.814656      120      2.0      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed = data_with_dummies[cols]\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EXERCISE\n",
+    "\n",
+    "### Part 1\n",
+    "Calculate the variance inflation factors for all variables contained in data_preprocessed. Anything strange?\n",
+    "\n",
+    "### Part 2\n",
+    "As mentioned in the lecture, your task is to calculate the variance inflation factor (VIF) of all variables including the dummies (but without the dependent variable).\n",
+    "\n",
+    "### Part 3\n",
+    "Now calculate the VIFs for a data frame where we include the dummies, without 'log_price', but DO NOT DROP THE FIRST DUMMY. Anything strange now?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 1 \n",
+    "\n",
+    "\n",
+    "The data_preprocessed and the VIF code from above\n",
+    "variables = data_preprocessed\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0       True                False   \n",
+       "1   8.974618      427      2.9      False                 True   \n",
+       "2   9.495519      358      5.0      False                 True   \n",
+       "3  10.043249      240      4.2      False                False   \n",
+       "4   9.814656      120      2.0      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variables = data_preprocessed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0       True                False   \n",
+       "1   8.974618      427      2.9      False                 True   \n",
+       "2   9.495519      358      5.0      False                 True   \n",
+       "3  10.043249      240      4.2      False                False   \n",
+       "4   9.814656      120      2.0      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variables.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['log_price', 'Mileage', 'EngineV', 'Brand_BMW', 'Brand_Mercedes-Benz',\n",
+       "       'Brand_Mitsubishi', 'Brand_Renault', 'Brand_Toyota', 'Brand_Volkswagen',\n",
+       "       'Body_hatch', 'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+       "       'Engine Type_Gas', 'Engine Type_Other', 'Engine Type_Petrol',\n",
+       "       'Registration_yes'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert all boolean columns to int64\n",
+    "bool_columns = ['Brand_BMW','Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']\n",
+    "variables[bool_columns] = variables[bool_columns].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0          1                    0   \n",
+       "1   8.974618      427      2.9          0                    1   \n",
+       "2   9.495519      358      5.0          0                    1   \n",
+       "3  10.043249      240      4.2          0                    0   \n",
+       "4   9.814656      120      2.0          0                    0   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                 0              0             0                 0   \n",
+       "1                 0              0             0                 0   \n",
+       "2                 0              0             0                 0   \n",
+       "3                 0              0             0                 0   \n",
+       "4                 0              0             1                 0   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0           0           0           1           0         0                0   \n",
+       "1           0           0           0           0         1                0   \n",
+       "2           0           0           1           0         0                1   \n",
+       "3           0           0           0           0         0                0   \n",
+       "4           0           0           0           0         0                0   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0                  0                   1                 1  \n",
+       "1                  0                   0                 1  \n",
+       "2                  0                   0                 1  \n",
+       "3                  0                   1                 1  \n",
+       "4                  0                   1                 1  "
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variables.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41.981260</td>\n",
+       "      <td>log_price</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.460434</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.445639</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.603990</td>\n",
+       "      <td>Brand_BMW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.084356</td>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.830297</td>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.281498</td>\n",
+       "      <td>Brand_Renault</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.406546</td>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3.312814</td>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.583516</td>\n",
+       "      <td>Body_hatch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.597487</td>\n",
+       "      <td>Body_other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>3.455354</td>\n",
+       "      <td>Body_sedan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.810633</td>\n",
+       "      <td>Body_vagon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.579105</td>\n",
+       "      <td>Body_van</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.711589</td>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.082223</td>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.506715</td>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>15.167906</td>\n",
+       "      <td>Registration_yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          VIF             features\n",
+       "0   41.981260            log_price\n",
+       "1    4.460434              Mileage\n",
+       "2   13.445639              EngineV\n",
+       "3    2.603990            Brand_BMW\n",
+       "4    3.084356  Brand_Mercedes-Benz\n",
+       "5    1.830297     Brand_Mitsubishi\n",
+       "6    2.281498        Brand_Renault\n",
+       "7    2.406546         Brand_Toyota\n",
+       "8    3.312814     Brand_Volkswagen\n",
+       "9    1.583516           Body_hatch\n",
+       "10   1.597487           Body_other\n",
+       "11   3.455354           Body_sedan\n",
+       "12   1.810633           Body_vagon\n",
+       "13   2.579105             Body_van\n",
+       "14   1.711589      Engine Type_Gas\n",
+       "15   1.082223    Engine Type_Other\n",
+       "16   2.506715   Engine Type_Petrol\n",
+       "17  15.167906     Registration_yes"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's simply use the data_preprocessed and the VIF code from above\n",
+    "\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Obviously, 'log_price' has a very high VIF. This implies it is most definitely **linearly correlated** with all the other variables. And this is no surprise! We are using a linear regression to determine 'log_price' given values of the independent variables! This is exactly what we expect - a linear relationship!\n",
+    "\n",
+    "However, to actually assess multicollinearity for the predictors, we have to drop 'log_price'. The multicollinearity assumption refers only to the idea that the **independent variables** shoud not be collinear."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.459662</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.841729</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.294007</td>\n",
+       "      <td>Brand_BMW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.868649</td>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.641712</td>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.086774</td>\n",
+       "      <td>Brand_Renault</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.162166</td>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.844515</td>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.464260</td>\n",
+       "      <td>Body_hatch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.534059</td>\n",
+       "      <td>Body_other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>3.120431</td>\n",
+       "      <td>Body_sedan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.581933</td>\n",
+       "      <td>Body_vagon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.470096</td>\n",
+       "      <td>Body_van</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.689146</td>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.082037</td>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2.498172</td>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>9.641446</td>\n",
+       "      <td>Registration_yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF             features\n",
+       "0   4.459662              Mileage\n",
+       "1   7.841729              EngineV\n",
+       "2   2.294007            Brand_BMW\n",
+       "3   2.868649  Brand_Mercedes-Benz\n",
+       "4   1.641712     Brand_Mitsubishi\n",
+       "5   2.086774        Brand_Renault\n",
+       "6   2.162166         Brand_Toyota\n",
+       "7   2.844515     Brand_Volkswagen\n",
+       "8   1.464260           Body_hatch\n",
+       "9   1.534059           Body_other\n",
+       "10  3.120431           Body_sedan\n",
+       "11  1.581933           Body_vagon\n",
+       "12  2.470096             Body_van\n",
+       "13  1.689146      Engine Type_Gas\n",
+       "14  1.082037    Engine Type_Other\n",
+       "15  2.498172   Engine Type_Petrol\n",
+       "16  9.641446     Registration_yes"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variables = variables.drop(['log_price'], axis=1)\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, all VIFs are pretty much acceptable. The ones that are particularly high are 'EngineV' and 'Registration_yes'. \n",
+    "\n",
+    "We already discussed 'EngineV' in the lecture.\n",
+    "\n",
+    "In the case of registration, the main issue is that **most values are 'yes'** so all types of problems come from there. One way this imbalance manifests is in multicollinearity. Remember that all independent variables are pretty good at determining 'log_price'? Well, if 'registration' is always 'yes', then if we predict 'log_price' we are predicting registration, too (it is going to be 'yes'). That is why, whenever a single category is so predominant, we may just drop the variable. \n",
+    "\n",
+    "Note that it will most probably be insignificant anyways."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solution 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_Audi</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Diesel</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_no</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_Audi  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840       False       True                False   \n",
+       "1      427      2.9   8.974618       False      False                 True   \n",
+       "2      358      5.0   9.495519       False      False                 True   \n",
+       "3      240      4.2  10.043249        True      False                False   \n",
+       "4      120      2.0   9.814656       False      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  ...  \\\n",
+       "0             False          False         False             False  ...   \n",
+       "1             False          False         False             False  ...   \n",
+       "2             False          False         False             False  ...   \n",
+       "3             False          False         False             False  ...   \n",
+       "4             False          False          True             False  ...   \n",
+       "\n",
+       "   Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Diesel  \\\n",
+       "0       False        True       False     False               False   \n",
+       "1       False       False       False      True                True   \n",
+       "2       False        True       False     False               False   \n",
+       "3       False       False       False     False               False   \n",
+       "4       False       False       False     False               False   \n",
+       "\n",
+       "   Engine Type_Gas  Engine Type_Other  Engine Type_Petrol  Registration_no  \\\n",
+       "0            False              False                True            False   \n",
+       "1            False              False               False            False   \n",
+       "2             True              False               False            False   \n",
+       "3            False              False                True            False   \n",
+       "4            False              False                True            False   \n",
+       "\n",
+       "   Registration_yes  \n",
+       "0              True  \n",
+       "1              True  \n",
+       "2              True  \n",
+       "3              True  \n",
+       "4              True  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To solve this one, we must create a new variable with dummies, without dropping the first one\n",
+    "data_with_dummies_new = pd.get_dummies(data_no_multicollinearity)#, drop_first=True)\n",
+    "data_with_dummies_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert all boolean columns to int64\n",
+    "bool_columns = ['Brand_Audi', 'Brand_BMW', 'Brand_Mercedes-Benz',\n",
+    "       'Brand_Mitsubishi', 'Brand_Renault', 'Brand_Toyota', 'Brand_Volkswagen',\n",
+    "       'Body_crossover', 'Body_hatch', 'Body_other', 'Body_sedan',\n",
+    "       'Body_vagon', 'Body_van', 'Engine Type_Diesel', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_no',\n",
+    "       'Registration_yes']\n",
+    "data_with_dummies_new[bool_columns] = data_with_dummies_new[bool_columns].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_Audi</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Diesel</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_no</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_Audi  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840           0          1                    0   \n",
+       "1      427      2.9   8.974618           0          0                    1   \n",
+       "2      358      5.0   9.495519           0          0                    1   \n",
+       "3      240      4.2  10.043249           1          0                    0   \n",
+       "4      120      2.0   9.814656           0          0                    0   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  ...  \\\n",
+       "0                 0              0             0                 0  ...   \n",
+       "1                 0              0             0                 0  ...   \n",
+       "2                 0              0             0                 0  ...   \n",
+       "3                 0              0             0                 0  ...   \n",
+       "4                 0              0             1                 0  ...   \n",
+       "\n",
+       "   Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Diesel  \\\n",
+       "0           0           1           0         0                   0   \n",
+       "1           0           0           0         1                   1   \n",
+       "2           0           1           0         0                   0   \n",
+       "3           0           0           0         0                   0   \n",
+       "4           0           0           0         0                   0   \n",
+       "\n",
+       "   Engine Type_Gas  Engine Type_Other  Engine Type_Petrol  Registration_no  \\\n",
+       "0                0                  0                   1                0   \n",
+       "1                0                  0                   0                0   \n",
+       "2                1                  0                   0                0   \n",
+       "3                0                  0                   1                0   \n",
+       "4                0                  0                   1                0   \n",
+       "\n",
+       "   Registration_yes  \n",
+       "0                 1  \n",
+       "1                 1  \n",
+       "2                 1  \n",
+       "3                 1  \n",
+       "4                 1  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Dummy_variables = data_with_dummies_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Mileage', 'EngineV', 'log_price', 'Brand_Audi', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_crossover', 'Body_hatch',\n",
+       "       'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+       "       'Engine Type_Diesel', 'Engine Type_Gas', 'Engine Type_Other',\n",
+       "       'Engine Type_Petrol', 'Registration_no', 'Registration_yes'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Dummy_variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#variables .drop(['log_price'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Dummy_variables' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\sklearn+-+Dummies+and+VIF+-+Exercise.ipynb Cell 74\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BDummies%2Band%2BVIF%2B-%2BExercise.ipynb#Y133sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m Dummy_variables \u001b[39m=\u001b[39m Dummy_variables\u001b[39m.\u001b[39mdrop([\u001b[39m'\u001b[39m\u001b[39mlog_price\u001b[39m\u001b[39m'\u001b[39m], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BDummies%2Band%2BVIF%2B-%2BExercise.ipynb#Y133sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m vif \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame()\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BDummies%2Band%2BVIF%2B-%2BExercise.ipynb#Y133sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m vif[\u001b[39m\"\u001b[39m\u001b[39mVIF\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m [variance_inflation_factor(Dummy_variables\u001b[39m.\u001b[39mvalues, i) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(Dummy_variables\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m])]\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'Dummy_variables' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "Dummy_variables = Dummy_variables.drop(['log_price'], axis=1)\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(Dummy_variables.values, i) for i in range(Dummy_variables.shape[1])]\n",
+    "vif[\"features\"] = Dummy_variables.columns\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result that we get is that very interesting indeed. Most VIFs are equal to **inf**, or plus infinity. \n",
+    "\n",
+    "We even got an warning: *RuntimeWarning: divide by zero encountered in double_scalars, vif = 1. / (1. - r_squared_i)*\n",
+    "\n",
+    "The main reason is what we've discussed before. When a car is an 'Audi' all other brand dummies are 0. When a car is not 'Audi', at least one of them will be 1. By including all dummies have introduced multicollinearity (**perfect multicollinearity**)!!! \n",
+    "\n",
+    "If we run a regression including all these dummies, the coefficients would be inflated and completely off-mark. \n",
+    "\n",
+    "Now you see why we need to drop one of the dummy variables for each feature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Feature+Scaling+Exercise+Solution.ipynb b/Udemy DataScience Workbook/sklearn+-+Feature+Scaling+Exercise+Solution.ipynb
new file mode 100644
index 0000000..3bb365c
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Feature+Scaling+Exercise+Solution.ipynb	
@@ -0,0 +1,700 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature scaling with sklearn - Exercise Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. This exercise is very similar to a previous one. This time, however, **please standardize the data**.\n",
+    "\n",
+    "Apart from that, please:\n",
+    "-  Display the intercept and coefficient(s)\n",
+    "-  Find the R-squared and Adjusted R-squared\n",
+    "-  Compare the R-squared and the Adjusted R-squared\n",
+    "-  Compare the R-squared of this regression and the simple linear regression where only 'size' was used\n",
+    "-  Using the model make a prediction about an apartment with size 750 sq.ft. from 2009\n",
+    "-  Find the univariate (or multivariate if you wish - see the article) p-values of the two variables. What can you say about them?\n",
+    "-  Create a summary table with your findings\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size' and 'year'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size  year\n",
+       "0  234314.144   643.09  2015\n",
+       "1  228581.528   656.22  2009\n",
+       "2  281626.336   487.29  2018\n",
+       "3  401255.608  1504.75  2015\n",
+       "4  458674.256  1275.46  2009"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size_year.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "      <td>2012.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "      <td>4.729021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "      <td>2006.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size         year\n",
+       "count     100.000000   100.000000   100.000000\n",
+       "mean   292289.470160   853.024200  2012.600000\n",
+       "std     77051.727525   297.941951     4.729021\n",
+       "min    154282.128000   479.750000  2006.000000\n",
+       "25%    234280.148000   643.330000  2009.000000\n",
+       "50%    280590.716000   696.405000  2015.000000\n",
+       "75%    335723.696000  1029.322500  2018.000000\n",
+       "max    500681.128000  1842.510000  2018.000000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data[['size','year']]\n",
+    "y = data['price']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scale the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(x)\n",
+    "x_scaled = scaler.transform(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x_scaled,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "292289.4701599997"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([67501.57614152, 13724.39708231])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7764803683276793"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_scaled,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's use the handy function we created\n",
+    "def adj_r2(x,y):\n",
+    "    r2 = reg.score(x,y)\n",
+    "    n = x.shape[0]\n",
+    "    p = x.shape[1]\n",
+    "    adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
+    "    return adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.77187171612825"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adj_r2(x_scaled,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the R-squared and the Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems the the R-squared is only slightly larger than the Adjusted R-squared, implying that we were not penalized a lot for the inclusion of 2 independent variables. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the Adjusted R-squared with the R-squared of the simple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comparing the Adjusted R-squared with the R-squared of the simple linear regression (when only 'size' was used - a couple of lectures ago), we realize that 'Year' is not bringing too much value to the result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making predictions\n",
+    "\n",
+    "Find the predicted price of an apartment that has a size of 750 sq.ft. from 2009."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_data = [[750,2009]]\n",
+    "new_data_scaled = scaler.transform(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([258330.34465995])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.predict(new_data_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the univariate p-values of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import f_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([285.92105192,   0.85525799]), array([8.12763222e-31, 3.57340758e-01]))"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f_regression(x_scaled,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([8.12763222e-31, 3.57340758e-01])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values = f_regression(x,y)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.357])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a summary table with your findings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Coefficients</th>\n",
+       "      <th>p-values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>size</td>\n",
+       "      <td>67501.576142</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>year</td>\n",
+       "      <td>13724.397082</td>\n",
+       "      <td>0.357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Features  Coefficients  p-values\n",
+       "0     size  67501.576142     0.000\n",
+       "1     year  13724.397082     0.357"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary = pd.DataFrame(data = x.columns.values, columns=['Features'])\n",
+    "reg_summary ['Coefficients'] = reg.coef_\n",
+    "reg_summary ['p-values'] = p_values.round(3)\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems that 'Year' is not event significant, therefore we should remove it from the model.\n",
+    "\n",
+    "Note that this dataset is extremely clean and probably artificially created, therefore standardization does not really bring any value to it."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Feature+Scaling+Exercise.ipynb b/Udemy DataScience Workbook/sklearn+-+Feature+Scaling+Exercise.ipynb
new file mode 100644
index 0000000..e247fdf
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Feature+Scaling+Exercise.ipynb	
@@ -0,0 +1,680 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature scaling with sklearn - Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. This exercise is very similar to a previous one. This time, however, **please standardize the data**.\n",
+    "\n",
+    "Apart from that, please:\n",
+    "-  Display the intercept and coefficient(s)\n",
+    "-  Find the R-squared and Adjusted R-squared\n",
+    "-  Compare the R-squared and the Adjusted R-squared\n",
+    "-  Compare the R-squared of this regression and the simple linear regression where only 'size' was used\n",
+    "-  Using the model make a prediction about an apartment with size 750 sq.ft. from 2009\n",
+    "-  Find the univariate (or multivariate if you wish - see the article) p-values of the two variables. What can you say about them?\n",
+    "-  Create a summary table with your findings\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size' and 'year'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy  as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size_year.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "      <td>2012.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "      <td>4.729021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "      <td>2006.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size         year\n",
+       "count     100.000000   100.000000   100.000000\n",
+       "mean   292289.470160   853.024200  2012.600000\n",
+       "std     77051.727525   297.941951     4.729021\n",
+       "min    154282.128000   479.750000  2006.000000\n",
+       "25%    234280.148000   643.330000  2009.000000\n",
+       "50%    280590.716000   696.405000  2015.000000\n",
+       "75%    335723.696000  1029.322500  2018.000000\n",
+       "max    500681.128000  1842.510000  2018.000000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['price'] #dependent\n",
+    "x = data[['size', 'year']] #independent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scale the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(x,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_scaled = scaler.transform(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x_scaled,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "292289.4701599997"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([67501.57614152, 13724.39708231])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7764803683276793"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_scaled,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100, 2)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_scaled.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.77187171612825"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2 = reg.score(x_scaled,y)\n",
+    "n = x_scaled.shape[0]\n",
+    "p = x_scaled.shape[1]\n",
+    "adj_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
+    "adj_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the R-squared and the Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems the the R-squared is only slightly larger than the Adjusted R-squared, implying that we were not penalized a lot for the inclusion of 2 independent variables. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the Adjusted R-squared with the R-squared of the simple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comparing the Adjusted R-squared with the R-squared of the simple linear regression (when only 'size' was used - a couple of lectures ago), we realize that 'Year' is not bringing too much value to the result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making predictions\n",
+    "\n",
+    "Find the predicted price of an apartment that has a size of 750 sq.ft. from 2009."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_data = [[750, 2009]]\n",
+    "new_data_scaled = scaler.transform(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([258330.34465995])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.predict(new_data_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the univariate p-values of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import f_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([285.92105192,   0.85525799]), array([8.12763222e-31, 3.57340758e-01]))"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f_regression(x_scaled,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([8.12763222e-31, 3.57340758e-01])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values = f_regression(x_scaled,y)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.357])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a summary table with your findings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Coefficients</th>\n",
+       "      <th>p_values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>size</td>\n",
+       "      <td>67501.576142</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>year</td>\n",
+       "      <td>13724.397082</td>\n",
+       "      <td>0.357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Features  Coefficients  p_values\n",
+       "0     size  67501.576142     0.000\n",
+       "1     year  13724.397082     0.357"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary = pd.DataFrame(data = x.columns.values, columns= ['Features'])\n",
+    "reg_summary['Coefficients'] = reg.coef_\n",
+    "reg_summary['p_values'] = p_values.round(3)\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems that 'Year' is not event significant, therefore we should remove it from the model.\n",
+    "\n",
+    "Note that this dataset is extremely clean and probably artificially created, therefore standardization does not really bring any value to it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Feature+Selection+with+F-regression.ipynb b/Udemy DataScience Workbook/sklearn+-+Feature+Selection+with+F-regression.ipynb
new file mode 100644
index 0000000..bb96f83
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Feature+Selection+with+F-regression.ipynb	
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1714</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1664</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1760</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1685</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1693</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    SAT  Rand 1,2,3   GPA\n",
+       "0  1714           1  2.40\n",
+       "1  1664           3  2.52\n",
+       "2  1760           3  2.54\n",
+       "3  1685           3  2.74\n",
+       "4  1693           2  2.83"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('1.02. Multiple linear regression.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1845.273810</td>\n",
+       "      <td>2.059524</td>\n",
+       "      <td>3.330238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>104.530661</td>\n",
+       "      <td>0.855192</td>\n",
+       "      <td>0.271617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1634.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1772.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1846.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.380000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1934.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.502500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2050.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.810000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               SAT  Rand 1,2,3        GPA\n",
+       "count    84.000000   84.000000  84.000000\n",
+       "mean   1845.273810    2.059524   3.330238\n",
+       "std     104.530661    0.855192   0.271617\n",
+       "min    1634.000000    1.000000   2.400000\n",
+       "25%    1772.000000    1.000000   3.190000\n",
+       "50%    1846.000000    2.000000   3.380000\n",
+       "75%    1934.000000    3.000000   3.502500\n",
+       "max    2050.000000    3.000000   3.810000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the multiple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data[['SAT','Rand 1,2,3']]\n",
+    "y = data['GPA']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00165354, -0.00826982])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.29603261264909486"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculating the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4066811952814285"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(84, 2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.39203134825134023"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2 = reg.score(x,y)\n",
+    "n = x.shape[0]\n",
+    "p = x.shape[1]\n",
+    "\n",
+    "adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
+    "adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import f_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([56.04804786,  0.17558437]), array([7.19951844e-11, 6.76291372e-01]))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f_regression(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7.19951844e-11, 6.76291372e-01])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values = f_regression(x,y)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.676])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values.round(3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Feature+Selection+with+F-regression_with_comments.ipynb b/Udemy DataScience Workbook/sklearn+-+Feature+Selection+with+F-regression_with_comments.ipynb
new file mode 100644
index 0000000..53d8a0a
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Feature+Selection+with+F-regression_with_comments.ipynb	
@@ -0,0 +1,538 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For these lessons we will need NumPy, pandas, matplotlib and seaborn\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "# and of course the actual regression (machine learning) module\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1714</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1664</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1760</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1685</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1693</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    SAT  Rand 1,2,3   GPA\n",
+       "0  1714           1  2.40\n",
+       "1  1664           3  2.52\n",
+       "2  1760           3  2.54\n",
+       "3  1685           3  2.74\n",
+       "4  1693           2  2.83"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "data = pd.read_csv('1.02. Multiple linear regression.csv')\n",
+    "\n",
+    "# Let's explore the top 5 rows of the df\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1845.273810</td>\n",
+       "      <td>2.059524</td>\n",
+       "      <td>3.330238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>104.530661</td>\n",
+       "      <td>0.855192</td>\n",
+       "      <td>0.271617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1634.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1772.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1846.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.380000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1934.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.502500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2050.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.810000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               SAT  Rand 1,2,3        GPA\n",
+       "count    84.000000   84.000000  84.000000\n",
+       "mean   1845.273810    2.059524   3.330238\n",
+       "std     104.530661    0.855192   0.271617\n",
+       "min    1634.000000    1.000000   2.400000\n",
+       "25%    1772.000000    1.000000   3.190000\n",
+       "50%    1846.000000    2.000000   3.380000\n",
+       "75%    1934.000000    3.000000   3.502500\n",
+       "max    2050.000000    3.000000   3.810000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This method gives us very nice descriptive statistics. We don't need this for now, but will later on!\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the multiple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are two independent variables: 'SAT' and 'Rand 1,2,3'\n",
+    "x = data[['SAT','Rand 1,2,3']]\n",
+    "\n",
+    "# and a single depended variable: 'GPA'\n",
+    "y = data['GPA']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We start by creating a linear regression object\n",
+    "reg = LinearRegression()\n",
+    "\n",
+    "# The whole learning process boils down to fitting the regression\n",
+    "reg.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00165354, -0.00826982])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Getting the coefficients of the regression\n",
+    "reg.coef_\n",
+    "# Note that the output is an array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.29603261264909486"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Getting the intercept of the regression\n",
+    "reg.intercept_\n",
+    "# Note that the result is a float as we usually expect a single value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculating the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4066811952814285"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the R-squared of the regression\n",
+    "reg.score(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(84, 2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the shape of x, to facilitate the creation of the Adjusted R^2 metric\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.39203134825134023"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If we want to find the Adjusted R-squared we can do so by knowing the r2, the # observations, the # features\n",
+    "r2 = reg.score(x,y)\n",
+    "# Number of observations is the shape along axis 0\n",
+    "n = x.shape[0]\n",
+    "# Number of features (predictors, p) is the shape along axis 1\n",
+    "p = x.shape[1]\n",
+    "\n",
+    "# We find the Adjusted R-squared using the formula\n",
+    "adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
+    "adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature selection\n",
+    "Full documentation: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the feature selection module from sklearn\n",
+    "# This module allows us to select the most appopriate features for our regression\n",
+    "# There exist many different approaches to feature selection, however, we will use one of the simplest\n",
+    "from sklearn.feature_selection import f_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([56.04804786,  0.17558437]), array([7.19951844e-11, 6.76291372e-01]))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We will look into: f_regression\n",
+    "# f_regression finds the F-statistics for the *simple* regressions created with each of the independent variables\n",
+    "# In our case, this would mean running a simple linear regression on GPA where SAT is the independent variable\n",
+    "# and a simple linear regression on GPA where Rand 1,2,3 is the indepdent variable\n",
+    "# The limitation of this approach is that it does not take into account the mutual effect of the two features\n",
+    "f_regression(x,y)\n",
+    "\n",
+    "# There are two output arrays\n",
+    "# The first one contains the F-statistics for each of the regressions\n",
+    "# The second one contains the p-values of these F-statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7.19951844e-11, 6.76291372e-01])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Since we are more interested in the latter (p-values), we can just take the second array\n",
+    "p_values = f_regression(x,y)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.676])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To be able to quickly evaluate them, we can round the result to 3 digits after the dot\n",
+    "p_values.round(3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+How+to+properly+include+p-values.ipynb b/Udemy DataScience Workbook/sklearn+-+How+to+properly+include+p-values.ipynb
new file mode 100644
index 0000000..359f26f
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+How+to+properly+include+p-values.ipynb	
@@ -0,0 +1,462 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to (properly) include p-values with sklearn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For these lessons we will need NumPy, pandas, matplotlib and seaborn\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "# and of course the actual regression (machine learning) module\n",
+    "from sklearn import linear_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1714</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1664</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1760</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1685</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1693</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    SAT  Rand 1,2,3   GPA\n",
+       "0  1714           1  2.40\n",
+       "1  1664           3  2.52\n",
+       "2  1760           3  2.54\n",
+       "3  1685           3  2.74\n",
+       "4  1693           2  2.83"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "data = pd.read_csv('1.02. Multiple linear regression.csv')\n",
+    "\n",
+    "# Let's explore the top 5 rows of the df\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1845.273810</td>\n",
+       "      <td>2.059524</td>\n",
+       "      <td>3.330238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>104.530661</td>\n",
+       "      <td>0.855192</td>\n",
+       "      <td>0.271617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1634.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1772.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1846.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.380000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1934.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.502500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2050.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.810000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               SAT  Rand 1,2,3        GPA\n",
+       "count    84.000000   84.000000  84.000000\n",
+       "mean   1845.273810    2.059524   3.330238\n",
+       "std     104.530661    0.855192   0.271617\n",
+       "min    1634.000000    1.000000   2.400000\n",
+       "25%    1772.000000    1.000000   3.190000\n",
+       "50%    1846.000000    2.000000   3.380000\n",
+       "75%    1934.000000    3.000000   3.502500\n",
+       "max    2050.000000    3.000000   3.810000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This method gives us very nice descriptive statistics. We don't need this for now, but will later on!\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create your first multiple regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are two independent variables: 'SAT' and 'Rand 1,2,3'\n",
+    "x = data[['SAT','Rand 1,2,3']]\n",
+    "\n",
+    "# and a single depended variable: 'GPA'\n",
+    "y = data['GPA']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to (properly) include p-values in sklearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since the p-values are obtained through certain statistics, we need the 'stat' module from scipy.stats\n",
+    "import scipy.stats as stat\n",
+    "\n",
+    "# Since we are using an object oriented language such as Python, we can simply define our own \n",
+    "# LinearRegression class (the same one from sklearn)\n",
+    "# By typing the code below we will ovewrite a part of the class with one that includes p-values\n",
+    "# Here's the full source code of the ORIGINAL class: https://github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/linear_model/base.py#L362\n",
+    "\n",
+    "\n",
+    "class LinearRegression(linear_model.LinearRegression):\n",
+    "    \"\"\"\n",
+    "    LinearRegression class after sklearn's, but calculate t-statistics\n",
+    "    and p-values for model coefficients (betas).\n",
+    "    Additional attributes available after .fit()\n",
+    "    are `t` and `p` which are of the shape (y.shape[1], X.shape[1])\n",
+    "    which is (n_features, n_coefs)\n",
+    "    This class sets the intercept to 0 by default, since usually we include it\n",
+    "    in X.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # nothing changes in __init__\n",
+    "    def __init__(self, fit_intercept=True, normalize=False, copy_X=True,\n",
+    "                 n_jobs=1):\n",
+    "        self.fit_intercept = fit_intercept\n",
+    "        self.normalize = normalize\n",
+    "        self.copy_X = copy_X\n",
+    "        self.n_jobs = n_jobs\n",
+    "\n",
+    "    \n",
+    "    def fit(self, X, y, n_jobs=1):\n",
+    "        self = super(LinearRegression, self).fit(X, y, n_jobs)\n",
+    "        \n",
+    "        # Calculate SSE (sum of squared errors)\n",
+    "        # and SE (standard error)\n",
+    "        sse = np.sum((self.predict(X) - y) ** 2, axis=0) / float(X.shape[0] - X.shape[1])\n",
+    "        se = np.array([np.sqrt(np.diagonal(sse * np.linalg.inv(np.dot(X.T, X))))])\n",
+    "\n",
+    "        # compute the t-statistic for each feature\n",
+    "        self.t = self.coef_ / se\n",
+    "        # find the p-value for each feature\n",
+    "        self.p = np.squeeze(2 * (1 - stat.t.cdf(np.abs(self.t), y.shape[0] - X.shape[1])))\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# When we create the regression everything is the same\n",
+    "reg_with_pvalues = LinearRegression()\n",
+    "reg_with_pvalues.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.        , 0.75717067])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The difference is that we can check what's contained in the local variable 'p' in an instance of the LinearRegression() class\n",
+    "reg_with_pvalues.p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's create a new data frame with the names of the features\n",
+    "reg_summary = pd.DataFrame([['SAT'],['Rand 1,2,3']],columns =['Features'])\n",
+    "# Then we create and fill a second column, called 'Coefficients' with the coefficients of the regression\n",
+    "reg_summary['Coefficients'] = reg_with_pvalues.coef_\n",
+    "# Finally, we add the p-values we just calculated\n",
+    "reg_summary['p-values'] = reg_with_pvalues.p.round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Coefficients</th>\n",
+       "      <th>p-values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SAT</td>\n",
+       "      <td>0.001654</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Rand 1,2,3</td>\n",
+       "      <td>-0.008270</td>\n",
+       "      <td>0.757</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Features  Coefficients  p-values\n",
+       "0         SAT      0.001654     0.000\n",
+       "1  Rand 1,2,3     -0.008270     0.757"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This result is identical to the one from StatsModels\n",
+    "reg_summary"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+1).ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+1).ipynb
new file mode 100644
index 0000000..e2ed102
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+1).ipynb	
@@ -0,0 +1,1558 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('1.04. Real-life example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19e03980898>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19e03966eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19e03d7e390>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19e03d2d2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19e03d87780>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19e03d89588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19e03974240>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19e03f01a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19e03e33588>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEFCAYAAAD+A2xwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAHPtJREFUeJzt3X9wVFf9N/D3/bGbkGz4ERrEIulDUgLFTr9psGinBDoyQOvPeYAS7Jgy01GpbaxatCCVgBgZSnFai05HR037ULRhrA9gHdRG6CShgo/5NrTx24LQTpSEYgIxZDcku3vvef7Y7E022WRDupvl3PN+jU6ye3ez59T4zunn/LiaEEKAiIikpae7AURE9MEwyImIJMcgJyKSHIOciEhyDHIiIsmZE/2B7e3d437vtGlZ6OzsSWJrrn+q9Vm1/gLq9Vm1/gLJ6XNeXs6I16QakZumke4mTDjV+qxafwH1+qxaf4HU91mqICciouEY5EREkmOQExFJjkFORCQ5BjkRkeQY5EREkmOQExFJzjVBHugNpbsJRERp4Yogf/PcJXztmXq83dKZ7qYQEU04VwR5R9dVAMClrt40t4SIaOK5IsjDYRsAYPNmR0SkIFcEeciKBLnV/5WISCWuCPKwFRmJWzZH5ESkHpcEef+InEFORApikBMRSc4dQR5maYWI1OWKIOdkJxGpzBVBztIKEanMVUFuM8iJSEEuCXLWyIlIXS4J8miNnEFOROpxRZCH+rfoW9yiT0QKckWQh7lqhYgU5qog52QnEanIJUHOyU4iUpdLgjwyIg8zyIlIQa4I8uhkJ0srRKQiVwQ5JzuJSGUuCXLWyIlIXS4Jcp61QkTqYpATEUlO+iAXQgwqrbBGTkTqkT7Iw4POV+GqFSJSkQuCfGAUzkOziEhFCYPctm1UVlairKwM5eXlaGlpifuaL33pS/j1r3+dkkaOJibIOSInIgUlDPLa2loEg0HU1NRg48aN2LVr17DXPPPMM+jq6kpJAxMZXFphkBORisxEL2hsbERpaSkAoLi4GM3NzTHX//CHP0DTNCxZsmRMHzhtWhZM0xhHUyPy8nJiHlv6oL9F2vDrbuDGPo1Gtf4C6vVZtf4Cqe1zwiD3+/3w+XzOY8MwEA6HYZomzpw5g1deeQXPPvssfvKTn4zpAzs7e8bd2Ly8HLS3d8c8d7Ej4HwfClnDrssuXp/dTLX+Aur1WbX+Asnp82h/CBIGuc/nQyAwEJa2bcM0I287ePAgLl68iPXr16O1tRUejwezZs0a8+g8GQbXyHloFhGpKGGQl5SU4NixY/jUpz6FpqYmFBUVOdcef/xx5/u9e/fihhtumNAQB4AQV60QkeISBvny5ctx/PhxrFu3DkII7Ny5E9XV1cjPz8eyZcsmoo2jsriOnIgUlzDIdV3Hjh07Yp4rLCwc9rqvfe1ryWvVNQhx+SERKU7+DUHhwUHOLfpEpB75g5wjciJSnPRBPri0IgRgC4Y5EalF+iAfulKFK1eISDXSB3loyO3duHKFiFQjfZAPnuwEOOFJROqRP8j7SymmoQHghCcRqUf6II+WVjI8kYO4GOREpBrpg9yKBrm3P8g52UlEipE+yIeNyLn8kIgUI32Qh8OR4PZGg9ziZCcRqUX+IO8P7kzWyIlIUdIHeWhIjZzryIlINdIHucVVK0SkOOmDPNS/SsUJcq5aISLFSB/k0Z2dzvJD7uwkIsXIH+QsrRCR4lwU5JGuMMiJSDUuCHIBQ9dgmgxyIlKT9EEesmyYhg5D6z80i5OdRKQY6YM8bNkwDQ2GEekK7xBERKox092A8XitqdX5/kogCFsInGvtAsAt+kSkHulH5LYtoGsatP6esEZORKqRP8hFZLJT13hjCSJSk/RBbtkCOoOciBQmfZDbdmRErunRVSuskRORWqQP8oEReeQxTz8kItVIHeS2EBACMHSdpRUiUpbcQd4f2rquQesP8jCDnIgU44ogN3QNuh77HBGRKqQOcmvQiHygtMLJTiJSiyuCnOvIiUhlUgd5TI1c56FZRKQmVwS5MWj5IUfkRKQaqYPcqZFrA6UVTnYSkWqkDvLBpRVd52QnEakpYZDbto3KykqUlZWhvLwcLS0tMdf379+P1atXY82aNTh27FjKGhrP4MlOLVpaYY2ciBST8Dzy2tpaBINB1NTUoKmpCbt27cJzzz0HALh8+TJ+9atf4eDBg+jr68OnP/1p3H333c7mnFSLu2qFN5YgIsUkHJE3NjaitLQUAFBcXIzm5mbnWm5uLg4dOgSPx4OOjg5Mnjx5wkIcGLgbUExphSNyIlJMwhG53++Hz+dzHhuGgXA4DNOMvNU0Tbz44ovYu3cvysvLE37gtGlZME1j3A3Oy8tBji8TAOD9Ty8AIGuSx3nO9BjIy8sZ98+/HrmtP4mo1l9AvT6r1l8gtX1OGOQ+nw+BQMB5bNu2E+JRX/ziF7F27Vp8+ctfxokTJ/CJT3xixJ/X2dkz7sbm5eWgvb0b3f5IgAd6ggCAUMhCT08fAODq1RDa27vH/RnXm2ifVaFafwH1+qxaf4Hk9Hm0PwQJSyslJSWoq6sDADQ1NaGoqMi59u6776KiogJCCHg8Hni9Xuj6xC2EiamR69FDs7hqhYjUknBEvnz5chw/fhzr1q2DEAI7d+5EdXU18vPzsWzZMsyfPx9lZWXQNA2lpaVYtGjRRLQbQOTfDoDY0w9ZIyci1SQMcl3XsWPHjpjnCgsLne8rKipQUVGR/JaNQXTwbfDGEkSkMKk3BEU3/+haZESuaxq36BORcqQO8sE7O6NfGeREpBqpg3zwZCcAGIbGLfpEpBxXBbnJETkRKUjqIB+8szP6lZOdRKQauYN8SI3c0DUuPyQi5Ugd5MNq5Dpr5ESkHpcFuc4aOREpR+ogH1ZaMTjZSUTqkTrIrXjryFkjJyLFSB3kdrwaOW8sQUSKcUWQR+8OZOg6R+REpBypg9yyhXPOChAZkXMdORGpRuogt4XA4OPPDV2DLQQEyytEpBC5g9wWzkQnEFm1AoArV4hIKXIHuRiojwMDq1cY5ESkErmDfMiI3Oyvs3DCk4hUIneQCxEzIjecETm36ROROuQOcltg0IDcGZ1z5QoRqUTuIBec7CQikjvIh65a6S+zhBnkRKQQuYN8yKqV6IicpRUiUonUQS5s4ezqBCJb9AHAsjjZSUTqkDbIbSEggJidnVxHTkQqkjbIxZADs4DByw8Z5ESkDmmDPJrVXLVCRKqTN8hHGZFzspOIVCJvkIvYuwMBnOwkIjXJG+TOiHzgOdbIiUhF8gZ53BE5g5yI1CNvkPdXT7hqhYhUJ2+QxxuRG/01cgY5ESlE3iCPs2pF5zG2RKQgeYPcGZEPPOeUVnhjCSJSiPxBzho5ESlO2iAX/dUTjRuCiEhx0gZ5dNRtcLKTiBRnJnqBbdvYvn07Tp8+Da/Xi6qqKtx0003O9eeffx6///3vAQBLly5FRUVF6lo7iOgvrWiDgjxaZmGQE5FKEo7Ia2trEQwGUVNTg40bN2LXrl3OtX/96184fPgwXnrpJdTU1KChoQHvvPNOShscNVAjH3hu4NAsrlohInUkHJE3NjaitLQUAFBcXIzm5mbn2syZM/Hzn/8chmEAAMLhMDIyMlLU1Fjxlh+aXLVCRApKGOR+vx8+n895bBgGwuEwTNOEx+NBbm4uhBDYvXs3FixYgDlz5oz686ZNy4JpGuNucF5eDnJ8mfB6rwIAsiZ5kePLBADkTssGAGRO8iIvL2fcn3G9cVNfxkK1/gLq9Vm1/gKp7XPCIPf5fAgEAs5j27ZhmgNv6+vrw5YtW5CdnY1t27Yl/MDOzp5xNjXyD6K9vRvd/l70XA0CAILBMLr9vZG2eiOVoivdvWhv7x7351xPon1WhWr9BdTrs2r9BZLT59H+ECSskZeUlKCurg4A0NTUhKKiIueaEAIPP/ww5s2bhx07djgllokw+hZ91siJSB0JR+TLly/H8ePHsW7dOgghsHPnTlRXVyM/Px+2beOvf/0rgsEg6uvrAQCPPfYYbr/99pQ3fLRjbLmOnIhUkjDIdV3Hjh07Yp4rLCx0vn/rrbeS36oxGPUYW052EpFCpN0QNNqt3riOnIhUIm+Qx7n5ss4gJyIFyRvk8UbknOwkIgXJG+TOFv2B50xOdhKRguQN8uihWXFvLMEgJyJ1SBvk/QPymEOzuGqFiFQkbZDzxhJERBHSBrkVd/khzyMnIvVIG+QiGuSD79nJY2yJSEHSBnm8nZ06a+REpCCJgzzydXBpRdc0mIaOYNhKU6uIiCaevEEep0YOAJleA71BBjkRqUPeII9TWgEY5ESkHnmDPM5kJwBkek30BsNpaBERUXrIG+Rx1pEDQGZGZEQuBCc8iUgN8ga5LaAB0OLUyIUAgiEuQSQiNcgb5GJ4fRyIlFYAoDfEOjkRqUHeILfFsLIKEBmRA2CdnIiUIW2QCyFijrCNcoK8jyNyIlKDtEFujTgi7y+tcERORIqQNsjFCDXySU5phSNyIlKDtEGeuEbOICciNcgb5EKMvmqFpRUiUoS8QW4LxMlxZHBETkSKMdPdgPGKNyJ/rakVFy4FAADv/LPTCfW7i2dNePuIiCaKxCPy4dvzAcBjRLoUCnNnJxGpQcogF0KMWCM3TQY5EalF0iCPfI07Io8GucUgJyI1SBnkA2eRD78WLa2EOSInIkXIHeRxRuQsrRCRauQM8v6Mjlcjj9y3U2NphYiUIWmQR0bkQ88ijzINnSNyIlKGnEHeX1ox4u0IQmTCM8wROREpQsogj97GbYQBOTwmR+REpA4pg9y58fIISe4xdIQtwft2EpES5AxyZ/nhCDXy/pUrYYtBTkTuJ2eQR1etjDIiB7gEkYjUkDDIbdtGZWUlysrKUF5ejpaWlmGvuXz5MlasWIG+vr6UNHJYmxKMyD1cS05ECkkY5LW1tQgGg6ipqcHGjRuxa9eumOv19fV48MEH0dHRkbJGDjVQI49/ndv0iUglCYO8sbERpaWlAIDi4mI0NzfH/gBdR3V1NaZOnZqaFsaRsEbObfpEpJCE55H7/X74fD7nsWEYCIfDMM3IW++6665r+sBp07JgmsY1NnNAXl4OMjI8AIBJmR7k+DKHvcaX7QUAmB4DOb5M5OXljPvzrgeyt/9aqdZfQL0+q9ZfILV9ThjkPp8PgUDAeWzbthPi49HZ2TPu9+bl5aC9vRuBniAAIBSy0O3vHfY6u7+kcsXfh25/L9rbu8f9mekW7bMqVOsvoF6fVesvkJw+j/aHIGFppaSkBHV1dQCApqYmFBUVfaDGJMNY1pEDnOwkIjUkHFovX74cx48fx7p16yCEwM6dO1FdXY38/HwsW7ZsIto4TLRGro3wZ4iTnUSkkoRBrus6duzYEfNcYWHhsNcdPXo0ea1KIDoiN0Y6NMvkZCcRqUPODUHROwSNtI6cpRUiUoiUQS4SHGPL0goRqUTKILfGuI6cI3IiUoGUQS7GuLOTNXIiUoGUQZ54Z2fkeZZWiEgFkgZ55OtI68g1TYOHt3sjIkXIGeQJNgQBkSWIDHIiUoHcQT5SkRy8bycRqUPOIHdq5CO/hqUVIlKF3EE+SmnFY+qwbOGM3omI3ErOIB9jjRwAyytE5HpyBnmCLfoA4IkuQWR5hYhcTs4gH8OI3DcpcvOJ9y+P//xzIiIZyBnkY5jsnDt7KjQN+Pt7l53XExG5kZxBnuDQLCAyIp/z4cn4jz+It85dmqimERFNODmDvH+AbYxSIweAj87JBQAcOdGS6iYREaWNlEGe6BjbqGk5GZh1QzbOnO/C2dauiWgaEdGEkzLIEx2aNdiCOdMAAK83v5/SNhERpYucQZ7gGNvBZkzLgmnoeLeNI3Iicic5g1wIaFri0goQqaPf9CEfzv87gGDImoDWERFNLDmD3B59DflQc26cDFsItFzsTmGriIjSQ84gF2JM9fGoghsnAwDebbuSqiYREaWNnEFui2sakRd8OBLk711gkBOR+8gZ5EKMuqtzqLypk+Cb5OGInIhcSc4gv8YRuaZpKLhxMjq6enElEExhy4iIJp6cQS7GtoZ8sDn95ZV3WV4hIpeRM8ivcUQOcMKTiNxLziC/xlUrwMCI/D1uDCIilzHT3YDxEEKMaVdn1GtNrQCAnCwPzpzvwtH/Pg9d13B38awUtZCIaOLIOSK3xZh2dQ714elZCIVtdHRdTUGriIjSQ7ogF0KMa7ITAGbl+QAAre2BZDeLiChtpAtyawy3eRvJzNws6JqG8wxyInIReYN8HCNyj6njQ7mT0Nndh57ecLKbRkSUFtIE+ZWeIP7f/7wPyxr7EbbxzMrLBgC0dnBUTkTuIE2QH208jx2/OIlT5zoAjG9EDgCzbojUydva/UlrGxFROkkT5MVzbwAwcP/N8dTIAWBytge+SR60XepB2LKT1j4ionSRJsj/18zJuLVwujNROd4RuaZpmJWXjVDYxqmzHclsIhFRjHNtXfjd8fcQCqf2pjYJg9y2bVRWVqKsrAzl5eVoaYm9I/2BAwewatUqrF27FseOHUtZQwHgfy+92fl+vCNyALh51hTomoaf//5tHm1LRClxtrULe37dhP9b/x6eerERlp26CkDCnZ21tbUIBoOoqalBU1MTdu3aheeeew4A0N7ejn379uHll19GX18f7r//ftx1113wer0paezHbvkQPpSbhYuXe67pGNuhpk/JROl/fRh1p9rw9IFT+PJnFyBv6iR4DB2XrvSis7sP2Zkmpk/JRKbXRE9fGMGQhUyvgUkZJoQA+kIWLMuG12PA6zFgWTaCYRtCCGR4DOi6hu6eEK4EgjANDVN8GcjONIdtZBJCQAggbNno9Peh80ofwrYNQ9fhMXTk94RgBcMwxvhvIOPZKDX6zxvj65L0A83MazihMtltuwa2AEIhC31h2/mM6O0Hna/R56DFvQYt8j6vvw/dPeM7lTMUttEbtBC2bBiGDtPQYOqRr5quQQgAQqD/C4DI7xz6HwsI9P8HAgOvhRi4ybkQsddsO/I7a+gaMr0GvF7D+WcsMPSbmG8BIWBmeNDl73OeFzEvGPzS4RdEzM8d/iFD32HoGgxDh2XZ6O4Joac3hEmZJnImeaFpkf8fhy2BDI8Or8dAMGQh0BuGLQSyMz3I8Bi4EgjiP/4+mIaOqTkZMA0Nre0BXLgUwJTsDMye4UN3TxAn/ucizrVdwbzZUzFv9lTsf/UMQmEbs2f48Je3LkATAg9++pYPNAgdScIgb2xsRGlpKQCguLgYzc3NzrU333wTt99+O7xeL7xeL/Lz8/HOO+/gtttuS3pDgUg5ZcUds7Hvj6fHXVqJumlmDspnzsP/+cNpPH3gVJJaSEQq03UNFy/3oO5UGwBg8W0zMXtGDv77TAdeb34fRbOnYsl/3Zj0z00Y5H6/Hz6fz3lsGAbC4TBM04Tf70dOTo5zLTs7G37/6KtB8vJyRr2eyNoV87F2xfwP9DMGu2958n4WEVE899+b2p+fsEDh8/kQCAysubZtG6Zpxr0WCARigp2IiFIvYZCXlJSgrq4OANDU1ISioiLn2m233YbGxkb09fWhu7sb586di7lORESpp4l4MwqD2LaN7du348yZMxBCYOfOnairq0N+fj6WLVuGAwcOoKamBkIIbNiwAStXrpyothMREcYQ5EREdH2TZkMQERHFxyAnIpIcg5yISHJS3LMzOuF6+vRpeL1eVFVV4aabbkp3s5IqFAphy5YtaG1tRTAYxFe/+lXcfPPN2Lx5MzRNw9y5c7Ft2zboH2RL63Xq0qVLWLVqFX75y1/CNE3X9/mnP/0pjh49ilAohC984QtYtGiRa/scCoWwefNmtLa2Qtd1fP/733f1/8anTp3Cnj17sG/fPrS0tMTt549//GO89tprME0TW7ZsSc4GSiGBP/7xj2LTpk1CCCHeeOMN8dBDD6W5Rcn3m9/8RlRVVQkhhLh8+bJYunSp2LBhgzhx4oQQQoitW7eKP/3pT+lsYkoEg0Hx8MMPixUrVoizZ8+6vs8nTpwQGzZsEJZlCb/fL5599llX9/nVV18Vjz76qBBCiIaGBlFRUeHa/v7sZz8Tn/nMZ8R9990nhBBx+9nc3CzKy8uFbduitbVVrFq1KimfLcWfwdGOCXCLe+65B1//+tedx4Zh4O9//zsWLVoEAFiyZAlef/31dDUvZZ588kmsW7cOM2bMAADX97mhoQFFRUV45JFH8NBDD+Huu+92dZ/nzJkDy7Jg2zb8fj9M03Rtf/Pz87F3717ncbx+NjY2YvHixdA0DTfeeCMsy8Lly5c/8GdLEeQjHRPgJtnZ2fD5fPD7/Xj00UfxjW98A0II5xCs7OxsdHd3p7mVyfXb3/4Wubm5zh9pAK7vc2dnJ5qbm/GjH/0I3/ve9/Ctb33L1X3OyspCa2sr7r33XmzduhXl5eWu7e/KlSudXe9A/N/loVmWrP5LUSMf7ZgAN7lw4QIeeeQR3H///fjsZz+Lp556yrkWCAQwefLkNLYu+V5++WVomoa//OUvePvtt7Fp06aY0Ykb+zx16lQUFBTA6/WioKAAGRkZeP/9953rbuvz888/j8WLF2Pjxo24cOEC1q9fj1Ao5Fx3W38HG1z3j/YzVceaSDEiH+2YALfo6OjAgw8+iG9/+9tYs2YNAGDBggU4efIkAKCurg4f+9jH0tnEpNu/fz9efPFF7Nu3D7fccguefPJJLFmyxNV9XrhwIerr6yGEwMWLF3H16lXceeedru3z5MmTnaCaMmUKwuGw63+vo+L1s6SkBA0NDbBtG21tbbBtG7m5uR/4s6TY2RnvmIDCwsJ0NyupqqqqcOTIERQUFDjPPfHEE6iqqkIoFEJBQQGqqqpgGEYaW5k65eXl2L59O3Rdx9atW13d5927d+PkyZMQQuCb3/wmPvKRj7i2z4FAAFu2bEF7eztCoRAeeOAB3Hrrra7t7/nz5/HYY4/hwIEDeO+99+L2c+/evairq4Nt2/jOd76TlD9kUgQ5ERGNTIrSChERjYxBTkQkOQY5EZHkGORERJJjkBMRSY5BTlI7f/48br31Vnz+85+P+e/+/fuv+Wc98cQTeOutt675fU8//TQqKyuHPV9eXo4///nP1/zziK4Vlx+S1M6fP48HHngAR48eTVsb/vnPf+K+++5DQ0MDPB4PAKCtrQ1lZWU4duyYK3ch0/WFv2HkWosXL8bKlSvR2NgIwzDwzDPPYPbs2Th58qSzOaO4uBjnzp3Dvn37UF5ejoqKCgCRo2YzMzNx7tw5zJs3D3v27IHX68XBgwfxwgsvwLZtfPSjH8W2bduQn5+PuXPnor6+Hp/85CcBAIcPH8bnPvc5hjhNCJZWSHr//ve/h5VWTp8+jfb2dtx55504ePAg7rjjDuzfvx+hUAiPP/44nnrqKRw8eHDEoH3jjTdQWVmJI0eOoK2tDQ0NDfjHP/6BAwcO4KWXXsKhQ4cwffp0/OIXvwAArF69Gq+88orz/sOHD2P16tUT0n8iDhdIejNmzMChQ4fiXouerDh37lz87W9/w5kzZzB9+nTMnz8fALBmzRr84Ac/GPa+uXPnYubMmQCAwsJCdHV1oa2tDS0tLVi7di2AyE0TFixYACBy8t0Pf/hD9PT04OzZs5gyZUrMcQtEqcQgJ1fLyMgAAGiaBiEEDMOAbdtjft/g91qWhXvvvRff/e53AUTOEbEsC0DkuNalS5eitrYWb775pnPwGdFEYGmFlFJQUIArV67g9OnTAIDf/e53Y37vxz/+cbz66qu4dOkShBDYvn07XnjhBef6qlWrcOTIEdTX1+Oee+5JetuJRsIROUkvWiMf7I477oj7Wq/Xi927d2PTpk3QdR1z5sxBZmbmmD5n/vz5qKiowPr162HbNm655RZ85Stfca4vXLgQW7duxcKFC5GdnT3+DhFdIy4/JKXYto09e/agoqICWVlZqK6uxsWLF7F58+Z0N41o3DgiJ6Xouo6pU6dizZo18Hg8mDVrVtzJTiKZcERORCQ5TnYSEUmOQU5EJDkGORGR5BjkRESSY5ATEUnu/wM0M+WEXnK8FQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19e03f3a400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19e04010240>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19e03fd3400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19e040b5eb8>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19e04136668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x19e040943c8>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19e04204198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+1)_with_comments.ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+1)_with_comments.ipynb
new file mode 100644
index 0000000..7e99d2e
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+1)_with_comments.ipynb	
@@ -0,0 +1,1620 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For this practical example we will need the following libraries and modules\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "raw_data = pd.read_csv('1.04. Real-life example.csv')\n",
+    "\n",
+    "# Let's explore the top 5 rows of the df\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics are very useful for initial exploration of the variables\n",
+    "# By default, only descriptives for the numerical variables are shown\n",
+    "# To include the categorical ones, you should specify this with an argument\n",
+    "raw_data.describe(include='all')\n",
+    "\n",
+    "# Note that categorical variables don't have some types of numerical descriptives\n",
+    "# and numerical variables don't have some types of categorical descriptives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For these several lessons, we will create the regression without 'Model'\n",
+    "# Certainly, when you work on the problem on your own, you could create a regression with 'Model'\n",
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "\n",
+    "# Let's check the descriptives without 'Model'\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data.isnull() # shows a df with the information whether a data point is null \n",
+    "# Since True = the data point is missing, while False = the data point is not missing, we can sum them\n",
+    "# This will give us the total number of missing values feature-wise\n",
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's simply drop all missing values\n",
+    "# This is not always recommended, however, when we remove less than 5% of the data, it is okay\n",
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's check the descriptives without the missing values\n",
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897ab4940>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b897a96438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A great step in the data exploration is to display the probability distribution function (PDF) of a variable\n",
+    "# The PDF will show us how that variable is distributed \n",
+    "# This makes it very easy to spot anomalies, such as outliers\n",
+    "# The PDF is often the basis on which we decide whether we want to transform a feature\n",
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obviously there are some outliers present \n",
+    "\n",
+    "# Without diving too deep into the topic, we can deal with the problem easily by removing 0.5%, or 1% of the problematic samples\n",
+    "# Here, the outliers are situated around the higher prices (right side of the graph)\n",
+    "# Logic should also be applied\n",
+    "# This is a dataset about used cars, therefore one can imagine how $300,000 is an excessive price\n",
+    "\n",
+    "# Outliers are a great issue for OLS, thus we must deal with them in some way\n",
+    "# It may be a useful exercise to try training a model without removing the outliers\n",
+    "\n",
+    "# Let's declare a variable that will be equal to the 99th percentile of the 'Price' variable\n",
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "# Then we can create a new df, with the condition that all prices must be below the 99 percentile of 'Price'\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "# In this way we have essentially removed the top 1% of the data about 'Price'\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897eacfd0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b897e699e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can check the PDF once again to ensure that the result is still distributed in the same way overall\n",
+    "# however, there are much fewer outliers\n",
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897e36e80>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b897ec4320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can treat the other numerical variables in a similar way\n",
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b8980359b0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b89803a198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This plot looks kind of normal, doesn't it?\n",
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897f681d0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b89806f400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The situation with engine volume is very strange\n",
+    "# In such cases it makes sense to manually check what may be causing the problem\n",
+    "# In our case the issue comes from the fact that most missing values are indicated with 99.99 or 99\n",
+    "# There are also some incorrect entries like 75\n",
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A simple Google search can indicate the natural domain of this variable\n",
+    "# Car engine volumes are usually (always?) below 6.5l\n",
+    "# This is a prime example of the fact that a domain expert (a person working in the car industry)\n",
+    "# may find it much easier to determine problems with the data than an outsider\n",
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b8981a0b00>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b898106240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Following this graph, we realize we can actually treat EngineV as a categorical variable\n",
+    "# Even so, in this course we won't, but that's yet something else you may try on your own\n",
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b89825e6a0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vRFF5M26eEzus2nyNy3CXy+XYunVrn/sSEhKcP69atQqrVq3qsz00NBSvv/76CJVIRN7uVEULZAAmRAwv3AFg1pRw/PK7C/HnD0sRHRqIu27UYcGcCUMauoiLUiMiOACnKo2wOwQoFePn0h5O+UtEw9LVY0d5XQciQgLcuirVHRMi1PjFYwuH/TwymQzzEyNw8GQdztW2DzhLpRSNn48xIhoVZ6qNEEQRccMckhktKYm958QXlTd7uJKxxXAnomG5/GXlxEjvDPcZujD4+ylQXDG+vlRluBPRkImiiKKKFmgC/RAxzBWQRoufUo6kyaFoNHa5nN5AShjuRDRkF5pMaDdZkZwQAfkQT4EcC5fH2s9UGz1cydhhuBPRkF0ex56feP25Xjxt9pRL4X6+1cOVjB2eLUNEQ1ZU0QyFXIY5U8LxeWmTp8u5xuXFrkVRhL+fAl+WN+PQl7WQyWQ+t+D1YPHInYiGpN1kQVVDJ6ZPCkFQgHcfJ8pkMsSGB6Krx47OLvemLvB1DHciGpLiS2fJePuQzGWxly6wamjxnXmshoPhTkRDUlzuW+E+IaJ3cY/GFrOLPaWB4U5Eg2azCyg5b0RMWCBiwwdeEcmbaIP8EBSgRKOxe1ysrcpwJ6JBK6lsgcXqwILp1y5Q7a1kMhkmhAfBYnOMi/PdGe5ENGifnek9M+bG2dEermRwYi8NzYyHcXeGOxENisXqQGF5M6LDAhE/hCX1POnyrJWNDHcior6KKpphtQm4cVbMkBfm8JSgACVC1Co0tXbB7hA8Xc6oYrgT0aB89lXvkMxNs2M8XMnQxEYEwe4QUdXQ4elSRhXDnYjc1tVjw6nKFkyKUiPOS2eBdOXy2T1Sn4qA4U5EbtOXGWB3iLhxlm8etQNfh/tX1Qx3IiKIoohDX9ZBJgMW++iQDAD4qxSICPZHRV07LDaHp8sZNQx3InJLZX0Hzjd2IiUxEpGhgZ4uZ1hiI4LgEEScq23zdCmjhuFORG45oK8FACxfOMnDlQxfbHjv9wVSHndnuBORS+0mC06UXsTESLUkFpmOCQ+EUiHHqUrpLt7BcCcilw4X1sMhiFieGudz57b3R6mQY4YuFLUGk2SnImC4E9F19VjtOHSyFoH+Ctw8N9bT5YyYedMiAHy9wLfUMNyJ6Lo+/rwGHV023HmDDgEq716UYzDmTQsHwHAnonGoo8uKDz+/AG2QH+68YbKnyxlRseFBiAwJwFfnjZKcioDhTkQDev+T87BYHXhgyRQE+kvnqB3onQJ4XkIEui0OVNS1e7qcEecy3AVBwJYtW5Ceno6MjAxUV1f32b5v3z6sXLkSq1atwqFDh/psO3HiBG677baRrZiIxsTF1i4cOlmHyJAA3L5AmotJfz3uLr2zZlyGe15eHqxWK3Jzc7Fx40bs2LHDuc1gMCAnJwd79+7Fnj17kJWVBavVCgBoaGjAn/70J9jt9tGrnohGhSCKyP6gFA5BxLdvT4BSIc0/8mfpwqBUyCQ57u7yN6bX65GWlgYASElJQUlJiXNbcXExFixYAJVKBa1WC51Oh9LSUlgsFrzwwgt48cUXR61wIho9h07WoaymDQumR+KGmb61IMdg+KsUmKELQ81FE5rbuz1dzohyOYhmMpmg0WictxUKBex2O5RKJUwmE7TaryfrV6vVMJlM2Lp1K9atW4eYGPfnnwgLC4JSqRhk+Z4TFeVbixQMBnvzXSPRX2OLGX87UgFNoB82rFmIsOAAtx6n1bi335UGU29UlHZIr+HqtW9bOBmnq4woq+vErMToAV9jNN87o/HcLsNdo9HAbP56tXBBEKBUKvvdZjab4efnhy+++AIXLlzAG2+8gfb2dmzYsAG//e1vr/s6ra2+szJKVJQWBkOnp8sYFezNd41Efza7gFfePokeqwOPPzADdosNBoPNrcd2mnoG/Xru1nu5t6G8hqvXnj5BCxmAI/oaLJ0dPeBrjNZ7Z7i/t4E+GFyGe2pqKg4dOoR7770XhYWFSEpKcm5LTk7Ga6+9BovFAqvVioqKCiQnJ+Ojjz5y7rN06VKXwU5EnieKInI+LkNFfQcWz4kZcObHw4V1Y1zZ6ApRq5A0ORRlNW2SulrVZbivWLECBQUFWL16NURRRGZmJrKzs6HT6bB8+XJkZGRgzZo1EEURGzZsgL+//1jUTUQj7ODJOhwrbkB8rBZr754piWkG3LVwRhTKatpw8qwBCoU0+nYZ7nK5HFu3bu1zX0JCgvPnVatWYdWqVQM+vqCgYBjlEdFY+PxME/6adw7BQX746cp5UPn5zvdfI2HhjGi8nXcO+rKLuNGH56q/krSuSiCiQfui9CJ2/+MrqPzkeOrb8xHu5heoo+nqoR+tJmBEx9uvFqb1R8LEYJTVtCE5MUIS0yxI8+RVInJLwakGvPmP0/Dzk+Pn6SmYNjHY0yV5zMIZ0RBF4EKTydOljAiGO9E45BAE/DXvHPb88wxUfgr8fNV8JMaFeLosj7p8Pn9VfYeHKxkZvv+3BxENSqOxC3/+4AzO1rZjQkQQnno4GTGXFo0ezyJCAjDj0lkzpm4bNIF+ni5pWBjuROOE1ebAvz6/gPc/qYbdIWDhjCisu3fWgBOCSe2UR3csnhODspo2VDV0OOed8VUMdyKJszsEHDvVgP8rOI/WTgtCNCo8tiIJqUlR4+p0R3csmhmNnI/Ooqq+A3Onhvv0vw/DnUiiBFHEiTMXsf9oJZpau6FSynHv4njcuzgeQQFf/19/PB6hD0Qd4IdJ0WpcaOpdfs8bzhwaKoY7kRcYKGBvTxnaVLsXmjqR/WEpqhs7oZDLsCw1Dg8smYJQDS8ydGXqhGBcaDKhqqGD4U5E3sHuEPCPgvP48NNqOAQRi2fH4MG0qYgO4xemVxvoA3VSlBp+Sjmq6jt9euiK4U4kEeZuG367rwhnqlsREeyP790zE3On+vaXgp6gUMgRH6tFeW07mozdiI3wzQ9GhjuRD7r6qNPcbcOhL+th7OjBgumR+OH9syW3LN5YmjYhGOW17ahs6PDZcOdFTEQ+rttix0ef18DY0YPlCyfhyYfmMdiHKSY8EEEBSlQ3dsLho4tnM9yJfJjNLuCAvhambhsWzYrBmm9Mh1zum2PE3kQmk2HqhGDY7AJqDWbXD/BC/Hgn8lGCIOLwl3UwdlgwfVIIbpwdgyNF9QPuP9Qzb8araRODcbrKiMr6DsTH+t4KXjxyJ/JRReXNaGjpQlyUGjfNjvHZszq8VZjWH6EaFeoMJlisDk+XM2gMdyIf1NjShVOVRmgC/ZA2fwKHYkbJ1InBEESg5qLvzRTJYRkiH2PqtuFYcQNkMiBt/gSo3FxY3huvRPXGmq4UH6PFl2ebUd3ke2vv8sidyIeIoojsD86gy2JHSmIkokIDPV2SpAWrVQjT+qOh2YyuHvcWCvcWDHciH3K4sB5fnmtGTHgg5kwL93Q540J8rBaCCBSWN3u6lEFhuBP5iLpmM/YeOAd1gBK3JE+AnF+gjon4mN4zZb4oNXi4ksFhuBP5AJvdgTf/XgKbXcD3750FdYBvLyThS0I0KoRqVCipMqLbYvd0OW5juBP5gH2HKlBrMOP2BXFITYrydDnjTnysFnaHgKIK3xmaYbgTebnC8mYc0NdiYqQa6Xckerqcceny0MzJMt8ZmmG4E3mxNpMFf/rnGSgVcqz/5hz4+7l32iONrBCNClGhASipMsLuI3PNMNyJvJQgiHjz76dh6rZh1bIETI7WeLqkcUsmk2F+YiR6rA6UXWjzdDlu4UVMRF6qqLwZZTVt0MVooFDIvP6CH6lLSYxE3he1KCpvxpyp3n8aKo/cibxQncHknF5gydxYzhvjBZImhyLQX4HC8maIoujpclxyGe6CIGDLli1IT09HRkYGqqur+2zft28fVq5ciVWrVuHQoUMAgPr6eqxduxYZGRl47LHHUFlZOTrVE0lQh9mKo8UNkMtkuC1lIlQcZ/cKSoUcc6dGoLm9B/XN3j8NsMtwz8vLg9VqRW5uLjZu3IgdO3Y4txkMBuTk5GDv3r3Ys2cPsrKyYLVa8bvf/Q6PPfYYcnJysH79emRlZY1qE0RSYbE5cFBfC6tNwOI5MYgI8d0FmqVofmLvsoW+cLWqyzF3vV6PtLQ0AEBKSgpKSkqc24qLi7FgwQKoVCqoVCrodDqUlpZi06ZN0Gp7Tx1yOBzw9+eK60SuCIKI/MJ6dHTZMGdqGBInhXi6JLpKckIkZDKgqLwF9908xdPlXJfLcDeZTNBovv6WXqFQwG63Q6lUwmQyOUMcANRqNUwmE8LDe79sqKysxMsvv4w33njDZSFhYUFQujm7nTeIivK9yfvdxd7GnkbtjwNf1KChpQtTJgTj1tTJQ5peQKuR7pG+p3uLitIiCsCsKeE4c94IVaAKIZqROXAdjfely3DXaDQwm78eXxIEAUqlst9tZrPZGfaffvopfv3rX+OVV17BtGnTXBbS2to16OI9JSpKC4PB96YAdQd7G3uiKOKwvgZl1a2IDAnAzXNiYDZbBv08Wk0AOk09o1Ch53lDb5ffO3Piw/BVlRGHPq/G0nkThv28w31fDvTB4HLMPTU1Ffn5+QCAwsJCJCUlObclJydDr9fDYrGgs7MTFRUVSEpKwqeffoqXXnoJ//3f/4158+YNuWii8eCfx6vx1flWhKhVuGPhJPgpeRKbN5ufGAnA+8fdXR65r1ixAgUFBVi9ejVEUURmZiays7Oh0+mwfPlyZGRkYM2aNRBFERs2bIC/vz8yMzNhs9nw3HPPAQCmTp2KrVu3jnozRL7mX59dwP/mV0IdoMQ3Fk1CgMp3hibHqwkRQYgODXRerapUeOeHsUz0khM2vfHP5YF465/3I4G9jZ2PT9Rg74FzCNP64/YFE6ENUg3r+bxh6GK0eENvVy4w/nbeWeR9UYuN6SnDvqDJY8MyRDTyPvysGnsPnEOIRoVnH10w7GCnsZXiA0MzDHeiMSSKIv5RUIV3DlUgTOuPTWtSERMe5OmyaJAuX61a5MVXqzLcicaIKIr43/xK7D9ahciQADz3nVTEMth90pVXq9Z56dWqDHeiMSCKIvYeKMc/j1cjJiwQz30nlYtb+7jLQzNFXjo0w3AnGmWCICLnozL8+4saTIxUY9N3UhEeLN2LjcaLeQkRkMm8d9ydU/4SjSKb3YHd//cV9GUGTI7W4Oa5MV4bBjQ4mkA/JMaFoLy2HR1dVgR72ZfiPHInGiVdPXZk5RZBX2bATF0oNq1JRYCKx1NSkpIYCRHAqYoWT5dyDYY70ShoM1nw8tsnUVbThkUzorBh1XwEBTDYpcabr1blu41ohDUau5CVW4jm9h4sS43Dd76RBLmci21I0ZVXq9rsgldNHeE9lRBJwOkqI7b9vy/Q3N6DB9Om4rEVDHYpu7y2qsXqQFlNq6fL6YPhTjQCRFHExydqkLWvEFa7Az+4bxa+uXQql8cbBy4v4FF0zrvG3RnuRMNk7rHhjfdKsPfAOQQHqbBpTeqITAVLviFpciiC/JXQn70IQfCeq1U55k40DGfOG/GnD86gpcOCmbpQPP7AHIRpufLYeKJUyHHDrGgcKazHV9VGzJ0a4emSADDciYbE1G1D7oFzKChphEwGPHjLVNy/ZArkchkOF9Z5ujwaY0vmxuJIYT0+KWlkuBMNxkCBeeU0rGOh22JH3hc1+NfnF9BtcUAXo8Hae2ZiSmzwmNZB3iUxLgTRoYE4WWZA9512BPp7Plo9XwGRD2g0diG/sB7HTjXA1G2DJtAPi2aGY6YuDOcbO3G+0XvmiaexJ5PJsGRuLPYfq4K+zIBbkj3/nQvDnagfFqsD5xs7cPp8K4ormnGhyQSg95LzB2+ZihU3TMZnZ5o8XCV5k8WXwv2TkgaGO5Gn2R0CmoxdaGjpQn2LGQ0tXagzmFDXbMblaboVchnmTA1HWvIELJge5VUXqpD3iA4NRNKkEJReaENTaxdiwjw7nTPDnSRPFEWYum1ovdCKMxUGfPpVE9pNVrSbLOjstuHqtRZUSjkS4kIwbUIwkiaHYlZ8mFeMoZL3W5Y6CWdr2/Hhp9VYe88sj9bCdyxJgt0hoKW9B02tXTC09cDQ1n3pfz1obu9Gj9VxzWNUfnJEhgSO4RbxAAAM/ElEQVQgROOPELUKackTMCFCjYiQAMh58RENwQ0zo7H/WBUKTjXi/iVTEBniuTn7Ge7kc8zdNmdw68sMuNjahZZ2C4R+ljvzVykQFRKAqNBA6CaEIDhAgabWboRoVAhQKfpcQWrstMDYaRnLVkhi5HIZ7r85Hnv+eQYffnoBGXfN8FgtDHfyei3tPSi70IrGlt6j8i6Lvc/2AJUCESEBCFb7QRukgjbQD5ogP9x1ow7aQD9ngF9eZZ7nodNoWjwnBv8oqMLR4nrcv2SKxy5qY7iT1xEEEVUNHSiqaEbhuRbUGkzObQEqBSZHaxAV2ns0Hh4cMOAXnN62eAKNDwq5HPffPAXZH5bi3cPlePyBOR6pg+FOXqHbYsfpKiOKKppRXNGCzi4bAECpkGHutHAEBSgRF6mG5oojcVeuPkLXagLQaeoZ8dqJrnbz3FgcLqzD8dNNmDM1HEvmjv2pkQx38hhDWzcKy5tRXN6M0gttcFyadOnyl5spiZGYNSUMASolh1LIpygVcqz/5hy8mH0COR+dxbSJIYgNH9tTIxnuNGasNgfKatpwqrIFJ85cRLvZ6twWHuyPSVEafOuWqYiP1fJsFfJ50WFB+N7dM/HmP07jjfdO4eerUsZ0/J3hTqOm22JHVUMHKuracbamDWU17bA7BAC9wy2TotSYFK3BpCg1ggL8AADVTZ2obuKl/CQNN82OQUVdO/L0tdj65xN4cuU8JMaFjMlruwx3QRDw4osvoqysDCqVCtu2bUN8fLxz+759+7B3714olUo88cQTWLZsGYxGI55++mn09PQgOjoa27dvR2Cg5873pNEjiiLMPXY0t3c7zy9vMnahsqED9QYzrjw5cVKUBvOmhWPu1HDUG81QyHmlJ0nfo9+YjsjQQOQePIdX3j6JW+ZNwPJFkxEXqR7V13UZ7nl5ebBarcjNzUVhYSF27NiBP/7xjwAAg8GAnJwc/O1vf4PFYsGaNWuwdOlS7Nq1C/fffz9WrlyJ3bt3Izc3F2vXrh2VBsw9NrS090Ahl0GpkEMhl0GhkOPyX/X9nPoMsb87r9pXRJ8bV/4HDrkcLW3d/T5QvOaHPj/2+9riAPsO9Lw2hwCrzQGL7fJ/HbDaBVisDlhtDvRYHeix2i/914FGYxdsdgF2hwCbXYDNIQAioFD0HfpQyGXw81NALuv9WSGXQ6no/a9CIYNSLoNDFGGxCrDY7LBYHeixOWC1Cdf0pPKTI2lyKBLiQpAwMRjT4kIQov767JWmK//9iCRMJpPhzhsmIy5KjT9/UIrDhfU4XFgPXYwG0yYEY0lKHBJiNCO+apfLcNfr9UhLSwMApKSkoKSkxLmtuLgYCxYsgEqlgkqlgk6nQ2lpKfR6PdavXw8AuPXWW5GVlTVq4Z6Zo0dDS9eoPLeUKOQy+CnlUCrkUPspr1nXUxQvffCIvRdi2B0iLFYbHIIIu0OEQxBgd4iQyQA/hRzKS8+lDfRDYKgS2iAVNJfOL9cG+iFYrXK+RnuXFV+eM3iibSKvMWdKOHb8eDEKzzXj4Mk6nKttx4UmE44U1eMPP0tzDk2OFJfhbjKZoNFonLcVCgXsdjuUSiVMJhO0Wq1zm1qthslk6nO/Wq1GZ6frMdSoKK3Lffqze/OKIT2OiMgT7o4Jwd23JIz667gc9NRoNDCbzc7bgiBAqVT2u81sNkOr1fa532w2IziYCxkQEY0ll+GempqK/Px8AEBhYSGSkpKc25KTk6HX62GxWNDZ2YmKigokJSUhNTUVR44cAQDk5+dj4cKFo1Q+ERH1RyYO9O3iJZfPljl79ixEUURmZiby8/Oh0+mwfPly7Nu3D7m5uRBFEevXr8ddd92F5uZmbNq0CWazGWFhYdi5cyeCgjw7tzER0XjiMtyJiMj38ERjIiIJYrgTEUkQw52ISII4t8xVioqK8OqrryInJwenT5/GCy+8AJVKhVmzZuGXv/wljh07hrfeegtA70U/er0e77//PpRKJZ577jnIZDJMnz4dL7zwAuRednm9q97kcjm2b98OvV4PuVyOTZs2YeHChT4xnYQ7vW3btg0nT56EWq3G008/jfnz56O6utqrf282mw2bN29GXV0drFYrnnjiCSQmJvZb8+uvv47Dhw9DqVRi8+bNSE5O9ur+BtMbAFRXV+PJJ5/E+++/DwBe/b4cTG8vv/wyTp48CbvdjvT0dKxatWpkehPJaffu3eL9998vPvLII6IoiuJDDz0k6vV6URRFMSsrS9y/f3+f/d966y1x586doiiK4vr168VPP/1UFEVRfP7558WPP/54DCt3zZ3ezpw5Iz7yyCOiIAhiVVWV+NBDD4miKIq/+c1vxL/97W+iKIrim2++KWZnZ3ukh4G409vBgwfFdevWiQ6HQ2xpaXH25u2/t3fffVfctm2bKIqiaDQaxdtuu63fmktKSsSMjAxREASxrq5OXLlypSiK3t2fu72Joii+99574kMPPSQuWbLE+Xhvfl+629vx48fF//iP/xBFURQtFov4jW98Q2xraxuR3rzjI9xL6HQ6/OEPf3DebmpqQmpqKoDe8/31er1zW2NjI/7+97/jJz/5CQDg9OnTuPHGGwH0TrnwySefjGHlrrnTW3R0NAICAmC1WmEymZwXq105BYWv9lZeXo60tDTI5XKEh4dDoVDAYDB4/e/t7rvvxs9+9jPnbYVC0W/Ner0et9xyC2QyGSZOnAiHwwGj0ejV/bnbGwCEhITgL3/5S5/He/P70t3eFixYgMzMTOd+DocDSqVyRHpjuF/hrrvucgYaAEyePBmff/45AODQoUPo7v56sqvs7GysXbsWKlXvZFiiKDon/nF3yoWx5E5vSqUScrkc99xzD77//e9j3bp1ADCk6STGkju9zZo1C0ePHoXNZkNNTQ3Ky8vR3d3t9b83tVoNjUYDk8mEp556Cv/5n//Zb81XTxNy+X5v7s/d3gBg2bJl11wr483vS3d78/f3R0hICGw2G5577jmkp6c7p3EZbm8M9+vIzMzEm2++iR/96EeIiIhAWFgYgN4Luw4fPoz77rvPue+V45i+MOVCf73t378fkZGR+Pe//40DBw7g9ddfR1NTk89NJ9Ffb7fccgsWLVqE733ve8jOzsacOXMQGhrqE7+3hoYGfPe738W3vvUtPPDAA/3WPNBUIN7enzu9DcTb35fu9tbe3o4f/vCHSEhIcE64OBK9Mdyv48iRI8jMzMTu3bvR1taGpUuXAgDOnj2LqVOnIiAgwLnv7Nmz8dlnnwHonXJh0aJFHqnZXf31FhwcjKCgICgUCqjVaqhUKpjNZp+bTqK/3qqqqhAREYG3334bjz/+OGQyGYKDg73+99bc3Ix169bhmWeewbe//W0A/b/XUlNTcezYMQiCgPr6egiCgPDwcK/uz93eBuLN70t3e+vp6cHatWvx8MMP48knn3Q+fiR649ky1xEfH48f/ehHCAwMxE033YTbbrsNAFBVVYXJkyf32XfTpk14/vnnkZWVhWnTpuGuu+7yRMlu6683h8OBkydPYvXq1XA4HHjggQcwbdo0PPHEE9i0aRP27dvnnE7Cm/XXm8ViwdGjR/Huu+/C398fW7ZsAeD9v7f/+q//QkdHB3bt2oVdu3YBAH75y19i27ZtfWpWKBRYtGgR0tPTIQiCT/Tnbm8D8eb3pbu95eTkoKamBu+88w7eeecdAL1/eY5Eb5x+gIhIgjgsQ0QkQQx3IiIJYrgTEUkQw52ISIIY7kREEsRwp3Fp69ateOqpp/rcd+zYMSxfvhwmk8lDVRGNHIY7jUsbN25ESUkJDhw4AADo6urCiy++iMzMzD6X8RP5Kp7nTuPWJ598gs2bN+ODDz7A73//ewiCgM2bN6OoqAjbt2+HxWJBeHg4tm7diri4OBw/fhy/+93vnAvCb968GXfccQeefvppmEwm5/S6ly92I/IkhjuNa7/61a/Q2dmJyspKvPPOO5DL5Xj44Yfx1ltvITY2FocPH0ZOTg727NmDJ598Es888wymTJmCY8eO4dVXX8X+/fvx9NNPw9/fHy+99JKn2yFy4vQDNK4999xzuP322/HGG28gICAAZ86cQW1trXMCJ1EUYbFYAABZWVk4ePAg3n//fRQVFaGrq8v5PPPnz/dI/UQDYbjTuKbRaBAcHIy4uDgAvfNpT5kyBe+9957zdktLC0RRxKOPPoolS5bghhtuwOLFi/GLX/zC+Tz+/v4eqZ9oIPxClegKiYmJMBgMOHnyJAAgNzcXzz77LIxGI2pra/HUU0/h1ltvxYEDB+BwODxcLdHAeOROdIWAgAC89tpreOmll2C1WhEcHIwdO3YgIiIC3/zmN3HfffdBoVDg5ptvhtlsRk9Pj6dLJuoXv1AlIpIgDssQEUkQw52ISIIY7kREEsRwJyKSIIY7EZEEMdyJiCSI4U5EJEH/H5VzTsZX9sQsAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b89827c978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Finally, the situation with 'Year' is similar to 'Price' and 'Mileage'\n",
+    "# However, the outliers are on the low end\n",
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I'll simply remove them\n",
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b898296d30>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b898339160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here's the new result\n",
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When we remove observations, the original indexes are preserved\n",
+    "# If we remove observations with indexes 2 and 3, the indexes will go as: 0,1,4,5,6\n",
+    "# That's very problematic as we tend to forget about it (later you will see an example of such a problem)\n",
+    "\n",
+    "# Finally, once we reset the index, a new column will be created containing the old index (just in case)\n",
+    "# We won't be needing it, thus 'drop=True' to completely forget about it\n",
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's see what's left\n",
+    "data_cleaned.describe(include='all')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+2).ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+2).ipynb
new file mode 100644
index 0000000..aade491
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+2).ipynb	
@@ -0,0 +1,2703 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('1.04. Real-life example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92e3e5780>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92a32eeb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92e7d0d68>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92e501080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92e7d0c50>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92e7dde80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92e8eb080>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92e959ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92e970cc0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92e9fc278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92e95f940>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92ea23b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92eb7a208>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92eb97940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92eb894e0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92ec57128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92ed246d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e92fd27a90>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92ece8080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>14200.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>9.560997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10799.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>193</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.287209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>1400.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>212</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>7.244228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>11950.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>177</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.388487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>260</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>7.824046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>9500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>165</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.159047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>16000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>250</td>\n",
+       "      <td>4.70</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>9.680344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.059517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>2990.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>203</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>8.003029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>26500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.184900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>3500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>250</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>8.160518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>38233.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>10.551454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>132</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>8.922658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>6800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>225</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>8.824678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>130</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>24900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>163</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.122623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>20800.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>151</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.942708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>330</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13566.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>171</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.515322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>8500.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9.047821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>7.972466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>21500.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>72</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>9.975808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>87</td>\n",
+       "      <td>3.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>28500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>160</td>\n",
+       "      <td>4.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.257659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3837</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>180</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3838</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>9700.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>247</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.179881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3839</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>185</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3840</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10900.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>180</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.296518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3841</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>25500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>77</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.146434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3842</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>15500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.648595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3843</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>9750.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>159</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>9.185023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3844</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>16100.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>194</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>9.686575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3845</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>9200.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>171</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.126959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3846</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>5150.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>240</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>8.546752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3847</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>11.512925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3848</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>8999.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>126</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.104869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3849</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>16800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.729134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3850</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>8200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>280</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>9.011889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3851</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>24950.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.124629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3852</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>80999.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>11.302192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3853</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7300.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>207</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>8.895630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3854</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>21335.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>105</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.968104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3855</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>45000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>10.714418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3856</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>6750.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>155</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>8.817298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3857</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>7000.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>210</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>8.853665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3858</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>12090.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>145</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9.400134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3859</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>27900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>38</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.236382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3860</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>2100.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>237</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>7.649693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3861</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>6800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>152</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>8.824678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277     2.00      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427     2.90      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358     5.00         Gas   \n",
+       "3              Audi   23000.0  crossover      240     4.20      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120     2.00      Petrol   \n",
+       "5              Audi   14200.0      vagon      200     2.70      Diesel   \n",
+       "6           Renault   10799.0      vagon      193     1.50      Diesel   \n",
+       "7        Volkswagen    1400.0      other      212     1.80         Gas   \n",
+       "8           Renault   11950.0      vagon      177     1.50      Diesel   \n",
+       "9           Renault    2500.0      sedan      260     1.79      Petrol   \n",
+       "10             Audi    9500.0      vagon      165     2.70         Gas   \n",
+       "11       Volkswagen   10500.0      sedan      100     1.80      Petrol   \n",
+       "12           Toyota   16000.0  crossover      250     4.70         Gas   \n",
+       "13          Renault    8600.0      hatch       84     1.50      Diesel   \n",
+       "14              BMW    2990.0      other      203     2.00      Petrol   \n",
+       "15           Toyota   26500.0  crossover       21     2.00      Petrol   \n",
+       "16             Audi    3500.0      vagon      250     2.50      Diesel   \n",
+       "17           Toyota   38233.0      other        0     2.40      Diesel   \n",
+       "18       Volkswagen    7500.0      hatch      132     1.40      Diesel   \n",
+       "19             Audi    6800.0      sedan      225     2.40         Gas   \n",
+       "20       Mitsubishi   10500.0  crossover      130     2.40         Gas   \n",
+       "21             Audi   24900.0      sedan      163     4.20      Diesel   \n",
+       "22       Volkswagen   20800.0  crossover      151     3.00      Diesel   \n",
+       "23             Audi    6500.0      sedan      330     2.40      Petrol   \n",
+       "24    Mercedes-Benz   13566.0      other      171     2.20       Other   \n",
+       "25       Mitsubishi    8500.0      hatch       65     1.30      Petrol   \n",
+       "26             Audi    2900.0      sedan        1     2.30         Gas   \n",
+       "27              BMW   21500.0      other       72     3.00      Petrol   \n",
+       "28       Mitsubishi   17900.0  crossover       87     3.80         Gas   \n",
+       "29              BMW   28500.0  crossover      160     4.80         Gas   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3837  Mercedes-Benz   11500.0        van      180     2.20      Diesel   \n",
+       "3838     Mitsubishi    9700.0        van      247     2.40         Gas   \n",
+       "3839        Renault   10500.0      vagon      185     1.50      Diesel   \n",
+       "3840        Renault   10900.0      vagon      180     1.50      Diesel   \n",
+       "3841  Mercedes-Benz   25500.0  crossover       77     3.50      Petrol   \n",
+       "3842     Volkswagen   15500.0      sedan       80     1.40      Petrol   \n",
+       "3843     Volkswagen    9750.0        van      159     1.90      Diesel   \n",
+       "3844            BMW   16100.0  crossover      194     3.00      Diesel   \n",
+       "3845     Volkswagen    9200.0      vagon      171     1.60      Petrol   \n",
+       "3846     Volkswagen    5150.0        van      240     2.00      Diesel   \n",
+       "3847         Toyota  100000.0  crossover        0     4.50      Diesel   \n",
+       "3848        Renault    8999.0      other      126     2.00      Diesel   \n",
+       "3849  Mercedes-Benz   16800.0      sedan      125     1.80      Petrol   \n",
+       "3850  Mercedes-Benz    8200.0      sedan      280     2.40         Gas   \n",
+       "3851  Mercedes-Benz   24950.0      other       60     1.80      Petrol   \n",
+       "3852           Audi   80999.0  crossover        0     3.00      Diesel   \n",
+       "3853  Mercedes-Benz    7300.0        van      207     2.20      Diesel   \n",
+       "3854            BMW   21335.0      other      105     3.00      Petrol   \n",
+       "3855            BMW   45000.0  crossover       80     3.00      Petrol   \n",
+       "3856        Renault    6750.0        van      155     1.50      Diesel   \n",
+       "3857        Renault    7000.0        van      210     1.50      Diesel   \n",
+       "3858            BMW   12090.0      hatch      145     1.60      Petrol   \n",
+       "3859            BMW   27900.0      sedan       38     2.00      Petrol   \n",
+       "3860        Renault    2100.0      vagon      237     1.90      Diesel   \n",
+       "3861        Renault    6800.0      sedan      152     1.60      Petrol   \n",
+       "3862     Volkswagen   11500.0        van      163     2.50      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35     1.60      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9     3.00      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1     3.50      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124     2.00      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "5             yes  2006   9.560997  \n",
+       "6             yes  2012   9.287209  \n",
+       "7              no  1999   7.244228  \n",
+       "8             yes  2011   9.388487  \n",
+       "9             yes  1994   7.824046  \n",
+       "10            yes  2003   9.159047  \n",
+       "11            yes  2008   9.259131  \n",
+       "12            yes  2001   9.680344  \n",
+       "13            yes  2012   9.059517  \n",
+       "14             no  2001   8.003029  \n",
+       "15            yes  2013  10.184900  \n",
+       "16             no  1998   8.160518  \n",
+       "17            yes  2016  10.551454  \n",
+       "18            yes  2006   8.922658  \n",
+       "19            yes  1998   8.824678  \n",
+       "20            yes  2006   9.259131  \n",
+       "21            yes  2008  10.122623  \n",
+       "22            yes  2008   9.942708  \n",
+       "23            yes  1999   8.779557  \n",
+       "24             no  2011   9.515322  \n",
+       "25            yes  2010   9.047821  \n",
+       "26            yes  1989   7.972466  \n",
+       "27            yes  2007   9.975808  \n",
+       "28            yes  2008   9.792556  \n",
+       "29            yes  2008  10.257659  \n",
+       "...           ...   ...        ...  \n",
+       "3837          yes  2011   9.350102  \n",
+       "3838          yes  2008   9.179881  \n",
+       "3839          yes  2011   9.259131  \n",
+       "3840          yes  2012   9.296518  \n",
+       "3841          yes  2008  10.146434  \n",
+       "3842          yes  2013   9.648595  \n",
+       "3843          yes  2009   9.185023  \n",
+       "3844          yes  2004   9.686575  \n",
+       "3845          yes  2008   9.126959  \n",
+       "3846          yes  2005   8.546752  \n",
+       "3847          yes  2016  11.512925  \n",
+       "3848          yes  2012   9.104869  \n",
+       "3849          yes  2008   9.729134  \n",
+       "3850          yes  1997   9.011889  \n",
+       "3851          yes  2013  10.124629  \n",
+       "3852          yes  2016  11.302192  \n",
+       "3853          yes  2003   8.895630  \n",
+       "3854          yes  2008   9.968104  \n",
+       "3855          yes  2011  10.714418  \n",
+       "3856          yes  2012   8.817298  \n",
+       "3857          yes  2005   8.853665  \n",
+       "3858          yes  2010   9.400134  \n",
+       "3859          yes  2013  10.236382  \n",
+       "3860           no  2001   7.649693  \n",
+       "3861          yes  2007   8.824678  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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M1wefW2YU89yA4p5fPuc22tamYv6dM2EkfZ6R9FmAkfV5CvFZaGtT2k7ZG2+8gZdeegnr1q1DeXl5VhPjcDgcDofD4XA4nNFOWn3KQqEQHnnkEXg8Htx+++24+eab8dRTT+VrbhwOJ4cIgRA6ez0QAiHieI/dSxzncAoB67rlkOHf3ejBLwaJ6zdf2zmc4YGsSFltbW28nuyLL77I64Q4HE5muLwi2nrcqK02wVyijf89FA7jpQ+OYOchG+xuARaTDgtmWHH90nqolErmOIdTCGLX5ecHuuDyBmEuUWPhrDFFdV3GnJ5QIASdpnhqf2Lf3e5mG/pdAirMOsxv4Pf0SCQUDuNv7xzC3qN9cHkDqCiN/tZXXTAZTreIjTtOYu/RPvQPCCg1aTF5jBk3XDINAFBm0hXVdcvhjHaGpHk0h8PJH2IwiEee3YV2mxvhCKBUAOOtJtzzrTOhVavx/PvN2Ly7I/56u1vAxh1tCIbC+NbyGcxxDqcQ/PWdQ9i6ryv+b5c3iI072uATgrj1q7MKOLPid3pe+uAINu5oi/+7b0CI/3vlsoZCTYuTY3xiAD968hMEQ6f12mK/9Sd7O+EXkyNjTreIPUf6sOdIHwCgwqzDzIkW3HhpA0p03BzkcApN4Z8eHA5HFqQUlIef3YmTPVGHDADCEeBkjxsPP7sTQiCEjxIcrkQ+2t0Bl1ekjvN0F04hEAKhJIcska37ugp+Xcacnr4BAZHIaUP4pQ+O5PR9Uu95OWloQiCE3c02ybHdzb0F/+44ueOup7clOWSJpDpkUvS7BGz9sgt3/P4TrN/YjFA4HB+Tuvbaelxos7n5NcTh5Am+NcLhFDlJu/IDQjw95fql9fD6g2jr8Uge19bjwYHjfSD1vIgA+PxAF3X8ROcApk+w5OBTcIYzQiAEp1sYsnSnnYe6meOLZo/L6NzZfhaW03PNkqlZf0ep97zFrIXRoIXXHxi0BqRG5pxuAf0DguR57S4/nG4B1ZaSrObHKTx9Th88/tw4R34xHI+kXr+0PvnaK9WhRK+Gze6FEIg+LfRaFRbPHoMbLplWFJFhDmekwJ0yDqfIoaUinTGZ3pLiwIl+6vjxTroUbHe/lztlo5iYc7CrqQf9LhEVZi3OnF6d9zS9nU3STk/ieLpOGW1zI53PMhROT+o93+8S0e8S4/+mpSOWmXSoKNWhT2KOFrMeZSZdVnPjFAdNrY6cn3N3cy+CoXBSOnv/wODr3S+GsGlnOxQKBU+H5XByCN/i4HCKGNauvEqhoB5fazVRx2dOoLe1GFfFd9RHMy9sOoyNO9riDkG/S8TGHW14YdPhvL7vhBpjVuNSJKUcIvOUw5jTI0W2Tk+f04ctje3YcVA6dTMVqXREnUaF+Q1WydfPb6jKOIrHFfyKi/HW3K/NfQN+fEpIG5Zid7ONXw8cTg7hkTIOp0iQSqti7cqz6gZYD8wdjIjE5/s7UV/LI2XDgVynGAqBELbula433Lq3E9ddVE98n2zncrzTndW41HxylXIYc3oSI1kxMnV6fGIAdz/zKdy+YFrHkSJz1y+tBwDsarLB7hJgMetw5nRr/O+A/N8oVxFGTm758lhfzs9pMqjTugb7XQJPh+Vwcgh3yjicHJGpPDbN6Ckz6VBu0sDuDgw6rsyoJe7Yxyg10sdbuujpi/uO0dMfOYUnX0ZzYg1JKkIgDJvdi9pqc17m0mGjO12s8VRynXIYc252N/fC7vLDYtZjfkNVktOTDnf+fhu8QvoRB1ZkLhZITwyop5uSypUci5Ndzb05P6ffn96mQIVZx9NhOZwcwp0yDidLspXHZhk9AYK6ViAUxjirCTqNEkIgPGhcp1FiWm0Z9b2VisHOXiKhUHoPac7Qky+jWQwNvqZY47mai06vACj7BTo9PW03lVzXWamUSqxc1oBrlkyFSqtBSAxkHJ3sc/oycsgAcmSO9juEIxF8sLM9PhZLSQ1HIvjmpdOTzjMUoiaczNCqcx+lDJJUnwjMb7Dy35/DySE894DDyRK58thSNRkso6fP6YOXsHvp9QchBkI4f85YyfHz54xFKEx/yuo0Gup4iV5LHecUlnzKn2vVdGMrdTyXc5k/tTqr8VTyVWel06gwtsqYVZ3WJok0SBJKBaAAUFmqx7KzaiUjc/TfwYatezslx7ZJtBqQE2HkFIYgY23PNcqEfRC9VoVLFoyPX3+83pDDyQ08UsbhZIGcnWS1SkFM6WIZPU2tDpCeveEI0Nbjxg2XTINCoZBMRwqGItCoAKlnpUYFBMP0iINfIgLHKR7yqQRoLTcQo7B6rRLWckPe5iIytuxZ41LkOuUwGxKj61LROxIRAHfcMA9TxpcRHUHa70B7L78YGpSSypUcixevQM9yYFFqUGOAUj9m0qvg9odgMWmxYEY1rrpgCvqdPkChOLU2qBAKh7F+YzOvN+RwcgR3yjicLJBjiG7c2UZMJbpmyVSq0TN9QjmUCkg6ZkoFUFt9Wl0xAkXS/2OQNi8DIeD/O7sO6zcdJX6+yxZOJI5xCk8+jWadRoWqcgPabYP74FWWGQY5Bbmcy9z6SrxHiSDNra+Ufa4YiSmHQ9lzTYrU9EK5VJj1VIcMoP8O5SYtHG5R4qhTpKi56jQqzJ1WlZTuGGPutEqeulZALjtnIv78TlPGx5OaTseI9UCLXRI6jXJQDSkpTVYMBLFw5hjUVptgLuHZFhyOXPhWBoeTBSx5bINOTY2kAaCmVVWWGTCuSlr+e1yVEeYSLV48JVtud0WNMLsr+mB8cdNhnOh0UuevUtGNqgljS6njnMKSr7Q8IBoF7nV4Jcd6Hb68SrF7GU1xWeM0dBoVqi0lBXMoaNF1FnK+R+rvMK0Keq308XqtalD0EwBIsfT0qvo4ucZakZ3ioVcMgRbMirlssZpDqXR80nW8pbELj7+4Bz9Z8wlW/fkLiEFem8zhyIE7ZRxOFrAMUZ8QZEbSrl9aj2Vn1aKyVAeFAqgs1SXVizTUSYt1NNSVQQiE8AmhRmTrvk7sP05XT9zT3EMdP9aW+walnNxy+vrRQ6mg1xulgxz1xXzN5WQPfTOBNV7M0KLrJNL9Hkm/w8pLG7B49hjJYxbPHjPI4RMCIew5LK3yt+dwH68hKiAnOgeyPkc4jez01LpQOddxOAKc7HHjkWd3ZTpFDmdUwdMXOaOGXPdxikGrVQmGIrJTuiKRCCKR6P8T59x4RLofTeORfiw+wy1Z8wMAfjGMAQkp/UT6BiipTABautOTHucMPXlLy2M0Jpcaz9VcDrXQNwNY48VMmUkHjVohuy7um5dOw9kza9JKA6OpQ8ZqUKXUYlPJZ81iOri8Itp63DwdLgEdQ4gn19hdftgcPmjVShh0aojBMCxmbbyxPI12mxsur8h/Ow6HAXfKOCOeXPVOIjl1NANIpQSz0ez6jc1J47F0EQBYtqCWaBT1u/zotvuoc55WV4Yt+6QjadFxMzr6pVPUAMBipqszcoqHWFperqAJfegkhD5yidGoA00T38jov1fspCNU8tz7h/HO560ZrVk6jQrWKiNsttPfZTqOc6GFPsRgEI88uwvtNjfCkWgd7XirCfd860xo1aPbfCGloeYLrUaF3760G3Z3gFjnTCImSjVzUgXztbHnrLksf+sLh1OsjO5VjTMqkNs7ieR0yXXqxEAILk8AZq0y6fjTkbTBO9NCIIRdTdIphLuabFixaBKUSoWktL1SoYDbS9+lDATp+SlzplrxUWM3cXzy2HLq8ZyRgcsrouOwDWatMr6brdOooFJKR8tUCoWkIZ9uY2ISc6dUxGsuSeOZkq+IuVxaMkg7y7TfG62hvRwnXqdRYd60KmySEPqYl6XQR5/Th30tDowr16GSYIA//OxOtPWcFpqJpcM9/OxOPHjLwozfeyRQk2VNWbr4xRD8YjR9MV01fgUAjxCIR8uk7sHU56zVYsCcqZVcyZEzquBOGWdEk61kvUqpZDp1cndzpdITnW6BmP7R7xJgc3iJvcZC4QhauikddgHsO042bAHgWAe9NsdDkUzmDH9i1+7JntNpqnXV0WtXEMPEpsZeISSZjvTCpsOyGxPT6BvwZzUuRa4i5tlyqJVe50lDbsPmbBvaJ0KyvzPtkuUTA7j7mU/hTlhbTAY1fvWD82DQno7MR1MWByt/AkBbj2fUp8OVGofPZ48AePq1/VAAMBrU0GlUg+7B1Odsj92X0UYEhzOc4dsPnBGNnJqIpObPSG7+LKchbsyojflOqcXNsfPHnK9ENSuDjr4vYmfk63t89Jqx9h56TdhOhtBHZy/d6eMUD5k0cH3gL9uTHDIgeu0+8JftaCQIPMRIHRcCIWwjpMpu29eZ1rxsjLRc1rgUtPt8KGFFr2nIbdgst6E9CyEQIl4HjRkKfaQ6ZADg9gVx9zOfJv3tOCOiyBof6ew7Sr8/i5EIor916j34f/88SMwYSafxPG9izRnu8EgZZ0TDqolgSdZfOGcs1alr73Wj3Sbt+LTb3Ohz+qjnX9BQRZ1/KEQ34Cwmes1XmVGHHifFsWPYh30yirg5hSXTCJDLK6KzT9q56ezz4aSN7pB3O5KPtdm98Itk0ZnUxsQ0NBr6fiFrPBU5EfN8pDJKpWnNmlSBf352MqPzyanjyuVnzbXQR5/TN8ghi+H2BdHn9MVTGXWM35g1PtI53jVyNsw+209OoWddZ0IghP4BPzbubMPeI71prYFS92eh05s5oxvulHFGNDHJepLQBkuyHgoFxanTwe0Rifn14QjQ1Oqgnp8l1NHjoI/3MtQTwxFGkhFDYM8v0CNxnMIjt2Yylc/3d1HPKwbpu83TU1o1sCJA6USIqA2OZYynMtQqgjRHmRX9pjFnagXTYMzlZ6VvaunSFvpoaqWrZja1OrBodtQpMxro6Xms8ZGOuWR0iDBpNSqYJOrQEu+x1OszdQ2kHRu7P+dOq4ICwJ7D6Tl2HE4ukeWUNTY2YvXq1Vi3bl38b48++igmT56MG2+8MW+T43ByQeaS9TpYyw0o0Wskx0v0GkweV0ZUolIqgOkTyomyweUmHSbWmKhzryrVU8drynXYRxmfMbEcRzvJKYyiSHe6aMqMnMKTTVSk8ah0q4UYPUxlT0vSvyOMKiPWeCJGA93gZI2nki8VQZKQBs1RvnjeuLTfx2LSwlSiReORXny4u4MqoJLLz6rTqGDQqwGJcxn06rQjCdMn0IWDEscLqf45HCA1dh9p+MUQfvX8Lnj9gSRnKRyJJNWvSrG72YZQKIy9R/uox/YNCIPOlam4DoeTDUynbO3atXjzzTdhMEQXwP7+ftx11104ceIEbr311rxPkMPJFpoEtEoJqtMFAB6f9M62xxeAVqPCOKtRsiB9nNWIyjIDjAZpp8xo0MDJ6CP22QF6NMPOiBjUVNKdPgUjVKZSMUJpnIKSTVQkEqJHwiLBMJRK6QazUhvHWg39cUIbT93J1qroO9Os8VRyrSJIE9IIhiL0lOi56TtlXiGYdK/TBFRY2QHpfFYhEJJsEg5E6/oECVVHGpVlBpgMaskURpNBnaTCqNOosHj2GHywq2PQaxefMbjR9WgjEinM2qxA5iIvmZJY9xpzlvRa9hrQNyDgw90dSf+We2yMfKY3czipMK/MCRMmYM2aNfF/ezwe3H777bjyyivzOjEOJ9fEJKATF1chEILLI63k5vIIsNm9RHVEu0uA0y2gvrZMcry+tgxCIERx6kSEpCzeBHwCXf0wzHCqDFq6oVxeRk9jqq00Usc5haXMpIOO0K9Iq1FRoyLja+j1XZZyvaRDBkQdtVRjPRbZkEKnkY5shMJhrN/YjHvXfoaf/+9nuHftZ1i/sRluhoCNh3FfSJFLFUGakAbLUUYkGp1PB1KD+G37uiRFDa5fWo9lZ9WislQPpQKoLNVj2Vm1kg2iadjsXggB6W9ICISJDhuNR793LtQpmz1qlQKPfu/cwS8mNTBnNTYfBUyrLUy7kqF2yEiQ6ldzfaxccR0OJxcwI2XLly9HW9vpHbe6ujrU1dVhy5Ytst/EYimBeoi7z1ut8grKCwWfX+bkcm6dvR44PNIGnsMTgKXCCINOBZ+ENLhep0LtuHIcON4oefyB43YElyookvcinH56tGJyrRmH28kF3RfHiFWHAAAgAElEQVTOr8Xuw+Q0tGmTKqnnnzGlAkc7yOefOcVa1NdCLhjq9SmX36dfDFLt1qoqE/QEx3xWvRXv7yCn/0wcW45P90tHfADAaDYM+iyXLpyItz45Pui1ly6ciNpxg43Ita/vk0zzmzWRbnDOa6hJ63v0i0F8eUxaiv7LY/0wlxmI35PUufYSUj/3Hu3Dt1ecgapyPWyOwZs9lWV6zJxWjfPnjsebHx+TPX/yXEIIKpSolfgufnTjAvjFIOwDAiylOtmfLxEPow7QUmFM+3p+/fV9CIaSTftgKIKNOztw21Wz43/L5W82XKGtTbOn1wCbhlY5dDRSVW7A1EmVWV9rI+05OpI+TzF9liFZ0ewZ7KZlg9Vqho2hHFZI+PwyhzY3lmqS1LjTSa+bGXB4iWIZkUgEx1r6iPLcvQ4funvoss1eQpQuxqFjdur4v7bRDbtPdrVSx5WMbU+LUUu9FoppMcuUoVyfcn1v9di9khsGAOAXQjh6oo+YvtjXR2+X4GDcG3a7GzZD8iPkykUT4fcHBqX1Xblo4qDPLQRC2Noo7RSyBCEOneiFzVZDfU0iPXYv9T6lfU/pnqutw0E04PRaNVxOH1acNwFen4jdzb3od/nB0uOhYbd7YFSTI0djT11zmVx1fg89QuD3CGldz7TffGtjBy47py6+Nmf7m430tSng58q4Q8GcqZVwOX0Z3T8xcr3uF1ohsphtxHQpxGehrU0je5uJMypgSYLHxnce6obdHYDFpMGCGTW4fmk9TjBkhQ+02CFQZL7p6ox6ZpqNlZE+6BPpkbROhhCHl5HmZdTRF3SVkqcJFTNlJh0qCddfRSld1OFgC93xae2h3xtSNWK0+s1UaGl+IYaT0mNPr3l0mUlHEdzRpiV+IafNhtcvnX7p9QfidVgrlzVgxaJJON7hxO9f24dMWiuplAqq4AVJiEQu7b3SzZsTxxPrwFikUwMZS831S6yBrNTc0YDVUkIUmeKkh1IBXDBvLNRKJfYc7hskCJZKoZyiTNufcIYP3CnjDHtYkuDPb2zG5oRicbs7gI072hAMh/FvEyqo59apVVR1xTKjllpUz+JEl5M6XmXWw0bpMzatrhw7m8jpi+VGumx0aQndsLGW09UfOYUlG1GHBQ1W7GwmN6A9d9YYHGghX5+JzkCqkRKr36RBc25YiIH0WjXoNCqIhFQ8MRhOy7DSaVSYXV+ZtKbEmF1fAZ8QJH6mvoFoHWplmT7JuMrUrk6tzYoRM952NfWg3yVS1RppmAx0E4E1nkr6ypDc46DBHTI2F84dgy+P2dHvIq8zEQCXnTMR1ZYSXHsRuXeZqUSD1z8+XjCnKNP2J5zhg6wVtba2Fhs2bEj62+23356XCXE46cCSBF+xaBI+3jPYeAKAj/d04KsLJ1LPP85aQu1D5hOC8Z00KQOos48eyeplpIj1MwqMuxmRss8OkJtyAsDO5h7quMMtYizbt+QUkGsvmoKmVgfaetyIIKqOVlttwrUXTaEeJzIak5OcmESy2bmlOZQs2nrSSzl1eUV4/dJRY68/CJdXhLlEft+rI23SzuqRNicM59MfqwadepBxlSliICypsPnCpsNJEt80tUYa461mqgLn+DRTBNPZRHC6BaIggyCGct5bbrhhY/Sw5EQTVW5cNh0bPjyCD3eR62d1CZHXxA2l1PUtNXI7lE5RLpvCc4oXHu/kDGtY6TDNrQ6QbM9QGHB6BGIKn1GnwnirGRVmaWOt4lTz1FA4jKZWR7wprN0loqnVgVA4DJaifK2VLlnPyh4MMHKeaiz01KIxFfTxAKOBMGfoEAIh9Ni9g9T2Nnx4FCdPOWRAdNf3ZI8bGz48Sj3WzOj1NeClbwgcPulIViFEsgqhHGIqgbF7rMKsxbKzamFiZMQZ0mxR1dbjpm6utPXQ6+sScXlFyRYY0ffxoKOXfi6bw0s0rlLRaqILAGkZkEpRFQIhbNvXKfl6klojCZ1GhbEV0o7P2IqSjIxAucqQsdRcKVipuaOBls6RUdOTTyKRqILo3iPkjIDo66QXh9T1TSqVFog6RencV5kgJ/WXM/zh6YucYQ0rHYahGI9+lwi9Tg2PpLpi9PaY12CVbFI579TO7n3/9znabaeNtJhR/NDfdjIbxZ7ophtwZUYdOu3kxdZcokW3g5zeWMWo92i30SMOWxo7MKfeSn0NJ7/QolHBUIRqgH/9winEdJteJ70u61g7XaSmqbWfsnNrw4VzxsJqoRvusQ0Nx6keXA53dEPDpNVQZfFNOvlRLSAaOcxmPBGWA5e4Fkhhd4nMlM3KUh1K9Bq4vQLEQABaQhNlqRRVm91LjDD5xWjfsdpqeREuIRAi1rX6xVDafcqA5LpDlVaDkBgYdI5YuticqZVJfaZipNtvbSSy/7g8x3608z+v7WPeb0IgjOfebcK3L58Rj/DTIlOpsHpC5oJcNoXnFC/cKeMMa1jpMA11dGntilIdZfcpWv9BlEaLRODyikQjrN3mwQBDvYxVM2Ep0wGULCcjI9rhZSh0RRhFCSqKqhtnaKDVEVw4ZyzVAF/3bjM+T0hhTTzWS+ifFyPEuDZqKo3Ee6dvQMAv/rwdlYx0xkee3ZXUGDYciW5osOxtqyW9/nkiYxebNZ5IuYnuEFrMdONIL8OZcPsCScZXzCHTa1UQAyGqCAGzh1caPb5ou/Ox+rhMDVGdRgVrlTFJ+Sy1Fq7cqIZapUiS0DcZ1Lj6wskZvedIYsq4cnx2kB4B4sgXBdr6ZRcMenU8DZF27acyFE5RLpvCc4oXnr7IySmkFKt8cu1FU1BXbYqn+ikVQN2pmhpziRa1VmkDrtZqxPgqE7GRq8Wsg0GnxtYvuyTHt33ZhS+Pk0U2AKCFsasuEhqzxnB56eqJAx66YW1z0h8qYYZTWE1RdhvNDNV1Tq8jsDHrwg6ekL4+dzXZoGTk1lYyRF7mTq1iNkGmpTNGNzSk7w/W12pj1GKmwpLYZ40nEovqkWD0gyemQCVCahZt1Ktx/3fOxsO3LcTKZQ2Sjq613AC9VvrRrtdKN/EmwVJfTRx3eUUcPNEPlzdzqfYXNh3Gxh1tcZVMhyc4qKeZ2xfEL9ftyvg9RgrT6soyOu7rF07K7URGEJ/s7YRXiEboY5EpOQiBIBSKSMrfcv+MyFVTeE7xwiNlnJxQSKnWlzcfk9xtf3nzMaxc1oD6ujK0SUSz6uvKoNOoIBCMJEEMwekRqZL4rJ00kSAuEMPnox+vVNCdJp2Wvjtm1NPHWQbmUUYK22hjqK9zp1ugKvlp1SroCKltOrUSAwSnvt8lwO2hKxgKYogq8qDVqGQbHFKF6LQ6LxYBGY5NItMn0CPmrPFEqhl1muYSevSadc/S6B8Q4PIGYLVQzq9RobLMIBnBrywzpLWjfryLfv8f7xqA2agZFPGsqzbhnm+dCa1avolBq4VLpc3mSVucZaTR0iW/DjKRV7ecyO1ERhB+MYT17x/Gd782CzqNCiV6jSx1WLcviPv/vB0//+YCuH0BbNxxEnuP9sWfEYvnjseK8ybIekbQ5PbTaTnCGZ5wp4yTE3Il1Zpu/w856ov7jkhHC/Yd6UffQh/cBMfJ7Q/CSZHRBdhCG22MBr1tvfSaLg3jO6hkpErpCE1sY4yvNuIkpa5sHEMIZLQx1JLEBp2a2ZJBrZJ2ytRqJQSKgqKacXuJYogY9QmHgROdA3D76JsOMaRqLtKp40olJoAhl8oyA0wGteR8TQZ1Wr22WNFpD6FHWYzeLFTzFApg9Yt7qJsBQiAEm106pdp2auderiHXS2jenDj+0F93oD1lHTvZ48ZDf92Bh757rqz3ic2NlIorRfNJBxZMr5b9+pEG70uVH/Yf70ef0wefEISbkeKdSFe/Dz9Z88mgtbpvQMCbHx+D1ydSnxHpbPjRWo4UurE0Jzu4U8ZJC6kbPhdSrXIWJKlGqCxForYeN3V8//F+6ryOttP7iB0+SR8XBIaRwYgU+CUESBKxD9AfGh3ddNEBWx89397LeP/RRCEkiX1CkKoaaHP64CFsKpD+HqON0Ri4m2DYx2hhRFESkaq50GpUUCDTTlSnv2e5RshDty3ET5/amvR+ilN/TweXl+50sYQ++gYyd8pi1wJtM8Bm90Ik/PRiMJKW0EcPw4Fs6/UMcshitPd6qdGsQet5GrVuAEZ9C7PWHp7FkA+cHhF3PvNpRsfSIv+sZwRrw4+1zvHG0iMD7pRxZEG74eVItbKKwWkL0vVL60+/t0tAhfn0e7MUiWqrTdRxVs1Et50e6XL56U5NmUkN2wDZOK4q16Ojn3wOlp0iMgpY/IxeU0ol3bIp0fMlIkYurvN0KTPpUGHWxmtsErGYNDjekblhFgrSf3sFQ7rUzGhMnkhqIboQCKGp1Z6xXa1VK9I2Qn7zwp5B7xc59fcHb5XvmE0eV0odXzDdilc/Pk4cr5PpECVCipZKGXrM/nOM8URmTKzA5j3klEIToXYtRnOrAwtmJEezkn63hPX8qgumQJ/SB4pGQxoppyMRlkgTp7igPSNYtcOhUDgpHVJqneONpUcG3H3myILWj4hWEJu6Qy5V/MqKQKx/v/n0e0eS3zumSCTF/IYqmEu0mDtNuvvx3GmVsDN6ewj0TXGEQ3TDldSwNgZN9htg78orGPbVxDH0FLFFs8dTx/9tCu8cHSOd6zxX6DQqlBAUNge8ATz//uGMz13NSNmLFZNLoVQADbVso9hi0iYVoofCYazf2Ix7136G3/19b9pzjtHW602rR5rLK0rWlQLR+qRjHQ7Z9XFaRjS04lSqpBQmgxp6RkpxKretmEkUgM13f6JxVXSVy4pyhgqmxPWT9LslrOevf3wMi2ePkTWv8VUlo7qeDABmTq4s9BQ4aWA51ddUCpbK6Ye7O6jrHMuGGkrhNU52cKeMw4R1wwOgOkY6jSrJGPv5/36Ge9d+hvUbmxEKh6kLUr/Lj92HpWV/Y4sNS5GI5DYpAExl7HrPnEg3PEMhutPFKD9BKExfLFmK9BHGHVxeSlfQqyyjj2tUfImIwdoAyEf+frQ+SDqFTE7AgySwqFYpYCyhOwdarYr4+2tUSmL/qkRifcdCpyK6L55S15NTPE8jFAZ2UXaWU42Q5pN0dcWHn92VtCbRsDFS+jpsbuK1oNOoYGWoWiZi1KtQV21GuUnaMS83DTb0AgTlRrnjiRgYoiTTJ9AVAFNbkrCeJVdfOOXUWq6DQhFtJp7q4NZVm3Dft8+SMfuRDV+bhxcefwCvfHRUcn2hbfiRNsYSnS26U+dH/4C8tgCcwsNzk9JkNBZRyknbijlAu5pssLsEWMw6nDndGv87LbR+zZKpxBTDcqOOGM1KTAdYuawBKxZNQluPG7XVpvguqhAIYQ/BqdtzuA9VDKeE1WdMpVMDIEezlABoJlCQJf3NqD+xMxoAC4w+Za3d9PS3HrsXAN+RjRG7nnc398Lu8tP7ReUAm8NHlEeXAynDKRSO4BijJqyly0UUChGCYSASgV6rpIozxBqpP/y3nbjnW2fhk73y1PVYaFVIq3+WHEeElO6TuuazxH/abR5q70OWUxdDrVJAp1Vj1f99ASXBMjMaNIOeQ/2M+bHGE2HVx/UyWm6kRhVZzxK3NyCpLtfn9KGp1YHpE8rTEmUZyXBDe3jhF8PEdEK1SkFUeiSt4Yn2T5lJBx0l9XfjjpO4efmM7D4AZ0jgTplMRnMRZTqd5GM1UIm1UEIghF1NPZLn3tVkwzVLphKbIs5rqMLeI73U98603q3f5Yed8WA72kY3XDUK+m9fWa6GzUGpKbPo0G4jGzaszdAIoyrHzpI9Z6Q1VFfktkZquDPkksSkvDUZaFTkfl+RCCCKjNRZRmotqx4zkTabBy2dA1k5mInUWHQ42Uu+b0IpYUSLzH5DwOk6LbVKIbmujGPcE2IwRF0vTYxavK9fOBk9dh8+2dcVX7tIjby9/sAgNUUto/8cazwRlrx/gBGutTl8qLWeTqGW+yyJqcvFMixG43OXxbTazPqUcXIPqeZTisQ60NiGz7tftCa1lIhRazXC4xNhdw9eiwenQ5In0HikD99YKl91lVM4uFMmk9FcRCmnk/z6jc3E72fZglpJoQIgumubGGmTikColIqM3/uaJVOhVCokjRqlQgE/SabsFG6GvDVL/loU6Su1yFBnVDBWerWabpiwisFVDKfSqONLhBQ0SeJckk2dWoTh/2hVKgBkp5xxa2D3YVtaEuaH2+U3aGbBaop+uM2BsVWnnQFjGoI1sR3ojTvbJNeV8+fQ657m1lehy+4jrllWRqTnvH8bg8eel9ccud8l4Fi7E1PGl8UNroYJlCZmMsYTGWel16RWs1IxUzYV5DxLEhnNz10WJEedM/TEfgqtWgmRIa5ld0XTCTfubMOe5l443AJR0KvX6UeAkE5Toj8dJXe6BepabHcNzh7gFCfc4pJBIaSwiw2a08T6fpafXUc9t0GnpkYgrr1oCppaHWi3RZvNKhXAeKsJ1140RdZ7kx5eoXCEuRNcP96MVkofr/FVJvQ6ybL6GrUSNMM3GGI4bRH68aTG1jG8At2yLjXSP7+G4fRx8ouP8fvRYIgryqoJo+F0i6gkRD2kELN8v0TKzVp09ZNTc2sqkwUorGkYIxazHgadmriuHDxhpx5vKtEmrJeDFWM7++i9Cdt7yemPqSgAPP7iHlhMGiyYUYPrl9bDXKKFWglI2YZqJdISyHB76enPBq2amMKq16okv3e5KcD8uUuH1fKCM/SwHDIgGuF65vUvk4SHSAkRNCVSjy8aJVerFHj3i1bme+ZDiIqTe7hTJoNCSGEXGzSnqc/ppX4/rLoEp1uIGwpSEYi/f3g0KbQfjkTrVP7+4VFcelYd9b2bWum784dO0MdP9tDnXllGX+jCDNEAgRGO0KnCoInyGwxKOCk2npIRCevodVHHXQyjjJNfUtPwUsm8zxegyHKj3VKqJUY9pEhnZ1+rAmg+nMjYjKhKEbhpaqH3I0xkfkMVfEKQWrNGo73XjYk1Udn7SCSCSCT6/ziMlFSTQU1M8Usl9pXa3QFs3NGGYDiMq8+fIumQAVFHjdY7LJFQOIw/v32Q+prDbQ4smj0WH+xsHzS2aPYYSacp8Vmi0moQEgOSr3O6BeJ30D8wOp67NE5209duTnGiUimISrDp4HAL8Yj+h7s7qK+dW1+Z1Fd2tOkiDCf4NrgMCiGFXazEnKbEm5n1/bCiUYmx+1TJfCEQwtZ9XZKHbd3XBYNOTX3vags9vUbFqK8QGTUwTjfdaQkzDFFWNMMjMNIXFfR9FRVjzd13jO6Ufrq/m34CTl453kk3vM6eIa0GKQdNmtLsqbg8wSTlUxZ+GVE/BaLqehPH0Ht5aRmqgCe6kr+3tz9rYb53omorbU1jrWdujxhPu4ulbfe7xLiMtdVSAj2hv5deq4RWrcKc+sxaUWzZ04EmhtLk8Q56w/sYL31wBAdb6OcyG3W48ZJpWHZWLSymqKMXa4Nw4yXTqMfqNCqMrTISDcMyk474Pem0qlH13JWioy97w54z9PTYcyPQworoJ7LsrDqqAjaneOBOmQzSkcKW6sM1nBACIXT2etKaP+v7GWc1QU8wovRaFazlhviCcc8fP8XP/vcz3PPHT7F+YzO6+r3EEL5fDMHpEanvvfeotPJijAE/fTdayVDaUDDEDlhOHUNRH6ySLlYkjlUMX1lK3zGfMcobtBaa6Yzvf9lZtRmf2+3NLv3J7RPjUY+Hb1uISxeMo75eJ8MJjKk1Or30+9Kop1+3qXUYFWa6AT9vagUevm0hVi5rgEqppK5psybSa7JK9GRDKfb3RbPHSo5HIhGs+vN2NB62oa7ahMpSHZQKEJ2TVMJhwM5QbJUjkEFLHUwkMSVVqYy2GycpRWZGLs81spg9havijjRit06ZUQudhn6fsiL6MSwmHUwGTVp9HTmFg6cvyoSVBz/c1RmT5p9SAyFn/nShDiUWnVGDD3YNDrEvOqMGOo0Kz73flJQCE9tZdjEk6RGJ4IrzJ2Hz7vak+iy1SoErzp+Ede8coh4+4KILdbhFeiRsQrUJOw6RHb+qcgNabWQjyVSihpNiHGvUGtAk9zWMiIGZUTNWX1uOA61khckxjOaxnPxSeaoRsds3+BoxGdSor5Uv2pCKgl6uyGTetNNOi06jgqmE7viwUjETsdnp9x1LgMaSErlbNHssPjtIdjKWLqgdFLEhrWkVJi0+PyitJgsAewhqscBpuf5vXDwVh08643WyMYRA9B/9LhH9LhEXzx+Hi8+sxcN/2079vIkMMJxtOTvjtJT9RGIKlfkQ43C6BQiEDTnxVArWaE5frJfRvJ0zvBhTWQKfPwSHW4CO8mzXa1WIRCIwlWhgMWuJQmoAYHcLeOAvX8BDyFTY3Wwb9fWZxQR3ymTCksIe7ipR2c6fKRVOkhdSKCAEQti2T7p/UePRPiiV0R3gwe8ZLeC/8+mtgwQzgqEI/vt/P8OCaRXUeSsZFTmqCH2n1uWlO3WTxpYynDIV1SlTM0o/xlQa0dJFTmNhqeN19dNTKbpsXkwZy6WXC8mvfnAe7n7m0yTHzGRQ41c/OA99Tnk9r6QoLVGi15V56sqEmtMphn1OHxqP0CMr/R75wiCs6jNWp4DUermxlfTNhY5eD8ZWGpN6YJHWtOffo2/0uBgd4w06NV7efExSAjuVvUf7sXj2GIisPOcEJjAUEyePi97PtNoSmnR98uu0eRPjSKcVy2jE5qALxnCGHx29p3/TWIaQTqMc1ErEL4awaWc7mlod8ArsnTWa0ybV15FTOLhTliZSQhTDXSUql/MnfT+NhAbOjYf7sOiMMUTnwS+GozKzEl6ZRq1Ev9MnGUUAALcviC+P05XSGCVhWdeUBRnGlEui/0gifj/9+DHldHltM0MKvK2H3oft80NdWDRHOtWKMzQYtBo89aMLcaTNjq37urB49ph4hOxfn57I+LxBxoYDi/ZeD0oM6kEOI4lIKAS9Tg1AfvNiEn0uujM64Eu+L4930WvzXvzgKF784Gjc2TVoT0eYU9c0m51uDHf102t9bA6vrNRAICpWZKcYVFL4A0HiRpZSCahUSmbvL5p0fSJ7jvTmRQQr5jDOqa/Ch7sGi4hIyeePNrpzVJvEKW4CFIVmKcEQnUaBCAAxIG8jR6mIbhRxigNZv0RjYyNWr16NdevWoaWlBT/72c+gUCgwbdo0rFq1CsphkJ6XT4a7OmO+5886v5uxs0ySmRUDYTQe6WO8O31hUgKguV1aDd1wrR9fii8OkQ0sWgoCAAQYqUQiQxyhhaHA5RHoTh+rziedBsGc7CBFLnxiIMnx+aixEyaDGg999xy0yoi2kAgS+t/E0CgB2p5EKBTGXU9vg8cvLwdywBtgKrHKpcZiQEcf2SitSElflCMyAkQ3cu5+5lM89aMLia8xGugpwWHG13Gsc0C25L3FrIfFLF/CHgAcLkHSIQOijtoL7zdj65enxZNIWRHXL63HsXYnjlHEZlQKZU6jWallABazFnXVJnj9gUFp9aMd3q5kdMASC0tFr9MwN4uTzh+Jtl5Jp1UGJ38w7+q1a9fi3nvvhSBEF91f/vKX+PGPf4z169cjEolg06ZNeZ9ksTPc1RlzOX8poRPW+SePKyMWtWrVClRSjp05iZ5Xz+oDxkreYknW726mC4nsP0F3Glmy5CrGtkkPY1few4hgzJtGV3m79Cx6jzlO9pBEbmK1P1KRKLcviJ+s2YbGo/RIMA2Gxg3VIQOApha7bIcMALyMRuuJ1Fjoa46WUQQ/vio5hc/A2BxJxO0LUtNCzQa68eJjiCS1dbmJ62Eq8xuqMN5qRjpCmWFEQNpLUQA40CJ9zexu7k1at1VKJS6aRxdvsZbrZYtgpSIlKpUqRtDvEnGyxw23LzC4tcAoZ3odrynjDMbpFonVIlJUpPQwS7Thhrtw3XCEudRPmDABa9aswV133QUA2L9/P8455xwAwIUXXoitW7fi0ksvze8sixxaqsdwSLPQaVSYO61KstfM3GmVsuZPEzrRaVSYXV+JzRJCH7PrK2Au0cJqMaBNoidYdUUJGmrLJEVC5tZXEHeEY/h82S0mHoH+Br4A3ekZYPT5YtmpDBE6ZgPgmgoDcIxsuE9m1Ivx3bP888Kmw5IiN+FIBJedM0FWamAmMPYrmLT1phelk9NYNdYY3uujX/iHT8qTdY9RZkrvOm5qdWDRbOnUYKlU6kT0jPWyprIEWp2amhoY+x6uvWgKtGoVNGo1xKC866CsRAfS5noE0UiaFFJZEeOq6fVp46pNOO+UkiSrGXSM2LNiV1MP+l0iKsxanDm9GlddMIWY1hmrqYndG8DwqNXOJ+YSLZQKEH9rzuglnb2LM6dbodOokmy4vgHhlOKrAoIYGnbCdcMZplO2fPlytLWdfnhEIhEoTrnhRqMRLhe7gaHFUgK1emgdE6uV3ucm1/zHN+ajxKDFZ192otfhQ1W5AeeeMRa3rPg3qCRk1fM1P78YhH1AgKVUB30a26slhN3fEoM2aa6k8//va3slhUL0eg2+d/UcnOiSNuBOdLlhLjMQVbYEMYTD7dIG2NGOAcyYTI/0ZGt4snacSvRaAORoVVmJDn6RbGCyNrRYi2u5SQe7m2ysTRpfAewgN5b0MxTxIirVkN9LQ81Qr0+p99OnX0r34fv0yy40TMhcXZFFluKLWDBzLA60yHeOlDK+4lhjeEbWMAIh+swdviDOGHc6kvCv7SflTDHOefNqYa2IOicnu13YfqAbZ8+qQV2NGTWVdEelslyPY4T1DgDG1Zhx0+UTUGLQ4tN9Heh1+KHTqpJaf8S+h7c+O4kbLp0Oj1++Y15tNUGlBKRubZUSqCgzwGYfHAmsKjdg6qTKpHXdXEavWZ07Ywz0WjV+dOMC2c+e1GdFzNEKRoB+gsOYyt6jffjeNYa0nnHDEdra5HQL3CHjpKFouU8AACAASURBVE1FqQ4OlzDIRl37+r6k+zKxzj9mz5UYtLjtqtkA2DZsprZoISgmGyftbyqxfszj8aC0tJR5jJ1RGJ1rrFYzbLah73Z/1eJJuOycuqS6kH6J9LJ8zE+uJL9U3YoQCOHTvdKG+6d7O/HVhRPi0sdS5w+GItj4Ravk8Ru/aMWS2WPQ2iktKNHaOYC9B7tgc0jXh5D+DgAtXW58vo/eyT5b9FoVNXQ/vlKPAyfIx4cZBSYso1irAmgZYlIOfyJdDCGPYyfp6W/HT9pRV0muJyymxSxThnJ9Sr3323pc8BHUs3xCCAOu/BXzZ5uQYmX0uEtFpVADkFfrwKpR12vVcPnIYeb9h3tQk5AiKAryaywUABShEI6f7MNP15xWdv3LW/uhVinwjYumUI/XMhx8l0uArdcFr09EMBRBBCD2Ytz4RSsmWNOr5/W4BEmHDIg6ajMnlks6ZXOmVsLl9CHxyeRiRPo7Op1J0XQ1MOgciQiBEDZ+Id3I+7N9HbCY6PLeMXodPhw90UetdR7pa9PBE/1DOBPOSECpAM6YUoHlZ09ARak+bqMKgRC2Ng7OlEpla2MHLjunDrXjyok2bGokvNyoxfzpVqxcNq0oo2yF8Bdoa1PaTtmsWbPw+eefY+HChdiyZQvOPffcrCY30pBSHxwKWJL2NKdNjtDHxp1txPNfOHcctcHz/uP9xB29cASyi96laLfRU6iMBgUGfGQLr6pUhd4BsnlaXWqA00N+Dz+j8MZBiWLJQadTwe8lz4/V+0lkiDlUl9Ov1Wm1XA4/rzBCsekWeQ8l6aZVNtSWoqMvNw7whJpS2Jzkes3x1mQJ/Loa9uZhjAiizsh//V661cZLHx6lHl/KEAJBBHhx02FskkgXT8UvhpgKrql0O+h1pmdNr4ZWrZKVbtjGEJJp63Fj5iR625FEbHYvVWl3br0Fnx/oZp5nONRq55tqCz2KGUNKUp0zOglHgC17OqFVq+Lpv6FwGOvebZLVqiRmD9ZSXpOaju/wiPhwVzsOn3Rg1XfOLkrHrJhI+9u5++67sWbNGlx//fUIBAJYvnx5PubFSQOWpL0QCFG7ubOEOAw6NfX8LMN/TAXd8DeVMIwYCiWMAn6djn5ullPjZwh9lDJqrioZ0QT2R6fPj9WPl1UzVsKQzB9g9GHjZIe13EAUuVEqgXe+SC/tbijxyVQ0jCFX3AIAWJmOJTr6K8alCH3IaZicSOPRXqJIEOue0zPkpYOhELbuk05ZlSRNcYsZjJTXSrMOK5c14OHbFuLR//9cPHzbQqxc1iBpLLEMf7mOQRzGJsRXFtZh2Vm1qCzVQ6mIZipIMRxqtfPNAEO1OIZRr0G1Rc9+IWfU8MneTnhPKTO/9MERbCOk0KcitRmSKgxC6jnbZvNg/fvN2U18FCArUlZbW4sNGzYAACZPnoznnnsur5MqZmgNNwsFK9Jls5P74sT6kNGESnxCkHp+rUoJvVYpuQOq16owtspILEhWKgCNOnPZdbfI6PPFeHA5PHSDp4eSPglEm87SEBlCHCyhD3avEbqFyJK0Z82/3ebG9DzWNY12dBoVKst06OgdnE4WDrP74BUSVr+uVNKJkhn0gJty6x0h1JnGsDm8SWl1Bk16SSHH2h1pvT6RcjPd+fQIQWJmgRR/fOug7NcqlWA2k3W4RYytkpfVwXK803XMreUG6FPq52LotSqMqTAmNew2lWjx+sfHZIuIjCZcMjfM5NbpcUYPfjGE599vxreWz5DdMxFI3gyRyr6qry0nRsIBYPuhHlx1wZQhFRArRpudRnFX3xURcmu2CkEs0kXqFQOFgpmeGHvIST38gqEI9fxWSwkWzR4rqd64aPYY+IQgNX2R1hyRRZiRlsF6brHemaEngG6GYeqipB4CbEl+ltCImnHtKZUKWEwa2CWaVFtMWuZOt4mVisXJCiEQknTIhgN9NK9JAj/DWQCAytLourNlN71pcXc/3dDsd4lIrPxiydSnsnlPGpGsFEwMyfyykvyl3YXDgMlAf6zXMhQVk2AtQOlobyPqCC6ePUYydXPx7DFxoynRYUx00oaLYTUUWMt59IuTObuabLjsnAnUtEWdRolAMCy5GSJVMtPHSD12+4K4/8/bsWBG/m3nYrbZaXCnTCasmi25xHqzhAKhjB4uUl4/S5LfWm4YpO4VQ6tRocykg0qpxMplDVixaBJcYhhmrTK+m6FSRvtZST1I552SzL/xkmlQKhTY1WSD3SXAYtbhzOnRG4AkwRyjtSvzIkuDTgNQ6rayFTNg7QMHWWlRquwmEWA4lWrGNdRmc2PBjBrJa2PBjGpmXZCjiCM1I4EWggDOcECVZoC7x0HfwGioLcWNy6Zh4pgyqlw8wN7MMKfkBVek2YA5G6RENBIplzEXBdgbRlLotSpYy0uomQnaNJ47rMiWtTzN9EUAN1wyDQqFImosyWwIXaha7WImV/WZnNGJEAjD4w+S1wol8OsfLIJPCMKgU8MnBBEMRaBSRss60omwJWJ3Z2Y7p4scmz1bezwfcKdMBqyarWuWTGX+oElee8qDSI7XzvL6WZEu1iOe1Dsmdn7S0bG/x5w6qR3NHoaRcrwjC8OUYRgaNABFpI2JRklvoju2wogeO9lxKTWq0EcREqk0AX2UWnpLqRo9TrLj5GF8ODEQol4bnzJqW0oY9TGc7OjqL5xhpQZ900HPUP787AC9cXoqHX30yFpz2wAe+OtOqFUKqBUATd+CudeRcmyZcehEIViNrVkpqd/+ynT89V9NGb13JBKhSqWHIxjUi4yG3MhWOiQ+K1RaDUJioGgMomFF8WoAcYYJv3lxN3mtCEczgT79sgt7j/bF7c45Uytx8dn0CJsc5NrOmcCy2a+6YDJe//h4xvZ4PuEWlwzkqBOyHnLZRtpYx9Ocoj4nWfFKEKORt/d2nCQ2sL3uono0HpY2wBoP9+G6i+i7DKwUOQOjaJ+GRk2/gSzlBvhsZKfQYgTslLKqOqsBx7rJx5cx6kdKdDr0gWx463X04pkJNaXocZKlj5fMHYf1m8hqcGfNqKYaQUZGqhNrnJMdhVS3rLJo0UXZUMi6kVmGkAQ2Epk4xohjXeQb152St3y4Lb1m09mgZ4jnCIyG87U1JlQS0sVZCIEwRIYSiSHNjZZMIlty0GlUsFYZC9K+ZiRQVc7eaCgzauD0cLEmjjRBRsrBo+t2Jf27b0DAh7s78OHuDmo0Xo5osFzbORG59WEsm339+4eTxE0yzXzLB9zikgGrZoslzZttpC2d46XSPMpMOmIKik6rgkGnJirmbNvXhYvmjmM6pZVlerz0wRHsPNQNuzsAi0mDBTNqcP3SemYxeDqqbKkYGQaGgrE6OOk6F+jsp0f59IwcLr9AfyA6GH2onBK1YIkIDLlslYrRM4lRdCe3mJyTGSz/g9QEOBf0OegRm0gBHDK5CAzF19TNzm7azkua1Fj06LaT71sTwymbOr6MIoykxPgqEzEdnYVSAQQYdbZOj5hWoT1tw49TONpt7Ch7Q105th/KLM2Mw6FBi8YnolMrIUh4fyzbOdEBo/XJlYps0W12HQ61SG905zN6JxfulMmAVbPF+gGzjbTlIlJHy3WwOX2U3jEhiKEwLGbppp7lJh3KTDo8934TPtp92rGzuwPYuKMNgVAIl5xZR53ZwZbMlc5aGM2R2xkpUyx7l5X6GIzQnTJjiQa2AfJJtColPJRZDHjou+VHO+jfnRgMUVNnWZEa3qcsv4iMqEm+HDKA3aBZKOLWRk43/b442ePC/Ibq+L/djPtIDgpERTL+89o5uPPpbcTXsXpCef0hVJUZ0GYb7ChWlRmg06hw7UVT0NTqQLvNLWvXOUY4AtgZNbzeDPO5eV1XcTF9QjnzNTGVUrnRiwqzFg115TjUYoeDR9g4MohdW6RrjFR3T7KdpUp1SvQanEzomciKbNFs9hkTLMQWAJlE73INd8pkQqvLYZFtpC3b451ugZq+6GbIxmvVKpToNZJOWazPVaJDlshHuzvxtXMnUc+v12aew8syWjUKtvGZDWGG0EeZSQ9Q0hdZqUY9Tvpv0yVh2CURiVBTXy+cM5Z6eKiImxcPN1KLioVASPKe4rCJMK5LX0pWgNObeRP3shIVvnP5LEweVwZziRY9DMVVlvphuUkLL6EXhtcfgBAI4ZWPjiUZIXKpLNVBo6Kvp+nI8XOKF5OMaKf91PoidxmfU18JtUqVrqgmZxQTjgD/NtGC/S12yfFQGBhbUQIxGJZlO0uqOhKCErTIFslmv+qCKTjUas/Yns433CmTSTYpHNlG2rI9vsykI9YoVJTqMd5Kl0g2aFXodUqn8fU6fThykh6tYan0BCh9LVgwSsqg1QABit3LUjkr0dBl9VlKa0Y9XVJerQGQjV2uZKRnukVq6utZDVbq8QFW0jmHSWqk0mLSwmiIGuakCDiHDmszpqE2OYowdVwZdjSlJ0wSf6+IAnPqT98nrN5/Cob6kMMtEp3xfpcIm8OXsbLZ/AYrxlXRd3m5lPrIoL03faedhccXwPZD0husHA4JkkMWQwiEsOo7Z8MnBKm2M61URwpaZCtRUbytx43aalM8bTtTe3ooep5xpyxNMk3hyCbSBmBQOotSAYy3mnDtRVOYx7KcOlY0pN3moaQ3hnGole6U7WzuoY73ZtHcktVc2cdweFgbiKz2RiKj91IfY1c9xNjAZ81vgKHk1tLtoqa+snb9Pazu1hwmqTt//S6yUc6Rh1IFqnRkmSk5isBq/UDD6w/C5T1dh9VuoxvDfS4/MZVHoWBHn8VAMC1nXaEAKhKeJ9sP0tfblm43xlal0assTxSjHPVwoiODSCqLPYf7cn5ODsfhFuATgkzbmVaqI4VGoyRGtmiK5ena40PZ84w7ZUNEtjLAL29OTmcJR4CTPW68vPmYLLWY2MUm1UfM66cbLHotfZ5GPX3c4aXXdfmFzJ0y1v2QbfIdozwEAwynxSXQjW+RYZtrlQAtkFhbaUJzJ/nhPKmmlFrwKjIiYf1ZOMyc9Hf+OPKoKjOgtYccpU5tSF9mzPxRF44AbT1uzJxUAQDQa+nnUkJBTBeLRIAnNjTS31ChIN6zqVhMWvzk+nnRXpSnniesjZRCb7Rk2x6GE4W1dkuhVAAalbTwglatzOicHA4LuWmBtFIdSQiXqxAIYd27TVSFxXTs8Vz1KZYDXwGHGJ1GhbFVRmr4tsfuhZAQomGpLwqscE4CsVzxxJxxJ6OmjAUrxc2so+e+G/WZ5/CWMBzCfFdEaRly/t199MVFZE2QcYeOYaSeVlkMKCGkUJboNZg50UI9ftp4LvSRDU43OR+ekzl6Fd0x6kiptWToqVBRKqICHzFKGOqKVos+qzpZrTqa2SCHBTOqUWs1JT1P6seXUo9hjeebmIHTNyAgEjlt4Lz0wZGCzmu4wVq7pQhHgHnTqyTH5k6rzHZKHI4kcspsgNNZXXIRgmH0D0Q3/YVACJ19Hqx7rwn3rv2MKOaRaDOz7PHYeXNlf8uBR8qKBFp4NB31RVLOazZiD/0M2XYPQ/K+REuvqyor0aCtj16bRSIcKVAzpVNEWJrmWXqFYcZHY5S3QKUA3F7pa8ftFZlF/zJaRnEopNsTiiMPt0iP9nT1JjtlNlbvCwrjraYkCXmmMFMWjapj9/NVF0wGkJheE1Ug8/gCcLgFarpNr4O+Xvc6/Jg4pjCbLUIghJ2HuiXHdh7qKbgc9XAik7XZYtJBSyjENurV0GmUTPVQDicd9FoVIpEIQuGwrEh4amqhRk2/Jt/fcRJqlRK7m22yNkDTVVjMjfq5fLjFUCS8uOkwNiU0b445TZFIBNdeVM9UX6Q5dcFQhOrpLz+bLlkfZPTC8jB6WdkZdU+tfZnnxovBLLbAZaAEXTa/f4DuTGbrLrIevDYH/f2Pd7pgJ/Q6s7vF+C4TCZHRD4pDJ9soNEcaB+O6TX32148vx+Y90junxHOcqtu951tnJv2d1Xexx05uMcIiHAF+8X9foPLU+v3ArefA7RXjm2xyCs1ZzbflNOfOF063QF2PCi1HPZxgtdOQwu4WsHWv9H2w76gd550xBpt3d2Q7NQ4njl8MYdPOdigUClmpfqmieqYSDX705MdEcafP9nenpSibrsJiturn6cKdsiJACISwdZ/0Qrl1XxeuvaieqRazfmMzMRK2bEEt1dNv76XvIrsZTpeRIQGtZlxlakUWRkIkvwaGWkFPMVSr1YCYP8eQ9elMjF35yWPNRIVJBcBsIqtlyGtzGOT5+hypsFRRNVolteAz1XEyGdjy4TFH6PJzJ6Kz15Ok1pUITc22slSHaouB+V4sSDULcoSmys30z8oazycs5UrWOOc0Wg3bfDMZ1INEbkj1jnaXH5eeVQelAvhoTydvh8LJKXIaMyduOsXw+YOgdR5Kt8WH3FTKGNmqn6cLd8qKAJvDR7yw/GIINocvIaQ7uDhaCISwq0lacWtXUw9WLJpE9fR1DCGPTjvdaetk9MryCXSnLhxmmWBkPPQN86wx6JQQ/eQVobpMjwEvOdKX7z5pc6dU4vMDZLW1UIT8zUYAqNV0I6jQPTuGO/z7y4xSowZOSvNapYIew1anpGiNtxqp73f3yvmYNLY0/oAtp/xu9Ie0dVCPtGyQY8gMhuXYFM7x6WG0EOmx+1BZlr1TOxqwlhugUtLbQ1x9wRT8Y9sJOBjZKgBQatTCZNBAqVRyh4yTc/oG/Ogf8GNsZfJaLARC6B/wY+PONuw90ov+AeGUTRqBXwzDXKLJiTZARYK4XeJ7y1GAzVY9PR24U1YMsHbTE8bD4TAikeSmxU63QO174xOCmFtfiQ92DU5LmFtfASOj7qWOISYRpib4AUoV3aCoqSiBsz2zFMZ8mxceRhqSgtFlM9+Ptr1H+6njvQwjiNVTyScEmdE0DhlWqhsnGa1aAaVSSXXIAKBEp4HdTf5ua1PWLJaRaTGn13eG9pDuZGQepLMFlUnNgsBwClnj+aS22kRsF5AqqMKho9OoMGOCBftPkHtEBQIhWQ4ZEO2fd9cz23I1vf/X3puGuVGded//0lKlXS11q+3e7PZux3hrO9jYxuDQ4AnvMGFLABNMhgwQJjPJzJNk2BwIwUC4MpNnmEwyITwkvIE4CRckzCTv9QwTY0yMlwDe24C3Nl56cW/qbqmlLm31fpAlS+o655S2VrV9fl+4cKmlo1Kd5d7+N4czhi17zuKu6+YAyNZRyA0YZAYpAoxMLcmkriaayarLJuOL6+ak1/h8FWCL6VOcLzw3aZxJWeaZii0+j42o1mURjfB5bPjVW8ew5YOz6Xx8fzCKLR+cxa/eOqYpJeTomSHVa0fPDCFOiw0DSCj065MYB4ZqJ71Zqa0IMQQbw6la7APOalLbwGjUWuOlR0pY05plD/kD9D5jJzrpPeQUKJCIzx65BwhHG6lUNzWqXRLu+eyccR6RvonEFE3pKPOnVlGv1+f04WIJruQryJLapB/70jJ89/6VeOxLy7C+dTaMBgN1PZfMBjy4fonmzymkZmFaPV1dkXW9nDhtIlG90mYxcQdQnly/Yir1+pGzQ0wxqEzkaIILfXDKxoHjfTjbE4AcjWepsBaKw2rCqkVkobpqlwWtyxpxx7WzMRSU0+fuQhVgU+nj5RQj4pGycYJmmUtmI1YuqMPWDKGPFCsXTAYA7DjQpfq+Ow50Ye4U+gHl2NlBnCWkGJ7tHcG+Y33Uv99/nN5Q8tQ5epSru5/uOT7ZPUy9TiPMSF8sdnthebSPd9LHHmJ4eRhZWBBFE0KUmjUrQe4+hZ3RjkCgxsp4fUexsFLdWMqlHHU6GRHg3LRcluDK0MiF5tBaxDRY6/mKyyZjm0pmwtJ5PnQzGrZnsmhWdd4HAKdNRH2NFZ19Y+9RfY21ooaPHI3DbFRfV8xGA2TeSDovJEZLFtreLpoERBgiXhxOKRkYlvHYz95HtUsqul+iVTTiu3+zHE6rGQZByMpaWDjDi9ZlTXA7RLyx/SQe+z+7MRCIwOsUsWhmDQ6eUD/TFpYuXlq4UTZOsJrP3XHNLBgEAXs+7oE/GIHHIWLp3Np0OgwpPCvHEkx1xBDj4GdhPICN1VZ8fFo90gak6jvIxFmmURFCH6IZiFVQ4C4Wp3v1E4y0KbMRoEXe6zw2DAbJhl/TJCf2HyenMIqSARbRqBp9sIhGmE0GolKcHIlzNbQScOvV03Hk9CA6eoNIKBdU/W69ejreO6wuD86h0+Szo+0kOQo8RgRBQ4o4TcE2N6WFtZ4bCWnNOw+ew86D2n/zQt0ipLRqVrp1uRkKyhgkqC8OjXD1xXwpRoiJG2ScSlGK3p3hSBwP/2QXls6pxfprZ6mmFr7yxyNZwY6BQARvU9RFyyFxny88fXEcyKf5nMGQjFwYMnIORkbpRpVkpv+MNS56+mBXHz3S1cuYQF4X3fPKSq+UWPKMtPdmnDHKfQQJMn4blvy0iR7oQoSRK+220d9g3hQPVp2PtuayasFk+Dw2Ynqd11V6uddLkde2teNMTzBdR5NQgDM9Qby2rR1VTn5/C2F6PT07wG3PXpO0pC9mpbSAnNLCWs8DoQj2M7IPtLL/WH/ezUkDoQg6etWjcR29IQRClfNipeSl1SiHvPTFjs9jg8hd65xLFDmawM62bnzzRzvx+jsnUO22pA0yORrHzkPqGWYktK5BcjSOHn+o5I2jgQIjZZFIBA8//DDOnDkDh8OBxx57DM3NzSUe2sRELfVFS/O5LXvOEj2vc5roB5CuAXp64J6jZHU+APjwDLlQGACOnKZfP9hOT28cGGT0MRsqPIwdZDhcyt5amuGBN5oAUOw2RkkYTnXTDea2dvpvE47EcPs1syAIArGodTzlXi81WMqo0+oqV98zkdl1mN5zLDMdEWCr/nX0jlANrcyUFtZ6frYnSLyeL4V4bk920VOqT3YNY+GMmmKHVhDjLS99sSOZjaj12nG2Z+wZgKXMyOGUCrXWC+PJaCSOLR+cRXg0hi+uS9ZpHznlz7tfJGsNyiebolAKMspeffVV2Gw2vPrqq2hvb8eTTz6JF198sSQDmqjQfixW8zmrZMJe4oGgFzMZhdkjjMnA0PFAnFHYy0r9jdAaeQEwm4E45T0EIwq2nFwOAUNB8ufbJCBQxPmI1Ty63ufA0TMB4nWLaKbmTluMwCjluzvtAvyU7xdniLB09o1gen1VWjnIKJoRj0SzFp7xlHu91GApo8YYkVCOOsER+qQeDMpZCoys3mGSaGQ6zlKGEX09l9BY64BESBnOl0KiRw5G30jW9XLD15vSIUfjCBOyNcwmA+IFNjHncPIhcj5iVHhzo9Kwo60bHxzpQSKRACuItfxTtTh+dvhCHdrMaqxd0kCta2WlrZeCglbn48ePY82aNQCA6dOn48SJEyUZzESG9WPRvINhOUY8EPQPy+hkCGXEGE+fi5HiNsljw2CIbFg4rQIC4cINH6fVjNEo2TBJFHF2ERg9zlh7EmsR8XnMOOcnj90s0D27iRgj9VQUMEq5t811HviPkWvGWL9tKGPDlsxG+Grs6O3N/q3HU+71UoOVulvhEp8JS5jhSBoYyo6MsXqHmc0GquMs0zCSzEbYLGbV19osZojmZI+dUlBI9KjB5yzqernJXG/UnEQc7dCitnIkgZbZNdh7lJ5KW2UX8fe3LIACBT/63WH4i/Fici5JUvWJeqhS1KoearOYsene5YDJiFf/52McPN6HbXs7iNEvVtp6qQRCCjLK5s2bh7fffhutra04cOAAzp07h3g8DiOhH5XHY4PJNL6Lrm8cN57RSIyo5nLwRD/uv8WKv/vCEtisInYe7EDfkIwat4SVCxtwzw3z0cnoazOl3k293tzkBVSUvlK4GM04WX0iLZIZgTC5DsFmlxCQaQs5o9cXKxxFgRWeZvStZi4iFlECQH6TxjonDp8mCw401nngp/QSm+xzY4jy9yw9Y6+bntY0b4ZvzFygzY1G6rtdnJRzffqkj56f6r2ERA3qvBZ0DZSm23t9jQ1nCHVTAHDl0qnweS/c2xGGqMEknwurFjXgv7a3j7m2alE9GjNq2EYjMYwSFFFHIzHEBLJ4jhYEIdkYeMVldbjnhvkw5inmMMTI6Xa6rLx2a4LAWpucbitqqizoHRw7r6rdEv7XncvwD/97G/pUrqdYuagely9Krvw7P+zF1g/OFD9wDkfnHD45AOfnrPj57w9niX+kAio2q4h7b1yQ/veuvhEMEBwW/sAojKIZvhq76vV8KMgou+WWW3DixAls2LABLS0tmD9/PtEgAwB/HhLApcDnc46JBpSTHn+IWLPQ6w/jxCf9qHKK2HekJ+3VGhiWse9ID7qXN2LvR+rWd4rjp+h1Q+cY3zUwTD8IRRgKgqxI3DDDs8byXFhFAdHRwnwsrEBDEZmRAAB/gG4wexjy0tcsacAhilFWzRBJ6R+k18IMDjOaQycSWXOh1HNjPJ0f5aKc65Mcps+NRCwOg4GdYnwxUCqDDAASCn3mR0Yj6O29MPNHR+ifPToyiuuXN2LfkZ4xKpnXL2/MmjM9/hDxkNs/NAq/fwTVhKibFjbetRT1PgcksxEDjHphNT76hN5Q/sBH3ZjX7C1obKWmnHv1pbI2kRrU9w/JeOn3bVg4o1q13Q4ANNU6cNPq5vRvcPOV07DzYEdRTgUOZyLQ4w/j776/ldhcfceBTnz28qYLDaejcXid5GyKeCSqeS2jrU0FVaYdOnQIS5cuxcsvv4zW1lY0NTUV8jYVoRyqKW6HRG4WKhrhdkh46hd7VRXYnvrFXsxh9BljSda3MxoEk7y6KeqqHdTrrF5YopnVvJr+mFmKaB6tMPK/iv2VR2W6sdjO6LEWYVRa9zMMZlYEcXaTl6i+KZkN8FUxumtzyoqdMXcSCeWSMMhKja+Krijbm3OYZQl99PjDVJXMTFgKgr4qKxbNKlxIw241F5UG01jr5gxMDwAAIABJREFUIAbYDULyOufiIBCKEAUWFCBZMqEoaKzN9uAbDMCaxZPx2JeWZe3PNsmE1QvryzlkDkc3kAwy4EItcSZzpnhUX7twhjerOXUxFHQanjp1Kp577jn87Gc/g9PpxFNPPVX0QMpNuVVTyEd3BcFQBGd71FX0zvYE00WShbw7AKZYQM8A3dsWY9Q9OSxmAGTjYbLXgSFKTdq8qR7s/JCc1+6ySegpUIGxzmPByR7ygctmBmj9m82gJScCjT4H2ikKiM2THHiPEulk9ZAbpqSFAkCAEWlRFAWrF9bhLRVP6OqFdbxWo8LkSrPn0lBjLyqqUk4cIkDZsyqKxEg3jeasiSyhjyqHqLleQIuCYLxAS9sgsOX7WThtIhp8DpxR2XMafI6KNo/mlBaW0iYA7Gw7N0Z0JpEARJNJ9exz22dmIhaPY9s+bXLiHqeEkXCU2b6Fw5lIeJwS3A5pjO1gEc9L7kfi8Dgl2K1mHDzRj237OktiVxS0+nu9Xrz00ksFfWCxqEnOa6GcqilDQRkyIdw/GkngyOlBolmlADhwnC4pv/84vVCXVZN26hw9pHrkDLkxNAB8wvh7ksGZYjhENzrPMbzYNAIyI/WSYe9KooAoRT1yRoOTapQ11tJTZFg95GJRhnImI7U0EotTJe85lYWUWpQinlCIB/xKM1Jmg6zaJaJ/uLAPGWb02jKbsuddnFE4OxiMaFZfBOgKgnI0jncP5NcfJ0VCST4zxRpOj25owVO/2DsmFfPRDS1FvS9HXzBKjgGAqAJKEicwGgzYsG4eBMGAt/eqpz1mMqepCrs/1N4QncOZCNgsyYyFzVuOZu3Pqfm06rLJEEVj1hwphV0xYdoOFhPpKrdqilUywSCoC2YYBMDjpG+wbAljegqU227G8CjZ017nsSHQRTYsXHYzAmHy4d9hMWEgSD5c1vvsONZBfv/L59Wi7RNyXdzkahsCHYXVFbhsEvqGyAe0STU2nOkhRwqnTHbiw9Nkb+PUyW4I6FI1qgWw6+VOdtI9mUqC/uy6HDYMhcnjXzSzhqsn6hi3Q4LXKarK4nvPe+JuvXo6jpweTB+g9YLdAgRLVwY2hkINMgCYUe/GWxRxo1whC5YKZpVD1Ky+mHw/soLgqe7BgvtDGQQgOBqFmyLLrAWjwYA5U6owEo5gIBBBlUPEnClVJeulw9EHgSJ6Q7F64K1vnQUoCt7Z30ldl46dHYRkFiBHdbR4cThFMhKOYjAo492D6vvMx6f9UAh9aouxK3S3QpNqvlKRrv5hGQouWKS/2Xqc+Z5amjcXQ1iOERethAIcPUOv+WI1dx5iuKwjcfpiGGB460OEPicpcr3OuUxiKMiNMD7fJhZ++PC66bUlkxljczDSywYDEWqU0+uUqPUbQwyPvshIw5rd6Ibdom602y0mVDOUNTmVJZXqpkYq1S23lkkvGA36NexPdNKj+705kvgnu+lOn47+EPN3UkMyG1FXY8+67if0pdNCQgGe+n/3YOMLu7F5y9GC0yBT+2XKGTAQiGjeLzkTh2JaamQ6G9TOXUaDAXetm4urljRQ32cgIENhSm5xOBOLwaCMV/7nCFH0ZiBA7kFajF2hm0gZLRIWiytFRbrcDonYzFM0G7O8oKz0yMD5+rDG2gu5+awaAJI1ncIi0SNhzXVOqvxz4yQXzg2SUxxr3HQp6sYaB/xBsuFYV23HuUHyQcPKGH8oTK8XY/UQonGmm344izJqugIMg3c0GqNEOkTU+xzU+o0lM33YfrCb+P7zmqvQOUBO35w62YWb187Eg/+xK6ug22E14dkHrgAwPl3mOYVDM+ppUfxK0zzZhQPtdOXXSlHtpDsjBoZlTK+78P8CYw0WFKVkTY1ZmREsMp2OQP5pMOPVT4dTeRwMISEaS2bXwGQUsHnLUeresb51FgDgnX0dqo6jKrsEfx4H0Eo3GObonyK6JJWMKoeEk11kZ57HkVzn1c6GatkVWtGNUUar+Wpd2phXvr869GUgdbDde6QHA4EIvE4RLXNq04tTJBYj5uizIllDNKUJsOuGpk12YftBcs725XNrsecI2SjzuOjRJJa3zWRk9MpivH+M8f0MRXjZRhiRKJlRfCwyooAW0Qi7Vd0os1tFSGYjvrV+Mf7XD3cglhGxNBkFfGv9YpgprSIAYNm8yXhrL7n+pL7WAatoxr99fQ36h8I4cnoQc6ZUZUXIxqPLPKcw5GgcB46pz80Dx/px9aJ64tpWaWSGamslqXLSNzxnTkr4bIJqVub1UqUBN/icxDYHBgF4ZMNSfH/zPk1NTgsxorRkhrD3S85EIFFAeL3KIWLZ3OTZRsveYTQYcNd1cwBFyernlGLx7BocPN6nWaxozeJ6fHb5FIyEo9j0iz3cQONkIQB49oEr8Ptdn+BP+wurzS0Fc6d6sKuN7FCfO9ULm8VEFXwqBF240VmePatkokoQsyzSoaBMDEHKkWRk7FdvHVNN9/jVW8cAgCppP8JQ0GueRBeDmN1E7xlD6wEHAAybByZGPYVFor9/SKYfHj5hpAaNMMQ4WCmINOw2uqcwItN/m2oX3eNeX+1AaFTdqA6NRiFH4/j+5v1ZBhkAxOIKvr95PySzEVcumqT691cumoTmyS6iSSoAaKi5IF/tsImY2eiGI0MEgDV3Stn6gZM/rAMyBIG4tlWa7gJ6ZI0Xk7z0eWu3ZkerRLORmmYsZmygktmIWo+t4E1VMhuxZrG6rPiaJfVwWMyIaDDIgMLSYFiS/bxx9MXDtHpXXq/3OCQ8cc/lWN86m5GB1Dtm71h/7Wy0LmtEtcsCgwBUuyxoXdaIW66agbkMp0eKploHvnjdbNR6bKj3OXS79nEqR2OtAw6biMPt9H6L5aTBZ8f6a2cRn0+LaMRt18yEoihpNcbUv39maUNRImu6MMpYB5ewHCso3z+F2yHB41A/vFc5zLBKJuw8pG6R7zzUhf6hMDp61YUsOnqDGGTUEIwyNuAYo24gzqgaZ/UhSzBSd1jph59qpvdRq7LT7/+oTI8UsiJtNHwees+dyTX065dNp/cTMhhAeTZldPYGqc9GIBTBndfOQVNOb6CmWgfuvHYOJLMRV7eoH+CubqlPy2tv3nIUG1/YjYef351Vb1LueklOcbgdEjGdrcohwVdlJa5tlcZk1MX2oIqFUYea24pgKCiDtAwq56+XkjtbkwfYVIqLxyGidVkj7mydTTWacinEiNJSx8i5OHDaRDTUaI96Lp3rS5dd0PaO/mEZL795JKumMRVJ3nTvcjx93wo88eVPAwAef/HP2NHWDYtogEU0njfYJDTW2rPUhyWzgFlN7oz/Jz+nKUQTr1WbCOT7K1lEA65cNBl1XlvaWSYgaQw99MUlePnNIxVtEzMSjsJoMBCfz9UL6/D7HZ/grT0dWWVRo5E4DIJQVNmILnZdLZ692z4zU9VLk2uRytE4uvpGsrw8ktlIVCkKhGPUSNpoJIHDJweoQh6smjEbo+aM1WA2xDBqZIbu+8rL6qjXWZEqVl0WQ8eDKUtfjLdsZgM9CrmAYXS5HYw+UhRvnsdpQSAUpT4bZ883n82tOctsSrv+/AHOe/7w7nUmD3Cp9BGayA33iusbyWwcE7VJkWoSnLm2FVO4X2pY7R4qyb6j9DYhuUIftHniLcM8SR1gn77/Cnzv/hV4+v4rsL51NowGg6bDaIpCjSit+yXn4sVkuCCZbxCSjsBbr56evs5yDuxs61YVhklFkt/YfjK9LwHJs9JoJI4V8ydj070rMHeKJytFV44q2LqnI/2ekVgMH5+m16xGYgome6yapP85lSPfFNTRSAKJOPDo3cuwcsFkOCwmKABG5RieeWUvdlLSBseDoWAEQ0GZuI7eeOW0smUo6aKmTEszTgDUfP8ssYOcXk2DAXlMelmKWFxBVz89TcfKSO+TGF5blsM5wYiUsSJdFsamHQjRrSaXlX4gsTCMSpbC4ZRJDuw9Ru7FFmSkf9Lo7Kf3OBseoXtbWMqTtD5SS2bXYFq9i9oOodZj1VR0T3q2tRTta5k7nMogR+PM9NfM37+9Ywjf//X+cR6lOqMMZ0wlGQiQhY8AIJhT56t1j8mXQCiCzmO9cIoG1d5iqQNsLrmiIlWOZBPS0GgU/oBcsMhICppkP+fiIRCKoLNPfS5kllOnyi1e29aedvbR5kQKUk0jbV86cnoQEQ371tMv78XZHnaKdLc/DLvFhBHGXs2ZWOxo68auw91ZZ6f+YRnQQY212yHCKpmIdcY9/lDZ6nZ1YZQB9GacmZA2OVrBaiMjvH/oJD13tZNhtJ3sovei+pDhDWI1bx5mpEeeZMhDv/cx3evQ3kX/++Md9OunCel76evn6NdZNWk0GmrsAMjqde1d9HYEpxifbZVM1GfTaDBQ1RfjCUXz5FV7trWkJ5ZKNY5TeoaCMjENo39YHvP7T29wE9U+x5uOHvq8rySSmb51+arG1pyVcp7QhJ9EE3tbJW32LPXffJHMRvhq7OjtLXyN5eiXsz3BvKIUmUZWPJFAQlEgmQxEQSzSAZO1L53tCVKvd/QGiGn/anCD7OKk0DYw5VbwHAxG8N2X3k8HdnLPZqkos9a+lvmgG6MstUmt+3STqsIcDXo0oRcz62dQ/97JSB+UGUIVfoZlrzCUOGoY6XtzpnqwRUX1KIWXIVahMJ58q8jqQ0Z/f2btCSP1wFPEA+yrohvcrMNbLEH/bYZGInDaRGqU9tENLcQDmqIImiev2oFMy+TnzaP1C6uxfGY7jdTvb7OYdWGU2awiAuHKey3VsIj0eT0YjKAuJ3M5NU9uWNk8pq1JvqSEn1JkCj89cc/lxL/LneO5mz3J6cjhqFHFSL/PJdPI+s3W49i6p4Px/pLqHpUSXyPtS421Dur1YJjc25Uz8bGIRiiKoklhthDG49GhqViXK/MC0JFRVoznkeWNHmQUcat5VTNprnMDIIf4qxk1WQYjffxHT9EjaUdP0yN5i2ZV440dnxCvz2yownsfk2swRDPdqJpaS1d4mlnvxp8OkKVLWbLzrPRPGg4r3aCeWucCDpAjhV5Gv6NMdQDSgUk0mfDEPZcjEIogEEmMSWViTV5an7F8Jj8/0OkPVmP5sByDzWJK//79w7Ju6icafU6cG9CnUcbqDVmr4kgqVT+/QCjCFPfJNfZ4L0FOOegdJPcfVSPlyAvJMbx7kOzoTZGqe1V7fm0Ws+q5a8nsGjhtInXfmjKJLsCVC+9tNnGo9VjwwI0L4LCY8Kstx7CX0BJmorD3SI9qCm+5MpR0Y5Rp9TySmjfTvNEso8DEUPiJMJQqWDVRAmM5MTLGF2RE6tRS5zJhGY2CQDeK/MFRGAVArSzPKAAzGuhGm9tON5x6h/PbWDIZpRRUCgCmMsQKGnx2WESDqtCLRTTCl4eR47SJmD7VOSZViDV5Wb1ibr16Oo6cHhzjsMgs2ubok5Tyqz84tq7M4xDhdkhjfn+9eJB1YhuqwqqzVWtIX6p+fmczWqOMHVfy+rzm7DYnvJcgpxzk69BMOfJe/MOHRHGzTFJ1r6+/c2LM89s/LKOp1oHQaEx1X6Pte2oCIjQMBgFxlUlnJPw76TzIKT89/lE88fP307+BZDYgFk+AISKuWwYCEbz85hH89fVzsxxo5cpQ0oVRpsXzKIkGYiSN5Y22MCTfY4yCdn+AbjQYGJJprGjOotm1+PA0uX5jZkMVdh3uIV7vYNQLnO6hX48xjM5EQlE1yICkoXaik15Td5pRzNuVR255Lk6ridioVTCwpbNddhErF9SppnGsXDC5JJOMNnm1CHm8/k67qsMis2ibMz7kW/MjmY2w20RVoyzVY4/0+1eaAca6V0nqfQxPe47RpmWeaZ3rjbX0z869XsrP5nAyYTlOXDYTguFYlkEkR+NM1cMU/oCM3sEw8fkNjcbw2JeWISzHxqyJtLpJ0vsZDEm58fc+PJdlNOYaXgKAep8dc5rc2Lp3bMTvqiUNCI3G8OcPz2n6npzSk/rJUimMk7029A+FMBFbp+5s64YkGrHu001jnvNSZyjpwijT4nn89dbjxEjaI3cthWQ2qOavSmYDHFb61+zx042GkRF6JGxmkwdbVBaGFMs/NQn//R45/XHaZEavLUajVK/bTr0uM5o/hxia9t0DdKWzM910QQBWTV6siBreaExRNciApKF2jCFS0uMP445rZsEgCNh7pPe88pmEljm+kgtlFCLk0esP8QOdDig0/UyOxtE3qK4Q2jcYRi9FxanSmPWSR6mCmVHHmltorUUwR+vGKpqNRA+90SBkNaIu9WdzOJmMqkSEM7nrujlomuTMOkj2D2lfczxOC6DQxarCcoz6/Obue7T5oCSASCTBbK6uAOjoHYEgJB2vqftgEY1YuWAybl4zA4+/+GfGt9OO2SggFlfgsJkRCNFbFHHUYZ0j9c47+zrw9t4OVJc59VwXRlljrYOafljlEKmRtGAoghghNhqLs4sNR6P0SFmEEQc3MA4vpChTijaG+uOBE2Q5eQDwOulCGbOaqvDf758hXmdkAmGEIOmdhpH+WFdtQ3s3ORo2vcGJ7jxz41MMMFIfJ3msxHx0Aclnr5JCGSwhDwgCP9DpgELTz3oHw9QeiNFYgvj7VxpW3VYl6fXTW2GE5VhWXZfbIUHKOLxlIpqNeallDQVlJAh7QkJRxszJcip1cS5tptXTSwdmT/FkzYN4IoE33zsNQWDv+0Ay3dHnsZX0+aXNB9FswO48olu5kvqp5r3BUKSkzq7v/PWnYTQa4A+M4tnN+mhZcilSitpCrc9+Lqklv9yp57qoMHbaRNT71KM99T47BoMRaiTt8CcDxHzVeCLp3aDx6bn0Rp7zc+oDxoyBYXUdY6QKxBhGY72XHglj1WyxauoYOh/MPmrhCN1oixDkdlNUu+hGBSk71CAA86d5IRG+n2QyoLnOTUw3ylVfS3n0xjPyRGsku2R2DXxVVt4cusKw0s+ojSIZq7/ZZNDcSHi8qavWr7E/HKIfuNQNytIUmeTbiJo1x3mkm1MoTpuI+hr1TBqbZIDNkj0PfrP1ON7e18mst8psNl2K51eOxtHjD6X7MpLeL8o4K2hh39G+tDpkqTAaDaj12FBf4wDX5akcpUjeUBTymTIfim0STUI3j9esRjfx39WUtDJxMmq2zEYBDou61zf57/RfiKVw1NVfXB+ubj89rNs3RPcKt52kR9I6+ujpmcOMSNhAgH4A6mN4pCSG1We30hf1GoJQSb3Pjmq3FTGC0RhLJCCZjXh0QwuazkdjgQsbzqMbWqifO16QusZnqi+qwQ9044OW9DMSPo8NFkLLiZSQTObvLwil2XhKwfGz2vuUjfeQbYw64aGclPOhoEyMWMqROPU3zKWQOUmb4xxOMQiC+voSkhNZgho051IumXXLQOHPbzyRwOYtR7Hxhd14+Pnd2PjCbmzechS3Xj19zPst/9SkkohzpFIqac6uKoeIBkIgIBevU4TDJmLzlqP47kvvE8slOOWHlXWmlUIiZbmkyktSzoZSoYv8FDkaxwGCbOaBY/1Yu6SR+vcjYXpR0nAwitGI+mtGIzGq0ScAmNlITxHwMNIH507x4M8fkRfDSQxJfj/jwOCw0D+fdciLMZ50Vn63P0A3Km2MNKilcybh11vbiddnNjpVDeNp9U70D4UpUVKgfyiMarc1LVlfbH+icsBKn+TNoStLMelnktmoSUgm1T/r0Il+/J//76PSDb4I4gmdWIcqGFm9EXN2XbdDQjXhN/S68o8456uIynsJcspBIBRBF8Xp+sFH57BmUT18VVaqc4lEZt1yIc8vK+078/06+oIlEeZIpSOr7ZsLZ3jRuqwJXpcFJqOAzX88in3H+jAUjBDTmxfN8uGN7e2q8v6ciwfJbEAkmkCVQ4RFNEGOxuAPRiBAvbRKNBvx3GsHS97iRBdG2VBQJjZLHQjIgKJAMguQVWq/JLPAlKz/5NwwSFHxWCJZf0DLVQ0xusmzIml2mxmm84WiuZiMAhbOqsH/fZ884adOcmBHG3mxYgmZMGveGEYXK9RL8tSlGGW8P6tw/s8fqhu0Ow92Y0adeoQ1xZHTg1i5IGn0Om3iGKlqPUFS8eEHuspSbKNIlpBMpoiInmrLWCmCmWhxPE6fbEd7Nz1qrxWbZMyrlUWpm32+tq0wRVTeS5BTSmgiaQAwOBLFYy++h2qXhIUza+Bxink1ps+tW87n+dWqOpp6P9FU2j2Ntm+m1tyDJ/oxFIygyiFh0axqGAwCDhzrS/eLTCjA/qM9CGtoH8DRN6xasiWzfbhhZTO8LktaJXQoKOPN907j7X1jhfxGI/G0EV/KOrOCTLpoNIpvfOMbuP3227F+/XqcOHGiqEFYJRMx/UVAqj6A/ApWI0Kvg96nq6svRDxUKABkxoSsdtHf3+uy4Ad/vwomY/Z3MBkF/ODvV6G+hqG+WE0Ps3tcFpDKxkwGYN5UD/XvFzNqWuY20Q2fOkZ6KcuoO3JmUNUgA5I1gaR0gXgCsDAOU9Pq6H3KJhKVqHnjJCkm/Sx1OHjqvhV4/uFWPHXfCqxvnZ32qKW8yXoyyABoTu/RCktFNh+a61xYuaBO9RqplUWpUgiLqjHkcEoIqz1Div5hGW/v7YCNUeqRSzF1y/mmffuqrMRU73zITUdW2zcz11wFyWykbfs6YRAELJxRDeBCdMQfjDJVLjn6h5WyuPvwOby9ryP9nKSem/XXzs7aN7xOidhqqRTrf0GRsnfeeQexWAy//vWvsWPHDvzrv/4rfvjDHxY8iLAcoxpFPf4wUUFRjibQ2UdPn+saoNd8fXSaXpN1mtFHy+2gp8L53FZYRROuXtKA9w53YjicgMtqwOXz62EVTegfokfaWDnMZrOBev9YUqRHGbUjnQP0mjYzqzk3Y50Nj8aIXm+zCYhSApW05tGAhjQnDkcDpYhWSmYjfDX2rObi+dR5jDdCiSvFgmHt3mZSZkGKcCSedyuLUkWcJ5LEvRyNo6tvBPHzAguci4tklgk0N+btGwxj7ZJ6HDwxAH9gFKJZPWUvRTF1y/mmfUtmI1YtqMNbKqne+aCWjpzZXxIg94bcd7QXSikKjjgTErU2Q6l944aVzTjbE4RFNGLTL/ao/n3/8CgGhkdRxwik0CjIKJs2bRri8TgSiQSCwSBMpuKyIFnSy1UMo4elsmNmjM/jpHtwXTb63/sZQhhhOYb/3HEyq65kOJzAlg/OIqEo+PzVM4n1DtUuiSl7C0Wh1lXtbOui/nkfQ2jEyAioBsJ0w2iE0QctnkiAFAkVGCKo85u98BJSMrxOkasTckpKqdPPCqnzGC9YrUTyxWoxEFuf5MKqc4WiFGxkFfsbTgSJ+6y+egEZXmd5e+twKsNQUNZskAHJNhxrWxrxhc/MwlBQhsMm4o3t7dh7JPmcpOZnZi+mQikkZfj2a2ZBEATsPd8PshAy31utv+ScKR7iew+cj5xxJh4ehwgFwGBQe3puLmpOtdxnyOMUifWHALDlgzO4a93cgsdQkDVls9nQ0dGBz372s/D7/fjJT35Cfb3HY4OJki880sVo8MvIgbba6UbbjClVwK5TxOu1jPTB5kZ6+t+s5moIwjHV8KggAL4aJ3a1dav+7a62bjxw62K47KLqJu+yi6iupo/P7qAblXKMscwY6QcZK8ModTEOIQ2T3MCH6kIuAGCSRGLINxZX0FjrwNmesdHK5noX5s6sxerFjfiv7WOFQlYvbkRjfRV1bOXA59N3yqTexzfesNanUjEaiaGrbwQetxUWMTmnnG4rfB4rehh9typBFSMtO1+8Hjua6yJo7xwu6n2MBgHzZtWm7yEA0KWgSs+qRQ2qa86qRfUVWXNyeeGNQ6oCCzariHtvXFDBkY2Fr0dkWGuTwti71d/TjsY6V3rOfP2OpRiNxOAflmGzmBAajcHjkrLmV6H83ReWwGYVsbutC32DYdRUWbHisjrcc8P8rCyW1Od7PNb0eH7y+kG89QG5v2ouBgPwFyuacd+NC9LvrTYPdrZ1w2QQEFPxDhkMAhIJRdUws0rJex2WeSqjHrnyvCCg2rqsFa9Lwozm6qxnP/cZYtVkHv7ED2fGHp8vBf3VSy+9hNWrV+Mb3/gGurq6cPfdd+P3v/89JEn9cO5nRGLOdg5Srx9pp6f37PtI3eBJYWLYJDMnO6jRlsluC7W5tZBIEPNVFQU4dKSbOJHDchwHPupCZ696AXxn7wh27aMvTCdP05tPT5/sQFs7+TVWQn5sClb6ZDUjkmlneK+rLAZ4nWTP87fuWIJ//tW+MUpnD61fjN7eAK5f3oh9R3rGXL9+eeOYVLFyC2X4fM6sz9QbpR7fxXCgYq1PxcKKWiycUa1LZa9AHjLxWvA5JHxu/WI89Yu96blaCIqioK8vmJ7D4zGvc7nhiikIhSNjFFFvuGJKxee/HI1jxwH1FLAdBzrx2cubdJPKWM718lJYmz7+hL735yKZDTApCdV7bgIQCUdgAhAYCiP1imLn142rmvHZy5uy3mNgIHneUYtkpdbGv//CYiTicew71qcp+nHVonrcumZ6+r1p80DNIEuOh7woLZnlw01rpuMHv96Pc/5Q+qxRV2PHqBzTXU3wpYLXJWHeFA+uW9YIyWw4vy4XJpoVluM48Ul/+jmlPUMk+gbDOPFJPzUbg7Y2FWSUuVwumM3JglG3241YLIZ4vHDvwSDD8nTa6B7bGhc9UhRXFDTU2NChUnvWUGNDtduKJbN9qrnMS2b74LSJuGpJPd7eO1aB5aol9ekCVXUlMAMcjEiePxCBTJCHlGMJBEL0PmKsItQ6RqStyWfHIYrRVuWgFwez0ktJRZEX/l7Colk1qrLhi2ZVw2k1pyXtA5EEnKIhS9KepYRGW/h5Kg+n3LBkoTOlmweGR3WTPuMtcaTMbjVBNJnSc3nf0V689N9H8n6fhAL0+kOoq7FXbF5npk4aRTPikagER3XBAAAgAElEQVRuDJ2JVPPGKQ5WD9dcfFVWzc9pKfdNUsowaW1MKAocNgkHT/QzDTIvoZa0lKnhktmAXW3d2Hu0J+ucl1CAjt4RNNU6LmmjjJWWnk/dY74MDMvY0daNj04NoGVOLW77zEysWViHx372ft7vNTIaw0PP706n765d0pD3M1RsCntBO9eXvvQlHD58GOvXr8fdd9+Nf/zHf4TNVvgizyqsNBrp1+tq6EV1DTV2PLJh6RjpeIfVhEc2LE2OgTS28/9d35pUYPE6k8aA1ymidVljWv4ySkgRjMYU+NxWkPQmjAbAwVBE8gfoQiD+IP06sw9ZhG70hRjh+kCY/veSaARpDTcYkg12aeqbKZw2EYtm+bIMMi1KaLlKS6mFP7OxJodTDrQ8n6kD/qZ7l2Pj+fVID8xqpKuu5su+YxcElZw2EdMbinh/QdDFvJbMRtTV2HVjkAEXat7U0EvNG6c0hBn12mqv16oOV+75RVsbdx7qxn9tb2caOoIAPP7Xn85Ss01Bmwes3q1jx5qAAhAb0I+EI1jb0pCh7CoRz3wXI6ysh3IZZJkMBCLp59PtkOApYp1LPetbPjhDfIZIwYZixHGAAiNldrsdzz33XMEfmsskL92gmzqZtXnTZ9jwSARvvn8GwZwm08FwDL/700ncctUMavPqz18dpzZP7OoPUiXd+wbDMJsMiKuqCxpgNtNnb5QRhRxmeJLe+3BshC+TfYTvnqKPod44xIh09g6GYTIAauuZyQBEonHsJ4xh/7F+3Ho1WTmM5RXu9Yc09UrhcMpBPlELyWzEtHo3HFbTmLWKxA2rpuD3O06XbLyZjGgcg1Y+6cpOU3czMghoGAWagtqlPa9L3ZONo19YPVRz8Qfk9JpDS0vU2mOsGGhro1YJekUBeofCWY7aFLR50OBzZGXXFMtAIAI5EsejG1rQ1ReCwQA8u3l/yd7/UoGltq2Fdw92Yd/RXvhLkH5/8MQAFs6swdt7x2ZxrVowGYIgjElhL0YcB9BJ8+h6nwMGg3rtksEAuGz0SFIvo0h+ICBjz8fqzZf3fNyDNQvr8jo4jQnDMyJ9gVCU2OssokHhbFajB//9HjmvVWQsjn3D9EiazGi+zWr+bGQ8RTbJjAhhokViyQaYhabbsJTQIAg8lYdTMfJV6pPPS5drNcr6BssnEBJXEkRV2ELI9Vvl6+XP5NjZQT6vKWSmxJbywMDRFwOMLJpcRLMRDpsZm7ccpaYl0gymgcAoegfDaPRp65FGgrY25sPAsIzp6i0LifPg1qun47Vt7el/r3KIGAxGCq5zBYCdbd3Ye7QXciQOZxEOp0uZxTNr8P7H9CABi8ymzpmkUiy9TglyNIaRUbbh7w+MYu2SBhw/OzRGs+Dza2dANJmKbrEyZpxFv0MJkMxGrFlUr3ptzaJ6nOyiFwJHGEaFxynCH1RPsfMHI4AgFJXu4fPYIBGiXZLZgGn1Lur7i4w4t6+Knjc+g5EGtPxTk6jX5zfXUK831dILpqfX0dXGqpz0+1flECn3R6Le/5Q3TI0ls2vgq7LyVB5OxWA9n7mLeD51EEaDgLVLyqc7KEfjWDSzumTvt2Batoqt2yGl08HzZVK1nc9rCpkpsT95qBWb7l2umuLFmdiYC1CNff2ddmZaIi31T1GAf311PzZvOXq+nU1h0NZGVh16Jr/64xHiWDLnwdP3rUjPA9FkwvrW2Xjiy5/GivmTAWhr1cFiNBKHgmR2VikobadIfdNU68D1VzRTXyMUeEOqHCKe/coVeOre5Vg0qwaSRmVEj9OCt/d14EzPBWGqTM0CQL05eTHoZ4WmFBVNq6MbBfOmetDoU68ra/TZ4bbTN2irmN/BKRfJbCQaTr4qK5w2kW44eGwgfYTZCOYBg7Uu2iT6wWfuVLrkP+tg1lhLr+kLjtJrzgaDEdgs6tFQm8XMvP+3fWZmVsf1apcFrcsacdtnZuZ9KOZwSg3t+cyFdhgai4JJ3sKbVLJYMqsGR8/Q25XkQ8ucbOeQZDaiZU5tQe9VX23n81oDeqx545SO2U35tV8YjcSx/6h6JCJV4wrQDSYgu36nGEhr46oFkzW/hz8YZY6FdHB+Y/tJ7GzrVlXeJjnaxxO7VRfJbGVFMhtw9ZJ6PPalZfA66eJShfb1Hh6JIJ5Q8Pa+Dry9t0Oz43PhDC8OHmfPl1Kii19cjsax6xChj9ehbqy+jD5B4wkFG+9eiqd+sRdne4JQkLTxGmsdeHRDCw6doMvGftIdKCrdQ47Giak4qcLa1PvsPdILf0CGJ0MxKBZXQPpto3Ggs4+e+zxKyg08z6lu+t8f66QfvKIEZcgUxzvofYdGZbpRZrMYMRJW9yyNhKPplC4SrCayPJWHU0nyUeqTzEaiEmku8QRw9Ay9nUgxnBsIo7NPvVVHIXz4iR/zmrMdPJnr4kBAexpTWI7xec255HHaRDT4bOjo1dbWI5mmpy3t98L8IsuLF1tfRtq744kE7DYJOw50np/bEuRIHEFKDV2+Y6HVzSVLZhTIGspLCmFukxu9Q6PE+2oRk47+Uta96Y0Vn5qE66+YmqUIWkhKu4CkIJ/HISJMSF30OC2wSibi751LtSu5l6xd0oBt+9Q1GcqVJq8Lo6zXHyI+/HI0AT9DSAKCkCW1nCubrt4K8AIKFObBnga9mD9ZWFvttqSGmvVfAOhg9Gk5dY4+MVk1YV43Q7Ke4REaGqHnrbOUjFgLW0dviPgbDwZlzQ8+SXa3mN+WwykVktkIX42d2ZcprwyNMurnt7UPMFN6WFLImZhV0rRTc3PNono89uJ7mt7Hez6lmc9rDgf49t3LsOkXe3C2h+1AWTKrBgdP9GuqcU3PTYq8eKkOprl7dyyu4C9XT8c1S+oRlmPpud3eOYhNv9hbkrHQzm3DjDZExWKzmaFQ6oGtItlRPdGxiEasXDAZd1wza0w6tVUy5bWnAMDGDUtht5rhdkh4/Z0TRIGjsBzTFCH7m/9nHpbOrU33KcunJrwU6MIoY0ViHFYzJNGgKpZhEQ1ZqYNOm4jpU7MbUs5poqfnZV4nHexpuB0SPITm01WO5AGC1qtoep2L+v6sIDpL+rOZoV45v7kaW/d2Ea+LZvpjIjLy2pvrXGlvRi4CgDlTqsblwS/kt+VwxhOZokSai0U0opmR2l0My+b68G6begYDANzz2bn4+f/9WPP7TZlEHqvbLmrejFvm+LKMLz6vOZcyosmE796zHIrRiO17TmFStR1NtQ68sf2kahTZaDyelzKnz2MjCv6U+mCa1RstIMPrvCBCAgANPidxLC67CKvEPtKmVCetkqkkQiOFsPcIfY0fDEY0+9tEs0GTYFylEc0C/umOJWjwOYnOs7Acy8sgMwhAzfkSIYCeFRWLK5p+7zlTqtLjq4SSrS6MMrOJbnbYLSb43Fac7R3rCapxs5shJkP8dnSo/H2Dz64qp5oPktkIu1XdKLOf70FGk5edwlAxYj2krPTFBKPozM1o/swSUkkogGQWIEfHDlQyC2ie7EJjrboEbWOtA9VuK7V5NPd+cy4V8hH6WHHZpKIUDGmYjGwRAZddzOtQYzCSY4CszVhAspk1T0/kcLJJGTIHT/Sj1x/OUlNUiyLnm/Y7ngdTmvN6fets6lgGgxF896X3iQ2u1Zph2yzmkhllTbUOhEZj6XsalqOqPV5JDupMqhwihkbYapDVLgse+uIS/ODXB3DOH0qrA5ZCtKTURKMKRJORmtXgdkh5Kf4mlOTekTrD07InjAYQn51McttbjXeavC6MMp/HRuz4bTQkf6heQqi3dzDMrDkCgG+frznLlbV8dENL1utovTtIyNE4QgQxi9BoFL3+EFW+2WKhf04tQ31x6mS6t9zBMDoFCMSJbDCAWXxptRixamG9qlG1amE9JLMRj25oIdb8AVSdFw7nksHtkCCJRk19eoyCwMwyKAQBwPe/ugoGxuybVu/StMml8LnJ60hKiVHNseV1inji/lUwKQnuoOFwcmAZMrlR5ELSfsfjYKq1N1rmWPpz2v3kfvdM1O5T/7CcNqYGhkc1RaeqHCLmT/fiw3Y/BkdkeHOiMako3IM/2an691o+Y16zFzspWQoplsyuwZvvnUFXRi9ZPRpkACCJRjz32kFiGwaA7gBQw0tQ5yZlT9z2mZmIJxS8s69D9T5Vu8a+33inyevCKAMAE6W5cu9QmFpzpqVnRmbN2dmeIBprHVkRMjUvCsnjkgurpiwluU8K/0+bTE9fDIbp+c19gzLqa2zo7Btb7FtfYwNrGRihedsVYFYjXeFpTpMHS2b5YBAEVSETIPlgz5lShWA4Cn9ARpVTwpwpVTAaDNSULVbzaA7n4kPbrrrvaC8WzSidZH3mp//nuyex4bq5aCJEuJvOr5+0A1IuvYOjqKtRX6dTSoxqm3HLnFo017mYtXgczqVGMU2e80n7HY+DKf0cdaFeLDWWG1Y24zs/e1+1SXDud6fdp9BoDI99aRmGgjKee+0gM0rjtIn4+BM//EEZVQ4RC2d40+dEowGo9dhwtieAUUJvWhre8+emG6+chiOn/cSxVJ8/n96wahoe/I8deX9OJcjsH0YzntUcADaLSXUfyk1lZ2E0GHDXdXMARcHbKgIeS2aT32+80uR1YZQNBWVEKM2Vg6yeD3noZIpmI6rdljENl1neJhqsBrG+KisWzqzG23vHPgQLZ3oRZnjFQ6wUJYHcv0EQ2EIb5wZCRO9KKjysJf2Ttmjn3l9/4ML9bV3ayBvBcjhIroVaN/OBQCQvxcJ82L6/E7etnZWOcJMyDDIPa23t/fjR79qI7+m0qbe9SEHyxt945TR09Y0griEjgsO5lNBqyJSKch5MWeeo3AhGWI5pVpJk3aewHIPPY8OcKR5ihKraNdY4GAxG0of7dZdPSZ97CslgWDl/Eu76i7npNY4UMVp52WTctW4OJLMRL/7hQ037hWQyQC5DVkUKt0NEYCQCj1PCnCkemE0C2tr95xtzSwjJMdXsj71HerFmUX2WAqOaA8BkFM4HTUoTqV1/7WwYjQZdKvfqwihjTcZp9W5KzZIBvoxFQo7GVTdwWiQsFlcK9jYlx8DOuT52Vl12/tjZIaxaQGhHf56GGnovovpqsiRuR28Ibjv9MDTJy1hkBUFz+qfaos3y5t2wsnncFW44HD1CS+NTg5YSWAzxRFIVdnp9FTXDIIVkNuKy6dUwGoQxOflAstF1PSObIXczdthEvLG9HY+/+N6Yon/eBJnDyd+Q0TP51q7l893pr5Xw5nuncfBEPwaGZUhmAxQFiMQSqHZZsHBmNVqXNsJhNeO7L6mrUL6zvxPb9nWmz5WXzyP3eCNhtSSP4z3+ENwOScVJJWHuFA/uuHZ2Whnww1N+Te8txxJlrTXbeNdSxBNKljM+VQoUiSXwOEFZdyAg4/EX31PNTMs9S5YyUptPm5rxRhdGmWQ2YvGsGrylUpO0eFY1nDYRPo9NVfLV57Gm+1qQVHuMBgM1Eta6tJEYJh4Y1uZtumnNNOw+3I1g+EJUy2E14aY10xAIRdBBkKvt6BlhelXcDhGNPruq0Emjz06st0txqpue9lPlkKg1fb4qK0STUdPhTA0tXqrxVrjhcPQITTRIDVYPwmLIXMucNhHzmr3U10tmI65cVKfa1+XKRXXUeZxby1vrsWHzlqMFZy9wOJcCrLPTRNs7af1cc8nHiKO91mYxZ6WyZWYWWSQjbvvMDIgmE3oo2gApYye1RrFKTtTYcagb+4/1jQka3HjlNGz+4zF8fGoAO9u68fFpP5bM9uGqxXXJ8hiN5GuQGQBoja3FE8qYM3JqHafJygPJdHmta3upI7Va29SMJ7pxNyYIKYgJRUk2ZyY0DQyPJpszp4yu/mEZinLhR/7N1uPMSI1VMkEyq+f/iWZDlsdFjsbR4w+N6eT9vVf2ZR1igOSh5nuv7EuLW6ihAJDlGLHXl0FICqFsvHspmmod6dcZhGRdx8a7l8JhpxtHk6rtxO70kmiA2y4SFTBz/z11OMtHsTLlpVIj5dG67TMz0bqsEdUuCwxCMlWgdVmjLsLJHM54QRMNUmPRzNLXlKWYxmjVoYaBsJCR/j2eSGDzlqPY+MJuPPz8bmx8YTc2bzmKkBylrtm56y+Hc6lCO1tMVNT6uaqRz7lB7bVrWxqo/cA6ekfw1Pm+aLRzTC5HTw8i32D+aCSePL8i+/z6xvaT2NnWjYFAJOvaf/zn4fw+gEKd15Z1tqzz2vDs365EtYbvqyaOkUnKINYCX9t1EimTo3Hsajunem1X2zmsXdxAFdLo9YeoG/iahXX0vOuRCGJx9SUsdj58REt/DI3G0NGr7rHu6A3CxlBX9FVZyTqp5ycKTajEyvCGWcwGcs0ZgKGRiGoPOCBZ01dsXrpWjxZvBMu51MlHEt9oALxuKxxW0xiHEAAYBYCwrGkikufmKEfjOEAQ7DlwrB+fVxHsIWUwJNXQeJ0ph0OjkDmnZ/Kt7c9HgETttUNBGdv2jo0yZtLRG0QgFIHTJmpWBvQHZKZBqYV9R3uhEAIWnYSSlXwxCMBDX0yWoeSeLbV8X5o4RorMVEyayiVrbS9EHZ0GqdypkujCKOsdDBMloEcjcURjCWo+MASBuoGz1A8j0Zhq6h6QTOnr9Yfwp4NdxMViycwaqlAGqd4rRe/QKEitxBIJZKlLqqURneyih14/+mSQWAw6GkkACrmpXqny0rVK6vJGsJxLGVrtQy6ptUEkRLmNBiBehNPxyOlBrFxAb8eRCc2gHFDZbGkZDB+f8l80tTIcTrkYb6GPcjJeSpKZr9Wy3iaUpLEyr9k7xrgQCHVaLnuyz1ixDATkfHTsCiIl5lbrsY05W+Z+X0lM3v9INJ6XOEamQdzrDxFVLklrezHq6Gqwyp0qiS6MMtZTZzYZiE3+bBYzfFVWpvohLVIjmum3IRpLUBeLdZ9uov79nClVxIZ41S4p/aCTYDVvnjOFLlk/r7mKGohzO6Sy13SNd68HDmciIpmNmhuael0WQFHgJ9SfaWh1RoW1ruTidkgwEIQ+DIIwZrOlHSgHgzKumD8ZO1SU0HidKYeT5GIS+qiEgamlL5ZBSPZUBcaeY958/wzeVom0zZ3qwZ8/VM/+AgCzUUBUQxqD1ylBURTNNcaFQEs/VDu3AShYHEMyG9FY68z7vFmMOroapX6/UqKLmjKfxwaLqD4Ui2iE2yER835HzhdUknJWUz8yLffYzajJUqDQhSoicRgJd9JoSDZvJo/PB7OREedmGK3V51OY1HBYTbBKZmreeViOjVtNV8pLxQ9VHM5Y5GicWuOQyZLZNfB5bMQ6BzsjbZqGw2pCtVt7lAxIek/VDDIgWQiemw7JqjW949rZvM6Uw6FAq9eZaM4LLbXn5SB19jES6l4bfGNFzVLnmPWts1TXqLvWzSaeaSWTQZNBBiTPhy1zavP7QnmiJf0w89wmmY2oq7EX9Wzlc95kRVDzrUEr9fuVGl1EyiSzESsX1GGrioLQygWTEZZjRG/wYFDGUFBmpsfRIjVhRh8w0WyieqOgKMT0x0QCzPF19dPTG1mRPAB49oEr8OB/7Bqj/vjsA1cgFlOIcqgGAbBKpqyGjIFIAk7RkJeYB4fDKZ6hoExc64BkZDs3dYPkdVw4s4ZYq0sjtW7kgxyN49CJfuprUilAKVi1pjbJpFvZYg5HL6TOFgdP9KNvMKyrnkv5kK8kfqlInX3+atVUPPPyPnQPhKCA3PZH7W/VzpWkM+2KBZPRdqJf9TxpEJKOcq/Kb/jBxz0YDJYuYiYAuLqlIe/npBR1WPlkTpU6gqr3lF9dGGUA8IW1M3DszFBaqVBAMmT8hbUzoCj0mjC3Q9Lcd0At95jWG8jrlJjpjz6Pjfj3HqfEHF+ycZ5BtcmzZDYkhUAYWEUz/u3ra9A/FMaR04PJlMnznu6eEXZzaJvFpNscWw7nUoGWjpRKQc4t/M51+FQ5JNitZhw9PZjXZ991/VwsnOrJK0KWm+tPIjMFiDZ2tQOlHmWLORy9kDpb3H+LFSc+6Z/QpQFaa8/LgcMq4an7VhTU9kftXHnHNbNgEARVef/fGI+rnievWlyf1YQ6Rcph/tiLf8bQSP5y+yTWfbpJ8/muHHVYWmoBS52iq/eUX90YZa9ta8/qlK4AONMTxGvb2rG+dXZe/Sjy3cAlsxEtc2pV379lji+d/gioLxZGg4HYW8huNTPHJ5mNWL2wTrXXyOqF9P4+uVS7rWOK890OCRbRoCr2YRGTkv96zrHlcC4VaN7ilCk2EIhkzU2tdQ40jAYBf3XlDASG6D0Pc8ldN0iopQAlP5fXmnI4pcAimiaMqAcJPTT11dKTUQu078I6T5LGRUoPV6PRZ0+q2BJ6mXld+RkglTojljqCWqmIrFYKMsp++9vf4ne/+x0AQJZlfPTRR9ixYwdcrvz72gDaVHfK7UEpJv2R1lsoNBqFrCHMe/s1syAIgqoXojSQ6tYERIpQPeJwOKVFq8JX7tyUzMn624PH1SWyHRYT5GhctZ7BbMpfv5m2bqfQkgIEcNVVDodzgYspOq72XQpxRgVCEYQI/XrVCMtxPP7Xn8arW48XLZhUjDJmKSj1+b+SEVkWBRllN998M26++WYAwBNPPIFbbrmlYIMM0J7jWU6PqtZJonZ4oI9f1pSjWk6P8VBQhkyQYotE4zjbE9R1ji2HcymRuRa0dwzhn3+9X/V1anOTthaNyDGiZpAcScA/LOe1IdA+SxCAL18/DwtmVPPaVA6Hw1EhH2fU2Z4gsQxFDX9gFGE5hi9dPxdWi6koA6TSdVilPh/rISJLoqj0xUOHDuH48eN4/PHHixpEPjme5faoFvL+pcxRLcf3Y42vsdah6xxbDudSRDIbMb3BndfcpM11mryyxylBjsYQVxTNmxP9syxYOrdWNxsdh8PhTGQaax1EwTY11PQWCjVo9FKHVerzcbER2VI3swaKNMqef/55fPWrX2W+zuOxwWSiD/iKhfX4w7snVf69Do31+fXLAQCfz5n33xTDqkUN+K/t7Sr/Xq86fj2Nb/rU6rzHX0nG+97lCx/fxELL+lRK8r3/+c5N8usbAED1WjgSx9f+ZRt8VVasuKwO99wwH0ZSn48ixlYMen5u9Tw2QN/j0/PYKk2+a9PFdi8vpu9T7HfxAWiuc6G9c1jT69XW4MYiPn8inRHzJd/fJh5P4Ge/P4zdbV3oHQznvW/SKNgoGx4eRnt7O1asWMF8rd9Pl3wHgBChN08oHMnbivX5nOOei3zDFVMQCkfGhIhvuGLKmLHocXz5jL+SVOLe5cOlNr6LYdPUsj6VikLuf75zk/Z6AFnXRLMRo5F4ui1Ijz+M/9rejlA4oql4e7zWDT3PKz2PDdD3+Mo5tkttbdLz71wIF9P3KdV3+af1i/HUL/aiozeZymgQgHqfHTMb3Th0fKCsa/BEOSPmSyG/zeYtR7OEQvLdN2lrk6Dk6itr5K233sKuXbuwceNG5mtZX1iOxrHxhd2qodFqlwWb7l2eV2iwkpNZSzhTz+OTo3Hd5dhmoveF+lIb38Vw8BnP36uY+59vqgTt9XI0jl5/CM+9drAk62450jgy0fO80vPYAH2PjxtldPK5N3r+nQvhYvo+pf4uarL95V6DU+j9jJgv+f42pbBXaGtTwXG2kydPorGxmGDoBbQUEU4UMjuf6xHW+ErRrZ3D4ZSefNcW2uslsxGi2ViydVfv6x6Hw+FcLKRk+zNFlMZrDb7Uz4jltlcKTl/8m7/5m6I+OBO9FBFyOBzOpQJfdzkcDofD0U65983iKtJKRKqZmxp6aOamJ+RoHD3+EOSousQ9h8PhsEiluiycUa16na+7HA6HUzxyNI6uvhF+ZrtIKLe9UpT6YinRczM3PRBPJPCbrceTzaWHZXhdF5pLkzrAczgcTiZq60hTrQMj4SgGgzJfdzkcDqcEZK21ARleJz+zXSyU017RjVFWzubJFwO/2Xo8S+2lf1hO/78WtRcOh8NRW0f6h2WsbWnAHevmXTTF2xwOh1NJ+Jnt4qWc9oruzHVeMD4WORrHvqO9qtf2He3jYXEOh8OEto4cPN4Pj4s7wjgcDqdY+Jnt0qAc9orujDLOWC4mdUoOh1MZWOuIn3CNw+FwONrhZzZOoXCjbAKQUntRg6ukcTgcLbDWEQ/hGofD4XC0w89snELhRtkEgKtTcjicYmGtIxZRNyXGHA6HM2HhZzZOofBdeILA1Sk5HE6x8HWEw+Fwyg9fazmFwI2yCQJXp+RwOMXC1xEOh8MpP5lrrVE0c2VbjiZ4+uIEg6tTcjicYuHrCIfD4ZQfyWxEXY2dr7UcTXCjjMPhcDgcDofD4XAqCDfKOBwOh8PhcDgcDqeCcKOMw+FwOBwOh8PhcCoIN8o4HA6Hw+FwOBwOp4Jwo4zD4XA4HA6Hw+FwKoigKIpS6UFwOBwOh8PhcDgczqUKj5RxOBwOh8PhcDgcTgXhRhmHw+FwOBwOh8PhVBBulHE4HA6Hw+FwOBxOBeFGGYfD4XA4HA6Hw+FUEG6UcTgcDofD4XA4HE4F4UYZh8PhcDgcDofD4VQQU6UHkC8HDhzAP//zP+Pll1/G4cOH8fjjj0MURcybNw+PPvooDAYDNm3ahL1798Jut+Ob3/wmFi1ahFOnTuGhhx6CIAiYNWsWHn/8cRgMpbdJCx3f4cOH8ZWvfAXNzc0AgDvuuAPXX399ycYVjUbxyCOPoKOjA5FIBA888ABmzpypek/+/d//Hdu2bYPJZMIjjzyChQsXlv3+FTs+Pd0/ADh16hS++tWv4g9/+AMAYGBgAN/85jcxOjqK2tpaPPPMM7BarboZ3+DgINatW4fZs2cDAFpbW3H33XeXbHycJJnrg15Qe3auueaaSg8LABCPx7Fx40acPHkSRqMRzzzzDKZMmVLpYWXR39+Pm2++GT/72c8wY8aMSg8nzY033q9YWHwAAAgOSURBVAin0wkAaGxsxDPPPFPhEV3g+eefx9atWxGNRnHHHXfg85//fKWHNGFJJBL4zne+gyNHjkAURWzatAlTp06t9LA0k7kmks4Zanu+nij2/KI31NZdRVEm7PcBstdpk8mk3++iTCB++tOfKn/5l3+pfP7zn1cURVFuuukmZc+ePYqiKMoPfvAD5Y033lC2bt2q3HPPPUo8Hlf6+/uVm266SVEURbn//vuV3bt3K4qiKN/+9reV//mf/9HV+F599VXlxRdfLPmYUrz22mvKpk2bFEVRlIGBAeWqq65SvSdtbW3KXXfdpSQSCaWjo0O5+eabFUUp//0rdnx6uX+Koii/+93vlJtuuklZuXJl+u+ffPJJ5fXXX1cURVGef/555ec//7muxrdjxw7lu9/9bknHxMkmd33QC2rPjl744x//qDz00EOKoijK7t27la985SsVHlE2kUhE+du//VvluuuuU44fP17p4aQZHR1VPve5z1V6GKrs3r1buf/++5V4PK4Eg0Hl3/7t3yo9pAnNm2++qTz44IOKoijKvn37dDdHaOSuifns+Xqi2POL3lBbdyfy98ldp/X8XSZU+uKUKVPwwx/+MP3/586dQ0tLCwCgpaUFe/bswfHjx3HllVfCYDDA6/XCaDSit7cXhw8fxuWXXw4AWLNmDXbu3Kmr8bW1tWHbtm2488478cgjjyAYDJZ0bH/xF3+Br3/96+n/NxqNqvdkz549WL16NQRBQH19PeLxOAYGBsp+/4odn17uHwC43W688sorWX+/Z88eXHnllWNeq5fxtbW14fDhw/jiF7+Ir33ta+jp6Snp+Dhj1we9oPbs6IXW1lY8+eSTAIDOzk7U1NRUeETZPPvss7j99ttRW1tb6aFk8fHHHyMcDuOee+7Bhg0bsH///koPKc27776L2bNn46tf/Sq+8pWv4Oqrr670kCY0mXvL4sWL0dbWVuERaSd3Tcxnz9cTxZ5f9IbaujuRv0/uOq3n7zKhjLJ169bBZLqQcdnU1IT33nsPAPD2228jHA5j3rx52L59O6LRKM6cOYPjx48jHA5DURQIggAAsNvtCAQCuhrfwoUL8U//9E/45S9/iaamJvzoRz8q6djsdjscDgeCwSC+9rWv4R/+4R9U70kwGITD4cj6u0AgUPb7V+z49HL/AGDt2rWw2WxZfx8MBtOpRJW8f6TxTZ8+HV/72tfwyiuvoLW1FZs2bSrp+Dhj1we9oPbs6AmTyYQHH3wQTz75JNatW1fp4aT57W9/C6/Xmz4Q6wmLxYIvf/nLePHFF/HEE0/gm9/8JmKxWKWHBQDw+/1oa2vDc889lx6boiiVHtaEJXdPNBqNuvmtWeSuifns+Xqi2POLHslddyfq91Fbp/X8XSaUUZbL008/jeeffx733Xcfqqur4fF4sHr1aixbtgx33303fv7zn2P+/PmoqqrKqn8aGRmBy+XS1fiuvfZaXHbZZQCAa6+9Fh9++GHJx9PV1YUNGzbgc5/7HG644QbVe+JwODAyMpL1706nc1zuXzHj08v9I5E57krePxIrVqzA8uXLAZTv/nH0S+6zozeeffZZvPnmm/j2t7+NUChU6eEAAF5//XXs3LkTd911Fz766CM8+OCD6O3trfSwAADTpk3DX/3VX0EQBEybNg1VVVW6GVtVVRVWr14NURQxffp0SJKkS+/6RCF3T0wkErp0/mghnz1fbxRzftErmeuuLMvpf59I30dtnc5cb/T2XSa0UfbOO+/g6aefxk9/+lMMDg5i1apVOHnyJKqrq7F582bce++9EAQBLpcLn/rUp/DnP/8ZAPCnP/0Jy5Yt09X4vvzlL+PgwYMAgF27dmH+/PklHUtfXx/uuecefOtb38Ktt94KAKr3pKWlBe+++y4SiQQ6OzuRSCTg9XrLfv+KHZ9e7h+JlpYWvPPOO+nXLl26VFfj27hxI958800A5bl/HP2i9uzohTfeeAPPP/88AMBqtUIQBN2kV/7yl7/EK6+8gpdffhnz5s3Ds88+C5/PV+lhAQBee+01fO973wOQTKMPBoO6GdvSpUuxfft2KIqCc+fOIRwOo6qqqtLDmrC0tLTgT3/6EwBg//79abGmiUg+e76eKPb8ojfU1t3LLrtsQn4ftXV6zZo1uv0uE9Odcp6pU6fivvvug9VqxfLly3HVVVdBlmVs374dr732GiRJwmOPPQYAePDBB/Htb38bP/jBDzB9+vRxSYPJZ3zf+c538OSTT8JsNqOmpiadz1sqfvKTn2B4eBg//vGP8eMf/xgA8Oijj2LTpk1Z98RoNGLZsmW47bbbkEgkxu3+FTs+vdw/Eg888AAefPBBvPrqq/B4PPiXf/kXXY3vG9/4Bh555BH86le/gtVq5emLlxBqz84LL7wAi8VS4ZEB1113HR5++GHceeediMVieOSRRyBJUqWHpXtuvfVWPPzww7jjjjsgCAKefvpp3URP1q5di/fffx+33norFEXBY489phtDeyJy7bXXYseOHbj99tuhKAqefvrpSg+pYNTOGaQ9X08Ue37RG2rr7owZMybkb6OGnp8zQeHJ3BwOh8PhcDgcDodTMSZ0+iKHw+FwOBwOh8PhTHS4UcbhcDgcDofD4XA4FYQbZRwOh8PhcDgcDodTQbhRxuFwOBwOh8PhcDgVhBtlHA6Hw+FwOBwOh1NBuFHG4XA4HA6Hw+FwOBWEG2UcDofD4XA4HA6HU0G4UcbhcDgcDofD4XA4FeT/B3KNfyocEwLvAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e92feb0ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+2)_with_comments.ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+2)_with_comments.ipynb
new file mode 100644
index 0000000..c625d40
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+2)_with_comments.ipynb	
@@ -0,0 +1,2254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For this practical example we will need the following libraries and modules\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "raw_data = pd.read_csv('Real-life+example.csv')\n",
+    "\n",
+    "# Let's explore the top 5 rows of the df\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics are very useful for initial exploration of the variables\n",
+    "# By default, only descriptives for the numerical variables are shown\n",
+    "# To include the categorical ones, you should specify this with an argument\n",
+    "raw_data.describe(include='all')\n",
+    "\n",
+    "# Note that categorical variables don't have some types of numerical descriptives\n",
+    "# and numerical variables don't have some types of categorical descriptives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For these several lessons, we will create the regression without 'Model'\n",
+    "# Certainly, when you work on the problem on your own, you could create a regression with 'Model'\n",
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "\n",
+    "# Let's check the descriptives without 'Model'\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data.isnull() # shows a df with the information whether a data point is null \n",
+    "# Since True = the data point is missing, while False = the data point is not missing, we can sum them\n",
+    "# This will give us the total number of missing values feature-wise\n",
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's simply drop all missing values\n",
+    "# This is not always recommended, however, when we remove less than 5% of the data, it is okay\n",
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's check the descriptives without the missing values\n",
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\3514801760.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A great step in the data exploration is to display the probability distribution function (PDF) of a variable\n",
+    "# The PDF will show us how that variable is distributed \n",
+    "# This makes it very easy to spot anomalies, such as outliers\n",
+    "# The PDF is often the basis on which we decide whether we want to transform a feature\n",
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obviously there are some outliers present \n",
+    "\n",
+    "# Without diving too deep into the topic, we can deal with the problem easily by removing 0.5%, or 1% of the problematic samples\n",
+    "# Here, the outliers are situated around the higher prices (right side of the graph)\n",
+    "# Logic should also be applied\n",
+    "# This is a dataset about used cars, therefore one can imagine how $300,000 is an excessive price\n",
+    "\n",
+    "# Outliers are a great issue for OLS, thus we must deal with them in some way\n",
+    "# It may be a useful exercise to try training a model without removing the outliers\n",
+    "\n",
+    "# Let's declare a variable that will be equal to the 99th percentile of the 'Price' variable\n",
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "# Then we can create a new df, with the condition that all prices must be below the 99 percentile of 'Price'\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "# In this way we have essentially removed the top 1% of the data about 'Price'\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\4102241817.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_1['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Ho+FI/jUZZYwekosMqwlCiDi/ChER0YlhiEkTkiTB6w2gYndDzJyVQ+G7idy+AIKKCgnxD/1kOcwAgHqXF/4A+16IiEhbDDFpJhBUowFDUdXo7dUOq6nH184Obz/gavP3+FpEREQ9xTkxaazVHRpaMhok2Czx314dkeUIhxh3gMNGRESkOYaYNNYSDjGZdnNC7iDKsJsgS4CqimgPDxERkVYYYtLYkRDT86EkAJAlCZn20LwYDikREZHWGGLSWIsnFDQybIkJMUBo5V+AIYaIiLSXUiGmqqoKZWVleO2117QuJS20thtOSpRIr47LzRBDRETaSpkQEwgEsHjxYrjdbq1LSRutnlCI6Z2emEAXzyQiIupdKRNiHnvsMWRkZGhdRlpxR2+vTtyd9JEQ08KeGCIi0lhKhJgNGzbg5Zdfxv333691KWkjEFThDy96Z7clMMSEh6Za3QEoKm+zJiIi7Wi+2J3L5cLNN9+M2267DSUlJQm5ptGYEtksYQzhJf4NhmO3S5IASZZgCD9a/KFeGJNRhs1shBzeS0k2AAal4+3Wxzvf/lyGzQijQUZQUeH2BpCdYYFBliDJEoxGCUL0/Fbu7rQ3nbC96Y3tTW9sr7Y0DzF33XUXysrKcP755yfkerIsISfHkZBrpRqn8/ibOPpVN2w2M4wmFXUuH4DQpF673QKb1Qij0QCb1QyjseMuSsc7H3vOiOxMC+qaPPAFBex2SygoWc3IzrYnrrHour3phu1Nb2xvemN7taFpiFm9ejU2btyINWvWJOyaqirgcqXX5GCDQYbTaYPL5YGidL6NoyQBHm8AHo8f/oCChubQ7tU2iwFutw+SUBEMKvB4/fD7O+57dLzzR5/LtBlR1wTUNrShKNsKs8kAj9ePpiaBRCzk2532phO2N72xvemN7U0Mp9MWV++OpiHm1VdfRX19PWbOnBlz/M4778Tbb7+NP//5z3FdNxhMz79IiqIes22SJEGoAkr4EVnozm4xQlEFVCEghICqoNO5LMc7f/S5yC3bTa3+6NcTqkAwKBK6HcHx2puO2N70xvamN7ZXG5qGmOXLl8Pr9cYcmzNnDm644QZccMEFGlWVHtq8oRDjSODt1RFOB9eKISIi7WkaYoqKijo9npeXd8xz1D29cXt1xJGtB7hWDBERaSc1phdTwkU2aLT3QoiJrBXj8QURSIHuRCIi6ps0vzvpaNu2bdO6BN0TQsAdGU6yJn44yWIywGyS4Q+oaPX4e2XIioiIqCvsiUlDvoCKoBKaYNsbw0nAkSGlyARiIiKiZGOISUORXhir2dBrCxJlhntfGGKIiEgrDDFpqDfnw0Rkcg8lIiLSGENMGmrrxfkwEeyJISIirTHEpCG3p/dur47IdDDEEBGRthhi0lCkJ8bei3cNZdpCw0ltHu5mTURE2mCISUO9udBdhM1igNEgQQBo5bwYIiLSAENMGmpLQoiRJAkZ4Z4eVxtDDBERJR9DTJppv9CdvRcn9gLtth/gvBgiItIAQ0ya8foVRKao2C29uyBzpp09MUREpB2GmDQTmQ9jNRsgy1Kvfq0jPTEMMURElHwMMWkmMpRk6+VeGOBIT0wLe2KIiEgDDDFpxu3r/dV6I6Ihxh2AKnibNRERJRdDTJqJDCcloyfGYTVBkgBFFWhu9fX61yMiImqPISbNREJMb0/qBQBZPnKbdW2Tt9e/HhERUXsMMWkmmT0xwJHJvXVNnqR8PSIiogiGmDTj9kXWiElWiAn1xNQ1syeGiIiSiyEmzbQlvScmMpzEnhgiIkouhpg0oqoCHl/y5sQA7YaT2BNDRERJxhCTRlo9AUTudLaaDUn5mtHhpCYPBG+zJiKiJGKISSPN4UXnbJbeX603IjN8d5LXr6DFwz2UiIgoeRhi0khkrZZkzYcBAINBju6WXdPAeTFERJQ8DDFpJNITk6z5MBFOR2heTE2TO6lfl4iI+jaGmDTS3BoZTkpuiMmMhJhG9sQQEVHyMMSkEVebNiEmy84QQ0REyccQk0aa20JzYpK10F1EtCeGa8UQEVESMcSkkchwUvLnxITuUKpp5JwYIiJKHoaYNNKs0XCSMzyc1OIORBfbIyIi6m1xhZjq6upE10E9pKgqWtzahBizydBuN2sOKRERUXLEFWJmzZqFBQsW4O2334bf7090TRQHV1totV4JgNWSnNV628vPtgLg5F4iIkqeuELMfffdB1VVsXjxYpx55plYtmwZvvnmm0TXRicgutCd1QhZSs5qve3lZ9kAcHIvERElT1zjDhdeeCEuvPBCVFdX4/XXX8cbb7yBv/71rxgxYgTmzZuHCy64APn5+YmulY6jSaNJvREF0Z4YTu4lIqLk6NHE3qKiIvziF7/AO++8g1dffRU5OTl44IEHMHPmTPzqV79CeXl5ouqkLkRur072fJiIaE8Mh5OIiChJenx30saNG3H77bdj/vz52LRpE8444wwsXboUHo8Hl1xyCVauXJmAMqkrWi10FxGZE8OJvURElCxxfeLt2bMHb7zxBt58800cOHAA/fv3x+WXX4558+ahpKQEAHDZZZdh8eLFWLFiBa688spE1kydaL+DtRYKskM9MQ0uHwJBFSYj794nIqLeFVeIOffcc2GxWHD22Wfj7rvvxrRp0zp93rBhw7B79+6e1EfdpHVPTIbNBKvZAK9fQV2zByV5Dk3qICKiviOuT7zbb78dF1xwATIzM4/7vOuuuw7XXXddXIXRidFqobsISZJQmGPD3upWVDcyxBARUe+Lq8//3XffRU1NTafntm7divPPP79HRdGJi/TEaHV3EgAUhoeUajm5l4iIkqDbn3gbN26EEAIAsH79emzYsAENDQ0dnvfRRx9h3759iauQukXrnhgAKMixA+AdSkRElBzd/sT7+9//jjfeeAOSJEGSJCxbtqzDcyIhZ+7cuYmrkLoUCCrRPYtsSd7Bur2iHC54R0REydPtT7zbbrsNP/jBDyCEwBVXXIE77rgDI0aMiHmOLMtwOp046aSTEl4oHVukF8ZokGA2yggEVU3qiNyhxBBDRETJ0O0Qk5mZialTpwIA/vKXv2DMmDFwODh5MxVEQkym3QxJgy0HIorCw0l1TR6oqoAsa1cLERGlv26HmNWrV2PGjBnIycnBwYMHcfDgweM+/6KLLuppbdRNrnYhRks5mRYYDRKCikCDy4v8cM8MERFRb+h2iFm6dCn+9re/IScnB0uXLj3ucyVJYohJokhPjNNu0rQOWZZQkG3DoXo3qps8DDFERNSruh1i/vWvf6GgoCD6e0od0Z4Yh7Y9MQCiIaa20QMM0boaIiJKZ90OMf379+/09xHBYBCtra3Izs5OSGHUfanSEwMcWSuGk3uJiKi3xbXYXTAYxOOPP441a9YAANatW4czzjgD06ZNwxVXXIHm5uaEFknHl0o9MYU53M2aiIiSI64Q8+ijj2LFihVwuVwAgHvuuQfZ2dm45ZZbsHfvXvzxj39MaJEUK7JWT+ThivbEpFKIcWtcCRERpbu4Qsxbb72FRYsW4dJLL0VlZSV27NiBa6+9Fj//+c/x61//Gh9++GGi66QwBUCrNxDzaGr1AQAyUiDEFOUeWbVXDS9+SERE1BviWt61pqYGEyZMAAB8/PHHkGUZ06dPBwAUFxejpaWl29eqr6/H/fffj08//RQ+nw9TpkzBkiVLMHz48HhKS2uSBHh9QVTsbohZ0K6pNbxvktUIjzeoVXkAgPwsKwyyBH9QRVOLD7lOq6b1EBFR+oqrJ6awsBD79+8HAHz44YcYNWoUcnNzAQBffvkliouLu32t66+/Hnv27MHTTz+NV155BVarFVdeeSU8Hs6pOJZAUIU/oMAfUOD2BqKBJjMFJvYaZDl6a/XhBg4pERFR74krxMydOxf33Xcf5s+fj02bNuEHP/gBAODee+/FY4891u1drJubm9G/f3/cc889GD9+PIYPH47rrrsONTU12LFjRzyl9TmRPZMMsgSL2aBxNSHF4Xkx1ZzcS0REvSiu4aQbb7wRdrsdGzZswE033YSf/exnAIBvvvkGV199Na699tpuXScrKytmEnBDQwNWrlyJ4uLiDvsyUee8PgVAaChJyy0H2ivKtQOV9ahmTwwREfWiuEKMJElYuHAhFi5cGHP8pZdeiruQ22+/HX/7299gNpuxYsUK2O32uK9lNMbVwZSyDAY5+qskSzCEHwDgC8SGGNkAGJSOYUYO38kUz/muXmuQJUiyBKNRghAS+uWH9tSqbvTE9V60b29fwPamN7Y3vbG92oorxABAS0sLPv/8c7jdbohO7kI50W0HrrjiCvzkJz/Biy++iOuvvx6rVq3CmDFjTrguWZaQk5OeG1NmZtrgCQjYbGYYTaF5MIoITaLOtJthNBpgs5phNHbcxdpmNcZ9vqvXmowybFYzsrNDwXPE4ND8qNomT4/eC6ezb21bwPamN7Y3vbG92ogrxHz66ae44YYbjjn5Np69kyLDR/feey/Ky8vxwgsv4L777jvh2lRVwOVKr2EMg0GG02lDS4sHHq8fHo8f/nAPTHP49mqLyYBgUIHH64ffr3S4hiTUuM939VqzyQCP14+mJgEhgAxzKKFXN7hRW9cC4wkm9kh7XS4PFKVjaEo3bG96Y3vTG9ubGE6nLa7enbhCzB//+EcMGzYMt9xyC4qKiiDL8XUrNTQ0YO3atTj33HNhNIZKkWUZI0aMQE1NTVzXBIBgMD3/IimKCqEKKOEHALjDt1TbLAYIIaAqiJ5rTxUi7vNdvVZRBYQqEAyGnpdhM8FskuEPqKiud0fXjomnven6XnaG7U1vbG96Y3u1EVf6qKysxI033ojJkydj4MCB6N+/f4dHd9TV1WHRokVYu3Zt9FggEEBFRQXXiekmrz8UYuyWuEcGE06WJBTlhIILb7MmIqLeEleI6devH1pbW3v8xUeOHInp06fjnnvuwYYNG7B9+3YsXboULpcLV155ZY+v3xdEbrG2WVMnxABHVu7lHUpERNRb4goxCxcuxBNPPBFd8K4nHnzwQUybNg2//vWv8aMf/QhNTU148cUX0a9fvx5fuy/wtLvFOpUUhdeKOcy1YoiIqJfE9cm3Zs0aVFdX45xzzkFubi6s1til5SVJwgcffNCta2VmZuKuu+7CXXfdFU8pfV4qDicBQDF7YoiIqJfF9clXXFx8QlsLUO8IBFUEldBE25TriYmEGO5mTUREvSSuT754bn2mxIv0whgNEkzG1NhyICLSE9Pg8sEXUGAxpVZ9RESkfz3673tlZSU+++wz1NTU4PLLL8e+fftQWlqKjIyMRNVHxxGZ1Gs1p1YvDABk2EzIsJnQ6gngcL0bg4sztS6JiIjSTFyffqqq4o477sCrr74KIQQkScJ3v/td/OlPf8LevXvxwgsvcLgpCSKTem2W1OzlKMmzY8f+Zhyqb2OIISKihIvr7qQ//elPWLNmDe655x589tln0W0HfvOb30BVVTz00EMJLZI6FxlOSsWeGAAoyQttOXCwnvNiiIgo8eIKMa+++ipuuOEG/OAHP0B2dnb0+KhRo3DDDTfgs88+S1R9dByp3hPTLy80L+ZQfZvGlRARUTqKK8TU1dVh1KhRnZ4rKiqCy+XqUVHUPSnfExPezfoQe2KIiKgXxBViBg8ejE8++aTTc+vXr8fgwYN7VBR1z5GemBQNMXlH1opRVO332CAiovQS16ffFVdcgTvuuAOBQACzZs2CJEnYs2cP1q1bh2effRZLly5NdJ3UieiWAyk6nJTrtEY3gqxp9ETnyBARESVCXCHmRz/6ERoaGrBixQqsWrUKALBo0SKYTCYsWLAAl1xySUKLpM55/eGemBQdTpIlCSW5DuypbsGhejdDDBERJVTcn37XXHMNzj//fKxfvx5GoxGZmZmYMGFCzERf6j1CiCPrxKRoTwwAlOTbwyGmDUCB1uUQEVEaOeEQ849//AMvvfQSysvLEQyGP0StVkyaNAmXXHIJzj777IQXSR0FFQFFDd3angoTeyUp9ACkmOP9IrdZ13FyLxERJVa3P/0URcFNN92Ef/7znygqKsL3v/995OfnQwiBw4cPY/369fjVr36FCy+8EPfff39v1kw4Mh8mtOVAXPOzE8ZgkCDLMlo8QQAi5lyOM7Q56EHeZk1ERAnW7RCzatUqvPfee7j11ltx2WWXQZJi/8etKApeeukl/O53v8PkyZPxwx/+MOHF0hEef2RSr/a9MAZZgscfROW+ZviDSsy5plYfAOAwb7MmIqIE6/Z/4VevXo2f/vSnuPzyyzsEGAAwGAy49NJL8eMf/xivv/56Qoukjrzh26tTYSgpIhBU4Q8oMQ+b2QBJAnwBBQ0tPq1LJCKiNNLtEFNVVYXp06d3+byzzjoL27dv71FR1LVUv706QpYlOB1mAMChOg4pERFR4nQ7xHg8HmRlZXX5vJycHLS18cOqt3n8qb3QXXs5GRYAwAGGGCIiSqBuhxghBAyGrv/XL8tydENI6j3eyO3V5tTuiQFCi94BwP6aVo0rISKidKLtbS0UtyPDSTroiXGGemL21TLEEBFR4pzQJ+Bdd92FjIyM4z6ntZUfVMkQWa1XTz0xB+vaoKoCstxxYjgREdGJ6nZPzJQpU+BwOCCEOO7D4XBg8uTJvVkzQV89MU67CWajjEBQRXUjb7UmIqLE6PYn4PPPP9+bddAJEEIcmdibQrdYH4skSSjOc2BvdQv217ZxDyUiIkoIzonRIX9QhRrZciDFb7GO6F8QCi6c3EtERInCEKNDkaEkk1GG0aCPtzCyh9J+Tu4lIqIE0ccnIMXweMPzYXQwqTeiJN8OgCGGiIgShyFGh9w6mtQb0S8/1BNT2+SFN7zvExERUU8wxOhQZDjJqqMQk2k3Iyu8/QBX7iUiokRgiNGh6O3VOhpOAoABhaE1hji5l4iIEoEhRoc8kR2sddQTAwADCiIhhj0xRETUcwwxOqSXHayPNqgoFGL21LRoXAkREaUDhhgdik7s1cFCd+0NKXYCAPZWt0TXuSEiIooXQ4wO6XFiLwAU59phMRngD6g41MDtB4iIqGcYYnRGFUK3w0myLGFgZEjpsEvjaoiISO8YYnTG4w1ChEdirDobTgKAIUWZAIDdhzkvhoiIeoYhRmdcbX4AgNkkwyBLGldz4gYXh0LMHoYYIiLqIYYYnXG5QyFGT6v1tjckHGL2Vrdyci8REfUIQ4zOtLgDAPR3Z1JESZ4DZpMMX0DBYU7uJSKiHmCI0ZnIcJJVZ5N6I2RZwqDC8JBSNYeUiIgofgwxOhMJMXrtiQE4L4aIiBKDIUZnWqJzYvTZEwMAg3mHEhERJQBDjM64InNidDqxFwCGlBzpiVFUVeNqiIhIrxhidCbSE6PHNWIi+uU7YLMY4Aso3AySiIjixhCjM9E5MToeTpIlCcP6ZQEAKg82a1wNERHpFUOMjqiqQKtH/8NJADC8X2gzyJ0HGGKIiCg+DDE60uL2R7ccsJj02xMDACMGhHtiGGKIiChODDE60tQamQ9jgKzDLQfaG1aSBQlAbZMXzeEhMiIiohPBEKMjR+bD6HsoCQDsViP6FTgAsDeGiIjiwxCjI82tPgCAPQ1CDACM6M8hJSIiih9DjI40RXpirOkRYoaH71Di5F4iIoqH5iGmqakJd9xxB6ZPn45JkybhkksuwcaNG7UuKyWl03AScGRy7+7DLQgqXPSOiIhOjOYhZtGiRfjyyy/x4IMP4tVXX8WoUaMwf/587Nq1S+vSUk5kYm+6hJiiHBsybCYEgiq3ICAiohOmaYjZs2cPPvvsM9x1112YPHkyhg4dittvvx2FhYVYs2aNlqWlJD0vdCdJkYcUfciyjJMHZQMAtu5p1LZAIiLSHU1DTE5ODp5++mmMGzcueizyAedyuTSsLDU16XRir8EQCiwtniBavYGYx9Dwondb9jLEEBHRidH009DpdGLGjBkxx959913s2bMH//d//xf3dY1GzUfJEspgCLWnOTyclGEzwXDUOjFypHfDABiUjmvI9OR8T69tMsjwBhTsOtCEQCB27kvk2Tv3NwMSYDTI0fZGfk13bG96Y3vTG9urrZT6L/0XX3yBW265BXPmzMHMmTPjuoYsS8jJcSS2sBQQVNTolgM5WTaYjLFDSjarEUajATarGUZjx0myPTmfqGsbjUYIxJ4vyHXAbjHC7QuixuXHmGF50XNOp+0435H0w/amN7Y3vbG92kiZEPPBBx9g8eLFmDRpEpYvXx73dVRVwOVyJ7Ay7RkMMgLh7QZkCYCqwu0OxjxHEiqCQQUerx9+v9LhGj0535vXBoDiPDt2HXRh/bcH0S/HCoNBhtNpg8vlgdIH7lpie9Mb25ve2N7EcDptcfXupESIeeGFF3DvvffivPPOw+9//3uYzeYeXS8YTL+/SI2toV6YDLsZqgAUVcScV4WAEAKq0vFcT8/35rUBoCQcYiqqGjB32pDocUVR0/K9PBa2N72xvemN7dWG5oNaq1atwt13341LL70UDz74YI8DTLqKTOp1OtLv+1OSHxr+23nAhUCwY08NERFRZzQNMVVVVfjd736Hc845BwsXLkRdXR1qa2tRW1uLlhauG9Jeo8sLAHDaTRpXknhZDjOcDjOCihqa4EtERNQNmg4nvfvuuwgEAnj//ffx/vvvx5y7+OKLcf/992tUWeqJ9MRk2tOvJ0aSJIwcmI2NW2uweXcjxo3I17okIiLSAU1DzC9+8Qv84he/0LIE3WhsSd/hJAAYPSQHG7fW4Jtd9fgpTtK6HCIi0gHN58RQ90SGkzLTcDgJAEoH50ACsK+mNRrYiIiIjochRifSeWIvEBomG1ISWr3368o6jashIiI9YIjRiUZXOMSk4ZyYiHHhhe6+rqzXuBIiItIDhhidSOeJvRHjh4dCzOZdDX1i0SgiIuoZhhgd8AcVtIW3HHA60nNODAAMLXEiw2aC2xfkrtZERNQlhhgdcIU3fjQaJNh0toP1iZBlCWOG5gIANm2t1rgaIiJKdQwxOtDcFgoxWQ4zJKnjLtHpZHx4Xsy6zYc1roSIiFIdQ4wOREKM02HRuJLeN35EHgyyhL2HW3C4Ib028iQiosRiiNGBpvC6KTmZ6TupN8JhNWHU4BwAwKatNRpXQ0REqYwhRgcii79lZ6Z/TwwAnFJaCADYuK1W40qIiCiVMcToQOT26pyMvhFiJo0sAABUHmjm6r1ERHRMDDE60Nd6YnIyLTg5PKT05Q72xhARUecYYnQg0hOT20dCDABMG1sCAPhiO0MMERF1jiFGB/pCT4wkRR4SJAmYNj4UYrbuaYLL7de4OiIiSkUMMSkuEFTR4g6t1puTpiHGYJAgyzJaPEG0egNodgdglGUMLMqAKgQ28C4lIiLqRPou/5ommttCvTBGg4QMmwnNbQGNK0o8gyzB4w+icl8z/EEFBlmCzWZG/3wH9lW34vPNh3H2KQMhhNC6VCIiSiHsiUlxTeEtB3Kd1rRfrTcQVOEPKPAHFASCKgYVZgAAKg+4UN/s1bg6IiJKNQwxKS6y0F2u06pxJcnnsJlQnGsHAKzfwr2UiIgoFkNMiovemZTV90IMAAzvnwUA+LyCIYaIiGIxxKS4yHBSXpZN40q0MbRfJmRZwt7qFhysa9O6HCIiSiEMMSmusQ8PJwGA1WzE6CGhhe8+++aQxtUQEVEqYYhJcdHhpD4aYgDgtDHFAID/fnsYiqpqXA0REaUKhpgUFwkxeX04xIwZmhu+vdyPb3c1aF0OERGlCIaYFBe9xbqPTuwFAKNBxrRwb8x/OKRERERhDDEpzOdX4PEFAfTt4SQAODO8DcFXO+rQwm0IiIgIDDEprSm8Wq/ZJMNu7duLKw8qysSgogwoqsDnm3m7NRERMcSktMhCdzmZ6b9ab3dMn9APAPDxVwe4BQERETHEpLLG8KTenAyzxpWkhmljimExGXCo3o3t+5q0LoeIiDTGEJPCmlpCcz+y03T36hNlsxhx6ugiAMBHXx7QuBoiItIaQ0wKi9xencMQEzWrrD8AYNO2WrjaOMGXiKgvY4hJYQ0tDDFHG1yciaElTiiqwKdfH9S6HCIi0hBDTAprdHkB9O2F7jozsyw8wffLg1zBl4ioD2OISWH1kRDThxe668ypo4qQYTOh3uXFl9vrtC6HiIg0whCTooKKiubIar19vCdGkiIPCZIkwWI2RufGvL9xn8bVERGRVhhiUlRTiw8CgNEgwenou7dYGwwSZFlGiyeIVm8g+jh1TBEMsoQd+5tRecildZlERKSBvr0MbAprP6lX7sML3RlkCR5/EJX7muEPKjHnhpY4sfNAM95btxfXXTyOC+AREfUx7IlJUfWc1BsjEFThDygxj5MHZQMANm2vjX6/iIio72CISVEN4Q/lnEyGmGPJy7KiJM8OVRV4b/1ercshIqIkY4hJUQ2u0HBSXhbXiDme8SPyAQCffHUQrZ6AxtUQEVEyMcSkqEhPTC57Yo5rQIED/Qsc8AUUfPjFfq3LISKiJGKISVH14Z6Yvn57dVckScI5UwYCAD7YuB8+v9LFK4iIKF0wxKSoxpZwT4yTw0ldmXhSAQqybWj1BPDxV9wYkoior2CISUFefxBt3iAA3p3UHQZZwtzThwAA3vl8D3wB9sYQEfUFDDEpKDKp12YxwmbhUj7dcfrYYuRnWeFyB/DRF+yNISLqCxhiUlB0Ui+HkrrNaJBxfqQ3Zt0ezo0hIuoDGGJSUGS1Xg4ldU9kb6XTx5WgINuGFncA//pif3SvJSIiSk8MMSmovjlyezV7YrrSfm8lb0DBuVNDdyq9tXYPqpvcaPUGwD4ZIqL0xBCTghqidyaxJ6Yrkb2VtuxuQPnOOpjNBuRkWuDxBfHiu9tQsbsBXl+QPTJERGmIISYFNUTXiGFPTHdF9lYKBlVMGhlaxXdzVWN0fhEREaWflAoxTz31FC6//HKty9BcPVfr7ZF++Q4U59qhCoGNW2u0LoeIiHpJyoSYF198EQ8//LDWZWhOVUV0TkxBtk3javRJkiSccnIBAKDygAuVB5o1roiIiHqD5iGmuroav/jFL7B8+XIMGTJE63I01+DyQlEFjAYJOZzYG7e8LCtGDMgCALzy0U6oqtC4IiIiSjTNQ8zmzZthMpnw5ptvYsKECVqXo7naJg8AID/LBlnmZNSemDQyH2aTjP21bdyOgIgoDWm+HOzs2bMxe/bshF7TaNQ8m8WtLjyptzDHFm2HwXDkV0mWYAg/2pPDa6LIBsCgdAw/PTnfm9fu7Lwsh9ory3KPru2wmjCltBCffXMYr31SiamjCpGVkXq9W+3f376A7U1vbG96S7X2ah5iEk2WJeTkOLQuI24uTwAAMKjY2aEdmZk2eAICNpsZRpMac85mNcJoNMBmNcNojD3X0/O9ee3jnbdaTT2+9imjirGvpg17q1vw0keVWPrzKR2ekyqczr41B4rtTW9sb3pLlfamXYhRVQGXy611GXHbd9gFAHDaTWhsbAMQSrxOpw0tLR54vH54PH74j9rkUBIqgkEFHq8f/k6W3O/J+d68dmfnZVmG1WqC1xvo8bXNJgMuOeckPPDil/is/CDeX1uFyaWFHZ6npcj763J5oCgdg1i6YXvTG9ub3nqrvU6nLa7enbQLMQAQDOr3L1J1Q2hOTJ7T0qEdiqJCqAJK+NGeKgSEEFAVdDjX0/O9ee3Oz4faraoqVCH36NqKKjCgIAPfPW0Q3lq7B//vna04aUAWHFZTh+dqTVFUXf/dPVFsb3pje9NbqrQ3NQa1CAAghEBNeGJvIW+vTqgLzxyKkjw7mtv8+Ms/t0EI3q1ERKR3DDEppM0bhMcXBADkM8QklMlowIK5o2GQJWzYWoP/fHNI65KIiKiHGGJSSOT26qwMMywmg8bVpJ+hJU5cdNZQAMCq93egukG/c6eIiCjFQsz999+P559/XusyNFPTyKGk3vbdUwejdFA2fAEFT7z+DXydTAYmIiJ9SKkQ09fVcj5Mr5NlCdecPwZOhxn7a9uw8p9bOT+GiEinGGJSSGRSL/dM6l05mRZcd9FYGGQJ6yqq8d6GfVqXREREcWCISSG14eGkghyGmN42cmA2fjx7BADgbx/txJc7ajWuiIiIThRDTAqpbeZwUm+QpMhDinmcM3kgpk/oByGAp97YjKpDLq1LJSKiE8AQkyICQRWN4X2TOJyUOAZDaC+mFk8Qrd5AzKPNF8TPzh2JscNy4Q+qePjv5ThU36Z1yURE1E0MMSmipskDAcBqNiDTnnqryeqVQZbg8QexZXcDynfWxTwqdjcgEFBx3UXjMKgoAy3uAJa/9FV0bhIREaU2hpgUcbAu1APQL98BSeq4WzP1TCCowh9QYh6BoApJAuxWI276aRn65TvQ2OLD8r9+iXqXl+8DEVGKY4hJEdEQk6ffHbj1pv1QkyxLuO7isSjItqKu2Yt7/7IRlQebwVVkiIhSF0NMijjQrieGkuPooabdh1tw9uSByM4wo6nVj+V//RI79jWxR4aIKEUxxKSIQwwxmmk/1GQyypgzdSBynRZ4/Qoe/ttXKN9Zp3WJRETUCYaYFBBUVBwO7+PTnyFGc1azEXOmDES/fAf8ARWPvFKO9zbs48q+REQphiEmBdQ0eqCoAhazAblOi9blEACzyYDzTh2EaWOLIQTw0r924Ok1FdxriYgohTDEpID2k3o5/yJ1yLKES84+CZecfVJ0i4J7/rIRe6tbtC6NiIjAEJMSjtxebde4EjqaLEs4d+og3PyzSchymHGgrg13/7+N+Md/d0Pl8BIRkaYYYlLAwfAqsf3zMzSuhNprfwt2vwIHllw2CeNH5EFRBV779y78duVG7OcKv0REmjFqXQC1v72aPTGpJHILduW+ZviDobkwU0oLkZtpxX+/PYS91S24+9kNuPCsoThn8kCYjPw/ARFRMvGnrsYUVcXh+tCdSVzoLjW1vwU7EFQxqCgDF5wxBAMKHAgoKl75uBJ3PLMOX1fWa10qEVGfwhCjseidSSYDcrOsWpdD3WS3mnDuqYPw8/NORpbDjOpGDx7+ezkefeVr1DZ5ojtlExFR7+FwksYO1IaGkkry7JD5oacrRqOMU0qLMHZYPv65bg8+/vIAvtpZh2+r6jFr0gDMmToQ2ZlWGLQulIgoTbEnRmN7wrfrDijkpF69icyZ2X3IhWH9nLh4+jD0z3cgqAi8v2Ef7vjzeny0aR94ExMRUe9giNFY1SEXAGBYP6fGlVC8InNmHFYjZp/SH7Mm9Uem3QSPL4hV7+/AspXrsW1vo9ZlEhGlHQ4naUgVAlWHQj0xw0oYYtKBJEkYWJiBfvkO7NzfjK8r67C3uhW/X/UlThlZgB/NHoHCbJvWZRIRpQWGGA1VN7jh8QVhNsrc+DHNGGQJ44bn4cKzhuKDDfvw0ZcHsGl7Lcor63DO5IGYe/oQ2Cz850dE1BMcTtJQZChpUHEmjAa+FenI6TDj5+eV4rfzT8WYIbkIKgLvrNuLW55ai3+XH4SqcsIMEVG8+MmpoaqDHEpKZ+1X/M3OtGDhRWOw8MIxKMyxweUOYOU7W7Fs5QZs3cP5MkRE8WB/toZ2hXtihjLEpKXOVvwFgO+fPgTb9zbii+112FfTit89vwnTvzmMedOHwmk3a1gxEZG+sCdGI4Ggin01oZ6YobwzKa21X/HXH1CgKCpGDcnFnVdNwayy/pAA/PurA1iyYi3eWrsbgaCqdclERLrAEKOR/bWtCCoCGTYTCrhSb5+UYTfhiu+W4rcLpmLUkFz4Agpe/WQX7nhmHb7d1aB1eUREKY8hRiO7Dh5ZH4bL0/c97efL5GTZcNPPJuHn3y1Fpt2E6kYPHvzbV3jkla9R0+TRulQiopTFOTEaqTzYDAAYUpypcSWkhfbzZRRVhc1mhs1swMXTh+GL7bWoqGpA+c46bK5qwPdOG4TvTRsCiyl2AwPBpYCJqI9jiNGAKgQqqkLDBaWDcjSuhrQUCKoIKgqMptC8GQCYNLIAo4fkoHxnPbbtbcKbn+3Gx18dxPdOG4zTxhbDIId67qwWI/dlIqI+jcNJGthX3QqXOwCL2YARA7K0LodSUH62DddcOAbnTh2EDJsJrjY/XvrXDtz553VY/ekubK6qh9cX5FAkEfVp7InRwDe76gEAowfncJE7OiZJkjCoKBP52RZs39uMryvr0dzmx7827sfXO+ugqAKnjS4GYwwR9VX8BNXAt+EQM3ZYnsaVkB4YZBmjhuTg4hlDMX54HowGCbVNXvx/b1bg9j+vw2ffHEJQ4W3ZRNT3sCcmydzeIHYeCN2ZNHZorsbVkJ6YjQZMPCkfJw/KxvZ9Tdi2twkH69rwzFtbsPrTXZh9ygCcMbYETgcXzCOivoEhJsm27GmAKgSKc+0o4G7GFAebxYgpo4pw6ZyTsb6iGu9t2Id6lw9//6gSr32yC5NGFmDGxH4oHZwDmXNmiCiNMcQk2TfhRczGDmMvDPWMzWLE96cNwdmnDMDnFdX45KuDqDrkwoatNdiwtQb5WVZMHVWEKaWFGFSUwUnARJR2GGKSSFVFdFLvOM6HoR6SpNDDYjZixsT+mDGxP/ZWt+CTrw5g7ebDqGv24u3P9+Dtz/egMMeGqaOKUHZSAYaUZEICjhtqurMGzbFez/VriChZGGKSaPPuBjS2+OCwGlE6KFvrckjH2q/4CxwJDblZVsybORwXzxyBL7bW4Mvttdhc1YCaRg/+8d/d+Md/dyPDZsKYYbkoHZSD0sE5yLCZOly/qzVoFABeb6DTc1qvX9PTcEZE+sEQk0Sflh8EAJw2phgmI5cpo/gda4dsALBbjRhc4oTNYsTkUYWYcFI+9la3oOqQCwdq29DqCWDd5mqs21wNAMjJtKA4z47iXDuK8+zIzrBg9JBcZFhNnX7oS5IErzeAit0NHTarNBnl4762tx0vXAHaBywiSiyGmCRxtfnx5Y46AMD0Cf00robSRWSH7PbMJrnDuYGFGRhYmAFVFWjxBNDiDuCrHXVocHnR2OJDY4sPW3Y3AgCcdhNGDcnF6CG5GFqSiX55Dshyx96Nzr62lo4XrgDtAxYRJR5DTJL899vDUFSBoSWZGFiYoXU51EfJsoT+BQ4MH5CN4f2y0NzmRU2jB9UNHlQ3utHo8sHlDmBdRTXWVYR6aqxmA4YUZ2JYvywM6+fE8P5ZMBpTd4mpVAtXRNR7GGKSQAiBT78ODSWdxV4YSiFWsxGDijIxqCi0Eak/qKCxxQ9VFdhb3YLdh1rg9SvYurcJW/c2RV+Xk2lBdoYZuU4r8rOsyMuycvVpIko6hpgk+LqyHofq3TCbZJw6qkjrcoiOyWw0YGBhBiaMyEeG1QRFUXGgrg27DjZj10EXdh1y4WBtW3QIqupQC4DQXVLZGRYU5drR0ubHsH5OlOQ5Op00TESUKAwxvSyoqPjbRzsBALMnDYDNwm856YcsS9H5NDMm9gcAeP0KtuxtxOffHsbhBjfqmj3w+JRosNm6pzH6+iyHGcW5dmSHe26yHBbkZllRkOuAx+OHBMBokCBJEgIBBQFFhT+gIhBU4Q0o8PkVeP1BeGN+VTr8ObrtggAUNTTfxWiQYDLKMBkNMBtlWC0G7D7kQmG2HTmZZuRkWlGQbUWmnSscE+kVP1F72SdfHcShejcy7SbMnTZE63KIuiWyBg062V7SbjVi5MBseHxBjAooEELA7Q2irjk0STioClQ3uFHf7EVzmx/Nbf6k1w8Avk5uUqoMb/nRnsNqjN6d1S/PER1eO3r7Bk4GJko9DDG9qM0bwBv/qQIAXHTWMNit/HZT6jvWGjQRsiyh/b0/kiTBYTPBYTPhpIHZ0aEotzeAQ/Vu1DS60dTqR3ObL/Rrqw++oIpAQEVQUaGoKlQ1dPeQySTDbDLAKEuwmAyhh9kAq9kAq8UIk9GARpcXsgyYDKFeFpNRhkGWYLUY0K8gA7v2u+APBBFUVPiDoV6dQFBFUFVht5jQ6gmg0eVFQ7jnqM0bROUBV4eA43SYMaDAgQGFGRhQkIHhA7JQlG3jVg5EKYSfqr1EVQWefWsLWj0B9M93YPqEEq1LIuqW461BAxxZh0bqpJemPZvFiGH9nBjWzxk9JkkS2vxB7KluhavFGx36Ofrax1v/ZmtVI3yBYIevl2E3oSjXjsZmH3yBjqvBmE2GaMCK9Kr4/AqqG9043BB67KluRdXBZjS1+uFq86OizY+K3UeGx6xmAwYVZWJwUSYGF2dgcFEmivPsMMh9e1Jz+wUGI79l1qNkYIjpJa98XIkvd9TBaJBx5XdL+/wPOdKfY92qHFmHJhHXPjrEdLbGTSK/7tHDZFaLEYOLnRhc7IQkAS2eAMp31qHNE0BTiw8NLV40uHxoavWhqcUPr1/B9n1N2L6v6UhdRhkDCzMwqCi0fEJRrh1FOTbkZFoSul9Vqq5EfPQCg5Iswa+64fEGYDEZulxckNtfUE9oHmJUVcXjjz+Ov//972hpacGUKVNwxx13YODAgVqXFhchBP65bi/+uX4vAODq75dieP8sjasiohMZJjMZZRTk2FCQE9pp3mY1YuywPBysdWNfTQv217SGf22DL6Cg8qALlQdjh6PMJhmF2XbkOi0oyLXDapKRaQ0Nu1lMBphNBljCw2cGWYLBEBoWiz7a/RmyhEBQhSx1/qGt1UrE7RcY9AdCk6x9AQWSLMPrDaAwxwYICf5gaO6UEKFeaiEEVAGoEAgEVEiyhFAzJUiyBIMkQZYBs8kIoxw6LstS6LwU+p6YjDIMRhkQAiZTaPK2OfyrySjDZjVxdeY+QPMQ86c//QmrVq3C/fffj+LiYjzwwANYsGAB1qxZA7NZX3cNtHkDeO7trfhiey0A4KIzh+K00cUaV0WUXMebFKzlEENPhskMsgR/UEVzqw9WswEjBmRhxIAsCCHgavOjqTW0tk5dkyd8x5YX/oCK/bWt2F/bClTWJ7QdFrMhOmfIajGgKMeOLIcZGTYTMu0mOO1mZNrNyHSYkGkzw2YxxN0rFAiqaHGHJmg3tfrQ3Br+tc2P5lY/Glq8qG/2wu0LIlU6P2RJgsNmhMNqiv5qt4b/bDXCbjUhw2ZEhi30PXPYTMgIPyeyOnVv9+Skas+a3mgaYvx+P5599lksXrwYM2fOBAA89NBDOOuss/Dee+9h7ty5WpbXbS63Hx99cQAffrEfLe4AjAYJP5o1AmefMkDr0oiSqju9HVr/fO7JMFlnr7VZjMjKsGDiSZH5NqGlFeqavahpdKPF44dfAQ7WtqLB5YXbG4Q/oISuFVTgD6hQVQEl+lChKKLDUFuEoobuBnN7j8wL2h1er+dYjAYpFGpsJtgsRphMMkyG8CRqgwRFEQgoKoLB0GRrt09Bi9uPFk8APv+JrX4cmYhtNMjIzjDDZjHCbDRAliVIEiAj9KskS1AUNXRHm6JCiNDfmEiPDSQg02aCLElQhYCqhntv1NDvA4oKr19BmzeAoKIiqISOA4AqBFrcoe01ToSEUK+bw2pCht2ETJspGoQybCbYLUaYTYbwpHIZRqMMo0GGw9GCllYvgsFwO4SAwJFaFVVE26CoAioAVVGj/14M4YnsNktoAntOpgUZFiOsFiMnkndB0xCzdetWtLW1Ydq0adFjTqcTo0ePxoYNG1IuxCiqilZPEE0tPlQ3urG/tg1b9zRi10EX1PBP5qIcGxZeOAZDip1dXI0o/XSnt2NIv/QbXu0svGXYTciwZ0GSJdhtZrS5/aioqu+wr5PVbMDooXlQFIH2wS/yYS4A+IIKvtlZD58/fNdVQIUvoMAfUCAA5DqtaHUH0OYNoNUdQIsn9GurJwBfQEFQEdF1fOIhyxKcdhOcDguyHGY4HWY4HSZkZViQYTejttENo0GCzWyEySjDbrdAKArGDs/v0K721wyqAt/srOs0VNqsRkwYUXBCr1eFgKIIyLKEwcVOtLoDcPsC4dAXgNsXCn8eXxCtngDqXV54faEhsEBQhQCiAbG2yRPX9yrRbBYD7JZQD5LDFulRate7FA5adqsRGVYTrObQHXtmkyG8TpKc1kFIEhr2W7333nv41a9+hfLyclit1ujx//3f/4XX68VTTz11wtcU4kgaT6TmNv9x92MxGmTYrUZYTIkfhZUkQJZlqKoKRRWhf2xHNVGWAKNR7vRcT8/35rU7Oy9JgAQJAiK0GFqatOtY54Aj7Y08Lx3adbzXBhVx5H/c6dSu8Adhh/PhuS3Hu/axXitJEoyGzl/bndcDoXApROhDvn04iryg/R1FUrueksiHn6J0v7bINSCFeoB6q13He318rw1/XyL/BmUJQKj3J/K5cuT7JsK9Rp1cHJ0Pm0a+JxHtP6ciH8Pte6MSTZKOGiiVYn5p97zOjsayWYywWQwJ7VUN9dSdeNjStCfG4wkl3aPnvlgsFjQ3N8d1TUmSYDAkPnXmOq1dP6mXybIcWh/DeOygdLxzPT3fm9fW8muzXVp87eOe1nG7UvPaPdbFf87S9f2i1Kfpfb+R3he/P3ZFT5/PB5vNpkVJREREpBOahpiSktACcDU1NTHHa2pqUFTEjRKJiIjo2DQNMaWlpcjIyMC6deuix1wuFyoqKjBlyhQNKyMiIqJUp+mcGLPZjMsuuwzLly9Hbm4u+vfvjwceeADFxcWYM2eOlqURERFRitN8sbsbbrgBwWAQt912G7xeL6ZMmYJnnnkGJpNJ69KIiIgohWl6izURERFRvLgrIREREekSQwwRERHpEkMMERER6RJDDBEREekSQwwRERHpEkMMERER6RJDDBEREekSQ0wKU1UVjz76KM466yxMnDgR11xzDfbt26d1WZ1qamrCHXfcgenTp2PSpEm45JJLsHHjxuj5tWvXYt68eZgwYQLOO+88vPXWWzGv9/l8WLZsGaZNm4aysjLcdNNNaGhoiHlOIq7RG6qqqlBWVobXXnstemzLli247LLLMHHiRMyePRt/+ctfYl7Tnfc2EddIpNWrV+N73/sexo0bh+9///t45513ouf279+PhQsXYtKkSTjzzDPx8MMPQ1GUmNe/+OKL+M53voPx48fjZz/7GSoqKmLOJ+IaiRIMBvHII49g1qxZKCsrw6WXXoqvvvoqej6d3t+nnnoKl19+ecJrS8Y1EtXeDz/8ED/4wQ9QVlaG2bNn4/e//z28Xm/0fLJ+PnV1jUS1t73bbrsNs2fPjjmmq/dXUMp67LHHxKmnnio++ugjsWXLFnH11VeLOXPmCJ/Pp3VpHVx11VVi7ty5YsOGDWLXrl1i2bJlYvz48aKyslLs3LlTjBs3Tjz44INi586d4s9//rMYPXq0+O9//xt9/dKlS8XZZ58tNmzYIMrLy8VFF10kLr300uj5RFyjN/j9fjFv3jwxcuRI8eqrrwohhGhoaBCnnnqquOWWW8TOnTvFK6+8IsaNGydeeeWV6Ou6em8TcY1EWr16tRg9erR44YUXxJ49e8Sf/vQnUVpaKr744gvh9/vFnDlzxP/8z/+Ibdu2iffff19MnTpVPPLII9HXv/baa2L8+PHijTfeEDt27BC/+c1vxNSpU0V9fX30+9jTayTSo48+Ks444wzx6aefit27d4tbb71VnHLKKaK6ujqt3t8XXnhBlJaWissuuyx6LFXa151rJKK9GzZsEKNGjRIrVqwQVVVV4uOPPxbTp08XS5cujT4nGT+funONRLS3vffff1+MHDlSzJo1K+a4nt5fhpgU5fP5RFlZmXjxxRejx5qbm8X48ePFmjVrNKyso927d4uRI0eKjRs3Ro+pqirOPvts8fDDD4vbb79d/PCHP4x5zaJFi8TVV18thBDi8OHDorS0VHz88cfR87t27RIjR44UX3zxhRBCJOQaveGPf/yj+PnPfx4TYp588klx5plnikAgEPO8OXPmCCG6994m4hqJoqqqmDVrlrj//vtjjl999dXiySefFGvWrBFjx44VTU1N0XMvvfSSmDRpUvQH1pw5c8Qf/vCH6PlAICBmzJghnnzySSGESMg1EumCCy4Q9913X/TPLS0tYuTIkeLdd99Ni/f38OHDYuHChWLixInivPPOi/mQS5X2dXWNRLX3pptuEldeeWXM819//XUxZswY4fP5kvbzqatrJKq9EdXV1eK0004Tl112WUyI0dv7y+GkFLV161a0tbVh2rRp0WNOpxOjR4/Ghg0bNKyso5ycHDz99NMYN25c9JgkSZAkCS6XCxs3boxpBwCcdtpp2LRpE4QQ2LRpU/RYxNChQ1FUVBRtayKukWgbNmzAyy+/jPvvvz/m+MaNGzF16lQYjUe2JjvttNOwe/du1NXVdeu9TcQ1EqWqqgoHDhzA+eefH3P8mWeewcKFC7Fx40aMGTMGWVlZMbW2trZiy5YtqK+vx+7du2NqNRqNmDx5ckx7e3qNRMrLy8NHH32E/fv3Q1EUvPzyyzCbzSgtLU2L93fz5s0wmUx48803MWHChJhzqdK+rq6RqPZeffXVWLJkScwxWZYRCATQ2tqatJ9PXV0jUe0FACEEli5digsvvBBTp06NOae395chJkUdPnwYAFBSUhJzvLCwMHouVTidTsyYMQNmszl67N1338WePXtw1lln4fDhwyguLo55TWFhITweDxobG1FdXY2cnBxYLJYOz4m0NRHXSCSXy4Wbb74Zt912W4f36Fi1AsChQ4e69d4m4hqJUlVVBQBwu92YP38+pk2bhh/96Ef48MMPE1ZrKrUXAG699VaYTCZ85zvfwbhx4/DQQw/h0UcfxaBBg9KivbNnz8Zjjz2GgQMHdjiXKu3r6hon4njtHT16NEpLS6N/DgQCWLlyJcaOHYvc3Nyk/Xzq6hqJai8ArFy5ErW1tVi0aFGHc3p7fxliUpTH4wGAmGAAABaLBT6fT4uSuu2LL77ALbfcgjlz5mDmzJnwer0d2hH5s9/vh8fj6XAeiG1rIq6RSHfddRfKyso69E4cq9bIDy+fz9et9zYR10iU1tZWAMCSJUswd+5cPPvsszjjjDNw3XXXYe3atWnXXgDYuXMnMjMz8cQTT+Dll1/GvHnzsHjxYmzZsiUt29teqrSvq2v0hmAwiJtvvhk7duzAnXfeCQBJ+/nU1TUSZevWrXj88cfxwAMPdFqT3t5fY9dPIS1YrVYAob+8kd8DoTfXZrNpVVaXPvjgAyxevBiTJk3C8uXLAYT+Yh79jzDyZ5vNBqvV2uk/0vZtTcQ1EmX16tXYuHEj1qxZ0+n5zmqJ/KO02+3dem8TcY1EMZlMAID58+fj4osvBgCMGjUKFRUVeO65506o1qOfE097j3WNRDl06BBuuukmrFy5EpMnTwYAjBs3Djt37sRjjz2Wdu/v0VKlfV1dI9FaW1tx4403Yv369Xj88ccxfvz4Y9ZxdK3J+BmXCD6fD4sXL8a1114b0/vUnt7eX/bEpKhIN1xNTU3M8ZqaGhQVFWlRUpdeeOEF/OpXv8KsWbPw5JNPRlN1SUlJp+2w2+3IzMxEcXExmpqaOvyFbt/WRFwjUV599VXU19dj5syZKCsrQ1lZGQDgzjvvxIIFC1BcXNxprQBQVFTUrfc2EddIlMj1Ro4cGXN8xIgR2L9/f9q1t7y8HIFAIGaOFwBMmDABe/bsSbv2Hi1V2tfVNRKppqYmehv9M888gxkzZkTPJevnU1fXSITy8nLs2LEDjz/+ePRn11NPPYWDBw+irKwMGzdu1N37yxCTokpLS5GRkYF169ZFj7lcLlRUVGDKlCkaVta5VatW4e6778all16KBx98MKabcPLkyVi/fn3M8z///HNMmjQJsizjlFNOgaqq0clvQGgeRnV1dbStibhGoixfvhxvv/02Vq9eHX0AwA033IB7770XU6ZMwaZNm2LWOPn8888xdOhQ5OXldeu9TcQ1EmXMmDFwOBwoLy+POb59+3YMGjQIU6ZMQUVFRXTYKVKrw+FAaWkp8vLyMHTo0Jhag8EgNm7cGNPenl4jUSLj9Nu2bevQ3iFDhqTd+3u0VGlfV9dIlObmZlxxxRVoaGjAiy++2OH7m6yfT11dIxHGjx+P9957D2+88Ub0Z9dPf/pTFBYWYvXq1Rg7dqz+3t8TupeJkurBBx8UU6dOFR988EHMffZ+v1/r0mLs2rVLjBkzRlx//fWipqYm5uFyucT27dvFmDFjxAMPPCB27twpnnnmmQ7rHyxatEjMnj1bfP7559E1FNrfFpiIa/Sm9rdY19XViSlTpoglS5aIHTt2iFdffVWMGzdOvPbaa9Hnd/XeJuIaifTEE0+IsrIysWbNmph1Yj7//HPh9XrF2WefLebPny+2bNkSXePlsccei77+5ZdfFuPHjxevvfZadI2XU089NbrGSyKukSiKoohLLrlEnHfeeWLt2rWiqqpKPPTQQ2LUqFHiq6++Srv3d8mSJTH/TlKlfd25RiLau2TJEjFmzBixdu3aDj+/gsGgECI5P5+6c41EtPdojz76aId1YvT0/jLEpLBgMCj+8Ic/iNNOO01MnDhRXHPNNWLfvn1al9XBihUrxMiRIzt9LFmyRAghxCeffCLmzp0rxo4dK8477zzx1ltvxVyjra1N3HrrrWLy5Mli8uTJYtGiRaKhoSHmOYm4Rm9pH2KEEKK8vFz8+Mc/FmPHjhWzZs0Szz//fMzzu/PeJuIaifTss8+K2bNnizFjxogLLrhAvP/++9Fzu3fvFldddZUYN26cOPPMM8XDDz8sFEWJef2f//xnMX36dDF+/Hjxs5/9TFRUVMScT8Q1EqWpqUncddddYubMmaKsrEz85Cc/EevWrYueT6f3t7MPuVRpX1fX6Gl7g8GgGDdu3DF/fkXqSdbPp66u0dP2dqazEKOn91cS4gRvQCciIiJKAZwTQ0RERLrEEENERES6xBBDREREusQQQ0RERLrEEENERES6xBBDREREusQQQ0RERLrEEENERES6xBBDRElx+eWX4+STT455jB07FjNnzsSyZcvQ3Nx8zNfu378fJ598Ml577bUkVkxEqc6odQFE1HeMHj0ad955Z/TPgUAAmzdvxoMPPogtW7bgr3/9KyRJ6vC6wsJCvPzyyxg0aFAyyyWiFMcQQ0RJk5GRgYkTJ8YcmzJlCtra2vDoo4+ivLy8w3kAMJvNnR4nor6Nw0lEpLmxY8cCAA4ePIjLL78cixcvxg033ICJEyfiqquu6nQ4adeuXfjlL3+JqVOnYsqUKVi4cCEqKyuj530+H/7whz9gxowZGDt2LM4//3y8/fbbSW8bEfUe9sQQkeaqqqoAAAMHDgQAvPPOO7jggguwYsUKqKra4fnV1dX4yU9+gqKiItx1112w2+147LHHcMUVV+Af//gHsrKycP311+OLL77ADTfcgOHDh+P999/Hr3/9a/j9flx00UXJbB4R9RKGGCJKGiEEgsFg9M/Nzc1Yv349VqxYgbKysmiPjMlkwrJly2A2mwGEJva2t3LlSvj9fjz33HMoKCgAAJSWluKSSy5BeXk5jEYjPv30Uzz00EP43ve+BwA466yz4PF4sHz5csydOxdGI3/8Eekd/xUTUdJs2LABY8aMiTkmyzJOP/10/Pa3v41O6h02bFg0wHRm06ZNmDhxYjTAAEBxcTE++ugjAMDy5cshSRJmzJgRE5pmz56NN998Ezt27MCoUaMS2TQi0gBDDBElzZgxY7Bs2TIAgCRJsFgsKCkpQUZGRszzHA7Hca/T1NSEAQMGHPe8EAKTJk3q9HxNTQ1DDFEaYIghoqRxOBwYN25cj6+TmZmJhoaGDsfXrl2LAQMGIDMzE3a7HX/5y186ff3gwYN7XAMRaY93JxGR7kyePBnl5eUxQaa+vh4LFizAJ598gqlTp8LtdkMIgXHjxkUf27dvxxNPPBEzxERE+sWeGCLSnSuvvBKrV6/GggULsHDhQphMJqxYsQLFxcU4//zzkZmZiSlTpuC6667Dddddh+HDh+Prr7/Go48+irPOOgu5ublaN4GIEoAhhoh0p6SkBKtWrcIDDzyApUuXwmw249RTT8VDDz2ErKwsAMDTTz+NRx55BE899RTq6+tRVFSEq666Ctdff73G1RNRokhCCKF1EUREREQninNiiIiISJcYYoiIiEiXGGKIiIhIlxhiiIiISJcYYoiIiEiXGGKIiIhIlxhiiIiISJcYYoiIiEiXGGKIiIhIlxhiiIiISJcYYoiIiEiX/n+xUqZjGOUoUQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can check the PDF once again to ensure that the result is still distributed in the same way overall\n",
+    "# however, there are much fewer outliers\n",
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\1674007885.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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FFXOTDdX1VoiCgNw+Xk7gTInxOqhV3vWYKk+2KloLERH1LgYmCiu+5QSGZybAoFcrWosoCkhN5LAcEVE0YGCisHJqOE6Z5QTOlJbkXcDyYEWzwpUQEVFvYmCisOFweVB6vBEAMPaiUAlMvFKOiCgaMDBR2Cgtb4TLLSHZqEf/frFKlwMASE2MgSAAdc121DfblC6HiIh6CQMThQVBEPCdf7HKZIiiCEEQoOCqAgAArVqFAe3hbV97fUREFHkYmCjkeQC02l34tn39pazMBLTaXWi1u9DmcENStjxcNCABALD/GAMTEVGkYmCikCYIAuwON3bsq4G50QZBAJwuD/YersPew3U4cLwRbo8EAcp1NV3U3wgA2He0XrEaiIiodyl7XTZRN5XXtAAA+iXoAXhDEwBoNcpnfl9gOnrCArvTDbWofE1ERBRc/JedwkJl+w1uM5JDY7L36RLj9Ug26iBJMsqqLEqXQ0REvYCBiUKeJMs40R6Y+vczKFxNZ4IADB9oAgAcqmxqn4x+6kFEROGPgYlCXqW5FQ6XBxqViH4JMUqX04FKJUAURQxKjwcA7DvW6J+Q7nt4FK6RiIguHOcwUcgrLfcuVpmebIAohlaPjUoUYHO6/csblFU1Y8+hkxDbN2jUInKGJCFOr4Es8wa9REThij1MFPJ8q3tnJIfecJyPMVYLjVqEyy3B3GCF0+WB0+WBy630ogdERBQMDEwU0hxOD460T6QOldW9uyIKAtLbb5NibuSK30REkYaBiULagYpGeCQZcTEaxBs0SpdzTuntgc7cxMBERBRpGJgopH3ffruRASmxIX/FmW/Jg5PsYSIiijgMTBTSTgWmOIUrCSwt2QBBANrsbrTZXUqXQ0REQcTARCGrscWBqro2CAjN9ZfOpFWrkBSvA8B5TEREkYaBiULWvvab2Q5Mi4NeGx4rYKQmcuI3EVEkYmCikPV9e2DKHpyocCXdl5roXVjzJCd+ExFFFAYmCkmyLGPfMe/6S9mDwicwpbUHpkaLg2swERFFEAYmCkkn6q2wtDmhVYsYkmFUupxui43RIFavhgz2MhERRRIGJgpJB9pX9x6WmQCNOrw+phyWIyKKPOH1k4iiRunxJgDAyDAajvNJaQ9MnPhNRBQ5GJgo5Miy7O9hCqf5Sz6pplM9TJLEG+4SEUUCBiYKOSfqrWixuqBVixgaRvOXfEzxOmjUItweGQ0tdqXLISKiIGBgopBTWh6+85cA7414U0x6AEBtA4fliIgiQfj9NKKI5xuOC8f5Sz6+YbnaBqvClRARUTAwMFFIkWXZP+E7e5BJ0VouRFqSd8Xv6vo2yDLnMRERhTsGJgopJ+ra0GoL3/lLPv1MeqhEATaHBzX17GUiIgp3igcmSZKwevVqXHrppRg/fjzuvPNOVFRUnPX4xsZG3HvvvcjPz0dBQQFWrFgBm63jPJFt27bh6quvxtixYzFnzhxs3769w/7vv/8e8+bNw4QJE3DxxRdj2bJlaGlp6ZX3R+fH17s0LDMBapXiH88eU4mifz2mgxVNyhZDREQXTPGfSOvWrcPrr7+ORx99FG+88QYkScKCBQvgdDq7PH7RokUoLy/HK6+8ghdeeAGff/45li9f7t9fVFSEJUuW4MYbb8Q777yDwsJCLFy4EGVlZQCAuro6zJ8/HwMGDMDbb7+NdevWYdeuXXjggQf64u1SAJEwf8knPdk7LMfAREQU/hQNTE6nExs3bsSiRYswffp0ZGdnY9WqVaipqcFHH33U6fg9e/aguLgYTz31FHJzc1FYWIhHHnkEW7ZsQW1tLQDgpZdewsyZM3HrrbciKysLS5cuRW5uLl599VUAQFVVFaZOnYpHHnkEQ4cORV5eHn7605/iyy+/7NP3Tp1Fyvwln4z2eUyHKpu5HhMRUZhTNDCVlpaira0NhYWF/m1GoxE5OTnYuXNnp+NLSkqQkpKCrKws/7aCggIIgoBdu3ZBkiTs3r27w/kAYPLkyf7zjRs3Ds899xzUajUAoKysDFu2bMGUKVN64y3SeYiU+Us+SUY9NGoRNocbx2s55EtEFM7USr54TU0NACAjI6PD9tTUVP++09XW1nY6VqvVwmQyobq6GhaLBVarFenp6d0635VXXoljx45hwIABWLt27YW+HajDcM0glUoEJBmiKEAlCp33iwIEUYBaLUCWO+8PpoOVzQCA4QNN0Ou8H01BAIT22rqqTxQECIIAUQWoPAruF7z7RFEEIAHwtl3/frEor2nBgYpGZGUmdL8xopSqfd6aKoznr4UCtmNwsB2DJxLaUtHA5JusrdVqO2zX6XRobm7u8vgzj/Ud73A4YLfbz3o+h8PR6XkrV66EzWbDM888g1tvvRVbtmxBbGxsj96LKApITOzZc5Vma7BCp9NAVKk67dOoRcTotTCZDL1eR1m1BQCQl53WoS2dkhUxMVqoNVKn58To1VCrVYjRa6FWK7dfp9PA5Zag12s67B+cYUR5TQsOVllwc5h+PpRgNMYoXUJEYDsGB9sxeMK5LRUNTHq9dzVkp9Pp/xoAHA4HYmI6N6per+9yMrjD4YDBYIBOp/Of78z9XZ1vzJgxAIC1a9fisssuwz//+U/MmTOnR+9FkmRYLOF3+bivV8zhcMHucHfar9WoYLM70dQko7eWE3JLMlrtLnxzqA4AkJKgQ/mJJgDeIOrxyLDZnHC6PJ2eK8gS3G4PbHYnnE7l9jscLogqFex2FyTpVLBKTfB+rr8rq0PtSQu06s6hlE5RqUQYjTGwWGzweDoHVOoetmNwsB2DJ1Tb0miM6Xavl6KByTe8ZjabMWjQIP92s9mMkSNHdjo+PT0dH3/8cYdtTqcTTU1NSE1NhclkgsFggNls7nCM2WxGWloaAODIkSM4fvw4pk+f7t+flpYGk8nknzjeU2536HwIuss7kiRAkmR4upiY7JFkyJIMt1vulQUYBUFAm92F/3xzAq02F9QqAY0tDnx98CQAwKBXY3CGEZKELuuTZG9dkkfh/bIMEd5lMk4/LiFOC1OcFk2tTnxX1oCxWcnn0zxRy+ORwvL/p1DDdgwOtmPwhHNbKjqYmJ2djbi4OOzYscO/zWKxYN++fcjPz+90fH5+PmpqalBeXu7fVlxcDACYOHEiBEFAXl6ef5vPjh07MGnSJADAV199hUWLFsFisfj3Hz9+HI2NjR0mk1PfqqhtBQCkmGLg8Uhwujxwujxwh9BvIj0hCAJyh3pD0t7DdQpXQ0REPaVoYNJqtZg7dy5WrlyJTz75BKWlpVi8eDHS09Mxa9YseDwenDx50j83ady4ccjLy8PixYvxzTffoKioCMuWLcOcOXP8PUjz58/H+++/j5dffhllZWV4+umnsX//fsybNw8AMHv2bJhMJixZsgSHDh1CSUkJFi1ahLFjx+Lyyy9XrC2ine+ea75bikSS0RclAQD2ltXxNilERGFK8enqixYtwvXXX4+HH34YN910E1QqFTZs2ACNRoPq6mpMnToVW7duBeD9bX3t2rXIzMzEvHnzcPfdd2PatGkdFq6cOnUqHn/8cfztb3/Dj3/8YxQVFWH9+vX+3iOTyeRfk+mmm27Cr371K+Tk5GDDhg1QdTHpmfqGLzD5blobSUYMMkGrEdFgcaDC3Kp0OURE1AOKzmECAJVKhSVLlmDJkiWd9mVmZuLAgQMdtiUnJ2P16tXnPOecOXPOOXl76NCh+MMf/tCjein4Gix2tNndEAQgOUEf+AlhRqtWIXdIEvYcqsPXh+swKC1e6ZKIiOg8Kd7DRHS0fTmBpHgdNGG4llV3jB+eAoDzmIiIwlVk/nSisHLkhDcwpSRG3nCcz7j2q+OOVregsaXzmmBERBTaGJhIcUd9gSkC5y/5JMTpkDXAe7uXnaXmAEcTEVGoYWAiRdmdblSd9E6ETo3QHiZB8D4Kc7237Cn6vgZC+y1VBKF3bzdDRETBwcBEijpywgJJBuJiNIg947YikUClEiCKIlpsbuRelAxRFHCspgWHq5rRaneh1e5C5/XDiYgo1DAwkaIOt99wN1J7l1SiAJvTjf3HGlBW1YzMFO/95P7x1VHsPVyHfccaYHe42dNERBTiGJhIUYcqmwBE5oKVp3O5vauXD8nwzmM6XNkMh9MNV5jeIoCIKNowMJFiJElG2QlvD1NaUmT2MJ1pYEosNGoRbXY3ahttSpdDRETdxMBEijlR1wabwwOtRkRSfOQtWNkVlUrE4HTvwpWHKpoAnJoUfvpEcE4KJyIKLYqv9E3R61CVt3dpSLoRoiggWmY/jxxowuHKZpTXtMDucvsnhQNd32dOr1ODN+0hIlIWAxMp5nD7/KWL+huVLaSPJSfokWLS42STHaXHGjFhRCrKKprhdHdOjBq1iJwhSYjTa3jjXiIiBfVoSK62tjbYdVAUOtzewxRtgQkAsgcnAgC+P9oIt0fyTwo/88FJ4UREoaFHgenyyy/HggULsHXrVjidzmDXRFGgudWBk012CID/yrFoMjgtHjE6NWwON/Ye4v3liIhCXY8C0xNPPAFJknDfffdh6tSpWLFiBb799ttg10YR7FD7+ksDUuIQo4u+kWFRFDBykAkA8MXXJzjcRkQU4noUmP7rv/4LGzduxGeffYbbb78dRUVFuOGGGzB79mxs3LgRdXX8jZnOzTccNzwzQeFKlDNiYAJUooAKcyuq661Kl0NEROdwQcsKpKWl4ec//zm2bduGTZs2ITExEc888wymT5+OX//619i7d2+w6qQI4wtMw6I4MOm1av9cpm/K+EsGEVEou+B1mEpKSvDb3/4Wd9xxB3bt2oUpU6bggQcegM1mw0033YRXXnklCGVSJHG6PCivaQEADBtgUrYYhY0fngwBQIW5FY0tDqXLISKis+jR5JHy8nJs2bIF7777LqqqqjBgwADccsstuO6665CRkQEAmDt3Lu677z68+OKLuO2224JZM4W5YzUt8EgyEmK1SDHp0eZwK12SYhLidBiTlYxvyuqx72gDpozNULokIiLqQo8C05VXXgmdToeZM2fi0UcfRWFhYZfHXXTRRTh27NiF1EcRyHf/uGGZCVzJGsD0iZn4pqweR6stmDCiHwx6jdIlERHRGXoUmH7729/i2muvRXx8/DmP++Uvf4lf/vKXPSqMItfh9ivkhg2I3vlLpxucHo/0JANqGqzYX96IiSNTlS6JiIjO0KM5TB9++CHMZnOX+0pLS/GjH/3ogoqiyHP6fdFOXSFnAjuYvMYO6wcAOFjRDKcrSu4RQ0QURrrdw1RSUuJfK6a4uBg7d+5EQ0NDp+M+++wzVFRUBK9CCnseAHa7CwBQ02BFm90NjUpEcvv8Ja5lDQxKjUNCrBbNbU4crGzG6KFJSpdERESn6XZg+vvf/44tW7b4ewpWrFjR6RhfoJo9e3bwKqSwJggC7HYX9h1rgMst4cDxRgDe+6l9f7QBBr0agzOMEBDdXU2CICB3aBK++q4GpccaMWpwIlRidLcJEVEo6XZgevjhh/GTn/wEsixj3rx5WLZsGYYNG9bhGFEUYTQaMXz48KAXSuHNd6+06jrvAo39EvRwujzQai54ZYuIMbR/PPYcOgmrw43ympaovMceEVGo6nZgio+PR0FBAQDgz3/+M3JzcxEbG9trhVFkMjfZAAApiTEKVxJ6VKKIEQNN2Hu4HocqmhiYiIhCSLcD0+bNm3HZZZchMTERJ06cwIkTJ855/Jw5cy60NoowdqcbljbvzZpTTAxMXRmWmYBvDtejttGG5lYngyURUYjodmB64IEH8H//939ITEzEAw88cM5jBUFgYKJOTjbZAQAJsVrotSqFqwlNsXoNBqTEovJkGw5VNjEwERGFiG4Hpk8++QQpKSn+r4nOl7mxfTiOvUvnNGKgCZUn21BWZUFBTprS5RAREc4jMA0YMKDLr33cbjdaW1thMpmCUhhFnpOcv9Qt/VNiYdCrYbW7cay6hQtZEhGFgB5douR2u7F27Vq89957AIAdO3ZgypQpKCwsxLx589Dc3BzUIin8eSQZdc3eIblU9jCdkygI/lXQD7ffRoaIiJTVo8C0evVqvPjii7BYLACA3/3udzCZTHjwwQdx/PhxPPvss0EtksJffbMNkiRDp1HBGMt7pQXiu0Kuqq4NLVanwtUQEVGPAtP777+Pe+65BzfffDPKyspw6NAh/OIXv8Ctt96KxYsX49NPPw12nRTmaht885f0vOFuNxhjtUg26iDLwJ5DdUqXQ0QU9XoUmMxmM8aNGwcA+Ne//gVRFDFt2jQAQHp6OlpaWoJXIUWE2gbvgpWpnL/UbUMyvL1Mu0q7vm8jERH1nR4FptTUVFRWVgIAPv30U4waNQpJSd57X+3Zswfp6enBq5DCnizLqG30BiZeIdd9QzLiAQBHTlhQ3z7/i4iIlNGjwDR79mw88cQTuOOOO7Br1y785Cc/AQA89thjWLNmDX70ox8FtUgKb/XNdtgcHoiC9x5y1D2xeg3SkwwAgOL9tQpXQ0QU3XoUmO6++27cfvvtEAQB9957L372s58BAL799lvcfvvt+MUvfhHUIim8HTnhvTggyaiHWsV7x52PrAHeYTkGJiIiZXV7HabTCYKAu+66C3fddVeH7W+88UZQiqLI4gtMnL90/oZkGPHVtzU4VtOCBosdSUb20BERKaFHgQkAWlpaUFRUBKvVClmWO+3nrVHIp6zKuy4XA9P5i9GpMSTDiKPVFnx9uA4z8jKVLomIKCr1KDB98cUXWLRoEWw2W5f7eS858rFYnajhFXIXZGxWMo5WW7DnEAMTEZFSehSYnn32WVx00UV48MEHkZaWBlHkvBTq2qGKJgCAKU4HvbbHHZpRbUxWMrb85yhKyxthtbth0LMdiYj6Wo/+5S0rK8O6deswadKkYNdDEeZge2BKTzYoW0gYS0syID3JgJoGK747Wo+CUbwhLxFRX+tR11D//v3R2toa7FooAvkCUwYD0wWZMLwfAOBrrvpNRKSIHgWmu+66C7///e/9i1cSdcXmcKO81rvqu289IeqZCSNSAAB7y+rh9kgKV0NEFH16NCT33nvvoba2Fj/4wQ+QlJQEvb7jpc6CIODjjz8OSoEUvsqqmiHLQLJRj9gYDZwuj9Ilha2s/gmIi9Gg1ebCkRMWjBhoUrokIqKo0qPAlJ6eztufUEAHK5sAAFmZCcoWEgFEUUDu0CTs2FeL747WMzAREfWxHgWmJ554Ith1UAQ6eLwJADCsfbVqujCjfYHpSAOum5aldDlERFHlgq5PLisrw5dffgmz2YxbbrkFFRUVyM7ORlxcXLDqozBld7pR1r7C97BME07UtSlcUfjLHeq9wXV5TQtarE7EG7QKV0REFD16FJgkScKyZcuwadMmyLIMQRDwwx/+EOvWrcPx48fx2muvccguyh043gSPJCPFpEeKKYaBKQhMcTpkpsSh8mQrvj/WgItz+P8YEVFf6dFVcuvWrcN7772H3/3ud/jyyy/9t0ZZsmQJJEnCqlWrglokhZ/vjzUAAHKGJClcSWQZfZG3Pb8/0qBwJURE0aVHgWnTpk1YtGgRfvKTn8BkMvm3jxo1CosWLcKXX34ZrPooTO071ggAGD00WeFKIsvo9mG57441dHkPRyIi6h09Ckx1dXUYNWpUl/vS0tJgsVguqCgKb40tDpyoa4MgAKMGJypdTkQZnmmCViOiudWJypMc5iQi6is9CkyDBw/G559/3uW+4uJiDB48+IKKImXVW+w42dj1jZW74/uj3uGiIelGxMZoglUWAdCoRWQP8obQ747WK1wNEVH06NGk73nz5mHZsmVwuVy4/PLLIQgCysvLsWPHDmzcuBEPPPBAsOukPmB3erDrgBllVd4ewismZmJo//NfEmBf+/yl3KHsXeoNuUOT8E1ZPb470oAfTuYvJ0REfaFHgemGG25AQ0MDXnzxRbz++usAgHvuuQcajQYLFizATTfdFNQiqffZHG689+Ux2Bxu/7Yvv61G2nne0kSS5VOBiRO+e4VvHtOhyiY4nB7otCqFKyIiinw9XofpzjvvxI9+9CMUFxdDrVYjPj4e48aN6zAJnMLH0WoLbA43YvVqXDImHTv3m9HU6sTne6owOTftvM5jsbqg06qQNYArfPeG9CQDko161FvsOFDRiLFZ/ZQuiYgo4p13YPrHP/6BN954A3v37oXb7e2N0Ov1yMvLw0033YSZM2cGvUjqfRW1rQCA4QNNyEiOxbTx/fH+V+WoqmtD8b5azJw4sFvn2bnfDACYMKwf1KoeTZGjAARBwOiLkvD51yfw3ZEGBiYioj7Q7cDk8Xhw77334oMPPkBaWhquueYa9OvXD7Iso6amBsXFxfj1r3+N//qv/8KTTz7ZmzVTkLk9Eqrar7ga0C8WgHeRxHHDkrH7YB0+LqnAjLxMCAHOI8kydpZ6A1P+qNTeLDnqjR7aHpiOcj0mIqK+0O3A9Prrr+Ojjz7CQw89hLlz50IQOv749Hg8eOONN/D4449j0qRJuP7664NeLPWOwxVNcHkk6LUqJBl1/u0jBpnw3ZEG1DbYsPdwHcYPO3dPxuHKZjS2OBCjU3H9pV42anASREFATYMVdc029EuIUbokIqKI1u0xk82bN+PGG2/ELbfc0iksAYBKpcLNN9+Mn/70p3jnnXeCWiT1rm/L6gAA/fvFdvjeatUqjBrivdJt6/bygOfx9S5NGJ4CjZrDccEiCL6H4H/ExmiQ1X5T4++PNipcIRFR5Ov2T7WjR49i2rRpAY+79NJLcfDgwQsqivrWt2Xe9Xx8w3Gnyx2aBLVKwOGqZhyqbDrrOSRJRkl7YCrgcFzQqFQCRFFEi82NVrurw2PEQBMAYO/hOniULZOIKOJ1e0jOZrMhISHwVU+JiYloa+MKxOGiwWJH1clWCAAyughMBr0G+aPSsP27Grz75THc+9/juzxP6fFGNLc5EatX8/5xQaQSBdicbpRVNMPp7hiLRNHbG7i/vBFWuwvGGC1vl0JE1Eu63cMkyzJUqsDrvYiiyH+0w8g37b1LKYkx0J9lPZ8f5A+EShTw/dEG/xpLp5NlGe9+eQwAUDAqjVfH9QKXW4LT5enwSIjVQqMS4XB5UGVuVbpEIqKIxp9sUa68pgUAkJF89gUqU0wxuHzCAADA3/9VBumMQPzd0QYcrGiCWiXimkKuPN1XRFFAWpJ3sveBiiZliyEiinDntQ7T8uXLERcXd85jWlv5m244qa63AvAuI3AuP5oyFF98W43ymhaUlJpRMMq7mKUky9j0rzIAwIy8AUgy6nu3YOogPdmAypNtOHi8SelSiIgiWrd7mPLz8xEbGwtZls/5iI2NxaRJk7pdgCRJWL16NS699FKMHz8ed955JyoqKs56fGNjI+69917k5+ejoKAAK1asgM3W8Uax27Ztw9VXX42xY8dizpw52L59e4f9hw4dwsKFCzF58mQUFhZi0aJFOHHiRLdrjiQ1Dd0LTMZYLX44eRAA4K//PIiDFU2QZBkfFVfguLkVeq2KvUsKyEj2zjsrq2qG2yMpXA0RUeTqdg/TX/7yl14pYN26dXj99dfx5JNPIj09Hc888wwWLFiA9957D1qtttPxixYtgs1mwyuvvAKLxYKHHnoIVqsVTz31FACgqKgIS5Yswf33348pU6bgrbfewsKFC7F582ZkZWWhsbER8+fPR15eHv7yl7/A6XTiySefxIIFC/DOO+9Apzt3cIgkTpcH9c12AEBCXOe2PtOs/IHYdeAkKsyteOZve5DR3rsBAD+cPAjxhsDnoOAyxWmh16pgd3pQVtXsv3KOiIiCS9E5TE6nExs3bsSiRYswffp0ZGdnY9WqVaipqcFHH33U6fg9e/aguLgYTz31FHJzc1FYWIhHHnkEW7ZsQW1tLQDgpZdewsyZM3HrrbciKysLS5cuRW5uLl599VUAwMcffwyr1Yqnn34aI0aMwOjRo/HMM8+grKwMu3fv7tP3r7TaRm/PnEGvPuuE79PptWo8ODcPBaNS4ZFkVJ5sg06rwk8uuwjXFA7p5WqpK4IgoH/71Y37jnE9JiKi3qJoYCotLUVbWxsKCwv924xGI3JycrBz585Ox5eUlCAlJQVZWVn+bQUFBRAEAbt27YIkSdi9e3eH8wHA5MmT/ecrLCzEunXroNefmmsjit5msFgsQX1/oa62fTguPSm2y8VIu6LXqnHXtbmYd9VIXFkwEE8svBjXFA7xX+JOfc8XmPaX8zYpRES95bxvvhtMNTU1AICMjIwO21NTU/37TldbW9vpWK1WC5PJhOrqalgsFlitVqSnp5/1fJmZmcjMzOyw/49//CP0ej3y8/Mv6P2ow2x1a3OTt4cpPdkAURSg6iL0qEQBgihArRYgy6f2XzGp4814z5a3BAEQnN5zd3V+sX3lalEFqDxhvL+9AbzhWzr/5/dwPwAMTPVeiHHkhAVuSYJeq+j/1hdE1b4khYpLU1wQtmNwsB2DJxLaUtF/WX2Ttc+cq6TT6dDc3Nzl8V3Na9LpdHA4HLDb7Wc9n8Ph6LKGv/zlL3jttdfw8MMPIymp5wsuiqKAxMTOCz+GsoZWJwAgLckAnU4DsYt1tjRqETF6LUymsy870Gp1wmp3d7lPELy3zYmJ0UKt6TwpOUavhlqtQoxeC7U6fPfrdBq43BL0ek2fvj4AJBhF9DPFoK7JhqoGOya1X8EYzoxG3hsvGNiOwcF2DJ5wbktFA5NvWMzpdHYYInM4HIiJ6dyoer0eTqez03aHwwGDweCfsH3mMV2dT5ZlvPDCC3jxxRfxi1/8ArfccssFvRdJkmGxWC/oHH2tvNo7BJmWHAuHwwW7o3Po0WpUsNmdaGqS0dV6pIIANFtdKD3WAJe78w90g16NgelG2OxOOJ2db+AhyBLcbk/Y73c4XBBVKtjtLkiSdN7P7+l+wPs9GjEwAXVNNuz49gSy0s+99EcoU6lEGI0xsFhs8PCqvx5jOwYH2zF4QrUtjcaYbvd6KRqYfMNrZrMZgwYN8m83m80YOXJkp+PT09Px8ccfd9jmdDrR1NSE1NRUmEwmGAwGmM3mDseYzWakpZ36rdvlcuHBBx/EP/7xDzz44IO47bbbgvJ+3F0EhlBWU++9wi09yYDqky3wSJ0TkUeSIUsy3G65yxXcBUGALMmwO72rT59JpRIgyzIkD7o8v9S+HEXY75dliPAuk3H6cb39+oB3+4iBJnz1bQ2+P9oQdp/Drng8UkS8D6WxHYOD7Rg84dyWig4mZmdnIy4uDjt27PBvs1gs2LdvX5fzifLz81FTU4Py8nL/tuLiYgDAxIkTIQgC8vLy/Nt8duzY0WFtqPvvvx8ffPABnn322aCFpXDTYnWirX0YLTXp7MNtFB58ywlUmFthsXbuhSUiogujaGDSarWYO3cuVq5ciU8++QSlpaVYvHgx0tPTMWvWLHg8Hpw8edI/N2ncuHHIy8vD4sWL8c0336CoqAjLli3DnDlz/D1I8+fPx/vvv4+XX34ZZWVlePrpp7F//37MmzcPAPD2229j69atWLx4MQoKCnDy5En/w/c60aC2wTt/LNmog04TeEkBCm3xBi0yU7xDcaXlXF6AiCjYFJ+uvmjRIlx//fV4+OGHcdNNN0GlUmHDhg3QaDSorq7G1KlTsXXrVgDe4Z+1a9ciMzMT8+bNw913341p06Zh+fLl/vNNnToVjz/+OP72t7/hxz/+MYqKirB+/Xr/UgT/+Mc/AABPP/00pk6d2uHhe51o4FvhOz05vCaq09nlDEkEAOxnYCIiCjrFrz9WqVRYsmQJlixZ0mlfZmYmDhw40GFbcnIyVq9efc5zzpkzB3PmzOly38aNG3tcayQ5FZg4HBcpRg1Owkc7K7CfC1gSEQWd4j1MpAzfopUZnL8UMUYOMkEUBJibbKhrtgV+AhERdRsDU5Q62f4DNTUxfNfEoI5idGoMyYgHABysaFK2GCKiCMPAFKUaW7wLeSYZ9QGOpHDiu1ruwPEmResgIoo0DExRyOWW0GJ1AQCSjDqFq6FgGukLTOxhIiIKKgamKNTc6u1dUqtExMVoAhxN4WR4pgmCAJgbbf5eRCIiunAMTFGoof0HaWK8FsLZ7ppLYcmgV2NQKucxEREFGwNTFGpq72FKjONwXCQawWE5IqKgY2CKQg2W9sDECd8RaeQgEwDgwHGux0REFCwMTFGIPUyRzdfDVF1vhaWN95UjIgoGBqYodGoOEwNTJIqL0SAzxXvLG85jIiIKDgamKNTEwBTxOI+JiCi4GJiiUGOLHQADUyQbOch7I14uYElEFBwMTFFGkmU0tXrntTAwRS5fD1PVyVa02lzKFkNEFAEYmKJMS5sTHkmGIADGWK3S5VCQCILvIUAQBJjidMhINkAGcLiqWenyiIjCHgNTlGlsv0LOGKuFWsVvfyRQqQSIoogWmxutdpf/cVF/IwDg2yP18ChcIxFRuFMrXQD1rUYLlxSINCpRgM3pRllFM5zuU9FIo1YBAL47Ug+7w404vQayLCtVJhFRWGNgijK+HibOX4o8LrcEp+tUYOqX4P0e1zXZYWsPTERE1DMck4kyjVxSIGoY9BrEGzSQARw5YVG6HCKisMbAFGUYmKJLamIMAKCsqrnDpPAzH0REdG4ckosyDEzRJS3RgLIqC46caEaLzQ2g6zlMep0aqr4tjYgorDAwRZlTgan7N971XbIOdO6JYOdEaPP1MJXXtOC7I3XwSJ0Dk0YtImdIEieFExGdAwNTFJFl+bx7mE6/ZL2r3glRFCAFs0gKqniDBnqtCnanBzX1ViTEce0tIqKeYGCKInanB472q6hM3fzBebZL1n0MejUGZxghdNH7RMoTBAFpSQaU17SgttHGwERE1EOc9B1FLG3eW6LotCroteeXlX2XrJ/5cHvYvxTq0pO8w3LmRqvClRARhS8GpijS3B6YEgzsZYgm6ckGAEBtAwMTEVFPMTBFEV8Pk5HDMlElNdEAUQDa7G602XkjXiKinmBgiiLsYYpOGrWI/v1iAQAnm+wKV0NEFJ4YmKJIM3uYotbgDO+NeE822hSuhIgoPDEwRRFLm3dJAfYwRZ/B6fEAgJNNDExERD3BwBRFLG3e+SvsYYo+Q9p7mBosdnh4ZSMR0XljYIoizexhilpJRh1idGpIMlBv4TwmIqLzxcAURXiVXPQSBMF/mxRO/CYiOn8MTFFCluVTV8nFMjBFo7RE73pMnMdERHT+GJiihM3hhtvjvRccA1N0SvP3MNl4k10iovPEwBQlfL1LMTo1NGqVwtWQEvqZYiAKgM3hQZvNrXQ5RERhhYEpSvjnL7F3KWqpVSKSjHoAgJnDckRE54WBKUpw/hIBQIrp1LAcERF1HwNTlGhuZQ8TASkmbw9THQMTEdF5YWCKEhYre5joVA9TQ4sDLjcXsCQi6i4GpijBHiYCgNgYDQw6NWQuYElEdF4YmKIEe5jIxzcsx3lMRETdx8AUJdjDRD6nJn6zh4mIqLsYmKIEe5jIxxeY6riAJRFRtzEwRQFJlv3rMDEwUVKCDqIgwO70oNXmUrocIqKwwMAUBax2NzyStych3sDAFO1Uoogkow4A5zEREXUXA1MUaG51AABi9Wpo1PyWE+cxERGdL/70jAK8LQqdKSWRK34TEZ0PBqYowNui0JlSErxLCzRyAUsiom5hYIoC7GGiM52+gCV7mYiIAmNgigKneph0CldCocS3gKW5kYGJiCgQBqYocKqHSaNwJRRKfBO/zY1WhSshIgp9DExRgD1M1JVTgYkLWBIRBcLAFAWaOYeJunD6ApZ1zVxegIjoXBiYogBX+aaunL6A5bFqi8LVEBGFNgamCCdJsv8+cuxhojP5huWOMjAREZ0TA1OEa7W5IMuAACDewEnf1JHvSrmj1S0KV0JEFNoYmCKcb/5SbIwGahW/3dSRr4fpxMlWOJwehashIgpd/Aka4fzzl+I4HEedxcZoYNCrIckcliMiOhcGpgjX3Oa98S4nfNPZpLXfV+5wVbPClRARhS4GpghnaXMB4IRvOrvURAMABiYionNhYIpw7GGiQNKS2gNTZRMkLmBJRNQlBqYIxxvvUiD9EvTQaVRos7tRaW5VuhwiopDEwBThmrloJQUgigIu6m8EABw43qRsMUREIUrxwCRJElavXo1LL70U48ePx5133omKioqzHt/Y2Ih7770X+fn5KCgowIoVK2Czdbzb+rZt23D11Vdj7NixmDNnDrZv337W116wYAHWrFkT1PcUStjDRN0xLDMBAHCgoknZQoiIQpTigWndunV4/fXX8eijj+KNN97whxin09nl8YsWLUJ5eTleeeUVvPDCC/j888+xfPly//6ioiIsWbIEN954I9555x0UFhZi4cKFKCsr63Aep9OJ3/zmN/jiiy968+0pjjfepe4YnmkCABw43sh5TEREXVA0MDmdTmzcuBGLFi3C9OnTkZ2djVWrVqGmpgYfffRRp+P37NmD4uJiPPXUU8jNzUVhYSEeeeQRbNmyBbW1tQCAl156CTNnzsStt96KrKwsLF26FLm5uXj11Vf959m9ezeuu+46lJSUwGg09tn77WseSUKrlVfJUWCD0uKg1Yhos7tx4mSb0uUQEYUcRQNTaWkp2traUFhY6N9mNBqRk5ODnTt3djq+pKQEKSkpyMrK8m8rKCiAIAjYtWsXJEnC7t27O5wPACZPntzhfJ9//jkuvfRSbN68GfHx8b3wzkJDi9UFGYAgAPExvC0KnZ1KJWLYABMADssREXVFreSL19TUAAAyMjI6bE9NTfXvO11tbW2nY7VaLUwmE6qrq2GxWGC1WpGenn7O8y1evDhYbyGkNbd6h+PiDVqIoqBwNRTqsgeZsO9YAw4cb8QVEzOVLoeIKKQoGph8k7W12o7DRTqdDs3NnRfRs9lsnY71He9wOGC32896PofDEayyz0qtVnxKWAdtDu9wnClOe9baVCoRkGSIogBVF6FKFAQIggBRBag83H/W/YJ3nyiKAKQ+e/1gnEMlChBEATlDk/D2v4/gQEUTRNWp99SXVO33O1TxvocXhO0YHGzH4ImEtlQ0MOn13julO51O/9cA4HA4EBMT0+XxXU0GdzgcMBgM0Ol0/vOdub+r8wWTKApITIzt1dc4Xy6pHgCQnBBzztpsDVbodBqIKlWnfTF6NdRqFWL0WqjVEvefZb9Op4HLLUGv1/To+T3dH4xzaNQiYvRa5OUYodeq0GJ1ocnqRlb7RHAlGI29+/9rtGA7BgfbMXjCuS0VDUy+4TWz2YxBgwb5t5vNZowcObLT8enp6fj44487bHM6nWhqakJqaipMJhMMBgPMZnOHY8xmM9LS0nrhHZwiSTIsFmuvvsb5qj7ZAgAw6NRobOx6Iq+v58nhcMHucHfaL8gS3G4PbHYnnF3czZ77vfsdDhdElQp2uwuSJJ3383u6Pxjn0GpUsNmd0IoyRg1OxJ5DdfjPnkokxfb9vDeVSoTRGAOLxQaPp+uASIGxHYOD7Rg8odqWRmNMt3u9FA1M2dnZiIuLw44dO/yByWKxYN++fZg7d26n4/Pz87Fy5UqUl5dj8ODBAIDi4mIAwMSJEyEIAvLy8lBcXIwbbrjB/7wdO3Zg0qRJvf5+3O7Q+RAAQKPFOwwZb9CctTbvqIsASZLhkTpfTi7JMmRZhuQB959rvyxDhHdtr9OP6+3XD8Y5PJIMWZLhdssYPTQJew7VYe/hOlx98eAuX68veDxSyP3/FI7YjsHBdgyecG5LRQOTVqvF3LlzsXLlSiQlJWHAgAF45plnkJ6ejlmzZsHj8aChoQHx8fHQ6/UYN24c8vLysHjxYixfvhxWqxXLli3DnDlz/D1I8+fPx8KFC5GTk4Np06Zh06ZN2L9/Px577DEl36oimlq9gckUxzWYqHvGXJQMACirssBqd8Gg59WVRERACCxcuWjRIlx//fV4+OGHcdNNN0GlUmHDhg3QaDSorq7G1KlTsXXrVgCAIAhYu3YtMjMzMW/ePNx9992YNm1ah4Urp06discffxx/+9vf8OMf/xhFRUVYv359h6UIooUvMCXGMzBR9/QzxSAj2QBJlvH9sUalyyEiChmK9jABgEqlwpIlS7BkyZJO+zIzM3HgwIEO25KTk7F69epznnPOnDmYM2dOt17/008/7Xat4eZUDxMXraTuG3NRMqrrrfi2rB752alKl0NEFBIU72Gi3iHLMpra12HikBydD9+w3LdH6yHzNilERAAYmCKW1eGGq31iHXuY6HyMGGiCViOiudWJ47WtSpdDRBQSGJgiVGOLdzguVq+GRt15fSWis9GoRYwe6u1l2llqDnA0EVF0YGCKUP75S5zwTT1QMMo7d6l4fy2H5YiIwMAUsZpaOH+Jem7csH7QaVSoa7bjyAmL0uUQESmOgSlC+ZcUYGCibhAE38N73zm9Vo0Jw/sBAHbsr1W4OiIi5TEwRahTQ3Kc8E3nplIJEEURLTY3Wu0u/2Nce2Aq3meG6yyrjBMRRQvF12Gi3sElBai7VKIAm9ONsopmON2n7jXnkWToNCIsVie+LavDxBGpnM9ERFGLPUwRirdFofPlcktwujz+h8cjYVBaPACg6PsahasjIlIWA1OE8i0rwMBEF2LEQBMAYPfBOv9niogoGjEwRSBJltHsH5LjHCbqueQEPdKTDJAkGZ/urlS6HCIixTAwRaAWqwuSLEMAYIxlYKILk3tREgDgs92VcLg8AY4mIopMDEwRqKl96MQYq4VaxW8xXZjB6fFIMurQZndjO+cyEVGU4k/TCMQJ3xRMoiDgsgkDAAAfFB3336OQiCiaMDBFoFOBicNxFBxTRmcgIVYLc5MNn+ziXCYiij4MTBHIvwYT7yNHQaLTqnD99CwAwHtfHYXF6lS4IiKivsXAFIG4pAD1hkvGZGBwWjxsDg82f3FU6XKIiPoUA1ME4pAcBZsgeFcE/9kPhgMAPv+6Cgcqmvz3niMiinQMTBGIk74pmE6/11z/lDhcnJsGWQb+sOU71DZa0Wp3gYsNEFGkY2CKQPXNdgBAslGvcCUUCXz3mtt/rAF7D9dh5KBEJMRq0dTqxLp3vsX3R+thd7jZ00REEY2BKcLYHG602d0AvKs0EwWL715zsizj0nEZEAUBx2tbsWNfrdKlERH1OgamCFPX3rsUF6NBjE6tcDUUqZKMekzOSQUA7DlYh/98c0LhioiIehcDU4Spa7IBAPqxd4l62fCBJozNSgYA/N+nh1FSala4IiKi3sPAFGF8PUwMTNQXxg1LxohBJsgy8OLm71C0j7dOIaLIxMAUYU42+3qYYhSuhKKBIAiYOjYDBaNSIckyXnp3H77Yy+E5Ioo8DEwRxneFXD8Te5iob4iCgJuvHInpEwZABvDytlJs+rwMkiwrXRoRUdAwMEUYDsmREkRBwK1XjsQ1hYMBAO9vL8eLm7+Dw8UVmogoMjAwRRBZllHHITlSiCAI+MllWbjjmlFQiQJ2HTiJp/6627+QKhFROGNgiiBWhxs2h/c3eq7BRH1JEHwPAVPH9sf9P5uAuBgNjtW04NFXS3CsxqJ0iUREF4SBKYLUNXmH44wGDXQalcLVULQ4/dYprXYXWu0u9E+Jwz03jkdaUgwaWxx4/C+78eW31UqXSkTUY1zZMIL4h+NMHI6jvuO7dUpZRTOc7o5zlmYVDMK/95xAeW0LNry/H+U1LfjpjGFQq/i7GhGFF/6rFUE44ZuU5Lt1yukPAcDM/ExcNXkQAODjXZV49o2vYWlzKlssEdF5YmCKIL4hOU74plAiCAKuuWQIfv2TsdBrVThQ0YQVr+zE0WrOayKi8MHAFEFOXSHHHiYKPXkjUvDwrZOQnmRAY4sDT7y2G//55tS8JkEQIAi+r31/9z6IiJTGwBRBOCRHoa5/v1g8fOskjB/WD26PhI1b9+P97cfgAdBqd6HZ6oK5wYpmq8s/gbzV7gJXcyIipXHSd4TwrsHkW+WbQ3IUWnzLDgACYmM0+PX1Y/HOv4/gH18dw6bPj6DF5sJF/Y2QZSAmRgubzQmP5F0pXKMWkTMkCXF6DWSuHk5ECmFgihAtVpd/VeVko07haohOOX3ZAeBU4Lly8iCoVAK2fHEUHxVXYMLwfhg/vB/UGu/kcV9gIiIKBQxMEaKqrg0AkGLSQ6PmGkwUOs617ECKKQaXTeiPz/ecwJ5DddDrVMjLTleoUiKis2NgihBVJ1sBAAP6xSlcCVHXfMsOnCl3aBJ0GhU+Kq5A0Xe1SDTGII03jyaiEMNJ3xGi8qS3hykzNVbhSojO36zJg5A9KBEygH8WH+f954go5DAwRYiqOvYwUfgSBAFTxmQgPdkAl1vCp7uqOg3fEREpiYEpAsiyjKr2HqYBKexhovAkigKmj++PuBgNmtuc+OrbGl4VR0Qhg4EpAtRb7LA7PVCJAtKTDEqXQ9RjMTo1rrx4MERRwPHaVhw43qR0SUREABiYIoKvdyk92cCbmlLYS0+ORX52CgCgpPQk6tvXFyMiUhJ/ukaAyvYr5DJTOH+JIsOowYnITImFJMv4dFclHE7OZyIiZTEwRQDfGkwD+nH+EkUGQRBwyZgMGHRqNLc58X+fHVa6JCKKcgxMEYATvikS6bUqXDouAwKA4n21+Orb6oDPISLqLQxMYc7tkVBd374GE4fkKMKkJRkwYaR3PtOfPzyAmgarwhURUbRiYApz5kYb3B4ZOo0KyQlcHZkiz/jh/TAsMwEOlwe/f/tb2J1upUsioijEwBTmKszeCd/9+8VC9N4OniiiiIKA236YDVOcFlV1bdj4/n6uz0REfY6BKcwdON4IAMgaYFS4EqLekxCnwy9/PAYqUUDJgZP4x/ZypUsioijDwBTm9pV7A1PO4CSFKyHqXcMzTbj5ByMAAO/8+wg+/7pK4YqIKJowMIWx+mY7zI02iIKAkYNMSpdD1OumTxiAH148CADw5w8OoHh/rcIVEVG0YGAKY/vKGwAAQzPiEaNTK1wNUd+4/rIsTB/fHzKAP767D//aw54mIup9DExhbH/7cNyoIYkKV0LUdwRBwNxZIzF1TAYkWcafPzyANz89BI8kKV0aEUUwBqYwJcsy9h9rD0yDGJgouoiigPlXZ+PHlw4FAHxYXIHH/7IbVXVtEASh04OI6EIxMIWp6normtuc0KhFDMtMULocoj4nCAJ+NGUofv5fuYjRqXG02oLlG4vx2kcHUN3Qhla7y//gneiI6EJx4kuY8g3HDRuQAI1apXA1RMqZnJOOAalx+NN7+1Be04JPdlXi31+fQPZgE7IHJ6KfKQY5Q5IQp9dw/SYi6jEGpjC1s/3qoBzOXyKCKU6HmZMycbTagq8P1aHB4sC3Rxrw7ZEG9O8XC4fTg8LcdKhEDs8RUc8wMIWh8poWHKxshkoUcMnoDKXLIep1guB9AJ0Dj2+KkiAIyEyJw4B+sag62YYDFU2oOtmGE+2rg7/1rzJcMjodU0anYwDvu0hE54mBKQx9sqsSADBxZAoS43UKV0PUu1QqAaIoosXmBtB5SE0UBZx+fZwgCMhMjUNmahxabS4cOWFBWVUzLG1OfLDjOD7YcRxD0uMxZUwGJuekIS5G02fvhYjCFwNTmLFYnSja5x2OmzlpoMLVEPU+lSjA5nSjrKIZTnfn6dsGvRqDM4wQuuh9iovRYFJ2KuZfnY2yKgv+80019h6uw7GaFhyracGbnx7C+GH9cMmYDIy5KAkqkdfBEFHXGJjCzOdfn4DbI2FIejyy+vP+cRQ9XG4JTlfnwKTVnDvkqFQCNBo1hg9MxPCBJrRYndhVehI79tei0tyKkgMnUXLgJIyxWlyck4ZJ2am4qL+RN7Mmog4YmMJIfbMdHxUfBwD8YNJAri9D1A1d9VAlGnW4avIg1DfbUVbVjCMnLLC0OfHRzgp8tLMCCbFa5AxJQvZgE4ZnmpCaGMMARRTlFA9MkiRh7dq1+Pvf/46Wlhbk5+dj2bJlGDiw6+GmxsZG/O53v8O///1vCIKAa665Bvfffz9iYmL8x2zbtg1r1qxBZWUlLrroIixduhSFhYXndY5Q43JLWLf5W7TZ3RicHo/8UalKl0QUVrrqoYo3aFCQk4bbrxmFoycs2P59Db49Uo/mNie2f1+D7d/XAAD0WhUGpMShX4Ie/RL0SE7Qo58xBglxWsQbNIjVq7m8B1GEUzwwrVu3Dq+//jqefPJJpKen45lnnsGCBQvw3nvvQavVdjp+0aJFsNlseOWVV2CxWPDQQw/BarXiqaeeAgAUFRVhyZIluP/++zFlyhS89dZbWLhwITZv3oysrKxunSPUSLKM1z8+iKPVLYjVq/GrOaOhVnGuBVGwqNUiJoxIwfjhKXB7JBysaML+8kaUljeivLYFdqcHZVXNKKtqPus59FoV4mI0iIvRIN6gbf9T49/W4e8GLeJi1JwzRRRGFA1MTqcTGzduxH333Yfp06cDAFatWoVLL70UH330EWbPnt3h+D179qC4uBhbt271h59HHnkECxYswD333IO0tDS89NJLmDlzJm699VYAwNKlS7Fnzx68+uqreOSRR7p1jlBibrTilW2lKD3eBAHAwmtz0c8Uuj1hROGmq6vwBqXHY1B6PK6cPAgygKq6Nuw5YEZTqwOtVhdabC60Wl2wO92wOz2QZcDu9MDu9KCu2d7t1zbo1YiP0SDOoEF8jLY9TGm8207fbvCGMINe3eXQYF8syBloCgAXBaVIp2hgKi0tRVtbW4fhMqPRiJycHOzcubNTYCopKUFKSoo/6ABAQUEBBEHArl27cNVVV2H37t144IEHOjxv8uTJ+Oijj7p1jquvvro33mqPVJ1sxaN/LoHTJUGrEfGzmSMw5qJkpcsiiijdvQpvcJoR6UnuTvtjY9QYkBqP/Uca0GJz+oOTL0y53RLUahEtbS602V1otblgtXvPY7W7YbW7Udto61atggAYdGrotWrodSroNCrodWrE6tWI0aqh16oQo1PDoFcjOTEWktsNSZIhy4AMGZABjyR7hyfdElxuj/9rt3+bd7vva9+fogho1SpoNaL/zxid2v8wxmoR1/61QX9qu0GnhhhiC4a6PZJ/iNZ52p+nv1+HywOPJEGlVqO5xQab3Q1JBjQqsUMbaNQq6DQi1GoROo2q/eH9Wtv+91B7/+dDlmV4JBkej/dPtyTB4/F+piRZhizLkCRAkiVIkgxBEKASBYii90/f11qtCnqHGy63BFmS29dWC692UTQw1dR45wdkZHRcfDE1NdW/73S1tbWdjtVqtTCZTKiurobFYoHVakV6evpZzxfoHD0ligKSkmJ7/PyuxCfE4PdLZkCjFhFv0PbKKsWCAMgyMKNgMLr6BVEUvMMVmWlG7g+wX4AAGXKH43r79UOtDS50P4BO7RhK9Z1r//BBSefc73ZLHVaRkmXvDyMZgCgI3h88cvsPIum0r9t/KIVrB44MeNfJEtoXfvAtNNq+P7g/NDu2k+z/j+8P735BFKHVANo+6KyXAXiA9htBn9re4V0L6HJZjJ6/4hlfdfOz42sv75+y/2mCClBrvIHhQlb+a251AKIIiB1L8i1K628BoWP7+NouLkYT9Oko5xNmFQ1MNpv3t6oz5yrpdDo0N3eeK2Cz2bqc16TT6eBwOGC32896PofD0a1z9JQgCFCpghtoVCoR6cl98y0yBPgQBprQyv3K7g+FGrj/wr+HRBS6FJ1xqNfrAXjnMp3O4XB0ecWaXq/vdKzveIPBAJ1OF/B8gc5BREREdCZFA5NvaMxsNnfYbjabu5x8nZ6e3ulYp9OJpqYmpKamwmQywWAwnPN8gc5BREREdCZFA1N2djbi4uKwY8cO/zaLxYJ9+/YhPz+/0/H5+fmoqalBeXm5f1txcTEAYOLEiRAEAXl5ef5tPjt27MCkSZO6dQ4iIiKiMykamLRaLebOnYuVK1fik08+QWlpKRYvXoz09HTMmjULHo8HJ0+e9M9NGjduHPLy8rB48WJ88803KCoqwrJlyzBnzhx/D9L8+fPx/vvv4+WXX0ZZWRmefvpp7N+/H/Pmzev2OYiIiIhOJ8gKL57h8Xjw3HPP4e2334bdbvev9J2ZmYnKykpcccUVeOKJJ3DdddcBAOrr67FixQp88cUX0Ol0uOqqq/Dggw/65y8BwObNm7Fu3TrU1NRg2LBhWLJkSYelC7pzDiIiIiIfxQMTERERUajjuvxEREREATAwEREREQXAwEREREQUAAMTERERUQAMTEREREQBMDARERERBcDARERERBQAA1OUkiQJq1evxqWXXorx48fjzjvvREVFhdJlhZympiYsW7YM06ZNQ15eHm666SaUlJT492/fvh3XXXcdxo0bh6uuugrvv/9+h+c7HA6sWLEChYWFmDBhAu699140NDT09dsIKUePHsWECRPw9ttv+7ft378fc+fOxfjx4zFjxgz8+c9/7vAcfl5P2bx5M66++mqMGTMG11xzDbZt2+bfV1lZibvuugt5eXmYOnUqnn/+eXg8ng7P/+tf/4orrrgCY8eOxc9+9jPs27evr9+C4txuN1544QVcfvnlmDBhAm6++WZ8/fXX/v38PAb2hz/8AbfcckuHbcFot0DnUJRMUWnNmjXy5MmT5c8++0zev3+/fPvtt8uzZs2SHQ6H0qWFlPnz58uzZ8+Wd+7cKR85ckResWKFPHbsWLmsrEw+fPiwPGbMGPm5556TDx8+LP/pT3+Sc3Jy5K+++sr//AceeECeOXOmvHPnTnnv3r3ynDlz5JtvvlnBd6Qsp9MpX3fddfKIESPkTZs2ybIsyw0NDfLkyZPlBx98UD58+LD81ltvyWPGjJHfeust//P4efXavHmznJOTI7/22mtyeXm5vG7dOjk7O1vevXu37HQ65VmzZskLFy6UDxw4IP/zn/+UCwoK5BdeeMH//LffflseO3asvGXLFvnQoUPykiVL5IKCArm+vl7Bd9X3Vq9eLU+ZMkX+4osv5GPHjskPPfSQPHHiRLm2tpafx2547bXX5OzsbHnu3Ln+bcFot+6cQ0kMTFHI4XDIEyZMkP/617/6tzU3N8tjx46V33vvPQUrCy3Hjh2TR4wYIZeUlPi3SZIkz5w5U37++efl3/72t/L111/f4Tn33HOPfPvtt8uyLMs1NTVydna2/K9//cu//8iRI/KIESPk3bt3982bCDHPPvusfOutt3YITOvXr5enTp0qu1yuDsfNmjVLlmV+Xn0kSZIvv/xy+cknn+yw/fbbb5fXr18vv/fee/Lo0aPlpqYm/7433nhDzsvL8/9AmjVrlvz000/797tcLvmyyy6T169f3zdvIkRce+218hNPPOH/e0tLizxixAj5ww8/5OfxHGpqauS77rpLHj9+vHzVVVd1CEzBaLdA51Aah+SiUGlpKdra2jrcX89oNCInJwc7d+5UsLLQkpiYiD/+8Y8YM2aMf5sgCBAEARaLBSUlJR3aEAAuvvhi7Nq1C7IsY9euXf5tPkOHDkVaWlpUtvPOnTvx5ptv4sknn+ywvaSkBAUFBVCr1f5tF198MY4dO4a6ujp+XtsdPXoUVVVV+NGPftRh+4YNG3DXXXehpKQEubm5SEhI8O+7+OKL0draiv3796O+vh7Hjh3r0I5qtRqTJk2KqnYEgOTkZHz22WeorKyEx+PBm2++Ca1Wi+zsbH4ez+H777+HRqPBu+++i3HjxnXYF4x2C3QOpTEwRaGamhoAQEZGRoftqamp/n3k/Z/5sssug1ar9W/78MMPUV5ejksvvRQ1NTVIT0/v8JzU1FTYbDY0NjaitrYWiYmJnW7qHI3tbLFYcP/99+Phhx/u9Lk7WzsCQHV1NT+v7Y4ePQoAsFqtuOOOO1BYWIgbbrgBn376KQC24/l46KGHoNFocMUVV2DMmDFYtWoVVq9ejUGDBrEdz2HGjBlYs2YNBg4c2GlfMNot0DmUxsAUhWw2GwB0CAIAoNPp4HA4lCgpLOzevRsPPvggZs2ahenTp8Nut3dqQ9/fnU4nbDZbp/1AdLbz8uXLMWHChE69IwC6bEdfyHQ4HPy8tmttbQUALF26FLNnz8bGjRsxZcoU/PKXv8T27dvZjufh8OHDiI+Px+9//3u8+eabuO6663Dfffdh//79bMceCka7BTqH0tSBD6FIo9frAXh/qPu+BrwfyJiYGKXKCmkff/wx7rvvPuTl5WHlypUAvP8jO53ODsf5/h4TEwO9Xt9pPxB97bx582aUlJTgvffe63J/V+3k+8fRYDDw89pOo9EAAO644w78+Mc/BgCMGjUK+/btw8svv3xe7XjmMdHUjtXV1bj33nvxyiuvYNKkSQCAMWPG4PDhw1izZg0/jz0UjHYLdA6lsYcpCvm6RM1mc4ftZrMZaWlpSpQU0l577TX8+te/xuWXX47169f7f+PJyMjosg0NBgPi4+ORnp6OpqamTv8ARFs7b9q0CfX19Zg+fTomTJiACRMmAAD+93//FwsWLEB6enqX7QgAaWlp/Ly2873XESNGdNg+bNgwVFZWsh27ae/evXC5XB3mJgLAuHHjUF5eznbsoWC0W6BzKI2BKQplZ2cjLi4OO3bs8G+zWCzYt28f8vPzFaws9Lz++ut49NFHcfPNN+O5557r0F08adIkFBcXdzi+qKgIeXl5EEUREydOhCRJ/snfgHceSm1tbVS188qVK7F161Zs3rzZ/wCARYsW4bHHHkN+fj527drVYb2goqIiDB06FMnJyfy8tsvNzUVsbCz27t3bYfvBgwcxaNAg5OfnY9++ff6hO8DbjrGxscjOzkZycjKGDh3aoR3dbjdKSkqiqh19c2QOHDjQYfvBgwcxZMgQfh57KBjtFugcilP6Mj1SxnPPPScXFBTIH3/8cYf1MJxOp9KlhYwjR47Iubm58q9+9SvZbDZ3eFgsFvngwYNybm6u/Mwzz8iHDx+WN2zY0GkdpnvuuUeeMWOGXFRU5F+H6fRLcaPV6csK1NXVyfn5+fLSpUvlQ4cOyZs2bZLHjBkjv/322/7j+Xn1+v3vfy9PmDBBfu+99zqsw1RUVCTb7XZ55syZ8h133CHv37/fvw7TmjVr/M9/88035bFjx8pvv/22fx2myZMnR9U6TB6PR77pppvkq666St6+fbt89OhRedWqVfKoUaPkr7/+mp/Hblq6dGmHf8uC0W7dOYeSGJiilNvtlp9++mn54osvlsePHy/feeedckVFhdJlhZQXX3xRHjFiRJePpUuXyrIsy59//rk8e/ZsefTo0fJVV10lv//++x3O0dbWJj/00EPypEmT5EmTJsn33HOP3NDQoMTbCSmnByZZluW9e/fKP/3pT+XRo0fLl19+ufyXv/ylw/H8vJ6yceNGecaMGXJubq587bXXyv/85z/9+44dOybPnz9fHjNmjDx16lT5+eeflz0eT4fn/+lPf5KnTZsmjx07Vv7Zz34m79u3r6/fguKamprk5cuXy9OnT5cnTJgg//d//7e8Y8cO/35+HgM7MzDJcnDaLdA5lCTIsiwr3ctFREREFMo4h4mIiIgoAAYmIiIiogAYmIiIiIgCYGAiIiIiCoCBiYiIiCgABiYiIiKiABiYiIiIiAJgYCKisHPLLbdg5MiRuPHGG896zOLFizFy5Eg88MADAICRI0dizZo1AIC3334bI0eORGVlZZ/US0ThT610AUREPSGKIr7++mvU1NT47w/mY7Va8dlnn3XY9uabb3Y6joiou9jDRERhKScnBzqdDh988EGnfZ999hliYmI63OF8/PjxDExE1GMMTEQUlgwGAy677LIuA9PWrVtx5ZVXQq0+1Yl++pBcV0pKSjB37lyMGzcOBQUFWLp0KRoaGjocs3PnTtxxxx3Iz8/H6NGjMWPGDKxZswaSJPmPMZvNWLx4MQoKCpCfn49ly5Zh1apVmDFjRodz/f3vf8c111yD0aNHY/r06VizZk2Hu7QTUWhhYCKisHX11Vf7h+V8Wltb8e9//xuzZ8/u9nl27tyJ2267DXq9Hs8//zx+85vfoLi4GLfeeivsdjsAoLS0FLfddhtMJhNWrVqFF198EZMmTcLatWuxbds2AIDT6cS8efOwe/du/OY3v8ETTzyB0tJSbNy4scPr/eEPf8Bvf/tbFBYWYv369bj55pvx0ksv4be//W0QWoWIegPnMBFR2Jo+fTpiYmLwwQcf4LbbbgMA/POf/0RycjImTpzY7fM8++yzGDp0KP7whz9ApVIBAMaNG4drrrkGmzZtws0334zS0lJccskleOaZZyCK3t81p0yZgk8//RQ7duzANddcg3fffRdHjhzBpk2bMHr0aADAxRdfjJkzZ/pfq6WlBevWrcN///d/4+GHHwYATJ06FSaTCQ8//DDmz5+P4cOHB6N5iCiI2MNERGFLr9djxowZHYbl3n//ffzwhz+EIAjdOofNZsPevXtx2WWXQZZluN1uuN1uDBw4EFlZWfjyyy8BAHPmzMFLL70El8uF0tJSfPjhh1i9ejU8Hg9cLhcAoKioCAMHDvSHJQCIi4vD5Zdf7v/7nj17YLfbMWPGDP9rud1u/5Cd7/WIKLSwh4mIwtoPf/hD/L//9/9QU1MDnU6H7du34+677+728y0WCyRJwksvvYSXXnqp036dTgcAsNvtePTRR7Flyxa43W5kZmZiwoQJUKvVkGUZANDY2Ijk5ORO5zh9W1NTEwBg4cKFXdZjNpu7XTsR9R0GJiIKa9OmTUNsbCw++OADGAwGZGZmdujhCSQ2NhaCIOC2227DNddc02l/TEwMAOCxxx7Dhx9+iOeffx6XXHIJDAYDAKCwsNB/bFpaGo4dO9bpHPX19f6vjUYjAGDlypUYMmRIp2P79evX7dqJqO9wSI6IwppWq8XMmTPx4YcfYtu2bV2GnnOJi4tDTk4Ojhw5gjFjxvgfw4cPx5o1a7Bjxw4AwK5duzB58mTMnDnTH5a+++47NDQ0+K+SKygoQGVlJfbv3+8/v91uxxdffOH/+7hx46DRaFBbW9vh9dRqNZ577jkupkkUotjDRERh7+qrr8Zdd90FURT9E6nPxz333IOFCxfi3nvvxbXXXguPx4ONGzdi7969+OUvfwkAGDt2LLZt24a//e1vyMrKQmlpKV588UUIggCbzQYAmD17Nv74xz/iV7/6Ff7nf/4HRqMRL7/8Murr69G/f38AQGJiIhYsWIAXXngBra2tmDx5Mmpra/HCCy9AEARkZ2cHr2GIKGgYmIgo7F1yySUwGo3IyMhAVlbWeT9/6tSp2LBhA9auXYtFixZBo9EgNzcXL7/8MsaPHw8AeOCBB+ByufD888/D6XQiMzMTv/jFL3D48GF8+umn8Hg8UKvV2LBhAx577DEsX74carUa1157LUwmE44ePep/vbvvvhspKSl4/fXX8ac//QkJCQkoLCzEPffcg/j4+GA1CxEFkSD7ZisSEdEFOXToEI4cOYJZs2Z1uErv+uuvR3p6OtauXatgdUR0IdjDREQUJFarFf/zP/+Dn/3sZ/jBD34Aj8eDrVu34rvvvsN9992ndHlEdAHYw0REFEQffPABNmzYgLKyMsiyjJycHPziF7/A1KlTlS6NiC4AAxMRERFRAFxWgIiIiCgABiYiIiKiABiYiIiIiAJgYCIiIiIKgIGJiIiIKAAGJiIiIqIAGJiIiIiIAmBgIiIiIgqAgYmIiIgogP8PiYS77o9ysQwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can treat the other numerical variables in a similar way\n",
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\1666233205.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_2['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This plot looks kind of normal, doesn't it?\n",
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\2097752121.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The situation with engine volume is very strange\n",
+    "# In such cases it makes sense to manually check what may be causing the problem\n",
+    "# In our case the issue comes from the fact that most missing values are indicated with 99.99 or 99\n",
+    "# There are also some incorrect entries like 75\n",
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A simple Google search can indicate the natural domain of this variable\n",
+    "# Car engine volumes are usually (always?) below 6.5l\n",
+    "# This is a prime example of the fact that a domain expert (a person working in the car industry)\n",
+    "# may find it much easier to determine problems with the data than an outsider\n",
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\1910130346.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_3['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Following this graph, we realize we can actually treat EngineV as a categorical variable\n",
+    "# Even so, in this course we won't, but that's yet something else you may try on your own\n",
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\2257603476.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Finally, the situation with 'Year' is similar to 'Price' and 'Mileage'\n",
+    "# However, the outliers are on the low end\n",
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I'll simply remove them\n",
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\730772117.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_4['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here's the new result\n",
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When we remove observations, the original indexes are preserved\n",
+    "# If we remove observations with indexes 2 and 3, the indexes will go as: 0,1,4,5,6\n",
+    "# That's very problematic as we tend to forget about it (later you will see an example of such a problem)\n",
+    "\n",
+    "# Finally, once we reset the index, a new column will be created containing the old index (just in case)\n",
+    "# We won't be needing it, thus 'drop=True' to completely forget about it\n",
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's see what's left\n",
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we decided to use some matplotlib code, without explaining it\n",
+    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
+    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3)) #sharey -> share 'Price' as y\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_7480\\2845240188.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_cleaned['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# From the subplots and the PDF of price, we can easily determine that 'Price' is exponentially distributed\n",
+    "# A good transformation in that case is a log transformation\n",
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "...           ...   ...        ...  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's transform 'Price' with a log transformation\n",
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "\n",
+    "# Then we add it to our data frame\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# The relationships show a clear linear relationship\n",
+    "# This is some good linear regression material\n",
+    "\n",
+    "# Alternatively we could have transformed each of the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since we will be using the log price variable, we can drop the old 'Price' one\n",
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's quickly see the columns of our data frame\n",
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn does not have a built-in way to check for multicollinearity\n",
+    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
+    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
+    "\n",
+    "# Here's the relevant module\n",
+    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# To make this as easy as possible to use, we declare a variable where we put\n",
+    "# all features where we want to check for multicollinearity\n",
+    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "\n",
+    "# we create a new data frame which will include all the VIFs\n",
+    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
+    "vif = pd.DataFrame()\n",
+    "\n",
+    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "# Finally, I like to include names so it is easier to explore the result\n",
+    "vif[\"Features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF Features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's explore the result\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since Year has the highest VIF, I will remove it from the model\n",
+    "# This will drive the VIF of other variables down!!! \n",
+    "# So even if EngineV seems with a high VIF, too, once 'Year' is gone that will no longer be the case\n",
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+3).ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+3).ipynb
new file mode 100644
index 0000000..5f8bf4e
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+3).ipynb	
@@ -0,0 +1,3155 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('1.04. Real-life example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb673e2780>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb673c5cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb677be0f0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb676f85c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb677fd518>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb677c9e48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb6794d518>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb673d47b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb6794dc88>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb6797c4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb67a03e10>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb67a13198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb67ae8710>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb679e4080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb67b74e10>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb67c45630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb67d0c860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb67d1b3c8>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb67cd6a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>14200.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>9.560997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10799.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>193</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.287209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>1400.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>212</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>7.244228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>11950.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>177</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.388487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>260</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>7.824046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>9500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>165</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.159047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>16000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>250</td>\n",
+       "      <td>4.70</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>9.680344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.059517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>2990.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>203</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>8.003029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>26500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.184900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>3500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>250</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>8.160518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>38233.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>10.551454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>132</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>8.922658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>6800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>225</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>8.824678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>130</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>24900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>163</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.122623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>20800.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>151</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.942708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>330</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13566.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>171</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.515322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>8500.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9.047821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>7.972466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>21500.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>72</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>9.975808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>87</td>\n",
+       "      <td>3.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>28500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>160</td>\n",
+       "      <td>4.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.257659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3837</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>180</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3838</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>9700.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>247</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.179881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3839</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>185</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3840</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10900.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>180</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.296518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3841</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>25500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>77</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.146434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3842</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>15500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.648595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3843</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>9750.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>159</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>9.185023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3844</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>16100.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>194</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>9.686575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3845</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>9200.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>171</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.126959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3846</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>5150.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>240</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>8.546752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3847</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>11.512925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3848</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>8999.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>126</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.104869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3849</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>16800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.729134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3850</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>8200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>280</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>9.011889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3851</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>24950.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.124629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3852</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>80999.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>11.302192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3853</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7300.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>207</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>8.895630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3854</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>21335.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>105</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.968104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3855</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>45000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>10.714418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3856</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>6750.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>155</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>8.817298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3857</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>7000.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>210</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>8.853665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3858</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>12090.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>145</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9.400134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3859</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>27900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>38</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.236382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3860</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>2100.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>237</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>7.649693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3861</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>6800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>152</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>8.824678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277     2.00      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427     2.90      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358     5.00         Gas   \n",
+       "3              Audi   23000.0  crossover      240     4.20      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120     2.00      Petrol   \n",
+       "5              Audi   14200.0      vagon      200     2.70      Diesel   \n",
+       "6           Renault   10799.0      vagon      193     1.50      Diesel   \n",
+       "7        Volkswagen    1400.0      other      212     1.80         Gas   \n",
+       "8           Renault   11950.0      vagon      177     1.50      Diesel   \n",
+       "9           Renault    2500.0      sedan      260     1.79      Petrol   \n",
+       "10             Audi    9500.0      vagon      165     2.70         Gas   \n",
+       "11       Volkswagen   10500.0      sedan      100     1.80      Petrol   \n",
+       "12           Toyota   16000.0  crossover      250     4.70         Gas   \n",
+       "13          Renault    8600.0      hatch       84     1.50      Diesel   \n",
+       "14              BMW    2990.0      other      203     2.00      Petrol   \n",
+       "15           Toyota   26500.0  crossover       21     2.00      Petrol   \n",
+       "16             Audi    3500.0      vagon      250     2.50      Diesel   \n",
+       "17           Toyota   38233.0      other        0     2.40      Diesel   \n",
+       "18       Volkswagen    7500.0      hatch      132     1.40      Diesel   \n",
+       "19             Audi    6800.0      sedan      225     2.40         Gas   \n",
+       "20       Mitsubishi   10500.0  crossover      130     2.40         Gas   \n",
+       "21             Audi   24900.0      sedan      163     4.20      Diesel   \n",
+       "22       Volkswagen   20800.0  crossover      151     3.00      Diesel   \n",
+       "23             Audi    6500.0      sedan      330     2.40      Petrol   \n",
+       "24    Mercedes-Benz   13566.0      other      171     2.20       Other   \n",
+       "25       Mitsubishi    8500.0      hatch       65     1.30      Petrol   \n",
+       "26             Audi    2900.0      sedan        1     2.30         Gas   \n",
+       "27              BMW   21500.0      other       72     3.00      Petrol   \n",
+       "28       Mitsubishi   17900.0  crossover       87     3.80         Gas   \n",
+       "29              BMW   28500.0  crossover      160     4.80         Gas   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3837  Mercedes-Benz   11500.0        van      180     2.20      Diesel   \n",
+       "3838     Mitsubishi    9700.0        van      247     2.40         Gas   \n",
+       "3839        Renault   10500.0      vagon      185     1.50      Diesel   \n",
+       "3840        Renault   10900.0      vagon      180     1.50      Diesel   \n",
+       "3841  Mercedes-Benz   25500.0  crossover       77     3.50      Petrol   \n",
+       "3842     Volkswagen   15500.0      sedan       80     1.40      Petrol   \n",
+       "3843     Volkswagen    9750.0        van      159     1.90      Diesel   \n",
+       "3844            BMW   16100.0  crossover      194     3.00      Diesel   \n",
+       "3845     Volkswagen    9200.0      vagon      171     1.60      Petrol   \n",
+       "3846     Volkswagen    5150.0        van      240     2.00      Diesel   \n",
+       "3847         Toyota  100000.0  crossover        0     4.50      Diesel   \n",
+       "3848        Renault    8999.0      other      126     2.00      Diesel   \n",
+       "3849  Mercedes-Benz   16800.0      sedan      125     1.80      Petrol   \n",
+       "3850  Mercedes-Benz    8200.0      sedan      280     2.40         Gas   \n",
+       "3851  Mercedes-Benz   24950.0      other       60     1.80      Petrol   \n",
+       "3852           Audi   80999.0  crossover        0     3.00      Diesel   \n",
+       "3853  Mercedes-Benz    7300.0        van      207     2.20      Diesel   \n",
+       "3854            BMW   21335.0      other      105     3.00      Petrol   \n",
+       "3855            BMW   45000.0  crossover       80     3.00      Petrol   \n",
+       "3856        Renault    6750.0        van      155     1.50      Diesel   \n",
+       "3857        Renault    7000.0        van      210     1.50      Diesel   \n",
+       "3858            BMW   12090.0      hatch      145     1.60      Petrol   \n",
+       "3859            BMW   27900.0      sedan       38     2.00      Petrol   \n",
+       "3860        Renault    2100.0      vagon      237     1.90      Diesel   \n",
+       "3861        Renault    6800.0      sedan      152     1.60      Petrol   \n",
+       "3862     Volkswagen   11500.0        van      163     2.50      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35     1.60      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9     3.00      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1     3.50      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124     2.00      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "5             yes  2006   9.560997  \n",
+       "6             yes  2012   9.287209  \n",
+       "7              no  1999   7.244228  \n",
+       "8             yes  2011   9.388487  \n",
+       "9             yes  1994   7.824046  \n",
+       "10            yes  2003   9.159047  \n",
+       "11            yes  2008   9.259131  \n",
+       "12            yes  2001   9.680344  \n",
+       "13            yes  2012   9.059517  \n",
+       "14             no  2001   8.003029  \n",
+       "15            yes  2013  10.184900  \n",
+       "16             no  1998   8.160518  \n",
+       "17            yes  2016  10.551454  \n",
+       "18            yes  2006   8.922658  \n",
+       "19            yes  1998   8.824678  \n",
+       "20            yes  2006   9.259131  \n",
+       "21            yes  2008  10.122623  \n",
+       "22            yes  2008   9.942708  \n",
+       "23            yes  1999   8.779557  \n",
+       "24             no  2011   9.515322  \n",
+       "25            yes  2010   9.047821  \n",
+       "26            yes  1989   7.972466  \n",
+       "27            yes  2007   9.975808  \n",
+       "28            yes  2008   9.792556  \n",
+       "29            yes  2008  10.257659  \n",
+       "...           ...   ...        ...  \n",
+       "3837          yes  2011   9.350102  \n",
+       "3838          yes  2008   9.179881  \n",
+       "3839          yes  2011   9.259131  \n",
+       "3840          yes  2012   9.296518  \n",
+       "3841          yes  2008  10.146434  \n",
+       "3842          yes  2013   9.648595  \n",
+       "3843          yes  2009   9.185023  \n",
+       "3844          yes  2004   9.686575  \n",
+       "3845          yes  2008   9.126959  \n",
+       "3846          yes  2005   8.546752  \n",
+       "3847          yes  2016  11.512925  \n",
+       "3848          yes  2012   9.104869  \n",
+       "3849          yes  2008   9.729134  \n",
+       "3850          yes  1997   9.011889  \n",
+       "3851          yes  2013  10.124629  \n",
+       "3852          yes  2016  11.302192  \n",
+       "3853          yes  2003   8.895630  \n",
+       "3854          yes  2008   9.968104  \n",
+       "3855          yes  2011  10.714418  \n",
+       "3856          yes  2012   8.817298  \n",
+       "3857          yes  2005   8.853665  \n",
+       "3858          yes  2010   9.400134  \n",
+       "3859          yes  2013  10.236382  \n",
+       "3860           no  2001   7.649693  \n",
+       "3861          yes  2007   8.824678  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eb68e43b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840          1                    0   \n",
+       "1      427      2.9   8.974618          0                    1   \n",
+       "2      358      5.0   9.495519          0                    1   \n",
+       "3      240      4.2  10.043249          0                    0   \n",
+       "4      120      2.0   9.814656          0                    0   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                 0              0             0                 0   \n",
+       "1                 0              0             0                 0   \n",
+       "2                 0              0             0                 0   \n",
+       "3                 0              0             0                 0   \n",
+       "4                 0              0             1                 0   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0           0           0           1           0         0                0   \n",
+       "1           0           0           0           0         1                0   \n",
+       "2           0           0           1           0         0                1   \n",
+       "3           0           0           0           0         0                0   \n",
+       "4           0           0           0           0         0                0   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0                  0                   1                 1  \n",
+       "1                  0                   0                 1  \n",
+       "2                  0                   0                 1  \n",
+       "3                  0                   1                 1  \n",
+       "4                  0                   1                 1  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0          1                    0   \n",
+       "1   8.974618      427      2.9          0                    1   \n",
+       "2   9.495519      358      5.0          0                    1   \n",
+       "3  10.043249      240      4.2          0                    0   \n",
+       "4   9.814656      120      2.0          0                    0   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                 0              0             0                 0   \n",
+       "1                 0              0             0                 0   \n",
+       "2                 0              0             0                 0   \n",
+       "3                 0              0             0                 0   \n",
+       "4                 0              0             1                 0   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0           0           0           1           0         0                0   \n",
+       "1           0           0           0           0         1                0   \n",
+       "2           0           0           1           0         0                1   \n",
+       "3           0           0           0           0         0                0   \n",
+       "4           0           0           0           0         0                0   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0                  0                   1                 1  \n",
+       "1                  0                   0                 1  \n",
+       "2                  0                   0                 1  \n",
+       "3                  0                   1                 1  \n",
+       "4                  0                   1                 1  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed = data_with_dummies[cols]\n",
+    "data_preprocessed.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+3)_with_comments.ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+3)_with_comments.ipynb
new file mode 100644
index 0000000..b721db1
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+3)_with_comments.ipynb	
@@ -0,0 +1,2717 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For this practical example we will need the following libraries and modules\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "raw_data = pd.read_csv('Real-life+example.csv')\n",
+    "\n",
+    "# Let's explore the top 5 rows of the df\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics are very useful for initial exploration of the variables\n",
+    "# By default, only descriptives for the numerical variables are shown\n",
+    "# To include the categorical ones, you should specify this with an argument\n",
+    "raw_data.describe(include='all')\n",
+    "\n",
+    "# Note that categorical variables don't have some types of numerical descriptives\n",
+    "# and numerical variables don't have some types of categorical descriptives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For these several lessons, we will create the regression without 'Model'\n",
+    "# Certainly, when you work on the problem on your own, you could create a regression with 'Model'\n",
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "\n",
+    "# Let's check the descriptives without 'Model'\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data.isnull() # shows a df with the information whether a data point is null \n",
+    "# Since True = the data point is missing, while False = the data point is not missing, we can sum them\n",
+    "# This will give us the total number of missing values feature-wise\n",
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's simply drop all missing values\n",
+    "# This is not always recommended, however, when we remove less than 5% of the data, it is okay\n",
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's check the descriptives without the missing values\n",
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\3514801760.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+7JwrSTXp5dBMzgUAlAmCS5GIJ5JKJNNXhu5rj0v3UBE9LgCAMkFwKRLp3hbLkrzuPva4dA8VhaOJzJWlAQAoZQSXIhHdZ9fcA10ZOs3vc8vjTj2W4SIAQDkguBSJSLR/K4qk1PWKMrvnhhguAgCUPoJLkYjGUz0uXk//fiTp4MKSaABAOSC4FIlYPNXj0t/gUsuSaABAGSG4FIl0cPENtMeF4AIAKAMElyKR6XHpxxwXae/Kog4m5wIAygDBpUhE08Glj0uh0zKTcwkuAIAyQHApErF4/3bNTeMK0QCAckJwKRLRAU7OTfe4dITocQEAlD6CS5HYu6poYHNcGCoCAJQDgkuRGOiqour0hRbZgA4AUAYILkVioPu4pIeKQpGE4olkzusCAKCYEFyKRPpaRf0NLpV+j1wW1ysCAJSHAQWXxsbGXNdR9gY6VOSyLFUHPJJYWQQAKH0DCi5nnnmmrrjiCj3//POKRnmzPFjGmAFPzpX22cuFlUUAgBI3oOBy5513KplM6oYbbtCpp56qBQsW6K233sp1bWUjnjAy3Z/3d6hIYvdcAED58AzkSZ///Of1+c9/Xo2NjfrNb36j3/72t/rf//1fjR07VhdccIHOO+88DRkyJNe1lqx0b4tlSR631e/nV2d2z6X3CwBQ2g5qcu7w4cP19a9/Xb///e/15JNPqqGhQXfffbfOOOMMXXvttVqzZk2u6ixp0fjeibmW1f/gUhNgLxcAQHk46FVFq1at0s0336yvfvWrWr16tU455RTNmzdPoVBIF198sR577LEclFnaYgO8TlFaZqiIOS4AgBI3oKGibdu26be//a2eeeYZffDBBxo1apTmzJmjCy64QCNHjpQkXXrppbrhhhu0aNEiXX755bmsueRkVhT188rQaTUMFQEAysSAgstnP/tZVVRU6KyzztJ3vvMdnXTSSb0+bvTo0dq6det+z7V7927ddddd+vOf/6xIJKIZM2boxhtv1JgxYwZSmiMNdPO5tGqGigAAZWJAweXmm2/Weeedp5qamv0+7uqrr9bVV1+938dcc801SiaTeuSRR1RVVaUf/OAHuvzyy/Xiiy8qEAgMpDzH2XeOy0BkrlfEUBEAoMQN6J3yhRdeUFNTU6/H3nvvPc2ePbtP52lra9OoUaN0xx13aNKkSRozZoyuvvpqNTU1acOGDQMpzZEOtsclc4VohooAACWuzz0uq1atkjGp3UZWrFihlStXqqWlJetxL730knbs2NGnc9bV1enee+/NfN3S0qLHHntMI0aM0NixY/tamuNFYwPbNTctPVTUEYoraUzmEgAAAJSaPgeXX/3qV/rtb38ry7JkWZYWLFiQ9Zh0sDn33HP7XcjNN9+sX/7yl/L5fFq0aJEqKyv7fY40zwADQKG4u1cPuSyX3C5LicTeyblul9V9LNXOLrfkTmQHEbfLkuWy5PFYaqitkCQljVEklsj0wDhJuk3cA1xZVWpoj2y0SU+0RzbaJFsptkmfg8tNN92kCy+8UMYYXXbZZbrllluyekVcLpdqa2t19NFH97uQyy67TBdddJGeeOIJXXPNNVqyZImOP/74fp/H5bLU0FDV7+fZoaLCo0DAp6RSwaQq4FNlZSqEBPweeTxuBfw+eTzZV332elwK+H2qr08FvCq/R53huCyPxzGvvze1teUxr6mvaI9stElPtEc22iRbKbVJn4NLTU2NTjjhBEnSz372Mx1//PGqqsrdG2Q6BH33u9/VmjVrtHjxYt155539Pk8yaRQMduWsrnxwu12qrQ0oEokrFIoqFO6eVGuMuroikiTLJBWPJxQKRxWNJrLO4fO6FQpH1dpqZIxUFfCqMxzXBx8FVe1zXrJOt0kwGMr0QJUz2iMbbdIT7ZGNNsnmlDaprQ30uVeoz8Hl6aef1umnn66Ghgbt3LlTO3fu3O/jzz///AOes6WlRcuWLdNnP/tZeTypUlwul8aOHfuJk3/7Ih4v3h/OvpImqUQyNbwjpbb7TyRN9zEjY4ySCWXu21ciaWSSRvF46nE1Aa+a9oTU2h5xzOvvTSKRdHT9uUZ7ZKNNeqI9stEm2UqpTfocXObNm6df/vKXamho0Lx58/b7WMuy+hRcmpubNXfuXD366KM67bTTJEmxWExr167VrFmz+lqa4x3sqiJp3ytEs7IIAFC6+hxc/u///k9Dhw7NfJ4L48aN08yZM3XHHXfojjvuUF1dnR5++GEFg8Gy2m03s3OuZ2A750pSNVeIBgCUgT4Hl1GjRvX6eVo8HldHR4fq6+v7VcDChQt177336vrrr1d7e7umT5+uJ554Qocccki/zuNk0Vz0uLB7LgCgDAxo59x4PK6HHnpIRxxxhGbPnq3ly5fruuuuUzAY1AknnKAHHnhAdXV1fTpXTU2NbrvtNt12220DKaUkxLp3zvV5GSoCAGB/BvRO+cADD2jRokUKBoOSpDvuuEP19fWaP3++tm/f3mNTOexfMmkUT6Qm3x7cHBd6XAAApW9A75TPPfec5s6dq0suuUSbNm3Shg0bdNVVV+nf/u3fdP311+tPf/pTrussWbF9Znl7D2aOS4A5LgCA0jeg4NLU1KTJkydLkl5++WW5XC7NnDlTkjRixAi1t7fnrsISl77AottlZXbNHQiGigAA5WBAwWXYsGF6//33JUl/+tOfdOyxx2rQoEGSpL/97W8aMWJE7ioscbmYmCv1HCpKX3oBAIBSM6B3y3PPPVd33nmnvvrVr2r16tW68MILJaV2vX3wwQf7fHVoSLHuzecONrikh4pi8WTmoo0AAJSaAa0q+sY3vqHKykqtXLlS3/zmN/Wv//qvkqS33npLX/nKV3TVVVfltMhSFo0f3JWh0/w+tzxul+KJpNq7oqrwlc51KQAASBtQcLEsS1deeaWuvPLKHvf//Oc/z0lR5STdO3IwE3Ol1M+kptKrPe0RtYdiGlJPcAEAlJ4BBRdJam9v1+uvv66urq5e51T0Zct/7N3D5WCHiqTUJnR72iNq72KCLgCgNA0ouPz5z3/Wddddp1Ao1Ovxvl6rCLkbKpLYywUAUPoGFFzuvfdejR49WvPnz9fw4cPlch38m265Sg8VeXISXLqXRBNcAAAlakDBZdOmTfrxj3+s6dOn57qespPZ7j8HwSV9oUX2cgEAlKoBvVsecsgh6ujoyHUtZSmWo31cpL0XWmT3XABAqRrQu+WVV16pH/3oR5lN6DBwudqATmKoCABQ+gY0VPTss8+qsbFRn/nMZzRo0CD5/f4exy3L0tKlS3NSYKnLaY8LQ0UAgBI3oOAyYsQItvXPkb3LoQ9uHxdp7+659LgAAErVgILLnXfemes6ylamx8Wdu6Ei5rgAAErVgDegk1Kri1577TU1NTVpzpw52rFjh8aPH6/q6upc1VfycjvHJdXj0hWJK55IypODMAQAQDEZUHBJJpO65ZZb9OSTT8oYI8uy9I//+I/68Y9/rO3bt2vx4sUMJfVRLue4VPm9siQZSR2hmOqrKw76nAAAFJMBvVv++Mc/1rPPPqs77rhDr732WmbL/29961tKJpO67777clpkqTLG5DS4uFyWqlgSDQAoYQN6t3zyySd13XXX6cILL1R9fX3m/mOPPVbXXXedXnvttVzVV9LSu+ZKytmwzt5t/1lZBAAoPQN6t2xubtaxxx7b67Hhw4crGAweVFHlIhSNS5IsSR631e/nW1b6ZmVumb1cQnFZVv/PCQBAMRtQcDniiCP0yiuv9HpsxYoVOuKIIw6qqHIRiaaWQns8rn6HDLfbksvlUnsoro5wLHMLVKSmLTUHQ+oIx5TIedUAANhnQJNzL7vsMt1yyy2KxWI688wzZVmWtm3bpuXLl+t//ud/NG/evFzXWZLC3T0uA5nf4nZZCkXj2rSjTdH43ngSiaU+3/RBm4bWB3TckYNU7fdm5iEBAOBkAwou//zP/6yWlhYtWrRIS5YskSTNnTtXXq9XV1xxhS6++OKcFlmqwtH05nMDn98SiycVje0NLulzdYXjmYm/AACUigHv4/K1r31Ns2fP1ooVK+TxeFRTU6PJkyf3mKyL/QtHuoNLDvdb8XtTO/CmQxEAAKWk38Hld7/7nX7+859rzZo1isdTQx1+v1/Tpk3TxRdfrLPOOivnRZaq9LBOLpZCp1X4UsElQnABAJSgPgeXRCKhb37zm/rDH/6g4cOH65/+6Z80ZMgQGWP00UcfacWKFbr22mv1+c9/XnfddVc+ay4ZBzPH5ZP4fekel3jOzgkAQLHoc3BZsmSJXnzxRf3Xf/2XLr300qxVMIlEQj//+c/1ve99T9OnT9cXv/jFnBdbajJDRXnocWGoCABQivr8jvn000/rX/7lXzRnzpxel+663W5dcskl+tKXvqTf/OY3OS2yVIXzMFQUSA8VxRKsJAIAlJw+v2Nu2bJFM2fOPODjTjvtNK1fv/6giioX4Uj3UFEuJ+f6Up1oxtDrAgAoPX1+xwyFQqqrqzvg4xoaGtTZ2XlQRZWLfEzOdbksVXSvLApFmOcCACgtfX7HNMbI7XYf+IQuF0MUfZSPOS6SFKhI/Zy6CC4AgBKT23dM9MveVUUHDoT9kd72PxQmuAAASku/9nG57bbbVF1dvd/HdHR0HFRB5SQXO+f2JhNc6HEBAJSYPgeXGTNmSNIBh4Gqqqo0ffr0g6uqTKQ3icvl5Fxp714uDBUBAEpNn4PL448/ns86ylI+NqCT6HEBAJQu5rjYiKEiAAD6h+BiE2PM3qGifK0qCrOPCwCgtBBcbBKOJpSeLZTz4OKjxwUAUJoILjbpCsckSZYluV3Zl1A4GP7uoaJILKFYPJnTcwMAYCeCi03SvSFej6vXaz8djAqvS+ks1BGK5vTcAADYieBik67uzeF8Od58TpIsy8r0ugQ7Yzk/PwAAdiG42GTfHpd8CGSCCz0uAIDSQXCxSbrHJW/BpXsTumAXwQUAUDoILjYpVI9LOz0uAIASQnCxSah7VZEv30NF9LgAAEoIwcUmXZH8XBk6zd+9CV0bPS4AgBJCcLFJ3oeKfOmhIlYVAQBKB8HFJqF8T85lVREAoAQRXGySHirK3xyXvauKjDEHeDQAAM5AcLFJvoeK/N1DRbF4MnMVagAAnI7gYpP0tYryNTnX63FlQlFbB8NFAIDSQHCxSb57XCSpsnueS2tHJG/fAwCAQiK42KQz1D3HxZu/H0FVIBVc9rQTXAAApcH24NLa2qpbbrlFM2fO1LRp03TxxRdr1apVdpeVd6FIegO6/AwVSVKV3ytJamkP5+17AABQSLYHl7lz5+pvf/ubFi5cqCeffFLHHnusvvrVr2rz5s12l5ZX+b5WkSRVBbqDS5DgAgAoDbYGl23btum1117TbbfdpunTp+uoo47SzTffrGHDhunZZ5+1s7S8SiT3rvTJ61CRPzVU1MJQEQCgRNgaXBoaGvTII49o4sSJmfssy5JlWQoGgzZWll/hyN7lyflaVSTt2+NCcAEAlAaPnd+8trZWp59+eo/7XnjhBW3btk3/7//9vwGf15PH4ZdcCIbSS6Fd8nlcivayQZyrO8C53JI7YfX7uCTVVvokSXvaw0XfJm63q8fHckd7ZKNNeqI9stEm2UqxTWwNLh/3xhtvaP78+Tr77LN1xhlnDOgcLpelhoaq3BaWY63dK4oCFR4FAj55vMmsxwT8Hnk8bgX8Pnk8/T8uSRUVqR6X9q6Yqqr98nnz17uTK7W1AbtLKCq0RzbapCfaIxttkq2U2qRogsvSpUt1ww03aNq0abrnnnsGfJ5k0igY7MphZbnX3NIpSfL73AqFoorGsne2tUxS8XhCoXBU0V52vj3QcWmfHp14Upu3t2j4oMrcvpAccrtdqq0NKBgMKZHoPYiVE9ojG23SE+2RjTbJ5pQ2qa0N9LlXqCiCy+LFi/Xd735X55xzjv77v/9bPp/voM4XjxfvD0eSOruHiip8biWSRolk9lBR0hgZY5RMaEDHJcltpPqaCjXtCalpT0iDa/25fSF5kEgki/7nV0i0RzbapCfaIxttkq2U2sT2Qa8lS5boO9/5ji655BItXLjwoEOLE6R3zQ348p8b66srJKXmuQAA4HS29rhs2bJF3/ve9/SZz3xGV155pZqbmzPH/H6/ampqbKwuf9J7uPgr8j/npL4mFVxYWQQAKAW2BpcXXnhBsVhMf/zjH/XHP/6xx7EvfOELuuuuu2yqLL9C0e7gUoAel4Z0cGEvFwBACbA1uHz961/X17/+dTtLsEWoex8Xvy//PS4N6aEids8FAJQA2+e4lKPMHJeKAsxxoccFAFBCCC42SAeXgvS41KQn5xJcAADOR3CxQSa4FKLHpTq1SqsjFFOkl/1iAABwEoKLDQq5HDpQ4VFF94659LoAAJyO4GKDzOTcAiyHtixLg2qZoAsAKA0EFxsUco6LJDXUpHbMZYIuAMDpCC42KOQ+LpIyPS676XEBADgcwcUGoXDhlkNL0pC6VI9LcyvBBQDgbASXAosnkop2X+iqUENFw+pTV4Vuag0V5PsBAJAvBJcCC0f3LkkuWHBpCEiSdhFcAAAOR3ApsPTE3AqfW253YZp/aHdw2dMeUSzOXi4AAOciuBRYOrhUFmh+iyTVBLyq6O7d2cU8FwCAgxFcCiw9VFTpL1xwsSxLw+pTvS7McwEAOBnBpcC60j0ufm9Bv286uOzaQ3ABADgXwaXAwpngUrgeF2nvPBd6XAAATkZwKbBQZqjIph4XggsAwMEILgWW7nEp1OZzaUMJLgCAEkBwKbD0HJeqQGF7XIZm9nIJK2lMQb83AAC5QnApsHD3laELuRxakgbXVsjtshRPJNXKxRYBAA5FcCmw9AUWCz051+1yaXBt6ppFDBcBAJyK4FJgIZuWQ0v7rCxiSTQAwKEILgUWsmk5tLR3gi5LogEATkVwKTC7lkNLLIkGADgfwaXA7OxxSV8lurGF4AIAcCaCS4GFbZzjMnJwpSTpw5ZOlkQDAByJ4FJgmaGiAi2Htqz0zdLwQZXyuC1FY0m1BCOyLKsgNQAAkCsElwKKJ5KKxZOSCjNU5HZbcrlcag/F1RGOKRRNaFhDqtdl0842dYRjSuS9CgAAcqfwEy3KWFc4nvk8UIChIrfLUiga16YdbYrGUxHF73NLkt5Yv0sul6Xjjhykar9XhqEjAIADEFwKqDMck5QaJnK7CjdME4snFY2lgkttZSowtbSFM70/AAA4BUNFBZTucbFjRVFaXXWFJKm1M2pbDQAADBTBpYA6w/ZcYHFf9dU+SVJbR4ThIQCA4xBcCqire6ioysYel5pKn1yWFE8YdYRittUBAMBAEFwKKNPjYsMeLmkul6XaqlSvC1eJBgA4DcGlgNI9LnbOcZH2znPZ00FwAQA4C8GlgIphjou0d55LazsTdAEAzkJwKaCuzFCRvT0u9ekeF4aKAAAOQ3ApoM7M5Fx7e1zq0j0urCwCADgMwaWAimEfF0mqrfTJ5bIUiyfV3Ba2tRYAAPqD4FJAxbCqSEqtLGqoSQ0X7WjssLUWAAD6g+BSQF2R7qGigP1XWhhc65ckbW9st7kSAAD6juBSQJ2h4uhxkaTBdd09Lk30uAAAnIPgUiDxRFKR7gsd2j3HRdrb47KjqZ0JugAAxyC4FEh6Yq5UHD0u9dUVcrsshSIJNe0J2V0OAAB9QnApkPRS6ECFWy6XZXM1qQm6g2pTw0VbP2KeCwDAGQguBZJZCl1hf29L2pC6gCRp60dBmysBAKBvCC4F0lkku+bua0h9ap4LPS4AAKcguBRIsVxgcV/pHpdtHwWVZIIuAMABCC4FUiybz+2roaZCXrdLoUhCu5igCwBwAIJLgRRjj4vLZWnUsCpJ0qadbTZXAwDAgRFcCqQYe1wkacwhdZKk9TsILgCA4kdwKZBiucDix40ZlQ4urfYWAgBAHxBcCiS9j0tVoMh6XEbVypL0UUuX2jqjdpcDAMB+EVwKpKsIl0NLUqXfq0OHVUuSNtDrAgAockUVXB5++GHNmTPH7jLyorNIh4ok6ZjD6iVJ6wguAIAiVzTB5YknntD9999vdxl50xXpHioqssm5kjTu8HpJzHMBABQ/2//539jYqFtvvVXLly/XkUceaXc5eVPMPS7juntc3m/qUGc4VpThCgAAqQh6XN555x15vV4988wzmjx5st3l5EU8kVQkmpBUnD0udVUVGjGoUkbShvdZFg0AKF62//N/1qxZmjVrVk7P6fHYnsd66IrEM5/XVvnkdqfqc1kuuV2W3L1cLdplWbIsSy635E70/3hfHuN2WbJcljweS+OPaNBHLV1av6NV08cPG+hLHbB0m6Q/ljvaIxtt0hPtkY02yVaKbWJ7cMk1l8tSQ0OV3WX00BVPXQeo0u/R4MHVmfsrKjwKBHzyeJNZzwn4PfJ43Ar4ffJ4+n+8L4/xelwK+H2qr6/UCRNG6uW/faC3t7TY2n61tQHbvncxoj2y0SY90R7ZaJNspdQmJRdckkmjYLDL7jJ6+LAxKEmqrPBoz55Oud0u1dYGFInEFQpFFY0lsp5jmaTi8YRC4aii0f4f78tjfF63QuGoWluNjhpeJZdl6f2mDq3bvEvDGioP8lX3T7pNgsGQEoneg1g5oT2y0SY90R7ZaJNsTmmT2tpAn3uFSi64SFI8Xlw/nGD3xm6VFZ4etSVNUomkUSKZfWXmpDEyxiiZ0ICO9+UxiaSRSRrF40YVHreOPrRO63a06o11u3TW9MMG+nIPSiKRLLqfn51oj2y0SU+0RzbaJFsptUnpDHoVsfauVHAptl1zP27S2MGSpDc37ba5EgAAekdwKYBgZ2oPl7oqn82V7N/kMUMkSe9tb82sggIAoJgQXAog2N3jUlvkwWXk4EoNqfMrnkhq7bYWu8sBACBLUc1xueuuu+wuIS/Sc1yKPbhYlqXJY4bo/954X29u2q2pRw+1uyQAAHqgx6UA0sGlprL45rhYVvqW2vNl8tGp4aK/b2iW6X3OLwAAtiG4FEB6qKjY5ri43ZZcLpfaQ3F1hGPqCMd0+PBqVfk9auuM6o0Nu8RMFwBAMSmqoaJSVaxDRW6XpVA0rk072hSN740ohw+v0bvb9mjpqh0af0SDqv1eGbpfAABFgB6XPEsao/au1Kqi2sriCi5psXhS0VgicztiRGp33807gz0CDQAAdiO45FlXOJ7Z/K2mSIPLxw2tD6jK71EsntTbm1hdBAAoHgSXPNt311xvkV388ZNYlqWjDqmVJK18r9HmagAA2MsZ76QOVqzzWw5k9MhUcFm7dU/mNQAAYDeCS55lNp8rwqXQ+1NfU6Gh9X4lk0Yv//0Du8sBAEASwSXvnNrjIknHH5W6dtFLb7yveBFfVRQAUD4ILnnmlO3+e3PUIbWqrfSptSOq1et22V0OAAAEl3xLX2CxWJdC74/bZenUySMlSUtX7bC5GgAACC555+ShIkk6ZeJIuV2WNu0MatPONrvLAQCUOYJLnjl5qEhK1X3S8SMkSc/8Zau9xQAAyh7BJc8yPS4OHCpKm33KkXJZlt7avFsb3m+1uxwAQBkjuOTZ3h4XZy2H3tewhkqdOinV6/KbVzfbXA0AoJwRXPIoHI0rGkstI3bqUFHa7JOPksdt6b3trXp3K5cBAADYg+CSR+lhIp/XJb/P2RfiHlzn1+mTR0mSfvHSRiWTXC0aAFB4BJc8Chb5VaH7a/YpR6qywqPtjR3spgsAsAXBJY+cvhRakiwrfbNUV12hL8wcLUl66tXNag/FbK4OAFBuCC555PQVRW63JZfLpfZQXB3hmDrCMc04brhGDa1SVziun//fBiXsLhIAUFacPfGiyDl9RZHbZSkUjWvTjjZF43sjytRxQ/XBrk4te/sjfeqYofrUuGEyhjkvAID8o8clj9o6nD9UJEmxeFLRWCJzG1RToWMOr5ckLfnjeoUicXsLBACUDYJLHu1qC0mShtQFbK4k96aNG6rqgFctwYh+9dJGu8sBAJQJgkseNbeGJUlD6/w2V5J7Xo9LM6ccIkl66W8f6M1NzTZXBAAoBwSXPEkao+buHpeh9aXX4yJJhwyp0und4eUnz72r1o6IzRUBAEodwSVPWtsjiieM3C5LDbUVdpeTN58/bbQOG1at9q6YHv3dWiWZpAsAyCOCS57sak31tgyu9cvtKt1m9npcuur8CfJ5XVq7dY/+sHy73SUBAEpY6b6j2qy5LTW/ZUh96c1v2ZdlpYaMLvnMMZJSF2HcvDMoy7IyNwAAcoXgkifpHpdSnd8i9dygbuq4IZo2bqgSSaNFT7+tXW2hzKZ1bFIHAMgVgkuelEVw6d6g7t2tLXpz025NGD1I1QGvdgfDWvSbt/X3Dbu0dmuLwpE4PS8AgJwguOTJru6l0ENKcCn0x6U3qJOk0yaPlGVJWz4Mas3GZsXiSZurAwCUEoJLnuwq8aXQn2RofUAzxg+TJK1et0sf7u60uSIAQCkhuORBJJbIbPdfbsFFko45vF5HjayRMdL/rXpfLcGw3SUBAEoEwSUP0iuKAhUeVfnL7zqWlmXp08ePUENNhcLRhB555h2Fo1zPCABw8AgueZCZmFvnL9tJqV6PS2dOGyW/z60PdnXqkWfeUTLJ5nQAgINDcMmD5jJYUdQX1QGvPjPjMHnclv62oVlLlq6XYWddAMBBILjkQXpFUbkHF0kaPqhSl372GFmS/vTGB3rmta12lwQAcDCCSx7s3cOl9JdC98WnjhmmS89O7az7279s4bIAAIABI7jkQVN3cBlCj0vGrE8dqvNPPUqS9MuXNurZv261tyAAgCMRXHIsFInrw+bU3iWHDau2uZriYFmp2+dPG60vzBwtKXVNo1+9tFHMeAEA9AfBJce2fhiUUeqq0PXVFXaXY7t9r2fUEY6lel66w8vvl2/XD361Rp0slQYA9BHBJcc27QxKksaMqrW5kuKw7/WM1mxs1pqNzRpS59cZU0fJ7bL05qbduvNnq7WzmR12AQAHRnDJsc3dwWX0IXU2V1Jc0tczSt8OH16ts2ccpkCFWzubO3XLo8v1h2VbWS4NANgvgksOGWO0aWebJGnMIfS4HMjQhoC+MHOMxh9er2g8qR/9eo3++4m/qXFPl92lAQCKFMElh3a1hdXeFZPHbenw4TV2l+MIlX6Prrpgoi4+62j5PC6t3dqimx9doade3axQhLkvAICeCC45tPmDVG/L4cNr5PXQtH3lsix97qQj9MNvzdKEowYpnkjqd3/dqvkPL9PSVTsUjSXsLhEAUCR4d82hTZn5LQwT9Ud6ufTIIVX69iVTde2FkzS8IaBgV0xLlm7Qtx9apueWbVWwK2p3qQAAm5XfpYvzaHNmfgsTc/sqvVy6rSumSKJLoXBc4w6v1/w5n9Kytz/SH1ftUEswoidf2azf/mWLpo0bqunHDNPxRw1SoIJfXwAoN/zlz5FILKHtjR2SmJjbH+nl0lt2tsnj8SgUiirRfRXpmiqfvnjmWIUicb325ofavDOoFe82acW7TZl5REeOqNXIwZWqr/apOuCVx+1K3Twued3WPp+75fWkvu7tit2sZgIAZyC45MjqdU1KJI0G1/o1uI5rFPVXLJ6UUWrJdDq4SFLA79FJE0bqhGNHaEdTu1a/16Q1G5vV3BbW5p3BzPLzvvK6XRraENDwQZUaPbJWYw6t06ihVar0e+XO9YsCAOQcwSVHXv77TknSzMkje/0XPQYm3SOzaUebovGEjjqkVkeOrFGwK6bm1pD2tEckSa0dEXWF44onkoonTPfHpBIJ0yMIxRJJ7Wzu1M7mTv1t/S5JUpXfo+njh+mUiSM1+pBaufj5AUDRIrjkwPu7OrTx/Ta5LEunTT7E7nJKUnoDu7SAz63DhlXr2CMbNObQ+kyw+bhKv0eHjajR2k0tCkViCkcTCnZGtac9oqY9ITXtCakzHNcrf9+pV/6+Uw01FZoxfpg+ffwIHTmiJhNCGUoCgOJAcMmBV/6W6m2ZOm4I1yeyyceDTZrP65LLSs1t8Xnd8nndqq3y6dDuC2AmkkntDkbUEozozY3N2tMe0Ysrd+jFlTs0rCGgTx0zTNPHD9XhI2oZSgKAIkBwOUiRaEJ/fedDSdIZU0bZXA36y+1yafQhtfrMCfWaMnaINu9s0+adQW37qF1Ne0L6/evb9PvXt2nk4EpNGjNExx5Rr6NG1qqm0md36QBQlmwPLslkUj/84Q/1q1/9Su3t7ZoxY4ZuueUWHXbYYXaX1ie//csWhSIJDasP6NgjG+wuBwfBGOmQIVU6ZEiVTjxuuHY0tWvLznbt3N2pD3d36cPd2/XCiu2SUlf/HjEooGENlRpaH9DwhoCG1AfUUFOh6oA3a55TX4aa9n1OLJ7QB7s6tGVHi3a3RRSJJhSJJRSNJxSNJWWMkdfjls/rktfjUoXXrZqAV3XVFaqr8qmu2iePm22aAJQe24PLj3/8Yy1ZskR33XWXRowYobvvvltXXHGFnn32Wfl8xf2v2j+/uVN/6H4ju/CMMUzqLCFej0ujD6nT6EPqlDRGHrdLG99v08b32/RRS5d2B8PaHQzrna17sp7rcVuqr65QXbVPddUVaqiu0JD6gAbXVCjg98jbHSgisYRCkbhaghHtDoa1q3uy8Z72iDpCsYN+DTWVXg2tC2hwvV9D6gIaUufvcfN6GPwC4Dy2BpdoNKr/+Z//0Q033KAzzjhDknTffffptNNO04svvqhzzz3XzvI+kTFGK99r0s/+sE6SdN4pR2rG+GE2V4V88fs8mnL0EJ0xZZSMkbrCMe1o6lBTa6h7gm+XPtzdpea2kEKRhOIJo+a2sJrbwgf1fb0el6oDXlVWeOSv8Ki+xqdwJJHaaVhSPGmU6F5BlTRGMlJbZ1TBztReOO1dMbV3xbT5w96XjNdX+1RfXaEKn1t+r1sVPrcqvO7MXjdulyVX+malLs2Q+bz7fq/blXq+z6MKr1v+7nOkz5X+2uUi1APIDVuDy3vvvafOzk6ddNJJmftqa2t13HHHaeXKlUUVXJJJow93d2rzh0H96Y0PtO2jdknS9PHDdN6pR9lcHfIpvbtveyguKTXkM2pYtUZ1T/B1uSzFk0ZvbWxWKBJTKJxQVySmznBcoXBc4VhCPo9bwa6owpGE4onUUE/6DX9QTYWqK33qisTk97pV6feotsqn+tpAZkO+6kqvxhxar/e27FEkln3xyfTxTTvaFInFFYkm1BmOq70rqvauWOaClc1tYe1uCysSS6i1I6rWjsJcRsHncfUIM5V+r6r8HlUFvKoO7PO536uq7q/TIcrrSW0qWOFzqyscU2c4plgsFdZM0ihpUv9/JpJJJbrDXCKZvnUviTfp+5NKJvc/bGeM9rulgTGm+zIVlix1P9aSUtnMylzCwkp/3v2YRNIoFk8t048lkorHe35MHUs9Jv11LJ7o/rj3Pkndk81dCvi9soyR3+dRpd+jygqPqvxeBSrc3V97Ux+7j3k9vW/A+PHXl+xur6SREkmzt333aVvJyO2y5Ha55HFbcrtd3V9bqf9nLKsgW0MYY2S664wnk+oMxRSJJmRMqr5S3J4iuc/vc+Z3PbHvx9T98YSRyyXVd8TU1RWRunuPPW6X3G5Lnu6fncftctQ/Lixj4zrPF198Uddee63WrFkjv3/vpm3/+Z//qXA4rIcffrjf5zTGHPAP00C0BMM99gOxlNocrdLvVX9/3JYluVwuxeNJReMJ9fYTcFmSx+NKbcw2gOO5OEeha7Bkycj0eFwx1RiPJ9XbKSzLksdtfeLzLUvyedySTO/P737d+z4//caXbo9ctUP6NRiT+uOX/qOffh3JZM/nmx7nsHq+hlQnT2b+jjF730Q+/nNE8dj7Pr7vXy6T+Xnm/Pt1f1Nrn2+Z/s77hgrTSwGmly9M+r/9rNdKB8te6tjf4w+O2fvfATRu+nnpsxTi/6n0357UF8q0l7VPWwQqPPL7cjvU7OpHyLS1xyUUCklS1lyWiooKtbW1DeiclpVK+7k2tKEy5+f0eFLb0e/PgeYh9GWewsGeoxRqKIYaD/b756IG5rUAcDpblx2ke1mi0Z7d1ZFIRIFAwI6SAABAEbM1uIwcOVKS1NTU1OP+pqYmDR8+3I6SAABAEbM1uIwfP17V1dVavnx55r5gMKi1a9dqxowZNlYGAACKka1zXHw+ny699FLdc889GjRokEaNGqW7775bI0aM0Nlnn21naQAAoAjZvgHdddddp3g8rptuuknhcFgzZszQT37yE3m9XrtLAwAARcbW5dAAAAD9wcVMAACAYxBcAACAYxBcAACAYxBcAACAYxBcAACAYxBcAACAYxBcAACAYxBcCiyZTOqBBx7QaaedpilTpuhrX/uaduzYYXdZA9LY2Khjjjkm6/bUU09Jkt59911deumlmjJlimbNmqWf/exnPZ7fl7bIxTkK5eGHH9acOXN63FcsbXCgc+RDb+1x0003Zf2+zJo1K6evpZjao7W1VbfccotmzpypadOm6eKLL9aqVasyx5ctW6YLLrhAkydP1jnnnKPnnnuux/MjkYgWLFigk046SVOnTtU3v/lNtbS09HhMIc6RSwdqky9/+ctZvyP7/h6VYpvs3r1b3/rWt/TpT39aU6dO1b//+79r06ZNmePl/HekVwYF9eCDD5oTTzzRvPTSS+bdd981X/nKV8zZZ59tIpGI3aX128svv2wmTpxoGhsbTVNTU+YWCoVMS0uLOfHEE838+fPNxo0bza9//WszceJE8+tf/zrz/AO1RS7OUSiLFy8248ePN5deemnmvmJpg76coxDtYYwxX/ziF83ChQt7/L7s3r27ZNvjy1/+sjn33HPNypUrzebNm82CBQvMpEmTzKZNm8zGjRvNxIkTzcKFC83GjRvNo48+ao477jjz17/+NfP8efPmmbPOOsusXLnSrFmzxpx//vnmkksuyRwv1DkK1SbGGHPSSSeZJUuW9Pgd2bNnT0m3yUUXXWT++Z//2axZs8Zs3LjRXHvttebUU081XV1dZf135JMQXAooEomYqVOnmieeeCJzX1tbm5k0aZJ59tlnbaxsYB555BEze/bsXo899NBD5tRTTzWxWCxz37333mvOPvtsY0zf2iIX58i3jz76yFx55ZVmypQp5pxzzunxRl0sbXCgc+TS/tojmUyaKVOmmBdffLHX55Zae2zdutWMGzfOrFq1KnNfMpk0Z511lrn//vvNzTffbL74xS/2eM7cuXPNV77yFWNMqi3Hjx9vXn755czxzZs3m3Hjxpk33njDGGMKco5cOlCbNDc3m3Hjxpl33nmn1+eXYpu0traauXPnmnXr1mXue/fdd824cePMmjVryvLvyIEwVFRA7733njo7O3XSSSdl7qutrdVxxx2nlStX2ljZwKxbt05jxozp9diqVat0wgknyOPZezmsT3/609q6dauam5v71Ba5OEe+vfPOO/J6vXrmmWc0efLkHseKpQ0OdI5Ctcf27dvV1dWl0aNH9/rcUmuPhoYGPfLII5o4cWLmPsuyZFmWgsGgVq1a1aPOdB2rV6+WMUarV6/O3Jd21FFHafjw4T1eS77PkUsHapN169bJsiwdddRRvT6/FNukrq5O9957r8aNGydJamlp0WOPPaYRI0Zo7NixZfl35EAILgX00UcfSZJGjhzZ4/5hw4ZljjnJ+vXr1dLSoksuuUQnn3yyLr74Yr366quSUq91xIgRPR4/bNgwSdKHH37Yp7bIxTnybdasWXrwwQd12GGHZR0rljY40DlyaX/tsX79eknS448/rlmzZumss87S7bffrvb29kydB/taiqk9amtrdfrpp8vn82Xue+GFF7Rt2zaddtppn1hHKBTSnj171NjYqIaGBlVUVPT7teTyHLl0oDZZv369ampqdPvtt2vmzJk655xzdP/99ysajUpSSbbJvm6++WaddNJJeu655/Td735XlZWVZfl35EAILgUUCoUkqcf/tJJUUVGhSCRiR0kDFo/HtXnzZrW1tenaa6/VI488oilTpujf//3ftWzZMoXD4V5fp5SaGNeXtsjFOexULG1woHMUyvr16+VyuTRs2DA99NBDmjdvnv7yl7/o6quvVjKZLPn2eOONNzR//nydffbZOuOMM3qtI/11NBpVKBTKOp6udX+vJdfnyKePt8n69esViUQ0adIkPfroo7rqqqv0q1/9SjfddJMklXybXHbZZXryySd17rnn6pprrtE777zD35FeeA78EOSK3++XlPrFT38upX7ogUDArrIGxOPxaPny5XK73ZnXMmHCBG3YsEE/+clP5Pf7s/4HT/9yV1ZW9qktcnEOOxVLGxzoHIVy1VVX6V//9V/V0NAgSRo3bpyGDh2qL33pS3rrrbdKuj2WLl2qG264QdOmTdM999wjKfVH/+N1pL8OBAK91pmuNf1aCnGOfOmtTW6//XbdeOONqqurk5T6HfF6vbr++uv17W9/u+TbZOzYsZKk7373u1qzZo0WL17M35Fe0ONSQOluuKamph73NzU1afjw4XaUdFCqqqp6/JJL0tFHH63GxkaNGDGi19cpScOHD+9TW+TiHHYqljY40DkKxeVyZUJL2tFHHy0p1Q1dqu2xePFiXXvttTrzzDP10EMPZf6VOnLkyF7rqKysVE1NjUaMGKHW1tasN4t9X0shzpEPn9QmHo8nE1rS9v0dKcU2aWlp0XPPPad4PJ65z+VyaezYsWpqauLvSC8ILgU0fvx4VVdXa/ny5Zn7gsGg1q5dqxkzZthYWf9t2LBB06ZN6/FaJOntt9/W2LFjNWPGDK1evVqJRCJz7PXXX9dRRx2lwYMH96ktcnEOOxVLGxzoHIXy7W9/W5dffnmP+9566y1JqX9plmJ7LFmyRN/5znd0ySWXaOHChT262qdPn64VK1b0ePzrr7+uadOmyeVy6VOf+pSSyWRmMqkkbdmyRY2NjZnXUohz5Nr+2mTOnDmaP39+j8e/9dZb8nq9OvLII0uyTZqbmzV37lwtW7Ysc18sFtPatWs1ZswY/o70puDrmMrcwoULzQknnGCWLl3aY618NBq1u7R+SSQS5sILLzSf+9znzMqVK83GjRvN9773PTNhwgSzbt0609zcbGbMmGFuvPFGs2HDBvPkk0+aiRMnmqeeeipzjgO1RS7OUUg33nhjj+W/xdIGfTlHIdpj6dKlZty4cebBBx8027ZtMy+//LKZNWuWmTt3bkm2x+bNm83xxx9vrrnmmh57kjQ1NZlgMGjWr19vjj/+eHP33XebjRs3mp/85CdZe4XMnTvXzJo1y7z++uuZ/Ub2bdNCnaNQbfL444+bY4891ixZssRs377dPPfcc+bEE080CxcuLNk2McaYK664wpx99tlmxYoVZt26dWbu3LlmxowZ5oMPPij7vyO9IbgUWDweN9///vfNpz/9aTNlyhTzta99zezYscPusgZk165dZt68eeaUU04xEydONBdddJFZuXJl5viaNWvMl770JTNhwgRz5plnmscff7zH8/vSFrk4R6F8/I3amOJpgwOdIx96a4/nn3/enH/++WbSpEnmlFNOMXfddZcJh8M5fS3F0h6LFi0y48aN6/V24403GmOMeeWVV8y5555rJkyYYM455xzz3HPP9ThHZ2en+a//+i8zffp0M336dDN37lzT0tLS4zGFOEeu9KVNFi9ebP7xH/8x87NZtGiRSSQSJdsmxhgTDAbNrbfeak455RQzadIk85WvfMWsX78+c7yc/470xjImx4vSAQAA8oQ5LgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAKYs6cOTrmmGN63CZMmKAzzjhDCxYsUFtb2yc+9/3339cxxxyjp556qoAVAyhGHrsLAFA+jjvuON16662Zr2OxmN555x0tXLhQ7777rv73f/9XlmVlPW/YsGH6xS9+ocMPP7yQ5QIoQgQXAAVTXV2tKVOm9LhvxowZ6uzs1AMPPKA1a9ZkHZckn8/X6/0Ayg9DRQBsN2HCBEnSzp07NWfOHN1www267rrrNGXKFH35y1/udaho8+bN+o//+A+dcMIJmjFjhq688kpt2rQpczwSiej73/++Tj/9dE2YMEGzZ8/W888/X/DXBiC36HEBYLstW7ZIkg477DBJ0u9//3udd955WrRokZLJZNbjGxsbddFFF2n48OG67bbbVFlZqQcffFCXXXaZfve736murk7XXHON3njjDV133XUaM2aM/vjHP+r6669XNBrV+eefX8iXByCHCC4ACsYYo3g8nvm6ra1NK1as0KJFizR16tRMz4vX69WCBQvk8/kkpSbn7uuxxx5TNBrVT3/6Uw0dOlSSNH78eF188cVas2aNPB6P/vznP+u+++7T5z73OUnSaaedplAopHvuuUfnnnuuPB7+/AFOxP+5AApm5cqVOv7443vc53K5dPLJJ+v222/PTMwdPXp0JrT0ZvXq1ZoyZUomtEjSiBEj9NJLL0mS7rnnHlmWpdNPP71HUJo1a5aeeeYZbdiwQccee2wuXxqAAiG4ACiY448/XgsWLJAkWZaliooKjRw5UtXV1T0eV1VVtd/ztLa26tBDD93vcWOMpk2b1uvxpqYmggvgUAQXAAVTVVWliRMnHvR5ampq1NLSknX/smXLdOihh6qmpkaVlZX62c9+1uvzjzjiiIOuAYA9WFUEwHGmT5+uNWvW9Agvu3fv1hVXXKFXXnlFJ5xwgrq6umSM0cSJEzO39evX60c/+lGP4SMAzkKPCwDHufzyy/X000/riiuu0JVXXimv16tFixZpxIgRmj17tmpqajRjxgxdffXVuvrqqzVmzBi9+eabeuCBB3Taaadp0KBBdr8EAANEcAHgOCNHjtSSJUt09913a968efL5fDrxxBN13333qa6uTpL0yCOP6Ac/+IEefvhh7d69W8OHD9eXv/xlXXPNNTZXD+BgWMYYY3cRAAAAfcEcFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4Bj/H7HMP+BC6CSAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A great step in the data exploration is to display the probability distribution function (PDF) of a variable\n",
+    "# The PDF will show us how that variable is distributed \n",
+    "# This makes it very easy to spot anomalies, such as outliers\n",
+    "# The PDF is often the basis on which we decide whether we want to transform a feature\n",
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obviously there are some outliers present \n",
+    "\n",
+    "# Without diving too deep into the topic, we can deal with the problem easily by removing 0.5%, or 1% of the problematic samples\n",
+    "# Here, the outliers are situated around the higher prices (right side of the graph)\n",
+    "# Logic should also be applied\n",
+    "# This is a dataset about used cars, therefore one can imagine how $300,000 is an excessive price\n",
+    "\n",
+    "# Outliers are a great issue for OLS, thus we must deal with them in some way\n",
+    "# It may be a useful exercise to try training a model without removing the outliers\n",
+    "\n",
+    "# Let's declare a variable that will be equal to the 99th percentile of the 'Price' variable\n",
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "# Then we can create a new df, with the condition that all prices must be below the 99 percentile of 'Price'\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "# In this way we have essentially removed the top 1% of the data about 'Price'\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\4102241817.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_1['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can check the PDF once again to ensure that the result is still distributed in the same way overall\n",
+    "# however, there are much fewer outliers\n",
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\1674007885.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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FFXOTDdX1VoiCgNw+Xk7gTInxOqhV3vWYKk+2KloLERH1LgYmCiu+5QSGZybAoFcrWosoCkhN5LAcEVE0YGCisHJqOE6Z5QTOlJbkXcDyYEWzwpUQEVFvYmCisOFweVB6vBEAMPaiUAlMvFKOiCgaMDBR2Cgtb4TLLSHZqEf/frFKlwMASE2MgSAAdc121DfblC6HiIh6CQMThQVBEPCdf7HKZIiiCEEQoOCqAgAArVqFAe3hbV97fUREFHkYmCjkeQC02l34tn39pazMBLTaXWi1u9DmcENStjxcNCABALD/GAMTEVGkYmCikCYIAuwON3bsq4G50QZBAJwuD/YersPew3U4cLwRbo8EAcp1NV3U3wgA2He0XrEaiIiodyl7XTZRN5XXtAAA+iXoAXhDEwBoNcpnfl9gOnrCArvTDbWofE1ERBRc/JedwkJl+w1uM5JDY7L36RLj9Ug26iBJMsqqLEqXQ0REvYCBiUKeJMs40R6Y+vczKFxNZ4IADB9oAgAcqmxqn4x+6kFEROGPgYlCXqW5FQ6XBxqViH4JMUqX04FKJUAURQxKjwcA7DvW6J+Q7nt4FK6RiIguHOcwUcgrLfcuVpmebIAohlaPjUoUYHO6/csblFU1Y8+hkxDbN2jUInKGJCFOr4Es8wa9REThij1MFPJ8q3tnJIfecJyPMVYLjVqEyy3B3GCF0+WB0+WBy630ogdERBQMDEwU0hxOD460T6QOldW9uyIKAtLbb5NibuSK30REkYaBiULagYpGeCQZcTEaxBs0SpdzTuntgc7cxMBERBRpGJgopH3ffruRASmxIX/FmW/Jg5PsYSIiijgMTBTSTgWmOIUrCSwt2QBBANrsbrTZXUqXQ0REQcTARCGrscWBqro2CAjN9ZfOpFWrkBSvA8B5TEREkYaBiULWvvab2Q5Mi4NeGx4rYKQmcuI3EVEkYmCikPV9e2DKHpyocCXdl5roXVjzJCd+ExFFFAYmCkmyLGPfMe/6S9mDwicwpbUHpkaLg2swERFFEAYmCkkn6q2wtDmhVYsYkmFUupxui43RIFavhgz2MhERRRIGJgpJB9pX9x6WmQCNOrw+phyWIyKKPOH1k4iiRunxJgDAyDAajvNJaQ9MnPhNRBQ5GJgo5Miy7O9hCqf5Sz6pplM9TJLEG+4SEUUCBiYKOSfqrWixuqBVixgaRvOXfEzxOmjUItweGQ0tdqXLISKiIGBgopBTWh6+85cA7414U0x6AEBtA4fliIgiQfj9NKKI5xuOC8f5Sz6+YbnaBqvClRARUTAwMFFIkWXZP+E7e5BJ0VouRFqSd8Xv6vo2yDLnMRERhTsGJgopJ+ra0GoL3/lLPv1MeqhEATaHBzX17GUiIgp3igcmSZKwevVqXHrppRg/fjzuvPNOVFRUnPX4xsZG3HvvvcjPz0dBQQFWrFgBm63jPJFt27bh6quvxtixYzFnzhxs3769w/7vv/8e8+bNw4QJE3DxxRdj2bJlaGlp6ZX3R+fH17s0LDMBapXiH88eU4mifz2mgxVNyhZDREQXTPGfSOvWrcPrr7+ORx99FG+88QYkScKCBQvgdDq7PH7RokUoLy/HK6+8ghdeeAGff/45li9f7t9fVFSEJUuW4MYbb8Q777yDwsJCLFy4EGVlZQCAuro6zJ8/HwMGDMDbb7+NdevWYdeuXXjggQf64u1SAJEwf8knPdk7LMfAREQU/hQNTE6nExs3bsSiRYswffp0ZGdnY9WqVaipqcFHH33U6fg9e/aguLgYTz31FHJzc1FYWIhHHnkEW7ZsQW1tLQDgpZdewsyZM3HrrbciKysLS5cuRW5uLl599VUAQFVVFaZOnYpHHnkEQ4cORV5eHn7605/iyy+/7NP3Tp1Fyvwln4z2eUyHKpu5HhMRUZhTNDCVlpaira0NhYWF/m1GoxE5OTnYuXNnp+NLSkqQkpKCrKws/7aCggIIgoBdu3ZBkiTs3r27w/kAYPLkyf7zjRs3Ds899xzUajUAoKysDFu2bMGUKVN64y3SeYiU+Us+SUY9NGoRNocbx2s55EtEFM7USr54TU0NACAjI6PD9tTUVP++09XW1nY6VqvVwmQyobq6GhaLBVarFenp6d0635VXXoljx45hwIABWLt27YW+HajDcM0glUoEJBmiKEAlCp33iwIEUYBaLUCWO+8PpoOVzQCA4QNN0Ou8H01BAIT22rqqTxQECIIAUQWoPAruF7z7RFEEIAHwtl3/frEor2nBgYpGZGUmdL8xopSqfd6aKoznr4UCtmNwsB2DJxLaUtHA5JusrdVqO2zX6XRobm7u8vgzj/Ud73A4YLfbz3o+h8PR6XkrV66EzWbDM888g1tvvRVbtmxBbGxsj96LKApITOzZc5Vma7BCp9NAVKk67dOoRcTotTCZDL1eR1m1BQCQl53WoS2dkhUxMVqoNVKn58To1VCrVYjRa6FWK7dfp9PA5Zag12s67B+cYUR5TQsOVllwc5h+PpRgNMYoXUJEYDsGB9sxeMK5LRUNTHq9dzVkp9Pp/xoAHA4HYmI6N6per+9yMrjD4YDBYIBOp/Of78z9XZ1vzJgxAIC1a9fisssuwz//+U/MmTOnR+9FkmRYLOF3+bivV8zhcMHucHfar9WoYLM70dQko7eWE3JLMlrtLnxzqA4AkJKgQ/mJJgDeIOrxyLDZnHC6PJ2eK8gS3G4PbHYnnE7l9jscLogqFex2FyTpVLBKTfB+rr8rq0PtSQu06s6hlE5RqUQYjTGwWGzweDoHVOoetmNwsB2DJ1Tb0miM6Xavl6KByTe8ZjabMWjQIP92s9mMkSNHdjo+PT0dH3/8cYdtTqcTTU1NSE1NhclkgsFggNls7nCM2WxGWloaAODIkSM4fvw4pk+f7t+flpYGk8nknzjeU2536HwIuss7kiRAkmR4upiY7JFkyJIMt1vulQUYBUFAm92F/3xzAq02F9QqAY0tDnx98CQAwKBXY3CGEZKELuuTZG9dkkfh/bIMEd5lMk4/LiFOC1OcFk2tTnxX1oCxWcnn0zxRy+ORwvL/p1DDdgwOtmPwhHNbKjqYmJ2djbi4OOzYscO/zWKxYN++fcjPz+90fH5+PmpqalBeXu7fVlxcDACYOHEiBEFAXl6ef5vPjh07MGnSJADAV199hUWLFsFisfj3Hz9+HI2NjR0mk1PfqqhtBQCkmGLg8Uhwujxwujxwh9BvIj0hCAJyh3pD0t7DdQpXQ0REPaVoYNJqtZg7dy5WrlyJTz75BKWlpVi8eDHS09Mxa9YseDwenDx50j83ady4ccjLy8PixYvxzTffoKioCMuWLcOcOXP8PUjz58/H+++/j5dffhllZWV4+umnsX//fsybNw8AMHv2bJhMJixZsgSHDh1CSUkJFi1ahLFjx+Lyyy9XrC2ine+ea75bikSS0RclAQD2ltXxNilERGFK8enqixYtwvXXX4+HH34YN910E1QqFTZs2ACNRoPq6mpMnToVW7duBeD9bX3t2rXIzMzEvHnzcPfdd2PatGkdFq6cOnUqHn/8cfztb3/Dj3/8YxQVFWH9+vX+3iOTyeRfk+mmm27Cr371K+Tk5GDDhg1QdTHpmfqGLzD5blobSUYMMkGrEdFgcaDC3Kp0OURE1AOKzmECAJVKhSVLlmDJkiWd9mVmZuLAgQMdtiUnJ2P16tXnPOecOXPOOXl76NCh+MMf/tCjein4Gix2tNndEAQgOUEf+AlhRqtWIXdIEvYcqsPXh+swKC1e6ZKIiOg8Kd7DRHS0fTmBpHgdNGG4llV3jB+eAoDzmIiIwlVk/nSisHLkhDcwpSRG3nCcz7j2q+OOVregsaXzmmBERBTaGJhIcUd9gSkC5y/5JMTpkDXAe7uXnaXmAEcTEVGoYWAiRdmdblSd9E6ETo3QHiZB8D4Kc7237Cn6vgZC+y1VBKF3bzdDRETBwcBEijpywgJJBuJiNIg947YikUClEiCKIlpsbuRelAxRFHCspgWHq5rRaneh1e5C5/XDiYgo1DAwkaIOt99wN1J7l1SiAJvTjf3HGlBW1YzMFO/95P7x1VHsPVyHfccaYHe42dNERBTiGJhIUYcqmwBE5oKVp3O5vauXD8nwzmM6XNkMh9MNV5jeIoCIKNowMJFiJElG2QlvD1NaUmT2MJ1pYEosNGoRbXY3ahttSpdDRETdxMBEijlR1wabwwOtRkRSfOQtWNkVlUrE4HTvwpWHKpoAnJoUfvpEcE4KJyIKLYqv9E3R61CVt3dpSLoRoiggWmY/jxxowuHKZpTXtMDucvsnhQNd32dOr1ODN+0hIlIWAxMp5nD7/KWL+huVLaSPJSfokWLS42STHaXHGjFhRCrKKprhdHdOjBq1iJwhSYjTa3jjXiIiBfVoSK62tjbYdVAUOtzewxRtgQkAsgcnAgC+P9oIt0fyTwo/88FJ4UREoaFHgenyyy/HggULsHXrVjidzmDXRFGgudWBk012CID/yrFoMjgtHjE6NWwON/Ye4v3liIhCXY8C0xNPPAFJknDfffdh6tSpWLFiBb799ttg10YR7FD7+ksDUuIQo4u+kWFRFDBykAkA8MXXJzjcRkQU4noUmP7rv/4LGzduxGeffYbbb78dRUVFuOGGGzB79mxs3LgRdXX8jZnOzTccNzwzQeFKlDNiYAJUooAKcyuq661Kl0NEROdwQcsKpKWl4ec//zm2bduGTZs2ITExEc888wymT5+OX//619i7d2+w6qQI4wtMw6I4MOm1av9cpm/K+EsGEVEou+B1mEpKSvDb3/4Wd9xxB3bt2oUpU6bggQcegM1mw0033YRXXnklCGVSJHG6PCivaQEADBtgUrYYhY0fngwBQIW5FY0tDqXLISKis+jR5JHy8nJs2bIF7777LqqqqjBgwADccsstuO6665CRkQEAmDt3Lu677z68+OKLuO2224JZM4W5YzUt8EgyEmK1SDHp0eZwK12SYhLidBiTlYxvyuqx72gDpozNULokIiLqQo8C05VXXgmdToeZM2fi0UcfRWFhYZfHXXTRRTh27NiF1EcRyHf/uGGZCVzJGsD0iZn4pqweR6stmDCiHwx6jdIlERHRGXoUmH7729/i2muvRXx8/DmP++Uvf4lf/vKXPSqMItfh9ivkhg2I3vlLpxucHo/0JANqGqzYX96IiSNTlS6JiIjO0KM5TB9++CHMZnOX+0pLS/GjH/3ogoqiyHP6fdFOXSFnAjuYvMYO6wcAOFjRDKcrSu4RQ0QURrrdw1RSUuJfK6a4uBg7d+5EQ0NDp+M+++wzVFRUBK9CCnseAHa7CwBQ02BFm90NjUpEcvv8Ja5lDQxKjUNCrBbNbU4crGzG6KFJSpdERESn6XZg+vvf/44tW7b4ewpWrFjR6RhfoJo9e3bwKqSwJggC7HYX9h1rgMst4cDxRgDe+6l9f7QBBr0agzOMEBDdXU2CICB3aBK++q4GpccaMWpwIlRidLcJEVEo6XZgevjhh/GTn/wEsixj3rx5WLZsGYYNG9bhGFEUYTQaMXz48KAXSuHNd6+06jrvAo39EvRwujzQai54ZYuIMbR/PPYcOgmrw43ympaovMceEVGo6nZgio+PR0FBAQDgz3/+M3JzcxEbG9trhVFkMjfZAAApiTEKVxJ6VKKIEQNN2Hu4HocqmhiYiIhCSLcD0+bNm3HZZZchMTERJ06cwIkTJ855/Jw5cy60NoowdqcbljbvzZpTTAxMXRmWmYBvDtejttGG5lYngyURUYjodmB64IEH8H//939ITEzEAw88cM5jBUFgYKJOTjbZAQAJsVrotSqFqwlNsXoNBqTEovJkGw5VNjEwERGFiG4Hpk8++QQpKSn+r4nOl7mxfTiOvUvnNGKgCZUn21BWZUFBTprS5RAREc4jMA0YMKDLr33cbjdaW1thMpmCUhhFnpOcv9Qt/VNiYdCrYbW7cay6hQtZEhGFgB5douR2u7F27Vq89957AIAdO3ZgypQpKCwsxLx589Dc3BzUIin8eSQZdc3eIblU9jCdkygI/lXQD7ffRoaIiJTVo8C0evVqvPjii7BYLACA3/3udzCZTHjwwQdx/PhxPPvss0EtksJffbMNkiRDp1HBGMt7pQXiu0Kuqq4NLVanwtUQEVGPAtP777+Pe+65BzfffDPKyspw6NAh/OIXv8Ctt96KxYsX49NPPw12nRTmaht885f0vOFuNxhjtUg26iDLwJ5DdUqXQ0QU9XoUmMxmM8aNGwcA+Ne//gVRFDFt2jQAQHp6OlpaWoJXIUWE2gbvgpWpnL/UbUMyvL1Mu0q7vm8jERH1nR4FptTUVFRWVgIAPv30U4waNQpJSd57X+3Zswfp6enBq5DCnizLqG30BiZeIdd9QzLiAQBHTlhQ3z7/i4iIlNGjwDR79mw88cQTuOOOO7Br1y785Cc/AQA89thjWLNmDX70ox8FtUgKb/XNdtgcHoiC9x5y1D2xeg3SkwwAgOL9tQpXQ0QU3XoUmO6++27cfvvtEAQB9957L372s58BAL799lvcfvvt+MUvfhHUIim8HTnhvTggyaiHWsV7x52PrAHeYTkGJiIiZXV7HabTCYKAu+66C3fddVeH7W+88UZQiqLI4gtMnL90/oZkGPHVtzU4VtOCBosdSUb20BERKaFHgQkAWlpaUFRUBKvVClmWO+3nrVHIp6zKuy4XA9P5i9GpMSTDiKPVFnx9uA4z8jKVLomIKCr1KDB98cUXWLRoEWw2W5f7eS858rFYnajhFXIXZGxWMo5WW7DnEAMTEZFSehSYnn32WVx00UV48MEHkZaWBlHkvBTq2qGKJgCAKU4HvbbHHZpRbUxWMrb85yhKyxthtbth0LMdiYj6Wo/+5S0rK8O6deswadKkYNdDEeZge2BKTzYoW0gYS0syID3JgJoGK747Wo+CUbwhLxFRX+tR11D//v3R2toa7FooAvkCUwYD0wWZMLwfAOBrrvpNRKSIHgWmu+66C7///e/9i1cSdcXmcKO81rvqu289IeqZCSNSAAB7y+rh9kgKV0NEFH16NCT33nvvoba2Fj/4wQ+QlJQEvb7jpc6CIODjjz8OSoEUvsqqmiHLQLJRj9gYDZwuj9Ilha2s/gmIi9Gg1ebCkRMWjBhoUrokIqKo0qPAlJ6eztufUEAHK5sAAFmZCcoWEgFEUUDu0CTs2FeL747WMzAREfWxHgWmJ554Ith1UAQ6eLwJADCsfbVqujCjfYHpSAOum5aldDlERFHlgq5PLisrw5dffgmz2YxbbrkFFRUVyM7ORlxcXLDqozBld7pR1r7C97BME07UtSlcUfjLHeq9wXV5TQtarE7EG7QKV0REFD16FJgkScKyZcuwadMmyLIMQRDwwx/+EOvWrcPx48fx2muvccguyh043gSPJCPFpEeKKYaBKQhMcTpkpsSh8mQrvj/WgItz+P8YEVFf6dFVcuvWrcN7772H3/3ud/jyyy/9t0ZZsmQJJEnCqlWrglokhZ/vjzUAAHKGJClcSWQZfZG3Pb8/0qBwJURE0aVHgWnTpk1YtGgRfvKTn8BkMvm3jxo1CosWLcKXX34ZrPooTO071ggAGD00WeFKIsvo9mG57441dHkPRyIi6h09Ckx1dXUYNWpUl/vS0tJgsVguqCgKb40tDpyoa4MgAKMGJypdTkQZnmmCViOiudWJypMc5iQi6is9CkyDBw/G559/3uW+4uJiDB48+IKKImXVW+w42dj1jZW74/uj3uGiIelGxMZoglUWAdCoRWQP8obQ747WK1wNEVH06NGk73nz5mHZsmVwuVy4/PLLIQgCysvLsWPHDmzcuBEPPPBAsOukPmB3erDrgBllVd4ewismZmJo//NfEmBf+/yl3KHsXeoNuUOT8E1ZPb470oAfTuYvJ0REfaFHgemGG25AQ0MDXnzxRbz++usAgHvuuQcajQYLFizATTfdFNQiqffZHG689+Ux2Bxu/7Yvv61G2nne0kSS5VOBiRO+e4VvHtOhyiY4nB7otCqFKyIiinw9XofpzjvvxI9+9CMUFxdDrVYjPj4e48aN6zAJnMLH0WoLbA43YvVqXDImHTv3m9HU6sTne6owOTftvM5jsbqg06qQNYArfPeG9CQDko161FvsOFDRiLFZ/ZQuiYgo4p13YPrHP/6BN954A3v37oXb7e2N0Ov1yMvLw0033YSZM2cGvUjqfRW1rQCA4QNNyEiOxbTx/fH+V+WoqmtD8b5azJw4sFvn2bnfDACYMKwf1KoeTZGjAARBwOiLkvD51yfw3ZEGBiYioj7Q7cDk8Xhw77334oMPPkBaWhquueYa9OvXD7Iso6amBsXFxfj1r3+N//qv/8KTTz7ZmzVTkLk9Eqrar7ga0C8WgHeRxHHDkrH7YB0+LqnAjLxMCAHOI8kydpZ6A1P+qNTeLDnqjR7aHpiOcj0mIqK+0O3A9Prrr+Ojjz7CQw89hLlz50IQOv749Hg8eOONN/D4449j0qRJuP7664NeLPWOwxVNcHkk6LUqJBl1/u0jBpnw3ZEG1DbYsPdwHcYPO3dPxuHKZjS2OBCjU3H9pV42anASREFATYMVdc029EuIUbokIqKI1u0xk82bN+PGG2/ELbfc0iksAYBKpcLNN9+Mn/70p3jnnXeCWiT1rm/L6gAA/fvFdvjeatUqjBrivdJt6/bygOfx9S5NGJ4CjZrDccEiCL6H4H/ExmiQ1X5T4++PNipcIRFR5Ov2T7WjR49i2rRpAY+79NJLcfDgwQsqivrWt2Xe9Xx8w3Gnyx2aBLVKwOGqZhyqbDrrOSRJRkl7YCrgcFzQqFQCRFFEi82NVrurw2PEQBMAYO/hOniULZOIKOJ1e0jOZrMhISHwVU+JiYloa+MKxOGiwWJH1clWCAAyughMBr0G+aPSsP27Grz75THc+9/juzxP6fFGNLc5EatX8/5xQaQSBdicbpRVNMPp7hiLRNHbG7i/vBFWuwvGGC1vl0JE1Eu63cMkyzJUqsDrvYiiyH+0w8g37b1LKYkx0J9lPZ8f5A+EShTw/dEG/xpLp5NlGe9+eQwAUDAqjVfH9QKXW4LT5enwSIjVQqMS4XB5UGVuVbpEIqKIxp9sUa68pgUAkJF89gUqU0wxuHzCAADA3/9VBumMQPzd0QYcrGiCWiXimkKuPN1XRFFAWpJ3sveBiiZliyEiinDntQ7T8uXLERcXd85jWlv5m244qa63AvAuI3AuP5oyFF98W43ymhaUlJpRMMq7mKUky9j0rzIAwIy8AUgy6nu3YOogPdmAypNtOHi8SelSiIgiWrd7mPLz8xEbGwtZls/5iI2NxaRJk7pdgCRJWL16NS699FKMHz8ed955JyoqKs56fGNjI+69917k5+ejoKAAK1asgM3W8Uax27Ztw9VXX42xY8dizpw52L59e4f9hw4dwsKFCzF58mQUFhZi0aJFOHHiRLdrjiQ1Dd0LTMZYLX44eRAA4K//PIiDFU2QZBkfFVfguLkVeq2KvUsKyEj2zjsrq2qG2yMpXA0RUeTqdg/TX/7yl14pYN26dXj99dfx5JNPIj09Hc888wwWLFiA9957D1qtttPxixYtgs1mwyuvvAKLxYKHHnoIVqsVTz31FACgqKgIS5Yswf33348pU6bgrbfewsKFC7F582ZkZWWhsbER8+fPR15eHv7yl7/A6XTiySefxIIFC/DOO+9Apzt3cIgkTpcH9c12AEBCXOe2PtOs/IHYdeAkKsyteOZve5DR3rsBAD+cPAjxhsDnoOAyxWmh16pgd3pQVtXsv3KOiIiCS9E5TE6nExs3bsSiRYswffp0ZGdnY9WqVaipqcFHH33U6fg9e/aguLgYTz31FHJzc1FYWIhHHnkEW7ZsQW1tLQDgpZdewsyZM3HrrbciKysLS5cuRW5uLl599VUAwMcffwyr1Yqnn34aI0aMwOjRo/HMM8+grKwMu3fv7tP3r7TaRm/PnEGvPuuE79PptWo8ODcPBaNS4ZFkVJ5sg06rwk8uuwjXFA7p5WqpK4IgoH/71Y37jnE9JiKi3qJoYCotLUVbWxsKCwv924xGI3JycrBz585Ox5eUlCAlJQVZWVn+bQUFBRAEAbt27YIkSdi9e3eH8wHA5MmT/ecrLCzEunXroNefmmsjit5msFgsQX1/oa62fTguPSm2y8VIu6LXqnHXtbmYd9VIXFkwEE8svBjXFA7xX+JOfc8XmPaX8zYpRES95bxvvhtMNTU1AICMjIwO21NTU/37TldbW9vpWK1WC5PJhOrqalgsFlitVqSnp5/1fJmZmcjMzOyw/49//CP0ej3y8/Mv6P2ow2x1a3OTt4cpPdkAURSg6iL0qEQBgihArRYgy6f2XzGp4814z5a3BAEQnN5zd3V+sX3lalEFqDxhvL+9AbzhWzr/5/dwPwAMTPVeiHHkhAVuSYJeq+j/1hdE1b4khYpLU1wQtmNwsB2DJxLaUtF/WX2Ttc+cq6TT6dDc3Nzl8V3Na9LpdHA4HLDb7Wc9n8Ph6LKGv/zlL3jttdfw8MMPIymp5wsuiqKAxMTOCz+GsoZWJwAgLckAnU4DsYt1tjRqETF6LUymsy870Gp1wmp3d7lPELy3zYmJ0UKt6TwpOUavhlqtQoxeC7U6fPfrdBq43BL0ek2fvj4AJBhF9DPFoK7JhqoGOya1X8EYzoxG3hsvGNiOwcF2DJ5wbktFA5NvWMzpdHYYInM4HIiJ6dyoer0eTqez03aHwwGDweCfsH3mMV2dT5ZlvPDCC3jxxRfxi1/8ArfccssFvRdJkmGxWC/oHH2tvNo7BJmWHAuHwwW7o3Po0WpUsNmdaGqS0dV6pIIANFtdKD3WAJe78w90g16NgelG2OxOOJ2db+AhyBLcbk/Y73c4XBBVKtjtLkiSdN7P7+l+wPs9GjEwAXVNNuz49gSy0s+99EcoU6lEGI0xsFhs8PCqvx5jOwYH2zF4QrUtjcaYbvd6KRqYfMNrZrMZgwYN8m83m80YOXJkp+PT09Px8ccfd9jmdDrR1NSE1NRUmEwmGAwGmM3mDseYzWakpZ36rdvlcuHBBx/EP/7xDzz44IO47bbbgvJ+3F0EhlBWU++9wi09yYDqky3wSJ0TkUeSIUsy3G65yxXcBUGALMmwO72rT59JpRIgyzIkD7o8v9S+HEXY75dliPAuk3H6cb39+oB3+4iBJnz1bQ2+P9oQdp/Drng8UkS8D6WxHYOD7Rg84dyWig4mZmdnIy4uDjt27PBvs1gs2LdvX5fzifLz81FTU4Py8nL/tuLiYgDAxIkTIQgC8vLy/Nt8duzY0WFtqPvvvx8ffPABnn322aCFpXDTYnWirX0YLTXp7MNtFB58ywlUmFthsXbuhSUiogujaGDSarWYO3cuVq5ciU8++QSlpaVYvHgx0tPTMWvWLHg8Hpw8edI/N2ncuHHIy8vD4sWL8c0336CoqAjLli3DnDlz/D1I8+fPx/vvv4+XX34ZZWVlePrpp7F//37MmzcPAPD2229j69atWLx4MQoKCnDy5En/w/c60aC2wTt/LNmog04TeEkBCm3xBi0yU7xDcaXlXF6AiCjYFJ+uvmjRIlx//fV4+OGHcdNNN0GlUmHDhg3QaDSorq7G1KlTsXXrVgDe4Z+1a9ciMzMT8+bNw913341p06Zh+fLl/vNNnToVjz/+OP72t7/hxz/+MYqKirB+/Xr/UgT/+Mc/AABPP/00pk6d2uHhe51o4FvhOz05vCaq09nlDEkEAOxnYCIiCjrFrz9WqVRYsmQJlixZ0mlfZmYmDhw40GFbcnIyVq9efc5zzpkzB3PmzOly38aNG3tcayQ5FZg4HBcpRg1Owkc7K7CfC1gSEQWd4j1MpAzfopUZnL8UMUYOMkEUBJibbKhrtgV+AhERdRsDU5Q62f4DNTUxfNfEoI5idGoMyYgHABysaFK2GCKiCMPAFKUaW7wLeSYZ9QGOpHDiu1ruwPEmResgIoo0DExRyOWW0GJ1AQCSjDqFq6FgGukLTOxhIiIKKgamKNTc6u1dUqtExMVoAhxN4WR4pgmCAJgbbf5eRCIiunAMTFGoof0HaWK8FsLZ7ppLYcmgV2NQKucxEREFGwNTFGpq72FKjONwXCQawWE5IqKgY2CKQg2W9sDECd8RaeQgEwDgwHGux0REFCwMTFGIPUyRzdfDVF1vhaWN95UjIgoGBqYodGoOEwNTJIqL0SAzxXvLG85jIiIKDgamKNTEwBTxOI+JiCi4GJiiUGOLHQADUyQbOch7I14uYElEFBwMTFFGkmU0tXrntTAwRS5fD1PVyVa02lzKFkNEFAEYmKJMS5sTHkmGIADGWK3S5VCQCILvIUAQBJjidMhINkAGcLiqWenyiIjCHgNTlGlsv0LOGKuFWsVvfyRQqQSIoogWmxutdpf/cVF/IwDg2yP18ChcIxFRuFMrXQD1rUYLlxSINCpRgM3pRllFM5zuU9FIo1YBAL47Ug+7w404vQayLCtVJhFRWGNgijK+HibOX4o8LrcEp+tUYOqX4P0e1zXZYWsPTERE1DMck4kyjVxSIGoY9BrEGzSQARw5YVG6HCKisMbAFGUYmKJLamIMAKCsqrnDpPAzH0REdG4ckosyDEzRJS3RgLIqC46caEaLzQ2g6zlMep0aqr4tjYgorDAwRZlTgan7N971XbIOdO6JYOdEaPP1MJXXtOC7I3XwSJ0Dk0YtImdIEieFExGdAwNTFJFl+bx7mE6/ZL2r3glRFCAFs0gKqniDBnqtCnanBzX1ViTEce0tIqKeYGCKInanB472q6hM3fzBebZL1n0MejUGZxghdNH7RMoTBAFpSQaU17SgttHGwERE1EOc9B1FLG3eW6LotCroteeXlX2XrJ/5cHvYvxTq0pO8w3LmRqvClRARhS8GpijS3B6YEgzsZYgm6ckGAEBtAwMTEVFPMTBFEV8Pk5HDMlElNdEAUQDa7G602XkjXiKinmBgiiLsYYpOGrWI/v1iAQAnm+wKV0NEFJ4YmKJIM3uYotbgDO+NeE822hSuhIgoPDEwRRFLm3dJAfYwRZ/B6fEAgJNNDExERD3BwBRFLG3e+SvsYYo+Q9p7mBosdnh4ZSMR0XljYIoizexhilpJRh1idGpIMlBv4TwmIqLzxcAURXiVXPQSBMF/mxRO/CYiOn8MTFFCluVTV8nFMjBFo7RE73pMnMdERHT+GJiihM3hhtvjvRccA1N0SvP3MNl4k10iovPEwBQlfL1LMTo1NGqVwtWQEvqZYiAKgM3hQZvNrXQ5RERhhYEpSvjnL7F3KWqpVSKSjHoAgJnDckRE54WBKUpw/hIBQIrp1LAcERF1HwNTlGhuZQ8TASkmbw9THQMTEdF5YWCKEhYre5joVA9TQ4sDLjcXsCQi6i4GpijBHiYCgNgYDQw6NWQuYElEdF4YmKIEe5jIxzcsx3lMRETdx8AUJdjDRD6nJn6zh4mIqLsYmKIEe5jIxxeY6riAJRFRtzEwRQFJlv3rMDEwUVKCDqIgwO70oNXmUrocIqKwwMAUBax2NzyStych3sDAFO1Uoogkow4A5zEREXUXA1MUaG51AABi9Wpo1PyWE+cxERGdL/70jAK8LQqdKSWRK34TEZ0PBqYowNui0JlSErxLCzRyAUsiom5hYIoC7GGiM52+gCV7mYiIAmNgigKneph0CldCocS3gKW5kYGJiCgQBqYocKqHSaNwJRRKfBO/zY1WhSshIgp9DExRgD1M1JVTgYkLWBIRBcLAFAWaOYeJunD6ApZ1zVxegIjoXBiYogBX+aaunL6A5bFqi8LVEBGFNgamCCdJsv8+cuxhojP5huWOMjAREZ0TA1OEa7W5IMuAACDewEnf1JHvSrmj1S0KV0JEFNoYmCKcb/5SbIwGahW/3dSRr4fpxMlWOJwehashIgpd/Aka4fzzl+I4HEedxcZoYNCrIckcliMiOhcGpgjX3Oa98S4nfNPZpLXfV+5wVbPClRARhS4GpghnaXMB4IRvOrvURAMABiYionNhYIpw7GGiQNKS2gNTZRMkLmBJRNQlBqYIxxvvUiD9EvTQaVRos7tRaW5VuhwiopDEwBThmrloJQUgigIu6m8EABw43qRsMUREIUrxwCRJElavXo1LL70U48ePx5133omKioqzHt/Y2Ih7770X+fn5KCgowIoVK2Czdbzb+rZt23D11Vdj7NixmDNnDrZv337W116wYAHWrFkT1PcUStjDRN0xLDMBAHCgoknZQoiIQpTigWndunV4/fXX8eijj+KNN97whxin09nl8YsWLUJ5eTleeeUVvPDCC/j888+xfPly//6ioiIsWbIEN954I9555x0UFhZi4cKFKCsr63Aep9OJ3/zmN/jiiy968+0pjjfepe4YnmkCABw43sh5TEREXVA0MDmdTmzcuBGLFi3C9OnTkZ2djVWrVqGmpgYfffRRp+P37NmD4uJiPPXUU8jNzUVhYSEeeeQRbNmyBbW1tQCAl156CTNnzsStt96KrKwsLF26FLm5uXj11Vf959m9ezeuu+46lJSUwGg09tn77WseSUKrlVfJUWCD0uKg1Yhos7tx4mSb0uUQEYUcRQNTaWkp2traUFhY6N9mNBqRk5ODnTt3djq+pKQEKSkpyMrK8m8rKCiAIAjYtWsXJEnC7t27O5wPACZPntzhfJ9//jkuvfRSbN68GfHx8b3wzkJDi9UFGYAgAPExvC0KnZ1KJWLYABMADssREXVFreSL19TUAAAyMjI6bE9NTfXvO11tbW2nY7VaLUwmE6qrq2GxWGC1WpGenn7O8y1evDhYbyGkNbd6h+PiDVqIoqBwNRTqsgeZsO9YAw4cb8QVEzOVLoeIKKQoGph8k7W12o7DRTqdDs3NnRfRs9lsnY71He9wOGC32896PofDEayyz0qtVnxKWAdtDu9wnClOe9baVCoRkGSIogBVF6FKFAQIggBRBag83H/W/YJ3nyiKAKQ+e/1gnEMlChBEATlDk/D2v4/gQEUTRNWp99SXVO33O1TxvocXhO0YHGzH4ImEtlQ0MOn13julO51O/9cA4HA4EBMT0+XxXU0GdzgcMBgM0Ol0/vOdub+r8wWTKApITIzt1dc4Xy6pHgCQnBBzztpsDVbodBqIKlWnfTF6NdRqFWL0WqjVEvefZb9Op4HLLUGv1/To+T3dH4xzaNQiYvRa5OUYodeq0GJ1ocnqRlb7RHAlGI29+/9rtGA7BgfbMXjCuS0VDUy+4TWz2YxBgwb5t5vNZowcObLT8enp6fj44487bHM6nWhqakJqaipMJhMMBgPMZnOHY8xmM9LS0nrhHZwiSTIsFmuvvsb5qj7ZAgAw6NRobOx6Iq+v58nhcMHucHfaL8gS3G4PbHYnnF3czZ77vfsdDhdElQp2uwuSJJ3383u6Pxjn0GpUsNmd0IoyRg1OxJ5DdfjPnkokxfb9vDeVSoTRGAOLxQaPp+uASIGxHYOD7Rg8odqWRmNMt3u9FA1M2dnZiIuLw44dO/yByWKxYN++fZg7d26n4/Pz87Fy5UqUl5dj8ODBAIDi4mIAwMSJEyEIAvLy8lBcXIwbbrjB/7wdO3Zg0qRJvf5+3O7Q+RAAQKPFOwwZb9CctTbvqIsASZLhkTpfTi7JMmRZhuQB959rvyxDhHdtr9OP6+3XD8Y5PJIMWZLhdssYPTQJew7VYe/hOlx98eAuX68veDxSyP3/FI7YjsHBdgyecG5LRQOTVqvF3LlzsXLlSiQlJWHAgAF45plnkJ6ejlmzZsHj8aChoQHx8fHQ6/UYN24c8vLysHjxYixfvhxWqxXLli3DnDlz/D1I8+fPx8KFC5GTk4Np06Zh06ZN2L9/Px577DEl36oimlq9gckUxzWYqHvGXJQMACirssBqd8Gg59WVRERACCxcuWjRIlx//fV4+OGHcdNNN0GlUmHDhg3QaDSorq7G1KlTsXXrVgCAIAhYu3YtMjMzMW/ePNx9992YNm1ah4Urp06discffxx/+9vf8OMf/xhFRUVYv359h6UIooUvMCXGMzBR9/QzxSAj2QBJlvH9sUalyyEiChmK9jABgEqlwpIlS7BkyZJO+zIzM3HgwIEO25KTk7F69epznnPOnDmYM2dOt17/008/7Xat4eZUDxMXraTuG3NRMqrrrfi2rB752alKl0NEFBIU72Gi3iHLMpra12HikBydD9+w3LdH6yHzNilERAAYmCKW1eGGq31iHXuY6HyMGGiCViOiudWJ47WtSpdDRBQSGJgiVGOLdzguVq+GRt15fSWis9GoRYwe6u1l2llqDnA0EVF0YGCKUP75S5zwTT1QMMo7d6l4fy2H5YiIwMAUsZpaOH+Jem7csH7QaVSoa7bjyAmL0uUQESmOgSlC+ZcUYGCibhAE38N73zm9Vo0Jw/sBAHbsr1W4OiIi5TEwRahTQ3Kc8E3nplIJEEURLTY3Wu0u/2Nce2Aq3meG6yyrjBMRRQvF12Gi3sElBai7VKIAm9ONsopmON2n7jXnkWToNCIsVie+LavDxBGpnM9ERFGLPUwRirdFofPlcktwujz+h8cjYVBaPACg6PsahasjIlIWA1OE8i0rwMBEF2LEQBMAYPfBOv9niogoGjEwRSBJltHsH5LjHCbqueQEPdKTDJAkGZ/urlS6HCIixTAwRaAWqwuSLEMAYIxlYKILk3tREgDgs92VcLg8AY4mIopMDEwRqKl96MQYq4VaxW8xXZjB6fFIMurQZndjO+cyEVGU4k/TCMQJ3xRMoiDgsgkDAAAfFB3336OQiCiaMDBFoFOBicNxFBxTRmcgIVYLc5MNn+ziXCYiij4MTBHIvwYT7yNHQaLTqnD99CwAwHtfHYXF6lS4IiKivsXAFIG4pAD1hkvGZGBwWjxsDg82f3FU6XKIiPoUA1ME4pAcBZsgeFcE/9kPhgMAPv+6Cgcqmvz3niMiinQMTBGIk74pmE6/11z/lDhcnJsGWQb+sOU71DZa0Wp3gYsNEFGkY2CKQPXNdgBAslGvcCUUCXz3mtt/rAF7D9dh5KBEJMRq0dTqxLp3vsX3R+thd7jZ00REEY2BKcLYHG602d0AvKs0EwWL715zsizj0nEZEAUBx2tbsWNfrdKlERH1OgamCFPX3rsUF6NBjE6tcDUUqZKMekzOSQUA7DlYh/98c0LhioiIehcDU4Spa7IBAPqxd4l62fCBJozNSgYA/N+nh1FSala4IiKi3sPAFGF8PUwMTNQXxg1LxohBJsgy8OLm71C0j7dOIaLIxMAUYU42+3qYYhSuhKKBIAiYOjYDBaNSIckyXnp3H77Yy+E5Ioo8DEwRxneFXD8Te5iob4iCgJuvHInpEwZABvDytlJs+rwMkiwrXRoRUdAwMEUYDsmREkRBwK1XjsQ1hYMBAO9vL8eLm7+Dw8UVmogoMjAwRRBZllHHITlSiCAI+MllWbjjmlFQiQJ2HTiJp/6627+QKhFROGNgiiBWhxs2h/c3eq7BRH1JEHwPAVPH9sf9P5uAuBgNjtW04NFXS3CsxqJ0iUREF4SBKYLUNXmH44wGDXQalcLVULQ4/dYprXYXWu0u9E+Jwz03jkdaUgwaWxx4/C+78eW31UqXSkTUY1zZMIL4h+NMHI6jvuO7dUpZRTOc7o5zlmYVDMK/95xAeW0LNry/H+U1LfjpjGFQq/i7GhGFF/6rFUE44ZuU5Lt1yukPAcDM/ExcNXkQAODjXZV49o2vYWlzKlssEdF5YmCKIL4hOU74plAiCAKuuWQIfv2TsdBrVThQ0YQVr+zE0WrOayKi8MHAFEFOXSHHHiYKPXkjUvDwrZOQnmRAY4sDT7y2G//55tS8JkEQIAi+r31/9z6IiJTGwBRBOCRHoa5/v1g8fOskjB/WD26PhI1b9+P97cfgAdBqd6HZ6oK5wYpmq8s/gbzV7gJXcyIipXHSd4TwrsHkW+WbQ3IUWnzLDgACYmM0+PX1Y/HOv4/gH18dw6bPj6DF5sJF/Y2QZSAmRgubzQmP5F0pXKMWkTMkCXF6DWSuHk5ECmFgihAtVpd/VeVko07haohOOX3ZAeBU4Lly8iCoVAK2fHEUHxVXYMLwfhg/vB/UGu/kcV9gIiIKBQxMEaKqrg0AkGLSQ6PmGkwUOs617ECKKQaXTeiPz/ecwJ5DddDrVMjLTleoUiKis2NgihBVJ1sBAAP6xSlcCVHXfMsOnCl3aBJ0GhU+Kq5A0Xe1SDTGII03jyaiEMNJ3xGi8qS3hykzNVbhSojO36zJg5A9KBEygH8WH+f954go5DAwRYiqOvYwUfgSBAFTxmQgPdkAl1vCp7uqOg3fEREpiYEpAsiyjKr2HqYBKexhovAkigKmj++PuBgNmtuc+OrbGl4VR0Qhg4EpAtRb7LA7PVCJAtKTDEqXQ9RjMTo1rrx4MERRwPHaVhw43qR0SUREABiYIoKvdyk92cCbmlLYS0+ORX52CgCgpPQk6tvXFyMiUhJ/ukaAyvYr5DJTOH+JIsOowYnITImFJMv4dFclHE7OZyIiZTEwRQDfGkwD+nH+EkUGQRBwyZgMGHRqNLc58X+fHVa6JCKKcgxMEYATvikS6bUqXDouAwKA4n21+Orb6oDPISLqLQxMYc7tkVBd374GE4fkKMKkJRkwYaR3PtOfPzyAmgarwhURUbRiYApz5kYb3B4ZOo0KyQlcHZkiz/jh/TAsMwEOlwe/f/tb2J1upUsioijEwBTmKszeCd/9+8VC9N4OniiiiIKA236YDVOcFlV1bdj4/n6uz0REfY6BKcwdON4IAMgaYFS4EqLekxCnwy9/PAYqUUDJgZP4x/ZypUsioijDwBTm9pV7A1PO4CSFKyHqXcMzTbj5ByMAAO/8+wg+/7pK4YqIKJowMIWx+mY7zI02iIKAkYNMSpdD1OumTxiAH148CADw5w8OoHh/rcIVEVG0YGAKY/vKGwAAQzPiEaNTK1wNUd+4/rIsTB/fHzKAP767D//aw54mIup9DExhbH/7cNyoIYkKV0LUdwRBwNxZIzF1TAYkWcafPzyANz89BI8kKV0aEUUwBqYwJcsy9h9rD0yDGJgouoiigPlXZ+PHlw4FAHxYXIHH/7IbVXVtEASh04OI6EIxMIWp6normtuc0KhFDMtMULocoj4nCAJ+NGUofv5fuYjRqXG02oLlG4vx2kcHUN3Qhla7y//gneiI6EJx4kuY8g3HDRuQAI1apXA1RMqZnJOOAalx+NN7+1Be04JPdlXi31+fQPZgE7IHJ6KfKQY5Q5IQp9dw/SYi6jEGpjC1s/3qoBzOXyKCKU6HmZMycbTagq8P1aHB4sC3Rxrw7ZEG9O8XC4fTg8LcdKhEDs8RUc8wMIWh8poWHKxshkoUcMnoDKXLIep1guB9AJ0Dj2+KkiAIyEyJw4B+sag62YYDFU2oOtmGE+2rg7/1rzJcMjodU0anYwDvu0hE54mBKQx9sqsSADBxZAoS43UKV0PUu1QqAaIoosXmBtB5SE0UBZx+fZwgCMhMjUNmahxabS4cOWFBWVUzLG1OfLDjOD7YcRxD0uMxZUwGJuekIS5G02fvhYjCFwNTmLFYnSja5x2OmzlpoMLVEPU+lSjA5nSjrKIZTnfn6dsGvRqDM4wQuuh9iovRYFJ2KuZfnY2yKgv+80019h6uw7GaFhyracGbnx7C+GH9cMmYDIy5KAkqkdfBEFHXGJjCzOdfn4DbI2FIejyy+vP+cRQ9XG4JTlfnwKTVnDvkqFQCNBo1hg9MxPCBJrRYndhVehI79tei0tyKkgMnUXLgJIyxWlyck4ZJ2am4qL+RN7Mmog4YmMJIfbMdHxUfBwD8YNJAri9D1A1d9VAlGnW4avIg1DfbUVbVjCMnLLC0OfHRzgp8tLMCCbFa5AxJQvZgE4ZnmpCaGMMARRTlFA9MkiRh7dq1+Pvf/46Wlhbk5+dj2bJlGDiw6+GmxsZG/O53v8O///1vCIKAa665Bvfffz9iYmL8x2zbtg1r1qxBZWUlLrroIixduhSFhYXndY5Q43JLWLf5W7TZ3RicHo/8UalKl0QUVrrqoYo3aFCQk4bbrxmFoycs2P59Db49Uo/mNie2f1+D7d/XAAD0WhUGpMShX4Ie/RL0SE7Qo58xBglxWsQbNIjVq7m8B1GEUzwwrVu3Dq+//jqefPJJpKen45lnnsGCBQvw3nvvQavVdjp+0aJFsNlseOWVV2CxWPDQQw/BarXiqaeeAgAUFRVhyZIluP/++zFlyhS89dZbWLhwITZv3oysrKxunSPUSLKM1z8+iKPVLYjVq/GrOaOhVnGuBVGwqNUiJoxIwfjhKXB7JBysaML+8kaUljeivLYFdqcHZVXNKKtqPus59FoV4mI0iIvRIN6gbf9T49/W4e8GLeJi1JwzRRRGFA1MTqcTGzduxH333Yfp06cDAFatWoVLL70UH330EWbPnt3h+D179qC4uBhbt271h59HHnkECxYswD333IO0tDS89NJLmDlzJm699VYAwNKlS7Fnzx68+uqreOSRR7p1jlBibrTilW2lKD3eBAHAwmtz0c8Uuj1hROGmq6vwBqXHY1B6PK6cPAgygKq6Nuw5YEZTqwOtVhdabC60Wl2wO92wOz2QZcDu9MDu9KCu2d7t1zbo1YiP0SDOoEF8jLY9TGm8207fbvCGMINe3eXQYF8syBloCgAXBaVIp2hgKi0tRVtbW4fhMqPRiJycHOzcubNTYCopKUFKSoo/6ABAQUEBBEHArl27cNVVV2H37t144IEHOjxv8uTJ+Oijj7p1jquvvro33mqPVJ1sxaN/LoHTJUGrEfGzmSMw5qJkpcsiiijdvQpvcJoR6UnuTvtjY9QYkBqP/Uca0GJz+oOTL0y53RLUahEtbS602V1otblgtXvPY7W7YbW7Udto61atggAYdGrotWrodSroNCrodWrE6tWI0aqh16oQo1PDoFcjOTEWktsNSZIhy4AMGZABjyR7hyfdElxuj/9rt3+bd7vva9+fogho1SpoNaL/zxid2v8wxmoR1/61QX9qu0GnhhhiC4a6PZJ/iNZ52p+nv1+HywOPJEGlVqO5xQab3Q1JBjQqsUMbaNQq6DQi1GoROo2q/eH9Wtv+91B7/+dDlmV4JBkej/dPtyTB4/F+piRZhizLkCRAkiVIkgxBEKASBYii90/f11qtCnqHGy63BFmS29dWC692UTQw1dR45wdkZHRcfDE1NdW/73S1tbWdjtVqtTCZTKiurobFYoHVakV6evpZzxfoHD0ligKSkmJ7/PyuxCfE4PdLZkCjFhFv0PbKKsWCAMgyMKNgMLr6BVEUvMMVmWlG7g+wX4AAGXKH43r79UOtDS50P4BO7RhK9Z1r//BBSefc73ZLHVaRkmXvDyMZgCgI3h88cvsPIum0r9t/KIVrB44MeNfJEtoXfvAtNNq+P7g/NDu2k+z/j+8P735BFKHVANo+6KyXAXiA9htBn9re4V0L6HJZjJ6/4hlfdfOz42sv75+y/2mCClBrvIHhQlb+a251AKIIiB1L8i1K628BoWP7+NouLkYT9Oko5xNmFQ1MNpv3t6oz5yrpdDo0N3eeK2Cz2bqc16TT6eBwOGC32896PofD0a1z9JQgCFCpghtoVCoR6cl98y0yBPgQBprQyv3K7g+FGrj/wr+HRBS6FJ1xqNfrAXjnMp3O4XB0ecWaXq/vdKzveIPBAJ1OF/B8gc5BREREdCZFA5NvaMxsNnfYbjabu5x8nZ6e3ulYp9OJpqYmpKamwmQywWAwnPN8gc5BREREdCZFA1N2djbi4uKwY8cO/zaLxYJ9+/YhPz+/0/H5+fmoqalBeXm5f1txcTEAYOLEiRAEAXl5ef5tPjt27MCkSZO6dQ4iIiKiMykamLRaLebOnYuVK1fik08+QWlpKRYvXoz09HTMmjULHo8HJ0+e9M9NGjduHPLy8rB48WJ88803KCoqwrJlyzBnzhx/D9L8+fPx/vvv4+WXX0ZZWRmefvpp7N+/H/Pmzev2OYiIiIhOJ8gKL57h8Xjw3HPP4e2334bdbvev9J2ZmYnKykpcccUVeOKJJ3DdddcBAOrr67FixQp88cUX0Ol0uOqqq/Dggw/65y8BwObNm7Fu3TrU1NRg2LBhWLJkSYelC7pzDiIiIiIfxQMTERERUajjuvxEREREATAwEREREQXAwEREREQUAAMTERERUQAMTEREREQBMDARERERBcDARERERBQAA1OUkiQJq1evxqWXXorx48fjzjvvREVFhdJlhZympiYsW7YM06ZNQ15eHm666SaUlJT492/fvh3XXXcdxo0bh6uuugrvv/9+h+c7HA6sWLEChYWFmDBhAu699140NDT09dsIKUePHsWECRPw9ttv+7ft378fc+fOxfjx4zFjxgz8+c9/7vAcfl5P2bx5M66++mqMGTMG11xzDbZt2+bfV1lZibvuugt5eXmYOnUqnn/+eXg8ng7P/+tf/4orrrgCY8eOxc9+9jPs27evr9+C4txuN1544QVcfvnlmDBhAm6++WZ8/fXX/v38PAb2hz/8AbfcckuHbcFot0DnUJRMUWnNmjXy5MmT5c8++0zev3+/fPvtt8uzZs2SHQ6H0qWFlPnz58uzZ8+Wd+7cKR85ckResWKFPHbsWLmsrEw+fPiwPGbMGPm5556TDx8+LP/pT3+Sc3Jy5K+++sr//AceeECeOXOmvHPnTnnv3r3ynDlz5JtvvlnBd6Qsp9MpX3fddfKIESPkTZs2ybIsyw0NDfLkyZPlBx98UD58+LD81ltvyWPGjJHfeust//P4efXavHmznJOTI7/22mtyeXm5vG7dOjk7O1vevXu37HQ65VmzZskLFy6UDxw4IP/zn/+UCwoK5BdeeMH//LffflseO3asvGXLFvnQoUPykiVL5IKCArm+vl7Bd9X3Vq9eLU+ZMkX+4osv5GPHjskPPfSQPHHiRLm2tpafx2547bXX5OzsbHnu3Ln+bcFot+6cQ0kMTFHI4XDIEyZMkP/617/6tzU3N8tjx46V33vvPQUrCy3Hjh2TR4wYIZeUlPi3SZIkz5w5U37++efl3/72t/L111/f4Tn33HOPfPvtt8uyLMs1NTVydna2/K9//cu//8iRI/KIESPk3bt3982bCDHPPvusfOutt3YITOvXr5enTp0qu1yuDsfNmjVLlmV+Xn0kSZIvv/xy+cknn+yw/fbbb5fXr18vv/fee/Lo0aPlpqYm/7433nhDzsvL8/9AmjVrlvz000/797tcLvmyyy6T169f3zdvIkRce+218hNPPOH/e0tLizxixAj5ww8/5OfxHGpqauS77rpLHj9+vHzVVVd1CEzBaLdA51Aah+SiUGlpKdra2jrcX89oNCInJwc7d+5UsLLQkpiYiD/+8Y8YM2aMf5sgCBAEARaLBSUlJR3aEAAuvvhi7Nq1C7IsY9euXf5tPkOHDkVaWlpUtvPOnTvx5ptv4sknn+ywvaSkBAUFBVCr1f5tF198MY4dO4a6ujp+XtsdPXoUVVVV+NGPftRh+4YNG3DXXXehpKQEubm5SEhI8O+7+OKL0draiv3796O+vh7Hjh3r0I5qtRqTJk2KqnYEgOTkZHz22WeorKyEx+PBm2++Ca1Wi+zsbH4ez+H777+HRqPBu+++i3HjxnXYF4x2C3QOpTEwRaGamhoAQEZGRoftqamp/n3k/Z/5sssug1ar9W/78MMPUV5ejksvvRQ1NTVIT0/v8JzU1FTYbDY0NjaitrYWiYmJnW7qHI3tbLFYcP/99+Phhx/u9Lk7WzsCQHV1NT+v7Y4ePQoAsFqtuOOOO1BYWIgbbrgBn376KQC24/l46KGHoNFocMUVV2DMmDFYtWoVVq9ejUGDBrEdz2HGjBlYs2YNBg4c2GlfMNot0DmUxsAUhWw2GwB0CAIAoNPp4HA4lCgpLOzevRsPPvggZs2ahenTp8Nut3dqQ9/fnU4nbDZbp/1AdLbz8uXLMWHChE69IwC6bEdfyHQ4HPy8tmttbQUALF26FLNnz8bGjRsxZcoU/PKXv8T27dvZjufh8OHDiI+Px+9//3u8+eabuO6663Dfffdh//79bMceCka7BTqH0tSBD6FIo9frAXh/qPu+BrwfyJiYGKXKCmkff/wx7rvvPuTl5WHlypUAvP8jO53ODsf5/h4TEwO9Xt9pPxB97bx582aUlJTgvffe63J/V+3k+8fRYDDw89pOo9EAAO644w78+Mc/BgCMGjUK+/btw8svv3xe7XjmMdHUjtXV1bj33nvxyiuvYNKkSQCAMWPG4PDhw1izZg0/jz0UjHYLdA6lsYcpCvm6RM1mc4ftZrMZaWlpSpQU0l577TX8+te/xuWXX47169f7f+PJyMjosg0NBgPi4+ORnp6OpqamTv8ARFs7b9q0CfX19Zg+fTomTJiACRMmAAD+93//FwsWLEB6enqX7QgAaWlp/Ly2873XESNGdNg+bNgwVFZWsh27ae/evXC5XB3mJgLAuHHjUF5eznbsoWC0W6BzKI2BKQplZ2cjLi4OO3bs8G+zWCzYt28f8vPzFaws9Lz++ut49NFHcfPNN+O5557r0F08adIkFBcXdzi+qKgIeXl5EEUREydOhCRJ/snfgHceSm1tbVS188qVK7F161Zs3rzZ/wCARYsW4bHHHkN+fj527drVYb2goqIiDB06FMnJyfy8tsvNzUVsbCz27t3bYfvBgwcxaNAg5OfnY9++ff6hO8DbjrGxscjOzkZycjKGDh3aoR3dbjdKSkqiqh19c2QOHDjQYfvBgwcxZMgQfh57KBjtFugcilP6Mj1SxnPPPScXFBTIH3/8cYf1MJxOp9KlhYwjR47Iubm58q9+9SvZbDZ3eFgsFvngwYNybm6u/Mwzz8iHDx+WN2zY0GkdpnvuuUeeMWOGXFRU5F+H6fRLcaPV6csK1NXVyfn5+fLSpUvlQ4cOyZs2bZLHjBkjv/322/7j+Xn1+v3vfy9PmDBBfu+99zqsw1RUVCTb7XZ55syZ8h133CHv37/fvw7TmjVr/M9/88035bFjx8pvv/22fx2myZMnR9U6TB6PR77pppvkq666St6+fbt89OhRedWqVfKoUaPkr7/+mp/Hblq6dGmHf8uC0W7dOYeSGJiilNvtlp9++mn54osvlsePHy/feeedckVFhdJlhZQXX3xRHjFiRJePpUuXyrIsy59//rk8e/ZsefTo0fJVV10lv//++x3O0dbWJj/00EPypEmT5EmTJsn33HOP3NDQoMTbCSmnByZZluW9e/fKP/3pT+XRo0fLl19+ufyXv/ylw/H8vJ6yceNGecaMGXJubq587bXXyv/85z/9+44dOybPnz9fHjNmjDx16lT5+eeflz0eT4fn/+lPf5KnTZsmjx07Vv7Zz34m79u3r6/fguKamprk5cuXy9OnT5cnTJgg//d//7e8Y8cO/35+HgM7MzDJcnDaLdA5lCTIsiwr3ctFREREFMo4h4mIiIgoAAYmIiIiogAYmIiIiIgCYGAiIiIiCoCBiYiIiCgABiYiIiKiABiYiIiIiAJgYCKisHPLLbdg5MiRuPHGG896zOLFizFy5Eg88MADAICRI0dizZo1AIC3334bI0eORGVlZZ/US0ThT610AUREPSGKIr7++mvU1NT47w/mY7Va8dlnn3XY9uabb3Y6joiou9jDRERhKScnBzqdDh988EGnfZ999hliYmI63OF8/PjxDExE1GMMTEQUlgwGAy677LIuA9PWrVtx5ZVXQq0+1Yl++pBcV0pKSjB37lyMGzcOBQUFWLp0KRoaGjocs3PnTtxxxx3Iz8/H6NGjMWPGDKxZswaSJPmPMZvNWLx4MQoKCpCfn49ly5Zh1apVmDFjRodz/f3vf8c111yD0aNHY/r06VizZk2Hu7QTUWhhYCKisHX11Vf7h+V8Wltb8e9//xuzZ8/u9nl27tyJ2267DXq9Hs8//zx+85vfoLi4GLfeeivsdjsAoLS0FLfddhtMJhNWrVqFF198EZMmTcLatWuxbds2AIDT6cS8efOwe/du/OY3v8ETTzyB0tJSbNy4scPr/eEPf8Bvf/tbFBYWYv369bj55pvx0ksv4be//W0QWoWIegPnMBFR2Jo+fTpiYmLwwQcf4LbbbgMA/POf/0RycjImTpzY7fM8++yzGDp0KP7whz9ApVIBAMaNG4drrrkGmzZtws0334zS0lJccskleOaZZyCK3t81p0yZgk8//RQ7duzANddcg3fffRdHjhzBpk2bMHr0aADAxRdfjJkzZ/pfq6WlBevWrcN///d/4+GHHwYATJ06FSaTCQ8//DDmz5+P4cOHB6N5iCiI2MNERGFLr9djxowZHYbl3n//ffzwhz+EIAjdOofNZsPevXtx2WWXQZZluN1uuN1uDBw4EFlZWfjyyy8BAHPmzMFLL70El8uF0tJSfPjhh1i9ejU8Hg9cLhcAoKioCAMHDvSHJQCIi4vD5Zdf7v/7nj17YLfbMWPGDP9rud1u/5Cd7/WIKLSwh4mIwtoPf/hD/L//9/9QU1MDnU6H7du34+677+728y0WCyRJwksvvYSXXnqp036dTgcAsNvtePTRR7Flyxa43W5kZmZiwoQJUKvVkGUZANDY2Ijk5ORO5zh9W1NTEwBg4cKFXdZjNpu7XTsR9R0GJiIKa9OmTUNsbCw++OADGAwGZGZmdujhCSQ2NhaCIOC2227DNddc02l/TEwMAOCxxx7Dhx9+iOeffx6XXHIJDAYDAKCwsNB/bFpaGo4dO9bpHPX19f6vjUYjAGDlypUYMmRIp2P79evX7dqJqO9wSI6IwppWq8XMmTPx4YcfYtu2bV2GnnOJi4tDTk4Ojhw5gjFjxvgfw4cPx5o1a7Bjxw4AwK5duzB58mTMnDnTH5a+++47NDQ0+K+SKygoQGVlJfbv3+8/v91uxxdffOH/+7hx46DRaFBbW9vh9dRqNZ577jkupkkUotjDRERh7+qrr8Zdd90FURT9E6nPxz333IOFCxfi3nvvxbXXXguPx4ONGzdi7969+OUvfwkAGDt2LLZt24a//e1vyMrKQmlpKV588UUIggCbzQYAmD17Nv74xz/iV7/6Ff7nf/4HRqMRL7/8Murr69G/f38AQGJiIhYsWIAXXngBra2tmDx5Mmpra/HCCy9AEARkZ2cHr2GIKGgYmIgo7F1yySUwGo3IyMhAVlbWeT9/6tSp2LBhA9auXYtFixZBo9EgNzcXL7/8MsaPHw8AeOCBB+ByufD888/D6XQiMzMTv/jFL3D48GF8+umn8Hg8UKvV2LBhAx577DEsX74carUa1157LUwmE44ePep/vbvvvhspKSl4/fXX8ac//QkJCQkoLCzEPffcg/j4+GA1CxEFkSD7ZisSEdEFOXToEI4cOYJZs2Z1uErv+uuvR3p6OtauXatgdUR0IdjDREQUJFarFf/zP/+Dn/3sZ/jBD34Aj8eDrVu34rvvvsN9992ndHlEdAHYw0REFEQffPABNmzYgLKyMsiyjJycHPziF7/A1KlTlS6NiC4AAxMRERFRAFxWgIiIiCgABiYiIiKiABiYiIiIiAJgYCIiIiIKgIGJiIiIKAAGJiIiIqIAGJiIiIiIAmBgIiIiIgqAgYmIiIgogP8PiYS77o9ysQwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can treat the other numerical variables in a similar way\n",
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\1666233205.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_2['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This plot looks kind of normal, doesn't it?\n",
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\2097752121.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The situation with engine volume is very strange\n",
+    "# In such cases it makes sense to manually check what may be causing the problem\n",
+    "# In our case the issue comes from the fact that most missing values are indicated with 99.99 or 99\n",
+    "# There are also some incorrect entries like 75\n",
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A simple Google search can indicate the natural domain of this variable\n",
+    "# Car engine volumes are usually (always?) below 6.5l\n",
+    "# This is a prime example of the fact that a domain expert (a person working in the car industry)\n",
+    "# may find it much easier to determine problems with the data than an outsider\n",
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\1910130346.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_3['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Following this graph, we realize we can actually treat EngineV as a categorical variable\n",
+    "# Even so, in this course we won't, but that's yet something else you may try on your own\n",
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\2257603476.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Finally, the situation with 'Year' is similar to 'Price' and 'Mileage'\n",
+    "# However, the outliers are on the low end\n",
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I'll simply remove them\n",
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\730772117.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_4['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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noPF1/rIWRG2doTUaTYflWq0WTU1NXa7/83Xb1rdYLACAiooKNDQ0YPny5RgzZgx27NiBF198EQMHDsR1113ncawKhYCIiGCPt/eUwaD3+TH9SaDnD/AcMH/X87eKrdDrNVCpRbeOodepoFIpoddpoFK5vq1apYBep0F4eJBbx3MXrwHm7wuyFkQ6nQ6Asy9R298BwGKxQK/vfAJ0Ol2Xna0tFguCgpz/Ie+77z7MnTsXN998MwBg+PDhaGpqwurVq7FgwQIoFJ5VmqIowWhs9WhbTyiVChgMehiNJjgc7n249QWBnj/Ac8D83ctfEACT2QaTyQqrzeHWsQRJhN3ugMlshdXq+rYatRImsxWNjRIkya1DuoTXAPPvbv4Gg97lFiZZC6K221/V1dVITExsX15dXY3U1NRO68fFxeHLL7/ssMxqtaKxsRExMTGor69HcXExRo4c2WGdrKwsvPrqq2hsbERkZKTH8drtvr8gHQ5RluP6i0DPH+A5YP6u5S8IAiRRguPsjztESYIkSRAdcGtbhyhBEiXY7c7tvYXXAPP3Rf6y3phMS0tDSEgIcnNz25cZjUYcPnwY2dnZndbPzs5GZWUlSkpK2pfl5eUBAMaOHYuwsDDo9XocPXq0w3ZHjx6FwWDoVjFEREREfZesLUQajQYLFy7E888/j8jISCQkJGD16tWIi4vDrFmz4HA4UF9fj9DQUOh0OmRmZmLMmDF44IEHsGLFCrS2tmL58uWYP38+YmNjAQC33norXn31VURHR2Ps2LHIz8/HunXrcO+998qZKhEREfkxWQsiwDnQot1uxxNPPAGz2Yzs7Gy8/vrrUKvVKCsrw2WXXYZnnnkGCxYsgCAIeOWVV7By5UosWrQIWq0Ws2fPxrJly9r3d//99yMiIgLr1q1DRUUFBgwYgKVLl+LGG2+UMUsiIiLyZ4LkzRu/fYjDIaK+vsVnx1OpFIiICEZDQ0tA3jsO9PwBngPm717+giCg2WzDvsJatztVhwSpkTwgHAUnGmCx2V3eTqNWInNoP4To1F7pQ8RrgPl3N//IyGCXO1UH5uAGREREROeQ/ZYZERGRrwiCZwNI8mZK38eCiIiIAoIDgNls82hbnVYFZc+GQ36GBREREfV5giDAbLbh8Ml62Nzsj6JWKZCeFOm1vlLkH1gQERFRwLDZRbc7nVNgYKdqIiIiCngsiIiIiCjgsSAiIiKigMeCiIiIiAIeO1UTEVE7u0PE8bImmC12KJUKhIdoMDAmxOPxe4h6CxZEREQEAKiqb8W3e0/D2GLtsDwxNgQ5I+KgVXMkHuq7eMuMiIiQX1CNj344AWOLFXqtEqmJ4RjS3wCFAJyqasYnP5zsVCgR9SVsISIiCnB1TWb8Z2sxAGBwfCjGp8e2twalDYrAtn3lONNqw7Z95Zg9cRCUCt4+o76HLURERAHMZhfxee4p2OwiBkSHYMqo+A63xvqF6XDF+IHQqBWoM1qw93iNjNESeQ8LIiKiALbzSDUam60IC9bgktEJXXaeDtKpMWlEHADg0IkGlNe2+DpMIq/zqCCqqqrq6TiIiMjHGs5YUHi6CQCwcHYqdNrz96JIjA1FysAwAMDOgmqInNOL+hiPCqJLLrkEixcvxubNm2G1spMdEVFvtOd4LQAgOcGAIQlhF11/TEo0NCoFmpqtOFlxxtvhEfmURwXRM888A1EU8fDDD2PKlClYuXIlDhw40NOxERGRl9Q0mFBW3QwBwPj0WJe20aiVSB8cCQDYX1gLUWQrEfUdHhVEV199NdavX49vvvkGd9xxB3bs2IHrr78ec+fOxfr161FbW9vTcRIRUQ/afbZzdHJCGCJCtS5vN3xQBDRqBYyttvbbbUR9Qbc6VcfGxuJ///d/8dlnn+H9999HREQEVq9ejRkzZuC+++7Dvn37eipOIiLqIbVNJlTVm6AQgFFDo9zaVq1SYMTZVqK9x2vZl4j6jG4/ZbZr1y48+eST+NWvfoX8/HxMnjwZjz32GEwmE2666Sa88cYbPRAmERH1lKMljQCApHgDQvRqt7dPTYyAWqWAscWKgpKGHo6OSB4eDcxYUlKCDz/8EB999BFOnz6NhIQE3HLLLViwYAHi4+MBAAsXLsTDDz+MV199FbfddltPxkxERB4yW+04UensEJ2aGO7RPtQqBYYmhOFISQO27SvH+DTX+iD5E1GUoOAAk3QOjwqiK664AlqtFjNnzsSqVauQk5PT5XpDhgzByZMnuxMfERH1oONlTRBFCVEGLfqF6TzeT8rAcBwpacCh4nrUNpoQ1Y19+cqx0kZ8s/s0qupb0WyyITZCj8H9DRgcb4BaxWH5Ap1HBdGTTz6JX/ziFwgNDb3ger/+9a/x61//2qPAiIioZ4mShGOnGgE4b3t1Zwb7sBAN+vcLRnltC77dexrXTk/uoSh7nihJ+PSHk9i0rRjn9niqajChqsGEgpIGzBw3EEE6zmYVyDwqiT///HNUV1d3+VpBQQHmzZvXraCIiKjnlde0oMVsh1atRFL8hX+hdUV6UgQA4Lt95bDZxW7vzxvsDhFrPziID84WQ8MGhmHmuAGYP3UwxqT0g16rRGOzFZ/nnUKLySZ3uCQjl8vhXbt2QTr7NEFeXh527tyJ+vr6Tut98803KC0t7bkIiYioRxSVGwEAQ/oboFJ2/xZRYmwowkM0aGy2Yn9RLcamxnR7nz3tvW+LsPtYDVRKBW64bCj0WhWsNgcAYMSQKAyKC8WWvFKcabXh87xSzJucxNtnAcrlgujdd9/Fhx9+CEEQIAgCVq5c2WmdtoJp7ty5PRchERF1m9XmQGl1MwBgSIKhR/apUAgYlxaDL3eVYfuhKp8WRK7c7ttZUIUtO52/oN8zPwMpiRHYV9hxnLzQIA1mT0jEf3NPodlkw+5jNZjg4kCV1Le4XBA98cQTuPbaayFJEhYtWoTly5dj6NChHdZRKBQwGAwYNmxYjwdKRESeK6k8A1GUEB6iQaQbAzFeTPbwWHy5qwz7CmvRbLJ59Bi/uxwAzOYL396qaTTh9U+OAAAuGzsAaUmRsJ9nZO1gvRqTRsbhi51lOHqqEUlxoYiNDOrpsMnPuVwQhYaGYvz48QCAN998ExkZGQgODvZaYERE1HPOvV3Wnc7UP9e/XzAGxoSgtLoZuwqqMWN0Qo/tuyuCIMBstuHwyfoL9lv6b+4pWGwOxEUGYVBcKI6easCgeAMEdJ17fFQwhg4IQ2FZE348WIl5k5N65LYi9R4uF0SbNm3C9OnTERERgfLycpSXl19w/fnz53c3NiIi6gHNrTZUN5gAAIP798ztsnPljIhD6deF2H6o0usFURubXWzvC/RzZdXNKKtuhkIAJmbEwu4QYXdcvNP3uNRonK5pwZlWG46VNiI9KbKnwyY/5nJB9Nhjj+Hf//43IiIi8Nhjj11wXUEQWBAREfmJ4gpn61B8VBCCdT1/S2tiehze/boQx8uaUNNoQnS4vseP4SqHKGJngfMp6OFJkTAEa1zeVqNWImtoFLYfqsKhE/VIHRgOJVuJAobLBdFXX32F6Ojo9r8TEVHvcPJsQTQ4vudbhwAgIlSL4UkROHyyATsOVWLe5MFeOY4rjpQ04kyrDXqtEqOS3ZunDQCGJIRhX1EdWs12FJ5uQmpihBeiJH/kckGUkJDQ5d/b2O12NDc3Izw8vEcCIyKi7mtstqCx2QqFAAyMDfHacXIy4nD4ZAO2H6rC3ElJPdpPyVU2u4hDxc7hYEYPi/bo8XmlQsCIwZHIO1KNg8X1GDYgvIejJH/lUVug3W7HK6+8go8//hgAkJubi8mTJyMnJweLFi1CU1NTjwZJRESeKTk7b1l8v2Bo1UqvHWdMSjQ0KgUq61tx8uwxfa3gVAMsNgcMQepuDS0wdEAYdBolWsx2FJ/tjE59n0cF0Zo1a/Dqq6/CaHReKL///e8RHh6OZcuW4dSpU3jhhRd6NEgiIvJMW0GUFNf9kakvRK9VYXSKs1vF9oOVXj1WV2x2EYdPNAAARiZHQdGNFiqVUtE+CvfRs1OdUN/nUUH06aef4sEHH8TNN9+MoqIiHD9+HPfccw9uvfVWPPDAA/j66697Ok4iInJTh9tlMd67XdYmJyMOAJB7pMqlp7p60tGzrUOhQeoe6Ss1dEAYFAJQZzSjrsncAxGSv/OoIKqurkZmZiYA4Ntvv4VCocC0adMAAHFxcThzRp7mUiIi+sm5t8s0Xrxd1iZjcARCg9Q40+ocJ8hX7A4Rh086W4dGJUdBoeh+/yWdRoWBsc5WtYJTDd3eH/k/jwqimJgYlJWVAQC+/vprDB8+HJGRzvEa9uzZg7i4uJ6LkIiIPOKr22VtlAoFJgx3Tnvxow9vmxWfNsJsdSBYp+rRJ+mGDQgDABSVNZ13zCPqOzwqiObOnYtnnnkGv/rVr5Cfn49rr70WAPD000/j5Zdf5mz3REQya2q2orHZCsFHt8va5Ixw/kK893gtTBa7148nShIOnW2NSk+K7JHWoTbxUUEI0athtYvYc6ymx/ZL/smjgui3v/0t7rjjDgiCgIceegi//OUvAQAHDhzAHXfcgXvuuadHgyQiIvecqj57uywqyCe3y9okxYUiLjIIVruI3T4oIsqqm3Gm1QaNSoGhZ1t0eoogCO2tRNsP+b6jOPmWy+MQnUsQBNx99924++67OyzfuHFjjwRFRETdc6rKObN9Yoxvbpe1EQQBORmx+GDbCWw/VInJI+O9erxDZ58sS0kM92jcoYtJTjBgz/FaFJ02os5o7tGJccm/eFQQAcCZM2ewY8cOtLa2QpI6zyDMqTuIiOTRYrK1PxnlzcEYz2diRhw+2HYCR042oOGMBRFeKiJqGkyoaTRBIQBpXhpROkinRlxkECrrW7HzSBWuGJ/oleOQ/DwqiLZt24YlS5bAZDJ1+TrnMiMiks+pamfrUEyEHnqtx7/3eiw6XI9hA8JwvKwJuYerMHuCd4qItr5Dg/sbEKTzXp5DEgyorG9F3pFqFkR9mEdX0AsvvIAhQ4Zg2bJliI2NhULBye+IiPzFqSpn/6FEGVqH2uRkxOF4WRN+PFjplYKoqcXaflsww8uz0g+ON2D7wUqcqDCiuqEVMRFBXj0eycOjgqioqAhr167FuHHjejoeIiLqBpPFjup6Z+u9r/sPnWtcWgw2fHkcZTXNOFlpRFJcz04se7CoDgCQEB2McC/369FrVUgdGI6CU43IO1KNuZOSvHo8kodHTTv9+/dHc3NzT8dCRETddKrqDCQAkQYtQoLUssURoldjbKpzKo/v9pb36L7PtFpxrLQRgPdbh9qMOZtL3pFqnxzvXILg7Iri6g95xqOC6O6778af//zn9sEZu0MURaxZswZTp05FVlYW7rzzTpSWlp53/YaGBjz00EPIzs7G+PHjsXLlyk59mfbv34+bb74Zo0aNwvTp07FmzRqIom+HkSciksPJirbbZfK1DrWZltkfALDjcBXM1p4bk2jbvnI4RAmRBi1iI/U9tt8LGTW0H5QKAWU1zaioa/HJMe2ihOr6VjS12tBsdv2HQ0h6xqNbZh9//DGqqqpw+eWXIzIyEjqdrsPrgiDgyy+/dGlfa9euxYYNG/Dss88iLi4Oq1evxuLFi/Hxxx9Do9F0Wr+tM/cbb7wBo9GIxx9/HK2trXjuuecAACdOnMCtt96KX/ziF3j66adx9OhRLFu2DHq9Hnfeeacn6RIR9Qomix2na51f1nL2H2qTlhiOmAg9qhtM2HmkGlPPFkjdYbLYsXWPs8VpxOBIn7WIBOvUSEuMwKGT9dh7vBbxUcFePZ4gCGix2FFS1QzjGTMcYuenubuiVimQnhSJEJ26yyfA6fw8Koji4uJ6ZHoOq9WK9evX4+GHH8aMGTMAAC+++CKmTp2KLVu2YO7cuR3W37NnD/Ly8rB582YkJycDAJ566iksXrwYDz74IGJjY7Fu3ToMHToUK1euhCAISEpKwtGjR7F79+5ux0tE5M8On6yHKEowBGsQHiL/eDmCIGBaZn+8920Rtu4r75GC6Nu9p9FqsSMsWINEH01J0mZMSjQOnazHnuO1uHLiIJ8c02YXYbU5XC6IyHMeFUTPPPNMjxy8oKAALS0tyMnJaV9mMBiQnp6OnTt3diqIdu3ahejo6PZiCADGjx8PQRCQn5+POXPm4Pvvv8fixYs7/NawZMmSHomXiMif7TteC8A/WofaTB4Zjw++K0ZxuRGnqs5061aeze7A57mnAACZQ/tB4eP+MlnD+uGtLUdRdLoJTS1WhAV3votBvVe3Bm4oKirCDz/8gOrqatxyyy0oLS1FWloaQkJc+89YWekcCj0+vuNIpjExMe2vnauqqqrTuhqNBuHh4aioqEBzczNqamoQGhqK3/3ud/juu+9gMBgwf/58/OpXv4JS2b3h61VeGAX1fJRKRYc/A02g5w/wHDB/9/K32R0/jcsTFwqlG3N6Kc52xlUoAaXD9e2UCgGCQoBKJUCSut4uKkyHcWkxyD1chc/zSnHPNSNc3//PzsF3+8rR1GJFRKgWKYlhcDhcbzXxNEfgpzxjIvUYHG/AiQojDhTXYcboBLf24w5BABQ2Z97OoW1c6wfrynvSW/j6M8CjgkgURSxfvhzvv/8+JEmCIAi48sorsXbtWpw6dQpvv/22S7fU2jpD/7yvkFarRVNTU5frd9WvSKvVwmKxtD/59txzz+HWW2/F3/72Nxw5cgRPP/00Wltb8dvf/taDbJ0UCgEREd69Z9wVg8E3HQb9VaDnD/AcMH/X8s89WAGrTURokBoD48Pc6luj16mgUimh12mgUrn+AIpapYBep0F4+IXH5bnpijTkHq5C7pEq3HH1CMS52f/GYNDDbLXj4x9PAgCuzElCSLAONrvrsXqaI9Axz8mZ/XGiwoj9xfW45tIUt/bjLqvYCgDQ6Vx/WtDV96Q38dVngEcF0dq1a/Hxxx/j97//PWbMmIHJkycDAJYuXYp7770XL774Ynsn5wtp64xttVo7dMy2WCzQ6zufAJ1OB6vV2mm5xWJBUFAQVCpnOpMmTcJvfvMbAMDw4cNRX1+PP//5z7j//vs97oAnihKMxlaPtvWEUqmAwaCH0WiCwxF4T8gFev4AzwHzdy//b3Y5byUlxYXCZOr8OXkhgiTCbnfAZLbCanX9GSWNWgmT2YrGRgkX6r8bGazGiCGROFhcj39tKcCts9Nc2v+552DTd0WoazKjX5gO2cNjUHCyHlab67F6miPQMc/0xHAAwL5jNaioaoJO450RsgUBsFiccZrNNpeflHb1PekNeuIzwGDQu9zC5NE7+f7772PJkiW49tpr4XD8dGENHz4cS5YswfPPP+/Sftpuf1VXVyMx8aeRTKurq5Gamtpp/bi4uE5Pr1mtVjQ2NiImJgYRERHQarVISelYtQ8bNgytra2or69HVFSUy3n+nN2N30Z6isMhynJcfxHo+QM8B8z/4vnb7A7kH3XOLJ8Ub3C7A64oSZAkCaIDbm3rECVIogS7XbroE01XThiEg8X12Lq3HHNzkmBwo/9Ng9GMj384CQBYMC0ZKoUAhyi5FaunOQId84yN0CM6XIeaRjP2HqvFuLQYt/blKkEQIErO910URZdjduc96S189Rng0Y252tpaDB8+vMvXYmNjYTQaXdpPW3+j3Nzc9mVGoxGHDx9GdnZ2p/Wzs7NRWVmJkpKS9mV5eXkAgLFjx0KpVGLMmDHYt29fh+2OHj0Kg8GA8PBwl+IiIupNDp6oh9nqQHiIBjER/nmLMS0xHIPjQ2Gzi/hk+0m3tv3w+xMwWx0YFBuKCRmx3gnQRYIgIHNoPwDA/uI6WWOhnuVRQTRo0CBs3bq1y9fy8vIwaJBrjyNqNBosXLgQzz//PL766isUFBTggQceQFxcHGbNmgWHw4GamhqYzc5ZmzMzMzFmzBg88MAD2L9/P3bs2IHly5dj/vz5iI11/ie55557sG3bNrz88ss4deoUNm/ejL/+9a9YtGhRtztVExH5o10FztGTs4ZF++1IxYIg4JqpQwAAX+WXoaTyjEvbFZysx1e7nIMA/88lyT5/sqwro5KddxoOFNf1mVYY8rAgWrRoEd5880089dRT+PHHHyEIAkpKSrB+/XqsX78ev/zlL13e15IlS3DdddfhiSeewE033QSlUonXX38darUaFRUVmDJlCjZv3gzA+R/qlVdewYABA7Bo0SL89re/xbRp07BixYr2/U2YMAHr1q3DN998gzlz5mD16tW466678Otf/9qTVImI/JrN7sCes4/bj07pJ3M0FzZiSBSy02IgScCbnxdAvMhtILPVjj9u2A1RkjAxPRbDfTRNx8WkDgyHRq1AU7MVpdWcxqqv8KgP0fXXX4/6+nq8+uqr2LBhAwDgwQcfhFqtxuLFi3HTTTe5vC+lUomlS5di6dKlnV4bMGAAjh492mFZVFQU1qxZc8F9Tp06FVOnTnU5BiKi3qrtdllEqBZJ8QYcKPLv2zg3XjYMB0/U4UTFGXy9uwwzxw0877r/3HIMFXUtiDLosHCWd5/ocodapcTwxAjsK6rDgeI6v5gmhbrP4+7xd955J+bNm4e8vDyoVCqEhoYiMzOT/XSIiHyo7XbZuNQYv7iddDERoVosmJaMf35xDBu/KoQhWIPxwzv2C5IkCR9+fwJb95ZDEIC7r85AkBuPnvvCqOQo7Cuqw/6iOlyVkyR3ONQD3C6IPvnkE2zcuBH79u2D3e6crE+n02HMmDG46aabMHPmzB4PkoiIOrPZHdhb6Lxdlj3cO087ecMlYxJwstKIHw5U4q8fHYbF5sCkEXFQKhSw2Bz411fH8e1e53xlt1w5HGmDIvzuScORQ5z9iIpOG9FitiHYzwo2cp/LBZHD4cBDDz2E//73v4iNjcVVV12Ffv36QZIkVFZWIi8vD/fddx+uvvpqPPvss96MmYiI4LxdZrI4b5clJ4Sh1dJzM8p7k0IQcPuVwwEJ+OFgJf6+uQDvby3GgOhgHC9rgs0uQgBw65VpuO6yFDQ0+GZ2eXf0C9cjPioIFXWtOHSivlMrF/U+LhdEGzZswJYtW/D4449j4cKFnZ5kcDgc2LhxI/7whz9g3LhxuO6663o8WCIi+knb7bKxqdG94nbZuRQKAbfPGY7wUC2+3XMaxhYrDrc4B5SMMuhw42XDZH/E/mJGJUehoq4VB4rrWBD1AS4XRJs2bcKNN96IW265pcvXlUolbr75ZhQWFuKDDz5gQURE5EU2u9h+u2x8Wu/8MlYoBFw7PRlXTxmMQyfqUdNoQlpiBBKig/12+IBzjRoShc/zSnGguB6iJPW6opQ6cvmx+xMnTmDatGkXXW/q1Kk4duxYt4IiIqILO3TO7bIhCQa5w+kWlVKBzKH9MHPcQAyICekVxRAADBsYDq1GCWOLFaeqXBtXifyXywWRyWRCWFjYRdeLiIhAS4v/3e8lIupLdhZUAeidt8v6CpVSgfRBEQDg98Md0MW5XBBJkuTSSM8KhYIjdxIReZHF9tNgjNlemkuLOhKEth+hw89P03jUd3qtt7R0kZN3puklIiKv2XO8BmarA/3CdEhOuHjLPXWPUilAoVDgjMkOoOMv/EPOnv/i8iZUNbQiWN/x8XudVgVOGtU7uFUQrVixAiEhIRdcp7mZw5gTEXnT9oPO22U5GXG8XeYDSoUAk9WOotImWO2OTq9HhGrRcMaCz/NOdShQ1SoF0pMiEaJT885JL+ByQdQ2+/zF3tTg4GCMGzeue1EREVGXmpotOHjC2V9l0og4maMJLDa7CKutc0HUv18wGs5YUFJ5BgNjLtxoQP7L5YLorbfe8mYcRETkgtzDVZAkILm/AbGRQXKHQwASooNx6EQ9ymtbIEkS+w71Uh7Ndk9ERPL48VAlALYO+ZOYcD3UKgXMVgfqjGa5wyEPsSAiIuolSqubcaqqGUqFgGyOjOw3FAoB8VHO1rrTNRx2prdiQURE1Et8d3bC09HD+iFEz8lE/UlCv2AALIh6MxZERES9gMXmaL9dNj0rQeZo6Of6RzsLotomM8zW3jHJLnXEgoiIqBfYVVANk8WOfmE6DE+KkDsc+plgnRrhIRoAQHltq8zRkCdYEBER9QJb9zlvl03P6s+xh/xUQrTzkfvyWt42641YEBER+bnTNc0oLGuCQhAweWS83OHQebT1I2p7/J56FxZERER+7uvdpwEAWcP6ITxEK3M0dD4xEXqolXz8vrdiQURE5MeaTTb8cLACADBz7ACZo6ELUSgExPfj4/e9FQsiIiI/tnXPaVhtIgbGhCA1MVzucOgi+Ph978WCiIjIT9kdIr7YWQoAuHzcQE4J0Qvw8fveiwUREZGf2r6/AvVnLDAEqTEhPUbucMgF5z5+z1ai3oUFERGRH5IkCe9/exwAMGN0AtQqpcwRkavaHr8vrWqWORJyBwsiIiI/tK+wDkVlTdCqlbiMnal7lbZ+RGU1zRD5+H2vwYKIiMjPSJKED78vBgBcOnYAQoM0MkdE7jj38Xu2EvUeLIiIiPzMoZP1KDpthEalwJyJiXKHQ2469/H7QyfqZI6GXMWCiIjIj0iShI9+OAkAmJ2ThDAOxNgrDTjbj+hgcb3MkZCrWBAREfmRvYW1KCxrglqlwIJLhsodDnko4ezj96XVzajnqNW9AgsiIiI/4RBFvPdtEQDgivGJiArTyxwReUqvVSEmwvn+7SuslTkacgULIiIiP/HdvgpU1LUiRK/G3ElJcodD3ZQYGwrA2epH/o8FERGRHzBZ7Phwm/PJsqunDEaQTiVzRNRdibHOfkSHTzbAYnXIHA1dDAsiIiI/8MF3xTC22hAbocf0rP5yh0M9ICJUiyiDDnaHiEMn2bna37EgIiKS2YkKI77KLwMALJyVCpWSH819gSAIGDEkEgCw9zhvm/k7/q8jIpKRQxTx5n+PQgIwMSMWGYMj5Q6JetDI5CgAzn5EDlGUORq6EBZEREQy2rKzFCVVZxCsU+HGS4fJHQ71sKEDwhGiV6PZZMOxU41yh0MXwIKIiEgmp6rO4D9bnR2p/+eSoTAEc4qOvkapEDB6WD8AQP6xGpmjoQthQUREJAOrzYF1Hx2CQ5Qwelg/TBkVL3dI5CVjU2MAOAsiTvbqv1gQERHJ4F/fFKKirhVhwRrcdmUaBEGQOyTykvSkSOi1SjQ1W1FcbpQ7HDoPFkRERD72w4EKfLP7NADgjquGczb7Pk6tUiAz+exts6PVMkdD58OCiIjIh0oqz+DNz48CAH4xOQkjh0TJHBH5wtjUaABA/tEaSLxt5pdYEBER+UhjswWv/Gc/bHYRo5Kj8Ispg+UOiXxkxJAoaNVK1DaZUVzB22b+iAUREZEPmCx2/OndfagzWhAbocdd89KhYL+hgKFVKzE6xXnbbMehKpmjoa6wICIi8jK7Q8Srmw7iVFUzDEFqPHBDFoJ0arnDIh+bmB4LANh5pIqDNPohFkRERF7kEEWs++gQDp6oh0atwP3XZyImXC93WCSD9KRIhOjVMLbacKSkQe5w6GdkL4hEUcSaNWswdepUZGVl4c4770Rpael5129oaMBDDz2E7OxsjB8/HitXroTJZOpyXavVinnz5uGxxx7zVvhEROclihL+9vFh5B+tgUop4N5rRmJwvEHusEgmKqUC2WnOMYlyedvM78heEK1duxYbNmzAqlWrsHHjRoiiiMWLF8NqtXa5/pIlS1BSUoI33ngDL730ErZu3YoVK1Z0ue7//d//4dixY16Mnoioa3aHiL9+fAh5R6qhVAj49fyRfKKMMOHsbbP8YzWw2hwyR0PnkrUgslqtWL9+PZYsWYIZM2YgLS0NL774IiorK7Fly5ZO6+/Zswd5eXl47rnnkJGRgZycHDz11FP48MMPUVXVsdretm0bPvvsMwwbxrmBiMi3rDYH/vyfA+3F0N2/yEDW2ekbKLANHRCGKIMWZqsDu49zKg9/ImtBVFBQgJaWFuTk5LQvMxgMSE9Px86dOzutv2vXLkRHRyM5Obl92fjx4yEIAvLz89uX1dfXY9myZVi1ahUiIiK8mwQR0TlazDa8+O992FdUB7VKgfuuHYlxZ2+TECkEAZNHOqdp2bavQuZo6FwqOQ9eWVkJAIiP7ziHT0xMTPtr56qqquq0rkajQXh4OCoqfrqwHn/8cVxyySW49NJL8fe//73H4lWpfFc/KpWKDn8GmkDPH+A56I351zSa8MLGvSivbYFOo8SDN2QhbZBnv5S5m78gAIJCgPLsjzsUggBBEKBQAkqH69sqFQIEhQCVSoAk9fwQAl2dA0/z9DTH7mx7vvMzfXQCPv7hJI6UNKDhjAXREZ072QsCoLA581YoFABceyrN2++JL/n6M0DWgqitM7RG03HYeq1Wi6ampi7X//m6betbLBYAwMaNG1FUVIQXXnihR2NVKARERAT36D5dYTAE9tMogZ4/wHPQW/I/fKIOz/xjFxrPWBAVpsP/WzwRg/uHdXu/7uRvFVuh12ugUrv3SLdep4JKpYRep4FK5fq2apUCep0G4eFBbh3PXT8/B57k6WmO3dn2fOcnIiIYmSnR2HusBnlHa7DwyuFdbm8VWwEAOjeGaPDVe+JLvvoMkLUg0ul0AJx9idr+DgAWiwV6fecToNPpuuxsbbFYEBQUhOLiYqxevRqvv/46goJ69mIQRQlGY2uP7vNClEoFDAY9jEYTHI7AG68i0PMHeA56U/5f55fhrc+PwiFKGBgTgoduzEK4XoWGhhaP9mcXJZitIrRaFSwWO0Tp4vkrFAIcDgkmk9XtzrqCJMJud8BktsJqdX1bjVoJk9mKxkYJ3piNoqtrQBAAk9nmdp6e5tidbS90fiaPiMPeYzXYkluCK8cPhOJnrV2CAFgszmOZzTaILo5b5O33xJd64jPAYNC73MIka0HUdvururoaiYmJ7curq6uRmpraaf24uDh8+eWXHZZZrVY0NjYiJiYGmzdvRktLC26//fb2181mM3bv3o3PP/8ce/bs6Va8drvvP5QdDlGW4/qLQM8f4Dnw5/xtdhH//OIYvttXDgAYlxaDX80ZDq1G6XHMgiCgxWzD0VMNUKlVMJmscIgX/2YL0qkwKN4AUYRL659LlCRIkgTR4d62DlGCJEqw2yWvzs917jUgCAIkUYLj7I+rPM2xO9te6PyMGhKFEL0aDWcs2Hu8BqOSO3a6FwShvRAWRdHl4/rqPfElX30GyFoQpaWlISQkBLm5ue0FkdFoxOHDh7Fw4cJO62dnZ+P5559HSUkJBg0aBADIy8sDAIwdOxaTJk3CvHnzOmzz8MMPIy4uDg8//LCXsyGiQNJwxoK1mw6g6LQRAoBrZyTjygmJEHpoOg6bXYQkiLDaHC59GWrUvaevFTlvbU0aEYctO0vx9e7TnQoi8j1ZCyKNRoOFCxfi+eefR2RkJBISErB69WrExcVh1qxZcDgcqK+vR2hoKHQ6HTIzMzFmzBg88MADWLFiBVpbW7F8+XLMnz8fsbHOsR3Cw8M7HEOn0yE4OLi9gCIi6q49x2vw980FaDbZEKRV4e6rMzjGkAcuVjy2vSwIP63bl6Z/u2RMAr7YWYr9RXWorG9FXGTf6ffTG8laEAHOgRbtdjueeOIJmM1mZGdn4/XXX4darUZZWRkuu+wyPPPMM1iwYAEEQcArr7yClStXYtGiRdBqtZg9ezaWLVsmdxpEFACsNgf+9U0hvtl9GgCQGBuCe+aPQGwEv8jc5YCzb8yFCAoBVrEVJrMN0tlWMoVCcPF5K/8XGxGEUclR2FdUhy93lWLhrM5dRch3ZC+IlEolli5diqVLl3Z6bcCAATh69GiHZVFRUVizZo3L+3/rrbe6HSMRUVl1M9Z9dAina50dpWePT8Q104ZA7cPhOPoKQRBgNttw+GQ9bBfoG6JUCNDrNR36UbX1lRLQN5qKZmYPxL6iOvxwoBILpg3hpL8ykr0gIiLyZza7iM07SvDp9pOwOyQYgjVYPHc4RgzmLbLustnFCz4pplQIUKk79qPqa32l0gdFIKFfME7XtmDb/gpcMT7x4huRV/StK4uIqAcdL2vEir/n4cPvT8DukJCZHIWn7hjPYoh6jCAImDluAADgi12lsPv5EBN9GVuIiIh+ptVsw3tbi/HtHmdfIUOQGjfNTMH44TE99hQZUZtJI+KwadsJ1Bst+PFgJaZl9pc7pIDEgoiI6Cyb3YGv8k/j0+0n0WK2AwCmjIrH/1wyFCF69/t2eFI8sd4KPGqVErMnJOJfXxdi8/YSTB4ZB5VSKXdYAYcFEREFPFGUsP1QJT7YVox6o3MaoPioICyclYrhHs5F5spTVF3pS09RketmZCXg0+0lqG40Ie9INSaNiL/4RtSjWBARUcCy2R3YcagKn+8sRfnZp8ciQrWYP2UwJo2Mg1LhWTdLV5+i6krbU1R95CEqcpFWo8Ss7IH4z3fF+OTHk5iYHid3SAGHBRERBZzGZgu+2X0a3+w5jWaTsxUnSKvCVZMG4bIxA6BR98ztios9RdWVvvYUFbnu0jED8HneKVTUtWL7oUpkpUbLHVJAYUFERAHBZndgf1Eddhyuwt7jte2PcUcZtLhs7EBMy4w/7xgw7vYFYj8g8kSQToU5Ewfh3W+L8MF3xchI5tOMvsSCiIj6LFGUcORUA3IPVSH/WDVMlp9aa4YOCMOscQMxOqXfBW+NedIXiP2AyFOXjR2AL/PLUGc0Y9u+ciTGGeQOKWCwICKiPkWSJBRXGJF7qAp5BdUwtljbX4sI1WJCeiwmpsciMTb0ovvytC9QXxtNmXxHo1bi6imD8cZnBfh8RwlunZMud0gBgwUREfUJ9UYzvt9fgR8OVqCm0dy+PFinQnZaDCakx2LYwHAoPLif5W5fIPYDou6YPDKuvS9R3uFKjBoSKXdIAYEFERH1WqIo4UBxHbbuLce+olpIzm5B0KgVGDMsGhPSY5ExOBIqJQsU6j2UCgVuuHQY/vTuPuw5VoNBsSEejYNF7mFBRES9js3uwHf7KvDf3BLUnR03CABSBoZjemZ/jEmJhlbDge1IfoLQ1snevZbJrGH9kD44EodP1GPnkWpcMibBK/HRT1gQEZFHIypLbc0xPiSKEr4/UIFN24rR2OzsGxSsU2HyyHhMz+qP+Khgn8dEdD5KpQCFQoEzJjsA9/6/KBQCrpmejIKT9SitbsbpmhYkRPP69iYWREQBztMRlXVaFXzZBlN4ugn/3HIMJVVnADg7SF+VMwhTR8VDrWJrEPkfpUKAyWpHUWkTrHb3xqMK0qmQ1D8MmSnR2HO0BrsKqhEflQSFgh31vYUFEVEA8/QpKrVKgfSkSITo1B63FF2sVartZZvDgfe+KcJ/c09BAqDXqnD15CRcMmYA1Cr2DSL/150BOidkxOHIiXo0tVhx9FQjhid5NpUMXRwLIiLy6AO7O1xplRIUAkqqa/Dn9/eh4uy0Gjkj4nDDJUNhCNb4IEoi+ek0KoxJicaPByuxt7AWg/uHQqfhV7c38KwSkU+52ipVUnkGW/echtUuQq9VYvroBFyVk9StVimi3mjYwDAcKWlAwxkL9h6vxcQMznPmDWxvJiJZtLVK/fzHYrVjV0E1vthZCqtdRFykHnMnJWFAdIjcIRPJQiEIGD88BgBwrLQJ9UbzRbYgT7AgIiK/IYoSdhxyzjUGAJnD+uGK8YnQa9mYTYEtNjIIg+Kco6vvPFLNVlIvYEFERH7BIUrYurccx8uaIACYmBGLKZkJHZ6qaRvTRRAEt36I+oKxqdFQKgRUNZhwqqpZ7nD6HP7aRUSyc4gitu4pR1lNCxQKAdMy4zE4vuOklt0Z08XXQwQQeUOIXo2MwZHYX1SHXQXVSIgO5ijsPYgFERHJyiFK+HZPOU7XtECpEHDJmAT079d5ADpPx3TpiSECiPzFiCGRKDzdhBazHYdP1GPU0H5yh9RnsLQkItmIkoTv91e0F0OXju26GDrX+Tpjn+/HnfGVyH2e3caUO+reS6VUYGxqNADgQHE9WkzuD6pKXWMLERHJQpIk7DhYhZLKM1AIzpYhTr3Ru3h6G1OhEMAy1XNJcaE4eqoR1Q0m7D5Wg6mZ/eUOqU9gQUREsthzrBaFp50dqKdmxl+0Zag7PJ1gky0ZF+bpbcwgnQqD4g0Q3Hw/yEkQBGSnxeDT7SU4UXEGaYkmREfo5Q6r12NBREQ+t6ugGruP1QAAJmTEtj9O7A3dnWCTLRkX5+5I523TUpDnosJ0GJoQhsLTTdhzvBazxg+UO6RejwUREfnU8bJG/HPLUQBAelIEUgaGe/V43Z1gky0Z5K9GDY1CcbkRlfWtqKhr4S3nbmJBREQ+U9Nowsvv74fdIWFQbCjGnO0c6gvdmWCTyB+F6NVIGRiGglON2Hu8FnGRQXKH1KvxfzsR+USr2Y6X3tuPM602DIgJwYwxCVCwkw5Rt4xMjoJSIaCm0YzTNS1yh9OrsSAiIq9ziCL+8uFBlNe2IDxEi7uvzoBaxY8fou7Sa1VIGxQBANhfVMextrqBn0hE5HXvfHkcB0/UQ6NW4P7rRyE8RCt3SER9RnpSBJQKAbVNZlTUtcodTq/FgoiIvOrLXaX4evdpCADunJuBpDjDRbchItfptSoMHRAGAO0TI5P7WBARkdfsL6rFO18dBwBcNyO5fYRdIupZGYMjIQhAeW0LSirPyB1Or8SCiIi8oqy6GX/58BAkCZgyKh6zJyTKHRJRnxWiV7dPiLxl5ymZo+mdWBARUY9rarbgpff2wWx1IC0xHLdekQqBT5QRedWIIZEAgAOFdahuMMkcTe/DgoiIepTV5sDL/zmAOqMFsZFB+PU1I6FS8qOGyNvCQ7QYEB0MCcBX+aVyh9Pr8FOKiHqMKElYv/kIisuNCNap8NvrRiFEr5Y7LKKAkTHY2Uq0bX85zFa7zNH0LiyIiKjHfPBdMfKOVEOpEPCbBSMRy5FziXxqQEwIYiL0MFkc+OFApdzh9CosiIioR3yz5zQ+3V4CALh1dipSEyNkjogo8AiCgGlZ/QEAX+aXQeRAjS5jQURE3bbnWA3ePjth6/wpgzF1VH+ZIyIKXBPSY6HXKlFV34ojJxvkDqfXYEFERB4RBOdPcbkR6z5yPl4/Pas/fjFlMARBuMCP3JET9W06jQo5GfEAgG/3npY5mt6Ds90TkduUSgEKhQJF5Ub86d19sNpFZAyOxDXTk9FiuXBHToVCgOijOIkC1YzRCfh6dxn2Hq9FU7MFYZwu56JYEBGR25QKARV1LXjp33vRbLIhOlyHcWkxOFhcd9Ftg3QqDIo3QACbioi8ZWBMCJITDCg6bcT3BypwVU6S3CH5Pd4yIyK3NZtsePU/B2BstSE0SI0ZoxMgSRKsNsdFf+wOtg8R+cKMrAQAwNa95exc7QK/KIhEUcSaNWswdepUZGVl4c4770Rp6fkHlWpoaMBDDz2E7OxsjB8/HitXroTJZOqwv9deew1XXHEFsrKycNVVV+Hdd9/1RSpEfV6r2Y6Ptp1AbZMZoUFqXJ49EHotG5uJ/M24tBjotSrUNplx+ES93OH4Pb8oiNauXYsNGzZg1apV2LhxI0RRxOLFi2G1Wrtcf8mSJSgpKcEbb7yBl156CVu3bsWKFSvaX1+3bh3WrVuH+++/Hx999BFuvfVWrFixAps2bfJNQkR9lMlixxc7S9HYbEVEqBZX5SRx4EUiP6VVKzEpIw4A8P2BCpmj8X+yF0RWqxXr16/HkiVLMGPGDKSlpeHFF19EZWUltmzZ0mn9PXv2IC8vD8899xwyMjKQk5ODp556Ch9++CGqqqoAAO+88w7uuOMOzJkzB4mJibjhhhtw9dVXs5WIqBtMFju27CxFU4sVIXo17lkwEqFBGrnDIqILmDzKWRDtPlaLVrNN5mj8m+wFUUFBAVpaWpCTk9O+zGAwID09HTt37uy0/q5duxAdHY3k5OT2ZePHj4cgCMjPz4coinjuuedwzTXXdNhOoVDAaDR6LxGiPuxMqxX/zT2FpmYrgrQqXD01CVFhOrnDIqKLGBQbioR+wbA7ROQVVMsdjl+TvSCqrHQOLR4fH99heUxMTPtr56qqquq0rkajQXh4OCoqKqBQKJCTk4O4uLj218vLy/Hpp59iypQpXsiAqG+rbTLjsx2ncKbVhhC9GrPGD+QjvES9hCAImDzS+Z35A2+bXZDsPSHbOkNrNB2b3rVaLZqamrpc/+frtq1vsVg6La+trcWdd96JqKgo3HPPPd2KVaXyXf2oPDs7uDJAZwkP9PwB35wDQQAEhQDl2Z+fK6tuxjd7TsPukBBp0OLy7IEI0qqgODvIokIJKB3uPT7v6rYKheKcP0WPj+mLWL1yzLMjWLbl75Nj+ihPT68BOWKV9Zhw7xoAnENiCAoBKpUASXJuP2VUPN79thBFp42oaTIhPirYrVjk4uvvAdkLIp3O2exutVrb/w4AFosFer2+y/W76mxtsVgQFNRxIsni4mLcddddcDgcePPNN2EwGDyOU6EQEBHh+4vIYOh8DgJJoOcPeP8cWMVW6PUaqNQdP3CPnKzHN/llkCTnmCazc5KgUSsBAHqdCiqVEnqdBiqVe4/Ru7utTqfu1jF9GWtPHlOrVcNmF9vz98UxfZWnp9eAHLHKeUzl2V/CXb0GAECtUkCv0yA8/Kfvw4iIYIxJjUF+QTXyj9fhlqExbsUiN199D8heELXd/qqurkZiYmL78urqaqSmpnZaPy4uDl9++WWHZVarFY2NjYiJ+elNzs/Pxz333IPY2Fi89tpriI2N7VacoijBaGzt1j7coVQqYDDoYTSa4AjAcVsCPX/AN+dAEACT2QaTyQqrzQHAea3vLKjG4bNzICUnGDB5ZDzsNjvsNuco1IIkwm53wGS2wmp1uHdMF7dVKBTQ6dQwm20QRdHjY/oiVm8c02KxQaFUtufvi2P6Kk9PrwE5YpXzmA67M2dXrwEA0KiVMJmtaGyUcO7QQxPTY5FfUI0v80owZ8LA9hZIf9YTn4EGg97lFibZC6K0tDSEhIQgNze3vSAyGo04fPgwFi5c2Gn97OxsPP/88ygpKcGgQYMAAHl5eQCAsWPHAgD279+PxYsXIz09Ha+++mq3WobOZbf7/ovZ4RBlOa6/CPT8Ae+eA0EQIIkSHGd/TBY7tu4tR3WD81b2yOQoZA2NcsYh/vTpKkoSJEmC6Oi43BWub+vMWRRFOETJ42P6JlYvHFOSoMBP+fvkmD7K09NrQI5YZT0mzubs4jUAOI8jiRLsduex24waEokgrQr1RgsOFtUhPSnSrXjk5KvvAdk7aGg0GixcuBDPP/88vvrqKxQUFOCBBx5AXFwcZs2aBYfDgZqaGpjNZgBAZmYmxowZgwceeAD79+/Hjh07sHz5csyfPx+xsbGw2+14+OGHERUVhWeffRYWiwU1NTWoqalBfT0HpiI6n5oGEz75sQTVDSaolQrMGN0fo4f1g9ALfpMkogtTq5QYn+68U/LDgc4PLJEftBABzoEW7XY7nnjiCZjNZmRnZ+P111+HWq1GWVkZLrvsMjzzzDNYsGABBEHAK6+8gpUrV2LRokXQarWYPXs2li1bBsDZOlRSUgIAmDlzZofjJCQk4Ouvv/Z5fkT+TJIkHCyuQ97hKogSEBaiwYysBISFcIwhor5k8og4fLvnNPKPVWOhJYUjzP+MX5wNpVKJpUuXYunSpZ1eGzBgAI4ePdphWVRUFNasWdPlvsaMGdNpfSLqmrHFinUfH2of1n9QbAgmjYyH2odPVBKRbwzpb0BsZBCq6lux62g1po7qL3dIfoWfekQB6kBxHZ58PReHT9RDqRAwfngMpmX1ZzFE1EcJgoApI51j9PG2WWd+0UJERL5jszvw/tZibNnpnEA5PioIOSPiOCcZUQDIyYjDf7YW41hpI2obTegXzqFN2vBXQaIAUlxuxIq/72wvhmaOHYCHfzkakQZOw0EUCCINOqQNigAAbD/EVqJzsYWIKADYHSI++uEENm8/BVGSEBaswW1XpiFrWDSaOeEjUUCZNCIOR0oa8OPBSsydlMQnSc9iQUTUx52qOoPXPjmCsppmAMCE9FjcfHkKb5ERBaixqdF4a8tRVDWYUFxuRHJCmNwh+QUWRER9lN0hYvOOEnz8w0k4RAkhejVuvSIV49J617D9RNSzdBoVxqbEYPuhSvx4sJIF0VksiIj6oNO1LXj9k8M4WXkGADA2JRq3XJEKQzDHFiIi522z7YcqkXekCjdeNoxPl4IFEVGfIooSPt95Ch98dwJ2h4ggrQo3z0rBxPRY9hMgonbDB0UgPESDxmYr9hfVYWxqtNwhyY4FEVEfUVXfitc/PYLC000AgFHJUVg0Ow0RoVqZIyMif6NQCMjJiMNnuafw48EKFkRgQUTU64mShK/zy/Det0Ww2kXoNErcdNkwTBkVz1YhIjqvnBHOgmh/UR2aTbaAf9CCBRFRL1bbaML6zUdQcKoRgLMZ/PY5aegXxsHWiOjCBkSHIDE2BKeqmpF3pAqXjhkgd0iyYkFE1Au1tQq9v7UYFpsDGrUC/3PJUMwYnQAFW4WIyEWTRsTjVNVx/HiwkgWR3AEQkXsq6lrw988KUFjm7CuUMiAMd1w1HLGRwW7vi7UTUWCbkB6Lf39diOJyIyrqWhAf5f7nSF/Bgoiol3CIIv6bewoffn8SdocIrUaJ62ckY8boBEiC4NGI0wqFANELsRJR7xAWrMGIIZHYX1SHHw9W4trpyXKHJBsWRES9wKmqM/j75gKUVDnHFRoxOBK3zk5FvzA9hLPF0OGT9bDZ3StvgnQqDIo3QACbiogC1ZSR8dhfVIcfDlRg/tTBUCoCc0wiFkREfsxstePfXxfisx0lcIgSgnUq3HjZMEwaEdfpCTKbXYTV5nBr/xp1YH7wEdFPsob1Q4hejcZmKw4W1yNzaD+5Q5IFCyIiP7XnWA3++cUxVDeYADhHm144KwVhIRxXiIh6jkqpwKQRcdiysxTb9lewICIi/1DXZMaGL49hz/FaAECUQYdfzhyG0SkcOI2IvGPKqHhs2VmKfYW1aGqxIiwAp/lhQUTkJyxWBz7LLcF/c0/BahehVAiYPz0Zs7MHQqlgHx8i8p4B0SEYHG/AiQojth+sxOwJiXKH5HMsiIhkJkoSth+sxPtbi9DYbAUApAwMx21z0jAyJRYNDS2wu9lZmojIXVMz43Giwoit+8pxxfiBATfSPQsiIhkdKWnAu98Uts9K3y9Mh/+5ZCjGpkZDrVbKHB0RBZIJw51jElXVt+JwSQMykiLlDsmnWBARyaCwrAn/+a6ofcoNnUaJeZOSMHPcAKhVLISIyPf0WhUmjYjD17tP45vdp1kQEZH3nKgwYtO2EzhQXAcAUCoETM/qj3mTBwdkJ0Yi8i+XjE7A17tPY8/xGtQbzYg06OQOyWdYEBF5mSRJOHyyAZ/lluDwyQYAgEIQMGVUHOZOSuJErETkNxKiQ5A6MBxHSxuxdW85rpk2RO6QfIYFEXnE0852kiT1cCT+yyGK2FlQjf/uOIVT1c0AnIXQhPQY/GLKYMRGBHW5Xdu5bTvFgnDh8x1g/R6JyMsuHTvAWRDtK8fcSUlQqwJjAFcWROQ2BwCzB/NmAYBOq0Jf7yFT22TCd/sqsG1/OZrOPjWmUSswbVR/zMoeiH7h528ROvfcCgoBVrEVJrMNknj+QpLzkRFRTxo9rB8iQrVoOGPBjkOVmJrZX+6QfIIFEblFEASYPZw3S61SID0pEiE6dZ9rKbLZRewvqsN3+8pxsLgObdkZgtS4bNxAXDp6AEKC1BfchyAALaafzq1SIUCv18BkssJxgYKI85ERUU9SKRW4fNxA/PubQvw37xQmj4qHIgCaolkQkUc8mTerr7E7RBw6UY+dBdXYc7wGJstP52N4UgRyMuIwMjkKKqWzuflis9G3tfS0nVulQoBK7fz7hQoizkdGRD1telZ/fPzjCVTUtWJ/UR2yAmA6DxZERG5oOGPBoRP1OHSyHgeK6tBqsbe/FhGqxcT0WEzPSkBwkBqHT9bj0Il6l/fNlh4i8hd6rQrTsxLw39xT+G/uKRZERIFMkiRUN5pwosKI4tNGHClpwOnalg7rhAVrMC4tBuOHxyA5IQwKQYAgCGg229xuRWNLDxH5k8vHDcQXO0txrLQRhWVNGDogTO6QvIoFERGAFrMNFXWtqKhrQUVdK8qqm3GiwogWs73DegKApHgDMgZHYsTgSAxNCIOC84wRUR8UEarFpBFx2La/Ah9sK8bSm0bLHZJXsSCiPk+SJLSY7ahrMqPOePanyYz6s3+vbTLjTGvX/XtUSgGJsaEYHGdASmI4hg+KQIj+wp2jiYj6inmTk/DjwUocKWnAkZP1GN6HR69mQUS9xvnG4rE7RDScsbQXOnXGs8VOe9FjgcWFW1cRoVrERwUhPjIY/fsFISnegIExIe2doomIAk2/MD1mZCXgq91l+M+2YvxuUESfnfSVBRH1CjZRQkVtM6obW1HTYEZ1QyuqG02oaTChzmiGK0/xG4LUiArTIcqgQ6RBh6gwHfqd/XtMhB56Lf87EBH93FWTBmHb/nIUnTZiXx9+4ozfAORXWs12lNe2oLKhFY0tNpRUNKGirhXVDa2wO85f9SgVAkL0agTr1Qg55ycsRIOsYdEY0C/Yo9FWPflNqI/+8kREASo8RIvLxg7AZ7mn8K+vjiMjKbJPjl7NgohkYbI4C5/TtS0d/mw4YznvNgqFgNAgNQxBGhiCnX+GBmtgCNJAr1V2Wbxo1ErERuqh1ShdakU6l12SPBqRmyNHE1FfM3dSEn48VImqBhP+m1uCeZMHyx1Sj2NBRF5nttpR12RBY7MFeYercLqmBXVG83nXjwjVIiE6GEn9wxARrEFMuB4hwWqUVjfD7ubo2EqlAIVCgTMmOwDXKyKFQoBdlFDgwYjcHE+IiPoavVaFGy4dir9+dBifbC9BTkbcBach6o1YEFGPazbZUFXfisr6VlQ3mM77BFdYsAYJ0cHo3y8YCf2CkdAvBP37BSFIp4ZKpUBERDAaGlrgcEhoNttwuqaly/1ciFIhwGS1o6i0CVa762MCtRU1drvk9ojcHE+IiPqiCcNj8d3echScasRbW47ht9eP6lMdrFkQUbc5RBGVda0orW5GeW0rmk2dCyBDkBrREXpkDu2HlAHhSIgOueDj6x1neu9+jBwkkYioewRBwMJZqVjx9504UFyHr/LLMHPcQLnD6jEsiMgjNruI4vImnKpqRnltS4cOz4IARBl0iI0MQlxkEKLDddColdDrVMgcGg2HQwIgXXBur3NnehcA9skhIvID/fsF438uScaGL4/j398UITUxAgNjQuQOq0ewICKXSZKEwtNN+GbPaew6Ug2b46cyRa9VYWBMMAbEhCA2IqjLJxDcuX117kzvWo2SfXKIiPzEZWMH4NCJeuwrqsNfPjyIx28ZhyBd7y8nen8G5HUNZyz48WAFvj9Qiar61vbloUFqDD47eGGkQevyvWRXbl+dO9O7UslCiIjIXwiCgNuvGo4V6/NQUdeKP39wAA/8T2avH8SWBRF1yWYXsbewFt/vr8DBE3Xtj6xr1UpkDeuHfuE6RIa6XgQREVHfYQjS4P7rMvHsht04UtKA9Z8eweJ56VD04u8EFkTUTpIknKpqxvf7K7DjcGWHiU2HDQjDlFHxyE6LhUOSsK+w1u2nr4iIqO8YFBeKe68ZgZfe3Y8dh6sAAbhjzvBe21LEgohgbLUi91AVtu2vQFlNc/vytpmOp4yMR2xkEABnU+mFOkMTEVHgGDE4CovnpuO1Tw5jx6EqGFusuPeakb1yKqTeFzH1CLPVjj3HarHjcBUOnaiHePaemEqpwJiUfpgyMh7pSZFQKHpv8ycREXnfhPRYBOtU+PMHB3H4ZAP+3/o83DkvHcMGhMsdmltkL4hEUcQrr7yCd999F2fOnEF2djaWL1+OgQO7HtugoaEBv//97/Hdd99BEARcddVVeOSRR6DX/zRi5meffYaXX34ZZWVlGDJkCB599FHk5OT4KiW/1Wyy4WBxHfYW1mJvYS2stp+eEkuKC8XkkfGYkB57wfGBiIiIfm7EkCg88svRWPvBAdQ2mfHsP3dj5tiBmDtpEEKDNHKH5xLZC6K1a9diw4YNePbZZxEXF4fVq1dj8eLF+Pjjj6HRdD6JS5YsgclkwhtvvAGj0YjHH38cra2teO655wAAO3bswNKlS/HII49g8uTJeO+993DXXXdh06ZNSE5O9nV6srI7RJRWN+PwSefjkUWnmzrM5xUTocfE9FhMSI9FfFSwfIESEVGvNzjegJV3TMA7Xx7DDwcr8cWuUmzbX46Z4wZgemYCosJ0cod4QbIWRFarFevXr8fDDz+MGTNmAABefPFFTJ06FVu2bMHcuXM7rL9nzx7k5eVh8+bN7cXNU089hcWLF+PBBx9EbGws/va3v2HmzJm49dZbAQCPPvoo9uzZg3/84x946qmnfJqfL1ltDlQ3mFBe14LiciOKy404WXkGdkfHIQ0TooMxKjkKY1NiMDg+lE+JERFRjwnSqfCruekYnx6L97cW4VRVMz75sQSf/liC9KQIZA7thxFDohAbofe77x9ZC6KCggK0tLR0uJ1lMBiQnp6OnTt3diqIdu3ahejo6A4tPePHj4cgCMjPz8fs2bOxe/duPPbYYx22mzBhArZs2eLdZHqYJDnn0DK2WNHcaoXZ6oDJYoex1YamZguaWqwwtlhRZzSjqr4VdcauZ4kP1qkwbEA4Rg6JxMjkKPQL61uT8RERkf8ZOSQKGYMjsftoDb7ZcxpHShpw6KTzBziOEL0ag2JDEN8vGDHhegwbEI5BcaGyxixrQVRZWQkAiI+P77A8Jiam/bVzVVVVdVpXo9EgPDwcFRUVMBqNaG1tRVxcnEv7c4dCISAysmdvK5mtDrSabc452KW2udglSBLgkIB6oxlavQZavWv3XwVBgFIhQK1SQKVUQK1SQOmFTtFhkoSYfiEdbr+5QiEAKpUCA2INF91WEAABAiRIEOD6dt05Zk9s15PHPPccXGg//hCrN4758/z9OVZvHdOV97+nj+mLPD29BuSI1T+O6do1ADjPmVql8JvxgGZNCsGsSYPhECVYbA5YbQ7Y7F1PxhQepusQd9tfw8L0bp+/Nu48GCRrQWQymQCgU18hrVaLpqamLtfvql+RVquFxWKB2Ww+7/4slq5bUFwlCEKPj5gcrFcguBd2YFYCUKuUHm/v6bY8pne2C5Rj9qZYA+WYvSlW+Y7p8aZ+RakENGrPzoNC4ZtxjWQdPUmnc3awslqtHZZbLJYOT42du/7P121bPygoCFqt1q39EREREQEyF0Rtt7+qq6s7LK+urkZsbGyn9ePi4jqta7Va0djYiJiYGISHhyMoKMjl/REREREBMhdEaWlpCAkJQW5ubvsyo9GIw4cPIzs7u9P62dnZqKysRElJSfuyvLw8AMDYsWMhCALGjBnTvqxNbm4uxo0b56UsiIiIqLeT9e6kRqPBwoUL8fzzzyMyMhIJCQlYvXo14uLiMGvWLDgcDtTX1yM0NBQ6nQ6ZmZkYM2YMHnjgAaxYsQKtra1Yvnw55s+f394CdPvtt+Ouu+5Ceno6pk2bhvfffx9HjhzB008/LWeqRERE5McESfK073bPcDgc+OMf/4j//Oc/MJvN7SNVDxgwAGVlZbjsssvwzDPPYMGCBQCAuro6rFy5Etu2bYNWq8Xs2bOxbNmy9v5DALBp0yasXbsWlZWVGDp0KJYuXcqRqomIiOi8ZC+IiIiIiOQmax8iIiIiIn/AgoiIiIgCHgsiIiIiCngsiIiIiCjgsSAiIiKigMeCiIiIiAIeCyIiIiIKeCyIfGjdunW45ZZbOizbtm0brr32WowePRrz5s3DJ5980uH1lpYWrFy5ElOmTMG4ceNw5513oqioqMM6t99+O1JTUzv8/Pw4/sCT/GtqavDggw9i4sSJmDRpElatWoXW1tYO63z22WeYM2cORo0ahfnz52P79u1ez8UT3sp/1qxZnd7/xx57zOv5uKKxsRHLly/HtGnTMGbMGNx0003YtWtX++vbt2/HggULkJmZidmzZ+PTTz/tsL3FYsHKlSuRk5OD0aNH46GHHkJ9fX2HdS62D7n54hz482dAd/M/1/Lly7u8tvv6NXCu852DvnwNVFRU4MEHH8TkyZORnZ2NX/3qVzh+/HiHdXrke0Ain3j77beltLQ0aeHChe3Ldu3aJaWmpkpPPfWUVFhYKH3yySfS6NGjpQ8++KB9nUcffVS68sorpfz8fKmwsFC6++67pRkzZkhms7l9nZycHGnDhg1SdXV1+09DQ4MPs7s4T/K3Wq3S3Llzpblz50o7d+6UDh48KN1www3SokWL2vexfft2KSMjQ/rHP/4hFRYWSs8++6w0YsQIqbCw0McZXpi38m9paZHS0tKkb775psP7bzQafZxh126//fb2+IuLi6WVK1dKo0aNkoqKiqTCwkJp5MiR0h//+EepsLBQeu2116T09HTpxx9/bN/+sccek2bOnCnt3LlT2rdvnzR//nzp5ptvbn/dlX3IzdvnQJL8+zOgu/lLkiQ5HA7phRdekFJSUqRHH320w2uBcA1I0oXPgST13WvAYrFIc+fOlRYuXCjt379fOnbsmHTfffdJOTk5Ul1dnSRJPfc9wILIyyorK6W7775bysrKkmbPnt3hC/Gee+6Rrr/++g7rr127Vrrkkkva/z127FjpzTffbP/3kSNHpJSUFOngwYOSJElSbW2tlJKSIh06dMjLmXimO/l/8cUXUkpKSoeLuqKiQkpNTZVyc3MlSZKkO+64Q7r//vs77OOGG26QnnzySS9l5B5v579v3z4pJSVFamxs9EE27jl58qSUkpIi7dq1q32ZKIrSzJkzpT/96U/Sk08+KV133XUdtnnwwQelO+64Q5Ik57lLS0uTvv322/bXi4uLpZSUFGn37t2SJEkX3YfcfHEO/PkzoLv5S5Kz4LnhhhukiRMnSjNmzOhUDPT1a0CSLn4O+vI18MMPP0gpKSlSZWVl++tms1nKzMyU3n33XUmSeu57gLfMvOzQoUNQq9X46KOPkJmZ2eG1kpISjB07tsOy9PR0nD59GuXl5QCAqKgobN68GXV1dbBarXjvvfcQHh6OxMREAMDRo0chCAIGDx7sm4Tc1J38T548iYiICCQnJ7e/HhcXh4iICOTl5UEURezevbvTPHUTJkzAzp07vZeUG7yZP+B8//v164ewsDDvJ+OmiIgI/PWvf8XIkSPblwmCAEEQYDQasWvXrk7v3cSJE5Gfnw9JkpCfn9++rM3gwYMRGxvb/v5ebB9y88U58OfPgO7mDwA7duxAcnIyPvnkEwwYMKDTMfr6NQBc/Bz05Wtg2LBh+Otf/9o+gTsAKBTO0sVoNPbo9wALIi+79NJL8fLLL2PgwIGdXouJiUFFRUWHZWVlZQCck9gCwNNPP42qqipMmjQJWVlZ2LRpE/72t78hNDQUAHDs2DGEhobiqaeewrRp0zB79mz86U9/gtVq9XJmrulO/jExMThz5gyam5vbX29ubkZTUxPq6+thNBrR2tqKuLi4TvutrKz0Qjbu82b+gPODMCgoCEuWLMGUKVMwb948vPHGGxBF0YtZucZgMGD69OnQaDTtyz7//HOUlJRg6tSpqKys7PK9M5lMaGhoQFVVFSIiIjpM3Ny2Ttv7e7F9yM0X58CfPwO6mz8A3HzzzXj66acRFRXV5TH6+jUAXPwc9OVrIDo6GtOnT+/w+ltvvQWz2YzJkyf36PcACyIZXX311diyZQs++ugj2O12HDlyBOvXrwcA2Gw2AM4vvIEDB+Lvf/87NmzYgAkTJuA3v/lN+xfpsWPHYLFYMGrUKLz22mu455578O677+KJJ56QLS9XXSz/adOmITQ0FI8//jgaGxvR3NyM//f//h8EQYDNZoPZbAaADv/RAECr1cJisfg8H3d1N38AOH78OIxGI6644gq8/vrruOmmm/DSSy/h5ZdfljO1Lu3evRvLli3DrFmzMGPGDJjN5k7vXdu/rVYrTCZTp9eBju/vxfbhb7xxDnrTZ4C7+buir18Drgika+CLL77ACy+8gNtuuw2pqak9+j3AgkhG8+fPx7333osnn3wSI0eOxL333ovFixcDAEJDQ7F3716sWrUKzzzzTHsL0Z/+9CdoNJr2L86nnnoK27Ztw0033YSUlBRcffXVePzxx/Hhhx+itrZWzvQu6mL5h4eH49VXX0VBQQEmTpyIKVOmID4+HhkZGQgJCWn/rfnn/2ksFgv0er3P83FXd/MHgL/97W/48ssvcdVVVyE1NRW//OUvcc899/hNK1GbL7/8EnfccQeysrLw/PPPA3B+YP38vWv7t16vh06n6/ID8dz392L78CfeOge95TPAk/xd0devAVcEyjXwzjvv4P7778e8efPwyCOPtG9/7jZtPPkeYEEks3vvvRe7d+/Gt99+iy+//BL9+/eHUqlE//79kZ+fj6ioKPTv3799fbVajfT0dJSUlAAAVCpVp/4jw4YNAwC/uW10IRfKHwBGjx6Nzz//HD/88AN27NiBhx9+GKWlpUhMTER4eDiCgoJQXV3dYZ/V1dUd7jf7s+7kDzh/K2q7fdomJSUFra2taGpq8nk+XXn77bdx33334ZJLLsFf/vKX9g+w+Pj4Lt+7oKAghIaGIi4uDo2NjZ0+6M59fy+2D3/hzXPQGz4DPM3fFX39GnBFIFwDq1evxooVK3DrrbfimWeeae9H1JPfAyyIZPT2229j1apVUCqViI2NhUKhwOeff47Ro0cjODgYcXFxaGho6PBGi6KIwsJCJCUlAQBuueUWLFu2rMN+Dxw4ALVa3b6Ov7pY/iUlJbjxxhtRX1+PqKgo6HQ67Nq1C42NjZg0aRIEQcCYMWPaOxi3yc3Nxbhx42TKynXdzV+SJMycOROvvPJKh/0eOHAA0dHRiIiIkCmzn2zYsAGrVq3CzTffjD/+8Y8dmrXHjRvX6b3bsWMHxowZA4VCgbFjx0IUxfaOxQBw4sQJVFVVITs726V9+ANvnwN//wzoTv6u6OvXgCv6+jWwevVqvPbaa3j00Ufx2GOPQRCE9nV79HvArWfSqFseffTRDo9d//jjj1J6err0wQcfSKWlpdK6deukjIyM9keqW1papFmzZkk33HCDtHfvXqmwsFD63e9+J2VlZUmlpaWSJEnSW2+9JQ0fPlzasGGDdOrUKenTTz+VJkyYIP3xj3+UJccLcTd/m80mXXrppdJvf/tb6cSJE9KOHTukadOmSU888UT7PrZt2yYNHz5cWr9+vVRYWCg999xz0qhRo/xuHCJJ8k7+zz77rJSVlSV9+umnUklJibRx40Zp1KhR0r/+9S+f5/dzxcXFUkZGhnTvvfd2GBulbZykY8eOSRkZGdLq1aulwsJC6fXXX+80/sqDDz4oXXrppdKOHTvax+A59xy6sg85+eIc+PNnQE/kf66FCxd2euQ8EK6Bc3V1DvryNbBjxw4pJSVFWrVqVaftm5ubJUnque8BFkQ+9PMvREmSpHfffVe6/PLLpVGjRkkLFiyQvvvuuw6vV1ZWSg8++KA0efJkady4cdLtt98uHTlypMM6b7/9tnTllVdKI0aMkC655BLp1VdflRwOh9fzcZcn+RcWFkqLFi2SsrKypGnTpkl//OMfJZvN1mGdDz74QLr88sulkSNHStdcc43ffBD+nDfyt9ls0iuvvCJddtllUkZGhnTFFVf4RTEkSZL06quvSikpKV3+tH2gb926VZo7d640YsQIafbs2dKnn37aYR8tLS3S448/Lo0bN04aN26c9OCDD0r19fUd1rnYPuTkq3Pgr58BPZH/uboqBtzdh6/56hz01WvgiSeeOO/2a9asaV+vJ74HBEnyg4EaiIiIiGTkHzdYiYiIiGTEgoiIiIgCHgsiIiIiCngsiIiIiCjgsSAiIiKigMeCiIiIiAIeCyIiIiIKeCyIiKhXW7ZsGVJTU/H99993+fq2bduQmpraPpkkEVFXODAjEfVqRqMRV111FdRqNT755BMEBQW1v9bc3Ix58+YhNDQU7733Xoc5lIiIzsUWIiLq1QwGA1auXInTp0/jxRdf7PDaCy+8gJqaGvzf//0fiyEiuiAWRETU61166aWYN28e3n77bezbtw8AkJ+fj3feeQdLlixBWloaysvL8eCDD2L8+PHIzMzEokWLcPjw4Q77KSsrwyOPPIIpU6YgIyMDOTk5eOSRR9DQ0NDhWH/4wx+waNEijBo1Co8//rhPcyUi7+AtMyLqExobG3HVVVchPj4eGzZswLXXXovg4GD885//RFNTE+bPnw+9Xo/f/OY30Ov1+Mc//oGDBw/ivffeQ3JyMkwmE6666ipERETgf//3fxEaGoo9e/bglVdewbXXXounnnoKgLMgqqqqwu23346JEyciODgYo0ePljl7IuouldwBEBH1hPDwcKxYsQK/+c1vcMcdd6CsrAybNm2CUqnEP/7xDzQ2NuKdd95BQkICAGDatGmYM2cOXnrpJaxZswYnT55EXFwcnnvuOQwcOBAAMHHiROzbtw95eXkdjtW/f388/PDDPs+RiLyHBRER9RmXX3455syZg82bN2P58uUYNGgQAGD79u0YPnw4YmNjYbfbAQAKhQLTpk3DRx99BAAYPnw4NmzYAFEUcfLkSZSUlKCwsBDFxcXt27QZPny4bxMjIq9jQUREfcrUqVOxefNmTJ8+vX1ZY2MjSkpKkJGR0eU2JpMJer0ef//73/GXv/wFjY2N6NevH0aMGAG9Xo8zZ850WP/cJ9mIqG9gQUREfV5oaCjGjx+PRx55pMvXNRoNPv74Yzz77LNYunQpFixYgMjISADA/fffjwMHDvgyXCKSAQsiIurzxo8fj48//hiDBw9GSEhI+/Lf//73sNlsWLlyJfLz82EwGLB48eL211taWpCfnw+Vih+VRH0dH7snoj7vtttugyiKuO2227B582Zs374dTz75JN566y0MHjwYADBq1CgYjUY8++yzyM3Nxccff4ybb74ZtbW1MJlMMmdARN7GX3uIqM+LjY3Fxo0b8cILL2DFihWwWCxISkrC008/jeuuuw4AcM0116CsrAzvv/8+NmzYgNjYWEyfPh2//OUv8eSTT6KoqAjJyckyZ0JE3sJxiIiIiCjg8ZYZERERBTwWRERERBTwWBARERFRwGNBRERERAGPBREREREFPBZEREREFPBYEBEREVHAY0FEREREAY8FEREREQU8FkREREQU8FgQERERUcBjQUREREQB7/8Db7z2WnBqtHwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here's the new result\n",
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When we remove observations, the original indexes are preserved\n",
+    "# If we remove observations with indexes 2 and 3, the indexes will go as: 0,1,4,5,6\n",
+    "# That's very problematic as we tend to forget about it (later you will see an example of such a problem)\n",
+    "\n",
+    "# Finally, once we reset the index, a new column will be created containing the old index (just in case)\n",
+    "# We won't be needing it, thus 'drop=True' to completely forget about it\n",
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's see what's left\n",
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we decided to use some matplotlib code, without explaining it\n",
+    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
+    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3)) #sharey -> share 'Price' as y\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_4840\\2845240188.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_cleaned['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# From the subplots and the PDF of price, we can easily determine that 'Price' is exponentially distributed\n",
+    "# A good transformation in that case is a log transformation\n",
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "...           ...   ...        ...  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's transform 'Price' with a log transformation\n",
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "\n",
+    "# Then we add it to our data frame\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# The relationships show a clear linear relationship\n",
+    "# This is some good linear regression material\n",
+    "\n",
+    "# Alternatively we could have transformed each of the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since we will be using the log price variable, we can drop the old 'Price' one\n",
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's quickly see the columns of our data frame\n",
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn does not have a built-in way to check for multicollinearity\n",
+    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
+    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
+    "\n",
+    "# Here's the relevant module\n",
+    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# To make this as easy as possible to use, we declare a variable where we put\n",
+    "# all features where we want to check for multicollinearity\n",
+    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "\n",
+    "# we create a new data frame which will include all the VIFs\n",
+    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
+    "vif = pd.DataFrame()\n",
+    "\n",
+    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "# Finally, I like to include names so it is easier to explore the result\n",
+    "vif[\"Features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF Features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's explore the result\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since Year has the highest VIF, I will remove it from the model\n",
+    "# This will drive the VIF of other variables down!!! \n",
+    "# So even if EngineV seems with a high VIF, too, once 'Year' is gone that will no longer be the case\n",
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To include the categorical data in the regression, let's create dummies\n",
+    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
+    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840       True                False   \n",
+       "1      427      2.9   8.974618      False                 True   \n",
+       "2      358      5.0   9.495519      False                 True   \n",
+       "3      240      4.2  10.043249      False                False   \n",
+       "4      120      2.0   9.814656      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here's the result\n",
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df\n",
+    "# Since each problem is different, that must be done manually\n",
+    "# We can display all possible features and then choose the desired order\n",
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To make the code a bit more parametrized, let's declare a new variable that will contain the preferred order\n",
+    "# If you want a different order, just specify it here\n",
+    "# Conventionally, the most intuitive order is: dependent variable, indepedendent numerical variables, dummies\n",
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0       True                False   \n",
+       "1   8.974618      427      2.9      False                 True   \n",
+       "2   9.495519      358      5.0      False                 True   \n",
+       "3  10.043249      240      4.2      False                False   \n",
+       "4   9.814656      120      2.0      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To implement the reordering, we will create a new df, which is equal to the old one but with the new order of features\n",
+    "data_preprocessed = data_with_dummies[cols]\n",
+    "data_preprocessed.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+4).ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+4).ipynb
new file mode 100644
index 0000000..630db6f
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+4).ipynb	
@@ -0,0 +1,3572 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('1.04. Real-life example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afcf61978>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afcf462b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afd35d2e8>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23af8eafa20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afd366748>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afd368828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afcf46a20>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afd4e89e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afd4e2240>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afd519898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afd4f2d68>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afd5b3630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afd70aeb8>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afd727c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afd74f5c0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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a8sYNwOnysHN/jV+22Vf5HJax2WyYTOdPa9LpdLhcLvR6PTabDbP5/BhbVFQUNpuN+vp6mpqaWLVqFe+++y7PP/88v/71ry/5OnFxkeg7MWXp5UpM9N8XT4EmWQOjt2d1uT1sKz2NMUzHFZmJfhlv72ifJCaa+cFNo1j57l5+//e9LP9p3kWPyD/ZWcE/Co+RGBfBrIlpXf4S9WKvf+v0Ybzz2TG27a9hzvUju7U9syn8gv929JqhxF95fJa7yWTCbj9/WbDH40Gv1190md1ux2w2Exsby7XXXgvAjBkzWLlypc8g9V08BetSEhPN1NZa/b7dQJCsgaGGrEUHa6m3OhiRGktLi9Mvr3WpfXLV8ATKcwexcXclv1q5jYVzxhH2tQOvDV+c5I0NhzFFhPGft13BsdNNOJ3+GUI5l2tcRj++PFzHF3urSOvGl7RWW6vPMfdQ+v3oyu9rRx8GPg8BcnNzKSgoAKC4uJisrCzvsrFjx1JUVITD4cBqtVJeXk5WVhbjx49ny5YtAHz++edkZmZeVlghxHlb9rTPlz7MD18wdoZGo+GeWcPIGZZA2YkGHnl1G38vKOeTL07y6zd288aGw8SZjTw2L6dbxXspU8cNBM4PR4nL5/PIffbs2RQWFjJ37lwURSE/P5/Vq1eTmprKzJkzmT9/PvPmzUNRFBYuXIjRaOT+++/niSee4O6770av1/P888/3xHsRQnXqGlrYd9RCRkq0X65I7SytVsP9t47m3a3HKCiu4v1tFd5lWYNiWHTvBMII3LnoVwyNJybKwK4DNdwza5jfT/3sC3yWu1arZenSpRc8lpGR4f3/OXPmMGfOnAuWx8bG8vLLL/spohB915Y9VSjAtHEpuHp4Ui1DmI45MzK57ZohfFF2Bpfbw7jMBGJNRhITTQEdztBptVw1qj8ff36S0qMWsoddenZJ8W3ycShEiGpzeSjYU0VUuJ6JIy//kn5/MYbpmDJmANOyU4g19dxfD5NHJwOwfd/pHntNNZFyFyJEfVF2BmtzG3ljB2II8/+ZZKEutb+JAf0iKT5SR3OrK9hxeh0pdyFC1KbdlWiA6bnqvILSF41Gw+TRybS5PBQdkqmAL5eUuxAhqOK0lfKqJsZk9CMpNiLYcYJm0qj+AOzYJxc0Xa6u38ZFCBEwG3dXAnBtHztq31x86luPJcVFcKCinn/uOE5UePsFVWqdD8af5MhdiBDTaHOwY18NSbER3mlw+7KhA9qnNjhWHToXG/UGUu5ChJgNRZW43B6unzi4UzMzql1ashmtBo5VNQU7Sq8i5S5ECGlxuPh09ynMkWFMGTMg2HFCgtGgIyXRRL3VQb310pOZifOk3IUIIZ/tqaLZ4WLm+EF98vTHjgwd2D40c7RKhmY6S8pdiBDhcnv46POTGMK0XJs7KNhxQsqgxCjC9FqOVTXJLfg6ScpdiBDxya4T1FsdTB070C83vlATnU5LWrKZZoeLGktLsOP0ClLuQoQAZ5ubtR8fxKDXcuPkNN8r9EHnh2bki9XOkHIXIgRs3F2JpamVWRMG9+j8Lb1J/7gIIsP1VNRYaXO5gx0n5Em5CxFkza1tfLC9gqiIMG6YlBrsOCFLo9EwdEA0bS4Pe46cDXackCflLkSQfbDjBPZWF3fOyPRegSku7tzQjMwU6ZuUuxBBVFVn56NdJ4iPNnLLNUODHSfkxZqNxJmNlJSfxdbin1v7qZWUuxBBoigK/++jg7g9Ct+fnUW4UaZ66oyhA6NxexQ+L5OZIi9Fyl2IICnce5pDJxvIGZZAzrDEYMfpNYYMMKNBhmZ8kXIXIgjqrQ7WbTqM0aDj+7OzfK8gvCLDwxiRFseRykbONMg57x2Rcheih7k9Hv74j33YW13MmZ5BfHR4sCP1OuduwbdTjt47JOUuRA97r/A4h042MH54ItNzZF7yrhg/PJEwvZbt+2pkOoIOSLkL0YNKj57lvcLjJMSE88MbRqCRKX27JMKoJ2dYAqctzRw/LZOJXYyUuxA95ESNlVfeLUWn03L/baOJlHPau2XSV0Mz8sXqxUm5C9EDzja28ts39+B0uvnJLaPIGBgT7Ei93hVD4jFFhLFrfw1ujyfYcUKOlLsQAVZvdfCbdcU02pzcfW0mV45ICnYkVdDrtEwcmURTcxslMh3Bt0i5CxFAdY0tLH+9iBpLMzdOSuO6iTJ3jD+du1H2hqLKICcJPT7L3ePx8NRTT3H33Xczf/58KioqLli+fv167rjjDubMmcOnn356wbLPP/+cadOm+TexEL3EqVoby1/fTW1DK7ddM4Q7p8n0Av42KMnEiNRYDlTUc6rOHuw4IcVnuW/YsAGn08m6detYtGgRy5cv9y6rra1lzZo1rF27llWrVrFixQqcTicA1dXV/OUvf8HlcgUuvRAhqqS8jmfXFGFpcnDX9Axuu2aInBkTILMmDAZgoxy9X8BnuRcVFZGXlwdAdnY2paWl3mUlJSXk5ORgMBgwm82kpqZSVlaGw+Hg6aefZsmSJQELLkQo8igKH+yo4KW3SnB7FP7jttHcMEluvhFI2ZkJ9IsOZ1tpNfZWmUzsHJ8zFdlsNkwmk/dnnU6Hy+VCr9djs9kwm83eZVFRUdhsNpYuXcqCBQvo379/p4PExUWi1/v/hsCJiWbfTwoRkjUweipro83Bir/tZnfZGeKjw/nlDyeSlRp3Wdu4VFazyX9Xsvpjn5zbhj9zXe5rn3Pr1AxWv7+P3UfOcseMYd7Hz2W7VMZQ+132Vx6f5W4ymbDbz49leTwe9Hr9RZfZ7XbCwsL44osvOHHiBK+88gqNjY0sXLiQ3/72t5d8nfr65q6+hw4lJpqpre0dFzhI1sDoqazFh+v433+V0Wh3csXQeH588yiiI/Sdeu3NxaeA9gKy2loDHRWg2/vk6/u1pzJ/3Tfz52bG84ZBx983H2HSiETCvjpQtNpafe7XUPpd7srva0cfBj7LPTc3l08//ZQbb7yR4uJisrLOT3I0duxYXnzxRRwOB06nk/LycsaOHctHH33kfc6UKVN8FrsQvZWtpY11Gw9TWHoavU7DXdMzuP6qVLQyvt6josLDuDY3hQ93nKBgTzUzxw8KdqSg81nus2fPprCwkLlz56IoCvn5+axevZrU1FRmzpzJ/PnzmTdvHoqisHDhQoxGuf+jUD+PorC1pJq3Npdja2kjLdnMj24ayaBEk++Ve5Fzf1V8U0/+ldFZ11+ZysYvKvlgRwVTxw0kTN+3z/T2We5arZalS5de8FhGRob3/+fMmcOcOXM6XL+wsLAb8YQIPQdP1LP+0yMcq7ZiDNMxZ0YmsyYMQq/r22USbNFRBqbnpPDx5ycpLK32ngPfV8mtX4TopKNVTfyj8Bgl5e1XQ04cmcScGZkyZW8I+c5VqWzafYoPtldwzZgBwY4TVFLuQlyCR1HYf8zCv3adYP/xegCGD47lrhmZ3ps1i9ARazIyPXsgG4oq+XT3KcLC+u5fU1LuQlxEg83Bzv01bP7yFDX17Xf7GZUex02T0xmRGtvpC5I6GrMWgXPLlHQKS6v5R+ExbpmSHuw4QSPlLsRXWp0udh+qZfu+GvYft6Ao7ZNTTRmTzMzxg0hPliP13sAcaeCmyem8tbmcvUctTB8fFexIQSHlLvo0j6JQVlFP4d5qig7V4mxrnzp26MBoJo9OZuLIJMyRhm+tJ0fkoW3W+EFs2l3JgYp6ckf0py+emCrlLvqkmvpmCveeZltpNZYmBwCJseFMHp3M5NHJ9I+PDHJC0R2GMB13Ts3gT+/vZ/veKq6+IjnYkXqclLvoMxRF4dDJBv65o4LSoxYAwg06po4bwJQxA8hMiZHJvVTkqtH9eeezoxypbGTogGiS+/WtD2wpdxEUnRnW8Od5yhWnrfxt42EOnWwAIGtQDNNyUsjNSsQY5v85jURgdXZYbOKoJD7YfoJdB2q4+ep0tNq+8+Et5S5Uzdrs5K8fHuCzPdUowNiMftw8OZ3MQXKbu74gISaCkenxHDhu4dDJBkakXd5Ebr2ZlLvo9To6iquqs7N9Xw32ljZiTQZ+fPMoRqXH93A6EWyTrkjmSGUDxUfqSB9gJtzQN2qv757hL1TL41EoOniGDV9U0tLaRvawBG6+Ol2KvY+KDA9jXGY/nG0eig/XBTtOj+kbH2Giz3C0uSkorqL6bDPRkWFcPzmdiK+uUuzpcX4ROkakxnH4ZCOHTjYybHAs/frAlBFy5C5Uw9rs5IPtFVSfbWZQYhQ3Xp1GUlzfOkNCXJxWq+HKkUkA7Np/BkVRgpwo8OTIXaiCpamVDV9U0up0M3pIPDlZCTKn+iX0xYuwBiZEkdrfxIkaG8eqraqfG0jKXfR6NZZmNu0+RZvLw8SRSd06I6Ivll5fMmF4Eqdq7RQdrGVwkknVc76r952JPuHgiXo2FlXicnvIGzegT53qJi6fKTKM0UPiaXG42Hv0bLDjBJSUu+i1yirq+e2be/B4FKbnpDBkgLr/zBb+ccXQeCLD9ew/Vk+T3RnsOAEj5S56pbKKel58aw9ut8K0nBQGJ6nr9nYicPQ6LRNGJOFRFHYfqg12nICRche9zoGKel58s73YH7x9jBS7uGxp/U0kxIRzosbG8dNNwY4TEFLuolc5cNzCS2/uwaMoPHjHGLKHJQQ7kuiFNBoNOVntvzt/Lzga5DSBIeUueo39xy28+FZJe7HfPobsTCl20XUD+kWRHB9J6VGLd0I5NZFyF73CvuMWXnqrBEVR+OkdYxgnxS78IGfY+aN3tV3YJOUuQt7uQ7W89OYeb7GPzZBiF/6RGBfB2Ix+HDrZoLqjdyl3EdK2llTzyjt70Wm1/OyucVLswu9uvjodgH9urwhuED+TK1RFyHr13b18UVaLIUzLteNTqG1okStIhd9lpsQwIjWW0mMWjp9uUs2N0OXIXYQcRVEoPlzHF2W1RBj1fGdiKomxEcGOJVTsJhUevfs8cvd4PCxZsoSDBw9iMBhYtmwZaWlp3uXr169n7dq16PV6HnjgAWbMmEFVVRWLFy/G7XajKApLly5l6NChAX0jQh3cHoWd+2s4UtmIOTKMWRMGYY40BDuWULlRaXEMGWBm98FaqursDEyICnakbvN55L5hwwacTifr1q1j0aJFLF++3LustraWNWvWsHbtWlatWsWKFStwOp289NJL3HvvvaxZs4b777+fFStWBPRNCHVodbr45POTHKlsJD7ayHeuSpViFz1Co9Fw46R0FOCjXSeCHccvfB65FxUVkZeXB0B2djalpaXeZSUlJeTk5GAwGDAYDKSmplJWVsZjjz2G2WwGwO12YzQaAxRfqEVtQwsFxVXYW12kJZuZMiYZvU5GDUXPyRmWQP+4CLbvO80dU4cSY+rdveWz3G02GybT+cu7dTodLpcLvV6PzWbzljhAVFQUNpuN+Pj225kdPXqU559/nldeecVnkLi4SPR6/9+FPjHR7PtJIaIvZTWb2u+EoygKJUfq2FZSjUdRuGp0MuNHJKHx41zs516rN5CsgXGprF//Xb7j2mH84e0SdpTVcu8NI3si2iXzdIfPcjeZTNjtdu/PHo8HvV5/0WV2u91b9jt27OBXv/oVv/71rzs13l5f33zZ4X1JTDRTW2v1+3YDoa9ltdpasbe2sb30NFV1zYQbdOSNG8CAflHY7A4/JW3/R221tfpte4EkWQPDV9av/y6PTY/DFBHG+1uPMn3sAIwG/x9wXkpX/m119GHg8+/e3NxcCgoKACguLiYrK8u7bOzYsRQVFeFwOLBarZSXl5OVlcWOHTt49tln+fOf/8yYMWMuK6hQP0VROFLZyD+2HqeqrpmBCZHcfHU6A/r1/i+xRO9mDNMxIycFe6uLwtLqYMfpFp9H7rNnz6awsJC5c+eiKAr5+fmsXr2a1NRUZs6cyfz585k3bx6KorBw4UKMRiP5+fm0tbXx+OOPAzBkyBCWLl0a8DcjQl+DzcH/fljGnvKzhOm0TB7dn8xBMX4dhhGiO64dP4gPd57g410nmZ6dglbbO383NUqITKgQiCGJvjbU0VO6ktWjKBTsqeLtzeXYW10kx0dy9ZhkTBFhAUrZTk3DB6FETVmnZ6d867G/flhGwZ4qHrz9CsYPTwqtDZZVAAARYElEQVRkvAv4c1hGrlAVAVdx2sqajw9ytKqJcIOO78/OQqtFjtZFyLp+4mAK9lTx0a6TPVru/iTlLgKmxeHinYKjbNxdiaLAxJFJ3H3tMOLMRplGQIS0Af2iGJfRjz3lZzlS2UjmoJhgR7psUu7C7zyKwvbS07y1uZxGu5P+cRHce91wRg+JD3Y0ITrt+omp7Ck/y0e7TpA5qPedGCLlLvzqWHUTr39yiKNVTRj0Wr57zRBumJRKWACuYRAikIanxpKWbGb3oVrO1DeTFBcZ7EiXRcpd+EWj3cnbW8rZWtJ++tiVI5KYMyOTfjG950IXIb5Oo9Fw/cTBrPzHfj7+/CT3Xjc82JEui5S76BaX28PGokr+UXiMFoebQYlRzJuVxYi0uGBHE6LbJgxP4u3ocrburea7eUMDfnaXP0m5iy45Ny3vm5vLOW1pJipcz73XtV/gdrq+mdMBuOJYiJ6m12mZPWEwazcd4dMvT3HLV1MD9wZS7uKylVVYWPn3Eg5XNqLVaJiRk8LtU9uPauQsGKE2eeMG8n+Fx9hYVMl3Jg7uNd8fSbmLTjttaebvW8r54mAt0D6L3vemZ8i0AULVIox6pmWn8K+dJ9i+r4ap4wYGO1KnSLkLn07V2fnntuPsPFCDosDwtDhuv2YIWYNjgx1NiB4xa/wgPvn8JB9sr+DqK3rHdNRS7qJDJ2qsvLftOLsP1qIAgxJN3Dolne9cM5S6Oluw4wnRY+Kjw5maPZBPd59ie+lp8nrB0buUu7iA2+Oh5MhZNn15in3HLACkJ5u5ZUo64zIT0Go0Mm2A6JNumpTGZ3uqeW/bcSb3gqN3KXcBtM/WWLCnii3FVdRb2+dTzxoUw01Xp3PFkHgpdNHnxUeHMz17IBuKKtm6t/qiE46FEin3Psza7OTLw3UUHaxl/3ELbo+C0dA+n/WMnBQGJZkueP65M2F604yAQvjTjZPT2LKnivcKjzN5dDLGsNA9c0bK3Q86c/pfKHzKK4pCTX0LB45bKDpUS1lFA56vZnxOSzYzddxAJo3qT4RRfi2EuJhYk5FZEwbx4Y4TfLijgu/m+b7LXLDIv2IVc7S5qT5r50hlI4dONnCospEmu9O7fOjAaCYMT6LN7cYcaQBg54GaYMUVole4eXI620pP8+HOE1wzZgAJsRHBjnRRUu69XIvDRb3VgcXaiqXJQU19M9V1zZyqs1HX0MrX78QSYzIwcWQSWYNjGZeR4J33RS48EqLzIox65szI5E/v7WftpiP89I7QnDFSyj2EuT0e6q0Ozja2UtfYiqWpFYvVgaXpfJm3OFwXXdccGUbW4FgGJkSRPsDM8MGxJMZGyBejQvjBpFH92fzlKXYfqmXPkTrGZSYEO9K3SLkHWXNrG6ctLeytaKD8hIV9xyzYWtqwtbTR7HDR0U0QI4w64s3hxKVEE28OJ95sJC7aSFJsBAMSooj+aphFCOF/Go2Ge68bzjP/+zmrPzjArxZMJMZkDHasC0i595AGm4OK01aq6uyctjRz2tJMjaWZpua2iz4/0qgnISYCU4QeU0QYURFhRIWHERWuJzJCz3UTUn2+pgy3CBE4g5NMfG96Jms3HubP/zzAwjnj0IbQX8ZS7gHgdLk5Y2mhtqEFS5ODs02t/L9/HbzgORoNJMZEkJYcTXJ8JJlpcUSGaTla1UhkeBg6H3dcl+IWIvhmTxjE/uMWSsrP8q+dJ7hxUlqwI3lJufuB2+2hpr6F6rPtR+Nnm1ovGE6JCteTMyyBtP5mBiWZSI6PJCku4oIr3M7d9by2oSUI70AI0RUajYYFN47k6dW7eGtzOTFRBqaMGRDsWICUe5e1OFyUlJ9l96Favjxci8vd3uYaDSTEhJMcH0n/+Ejio8MJN5y/0KGp2UlTs5NDlQ0XbE8uDBKid4qOMvDzOdn8+o3d/OWDAxjDdEwYkRTsWFLul6PR7uTLw7V8eaiOAxUWb6GbIsJI7W9iQL8okuIiCNOH9pwTQgj/GpxkYuGcbP5n7Zf88R/7aLQ7uTY3Jahnp0m5+1BT38yXh+rYfbiW8spG73njg5NM5GYlkpuVyJFTDXKKoRB93NCB0Sy8axwv/30vr39yiCOnGvm364cH7YpvKfdvaHG4OFBRz75jFkqPnaW2oX2oRAMMGxRDblYiOVmJJH7tqrTyqsYgpRVChJKswbEs+eGV/OH/Stm5v4b9xy3cNDmdGTkDe/wOTj7L3ePxsGTJEg4ePIjBYGDZsmWkpZ3/Rnj9+vWsXbsWvV7PAw88wIwZM7BYLDz88MO0traSlJTEc889R0RE6F2i62hzU1lr48RpKxU1Nk7UWDl5xobb0358HmHUkZuVyNiMfmRnJhAdJeeOCyEuLT46nMfm5fLhzhP8a2cFazce5p/bjzNheBJXjkgiIyW6R4reZ7lv2LABp9PJunXrKC4uZvny5fzhD38AoLa2ljVr1vD222/jcDiYN28eU6ZM4dVXX+Xmm2/mjjvuYOXKlaxbt4777rsvIG/A0tRK9dlmNJr2b67PnUHY5vYQftrKmTobjjYPrQ4X9TYH9VYHDVYHFquDBpvjgrNadFoNaclmRqfHM3pIPEMHRof8nM1CiNCj12m55ep0ZuSk8OGOCgr3VvPpl6f49MtT6LQaBiWaGJgQRb8YI+nJ0eRmJfo/g68nFBUVkZeXB0B2djalpaXeZSUlJeTk5GAwGDAYDKSmplJWVkZRURH3338/AFOnTmXFihUBK/f/+duX1NRf3umDOq2GWJORYSkxDO5vJrW/ibT+ZgYmREmZCyH8xhQRxl0zMrlj2lAOnmjgy8N1HKtu4kSNjYoaK9A+5Pv7/84jMjzMr6/ts9xtNhsm0/l5vXU6HS6XC71ej81mw2w2e5dFRUVhs9kueDwqKgqr1eozSGKi2edzLubPT1zXpfX86a7ZI4IdQQgR4pL7xzDtSt8XOXW1C7/J52GqyWTCbrd7f/Z4POj1+osus9vtmM3mCx632+1ER0f7JawQQojO8Vnuubm5FBQUAFBcXExWVpZ32dixYykqKsLhcGC1WikvLycrK4vc3Fy2bNkCQEFBAePHjw9QfCGEEBejUZSO5h1sd+5smUOHDqEoCvn5+RQUFJCamsrMmTNZv34969atQ1EU7r//fq6//nrq6up47LHHsNvtxMXF8cILLxAZGdlT70kIIfo8n+UuhBCi95FTQ4QQQoWk3IUQQoWk3IUQQoV67dwye/bs4Te/+Q1r1qxh3759PP300xgMBkaOHMkvf/lLtFotzz33HEVFRWi1Wh577DHGjx9PQ0MD119/vfesn1mzZvGDH/wg6FmXLVvG7t27iYqK4uGHH2bcuHFUVFTw+OOPo9FoGDZsGE8//TRabWA/j7uadd++ffzHf/wH6enpANxzzz3ceOONAcnY1tbG4sWLOXXqFE6nkwceeIDMzMyL7quXX36ZzZs3o9frWbx4MWPHju3R/drdrKG6XwEqKip48MEHef/99wF6dNqR7mbtyR64nKzPP/88u3fvxuVycffddzNnzpyu71elF1q5cqVy8803K3fddZeiKIpy++23K0VFRYqiKMqKFSuUd999Vzlw4IBy1113KR6PRzl27Jhy++23K4qiKIWFhcrSpUtDKuumTZuUBQsWKG63Wzl79qw36/3336/s2LFDURRFefLJJ5WPP/44ZLOuX79eWbVqVUDznfPWW28py5YtUxRFUSwWizJt2rSL7qvS0lJl/vz5isfjUU6dOqXccccdiqL07H7tbtZQ3K+KoijvvPOOcvvttytXX321d/1nnnlGefvttxVFUZQ//vGPyurVq0M2a0/2QGezbt++XfnP//xPRVEUxeFwKLNmzVIaGhq6vF975bBMamoqv//9770/19TUkJubC7Sfl19UVERSUhLh4eE4nU5sNpv3wqvS0lL27dvHvffey0MPPcSZM2eCnvXIkSPk5eWh1WqJj49Hp9NRW1vLvn37mDhxItA+jcO2bdtCNmtpaSmbN2/m+9//PosXL8ZmswUs53e+8x1+9rOfeX/W6XQX3VdFRUVcc801aDQaBg4ciNvtxmKx9Oh+7W7WUNyvADExMbz22msXrP/1qUpCZb92lLUne6CzWXNycsjPz/c+z+12o9fru7xfe2W5X3/99d6yBhg8eDC7du0C4NNPP6WlpQW9Xo9Wq+WGG27ghz/8IQsWLABg6NChPPTQQ7z22mvMmjWLZcuWBT3ryJEj+eyzz2hra+PkyZMcOXKElpYWFEXxzhPf2WkcgpV17NixPProo7z++usMHjyYV155JWA5o6KiMJlM2Gw2HnroIf77v//7ovvqm1NnnHu8J/drd7OG4n4FmDFjxreuXenKtCPBytqTPdDZrEajkZiYGNra2nj88ce5++67vVO6dGW/9spy/6b8/Hz++Mc/8u///u/069ePuLg43n33XRISEvjkk0/YuHEjL7/8MjU1NUyaNImrrroKgNmzZ7N///6gZ73mmmuYMGECP/jBD1i9ejWjR48mNjb2gnHgYEzjcDlZZ8+ezRVXXAH0zH6trq7m3/7t37jtttu45ZZbLrqvOpoeo6f3a3eyhuJ+7UhPTzvSnaw93QOdzdrY2MiPf/xjMjIyvJMvdnW/qqLct2zZQn5+PitXrqShoYEpU6YQHR1NZGQkOp2OqKgoDAYDdrudJ554go8++giA7du3M3r06KBnPXbsGP369eONN97gJz/5CRqNhujoaEaNGsXOnTuB9mkcJkyYELJZf/SjH1FSUgIEfr/W1dWxYMECHnnkEb73ve8BXHRf5ebmsnXrVjweD1VVVXg8HuLj43t0v3Y3ayju14705LQj3c3akz3Q2aytra3cd9993HnnnTz44IPe9bu6X3vtFaqVlZX8/Oc/Z/369WzatImXXnqJiIgIrrrqKhYuXIjb7eZXv/oVhw4dwu12c+ONN/LDH/6QkydPsnjxYgAiIiJYtmwZSUmBvZmtr6wOh4OHH36YmpoajEYjTz31FMOGDePYsWM8+eSTtLW1MXToUJYtW4ZOF9hJ/ruadd++fTzzzDOEhYWRkJDAM888c8Ewgz8tW7aMDz/8kKFDh3of++Uvf8myZcu+ta9+//vfU1BQgMfj4Re/+AUTJkzo0f3a3ayhul/PmTJlCoWFhQA9Ou1Id7P2ZA90NuuaNWt4+eWXGTlypPd5+fn5REREdGm/9tpyF0II0TFVDMsIIYS4kJS7EEKokJS7EEKokJS7EEKokJS7EEKokJS76JOWLl3KQw89dMFjW7duZebMmQG9xF+IniLlLvqkRYsWUVpaysaNGwFobm5myZIl5OfnB+w8ciF6kpznLvqsbdu2sXjxYj744AN+97vf4fF4WLx4MXv27OG5557D4XAQHx/P0qVLSUlJYfv27bz00kveG8IvXryYa6+9locffhibzeadSnjatGnBfmtCSLmLvu2JJ57AarVy9OhR3nzzTbRaLXfeeSd/+tOfSE5OZvPmzaxZs4ZVq1bx4IMP8sgjj5Cens7WrVv5zW9+w7vvvsvDDz+M0Wjk2WefDfbbEcKr196sQwh/ePzxx5k+fTqvvPIK4eHhHDhwgMrKSu+kTYqi4HA4AFixYgWbNm3i/fffZ8+ePTQ3N3u3M27cuKDkF6IjUu6iTzOZTERHR5OSkgK0z6Gdnp7OO++84/357NmzKIrCPffcw9VXX82VV17JpEmT+MUvfuHdjtFoDEp+IToiX6gK8TWZmZnU1taye/duANatW8ejjz6KxWKhsrKShx56iKlTp7Jx40bcbneQ0wrRMTlyF+JrwsPDefHFF3n22WdxOp1ER0ezfPly+vXrx6233spNN92ETqdj8uTJ2O12Wltbgx1ZiIuSL1SFEEKFZFhGCCFUSMpdCCFUSMpdCCFUSMpdCCFUSMpdCCFUSMpdCCFUSMpdCCFU6P8DVmHTtwluTawAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afd7e4780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afd8ac908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x23afd88cac8>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afd892550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>14200.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>9.560997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10799.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>193</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.287209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>1400.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>212</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>7.244228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>11950.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>177</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.388487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>260</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>7.824046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>9500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>165</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.159047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>16000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>250</td>\n",
+       "      <td>4.70</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>9.680344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.059517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>2990.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>203</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>8.003029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>26500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.184900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>3500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>250</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>8.160518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>38233.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>10.551454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>132</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>8.922658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>6800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>225</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>8.824678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>130</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>24900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>163</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.122623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>20800.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>151</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.942708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>330</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13566.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>171</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.515322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>8500.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9.047821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>7.972466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>21500.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>72</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>9.975808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>87</td>\n",
+       "      <td>3.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>28500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>160</td>\n",
+       "      <td>4.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.257659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3837</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>180</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3838</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>9700.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>247</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.179881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3839</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>185</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3840</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10900.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>180</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.296518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3841</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>25500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>77</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.146434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3842</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>15500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.648595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3843</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>9750.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>159</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>9.185023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3844</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>16100.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>194</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>9.686575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3845</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>9200.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>171</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.126959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3846</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>5150.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>240</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>8.546752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3847</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>11.512925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3848</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>8999.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>126</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.104869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3849</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>16800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.729134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3850</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>8200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>280</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>9.011889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3851</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>24950.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.124629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3852</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>80999.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>11.302192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3853</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7300.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>207</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>8.895630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3854</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>21335.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>105</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.968104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3855</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>45000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>10.714418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3856</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>6750.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>155</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>8.817298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3857</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>7000.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>210</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>8.853665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3858</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>12090.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>145</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9.400134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3859</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>27900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>38</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.236382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3860</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>2100.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>237</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>7.649693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3861</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>6800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>152</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>8.824678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277     2.00      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427     2.90      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358     5.00         Gas   \n",
+       "3              Audi   23000.0  crossover      240     4.20      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120     2.00      Petrol   \n",
+       "5              Audi   14200.0      vagon      200     2.70      Diesel   \n",
+       "6           Renault   10799.0      vagon      193     1.50      Diesel   \n",
+       "7        Volkswagen    1400.0      other      212     1.80         Gas   \n",
+       "8           Renault   11950.0      vagon      177     1.50      Diesel   \n",
+       "9           Renault    2500.0      sedan      260     1.79      Petrol   \n",
+       "10             Audi    9500.0      vagon      165     2.70         Gas   \n",
+       "11       Volkswagen   10500.0      sedan      100     1.80      Petrol   \n",
+       "12           Toyota   16000.0  crossover      250     4.70         Gas   \n",
+       "13          Renault    8600.0      hatch       84     1.50      Diesel   \n",
+       "14              BMW    2990.0      other      203     2.00      Petrol   \n",
+       "15           Toyota   26500.0  crossover       21     2.00      Petrol   \n",
+       "16             Audi    3500.0      vagon      250     2.50      Diesel   \n",
+       "17           Toyota   38233.0      other        0     2.40      Diesel   \n",
+       "18       Volkswagen    7500.0      hatch      132     1.40      Diesel   \n",
+       "19             Audi    6800.0      sedan      225     2.40         Gas   \n",
+       "20       Mitsubishi   10500.0  crossover      130     2.40         Gas   \n",
+       "21             Audi   24900.0      sedan      163     4.20      Diesel   \n",
+       "22       Volkswagen   20800.0  crossover      151     3.00      Diesel   \n",
+       "23             Audi    6500.0      sedan      330     2.40      Petrol   \n",
+       "24    Mercedes-Benz   13566.0      other      171     2.20       Other   \n",
+       "25       Mitsubishi    8500.0      hatch       65     1.30      Petrol   \n",
+       "26             Audi    2900.0      sedan        1     2.30         Gas   \n",
+       "27              BMW   21500.0      other       72     3.00      Petrol   \n",
+       "28       Mitsubishi   17900.0  crossover       87     3.80         Gas   \n",
+       "29              BMW   28500.0  crossover      160     4.80         Gas   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3837  Mercedes-Benz   11500.0        van      180     2.20      Diesel   \n",
+       "3838     Mitsubishi    9700.0        van      247     2.40         Gas   \n",
+       "3839        Renault   10500.0      vagon      185     1.50      Diesel   \n",
+       "3840        Renault   10900.0      vagon      180     1.50      Diesel   \n",
+       "3841  Mercedes-Benz   25500.0  crossover       77     3.50      Petrol   \n",
+       "3842     Volkswagen   15500.0      sedan       80     1.40      Petrol   \n",
+       "3843     Volkswagen    9750.0        van      159     1.90      Diesel   \n",
+       "3844            BMW   16100.0  crossover      194     3.00      Diesel   \n",
+       "3845     Volkswagen    9200.0      vagon      171     1.60      Petrol   \n",
+       "3846     Volkswagen    5150.0        van      240     2.00      Diesel   \n",
+       "3847         Toyota  100000.0  crossover        0     4.50      Diesel   \n",
+       "3848        Renault    8999.0      other      126     2.00      Diesel   \n",
+       "3849  Mercedes-Benz   16800.0      sedan      125     1.80      Petrol   \n",
+       "3850  Mercedes-Benz    8200.0      sedan      280     2.40         Gas   \n",
+       "3851  Mercedes-Benz   24950.0      other       60     1.80      Petrol   \n",
+       "3852           Audi   80999.0  crossover        0     3.00      Diesel   \n",
+       "3853  Mercedes-Benz    7300.0        van      207     2.20      Diesel   \n",
+       "3854            BMW   21335.0      other      105     3.00      Petrol   \n",
+       "3855            BMW   45000.0  crossover       80     3.00      Petrol   \n",
+       "3856        Renault    6750.0        van      155     1.50      Diesel   \n",
+       "3857        Renault    7000.0        van      210     1.50      Diesel   \n",
+       "3858            BMW   12090.0      hatch      145     1.60      Petrol   \n",
+       "3859            BMW   27900.0      sedan       38     2.00      Petrol   \n",
+       "3860        Renault    2100.0      vagon      237     1.90      Diesel   \n",
+       "3861        Renault    6800.0      sedan      152     1.60      Petrol   \n",
+       "3862     Volkswagen   11500.0        van      163     2.50      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35     1.60      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9     3.00      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1     3.50      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124     2.00      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "5             yes  2006   9.560997  \n",
+       "6             yes  2012   9.287209  \n",
+       "7              no  1999   7.244228  \n",
+       "8             yes  2011   9.388487  \n",
+       "9             yes  1994   7.824046  \n",
+       "10            yes  2003   9.159047  \n",
+       "11            yes  2008   9.259131  \n",
+       "12            yes  2001   9.680344  \n",
+       "13            yes  2012   9.059517  \n",
+       "14             no  2001   8.003029  \n",
+       "15            yes  2013  10.184900  \n",
+       "16             no  1998   8.160518  \n",
+       "17            yes  2016  10.551454  \n",
+       "18            yes  2006   8.922658  \n",
+       "19            yes  1998   8.824678  \n",
+       "20            yes  2006   9.259131  \n",
+       "21            yes  2008  10.122623  \n",
+       "22            yes  2008   9.942708  \n",
+       "23            yes  1999   8.779557  \n",
+       "24             no  2011   9.515322  \n",
+       "25            yes  2010   9.047821  \n",
+       "26            yes  1989   7.972466  \n",
+       "27            yes  2007   9.975808  \n",
+       "28            yes  2008   9.792556  \n",
+       "29            yes  2008  10.257659  \n",
+       "...           ...   ...        ...  \n",
+       "3837          yes  2011   9.350102  \n",
+       "3838          yes  2008   9.179881  \n",
+       "3839          yes  2011   9.259131  \n",
+       "3840          yes  2012   9.296518  \n",
+       "3841          yes  2008  10.146434  \n",
+       "3842          yes  2013   9.648595  \n",
+       "3843          yes  2009   9.185023  \n",
+       "3844          yes  2004   9.686575  \n",
+       "3845          yes  2008   9.126959  \n",
+       "3846          yes  2005   8.546752  \n",
+       "3847          yes  2016  11.512925  \n",
+       "3848          yes  2012   9.104869  \n",
+       "3849          yes  2008   9.729134  \n",
+       "3850          yes  1997   9.011889  \n",
+       "3851          yes  2013  10.124629  \n",
+       "3852          yes  2016  11.302192  \n",
+       "3853          yes  2003   8.895630  \n",
+       "3854          yes  2008   9.968104  \n",
+       "3855          yes  2011  10.714418  \n",
+       "3856          yes  2012   8.817298  \n",
+       "3857          yes  2005   8.853665  \n",
+       "3858          yes  2010   9.400134  \n",
+       "3859          yes  2013  10.236382  \n",
+       "3860           no  2001   7.649693  \n",
+       "3861          yes  2007   8.824678  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afe9ca940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840          1                    0   \n",
+       "1      427      2.9   8.974618          0                    1   \n",
+       "2      358      5.0   9.495519          0                    1   \n",
+       "3      240      4.2  10.043249          0                    0   \n",
+       "4      120      2.0   9.814656          0                    0   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                 0              0             0                 0   \n",
+       "1                 0              0             0                 0   \n",
+       "2                 0              0             0                 0   \n",
+       "3                 0              0             0                 0   \n",
+       "4                 0              0             1                 0   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0           0           0           1           0         0                0   \n",
+       "1           0           0           0           0         1                0   \n",
+       "2           0           0           1           0         0                1   \n",
+       "3           0           0           0           0         0                0   \n",
+       "4           0           0           0           0         0                0   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0                  0                   1                 1  \n",
+       "1                  0                   0                 1  \n",
+       "2                  0                   0                 1  \n",
+       "3                  0                   1                 1  \n",
+       "4                  0                   1                 1  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0          1                    0   \n",
+       "1   8.974618      427      2.9          0                    1   \n",
+       "2   9.495519      358      5.0          0                    1   \n",
+       "3  10.043249      240      4.2          0                    0   \n",
+       "4   9.814656      120      2.0          0                    0   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                 0              0             0                 0   \n",
+       "1                 0              0             0                 0   \n",
+       "2                 0              0             0                 0   \n",
+       "3                 0              0             0                 0   \n",
+       "4                 0              0             1                 0   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0           0           0           1           0         0                0   \n",
+       "1           0           0           0           0         1                0   \n",
+       "2           0           0           1           0         0                1   \n",
+       "3           0           0           0           0         0                0   \n",
+       "4           0           0           0           0         0                0   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0                  0                   1                 1  \n",
+       "1                  0                   0                 1  \n",
+       "2                  0                   0                 1  \n",
+       "3                  0                   1                 1  \n",
+       "4                  0                   1                 1  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed = data_with_dummies[cols]\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the inputs and the targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "targets = data_preprocessed['log_price']\n",
+    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scale the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StandardScaler(copy=True, with_mean=True, with_std=True)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs_scaled = scaler.transform(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, targets, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat = reg.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afe948940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_train, y_hat)\n",
+    "plt.xlabel('Targets (y_train)',size=18)\n",
+    "plt.ylabel('Predictions (y_hat)',size=18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'Residuals PDF')"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23afe8f5cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(y_train - y_hat)\n",
+    "plt.title(\"Residuals PDF\", size=18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.744996578792662"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.415239458021299"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.44871341,  0.20903483,  0.0142496 ,  0.01288174, -0.14055166,\n",
+       "       -0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,\n",
+       "       -0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,\n",
+       "       -0.14690868,  0.32047333])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-0.448713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>0.209035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>0.014250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>0.012882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>-0.140552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>-0.179909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>-0.060550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>-0.089924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>-0.145469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>-0.101444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-0.200630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-0.129887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-0.168597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-0.121490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-0.033368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-0.146909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>0.320473</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features   Weights\n",
+       "0               Mileage -0.448713\n",
+       "1               EngineV  0.209035\n",
+       "2             Brand_BMW  0.014250\n",
+       "3   Brand_Mercedes-Benz  0.012882\n",
+       "4      Brand_Mitsubishi -0.140552\n",
+       "5         Brand_Renault -0.179909\n",
+       "6          Brand_Toyota -0.060550\n",
+       "7      Brand_Volkswagen -0.089924\n",
+       "8            Body_hatch -0.145469\n",
+       "9            Body_other -0.101444\n",
+       "10           Body_sedan -0.200630\n",
+       "11           Body_vagon -0.129887\n",
+       "12             Body_van -0.168597\n",
+       "13      Engine Type_Gas -0.121490\n",
+       "14    Engine Type_Other -0.033368\n",
+       "15   Engine Type_Petrol -0.146909\n",
+       "16     Registration_yes  0.320473"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
+    "reg_summary['Weights'] = reg.coef_\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned['Brand'].unique()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+4)_with_comments.ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+4)_with_comments.ipynb
new file mode 100644
index 0000000..edb5cc8
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+4)_with_comments.ipynb	
@@ -0,0 +1,3622 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For this practical example we will need the following libraries and modules\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '1.04. Real-life example.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\sklearn+-+Linear+Regression+-+Practical+Example+(Part+4)_with_comments.ipynb Cell 5\u001b[0m line \u001b[0;36m2\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2B%28Part%2B4%29_with_comments.ipynb#W4sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Load the data from a .csv in the same folder\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2B%28Part%2B4%29_with_comments.ipynb#W4sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m raw_data \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(\u001b[39m'\u001b[39;49m\u001b[39m1.04. Real-life example.csv\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2B%28Part%2B4%29_with_comments.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m# Let's explore the top 5 rows of the df\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2B%28Part%2B4%29_with_comments.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m raw_data\u001b[39m.\u001b[39mhead()\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:948\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    944\u001b[0m     dtype_backend\u001b[39m=\u001b[39mdtype_backend,\n\u001b[0;32m    945\u001b[0m )\n\u001b[0;32m    946\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 948\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:611\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    608\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[0;32m    610\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 611\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m    613\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[0;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1448\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1445\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m   1447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m-> 1448\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1705\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1703\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[0;32m   1704\u001b[0m         mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m-> 1705\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[0;32m   1706\u001b[0m     f,\n\u001b[0;32m   1707\u001b[0m     mode,\n\u001b[0;32m   1708\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1709\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1710\u001b[0m     memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[0;32m   1711\u001b[0m     is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[0;32m   1712\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[0;32m   1713\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1714\u001b[0m )\n\u001b[0;32m   1715\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m   1716\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\io\\common.py:863\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    858\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[0;32m    859\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    860\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    861\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[0;32m    862\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[1;32m--> 863\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[0;32m    864\u001b[0m             handle,\n\u001b[0;32m    865\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[0;32m    866\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[0;32m    867\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[0;32m    868\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    869\u001b[0m         )\n\u001b[0;32m    870\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    871\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m    872\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '1.04. Real-life example.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "raw_data = pd.read_csv('1.04. Real-life example.csv')\n",
+    "\n",
+    "# Let's explore the top 5 rows of the df\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics are very useful for initial exploration of the variables\n",
+    "# By default, only descriptives for the numerical variables are shown\n",
+    "# To include the categorical ones, you should specify this with an argument\n",
+    "raw_data.describe(include='all')\n",
+    "\n",
+    "# Note that categorical variables don't have some types of numerical descriptives\n",
+    "# and numerical variables don't have some types of categorical descriptives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For these several lessons, we will create the regression without 'Model'\n",
+    "# Certainly, when you work on the problem on your own, you could create a regression with 'Model'\n",
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "\n",
+    "# Let's check the descriptives without 'Model'\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data.isnull() # shows a df with the information whether a data point is null \n",
+    "# Since True = the data point is missing, while False = the data point is not missing, we can sum them\n",
+    "# This will give us the total number of missing values feature-wise\n",
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's simply drop all missing values\n",
+    "# This is not always recommended, however, when we remove less than 5% of the data, it is okay\n",
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's check the descriptives without the missing values\n",
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897ab4940>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b897a96438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A great step in the data exploration is to display the probability distribution function (PDF) of a variable\n",
+    "# The PDF will show us how that variable is distributed \n",
+    "# This makes it very easy to spot anomalies, such as outliers\n",
+    "# The PDF is often the basis on which we decide whether we want to transform a feature\n",
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obviously there are some outliers present \n",
+    "\n",
+    "# Without diving too deep into the topic, we can deal with the problem easily by removing 0.5%, or 1% of the problematic samples\n",
+    "# Here, the outliers are situated around the higher prices (right side of the graph)\n",
+    "# Logic should also be applied\n",
+    "# This is a dataset about used cars, therefore one can imagine how $300,000 is an excessive price\n",
+    "\n",
+    "# Outliers are a great issue for OLS, thus we must deal with them in some way\n",
+    "# It may be a useful exercise to try training a model without removing the outliers\n",
+    "\n",
+    "# Let's declare a variable that will be equal to the 99th percentile of the 'Price' variable\n",
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "# Then we can create a new df, with the condition that all prices must be below the 99 percentile of 'Price'\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "# In this way we have essentially removed the top 1% of the data about 'Price'\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897eacfd0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b897e699e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can check the PDF once again to ensure that the result is still distributed in the same way overall\n",
+    "# however, there are much fewer outliers\n",
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897e36e80>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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s9UI4Br0WU/LT0esPocHB7ptEiY6hHwNNbf1dNeN8q+bFTBlng0YQ8PkJF6T47c9HRJeBoR8Dg/3z47T1QjgpBhETc61wdwdwutmjdDlEdA0Y+lEmSTKa2rphTtHBkqpXupyrNrDr6PBxF++sRZTAGPpR1tbZi0BQSthZ/oA0swF52Wa0dvaipb1H6XKI6Cox9KPsTIKv5w81MNvn9k2ixMXQj7Kmtm4IAEbH0f1wr1a2LQWWVB1ONXvQ3RtQuhwiugoM/Sjq8QXh7OhBZpoRBt2FnSoTjSAImJybhpAk4+CRFqXLIaKrwNCPoqOnOiDLwJgkmOUPmJRrhQBgb3WT0qUQ0VVg6EfR4f5WymOyEn89f0CqUYecLBOON3XB0epVuhwiukIM/Sj6/IQLolaAPT1F6VIialJeGgBgP2f7RAmHoR8lLR09aGrrxqiMVGg1idV6IZyx2SaYjCI+OHyW79AlSjAM/Sj55KgTAJA/yqxwJZGn1WhQXGBHp9eP+sZOpcshoivA0I+SyqMtEARgbHbyhT4AzCrMBgBU9n9zI6LEwNCPAldXLxrOdKFwbDqM+uS89/y08TakGER8fLSFSzxECYShHwWfftEK4NxsOBmJWg1mXpeFdrcPx5vYcpkoUTD0o6DyaN8bl4oL7ApXEl03DSzx1HKJhyhRMPQjrKvbj6OnOzA5Nw02i0HpcqLq+gk2GPVafHy0hZ03iRIEQz/CPjrSAllO/lk+AOhELYomZ6G1sxen2GefKCEw9K9Ad2/wkveJDYYkvHXgJHSiBl++flQMK1NO0XVZAIBD9a0KV0JEl4OhfxkkWUbNsTa8sqcBf99/Au1u34jXvV9zFm1dPswtykGaObmXdgZMn5ABjSDgUEOb0qUQ0WVg6IfhC4Tw7sHT+KSuFRqNAHd3AM+89AnaOnuHXRcMSfjH+ycgajVYcOs4haqNvVSjDgVj03C8qQudXr/S5RBRGAz9MOpOdaC5vQd5dhMWzZ2IGyZloqWjB8/s+ASNznPr2B8cPovWzl7MvTEn6V/ABYDdVY7B/0wpOgDAZ5ztE8U9hn4YAzc1/8oNo2HUi5h5XRb+1+wJaO3sxbo/f4y91WdQ9t4X2P52HUStgAVfyle44tjLs/d1Ea1u4Lo+UbxLzreLRkgwJMHZ0QObxTDsnbX33DYBYzJN2PrGEWx7oxYAkGk1YNnXCpBhNSpVrmKsJj3MKTrUHHchGJIgajmXIIpXYUNfkiQ89dRTOHr0KPR6PdatW4dx486tWZeXl6O0tBSiKGLlypW488474XK58Nhjj6G3txfZ2dn41a9+hZSUFLz44ov4n//5HwDA3Llz8cgjj0RvZBHQ3NaNkCRjdMaFN0G5aUo2xmSZ8PLuBhTmp+OrxbnQiYl/d6yrIQgC8uwm1J7qwBenOzB1fIbSJRHRRYSdku3cuRN+vx9lZWVYvXo1NmzYMHjO6XRi+/btKC0txdatW7Fp0yb4/X5s2bIFCxcuxI4dOzBt2jSUlZXh9OnTeP3111FaWoqysjLs27cPtbW1UR3ctRpYs7/Y/W1zs0z40b0zcNct+aoN/AF5/Y3luIuHKL6FDf3KykrMnj0bAFBUVISamprBc9XV1Zg5cyb0ej0sFgvy8/NRW1s77HPmzJmD999/H6NHj8Z//ud/QqvVQqPRIBgMwmCI7xc8HU4PBACjbMl1E5RoGJWRAoNOy9AninNhl3c8Hg/M5nPtgbVaLYLBIERRhMfjgcViGTxnMpng8XiGHTeZTHC73dDpdMjIyIAsy3j22Wcxbdo0TJgw4ZLPbbOlQlRoBt3rD6K5rRt2WwoybcNvd2i3Wy64/q0PToz4OBZzfK/xR7K+ogI7Dhw+i4AgICdL+ZbSI32dkgXHlpjiYWxhQ99sNsPrPXcvVEmSIIriiOe8Xi8sFsvgcaPRCK/XC6vVCgDw+Xx44oknYDKZ8POf/zxsce3t3Vc8oEg5fNwFSZZhTzfC7Rm+J9/pdF9w/fnXJAKL+cKxXYup+ek4cPgsdh88hfk3j43Y414Nu90y4tcpGXBsiSmWY7vUN5ewyzvFxcWoqKgAAFRVVaGgoGDw3IwZM1BZWQmfzwe3242GhgYUFBSguLgYe/bsAQBUVFRg1qxZkGUZDz/8MAoLC/H0009Dq43vNfAjJ9sBAKMzkuem5tF2w8RMAMAhbt0kilthZ/rz58/H/v37sXTpUsiyjPXr12Pbtm3Iz8/HvHnzUFJSgmXLlkGWZaxatQoGgwErV67EmjVrUF5eDpvNho0bN2Lnzp04ePAg/H4/9u7dCwD4yU9+gpkzZ0Z9kFej7nQHBAHI5nr+ZbNZDMgfZcbRUx3o8QWRYuCOYKJ4E/ZfpUajwdNPPz3s2KRJkwZ/v2TJEixZsmTY+aysLGzdunXYsfnz5+Ozzz67llpjqqnNi3SzATqRe86vxI2TsnCq2YPPT7RjVmHydxolSjRMtBF4egLw9gZV0zQtkmZM5hIPUTxj6I/A2dEDAEgz6RWuJPFMGGOFJVWHzxraeO9cojjE0B9Bc/+uIc70r5xGEDB9QiY6vX40tvDGKkTxhqE/gpb2/pm+mTP9q3H9BBsA4PMT7QpXQkTnY+iP4Fzoc6Z/Nab19945fMKlcCVEdD6G/gha2nug1QiwpHKmfzXSzQbk2k2oO92BQDCkdDlENARDfwQt7d3ITDNCoxGULiVhXT8+A4GghPrGTqVLIaIh+O6Z8/T4gujqDiB/tPI9MhLN7irH4O9DUt8N5A+faGerZaI4wpn+eQbW80elj9xOmS5Pti0VGoHr+kTxhqF/npb+Pfpsv3BtdKIG9vQUnDrrhqcnoHQ5RNSPyzvnaenfo59tS0FQ4VoS3ZgsE5rbe/C33fUYP8Y67NwdRbkKVUWkbpzpn6e5nTP9SMnpv+PYmTblWmQT0XCc6Z+npb0HggBkpaXgTPvIveaHvmBJF5eRZoRep0FTqxeyLEMQuBuKSGmc6Z+npb0bmVYju2tGgEYQMDojFd7eINzdXNcnigdMtiF8/hA6PH4u7URQTmbfTWia2rxhriSiWGDoDzHQXTM7naEfKWOy+tb1m7iuTxQXGPpDtHb1reFnMfQjxpKqhzlFh6a2bkgSWy0TKY2hP0R7f+hnWNhoLZLGZKYiEJTQ1pV4N48nSjYM/SHaunwAgAyrUeFKkktOVv+6fivX9YmUxtAfwjUw07dyph9JozO4X58oXjD0h3B19UIQ+loDU+QY9FpkWo1o7ehBICgpXQ6RqjH0h2jr8iHdbICo5R9LpI3OTIUkn2toR0TKYLr1kyQZHR4fl3aiZEzmwNZNrusTKYmh36/T60dIkpHJF3GjItuWAo0g4KyL6/pESmLo92sb3K7J0I8GUauBPd0IV5cPPj9voUikFIZ+P+7cib6BJR7O9omUw9Dv5+rfo8/lnegZPdiHh6FPpBSGfr9zM32GfrRkpRkhagWc5Yu5RIph6Pdr4/JO1Gk0AkZlpKKrOzD4TZaIYouh38/V5YNO1MCcolO6lKQ2sK5/+DhvmE6kBIZ+P5e7FxlWI+/uFGW5/X14PmPoEymCoQ/AHwjB3R1gd80YsJr0MBlFHDnhQkhiSwaiWGPoA3C5uXMnVgRBQK7dBG9vEMeb3EqXQ6Q6DH1wj36sDbRarjnWpnAlROrD0MfQnTuc6cfC6IxUaDUCPjvGdX2iWGPoA2jnG7NiSq/TYlJuGk40dcHTE1C6HCJVYeiDe/SVcMPEDMjg1k2iWAsb+pIkYe3atbjvvvtQUlKCkydPDjtfXl6ORYsWYcmSJdi1axcAwOVyYfny5Vi2bBkeffRR9PSc66Hucrnw9a9/HT6fL8JDuXqtnX2hz5l+7EyfkAkAONTQqnAlROoSNvR37twJv9+PsrIyrF69Ghs2bBg853Q6sX37dpSWlmLr1q3YtGkT/H4/tmzZgoULF2LHjh2YNm0aysrKAAB79+7F8uXL0doaX//Q2zp7YTXpoddplS5FNfJHmZFpNeJQfSvvpkUUQ2K4CyorKzF79mwAQFFREWpqagbPVVdXY+bMmdDr9dDr9cjPz0dtbS0qKyuxYsUKAMCcOXOwadMmPPjgg9BoNNi2bRsWL158WcXZbKkQxegGcUiS4XL3YlJeOux2y/CT9W2wmJN79q/U+LKzrZhTnIf/3l2PRlcPbrl+dMSf44KvZxLh2BJTPIwtbOh7PB6YzebBj7VaLYLBIERRhMfjgcVybhAmkwkej2fYcZPJBLe7bz/2bbfddkXFtbdHvxujq6sXwZCMtFQdnM4L9427PcnbI8ZiNio2PqfTjevz0/HfAHYeOIEJ2aaIPr7dbhnx65kMOLbEFMuxXeqbS9jlHbPZDK/3XFdESZIgiuKI57xeLywWy7DjXq8XVqv1qouPtoH1/Ky0FIUrUZ8JYyzItBpRVd+KQJA3ViGKhbChX1xcjIqKCgBAVVUVCgoKBs/NmDEDlZWV8Pl8cLvdaGhoQEFBAYqLi7Fnzx4AQEVFBWbNmhWl8q9da2ffi8xZacm9jBOPBEHAzVOy0eMLoYa7eIhiIuzyzvz587F//34sXboUsixj/fr12LZtG/Lz8zFv3jyUlJRg2bJlkGUZq1atgsFgwMqVK7FmzRqUl5fDZrNh48aNsRjLVTk302foK+GmKdl46+ApfFzbgpnX2ZUuhyjphQ19jUaDp59+etixSZMmDf5+yZIlWLJkybDzWVlZ2Lp160Uf87333rvSOqNmMPTTubyjhIElnk+/aEWvPwijPuxfSSK6Bqp/c1bb4B59vjFLCYIg4PYZY9DrD2H/Z2eVLoco6ak+9J0dPUgz66GL8tZQurg7Z+ZC1Grw7senIcmy0uUQJTVV/ywdkiS0u30YP0b5vbNqs7vKMezj8aMtqHd0orq+DUXXZSlUFVHyU/VMv8PtR0iSuV0zDkwdbwMAvPPRKYUrIUpuqg59bteMHzaLAdPG21B7qgMnzybnm3OI4oHKQ5/bNePJ3bfmAwD+urOOa/tEUaLq0G/ju3HjyvQJmSgusKOusRP7q5uULocoKak69DnTjz/LvnYdDHotynfVo8vrV7ocoqSj8tDvW9PnbRLjR4bViEVzJsLbG8Rf3jkKmcs8RBGl8tDvRbpZD52o6j+GuLG7yoHdVQ5otQLs6Sn4+KgT/7f8kNJlESUV1aZdMCTB1eXjen4c0ggC7izOgSVVh5rjLvyzslHpkoiShmpD/6yrG5IsIycrVelSaARGvYiv3ZQHo16LHe/W4d2PTnOphygCVBv6Dmdfv/9cuznMlaQUS6oeX7spD1azHn/95xd46d06hCTeWpHoWqg29BudHgBAXlZk79hEkZVhNeLJ+29Cnt2M9z5xYGNpFdrdPqXLIkpYqg19zvQTR4bViJ9+vxjFBXbUnurA2q0H8OkXTqXLIkpI6g39Vg8sqTpYTXqlS6Ewdlc5cOBIM66fYMOt00ah1x/C5pc/w0vv1PE2i0RXSJWh7/OH4OzoRR5n+QlFEAQU5qfjG18eh9wsE/75SSN+8adKnGn1hv9kIgKg0tB39IdELtfzE5LNYsC/PXAT7piZi0anB0+/+BEqDp3h7h6iy6DKfvqO/hdxc+0M/URl0Glx/12FuH68DdveqMWLb9ai4tAZfGX6aGg0AixmI9yevjYbdxTlKlwtUfxQZ+j3z/S5vJO4ht6E5e4v5aOi6gyOnemCLxDC3KIcBSsjim/qXN7pn+nncHknKZhTdJh/81iMyUyFw+nFPz9u5Au8RBehytBvdHqRaTUixaDKH3SSkk7U4KuzcpE/yozm9h68+cEJhCSu8ROdT3Wh7+72o9Pr53p+EtJqNJhzYw5y7SacbvZgX3UTb8ZCdB7Vhf4ZrucnNY1GwNyiHIzJNOHkWTcOft7MXT1EQ6gu9I81dQEA8jjTT1qiVoNv3jYBNosBdac78d97jyldElHcUF3of9bQBgCYOj5D4Uoomgx6Lb52Ux4sqTr84/2TeOfgKaVLIooLqgr97t4gvmjsxIQxFqSx/ULSSzH0tWdOM+tR+l499n/G++4SqSr0D59wISTJmDEpS+lSKEYsqXqsvq8IJqOIbW/UslEbqZ6qQv9QfSsA4MbJmQpXQrGUZzfjx9+9EaIo4A8Wdn2jAAAMnklEQVSvHsbHtS1Kl0SkGNWEviTL+OxYG9JMeuSPsihdDsXY5Nw0PLLoBmg0wJZXa1D6zy8QDPGGLKQ+qgn9401dcHcHcMOkTGgEQelySAHTJ2TiyftvwpjMVLzz0Wk8/eLHqG5o45ZOUhXVvCW1ur5v186Nk7i0o2a5djOefOAmbCo/hPrGTjz3/w4h25aCqeNsyMs2Y15xntIlEkWVKkI/EAzhw8/PQqsRMI1bNVXPqBfxlemjMXVcOj6pa4XD6UVLew+Mei2aXd24ZeooTMqxQuBPhJSEVBH6bx44BWdHL+bfNJb9dlRoaEfOoWwWI+bNykOHx4cvTnei4Uwndn7ciJ0fNyLTasDNU0bh5qnZGD/awm8AlDSSPgFb2rvxj/dPIs2sx3dmT1C6HIpD6WYDbp6ajeJCO5ravDjR5MbpFg/eOngKbx08BZvFgKLJWbhhYiYKxqYh1ahTumSiq5bUoS/LMnbs7Nul8b1513GWT5ek1QjIs5uRZzcjFJLgaPXiVLMHza5u7PrUgV2fOiCg7+Y7Y7P7rstMMyLNpEea2YA0kx5GvZY/FVBcC5uCkiThqaeewtGjR6HX67Fu3TqMGzdu8Hx5eTlKS0shiiJWrlyJO++8Ey6XC4899hh6e3uRnZ2NX/3qV0hJSRnx2mhxdvTgxTdrceRkO6aOs+HmKdlRey5KPlqtBvmjLMgfZcHsGWNQ39iJIyfbcfRUB443daHR6QXQfMHn6UUNUo0iTEbdsF9TjSJSDRceNxlFpPb/Xi9q+A2Doi5s6O/cuRN+vx9lZWWoqqrChg0b8Ic//AEA4HQ6sX37drz88svw+XxYtmwZbrvtNmzZsgULFy7EokWL8MILL6CsrAzf/OY3R7xWr498O4TPT7jw/MvV8AckzJiUif/zjan8x0RXbW91X/uGdIsBt14/CjdPy4anO4AOjw/dvUH0+EPo8QXR4wui1xeELyDB2dmDQKuEK9kMKmoFpBp1/d8IhnzDMIgw6kWIWgGiVoP0tBT4egOQJBlBSUIwKCEQkhEM9f0+KMmQJAmSDEAG5L7/QasVoNNqodNpoNNqoBP7/tOLGoiDv9f2HR9y/tx/2r5CZRly3y/9H8oDhwefq/+p+66VgZDUV18gNFCvhGCw/9jAxyEJBqMeHZ3dEAShr4b+WvU67bCPtZq+PwutRoBWK0Cr1UDUCNBqLv7v/FJfi3C7dgfGKPWPRx7yZyDLcv+fdd85acifx+D1kNHRG0RXZw80/XVqNAK0Qt+vA9cHJQk9viCCIRnjR1sgaiO/qz5s6FdWVmL27NkAgKKiItTU1Ayeq66uxsyZM6HX66HX65Gfn4/a2lpUVlZixYoVAIA5c+Zg06ZNGDt27IjXzpgxI+KD6vL6YU3V43/NmYgvTRvFwKeI0ggCrCY9rGH6N8myjEBQgj8gwRcMwR8IwR+Q+n4N9v3qG/y475wvEEK724ezru6wQUTJ7X/PL8C8WZHfQhw29D0eD8zmc73ntVotgsEgRFGEx+OBxXLu3a0mkwkej2fYcZPJBLfbfdFrL8Vuv7p3zt5zhwX33HHdVX3uUHdf5fMTEcWrsD87mM1meL3ewY8lSYIoiiOe83q9sFgsw457vV5YrdaLXktERLETNvSLi4tRUVEBAKiqqkJBQcHguRkzZqCyshI+nw9utxsNDQ0oKChAcXEx9uzZAwCoqKjArFmzLnotERHFjiCHaTwysHunrq4Osixj/fr1qKioQH5+PubNm4fy8nKUlZVBlmWsWLECd911F1pbW7FmzRp4vV7YbDZs3LgRqampI15LRESxEzb0iYgoeaimyyYRETH0iYhUhaFPRKQibEYzgnCtJxJFIBDAE088AYfDAb/fj5UrV2Ly5Ml4/PHHIQgCrrvuOvz85z+HRqPB7373O+zevRuiKOKJJ56IypvmIq2trQ2LFi3Cf/3Xf0EUxaQZFwD8x3/8B9577z0EAgF873vfwy233JIU4wsEAnj88cfhcDig0Wjwi1/8Iim+docOHcJvfvMbbN++HSdPnrzs8Vzs2qiS6QJvv/22vGbNGlmWZfnTTz+VH3roIYUrujp/+9vf5HXr1smyLMsul0ueO3euvGLFCvnDDz+UZVmWn3zySfmdd96Ra2pq5JKSElmSJNnhcMiLFi1SsuzL4vf75Ycfflj++te/LtfX1yfNuGRZlj/88EN5xYoVcigUkj0ej/z8888nzfjeffdd+Uc/+pEsy7K8b98++ZFHHkn4sb3wwgvywoUL5e9+97uyLMtXNJ6Rro02Lu+M4FKtJxLJ3XffjR//+MeDH2u1Whw+fBi33HILgL4WGe+//z4qKytx++23QxAE5OTkIBQKweVyKVX2ZXnmmWewdOlSZGf3NdJLlnEBwL59+1BQUIAf/vCHeOihh3DHHXckzfgmTJiAUCgESZLg8XggimLCjy0/Px+bN28e/PhKxjPStdHG0B/BxVpPJBqTyQSz2QyPx4Mf/ehHePTRRyHL8mAvoqEtMoaOd+B4vHrllVeQkZEx+I0ZQFKMa0B7eztqamrw29/+Fv/+7/+Oxx57LGnGl5qaCofDgQULFuDJJ59ESUlJwo/trrvuGuxSAFzZ38WRro02rumP4FKtJxJNU1MTfvjDH2LZsmW455578Otf/3rwXKK2yHj55ZchCAI++OADHDlyBGvWrBk2C0zUcQ1IT0/HxIkTodfrMXHiRBgMBpw9e3bwfCKP78UXX8Ttt9+O1atXo6mpCQ888AACgcDg+UQe24Cha/LhxjPStVGvL+rPkIAu1XoikbS2tmL58uX413/9V9x7770AgGnTpuHAgQMA+lpk3HTTTSguLsa+ffsgSRLOnDkDSZKQkRG/9xJ+6aWX8Je//AXbt2/H1KlT8cwzz2DOnDkJP64Bs2bNwt69eyHLMpqbm9HT04Mvf/nLSTE+q9U6GN5paWkIBoNJ8XdyqCsZz0jXRhvfkTuCkVpPTJo0Semyrti6devw5ptvYuLEiYPHfvazn2HdunUIBAKYOHEi1q1bB61Wi82bN6OiogKSJOGnP/1pTP7yRUJJSQmeeuopaDQaPPnkk0kzrmeffRYHDhyALMtYtWoV8vLykmJ8Xq8XTzzxBJxOJwKBAO6//35Mnz494cfW2NiIn/zkJygvL8fx48cvezwXuzaaGPpERCrC5R0iIhVh6BMRqQhDn4hIRRj6REQqwtAnIlIRhj6pSmNjIwoLC7F27dphx48cOYLCwkK88sor+Pa3vw0A2Lx587C31xMlA4Y+qU56ejr27t2LUCg0eOyNN94YfPPPa6+9plRpRFHH0CfVMZlMmDp1Kj766KPBY/v378dXvvIVAEBhYeEFn1NRUYF7770X3/nOd/DII4+gvb0dAPDmm29iyZIl+Na3voW7774bn3zyCQCgrq4OixYtwre//W384he/wPz58wH0vUv64YcfxqJFi7B48eKYNNgiGoqhT6q0YMECvP322wCA6upqFBYWQqfTjXity+XCxo0bsXXrVrz66qu4/fbb8Zvf/AaSJKG0tBR//OMf8frrr+MHP/gBXnjhBQDA448/jh//+Md47bXXMHbs2MGfKn75y19i8eLFeOWVV/CHP/wBa9euhcfjic2gicCGa6RSX/3qV/Hcc89BkiS8+eabWLBgAd54440Rrz106BCamppw//33A+hr05GWlgaNRoPf//73eO+993D8+HEcPHgQGo0GHR0dcDgcmDt3LgBg8eLF+POf/wwAeP/993Hs2DE8//zzAIBgMIjTp09j6tSpMRg1EUOfVMpkMmHKlCmorKzEhx9+iNWrV1809EOhEIqLi/HHP/4RAODz+eD1euH1enHvvffiW9/6Fm6++WYUFhbipZdeglarxcW6m0iShD/96U9IT08HALS0tCAzMzM6gyQaAZd3SLUWLFiAjRs3Yvr06ZdsnX3jjTeiqqoKx48fBwBs2bIFzz77LE6cOAFBEPDQQw/h1ltvxbvvvotQKASLxYKxY8diz549AIC///3vg4/1pS99CTt27AAA1NfX45577kFPT08UR0k0HGf6pFp33nknfvaznw27u9hI7HY71q9fj0cffRSSJGHUqFH49a9/DavViqlTp2LBggUQBAG33347KisrAfR1yXziiSfw3HPPobCwEEajEQDwb//2b1i7di3uueeeweuG3lyDKNrYZZMoCn73u99hyZIlyM7OxjvvvIO///3v3PNPcYEzfaIoyMnJwfLlyyGKIqxWK375y18qXRIRAM70iYhUhS/kEhGpCEOfiEhFGPpERCrC0CciUhGGPhGRivx/F/+NcUqWFfoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b897ec4320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can treat the other numerical variables in a similar way\n",
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b8980359b0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b89803a198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This plot looks kind of normal, doesn't it?\n",
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897f681d0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b89806f400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The situation with engine volume is very strange\n",
+    "# In such cases it makes sense to manually check what may be causing the problem\n",
+    "# In our case the issue comes from the fact that most missing values are indicated with 99.99 or 99\n",
+    "# There are also some incorrect entries like 75\n",
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A simple Google search can indicate the natural domain of this variable\n",
+    "# Car engine volumes are usually (always?) below 6.5l\n",
+    "# This is a prime example of the fact that a domain expert (a person working in the car industry)\n",
+    "# may find it much easier to determine problems with the data than an outsider\n",
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b8981a0b00>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b898106240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Following this graph, we realize we can actually treat EngineV as a categorical variable\n",
+    "# Even so, in this course we won't, but that's yet something else you may try on your own\n",
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b89825e6a0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b89827c978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Finally, the situation with 'Year' is similar to 'Price' and 'Mileage'\n",
+    "# However, the outliers are on the low end\n",
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I'll simply remove them\n",
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b898296d30>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b898339160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here's the new result\n",
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When we remove observations, the original indexes are preserved\n",
+    "# If we remove observations with indexes 2 and 3, the indexes will go as: 0,1,4,5,6\n",
+    "# That's very problematic as we tend to forget about it (later you will see an example of such a problem)\n",
+    "\n",
+    "# Finally, once we reset the index, a new column will be created containing the old index (just in case)\n",
+    "# We won't be needing it, thus 'drop=True' to completely forget about it\n",
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's see what's left\n",
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b898401828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we decided to use some matplotlib code, without explaining it\n",
+    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
+    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3)) #sharey -> share 'Price' as y\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2b8994ccdd8>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b8983c59b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# From the subplots and the PDF of price, we can easily determine that 'Price' is exponentially distributed\n",
+    "# A good transformation in that case is a log transformation\n",
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>14200.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>9.560997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10799.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>193</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.287209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>1400.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>212</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>7.244228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>11950.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>177</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.388487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>260</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>7.824046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>9500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>165</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.159047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>16000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>250</td>\n",
+       "      <td>4.70</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>9.680344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>8600.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.059517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>2990.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>203</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>8.003029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>26500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.184900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>3500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>250</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>8.160518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>38233.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>10.551454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>132</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>8.922658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>6800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>225</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>8.824678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>130</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>24900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>163</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.122623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>20800.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>151</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.942708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>330</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13566.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>171</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.515322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>8500.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9.047821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>7.972466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>21500.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>72</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>9.975808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>87</td>\n",
+       "      <td>3.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>28500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>160</td>\n",
+       "      <td>4.80</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.257659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3837</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>180</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3838</th>\n",
+       "      <td>Mitsubishi</td>\n",
+       "      <td>9700.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>247</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.179881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3839</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10500.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>185</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.259131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3840</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>10900.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>180</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.296518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3841</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>25500.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>77</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>10.146434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3842</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>15500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1.40</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.648595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3843</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>9750.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>159</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>9.185023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3844</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>16100.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>194</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>9.686575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3845</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>9200.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>171</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.126959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3846</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>5150.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>240</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>8.546752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3847</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>11.512925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3848</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>8999.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>126</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>9.104869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3849</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>16800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>125</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.729134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3850</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>8200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>280</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>9.011889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3851</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>24950.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.124629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3852</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>80999.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>11.302192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3853</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7300.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>207</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>8.895630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3854</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>21335.0</td>\n",
+       "      <td>other</td>\n",
+       "      <td>105</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.968104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3855</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>45000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>10.714418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3856</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>6750.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>155</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>8.817298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3857</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>7000.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>210</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>8.853665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3858</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>12090.0</td>\n",
+       "      <td>hatch</td>\n",
+       "      <td>145</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>9.400134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3859</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>27900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>38</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>10.236382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3860</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>2100.0</td>\n",
+       "      <td>vagon</td>\n",
+       "      <td>237</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>7.649693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3861</th>\n",
+       "      <td>Renault</td>\n",
+       "      <td>6800.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>152</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>8.824678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277     2.00      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427     2.90      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358     5.00         Gas   \n",
+       "3              Audi   23000.0  crossover      240     4.20      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120     2.00      Petrol   \n",
+       "5              Audi   14200.0      vagon      200     2.70      Diesel   \n",
+       "6           Renault   10799.0      vagon      193     1.50      Diesel   \n",
+       "7        Volkswagen    1400.0      other      212     1.80         Gas   \n",
+       "8           Renault   11950.0      vagon      177     1.50      Diesel   \n",
+       "9           Renault    2500.0      sedan      260     1.79      Petrol   \n",
+       "10             Audi    9500.0      vagon      165     2.70         Gas   \n",
+       "11       Volkswagen   10500.0      sedan      100     1.80      Petrol   \n",
+       "12           Toyota   16000.0  crossover      250     4.70         Gas   \n",
+       "13          Renault    8600.0      hatch       84     1.50      Diesel   \n",
+       "14              BMW    2990.0      other      203     2.00      Petrol   \n",
+       "15           Toyota   26500.0  crossover       21     2.00      Petrol   \n",
+       "16             Audi    3500.0      vagon      250     2.50      Diesel   \n",
+       "17           Toyota   38233.0      other        0     2.40      Diesel   \n",
+       "18       Volkswagen    7500.0      hatch      132     1.40      Diesel   \n",
+       "19             Audi    6800.0      sedan      225     2.40         Gas   \n",
+       "20       Mitsubishi   10500.0  crossover      130     2.40         Gas   \n",
+       "21             Audi   24900.0      sedan      163     4.20      Diesel   \n",
+       "22       Volkswagen   20800.0  crossover      151     3.00      Diesel   \n",
+       "23             Audi    6500.0      sedan      330     2.40      Petrol   \n",
+       "24    Mercedes-Benz   13566.0      other      171     2.20       Other   \n",
+       "25       Mitsubishi    8500.0      hatch       65     1.30      Petrol   \n",
+       "26             Audi    2900.0      sedan        1     2.30         Gas   \n",
+       "27              BMW   21500.0      other       72     3.00      Petrol   \n",
+       "28       Mitsubishi   17900.0  crossover       87     3.80         Gas   \n",
+       "29              BMW   28500.0  crossover      160     4.80         Gas   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3837  Mercedes-Benz   11500.0        van      180     2.20      Diesel   \n",
+       "3838     Mitsubishi    9700.0        van      247     2.40         Gas   \n",
+       "3839        Renault   10500.0      vagon      185     1.50      Diesel   \n",
+       "3840        Renault   10900.0      vagon      180     1.50      Diesel   \n",
+       "3841  Mercedes-Benz   25500.0  crossover       77     3.50      Petrol   \n",
+       "3842     Volkswagen   15500.0      sedan       80     1.40      Petrol   \n",
+       "3843     Volkswagen    9750.0        van      159     1.90      Diesel   \n",
+       "3844            BMW   16100.0  crossover      194     3.00      Diesel   \n",
+       "3845     Volkswagen    9200.0      vagon      171     1.60      Petrol   \n",
+       "3846     Volkswagen    5150.0        van      240     2.00      Diesel   \n",
+       "3847         Toyota  100000.0  crossover        0     4.50      Diesel   \n",
+       "3848        Renault    8999.0      other      126     2.00      Diesel   \n",
+       "3849  Mercedes-Benz   16800.0      sedan      125     1.80      Petrol   \n",
+       "3850  Mercedes-Benz    8200.0      sedan      280     2.40         Gas   \n",
+       "3851  Mercedes-Benz   24950.0      other       60     1.80      Petrol   \n",
+       "3852           Audi   80999.0  crossover        0     3.00      Diesel   \n",
+       "3853  Mercedes-Benz    7300.0        van      207     2.20      Diesel   \n",
+       "3854            BMW   21335.0      other      105     3.00      Petrol   \n",
+       "3855            BMW   45000.0  crossover       80     3.00      Petrol   \n",
+       "3856        Renault    6750.0        van      155     1.50      Diesel   \n",
+       "3857        Renault    7000.0        van      210     1.50      Diesel   \n",
+       "3858            BMW   12090.0      hatch      145     1.60      Petrol   \n",
+       "3859            BMW   27900.0      sedan       38     2.00      Petrol   \n",
+       "3860        Renault    2100.0      vagon      237     1.90      Diesel   \n",
+       "3861        Renault    6800.0      sedan      152     1.60      Petrol   \n",
+       "3862     Volkswagen   11500.0        van      163     2.50      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35     1.60      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9     3.00      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1     3.50      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124     2.00      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "5             yes  2006   9.560997  \n",
+       "6             yes  2012   9.287209  \n",
+       "7              no  1999   7.244228  \n",
+       "8             yes  2011   9.388487  \n",
+       "9             yes  1994   7.824046  \n",
+       "10            yes  2003   9.159047  \n",
+       "11            yes  2008   9.259131  \n",
+       "12            yes  2001   9.680344  \n",
+       "13            yes  2012   9.059517  \n",
+       "14             no  2001   8.003029  \n",
+       "15            yes  2013  10.184900  \n",
+       "16             no  1998   8.160518  \n",
+       "17            yes  2016  10.551454  \n",
+       "18            yes  2006   8.922658  \n",
+       "19            yes  1998   8.824678  \n",
+       "20            yes  2006   9.259131  \n",
+       "21            yes  2008  10.122623  \n",
+       "22            yes  2008   9.942708  \n",
+       "23            yes  1999   8.779557  \n",
+       "24             no  2011   9.515322  \n",
+       "25            yes  2010   9.047821  \n",
+       "26            yes  1989   7.972466  \n",
+       "27            yes  2007   9.975808  \n",
+       "28            yes  2008   9.792556  \n",
+       "29            yes  2008  10.257659  \n",
+       "...           ...   ...        ...  \n",
+       "3837          yes  2011   9.350102  \n",
+       "3838          yes  2008   9.179881  \n",
+       "3839          yes  2011   9.259131  \n",
+       "3840          yes  2012   9.296518  \n",
+       "3841          yes  2008  10.146434  \n",
+       "3842          yes  2013   9.648595  \n",
+       "3843          yes  2009   9.185023  \n",
+       "3844          yes  2004   9.686575  \n",
+       "3845          yes  2008   9.126959  \n",
+       "3846          yes  2005   8.546752  \n",
+       "3847          yes  2016  11.512925  \n",
+       "3848          yes  2012   9.104869  \n",
+       "3849          yes  2008   9.729134  \n",
+       "3850          yes  1997   9.011889  \n",
+       "3851          yes  2013  10.124629  \n",
+       "3852          yes  2016  11.302192  \n",
+       "3853          yes  2003   8.895630  \n",
+       "3854          yes  2008   9.968104  \n",
+       "3855          yes  2011  10.714418  \n",
+       "3856          yes  2012   8.817298  \n",
+       "3857          yes  2005   8.853665  \n",
+       "3858          yes  2010   9.400134  \n",
+       "3859          yes  2013  10.236382  \n",
+       "3860           no  2001   7.649693  \n",
+       "3861          yes  2007   8.824678  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's transform 'Price' with a log transformation\n",
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "\n",
+    "# Then we add it to our data frame\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b89959bfd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# The relationships show a clear linear relationship\n",
+    "# This is some good linear regression material\n",
+    "\n",
+    "# Alternatively we could have transformed each of the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since we will be using the log price variable, we can drop the old 'Price' one\n",
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's quickly see the columns of our data frame\n",
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn does not have a built-in way to check for multicollinearity\n",
+    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
+    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
+    "\n",
+    "# Here's the relevant module\n",
+    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# To make this as easy as possible to use, we declare a variable where we put\n",
+    "# all features where we want to check for multicollinearity\n",
+    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "\n",
+    "# we create a new data frame which will include all the VIFs\n",
+    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
+    "vif = pd.DataFrame()\n",
+    "\n",
+    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "# Finally, I like to include names so it is easier to explore the result\n",
+    "vif[\"Features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF Features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's explore the result\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since Year has the highest VIF, I will remove it from the model\n",
+    "# This will drive the VIF of other variables down!!! \n",
+    "# So even if EngineV seems with a high VIF, too, once 'Year' is gone that will no longer be the case\n",
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To include the categorical data in the regression, let's create dummies\n",
+    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
+    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840          1                    0   \n",
+       "1      427      2.9   8.974618          0                    1   \n",
+       "2      358      5.0   9.495519          0                    1   \n",
+       "3      240      4.2  10.043249          0                    0   \n",
+       "4      120      2.0   9.814656          0                    0   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                 0              0             0                 0   \n",
+       "1                 0              0             0                 0   \n",
+       "2                 0              0             0                 0   \n",
+       "3                 0              0             0                 0   \n",
+       "4                 0              0             1                 0   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0           0           0           1           0         0                0   \n",
+       "1           0           0           0           0         1                0   \n",
+       "2           0           0           1           0         0                1   \n",
+       "3           0           0           0           0         0                0   \n",
+       "4           0           0           0           0         0                0   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0                  0                   1                 1  \n",
+       "1                  0                   0                 1  \n",
+       "2                  0                   0                 1  \n",
+       "3                  0                   1                 1  \n",
+       "4                  0                   1                 1  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here's the result\n",
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df\n",
+    "# Since each problem is different, that must be done manually\n",
+    "# We can display all possible features and then choose the desired order\n",
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To make the code a bit more parametrized, let's declare a new variable that will contain the preferred order\n",
+    "# If you want a different order, just specify it here\n",
+    "# Conventionally, the most intuitive order is: dependent variable, indepedendent numerical variables, dummies\n",
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0          1                    0   \n",
+       "1   8.974618      427      2.9          0                    1   \n",
+       "2   9.495519      358      5.0          0                    1   \n",
+       "3  10.043249      240      4.2          0                    0   \n",
+       "4   9.814656      120      2.0          0                    0   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                 0              0             0                 0   \n",
+       "1                 0              0             0                 0   \n",
+       "2                 0              0             0                 0   \n",
+       "3                 0              0             0                 0   \n",
+       "4                 0              0             1                 0   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0           0           0           1           0         0                0   \n",
+       "1           0           0           0           0         1                0   \n",
+       "2           0           0           1           0         0                1   \n",
+       "3           0           0           0           0         0                0   \n",
+       "4           0           0           0           0         0                0   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0                  0                   1                 1  \n",
+       "1                  0                   0                 1  \n",
+       "2                  0                   0                 1  \n",
+       "3                  0                   1                 1  \n",
+       "4                  0                   1                 1  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To implement the reordering, we will create a new df, which is equal to the old one but with the new order of features\n",
+    "data_preprocessed = data_with_dummies[cols]\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the inputs and the targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The target(s) (dependent variable) is 'log price'\n",
+    "targets = data_preprocessed['log_price']\n",
+    "\n",
+    "# The inputs are everything BUT the dependent variable, so we can simply drop it\n",
+    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scale the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StandardScaler(copy=True, with_mean=True, with_std=True)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import the scaling module\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# Create a scaler object\n",
+    "scaler = StandardScaler()\n",
+    "# Fit the inputs (calculate the mean and standard deviation feature-wise)\n",
+    "scaler.fit(inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale the features and store them in a new variable (the actual scaling procedure)\n",
+    "inputs_scaled = scaler.transform(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the module for the split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Split the variables with an 80-20 split and some random state\n",
+    "# To have the same split as mine, use random_state = 365\n",
+    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, targets, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a linear regression object\n",
+    "reg = LinearRegression()\n",
+    "# Fit the regression with the scaled TRAIN inputs and targets\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's check the outputs of the regression\n",
+    "# I'll store them in y_hat as this is the 'theoretical' name of the predictions\n",
+    "y_hat = reg.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b8983e8320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot\n",
+    "# The closer the points to the 45-degree line, the better the prediction\n",
+    "plt.scatter(y_train, y_hat)\n",
+    "# Let's also name the axes\n",
+    "plt.xlabel('Targets (y_train)',size=18)\n",
+    "plt.ylabel('Predictions (y_hat)',size=18)\n",
+    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
+    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
+    "# We want the x-axis and the y-axis to be the same\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'Residuals PDF')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2b8996b8898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Another useful check of our model is a residual plot\n",
+    "# We can plot the PDF of the residuals and check for anomalies\n",
+    "sns.distplot(y_train - y_hat)\n",
+    "\n",
+    "# Include a title\n",
+    "plt.title(\"Residuals PDF\", size=18)\n",
+    "\n",
+    "# In the best case scenario this plot should be normally distributed\n",
+    "# In our case we notice that there are many negative residuals (far away from the mean)\n",
+    "# Given the definition of the residuals (y_train - y_hat), negative values imply\n",
+    "# that y_hat (predictions) are much higher than y_train (the targets)\n",
+    "# This is food for thought to improve our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.744996578792662"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find the R-squared of the model\n",
+    "reg.score(x_train,y_train)\n",
+    "\n",
+    "# Note that this is NOT the adjusted R-squared\n",
+    "# in other words... find the Adjusted R-squared to have the appropriate measure :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.415239458021299"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the bias (intercept) of the regression\n",
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.44871341,  0.20903483,  0.0142496 ,  0.01288174, -0.14055166,\n",
+       "       -0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,\n",
+       "       -0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,\n",
+       "       -0.14690868,  0.32047333])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the weights (coefficients) of the regression\n",
+    "reg.coef_\n",
+    "\n",
+    "# Note that they are barely interpretable if at all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-0.448713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>0.209035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>0.014250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>0.012882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>-0.140552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>-0.179909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>-0.060550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>-0.089924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>-0.145469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>-0.101444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-0.200630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-0.129887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-0.168597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-0.121490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-0.033368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-0.146909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>0.320473</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features   Weights\n",
+       "0               Mileage -0.448713\n",
+       "1               EngineV  0.209035\n",
+       "2             Brand_BMW  0.014250\n",
+       "3   Brand_Mercedes-Benz  0.012882\n",
+       "4      Brand_Mitsubishi -0.140552\n",
+       "5         Brand_Renault -0.179909\n",
+       "6          Brand_Toyota -0.060550\n",
+       "7      Brand_Volkswagen -0.089924\n",
+       "8            Body_hatch -0.145469\n",
+       "9            Body_other -0.101444\n",
+       "10           Body_sedan -0.200630\n",
+       "11           Body_vagon -0.129887\n",
+       "12             Body_van -0.168597\n",
+       "13      Engine Type_Gas -0.121490\n",
+       "14    Engine Type_Other -0.033368\n",
+       "15   Engine Type_Petrol -0.146909\n",
+       "16     Registration_yes  0.320473"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a regression summary where we can compare them with one-another\n",
+    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
+    "reg_summary['Weights'] = reg.coef_\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the different categories in the 'Brand' variable\n",
+    "data_cleaned['Brand'].unique()\n",
+    "\n",
+    "# In this way we can see which 'Brand' is actually the benchmark"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+5).ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+5).ipynb
new file mode 100644
index 0000000..477f82b
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+5).ipynb	
@@ -0,0 +1,9950 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Real-life+example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\216991009.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\2589761964.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_1['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\534165355.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\1950307955.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_2['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\823394628.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\1902427236.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_3['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\534082538.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\2748958191.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_4['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\462586877.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_cleaned['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "...           ...   ...        ...  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "vif = pd.DataFrame()\n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "vif[\"features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0   8.342840       True                False   \n",
+       "1      427      2.9   8.974618      False                 True   \n",
+       "2      358      5.0   9.495519      False                 True   \n",
+       "3      240      4.2  10.043249      False                False   \n",
+       "4      120      2.0   9.814656      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0       True                False   \n",
+       "1   8.974618      427      2.9      False                 True   \n",
+       "2   9.495519      358      5.0      False                 True   \n",
+       "3  10.043249      240      4.2      False                False   \n",
+       "4   9.814656      120      2.0      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed = data_with_dummies[cols]\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the inputs and the targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "targets = data_preprocessed['log_price']\n",
+    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scale the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs_scaled = scaler.transform(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, targets, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat = reg.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_train, y_hat)\n",
+    "plt.xlabel('Targets (y_train)',size=18)\n",
+    "plt.ylabel('Predictions (y_hat)',size=18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_764\\329104437.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(y_train - y_hat)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Residuals PDF')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(y_train - y_hat)\n",
+    "plt.title(\"Residuals PDF\", size=18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.744996578792662"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.415239458021299"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.44871341,  0.20903483,  0.0142496 ,  0.01288174, -0.14055166,\n",
+       "       -0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,\n",
+       "       -0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,\n",
+       "       -0.14690868,  0.32047333])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-0.448713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>0.209035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>0.014250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>0.012882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>-0.140552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>-0.179909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>-0.060550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>-0.089924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>-0.145469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>-0.101444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-0.200630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-0.129887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-0.168597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-0.121490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-0.033368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-0.146909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>0.320473</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features   Weights\n",
+       "0               Mileage -0.448713\n",
+       "1               EngineV  0.209035\n",
+       "2             Brand_BMW  0.014250\n",
+       "3   Brand_Mercedes-Benz  0.012882\n",
+       "4      Brand_Mitsubishi -0.140552\n",
+       "5         Brand_Renault -0.179909\n",
+       "6          Brand_Toyota -0.060550\n",
+       "7      Brand_Volkswagen -0.089924\n",
+       "8            Body_hatch -0.145469\n",
+       "9            Body_other -0.101444\n",
+       "10           Body_sedan -0.200630\n",
+       "11           Body_vagon -0.129887\n",
+       "12             Body_van -0.168597\n",
+       "13      Engine Type_Gas -0.121490\n",
+       "14    Engine Type_Other -0.033368\n",
+       "15   Engine Type_Petrol -0.146909\n",
+       "16     Registration_yes  0.320473"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
+    "reg_summary['Weights'] = reg.coef_\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned['Brand'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat_test = reg.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
+    "plt.xlabel('Targets (y_test)',size=18)\n",
+    "plt.ylabel('Predictions (y_hat_test)',size=18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.501696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction\n",
+       "0  10685.501696\n",
+       "1   3499.255242\n",
+       "2   7553.285218\n",
+       "3   7463.963017\n",
+       "4  11353.490075"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.501696</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "      <td>7900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.726363</td>\n",
+       "      <td>6950.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.071179</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.446953</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.751303</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.163043</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Prediction  Target\n",
+       "0    10685.501696     NaN\n",
+       "1     3499.255242  7900.0\n",
+       "2     7553.285218     NaN\n",
+       "3     7463.963017     NaN\n",
+       "4    11353.490075     NaN\n",
+       "..            ...     ...\n",
+       "769  29651.726363  6950.0\n",
+       "770  10732.071179     NaN\n",
+       "771  13922.446953     NaN\n",
+       "772  27487.751303     NaN\n",
+       "773  13491.163043     NaN\n",
+       "\n",
+       "[774 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    7.740664\n",
+       "1    7.937375\n",
+       "2    7.824046\n",
+       "3    8.764053\n",
+       "4    9.121509\n",
+       "Name: log_price, dtype: float64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test = y_test.reset_index(drop=True)\n",
+    "y_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "      <td>2800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "      <td>2500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "      <td>6400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "      <td>9150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.726363</td>\n",
+       "      <td>29500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.071179</td>\n",
+       "      <td>9600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.446953</td>\n",
+       "      <td>18300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.751303</td>\n",
+       "      <td>68500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.163043</td>\n",
+       "      <td>10800.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Prediction   Target\n",
+       "0    10685.501696   2300.0\n",
+       "1     3499.255242   2800.0\n",
+       "2     7553.285218   2500.0\n",
+       "3     7463.963017   6400.0\n",
+       "4    11353.490075   9150.0\n",
+       "..            ...      ...\n",
+       "769  29651.726363  29500.0\n",
+       "770  10732.071179   9600.0\n",
+       "771  13922.446953  18300.0\n",
+       "772  27487.751303  68500.0\n",
+       "773  13491.163043  10800.0\n",
+       "\n",
+       "[774 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_pf['Residual'] = df_pf['Target'] - df_pf['Prediction']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.501696</td>\n",
+       "      <td>2300.0</td>\n",
+       "      <td>-8385.501696</td>\n",
+       "      <td>364.587030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "      <td>2800.0</td>\n",
+       "      <td>-699.255242</td>\n",
+       "      <td>24.973402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>-5053.285218</td>\n",
+       "      <td>202.131409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "      <td>6400.0</td>\n",
+       "      <td>-1063.963017</td>\n",
+       "      <td>16.624422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "      <td>9150.0</td>\n",
+       "      <td>-2203.490075</td>\n",
+       "      <td>24.081859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.726363</td>\n",
+       "      <td>29500.0</td>\n",
+       "      <td>-151.726363</td>\n",
+       "      <td>0.514327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.071179</td>\n",
+       "      <td>9600.0</td>\n",
+       "      <td>-1132.071179</td>\n",
+       "      <td>11.792408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.446953</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>4377.553047</td>\n",
+       "      <td>23.921055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.751303</td>\n",
+       "      <td>68500.0</td>\n",
+       "      <td>41012.248697</td>\n",
+       "      <td>59.871896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.163043</td>\n",
+       "      <td>10800.0</td>\n",
+       "      <td>-2691.163043</td>\n",
+       "      <td>24.918176</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Prediction   Target      Residual  Difference%\n",
+       "0    10685.501696   2300.0  -8385.501696   364.587030\n",
+       "1     3499.255242   2800.0   -699.255242    24.973402\n",
+       "2     7553.285218   2500.0  -5053.285218   202.131409\n",
+       "3     7463.963017   6400.0  -1063.963017    16.624422\n",
+       "4    11353.490075   9150.0  -2203.490075    24.081859\n",
+       "..            ...      ...           ...          ...\n",
+       "769  29651.726363  29500.0   -151.726363     0.514327\n",
+       "770  10732.071179   9600.0  -1132.071179    11.792408\n",
+       "771  13922.446953  18300.0   4377.553047    23.921055\n",
+       "772  27487.751303  68500.0  41012.248697    59.871896\n",
+       "773  13491.163043  10800.0  -2691.163043    24.918176\n",
+       "\n",
+       "[774 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
+    "df_pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>774.000000</td>\n",
+       "      <td>774.000000</td>\n",
+       "      <td>774.000000</td>\n",
+       "      <td>774.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>15946.760167</td>\n",
+       "      <td>18165.817106</td>\n",
+       "      <td>2219.056939</td>\n",
+       "      <td>36.256693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>13133.197604</td>\n",
+       "      <td>19967.858908</td>\n",
+       "      <td>10871.218143</td>\n",
+       "      <td>55.066507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1320.562768</td>\n",
+       "      <td>1200.000000</td>\n",
+       "      <td>-29456.498331</td>\n",
+       "      <td>0.062794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7413.644234</td>\n",
+       "      <td>6900.000000</td>\n",
+       "      <td>-2044.191251</td>\n",
+       "      <td>12.108022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>11568.168859</td>\n",
+       "      <td>11600.000000</td>\n",
+       "      <td>142.518577</td>\n",
+       "      <td>23.467728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>20162.408805</td>\n",
+       "      <td>20500.000000</td>\n",
+       "      <td>3147.343497</td>\n",
+       "      <td>39.563570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>77403.055224</td>\n",
+       "      <td>126000.000000</td>\n",
+       "      <td>85106.162329</td>\n",
+       "      <td>512.688080</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Prediction         Target      Residual  Difference%\n",
+       "count    774.000000     774.000000    774.000000   774.000000\n",
+       "mean   15946.760167   18165.817106   2219.056939    36.256693\n",
+       "std    13133.197604   19967.858908  10871.218143    55.066507\n",
+       "min     1320.562768    1200.000000 -29456.498331     0.062794\n",
+       "25%     7413.644234    6900.000000  -2044.191251    12.108022\n",
+       "50%    11568.168859   11600.000000    142.518577    23.467728\n",
+       "75%    20162.408805   20500.000000   3147.343497    39.563570\n",
+       "max    77403.055224  126000.000000  85106.162329   512.688080"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>30480.85</td>\n",
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+       "      <td>19.15</td>\n",
+       "      <td>0.06</td>\n",
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+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>16960.31</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>38.69</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>12469.21</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>30.79</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>25614.14</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>-114.14</td>\n",
+       "      <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>42703.68</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>-203.68</td>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>3084.69</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>15.31</td>\n",
+       "      <td>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.73</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>-151.73</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>9749.53</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>50.47</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>23118.07</td>\n",
+       "      <td>22999.00</td>\n",
+       "      <td>-119.07</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>8734.58</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>65.42</td>\n",
+       "      <td>0.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>3473.79</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>26.21</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>21174.10</td>\n",
+       "      <td>21335.00</td>\n",
+       "      <td>160.90</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>8967.74</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-67.74</td>\n",
+       "      <td>0.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>665</th>\n",
+       "      <td>17858.02</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>141.98</td>\n",
+       "      <td>0.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>17654.84</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>145.16</td>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>11391.95</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>108.05</td>\n",
+       "      <td>0.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>28625.56</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>274.44</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>75.83</td>\n",
+       "      <td>0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>6429.03</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>70.97</td>\n",
+       "      <td>1.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>7597.39</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-97.39</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>18555.09</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>244.91</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>7396.87</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-96.87</td>\n",
+       "      <td>1.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>16178.14</td>\n",
+       "      <td>16400.00</td>\n",
+       "      <td>221.86</td>\n",
+       "      <td>1.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>12327.10</td>\n",
+       "      <td>12499.00</td>\n",
+       "      <td>171.90</td>\n",
+       "      <td>1.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>51287.19</td>\n",
+       "      <td>52055.25</td>\n",
+       "      <td>768.06</td>\n",
+       "      <td>1.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>6009.63</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>90.37</td>\n",
+       "      <td>1.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>4973.17</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-73.17</td>\n",
+       "      <td>1.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>27716.14</td>\n",
+       "      <td>27300.00</td>\n",
+       "      <td>-416.14</td>\n",
+       "      <td>1.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>28950.00</td>\n",
+       "      <td>451.09</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>2953.17</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>46.83</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>26425.14</td>\n",
+       "      <td>25999.00</td>\n",
+       "      <td>-426.14</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>13111.91</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-211.91</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>12858.08</td>\n",
+       "      <td>12650.00</td>\n",
+       "      <td>-208.08</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>13421.16</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>-221.16</td>\n",
+       "      <td>1.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>7327.18</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-127.18</td>\n",
+       "      <td>1.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>3816.33</td>\n",
+       "      <td>3750.00</td>\n",
+       "      <td>-66.33</td>\n",
+       "      <td>1.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>9721.50</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>178.50</td>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>44173.72</td>\n",
+       "      <td>44999.00</td>\n",
+       "      <td>825.28</td>\n",
+       "      <td>1.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>40753.58</td>\n",
+       "      <td>40000.00</td>\n",
+       "      <td>-753.58</td>\n",
+       "      <td>1.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>22262.80</td>\n",
+       "      <td>22711.65</td>\n",
+       "      <td>448.85</td>\n",
+       "      <td>1.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>18231.44</td>\n",
+       "      <td>18600.00</td>\n",
+       "      <td>368.56</td>\n",
+       "      <td>1.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>9596.94</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>-196.94</td>\n",
+       "      <td>2.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>6067.79</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>132.21</td>\n",
+       "      <td>2.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>35371.16</td>\n",
+       "      <td>34600.00</td>\n",
+       "      <td>-771.16</td>\n",
+       "      <td>2.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>11967.39</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-267.39</td>\n",
+       "      <td>2.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>17908.00</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>-408.00</td>\n",
+       "      <td>2.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8707.13</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-207.13</td>\n",
+       "      <td>2.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>29049.27</td>\n",
+       "      <td>28350.00</td>\n",
+       "      <td>-699.27</td>\n",
+       "      <td>2.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>14513.46</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>386.54</td>\n",
+       "      <td>2.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>20453.89</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>546.11</td>\n",
+       "      <td>2.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>15383.35</td>\n",
+       "      <td>14990.00</td>\n",
+       "      <td>-393.35</td>\n",
+       "      <td>2.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>24642.50</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-642.50</td>\n",
+       "      <td>2.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>50099.92</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>1400.08</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>16133.86</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-433.86</td>\n",
+       "      <td>2.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>17489.92</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>510.08</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>1894.40</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>55.60</td>\n",
+       "      <td>2.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>30810.25</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>-910.25</td>\n",
+       "      <td>3.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>8721.97</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>277.03</td>\n",
+       "      <td>3.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>18866.50</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>-566.50</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>5958.63</td>\n",
+       "      <td>6150.00</td>\n",
+       "      <td>191.37</td>\n",
+       "      <td>3.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>5811.74</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>188.26</td>\n",
+       "      <td>3.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>9800.43</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-300.43</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>7324.63</td>\n",
+       "      <td>7100.00</td>\n",
+       "      <td>-224.63</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>12583.52</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>416.48</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>10115.13</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-315.13</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>11156.38</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-356.38</td>\n",
+       "      <td>3.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>9279.28</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>320.72</td>\n",
+       "      <td>3.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>31369.37</td>\n",
+       "      <td>32500.00</td>\n",
+       "      <td>1130.63</td>\n",
+       "      <td>3.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>2315.71</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>84.29</td>\n",
+       "      <td>3.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5175.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-175.77</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>21611.83</td>\n",
+       "      <td>22400.00</td>\n",
+       "      <td>788.17</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>7976.26</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-276.26</td>\n",
+       "      <td>3.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>24874.86</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-875.86</td>\n",
+       "      <td>3.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>36516.35</td>\n",
+       "      <td>37900.00</td>\n",
+       "      <td>1383.65</td>\n",
+       "      <td>3.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>4666.05</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-166.05</td>\n",
+       "      <td>3.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>18672.68</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>-672.68</td>\n",
+       "      <td>3.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>14815.83</td>\n",
+       "      <td>15400.00</td>\n",
+       "      <td>584.17</td>\n",
+       "      <td>3.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>10581.62</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>417.38</td>\n",
+       "      <td>3.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>12663.54</td>\n",
+       "      <td>12200.00</td>\n",
+       "      <td>-463.54</td>\n",
+       "      <td>3.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>1825.44</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>74.56</td>\n",
+       "      <td>3.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>12247.90</td>\n",
+       "      <td>12750.00</td>\n",
+       "      <td>502.10</td>\n",
+       "      <td>3.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>5930.73</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>-230.73</td>\n",
+       "      <td>4.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>9782.47</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>417.53</td>\n",
+       "      <td>4.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>12452.22</td>\n",
+       "      <td>11960.00</td>\n",
+       "      <td>-492.22</td>\n",
+       "      <td>4.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>23163.87</td>\n",
+       "      <td>24171.42</td>\n",
+       "      <td>1007.55</td>\n",
+       "      <td>4.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>3246.94</td>\n",
+       "      <td>3390.00</td>\n",
+       "      <td>143.06</td>\n",
+       "      <td>4.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>12354.16</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>545.84</td>\n",
+       "      <td>4.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>33499.44</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>1500.56</td>\n",
+       "      <td>4.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>16257.03</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>741.97</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>8415.81</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>384.19</td>\n",
+       "      <td>4.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>12318.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>581.61</td>\n",
+       "      <td>4.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>2765.14</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>134.86</td>\n",
+       "      <td>4.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>11420.95</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>578.05</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>2725.40</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-125.40</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>23624.69</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-1124.69</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11377.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>621.16</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>12096.18</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-596.18</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>9363.27</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-463.27</td>\n",
+       "      <td>5.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>12311.47</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-611.47</td>\n",
+       "      <td>5.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>14218.94</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-718.94</td>\n",
+       "      <td>5.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>8431.89</td>\n",
+       "      <td>7999.00</td>\n",
+       "      <td>-432.89</td>\n",
+       "      <td>5.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>37600.15</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>2299.85</td>\n",
+       "      <td>5.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>39882.67</td>\n",
+       "      <td>37700.00</td>\n",
+       "      <td>-2182.67</td>\n",
+       "      <td>5.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>20588.57</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>1311.43</td>\n",
+       "      <td>5.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>6861.84</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>438.16</td>\n",
+       "      <td>6.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>18982.15</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-1082.15</td>\n",
+       "      <td>6.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>15067.85</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>-867.85</td>\n",
+       "      <td>6.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>797.71</td>\n",
+       "      <td>6.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>11147.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-647.91</td>\n",
+       "      <td>6.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>20183.30</td>\n",
+       "      <td>18999.00</td>\n",
+       "      <td>-1184.30</td>\n",
+       "      <td>6.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>17334.11</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>1165.89</td>\n",
+       "      <td>6.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>21289.80</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-1289.80</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>6826.78</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>473.22</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>4472.47</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-272.47</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>53294.61</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>3705.39</td>\n",
+       "      <td>6.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>6658.73</td>\n",
+       "      <td>6250.00</td>\n",
+       "      <td>-408.73</td>\n",
+       "      <td>6.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>19001.29</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>-1201.29</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>-290.49</td>\n",
+       "      <td>6.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>43236.92</td>\n",
+       "      <td>40500.00</td>\n",
+       "      <td>-2736.92</td>\n",
+       "      <td>6.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>9076.42</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-576.42</td>\n",
+       "      <td>6.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>10159.18</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>740.82</td>\n",
+       "      <td>6.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>16027.02</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-1027.02</td>\n",
+       "      <td>6.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>13502.35</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>997.65</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>6413.26</td>\n",
+       "      <td>5999.00</td>\n",
+       "      <td>-414.26</td>\n",
+       "      <td>6.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>49221.63</td>\n",
+       "      <td>52999.00</td>\n",
+       "      <td>3777.37</td>\n",
+       "      <td>7.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>21816.43</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>1683.57</td>\n",
+       "      <td>7.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>11969.66</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>930.34</td>\n",
+       "      <td>7.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>8263.95</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-563.95</td>\n",
+       "      <td>7.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11581.54</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>918.46</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>6977.90</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-477.90</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>6978.91</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-478.91</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>16392.22</td>\n",
+       "      <td>17700.00</td>\n",
+       "      <td>1307.78</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>15742.41</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>1256.59</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>7869.47</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>630.53</td>\n",
+       "      <td>7.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>21297.65</td>\n",
+       "      <td>19800.00</td>\n",
+       "      <td>-1497.65</td>\n",
+       "      <td>7.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>6893.62</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-493.62</td>\n",
+       "      <td>7.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>15627.03</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>-1127.03</td>\n",
+       "      <td>7.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>3411.53</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>288.47</td>\n",
+       "      <td>7.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>11336.71</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>963.29</td>\n",
+       "      <td>7.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>8195.34</td>\n",
+       "      <td>7600.00</td>\n",
+       "      <td>-595.34</td>\n",
+       "      <td>7.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>16717.08</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-1217.08</td>\n",
+       "      <td>7.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>7772.57</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-572.57</td>\n",
+       "      <td>7.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>16197.42</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-1197.42</td>\n",
+       "      <td>7.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>6102.36</td>\n",
+       "      <td>5650.00</td>\n",
+       "      <td>-452.36</td>\n",
+       "      <td>8.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>4488.43</td>\n",
+       "      <td>4150.00</td>\n",
+       "      <td>-338.43</td>\n",
+       "      <td>8.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>15885.66</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>1414.34</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>17983.10</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>1616.90</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>7045.72</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-545.72</td>\n",
+       "      <td>8.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-295.06</td>\n",
+       "      <td>8.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>6130.96</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>569.04</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>5947.51</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>552.49</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>7609.28</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-609.28</td>\n",
+       "      <td>8.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8117.44</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>782.56</td>\n",
+       "      <td>8.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>11098.18</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-898.18</td>\n",
+       "      <td>8.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>17028.45</td>\n",
+       "      <td>18700.00</td>\n",
+       "      <td>1671.55</td>\n",
+       "      <td>8.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19847.25</td>\n",
+       "      <td>18200.00</td>\n",
+       "      <td>-1647.25</td>\n",
+       "      <td>9.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>682</th>\n",
+       "      <td>5454.34</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-454.34</td>\n",
+       "      <td>9.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>20662.46</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>-1762.46</td>\n",
+       "      <td>9.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11696.25</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>1203.75</td>\n",
+       "      <td>9.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>10716.84</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-916.84</td>\n",
+       "      <td>9.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>13459.92</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>-1159.92</td>\n",
+       "      <td>9.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>8767.54</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>932.46</td>\n",
+       "      <td>9.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>23272.83</td>\n",
+       "      <td>25749.75</td>\n",
+       "      <td>2476.92</td>\n",
+       "      <td>9.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>5484.97</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-485.97</td>\n",
+       "      <td>9.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>9495.62</td>\n",
+       "      <td>8650.00</td>\n",
+       "      <td>-845.62</td>\n",
+       "      <td>9.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>11858.48</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1058.48</td>\n",
+       "      <td>9.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>48535.50</td>\n",
+       "      <td>-4777.31</td>\n",
+       "      <td>9.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>47047.70</td>\n",
+       "      <td>52300.00</td>\n",
+       "      <td>5252.30</td>\n",
+       "      <td>10.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>6717.17</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>-617.17</td>\n",
+       "      <td>10.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>5391.77</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>608.23</td>\n",
+       "      <td>10.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>2008.91</td>\n",
+       "      <td>10.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>2008.91</td>\n",
+       "      <td>10.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>25576.58</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>2923.42</td>\n",
+       "      <td>10.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>20367.25</td>\n",
+       "      <td>22700.00</td>\n",
+       "      <td>2332.75</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>7267.36</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>832.64</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>1792.36</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>206.64</td>\n",
+       "      <td>10.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>8248.67</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>951.33</td>\n",
+       "      <td>10.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>5643.45</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>656.55</td>\n",
+       "      <td>10.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>6839.57</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>810.43</td>\n",
+       "      <td>10.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>41503.90</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-4003.90</td>\n",
+       "      <td>10.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>9310.69</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>-910.69</td>\n",
+       "      <td>10.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>35662.26</td>\n",
+       "      <td>40000.00</td>\n",
+       "      <td>4337.74</td>\n",
+       "      <td>10.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>16407.29</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-1607.29</td>\n",
+       "      <td>10.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>21303.57</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>2596.43</td>\n",
+       "      <td>10.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>15872.91</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-1572.91</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>10332.54</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>-1032.54</td>\n",
+       "      <td>11.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>18575.43</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>2324.57</td>\n",
+       "      <td>11.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>8882.65</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1117.35</td>\n",
+       "      <td>11.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>7797.18</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-798.18</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10621.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>1377.16</td>\n",
+       "      <td>11.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.07</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1132.07</td>\n",
+       "      <td>11.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>2381.01</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>318.99</td>\n",
+       "      <td>11.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>11200.20</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-1201.20</td>\n",
+       "      <td>12.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>2373.66</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>326.34</td>\n",
+       "      <td>12.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>10696.56</td>\n",
+       "      <td>12179.00</td>\n",
+       "      <td>1482.44</td>\n",
+       "      <td>12.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>3590.97</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-390.97</td>\n",
+       "      <td>12.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>60500.00</td>\n",
+       "      <td>7436.78</td>\n",
+       "      <td>12.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>7300.05</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-800.05</td>\n",
+       "      <td>12.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>6392.70</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>907.30</td>\n",
+       "      <td>12.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>6746.43</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-746.43</td>\n",
+       "      <td>12.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>9192.06</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>1307.94</td>\n",
+       "      <td>12.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>11034.66</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-1234.66</td>\n",
+       "      <td>12.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>16939.86</td>\n",
+       "      <td>19400.00</td>\n",
+       "      <td>2460.14</td>\n",
+       "      <td>12.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2479.43</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-279.43</td>\n",
+       "      <td>12.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9593.52</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>1406.48</td>\n",
+       "      <td>12.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>23967.81</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>3532.19</td>\n",
+       "      <td>12.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>7491.01</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>1108.99</td>\n",
+       "      <td>12.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-942.62</td>\n",
+       "      <td>12.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>9489.56</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>1410.44</td>\n",
+       "      <td>12.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>44312.98</td>\n",
+       "      <td>50900.00</td>\n",
+       "      <td>6587.02</td>\n",
+       "      <td>12.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>9829.53</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1129.53</td>\n",
+       "      <td>12.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>9269.68</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-1069.68</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>-525.77</td>\n",
+       "      <td>13.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>23650.15</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-2750.15</td>\n",
+       "      <td>13.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>8850.21</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1349.79</td>\n",
+       "      <td>13.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>4770.52</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>729.48</td>\n",
+       "      <td>13.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>29479.97</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>4520.03</td>\n",
+       "      <td>13.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>8316.92</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>1283.08</td>\n",
+       "      <td>13.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>24974.57</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>3925.43</td>\n",
+       "      <td>13.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>14943.75</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>2356.25</td>\n",
+       "      <td>13.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>33000.00</td>\n",
+       "      <td>4501.09</td>\n",
+       "      <td>13.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>14591.35</td>\n",
+       "      <td>16900.00</td>\n",
+       "      <td>2308.65</td>\n",
+       "      <td>13.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10358.58</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>1641.42</td>\n",
+       "      <td>13.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>13074.74</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-1574.74</td>\n",
+       "      <td>13.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>9097.13</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1097.13</td>\n",
+       "      <td>13.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>15010.51</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>-1810.51</td>\n",
+       "      <td>13.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>12593.00</td>\n",
+       "      <td>14600.00</td>\n",
+       "      <td>2007.00</td>\n",
+       "      <td>13.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>20246.04</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>3253.96</td>\n",
+       "      <td>13.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>8768.29</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-1068.29</td>\n",
+       "      <td>13.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>39744.80</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>-4844.80</td>\n",
+       "      <td>13.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>9909.62</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1209.62</td>\n",
+       "      <td>13.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>44680.77</td>\n",
+       "      <td>51900.00</td>\n",
+       "      <td>7219.23</td>\n",
+       "      <td>13.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>21942.05</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>3557.95</td>\n",
+       "      <td>13.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>12900.31</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>2099.69</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>18128.32</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-2228.32</td>\n",
+       "      <td>14.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>4296.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>703.23</td>\n",
+       "      <td>14.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>6006.27</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>992.73</td>\n",
+       "      <td>14.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>1972.28</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>327.72</td>\n",
+       "      <td>14.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>8236.36</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1036.36</td>\n",
+       "      <td>14.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>10612.51</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>1787.49</td>\n",
+       "      <td>14.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>14197.26</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>-1797.26</td>\n",
+       "      <td>14.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>47013.28</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>7986.72</td>\n",
+       "      <td>14.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>12028.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-1528.91</td>\n",
+       "      <td>14.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>3666.31</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-466.31</td>\n",
+       "      <td>14.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>2860.97</td>\n",
+       "      <td>3350.00</td>\n",
+       "      <td>489.03</td>\n",
+       "      <td>14.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>7256.86</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1243.14</td>\n",
+       "      <td>14.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>10499.60</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>1800.40</td>\n",
+       "      <td>14.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>12383.35</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1583.35</td>\n",
+       "      <td>14.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>11937.31</td>\n",
+       "      <td>13995.00</td>\n",
+       "      <td>2057.69</td>\n",
+       "      <td>14.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2810.71</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-360.71</td>\n",
+       "      <td>14.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>7664.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>1335.68</td>\n",
+       "      <td>14.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>8041.62</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-1042.62</td>\n",
+       "      <td>14.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>13334.19</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>-1734.19</td>\n",
+       "      <td>14.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>13680.48</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1780.48</td>\n",
+       "      <td>14.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>13500.69</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>2399.31</td>\n",
+       "      <td>15.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>11638.89</td>\n",
+       "      <td>13708.50</td>\n",
+       "      <td>2069.61</td>\n",
+       "      <td>15.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>15700.53</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>2799.47</td>\n",
+       "      <td>15.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>11425.01</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>2074.99</td>\n",
+       "      <td>15.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>63000.00</td>\n",
+       "      <td>9687.19</td>\n",
+       "      <td>15.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>6599.62</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>1200.38</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>61639.73</td>\n",
+       "      <td>72900.00</td>\n",
+       "      <td>11260.27</td>\n",
+       "      <td>15.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1301.47</td>\n",
+       "      <td>15.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>10889.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>2010.61</td>\n",
+       "      <td>15.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>2613.97</td>\n",
+       "      <td>3099.00</td>\n",
+       "      <td>485.03</td>\n",
+       "      <td>15.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>23721.58</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-3221.58</td>\n",
+       "      <td>15.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>10133.68</td>\n",
+       "      <td>8750.00</td>\n",
+       "      <td>-1383.68</td>\n",
+       "      <td>15.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>22261.50</td>\n",
+       "      <td>19200.00</td>\n",
+       "      <td>-3061.50</td>\n",
+       "      <td>15.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>10448.50</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-1449.50</td>\n",
+       "      <td>16.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>24271.90</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-3371.90</td>\n",
+       "      <td>16.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>14926.69</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>2873.31</td>\n",
+       "      <td>16.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>11619.16</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>-1619.16</td>\n",
+       "      <td>16.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>46465.02</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>-6565.02</td>\n",
+       "      <td>16.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>50954.68</td>\n",
+       "      <td>61000.00</td>\n",
+       "      <td>10045.32</td>\n",
+       "      <td>16.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6848.88</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>1351.12</td>\n",
+       "      <td>16.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>16547.03</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>-2347.03</td>\n",
+       "      <td>16.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>4590.16</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>909.84</td>\n",
+       "      <td>16.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>13348.91</td>\n",
+       "      <td>15999.00</td>\n",
+       "      <td>2650.09</td>\n",
+       "      <td>16.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>11191.05</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1591.05</td>\n",
+       "      <td>16.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>2126.49</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>423.51</td>\n",
+       "      <td>16.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.96</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-1063.96</td>\n",
+       "      <td>16.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>2749.08</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>550.92</td>\n",
+       "      <td>16.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>18282.75</td>\n",
+       "      <td>21950.00</td>\n",
+       "      <td>3667.25</td>\n",
+       "      <td>16.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>19071.43</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>3828.57</td>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>32886.86</td>\n",
+       "      <td>39500.00</td>\n",
+       "      <td>6613.14</td>\n",
+       "      <td>16.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>25752.19</td>\n",
+       "      <td>30990.00</td>\n",
+       "      <td>5237.81</td>\n",
+       "      <td>16.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>7719.69</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>1580.31</td>\n",
+       "      <td>16.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>25986.31</td>\n",
+       "      <td>31310.00</td>\n",
+       "      <td>5323.69</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>8250.47</td>\n",
+       "      <td>9950.00</td>\n",
+       "      <td>1699.53</td>\n",
+       "      <td>17.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>28937.50</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>5962.50</td>\n",
+       "      <td>17.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8392.42</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>-1242.42</td>\n",
+       "      <td>17.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>8033.61</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>1716.39</td>\n",
+       "      <td>17.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>8156.11</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>1743.89</td>\n",
+       "      <td>17.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>2470.61</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>529.39</td>\n",
+       "      <td>17.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>19659.97</td>\n",
+       "      <td>16700.00</td>\n",
+       "      <td>-2959.97</td>\n",
+       "      <td>17.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>17540.72</td>\n",
+       "      <td>14899.00</td>\n",
+       "      <td>-2641.72</td>\n",
+       "      <td>17.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>7814.16</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1685.84</td>\n",
+       "      <td>17.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>21097.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-3197.66</td>\n",
+       "      <td>17.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>4266.27</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>933.73</td>\n",
+       "      <td>17.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>9380.46</td>\n",
+       "      <td>7950.00</td>\n",
+       "      <td>-1430.46</td>\n",
+       "      <td>17.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>7385.38</td>\n",
+       "      <td>18.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>7385.38</td>\n",
+       "      <td>18.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>9089.35</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-1389.35</td>\n",
+       "      <td>18.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>27740.81</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-4240.81</td>\n",
+       "      <td>18.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>3542.72</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-542.72</td>\n",
+       "      <td>18.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>25037.87</td>\n",
+       "      <td>21200.00</td>\n",
+       "      <td>-3837.87</td>\n",
+       "      <td>18.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>35991.51</td>\n",
+       "      <td>43999.00</td>\n",
+       "      <td>8007.49</td>\n",
+       "      <td>18.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>13733.33</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>3066.67</td>\n",
+       "      <td>18.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>11431.35</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>2567.65</td>\n",
+       "      <td>18.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>6082.85</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>1367.15</td>\n",
+       "      <td>18.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>12674.57</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-1974.57</td>\n",
+       "      <td>18.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25975.12</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>-4075.12</td>\n",
+       "      <td>18.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3026.25</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>-476.25</td>\n",
+       "      <td>18.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>33172.20</td>\n",
+       "      <td>40800.00</td>\n",
+       "      <td>7627.80</td>\n",
+       "      <td>18.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>13159.16</td>\n",
+       "      <td>16200.00</td>\n",
+       "      <td>3040.84</td>\n",
+       "      <td>18.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>20216.67</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>4683.33</td>\n",
+       "      <td>18.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>10220.74</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>-1620.74</td>\n",
+       "      <td>18.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>29127.72</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>-4627.72</td>\n",
+       "      <td>18.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>5839.08</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1360.92</td>\n",
+       "      <td>18.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>65500.00</td>\n",
+       "      <td>12436.78</td>\n",
+       "      <td>18.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>28256.79</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>6643.21</td>\n",
+       "      <td>19.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-724.77</td>\n",
+       "      <td>19.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>2543.57</td>\n",
+       "      <td>3149.25</td>\n",
+       "      <td>605.68</td>\n",
+       "      <td>19.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>60493.51</td>\n",
+       "      <td>75000.00</td>\n",
+       "      <td>14506.49</td>\n",
+       "      <td>19.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>8962.29</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-1462.29</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>14832.32</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>-2432.32</td>\n",
+       "      <td>19.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>19676.51</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>4823.49</td>\n",
+       "      <td>19.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20349.62</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-3350.62</td>\n",
+       "      <td>19.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>1926.60</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>473.40</td>\n",
+       "      <td>19.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>8381.60</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-1382.60</td>\n",
+       "      <td>19.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>38334.67</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>-6334.67</td>\n",
+       "      <td>19.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>20972.54</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>-3472.54</td>\n",
+       "      <td>19.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>6732.97</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1667.03</td>\n",
+       "      <td>19.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>10419.20</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>2580.80</td>\n",
+       "      <td>19.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>688</th>\n",
+       "      <td>24572.28</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-4072.28</td>\n",
+       "      <td>19.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>10014.71</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>2485.29</td>\n",
+       "      <td>19.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-709.17</td>\n",
+       "      <td>20.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>34678.95</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>8821.05</td>\n",
+       "      <td>20.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>18893.76</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-3193.76</td>\n",
+       "      <td>20.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>8121.22</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>2078.78</td>\n",
+       "      <td>20.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>11443.85</td>\n",
+       "      <td>9499.00</td>\n",
+       "      <td>-1944.85</td>\n",
+       "      <td>20.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>33392.40</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>8607.60</td>\n",
+       "      <td>20.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1753.52</td>\n",
+       "      <td>20.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>17251.60</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-2951.60</td>\n",
+       "      <td>20.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>6981.34</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1818.66</td>\n",
+       "      <td>20.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>2379.58</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>620.42</td>\n",
+       "      <td>20.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>10932.17</td>\n",
+       "      <td>13800.00</td>\n",
+       "      <td>2867.83</td>\n",
+       "      <td>20.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>2779.04</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-479.04</td>\n",
+       "      <td>20.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>11002.01</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>2897.99</td>\n",
+       "      <td>20.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>5144.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1355.98</td>\n",
+       "      <td>20.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>7110.32</td>\n",
+       "      <td>8990.00</td>\n",
+       "      <td>1879.68</td>\n",
+       "      <td>20.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>67431.00</td>\n",
+       "      <td>14118.19</td>\n",
+       "      <td>20.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7745.02</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-1345.02</td>\n",
+       "      <td>21.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>63921.33</td>\n",
+       "      <td>80999.00</td>\n",
+       "      <td>17077.67</td>\n",
+       "      <td>21.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>3940.49</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1058.51</td>\n",
+       "      <td>21.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>23630.85</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>-4130.85</td>\n",
+       "      <td>21.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>14792.29</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>4007.71</td>\n",
+       "      <td>21.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>11770.64</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-2070.64</td>\n",
+       "      <td>21.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>9424.34</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>2575.66</td>\n",
+       "      <td>21.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>8139.48</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-1439.48</td>\n",
+       "      <td>21.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>22367.52</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>6132.48</td>\n",
+       "      <td>21.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>13004.34</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-2304.34</td>\n",
+       "      <td>21.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>8512.03</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-1512.03</td>\n",
+       "      <td>21.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>32121.20</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>8878.80</td>\n",
+       "      <td>21.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>12523.73</td>\n",
+       "      <td>16000.00</td>\n",
+       "      <td>3476.27</td>\n",
+       "      <td>21.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>19845.36</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>-3545.36</td>\n",
+       "      <td>21.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13292.71</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>3706.29</td>\n",
+       "      <td>21.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>8467.17</td>\n",
+       "      <td>6950.00</td>\n",
+       "      <td>-1517.17</td>\n",
+       "      <td>21.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11876.82</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>3323.18</td>\n",
+       "      <td>21.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>22547.55</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>-4047.55</td>\n",
+       "      <td>21.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>50728.89</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>14271.11</td>\n",
+       "      <td>21.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2625.10</td>\n",
+       "      <td>2150.00</td>\n",
+       "      <td>-475.10</td>\n",
+       "      <td>22.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>5863.41</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1063.41</td>\n",
+       "      <td>22.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>9175.06</td>\n",
+       "      <td>7499.00</td>\n",
+       "      <td>-1676.06</td>\n",
+       "      <td>22.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>8453.28</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2446.72</td>\n",
+       "      <td>22.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>5350.91</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>1549.09</td>\n",
+       "      <td>22.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>6819.44</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1980.56</td>\n",
+       "      <td>22.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>10303.21</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>2996.79</td>\n",
+       "      <td>22.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>9139.04</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>2660.96</td>\n",
+       "      <td>22.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>18621.12</td>\n",
+       "      <td>24100.00</td>\n",
+       "      <td>5478.88</td>\n",
+       "      <td>22.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>15954.72</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-2955.72</td>\n",
+       "      <td>22.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>12270.01</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>3629.99</td>\n",
+       "      <td>22.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>11490.44</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>3409.56</td>\n",
+       "      <td>22.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>14136.01</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-2636.01</td>\n",
+       "      <td>22.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>11095.57</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-2096.57</td>\n",
+       "      <td>23.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>10350.25</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>3149.75</td>\n",
+       "      <td>23.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>20680.40</td>\n",
+       "      <td>27000.00</td>\n",
+       "      <td>6319.60</td>\n",
+       "      <td>23.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>34446.96</td>\n",
+       "      <td>27900.00</td>\n",
+       "      <td>-6546.96</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1290.83</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>3086.38</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-587.38</td>\n",
+       "      <td>23.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>16427.12</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-3127.12</td>\n",
+       "      <td>23.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>43163.25</td>\n",
+       "      <td>-10149.56</td>\n",
+       "      <td>23.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>3747.21</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>1152.79</td>\n",
+       "      <td>23.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>14410.15</td>\n",
+       "      <td>18932.55</td>\n",
+       "      <td>4522.40</td>\n",
+       "      <td>23.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.45</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>4377.55</td>\n",
+       "      <td>23.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>9232.79</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>-1782.79</td>\n",
+       "      <td>23.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>18165.16</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>5734.84</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.49</td>\n",
+       "      <td>9150.00</td>\n",
+       "      <td>-2203.49</td>\n",
+       "      <td>24.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>6205.48</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-1205.48</td>\n",
+       "      <td>24.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>11004.05</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>3495.95</td>\n",
+       "      <td>24.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>10505.98</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>3394.02</td>\n",
+       "      <td>24.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>19920.63</td>\n",
+       "      <td>16000.00</td>\n",
+       "      <td>-3920.63</td>\n",
+       "      <td>24.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>3621.08</td>\n",
+       "      <td>4799.00</td>\n",
+       "      <td>1177.92</td>\n",
+       "      <td>24.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>17316.59</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-3416.59</td>\n",
+       "      <td>24.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>8471.55</td>\n",
+       "      <td>6799.00</td>\n",
+       "      <td>-1672.55</td>\n",
+       "      <td>24.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>36883.77</td>\n",
+       "      <td>49000.00</td>\n",
+       "      <td>12116.23</td>\n",
+       "      <td>24.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-1642.62</td>\n",
+       "      <td>24.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.16</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-2691.16</td>\n",
+       "      <td>24.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>3001.37</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>998.63</td>\n",
+       "      <td>24.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.26</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>-699.26</td>\n",
+       "      <td>24.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>23485.45</td>\n",
+       "      <td>18777.00</td>\n",
+       "      <td>-4708.45</td>\n",
+       "      <td>25.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>10835.95</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>3664.05</td>\n",
+       "      <td>25.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>6013.05</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1213.05</td>\n",
+       "      <td>25.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>3735.04</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1263.96</td>\n",
+       "      <td>25.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>15623.39</td>\n",
+       "      <td>20999.00</td>\n",
+       "      <td>5375.61</td>\n",
+       "      <td>25.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>8667.12</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1767.12</td>\n",
+       "      <td>25.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>8916.19</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>3082.81</td>\n",
+       "      <td>25.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>8688.92</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>3011.08</td>\n",
+       "      <td>25.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>12027.56</td>\n",
+       "      <td>16200.00</td>\n",
+       "      <td>4172.44</td>\n",
+       "      <td>25.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>12976.15</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>4523.85</td>\n",
+       "      <td>25.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>44407.14</td>\n",
+       "      <td>59999.00</td>\n",
+       "      <td>15591.86</td>\n",
+       "      <td>25.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>31059.18</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>10940.82</td>\n",
+       "      <td>26.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>8110.06</td>\n",
+       "      <td>10990.00</td>\n",
+       "      <td>2879.94</td>\n",
+       "      <td>26.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>672</th>\n",
+       "      <td>5679.39</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-1179.39</td>\n",
+       "      <td>26.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>4255.62</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>1544.38</td>\n",
+       "      <td>26.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>3164.66</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-665.66</td>\n",
+       "      <td>26.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>3154.48</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>1145.52</td>\n",
+       "      <td>26.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>4307.68</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>-907.68</td>\n",
+       "      <td>26.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>5195.69</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-1095.69</td>\n",
+       "      <td>26.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>47600.00</td>\n",
+       "      <td>12736.47</td>\n",
+       "      <td>26.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>8638.63</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>3161.37</td>\n",
+       "      <td>26.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>683</th>\n",
+       "      <td>32771.45</td>\n",
+       "      <td>44800.00</td>\n",
+       "      <td>12028.55</td>\n",
+       "      <td>26.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>28154.84</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>10345.16</td>\n",
+       "      <td>26.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>67952.07</td>\n",
+       "      <td>53500.00</td>\n",
+       "      <td>-14452.07</td>\n",
+       "      <td>27.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>13471.84</td>\n",
+       "      <td>10600.00</td>\n",
+       "      <td>-2871.84</td>\n",
+       "      <td>27.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>23227.15</td>\n",
+       "      <td>31900.00</td>\n",
+       "      <td>8672.85</td>\n",
+       "      <td>27.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>7131.92</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>2668.08</td>\n",
+       "      <td>27.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>8071.70</td>\n",
+       "      <td>11100.00</td>\n",
+       "      <td>3028.30</td>\n",
+       "      <td>27.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>26478.15</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>-5678.15</td>\n",
+       "      <td>27.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>7116.68</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>2683.32</td>\n",
+       "      <td>27.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>9312.21</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-2012.21</td>\n",
+       "      <td>27.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>10848.85</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-2348.85</td>\n",
+       "      <td>27.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>10597.72</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-2297.72</td>\n",
+       "      <td>27.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7940.44</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>3059.56</td>\n",
+       "      <td>27.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>5040.46</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1959.54</td>\n",
+       "      <td>27.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>10716.00</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>4184.00</td>\n",
+       "      <td>28.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>9205.10</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>3594.90</td>\n",
+       "      <td>28.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>31656.01</td>\n",
+       "      <td>24700.00</td>\n",
+       "      <td>-6956.01</td>\n",
+       "      <td>28.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>13853.36</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-3053.36</td>\n",
+       "      <td>28.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>2801.47</td>\n",
+       "      <td>28.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>8591.66</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>3408.34</td>\n",
+       "      <td>28.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-2701.29</td>\n",
+       "      <td>28.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>50074.56</td>\n",
+       "      <td>69990.00</td>\n",
+       "      <td>19915.44</td>\n",
+       "      <td>28.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>11432.63</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-2532.63</td>\n",
+       "      <td>28.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6437.21</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>2561.79</td>\n",
+       "      <td>28.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>8351.34</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-1851.34</td>\n",
+       "      <td>28.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>35336.33</td>\n",
+       "      <td>49500.00</td>\n",
+       "      <td>14163.67</td>\n",
+       "      <td>28.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>74232.55</td>\n",
+       "      <td>103999.00</td>\n",
+       "      <td>29766.45</td>\n",
+       "      <td>28.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>45664.45</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>-10164.45</td>\n",
+       "      <td>28.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>19298.61</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-4299.61</td>\n",
+       "      <td>28.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>11580.54</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-2580.54</td>\n",
+       "      <td>28.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>23496.36</td>\n",
+       "      <td>33000.00</td>\n",
+       "      <td>9503.64</td>\n",
+       "      <td>28.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>31557.80</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>-7057.80</td>\n",
+       "      <td>28.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>24323.48</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>-5523.48</td>\n",
+       "      <td>29.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>18285.07</td>\n",
+       "      <td>25900.00</td>\n",
+       "      <td>7614.93</td>\n",
+       "      <td>29.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>25640.67</td>\n",
+       "      <td>19800.00</td>\n",
+       "      <td>-5840.67</td>\n",
+       "      <td>29.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>5983.44</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>2516.56</td>\n",
+       "      <td>29.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>8973.16</td>\n",
+       "      <td>12750.00</td>\n",
+       "      <td>3776.84</td>\n",
+       "      <td>29.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>23336.06</td>\n",
+       "      <td>33200.00</td>\n",
+       "      <td>9863.94</td>\n",
+       "      <td>29.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>6110.94</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>2589.06</td>\n",
+       "      <td>29.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>3275.83</td>\n",
+       "      <td>29.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>10094.58</td>\n",
+       "      <td>7777.00</td>\n",
+       "      <td>-2317.58</td>\n",
+       "      <td>29.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>21690.99</td>\n",
+       "      <td>30900.00</td>\n",
+       "      <td>9209.01</td>\n",
+       "      <td>29.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>10809.48</td>\n",
+       "      <td>15400.00</td>\n",
+       "      <td>4590.52</td>\n",
+       "      <td>29.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>11018.79</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>4681.21</td>\n",
+       "      <td>29.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>13377.99</td>\n",
+       "      <td>10300.00</td>\n",
+       "      <td>-3077.99</td>\n",
+       "      <td>29.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>12731.26</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-2931.26</td>\n",
+       "      <td>29.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>11178.46</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>-2578.46</td>\n",
+       "      <td>29.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>21681.00</td>\n",
+       "      <td>30999.00</td>\n",
+       "      <td>9318.00</td>\n",
+       "      <td>30.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>27293.13</td>\n",
+       "      <td>39040.00</td>\n",
+       "      <td>11746.87</td>\n",
+       "      <td>30.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>6510.80</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-1510.80</td>\n",
+       "      <td>30.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>8298.32</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>3601.68</td>\n",
+       "      <td>30.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>10821.19</td>\n",
+       "      <td>8299.00</td>\n",
+       "      <td>-2522.19</td>\n",
+       "      <td>30.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>5346.97</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-1246.97</td>\n",
+       "      <td>30.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5484.02</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>2415.98</td>\n",
+       "      <td>30.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>5238.05</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-1238.05</td>\n",
+       "      <td>30.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>17597.10</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>7902.90</td>\n",
+       "      <td>30.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>7727.53</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>3472.47</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>6358.55</td>\n",
+       "      <td>4850.00</td>\n",
+       "      <td>-1508.55</td>\n",
+       "      <td>31.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>2886.35</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>1313.65</td>\n",
+       "      <td>31.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>14049.76</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-3349.76</td>\n",
+       "      <td>31.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>12974.89</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>5925.11</td>\n",
+       "      <td>31.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>6858.89</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>3140.11</td>\n",
+       "      <td>31.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>15691.29</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>7208.71</td>\n",
+       "      <td>31.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>2393.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1106.66</td>\n",
+       "      <td>31.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>6425.00</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>2975.00</td>\n",
+       "      <td>31.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>50206.83</td>\n",
+       "      <td>73500.00</td>\n",
+       "      <td>23293.17</td>\n",
+       "      <td>31.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>25685.67</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>-6185.67</td>\n",
+       "      <td>31.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>19692.08</td>\n",
+       "      <td>-6259.23</td>\n",
+       "      <td>31.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>28910.96</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>13589.04</td>\n",
+       "      <td>31.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>9919.19</td>\n",
+       "      <td>14600.00</td>\n",
+       "      <td>4680.81</td>\n",
+       "      <td>32.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>9113.05</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-2213.05</td>\n",
+       "      <td>32.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>24427.14</td>\n",
+       "      <td>36000.00</td>\n",
+       "      <td>11572.86</td>\n",
+       "      <td>32.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>4738.89</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>2261.11</td>\n",
+       "      <td>32.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>25040.42</td>\n",
+       "      <td>37000.00</td>\n",
+       "      <td>11959.58</td>\n",
+       "      <td>32.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>18403.35</td>\n",
+       "      <td>13893.75</td>\n",
+       "      <td>-4509.60</td>\n",
+       "      <td>32.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>14576.33</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-3576.33</td>\n",
+       "      <td>32.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>6610.04</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>3189.96</td>\n",
+       "      <td>32.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>10105.70</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>4894.30</td>\n",
+       "      <td>32.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>5041.20</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-1241.20</td>\n",
+       "      <td>32.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>6503.62</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-1603.62</td>\n",
+       "      <td>32.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>13542.79</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-3342.79</td>\n",
+       "      <td>32.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4364.08</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>2135.92</td>\n",
+       "      <td>32.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>12006.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>5893.34</td>\n",
+       "      <td>32.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>3787.35</td>\n",
+       "      <td>32.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>31251.89</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-7751.89</td>\n",
+       "      <td>32.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>7011.74</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>3488.26</td>\n",
+       "      <td>33.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>5792.10</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>2907.90</td>\n",
+       "      <td>33.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>8493.04</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>4306.96</td>\n",
+       "      <td>33.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>7950.27</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>4049.73</td>\n",
+       "      <td>33.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>8940.39</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>4559.61</td>\n",
+       "      <td>33.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>4569.15</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>2330.85</td>\n",
+       "      <td>33.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>13041.39</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>6658.61</td>\n",
+       "      <td>33.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>20163.48</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>10336.52</td>\n",
+       "      <td>33.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>2839.03</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>1460.97</td>\n",
+       "      <td>33.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>20433.23</td>\n",
+       "      <td>30999.00</td>\n",
+       "      <td>10565.77</td>\n",
+       "      <td>34.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>1746.19</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>903.81</td>\n",
+       "      <td>34.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>19855.31</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-5055.31</td>\n",
+       "      <td>34.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>19741.65</td>\n",
+       "      <td>29999.00</td>\n",
+       "      <td>10257.35</td>\n",
+       "      <td>34.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>65613.31</td>\n",
+       "      <td>99999.00</td>\n",
+       "      <td>34385.69</td>\n",
+       "      <td>34.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>10950.00</td>\n",
+       "      <td>3771.80</td>\n",
+       "      <td>34.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>9143.17</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2343.17</td>\n",
+       "      <td>34.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>2285.24</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1214.76</td>\n",
+       "      <td>34.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>22835.48</td>\n",
+       "      <td>16950.00</td>\n",
+       "      <td>-5885.48</td>\n",
+       "      <td>34.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>10786.38</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-2786.38</td>\n",
+       "      <td>34.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>11477.47</td>\n",
+       "      <td>17650.00</td>\n",
+       "      <td>6172.53</td>\n",
+       "      <td>34.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>23972.56</td>\n",
+       "      <td>36900.00</td>\n",
+       "      <td>12927.44</td>\n",
+       "      <td>35.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>3648.96</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-948.96</td>\n",
+       "      <td>35.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>11706.93</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>6393.07</td>\n",
+       "      <td>35.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>11100.00</td>\n",
+       "      <td>3921.80</td>\n",
+       "      <td>35.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>12286.47</td>\n",
+       "      <td>35.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>12286.47</td>\n",
+       "      <td>35.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>12897.92</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-3397.92</td>\n",
+       "      <td>35.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>12904.68</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-3404.68</td>\n",
+       "      <td>35.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>9611.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>5388.20</td>\n",
+       "      <td>35.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>20236.85</td>\n",
+       "      <td>31600.00</td>\n",
+       "      <td>11363.15</td>\n",
+       "      <td>35.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>4151.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>2348.98</td>\n",
+       "      <td>36.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>28951.20</td>\n",
+       "      <td>45500.00</td>\n",
+       "      <td>16548.80</td>\n",
+       "      <td>36.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>18988.13</td>\n",
+       "      <td>-6963.18</td>\n",
+       "      <td>36.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>5570.46</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>3229.54</td>\n",
+       "      <td>36.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>22454.74</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>13045.26</td>\n",
+       "      <td>36.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>41711.18</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>-11211.18</td>\n",
+       "      <td>36.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>3152.62</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1846.38</td>\n",
+       "      <td>36.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>8902.52</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-2402.52</td>\n",
+       "      <td>36.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>48699.98</td>\n",
+       "      <td>77500.00</td>\n",
+       "      <td>28800.02</td>\n",
+       "      <td>37.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>16821.54</td>\n",
+       "      <td>26800.00</td>\n",
+       "      <td>9978.46</td>\n",
+       "      <td>37.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>5521.28</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>3278.72</td>\n",
+       "      <td>37.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>5268.17</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>3131.83</td>\n",
+       "      <td>37.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>65825.25</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>39173.75</td>\n",
+       "      <td>37.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>12459.85</td>\n",
+       "      <td>19900.00</td>\n",
+       "      <td>7440.15</td>\n",
+       "      <td>37.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>65723.62</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>39275.38</td>\n",
+       "      <td>37.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>9070.23</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>5429.77</td>\n",
+       "      <td>37.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>9348.90</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2548.90</td>\n",
+       "      <td>37.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>9369.66</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>5630.34</td>\n",
+       "      <td>37.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>32954.19</td>\n",
+       "      <td>52777.00</td>\n",
+       "      <td>19822.81</td>\n",
+       "      <td>37.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>17195.57</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-4695.57</td>\n",
+       "      <td>37.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>3425.23</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>2074.77</td>\n",
+       "      <td>37.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>3173.48</td>\n",
+       "      <td>5100.00</td>\n",
+       "      <td>1926.52</td>\n",
+       "      <td>37.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>10337.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-2837.11</td>\n",
+       "      <td>37.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>105999.00</td>\n",
+       "      <td>40256.43</td>\n",
+       "      <td>37.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>8661.95</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>5337.05</td>\n",
+       "      <td>38.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>26062.23</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>16437.77</td>\n",
+       "      <td>38.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>7630.64</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-2130.64</td>\n",
+       "      <td>38.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11378.43</td>\n",
+       "      <td>18600.00</td>\n",
+       "      <td>7221.57</td>\n",
+       "      <td>38.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>66032.76</td>\n",
+       "      <td>107999.00</td>\n",
+       "      <td>41966.24</td>\n",
+       "      <td>38.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>7279.97</td>\n",
+       "      <td>11950.00</td>\n",
+       "      <td>4670.03</td>\n",
+       "      <td>39.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>18577.88</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>11922.12</td>\n",
+       "      <td>39.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>4043.74</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>-1143.74</td>\n",
+       "      <td>39.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>2178.94</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>1421.06</td>\n",
+       "      <td>39.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>23286.27</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>15213.73</td>\n",
+       "      <td>39.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12558.90</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-3559.90</td>\n",
+       "      <td>39.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>18432.63</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>12067.37</td>\n",
+       "      <td>39.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>9057.77</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>5941.23</td>\n",
+       "      <td>39.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>13476.90</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>9023.10</td>\n",
+       "      <td>40.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>37704.71</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>-10804.71</td>\n",
+       "      <td>40.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>32945.80</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-9445.80</td>\n",
+       "      <td>40.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-2212.65</td>\n",
+       "      <td>40.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>109999.00</td>\n",
+       "      <td>44256.43</td>\n",
+       "      <td>40.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36764.38</td>\n",
+       "      <td>62000.00</td>\n",
+       "      <td>25235.62</td>\n",
+       "      <td>40.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>77403.06</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-22403.06</td>\n",
+       "      <td>40.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>8530.54</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>5969.46</td>\n",
+       "      <td>41.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>15259.81</td>\n",
+       "      <td>26000.00</td>\n",
+       "      <td>10740.19</td>\n",
+       "      <td>41.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>16253.48</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-4753.48</td>\n",
+       "      <td>41.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>1815.28</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>1284.72</td>\n",
+       "      <td>41.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>44193.38</td>\n",
+       "      <td>75500.00</td>\n",
+       "      <td>31306.62</td>\n",
+       "      <td>41.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>45272.25</td>\n",
+       "      <td>31990.00</td>\n",
+       "      <td>-13282.25</td>\n",
+       "      <td>41.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>9198.08</td>\n",
+       "      <td>15800.00</td>\n",
+       "      <td>6601.92</td>\n",
+       "      <td>41.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>19854.79</td>\n",
+       "      <td>14000.00</td>\n",
+       "      <td>-5854.79</td>\n",
+       "      <td>41.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>10031.96</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>7268.04</td>\n",
+       "      <td>42.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>9661.78</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2861.78</td>\n",
+       "      <td>42.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>2539.57</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>1860.43</td>\n",
+       "      <td>42.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>11474.22</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>8524.78</td>\n",
+       "      <td>42.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>65887.51</td>\n",
+       "      <td>114999.00</td>\n",
+       "      <td>49111.49</td>\n",
+       "      <td>42.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>2784.46</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>-834.46</td>\n",
+       "      <td>42.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>15015.22</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-4515.22</td>\n",
+       "      <td>43.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>31216.83</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>23783.17</td>\n",
+       "      <td>43.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>5374.74</td>\n",
+       "      <td>3750.00</td>\n",
+       "      <td>-1624.74</td>\n",
+       "      <td>43.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>5558.93</td>\n",
+       "      <td>43.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>3450.69</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>2649.31</td>\n",
+       "      <td>43.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>16724.45</td>\n",
+       "      <td>29600.00</td>\n",
+       "      <td>12875.55</td>\n",
+       "      <td>43.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>65210.35</td>\n",
+       "      <td>115800.00</td>\n",
+       "      <td>50589.65</td>\n",
+       "      <td>43.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>10740.09</td>\n",
+       "      <td>19100.00</td>\n",
+       "      <td>8359.91</td>\n",
+       "      <td>43.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>12234.68</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-3734.68</td>\n",
+       "      <td>43.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>14402.34</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>-4402.34</td>\n",
+       "      <td>44.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>10807.78</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-3307.78</td>\n",
+       "      <td>44.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>39074.36</td>\n",
+       "      <td>69999.00</td>\n",
+       "      <td>30924.64</td>\n",
+       "      <td>44.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>14512.61</td>\n",
+       "      <td>25999.00</td>\n",
+       "      <td>11486.39</td>\n",
+       "      <td>44.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3316.44</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1016.44</td>\n",
+       "      <td>44.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>12255.45</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>9744.55</td>\n",
+       "      <td>44.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>6781.86</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>-2081.86</td>\n",
+       "      <td>44.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>3752.54</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-1152.54</td>\n",
+       "      <td>44.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>2389.14</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>-739.14</td>\n",
+       "      <td>44.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>34761.74</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-10761.74</td>\n",
+       "      <td>44.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>11881.05</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-3681.05</td>\n",
+       "      <td>44.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1873.03</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>1526.97</td>\n",
+       "      <td>44.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>13212.52</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>10786.48</td>\n",
+       "      <td>44.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>17282.06</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-5382.06</td>\n",
+       "      <td>45.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>17011.43</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-5311.43</td>\n",
+       "      <td>45.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>2020.25</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>1679.75</td>\n",
+       "      <td>45.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>18942.34</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-5943.34</td>\n",
+       "      <td>45.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>11337.51</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>9562.49</td>\n",
+       "      <td>45.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>10547.82</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>8952.18</td>\n",
+       "      <td>45.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>6421.18</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-2021.18</td>\n",
+       "      <td>45.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>12414.72</td>\n",
+       "      <td>23000.00</td>\n",
+       "      <td>10585.28</td>\n",
+       "      <td>46.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>24796.09</td>\n",
+       "      <td>46000.00</td>\n",
+       "      <td>21203.91</td>\n",
+       "      <td>46.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>4172.84</td>\n",
+       "      <td>7750.00</td>\n",
+       "      <td>3577.16</td>\n",
+       "      <td>46.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>15956.95</td>\n",
+       "      <td>29999.00</td>\n",
+       "      <td>14042.05</td>\n",
+       "      <td>46.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>1858.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1641.66</td>\n",
+       "      <td>46.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>8522.39</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>-2722.39</td>\n",
+       "      <td>46.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>13235.55</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-4236.55</td>\n",
+       "      <td>47.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>13600.39</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>12199.61</td>\n",
+       "      <td>47.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>4032.10</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>3617.90</td>\n",
+       "      <td>47.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>12414.41</td>\n",
+       "      <td>8420.00</td>\n",
+       "      <td>-3994.41</td>\n",
+       "      <td>47.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>2573.48</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>2326.52</td>\n",
+       "      <td>47.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>14572.70</td>\n",
+       "      <td>27900.00</td>\n",
+       "      <td>13327.30</td>\n",
+       "      <td>47.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>15144.28</td>\n",
+       "      <td>29000.00</td>\n",
+       "      <td>13855.72</td>\n",
+       "      <td>47.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>20159.19</td>\n",
+       "      <td>38888.00</td>\n",
+       "      <td>18728.81</td>\n",
+       "      <td>48.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>14000.00</td>\n",
+       "      <td>6758.93</td>\n",
+       "      <td>48.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>25682.58</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>-8382.58</td>\n",
+       "      <td>48.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>10563.39</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>9936.61</td>\n",
+       "      <td>48.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>6546.81</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-2146.81</td>\n",
+       "      <td>48.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "      <td>37294.64</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "      <td>37294.64</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>13443.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-4443.32</td>\n",
+       "      <td>49.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>14933.54</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>14566.46</td>\n",
+       "      <td>49.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>13884.26</td>\n",
+       "      <td>27800.00</td>\n",
+       "      <td>13915.74</td>\n",
+       "      <td>50.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>31373.47</td>\n",
+       "      <td>20859.15</td>\n",
+       "      <td>-10514.32</td>\n",
+       "      <td>50.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>47014.52</td>\n",
+       "      <td>95000.00</td>\n",
+       "      <td>47985.48</td>\n",
+       "      <td>50.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>41075.73</td>\n",
+       "      <td>27200.00</td>\n",
+       "      <td>-13875.73</td>\n",
+       "      <td>51.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>10125.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-3425.27</td>\n",
+       "      <td>51.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>60603.06</td>\n",
+       "      <td>124000.00</td>\n",
+       "      <td>63396.94</td>\n",
+       "      <td>51.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>14363.76</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-4863.76</td>\n",
+       "      <td>51.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>25302.43</td>\n",
+       "      <td>52000.00</td>\n",
+       "      <td>26697.57</td>\n",
+       "      <td>51.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>7391.67</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>7808.33</td>\n",
+       "      <td>51.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>9753.44</td>\n",
+       "      <td>20400.00</td>\n",
+       "      <td>10646.56</td>\n",
+       "      <td>52.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>32925.60</td>\n",
+       "      <td>69500.00</td>\n",
+       "      <td>36574.40</td>\n",
+       "      <td>52.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>22941.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-7941.80</td>\n",
+       "      <td>52.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>6199.59</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>7000.41</td>\n",
+       "      <td>53.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>5597.06</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>6401.94</td>\n",
+       "      <td>53.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>13409.47</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-4709.47</td>\n",
+       "      <td>54.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>6635.73</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>-2335.73</td>\n",
+       "      <td>54.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>33707.27</td>\n",
+       "      <td>73900.00</td>\n",
+       "      <td>40192.73</td>\n",
+       "      <td>54.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>19309.90</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-6809.90</td>\n",
+       "      <td>54.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>19624.73</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>-6924.73</td>\n",
+       "      <td>54.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>13757.58</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-4857.58</td>\n",
+       "      <td>54.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>12354.92</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>15145.08</td>\n",
+       "      <td>55.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>17714.62</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-6314.62</td>\n",
+       "      <td>55.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>11555.80</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>-4155.80</td>\n",
+       "      <td>56.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>22011.47</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>-8012.47</td>\n",
+       "      <td>57.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>15457.91</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-5657.91</td>\n",
+       "      <td>57.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>11099.09</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-4100.09</td>\n",
+       "      <td>58.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>35039.40</td>\n",
+       "      <td>85555.00</td>\n",
+       "      <td>50515.60</td>\n",
+       "      <td>59.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>34376.16</td>\n",
+       "      <td>85000.00</td>\n",
+       "      <td>50623.84</td>\n",
+       "      <td>59.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>16773.62</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-6273.62</td>\n",
+       "      <td>59.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.75</td>\n",
+       "      <td>68500.00</td>\n",
+       "      <td>41012.25</td>\n",
+       "      <td>59.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>7996.00</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-2997.00</td>\n",
+       "      <td>59.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>5284.06</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-1984.06</td>\n",
+       "      <td>60.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>87777.00</td>\n",
+       "      <td>52913.47</td>\n",
+       "      <td>60.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>19701.70</td>\n",
+       "      <td>49999.00</td>\n",
+       "      <td>30297.30</td>\n",
+       "      <td>60.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>30409.09</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>-11509.09</td>\n",
+       "      <td>60.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>14486.38</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-5486.38</td>\n",
+       "      <td>60.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>11601.33</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-4401.33</td>\n",
+       "      <td>61.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>10824.89</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-4124.89</td>\n",
+       "      <td>61.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>65448.37</td>\n",
+       "      <td>40500.00</td>\n",
+       "      <td>-24948.37</td>\n",
+       "      <td>61.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>5351.86</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-2051.86</td>\n",
+       "      <td>62.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>25864.65</td>\n",
+       "      <td>69990.00</td>\n",
+       "      <td>44125.35</td>\n",
+       "      <td>63.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>15682.82</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-6082.82</td>\n",
+       "      <td>63.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>40765.33</td>\n",
+       "      <td>112000.00</td>\n",
+       "      <td>71234.67</td>\n",
+       "      <td>63.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>1320.56</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>2379.44</td>\n",
+       "      <td>64.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1495.06</td>\n",
+       "      <td>65.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>19335.34</td>\n",
+       "      <td>55555.00</td>\n",
+       "      <td>36219.66</td>\n",
+       "      <td>65.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>26736.21</td>\n",
+       "      <td>16100.00</td>\n",
+       "      <td>-10636.21</td>\n",
+       "      <td>66.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>26441.03</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-10541.03</td>\n",
+       "      <td>66.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>5002.39</td>\n",
+       "      <td>2999.00</td>\n",
+       "      <td>-2003.39</td>\n",
+       "      <td>66.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>18317.20</td>\n",
+       "      <td>10974.21</td>\n",
+       "      <td>-7342.99</td>\n",
+       "      <td>66.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>40893.84</td>\n",
+       "      <td>126000.00</td>\n",
+       "      <td>85106.16</td>\n",
+       "      <td>67.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>38473.71</td>\n",
+       "      <td>119000.00</td>\n",
+       "      <td>80526.29</td>\n",
+       "      <td>67.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>109999.00</td>\n",
+       "      <td>75135.47</td>\n",
+       "      <td>68.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>10946.44</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-4446.44</td>\n",
+       "      <td>68.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>3205.38</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-1305.38</td>\n",
+       "      <td>68.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>16924.74</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-6925.74</td>\n",
+       "      <td>69.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>9356.43</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-3856.43</td>\n",
+       "      <td>70.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15374.08</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-6474.08</td>\n",
+       "      <td>72.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>11056.52</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-4656.52</td>\n",
+       "      <td>72.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>2440.90</td>\n",
+       "      <td>1400.00</td>\n",
+       "      <td>-1040.90</td>\n",
+       "      <td>74.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>3939.91</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>11560.09</td>\n",
+       "      <td>74.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>15380.04</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-6580.04</td>\n",
+       "      <td>74.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>35093.27</td>\n",
+       "      <td>19990.00</td>\n",
+       "      <td>-15103.27</td>\n",
+       "      <td>75.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>8270.38</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>-3570.38</td>\n",
+       "      <td>75.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>6916.13</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>-3016.13</td>\n",
+       "      <td>77.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-2947.48</td>\n",
+       "      <td>77.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>2555.02</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>9044.98</td>\n",
+       "      <td>77.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>13355.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-5855.11</td>\n",
+       "      <td>78.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>3948.74</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-1748.74</td>\n",
+       "      <td>79.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>9024.05</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-4124.05</td>\n",
+       "      <td>84.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>15292.10</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-6992.10</td>\n",
+       "      <td>84.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>7621.25</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-3521.25</td>\n",
+       "      <td>85.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>2975.40</td>\n",
+       "      <td>1600.00</td>\n",
+       "      <td>-1375.40</td>\n",
+       "      <td>85.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>14915.98</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-7015.98</td>\n",
+       "      <td>88.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>5716.14</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-2716.14</td>\n",
+       "      <td>90.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15819.50</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-7519.50</td>\n",
+       "      <td>90.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>13420.15</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-6421.15</td>\n",
+       "      <td>91.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>23576.99</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>-11576.99</td>\n",
+       "      <td>96.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>8648.80</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-4248.80</td>\n",
+       "      <td>96.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>15559.43</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-7659.43</td>\n",
+       "      <td>96.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>4004.65</td>\n",
+       "      <td>2000.00</td>\n",
+       "      <td>-2004.65</td>\n",
+       "      <td>100.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>27225.34</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-13725.34</td>\n",
+       "      <td>101.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>5462.52</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-2762.52</td>\n",
+       "      <td>102.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>8131.92</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>-4132.92</td>\n",
+       "      <td>103.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>10810.62</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-5810.62</td>\n",
+       "      <td>116.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>7973.87</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-4323.87</td>\n",
+       "      <td>118.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>5948.04</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-3348.04</td>\n",
+       "      <td>128.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>6891.99</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-3891.99</td>\n",
+       "      <td>129.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>26331.41</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-15131.41</td>\n",
+       "      <td>135.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>21493.44</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-12493.44</td>\n",
+       "      <td>138.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>6537.82</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-3837.82</td>\n",
+       "      <td>142.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>12891.96</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-7591.96</td>\n",
+       "      <td>143.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>8340.94</td>\n",
+       "      <td>3350.00</td>\n",
+       "      <td>-4990.94</td>\n",
+       "      <td>148.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>3040.77</td>\n",
+       "      <td>1200.00</td>\n",
+       "      <td>-1840.77</td>\n",
+       "      <td>153.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>9631.08</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-5832.08</td>\n",
+       "      <td>153.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>13967.55</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-8467.55</td>\n",
+       "      <td>153.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>3818.71</td>\n",
+       "      <td>1450.00</td>\n",
+       "      <td>-2368.71</td>\n",
+       "      <td>163.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>7809.13</td>\n",
+       "      <td>2899.00</td>\n",
+       "      <td>-4910.13</td>\n",
+       "      <td>169.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-2890.49</td>\n",
+       "      <td>170.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>7320.42</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-4720.42</td>\n",
+       "      <td>181.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>12261.19</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-8161.19</td>\n",
+       "      <td>199.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.29</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-5053.29</td>\n",
+       "      <td>202.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>9817.06</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-6617.06</td>\n",
+       "      <td>206.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>7161.67</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-4961.67</td>\n",
+       "      <td>225.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>7918.89</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-5518.89</td>\n",
+       "      <td>229.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>9984.87</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-6984.87</td>\n",
+       "      <td>232.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>10381.62</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-7381.62</td>\n",
+       "      <td>246.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>8843.22</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-6343.22</td>\n",
+       "      <td>253.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>10123.04</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>-7323.04</td>\n",
+       "      <td>261.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>16165.79</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-11765.79</td>\n",
+       "      <td>267.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>17937.36</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-13137.36</td>\n",
+       "      <td>273.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>7319.77</td>\n",
+       "      <td>1850.00</td>\n",
+       "      <td>-5469.77</td>\n",
+       "      <td>295.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>16095.32</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-12495.32</td>\n",
+       "      <td>347.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.50</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-8385.50</td>\n",
+       "      <td>364.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>9664.46</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-7764.46</td>\n",
+       "      <td>408.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>32481.05</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-26481.05</td>\n",
+       "      <td>441.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>9954.42</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>-8154.42</td>\n",
+       "      <td>453.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>35956.50</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-29456.50</td>\n",
+       "      <td>453.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>10019.90</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>-8219.90</td>\n",
+       "      <td>456.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>30628.28</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-25629.28</td>\n",
+       "      <td>512.69</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction    Target  Residual  Difference%\n",
+       "698    30480.85  30500.00     19.15         0.06\n",
+       "742    16960.31  16999.00     38.69         0.23\n",
+       "60     12469.21  12500.00     30.79         0.25\n",
+       "110    25614.14  25500.00   -114.14         0.45\n",
+       "367    42703.68  42500.00   -203.68         0.48\n",
+       "369     3084.69   3100.00     15.31         0.49\n",
+       "769    29651.73  29500.00   -151.73         0.51\n",
+       "272     9749.53   9800.00     50.47         0.52\n",
+       "714    23118.07  22999.00   -119.07         0.52\n",
+       "630     8734.58   8800.00     65.42         0.74\n",
+       "380     3473.79   3500.00     26.21         0.75\n",
+       "648    21174.10  21335.00    160.90         0.75\n",
+       "308     8967.74   8900.00    -67.74         0.76\n",
+       "665    17858.02  18000.00    141.98         0.79\n",
+       "379    17654.84  17800.00    145.16         0.82\n",
+       "719    11391.95  11500.00    108.05         0.94\n",
+       "102    28625.56  28900.00    274.44         0.95\n",
+       "94      7724.17   7800.00     75.83         0.97\n",
+       "561     6429.03   6500.00     70.97         1.09\n",
+       "242     7597.39   7500.00    -97.39         1.30\n",
+       "528    18555.09  18800.00    244.91         1.30\n",
+       "61      7396.87   7300.00    -96.87         1.33\n",
+       "19     16178.14  16400.00    221.86         1.35\n",
+       "280    12327.10  12499.00    171.90         1.38\n",
+       "311    51287.19  52055.25    768.06         1.48\n",
+       "723     6009.63   6100.00     90.37         1.48\n",
+       "49      4973.17   4900.00    -73.17         1.49\n",
+       "114    27716.14  27300.00   -416.14         1.52\n",
+       "636    28498.91  28950.00    451.09         1.56\n",
+       "612     2953.17   3000.00     46.83         1.56\n",
+       "47     26425.14  25999.00   -426.14         1.64\n",
+       "23     13111.91  12900.00   -211.91         1.64\n",
+       "31     12858.08  12650.00   -208.08         1.64\n",
+       "91     13421.16  13200.00   -221.16         1.68\n",
+       "329     7327.18   7200.00   -127.18         1.77\n",
+       "549     3816.33   3750.00    -66.33         1.77\n",
+       "252     9721.50   9900.00    178.50         1.80\n",
+       "387    44173.72  44999.00    825.28         1.83\n",
+       "267    40753.58  40000.00   -753.58         1.88\n",
+       "467    22262.80  22711.65    448.85         1.98\n",
+       "556    18231.44  18600.00    368.56         1.98\n",
+       "165     9596.94   9400.00   -196.94         2.10\n",
+       "259     6067.79   6200.00    132.21         2.13\n",
+       "601    35371.16  34600.00   -771.16         2.23\n",
+       "708    11967.39  11700.00   -267.39         2.29\n",
+       "593    17908.00  17500.00   -408.00         2.33\n",
+       "398     8707.13   8500.00   -207.13         2.44\n",
+       "526    29049.27  28350.00   -699.27         2.47\n",
+       "603    14513.46  14900.00    386.54         2.59\n",
+       "53     20453.89  21000.00    546.11         2.60\n",
+       "632    15383.35  14990.00   -393.35         2.62\n",
+       "533    24642.50  24000.00   -642.50         2.68\n",
+       "497    50099.92  51500.00   1400.08         2.72\n",
+       "212    16133.86  15700.00   -433.86         2.76\n",
+       "130    17489.92  18000.00    510.08         2.83\n",
+       "290     1894.40   1950.00     55.60         2.85\n",
+       "78     30810.25  29900.00   -910.25         3.04\n",
+       "642     8721.97   8999.00    277.03         3.08\n",
+       "437    18866.50  18300.00   -566.50         3.10\n",
+       "101     5958.63   6150.00    191.37         3.11\n",
+       "314     5811.74   6000.00    188.26         3.14\n",
+       "150     9800.43   9500.00   -300.43         3.16\n",
+       "565     7324.63   7100.00   -224.63         3.16\n",
+       "574    12583.52  13000.00    416.48         3.20\n",
+       "591    10115.13   9800.00   -315.13         3.22\n",
+       "172    11156.38  10800.00   -356.38         3.30\n",
+       "133     9279.28   9600.00    320.72         3.34\n",
+       "480    31369.37  32500.00   1130.63         3.48\n",
+       "87      2315.71   2400.00     84.29         3.51\n",
+       "11      5175.77   5000.00   -175.77         3.52\n",
+       "43     21611.83  22400.00    788.17         3.52\n",
+       "96      7976.26   7700.00   -276.26         3.59\n",
+       "406    24874.86  23999.00   -875.86         3.65\n",
+       "173    36516.35  37900.00   1383.65         3.65\n",
+       "540     4666.05   4500.00   -166.05         3.69\n",
+       "40     18672.68  18000.00   -672.68         3.74\n",
+       "340    14815.83  15400.00    584.17         3.79\n",
+       "239    10581.62  10999.00    417.38         3.79\n",
+       "109    12663.54  12200.00   -463.54         3.80\n",
+       "256     1825.44   1900.00     74.56         3.92\n",
+       "317    12247.90  12750.00    502.10         3.94\n",
+       "77      5930.73   5700.00   -230.73         4.05\n",
+       "301     9782.47  10200.00    417.53         4.09\n",
+       "333    12452.22  11960.00   -492.22         4.12\n",
+       "570    23163.87  24171.42   1007.55         4.17\n",
+       "581     3246.94   3390.00    143.06         4.22\n",
+       "693    12354.16  12900.00    545.84         4.23\n",
+       "381    33499.44  35000.00   1500.56         4.29\n",
+       "438    16257.03  16999.00    741.97         4.36\n",
+       "368     8415.81   8800.00    384.19         4.37\n",
+       "273    12318.39  12900.00    581.61         4.51\n",
+       "235     2765.14   2900.00    134.86         4.65\n",
+       "168    11420.95  11999.00    578.05         4.82\n",
+       "707     2725.40   2600.00   -125.40         4.82\n",
+       "72     23624.69  22500.00  -1124.69         5.00\n",
+       "559    11377.84  11999.00    621.16         5.18\n",
+       "134    12096.18  11500.00   -596.18         5.18\n",
+       "446     9363.27   8900.00   -463.27         5.21\n",
+       "127    12311.47  11700.00   -611.47         5.23\n",
+       "450    14218.94  13500.00   -718.94         5.33\n",
+       "293     8431.89   7999.00   -432.89         5.41\n",
+       "18     37600.15  39900.00   2299.85         5.76\n",
+       "452    39882.67  37700.00  -2182.67         5.79\n",
+       "195    20588.57  21900.00   1311.43         5.99\n",
+       "676     6861.84   7300.00    438.16         6.00\n",
+       "766    18982.15  17900.00  -1082.15         6.05\n",
+       "328    15067.85  14200.00   -867.85         6.11\n",
+       "762    12201.29  12999.00    797.71         6.14\n",
+       "649    11147.91  10500.00   -647.91         6.17\n",
+       "299    20183.30  18999.00  -1184.30         6.23\n",
+       "641    17334.11  18500.00   1165.89         6.30\n",
+       "5      21289.80  20000.00  -1289.80         6.45\n",
+       "545     6826.78   7300.00    473.22         6.48\n",
+       "622     4472.47   4200.00   -272.47         6.49\n",
+       "422    53294.61  57000.00   3705.39         6.50\n",
+       "740     6658.73   6250.00   -408.73         6.54\n",
+       "85     19001.29  17800.00  -1201.29         6.75\n",
+       "315     4590.49   4300.00   -290.49         6.76\n",
+       "462    43236.92  40500.00  -2736.92         6.76\n",
+       "394     9076.42   8500.00   -576.42         6.78\n",
+       "188    10159.18  10900.00    740.82         6.80\n",
+       "731    16027.02  15000.00  -1027.02         6.85\n",
+       "448    13502.35  14500.00    997.65         6.88\n",
+       "254     6413.26   5999.00   -414.26         6.91\n",
+       "667    49221.63  52999.00   3777.37         7.13\n",
+       "600    21816.43  23500.00   1683.57         7.16\n",
+       "144    11969.66  12900.00    930.34         7.21\n",
+       "270     8263.95   7700.00   -563.95         7.32\n",
+       "8      11581.54  12500.00    918.46         7.35\n",
+       "433     6977.90   6500.00   -477.90         7.35\n",
+       "251     6978.91   6500.00   -478.91         7.37\n",
+       "553    16392.22  17700.00   1307.78         7.39\n",
+       "725    15742.41  16999.00   1256.59         7.39\n",
+       "615     7869.47   8500.00    630.53         7.42\n",
+       "518    21297.65  19800.00  -1497.65         7.56\n",
+       "268     6893.62   6400.00   -493.62         7.71\n",
+       "71     15627.03  14500.00  -1127.03         7.77\n",
+       "579     3411.53   3700.00    288.47         7.80\n",
+       "330    11336.71  12300.00    963.29         7.83\n",
+       "59      8195.34   7600.00   -595.34         7.83\n",
+       "354    16717.08  15500.00  -1217.08         7.85\n",
+       "147     7772.57   7200.00   -572.57         7.95\n",
+       "508    16197.42  15000.00  -1197.42         7.98\n",
+       "22      6102.36   5650.00   -452.36         8.01\n",
+       "103     4488.43   4150.00   -338.43         8.16\n",
+       "755    15885.66  17300.00   1414.34         8.18\n",
+       "198    17983.10  19600.00   1616.90         8.25\n",
+       "470     7045.72   6500.00   -545.72         8.40\n",
+       "710     3795.06   3500.00   -295.06         8.43\n",
+       "185     6130.96   6700.00    569.04         8.49\n",
+       "542     5947.51   6500.00    552.49         8.50\n",
+       "412     7609.28   7000.00   -609.28         8.70\n",
+       "415     8117.44   8900.00    782.56         8.79\n",
+       "200    11098.18  10200.00   -898.18         8.81\n",
+       "754    17028.45  18700.00   1671.55         8.94\n",
+       "65     19847.25  18200.00  -1647.25         9.05\n",
+       "682     5454.34   5000.00   -454.34         9.09\n",
+       "222    20662.46  18900.00  -1762.46         9.33\n",
+       "89     11696.25  12900.00   1203.75         9.33\n",
+       "52     10716.84   9800.00   -916.84         9.36\n",
+       "126    13459.92  12300.00  -1159.92         9.43\n",
+       "598     8767.54   9700.00    932.46         9.61\n",
+       "504    23272.83  25749.75   2476.92         9.62\n",
+       "493     5484.97   4999.00   -485.97         9.72\n",
+       "449     9495.62   8650.00   -845.62         9.78\n",
+       "516    11858.48  10800.00  -1058.48         9.80\n",
+       "261    53312.81  48535.50  -4777.31         9.84\n",
+       "531    47047.70  52300.00   5252.30        10.04\n",
+       "68      6717.17   6100.00   -617.17        10.12\n",
+       "376     5391.77   6000.00    608.23        10.14\n",
+       "538    17591.09  19600.00   2008.91        10.25\n",
+       "419    17591.09  19600.00   2008.91        10.25\n",
+       "595    25576.58  28500.00   2923.42        10.26\n",
+       "472    20367.25  22700.00   2332.75        10.28\n",
+       "386     7267.36   8100.00    832.64        10.28\n",
+       "473     1792.36   1999.00    206.64        10.34\n",
+       "14      8248.67   9200.00    951.33        10.34\n",
+       "123     5643.45   6300.00    656.55        10.42\n",
+       "142     6839.57   7650.00    810.43        10.59\n",
+       "257    41503.90  37500.00  -4003.90        10.68\n",
+       "194     9310.69   8400.00   -910.69        10.84\n",
+       "637    35662.26  40000.00   4337.74        10.84\n",
+       "410    16407.29  14800.00  -1607.29        10.86\n",
+       "395    21303.57  23900.00   2596.43        10.86\n",
+       "213    15872.91  14300.00  -1572.91        11.00\n",
+       "653    10332.54   9300.00  -1032.54        11.10\n",
+       "54     18575.43  20900.00   2324.57        11.12\n",
+       "105     8882.65  10000.00   1117.35        11.17\n",
+       "597     7797.18   6999.00   -798.18        11.40\n",
+       "15     10621.84  11999.00   1377.16        11.48\n",
+       "770    10732.07   9600.00  -1132.07        11.79\n",
+       "674     2381.01   2700.00    318.99        11.81\n",
+       "132    11200.20   9999.00  -1201.20        12.01\n",
+       "525     2373.66   2700.00    326.34        12.09\n",
+       "730    10696.56  12179.00   1482.44        12.17\n",
+       "506     3590.97   3200.00   -390.97        12.22\n",
+       "278    53063.22  60500.00   7436.78        12.29\n",
+       "108     7300.05   6500.00   -800.05        12.31\n",
+       "503     6392.70   7300.00    907.30        12.43\n",
+       "536     6746.43   6000.00   -746.43        12.44\n",
+       "156     9192.06  10500.00   1307.94        12.46\n",
+       "765    11034.66   9800.00  -1234.66        12.60\n",
+       "534    16939.86  19400.00   2460.14        12.68\n",
+       "50      2479.43   2200.00   -279.43        12.70\n",
+       "562     9593.52  11000.00   1406.48        12.79\n",
+       "131    23967.81  27500.00   3532.19        12.84\n",
+       "157     7491.01   8600.00   1108.99        12.90\n",
+       "638     8242.62   7300.00   -942.62        12.91\n",
+       "543     9489.56  10900.00   1410.44        12.94\n",
+       "724    44312.98  50900.00   6587.02        12.94\n",
+       "527     9829.53   8700.00  -1129.53        12.98\n",
+       "610     9269.68   8200.00  -1069.68        13.04\n",
+       "359     4524.77   3999.00   -525.77        13.15\n",
+       "24     23650.15  20900.00  -2750.15        13.16\n",
+       "402     8850.21  10200.00   1349.79        13.23\n",
+       "360     4770.52   5500.00    729.48        13.26\n",
+       "184    29479.97  34000.00   4520.03        13.29\n",
+       "324     8316.92   9600.00   1283.08        13.37\n",
+       "307    24974.57  28900.00   3925.43        13.58\n",
+       "118    14943.75  17300.00   2356.25        13.62\n",
+       "607    28498.91  33000.00   4501.09        13.64\n",
+       "332    14591.35  16900.00   2308.65        13.66\n",
+       "140    10358.58  12000.00   1641.42        13.68\n",
+       "627    13074.74  11500.00  -1574.74        13.69\n",
+       "761     9097.13   8000.00  -1097.13        13.71\n",
+       "207    15010.51  13200.00  -1810.51        13.72\n",
+       "685    12593.00  14600.00   2007.00        13.75\n",
+       "350    20246.04  23500.00   3253.96        13.85\n",
+       "143     8768.29   7700.00  -1068.29        13.87\n",
+       "318    39744.80  34900.00  -4844.80        13.88\n",
+       "377     9909.62   8700.00  -1209.62        13.90\n",
+       "152    44680.77  51900.00   7219.23        13.91\n",
+       "57     21942.05  25500.00   3557.95        13.95\n",
+       "277    12900.31  15000.00   2099.69        14.00\n",
+       "253    18128.32  15900.00  -2228.32        14.01\n",
+       "384     4296.77   5000.00    703.23        14.06\n",
+       "113     6006.27   6999.00    992.73        14.18\n",
+       "287     1972.28   2300.00    327.72        14.25\n",
+       "650     8236.36   7200.00  -1036.36        14.39\n",
+       "319    10612.51  12400.00   1787.49        14.42\n",
+       "430    14197.26  12400.00  -1797.26        14.49\n",
+       "95     47013.28  55000.00   7986.72        14.52\n",
+       "757    12028.91  10500.00  -1528.91        14.56\n",
+       "153     3666.31   3200.00   -466.31        14.57\n",
+       "720     2860.97   3350.00    489.03        14.60\n",
+       "631     7256.86   8500.00   1243.14        14.63\n",
+       "36     10499.60  12300.00   1800.40        14.64\n",
+       "763    12383.35  10800.00  -1583.35        14.66\n",
+       "344    11937.31  13995.00   2057.69        14.70\n",
+       "389     2810.71   2450.00   -360.71        14.72\n",
+       "204     7664.32   9000.00   1335.68        14.84\n",
+       "718     8041.62   6999.00  -1042.62        14.90\n",
+       "484    13334.19  11600.00  -1734.19        14.95\n",
+       "258    13680.48  11900.00  -1780.48        14.96\n",
+       "616    13500.69  15900.00   2399.31        15.09\n",
+       "281    11638.89  13708.50   2069.61        15.10\n",
+       "476    15700.53  18500.00   2799.47        15.13\n",
+       "716    11425.01  13500.00   2074.99        15.37\n",
+       "529    53312.81  63000.00   9687.19        15.38\n",
+       "481     6599.62   7800.00   1200.38        15.39\n",
+       "706    61639.73  72900.00  11260.27        15.45\n",
+       "558     7098.53   8400.00   1301.47        15.49\n",
+       "151    10889.39  12900.00   2010.61        15.59\n",
+       "663     2613.97   3099.00    485.03        15.65\n",
+       "16     23721.58  20500.00  -3221.58        15.72\n",
+       "356    10133.68   8750.00  -1383.68        15.81\n",
+       "297    22261.50  19200.00  -3061.50        15.95\n",
+       "119    10448.50   8999.00  -1449.50        16.11\n",
+       "390    24271.90  20900.00  -3371.90        16.13\n",
+       "439    14926.69  17800.00   2873.31        16.14\n",
+       "441    11619.16  10000.00  -1619.16        16.19\n",
+       "548    46465.02  39900.00  -6565.02        16.45\n",
+       "460    50954.68  61000.00  10045.32        16.47\n",
+       "211     6848.88   8200.00   1351.12        16.48\n",
+       "106    16547.03  14200.00  -2347.03        16.53\n",
+       "599     4590.16   5500.00    909.84        16.54\n",
+       "220    13348.91  15999.00   2650.09        16.56\n",
+       "226    11191.05   9600.00  -1591.05        16.57\n",
+       "197     2126.49   2550.00    423.51        16.61\n",
+       "3       7463.96   6400.00  -1063.96        16.62\n",
+       "378     2749.08   3300.00    550.92        16.69\n",
+       "507    18282.75  21950.00   3667.25        16.71\n",
+       "186    19071.43  22900.00   3828.57        16.72\n",
+       "100    32886.86  39500.00   6613.14        16.74\n",
+       "620    25752.19  30990.00   5237.81        16.90\n",
+       "229     7719.69   9300.00   1580.31        16.99\n",
+       "400    25986.31  31310.00   5323.69        17.00\n",
+       "336     8250.47   9950.00   1699.53        17.08\n",
+       "483    28937.50  34900.00   5962.50        17.08\n",
+       "171     8392.42   7150.00  -1242.42        17.38\n",
+       "421     8033.61   9750.00   1716.39        17.60\n",
+       "643     8156.11   9900.00   1743.89        17.62\n",
+       "696     2470.61   3000.00    529.39        17.65\n",
+       "286    19659.97  16700.00  -2959.97        17.72\n",
+       "496    17540.72  14899.00  -2641.72        17.73\n",
+       "655     7814.16   9500.00   1685.84        17.75\n",
+       "407    21097.66  17900.00  -3197.66        17.86\n",
+       "464     4266.27   5200.00    933.73        17.96\n",
+       "758     9380.46   7950.00  -1430.46        17.99\n",
+       "670    33614.62  41000.00   7385.38        18.01\n",
+       "9      33614.62  41000.00   7385.38        18.01\n",
+       "263     9089.35   7700.00  -1389.35        18.04\n",
+       "521    27740.81  23500.00  -4240.81        18.05\n",
+       "46      3542.72   3000.00   -542.72        18.09\n",
+       "237    25037.87  21200.00  -3837.87        18.10\n",
+       "28     35991.51  43999.00   8007.49        18.20\n",
+       "125    13733.33  16800.00   3066.67        18.25\n",
+       "721    11431.35  13999.00   2567.65        18.34\n",
+       "749     6082.85   7450.00   1367.15        18.35\n",
+       "55     12674.57  10700.00  -1974.57        18.45\n",
+       "466    25975.12  21900.00  -4075.12        18.61\n",
+       "651     3026.25   2550.00   -476.25        18.68\n",
+       "560    33172.20  40800.00   7627.80        18.70\n",
+       "247    13159.16  16200.00   3040.84        18.77\n",
+       "738    20216.67  24900.00   4683.33        18.81\n",
+       "715    10220.74   8600.00  -1620.74        18.85\n",
+       "608    29127.72  24500.00  -4627.72        18.89\n",
+       "509     5839.08   7200.00   1360.92        18.90\n",
+       "435    53063.22  65500.00  12436.78        18.99\n",
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+       "689     4524.77   3800.00   -724.77        19.07\n",
+       "51      2543.57   3149.25    605.68        19.23\n",
+       "501    60493.51  75000.00  14506.49        19.34\n",
+       "292     8962.29   7500.00  -1462.29        19.50\n",
+       "193    14832.32  12400.00  -2432.32        19.62\n",
+       "711    19676.51  24500.00   4823.49        19.69\n",
+       "7      20349.62  16999.00  -3350.62        19.71\n",
+       "569     1926.60   2400.00    473.40        19.72\n",
+       "453     8381.60   6999.00  -1382.60        19.75\n",
+       "444    38334.67  32000.00  -6334.67        19.80\n",
+       "454    20972.54  17500.00  -3472.54        19.84\n",
+       "176     6732.97   8400.00   1667.03        19.85\n",
+       "240    10419.20  13000.00   2580.80        19.85\n",
+       "688    24572.28  20500.00  -4072.28        19.86\n",
+       "313    10014.71  12500.00   2485.29        19.88\n",
+       "471     4209.17   3500.00   -709.17        20.26\n",
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+       "79     18893.76  15700.00  -3193.76        20.34\n",
+       "409     8121.22  10200.00   2078.78        20.38\n",
+       "154    11443.85   9499.00  -1944.85        20.47\n",
+       "494    33392.40  42000.00   8607.60        20.49\n",
+       "98      6746.48   8500.00   1753.52        20.63\n",
+       "227    17251.60  14300.00  -2951.60        20.64\n",
+       "115     6981.34   8800.00   1818.66        20.67\n",
+       "744     2379.58   3000.00    620.42        20.68\n",
+       "474    10932.17  13800.00   2867.83        20.78\n",
+       "459     2779.04   2300.00   -479.04        20.83\n",
+       "331    11002.01  13900.00   2897.99        20.85\n",
+       "76      5144.02   6500.00   1355.98        20.86\n",
+       "244     7110.32   8990.00   1879.68        20.91\n",
+       "644    53312.81  67431.00  14118.19        20.94\n",
+       "174     7745.02   6400.00  -1345.02        21.02\n",
+       "358    63921.33  80999.00  17077.67        21.08\n",
+       "425     3940.49   4999.00   1058.51        21.17\n",
+       "321    23630.85  19500.00  -4130.85        21.18\n",
+       "312    14792.29  18800.00   4007.71        21.32\n",
+       "17     11770.64   9700.00  -2070.64        21.35\n",
+       "517     9424.34  12000.00   2575.66        21.46\n",
+       "392     8139.48   6700.00  -1439.48        21.48\n",
+       "416    22367.52  28500.00   6132.48        21.52\n",
+       "335    13004.34  10700.00  -2304.34        21.54\n",
+       "45      8512.03   7000.00  -1512.03        21.60\n",
+       "515    32121.20  41000.00   8878.80        21.66\n",
+       "557    12523.73  16000.00   3476.27        21.73\n",
+       "41     19845.36  16300.00  -3545.36        21.75\n",
+       "13     13292.71  16999.00   3706.29        21.80\n",
+       "691     8467.17   6950.00  -1517.17        21.83\n",
+       "20     11876.82  15200.00   3323.18        21.86\n",
+       "275    22547.55  18500.00  -4047.55        21.88\n",
+       "120    50728.89  65000.00  14271.11        21.96\n",
+       "647     2625.10   2150.00   -475.10        22.10\n",
+       "524     5863.41   4800.00  -1063.41        22.15\n",
+       "547     9175.06   7499.00  -1676.06        22.35\n",
+       "747     8453.28  10900.00   2446.72        22.45\n",
+       "295     5350.91   6900.00   1549.09        22.45\n",
+       "80      6819.44   8800.00   1980.56        22.51\n",
+       "661    10303.21  13300.00   2996.79        22.53\n",
+       "537     9139.04  11800.00   2660.96        22.55\n",
+       "626    18621.12  24100.00   5478.88        22.73\n",
+       "576    15954.72  12999.00  -2955.72        22.74\n",
+       "499    12270.01  15900.00   3629.99        22.83\n",
+       "218    11490.44  14900.00   3409.56        22.88\n",
+       "458    14136.01  11500.00  -2636.01        22.92\n",
+       "138    11095.57   8999.00  -2096.57        23.30\n",
+       "320    10350.25  13500.00   3149.75        23.33\n",
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+       "709    34446.96  27900.00  -6546.96        23.47\n",
+       "75      4209.17   5500.00   1290.83        23.47\n",
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+       "602    16427.12  13300.00  -3127.12        23.51\n",
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+       "399    14410.15  18932.55   4522.40        23.89\n",
+       "771    13922.45  18300.00   4377.55        23.92\n",
+       "662     9232.79   7450.00  -1782.79        23.93\n",
+       "634    18165.16  23900.00   5734.84        24.00\n",
+       "4      11353.49   9150.00  -2203.49        24.08\n",
+       "580     6205.48   5000.00  -1205.48        24.11\n",
+       "687    11004.05  14500.00   3495.95        24.11\n",
+       "305    10505.98  13900.00   3394.02        24.42\n",
+       "206    19920.63  16000.00  -3920.63        24.50\n",
+       "659     3621.08   4799.00   1177.92        24.55\n",
+       "178    17316.59  13900.00  -3416.59        24.58\n",
+       "692     8471.55   6799.00  -1672.55        24.60\n",
+       "479    36883.77  49000.00  12116.23        24.73\n",
+       "209     8242.62   6600.00  -1642.62        24.89\n",
+       "773    13491.16  10800.00  -2691.16        24.92\n",
+       "594     3001.37   4000.00    998.63        24.97\n",
+       "1       3499.26   2800.00   -699.26        24.97\n",
+       "625    23485.45  18777.00  -4708.45        25.08\n",
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+       "83      6013.05   4800.00  -1213.05        25.27\n",
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+       "519     8667.12   6900.00  -1767.12        25.61\n",
+       "177     8916.19  11999.00   3082.81        25.69\n",
+       "361     8688.92  11700.00   3011.08        25.74\n",
+       "482    12027.56  16200.00   4172.44        25.76\n",
+       "705    12976.15  17500.00   4523.85        25.85\n",
+       "135    44407.14  59999.00  15591.86        25.99\n",
+       "264    31059.18  42000.00  10940.82        26.05\n",
+       "372     8110.06  10990.00   2879.94        26.21\n",
+       "672     5679.39   4500.00  -1179.39        26.21\n",
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+       "145     4307.68   3400.00   -907.68        26.70\n",
+       "681     5195.69   4100.00  -1095.69        26.72\n",
+       "175    34863.53  47600.00  12736.47        26.76\n",
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+       "683    32771.45  44800.00  12028.55        26.85\n",
+       "592    28154.84  38500.00  10345.16        26.87\n",
+       "429    67952.07  53500.00 -14452.07        27.01\n",
+       "294    13471.84  10600.00  -2871.84        27.09\n",
+       "623    23227.15  31900.00   8672.85        27.19\n",
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+       "37     26478.15  20800.00  -5678.15        27.30\n",
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+       "572    10597.72   8300.00  -2297.72        27.68\n",
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+       "752     8591.66  12000.00   3408.34        28.40\n",
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+       "58     35336.33  49500.00  14163.67        28.61\n",
+       "423    74232.55 103999.00  29766.45        28.62\n",
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+       "129    11580.54   9000.00  -2580.54        28.67\n",
+       "303    23496.36  33000.00   9503.64        28.80\n",
+       "21     31557.80  24500.00  -7057.80        28.81\n",
+       "767    24323.48  18800.00  -5523.48        29.38\n",
+       "192    18285.07  25900.00   7614.93        29.40\n",
+       "700    25640.67  19800.00  -5840.67        29.50\n",
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+       "734    23336.06  33200.00   9863.94        29.71\n",
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+       "357    11018.79  15700.00   4681.21        29.82\n",
+       "190    13377.99  10300.00  -3077.99        29.88\n",
+       "234    12731.26   9800.00  -2931.26        29.91\n",
+       "695    11178.46   8600.00  -2578.46        29.98\n",
+       "364    21681.00  30999.00   9318.00        30.06\n",
+       "362    27293.13  39040.00  11746.87        30.09\n",
+       "309     6510.80   5000.00  -1510.80        30.22\n",
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+       "373    10821.19   8299.00  -2522.19        30.39\n",
+       "513     5346.97   4100.00  -1246.97        30.41\n",
+       "12      5484.02   7900.00   2415.98        30.58\n",
+       "352     5238.05   4000.00  -1238.05        30.95\n",
+       "181    17597.10  25500.00   7902.90        30.99\n",
+       "128     7727.53  11200.00   3472.47        31.00\n",
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+       "764    14049.76  10700.00  -3349.76        31.31\n",
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+       "405     6425.00   9400.00   2975.00        31.65\n",
+       "70     50206.83  73500.00  23293.17        31.69\n",
+       "596    25685.67  19500.00  -6185.67        31.72\n",
+       "351    25951.31  19692.08  -6259.23        31.79\n",
+       "489    28910.96  42500.00  13589.04        31.97\n",
+       "38      9919.19  14600.00   4680.81        32.06\n",
+       "274     9113.05   6900.00  -2213.05        32.07\n",
+       "374    24427.14  36000.00  11572.86        32.15\n",
+       "302     4738.89   7000.00   2261.11        32.30\n",
+       "148    25040.42  37000.00  11959.58        32.32\n",
+       "164    18403.35  13893.75  -4509.60        32.46\n",
+       "420    14576.33  11000.00  -3576.33        32.51\n",
+       "555     6610.04   9800.00   3189.96        32.55\n",
+       "505    10105.70  15000.00   4894.30        32.63\n",
+       "250     5041.20   3800.00  -1241.20        32.66\n",
+       "491     6503.62   4900.00  -1603.62        32.73\n",
+       "426    13542.79  10200.00  -3342.79        32.77\n",
+       "84      4364.08   6500.00   2135.92        32.86\n",
+       "136    12006.66  17900.00   5893.34        32.92\n",
+       "139     7712.65  11500.00   3787.35        32.93\n",
+       "618    31251.89  23500.00  -7751.89        32.99\n",
+       "702     7011.74  10500.00   3488.26        33.22\n",
+       "728     5792.10   8700.00   2907.90        33.42\n",
+       "751     8493.04  12800.00   4306.96        33.65\n",
+       "498     7950.27  12000.00   4049.73        33.75\n",
+       "605     8940.39  13500.00   4559.61        33.77\n",
+       "678     4569.15   6900.00   2330.85        33.78\n",
+       "205    13041.39  19700.00   6658.61        33.80\n",
+       "265    20163.48  30500.00  10336.52        33.89\n",
+       "403     2839.03   4300.00   1460.97        33.98\n",
+       "291    20433.23  30999.00  10565.77        34.08\n",
+       "74      1746.19   2650.00    903.81        34.11\n",
+       "741    19855.31  14800.00  -5055.31        34.16\n",
+       "170    19741.65  29999.00  10257.35        34.19\n",
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+       "289     7178.20  10950.00   3771.80        34.45\n",
+       "90      9143.17   6800.00  -2343.17        34.46\n",
+       "563     2285.24   3500.00   1214.76        34.71\n",
+       "112    22835.48  16950.00  -5885.48        34.72\n",
+       "189    10786.38   8000.00  -2786.38        34.83\n",
+       "669    11477.47  17650.00   6172.53        34.97\n",
+       "163    23972.56  36900.00  12927.44        35.03\n",
+       "97      3648.96   2700.00   -948.96        35.15\n",
+       "690    11706.93  18100.00   6393.07        35.32\n",
+       "117     7178.20  11100.00   3921.80        35.33\n",
+       "673    22213.53  34500.00  12286.47        35.61\n",
+       "739    22213.53  34500.00  12286.47        35.61\n",
+       "341    12897.92   9500.00  -3397.92        35.77\n",
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+       "694     8131.92   3999.00  -4132.92       103.35\n",
+       "727    10810.62   5000.00  -5810.62       116.21\n",
+       "397     7973.87   3650.00  -4323.87       118.46\n",
+       "196     5948.04   2600.00  -3348.04       128.77\n",
+       "704     6891.99   3000.00  -3891.99       129.73\n",
+       "571    26331.41  11200.00 -15131.41       135.10\n",
+       "680    21493.44   9000.00 -12493.44       138.82\n",
+       "465     6537.82   2700.00  -3837.82       142.14\n",
+       "81     12891.96   5300.00  -7591.96       143.24\n",
+       "554     8340.94   3350.00  -4990.94       148.98\n",
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+       "635     3818.71   1450.00  -2368.71       163.36\n",
+       "660     7809.13   2899.00  -4910.13       169.37\n",
+       "664     4590.49   1700.00  -2890.49       170.03\n",
+       "82      7320.42   2600.00  -4720.42       181.55\n",
+       "282    12261.19   4100.00  -8161.19       199.05\n",
+       "2       7553.29   2500.00  -5053.29       202.13\n",
+       "729     9817.06   3200.00  -6617.06       206.78\n",
+       "492     7161.67   2200.00  -4961.67       225.53\n",
+       "382     7918.89   2400.00  -5518.89       229.95\n",
+       "502     9984.87   3000.00  -6984.87       232.83\n",
+       "750    10381.62   3000.00  -7381.62       246.05\n",
+       "434     8843.22   2500.00  -6343.22       253.73\n",
+       "182    10123.04   2800.00  -7323.04       261.54\n",
+       "396    16165.79   4400.00 -11765.79       267.40\n",
+       "298    17937.36   4800.00 -13137.36       273.69\n",
+       "629     7319.77   1850.00  -5469.77       295.66\n",
+       "619    16095.32   3600.00 -12495.32       347.09\n",
+       "0      10685.50   2300.00  -8385.50       364.59\n",
+       "485     9664.46   1900.00  -7764.46       408.66\n",
+       "657    32481.05   6000.00 -26481.05       441.35\n",
+       "162     9954.42   1800.00  -8154.42       453.02\n",
+       "451    35956.50   6500.00 -29456.50       453.18\n",
+       "532    10019.90   1800.00  -8219.90       456.66\n",
+       "639    30628.28   4999.00 -25629.28       512.69"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.options.display.max_rows = 999\n",
+    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
+    "df_pf.sort_values(by=['Difference%'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+5)_with_comments.ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+5)_with_comments.ipynb
new file mode 100644
index 0000000..bd0f293
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+(Part+5)_with_comments.ipynb	
@@ -0,0 +1,25381 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For this practical example we will need the following libraries and modules\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.00</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW  4200.00      sedan      277     2.00      Petrol   \n",
+       "1  Mercedes-Benz  7900.00        van      427     2.90      Diesel   \n",
+       "2  Mercedes-Benz 13300.00      sedan      358     5.00         Gas   \n",
+       "3           Audi 23000.00  crossover      240     4.20      Petrol   \n",
+       "4         Toyota 18300.00  crossover      120     2.00      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data from a .csv in the same folder\n",
+    "raw_data = pd.read_csv('Real-life+example.csv')\n",
+    "\n",
+    "# Let's explore the top 5 rows of the df\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.00</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.00</td>\n",
+       "      <td>4195.00</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.00</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.75</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.24</td>\n",
+       "      <td>2.79</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.55</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.71</td>\n",
+       "      <td>5.07</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.72</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.00</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.00</td>\n",
+       "      <td>99.99</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand     Price   Body  Mileage  EngineV Engine Type  \\\n",
+       "count         4345   4173.00   4345  4345.00  4195.00        4345   \n",
+       "unique           7       NaN      6      NaN      NaN           4   \n",
+       "top     Volkswagen       NaN  sedan      NaN      NaN      Diesel   \n",
+       "freq           936       NaN   1649      NaN      NaN        2019   \n",
+       "mean           NaN  19418.75    NaN   161.24     2.79         NaN   \n",
+       "std            NaN  25584.24    NaN   105.71     5.07         NaN   \n",
+       "min            NaN    600.00    NaN     0.00     0.60         NaN   \n",
+       "25%            NaN   6999.00    NaN    86.00     1.80         NaN   \n",
+       "50%            NaN  11500.00    NaN   155.00     2.20         NaN   \n",
+       "75%            NaN  21700.00    NaN   230.00     3.00         NaN   \n",
+       "max            NaN 300000.00    NaN   980.00    99.99         NaN   \n",
+       "\n",
+       "       Registration    Year    Model  \n",
+       "count          4345 4345.00     4345  \n",
+       "unique            2     NaN      312  \n",
+       "top             yes     NaN  E-Class  \n",
+       "freq           3947     NaN      199  \n",
+       "mean            NaN 2006.55      NaN  \n",
+       "std             NaN    6.72      NaN  \n",
+       "min             NaN 1969.00      NaN  \n",
+       "25%             NaN 2003.00      NaN  \n",
+       "50%             NaN 2008.00      NaN  \n",
+       "75%             NaN 2012.00      NaN  \n",
+       "max             NaN 2016.00      NaN  "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics are very useful for initial exploration of the variables\n",
+    "# By default, only descriptives for the numerical variables are shown\n",
+    "# To include the categorical ones, you should specify this with an argument\n",
+    "raw_data.describe(include='all')\n",
+    "\n",
+    "# Note that categorical variables don't have some types of numerical descriptives\n",
+    "# and numerical variables don't have some types of categorical descriptives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.00</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.00</td>\n",
+       "      <td>4195.00</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.75</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.24</td>\n",
+       "      <td>2.79</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.71</td>\n",
+       "      <td>5.07</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.00</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.00</td>\n",
+       "      <td>99.99</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand     Price   Body  Mileage  EngineV Engine Type  \\\n",
+       "count         4345   4173.00   4345  4345.00  4195.00        4345   \n",
+       "unique           7       NaN      6      NaN      NaN           4   \n",
+       "top     Volkswagen       NaN  sedan      NaN      NaN      Diesel   \n",
+       "freq           936       NaN   1649      NaN      NaN        2019   \n",
+       "mean           NaN  19418.75    NaN   161.24     2.79         NaN   \n",
+       "std            NaN  25584.24    NaN   105.71     5.07         NaN   \n",
+       "min            NaN    600.00    NaN     0.00     0.60         NaN   \n",
+       "25%            NaN   6999.00    NaN    86.00     1.80         NaN   \n",
+       "50%            NaN  11500.00    NaN   155.00     2.20         NaN   \n",
+       "75%            NaN  21700.00    NaN   230.00     3.00         NaN   \n",
+       "max            NaN 300000.00    NaN   980.00    99.99         NaN   \n",
+       "\n",
+       "       Registration    Year  \n",
+       "count          4345 4345.00  \n",
+       "unique            2     NaN  \n",
+       "top             yes     NaN  \n",
+       "freq           3947     NaN  \n",
+       "mean            NaN 2006.55  \n",
+       "std             NaN    6.72  \n",
+       "min             NaN 1969.00  \n",
+       "25%             NaN 2003.00  \n",
+       "50%             NaN 2008.00  \n",
+       "75%             NaN 2012.00  \n",
+       "max             NaN 2016.00  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For these several lessons, we will create the regression without 'Model'\n",
+    "# Certainly, when you work on the problem on your own, you could create a regression with 'Model'\n",
+    "data = raw_data.drop(['Model'],axis=1)\n",
+    "\n",
+    "# Let's check the descriptives without 'Model'\n",
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data.isnull() # shows a df with the information whether a data point is null \n",
+    "# Since True = the data point is missing, while False = the data point is not missing, we can sum them\n",
+    "# This will give us the total number of missing values feature-wise\n",
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's simply drop all missing values\n",
+    "# This is not always recommended, however, when we remove less than 5% of the data, it is okay\n",
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.00</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.00</td>\n",
+       "      <td>4025.00</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.57</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.73</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.39</td>\n",
+       "      <td>4.94</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.00</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.00</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.00</td>\n",
+       "      <td>99.99</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand     Price   Body  Mileage  EngineV Engine Type  \\\n",
+       "count         4025   4025.00   4025  4025.00  4025.00        4025   \n",
+       "unique           7       NaN      6      NaN      NaN           4   \n",
+       "top     Volkswagen       NaN  sedan      NaN      NaN      Diesel   \n",
+       "freq           880       NaN   1534      NaN      NaN        1861   \n",
+       "mean           NaN  19552.31    NaN   163.57     2.76         NaN   \n",
+       "std            NaN  25815.73    NaN   103.39     4.94         NaN   \n",
+       "min            NaN    600.00    NaN     0.00     0.60         NaN   \n",
+       "25%            NaN   6999.00    NaN    90.00     1.80         NaN   \n",
+       "50%            NaN  11500.00    NaN   158.00     2.20         NaN   \n",
+       "75%            NaN  21900.00    NaN   230.00     3.00         NaN   \n",
+       "max            NaN 300000.00    NaN   980.00    99.99         NaN   \n",
+       "\n",
+       "       Registration    Year  \n",
+       "count          4025 4025.00  \n",
+       "unique            2     NaN  \n",
+       "top             yes     NaN  \n",
+       "freq           3654     NaN  \n",
+       "mean            NaN 2006.38  \n",
+       "std             NaN    6.70  \n",
+       "min             NaN 1969.00  \n",
+       "25%             NaN 2003.00  \n",
+       "50%             NaN 2007.00  \n",
+       "75%             NaN 2012.00  \n",
+       "max             NaN 2016.00  "
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's check the descriptives without the missing values\n",
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\3514801760.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+7JwrSTXp5dBMzgUAlAmCS5GIJ5JKJNNXhu5rj0v3UBE9LgCAMkFwKRLp3hbLkrzuPva4dA8VhaOJzJWlAQAoZQSXIhHdZ9fcA10ZOs3vc8vjTj2W4SIAQDkguBSJSLR/K4qk1PWKMrvnhhguAgCUPoJLkYjGUz0uXk//fiTp4MKSaABAOSC4FIlYPNXj0t/gUsuSaABAGSG4FIl0cPENtMeF4AIAKAMElyKR6XHpxxwXae/Kog4m5wIAygDBpUhE08Glj0uh0zKTcwkuAIAyQHApErF4/3bNTeMK0QCAckJwKRLRAU7OTfe4dITocQEAlD6CS5HYu6poYHNcGCoCAJQDgkuRGOiqour0hRbZgA4AUAYILkVioPu4pIeKQpGE4olkzusCAKCYEFyKRPpaRf0NLpV+j1wW1ysCAJSHAQWXxsbGXNdR9gY6VOSyLFUHPJJYWQQAKH0DCi5nnnmmrrjiCj3//POKRnmzPFjGmAFPzpX22cuFlUUAgBI3oOBy5513KplM6oYbbtCpp56qBQsW6K233sp1bWUjnjAy3Z/3d6hIYvdcAED58AzkSZ///Of1+c9/Xo2NjfrNb36j3/72t/rf//1fjR07VhdccIHOO+88DRkyJNe1lqx0b4tlSR631e/nV2d2z6X3CwBQ2g5qcu7w4cP19a9/Xb///e/15JNPqqGhQXfffbfOOOMMXXvttVqzZk2u6ixp0fjeibmW1f/gUhNgLxcAQHk46FVFq1at0s0336yvfvWrWr16tU455RTNmzdPoVBIF198sR577LEclFnaYgO8TlFaZqiIOS4AgBI3oKGibdu26be//a2eeeYZffDBBxo1apTmzJmjCy64QCNHjpQkXXrppbrhhhu0aNEiXX755bmsueRkVhT188rQaTUMFQEAysSAgstnP/tZVVRU6KyzztJ3vvMdnXTSSb0+bvTo0dq6det+z7V7927ddddd+vOf/6xIJKIZM2boxhtv1JgxYwZSmiMNdPO5tGqGigAAZWJAweXmm2/Weeedp5qamv0+7uqrr9bVV1+938dcc801SiaTeuSRR1RVVaUf/OAHuvzyy/Xiiy8qEAgMpDzH2XeOy0BkrlfEUBEAoMQN6J3yhRdeUFNTU6/H3nvvPc2ePbtP52lra9OoUaN0xx13aNKkSRozZoyuvvpqNTU1acOGDQMpzZEOtsclc4VohooAACWuzz0uq1atkjGp3UZWrFihlStXqqWlJetxL730knbs2NGnc9bV1enee+/NfN3S0qLHHntMI0aM0NixY/tamuNFYwPbNTctPVTUEYoraUzmEgAAAJSaPgeXX/3qV/rtb38ry7JkWZYWLFiQ9Zh0sDn33HP7XcjNN9+sX/7yl/L5fFq0aJEqKyv7fY40zwADQKG4u1cPuSyX3C5LicTeyblul9V9LNXOLrfkTmQHEbfLkuWy5PFYaqitkCQljVEklsj0wDhJuk3cA1xZVWpoj2y0SU+0RzbaJFsptkmfg8tNN92kCy+8UMYYXXbZZbrllluyekVcLpdqa2t19NFH97uQyy67TBdddJGeeOIJXXPNNVqyZImOP/74fp/H5bLU0FDV7+fZoaLCo0DAp6RSwaQq4FNlZSqEBPweeTxuBfw+eTzZV332elwK+H2qr08FvCq/R53huCyPxzGvvze1teUxr6mvaI9stElPtEc22iRbKbVJn4NLTU2NTjjhBEnSz372Mx1//PGqqsrdG2Q6BH33u9/VmjVrtHjxYt155539Pk8yaRQMduWsrnxwu12qrQ0oEokrFIoqFO6eVGuMuroikiTLJBWPJxQKRxWNJrLO4fO6FQpH1dpqZIxUFfCqMxzXBx8FVe1zXrJOt0kwGMr0QJUz2iMbbdIT7ZGNNsnmlDaprQ30uVeoz8Hl6aef1umnn66Ghgbt3LlTO3fu3O/jzz///AOes6WlRcuWLdNnP/tZeTypUlwul8aOHfuJk3/7Ih4v3h/OvpImqUQyNbwjpbb7TyRN9zEjY4ySCWXu21ciaWSSRvF46nE1Aa+a9oTU2h5xzOvvTSKRdHT9uUZ7ZKNNeqI9stEm2UqpTfocXObNm6df/vKXamho0Lx58/b7WMuy+hRcmpubNXfuXD366KM67bTTJEmxWExr167VrFmz+lqa4x3sqiJp3ytEs7IIAFC6+hxc/u///k9Dhw7NfJ4L48aN08yZM3XHHXfojjvuUF1dnR5++GEFg8Gy2m03s3OuZ2A750pSNVeIBgCUgT4Hl1GjRvX6eVo8HldHR4fq6+v7VcDChQt177336vrrr1d7e7umT5+uJ554Qocccki/zuNk0Vz0uLB7LgCgDAxo59x4PK6HHnpIRxxxhGbPnq3ly5fruuuuUzAY1AknnKAHHnhAdXV1fTpXTU2NbrvtNt12220DKaUkxLp3zvV5GSoCAGB/BvRO+cADD2jRokUKBoOSpDvuuEP19fWaP3++tm/f3mNTOexfMmkUT6Qm3x7cHBd6XAAApW9A75TPPfec5s6dq0suuUSbNm3Shg0bdNVVV+nf/u3fdP311+tPf/pTrussWbF9Znl7D2aOS4A5LgCA0jeg4NLU1KTJkydLkl5++WW5XC7NnDlTkjRixAi1t7fnrsISl77AottlZXbNHQiGigAA5WBAwWXYsGF6//33JUl/+tOfdOyxx2rQoEGSpL/97W8aMWJE7ioscbmYmCv1HCpKX3oBAIBSM6B3y3PPPVd33nmnvvrVr2r16tW68MILJaV2vX3wwQf7fHVoSLHuzecONrikh4pi8WTmoo0AAJSaAa0q+sY3vqHKykqtXLlS3/zmN/Wv//qvkqS33npLX/nKV3TVVVfltMhSFo0f3JWh0/w+tzxul+KJpNq7oqrwlc51KQAASBtQcLEsS1deeaWuvPLKHvf//Oc/z0lR5STdO3IwE3Ol1M+kptKrPe0RtYdiGlJPcAEAlJ4BBRdJam9v1+uvv66urq5e51T0Zct/7N3D5WCHiqTUJnR72iNq72KCLgCgNA0ouPz5z3/Wddddp1Ao1Ovxvl6rCLkbKpLYywUAUPoGFFzuvfdejR49WvPnz9fw4cPlch38m265Sg8VeXISXLqXRBNcAAAlakDBZdOmTfrxj3+s6dOn57qespPZ7j8HwSV9oUX2cgEAlKoBvVsecsgh6ujoyHUtZSmWo31cpL0XWmT3XABAqRrQu+WVV16pH/3oR5lN6DBwudqATmKoCABQ+gY0VPTss8+qsbFRn/nMZzRo0CD5/f4exy3L0tKlS3NSYKnLaY8LQ0UAgBI3oOAyYsQItvXPkb3LoQ9uHxdp7+659LgAAErVgILLnXfemes6ylamx8Wdu6Ei5rgAAErVgDegk1Kri1577TU1NTVpzpw52rFjh8aPH6/q6upc1VfycjvHJdXj0hWJK55IypODMAQAQDEZUHBJJpO65ZZb9OSTT8oYI8uy9I//+I/68Y9/rO3bt2vx4sUMJfVRLue4VPm9siQZSR2hmOqrKw76nAAAFJMBvVv++Mc/1rPPPqs77rhDr732WmbL/29961tKJpO67777clpkqTLG5DS4uFyWqlgSDQAoYQN6t3zyySd13XXX6cILL1R9fX3m/mOPPVbXXXedXnvttVzVV9LSu+ZKytmwzt5t/1lZBAAoPQN6t2xubtaxxx7b67Hhw4crGAweVFHlIhSNS5IsSR631e/nW1b6ZmVumb1cQnFZVv/PCQBAMRtQcDniiCP0yiuv9HpsxYoVOuKIIw6qqHIRiaaWQns8rn6HDLfbksvlUnsoro5wLHMLVKSmLTUHQ+oIx5TIedUAANhnQJNzL7vsMt1yyy2KxWI688wzZVmWtm3bpuXLl+t//ud/NG/evFzXWZLC3T0uA5nf4nZZCkXj2rSjTdH43ngSiaU+3/RBm4bWB3TckYNU7fdm5iEBAOBkAwou//zP/6yWlhYtWrRIS5YskSTNnTtXXq9XV1xxhS6++OKcFlmqwtH05nMDn98SiycVje0NLulzdYXjmYm/AACUigHv4/K1r31Ns2fP1ooVK+TxeFRTU6PJkyf3mKyL/QtHuoNLDvdb8XtTO/CmQxEAAKWk38Hld7/7nX7+859rzZo1isdTQx1+v1/Tpk3TxRdfrLPOOivnRZaq9LBOLpZCp1X4UsElQnABAJSgPgeXRCKhb37zm/rDH/6g4cOH65/+6Z80ZMgQGWP00UcfacWKFbr22mv1+c9/XnfddVc+ay4ZBzPH5ZP4fekel3jOzgkAQLHoc3BZsmSJXnzxRf3Xf/2XLr300qxVMIlEQj//+c/1ve99T9OnT9cXv/jFnBdbajJDRXnocWGoCABQivr8jvn000/rX/7lXzRnzpxel+663W5dcskl+tKXvqTf/OY3OS2yVIXzMFQUSA8VxRKsJAIAlJw+v2Nu2bJFM2fOPODjTjvtNK1fv/6giioX4Uj3UFEuJ+f6Up1oxtDrAgAoPX1+xwyFQqqrqzvg4xoaGtTZ2XlQRZWLfEzOdbksVXSvLApFmOcCACgtfX7HNMbI7XYf+IQuF0MUfZSPOS6SFKhI/Zy6CC4AgBKT23dM9MveVUUHDoT9kd72PxQmuAAASku/9nG57bbbVF1dvd/HdHR0HFRB5SQXO+f2JhNc6HEBAJSYPgeXGTNmSNIBh4Gqqqo0ffr0g6uqTKQ3icvl5Fxp714uDBUBAEpNn4PL448/ns86ylI+NqCT6HEBAJQu5rjYiKEiAAD6h+BiE2PM3qGifK0qCrOPCwCgtBBcbBKOJpSeLZTz4OKjxwUAUJoILjbpCsckSZYluV3Zl1A4GP7uoaJILKFYPJnTcwMAYCeCi03SvSFej6vXaz8djAqvS+ks1BGK5vTcAADYieBik67uzeF8Od58TpIsy8r0ugQ7Yzk/PwAAdiG42GTfHpd8CGSCCz0uAIDSQXCxSbrHJW/BpXsTumAXwQUAUDoILjYpVI9LOz0uAIASQnCxSah7VZEv30NF9LgAAEoIwcUmXZH8XBk6zd+9CV0bPS4AgBJCcLFJ3oeKfOmhIlYVAQBKB8HFJqF8T85lVREAoAQRXGySHirK3xyXvauKjDEHeDQAAM5AcLFJvoeK/N1DRbF4MnMVagAAnI7gYpP0tYryNTnX63FlQlFbB8NFAIDSQHCxSb57XCSpsnueS2tHJG/fAwCAQiK42KQz1D3HxZu/H0FVIBVc9rQTXAAApcH24NLa2qpbbrlFM2fO1LRp03TxxRdr1apVdpeVd6FIegO6/AwVSVKV3ytJamkP5+17AABQSLYHl7lz5+pvf/ubFi5cqCeffFLHHnusvvrVr2rz5s12l5ZX+b5WkSRVBbqDS5DgAgAoDbYGl23btum1117TbbfdpunTp+uoo47SzTffrGHDhunZZ5+1s7S8SiT3rvTJ61CRPzVU1MJQEQCgRNgaXBoaGvTII49o4sSJmfssy5JlWQoGgzZWll/hyN7lyflaVSTt2+NCcAEAlAaPnd+8trZWp59+eo/7XnjhBW3btk3/7//9vwGf15PH4ZdcCIbSS6Fd8nlcivayQZyrO8C53JI7YfX7uCTVVvokSXvaw0XfJm63q8fHckd7ZKNNeqI9stEm2UqxTWwNLh/3xhtvaP78+Tr77LN1xhlnDOgcLpelhoaq3BaWY63dK4oCFR4FAj55vMmsxwT8Hnk8bgX8Pnk8/T8uSRUVqR6X9q6Yqqr98nnz17uTK7W1AbtLKCq0RzbapCfaIxttkq2U2qRogsvSpUt1ww03aNq0abrnnnsGfJ5k0igY7MphZbnX3NIpSfL73AqFoorGsne2tUxS8XhCoXBU0V52vj3QcWmfHp14Upu3t2j4oMrcvpAccrtdqq0NKBgMKZHoPYiVE9ojG23SE+2RjTbJ5pQ2qa0N9LlXqCiCy+LFi/Xd735X55xzjv77v/9bPp/voM4XjxfvD0eSOruHiip8biWSRolk9lBR0hgZY5RMaEDHJcltpPqaCjXtCalpT0iDa/25fSF5kEgki/7nV0i0RzbapCfaIxttkq2U2sT2Qa8lS5boO9/5ji655BItXLjwoEOLE6R3zQ348p8b66srJKXmuQAA4HS29rhs2bJF3/ve9/SZz3xGV155pZqbmzPH/H6/ampqbKwuf9J7uPgr8j/npL4mFVxYWQQAKAW2BpcXXnhBsVhMf/zjH/XHP/6xx7EvfOELuuuuu2yqLL9C0e7gUoAel4Z0cGEvFwBACbA1uHz961/X17/+dTtLsEWoex8Xvy//PS4N6aEids8FAJQA2+e4lKPMHJeKAsxxoccFAFBCCC42SAeXgvS41KQn5xJcAADOR3CxQSa4FKLHpTq1SqsjFFOkl/1iAABwEoKLDQq5HDpQ4VFF94659LoAAJyO4GKDzOTcAiyHtixLg2qZoAsAKA0EFxsUco6LJDXUpHbMZYIuAMDpCC42KOQ+LpIyPS676XEBADgcwcUGoXDhlkNL0pC6VI9LcyvBBQDgbASXAosnkop2X+iqUENFw+pTV4Vuag0V5PsBAJAvBJcCC0f3LkkuWHBpCEiSdhFcAAAOR3ApsPTE3AqfW253YZp/aHdw2dMeUSzOXi4AAOciuBRYOrhUFmh+iyTVBLyq6O7d2cU8FwCAgxFcCiw9VFTpL1xwsSxLw+pTvS7McwEAOBnBpcC60j0ufm9Bv286uOzaQ3ABADgXwaXAwpngUrgeF2nvPBd6XAAATkZwKbBQZqjIph4XggsAwMEILgWW7nEp1OZzaUMJLgCAEkBwKbD0HJeqQGF7XIZm9nIJK2lMQb83AAC5QnApsHD3laELuRxakgbXVsjtshRPJNXKxRYBAA5FcCmw9AUWCz051+1yaXBt6ppFDBcBAJyK4FJgIZuWQ0v7rCxiSTQAwKEILgUWsmk5tLR3gi5LogEATkVwKTC7lkNLLIkGADgfwaXA7OxxSV8lurGF4AIAcCaCS4GFbZzjMnJwpSTpw5ZOlkQDAByJ4FJgmaGiAi2Htqz0zdLwQZXyuC1FY0m1BCOyLKsgNQAAkCsElwKKJ5KKxZOSCjNU5HZbcrlcag/F1RGOKRRNaFhDqtdl0842dYRjSuS9CgAAcqfwEy3KWFc4nvk8UIChIrfLUiga16YdbYrGUxHF73NLkt5Yv0sul6Xjjhykar9XhqEjAIADEFwKqDMck5QaJnK7CjdME4snFY2lgkttZSowtbSFM70/AAA4BUNFBZTucbFjRVFaXXWFJKm1M2pbDQAADBTBpYA6w/ZcYHFf9dU+SVJbR4ThIQCA4xBcCqire6ioysYel5pKn1yWFE8YdYRittUBAMBAEFwKKNPjYsMeLmkul6XaqlSvC1eJBgA4DcGlgNI9LnbOcZH2znPZ00FwAQA4C8GlgIphjou0d55LazsTdAEAzkJwKaCuzFCRvT0u9ekeF4aKAAAOQ3ApoM7M5Fx7e1zq0j0urCwCADgMwaWAimEfF0mqrfTJ5bIUiyfV3Ba2tRYAAPqD4FJAxbCqSEqtLGqoSQ0X7WjssLUWAAD6g+BSQF2R7qGigP1XWhhc65ckbW9st7kSAAD6juBSQJ2h4uhxkaTBdd09Lk30uAAAnIPgUiDxRFKR7gsd2j3HRdrb47KjqZ0JugAAxyC4FEh6Yq5UHD0u9dUVcrsshSIJNe0J2V0OAAB9QnApkPRS6ECFWy6XZXM1qQm6g2pTw0VbP2KeCwDAGQguBZJZCl1hf29L2pC6gCRp60dBmysBAKBvCC4F0lkku+bua0h9ap4LPS4AAKcguBRIsVxgcV/pHpdtHwWVZIIuAMABCC4FUiybz+2roaZCXrdLoUhCu5igCwBwAIJLgRRjj4vLZWnUsCpJ0qadbTZXAwDAgRFcCqQYe1wkacwhdZKk9TsILgCA4kdwKZBiucDix40ZlQ4urfYWAgBAHxBcCiS9j0tVoMh6XEbVypL0UUuX2jqjdpcDAMB+EVwKpKsIl0NLUqXfq0OHVUuSNtDrAgAockUVXB5++GHNmTPH7jLyorNIh4ok6ZjD6iVJ6wguAIAiVzTB5YknntD9999vdxl50xXpHioqssm5kjTu8HpJzHMBABQ/2//539jYqFtvvVXLly/XkUceaXc5eVPMPS7juntc3m/qUGc4VpThCgAAqQh6XN555x15vV4988wzmjx5st3l5EU8kVQkmpBUnD0udVUVGjGoUkbShvdZFg0AKF62//N/1qxZmjVrVk7P6fHYnsd66IrEM5/XVvnkdqfqc1kuuV2W3L1cLdplWbIsSy635E70/3hfHuN2WbJcljweS+OPaNBHLV1av6NV08cPG+hLHbB0m6Q/ljvaIxtt0hPtkY02yVaKbWJ7cMk1l8tSQ0OV3WX00BVPXQeo0u/R4MHVmfsrKjwKBHzyeJNZzwn4PfJ43Ar4ffJ4+n+8L4/xelwK+H2qr6/UCRNG6uW/faC3t7TY2n61tQHbvncxoj2y0SY90R7ZaJNspdQmJRdckkmjYLDL7jJ6+LAxKEmqrPBoz55Oud0u1dYGFInEFQpFFY0lsp5jmaTi8YRC4aii0f4f78tjfF63QuGoWluNjhpeJZdl6f2mDq3bvEvDGioP8lX3T7pNgsGQEoneg1g5oT2y0SY90R7ZaJNsTmmT2tpAn3uFSi64SFI8Xlw/nGD3xm6VFZ4etSVNUomkUSKZfWXmpDEyxiiZ0ICO9+UxiaSRSRrF40YVHreOPrRO63a06o11u3TW9MMG+nIPSiKRLLqfn51oj2y0SU+0RzbaJFsptUnpDHoVsfauVHAptl1zP27S2MGSpDc37ba5EgAAekdwKYBgZ2oPl7oqn82V7N/kMUMkSe9tb82sggIAoJgQXAog2N3jUlvkwWXk4EoNqfMrnkhq7bYWu8sBACBLUc1xueuuu+wuIS/Sc1yKPbhYlqXJY4bo/954X29u2q2pRw+1uyQAAHqgx6UA0sGlprL45rhYVvqW2vNl8tGp4aK/b2iW6X3OLwAAtiG4FEB6qKjY5ri43ZZcLpfaQ3F1hGPqCMd0+PBqVfk9auuM6o0Nu8RMFwBAMSmqoaJSVaxDRW6XpVA0rk072hSN740ohw+v0bvb9mjpqh0af0SDqv1eGbpfAABFgB6XPEsao/au1Kqi2sriCi5psXhS0VgicztiRGp33807gz0CDQAAdiO45FlXOJ7Z/K2mSIPLxw2tD6jK71EsntTbm1hdBAAoHgSXPNt311xvkV388ZNYlqWjDqmVJK18r9HmagAA2MsZ76QOVqzzWw5k9MhUcFm7dU/mNQAAYDeCS55lNp8rwqXQ+1NfU6Gh9X4lk0Yv//0Du8sBAEASwSXvnNrjIknHH5W6dtFLb7yveBFfVRQAUD4ILnnmlO3+e3PUIbWqrfSptSOq1et22V0OAAAEl3xLX2CxWJdC74/bZenUySMlSUtX7bC5GgAACC555+ShIkk6ZeJIuV2WNu0MatPONrvLAQCUOYJLnjl5qEhK1X3S8SMkSc/8Zau9xQAAyh7BJc8yPS4OHCpKm33KkXJZlt7avFsb3m+1uxwAQBkjuOTZ3h4XZy2H3tewhkqdOinV6/KbVzfbXA0AoJwRXPIoHI0rGkstI3bqUFHa7JOPksdt6b3trXp3K5cBAADYg+CSR+lhIp/XJb/P2RfiHlzn1+mTR0mSfvHSRiWTXC0aAFB4BJc8Chb5VaH7a/YpR6qywqPtjR3spgsAsAXBJY+cvhRakiwrfbNUV12hL8wcLUl66tXNag/FbK4OAFBuCC555PQVRW63JZfLpfZQXB3hmDrCMc04brhGDa1SVziun//fBiXsLhIAUFacPfGiyDl9RZHbZSkUjWvTjjZF43sjytRxQ/XBrk4te/sjfeqYofrUuGEyhjkvAID8o8clj9o6nD9UJEmxeFLRWCJzG1RToWMOr5ckLfnjeoUicXsLBACUDYJLHu1qC0mShtQFbK4k96aNG6rqgFctwYh+9dJGu8sBAJQJgkseNbeGJUlD6/w2V5J7Xo9LM6ccIkl66W8f6M1NzTZXBAAoBwSXPEkao+buHpeh9aXX4yJJhwyp0und4eUnz72r1o6IzRUBAEodwSVPWtsjiieM3C5LDbUVdpeTN58/bbQOG1at9q6YHv3dWiWZpAsAyCOCS57sak31tgyu9cvtKt1m9npcuur8CfJ5XVq7dY/+sHy73SUBAEpY6b6j2qy5LTW/ZUh96c1v2ZdlpYaMLvnMMZJSF2HcvDMoy7IyNwAAcoXgkifpHpdSnd8i9dygbuq4IZo2bqgSSaNFT7+tXW2hzKZ1bFIHAMgVgkuelEVw6d6g7t2tLXpz025NGD1I1QGvdgfDWvSbt/X3Dbu0dmuLwpE4PS8AgJwguOTJru6l0ENKcCn0x6U3qJOk0yaPlGVJWz4Mas3GZsXiSZurAwCUEoJLnuwq8aXQn2RofUAzxg+TJK1et0sf7u60uSIAQCkhuORBJJbIbPdfbsFFko45vF5HjayRMdL/rXpfLcGw3SUBAEoEwSUP0iuKAhUeVfnL7zqWlmXp08ePUENNhcLRhB555h2Fo1zPCABw8AgueZCZmFvnL9tJqV6PS2dOGyW/z60PdnXqkWfeUTLJ5nQAgINDcMmD5jJYUdQX1QGvPjPjMHnclv62oVlLlq6XYWddAMBBILjkQXpFUbkHF0kaPqhSl372GFmS/vTGB3rmta12lwQAcDCCSx7s3cOl9JdC98WnjhmmS89O7az7279s4bIAAIABI7jkQVN3cBlCj0vGrE8dqvNPPUqS9MuXNurZv261tyAAgCMRXHIsFInrw+bU3iWHDau2uZriYFmp2+dPG60vzBwtKXVNo1+9tFHMeAEA9AfBJce2fhiUUeqq0PXVFXaXY7t9r2fUEY6lel66w8vvl2/XD361Rp0slQYA9BHBJcc27QxKksaMqrW5kuKw7/WM1mxs1pqNzRpS59cZU0fJ7bL05qbduvNnq7WzmR12AQAHRnDJsc3dwWX0IXU2V1Jc0tczSt8OH16ts2ccpkCFWzubO3XLo8v1h2VbWS4NANgvgksOGWO0aWebJGnMIfS4HMjQhoC+MHOMxh9er2g8qR/9eo3++4m/qXFPl92lAQCKFMElh3a1hdXeFZPHbenw4TV2l+MIlX6Prrpgoi4+62j5PC6t3dqimx9doade3axQhLkvAICeCC45tPmDVG/L4cNr5PXQtH3lsix97qQj9MNvzdKEowYpnkjqd3/dqvkPL9PSVTsUjSXsLhEAUCR4d82hTZn5LQwT9Ud6ufTIIVX69iVTde2FkzS8IaBgV0xLlm7Qtx9apueWbVWwK2p3qQAAm5XfpYvzaHNmfgsTc/sqvVy6rSumSKJLoXBc4w6v1/w5n9Kytz/SH1ftUEswoidf2azf/mWLpo0bqunHDNPxRw1SoIJfXwAoN/zlz5FILKHtjR2SmJjbH+nl0lt2tsnj8SgUiirRfRXpmiqfvnjmWIUicb325ofavDOoFe82acW7TZl5REeOqNXIwZWqr/apOuCVx+1K3Twued3WPp+75fWkvu7tit2sZgIAZyC45MjqdU1KJI0G1/o1uI5rFPVXLJ6UUWrJdDq4SFLA79FJE0bqhGNHaEdTu1a/16Q1G5vV3BbW5p3BzPLzvvK6XRraENDwQZUaPbJWYw6t06ihVar0e+XO9YsCAOQcwSVHXv77TknSzMkje/0XPQYm3SOzaUebovGEjjqkVkeOrFGwK6bm1pD2tEckSa0dEXWF44onkoonTPfHpBIJ0yMIxRJJ7Wzu1M7mTv1t/S5JUpXfo+njh+mUiSM1+pBaufj5AUDRIrjkwPu7OrTx/Ta5LEunTT7E7nJKUnoDu7SAz63DhlXr2CMbNObQ+kyw+bhKv0eHjajR2k0tCkViCkcTCnZGtac9oqY9ITXtCakzHNcrf9+pV/6+Uw01FZoxfpg+ffwIHTmiJhNCGUoCgOJAcMmBV/6W6m2ZOm4I1yeyyceDTZrP65LLSs1t8Xnd8nndqq3y6dDuC2AmkkntDkbUEozozY3N2tMe0Ysrd+jFlTs0rCGgTx0zTNPHD9XhI2oZSgKAIkBwOUiRaEJ/fedDSdIZU0bZXA36y+1yafQhtfrMCfWaMnaINu9s0+adQW37qF1Ne0L6/evb9PvXt2nk4EpNGjNExx5Rr6NG1qqm0md36QBQlmwPLslkUj/84Q/1q1/9Su3t7ZoxY4ZuueUWHXbYYXaX1ie//csWhSIJDasP6NgjG+wuBwfBGOmQIVU6ZEiVTjxuuHY0tWvLznbt3N2pD3d36cPd2/XCiu2SUlf/HjEooGENlRpaH9DwhoCG1AfUUFOh6oA3a55TX4aa9n1OLJ7QB7s6tGVHi3a3RRSJJhSJJRSNJxSNJWWMkdfjls/rktfjUoXXrZqAV3XVFaqr8qmu2iePm22aAJQe24PLj3/8Yy1ZskR33XWXRowYobvvvltXXHGFnn32Wfl8xf2v2j+/uVN/6H4ju/CMMUzqLCFej0ujD6nT6EPqlDRGHrdLG99v08b32/RRS5d2B8PaHQzrna17sp7rcVuqr65QXbVPddUVaqiu0JD6gAbXVCjg98jbHSgisYRCkbhaghHtDoa1q3uy8Z72iDpCsYN+DTWVXg2tC2hwvV9D6gIaUufvcfN6GPwC4Dy2BpdoNKr/+Z//0Q033KAzzjhDknTffffptNNO04svvqhzzz3XzvI+kTFGK99r0s/+sE6SdN4pR2rG+GE2V4V88fs8mnL0EJ0xZZSMkbrCMe1o6lBTa6h7gm+XPtzdpea2kEKRhOIJo+a2sJrbwgf1fb0el6oDXlVWeOSv8Ki+xqdwJJHaaVhSPGmU6F5BlTRGMlJbZ1TBztReOO1dMbV3xbT5w96XjNdX+1RfXaEKn1t+r1sVPrcqvO7MXjdulyVX+malLs2Q+bz7fq/blXq+z6MKr1v+7nOkz5X+2uUi1APIDVuDy3vvvafOzk6ddNJJmftqa2t13HHHaeXKlUUVXJJJow93d2rzh0H96Y0PtO2jdknS9PHDdN6pR9lcHfIpvbtveyguKTXkM2pYtUZ1T/B1uSzFk0ZvbWxWKBJTKJxQVySmznBcoXBc4VhCPo9bwa6owpGE4onUUE/6DX9QTYWqK33qisTk97pV6feotsqn+tpAZkO+6kqvxhxar/e27FEkln3xyfTxTTvaFInFFYkm1BmOq70rqvauWOaClc1tYe1uCysSS6i1I6rWjsJcRsHncfUIM5V+r6r8HlUFvKoO7PO536uq7q/TIcrrSW0qWOFzqyscU2c4plgsFdZM0ihpUv9/JpJJJbrDXCKZvnUviTfp+5NKJvc/bGeM9rulgTGm+zIVlix1P9aSUtnMylzCwkp/3v2YRNIoFk8t048lkorHe35MHUs9Jv11LJ7o/rj3Pkndk81dCvi9soyR3+dRpd+jygqPqvxeBSrc3V97Ux+7j3k9vW/A+PHXl+xur6SREkmzt333aVvJyO2y5Ha55HFbcrtd3V9bqf9nLKsgW0MYY2S664wnk+oMxRSJJmRMqr5S3J4iuc/vc+Z3PbHvx9T98YSRyyXVd8TU1RWRunuPPW6X3G5Lnu6fncftctQ/Lixj4zrPF198Uddee63WrFkjv3/vpm3/+Z//qXA4rIcffrjf5zTGHPAP00C0BMM99gOxlNocrdLvVX9/3JYluVwuxeNJReMJ9fYTcFmSx+NKbcw2gOO5OEeha7Bkycj0eFwx1RiPJ9XbKSzLksdtfeLzLUvyedySTO/P737d+z4//caXbo9ctUP6NRiT+uOX/qOffh3JZM/nmx7nsHq+hlQnT2b+jjF730Q+/nNE8dj7Pr7vXy6T+Xnm/Pt1f1Nrn2+Z/s77hgrTSwGmly9M+r/9rNdKB8te6tjf4w+O2fvfATRu+nnpsxTi/6n0357UF8q0l7VPWwQqPPL7cjvU7OpHyLS1xyUUCklS1lyWiooKtbW1DeiclpVK+7k2tKEy5+f0eFLb0e/PgeYh9GWewsGeoxRqKIYaD/b756IG5rUAcDpblx2ke1mi0Z7d1ZFIRIFAwI6SAABAEbM1uIwcOVKS1NTU1OP+pqYmDR8+3I6SAABAEbM1uIwfP17V1dVavnx55r5gMKi1a9dqxowZNlYGAACKka1zXHw+ny699FLdc889GjRokEaNGqW7775bI0aM0Nlnn21naQAAoAjZvgHdddddp3g8rptuuknhcFgzZszQT37yE3m9XrtLAwAARcbW5dAAAAD9wcVMAACAYxBcAACAYxBcAACAYxBcAACAYxBcAACAYxBcAACAYxBcAACAYxBcCiyZTOqBBx7QaaedpilTpuhrX/uaduzYYXdZA9LY2Khjjjkm6/bUU09Jkt59911deumlmjJlimbNmqWf/exnPZ7fl7bIxTkK5eGHH9acOXN63FcsbXCgc+RDb+1x0003Zf2+zJo1K6evpZjao7W1VbfccotmzpypadOm6eKLL9aqVasyx5ctW6YLLrhAkydP1jnnnKPnnnuux/MjkYgWLFigk046SVOnTtU3v/lNtbS09HhMIc6RSwdqky9/+ctZvyP7/h6VYpvs3r1b3/rWt/TpT39aU6dO1b//+79r06ZNmePl/HekVwYF9eCDD5oTTzzRvPTSS+bdd981X/nKV8zZZ59tIpGI3aX128svv2wmTpxoGhsbTVNTU+YWCoVMS0uLOfHEE838+fPNxo0bza9//WszceJE8+tf/zrz/AO1RS7OUSiLFy8248ePN5deemnmvmJpg76coxDtYYwxX/ziF83ChQt7/L7s3r27ZNvjy1/+sjn33HPNypUrzebNm82CBQvMpEmTzKZNm8zGjRvNxIkTzcKFC83GjRvNo48+ao477jjz17/+NfP8efPmmbPOOsusXLnSrFmzxpx//vnmkksuyRwv1DkK1SbGGHPSSSeZJUuW9Pgd2bNnT0m3yUUXXWT++Z//2axZs8Zs3LjRXHvttebUU081XV1dZf135JMQXAooEomYqVOnmieeeCJzX1tbm5k0aZJ59tlnbaxsYB555BEze/bsXo899NBD5tRTTzWxWCxz37333mvOPvtsY0zf2iIX58i3jz76yFx55ZVmypQp5pxzzunxRl0sbXCgc+TS/tojmUyaKVOmmBdffLHX55Zae2zdutWMGzfOrFq1KnNfMpk0Z511lrn//vvNzTffbL74xS/2eM7cuXPNV77yFWNMqi3Hjx9vXn755czxzZs3m3Hjxpk33njDGGMKco5cOlCbNDc3m3Hjxpl33nmn1+eXYpu0traauXPnmnXr1mXue/fdd824cePMmjVryvLvyIEwVFRA7733njo7O3XSSSdl7qutrdVxxx2nlStX2ljZwKxbt05jxozp9diqVat0wgknyOPZezmsT3/609q6dauam5v71Ba5OEe+vfPOO/J6vXrmmWc0efLkHseKpQ0OdI5Ctcf27dvV1dWl0aNH9/rcUmuPhoYGPfLII5o4cWLmPsuyZFmWgsGgVq1a1aPOdB2rV6+WMUarV6/O3Jd21FFHafjw4T1eS77PkUsHapN169bJsiwdddRRvT6/FNukrq5O9957r8aNGydJamlp0WOPPaYRI0Zo7NixZfl35EAILgX00UcfSZJGjhzZ4/5hw4ZljjnJ+vXr1dLSoksuuUQnn3yyLr74Yr366quSUq91xIgRPR4/bNgwSdKHH37Yp7bIxTnybdasWXrwwQd12GGHZR0rljY40DlyaX/tsX79eknS448/rlmzZumss87S7bffrvb29kydB/taiqk9amtrdfrpp8vn82Xue+GFF7Rt2zaddtppn1hHKBTSnj171NjYqIaGBlVUVPT7teTyHLl0oDZZv369ampqdPvtt2vmzJk655xzdP/99ysajUpSSbbJvm6++WaddNJJeu655/Td735XlZWVZfl35EAILgUUCoUkqcf/tJJUUVGhSCRiR0kDFo/HtXnzZrW1tenaa6/VI488oilTpujf//3ftWzZMoXD4V5fp5SaGNeXtsjFOexULG1woHMUyvr16+VyuTRs2DA99NBDmjdvnv7yl7/o6quvVjKZLPn2eOONNzR//nydffbZOuOMM3qtI/11NBpVKBTKOp6udX+vJdfnyKePt8n69esViUQ0adIkPfroo7rqqqv0q1/9SjfddJMklXybXHbZZXryySd17rnn6pprrtE777zD35FeeA78EOSK3++XlPrFT38upX7ogUDArrIGxOPxaPny5XK73ZnXMmHCBG3YsEE/+clP5Pf7s/4HT/9yV1ZW9qktcnEOOxVLGxzoHIVy1VVX6V//9V/V0NAgSRo3bpyGDh2qL33pS3rrrbdKuj2WLl2qG264QdOmTdM999wjKfVH/+N1pL8OBAK91pmuNf1aCnGOfOmtTW6//XbdeOONqqurk5T6HfF6vbr++uv17W9/u+TbZOzYsZKk7373u1qzZo0WL17M35Fe0ONSQOluuKamph73NzU1afjw4XaUdFCqqqp6/JJL0tFHH63GxkaNGDGi19cpScOHD+9TW+TiHHYqljY40DkKxeVyZUJL2tFHHy0p1Q1dqu2xePFiXXvttTrzzDP10EMPZf6VOnLkyF7rqKysVE1NjUaMGKHW1tasN4t9X0shzpEPn9QmHo8nE1rS9v0dKcU2aWlp0XPPPad4PJ65z+VyaezYsWpqauLvSC8ILgU0fvx4VVdXa/ny5Zn7gsGg1q5dqxkzZthYWf9t2LBB06ZN6/FaJOntt9/W2LFjNWPGDK1evVqJRCJz7PXXX9dRRx2lwYMH96ktcnEOOxVLGxzoHIXy7W9/W5dffnmP+9566y1JqX9plmJ7LFmyRN/5znd0ySWXaOHChT262qdPn64VK1b0ePzrr7+uadOmyeVy6VOf+pSSyWRmMqkkbdmyRY2NjZnXUohz5Nr+2mTOnDmaP39+j8e/9dZb8nq9OvLII0uyTZqbmzV37lwtW7Ysc18sFtPatWs1ZswY/o70puDrmMrcwoULzQknnGCWLl3aY618NBq1u7R+SSQS5sILLzSf+9znzMqVK83GjRvN9773PTNhwgSzbt0609zcbGbMmGFuvPFGs2HDBvPkk0+aiRMnmqeeeipzjgO1RS7OUUg33nhjj+W/xdIGfTlHIdpj6dKlZty4cebBBx8027ZtMy+//LKZNWuWmTt3bkm2x+bNm83xxx9vrrnmmh57kjQ1NZlgMGjWr19vjj/+eHP33XebjRs3mp/85CdZe4XMnTvXzJo1y7z++uuZ/Ub2bdNCnaNQbfL444+bY4891ixZssRs377dPPfcc+bEE080CxcuLNk2McaYK664wpx99tlmxYoVZt26dWbu3LlmxowZ5oMPPij7vyO9IbgUWDweN9///vfNpz/9aTNlyhTzta99zezYscPusgZk165dZt68eeaUU04xEydONBdddJFZuXJl5viaNWvMl770JTNhwgRz5plnmscff7zH8/vSFrk4R6F8/I3amOJpgwOdIx96a4/nn3/enH/++WbSpEnmlFNOMXfddZcJh8M5fS3F0h6LFi0y48aN6/V24403GmOMeeWVV8y5555rJkyYYM455xzz3HPP9ThHZ2en+a//+i8zffp0M336dDN37lzT0tLS4zGFOEeu9KVNFi9ebP7xH/8x87NZtGiRSSQSJdsmxhgTDAbNrbfeak455RQzadIk85WvfMWsX78+c7yc/470xjImx4vSAQAA8oQ5LgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAAwDEILgAKYs6cOTrmmGN63CZMmKAzzjhDCxYsUFtb2yc+9/3339cxxxyjp556qoAVAyhGHrsLAFA+jjvuON16662Zr2OxmN555x0tXLhQ7777rv73f/9XlmVlPW/YsGH6xS9+ocMPP7yQ5QIoQgQXAAVTXV2tKVOm9LhvxowZ6uzs1AMPPKA1a9ZkHZckn8/X6/0Ayg9DRQBsN2HCBEnSzp07NWfOHN1www267rrrNGXKFH35y1/udaho8+bN+o//+A+dcMIJmjFjhq688kpt2rQpczwSiej73/++Tj/9dE2YMEGzZ8/W888/X/DXBiC36HEBYLstW7ZIkg477DBJ0u9//3udd955WrRokZLJZNbjGxsbddFFF2n48OG67bbbVFlZqQcffFCXXXaZfve736murk7XXHON3njjDV133XUaM2aM/vjHP+r6669XNBrV+eefX8iXByCHCC4ACsYYo3g8nvm6ra1NK1as0KJFizR16tRMz4vX69WCBQvk8/kkpSbn7uuxxx5TNBrVT3/6Uw0dOlSSNH78eF188cVas2aNPB6P/vznP+u+++7T5z73OUnSaaedplAopHvuuUfnnnuuPB7+/AFOxP+5AApm5cqVOv7443vc53K5dPLJJ+v222/PTMwdPXp0JrT0ZvXq1ZoyZUomtEjSiBEj9NJLL0mS7rnnHlmWpdNPP71HUJo1a5aeeeYZbdiwQccee2wuXxqAAiG4ACiY448/XgsWLJAkWZaliooKjRw5UtXV1T0eV1VVtd/ztLa26tBDD93vcWOMpk2b1uvxpqYmggvgUAQXAAVTVVWliRMnHvR5ampq1NLSknX/smXLdOihh6qmpkaVlZX62c9+1uvzjzjiiIOuAYA9WFUEwHGmT5+uNWvW9Agvu3fv1hVXXKFXXnlFJ5xwgrq6umSM0cSJEzO39evX60c/+lGP4SMAzkKPCwDHufzyy/X000/riiuu0JVXXimv16tFixZpxIgRmj17tmpqajRjxgxdffXVuvrqqzVmzBi9+eabeuCBB3Taaadp0KBBdr8EAANEcAHgOCNHjtSSJUt09913a968efL5fDrxxBN13333qa6uTpL0yCOP6Ac/+IEefvhh7d69W8OHD9eXv/xlXXPNNTZXD+BgWMYYY3cRAAAAfcEcFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4BgEFwAA4Bj/H7HMP+BC6CSAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A great step in the data exploration is to display the probability distribution function (PDF) of a variable\n",
+    "# The PDF will show us how that variable is distributed \n",
+    "# This makes it very easy to spot anomalies, such as outliers\n",
+    "# The PDF is often the basis on which we decide whether we want to transform a feature\n",
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.00</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.00</td>\n",
+       "      <td>3984.00</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.12</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.27</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.77</td>\n",
+       "      <td>4.96</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.00</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.00</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.00</td>\n",
+       "      <td>99.99</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand     Price   Body  Mileage  EngineV Engine Type  \\\n",
+       "count         3984   3984.00   3984  3984.00  3984.00        3984   \n",
+       "unique           7       NaN      6      NaN      NaN           4   \n",
+       "top     Volkswagen       NaN  sedan      NaN      NaN      Diesel   \n",
+       "freq           880       NaN   1528      NaN      NaN        1853   \n",
+       "mean           NaN  17837.12    NaN   165.12     2.74         NaN   \n",
+       "std            NaN  18976.27    NaN   102.77     4.96         NaN   \n",
+       "min            NaN    600.00    NaN     0.00     0.60         NaN   \n",
+       "25%            NaN   6980.00    NaN    93.00     1.80         NaN   \n",
+       "50%            NaN  11400.00    NaN   160.00     2.20         NaN   \n",
+       "75%            NaN  21000.00    NaN   230.00     3.00         NaN   \n",
+       "max            NaN 129222.00    NaN   980.00    99.99         NaN   \n",
+       "\n",
+       "       Registration    Year  \n",
+       "count          3984 3984.00  \n",
+       "unique            2     NaN  \n",
+       "top             yes     NaN  \n",
+       "freq           3613     NaN  \n",
+       "mean            NaN 2006.29  \n",
+       "std             NaN    6.67  \n",
+       "min             NaN 1969.00  \n",
+       "25%             NaN 2002.75  \n",
+       "50%             NaN 2007.00  \n",
+       "75%             NaN 2011.00  \n",
+       "max             NaN 2016.00  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obviously there are some outliers present \n",
+    "\n",
+    "# Without diving too deep into the topic, we can deal with the problem easily by removing 0.5%, or 1% of the problematic samples\n",
+    "# Here, the outliers are situated around the higher prices (right side of the graph)\n",
+    "# Logic should also be applied\n",
+    "# This is a dataset about used cars, therefore one can imagine how $300,000 is an excessive price\n",
+    "\n",
+    "# Outliers are a great issue for OLS, thus we must deal with them in some way\n",
+    "# It may be a useful exercise to try training a model without removing the outliers\n",
+    "\n",
+    "# Let's declare a variable that will be equal to the 99th percentile of the 'Price' variable\n",
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "# Then we can create a new df, with the condition that all prices must be below the 99 percentile of 'Price'\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "# In this way we have essentially removed the top 1% of the data about 'Price'\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\4102241817.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_1['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can check the PDF once again to ensure that the result is still distributed in the same way overall\n",
+    "# however, there are much fewer outliers\n",
+    "sns.distplot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\1674007885.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can treat the other numerical variables in a similar way\n",
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\1666233205.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_2['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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mJtK3b1/uv/9+UlJSmDVrFoFAgNLSUmJjY3G73YwdO5YJEyZwww03cMcdd1BdXc3ixYuZM2dOqAfpmmuuYf78+WRmZjJ9+nSWLFnC1q1bueeeewDo379/gxjqB4P36dOH+Pj4dr1+kY5i/Y7D5B6sxDDg7HF9SOsd067ndzpszJs9gh4xLt5evY+XP8xlcmYyw9Lj2zUOEZGTsXxo/oIFC7j00ku5/fbbufzyy7Hb7TzzzDM4nU4KCgqYOnUqS5cuBepuKzz22GOkpaUxd+5crr/+eqZPn84dd9wROt7UqVO59957efHFF7noootYtWoVTz755AlFK0Wkzo78cr46NsB7ysgU0pLClywZRv3LaPJls9n4/jmD+fZZAwBYvaWInftPfPhDRMQKhqmBImERCAQpLT1qdRgdgtNpwxc0WPPlQTytGPQ9dnAvYtxOjWGibgB9QkI0ZWVHT3sMU1VNLRt3HW4wkLq4zMO7a/YRNGHckF6MyejZYL+YKCcZafFs21OGt7ZlP8tIt4Oxg3sTCJhA83+WNpvBqx/vZvnafAwDzhnft1lJXFPvnXC1ZXendgwftWX4tLYtExOjO8egbxGxTnWNnxVfHCBoQv/kGEYfG+AdLnabgcfnJze/Ap+/+U+7RbkdnH9GOnkFR9i5v5wVXxzk/Mnp9OzhbnpnEZE2YvktORFpf0HT5JONB/F4A8THuDhzdGqLJ+ZtrpZWF/cHghiGwfSxfejTK5pA0OTDDQeo8bWsh0tEJJyUMIl0Q5tzSygq8+CwG8wY37dDzulmsxlMH5tKbJST6ho/H39RQLCFpQ1ERMKl4/2VFJE2VVRWzaZddXO4nTEymbjoE6vndxQup51zJvTFYTcoLK3mi12HrQ5JRLopJUwi3YivNsCnGwswgUF94hjUp4fVITUpPiaCM0fXFbn9cncpRaWqqC8i7U8Jk0g3snpLEUdr/MREOpmcmWx1OM02ICWWjL5xAHy6qaBFU6aIiISDEiaRbmLr3lK27ysH6uaI64jjlk4le0QysVFOjtb4WbO1uOkdRETCqHP9xRSRVvF4/bzw3g4AhvePJyUxyuKIWs7psDF1TCoGsPtgJQcOqe6ZiLQfJUwi3cArH+dSXuUjLsrJ+CG9rQ6n1XrHRzK8fwJQd3uxVsX+RKSdKGES6eL2Flby/rr9AJw5JrXT3Yr7unFDehHtdlDlqatcLiLSHjr3X04ROaVg0OT5t7djmjBxWO92n1S3LTgdttCA9a15ZZQd8VockYh0B0qYRLqwD9bvJ6/wCJERDi4+u+tMQJ2WFEN6cgymCTnbijXvoIi0OSVMIl1U2REvr3y8G4Dvzcjo0AUqW2PisN7YDIOCkmr2awC4iLQxJUwiXdSL7++kxhcgo08cZ4/va3U4YRcb5WLEgLoB4Dnbiglo2hQRaUNKmES6oE25JeRsK8ZmGPzovGHY2mhiXauNyeiJ22XnSHUtW/eWWh2OiHRhSphEuphaf5AXltfVXPpmVhrpybEWR9R2nA4b4wb3AuCLnYfx+lQBXETahhImkS5m+bp8iss89Ihx8Z2zBlodTpsbnNaD2CgnNb4AH204YHU4ItJFKWES6UIqqry8+dleAC49O4PICIe1AbUDm80I9TK9vy6fKk+txRGJSFekhEmkC1myYjc1vgADU+OYMirF6nDazYDUWBLjIvB4AyxbnWd1OCLSBSlhEuki9hRU8unmAgB++M0hXXagd2MMw2DisCQAPli3X71MIhJ2SphEugDTNEMDvc8clUJGnx4WR9T+0pNjSOsdTY0vwHtr860OR0S6GCVMIl3Aqi1F5B6oJMJp55IuVNG7JQzD4LzJ6UDdwPfqGvUyiUj4KGES6eS8vgAvf5QLwOwz+5MQG2FxRNYZM7gXfXtF4/EGWH5swmERkXBQwiTSyb29Zh9lR7z06uFmVlY/q8OxlM0wmH3mAADeW5uvukwiEjZKmEQ6sfIqb+ipsO+fMxinw25xRNbLHpFMrx5ujtb4+ezLAqvDEZEuQgmTSCf26se78dUGyegbx8Rhva0Op0Ow2QzOy64by/Tu2nyCmmNORMJACZNIJ5VfXMWnm+p6UH5w7hCMblRGoClTR6cS7XZQXOZhw87DVocjIl2AEiaRTuqfH+7CBLKGJzG4b/crI3AqES47M8b3BeCdNfssjkZEugIlTCKd0ObdJXy1pxSH3eCSGd2zjEBTvjExDYfdYNeBCnYdqLA6HBHp5JQwiXQygWCQf36wC6hLCpLiIy2OqGOKj4ngjJF108Ool0lETpcSJpFO5tNNBRw4fJRotyP0CL007rxjZRbWbz9EUWm1xdGISGemhEnEIoZhNONVv23d9t7aAK9+sgeA75w1kGi308Ir6Pj69o5hTEZPTNTLJCKnx2F1ACLdUQCoacbUHYbNwBesxlNTixk0eevzvVQe9ZGUEMk5E/q2faBdwHlZ/diUW8LHXxzkmu+MtjocEemklDCJtDPDMKipqWXL3lJq/cFTbmu3GURGuvB4fFQe9bE8p25S2ZYWqezOFQeG908gPTmGfUVVLFu5h1kT06wOSUQ6ISVMIhap9Qfx1Z566g67zcDhrNtuzdYi/AGTjL49GJqeQFULJpe12QxOnZp1XYZhcF5WOk//ewtLP9vLueP6WB2SiHRCSphEOoHSyhpyD1QCcOFZA9iWV4bP3/x50qLcDvqnxmHQPbuaskYk8c+PdlFaWcOarcVkD0+yOiQR6WQ06FukE1i3/RAAGX3j6J8SG+qdau7LH+iu/Ut1HHYbM4/dintn9T5MU9OliEjLKGES6eAOHqpi/6GjGAZMzky2OpxO65wJaTgdNvYUVIZ660REmksJk0gHZpomKzfXzRc3JK0H8bERFkfUsdWVX2i8ZEOPGBczJtT1Mr2Xk3/CehGRU9EYJpEObF9xFYWl1TjsBmMyelkdTodmtxvYbDaOePzAibfcDJvB1LF9eG/NPnK2F7Ov+AiJce7QeneEg+Y/dygi3Y0SJpEOKmiarD82dilzQCJRbv26nordZuDx+cnNr2h0QHx9iYa+vaI5cPgoL3+UG7rF6XTYyByQSIzbqfFNItIo3ZIT6aB2H6ikvMpHhMvO6EGJVofTaZxqQHytP8iIAQkAbMsr46inNrRcRORUlDCJdECBQJAvdh0GYOLwJFxO3SwKl7Te0cRGOan1B8k9UGF1OCLSSShhEumAtu0rp7rGT7TbwWiNXQorwzAY0b+ul2lrXpluwYlIsyhhEulgfLUBNu8uAWD8kF447Po1DbeMvj1wOmwcqa7lwKGjVocjIp2A/hKLdDBf7inFVxukR4yLjLQeVofTJTkdNoYca9uteWUWRyMinYESJpEOpLrGz9a9dR/gE4b2xqb6QG1meP8EDKCgpJrSyhqrwxGRDk4Jk0gHsin3MIGgSe/4SNJ6R1sdTpcWE+kkPTkGgK/2lFocjYh0dEqYRDqII9U+du6ve2prwtBeqj7dDupLDOzaX8GRap/F0YhIR6aESaSD2JRbgmlCn15RJCdGWR1Ot9A7PpKecW4CQZPPNxdaHY6IdGBKmEQ6gMqjPnYfmxB23GCVEWgvhmGEepk+3ngQf0AFLEWkcUqYRDqAjbsOY1JXVLFXfKTV4XQr/VNiiYpwUHnUx9qtxVaHIyIdlBImEYuVV3nZU3AEgLFD1LvU3uy2//Qyvbt2nwpZikijlDCJWGzjrroilenJMfSMc1scTfc0vH8CTruNvYVHQgPvRUSOp4RJxEKllTXkFR7rXdLYJctERjjIGpEEwLJVeRZHIyIdkRImEQvV9y4NSIklITbC4mi6t29M6odhwMbcEvYVHbE6HBHpYJQwiVjkcLmH/OIqDGDM4J5Wh9PtJSVEkjW8rpdpqXqZRORrlDCJWGTDjsMADEiNJT5GvUsdwYVTBgCwdlsxRaXV1gYjIh2KEiYRCxw4VEXesds+YzLUu9RRpCfHMiajJ6YJb6mXSUSOo4RJxALvrMkH6moA9VDvUocy+8wBAHy+uZDiMvUyiUgdJUwi7ezg4aN8seMQAGMyEi2ORr5ucN8ejBqUSNA0eeOzvVaHIyIdhBImkXb278/3YgL9k2NJiFXdpY7oommDAFj5VSEFJUctjkZEOgLLE6ZgMMgjjzzCtGnTGDduHNdeey35+fkn3b6srIwbb7yRrKwssrOzufPOO/F4PA22WbZsGRdccAFjxoxhzpw5rFy5ssH6r776irlz5zJ+/HjOOOMMFi9ezJEjeoxY2l5RWTWrttRN8jp+qOoudVQDU+MYN7gXpgmvf7qnTc9lGEaLXyLS/ixPmB5//HFeeOEF7r77bl566SWCwSDz5s3D5/M1uv2CBQvIy8vjueee4+GHH2bFihXccccdofWrVq1i0aJFXHbZZbz66qtMmTKF+fPnk5ubC8Dhw4e55ppr6Nu3L6+88gqPP/4469at49Zbb22Py5VubunKPEwTMgckaM64Dm7OtIEArNlazN7CyjY5RwCoqqlt8SvQJtGIyKk4rDy5z+fj2Wef5aabbmLGjBkAPPTQQ0ybNo13332X2bNnN9h+w4YNrFmzhqVLl5KRkQHAXXfdxbx581i4cCHJyck8/fTTzJw5k6uuugqAW265hQ0bNvD8889z1113ceDAAaZOncpdd92Fw+Fg4MCBfP/73+ehhx5q12uX7udwhYfPv6zrXTp/cn8qqxv/UiAdQ3pyLGeMTGbVV0X84/1d3PzD8WHt3TEMg5qaWrbsLaXWH2z2fk6HjcwBicS4nZr3TqQdWdrDtG3bNo4ePcqUKVNCy+Li4sjMzGTt2rUnbJ+Tk0Pv3r1DyRJAdnY2hmGwbt06gsEg69evb3A8gMmTJ4eON3bsWP74xz/icNTlirm5ubz++uucddZZbXGJIiHLVu0jEDTJHJDAwD5xVocjzXDJ9AycDhvb88v5YufhNjlHrT+IrzbQ7FdLkisRCR9Le5gKC+u+baempjZYnpSUFFp3vKKiohO2dblcxMfHU1BQQGVlJdXV1aSkpDTreOeddx579+6lb9++PPbYY6d7OTgclt/h7BDsdhsETWw2A7utZd/I7TYDw2bgcBiYZtcZq1Fe5eXTTQVA3a0e41jbNNU+NpvtuP8GsR0bw2Kzgz3Q/PZp7/2sOGdT+329LY93svddcs8ozp+czpuf7eWfH+UyflhvHPbw/J4bBs1+HzQn1vZiP3b99jC1Q3emtgyf9mhLSxOm+sHaLperwfKIiAgqKk6cMdzj8Zywbf32Xq+Xmpqakx7P6/WesN8DDzyAx+Ph/vvv56qrruL1118nOjq6VddisxkkJLRu367IU1pNRIQTm93eov2cDhuRbhfx8VFtFJk13lyZR20gyLD+CZwxNo1DZR4iI104nM3rLXC7nQBEuh04HHYi3S4cjub3NLT3fh051vq2PN6p3ndXXpDJJxsLKCqtZsWmQi49d0izY2qKL1jdovdBU7G2p7g4jcELF7Vl+LRlW1qaMLnddY9U+3y+0L8BvF4vkZEnXrTb7W50MLjX6yUqKoqIiIjQ8b6+vrHjjR49GoDHHnuMs88+m/fee485c+a06lqCQZPKShW5g//0tHm9tdR4/S3a1+W046nxUV5u0lWGZ3i8ft469qTV+dn9qKioxlNTi8fjw1d76uG7NpsNt9tJTU0twWAQwwzi9wfw1Pjw+Zo/9Le997PinE3t9/W2PF5T77vvnZPB029u4cV3tjFmQAK9E07/j7Jh0Oz3QUtibWt2u424uEgqKz0EAro9eDrUluHT2raMi4tsdq+UpQlT/e214uJi0tPTQ8uLi4sZNmzYCdunpKSwfPnyBst8Ph/l5eUkJSURHx9PVFQUxcXFDbYpLi4mOTkZgN27d7Nv377QIHOA5ORk4uPjKSoqOq3r8WtsAVD3QQAGwaBJINiyv+iBoIkZNPH7zS4zoHV5Tj7VXj+pPaMYPahn3bUda5um26fuPRUMBgkETYJmXbsEA7Sobdt7PyvO2fR+DdvyeIGgCaZJINB4EjJlZAqfbDzItn3lPLdsGzd8f2xoAHhr36eGYbTgfdAw1o7wOxIIBPU3L0zUluHTlm1p6Y3T4cOHExMTw+rVq0PLKisr2bJlC1lZWSdsn5WVRWFhIXl5/5njac2aNQBMnDgRwzCYMGFCaFm91atXM2nSJAA+//xzFixYQGXlfx4T3rdvH2VlZQ0Gk4uEQ60/yLtr6+qKnT85HZtq6HRIdruBzWbjiMff6GP8R71+LjlnMA67webdJXy86aAe8RfpZiztYXK5XFx55ZU88MADJCYm0rdvX+6//35SUlKYNWsWgUCA0tJSYmNjcbvdjB07lgkTJnDDDTdwxx13UF1dzeLFi5kzZ06oB+maa65h/vz5ZGZmMn36dJYsWcLWrVu55557AJg9ezZPPfUUixYt4qabbqKiooLf/va3jBkzhnPOOcfK5pAuaOVXhVRU+UiIjWDKyJSmdxBL2G0GHp+f3PwKfP6Tp0CjM3qyYcdhXnxvJ97aAPExEXrEX6SbsHxo/oIFC7j00ku5/fbbufzyy7Hb7TzzzDM4nU4KCgqYOnUqS5cuBeq6sB977DHS0tKYO3cu119/PdOnT29QuHLq1Knce++9vPjii1x00UWsWrWKJ598MtR7FB8fz/PPPw/A5Zdfzi9/+UsyMzN55plnsLdwgLLIqQSDJstW7wPgm5P6he3pKmk7TT3inzkgkcS4CLy1AT5af6BFY49EpHOztIcJwG63s2jRIhYtWnTCurS0NLZv395gWc+ePXnkkUdOecw5c+accvD2wIED+fOf/9yqeEWaa8POQxSVVhMV4eDscX2sDkfCwG4zmDomlbc+z6OgpJqv9pQybkhvq8MSkXagr7wibcA0TZauqutdOmdCXyIjLP9uImESHxPBxGF1SdKaLUXsOdg206aISMeihEmkDWzfV86egkqcDhszJ/WzOhwJs2Hp8aQnxxA04Zl/b6Gi6sQ6byLStShhEmkD9U/GnTU6lR7RJxZblc7NMAzOGp1KfIyLiqM+/ufVzZqyRKSLU8IkEmZFpdVs3FU379g3J6VZHI20FafDxsysfrhddnbur+CZt7YQ1JNyIl2WEiaRMHsvJx8TGJPRk9Semi6nK4uPieAnszOx2wzWbC3mXx/usjokEWkjrUqYTrcitkhXVeWp5dPNdZPszsrS2KXuYHj/BH58wQgA3lmTzxvHpsERka6lVQnTOeecw7x581i6dGmjc7uJdFcfbzyIrzZIWu8YRvRPsDocaSdnjk7l++cMBuC1T/fw2ie7VchSpItpVcL0u9/9jmAwyE033cTUqVO588472bx5c7hjE+lU/IEg76/bD9T1LhmaBqVbOX9yOt87p65A7huf7eVVJU0iXUqrisN897vf5bvf/S5FRUW8+uqrvP7667z44osMHjyYiy++mO985zv06tUr3LGKdGg524spO+IlLtrF5Mxkq8MRC3xrcn/shsFLH+zi35/nEQzCJWcPUvIs0gWc1qDv5ORkfvazn7Fs2TKWLFlCQkIC999/PzNmzOC6665j48aN4YpTpEMzTZP3jpUSOHd8X5wOPU/RXc3KTufybwwBYOmqPP71Ya56mkS6gNP+q56Tk8NvfvMbfvKTn7Bu3TrOOussbr31VjweD5dffjnPPfdcGMIU6dh2HahgT8ERHHYbM8b3tTocsdg3s/pxxTeHAvD2mn387Z3tBINKmkQ6s1bdksvLy+P111/njTfe4MCBA/Tt25cf/ehHXHzxxaSmpgJw5ZVXctNNN/HEE09w9dVXhzNmkQ7n3TV1vUtTRiYTp0KVAnxjYhpOh43nl23joy8O4vEF+MmFIzQJs0gn1aqE6bzzziMiIoKZM2dy9913M2XKlEa3GzRoEHv37j2d+EQ6vEPlHtbvPATU9SyI1Js+tg9ul52n39zC6i1FeLx+fjFnFC6n3erQRKSFWpUw/eY3v+E73/kOsbGxp9zuF7/4Bb/4xS9aFZhIZ7E8Zz+mCSMHJpLWO8bqcKSDyR6RjNvl4H9e3cym3BL+9K+NXHfJGKLcTqtDE5EWaFXf8DvvvENxcXGj67Zt28a3v/3t0wpKpLPweP18sukgAOdlpWMYRjNeFgct7W5MRk8Wfn8sbpedbfvK+dO/NuLx+q0OS0RaoNk9TDk5OaEnPdasWcPatWspLS09YbsPP/yQ/Pz88EUo0oGt2HiQGl+AlMQoBvSJpaqmtsl9bDYDTdPa/QxLT2DR5eN54KUv2Lm/gof++QU//e4oq8MSkWZqdsL0r3/9i9dffz30LfnOO+88YZv6hGr27Nnhi1CkgwqaJstz6r4cDE7rwabckmbtF+V20D81DgN1NXU3A1PjuOmycTzw0gZ27q/gqTe+4qzRKVaHJSLN0OyE6fbbb+eSSy7BNE3mzp3L4sWLGTx4cINtbDYbcXFxDBkyJOyBinQ063ccorTSS4TTTnpyDL7aQLP2czn1lFR3NjA1joU/GMeDL33Bjvxyav0BzhqTik33akU6tGYnTLGxsWRnZwPw17/+lZEjRxIdrZnYpfuqLyUwYkCCHhWXFsno04P/d/EYHvrnF+wpOILLaSd7RJIqgot0YM1OmF577TXOPvtsEhISOHjwIAcPHjzl9nPmzDnd2EQ6rN0HK9l1oAK7zSBzgCbZlZYbOTCRq84fzl+WbmX7vnJ6RLsYrgmbRTqsZidMt956K//85z9JSEjg1ltvPeW2hmEoYZIu7d21+wCYOKw3UW5ns2/HiRxvwrDefLmnhLVbi1m7rZi4aBd9eqnnXqQjanbC9P7779O7d+/Qv0W6q9LKGnK21RWqPGdCGiWVNRZHJJ3ZmIyelFTUsPtgJR9/cZALz+xPbJSqxYt0NM1OmPr27dvov+v5/X6qqqqIj48PS2AiHdX76/YTNE2Gp8eTlhSjhElOi2EYTBmZTOVRH4cravj4iwLOP6MfdpvGxYl0JK36jfT7/Tz22GO8+eabAKxevZqzzjqLKVOmMHfuXCoqKsIapEhHUePz89EXxwpVZqdbHI10FXa7jenj+uBy2iiprGHdsR5MEek4WpUwPfLIIzzxxBNUVlYC8Nvf/pb4+Hhuu+029u3bx4MPPhjWIEU6is82F+Lx+klOiGTM4F5WhyNdSEykk6mj6yYv37avnH1FRyyOSESO16qE6a233mLhwoVcccUV5ObmsnPnTn7+859z1VVXccMNN/DBBx+EO04RywVNk/eOFaqcOamf6uZI2KUlxTByYN2Tcqu+KtL0KSIdSKsSpuLiYsaOHQvARx99hM1mY/r06QCkpKRw5Ii+GUnXs3HXYYrLPERFOFSdWdrMuCG9SIiNoMYXYOVXRaEZFETEWq1KmJKSkti/fz8AH3zwASNGjCAxMRGADRs2kJKiDxPpet5bW9e7dPa4PrhdzX5eQro4w6h/NWfi5aYnYbbbbEwdk4LNgP3FVeQeqGzfCxKRRrXqr/7s2bP53e9+x5tvvsm6detYvHgxAPfccw8vvvgiP/vZz8IapIjV9hUdYdu+cmyGwTcmplkdjnQQdruBzWbjiMcPtKwn6FSTMCfEuhk3pBfrdxwmZ1sxfXtHExmhJF3ESq36Dbz++uuJiopi7dq13Hjjjfzwhz8EYPPmzfz4xz/m5z//eViDFLHau8d6l7JGJJEY57Y4Guko7DYDj89Pbn4FPn/Lipc2NQlz5oBE8gqPUFLpZc3WYs4e1yccIYtIK7UqYTIMg5/+9Kf89Kc/bbD8pZdeCktQIh1JeZWX1VuKAJiV1c/iaKQjqvUHW1ztvalJmG02gzNGpbB0ZR55hUfIL66iX1LM6YQpIqeh1X28R44cYdWqVVRXVzc6KFFTo0hX8cH6/QSCJoPTejAwNc7qcKQb6RnnJnNAAl/tKWP1liJSEqNwOe1WhyXSLbUqYfrkk09YsGABHo+n0fWaS066Cm9tgI821BWqnDVJvUvS/sYO7kVeYRVVnlq+3FPK5Mxkq0MS6ZZalTA9+OCDDBo0iNtuu43k5GRsKuEvXdTnXxZS5amlVw8344eqUKW0P4fdxsRhvVnxxUG+2lPKiP4JVock0i21KmHKzc3l8ccfZ9KkSeGOR6TDCJpmaLD3N7M0t5dYJz05hpSeURSWVLN6SxHTxmoAuEh7a9UnQJ8+faiqqgp3LCIdysZdhykqrSYqwsG0MalWhyPdmGEYZA9PwjAgr/AI2/LKrA5JpNtpVcL005/+lP/5n/8JFa8U6YreWXOsUOV4FaoU68XHRjAsPR6AJR/l4g+crIqTiLSFVn0KvPnmmxQVFfHNb36TxMRE3O6GdWkMw2D58uVhCVDECnsKKtmRX47dZjBzogZ7S8cwdnAv9hYcobC0mg/W7+ebehBBpN20KmFKSUnR9CfSpb2zZh8A2SOSSYiNsDgakToRTjuThifx6aYCXvtkD5Mzk4mLclkdlki30KqE6Xe/+1244xDpMA5XeMjZdgiA87L1DV46lqHp8ewtPML+4ipe/2QPPzpvmNUhiXQLp/XYT25uLn/961954IEHKCoqIicnR4PBpdNbnrOfoGkyon8C6cmxVocj0oDNMLjk7EEArPjiIIWl1RZHJNI9tKqHKRgMsnjxYpYsWYJpmhiGwbe+9S0ef/xx9u3bx9///nfdspNOqbrGz8cb6wpVnpedbnE0Io0bnBbP2MG92LjrMEtW5PLLi0ZbHZJIl9eqHqbHH3+cN998k9/+9rd89tlnoalRFi1aRDAY5KGHHgprkCLtZcXGA9T4AvTpFc3oQYlWhyNyUt+bkYFhwLrth8g9UGF1OCJdXqsSpiVLlrBgwQIuueQS4uPjQ8tHjBjBggUL+Oyzz8IVn0i78dUGePdYKYHzsvphGI3PIi/SEfTtHcNZo+vqg/3zw12NzukpIuHTqoTp8OHDjBgxotF1ycnJVFZWnlZQIu3NMAw+3VxAxVEfPePcnDk6FcMwmnhZHbV0d3OmDsTpsLFzfwVf7DpsdTgiXVqrEqb+/fuzYsWKRtetWbOG/v37n1ZQIu0pAFQc9fLWyjwAzp2YRk1tgKqa2lO+jnr9qHSgWCkxzh2qxfTyR7kEgnpHirSVVg36njt3LosXL6a2tpZzzjkHwzDIy8tj9erVPPvss9x6663hjlOkTRiGQU1NLa9/uoeyI14iI+xERzrY2Ixv61FuB/1T4zBQV5NY54Iz0lnxxQEKSqr5bHMh0zXPnEibaFXC9L3vfY/S0lKeeOIJXnjhBQAWLlyI0+lk3rx5XH755WENUqQtBYMm67bX1V3KHJBIMGjiCwaa3M/l1GS8Yr0ot5NvnzmAlz7YxWuf7GZyZjIRTrvVYYl0Oa2eIOvaa6/l29/+NmvWrMHhcBAbG8vYsWMbDAIX6Qw27DxE5VEfLqeNof3irQ5HpMXOmZDG8nX7OVxRw3tr85l95gCrQxLpclqcMP373//mpZdeYuPGjfj9fgDcbjcTJkzg8ssvZ+bMmWEPUqStBE2Td1bXTYMyon8CTod6jaTzcTpsXDR9EE+/uYWlq/KYPq6PpkwRCbNmJ0yBQIAbb7yRt99+m+TkZC688EJ69eqFaZoUFhayZs0arrvuOr773e/y+9//vi1jFgmbL3YepqCkGqfDxvD+CVaHI9JqkzOTeWfNPvYVVfHvz/byw28OtTokkS6l2QnTCy+8wLvvvsuvf/1rrrzyyhNq1AQCAV566SXuvfdeJk2axKWXXhr2YEXCyTRN/v35XgAyByRo3Id0ajbD4HvnDObBl77gww0HmJnVj6T4SKvDEukymn3/4bXXXuOyyy7jRz/6UaMF/ex2O1dccQXf//73efXVV8MapEhb+GLXYfYUVOJ02Bg1qKfV4YictpEDEhk5MJFA0OS1T3ZbHY5Il9LshGnPnj1Mnz69ye2mTZvGjh07Tiso6bo8Xj/7io7wxc7DbNlTSo3Pb0kcwaDJKyvqPlBmjO9LZESrn38Q6VAuPTsDgNVfFbGv6IjF0Yh0Hc1OmDweDz169Ghyu4SEBI4ePXpaQUnXlHuggiUf5fLRhoNsyi0hZ/shXv5oN59tLsBb2/Rj/OG08qtCDhw+SpTbwcxJae16bpG21D8lluwRSZjAKx+rl0kkXJqdMJmmid3e9BgPm82mOY3kBDv2lfPZ5kKCJvSIdpHRN46ecREEgya5Byp5e9U+qqpr2yWWWn+Q1z7ZA8AFZ/Qnyu1sl/OKtJeLpg3CZhhsyi1h+74yq8MR6RL0DLW0ud0HK1m1pQiAYenxfGfqAM4ancoFU/pz3uR+REU4qDjqY+mqPEoqato8nuU5+ZRU1hAf42LmsWklRLqS5MQopo+tm5h3yYrd+hIrEgYtGrhxxx13EBMTc8ptqqqqTisg6Vpq/UFythUDdXWOJg3vHXpowDAMkhOiuGBKOu+vO0DZES9vr97HuCG9iGllr09jDyQcr+yIlzePPRl3ydkZuF12aj2af0u6nm+fNZDPvyxk14EKNu4qYdyQXlaHJNKpNbuHKSsri+joaEzTPOUrOjqaSZMmtWXM0ol8taeUGl+A2CgnE4b1bjShiXI7OS+7HwmxEXi8fp549UuOelp+ey4ATU6Y++LyHdT4AgxIjWXMkF6aQFe6rITYiFAP6pIVuQSD6mUSOR3N7mH629/+1pZxSBd01FPLlr2lAEwY2hu77eS9Py6nnXMn9mXZqn0Ullbz2CubWPiDcTjszcvp6yfR3bK3lFp/4ylQYUk1a4/1do0d3IvNuSWaQFe6tG+dkc5HGw5w4PBRVn5VyFmjU60OSaTT0hgmaTM52w/hD5j0jneTnnzqW7kA0W4n501Ox+2ys21fOX9/d3uLx17U+oP4agMnvKpravn4iwMADE7rQY9oF77aAP6A+pek64p2O7lgSn8AXvtkz0m/TIhI05QwSZuorqllZ345wElvxTWmZ5ybqy8YgWHAxxsLeG9tflji2bDjMJXVtURGOJg4rHdYjinSGXxjYhrxMS5KKmv46NiXBhFpOcsTpmAwyCOPPMK0adMYN24c1157Lfn5J/+QLCsr48YbbyQrK4vs7GzuvPNOPB5Pg22WLVvGBRdcwJgxY5gzZw4rV65ssH7nzp3Mnz+fyZMnM2XKFBYsWMDBgwfb5Pq6q7VbiggETeJjXC2enmHkwER+cO4QAP7xwS427jp8WrEUllSzNa/u0eozR6VoChTpViKcdr4zdSAA//58Lx6vNcViRTo7yxOmxx9/nBdeeIG7776bl156iWAwyLx58/D5fI1uv2DBAvLy8njuued4+OGHWbFiBXfccUdo/apVq1i0aBGXXXYZr776KlOmTGH+/Pnk5uYCdQnXNddcg9vt5m9/+xtPP/00paWlzJs3D6/X2x6X3C18vrkuAR3Ut0eze5eONyurH9PH9sEEnnzjK/Yfat3Tl9U1tXyyqQCAof160Ld3dKuOI9KZTR2dSnJCJEeqa3k3TL22It2NpQmTz+fj2WefZcGCBcyYMYPhw4fz0EMPUVhYyLvvvnvC9hs2bGDNmjX84Q9/YOTIkUyZMoW77rqL119/naKiujo/Tz/9NDNnzuSqq64iIyODW265hZEjR/L8888DsHz5cqqrq7nvvvsYOnQoo0aN4v777yc3N5f169e36/V3VYUl1ezaX4EBDEqNa9UxDMPgyllDGZ4ej9cX4OF/baK8qmUJba0/yAfrD+Dx+ukR42LisKRWxSLS2TnsNi6aPgiAt9fso7K68S+kInJyliZM27Zt4+jRo0yZMiW0LC4ujszMTNauXXvC9jk5OfTu3ZuMjIzQsuzsbAzDYN26dQSDQdavX9/geACTJ08OHW/KlCk8/vjjuN3u0Hqbra4ZKisrw3p93dWnx3p00pJiiHK3fo42h93GLy4aTVJCJCWVNfz+/9ZzuMLT9I5AIGjyyaYCSiu9uF12zp3QF6fD8g5VEctMGp5E/+RYvL4Ab32eZ3U4Ip2OpTOOFhYWApCa2vBR16SkpNC64xUVFZ2wrcvlIj4+noKCAiorK6muriYlJeWkx0tLSyMtreHcYU899RRut5usrKzTuh6HPpAJmiafba5LmIalx5+ylEBj7DYDw2bgcBiYpkF8bAS3/HACv/+/9RSXefj9/61n0eXj6du74VN3hgGGzcBuM/D7g3ywfj+FpR7sNuPYoNeIRs9nMwwMw8BmB3ugZbG2dt+W7FefzNf9N9gu5wzHflacs6n9vt6WVsZ6Ovt+/XekJX7wjcHc98IGPtywn2+dkU6vFo4vBLAfK/Vhb2bJDzk5tWX4tEdbWpow1Q/WdrlcDZZHRERQUVHR6PZf37Z+e6/XS01NzUmPd7LxSX/729/4+9//zu23305iYmKrrgPAZjNISND4mJ35ZRyuqCHCZWf4gJ60tFSe02Ej0u0iPj4qtCwhIZr7rpvG7U9+zoFDVfzmf9dw+axhXHzO4AZ1mryBoxSX1/Dhuv2UV3lxOmycf0Z/0lNOflsw0u3A4bAT6XbhcLTskevW7tua/dzHKp+35zlPZ7+OHKu7kSrynal9Gvsdaa6pE6J4e00+m3Yd5q3V+7j+sgktPka9uLiWJ1vSOLVl+LRlW1qaMNXfFvP5fA1ukXm9XiIjT7xot9vd6GBwr9dLVFQUERERoeN9ff3Xj2eaJg8//DBPPPEEP//5z/nRj350WtcSDJpUVlaf1jG6gs827Acgc0AigUCAmhY+keNy2vHU+CgvNzm+BJMduO3KCTz1xldsyi3hb8u28u9PdzMmoyd9e8dQedTH5t0l5BUeASDa7WDmpDQS4yKorj752CfDDOL3B/DU+PD5Ai2KtbX7tmQ/m82G2+2kpqaWYDDYLucMx35WnLOp/b7ellbGejr7nux3pLkumjaQTbsO80FOPt+Y0Je03k3XSDue3W4jLi6SykoPAdUxOy1qy/BpbVvGxUU2u1fK0oSp/vZacXEx6enpoeXFxcUMGzbshO1TUlJYvnx5g2U+n4/y8nKSkpKIj48nKiqK4uLiBtsUFxeTnJwc+v/a2lpuu+02/v3vf3Pbbbdx9dVXh+V6/CoKx8bcEgBGZfQiGDQJtHA6hkDQxAya+P3mCUUroyIc/NelY1j5VSEvvb+LsiNeVnzRsByE3WYwtF88ozMScbscTZ4/eGxKn2CAFsfa2n1btl/deyoYDBIImu10ztPfz4pzNr1fw7a0MtbT2fdUvyPN0T85lolDe7NuxyH+9cEurrtkTIuPARAIBPU3L0zUluHTlm1p6Y3T4cOHExMTw+rVq0PLKisr2bJlS6PjibKysigsLCQv7z8DFtesWQPAxIkTMQyDCRMmhJbVW716dYP57W6++WbefvttHnzwwbAlSwJHa2rJPVB3K3VURs82OYdhGJw5KpUHf3kmC78/lpmT0pg0rDczJ6Vx0dmDuGzmELJGJOF2WfpdQKRDu2j6IAwDNuw8zK4DJw5/EJETWfqp4nK5uPLKK3nggQdITEykb9++3H///aSkpDBr1iwCgQClpaXExsbidrsZO3YsEyZM4IYbbuCOO+6gurqaxYsXM2fOnFAP0jXXXMP8+fPJzMxk+vTpLFmyhK1bt3LPPfcA8Morr7B06VJuvvlmsrOzOXToUCie+vNI62zdW4ZpQp9e0fTqEcnu/LI2O5fTYWfUoJ6MGlSXmBmGQVVNLRt3HcZX27JbIyLdTZ9e0Uwdnconmwp4+aNcbvnh+FbVSxPpTiwfmr9gwQIuvfRSbr/9di6//HLsdjvPPPMMTqeTgoICpk6dytKlS4G6D8XHHnuMtLQ05s6dy/XXX8/06dMbFK6cOnUq9957Ly+++CIXXXQRq1at4sknnwyVIvj3v/8NwH333cfUqVMbvOrPI62zeXfd7bgxbdS7JCLh892pA3HYbezIL2fz7lKrwxHp8Cy/b2G321m0aBGLFi06YV1aWhrbt29vsKxnz5488sgjpzzmnDlzmDNnTqPrnn322VbHKidnmiZf7qn7o6uESaTjS4xz842JfXlnTT6vfryb0YMS1cskcgqW9zBJ13Dg0FHKjnhxOWwM6x9vdTgi0gwXnNGfCJedvKIjrN9xenM2inR1SpgkLLYcm9x2aL94XA5NbivSGcRGuZg5sa6Q7+uf7ibYmjoFIt2EEiYJi137y4G6hElEOo/zstOJjLCz/9BRcrYVN72DSDelhElOm2ma7Dz2aPKQtB4WRyMiLRET6WRWVl0dvNc/3UOwhfWkRLoLJUxy2g5X1FBR5cNuMxiYevJpSEQkPAyj/mW06HUy35zUj2i3g4KSalZvLWrHKxHpPJQwyWnbeex2XP+UWFxOjV8SaUt2u4HNZuOIx09VTW2LXierUBbldnBedl0v0xuf7iEQVNVpka+zvKyAdH679ut2nEh7sdsMPD4/ufkV+PzNL9LqdNjIHJBIjNvZ6JQqMyel8e7afIrKPHz+ZSHTxvQJZ9ginZ56mOS01Y9fGtw33tpARLqRWn8QX22g2a/aJubXcrscXHBGfwDe/Gwvfk0GK9KAEiY5LdU1tRw8dBSAwephEunQmhr7dO7ENOKiXRyuqGHVlqITxj/V/fv4Y7V+3JRIZ6NbcnJadh2oxASSEiLpEe2yOhwROYnjxz7ByZ+EO2dCX17/ZA9vfraXsYN7YbPVJUkulwNvTS2GzcAXrMZTU4vZjCfq3BEONLJRugIlTHJa6gd8a/ySSMfW3LFPPWJcRDjtHCr38Nonu8no24Mot4P+qXHk5lcQCAaJjHTh8fgINJEwNTVuSqQzUcIkp2VPQSUAGX2VMIl0BvVjn05leP94Nu4qYcOOQ6T1jsbltIX29QcCOJx1x2gqYRLpSjSGSVrNNE32FhwBYGCK6i+JdBXD+yfgtNsor/Kx/9gYRZHuTgmTtNqhcg/VXj8Ou42+vaOtDkdEwiTCaWdoejwAm3NLdDtNBCVMchr2Ftb1LvVLisZh11tJpCvJHJCA3WZwuKKG/cXqZRLRp5y0Wn3CNEC340S6nMgIR+hhjnXbNSmviBImabW9xwZ8D0iJtTgSEWkLIwcmYjPg4OFq9hystDocEUspYZJWCZomeUV1PUz9lTCJdEnRkU4GHXsC9v2cfIujEbGWEiZpleIyDx5vAKfDRp9eGvAt0lWNGpgIwNa9ZZQd8VocjYh1lDBJq+wtrOueT0+K0YBvkS4sLtrFwNS6XuTNuw9bHI2IdfRJJ61SX39JA75Fur5xQ3oBsGt/BR6v3+JoRKyhhElapf4JOY1fEun6UnpGkZ4cQyBosjWvzOpwRCyhhElaLGia7Cuq72FSwiTS1RmGwYwJaQBsyyun1h+0OCKR9qeESVrscEUNNb4ADrtBSs8oq8MRkXYwKqMnMZFOvLUBtueVWh2OSLtTwiQttr+4CoA+PVXhW6S7sNsMRg/qCcAXOw9puhTpdvRpJy2Wfyxh6pcUY3EkItKehqXH43LYqKjyse/Y3wGR7kIJk7RYfQ9TmhImkW7F6bAz7NikvF/t1m056V6UMEmLqYdJpPsaMSABm2FQVObhcIXH6nBE2o0SJmkRj9dPcXndH0n1MIl0P9FuJ0P6xQN11b9FugslTNIiBw4fBaBHjIu4KJfF0YiIFcYcK2SZV3iE6hoVspTuQQmTtIhux4lIUkIUSQmRBE3YkV9udTgi7UIJk7RI/YDvfr2VMIl0Z5kDEoC6hCkQVCFL6fqUMEmLtEcPk2HUv4wWvNosHBFpRP/kWKLcDmp8gdDckiJdmcPqAKTzCJom+YfaNmGy2w1sNhtHPH6g+YXxbDYDfccVaT82m8Gw9Hg27DjM1rwyBvWJw9A3F+nClDBJsx2uqMF7bEqU5MS2mRLFbjPw+Pzk5lfg8weavV+U20H/1DgM9AdbpL0MSYtn064SSiu9FJd52uzvgkhHoIRJmi2/6NiUKL3afkqUWn8QX23zEyaXU3eXRdqb22VnUJ84du6vYGtemRIm6dL0KSPNtv+QBnyLSEPD+9cN/s4vqqLKU2txNCJtRwmTNJtKCojI1yXERpDSMwoT2L5PhSyl61LCJM22XwmTiDRixLFepp35FdT69fiFdE1KmKRZNCWKiJxMWu9oYqOc+PxBdh+stDockTahhEma5cChuilR4mNcxGpKFBE5jmEYDE+v62XalleGaTa/JIhIZ6GESZqlvv6SepdEpDEZaXE47AYVR30UlFRbHY5I2ClhkmbRgG8RORWXw87gvj0A2Jqnwd/S9ShhkmbRHHIi0pT6EgMHDh2l8qjP4mhEwksJkzSpPaZEEZHOLy7aRd/e0UDdWCaRrkQJkzTpcLknNCVKSk9V8hWRk6svMbDrQEWLqvWLdHRKmKRJ+cV1T8j16RWN3aa3jIicXGrPKHrEuPAHTHbkl1sdjkjY6NNPmqQpUUSkuQzDYMSxEgNf7SklGFSJAekalDBJk1ThW0RaYlDfOFxOG0eqa/lqT6nV4YiEhRImaVJ9SQHVYBKR5nDYbQxJiwfgow0HrA1GJEyUMMkp1fg0JYqItNyw9HgMA3bkl4e+dIl0ZkqY5JTqp0TpEe0iTlOiiEgzxUQ6GZASC8D76/Itjkbk9ClhklNS/SURaa2RA3sC8PmXhVR5ai2ORuT0KGGSU9qv8Usi0krJiZGkJcVQ6w+y4guNZZLOTQmTnJKmRBGR1jIMgxnj+gDwwfoD+ANBiyMSaT0lTHJSpmmSf2wMk3qYRKQ1JgxLIi7KSdkRL+t3HLI6HJFWU8IkJ1Va6cXj9WO3GaRqShQRaQWnw8aM8WkALM/Zb3E0Iq2nhElOqn7Ad2rPKBx2vVVEpHXOndAXu81g14EK9hRUWh2OSKvoU1BOSgUrRSQcesREkD0iCYD3clRiQDonJUxyUhrwLSLh8s2sfgCs2VLM4WPFcEU6EyVMclL1k+6qh0lETteAlDhGDkggaJosW7PP6nBEWszyhCkYDPLII48wbdo0xo0bx7XXXkt+/sm7bMvKyrjxxhvJysoiOzubO++8E4+n4beVZcuWccEFFzBmzBjmzJnDypUrT3ruefPm8eijj4b1mroCX22AwtJqANLUwyQiYXDhlAEAfLKxgIoqr7XBiLSQ5QnT448/zgsvvMDdd9/NSy+9FEpifD5fo9svWLCAvLw8nnvuOR5++GFWrFjBHXfcEVq/atUqFi1axGWXXcarr77KlClTmD9/Prm5uQ2O4/P5+NWvfsUnn3zSlpfXaR0sOYpp1k1vEB+jKVFE5PQNS48no28c/kCQd9ZqLJN0LpYmTD6fj2effZYFCxYwY8YMhg8fzkMPPURhYSHvvvvuCdtv2LCBNWvW8Ic//IGRI0cyZcoU7rrrLl5//XWKiooAePrpp5k5cyZXXXUVGRkZ3HLLLYwcOZLnn38+dJz169dz8cUXk5OTQ1xcXLtdb2dSP+C7X1IMhmFYHI2IdAWGYTD7WC/ThxsOcKS68S/GIh2RpQnTtm3bOHr0KFOmTAkti4uLIzMzk7Vr156wfU5ODr179yYjIyO0LDs7G8MwWLduHcFgkPXr1zc4HsDkyZMbHG/FihVMmzaN1157jdjY2Da4ss5vf/GxgpW6HSciYTQmoyfpyTF4fQHeXq2xTNJ5OKw8eWFhIQCpqakNliclJYXWHa+oqOiEbV0uF/Hx8RQUFFBZWUl1dTUpKSmnPN4NN9wQrktowOGw/A5n2Bw4XNfD1D8ltsXXZbfbIGhisxnYbS3rnbIZBoZhYLODPdD8fdt7v/Y6p81mO+6/QbXPaez39ba0MlYrzhmuWG3mydvx6+w2A8Nm4HAYmOZ/znnJjAwe+sdG3l+3n29N6U98TESL4ukq7Mfq29lV5+60tUdbWpow1Q/WdrkajpGJiIigoqKi0e2/vm399l6vl5qampMez+tt2wGGNptBQkJ0m56jvZimSf6xHqaRg3u36ro8pdVERDix2e0t2i/S7cDhsBPpduFwNH/eqfber73P6XY72/2cp7NfR461vi2tjNWKc4Yr1lp/3b6NtePXOR02It0u4uMbzhRwTlYUS1fuY/u+MpavO8C1c0a3KJ6uJi4u0uoQuoy2bEtLEya32w3UjWWq/zeA1+slMvLEi3a73Y0OBvd6vURFRRERERE63tfXN3a8cAoGTSorq9v0HO2l7IiXI9U+DANiI2yUlR1t0f71PVJeby01Xn+L9jXMIH5/AE+ND58v0GH3a69z2mw23G4nNTW1BINBtc9p7Pf1trQyVivOGa5Y/X7zpO34dS6nHU+Nj/JyE9NsuO67Uwdw3wtlLPt8L+eO70NinLvxg3RhdruNuLhIKis9BDQx8WlpbVvGxUU2u1fK0oSp/vZacXEx6enpoeXFxcUMGzbshO1TUlJYvnx5g2U+n4/y8nKSkpKIj48nKiqK4uLiBtsUFxeTnJzcBlfQkN/fNd7we49NXZCSGIXNMFp8XXVjxA2CQZNA0Gxq8waCpolpmgQDtGjf9t6v/c5Z1/bBYJBA0FT7nNZ+DdvSylitOGe4Yq1Pkhprx68LBE3MoInfX7f/8Yb1i2dov3h25Jfzrw938ZMLM1sUU1cSCAS7zOeH1dqyLS29cTp8+HBiYmJYvXp1aFllZSVbtmwhKyvrhO2zsrIoLCwkLy8vtGzNmjUATJw4EcMwmDBhQmhZvdWrVzNp0qQ2uoqup77CtwZ8i0hbMQyD751T9wDP55sLySs8YnFEIqdmacLkcrm48soreeCBB3j//ffZtm0bN9xwAykpKcyaNYtAIMChQ4dCY5PGjh3LhAkTuOGGG9i0aROrVq1i8eLFzJkzJ9SDdM011/DWW2/xl7/8hdzcXO677z62bt3K3LlzrbzUTiVfFb5FJEwMo/5lnPAa3DeeyZnJmMA/Ptx1bPv/rBfpSCwfmr9gwQIuvfRSbr/9di6//HLsdjvPPPMMTqeTgoICpk6dytKlS4G6X6THHnuMtLQ05s6dy/XXX8/06dMbFK6cOnUq9957Ly+++CIXXXQRq1at4sknn2xQikBOTXPIiUg42O0GNpuNIx4/VTW1jb4umNIfh91gW14Zq7YUNVjXspFWIm3L0jFMAHa7nUWLFrFo0aIT1qWlpbF9+/YGy3r27MkjjzxyymPOmTOHOXPmNOv8H3zwQbNj7Q5q/UEKSo5NiZLUNZ76ExFr2G0GHp+f3PwKfP6Tpz8jByaycVcJL7y3g0trM3A4bDgdNjIHJBLjdp4w/knECpb3MEnHcuBwFYGgSbTbQc9u+NSKiIRfrT+IrzZw0lfmgESi3A6qPLXkbCvGVxsIlS8Q6SiUMEkD9QMv+6fEagyBiLQLp8NG9ogkAL7aW0rZEU3MKx2PEiZpIK/oWIXvZE0ZIyLtJz05ln5JMZgmrPqqkKBuw0kHo4RJGji+h0lEpD1lj0jCabdxqLyGTbtKrA5HpAElTBLiDwTJL1YPk4hYIzrSSXZm3a25dduLyS9SbSbpOJQwSUhhSTX+QBC3y07vBM1tJCLtb1CfONKT627N/fXt7XhbOI2LSFtRwiQhece+zaUnxWDTgG8RsYBhGJwxMpmoCAeFpdU8u3SLygpIh6CESUJCCZPGL4mIhdwuB+dOTMNmM1iztZh31uRbHZKIEib5j331A741fklELJbSM4pLZtTN0PCvj3axKfewxRFJd6eESYC62cjz6gd8q4dJRDqAaWNSmTYmFdOEx1/9kl0HKqwOSboxJUwCQHGZB68vgNNhI7VnlNXhiIhgGAZXnT+cUYMS8fmDPPyvjew/Njm4SHtTwiQA7CmoBKBfUgx2m94WItIxOOw2fjlnNBl94zha4+e+FzaE6sWdimEYLX6JnIo+GQWA3QfrEqZBqXEWRyIi0lCEy85/XTqWASmxVHlque/F9ezILz/p9gGgqqa2xS8VMJBTcVgdgHQMoYSpjxImEel4YiKdLLp8PA+/vIkd+eU88NIG5p4/nLNGpzbYzjAMampq2bK3tEUT+DodNjIHJBLjdqqMgTRKPUxCrT9IfnFdF7cSJhHpqCIjHCz8/ljGD+mFP2DyzFtbeWH5DvyBExOjWn8QX22g2a+WJFfSPSlhEvYVH8EfMImJdNI7XhW+RaTjcjnt/PLi0XznrAEALM/Zzz1/XUdByVFrA5MuTwmTsOe423Ea+CgiHZ3NMJgzbRDXXTyaaLeDvKIj3PmXtSxbnddob5NIOChhEnYXaMC3iHQ+44f25q6fTGbkwLqyA//6MJc7/7KWXfvLLYmn5U/mWRKmtJIGfYsGfItIp5UQG8HC74/l080F/OvDXPYfquLhf22iX1IM44b0JCHW3S5xBICamtoW7WPYDBwuX9sEJGGnhKmbq/LUUlzmAWCAephEpBMyDINpY/owbnAvXv1kDx9/cYD84iryi6sY1CeOsYN7EhvlatPzt+bJPLfLzvjhLly619MpKGHq5up7l5ITIomJdFocjYhI68VGuZh7/nCmje3D39/dzp6Dlew+WMmegkoG9Ylj5MBE4mMi2uz89U/mNZfdpntynYkSpm5u98G6uZl0O05EuoqkhEi+MTGNgv5HWb/jEAUl1eQeqCT3QCX9kmIYNShRTwRLiylh6ubqq+UOSYu3NA4RkXDr2cPNN7P6cajcw1d7StlXVBW6VZeUEMmI/gn0S4rBpp4eaQYlTN2YPxAk99gtuaH94q0NRkSkjfSOj2TG+L6UV3n5ak8puw9WUlzmobjMQ5TbwbD0eEYOSLQ6TOnglDB1Y3sLjlDrDxIT6SS1Z5TV4YiItKn4mAjOGp3K+CG92J5fwc78cqpr/GzYcZhNu0rIPVjJ+dnp9EuKsTpU6YCUMHVj2/PLABjWL14FK0Wk24hyOxk/pBdjBiWyt/AIW/PKKK30svLLQlZ+WUhG3zimjelD9ogk3C59TEodvRO6sR35dQO+dTtORLoju91GRt8eDOoTR3mVjwOHjvLFzsOhAeIvLt9J1ogkpo/tQ4ZmQuj2lDB1U8Ggyc5j1XCVMIlId2YYBsmJUczKTifgD/LZ5gI+3lRAUWk1n24q4NNNBaT2jGLamD5MGZlMjzYsTSAdlxKmbiq/uIoaX4DICLvu14uIHNMjJoJvndGf8yens3N/BZ9sPMjabcUUlFTzzw938fJHuYwcmMiZo1IYP6QXLqfd6pClnShh6qa2H1dOQI/UioiAYXBsfre6ud6GpScwLD2BK2YNY/WWIj7ddJDcg5Vs3l3C5t0lREbYmTQ8mamjU0jpFW11+NLGlDB1U9v31Q341u04ERGw2w1sNhtHPH7APGH9pBFJTBqRRHFZNWu2FrN2axGllV4+2XiQTzYeJDEugv7JsfRPiSUuuu2mYRHrKGHqhvyBINuOJUzD0uOtDUZEpAOw2ww8Pj+5+RX4/Kee3qRfUgxpvaMpLKlm5/4K9hRUUlrppbTSy4adh0ntGcXw/gn07R2NTQPFuwwlTN3Q7oOVeLwBYiKdDEzRlCgiIvVaMh9czx5uevZwc/b4PlR7A6xYf4ADh6ooKKmmoKSamEgnw/vHM7hvD4116gKUMHVDX+4pAWDkwESNXxIROU1Oh40JAxKJcjkoqfCwPb+MnfkVVHlqydl2iI27ShjRP4HMAQlKnDoxJUzd0ObdpQCMGqipAEREwikmysnEYUmMyejFnoJKtu4to+Koj025JWzLK2N0Rk+G94/HbrNZHaq0kBKmbqbyqI+8wiOAEiYR6diOf2qtJft0BE6HjaH94hmS1oO8oio27TpMeZWPddsPsSO/nKwRSZr0vJNRwtTN1N+OS0+OUfE1Eemwmnpq7WRsNoNg24XVYoZhMCAllvTkGHIPVPLFzkMcqa7lg3UH2FtwhKH9e5LUQ0/VdQZKmLqZL4/djhs9qKfFkYiInFxLnlo7XpTbQf/UOIwW9Eq1B5thMCStBwNSYtm46zBb88rYfbCS/356Jdd+O1N/kzsBJUzdSDBo8uUejV8Skc6jJU+tAbicHXtskNNhY9LwJAb2iWPVV0WUVNTw0D83cs6EvvzgnMEaFN6Bdex3loTVjvxyqjy1RLsdZPTtYXU4IiLdVs84NxdNG8isyf0B+HD9Ae5+Pof9xVUWRyYno4SpG1mzrRiA8UN747DrRy8iYiW73cYPZg5l0Q/HExft4sDho9z1fA6fbS6wOjRphD41u4lAMMi67XUJU/bwJIujERGRemMyenLXj7MZPagn/kCQZ97ayovLdxIIdqTh66KEqZvYvq+cI9W1xyrPJlgdjoiIHCcu2sV/fW8M3zlrAADv5eTzx39spMpTa21gEqKEqZtYs7Wud2mCbseJiHRINsNgzrRB/GLOKCKcdrbmlXH382vZf0jjmjoCfXJ2A/5AkPU7DgGQNUK340REOrJJw5P41Y8m0quHm0PlNdzzt3V8seuw1WF1e0qYuoEte0up8tQSG+VkeHq81eGIiEgT+iXFsPjqLIanx+P1BXj05U28vXofptn8Ip4SXkqYuoEP1x8AYHJmsuYvEhHpJGIinSz8wTimj+2DCfzzw138Zdk2/AENBreCPj27uMPlHjbl1k2Hcs74vhZHIyIiLeGw25h7/jAu/8YQDAM+3VTAAy99wZFqn9WhdTtKmLq4j744iAlkDkggtWe01eGIiEgLGYbBN7P6cf33xhIZYWdHfjl3P5/DgcNHrQ6tW1HC1IXV+oN8vPEgAOdOSLM4GhEROR2jB/XkVz+aRO94N4crarj3bzmhOwjS9pQwdWE524qp8tSSEBvB2MGa2FFEpLPr2yua26+axNB+8Xi8AR5+eSPvrs3XYPB2oISpiwoEg/x75V4AZozvq8HeIiJdRGyUi5suG8fUMamYJrz0/k6eW7atRZMUS8vpU7SLWvllEQUl1US7HcycqNtxIiJdicNu45pvDecH5w7GAD7ZVMBv/7qOghKNa2orSpi6oFp/kNc/3QPAhVMGEBnhsDgiEREJN8MwOH9yf268bBxxUU72H6rirudyWPVVEYZhnPQlraOEqQv6eONBSipriI9xce4ElRIQEemKAkBVTS39U+O4+YoJDOnXA29tgKfe/Iqn3vyKkiM1VNXUnvDSjbvWUddDF1Ne5eW1T3YD8O0zB+By2i2OSEREws0wDGpqatmyt5Raf10hy2lj+xAT6WTDjsOs/LKQzbklTB2TSlpSTGg/p8NG5oBEYtxODRRvISVMXYhpmjy/bBtHa/ykJ8cwbWwfq0MSEZE2VOsPNhjsPXpQT3r3iOSzzQVUeWp5e/U+BvWJY9LwJNwufYE+Hbol14V8trmQjbklOOwG82Zn4rDrxysi0pEZgGFwyjFHjb9OfsyUnlF8Z+pAhvePB2D3wUre+HQPuw9WqFfpNKiHqYvYX1zFC8t3ADBn2iDSesc0sYeIiFjJbjPwB4N4vH6CgZYlMjabwalmlHM6bGSPSGZgShyff1VIRZWPTzcVsnVvOS6nnaxhSacXfDekhKkLOFzh4Y///IIaX4Bh/eI5Pzvd6pBERKQJNptBjS/AjrxSarwtG4od5XbQPzUOg1M/9dY7IZLZZ/Zny54yvtxdSkllDU+8+iUf9IvnkrMzGJzW43QuoVtRwtTJVVR5eeifGymv8tG3VzT/75LR2Gx6bFREpLP4+jik5nA5mz/kwm6zMTqjJ0P69WDr3jK25pWxPb+ce/++jqH94vnGxDTGD+mlYRxNUMLUie0pqOSxVzZTdsRLQmwEN3x/LNFup9VhiYhIB+R2OZg8MoXvnTuY99bk89nmQnbkl7Mjv5yE2AhmjOvD9LF96BETYXWoHZLl6WQwGOSRRx5h2rRpjBs3jmuvvZb8/PyTbl9WVsaNN95IVlYW2dnZ3HnnnXg8ngbbLFu2jAsuuIAxY8YwZ84cVq5c2eJjdGSBYJD3cvL5/f+tp+yIl9SeUdz8w/EkxrmtDk1ERDq4hFg3P74wk/t+PoXZZw4gLspJ2REvr36yh4X/8xm//7/1vJeTT2lljdWhdiiW9zA9/vjjvPDCC/z+978nJSWF+++/n3nz5vHmm2/icrlO2H7BggV4PB6ee+45Kisr+fWvf011dTV/+MMfAFi1ahWLFi3i5ptv5qyzzuLll19m/vz5vPbaa2RkZDTrGB1VMGiyaXcJSz7K5cDhuvL3YzJ68tPvjFQ1bxERaZHEODcXTx/Et88cQM72Yj5Yv5/cA5WhXqcXl+9kYGocmQMSGNovnsF9e3TrzxpLr9zn8/Hss89y0003MWPGDAAeeughpk2bxrvvvsvs2bMbbL9hwwbWrFnD0qVLQ8nPXXfdxbx581i4cCHJyck8/fTTzJw5k6uuugqAW265hQ0bNvD8889z1113NesYHUnQNMkrPMKm3BI+3VRAybGMPybSyUXTB3H22D4asyQiIq3mdNiYMjKFKSNTOFzhYf2Ow6zbXsyu/RXsKahkT0Elb63MwzCgX1IMGX160KdXdOgVF+XsFlOuWJowbdu2jaNHjzJlypTQsri4ODIzM1m7du0JCVNOTg69e/cOJToA2dnZGIbBunXrOP/881m/fj233nprg/0mT57Mu+++26xjXHDBBW1xqa1SUHKUB176grIj3tCyaLeDaWP6cMGU/sREarySiIiET68ekczK6sesrH6UV3nZlFvCzvxyduwv51B5DfuKqthXVNVgn2i3g5493CTERJAQ5yYhxkWPmAgiIxxERtiJdDmO/bvu/11OO7ZOmGBZmjAVFhYCkJqa2mB5UlJSaN3xioqKTtjW5XIRHx9PQUEBlZWVVFdXk5KSctLjNXWM1rLZDBITo1u9f2Ni4yK577ppGAa4HHVvsgiXvYmHSK1nGGCacG52f1paI81mgMNhIy05rkX7tvd+7XVOwwADAxMT01T7nM5+X29LK2O14pzhihVO3o5tdc7O1D4tOqfNwGG3kZwY1W7nNIy6HqXmJCyJidEMSk8M/X/QNKn1B/EHgvgDJoFAkECw5YUwTQjNZ1cXxrHiCMdCCkVm1P+7rlBnTKTzpE/y1V9Ojx6RLf4ZNJelCVP9QOuvj1WKiIigoqKi0e0bG9cUERGB1+ulpqbmpMfzer3NOkZrGYaB3R7eVMZut5HSie8XR53GI6pOR+tK+Lf3ft3lnIq165yzM8VqxTmtibX9/1a2hr2dz9caNlvbPctm6VNybnfdU10+n6/Bcq/XS2RkZKPbf33b+u2joqKIiIho8nhNHUNERETk6yxNmOpvjRUXFzdYXlxc3Ojg65SUlBO29fl8lJeXk5SURHx8PFFRUac8XlPHEBEREfk6SxOm4cOHExMTw+rVq0PLKisr2bJlC1lZWSdsn5WVRWFhIXl5eaFla9asAWDixIkYhsGECRNCy+qtXr2aSZMmNesYIiIiIl9nacLkcrm48soreeCBB3j//ffZtm0bN9xwAykpKcyaNYtAIMChQ4dCY5PGjh3LhAkTuOGGG9i0aROrVq1i8eLFzJkzJ9SDdM011/DWW2/xl7/8hdzcXO677z62bt3K3Llzm30MERERkeMZptnSsfnhFQgE+OMf/8grr7xCTU0NWVlZLF68mLS0NPbv3883vvENfve733HxxRcDUFJSwp133sknn3xCREQE559/Prfddlto/BLAa6+9xuOPP05hYSGDBw9m0aJFDUoXNOcYIiIiIvUsT5hEREREOjrL55ITERER6eiUMImIiIg0QQmTiIiISBOUMImIiIg0QQmTiIiISBOUMImIiIg0QQmTiIiISBOUMElYBYNBHnnkEaZNm8a4ceO49tpryc/PtzqsDu3Pf/4zP/rRjxos27p1K1deeSXjxo3j3HPP5a9//WuD9WrnOuXl5SxevJjp06czYcIELr/8cnJyckLrV65cycUXX8zYsWM5//zzeeuttxrs7/V6ufPOO5kyZQrjx4/nxhtvpLS0tL0vo0MoKSlh0aJFnHHGGYwfP5758+eTm5sbWq/3ZOvs2bOH8ePH88orr4SWqS2br6ioiGHDhp3wqm/Pdm1LUySMHn30UXPy5Mnmhx9+aG7dutX88Y9/bM6aNcv0er1Wh9Yh/f3vfzeHDx9uXnnllaFlpaWl5uTJk83bbrvN3LVrl/nyyy+bo0ePNl9++eXQNmrnOtdcc405e/Zsc+3atebu3bvNO++80xwzZoyZm5tr7tq1yxw9erT5xz/+0dy1a5f5v//7v2ZmZqb5+eefh/a/9dZbzZkzZ5pr1641N27caM6ZM8e84oorLLwi6/zgBz8wv/e975kbN240d+3aZV533XXm1KlTzerqar0nW8nn85kXX3yxOXToUHPJkiWmaer3u6U++ugjc/To0WZRUZFZXFwcenk8nnZvSyVMEjZer9ccP368+X//93+hZRUVFeaYMWPMN99808LIOp7CwkLzpz/9qTlu3Djz/PPPb5AwPfnkk+bUqVPN2tra0LIHH3zQnDVrlmmaaud6e/fuNYcOHWrm5OSElgWDQXPmzJnmn/70J/M3v/mNeemllzbYZ+HCheaPf/xj0zTrfgbDhw83P/roo9D63bt3m0OHDjXXr1/fPhfRQZSXl5sLFy40t2/fHlq2detWc+jQoebGjRv1nmylBx980LzqqqsaJExqy5Z56qmnzG9/+9uNrmvvttQtOQmbbdu2cfTo0Qbz9sXFxZGZmcnatWstjKzj+eqrr3A6nbzxxhuMHTu2wbqcnByys7NxOByhZWeccQZ79+7l8OHDaudjEhISeOqppxg9enRomWEYGIZBZWUlOTk5DdoI6tpx3bp1mKbJunXrQsvqDRw4kOTk5G7VjgA9evTgwQcfZOjQoQCUlpby3HPPkZKSwuDBg/WebIW1a9fyj3/8g9///vcNlqstW2b79u1kZGQ0uq6921IJk4RNYWEhAKmpqQ2WJyUlhdZJnXPPPZdHH32Ufv36nbCusLCQlJSUBsuSkpIAKCgoUDsfExcXx9lnn43L5Qote+edd8jLy2PatGknbUePx0NZWRlFRUUkJCScMOl2d2vHr/vNb37DlClTeOutt7jnnnuIiorSe7KFKisrufnmm7n99ttPaBO1Zcvs2LGD0tJSrrjiCs4880wuv/xyPv74Y6D921IJk4SNx+MBaPABBhAREYHX67UipE6ppqam0TaEukHKaufGrV+/nttuu41Zs2YxY8aMRtux/v99Ph8ej+eE9aB2nDt3LkuWLGH27Nn88pe/5KuvvtJ7soXuuOMOxo8fz7e//e0T1qktm8/v97N7924qKiq47rrreOqppxg3bhzz589n5cqV7d6WjqY3EWket9sN1H0Y1f8b6t64kZGRVoXV6bjdbnw+X4Nl9b/cUVFRaudGLF++nJtuuokJEybwwAMPAHV/FL/ejvX/HxkZ2Wg7Q/duR4DBgwcDcM8997Bx40b+/ve/6z3ZAq+99ho5OTm8+eabja5XWzafw+Fg9erV2O32UFuMGjWKnTt38swzz7R7W6qHScKmvtuzuLi4wfLi4mKSk5OtCKlTSklJabQNAZKTk9XOX/P3v/+d6667jnPOOYcnn3wy9A0zNTW10TaKiooiNjaWlJQUysvLT/iD2x3bsbS0lLfeegu/3x9aZrPZGDx4MMXFxXpPtsCSJUsoKSlhxowZjB8/nvHjxwPw3//938ybN09t2ULR0dENkh2AIUOGUFRU1O5tqYRJwmb48OHExMSwevXq0LLKykq2bNlCVlaWhZF1LllZWaxbt45AIBBatmrVKgYOHEjPnj3Vzsd54YUXuPvuu7niiiv44x//2KDrfdKkSaxZs6bB9qtWrWLChAnYbDYmTpxIMBgMDf6Gupo5RUVF3a4dDx8+zMKFC1m5cmVoWW1tLVu2bCEjI0PvyRZ44IEHWLp0Ka+99lroBbBgwQLuuecetWUL7Ny5kwkTJjRoC4Avv/ySwYMHt39btvi5OpFT+OMf/2hmZ2eby5cvb1DzwufzWR1ah3XLLbc0KCtw+PBhMysry7zlllvMnTt3mkuWLDFHjx5tvvLKK6Ft1M51JQBGjhxp/vKXv2xQn6W4uNisrKw0d+zYYY4cOdK8//77zV27dpnPPPPMCXWYFi5caJ577rnmqlWrQnWYjv9ZdCfz5s0zZ82aZa5Zs8bcvn27uXDhQjMrK8s8cOCA3pOn6fiyAmrL5gsEAuYll1xiXnDBBebatWvNXbt2mffee685atQoc/v27e3elkqYJKz8fr953333mWeccYY5btw489prrzXz8/OtDqtD+3rCZJqmuXHjRvP73/++OWrUKPOcc84x//a3vzVYr3Y2zSeeeMIcOnRoo69bbrnFNE3TXLFihTl79mxz1KhR5vnnn2++9dZbDY5x9OhR89e//rU5adIkc9KkSebChQvN0tJSKy7HcpWVleZ///d/m2eddZY5ZswY88c//rG5Y8eO0Hq9J1vv+ITJNNWWLXHo0CHz1ltvNc866yxz9OjR5g9+8ANz7dq1ofXt2ZaGaZpmq/vLRERERLoBjWESERERaYISJhEREZEmKGESERERaYISJhEREZEmKGESERERaYISJhEREZEmKGESERERaYISJhEREZEmKGESkU7nRz/6EcOGDeOyyy476TY33HADw4YN49ZbbwVg2LBhPProowC88sorDBs2jP3797dLvCLS+TmsDkBEpDVsNhtffPEFhYWFpKSkNFhXXV3Nhx9+2GDZP/7xjxO2ExFpLvUwiUinlJmZSUREBG+//fYJ6z788EMiIyNJTk4OLRs3bpwSJhFpNSVMItIpRUVFcfbZZzeaMC1dupTzzjsPh+M/nejH35JrTE5ODldeeSVjx44lOzubW265hdLS0gbbrF27lp/85CdkZWUxatQozj33XB599FGCwWBom+LiYm644Qays7PJyspi8eLFPPTQQ5x77rkNjvWvf/2LCy+8kFGjRjFjxgweffRRAoFAa5tDRNqYEiYR6bQuuOCC0G25elVVVXz88cfMnj272cdZu3YtV199NW63mz/96U/86le/Ys2aNVx11VXU1NQAsG3bNq6++mri4+N56KGHeOKJJ5g0aRKPPfYYy5YtA8Dn8zF37lzWr1/Pr371K373u9+xbds2nn322Qbn+/Of/8xvfvMbpkyZwpNPPskVV1zB008/zW9+85swtIqItAWNYRKRTmvGjBlERkby9ttvc/XVVwPw3nvv0bNnTyZOnNjs4zz44IMMHDiQP//5z9jtdgDGjh3LhRdeyJIlS7jiiivYtm0bZ555Jvfffz82W913zbPOOosPPviA1atXc+GFF/LGG2+we/dulixZwqhRowA444wzmDlzZuhcR44c4fHHH+cHP/gBt99+OwBTp04lPj6e22+/nWuuuYYhQ4aEo3lEJIzUwyQinZbb7ebcc89tcFvurbfe4lvf+haGYTTrGB6Ph40bN3L22WdjmiZ+vx+/30+/fv3IyMjgs88+A2DOnDk8/fTT1NbWsm3bNt555x0eeeQRAoEAtbW1AKxatYp+/fqFkiWAmJgYzjnnnND/b9iwgZqaGs4999zQufx+f+iWXf35RKRjUQ+TiHRq3/rWt/h//+//UVhYSEREBCtXruT6669v9v6VlZUEg0Gefvppnn766RPWR0REAFBTU8Pdd9/N66+/jt/vJy0tjfHjx+NwODBNE4CysjJ69ux5wjGOX1ZeXg7A/PnzG42nuLi42bGLSPtRwiQindr06dOJjo7m7bffJioqirS0tAY9PE2Jjo7GMAyuvvpqLrzwwhPWR0ZGAnDPPffwzjvv8Kc//YkzzzyTqKgoAKZMmRLaNjk5mb17955wjJKSktC/4+LiAHjggQcYMGDACdv26tWr2bGLSPvRLTkR6dRcLhczZ87knXfeYdmyZY0mPacSExNDZmYmu3fvZvTo0aHXkCFDePTRR1m9ejUA69atY/LkycycOTOULH355ZeUlpaGnpLLzs5m//79bN26NXT8mpoaPvnkk9D/jx07FqfTSVFRUYPzORwO/vjHP6qYpkgHpR4mEen0LrjgAn76059is9lCA6lbYuHChcyfP58bb7yR73znOwQCAZ599lk2btzIL37xCwDGjBnDsmXLePHFF8nIyGDbtm088cQTGIaBx+MBYPbs2Tz11FP88pe/5L/+67+Ii4vjL3/5CyUlJfTp0weAhIQE5s2bx8MPP0xVVRWTJ0+mqKiIhx9+GMMwGD58ePgaRkTCRgmTiHR6Z555JnFxcaSmppKRkdHi/adOncozzzzDY489xoIFC3A6nYwcOZK//OUvjBs3DoBbb72V2tpa/vSnP+Hz+UhLS+PnP/85u3bt4oMPPiAQCOBwOHjmmWe45557uOOOO3A4HHznO98hPj6ePXv2hM53/fXX07t3b1544QX+93//lx49ejBlyhQWLlxIbGxsuJpFRMLIMOtHK4qIyGnZuXMnu3fvZtasWQ2e0rv00ktJSUnhscceszA6ETkd6mESEQmT6upq/uu//osf/vCHfPOb3yQQCLB06VK+/PJLbrrpJqvDE5HToB4mEZEwevvtt3nmmWfIzc3FNE0yMzP5+c9/ztSpU60OTUROgxImERERkSaorICIiIhIE5QwiYiIiDRBCZOIiIhIE5QwiYiIiDRBCZOIiIhIE5QwiYiIiDRBCZOIiIhIE5QwiYiIiDTh/wMrsgLWChN2KQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This plot looks kind of normal, doesn't it?\n",
+    "sns.distplot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\2097752121.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The situation with engine volume is very strange\n",
+    "# In such cases it makes sense to manually check what may be causing the problem\n",
+    "# In our case the issue comes from the fact that most missing values are indicated with 99.99 or 99\n",
+    "# There are also some incorrect entries like 75\n",
+    "sns.distplot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A simple Google search can indicate the natural domain of this variable\n",
+    "# Car engine volumes are usually (always?) below 6.5l\n",
+    "# This is a prime example of the fact that a domain expert (a person working in the car industry)\n",
+    "# may find it much easier to determine problems with the data than an outsider\n",
+    "data_3 = data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\1910130346.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_3['EngineV'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='EngineV', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Following this graph, we realize we can actually treat EngineV as a categorical variable\n",
+    "# Even so, in this course we won't, but that's yet something else you may try on your own\n",
+    "sns.distplot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\2257603476.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Finally, the situation with 'Year' is similar to 'Price' and 'Mileage'\n",
+    "# However, the outliers are on the low end\n",
+    "sns.distplot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I'll simply remove them\n",
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\730772117.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_4['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here's the new result\n",
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When we remove observations, the original indexes are preserved\n",
+    "# If we remove observations with indexes 2 and 3, the indexes will go as: 0,1,4,5,6\n",
+    "# That's very problematic as we tend to forget about it (later you will see an example of such a problem)\n",
+    "\n",
+    "# Finally, once we reset the index, a new column will be created containing the old index (just in case)\n",
+    "# We won't be needing it, thus 'drop=True' to completely forget about it\n",
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.00</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.00</td>\n",
+       "      <td>3867.00</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.46</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.54</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.86</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.63</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.00</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.00</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.00</td>\n",
+       "      <td>6.30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand     Price   Body  Mileage  EngineV Engine Type  \\\n",
+       "count         3867   3867.00   3867  3867.00  3867.00        3867   \n",
+       "unique           7       NaN      6      NaN      NaN           4   \n",
+       "top     Volkswagen       NaN  sedan      NaN      NaN      Diesel   \n",
+       "freq           848       NaN   1467      NaN      NaN        1807   \n",
+       "mean           NaN  18194.46    NaN   160.54     2.45         NaN   \n",
+       "std            NaN  19085.86    NaN    95.63     0.95         NaN   \n",
+       "min            NaN    800.00    NaN     0.00     0.60         NaN   \n",
+       "25%            NaN   7200.00    NaN    91.00     1.80         NaN   \n",
+       "50%            NaN  11700.00    NaN   157.00     2.20         NaN   \n",
+       "75%            NaN  21700.00    NaN   225.00     3.00         NaN   \n",
+       "max            NaN 129222.00    NaN   435.00     6.30         NaN   \n",
+       "\n",
+       "       Registration    Year  \n",
+       "count          3867 3867.00  \n",
+       "unique            2     NaN  \n",
+       "top             yes     NaN  \n",
+       "freq           3505     NaN  \n",
+       "mean            NaN 2006.71  \n",
+       "std             NaN    6.10  \n",
+       "min             NaN 1988.00  \n",
+       "25%             NaN 2003.00  \n",
+       "50%             NaN 2008.00  \n",
+       "75%             NaN 2012.00  \n",
+       "max             NaN 2016.00  "
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's see what's left\n",
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we decided to use some matplotlib code, without explaining it\n",
+    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
+    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3)) #sharey -> share 'Price' as y\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
+    "ax2.set_title('Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\2845240188.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_cleaned['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# From the subplots and the PDF of price, we can easily determine that 'Price' is exponentially distributed\n",
+    "# A good transformation in that case is a log transformation\n",
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.90</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.00</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.00</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.51</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW   4200.00      sedan      277     2.00      Petrol   \n",
+       "1     Mercedes-Benz   7900.00        van      427     2.90      Diesel   \n",
+       "2     Mercedes-Benz  13300.00      sedan      358     5.00         Gas   \n",
+       "3              Audi  23000.00  crossover      240     4.20      Petrol   \n",
+       "4            Toyota  18300.00  crossover      120     2.00      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3862     Volkswagen  11500.00        van      163     2.50      Diesel   \n",
+       "3863         Toyota  17900.00      sedan       35     1.60      Petrol   \n",
+       "3864  Mercedes-Benz 125000.00      sedan        9     3.00      Diesel   \n",
+       "3865            BMW   6500.00      sedan        1     3.50      Petrol   \n",
+       "3866     Volkswagen  13500.00        van      124     2.00      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991       8.34  \n",
+       "1             yes  1999       8.97  \n",
+       "2             yes  2003       9.50  \n",
+       "3             yes  2007      10.04  \n",
+       "4             yes  2011       9.81  \n",
+       "...           ...   ...        ...  \n",
+       "3862          yes  2008       9.35  \n",
+       "3863          yes  2014       9.79  \n",
+       "3864          yes  2014      11.74  \n",
+       "3865          yes  1999       8.78  \n",
+       "3866          yes  2013       9.51  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's transform 'Price' with a log transformation\n",
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "\n",
+    "# Then we add it to our data frame\n",
+    "data_cleaned['log_price'] = log_price\n",
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
+    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
+    "ax1.set_title('Log Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
+    "ax2.set_title('Log Price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
+    "ax3.set_title('Log Price and Mileage')\n",
+    "\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# The relationships show a clear linear relationship\n",
+    "# This is some good linear regression material\n",
+    "\n",
+    "# Alternatively we could have transformed each of the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#price variable, we can drop the old 'Price' one\n",
+    "data_cleaned = data_cleaned.drop(['Price'],axis=1)# Since we will be using the log "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's quickly see the columns of our data frame\n",
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn does not have a built-in way to check for multicollinearity\n",
+    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
+    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
+    "\n",
+    "# Here's the relevant module\n",
+    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# To make this as easy as possible to use, we declare a variable where we put\n",
+    "# all features where we want to check for multicollinearity\n",
+    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
+    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
+    "\n",
+    "# we create a new data frame which will include all the VIFs\n",
+    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
+    "vif = pd.DataFrame()\n",
+    "\n",
+    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
+    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "# Finally, I like to include names so it is easier to explore the result\n",
+    "vif[\"Features\"] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.79</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.35</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.66</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    VIF Features\n",
+       "0  3.79  Mileage\n",
+       "1 10.35     Year\n",
+       "2  7.66  EngineV"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's explore the result\n",
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since Year has the highest VIF, I will remove it from the model\n",
+    "# This will drive the VIF of other variables down!!! \n",
+    "# So even if EngineV seems with a high VIF, too, once 'Year' is gone that will no longer be the case\n",
+    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To include the categorical data in the regression, let's create dummies\n",
+    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
+    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.90</td>\n",
+       "      <td>8.97</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>10.04</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>9.81</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277     2.00       8.34       True                False   \n",
+       "1      427     2.90       8.97      False                 True   \n",
+       "2      358     5.00       9.50      False                 True   \n",
+       "3      240     4.20      10.04      False                False   \n",
+       "4      120     2.00       9.81      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here's the result\n",
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df\n",
+    "# Since each problem is different, that must be done manually\n",
+    "# We can display all possible features and then choose the desired order\n",
+    "data_with_dummies.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To make the code a bit more parametrized, let's declare a new variable that will contain the preferred order\n",
+    "# If you want a different order, just specify it here\n",
+    "# Conventionally, the most intuitive order is: dependent variable, indepedendent numerical variables, dummies\n",
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
+    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.34</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.97</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.90</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.50</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.04</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.81</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0       8.34      277     2.00       True                False   \n",
+       "1       8.97      427     2.90      False                 True   \n",
+       "2       9.50      358     5.00      False                 True   \n",
+       "3      10.04      240     4.20      False                False   \n",
+       "4       9.81      120     2.00      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To implement the reordering, we will create a new df, which is equal to the old one but with the new order of features\n",
+    "data_preprocessed = data_with_dummies[cols]\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the inputs and the targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The target(s) (dependent variable) is 'log price'\n",
+    "targets = data_preprocessed['log_price']\n",
+    "\n",
+    "# The inputs are everything BUT the dependent variable, so we can simply drop it\n",
+    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scale the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import the scaling module\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# Create a scaler object\n",
+    "scaler = StandardScaler()\n",
+    "# Fit the inputs (calculate the mean and standard deviation feature-wise)\n",
+    "scaler.fit(inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale the features and store them in a new variable (the actual scaling procedure)\n",
+    "inputs_scaled = scaler.transform(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the module for the split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Split the variables with an 80-20 split and some random state\n",
+    "# To have the same split as mine, use random_state = 365\n",
+    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, targets, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a linear regression object\n",
+    "reg = LinearRegression()\n",
+    "# Fit the regression with the scaled TRAIN inputs and targets\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's check the outputs of the regression\n",
+    "# I'll store them in y_hat as this is the 'theoretical' name of the predictions\n",
+    "y_hat = reg.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1VoiLi0NcXJzBx77//nsAwB9//CH5+ob4+nrIer3mhH1jGPvFOPaNcfbSN/39PKHadAYFxeZHbtbsSMeI/uFwMjHy8eTEHnjnmyOi719QUokdh7NFn28Lbi5KjBvSCQU3y1FWUYWaWjWyr5aitLwa3u4u6BXZCr4+HnBWKtCtcyt079QKT07siZQLN1BQXAGVrztiOgaa7Dex7OV101xJDo5iYmLw+++/48yZM+jatSuAupEjQRBw8uRJjBkzBkDdcv6cnByH2z6kuLgctbWOO5/dGJyclPD19WDf1NNY/aJWC0i7VIii0ir4e7siqn2Aww3D8zVjnL31zZHUfFSJzOG5cbMCK3/+E/F3dDJ6TlRbX7i7OqGiqtboOY6mslqNjbszcNfAMDxydw+9x46k5mP19jTtKNH6pHSofNwwfUzdKFG7wLpgpvjmLavaYG+vG3vi5+cBpVKeETXJwdHYsWPx22+/4YknnsCzzz6LKVOmoFevXnB2dsbatWsxfPhwxMTEYPHixSgqKkKPHj3MX9SO1NaqHapmRlNi3xgmZ7/IVWvGXvA1Y5w99M2R1HwsTbQs9eGHPRdxW6CX0ddjSmaBXQVGvToHYnTf9jiecQ07kq1b1bz1YBbCQ7zRNzoEgPESCAUllVi04ZToEgiWsIfXjb0RUVVINMkh1vjx49GvXz8UFBTgrbfegiAI8PX1xfjx41FeXo6HHnoIffr0QUJCAhQKBe677z75Wk3UjGneaLntgOOQskWGvTiSehXLfpKWE2pqaX9qVqHB47Zy6WopIkP9MXVkJOKHdLD6equ2p0OtFmQtgUD2Q/LIkZOTEz7//HOsWLECBw8ehJOTEwDgn//8J3Jzc3H48GHtuXfffTcmT55sfWuJmjlri+dR03PkUb6jaflYmnhG8vPrV3nW3XbkaLr5VW1NSbetwSrr83VKyqu1ZQ3ElkBgNWzHYdX2IW5ubpgzZw7mzJmjPebl5YWVK1fi5MmTyMnJQceOHdGlSxerG0rUElhSa4ZvtLYnV0VpW7CkppApyf8bySwpr8a6nRl2vRRfU65AruKLllS4LiqrlG3POmp8jbbxbM+ePdGzZ8/GujxRsyS1eF5LYw8fMlJG+XTbHejrjv5+0haqyPHzy1VTaNexXOw6lmv+RDvg61G3Z1tkqD8CfNys/vktCbKOpl7D2h0ZKCmv1h5zlBHGlkjW4Kj+xrP1yZFFvmDBAoPH+/fvb1W5ACJ7IPbNtiVvO2Av01iWjvIZanfgzymYNioSsZ1bib6vXD9/SwywU7MLkXOtFPlF5YgJC8D+03mSrxWgU+FaTKBlaJrREUYYWyqrgqPz589jyZIlSE5ORmFhIWpqjO9vo1AokJKSYs3tiJo9Md9oW/K2A/Y0jWXJKJ+xdt+4WWHRaiY5f/6WGGBvPpDV4JiLkwLVtX8lS/t4uqDkVnWD8+qLaOunHa2bNjLC4O9FLOYR2h/JQzlZWVm4//77sXXrVuTn56O6uhqCIJj8j4hMUyoVmDYywuQ5LXXbAXtbFSQ2uPD1cJWl3XL//JGh/vD3dhV1bnNWXSvAxVmJUX3aYd7UWDwworOo553JLND2de+oYDwzoRsCfKQFnGL3rKOmI3nkaOnSpSgrK0NAQAAefvhhREVFOVyhRyJ7pHmjrT91ovJxw9QWnJ9gb8nqYkf5oJBnNZPcP//xjGuoZp0cAEB1jRo7knMQGeoPlbe4bVTKKmr0+rp3VDBiI4KQnl2ElKwCg6NUprTEaU57Jjk4OnDgAJydnZGQkICoqCg520TU4um+0XJlSx17S1bXjPKZmk6ZOjICxbeqRF3PXLvl/PmlFH1sCdYmZWDBkwPh5e6MsgrjaSIa9ftaqVQgOixA0muwJU5z2jPJ02qFhYUICwtjYETUSDRvtANiWiM6zPG2DpGTWi2guFRckNGUHzLGplNUPm7a/B+5kuwtmcYzVZDSmqKPzV1BSSXO5d7EqD7tRJ1v7Hdi6WuwJecR2ivJI0eBgYFQyLkFLpGN2MOycDLO0OosY2zxIWNulE+uJHsx1/Fyc8KyTWdQamS5uLVFH1uCorJKjBvUATuSc0yOHpn6nVlaKqCl5hHaM8kjR0OHDsWFCxeQlWXZvCqRPTmalo+Xlx7Ae2uPY8WmFLy39jheXnqAW3TYCWNbqRhjqw8Zc6N8w3q2Mfl8Me0Wk6xfVlmrFxgBf61kO5J6VZaij82dv5cblEoFHr4r2uR5pn5nYn5XgP4II9kXhSBxGdnly5cRHx+P0NBQfPbZZwgJCZG7bTZVWFjGTf3qcXZWIiDAq9n0jbFl0Rpi37SaW7/IyZq+UasFvLz0gOgRI3tMVjc36hXo5260zpGxEc0jqVfx7fZ0vSBITI6Mj4eLXgFCasjHwwVTRkRoaxgdz7hm1cKI73ZlYPuR7AYbovbopMLYfmGSRqn5fmOcSuUFJyfr6ykCIqfV5s2bZ/B469atcebMGYwYMQLdunVDSEgI3NwMz7UqFAq8++670ltKJCPuYWb/xFZwnhLXGSP7hNrd78lc8A0ABTcrcC73ZoPgyFihx/5dgvHH2Xy9wMjb3Rk1IpbvMzAyr6S8Gp9vrqvHp5mOfH/2IEnT7kfT8rHtcLbBx06dL8DQHm3s7jVLfxEVHG3atAkKhcJoraKamhqcOHHC4GOa5zE4Intib8vCqSGxK358vV3t7kNG7L5lAoCtB7Mg1Kpxf1zdNIypQo+GPmxLRayqIstZU1iUX74cn6jgKD4+nsnX1KzY27JwasiRt1KxdN+ybYezMfGOTlAqFcwLsjOWBDGaqdBdx7L55cvBiQqOjO1nRuSoHPmDt6Vw5K1UpATV32xLxeDut9n1rvYtkdggxpJVlRr5xbcQDQZH9kiezCUJpk2bhpiYGFvdnlo4zQevKfb6wWuIWi2YrG3jiBx5KxUpQfX+03nYedRwjgrZVnJavsm/K0tXVWqcSLsuR/OoEVi18ay1uN8a2YrY6sb2+MFbn73sUt8YHHUrFUvr3GgcTeeHpT3adSwXu47lGvy7UqsFJGxNlXTdqmquNrNXNg2OiGzJUT94ddnTLvWNxRG3UhETfJPjqf93pVYL+PqXFFFbjRgSovKQuYUkFwZH1KI54gevRktaEaMpsuhIenZqhcHdWiM5LR+VHCFoVtYmZUAtAOt2WpZjVN8Dw80XiiTbYHBELZ4jfvACTVuOgFusWMZY8T+yL17uzpgd3w1lFdVYt/Oc6ECnoKTS6o17e3UOhKurk1XXoMbD4IjIQTVVOYLmnNMkJ00Aue1wFk6dL7B1c0iEQd1aQ6lQoHdkMHpHBiM9uwjJafnYdSy3Ue/bq3Mgnpvcs1HvQdZhcESkw5FGSJqiHIHUnCZNP5aUVyP0Nj+0CXCX3AZHIGUZN9mOQgEIArAjOQc7knMaBPuNERw5OykQHeqPZyf24IiRA2BwRPQ/jjZC0th1gKTmNBnqx6ZOcm/KIFfMNiFkX+pPd+oG+7ERQfD3dkVRaZVs97t/eCeM7tvebr9oUUM2q3NEZE+M1SnRvGkeTcu3UcuME1MH6IER0ssRWJLTpGGsHwuasB+PpuXj5aUH8N7a41ixKQXvrT2Ol5ceaJR7i90mhBzD2v/9Lu/s1Ua2a3p7uDAwckAMjqjFEztCYo+FFTXlCIwVtFy3M0NyUHA845qo8zQ5TfbQj00d5Fq6TQjZN02wH6zylO2aM8dGMTByQAyOqMWTMkJiT3pHBWPKiM4GH5MaFBxNy8eO5BxR52pymmzdjzU1anyzLc3kOXIHZ9x7r/nRTMVaK8DHrVnUGWupmHNELZ6jb0KrVgtYt/OcyXMs3TxT7FSRbk6Ttf1oTZ7Q0bR8rNyWhtLyapPnyb3ZJ/fesx1vd2eUSiy+aIq/lxs6t/XTJm1bamSfdrg9IsiuF3OQeQyOqMVz9E1o5a53ZMlUke4WK9b0ozXJ8JYmROsGZ9YmbkvdJoSsV12rRvyQDghWeeDytTJsPphl9TU1wX56dpGkwGh2fFf0jQ6xuh1kewyOqMVz5N3fActGbAwFAwD0jon9oB/Rp61e4CK1H63ZAkVKQrQmOJNjdSK3CbGdymo1EvddxDMTuiEmXCVLcKQJ9i0dJXakLYdIHMnB0datWzFy5Ei4uLhIev6wYcMQFhYm9fZEsnH0TWjFjtjkF9zCy0sP6AUDXu51bwG6e0Npjplz+Ew+okMDtB8IUvrR2i1QLE2I1gRncu5J1zsqGLPju2HV9jSUmJnWI/mtTcrAgicHWjWC5+PpgofGRGl/52L/psYNCkNMmIpTaM2Q5ODoxRdfhL+/P8aNG4cJEyaga9euFj3/ySeflHprItnJtQmtLYpIihmx8fZwQeK+zAbHDW2YKXYTzZLy6gaBhNF+9HXD1BEN+9GaKUG1WkBKpmWVqKf+r/SBNQGZ7u/Y19MV6dlF2Hk0R/Lmo/QXL3dnzL63G05evIEdh7NFPaegpBK/HMpEVXWt5Ps+MKKz3mtTTM6RQgHcM6gDnJ25rqk5khwchYWFISsrC6tWrcLq1asRGRmJiRMnYvz48VCpVHK2kahJWLsJ7ZHUfKzantbkRSTFjNgIjbjJV/1AQrcfdStkG1olJjWJ29KK1D4eLnhobN3IQGpWoeSA7EjqVXy7Pd1s4jdJU1ZRA6VSgT6RQaKDIwAGA39LqLz1K7ify71pNudIEOrOc8R9Gck8ySHv9u3bsXbtWtx///3w8fFBWloaFixYgDvuuANz5szB7t27oVZzJ2pyLJpNaAfEtNYmZh5KyUNqVqHJJeAHTl3Gog2nzNbXUasFpGYVirqmJYzVO1L5uCF+SIdGHdUwtDxf048Du7VG986tjAaYUpK4jdUyMsbH0wUfPjNYG6BKDci+25WBpYlnGBg1sqKySkS1D0CgX9NsOWMoD87RV7CS9axKyI6NjUVsbCz++c9/IikpCYmJidi/fz927NiBpKQkBAYG4p577sHEiRPRubPhOixE9siSZF21WsCKxD9NXm9tUgbUQl1RxsYaWTI28nU49arV1zZH6oeEpUncUhKwHxoTpTf1IT5Hq1z7/0dS87HNgpEMks7fyw1KpQKz4rvjnW+ONPr9DOUTOvoKVrKeLJOlrq6uuPvuu7FixQr8/vvvePXVV3H77bejsLAQX3/9NcaPH4/77rsP69atQ2lpqRy3JGo0llZZTrtUiBs3K0xes6CkEksTG79ys+7IV3RYAJRKRZO8gUu9h5gtUHQ/vCxJwFbVK8KnGbUrLKmEt4f5hSSJ+y5i074LOHDmChK2nhV1T7KOJhBWqwX4eLpiTL9QuMuwSeuoPu0MjqoaS7zXBO1i2krNk0JopGSEmzdv4ocffsCiRYtw69atupspFHB3d8fkyZPx7LPPws/PrzFuLYvCwjLU1HBaUJezsxIBAV7Num/UaqHBiq76VD5ueG/2IAB1H9bHMq4hSWQ1aXPXbIzkbTE/kzVMtV3sa0bsZrWHUvKwYlOK2TaNGxSG+CEdtW2yNEeJbOOZCd0A1I20Fuj8rtxdnSAIQKVO0rXKxw139GyDxH0XzV533tRY7TS52HxCc/WzbFX9uiW8D0ulUnnByUmeBHlZ6xzdunUL27dvx88//4zDhw+jtrYWgiBApVJh3LhxuHLlCn777TesWrUK27dvx8qVKxEeHi5nE4isInb11OYDF/H7ySuyfdjKXblZV2PX4pGjzIFmSjA1qxCp2YWAAESHBSC6vX5/iB2higlT6QVGrENk3wJ83DCsZxukZxcZ3LamoqouKOoWHoCgAA+EBHgi7vZ2UCoV+P3kZVHTsppRVbHkWsFKjsnq4EgQBOzbtw+JiYnYtWsXKioqIAgCnJycMGzYMEyaNAnDhw+Hs3Pdra5du4Y5c+bgxIkTePPNN/Hll19a/UMQyUVs7oy1q2OsuXd9YsoHGHuj9/ZwgSAIohK2vdyd9c6z5ENCM6Vlqo3HM67ptW/zwawGOVkl5dVml1hbm6NETatdK08UllaJGgE6nVkIZBYCALYfyca0kRGNWqPM2hWs5LgkT6udPXsWP/30EzZv3owbN25olwqHhYVh0qRJiI+PR3Cw4TfN1NRUxMfHw8PDA8ePH5fe+kbEIcuGWsJwbmpWId5ba5vX5LypsRaPHFla5dlchez8gvIG38RVPm54YERneLm7IDWrEFAA0aEB2pwmU5ydlTibfRPLfzilN01Sv41ipjAAiBoB0p3usOXvk5qG5rXRUkZ4WsL7sFR2Ma02YcIEKBQKCIIADw8PjB07FpMmTUKfPn3MPtfT0xMA4OXlJfX2RI1C7r2yNIHFup3nZN+eREqVZ2NTC7rHxg0K1wugSsqrG6yy2++TJ2qV3ZHUfCzacMpkG2MjgsyO7qzZkV5Xdc+MMX3b6bWJS62bv7VJGXhv9iCO8JCsrJpW69WrFyZNmoS7775bG/CI4e7ujg8//BAdO3a05vZEspMrPyfu9rboExWsfYNWKhSihv7FVti2dtsNU3QDqKNp+ViaKH3fs683m06eXpuUAQ83Z7PBaGFplai2Hzh9FfcNt3wzXHJcuvl6LMhIcrFqb7UOHTpIem5wcDD+9re/Sb01UaMylYgpdnVMn6hgvTdqMcmdhqbIvD1cMLBrCGIjgvQCJWu23RDL2gBs84FMlJrJZSooqaybqpNJSXm13s/cua0f3F2dtAm91DxxhJDkJio4KikpgY+Pj94xqYGRruLiYvj6+lp9HSK5GUvEBCB6dYzYayqVCqNTZKXl1diRnIMdyTl6eTpip/2s+dCwdt+zHckiiybKPPOh+Zk1wSYDo+aPI4QkN1GZS2PGjMG6detk2w6kpqYGX3/9NUaNGmXR85YvX44ZM2boHTt79iwefPBB9OrVC3FxcVi5cqUsbSQyVFDR0qKFYq4pdkWVZirru10ZWLtT3Aosaz40rNlCIT27SPSWJdGhAWYL7gV4u4oq3AjU/cyWbjFCjovFGKkxiAqOBg4ciH/9618YN24cfv75Z1RViZv/r6+4uBgrV67EXXfdhXfffRd33HGH6OeuXr0aCxcu1DtWWFiIRx55BO3bt8fGjRvxzDPP4IMPPsDGjRsltY9IDKP7mPkar7irYWhvNUuqPgPAtsPZovb3svZDw5otFMQGVm4uSkABTBlhOuCcNioSM8ZEmb2ej6cLOt7my+X7LYgcdbaI6hM1rfbhhx9i+PDhWLBgAebNm4f//ve/uOuuuzBs2DD07t3b5NRYTk4Ojh49ij179iApKQlVVVXw9/fHJ598gjFjxpi999WrVzF//nz88ccfDQpGfvfdd3BxccF//vMfODs7o1OnTsjKysKKFSswadIkMT8akSSW7DyvYWzZfZ+ooEZpo7UfGpbue6ZLbGBVWa3GB+tOIMDHDWP7heKPs/kml2Nf7Bdqco+zklvVeGnpAW4O6+BUvm54ckIPlJZVYtX2NIOvwea6VJ/sg+iE7HHjxmHYsGFYvnw5Vq9ejXXr1mH9+vUAgKCgIISFhcHHxwceHh4oLi5GYWEh8vLycOPGDQB1xSJ9fX3xxBNPYObMmQ1ymIw5c+YMXFxcsGnTJixZsgS5ubnax5KTk9GvXz9tgUkAGDBgAJYvX47r16+jVatWYn88Iotppsh0644YC45MLbs3VBHYGj6eLnhoTJTVHxpiVu4ZC8AsLYlQWFKJbYezMTu+K3w8XI2u1rs/LgId2vji2+3pRgMgBkaOoX5RUR9PFwyIqVt8ENNBhcBAbxQWlqFnx0Dt6GrJrSp4e7lA5e3OpfrUqCxarebj44OXXnoJjz32GL7//nts3boVqampyM/PR36+8Y0zo6KicO+99+L++++Ht7e3RQ2Mi4tDXFycwcfy8vIQGRmpd0xTePLKlStWBUdyFZJqTjR9wr7RZ65f1GoBa81M85ir+myJaaMi0b9ra1mu1b9rayidlFi9PU2viKPK1w3TR0ehb7TxAOzBMVEGaxyZsn7XOXz07JAGH3pqtYC0S4UoKKlESVkVpo6IwKpf01DOZGuHdNfAMDwwvDNOnbuOH34/j9KKGoQGeeG+OzvD3d1Z72/K1RXo1inQxi22H3wfNk5EKTTRJC3lDwgIwKxZszBr1iwUFhbi8OHDyMnJwY0bN1BcXAw3Nze0atUKHTp0wO233260Ura1Kioq4OrqqnfMza1uOL+y0rpETF9fD6ue35yxbwwz1i9/nruuF1gYIuf2z2Ft/BEQIF+B1dEDO2BE/3CkXLiBguIKqHzdEdMxEE5mvrWPHtgB3l5uWJH4J27crBB1r4LiSlwurEBMx0Dt/S5fL8W2g5koKGZytaPzcHPGcw/0wpCebfH3hb8hI/um9rHrNysw64PfEBHqh49euBMA32tMYd80Lqv3VgsICBCVO9QY3N3dGySHa4IiS4pSGlJcXI7aWpZm1+XkpISvrwf7ph5z/ZJ95aaBZzU0pl8ojpzNNxtImaLydUObAHcUFpZJvoYx7QI90C6w7g25+OYtg+doRniKSqvg7+2KmA6B+PKfo3Hk9GX8cSYPSSKmENf9ehYXL5eghNNjzc5zk7qja3t/PPfBbly8UmzwnIzsm3j+w934ZO5wvtcYwPdh4/z8PKBU2nj7EHvQunXrBtN5mn+HhIRYde3aWjX3rTGCfWOYsX7xEbkEvWenVrjvzs5Izy7C8YxrOHTmql6AoPJxQ78uwSYTkqeOiIBaLZhMDG8shhLOVT5ueHJiD3QJ9Ud1Va2o4OjU+YLGbCbZiMrHDRHt/FFaWmU0MNK4cLkYZRU1fK8xgX3TkJwj8A4dHPXt2xfr1q1DbW0tnJycAACHDh1Chw4dEBjIOWpqXJpl+Lqr1QyJDPVvkHxan7e7szbBVLMNwgNxEQYLRnZq62d3m2waSzgvKKnEO98cwZzJPdCzY6Cs+9aRY7mjZxscTr2KnSIXIHy8JhnPTOjeyK0iMsyhg6NJkybhiy++wOuvv47HH38cp06dQkJCAv7973/bumnUzBkbJZEcoBjIJDS2SaypStu2IKaIZcIvZ/Hh04MxZUSEwb3ayLFNiesMX29X5Bfcwm8nLqNIZy88bw8XCIIgatsdXXk3DE/dEjUFhw6OAgMD8cUXX+Ctt97ChAkTEBQUhHnz5mHChAm2bho1Y6ZGSQxtxiqmWnRpvT3BHImYIpYlt6rx3Cd7uMKmGfJyd0a7YG9Et6+r+D5uUAdt4J5fcAuJ+zIlXTdEZV3eKJE1HCo4WrBgQYNjPXr00NZbImpsUjZjLSgVuVLLzHmaabzjGddw8MxVvXo+uvuuSaG5tpSRKLHVsCuq1UA1cySam7KKGm0hT81rMDosAGq1gJeXHpB83TEDwuVrJJGFHCo4IrI1KZuxlpaJW3Vl7Dy1WsDmAxexIznH6AhUoZFRKzGMVe4WG2xx008CGr4GLd0Wp77ySnF78xE1Bo5xE1lAymasPp6uJs78i6Hzjqbl4/lP9yJxX6aojVzXJmVYtFLN2Aatmg+6o2nGi7tqaKphEwF/vQbF/q0Yo/I1vMCBqCkwOCKygJTNWP28xQVH9c/TBC5id7cH/hq1EkPsFGH9YKv+5rkAMG2k6Y1jqeXQvAatGVFU+bohpiNXHJPtWD2tdvXqVRQXFyMi4q83x4SEBGzatAm1tbW488478eSTT1pdlJHIHojZM0yhAErKdYqTih3I0TlPTOBiTEpWgaicIbFThEnJ2fDxdEXJrSpcv1mOP1Ly9eov+Xi44MExUXhmQjes3JbG4o2EorJK9IsOkVy6YfroKLMV2Ikak1UjR59++ilGjBiBr776Snts2bJlePfdd5GSkoK0tDSsWLECjz76KGpruQcSOT7NZqymCAKwNPGMdkqquLzK5PkauudZk6+x+UAWXl56wOyUmNhpj3W7zuHzzSlYt+scko7mNgh+SsqrsTTxNM7lFGFgN+uKr1Lz4O/lJupvxctd//u5yscNz0zoZnLPPqKmIHnk6LfffsNnn30GoG6PMwCoqqrCF198AQAYPnw4+vXrh5UrV+LkyZP47rvvMHXqVBmaTM2RNaulmlrvqGDMju+GZT+dNlmRVbNqTcpUnLX5GmIStOVOpN5+RFxxP2reVD51f79A3d/KMxO6Ga0JZk/1uoh0SQ6ONmzYAIVCgRdffBGzZs0CABw8eBClpaVo1aoVFi9eDCcnJwwZMgT33HMPfvnlFwZHZJC1q6XkJDZI8/FwMVuqXpN7IWYqLsDbFWpBwKGUPPh7ucHXQ1yekjn1ywroEtMuIktNHRmh93ozV7TUEWt7UfMnOTg6efIkVCoVnnjiCe2xvXv3AgCGDRum3c4jIiIC7du3R3p6upVNpebIWEFFa5amW9MWsUGaJavWNNMLhn5OjaoaNT5Yd0Lvvua2HBGjflkBXUqlAv3N7NVGZIi3uzNcXJxEV4g3Vu2dyF5JDo4KCwvRpUsXKHS2PThw4AAUCgX69++vd663tzdyc3Olt5KaJSkFFRuLpUGapVNlxqYXvN2dUVpR0yAIknM0x1ggp1YL+OOs+aX6RPXNvCsasRFBSL1UWLdiUQFEhwYwAKJmQ3Jw5O7ujuLiv3ZWzsvLw4ULFwwGR1euXIGPj4/0VlKzopm6SskqsLigYmO1x9IgTcyUlG7uBdBwesHXwxVf/nIWsHJ0yBxjgZy1Rfqoeak/UqnZE033mO7oUP2R1s3Istl0OJHcJAdHEREROHHiBM6dO4fOnTtj06ZNAIDIyEiEhPy1YuWnn35CQUEBBgwYYH1ryeEZmroyx9rkZHOkVL0WM1VWP/dC8zzN1gpJydmNHpzUD9B0NXa/kmO5M7YturQPQHF5lTYvCIDBXCF7mg4nagySg6Px48fj+PHjmDlzJmJjY/Hbb79BoVBoN33Ny8vDF198gXXr1kGhUCA+Pl6uNpODMvaGak5jb08hpeo18NdU2dqkDBSIzL0ApAWIUnVu62v0MW77Qbq2HMzCgdN5mDYyQm+ktv6orT1NhxM1FsnB0ZQpU3Do0CH8+uuvSEpKAgD069cPDz74IIC64pCrVq0CANx///0Mjlo4qUUNTY18yEXKUnuN3lHB6NslBJcLK5B95SZ8PFxMLkeWGiBKdTj1GjJyDxic6uBqNapPzMiPlJFWIkcjOThSKpX49NNPsXfvXqSmpiI8PBxxcXHaVWodOnTAyJEjce+992LUqFGyNZgck9T8FkNTU1IZW6YvJX9Il1KpQPfOrdAu0AM1NcZ3nZcSIKp83PDAiM5Yt/Oc5CBG9wOv/pLqKSMisDRRnmBNAfHFwMm+fbM11ejIj9SRViJHYvX2IUOHDsXQoUMbHPf19cXixYutvTw1E5a+UZqbmrKUuWX6UvKHLCUlQNT0Qe/IYKRmFeKzxD9xq1JatflvtqVh9Y50FJX+VYnb39sVndr64nxusYlniuPmokRFtfHgkBxHaUUNNh/IxD1DOjR4zJqRViJHwY1nqUmIfaMcNzAM86bG4r3Zg2QNjMztPK/JH6q/u7xmOwM52mJJgFj/vkqlAjEdVHjk7i6S719aXq0XGAFAUWmVLIERAAZGzcyO5GzU1Kj1NhlWqwXtSKspTTEdTtSYrB452rlzJ44dO4aSkhLU1NRAMFI2WKFQ4O2337b2duSgxE5dxQ/tKGsSpyXJo+Yq+VpLbIA4Ja4zRvYJBQCkZhXqtSU2IghuLkpUMhChRlZWUYO/L9mPUp299DSjrU0x0kpkS5KDo1u3buHxxx/H8ePHtccMBUYKhQKCIDA4auGkLn23lqXJo41ZyVfUNiI+bhjZJxTHM64ZnAaMaOvHwKiFUyhgdusauZTW22RYN3/N1J5pXMZPjk5ycPTll1/i2LFjAOpqG3Xq1Anu7u6yNYyaH3ObUDbGG6o9JY+K2kakuhYbfjtncEuPwpJKHE5lReuWrqkCI1PWJmXgvdmDuHEsNVuSg6OtW7dCoVDgjTfe4IayJFpjT13VJyV5VOzms1JoAsSErakG900rq6jhXmckitQRJIUCGN03FPtOXZG8d5/uaCuX61NzJDk4ysnJQevWrRkYkcWachNKS5fpW7L5rFSxEUFYvYMbMZN1BAEYHtsGgAL7/ryCahNlJOo/b/vhbIztF2pVIM6l+tScSV6t5uHhAT8/PznbQiQ7zVSWKZpcJzGr2uSQnl3UYNUYkRS7j1/G7uO5ogMjXftP52Fk77bw9nDRO+7j6WLkGfq4VJ+aM8nBUc+ePZGZmYnS0lI520MkOzHL9MWualOrrU/44Ddusgclt6qRdDQXpeXV8PFwwag+7TBvaiw+fHqwbEv11WqhQSkAIkcgeVrt8ccfx969e7FgwQL897//lbNNRLIzl+skx5YImg8Cc7lK/MZNjcnH0wV9ooOw+9hl0c8pKa/GjuQcRIb6w9lZKcvK0qaYoiZqLJKDo6CgIDz88MNISEjAmTNnMGzYMISEhMDFxfiQ7OTJk6XejshqpnKdrF3VduDUZSz/4ZTeBrTGPgis3dPM3UUJD3cX7olGBpXcqkaIv6ek5+rW/LJmZamxPQTF7N1GZA8kB0d33XWXtoZRamoqUlNTzT6HwRHZi/or0nw9XUU9z9Coz5HUfCzacKrBcWMfBGKW9JtSUa3GsxO74FxuERL3ZUq6BjVv3l4ukgJw3dFRqStLLSm8ymX/ZK8kB0dt2rSRsx1ETcbQcL+/tyu83J1NLm02lGehVgtYvT3N5P0MfRD0jgrG7PiuWPbTGUnLsQtLK7HzaK7lT6QWQeXtLjkA1x0dlbKyVI4paiJbkxwc7dq1S852EDUJY8P9YlaPGcqzSM8u0ptKM8TYB0Hf6BAACixNtPwDbPWOdFRUSduAlpq3AG9XqAUB1bVqxA/pgN9PXrZoBMnanDh7KrxKJJXVe6sROQoxw/3GjO0XajBHwtoPgr7RwVCaKAppDAOjlknl44Z+XYJN1ieqqlHjg3UntP/293ZF/JBwBPl7Yu3OjAZbgtS/vrUbxkopvEpkb2QJji5fvozdu3fj4sWLKCsrg5eXF8LDwzF06FCEhYXJcQsiq4kZ7jfm8Nl8TL6zc4ORIzk+CHpHBcPDzVnvA41IY9ygMLRp5aWX89OprV+DqWFvd2eUVtQ0CLKLSquQuC8Tz0zohpljoxp9f0NLC68S2SOrgqPa2lq8++67WLNmDWpr677JajaZBeo2nX3ggQfw2muvwdVVXMIrUWOxZhjf2NRYZKg/VD5uJqfWAnzcoFYLOJSSZzSpNbp9gFUr2Kj5iglTNXjd1U+W9vVwxZe/nAVMjD5q9kNr7P0NbbXJNJGcrAqOXnrpJWzbtg2CICAkJAQxMTHw9vZGcXExUlJScO3aNaxbtw5FRUX4+OOP5WozkSSNkUuhVCowfUyUwdVqGlXVtfhg/Qntv91dndC5rR+6dVDhzl5tceFKMYrKKjGsZxsk7rtoVRupeTE1wqKbLJ2aVSg6Cbop9je0xSbTRHKSHBzt2LEDW7duhZeXF958803cfffdeo8LgoDNmzdj/vz52LZtG+655x4MHz7c6gYTSWVtfSFjwVXf6GC8NrNvgzpHbi5OqKyubTDNUVFVi9MXC3D6YgHW7Tqn95ibixJqtYDqWlYSJvEjLJbmvjXF/oZNvck0kZwkbx/y3XffQaFQ4O23324QGAF1U2rjx4/HO++8A0EQsGHDBqsaSmQtMfusGWMuR2JQjzb4aM4QxA/pAC/3uu8cldWWJ01XVqsZGBEAYFSfdqJHWOw1CVoThA2IaY3osAAGRuQwJAdHp0+fRnBwMMaMGWPyvDFjxiA4OBinT0sreEckJ2P7rHm7mx5EFbVdQvo1JO67aNGqMyJjYiOCRJ+rGRU1hUnQROJJnlYrKSlBTEyMqHNbt26Ns2fPSr0VkayMDfcfz7gmOUeiVkQxSLk4OyngrFSgotryndjJMVgayDAJmkhekoMjf39/ZGcbr7WhIQgCsrOz4efnJ/VWRLIzlHNhTY5EyoUbZotByqWmVkANp96aNSmBDJOgieQjOTiKjY1FUlIS1q1bhylTphg9b+3atSgsLMSoUaOk3oqoyUhNVD10+kojtIaau/pb1lgbyDAJmkgekoOjadOmYceOHfjvf/+LkpISTJs2DV5eXtrHy8rKsHr1anz66adQKBSYNm2aLA0mklv9TWiNfZgYO+9Iaj427b1gg5aTo/t6/lgcP3MZN4orZAtkmmIlGlFzJzk4GjhwIKZPn47Vq1fjo48+wieffILw8HB4e3ujtLQUmZmZqK2thSAImDZtGgYOHChnu4lkYWgT2gAfN0zT+fauVgvYfCATO5Kz9b7lB/i4YcqICKzfKW1LEmrZ2od4w9VZ8poYImpECkGQsif4X7766issW7YMxcXFDR7z8/PDrFmz8Nhjj1lzC5soLCxDTQ0TXnU5OysREODVbPrG2Ca0Gs9M6AYAFu971pSkbCxK9uGp+K74fvd53LhZoT1WPzBvqZrbe42c2DfGqVRecHKS5wuH1cERAFRVVSE5ORkXLlxAaWkpvLy80LFjR/Tu3Rvu7u5ytNOk0tJSvP/++9i5cyeqqqpwxx134LXXXkNgYKDka/KF11Bz+qNUqwW8vPSAyaDC28PF5CadtqbyccN7swehpkaNF5fsQ3klN6N1FOGtvZGZV2r08WcmdGvRAVJzeq+RG/vGODmDI1k2nnV1dcWgQYMwaNAgOS5nseeffx7nz5/HW2+9hTZt2mDhwoV46KGH8OOPP3JPtxbOWJ6QmE1obRUYuTkrUSniTW/qyAgcz7iGb7alMTCyM2P7hSKv4BZOnLvR4LFenQORddV4YATU7YMWGxHERGoiG5ElOLKls2fPYt++ffj8889xxx13AADee+893HnnndiyZQsmTJhg4xaSrZjKJ6qutd9vXOYCI82KJgAmpwXJNlr5ueH+uLrfT1VVLdbvzsDVwnKEBHjggeERuHClGO+tPW7yGsY2OiaipiEqOLrzzjuhUCiwcuVKhIaGao9ZQqFQYPfu3RY30JzMzEwAQJ8+fbTHvLy8EBYWhsOHDzM4aqGM5RMVllRiyY+nMbhbaxu0yjpe7s6YHd8N0e0DoFYLmLtkv62bRAZcv1mJqqpauLo6wdXVCTPGROs9buk+aETU9EQFR3l5eVAoFKipqdE7ZgmFonGGh4OD6+blr1y5gk6dOgEAamtrkZeXZ1XOkVzzls2Jpk/svW/UagFrk0yvINt/2rLXrz0oq6iBi7MSJy/cQMKWsyix43yolu77385h5t1dDD4W6CsuDzPQ1x3OLXQ1m6O819gC+8Y4OcMMUcHRO++8AwAICgpqcMzWunfvjo4dO2L+/Pn48MMP4efnh08//RSFhYWorpb+4eHr6yFjK5sXe++bP89db7Jq1U1tR3IOklPzbd0MMuNGaRUCArwMPtbfzxOBP6forVKrr5W/B/r3bAenFp5zZO/vNbbEvmlcsqxWs7Xz589j3rx5OH36NFxcXDB+/HiUlJRAqVTi008/lXTN4uJy1NpxXootODkp4evrYfd9c/B0HpYmMheHbGfE7W2NjhwBwJHUfCzacMro43Mm90Df6Ja7Ws1R3mtsgX1jnJ+fB5RKG69WW7x4Mdq0aYOJEyeaPXfZsmW4cOEC3nvvPam3M6lTp07YuHEjioqK4OzsDG9vb0yePBkDBgyQfM3aWjWXSRohZ9+IrU5tCR8PF8nPHRgTgoMpV626P9F9d3Y2+TcS27kV5kzugTU70vVGkDTJ9rGdW/H9B3wfNoV905CcQz1WBUe9e/cWFRxt375dmzgtt9LSUjz11FP45z//iejousTHnJwcpKSkYO7cuY1yT5KHmOrUhpgLqCJD/RHg4yapMKJKZD4IkTG9OgfC1dXJ7Hl9o4Mxon84/jiZI+v2IURkPVHBUW5uLg4ePNjg+PXr17FhwwajzxMEAZcvX0ZGRgY8PT2lt9IEb29vCIKAt956C2+88QYqKirwj3/8AwMGDOCWJXbM3GqyUX3aITYiqMGHhZiASqlUYNrICEnL3D3czX+omVN/M1FqHgJ83PDY3V1w4tw17DqWC7WBb6m9Ogfiuck9RV/TSalAl3AVRwCI7IyonKOKigqMGTMG+fnSEkEFQcCYMWPwySefSHq+OVevXsWbb76JQ4cOwdXVFaNHj8bLL7+stxGupVh9tCG5KrOKqU6toRv4iNnuQ3fEyVAgZY6rswJVNZaPzXq6O2PGmCj4ebpCLQj4YN0Ji69B9k339aVWCzh94Qa2H8nGrcoadGjtgylxEaJGjDRY6dg49o1x7BvjmrxCtru7O1566SV8/PHH2mOXL1+Gq6srWrVqZfR5SqUSnp6eiImJwbx586xvrREhISFYvHhxo12f5CWmOrWGZiRpdnw3rDOzwWv9qsK9o4IRGxGknYLLu3ELm/ZnmryGlMAIAG5V1EDl44aIdv5QqwXJ03pkfzR5QLqBt1KpQI/OrdCjs/H3PyJyXKJzjsaPH4/x48dr/x0dHY3u3btj9erVjdIwar6kFLdb9WsaSm6ZLs1gqKqwUqnQ/js1q9BscGSNotIq7T2lTuuRbfh7u2p/f0BdUv+AriEGp3aJqPmTnJD97LPP4rbbbpOzLdRC+Hu5Wfwcc4GRhqnAq7ErDvt7/7WPX++oYDwzoRu+3HIWFVXc98zeje4bivDWvrKumiQixyV5cu7ZZ5/FpEmTUFJSgu+++67B419//TUWL16M69evW9VAan40q8kag6nAS0pQJpavlysKSyqRmlUI9f8ydXtHBePT54bC3YWVbO3d9ZsViA4LwICY1ogOC2BgRNTCWfWuvXfvXtx5552YP38+rl7Vrw2zZ88eLFmyBH/729+wb98+qxpJzYtm2slS3mbqF6l86r7xGxMZ6g8vd8sHS73cnXHP4HCT5xSXVWHZT2fw3trjeHnpARxNq1u84OysxGPjYiy+JzWtVn4s4UBEf5EcHJ05cwazZ89GWVkZwsPDUVVVpff4mDFjEBMTg5s3b2LOnDnIysqyurHUfGimncSOIKl83DBjTKTJc6aOjGiUb/xlFTWIbh8gur2aJHJNgKT5WaUEZtQ02gV527oJRGRHJAdHX3zxBWpqajBt2jT88ssvCA0N1Xt8ypQp2LBhA6ZPn47y8nKsWLHC6sZS89I7Khjvzx6EeVNjMapPO5PnTh0Zgb7RIQYDFJWPW4Nl/IakZxdJrj9UVFap197Hx3cxW4l7bVIGamrUSM0qRHWtGiN7t4WbM6dr7FFpBTfxJaK/SP4qm5ycDD8/P7z66qtQGNkKV6FQYN68eUhMTMT+/fslN5KaL81qsuiwAESG+jeoS1R/GXX95fmWJM9ak5CtyVfStDc1qxAl5eZXz/19yX6UmjmPbM9cPlpjbHNDRPZLcnBUWFiI6OhouLq6mjzPzc0NYWFhSE9Pl3oraiHEBj66y/MtITUh21Auk9hAi4GR/TOXqyZ1mxsiclySg6PAwMAGSdjGFBYWwtubc/rUkKFv5FICHzGk7rlmKJepMVe+UdMylatmbpsbMdO5ROR4JAdHMTEx+O2337B161bcddddRs/bvXs3rly5gsGDB0u9FTVTTf2N3NLijAoF8NS9hj/8rNncluyDocrXutRqAWuSLKvKTkTNg+SE7Pvuuw+CIOAf//gH1q9f32C1WlVVFX744QfMmzcPCoUC9913n9WNpeZD8428fnBRf6WX3CxZJffUvV3RN9rwB6fUcgRke+MGhWHe1Fi8N3uQySBczDY3mqrsRNS8SB45iouLwz333INNmzbhX//6F95++22EhYXB09MTZWVluHTpEqqqqiAIAu6++26MHTtWznaTA7P1N3Ld3KbjGddw8MxVvdwgcyMKGrERQYgfEo6tf2SjsppVsB1Fm1ZeoqZuxeaVNXbldSJqelYVXlmwYAE6d+6Mzz//HCUlJQ2Srj09PfHwww/jmWeesaqR1LxY8o28sfKPdFfJPRAXYfFKJENTguQYxOaLyX0eETkOq4IjpVKJWbNm4ZFHHkFycjKysrJQVFQEDw8PhIeHo0+fPvDy8pKrrdRM2Ns3cktXvxlL0iX7Z25lmi4xeWWWXI+IHIcsJXtdXFwwcOBADBw4UI7LUTNnq2/kUmrV1H9O57Z+ZqcEyX5ZUkVdTAJ/Y1VlJyLb4n4G1ORs8Y1cysq4I6lX8e32dL18JG8PF9YuckBi88jq0yTwmytOSkTNi6jgaPr06VAoFPjggw/QunVr7TFLKBQKrFq1yvIWUrPT1N/IpdSq+W5XBrYdzm7wHAZGjsPHwwUDuoYgNiJIO0ooZfTQmqrsROSYRAVHR48ehUKhQHl5ud4xSxjbYoRapqb6Rm7pyji1WsCm/RcNBkbkOMYNCkP8kI56AYw1dbWkVmUnIsckKjh65plnoFAoEBDw15vDs88+22iNopahKb6RW7IyrqyiGqt3pKOotMrk+WT/YsJUDQIjVromIrFEBUdz5sxpcIzBEclByjdyS6ZGxK54O55xDTuScyxqB9mn+vlqtq6rRUSOhwnZZJWm3q3c0qkRsSveDp0Rt08g2b/6+Wr2UFeLiByLqOBIrVbLcjOlUvJuJWSHmnpvNClTI2JWxvl4uKCEidYOz1i+mr3V1SIi+ycqOOratavVN1IoFEhJSbH6OmQfmjqHQ+zUSM9OrXAu96beSJa5lXEDuoZwSs1B9Oikwui+7QEBuFlWhZJbVfD2coHK293oqKWldbWaejSUiOyPqOBIEASrbyTHNcg+2CKHQ+zUyNwl+/VGgTQjWaZWxnm5uzA4cgD9ooPxVHw3i59nSV2tph4NJSL7JCo4WrlypcHjixcvxuHDhzFgwABMmTIFXbp0ga+vLyorK5GRkYHvvvsOO3bswOjRo/GPf/xD1oaT7aRdKhSdwxEZ6i/Lt3CxUx71p8d0R7Lenz3IYFvUasHshyfZlr+XC2bdI20EW2xdreMZ1yweDeUoE1HzJCo46tevX4NjW7ZswZEjRzBjxgy8/vrrDR5v3bo1hg4dik8++QTLli3DgAEDMG3aNOtbTDYndqn78Yxr+Hxziizfwq3dSiRhayo83JwR3T6gwYeXmA/P6FB/pGYXWdUGkm766Cirgg5zdbViI4Lw8tIDJq9RfzSUo0xEzZdCkDjfdd999yErKwv79u2Dq6ur0fNqamowZMgQBAUF4eeff5bc0KZWWFiGmhp5EtGbC2dnJQICvLDv6CW8u+a4VdeyNCdJrRbw8tIDVo/uGKuavPnARWw+mIWaWk7/NqX4IeEIVnnC38sNJeVVWLfzXKMXBTU00pOaVYj31pp/Tc+bGovosACzmw+LeX1r/p74XtMQ+8Y49o1xKpUXnJzkWfgleSl/RkYGIiIiTAZGAODs7IzQ0FCkp6dLvRXZGxmqnVuakyRmdEeMkvJq7EjOwY7kHAT4uKF/l2DsPXUFZRU1Vl2XLGMs6OkdGdyo01TG6mpZsqKNdZOImj/JwZG3tzeuXLli9rzq6mpcunRJr7o2ObbiMusrSEupK6OZGknYmipLMFNYUsltQpqYm4sT5kzsjuiwhtObgO226bBkRRvrJhE1f5LHn7p164YbN24gISHB5HmffPIJbt68iT59+ki9FdkZf2/To4Viab6Fp2YV4lBKHlKzCqFWm57Wio0IgquLkyz3p6b3+LguiOmgsrsRFc2KNlM0K9pYN4mo+ZM8cvTII4/g999/x3vvvYeMjAxMmDABERER8PT0RGlpKc6ePYu1a9ciKSkJrq6uePzxx+VsN9lQVPsAWVZ35RfcapBHZC6hVcy3drI9d1cnVFTV6h3zcrffgvxiV7QplQqL6yYRkeORnJANACtWrMBHH30EhZEcFEEQ4OLigrfffhvjx4+X3EhbYLJbQ7qJgH+cyTP5QeLl7mxy6svbwwWlJqpSG0toPZSShxWbWEzUnkn93doDQyvQ6udHiVkcoPJxw3uzB5kcIWNirXHsG+PYN8bZRUI2AMyaNQu9e/fGsmXL8Mcff6Cq6q9cFDc3NwwbNgxz5sxBRESE1Q0l+2JuaTQAk8GTuZjcWEIrv43bP6m/W3vQOyoYsRFBJpPCLRllIiLHZNXIka6Kigrk5uaiuLgY/v7+aNOmDdzcHPeDjFF5Q4a+sZgqgmfsW/gdPdsgcd9Fs/fTLJvWJdeSfrJe/akza3+3jkbMKJMpHAEwjn1jHPvGOLsZOdLl7u6OTp06yXU5chCmVhcZ+xZ+OPWqqGsbSmiVa0k/SadbOFHO362jETPKRESOSZbgaO/evdi9ezcuXLiAkpISbNy4EcXFxVi5ciWmTZsGlUolx23IARkKnqRuBFpYUqndaDR+SAckHc0xmdvSnHm5OaGsstb8iTLz9nDBAyM6a0dGrP3dOjpblR4gosZlVXB048YNvPDCC0hOTgZQl2ugSc6+fPkyFi9ejG+//RYrVqxAz549rW8tNQvWbgSq4eflAldnJapa4NDywO63IckGm+WWlldjaeIZXBlSpq1srTtaYsnvlojIXkmenKuqqsJjjz2GI0eOwMvLC6NGjUJISMhfF1Yq4efnh5s3b+KRRx5Bbm6uLA0mx6eZGjNFdyNQYx+0N8uqW2RgBAC3Kmw7Ypa4LxMrNqXgvbXH8fLSAzialg9A/O+WU09EZM8kB0erV69GamoqevXqhV9//RWffvop2rZtq308MjISO3fuRGxsLMrLy/H111/L0mBqHjSr3eoX3lP5uOGZCd0QGxFkdouGluzAaXG5PU1Bs2u9JkAy97vtHRVscfFPIqKmJHlabcuWLVAqlXj//feN5hR5e3vjgw8+wJgxY7B3717JjaTmyVRCa2pWIVek2UhYax9k5ZVY/DzdJfqa3+35yzdRLSjgohDQqY0flEoFd7MnIrsneeTowoUL6NSpE0JDQ02e17ZtW4SHh4vah41aHk1Ca7/ouinZw6lXGRhZSaEA7hkcjln3xOBvA9pb/PyBMSHmTzJAs5+YhlKpQJdwFYbd3g5dwlXawMjQVGn90SciIluSPHKkVovP9XBxcYGTU+Pth1VTU4MlS5YgMTERRUVFiImJwcsvv4xevXo12j1JHmq1gM0HLmJHco5eRW1vDxcbtqpxqXxcUFDSeDlDggBEtw9AdFgAtpVkWfRclY8b4m5vh+1HsiUFqKaW6HM3eyJyFJJHjtq2bYvMzEyUlpaaPK+wsBAZGRl6+UhyW7p0Kb7//nu8+eabSExMRIcOHfD4448jP5/fQu3Z0bR8PP/pXiTuy2yw1UhzXqLfmIGRhiZIKSs3voWLIVNHRsDZWWk2qdoYU0v0LdnNnojIliQHR8OGDUN1dTXef/99k+f997//RW1tLYYOHSr1VmYlJSVh3LhxGDJkCMLCwvDqq6+ipKQEJ06caLR7knU00yum9l8j6TRBipFtDxtwd3HS2/PMWFK1KeaW6HM3eyJyFJKn1R577DFs3LgR3333HW7cuIHx48ejpKQuifP8+fNIT0/H6tWrcfToUXh5eeHhhx+Wq80NBAYGYvfu3XjwwQdx2223Yf369XB1dUV0dHSj3ZOkEzO9osvHwwUlzXgkSW7e7s7aICU6LACbD5qfWntmQjd07Riod6x+wnx+QbnJrUEMLdFXqwX8ee46sq/cRHFplZFn6msuBSKJyHFJDo4CAwPx2Wef4emnn0ZSUhJ27typfWzcuHEA6opCenp64qOPPtKrgSS3119/Hc8//zxGjBgBJycnKJVKLFq0CO3bW56MqiHX/izNiaZPrO2bs5kFFuWzTBkVgbJb1bhaWA4IAjq284PK2w0rfk5h4rYBM//WBa6udTl+MeEqeLg6obzKeDVtbw8XdO/cymieT7dOdUGTWi1AoVTg18OX9Eb8VL5umD46Cn2j9VeaHUnNx+pf01BQ/NfvSKGoy4kyRuXrhpgOqhaRcyTX31NzxL4xjn1jnNiRclHXsnbj2atXr+LLL79EUlISLl++rD3eqlUr3HnnnZg1a5ZVQYoY27dvR0JCAh577DGEhITg+++/x5YtW7Bq1Sp06dKlUe9Nlvv9WA4+WH1U9Pm+Xq4oLvtr1CHQzx2z4rsDAN755ojs7bNH08ZEoU0rb1y+Xoo129OMnjfxzk54ZHw3AMCBU5exIvFP3LhZYfLar83si0E92pg8x9C1vD1dcM/QTrh/ZCSc6gUzB05dlvS7EdMWIqLGJjk4unjxIsLDw7XbhQDArVu3UFJSAk9PT/j4+MjWSFOuXLmCUaNGISEhAX369NEenzZtGvz9/fHZZ59Jum5xcTlqa1tm9WVjnJyU8PX1sLpvzmYW4J1Vx6xuz5zJPQAAq7enoaAZjyC5uzrhiXu6akdmjqTmY9W2VBTqTFN5ezjj4bu6oN//luEfSc3Hog2nTF7X2IhPfeauNWdyD71rqNUC/r5on8nfSf0RJLFtaU7k+ntqjtg3xrFvjPPz84BSKc+ImuRpteeeew5lZWXYuHEjAgLqNl709PSEp6enLA0T6+TJk6iurkb37t31jvfs2RN79uyRfN3aWjVqWujWFOZY2zed2vjBy93Z6mTs1dvT8N7sQeg5exA2H8g0mQ/jyCqqarFowyltwnRs51bo+fRgg8Uza2rUUKsFrDIxugTU5XEtmDUQzs5Kk79LMddavT0NPTsGaqfCUrMKzQarggDcP7wT/H3cGrS/peF7jXHsG+PYNw1ZNw+mT3KIlZ2dDTc3N21gZCutW7cGAKSl6b+Bp6enIzw83AYtoqZSUFKJ1TvScPBMHnYebfpNWJvamqQM7TYbmuKZA2JaIzosQC9HJ/WS+SKaJeXVOJd70+w9pSy/F7vabMvBLLg4KRu0n4jI1iQHR15eXhYVgmwsPXr0QO/evfHKK6/g0KFDyMzMxMKFC3Hw4EHMmjXL1s0jA9Kzi2Rbwr/7+GV8ueVsi1jNVlhSiaTkbJP7kB1Ny8fSxNOiricmiJGy/N7X01XUc8oqalgVm4jskuTgaNq0acjKysJXX30lZ3ssplQqsXTpUgwYMACvvfYaJk6ciEOHDiEhIQE9e/a0adtIX02NGr8evoTNBzNt3RSHtW7XOby89IDBgMLS2lFilsyLXVavOe9oWj6+2Jwi6jkaa3VGxIiI7IHknKPu3bvj9ttvx/vvv4/Vq1cjNjYWQUFBcHd3N/qc559/XurtTPLz88P8+fMxf/78Rrk+SadWC0jPLsK2w1k4db7Aouc2dn0jf29X3NmrDRL3ZTbaPRqDZh8y3aKNltaO8vZwMVmwUSMy1B8BPm4mp9Y0xR81wZmlNNNy0WG2naInItKQHBzNmjULCoUCgiAgNzdXbxl/fYIgQKFQNFpwRPbJ0O7rYql83HD/8M5YtulMI7SsTllFDWrVAvy8XHCzzPGm5XT3IROTG6SrpqYWarVgNtdHqVRg2sgIk0HP1P9tNWJJcFYfq2ITkT2RHBz17dtXznaQDDSjNPVXMNninlJHETT6dQnGul3SP2zFqK5R4+cDWQ1q9DgK3REXS4OLimo15i7Zj4fGRulVwTb0utFsJVI/0FX5uGHqyAj0jgpGapb5JHBTWBWbiOyJ5ODo22+/lbMdZCVDozQBPm6Y9r8Pr6a855QRnbFu5zlJ11QogJ6dArHtcLZczTSr1oHzXTRBkZTgoqS8Gkt+PN2grIKh1039rUTqB1HWjPyY25ONiKipsf54M6AZpan/zV2Tm9IYq4FM3XNp4hnJowiCAJw4d0OOJrYImqDImtys+gncxl43psoH5BeUS76/oT3ZiIhsyeKRo5SUFJw4cQJlZWW47bbbMGjQIKhUqsZoG4kgJhFXNzelqe5JjU8z4qJWC1i3U/7fh9jXjVot4PeTxnMOjdGdliMisieig6Ps7Gy88sorOH78uN5xFxcXPPLII3juuefg5OQkewPJNEuK9Mm1GihNRJFBspwCgCUTfJoRF2vzfYwR+7oRmwx+z+BwRLcPaNKcOCIiKUQFR6WlpZg5cyauXLmC+luxVVVVYcWKFSgsLMR//vOfRmkkGSelSJ/V99TZ04vkIzYwqj/i0pgrveQsFNk60JPL9YnIIYjKOVq9ejUuX74MLy8vvPHGG9i7dy9OnDiBxMRE3HvvvRAEAd9//z3Onz/f2O2leiwt0ifLPb3FVUBu7mwx5jFuYBjemz1IbyqqMVd6NUahSCIieycqOPrtt9+gUCiwdOlSTJs2TVvsMTo6Gu+++y4eeOABAMDOnTsbtbHUkKZInylyrwaKah8g6p6z47s1OM/TzRldO/jD1dnx1wLYYo1bTLiqwVSUmNeAFGJfN6Jeg75ckUZEjkPUJ1RmZibatGljtLbRlClTIAhCg81fqfFpivSZIvdqILH37BsdjPdnD8K8qbGYdU8M5k2Nxcy7onD5ejmqmvFu0goFMKZfqOwBi4+n4arWYn4fYw20x9vDxeRzxL5uxNx/+ugo5hcRkcMQnXMUGhpq9PGOHTsCAIqKimRpFFlGTJE+W91Ts/wb0GyKarridYC3K6aNigQAJGxNlW2D2qYkCEDPTq1w352d9eoClZRXYd3Ocw36q3dUEHYk55i97oCYEKMBhpjfx+R67YkM9cfxjGuyvG6M3t/XDU9O6IEuoX6oacYBMRE1L6KCo+rqari4GP+W6eZW9420spIrmGzFXJE+W99T/PL/uufGRgRh9Y50mVtsvR6dVKL2iCsqq9QLDDV6RwY36K/NBzJF3Ts2Isjk4+Z+HwbbI+PrxtC1YjqoEBjojcLCMouvR0RkK5IrZBtSfyUbNS1DH372ck+xy70LS+sKEMYP6SBqVdyUuM4oLa/G5oNZotorlauLEp/OGYoLV4pFBUfGko/r99fRtHwk7rsoqg1iCj1KeQ3I+bqpfy1OpRGRI3L8rFhyCJYuN9+RLG77EF9vV7QJ8pLSJItUVatx4UqxrAnwlhbTXL8zA2oH3uqEiMhRMDgiLbVaQGpWIQ6l5CE1q1DWD2JLl3GLzTXy93JrsiXiyWn5SM8uwpQR8iTAix1N09AUZSQiosYlelqtpKQER44cseocY6vdyPYae+NazYiLJcFA/Q1R69PdPkOhqEuEbky7juVi17FcBPi4YWy/UPxxNr9B8vHUEeL7S0rxxsYs+EhERHVEB0cZGRl46KGHjD6uUChMnqNQKJCSkmJ5C6nRaTaRrU+zAekzE7pZHSBplnsbuo8xo/q0Q+K+TKOPa0Zo0rOLGj0w0lVYUolth7MxO74rfDxcUVJejdDb/NAmwN2i0TYpI14spEhE1PhET6sJgmD1f2R/xG5ca+kUm6EpOs1ybzEVtr3cndG5rT9mx3dtkOOj8nHTC9hsNZqy6td0dG7rh4HdWqN751YWJx9bWrxR7mKeRERkmKiRI1a+br4s2bi2W6dAUdc0N0UXGxGEzQcyTa7SKquowQfrTyDAxw1TRkTAx8PF6FJzW42mlNyqxt+X7Mcjf+uC0QM7WPx8S0fTHhghbzFPIiIyTFRw1LZt28ZuB9mI3BvXmpuimx3fFX2jQ3DPkA5oG+TVIIgy9LyliXVTewNiWhs8R0o+kzndOqhw+qL5Jful5dVYtOEUvL3c0CXUz+L7GCueaMi6nRlQKtAoRT2JiOgvXK3Wwsm5aaiYKbplP53BkdR8AHUf8u/PHoSXpvSCl7vpON3U1J6Y7SvE0kzZ3T0gzKLnff7Tacmr+zT9oNlm5d7Bhu+tCTCPpuVLug8REYnD4KiFk7Nuj5gpOkEAlib+9QGvVCqgVCjMLt03t4w9NiII8UPCzQZZhvh4uOCJcXV7v2l2vLc0H+h6UTnSLhVafG8NTfHEftEh2HMqz+S5UnLAiIhIPAZHLZycG9dakhit+wF/POOaqOckp+UbrL90NC0fLy89gMR9mZL2YhvcvTUGdmuN6LAAva02LB2NElPR2xxLcsCIiKhxMDgibd6LuVVh5liSGK35gD+ali9q01Wgrs7Qe2uP46XP9mtHnjQ5TtbkGx0+m29wJEbTLz5mdq/XELMKzxy5c8CIiMhysu6tRo5Ljg1ILU2MLiitwMbfLljc1qLSKm1y97qd5yx+foN2/C9QM7S/WO+oYPTs1Ap/X7IfpSb2Nmvl74Go9gFWT3fJmQNGRETScOSItDR5LwNi9KeYLHm+JVNRpWXVVo34fL3lrGwr1EyNxDg7KzFzbJTJ5z9xbzdZltnLmQNGRETSMDgiWfWOCsbs+K5QmIkTVD5u8BI5XWVMRbXaqufrMjcSY2rqcc7kHhjUo40s7ZAzB4yIiKThtBrJrm90CAAFliYaL244dWQEbtysaLpGmaA7EqNWC0anFo1NPbq6OsnaHmO1j1Q+bpgq0153RERkHIMjahR9o4OhNPMBf/C06SXrYri7OqGiqtaqa2hGYgxV9vb2cMHAriGIjQjSBkqGcpPkJkcOGBERScPgiBqNuQ94S+oIGTO6byg27c80e563hwucnRR6y+11AzVjlb1Ly6uxIzkHO5Jz9LZAaQpNFYgREZE+BkfUqEx9wFu77Ye3hwvuGdwBocHeSNiaarLG0cyxUUYDNTGVvYG/KlRbUt6AiIgcDxOyyWas3fZj5tgoKJUK9I4KxifPDTVYIVu3VpOx1XhiCi/qYoVqIqLmjSNHZFPmko8BNHjM0PSWUqnAPUM6YtygDhbn6VhaULGgpBJJydnw9XaFv5cbYjqoLHo+ERHZNwZHZHPmcpMsSUyWkqcjpaDiul1/FZ9U+bjhyYk90CXUz+LrEBGR/WFwRHbBVFDT2InJ1uY+FZRU4p1vjuCZSd3ROyJI5tYREVFTY84RtXjW5j5pfPbDnziSmi9Di4iIyJYYHBHBeAVsSwgCsDTxtHZTXCIickycViP6H93cp+MZ13DozFWUmNhs1pi1SRmIjQhiwUYiIgfF4IhIhya/KTosAA/ERWgTwYtLq/SSsE0pKKlEenYRCzgSETkoBkfUJEztWWavdBPB1WoB249ki07atrQ8ABER2Q8GR9ToDO1Z1tRbcVhLk7RtaIsRQ6SUByAiIvvg8AnZf/zxB6Kiogz+N2LECFs3r8XT7FlWf8RFsxWHIyUv944Kxuz4rlCYGfBS+dSNjBERkWNy+JGj2NhY7Nu3T+/YiRMnMGfOHDz99NM2ahUBELVnmaMlL/eNDgGgwNJE4yNIU0dGOMzPQ0REDTn8yJGrqyuCgoK0/3l5eeGdd97BhAkTMGnSJFs3r0UTs2eZJnnZkfSNNrzsX+Xrxk1piYiaAYcfOapv2bJlKC8vxyuvvGLVdZycHD5ulJ2mT8T2jdhl8CXl1XB2dqz+7t+1Nfp2CUHapUIU36pG2xBfhLbyhCBwQ1pdlr5mWhL2jXHsG+PYN8aZS3mwRLMKjgoKCpCQkIC5c+fC39/fqmv5+nrI06hmSGzfhN4mbq+x0Nv8EBDgZU2TJKtVC0i5cAMFxRVQ+bojpmMgnCyYEhsU6N2IrWs++PdkHPvGOPaNceybxtWsgqM1a9bAx8cHDzzwgNXXKi4uR22tWoZWNR9OTkr4+nqI7ps2Ae5Q+bihwMTUmsrXDW0C3FFYWCZnU0U5kpqP1dvT9Nqn8nHD9DFR6BstfmrM0n5pSdg3xrFvjGPfGMe+Mc7PzwNKpTwjas0qOEpMTER8fDzc3d2tvlZtrRo1NXzhGWJJ30w1s/x96ogIqNUC1OqmnY7SrKKrr6CkEos2nJKUO8TXjHHsG+PYN8axb4xj3zQkZ1ZDs5m0TE1NRXZ2NsaPH2/rppAOY3uWqXxsl7wsdhVdUwdsRERkH5rNyFFycjICAwMRHR1t66ZQPbp7ltlDhWxLVtFxCxAiopan2QRHKSkpiIqKsnUzyAjdrThsTezWHtwChIioZWo202rXrl2zeoUatQxit/bgFiBERC1Tsxk5+vzzz23dBHIQkaH+CPBxMzm1xi1AiIharmYzckQklmYTWVO4BQgRUcvF4IhaJHtcRUdERPah2UyrEVnK3lbRERGRfWBwRC2aPa2iIyIi+8BpNSIiIiIdDI6IiIiIdDA4IiIiItLB4IiIiIhIB4MjIiIiIh0MjoiIiIh0MDgiIiIi0sHgiIiIiEgHgyMiIiIiHQyOiIiIiHQwOCIiIiLSweCIiIiISAeDIyIiIiIdDI6IiIiIdDA4IiIiItLB4IiIiIhIB4MjIiIiIh0MjoiIiIh0MDgiIiIi0sHgiIiIiEgHgyMiIiIiHQyOiIiIiHQwOCIiIiLSweCIiIiISAeDIyIiIiIdDI6IiIiIdDA4IiIiItLB4IiIiIhIB4MjIiIiIh0MjoiIiIh0MDgiIiIi0sHgiIiIiEgHgyMiIiIiHQyOiIiIiHQwOCIiIiLSweCIiIiISAeDIyIiIiIdzSY4SkxMxN13343u3bvjb3/7G7Zu3WrrJhEREZEDahbB0U8//YTXX38d06dPx5YtWzBu3Dj8/e9/x/Hjx23dNCIiInIwDh8cCYKATz75BA899BCmT5+O9u3bY/bs2Rg0aBAOHz5s6+YRERGRg1EIgiDYuhHWuHDhAu666y78+OOPiImJke26arUajt0z8lMoAKVSyb6ph/1iHPvGOPaNcewb49g3ximVCigUClmu5SzLVWzo4sWLAIBbt27hscceQ0pKCtq1a4fZs2cjLi5O8nWVSocfVGs07BvD2C/GsW+MY98Yx74xjn3TuBy+d0tLSwEAr7zyCsaNG4evvvoKgwcPxtNPP42DBw/auHVERETkaBx+5MjFxQUA8Nhjj2HChAkAgC5duiAlJQVff/01Bg4caMvmERERkYNx+JGjkJAQAEBkZKTe8c6dOyMnJ8cWTSIiIiIH5vDBUdeuXeHl5YWTJ0/qHU9PT0f79u1t1CoiIiJyVA4/rebu7o7HH38cS5YsQUhICHr06IEtW7Zg//79SEhIsHXziIiIyME4/FJ+ja+//hqrVq3C1atX0alTJ8yZMwcjR460dbOIiIjIwTSb4IiIiIhIDg6fc0REREQkJwZHRERERDoYHBERERHpYHBEREREpIPBEREREZEOBkdEREREOhgc6UhMTMTdd9+N7t27429/+xu2bt1q6ybZ3B9//IGoqCiD/40YMcLWzbO5mpoafPLJJxg+fDhiY2Mxffp0nDhxwtbNsgulpaWYP38+hgwZgn79+uGll17CjRs3bN0sm1q+fDlmzJihd+zs2bN48MEH0atXL8TFxWHlypU2ap3tGeofAMjKykKvXr1a7JZQhvpl165dmDRpEmJjYxEXF4d3330XFRUVNmqh7Rjqm19++QXjx49Hjx49MHLkSHz++eewuGqRQIIgCEJiYqIQExMjrFq1SsjKyhI+++wzITo6Wjh27Jitm2ZTlZWVQn5+vt5/v/76qxAVFSVs2LDB1s2zuU8//VQYPHiwsHfvXiEzM1N4/fXXhd69ewtXr161ddNs7tFHHxWGDRsm/Pbbb0J6errw9NNPC3fffbdQWVlp66bZxKpVq4To6GjhwQcf1B4rKCgQ+vfvL7z22mvCuXPnhA0bNgjdu3dvkX9bhvpHEATh3LlzQlxcnBAZGSlkZ2fbqHW2Y6hfjhw5InTp0kVYunSpcPHiReG3334T7rjjDuHVV1+1YUubnqG+2bNnj9ClSxdh5cqVwqVLl4Tt27cLvXr1EhISEiy6NoMjQRDUarUwfPhwYcGCBXrHH330UWHZsmU2apV9KisrE4YPH97i/giNueeee4R33nlH+++SkhIhMjJS2L59uw1bZXspKSlCZGSk8Pvvv2uPlZaWCn369BF++OEHG7as6eXl5QlPPvmk0KtXL2Hs2LF6b+TLli0ThgwZIlRXV2uPffjhh8Lo0aNt0VSbMNc/vXr1EiZMmNDigiNT/TJ37lzh4Ycf1jv/xx9/FLp27doivnyY6puNGzcKH3/8sd75Tz/9tPDEE09YdA9OqwG4ePEicnNzMX78eL3jX375JZ588kkbtco+LVu2DOXl5XjllVds3RS7EBgYiN27dyMnJwe1tbVYv349XF1dER0dbeum2VRmZiYAoE+fPtpjXl5eCAsLw+HDh23UKts4c+YMXFxcsGnTJvTs2VPvseTkZPTr1w/Ozn9tczlgwABkZmbi+vXrTd1UmzDVP0lJSXjnnXda5PuNqX559NFHG/SJUqlEdXU1SktLm7KZNmGqbyZOnIgXXngBAKBWq3HgwAEcOXIEgwcPtugeDr/xrBwuXrwIALh16xYee+wxpKSkoF27dpg9ezbi4uJs3Dr7UVBQgISEBMydOxf+/v62bo5deP311/H8889jxIgRcHJyglKpxKJFi9C+fXtbN82mgoODAQBXrlxBp06dAAC1tbXIy8tDYGCgLZvW5OLi4oy+j+Tl5SEyMlLvmG7ftWrVqtHbZ2um+uf7778HUJf72NKY6peYmBi9f1dXVyMhIQHdunWDSqVqiubZlKm+0bh8+TJGjRqFmpoaDBkyBFOnTrXoHhw5ArSR9iuvvIJx48bhq6++wuDBg/H000/j4MGDNm6d/VizZg18fHzwwAMP2LopduPcuXPw8fHBkiVLsH79ekycOBEvvfQSzp49a+um2VT37t3RsWNHzJ8/H1evXkVFRQU+/PBDFBYWorq62tbNsxsVFRVwdXXVO+bm5gYAqKystEWTyMHU1NRg3rx5yMjIwPz5823dHLvh6+uL77//HgsXLkRqairmzZtn0fM5cgTAxcUFAPDYY49hwoQJAIAuXbogJSUFX3/9NQYOHGjL5tmNxMRExMfHw93d3dZNsQtXrlzB3LlzkZCQoJ0+6t69O86dO4dFixbhs88+s3ELbcfV1RWLFy/GvHnzcMcdd8DFxQXjx4/H8OHDoVTyO5mGu7s7qqqq9I5pgiJPT09bNIkcSGlpKV544QUcPnwYixcvRo8ePWzdJLvh7e2NmJgYxMTEoLa2FnPnzsXLL7+Mtm3bino+gyMAISEhANBgeLtz58747bffbNAi+5Oamors7OwGeVkt2cmTJ1FdXY3u3bvrHe/Zsyf27Nljo1bZj06dOmHjxo0oKiqCs7MzvL29MXnyZAwYMMDWTbMbrVu3Rn5+vt4xzb8170tEhuTn5+OJJ55Abm4uvvzyS/Tt29fWTbILycnJcHV11QsUo6KiANT1mdjgiF/hAHTt2hVeXl44efKk3vH09PQWnzuikZycjMDAwBafaKyrdevWAIC0tDS94+np6QgPD7dBi+xHaWkpHnzwQaSmpsLf3x/e3t7IyclBSkqKxYmRzVnfvn1x9OhR1NbWao8dOnQIHTp0aHG5WSTezZs3MXPmTBQUFGD16tUMjHSsXLkSb7/9tt6xkydPwtnZ2aL3ZQZHqBvafvzxx7FkyRJs3rwZly5dwtKlS7F//3488sgjtm6eXUhJSdFG31SnR48e6N27N1555RUcOnQImZmZWLhwIQ4ePIhZs2bZunk25e3tDUEQ8NZbbyEjIwN//vknZs+ejQEDBnCaWsekSZNQWlqK119/HefOncMPP/yAhIQErpIlk9555x1kZ2fj/fffh0qlwrVr17T/6QbaLdHDDz+MU6dO4eOPP0ZWVha2bt2K999/Hw899BACAgJEX4fTav/z9NNPw8PDAx9//DGuXr2KTp06YdGiRejfv7+tm2YXrl27xhVq9SiVSixduhQLFy7Ea6+9hps3byIyMhIJCQkNlpe2RB999BHefPNNTJ06Fa6urhg9ejRefvllWzfLrgQGBuKLL77AW2+9hQkTJiAoKAjz5s3T5j4S1VdbW4tffvkF1dXVmDlzZoPHd+7ciXbt2tmgZfbh9ttvx/Lly7Fw4UIkJCRApVLh0UcfxRNPPGHRdRSCYGlNbSIiIqLmi9NqRERERDoYHBERERHpYHBEREREpIPBEREREZEOBkdEREREOhgcEREREelgcERERESkg8ERERERkQ5WyCayAz/88ANee+01i5/Xr18/fPvtt43QItvIyMhAREREk91v7ty52L9/P7Zv3w4/P78mu6+uvLw8eHt7w9vbu9HvpXmdhYSEyLI58syZM5GXl4cff/wRnp6eMrSQyD4wOCKyA4GBgbj99tsbHL9y5QquXLkCV1dXdOvWrcHjkZGRTdG8Rnfx4kX897//xa1bt7B27domuefmzZuxefNmvPHGGzYJjKqqqrB06VJ89dVX2LRpU5MER3J7/fXXER8fjwULFuA///mPrZtDJBtuH0JkxxYtWoTFixejbdu22LVrl62b02g0P+ftt9/eJMFRcXEx7rrrLnh7e2PLli1wdm7674k5OTkYMWIEAODXX39FWFhYo9+zpKQE+fn5cHFxQfv27WW55j//+U9s2LABa9asMRjgEzki5hwRUYuzYsUKXL9+HbNnz7ZJYGQrPj4+6NSpk2yBEQA89dRTUCgUeOedd2S7JpGtMTgiohaloKAAq1atgp+fH+666y5bN8fhtWvXDkOHDsWpU6fw+++/27o5RLJgcETk4IqLi7FixQpMnz4d/fv3R9euXdGnTx9MnDgRixYtws2bNxs8JyoqClFRUbh+/TpeeuklxMbGonfv3njooYdQU1OjPW/btm2YMWMGBgwYgF69euGBBx7A5s2bkZOTg6ioKMTFxRlsU1JSEmbNmoWBAweiW7duGDp0KObOnYszZ87onae5zuLFiwEAx44da3Dd2tparFmzBlOnTkXv3r3RrVs3DBkyBM888wx2795tcX9t2LAB5eXlGDt2LNzc3LTHL1y4oO2X+u3UNWbMGERFReGXX36x+N4aM2bM0E6pAcDo0aMRFRWFP/74AwDw6quvIioqCmvXrsX69etx5513onv37hg9erT2HAA4e/Ys/u///g933XUXbr/9dnTr1g2DBg3CE088gW3btjW47w8//ICoqCjccccdDdoTFRWFPXv2IDU1Fc8//zwGDRqEbt26YcSIEXj77bdRUFBg9OeJj48HgGa1OIBatpYznkzUDGVmZuLhhx/GlStX4OzsjPbt26Nt27bIzc3FmTNncObMGWzZsgUbN26El5dXg+fPmTMHx48fR2RkJAoKChAUFARnZ2eo1WrMnz8f3333HQDgtttuQ5s2bXD27FnMnTsXo0ePNtiempoavPrqq/j5558B1CWaR0VFIScnB5s3b8bWrVvxj3/8Aw8++CAAwM3NDbfffrs28dzb2xuRkZEICgoCAAiCgBdffBHbt28HAISFhcHHxweXL19GUlISkpKS8PTTT+P5558X3WeaoGbYsGF6xzt27IjY2FgcP34cP/30E7p27drguceOHUNmZib8/PwwcuRI0fesLzIyErdu3cLp06cBAF27doWbmxt8fHz0ztu0aROOHTuG1q1bIzw8HDk5OejSpQsAYM2aNXjzzTehVqvh5+eHsLAwVFRUICcnB3v27MGePXvw1FNP4cUXXxTdrj179mDdunUQBAHh4eHw8vLCpUuX8M033+C3337DDz/8YDBxfNCgQVAqldi/fz+Kiorg7+8vuW+I7IJARHbr008/FSIjI4Xhw4cbfPzBBx8UIiMjhfvvv1+4evWq9rharRZ+/PFHITo6WoiMjBRWrVql97zIyEghMjJS6Natm3D48GFBEAShtrZWKCwsFARBENatWydERkYKPXr0ELZs2aJ9XlFRkTBnzhzt8+u364MPPhAiIyOFO+64Q9izZ4/2eE1NjbBy5UohJiZGiIqKEvbt22fw55wyZYre8d9//12IjIwUBgwYIKSmpupdb9myZUJkZKTQpUsX4cqVK+a6UhAEQbhx44YQFRUlREZGan9WXd99950QGRkpDBo0SKipqWnw+P/93/8JkZGRwr/+9S9R9zMlOztb24+ZmZl6j73yyivax/7zn/9o23Ljxg1BEATh4sWLQteuXYXIyEjhs88+E6qqqrTPLSwsFJ5//nkhMjJS6Nq1q1BUVKR9bOPGjUJkZKQwdOhQvftpXkeRkZHCrFmz9F5LSUlJQpcuXYTIyEjh66+/NvrzjBs3ToiMjBS2bt0quU+I7AWn1Ygc1PXr15GRkQEAePPNNxEcHKx9TKFQID4+Hv369QMApKWlGbzGXXfdhb59+wIAlEol/P39oVarsWjRIgDAvHnzcPfdd2vP9/Pzw4cffoioqCiD7UlISAAAfPbZZxg6dKj2MScnJ8yYMQMPP/wwBEHAwoULRf2MqampAIDY2Fi9ezo5OeHJJ5/E2LFjMW7cOINTh4YcPnwYgiAgKCjI4OjG3XffDU9PT1y/fh379u3Te6yyshJbt24FAEycOFHU/azl5uaGuXPnwsnJCQCgUqkAAPv374eTkxO6du2K2bNnw8XFRfscf39/vPLKKwCA6upqXLx4UfT9AgMD8emnn+q9lkaMGKGdhjt27JjR52rKShw6dEj0/YjsFYMjIgfVqlUrHDp0CCdPnjRY76i2tlY7BVJRUWHwGr17925w7MSJE7h27RpcXV0xadKkBo+7uLhg+vTpDY7v2bMHVVVV6Ny5s8EpKQC49957AQCnTp3CjRs3jP9w/xMeHg4A+P3337F8+XJcuXJF7/FPPvkE7733nsFgzZCcnBwAMLps3svLC2PHjgUA/PTTT3qP7dy5E8XFxYiMjET37t1F3c9aMTExBosrTp8+HSdPnsSaNWsMPs/d3V37/+Xl5aLvN3DgQL08LI1OnToBqCsFYEyHDh0AANnZ2aLvR2SvmHNE5ODc3d2Rm5uLP//8E5cuXUJ2djbOnz+Ps2fP4tatWwAAtVpt8Lma3B5dmtGo8PBwvQ9ZXYYKUmqel5eXh6lTpxp8nqBTVu3ChQsIDAw08ZMBcXFx6NevHw4fPoyPPvoIH330ETp27IhBgwZh6NChRj/MjdEEZPVze3RNmjQJP/zwA3bu3InS0lJtgJmYmAig6UaNAMO/H10uLi44deoU0tPTkZ2djUuXLiE9PR0XLlzQniNYUMouJCTE4HHN60A3Wb8+TZ+aStwmchQMjogc2IULFzB//nwcPnxY77i3tzf69OmD/Px87dSUIYaCn8LCQgAwuR2EoaRczahCaWmpyekXjeLiYrPnODs748svv8Tq1avxww8/aD/4L1y4gFWrVsHb2xuPP/64ttaO2Ht6eHgYPadPnz4IDw9HZmYmtm/fjkmTJuHatWvYt28fnJ2dcc8995i9j1xMBX4//vgjPvzwQ1y7dk3veLt27TB58mRtMr0ldKfnLKV5vYid4iSyZwyOiBzUjRs38OCDD+LGjRto06YN7r//fsTExKBjx45o164dFAoF5s6dazI4MkQTOJSWlho9p6yszOjzxowZg08//dSie5ri6uqKRx55BI888gjy8vJw6NAh/PHHH9izZw+uX7+OhQsXwt3dHY888ojZa2mCDXOB2cSJE/HRRx/h559/xqRJk7BlyxbU1tZixIgRZke7msKPP/6IV199FQAwdOhQjBo1ChEREejUqRP8/PxQXV0tKTiyhiYoMjbaSORIGBwROaiNGzfixo0b8Pf3x8aNG7XJurquXr1q8XU1+UtZWVmoqKgw+GFnKODS5JxoptcMKS8vx59//qktDaBJNDbm5s2buHjxIkJCQnDbbbehdevWiI+PR3x8PKqrqzFnzhzs3r0bP/30k6jgqFWrVgD+Gh0zJj4+Hp988gmOHDmCoqIi7NixA0DTTqmZsnz5cgB17Xz33XcbPJ6Xl9fUTdL2qT0Ej0TWYkI2kYPSJBe3adPGYGB07tw5nDhxAkBdcrZYvXv3hkqlQnV1dYOkZKAuh8XQqMSwYcPg5OSECxcuYP/+/QavnZCQgBkzZuDee+/VSxTWTInVz4/5xz/+gQceeACff/55g2u5uLhoV+OJ/fk0AZy5oDEkJARDhw5FTU0NfvjhBxw/fhyBgYG48847Rd1HDKXyr7dfS/KCgL9+98YS3zds2KD9f1N5QnLSBGQdO3ZskvsRNSYGR0QOSvMhlJqaqi2SCNR90O7ZswePP/44qqurAVi2YsnV1RVPPfUUAGDBggXYuXOn9rFbt27hjTfewPHjxxs8r23btrjvvvsAAH//+9/1NspVq9X4/vvvtZWwp0+frpe3pClQmZ+fr/dhrlndtn79eiQmJuoFERkZGdqKzPULOhqj2Rj1+vXrZldVaVbqLVq0CLW1tbjnnntk3YdNN6fr8uXLFj1X87tfv369XqBXWlqKRYsWYcWKFdpjxlYqyk3zmjC0ApLI0XBajchBTZ48GWvWrEFWVhaee+45tG3bFgEBAbhy5Qpu3LihHVk5fPiwxdNrM2bMwMmTJ7FlyxY8/fTTaNOmDQIDA3H+/HncunULPXr0wKlTpxpMi/3jH//A1atXsXv3bsyePRvBwcEICQlBbm6udhXTmDFj8MILL+g9T1P1OTc3F6NHj0ZwcDDWrl2L0aNH4/7778d3332HV155Be+++y5uu+02lJaW4tKlSxAEAT169NAGc+aEhIQgKioKaWlpOHr0KEJDQ42eO3z4cKhUKm275Z5S8/f311Yzf+aZZ9CxY0c8//zzDbb2MOTFF1/E008/jXPnzmHEiBHaEbGsrCxUVlYiNDQUCoUCly5dapIptry8POTm5sLZ2RmDBw9u9PsRNTaOHBE5KG9vb2zYsAGzZs1CREQECgoKkJGRAW9vb0yaNAkbN27E22+/DaBudMmS0QmlUokPP/wQb7/9NmJjY3Hz5k2cO3cOUVFRWLRoEWbOnAmgYfKtm5sbli5dio8//hhDhw5FdXU1zp49i9raWvTv3x/vvvsuFi5c2CCoGjBgAObNm4e2bdsiPz8fOTk5uH79OgDg3//+N9555x30798farUaaWlpKCoqQu/evfHGG29gzZo1BlfPGaMZjdqzZ4/J81xcXDBu3DgAdaULDNWSstYnn3yC2NhYqNVqZGZm4tKlS6KeN3z4cGzYsAEjR45EUFAQLly4gCtXriAyMhJz587FTz/9hPHjxwOApP3nLKXpyyFDhjDniJoFhWDpZDcRtXirV6/Gf/7zHwwePBhfffWVrZtjkZKSEsTFxaGiogJ79+41uQ/YnDlz8Ouvv+KNN94wWPiS6kyePBl//vknVq1apa24TuTIOHJERHoqKysxZMgQTJs2zeh03O+//w6groKzo/Hx8cFDDz2EqqoqbWFHQ65du4bdu3fDw8OjSWsbOZrU1FT8+eef6NevHwMjajYYHBGRHjc3NwQFBeHo0aNYsGCBXk2gyspKLF68GL///jvc3d0Nbi/iCGbOnImAgAB8++23qKqq0h6/evUqsrKycPbsWcyZMwfV1dWYNGmSyYraLd0XX3wBhUKB559/3tZNIZINp9WIqIHk5GQ88cQTuHXrFtzd3dG+fXsolUpkZ2ejrKwMbm5uePvtt7U5OY5o27ZteP755/HKK6/g0UcfBQBs3rwZc+fO1Z4TFBSEn3/+GQEBAQ2ev2zZMu0ImqXWrl0rrdF25s8//8R9992H6dOn4//+7/9s3Rwi2TA4IiKD8vLy8M0332D//v24cuUKampqEBwcjEGDBmH69Ono3LmzrZtotZdeegl79uzBr7/+Cn9/f6SkpODxxx9HWVkZYmNj8cYbbxit2/Pqq6/ixx9/lHTftLQ0a5ptN2bMmIH8/HwkJiaa3JKFyNEwOCIiIiLSwZwjIiIiIh0MjoiIiIh0MDgiIiIi0sHgiIiIiEgHgyMiIiIiHQyOiIiIiHQwOCIiIiLSweCIiIiISMf/A4Vw+LwfZdBiAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot\n",
+    "# The closer the points to the 45-degree line, the better the prediction\n",
+    "plt.scatter(y_train, y_hat)\n",
+    "# Let's also name the axes\n",
+    "plt.xlabel('Targets (y_train)',size=18)\n",
+    "plt.ylabel('Predictions (y_hat)',size=18)\n",
+    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
+    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
+    "# We want the x-axis and the y-axis to be the same\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8332\\1719987585.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(y_train - y_hat)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Residuals PDF')"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Another useful check of our model is a residual plot\n",
+    "# We can plot the PDF of the residuals and check for anomalies\n",
+    "sns.distplot(y_train - y_hat)\n",
+    "\n",
+    "# Include a title\n",
+    "plt.title(\"Residuals PDF\", size=18)\n",
+    "\n",
+    "# In the best case scenario this plot should be normally distributed\n",
+    "# In our case we notice that there are many negative residuals (far away from the mean)\n",
+    "# Given the definition of the residuals (y_train - y_hat), negative values imply\n",
+    "# that y_hat (predictions) are much higher than y_train (the targets)\n",
+    "# This is food for thought to improve our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.744996578792662"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find the R-squared of the model\n",
+    "reg.score(x_train,y_train)\n",
+    "\n",
+    "# Note that this is NOT the adjusted R-squared\n",
+    "# in other words... find the Adjusted R-squared to have the appropriate measure :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.415239458021299"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the bias (intercept) of the regression\n",
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.44871341,  0.20903483,  0.0142496 ,  0.01288174, -0.14055166,\n",
+       "       -0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,\n",
+       "       -0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,\n",
+       "       -0.14690868,  0.32047333])"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the weights (coefficients) of the regression\n",
+    "reg.coef_\n",
+    "\n",
+    "# Note that they are barely interpretable if at all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>-0.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>-0.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>-0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>-0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>-0.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>-0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-0.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-0.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-0.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>0.32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features  Weights\n",
+       "0               Mileage    -0.45\n",
+       "1               EngineV     0.21\n",
+       "2             Brand_BMW     0.01\n",
+       "3   Brand_Mercedes-Benz     0.01\n",
+       "4      Brand_Mitsubishi    -0.14\n",
+       "5         Brand_Renault    -0.18\n",
+       "6          Brand_Toyota    -0.06\n",
+       "7      Brand_Volkswagen    -0.09\n",
+       "8            Body_hatch    -0.15\n",
+       "9            Body_other    -0.10\n",
+       "10           Body_sedan    -0.20\n",
+       "11           Body_vagon    -0.13\n",
+       "12             Body_van    -0.17\n",
+       "13      Engine Type_Gas    -0.12\n",
+       "14    Engine Type_Other    -0.03\n",
+       "15   Engine Type_Petrol    -0.15\n",
+       "16     Registration_yes     0.32"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a regression summary where we can compare them with one-another\n",
+    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
+    "reg_summary['Weights'] = reg.coef_\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the different categories in the 'Brand' variable\n",
+    "data_cleaned['Brand'].unique()\n",
+    "\n",
+    "# In this way we can see which 'Brand' is actually the benchmark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Once we have trained and fine-tuned our model, we can proceed to testing it\n",
+    "# Testing is done on a dataset that the algorithm has never seen\n",
+    "# Luckily we have prepared such a dataset\n",
+    "# Our test inputs are 'x_test', while the outputs: 'y_test' \n",
+    "# We SHOULD NOT TRAIN THE MODEL ON THEM, we just feed them and find the predictions\n",
+    "# If the predictions are far off, we will know that our model overfitted\n",
+    "y_hat_test = reg.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot with the test targets and the test predictions\n",
+    "# You can include the argument 'alpha' which will introduce opacity to the graph\n",
+    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
+    "plt.xlabel('Targets (y_test)',size=18)\n",
+    "plt.ylabel('Predictions (y_hat_test)',size=18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.49</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Prediction\n",
+       "0    10685.50\n",
+       "1     3499.26\n",
+       "2     7553.29\n",
+       "3     7463.96\n",
+       "4    11353.49"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, let's manually check these predictions\n",
+    "# To obtain the actual prices, we take the exponential of the log_price\n",
+    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.26</td>\n",
+       "      <td>7900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.29</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.96</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.49</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>21289.80</td>\n",
+       "      <td>14200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>20159.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20349.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11581.54</td>\n",
+       "      <td>11950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5175.77</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5484.02</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13292.71</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>8248.67</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10621.84</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>23721.58</td>\n",
+       "      <td>3500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>11770.64</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>37600.15</td>\n",
+       "      <td>7500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>16178.14</td>\n",
+       "      <td>6800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11876.82</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>31557.80</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>6102.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>13111.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>23650.15</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>45272.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>2178.94</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>2555.02</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>35991.51</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>26062.23</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>15559.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>12858.08</td>\n",
+       "      <td>14500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>12459.85</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>22941.80</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>19854.79</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>11337.51</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>10499.60</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>26478.15</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>9919.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>16724.45</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>18672.68</td>\n",
+       "      <td>41000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>19845.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>23286.27</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>21611.83</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>8512.03</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>3542.72</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>26425.14</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>77403.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>4973.17</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2479.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>2543.57</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>10716.84</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>20453.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>18575.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>12674.57</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>15380.04</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>21942.05</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>35336.33</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>8195.34</td>\n",
+       "      <td>4300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>12469.21</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>7396.87</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>10848.85</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>5351.86</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>9611.80</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19847.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3316.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>7621.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>6717.17</td>\n",
+       "      <td>3700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>2440.90</td>\n",
+       "      <td>15700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>50206.83</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>15627.03</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>23624.69</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>18577.88</td>\n",
+       "      <td>13708.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>1746.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>5144.02</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>5930.73</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>30810.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>18893.76</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>6819.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>12891.96</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>7320.42</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>6013.05</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4364.08</td>\n",
+       "      <td>4000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>19001.29</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>8071.70</td>\n",
+       "      <td>14500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>2315.71</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>10807.78</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11696.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>9143.17</td>\n",
+       "      <td>6700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>13421.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>7116.68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>18317.20</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>47013.28</td>\n",
+       "      <td>13500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>7976.26</td>\n",
+       "      <td>6150.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>3648.96</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>4900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6437.21</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>32886.86</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>5958.63</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>28625.56</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>4488.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>5521.28</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>8882.65</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>16547.03</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>7131.92</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>7300.05</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>12663.54</td>\n",
+       "      <td>12700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>25614.14</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>3152.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>22835.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>6006.27</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>27716.14</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>6981.34</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15819.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>8990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>14943.75</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>10448.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>50728.89</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>41075.73</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>19335.34</td>\n",
+       "      <td>22700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>5643.45</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>15691.29</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>13733.33</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>13459.92</td>\n",
+       "      <td>87777.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>12311.47</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>7727.53</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>11580.54</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>17489.92</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>23967.81</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>11200.20</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>9279.28</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>12096.18</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>44407.14</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>12006.66</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>66032.76</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>11095.57</td>\n",
+       "      <td>17300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10358.58</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>1815.28</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>6839.57</td>\n",
+       "      <td>31600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>8768.29</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>11969.66</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>4307.68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>8298.32</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>7772.57</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>25040.42</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>8351.34</td>\n",
+       "      <td>6100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>9800.43</td>\n",
+       "      <td>1400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>10889.39</td>\n",
+       "      <td>8800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>44680.77</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>3666.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>11443.85</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>3086.38</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>9192.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>7491.01</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>15623.39</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>12904.68</td>\n",
+       "      <td>8000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>32954.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>13967.55</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>9954.42</td>\n",
+       "      <td>1900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>23972.56</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>18403.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>9596.94</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>15292.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>13212.52</td>\n",
+       "      <td>38888.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>11420.95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>8522.39</td>\n",
+       "      <td>17650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>19741.65</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8392.42</td>\n",
+       "      <td>18500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>11156.38</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>36516.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7745.02</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>6800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>6732.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>8916.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>17316.59</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>14933.54</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>19701.70</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>17597.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>10123.04</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>47014.52</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>29479.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>6130.96</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>19071.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>28256.79</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>10159.18</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>10786.38</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>13377.99</td>\n",
+       "      <td>19999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>15956.95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>18285.07</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>14832.32</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>9310.69</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>20588.57</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>5948.04</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>2126.49</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>17983.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>2784.46</td>\n",
+       "      <td>4999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>11098.18</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>10094.58</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>5040.46</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>65613.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>7664.32</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>13041.39</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>19920.63</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>15010.51</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>5716.14</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>31216.83</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6848.88</td>\n",
+       "      <td>33200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>16133.86</td>\n",
+       "      <td>10200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>15872.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>2393.34</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>5284.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>12974.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>8661.95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>11490.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>34376.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>13348.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>20662.46</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>6199.59</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>3939.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>6916.13</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>11191.05</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>17251.60</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>15682.82</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>7719.69</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>9198.08</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>9312.21</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>1320.56</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>6110.94</td>\n",
+       "      <td>16900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>12731.26</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>2765.14</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>9024.05</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>25037.87</td>\n",
+       "      <td>18800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>26736.21</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>10581.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>10419.20</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>18432.63</td>\n",
+       "      <td>34900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>7597.39</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>32925.60</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>7110.32</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>15457.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>41711.18</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>13159.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>22011.47</td>\n",
+       "      <td>44999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>7391.67</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>5041.20</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>6978.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>9721.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>18128.32</td>\n",
+       "      <td>61000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>6413.26</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>24796.09</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>1825.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>41503.90</td>\n",
+       "      <td>18000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>13680.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>6067.79</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>6858.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>12414.41</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>9089.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>31059.18</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>20163.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>18800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>40753.58</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>6893.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>4151.02</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>8263.95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>3948.74</td>\n",
+       "      <td>13900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>9749.53</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>12318.39</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>9113.05</td>\n",
+       "      <td>3600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>22547.55</td>\n",
+       "      <td>7500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>5002.39</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>12900.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>3154.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>12327.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>11638.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>12261.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>7630.64</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>40765.33</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>34761.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>19659.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>1972.28</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>6635.73</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>1894.40</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>20433.23</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>8962.29</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>8431.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>13471.84</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>5350.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>21690.99</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>22261.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>17937.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>20183.30</td>\n",
+       "      <td>2999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>20236.85</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>9782.47</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>4738.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>23496.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>6546.81</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>10505.98</td>\n",
+       "      <td>13999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>3164.66</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>24974.57</td>\n",
+       "      <td>11100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>8967.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>6510.80</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>9205.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>51287.19</td>\n",
+       "      <td>20500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>14792.29</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>10014.71</td>\n",
+       "      <td>11600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>5811.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>65887.51</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>12247.90</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>39744.80</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>10612.51</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>10350.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>23630.85</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>65210.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>8316.92</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>9057.77</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>9356.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>10716.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>15067.85</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>7327.18</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>11336.71</td>\n",
+       "      <td>10800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>11002.01</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>14591.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>12452.22</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>12414.72</td>\n",
+       "      <td>19600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>13004.34</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>8250.47</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>5374.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>11099.09</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>14815.83</td>\n",
+       "      <td>10200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>12897.92</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>14572.70</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>60603.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>11937.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>28951.20</td>\n",
+       "      <td>11700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>32945.80</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>26441.03</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>2020.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>20246.04</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>5238.05</td>\n",
+       "      <td>11500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>9753.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>16717.08</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>22454.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>10133.68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>11018.79</td>\n",
+       "      <td>29900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>63921.33</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>4770.52</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>8688.92</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>27293.13</td>\n",
+       "      <td>13893.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>44193.38</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>21681.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>10547.82</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>13420.15</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>42703.68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>8415.81</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>3084.69</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>10835.95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>9661.78</td>\n",
+       "      <td>12000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>8110.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>10821.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>24427.14</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>5391.77</td>\n",
+       "      <td>4300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>9909.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>2749.08</td>\n",
+       "      <td>9600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>17654.84</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>3473.79</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>33499.44</td>\n",
+       "      <td>4999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>7918.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>13757.58</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>4296.77</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>13235.55</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>7267.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>44173.72</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>11474.22</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2810.71</td>\n",
+       "      <td>22400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>24271.90</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>25302.43</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>8139.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>13600.39</td>\n",
+       "      <td>36900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>9076.42</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>21303.57</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>16165.79</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>7973.87</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8707.13</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>14410.15</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>25986.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>9070.23</td>\n",
+       "      <td>12300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>8850.21</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>2839.03</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>8648.80</td>\n",
+       "      <td>6999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>6425.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>24874.86</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>21097.66</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>65448.37</td>\n",
+       "      <td>2450.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>8121.22</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>16407.29</td>\n",
+       "      <td>4900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>13409.47</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>7609.28</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>12234.68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>14363.76</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8117.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>22367.52</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11378.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>14576.33</td>\n",
+       "      <td>1850.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>8033.61</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>53294.61</td>\n",
+       "      <td>3149.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>74232.55</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>50074.56</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>3940.49</td>\n",
+       "      <td>18988.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>13542.79</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>5462.52</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>2389.14</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>67952.07</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>14197.26</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>16253.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>2975.40</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>6977.90</td>\n",
+       "      <td>6900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>8843.22</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>40893.84</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>18866.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>16257.03</td>\n",
+       "      <td>9900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>14926.69</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>8638.63</td>\n",
+       "      <td>8200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>11619.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>3735.04</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>38334.67</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>9363.27</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>13502.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>9495.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>14218.94</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>35956.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>39882.67</td>\n",
+       "      <td>21900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>8381.60</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>20972.54</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>10946.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>19624.73</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>16821.54</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>14136.01</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>2779.04</td>\n",
+       "      <td>18800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>50954.68</td>\n",
+       "      <td>7650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>31656.01</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>43236.92</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>35093.27</td>\n",
+       "      <td>28500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>4266.27</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>6537.82</td>\n",
+       "      <td>16999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25975.12</td>\n",
+       "      <td>34500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>22262.80</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12558.90</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>65825.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>7045.72</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>20367.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>1792.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>10932.17</td>\n",
+       "      <td>3400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>5570.46</td>\n",
+       "      <td>27800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>15700.53</td>\n",
+       "      <td>2300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15374.08</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36764.38</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>36883.77</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>31369.37</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>6599.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>12027.56</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>28937.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>13334.19</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>9664.46</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>13884.26</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>10824.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>4043.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>28910.96</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>8902.52</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>6503.62</td>\n",
+       "      <td>25749.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>7161.67</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>5484.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>33392.40</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>3747.21</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>17540.72</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>50099.92</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>7950.27</td>\n",
+       "      <td>12300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>12270.01</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>8973.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>60493.51</td>\n",
+       "      <td>18999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>9984.87</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>6392.70</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>23272.83</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>10105.70</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>3590.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>18282.75</td>\n",
+       "      <td>8420.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>16197.42</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>5839.08</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1873.03</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>17282.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>15144.28</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>5346.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>15015.22</td>\n",
+       "      <td>10800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>32121.20</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>11858.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>9424.34</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>21297.65</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>8667.12</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>16773.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>27740.81</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>17011.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>10809.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>5863.41</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>2373.66</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>29049.27</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>9829.53</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>18555.09</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>17714.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>47047.70</td>\n",
+       "      <td>14600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>10019.90</td>\n",
+       "      <td>34900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>24642.50</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>16939.86</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>34678.95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>6746.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>9139.04</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>6781.86</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>4666.05</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>9369.66</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>5947.51</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>9489.56</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>12354.92</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>6826.78</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>3100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>9175.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>46465.02</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>3816.33</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>14915.98</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>11555.80</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>5983.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>16392.22</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>8340.94</td>\n",
+       "      <td>13500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>6610.04</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>18231.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>12523.73</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11377.84</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>33172.20</td>\n",
+       "      <td>10000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>6429.03</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9593.52</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>2285.24</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>25682.58</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>7324.63</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>11881.05</td>\n",
+       "      <td>51500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>7279.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7940.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>1926.60</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>23163.87</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>26331.41</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>10597.72</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>33707.27</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>12583.52</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>1858.34</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>15954.72</td>\n",
+       "      <td>11900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>14486.38</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>3205.38</td>\n",
+       "      <td>2200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>3411.53</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>6205.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>3246.94</td>\n",
+       "      <td>8650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>3425.23</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>11432.63</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>39074.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>9348.90</td>\n",
+       "      <td>19500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>15259.81</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>12255.45</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>3173.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>14512.61</td>\n",
+       "      <td>4799.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>10115.13</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>28154.84</td>\n",
+       "      <td>14900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>17908.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>3001.37</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>25576.58</td>\n",
+       "      <td>2700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>25685.67</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>7797.18</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>8767.54</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>4590.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>21816.43</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>35371.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>16427.12</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>14513.46</td>\n",
+       "      <td>5000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>31373.47</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>8940.39</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>20680.40</td>\n",
+       "      <td>20500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>29127.72</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>3040.77</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>9269.68</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>27225.34</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>2953.17</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>13853.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>19298.61</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>7869.47</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>13500.69</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>2573.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>31251.89</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>16095.32</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>25752.19</td>\n",
+       "      <td>5100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>38473.71</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>4472.47</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>23227.15</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>16924.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>23485.45</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>18621.12</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>13074.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>2539.57</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>7319.77</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>8734.58</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>7256.86</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>15383.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>11056.52</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>18165.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>3818.71</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>35662.26</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>30628.28</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>14402.34</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>17334.11</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>8721.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>8156.11</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>10740.09</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>3450.69</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2625.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>21174.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>11147.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>8236.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3026.25</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>17195.57</td>\n",
+       "      <td>3200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>10332.54</td>\n",
+       "      <td>19400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>4032.10</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>7814.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>35039.40</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>32481.05</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>10563.39</td>\n",
+       "      <td>109999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>3621.08</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>7809.13</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>10303.21</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>9232.79</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>2613.97</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>17858.02</td>\n",
+       "      <td>10700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>65723.62</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>49221.63</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>8270.38</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
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+       "      <td>11477.47</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
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+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
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+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
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+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
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+       "      <td>22213.53</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
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+       "      <td>2381.01</td>\n",
+       "      <td>24000.00</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
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+       "      <td>21493.44</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
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+       "      <td>1900.00</td>\n",
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+       "      <td>8999.00</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>11178.46</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>2470.61</td>\n",
+       "      <td>15500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>10031.96</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>30480.85</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>4255.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>25640.67</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>7996.00</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>7011.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>5597.06</td>\n",
+       "      <td>7500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>6891.99</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>12976.15</td>\n",
+       "      <td>18700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>61639.73</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>2725.40</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>11967.39</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>34446.96</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>20859.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>19676.51</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>8530.54</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>4004.65</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>23118.07</td>\n",
+       "      <td>19700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>10220.74</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>11425.01</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>4172.84</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>8041.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>11391.95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>2860.97</td>\n",
+       "      <td>9200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>11431.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>30409.09</td>\n",
+       "      <td>16950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>6009.63</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>44312.98</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>15742.41</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>45664.45</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>10810.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>5792.10</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>9817.06</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>10696.56</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>16027.02</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>19309.90</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>11601.33</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>23336.06</td>\n",
+       "      <td>13999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>10337.11</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>37704.71</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>20216.67</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>6658.73</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>19855.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>16960.31</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>5268.17</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>2379.58</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>6421.18</td>\n",
+       "      <td>7777.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>13355.11</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>8453.28</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>48699.98</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>6082.85</td>\n",
+       "      <td>4100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>10381.62</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>8493.04</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>8591.66</td>\n",
+       "      <td>13999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>6358.55</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>17028.45</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>15885.66</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>3752.54</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>12028.91</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>9380.46</td>\n",
+       "      <td>16999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>10125.27</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>13443.32</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>9097.13</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>4700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>12383.35</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>14049.76</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>11034.66</td>\n",
+       "      <td>3750.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>18982.15</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>24323.48</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.73</td>\n",
+       "      <td>6950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.07</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.45</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.75</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.16</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction    Target\n",
+       "0      10685.50       NaN\n",
+       "1       3499.26   7900.00\n",
+       "2       7553.29       NaN\n",
+       "3       7463.96       NaN\n",
+       "4      11353.49       NaN\n",
+       "5      21289.80  14200.00\n",
+       "6      20159.19       NaN\n",
+       "7      20349.62       NaN\n",
+       "8      11581.54  11950.00\n",
+       "9      33614.62       NaN\n",
+       "10      7241.07       NaN\n",
+       "11      5175.77  10500.00\n",
+       "12      5484.02       NaN\n",
+       "13     13292.71       NaN\n",
+       "14      8248.67       NaN\n",
+       "15     10621.84       NaN\n",
+       "16     23721.58   3500.00\n",
+       "17     11770.64       NaN\n",
+       "18     37600.15   7500.00\n",
+       "19     16178.14   6800.00\n",
+       "20     11876.82       NaN\n",
+       "21     31557.80       NaN\n",
+       "22      6102.36       NaN\n",
+       "23     13111.91       NaN\n",
+       "24     23650.15       NaN\n",
+       "25     45272.25       NaN\n",
+       "26      2178.94       NaN\n",
+       "27      2555.02       NaN\n",
+       "28     35991.51       NaN\n",
+       "29     26062.23       NaN\n",
+       "30     15559.43       NaN\n",
+       "31     12858.08  14500.00\n",
+       "32     12459.85       NaN\n",
+       "33     22941.80       NaN\n",
+       "34     19854.79       NaN\n",
+       "35     11337.51       NaN\n",
+       "36     10499.60       NaN\n",
+       "37     26478.15       NaN\n",
+       "38      9919.19       NaN\n",
+       "39     16724.45       NaN\n",
+       "40     18672.68  41000.00\n",
+       "41     19845.36       NaN\n",
+       "42     23286.27       NaN\n",
+       "43     21611.83   6500.00\n",
+       "44      7241.07       NaN\n",
+       "45      8512.03       NaN\n",
+       "46      3542.72       NaN\n",
+       "47     26425.14       NaN\n",
+       "48     77403.06       NaN\n",
+       "49      4973.17       NaN\n",
+       "50      2479.43       NaN\n",
+       "51      2543.57   8900.00\n",
+       "52     10716.84       NaN\n",
+       "53     20453.89       NaN\n",
+       "54     18575.43       NaN\n",
+       "55     12674.57       NaN\n",
+       "56     15380.04       NaN\n",
+       "57     21942.05       NaN\n",
+       "58     35336.33       NaN\n",
+       "59      8195.34   4300.00\n",
+       "60     12469.21       NaN\n",
+       "61      7396.87       NaN\n",
+       "62     10848.85       NaN\n",
+       "63      5351.86       NaN\n",
+       "64      9611.80       NaN\n",
+       "65     19847.25       NaN\n",
+       "66      3316.44       NaN\n",
+       "67      7621.25       NaN\n",
+       "68      6717.17   3700.00\n",
+       "69      2440.90  15700.00\n",
+       "70     50206.83       NaN\n",
+       "71     15627.03       NaN\n",
+       "72     23624.69       NaN\n",
+       "73     18577.88  13708.50\n",
+       "74      1746.19       NaN\n",
+       "75      4209.17       NaN\n",
+       "76      5144.02       NaN\n",
+       "77      5930.73       NaN\n",
+       "78     30810.25       NaN\n",
+       "79     18893.76       NaN\n",
+       "80      6819.44       NaN\n",
+       "81     12891.96       NaN\n",
+       "82      7320.42       NaN\n",
+       "83      6013.05       NaN\n",
+       "84      4364.08   4000.00\n",
+       "85     19001.29       NaN\n",
+       "86      8071.70  14500.00\n",
+       "87      2315.71       NaN\n",
+       "88     10807.78       NaN\n",
+       "89     11696.25       NaN\n",
+       "90      9143.17   6700.00\n",
+       "91     13421.16       NaN\n",
+       "92      7116.68       NaN\n",
+       "93     18317.20       NaN\n",
+       "94      7724.17       NaN\n",
+       "95     47013.28  13500.00\n",
+       "96      7976.26   6150.00\n",
+       "97      3648.96       NaN\n",
+       "98      6746.48   4900.00\n",
+       "99      6437.21       NaN\n",
+       "100    32886.86       NaN\n",
+       "101     5958.63       NaN\n",
+       "102    28625.56       NaN\n",
+       "103     4488.43       NaN\n",
+       "104     5521.28       NaN\n",
+       "105     8882.65       NaN\n",
+       "106    16547.03       NaN\n",
+       "107     7131.92       NaN\n",
+       "108     7300.05       NaN\n",
+       "109    12663.54  12700.00\n",
+       "110    25614.14       NaN\n",
+       "111     3152.62       NaN\n",
+       "112    22835.48       NaN\n",
+       "113     6006.27       NaN\n",
+       "114    27716.14       NaN\n",
+       "115     6981.34       NaN\n",
+       "116    15819.50       NaN\n",
+       "117     7178.20   8990.00\n",
+       "118    14943.75       NaN\n",
+       "119    10448.50       NaN\n",
+       "120    50728.89   8900.00\n",
+       "121    41075.73       NaN\n",
+       "122    19335.34  22700.00\n",
+       "123     5643.45       NaN\n",
+       "124    15691.29       NaN\n",
+       "125    13733.33       NaN\n",
+       "126    13459.92  87777.00\n",
+       "127    12311.47       NaN\n",
+       "128     7727.53   9800.00\n",
+       "129    11580.54       NaN\n",
+       "130    17489.92       NaN\n",
+       "131    23967.81       NaN\n",
+       "132    11200.20       NaN\n",
+       "133     9279.28       NaN\n",
+       "134    12096.18       NaN\n",
+       "135    44407.14       NaN\n",
+       "136    12006.66       NaN\n",
+       "137    66032.76       NaN\n",
+       "138    11095.57  17300.00\n",
+       "139     7712.65       NaN\n",
+       "140    10358.58       NaN\n",
+       "141     1815.28       NaN\n",
+       "142     6839.57  31600.00\n",
+       "143     8768.29       NaN\n",
+       "144    11969.66       NaN\n",
+       "145     4307.68       NaN\n",
+       "146     8298.32       NaN\n",
+       "147     7772.57       NaN\n",
+       "148    25040.42   8900.00\n",
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+       "641    17334.11       NaN\n",
+       "642     8721.97       NaN\n",
+       "643     8156.11       NaN\n",
+       "644    53312.81       NaN\n",
+       "645    10740.09       NaN\n",
+       "646     3450.69       NaN\n",
+       "647     2625.10       NaN\n",
+       "648    21174.10       NaN\n",
+       "649    11147.91       NaN\n",
+       "650     8236.36       NaN\n",
+       "651     3026.25       NaN\n",
+       "652    17195.57   3200.00\n",
+       "653    10332.54  19400.00\n",
+       "654     4032.10       NaN\n",
+       "655     7814.16       NaN\n",
+       "656    35039.40       NaN\n",
+       "657    32481.05       NaN\n",
+       "658    10563.39 109999.00\n",
+       "659     3621.08       NaN\n",
+       "660     7809.13       NaN\n",
+       "661    10303.21       NaN\n",
+       "662     9232.79       NaN\n",
+       "663     2613.97       NaN\n",
+       "664     4590.49       NaN\n",
+       "665    17858.02  10700.00\n",
+       "666    65723.62       NaN\n",
+       "667    49221.63       NaN\n",
+       "668     8270.38       NaN\n",
+       "669    11477.47       NaN\n",
+       "670    33614.62       NaN\n",
+       "671     2886.35       NaN\n",
+       "672     5679.39       NaN\n",
+       "673    22213.53       NaN\n",
+       "674     2381.01  24000.00\n",
+       "675    13476.90       NaN\n",
+       "676     6861.84       NaN\n",
+       "677     9631.08       NaN\n",
+       "678     4569.15       NaN\n",
+       "679    25864.65       NaN\n",
+       "680    21493.44       NaN\n",
+       "681     5195.69   1900.00\n",
+       "682     5454.34   8999.00\n",
+       "683    32771.45       NaN\n",
+       "684    18942.34       NaN\n",
+       "685    12593.00       NaN\n",
+       "686    23576.99       NaN\n",
+       "687    11004.05   8999.00\n",
+       "688    24572.28       NaN\n",
+       "689     4524.77       NaN\n",
+       "690    11706.93       NaN\n",
+       "691     8467.17       NaN\n",
+       "692     8471.55       NaN\n",
+       "693    12354.16       NaN\n",
+       "694     8131.92       NaN\n",
+       "695    11178.46       NaN\n",
+       "696     2470.61  15500.00\n",
+       "697    10031.96       NaN\n",
+       "698    30480.85       NaN\n",
+       "699     4255.62       NaN\n",
+       "700    25640.67       NaN\n",
+       "701     7996.00       NaN\n",
+       "702     7011.74       NaN\n",
+       "703     5597.06   7500.00\n",
+       "704     6891.99       NaN\n",
+       "705    12976.15  18700.00\n",
+       "706    61639.73       NaN\n",
+       "707     2725.40       NaN\n",
+       "708    11967.39       NaN\n",
+       "709    34446.96       NaN\n",
+       "710     3795.06  20859.15\n",
+       "711    19676.51       NaN\n",
+       "712     8530.54       NaN\n",
+       "713     4004.65       NaN\n",
+       "714    23118.07  19700.00\n",
+       "715    10220.74       NaN\n",
+       "716    11425.01       NaN\n",
+       "717     4172.84       NaN\n",
+       "718     8041.62       NaN\n",
+       "719    11391.95       NaN\n",
+       "720     2860.97   9200.00\n",
+       "721    11431.35       NaN\n",
+       "722    30409.09  16950.00\n",
+       "723     6009.63       NaN\n",
+       "724    44312.98       NaN\n",
+       "725    15742.41       NaN\n",
+       "726    45664.45       NaN\n",
+       "727    10810.62       NaN\n",
+       "728     5792.10   9800.00\n",
+       "729     9817.06       NaN\n",
+       "730    10696.56       NaN\n",
+       "731    16027.02       NaN\n",
+       "732    19309.90       NaN\n",
+       "733    11601.33       NaN\n",
+       "734    23336.06  13999.00\n",
+       "735    10337.11       NaN\n",
+       "736    53312.81   8500.00\n",
+       "737    37704.71       NaN\n",
+       "738    20216.67       NaN\n",
+       "739    22213.53       NaN\n",
+       "740     6658.73       NaN\n",
+       "741    19855.31       NaN\n",
+       "742    16960.31       NaN\n",
+       "743     5268.17       NaN\n",
+       "744     2379.58       NaN\n",
+       "745     6421.18   7777.00\n",
+       "746    13355.11       NaN\n",
+       "747     8453.28  10500.00\n",
+       "748    48699.98       NaN\n",
+       "749     6082.85   4100.00\n",
+       "750    10381.62       NaN\n",
+       "751     8493.04       NaN\n",
+       "752     8591.66  13999.00\n",
+       "753     6358.55       NaN\n",
+       "754    17028.45       NaN\n",
+       "755    15885.66       NaN\n",
+       "756     3752.54       NaN\n",
+       "757    12028.91       NaN\n",
+       "758     9380.46  16999.00\n",
+       "759    10125.27       NaN\n",
+       "760    13443.32       NaN\n",
+       "761     9097.13       NaN\n",
+       "762    12201.29   4700.00\n",
+       "763    12383.35       NaN\n",
+       "764    14049.76       NaN\n",
+       "765    11034.66   3750.00\n",
+       "766    18982.15       NaN\n",
+       "767    24323.48       NaN\n",
+       "768    38260.36       NaN\n",
+       "769    29651.73   6950.00\n",
+       "770    10732.07       NaN\n",
+       "771    13922.45       NaN\n",
+       "772    27487.75       NaN\n",
+       "773    13491.16       NaN"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can also include the test targets in that data frame (so we can manually compare them)\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf\n",
+    "\n",
+    "# Note that we have a lot of missing values\n",
+    "# There is no reason to have ANY missing values, though\n",
+    "# This suggests that something is wrong with the data frame / indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   7.74\n",
+       "1   7.94\n",
+       "2   7.82\n",
+       "3   8.76\n",
+       "4   9.12\n",
+       "Name: log_price, dtype: float64"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# After displaying y_test, we find what the issue is\n",
+    "# The old indexes are preserved (recall earlier in that code we made a note on that)\n",
+    "# The code was: data_cleaned = data_4.reset_index(drop=True)\n",
+    "\n",
+    "# Therefore, to get a proper result, we must reset the index and drop the old indexing\n",
+    "y_test = y_test.reset_index(drop=True)\n",
+    "\n",
+    "# Check the result\n",
+    "y_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.50</td>\n",
+       "      <td>2300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.26</td>\n",
+       "      <td>2800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.29</td>\n",
+       "      <td>2500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.96</td>\n",
+       "      <td>6400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.49</td>\n",
+       "      <td>9150.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>21289.80</td>\n",
+       "      <td>20000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>20159.19</td>\n",
+       "      <td>38888.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20349.62</td>\n",
+       "      <td>16999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11581.54</td>\n",
+       "      <td>12500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>12800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5175.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5484.02</td>\n",
+       "      <td>7900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13292.71</td>\n",
+       "      <td>16999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>8248.67</td>\n",
+       "      <td>9200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10621.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>23721.58</td>\n",
+       "      <td>20500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>11770.64</td>\n",
+       "      <td>9700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>37600.15</td>\n",
+       "      <td>39900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>16178.14</td>\n",
+       "      <td>16400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11876.82</td>\n",
+       "      <td>15200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>31557.80</td>\n",
+       "      <td>24500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>6102.36</td>\n",
+       "      <td>5650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>13111.91</td>\n",
+       "      <td>12900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>23650.15</td>\n",
+       "      <td>20900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>45272.25</td>\n",
+       "      <td>31990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>2178.94</td>\n",
+       "      <td>3600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>2555.02</td>\n",
+       "      <td>11600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>35991.51</td>\n",
+       "      <td>43999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>26062.23</td>\n",
+       "      <td>42500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>15559.43</td>\n",
+       "      <td>7900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>12858.08</td>\n",
+       "      <td>12650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>12459.85</td>\n",
+       "      <td>19900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>22941.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>19854.79</td>\n",
+       "      <td>14000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>11337.51</td>\n",
+       "      <td>20900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>10499.60</td>\n",
+       "      <td>12300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>26478.15</td>\n",
+       "      <td>20800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>9919.19</td>\n",
+       "      <td>14600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>16724.45</td>\n",
+       "      <td>29600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>18672.68</td>\n",
+       "      <td>18000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>19845.36</td>\n",
+       "      <td>16300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>23286.27</td>\n",
+       "      <td>38500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>21611.83</td>\n",
+       "      <td>22400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>14000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>8512.03</td>\n",
+       "      <td>7000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>3542.72</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>26425.14</td>\n",
+       "      <td>25999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>77403.06</td>\n",
+       "      <td>55000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>4973.17</td>\n",
+       "      <td>4900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2479.43</td>\n",
+       "      <td>2200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>2543.57</td>\n",
+       "      <td>3149.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>10716.84</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>20453.89</td>\n",
+       "      <td>21000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>18575.43</td>\n",
+       "      <td>20900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>12674.57</td>\n",
+       "      <td>10700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>15380.04</td>\n",
+       "      <td>8800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>21942.05</td>\n",
+       "      <td>25500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>35336.33</td>\n",
+       "      <td>49500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>8195.34</td>\n",
+       "      <td>7600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>12469.21</td>\n",
+       "      <td>12500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>7396.87</td>\n",
+       "      <td>7300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>10848.85</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>5351.86</td>\n",
+       "      <td>3300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>9611.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19847.25</td>\n",
+       "      <td>18200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3316.44</td>\n",
+       "      <td>2300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>7621.25</td>\n",
+       "      <td>4100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>6717.17</td>\n",
+       "      <td>6100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>2440.90</td>\n",
+       "      <td>1400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>50206.83</td>\n",
+       "      <td>73500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>15627.03</td>\n",
+       "      <td>14500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>23624.69</td>\n",
+       "      <td>22500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>18577.88</td>\n",
+       "      <td>30500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>1746.19</td>\n",
+       "      <td>2650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>5144.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>5930.73</td>\n",
+       "      <td>5700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>30810.25</td>\n",
+       "      <td>29900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>18893.76</td>\n",
+       "      <td>15700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>6819.44</td>\n",
+       "      <td>8800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>12891.96</td>\n",
+       "      <td>5300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>7320.42</td>\n",
+       "      <td>2600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>6013.05</td>\n",
+       "      <td>4800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4364.08</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>19001.29</td>\n",
+       "      <td>17800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>8071.70</td>\n",
+       "      <td>11100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>2315.71</td>\n",
+       "      <td>2400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>10807.78</td>\n",
+       "      <td>7500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11696.25</td>\n",
+       "      <td>12900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>9143.17</td>\n",
+       "      <td>6800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>13421.16</td>\n",
+       "      <td>13200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>7116.68</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>18317.20</td>\n",
+       "      <td>10974.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>7800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>47013.28</td>\n",
+       "      <td>55000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>7976.26</td>\n",
+       "      <td>7700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>3648.96</td>\n",
+       "      <td>2700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6437.21</td>\n",
+       "      <td>8999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>32886.86</td>\n",
+       "      <td>39500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>5958.63</td>\n",
+       "      <td>6150.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>28625.56</td>\n",
+       "      <td>28900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>4488.43</td>\n",
+       "      <td>4150.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>5521.28</td>\n",
+       "      <td>8800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>8882.65</td>\n",
+       "      <td>10000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>16547.03</td>\n",
+       "      <td>14200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>7131.92</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>7300.05</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>12663.54</td>\n",
+       "      <td>12200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>25614.14</td>\n",
+       "      <td>25500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>3152.62</td>\n",
+       "      <td>4999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>22835.48</td>\n",
+       "      <td>16950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>6006.27</td>\n",
+       "      <td>6999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>27716.14</td>\n",
+       "      <td>27300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>6981.34</td>\n",
+       "      <td>8800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15819.50</td>\n",
+       "      <td>8300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>11100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>14943.75</td>\n",
+       "      <td>17300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>10448.50</td>\n",
+       "      <td>8999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>50728.89</td>\n",
+       "      <td>65000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>41075.73</td>\n",
+       "      <td>27200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>19335.34</td>\n",
+       "      <td>55555.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>5643.45</td>\n",
+       "      <td>6300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>15691.29</td>\n",
+       "      <td>22900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>13733.33</td>\n",
+       "      <td>16800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>13459.92</td>\n",
+       "      <td>12300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>12311.47</td>\n",
+       "      <td>11700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>7727.53</td>\n",
+       "      <td>11200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>11580.54</td>\n",
+       "      <td>9000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>17489.92</td>\n",
+       "      <td>18000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>23967.81</td>\n",
+       "      <td>27500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>11200.20</td>\n",
+       "      <td>9999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>9279.28</td>\n",
+       "      <td>9600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>12096.18</td>\n",
+       "      <td>11500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>44407.14</td>\n",
+       "      <td>59999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>12006.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>66032.76</td>\n",
+       "      <td>107999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>11095.57</td>\n",
+       "      <td>8999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>11500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10358.58</td>\n",
+       "      <td>12000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>1815.28</td>\n",
+       "      <td>3100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>6839.57</td>\n",
+       "      <td>7650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>8768.29</td>\n",
+       "      <td>7700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>11969.66</td>\n",
+       "      <td>12900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>4307.68</td>\n",
+       "      <td>3400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>8298.32</td>\n",
+       "      <td>11900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>7772.57</td>\n",
+       "      <td>7200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>25040.42</td>\n",
+       "      <td>37000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>8351.34</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>9800.43</td>\n",
+       "      <td>9500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>10889.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>44680.77</td>\n",
+       "      <td>51900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>3666.31</td>\n",
+       "      <td>3200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>11443.85</td>\n",
+       "      <td>9499.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>3086.38</td>\n",
+       "      <td>2499.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>9192.06</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>7491.01</td>\n",
+       "      <td>8600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>15623.39</td>\n",
+       "      <td>20999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>12904.68</td>\n",
+       "      <td>9500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>32954.19</td>\n",
+       "      <td>52777.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>13967.55</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>9954.42</td>\n",
+       "      <td>1800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>23972.56</td>\n",
+       "      <td>36900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>18403.35</td>\n",
+       "      <td>13893.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>9596.94</td>\n",
+       "      <td>9400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>15292.10</td>\n",
+       "      <td>8300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>13212.52</td>\n",
+       "      <td>23999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>11420.95</td>\n",
+       "      <td>11999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>8522.39</td>\n",
+       "      <td>5800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>19741.65</td>\n",
+       "      <td>29999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8392.42</td>\n",
+       "      <td>7150.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>11156.38</td>\n",
+       "      <td>10800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>36516.35</td>\n",
+       "      <td>37900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7745.02</td>\n",
+       "      <td>6400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>47600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>6732.97</td>\n",
+       "      <td>8400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>8916.19</td>\n",
+       "      <td>11999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>17316.59</td>\n",
+       "      <td>13900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>14933.54</td>\n",
+       "      <td>29500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>19701.70</td>\n",
+       "      <td>49999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>17597.10</td>\n",
+       "      <td>25500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>10123.04</td>\n",
+       "      <td>2800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>47014.52</td>\n",
+       "      <td>95000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>29479.97</td>\n",
+       "      <td>34000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>6130.96</td>\n",
+       "      <td>6700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>19071.43</td>\n",
+       "      <td>22900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>28256.79</td>\n",
+       "      <td>34900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>10159.18</td>\n",
+       "      <td>10900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>10786.38</td>\n",
+       "      <td>8000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>13377.99</td>\n",
+       "      <td>10300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>15956.95</td>\n",
+       "      <td>29999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>18285.07</td>\n",
+       "      <td>25900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>14832.32</td>\n",
+       "      <td>12400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>9310.69</td>\n",
+       "      <td>8400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>20588.57</td>\n",
+       "      <td>21900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>5948.04</td>\n",
+       "      <td>2600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>2126.49</td>\n",
+       "      <td>2550.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>17983.10</td>\n",
+       "      <td>19600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>2784.46</td>\n",
+       "      <td>1950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>11098.18</td>\n",
+       "      <td>10200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>10094.58</td>\n",
+       "      <td>7777.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>5040.46</td>\n",
+       "      <td>7000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>65613.31</td>\n",
+       "      <td>99999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>7664.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>13041.39</td>\n",
+       "      <td>19700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>19920.63</td>\n",
+       "      <td>16000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>15010.51</td>\n",
+       "      <td>13200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>5716.14</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>6600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>31216.83</td>\n",
+       "      <td>55000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6848.88</td>\n",
+       "      <td>8200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>16133.86</td>\n",
+       "      <td>15700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>15872.91</td>\n",
+       "      <td>14300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>2393.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>5284.06</td>\n",
+       "      <td>3300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>12974.89</td>\n",
+       "      <td>18900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>8661.95</td>\n",
+       "      <td>13999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>11490.44</td>\n",
+       "      <td>14900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>34376.16</td>\n",
+       "      <td>85000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>13348.91</td>\n",
+       "      <td>15999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>109999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>20662.46</td>\n",
+       "      <td>18900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>6199.59</td>\n",
+       "      <td>13200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>3939.91</td>\n",
+       "      <td>15500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>6916.13</td>\n",
+       "      <td>3900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>11191.05</td>\n",
+       "      <td>9600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>17251.60</td>\n",
+       "      <td>14300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>15682.82</td>\n",
+       "      <td>9600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>7719.69</td>\n",
+       "      <td>9300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>9198.08</td>\n",
+       "      <td>15800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>9312.21</td>\n",
+       "      <td>7300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>1320.56</td>\n",
+       "      <td>3700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>6110.94</td>\n",
+       "      <td>8700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>12731.26</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>2765.14</td>\n",
+       "      <td>2900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>9024.05</td>\n",
+       "      <td>4900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>25037.87</td>\n",
+       "      <td>21200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>26736.21</td>\n",
+       "      <td>16100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>10581.62</td>\n",
+       "      <td>10999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>10419.20</td>\n",
+       "      <td>13000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>18432.63</td>\n",
+       "      <td>30500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>7597.39</td>\n",
+       "      <td>7500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>32925.60</td>\n",
+       "      <td>69500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>7110.32</td>\n",
+       "      <td>8990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>15457.91</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>41711.18</td>\n",
+       "      <td>30500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>13159.16</td>\n",
+       "      <td>16200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>22011.47</td>\n",
+       "      <td>13999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>7391.67</td>\n",
+       "      <td>15200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>5041.20</td>\n",
+       "      <td>3800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>6978.91</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>9721.50</td>\n",
+       "      <td>9900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>18128.32</td>\n",
+       "      <td>15900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>6413.26</td>\n",
+       "      <td>5999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>24796.09</td>\n",
+       "      <td>46000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>1825.44</td>\n",
+       "      <td>1900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>41503.90</td>\n",
+       "      <td>37500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>13680.48</td>\n",
+       "      <td>11900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>6067.79</td>\n",
+       "      <td>6200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>6858.89</td>\n",
+       "      <td>9999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>48535.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>12414.41</td>\n",
+       "      <td>8420.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>9089.35</td>\n",
+       "      <td>7700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>31059.18</td>\n",
+       "      <td>42000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>20163.48</td>\n",
+       "      <td>30500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>109999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>40753.58</td>\n",
+       "      <td>40000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>6893.62</td>\n",
+       "      <td>6400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>4151.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>8263.95</td>\n",
+       "      <td>7700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>3948.74</td>\n",
+       "      <td>2200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>9749.53</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>12318.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>9113.05</td>\n",
+       "      <td>6900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>22547.55</td>\n",
+       "      <td>18500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>5002.39</td>\n",
+       "      <td>2999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>12900.31</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>60500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>3154.48</td>\n",
+       "      <td>4300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>12327.10</td>\n",
+       "      <td>12499.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>11638.89</td>\n",
+       "      <td>13708.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>12261.19</td>\n",
+       "      <td>4100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>7630.64</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>40765.33</td>\n",
+       "      <td>112000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>34761.74</td>\n",
+       "      <td>24000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>19659.97</td>\n",
+       "      <td>16700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>1972.28</td>\n",
+       "      <td>2300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>6635.73</td>\n",
+       "      <td>4300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>10950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>1894.40</td>\n",
+       "      <td>1950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>20433.23</td>\n",
+       "      <td>30999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>8962.29</td>\n",
+       "      <td>7500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>8431.89</td>\n",
+       "      <td>7999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>13471.84</td>\n",
+       "      <td>10600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>5350.91</td>\n",
+       "      <td>6900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>21690.99</td>\n",
+       "      <td>30900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>22261.50</td>\n",
+       "      <td>19200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>17937.36</td>\n",
+       "      <td>4800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>20183.30</td>\n",
+       "      <td>18999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>20236.85</td>\n",
+       "      <td>31600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>9782.47</td>\n",
+       "      <td>10200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>4738.89</td>\n",
+       "      <td>7000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>23496.36</td>\n",
+       "      <td>33000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>6546.81</td>\n",
+       "      <td>4400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>10505.98</td>\n",
+       "      <td>13900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>3164.66</td>\n",
+       "      <td>2499.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>24974.57</td>\n",
+       "      <td>28900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>8967.74</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>6510.80</td>\n",
+       "      <td>5000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>9205.10</td>\n",
+       "      <td>12800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>51287.19</td>\n",
+       "      <td>52055.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>14792.29</td>\n",
+       "      <td>18800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>10014.71</td>\n",
+       "      <td>12500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>5811.74</td>\n",
+       "      <td>6000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>4300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>65887.51</td>\n",
+       "      <td>114999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>12247.90</td>\n",
+       "      <td>12750.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>39744.80</td>\n",
+       "      <td>34900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>10612.51</td>\n",
+       "      <td>12400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>10350.25</td>\n",
+       "      <td>13500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>23630.85</td>\n",
+       "      <td>19500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>65210.35</td>\n",
+       "      <td>115800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>9900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>8316.92</td>\n",
+       "      <td>9600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>9057.77</td>\n",
+       "      <td>14999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>9356.43</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>10716.00</td>\n",
+       "      <td>14900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>15067.85</td>\n",
+       "      <td>14200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>7327.18</td>\n",
+       "      <td>7200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>11336.71</td>\n",
+       "      <td>12300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>11002.01</td>\n",
+       "      <td>13900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>14591.35</td>\n",
+       "      <td>16900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>12452.22</td>\n",
+       "      <td>11960.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>12414.72</td>\n",
+       "      <td>23000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>13004.34</td>\n",
+       "      <td>10700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>8250.47</td>\n",
+       "      <td>9950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>5374.74</td>\n",
+       "      <td>3750.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>11099.09</td>\n",
+       "      <td>6999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>14815.83</td>\n",
+       "      <td>15400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>12897.92</td>\n",
+       "      <td>9500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>14572.70</td>\n",
+       "      <td>27900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>60603.06</td>\n",
+       "      <td>124000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>11937.31</td>\n",
+       "      <td>13995.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>28951.20</td>\n",
+       "      <td>45500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>32945.80</td>\n",
+       "      <td>23500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>26441.03</td>\n",
+       "      <td>15900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>9500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>2020.25</td>\n",
+       "      <td>3700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>20246.04</td>\n",
+       "      <td>23500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>19692.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>5238.05</td>\n",
+       "      <td>4000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>9753.44</td>\n",
+       "      <td>20400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>16717.08</td>\n",
+       "      <td>15500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>22454.74</td>\n",
+       "      <td>35500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>10133.68</td>\n",
+       "      <td>8750.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>11018.79</td>\n",
+       "      <td>15700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>63921.33</td>\n",
+       "      <td>80999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>4770.52</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>8688.92</td>\n",
+       "      <td>11700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>27293.13</td>\n",
+       "      <td>39040.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>44193.38</td>\n",
+       "      <td>75500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>21681.00</td>\n",
+       "      <td>30999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>10547.82</td>\n",
+       "      <td>19500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>13420.15</td>\n",
+       "      <td>6999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>42703.68</td>\n",
+       "      <td>42500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>8415.81</td>\n",
+       "      <td>8800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>3084.69</td>\n",
+       "      <td>3100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>10835.95</td>\n",
+       "      <td>14500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>9661.78</td>\n",
+       "      <td>6800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>8110.06</td>\n",
+       "      <td>10990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>10821.19</td>\n",
+       "      <td>8299.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>24427.14</td>\n",
+       "      <td>36000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>105999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>5391.77</td>\n",
+       "      <td>6000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>9909.62</td>\n",
+       "      <td>8700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>2749.08</td>\n",
+       "      <td>3300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>17654.84</td>\n",
+       "      <td>17800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>3473.79</td>\n",
+       "      <td>3500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>33499.44</td>\n",
+       "      <td>35000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>7918.89</td>\n",
+       "      <td>2400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>13757.58</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>4296.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>13235.55</td>\n",
+       "      <td>8999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>7267.36</td>\n",
+       "      <td>8100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>44173.72</td>\n",
+       "      <td>44999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>11474.22</td>\n",
+       "      <td>19999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2810.71</td>\n",
+       "      <td>2450.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>24271.90</td>\n",
+       "      <td>20900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>25302.43</td>\n",
+       "      <td>52000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>8139.48</td>\n",
+       "      <td>6700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>13600.39</td>\n",
+       "      <td>25800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>9076.42</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>21303.57</td>\n",
+       "      <td>23900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>16165.79</td>\n",
+       "      <td>4400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>7973.87</td>\n",
+       "      <td>3650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8707.13</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>14410.15</td>\n",
+       "      <td>18932.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>25986.31</td>\n",
+       "      <td>31310.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>9070.23</td>\n",
+       "      <td>14500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>8850.21</td>\n",
+       "      <td>10200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>2839.03</td>\n",
+       "      <td>4300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>8648.80</td>\n",
+       "      <td>4400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>6425.00</td>\n",
+       "      <td>9400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>24874.86</td>\n",
+       "      <td>23999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>21097.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>65448.37</td>\n",
+       "      <td>40500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>8121.22</td>\n",
+       "      <td>10200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>16407.29</td>\n",
+       "      <td>14800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>13409.47</td>\n",
+       "      <td>8700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>7609.28</td>\n",
+       "      <td>7000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>12234.68</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>14363.76</td>\n",
+       "      <td>9500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8117.44</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>22367.52</td>\n",
+       "      <td>28500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>2300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11378.43</td>\n",
+       "      <td>18600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>14576.33</td>\n",
+       "      <td>11000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>8033.61</td>\n",
+       "      <td>9750.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>53294.61</td>\n",
+       "      <td>57000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>74232.55</td>\n",
+       "      <td>103999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>50074.56</td>\n",
+       "      <td>69990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>3940.49</td>\n",
+       "      <td>4999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>13542.79</td>\n",
+       "      <td>10200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>5462.52</td>\n",
+       "      <td>2700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>2389.14</td>\n",
+       "      <td>1650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>67952.07</td>\n",
+       "      <td>53500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>14197.26</td>\n",
+       "      <td>12400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>16253.48</td>\n",
+       "      <td>11500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>2975.40</td>\n",
+       "      <td>1600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>6977.90</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>8843.22</td>\n",
+       "      <td>2500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>65500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>40893.84</td>\n",
+       "      <td>126000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>18866.50</td>\n",
+       "      <td>18300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>16257.03</td>\n",
+       "      <td>16999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>14926.69</td>\n",
+       "      <td>17800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>8638.63</td>\n",
+       "      <td>11800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>11619.16</td>\n",
+       "      <td>10000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>18988.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>3735.04</td>\n",
+       "      <td>4999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>38334.67</td>\n",
+       "      <td>32000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>9363.27</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>87777.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>13502.35</td>\n",
+       "      <td>14500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>9495.62</td>\n",
+       "      <td>8650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>14218.94</td>\n",
+       "      <td>13500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>35956.50</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>39882.67</td>\n",
+       "      <td>37700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>8381.60</td>\n",
+       "      <td>6999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>20972.54</td>\n",
+       "      <td>17500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>10946.44</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>19624.73</td>\n",
+       "      <td>12700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>16821.54</td>\n",
+       "      <td>26800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>14136.01</td>\n",
+       "      <td>11500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>2779.04</td>\n",
+       "      <td>2300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>50954.68</td>\n",
+       "      <td>61000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>31656.01</td>\n",
+       "      <td>24700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>43236.92</td>\n",
+       "      <td>40500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>35093.27</td>\n",
+       "      <td>19990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>4266.27</td>\n",
+       "      <td>5200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>6537.82</td>\n",
+       "      <td>2700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25975.12</td>\n",
+       "      <td>21900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>22262.80</td>\n",
+       "      <td>22711.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12558.90</td>\n",
+       "      <td>8999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>65825.25</td>\n",
+       "      <td>104999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>7045.72</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>3500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>20367.25</td>\n",
+       "      <td>22700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>1792.36</td>\n",
+       "      <td>1999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>10932.17</td>\n",
+       "      <td>13800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>5570.46</td>\n",
+       "      <td>8800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>15700.53</td>\n",
+       "      <td>18500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15374.08</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36764.38</td>\n",
+       "      <td>62000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>36883.77</td>\n",
+       "      <td>49000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>31369.37</td>\n",
+       "      <td>32500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>6599.62</td>\n",
+       "      <td>7800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>12027.56</td>\n",
+       "      <td>16200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>28937.50</td>\n",
+       "      <td>34900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>13334.19</td>\n",
+       "      <td>11600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>9664.46</td>\n",
+       "      <td>1900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>13884.26</td>\n",
+       "      <td>27800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>10824.89</td>\n",
+       "      <td>6700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>4043.74</td>\n",
+       "      <td>2900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>28910.96</td>\n",
+       "      <td>42500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>8902.52</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>6503.62</td>\n",
+       "      <td>4900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>7161.67</td>\n",
+       "      <td>2200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>5484.97</td>\n",
+       "      <td>4999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>33392.40</td>\n",
+       "      <td>42000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>3747.21</td>\n",
+       "      <td>4900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>17540.72</td>\n",
+       "      <td>14899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>50099.92</td>\n",
+       "      <td>51500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>7950.27</td>\n",
+       "      <td>12000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>12270.01</td>\n",
+       "      <td>15900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>8973.16</td>\n",
+       "      <td>12750.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>60493.51</td>\n",
+       "      <td>75000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>9984.87</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>6392.70</td>\n",
+       "      <td>7300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>23272.83</td>\n",
+       "      <td>25749.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>10105.70</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>3590.97</td>\n",
+       "      <td>3200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>18282.75</td>\n",
+       "      <td>21950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>16197.42</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>5839.08</td>\n",
+       "      <td>7200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1873.03</td>\n",
+       "      <td>3400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>17282.06</td>\n",
+       "      <td>11900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>15144.28</td>\n",
+       "      <td>29000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>5346.97</td>\n",
+       "      <td>4100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>15015.22</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>32121.20</td>\n",
+       "      <td>41000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>11858.48</td>\n",
+       "      <td>10800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>9424.34</td>\n",
+       "      <td>12000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>21297.65</td>\n",
+       "      <td>19800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>8667.12</td>\n",
+       "      <td>6900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>16773.62</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>27740.81</td>\n",
+       "      <td>23500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>17011.43</td>\n",
+       "      <td>11700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>10809.48</td>\n",
+       "      <td>15400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>5863.41</td>\n",
+       "      <td>4800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>2373.66</td>\n",
+       "      <td>2700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>29049.27</td>\n",
+       "      <td>28350.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>9829.53</td>\n",
+       "      <td>8700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>18555.09</td>\n",
+       "      <td>18800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>63000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>17714.62</td>\n",
+       "      <td>11400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>47047.70</td>\n",
+       "      <td>52300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>10019.90</td>\n",
+       "      <td>1800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>24642.50</td>\n",
+       "      <td>24000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>16939.86</td>\n",
+       "      <td>19400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>34678.95</td>\n",
+       "      <td>43500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>6746.43</td>\n",
+       "      <td>6000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>9139.04</td>\n",
+       "      <td>11800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>6781.86</td>\n",
+       "      <td>4700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>4666.05</td>\n",
+       "      <td>4500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>9369.66</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>5947.51</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>9489.56</td>\n",
+       "      <td>10900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>12354.92</td>\n",
+       "      <td>27500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>6826.78</td>\n",
+       "      <td>7300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>3799.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>9175.06</td>\n",
+       "      <td>7499.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>46465.02</td>\n",
+       "      <td>39900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>3816.33</td>\n",
+       "      <td>3750.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>14915.98</td>\n",
+       "      <td>7900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>11555.80</td>\n",
+       "      <td>7400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>5983.44</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>16392.22</td>\n",
+       "      <td>17700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>8340.94</td>\n",
+       "      <td>3350.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>6610.04</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>18231.44</td>\n",
+       "      <td>18600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>12523.73</td>\n",
+       "      <td>16000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>8400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11377.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>33172.20</td>\n",
+       "      <td>40800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>6429.03</td>\n",
+       "      <td>6500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9593.52</td>\n",
+       "      <td>11000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>2285.24</td>\n",
+       "      <td>3500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>25682.58</td>\n",
+       "      <td>17300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>7324.63</td>\n",
+       "      <td>7100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>11881.05</td>\n",
+       "      <td>8200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>7279.97</td>\n",
+       "      <td>11950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7940.44</td>\n",
+       "      <td>11000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>1926.60</td>\n",
+       "      <td>2400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>23163.87</td>\n",
+       "      <td>24171.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>26331.41</td>\n",
+       "      <td>11200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>10597.72</td>\n",
+       "      <td>8300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>33707.27</td>\n",
+       "      <td>73900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>12583.52</td>\n",
+       "      <td>13000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>1858.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>15954.72</td>\n",
+       "      <td>12999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>14486.38</td>\n",
+       "      <td>9000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>3205.38</td>\n",
+       "      <td>1900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>3411.53</td>\n",
+       "      <td>3700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>6205.48</td>\n",
+       "      <td>5000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>3246.94</td>\n",
+       "      <td>3390.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>11000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>3425.23</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>11432.63</td>\n",
+       "      <td>8900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>39074.36</td>\n",
+       "      <td>69999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>9348.90</td>\n",
+       "      <td>6800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>15259.81</td>\n",
+       "      <td>26000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>12255.45</td>\n",
+       "      <td>22000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>3173.48</td>\n",
+       "      <td>5100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>14512.61</td>\n",
+       "      <td>25999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>10115.13</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>28154.84</td>\n",
+       "      <td>38500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>17908.00</td>\n",
+       "      <td>17500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>3001.37</td>\n",
+       "      <td>4000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>25576.58</td>\n",
+       "      <td>28500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>25685.67</td>\n",
+       "      <td>19500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>7797.18</td>\n",
+       "      <td>6999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>8767.54</td>\n",
+       "      <td>9700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>4590.16</td>\n",
+       "      <td>5500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>21816.43</td>\n",
+       "      <td>23500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>35371.16</td>\n",
+       "      <td>34600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>16427.12</td>\n",
+       "      <td>13300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>14513.46</td>\n",
+       "      <td>14900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>31373.47</td>\n",
+       "      <td>20859.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>8940.39</td>\n",
+       "      <td>13500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>20680.40</td>\n",
+       "      <td>27000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>33000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>29127.72</td>\n",
+       "      <td>24500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>3040.77</td>\n",
+       "      <td>1200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>9269.68</td>\n",
+       "      <td>8200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>27225.34</td>\n",
+       "      <td>13500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>2953.17</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>13853.36</td>\n",
+       "      <td>10800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>19298.61</td>\n",
+       "      <td>14999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>7869.47</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>13500.69</td>\n",
+       "      <td>15900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>2573.48</td>\n",
+       "      <td>4900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>31251.89</td>\n",
+       "      <td>23500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>16095.32</td>\n",
+       "      <td>3600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>25752.19</td>\n",
+       "      <td>30990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>38473.71</td>\n",
+       "      <td>119000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>4472.47</td>\n",
+       "      <td>4200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>23227.15</td>\n",
+       "      <td>31900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>16924.74</td>\n",
+       "      <td>9999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>23485.45</td>\n",
+       "      <td>18777.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>18621.12</td>\n",
+       "      <td>24100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>13074.74</td>\n",
+       "      <td>11500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>2539.57</td>\n",
+       "      <td>4400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>7319.77</td>\n",
+       "      <td>1850.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>8734.58</td>\n",
+       "      <td>8800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>7256.86</td>\n",
+       "      <td>8500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>15383.35</td>\n",
+       "      <td>14990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>11056.52</td>\n",
+       "      <td>6400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>18165.16</td>\n",
+       "      <td>23900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>3818.71</td>\n",
+       "      <td>1450.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>28950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>35662.26</td>\n",
+       "      <td>40000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>7300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>30628.28</td>\n",
+       "      <td>4999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>14402.34</td>\n",
+       "      <td>10000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>17334.11</td>\n",
+       "      <td>18500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>8721.97</td>\n",
+       "      <td>8999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>8156.11</td>\n",
+       "      <td>9900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>67431.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>10740.09</td>\n",
+       "      <td>19100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>3450.69</td>\n",
+       "      <td>6100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2625.10</td>\n",
+       "      <td>2150.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>21174.10</td>\n",
+       "      <td>21335.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>11147.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>8236.36</td>\n",
+       "      <td>7200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3026.25</td>\n",
+       "      <td>2550.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>17195.57</td>\n",
+       "      <td>12500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>10332.54</td>\n",
+       "      <td>9300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>4032.10</td>\n",
+       "      <td>7650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>7814.16</td>\n",
+       "      <td>9500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>35039.40</td>\n",
+       "      <td>85555.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>32481.05</td>\n",
+       "      <td>6000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>10563.39</td>\n",
+       "      <td>20500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>3621.08</td>\n",
+       "      <td>4799.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>7809.13</td>\n",
+       "      <td>2899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>10303.21</td>\n",
+       "      <td>13300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>9232.79</td>\n",
+       "      <td>7450.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>2613.97</td>\n",
+       "      <td>3099.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>1700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>665</th>\n",
+       "      <td>17858.02</td>\n",
+       "      <td>18000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>65723.62</td>\n",
+       "      <td>104999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>49221.63</td>\n",
+       "      <td>52999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>8270.38</td>\n",
+       "      <td>4700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>11477.47</td>\n",
+       "      <td>17650.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>2886.35</td>\n",
+       "      <td>4200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>672</th>\n",
+       "      <td>5679.39</td>\n",
+       "      <td>4500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>2381.01</td>\n",
+       "      <td>2700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>13476.90</td>\n",
+       "      <td>22500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>6861.84</td>\n",
+       "      <td>7300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>9631.08</td>\n",
+       "      <td>3799.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>4569.15</td>\n",
+       "      <td>6900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>25864.65</td>\n",
+       "      <td>69990.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>21493.44</td>\n",
+       "      <td>9000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>5195.69</td>\n",
+       "      <td>4100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>682</th>\n",
+       "      <td>5454.34</td>\n",
+       "      <td>5000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>683</th>\n",
+       "      <td>32771.45</td>\n",
+       "      <td>44800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>18942.34</td>\n",
+       "      <td>12999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>12593.00</td>\n",
+       "      <td>14600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>23576.99</td>\n",
+       "      <td>12000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>11004.05</td>\n",
+       "      <td>14500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>688</th>\n",
+       "      <td>24572.28</td>\n",
+       "      <td>20500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>11706.93</td>\n",
+       "      <td>18100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>8467.17</td>\n",
+       "      <td>6950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>8471.55</td>\n",
+       "      <td>6799.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>12354.16</td>\n",
+       "      <td>12900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>8131.92</td>\n",
+       "      <td>3999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>11178.46</td>\n",
+       "      <td>8600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>2470.61</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>10031.96</td>\n",
+       "      <td>17300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>30480.85</td>\n",
+       "      <td>30500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>4255.62</td>\n",
+       "      <td>5800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>25640.67</td>\n",
+       "      <td>19800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>7996.00</td>\n",
+       "      <td>4999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>7011.74</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>5597.06</td>\n",
+       "      <td>11999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>6891.99</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>12976.15</td>\n",
+       "      <td>17500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>61639.73</td>\n",
+       "      <td>72900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>2725.40</td>\n",
+       "      <td>2600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>11967.39</td>\n",
+       "      <td>11700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>34446.96</td>\n",
+       "      <td>27900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>3500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>19676.51</td>\n",
+       "      <td>24500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>8530.54</td>\n",
+       "      <td>14500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>4004.65</td>\n",
+       "      <td>2000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>23118.07</td>\n",
+       "      <td>22999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>10220.74</td>\n",
+       "      <td>8600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>11425.01</td>\n",
+       "      <td>13500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>4172.84</td>\n",
+       "      <td>7750.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>8041.62</td>\n",
+       "      <td>6999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>11391.95</td>\n",
+       "      <td>11500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>2860.97</td>\n",
+       "      <td>3350.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>11431.35</td>\n",
+       "      <td>13999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>30409.09</td>\n",
+       "      <td>18900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>6009.63</td>\n",
+       "      <td>6100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>44312.98</td>\n",
+       "      <td>50900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>15742.41</td>\n",
+       "      <td>16999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>45664.45</td>\n",
+       "      <td>35500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>10810.62</td>\n",
+       "      <td>5000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>5792.10</td>\n",
+       "      <td>8700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>9817.06</td>\n",
+       "      <td>3200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>10696.56</td>\n",
+       "      <td>12179.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>16027.02</td>\n",
+       "      <td>15000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>19309.90</td>\n",
+       "      <td>12500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>11601.33</td>\n",
+       "      <td>7200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>23336.06</td>\n",
+       "      <td>33200.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>10337.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>43163.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>37704.71</td>\n",
+       "      <td>26900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>20216.67</td>\n",
+       "      <td>24900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>6658.73</td>\n",
+       "      <td>6250.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>19855.31</td>\n",
+       "      <td>14800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>16960.31</td>\n",
+       "      <td>16999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>5268.17</td>\n",
+       "      <td>8400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>2379.58</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>6421.18</td>\n",
+       "      <td>4400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>13355.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>8453.28</td>\n",
+       "      <td>10900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>48699.98</td>\n",
+       "      <td>77500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>6082.85</td>\n",
+       "      <td>7450.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>10381.62</td>\n",
+       "      <td>3000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>8493.04</td>\n",
+       "      <td>12800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>8591.66</td>\n",
+       "      <td>12000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>6358.55</td>\n",
+       "      <td>4850.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>17028.45</td>\n",
+       "      <td>18700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>15885.66</td>\n",
+       "      <td>17300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>3752.54</td>\n",
+       "      <td>2600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>12028.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>9380.46</td>\n",
+       "      <td>7950.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>10125.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>13443.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>9097.13</td>\n",
+       "      <td>8000.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>12999.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>12383.35</td>\n",
+       "      <td>10800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>14049.76</td>\n",
+       "      <td>10700.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>11034.66</td>\n",
+       "      <td>9800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>18982.15</td>\n",
+       "      <td>17900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>24323.48</td>\n",
+       "      <td>18800.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.73</td>\n",
+       "      <td>29500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.07</td>\n",
+       "      <td>9600.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.45</td>\n",
+       "      <td>18300.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.75</td>\n",
+       "      <td>68500.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.16</td>\n",
+       "      <td>10800.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction    Target\n",
+       "0      10685.50   2300.00\n",
+       "1       3499.26   2800.00\n",
+       "2       7553.29   2500.00\n",
+       "3       7463.96   6400.00\n",
+       "4      11353.49   9150.00\n",
+       "5      21289.80  20000.00\n",
+       "6      20159.19  38888.00\n",
+       "7      20349.62  16999.00\n",
+       "8      11581.54  12500.00\n",
+       "9      33614.62  41000.00\n",
+       "10      7241.07  12800.00\n",
+       "11      5175.77   5000.00\n",
+       "12      5484.02   7900.00\n",
+       "13     13292.71  16999.00\n",
+       "14      8248.67   9200.00\n",
+       "15     10621.84  11999.00\n",
+       "16     23721.58  20500.00\n",
+       "17     11770.64   9700.00\n",
+       "18     37600.15  39900.00\n",
+       "19     16178.14  16400.00\n",
+       "20     11876.82  15200.00\n",
+       "21     31557.80  24500.00\n",
+       "22      6102.36   5650.00\n",
+       "23     13111.91  12900.00\n",
+       "24     23650.15  20900.00\n",
+       "25     45272.25  31990.00\n",
+       "26      2178.94   3600.00\n",
+       "27      2555.02  11600.00\n",
+       "28     35991.51  43999.00\n",
+       "29     26062.23  42500.00\n",
+       "30     15559.43   7900.00\n",
+       "31     12858.08  12650.00\n",
+       "32     12459.85  19900.00\n",
+       "33     22941.80  15000.00\n",
+       "34     19854.79  14000.00\n",
+       "35     11337.51  20900.00\n",
+       "36     10499.60  12300.00\n",
+       "37     26478.15  20800.00\n",
+       "38      9919.19  14600.00\n",
+       "39     16724.45  29600.00\n",
+       "40     18672.68  18000.00\n",
+       "41     19845.36  16300.00\n",
+       "42     23286.27  38500.00\n",
+       "43     21611.83  22400.00\n",
+       "44      7241.07  14000.00\n",
+       "45      8512.03   7000.00\n",
+       "46      3542.72   3000.00\n",
+       "47     26425.14  25999.00\n",
+       "48     77403.06  55000.00\n",
+       "49      4973.17   4900.00\n",
+       "50      2479.43   2200.00\n",
+       "51      2543.57   3149.25\n",
+       "52     10716.84   9800.00\n",
+       "53     20453.89  21000.00\n",
+       "54     18575.43  20900.00\n",
+       "55     12674.57  10700.00\n",
+       "56     15380.04   8800.00\n",
+       "57     21942.05  25500.00\n",
+       "58     35336.33  49500.00\n",
+       "59      8195.34   7600.00\n",
+       "60     12469.21  12500.00\n",
+       "61      7396.87   7300.00\n",
+       "62     10848.85   8500.00\n",
+       "63      5351.86   3300.00\n",
+       "64      9611.80  15000.00\n",
+       "65     19847.25  18200.00\n",
+       "66      3316.44   2300.00\n",
+       "67      7621.25   4100.00\n",
+       "68      6717.17   6100.00\n",
+       "69      2440.90   1400.00\n",
+       "70     50206.83  73500.00\n",
+       "71     15627.03  14500.00\n",
+       "72     23624.69  22500.00\n",
+       "73     18577.88  30500.00\n",
+       "74      1746.19   2650.00\n",
+       "75      4209.17   5500.00\n",
+       "76      5144.02   6500.00\n",
+       "77      5930.73   5700.00\n",
+       "78     30810.25  29900.00\n",
+       "79     18893.76  15700.00\n",
+       "80      6819.44   8800.00\n",
+       "81     12891.96   5300.00\n",
+       "82      7320.42   2600.00\n",
+       "83      6013.05   4800.00\n",
+       "84      4364.08   6500.00\n",
+       "85     19001.29  17800.00\n",
+       "86      8071.70  11100.00\n",
+       "87      2315.71   2400.00\n",
+       "88     10807.78   7500.00\n",
+       "89     11696.25  12900.00\n",
+       "90      9143.17   6800.00\n",
+       "91     13421.16  13200.00\n",
+       "92      7116.68   9800.00\n",
+       "93     18317.20  10974.21\n",
+       "94      7724.17   7800.00\n",
+       "95     47013.28  55000.00\n",
+       "96      7976.26   7700.00\n",
+       "97      3648.96   2700.00\n",
+       "98      6746.48   8500.00\n",
+       "99      6437.21   8999.00\n",
+       "100    32886.86  39500.00\n",
+       "101     5958.63   6150.00\n",
+       "102    28625.56  28900.00\n",
+       "103     4488.43   4150.00\n",
+       "104     5521.28   8800.00\n",
+       "105     8882.65  10000.00\n",
+       "106    16547.03  14200.00\n",
+       "107     7131.92   9800.00\n",
+       "108     7300.05   6500.00\n",
+       "109    12663.54  12200.00\n",
+       "110    25614.14  25500.00\n",
+       "111     3152.62   4999.00\n",
+       "112    22835.48  16950.00\n",
+       "113     6006.27   6999.00\n",
+       "114    27716.14  27300.00\n",
+       "115     6981.34   8800.00\n",
+       "116    15819.50   8300.00\n",
+       "117     7178.20  11100.00\n",
+       "118    14943.75  17300.00\n",
+       "119    10448.50   8999.00\n",
+       "120    50728.89  65000.00\n",
+       "121    41075.73  27200.00\n",
+       "122    19335.34  55555.00\n",
+       "123     5643.45   6300.00\n",
+       "124    15691.29  22900.00\n",
+       "125    13733.33  16800.00\n",
+       "126    13459.92  12300.00\n",
+       "127    12311.47  11700.00\n",
+       "128     7727.53  11200.00\n",
+       "129    11580.54   9000.00\n",
+       "130    17489.92  18000.00\n",
+       "131    23967.81  27500.00\n",
+       "132    11200.20   9999.00\n",
+       "133     9279.28   9600.00\n",
+       "134    12096.18  11500.00\n",
+       "135    44407.14  59999.00\n",
+       "136    12006.66  17900.00\n",
+       "137    66032.76 107999.00\n",
+       "138    11095.57   8999.00\n",
+       "139     7712.65  11500.00\n",
+       "140    10358.58  12000.00\n",
+       "141     1815.28   3100.00\n",
+       "142     6839.57   7650.00\n",
+       "143     8768.29   7700.00\n",
+       "144    11969.66  12900.00\n",
+       "145     4307.68   3400.00\n",
+       "146     8298.32  11900.00\n",
+       "147     7772.57   7200.00\n",
+       "148    25040.42  37000.00\n",
+       "149     8351.34   6500.00\n",
+       "150     9800.43   9500.00\n",
+       "151    10889.39  12900.00\n",
+       "152    44680.77  51900.00\n",
+       "153     3666.31   3200.00\n",
+       "154    11443.85   9499.00\n",
+       "155     3086.38   2499.00\n",
+       "156     9192.06  10500.00\n",
+       "157     7491.01   8600.00\n",
+       "158    15623.39  20999.00\n",
+       "159    12904.68   9500.00\n",
+       "160    32954.19  52777.00\n",
+       "161    13967.55   5500.00\n",
+       "162     9954.42   1800.00\n",
+       "163    23972.56  36900.00\n",
+       "164    18403.35  13893.75\n",
+       "165     9596.94   9400.00\n",
+       "166    15292.10   8300.00\n",
+       "167    13212.52  23999.00\n",
+       "168    11420.95  11999.00\n",
+       "169     8522.39   5800.00\n",
+       "170    19741.65  29999.00\n",
+       "171     8392.42   7150.00\n",
+       "172    11156.38  10800.00\n",
+       "173    36516.35  37900.00\n",
+       "174     7745.02   6400.00\n",
+       "175    34863.53  47600.00\n",
+       "176     6732.97   8400.00\n",
+       "177     8916.19  11999.00\n",
+       "178    17316.59  13900.00\n",
+       "179    14933.54  29500.00\n",
+       "180    19701.70  49999.00\n",
+       "181    17597.10  25500.00\n",
+       "182    10123.04   2800.00\n",
+       "183    47014.52  95000.00\n",
+       "184    29479.97  34000.00\n",
+       "185     6130.96   6700.00\n",
+       "186    19071.43  22900.00\n",
+       "187    28256.79  34900.00\n",
+       "188    10159.18  10900.00\n",
+       "189    10786.38   8000.00\n",
+       "190    13377.99  10300.00\n",
+       "191    15956.95  29999.00\n",
+       "192    18285.07  25900.00\n",
+       "193    14832.32  12400.00\n",
+       "194     9310.69   8400.00\n",
+       "195    20588.57  21900.00\n",
+       "196     5948.04   2600.00\n",
+       "197     2126.49   2550.00\n",
+       "198    17983.10  19600.00\n",
+       "199     2784.46   1950.00\n",
+       "200    11098.18  10200.00\n",
+       "201    10094.58   7777.00\n",
+       "202     5040.46   7000.00\n",
+       "203    65613.31  99999.00\n",
+       "204     7664.32   9000.00\n",
+       "205    13041.39  19700.00\n",
+       "206    19920.63  16000.00\n",
+       "207    15010.51  13200.00\n",
+       "208     5716.14   3000.00\n",
+       "209     8242.62   6600.00\n",
+       "210    31216.83  55000.00\n",
+       "211     6848.88   8200.00\n",
+       "212    16133.86  15700.00\n",
+       "213    15872.91  14300.00\n",
+       "214     2393.34   3500.00\n",
+       "215     5284.06   3300.00\n",
+       "216    12974.89  18900.00\n",
+       "217     8661.95  13999.00\n",
+       "218    11490.44  14900.00\n",
+       "219    34376.16  85000.00\n",
+       "220    13348.91  15999.00\n",
+       "221    34863.53 109999.00\n",
+       "222    20662.46  18900.00\n",
+       "223     6199.59  13200.00\n",
+       "224     3939.91  15500.00\n",
+       "225     6916.13   3900.00\n",
+       "226    11191.05   9600.00\n",
+       "227    17251.60  14300.00\n",
+       "228    15682.82   9600.00\n",
+       "229     7719.69   9300.00\n",
+       "230     9198.08  15800.00\n",
+       "231     9312.21   7300.00\n",
+       "232     1320.56   3700.00\n",
+       "233     6110.94   8700.00\n",
+       "234    12731.26   9800.00\n",
+       "235     2765.14   2900.00\n",
+       "236     9024.05   4900.00\n",
+       "237    25037.87  21200.00\n",
+       "238    26736.21  16100.00\n",
+       "239    10581.62  10999.00\n",
+       "240    10419.20  13000.00\n",
+       "241    18432.63  30500.00\n",
+       "242     7597.39   7500.00\n",
+       "243    32925.60  69500.00\n",
+       "244     7110.32   8990.00\n",
+       "245    15457.91   9800.00\n",
+       "246    41711.18  30500.00\n",
+       "247    13159.16  16200.00\n",
+       "248    22011.47  13999.00\n",
+       "249     7391.67  15200.00\n",
+       "250     5041.20   3800.00\n",
+       "251     6978.91   6500.00\n",
+       "252     9721.50   9900.00\n",
+       "253    18128.32  15900.00\n",
+       "254     6413.26   5999.00\n",
+       "255    24796.09  46000.00\n",
+       "256     1825.44   1900.00\n",
+       "257    41503.90  37500.00\n",
+       "258    13680.48  11900.00\n",
+       "259     6067.79   6200.00\n",
+       "260     6858.89   9999.00\n",
+       "261    53312.81  48535.50\n",
+       "262    12414.41   8420.00\n",
+       "263     9089.35   7700.00\n",
+       "264    31059.18  42000.00\n",
+       "265    20163.48  30500.00\n",
+       "266    65742.57 109999.00\n",
+       "267    40753.58  40000.00\n",
+       "268     6893.62   6400.00\n",
+       "269     4151.02   6500.00\n",
+       "270     8263.95   7700.00\n",
+       "271     3948.74   2200.00\n",
+       "272     9749.53   9800.00\n",
+       "273    12318.39  12900.00\n",
+       "274     9113.05   6900.00\n",
+       "275    22547.55  18500.00\n",
+       "276     5002.39   2999.00\n",
+       "277    12900.31  15000.00\n",
+       "278    53063.22  60500.00\n",
+       "279     3154.48   4300.00\n",
+       "280    12327.10  12499.00\n",
+       "281    11638.89  13708.50\n",
+       "282    12261.19   4100.00\n",
+       "283     7630.64   5500.00\n",
+       "284    40765.33 112000.00\n",
+       "285    34761.74  24000.00\n",
+       "286    19659.97  16700.00\n",
+       "287     1972.28   2300.00\n",
+       "288     6635.73   4300.00\n",
+       "289     7178.20  10950.00\n",
+       "290     1894.40   1950.00\n",
+       "291    20433.23  30999.00\n",
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+       "719    11391.95  11500.00\n",
+       "720     2860.97   3350.00\n",
+       "721    11431.35  13999.00\n",
+       "722    30409.09  18900.00\n",
+       "723     6009.63   6100.00\n",
+       "724    44312.98  50900.00\n",
+       "725    15742.41  16999.00\n",
+       "726    45664.45  35500.00\n",
+       "727    10810.62   5000.00\n",
+       "728     5792.10   8700.00\n",
+       "729     9817.06   3200.00\n",
+       "730    10696.56  12179.00\n",
+       "731    16027.02  15000.00\n",
+       "732    19309.90  12500.00\n",
+       "733    11601.33   7200.00\n",
+       "734    23336.06  33200.00\n",
+       "735    10337.11   7500.00\n",
+       "736    53312.81  43163.25\n",
+       "737    37704.71  26900.00\n",
+       "738    20216.67  24900.00\n",
+       "739    22213.53  34500.00\n",
+       "740     6658.73   6250.00\n",
+       "741    19855.31  14800.00\n",
+       "742    16960.31  16999.00\n",
+       "743     5268.17   8400.00\n",
+       "744     2379.58   3000.00\n",
+       "745     6421.18   4400.00\n",
+       "746    13355.11   7500.00\n",
+       "747     8453.28  10900.00\n",
+       "748    48699.98  77500.00\n",
+       "749     6082.85   7450.00\n",
+       "750    10381.62   3000.00\n",
+       "751     8493.04  12800.00\n",
+       "752     8591.66  12000.00\n",
+       "753     6358.55   4850.00\n",
+       "754    17028.45  18700.00\n",
+       "755    15885.66  17300.00\n",
+       "756     3752.54   2600.00\n",
+       "757    12028.91  10500.00\n",
+       "758     9380.46   7950.00\n",
+       "759    10125.27   6700.00\n",
+       "760    13443.32   9000.00\n",
+       "761     9097.13   8000.00\n",
+       "762    12201.29  12999.00\n",
+       "763    12383.35  10800.00\n",
+       "764    14049.76  10700.00\n",
+       "765    11034.66   9800.00\n",
+       "766    18982.15  17900.00\n",
+       "767    24323.48  18800.00\n",
+       "768    38260.36  75555.00\n",
+       "769    29651.73  29500.00\n",
+       "770    10732.07   9600.00\n",
+       "771    13922.45  18300.00\n",
+       "772    27487.75  68500.00\n",
+       "773    13491.16  10800.00"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's overwrite the 'Target' column with the appropriate values\n",
+    "# Again, we need the exponential of the test log price\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Additionally, we can calculate the difference between the targets and the predictions\n",
+    "# Note that this is actually the residual (we already plotted the residuals)\n",
+    "df_pf['Residual'] = df_pf['Target'] - df_pf['Prediction']\n",
+    "\n",
+    "# Since OLS is basically an algorithm which minimizes the total sum of squared errors (residuals),\n",
+    "# this comparison makes a lot of sense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.50</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-8385.50</td>\n",
+       "      <td>364.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.26</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>-699.26</td>\n",
+       "      <td>24.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.29</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-5053.29</td>\n",
+       "      <td>202.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.96</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-1063.96</td>\n",
+       "      <td>16.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.49</td>\n",
+       "      <td>9150.00</td>\n",
+       "      <td>-2203.49</td>\n",
+       "      <td>24.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>21289.80</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-1289.80</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>20159.19</td>\n",
+       "      <td>38888.00</td>\n",
+       "      <td>18728.81</td>\n",
+       "      <td>48.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20349.62</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-3350.62</td>\n",
+       "      <td>19.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11581.54</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>918.46</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>7385.38</td>\n",
+       "      <td>18.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>5558.93</td>\n",
+       "      <td>43.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5175.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-175.77</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5484.02</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>2415.98</td>\n",
+       "      <td>30.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13292.71</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>3706.29</td>\n",
+       "      <td>21.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>8248.67</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>951.33</td>\n",
+       "      <td>10.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10621.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>1377.16</td>\n",
+       "      <td>11.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>23721.58</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-3221.58</td>\n",
+       "      <td>15.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>11770.64</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-2070.64</td>\n",
+       "      <td>21.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>37600.15</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>2299.85</td>\n",
+       "      <td>5.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>16178.14</td>\n",
+       "      <td>16400.00</td>\n",
+       "      <td>221.86</td>\n",
+       "      <td>1.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11876.82</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>3323.18</td>\n",
+       "      <td>21.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>31557.80</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>-7057.80</td>\n",
+       "      <td>28.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>6102.36</td>\n",
+       "      <td>5650.00</td>\n",
+       "      <td>-452.36</td>\n",
+       "      <td>8.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>13111.91</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-211.91</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>23650.15</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-2750.15</td>\n",
+       "      <td>13.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>45272.25</td>\n",
+       "      <td>31990.00</td>\n",
+       "      <td>-13282.25</td>\n",
+       "      <td>41.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>2178.94</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>1421.06</td>\n",
+       "      <td>39.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>2555.02</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>9044.98</td>\n",
+       "      <td>77.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>35991.51</td>\n",
+       "      <td>43999.00</td>\n",
+       "      <td>8007.49</td>\n",
+       "      <td>18.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>26062.23</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>16437.77</td>\n",
+       "      <td>38.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>15559.43</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-7659.43</td>\n",
+       "      <td>96.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>12858.08</td>\n",
+       "      <td>12650.00</td>\n",
+       "      <td>-208.08</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>12459.85</td>\n",
+       "      <td>19900.00</td>\n",
+       "      <td>7440.15</td>\n",
+       "      <td>37.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>22941.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-7941.80</td>\n",
+       "      <td>52.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>19854.79</td>\n",
+       "      <td>14000.00</td>\n",
+       "      <td>-5854.79</td>\n",
+       "      <td>41.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>11337.51</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>9562.49</td>\n",
+       "      <td>45.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>10499.60</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>1800.40</td>\n",
+       "      <td>14.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>26478.15</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>-5678.15</td>\n",
+       "      <td>27.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>9919.19</td>\n",
+       "      <td>14600.00</td>\n",
+       "      <td>4680.81</td>\n",
+       "      <td>32.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>16724.45</td>\n",
+       "      <td>29600.00</td>\n",
+       "      <td>12875.55</td>\n",
+       "      <td>43.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>18672.68</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>-672.68</td>\n",
+       "      <td>3.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>19845.36</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>-3545.36</td>\n",
+       "      <td>21.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>23286.27</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>15213.73</td>\n",
+       "      <td>39.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>21611.83</td>\n",
+       "      <td>22400.00</td>\n",
+       "      <td>788.17</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>14000.00</td>\n",
+       "      <td>6758.93</td>\n",
+       "      <td>48.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>8512.03</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-1512.03</td>\n",
+       "      <td>21.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>3542.72</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-542.72</td>\n",
+       "      <td>18.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>26425.14</td>\n",
+       "      <td>25999.00</td>\n",
+       "      <td>-426.14</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>77403.06</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-22403.06</td>\n",
+       "      <td>40.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>4973.17</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-73.17</td>\n",
+       "      <td>1.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2479.43</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-279.43</td>\n",
+       "      <td>12.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>2543.57</td>\n",
+       "      <td>3149.25</td>\n",
+       "      <td>605.68</td>\n",
+       "      <td>19.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>10716.84</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-916.84</td>\n",
+       "      <td>9.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>20453.89</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>546.11</td>\n",
+       "      <td>2.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>18575.43</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>2324.57</td>\n",
+       "      <td>11.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>12674.57</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-1974.57</td>\n",
+       "      <td>18.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>15380.04</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-6580.04</td>\n",
+       "      <td>74.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>21942.05</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>3557.95</td>\n",
+       "      <td>13.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>35336.33</td>\n",
+       "      <td>49500.00</td>\n",
+       "      <td>14163.67</td>\n",
+       "      <td>28.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>8195.34</td>\n",
+       "      <td>7600.00</td>\n",
+       "      <td>-595.34</td>\n",
+       "      <td>7.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>12469.21</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>30.79</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>7396.87</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-96.87</td>\n",
+       "      <td>1.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>10848.85</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-2348.85</td>\n",
+       "      <td>27.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>5351.86</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-2051.86</td>\n",
+       "      <td>62.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>9611.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>5388.20</td>\n",
+       "      <td>35.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19847.25</td>\n",
+       "      <td>18200.00</td>\n",
+       "      <td>-1647.25</td>\n",
+       "      <td>9.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3316.44</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1016.44</td>\n",
+       "      <td>44.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>7621.25</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-3521.25</td>\n",
+       "      <td>85.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>6717.17</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>-617.17</td>\n",
+       "      <td>10.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>2440.90</td>\n",
+       "      <td>1400.00</td>\n",
+       "      <td>-1040.90</td>\n",
+       "      <td>74.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>50206.83</td>\n",
+       "      <td>73500.00</td>\n",
+       "      <td>23293.17</td>\n",
+       "      <td>31.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>15627.03</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>-1127.03</td>\n",
+       "      <td>7.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>23624.69</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-1124.69</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>18577.88</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>11922.12</td>\n",
+       "      <td>39.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>1746.19</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>903.81</td>\n",
+       "      <td>34.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1290.83</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>5144.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1355.98</td>\n",
+       "      <td>20.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>5930.73</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>-230.73</td>\n",
+       "      <td>4.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>30810.25</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>-910.25</td>\n",
+       "      <td>3.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>18893.76</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-3193.76</td>\n",
+       "      <td>20.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>6819.44</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1980.56</td>\n",
+       "      <td>22.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>12891.96</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-7591.96</td>\n",
+       "      <td>143.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>7320.42</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-4720.42</td>\n",
+       "      <td>181.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>6013.05</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1213.05</td>\n",
+       "      <td>25.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4364.08</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>2135.92</td>\n",
+       "      <td>32.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>19001.29</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>-1201.29</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>8071.70</td>\n",
+       "      <td>11100.00</td>\n",
+       "      <td>3028.30</td>\n",
+       "      <td>27.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>2315.71</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>84.29</td>\n",
+       "      <td>3.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>10807.78</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-3307.78</td>\n",
+       "      <td>44.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11696.25</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>1203.75</td>\n",
+       "      <td>9.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>9143.17</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2343.17</td>\n",
+       "      <td>34.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>13421.16</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>-221.16</td>\n",
+       "      <td>1.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>7116.68</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>2683.32</td>\n",
+       "      <td>27.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>18317.20</td>\n",
+       "      <td>10974.21</td>\n",
+       "      <td>-7342.99</td>\n",
+       "      <td>66.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>75.83</td>\n",
+       "      <td>0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>47013.28</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>7986.72</td>\n",
+       "      <td>14.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>7976.26</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-276.26</td>\n",
+       "      <td>3.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>3648.96</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-948.96</td>\n",
+       "      <td>35.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1753.52</td>\n",
+       "      <td>20.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6437.21</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>2561.79</td>\n",
+       "      <td>28.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>32886.86</td>\n",
+       "      <td>39500.00</td>\n",
+       "      <td>6613.14</td>\n",
+       "      <td>16.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>5958.63</td>\n",
+       "      <td>6150.00</td>\n",
+       "      <td>191.37</td>\n",
+       "      <td>3.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>28625.56</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>274.44</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>4488.43</td>\n",
+       "      <td>4150.00</td>\n",
+       "      <td>-338.43</td>\n",
+       "      <td>8.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>5521.28</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>3278.72</td>\n",
+       "      <td>37.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>8882.65</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1117.35</td>\n",
+       "      <td>11.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>16547.03</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>-2347.03</td>\n",
+       "      <td>16.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>7131.92</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>2668.08</td>\n",
+       "      <td>27.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>7300.05</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-800.05</td>\n",
+       "      <td>12.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>12663.54</td>\n",
+       "      <td>12200.00</td>\n",
+       "      <td>-463.54</td>\n",
+       "      <td>3.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>25614.14</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>-114.14</td>\n",
+       "      <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>3152.62</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1846.38</td>\n",
+       "      <td>36.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>22835.48</td>\n",
+       "      <td>16950.00</td>\n",
+       "      <td>-5885.48</td>\n",
+       "      <td>34.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>6006.27</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>992.73</td>\n",
+       "      <td>14.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>27716.14</td>\n",
+       "      <td>27300.00</td>\n",
+       "      <td>-416.14</td>\n",
+       "      <td>1.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>6981.34</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1818.66</td>\n",
+       "      <td>20.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15819.50</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-7519.50</td>\n",
+       "      <td>90.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>11100.00</td>\n",
+       "      <td>3921.80</td>\n",
+       "      <td>35.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>14943.75</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>2356.25</td>\n",
+       "      <td>13.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>10448.50</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-1449.50</td>\n",
+       "      <td>16.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>50728.89</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>14271.11</td>\n",
+       "      <td>21.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>41075.73</td>\n",
+       "      <td>27200.00</td>\n",
+       "      <td>-13875.73</td>\n",
+       "      <td>51.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>19335.34</td>\n",
+       "      <td>55555.00</td>\n",
+       "      <td>36219.66</td>\n",
+       "      <td>65.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>5643.45</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>656.55</td>\n",
+       "      <td>10.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>15691.29</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>7208.71</td>\n",
+       "      <td>31.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>13733.33</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>3066.67</td>\n",
+       "      <td>18.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>13459.92</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>-1159.92</td>\n",
+       "      <td>9.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>12311.47</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-611.47</td>\n",
+       "      <td>5.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>7727.53</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>3472.47</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>11580.54</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-2580.54</td>\n",
+       "      <td>28.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>17489.92</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>510.08</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>23967.81</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>3532.19</td>\n",
+       "      <td>12.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>11200.20</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-1201.20</td>\n",
+       "      <td>12.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>9279.28</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>320.72</td>\n",
+       "      <td>3.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>12096.18</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-596.18</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>44407.14</td>\n",
+       "      <td>59999.00</td>\n",
+       "      <td>15591.86</td>\n",
+       "      <td>25.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>12006.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>5893.34</td>\n",
+       "      <td>32.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>66032.76</td>\n",
+       "      <td>107999.00</td>\n",
+       "      <td>41966.24</td>\n",
+       "      <td>38.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>11095.57</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-2096.57</td>\n",
+       "      <td>23.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>3787.35</td>\n",
+       "      <td>32.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10358.58</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>1641.42</td>\n",
+       "      <td>13.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>1815.28</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>1284.72</td>\n",
+       "      <td>41.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>6839.57</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>810.43</td>\n",
+       "      <td>10.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>8768.29</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-1068.29</td>\n",
+       "      <td>13.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>11969.66</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>930.34</td>\n",
+       "      <td>7.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>4307.68</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>-907.68</td>\n",
+       "      <td>26.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>8298.32</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>3601.68</td>\n",
+       "      <td>30.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>7772.57</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-572.57</td>\n",
+       "      <td>7.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>25040.42</td>\n",
+       "      <td>37000.00</td>\n",
+       "      <td>11959.58</td>\n",
+       "      <td>32.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>8351.34</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-1851.34</td>\n",
+       "      <td>28.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>9800.43</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-300.43</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>10889.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>2010.61</td>\n",
+       "      <td>15.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>44680.77</td>\n",
+       "      <td>51900.00</td>\n",
+       "      <td>7219.23</td>\n",
+       "      <td>13.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>3666.31</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-466.31</td>\n",
+       "      <td>14.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>11443.85</td>\n",
+       "      <td>9499.00</td>\n",
+       "      <td>-1944.85</td>\n",
+       "      <td>20.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>3086.38</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-587.38</td>\n",
+       "      <td>23.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>9192.06</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>1307.94</td>\n",
+       "      <td>12.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>7491.01</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>1108.99</td>\n",
+       "      <td>12.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>15623.39</td>\n",
+       "      <td>20999.00</td>\n",
+       "      <td>5375.61</td>\n",
+       "      <td>25.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>12904.68</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-3404.68</td>\n",
+       "      <td>35.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>32954.19</td>\n",
+       "      <td>52777.00</td>\n",
+       "      <td>19822.81</td>\n",
+       "      <td>37.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>13967.55</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-8467.55</td>\n",
+       "      <td>153.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>9954.42</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>-8154.42</td>\n",
+       "      <td>453.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>23972.56</td>\n",
+       "      <td>36900.00</td>\n",
+       "      <td>12927.44</td>\n",
+       "      <td>35.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>18403.35</td>\n",
+       "      <td>13893.75</td>\n",
+       "      <td>-4509.60</td>\n",
+       "      <td>32.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>9596.94</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>-196.94</td>\n",
+       "      <td>2.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>15292.10</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-6992.10</td>\n",
+       "      <td>84.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>13212.52</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>10786.48</td>\n",
+       "      <td>44.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>11420.95</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>578.05</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>8522.39</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>-2722.39</td>\n",
+       "      <td>46.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>19741.65</td>\n",
+       "      <td>29999.00</td>\n",
+       "      <td>10257.35</td>\n",
+       "      <td>34.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8392.42</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>-1242.42</td>\n",
+       "      <td>17.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>11156.38</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-356.38</td>\n",
+       "      <td>3.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>36516.35</td>\n",
+       "      <td>37900.00</td>\n",
+       "      <td>1383.65</td>\n",
+       "      <td>3.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7745.02</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-1345.02</td>\n",
+       "      <td>21.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>47600.00</td>\n",
+       "      <td>12736.47</td>\n",
+       "      <td>26.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>6732.97</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1667.03</td>\n",
+       "      <td>19.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>8916.19</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>3082.81</td>\n",
+       "      <td>25.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>17316.59</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-3416.59</td>\n",
+       "      <td>24.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>14933.54</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>14566.46</td>\n",
+       "      <td>49.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>19701.70</td>\n",
+       "      <td>49999.00</td>\n",
+       "      <td>30297.30</td>\n",
+       "      <td>60.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>17597.10</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>7902.90</td>\n",
+       "      <td>30.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>10123.04</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>-7323.04</td>\n",
+       "      <td>261.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>47014.52</td>\n",
+       "      <td>95000.00</td>\n",
+       "      <td>47985.48</td>\n",
+       "      <td>50.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>29479.97</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>4520.03</td>\n",
+       "      <td>13.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>6130.96</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>569.04</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>19071.43</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>3828.57</td>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>28256.79</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>6643.21</td>\n",
+       "      <td>19.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>10159.18</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>740.82</td>\n",
+       "      <td>6.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>10786.38</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-2786.38</td>\n",
+       "      <td>34.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>13377.99</td>\n",
+       "      <td>10300.00</td>\n",
+       "      <td>-3077.99</td>\n",
+       "      <td>29.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>15956.95</td>\n",
+       "      <td>29999.00</td>\n",
+       "      <td>14042.05</td>\n",
+       "      <td>46.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>18285.07</td>\n",
+       "      <td>25900.00</td>\n",
+       "      <td>7614.93</td>\n",
+       "      <td>29.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>14832.32</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>-2432.32</td>\n",
+       "      <td>19.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>9310.69</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>-910.69</td>\n",
+       "      <td>10.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>20588.57</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>1311.43</td>\n",
+       "      <td>5.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>5948.04</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-3348.04</td>\n",
+       "      <td>128.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>2126.49</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>423.51</td>\n",
+       "      <td>16.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>17983.10</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>1616.90</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>2784.46</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>-834.46</td>\n",
+       "      <td>42.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>11098.18</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-898.18</td>\n",
+       "      <td>8.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>10094.58</td>\n",
+       "      <td>7777.00</td>\n",
+       "      <td>-2317.58</td>\n",
+       "      <td>29.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>5040.46</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1959.54</td>\n",
+       "      <td>27.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>65613.31</td>\n",
+       "      <td>99999.00</td>\n",
+       "      <td>34385.69</td>\n",
+       "      <td>34.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>7664.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>1335.68</td>\n",
+       "      <td>14.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>13041.39</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>6658.61</td>\n",
+       "      <td>33.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>19920.63</td>\n",
+       "      <td>16000.00</td>\n",
+       "      <td>-3920.63</td>\n",
+       "      <td>24.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>15010.51</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>-1810.51</td>\n",
+       "      <td>13.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>5716.14</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-2716.14</td>\n",
+       "      <td>90.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-1642.62</td>\n",
+       "      <td>24.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>31216.83</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>23783.17</td>\n",
+       "      <td>43.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6848.88</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>1351.12</td>\n",
+       "      <td>16.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>16133.86</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-433.86</td>\n",
+       "      <td>2.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>15872.91</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-1572.91</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>2393.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1106.66</td>\n",
+       "      <td>31.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>5284.06</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-1984.06</td>\n",
+       "      <td>60.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>12974.89</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>5925.11</td>\n",
+       "      <td>31.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>8661.95</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>5337.05</td>\n",
+       "      <td>38.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>11490.44</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>3409.56</td>\n",
+       "      <td>22.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>34376.16</td>\n",
+       "      <td>85000.00</td>\n",
+       "      <td>50623.84</td>\n",
+       "      <td>59.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>13348.91</td>\n",
+       "      <td>15999.00</td>\n",
+       "      <td>2650.09</td>\n",
+       "      <td>16.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>109999.00</td>\n",
+       "      <td>75135.47</td>\n",
+       "      <td>68.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>20662.46</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>-1762.46</td>\n",
+       "      <td>9.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>6199.59</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>7000.41</td>\n",
+       "      <td>53.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>3939.91</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>11560.09</td>\n",
+       "      <td>74.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>6916.13</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>-3016.13</td>\n",
+       "      <td>77.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>11191.05</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1591.05</td>\n",
+       "      <td>16.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>17251.60</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-2951.60</td>\n",
+       "      <td>20.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>15682.82</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-6082.82</td>\n",
+       "      <td>63.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>7719.69</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>1580.31</td>\n",
+       "      <td>16.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>9198.08</td>\n",
+       "      <td>15800.00</td>\n",
+       "      <td>6601.92</td>\n",
+       "      <td>41.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>9312.21</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-2012.21</td>\n",
+       "      <td>27.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>1320.56</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>2379.44</td>\n",
+       "      <td>64.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>6110.94</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>2589.06</td>\n",
+       "      <td>29.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>12731.26</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-2931.26</td>\n",
+       "      <td>29.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>2765.14</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>134.86</td>\n",
+       "      <td>4.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>9024.05</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-4124.05</td>\n",
+       "      <td>84.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>25037.87</td>\n",
+       "      <td>21200.00</td>\n",
+       "      <td>-3837.87</td>\n",
+       "      <td>18.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>26736.21</td>\n",
+       "      <td>16100.00</td>\n",
+       "      <td>-10636.21</td>\n",
+       "      <td>66.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>10581.62</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>417.38</td>\n",
+       "      <td>3.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>10419.20</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>2580.80</td>\n",
+       "      <td>19.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>18432.63</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>12067.37</td>\n",
+       "      <td>39.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>7597.39</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-97.39</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>32925.60</td>\n",
+       "      <td>69500.00</td>\n",
+       "      <td>36574.40</td>\n",
+       "      <td>52.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>7110.32</td>\n",
+       "      <td>8990.00</td>\n",
+       "      <td>1879.68</td>\n",
+       "      <td>20.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>15457.91</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-5657.91</td>\n",
+       "      <td>57.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>41711.18</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>-11211.18</td>\n",
+       "      <td>36.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>13159.16</td>\n",
+       "      <td>16200.00</td>\n",
+       "      <td>3040.84</td>\n",
+       "      <td>18.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>22011.47</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>-8012.47</td>\n",
+       "      <td>57.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>7391.67</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>7808.33</td>\n",
+       "      <td>51.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>5041.20</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-1241.20</td>\n",
+       "      <td>32.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>6978.91</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-478.91</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>9721.50</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>178.50</td>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>18128.32</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-2228.32</td>\n",
+       "      <td>14.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>6413.26</td>\n",
+       "      <td>5999.00</td>\n",
+       "      <td>-414.26</td>\n",
+       "      <td>6.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>24796.09</td>\n",
+       "      <td>46000.00</td>\n",
+       "      <td>21203.91</td>\n",
+       "      <td>46.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>1825.44</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>74.56</td>\n",
+       "      <td>3.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>41503.90</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-4003.90</td>\n",
+       "      <td>10.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>13680.48</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1780.48</td>\n",
+       "      <td>14.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>6067.79</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>132.21</td>\n",
+       "      <td>2.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>6858.89</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>3140.11</td>\n",
+       "      <td>31.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>48535.50</td>\n",
+       "      <td>-4777.31</td>\n",
+       "      <td>9.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>12414.41</td>\n",
+       "      <td>8420.00</td>\n",
+       "      <td>-3994.41</td>\n",
+       "      <td>47.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>9089.35</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-1389.35</td>\n",
+       "      <td>18.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>31059.18</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>10940.82</td>\n",
+       "      <td>26.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>20163.48</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>10336.52</td>\n",
+       "      <td>33.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>109999.00</td>\n",
+       "      <td>44256.43</td>\n",
+       "      <td>40.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>40753.58</td>\n",
+       "      <td>40000.00</td>\n",
+       "      <td>-753.58</td>\n",
+       "      <td>1.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>6893.62</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-493.62</td>\n",
+       "      <td>7.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>4151.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>2348.98</td>\n",
+       "      <td>36.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>8263.95</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-563.95</td>\n",
+       "      <td>7.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>3948.74</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-1748.74</td>\n",
+       "      <td>79.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>9749.53</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>50.47</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>12318.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>581.61</td>\n",
+       "      <td>4.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>9113.05</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-2213.05</td>\n",
+       "      <td>32.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>22547.55</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>-4047.55</td>\n",
+       "      <td>21.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>5002.39</td>\n",
+       "      <td>2999.00</td>\n",
+       "      <td>-2003.39</td>\n",
+       "      <td>66.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>12900.31</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>2099.69</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>60500.00</td>\n",
+       "      <td>7436.78</td>\n",
+       "      <td>12.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>3154.48</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>1145.52</td>\n",
+       "      <td>26.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>12327.10</td>\n",
+       "      <td>12499.00</td>\n",
+       "      <td>171.90</td>\n",
+       "      <td>1.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>11638.89</td>\n",
+       "      <td>13708.50</td>\n",
+       "      <td>2069.61</td>\n",
+       "      <td>15.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>12261.19</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-8161.19</td>\n",
+       "      <td>199.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>7630.64</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-2130.64</td>\n",
+       "      <td>38.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>40765.33</td>\n",
+       "      <td>112000.00</td>\n",
+       "      <td>71234.67</td>\n",
+       "      <td>63.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>34761.74</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-10761.74</td>\n",
+       "      <td>44.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>19659.97</td>\n",
+       "      <td>16700.00</td>\n",
+       "      <td>-2959.97</td>\n",
+       "      <td>17.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>1972.28</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>327.72</td>\n",
+       "      <td>14.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>6635.73</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>-2335.73</td>\n",
+       "      <td>54.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>10950.00</td>\n",
+       "      <td>3771.80</td>\n",
+       "      <td>34.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>1894.40</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>55.60</td>\n",
+       "      <td>2.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>20433.23</td>\n",
+       "      <td>30999.00</td>\n",
+       "      <td>10565.77</td>\n",
+       "      <td>34.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>8962.29</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-1462.29</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>8431.89</td>\n",
+       "      <td>7999.00</td>\n",
+       "      <td>-432.89</td>\n",
+       "      <td>5.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>13471.84</td>\n",
+       "      <td>10600.00</td>\n",
+       "      <td>-2871.84</td>\n",
+       "      <td>27.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>5350.91</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>1549.09</td>\n",
+       "      <td>22.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>21690.99</td>\n",
+       "      <td>30900.00</td>\n",
+       "      <td>9209.01</td>\n",
+       "      <td>29.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>22261.50</td>\n",
+       "      <td>19200.00</td>\n",
+       "      <td>-3061.50</td>\n",
+       "      <td>15.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>17937.36</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-13137.36</td>\n",
+       "      <td>273.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>20183.30</td>\n",
+       "      <td>18999.00</td>\n",
+       "      <td>-1184.30</td>\n",
+       "      <td>6.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>20236.85</td>\n",
+       "      <td>31600.00</td>\n",
+       "      <td>11363.15</td>\n",
+       "      <td>35.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>9782.47</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>417.53</td>\n",
+       "      <td>4.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>4738.89</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>2261.11</td>\n",
+       "      <td>32.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>23496.36</td>\n",
+       "      <td>33000.00</td>\n",
+       "      <td>9503.64</td>\n",
+       "      <td>28.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>6546.81</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-2146.81</td>\n",
+       "      <td>48.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>10505.98</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>3394.02</td>\n",
+       "      <td>24.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>3164.66</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-665.66</td>\n",
+       "      <td>26.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>24974.57</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>3925.43</td>\n",
+       "      <td>13.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>8967.74</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-67.74</td>\n",
+       "      <td>0.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>6510.80</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-1510.80</td>\n",
+       "      <td>30.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>9205.10</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>3594.90</td>\n",
+       "      <td>28.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>51287.19</td>\n",
+       "      <td>52055.25</td>\n",
+       "      <td>768.06</td>\n",
+       "      <td>1.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>14792.29</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>4007.71</td>\n",
+       "      <td>21.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>10014.71</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>2485.29</td>\n",
+       "      <td>19.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>5811.74</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>188.26</td>\n",
+       "      <td>3.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>-290.49</td>\n",
+       "      <td>6.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>65887.51</td>\n",
+       "      <td>114999.00</td>\n",
+       "      <td>49111.49</td>\n",
+       "      <td>42.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>12247.90</td>\n",
+       "      <td>12750.00</td>\n",
+       "      <td>502.10</td>\n",
+       "      <td>3.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>39744.80</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>-4844.80</td>\n",
+       "      <td>13.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>10612.51</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>1787.49</td>\n",
+       "      <td>14.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>10350.25</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>3149.75</td>\n",
+       "      <td>23.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>23630.85</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>-4130.85</td>\n",
+       "      <td>21.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>65210.35</td>\n",
+       "      <td>115800.00</td>\n",
+       "      <td>50589.65</td>\n",
+       "      <td>43.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>2801.47</td>\n",
+       "      <td>28.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>8316.92</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>1283.08</td>\n",
+       "      <td>13.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>9057.77</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>5941.23</td>\n",
+       "      <td>39.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>9356.43</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-3856.43</td>\n",
+       "      <td>70.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>10716.00</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>4184.00</td>\n",
+       "      <td>28.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>15067.85</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>-867.85</td>\n",
+       "      <td>6.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>7327.18</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-127.18</td>\n",
+       "      <td>1.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>11336.71</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>963.29</td>\n",
+       "      <td>7.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>11002.01</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>2897.99</td>\n",
+       "      <td>20.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>14591.35</td>\n",
+       "      <td>16900.00</td>\n",
+       "      <td>2308.65</td>\n",
+       "      <td>13.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>12452.22</td>\n",
+       "      <td>11960.00</td>\n",
+       "      <td>-492.22</td>\n",
+       "      <td>4.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>12414.72</td>\n",
+       "      <td>23000.00</td>\n",
+       "      <td>10585.28</td>\n",
+       "      <td>46.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>13004.34</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-2304.34</td>\n",
+       "      <td>21.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>8250.47</td>\n",
+       "      <td>9950.00</td>\n",
+       "      <td>1699.53</td>\n",
+       "      <td>17.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>5374.74</td>\n",
+       "      <td>3750.00</td>\n",
+       "      <td>-1624.74</td>\n",
+       "      <td>43.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "      <td>37294.64</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>11099.09</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-4100.09</td>\n",
+       "      <td>58.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>14815.83</td>\n",
+       "      <td>15400.00</td>\n",
+       "      <td>584.17</td>\n",
+       "      <td>3.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>12897.92</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-3397.92</td>\n",
+       "      <td>35.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>14572.70</td>\n",
+       "      <td>27900.00</td>\n",
+       "      <td>13327.30</td>\n",
+       "      <td>47.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>60603.06</td>\n",
+       "      <td>124000.00</td>\n",
+       "      <td>63396.94</td>\n",
+       "      <td>51.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>11937.31</td>\n",
+       "      <td>13995.00</td>\n",
+       "      <td>2057.69</td>\n",
+       "      <td>14.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>28951.20</td>\n",
+       "      <td>45500.00</td>\n",
+       "      <td>16548.80</td>\n",
+       "      <td>36.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>32945.80</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-9445.80</td>\n",
+       "      <td>40.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>26441.03</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-10541.03</td>\n",
+       "      <td>66.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-2701.29</td>\n",
+       "      <td>28.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>2020.25</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>1679.75</td>\n",
+       "      <td>45.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>20246.04</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>3253.96</td>\n",
+       "      <td>13.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>19692.08</td>\n",
+       "      <td>-6259.23</td>\n",
+       "      <td>31.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>5238.05</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-1238.05</td>\n",
+       "      <td>30.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>9753.44</td>\n",
+       "      <td>20400.00</td>\n",
+       "      <td>10646.56</td>\n",
+       "      <td>52.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>16717.08</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-1217.08</td>\n",
+       "      <td>7.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>22454.74</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>13045.26</td>\n",
+       "      <td>36.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>10133.68</td>\n",
+       "      <td>8750.00</td>\n",
+       "      <td>-1383.68</td>\n",
+       "      <td>15.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>11018.79</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>4681.21</td>\n",
+       "      <td>29.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>63921.33</td>\n",
+       "      <td>80999.00</td>\n",
+       "      <td>17077.67</td>\n",
+       "      <td>21.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>-525.77</td>\n",
+       "      <td>13.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>4770.52</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>729.48</td>\n",
+       "      <td>13.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>8688.92</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>3011.08</td>\n",
+       "      <td>25.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>27293.13</td>\n",
+       "      <td>39040.00</td>\n",
+       "      <td>11746.87</td>\n",
+       "      <td>30.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>44193.38</td>\n",
+       "      <td>75500.00</td>\n",
+       "      <td>31306.62</td>\n",
+       "      <td>41.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>21681.00</td>\n",
+       "      <td>30999.00</td>\n",
+       "      <td>9318.00</td>\n",
+       "      <td>30.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>10547.82</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>8952.18</td>\n",
+       "      <td>45.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>13420.15</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-6421.15</td>\n",
+       "      <td>91.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>42703.68</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>-203.68</td>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>8415.81</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>384.19</td>\n",
+       "      <td>4.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>3084.69</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>15.31</td>\n",
+       "      <td>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>10835.95</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>3664.05</td>\n",
+       "      <td>25.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>9661.78</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2861.78</td>\n",
+       "      <td>42.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>8110.06</td>\n",
+       "      <td>10990.00</td>\n",
+       "      <td>2879.94</td>\n",
+       "      <td>26.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>10821.19</td>\n",
+       "      <td>8299.00</td>\n",
+       "      <td>-2522.19</td>\n",
+       "      <td>30.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>24427.14</td>\n",
+       "      <td>36000.00</td>\n",
+       "      <td>11572.86</td>\n",
+       "      <td>32.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>105999.00</td>\n",
+       "      <td>40256.43</td>\n",
+       "      <td>37.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>5391.77</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>608.23</td>\n",
+       "      <td>10.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>9909.62</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1209.62</td>\n",
+       "      <td>13.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>2749.08</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>550.92</td>\n",
+       "      <td>16.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>17654.84</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>145.16</td>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>3473.79</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>26.21</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>33499.44</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>1500.56</td>\n",
+       "      <td>4.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>7918.89</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-5518.89</td>\n",
+       "      <td>229.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>13757.58</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-4857.58</td>\n",
+       "      <td>54.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>4296.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>703.23</td>\n",
+       "      <td>14.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>13235.55</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-4236.55</td>\n",
+       "      <td>47.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>7267.36</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>832.64</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>44173.72</td>\n",
+       "      <td>44999.00</td>\n",
+       "      <td>825.28</td>\n",
+       "      <td>1.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>11474.22</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>8524.78</td>\n",
+       "      <td>42.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2810.71</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-360.71</td>\n",
+       "      <td>14.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>24271.90</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-3371.90</td>\n",
+       "      <td>16.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>25302.43</td>\n",
+       "      <td>52000.00</td>\n",
+       "      <td>26697.57</td>\n",
+       "      <td>51.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>8139.48</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-1439.48</td>\n",
+       "      <td>21.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>13600.39</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>12199.61</td>\n",
+       "      <td>47.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>9076.42</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-576.42</td>\n",
+       "      <td>6.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>21303.57</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>2596.43</td>\n",
+       "      <td>10.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>16165.79</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-11765.79</td>\n",
+       "      <td>267.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>7973.87</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-4323.87</td>\n",
+       "      <td>118.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8707.13</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-207.13</td>\n",
+       "      <td>2.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>14410.15</td>\n",
+       "      <td>18932.55</td>\n",
+       "      <td>4522.40</td>\n",
+       "      <td>23.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>25986.31</td>\n",
+       "      <td>31310.00</td>\n",
+       "      <td>5323.69</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>9070.23</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>5429.77</td>\n",
+       "      <td>37.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>8850.21</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1349.79</td>\n",
+       "      <td>13.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>2839.03</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>1460.97</td>\n",
+       "      <td>33.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>8648.80</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-4248.80</td>\n",
+       "      <td>96.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>6425.00</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>2975.00</td>\n",
+       "      <td>31.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>24874.86</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-875.86</td>\n",
+       "      <td>3.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>21097.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-3197.66</td>\n",
+       "      <td>17.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>65448.37</td>\n",
+       "      <td>40500.00</td>\n",
+       "      <td>-24948.37</td>\n",
+       "      <td>61.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>8121.22</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>2078.78</td>\n",
+       "      <td>20.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>16407.29</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-1607.29</td>\n",
+       "      <td>10.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>13409.47</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-4709.47</td>\n",
+       "      <td>54.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>7609.28</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-609.28</td>\n",
+       "      <td>8.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>12234.68</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-3734.68</td>\n",
+       "      <td>43.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>14363.76</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-4863.76</td>\n",
+       "      <td>51.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8117.44</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>782.56</td>\n",
+       "      <td>8.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>22367.52</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>6132.48</td>\n",
+       "      <td>21.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1495.06</td>\n",
+       "      <td>65.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11378.43</td>\n",
+       "      <td>18600.00</td>\n",
+       "      <td>7221.57</td>\n",
+       "      <td>38.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>2008.91</td>\n",
+       "      <td>10.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>14576.33</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-3576.33</td>\n",
+       "      <td>32.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>8033.61</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>1716.39</td>\n",
+       "      <td>17.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>53294.61</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>3705.39</td>\n",
+       "      <td>6.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>74232.55</td>\n",
+       "      <td>103999.00</td>\n",
+       "      <td>29766.45</td>\n",
+       "      <td>28.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>50074.56</td>\n",
+       "      <td>69990.00</td>\n",
+       "      <td>19915.44</td>\n",
+       "      <td>28.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>3940.49</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1058.51</td>\n",
+       "      <td>21.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>13542.79</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-3342.79</td>\n",
+       "      <td>32.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>5462.52</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-2762.52</td>\n",
+       "      <td>102.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>2389.14</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>-739.14</td>\n",
+       "      <td>44.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>67952.07</td>\n",
+       "      <td>53500.00</td>\n",
+       "      <td>-14452.07</td>\n",
+       "      <td>27.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>14197.26</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>-1797.26</td>\n",
+       "      <td>14.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>16253.48</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-4753.48</td>\n",
+       "      <td>41.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>2975.40</td>\n",
+       "      <td>1600.00</td>\n",
+       "      <td>-1375.40</td>\n",
+       "      <td>85.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>6977.90</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-477.90</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>8843.22</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-6343.22</td>\n",
+       "      <td>253.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>65500.00</td>\n",
+       "      <td>12436.78</td>\n",
+       "      <td>18.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>40893.84</td>\n",
+       "      <td>126000.00</td>\n",
+       "      <td>85106.16</td>\n",
+       "      <td>67.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>18866.50</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>-566.50</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>16257.03</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>741.97</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>14926.69</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>2873.31</td>\n",
+       "      <td>16.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>8638.63</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>3161.37</td>\n",
+       "      <td>26.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>11619.16</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>-1619.16</td>\n",
+       "      <td>16.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>18988.13</td>\n",
+       "      <td>-6963.18</td>\n",
+       "      <td>36.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>3735.04</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1263.96</td>\n",
+       "      <td>25.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>38334.67</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>-6334.67</td>\n",
+       "      <td>19.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-2212.65</td>\n",
+       "      <td>40.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>9363.27</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-463.27</td>\n",
+       "      <td>5.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>87777.00</td>\n",
+       "      <td>52913.47</td>\n",
+       "      <td>60.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>13502.35</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>997.65</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>9495.62</td>\n",
+       "      <td>8650.00</td>\n",
+       "      <td>-845.62</td>\n",
+       "      <td>9.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>14218.94</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-718.94</td>\n",
+       "      <td>5.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>35956.50</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-29456.50</td>\n",
+       "      <td>453.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>39882.67</td>\n",
+       "      <td>37700.00</td>\n",
+       "      <td>-2182.67</td>\n",
+       "      <td>5.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>8381.60</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-1382.60</td>\n",
+       "      <td>19.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>20972.54</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>-3472.54</td>\n",
+       "      <td>19.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>10946.44</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-4446.44</td>\n",
+       "      <td>68.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>19624.73</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>-6924.73</td>\n",
+       "      <td>54.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>16821.54</td>\n",
+       "      <td>26800.00</td>\n",
+       "      <td>9978.46</td>\n",
+       "      <td>37.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>14136.01</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-2636.01</td>\n",
+       "      <td>22.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>2779.04</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-479.04</td>\n",
+       "      <td>20.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>50954.68</td>\n",
+       "      <td>61000.00</td>\n",
+       "      <td>10045.32</td>\n",
+       "      <td>16.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>31656.01</td>\n",
+       "      <td>24700.00</td>\n",
+       "      <td>-6956.01</td>\n",
+       "      <td>28.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>43236.92</td>\n",
+       "      <td>40500.00</td>\n",
+       "      <td>-2736.92</td>\n",
+       "      <td>6.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>35093.27</td>\n",
+       "      <td>19990.00</td>\n",
+       "      <td>-15103.27</td>\n",
+       "      <td>75.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>4266.27</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>933.73</td>\n",
+       "      <td>17.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>6537.82</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-3837.82</td>\n",
+       "      <td>142.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25975.12</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>-4075.12</td>\n",
+       "      <td>18.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>22262.80</td>\n",
+       "      <td>22711.65</td>\n",
+       "      <td>448.85</td>\n",
+       "      <td>1.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12558.90</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-3559.90</td>\n",
+       "      <td>39.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>65825.25</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>39173.75</td>\n",
+       "      <td>37.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>7045.72</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-545.72</td>\n",
+       "      <td>8.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-709.17</td>\n",
+       "      <td>20.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>20367.25</td>\n",
+       "      <td>22700.00</td>\n",
+       "      <td>2332.75</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>1792.36</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>206.64</td>\n",
+       "      <td>10.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>10932.17</td>\n",
+       "      <td>13800.00</td>\n",
+       "      <td>2867.83</td>\n",
+       "      <td>20.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>5570.46</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>3229.54</td>\n",
+       "      <td>36.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>15700.53</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>2799.47</td>\n",
+       "      <td>15.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15374.08</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-6474.08</td>\n",
+       "      <td>72.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36764.38</td>\n",
+       "      <td>62000.00</td>\n",
+       "      <td>25235.62</td>\n",
+       "      <td>40.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>36883.77</td>\n",
+       "      <td>49000.00</td>\n",
+       "      <td>12116.23</td>\n",
+       "      <td>24.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>31369.37</td>\n",
+       "      <td>32500.00</td>\n",
+       "      <td>1130.63</td>\n",
+       "      <td>3.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>6599.62</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>1200.38</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>12027.56</td>\n",
+       "      <td>16200.00</td>\n",
+       "      <td>4172.44</td>\n",
+       "      <td>25.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>28937.50</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>5962.50</td>\n",
+       "      <td>17.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>13334.19</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>-1734.19</td>\n",
+       "      <td>14.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>9664.46</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-7764.46</td>\n",
+       "      <td>408.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>13884.26</td>\n",
+       "      <td>27800.00</td>\n",
+       "      <td>13915.74</td>\n",
+       "      <td>50.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>10824.89</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-4124.89</td>\n",
+       "      <td>61.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>4043.74</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>-1143.74</td>\n",
+       "      <td>39.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>28910.96</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>13589.04</td>\n",
+       "      <td>31.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>8902.52</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-2402.52</td>\n",
+       "      <td>36.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>6503.62</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-1603.62</td>\n",
+       "      <td>32.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>7161.67</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-4961.67</td>\n",
+       "      <td>225.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>5484.97</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-485.97</td>\n",
+       "      <td>9.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>33392.40</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>8607.60</td>\n",
+       "      <td>20.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>3747.21</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>1152.79</td>\n",
+       "      <td>23.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>17540.72</td>\n",
+       "      <td>14899.00</td>\n",
+       "      <td>-2641.72</td>\n",
+       "      <td>17.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>50099.92</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>1400.08</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>7950.27</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>4049.73</td>\n",
+       "      <td>33.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>12270.01</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>3629.99</td>\n",
+       "      <td>22.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>8973.16</td>\n",
+       "      <td>12750.00</td>\n",
+       "      <td>3776.84</td>\n",
+       "      <td>29.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>60493.51</td>\n",
+       "      <td>75000.00</td>\n",
+       "      <td>14506.49</td>\n",
+       "      <td>19.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>9984.87</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-6984.87</td>\n",
+       "      <td>232.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>6392.70</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>907.30</td>\n",
+       "      <td>12.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>23272.83</td>\n",
+       "      <td>25749.75</td>\n",
+       "      <td>2476.92</td>\n",
+       "      <td>9.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>10105.70</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>4894.30</td>\n",
+       "      <td>32.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>3590.97</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-390.97</td>\n",
+       "      <td>12.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>18282.75</td>\n",
+       "      <td>21950.00</td>\n",
+       "      <td>3667.25</td>\n",
+       "      <td>16.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>16197.42</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-1197.42</td>\n",
+       "      <td>7.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>5839.08</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1360.92</td>\n",
+       "      <td>18.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1873.03</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>1526.97</td>\n",
+       "      <td>44.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>17282.06</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-5382.06</td>\n",
+       "      <td>45.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>15144.28</td>\n",
+       "      <td>29000.00</td>\n",
+       "      <td>13855.72</td>\n",
+       "      <td>47.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>5346.97</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-1246.97</td>\n",
+       "      <td>30.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>15015.22</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-4515.22</td>\n",
+       "      <td>43.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>32121.20</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>8878.80</td>\n",
+       "      <td>21.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>11858.48</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1058.48</td>\n",
+       "      <td>9.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>9424.34</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>2575.66</td>\n",
+       "      <td>21.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>21297.65</td>\n",
+       "      <td>19800.00</td>\n",
+       "      <td>-1497.65</td>\n",
+       "      <td>7.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>8667.12</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1767.12</td>\n",
+       "      <td>25.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>16773.62</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-6273.62</td>\n",
+       "      <td>59.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>27740.81</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-4240.81</td>\n",
+       "      <td>18.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>17011.43</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-5311.43</td>\n",
+       "      <td>45.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>10809.48</td>\n",
+       "      <td>15400.00</td>\n",
+       "      <td>4590.52</td>\n",
+       "      <td>29.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>5863.41</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1063.41</td>\n",
+       "      <td>22.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>2373.66</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>326.34</td>\n",
+       "      <td>12.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>29049.27</td>\n",
+       "      <td>28350.00</td>\n",
+       "      <td>-699.27</td>\n",
+       "      <td>2.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>9829.53</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1129.53</td>\n",
+       "      <td>12.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>18555.09</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>244.91</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>63000.00</td>\n",
+       "      <td>9687.19</td>\n",
+       "      <td>15.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>17714.62</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-6314.62</td>\n",
+       "      <td>55.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>47047.70</td>\n",
+       "      <td>52300.00</td>\n",
+       "      <td>5252.30</td>\n",
+       "      <td>10.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>10019.90</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>-8219.90</td>\n",
+       "      <td>456.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>24642.50</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-642.50</td>\n",
+       "      <td>2.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>16939.86</td>\n",
+       "      <td>19400.00</td>\n",
+       "      <td>2460.14</td>\n",
+       "      <td>12.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>34678.95</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>8821.05</td>\n",
+       "      <td>20.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>6746.43</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-746.43</td>\n",
+       "      <td>12.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>9139.04</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>2660.96</td>\n",
+       "      <td>22.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>2008.91</td>\n",
+       "      <td>10.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>6781.86</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>-2081.86</td>\n",
+       "      <td>44.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>4666.05</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-166.05</td>\n",
+       "      <td>3.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>9369.66</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>5630.34</td>\n",
+       "      <td>37.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>5947.51</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>552.49</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>9489.56</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>1410.44</td>\n",
+       "      <td>12.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>12354.92</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>15145.08</td>\n",
+       "      <td>55.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>6826.78</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>473.22</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-2947.48</td>\n",
+       "      <td>77.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>9175.06</td>\n",
+       "      <td>7499.00</td>\n",
+       "      <td>-1676.06</td>\n",
+       "      <td>22.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>46465.02</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>-6565.02</td>\n",
+       "      <td>16.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>3816.33</td>\n",
+       "      <td>3750.00</td>\n",
+       "      <td>-66.33</td>\n",
+       "      <td>1.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>14915.98</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-7015.98</td>\n",
+       "      <td>88.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>11555.80</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>-4155.80</td>\n",
+       "      <td>56.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>5983.44</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>2516.56</td>\n",
+       "      <td>29.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>16392.22</td>\n",
+       "      <td>17700.00</td>\n",
+       "      <td>1307.78</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>8340.94</td>\n",
+       "      <td>3350.00</td>\n",
+       "      <td>-4990.94</td>\n",
+       "      <td>148.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>6610.04</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>3189.96</td>\n",
+       "      <td>32.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>18231.44</td>\n",
+       "      <td>18600.00</td>\n",
+       "      <td>368.56</td>\n",
+       "      <td>1.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>12523.73</td>\n",
+       "      <td>16000.00</td>\n",
+       "      <td>3476.27</td>\n",
+       "      <td>21.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1301.47</td>\n",
+       "      <td>15.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11377.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>621.16</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>33172.20</td>\n",
+       "      <td>40800.00</td>\n",
+       "      <td>7627.80</td>\n",
+       "      <td>18.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>6429.03</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>70.97</td>\n",
+       "      <td>1.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9593.52</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>1406.48</td>\n",
+       "      <td>12.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>2285.24</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1214.76</td>\n",
+       "      <td>34.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>25682.58</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>-8382.58</td>\n",
+       "      <td>48.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>7324.63</td>\n",
+       "      <td>7100.00</td>\n",
+       "      <td>-224.63</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>11881.05</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-3681.05</td>\n",
+       "      <td>44.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>7279.97</td>\n",
+       "      <td>11950.00</td>\n",
+       "      <td>4670.03</td>\n",
+       "      <td>39.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7940.44</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>3059.56</td>\n",
+       "      <td>27.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>1926.60</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>473.40</td>\n",
+       "      <td>19.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>23163.87</td>\n",
+       "      <td>24171.42</td>\n",
+       "      <td>1007.55</td>\n",
+       "      <td>4.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>26331.41</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-15131.41</td>\n",
+       "      <td>135.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>10597.72</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-2297.72</td>\n",
+       "      <td>27.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>33707.27</td>\n",
+       "      <td>73900.00</td>\n",
+       "      <td>40192.73</td>\n",
+       "      <td>54.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>12583.52</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>416.48</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>1858.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1641.66</td>\n",
+       "      <td>46.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>15954.72</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-2955.72</td>\n",
+       "      <td>22.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>14486.38</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-5486.38</td>\n",
+       "      <td>60.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>3205.38</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-1305.38</td>\n",
+       "      <td>68.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>3411.53</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>288.47</td>\n",
+       "      <td>7.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>6205.48</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-1205.48</td>\n",
+       "      <td>24.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>3246.94</td>\n",
+       "      <td>3390.00</td>\n",
+       "      <td>143.06</td>\n",
+       "      <td>4.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>3275.83</td>\n",
+       "      <td>29.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>3425.23</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>2074.77</td>\n",
+       "      <td>37.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>11432.63</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-2532.63</td>\n",
+       "      <td>28.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>39074.36</td>\n",
+       "      <td>69999.00</td>\n",
+       "      <td>30924.64</td>\n",
+       "      <td>44.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>9348.90</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2548.90</td>\n",
+       "      <td>37.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>15259.81</td>\n",
+       "      <td>26000.00</td>\n",
+       "      <td>10740.19</td>\n",
+       "      <td>41.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>12255.45</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>9744.55</td>\n",
+       "      <td>44.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>3173.48</td>\n",
+       "      <td>5100.00</td>\n",
+       "      <td>1926.52</td>\n",
+       "      <td>37.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>14512.61</td>\n",
+       "      <td>25999.00</td>\n",
+       "      <td>11486.39</td>\n",
+       "      <td>44.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>10115.13</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-315.13</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>28154.84</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>10345.16</td>\n",
+       "      <td>26.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>17908.00</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>-408.00</td>\n",
+       "      <td>2.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>3001.37</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>998.63</td>\n",
+       "      <td>24.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>25576.58</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>2923.42</td>\n",
+       "      <td>10.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>25685.67</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>-6185.67</td>\n",
+       "      <td>31.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>7797.18</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-798.18</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>8767.54</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>932.46</td>\n",
+       "      <td>9.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>4590.16</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>909.84</td>\n",
+       "      <td>16.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>21816.43</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>1683.57</td>\n",
+       "      <td>7.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>35371.16</td>\n",
+       "      <td>34600.00</td>\n",
+       "      <td>-771.16</td>\n",
+       "      <td>2.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>16427.12</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-3127.12</td>\n",
+       "      <td>23.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>14513.46</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>386.54</td>\n",
+       "      <td>2.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>31373.47</td>\n",
+       "      <td>20859.15</td>\n",
+       "      <td>-10514.32</td>\n",
+       "      <td>50.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>8940.39</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>4559.61</td>\n",
+       "      <td>33.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>20680.40</td>\n",
+       "      <td>27000.00</td>\n",
+       "      <td>6319.60</td>\n",
+       "      <td>23.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>33000.00</td>\n",
+       "      <td>4501.09</td>\n",
+       "      <td>13.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>29127.72</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>-4627.72</td>\n",
+       "      <td>18.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>3040.77</td>\n",
+       "      <td>1200.00</td>\n",
+       "      <td>-1840.77</td>\n",
+       "      <td>153.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>9269.68</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-1069.68</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>27225.34</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-13725.34</td>\n",
+       "      <td>101.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>2953.17</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>46.83</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>13853.36</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-3053.36</td>\n",
+       "      <td>28.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>19298.61</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-4299.61</td>\n",
+       "      <td>28.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>7869.47</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>630.53</td>\n",
+       "      <td>7.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>13500.69</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>2399.31</td>\n",
+       "      <td>15.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>2573.48</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>2326.52</td>\n",
+       "      <td>47.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>31251.89</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-7751.89</td>\n",
+       "      <td>32.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>16095.32</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-12495.32</td>\n",
+       "      <td>347.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>25752.19</td>\n",
+       "      <td>30990.00</td>\n",
+       "      <td>5237.81</td>\n",
+       "      <td>16.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>38473.71</td>\n",
+       "      <td>119000.00</td>\n",
+       "      <td>80526.29</td>\n",
+       "      <td>67.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>4472.47</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-272.47</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>23227.15</td>\n",
+       "      <td>31900.00</td>\n",
+       "      <td>8672.85</td>\n",
+       "      <td>27.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>16924.74</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-6925.74</td>\n",
+       "      <td>69.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>23485.45</td>\n",
+       "      <td>18777.00</td>\n",
+       "      <td>-4708.45</td>\n",
+       "      <td>25.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>18621.12</td>\n",
+       "      <td>24100.00</td>\n",
+       "      <td>5478.88</td>\n",
+       "      <td>22.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>13074.74</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-1574.74</td>\n",
+       "      <td>13.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>2539.57</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>1860.43</td>\n",
+       "      <td>42.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>7319.77</td>\n",
+       "      <td>1850.00</td>\n",
+       "      <td>-5469.77</td>\n",
+       "      <td>295.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>8734.58</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>65.42</td>\n",
+       "      <td>0.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>7256.86</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1243.14</td>\n",
+       "      <td>14.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>15383.35</td>\n",
+       "      <td>14990.00</td>\n",
+       "      <td>-393.35</td>\n",
+       "      <td>2.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>11056.52</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-4656.52</td>\n",
+       "      <td>72.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>18165.16</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>5734.84</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>3818.71</td>\n",
+       "      <td>1450.00</td>\n",
+       "      <td>-2368.71</td>\n",
+       "      <td>163.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>28950.00</td>\n",
+       "      <td>451.09</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>35662.26</td>\n",
+       "      <td>40000.00</td>\n",
+       "      <td>4337.74</td>\n",
+       "      <td>10.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-942.62</td>\n",
+       "      <td>12.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>30628.28</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-25629.28</td>\n",
+       "      <td>512.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>14402.34</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>-4402.34</td>\n",
+       "      <td>44.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>17334.11</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>1165.89</td>\n",
+       "      <td>6.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>8721.97</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>277.03</td>\n",
+       "      <td>3.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>8156.11</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>1743.89</td>\n",
+       "      <td>17.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>67431.00</td>\n",
+       "      <td>14118.19</td>\n",
+       "      <td>20.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>10740.09</td>\n",
+       "      <td>19100.00</td>\n",
+       "      <td>8359.91</td>\n",
+       "      <td>43.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>3450.69</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>2649.31</td>\n",
+       "      <td>43.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2625.10</td>\n",
+       "      <td>2150.00</td>\n",
+       "      <td>-475.10</td>\n",
+       "      <td>22.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>21174.10</td>\n",
+       "      <td>21335.00</td>\n",
+       "      <td>160.90</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>11147.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-647.91</td>\n",
+       "      <td>6.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>8236.36</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1036.36</td>\n",
+       "      <td>14.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3026.25</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>-476.25</td>\n",
+       "      <td>18.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>17195.57</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-4695.57</td>\n",
+       "      <td>37.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>10332.54</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>-1032.54</td>\n",
+       "      <td>11.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>4032.10</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>3617.90</td>\n",
+       "      <td>47.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>7814.16</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1685.84</td>\n",
+       "      <td>17.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>35039.40</td>\n",
+       "      <td>85555.00</td>\n",
+       "      <td>50515.60</td>\n",
+       "      <td>59.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>32481.05</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-26481.05</td>\n",
+       "      <td>441.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>10563.39</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>9936.61</td>\n",
+       "      <td>48.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>3621.08</td>\n",
+       "      <td>4799.00</td>\n",
+       "      <td>1177.92</td>\n",
+       "      <td>24.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>7809.13</td>\n",
+       "      <td>2899.00</td>\n",
+       "      <td>-4910.13</td>\n",
+       "      <td>169.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>10303.21</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>2996.79</td>\n",
+       "      <td>22.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>9232.79</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>-1782.79</td>\n",
+       "      <td>23.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>2613.97</td>\n",
+       "      <td>3099.00</td>\n",
+       "      <td>485.03</td>\n",
+       "      <td>15.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-2890.49</td>\n",
+       "      <td>170.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>665</th>\n",
+       "      <td>17858.02</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>141.98</td>\n",
+       "      <td>0.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>65723.62</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>39275.38</td>\n",
+       "      <td>37.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>49221.63</td>\n",
+       "      <td>52999.00</td>\n",
+       "      <td>3777.37</td>\n",
+       "      <td>7.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>8270.38</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>-3570.38</td>\n",
+       "      <td>75.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>11477.47</td>\n",
+       "      <td>17650.00</td>\n",
+       "      <td>6172.53</td>\n",
+       "      <td>34.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>7385.38</td>\n",
+       "      <td>18.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>2886.35</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>1313.65</td>\n",
+       "      <td>31.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>672</th>\n",
+       "      <td>5679.39</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-1179.39</td>\n",
+       "      <td>26.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>12286.47</td>\n",
+       "      <td>35.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>2381.01</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>318.99</td>\n",
+       "      <td>11.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>13476.90</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>9023.10</td>\n",
+       "      <td>40.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>6861.84</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>438.16</td>\n",
+       "      <td>6.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>9631.08</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-5832.08</td>\n",
+       "      <td>153.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>4569.15</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>2330.85</td>\n",
+       "      <td>33.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>25864.65</td>\n",
+       "      <td>69990.00</td>\n",
+       "      <td>44125.35</td>\n",
+       "      <td>63.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>21493.44</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-12493.44</td>\n",
+       "      <td>138.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>5195.69</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-1095.69</td>\n",
+       "      <td>26.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>682</th>\n",
+       "      <td>5454.34</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-454.34</td>\n",
+       "      <td>9.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>683</th>\n",
+       "      <td>32771.45</td>\n",
+       "      <td>44800.00</td>\n",
+       "      <td>12028.55</td>\n",
+       "      <td>26.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>18942.34</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-5943.34</td>\n",
+       "      <td>45.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>12593.00</td>\n",
+       "      <td>14600.00</td>\n",
+       "      <td>2007.00</td>\n",
+       "      <td>13.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>23576.99</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>-11576.99</td>\n",
+       "      <td>96.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>11004.05</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>3495.95</td>\n",
+       "      <td>24.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>688</th>\n",
+       "      <td>24572.28</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-4072.28</td>\n",
+       "      <td>19.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-724.77</td>\n",
+       "      <td>19.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>11706.93</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>6393.07</td>\n",
+       "      <td>35.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>8467.17</td>\n",
+       "      <td>6950.00</td>\n",
+       "      <td>-1517.17</td>\n",
+       "      <td>21.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>8471.55</td>\n",
+       "      <td>6799.00</td>\n",
+       "      <td>-1672.55</td>\n",
+       "      <td>24.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>12354.16</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>545.84</td>\n",
+       "      <td>4.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>8131.92</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>-4132.92</td>\n",
+       "      <td>103.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>11178.46</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>-2578.46</td>\n",
+       "      <td>29.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>2470.61</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>529.39</td>\n",
+       "      <td>17.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>10031.96</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>7268.04</td>\n",
+       "      <td>42.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>30480.85</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>19.15</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>4255.62</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>1544.38</td>\n",
+       "      <td>26.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>25640.67</td>\n",
+       "      <td>19800.00</td>\n",
+       "      <td>-5840.67</td>\n",
+       "      <td>29.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>7996.00</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-2997.00</td>\n",
+       "      <td>59.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>7011.74</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>3488.26</td>\n",
+       "      <td>33.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>5597.06</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>6401.94</td>\n",
+       "      <td>53.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>6891.99</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-3891.99</td>\n",
+       "      <td>129.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>12976.15</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>4523.85</td>\n",
+       "      <td>25.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>61639.73</td>\n",
+       "      <td>72900.00</td>\n",
+       "      <td>11260.27</td>\n",
+       "      <td>15.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>2725.40</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-125.40</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>11967.39</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-267.39</td>\n",
+       "      <td>2.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>34446.96</td>\n",
+       "      <td>27900.00</td>\n",
+       "      <td>-6546.96</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-295.06</td>\n",
+       "      <td>8.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>19676.51</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>4823.49</td>\n",
+       "      <td>19.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>8530.54</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>5969.46</td>\n",
+       "      <td>41.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>4004.65</td>\n",
+       "      <td>2000.00</td>\n",
+       "      <td>-2004.65</td>\n",
+       "      <td>100.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>23118.07</td>\n",
+       "      <td>22999.00</td>\n",
+       "      <td>-119.07</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>10220.74</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>-1620.74</td>\n",
+       "      <td>18.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>11425.01</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>2074.99</td>\n",
+       "      <td>15.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>4172.84</td>\n",
+       "      <td>7750.00</td>\n",
+       "      <td>3577.16</td>\n",
+       "      <td>46.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>8041.62</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-1042.62</td>\n",
+       "      <td>14.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>11391.95</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>108.05</td>\n",
+       "      <td>0.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>2860.97</td>\n",
+       "      <td>3350.00</td>\n",
+       "      <td>489.03</td>\n",
+       "      <td>14.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>11431.35</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>2567.65</td>\n",
+       "      <td>18.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>30409.09</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>-11509.09</td>\n",
+       "      <td>60.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>6009.63</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>90.37</td>\n",
+       "      <td>1.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>44312.98</td>\n",
+       "      <td>50900.00</td>\n",
+       "      <td>6587.02</td>\n",
+       "      <td>12.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>15742.41</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>1256.59</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>45664.45</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>-10164.45</td>\n",
+       "      <td>28.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>10810.62</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-5810.62</td>\n",
+       "      <td>116.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>5792.10</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>2907.90</td>\n",
+       "      <td>33.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>9817.06</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-6617.06</td>\n",
+       "      <td>206.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>10696.56</td>\n",
+       "      <td>12179.00</td>\n",
+       "      <td>1482.44</td>\n",
+       "      <td>12.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>16027.02</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-1027.02</td>\n",
+       "      <td>6.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>19309.90</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-6809.90</td>\n",
+       "      <td>54.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>11601.33</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-4401.33</td>\n",
+       "      <td>61.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>23336.06</td>\n",
+       "      <td>33200.00</td>\n",
+       "      <td>9863.94</td>\n",
+       "      <td>29.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>10337.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-2837.11</td>\n",
+       "      <td>37.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>43163.25</td>\n",
+       "      <td>-10149.56</td>\n",
+       "      <td>23.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>37704.71</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>-10804.71</td>\n",
+       "      <td>40.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>20216.67</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>4683.33</td>\n",
+       "      <td>18.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>12286.47</td>\n",
+       "      <td>35.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>6658.73</td>\n",
+       "      <td>6250.00</td>\n",
+       "      <td>-408.73</td>\n",
+       "      <td>6.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>19855.31</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-5055.31</td>\n",
+       "      <td>34.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>16960.31</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>38.69</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>5268.17</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>3131.83</td>\n",
+       "      <td>37.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>2379.58</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>620.42</td>\n",
+       "      <td>20.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>6421.18</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-2021.18</td>\n",
+       "      <td>45.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>13355.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-5855.11</td>\n",
+       "      <td>78.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>8453.28</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2446.72</td>\n",
+       "      <td>22.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>48699.98</td>\n",
+       "      <td>77500.00</td>\n",
+       "      <td>28800.02</td>\n",
+       "      <td>37.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>6082.85</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>1367.15</td>\n",
+       "      <td>18.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>10381.62</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-7381.62</td>\n",
+       "      <td>246.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>8493.04</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>4306.96</td>\n",
+       "      <td>33.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>8591.66</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>3408.34</td>\n",
+       "      <td>28.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>6358.55</td>\n",
+       "      <td>4850.00</td>\n",
+       "      <td>-1508.55</td>\n",
+       "      <td>31.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>17028.45</td>\n",
+       "      <td>18700.00</td>\n",
+       "      <td>1671.55</td>\n",
+       "      <td>8.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>15885.66</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>1414.34</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>3752.54</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-1152.54</td>\n",
+       "      <td>44.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>12028.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-1528.91</td>\n",
+       "      <td>14.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>9380.46</td>\n",
+       "      <td>7950.00</td>\n",
+       "      <td>-1430.46</td>\n",
+       "      <td>17.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>10125.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-3425.27</td>\n",
+       "      <td>51.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>13443.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-4443.32</td>\n",
+       "      <td>49.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>9097.13</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1097.13</td>\n",
+       "      <td>13.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>797.71</td>\n",
+       "      <td>6.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>12383.35</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1583.35</td>\n",
+       "      <td>14.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>14049.76</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-3349.76</td>\n",
+       "      <td>31.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>11034.66</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-1234.66</td>\n",
+       "      <td>12.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>18982.15</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-1082.15</td>\n",
+       "      <td>6.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>24323.48</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>-5523.48</td>\n",
+       "      <td>29.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "      <td>37294.64</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.73</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>-151.73</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.07</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1132.07</td>\n",
+       "      <td>11.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.45</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>4377.55</td>\n",
+       "      <td>23.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.75</td>\n",
+       "      <td>68500.00</td>\n",
+       "      <td>41012.25</td>\n",
+       "      <td>59.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.16</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-2691.16</td>\n",
+       "      <td>24.92</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction    Target  Residual  Difference%\n",
+       "0      10685.50   2300.00  -8385.50       364.59\n",
+       "1       3499.26   2800.00   -699.26        24.97\n",
+       "2       7553.29   2500.00  -5053.29       202.13\n",
+       "3       7463.96   6400.00  -1063.96        16.62\n",
+       "4      11353.49   9150.00  -2203.49        24.08\n",
+       "5      21289.80  20000.00  -1289.80         6.45\n",
+       "6      20159.19  38888.00  18728.81        48.16\n",
+       "7      20349.62  16999.00  -3350.62        19.71\n",
+       "8      11581.54  12500.00    918.46         7.35\n",
+       "9      33614.62  41000.00   7385.38        18.01\n",
+       "10      7241.07  12800.00   5558.93        43.43\n",
+       "11      5175.77   5000.00   -175.77         3.52\n",
+       "12      5484.02   7900.00   2415.98        30.58\n",
+       "13     13292.71  16999.00   3706.29        21.80\n",
+       "14      8248.67   9200.00    951.33        10.34\n",
+       "15     10621.84  11999.00   1377.16        11.48\n",
+       "16     23721.58  20500.00  -3221.58        15.72\n",
+       "17     11770.64   9700.00  -2070.64        21.35\n",
+       "18     37600.15  39900.00   2299.85         5.76\n",
+       "19     16178.14  16400.00    221.86         1.35\n",
+       "20     11876.82  15200.00   3323.18        21.86\n",
+       "21     31557.80  24500.00  -7057.80        28.81\n",
+       "22      6102.36   5650.00   -452.36         8.01\n",
+       "23     13111.91  12900.00   -211.91         1.64\n",
+       "24     23650.15  20900.00  -2750.15        13.16\n",
+       "25     45272.25  31990.00 -13282.25        41.52\n",
+       "26      2178.94   3600.00   1421.06        39.47\n",
+       "27      2555.02  11600.00   9044.98        77.97\n",
+       "28     35991.51  43999.00   8007.49        18.20\n",
+       "29     26062.23  42500.00  16437.77        38.68\n",
+       "30     15559.43   7900.00  -7659.43        96.95\n",
+       "31     12858.08  12650.00   -208.08         1.64\n",
+       "32     12459.85  19900.00   7440.15        37.39\n",
+       "33     22941.80  15000.00  -7941.80        52.95\n",
+       "34     19854.79  14000.00  -5854.79        41.82\n",
+       "35     11337.51  20900.00   9562.49        45.75\n",
+       "36     10499.60  12300.00   1800.40        14.64\n",
+       "37     26478.15  20800.00  -5678.15        27.30\n",
+       "38      9919.19  14600.00   4680.81        32.06\n",
+       "39     16724.45  29600.00  12875.55        43.50\n",
+       "40     18672.68  18000.00   -672.68         3.74\n",
+       "41     19845.36  16300.00  -3545.36        21.75\n",
+       "42     23286.27  38500.00  15213.73        39.52\n",
+       "43     21611.83  22400.00    788.17         3.52\n",
+       "44      7241.07  14000.00   6758.93        48.28\n",
+       "45      8512.03   7000.00  -1512.03        21.60\n",
+       "46      3542.72   3000.00   -542.72        18.09\n",
+       "47     26425.14  25999.00   -426.14         1.64\n",
+       "48     77403.06  55000.00 -22403.06        40.73\n",
+       "49      4973.17   4900.00    -73.17         1.49\n",
+       "50      2479.43   2200.00   -279.43        12.70\n",
+       "51      2543.57   3149.25    605.68        19.23\n",
+       "52     10716.84   9800.00   -916.84         9.36\n",
+       "53     20453.89  21000.00    546.11         2.60\n",
+       "54     18575.43  20900.00   2324.57        11.12\n",
+       "55     12674.57  10700.00  -1974.57        18.45\n",
+       "56     15380.04   8800.00  -6580.04        74.77\n",
+       "57     21942.05  25500.00   3557.95        13.95\n",
+       "58     35336.33  49500.00  14163.67        28.61\n",
+       "59      8195.34   7600.00   -595.34         7.83\n",
+       "60     12469.21  12500.00     30.79         0.25\n",
+       "61      7396.87   7300.00    -96.87         1.33\n",
+       "62     10848.85   8500.00  -2348.85        27.63\n",
+       "63      5351.86   3300.00  -2051.86        62.18\n",
+       "64      9611.80  15000.00   5388.20        35.92\n",
+       "65     19847.25  18200.00  -1647.25         9.05\n",
+       "66      3316.44   2300.00  -1016.44        44.19\n",
+       "67      7621.25   4100.00  -3521.25        85.88\n",
+       "68      6717.17   6100.00   -617.17        10.12\n",
+       "69      2440.90   1400.00  -1040.90        74.35\n",
+       "70     50206.83  73500.00  23293.17        31.69\n",
+       "71     15627.03  14500.00  -1127.03         7.77\n",
+       "72     23624.69  22500.00  -1124.69         5.00\n",
+       "73     18577.88  30500.00  11922.12        39.09\n",
+       "74      1746.19   2650.00    903.81        34.11\n",
+       "75      4209.17   5500.00   1290.83        23.47\n",
+       "76      5144.02   6500.00   1355.98        20.86\n",
+       "77      5930.73   5700.00   -230.73         4.05\n",
+       "78     30810.25  29900.00   -910.25         3.04\n",
+       "79     18893.76  15700.00  -3193.76        20.34\n",
+       "80      6819.44   8800.00   1980.56        22.51\n",
+       "81     12891.96   5300.00  -7591.96       143.24\n",
+       "82      7320.42   2600.00  -4720.42       181.55\n",
+       "83      6013.05   4800.00  -1213.05        25.27\n",
+       "84      4364.08   6500.00   2135.92        32.86\n",
+       "85     19001.29  17800.00  -1201.29         6.75\n",
+       "86      8071.70  11100.00   3028.30        27.28\n",
+       "87      2315.71   2400.00     84.29         3.51\n",
+       "88     10807.78   7500.00  -3307.78        44.10\n",
+       "89     11696.25  12900.00   1203.75         9.33\n",
+       "90      9143.17   6800.00  -2343.17        34.46\n",
+       "91     13421.16  13200.00   -221.16         1.68\n",
+       "92      7116.68   9800.00   2683.32        27.38\n",
+       "93     18317.20  10974.21  -7342.99        66.91\n",
+       "94      7724.17   7800.00     75.83         0.97\n",
+       "95     47013.28  55000.00   7986.72        14.52\n",
+       "96      7976.26   7700.00   -276.26         3.59\n",
+       "97      3648.96   2700.00   -948.96        35.15\n",
+       "98      6746.48   8500.00   1753.52        20.63\n",
+       "99      6437.21   8999.00   2561.79        28.47\n",
+       "100    32886.86  39500.00   6613.14        16.74\n",
+       "101     5958.63   6150.00    191.37         3.11\n",
+       "102    28625.56  28900.00    274.44         0.95\n",
+       "103     4488.43   4150.00   -338.43         8.16\n",
+       "104     5521.28   8800.00   3278.72        37.26\n",
+       "105     8882.65  10000.00   1117.35        11.17\n",
+       "106    16547.03  14200.00  -2347.03        16.53\n",
+       "107     7131.92   9800.00   2668.08        27.23\n",
+       "108     7300.05   6500.00   -800.05        12.31\n",
+       "109    12663.54  12200.00   -463.54         3.80\n",
+       "110    25614.14  25500.00   -114.14         0.45\n",
+       "111     3152.62   4999.00   1846.38        36.93\n",
+       "112    22835.48  16950.00  -5885.48        34.72\n",
+       "113     6006.27   6999.00    992.73        14.18\n",
+       "114    27716.14  27300.00   -416.14         1.52\n",
+       "115     6981.34   8800.00   1818.66        20.67\n",
+       "116    15819.50   8300.00  -7519.50        90.60\n",
+       "117     7178.20  11100.00   3921.80        35.33\n",
+       "118    14943.75  17300.00   2356.25        13.62\n",
+       "119    10448.50   8999.00  -1449.50        16.11\n",
+       "120    50728.89  65000.00  14271.11        21.96\n",
+       "121    41075.73  27200.00 -13875.73        51.01\n",
+       "122    19335.34  55555.00  36219.66        65.20\n",
+       "123     5643.45   6300.00    656.55        10.42\n",
+       "124    15691.29  22900.00   7208.71        31.48\n",
+       "125    13733.33  16800.00   3066.67        18.25\n",
+       "126    13459.92  12300.00  -1159.92         9.43\n",
+       "127    12311.47  11700.00   -611.47         5.23\n",
+       "128     7727.53  11200.00   3472.47        31.00\n",
+       "129    11580.54   9000.00  -2580.54        28.67\n",
+       "130    17489.92  18000.00    510.08         2.83\n",
+       "131    23967.81  27500.00   3532.19        12.84\n",
+       "132    11200.20   9999.00  -1201.20        12.01\n",
+       "133     9279.28   9600.00    320.72         3.34\n",
+       "134    12096.18  11500.00   -596.18         5.18\n",
+       "135    44407.14  59999.00  15591.86        25.99\n",
+       "136    12006.66  17900.00   5893.34        32.92\n",
+       "137    66032.76 107999.00  41966.24        38.86\n",
+       "138    11095.57   8999.00  -2096.57        23.30\n",
+       "139     7712.65  11500.00   3787.35        32.93\n",
+       "140    10358.58  12000.00   1641.42        13.68\n",
+       "141     1815.28   3100.00   1284.72        41.44\n",
+       "142     6839.57   7650.00    810.43        10.59\n",
+       "143     8768.29   7700.00  -1068.29        13.87\n",
+       "144    11969.66  12900.00    930.34         7.21\n",
+       "145     4307.68   3400.00   -907.68        26.70\n",
+       "146     8298.32  11900.00   3601.68        30.27\n",
+       "147     7772.57   7200.00   -572.57         7.95\n",
+       "148    25040.42  37000.00  11959.58        32.32\n",
+       "149     8351.34   6500.00  -1851.34        28.48\n",
+       "150     9800.43   9500.00   -300.43         3.16\n",
+       "151    10889.39  12900.00   2010.61        15.59\n",
+       "152    44680.77  51900.00   7219.23        13.91\n",
+       "153     3666.31   3200.00   -466.31        14.57\n",
+       "154    11443.85   9499.00  -1944.85        20.47\n",
+       "155     3086.38   2499.00   -587.38        23.50\n",
+       "156     9192.06  10500.00   1307.94        12.46\n",
+       "157     7491.01   8600.00   1108.99        12.90\n",
+       "158    15623.39  20999.00   5375.61        25.60\n",
+       "159    12904.68   9500.00  -3404.68        35.84\n",
+       "160    32954.19  52777.00  19822.81        37.56\n",
+       "161    13967.55   5500.00  -8467.55       153.96\n",
+       "162     9954.42   1800.00  -8154.42       453.02\n",
+       "163    23972.56  36900.00  12927.44        35.03\n",
+       "164    18403.35  13893.75  -4509.60        32.46\n",
+       "165     9596.94   9400.00   -196.94         2.10\n",
+       "166    15292.10   8300.00  -6992.10        84.24\n",
+       "167    13212.52  23999.00  10786.48        44.95\n",
+       "168    11420.95  11999.00    578.05         4.82\n",
+       "169     8522.39   5800.00  -2722.39        46.94\n",
+       "170    19741.65  29999.00  10257.35        34.19\n",
+       "171     8392.42   7150.00  -1242.42        17.38\n",
+       "172    11156.38  10800.00   -356.38         3.30\n",
+       "173    36516.35  37900.00   1383.65         3.65\n",
+       "174     7745.02   6400.00  -1345.02        21.02\n",
+       "175    34863.53  47600.00  12736.47        26.76\n",
+       "176     6732.97   8400.00   1667.03        19.85\n",
+       "177     8916.19  11999.00   3082.81        25.69\n",
+       "178    17316.59  13900.00  -3416.59        24.58\n",
+       "179    14933.54  29500.00  14566.46        49.38\n",
+       "180    19701.70  49999.00  30297.30        60.60\n",
+       "181    17597.10  25500.00   7902.90        30.99\n",
+       "182    10123.04   2800.00  -7323.04       261.54\n",
+       "183    47014.52  95000.00  47985.48        50.51\n",
+       "184    29479.97  34000.00   4520.03        13.29\n",
+       "185     6130.96   6700.00    569.04         8.49\n",
+       "186    19071.43  22900.00   3828.57        16.72\n",
+       "187    28256.79  34900.00   6643.21        19.03\n",
+       "188    10159.18  10900.00    740.82         6.80\n",
+       "189    10786.38   8000.00  -2786.38        34.83\n",
+       "190    13377.99  10300.00  -3077.99        29.88\n",
+       "191    15956.95  29999.00  14042.05        46.81\n",
+       "192    18285.07  25900.00   7614.93        29.40\n",
+       "193    14832.32  12400.00  -2432.32        19.62\n",
+       "194     9310.69   8400.00   -910.69        10.84\n",
+       "195    20588.57  21900.00   1311.43         5.99\n",
+       "196     5948.04   2600.00  -3348.04       128.77\n",
+       "197     2126.49   2550.00    423.51        16.61\n",
+       "198    17983.10  19600.00   1616.90         8.25\n",
+       "199     2784.46   1950.00   -834.46        42.79\n",
+       "200    11098.18  10200.00   -898.18         8.81\n",
+       "201    10094.58   7777.00  -2317.58        29.80\n",
+       "202     5040.46   7000.00   1959.54        27.99\n",
+       "203    65613.31  99999.00  34385.69        34.39\n",
+       "204     7664.32   9000.00   1335.68        14.84\n",
+       "205    13041.39  19700.00   6658.61        33.80\n",
+       "206    19920.63  16000.00  -3920.63        24.50\n",
+       "207    15010.51  13200.00  -1810.51        13.72\n",
+       "208     5716.14   3000.00  -2716.14        90.54\n",
+       "209     8242.62   6600.00  -1642.62        24.89\n",
+       "210    31216.83  55000.00  23783.17        43.24\n",
+       "211     6848.88   8200.00   1351.12        16.48\n",
+       "212    16133.86  15700.00   -433.86         2.76\n",
+       "213    15872.91  14300.00  -1572.91        11.00\n",
+       "214     2393.34   3500.00   1106.66        31.62\n",
+       "215     5284.06   3300.00  -1984.06        60.12\n",
+       "216    12974.89  18900.00   5925.11        31.35\n",
+       "217     8661.95  13999.00   5337.05        38.12\n",
+       "218    11490.44  14900.00   3409.56        22.88\n",
+       "219    34376.16  85000.00  50623.84        59.56\n",
+       "220    13348.91  15999.00   2650.09        16.56\n",
+       "221    34863.53 109999.00  75135.47        68.31\n",
+       "222    20662.46  18900.00  -1762.46         9.33\n",
+       "223     6199.59  13200.00   7000.41        53.03\n",
+       "224     3939.91  15500.00  11560.09        74.58\n",
+       "225     6916.13   3900.00  -3016.13        77.34\n",
+       "226    11191.05   9600.00  -1591.05        16.57\n",
+       "227    17251.60  14300.00  -2951.60        20.64\n",
+       "228    15682.82   9600.00  -6082.82        63.36\n",
+       "229     7719.69   9300.00   1580.31        16.99\n",
+       "230     9198.08  15800.00   6601.92        41.78\n",
+       "231     9312.21   7300.00  -2012.21        27.56\n",
+       "232     1320.56   3700.00   2379.44        64.31\n",
+       "233     6110.94   8700.00   2589.06        29.76\n",
+       "234    12731.26   9800.00  -2931.26        29.91\n",
+       "235     2765.14   2900.00    134.86         4.65\n",
+       "236     9024.05   4900.00  -4124.05        84.16\n",
+       "237    25037.87  21200.00  -3837.87        18.10\n",
+       "238    26736.21  16100.00 -10636.21        66.06\n",
+       "239    10581.62  10999.00    417.38         3.79\n",
+       "240    10419.20  13000.00   2580.80        19.85\n",
+       "241    18432.63  30500.00  12067.37        39.57\n",
+       "242     7597.39   7500.00    -97.39         1.30\n",
+       "243    32925.60  69500.00  36574.40        52.63\n",
+       "244     7110.32   8990.00   1879.68        20.91\n",
+       "245    15457.91   9800.00  -5657.91        57.73\n",
+       "246    41711.18  30500.00 -11211.18        36.76\n",
+       "247    13159.16  16200.00   3040.84        18.77\n",
+       "248    22011.47  13999.00  -8012.47        57.24\n",
+       "249     7391.67  15200.00   7808.33        51.37\n",
+       "250     5041.20   3800.00  -1241.20        32.66\n",
+       "251     6978.91   6500.00   -478.91         7.37\n",
+       "252     9721.50   9900.00    178.50         1.80\n",
+       "253    18128.32  15900.00  -2228.32        14.01\n",
+       "254     6413.26   5999.00   -414.26         6.91\n",
+       "255    24796.09  46000.00  21203.91        46.10\n",
+       "256     1825.44   1900.00     74.56         3.92\n",
+       "257    41503.90  37500.00  -4003.90        10.68\n",
+       "258    13680.48  11900.00  -1780.48        14.96\n",
+       "259     6067.79   6200.00    132.21         2.13\n",
+       "260     6858.89   9999.00   3140.11        31.40\n",
+       "261    53312.81  48535.50  -4777.31         9.84\n",
+       "262    12414.41   8420.00  -3994.41        47.44\n",
+       "263     9089.35   7700.00  -1389.35        18.04\n",
+       "264    31059.18  42000.00  10940.82        26.05\n",
+       "265    20163.48  30500.00  10336.52        33.89\n",
+       "266    65742.57 109999.00  44256.43        40.23\n",
+       "267    40753.58  40000.00   -753.58         1.88\n",
+       "268     6893.62   6400.00   -493.62         7.71\n",
+       "269     4151.02   6500.00   2348.98        36.14\n",
+       "270     8263.95   7700.00   -563.95         7.32\n",
+       "271     3948.74   2200.00  -1748.74        79.49\n",
+       "272     9749.53   9800.00     50.47         0.52\n",
+       "273    12318.39  12900.00    581.61         4.51\n",
+       "274     9113.05   6900.00  -2213.05        32.07\n",
+       "275    22547.55  18500.00  -4047.55        21.88\n",
+       "276     5002.39   2999.00  -2003.39        66.80\n",
+       "277    12900.31  15000.00   2099.69        14.00\n",
+       "278    53063.22  60500.00   7436.78        12.29\n",
+       "279     3154.48   4300.00   1145.52        26.64\n",
+       "280    12327.10  12499.00    171.90         1.38\n",
+       "281    11638.89  13708.50   2069.61        15.10\n",
+       "282    12261.19   4100.00  -8161.19       199.05\n",
+       "283     7630.64   5500.00  -2130.64        38.74\n",
+       "284    40765.33 112000.00  71234.67        63.60\n",
+       "285    34761.74  24000.00 -10761.74        44.84\n",
+       "286    19659.97  16700.00  -2959.97        17.72\n",
+       "287     1972.28   2300.00    327.72        14.25\n",
+       "288     6635.73   4300.00  -2335.73        54.32\n",
+       "289     7178.20  10950.00   3771.80        34.45\n",
+       "290     1894.40   1950.00     55.60         2.85\n",
+       "291    20433.23  30999.00  10565.77        34.08\n",
+       "292     8962.29   7500.00  -1462.29        19.50\n",
+       "293     8431.89   7999.00   -432.89         5.41\n",
+       "294    13471.84  10600.00  -2871.84        27.09\n",
+       "295     5350.91   6900.00   1549.09        22.45\n",
+       "296    21690.99  30900.00   9209.01        29.80\n",
+       "297    22261.50  19200.00  -3061.50        15.95\n",
+       "298    17937.36   4800.00 -13137.36       273.69\n",
+       "299    20183.30  18999.00  -1184.30         6.23\n",
+       "300    20236.85  31600.00  11363.15        35.96\n",
+       "301     9782.47  10200.00    417.53         4.09\n",
+       "302     4738.89   7000.00   2261.11        32.30\n",
+       "303    23496.36  33000.00   9503.64        28.80\n",
+       "304     6546.81   4400.00  -2146.81        48.79\n",
+       "305    10505.98  13900.00   3394.02        24.42\n",
+       "306     3164.66   2499.00   -665.66        26.64\n",
+       "307    24974.57  28900.00   3925.43        13.58\n",
+       "308     8967.74   8900.00    -67.74         0.76\n",
+       "309     6510.80   5000.00  -1510.80        30.22\n",
+       "310     9205.10  12800.00   3594.90        28.09\n",
+       "311    51287.19  52055.25    768.06         1.48\n",
+       "312    14792.29  18800.00   4007.71        21.32\n",
+       "313    10014.71  12500.00   2485.29        19.88\n",
+       "314     5811.74   6000.00    188.26         3.14\n",
+       "315     4590.49   4300.00   -290.49         6.76\n",
+       "316    65887.51 114999.00  49111.49        42.71\n",
+       "317    12247.90  12750.00    502.10         3.94\n",
+       "318    39744.80  34900.00  -4844.80        13.88\n",
+       "319    10612.51  12400.00   1787.49        14.42\n",
+       "320    10350.25  13500.00   3149.75        23.33\n",
+       "321    23630.85  19500.00  -4130.85        21.18\n",
+       "322    65210.35 115800.00  50589.65        43.69\n",
+       "323     7098.53   9900.00   2801.47        28.30\n",
+       "324     8316.92   9600.00   1283.08        13.37\n",
+       "325     9057.77  14999.00   5941.23        39.61\n",
+       "326     9356.43   5500.00  -3856.43        70.12\n",
+       "327    10716.00  14900.00   4184.00        28.08\n",
+       "328    15067.85  14200.00   -867.85         6.11\n",
+       "329     7327.18   7200.00   -127.18         1.77\n",
+       "330    11336.71  12300.00    963.29         7.83\n",
+       "331    11002.01  13900.00   2897.99        20.85\n",
+       "332    14591.35  16900.00   2308.65        13.66\n",
+       "333    12452.22  11960.00   -492.22         4.12\n",
+       "334    12414.72  23000.00  10585.28        46.02\n",
+       "335    13004.34  10700.00  -2304.34        21.54\n",
+       "336     8250.47   9950.00   1699.53        17.08\n",
+       "337     5374.74   3750.00  -1624.74        43.33\n",
+       "338    38260.36  75555.00  37294.64        49.36\n",
+       "339    11099.09   6999.00  -4100.09        58.58\n",
+       "340    14815.83  15400.00    584.17         3.79\n",
+       "341    12897.92   9500.00  -3397.92        35.77\n",
+       "342    14572.70  27900.00  13327.30        47.77\n",
+       "343    60603.06 124000.00  63396.94        51.13\n",
+       "344    11937.31  13995.00   2057.69        14.70\n",
+       "345    28951.20  45500.00  16548.80        36.37\n",
+       "346    32945.80  23500.00  -9445.80        40.19\n",
+       "347    26441.03  15900.00 -10541.03        66.30\n",
+       "348    12201.29   9500.00  -2701.29        28.43\n",
+       "349     2020.25   3700.00   1679.75        45.40\n",
+       "350    20246.04  23500.00   3253.96        13.85\n",
+       "351    25951.31  19692.08  -6259.23        31.79\n",
+       "352     5238.05   4000.00  -1238.05        30.95\n",
+       "353     9753.44  20400.00  10646.56        52.19\n",
+       "354    16717.08  15500.00  -1217.08         7.85\n",
+       "355    22454.74  35500.00  13045.26        36.75\n",
+       "356    10133.68   8750.00  -1383.68        15.81\n",
+       "357    11018.79  15700.00   4681.21        29.82\n",
+       "358    63921.33  80999.00  17077.67        21.08\n",
+       "359     4524.77   3999.00   -525.77        13.15\n",
+       "360     4770.52   5500.00    729.48        13.26\n",
+       "361     8688.92  11700.00   3011.08        25.74\n",
+       "362    27293.13  39040.00  11746.87        30.09\n",
+       "363    44193.38  75500.00  31306.62        41.47\n",
+       "364    21681.00  30999.00   9318.00        30.06\n",
+       "365    10547.82  19500.00   8952.18        45.91\n",
+       "366    13420.15   6999.00  -6421.15        91.74\n",
+       "367    42703.68  42500.00   -203.68         0.48\n",
+       "368     8415.81   8800.00    384.19         4.37\n",
+       "369     3084.69   3100.00     15.31         0.49\n",
+       "370    10835.95  14500.00   3664.05        25.27\n",
+       "371     9661.78   6800.00  -2861.78        42.09\n",
+       "372     8110.06  10990.00   2879.94        26.21\n",
+       "373    10821.19   8299.00  -2522.19        30.39\n",
+       "374    24427.14  36000.00  11572.86        32.15\n",
+       "375    65742.57 105999.00  40256.43        37.98\n",
+       "376     5391.77   6000.00    608.23        10.14\n",
+       "377     9909.62   8700.00  -1209.62        13.90\n",
+       "378     2749.08   3300.00    550.92        16.69\n",
+       "379    17654.84  17800.00    145.16         0.82\n",
+       "380     3473.79   3500.00     26.21         0.75\n",
+       "381    33499.44  35000.00   1500.56         4.29\n",
+       "382     7918.89   2400.00  -5518.89       229.95\n",
+       "383    13757.58   8900.00  -4857.58        54.58\n",
+       "384     4296.77   5000.00    703.23        14.06\n",
+       "385    13235.55   8999.00  -4236.55        47.08\n",
+       "386     7267.36   8100.00    832.64        10.28\n",
+       "387    44173.72  44999.00    825.28         1.83\n",
+       "388    11474.22  19999.00   8524.78        42.63\n",
+       "389     2810.71   2450.00   -360.71        14.72\n",
+       "390    24271.90  20900.00  -3371.90        16.13\n",
+       "391    25302.43  52000.00  26697.57        51.34\n",
+       "392     8139.48   6700.00  -1439.48        21.48\n",
+       "393    13600.39  25800.00  12199.61        47.29\n",
+       "394     9076.42   8500.00   -576.42         6.78\n",
+       "395    21303.57  23900.00   2596.43        10.86\n",
+       "396    16165.79   4400.00 -11765.79       267.40\n",
+       "397     7973.87   3650.00  -4323.87       118.46\n",
+       "398     8707.13   8500.00   -207.13         2.44\n",
+       "399    14410.15  18932.55   4522.40        23.89\n",
+       "400    25986.31  31310.00   5323.69        17.00\n",
+       "401     9070.23  14500.00   5429.77        37.45\n",
+       "402     8850.21  10200.00   1349.79        13.23\n",
+       "403     2839.03   4300.00   1460.97        33.98\n",
+       "404     8648.80   4400.00  -4248.80        96.56\n",
+       "405     6425.00   9400.00   2975.00        31.65\n",
+       "406    24874.86  23999.00   -875.86         3.65\n",
+       "407    21097.66  17900.00  -3197.66        17.86\n",
+       "408    65448.37  40500.00 -24948.37        61.60\n",
+       "409     8121.22  10200.00   2078.78        20.38\n",
+       "410    16407.29  14800.00  -1607.29        10.86\n",
+       "411    13409.47   8700.00  -4709.47        54.13\n",
+       "412     7609.28   7000.00   -609.28         8.70\n",
+       "413    12234.68   8500.00  -3734.68        43.94\n",
+       "414    14363.76   9500.00  -4863.76        51.20\n",
+       "415     8117.44   8900.00    782.56         8.79\n",
+       "416    22367.52  28500.00   6132.48        21.52\n",
+       "417     3795.06   2300.00  -1495.06        65.00\n",
+       "418    11378.43  18600.00   7221.57        38.83\n",
+       "419    17591.09  19600.00   2008.91        10.25\n",
+       "420    14576.33  11000.00  -3576.33        32.51\n",
+       "421     8033.61   9750.00   1716.39        17.60\n",
+       "422    53294.61  57000.00   3705.39         6.50\n",
+       "423    74232.55 103999.00  29766.45        28.62\n",
+       "424    50074.56  69990.00  19915.44        28.45\n",
+       "425     3940.49   4999.00   1058.51        21.17\n",
+       "426    13542.79  10200.00  -3342.79        32.77\n",
+       "427     5462.52   2700.00  -2762.52       102.32\n",
+       "428     2389.14   1650.00   -739.14        44.80\n",
+       "429    67952.07  53500.00 -14452.07        27.01\n",
+       "430    14197.26  12400.00  -1797.26        14.49\n",
+       "431    16253.48  11500.00  -4753.48        41.33\n",
+       "432     2975.40   1600.00  -1375.40        85.96\n",
+       "433     6977.90   6500.00   -477.90         7.35\n",
+       "434     8843.22   2500.00  -6343.22       253.73\n",
+       "435    53063.22  65500.00  12436.78        18.99\n",
+       "436    40893.84 126000.00  85106.16        67.54\n",
+       "437    18866.50  18300.00   -566.50         3.10\n",
+       "438    16257.03  16999.00    741.97         4.36\n",
+       "439    14926.69  17800.00   2873.31        16.14\n",
+       "440     8638.63  11800.00   3161.37        26.79\n",
+       "441    11619.16  10000.00  -1619.16        16.19\n",
+       "442    25951.31  18988.13  -6963.18        36.67\n",
+       "443     3735.04   4999.00   1263.96        25.28\n",
+       "444    38334.67  32000.00  -6334.67        19.80\n",
+       "445     7712.65   5500.00  -2212.65        40.23\n",
+       "446     9363.27   8900.00   -463.27         5.21\n",
+       "447    34863.53  87777.00  52913.47        60.28\n",
+       "448    13502.35  14500.00    997.65         6.88\n",
+       "449     9495.62   8650.00   -845.62         9.78\n",
+       "450    14218.94  13500.00   -718.94         5.33\n",
+       "451    35956.50   6500.00 -29456.50       453.18\n",
+       "452    39882.67  37700.00  -2182.67         5.79\n",
+       "453     8381.60   6999.00  -1382.60        19.75\n",
+       "454    20972.54  17500.00  -3472.54        19.84\n",
+       "455    10946.44   6500.00  -4446.44        68.41\n",
+       "456    19624.73  12700.00  -6924.73        54.53\n",
+       "457    16821.54  26800.00   9978.46        37.23\n",
+       "458    14136.01  11500.00  -2636.01        22.92\n",
+       "459     2779.04   2300.00   -479.04        20.83\n",
+       "460    50954.68  61000.00  10045.32        16.47\n",
+       "461    31656.01  24700.00  -6956.01        28.16\n",
+       "462    43236.92  40500.00  -2736.92         6.76\n",
+       "463    35093.27  19990.00 -15103.27        75.55\n",
+       "464     4266.27   5200.00    933.73        17.96\n",
+       "465     6537.82   2700.00  -3837.82       142.14\n",
+       "466    25975.12  21900.00  -4075.12        18.61\n",
+       "467    22262.80  22711.65    448.85         1.98\n",
+       "468    12558.90   8999.00  -3559.90        39.56\n",
+       "469    65825.25 104999.00  39173.75        37.31\n",
+       "470     7045.72   6500.00   -545.72         8.40\n",
+       "471     4209.17   3500.00   -709.17        20.26\n",
+       "472    20367.25  22700.00   2332.75        10.28\n",
+       "473     1792.36   1999.00    206.64        10.34\n",
+       "474    10932.17  13800.00   2867.83        20.78\n",
+       "475     5570.46   8800.00   3229.54        36.70\n",
+       "476    15700.53  18500.00   2799.47        15.13\n",
+       "477    15374.08   8900.00  -6474.08        72.74\n",
+       "478    36764.38  62000.00  25235.62        40.70\n",
+       "479    36883.77  49000.00  12116.23        24.73\n",
+       "480    31369.37  32500.00   1130.63         3.48\n",
+       "481     6599.62   7800.00   1200.38        15.39\n",
+       "482    12027.56  16200.00   4172.44        25.76\n",
+       "483    28937.50  34900.00   5962.50        17.08\n",
+       "484    13334.19  11600.00  -1734.19        14.95\n",
+       "485     9664.46   1900.00  -7764.46       408.66\n",
+       "486    13884.26  27800.00  13915.74        50.06\n",
+       "487    10824.89   6700.00  -4124.89        61.57\n",
+       "488     4043.74   2900.00  -1143.74        39.44\n",
+       "489    28910.96  42500.00  13589.04        31.97\n",
+       "490     8902.52   6500.00  -2402.52        36.96\n",
+       "491     6503.62   4900.00  -1603.62        32.73\n",
+       "492     7161.67   2200.00  -4961.67       225.53\n",
+       "493     5484.97   4999.00   -485.97         9.72\n",
+       "494    33392.40  42000.00   8607.60        20.49\n",
+       "495     3747.21   4900.00   1152.79        23.53\n",
+       "496    17540.72  14899.00  -2641.72        17.73\n",
+       "497    50099.92  51500.00   1400.08         2.72\n",
+       "498     7950.27  12000.00   4049.73        33.75\n",
+       "499    12270.01  15900.00   3629.99        22.83\n",
+       "500     8973.16  12750.00   3776.84        29.62\n",
+       "501    60493.51  75000.00  14506.49        19.34\n",
+       "502     9984.87   3000.00  -6984.87       232.83\n",
+       "503     6392.70   7300.00    907.30        12.43\n",
+       "504    23272.83  25749.75   2476.92         9.62\n",
+       "505    10105.70  15000.00   4894.30        32.63\n",
+       "506     3590.97   3200.00   -390.97        12.22\n",
+       "507    18282.75  21950.00   3667.25        16.71\n",
+       "508    16197.42  15000.00  -1197.42         7.98\n",
+       "509     5839.08   7200.00   1360.92        18.90\n",
+       "510     1873.03   3400.00   1526.97        44.91\n",
+       "511    17282.06  11900.00  -5382.06        45.23\n",
+       "512    15144.28  29000.00  13855.72        47.78\n",
+       "513     5346.97   4100.00  -1246.97        30.41\n",
+       "514    15015.22  10500.00  -4515.22        43.00\n",
+       "515    32121.20  41000.00   8878.80        21.66\n",
+       "516    11858.48  10800.00  -1058.48         9.80\n",
+       "517     9424.34  12000.00   2575.66        21.46\n",
+       "518    21297.65  19800.00  -1497.65         7.56\n",
+       "519     8667.12   6900.00  -1767.12        25.61\n",
+       "520    16773.62  10500.00  -6273.62        59.75\n",
+       "521    27740.81  23500.00  -4240.81        18.05\n",
+       "522    17011.43  11700.00  -5311.43        45.40\n",
+       "523    10809.48  15400.00   4590.52        29.81\n",
+       "524     5863.41   4800.00  -1063.41        22.15\n",
+       "525     2373.66   2700.00    326.34        12.09\n",
+       "526    29049.27  28350.00   -699.27         2.47\n",
+       "527     9829.53   8700.00  -1129.53        12.98\n",
+       "528    18555.09  18800.00    244.91         1.30\n",
+       "529    53312.81  63000.00   9687.19        15.38\n",
+       "530    17714.62  11400.00  -6314.62        55.39\n",
+       "531    47047.70  52300.00   5252.30        10.04\n",
+       "532    10019.90   1800.00  -8219.90       456.66\n",
+       "533    24642.50  24000.00   -642.50         2.68\n",
+       "534    16939.86  19400.00   2460.14        12.68\n",
+       "535    34678.95  43500.00   8821.05        20.28\n",
+       "536     6746.43   6000.00   -746.43        12.44\n",
+       "537     9139.04  11800.00   2660.96        22.55\n",
+       "538    17591.09  19600.00   2008.91        10.25\n",
+       "539     6781.86   4700.00  -2081.86        44.29\n",
+       "540     4666.05   4500.00   -166.05         3.69\n",
+       "541     9369.66  15000.00   5630.34        37.54\n",
+       "542     5947.51   6500.00    552.49         8.50\n",
+       "543     9489.56  10900.00   1410.44        12.94\n",
+       "544    12354.92  27500.00  15145.08        55.07\n",
+       "545     6826.78   7300.00    473.22         6.48\n",
+       "546     6746.48   3799.00  -2947.48        77.59\n",
+       "547     9175.06   7499.00  -1676.06        22.35\n",
+       "548    46465.02  39900.00  -6565.02        16.45\n",
+       "549     3816.33   3750.00    -66.33         1.77\n",
+       "550    14915.98   7900.00  -7015.98        88.81\n",
+       "551    11555.80   7400.00  -4155.80        56.16\n",
+       "552     5983.44   8500.00   2516.56        29.61\n",
+       "553    16392.22  17700.00   1307.78         7.39\n",
+       "554     8340.94   3350.00  -4990.94       148.98\n",
+       "555     6610.04   9800.00   3189.96        32.55\n",
+       "556    18231.44  18600.00    368.56         1.98\n",
+       "557    12523.73  16000.00   3476.27        21.73\n",
+       "558     7098.53   8400.00   1301.47        15.49\n",
+       "559    11377.84  11999.00    621.16         5.18\n",
+       "560    33172.20  40800.00   7627.80        18.70\n",
+       "561     6429.03   6500.00     70.97         1.09\n",
+       "562     9593.52  11000.00   1406.48        12.79\n",
+       "563     2285.24   3500.00   1214.76        34.71\n",
+       "564    25682.58  17300.00  -8382.58        48.45\n",
+       "565     7324.63   7100.00   -224.63         3.16\n",
+       "566    11881.05   8200.00  -3681.05        44.89\n",
+       "567     7279.97  11950.00   4670.03        39.08\n",
+       "568     7940.44  11000.00   3059.56        27.81\n",
+       "569     1926.60   2400.00    473.40        19.72\n",
+       "570    23163.87  24171.42   1007.55         4.17\n",
+       "571    26331.41  11200.00 -15131.41       135.10\n",
+       "572    10597.72   8300.00  -2297.72        27.68\n",
+       "573    33707.27  73900.00  40192.73        54.39\n",
+       "574    12583.52  13000.00    416.48         3.20\n",
+       "575     1858.34   3500.00   1641.66        46.90\n",
+       "576    15954.72  12999.00  -2955.72        22.74\n",
+       "577    14486.38   9000.00  -5486.38        60.96\n",
+       "578     3205.38   1900.00  -1305.38        68.70\n",
+       "579     3411.53   3700.00    288.47         7.80\n",
+       "580     6205.48   5000.00  -1205.48        24.11\n",
+       "581     3246.94   3390.00    143.06         4.22\n",
+       "582     7724.17  11000.00   3275.83        29.78\n",
+       "583     3425.23   5500.00   2074.77        37.72\n",
+       "584    11432.63   8900.00  -2532.63        28.46\n",
+       "585    39074.36  69999.00  30924.64        44.18\n",
+       "586     9348.90   6800.00  -2548.90        37.48\n",
+       "587    15259.81  26000.00  10740.19        41.31\n",
+       "588    12255.45  22000.00   9744.55        44.29\n",
+       "589     3173.48   5100.00   1926.52        37.77\n",
+       "590    14512.61  25999.00  11486.39        44.18\n",
+       "591    10115.13   9800.00   -315.13         3.22\n",
+       "592    28154.84  38500.00  10345.16        26.87\n",
+       "593    17908.00  17500.00   -408.00         2.33\n",
+       "594     3001.37   4000.00    998.63        24.97\n",
+       "595    25576.58  28500.00   2923.42        10.26\n",
+       "596    25685.67  19500.00  -6185.67        31.72\n",
+       "597     7797.18   6999.00   -798.18        11.40\n",
+       "598     8767.54   9700.00    932.46         9.61\n",
+       "599     4590.16   5500.00    909.84        16.54\n",
+       "600    21816.43  23500.00   1683.57         7.16\n",
+       "601    35371.16  34600.00   -771.16         2.23\n",
+       "602    16427.12  13300.00  -3127.12        23.51\n",
+       "603    14513.46  14900.00    386.54         2.59\n",
+       "604    31373.47  20859.15 -10514.32        50.41\n",
+       "605     8940.39  13500.00   4559.61        33.77\n",
+       "606    20680.40  27000.00   6319.60        23.41\n",
+       "607    28498.91  33000.00   4501.09        13.64\n",
+       "608    29127.72  24500.00  -4627.72        18.89\n",
+       "609     3040.77   1200.00  -1840.77       153.40\n",
+       "610     9269.68   8200.00  -1069.68        13.04\n",
+       "611    27225.34  13500.00 -13725.34       101.67\n",
+       "612     2953.17   3000.00     46.83         1.56\n",
+       "613    13853.36  10800.00  -3053.36        28.27\n",
+       "614    19298.61  14999.00  -4299.61        28.67\n",
+       "615     7869.47   8500.00    630.53         7.42\n",
+       "616    13500.69  15900.00   2399.31        15.09\n",
+       "617     2573.48   4900.00   2326.52        47.48\n",
+       "618    31251.89  23500.00  -7751.89        32.99\n",
+       "619    16095.32   3600.00 -12495.32       347.09\n",
+       "620    25752.19  30990.00   5237.81        16.90\n",
+       "621    38473.71 119000.00  80526.29        67.67\n",
+       "622     4472.47   4200.00   -272.47         6.49\n",
+       "623    23227.15  31900.00   8672.85        27.19\n",
+       "624    16924.74   9999.00  -6925.74        69.26\n",
+       "625    23485.45  18777.00  -4708.45        25.08\n",
+       "626    18621.12  24100.00   5478.88        22.73\n",
+       "627    13074.74  11500.00  -1574.74        13.69\n",
+       "628     2539.57   4400.00   1860.43        42.28\n",
+       "629     7319.77   1850.00  -5469.77       295.66\n",
+       "630     8734.58   8800.00     65.42         0.74\n",
+       "631     7256.86   8500.00   1243.14        14.63\n",
+       "632    15383.35  14990.00   -393.35         2.62\n",
+       "633    11056.52   6400.00  -4656.52        72.76\n",
+       "634    18165.16  23900.00   5734.84        24.00\n",
+       "635     3818.71   1450.00  -2368.71       163.36\n",
+       "636    28498.91  28950.00    451.09         1.56\n",
+       "637    35662.26  40000.00   4337.74        10.84\n",
+       "638     8242.62   7300.00   -942.62        12.91\n",
+       "639    30628.28   4999.00 -25629.28       512.69\n",
+       "640    14402.34  10000.00  -4402.34        44.02\n",
+       "641    17334.11  18500.00   1165.89         6.30\n",
+       "642     8721.97   8999.00    277.03         3.08\n",
+       "643     8156.11   9900.00   1743.89        17.62\n",
+       "644    53312.81  67431.00  14118.19        20.94\n",
+       "645    10740.09  19100.00   8359.91        43.77\n",
+       "646     3450.69   6100.00   2649.31        43.43\n",
+       "647     2625.10   2150.00   -475.10        22.10\n",
+       "648    21174.10  21335.00    160.90         0.75\n",
+       "649    11147.91  10500.00   -647.91         6.17\n",
+       "650     8236.36   7200.00  -1036.36        14.39\n",
+       "651     3026.25   2550.00   -476.25        18.68\n",
+       "652    17195.57  12500.00  -4695.57        37.56\n",
+       "653    10332.54   9300.00  -1032.54        11.10\n",
+       "654     4032.10   7650.00   3617.90        47.29\n",
+       "655     7814.16   9500.00   1685.84        17.75\n",
+       "656    35039.40  85555.00  50515.60        59.04\n",
+       "657    32481.05   6000.00 -26481.05       441.35\n",
+       "658    10563.39  20500.00   9936.61        48.47\n",
+       "659     3621.08   4799.00   1177.92        24.55\n",
+       "660     7809.13   2899.00  -4910.13       169.37\n",
+       "661    10303.21  13300.00   2996.79        22.53\n",
+       "662     9232.79   7450.00  -1782.79        23.93\n",
+       "663     2613.97   3099.00    485.03        15.65\n",
+       "664     4590.49   1700.00  -2890.49       170.03\n",
+       "665    17858.02  18000.00    141.98         0.79\n",
+       "666    65723.62 104999.00  39275.38        37.41\n",
+       "667    49221.63  52999.00   3777.37         7.13\n",
+       "668     8270.38   4700.00  -3570.38        75.97\n",
+       "669    11477.47  17650.00   6172.53        34.97\n",
+       "670    33614.62  41000.00   7385.38        18.01\n",
+       "671     2886.35   4200.00   1313.65        31.28\n",
+       "672     5679.39   4500.00  -1179.39        26.21\n",
+       "673    22213.53  34500.00  12286.47        35.61\n",
+       "674     2381.01   2700.00    318.99        11.81\n",
+       "675    13476.90  22500.00   9023.10        40.10\n",
+       "676     6861.84   7300.00    438.16         6.00\n",
+       "677     9631.08   3799.00  -5832.08       153.52\n",
+       "678     4569.15   6900.00   2330.85        33.78\n",
+       "679    25864.65  69990.00  44125.35        63.05\n",
+       "680    21493.44   9000.00 -12493.44       138.82\n",
+       "681     5195.69   4100.00  -1095.69        26.72\n",
+       "682     5454.34   5000.00   -454.34         9.09\n",
+       "683    32771.45  44800.00  12028.55        26.85\n",
+       "684    18942.34  12999.00  -5943.34        45.72\n",
+       "685    12593.00  14600.00   2007.00        13.75\n",
+       "686    23576.99  12000.00 -11576.99        96.47\n",
+       "687    11004.05  14500.00   3495.95        24.11\n",
+       "688    24572.28  20500.00  -4072.28        19.86\n",
+       "689     4524.77   3800.00   -724.77        19.07\n",
+       "690    11706.93  18100.00   6393.07        35.32\n",
+       "691     8467.17   6950.00  -1517.17        21.83\n",
+       "692     8471.55   6799.00  -1672.55        24.60\n",
+       "693    12354.16  12900.00    545.84         4.23\n",
+       "694     8131.92   3999.00  -4132.92       103.35\n",
+       "695    11178.46   8600.00  -2578.46        29.98\n",
+       "696     2470.61   3000.00    529.39        17.65\n",
+       "697    10031.96  17300.00   7268.04        42.01\n",
+       "698    30480.85  30500.00     19.15         0.06\n",
+       "699     4255.62   5800.00   1544.38        26.63\n",
+       "700    25640.67  19800.00  -5840.67        29.50\n",
+       "701     7996.00   4999.00  -2997.00        59.95\n",
+       "702     7011.74  10500.00   3488.26        33.22\n",
+       "703     5597.06  11999.00   6401.94        53.35\n",
+       "704     6891.99   3000.00  -3891.99       129.73\n",
+       "705    12976.15  17500.00   4523.85        25.85\n",
+       "706    61639.73  72900.00  11260.27        15.45\n",
+       "707     2725.40   2600.00   -125.40         4.82\n",
+       "708    11967.39  11700.00   -267.39         2.29\n",
+       "709    34446.96  27900.00  -6546.96        23.47\n",
+       "710     3795.06   3500.00   -295.06         8.43\n",
+       "711    19676.51  24500.00   4823.49        19.69\n",
+       "712     8530.54  14500.00   5969.46        41.17\n",
+       "713     4004.65   2000.00  -2004.65       100.23\n",
+       "714    23118.07  22999.00   -119.07         0.52\n",
+       "715    10220.74   8600.00  -1620.74        18.85\n",
+       "716    11425.01  13500.00   2074.99        15.37\n",
+       "717     4172.84   7750.00   3577.16        46.16\n",
+       "718     8041.62   6999.00  -1042.62        14.90\n",
+       "719    11391.95  11500.00    108.05         0.94\n",
+       "720     2860.97   3350.00    489.03        14.60\n",
+       "721    11431.35  13999.00   2567.65        18.34\n",
+       "722    30409.09  18900.00 -11509.09        60.89\n",
+       "723     6009.63   6100.00     90.37         1.48\n",
+       "724    44312.98  50900.00   6587.02        12.94\n",
+       "725    15742.41  16999.00   1256.59         7.39\n",
+       "726    45664.45  35500.00 -10164.45        28.63\n",
+       "727    10810.62   5000.00  -5810.62       116.21\n",
+       "728     5792.10   8700.00   2907.90        33.42\n",
+       "729     9817.06   3200.00  -6617.06       206.78\n",
+       "730    10696.56  12179.00   1482.44        12.17\n",
+       "731    16027.02  15000.00  -1027.02         6.85\n",
+       "732    19309.90  12500.00  -6809.90        54.48\n",
+       "733    11601.33   7200.00  -4401.33        61.13\n",
+       "734    23336.06  33200.00   9863.94        29.71\n",
+       "735    10337.11   7500.00  -2837.11        37.83\n",
+       "736    53312.81  43163.25 -10149.56        23.51\n",
+       "737    37704.71  26900.00 -10804.71        40.17\n",
+       "738    20216.67  24900.00   4683.33        18.81\n",
+       "739    22213.53  34500.00  12286.47        35.61\n",
+       "740     6658.73   6250.00   -408.73         6.54\n",
+       "741    19855.31  14800.00  -5055.31        34.16\n",
+       "742    16960.31  16999.00     38.69         0.23\n",
+       "743     5268.17   8400.00   3131.83        37.28\n",
+       "744     2379.58   3000.00    620.42        20.68\n",
+       "745     6421.18   4400.00  -2021.18        45.94\n",
+       "746    13355.11   7500.00  -5855.11        78.07\n",
+       "747     8453.28  10900.00   2446.72        22.45\n",
+       "748    48699.98  77500.00  28800.02        37.16\n",
+       "749     6082.85   7450.00   1367.15        18.35\n",
+       "750    10381.62   3000.00  -7381.62       246.05\n",
+       "751     8493.04  12800.00   4306.96        33.65\n",
+       "752     8591.66  12000.00   3408.34        28.40\n",
+       "753     6358.55   4850.00  -1508.55        31.10\n",
+       "754    17028.45  18700.00   1671.55         8.94\n",
+       "755    15885.66  17300.00   1414.34         8.18\n",
+       "756     3752.54   2600.00  -1152.54        44.33\n",
+       "757    12028.91  10500.00  -1528.91        14.56\n",
+       "758     9380.46   7950.00  -1430.46        17.99\n",
+       "759    10125.27   6700.00  -3425.27        51.12\n",
+       "760    13443.32   9000.00  -4443.32        49.37\n",
+       "761     9097.13   8000.00  -1097.13        13.71\n",
+       "762    12201.29  12999.00    797.71         6.14\n",
+       "763    12383.35  10800.00  -1583.35        14.66\n",
+       "764    14049.76  10700.00  -3349.76        31.31\n",
+       "765    11034.66   9800.00  -1234.66        12.60\n",
+       "766    18982.15  17900.00  -1082.15         6.05\n",
+       "767    24323.48  18800.00  -5523.48        29.38\n",
+       "768    38260.36  75555.00  37294.64        49.36\n",
+       "769    29651.73  29500.00   -151.73         0.51\n",
+       "770    10732.07   9600.00  -1132.07        11.79\n",
+       "771    13922.45  18300.00   4377.55        23.92\n",
+       "772    27487.75  68500.00  41012.25        59.87\n",
+       "773    13491.16  10800.00  -2691.16        24.92"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, it makes sense to see how far off we are from the result percentage-wise\n",
+    "# Here, we take the absolute difference in %, so we can easily order the data frame\n",
+    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
+    "df_pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>774.00</td>\n",
+       "      <td>774.00</td>\n",
+       "      <td>774.00</td>\n",
+       "      <td>774.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>15946.76</td>\n",
+       "      <td>18165.82</td>\n",
+       "      <td>2219.06</td>\n",
+       "      <td>36.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>13133.20</td>\n",
+       "      <td>19967.86</td>\n",
+       "      <td>10871.22</td>\n",
+       "      <td>55.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1320.56</td>\n",
+       "      <td>1200.00</td>\n",
+       "      <td>-29456.50</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7413.64</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-2044.19</td>\n",
+       "      <td>12.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>11568.17</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>142.52</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>20162.41</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>3147.34</td>\n",
+       "      <td>39.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>77403.06</td>\n",
+       "      <td>126000.00</td>\n",
+       "      <td>85106.16</td>\n",
+       "      <td>512.69</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Prediction    Target  Residual  Difference%\n",
+       "count      774.00    774.00    774.00       774.00\n",
+       "mean     15946.76  18165.82   2219.06        36.26\n",
+       "std      13133.20  19967.86  10871.22        55.07\n",
+       "min       1320.56   1200.00 -29456.50         0.06\n",
+       "25%       7413.64   6900.00  -2044.19        12.11\n",
+       "50%      11568.17  11600.00    142.52        23.47\n",
+       "75%      20162.41  20500.00   3147.34        39.56\n",
+       "max      77403.06 126000.00  85106.16       512.69"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exploring the descriptives here gives us additional insights\n",
+    "df_pf.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>30480.85</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>19.15</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>16960.31</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>38.69</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>12469.21</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>30.79</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>25614.14</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>-114.14</td>\n",
+       "      <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>42703.68</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>-203.68</td>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>3084.69</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>15.31</td>\n",
+       "      <td>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.73</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>-151.73</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>9749.53</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>50.47</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>23118.07</td>\n",
+       "      <td>22999.00</td>\n",
+       "      <td>-119.07</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>8734.58</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>65.42</td>\n",
+       "      <td>0.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>3473.79</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>26.21</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>21174.10</td>\n",
+       "      <td>21335.00</td>\n",
+       "      <td>160.90</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>8967.74</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-67.74</td>\n",
+       "      <td>0.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>665</th>\n",
+       "      <td>17858.02</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>141.98</td>\n",
+       "      <td>0.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>17654.84</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>145.16</td>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>11391.95</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>108.05</td>\n",
+       "      <td>0.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>28625.56</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>274.44</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>75.83</td>\n",
+       "      <td>0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>6429.03</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>70.97</td>\n",
+       "      <td>1.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>7597.39</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-97.39</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>18555.09</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>244.91</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>7396.87</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-96.87</td>\n",
+       "      <td>1.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>16178.14</td>\n",
+       "      <td>16400.00</td>\n",
+       "      <td>221.86</td>\n",
+       "      <td>1.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>12327.10</td>\n",
+       "      <td>12499.00</td>\n",
+       "      <td>171.90</td>\n",
+       "      <td>1.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>51287.19</td>\n",
+       "      <td>52055.25</td>\n",
+       "      <td>768.06</td>\n",
+       "      <td>1.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>6009.63</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>90.37</td>\n",
+       "      <td>1.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>4973.17</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-73.17</td>\n",
+       "      <td>1.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>27716.14</td>\n",
+       "      <td>27300.00</td>\n",
+       "      <td>-416.14</td>\n",
+       "      <td>1.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>28950.00</td>\n",
+       "      <td>451.09</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>2953.17</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>46.83</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>26425.14</td>\n",
+       "      <td>25999.00</td>\n",
+       "      <td>-426.14</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>13111.91</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-211.91</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>12858.08</td>\n",
+       "      <td>12650.00</td>\n",
+       "      <td>-208.08</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>13421.16</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>-221.16</td>\n",
+       "      <td>1.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>7327.18</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-127.18</td>\n",
+       "      <td>1.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>3816.33</td>\n",
+       "      <td>3750.00</td>\n",
+       "      <td>-66.33</td>\n",
+       "      <td>1.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>9721.50</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>178.50</td>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>44173.72</td>\n",
+       "      <td>44999.00</td>\n",
+       "      <td>825.28</td>\n",
+       "      <td>1.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>40753.58</td>\n",
+       "      <td>40000.00</td>\n",
+       "      <td>-753.58</td>\n",
+       "      <td>1.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>22262.80</td>\n",
+       "      <td>22711.65</td>\n",
+       "      <td>448.85</td>\n",
+       "      <td>1.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>18231.44</td>\n",
+       "      <td>18600.00</td>\n",
+       "      <td>368.56</td>\n",
+       "      <td>1.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>9596.94</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>-196.94</td>\n",
+       "      <td>2.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>6067.79</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>132.21</td>\n",
+       "      <td>2.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>35371.16</td>\n",
+       "      <td>34600.00</td>\n",
+       "      <td>-771.16</td>\n",
+       "      <td>2.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>11967.39</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-267.39</td>\n",
+       "      <td>2.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>17908.00</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>-408.00</td>\n",
+       "      <td>2.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8707.13</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-207.13</td>\n",
+       "      <td>2.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>29049.27</td>\n",
+       "      <td>28350.00</td>\n",
+       "      <td>-699.27</td>\n",
+       "      <td>2.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>14513.46</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>386.54</td>\n",
+       "      <td>2.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>20453.89</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>546.11</td>\n",
+       "      <td>2.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>15383.35</td>\n",
+       "      <td>14990.00</td>\n",
+       "      <td>-393.35</td>\n",
+       "      <td>2.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>24642.50</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-642.50</td>\n",
+       "      <td>2.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>50099.92</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>1400.08</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>16133.86</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-433.86</td>\n",
+       "      <td>2.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>17489.92</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>510.08</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>1894.40</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>55.60</td>\n",
+       "      <td>2.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>30810.25</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>-910.25</td>\n",
+       "      <td>3.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>8721.97</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>277.03</td>\n",
+       "      <td>3.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>18866.50</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>-566.50</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>5958.63</td>\n",
+       "      <td>6150.00</td>\n",
+       "      <td>191.37</td>\n",
+       "      <td>3.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>5811.74</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>188.26</td>\n",
+       "      <td>3.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>9800.43</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-300.43</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>7324.63</td>\n",
+       "      <td>7100.00</td>\n",
+       "      <td>-224.63</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>12583.52</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>416.48</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>10115.13</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-315.13</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>11156.38</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-356.38</td>\n",
+       "      <td>3.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>9279.28</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>320.72</td>\n",
+       "      <td>3.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>31369.37</td>\n",
+       "      <td>32500.00</td>\n",
+       "      <td>1130.63</td>\n",
+       "      <td>3.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>2315.71</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>84.29</td>\n",
+       "      <td>3.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5175.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-175.77</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>21611.83</td>\n",
+       "      <td>22400.00</td>\n",
+       "      <td>788.17</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>7976.26</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-276.26</td>\n",
+       "      <td>3.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>24874.86</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-875.86</td>\n",
+       "      <td>3.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>36516.35</td>\n",
+       "      <td>37900.00</td>\n",
+       "      <td>1383.65</td>\n",
+       "      <td>3.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>4666.05</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-166.05</td>\n",
+       "      <td>3.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>18672.68</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>-672.68</td>\n",
+       "      <td>3.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>14815.83</td>\n",
+       "      <td>15400.00</td>\n",
+       "      <td>584.17</td>\n",
+       "      <td>3.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>10581.62</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>417.38</td>\n",
+       "      <td>3.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>12663.54</td>\n",
+       "      <td>12200.00</td>\n",
+       "      <td>-463.54</td>\n",
+       "      <td>3.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>1825.44</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>74.56</td>\n",
+       "      <td>3.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>12247.90</td>\n",
+       "      <td>12750.00</td>\n",
+       "      <td>502.10</td>\n",
+       "      <td>3.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>5930.73</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>-230.73</td>\n",
+       "      <td>4.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>9782.47</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>417.53</td>\n",
+       "      <td>4.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>12452.22</td>\n",
+       "      <td>11960.00</td>\n",
+       "      <td>-492.22</td>\n",
+       "      <td>4.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>23163.87</td>\n",
+       "      <td>24171.42</td>\n",
+       "      <td>1007.55</td>\n",
+       "      <td>4.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>3246.94</td>\n",
+       "      <td>3390.00</td>\n",
+       "      <td>143.06</td>\n",
+       "      <td>4.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>12354.16</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>545.84</td>\n",
+       "      <td>4.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>33499.44</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>1500.56</td>\n",
+       "      <td>4.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>16257.03</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>741.97</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>8415.81</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>384.19</td>\n",
+       "      <td>4.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>12318.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>581.61</td>\n",
+       "      <td>4.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>2765.14</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>134.86</td>\n",
+       "      <td>4.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>11420.95</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>578.05</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>2725.40</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-125.40</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>23624.69</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-1124.69</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11377.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>621.16</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>12096.18</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-596.18</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>9363.27</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-463.27</td>\n",
+       "      <td>5.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>12311.47</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-611.47</td>\n",
+       "      <td>5.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>14218.94</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-718.94</td>\n",
+       "      <td>5.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>8431.89</td>\n",
+       "      <td>7999.00</td>\n",
+       "      <td>-432.89</td>\n",
+       "      <td>5.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>37600.15</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>2299.85</td>\n",
+       "      <td>5.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>39882.67</td>\n",
+       "      <td>37700.00</td>\n",
+       "      <td>-2182.67</td>\n",
+       "      <td>5.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>20588.57</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>1311.43</td>\n",
+       "      <td>5.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>6861.84</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>438.16</td>\n",
+       "      <td>6.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>18982.15</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-1082.15</td>\n",
+       "      <td>6.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>15067.85</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>-867.85</td>\n",
+       "      <td>6.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>797.71</td>\n",
+       "      <td>6.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>11147.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-647.91</td>\n",
+       "      <td>6.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>20183.30</td>\n",
+       "      <td>18999.00</td>\n",
+       "      <td>-1184.30</td>\n",
+       "      <td>6.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>17334.11</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>1165.89</td>\n",
+       "      <td>6.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>21289.80</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-1289.80</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>6826.78</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>473.22</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>4472.47</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-272.47</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>53294.61</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>3705.39</td>\n",
+       "      <td>6.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>6658.73</td>\n",
+       "      <td>6250.00</td>\n",
+       "      <td>-408.73</td>\n",
+       "      <td>6.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>19001.29</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>-1201.29</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>-290.49</td>\n",
+       "      <td>6.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>43236.92</td>\n",
+       "      <td>40500.00</td>\n",
+       "      <td>-2736.92</td>\n",
+       "      <td>6.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>9076.42</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-576.42</td>\n",
+       "      <td>6.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>10159.18</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>740.82</td>\n",
+       "      <td>6.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>16027.02</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-1027.02</td>\n",
+       "      <td>6.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>13502.35</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>997.65</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>6413.26</td>\n",
+       "      <td>5999.00</td>\n",
+       "      <td>-414.26</td>\n",
+       "      <td>6.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>49221.63</td>\n",
+       "      <td>52999.00</td>\n",
+       "      <td>3777.37</td>\n",
+       "      <td>7.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>21816.43</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>1683.57</td>\n",
+       "      <td>7.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>11969.66</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>930.34</td>\n",
+       "      <td>7.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>8263.95</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-563.95</td>\n",
+       "      <td>7.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11581.54</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>918.46</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>6977.90</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-477.90</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>6978.91</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-478.91</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>16392.22</td>\n",
+       "      <td>17700.00</td>\n",
+       "      <td>1307.78</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>15742.41</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>1256.59</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>7869.47</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>630.53</td>\n",
+       "      <td>7.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>21297.65</td>\n",
+       "      <td>19800.00</td>\n",
+       "      <td>-1497.65</td>\n",
+       "      <td>7.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>6893.62</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-493.62</td>\n",
+       "      <td>7.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>15627.03</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>-1127.03</td>\n",
+       "      <td>7.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>3411.53</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>288.47</td>\n",
+       "      <td>7.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>11336.71</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>963.29</td>\n",
+       "      <td>7.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>8195.34</td>\n",
+       "      <td>7600.00</td>\n",
+       "      <td>-595.34</td>\n",
+       "      <td>7.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>16717.08</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-1217.08</td>\n",
+       "      <td>7.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>7772.57</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-572.57</td>\n",
+       "      <td>7.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>16197.42</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-1197.42</td>\n",
+       "      <td>7.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>6102.36</td>\n",
+       "      <td>5650.00</td>\n",
+       "      <td>-452.36</td>\n",
+       "      <td>8.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>4488.43</td>\n",
+       "      <td>4150.00</td>\n",
+       "      <td>-338.43</td>\n",
+       "      <td>8.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>15885.66</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>1414.34</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>17983.10</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>1616.90</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>7045.72</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-545.72</td>\n",
+       "      <td>8.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-295.06</td>\n",
+       "      <td>8.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>6130.96</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>569.04</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>5947.51</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>552.49</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>7609.28</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-609.28</td>\n",
+       "      <td>8.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8117.44</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>782.56</td>\n",
+       "      <td>8.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>11098.18</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-898.18</td>\n",
+       "      <td>8.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>17028.45</td>\n",
+       "      <td>18700.00</td>\n",
+       "      <td>1671.55</td>\n",
+       "      <td>8.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19847.25</td>\n",
+       "      <td>18200.00</td>\n",
+       "      <td>-1647.25</td>\n",
+       "      <td>9.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>682</th>\n",
+       "      <td>5454.34</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-454.34</td>\n",
+       "      <td>9.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>20662.46</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>-1762.46</td>\n",
+       "      <td>9.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11696.25</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>1203.75</td>\n",
+       "      <td>9.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>10716.84</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-916.84</td>\n",
+       "      <td>9.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>13459.92</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>-1159.92</td>\n",
+       "      <td>9.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>8767.54</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>932.46</td>\n",
+       "      <td>9.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>23272.83</td>\n",
+       "      <td>25749.75</td>\n",
+       "      <td>2476.92</td>\n",
+       "      <td>9.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>5484.97</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-485.97</td>\n",
+       "      <td>9.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>9495.62</td>\n",
+       "      <td>8650.00</td>\n",
+       "      <td>-845.62</td>\n",
+       "      <td>9.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>11858.48</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1058.48</td>\n",
+       "      <td>9.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>48535.50</td>\n",
+       "      <td>-4777.31</td>\n",
+       "      <td>9.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>47047.70</td>\n",
+       "      <td>52300.00</td>\n",
+       "      <td>5252.30</td>\n",
+       "      <td>10.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>6717.17</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>-617.17</td>\n",
+       "      <td>10.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>5391.77</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>608.23</td>\n",
+       "      <td>10.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>2008.91</td>\n",
+       "      <td>10.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>2008.91</td>\n",
+       "      <td>10.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>25576.58</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>2923.42</td>\n",
+       "      <td>10.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>20367.25</td>\n",
+       "      <td>22700.00</td>\n",
+       "      <td>2332.75</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>7267.36</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>832.64</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>1792.36</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>206.64</td>\n",
+       "      <td>10.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>8248.67</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>951.33</td>\n",
+       "      <td>10.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>5643.45</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>656.55</td>\n",
+       "      <td>10.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>6839.57</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>810.43</td>\n",
+       "      <td>10.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>41503.90</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-4003.90</td>\n",
+       "      <td>10.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>9310.69</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>-910.69</td>\n",
+       "      <td>10.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>35662.26</td>\n",
+       "      <td>40000.00</td>\n",
+       "      <td>4337.74</td>\n",
+       "      <td>10.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>16407.29</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-1607.29</td>\n",
+       "      <td>10.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>21303.57</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>2596.43</td>\n",
+       "      <td>10.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>15872.91</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-1572.91</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>10332.54</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>-1032.54</td>\n",
+       "      <td>11.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>18575.43</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>2324.57</td>\n",
+       "      <td>11.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>8882.65</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1117.35</td>\n",
+       "      <td>11.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>7797.18</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-798.18</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10621.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>1377.16</td>\n",
+       "      <td>11.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.07</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1132.07</td>\n",
+       "      <td>11.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>2381.01</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>318.99</td>\n",
+       "      <td>11.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>11200.20</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-1201.20</td>\n",
+       "      <td>12.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>2373.66</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>326.34</td>\n",
+       "      <td>12.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>10696.56</td>\n",
+       "      <td>12179.00</td>\n",
+       "      <td>1482.44</td>\n",
+       "      <td>12.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>3590.97</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-390.97</td>\n",
+       "      <td>12.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>60500.00</td>\n",
+       "      <td>7436.78</td>\n",
+       "      <td>12.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>7300.05</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-800.05</td>\n",
+       "      <td>12.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>6392.70</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>907.30</td>\n",
+       "      <td>12.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>6746.43</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-746.43</td>\n",
+       "      <td>12.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>9192.06</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>1307.94</td>\n",
+       "      <td>12.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>11034.66</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-1234.66</td>\n",
+       "      <td>12.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>16939.86</td>\n",
+       "      <td>19400.00</td>\n",
+       "      <td>2460.14</td>\n",
+       "      <td>12.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2479.43</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-279.43</td>\n",
+       "      <td>12.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9593.52</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>1406.48</td>\n",
+       "      <td>12.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>23967.81</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>3532.19</td>\n",
+       "      <td>12.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>7491.01</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>1108.99</td>\n",
+       "      <td>12.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-942.62</td>\n",
+       "      <td>12.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>9489.56</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>1410.44</td>\n",
+       "      <td>12.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>44312.98</td>\n",
+       "      <td>50900.00</td>\n",
+       "      <td>6587.02</td>\n",
+       "      <td>12.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>9829.53</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1129.53</td>\n",
+       "      <td>12.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>9269.68</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-1069.68</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>-525.77</td>\n",
+       "      <td>13.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>23650.15</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-2750.15</td>\n",
+       "      <td>13.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>8850.21</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1349.79</td>\n",
+       "      <td>13.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>4770.52</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>729.48</td>\n",
+       "      <td>13.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>29479.97</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>4520.03</td>\n",
+       "      <td>13.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>8316.92</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>1283.08</td>\n",
+       "      <td>13.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>24974.57</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>3925.43</td>\n",
+       "      <td>13.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>14943.75</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>2356.25</td>\n",
+       "      <td>13.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>33000.00</td>\n",
+       "      <td>4501.09</td>\n",
+       "      <td>13.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>14591.35</td>\n",
+       "      <td>16900.00</td>\n",
+       "      <td>2308.65</td>\n",
+       "      <td>13.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10358.58</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>1641.42</td>\n",
+       "      <td>13.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>13074.74</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-1574.74</td>\n",
+       "      <td>13.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>9097.13</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1097.13</td>\n",
+       "      <td>13.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>15010.51</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>-1810.51</td>\n",
+       "      <td>13.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>12593.00</td>\n",
+       "      <td>14600.00</td>\n",
+       "      <td>2007.00</td>\n",
+       "      <td>13.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>20246.04</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>3253.96</td>\n",
+       "      <td>13.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>8768.29</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-1068.29</td>\n",
+       "      <td>13.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>39744.80</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>-4844.80</td>\n",
+       "      <td>13.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>9909.62</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1209.62</td>\n",
+       "      <td>13.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>44680.77</td>\n",
+       "      <td>51900.00</td>\n",
+       "      <td>7219.23</td>\n",
+       "      <td>13.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>21942.05</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>3557.95</td>\n",
+       "      <td>13.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>12900.31</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>2099.69</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>18128.32</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-2228.32</td>\n",
+       "      <td>14.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>4296.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>703.23</td>\n",
+       "      <td>14.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>6006.27</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>992.73</td>\n",
+       "      <td>14.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>1972.28</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>327.72</td>\n",
+       "      <td>14.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>8236.36</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1036.36</td>\n",
+       "      <td>14.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>10612.51</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>1787.49</td>\n",
+       "      <td>14.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>14197.26</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>-1797.26</td>\n",
+       "      <td>14.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>47013.28</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>7986.72</td>\n",
+       "      <td>14.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>12028.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-1528.91</td>\n",
+       "      <td>14.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>3666.31</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-466.31</td>\n",
+       "      <td>14.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>2860.97</td>\n",
+       "      <td>3350.00</td>\n",
+       "      <td>489.03</td>\n",
+       "      <td>14.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>7256.86</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1243.14</td>\n",
+       "      <td>14.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>10499.60</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>1800.40</td>\n",
+       "      <td>14.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>12383.35</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1583.35</td>\n",
+       "      <td>14.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>11937.31</td>\n",
+       "      <td>13995.00</td>\n",
+       "      <td>2057.69</td>\n",
+       "      <td>14.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2810.71</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-360.71</td>\n",
+       "      <td>14.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>7664.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>1335.68</td>\n",
+       "      <td>14.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>8041.62</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-1042.62</td>\n",
+       "      <td>14.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>13334.19</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>-1734.19</td>\n",
+       "      <td>14.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>13680.48</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1780.48</td>\n",
+       "      <td>14.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>13500.69</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>2399.31</td>\n",
+       "      <td>15.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>11638.89</td>\n",
+       "      <td>13708.50</td>\n",
+       "      <td>2069.61</td>\n",
+       "      <td>15.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>15700.53</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>2799.47</td>\n",
+       "      <td>15.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>11425.01</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>2074.99</td>\n",
+       "      <td>15.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>63000.00</td>\n",
+       "      <td>9687.19</td>\n",
+       "      <td>15.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>6599.62</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>1200.38</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>61639.73</td>\n",
+       "      <td>72900.00</td>\n",
+       "      <td>11260.27</td>\n",
+       "      <td>15.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1301.47</td>\n",
+       "      <td>15.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>10889.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>2010.61</td>\n",
+       "      <td>15.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>2613.97</td>\n",
+       "      <td>3099.00</td>\n",
+       "      <td>485.03</td>\n",
+       "      <td>15.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>23721.58</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-3221.58</td>\n",
+       "      <td>15.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>10133.68</td>\n",
+       "      <td>8750.00</td>\n",
+       "      <td>-1383.68</td>\n",
+       "      <td>15.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>22261.50</td>\n",
+       "      <td>19200.00</td>\n",
+       "      <td>-3061.50</td>\n",
+       "      <td>15.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>10448.50</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-1449.50</td>\n",
+       "      <td>16.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>24271.90</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-3371.90</td>\n",
+       "      <td>16.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>14926.69</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>2873.31</td>\n",
+       "      <td>16.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>11619.16</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>-1619.16</td>\n",
+       "      <td>16.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>46465.02</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>-6565.02</td>\n",
+       "      <td>16.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>50954.68</td>\n",
+       "      <td>61000.00</td>\n",
+       "      <td>10045.32</td>\n",
+       "      <td>16.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6848.88</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>1351.12</td>\n",
+       "      <td>16.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>16547.03</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>-2347.03</td>\n",
+       "      <td>16.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>4590.16</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>909.84</td>\n",
+       "      <td>16.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>13348.91</td>\n",
+       "      <td>15999.00</td>\n",
+       "      <td>2650.09</td>\n",
+       "      <td>16.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>11191.05</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1591.05</td>\n",
+       "      <td>16.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>2126.49</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>423.51</td>\n",
+       "      <td>16.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.96</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-1063.96</td>\n",
+       "      <td>16.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>2749.08</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>550.92</td>\n",
+       "      <td>16.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>18282.75</td>\n",
+       "      <td>21950.00</td>\n",
+       "      <td>3667.25</td>\n",
+       "      <td>16.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>19071.43</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>3828.57</td>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>32886.86</td>\n",
+       "      <td>39500.00</td>\n",
+       "      <td>6613.14</td>\n",
+       "      <td>16.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>25752.19</td>\n",
+       "      <td>30990.00</td>\n",
+       "      <td>5237.81</td>\n",
+       "      <td>16.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>7719.69</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>1580.31</td>\n",
+       "      <td>16.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>25986.31</td>\n",
+       "      <td>31310.00</td>\n",
+       "      <td>5323.69</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>8250.47</td>\n",
+       "      <td>9950.00</td>\n",
+       "      <td>1699.53</td>\n",
+       "      <td>17.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>28937.50</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>5962.50</td>\n",
+       "      <td>17.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8392.42</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>-1242.42</td>\n",
+       "      <td>17.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>8033.61</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>1716.39</td>\n",
+       "      <td>17.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>8156.11</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>1743.89</td>\n",
+       "      <td>17.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>2470.61</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>529.39</td>\n",
+       "      <td>17.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>19659.97</td>\n",
+       "      <td>16700.00</td>\n",
+       "      <td>-2959.97</td>\n",
+       "      <td>17.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>17540.72</td>\n",
+       "      <td>14899.00</td>\n",
+       "      <td>-2641.72</td>\n",
+       "      <td>17.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>7814.16</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1685.84</td>\n",
+       "      <td>17.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>21097.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-3197.66</td>\n",
+       "      <td>17.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>4266.27</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>933.73</td>\n",
+       "      <td>17.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>9380.46</td>\n",
+       "      <td>7950.00</td>\n",
+       "      <td>-1430.46</td>\n",
+       "      <td>17.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>7385.38</td>\n",
+       "      <td>18.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>7385.38</td>\n",
+       "      <td>18.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>9089.35</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-1389.35</td>\n",
+       "      <td>18.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>27740.81</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-4240.81</td>\n",
+       "      <td>18.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>3542.72</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-542.72</td>\n",
+       "      <td>18.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>25037.87</td>\n",
+       "      <td>21200.00</td>\n",
+       "      <td>-3837.87</td>\n",
+       "      <td>18.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>35991.51</td>\n",
+       "      <td>43999.00</td>\n",
+       "      <td>8007.49</td>\n",
+       "      <td>18.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>13733.33</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>3066.67</td>\n",
+       "      <td>18.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>11431.35</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>2567.65</td>\n",
+       "      <td>18.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>6082.85</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>1367.15</td>\n",
+       "      <td>18.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>12674.57</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-1974.57</td>\n",
+       "      <td>18.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25975.12</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>-4075.12</td>\n",
+       "      <td>18.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3026.25</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>-476.25</td>\n",
+       "      <td>18.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>33172.20</td>\n",
+       "      <td>40800.00</td>\n",
+       "      <td>7627.80</td>\n",
+       "      <td>18.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>13159.16</td>\n",
+       "      <td>16200.00</td>\n",
+       "      <td>3040.84</td>\n",
+       "      <td>18.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>20216.67</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>4683.33</td>\n",
+       "      <td>18.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>10220.74</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>-1620.74</td>\n",
+       "      <td>18.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>29127.72</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>-4627.72</td>\n",
+       "      <td>18.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>5839.08</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1360.92</td>\n",
+       "      <td>18.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>65500.00</td>\n",
+       "      <td>12436.78</td>\n",
+       "      <td>18.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>28256.79</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>6643.21</td>\n",
+       "      <td>19.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-724.77</td>\n",
+       "      <td>19.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>2543.57</td>\n",
+       "      <td>3149.25</td>\n",
+       "      <td>605.68</td>\n",
+       "      <td>19.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>60493.51</td>\n",
+       "      <td>75000.00</td>\n",
+       "      <td>14506.49</td>\n",
+       "      <td>19.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>8962.29</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-1462.29</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>14832.32</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>-2432.32</td>\n",
+       "      <td>19.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>19676.51</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>4823.49</td>\n",
+       "      <td>19.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20349.62</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-3350.62</td>\n",
+       "      <td>19.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>1926.60</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>473.40</td>\n",
+       "      <td>19.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>8381.60</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-1382.60</td>\n",
+       "      <td>19.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>38334.67</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>-6334.67</td>\n",
+       "      <td>19.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>20972.54</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>-3472.54</td>\n",
+       "      <td>19.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>6732.97</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1667.03</td>\n",
+       "      <td>19.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>10419.20</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>2580.80</td>\n",
+       "      <td>19.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>688</th>\n",
+       "      <td>24572.28</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-4072.28</td>\n",
+       "      <td>19.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>10014.71</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>2485.29</td>\n",
+       "      <td>19.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-709.17</td>\n",
+       "      <td>20.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>34678.95</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>8821.05</td>\n",
+       "      <td>20.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>18893.76</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-3193.76</td>\n",
+       "      <td>20.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>8121.22</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>2078.78</td>\n",
+       "      <td>20.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>11443.85</td>\n",
+       "      <td>9499.00</td>\n",
+       "      <td>-1944.85</td>\n",
+       "      <td>20.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>33392.40</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>8607.60</td>\n",
+       "      <td>20.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1753.52</td>\n",
+       "      <td>20.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>17251.60</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-2951.60</td>\n",
+       "      <td>20.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>6981.34</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1818.66</td>\n",
+       "      <td>20.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>2379.58</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>620.42</td>\n",
+       "      <td>20.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>10932.17</td>\n",
+       "      <td>13800.00</td>\n",
+       "      <td>2867.83</td>\n",
+       "      <td>20.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>2779.04</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-479.04</td>\n",
+       "      <td>20.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>11002.01</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>2897.99</td>\n",
+       "      <td>20.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>5144.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1355.98</td>\n",
+       "      <td>20.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>7110.32</td>\n",
+       "      <td>8990.00</td>\n",
+       "      <td>1879.68</td>\n",
+       "      <td>20.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>67431.00</td>\n",
+       "      <td>14118.19</td>\n",
+       "      <td>20.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7745.02</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-1345.02</td>\n",
+       "      <td>21.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>63921.33</td>\n",
+       "      <td>80999.00</td>\n",
+       "      <td>17077.67</td>\n",
+       "      <td>21.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>3940.49</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1058.51</td>\n",
+       "      <td>21.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>23630.85</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>-4130.85</td>\n",
+       "      <td>21.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>14792.29</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>4007.71</td>\n",
+       "      <td>21.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>11770.64</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-2070.64</td>\n",
+       "      <td>21.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>9424.34</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>2575.66</td>\n",
+       "      <td>21.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>8139.48</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-1439.48</td>\n",
+       "      <td>21.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>22367.52</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>6132.48</td>\n",
+       "      <td>21.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>13004.34</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-2304.34</td>\n",
+       "      <td>21.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>8512.03</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-1512.03</td>\n",
+       "      <td>21.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>32121.20</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>8878.80</td>\n",
+       "      <td>21.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>12523.73</td>\n",
+       "      <td>16000.00</td>\n",
+       "      <td>3476.27</td>\n",
+       "      <td>21.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>19845.36</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>-3545.36</td>\n",
+       "      <td>21.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13292.71</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>3706.29</td>\n",
+       "      <td>21.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>8467.17</td>\n",
+       "      <td>6950.00</td>\n",
+       "      <td>-1517.17</td>\n",
+       "      <td>21.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11876.82</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>3323.18</td>\n",
+       "      <td>21.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>22547.55</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>-4047.55</td>\n",
+       "      <td>21.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>50728.89</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>14271.11</td>\n",
+       "      <td>21.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2625.10</td>\n",
+       "      <td>2150.00</td>\n",
+       "      <td>-475.10</td>\n",
+       "      <td>22.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>5863.41</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1063.41</td>\n",
+       "      <td>22.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>9175.06</td>\n",
+       "      <td>7499.00</td>\n",
+       "      <td>-1676.06</td>\n",
+       "      <td>22.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>8453.28</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2446.72</td>\n",
+       "      <td>22.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>5350.91</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>1549.09</td>\n",
+       "      <td>22.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>6819.44</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1980.56</td>\n",
+       "      <td>22.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>10303.21</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>2996.79</td>\n",
+       "      <td>22.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>9139.04</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>2660.96</td>\n",
+       "      <td>22.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>18621.12</td>\n",
+       "      <td>24100.00</td>\n",
+       "      <td>5478.88</td>\n",
+       "      <td>22.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>15954.72</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-2955.72</td>\n",
+       "      <td>22.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>12270.01</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>3629.99</td>\n",
+       "      <td>22.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>11490.44</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>3409.56</td>\n",
+       "      <td>22.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>14136.01</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-2636.01</td>\n",
+       "      <td>22.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>11095.57</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-2096.57</td>\n",
+       "      <td>23.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>10350.25</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>3149.75</td>\n",
+       "      <td>23.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>20680.40</td>\n",
+       "      <td>27000.00</td>\n",
+       "      <td>6319.60</td>\n",
+       "      <td>23.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>34446.96</td>\n",
+       "      <td>27900.00</td>\n",
+       "      <td>-6546.96</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1290.83</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>3086.38</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-587.38</td>\n",
+       "      <td>23.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>16427.12</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-3127.12</td>\n",
+       "      <td>23.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>43163.25</td>\n",
+       "      <td>-10149.56</td>\n",
+       "      <td>23.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>3747.21</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>1152.79</td>\n",
+       "      <td>23.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>14410.15</td>\n",
+       "      <td>18932.55</td>\n",
+       "      <td>4522.40</td>\n",
+       "      <td>23.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.45</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>4377.55</td>\n",
+       "      <td>23.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>9232.79</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>-1782.79</td>\n",
+       "      <td>23.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>18165.16</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>5734.84</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.49</td>\n",
+       "      <td>9150.00</td>\n",
+       "      <td>-2203.49</td>\n",
+       "      <td>24.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>6205.48</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-1205.48</td>\n",
+       "      <td>24.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>11004.05</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>3495.95</td>\n",
+       "      <td>24.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>10505.98</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>3394.02</td>\n",
+       "      <td>24.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>19920.63</td>\n",
+       "      <td>16000.00</td>\n",
+       "      <td>-3920.63</td>\n",
+       "      <td>24.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>3621.08</td>\n",
+       "      <td>4799.00</td>\n",
+       "      <td>1177.92</td>\n",
+       "      <td>24.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>17316.59</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-3416.59</td>\n",
+       "      <td>24.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>8471.55</td>\n",
+       "      <td>6799.00</td>\n",
+       "      <td>-1672.55</td>\n",
+       "      <td>24.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>36883.77</td>\n",
+       "      <td>49000.00</td>\n",
+       "      <td>12116.23</td>\n",
+       "      <td>24.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-1642.62</td>\n",
+       "      <td>24.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.16</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-2691.16</td>\n",
+       "      <td>24.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>3001.37</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>998.63</td>\n",
+       "      <td>24.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.26</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>-699.26</td>\n",
+       "      <td>24.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>23485.45</td>\n",
+       "      <td>18777.00</td>\n",
+       "      <td>-4708.45</td>\n",
+       "      <td>25.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>10835.95</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>3664.05</td>\n",
+       "      <td>25.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>6013.05</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1213.05</td>\n",
+       "      <td>25.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>3735.04</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1263.96</td>\n",
+       "      <td>25.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>15623.39</td>\n",
+       "      <td>20999.00</td>\n",
+       "      <td>5375.61</td>\n",
+       "      <td>25.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>8667.12</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1767.12</td>\n",
+       "      <td>25.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>8916.19</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>3082.81</td>\n",
+       "      <td>25.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>8688.92</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>3011.08</td>\n",
+       "      <td>25.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>12027.56</td>\n",
+       "      <td>16200.00</td>\n",
+       "      <td>4172.44</td>\n",
+       "      <td>25.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>12976.15</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>4523.85</td>\n",
+       "      <td>25.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>44407.14</td>\n",
+       "      <td>59999.00</td>\n",
+       "      <td>15591.86</td>\n",
+       "      <td>25.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>31059.18</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>10940.82</td>\n",
+       "      <td>26.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>8110.06</td>\n",
+       "      <td>10990.00</td>\n",
+       "      <td>2879.94</td>\n",
+       "      <td>26.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>672</th>\n",
+       "      <td>5679.39</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-1179.39</td>\n",
+       "      <td>26.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>4255.62</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>1544.38</td>\n",
+       "      <td>26.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>3164.66</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-665.66</td>\n",
+       "      <td>26.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>3154.48</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>1145.52</td>\n",
+       "      <td>26.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>4307.68</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>-907.68</td>\n",
+       "      <td>26.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>5195.69</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-1095.69</td>\n",
+       "      <td>26.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>47600.00</td>\n",
+       "      <td>12736.47</td>\n",
+       "      <td>26.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>8638.63</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>3161.37</td>\n",
+       "      <td>26.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>683</th>\n",
+       "      <td>32771.45</td>\n",
+       "      <td>44800.00</td>\n",
+       "      <td>12028.55</td>\n",
+       "      <td>26.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>28154.84</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>10345.16</td>\n",
+       "      <td>26.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>67952.07</td>\n",
+       "      <td>53500.00</td>\n",
+       "      <td>-14452.07</td>\n",
+       "      <td>27.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>13471.84</td>\n",
+       "      <td>10600.00</td>\n",
+       "      <td>-2871.84</td>\n",
+       "      <td>27.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>23227.15</td>\n",
+       "      <td>31900.00</td>\n",
+       "      <td>8672.85</td>\n",
+       "      <td>27.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>7131.92</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>2668.08</td>\n",
+       "      <td>27.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>8071.70</td>\n",
+       "      <td>11100.00</td>\n",
+       "      <td>3028.30</td>\n",
+       "      <td>27.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>26478.15</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>-5678.15</td>\n",
+       "      <td>27.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>7116.68</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>2683.32</td>\n",
+       "      <td>27.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>9312.21</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-2012.21</td>\n",
+       "      <td>27.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>10848.85</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-2348.85</td>\n",
+       "      <td>27.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>10597.72</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-2297.72</td>\n",
+       "      <td>27.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7940.44</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>3059.56</td>\n",
+       "      <td>27.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>5040.46</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1959.54</td>\n",
+       "      <td>27.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>10716.00</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>4184.00</td>\n",
+       "      <td>28.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>9205.10</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>3594.90</td>\n",
+       "      <td>28.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>31656.01</td>\n",
+       "      <td>24700.00</td>\n",
+       "      <td>-6956.01</td>\n",
+       "      <td>28.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>13853.36</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-3053.36</td>\n",
+       "      <td>28.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>2801.47</td>\n",
+       "      <td>28.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>8591.66</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>3408.34</td>\n",
+       "      <td>28.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-2701.29</td>\n",
+       "      <td>28.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>50074.56</td>\n",
+       "      <td>69990.00</td>\n",
+       "      <td>19915.44</td>\n",
+       "      <td>28.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>11432.63</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-2532.63</td>\n",
+       "      <td>28.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6437.21</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>2561.79</td>\n",
+       "      <td>28.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>8351.34</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-1851.34</td>\n",
+       "      <td>28.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>35336.33</td>\n",
+       "      <td>49500.00</td>\n",
+       "      <td>14163.67</td>\n",
+       "      <td>28.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>74232.55</td>\n",
+       "      <td>103999.00</td>\n",
+       "      <td>29766.45</td>\n",
+       "      <td>28.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>45664.45</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>-10164.45</td>\n",
+       "      <td>28.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>19298.61</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-4299.61</td>\n",
+       "      <td>28.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>11580.54</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-2580.54</td>\n",
+       "      <td>28.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>23496.36</td>\n",
+       "      <td>33000.00</td>\n",
+       "      <td>9503.64</td>\n",
+       "      <td>28.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>31557.80</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>-7057.80</td>\n",
+       "      <td>28.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>24323.48</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>-5523.48</td>\n",
+       "      <td>29.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>18285.07</td>\n",
+       "      <td>25900.00</td>\n",
+       "      <td>7614.93</td>\n",
+       "      <td>29.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>25640.67</td>\n",
+       "      <td>19800.00</td>\n",
+       "      <td>-5840.67</td>\n",
+       "      <td>29.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>5983.44</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>2516.56</td>\n",
+       "      <td>29.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>8973.16</td>\n",
+       "      <td>12750.00</td>\n",
+       "      <td>3776.84</td>\n",
+       "      <td>29.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>23336.06</td>\n",
+       "      <td>33200.00</td>\n",
+       "      <td>9863.94</td>\n",
+       "      <td>29.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>6110.94</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>2589.06</td>\n",
+       "      <td>29.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>3275.83</td>\n",
+       "      <td>29.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>10094.58</td>\n",
+       "      <td>7777.00</td>\n",
+       "      <td>-2317.58</td>\n",
+       "      <td>29.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>21690.99</td>\n",
+       "      <td>30900.00</td>\n",
+       "      <td>9209.01</td>\n",
+       "      <td>29.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>10809.48</td>\n",
+       "      <td>15400.00</td>\n",
+       "      <td>4590.52</td>\n",
+       "      <td>29.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>11018.79</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>4681.21</td>\n",
+       "      <td>29.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>13377.99</td>\n",
+       "      <td>10300.00</td>\n",
+       "      <td>-3077.99</td>\n",
+       "      <td>29.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>12731.26</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-2931.26</td>\n",
+       "      <td>29.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>11178.46</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>-2578.46</td>\n",
+       "      <td>29.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>21681.00</td>\n",
+       "      <td>30999.00</td>\n",
+       "      <td>9318.00</td>\n",
+       "      <td>30.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>27293.13</td>\n",
+       "      <td>39040.00</td>\n",
+       "      <td>11746.87</td>\n",
+       "      <td>30.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>6510.80</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-1510.80</td>\n",
+       "      <td>30.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>8298.32</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>3601.68</td>\n",
+       "      <td>30.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>10821.19</td>\n",
+       "      <td>8299.00</td>\n",
+       "      <td>-2522.19</td>\n",
+       "      <td>30.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>5346.97</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-1246.97</td>\n",
+       "      <td>30.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5484.02</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>2415.98</td>\n",
+       "      <td>30.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>5238.05</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-1238.05</td>\n",
+       "      <td>30.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>17597.10</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>7902.90</td>\n",
+       "      <td>30.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>7727.53</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>3472.47</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>6358.55</td>\n",
+       "      <td>4850.00</td>\n",
+       "      <td>-1508.55</td>\n",
+       "      <td>31.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>2886.35</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>1313.65</td>\n",
+       "      <td>31.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>14049.76</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-3349.76</td>\n",
+       "      <td>31.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>12974.89</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>5925.11</td>\n",
+       "      <td>31.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>6858.89</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>3140.11</td>\n",
+       "      <td>31.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>15691.29</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>7208.71</td>\n",
+       "      <td>31.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>2393.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1106.66</td>\n",
+       "      <td>31.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>6425.00</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>2975.00</td>\n",
+       "      <td>31.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>50206.83</td>\n",
+       "      <td>73500.00</td>\n",
+       "      <td>23293.17</td>\n",
+       "      <td>31.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>25685.67</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>-6185.67</td>\n",
+       "      <td>31.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>19692.08</td>\n",
+       "      <td>-6259.23</td>\n",
+       "      <td>31.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>28910.96</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>13589.04</td>\n",
+       "      <td>31.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>9919.19</td>\n",
+       "      <td>14600.00</td>\n",
+       "      <td>4680.81</td>\n",
+       "      <td>32.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>9113.05</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-2213.05</td>\n",
+       "      <td>32.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>24427.14</td>\n",
+       "      <td>36000.00</td>\n",
+       "      <td>11572.86</td>\n",
+       "      <td>32.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>4738.89</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>2261.11</td>\n",
+       "      <td>32.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>25040.42</td>\n",
+       "      <td>37000.00</td>\n",
+       "      <td>11959.58</td>\n",
+       "      <td>32.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>18403.35</td>\n",
+       "      <td>13893.75</td>\n",
+       "      <td>-4509.60</td>\n",
+       "      <td>32.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>14576.33</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-3576.33</td>\n",
+       "      <td>32.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>6610.04</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>3189.96</td>\n",
+       "      <td>32.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>10105.70</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>4894.30</td>\n",
+       "      <td>32.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>5041.20</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-1241.20</td>\n",
+       "      <td>32.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>6503.62</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-1603.62</td>\n",
+       "      <td>32.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>13542.79</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-3342.79</td>\n",
+       "      <td>32.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4364.08</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>2135.92</td>\n",
+       "      <td>32.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>12006.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>5893.34</td>\n",
+       "      <td>32.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>3787.35</td>\n",
+       "      <td>32.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>31251.89</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-7751.89</td>\n",
+       "      <td>32.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>7011.74</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>3488.26</td>\n",
+       "      <td>33.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>5792.10</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>2907.90</td>\n",
+       "      <td>33.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>8493.04</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>4306.96</td>\n",
+       "      <td>33.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>7950.27</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>4049.73</td>\n",
+       "      <td>33.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>8940.39</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>4559.61</td>\n",
+       "      <td>33.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>4569.15</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>2330.85</td>\n",
+       "      <td>33.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>13041.39</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>6658.61</td>\n",
+       "      <td>33.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>20163.48</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>10336.52</td>\n",
+       "      <td>33.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>2839.03</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>1460.97</td>\n",
+       "      <td>33.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>20433.23</td>\n",
+       "      <td>30999.00</td>\n",
+       "      <td>10565.77</td>\n",
+       "      <td>34.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>1746.19</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>903.81</td>\n",
+       "      <td>34.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>19855.31</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-5055.31</td>\n",
+       "      <td>34.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>19741.65</td>\n",
+       "      <td>29999.00</td>\n",
+       "      <td>10257.35</td>\n",
+       "      <td>34.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>65613.31</td>\n",
+       "      <td>99999.00</td>\n",
+       "      <td>34385.69</td>\n",
+       "      <td>34.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>10950.00</td>\n",
+       "      <td>3771.80</td>\n",
+       "      <td>34.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>9143.17</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2343.17</td>\n",
+       "      <td>34.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>2285.24</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1214.76</td>\n",
+       "      <td>34.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>22835.48</td>\n",
+       "      <td>16950.00</td>\n",
+       "      <td>-5885.48</td>\n",
+       "      <td>34.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>10786.38</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-2786.38</td>\n",
+       "      <td>34.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>11477.47</td>\n",
+       "      <td>17650.00</td>\n",
+       "      <td>6172.53</td>\n",
+       "      <td>34.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>23972.56</td>\n",
+       "      <td>36900.00</td>\n",
+       "      <td>12927.44</td>\n",
+       "      <td>35.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>3648.96</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-948.96</td>\n",
+       "      <td>35.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>11706.93</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>6393.07</td>\n",
+       "      <td>35.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>11100.00</td>\n",
+       "      <td>3921.80</td>\n",
+       "      <td>35.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>12286.47</td>\n",
+       "      <td>35.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>12286.47</td>\n",
+       "      <td>35.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>12897.92</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-3397.92</td>\n",
+       "      <td>35.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>12904.68</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-3404.68</td>\n",
+       "      <td>35.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>9611.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>5388.20</td>\n",
+       "      <td>35.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>20236.85</td>\n",
+       "      <td>31600.00</td>\n",
+       "      <td>11363.15</td>\n",
+       "      <td>35.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>4151.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>2348.98</td>\n",
+       "      <td>36.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>28951.20</td>\n",
+       "      <td>45500.00</td>\n",
+       "      <td>16548.80</td>\n",
+       "      <td>36.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>18988.13</td>\n",
+       "      <td>-6963.18</td>\n",
+       "      <td>36.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>5570.46</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>3229.54</td>\n",
+       "      <td>36.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>22454.74</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>13045.26</td>\n",
+       "      <td>36.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>41711.18</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>-11211.18</td>\n",
+       "      <td>36.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>3152.62</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1846.38</td>\n",
+       "      <td>36.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>8902.52</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-2402.52</td>\n",
+       "      <td>36.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>48699.98</td>\n",
+       "      <td>77500.00</td>\n",
+       "      <td>28800.02</td>\n",
+       "      <td>37.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>16821.54</td>\n",
+       "      <td>26800.00</td>\n",
+       "      <td>9978.46</td>\n",
+       "      <td>37.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>5521.28</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>3278.72</td>\n",
+       "      <td>37.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>5268.17</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>3131.83</td>\n",
+       "      <td>37.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>65825.25</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>39173.75</td>\n",
+       "      <td>37.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>12459.85</td>\n",
+       "      <td>19900.00</td>\n",
+       "      <td>7440.15</td>\n",
+       "      <td>37.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>65723.62</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>39275.38</td>\n",
+       "      <td>37.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>9070.23</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>5429.77</td>\n",
+       "      <td>37.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>9348.90</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2548.90</td>\n",
+       "      <td>37.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>9369.66</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>5630.34</td>\n",
+       "      <td>37.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>32954.19</td>\n",
+       "      <td>52777.00</td>\n",
+       "      <td>19822.81</td>\n",
+       "      <td>37.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>17195.57</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-4695.57</td>\n",
+       "      <td>37.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>3425.23</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>2074.77</td>\n",
+       "      <td>37.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>3173.48</td>\n",
+       "      <td>5100.00</td>\n",
+       "      <td>1926.52</td>\n",
+       "      <td>37.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>10337.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-2837.11</td>\n",
+       "      <td>37.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>105999.00</td>\n",
+       "      <td>40256.43</td>\n",
+       "      <td>37.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>8661.95</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>5337.05</td>\n",
+       "      <td>38.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>26062.23</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>16437.77</td>\n",
+       "      <td>38.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>7630.64</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-2130.64</td>\n",
+       "      <td>38.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11378.43</td>\n",
+       "      <td>18600.00</td>\n",
+       "      <td>7221.57</td>\n",
+       "      <td>38.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>66032.76</td>\n",
+       "      <td>107999.00</td>\n",
+       "      <td>41966.24</td>\n",
+       "      <td>38.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>7279.97</td>\n",
+       "      <td>11950.00</td>\n",
+       "      <td>4670.03</td>\n",
+       "      <td>39.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>18577.88</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>11922.12</td>\n",
+       "      <td>39.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>4043.74</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>-1143.74</td>\n",
+       "      <td>39.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>2178.94</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>1421.06</td>\n",
+       "      <td>39.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>23286.27</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>15213.73</td>\n",
+       "      <td>39.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12558.90</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-3559.90</td>\n",
+       "      <td>39.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>18432.63</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>12067.37</td>\n",
+       "      <td>39.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>9057.77</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>5941.23</td>\n",
+       "      <td>39.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>13476.90</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>9023.10</td>\n",
+       "      <td>40.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>37704.71</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>-10804.71</td>\n",
+       "      <td>40.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>32945.80</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-9445.80</td>\n",
+       "      <td>40.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-2212.65</td>\n",
+       "      <td>40.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>109999.00</td>\n",
+       "      <td>44256.43</td>\n",
+       "      <td>40.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36764.38</td>\n",
+       "      <td>62000.00</td>\n",
+       "      <td>25235.62</td>\n",
+       "      <td>40.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>77403.06</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-22403.06</td>\n",
+       "      <td>40.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>8530.54</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>5969.46</td>\n",
+       "      <td>41.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>15259.81</td>\n",
+       "      <td>26000.00</td>\n",
+       "      <td>10740.19</td>\n",
+       "      <td>41.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>16253.48</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-4753.48</td>\n",
+       "      <td>41.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>1815.28</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>1284.72</td>\n",
+       "      <td>41.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>44193.38</td>\n",
+       "      <td>75500.00</td>\n",
+       "      <td>31306.62</td>\n",
+       "      <td>41.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>45272.25</td>\n",
+       "      <td>31990.00</td>\n",
+       "      <td>-13282.25</td>\n",
+       "      <td>41.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>9198.08</td>\n",
+       "      <td>15800.00</td>\n",
+       "      <td>6601.92</td>\n",
+       "      <td>41.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>19854.79</td>\n",
+       "      <td>14000.00</td>\n",
+       "      <td>-5854.79</td>\n",
+       "      <td>41.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>10031.96</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>7268.04</td>\n",
+       "      <td>42.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>9661.78</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2861.78</td>\n",
+       "      <td>42.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>2539.57</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>1860.43</td>\n",
+       "      <td>42.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>11474.22</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>8524.78</td>\n",
+       "      <td>42.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>65887.51</td>\n",
+       "      <td>114999.00</td>\n",
+       "      <td>49111.49</td>\n",
+       "      <td>42.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>2784.46</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>-834.46</td>\n",
+       "      <td>42.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>15015.22</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-4515.22</td>\n",
+       "      <td>43.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>31216.83</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>23783.17</td>\n",
+       "      <td>43.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>5374.74</td>\n",
+       "      <td>3750.00</td>\n",
+       "      <td>-1624.74</td>\n",
+       "      <td>43.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>5558.93</td>\n",
+       "      <td>43.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>3450.69</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>2649.31</td>\n",
+       "      <td>43.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>16724.45</td>\n",
+       "      <td>29600.00</td>\n",
+       "      <td>12875.55</td>\n",
+       "      <td>43.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>65210.35</td>\n",
+       "      <td>115800.00</td>\n",
+       "      <td>50589.65</td>\n",
+       "      <td>43.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>10740.09</td>\n",
+       "      <td>19100.00</td>\n",
+       "      <td>8359.91</td>\n",
+       "      <td>43.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>12234.68</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-3734.68</td>\n",
+       "      <td>43.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>14402.34</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>-4402.34</td>\n",
+       "      <td>44.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>10807.78</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-3307.78</td>\n",
+       "      <td>44.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>39074.36</td>\n",
+       "      <td>69999.00</td>\n",
+       "      <td>30924.64</td>\n",
+       "      <td>44.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>14512.61</td>\n",
+       "      <td>25999.00</td>\n",
+       "      <td>11486.39</td>\n",
+       "      <td>44.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3316.44</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1016.44</td>\n",
+       "      <td>44.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>12255.45</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>9744.55</td>\n",
+       "      <td>44.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>6781.86</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>-2081.86</td>\n",
+       "      <td>44.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>3752.54</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-1152.54</td>\n",
+       "      <td>44.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>2389.14</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>-739.14</td>\n",
+       "      <td>44.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>34761.74</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-10761.74</td>\n",
+       "      <td>44.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>11881.05</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-3681.05</td>\n",
+       "      <td>44.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1873.03</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>1526.97</td>\n",
+       "      <td>44.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>13212.52</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>10786.48</td>\n",
+       "      <td>44.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>17282.06</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-5382.06</td>\n",
+       "      <td>45.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>17011.43</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-5311.43</td>\n",
+       "      <td>45.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>2020.25</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>1679.75</td>\n",
+       "      <td>45.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>18942.34</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-5943.34</td>\n",
+       "      <td>45.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>11337.51</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>9562.49</td>\n",
+       "      <td>45.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>10547.82</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>8952.18</td>\n",
+       "      <td>45.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>6421.18</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-2021.18</td>\n",
+       "      <td>45.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>12414.72</td>\n",
+       "      <td>23000.00</td>\n",
+       "      <td>10585.28</td>\n",
+       "      <td>46.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>24796.09</td>\n",
+       "      <td>46000.00</td>\n",
+       "      <td>21203.91</td>\n",
+       "      <td>46.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>4172.84</td>\n",
+       "      <td>7750.00</td>\n",
+       "      <td>3577.16</td>\n",
+       "      <td>46.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>15956.95</td>\n",
+       "      <td>29999.00</td>\n",
+       "      <td>14042.05</td>\n",
+       "      <td>46.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>1858.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1641.66</td>\n",
+       "      <td>46.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>8522.39</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>-2722.39</td>\n",
+       "      <td>46.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>13235.55</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-4236.55</td>\n",
+       "      <td>47.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>13600.39</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>12199.61</td>\n",
+       "      <td>47.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>4032.10</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>3617.90</td>\n",
+       "      <td>47.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>12414.41</td>\n",
+       "      <td>8420.00</td>\n",
+       "      <td>-3994.41</td>\n",
+       "      <td>47.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>2573.48</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>2326.52</td>\n",
+       "      <td>47.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>14572.70</td>\n",
+       "      <td>27900.00</td>\n",
+       "      <td>13327.30</td>\n",
+       "      <td>47.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>15144.28</td>\n",
+       "      <td>29000.00</td>\n",
+       "      <td>13855.72</td>\n",
+       "      <td>47.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>20159.19</td>\n",
+       "      <td>38888.00</td>\n",
+       "      <td>18728.81</td>\n",
+       "      <td>48.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>14000.00</td>\n",
+       "      <td>6758.93</td>\n",
+       "      <td>48.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>25682.58</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>-8382.58</td>\n",
+       "      <td>48.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>10563.39</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>9936.61</td>\n",
+       "      <td>48.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>6546.81</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-2146.81</td>\n",
+       "      <td>48.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "      <td>37294.64</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "      <td>37294.64</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>13443.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-4443.32</td>\n",
+       "      <td>49.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>14933.54</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>14566.46</td>\n",
+       "      <td>49.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>13884.26</td>\n",
+       "      <td>27800.00</td>\n",
+       "      <td>13915.74</td>\n",
+       "      <td>50.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>31373.47</td>\n",
+       "      <td>20859.15</td>\n",
+       "      <td>-10514.32</td>\n",
+       "      <td>50.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>47014.52</td>\n",
+       "      <td>95000.00</td>\n",
+       "      <td>47985.48</td>\n",
+       "      <td>50.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>41075.73</td>\n",
+       "      <td>27200.00</td>\n",
+       "      <td>-13875.73</td>\n",
+       "      <td>51.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>10125.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-3425.27</td>\n",
+       "      <td>51.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>60603.06</td>\n",
+       "      <td>124000.00</td>\n",
+       "      <td>63396.94</td>\n",
+       "      <td>51.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>14363.76</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-4863.76</td>\n",
+       "      <td>51.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>25302.43</td>\n",
+       "      <td>52000.00</td>\n",
+       "      <td>26697.57</td>\n",
+       "      <td>51.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>7391.67</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>7808.33</td>\n",
+       "      <td>51.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>9753.44</td>\n",
+       "      <td>20400.00</td>\n",
+       "      <td>10646.56</td>\n",
+       "      <td>52.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>32925.60</td>\n",
+       "      <td>69500.00</td>\n",
+       "      <td>36574.40</td>\n",
+       "      <td>52.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>22941.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-7941.80</td>\n",
+       "      <td>52.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>6199.59</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>7000.41</td>\n",
+       "      <td>53.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>5597.06</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>6401.94</td>\n",
+       "      <td>53.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>13409.47</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-4709.47</td>\n",
+       "      <td>54.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>6635.73</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>-2335.73</td>\n",
+       "      <td>54.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>33707.27</td>\n",
+       "      <td>73900.00</td>\n",
+       "      <td>40192.73</td>\n",
+       "      <td>54.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>19309.90</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-6809.90</td>\n",
+       "      <td>54.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>19624.73</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>-6924.73</td>\n",
+       "      <td>54.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>13757.58</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-4857.58</td>\n",
+       "      <td>54.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>12354.92</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>15145.08</td>\n",
+       "      <td>55.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>17714.62</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-6314.62</td>\n",
+       "      <td>55.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>11555.80</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>-4155.80</td>\n",
+       "      <td>56.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>22011.47</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>-8012.47</td>\n",
+       "      <td>57.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>15457.91</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-5657.91</td>\n",
+       "      <td>57.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>11099.09</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-4100.09</td>\n",
+       "      <td>58.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>35039.40</td>\n",
+       "      <td>85555.00</td>\n",
+       "      <td>50515.60</td>\n",
+       "      <td>59.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>34376.16</td>\n",
+       "      <td>85000.00</td>\n",
+       "      <td>50623.84</td>\n",
+       "      <td>59.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>16773.62</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-6273.62</td>\n",
+       "      <td>59.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.75</td>\n",
+       "      <td>68500.00</td>\n",
+       "      <td>41012.25</td>\n",
+       "      <td>59.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>7996.00</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-2997.00</td>\n",
+       "      <td>59.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>5284.06</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-1984.06</td>\n",
+       "      <td>60.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>87777.00</td>\n",
+       "      <td>52913.47</td>\n",
+       "      <td>60.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>19701.70</td>\n",
+       "      <td>49999.00</td>\n",
+       "      <td>30297.30</td>\n",
+       "      <td>60.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>30409.09</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>-11509.09</td>\n",
+       "      <td>60.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>14486.38</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-5486.38</td>\n",
+       "      <td>60.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>11601.33</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-4401.33</td>\n",
+       "      <td>61.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>10824.89</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-4124.89</td>\n",
+       "      <td>61.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>65448.37</td>\n",
+       "      <td>40500.00</td>\n",
+       "      <td>-24948.37</td>\n",
+       "      <td>61.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>5351.86</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-2051.86</td>\n",
+       "      <td>62.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>25864.65</td>\n",
+       "      <td>69990.00</td>\n",
+       "      <td>44125.35</td>\n",
+       "      <td>63.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>15682.82</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-6082.82</td>\n",
+       "      <td>63.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>40765.33</td>\n",
+       "      <td>112000.00</td>\n",
+       "      <td>71234.67</td>\n",
+       "      <td>63.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>1320.56</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>2379.44</td>\n",
+       "      <td>64.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1495.06</td>\n",
+       "      <td>65.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>19335.34</td>\n",
+       "      <td>55555.00</td>\n",
+       "      <td>36219.66</td>\n",
+       "      <td>65.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>26736.21</td>\n",
+       "      <td>16100.00</td>\n",
+       "      <td>-10636.21</td>\n",
+       "      <td>66.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>26441.03</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-10541.03</td>\n",
+       "      <td>66.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>5002.39</td>\n",
+       "      <td>2999.00</td>\n",
+       "      <td>-2003.39</td>\n",
+       "      <td>66.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>18317.20</td>\n",
+       "      <td>10974.21</td>\n",
+       "      <td>-7342.99</td>\n",
+       "      <td>66.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>40893.84</td>\n",
+       "      <td>126000.00</td>\n",
+       "      <td>85106.16</td>\n",
+       "      <td>67.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>38473.71</td>\n",
+       "      <td>119000.00</td>\n",
+       "      <td>80526.29</td>\n",
+       "      <td>67.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>109999.00</td>\n",
+       "      <td>75135.47</td>\n",
+       "      <td>68.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>10946.44</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-4446.44</td>\n",
+       "      <td>68.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>3205.38</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-1305.38</td>\n",
+       "      <td>68.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>16924.74</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-6925.74</td>\n",
+       "      <td>69.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>9356.43</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-3856.43</td>\n",
+       "      <td>70.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15374.08</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-6474.08</td>\n",
+       "      <td>72.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>11056.52</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-4656.52</td>\n",
+       "      <td>72.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>2440.90</td>\n",
+       "      <td>1400.00</td>\n",
+       "      <td>-1040.90</td>\n",
+       "      <td>74.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>3939.91</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>11560.09</td>\n",
+       "      <td>74.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>15380.04</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-6580.04</td>\n",
+       "      <td>74.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>35093.27</td>\n",
+       "      <td>19990.00</td>\n",
+       "      <td>-15103.27</td>\n",
+       "      <td>75.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>8270.38</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>-3570.38</td>\n",
+       "      <td>75.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>6916.13</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>-3016.13</td>\n",
+       "      <td>77.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-2947.48</td>\n",
+       "      <td>77.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>2555.02</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>9044.98</td>\n",
+       "      <td>77.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>13355.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-5855.11</td>\n",
+       "      <td>78.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>3948.74</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-1748.74</td>\n",
+       "      <td>79.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>9024.05</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-4124.05</td>\n",
+       "      <td>84.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>15292.10</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-6992.10</td>\n",
+       "      <td>84.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>7621.25</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-3521.25</td>\n",
+       "      <td>85.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>2975.40</td>\n",
+       "      <td>1600.00</td>\n",
+       "      <td>-1375.40</td>\n",
+       "      <td>85.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>14915.98</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-7015.98</td>\n",
+       "      <td>88.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>5716.14</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-2716.14</td>\n",
+       "      <td>90.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15819.50</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-7519.50</td>\n",
+       "      <td>90.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>13420.15</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-6421.15</td>\n",
+       "      <td>91.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>23576.99</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>-11576.99</td>\n",
+       "      <td>96.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>8648.80</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-4248.80</td>\n",
+       "      <td>96.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>15559.43</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-7659.43</td>\n",
+       "      <td>96.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>4004.65</td>\n",
+       "      <td>2000.00</td>\n",
+       "      <td>-2004.65</td>\n",
+       "      <td>100.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>27225.34</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-13725.34</td>\n",
+       "      <td>101.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>5462.52</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-2762.52</td>\n",
+       "      <td>102.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>8131.92</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>-4132.92</td>\n",
+       "      <td>103.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>10810.62</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-5810.62</td>\n",
+       "      <td>116.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>7973.87</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-4323.87</td>\n",
+       "      <td>118.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>5948.04</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-3348.04</td>\n",
+       "      <td>128.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>6891.99</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-3891.99</td>\n",
+       "      <td>129.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>26331.41</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-15131.41</td>\n",
+       "      <td>135.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>21493.44</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-12493.44</td>\n",
+       "      <td>138.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>6537.82</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-3837.82</td>\n",
+       "      <td>142.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>12891.96</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-7591.96</td>\n",
+       "      <td>143.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>8340.94</td>\n",
+       "      <td>3350.00</td>\n",
+       "      <td>-4990.94</td>\n",
+       "      <td>148.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>3040.77</td>\n",
+       "      <td>1200.00</td>\n",
+       "      <td>-1840.77</td>\n",
+       "      <td>153.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>9631.08</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-5832.08</td>\n",
+       "      <td>153.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>13967.55</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-8467.55</td>\n",
+       "      <td>153.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>3818.71</td>\n",
+       "      <td>1450.00</td>\n",
+       "      <td>-2368.71</td>\n",
+       "      <td>163.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>7809.13</td>\n",
+       "      <td>2899.00</td>\n",
+       "      <td>-4910.13</td>\n",
+       "      <td>169.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-2890.49</td>\n",
+       "      <td>170.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>7320.42</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-4720.42</td>\n",
+       "      <td>181.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>12261.19</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-8161.19</td>\n",
+       "      <td>199.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.29</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-5053.29</td>\n",
+       "      <td>202.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>9817.06</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-6617.06</td>\n",
+       "      <td>206.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>7161.67</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-4961.67</td>\n",
+       "      <td>225.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>7918.89</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-5518.89</td>\n",
+       "      <td>229.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>9984.87</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-6984.87</td>\n",
+       "      <td>232.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>10381.62</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-7381.62</td>\n",
+       "      <td>246.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>8843.22</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-6343.22</td>\n",
+       "      <td>253.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>10123.04</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>-7323.04</td>\n",
+       "      <td>261.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>16165.79</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-11765.79</td>\n",
+       "      <td>267.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>17937.36</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-13137.36</td>\n",
+       "      <td>273.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>7319.77</td>\n",
+       "      <td>1850.00</td>\n",
+       "      <td>-5469.77</td>\n",
+       "      <td>295.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>16095.32</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-12495.32</td>\n",
+       "      <td>347.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.50</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-8385.50</td>\n",
+       "      <td>364.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>9664.46</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-7764.46</td>\n",
+       "      <td>408.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>32481.05</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-26481.05</td>\n",
+       "      <td>441.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>9954.42</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>-8154.42</td>\n",
+       "      <td>453.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>35956.50</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-29456.50</td>\n",
+       "      <td>453.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>10019.90</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>-8219.90</td>\n",
+       "      <td>456.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>30628.28</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-25629.28</td>\n",
+       "      <td>512.69</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction    Target  Residual  Difference%\n",
+       "698    30480.85  30500.00     19.15         0.06\n",
+       "742    16960.31  16999.00     38.69         0.23\n",
+       "60     12469.21  12500.00     30.79         0.25\n",
+       "110    25614.14  25500.00   -114.14         0.45\n",
+       "367    42703.68  42500.00   -203.68         0.48\n",
+       "369     3084.69   3100.00     15.31         0.49\n",
+       "769    29651.73  29500.00   -151.73         0.51\n",
+       "272     9749.53   9800.00     50.47         0.52\n",
+       "714    23118.07  22999.00   -119.07         0.52\n",
+       "630     8734.58   8800.00     65.42         0.74\n",
+       "380     3473.79   3500.00     26.21         0.75\n",
+       "648    21174.10  21335.00    160.90         0.75\n",
+       "308     8967.74   8900.00    -67.74         0.76\n",
+       "665    17858.02  18000.00    141.98         0.79\n",
+       "379    17654.84  17800.00    145.16         0.82\n",
+       "719    11391.95  11500.00    108.05         0.94\n",
+       "102    28625.56  28900.00    274.44         0.95\n",
+       "94      7724.17   7800.00     75.83         0.97\n",
+       "561     6429.03   6500.00     70.97         1.09\n",
+       "242     7597.39   7500.00    -97.39         1.30\n",
+       "528    18555.09  18800.00    244.91         1.30\n",
+       "61      7396.87   7300.00    -96.87         1.33\n",
+       "19     16178.14  16400.00    221.86         1.35\n",
+       "280    12327.10  12499.00    171.90         1.38\n",
+       "311    51287.19  52055.25    768.06         1.48\n",
+       "723     6009.63   6100.00     90.37         1.48\n",
+       "49      4973.17   4900.00    -73.17         1.49\n",
+       "114    27716.14  27300.00   -416.14         1.52\n",
+       "636    28498.91  28950.00    451.09         1.56\n",
+       "612     2953.17   3000.00     46.83         1.56\n",
+       "47     26425.14  25999.00   -426.14         1.64\n",
+       "23     13111.91  12900.00   -211.91         1.64\n",
+       "31     12858.08  12650.00   -208.08         1.64\n",
+       "91     13421.16  13200.00   -221.16         1.68\n",
+       "329     7327.18   7200.00   -127.18         1.77\n",
+       "549     3816.33   3750.00    -66.33         1.77\n",
+       "252     9721.50   9900.00    178.50         1.80\n",
+       "387    44173.72  44999.00    825.28         1.83\n",
+       "267    40753.58  40000.00   -753.58         1.88\n",
+       "467    22262.80  22711.65    448.85         1.98\n",
+       "556    18231.44  18600.00    368.56         1.98\n",
+       "165     9596.94   9400.00   -196.94         2.10\n",
+       "259     6067.79   6200.00    132.21         2.13\n",
+       "601    35371.16  34600.00   -771.16         2.23\n",
+       "708    11967.39  11700.00   -267.39         2.29\n",
+       "593    17908.00  17500.00   -408.00         2.33\n",
+       "398     8707.13   8500.00   -207.13         2.44\n",
+       "526    29049.27  28350.00   -699.27         2.47\n",
+       "603    14513.46  14900.00    386.54         2.59\n",
+       "53     20453.89  21000.00    546.11         2.60\n",
+       "632    15383.35  14990.00   -393.35         2.62\n",
+       "533    24642.50  24000.00   -642.50         2.68\n",
+       "497    50099.92  51500.00   1400.08         2.72\n",
+       "212    16133.86  15700.00   -433.86         2.76\n",
+       "130    17489.92  18000.00    510.08         2.83\n",
+       "290     1894.40   1950.00     55.60         2.85\n",
+       "78     30810.25  29900.00   -910.25         3.04\n",
+       "642     8721.97   8999.00    277.03         3.08\n",
+       "437    18866.50  18300.00   -566.50         3.10\n",
+       "101     5958.63   6150.00    191.37         3.11\n",
+       "314     5811.74   6000.00    188.26         3.14\n",
+       "150     9800.43   9500.00   -300.43         3.16\n",
+       "565     7324.63   7100.00   -224.63         3.16\n",
+       "574    12583.52  13000.00    416.48         3.20\n",
+       "591    10115.13   9800.00   -315.13         3.22\n",
+       "172    11156.38  10800.00   -356.38         3.30\n",
+       "133     9279.28   9600.00    320.72         3.34\n",
+       "480    31369.37  32500.00   1130.63         3.48\n",
+       "87      2315.71   2400.00     84.29         3.51\n",
+       "11      5175.77   5000.00   -175.77         3.52\n",
+       "43     21611.83  22400.00    788.17         3.52\n",
+       "96      7976.26   7700.00   -276.26         3.59\n",
+       "406    24874.86  23999.00   -875.86         3.65\n",
+       "173    36516.35  37900.00   1383.65         3.65\n",
+       "540     4666.05   4500.00   -166.05         3.69\n",
+       "40     18672.68  18000.00   -672.68         3.74\n",
+       "340    14815.83  15400.00    584.17         3.79\n",
+       "239    10581.62  10999.00    417.38         3.79\n",
+       "109    12663.54  12200.00   -463.54         3.80\n",
+       "256     1825.44   1900.00     74.56         3.92\n",
+       "317    12247.90  12750.00    502.10         3.94\n",
+       "77      5930.73   5700.00   -230.73         4.05\n",
+       "301     9782.47  10200.00    417.53         4.09\n",
+       "333    12452.22  11960.00   -492.22         4.12\n",
+       "570    23163.87  24171.42   1007.55         4.17\n",
+       "581     3246.94   3390.00    143.06         4.22\n",
+       "693    12354.16  12900.00    545.84         4.23\n",
+       "381    33499.44  35000.00   1500.56         4.29\n",
+       "438    16257.03  16999.00    741.97         4.36\n",
+       "368     8415.81   8800.00    384.19         4.37\n",
+       "273    12318.39  12900.00    581.61         4.51\n",
+       "235     2765.14   2900.00    134.86         4.65\n",
+       "168    11420.95  11999.00    578.05         4.82\n",
+       "707     2725.40   2600.00   -125.40         4.82\n",
+       "72     23624.69  22500.00  -1124.69         5.00\n",
+       "559    11377.84  11999.00    621.16         5.18\n",
+       "134    12096.18  11500.00   -596.18         5.18\n",
+       "446     9363.27   8900.00   -463.27         5.21\n",
+       "127    12311.47  11700.00   -611.47         5.23\n",
+       "450    14218.94  13500.00   -718.94         5.33\n",
+       "293     8431.89   7999.00   -432.89         5.41\n",
+       "18     37600.15  39900.00   2299.85         5.76\n",
+       "452    39882.67  37700.00  -2182.67         5.79\n",
+       "195    20588.57  21900.00   1311.43         5.99\n",
+       "676     6861.84   7300.00    438.16         6.00\n",
+       "766    18982.15  17900.00  -1082.15         6.05\n",
+       "328    15067.85  14200.00   -867.85         6.11\n",
+       "762    12201.29  12999.00    797.71         6.14\n",
+       "649    11147.91  10500.00   -647.91         6.17\n",
+       "299    20183.30  18999.00  -1184.30         6.23\n",
+       "641    17334.11  18500.00   1165.89         6.30\n",
+       "5      21289.80  20000.00  -1289.80         6.45\n",
+       "545     6826.78   7300.00    473.22         6.48\n",
+       "622     4472.47   4200.00   -272.47         6.49\n",
+       "422    53294.61  57000.00   3705.39         6.50\n",
+       "740     6658.73   6250.00   -408.73         6.54\n",
+       "85     19001.29  17800.00  -1201.29         6.75\n",
+       "315     4590.49   4300.00   -290.49         6.76\n",
+       "462    43236.92  40500.00  -2736.92         6.76\n",
+       "394     9076.42   8500.00   -576.42         6.78\n",
+       "188    10159.18  10900.00    740.82         6.80\n",
+       "731    16027.02  15000.00  -1027.02         6.85\n",
+       "448    13502.35  14500.00    997.65         6.88\n",
+       "254     6413.26   5999.00   -414.26         6.91\n",
+       "667    49221.63  52999.00   3777.37         7.13\n",
+       "600    21816.43  23500.00   1683.57         7.16\n",
+       "144    11969.66  12900.00    930.34         7.21\n",
+       "270     8263.95   7700.00   -563.95         7.32\n",
+       "8      11581.54  12500.00    918.46         7.35\n",
+       "433     6977.90   6500.00   -477.90         7.35\n",
+       "251     6978.91   6500.00   -478.91         7.37\n",
+       "553    16392.22  17700.00   1307.78         7.39\n",
+       "725    15742.41  16999.00   1256.59         7.39\n",
+       "615     7869.47   8500.00    630.53         7.42\n",
+       "518    21297.65  19800.00  -1497.65         7.56\n",
+       "268     6893.62   6400.00   -493.62         7.71\n",
+       "71     15627.03  14500.00  -1127.03         7.77\n",
+       "579     3411.53   3700.00    288.47         7.80\n",
+       "330    11336.71  12300.00    963.29         7.83\n",
+       "59      8195.34   7600.00   -595.34         7.83\n",
+       "354    16717.08  15500.00  -1217.08         7.85\n",
+       "147     7772.57   7200.00   -572.57         7.95\n",
+       "508    16197.42  15000.00  -1197.42         7.98\n",
+       "22      6102.36   5650.00   -452.36         8.01\n",
+       "103     4488.43   4150.00   -338.43         8.16\n",
+       "755    15885.66  17300.00   1414.34         8.18\n",
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+       "470     7045.72   6500.00   -545.72         8.40\n",
+       "710     3795.06   3500.00   -295.06         8.43\n",
+       "185     6130.96   6700.00    569.04         8.49\n",
+       "542     5947.51   6500.00    552.49         8.50\n",
+       "412     7609.28   7000.00   -609.28         8.70\n",
+       "415     8117.44   8900.00    782.56         8.79\n",
+       "200    11098.18  10200.00   -898.18         8.81\n",
+       "754    17028.45  18700.00   1671.55         8.94\n",
+       "65     19847.25  18200.00  -1647.25         9.05\n",
+       "682     5454.34   5000.00   -454.34         9.09\n",
+       "222    20662.46  18900.00  -1762.46         9.33\n",
+       "89     11696.25  12900.00   1203.75         9.33\n",
+       "52     10716.84   9800.00   -916.84         9.36\n",
+       "126    13459.92  12300.00  -1159.92         9.43\n",
+       "598     8767.54   9700.00    932.46         9.61\n",
+       "504    23272.83  25749.75   2476.92         9.62\n",
+       "493     5484.97   4999.00   -485.97         9.72\n",
+       "449     9495.62   8650.00   -845.62         9.78\n",
+       "516    11858.48  10800.00  -1058.48         9.80\n",
+       "261    53312.81  48535.50  -4777.31         9.84\n",
+       "531    47047.70  52300.00   5252.30        10.04\n",
+       "68      6717.17   6100.00   -617.17        10.12\n",
+       "376     5391.77   6000.00    608.23        10.14\n",
+       "538    17591.09  19600.00   2008.91        10.25\n",
+       "419    17591.09  19600.00   2008.91        10.25\n",
+       "595    25576.58  28500.00   2923.42        10.26\n",
+       "472    20367.25  22700.00   2332.75        10.28\n",
+       "386     7267.36   8100.00    832.64        10.28\n",
+       "473     1792.36   1999.00    206.64        10.34\n",
+       "14      8248.67   9200.00    951.33        10.34\n",
+       "123     5643.45   6300.00    656.55        10.42\n",
+       "142     6839.57   7650.00    810.43        10.59\n",
+       "257    41503.90  37500.00  -4003.90        10.68\n",
+       "194     9310.69   8400.00   -910.69        10.84\n",
+       "637    35662.26  40000.00   4337.74        10.84\n",
+       "410    16407.29  14800.00  -1607.29        10.86\n",
+       "395    21303.57  23900.00   2596.43        10.86\n",
+       "213    15872.91  14300.00  -1572.91        11.00\n",
+       "653    10332.54   9300.00  -1032.54        11.10\n",
+       "54     18575.43  20900.00   2324.57        11.12\n",
+       "105     8882.65  10000.00   1117.35        11.17\n",
+       "597     7797.18   6999.00   -798.18        11.40\n",
+       "15     10621.84  11999.00   1377.16        11.48\n",
+       "770    10732.07   9600.00  -1132.07        11.79\n",
+       "674     2381.01   2700.00    318.99        11.81\n",
+       "132    11200.20   9999.00  -1201.20        12.01\n",
+       "525     2373.66   2700.00    326.34        12.09\n",
+       "730    10696.56  12179.00   1482.44        12.17\n",
+       "506     3590.97   3200.00   -390.97        12.22\n",
+       "278    53063.22  60500.00   7436.78        12.29\n",
+       "108     7300.05   6500.00   -800.05        12.31\n",
+       "503     6392.70   7300.00    907.30        12.43\n",
+       "536     6746.43   6000.00   -746.43        12.44\n",
+       "156     9192.06  10500.00   1307.94        12.46\n",
+       "765    11034.66   9800.00  -1234.66        12.60\n",
+       "534    16939.86  19400.00   2460.14        12.68\n",
+       "50      2479.43   2200.00   -279.43        12.70\n",
+       "562     9593.52  11000.00   1406.48        12.79\n",
+       "131    23967.81  27500.00   3532.19        12.84\n",
+       "157     7491.01   8600.00   1108.99        12.90\n",
+       "638     8242.62   7300.00   -942.62        12.91\n",
+       "543     9489.56  10900.00   1410.44        12.94\n",
+       "724    44312.98  50900.00   6587.02        12.94\n",
+       "527     9829.53   8700.00  -1129.53        12.98\n",
+       "610     9269.68   8200.00  -1069.68        13.04\n",
+       "359     4524.77   3999.00   -525.77        13.15\n",
+       "24     23650.15  20900.00  -2750.15        13.16\n",
+       "402     8850.21  10200.00   1349.79        13.23\n",
+       "360     4770.52   5500.00    729.48        13.26\n",
+       "184    29479.97  34000.00   4520.03        13.29\n",
+       "324     8316.92   9600.00   1283.08        13.37\n",
+       "307    24974.57  28900.00   3925.43        13.58\n",
+       "118    14943.75  17300.00   2356.25        13.62\n",
+       "607    28498.91  33000.00   4501.09        13.64\n",
+       "332    14591.35  16900.00   2308.65        13.66\n",
+       "140    10358.58  12000.00   1641.42        13.68\n",
+       "627    13074.74  11500.00  -1574.74        13.69\n",
+       "761     9097.13   8000.00  -1097.13        13.71\n",
+       "207    15010.51  13200.00  -1810.51        13.72\n",
+       "685    12593.00  14600.00   2007.00        13.75\n",
+       "350    20246.04  23500.00   3253.96        13.85\n",
+       "143     8768.29   7700.00  -1068.29        13.87\n",
+       "318    39744.80  34900.00  -4844.80        13.88\n",
+       "377     9909.62   8700.00  -1209.62        13.90\n",
+       "152    44680.77  51900.00   7219.23        13.91\n",
+       "57     21942.05  25500.00   3557.95        13.95\n",
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+       "253    18128.32  15900.00  -2228.32        14.01\n",
+       "384     4296.77   5000.00    703.23        14.06\n",
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+       "287     1972.28   2300.00    327.72        14.25\n",
+       "650     8236.36   7200.00  -1036.36        14.39\n",
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+       "95     47013.28  55000.00   7986.72        14.52\n",
+       "757    12028.91  10500.00  -1528.91        14.56\n",
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+       "720     2860.97   3350.00    489.03        14.60\n",
+       "631     7256.86   8500.00   1243.14        14.63\n",
+       "36     10499.60  12300.00   1800.40        14.64\n",
+       "763    12383.35  10800.00  -1583.35        14.66\n",
+       "344    11937.31  13995.00   2057.69        14.70\n",
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+       "204     7664.32   9000.00   1335.68        14.84\n",
+       "718     8041.62   6999.00  -1042.62        14.90\n",
+       "484    13334.19  11600.00  -1734.19        14.95\n",
+       "258    13680.48  11900.00  -1780.48        14.96\n",
+       "616    13500.69  15900.00   2399.31        15.09\n",
+       "281    11638.89  13708.50   2069.61        15.10\n",
+       "476    15700.53  18500.00   2799.47        15.13\n",
+       "716    11425.01  13500.00   2074.99        15.37\n",
+       "529    53312.81  63000.00   9687.19        15.38\n",
+       "481     6599.62   7800.00   1200.38        15.39\n",
+       "706    61639.73  72900.00  11260.27        15.45\n",
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+       "151    10889.39  12900.00   2010.61        15.59\n",
+       "663     2613.97   3099.00    485.03        15.65\n",
+       "16     23721.58  20500.00  -3221.58        15.72\n",
+       "356    10133.68   8750.00  -1383.68        15.81\n",
+       "297    22261.50  19200.00  -3061.50        15.95\n",
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+       "390    24271.90  20900.00  -3371.90        16.13\n",
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+       "441    11619.16  10000.00  -1619.16        16.19\n",
+       "548    46465.02  39900.00  -6565.02        16.45\n",
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+       "106    16547.03  14200.00  -2347.03        16.53\n",
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+       "3       7463.96   6400.00  -1063.96        16.62\n",
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+       "100    32886.86  39500.00   6613.14        16.74\n",
+       "620    25752.19  30990.00   5237.81        16.90\n",
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+       "643     8156.11   9900.00   1743.89        17.62\n",
+       "696     2470.61   3000.00    529.39        17.65\n",
+       "286    19659.97  16700.00  -2959.97        17.72\n",
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+       "407    21097.66  17900.00  -3197.66        17.86\n",
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+       "758     9380.46   7950.00  -1430.46        17.99\n",
+       "670    33614.62  41000.00   7385.38        18.01\n",
+       "9      33614.62  41000.00   7385.38        18.01\n",
+       "263     9089.35   7700.00  -1389.35        18.04\n",
+       "521    27740.81  23500.00  -4240.81        18.05\n",
+       "46      3542.72   3000.00   -542.72        18.09\n",
+       "237    25037.87  21200.00  -3837.87        18.10\n",
+       "28     35991.51  43999.00   8007.49        18.20\n",
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+       "55     12674.57  10700.00  -1974.57        18.45\n",
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+       "608    29127.72  24500.00  -4627.72        18.89\n",
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+       "7      20349.62  16999.00  -3350.62        19.71\n",
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+       "79     18893.76  15700.00  -3193.76        20.34\n",
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+       "115     6981.34   8800.00   1818.66        20.67\n",
+       "744     2379.58   3000.00    620.42        20.68\n",
+       "474    10932.17  13800.00   2867.83        20.78\n",
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+       "331    11002.01  13900.00   2897.99        20.85\n",
+       "76      5144.02   6500.00   1355.98        20.86\n",
+       "244     7110.32   8990.00   1879.68        20.91\n",
+       "644    53312.81  67431.00  14118.19        20.94\n",
+       "174     7745.02   6400.00  -1345.02        21.02\n",
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+       "321    23630.85  19500.00  -4130.85        21.18\n",
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+       "17     11770.64   9700.00  -2070.64        21.35\n",
+       "517     9424.34  12000.00   2575.66        21.46\n",
+       "392     8139.48   6700.00  -1439.48        21.48\n",
+       "416    22367.52  28500.00   6132.48        21.52\n",
+       "335    13004.34  10700.00  -2304.34        21.54\n",
+       "45      8512.03   7000.00  -1512.03        21.60\n",
+       "515    32121.20  41000.00   8878.80        21.66\n",
+       "557    12523.73  16000.00   3476.27        21.73\n",
+       "41     19845.36  16300.00  -3545.36        21.75\n",
+       "13     13292.71  16999.00   3706.29        21.80\n",
+       "691     8467.17   6950.00  -1517.17        21.83\n",
+       "20     11876.82  15200.00   3323.18        21.86\n",
+       "275    22547.55  18500.00  -4047.55        21.88\n",
+       "120    50728.89  65000.00  14271.11        21.96\n",
+       "647     2625.10   2150.00   -475.10        22.10\n",
+       "524     5863.41   4800.00  -1063.41        22.15\n",
+       "547     9175.06   7499.00  -1676.06        22.35\n",
+       "747     8453.28  10900.00   2446.72        22.45\n",
+       "295     5350.91   6900.00   1549.09        22.45\n",
+       "80      6819.44   8800.00   1980.56        22.51\n",
+       "661    10303.21  13300.00   2996.79        22.53\n",
+       "537     9139.04  11800.00   2660.96        22.55\n",
+       "626    18621.12  24100.00   5478.88        22.73\n",
+       "576    15954.72  12999.00  -2955.72        22.74\n",
+       "499    12270.01  15900.00   3629.99        22.83\n",
+       "218    11490.44  14900.00   3409.56        22.88\n",
+       "458    14136.01  11500.00  -2636.01        22.92\n",
+       "138    11095.57   8999.00  -2096.57        23.30\n",
+       "320    10350.25  13500.00   3149.75        23.33\n",
+       "606    20680.40  27000.00   6319.60        23.41\n",
+       "709    34446.96  27900.00  -6546.96        23.47\n",
+       "75      4209.17   5500.00   1290.83        23.47\n",
+       "155     3086.38   2499.00   -587.38        23.50\n",
+       "602    16427.12  13300.00  -3127.12        23.51\n",
+       "736    53312.81  43163.25 -10149.56        23.51\n",
+       "495     3747.21   4900.00   1152.79        23.53\n",
+       "399    14410.15  18932.55   4522.40        23.89\n",
+       "771    13922.45  18300.00   4377.55        23.92\n",
+       "662     9232.79   7450.00  -1782.79        23.93\n",
+       "634    18165.16  23900.00   5734.84        24.00\n",
+       "4      11353.49   9150.00  -2203.49        24.08\n",
+       "580     6205.48   5000.00  -1205.48        24.11\n",
+       "687    11004.05  14500.00   3495.95        24.11\n",
+       "305    10505.98  13900.00   3394.02        24.42\n",
+       "206    19920.63  16000.00  -3920.63        24.50\n",
+       "659     3621.08   4799.00   1177.92        24.55\n",
+       "178    17316.59  13900.00  -3416.59        24.58\n",
+       "692     8471.55   6799.00  -1672.55        24.60\n",
+       "479    36883.77  49000.00  12116.23        24.73\n",
+       "209     8242.62   6600.00  -1642.62        24.89\n",
+       "773    13491.16  10800.00  -2691.16        24.92\n",
+       "594     3001.37   4000.00    998.63        24.97\n",
+       "1       3499.26   2800.00   -699.26        24.97\n",
+       "625    23485.45  18777.00  -4708.45        25.08\n",
+       "370    10835.95  14500.00   3664.05        25.27\n",
+       "83      6013.05   4800.00  -1213.05        25.27\n",
+       "443     3735.04   4999.00   1263.96        25.28\n",
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+       "519     8667.12   6900.00  -1767.12        25.61\n",
+       "177     8916.19  11999.00   3082.81        25.69\n",
+       "361     8688.92  11700.00   3011.08        25.74\n",
+       "482    12027.56  16200.00   4172.44        25.76\n",
+       "705    12976.15  17500.00   4523.85        25.85\n",
+       "135    44407.14  59999.00  15591.86        25.99\n",
+       "264    31059.18  42000.00  10940.82        26.05\n",
+       "372     8110.06  10990.00   2879.94        26.21\n",
+       "672     5679.39   4500.00  -1179.39        26.21\n",
+       "699     4255.62   5800.00   1544.38        26.63\n",
+       "306     3164.66   2499.00   -665.66        26.64\n",
+       "279     3154.48   4300.00   1145.52        26.64\n",
+       "145     4307.68   3400.00   -907.68        26.70\n",
+       "681     5195.69   4100.00  -1095.69        26.72\n",
+       "175    34863.53  47600.00  12736.47        26.76\n",
+       "440     8638.63  11800.00   3161.37        26.79\n",
+       "683    32771.45  44800.00  12028.55        26.85\n",
+       "592    28154.84  38500.00  10345.16        26.87\n",
+       "429    67952.07  53500.00 -14452.07        27.01\n",
+       "294    13471.84  10600.00  -2871.84        27.09\n",
+       "623    23227.15  31900.00   8672.85        27.19\n",
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+       "37     26478.15  20800.00  -5678.15        27.30\n",
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+       "327    10716.00  14900.00   4184.00        28.08\n",
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+       "21     31557.80  24500.00  -7057.80        28.81\n",
+       "767    24323.48  18800.00  -5523.48        29.38\n",
+       "192    18285.07  25900.00   7614.93        29.40\n",
+       "700    25640.67  19800.00  -5840.67        29.50\n",
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+       "734    23336.06  33200.00   9863.94        29.71\n",
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+       "357    11018.79  15700.00   4681.21        29.82\n",
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+       "234    12731.26   9800.00  -2931.26        29.91\n",
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+       "309     6510.80   5000.00  -1510.80        30.22\n",
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+       "513     5346.97   4100.00  -1246.97        30.41\n",
+       "12      5484.02   7900.00   2415.98        30.58\n",
+       "352     5238.05   4000.00  -1238.05        30.95\n",
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+       "38      9919.19  14600.00   4680.81        32.06\n",
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+       "374    24427.14  36000.00  11572.86        32.15\n",
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+       "505    10105.70  15000.00   4894.30        32.63\n",
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+       "300    20236.85  31600.00  11363.15        35.96\n",
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+       "469    65825.25 104999.00  39173.75        37.31\n",
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+       "735    10337.11   7500.00  -2837.11        37.83\n",
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+       "675    13476.90  22500.00   9023.10        40.10\n",
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+       "334    12414.72  23000.00  10585.28        46.02\n",
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+       "6      20159.19  38888.00  18728.81        48.16\n",
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+       "414    14363.76   9500.00  -4863.76        51.20\n",
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+       "353     9753.44  20400.00  10646.56        52.19\n",
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+       "33     22941.80  15000.00  -7941.80        52.95\n",
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+       "703     5597.06  11999.00   6401.94        53.35\n",
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+       "551    11555.80   7400.00  -4155.80        56.16\n",
+       "248    22011.47  13999.00  -8012.47        57.24\n",
+       "245    15457.91   9800.00  -5657.91        57.73\n",
+       "339    11099.09   6999.00  -4100.09        58.58\n",
+       "656    35039.40  85555.00  50515.60        59.04\n",
+       "219    34376.16  85000.00  50623.84        59.56\n",
+       "520    16773.62  10500.00  -6273.62        59.75\n",
+       "772    27487.75  68500.00  41012.25        59.87\n",
+       "701     7996.00   4999.00  -2997.00        59.95\n",
+       "215     5284.06   3300.00  -1984.06        60.12\n",
+       "447    34863.53  87777.00  52913.47        60.28\n",
+       "180    19701.70  49999.00  30297.30        60.60\n",
+       "722    30409.09  18900.00 -11509.09        60.89\n",
+       "577    14486.38   9000.00  -5486.38        60.96\n",
+       "733    11601.33   7200.00  -4401.33        61.13\n",
+       "487    10824.89   6700.00  -4124.89        61.57\n",
+       "408    65448.37  40500.00 -24948.37        61.60\n",
+       "63      5351.86   3300.00  -2051.86        62.18\n",
+       "679    25864.65  69990.00  44125.35        63.05\n",
+       "228    15682.82   9600.00  -6082.82        63.36\n",
+       "284    40765.33 112000.00  71234.67        63.60\n",
+       "232     1320.56   3700.00   2379.44        64.31\n",
+       "417     3795.06   2300.00  -1495.06        65.00\n",
+       "122    19335.34  55555.00  36219.66        65.20\n",
+       "238    26736.21  16100.00 -10636.21        66.06\n",
+       "347    26441.03  15900.00 -10541.03        66.30\n",
+       "276     5002.39   2999.00  -2003.39        66.80\n",
+       "93     18317.20  10974.21  -7342.99        66.91\n",
+       "436    40893.84 126000.00  85106.16        67.54\n",
+       "621    38473.71 119000.00  80526.29        67.67\n",
+       "221    34863.53 109999.00  75135.47        68.31\n",
+       "455    10946.44   6500.00  -4446.44        68.41\n",
+       "578     3205.38   1900.00  -1305.38        68.70\n",
+       "624    16924.74   9999.00  -6925.74        69.26\n",
+       "326     9356.43   5500.00  -3856.43        70.12\n",
+       "477    15374.08   8900.00  -6474.08        72.74\n",
+       "633    11056.52   6400.00  -4656.52        72.76\n",
+       "69      2440.90   1400.00  -1040.90        74.35\n",
+       "224     3939.91  15500.00  11560.09        74.58\n",
+       "56     15380.04   8800.00  -6580.04        74.77\n",
+       "463    35093.27  19990.00 -15103.27        75.55\n",
+       "668     8270.38   4700.00  -3570.38        75.97\n",
+       "225     6916.13   3900.00  -3016.13        77.34\n",
+       "546     6746.48   3799.00  -2947.48        77.59\n",
+       "27      2555.02  11600.00   9044.98        77.97\n",
+       "746    13355.11   7500.00  -5855.11        78.07\n",
+       "271     3948.74   2200.00  -1748.74        79.49\n",
+       "236     9024.05   4900.00  -4124.05        84.16\n",
+       "166    15292.10   8300.00  -6992.10        84.24\n",
+       "67      7621.25   4100.00  -3521.25        85.88\n",
+       "432     2975.40   1600.00  -1375.40        85.96\n",
+       "550    14915.98   7900.00  -7015.98        88.81\n",
+       "208     5716.14   3000.00  -2716.14        90.54\n",
+       "116    15819.50   8300.00  -7519.50        90.60\n",
+       "366    13420.15   6999.00  -6421.15        91.74\n",
+       "686    23576.99  12000.00 -11576.99        96.47\n",
+       "404     8648.80   4400.00  -4248.80        96.56\n",
+       "30     15559.43   7900.00  -7659.43        96.95\n",
+       "713     4004.65   2000.00  -2004.65       100.23\n",
+       "611    27225.34  13500.00 -13725.34       101.67\n",
+       "427     5462.52   2700.00  -2762.52       102.32\n",
+       "694     8131.92   3999.00  -4132.92       103.35\n",
+       "727    10810.62   5000.00  -5810.62       116.21\n",
+       "397     7973.87   3650.00  -4323.87       118.46\n",
+       "196     5948.04   2600.00  -3348.04       128.77\n",
+       "704     6891.99   3000.00  -3891.99       129.73\n",
+       "571    26331.41  11200.00 -15131.41       135.10\n",
+       "680    21493.44   9000.00 -12493.44       138.82\n",
+       "465     6537.82   2700.00  -3837.82       142.14\n",
+       "81     12891.96   5300.00  -7591.96       143.24\n",
+       "554     8340.94   3350.00  -4990.94       148.98\n",
+       "609     3040.77   1200.00  -1840.77       153.40\n",
+       "677     9631.08   3799.00  -5832.08       153.52\n",
+       "161    13967.55   5500.00  -8467.55       153.96\n",
+       "635     3818.71   1450.00  -2368.71       163.36\n",
+       "660     7809.13   2899.00  -4910.13       169.37\n",
+       "664     4590.49   1700.00  -2890.49       170.03\n",
+       "82      7320.42   2600.00  -4720.42       181.55\n",
+       "282    12261.19   4100.00  -8161.19       199.05\n",
+       "2       7553.29   2500.00  -5053.29       202.13\n",
+       "729     9817.06   3200.00  -6617.06       206.78\n",
+       "492     7161.67   2200.00  -4961.67       225.53\n",
+       "382     7918.89   2400.00  -5518.89       229.95\n",
+       "502     9984.87   3000.00  -6984.87       232.83\n",
+       "750    10381.62   3000.00  -7381.62       246.05\n",
+       "434     8843.22   2500.00  -6343.22       253.73\n",
+       "182    10123.04   2800.00  -7323.04       261.54\n",
+       "396    16165.79   4400.00 -11765.79       267.40\n",
+       "298    17937.36   4800.00 -13137.36       273.69\n",
+       "629     7319.77   1850.00  -5469.77       295.66\n",
+       "619    16095.32   3600.00 -12495.32       347.09\n",
+       "0      10685.50   2300.00  -8385.50       364.59\n",
+       "485     9664.46   1900.00  -7764.46       408.66\n",
+       "657    32481.05   6000.00 -26481.05       441.35\n",
+       "162     9954.42   1800.00  -8154.42       453.02\n",
+       "451    35956.50   6500.00 -29456.50       453.18\n",
+       "532    10019.90   1800.00  -8219.90       456.66\n",
+       "639    30628.28   4999.00 -25629.28       512.69"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sometimes it is useful to check these outputs manually\n",
+    "# To see all rows, we use the relevant pandas syntax\n",
+    "pd.options.display.max_rows = 999\n",
+    "# Moreover, to make the dataset clear, we can display the result with only 2 digits after the dot \n",
+    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
+    "# Finally, we sort by difference in % and manually check the model\n",
+    "df_pf.sort_values(by=['Difference%'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+Semi-final.ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+Semi-final.ipynb
new file mode 100644
index 0000000..230d4bf
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+Semi-final.ipynb	
@@ -0,0 +1,10521 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practical example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "sns.set()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('Real-life+example.csv')\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determining the variables of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4345         4345  4345.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          2019         3947          NaN  \n",
+       "mean           NaN          NaN  2006.550058  \n",
+       "std            NaN          NaN     6.719097  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.drop(['Model'], axis=1)\n",
+    "data.describe(include= 'all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_no_mv = data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         4025         4025  4025.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1861         3654          NaN  \n",
+       "mean           NaN          NaN  2006.379627  \n",
+       "std            NaN          NaN     6.695595  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_mv.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring the PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8404\\216991009.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dealing with outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984.000000</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984</td>\n",
+       "      <td>3984.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853</td>\n",
+       "      <td>3613</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17837.117460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165.116466</td>\n",
+       "      <td>2.743770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.292922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18976.268315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.766126</td>\n",
+       "      <td>4.956057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.672745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6980.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11400.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2011.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1528          NaN          NaN   \n",
+       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
+       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
+       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
+       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3984         3984  3984.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1853         3613          NaN  \n",
+       "mean           NaN          NaN  2006.292922  \n",
+       "std            NaN          NaN     6.672745  \n",
+       "min            NaN          NaN  1969.000000  \n",
+       "25%            NaN          NaN  2002.750000  \n",
+       "50%            NaN          NaN  2007.000000  \n",
+       "75%            NaN          NaN  2011.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's declare a variable that will be equal to the 99th percentile of the 'Price' variable\n",
+    "q = data_no_mv['Price'].quantile(0.99)\n",
+    "# Then we can create a new df, with the condition that all prices must be below the 99 percentile of 'Price'\n",
+    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
+    "# In this way we have essentially removed the top 1% of the data about 'Price'\n",
+    "data_1.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x64b4593510>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(data_1['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8404\\1674007885.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_no_mv['Mileage'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Mileage', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can treat the other numerical variables in a similar way\n",
+    "sns.distplot(data_no_mv['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = data_1['Mileage'].quantile(0.99)\n",
+    "data_2 = data_1[data_1['Mileage']<q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x64b9853fd0>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(data_2['Mileage'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x64b6725ad0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The situation with engine volume is very strange\n",
+    "# In such cases it makes sense to manually check what may be causing the problem\n",
+    "# In our case the issue comes from the fact that most missing values are indicated with 99.99 or 99\n",
+    "# There are also some incorrect entries like 75\n",
+    "sns.displot(data_no_mv['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A simple Google search can indicate the natural domain of this variable\n",
+    "# Car engine volumes are usually (always?) below 6.5l\n",
+    "# This is a prime example of the fact that a domain expert (a person working in the car industry)\n",
+    "# may find it much easier to determine problems with the data than an outsider\n",
+    "data_3 =  data_2[data_2['EngineV']<6.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x64b992bd50>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Following this graph, we realize we can actually treat EngineV as a categorical variable\n",
+    "# Even so, in this course we won't, but that's yet something else you may try on your own\n",
+    "sns.displot(data_3['EngineV'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x64b9ebe810>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Finally, the situation with 'Year' is similar to 'Price' and 'Mileage'\n",
+    "# However, the outliers are on the low end\n",
+    "sns.displot(data_no_mv['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I'll simply remove them\n",
+    "q = data_3['Year'].quantile(0.01)\n",
+    "data_4 = data_3[data_3['Year']>q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8404\\2748958191.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_4['Year'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_4['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# When we remove observations, the original indexes are preserved\n",
+    "# If we remove observations with indexes 2 and 3, the indexes will go as: 0,1,4,5,6\n",
+    "# That's very problematic as we tend to forget about it (later you will see an example of such a problem)\n",
+    "\n",
+    "# Finally, once we reset the index, a new column will be created containing the old index (just in case)\n",
+    "# We won't be needing it, thus 'drop=True' to completely forget about it\n",
+    "data_cleaned = data_4.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867.000000</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867</td>\n",
+       "      <td>3867.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>848</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1807</td>\n",
+       "      <td>3505</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>18194.455679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>160.542539</td>\n",
+       "      <td>2.450440</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.709853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19085.855165</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95.633291</td>\n",
+       "      <td>0.949366</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.103870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1988.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7200.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>157.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>129222.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>6.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           848            NaN   1467          NaN          NaN   \n",
+       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
+       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
+       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
+       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
+       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
+       "\n",
+       "       Engine Type Registration         Year  \n",
+       "count         3867         3867  3867.000000  \n",
+       "unique           4            2          NaN  \n",
+       "top         Diesel          yes          NaN  \n",
+       "freq          1807         3505          NaN  \n",
+       "mean           NaN          NaN  2006.709853  \n",
+       "std            NaN          NaN     6.103870  \n",
+       "min            NaN          NaN  1988.000000  \n",
+       "25%            NaN          NaN  2003.000000  \n",
+       "50%            NaN          NaN  2008.000000  \n",
+       "75%            NaN          NaN  2012.000000  \n",
+       "max            NaN          NaN  2016.000000  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include= 'all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we decided to use some matplotlib code, without explaining it\n",
+    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
+    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
+    "f, (ax1, ax2, ax3)= plt.subplots(1, 3, sharey= True, figsize = (15,3))#sharey -> share 'Price' as y\n",
+    "ax1.scatter(data_cleaned['Year'], data_cleaned['Price'])\n",
+    "ax1.set_title('Price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'], data_cleaned['Price'])\n",
+    "ax2.set_title('Price and Year')\n",
+    "ax3.scatter(data_cleaned['Mileage'], data_cleaned['Price'])\n",
+    "ax3.set_title('Price and Price')\n",
+    "\n",
+    "plt.show()            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8404\\2845240188.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data_cleaned['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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I6nyAMRmVaIjhhnZERKQ1BpgUUFkfXvqc07yjbmf1zk0DABxkgCEiIo0xwKSAqua9W3K7GmDy7AAYYIiISHsMMElOVQWqG7wAEL0pY2f1yg0HmMiuvkRERFphgElytY1ehFQBRZY6vYQ6ojA7PIfmWK0LQoh4lEdERNQpDDBJruXwkSxLXbpWfrYVsiTB4wuhwRWAJEnRBxERUU9igElykQm8+V1YgQQAiiLBZDQgNzM8j2bv0QY0eQPRR6jLlRIREbWfQesCKP5a9ohUNc9/yc/q2vwXRZbg8QdhM4f/yXyzowpefzi2GA0yRg3IRprFyKElIiLqEQwwSSYEwNtio7mjzZvYZaSZoMbh+hlpZgCNqHF64Q+w34WIiLSRUENI5eXlKCkpwZtvvql1KbokSRK8viDK9tViy+5qbNldjQPNS559gRCCIRUSujZfJTIRuKHJ3+V6iYiIOithemACgQAWLVoEt9utdSm6Fwiq8AdCEELA6QoHjTSrMS7XzkwzAQjfIJKIiEgrCdMDs3z5cqSlpWldRlLxB1QEguGBI4fdFJdrRnpgPL4gh5CIiEgzCRFgNm7ciNdffx0PP/yw1qUklUZPuJfEalZgNMTnrTYZFVjNCgD2whARkXY0H0JyOp343e9+h3vuuQdFRUVxuaYhTh/WiUJR5JhfT0aSAEmWoDQ/XJ4gACDdZoLcvF+LrABKqPU8mI6cz0wzw+Nzo9EdQGG2DYosQZIlGAwShOjaHJv2tjVZsL3Jje1NbmyvtjQPMA888ABKSkowe/bsuFxPliVkZdnjcq1E43Ccfim0X3XDajXBYFThDYSHj7IclvAxgwKrxQSDofV6JKvF0O7zuZlWHK1xw+0LwmYzw2iQYbWYkJnZtb1mWmpPW5MJ25vc2N7kxvZqQ9MAs2bNGpSWlmLt2rVxu6aqCjidyTURWFFkOBxWOJ0ehEInXwwtSYDHG4DH44c/EEJtQ3gTO5tJgcfjRzAYgsfrh9/feu6KJNR2n4/sBVNV54Hb7YPJqMDj9aO+XqCr28C0t63Jgu1NbmxvcmN748fhsHa4Z0fTALN69WrU1NRg+vTpMcfvv/9+vPfee/jLX/7SqesGg8n5DykUUk/ZNkmSIFSBUPOj0R3eD8ZmMUAVAkIIqCEgpLZOGR0577CHVzTVN/miX0uoAsGgiNtGdqdra7Jhe5Mb25vc2F5taBpgli1bBq/XG3Ns5syZuP3223HppZdqVFXycDVvaBevJdQRGfbwSqQmd6DNsENERNTdNA0wBQUFbR7Pyck56TlqHyFEdBKv3RLfAGM1KzAoEoIhgSa3H1az5lOpiIgoxSTGVGKKO68/BLV5OMdmiW/AkCQpuq+M0x04zbOJiIjiL+F+dN6xY4fWJSSFyPCR1WyALHdtaXNbHDYTap0+7gVDRESaYA9Mkjo+fNQ9GTXaA8MAQ0REGmCASVKRHhh7nCfwRkQCTCMDDBERaYABJkn1WA+MmwGGiIh6HgNMknJHemDivAIpwmELX9fjC/GmjkRE1OMYYJKUy9vcA2Ptnh4Yk1GBxRS+qSPnwRARUU9jgElSrm7ugQGODyPVNzHAEBFRz2KASUIhVcDjCw/rxHsPmJYiAabB5eu2r0FERNQWBpgkFJn/IstSdJinO3ApNRERaYUBJgk1uSPDRwZIUvw3sYuITORt4BASERH1MAaYJNTk6f75L0DLISR/3O5CTURE1B4MMEno+CZ23XuniHSbERKAQFDlMBIREfUoBpgk1NRNd6E+kSLL0Z1+K+s93fq1iIiIWmKASUIuz/E5MN0to3kYqbKOAYaIiHoOA0wSis6B6ab7ILUUmQdTxQBDREQ9iAEmCUV6YLpzD5iIdHvzEBIDDBER9SAGmCTj9QfhD6oAun8ODAA4bOEemIo6d7d/LSIioggGmCQT2ZPFqMgwGrr/7Y0MIVU3eBFS1W7/ekREREAnA0xFRUW866A4qW8Kb+vfE8NHQHiisCJLUFWB6npvj3xNIiKiTgWY888/HzfeeCPee+89+P3c/yORRPZjsZp7JsBIkoSMtHAvzLFaDiMREVHP6FSAeeihh6CqKhYtWoRzzz0XS5cuxdatW+NdG3VC5M7QPdUDAxxfSl3BAENERD2kU59yl112GS677DJUVFTgrbfewttvv42///3vGDJkCObMmYNLL70Uubm58a6V2qEhMoTUQz0wAOCwmwE0sgeGiIh6TJdmeRYUFODXv/413n//faxevRpZWVl45JFHMH36dNx2223YsmVLvOqkdopM4rX2ZA8Mh5CIiKiHdXmZSmlpKe69917Mnz8fmzZtwjnnnIMlS5bA4/Hg6quvxsqVK+NQJrVXvavne2AiQ0gMMERE1FM69Sm3f/9+vP3223jnnXdw+PBh9O7dG/PmzcOcOXNQVFQEALjmmmuwaNEirFixAtddd108a6ZTaNBiDkyaGQBQ1+iDzx+C2aT02NcmIqLU1KlPuR//+Mcwm8248MIL8fvf/x5Tpkxp83mDBg3Cvn37ulIfdYAqBBqaVyH1ZA+MxaTAbjHA5Q2ios6NfgXpPfa1iYgoNXXqU+7ee+/FpZdeivT0U39Q3XLLLbjllls6VRh1XKPbD1UVAHpuGXVEfpYN5UedqKjzMMAQEVG369QcmA8++ACVlZVtntu+fTtmz57dpaKoc+obI3vAKJBlqUe/dl6WFQBwrMbVo1+XiIhSU7t/TC8tLYUQ4Z/uN2zYgI0bN6K2trbV8z799FMcPHgwfhVSu9U1Rnbh7f57IJ0oPxJganlTRyIi6n7tDjD//Oc/8fbbb0OSJEiShKVLl7Z6TiTgzJo1K34VUrvVNYW38rf34ATeiILmAMObOhIRUU9o9yfdPffcgyuuuAJCCFx77bW47777MGTIkJjnyLIMh8OBoUOHxr1QOr3IEJI2PTA2AMCxGjeEEJCknh3CIiKi1NLuAJOeno7JkycDAP76179i9OjRsNvt3VYYdVxd8y68WvTA5GZaAABuXxBNngDSbaYer4GIiFJHuz/p1qxZg/POOw9ZWVk4cuQIjhw5csrn//SnP+1qbdRB9Y09eyfqlkwGBTkOC2qcXhyrdTPAEBFRt2r3J92SJUvwj3/8A1lZWViyZMkpnytJEgOMBuqbtJvECwAF2bZogBnaJ1OTGoiIKDW0O8D87//+L/Ly8qK/p8QTWYWkxRASABRmW1G2D6jgSiQiIupm7f6k6927d5u/jwgGg2hqakJmZmZcCqOOCQRVNHkCALQZQgKAwuzwnKgK3hOJiIi6Wac2sgsGg3j66aexdu1aAMD69etxzjnnYMqUKbj22mvR0NAQ1yLp9CLDRwZFgtmozb2ICrObVyJxKTUREXWzTgWYp556CitWrIDT6QQA/OEPf0BmZibuuusuHDhwAI8++mhci6TTiwSYDLtZsyXMBc0BpqLWE72lARERUXfoVIB59913sXDhQsydOxd79uzBrl27cPPNN+NXv/oVfvOb3+CTTz6Jd510GvXNd6HOSNNu9U9uhgWKLCEYUlHr9GpWBxERJb9OBZjKykqMGzcOAPDZZ59BlmVMmzYNAFBYWIjGxsb4VUjtEumBcdi1CzCyLB2/pQCHkYiIqBt1KsDk5+fj0KFDAIBPPvkEI0eORHZ2NgDg22+/RWFhYfwqpHZxusI9MFoGGOD4PBiuRCIiou7UqQAza9YsPPTQQ5g/fz42bdqEK664AgDw4IMPYvny5bwbtQYSoQcGaDGRlyuRiIioG3Vqve0dd9wBm82GjRs34s4778Qvf/lLAMDWrVtxww034Oabb45rkXR6DZE5MBoHmAIGGCIi6gGdCjCSJGHBggVYsGBBzPHXXnstLkVRx0Um8TrsJgSCqmZ1HB9CYoAhIqLu0+kdzxobG/H111/D7Q7fffhEvJVAz2pwHR9CqmnQbgVQpAempsGLQDAEo0GbPWmIiCi5dSrAfPHFF7j99tvh8bQ9UZP3QupZwZCKJnd4F16HTdsA47AZYTUb4PEFUVnnQe+8NM1qISKi5NWpAPPoo49i0KBBuOuuu1BQUABZ7tRcYIqTRncAAoAsSUizaXMjxwhJklCYbUX50UYcq2WAISKi7tGpALNnzx48++yzmDhxYpcLqKmpwcMPP4wvvvgCPp8PkyZNwuLFizF48OAuXztVtFyBJGu0C29LBdm25gDjApCndTlERJSEOtV10qtXLzQ1NcWlgFtvvRX79+/HCy+8gDfeeAMWiwXXXXfdSYenqLXICqRMDXfhbakwi3vBEBFR9+pUgFmwYAGeeeaZ6GZ2ndXQ0IDevXvjD3/4A8aOHYvBgwfjlltuQWVlJXbt2tWla6eSyARerZdQRxTwpo5ERNTNOjWEtHbtWlRUVOBHP/oRsrOzYbFYYs5LkoSPP/74tNfJyMiIufFjbW0tVq5cicLCQgwZMqQzpaWk6B4waWaNKwnjUmoiIupunQowhYWFcb9dwL333ot//OMfMJlMWLFiBWw2W6evZTAk16RiRZFjfj2R0x0OMNkOMyRZgtL8aEmWJEiSBFkBlFDreTJdOa/IEiRZgsEgQQgJvfPtAMKTi32BEOzW9k8sPl1bkw3bm9zY3uTG9mqrUwHmoYceincduPbaa3HVVVfh1Vdfxa233opVq1Zh9OjRHb6OLEvIyrLHvb5E4HBY2zzu8oUAAEX56bBaTLBaTTAYYzezs1oMMBgUWC0mGAytN7rrynmjQYbVYkJm5vHQme2woNbphTso0KcT78fJ2pqs2N7kxvYmN7ZXG53eyA4Ir0b66quvUFlZiXnz5uHgwYMYMWIE0tI6vnQ2MmT04IMPYsuWLXjllVc6FZRUVcDpTK6hC0WR4XBY4XR6EAq1DhdVzXNNTDLg8frh8fjhD4RiniMJFcFgCB6vH35/qNU1unLeZFTg8fpRXy8Q2dOwIMuKWqcXO8trkJfe/rk5p2trsmF7kxvbm9zY3vhxOKwd7tnpVIBRVRX33XcfVq9eDSEEJEnCT37yEzz77LM4cOAAXnnllXYNMdXW1mLdunX48Y9/DIMhXIosyxgyZAgqKys7UxoAIKjhVvrdKRRS22xbQ/My6nSrCUIVCDU/WlKFgBACagitznX1fEgVEKpAMCiiuzLnZ1mxbX8dDle7OvV+nKytyYrtTW5sb3Jje7XRqYGsZ599FmvXrsUf/vAHfPXVV9EPrd/+9rdQVRWPP/54u65TXV2NhQsXYt26ddFjgUAAZWVl3AemnYQQaHBFJvEmxiokACjI4kReIiLqPp0KMKtXr8btt9+OK664ApmZmdHjI0eOxO23346vvvqqXdcZNmwYpk2bhj/84Q/YuHEjdu7ciSVLlsDpdOK6667rTGkpQWqeUCtJEtzeEIKhcIDMTJBVSABQmMMAQ0RE3adTAaa6uhojR45s81xBQQGcTme7r/XYY49hypQp+M1vfoMrr7wS9fX1ePXVV9GrV6/OlJb0QgCavIHo40itCwBgsxjgD6nQvlMvrLDFXjBt3eyTiIioKzo1B6Z///74/PPPcfbZZ7c6t2HDBvTv37/d10pPT8cDDzyABx54oDOlpBRJAry+IMr21SLQPP54uCq8I7LJoGDHgTr0L3JAgva3E8jNsECRJfgDKmqdPuRkWE7/IiIionbqVIC59tprcd999yEQCOD888+HJEnYv38/1q9fj5deeglLliyJd53UQiCoRlcZOZvnv1jMCoIJNAveoMjIz7LiaI0bR2pcDDBERBRXnQowV155JWpra7FixQqsWrUKALBw4UIYjUbceOONuPrqq+NaJJ2cxxcEANjMXVoR3y1659pxtMaNw1UuFA/K0bocIiJKIp3+1Lvpppswe/ZsbNiwAQaDAenp6Rg3blzMpF7qfp7mTeysZkXjSlrrnZeG0h1VOFwdnxt/EhERRXQ4wPzrX//Ca6+9hi1btiAYDP/0b7FYMGHCBFx99dW48MIL414knVykB8aaoD0wAHCk2qVxJURElGza/akXCoVw55134n/+539QUFCASy65BLm5uRBC4NixY9iwYQNuu+02XHbZZXj44Ye7s2ZqIRpgTIkXYHpFA4wbqhCQJe0nFxMRUXJo96feqlWr8OGHH+Luu+/GNddcA+mED6NQKITXXnsNf/zjHzFx4kT87Gc/i3ux1Foi98DkZ1mhyBJ8gRBqG7zIzUyM+2cQEZH+tXsfmDVr1uAXv/gF5s2b1yq8AICiKJg7dy5+/vOf46233oprkXRyx+fAJF6AMShydEO7wxxGIiKiOGp3gCkvL8e0adNO+7ypU6di586dXSqK2icQVBFoXjpttSTeJF6A82CIiKh7tDvAeDweZGRknPZ5WVlZcLn4YdUTvP7w8JEiSzB28C6ePSUyD4Y9MEREFE/t/tQTQkBRTv9TvizL3Dq+h7hbzH9pa1gvEfRmgCEiom6QmD+2U7sk8vyXiEgPzNEaF1QGWyIiipMOffI98MADSEtLO+Vzmpq4aVlP8Xgju/Am5vwXILwSyaCE74lU3eBFPlciERFRHLS7B2bSpEmw2+0QQpzyYbfbMXHixO6smZol8hLqCEWWUZjdPJG3isNIREQUH+3+5Pvb3/7WnXVQJ3j8iR9gAKBPvh2HqppwsKoJ44fmal0OERElAc6B0TE99MAAQL/8dADAwYpGjSshIqJkwQCjY3qYxAsAffPD86YOVnJ+FBERxQcDjI4d74FJ3Em8wPEAU1nnie5dQ0RE1BUMMDqlqgJevz56YBx2EzLSTBAADnEiLxERxQEDjE5FejIkCbCYErsHBuA8GCIiii8GGJ1yN89/sZgSdxfeljgPhoiI4okBRqci818SeRO7liIB5gADDBERxQEDjE7pZQl1RL+CcIA5VNUEVeUtBYiIqGsYYHQqEmAsOgkwBVk2mAwy/AEVFXVurcshIiKdY4DRqeNDSIkRYCQp8pDafMiyhN55nAdDRETxwQCjU8c3sdN+DoyiSJBlGY2eIJq8gTYfIXAiLxERxU9i/PhOHZZIc2AUWYLHH8Segw3wB0OtzhsNMkYNyEb/wnRgC7DvGJdSExFR12j/6UedkkgBJiIQVOEPtA4wEQOLHACAfUedEELoYvk3ERElJg4h6ZAQQjf3QWqpb34aDIoElzeIqnqP1uUQEZGOMcDokC8QgirCS5ETYQ5MexkUOToPpvwoh5GIiKjzGGB0yO0NDx+ZjDIUWV9v4YDIMNIxp8aVEBGRnunr048AAO4EW0LdEQMLwwGGPTBERNQVDDA65PHqaxO7lgYWhW/quP9YI3fkJSKiTmOA0SE998AU5dhhNirwBUI4WuPSuhwiItIpBhgdivTA6GkCb4QsS+H9YMBhJCIi6jwGGB1yJ+AeMB0RGUYq50ReIiLqJAYYHdJ/gDm+oR0REVFnMMDoUHQIyaTPADOoOcAcqGiC7xQ79xIREZ0MA4wO6b0HJifDgsw0E0KqYC8MERF1CgOMzvgCIQSCKgDAatHfJF4AkCQJQ/tkAgB2HWrQthgiItIlBhidcbr8AACDIsGo6PftG9onAwADDBERdY5+PwFTVCTAWEwGXd/NOdIDs/twQ/S+TkRERO3FAKMzkQCj1/kvEX3y7TCbFHh8QRyp4oZ2RETUMQwwOtPQHGBsOtvETpIiDwmSJMGgKBjcq3kY6TCHkYiIqGMYYHRGjz0wiiJBlmU0eoJo8gaij8iOvNv214KLqYmIqCP08ylIAHQaYGQJHn8Qew42wB9sGVXCc192HKiH1xdEmsWoTYFERKQ7+vkUJAD6DDARgaAKf4uN6zLTzJAkoNEdQF2jlwGGiIjajUNIOtOg4wBzIqNBRo7DAgDYeaBe22KIiEhXNA8w9fX1uO+++zBt2jRMmDABV199NUpLS7UuK2Ed74HR1yTekynMsQEAdhys17YQIiLSFc0DzMKFC/Htt9/isccew+rVqzFy5EjMnz8fe/fu1bq0hBMMqXB5AgCSowcGAIqaA8zOA/UQ3A+GiIjaSdMAs3//fnz11Vd44IEHMHHiRAwcOBD33nsv8vPzsXbtWi1LS0hOlx8C4eXIFlNy9MDkZVqhyBIaXH4cq3VrXQ4REemEpgEmKysLL7zwAoqLi6PHIvuEOJ28yd+J6hp9AACbWd+78LZkUGQUZFsBAD+U12pcDRER6YWm4xAOhwPnnXdezLEPPvgA+/fvx3/91391+roGg+YjY3GlNN/zqMEVHj6yW41Q5NYBRm4Of7ICKCGp3ee6er6r1+6dl4Yj1W5sP1CHi88eENPmZBdpJ9ubnNje5Mb2aiuhJlJ88803uOuuuzBz5kxMnz69U9eQZQlZWfb4FpYgPP7wEuR0uwk2m7nVeavFAINBgdVigsGgtvtcV8939dqD+mRi47ZKbD9QD3taeFWSw2E9xd9E8mF7kxvbm9zYXm0kTID5+OOPsWjRIkyYMAHLli3r9HVUVcDpTK65FIoiw+Gw4mh1EwDAYlTgdvtaPU8SKoLBEDxeP/z+ULvPdfV8V6/tsBhgMSlweQL4bkcFSkYWwun0IBRqHXaSTeS9ZXuTE9ub3Nje+HE4rB3u2UmIAPPKK6/gwQcfxEUXXYQ//elPMJlMXbpeMJic/5BqnV4A4SXUIbX1ih1VCAghoIbQ6vypznX1fFevrSjA0L6Z2LqnBt/trkbJyEKEQmrSvo9tYXuTG9ub3NhebWg+kLVq1Sr8/ve/x9y5c/HYY491Obwks/rIJN4k3LF21IAsAMDm3dUaV0JERHqgaQ9MeXk5/vjHP+JHP/oRFixYgOrq4x9eFosF6enpGlaXeKKrkCwJ0XEWV6MHZgMA9hxqQENT6+ExIiKiljT9JPzggw8QCATw0Ucf4aOPPoo5d/nll+Phhx/WqLLEFAkw9iQMMFnpFvTNT8PByiZ8s6MS4wdla10SERElME0/CX/961/j17/+tZYl6IYvEILLGwQA2MzJN4QEAOOG5OJgZRNKyyoYYIiI6JQ0nwND7VPXPIHXqMgwGZPzbRs7OAcAsGlHJUKq9hPEiIgocSXnJ2ESqmkIB5iMNFPS7MJ7osG9MpBmNcLlCWDXwQatyyEiogTGAKMTtS0CTLKSZSnaC7OFq5GIiOgUGGB0oqZ5CCnD3noH3mQyfmguAKB0RxXvTk1ERCfFAKMTkU3skrkHBghP5DUaZFTUunG4yqV1OURElKAYYHQiOoRkT+4AYzUbMGF4PgCgdEelxtUQEVGiYoDRiRqnBwCQkZbcQ0gAcPbYIgDAph1VGldCRESJigFGJ1JhEm/E5FGFUGQJh6tdOFrDYSQiImqNAUYHhBDH58Ak+SReAEizmaK3FihlLwwREbWBAUYHvP4QvP4QgOSfAxMxcUR4Hsym7ZwHQ0RErTHA6EDkHkhWswKzSdG4mp5xxvA8KLKEA5VNHEYiIqJWGGB0IBJgstItGlfSfSTp+AMAHHYTxjQPI339Q4WGlRERUSJigNGBusbw/Jes9OSc/6IoEmRZRqMniAZ3AJW1bjS4Axg/LA8A8H/fH0OQm9oREVELmt6Nmtqn1hnugcl2JGmAkSV4/EHsOdiAkKrCajXB4/FDQvjmlTVOL7btq8XYQbncnZeIiACwB0YXIiuQchzJO4QEAIGgCn8gFP1VFQJ9C9IAAKWczEtERC0wwOhATXMPTFaS9sCcyqBeDgDANzuqEAypGldDRESJggFGB1KlB6Ythdk2WM0GuLxBfLenRutyiIgoQTDA6EAkwGSnYICRZQlD+mQAAP695YjG1RARUaJggElwPn8ILm8QQGr2wADA8H6ZAIDv9lRHl5QTEVFqY4BJcLXNS6itZgVWc2psYneizDQzBvVyQAjgq61HtS6HiIgSAANMgqtt7nHIzbRCiuzyloLOHlMIAPjyu6NQuZSaiCjlMcAkuLrmFUi5GVaNK9HW+GF5sJgUVNZ7sONAvdblEBGRxhhgElxkCCk3M7UDjNmo4KzR4V6Yz749rHE1RESkNQaYBBfZhTfVAwwAnF/SGwDwzc4q1DdxMi8RUSpjgElwdY0MMBH9CtIxpE8GQqrA55u5pJqIKJUxwCQ4DiGFRe5UfcGEPgCAzzcfRkgVKT2xmYgolTHAJLjoEFJGau4BA8TerXrEgCyk24yob/Jj3Q/H0OQNIKR1gURE1OMYYBKYxxeExxfexC6Ve2Aid6vetq8WP5TXYnDv8M68763bj7J9tfD6guyJISJKMQwwCSwy/8VmNsBmMWpcjfYid6ke0jsDsgQcq3XjSLVL67KIiEgDDDAJLDL/JTsF70J9KjaLAQOKwnep3sobPBIRpSQGmAQW2cQuFW/ieDqjBmQBAMqPOqM3uyQiotTBAJPAIrcRYIBpLdthQWGODUJwYzsiolTEAJPAIj0LHEJq2+jmXph13x+DyxPQuBoiIupJDDAJrLohHGDyUvw+SCfTK9eOrHQzvP4Q/nfTIa3LISKiHsQAk8Cq6j0AUnsJ9alIkoTxQ3MBAB9uPACvP6hxRURE1FMYYBJUSFWjy6jzMjkH5mQG9nIgL9MKlzeIz77l7QWIiFIFA0yCqmv0IaQKGBQJmemcA3MysiRh5uS+AIAPNhyAP8B9eYmIUgEDTIKqrg/Pf8lxWCBzl9lTmjQiHzkOCxpcft7kkYgoRTDAJKjIBN5UvgdSeymKjNnnDAAAvLtuH3x+9sIQESU7BpgEVd3ACbwdcU5xEfIzrXC6A/h400GtyyEiom7GAJOgqurZA9MRBkXGZVMHAgDe//oA3F7uC0NElMwYYBJUTaQHhnvAtNuZIwvQK9cOty+I99cf0LocIiLqRgwwCaqKc2A6TJYlXDFtEADggw0Ho8NwRESUfBhgElAgqKK+eQ8YzoHpmPFDczGiXyaCIRVvfr5X63KIiKibMMAkoNpGLwQAk0GGw2bUuhxdkSQJV80YCgnA12UV2HOkQeuSiIioGzDAJKDoHjAZFkjcA6bD+hem4+ziQgDAax/vgiqExhUREVG8JVSAef755zFv3jyty9BcVfPcjTwOH3XanGmDYTYp2HPEiS+/O6p1OUREFGcJE2BeffVVPPHEE1qXkRBqGo73wNDpSVLkIUUf2Q4LLp8antD7z093o9Ht17hKIiKKJ80DTEVFBX79619j2bJlGDBggNblJITIXajzuIT6tBRFgizLaPQE0eQNxDzOGlOIXrl2uLxB/PPTPVqXSkREcaR5gPnhhx9gNBrxzjvvYNy4cVqXkxBquIS63RRZgscfxLZ9tdiyuzrm8f3eGpw5qgAA8OXWoyjbVxfTSxN5EBGR/hi0LmDGjBmYMWNGXK9pMGiey7qksrkHpjDHBoNBhqKE26MoMiRZgtL8OJHc/IEsK4ASktp9rqvn43ltWYTbKssyALXdXzskVIRCaqtrF+XYMG18L/x78xG8+G4Z/uvaibCaY//Z28wGGNr4++wJLd/bVMD2Jje2N7klWns1DzDxJssSsrLsWpfRaY1uPxrd4W3wRwzOi/mwTU+3whMQsFpNMBhbf1hbLQYYDAqsFhMMBrXd57p6Pp7XDgTD5y0WY7tef7pr2ywGzJ46CFt2V6Ou0Yf/773tuHByv+h5o0HGiP7ZyMqytXptT3I4Umu4kO1NbmxvckuU9iZdgFFVAafTrXUZnbb7UHjfkqx0M7xuH7xuHxRFhsNhRWOjBx6vHx6PH/5A6zsuS0JFMBiCx+uH/4Q7Mp/qXFfPx/PawaCAxWKE1xuAqqpx+dpKcy/M2i/34fu9NSjMsqJvQRoAwGRU4PH6UV8voMVq68h763R62uxBSjZsb3Jje5Nbd7bX4bB2uGcn6QIMAASD+v2HdLiqCQBQkGVt1Y5QSIVQBULNjxOpQkAIATWEVudPda6r5+N57UhoUVU1+tx4fO3CLDtG9s/Ctv11+OK7o5h1Tn/YLUaEVAGhCgSD4edpJRRSdf3vtqPY3uTG9ia3RGlvYgxkUdSx2nDvUWG2tkMayWjC8FxkO8zwBUL4YstRqG0EHiIi0gcGmARTwQDTbRRZxrRxvWBQJFTWebB5d7XWJRERUScxwCSYaA9MDgNMd3DYTZgyJnybge/31mIv75VERKRLCTUH5uGHH9a6BE2pQqCiLryEuoA9MN1mYJEDNQ1elO2rw783H8GkEQUY0S9L67KIiKgD2AOTQGqdXgSCKhRZ4iZ23WzC8DwU5dgQDAk8//b3qHV6tS6JiIg6gAEmgVTUhntf8rOsUGS+Nd1JliRMG98LGWkm1Df58dg/NsPtDWhdFhERtRM/JRMIVyD1LLNRwUVn9oPDbsLhKheeWr0Vvjb21yEiosTDAJNAIgGG8196TrrNhJsvHwOrWcHOg/V4evV3CAQZYoiIEh0DTAJhD4w2+uSl4Y4rx8NsVPDDvjosf3Nr9JYGRESUmBhgEgj3gNHOsL6ZuOPKsTAZZHy/txZP/HMLPL6g1mUREdFJMMAkCH8ghJqG8EoYBhhtDO+Xhf935TiYTQq27a/Dstc2o8nDib1ERImIASZBHKt1QwCwWwxItxlP+3zqHiP7Z+F3V5cgzWpE+VEnHvxraXRoj4iIEgcDTII4UBG+iWPf/DRIkqRxNaltYJEDi+dOQI7Dgoo6Dx78aym27avVuiwiImqBASZBHKwMB5g++WkaV5J6JCnykKKPPnlpuPe6iRjUywGXN4hHX9+C99fv1/SO1UREdBwDTII4WNkIINwDQz1HUSTIsoxGTxBN3kDMQ1Fk3Hl1CaaMLoQqBP756R48/eZWNLr9WpdNRJTyEupeSKlKCBHtgemXn65xNalFkSV4/EHsOdgA/wn7vxgNMkYNyMZNs0dhSJ8M/P3jnfh2VzX2HF6PeT8egTOG52lUNRERsQcmAdQ1+uDyBqHIEnrlcgWSFgJBFf5AKOYR2QtGkiScX9Ibd8+biN65djjdATzz1la8sPYHrlIiItIIA0wCONDc+1KYY4PRoGhcDZ1M/8J03HfdJFwypT8kCfj6hwrc+5f12LSjknNjiIh6GANMAogMH3H+S+IzGmRccd5g/Ne8M1CUY0ODy49n3voey17bjENVTVqXR0SUMhhgEsDBivAEXs5/STxtrVCSJAlDemfigesnY9bZA2BQZGzbX4cHXtqIVz/aCZc3GH0eERF1D07iTQDsgUlMLVcoAa2HiCQJuHTaIJwxIg9r/l2OLbur8b+bDmHdD8fwkzP749xxRUizmcBBQSKi+GOA0ZjPH0JlnQcAA0yiOdUKJQCwWQzoX+RAVZ0Xk0bmo3eeHeu+P4a6Rh9Wf74HH5UexM+mD8a5xUUaVE9ElNw4hKSxQ1VNEAAy7CY47Caty6E2tLVCyR8IIRhSY87nZlhwyZT+OGt0ASwmBU6XHy+9uw0P/m0Tdh9u0LgVRETJhQFGY/uONW9gV8Del2QgyxKG9c3E5dMGoWRYLkwGGXsON+CPf9uEZ9/aioo63leJiCgeOISksV2H6gEAQ3plaFsIxZXRIOOM4fm44rzB+GDDQXz53RGU7qjCt7uqcf6E3rj0nIFIs/KmnUREncUeGA0JIbDrUHhoYWjfTG2LoW6RmW7G/EtGYukNZ6J4UA5CqsDHpYew+Ll1eH/9/jbn1hAR0ekxwGioxulFXaMPiixhUJFD63IozlquYspymPEfl43GrXOK0TvPDo8viH9+ugdLVqzDZ98cgsqN8IiIOoRDSBradTDc+9KvIB1mExfbJpuTrWL68Zn9UH7EiW93VqG6wYtHX92EAUXpuOr8IRjeL0vDiomI9IM9MBqKzH8Z1pfzX5LZiauYgkEVg3tn4L7rJ+HK8wfDajZg39FG/GnVt3j2ra2oqvdyEzwiotNgD4yGovNf+mRqWwj1OEWRYDEbMf2MPjh7XB+s/nQnvvruKEp3VGHz7mpML+mDS6cORJqZ/0WJiNrC744aafIEcLjaBQAY0oc9MKkmMrxUfqQBBoMBowdkIz/Tiq9/qMCRahc+Lj2I9WXHcMV54Y3wZJk9MkRELXEISSO7m3tfinJscNi4gV2qCgTV6BBTmtWIC87ojRkTeiPDbkKjO4CV72/H0pUbUbavFkDsPZmIiFIZe2A0srN5/stQ9r5QC5IkoU9+Ggb1caC+0Y93/28/DlY2YdlrmzG0bwZmnzMQA5tXrFnMBt5niYhSFgOMRrburQEADO/LVSfUmsmgYEpxEdKtJmzYVoFt++uw62ADHnttM/oXpOPMMQWYOrYX0ixGCC7BJqIUxACjgYo6Nw5XuSBLEsYOydG6HEpgBkXGGcPzMLxfJr7bXYM9hxuwv6IR+ysasa28DrPPGYAhvdmLR0SphwFGA9/urAYADO+XCbuF28nT6aVZjTi7uBCjB2Zh8+4a7D/WiK17a7B1bw2G9M7AT87qh3FDciFzbgwRpQgGGA18s6sKADBhWJ7GlZDeZKSZcd74XnD7gjhS7cKGsgrsPtyA5au3IjfDgqnjemHq2CJkpVs4tERESY0Bpoc1uPzY07wCqWRorsbVkF7lZFgw44y+uPisAfh882F8+d0RVDd48da/92LNF3sxekA2po7vjfGDs2E0cKovESUfBpgetmV3NQSAAYXpyHZYtC6HdCqyj8yRahf6F6ajd+5QlB91YseBehyrdeP78lp8X14Li0nB2ME5mDg8H8WDcuJ6y4qTLeVmzw8R9QQGmB72zc7w8FEJh48oDiJ7yABA/8J09C9MR0OTH3uPOnGwohH1TX5s2FaJDdsqYTLIGNE/C6MHZmP0gGwU5dg6vZ9MCIDXG2jzHJd3E1FPYIDpQXWNPvxQHt6QjPNfqLtkpJlw5qgC3HTpKFTVerBxeyU27ahEVb0X3+2pwXd7wkv4s9LNGD0gG0P7ZmJI7wwU5tiik4BP1YsiSRK83gDK9tUiEFRjzhkNMkYNyObybiLqdgwwPeiTbw4hpAoM65OB3rl2rcuhJKYoEgyKgvxsOy45ewAuntIfh6td2L6vDtsP1GHPYSfqGn34cutRfLn1KADAZjZgQC8HBvVyYES/LAwucpxyyKll7w8RUU9jgOkh/kAIn28+AgD40aS+GldDyS4yR2bPwQb4g8dDRl6WFf2L0tErz44vNx/FwcpGVNR5UFXngdsXRFl5LcrKa/Gvr/ZBkoBeuXYMLHRgQFE6BhY50CcvDSZjYg8QnWpYjL1CRMmDAaaHfF1WgSZPADkOC0qGcviIekZbvSQmowyjQUFRjh3ZDjMAQFUFaht9qKrzoMbpRa3Ti/omPw5XuXC4yhXtpVFkCX3z09AnPw2SBGSmmZBhN8f9ZpOdnSB8qrk5AOfnECUTBpgeIITAR6UHAQAXnNGHdxamhCPLEnIzLMjNsMBkVDBuSC4CARXlRxuw72gjyo85se9oI5o8Aew71oh9xxqjr1VkCdkOM3IyLCjItiEnw4JBhQ4YDZ27V2xnJwifam4OwPk5RMmGAaYHbNxeicNVLpiMMqaOK9K6HKJ2yUo3IzMtL9pjKIRATYMX+441YuehevxQXovqBi8CQRVV9V5U1XuxfX89Pv/2CCSE96opyrGjIMuKjOaemuwMM/oWBeH3+gEBGBQJiiJDlgB/UEUgKNDg8mHHwXr4/CEEVRWhkEAgqEJAINdhBQB4/UH4AypCqgpVhGtTBRAMqWho8gEIBytFkWE0yLAYFaTZjHDYTCjIssJhM/EHCSKdY4DpZm5vAKs+3gUA+MmZ/XnrANItSZKQm2lFXpYNIwdmY0CRAz5/EE5XADVOD2oafKh1etHoDsDtC6K6wYvqBi+2al14C+9/fQBAi14jhwU5GRbkZ9nQK8eO3nl25GVaoMid6z0iop7DANPN3vh8L5wuPwqzbbj4rP5al0PULpIUfgCteylaTk+RJCncu5JmwqBegMkY3jhPqMCR6iYcrXGjqt4Dp8uPBpcfTnf4V58/hFBIIBhSIVpc12gIz8+BEJBlCQZFhtL8q8koIy/TCrvFCJNBhsmkwCDLkKTwEJgsSfAFQzhc1QR/oPn6qkAgEII3EII/oCIYUuHyBBFSRbTX6EQGRUJhth29c+3olWtHn/w09MlLQ06G+ZTBhsNSRD2LAaYble2rxWffHgYAXHvR8E7PCSDqSYoiQZZlNHqCAFp/KMuyhNYzTI6TJAnpdiPSbVkY3i8r5pzRKMOvSvh2+zF4/eHJxaoQEAKQJcBuNaJ/kQPby+vgCwRjXmu1GDBuSB5CIXHSuoKqwNbd1W0u7zYZFYwfmgubyYDaRh9qGjzRXqKKWjeOVLtwpNoFf1DFoaomHKpqinm92aSgV0441PTKs6NPrh1FuXZYzeFvo6k8QTgy6ToSbsMBWGKoo27FANNNyo86sfzNcOf51LFFrb6REyWqky3BjrBZDOhf5IDURu9Me7W1OiqE8ByW7qorEsxcvlA4jOSloVdeWvS8LEvwh1SUllWgqt6DukYf6ht9qHX6UNfkg88fQvlRJ8qPOmOum2Y1IjfDguH9w3vn9M6zoyDLljJzbFpOupZkCX7VDY83AKGKlA511P00DzCqquLpp5/GP//5TzQ2NmLSpEm477770LevfvdKOVDRiMde3wyfP4QR/TJxzcxhWpdE1GEn26jOZNS2J7GzdbU3ANnMRhRmSyjMtkXPWS0GOOxmbNpWico6N+qafKhr9MHtDaLJE4iuzoowKDKy08Mrswpz7UizGJBmMcJqNsBqNsBmVmC1GGA0KOFhM0WGyXj89yeGn0TtyThx5ZcsAQajAXUNHngDIRTl2GBSFARDKkKqQKh5UnZIFc2vB2RJhiyHryVLkV+lcC8OwqGo5XGDIh3/e2vxMBkUmE1Ku3aTpuSgeYB59tlnsWrVKjz88MMoLCzEI488ghtvvBFr166FyWTSurwOUdXwcunVn+9FMKRiUC8HbrtiLO8GTCmlvfNntNKZAKTIEgpzbBjcOwN98o/vou3zh1DX5EOjO9wDUVHjxqGqJviDKirrPais92Db/roO16jIEowGGQYl8uEsx35oK7Ef3rbmgGS3GpHW/LBbjx+zmJRO3/cqGFLR5AnA2TyHqdEVgNPd/Ht3AHWNPlTWueH2BuH1h6LhRAsSwkN5VrMCm8UIm9kAWzQ0GmC1GJBmMcBuPeHvymKEzWKAQQn/G+iJ8MOboXadpgHG7/fjpZdewqJFizB9+nQAwOOPP46pU6fiww8/xKxZs7Qsr90CwRA2bKvEx6WHsL8i/FPYmEHZ+I/Zo6Pj40Sp4HTzZ5SgDElKnrlgZpOCwmwb+hWE985JsxgRCqnRzQAb3AF4AioOVzjR4PLD5QnA4wvB4wvC6w8i2LxEPBBSobb44A+pAiF/COEBmq5TZCn6gW23GJDWHGpahkwBAZ8/XFukRpc3AJc3ePILn+LrWc0GZNhNMBllyHJ4Mnb0oYQnXweCKpxuP1Q1PA8KCC+Hj/zTsZjDPSqqKiAQ/iEx8vcVCKjwBoIxPToCaK4/iFqnr8N1W0xKOAxaTUizGmCPhsLw761mQ3P9EgyyDKNRRmaGDR63D6K5xkgdLX8jjv8GQPhd9QdCaJlVTAYZFrMBmelmpDWHMK6GOzVNP123b98Ol8uFKVOmRI85HA6MGjUKGzduTLgAo6oCLm8ADS4/quo8OFLjwq5DDdh5sD46IdFsUvCLGUMwbVyvTv/EQ6RXpxumSbcZMbBPVludM0lDliXkZVqRl2mFwSAjK8uO+noX6pv8bW6yFxm6KttTC7cvgJAqoKrNQy4hAZNJQWGOHQeONsZ8YEeGYwAgzWaCyxMIP5pDR+T3webnO11+OF3+TrVJksJzfdJtpuZfw79Pt4d7L2oavDAoEuwWA7IybQj4AjCbuzbp+lSTtk98rRDhNkZ61yRZQp+89HBg9IcDjdcXhNsXhNsb/tXlCaK+yQuvP7xCDQC8/hC8/lCnwk93MBvDgcpmNsBmOR6i7Jbwn20WY/T39uYeJ4tJgdGoRHvpDIqUtJ9FktCwv+rDDz/Ebbfdhi1btsBisUSP/7//9//g9Xrx/PPPd/iaQoiYn2TipcHlP+WN62RZgtVkiP7EEE/hZaJy9JtaIKiirXdNlgCDQW7z/KnOdfV8PK8NABIkCIjoc5OhXae69ontTZZ2tfna5iXRgWAooeru6teWpPBP0LIsxZxvz//deNUdDKqtYoIkhefjRHo4Iiu+Ir9GRvqkFs+XJClm7gkgIRRqfe3w88NzUiK1heethP89Szh5XW29tmPt6vxrT/Z6IZr7SUS4JxHRv6dwr1Dk7wwCrf6/tvoYlXA8c7XcdqBlj5cQsbWJ5peceDwOwm9ji68unXCu5XNPMvzb8rzZqMR9dEGWOx60NO2B8Xg8ANBqrovZbEZDQ0OnrilJ4e69eMt2WE7/pG4my+HJbqebU3Oq8115rZbX1vJr6/XaWn7t07/21F3jev07O5n2/N/t7nZ1yWlu4Jm4/840/DujbqfpAFuk18Xvj+3W9Pl8sFqtWpREREREOqBpgCkqCt8XqLKyMuZ4ZWUlCgoKtCiJiIiIdEDTADNixAikpaVh/fr10WNOpxNlZWWYNGmShpURERFRItN0DozJZMI111yDZcuWITs7G71798YjjzyCwsJCzJw5U8vSiIiIKIFpvknJ7bffjmAwiHvuuQderxeTJk3Ciy++CKORd20mIiKitmm6jJqIiIioM7jNHxEREekOAwwRERHpDgMMERER6Q4DDBEREekOAwwRERHpDgMMERER6Q4DDBEREekOA0wCU1UVTz31FKZOnYrx48fjpptuwsGDB7Uuq0319fW47777MG3aNEyYMAFXX301SktLo+fXrVuHOXPmYNy4cbjooovw7rvvxrze5/Nh6dKlmDJlCkpKSnDnnXeitrY25jnxuEa8lZeXo6SkBG+++Wb02LZt23DNNddg/PjxmDFjBv7617/GvKY972s8rhFva9aswcUXX4zi4mJccskleP/996PnDh06hAULFmDChAk499xz8cQTTyAUCsW8/tVXX8UFF1yAsWPH4pe//CXKyspizsfjGvESDAbx5JNP4vzzz0dJSQnmzp2LzZs3R88n03v8/PPPY968eXGvrSeuEa/2fvLJJ7jiiitQUlKCGTNm4E9/+hO8Xm/0fE99fzrdNeLV3pbuuecezJgxI+aYbt5fQQlr+fLl4swzzxSffvqp2LZtm7jhhhvEzJkzhc/n07q0Vq6//noxa9YssXHjRrF3716xdOlSMXbsWLFnzx6xe/duUVxcLB577DGxe/du8Ze//EWMGjVK/N///V/09UuWLBEXXnih2Lhxo9iyZYv46U9/KubOnRs9H49rxJvf7xdz5swRw4YNE6tXrxZCCFFbWyvOPPNMcdddd4ndu3eLN954QxQXF4s33ngj+rrTva/xuEa8rVmzRowaNUq88sorYv/+/eLZZ58VI0aMEN98843w+/1i5syZ4j/+4z/Ejh07xEcffSQmT54snnzyyejr33zzTTF27Fjx9ttvi127donf/va3YvLkyaKmpib6d9nVa8TTU089Jc455xzxxRdfiH379om7775bnHHGGaKioiKp3uNXXnlFjBgxQlxzzTXRY4nSvvZcIx7t3bhxoxg5cqRYsWKFKC8vF5999pmYNm2aWLJkSfQ5PfH9qT3XiEd7W/roo4/EsGHDxPnnnx9zXC/vLwNMgvL5fKKkpES8+uqr0WMNDQ1i7NixYu3atRpW1tq+ffvEsGHDRGlpafSYqqriwgsvFE888YS49957xc9+9rOY1yxcuFDccMMNQgghjh07JkaMGCE+++yz6Pm9e/eKYcOGiW+++UYIIeJyjXh79NFHxa9+9auYAPPcc8+Jc889VwQCgZjnzZw5UwjRvvc1HteIJ1VVxfnnny8efvjhmOM33HCDeO6558TatWvFmDFjRH19ffTca6+9JiZMmBD9ZjVz5kzx5z//OXo+EAiI8847Tzz33HNCCBGXa8TTpZdeKh566KHonxsbG8WwYcPEBx98kBTv8bFjx8SCBQvE+PHjxUUXXRTzAZco7TvdNeLV3jvvvFNcd911Mc9/6623xOjRo4XP5+ux70+nu0a82htRUVEhzjrrLHHNNdfEBBg9vb8cQkpQ27dvh8vlwpQpU6LHHA4HRo0ahY0bN2pYWWtZWVl44YUXUFxcHD0mSRIkSYLT6URpaWlMOwDgrLPOwqZNmyCEwKZNm6LHIgYOHIiCgoJoW+NxjXjauHEjXn/9dTz88MMxx0tLSzF58mQYDMdvM3bWWWdh3759qK6ubtf7Go9rxFN5eTkOHz6M2bNnxxx/8cUXsWDBApSWlmL06NHIyMiIqbepqQnbtm1DTU0N9u3bF1OvwWDAxIkTY9rc1WvEU05ODj799FMcOnQIoVAIr7/+OkwmE0aMGJEU7/EPP/wAo9GId955B+PGjYs5lyjtO9014tXeG264AYsXL445JssyAoEAmpqaeuz70+muEa/2AoAQAkuWLMFll12GyZMnx5zT0/vLAJOgjh07BgAoKiqKOZ6fnx89lygcDgfOO+88mEym6LEPPvgA+/fvx9SpU3Hs2DEUFhbGvCY/Px8ejwd1dXWoqKhAVlYWzGZzq+dE2hqPa8SL0+nE7373O9xzzz2t3p+T1QkAR48ebdf7Go9rxFN5eTkAwO12Y/78+ZgyZQquvPJKfPLJJ3GrN9HafPfdd8NoNOKCCy5AcXExHn/8cTz11FPo169fUrR3xowZWL58Ofr27dvqXKK073TX6IhTtXfUqFEYMWJE9M+BQAArV67EmDFjkJ2d3WPfn053jXi1FwBWrlyJqqoqLFy4sNU5Pb2/DDAJyuPxAEBMKAAAs9kMn8+nRUnt9s033+Cuu+7CzJkzMX36dHi93lbtiPzZ7/fD4/G0Og/EtjUe14iXBx54ACUlJa16JE5WZ+Sbls/na9f7Go9rxFNTUxMAYPHixZg1axZeeuklnHPOObjllluwbt26pGzz7t27kZ6ejmeeeQavv/465syZg0WLFmHbtm1J2d6WEqV9p7tGdwgGg/jd736HXbt24f777weAHvv+dLprxMv27dvx9NNP45FHHmmzJj29v4bTP4W0YLFYAIT/4UZ+D4TfWKvVqlVZp/Xxxx9j0aJFmDBhApYtWwYg/I/yxP+AkT9brVZYLJY2/4O2bGs8rhEPa9asQWlpKdauXdvm+bbqiPxntNls7Xpf43GNeDIajQCA+fPn4/LLLwcAjBw5EmVlZXj55Zc7VO+Jz+lMm092jXg5evQo7rzzTqxcuRITJ04EABQXF2P37t1Yvnx5Ur7HLSVK+053jXhramrCHXfcgQ0bNuDpp5/G2LFjT1rHibX2xPe4ePD5fFi0aBFuvvnmmF6nlvT0/rIHJkFFut4qKytjjldWVqKgoECLkk7rlVdewW233Ybzzz8fzz33XDRNFxUVtdkOm82G9PR0FBYWor6+vtU/5pZtjcc14mH16tWoqanB9OnTUVJSgpKSEgDA/fffjxtvvBGFhYVt1gkABQUF7Xpf43GNeIpcc9iwYTHHhwwZgkOHDiVdm7ds2YJAIBAzpwsAxo0bh/379ydde0+UKO073TXiqbKyMrpU/sUXX8R5550XPddT359Od4142LJlC3bt2oWnn346+v3r+eefx5EjR1BSUoLS0lJdvb8MMAlqxIgRSEtLw/r166PHnE4nysrKMGnSJA0ra9uqVavw+9//HnPnzsVjjz0W0zU4ceJEbNiwIeb5X3/9NSZMmABZlnHGGWdAVdXoRDcgPO+ioqIi2tZ4XCMeli1bhvfeew9r1qyJPgDg9ttvx4MPPohJkyZh06ZNMfuXfP311xg4cCBycnLa9b7G4xrxNHr0aNjtdmzZsiXm+M6dO9GvXz9MmjQJZWVl0aGmSL12ux0jRoxATk4OBg4cGFNvMBhEaWlpTJu7eo14iYzL79ixo1V7BwwYkJTvcUuJ0r7TXSNeGhoacO2116K2thavvvpqq7/fnvr+dLprxMPYsWPx4Ycf4u23345+//rFL36B/Px8rFmzBmPGjNHX+9vu9UrU4x577DExefJk8fHHH8eso/f7/VqXFmPv3r1i9OjR4tZbbxWVlZUxD6fTKXbu3ClGjx4tHnnkEbF7927x4osvttrfYOHChWLGjBni66+/ju6R0HLpXzyu0V1aLqOurq4WkyZNEosXLxa7du0Sq1evFsXFxeLNN9+MPv9072s8rhFvzzzzjCgpKRFr166N2Qfm66+/Fl6vV1x44YVi/vz5Ytu2bdE9XJYvXx59/euvvy7Gjh0r3nzzzegeLmeeeWZ0D5d4XCNeQqGQuPrqq8VFF10k1q1bJ8rLy8Xjjz8uRo4cKTZv3px07/HixYtj/p8kSvvac414tHfx4sVi9OjRYt26da2+fwWDQSFEz3x/as814tHeEz311FOt9oHRy/vLAJPAgsGg+POf/yzOOussMX78eHHTTTeJgwcPal1WKytWrBDDhg1r87F48WIhhBCff/65mDVrlhgzZoy46KKLxLvvvhtzDZfLJe6++24xceJEMXHiRLFw4UJRW1sb85x4XKM7tAwwQgixZcsW8fOf/1yMGTNGnH/++eJvf/tbzPPb877G4xrx9tJLL4kZM2aI0aNHi0svvVR89NFH0XP79u0T119/vSguLhbnnnuueOKJJ0QoFIp5/V/+8hcxbdo0MXbsWPHLX/5SlJWVxZyPxzXipb6+XjzwwANi+vTpoqSkRFx11VVi/fr10fPJ9B639QGXKO073TW62t5gMCiKi4tP+v0rUk9PfX863TW62t62tBVg9PL+SkJ0cIE5ERERkcY4B4aIiIh0hwGGiIiIdIcBhoiIiHSHAYaIiIh0hwGGiIiIdIcBhoiIiHSHAYaIiIh0hwGGiIiIdIcBhoh6xLx58zB8+PCYx5gxYzB9+nQsXboUDQ0NJ33toUOHMHz4cLz55ps9WDERJTKD1gUQUeoYNWoU7r///uifA4EAfvjhBzz22GPYtm0b/v73v0OSpFavy8/Px+uvv45+/fr1ZLlElMAYYIiox6SlpWH8+PExxyZNmgSXy4WnnnoKW7ZsaXUeAEwmU5vHiSh1cQiJiDQ3ZswYAMCRI0cwb948LFq0CLfffjvGjx+P66+/vs0hpL179+I///M/MXnyZEyaNAkLFizAnj17oud9Ph/+/Oc/47zzzsOYMWMwe/ZsvPfeez3eNiLqHuyBISLNlZeXAwD69u0LAHj//fdx6aWXYsWKFVBVtdXzKyoqcNVVV6GgoAAPPPAAbDYbli9fjmuvvRb/+te/kJGRgVtvvRXffPMNbr/9dgwePBgfffQRfvOb38Dv9+OnP/1pTzaPiLoBAwwR9RghBILBYPTPDQ0N2LBhA1asWIGSkpJoT4zRaMTSpUthMpkAhCfxtrRy5Ur4/X68/PLLyMvLAwCMGDECV199NbZs2QKDwYAvvvgCjz/+OC6++GIAwNSpU+HxeLBs2TLMmjULBgO//RHpGf8HE1GP2bhxI0aPHh1zTJZlnH322fjv//7v6ATeQYMGRcNLWzZt2oTx48dHwwsAFBYW4tNPPwUALFu2DJIk4bzzzosJTDNmzMA777yDXbt2YeTIkfFsGhH1MAYYIuoxo0ePxtKlSwEAkiTBbDajqKgIaWlpMc+z2+2nvE59fT369OlzyvNCCEyYMKHN85WVlQwwRDrHAENEPcZut6O4uLjL10lPT0dtbW2r4+vWrUOfPn2Qnp4Om82Gv/71r22+vn///l2ugYi0xVVIRKQ7EydOxJYtW2JCTE1NDW688UZ8/vnnmDx5MtxuN4QQKC4ujj527tyJZ555JmZYiYj0iT0wRKQ71113HdasWYMbb7wRCxYsgNFoxIoVK1BYWIjZs2cjPT0dkyZNwi233IJbbrkFgwcPxnfffYennnoKU6dORXZ2ttZNIKIuYoAhIt0pKirCqlWr8Mgjj2DJkiUwmUw488wz8fjjjyMjIwMA8MILL+DJJ5/E888/j5qaGhQUFOD666/HrbfeqnH1RBQPkhBCaF0EERERUUdwDgwRERHpDgMMERER6Q4DDBEREekOAwwRERHpDgMMERER6Q4DDBEREekOAwwRERHpDgMMERER6Q4DDBEREekOAwwRERHpDgMMERER6c7/D1nY/bBxx1gjAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# From the subplots and the PDF of price, we can easily determine that 'Price' is exponentially distributed\n",
+    "# A good transformation in that case is a log transformation\n",
+    "sns.distplot(data_cleaned['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's transform 'Price' with a log transformation\n",
+    "log_price = np.log(data_cleaned['Price'])\n",
+    "# Then we add it to our data frame\n",
+    "data_cleaned['log_price'] = log_price\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>11500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
+       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year  log_price  \n",
+       "0             yes  1991   8.342840  \n",
+       "1             yes  1999   8.974618  \n",
+       "2             yes  2003   9.495519  \n",
+       "3             yes  2007  10.043249  \n",
+       "4             yes  2011   9.814656  \n",
+       "...           ...   ...        ...  \n",
+       "3862          yes  2008   9.350102  \n",
+       "3863          yes  2014   9.792556  \n",
+       "3864          yes  2014  11.736069  \n",
+       "3865          yes  1999   8.779557  \n",
+       "3866          yes  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, (ax1, ax2, ax3)= plt.subplots(1, 3, sharey= True, figsize = (15,3))#sharey -> share 'Price' as y\n",
+    "ax1.scatter(data_cleaned['Year'], data_cleaned['log_price'])\n",
+    "ax1.set_title('log_price and Year')\n",
+    "ax2.scatter(data_cleaned['EngineV'], data_cleaned['log_price'])\n",
+    "ax2.set_title('log_price and EngineV')\n",
+    "ax3.scatter(data_cleaned['Mileage'], data_cleaned['log_price'])\n",
+    "ax3.set_title('Mileage and log_price')\n",
+    "\n",
+    "plt.show()        \n",
+    "# The relationships show a clear linear relationship\n",
+    "# This is some good linear regression material\n",
+    "\n",
+    "# Alternatively we could have transformed each of the independent variables    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since we will be using the log price variable,\n",
+    "data_cleaned=  data_cleaned.drop(['Price'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3862</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>van</td>\n",
+       "      <td>163</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3863</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3864</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3865</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3866</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3867 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand       Body  Mileage  EngineV Engine Type Registration  \\\n",
+       "0               BMW      sedan      277      2.0      Petrol          yes   \n",
+       "1     Mercedes-Benz        van      427      2.9      Diesel          yes   \n",
+       "2     Mercedes-Benz      sedan      358      5.0         Gas          yes   \n",
+       "3              Audi  crossover      240      4.2      Petrol          yes   \n",
+       "4            Toyota  crossover      120      2.0      Petrol          yes   \n",
+       "...             ...        ...      ...      ...         ...          ...   \n",
+       "3862     Volkswagen        van      163      2.5      Diesel          yes   \n",
+       "3863         Toyota      sedan       35      1.6      Petrol          yes   \n",
+       "3864  Mercedes-Benz      sedan        9      3.0      Diesel          yes   \n",
+       "3865            BMW      sedan        1      3.5      Petrol          yes   \n",
+       "3866     Volkswagen        van      124      2.0      Diesel          yes   \n",
+       "\n",
+       "      Year  log_price  \n",
+       "0     1991   8.342840  \n",
+       "1     1999   8.974618  \n",
+       "2     2003   9.495519  \n",
+       "3     2007  10.043249  \n",
+       "4     2011   9.814656  \n",
+       "...    ...        ...  \n",
+       "3862  2008   9.350102  \n",
+       "3863  2014   9.792556  \n",
+       "3864  2014  11.736069  \n",
+       "3865  1999   8.779557  \n",
+       "3866  2013   9.510445  \n",
+       "\n",
+       "[3867 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn does not have a built-in way to check for multicollinearity\n",
+    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
+    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
+    "\n",
+    "# Here's the relevant module\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# To make this as easy as possible to use, we declare a variable where we put\n",
+    "# all features where we want to check for multicollinearity\n",
+    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
+    "variables = data_cleaned[['Mileage','Year', 'EngineV']]\n",
+    "\n",
+    "# we create a new data frame which will include all the VIFs\n",
+    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
+    "\n",
+    "vif = pd.DataFrame()\n",
+    "\n",
+    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
+    "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+    "# Finally, I like to include names so it is easier to explore the result\n",
+    "vif['Features'] = variables.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.791584</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.354854</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.662068</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         VIF Features\n",
+       "0   3.791584  Mileage\n",
+       "1  10.354854     Year\n",
+       "2   7.662068  EngineV"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since Year has the highest VIF, I will remove it from the model\n",
+    "# This will drive the VIF of other variables down!!! \n",
+    "# So even if EngineV seems with a high VIF, too, once 'Year' is gone that will no longer be the case\n",
+    "data_no_multicollinearity = data_cleaned.drop(['Year'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type', 'Registration',\n",
+       "       'log_price'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_no_multicollinearity.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To include the categorical data in the regression, let's create dummies\n",
+    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
+    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
+    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Mileage', 'EngineV', 'log_price', 'Brand_BMW', 'Brand_Mercedes-Benz',\n",
+       "       'Brand_Mitsubishi', 'Brand_Renault', 'Brand_Toyota', 'Brand_Volkswagen',\n",
+       "       'Body_hatch', 'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+       "       'Engine Type_Gas', 'Engine Type_Other', 'Engine Type_Petrol',\n",
+       "       'Registration_yes'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3867, 18)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rearrange a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df\n",
+    "# Since each problem is different, that must be done manually\n",
+    "# I have displayed all possible features and then choose the desired order\n",
+    "\n",
+    "\n",
+    "# To make the code a bit more parametrized, let's declare a new variable that will contain the preferred order\n",
+    "# If you want a different order, just specify it here\n",
+    "# Conventionally, the most intuitive order is: dependent variable, indepedendent numerical variables, dummies\n",
+    "\n",
+    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW', 'Brand_Mercedes-Benz',\n",
+    "       'Brand_Mitsubishi', 'Brand_Renault', 'Brand_Toyota', 'Brand_Volkswagen',\n",
+    "       'Body_hatch', 'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+    "       'Engine Type_Gas', 'Engine Type_Other', 'Engine Type_Petrol',\n",
+    "       'Registration_yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0       True                False   \n",
+       "1   8.974618      427      2.9      False                 True   \n",
+       "2   9.495519      358      5.0      False                 True   \n",
+       "3  10.043249      240      4.2      False                False   \n",
+       "4   9.814656      120      2.0      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
+       "0              False                True              True  \n",
+       "1              False               False              True  \n",
+       "2              False               False              True  \n",
+       "3              False                True              True  \n",
+       "4              False                True              True  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To implement the reordering, we will create a new df, which is equal to the old one but with the new order of features\n",
+    "data_processed = data_with_dummies[cols]\n",
+    "data_processed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the inputs and the targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The target(s) (dependent variable) is 'log price'\n",
+    "targets = data_processed['log_price']\n",
+    "\n",
+    "inputs = data_processed.drop(['log_price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scale the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import the scaling module\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# Create a scaler object\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Fit the inputs (calculate the mean and standard deviation feature-wise)\n",
+    "scaler.fit(inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale the features and store them in a new variable (the actual scaling procedure)\n",
+    "inputs_scaled = scaler.transform(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the module for the split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Split the variables with an 80-20 split and some random state\n",
+    "# To have the same split as mine, use random_state = 365\n",
+    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, targets, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a linear regression object\n",
+    "reg = LinearRegression()\n",
+    "\n",
+    "# Fit the regression with the scaled TRAIN inputs and targets\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's check the outputs of the regression\n",
+    "# I'll store them in y_hat as this is the 'theoretical' name of the predictions\n",
+    "y_hat = reg.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot\n",
+    "# The closer the points to the 45-degree line, the better the prediction\n",
+    "plt.scatter(y_train,y_hat)\n",
+    "# Let's also name the axes\n",
+    "plt.xlabel('Targets (y_train)', size= 18)\n",
+    "plt.ylabel('Prediction (y_hat)', size= 18)\n",
+    "\n",
+    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
+    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
+    "# We want the x-axis and the y-axis to be the same\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_8404\\133299298.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(y_train- y_hat)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Residual PDF')"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Another useful check of our model is a residual plot\n",
+    "# We can plot the PDF of the residuals and check for anomalies\n",
+    "sns.distplot(y_train- y_hat)\n",
+    "\n",
+    "#Add title\n",
+    "plt.title('Residual PDF', size=18)\n",
+    "\n",
+    "# In the best case scenario this plot should be normally distributed\n",
+    "# In our case we notice that there are many negative residuals (far away from the mean)\n",
+    "# Given the definition of the residuals (y_train - y_hat), negative values imply\n",
+    "# that y_hat (predictions) are much higher than y_train (the targets)\n",
+    "# This is food for thought to improve our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.744996578792662"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find the R-squared of the model\n",
+    "reg.score(x_train,y_train)\n",
+    "\n",
+    "# Note that this is NOT the adjusted R-squared\n",
+    "# in other words... find the Adjusted R-squared to have the appropriate measure :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3093, 17)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r2 = reg.score(x_train,y_train)\n",
+    "n = x_train.shape[0]\n",
+    "p = x_train.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7435868037811093"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adjusted_r2 = 1 - (1- r2)*(n-1)/(n-p-1)\n",
+    "adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R2 and Adjusted R2 are almost similar with only 10% difference, suggest that the model is a reasonably good fit for the data, explaining a substantial portion of the variability in the dependent variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the weights and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.415239458021299"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the bias (intercept) of the regression\n",
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.44871341,  0.20903483,  0.0142496 ,  0.01288174, -0.14055166,\n",
+       "       -0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,\n",
+       "       -0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,\n",
+       "       -0.14690868,  0.32047333])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the weights (coefficients) of the regression\n",
+    "reg.coef_\n",
+    "\n",
+    "# Note that they are barely interpretable if at all, they are arrays of no."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-0.448713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>0.209035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>0.014250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>0.012882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>-0.140552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>-0.179909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>-0.060550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>-0.089924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>-0.145469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>-0.101444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-0.200630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-0.129887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-0.168597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-0.121490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-0.033368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-0.146909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>0.320473</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features    Weight\n",
+       "0               Mileage -0.448713\n",
+       "1               EngineV  0.209035\n",
+       "2             Brand_BMW  0.014250\n",
+       "3   Brand_Mercedes-Benz  0.012882\n",
+       "4      Brand_Mitsubishi -0.140552\n",
+       "5         Brand_Renault -0.179909\n",
+       "6          Brand_Toyota -0.060550\n",
+       "7      Brand_Volkswagen -0.089924\n",
+       "8            Body_hatch -0.145469\n",
+       "9            Body_other -0.101444\n",
+       "10           Body_sedan -0.200630\n",
+       "11           Body_vagon -0.129887\n",
+       "12             Body_van -0.168597\n",
+       "13      Engine Type_Gas -0.121490\n",
+       "14    Engine Type_Other -0.033368\n",
+       "15   Engine Type_Petrol -0.146909\n",
+       "16     Registration_yes  0.320473"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a regression summary where we can compare them with one-another\n",
+    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
+    "reg_summary['Weight'] = reg.coef_\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the different categories in the 'Brand' variable\n",
+    "data_cleaned['Brand'].unique()\n",
+    "# In this way we can see which 'Brand' is actually the benchmark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Once we have trained and fine-tuned our model, we can proceed to testing it\n",
+    "# Testing is done on a dataset that the algorithm has never seen\n",
+    "# Luckily we have prepared such a dataset\n",
+    "# Our test inputs are 'x_test', while the outputs: 'y_test' \n",
+    "# We SHOULD NOT TRAIN THE MODEL ON THEM, we just feed them and find the predictions\n",
+    "# If the predictions are far off, we will know that our model overfitted\n",
+    "y_hat_test = reg.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot with the test targets and the test predictions\n",
+    "# You can include the argument 'alpha' which will introduce opacity to the graph\n",
+    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
+    "plt.xlabel('Targets (y_test)', size= 18)\n",
+    "plt.ylabel('Prediction (y_hat_test)', size= 18)\n",
+    "plt.title('Targets Vs Prediction')\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.501696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Predictions\n",
+       "0  10685.501696\n",
+       "1   3499.255242\n",
+       "2   7553.285218\n",
+       "3   7463.963017\n",
+       "4  11353.490075"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, let's manually check these predictions\n",
+    "# To obtain the actual prices, we take the exponential of the log_price\n",
+    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns =['Predictions'])\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.501696</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "      <td>7900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.726363</td>\n",
+       "      <td>6950.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.071179</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.446953</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.751303</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.163043</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Predictions  Target\n",
+       "0    10685.501696     NaN\n",
+       "1     3499.255242  7900.0\n",
+       "2     7553.285218     NaN\n",
+       "3     7463.963017     NaN\n",
+       "4    11353.490075     NaN\n",
+       "..            ...     ...\n",
+       "769  29651.726363  6950.0\n",
+       "770  10732.071179     NaN\n",
+       "771  13922.446953     NaN\n",
+       "772  27487.751303     NaN\n",
+       "773  13491.163043     NaN\n",
+       "\n",
+       "[774 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can also include the test targets in that data frame (so we can manually compare them)\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.501696</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "      <td>7900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.726363</td>\n",
+       "      <td>6950.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.071179</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.446953</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.751303</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.163043</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Predictions  Target\n",
+       "0    10685.501696     NaN\n",
+       "1     3499.255242  7900.0\n",
+       "2     7553.285218     NaN\n",
+       "3     7463.963017     NaN\n",
+       "4    11353.490075     NaN\n",
+       "..            ...     ...\n",
+       "769  29651.726363  6950.0\n",
+       "770  10732.071179     NaN\n",
+       "771  13922.446953     NaN\n",
+       "772  27487.751303     NaN\n",
+       "773  13491.163043     NaN\n",
+       "\n",
+       "[774 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    7.740664\n",
+       "1    7.937375\n",
+       "2    7.824046\n",
+       "3    8.764053\n",
+       "4    9.121509\n",
+       "Name: log_price, dtype: float64"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# After displaying y_test, we find what the issue is\n",
+    "# The old indexes are preserved (recall earlier in that code we made a note on that)\n",
+    "# The code was: data_cleaned = data_4.reset_index(drop=True)\n",
+    "\n",
+    "# Therefore, to get a proper result, we must reset the index and drop the old indexing\n",
+    "\n",
+    "# Note that we have a lot of missing values\n",
+    "# There is no reason to have ANY missing values, though\n",
+    "# This suggests that something is wrong with the data frame / indexing\n",
+    "y_test = y_test.reset_index(drop = True)\n",
+    "\n",
+    "y_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.501696</td>\n",
+       "      <td>2300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "      <td>2800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "      <td>2500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "      <td>6400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "      <td>9150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.726363</td>\n",
+       "      <td>29500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.071179</td>\n",
+       "      <td>9600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.446953</td>\n",
+       "      <td>18300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.751303</td>\n",
+       "      <td>68500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.163043</td>\n",
+       "      <td>10800.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Predictions   Target\n",
+       "0    10685.501696   2300.0\n",
+       "1     3499.255242   2800.0\n",
+       "2     7553.285218   2500.0\n",
+       "3     7463.963017   6400.0\n",
+       "4    11353.490075   9150.0\n",
+       "..            ...      ...\n",
+       "769  29651.726363  29500.0\n",
+       "770  10732.071179   9600.0\n",
+       "771  13922.446953  18300.0\n",
+       "772  27487.751303  68500.0\n",
+       "773  13491.163043  10800.0\n",
+       "\n",
+       "[774 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's overwrite the 'Target' column with the appropriate values\n",
+    "# Again, we need the exponential of the test log price\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Additionally, we can calculate the difference between the targets and the predictions\n",
+    "# Note that this is actually the residual (we already plotted the residuals)\n",
+    "df_pf['Residual'] = df_pf['Target']-df_pf['Predictions']\n",
+    "# Since OLS is basically an algorithm which minimizes the total sum of squared errors (residuals),\n",
+    "# this comparison makes a lot of sense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.501696</td>\n",
+       "      <td>2300.0</td>\n",
+       "      <td>-8385.501696</td>\n",
+       "      <td>364.587030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.255242</td>\n",
+       "      <td>2800.0</td>\n",
+       "      <td>-699.255242</td>\n",
+       "      <td>24.973402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.285218</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>-5053.285218</td>\n",
+       "      <td>202.131409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.963017</td>\n",
+       "      <td>6400.0</td>\n",
+       "      <td>-1063.963017</td>\n",
+       "      <td>16.624422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.490075</td>\n",
+       "      <td>9150.0</td>\n",
+       "      <td>-2203.490075</td>\n",
+       "      <td>24.081859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.726363</td>\n",
+       "      <td>29500.0</td>\n",
+       "      <td>-151.726363</td>\n",
+       "      <td>0.514327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.071179</td>\n",
+       "      <td>9600.0</td>\n",
+       "      <td>-1132.071179</td>\n",
+       "      <td>11.792408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.446953</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>4377.553047</td>\n",
+       "      <td>23.921055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.751303</td>\n",
+       "      <td>68500.0</td>\n",
+       "      <td>41012.248697</td>\n",
+       "      <td>59.871896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.163043</td>\n",
+       "      <td>10800.0</td>\n",
+       "      <td>-2691.163043</td>\n",
+       "      <td>24.918176</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Predictions   Target      Residual  Difference%\n",
+       "0    10685.501696   2300.0  -8385.501696   364.587030\n",
+       "1     3499.255242   2800.0   -699.255242    24.973402\n",
+       "2     7553.285218   2500.0  -5053.285218   202.131409\n",
+       "3     7463.963017   6400.0  -1063.963017    16.624422\n",
+       "4    11353.490075   9150.0  -2203.490075    24.081859\n",
+       "..            ...      ...           ...          ...\n",
+       "769  29651.726363  29500.0   -151.726363     0.514327\n",
+       "770  10732.071179   9600.0  -1132.071179    11.792408\n",
+       "771  13922.446953  18300.0   4377.553047    23.921055\n",
+       "772  27487.751303  68500.0  41012.248697    59.871896\n",
+       "773  13491.163043  10800.0  -2691.163043    24.918176\n",
+       "\n",
+       "[774 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, it makes sense to see how far off we are from the result percentage-wise\n",
+    "# Here, we take the absolute difference in %, so we can easily order the data frame\n",
+    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target'] * 100)\n",
+    "df_pf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>774.000000</td>\n",
+       "      <td>774.000000</td>\n",
+       "      <td>774.000000</td>\n",
+       "      <td>774.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>15946.760167</td>\n",
+       "      <td>18165.817106</td>\n",
+       "      <td>2219.056939</td>\n",
+       "      <td>36.256693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>13133.197604</td>\n",
+       "      <td>19967.858908</td>\n",
+       "      <td>10871.218143</td>\n",
+       "      <td>55.066507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1320.562768</td>\n",
+       "      <td>1200.000000</td>\n",
+       "      <td>-29456.498331</td>\n",
+       "      <td>0.062794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7413.644234</td>\n",
+       "      <td>6900.000000</td>\n",
+       "      <td>-2044.191251</td>\n",
+       "      <td>12.108022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>11568.168859</td>\n",
+       "      <td>11600.000000</td>\n",
+       "      <td>142.518577</td>\n",
+       "      <td>23.467728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>20162.408805</td>\n",
+       "      <td>20500.000000</td>\n",
+       "      <td>3147.343497</td>\n",
+       "      <td>39.563570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>77403.055224</td>\n",
+       "      <td>126000.000000</td>\n",
+       "      <td>85106.162329</td>\n",
+       "      <td>512.688080</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Predictions         Target      Residual  Difference%\n",
+       "count    774.000000     774.000000    774.000000   774.000000\n",
+       "mean   15946.760167   18165.817106   2219.056939    36.256693\n",
+       "std    13133.197604   19967.858908  10871.218143    55.066507\n",
+       "min     1320.562768    1200.000000 -29456.498331     0.062794\n",
+       "25%     7413.644234    6900.000000  -2044.191251    12.108022\n",
+       "50%    11568.168859   11600.000000    142.518577    23.467728\n",
+       "75%    20162.408805   20500.000000   3147.343497    39.563570\n",
+       "max    77403.055224  126000.000000  85106.162329   512.688080"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exploring the descriptives here gives us additional insights\n",
+    "df_pf.describe(include = 'all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>30480.85</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>19.15</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>16960.31</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>38.69</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>12469.21</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>30.79</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>25614.14</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>-114.14</td>\n",
+       "      <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>42703.68</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>-203.68</td>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>3084.69</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>15.31</td>\n",
+       "      <td>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>29651.73</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>-151.73</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>9749.53</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>50.47</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>23118.07</td>\n",
+       "      <td>22999.00</td>\n",
+       "      <td>-119.07</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>8734.58</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>65.42</td>\n",
+       "      <td>0.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>3473.79</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>26.21</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>21174.10</td>\n",
+       "      <td>21335.00</td>\n",
+       "      <td>160.90</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>8967.74</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-67.74</td>\n",
+       "      <td>0.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>665</th>\n",
+       "      <td>17858.02</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>141.98</td>\n",
+       "      <td>0.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>17654.84</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>145.16</td>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>11391.95</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>108.05</td>\n",
+       "      <td>0.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>28625.56</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>274.44</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>75.83</td>\n",
+       "      <td>0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>6429.03</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>70.97</td>\n",
+       "      <td>1.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>7597.39</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-97.39</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>18555.09</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>244.91</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>7396.87</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-96.87</td>\n",
+       "      <td>1.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>16178.14</td>\n",
+       "      <td>16400.00</td>\n",
+       "      <td>221.86</td>\n",
+       "      <td>1.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>12327.10</td>\n",
+       "      <td>12499.00</td>\n",
+       "      <td>171.90</td>\n",
+       "      <td>1.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>51287.19</td>\n",
+       "      <td>52055.25</td>\n",
+       "      <td>768.06</td>\n",
+       "      <td>1.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>6009.63</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>90.37</td>\n",
+       "      <td>1.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>4973.17</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-73.17</td>\n",
+       "      <td>1.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>27716.14</td>\n",
+       "      <td>27300.00</td>\n",
+       "      <td>-416.14</td>\n",
+       "      <td>1.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>28950.00</td>\n",
+       "      <td>451.09</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>2953.17</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>46.83</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>26425.14</td>\n",
+       "      <td>25999.00</td>\n",
+       "      <td>-426.14</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>13111.91</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-211.91</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>12858.08</td>\n",
+       "      <td>12650.00</td>\n",
+       "      <td>-208.08</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>13421.16</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>-221.16</td>\n",
+       "      <td>1.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>7327.18</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-127.18</td>\n",
+       "      <td>1.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>3816.33</td>\n",
+       "      <td>3750.00</td>\n",
+       "      <td>-66.33</td>\n",
+       "      <td>1.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>9721.50</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>178.50</td>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>44173.72</td>\n",
+       "      <td>44999.00</td>\n",
+       "      <td>825.28</td>\n",
+       "      <td>1.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>40753.58</td>\n",
+       "      <td>40000.00</td>\n",
+       "      <td>-753.58</td>\n",
+       "      <td>1.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>22262.80</td>\n",
+       "      <td>22711.65</td>\n",
+       "      <td>448.85</td>\n",
+       "      <td>1.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>18231.44</td>\n",
+       "      <td>18600.00</td>\n",
+       "      <td>368.56</td>\n",
+       "      <td>1.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>9596.94</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>-196.94</td>\n",
+       "      <td>2.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>6067.79</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>132.21</td>\n",
+       "      <td>2.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>35371.16</td>\n",
+       "      <td>34600.00</td>\n",
+       "      <td>-771.16</td>\n",
+       "      <td>2.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>11967.39</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-267.39</td>\n",
+       "      <td>2.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>17908.00</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>-408.00</td>\n",
+       "      <td>2.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8707.13</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-207.13</td>\n",
+       "      <td>2.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>29049.27</td>\n",
+       "      <td>28350.00</td>\n",
+       "      <td>-699.27</td>\n",
+       "      <td>2.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>14513.46</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>386.54</td>\n",
+       "      <td>2.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>20453.89</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>546.11</td>\n",
+       "      <td>2.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>15383.35</td>\n",
+       "      <td>14990.00</td>\n",
+       "      <td>-393.35</td>\n",
+       "      <td>2.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>24642.50</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-642.50</td>\n",
+       "      <td>2.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>50099.92</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>1400.08</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>16133.86</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-433.86</td>\n",
+       "      <td>2.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>17489.92</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>510.08</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>1894.40</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>55.60</td>\n",
+       "      <td>2.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>30810.25</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>-910.25</td>\n",
+       "      <td>3.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>8721.97</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>277.03</td>\n",
+       "      <td>3.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>18866.50</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>-566.50</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>5958.63</td>\n",
+       "      <td>6150.00</td>\n",
+       "      <td>191.37</td>\n",
+       "      <td>3.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>5811.74</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>188.26</td>\n",
+       "      <td>3.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>9800.43</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-300.43</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>7324.63</td>\n",
+       "      <td>7100.00</td>\n",
+       "      <td>-224.63</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>12583.52</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>416.48</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>10115.13</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-315.13</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>11156.38</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-356.38</td>\n",
+       "      <td>3.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>9279.28</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>320.72</td>\n",
+       "      <td>3.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>31369.37</td>\n",
+       "      <td>32500.00</td>\n",
+       "      <td>1130.63</td>\n",
+       "      <td>3.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>2315.71</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>84.29</td>\n",
+       "      <td>3.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5175.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-175.77</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>21611.83</td>\n",
+       "      <td>22400.00</td>\n",
+       "      <td>788.17</td>\n",
+       "      <td>3.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>7976.26</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-276.26</td>\n",
+       "      <td>3.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>24874.86</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-875.86</td>\n",
+       "      <td>3.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>36516.35</td>\n",
+       "      <td>37900.00</td>\n",
+       "      <td>1383.65</td>\n",
+       "      <td>3.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>4666.05</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-166.05</td>\n",
+       "      <td>3.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>18672.68</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>-672.68</td>\n",
+       "      <td>3.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>14815.83</td>\n",
+       "      <td>15400.00</td>\n",
+       "      <td>584.17</td>\n",
+       "      <td>3.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>10581.62</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>417.38</td>\n",
+       "      <td>3.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>12663.54</td>\n",
+       "      <td>12200.00</td>\n",
+       "      <td>-463.54</td>\n",
+       "      <td>3.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>1825.44</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>74.56</td>\n",
+       "      <td>3.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>12247.90</td>\n",
+       "      <td>12750.00</td>\n",
+       "      <td>502.10</td>\n",
+       "      <td>3.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>5930.73</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>-230.73</td>\n",
+       "      <td>4.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>9782.47</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>417.53</td>\n",
+       "      <td>4.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>12452.22</td>\n",
+       "      <td>11960.00</td>\n",
+       "      <td>-492.22</td>\n",
+       "      <td>4.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>23163.87</td>\n",
+       "      <td>24171.42</td>\n",
+       "      <td>1007.55</td>\n",
+       "      <td>4.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>3246.94</td>\n",
+       "      <td>3390.00</td>\n",
+       "      <td>143.06</td>\n",
+       "      <td>4.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>12354.16</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>545.84</td>\n",
+       "      <td>4.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>33499.44</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>1500.56</td>\n",
+       "      <td>4.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>16257.03</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>741.97</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>8415.81</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>384.19</td>\n",
+       "      <td>4.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>12318.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>581.61</td>\n",
+       "      <td>4.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>2765.14</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>134.86</td>\n",
+       "      <td>4.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>11420.95</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>578.05</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>2725.40</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-125.40</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>23624.69</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-1124.69</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11377.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>621.16</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>12096.18</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-596.18</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>9363.27</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-463.27</td>\n",
+       "      <td>5.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>12311.47</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-611.47</td>\n",
+       "      <td>5.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>14218.94</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-718.94</td>\n",
+       "      <td>5.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>8431.89</td>\n",
+       "      <td>7999.00</td>\n",
+       "      <td>-432.89</td>\n",
+       "      <td>5.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>37600.15</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>2299.85</td>\n",
+       "      <td>5.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>39882.67</td>\n",
+       "      <td>37700.00</td>\n",
+       "      <td>-2182.67</td>\n",
+       "      <td>5.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>20588.57</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>1311.43</td>\n",
+       "      <td>5.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>6861.84</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>438.16</td>\n",
+       "      <td>6.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>18982.15</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-1082.15</td>\n",
+       "      <td>6.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>15067.85</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>-867.85</td>\n",
+       "      <td>6.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>797.71</td>\n",
+       "      <td>6.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>11147.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-647.91</td>\n",
+       "      <td>6.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>20183.30</td>\n",
+       "      <td>18999.00</td>\n",
+       "      <td>-1184.30</td>\n",
+       "      <td>6.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>17334.11</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>1165.89</td>\n",
+       "      <td>6.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>21289.80</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-1289.80</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>6826.78</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>473.22</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>4472.47</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-272.47</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>53294.61</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>3705.39</td>\n",
+       "      <td>6.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>6658.73</td>\n",
+       "      <td>6250.00</td>\n",
+       "      <td>-408.73</td>\n",
+       "      <td>6.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>19001.29</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>-1201.29</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>-290.49</td>\n",
+       "      <td>6.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>43236.92</td>\n",
+       "      <td>40500.00</td>\n",
+       "      <td>-2736.92</td>\n",
+       "      <td>6.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>9076.42</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-576.42</td>\n",
+       "      <td>6.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>10159.18</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>740.82</td>\n",
+       "      <td>6.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>16027.02</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-1027.02</td>\n",
+       "      <td>6.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>13502.35</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>997.65</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>6413.26</td>\n",
+       "      <td>5999.00</td>\n",
+       "      <td>-414.26</td>\n",
+       "      <td>6.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>49221.63</td>\n",
+       "      <td>52999.00</td>\n",
+       "      <td>3777.37</td>\n",
+       "      <td>7.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>21816.43</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>1683.57</td>\n",
+       "      <td>7.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>11969.66</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>930.34</td>\n",
+       "      <td>7.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>8263.95</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-563.95</td>\n",
+       "      <td>7.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11581.54</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>918.46</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>6977.90</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-477.90</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>6978.91</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-478.91</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>16392.22</td>\n",
+       "      <td>17700.00</td>\n",
+       "      <td>1307.78</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>15742.41</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>1256.59</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>7869.47</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>630.53</td>\n",
+       "      <td>7.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>21297.65</td>\n",
+       "      <td>19800.00</td>\n",
+       "      <td>-1497.65</td>\n",
+       "      <td>7.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>6893.62</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-493.62</td>\n",
+       "      <td>7.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>15627.03</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>-1127.03</td>\n",
+       "      <td>7.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>3411.53</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>288.47</td>\n",
+       "      <td>7.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>11336.71</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>963.29</td>\n",
+       "      <td>7.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>8195.34</td>\n",
+       "      <td>7600.00</td>\n",
+       "      <td>-595.34</td>\n",
+       "      <td>7.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>16717.08</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-1217.08</td>\n",
+       "      <td>7.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>7772.57</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-572.57</td>\n",
+       "      <td>7.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>16197.42</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-1197.42</td>\n",
+       "      <td>7.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>6102.36</td>\n",
+       "      <td>5650.00</td>\n",
+       "      <td>-452.36</td>\n",
+       "      <td>8.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>4488.43</td>\n",
+       "      <td>4150.00</td>\n",
+       "      <td>-338.43</td>\n",
+       "      <td>8.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>15885.66</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>1414.34</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>17983.10</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>1616.90</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>7045.72</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-545.72</td>\n",
+       "      <td>8.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-295.06</td>\n",
+       "      <td>8.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>6130.96</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>569.04</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>5947.51</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>552.49</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>7609.28</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-609.28</td>\n",
+       "      <td>8.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8117.44</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>782.56</td>\n",
+       "      <td>8.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>11098.18</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-898.18</td>\n",
+       "      <td>8.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>17028.45</td>\n",
+       "      <td>18700.00</td>\n",
+       "      <td>1671.55</td>\n",
+       "      <td>8.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19847.25</td>\n",
+       "      <td>18200.00</td>\n",
+       "      <td>-1647.25</td>\n",
+       "      <td>9.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>682</th>\n",
+       "      <td>5454.34</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-454.34</td>\n",
+       "      <td>9.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>20662.46</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>-1762.46</td>\n",
+       "      <td>9.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11696.25</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>1203.75</td>\n",
+       "      <td>9.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>10716.84</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-916.84</td>\n",
+       "      <td>9.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>13459.92</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>-1159.92</td>\n",
+       "      <td>9.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>8767.54</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>932.46</td>\n",
+       "      <td>9.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>23272.83</td>\n",
+       "      <td>25749.75</td>\n",
+       "      <td>2476.92</td>\n",
+       "      <td>9.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>5484.97</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-485.97</td>\n",
+       "      <td>9.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>9495.62</td>\n",
+       "      <td>8650.00</td>\n",
+       "      <td>-845.62</td>\n",
+       "      <td>9.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>11858.48</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1058.48</td>\n",
+       "      <td>9.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>48535.50</td>\n",
+       "      <td>-4777.31</td>\n",
+       "      <td>9.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>47047.70</td>\n",
+       "      <td>52300.00</td>\n",
+       "      <td>5252.30</td>\n",
+       "      <td>10.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>6717.17</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>-617.17</td>\n",
+       "      <td>10.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>5391.77</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>608.23</td>\n",
+       "      <td>10.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>2008.91</td>\n",
+       "      <td>10.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>17591.09</td>\n",
+       "      <td>19600.00</td>\n",
+       "      <td>2008.91</td>\n",
+       "      <td>10.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>25576.58</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>2923.42</td>\n",
+       "      <td>10.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>20367.25</td>\n",
+       "      <td>22700.00</td>\n",
+       "      <td>2332.75</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>7267.36</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>832.64</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>1792.36</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>206.64</td>\n",
+       "      <td>10.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>8248.67</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>951.33</td>\n",
+       "      <td>10.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>5643.45</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>656.55</td>\n",
+       "      <td>10.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>6839.57</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>810.43</td>\n",
+       "      <td>10.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>41503.90</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-4003.90</td>\n",
+       "      <td>10.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>9310.69</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>-910.69</td>\n",
+       "      <td>10.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>35662.26</td>\n",
+       "      <td>40000.00</td>\n",
+       "      <td>4337.74</td>\n",
+       "      <td>10.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>16407.29</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-1607.29</td>\n",
+       "      <td>10.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>21303.57</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>2596.43</td>\n",
+       "      <td>10.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>15872.91</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-1572.91</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>10332.54</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>-1032.54</td>\n",
+       "      <td>11.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>18575.43</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>2324.57</td>\n",
+       "      <td>11.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>8882.65</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1117.35</td>\n",
+       "      <td>11.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>7797.18</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-798.18</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10621.84</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>1377.16</td>\n",
+       "      <td>11.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>10732.07</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1132.07</td>\n",
+       "      <td>11.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>2381.01</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>318.99</td>\n",
+       "      <td>11.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>11200.20</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-1201.20</td>\n",
+       "      <td>12.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>2373.66</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>326.34</td>\n",
+       "      <td>12.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>10696.56</td>\n",
+       "      <td>12179.00</td>\n",
+       "      <td>1482.44</td>\n",
+       "      <td>12.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>3590.97</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-390.97</td>\n",
+       "      <td>12.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>60500.00</td>\n",
+       "      <td>7436.78</td>\n",
+       "      <td>12.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>7300.05</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-800.05</td>\n",
+       "      <td>12.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>6392.70</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>907.30</td>\n",
+       "      <td>12.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>6746.43</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-746.43</td>\n",
+       "      <td>12.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>9192.06</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>1307.94</td>\n",
+       "      <td>12.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>11034.66</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-1234.66</td>\n",
+       "      <td>12.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>16939.86</td>\n",
+       "      <td>19400.00</td>\n",
+       "      <td>2460.14</td>\n",
+       "      <td>12.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2479.43</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-279.43</td>\n",
+       "      <td>12.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9593.52</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>1406.48</td>\n",
+       "      <td>12.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>23967.81</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>3532.19</td>\n",
+       "      <td>12.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>7491.01</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>1108.99</td>\n",
+       "      <td>12.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-942.62</td>\n",
+       "      <td>12.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>9489.56</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>1410.44</td>\n",
+       "      <td>12.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>44312.98</td>\n",
+       "      <td>50900.00</td>\n",
+       "      <td>6587.02</td>\n",
+       "      <td>12.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>9829.53</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1129.53</td>\n",
+       "      <td>12.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>9269.68</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-1069.68</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>-525.77</td>\n",
+       "      <td>13.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>23650.15</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-2750.15</td>\n",
+       "      <td>13.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>8850.21</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1349.79</td>\n",
+       "      <td>13.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>4770.52</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>729.48</td>\n",
+       "      <td>13.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>29479.97</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>4520.03</td>\n",
+       "      <td>13.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>8316.92</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>1283.08</td>\n",
+       "      <td>13.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>24974.57</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>3925.43</td>\n",
+       "      <td>13.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>14943.75</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>2356.25</td>\n",
+       "      <td>13.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>28498.91</td>\n",
+       "      <td>33000.00</td>\n",
+       "      <td>4501.09</td>\n",
+       "      <td>13.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>14591.35</td>\n",
+       "      <td>16900.00</td>\n",
+       "      <td>2308.65</td>\n",
+       "      <td>13.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10358.58</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>1641.42</td>\n",
+       "      <td>13.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>13074.74</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-1574.74</td>\n",
+       "      <td>13.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>9097.13</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1097.13</td>\n",
+       "      <td>13.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>15010.51</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>-1810.51</td>\n",
+       "      <td>13.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>12593.00</td>\n",
+       "      <td>14600.00</td>\n",
+       "      <td>2007.00</td>\n",
+       "      <td>13.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>20246.04</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>3253.96</td>\n",
+       "      <td>13.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>8768.29</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-1068.29</td>\n",
+       "      <td>13.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>39744.80</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>-4844.80</td>\n",
+       "      <td>13.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>9909.62</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1209.62</td>\n",
+       "      <td>13.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>44680.77</td>\n",
+       "      <td>51900.00</td>\n",
+       "      <td>7219.23</td>\n",
+       "      <td>13.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>21942.05</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>3557.95</td>\n",
+       "      <td>13.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>12900.31</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>2099.69</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>18128.32</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-2228.32</td>\n",
+       "      <td>14.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>4296.77</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>703.23</td>\n",
+       "      <td>14.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>6006.27</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>992.73</td>\n",
+       "      <td>14.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>1972.28</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>327.72</td>\n",
+       "      <td>14.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>8236.36</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1036.36</td>\n",
+       "      <td>14.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>10612.51</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>1787.49</td>\n",
+       "      <td>14.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>14197.26</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>-1797.26</td>\n",
+       "      <td>14.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>47013.28</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>7986.72</td>\n",
+       "      <td>14.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>12028.91</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-1528.91</td>\n",
+       "      <td>14.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>3666.31</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-466.31</td>\n",
+       "      <td>14.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>2860.97</td>\n",
+       "      <td>3350.00</td>\n",
+       "      <td>489.03</td>\n",
+       "      <td>14.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>7256.86</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1243.14</td>\n",
+       "      <td>14.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>10499.60</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>1800.40</td>\n",
+       "      <td>14.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>12383.35</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1583.35</td>\n",
+       "      <td>14.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>11937.31</td>\n",
+       "      <td>13995.00</td>\n",
+       "      <td>2057.69</td>\n",
+       "      <td>14.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2810.71</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-360.71</td>\n",
+       "      <td>14.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>7664.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>1335.68</td>\n",
+       "      <td>14.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>8041.62</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-1042.62</td>\n",
+       "      <td>14.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>13334.19</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>-1734.19</td>\n",
+       "      <td>14.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>13680.48</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1780.48</td>\n",
+       "      <td>14.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>13500.69</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>2399.31</td>\n",
+       "      <td>15.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>11638.89</td>\n",
+       "      <td>13708.50</td>\n",
+       "      <td>2069.61</td>\n",
+       "      <td>15.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>15700.53</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>2799.47</td>\n",
+       "      <td>15.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>11425.01</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>2074.99</td>\n",
+       "      <td>15.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>63000.00</td>\n",
+       "      <td>9687.19</td>\n",
+       "      <td>15.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>6599.62</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>1200.38</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>61639.73</td>\n",
+       "      <td>72900.00</td>\n",
+       "      <td>11260.27</td>\n",
+       "      <td>15.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1301.47</td>\n",
+       "      <td>15.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>10889.39</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>2010.61</td>\n",
+       "      <td>15.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>2613.97</td>\n",
+       "      <td>3099.00</td>\n",
+       "      <td>485.03</td>\n",
+       "      <td>15.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>23721.58</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-3221.58</td>\n",
+       "      <td>15.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>10133.68</td>\n",
+       "      <td>8750.00</td>\n",
+       "      <td>-1383.68</td>\n",
+       "      <td>15.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>22261.50</td>\n",
+       "      <td>19200.00</td>\n",
+       "      <td>-3061.50</td>\n",
+       "      <td>15.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>10448.50</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-1449.50</td>\n",
+       "      <td>16.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>24271.90</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-3371.90</td>\n",
+       "      <td>16.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>14926.69</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>2873.31</td>\n",
+       "      <td>16.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>11619.16</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>-1619.16</td>\n",
+       "      <td>16.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>46465.02</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>-6565.02</td>\n",
+       "      <td>16.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>50954.68</td>\n",
+       "      <td>61000.00</td>\n",
+       "      <td>10045.32</td>\n",
+       "      <td>16.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6848.88</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>1351.12</td>\n",
+       "      <td>16.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>16547.03</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>-2347.03</td>\n",
+       "      <td>16.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>4590.16</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>909.84</td>\n",
+       "      <td>16.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>13348.91</td>\n",
+       "      <td>15999.00</td>\n",
+       "      <td>2650.09</td>\n",
+       "      <td>16.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>11191.05</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1591.05</td>\n",
+       "      <td>16.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>2126.49</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>423.51</td>\n",
+       "      <td>16.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7463.96</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-1063.96</td>\n",
+       "      <td>16.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>2749.08</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>550.92</td>\n",
+       "      <td>16.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>18282.75</td>\n",
+       "      <td>21950.00</td>\n",
+       "      <td>3667.25</td>\n",
+       "      <td>16.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>19071.43</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>3828.57</td>\n",
+       "      <td>16.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>32886.86</td>\n",
+       "      <td>39500.00</td>\n",
+       "      <td>6613.14</td>\n",
+       "      <td>16.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>25752.19</td>\n",
+       "      <td>30990.00</td>\n",
+       "      <td>5237.81</td>\n",
+       "      <td>16.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>7719.69</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>1580.31</td>\n",
+       "      <td>16.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>25986.31</td>\n",
+       "      <td>31310.00</td>\n",
+       "      <td>5323.69</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>8250.47</td>\n",
+       "      <td>9950.00</td>\n",
+       "      <td>1699.53</td>\n",
+       "      <td>17.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>28937.50</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>5962.50</td>\n",
+       "      <td>17.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8392.42</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>-1242.42</td>\n",
+       "      <td>17.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>8033.61</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>1716.39</td>\n",
+       "      <td>17.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>8156.11</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>1743.89</td>\n",
+       "      <td>17.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>2470.61</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>529.39</td>\n",
+       "      <td>17.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>19659.97</td>\n",
+       "      <td>16700.00</td>\n",
+       "      <td>-2959.97</td>\n",
+       "      <td>17.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>17540.72</td>\n",
+       "      <td>14899.00</td>\n",
+       "      <td>-2641.72</td>\n",
+       "      <td>17.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>7814.16</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1685.84</td>\n",
+       "      <td>17.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>21097.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-3197.66</td>\n",
+       "      <td>17.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>4266.27</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>933.73</td>\n",
+       "      <td>17.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>9380.46</td>\n",
+       "      <td>7950.00</td>\n",
+       "      <td>-1430.46</td>\n",
+       "      <td>17.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>7385.38</td>\n",
+       "      <td>18.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>33614.62</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>7385.38</td>\n",
+       "      <td>18.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>9089.35</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-1389.35</td>\n",
+       "      <td>18.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>27740.81</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-4240.81</td>\n",
+       "      <td>18.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>3542.72</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-542.72</td>\n",
+       "      <td>18.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>25037.87</td>\n",
+       "      <td>21200.00</td>\n",
+       "      <td>-3837.87</td>\n",
+       "      <td>18.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>35991.51</td>\n",
+       "      <td>43999.00</td>\n",
+       "      <td>8007.49</td>\n",
+       "      <td>18.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>13733.33</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>3066.67</td>\n",
+       "      <td>18.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>11431.35</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>2567.65</td>\n",
+       "      <td>18.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>6082.85</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>1367.15</td>\n",
+       "      <td>18.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>12674.57</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-1974.57</td>\n",
+       "      <td>18.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25975.12</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>-4075.12</td>\n",
+       "      <td>18.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3026.25</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>-476.25</td>\n",
+       "      <td>18.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>33172.20</td>\n",
+       "      <td>40800.00</td>\n",
+       "      <td>7627.80</td>\n",
+       "      <td>18.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>13159.16</td>\n",
+       "      <td>16200.00</td>\n",
+       "      <td>3040.84</td>\n",
+       "      <td>18.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>20216.67</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>4683.33</td>\n",
+       "      <td>18.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>10220.74</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>-1620.74</td>\n",
+       "      <td>18.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>29127.72</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>-4627.72</td>\n",
+       "      <td>18.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>5839.08</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1360.92</td>\n",
+       "      <td>18.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>53063.22</td>\n",
+       "      <td>65500.00</td>\n",
+       "      <td>12436.78</td>\n",
+       "      <td>18.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>28256.79</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>6643.21</td>\n",
+       "      <td>19.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>4524.77</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-724.77</td>\n",
+       "      <td>19.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>2543.57</td>\n",
+       "      <td>3149.25</td>\n",
+       "      <td>605.68</td>\n",
+       "      <td>19.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>60493.51</td>\n",
+       "      <td>75000.00</td>\n",
+       "      <td>14506.49</td>\n",
+       "      <td>19.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>8962.29</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-1462.29</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>14832.32</td>\n",
+       "      <td>12400.00</td>\n",
+       "      <td>-2432.32</td>\n",
+       "      <td>19.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>19676.51</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>4823.49</td>\n",
+       "      <td>19.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20349.62</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-3350.62</td>\n",
+       "      <td>19.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>1926.60</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>473.40</td>\n",
+       "      <td>19.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>8381.60</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-1382.60</td>\n",
+       "      <td>19.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>38334.67</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>-6334.67</td>\n",
+       "      <td>19.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>20972.54</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>-3472.54</td>\n",
+       "      <td>19.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>6732.97</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1667.03</td>\n",
+       "      <td>19.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>10419.20</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>2580.80</td>\n",
+       "      <td>19.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>688</th>\n",
+       "      <td>24572.28</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-4072.28</td>\n",
+       "      <td>19.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>10014.71</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>2485.29</td>\n",
+       "      <td>19.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-709.17</td>\n",
+       "      <td>20.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>34678.95</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>8821.05</td>\n",
+       "      <td>20.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>18893.76</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-3193.76</td>\n",
+       "      <td>20.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>8121.22</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>2078.78</td>\n",
+       "      <td>20.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>11443.85</td>\n",
+       "      <td>9499.00</td>\n",
+       "      <td>-1944.85</td>\n",
+       "      <td>20.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>33392.40</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>8607.60</td>\n",
+       "      <td>20.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1753.52</td>\n",
+       "      <td>20.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>17251.60</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-2951.60</td>\n",
+       "      <td>20.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>6981.34</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1818.66</td>\n",
+       "      <td>20.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>2379.58</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>620.42</td>\n",
+       "      <td>20.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>10932.17</td>\n",
+       "      <td>13800.00</td>\n",
+       "      <td>2867.83</td>\n",
+       "      <td>20.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>2779.04</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-479.04</td>\n",
+       "      <td>20.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>11002.01</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>2897.99</td>\n",
+       "      <td>20.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>5144.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1355.98</td>\n",
+       "      <td>20.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>7110.32</td>\n",
+       "      <td>8990.00</td>\n",
+       "      <td>1879.68</td>\n",
+       "      <td>20.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>67431.00</td>\n",
+       "      <td>14118.19</td>\n",
+       "      <td>20.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7745.02</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-1345.02</td>\n",
+       "      <td>21.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>63921.33</td>\n",
+       "      <td>80999.00</td>\n",
+       "      <td>17077.67</td>\n",
+       "      <td>21.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>3940.49</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1058.51</td>\n",
+       "      <td>21.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>23630.85</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>-4130.85</td>\n",
+       "      <td>21.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>14792.29</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>4007.71</td>\n",
+       "      <td>21.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>11770.64</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-2070.64</td>\n",
+       "      <td>21.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>9424.34</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>2575.66</td>\n",
+       "      <td>21.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>8139.48</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-1439.48</td>\n",
+       "      <td>21.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>22367.52</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>6132.48</td>\n",
+       "      <td>21.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>13004.34</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-2304.34</td>\n",
+       "      <td>21.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>8512.03</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-1512.03</td>\n",
+       "      <td>21.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>32121.20</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>8878.80</td>\n",
+       "      <td>21.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>12523.73</td>\n",
+       "      <td>16000.00</td>\n",
+       "      <td>3476.27</td>\n",
+       "      <td>21.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>19845.36</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>-3545.36</td>\n",
+       "      <td>21.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13292.71</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>3706.29</td>\n",
+       "      <td>21.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>8467.17</td>\n",
+       "      <td>6950.00</td>\n",
+       "      <td>-1517.17</td>\n",
+       "      <td>21.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11876.82</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>3323.18</td>\n",
+       "      <td>21.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>22547.55</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>-4047.55</td>\n",
+       "      <td>21.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>50728.89</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>14271.11</td>\n",
+       "      <td>21.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2625.10</td>\n",
+       "      <td>2150.00</td>\n",
+       "      <td>-475.10</td>\n",
+       "      <td>22.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>5863.41</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1063.41</td>\n",
+       "      <td>22.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>9175.06</td>\n",
+       "      <td>7499.00</td>\n",
+       "      <td>-1676.06</td>\n",
+       "      <td>22.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>8453.28</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2446.72</td>\n",
+       "      <td>22.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>5350.91</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>1549.09</td>\n",
+       "      <td>22.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>6819.44</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1980.56</td>\n",
+       "      <td>22.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>10303.21</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>2996.79</td>\n",
+       "      <td>22.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>9139.04</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>2660.96</td>\n",
+       "      <td>22.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>18621.12</td>\n",
+       "      <td>24100.00</td>\n",
+       "      <td>5478.88</td>\n",
+       "      <td>22.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>15954.72</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-2955.72</td>\n",
+       "      <td>22.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>12270.01</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>3629.99</td>\n",
+       "      <td>22.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>11490.44</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>3409.56</td>\n",
+       "      <td>22.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>14136.01</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-2636.01</td>\n",
+       "      <td>22.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>11095.57</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-2096.57</td>\n",
+       "      <td>23.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>10350.25</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>3149.75</td>\n",
+       "      <td>23.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>20680.40</td>\n",
+       "      <td>27000.00</td>\n",
+       "      <td>6319.60</td>\n",
+       "      <td>23.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>34446.96</td>\n",
+       "      <td>27900.00</td>\n",
+       "      <td>-6546.96</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>4209.17</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1290.83</td>\n",
+       "      <td>23.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>3086.38</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-587.38</td>\n",
+       "      <td>23.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>16427.12</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-3127.12</td>\n",
+       "      <td>23.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>53312.81</td>\n",
+       "      <td>43163.25</td>\n",
+       "      <td>-10149.56</td>\n",
+       "      <td>23.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>3747.21</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>1152.79</td>\n",
+       "      <td>23.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>14410.15</td>\n",
+       "      <td>18932.55</td>\n",
+       "      <td>4522.40</td>\n",
+       "      <td>23.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>13922.45</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>4377.55</td>\n",
+       "      <td>23.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>9232.79</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>-1782.79</td>\n",
+       "      <td>23.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>18165.16</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>5734.84</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11353.49</td>\n",
+       "      <td>9150.00</td>\n",
+       "      <td>-2203.49</td>\n",
+       "      <td>24.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>6205.48</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-1205.48</td>\n",
+       "      <td>24.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>11004.05</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>3495.95</td>\n",
+       "      <td>24.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>10505.98</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>3394.02</td>\n",
+       "      <td>24.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>19920.63</td>\n",
+       "      <td>16000.00</td>\n",
+       "      <td>-3920.63</td>\n",
+       "      <td>24.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>3621.08</td>\n",
+       "      <td>4799.00</td>\n",
+       "      <td>1177.92</td>\n",
+       "      <td>24.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>17316.59</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-3416.59</td>\n",
+       "      <td>24.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>8471.55</td>\n",
+       "      <td>6799.00</td>\n",
+       "      <td>-1672.55</td>\n",
+       "      <td>24.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>36883.77</td>\n",
+       "      <td>49000.00</td>\n",
+       "      <td>12116.23</td>\n",
+       "      <td>24.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>8242.62</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-1642.62</td>\n",
+       "      <td>24.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13491.16</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-2691.16</td>\n",
+       "      <td>24.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>3001.37</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>998.63</td>\n",
+       "      <td>24.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3499.26</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>-699.26</td>\n",
+       "      <td>24.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>23485.45</td>\n",
+       "      <td>18777.00</td>\n",
+       "      <td>-4708.45</td>\n",
+       "      <td>25.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>10835.95</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>3664.05</td>\n",
+       "      <td>25.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>6013.05</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1213.05</td>\n",
+       "      <td>25.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>3735.04</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1263.96</td>\n",
+       "      <td>25.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>15623.39</td>\n",
+       "      <td>20999.00</td>\n",
+       "      <td>5375.61</td>\n",
+       "      <td>25.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>8667.12</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1767.12</td>\n",
+       "      <td>25.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>8916.19</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>3082.81</td>\n",
+       "      <td>25.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>8688.92</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>3011.08</td>\n",
+       "      <td>25.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>12027.56</td>\n",
+       "      <td>16200.00</td>\n",
+       "      <td>4172.44</td>\n",
+       "      <td>25.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>12976.15</td>\n",
+       "      <td>17500.00</td>\n",
+       "      <td>4523.85</td>\n",
+       "      <td>25.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>44407.14</td>\n",
+       "      <td>59999.00</td>\n",
+       "      <td>15591.86</td>\n",
+       "      <td>25.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>31059.18</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>10940.82</td>\n",
+       "      <td>26.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>8110.06</td>\n",
+       "      <td>10990.00</td>\n",
+       "      <td>2879.94</td>\n",
+       "      <td>26.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>672</th>\n",
+       "      <td>5679.39</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-1179.39</td>\n",
+       "      <td>26.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>4255.62</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>1544.38</td>\n",
+       "      <td>26.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>3164.66</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-665.66</td>\n",
+       "      <td>26.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>3154.48</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>1145.52</td>\n",
+       "      <td>26.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>4307.68</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>-907.68</td>\n",
+       "      <td>26.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>5195.69</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-1095.69</td>\n",
+       "      <td>26.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>47600.00</td>\n",
+       "      <td>12736.47</td>\n",
+       "      <td>26.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>8638.63</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>3161.37</td>\n",
+       "      <td>26.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>683</th>\n",
+       "      <td>32771.45</td>\n",
+       "      <td>44800.00</td>\n",
+       "      <td>12028.55</td>\n",
+       "      <td>26.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>28154.84</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>10345.16</td>\n",
+       "      <td>26.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>67952.07</td>\n",
+       "      <td>53500.00</td>\n",
+       "      <td>-14452.07</td>\n",
+       "      <td>27.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>13471.84</td>\n",
+       "      <td>10600.00</td>\n",
+       "      <td>-2871.84</td>\n",
+       "      <td>27.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>23227.15</td>\n",
+       "      <td>31900.00</td>\n",
+       "      <td>8672.85</td>\n",
+       "      <td>27.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>7131.92</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>2668.08</td>\n",
+       "      <td>27.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>8071.70</td>\n",
+       "      <td>11100.00</td>\n",
+       "      <td>3028.30</td>\n",
+       "      <td>27.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>26478.15</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>-5678.15</td>\n",
+       "      <td>27.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>7116.68</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>2683.32</td>\n",
+       "      <td>27.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>9312.21</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-2012.21</td>\n",
+       "      <td>27.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>10848.85</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-2348.85</td>\n",
+       "      <td>27.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>10597.72</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-2297.72</td>\n",
+       "      <td>27.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7940.44</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>3059.56</td>\n",
+       "      <td>27.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>5040.46</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1959.54</td>\n",
+       "      <td>27.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>10716.00</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>4184.00</td>\n",
+       "      <td>28.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>9205.10</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>3594.90</td>\n",
+       "      <td>28.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>31656.01</td>\n",
+       "      <td>24700.00</td>\n",
+       "      <td>-6956.01</td>\n",
+       "      <td>28.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>13853.36</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-3053.36</td>\n",
+       "      <td>28.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>7098.53</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>2801.47</td>\n",
+       "      <td>28.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>8591.66</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>3408.34</td>\n",
+       "      <td>28.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>12201.29</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-2701.29</td>\n",
+       "      <td>28.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>50074.56</td>\n",
+       "      <td>69990.00</td>\n",
+       "      <td>19915.44</td>\n",
+       "      <td>28.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>11432.63</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-2532.63</td>\n",
+       "      <td>28.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6437.21</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>2561.79</td>\n",
+       "      <td>28.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>8351.34</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-1851.34</td>\n",
+       "      <td>28.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>35336.33</td>\n",
+       "      <td>49500.00</td>\n",
+       "      <td>14163.67</td>\n",
+       "      <td>28.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>74232.55</td>\n",
+       "      <td>103999.00</td>\n",
+       "      <td>29766.45</td>\n",
+       "      <td>28.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>45664.45</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>-10164.45</td>\n",
+       "      <td>28.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>19298.61</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-4299.61</td>\n",
+       "      <td>28.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>11580.54</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-2580.54</td>\n",
+       "      <td>28.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>23496.36</td>\n",
+       "      <td>33000.00</td>\n",
+       "      <td>9503.64</td>\n",
+       "      <td>28.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>31557.80</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>-7057.80</td>\n",
+       "      <td>28.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>24323.48</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>-5523.48</td>\n",
+       "      <td>29.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>18285.07</td>\n",
+       "      <td>25900.00</td>\n",
+       "      <td>7614.93</td>\n",
+       "      <td>29.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>25640.67</td>\n",
+       "      <td>19800.00</td>\n",
+       "      <td>-5840.67</td>\n",
+       "      <td>29.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>5983.44</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>2516.56</td>\n",
+       "      <td>29.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>8973.16</td>\n",
+       "      <td>12750.00</td>\n",
+       "      <td>3776.84</td>\n",
+       "      <td>29.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>23336.06</td>\n",
+       "      <td>33200.00</td>\n",
+       "      <td>9863.94</td>\n",
+       "      <td>29.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>6110.94</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>2589.06</td>\n",
+       "      <td>29.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>7724.17</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>3275.83</td>\n",
+       "      <td>29.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>10094.58</td>\n",
+       "      <td>7777.00</td>\n",
+       "      <td>-2317.58</td>\n",
+       "      <td>29.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>21690.99</td>\n",
+       "      <td>30900.00</td>\n",
+       "      <td>9209.01</td>\n",
+       "      <td>29.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>10809.48</td>\n",
+       "      <td>15400.00</td>\n",
+       "      <td>4590.52</td>\n",
+       "      <td>29.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>11018.79</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>4681.21</td>\n",
+       "      <td>29.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>13377.99</td>\n",
+       "      <td>10300.00</td>\n",
+       "      <td>-3077.99</td>\n",
+       "      <td>29.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>12731.26</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-2931.26</td>\n",
+       "      <td>29.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>11178.46</td>\n",
+       "      <td>8600.00</td>\n",
+       "      <td>-2578.46</td>\n",
+       "      <td>29.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>21681.00</td>\n",
+       "      <td>30999.00</td>\n",
+       "      <td>9318.00</td>\n",
+       "      <td>30.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>27293.13</td>\n",
+       "      <td>39040.00</td>\n",
+       "      <td>11746.87</td>\n",
+       "      <td>30.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>6510.80</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-1510.80</td>\n",
+       "      <td>30.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>8298.32</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>3601.68</td>\n",
+       "      <td>30.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>10821.19</td>\n",
+       "      <td>8299.00</td>\n",
+       "      <td>-2522.19</td>\n",
+       "      <td>30.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>5346.97</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-1246.97</td>\n",
+       "      <td>30.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5484.02</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>2415.98</td>\n",
+       "      <td>30.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>5238.05</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-1238.05</td>\n",
+       "      <td>30.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>17597.10</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>7902.90</td>\n",
+       "      <td>30.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>7727.53</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>3472.47</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>6358.55</td>\n",
+       "      <td>4850.00</td>\n",
+       "      <td>-1508.55</td>\n",
+       "      <td>31.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>2886.35</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>1313.65</td>\n",
+       "      <td>31.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>14049.76</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-3349.76</td>\n",
+       "      <td>31.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>12974.89</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>5925.11</td>\n",
+       "      <td>31.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>6858.89</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>3140.11</td>\n",
+       "      <td>31.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>15691.29</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>7208.71</td>\n",
+       "      <td>31.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>2393.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1106.66</td>\n",
+       "      <td>31.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>6425.00</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>2975.00</td>\n",
+       "      <td>31.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>50206.83</td>\n",
+       "      <td>73500.00</td>\n",
+       "      <td>23293.17</td>\n",
+       "      <td>31.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>25685.67</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>-6185.67</td>\n",
+       "      <td>31.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>19692.08</td>\n",
+       "      <td>-6259.23</td>\n",
+       "      <td>31.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>28910.96</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>13589.04</td>\n",
+       "      <td>31.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>9919.19</td>\n",
+       "      <td>14600.00</td>\n",
+       "      <td>4680.81</td>\n",
+       "      <td>32.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>9113.05</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-2213.05</td>\n",
+       "      <td>32.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>24427.14</td>\n",
+       "      <td>36000.00</td>\n",
+       "      <td>11572.86</td>\n",
+       "      <td>32.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>4738.89</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>2261.11</td>\n",
+       "      <td>32.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>25040.42</td>\n",
+       "      <td>37000.00</td>\n",
+       "      <td>11959.58</td>\n",
+       "      <td>32.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>18403.35</td>\n",
+       "      <td>13893.75</td>\n",
+       "      <td>-4509.60</td>\n",
+       "      <td>32.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>14576.33</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-3576.33</td>\n",
+       "      <td>32.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>6610.04</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>3189.96</td>\n",
+       "      <td>32.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>10105.70</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>4894.30</td>\n",
+       "      <td>32.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>5041.20</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-1241.20</td>\n",
+       "      <td>32.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>6503.62</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-1603.62</td>\n",
+       "      <td>32.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>13542.79</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-3342.79</td>\n",
+       "      <td>32.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4364.08</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>2135.92</td>\n",
+       "      <td>32.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>12006.66</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>5893.34</td>\n",
+       "      <td>32.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>3787.35</td>\n",
+       "      <td>32.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>31251.89</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-7751.89</td>\n",
+       "      <td>32.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>7011.74</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>3488.26</td>\n",
+       "      <td>33.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>5792.10</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>2907.90</td>\n",
+       "      <td>33.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>8493.04</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>4306.96</td>\n",
+       "      <td>33.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>7950.27</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>4049.73</td>\n",
+       "      <td>33.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>8940.39</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>4559.61</td>\n",
+       "      <td>33.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>4569.15</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>2330.85</td>\n",
+       "      <td>33.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>13041.39</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>6658.61</td>\n",
+       "      <td>33.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>20163.48</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>10336.52</td>\n",
+       "      <td>33.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>2839.03</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>1460.97</td>\n",
+       "      <td>33.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>20433.23</td>\n",
+       "      <td>30999.00</td>\n",
+       "      <td>10565.77</td>\n",
+       "      <td>34.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>1746.19</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>903.81</td>\n",
+       "      <td>34.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>19855.31</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-5055.31</td>\n",
+       "      <td>34.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>19741.65</td>\n",
+       "      <td>29999.00</td>\n",
+       "      <td>10257.35</td>\n",
+       "      <td>34.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>65613.31</td>\n",
+       "      <td>99999.00</td>\n",
+       "      <td>34385.69</td>\n",
+       "      <td>34.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>10950.00</td>\n",
+       "      <td>3771.80</td>\n",
+       "      <td>34.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>9143.17</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2343.17</td>\n",
+       "      <td>34.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>2285.24</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1214.76</td>\n",
+       "      <td>34.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>22835.48</td>\n",
+       "      <td>16950.00</td>\n",
+       "      <td>-5885.48</td>\n",
+       "      <td>34.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>10786.38</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-2786.38</td>\n",
+       "      <td>34.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>11477.47</td>\n",
+       "      <td>17650.00</td>\n",
+       "      <td>6172.53</td>\n",
+       "      <td>34.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>23972.56</td>\n",
+       "      <td>36900.00</td>\n",
+       "      <td>12927.44</td>\n",
+       "      <td>35.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>3648.96</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-948.96</td>\n",
+       "      <td>35.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>11706.93</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>6393.07</td>\n",
+       "      <td>35.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>7178.20</td>\n",
+       "      <td>11100.00</td>\n",
+       "      <td>3921.80</td>\n",
+       "      <td>35.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>12286.47</td>\n",
+       "      <td>35.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>22213.53</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>12286.47</td>\n",
+       "      <td>35.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>12897.92</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-3397.92</td>\n",
+       "      <td>35.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>12904.68</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-3404.68</td>\n",
+       "      <td>35.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>9611.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>5388.20</td>\n",
+       "      <td>35.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>20236.85</td>\n",
+       "      <td>31600.00</td>\n",
+       "      <td>11363.15</td>\n",
+       "      <td>35.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>4151.02</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>2348.98</td>\n",
+       "      <td>36.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>28951.20</td>\n",
+       "      <td>45500.00</td>\n",
+       "      <td>16548.80</td>\n",
+       "      <td>36.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>25951.31</td>\n",
+       "      <td>18988.13</td>\n",
+       "      <td>-6963.18</td>\n",
+       "      <td>36.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>5570.46</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>3229.54</td>\n",
+       "      <td>36.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>22454.74</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>13045.26</td>\n",
+       "      <td>36.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>41711.18</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>-11211.18</td>\n",
+       "      <td>36.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>3152.62</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>1846.38</td>\n",
+       "      <td>36.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>8902.52</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-2402.52</td>\n",
+       "      <td>36.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>48699.98</td>\n",
+       "      <td>77500.00</td>\n",
+       "      <td>28800.02</td>\n",
+       "      <td>37.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>16821.54</td>\n",
+       "      <td>26800.00</td>\n",
+       "      <td>9978.46</td>\n",
+       "      <td>37.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>5521.28</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>3278.72</td>\n",
+       "      <td>37.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>5268.17</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>3131.83</td>\n",
+       "      <td>37.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>65825.25</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>39173.75</td>\n",
+       "      <td>37.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>12459.85</td>\n",
+       "      <td>19900.00</td>\n",
+       "      <td>7440.15</td>\n",
+       "      <td>37.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>65723.62</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>39275.38</td>\n",
+       "      <td>37.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>9070.23</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>5429.77</td>\n",
+       "      <td>37.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>9348.90</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2548.90</td>\n",
+       "      <td>37.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>9369.66</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>5630.34</td>\n",
+       "      <td>37.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>32954.19</td>\n",
+       "      <td>52777.00</td>\n",
+       "      <td>19822.81</td>\n",
+       "      <td>37.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>17195.57</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-4695.57</td>\n",
+       "      <td>37.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>3425.23</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>2074.77</td>\n",
+       "      <td>37.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>3173.48</td>\n",
+       "      <td>5100.00</td>\n",
+       "      <td>1926.52</td>\n",
+       "      <td>37.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>10337.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-2837.11</td>\n",
+       "      <td>37.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>105999.00</td>\n",
+       "      <td>40256.43</td>\n",
+       "      <td>37.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>8661.95</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>5337.05</td>\n",
+       "      <td>38.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>26062.23</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>16437.77</td>\n",
+       "      <td>38.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>7630.64</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-2130.64</td>\n",
+       "      <td>38.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11378.43</td>\n",
+       "      <td>18600.00</td>\n",
+       "      <td>7221.57</td>\n",
+       "      <td>38.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>66032.76</td>\n",
+       "      <td>107999.00</td>\n",
+       "      <td>41966.24</td>\n",
+       "      <td>38.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>7279.97</td>\n",
+       "      <td>11950.00</td>\n",
+       "      <td>4670.03</td>\n",
+       "      <td>39.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>18577.88</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>11922.12</td>\n",
+       "      <td>39.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>4043.74</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>-1143.74</td>\n",
+       "      <td>39.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>2178.94</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>1421.06</td>\n",
+       "      <td>39.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>23286.27</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>15213.73</td>\n",
+       "      <td>39.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12558.90</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-3559.90</td>\n",
+       "      <td>39.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>18432.63</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>12067.37</td>\n",
+       "      <td>39.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>9057.77</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>5941.23</td>\n",
+       "      <td>39.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>13476.90</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>9023.10</td>\n",
+       "      <td>40.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>37704.71</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>-10804.71</td>\n",
+       "      <td>40.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>32945.80</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-9445.80</td>\n",
+       "      <td>40.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>7712.65</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-2212.65</td>\n",
+       "      <td>40.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>65742.57</td>\n",
+       "      <td>109999.00</td>\n",
+       "      <td>44256.43</td>\n",
+       "      <td>40.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36764.38</td>\n",
+       "      <td>62000.00</td>\n",
+       "      <td>25235.62</td>\n",
+       "      <td>40.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>77403.06</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-22403.06</td>\n",
+       "      <td>40.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>8530.54</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>5969.46</td>\n",
+       "      <td>41.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>15259.81</td>\n",
+       "      <td>26000.00</td>\n",
+       "      <td>10740.19</td>\n",
+       "      <td>41.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>16253.48</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-4753.48</td>\n",
+       "      <td>41.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>1815.28</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>1284.72</td>\n",
+       "      <td>41.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>44193.38</td>\n",
+       "      <td>75500.00</td>\n",
+       "      <td>31306.62</td>\n",
+       "      <td>41.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>45272.25</td>\n",
+       "      <td>31990.00</td>\n",
+       "      <td>-13282.25</td>\n",
+       "      <td>41.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>9198.08</td>\n",
+       "      <td>15800.00</td>\n",
+       "      <td>6601.92</td>\n",
+       "      <td>41.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>19854.79</td>\n",
+       "      <td>14000.00</td>\n",
+       "      <td>-5854.79</td>\n",
+       "      <td>41.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>10031.96</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>7268.04</td>\n",
+       "      <td>42.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>9661.78</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>-2861.78</td>\n",
+       "      <td>42.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>2539.57</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>1860.43</td>\n",
+       "      <td>42.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>11474.22</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>8524.78</td>\n",
+       "      <td>42.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>65887.51</td>\n",
+       "      <td>114999.00</td>\n",
+       "      <td>49111.49</td>\n",
+       "      <td>42.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>2784.46</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>-834.46</td>\n",
+       "      <td>42.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>15015.22</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-4515.22</td>\n",
+       "      <td>43.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>31216.83</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>23783.17</td>\n",
+       "      <td>43.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>5374.74</td>\n",
+       "      <td>3750.00</td>\n",
+       "      <td>-1624.74</td>\n",
+       "      <td>43.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>5558.93</td>\n",
+       "      <td>43.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>3450.69</td>\n",
+       "      <td>6100.00</td>\n",
+       "      <td>2649.31</td>\n",
+       "      <td>43.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>16724.45</td>\n",
+       "      <td>29600.00</td>\n",
+       "      <td>12875.55</td>\n",
+       "      <td>43.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>65210.35</td>\n",
+       "      <td>115800.00</td>\n",
+       "      <td>50589.65</td>\n",
+       "      <td>43.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>10740.09</td>\n",
+       "      <td>19100.00</td>\n",
+       "      <td>8359.91</td>\n",
+       "      <td>43.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>12234.68</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-3734.68</td>\n",
+       "      <td>43.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>14402.34</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>-4402.34</td>\n",
+       "      <td>44.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>10807.78</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-3307.78</td>\n",
+       "      <td>44.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>39074.36</td>\n",
+       "      <td>69999.00</td>\n",
+       "      <td>30924.64</td>\n",
+       "      <td>44.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>14512.61</td>\n",
+       "      <td>25999.00</td>\n",
+       "      <td>11486.39</td>\n",
+       "      <td>44.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3316.44</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1016.44</td>\n",
+       "      <td>44.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>12255.45</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>9744.55</td>\n",
+       "      <td>44.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>6781.86</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>-2081.86</td>\n",
+       "      <td>44.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>3752.54</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-1152.54</td>\n",
+       "      <td>44.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>2389.14</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>-739.14</td>\n",
+       "      <td>44.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>34761.74</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-10761.74</td>\n",
+       "      <td>44.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>11881.05</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-3681.05</td>\n",
+       "      <td>44.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1873.03</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>1526.97</td>\n",
+       "      <td>44.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>13212.52</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>10786.48</td>\n",
+       "      <td>44.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>17282.06</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-5382.06</td>\n",
+       "      <td>45.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>17011.43</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-5311.43</td>\n",
+       "      <td>45.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>2020.25</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>1679.75</td>\n",
+       "      <td>45.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>18942.34</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-5943.34</td>\n",
+       "      <td>45.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>11337.51</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>9562.49</td>\n",
+       "      <td>45.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>10547.82</td>\n",
+       "      <td>19500.00</td>\n",
+       "      <td>8952.18</td>\n",
+       "      <td>45.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>6421.18</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-2021.18</td>\n",
+       "      <td>45.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>12414.72</td>\n",
+       "      <td>23000.00</td>\n",
+       "      <td>10585.28</td>\n",
+       "      <td>46.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>24796.09</td>\n",
+       "      <td>46000.00</td>\n",
+       "      <td>21203.91</td>\n",
+       "      <td>46.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>4172.84</td>\n",
+       "      <td>7750.00</td>\n",
+       "      <td>3577.16</td>\n",
+       "      <td>46.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>15956.95</td>\n",
+       "      <td>29999.00</td>\n",
+       "      <td>14042.05</td>\n",
+       "      <td>46.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>1858.34</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>1641.66</td>\n",
+       "      <td>46.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>8522.39</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>-2722.39</td>\n",
+       "      <td>46.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>13235.55</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-4236.55</td>\n",
+       "      <td>47.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>13600.39</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>12199.61</td>\n",
+       "      <td>47.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>4032.10</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>3617.90</td>\n",
+       "      <td>47.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>12414.41</td>\n",
+       "      <td>8420.00</td>\n",
+       "      <td>-3994.41</td>\n",
+       "      <td>47.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>2573.48</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>2326.52</td>\n",
+       "      <td>47.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>14572.70</td>\n",
+       "      <td>27900.00</td>\n",
+       "      <td>13327.30</td>\n",
+       "      <td>47.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>15144.28</td>\n",
+       "      <td>29000.00</td>\n",
+       "      <td>13855.72</td>\n",
+       "      <td>47.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>20159.19</td>\n",
+       "      <td>38888.00</td>\n",
+       "      <td>18728.81</td>\n",
+       "      <td>48.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>7241.07</td>\n",
+       "      <td>14000.00</td>\n",
+       "      <td>6758.93</td>\n",
+       "      <td>48.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>25682.58</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>-8382.58</td>\n",
+       "      <td>48.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>10563.39</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>9936.61</td>\n",
+       "      <td>48.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>6546.81</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-2146.81</td>\n",
+       "      <td>48.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "      <td>37294.64</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>38260.36</td>\n",
+       "      <td>75555.00</td>\n",
+       "      <td>37294.64</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>13443.32</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-4443.32</td>\n",
+       "      <td>49.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>14933.54</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>14566.46</td>\n",
+       "      <td>49.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>13884.26</td>\n",
+       "      <td>27800.00</td>\n",
+       "      <td>13915.74</td>\n",
+       "      <td>50.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>31373.47</td>\n",
+       "      <td>20859.15</td>\n",
+       "      <td>-10514.32</td>\n",
+       "      <td>50.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>47014.52</td>\n",
+       "      <td>95000.00</td>\n",
+       "      <td>47985.48</td>\n",
+       "      <td>50.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>41075.73</td>\n",
+       "      <td>27200.00</td>\n",
+       "      <td>-13875.73</td>\n",
+       "      <td>51.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>10125.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-3425.27</td>\n",
+       "      <td>51.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>60603.06</td>\n",
+       "      <td>124000.00</td>\n",
+       "      <td>63396.94</td>\n",
+       "      <td>51.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>14363.76</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-4863.76</td>\n",
+       "      <td>51.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>25302.43</td>\n",
+       "      <td>52000.00</td>\n",
+       "      <td>26697.57</td>\n",
+       "      <td>51.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>7391.67</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>7808.33</td>\n",
+       "      <td>51.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>9753.44</td>\n",
+       "      <td>20400.00</td>\n",
+       "      <td>10646.56</td>\n",
+       "      <td>52.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>32925.60</td>\n",
+       "      <td>69500.00</td>\n",
+       "      <td>36574.40</td>\n",
+       "      <td>52.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>22941.80</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-7941.80</td>\n",
+       "      <td>52.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>6199.59</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>7000.41</td>\n",
+       "      <td>53.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>5597.06</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>6401.94</td>\n",
+       "      <td>53.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>13409.47</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-4709.47</td>\n",
+       "      <td>54.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>6635.73</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>-2335.73</td>\n",
+       "      <td>54.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>33707.27</td>\n",
+       "      <td>73900.00</td>\n",
+       "      <td>40192.73</td>\n",
+       "      <td>54.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>19309.90</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-6809.90</td>\n",
+       "      <td>54.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>19624.73</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>-6924.73</td>\n",
+       "      <td>54.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>13757.58</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-4857.58</td>\n",
+       "      <td>54.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>12354.92</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>15145.08</td>\n",
+       "      <td>55.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>17714.62</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-6314.62</td>\n",
+       "      <td>55.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>11555.80</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>-4155.80</td>\n",
+       "      <td>56.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>22011.47</td>\n",
+       "      <td>13999.00</td>\n",
+       "      <td>-8012.47</td>\n",
+       "      <td>57.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>15457.91</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-5657.91</td>\n",
+       "      <td>57.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>11099.09</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-4100.09</td>\n",
+       "      <td>58.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>35039.40</td>\n",
+       "      <td>85555.00</td>\n",
+       "      <td>50515.60</td>\n",
+       "      <td>59.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>34376.16</td>\n",
+       "      <td>85000.00</td>\n",
+       "      <td>50623.84</td>\n",
+       "      <td>59.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>16773.62</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-6273.62</td>\n",
+       "      <td>59.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>27487.75</td>\n",
+       "      <td>68500.00</td>\n",
+       "      <td>41012.25</td>\n",
+       "      <td>59.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>7996.00</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-2997.00</td>\n",
+       "      <td>59.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>5284.06</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-1984.06</td>\n",
+       "      <td>60.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>87777.00</td>\n",
+       "      <td>52913.47</td>\n",
+       "      <td>60.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>19701.70</td>\n",
+       "      <td>49999.00</td>\n",
+       "      <td>30297.30</td>\n",
+       "      <td>60.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>30409.09</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>-11509.09</td>\n",
+       "      <td>60.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>14486.38</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-5486.38</td>\n",
+       "      <td>60.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>11601.33</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-4401.33</td>\n",
+       "      <td>61.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>10824.89</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-4124.89</td>\n",
+       "      <td>61.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>65448.37</td>\n",
+       "      <td>40500.00</td>\n",
+       "      <td>-24948.37</td>\n",
+       "      <td>61.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>5351.86</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-2051.86</td>\n",
+       "      <td>62.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>25864.65</td>\n",
+       "      <td>69990.00</td>\n",
+       "      <td>44125.35</td>\n",
+       "      <td>63.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>15682.82</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-6082.82</td>\n",
+       "      <td>63.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>40765.33</td>\n",
+       "      <td>112000.00</td>\n",
+       "      <td>71234.67</td>\n",
+       "      <td>63.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>1320.56</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>2379.44</td>\n",
+       "      <td>64.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>3795.06</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1495.06</td>\n",
+       "      <td>65.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>19335.34</td>\n",
+       "      <td>55555.00</td>\n",
+       "      <td>36219.66</td>\n",
+       "      <td>65.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>26736.21</td>\n",
+       "      <td>16100.00</td>\n",
+       "      <td>-10636.21</td>\n",
+       "      <td>66.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>26441.03</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-10541.03</td>\n",
+       "      <td>66.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>5002.39</td>\n",
+       "      <td>2999.00</td>\n",
+       "      <td>-2003.39</td>\n",
+       "      <td>66.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>18317.20</td>\n",
+       "      <td>10974.21</td>\n",
+       "      <td>-7342.99</td>\n",
+       "      <td>66.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>40893.84</td>\n",
+       "      <td>126000.00</td>\n",
+       "      <td>85106.16</td>\n",
+       "      <td>67.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>38473.71</td>\n",
+       "      <td>119000.00</td>\n",
+       "      <td>80526.29</td>\n",
+       "      <td>67.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>34863.53</td>\n",
+       "      <td>109999.00</td>\n",
+       "      <td>75135.47</td>\n",
+       "      <td>68.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>10946.44</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-4446.44</td>\n",
+       "      <td>68.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>3205.38</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-1305.38</td>\n",
+       "      <td>68.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>16924.74</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-6925.74</td>\n",
+       "      <td>69.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>9356.43</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-3856.43</td>\n",
+       "      <td>70.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15374.08</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-6474.08</td>\n",
+       "      <td>72.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>11056.52</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-4656.52</td>\n",
+       "      <td>72.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>2440.90</td>\n",
+       "      <td>1400.00</td>\n",
+       "      <td>-1040.90</td>\n",
+       "      <td>74.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>3939.91</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>11560.09</td>\n",
+       "      <td>74.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>15380.04</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-6580.04</td>\n",
+       "      <td>74.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>35093.27</td>\n",
+       "      <td>19990.00</td>\n",
+       "      <td>-15103.27</td>\n",
+       "      <td>75.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>8270.38</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>-3570.38</td>\n",
+       "      <td>75.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>6916.13</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>-3016.13</td>\n",
+       "      <td>77.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>6746.48</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-2947.48</td>\n",
+       "      <td>77.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>2555.02</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>9044.98</td>\n",
+       "      <td>77.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>13355.11</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-5855.11</td>\n",
+       "      <td>78.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>3948.74</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-1748.74</td>\n",
+       "      <td>79.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>9024.05</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-4124.05</td>\n",
+       "      <td>84.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>15292.10</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-6992.10</td>\n",
+       "      <td>84.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>7621.25</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-3521.25</td>\n",
+       "      <td>85.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>2975.40</td>\n",
+       "      <td>1600.00</td>\n",
+       "      <td>-1375.40</td>\n",
+       "      <td>85.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>14915.98</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-7015.98</td>\n",
+       "      <td>88.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>5716.14</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-2716.14</td>\n",
+       "      <td>90.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15819.50</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-7519.50</td>\n",
+       "      <td>90.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>13420.15</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-6421.15</td>\n",
+       "      <td>91.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>23576.99</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>-11576.99</td>\n",
+       "      <td>96.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>8648.80</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-4248.80</td>\n",
+       "      <td>96.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>15559.43</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-7659.43</td>\n",
+       "      <td>96.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>4004.65</td>\n",
+       "      <td>2000.00</td>\n",
+       "      <td>-2004.65</td>\n",
+       "      <td>100.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>27225.34</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-13725.34</td>\n",
+       "      <td>101.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>5462.52</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-2762.52</td>\n",
+       "      <td>102.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>8131.92</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>-4132.92</td>\n",
+       "      <td>103.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>10810.62</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-5810.62</td>\n",
+       "      <td>116.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>7973.87</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-4323.87</td>\n",
+       "      <td>118.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>5948.04</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-3348.04</td>\n",
+       "      <td>128.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>6891.99</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-3891.99</td>\n",
+       "      <td>129.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>26331.41</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-15131.41</td>\n",
+       "      <td>135.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>21493.44</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-12493.44</td>\n",
+       "      <td>138.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>6537.82</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-3837.82</td>\n",
+       "      <td>142.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>12891.96</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-7591.96</td>\n",
+       "      <td>143.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>8340.94</td>\n",
+       "      <td>3350.00</td>\n",
+       "      <td>-4990.94</td>\n",
+       "      <td>148.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>3040.77</td>\n",
+       "      <td>1200.00</td>\n",
+       "      <td>-1840.77</td>\n",
+       "      <td>153.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>9631.08</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-5832.08</td>\n",
+       "      <td>153.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>13967.55</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-8467.55</td>\n",
+       "      <td>153.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>3818.71</td>\n",
+       "      <td>1450.00</td>\n",
+       "      <td>-2368.71</td>\n",
+       "      <td>163.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>7809.13</td>\n",
+       "      <td>2899.00</td>\n",
+       "      <td>-4910.13</td>\n",
+       "      <td>169.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>4590.49</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-2890.49</td>\n",
+       "      <td>170.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>7320.42</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-4720.42</td>\n",
+       "      <td>181.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>12261.19</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>-8161.19</td>\n",
+       "      <td>199.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7553.29</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-5053.29</td>\n",
+       "      <td>202.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>9817.06</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-6617.06</td>\n",
+       "      <td>206.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>7161.67</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-4961.67</td>\n",
+       "      <td>225.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>7918.89</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-5518.89</td>\n",
+       "      <td>229.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>9984.87</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-6984.87</td>\n",
+       "      <td>232.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>10381.62</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-7381.62</td>\n",
+       "      <td>246.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>8843.22</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-6343.22</td>\n",
+       "      <td>253.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>10123.04</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>-7323.04</td>\n",
+       "      <td>261.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>16165.79</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-11765.79</td>\n",
+       "      <td>267.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>17937.36</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-13137.36</td>\n",
+       "      <td>273.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>7319.77</td>\n",
+       "      <td>1850.00</td>\n",
+       "      <td>-5469.77</td>\n",
+       "      <td>295.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>16095.32</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-12495.32</td>\n",
+       "      <td>347.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10685.50</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-8385.50</td>\n",
+       "      <td>364.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>9664.46</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-7764.46</td>\n",
+       "      <td>408.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>32481.05</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-26481.05</td>\n",
+       "      <td>441.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>9954.42</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>-8154.42</td>\n",
+       "      <td>453.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>35956.50</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-29456.50</td>\n",
+       "      <td>453.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>10019.90</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>-8219.90</td>\n",
+       "      <td>456.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>30628.28</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>-25629.28</td>\n",
+       "      <td>512.69</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Predictions    Target  Residual  Difference%\n",
+       "698     30480.85  30500.00     19.15         0.06\n",
+       "742     16960.31  16999.00     38.69         0.23\n",
+       "60      12469.21  12500.00     30.79         0.25\n",
+       "110     25614.14  25500.00   -114.14         0.45\n",
+       "367     42703.68  42500.00   -203.68         0.48\n",
+       "369      3084.69   3100.00     15.31         0.49\n",
+       "769     29651.73  29500.00   -151.73         0.51\n",
+       "272      9749.53   9800.00     50.47         0.52\n",
+       "714     23118.07  22999.00   -119.07         0.52\n",
+       "630      8734.58   8800.00     65.42         0.74\n",
+       "380      3473.79   3500.00     26.21         0.75\n",
+       "648     21174.10  21335.00    160.90         0.75\n",
+       "308      8967.74   8900.00    -67.74         0.76\n",
+       "665     17858.02  18000.00    141.98         0.79\n",
+       "379     17654.84  17800.00    145.16         0.82\n",
+       "719     11391.95  11500.00    108.05         0.94\n",
+       "102     28625.56  28900.00    274.44         0.95\n",
+       "94       7724.17   7800.00     75.83         0.97\n",
+       "561      6429.03   6500.00     70.97         1.09\n",
+       "242      7597.39   7500.00    -97.39         1.30\n",
+       "528     18555.09  18800.00    244.91         1.30\n",
+       "61       7396.87   7300.00    -96.87         1.33\n",
+       "19      16178.14  16400.00    221.86         1.35\n",
+       "280     12327.10  12499.00    171.90         1.38\n",
+       "311     51287.19  52055.25    768.06         1.48\n",
+       "723      6009.63   6100.00     90.37         1.48\n",
+       "49       4973.17   4900.00    -73.17         1.49\n",
+       "114     27716.14  27300.00   -416.14         1.52\n",
+       "636     28498.91  28950.00    451.09         1.56\n",
+       "612      2953.17   3000.00     46.83         1.56\n",
+       "47      26425.14  25999.00   -426.14         1.64\n",
+       "23      13111.91  12900.00   -211.91         1.64\n",
+       "31      12858.08  12650.00   -208.08         1.64\n",
+       "91      13421.16  13200.00   -221.16         1.68\n",
+       "329      7327.18   7200.00   -127.18         1.77\n",
+       "549      3816.33   3750.00    -66.33         1.77\n",
+       "252      9721.50   9900.00    178.50         1.80\n",
+       "387     44173.72  44999.00    825.28         1.83\n",
+       "267     40753.58  40000.00   -753.58         1.88\n",
+       "467     22262.80  22711.65    448.85         1.98\n",
+       "556     18231.44  18600.00    368.56         1.98\n",
+       "165      9596.94   9400.00   -196.94         2.10\n",
+       "259      6067.79   6200.00    132.21         2.13\n",
+       "601     35371.16  34600.00   -771.16         2.23\n",
+       "708     11967.39  11700.00   -267.39         2.29\n",
+       "593     17908.00  17500.00   -408.00         2.33\n",
+       "398      8707.13   8500.00   -207.13         2.44\n",
+       "526     29049.27  28350.00   -699.27         2.47\n",
+       "603     14513.46  14900.00    386.54         2.59\n",
+       "53      20453.89  21000.00    546.11         2.60\n",
+       "632     15383.35  14990.00   -393.35         2.62\n",
+       "533     24642.50  24000.00   -642.50         2.68\n",
+       "497     50099.92  51500.00   1400.08         2.72\n",
+       "212     16133.86  15700.00   -433.86         2.76\n",
+       "130     17489.92  18000.00    510.08         2.83\n",
+       "290      1894.40   1950.00     55.60         2.85\n",
+       "78      30810.25  29900.00   -910.25         3.04\n",
+       "642      8721.97   8999.00    277.03         3.08\n",
+       "437     18866.50  18300.00   -566.50         3.10\n",
+       "101      5958.63   6150.00    191.37         3.11\n",
+       "314      5811.74   6000.00    188.26         3.14\n",
+       "150      9800.43   9500.00   -300.43         3.16\n",
+       "565      7324.63   7100.00   -224.63         3.16\n",
+       "574     12583.52  13000.00    416.48         3.20\n",
+       "591     10115.13   9800.00   -315.13         3.22\n",
+       "172     11156.38  10800.00   -356.38         3.30\n",
+       "133      9279.28   9600.00    320.72         3.34\n",
+       "480     31369.37  32500.00   1130.63         3.48\n",
+       "87       2315.71   2400.00     84.29         3.51\n",
+       "11       5175.77   5000.00   -175.77         3.52\n",
+       "43      21611.83  22400.00    788.17         3.52\n",
+       "96       7976.26   7700.00   -276.26         3.59\n",
+       "406     24874.86  23999.00   -875.86         3.65\n",
+       "173     36516.35  37900.00   1383.65         3.65\n",
+       "540      4666.05   4500.00   -166.05         3.69\n",
+       "40      18672.68  18000.00   -672.68         3.74\n",
+       "340     14815.83  15400.00    584.17         3.79\n",
+       "239     10581.62  10999.00    417.38         3.79\n",
+       "109     12663.54  12200.00   -463.54         3.80\n",
+       "256      1825.44   1900.00     74.56         3.92\n",
+       "317     12247.90  12750.00    502.10         3.94\n",
+       "77       5930.73   5700.00   -230.73         4.05\n",
+       "301      9782.47  10200.00    417.53         4.09\n",
+       "333     12452.22  11960.00   -492.22         4.12\n",
+       "570     23163.87  24171.42   1007.55         4.17\n",
+       "581      3246.94   3390.00    143.06         4.22\n",
+       "693     12354.16  12900.00    545.84         4.23\n",
+       "381     33499.44  35000.00   1500.56         4.29\n",
+       "438     16257.03  16999.00    741.97         4.36\n",
+       "368      8415.81   8800.00    384.19         4.37\n",
+       "273     12318.39  12900.00    581.61         4.51\n",
+       "235      2765.14   2900.00    134.86         4.65\n",
+       "168     11420.95  11999.00    578.05         4.82\n",
+       "707      2725.40   2600.00   -125.40         4.82\n",
+       "72      23624.69  22500.00  -1124.69         5.00\n",
+       "559     11377.84  11999.00    621.16         5.18\n",
+       "134     12096.18  11500.00   -596.18         5.18\n",
+       "446      9363.27   8900.00   -463.27         5.21\n",
+       "127     12311.47  11700.00   -611.47         5.23\n",
+       "450     14218.94  13500.00   -718.94         5.33\n",
+       "293      8431.89   7999.00   -432.89         5.41\n",
+       "18      37600.15  39900.00   2299.85         5.76\n",
+       "452     39882.67  37700.00  -2182.67         5.79\n",
+       "195     20588.57  21900.00   1311.43         5.99\n",
+       "676      6861.84   7300.00    438.16         6.00\n",
+       "766     18982.15  17900.00  -1082.15         6.05\n",
+       "328     15067.85  14200.00   -867.85         6.11\n",
+       "762     12201.29  12999.00    797.71         6.14\n",
+       "649     11147.91  10500.00   -647.91         6.17\n",
+       "299     20183.30  18999.00  -1184.30         6.23\n",
+       "641     17334.11  18500.00   1165.89         6.30\n",
+       "5       21289.80  20000.00  -1289.80         6.45\n",
+       "545      6826.78   7300.00    473.22         6.48\n",
+       "622      4472.47   4200.00   -272.47         6.49\n",
+       "422     53294.61  57000.00   3705.39         6.50\n",
+       "740      6658.73   6250.00   -408.73         6.54\n",
+       "85      19001.29  17800.00  -1201.29         6.75\n",
+       "315      4590.49   4300.00   -290.49         6.76\n",
+       "462     43236.92  40500.00  -2736.92         6.76\n",
+       "394      9076.42   8500.00   -576.42         6.78\n",
+       "188     10159.18  10900.00    740.82         6.80\n",
+       "731     16027.02  15000.00  -1027.02         6.85\n",
+       "448     13502.35  14500.00    997.65         6.88\n",
+       "254      6413.26   5999.00   -414.26         6.91\n",
+       "667     49221.63  52999.00   3777.37         7.13\n",
+       "600     21816.43  23500.00   1683.57         7.16\n",
+       "144     11969.66  12900.00    930.34         7.21\n",
+       "270      8263.95   7700.00   -563.95         7.32\n",
+       "8       11581.54  12500.00    918.46         7.35\n",
+       "433      6977.90   6500.00   -477.90         7.35\n",
+       "251      6978.91   6500.00   -478.91         7.37\n",
+       "553     16392.22  17700.00   1307.78         7.39\n",
+       "725     15742.41  16999.00   1256.59         7.39\n",
+       "615      7869.47   8500.00    630.53         7.42\n",
+       "518     21297.65  19800.00  -1497.65         7.56\n",
+       "268      6893.62   6400.00   -493.62         7.71\n",
+       "71      15627.03  14500.00  -1127.03         7.77\n",
+       "579      3411.53   3700.00    288.47         7.80\n",
+       "330     11336.71  12300.00    963.29         7.83\n",
+       "59       8195.34   7600.00   -595.34         7.83\n",
+       "354     16717.08  15500.00  -1217.08         7.85\n",
+       "147      7772.57   7200.00   -572.57         7.95\n",
+       "508     16197.42  15000.00  -1197.42         7.98\n",
+       "22       6102.36   5650.00   -452.36         8.01\n",
+       "103      4488.43   4150.00   -338.43         8.16\n",
+       "755     15885.66  17300.00   1414.34         8.18\n",
+       "198     17983.10  19600.00   1616.90         8.25\n",
+       "470      7045.72   6500.00   -545.72         8.40\n",
+       "710      3795.06   3500.00   -295.06         8.43\n",
+       "185      6130.96   6700.00    569.04         8.49\n",
+       "542      5947.51   6500.00    552.49         8.50\n",
+       "412      7609.28   7000.00   -609.28         8.70\n",
+       "415      8117.44   8900.00    782.56         8.79\n",
+       "200     11098.18  10200.00   -898.18         8.81\n",
+       "754     17028.45  18700.00   1671.55         8.94\n",
+       "65      19847.25  18200.00  -1647.25         9.05\n",
+       "682      5454.34   5000.00   -454.34         9.09\n",
+       "222     20662.46  18900.00  -1762.46         9.33\n",
+       "89      11696.25  12900.00   1203.75         9.33\n",
+       "52      10716.84   9800.00   -916.84         9.36\n",
+       "126     13459.92  12300.00  -1159.92         9.43\n",
+       "598      8767.54   9700.00    932.46         9.61\n",
+       "504     23272.83  25749.75   2476.92         9.62\n",
+       "493      5484.97   4999.00   -485.97         9.72\n",
+       "449      9495.62   8650.00   -845.62         9.78\n",
+       "516     11858.48  10800.00  -1058.48         9.80\n",
+       "261     53312.81  48535.50  -4777.31         9.84\n",
+       "531     47047.70  52300.00   5252.30        10.04\n",
+       "68       6717.17   6100.00   -617.17        10.12\n",
+       "376      5391.77   6000.00    608.23        10.14\n",
+       "538     17591.09  19600.00   2008.91        10.25\n",
+       "419     17591.09  19600.00   2008.91        10.25\n",
+       "595     25576.58  28500.00   2923.42        10.26\n",
+       "472     20367.25  22700.00   2332.75        10.28\n",
+       "386      7267.36   8100.00    832.64        10.28\n",
+       "473      1792.36   1999.00    206.64        10.34\n",
+       "14       8248.67   9200.00    951.33        10.34\n",
+       "123      5643.45   6300.00    656.55        10.42\n",
+       "142      6839.57   7650.00    810.43        10.59\n",
+       "257     41503.90  37500.00  -4003.90        10.68\n",
+       "194      9310.69   8400.00   -910.69        10.84\n",
+       "637     35662.26  40000.00   4337.74        10.84\n",
+       "410     16407.29  14800.00  -1607.29        10.86\n",
+       "395     21303.57  23900.00   2596.43        10.86\n",
+       "213     15872.91  14300.00  -1572.91        11.00\n",
+       "653     10332.54   9300.00  -1032.54        11.10\n",
+       "54      18575.43  20900.00   2324.57        11.12\n",
+       "105      8882.65  10000.00   1117.35        11.17\n",
+       "597      7797.18   6999.00   -798.18        11.40\n",
+       "15      10621.84  11999.00   1377.16        11.48\n",
+       "770     10732.07   9600.00  -1132.07        11.79\n",
+       "674      2381.01   2700.00    318.99        11.81\n",
+       "132     11200.20   9999.00  -1201.20        12.01\n",
+       "525      2373.66   2700.00    326.34        12.09\n",
+       "730     10696.56  12179.00   1482.44        12.17\n",
+       "506      3590.97   3200.00   -390.97        12.22\n",
+       "278     53063.22  60500.00   7436.78        12.29\n",
+       "108      7300.05   6500.00   -800.05        12.31\n",
+       "503      6392.70   7300.00    907.30        12.43\n",
+       "536      6746.43   6000.00   -746.43        12.44\n",
+       "156      9192.06  10500.00   1307.94        12.46\n",
+       "765     11034.66   9800.00  -1234.66        12.60\n",
+       "534     16939.86  19400.00   2460.14        12.68\n",
+       "50       2479.43   2200.00   -279.43        12.70\n",
+       "562      9593.52  11000.00   1406.48        12.79\n",
+       "131     23967.81  27500.00   3532.19        12.84\n",
+       "157      7491.01   8600.00   1108.99        12.90\n",
+       "638      8242.62   7300.00   -942.62        12.91\n",
+       "543      9489.56  10900.00   1410.44        12.94\n",
+       "724     44312.98  50900.00   6587.02        12.94\n",
+       "527      9829.53   8700.00  -1129.53        12.98\n",
+       "610      9269.68   8200.00  -1069.68        13.04\n",
+       "359      4524.77   3999.00   -525.77        13.15\n",
+       "24      23650.15  20900.00  -2750.15        13.16\n",
+       "402      8850.21  10200.00   1349.79        13.23\n",
+       "360      4770.52   5500.00    729.48        13.26\n",
+       "184     29479.97  34000.00   4520.03        13.29\n",
+       "324      8316.92   9600.00   1283.08        13.37\n",
+       "307     24974.57  28900.00   3925.43        13.58\n",
+       "118     14943.75  17300.00   2356.25        13.62\n",
+       "607     28498.91  33000.00   4501.09        13.64\n",
+       "332     14591.35  16900.00   2308.65        13.66\n",
+       "140     10358.58  12000.00   1641.42        13.68\n",
+       "627     13074.74  11500.00  -1574.74        13.69\n",
+       "761      9097.13   8000.00  -1097.13        13.71\n",
+       "207     15010.51  13200.00  -1810.51        13.72\n",
+       "685     12593.00  14600.00   2007.00        13.75\n",
+       "350     20246.04  23500.00   3253.96        13.85\n",
+       "143      8768.29   7700.00  -1068.29        13.87\n",
+       "318     39744.80  34900.00  -4844.80        13.88\n",
+       "377      9909.62   8700.00  -1209.62        13.90\n",
+       "152     44680.77  51900.00   7219.23        13.91\n",
+       "57      21942.05  25500.00   3557.95        13.95\n",
+       "277     12900.31  15000.00   2099.69        14.00\n",
+       "253     18128.32  15900.00  -2228.32        14.01\n",
+       "384      4296.77   5000.00    703.23        14.06\n",
+       "113      6006.27   6999.00    992.73        14.18\n",
+       "287      1972.28   2300.00    327.72        14.25\n",
+       "650      8236.36   7200.00  -1036.36        14.39\n",
+       "319     10612.51  12400.00   1787.49        14.42\n",
+       "430     14197.26  12400.00  -1797.26        14.49\n",
+       "95      47013.28  55000.00   7986.72        14.52\n",
+       "757     12028.91  10500.00  -1528.91        14.56\n",
+       "153      3666.31   3200.00   -466.31        14.57\n",
+       "720      2860.97   3350.00    489.03        14.60\n",
+       "631      7256.86   8500.00   1243.14        14.63\n",
+       "36      10499.60  12300.00   1800.40        14.64\n",
+       "763     12383.35  10800.00  -1583.35        14.66\n",
+       "344     11937.31  13995.00   2057.69        14.70\n",
+       "389      2810.71   2450.00   -360.71        14.72\n",
+       "204      7664.32   9000.00   1335.68        14.84\n",
+       "718      8041.62   6999.00  -1042.62        14.90\n",
+       "484     13334.19  11600.00  -1734.19        14.95\n",
+       "258     13680.48  11900.00  -1780.48        14.96\n",
+       "616     13500.69  15900.00   2399.31        15.09\n",
+       "281     11638.89  13708.50   2069.61        15.10\n",
+       "476     15700.53  18500.00   2799.47        15.13\n",
+       "716     11425.01  13500.00   2074.99        15.37\n",
+       "529     53312.81  63000.00   9687.19        15.38\n",
+       "481      6599.62   7800.00   1200.38        15.39\n",
+       "706     61639.73  72900.00  11260.27        15.45\n",
+       "558      7098.53   8400.00   1301.47        15.49\n",
+       "151     10889.39  12900.00   2010.61        15.59\n",
+       "663      2613.97   3099.00    485.03        15.65\n",
+       "16      23721.58  20500.00  -3221.58        15.72\n",
+       "356     10133.68   8750.00  -1383.68        15.81\n",
+       "297     22261.50  19200.00  -3061.50        15.95\n",
+       "119     10448.50   8999.00  -1449.50        16.11\n",
+       "390     24271.90  20900.00  -3371.90        16.13\n",
+       "439     14926.69  17800.00   2873.31        16.14\n",
+       "441     11619.16  10000.00  -1619.16        16.19\n",
+       "548     46465.02  39900.00  -6565.02        16.45\n",
+       "460     50954.68  61000.00  10045.32        16.47\n",
+       "211      6848.88   8200.00   1351.12        16.48\n",
+       "106     16547.03  14200.00  -2347.03        16.53\n",
+       "599      4590.16   5500.00    909.84        16.54\n",
+       "220     13348.91  15999.00   2650.09        16.56\n",
+       "226     11191.05   9600.00  -1591.05        16.57\n",
+       "197      2126.49   2550.00    423.51        16.61\n",
+       "3        7463.96   6400.00  -1063.96        16.62\n",
+       "378      2749.08   3300.00    550.92        16.69\n",
+       "507     18282.75  21950.00   3667.25        16.71\n",
+       "186     19071.43  22900.00   3828.57        16.72\n",
+       "100     32886.86  39500.00   6613.14        16.74\n",
+       "620     25752.19  30990.00   5237.81        16.90\n",
+       "229      7719.69   9300.00   1580.31        16.99\n",
+       "400     25986.31  31310.00   5323.69        17.00\n",
+       "336      8250.47   9950.00   1699.53        17.08\n",
+       "483     28937.50  34900.00   5962.50        17.08\n",
+       "171      8392.42   7150.00  -1242.42        17.38\n",
+       "421      8033.61   9750.00   1716.39        17.60\n",
+       "643      8156.11   9900.00   1743.89        17.62\n",
+       "696      2470.61   3000.00    529.39        17.65\n",
+       "286     19659.97  16700.00  -2959.97        17.72\n",
+       "496     17540.72  14899.00  -2641.72        17.73\n",
+       "655      7814.16   9500.00   1685.84        17.75\n",
+       "407     21097.66  17900.00  -3197.66        17.86\n",
+       "464      4266.27   5200.00    933.73        17.96\n",
+       "758      9380.46   7950.00  -1430.46        17.99\n",
+       "670     33614.62  41000.00   7385.38        18.01\n",
+       "9       33614.62  41000.00   7385.38        18.01\n",
+       "263      9089.35   7700.00  -1389.35        18.04\n",
+       "521     27740.81  23500.00  -4240.81        18.05\n",
+       "46       3542.72   3000.00   -542.72        18.09\n",
+       "237     25037.87  21200.00  -3837.87        18.10\n",
+       "28      35991.51  43999.00   8007.49        18.20\n",
+       "125     13733.33  16800.00   3066.67        18.25\n",
+       "721     11431.35  13999.00   2567.65        18.34\n",
+       "749      6082.85   7450.00   1367.15        18.35\n",
+       "55      12674.57  10700.00  -1974.57        18.45\n",
+       "466     25975.12  21900.00  -4075.12        18.61\n",
+       "651      3026.25   2550.00   -476.25        18.68\n",
+       "560     33172.20  40800.00   7627.80        18.70\n",
+       "247     13159.16  16200.00   3040.84        18.77\n",
+       "738     20216.67  24900.00   4683.33        18.81\n",
+       "715     10220.74   8600.00  -1620.74        18.85\n",
+       "608     29127.72  24500.00  -4627.72        18.89\n",
+       "509      5839.08   7200.00   1360.92        18.90\n",
+       "435     53063.22  65500.00  12436.78        18.99\n",
+       "187     28256.79  34900.00   6643.21        19.03\n",
+       "689      4524.77   3800.00   -724.77        19.07\n",
+       "51       2543.57   3149.25    605.68        19.23\n",
+       "501     60493.51  75000.00  14506.49        19.34\n",
+       "292      8962.29   7500.00  -1462.29        19.50\n",
+       "193     14832.32  12400.00  -2432.32        19.62\n",
+       "711     19676.51  24500.00   4823.49        19.69\n",
+       "7       20349.62  16999.00  -3350.62        19.71\n",
+       "569      1926.60   2400.00    473.40        19.72\n",
+       "453      8381.60   6999.00  -1382.60        19.75\n",
+       "444     38334.67  32000.00  -6334.67        19.80\n",
+       "454     20972.54  17500.00  -3472.54        19.84\n",
+       "176      6732.97   8400.00   1667.03        19.85\n",
+       "240     10419.20  13000.00   2580.80        19.85\n",
+       "688     24572.28  20500.00  -4072.28        19.86\n",
+       "313     10014.71  12500.00   2485.29        19.88\n",
+       "471      4209.17   3500.00   -709.17        20.26\n",
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+       "79      18893.76  15700.00  -3193.76        20.34\n",
+       "409      8121.22  10200.00   2078.78        20.38\n",
+       "154     11443.85   9499.00  -1944.85        20.47\n",
+       "494     33392.40  42000.00   8607.60        20.49\n",
+       "98       6746.48   8500.00   1753.52        20.63\n",
+       "227     17251.60  14300.00  -2951.60        20.64\n",
+       "115      6981.34   8800.00   1818.66        20.67\n",
+       "744      2379.58   3000.00    620.42        20.68\n",
+       "474     10932.17  13800.00   2867.83        20.78\n",
+       "459      2779.04   2300.00   -479.04        20.83\n",
+       "331     11002.01  13900.00   2897.99        20.85\n",
+       "76       5144.02   6500.00   1355.98        20.86\n",
+       "244      7110.32   8990.00   1879.68        20.91\n",
+       "644     53312.81  67431.00  14118.19        20.94\n",
+       "174      7745.02   6400.00  -1345.02        21.02\n",
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+       "425      3940.49   4999.00   1058.51        21.17\n",
+       "321     23630.85  19500.00  -4130.85        21.18\n",
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+       "17      11770.64   9700.00  -2070.64        21.35\n",
+       "517      9424.34  12000.00   2575.66        21.46\n",
+       "392      8139.48   6700.00  -1439.48        21.48\n",
+       "416     22367.52  28500.00   6132.48        21.52\n",
+       "335     13004.34  10700.00  -2304.34        21.54\n",
+       "45       8512.03   7000.00  -1512.03        21.60\n",
+       "515     32121.20  41000.00   8878.80        21.66\n",
+       "557     12523.73  16000.00   3476.27        21.73\n",
+       "41      19845.36  16300.00  -3545.36        21.75\n",
+       "13      13292.71  16999.00   3706.29        21.80\n",
+       "691      8467.17   6950.00  -1517.17        21.83\n",
+       "20      11876.82  15200.00   3323.18        21.86\n",
+       "275     22547.55  18500.00  -4047.55        21.88\n",
+       "120     50728.89  65000.00  14271.11        21.96\n",
+       "647      2625.10   2150.00   -475.10        22.10\n",
+       "524      5863.41   4800.00  -1063.41        22.15\n",
+       "547      9175.06   7499.00  -1676.06        22.35\n",
+       "747      8453.28  10900.00   2446.72        22.45\n",
+       "295      5350.91   6900.00   1549.09        22.45\n",
+       "80       6819.44   8800.00   1980.56        22.51\n",
+       "661     10303.21  13300.00   2996.79        22.53\n",
+       "537      9139.04  11800.00   2660.96        22.55\n",
+       "626     18621.12  24100.00   5478.88        22.73\n",
+       "576     15954.72  12999.00  -2955.72        22.74\n",
+       "499     12270.01  15900.00   3629.99        22.83\n",
+       "218     11490.44  14900.00   3409.56        22.88\n",
+       "458     14136.01  11500.00  -2636.01        22.92\n",
+       "138     11095.57   8999.00  -2096.57        23.30\n",
+       "320     10350.25  13500.00   3149.75        23.33\n",
+       "606     20680.40  27000.00   6319.60        23.41\n",
+       "709     34446.96  27900.00  -6546.96        23.47\n",
+       "75       4209.17   5500.00   1290.83        23.47\n",
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+       "602     16427.12  13300.00  -3127.12        23.51\n",
+       "736     53312.81  43163.25 -10149.56        23.51\n",
+       "495      3747.21   4900.00   1152.79        23.53\n",
+       "399     14410.15  18932.55   4522.40        23.89\n",
+       "771     13922.45  18300.00   4377.55        23.92\n",
+       "662      9232.79   7450.00  -1782.79        23.93\n",
+       "634     18165.16  23900.00   5734.84        24.00\n",
+       "4       11353.49   9150.00  -2203.49        24.08\n",
+       "580      6205.48   5000.00  -1205.48        24.11\n",
+       "687     11004.05  14500.00   3495.95        24.11\n",
+       "305     10505.98  13900.00   3394.02        24.42\n",
+       "206     19920.63  16000.00  -3920.63        24.50\n",
+       "659      3621.08   4799.00   1177.92        24.55\n",
+       "178     17316.59  13900.00  -3416.59        24.58\n",
+       "692      8471.55   6799.00  -1672.55        24.60\n",
+       "479     36883.77  49000.00  12116.23        24.73\n",
+       "209      8242.62   6600.00  -1642.62        24.89\n",
+       "773     13491.16  10800.00  -2691.16        24.92\n",
+       "594      3001.37   4000.00    998.63        24.97\n",
+       "1        3499.26   2800.00   -699.26        24.97\n",
+       "625     23485.45  18777.00  -4708.45        25.08\n",
+       "370     10835.95  14500.00   3664.05        25.27\n",
+       "83       6013.05   4800.00  -1213.05        25.27\n",
+       "443      3735.04   4999.00   1263.96        25.28\n",
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+       "519      8667.12   6900.00  -1767.12        25.61\n",
+       "177      8916.19  11999.00   3082.81        25.69\n",
+       "361      8688.92  11700.00   3011.08        25.74\n",
+       "482     12027.56  16200.00   4172.44        25.76\n",
+       "705     12976.15  17500.00   4523.85        25.85\n",
+       "135     44407.14  59999.00  15591.86        25.99\n",
+       "264     31059.18  42000.00  10940.82        26.05\n",
+       "372      8110.06  10990.00   2879.94        26.21\n",
+       "672      5679.39   4500.00  -1179.39        26.21\n",
+       "699      4255.62   5800.00   1544.38        26.63\n",
+       "306      3164.66   2499.00   -665.66        26.64\n",
+       "279      3154.48   4300.00   1145.52        26.64\n",
+       "145      4307.68   3400.00   -907.68        26.70\n",
+       "681      5195.69   4100.00  -1095.69        26.72\n",
+       "175     34863.53  47600.00  12736.47        26.76\n",
+       "440      8638.63  11800.00   3161.37        26.79\n",
+       "683     32771.45  44800.00  12028.55        26.85\n",
+       "592     28154.84  38500.00  10345.16        26.87\n",
+       "429     67952.07  53500.00 -14452.07        27.01\n",
+       "294     13471.84  10600.00  -2871.84        27.09\n",
+       "623     23227.15  31900.00   8672.85        27.19\n",
+       "107      7131.92   9800.00   2668.08        27.23\n",
+       "86       8071.70  11100.00   3028.30        27.28\n",
+       "37      26478.15  20800.00  -5678.15        27.30\n",
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+       "62      10848.85   8500.00  -2348.85        27.63\n",
+       "572     10597.72   8300.00  -2297.72        27.68\n",
+       "568      7940.44  11000.00   3059.56        27.81\n",
+       "202      5040.46   7000.00   1959.54        27.99\n",
+       "327     10716.00  14900.00   4184.00        28.08\n",
+       "310      9205.10  12800.00   3594.90        28.09\n",
+       "461     31656.01  24700.00  -6956.01        28.16\n",
+       "613     13853.36  10800.00  -3053.36        28.27\n",
+       "323      7098.53   9900.00   2801.47        28.30\n",
+       "752      8591.66  12000.00   3408.34        28.40\n",
+       "348     12201.29   9500.00  -2701.29        28.43\n",
+       "424     50074.56  69990.00  19915.44        28.45\n",
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+       "99       6437.21   8999.00   2561.79        28.47\n",
+       "149      8351.34   6500.00  -1851.34        28.48\n",
+       "58      35336.33  49500.00  14163.67        28.61\n",
+       "423     74232.55 103999.00  29766.45        28.62\n",
+       "726     45664.45  35500.00 -10164.45        28.63\n",
+       "614     19298.61  14999.00  -4299.61        28.67\n",
+       "129     11580.54   9000.00  -2580.54        28.67\n",
+       "303     23496.36  33000.00   9503.64        28.80\n",
+       "21      31557.80  24500.00  -7057.80        28.81\n",
+       "767     24323.48  18800.00  -5523.48        29.38\n",
+       "192     18285.07  25900.00   7614.93        29.40\n",
+       "700     25640.67  19800.00  -5840.67        29.50\n",
+       "552      5983.44   8500.00   2516.56        29.61\n",
+       "500      8973.16  12750.00   3776.84        29.62\n",
+       "734     23336.06  33200.00   9863.94        29.71\n",
+       "233      6110.94   8700.00   2589.06        29.76\n",
+       "582      7724.17  11000.00   3275.83        29.78\n",
+       "201     10094.58   7777.00  -2317.58        29.80\n",
+       "296     21690.99  30900.00   9209.01        29.80\n",
+       "523     10809.48  15400.00   4590.52        29.81\n",
+       "357     11018.79  15700.00   4681.21        29.82\n",
+       "190     13377.99  10300.00  -3077.99        29.88\n",
+       "234     12731.26   9800.00  -2931.26        29.91\n",
+       "695     11178.46   8600.00  -2578.46        29.98\n",
+       "364     21681.00  30999.00   9318.00        30.06\n",
+       "362     27293.13  39040.00  11746.87        30.09\n",
+       "309      6510.80   5000.00  -1510.80        30.22\n",
+       "146      8298.32  11900.00   3601.68        30.27\n",
+       "373     10821.19   8299.00  -2522.19        30.39\n",
+       "513      5346.97   4100.00  -1246.97        30.41\n",
+       "12       5484.02   7900.00   2415.98        30.58\n",
+       "352      5238.05   4000.00  -1238.05        30.95\n",
+       "181     17597.10  25500.00   7902.90        30.99\n",
+       "128      7727.53  11200.00   3472.47        31.00\n",
+       "753      6358.55   4850.00  -1508.55        31.10\n",
+       "671      2886.35   4200.00   1313.65        31.28\n",
+       "764     14049.76  10700.00  -3349.76        31.31\n",
+       "216     12974.89  18900.00   5925.11        31.35\n",
+       "260      6858.89   9999.00   3140.11        31.40\n",
+       "124     15691.29  22900.00   7208.71        31.48\n",
+       "214      2393.34   3500.00   1106.66        31.62\n",
+       "405      6425.00   9400.00   2975.00        31.65\n",
+       "70      50206.83  73500.00  23293.17        31.69\n",
+       "596     25685.67  19500.00  -6185.67        31.72\n",
+       "351     25951.31  19692.08  -6259.23        31.79\n",
+       "489     28910.96  42500.00  13589.04        31.97\n",
+       "38       9919.19  14600.00   4680.81        32.06\n",
+       "274      9113.05   6900.00  -2213.05        32.07\n",
+       "374     24427.14  36000.00  11572.86        32.15\n",
+       "302      4738.89   7000.00   2261.11        32.30\n",
+       "148     25040.42  37000.00  11959.58        32.32\n",
+       "164     18403.35  13893.75  -4509.60        32.46\n",
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+       "436     40893.84 126000.00  85106.16        67.54\n",
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+       "668      8270.38   4700.00  -3570.38        75.97\n",
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+       "546      6746.48   3799.00  -2947.48        77.59\n",
+       "27       2555.02  11600.00   9044.98        77.97\n",
+       "746     13355.11   7500.00  -5855.11        78.07\n",
+       "271      3948.74   2200.00  -1748.74        79.49\n",
+       "236      9024.05   4900.00  -4124.05        84.16\n",
+       "166     15292.10   8300.00  -6992.10        84.24\n",
+       "67       7621.25   4100.00  -3521.25        85.88\n",
+       "432      2975.40   1600.00  -1375.40        85.96\n",
+       "550     14915.98   7900.00  -7015.98        88.81\n",
+       "208      5716.14   3000.00  -2716.14        90.54\n",
+       "116     15819.50   8300.00  -7519.50        90.60\n",
+       "366     13420.15   6999.00  -6421.15        91.74\n",
+       "686     23576.99  12000.00 -11576.99        96.47\n",
+       "404      8648.80   4400.00  -4248.80        96.56\n",
+       "30      15559.43   7900.00  -7659.43        96.95\n",
+       "713      4004.65   2000.00  -2004.65       100.23\n",
+       "611     27225.34  13500.00 -13725.34       101.67\n",
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+       "694      8131.92   3999.00  -4132.92       103.35\n",
+       "727     10810.62   5000.00  -5810.62       116.21\n",
+       "397      7973.87   3650.00  -4323.87       118.46\n",
+       "196      5948.04   2600.00  -3348.04       128.77\n",
+       "704      6891.99   3000.00  -3891.99       129.73\n",
+       "571     26331.41  11200.00 -15131.41       135.10\n",
+       "680     21493.44   9000.00 -12493.44       138.82\n",
+       "465      6537.82   2700.00  -3837.82       142.14\n",
+       "81      12891.96   5300.00  -7591.96       143.24\n",
+       "554      8340.94   3350.00  -4990.94       148.98\n",
+       "609      3040.77   1200.00  -1840.77       153.40\n",
+       "677      9631.08   3799.00  -5832.08       153.52\n",
+       "161     13967.55   5500.00  -8467.55       153.96\n",
+       "635      3818.71   1450.00  -2368.71       163.36\n",
+       "660      7809.13   2899.00  -4910.13       169.37\n",
+       "664      4590.49   1700.00  -2890.49       170.03\n",
+       "82       7320.42   2600.00  -4720.42       181.55\n",
+       "282     12261.19   4100.00  -8161.19       199.05\n",
+       "2        7553.29   2500.00  -5053.29       202.13\n",
+       "729      9817.06   3200.00  -6617.06       206.78\n",
+       "492      7161.67   2200.00  -4961.67       225.53\n",
+       "382      7918.89   2400.00  -5518.89       229.95\n",
+       "502      9984.87   3000.00  -6984.87       232.83\n",
+       "750     10381.62   3000.00  -7381.62       246.05\n",
+       "434      8843.22   2500.00  -6343.22       253.73\n",
+       "182     10123.04   2800.00  -7323.04       261.54\n",
+       "396     16165.79   4400.00 -11765.79       267.40\n",
+       "298     17937.36   4800.00 -13137.36       273.69\n",
+       "629      7319.77   1850.00  -5469.77       295.66\n",
+       "619     16095.32   3600.00 -12495.32       347.09\n",
+       "0       10685.50   2300.00  -8385.50       364.59\n",
+       "485      9664.46   1900.00  -7764.46       408.66\n",
+       "657     32481.05   6000.00 -26481.05       441.35\n",
+       "162      9954.42   1800.00  -8154.42       453.02\n",
+       "451     35956.50   6500.00 -29456.50       453.18\n",
+       "532     10019.90   1800.00  -8219.90       456.66\n",
+       "639     30628.28   4999.00 -25629.28       512.69"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sometimes it is useful to check these outputs manually\n",
+    "# To see all rows, we use the relevant pandas syntax\n",
+    "pd.options.display.max_rows = 999\n",
+    "# Moreover, to make the dataset clear, we can display the result with only 2 digits after the dot \n",
+    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
+    "# Finally, we sort by difference in % and manually check the model\n",
+    "df_pf.sort_values(by=['Difference%'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df_pf.drop(['Difference'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>-0.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>-0.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>-0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>-0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>-0.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>-0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-0.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-0.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-0.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>0.32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features  Weight\n",
+       "0               Mileage   -0.45\n",
+       "1               EngineV    0.21\n",
+       "2             Brand_BMW    0.01\n",
+       "3   Brand_Mercedes-Benz    0.01\n",
+       "4      Brand_Mitsubishi   -0.14\n",
+       "5         Brand_Renault   -0.18\n",
+       "6          Brand_Toyota   -0.06\n",
+       "7      Brand_Volkswagen   -0.09\n",
+       "8            Body_hatch   -0.15\n",
+       "9            Body_other   -0.10\n",
+       "10           Body_sedan   -0.20\n",
+       "11           Body_vagon   -0.13\n",
+       "12             Body_van   -0.17\n",
+       "13      Engine Type_Gas   -0.12\n",
+       "14    Engine Type_Other   -0.03\n",
+       "15   Engine Type_Petrol   -0.15\n",
+       "16     Registration_yes    0.32"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['sedan', 'van', 'crossover', 'vagon', 'other', 'hatch'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned['Body'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Petrol', 'Diesel', 'Gas', 'Other'], dtype=object)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned['Engine Type'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['log_price', 'Mileage', 'EngineV', 'Brand_BMW', 'Brand_Mercedes-Benz',\n",
+       "       'Brand_Mitsubishi', 'Brand_Renault', 'Brand_Toyota', 'Brand_Volkswagen',\n",
+       "       'Body_hatch', 'Body_other', 'Body_sedan', 'Body_vagon', 'Body_van',\n",
+       "       'Engine Type_Gas', 'Engine Type_Other', 'Engine Type_Petrol',\n",
+       "       'Registration_yes'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_processed.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data_preprocessed' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\sklearn+-+Linear+Regression+-+Practical+Example+Semi-final.ipynb Cell 95\u001b[0m line \u001b[0;36m1\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2BSemi-final.ipynb#Y163sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m dummy_values\u001b[39m=\u001b[39m[brands,bodies,engine_types,registrations]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2BSemi-final.ipynb#Y163sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m dummy_strings\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mBrand\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mBody\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mEngine Type\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mRegistration\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2BSemi-final.ipynb#Y163sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m dummy_cols\u001b[39m=\u001b[39m data_preprocessed\u001b[39m.\u001b[39mcolumns\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2BSemi-final.ipynb#Y163sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39mfor\u001b[39;00m vals, label \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(dummy_values,dummy_strings):\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/sklearn%2B-%2BLinear%2BRegression%2B-%2BPractical%2BExample%2BSemi-final.ipynb#Y163sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m     \u001b[39mfor\u001b[39;00m val \u001b[39min\u001b[39;00m vals:\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'data_preprocessed' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "brands= data['Brand'].unique()\n",
+    "\n",
+    "bodies= data['Body'].unique()\n",
+    "\n",
+    "engine_types= data['Engine Type'].unique()\n",
+    "\n",
+    "registrations= data['Registration'].unique()\n",
+    "\n",
+    "dummy_values=[brands,bodies,engine_types,registrations]\n",
+    "\n",
+    "dummy_strings=['Brand','Body','Engine Type','Registration']\n",
+    "\n",
+    "dummy_cols= data_preprocessed.columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "for vals, label in zip(dummy_values,dummy_strings):\n",
+    "\n",
+    "    for val in vals:\n",
+    "\n",
+    "        found=False\n",
+    "\n",
+    "        for dummy_col in dummy_cols:\n",
+    "\n",
+    "            if dummy_col==label + '_' + val:\n",
+    "\n",
+    "                found=True\n",
+    "                break  # exit the inner loop when a match is found\n",
+    "\n",
+    "        if found==False:\n",
+    "\n",
+    "            print(label +' missing:',val)\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+final.ipynb b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+final.ipynb
new file mode 100644
index 0000000..5c43bfb
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Linear+Regression+-+Practical+Example+final.ipynb	
@@ -0,0 +1,19962 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Improving the model that predicts the price of a fairly used car"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import statsmodels.api as sum\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "sns.set()\n",
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('Real-life+example.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
+       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
+       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
+       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
+       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
+       "\n",
+       "  Registration  Year         Model  \n",
+       "0          yes  1991           320  \n",
+       "1          yes  1999  Sprinter 212  \n",
+       "2          yes  2003         S 500  \n",
+       "3          yes  2007            Q7  \n",
+       "4          yes  2011         Rav 4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preprocessing the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Exploring the descriptive statistics of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4345</td>\n",
+       "      <td>4173.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4195.000000</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345</td>\n",
+       "      <td>4345.000000</td>\n",
+       "      <td>4345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1649</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3947</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19418.746935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161.237284</td>\n",
+       "      <td>2.790734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.550058</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25584.242620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.705797</td>\n",
+       "      <td>5.066437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.719097</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21700.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           936            NaN   1649          NaN          NaN   \n",
+       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
+       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
+       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4345         4345  4345.000000     4345  \n",
+       "unique           4            2          NaN      312  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          2019         3947          NaN      199  \n",
+       "mean           NaN          NaN  2006.550058      NaN  \n",
+       "std            NaN          NaN     6.719097      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2008.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dealing with missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Brand             0\n",
+       "Price           172\n",
+       "Body              0\n",
+       "Mileage           0\n",
+       "EngineV         150\n",
+       "Engine Type       0\n",
+       "Registration      0\n",
+       "Year              0\n",
+       "Model             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19552.308065</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25815.734988</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11500.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21900.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>300000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand          Price   Body      Mileage      EngineV  \\\n",
+       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
+       "unique           7            NaN      6          NaN          NaN   \n",
+       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
+       "freq           880            NaN   1534          NaN          NaN   \n",
+       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
+       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
+       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
+       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
+       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
+       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
+       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
+       "\n",
+       "       Engine Type Registration         Year    Model  \n",
+       "count         4025         4025  4025.000000     4025  \n",
+       "unique           4            2          NaN      306  \n",
+       "top         Diesel          yes          NaN  E-Class  \n",
+       "freq          1861         3654          NaN      188  \n",
+       "mean           NaN          NaN  2006.379627      NaN  \n",
+       "std            NaN          NaN     6.695595      NaN  \n",
+       "min            NaN          NaN  1969.000000      NaN  \n",
+       "25%            NaN          NaN  2003.000000      NaN  \n",
+       "50%            NaN          NaN  2007.000000      NaN  \n",
+       "75%            NaN          NaN  2012.000000      NaN  \n",
+       "max            NaN          NaN  2016.000000      NaN  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring PDFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_1528\\4196895882.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(data['Price'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data['Price'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Outliers are not removed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Checking the OLS assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we decided to use some matplotlib code, without explaining it\n",
+    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
+    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1,3, sharey=True, figsize = (15,3))\n",
+    "ax1.scatter(data['Year'], data['Price'])\n",
+    "ax1.set_title('Year and Price')\n",
+    "ax2.scatter(data['EngineV'],data['Price'])\n",
+    "ax2.set_title('Engine and Price')\n",
+    "ax3.scatter(data['Mileage'], data['Price'])\n",
+    "ax3.set_title('Mileage and Price')\n",
+    "#ax4.scatter(data['Brand'], data['Price'])\n",
+    "#ax4.set_title('Brand and Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 3))\n",
+    "\n",
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=data['Year'], y=data['Price'], ax=ax1, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax1.set_title('Year and Price')\n",
+    "\n",
+    "# Scatter plot for EngineV and log_price with regression line\n",
+    "sns.regplot(x=data['EngineV'], y=data['Price'], ax=ax2, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax2.set_title('EngineV and Price')\n",
+    "\n",
+    "# Scatter plot for Mileage and log_price with regression line\n",
+    "sns.regplot(x=data['Mileage'], y=data['Price'], ax=ax3, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax3.set_title('Mileage and Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Relaxing the assumptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_1528\\1987317337.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  data['log_price'] = log_price\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>4200.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>320</td>\n",
+       "      <td>8.342840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>7900.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>Sprinter 212</td>\n",
+       "      <td>8.974618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>13300.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Gas</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>S 500</td>\n",
+       "      <td>9.495519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Audi</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>Q7</td>\n",
+       "      <td>10.043249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>18300.0</td>\n",
+       "      <td>crossover</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Rav 4</td>\n",
+       "      <td>9.814656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4339</th>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>17900.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>Corolla</td>\n",
+       "      <td>9.792556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4340</th>\n",
+       "      <td>Mercedes-Benz</td>\n",
+       "      <td>125000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>S 350</td>\n",
+       "      <td>11.736069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4341</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>6500.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>535</td>\n",
+       "      <td>8.779557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4342</th>\n",
+       "      <td>BMW</td>\n",
+       "      <td>8000.0</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>520</td>\n",
+       "      <td>8.987197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4344</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>van</td>\n",
+       "      <td>124</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>T5 (Transporter)</td>\n",
+       "      <td>9.510445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4025 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
+       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
+       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
+       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
+       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
+       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
+       "...             ...       ...        ...      ...      ...         ...   \n",
+       "4339         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
+       "4340  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
+       "4341            BMW    6500.0      sedan        1      3.5      Petrol   \n",
+       "4342            BMW    8000.0      sedan      194      2.0      Petrol   \n",
+       "4344     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
+       "\n",
+       "     Registration  Year              Model  log_price  \n",
+       "0             yes  1991                320   8.342840  \n",
+       "1             yes  1999       Sprinter 212   8.974618  \n",
+       "2             yes  2003              S 500   9.495519  \n",
+       "3             yes  2007                 Q7  10.043249  \n",
+       "4             yes  2011              Rav 4   9.814656  \n",
+       "...           ...   ...                ...        ...  \n",
+       "4339          yes  2014            Corolla   9.792556  \n",
+       "4340          yes  2014              S 350  11.736069  \n",
+       "4341          yes  1999                535   8.779557  \n",
+       "4342          yes  1985                520   8.987197  \n",
+       "4344          yes  2013  T5 (Transporter)    9.510445  \n",
+       "\n",
+       "[4025 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#Transform the 'Price' log transformation\n",
+    "log_price = np.log(data['Price'])\n",
+    "#Add log_price to the dataframe\n",
+    "\n",
+    "data['log_price'] = log_price\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1 , ax2, ax3) = plt.subplots(1,3, sharey=True, figsize= (15,3))\n",
+    "ax1.scatter(data['Year'],data['log_price'])\n",
+    "ax1.set_title('Year and log_price')\n",
+    "ax2.scatter(data['EngineV'], data['log_price'])\n",
+    "ax2.set_title('EngineV and log_price')\n",
+    "ax3.scatter(data['Mileage'],data['log_price'])\n",
+    "ax3.set_title('Mileage and log_price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's check the three scatters once again\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 3))\n",
+    "\n",
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=data['Year'], y=data['log_price'], ax=ax1, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax1.set_title('Year and log_price')\n",
+    "\n",
+    "# Scatter plot for EngineV and log_price with regression line\n",
+    "sns.regplot(x=data['EngineV'], y=data['log_price'], ax=ax2, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax2.set_title('EngineV and log_price')\n",
+    "\n",
+    "# Scatter plot for Mileage and log_price with regression line\n",
+    "sns.regplot(x=data['Mileage'], y=data['log_price'], ax=ax3, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "ax3.set_title('Mileage and log_price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cleaned = data.drop(['Price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Body</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Engine Type</th>\n",
+       "      <th>Registration</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>log_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "      <td>4025</td>\n",
+       "      <td>4025.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>306</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Volkswagen</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E-Class</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>880</td>\n",
+       "      <td>1534</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>3654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>188</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>163.572174</td>\n",
+       "      <td>2.764586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006.379627</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.409665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.394703</td>\n",
+       "      <td>4.935941</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.695595</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.933136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1969.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.396930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.853523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2007.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.350102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2012.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.994242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>980.000000</td>\n",
+       "      <td>99.990000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.611538</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand   Body      Mileage      EngineV Engine Type Registration  \\\n",
+       "count         4025   4025  4025.000000  4025.000000        4025         4025   \n",
+       "unique           7      6          NaN          NaN           4            2   \n",
+       "top     Volkswagen  sedan          NaN          NaN      Diesel          yes   \n",
+       "freq           880   1534          NaN          NaN        1861         3654   \n",
+       "mean           NaN    NaN   163.572174     2.764586         NaN          NaN   \n",
+       "std            NaN    NaN   103.394703     4.935941         NaN          NaN   \n",
+       "min            NaN    NaN     0.000000     0.600000         NaN          NaN   \n",
+       "25%            NaN    NaN    90.000000     1.800000         NaN          NaN   \n",
+       "50%            NaN    NaN   158.000000     2.200000         NaN          NaN   \n",
+       "75%            NaN    NaN   230.000000     3.000000         NaN          NaN   \n",
+       "max            NaN    NaN   980.000000    99.990000         NaN          NaN   \n",
+       "\n",
+       "               Year    Model    log_price  \n",
+       "count   4025.000000     4025  4025.000000  \n",
+       "unique          NaN      306          NaN  \n",
+       "top             NaN  E-Class          NaN  \n",
+       "freq            NaN      188          NaN  \n",
+       "mean    2006.379627      NaN     9.409665  \n",
+       "std        6.695595      NaN     0.933136  \n",
+       "min     1969.000000      NaN     6.396930  \n",
+       "25%     2003.000000      NaN     8.853523  \n",
+       "50%     2007.000000      NaN     9.350102  \n",
+       "75%     2012.000000      NaN     9.994242  \n",
+       "max     2016.000000      NaN    12.611538  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'Model', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cleaned.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn does not have a built-in way to check for multicollinearity\n",
+    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
+    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
+    "\n",
+    "# Here's the relevant module\n",
+    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# To make this as easy as possible to use, we declare a variable where we put\n",
+    "# all features where we want to check for multicollinearity\n",
+    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
+    "variables = data_cleaned[['Mileage', 'Year', 'EngineV']]\n",
+    "\n",
+    "# we create a new data frame which will include all the VIFs\n",
+    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
+    "vif = pd.DataFrame()\n",
+    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
+    "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range (variables.shape[1])]\n",
+    "# Finally, I like to include names so it is easier to explore the result\n",
+    "vif['Features'] = variables.columns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.481307</td>\n",
+       "      <td>Mileage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.748134</td>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.314535</td>\n",
+       "      <td>EngineV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        VIF Features\n",
+       "0  3.481307  Mileage\n",
+       "1  3.748134     Year\n",
+       "2  1.314535  EngineV"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With all vifs less than 5, It means that the correlation between your independent variables (Mileage, Year, and EngineV) is not excessively high.\n",
+    "So, it looks like your dataset is behaving well in terms of multicollinearity. You can proceed with your regression analysis without major concerns about the variables playing too nicely with each other. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To include the categorical data in the regression, let's create dummies\n",
+    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
+    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
+    "data_with_dummies = pd.get_dummies(data_cleaned,drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>8.974618</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Mileage  EngineV  Year  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0      277      2.0  1991   8.342840       True                False   \n",
+       "1      427      2.9  1999   8.974618      False                 True   \n",
+       "2      358      5.0  2003   9.495519      False                 True   \n",
+       "3      240      4.2  2007  10.043249      False                False   \n",
+       "4      120      2.0  2011   9.814656      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  Model_100  \\\n",
+       "0              False                True              True      False   \n",
+       "1              False               False              True      False   \n",
+       "2              False               False              True      False   \n",
+       "3              False                True              True      False   \n",
+       "4              False                True              True      False   \n",
+       "\n",
+       "   Model_11  Model_116  Model_118  Model_120  Model_19  Model_190  Model_200  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_21  Model_210  Model_220  Model_230  Model_25  Model_250  Model_300  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_316  Model_318  Model_320  Model_323  Model_324  Model_325  \\\n",
+       "0      False      False       True      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_328  Model_330  Model_335  Model_4 Series Gran Coupe  Model_428  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_4Runner  Model_5  Model_5 Series  Model_5 Series GT  Model_520  \\\n",
+       "0          False    False           False              False      False   \n",
+       "1          False    False           False              False      False   \n",
+       "2          False    False           False              False      False   \n",
+       "3          False    False           False              False      False   \n",
+       "4          False    False           False              False      False   \n",
+       "\n",
+       "   Model_523  Model_524  Model_525  Model_528  Model_530  Model_535  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_540  Model_545  Model_550  Model_6 Series Gran Coupe  Model_630  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_640  Model_645  Model_650  Model_730  Model_735  Model_740  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_745  Model_750  Model_760  Model_80  Model_9  Model_90  Model_A 140  \\\n",
+       "0      False      False      False     False    False     False        False   \n",
+       "1      False      False      False     False    False     False        False   \n",
+       "2      False      False      False     False    False     False        False   \n",
+       "3      False      False      False     False    False     False        False   \n",
+       "4      False      False      False     False    False     False        False   \n",
+       "\n",
+       "   Model_A 150  Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0        False        False        False     False     False     False   \n",
+       "1        False        False        False     False     False     False   \n",
+       "2        False        False        False     False     False     False   \n",
+       "3        False        False        False     False     False     False   \n",
+       "4        False        False        False     False     False     False   \n",
+       "\n",
+       "   Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  Model_A8  \\\n",
+       "0             False     False     False             False     False     False   \n",
+       "1             False     False     False             False     False     False   \n",
+       "2             False     False     False             False     False     False   \n",
+       "3             False     False     False             False     False     False   \n",
+       "4             False     False     False             False     False     False   \n",
+       "\n",
+       "   Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  Model_Avensis  \\\n",
+       "0      False         False        False         False          False   \n",
+       "1      False         False        False         False          False   \n",
+       "2      False         False        False         False          False   \n",
+       "3      False         False        False         False          False   \n",
+       "4      False         False        False         False          False   \n",
+       "\n",
+       "   Model_Aygo  Model_B 170  Model_B 180  Model_B 200  Model_Beetle  \\\n",
+       "0       False        False        False        False         False   \n",
+       "1       False        False        False        False         False   \n",
+       "2       False        False        False        False         False   \n",
+       "3       False        False        False        False         False   \n",
+       "4       False        False        False        False         False   \n",
+       "\n",
+       "   Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  Model_CL 55 AMG  \\\n",
+       "0       False          False         False         False            False   \n",
+       "1       False          False         False         False            False   \n",
+       "2       False          False         False         False            False   \n",
+       "3       False          False         False         False            False   \n",
+       "4       False          False         False         False            False   \n",
+       "\n",
+       "   Model_CL 550  Model_CL 63 AMG  Model_CLA 200  Model_CLA 220  \\\n",
+       "0         False            False          False          False   \n",
+       "1         False            False          False          False   \n",
+       "2         False            False          False          False   \n",
+       "3         False            False          False          False   \n",
+       "4         False            False          False          False   \n",
+       "\n",
+       "   Model_CLA-Class  Model_CLC 180  Model_CLC 200  Model_CLK 200  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 220  Model_CLK 230  Model_CLK 240  Model_CLK 280  Model_CLK 320  \\\n",
+       "0          False          False          False          False          False   \n",
+       "1          False          False          False          False          False   \n",
+       "2          False          False          False          False          False   \n",
+       "3          False          False          False          False          False   \n",
+       "4          False          False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 430  Model_CLS 350  Model_CLS 400  Model_CLS 500  \\\n",
+       "0          False          False          False          False   \n",
+       "1          False          False          False          False   \n",
+       "2          False          False          False          False   \n",
+       "3          False          False          False          False   \n",
+       "4          False          False          False          False   \n",
+       "\n",
+       "   Model_CLS 63 AMG  Model_Caddy  Model_Camry  Model_Captur  Model_Caravelle  \\\n",
+       "0             False        False        False         False            False   \n",
+       "1             False        False        False         False            False   \n",
+       "2             False        False        False         False            False   \n",
+       "3             False        False        False         False            False   \n",
+       "4             False        False        False         False            False   \n",
+       "\n",
+       "   Model_Carina  Model_Carisma  Model_Celica  Model_Clio  Model_Colt  \\\n",
+       "0         False          False         False       False       False   \n",
+       "1         False          False         False       False       False   \n",
+       "2         False          False         False       False       False   \n",
+       "3         False          False         False       False       False   \n",
+       "4         False          False         False       False       False   \n",
+       "\n",
+       "   Model_Corolla  Model_Corolla Verso  Model_Cross Touran  Model_Dokker  \\\n",
+       "0          False                False               False         False   \n",
+       "1          False                False               False         False   \n",
+       "2          False                False               False         False   \n",
+       "3          False                False               False         False   \n",
+       "4          False                False               False         False   \n",
+       "\n",
+       "   Model_Duster  Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  \\\n",
+       "0         False          False          False      False         False   \n",
+       "1         False          False          False      False         False   \n",
+       "2         False          False          False      False         False   \n",
+       "3         False          False          False      False         False   \n",
+       "4         False          False          False      False         False   \n",
+       "\n",
+       "   Model_FJ Cruiser  Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  \\\n",
+       "0             False          False           False        False        False   \n",
+       "1             False          False           False        False        False   \n",
+       "2             False          False           False        False        False   \n",
+       "3             False          False           False        False        False   \n",
+       "4             False          False           False        False        False   \n",
+       "\n",
+       "   Model_G 500  Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0        False           False           False         False         False   \n",
+       "1        False           False           False         False         False   \n",
+       "2        False           False           False         False         False   \n",
+       "3        False           False           False         False         False   \n",
+       "4        False           False           False         False         False   \n",
+       "\n",
+       "   Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  Model_Golf GTI  \\\n",
+       "0          False          False         False         False           False   \n",
+       "1          False          False         False         False           False   \n",
+       "2          False          False         False         False           False   \n",
+       "3          False          False         False         False           False   \n",
+       "4          False          False         False         False           False   \n",
+       "\n",
+       "   Model_Golf II  Model_Golf III  Model_Golf IV  Model_Golf Plus  \\\n",
+       "0          False           False          False            False   \n",
+       "1          False           False          False            False   \n",
+       "2          False           False          False            False   \n",
+       "3          False           False          False            False   \n",
+       "4          False           False          False            False   \n",
+       "\n",
+       "   Model_Golf V  Model_Golf VI  Model_Golf VII  Model_Golf Variant  \\\n",
+       "0         False          False           False               False   \n",
+       "1         False          False           False               False   \n",
+       "2         False          False           False               False   \n",
+       "3         False          False           False               False   \n",
+       "4         False          False           False               False   \n",
+       "\n",
+       "   Model_Grand Scenic  Model_Grandis  Model_Hiace  Model_Highlander  \\\n",
+       "0               False          False        False             False   \n",
+       "1               False          False        False             False   \n",
+       "2               False          False        False             False   \n",
+       "3               False          False        False             False   \n",
+       "4               False          False        False             False   \n",
+       "\n",
+       "   Model_Hilux  Model_I3  Model_IQ  Model_Jetta  Model_Kangoo  Model_Koleos  \\\n",
+       "0        False     False     False        False         False         False   \n",
+       "1        False     False     False        False         False         False   \n",
+       "2        False     False     False        False         False         False   \n",
+       "3        False     False     False        False         False         False   \n",
+       "4        False     False     False        False         False         False   \n",
+       "\n",
+       "   Model_L 200  Model_LT  Model_Laguna  Model_Lancer  Model_Lancer Evolution  \\\n",
+       "0        False     False         False         False                   False   \n",
+       "1        False     False         False         False                   False   \n",
+       "2        False     False         False         False                   False   \n",
+       "3        False     False         False         False                   False   \n",
+       "4        False     False         False         False                   False   \n",
+       "\n",
+       "   Model_Lancer X  Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0           False                     False                   False   \n",
+       "1           False                     False                   False   \n",
+       "2           False                     False                   False   \n",
+       "3           False                     False                   False   \n",
+       "4           False                     False                   False   \n",
+       "\n",
+       "   Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                   False                   False                  False   \n",
+       "1                   False                   False                  False   \n",
+       "2                   False                   False                  False   \n",
+       "3                   False                   False                  False   \n",
+       "4                   False                   False                  False   \n",
+       "\n",
+       "   Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                  False                     False           False   \n",
+       "1                  False                     False           False   \n",
+       "2                  False                     False           False   \n",
+       "3                  False                     False           False   \n",
+       "4                  False                     False           False   \n",
+       "\n",
+       "   Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0           False        False       False     False     False     False   \n",
+       "1           False        False       False     False     False     False   \n",
+       "2           False        False       False     False     False     False   \n",
+       "3           False        False       False     False     False     False   \n",
+       "4           False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0         False         False         False         False         False   \n",
+       "1         False         False         False         False         False   \n",
+       "2         False         False         False         False         False   \n",
+       "3         False         False         False         False         False   \n",
+       "4         False         False         False         False         False   \n",
+       "\n",
+       "   Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0          False         False         False         False        False   \n",
+       "1          False         False         False         False        False   \n",
+       "2          False         False         False         False        False   \n",
+       "3          False         False         False         False        False   \n",
+       "4          False         False         False         False        False   \n",
+       "\n",
+       "   Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0           False             False            False               False   \n",
+       "1           False             False            False               False   \n",
+       "2           False             False            False               False   \n",
+       "3           False             False            False               False   \n",
+       "4           False             False            False               False   \n",
+       "\n",
+       "   Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  Model_Pajero Wagon  \\\n",
+       "0         False               False               False               False   \n",
+       "1         False               False               False               False   \n",
+       "2         False               False               False               False   \n",
+       "3         False               False               False               False   \n",
+       "4         False               False               False               False   \n",
+       "\n",
+       "   Model_Passat B2  Model_Passat B3  Model_Passat B4  Model_Passat B5  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Passat B6  Model_Passat B7  Model_Passat B8  Model_Passat CC  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  Model_Prius  \\\n",
+       "0          False          False       False         False        False   \n",
+       "1          False          False       False         False        False   \n",
+       "2          False          False       False         False        False   \n",
+       "3          False          False       False         False        False   \n",
+       "4          False          False       False         False        False   \n",
+       "\n",
+       "   Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  Model_Rav 4  \\\n",
+       "0     False     False     False        False     False        False   \n",
+       "1     False     False     False        False     False        False   \n",
+       "2     False     False     False        False     False        False   \n",
+       "3     False     False      True        False     False        False   \n",
+       "4     False     False     False        False     False         True   \n",
+       "\n",
+       "   Model_S 140  Model_S 250  Model_S 280  Model_S 300  Model_S 320  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False        False   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 350  Model_S 400  Model_S 420  Model_S 430  Model_S 500  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False         True   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  Model_S 65 AMG  \\\n",
+       "0       False        False        False           False           False   \n",
+       "1       False        False        False           False           False   \n",
+       "2       False        False        False           False           False   \n",
+       "3       False        False        False           False           False   \n",
+       "4       False        False        False           False           False   \n",
+       "\n",
+       "   Model_S-Guard  Model_S4  Model_S5  Model_S8  Model_SL 500 (550)  \\\n",
+       "0          False     False     False     False               False   \n",
+       "1          False     False     False     False               False   \n",
+       "2          False     False     False     False               False   \n",
+       "3          False     False     False     False               False   \n",
+       "4          False     False     False     False               False   \n",
+       "\n",
+       "   Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  Model_Sandero  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_Sandero StepWay  Model_Scenic  Model_Scion  Model_Scirocco  \\\n",
+       "0                  False         False        False           False   \n",
+       "1                  False         False        False           False   \n",
+       "2                  False         False        False           False   \n",
+       "3                  False         False        False           False   \n",
+       "4                  False         False        False           False   \n",
+       "\n",
+       "   Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  Model_Space Star  \\\n",
+       "0          False         False         False        False             False   \n",
+       "1          False         False         False        False             False   \n",
+       "2          False         False         False        False             False   \n",
+       "3          False         False         False        False             False   \n",
+       "4          False         False         False        False             False   \n",
+       "\n",
+       "   Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  Model_Sprinter 210  \\\n",
+       "0              False           False               False               False   \n",
+       "1              False           False               False               False   \n",
+       "2              False           False               False               False   \n",
+       "3              False           False               False               False   \n",
+       "4              False           False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 211  Model_Sprinter 212  Model_Sprinter 213  \\\n",
+       "0               False               False               False   \n",
+       "1               False                True               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 311  Model_Sprinter 312  Model_Sprinter 313  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 315  Model_Sprinter 316  Model_Sprinter 318  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 319  Model_Sprinter 324  Model_Symbol  Model_Syncro  \\\n",
+       "0               False               False         False         False   \n",
+       "1               False               False         False         False   \n",
+       "2               False               False         False         False   \n",
+       "3               False               False         False         False   \n",
+       "4               False               False         False         False   \n",
+       "\n",
+       "   Model_T2 (Transporter)  Model_T3 (Transporter)  Model_T4 (Transporter)  \\\n",
+       "0                   False                   False                   False   \n",
+       "1                   False                   False                   False   \n",
+       "2                   False                   False                   False   \n",
+       "3                   False                   False                   False   \n",
+       "4                   False                   False                   False   \n",
+       "\n",
+       "   Model_T4 (Transporter)   Model_T5 (Transporter)  Model_T5 (Transporter)   \\\n",
+       "0                    False                   False                    False   \n",
+       "1                    False                   False                    False   \n",
+       "2                    False                   False                    False   \n",
+       "3                    False                   False                    False   \n",
+       "4                    False                   False                    False   \n",
+       "\n",
+       "   Model_T6 (Transporter)  Model_T6 (Transporter)   Model_TT  Model_Tacoma  \\\n",
+       "0                   False                    False     False         False   \n",
+       "1                   False                    False     False         False   \n",
+       "2                   False                    False     False         False   \n",
+       "3                   False                    False     False         False   \n",
+       "4                   False                    False     False         False   \n",
+       "\n",
+       "   Model_Tiguan  Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  \\\n",
+       "0         False          False         False         False         False   \n",
+       "1         False          False         False         False         False   \n",
+       "2         False          False         False         False         False   \n",
+       "3         False          False         False         False         False   \n",
+       "4         False          False         False         False         False   \n",
+       "\n",
+       "   Model_Up  Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0     False        False        False        False        False        False   \n",
+       "1     False        False        False        False        False        False   \n",
+       "2     False        False        False        False        False        False   \n",
+       "3     False        False        False        False        False        False   \n",
+       "4     False        False        False        False        False        False   \n",
+       "\n",
+       "   Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0         False        False       False     False     False     False   \n",
+       "1         False        False       False     False     False     False   \n",
+       "2         False        False       False     False     False     False   \n",
+       "3         False        False       False     False     False     False   \n",
+       "4         False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0       False     False       False        False     False     False  \n",
+       "1       False     False       False        False     False     False  \n",
+       "2       False     False       False        False     False     False  \n",
+       "3       False     False       False        False     False     False  \n",
+       "4       False     False       False        False     False     False  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Rearrange to have the dependent variable on first column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Mileage', 'EngineV', 'Year', 'log_price', 'Brand_BMW',\n",
+       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
+       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
+       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
+       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes',\n",
+       "       'Model_100', 'Model_11', 'Model_116', 'Model_118', 'Model_120',\n",
+       "       'Model_19', 'Model_190', 'Model_200', 'Model_21', 'Model_210',\n",
+       "       'Model_220', 'Model_230', 'Model_25', 'Model_250', 'Model_300',\n",
+       "       'Model_316', 'Model_318', 'Model_320', 'Model_323', 'Model_324',\n",
+       "       'Model_325', 'Model_328', 'Model_330', 'Model_335',\n",
+       "       'Model_4 Series Gran Coupe', 'Model_428', 'Model_4Runner',\n",
+       "       'Model_5', 'Model_5 Series', 'Model_5 Series GT', 'Model_520',\n",
+       "       'Model_523', 'Model_524', 'Model_525', 'Model_528', 'Model_530',\n",
+       "       'Model_535', 'Model_540', 'Model_545', 'Model_550',\n",
+       "       'Model_6 Series Gran Coupe', 'Model_630', 'Model_640', 'Model_645',\n",
+       "       'Model_650', 'Model_730', 'Model_735', 'Model_740', 'Model_745',\n",
+       "       'Model_750', 'Model_760', 'Model_80', 'Model_9', 'Model_90',\n",
+       "       'Model_A 140', 'Model_A 150', 'Model_A 170', 'Model_A 180',\n",
+       "       'Model_A1', 'Model_A3', 'Model_A4', 'Model_A4 Allroad', 'Model_A5',\n",
+       "       'Model_A6', 'Model_A6 Allroad', 'Model_A7', 'Model_A8',\n",
+       "       'Model_ASX', 'Model_Amarok', 'Model_Auris', 'Model_Avalon',\n",
+       "       'Model_Avensis', 'Model_Aygo', 'Model_B 170', 'Model_B 180',\n",
+       "       'Model_B 200', 'Model_Beetle', 'Model_Bora', 'Model_C-Class',\n",
+       "       'Model_CL 180', 'Model_CL 500', 'Model_CL 55 AMG', 'Model_CL 550',\n",
+       "       'Model_CL 63 AMG', 'Model_CLA 200', 'Model_CLA 220',\n",
+       "       'Model_CLA-Class', 'Model_CLC 180', 'Model_CLC 200',\n",
+       "       'Model_CLK 200', 'Model_CLK 220', 'Model_CLK 230', 'Model_CLK 240',\n",
+       "       'Model_CLK 280', 'Model_CLK 320', 'Model_CLK 430', 'Model_CLS 350',\n",
+       "       'Model_CLS 400', 'Model_CLS 500', 'Model_CLS 63 AMG',\n",
+       "       'Model_Caddy', 'Model_Camry', 'Model_Captur', 'Model_Caravelle',\n",
+       "       'Model_Carina', 'Model_Carisma', 'Model_Celica', 'Model_Clio',\n",
+       "       'Model_Colt', 'Model_Corolla', 'Model_Corolla Verso',\n",
+       "       'Model_Cross Touran', 'Model_Dokker', 'Model_Duster',\n",
+       "       'Model_E-Class', 'Model_Eclipse', 'Model_Eos', 'Model_Espace',\n",
+       "       'Model_FJ Cruiser', 'Model_Fluence', 'Model_Fortuner',\n",
+       "       'Model_G 320', 'Model_G 350', 'Model_G 500', 'Model_G 55 AMG',\n",
+       "       'Model_G 63 AMG', 'Model_GL 320', 'Model_GL 350', 'Model_GL 420',\n",
+       "       'Model_GL 450', 'Model_GL 500', 'Model_GL 550', 'Model_GLC-Class',\n",
+       "       'Model_GLE-Class', 'Model_GLK 220', 'Model_GLK 300',\n",
+       "       'Model_GLS 350', 'Model_GLS 400', 'Model_GLS 500', 'Model_GLS 63',\n",
+       "       'Model_Galant', 'Model_Golf GTI', 'Model_Golf II',\n",
+       "       'Model_Golf III', 'Model_Golf IV', 'Model_Golf Plus',\n",
+       "       'Model_Golf V', 'Model_Golf VI', 'Model_Golf VII',\n",
+       "       'Model_Golf Variant', 'Model_Grand Scenic', 'Model_Grandis',\n",
+       "       'Model_Hiace', 'Model_Highlander', 'Model_Hilux', 'Model_I3',\n",
+       "       'Model_IQ', 'Model_Jetta', 'Model_Kangoo', 'Model_Koleos',\n",
+       "       'Model_L 200', 'Model_LT', 'Model_Laguna', 'Model_Lancer',\n",
+       "       'Model_Lancer Evolution', 'Model_Lancer X',\n",
+       "       'Model_Lancer X Sportback', 'Model_Land Cruiser 100',\n",
+       "       'Model_Land Cruiser 105', 'Model_Land Cruiser 200',\n",
+       "       'Model_Land Cruiser 76', 'Model_Land Cruiser 80',\n",
+       "       'Model_Land Cruiser Prado', 'Model_Latitude', 'Model_Lite Ace',\n",
+       "       'Model_Logan', 'Model_Lupo', 'Model_M5', 'Model_M6', 'Model_MB',\n",
+       "       'Model_ML 250', 'Model_ML 270', 'Model_ML 280', 'Model_ML 320',\n",
+       "       'Model_ML 350', 'Model_ML 400', 'Model_ML 430', 'Model_ML 500',\n",
+       "       'Model_ML 550', 'Model_ML 63 AMG', 'Model_Mark II', 'Model_Master',\n",
+       "       'Model_Matrix', 'Model_Megane', 'Model_Modus', 'Model_Multivan',\n",
+       "       'Model_New Beetle', 'Model_Outlander', 'Model_Outlander XL',\n",
+       "       'Model_Pajero', 'Model_Pajero Pinin', 'Model_Pajero Sport',\n",
+       "       'Model_Pajero Wagon', 'Model_Passat B2', 'Model_Passat B3',\n",
+       "       'Model_Passat B4', 'Model_Passat B5', 'Model_Passat B6',\n",
+       "       'Model_Passat B7', 'Model_Passat B8', 'Model_Passat CC',\n",
+       "       'Model_Phaeton', 'Model_Pointer', 'Model_Polo', 'Model_Previa',\n",
+       "       'Model_Prius', 'Model_Q3', 'Model_Q5', 'Model_Q7', 'Model_R 320',\n",
+       "       'Model_R8', 'Model_Rav 4', 'Model_S 140', 'Model_S 250',\n",
+       "       'Model_S 280', 'Model_S 300', 'Model_S 320', 'Model_S 350',\n",
+       "       'Model_S 400', 'Model_S 420', 'Model_S 430', 'Model_S 500',\n",
+       "       'Model_S 55', 'Model_S 550', 'Model_S 600', 'Model_S 63 AMG',\n",
+       "       'Model_S 65 AMG', 'Model_S-Guard', 'Model_S4', 'Model_S5',\n",
+       "       'Model_S8', 'Model_SL 500 (550)', 'Model_SL 55 AMG',\n",
+       "       'Model_SLK 200', 'Model_SLK 350', 'Model_Sandero',\n",
+       "       'Model_Sandero StepWay', 'Model_Scenic', 'Model_Scion',\n",
+       "       'Model_Scirocco', 'Model_Sequoia', 'Model_Sharan', 'Model_Sienna',\n",
+       "       'Model_Smart', 'Model_Space Star', 'Model_Space Wagon',\n",
+       "       'Model_Sprinter', 'Model_Sprinter 208', 'Model_Sprinter 210',\n",
+       "       'Model_Sprinter 211', 'Model_Sprinter 212', 'Model_Sprinter 213',\n",
+       "       'Model_Sprinter 311', 'Model_Sprinter 312', 'Model_Sprinter 313',\n",
+       "       'Model_Sprinter 315', 'Model_Sprinter 316', 'Model_Sprinter 318',\n",
+       "       'Model_Sprinter 319', 'Model_Sprinter 324', 'Model_Symbol',\n",
+       "       'Model_Syncro', 'Model_T2 (Transporter)', 'Model_T3 (Transporter)',\n",
+       "       'Model_T4 (Transporter)', 'Model_T4 (Transporter) ',\n",
+       "       'Model_T5 (Transporter)', 'Model_T5 (Transporter) ',\n",
+       "       'Model_T6 (Transporter)', 'Model_T6 (Transporter) ', 'Model_TT',\n",
+       "       'Model_Tacoma', 'Model_Tiguan', 'Model_Touareg', 'Model_Touran',\n",
+       "       'Model_Trafic', 'Model_Tundra', 'Model_Up', 'Model_V 250',\n",
+       "       'Model_Vaneo', 'Model_Vento', 'Model_Venza', 'Model_Viano',\n",
+       "       'Model_Virage', 'Model_Vista', 'Model_Vito', 'Model_X1',\n",
+       "       'Model_X3', 'Model_X5', 'Model_X5 M', 'Model_X6', 'Model_X6 M',\n",
+       "       'Model_Yaris', 'Model_Z3', 'Model_Z4'], dtype=object)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df\n",
+    "# Since each problem is different, that must be done manually\n",
+    "# We can display all possible features and then choose the desired order\n",
+    "pd.options.display.max_columns = 999\n",
+    "data_with_dummies.columns.values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['log_price' 'Mileage' 'EngineV' 'Year' 'Brand_BMW' 'Brand_Mercedes-Benz'\n",
+      " 'Brand_Mitsubishi' 'Brand_Renault' 'Brand_Toyota' 'Brand_Volkswagen'\n",
+      " 'Body_hatch' 'Body_other' 'Body_sedan' 'Body_vagon' 'Body_van'\n",
+      " 'Engine Type_Gas' 'Engine Type_Other' 'Engine Type_Petrol'\n",
+      " 'Registration_yes' 'Model_100' 'Model_11' 'Model_116' 'Model_118'\n",
+      " 'Model_120' 'Model_19' 'Model_190' 'Model_200' 'Model_21' 'Model_210'\n",
+      " 'Model_220' 'Model_230' 'Model_25' 'Model_250' 'Model_300' 'Model_316'\n",
+      " 'Model_318' 'Model_320' 'Model_323' 'Model_324' 'Model_325' 'Model_328'\n",
+      " 'Model_330' 'Model_335' 'Model_4 Series Gran Coupe' 'Model_428'\n",
+      " 'Model_4Runner' 'Model_5' 'Model_5 Series' 'Model_5 Series GT'\n",
+      " 'Model_520' 'Model_523' 'Model_524' 'Model_525' 'Model_528' 'Model_530'\n",
+      " 'Model_535' 'Model_540' 'Model_545' 'Model_550'\n",
+      " 'Model_6 Series Gran Coupe' 'Model_630' 'Model_640' 'Model_645'\n",
+      " 'Model_650' 'Model_730' 'Model_735' 'Model_740' 'Model_745' 'Model_750'\n",
+      " 'Model_760' 'Model_80' 'Model_9' 'Model_90' 'Model_A 140' 'Model_A 150'\n",
+      " 'Model_A 170' 'Model_A 180' 'Model_A1' 'Model_A3' 'Model_A4'\n",
+      " 'Model_A4 Allroad' 'Model_A5' 'Model_A6' 'Model_A6 Allroad' 'Model_A7'\n",
+      " 'Model_A8' 'Model_ASX' 'Model_Amarok' 'Model_Auris' 'Model_Avalon'\n",
+      " 'Model_Avensis' 'Model_Aygo' 'Model_B 170' 'Model_B 180' 'Model_B 200'\n",
+      " 'Model_Beetle' 'Model_Bora' 'Model_C-Class' 'Model_CL 180' 'Model_CL 500'\n",
+      " 'Model_CL 55 AMG' 'Model_CL 550' 'Model_CL 63 AMG' 'Model_CLA 200'\n",
+      " 'Model_CLA 220' 'Model_CLA-Class' 'Model_CLC 180' 'Model_CLC 200'\n",
+      " 'Model_CLK 200' 'Model_CLK 220' 'Model_CLK 230' 'Model_CLK 240'\n",
+      " 'Model_CLK 280' 'Model_CLK 320' 'Model_CLK 430' 'Model_CLS 350'\n",
+      " 'Model_CLS 400' 'Model_CLS 500' 'Model_CLS 63 AMG' 'Model_Caddy'\n",
+      " 'Model_Camry' 'Model_Captur' 'Model_Caravelle' 'Model_Carina'\n",
+      " 'Model_Carisma' 'Model_Celica' 'Model_Clio' 'Model_Colt' 'Model_Corolla'\n",
+      " 'Model_Corolla Verso' 'Model_Cross Touran' 'Model_Dokker' 'Model_Duster'\n",
+      " 'Model_E-Class' 'Model_Eclipse' 'Model_Eos' 'Model_Espace'\n",
+      " 'Model_FJ Cruiser' 'Model_Fluence' 'Model_Fortuner' 'Model_G 320'\n",
+      " 'Model_G 350' 'Model_G 500' 'Model_G 55 AMG' 'Model_G 63 AMG'\n",
+      " 'Model_GL 320' 'Model_GL 350' 'Model_GL 420' 'Model_GL 450'\n",
+      " 'Model_GL 500' 'Model_GL 550' 'Model_GLC-Class' 'Model_GLE-Class'\n",
+      " 'Model_GLK 220' 'Model_GLK 300' 'Model_GLS 350' 'Model_GLS 400'\n",
+      " 'Model_GLS 500' 'Model_GLS 63' 'Model_Galant' 'Model_Golf GTI'\n",
+      " 'Model_Golf II' 'Model_Golf III' 'Model_Golf IV' 'Model_Golf Plus'\n",
+      " 'Model_Golf V' 'Model_Golf VI' 'Model_Golf VII' 'Model_Golf Variant'\n",
+      " 'Model_Grand Scenic' 'Model_Grandis' 'Model_Hiace' 'Model_Highlander'\n",
+      " 'Model_Hilux' 'Model_I3' 'Model_IQ' 'Model_Jetta' 'Model_Kangoo'\n",
+      " 'Model_Koleos' 'Model_L 200' 'Model_LT' 'Model_Laguna' 'Model_Lancer'\n",
+      " 'Model_Lancer Evolution' 'Model_Lancer X' 'Model_Lancer X Sportback'\n",
+      " 'Model_Land Cruiser 100' 'Model_Land Cruiser 105'\n",
+      " 'Model_Land Cruiser 200' 'Model_Land Cruiser 76' 'Model_Land Cruiser 80'\n",
+      " 'Model_Land Cruiser Prado' 'Model_Latitude' 'Model_Lite Ace'\n",
+      " 'Model_Logan' 'Model_Lupo' 'Model_M5' 'Model_M6' 'Model_MB'\n",
+      " 'Model_ML 250' 'Model_ML 270' 'Model_ML 280' 'Model_ML 320'\n",
+      " 'Model_ML 350' 'Model_ML 400' 'Model_ML 430' 'Model_ML 500'\n",
+      " 'Model_ML 550' 'Model_ML 63 AMG' 'Model_Mark II' 'Model_Master'\n",
+      " 'Model_Matrix' 'Model_Megane' 'Model_Modus' 'Model_Multivan'\n",
+      " 'Model_New Beetle' 'Model_Outlander' 'Model_Outlander XL' 'Model_Pajero'\n",
+      " 'Model_Pajero Pinin' 'Model_Pajero Sport' 'Model_Pajero Wagon'\n",
+      " 'Model_Passat B2' 'Model_Passat B3' 'Model_Passat B4' 'Model_Passat B5'\n",
+      " 'Model_Passat B6' 'Model_Passat B7' 'Model_Passat B8' 'Model_Passat CC'\n",
+      " 'Model_Phaeton' 'Model_Pointer' 'Model_Polo' 'Model_Previa' 'Model_Prius'\n",
+      " 'Model_Q3' 'Model_Q5' 'Model_Q7' 'Model_R 320' 'Model_R8' 'Model_Rav 4'\n",
+      " 'Model_S 140' 'Model_S 250' 'Model_S 280' 'Model_S 300' 'Model_S 320'\n",
+      " 'Model_S 350' 'Model_S 400' 'Model_S 420' 'Model_S 430' 'Model_S 500'\n",
+      " 'Model_S 55' 'Model_S 550' 'Model_S 600' 'Model_S 63 AMG'\n",
+      " 'Model_S 65 AMG' 'Model_S-Guard' 'Model_S4' 'Model_S5' 'Model_S8'\n",
+      " 'Model_SL 500 (550)' 'Model_SL 55 AMG' 'Model_SLK 200' 'Model_SLK 350'\n",
+      " 'Model_Sandero' 'Model_Sandero StepWay' 'Model_Scenic' 'Model_Scion'\n",
+      " 'Model_Scirocco' 'Model_Sequoia' 'Model_Sharan' 'Model_Sienna'\n",
+      " 'Model_Smart' 'Model_Space Star' 'Model_Space Wagon' 'Model_Sprinter'\n",
+      " 'Model_Sprinter 208' 'Model_Sprinter 210' 'Model_Sprinter 211'\n",
+      " 'Model_Sprinter 212' 'Model_Sprinter 213' 'Model_Sprinter 311'\n",
+      " 'Model_Sprinter 312' 'Model_Sprinter 313' 'Model_Sprinter 315'\n",
+      " 'Model_Sprinter 316' 'Model_Sprinter 318' 'Model_Sprinter 319'\n",
+      " 'Model_Sprinter 324' 'Model_Symbol' 'Model_Syncro'\n",
+      " 'Model_T2 (Transporter)' 'Model_T3 (Transporter)'\n",
+      " 'Model_T4 (Transporter)' 'Model_T4 (Transporter) '\n",
+      " 'Model_T5 (Transporter)' 'Model_T5 (Transporter) '\n",
+      " 'Model_T6 (Transporter)' 'Model_T6 (Transporter) ' 'Model_TT'\n",
+      " 'Model_Tacoma' 'Model_Tiguan' 'Model_Touareg' 'Model_Touran'\n",
+      " 'Model_Trafic' 'Model_Tundra' 'Model_Up' 'Model_V 250' 'Model_Vaneo'\n",
+      " 'Model_Vento' 'Model_Venza' 'Model_Viano' 'Model_Virage' 'Model_Vista'\n",
+      " 'Model_Vito' 'Model_X1' 'Model_X3' 'Model_X5' 'Model_X5 M' 'Model_X6'\n",
+      " 'Model_X6 M' 'Model_Yaris' 'Model_Z3' 'Model_Z4']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a list of column names with 'log_price' at the beginning\n",
+    "new_order = ['log_price'] + [col for col in data_with_dummies.columns if col != 'log_price']\n",
+    "\n",
+    "# Reorder the columns in the DataFrame\n",
+    "data_with_dummies = data_with_dummies[new_order]\n",
+    "\n",
+    "# Now, when you print the column names, 'log_price' should be the first column\n",
+    "print(data_with_dummies.columns.values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log_price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.342840</td>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.495519</td>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.043249</td>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.814656</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   log_price  Mileage  EngineV  Year  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0   8.342840      277      2.0  1991       True                False   \n",
+       "1   8.974618      427      2.9  1999      False                 True   \n",
+       "2   9.495519      358      5.0  2003      False                 True   \n",
+       "3  10.043249      240      4.2  2007      False                False   \n",
+       "4   9.814656      120      2.0  2011      False                False   \n",
+       "\n",
+       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0             False          False         False             False   \n",
+       "1             False          False         False             False   \n",
+       "2             False          False         False             False   \n",
+       "3             False          False         False             False   \n",
+       "4             False          False          True             False   \n",
+       "\n",
+       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
+       "0       False       False        True       False     False            False   \n",
+       "1       False       False       False       False      True            False   \n",
+       "2       False       False        True       False     False             True   \n",
+       "3       False       False       False       False     False            False   \n",
+       "4       False       False       False       False     False            False   \n",
+       "\n",
+       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  Model_100  \\\n",
+       "0              False                True              True      False   \n",
+       "1              False               False              True      False   \n",
+       "2              False               False              True      False   \n",
+       "3              False                True              True      False   \n",
+       "4              False                True              True      False   \n",
+       "\n",
+       "   Model_11  Model_116  Model_118  Model_120  Model_19  Model_190  Model_200  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_21  Model_210  Model_220  Model_230  Model_25  Model_250  Model_300  \\\n",
+       "0     False      False      False      False     False      False      False   \n",
+       "1     False      False      False      False     False      False      False   \n",
+       "2     False      False      False      False     False      False      False   \n",
+       "3     False      False      False      False     False      False      False   \n",
+       "4     False      False      False      False     False      False      False   \n",
+       "\n",
+       "   Model_316  Model_318  Model_320  Model_323  Model_324  Model_325  \\\n",
+       "0      False      False       True      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_328  Model_330  Model_335  Model_4 Series Gran Coupe  Model_428  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_4Runner  Model_5  Model_5 Series  Model_5 Series GT  Model_520  \\\n",
+       "0          False    False           False              False      False   \n",
+       "1          False    False           False              False      False   \n",
+       "2          False    False           False              False      False   \n",
+       "3          False    False           False              False      False   \n",
+       "4          False    False           False              False      False   \n",
+       "\n",
+       "   Model_523  Model_524  Model_525  Model_528  Model_530  Model_535  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_540  Model_545  Model_550  Model_6 Series Gran Coupe  Model_630  \\\n",
+       "0      False      False      False                      False      False   \n",
+       "1      False      False      False                      False      False   \n",
+       "2      False      False      False                      False      False   \n",
+       "3      False      False      False                      False      False   \n",
+       "4      False      False      False                      False      False   \n",
+       "\n",
+       "   Model_640  Model_645  Model_650  Model_730  Model_735  Model_740  \\\n",
+       "0      False      False      False      False      False      False   \n",
+       "1      False      False      False      False      False      False   \n",
+       "2      False      False      False      False      False      False   \n",
+       "3      False      False      False      False      False      False   \n",
+       "4      False      False      False      False      False      False   \n",
+       "\n",
+       "   Model_745  Model_750  Model_760  Model_80  Model_9  Model_90  Model_A 140  \\\n",
+       "0      False      False      False     False    False     False        False   \n",
+       "1      False      False      False     False    False     False        False   \n",
+       "2      False      False      False     False    False     False        False   \n",
+       "3      False      False      False     False    False     False        False   \n",
+       "4      False      False      False     False    False     False        False   \n",
+       "\n",
+       "   Model_A 150  Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0        False        False        False     False     False     False   \n",
+       "1        False        False        False     False     False     False   \n",
+       "2        False        False        False     False     False     False   \n",
+       "3        False        False        False     False     False     False   \n",
+       "4        False        False        False     False     False     False   \n",
+       "\n",
+       "   Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  Model_A8  \\\n",
+       "0             False     False     False             False     False     False   \n",
+       "1             False     False     False             False     False     False   \n",
+       "2             False     False     False             False     False     False   \n",
+       "3             False     False     False             False     False     False   \n",
+       "4             False     False     False             False     False     False   \n",
+       "\n",
+       "   Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  Model_Avensis  \\\n",
+       "0      False         False        False         False          False   \n",
+       "1      False         False        False         False          False   \n",
+       "2      False         False        False         False          False   \n",
+       "3      False         False        False         False          False   \n",
+       "4      False         False        False         False          False   \n",
+       "\n",
+       "   Model_Aygo  Model_B 170  Model_B 180  Model_B 200  Model_Beetle  \\\n",
+       "0       False        False        False        False         False   \n",
+       "1       False        False        False        False         False   \n",
+       "2       False        False        False        False         False   \n",
+       "3       False        False        False        False         False   \n",
+       "4       False        False        False        False         False   \n",
+       "\n",
+       "   Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  Model_CL 55 AMG  \\\n",
+       "0       False          False         False         False            False   \n",
+       "1       False          False         False         False            False   \n",
+       "2       False          False         False         False            False   \n",
+       "3       False          False         False         False            False   \n",
+       "4       False          False         False         False            False   \n",
+       "\n",
+       "   Model_CL 550  Model_CL 63 AMG  Model_CLA 200  Model_CLA 220  \\\n",
+       "0         False            False          False          False   \n",
+       "1         False            False          False          False   \n",
+       "2         False            False          False          False   \n",
+       "3         False            False          False          False   \n",
+       "4         False            False          False          False   \n",
+       "\n",
+       "   Model_CLA-Class  Model_CLC 180  Model_CLC 200  Model_CLK 200  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 220  Model_CLK 230  Model_CLK 240  Model_CLK 280  Model_CLK 320  \\\n",
+       "0          False          False          False          False          False   \n",
+       "1          False          False          False          False          False   \n",
+       "2          False          False          False          False          False   \n",
+       "3          False          False          False          False          False   \n",
+       "4          False          False          False          False          False   \n",
+       "\n",
+       "   Model_CLK 430  Model_CLS 350  Model_CLS 400  Model_CLS 500  \\\n",
+       "0          False          False          False          False   \n",
+       "1          False          False          False          False   \n",
+       "2          False          False          False          False   \n",
+       "3          False          False          False          False   \n",
+       "4          False          False          False          False   \n",
+       "\n",
+       "   Model_CLS 63 AMG  Model_Caddy  Model_Camry  Model_Captur  Model_Caravelle  \\\n",
+       "0             False        False        False         False            False   \n",
+       "1             False        False        False         False            False   \n",
+       "2             False        False        False         False            False   \n",
+       "3             False        False        False         False            False   \n",
+       "4             False        False        False         False            False   \n",
+       "\n",
+       "   Model_Carina  Model_Carisma  Model_Celica  Model_Clio  Model_Colt  \\\n",
+       "0         False          False         False       False       False   \n",
+       "1         False          False         False       False       False   \n",
+       "2         False          False         False       False       False   \n",
+       "3         False          False         False       False       False   \n",
+       "4         False          False         False       False       False   \n",
+       "\n",
+       "   Model_Corolla  Model_Corolla Verso  Model_Cross Touran  Model_Dokker  \\\n",
+       "0          False                False               False         False   \n",
+       "1          False                False               False         False   \n",
+       "2          False                False               False         False   \n",
+       "3          False                False               False         False   \n",
+       "4          False                False               False         False   \n",
+       "\n",
+       "   Model_Duster  Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  \\\n",
+       "0         False          False          False      False         False   \n",
+       "1         False          False          False      False         False   \n",
+       "2         False          False          False      False         False   \n",
+       "3         False          False          False      False         False   \n",
+       "4         False          False          False      False         False   \n",
+       "\n",
+       "   Model_FJ Cruiser  Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  \\\n",
+       "0             False          False           False        False        False   \n",
+       "1             False          False           False        False        False   \n",
+       "2             False          False           False        False        False   \n",
+       "3             False          False           False        False        False   \n",
+       "4             False          False           False        False        False   \n",
+       "\n",
+       "   Model_G 500  Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0        False           False           False         False         False   \n",
+       "1        False           False           False         False         False   \n",
+       "2        False           False           False         False         False   \n",
+       "3        False           False           False         False         False   \n",
+       "4        False           False           False         False         False   \n",
+       "\n",
+       "   Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  Model_Golf GTI  \\\n",
+       "0          False          False         False         False           False   \n",
+       "1          False          False         False         False           False   \n",
+       "2          False          False         False         False           False   \n",
+       "3          False          False         False         False           False   \n",
+       "4          False          False         False         False           False   \n",
+       "\n",
+       "   Model_Golf II  Model_Golf III  Model_Golf IV  Model_Golf Plus  \\\n",
+       "0          False           False          False            False   \n",
+       "1          False           False          False            False   \n",
+       "2          False           False          False            False   \n",
+       "3          False           False          False            False   \n",
+       "4          False           False          False            False   \n",
+       "\n",
+       "   Model_Golf V  Model_Golf VI  Model_Golf VII  Model_Golf Variant  \\\n",
+       "0         False          False           False               False   \n",
+       "1         False          False           False               False   \n",
+       "2         False          False           False               False   \n",
+       "3         False          False           False               False   \n",
+       "4         False          False           False               False   \n",
+       "\n",
+       "   Model_Grand Scenic  Model_Grandis  Model_Hiace  Model_Highlander  \\\n",
+       "0               False          False        False             False   \n",
+       "1               False          False        False             False   \n",
+       "2               False          False        False             False   \n",
+       "3               False          False        False             False   \n",
+       "4               False          False        False             False   \n",
+       "\n",
+       "   Model_Hilux  Model_I3  Model_IQ  Model_Jetta  Model_Kangoo  Model_Koleos  \\\n",
+       "0        False     False     False        False         False         False   \n",
+       "1        False     False     False        False         False         False   \n",
+       "2        False     False     False        False         False         False   \n",
+       "3        False     False     False        False         False         False   \n",
+       "4        False     False     False        False         False         False   \n",
+       "\n",
+       "   Model_L 200  Model_LT  Model_Laguna  Model_Lancer  Model_Lancer Evolution  \\\n",
+       "0        False     False         False         False                   False   \n",
+       "1        False     False         False         False                   False   \n",
+       "2        False     False         False         False                   False   \n",
+       "3        False     False         False         False                   False   \n",
+       "4        False     False         False         False                   False   \n",
+       "\n",
+       "   Model_Lancer X  Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0           False                     False                   False   \n",
+       "1           False                     False                   False   \n",
+       "2           False                     False                   False   \n",
+       "3           False                     False                   False   \n",
+       "4           False                     False                   False   \n",
+       "\n",
+       "   Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                   False                   False                  False   \n",
+       "1                   False                   False                  False   \n",
+       "2                   False                   False                  False   \n",
+       "3                   False                   False                  False   \n",
+       "4                   False                   False                  False   \n",
+       "\n",
+       "   Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                  False                     False           False   \n",
+       "1                  False                     False           False   \n",
+       "2                  False                     False           False   \n",
+       "3                  False                     False           False   \n",
+       "4                  False                     False           False   \n",
+       "\n",
+       "   Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0           False        False       False     False     False     False   \n",
+       "1           False        False       False     False     False     False   \n",
+       "2           False        False       False     False     False     False   \n",
+       "3           False        False       False     False     False     False   \n",
+       "4           False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0         False         False         False         False         False   \n",
+       "1         False         False         False         False         False   \n",
+       "2         False         False         False         False         False   \n",
+       "3         False         False         False         False         False   \n",
+       "4         False         False         False         False         False   \n",
+       "\n",
+       "   Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0         False         False         False         False            False   \n",
+       "1         False         False         False         False            False   \n",
+       "2         False         False         False         False            False   \n",
+       "3         False         False         False         False            False   \n",
+       "4         False         False         False         False            False   \n",
+       "\n",
+       "   Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0          False         False         False         False        False   \n",
+       "1          False         False         False         False        False   \n",
+       "2          False         False         False         False        False   \n",
+       "3          False         False         False         False        False   \n",
+       "4          False         False         False         False        False   \n",
+       "\n",
+       "   Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0           False             False            False               False   \n",
+       "1           False             False            False               False   \n",
+       "2           False             False            False               False   \n",
+       "3           False             False            False               False   \n",
+       "4           False             False            False               False   \n",
+       "\n",
+       "   Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  Model_Pajero Wagon  \\\n",
+       "0         False               False               False               False   \n",
+       "1         False               False               False               False   \n",
+       "2         False               False               False               False   \n",
+       "3         False               False               False               False   \n",
+       "4         False               False               False               False   \n",
+       "\n",
+       "   Model_Passat B2  Model_Passat B3  Model_Passat B4  Model_Passat B5  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Passat B6  Model_Passat B7  Model_Passat B8  Model_Passat CC  \\\n",
+       "0            False            False            False            False   \n",
+       "1            False            False            False            False   \n",
+       "2            False            False            False            False   \n",
+       "3            False            False            False            False   \n",
+       "4            False            False            False            False   \n",
+       "\n",
+       "   Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  Model_Prius  \\\n",
+       "0          False          False       False         False        False   \n",
+       "1          False          False       False         False        False   \n",
+       "2          False          False       False         False        False   \n",
+       "3          False          False       False         False        False   \n",
+       "4          False          False       False         False        False   \n",
+       "\n",
+       "   Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  Model_Rav 4  \\\n",
+       "0     False     False     False        False     False        False   \n",
+       "1     False     False     False        False     False        False   \n",
+       "2     False     False     False        False     False        False   \n",
+       "3     False     False      True        False     False        False   \n",
+       "4     False     False     False        False     False         True   \n",
+       "\n",
+       "   Model_S 140  Model_S 250  Model_S 280  Model_S 300  Model_S 320  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False        False   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 350  Model_S 400  Model_S 420  Model_S 430  Model_S 500  \\\n",
+       "0        False        False        False        False        False   \n",
+       "1        False        False        False        False        False   \n",
+       "2        False        False        False        False         True   \n",
+       "3        False        False        False        False        False   \n",
+       "4        False        False        False        False        False   \n",
+       "\n",
+       "   Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  Model_S 65 AMG  \\\n",
+       "0       False        False        False           False           False   \n",
+       "1       False        False        False           False           False   \n",
+       "2       False        False        False           False           False   \n",
+       "3       False        False        False           False           False   \n",
+       "4       False        False        False           False           False   \n",
+       "\n",
+       "   Model_S-Guard  Model_S4  Model_S5  Model_S8  Model_SL 500 (550)  \\\n",
+       "0          False     False     False     False               False   \n",
+       "1          False     False     False     False               False   \n",
+       "2          False     False     False     False               False   \n",
+       "3          False     False     False     False               False   \n",
+       "4          False     False     False     False               False   \n",
+       "\n",
+       "   Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  Model_Sandero  \\\n",
+       "0            False          False          False          False   \n",
+       "1            False          False          False          False   \n",
+       "2            False          False          False          False   \n",
+       "3            False          False          False          False   \n",
+       "4            False          False          False          False   \n",
+       "\n",
+       "   Model_Sandero StepWay  Model_Scenic  Model_Scion  Model_Scirocco  \\\n",
+       "0                  False         False        False           False   \n",
+       "1                  False         False        False           False   \n",
+       "2                  False         False        False           False   \n",
+       "3                  False         False        False           False   \n",
+       "4                  False         False        False           False   \n",
+       "\n",
+       "   Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  Model_Space Star  \\\n",
+       "0          False         False         False        False             False   \n",
+       "1          False         False         False        False             False   \n",
+       "2          False         False         False        False             False   \n",
+       "3          False         False         False        False             False   \n",
+       "4          False         False         False        False             False   \n",
+       "\n",
+       "   Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  Model_Sprinter 210  \\\n",
+       "0              False           False               False               False   \n",
+       "1              False           False               False               False   \n",
+       "2              False           False               False               False   \n",
+       "3              False           False               False               False   \n",
+       "4              False           False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 211  Model_Sprinter 212  Model_Sprinter 213  \\\n",
+       "0               False               False               False   \n",
+       "1               False                True               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 311  Model_Sprinter 312  Model_Sprinter 313  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 315  Model_Sprinter 316  Model_Sprinter 318  \\\n",
+       "0               False               False               False   \n",
+       "1               False               False               False   \n",
+       "2               False               False               False   \n",
+       "3               False               False               False   \n",
+       "4               False               False               False   \n",
+       "\n",
+       "   Model_Sprinter 319  Model_Sprinter 324  Model_Symbol  Model_Syncro  \\\n",
+       "0               False               False         False         False   \n",
+       "1               False               False         False         False   \n",
+       "2               False               False         False         False   \n",
+       "3               False               False         False         False   \n",
+       "4               False               False         False         False   \n",
+       "\n",
+       "   Model_T2 (Transporter)  Model_T3 (Transporter)  Model_T4 (Transporter)  \\\n",
+       "0                   False                   False                   False   \n",
+       "1                   False                   False                   False   \n",
+       "2                   False                   False                   False   \n",
+       "3                   False                   False                   False   \n",
+       "4                   False                   False                   False   \n",
+       "\n",
+       "   Model_T4 (Transporter)   Model_T5 (Transporter)  Model_T5 (Transporter)   \\\n",
+       "0                    False                   False                    False   \n",
+       "1                    False                   False                    False   \n",
+       "2                    False                   False                    False   \n",
+       "3                    False                   False                    False   \n",
+       "4                    False                   False                    False   \n",
+       "\n",
+       "   Model_T6 (Transporter)  Model_T6 (Transporter)   Model_TT  Model_Tacoma  \\\n",
+       "0                   False                    False     False         False   \n",
+       "1                   False                    False     False         False   \n",
+       "2                   False                    False     False         False   \n",
+       "3                   False                    False     False         False   \n",
+       "4                   False                    False     False         False   \n",
+       "\n",
+       "   Model_Tiguan  Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  \\\n",
+       "0         False          False         False         False         False   \n",
+       "1         False          False         False         False         False   \n",
+       "2         False          False         False         False         False   \n",
+       "3         False          False         False         False         False   \n",
+       "4         False          False         False         False         False   \n",
+       "\n",
+       "   Model_Up  Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0     False        False        False        False        False        False   \n",
+       "1     False        False        False        False        False        False   \n",
+       "2     False        False        False        False        False        False   \n",
+       "3     False        False        False        False        False        False   \n",
+       "4     False        False        False        False        False        False   \n",
+       "\n",
+       "   Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0         False        False       False     False     False     False   \n",
+       "1         False        False       False     False     False     False   \n",
+       "2         False        False       False     False     False     False   \n",
+       "3         False        False       False     False     False     False   \n",
+       "4         False        False       False     False     False     False   \n",
+       "\n",
+       "   Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0       False     False       False        False     False     False  \n",
+       "1       False     False       False        False     False     False  \n",
+       "2       False     False       False        False     False     False  \n",
+       "3       False     False       False        False     False     False  \n",
+       "4       False     False       False        False     False     False  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_preprocessed = data_with_dummies\n",
+    "data_preprocessed.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear regression model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Declare the inputs and the targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = data_preprocessed['log_price'] #  Dependent variable\n",
+    "#Input are everything but the dependent variable, so i'll drop 'log_price'\n",
+    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>EngineV</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Brand_BMW</th>\n",
+       "      <th>Brand_Mercedes-Benz</th>\n",
+       "      <th>Brand_Mitsubishi</th>\n",
+       "      <th>Brand_Renault</th>\n",
+       "      <th>Brand_Toyota</th>\n",
+       "      <th>Brand_Volkswagen</th>\n",
+       "      <th>Body_hatch</th>\n",
+       "      <th>Body_other</th>\n",
+       "      <th>Body_sedan</th>\n",
+       "      <th>Body_vagon</th>\n",
+       "      <th>Body_van</th>\n",
+       "      <th>Engine Type_Gas</th>\n",
+       "      <th>Engine Type_Other</th>\n",
+       "      <th>Engine Type_Petrol</th>\n",
+       "      <th>Registration_yes</th>\n",
+       "      <th>Model_100</th>\n",
+       "      <th>Model_11</th>\n",
+       "      <th>Model_116</th>\n",
+       "      <th>Model_118</th>\n",
+       "      <th>Model_120</th>\n",
+       "      <th>Model_19</th>\n",
+       "      <th>Model_190</th>\n",
+       "      <th>Model_200</th>\n",
+       "      <th>Model_21</th>\n",
+       "      <th>Model_210</th>\n",
+       "      <th>Model_220</th>\n",
+       "      <th>Model_230</th>\n",
+       "      <th>Model_25</th>\n",
+       "      <th>Model_250</th>\n",
+       "      <th>Model_300</th>\n",
+       "      <th>Model_316</th>\n",
+       "      <th>Model_318</th>\n",
+       "      <th>Model_320</th>\n",
+       "      <th>Model_323</th>\n",
+       "      <th>Model_324</th>\n",
+       "      <th>Model_325</th>\n",
+       "      <th>Model_328</th>\n",
+       "      <th>Model_330</th>\n",
+       "      <th>Model_335</th>\n",
+       "      <th>Model_4 Series Gran Coupe</th>\n",
+       "      <th>Model_428</th>\n",
+       "      <th>Model_4Runner</th>\n",
+       "      <th>Model_5</th>\n",
+       "      <th>Model_5 Series</th>\n",
+       "      <th>Model_5 Series GT</th>\n",
+       "      <th>Model_520</th>\n",
+       "      <th>Model_523</th>\n",
+       "      <th>Model_524</th>\n",
+       "      <th>Model_525</th>\n",
+       "      <th>Model_528</th>\n",
+       "      <th>Model_530</th>\n",
+       "      <th>Model_535</th>\n",
+       "      <th>Model_540</th>\n",
+       "      <th>Model_545</th>\n",
+       "      <th>Model_550</th>\n",
+       "      <th>Model_6 Series Gran Coupe</th>\n",
+       "      <th>Model_630</th>\n",
+       "      <th>Model_640</th>\n",
+       "      <th>Model_645</th>\n",
+       "      <th>Model_650</th>\n",
+       "      <th>Model_730</th>\n",
+       "      <th>Model_735</th>\n",
+       "      <th>Model_740</th>\n",
+       "      <th>Model_745</th>\n",
+       "      <th>Model_750</th>\n",
+       "      <th>Model_760</th>\n",
+       "      <th>Model_80</th>\n",
+       "      <th>Model_9</th>\n",
+       "      <th>Model_90</th>\n",
+       "      <th>Model_A 140</th>\n",
+       "      <th>Model_A 150</th>\n",
+       "      <th>Model_A 170</th>\n",
+       "      <th>Model_A 180</th>\n",
+       "      <th>Model_A1</th>\n",
+       "      <th>Model_A3</th>\n",
+       "      <th>Model_A4</th>\n",
+       "      <th>Model_A4 Allroad</th>\n",
+       "      <th>Model_A5</th>\n",
+       "      <th>Model_A6</th>\n",
+       "      <th>Model_A6 Allroad</th>\n",
+       "      <th>Model_A7</th>\n",
+       "      <th>Model_A8</th>\n",
+       "      <th>Model_ASX</th>\n",
+       "      <th>Model_Amarok</th>\n",
+       "      <th>Model_Auris</th>\n",
+       "      <th>Model_Avalon</th>\n",
+       "      <th>Model_Avensis</th>\n",
+       "      <th>Model_Aygo</th>\n",
+       "      <th>Model_B 170</th>\n",
+       "      <th>Model_B 180</th>\n",
+       "      <th>Model_B 200</th>\n",
+       "      <th>Model_Beetle</th>\n",
+       "      <th>Model_Bora</th>\n",
+       "      <th>Model_C-Class</th>\n",
+       "      <th>Model_CL 180</th>\n",
+       "      <th>Model_CL 500</th>\n",
+       "      <th>Model_CL 55 AMG</th>\n",
+       "      <th>Model_CL 550</th>\n",
+       "      <th>Model_CL 63 AMG</th>\n",
+       "      <th>Model_CLA 200</th>\n",
+       "      <th>Model_CLA 220</th>\n",
+       "      <th>Model_CLA-Class</th>\n",
+       "      <th>Model_CLC 180</th>\n",
+       "      <th>Model_CLC 200</th>\n",
+       "      <th>Model_CLK 200</th>\n",
+       "      <th>Model_CLK 220</th>\n",
+       "      <th>Model_CLK 230</th>\n",
+       "      <th>Model_CLK 240</th>\n",
+       "      <th>Model_CLK 280</th>\n",
+       "      <th>Model_CLK 320</th>\n",
+       "      <th>Model_CLK 430</th>\n",
+       "      <th>Model_CLS 350</th>\n",
+       "      <th>Model_CLS 400</th>\n",
+       "      <th>Model_CLS 500</th>\n",
+       "      <th>Model_CLS 63 AMG</th>\n",
+       "      <th>Model_Caddy</th>\n",
+       "      <th>Model_Camry</th>\n",
+       "      <th>Model_Captur</th>\n",
+       "      <th>Model_Caravelle</th>\n",
+       "      <th>Model_Carina</th>\n",
+       "      <th>Model_Carisma</th>\n",
+       "      <th>Model_Celica</th>\n",
+       "      <th>Model_Clio</th>\n",
+       "      <th>Model_Colt</th>\n",
+       "      <th>Model_Corolla</th>\n",
+       "      <th>Model_Corolla Verso</th>\n",
+       "      <th>Model_Cross Touran</th>\n",
+       "      <th>Model_Dokker</th>\n",
+       "      <th>Model_Duster</th>\n",
+       "      <th>Model_E-Class</th>\n",
+       "      <th>Model_Eclipse</th>\n",
+       "      <th>Model_Eos</th>\n",
+       "      <th>Model_Espace</th>\n",
+       "      <th>Model_FJ Cruiser</th>\n",
+       "      <th>Model_Fluence</th>\n",
+       "      <th>Model_Fortuner</th>\n",
+       "      <th>Model_G 320</th>\n",
+       "      <th>Model_G 350</th>\n",
+       "      <th>Model_G 500</th>\n",
+       "      <th>Model_G 55 AMG</th>\n",
+       "      <th>Model_G 63 AMG</th>\n",
+       "      <th>Model_GL 320</th>\n",
+       "      <th>Model_GL 350</th>\n",
+       "      <th>Model_GL 420</th>\n",
+       "      <th>Model_GL 450</th>\n",
+       "      <th>Model_GL 500</th>\n",
+       "      <th>Model_GL 550</th>\n",
+       "      <th>Model_GLC-Class</th>\n",
+       "      <th>Model_GLE-Class</th>\n",
+       "      <th>Model_GLK 220</th>\n",
+       "      <th>Model_GLK 300</th>\n",
+       "      <th>Model_GLS 350</th>\n",
+       "      <th>Model_GLS 400</th>\n",
+       "      <th>Model_GLS 500</th>\n",
+       "      <th>Model_GLS 63</th>\n",
+       "      <th>Model_Galant</th>\n",
+       "      <th>Model_Golf GTI</th>\n",
+       "      <th>Model_Golf II</th>\n",
+       "      <th>Model_Golf III</th>\n",
+       "      <th>Model_Golf IV</th>\n",
+       "      <th>Model_Golf Plus</th>\n",
+       "      <th>Model_Golf V</th>\n",
+       "      <th>Model_Golf VI</th>\n",
+       "      <th>Model_Golf VII</th>\n",
+       "      <th>Model_Golf Variant</th>\n",
+       "      <th>Model_Grand Scenic</th>\n",
+       "      <th>Model_Grandis</th>\n",
+       "      <th>Model_Hiace</th>\n",
+       "      <th>Model_Highlander</th>\n",
+       "      <th>Model_Hilux</th>\n",
+       "      <th>Model_I3</th>\n",
+       "      <th>Model_IQ</th>\n",
+       "      <th>Model_Jetta</th>\n",
+       "      <th>Model_Kangoo</th>\n",
+       "      <th>Model_Koleos</th>\n",
+       "      <th>Model_L 200</th>\n",
+       "      <th>Model_LT</th>\n",
+       "      <th>Model_Laguna</th>\n",
+       "      <th>Model_Lancer</th>\n",
+       "      <th>Model_Lancer Evolution</th>\n",
+       "      <th>Model_Lancer X</th>\n",
+       "      <th>Model_Lancer X Sportback</th>\n",
+       "      <th>Model_Land Cruiser 100</th>\n",
+       "      <th>Model_Land Cruiser 105</th>\n",
+       "      <th>Model_Land Cruiser 200</th>\n",
+       "      <th>Model_Land Cruiser 76</th>\n",
+       "      <th>Model_Land Cruiser 80</th>\n",
+       "      <th>Model_Land Cruiser Prado</th>\n",
+       "      <th>Model_Latitude</th>\n",
+       "      <th>Model_Lite Ace</th>\n",
+       "      <th>Model_Logan</th>\n",
+       "      <th>Model_Lupo</th>\n",
+       "      <th>Model_M5</th>\n",
+       "      <th>Model_M6</th>\n",
+       "      <th>Model_MB</th>\n",
+       "      <th>Model_ML 250</th>\n",
+       "      <th>Model_ML 270</th>\n",
+       "      <th>Model_ML 280</th>\n",
+       "      <th>Model_ML 320</th>\n",
+       "      <th>Model_ML 350</th>\n",
+       "      <th>Model_ML 400</th>\n",
+       "      <th>Model_ML 430</th>\n",
+       "      <th>Model_ML 500</th>\n",
+       "      <th>Model_ML 550</th>\n",
+       "      <th>Model_ML 63 AMG</th>\n",
+       "      <th>Model_Mark II</th>\n",
+       "      <th>Model_Master</th>\n",
+       "      <th>Model_Matrix</th>\n",
+       "      <th>Model_Megane</th>\n",
+       "      <th>Model_Modus</th>\n",
+       "      <th>Model_Multivan</th>\n",
+       "      <th>Model_New Beetle</th>\n",
+       "      <th>Model_Outlander</th>\n",
+       "      <th>Model_Outlander XL</th>\n",
+       "      <th>Model_Pajero</th>\n",
+       "      <th>Model_Pajero Pinin</th>\n",
+       "      <th>Model_Pajero Sport</th>\n",
+       "      <th>Model_Pajero Wagon</th>\n",
+       "      <th>Model_Passat B2</th>\n",
+       "      <th>Model_Passat B3</th>\n",
+       "      <th>Model_Passat B4</th>\n",
+       "      <th>Model_Passat B5</th>\n",
+       "      <th>Model_Passat B6</th>\n",
+       "      <th>Model_Passat B7</th>\n",
+       "      <th>Model_Passat B8</th>\n",
+       "      <th>Model_Passat CC</th>\n",
+       "      <th>Model_Phaeton</th>\n",
+       "      <th>Model_Pointer</th>\n",
+       "      <th>Model_Polo</th>\n",
+       "      <th>Model_Previa</th>\n",
+       "      <th>Model_Prius</th>\n",
+       "      <th>Model_Q3</th>\n",
+       "      <th>Model_Q5</th>\n",
+       "      <th>Model_Q7</th>\n",
+       "      <th>Model_R 320</th>\n",
+       "      <th>Model_R8</th>\n",
+       "      <th>Model_Rav 4</th>\n",
+       "      <th>Model_S 140</th>\n",
+       "      <th>Model_S 250</th>\n",
+       "      <th>Model_S 280</th>\n",
+       "      <th>Model_S 300</th>\n",
+       "      <th>Model_S 320</th>\n",
+       "      <th>Model_S 350</th>\n",
+       "      <th>Model_S 400</th>\n",
+       "      <th>Model_S 420</th>\n",
+       "      <th>Model_S 430</th>\n",
+       "      <th>Model_S 500</th>\n",
+       "      <th>Model_S 55</th>\n",
+       "      <th>Model_S 550</th>\n",
+       "      <th>Model_S 600</th>\n",
+       "      <th>Model_S 63 AMG</th>\n",
+       "      <th>Model_S 65 AMG</th>\n",
+       "      <th>Model_S-Guard</th>\n",
+       "      <th>Model_S4</th>\n",
+       "      <th>Model_S5</th>\n",
+       "      <th>Model_S8</th>\n",
+       "      <th>Model_SL 500 (550)</th>\n",
+       "      <th>Model_SL 55 AMG</th>\n",
+       "      <th>Model_SLK 200</th>\n",
+       "      <th>Model_SLK 350</th>\n",
+       "      <th>Model_Sandero</th>\n",
+       "      <th>Model_Sandero StepWay</th>\n",
+       "      <th>Model_Scenic</th>\n",
+       "      <th>Model_Scion</th>\n",
+       "      <th>Model_Scirocco</th>\n",
+       "      <th>Model_Sequoia</th>\n",
+       "      <th>Model_Sharan</th>\n",
+       "      <th>Model_Sienna</th>\n",
+       "      <th>Model_Smart</th>\n",
+       "      <th>Model_Space Star</th>\n",
+       "      <th>Model_Space Wagon</th>\n",
+       "      <th>Model_Sprinter</th>\n",
+       "      <th>Model_Sprinter 208</th>\n",
+       "      <th>Model_Sprinter 210</th>\n",
+       "      <th>Model_Sprinter 211</th>\n",
+       "      <th>Model_Sprinter 212</th>\n",
+       "      <th>Model_Sprinter 213</th>\n",
+       "      <th>Model_Sprinter 311</th>\n",
+       "      <th>Model_Sprinter 312</th>\n",
+       "      <th>Model_Sprinter 313</th>\n",
+       "      <th>Model_Sprinter 315</th>\n",
+       "      <th>Model_Sprinter 316</th>\n",
+       "      <th>Model_Sprinter 318</th>\n",
+       "      <th>Model_Sprinter 319</th>\n",
+       "      <th>Model_Sprinter 324</th>\n",
+       "      <th>Model_Symbol</th>\n",
+       "      <th>Model_Syncro</th>\n",
+       "      <th>Model_T2 (Transporter)</th>\n",
+       "      <th>Model_T3 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T4 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T5 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_T6 (Transporter)</th>\n",
+       "      <th>Model_TT</th>\n",
+       "      <th>Model_Tacoma</th>\n",
+       "      <th>Model_Tiguan</th>\n",
+       "      <th>Model_Touareg</th>\n",
+       "      <th>Model_Touran</th>\n",
+       "      <th>Model_Trafic</th>\n",
+       "      <th>Model_Tundra</th>\n",
+       "      <th>Model_Up</th>\n",
+       "      <th>Model_V 250</th>\n",
+       "      <th>Model_Vaneo</th>\n",
+       "      <th>Model_Vento</th>\n",
+       "      <th>Model_Venza</th>\n",
+       "      <th>Model_Viano</th>\n",
+       "      <th>Model_Virage</th>\n",
+       "      <th>Model_Vista</th>\n",
+       "      <th>Model_Vito</th>\n",
+       "      <th>Model_X1</th>\n",
+       "      <th>Model_X3</th>\n",
+       "      <th>Model_X5</th>\n",
+       "      <th>Model_X5 M</th>\n",
+       "      <th>Model_X6</th>\n",
+       "      <th>Model_X6 M</th>\n",
+       "      <th>Model_Yaris</th>\n",
+       "      <th>Model_Z3</th>\n",
+       "      <th>Model_Z4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>277</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>120</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4339</th>\n",
+       "      <td>35</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4340</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4341</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>True</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4344</th>\n",
+       "      <td>124</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4025 rows × 323 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Mileage  EngineV  Year  Brand_BMW  Brand_Mercedes-Benz  \\\n",
+       "0         277      2.0  1991       True                False   \n",
+       "1         427      2.9  1999      False                 True   \n",
+       "2         358      5.0  2003      False                 True   \n",
+       "3         240      4.2  2007      False                False   \n",
+       "4         120      2.0  2011      False                False   \n",
+       "...       ...      ...   ...        ...                  ...   \n",
+       "4339       35      1.6  2014      False                False   \n",
+       "4340        9      3.0  2014      False                 True   \n",
+       "4341        1      3.5  1999       True                False   \n",
+       "4342      194      2.0  1985       True                False   \n",
+       "4344      124      2.0  2013      False                False   \n",
+       "\n",
+       "      Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
+       "0                False          False         False             False   \n",
+       "1                False          False         False             False   \n",
+       "2                False          False         False             False   \n",
+       "3                False          False         False             False   \n",
+       "4                False          False          True             False   \n",
+       "...                ...            ...           ...               ...   \n",
+       "4339             False          False          True             False   \n",
+       "4340             False          False         False             False   \n",
+       "4341             False          False         False             False   \n",
+       "4342             False          False         False             False   \n",
+       "4344             False          False         False              True   \n",
+       "\n",
+       "      Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  \\\n",
+       "0          False       False        True       False     False   \n",
+       "1          False       False       False       False      True   \n",
+       "2          False       False        True       False     False   \n",
+       "3          False       False       False       False     False   \n",
+       "4          False       False       False       False     False   \n",
+       "...          ...         ...         ...         ...       ...   \n",
+       "4339       False       False        True       False     False   \n",
+       "4340       False       False        True       False     False   \n",
+       "4341       False       False        True       False     False   \n",
+       "4342       False       False        True       False     False   \n",
+       "4344       False       False       False       False      True   \n",
+       "\n",
+       "      Engine Type_Gas  Engine Type_Other  Engine Type_Petrol  \\\n",
+       "0               False              False                True   \n",
+       "1               False              False               False   \n",
+       "2                True              False               False   \n",
+       "3               False              False                True   \n",
+       "4               False              False                True   \n",
+       "...               ...                ...                 ...   \n",
+       "4339            False              False                True   \n",
+       "4340            False              False               False   \n",
+       "4341            False              False                True   \n",
+       "4342            False              False                True   \n",
+       "4344            False              False               False   \n",
+       "\n",
+       "      Registration_yes  Model_100  Model_11  Model_116  Model_118  Model_120  \\\n",
+       "0                 True      False     False      False      False      False   \n",
+       "1                 True      False     False      False      False      False   \n",
+       "2                 True      False     False      False      False      False   \n",
+       "3                 True      False     False      False      False      False   \n",
+       "4                 True      False     False      False      False      False   \n",
+       "...                ...        ...       ...        ...        ...        ...   \n",
+       "4339              True      False     False      False      False      False   \n",
+       "4340              True      False     False      False      False      False   \n",
+       "4341              True      False     False      False      False      False   \n",
+       "4342              True      False     False      False      False      False   \n",
+       "4344              True      False     False      False      False      False   \n",
+       "\n",
+       "      Model_19  Model_190  Model_200  Model_21  Model_210  Model_220  \\\n",
+       "0        False      False      False     False      False      False   \n",
+       "1        False      False      False     False      False      False   \n",
+       "2        False      False      False     False      False      False   \n",
+       "3        False      False      False     False      False      False   \n",
+       "4        False      False      False     False      False      False   \n",
+       "...        ...        ...        ...       ...        ...        ...   \n",
+       "4339     False      False      False     False      False      False   \n",
+       "4340     False      False      False     False      False      False   \n",
+       "4341     False      False      False     False      False      False   \n",
+       "4342     False      False      False     False      False      False   \n",
+       "4344     False      False      False     False      False      False   \n",
+       "\n",
+       "      Model_230  Model_25  Model_250  Model_300  Model_316  Model_318  \\\n",
+       "0         False     False      False      False      False      False   \n",
+       "1         False     False      False      False      False      False   \n",
+       "2         False     False      False      False      False      False   \n",
+       "3         False     False      False      False      False      False   \n",
+       "4         False     False      False      False      False      False   \n",
+       "...         ...       ...        ...        ...        ...        ...   \n",
+       "4339      False     False      False      False      False      False   \n",
+       "4340      False     False      False      False      False      False   \n",
+       "4341      False     False      False      False      False      False   \n",
+       "4342      False     False      False      False      False      False   \n",
+       "4344      False     False      False      False      False      False   \n",
+       "\n",
+       "      Model_320  Model_323  Model_324  Model_325  Model_328  Model_330  \\\n",
+       "0          True      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False      False      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_335  Model_4 Series Gran Coupe  Model_428  Model_4Runner  Model_5  \\\n",
+       "0         False                      False      False          False    False   \n",
+       "1         False                      False      False          False    False   \n",
+       "2         False                      False      False          False    False   \n",
+       "3         False                      False      False          False    False   \n",
+       "4         False                      False      False          False    False   \n",
+       "...         ...                        ...        ...            ...      ...   \n",
+       "4339      False                      False      False          False    False   \n",
+       "4340      False                      False      False          False    False   \n",
+       "4341      False                      False      False          False    False   \n",
+       "4342      False                      False      False          False    False   \n",
+       "4344      False                      False      False          False    False   \n",
+       "\n",
+       "      Model_5 Series  Model_5 Series GT  Model_520  Model_523  Model_524  \\\n",
+       "0              False              False      False      False      False   \n",
+       "1              False              False      False      False      False   \n",
+       "2              False              False      False      False      False   \n",
+       "3              False              False      False      False      False   \n",
+       "4              False              False      False      False      False   \n",
+       "...              ...                ...        ...        ...        ...   \n",
+       "4339           False              False      False      False      False   \n",
+       "4340           False              False      False      False      False   \n",
+       "4341           False              False      False      False      False   \n",
+       "4342           False              False       True      False      False   \n",
+       "4344           False              False      False      False      False   \n",
+       "\n",
+       "      Model_525  Model_528  Model_530  Model_535  Model_540  Model_545  \\\n",
+       "0         False      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False       True      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_550  Model_6 Series Gran Coupe  Model_630  Model_640  Model_645  \\\n",
+       "0         False                      False      False      False      False   \n",
+       "1         False                      False      False      False      False   \n",
+       "2         False                      False      False      False      False   \n",
+       "3         False                      False      False      False      False   \n",
+       "4         False                      False      False      False      False   \n",
+       "...         ...                        ...        ...        ...        ...   \n",
+       "4339      False                      False      False      False      False   \n",
+       "4340      False                      False      False      False      False   \n",
+       "4341      False                      False      False      False      False   \n",
+       "4342      False                      False      False      False      False   \n",
+       "4344      False                      False      False      False      False   \n",
+       "\n",
+       "      Model_650  Model_730  Model_735  Model_740  Model_745  Model_750  \\\n",
+       "0         False      False      False      False      False      False   \n",
+       "1         False      False      False      False      False      False   \n",
+       "2         False      False      False      False      False      False   \n",
+       "3         False      False      False      False      False      False   \n",
+       "4         False      False      False      False      False      False   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "4339      False      False      False      False      False      False   \n",
+       "4340      False      False      False      False      False      False   \n",
+       "4341      False      False      False      False      False      False   \n",
+       "4342      False      False      False      False      False      False   \n",
+       "4344      False      False      False      False      False      False   \n",
+       "\n",
+       "      Model_760  Model_80  Model_9  Model_90  Model_A 140  Model_A 150  \\\n",
+       "0         False     False    False     False        False        False   \n",
+       "1         False     False    False     False        False        False   \n",
+       "2         False     False    False     False        False        False   \n",
+       "3         False     False    False     False        False        False   \n",
+       "4         False     False    False     False        False        False   \n",
+       "...         ...       ...      ...       ...          ...          ...   \n",
+       "4339      False     False    False     False        False        False   \n",
+       "4340      False     False    False     False        False        False   \n",
+       "4341      False     False    False     False        False        False   \n",
+       "4342      False     False    False     False        False        False   \n",
+       "4344      False     False    False     False        False        False   \n",
+       "\n",
+       "      Model_A 170  Model_A 180  Model_A1  Model_A3  Model_A4  \\\n",
+       "0           False        False     False     False     False   \n",
+       "1           False        False     False     False     False   \n",
+       "2           False        False     False     False     False   \n",
+       "3           False        False     False     False     False   \n",
+       "4           False        False     False     False     False   \n",
+       "...           ...          ...       ...       ...       ...   \n",
+       "4339        False        False     False     False     False   \n",
+       "4340        False        False     False     False     False   \n",
+       "4341        False        False     False     False     False   \n",
+       "4342        False        False     False     False     False   \n",
+       "4344        False        False     False     False     False   \n",
+       "\n",
+       "      Model_A4 Allroad  Model_A5  Model_A6  Model_A6 Allroad  Model_A7  \\\n",
+       "0                False     False     False             False     False   \n",
+       "1                False     False     False             False     False   \n",
+       "2                False     False     False             False     False   \n",
+       "3                False     False     False             False     False   \n",
+       "4                False     False     False             False     False   \n",
+       "...                ...       ...       ...               ...       ...   \n",
+       "4339             False     False     False             False     False   \n",
+       "4340             False     False     False             False     False   \n",
+       "4341             False     False     False             False     False   \n",
+       "4342             False     False     False             False     False   \n",
+       "4344             False     False     False             False     False   \n",
+       "\n",
+       "      Model_A8  Model_ASX  Model_Amarok  Model_Auris  Model_Avalon  \\\n",
+       "0        False      False         False        False         False   \n",
+       "1        False      False         False        False         False   \n",
+       "2        False      False         False        False         False   \n",
+       "3        False      False         False        False         False   \n",
+       "4        False      False         False        False         False   \n",
+       "...        ...        ...           ...          ...           ...   \n",
+       "4339     False      False         False        False         False   \n",
+       "4340     False      False         False        False         False   \n",
+       "4341     False      False         False        False         False   \n",
+       "4342     False      False         False        False         False   \n",
+       "4344     False      False         False        False         False   \n",
+       "\n",
+       "      Model_Avensis  Model_Aygo  Model_B 170  Model_B 180  Model_B 200  \\\n",
+       "0             False       False        False        False        False   \n",
+       "1             False       False        False        False        False   \n",
+       "2             False       False        False        False        False   \n",
+       "3             False       False        False        False        False   \n",
+       "4             False       False        False        False        False   \n",
+       "...             ...         ...          ...          ...          ...   \n",
+       "4339          False       False        False        False        False   \n",
+       "4340          False       False        False        False        False   \n",
+       "4341          False       False        False        False        False   \n",
+       "4342          False       False        False        False        False   \n",
+       "4344          False       False        False        False        False   \n",
+       "\n",
+       "      Model_Beetle  Model_Bora  Model_C-Class  Model_CL 180  Model_CL 500  \\\n",
+       "0            False       False          False         False         False   \n",
+       "1            False       False          False         False         False   \n",
+       "2            False       False          False         False         False   \n",
+       "3            False       False          False         False         False   \n",
+       "4            False       False          False         False         False   \n",
+       "...            ...         ...            ...           ...           ...   \n",
+       "4339         False       False          False         False         False   \n",
+       "4340         False       False          False         False         False   \n",
+       "4341         False       False          False         False         False   \n",
+       "4342         False       False          False         False         False   \n",
+       "4344         False       False          False         False         False   \n",
+       "\n",
+       "      Model_CL 55 AMG  Model_CL 550  Model_CL 63 AMG  Model_CLA 200  \\\n",
+       "0               False         False            False          False   \n",
+       "1               False         False            False          False   \n",
+       "2               False         False            False          False   \n",
+       "3               False         False            False          False   \n",
+       "4               False         False            False          False   \n",
+       "...               ...           ...              ...            ...   \n",
+       "4339            False         False            False          False   \n",
+       "4340            False         False            False          False   \n",
+       "4341            False         False            False          False   \n",
+       "4342            False         False            False          False   \n",
+       "4344            False         False            False          False   \n",
+       "\n",
+       "      Model_CLA 220  Model_CLA-Class  Model_CLC 180  Model_CLC 200  \\\n",
+       "0             False            False          False          False   \n",
+       "1             False            False          False          False   \n",
+       "2             False            False          False          False   \n",
+       "3             False            False          False          False   \n",
+       "4             False            False          False          False   \n",
+       "...             ...              ...            ...            ...   \n",
+       "4339          False            False          False          False   \n",
+       "4340          False            False          False          False   \n",
+       "4341          False            False          False          False   \n",
+       "4342          False            False          False          False   \n",
+       "4344          False            False          False          False   \n",
+       "\n",
+       "      Model_CLK 200  Model_CLK 220  Model_CLK 230  Model_CLK 240  \\\n",
+       "0             False          False          False          False   \n",
+       "1             False          False          False          False   \n",
+       "2             False          False          False          False   \n",
+       "3             False          False          False          False   \n",
+       "4             False          False          False          False   \n",
+       "...             ...            ...            ...            ...   \n",
+       "4339          False          False          False          False   \n",
+       "4340          False          False          False          False   \n",
+       "4341          False          False          False          False   \n",
+       "4342          False          False          False          False   \n",
+       "4344          False          False          False          False   \n",
+       "\n",
+       "      Model_CLK 280  Model_CLK 320  Model_CLK 430  Model_CLS 350  \\\n",
+       "0             False          False          False          False   \n",
+       "1             False          False          False          False   \n",
+       "2             False          False          False          False   \n",
+       "3             False          False          False          False   \n",
+       "4             False          False          False          False   \n",
+       "...             ...            ...            ...            ...   \n",
+       "4339          False          False          False          False   \n",
+       "4340          False          False          False          False   \n",
+       "4341          False          False          False          False   \n",
+       "4342          False          False          False          False   \n",
+       "4344          False          False          False          False   \n",
+       "\n",
+       "      Model_CLS 400  Model_CLS 500  Model_CLS 63 AMG  Model_Caddy  \\\n",
+       "0             False          False             False        False   \n",
+       "1             False          False             False        False   \n",
+       "2             False          False             False        False   \n",
+       "3             False          False             False        False   \n",
+       "4             False          False             False        False   \n",
+       "...             ...            ...               ...          ...   \n",
+       "4339          False          False             False        False   \n",
+       "4340          False          False             False        False   \n",
+       "4341          False          False             False        False   \n",
+       "4342          False          False             False        False   \n",
+       "4344          False          False             False        False   \n",
+       "\n",
+       "      Model_Camry  Model_Captur  Model_Caravelle  Model_Carina  Model_Carisma  \\\n",
+       "0           False         False            False         False          False   \n",
+       "1           False         False            False         False          False   \n",
+       "2           False         False            False         False          False   \n",
+       "3           False         False            False         False          False   \n",
+       "4           False         False            False         False          False   \n",
+       "...           ...           ...              ...           ...            ...   \n",
+       "4339        False         False            False         False          False   \n",
+       "4340        False         False            False         False          False   \n",
+       "4341        False         False            False         False          False   \n",
+       "4342        False         False            False         False          False   \n",
+       "4344        False         False            False         False          False   \n",
+       "\n",
+       "      Model_Celica  Model_Clio  Model_Colt  Model_Corolla  \\\n",
+       "0            False       False       False          False   \n",
+       "1            False       False       False          False   \n",
+       "2            False       False       False          False   \n",
+       "3            False       False       False          False   \n",
+       "4            False       False       False          False   \n",
+       "...            ...         ...         ...            ...   \n",
+       "4339         False       False       False           True   \n",
+       "4340         False       False       False          False   \n",
+       "4341         False       False       False          False   \n",
+       "4342         False       False       False          False   \n",
+       "4344         False       False       False          False   \n",
+       "\n",
+       "      Model_Corolla Verso  Model_Cross Touran  Model_Dokker  Model_Duster  \\\n",
+       "0                   False               False         False         False   \n",
+       "1                   False               False         False         False   \n",
+       "2                   False               False         False         False   \n",
+       "3                   False               False         False         False   \n",
+       "4                   False               False         False         False   \n",
+       "...                   ...                 ...           ...           ...   \n",
+       "4339                False               False         False         False   \n",
+       "4340                False               False         False         False   \n",
+       "4341                False               False         False         False   \n",
+       "4342                False               False         False         False   \n",
+       "4344                False               False         False         False   \n",
+       "\n",
+       "      Model_E-Class  Model_Eclipse  Model_Eos  Model_Espace  Model_FJ Cruiser  \\\n",
+       "0             False          False      False         False             False   \n",
+       "1             False          False      False         False             False   \n",
+       "2             False          False      False         False             False   \n",
+       "3             False          False      False         False             False   \n",
+       "4             False          False      False         False             False   \n",
+       "...             ...            ...        ...           ...               ...   \n",
+       "4339          False          False      False         False             False   \n",
+       "4340          False          False      False         False             False   \n",
+       "4341          False          False      False         False             False   \n",
+       "4342          False          False      False         False             False   \n",
+       "4344          False          False      False         False             False   \n",
+       "\n",
+       "      Model_Fluence  Model_Fortuner  Model_G 320  Model_G 350  Model_G 500  \\\n",
+       "0             False           False        False        False        False   \n",
+       "1             False           False        False        False        False   \n",
+       "2             False           False        False        False        False   \n",
+       "3             False           False        False        False        False   \n",
+       "4             False           False        False        False        False   \n",
+       "...             ...             ...          ...          ...          ...   \n",
+       "4339          False           False        False        False        False   \n",
+       "4340          False           False        False        False        False   \n",
+       "4341          False           False        False        False        False   \n",
+       "4342          False           False        False        False        False   \n",
+       "4344          False           False        False        False        False   \n",
+       "\n",
+       "      Model_G 55 AMG  Model_G 63 AMG  Model_GL 320  Model_GL 350  \\\n",
+       "0              False           False         False         False   \n",
+       "1              False           False         False         False   \n",
+       "2              False           False         False         False   \n",
+       "3              False           False         False         False   \n",
+       "4              False           False         False         False   \n",
+       "...              ...             ...           ...           ...   \n",
+       "4339           False           False         False         False   \n",
+       "4340           False           False         False         False   \n",
+       "4341           False           False         False         False   \n",
+       "4342           False           False         False         False   \n",
+       "4344           False           False         False         False   \n",
+       "\n",
+       "      Model_GL 420  Model_GL 450  Model_GL 500  Model_GL 550  Model_GLC-Class  \\\n",
+       "0            False         False         False         False            False   \n",
+       "1            False         False         False         False            False   \n",
+       "2            False         False         False         False            False   \n",
+       "3            False         False         False         False            False   \n",
+       "4            False         False         False         False            False   \n",
+       "...            ...           ...           ...           ...              ...   \n",
+       "4339         False         False         False         False            False   \n",
+       "4340         False         False         False         False            False   \n",
+       "4341         False         False         False         False            False   \n",
+       "4342         False         False         False         False            False   \n",
+       "4344         False         False         False         False            False   \n",
+       "\n",
+       "      Model_GLE-Class  Model_GLK 220  Model_GLK 300  Model_GLS 350  \\\n",
+       "0               False          False          False          False   \n",
+       "1               False          False          False          False   \n",
+       "2               False          False          False          False   \n",
+       "3               False          False          False          False   \n",
+       "4               False          False          False          False   \n",
+       "...               ...            ...            ...            ...   \n",
+       "4339            False          False          False          False   \n",
+       "4340            False          False          False          False   \n",
+       "4341            False          False          False          False   \n",
+       "4342            False          False          False          False   \n",
+       "4344            False          False          False          False   \n",
+       "\n",
+       "      Model_GLS 400  Model_GLS 500  Model_GLS 63  Model_Galant  \\\n",
+       "0             False          False         False         False   \n",
+       "1             False          False         False         False   \n",
+       "2             False          False         False         False   \n",
+       "3             False          False         False         False   \n",
+       "4             False          False         False         False   \n",
+       "...             ...            ...           ...           ...   \n",
+       "4339          False          False         False         False   \n",
+       "4340          False          False         False         False   \n",
+       "4341          False          False         False         False   \n",
+       "4342          False          False         False         False   \n",
+       "4344          False          False         False         False   \n",
+       "\n",
+       "      Model_Golf GTI  Model_Golf II  Model_Golf III  Model_Golf IV  \\\n",
+       "0              False          False           False          False   \n",
+       "1              False          False           False          False   \n",
+       "2              False          False           False          False   \n",
+       "3              False          False           False          False   \n",
+       "4              False          False           False          False   \n",
+       "...              ...            ...             ...            ...   \n",
+       "4339           False          False           False          False   \n",
+       "4340           False          False           False          False   \n",
+       "4341           False          False           False          False   \n",
+       "4342           False          False           False          False   \n",
+       "4344           False          False           False          False   \n",
+       "\n",
+       "      Model_Golf Plus  Model_Golf V  Model_Golf VI  Model_Golf VII  \\\n",
+       "0               False         False          False           False   \n",
+       "1               False         False          False           False   \n",
+       "2               False         False          False           False   \n",
+       "3               False         False          False           False   \n",
+       "4               False         False          False           False   \n",
+       "...               ...           ...            ...             ...   \n",
+       "4339            False         False          False           False   \n",
+       "4340            False         False          False           False   \n",
+       "4341            False         False          False           False   \n",
+       "4342            False         False          False           False   \n",
+       "4344            False         False          False           False   \n",
+       "\n",
+       "      Model_Golf Variant  Model_Grand Scenic  Model_Grandis  Model_Hiace  \\\n",
+       "0                  False               False          False        False   \n",
+       "1                  False               False          False        False   \n",
+       "2                  False               False          False        False   \n",
+       "3                  False               False          False        False   \n",
+       "4                  False               False          False        False   \n",
+       "...                  ...                 ...            ...          ...   \n",
+       "4339               False               False          False        False   \n",
+       "4340               False               False          False        False   \n",
+       "4341               False               False          False        False   \n",
+       "4342               False               False          False        False   \n",
+       "4344               False               False          False        False   \n",
+       "\n",
+       "      Model_Highlander  Model_Hilux  Model_I3  Model_IQ  Model_Jetta  \\\n",
+       "0                False        False     False     False        False   \n",
+       "1                False        False     False     False        False   \n",
+       "2                False        False     False     False        False   \n",
+       "3                False        False     False     False        False   \n",
+       "4                False        False     False     False        False   \n",
+       "...                ...          ...       ...       ...          ...   \n",
+       "4339             False        False     False     False        False   \n",
+       "4340             False        False     False     False        False   \n",
+       "4341             False        False     False     False        False   \n",
+       "4342             False        False     False     False        False   \n",
+       "4344             False        False     False     False        False   \n",
+       "\n",
+       "      Model_Kangoo  Model_Koleos  Model_L 200  Model_LT  Model_Laguna  \\\n",
+       "0            False         False        False     False         False   \n",
+       "1            False         False        False     False         False   \n",
+       "2            False         False        False     False         False   \n",
+       "3            False         False        False     False         False   \n",
+       "4            False         False        False     False         False   \n",
+       "...            ...           ...          ...       ...           ...   \n",
+       "4339         False         False        False     False         False   \n",
+       "4340         False         False        False     False         False   \n",
+       "4341         False         False        False     False         False   \n",
+       "4342         False         False        False     False         False   \n",
+       "4344         False         False        False     False         False   \n",
+       "\n",
+       "      Model_Lancer  Model_Lancer Evolution  Model_Lancer X  \\\n",
+       "0            False                   False           False   \n",
+       "1            False                   False           False   \n",
+       "2            False                   False           False   \n",
+       "3            False                   False           False   \n",
+       "4            False                   False           False   \n",
+       "...            ...                     ...             ...   \n",
+       "4339         False                   False           False   \n",
+       "4340         False                   False           False   \n",
+       "4341         False                   False           False   \n",
+       "4342         False                   False           False   \n",
+       "4344         False                   False           False   \n",
+       "\n",
+       "      Model_Lancer X Sportback  Model_Land Cruiser 100  \\\n",
+       "0                        False                   False   \n",
+       "1                        False                   False   \n",
+       "2                        False                   False   \n",
+       "3                        False                   False   \n",
+       "4                        False                   False   \n",
+       "...                        ...                     ...   \n",
+       "4339                     False                   False   \n",
+       "4340                     False                   False   \n",
+       "4341                     False                   False   \n",
+       "4342                     False                   False   \n",
+       "4344                     False                   False   \n",
+       "\n",
+       "      Model_Land Cruiser 105  Model_Land Cruiser 200  Model_Land Cruiser 76  \\\n",
+       "0                      False                   False                  False   \n",
+       "1                      False                   False                  False   \n",
+       "2                      False                   False                  False   \n",
+       "3                      False                   False                  False   \n",
+       "4                      False                   False                  False   \n",
+       "...                      ...                     ...                    ...   \n",
+       "4339                   False                   False                  False   \n",
+       "4340                   False                   False                  False   \n",
+       "4341                   False                   False                  False   \n",
+       "4342                   False                   False                  False   \n",
+       "4344                   False                   False                  False   \n",
+       "\n",
+       "      Model_Land Cruiser 80  Model_Land Cruiser Prado  Model_Latitude  \\\n",
+       "0                     False                     False           False   \n",
+       "1                     False                     False           False   \n",
+       "2                     False                     False           False   \n",
+       "3                     False                     False           False   \n",
+       "4                     False                     False           False   \n",
+       "...                     ...                       ...             ...   \n",
+       "4339                  False                     False           False   \n",
+       "4340                  False                     False           False   \n",
+       "4341                  False                     False           False   \n",
+       "4342                  False                     False           False   \n",
+       "4344                  False                     False           False   \n",
+       "\n",
+       "      Model_Lite Ace  Model_Logan  Model_Lupo  Model_M5  Model_M6  Model_MB  \\\n",
+       "0              False        False       False     False     False     False   \n",
+       "1              False        False       False     False     False     False   \n",
+       "2              False        False       False     False     False     False   \n",
+       "3              False        False       False     False     False     False   \n",
+       "4              False        False       False     False     False     False   \n",
+       "...              ...          ...         ...       ...       ...       ...   \n",
+       "4339           False        False       False     False     False     False   \n",
+       "4340           False        False       False     False     False     False   \n",
+       "4341           False        False       False     False     False     False   \n",
+       "4342           False        False       False     False     False     False   \n",
+       "4344           False        False       False     False     False     False   \n",
+       "\n",
+       "      Model_ML 250  Model_ML 270  Model_ML 280  Model_ML 320  Model_ML 350  \\\n",
+       "0            False         False         False         False         False   \n",
+       "1            False         False         False         False         False   \n",
+       "2            False         False         False         False         False   \n",
+       "3            False         False         False         False         False   \n",
+       "4            False         False         False         False         False   \n",
+       "...            ...           ...           ...           ...           ...   \n",
+       "4339         False         False         False         False         False   \n",
+       "4340         False         False         False         False         False   \n",
+       "4341         False         False         False         False         False   \n",
+       "4342         False         False         False         False         False   \n",
+       "4344         False         False         False         False         False   \n",
+       "\n",
+       "      Model_ML 400  Model_ML 430  Model_ML 500  Model_ML 550  Model_ML 63 AMG  \\\n",
+       "0            False         False         False         False            False   \n",
+       "1            False         False         False         False            False   \n",
+       "2            False         False         False         False            False   \n",
+       "3            False         False         False         False            False   \n",
+       "4            False         False         False         False            False   \n",
+       "...            ...           ...           ...           ...              ...   \n",
+       "4339         False         False         False         False            False   \n",
+       "4340         False         False         False         False            False   \n",
+       "4341         False         False         False         False            False   \n",
+       "4342         False         False         False         False            False   \n",
+       "4344         False         False         False         False            False   \n",
+       "\n",
+       "      Model_Mark II  Model_Master  Model_Matrix  Model_Megane  Model_Modus  \\\n",
+       "0             False         False         False         False        False   \n",
+       "1             False         False         False         False        False   \n",
+       "2             False         False         False         False        False   \n",
+       "3             False         False         False         False        False   \n",
+       "4             False         False         False         False        False   \n",
+       "...             ...           ...           ...           ...          ...   \n",
+       "4339          False         False         False         False        False   \n",
+       "4340          False         False         False         False        False   \n",
+       "4341          False         False         False         False        False   \n",
+       "4342          False         False         False         False        False   \n",
+       "4344          False         False         False         False        False   \n",
+       "\n",
+       "      Model_Multivan  Model_New Beetle  Model_Outlander  Model_Outlander XL  \\\n",
+       "0              False             False            False               False   \n",
+       "1              False             False            False               False   \n",
+       "2              False             False            False               False   \n",
+       "3              False             False            False               False   \n",
+       "4              False             False            False               False   \n",
+       "...              ...               ...              ...                 ...   \n",
+       "4339           False             False            False               False   \n",
+       "4340           False             False            False               False   \n",
+       "4341           False             False            False               False   \n",
+       "4342           False             False            False               False   \n",
+       "4344           False             False            False               False   \n",
+       "\n",
+       "      Model_Pajero  Model_Pajero Pinin  Model_Pajero Sport  \\\n",
+       "0            False               False               False   \n",
+       "1            False               False               False   \n",
+       "2            False               False               False   \n",
+       "3            False               False               False   \n",
+       "4            False               False               False   \n",
+       "...            ...                 ...                 ...   \n",
+       "4339         False               False               False   \n",
+       "4340         False               False               False   \n",
+       "4341         False               False               False   \n",
+       "4342         False               False               False   \n",
+       "4344         False               False               False   \n",
+       "\n",
+       "      Model_Pajero Wagon  Model_Passat B2  Model_Passat B3  Model_Passat B4  \\\n",
+       "0                  False            False            False            False   \n",
+       "1                  False            False            False            False   \n",
+       "2                  False            False            False            False   \n",
+       "3                  False            False            False            False   \n",
+       "4                  False            False            False            False   \n",
+       "...                  ...              ...              ...              ...   \n",
+       "4339               False            False            False            False   \n",
+       "4340               False            False            False            False   \n",
+       "4341               False            False            False            False   \n",
+       "4342               False            False            False            False   \n",
+       "4344               False            False            False            False   \n",
+       "\n",
+       "      Model_Passat B5  Model_Passat B6  Model_Passat B7  Model_Passat B8  \\\n",
+       "0               False            False            False            False   \n",
+       "1               False            False            False            False   \n",
+       "2               False            False            False            False   \n",
+       "3               False            False            False            False   \n",
+       "4               False            False            False            False   \n",
+       "...               ...              ...              ...              ...   \n",
+       "4339            False            False            False            False   \n",
+       "4340            False            False            False            False   \n",
+       "4341            False            False            False            False   \n",
+       "4342            False            False            False            False   \n",
+       "4344            False            False            False            False   \n",
+       "\n",
+       "      Model_Passat CC  Model_Phaeton  Model_Pointer  Model_Polo  Model_Previa  \\\n",
+       "0               False          False          False       False         False   \n",
+       "1               False          False          False       False         False   \n",
+       "2               False          False          False       False         False   \n",
+       "3               False          False          False       False         False   \n",
+       "4               False          False          False       False         False   \n",
+       "...               ...            ...            ...         ...           ...   \n",
+       "4339            False          False          False       False         False   \n",
+       "4340            False          False          False       False         False   \n",
+       "4341            False          False          False       False         False   \n",
+       "4342            False          False          False       False         False   \n",
+       "4344            False          False          False       False         False   \n",
+       "\n",
+       "      Model_Prius  Model_Q3  Model_Q5  Model_Q7  Model_R 320  Model_R8  \\\n",
+       "0           False     False     False     False        False     False   \n",
+       "1           False     False     False     False        False     False   \n",
+       "2           False     False     False     False        False     False   \n",
+       "3           False     False     False      True        False     False   \n",
+       "4           False     False     False     False        False     False   \n",
+       "...           ...       ...       ...       ...          ...       ...   \n",
+       "4339        False     False     False     False        False     False   \n",
+       "4340        False     False     False     False        False     False   \n",
+       "4341        False     False     False     False        False     False   \n",
+       "4342        False     False     False     False        False     False   \n",
+       "4344        False     False     False     False        False     False   \n",
+       "\n",
+       "      Model_Rav 4  Model_S 140  Model_S 250  Model_S 280  Model_S 300  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4            True        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False        False        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_S 320  Model_S 350  Model_S 400  Model_S 420  Model_S 430  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4           False        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False         True        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_S 500  Model_S 55  Model_S 550  Model_S 600  Model_S 63 AMG  \\\n",
+       "0           False       False        False        False           False   \n",
+       "1           False       False        False        False           False   \n",
+       "2            True       False        False        False           False   \n",
+       "3           False       False        False        False           False   \n",
+       "4           False       False        False        False           False   \n",
+       "...           ...         ...          ...          ...             ...   \n",
+       "4339        False       False        False        False           False   \n",
+       "4340        False       False        False        False           False   \n",
+       "4341        False       False        False        False           False   \n",
+       "4342        False       False        False        False           False   \n",
+       "4344        False       False        False        False           False   \n",
+       "\n",
+       "      Model_S 65 AMG  Model_S-Guard  Model_S4  Model_S5  Model_S8  \\\n",
+       "0              False          False     False     False     False   \n",
+       "1              False          False     False     False     False   \n",
+       "2              False          False     False     False     False   \n",
+       "3              False          False     False     False     False   \n",
+       "4              False          False     False     False     False   \n",
+       "...              ...            ...       ...       ...       ...   \n",
+       "4339           False          False     False     False     False   \n",
+       "4340           False          False     False     False     False   \n",
+       "4341           False          False     False     False     False   \n",
+       "4342           False          False     False     False     False   \n",
+       "4344           False          False     False     False     False   \n",
+       "\n",
+       "      Model_SL 500 (550)  Model_SL 55 AMG  Model_SLK 200  Model_SLK 350  \\\n",
+       "0                  False            False          False          False   \n",
+       "1                  False            False          False          False   \n",
+       "2                  False            False          False          False   \n",
+       "3                  False            False          False          False   \n",
+       "4                  False            False          False          False   \n",
+       "...                  ...              ...            ...            ...   \n",
+       "4339               False            False          False          False   \n",
+       "4340               False            False          False          False   \n",
+       "4341               False            False          False          False   \n",
+       "4342               False            False          False          False   \n",
+       "4344               False            False          False          False   \n",
+       "\n",
+       "      Model_Sandero  Model_Sandero StepWay  Model_Scenic  Model_Scion  \\\n",
+       "0             False                  False         False        False   \n",
+       "1             False                  False         False        False   \n",
+       "2             False                  False         False        False   \n",
+       "3             False                  False         False        False   \n",
+       "4             False                  False         False        False   \n",
+       "...             ...                    ...           ...          ...   \n",
+       "4339          False                  False         False        False   \n",
+       "4340          False                  False         False        False   \n",
+       "4341          False                  False         False        False   \n",
+       "4342          False                  False         False        False   \n",
+       "4344          False                  False         False        False   \n",
+       "\n",
+       "      Model_Scirocco  Model_Sequoia  Model_Sharan  Model_Sienna  Model_Smart  \\\n",
+       "0              False          False         False         False        False   \n",
+       "1              False          False         False         False        False   \n",
+       "2              False          False         False         False        False   \n",
+       "3              False          False         False         False        False   \n",
+       "4              False          False         False         False        False   \n",
+       "...              ...            ...           ...           ...          ...   \n",
+       "4339           False          False         False         False        False   \n",
+       "4340           False          False         False         False        False   \n",
+       "4341           False          False         False         False        False   \n",
+       "4342           False          False         False         False        False   \n",
+       "4344           False          False         False         False        False   \n",
+       "\n",
+       "      Model_Space Star  Model_Space Wagon  Model_Sprinter  Model_Sprinter 208  \\\n",
+       "0                False              False           False               False   \n",
+       "1                False              False           False               False   \n",
+       "2                False              False           False               False   \n",
+       "3                False              False           False               False   \n",
+       "4                False              False           False               False   \n",
+       "...                ...                ...             ...                 ...   \n",
+       "4339             False              False           False               False   \n",
+       "4340             False              False           False               False   \n",
+       "4341             False              False           False               False   \n",
+       "4342             False              False           False               False   \n",
+       "4344             False              False           False               False   \n",
+       "\n",
+       "      Model_Sprinter 210  Model_Sprinter 211  Model_Sprinter 212  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False                True   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 213  Model_Sprinter 311  Model_Sprinter 312  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 313  Model_Sprinter 315  Model_Sprinter 316  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Sprinter 318  Model_Sprinter 319  Model_Sprinter 324  \\\n",
+       "0                  False               False               False   \n",
+       "1                  False               False               False   \n",
+       "2                  False               False               False   \n",
+       "3                  False               False               False   \n",
+       "4                  False               False               False   \n",
+       "...                  ...                 ...                 ...   \n",
+       "4339               False               False               False   \n",
+       "4340               False               False               False   \n",
+       "4341               False               False               False   \n",
+       "4342               False               False               False   \n",
+       "4344               False               False               False   \n",
+       "\n",
+       "      Model_Symbol  Model_Syncro  Model_T2 (Transporter)  \\\n",
+       "0            False         False                   False   \n",
+       "1            False         False                   False   \n",
+       "2            False         False                   False   \n",
+       "3            False         False                   False   \n",
+       "4            False         False                   False   \n",
+       "...            ...           ...                     ...   \n",
+       "4339         False         False                   False   \n",
+       "4340         False         False                   False   \n",
+       "4341         False         False                   False   \n",
+       "4342         False         False                   False   \n",
+       "4344         False         False                   False   \n",
+       "\n",
+       "      Model_T3 (Transporter)  Model_T4 (Transporter)  Model_T4 (Transporter)   \\\n",
+       "0                      False                   False                    False   \n",
+       "1                      False                   False                    False   \n",
+       "2                      False                   False                    False   \n",
+       "3                      False                   False                    False   \n",
+       "4                      False                   False                    False   \n",
+       "...                      ...                     ...                      ...   \n",
+       "4339                   False                   False                    False   \n",
+       "4340                   False                   False                    False   \n",
+       "4341                   False                   False                    False   \n",
+       "4342                   False                   False                    False   \n",
+       "4344                   False                   False                    False   \n",
+       "\n",
+       "      Model_T5 (Transporter)  Model_T5 (Transporter)   Model_T6 (Transporter)  \\\n",
+       "0                      False                    False                   False   \n",
+       "1                      False                    False                   False   \n",
+       "2                      False                    False                   False   \n",
+       "3                      False                    False                   False   \n",
+       "4                      False                    False                   False   \n",
+       "...                      ...                      ...                     ...   \n",
+       "4339                   False                    False                   False   \n",
+       "4340                   False                    False                   False   \n",
+       "4341                   False                    False                   False   \n",
+       "4342                   False                    False                   False   \n",
+       "4344                   False                     True                   False   \n",
+       "\n",
+       "      Model_T6 (Transporter)   Model_TT  Model_Tacoma  Model_Tiguan  \\\n",
+       "0                       False     False         False         False   \n",
+       "1                       False     False         False         False   \n",
+       "2                       False     False         False         False   \n",
+       "3                       False     False         False         False   \n",
+       "4                       False     False         False         False   \n",
+       "...                       ...       ...           ...           ...   \n",
+       "4339                    False     False         False         False   \n",
+       "4340                    False     False         False         False   \n",
+       "4341                    False     False         False         False   \n",
+       "4342                    False     False         False         False   \n",
+       "4344                    False     False         False         False   \n",
+       "\n",
+       "      Model_Touareg  Model_Touran  Model_Trafic  Model_Tundra  Model_Up  \\\n",
+       "0             False         False         False         False     False   \n",
+       "1             False         False         False         False     False   \n",
+       "2             False         False         False         False     False   \n",
+       "3             False         False         False         False     False   \n",
+       "4             False         False         False         False     False   \n",
+       "...             ...           ...           ...           ...       ...   \n",
+       "4339          False         False         False         False     False   \n",
+       "4340          False         False         False         False     False   \n",
+       "4341          False         False         False         False     False   \n",
+       "4342          False         False         False         False     False   \n",
+       "4344          False         False         False         False     False   \n",
+       "\n",
+       "      Model_V 250  Model_Vaneo  Model_Vento  Model_Venza  Model_Viano  \\\n",
+       "0           False        False        False        False        False   \n",
+       "1           False        False        False        False        False   \n",
+       "2           False        False        False        False        False   \n",
+       "3           False        False        False        False        False   \n",
+       "4           False        False        False        False        False   \n",
+       "...           ...          ...          ...          ...          ...   \n",
+       "4339        False        False        False        False        False   \n",
+       "4340        False        False        False        False        False   \n",
+       "4341        False        False        False        False        False   \n",
+       "4342        False        False        False        False        False   \n",
+       "4344        False        False        False        False        False   \n",
+       "\n",
+       "      Model_Virage  Model_Vista  Model_Vito  Model_X1  Model_X3  Model_X5  \\\n",
+       "0            False        False       False     False     False     False   \n",
+       "1            False        False       False     False     False     False   \n",
+       "2            False        False       False     False     False     False   \n",
+       "3            False        False       False     False     False     False   \n",
+       "4            False        False       False     False     False     False   \n",
+       "...            ...          ...         ...       ...       ...       ...   \n",
+       "4339         False        False       False     False     False     False   \n",
+       "4340         False        False       False     False     False     False   \n",
+       "4341         False        False       False     False     False     False   \n",
+       "4342         False        False       False     False     False     False   \n",
+       "4344         False        False       False     False     False     False   \n",
+       "\n",
+       "      Model_X5 M  Model_X6  Model_X6 M  Model_Yaris  Model_Z3  Model_Z4  \n",
+       "0          False     False       False        False     False     False  \n",
+       "1          False     False       False        False     False     False  \n",
+       "2          False     False       False        False     False     False  \n",
+       "3          False     False       False        False     False     False  \n",
+       "4          False     False       False        False     False     False  \n",
+       "...          ...       ...         ...          ...       ...       ...  \n",
+       "4339       False     False       False        False     False     False  \n",
+       "4340       False     False       False        False     False     False  \n",
+       "4341       False     False       False        False     False     False  \n",
+       "4342       False     False       False        False     False     False  \n",
+       "4344       False     False       False        False     False     False  \n",
+       "\n",
+       "[4025 rows x 323 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Train Test Spilt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the module for the split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# split the variables with an 80 - 20 split and some random state for example use random_state = 365\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(inputs, target, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create the regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create the regression object\n",
+    "reg = LinearRegression()\n",
+    "# fit the the regression with the trained inputs and the target\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's check the outputs of the regression\n",
+    "# I'll store them in y_hat as this is the 'theoretical' name of the predictions\n",
+    "y_hat = reg.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot\n",
+    "# The closer the points to the 45-degree line, the better the prediction\n",
+    "plt.scatter(y_train, y_hat, alpha= 0.2)\n",
+    "plt.xlabel('Target(y_train)', size= 18)\n",
+    "plt.ylabel('Prediction(Y_hat)', size= 18)\n",
+    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
+    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
+    "# We want the x-axis and the y-axis to be the same\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6.0, 13.0)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot for Year and log_price with regression line\n",
+    "sns.regplot(x=y_train, y=y_hat, scatter_kws={'s': 10}, line_kws={'color': 'red'})\n",
+    "plt.xlabel('Target(y_train)', size= 18)\n",
+    "plt.ylabel('Prediction(Y_hat)', size= 18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6,13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check for residue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_1528\\2934438678.py:3: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(y_train-y_hat)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Residuals PDF')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Another useful check of our model is a residual plot\n",
+    "# We can plot the PDF of the residuals and check for anomalies\n",
+    "sns.distplot(y_train-y_hat)\n",
+    "plt.title(\"Residuals PDF\", size=18)\n",
+    "\n",
+    "# In the best case scenario this plot should be normally distributed\n",
+    "#And our graph happened to be nornally distributed with the midpoint at zero, meaning that our prediction fits our target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# R-Squared of our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9410857085028603"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x_train, y_train)\n",
+    "# Note that this is NOT the adjusted R-squared\n",
+    "# in other words... find the Adjusted R-squared to have the appropriate measure :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adjusted R2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3220, 323)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the shape of x, to facilitate the creation of the Adjusted R^2 metric\n",
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9345148120409901"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If we want to find the Adjusted R-squared we can do so by knowing the r2, the # observations, the # features\n",
+    "r2 = reg.score(x_train, y_train)\n",
+    "# Number of observations is the shape along axis 0\n",
+    "n = x_train.shape[0]\n",
+    "# Number of features (predictors, p) is the shape along axis 1\n",
+    "p = x_train.shape[1]\n",
+    "\n",
+    "# We find the Adjusted R-squared using the formula\n",
+    "Adj_r2 = 1-(1-r2)* (n-1)/(n-p-1)\n",
+    "Adj_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.77503789e+03, 4.35676244e+00, 4.13082009e+03, 1.81202877e+01,\n",
+       "        9.46723457e+01, 1.90844651e+01, 1.30693718e+02, 5.91312636e+01,\n",
+       "        5.68044564e+01, 7.32783831e+01, 7.93890307e+00, 7.67267201e+01,\n",
+       "        9.95728691e+01, 4.19460543e+01, 3.00296891e+01, 6.02422631e+00,\n",
+       "        1.40106743e+01, 7.37501879e+02, 2.17908132e+01, 0.00000000e+00,\n",
+       "        8.57016665e-03, 2.52183205e-04, 1.77368305e+00, 2.85631239e+00,\n",
+       "        1.92264268e+01, 5.00553865e+00, 5.50707175e+00, 5.34654600e+00,\n",
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+       "        7.40340068e-001, 8.90623082e-001, 2.67587449e-001, 6.90853933e-001,\n",
+       "        1.00754811e-002, 6.89055296e-008, 4.32934445e-001, 2.04960304e-002,\n",
+       "        4.43239721e-002, 6.51183356e-001, 8.06907592e-008, 5.31985601e-001,\n",
+       "        6.63600657e-004, 2.22523086e-002, 4.31816253e-002, 1.00000000e+000,\n",
+       "        6.98283335e-001, 6.94986205e-002, 4.39863254e-003, 4.00579913e-004,\n",
+       "        6.26179589e-019, 2.36500444e-007, 3.39282802e-015, 4.84506304e-003,\n",
+       "        3.37943093e-002, 3.41070801e-001, 1.03219301e-001]))"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import the feature selection module from sklearn\n",
+    "# This module allows us to select the most appopriate features for our regression\n",
+    "# There exist many different approaches to feature selection, however, we will use one of the simplest\n",
+    "\n",
+    "from sklearn.feature_selection import f_regression\n",
+    "\n",
+    "# f_regression finds the F-statistics for the *simple* regressions created with each of the independent variables\n",
+    "# In our case, this would mean running a simple linear regression on GPA where SAT is the independent variable\n",
+    "# and a simple linear regression on GPA where Rand 1,2,3 is the indepdent variable\n",
+    "# The limitation of this approach is that it does not take into account the mutual effect of the two features\n",
+    "f_regression(x_train,y_train)\n",
+    "\n",
+    "#There are two output arrays\n",
+    "# The first one contains the F-statistics for each of the regressions\n",
+    "# The second one contains the p-values of these F-statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       4.13817142e-001, 5.91417515e-001, 1.25528377e-001, 8.14801678e-012,\n",
+       "       1.68556062e-001, 2.42028095e-001, 4.33383053e-001, 1.39233795e-001,\n",
+       "       6.48887602e-001, 1.63039120e-003, 3.85561860e-002, 5.17179158e-001,\n",
+       "       4.41821584e-004, 3.71343697e-001, 5.32761253e-001, 7.20978206e-001,\n",
+       "       4.09527800e-001, 8.64753022e-001, 5.63591273e-001, 1.42773118e-003,\n",
+       "       4.71586242e-001, 5.30703625e-009, 1.34748018e-002, 1.83021249e-001,\n",
+       "       2.16459985e-002, 7.34813292e-001, 9.89122411e-003, 3.68574535e-001,\n",
+       "       6.66441099e-001, 5.51175993e-001, 4.43305980e-004, 2.86342637e-001,\n",
+       "       3.35086125e-003, 8.80657206e-008, 7.35425272e-001, 2.50471868e-002,\n",
+       "       3.80894623e-002, 4.01958798e-001, 3.85931106e-001, 8.29302428e-001,\n",
+       "       8.27233532e-001, 6.71061839e-001, 5.46268783e-001, 9.68555856e-001,\n",
+       "       2.97385141e-001, 6.83120185e-001, 2.24054490e-001, 7.98829944e-002,\n",
+       "       7.74621412e-001, 1.00000000e+000, 4.64693438e-001, 1.07626058e-001,\n",
+       "       1.00000000e+000, 6.47070340e-003, 1.44611890e-001, 1.89630151e-001,\n",
+       "       1.00000000e+000, 1.00000000e+000, 6.81585635e-001, 7.89753766e-002,\n",
+       "       5.52414839e-001, 1.26031599e-001, 8.12262775e-001, 5.32761253e-001,\n",
+       "       6.20087988e-001, 2.44760711e-001, 9.28392404e-002, 1.00000000e+000,\n",
+       "       9.07988454e-002, 3.19204353e-002, 3.41680389e-004, 5.19563591e-002,\n",
+       "       1.00000000e+000, 4.42762754e-001, 4.06122042e-003, 3.99665611e-003,\n",
+       "       5.57008646e-001, 6.74717576e-002, 9.78408658e-003, 2.77099111e-003,\n",
+       "       6.02532109e-001, 1.00000000e+000, 7.11148676e-001, 5.01945642e-001,\n",
+       "       9.27493882e-004, 6.72904741e-001, 1.00000000e+000, 6.02532109e-001,\n",
+       "       3.24910587e-002, 8.26249124e-001, 8.34188757e-001, 1.00000000e+000,\n",
+       "       4.40746655e-004, 2.99839421e-006, 5.56621416e-004, 3.76191257e-010,\n",
+       "       1.01276166e-002, 1.68808863e-009, 4.34900697e-001, 1.31882699e-001,\n",
+       "       7.35283039e-002, 2.08717435e-001, 9.42660004e-002, 4.58042029e-009,\n",
+       "       2.91378789e-001, 3.42031569e-001, 1.58497540e-018, 4.88624552e-004,\n",
+       "       2.23462920e-004, 4.10323481e-011, 7.55702579e-009, 6.43900200e-001,\n",
+       "       9.50372855e-009, 6.07613924e-009, 2.96823255e-010, 5.09345354e-001,\n",
+       "       1.78926525e-001, 9.06121568e-001, 4.51607438e-001, 7.24165492e-001,\n",
+       "       5.91496727e-001, 9.04286267e-001, 3.01764465e-001, 4.36726460e-003,\n",
+       "       1.45529256e-003, 2.18784422e-001, 1.00000000e+000, 5.99223423e-002,\n",
+       "       9.74787951e-016, 9.07064650e-001, 7.03783041e-001, 2.06076823e-001,\n",
+       "       8.72593903e-008, 6.03304420e-006, 5.22102438e-001, 5.54278957e-002,\n",
+       "       7.34813292e-001, 7.61703682e-002, 1.00000000e+000, 6.12517681e-028,\n",
+       "       8.13086341e-002, 9.09954117e-001, 1.01974973e-009, 9.95704967e-001,\n",
+       "       1.03579279e-002, 8.84971529e-002, 3.29403271e-002, 2.66984999e-004,\n",
+       "       5.03783861e-002, 6.07751197e-002, 6.69335455e-002, 7.86322149e-001,\n",
+       "       6.08838773e-001, 7.57552813e-001, 1.82214428e-004, 7.23171120e-001,\n",
+       "       1.00000000e+000, 1.33597912e-001, 3.93444560e-001, 1.68935551e-002,\n",
+       "       1.00000000e+000, 3.60467380e-001, 7.43784280e-001, 1.68285534e-003,\n",
+       "       1.00000000e+000, 1.00901157e-005, 5.26457888e-001, 8.00001965e-001,\n",
+       "       4.62580458e-001, 1.11882697e-001, 6.41575085e-001, 1.88970570e-001,\n",
+       "       3.44770075e-002, 1.69454213e-002, 7.59920516e-007, 8.47328813e-006,\n",
+       "       1.34741492e-013, 3.35770762e-001, 3.63941304e-003, 1.96044222e-002,\n",
+       "       6.37819043e-003, 4.76202033e-002, 2.44760711e-001, 2.65676768e-002,\n",
+       "       6.01376927e-001, 6.85024613e-001, 7.95535047e-002, 1.75529365e-004,\n",
+       "       1.39028045e-014, 6.81975855e-001, 2.28028853e-004, 1.26486512e-001,\n",
+       "       3.73764587e-001, 1.09905717e-001, 1.33851176e-001, 6.70212905e-001,\n",
+       "       4.49327808e-001, 6.73575171e-011, 3.52668313e-002, 4.69537352e-001,\n",
+       "       9.67432151e-001, 6.31620457e-011, 1.00000000e+000, 4.65496452e-002,\n",
+       "       1.21390210e-004, 2.02025339e-008, 2.57302704e-001, 7.04803460e-004,\n",
+       "       8.91981750e-001, 3.40248085e-001, 6.66724461e-001, 5.83221602e-001,\n",
+       "       1.00000000e+000, 6.03919769e-001, 6.43900200e-001, 2.95109444e-001,\n",
+       "       9.28112666e-001, 2.15910154e-004, 1.00000000e+000, 3.75678769e-001,\n",
+       "       2.31400817e-001, 1.07557544e-001, 6.69175228e-002, 2.13495231e-001,\n",
+       "       1.77244755e-002, 2.55315670e-001, 1.00000000e+000, 2.62329963e-001,\n",
+       "       3.62648525e-001, 6.98283335e-001, 8.67631905e-001, 8.41694589e-001,\n",
+       "       5.82654840e-001, 2.25752785e-001, 9.54668885e-001, 8.12262775e-001,\n",
+       "       7.45977702e-001, 3.11392793e-001, 2.77867310e-002, 2.94550329e-003,\n",
+       "       5.15234482e-002, 8.70599844e-001, 1.70456776e-002, 2.32418760e-002,\n",
+       "       2.66085948e-003, 3.25914134e-002, 3.87907274e-001, 7.92210128e-001,\n",
+       "       7.40340068e-001, 8.90623082e-001, 2.67587449e-001, 6.90853933e-001,\n",
+       "       1.00754811e-002, 6.89055296e-008, 4.32934445e-001, 2.04960304e-002,\n",
+       "       4.43239721e-002, 6.51183356e-001, 8.06907592e-008, 5.31985601e-001,\n",
+       "       6.63600657e-004, 2.22523086e-002, 4.31816253e-002, 1.00000000e+000,\n",
+       "       6.98283335e-001, 6.94986205e-002, 4.39863254e-003, 4.00579913e-004,\n",
+       "       6.26179589e-019, 2.36500444e-007, 3.39282802e-015, 4.84506304e-003,\n",
+       "       3.37943093e-002, 3.41070801e-001, 1.03219301e-001])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Since we are more interested in the latter (p-values), we can just take the second array\n",
+    "p_values = f_regression(x_train,y_train)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.037, 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,\n",
+       "       0.   , 0.005, 0.   , 0.   , 0.   , 0.   , 0.014, 0.   , 0.   ,\n",
+       "       0.   , 1.   , 0.926, 0.987, 0.183, 0.091, 0.   , 0.025, 0.019,\n",
+       "       0.021, 0.487, 0.057, 0.001, 0.406, 0.108, 0.005, 0.   , 0.   ,\n",
+       "       0.491, 0.068, 0.827, 0.398, 0.232, 0.433, 0.123, 1.   , 0.19 ,\n",
+       "       1.   , 0.601, 0.038, 0.414, 0.591, 0.126, 0.   , 0.169, 0.242,\n",
+       "       0.433, 0.139, 0.649, 0.002, 0.039, 0.517, 0.   , 0.371, 0.533,\n",
+       "       0.721, 0.41 , 0.865, 0.564, 0.001, 0.472, 0.   , 0.013, 0.183,\n",
+       "       0.022, 0.735, 0.01 , 0.369, 0.666, 0.551, 0.   , 0.286, 0.003,\n",
+       "       0.   , 0.735, 0.025, 0.038, 0.402, 0.386, 0.829, 0.827, 0.671,\n",
+       "       0.546, 0.969, 0.297, 0.683, 0.224, 0.08 , 0.775, 1.   , 0.465,\n",
+       "       0.108, 1.   , 0.006, 0.145, 0.19 , 1.   , 1.   , 0.682, 0.079,\n",
+       "       0.552, 0.126, 0.812, 0.533, 0.62 , 0.245, 0.093, 1.   , 0.091,\n",
+       "       0.032, 0.   , 0.052, 1.   , 0.443, 0.004, 0.004, 0.557, 0.067,\n",
+       "       0.01 , 0.003, 0.603, 1.   , 0.711, 0.502, 0.001, 0.673, 1.   ,\n",
+       "       0.603, 0.032, 0.826, 0.834, 1.   , 0.   , 0.   , 0.001, 0.   ,\n",
+       "       0.01 , 0.   , 0.435, 0.132, 0.074, 0.209, 0.094, 0.   , 0.291,\n",
+       "       0.342, 0.   , 0.   , 0.   , 0.   , 0.   , 0.644, 0.   , 0.   ,\n",
+       "       0.   , 0.509, 0.179, 0.906, 0.452, 0.724, 0.591, 0.904, 0.302,\n",
+       "       0.004, 0.001, 0.219, 1.   , 0.06 , 0.   , 0.907, 0.704, 0.206,\n",
+       "       0.   , 0.   , 0.522, 0.055, 0.735, 0.076, 1.   , 0.   , 0.081,\n",
+       "       0.91 , 0.   , 0.996, 0.01 , 0.088, 0.033, 0.   , 0.05 , 0.061,\n",
+       "       0.067, 0.786, 0.609, 0.758, 0.   , 0.723, 1.   , 0.134, 0.393,\n",
+       "       0.017, 1.   , 0.36 , 0.744, 0.002, 1.   , 0.   , 0.526, 0.8  ,\n",
+       "       0.463, 0.112, 0.642, 0.189, 0.034, 0.017, 0.   , 0.   , 0.   ,\n",
+       "       0.336, 0.004, 0.02 , 0.006, 0.048, 0.245, 0.027, 0.601, 0.685,\n",
+       "       0.08 , 0.   , 0.   , 0.682, 0.   , 0.126, 0.374, 0.11 , 0.134,\n",
+       "       0.67 , 0.449, 0.   , 0.035, 0.47 , 0.967, 0.   , 1.   , 0.047,\n",
+       "       0.   , 0.   , 0.257, 0.001, 0.892, 0.34 , 0.667, 0.583, 1.   ,\n",
+       "       0.604, 0.644, 0.295, 0.928, 0.   , 1.   , 0.376, 0.231, 0.108,\n",
+       "       0.067, 0.213, 0.018, 0.255, 1.   , 0.262, 0.363, 0.698, 0.868,\n",
+       "       0.842, 0.583, 0.226, 0.955, 0.812, 0.746, 0.311, 0.028, 0.003,\n",
+       "       0.052, 0.871, 0.017, 0.023, 0.003, 0.033, 0.388, 0.792, 0.74 ,\n",
+       "       0.891, 0.268, 0.691, 0.01 , 0.   , 0.433, 0.02 , 0.044, 0.651,\n",
+       "       0.   , 0.532, 0.001, 0.022, 0.043, 1.   , 0.698, 0.069, 0.004,\n",
+       "       0.   , 0.   , 0.   , 0.   , 0.005, 0.034, 0.341, 0.103])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Finding weight and bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-271886118.8957569"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the bias (intercept) of the regression\n",
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-7.11168307e-04, -2.35976356e-03,  8.68792315e-02,  2.45087768e+07,\n",
+       "        2.45087768e+07,  3.35460065e+08,  2.30199764e+09,  1.45542358e+09,\n",
+       "        4.62245051e+08,  9.66255861e-03,  1.23547420e-02, -1.06618585e-01,\n",
+       "       -1.31590684e-01, -5.92526948e-02, -9.57940713e-02, -4.74364368e-02,\n",
+       "       -6.14403758e-02,  8.89392304e-01,  2.71885954e+08, -7.18996649e+09,\n",
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+       "        2.47377177e+08,  2.47377177e+08, -2.03011169e+09,  2.47377176e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08, -2.03011169e+09,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.59254546e+09,\n",
+       "       -1.18353763e+09, -2.65819554e+07,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377178e+08,  2.47377177e+08,\n",
+       "        2.47377178e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.71885953e+08, -2.03011169e+09,  2.71885954e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08,  2.47377176e+08,  2.47377176e+08,\n",
+       "        2.71885953e+08,  2.71885953e+08,  2.71885953e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08,  2.71885954e+08,  2.71885954e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08, -6.35741121e+07, -1.90359097e+08, -1.18353763e+09,\n",
+       "       -1.18353763e+09, -1.18353763e+09, -1.18353763e+09,  2.47377176e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08, -1.90359097e+08, -1.90359098e+08,\n",
+       "        2.47377177e+08, -8.93153042e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -7.09605288e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -1.08592764e+09, -7.86250753e+08,  2.47377177e+08,  2.47377176e+08,\n",
+       "        2.47377177e+08,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  1.82078318e+09,\n",
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+       "       -1.46384430e+09, -1.90359097e+08, -1.18353763e+09, -6.35741122e+07,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -6.35741125e+07, -1.18353763e+09,\n",
+       "       -1.18353763e+09, -1.58640664e+08, -2.03011169e+09, -2.03011169e+09,\n",
+       "        2.47377177e+08, -6.35741116e+07,  1.06343391e+09, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -1.18353763e+09,  5.60770216e+07,\n",
+       "        2.47377178e+08,  2.47377178e+08,  2.47377178e+08,  2.47377178e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377178e+08,  2.47377178e+08,\n",
+       "        2.47377178e+08,  2.47377178e+08, -6.35741121e+07, -1.90359098e+08,\n",
+       "       -1.90359097e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -6.35741119e+07, -1.18353763e+09, -1.18353763e+09,\n",
+       "       -1.18353763e+09,  2.47377177e+08, -2.56326823e+08, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -2.03011169e+09, -6.35741122e+07, -1.90359098e+08,\n",
+       "       -2.03011169e+09, -6.35741124e+07, -6.35741114e+07, -6.35741123e+07,\n",
+       "       -6.35741125e+07, -1.18353763e+09, -4.21205487e+03, -1.18353762e+09,\n",
+       "       -1.18353763e+09, -1.18353763e+09, -1.18353763e+09, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -2.03011169e+09, -1.90359098e+08,  2.47377178e+08,\n",
+       "        2.47377178e+08,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -1.69340410e-07,  2.47377177e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "       -2.77046898e-07, -2.03011169e+09, -1.18353763e+09, -2.03011169e+09,\n",
+       "        1.42292599e-06, -1.90359097e+08, -1.90359098e+08, -6.35741121e+07,\n",
+       "       -6.35741119e+07, -6.35741112e+07, -6.35741120e+07, -6.35741119e+07,\n",
+       "       -6.35741117e+07, -1.90359097e+08, -1.90359097e+08, -1.90359097e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08, -1.90359098e+08, -1.90359097e+08,\n",
+       "       -1.90359097e+08, -1.90359097e+08, -1.90359099e+08, -1.90359098e+08,\n",
+       "       -1.18353763e+09, -1.18353763e+09,  2.71885954e+08,  2.71885954e+08,\n",
+       "        2.71885954e+08,  2.47377177e+08,  2.71885955e+08, -1.18353763e+09,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377178e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377177e+08,  6.17630946e-14,  2.47377177e+08,\n",
+       "        2.47377178e+08,  2.47377178e+08,  2.47377178e+08,  2.47377179e+08,\n",
+       "        2.71885954e+08,  2.71885954e+08,  2.71885954e+08,  2.47377177e+08,\n",
+       "       -1.00867231e-13,  2.47377177e+08,  2.47377177e+08, -2.03011169e+09,\n",
+       "       -2.03011169e+09, -2.03011169e+09, -1.57485136e-13, -1.90359098e+08,\n",
+       "       -1.18353763e+09, -1.90359097e+08, -1.18353763e+09,  2.47377176e+08,\n",
+       "       -6.35741123e+07, -6.35741115e+07,  2.27283470e-14,  2.47377177e+08,\n",
+       "        2.47377176e+08,  2.47377176e+08,  2.47377177e+08,  2.47377176e+08,\n",
+       "        2.47377176e+08,  2.47377177e+08,  2.47377176e+08,  2.47377177e+08,\n",
+       "        2.47377176e+08,  2.47377177e+08,  2.47377177e+08,  2.47377179e+08,\n",
+       "       -2.03011169e+09, -1.90359097e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359097e+08, -1.90359098e+08, -1.90359098e+08, -1.90359098e+08,\n",
+       "       -1.90359098e+08, -1.90359098e+08,  2.71885954e+08, -1.18353763e+09,\n",
+       "       -1.90359098e+08, -1.90359097e+08, -1.90359098e+08, -2.03011169e+09,\n",
+       "       -1.18353763e+09, -1.90359099e+08,  2.47377178e+08,  2.47377176e+08,\n",
+       "       -1.90359098e+08, -1.18353763e+09,  2.47377177e+08,  1.11022302e-15,\n",
+       "       -1.18353763e+09,  2.47377176e+08,  2.47377177e+08,  2.47377177e+08,\n",
+       "        2.47377177e+08,  2.47377178e+08,  2.47377177e+08,  2.47377178e+08,\n",
+       "       -1.18353763e+09,  2.47377177e+08,  2.47377177e+08])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the weights (coefficients) of the regression\n",
+    "reg.coef_\n",
+    "# Note that they are barely interpretable if at all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-7.111683e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>-2.359764e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Year</td>\n",
+       "      <td>8.687923e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>Model_X6</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>Model_X6 M</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>Model_Yaris</td>\n",
+       "      <td>-1.183538e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>Model_Z3</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>Model_Z4</td>\n",
+       "      <td>2.473772e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>323 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Features       Weights\n",
+       "0                Mileage -7.111683e-04\n",
+       "1                EngineV -2.359764e-03\n",
+       "2                   Year  8.687923e-02\n",
+       "3              Brand_BMW  2.450878e+07\n",
+       "4    Brand_Mercedes-Benz  2.450878e+07\n",
+       "..                   ...           ...\n",
+       "318             Model_X6  2.473772e+08\n",
+       "319           Model_X6 M  2.473772e+08\n",
+       "320          Model_Yaris -1.183538e+09\n",
+       "321             Model_Z3  2.473772e+08\n",
+       "322             Model_Z4  2.473772e+08\n",
+       "\n",
+       "[323 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a regression summary where we can compare them with one another\n",
+    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
+    "\n",
+    "reg_summary['Weights'] = reg.coef_\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Weights</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mileage</td>\n",
+       "      <td>-7.111683e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EngineV</td>\n",
+       "      <td>-2.359764e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Year</td>\n",
+       "      <td>8.687923e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Brand_BMW</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Brand_Mercedes-Benz</td>\n",
+       "      <td>2.450878e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brand_Mitsubishi</td>\n",
+       "      <td>3.354601e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Brand_Renault</td>\n",
+       "      <td>2.301998e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Brand_Toyota</td>\n",
+       "      <td>1.455424e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Brand_Volkswagen</td>\n",
+       "      <td>4.622451e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Body_hatch</td>\n",
+       "      <td>9.662559e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Body_other</td>\n",
+       "      <td>1.235474e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Body_sedan</td>\n",
+       "      <td>-1.066186e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Body_vagon</td>\n",
+       "      <td>-1.315907e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Body_van</td>\n",
+       "      <td>-5.925269e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Engine Type_Gas</td>\n",
+       "      <td>-9.579407e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Engine Type_Other</td>\n",
+       "      <td>-4.743644e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Engine Type_Petrol</td>\n",
+       "      <td>-6.144038e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Registration_yes</td>\n",
+       "      <td>8.893923e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Model_100</td>\n",
+       "      <td>2.718860e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Model_11</td>\n",
+       "      <td>-7.189966e+09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Features       Weights\n",
+       "0               Mileage -7.111683e-04\n",
+       "1               EngineV -2.359764e-03\n",
+       "2                  Year  8.687923e-02\n",
+       "3             Brand_BMW  2.450878e+07\n",
+       "4   Brand_Mercedes-Benz  2.450878e+07\n",
+       "5      Brand_Mitsubishi  3.354601e+08\n",
+       "6         Brand_Renault  2.301998e+09\n",
+       "7          Brand_Toyota  1.455424e+09\n",
+       "8      Brand_Volkswagen  4.622451e+08\n",
+       "9            Body_hatch  9.662559e-03\n",
+       "10           Body_other  1.235474e-02\n",
+       "11           Body_sedan -1.066186e-01\n",
+       "12           Body_vagon -1.315907e-01\n",
+       "13             Body_van -5.925269e-02\n",
+       "14      Engine Type_Gas -9.579407e-02\n",
+       "15    Engine Type_Other -4.743644e-02\n",
+       "16   Engine Type_Petrol -6.144038e-02\n",
+       "17     Registration_yes  8.893923e-01\n",
+       "18            Model_100  2.718860e+08\n",
+       "19             Model_11 -7.189966e+09"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
+       "       'Mitsubishi'], dtype=object)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the different categories in the 'Brand' variable\n",
+    "data['Brand'].unique()\n",
+    "# In this way we can see which 'Brand' is actually the benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brand missing: Audi\n",
+      "Body missing: crossover\n",
+      "Engine Type missing: Diesel\n",
+      "Registration missing: no\n"
+     ]
+    }
+   ],
+   "source": [
+    "brands= data['Brand'].unique()\n",
+    "\n",
+    "bodies= data['Body'].unique()\n",
+    "\n",
+    "engine_types= data['Engine Type'].unique()\n",
+    "\n",
+    "registrations= data['Registration'].unique()\n",
+    "\n",
+    "dummy_values=[brands,bodies,engine_types,registrations]\n",
+    "\n",
+    "dummy_strings=['Brand','Body','Engine Type','Registration']\n",
+    "\n",
+    "dummy_cols= data_preprocessed.columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "for vals, label in zip(dummy_values,dummy_strings):\n",
+    "\n",
+    "    for val in vals:\n",
+    "\n",
+    "        found=False\n",
+    "\n",
+    "        for dummy_col in dummy_cols:\n",
+    "\n",
+    "            if dummy_col==label + '_' + val:\n",
+    "\n",
+    "                found=True\n",
+    "                break  # exit the inner loop when a match is found\n",
+    "\n",
+    "        if found==False:\n",
+    "\n",
+    "            print(label +' missing:',val)\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Once we have trained and fine-tuned our model, we can proceed to testing it\n",
+    "# Testing is done on a dataset that the algorithm has never seen\n",
+    "# Luckily we have prepared such a dataset\n",
+    "# Our test inputs are 'x_test', while the outputs: 'y_test' \n",
+    "# We SHOULD NOT TRAIN THE MODEL ON THEM, we just feed them and find the predictions\n",
+    "# If the predictions are far off, we will know that our model overfitted\n",
+    "y_hat_test = reg.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a scatter plot with the test targets and the test predictions\n",
+    "# You can include the argument 'alpha' which will introduce opacity to the graph\n",
+    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
+    "plt.xlabel('Target(y_test)', size= 18)\n",
+    "plt.ylabel('Prediction(y_hat_test', size= 18)\n",
+    "plt.xlim(6,13)\n",
+    "plt.ylim(6.13)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Chizy\\AppData\\Local\\Temp\\ipykernel_1528\\2906630971.py:3: RuntimeWarning: overflow encountered in exp\n",
+      "  df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction\n",
+       "0  30228.855333\n",
+       "1   9383.112639\n",
+       "2   6003.067434\n",
+       "3   8074.467696\n",
+       "4  11280.346946"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, let's manually check these predictions\n",
+    "# To obtain the actual prices, we take the exponential of the log_price\n",
+    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>7900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction  Target\n",
+       "0  30228.855333     NaN\n",
+       "1   9383.112639  7900.0\n",
+       "2   6003.067434     NaN\n",
+       "3   8074.467696     NaN\n",
+       "4  11280.346946     NaN"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can also include the test targets in that data frame (so we can manually compare them)\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf.head()\n",
+    "# Note that we have a lot of missing values\n",
+    "# There is no reason to have ANY missing values, though\n",
+    "# This suggests that something is wrong with the data frame / indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    10.257659\n",
+       "1     9.093807\n",
+       "2     8.787220\n",
+       "3     8.985946\n",
+       "4     9.323669\n",
+       "Name: log_price, dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# After displaying y_test, we find what the issue is\n",
+    "# The old indexes are preserved\n",
+    "# Therefore, to get a proper result, we must reset the index and drop the old indexing\n",
+    "y_test = y_test.reset_index(drop=True)\n",
+    "y_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>28500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>8900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>6550.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>7990.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>11200.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction   Target\n",
+       "0  30228.855333  28500.0\n",
+       "1   9383.112639   8900.0\n",
+       "2   6003.067434   6550.0\n",
+       "3   8074.467696   7990.0\n",
+       "4  11280.346946  11200.0"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's overwrite the 'Target' column with the appropriate values\n",
+    "# Again, we need the exponential of the test log price\n",
+    "df_pf['Target'] = np.exp(y_test)\n",
+    "df_pf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Additionally, we can calculate the difference between the targets and the predictions\n",
+    "# Note that this is actually the residual (we already plotted the residuals)\n",
+    "df_pf['Residual'] = df_pf['Target'] - df_pf['Prediction']\n",
+    "# Since OLS is basically an algorithm which minimizes the total sum of squared errors (residuals),\n",
+    "# this comparison makes a lot of sense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "        vertical-align: top;\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.855333</td>\n",
+       "      <td>28500.0</td>\n",
+       "      <td>-1728.855333</td>\n",
+       "      <td>6.066159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.112639</td>\n",
+       "      <td>8900.0</td>\n",
+       "      <td>-483.112639</td>\n",
+       "      <td>5.428232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.067434</td>\n",
+       "      <td>6550.0</td>\n",
+       "      <td>546.932566</td>\n",
+       "      <td>8.350116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.467696</td>\n",
+       "      <td>7990.0</td>\n",
+       "      <td>-84.467696</td>\n",
+       "      <td>1.057168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.346946</td>\n",
+       "      <td>11200.0</td>\n",
+       "      <td>-80.346946</td>\n",
+       "      <td>0.717383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4280.485476</td>\n",
+       "      <td>4600.0</td>\n",
+       "      <td>319.514524</td>\n",
+       "      <td>6.945968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11427.087287</td>\n",
+       "      <td>8350.0</td>\n",
+       "      <td>-3077.087287</td>\n",
+       "      <td>36.851345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2895.962467</td>\n",
+       "      <td>3300.0</td>\n",
+       "      <td>404.037533</td>\n",
+       "      <td>12.243562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>39228.722376</td>\n",
+       "      <td>43500.0</td>\n",
+       "      <td>4271.277624</td>\n",
+       "      <td>9.819029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22173.636762</td>\n",
+       "      <td>25500.0</td>\n",
+       "      <td>3326.363238</td>\n",
+       "      <td>13.044562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4899.198732</td>\n",
+       "      <td>5200.0</td>\n",
+       "      <td>300.801268</td>\n",
+       "      <td>5.784640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13110.051878</td>\n",
+       "      <td>15300.0</td>\n",
+       "      <td>2189.948122</td>\n",
+       "      <td>14.313386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20551.654463</td>\n",
+       "      <td>22500.0</td>\n",
+       "      <td>1948.345537</td>\n",
+       "      <td>8.659313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13015.274654</td>\n",
+       "      <td>13500.0</td>\n",
+       "      <td>484.725346</td>\n",
+       "      <td>3.590558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2718.022542</td>\n",
+       "      <td>2600.0</td>\n",
+       "      <td>-118.022542</td>\n",
+       "      <td>4.539329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3900.793694</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>-1000.793694</td>\n",
+       "      <td>34.510127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10857.592502</td>\n",
+       "      <td>9900.0</td>\n",
+       "      <td>-957.592502</td>\n",
+       "      <td>9.672652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>4330.566126</td>\n",
+       "      <td>4800.0</td>\n",
+       "      <td>469.433874</td>\n",
+       "      <td>9.779872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10710.004512</td>\n",
+       "      <td>13100.0</td>\n",
+       "      <td>2389.995488</td>\n",
+       "      <td>18.244240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9723.514589</td>\n",
+       "      <td>12650.0</td>\n",
+       "      <td>2926.485411</td>\n",
+       "      <td>23.134272</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Prediction   Target     Residual  Difference%\n",
+       "0   30228.855333  28500.0 -1728.855333     6.066159\n",
+       "1    9383.112639   8900.0  -483.112639     5.428232\n",
+       "2    6003.067434   6550.0   546.932566     8.350116\n",
+       "3    8074.467696   7990.0   -84.467696     1.057168\n",
+       "4   11280.346946  11200.0   -80.346946     0.717383\n",
+       "5    4280.485476   4600.0   319.514524     6.945968\n",
+       "6   11427.087287   8350.0 -3077.087287    36.851345\n",
+       "7    2895.962467   3300.0   404.037533    12.243562\n",
+       "8   39228.722376  43500.0  4271.277624     9.819029\n",
+       "9   22173.636762  25500.0  3326.363238    13.044562\n",
+       "10   4899.198732   5200.0   300.801268     5.784640\n",
+       "11  13110.051878  15300.0  2189.948122    14.313386\n",
+       "12  20551.654463  22500.0  1948.345537     8.659313\n",
+       "13  13015.274654  13500.0   484.725346     3.590558\n",
+       "14   2718.022542   2600.0  -118.022542     4.539329\n",
+       "15   3900.793694   2900.0 -1000.793694    34.510127\n",
+       "16  10857.592502   9900.0  -957.592502     9.672652\n",
+       "17   4330.566126   4800.0   469.433874     9.779872\n",
+       "18  10710.004512  13100.0  2389.995488    18.244240\n",
+       "19   9723.514589  12650.0  2926.485411    23.134272"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally, it makes sense to see how far off we are from the result percentage-wise\n",
+    "# Here, we take the absolute difference in %, so we can easily order the data frame\n",
+    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
+    "df_pf.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n",
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\pandas\\core\\nanops.py:1010: RuntimeWarning: invalid value encountered in subtract\n",
+      "  sqr = _ensure_numeric((avg - values) ** 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>8.050000e+02</td>\n",
+       "      <td>805.000000</td>\n",
+       "      <td>8.050000e+02</td>\n",
+       "      <td>805.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>18297.659888</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20081.200958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1150.000000</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>0.072466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>6.702651e+03</td>\n",
+       "      <td>6999.000000</td>\n",
+       "      <td>-1.574397e+03</td>\n",
+       "      <td>5.661504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.194286e+04</td>\n",
+       "      <td>11450.000000</td>\n",
+       "      <td>-1.598741e+01</td>\n",
+       "      <td>13.313279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.270802e+04</td>\n",
+       "      <td>22500.000000</td>\n",
+       "      <td>1.351802e+03</td>\n",
+       "      <td>24.983670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>199999.000000</td>\n",
+       "      <td>7.291851e+04</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Prediction         Target      Residual  Difference%\n",
+       "count  8.050000e+02     805.000000  8.050000e+02   805.000000\n",
+       "mean            inf   18297.659888          -inf          inf\n",
+       "std             NaN   20081.200958           NaN          NaN\n",
+       "min    0.000000e+00    1150.000000          -inf     0.072466\n",
+       "25%    6.702651e+03    6999.000000 -1.574397e+03     5.661504\n",
+       "50%    1.194286e+04   11450.000000 -1.598741e+01    13.313279\n",
+       "75%    2.270802e+04   22500.000000  1.351802e+03    24.983670\n",
+       "max             inf  199999.000000  7.291851e+04          inf"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pf.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Prediction</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Residual</th>\n",
+       "      <th>Difference%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>15211.01</td>\n",
+       "      <td>15200.00</td>\n",
+       "      <td>-11.01</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>6305.83</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>-5.83</td>\n",
+       "      <td>0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>6706.65</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-6.65</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>12486.63</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>13.37</td>\n",
+       "      <td>0.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>8715.33</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-15.33</td>\n",
+       "      <td>0.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>3206.26</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>-6.26</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>6513.15</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-13.15</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>17863.45</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>36.55</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>6715.99</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-15.99</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>8826.34</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-26.34</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>51340.32</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>159.68</td>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>14051.69</td>\n",
+       "      <td>14100.00</td>\n",
+       "      <td>48.31</td>\n",
+       "      <td>0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>2107.62</td>\n",
+       "      <td>2100.00</td>\n",
+       "      <td>-7.62</td>\n",
+       "      <td>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>5828.76</td>\n",
+       "      <td>5850.00</td>\n",
+       "      <td>21.24</td>\n",
+       "      <td>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>12653.40</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>46.60</td>\n",
+       "      <td>0.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>12447.09</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>52.91</td>\n",
+       "      <td>0.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>2837.86</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>12.14</td>\n",
+       "      <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>13541.54</td>\n",
+       "      <td>13600.00</td>\n",
+       "      <td>58.46</td>\n",
+       "      <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>757</th>\n",
+       "      <td>7734.58</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-34.58</td>\n",
+       "      <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>12836.82</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>63.18</td>\n",
+       "      <td>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618</th>\n",
+       "      <td>30492.98</td>\n",
+       "      <td>30650.00</td>\n",
+       "      <td>157.02</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>9551.54</td>\n",
+       "      <td>9499.00</td>\n",
+       "      <td>-52.54</td>\n",
+       "      <td>0.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>14318.96</td>\n",
+       "      <td>14400.00</td>\n",
+       "      <td>81.04</td>\n",
+       "      <td>0.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>781</th>\n",
+       "      <td>7543.03</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-43.03</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>25045.23</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>-145.23</td>\n",
+       "      <td>0.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>6538.30</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-38.30</td>\n",
+       "      <td>0.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>109346.97</td>\n",
+       "      <td>110000.00</td>\n",
+       "      <td>653.03</td>\n",
+       "      <td>0.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>5633.89</td>\n",
+       "      <td>5600.00</td>\n",
+       "      <td>-33.89</td>\n",
+       "      <td>0.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>7352.50</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>47.50</td>\n",
+       "      <td>0.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>15607.46</td>\n",
+       "      <td>15499.00</td>\n",
+       "      <td>-108.46</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11280.35</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-80.35</td>\n",
+       "      <td>0.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>11077.66</td>\n",
+       "      <td>10990.00</td>\n",
+       "      <td>-87.66</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>801</th>\n",
+       "      <td>39657.24</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>341.76</td>\n",
+       "      <td>0.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>23106.56</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-206.56</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>2219.87</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-19.87</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>25027.42</td>\n",
+       "      <td>24800.00</td>\n",
+       "      <td>-227.42</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>27755.46</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>-255.46</td>\n",
+       "      <td>0.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>20195.56</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-195.56</td>\n",
+       "      <td>0.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>7978.67</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-78.67</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>733</th>\n",
+       "      <td>9897.28</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>101.72</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>23557.83</td>\n",
+       "      <td>23800.00</td>\n",
+       "      <td>242.17</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>8182.71</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-82.71</td>\n",
+       "      <td>1.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>10094.92</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>105.08</td>\n",
+       "      <td>1.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8074.47</td>\n",
+       "      <td>7990.00</td>\n",
+       "      <td>-84.47</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>16675.21</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>-175.21</td>\n",
+       "      <td>1.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>7883.21</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-83.21</td>\n",
+       "      <td>1.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>9394.45</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>105.55</td>\n",
+       "      <td>1.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>17194.49</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-195.49</td>\n",
+       "      <td>1.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>7407.42</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>92.58</td>\n",
+       "      <td>1.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>768</th>\n",
+       "      <td>10863.14</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>135.86</td>\n",
+       "      <td>1.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>638</th>\n",
+       "      <td>2227.46</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-27.46</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>109352.13</td>\n",
+       "      <td>107999.00</td>\n",
+       "      <td>-1353.13</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>9014.15</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-114.15</td>\n",
+       "      <td>1.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>65500.00</td>\n",
+       "      <td>850.28</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>6807.32</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>92.68</td>\n",
+       "      <td>1.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>418</th>\n",
+       "      <td>11436.89</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>163.11</td>\n",
+       "      <td>1.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>688</th>\n",
+       "      <td>2464.71</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>35.29</td>\n",
+       "      <td>1.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>9947.38</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-147.38</td>\n",
+       "      <td>1.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>33474.09</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>525.91</td>\n",
+       "      <td>1.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>21559.42</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>340.58</td>\n",
+       "      <td>1.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>562</th>\n",
+       "      <td>9104.53</td>\n",
+       "      <td>9250.00</td>\n",
+       "      <td>145.47</td>\n",
+       "      <td>1.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>12087.91</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-187.91</td>\n",
+       "      <td>1.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>8939.11</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-139.11</td>\n",
+       "      <td>1.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>713</th>\n",
+       "      <td>35453.02</td>\n",
+       "      <td>34899.14</td>\n",
+       "      <td>-553.88</td>\n",
+       "      <td>1.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>34436.11</td>\n",
+       "      <td>34999.00</td>\n",
+       "      <td>562.89</td>\n",
+       "      <td>1.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>2897.71</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>-47.71</td>\n",
+       "      <td>1.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>3440.49</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>59.51</td>\n",
+       "      <td>1.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>735</th>\n",
+       "      <td>33320.06</td>\n",
+       "      <td>33900.00</td>\n",
+       "      <td>579.94</td>\n",
+       "      <td>1.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>784</th>\n",
+       "      <td>18817.25</td>\n",
+       "      <td>18500.00</td>\n",
+       "      <td>-317.25</td>\n",
+       "      <td>1.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>13857.09</td>\n",
+       "      <td>14100.00</td>\n",
+       "      <td>242.91</td>\n",
+       "      <td>1.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>20040.20</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>-340.20</td>\n",
+       "      <td>1.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>35262.29</td>\n",
+       "      <td>35900.00</td>\n",
+       "      <td>637.71</td>\n",
+       "      <td>1.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>24059.43</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>440.57</td>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>63500.00</td>\n",
+       "      <td>-1149.72</td>\n",
+       "      <td>1.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>19627.25</td>\n",
+       "      <td>19990.00</td>\n",
+       "      <td>362.75</td>\n",
+       "      <td>1.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>26993.16</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>506.84</td>\n",
+       "      <td>1.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>32584.87</td>\n",
+       "      <td>31990.00</td>\n",
+       "      <td>-594.87</td>\n",
+       "      <td>1.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>3056.57</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-56.57</td>\n",
+       "      <td>1.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>98085.79</td>\n",
+       "      <td>99999.00</td>\n",
+       "      <td>1913.21</td>\n",
+       "      <td>1.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>19170.78</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>-370.78</td>\n",
+       "      <td>1.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>10782.03</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>216.97</td>\n",
+       "      <td>1.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>17047.74</td>\n",
+       "      <td>17394.09</td>\n",
+       "      <td>346.35</td>\n",
+       "      <td>1.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1958.31</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>40.69</td>\n",
+       "      <td>2.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>29500.27</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>-600.27</td>\n",
+       "      <td>2.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>712</th>\n",
+       "      <td>23990.17</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>509.83</td>\n",
+       "      <td>2.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>422</th>\n",
+       "      <td>30909.33</td>\n",
+       "      <td>31600.00</td>\n",
+       "      <td>690.67</td>\n",
+       "      <td>2.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>11959.28</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-259.28</td>\n",
+       "      <td>2.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>11042.29</td>\n",
+       "      <td>10799.00</td>\n",
+       "      <td>-243.29</td>\n",
+       "      <td>2.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>56673.17</td>\n",
+       "      <td>58000.00</td>\n",
+       "      <td>1326.83</td>\n",
+       "      <td>2.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>43942.54</td>\n",
+       "      <td>45000.00</td>\n",
+       "      <td>1057.46</td>\n",
+       "      <td>2.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>11224.40</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>275.60</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>12978.30</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>321.70</td>\n",
+       "      <td>2.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>9166.68</td>\n",
+       "      <td>9400.00</td>\n",
+       "      <td>233.32</td>\n",
+       "      <td>2.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>8478.83</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>221.17</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>11184.55</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>-284.55</td>\n",
+       "      <td>2.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>8524.29</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>-224.29</td>\n",
+       "      <td>2.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>14494.88</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>405.12</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>37938.31</td>\n",
+       "      <td>39000.00</td>\n",
+       "      <td>1061.69</td>\n",
+       "      <td>2.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>18384.89</td>\n",
+       "      <td>18900.00</td>\n",
+       "      <td>515.11</td>\n",
+       "      <td>2.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>23828.27</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>671.73</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>11570.15</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>329.85</td>\n",
+       "      <td>2.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>532</th>\n",
+       "      <td>25592.54</td>\n",
+       "      <td>24900.00</td>\n",
+       "      <td>-692.54</td>\n",
+       "      <td>2.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>2004.72</td>\n",
+       "      <td>1950.00</td>\n",
+       "      <td>-54.72</td>\n",
+       "      <td>2.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>6374.72</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>-174.72</td>\n",
+       "      <td>2.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>194242.37</td>\n",
+       "      <td>199999.00</td>\n",
+       "      <td>5756.63</td>\n",
+       "      <td>2.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>58649.23</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>-1649.23</td>\n",
+       "      <td>2.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>3398.26</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>101.74</td>\n",
+       "      <td>2.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>711</th>\n",
+       "      <td>12864.69</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>-364.69</td>\n",
+       "      <td>2.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>64695.71</td>\n",
+       "      <td>66666.00</td>\n",
+       "      <td>1970.29</td>\n",
+       "      <td>2.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>29563.28</td>\n",
+       "      <td>28700.00</td>\n",
+       "      <td>-863.28</td>\n",
+       "      <td>3.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>726</th>\n",
+       "      <td>6787.74</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>211.26</td>\n",
+       "      <td>3.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>5769.23</td>\n",
+       "      <td>5600.00</td>\n",
+       "      <td>-169.23</td>\n",
+       "      <td>3.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>23184.61</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-684.61</td>\n",
+       "      <td>3.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>10824.58</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-324.58</td>\n",
+       "      <td>3.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>22372.19</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>-672.19</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>15019.12</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>480.88</td>\n",
+       "      <td>3.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>10058.12</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>-308.12</td>\n",
+       "      <td>3.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>8769.20</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-269.20</td>\n",
+       "      <td>3.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>10844.83</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>355.17</td>\n",
+       "      <td>3.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>742</th>\n",
+       "      <td>20522.18</td>\n",
+       "      <td>21200.00</td>\n",
+       "      <td>677.82</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>23223.72</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-723.72</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>1934.64</td>\n",
+       "      <td>1999.00</td>\n",
+       "      <td>64.36</td>\n",
+       "      <td>3.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>11770.33</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-370.33</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>43881.06</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>-1381.06</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>17450.09</td>\n",
+       "      <td>16900.00</td>\n",
+       "      <td>-550.09</td>\n",
+       "      <td>3.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>23341.27</td>\n",
+       "      <td>22600.00</td>\n",
+       "      <td>-741.27</td>\n",
+       "      <td>3.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>10919.19</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>380.81</td>\n",
+       "      <td>3.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>659</th>\n",
+       "      <td>21192.26</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-692.26</td>\n",
+       "      <td>3.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>12749.66</td>\n",
+       "      <td>13200.00</td>\n",
+       "      <td>450.34</td>\n",
+       "      <td>3.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>20283.08</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>716.92</td>\n",
+       "      <td>3.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>16201.55</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>598.45</td>\n",
+       "      <td>3.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>7974.91</td>\n",
+       "      <td>7699.00</td>\n",
+       "      <td>-275.91</td>\n",
+       "      <td>3.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13015.27</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>484.73</td>\n",
+       "      <td>3.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>6890.35</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>259.65</td>\n",
+       "      <td>3.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>557</th>\n",
+       "      <td>5483.10</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>216.90</td>\n",
+       "      <td>3.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>20200.76</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>799.24</td>\n",
+       "      <td>3.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>13917.01</td>\n",
+       "      <td>14474.00</td>\n",
+       "      <td>556.99</td>\n",
+       "      <td>3.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>11016.60</td>\n",
+       "      <td>10600.00</td>\n",
+       "      <td>-416.60</td>\n",
+       "      <td>3.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>3840.88</td>\n",
+       "      <td>3999.00</td>\n",
+       "      <td>158.12</td>\n",
+       "      <td>3.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>8524.71</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-324.71</td>\n",
+       "      <td>3.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>745</th>\n",
+       "      <td>11551.56</td>\n",
+       "      <td>11111.00</td>\n",
+       "      <td>-440.56</td>\n",
+       "      <td>3.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>759</th>\n",
+       "      <td>17687.48</td>\n",
+       "      <td>16999.00</td>\n",
+       "      <td>-688.48</td>\n",
+       "      <td>4.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>33095.36</td>\n",
+       "      <td>34500.00</td>\n",
+       "      <td>1404.64</td>\n",
+       "      <td>4.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>26864.09</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>-1064.09</td>\n",
+       "      <td>4.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>27698.29</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>1201.71</td>\n",
+       "      <td>4.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>31149.15</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>-1249.15</td>\n",
+       "      <td>4.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>12454.14</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>545.86</td>\n",
+       "      <td>4.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>35396.30</td>\n",
+       "      <td>33950.00</td>\n",
+       "      <td>-1446.30</td>\n",
+       "      <td>4.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>20103.15</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>896.85</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>473</th>\n",
+       "      <td>4170.89</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-170.89</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>64649.72</td>\n",
+       "      <td>62000.00</td>\n",
+       "      <td>-2649.72</td>\n",
+       "      <td>4.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>11964.00</td>\n",
+       "      <td>12500.00</td>\n",
+       "      <td>536.00</td>\n",
+       "      <td>4.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>25045.23</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-1046.23</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>3442.95</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>157.05</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>29647.28</td>\n",
+       "      <td>31000.00</td>\n",
+       "      <td>1352.72</td>\n",
+       "      <td>4.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>15012.39</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>687.61</td>\n",
+       "      <td>4.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>8143.20</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-343.20</td>\n",
+       "      <td>4.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>25063.05</td>\n",
+       "      <td>23999.00</td>\n",
+       "      <td>-1064.05</td>\n",
+       "      <td>4.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>12010.10</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-510.10</td>\n",
+       "      <td>4.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>11176.69</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>-476.69</td>\n",
+       "      <td>4.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>17286.75</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>813.25</td>\n",
+       "      <td>4.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>34276.07</td>\n",
+       "      <td>35900.00</td>\n",
+       "      <td>1623.93</td>\n",
+       "      <td>4.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>483</th>\n",
+       "      <td>25088.99</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-1088.99</td>\n",
+       "      <td>4.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2718.02</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-118.02</td>\n",
+       "      <td>4.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>3770.21</td>\n",
+       "      <td>3950.00</td>\n",
+       "      <td>179.79</td>\n",
+       "      <td>4.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>53853.04</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>-2353.04</td>\n",
+       "      <td>4.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>10463.07</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-464.07</td>\n",
+       "      <td>4.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>8008.22</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>391.78</td>\n",
+       "      <td>4.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>13401.92</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>-601.92</td>\n",
+       "      <td>4.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>13616.65</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>-616.65</td>\n",
+       "      <td>4.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>19805.25</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>994.75</td>\n",
+       "      <td>4.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>11005.68</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>-505.68</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>732</th>\n",
+       "      <td>7517.14</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>382.86</td>\n",
+       "      <td>4.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>17220.82</td>\n",
+       "      <td>18100.00</td>\n",
+       "      <td>879.18</td>\n",
+       "      <td>4.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>2307.20</td>\n",
+       "      <td>2200.00</td>\n",
+       "      <td>-107.20</td>\n",
+       "      <td>4.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>9130.01</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>469.99</td>\n",
+       "      <td>4.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>109346.97</td>\n",
+       "      <td>114999.00</td>\n",
+       "      <td>5652.03</td>\n",
+       "      <td>4.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>4748.92</td>\n",
+       "      <td>4999.00</td>\n",
+       "      <td>250.08</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>2677.67</td>\n",
+       "      <td>2550.00</td>\n",
+       "      <td>-127.67</td>\n",
+       "      <td>5.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>25642.46</td>\n",
+       "      <td>26999.00</td>\n",
+       "      <td>1356.54</td>\n",
+       "      <td>5.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>11203.43</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>596.57</td>\n",
+       "      <td>5.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>424</th>\n",
+       "      <td>3987.44</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>212.56</td>\n",
+       "      <td>5.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>6834.18</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-334.18</td>\n",
+       "      <td>5.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>709</th>\n",
+       "      <td>54169.36</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>-2669.36</td>\n",
+       "      <td>5.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>6540.19</td>\n",
+       "      <td>6899.00</td>\n",
+       "      <td>358.81</td>\n",
+       "      <td>5.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>5996.99</td>\n",
+       "      <td>5700.00</td>\n",
+       "      <td>-296.99</td>\n",
+       "      <td>5.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>19982.41</td>\n",
+       "      <td>18990.00</td>\n",
+       "      <td>-992.41</td>\n",
+       "      <td>5.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>651</th>\n",
+       "      <td>3001.45</td>\n",
+       "      <td>2850.00</td>\n",
+       "      <td>-151.45</td>\n",
+       "      <td>5.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>8216.39</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>-416.39</td>\n",
+       "      <td>5.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>11638.91</td>\n",
+       "      <td>12300.00</td>\n",
+       "      <td>661.09</td>\n",
+       "      <td>5.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>77117.34</td>\n",
+       "      <td>81500.00</td>\n",
+       "      <td>4382.66</td>\n",
+       "      <td>5.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9383.11</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-483.11</td>\n",
+       "      <td>5.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>6961.07</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-361.07</td>\n",
+       "      <td>5.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>27883.22</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>1616.78</td>\n",
+       "      <td>5.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>92087.91</td>\n",
+       "      <td>97500.00</td>\n",
+       "      <td>5412.09</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>14038.48</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-738.48</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>744</th>\n",
+       "      <td>17416.25</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>-916.25</td>\n",
+       "      <td>5.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>802</th>\n",
+       "      <td>12088.25</td>\n",
+       "      <td>11450.00</td>\n",
+       "      <td>-638.25</td>\n",
+       "      <td>5.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>40127.52</td>\n",
+       "      <td>42500.00</td>\n",
+       "      <td>2372.48</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>16792.66</td>\n",
+       "      <td>15900.00</td>\n",
+       "      <td>-892.66</td>\n",
+       "      <td>5.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>18167.73</td>\n",
+       "      <td>17200.00</td>\n",
+       "      <td>-967.73</td>\n",
+       "      <td>5.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>27263.83</td>\n",
+       "      <td>28900.00</td>\n",
+       "      <td>1636.17</td>\n",
+       "      <td>5.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>6555.59</td>\n",
+       "      <td>6950.00</td>\n",
+       "      <td>394.41</td>\n",
+       "      <td>5.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>459</th>\n",
+       "      <td>11415.57</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-615.57</td>\n",
+       "      <td>5.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4899.20</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>300.80</td>\n",
+       "      <td>5.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>630</th>\n",
+       "      <td>11642.12</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>-643.12</td>\n",
+       "      <td>5.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>59312.24</td>\n",
+       "      <td>63000.00</td>\n",
+       "      <td>3687.76</td>\n",
+       "      <td>5.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>5458.98</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>341.02</td>\n",
+       "      <td>5.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>445</th>\n",
+       "      <td>10059.50</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-559.50</td>\n",
+       "      <td>5.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>15882.42</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-883.42</td>\n",
+       "      <td>5.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>6869.42</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>430.58</td>\n",
+       "      <td>5.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>9004.49</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-504.49</td>\n",
+       "      <td>5.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>776</th>\n",
+       "      <td>6112.31</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>387.69</td>\n",
+       "      <td>5.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>10057.03</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>642.97</td>\n",
+       "      <td>6.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>5450.83</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>349.17</td>\n",
+       "      <td>6.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30228.86</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>-1728.86</td>\n",
+       "      <td>6.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>30536.34</td>\n",
+       "      <td>28787.85</td>\n",
+       "      <td>-1748.49</td>\n",
+       "      <td>6.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>10987.90</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>712.10</td>\n",
+       "      <td>6.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>8262.86</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>537.14</td>\n",
+       "      <td>6.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>28542.74</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>-1642.74</td>\n",
+       "      <td>6.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>4886.77</td>\n",
+       "      <td>4600.00</td>\n",
+       "      <td>-286.77</td>\n",
+       "      <td>6.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>10622.75</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-623.75</td>\n",
+       "      <td>6.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>1967.13</td>\n",
+       "      <td>2099.00</td>\n",
+       "      <td>131.87</td>\n",
+       "      <td>6.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>672</th>\n",
+       "      <td>17804.56</td>\n",
+       "      <td>19000.00</td>\n",
+       "      <td>1195.44</td>\n",
+       "      <td>6.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>5902.43</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>397.57</td>\n",
+       "      <td>6.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>58999.17</td>\n",
+       "      <td>62999.00</td>\n",
+       "      <td>3999.83</td>\n",
+       "      <td>6.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>46824.03</td>\n",
+       "      <td>43999.00</td>\n",
+       "      <td>-2825.03</td>\n",
+       "      <td>6.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>767</th>\n",
+       "      <td>2432.69</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>167.31</td>\n",
+       "      <td>6.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>9305.11</td>\n",
+       "      <td>9950.00</td>\n",
+       "      <td>644.89</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>4067.90</td>\n",
+       "      <td>4350.00</td>\n",
+       "      <td>282.10</td>\n",
+       "      <td>6.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>37165.88</td>\n",
+       "      <td>34900.00</td>\n",
+       "      <td>-2265.88</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>12248.10</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-748.10</td>\n",
+       "      <td>6.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>7572.68</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>527.32</td>\n",
+       "      <td>6.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>21837.03</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>-1337.03</td>\n",
+       "      <td>6.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>9919.12</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>-619.12</td>\n",
+       "      <td>6.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>96042.20</td>\n",
+       "      <td>89999.00</td>\n",
+       "      <td>-6043.20</td>\n",
+       "      <td>6.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>7739.78</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>560.22</td>\n",
+       "      <td>6.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>10247.89</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>751.11</td>\n",
+       "      <td>6.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>13573.72</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>-873.72</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>8274.39</td>\n",
+       "      <td>8888.00</td>\n",
+       "      <td>613.61</td>\n",
+       "      <td>6.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>2887.13</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-187.13</td>\n",
+       "      <td>6.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4280.49</td>\n",
+       "      <td>4600.00</td>\n",
+       "      <td>319.51</td>\n",
+       "      <td>6.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>16367.58</td>\n",
+       "      <td>15300.00</td>\n",
+       "      <td>-1067.58</td>\n",
+       "      <td>6.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6741.83</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>-441.83</td>\n",
+       "      <td>7.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>10274.74</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-674.74</td>\n",
+       "      <td>7.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>46581.86</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>-3081.86</td>\n",
+       "      <td>7.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>22293.90</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>1706.10</td>\n",
+       "      <td>7.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>8035.09</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-535.09</td>\n",
+       "      <td>7.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>3712.23</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>287.77</td>\n",
+       "      <td>7.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>13456.31</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>1043.69</td>\n",
+       "      <td>7.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>6673.96</td>\n",
+       "      <td>7199.00</td>\n",
+       "      <td>525.04</td>\n",
+       "      <td>7.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>17166.98</td>\n",
+       "      <td>15999.00</td>\n",
+       "      <td>-1167.98</td>\n",
+       "      <td>7.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>20763.53</td>\n",
+       "      <td>22400.00</td>\n",
+       "      <td>1636.47</td>\n",
+       "      <td>7.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>4634.43</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>365.57</td>\n",
+       "      <td>7.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>22540.34</td>\n",
+       "      <td>21000.00</td>\n",
+       "      <td>-1540.34</td>\n",
+       "      <td>7.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>33345.57</td>\n",
+       "      <td>36000.00</td>\n",
+       "      <td>2654.43</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>6205.20</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>494.80</td>\n",
+       "      <td>7.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>631</th>\n",
+       "      <td>13749.47</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>-949.47</td>\n",
+       "      <td>7.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>4906.11</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>393.89</td>\n",
+       "      <td>7.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>21489.76</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-1490.76</td>\n",
+       "      <td>7.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>736</th>\n",
+       "      <td>64619.43</td>\n",
+       "      <td>69999.00</td>\n",
+       "      <td>5379.57</td>\n",
+       "      <td>7.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>20892.73</td>\n",
+       "      <td>19400.00</td>\n",
+       "      <td>-1492.73</td>\n",
+       "      <td>7.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>8491.05</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>708.95</td>\n",
+       "      <td>7.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>3137.24</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>262.76</td>\n",
+       "      <td>7.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>722</th>\n",
+       "      <td>61406.17</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>-4406.17</td>\n",
+       "      <td>7.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>7008.81</td>\n",
+       "      <td>7600.00</td>\n",
+       "      <td>591.19</td>\n",
+       "      <td>7.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>17794.23</td>\n",
+       "      <td>19300.00</td>\n",
+       "      <td>1505.77</td>\n",
+       "      <td>7.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>778</th>\n",
+       "      <td>2913.09</td>\n",
+       "      <td>2700.00</td>\n",
+       "      <td>-213.09</td>\n",
+       "      <td>7.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>13922.03</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-1022.03</td>\n",
+       "      <td>7.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>25294.20</td>\n",
+       "      <td>23400.00</td>\n",
+       "      <td>-1894.20</td>\n",
+       "      <td>8.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>59458.46</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-4458.46</td>\n",
+       "      <td>8.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>20486.18</td>\n",
+       "      <td>18950.00</td>\n",
+       "      <td>-1536.18</td>\n",
+       "      <td>8.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>13047.55</td>\n",
+       "      <td>14200.00</td>\n",
+       "      <td>1152.45</td>\n",
+       "      <td>8.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>9352.13</td>\n",
+       "      <td>8650.00</td>\n",
+       "      <td>-702.13</td>\n",
+       "      <td>8.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>792</th>\n",
+       "      <td>32994.80</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>-2494.80</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>793</th>\n",
+       "      <td>6702.65</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>597.35</td>\n",
+       "      <td>8.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>23852.98</td>\n",
+       "      <td>26000.00</td>\n",
+       "      <td>2147.02</td>\n",
+       "      <td>8.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>5963.16</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>536.84</td>\n",
+       "      <td>8.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>38422.39</td>\n",
+       "      <td>41900.00</td>\n",
+       "      <td>3477.61</td>\n",
+       "      <td>8.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6003.07</td>\n",
+       "      <td>6550.00</td>\n",
+       "      <td>546.93</td>\n",
+       "      <td>8.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>665</th>\n",
+       "      <td>6688.38</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>611.62</td>\n",
+       "      <td>8.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>779</th>\n",
+       "      <td>19054.71</td>\n",
+       "      <td>20800.00</td>\n",
+       "      <td>1745.29</td>\n",
+       "      <td>8.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>3360.19</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>-260.19</td>\n",
+       "      <td>8.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>14929.05</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>1370.95</td>\n",
+       "      <td>8.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>22027.55</td>\n",
+       "      <td>20300.00</td>\n",
+       "      <td>-1727.55</td>\n",
+       "      <td>8.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>22027.55</td>\n",
+       "      <td>20300.00</td>\n",
+       "      <td>-1727.55</td>\n",
+       "      <td>8.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>8357.21</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>-657.21</td>\n",
+       "      <td>8.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>22153.23</td>\n",
+       "      <td>20400.00</td>\n",
+       "      <td>-1753.23</td>\n",
+       "      <td>8.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>17054.19</td>\n",
+       "      <td>15700.00</td>\n",
+       "      <td>-1354.19</td>\n",
+       "      <td>8.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>10417.13</td>\n",
+       "      <td>11403.99</td>\n",
+       "      <td>986.86</td>\n",
+       "      <td>8.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20551.65</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>1948.35</td>\n",
+       "      <td>8.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>26009.78</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>2490.22</td>\n",
+       "      <td>8.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>761</th>\n",
+       "      <td>12731.48</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-1031.48</td>\n",
+       "      <td>8.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>18506.54</td>\n",
+       "      <td>17000.00</td>\n",
+       "      <td>-1506.54</td>\n",
+       "      <td>8.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>3592.48</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-292.48</td>\n",
+       "      <td>8.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>23222.71</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>2277.29</td>\n",
+       "      <td>8.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>6826.47</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>673.53</td>\n",
+       "      <td>8.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>9482.59</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-782.59</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>6975.75</td>\n",
+       "      <td>6400.00</td>\n",
+       "      <td>-575.75</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>18326.60</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-1526.60</td>\n",
+       "      <td>9.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>27001.23</td>\n",
+       "      <td>24750.00</td>\n",
+       "      <td>-2251.23</td>\n",
+       "      <td>9.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>24552.29</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-2052.29</td>\n",
+       "      <td>9.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>25017.11</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-2117.11</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>14066.37</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>1433.63</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>25215.17</td>\n",
+       "      <td>27800.00</td>\n",
+       "      <td>2584.83</td>\n",
+       "      <td>9.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>52390.49</td>\n",
+       "      <td>57777.00</td>\n",
+       "      <td>5386.51</td>\n",
+       "      <td>9.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>24614.93</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-2114.93</td>\n",
+       "      <td>9.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>31509.05</td>\n",
+       "      <td>28800.00</td>\n",
+       "      <td>-2709.05</td>\n",
+       "      <td>9.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10857.59</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-957.59</td>\n",
+       "      <td>9.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>4605.75</td>\n",
+       "      <td>5100.00</td>\n",
+       "      <td>494.25</td>\n",
+       "      <td>9.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>7679.11</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-680.11</td>\n",
+       "      <td>9.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>13449.96</td>\n",
+       "      <td>14899.00</td>\n",
+       "      <td>1449.04</td>\n",
+       "      <td>9.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645</th>\n",
+       "      <td>19098.09</td>\n",
+       "      <td>17400.00</td>\n",
+       "      <td>-1698.09</td>\n",
+       "      <td>9.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>4330.57</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>469.43</td>\n",
+       "      <td>9.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>39228.72</td>\n",
+       "      <td>43500.00</td>\n",
+       "      <td>4271.28</td>\n",
+       "      <td>9.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>782</th>\n",
+       "      <td>2911.16</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>-261.16</td>\n",
+       "      <td>9.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>13959.83</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>1540.17</td>\n",
+       "      <td>9.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>739</th>\n",
+       "      <td>4184.64</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>-384.64</td>\n",
+       "      <td>10.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>12670.45</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-1170.45</td>\n",
+       "      <td>10.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5386.74</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>613.26</td>\n",
+       "      <td>10.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>39141.03</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>-3641.03</td>\n",
+       "      <td>10.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>10476.67</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-976.67</td>\n",
+       "      <td>10.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>4483.97</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>516.03</td>\n",
+       "      <td>10.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>8696.33</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>1003.67</td>\n",
+       "      <td>10.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>8964.70</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1035.30</td>\n",
+       "      <td>10.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>5739.40</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>-539.40</td>\n",
+       "      <td>10.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>469</th>\n",
+       "      <td>8512.76</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>987.24</td>\n",
+       "      <td>10.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>4305.44</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>-405.44</td>\n",
+       "      <td>10.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>33929.47</td>\n",
+       "      <td>37900.00</td>\n",
+       "      <td>3970.53</td>\n",
+       "      <td>10.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>5997.22</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>702.78</td>\n",
+       "      <td>10.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>27739.55</td>\n",
+       "      <td>31000.00</td>\n",
+       "      <td>3260.45</td>\n",
+       "      <td>10.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>9395.10</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-895.10</td>\n",
+       "      <td>10.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>23054.57</td>\n",
+       "      <td>25800.00</td>\n",
+       "      <td>2745.43</td>\n",
+       "      <td>10.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>734</th>\n",
+       "      <td>25323.00</td>\n",
+       "      <td>28350.00</td>\n",
+       "      <td>3027.00</td>\n",
+       "      <td>10.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>12411.94</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>1488.06</td>\n",
+       "      <td>10.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>30347.45</td>\n",
+       "      <td>34000.00</td>\n",
+       "      <td>3652.55</td>\n",
+       "      <td>10.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>17165.39</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-1665.39</td>\n",
+       "      <td>10.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>3927.19</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>472.81</td>\n",
+       "      <td>10.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>2228.68</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>270.32</td>\n",
+       "      <td>10.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>2584.33</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>315.67</td>\n",
+       "      <td>10.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>9974.83</td>\n",
+       "      <td>8990.00</td>\n",
+       "      <td>-984.83</td>\n",
+       "      <td>10.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>50737.64</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>6262.36</td>\n",
+       "      <td>10.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>4181.54</td>\n",
+       "      <td>4700.00</td>\n",
+       "      <td>518.46</td>\n",
+       "      <td>11.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>11824.42</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>1475.58</td>\n",
+       "      <td>11.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>34352.54</td>\n",
+       "      <td>30900.00</td>\n",
+       "      <td>-3452.54</td>\n",
+       "      <td>11.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>31072.01</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>3927.99</td>\n",
+       "      <td>11.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>9901.71</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1001.71</td>\n",
+       "      <td>11.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>8123.03</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>-823.03</td>\n",
+       "      <td>11.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>13300.81</td>\n",
+       "      <td>11950.00</td>\n",
+       "      <td>-1350.81</td>\n",
+       "      <td>11.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>513</th>\n",
+       "      <td>8867.20</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>1131.80</td>\n",
+       "      <td>11.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>13213.07</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>1686.93</td>\n",
+       "      <td>11.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>44560.45</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>-4561.45</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>44560.45</td>\n",
+       "      <td>39999.00</td>\n",
+       "      <td>-4561.45</td>\n",
+       "      <td>11.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>11484.51</td>\n",
+       "      <td>10300.00</td>\n",
+       "      <td>-1184.51</td>\n",
+       "      <td>11.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>7804.78</td>\n",
+       "      <td>6999.00</td>\n",
+       "      <td>-805.78</td>\n",
+       "      <td>11.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>22287.84</td>\n",
+       "      <td>25200.00</td>\n",
+       "      <td>2912.16</td>\n",
+       "      <td>11.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>24321.68</td>\n",
+       "      <td>21800.00</td>\n",
+       "      <td>-2521.68</td>\n",
+       "      <td>11.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>2343.59</td>\n",
+       "      <td>2100.00</td>\n",
+       "      <td>-243.59</td>\n",
+       "      <td>11.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>11932.57</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>1567.43</td>\n",
+       "      <td>11.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>10054.06</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-1054.06</td>\n",
+       "      <td>11.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>40781.07</td>\n",
+       "      <td>36500.00</td>\n",
+       "      <td>-4281.07</td>\n",
+       "      <td>11.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>2779.76</td>\n",
+       "      <td>3149.25</td>\n",
+       "      <td>369.49</td>\n",
+       "      <td>11.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>7056.08</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>943.92</td>\n",
+       "      <td>11.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>13196.07</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-1396.07</td>\n",
+       "      <td>11.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>8365.18</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1134.82</td>\n",
+       "      <td>11.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>2729.63</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>370.37</td>\n",
+       "      <td>11.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>641</th>\n",
+       "      <td>7279.35</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>-779.35</td>\n",
+       "      <td>11.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>6600.31</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>899.69</td>\n",
+       "      <td>12.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>8577.61</td>\n",
+       "      <td>9750.00</td>\n",
+       "      <td>1172.39</td>\n",
+       "      <td>12.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>13331.21</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1431.21</td>\n",
+       "      <td>12.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>689</th>\n",
+       "      <td>3074.51</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>425.49</td>\n",
+       "      <td>12.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>647</th>\n",
+       "      <td>2020.09</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>279.91</td>\n",
+       "      <td>12.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2895.96</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>404.04</td>\n",
+       "      <td>12.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>7808.12</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>1091.88</td>\n",
+       "      <td>12.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>21139.89</td>\n",
+       "      <td>24100.00</td>\n",
+       "      <td>2960.11</td>\n",
+       "      <td>12.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>24999.38</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>3500.62</td>\n",
+       "      <td>12.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>2718.83</td>\n",
+       "      <td>3100.00</td>\n",
+       "      <td>381.17</td>\n",
+       "      <td>12.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>42942.54</td>\n",
+       "      <td>38233.00</td>\n",
+       "      <td>-4709.54</td>\n",
+       "      <td>12.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>11131.10</td>\n",
+       "      <td>12700.00</td>\n",
+       "      <td>1568.90</td>\n",
+       "      <td>12.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>7447.84</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>1052.16</td>\n",
+       "      <td>12.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>4248.90</td>\n",
+       "      <td>4850.00</td>\n",
+       "      <td>601.10</td>\n",
+       "      <td>12.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>2802.47</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>397.53</td>\n",
+       "      <td>12.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>7351.03</td>\n",
+       "      <td>8400.00</td>\n",
+       "      <td>1048.97</td>\n",
+       "      <td>12.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>14528.10</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-1628.10</td>\n",
+       "      <td>12.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>22532.79</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-2533.79</td>\n",
+       "      <td>12.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>3832.33</td>\n",
+       "      <td>3400.00</td>\n",
+       "      <td>-432.33</td>\n",
+       "      <td>12.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>19197.99</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>2802.01</td>\n",
+       "      <td>12.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>18942.17</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-2142.17</td>\n",
+       "      <td>12.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>19190.84</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>2809.16</td>\n",
+       "      <td>12.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>73324.12</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>-8324.12</td>\n",
+       "      <td>12.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>12141.67</td>\n",
+       "      <td>10750.00</td>\n",
+       "      <td>-1391.67</td>\n",
+       "      <td>12.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>13444.25</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-1544.25</td>\n",
+       "      <td>12.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>32212.96</td>\n",
+       "      <td>28500.00</td>\n",
+       "      <td>-3712.96</td>\n",
+       "      <td>13.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>10173.73</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-1173.73</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22173.64</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>3326.36</td>\n",
+       "      <td>13.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>3216.30</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>483.70</td>\n",
+       "      <td>13.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>13061.64</td>\n",
+       "      <td>11550.00</td>\n",
+       "      <td>-1511.64</td>\n",
+       "      <td>13.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>7640.17</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1159.83</td>\n",
+       "      <td>13.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>8941.66</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-1041.66</td>\n",
+       "      <td>13.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>8848.20</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1351.80</td>\n",
+       "      <td>13.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>37294.36</td>\n",
+       "      <td>43000.00</td>\n",
+       "      <td>5705.64</td>\n",
+       "      <td>13.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>28900.44</td>\n",
+       "      <td>33333.00</td>\n",
+       "      <td>4432.56</td>\n",
+       "      <td>13.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>10084.88</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1184.88</td>\n",
+       "      <td>13.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>10766.99</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-1266.99</td>\n",
+       "      <td>13.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>19048.58</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>-2248.58</td>\n",
+       "      <td>13.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>23296.66</td>\n",
+       "      <td>26900.00</td>\n",
+       "      <td>3603.34</td>\n",
+       "      <td>13.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>27706.06</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>4293.94</td>\n",
+       "      <td>13.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>2856.79</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>443.21</td>\n",
+       "      <td>13.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>607</th>\n",
+       "      <td>8400.37</td>\n",
+       "      <td>7400.00</td>\n",
+       "      <td>-1000.37</td>\n",
+       "      <td>13.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>14874.09</td>\n",
+       "      <td>17200.00</td>\n",
+       "      <td>2325.91</td>\n",
+       "      <td>13.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>2853.47</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>446.53</td>\n",
+       "      <td>13.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>1929.25</td>\n",
+       "      <td>1699.00</td>\n",
+       "      <td>-230.25</td>\n",
+       "      <td>13.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>6740.68</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>1059.32</td>\n",
+       "      <td>13.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>15113.32</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-1813.32</td>\n",
+       "      <td>13.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>8146.17</td>\n",
+       "      <td>9450.00</td>\n",
+       "      <td>1303.83</td>\n",
+       "      <td>13.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>16956.75</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>-2056.75</td>\n",
+       "      <td>13.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>8616.78</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>1383.22</td>\n",
+       "      <td>13.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>27560.75</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>4439.25</td>\n",
+       "      <td>13.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>11782.11</td>\n",
+       "      <td>13700.00</td>\n",
+       "      <td>1917.89</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>6047.87</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-747.87</td>\n",
+       "      <td>14.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>3263.77</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>536.23</td>\n",
+       "      <td>14.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>25652.89</td>\n",
+       "      <td>29900.00</td>\n",
+       "      <td>4247.11</td>\n",
+       "      <td>14.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13110.05</td>\n",
+       "      <td>15300.00</td>\n",
+       "      <td>2189.95</td>\n",
+       "      <td>14.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>6154.50</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1045.50</td>\n",
+       "      <td>14.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>85955.91</td>\n",
+       "      <td>74999.00</td>\n",
+       "      <td>-10956.91</td>\n",
+       "      <td>14.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>13772.47</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>-1773.47</td>\n",
+       "      <td>14.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>7923.76</td>\n",
+       "      <td>9300.00</td>\n",
+       "      <td>1376.24</td>\n",
+       "      <td>14.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>32350.70</td>\n",
+       "      <td>37990.00</td>\n",
+       "      <td>5639.30</td>\n",
+       "      <td>14.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>504</th>\n",
+       "      <td>3043.80</td>\n",
+       "      <td>2650.00</td>\n",
+       "      <td>-393.80</td>\n",
+       "      <td>14.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>508</th>\n",
+       "      <td>9993.19</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-1293.19</td>\n",
+       "      <td>14.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>24702.61</td>\n",
+       "      <td>21500.00</td>\n",
+       "      <td>-3202.61</td>\n",
+       "      <td>14.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>63213.55</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>-8213.55</td>\n",
+       "      <td>14.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>12647.28</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>-1648.28</td>\n",
+       "      <td>14.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>11812.55</td>\n",
+       "      <td>13899.00</td>\n",
+       "      <td>2086.45</td>\n",
+       "      <td>15.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>3398.91</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>601.09</td>\n",
+       "      <td>15.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>12482.76</td>\n",
+       "      <td>10850.00</td>\n",
+       "      <td>-1632.76</td>\n",
+       "      <td>15.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>6099.48</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-799.48</td>\n",
+       "      <td>15.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>5248.40</td>\n",
+       "      <td>6200.00</td>\n",
+       "      <td>951.60</td>\n",
+       "      <td>15.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>6052.23</td>\n",
+       "      <td>7150.00</td>\n",
+       "      <td>1097.77</td>\n",
+       "      <td>15.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>9809.02</td>\n",
+       "      <td>8499.00</td>\n",
+       "      <td>-1310.02</td>\n",
+       "      <td>15.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>450</th>\n",
+       "      <td>6174.68</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>1125.32</td>\n",
+       "      <td>15.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>3635.06</td>\n",
+       "      <td>4300.00</td>\n",
+       "      <td>664.94</td>\n",
+       "      <td>15.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>3347.79</td>\n",
+       "      <td>2899.00</td>\n",
+       "      <td>-448.79</td>\n",
+       "      <td>15.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>20557.05</td>\n",
+       "      <td>17800.00</td>\n",
+       "      <td>-2757.05</td>\n",
+       "      <td>15.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>28877.80</td>\n",
+       "      <td>25000.00</td>\n",
+       "      <td>-3877.80</td>\n",
+       "      <td>15.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>5082.76</td>\n",
+       "      <td>4400.00</td>\n",
+       "      <td>-682.76</td>\n",
+       "      <td>15.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>17104.46</td>\n",
+       "      <td>14800.00</td>\n",
+       "      <td>-2304.46</td>\n",
+       "      <td>15.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>14842.29</td>\n",
+       "      <td>17595.00</td>\n",
+       "      <td>2752.71</td>\n",
+       "      <td>15.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>786</th>\n",
+       "      <td>3817.05</td>\n",
+       "      <td>3300.00</td>\n",
+       "      <td>-517.05</td>\n",
+       "      <td>15.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>9373.98</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-1273.98</td>\n",
+       "      <td>15.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>6888.50</td>\n",
+       "      <td>5950.00</td>\n",
+       "      <td>-938.50</td>\n",
+       "      <td>15.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>6903.35</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>1296.65</td>\n",
+       "      <td>15.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>34332.85</td>\n",
+       "      <td>40800.00</td>\n",
+       "      <td>6467.15</td>\n",
+       "      <td>15.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>16799.16</td>\n",
+       "      <td>14500.00</td>\n",
+       "      <td>-2299.16</td>\n",
+       "      <td>15.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>98480.67</td>\n",
+       "      <td>85000.00</td>\n",
+       "      <td>-13480.67</td>\n",
+       "      <td>15.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>6139.72</td>\n",
+       "      <td>7300.00</td>\n",
+       "      <td>1160.28</td>\n",
+       "      <td>15.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>15437.68</td>\n",
+       "      <td>13300.00</td>\n",
+       "      <td>-2137.68</td>\n",
+       "      <td>16.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>4064.76</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-564.76</td>\n",
+       "      <td>16.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>14087.55</td>\n",
+       "      <td>16800.00</td>\n",
+       "      <td>2712.45</td>\n",
+       "      <td>16.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>26137.11</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>-3637.11</td>\n",
+       "      <td>16.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>27304.45</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>-3804.45</td>\n",
+       "      <td>16.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>9637.17</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>1862.83</td>\n",
+       "      <td>16.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>605</th>\n",
+       "      <td>33099.94</td>\n",
+       "      <td>39500.00</td>\n",
+       "      <td>6400.06</td>\n",
+       "      <td>16.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>36023.63</td>\n",
+       "      <td>30990.00</td>\n",
+       "      <td>-5033.63</td>\n",
+       "      <td>16.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>37352.82</td>\n",
+       "      <td>44600.00</td>\n",
+       "      <td>7247.18</td>\n",
+       "      <td>16.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>10462.54</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>-1463.54</td>\n",
+       "      <td>16.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>31976.86</td>\n",
+       "      <td>27500.00</td>\n",
+       "      <td>-4476.86</td>\n",
+       "      <td>16.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>20915.39</td>\n",
+       "      <td>24996.00</td>\n",
+       "      <td>4080.61</td>\n",
+       "      <td>16.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>1673.12</td>\n",
+       "      <td>2000.00</td>\n",
+       "      <td>326.88</td>\n",
+       "      <td>16.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>14221.55</td>\n",
+       "      <td>17000.00</td>\n",
+       "      <td>2778.45</td>\n",
+       "      <td>16.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>34297.36</td>\n",
+       "      <td>41000.00</td>\n",
+       "      <td>6702.64</td>\n",
+       "      <td>16.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>19907.26</td>\n",
+       "      <td>17100.00</td>\n",
+       "      <td>-2807.26</td>\n",
+       "      <td>16.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>10362.29</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1462.29</td>\n",
+       "      <td>16.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>23291.25</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-3291.25</td>\n",
+       "      <td>16.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>2213.58</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-313.58</td>\n",
+       "      <td>16.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>8738.60</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>-1238.60</td>\n",
+       "      <td>16.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>6877.84</td>\n",
+       "      <td>5900.00</td>\n",
+       "      <td>-977.84</td>\n",
+       "      <td>16.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>10377.09</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-1477.09</td>\n",
+       "      <td>16.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>21349.94</td>\n",
+       "      <td>18300.00</td>\n",
+       "      <td>-3049.94</td>\n",
+       "      <td>16.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>2858.60</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-408.60</td>\n",
+       "      <td>16.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>11676.82</td>\n",
+       "      <td>9999.00</td>\n",
+       "      <td>-1677.82</td>\n",
+       "      <td>16.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>7892.51</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>1607.49</td>\n",
+       "      <td>16.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>5399.74</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1100.26</td>\n",
+       "      <td>16.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>25408.55</td>\n",
+       "      <td>21700.00</td>\n",
+       "      <td>-3708.55</td>\n",
+       "      <td>17.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>10609.93</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>2190.07</td>\n",
+       "      <td>17.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>28114.25</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>-4114.25</td>\n",
+       "      <td>17.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>39356.61</td>\n",
+       "      <td>47500.00</td>\n",
+       "      <td>8143.39</td>\n",
+       "      <td>17.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>50516.19</td>\n",
+       "      <td>61000.00</td>\n",
+       "      <td>10483.81</td>\n",
+       "      <td>17.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>9481.20</td>\n",
+       "      <td>11450.00</td>\n",
+       "      <td>1968.80</td>\n",
+       "      <td>17.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>13594.21</td>\n",
+       "      <td>16450.00</td>\n",
+       "      <td>2855.79</td>\n",
+       "      <td>17.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>12675.07</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-1875.07</td>\n",
+       "      <td>17.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>11505.09</td>\n",
+       "      <td>9800.00</td>\n",
+       "      <td>-1705.09</td>\n",
+       "      <td>17.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>9082.32</td>\n",
+       "      <td>10999.00</td>\n",
+       "      <td>1916.68</td>\n",
+       "      <td>17.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>31495.17</td>\n",
+       "      <td>26800.00</td>\n",
+       "      <td>-4695.17</td>\n",
+       "      <td>17.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>748</th>\n",
+       "      <td>11282.67</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-1682.67</td>\n",
+       "      <td>17.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>19281.74</td>\n",
+       "      <td>16400.00</td>\n",
+       "      <td>-2881.74</td>\n",
+       "      <td>17.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>658</th>\n",
+       "      <td>7992.08</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>1707.92</td>\n",
+       "      <td>17.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>758</th>\n",
+       "      <td>5881.36</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-881.36</td>\n",
+       "      <td>17.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>18117.61</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>3882.39</td>\n",
+       "      <td>17.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>11412.71</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-1712.71</td>\n",
+       "      <td>17.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>15774.43</td>\n",
+       "      <td>13400.00</td>\n",
+       "      <td>-2374.43</td>\n",
+       "      <td>17.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>4357.87</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>942.13</td>\n",
+       "      <td>17.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>8126.96</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1226.96</td>\n",
+       "      <td>17.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>11434.04</td>\n",
+       "      <td>9700.00</td>\n",
+       "      <td>-1734.04</td>\n",
+       "      <td>17.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>74745.66</td>\n",
+       "      <td>63333.00</td>\n",
+       "      <td>-11412.66</td>\n",
+       "      <td>18.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>18302.49</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>-2802.49</td>\n",
+       "      <td>18.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>3679.35</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>820.65</td>\n",
+       "      <td>18.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10710.00</td>\n",
+       "      <td>13100.00</td>\n",
+       "      <td>2390.00</td>\n",
+       "      <td>18.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>6293.06</td>\n",
+       "      <td>7700.00</td>\n",
+       "      <td>1406.94</td>\n",
+       "      <td>18.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>13370.87</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>-2070.87</td>\n",
+       "      <td>18.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>9355.15</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-1455.15</td>\n",
+       "      <td>18.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>26420.53</td>\n",
+       "      <td>32400.00</td>\n",
+       "      <td>5979.47</td>\n",
+       "      <td>18.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>3790.91</td>\n",
+       "      <td>4650.00</td>\n",
+       "      <td>859.09</td>\n",
+       "      <td>18.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>5459.66</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1240.34</td>\n",
+       "      <td>18.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>485</th>\n",
+       "      <td>4480.19</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1019.81</td>\n",
+       "      <td>18.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>8536.82</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1336.82</td>\n",
+       "      <td>18.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>40274.34</td>\n",
+       "      <td>49500.00</td>\n",
+       "      <td>9225.66</td>\n",
+       "      <td>18.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>89582.34</td>\n",
+       "      <td>75500.00</td>\n",
+       "      <td>-14082.34</td>\n",
+       "      <td>18.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>6884.55</td>\n",
+       "      <td>5800.00</td>\n",
+       "      <td>-1084.55</td>\n",
+       "      <td>18.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>51073.39</td>\n",
+       "      <td>43000.00</td>\n",
+       "      <td>-8073.39</td>\n",
+       "      <td>18.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>6090.18</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>1409.82</td>\n",
+       "      <td>18.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642</th>\n",
+       "      <td>19364.44</td>\n",
+       "      <td>16300.00</td>\n",
+       "      <td>-3064.44</td>\n",
+       "      <td>18.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>8280.51</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>1919.49</td>\n",
+       "      <td>18.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>5598.65</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>1301.35</td>\n",
+       "      <td>18.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>85858.88</td>\n",
+       "      <td>105999.00</td>\n",
+       "      <td>20140.12</td>\n",
+       "      <td>19.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>5506.55</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>1293.45</td>\n",
+       "      <td>19.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>8817.95</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2082.05</td>\n",
+       "      <td>19.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>11319.17</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-1819.17</td>\n",
+       "      <td>19.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>11445.18</td>\n",
+       "      <td>9599.00</td>\n",
+       "      <td>-1846.18</td>\n",
+       "      <td>19.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>5650.29</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1349.71</td>\n",
+       "      <td>19.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>4433.53</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>1066.47</td>\n",
+       "      <td>19.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>12724.62</td>\n",
+       "      <td>15800.00</td>\n",
+       "      <td>3075.38</td>\n",
+       "      <td>19.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>2092.87</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>507.13</td>\n",
+       "      <td>19.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>36593.81</td>\n",
+       "      <td>45500.00</td>\n",
+       "      <td>8906.19</td>\n",
+       "      <td>19.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>60274.39</td>\n",
+       "      <td>75000.00</td>\n",
+       "      <td>14725.61</td>\n",
+       "      <td>19.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>46983.85</td>\n",
+       "      <td>58500.00</td>\n",
+       "      <td>11516.15</td>\n",
+       "      <td>19.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>724</th>\n",
+       "      <td>8747.37</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>2152.63</td>\n",
+       "      <td>19.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>22708.02</td>\n",
+       "      <td>18950.00</td>\n",
+       "      <td>-3758.02</td>\n",
+       "      <td>19.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>13185.60</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2185.60</td>\n",
+       "      <td>19.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>9594.67</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1594.67</td>\n",
+       "      <td>19.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>16676.56</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-2776.56</td>\n",
+       "      <td>19.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>5399.84</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-899.84</td>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>6639.69</td>\n",
+       "      <td>8300.00</td>\n",
+       "      <td>1660.31</td>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>5676.99</td>\n",
+       "      <td>7100.00</td>\n",
+       "      <td>1423.01</td>\n",
+       "      <td>20.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>34847.46</td>\n",
+       "      <td>28950.00</td>\n",
+       "      <td>-5897.46</td>\n",
+       "      <td>20.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>3010.71</td>\n",
+       "      <td>2499.00</td>\n",
+       "      <td>-511.71</td>\n",
+       "      <td>20.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>5315.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1384.73</td>\n",
+       "      <td>20.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>6940.02</td>\n",
+       "      <td>8750.00</td>\n",
+       "      <td>1809.98</td>\n",
+       "      <td>20.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>6056.80</td>\n",
+       "      <td>7650.00</td>\n",
+       "      <td>1593.20</td>\n",
+       "      <td>20.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>8708.11</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>-1508.11</td>\n",
+       "      <td>20.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>16175.59</td>\n",
+       "      <td>20477.54</td>\n",
+       "      <td>4301.95</td>\n",
+       "      <td>21.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>5290.04</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>1409.96</td>\n",
+       "      <td>21.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>656</th>\n",
+       "      <td>1973.68</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>526.32</td>\n",
+       "      <td>21.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>4181.72</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>1118.28</td>\n",
+       "      <td>21.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>4732.08</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>1267.92</td>\n",
+       "      <td>21.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12222.81</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>3277.19</td>\n",
+       "      <td>21.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>32724.25</td>\n",
+       "      <td>41500.00</td>\n",
+       "      <td>8775.75</td>\n",
+       "      <td>21.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>13328.75</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2328.75</td>\n",
+       "      <td>21.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>36828.78</td>\n",
+       "      <td>46800.00</td>\n",
+       "      <td>9971.22</td>\n",
+       "      <td>21.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>494</th>\n",
+       "      <td>6920.60</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>1879.40</td>\n",
+       "      <td>21.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>19020.64</td>\n",
+       "      <td>24193.65</td>\n",
+       "      <td>5173.01</td>\n",
+       "      <td>21.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>24740.92</td>\n",
+       "      <td>31500.00</td>\n",
+       "      <td>6759.08</td>\n",
+       "      <td>21.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>18255.49</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>-3255.49</td>\n",
+       "      <td>21.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>775</th>\n",
+       "      <td>11692.08</td>\n",
+       "      <td>9600.00</td>\n",
+       "      <td>-2092.08</td>\n",
+       "      <td>21.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>17173.49</td>\n",
+       "      <td>22000.00</td>\n",
+       "      <td>4826.51</td>\n",
+       "      <td>21.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>11700.38</td>\n",
+       "      <td>15000.00</td>\n",
+       "      <td>3299.62</td>\n",
+       "      <td>22.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>2623.69</td>\n",
+       "      <td>2150.00</td>\n",
+       "      <td>-473.69</td>\n",
+       "      <td>22.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>2026.96</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>573.04</td>\n",
+       "      <td>22.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>8434.23</td>\n",
+       "      <td>6900.00</td>\n",
+       "      <td>-1534.23</td>\n",
+       "      <td>22.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>2717.11</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>782.89</td>\n",
+       "      <td>22.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>2713.87</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>786.13</td>\n",
+       "      <td>22.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>6200.75</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>1799.25</td>\n",
+       "      <td>22.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>10783.10</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-1983.10</td>\n",
+       "      <td>22.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>22566.89</td>\n",
+       "      <td>18400.00</td>\n",
+       "      <td>-4166.89</td>\n",
+       "      <td>22.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>18486.02</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>5413.98</td>\n",
+       "      <td>22.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>42831.62</td>\n",
+       "      <td>55555.00</td>\n",
+       "      <td>12723.38</td>\n",
+       "      <td>22.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>18116.63</td>\n",
+       "      <td>23500.00</td>\n",
+       "      <td>5383.37</td>\n",
+       "      <td>22.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>13535.73</td>\n",
+       "      <td>11000.00</td>\n",
+       "      <td>-2535.73</td>\n",
+       "      <td>23.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>9723.51</td>\n",
+       "      <td>12650.00</td>\n",
+       "      <td>2926.49</td>\n",
+       "      <td>23.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>479</th>\n",
+       "      <td>14919.17</td>\n",
+       "      <td>12114.35</td>\n",
+       "      <td>-2804.82</td>\n",
+       "      <td>23.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>447</th>\n",
+       "      <td>17573.11</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>5326.89</td>\n",
+       "      <td>23.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>20558.01</td>\n",
+       "      <td>16672.50</td>\n",
+       "      <td>-3885.51</td>\n",
+       "      <td>23.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>16030.55</td>\n",
+       "      <td>12999.00</td>\n",
+       "      <td>-3031.55</td>\n",
+       "      <td>23.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>17535.66</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>5364.34</td>\n",
+       "      <td>23.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>2977.77</td>\n",
+       "      <td>3900.00</td>\n",
+       "      <td>922.23</td>\n",
+       "      <td>23.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>22121.67</td>\n",
+       "      <td>29000.00</td>\n",
+       "      <td>6878.33</td>\n",
+       "      <td>23.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>29567.87</td>\n",
+       "      <td>38764.00</td>\n",
+       "      <td>9196.13</td>\n",
+       "      <td>23.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>22081.93</td>\n",
+       "      <td>28999.00</td>\n",
+       "      <td>6917.07</td>\n",
+       "      <td>23.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>8174.40</td>\n",
+       "      <td>6600.00</td>\n",
+       "      <td>-1574.40</td>\n",
+       "      <td>23.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>2813.36</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>886.64</td>\n",
+       "      <td>23.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>9728.36</td>\n",
+       "      <td>12800.00</td>\n",
+       "      <td>3071.64</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>23236.27</td>\n",
+       "      <td>18700.00</td>\n",
+       "      <td>-4536.27</td>\n",
+       "      <td>24.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>14539.53</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-2839.53</td>\n",
+       "      <td>24.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>4915.54</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>1584.46</td>\n",
+       "      <td>24.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>505</th>\n",
+       "      <td>22276.96</td>\n",
+       "      <td>17900.00</td>\n",
+       "      <td>-4376.96</td>\n",
+       "      <td>24.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>10082.85</td>\n",
+       "      <td>8100.00</td>\n",
+       "      <td>-1982.85</td>\n",
+       "      <td>24.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>3113.25</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>-613.25</td>\n",
+       "      <td>24.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>30902.33</td>\n",
+       "      <td>24800.00</td>\n",
+       "      <td>-6102.33</td>\n",
+       "      <td>24.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>12346.01</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-2446.01</td>\n",
+       "      <td>24.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>6161.68</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>2038.32</td>\n",
+       "      <td>24.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>616</th>\n",
+       "      <td>21017.12</td>\n",
+       "      <td>28000.00</td>\n",
+       "      <td>6982.88</td>\n",
+       "      <td>24.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>3748.29</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-748.29</td>\n",
+       "      <td>24.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>3061.17</td>\n",
+       "      <td>2450.00</td>\n",
+       "      <td>-611.17</td>\n",
+       "      <td>24.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>9998.69</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-1998.69</td>\n",
+       "      <td>24.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>3749.11</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>1250.89</td>\n",
+       "      <td>25.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>5390.58</td>\n",
+       "      <td>7200.00</td>\n",
+       "      <td>1809.42</td>\n",
+       "      <td>25.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>9709.07</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>3290.93</td>\n",
+       "      <td>25.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>803</th>\n",
+       "      <td>3760.79</td>\n",
+       "      <td>3000.00</td>\n",
+       "      <td>-760.79</td>\n",
+       "      <td>25.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>4400.37</td>\n",
+       "      <td>5900.00</td>\n",
+       "      <td>1499.63</td>\n",
+       "      <td>25.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>3203.55</td>\n",
+       "      <td>4299.00</td>\n",
+       "      <td>1095.45</td>\n",
+       "      <td>25.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>18827.10</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>-3828.10</td>\n",
+       "      <td>25.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>9674.76</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>3325.24</td>\n",
+       "      <td>25.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>5205.98</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>1794.02</td>\n",
+       "      <td>25.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>18944.86</td>\n",
+       "      <td>25500.00</td>\n",
+       "      <td>6555.14</td>\n",
+       "      <td>25.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>31179.60</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>10820.40</td>\n",
+       "      <td>25.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>804</th>\n",
+       "      <td>26319.25</td>\n",
+       "      <td>20900.00</td>\n",
+       "      <td>-5419.25</td>\n",
+       "      <td>25.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>2959.68</td>\n",
+       "      <td>2350.00</td>\n",
+       "      <td>-609.68</td>\n",
+       "      <td>25.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>8863.85</td>\n",
+       "      <td>12000.00</td>\n",
+       "      <td>3136.15</td>\n",
+       "      <td>26.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>652</th>\n",
+       "      <td>38039.83</td>\n",
+       "      <td>51500.00</td>\n",
+       "      <td>13460.17</td>\n",
+       "      <td>26.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>13761.17</td>\n",
+       "      <td>10900.00</td>\n",
+       "      <td>-2861.17</td>\n",
+       "      <td>26.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
+       "      <td>22356.85</td>\n",
+       "      <td>17700.00</td>\n",
+       "      <td>-4656.85</td>\n",
+       "      <td>26.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>10767.98</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>-2267.98</td>\n",
+       "      <td>26.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446</th>\n",
+       "      <td>37385.04</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>-7885.04</td>\n",
+       "      <td>26.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>10013.18</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-2113.18</td>\n",
+       "      <td>26.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>6589.41</td>\n",
+       "      <td>8999.00</td>\n",
+       "      <td>2409.59</td>\n",
+       "      <td>26.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>24995.51</td>\n",
+       "      <td>19700.00</td>\n",
+       "      <td>-5295.51</td>\n",
+       "      <td>26.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>7821.99</td>\n",
+       "      <td>10700.00</td>\n",
+       "      <td>2878.01</td>\n",
+       "      <td>26.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>6736.55</td>\n",
+       "      <td>5300.00</td>\n",
+       "      <td>-1436.55</td>\n",
+       "      <td>27.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>11445.85</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-2445.85</td>\n",
+       "      <td>27.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>2909.80</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>1090.20</td>\n",
+       "      <td>27.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>24627.39</td>\n",
+       "      <td>33900.00</td>\n",
+       "      <td>9272.61</td>\n",
+       "      <td>27.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>82760.24</td>\n",
+       "      <td>64900.00</td>\n",
+       "      <td>-17860.24</td>\n",
+       "      <td>27.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>5787.88</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>2212.12</td>\n",
+       "      <td>27.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>19048.95</td>\n",
+       "      <td>14900.00</td>\n",
+       "      <td>-4148.95</td>\n",
+       "      <td>27.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>730</th>\n",
+       "      <td>4668.85</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-1018.85</td>\n",
+       "      <td>27.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>561</th>\n",
+       "      <td>7564.39</td>\n",
+       "      <td>10500.00</td>\n",
+       "      <td>2935.61</td>\n",
+       "      <td>27.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>30477.38</td>\n",
+       "      <td>23800.00</td>\n",
+       "      <td>-6677.38</td>\n",
+       "      <td>28.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>11154.62</td>\n",
+       "      <td>8700.00</td>\n",
+       "      <td>-2454.62</td>\n",
+       "      <td>28.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>21802.49</td>\n",
+       "      <td>30385.00</td>\n",
+       "      <td>8582.51</td>\n",
+       "      <td>28.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>65745.19</td>\n",
+       "      <td>92222.00</td>\n",
+       "      <td>26476.81</td>\n",
+       "      <td>28.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>21739.14</td>\n",
+       "      <td>30500.00</td>\n",
+       "      <td>8760.86</td>\n",
+       "      <td>28.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>25245.24</td>\n",
+       "      <td>35500.00</td>\n",
+       "      <td>10254.76</td>\n",
+       "      <td>28.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>15218.48</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-3418.48</td>\n",
+       "      <td>28.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>55229.59</td>\n",
+       "      <td>77777.00</td>\n",
+       "      <td>22547.41</td>\n",
+       "      <td>28.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>55229.59</td>\n",
+       "      <td>77777.00</td>\n",
+       "      <td>22547.41</td>\n",
+       "      <td>28.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>8665.27</td>\n",
+       "      <td>6700.00</td>\n",
+       "      <td>-1965.27</td>\n",
+       "      <td>29.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>11514.96</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>-2614.96</td>\n",
+       "      <td>29.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>2951.62</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>1248.38</td>\n",
+       "      <td>29.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>5449.56</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-1249.56</td>\n",
+       "      <td>29.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>39888.50</td>\n",
+       "      <td>57000.00</td>\n",
+       "      <td>17111.50</td>\n",
+       "      <td>30.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>8386.38</td>\n",
+       "      <td>6450.00</td>\n",
+       "      <td>-1936.38</td>\n",
+       "      <td>30.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>5242.13</td>\n",
+       "      <td>7500.00</td>\n",
+       "      <td>2257.87</td>\n",
+       "      <td>30.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>17055.42</td>\n",
+       "      <td>24500.00</td>\n",
+       "      <td>7444.58</td>\n",
+       "      <td>30.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>783</th>\n",
+       "      <td>18140.29</td>\n",
+       "      <td>13900.00</td>\n",
+       "      <td>-4240.29</td>\n",
+       "      <td>30.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>15633.25</td>\n",
+       "      <td>22500.00</td>\n",
+       "      <td>6866.75</td>\n",
+       "      <td>30.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>10150.80</td>\n",
+       "      <td>7777.00</td>\n",
+       "      <td>-2373.80</td>\n",
+       "      <td>30.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774</th>\n",
+       "      <td>2485.85</td>\n",
+       "      <td>1900.00</td>\n",
+       "      <td>-585.85</td>\n",
+       "      <td>30.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>2073.96</td>\n",
+       "      <td>2999.00</td>\n",
+       "      <td>925.04</td>\n",
+       "      <td>30.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>19812.54</td>\n",
+       "      <td>28713.75</td>\n",
+       "      <td>8901.21</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>12983.41</td>\n",
+       "      <td>9900.00</td>\n",
+       "      <td>-3083.41</td>\n",
+       "      <td>31.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>7869.15</td>\n",
+       "      <td>5999.00</td>\n",
+       "      <td>-1870.15</td>\n",
+       "      <td>31.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>12071.60</td>\n",
+       "      <td>9200.00</td>\n",
+       "      <td>-2871.60</td>\n",
+       "      <td>31.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>8941.06</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>4058.94</td>\n",
+       "      <td>31.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>13336.31</td>\n",
+       "      <td>10150.00</td>\n",
+       "      <td>-3186.31</td>\n",
+       "      <td>31.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>14200.19</td>\n",
+       "      <td>10800.00</td>\n",
+       "      <td>-3400.19</td>\n",
+       "      <td>31.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>3105.70</td>\n",
+       "      <td>4550.00</td>\n",
+       "      <td>1444.30</td>\n",
+       "      <td>31.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>682</th>\n",
+       "      <td>4624.41</td>\n",
+       "      <td>6800.00</td>\n",
+       "      <td>2175.59</td>\n",
+       "      <td>31.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>3511.87</td>\n",
+       "      <td>5200.00</td>\n",
+       "      <td>1688.13</td>\n",
+       "      <td>32.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>4779.20</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-1179.20</td>\n",
+       "      <td>32.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>644</th>\n",
+       "      <td>11942.86</td>\n",
+       "      <td>17777.00</td>\n",
+       "      <td>5834.14</td>\n",
+       "      <td>32.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>16542.07</td>\n",
+       "      <td>12450.00</td>\n",
+       "      <td>-4092.07</td>\n",
+       "      <td>32.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>15827.38</td>\n",
+       "      <td>11900.00</td>\n",
+       "      <td>-3927.38</td>\n",
+       "      <td>33.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>4216.67</td>\n",
+       "      <td>6300.00</td>\n",
+       "      <td>2083.33</td>\n",
+       "      <td>33.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>9333.37</td>\n",
+       "      <td>7000.00</td>\n",
+       "      <td>-2333.37</td>\n",
+       "      <td>33.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>7732.20</td>\n",
+       "      <td>11600.00</td>\n",
+       "      <td>3867.80</td>\n",
+       "      <td>33.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>766</th>\n",
+       "      <td>12012.45</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-3012.45</td>\n",
+       "      <td>33.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>728</th>\n",
+       "      <td>19812.54</td>\n",
+       "      <td>29800.00</td>\n",
+       "      <td>9987.46</td>\n",
+       "      <td>33.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>5317.17</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>2682.83</td>\n",
+       "      <td>33.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>46006.01</td>\n",
+       "      <td>34419.45</td>\n",
+       "      <td>-11586.56</td>\n",
+       "      <td>33.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>36381.57</td>\n",
+       "      <td>55000.00</td>\n",
+       "      <td>18618.43</td>\n",
+       "      <td>33.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>23207.21</td>\n",
+       "      <td>17300.00</td>\n",
+       "      <td>-5907.21</td>\n",
+       "      <td>34.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>12083.74</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-3083.74</td>\n",
+       "      <td>34.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>1642.87</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>857.13</td>\n",
+       "      <td>34.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3900.79</td>\n",
+       "      <td>2900.00</td>\n",
+       "      <td>-1000.79</td>\n",
+       "      <td>34.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>67578.74</td>\n",
+       "      <td>49900.00</td>\n",
+       "      <td>-17678.74</td>\n",
+       "      <td>35.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>5030.79</td>\n",
+       "      <td>7800.00</td>\n",
+       "      <td>2769.21</td>\n",
+       "      <td>35.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>12682.44</td>\n",
+       "      <td>9350.00</td>\n",
+       "      <td>-3332.44</td>\n",
+       "      <td>35.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>44889.92</td>\n",
+       "      <td>69900.00</td>\n",
+       "      <td>25010.08</td>\n",
+       "      <td>35.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>5702.84</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>3297.16</td>\n",
+       "      <td>36.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11427.09</td>\n",
+       "      <td>8350.00</td>\n",
+       "      <td>-3077.09</td>\n",
+       "      <td>36.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>13828.77</td>\n",
+       "      <td>21900.00</td>\n",
+       "      <td>8071.23</td>\n",
+       "      <td>36.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>600</th>\n",
+       "      <td>5591.66</td>\n",
+       "      <td>8900.00</td>\n",
+       "      <td>3308.34</td>\n",
+       "      <td>37.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>16260.94</td>\n",
+       "      <td>11850.00</td>\n",
+       "      <td>-4410.94</td>\n",
+       "      <td>37.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>19627.05</td>\n",
+       "      <td>14300.00</td>\n",
+       "      <td>-5327.05</td>\n",
+       "      <td>37.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>2005.13</td>\n",
+       "      <td>3200.00</td>\n",
+       "      <td>1194.87</td>\n",
+       "      <td>37.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>727</th>\n",
+       "      <td>46345.58</td>\n",
+       "      <td>74000.00</td>\n",
+       "      <td>27654.42</td>\n",
+       "      <td>37.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>777</th>\n",
+       "      <td>51526.76</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-14026.76</td>\n",
+       "      <td>37.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>785</th>\n",
+       "      <td>29732.92</td>\n",
+       "      <td>47700.00</td>\n",
+       "      <td>17967.08</td>\n",
+       "      <td>37.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>46378.55</td>\n",
+       "      <td>74500.00</td>\n",
+       "      <td>28121.45</td>\n",
+       "      <td>37.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>16572.98</td>\n",
+       "      <td>11999.00</td>\n",
+       "      <td>-4573.98</td>\n",
+       "      <td>38.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>6644.61</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-1844.61</td>\n",
+       "      <td>38.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>460</th>\n",
+       "      <td>12197.10</td>\n",
+       "      <td>8800.00</td>\n",
+       "      <td>-3397.10</td>\n",
+       "      <td>38.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>64451.49</td>\n",
+       "      <td>104999.00</td>\n",
+       "      <td>40547.51</td>\n",
+       "      <td>38.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>39888.50</td>\n",
+       "      <td>65000.00</td>\n",
+       "      <td>25111.50</td>\n",
+       "      <td>38.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>17656.32</td>\n",
+       "      <td>28793.04</td>\n",
+       "      <td>11136.72</td>\n",
+       "      <td>38.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>15936.61</td>\n",
+       "      <td>11400.00</td>\n",
+       "      <td>-4536.61</td>\n",
+       "      <td>39.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>71675.00</td>\n",
+       "      <td>121500.00</td>\n",
+       "      <td>49825.00</td>\n",
+       "      <td>41.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>16226.28</td>\n",
+       "      <td>11500.00</td>\n",
+       "      <td>-4726.28</td>\n",
+       "      <td>41.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>8889.98</td>\n",
+       "      <td>6299.00</td>\n",
+       "      <td>-2590.98</td>\n",
+       "      <td>41.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>9669.09</td>\n",
+       "      <td>16500.00</td>\n",
+       "      <td>6830.91</td>\n",
+       "      <td>41.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>14822.55</td>\n",
+       "      <td>10450.00</td>\n",
+       "      <td>-4372.55</td>\n",
+       "      <td>41.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>16930.40</td>\n",
+       "      <td>29500.00</td>\n",
+       "      <td>12569.60</td>\n",
+       "      <td>42.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>5700.21</td>\n",
+       "      <td>10000.00</td>\n",
+       "      <td>4299.79</td>\n",
+       "      <td>43.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>32880.90</td>\n",
+       "      <td>22900.00</td>\n",
+       "      <td>-9980.90</td>\n",
+       "      <td>43.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>6477.68</td>\n",
+       "      <td>4500.00</td>\n",
+       "      <td>-1977.68</td>\n",
+       "      <td>43.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>10016.88</td>\n",
+       "      <td>18000.00</td>\n",
+       "      <td>7983.12</td>\n",
+       "      <td>44.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>11415.61</td>\n",
+       "      <td>7900.00</td>\n",
+       "      <td>-3515.61</td>\n",
+       "      <td>44.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>2050.79</td>\n",
+       "      <td>3700.00</td>\n",
+       "      <td>1649.21</td>\n",
+       "      <td>44.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>13804.82</td>\n",
+       "      <td>9500.00</td>\n",
+       "      <td>-4304.82</td>\n",
+       "      <td>45.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>24080.07</td>\n",
+       "      <td>16555.00</td>\n",
+       "      <td>-7525.07</td>\n",
+       "      <td>45.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>8443.28</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>7056.72</td>\n",
+       "      <td>45.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>17048.89</td>\n",
+       "      <td>11707.80</td>\n",
+       "      <td>-5341.09</td>\n",
+       "      <td>45.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>8419.88</td>\n",
+       "      <td>15500.00</td>\n",
+       "      <td>7080.12</td>\n",
+       "      <td>45.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>31124.28</td>\n",
+       "      <td>21300.00</td>\n",
+       "      <td>-9824.28</td>\n",
+       "      <td>46.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>13157.10</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-4157.10</td>\n",
+       "      <td>46.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>643</th>\n",
+       "      <td>1733.85</td>\n",
+       "      <td>1185.60</td>\n",
+       "      <td>-548.25</td>\n",
+       "      <td>46.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>38632.53</td>\n",
+       "      <td>73500.00</td>\n",
+       "      <td>34867.47</td>\n",
+       "      <td>47.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>62100.23</td>\n",
+       "      <td>42000.00</td>\n",
+       "      <td>-20100.23</td>\n",
+       "      <td>47.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>1196.37</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>1103.63</td>\n",
+       "      <td>47.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>794</th>\n",
+       "      <td>65898.02</td>\n",
+       "      <td>129222.00</td>\n",
+       "      <td>63323.98</td>\n",
+       "      <td>49.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>2313.02</td>\n",
+       "      <td>1550.00</td>\n",
+       "      <td>-763.02</td>\n",
+       "      <td>49.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>2972.03</td>\n",
+       "      <td>1990.00</td>\n",
+       "      <td>-982.03</td>\n",
+       "      <td>49.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>1417.83</td>\n",
+       "      <td>2800.00</td>\n",
+       "      <td>1382.17</td>\n",
+       "      <td>49.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>22471.17</td>\n",
+       "      <td>44700.00</td>\n",
+       "      <td>22228.83</td>\n",
+       "      <td>49.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>721</th>\n",
+       "      <td>71981.49</td>\n",
+       "      <td>144900.00</td>\n",
+       "      <td>72918.51</td>\n",
+       "      <td>50.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>50745.07</td>\n",
+       "      <td>33715.50</td>\n",
+       "      <td>-17029.57</td>\n",
+       "      <td>50.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577</th>\n",
+       "      <td>7343.52</td>\n",
+       "      <td>14999.00</td>\n",
+       "      <td>7655.48</td>\n",
+       "      <td>51.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>26609.57</td>\n",
+       "      <td>17600.00</td>\n",
+       "      <td>-9009.57</td>\n",
+       "      <td>51.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>17888.88</td>\n",
+       "      <td>11800.00</td>\n",
+       "      <td>-6088.88</td>\n",
+       "      <td>51.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>20479.37</td>\n",
+       "      <td>13500.00</td>\n",
+       "      <td>-6979.37</td>\n",
+       "      <td>51.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>12461.52</td>\n",
+       "      <td>8200.00</td>\n",
+       "      <td>-4261.52</td>\n",
+       "      <td>51.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>20828.11</td>\n",
+       "      <td>13700.00</td>\n",
+       "      <td>-7128.11</td>\n",
+       "      <td>52.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>18980.92</td>\n",
+       "      <td>39900.00</td>\n",
+       "      <td>20919.08</td>\n",
+       "      <td>52.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>780</th>\n",
+       "      <td>28362.55</td>\n",
+       "      <td>60500.00</td>\n",
+       "      <td>32137.45</td>\n",
+       "      <td>53.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>3521.95</td>\n",
+       "      <td>2300.00</td>\n",
+       "      <td>-1221.95</td>\n",
+       "      <td>53.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>9427.42</td>\n",
+       "      <td>20500.00</td>\n",
+       "      <td>11072.58</td>\n",
+       "      <td>54.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>5408.75</td>\n",
+       "      <td>3500.00</td>\n",
+       "      <td>-1908.75</td>\n",
+       "      <td>54.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>10854.85</td>\n",
+       "      <td>24000.00</td>\n",
+       "      <td>13145.15</td>\n",
+       "      <td>54.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>65041.89</td>\n",
+       "      <td>41900.00</td>\n",
+       "      <td>-23141.89</td>\n",
+       "      <td>55.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>7715.56</td>\n",
+       "      <td>4900.00</td>\n",
+       "      <td>-2815.56</td>\n",
+       "      <td>57.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>30316.04</td>\n",
+       "      <td>19200.00</td>\n",
+       "      <td>-11116.04</td>\n",
+       "      <td>57.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>20712.46</td>\n",
+       "      <td>13000.00</td>\n",
+       "      <td>-7712.46</td>\n",
+       "      <td>59.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>2719.53</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-1019.53</td>\n",
+       "      <td>59.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>3906.56</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-1506.56</td>\n",
+       "      <td>62.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>5055.93</td>\n",
+       "      <td>3099.00</td>\n",
+       "      <td>-1956.93</td>\n",
+       "      <td>63.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>4243.79</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-1643.79</td>\n",
+       "      <td>63.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>10676.78</td>\n",
+       "      <td>6499.00</td>\n",
+       "      <td>-4177.78</td>\n",
+       "      <td>64.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>16658.12</td>\n",
+       "      <td>10100.00</td>\n",
+       "      <td>-6558.12</td>\n",
+       "      <td>64.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>662</th>\n",
+       "      <td>6656.03</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-2656.03</td>\n",
+       "      <td>66.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>2486.42</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>5513.58</td>\n",
+       "      <td>68.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>13706.76</td>\n",
+       "      <td>8000.00</td>\n",
+       "      <td>-5706.76</td>\n",
+       "      <td>71.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>6524.07</td>\n",
+       "      <td>3799.00</td>\n",
+       "      <td>-2725.07</td>\n",
+       "      <td>71.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>41367.65</td>\n",
+       "      <td>23900.00</td>\n",
+       "      <td>-17467.65</td>\n",
+       "      <td>73.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>60487.88</td>\n",
+       "      <td>34600.00</td>\n",
+       "      <td>-25887.88</td>\n",
+       "      <td>74.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>9215.35</td>\n",
+       "      <td>5000.00</td>\n",
+       "      <td>-4215.35</td>\n",
+       "      <td>84.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>6870.33</td>\n",
+       "      <td>3600.00</td>\n",
+       "      <td>-3270.33</td>\n",
+       "      <td>90.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>9213.19</td>\n",
+       "      <td>4800.00</td>\n",
+       "      <td>-4413.19</td>\n",
+       "      <td>91.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>1444.13</td>\n",
+       "      <td>34999.00</td>\n",
+       "      <td>33554.87</td>\n",
+       "      <td>95.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>5130.28</td>\n",
+       "      <td>2600.00</td>\n",
+       "      <td>-2530.28</td>\n",
+       "      <td>97.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>44000.00</td>\n",
+       "      <td>44000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>35000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>3800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>38500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>4100.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>6500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>32000.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>8500.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>18800.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>723</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>1650.00</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>29058.70</td>\n",
+       "      <td>14250.00</td>\n",
+       "      <td>-14808.70</td>\n",
+       "      <td>103.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>20157.28</td>\n",
+       "      <td>9000.00</td>\n",
+       "      <td>-11157.28</td>\n",
+       "      <td>123.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>9463.58</td>\n",
+       "      <td>3650.00</td>\n",
+       "      <td>-5813.58</td>\n",
+       "      <td>159.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>4534.02</td>\n",
+       "      <td>1700.00</td>\n",
+       "      <td>-2834.02</td>\n",
+       "      <td>166.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>3200.86</td>\n",
+       "      <td>1200.00</td>\n",
+       "      <td>-2000.86</td>\n",
+       "      <td>166.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>3875.31</td>\n",
+       "      <td>1450.00</td>\n",
+       "      <td>-2425.31</td>\n",
+       "      <td>167.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>35212.93</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-22312.93</td>\n",
+       "      <td>172.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>14347.91</td>\n",
+       "      <td>4200.00</td>\n",
+       "      <td>-10147.91</td>\n",
+       "      <td>241.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>8975.55</td>\n",
+       "      <td>2400.00</td>\n",
+       "      <td>-6575.55</td>\n",
+       "      <td>273.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>48602.40</td>\n",
+       "      <td>11200.00</td>\n",
+       "      <td>-37402.40</td>\n",
+       "      <td>333.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>28240.95</td>\n",
+       "      <td>5500.00</td>\n",
+       "      <td>-22740.95</td>\n",
+       "      <td>413.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>19999.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>731</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>99000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>20996.24</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>1150.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>17639.47</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>12900.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>683</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>20000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>11300.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>458</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>7450.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>10200.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>37500.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>11700.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>6000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>inf</td>\n",
+       "      <td>4000.00</td>\n",
+       "      <td>-inf</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Prediction    Target  Residual  Difference%\n",
+       "681    15211.01  15200.00    -11.01         0.07\n",
+       "700     6305.83   6300.00     -5.83         0.09\n",
+       "540     6706.65   6700.00     -6.65         0.10\n",
+       "399    12486.63  12500.00     13.37         0.11\n",
+       "374     8715.33   8700.00    -15.33         0.18\n",
+       "74      3206.26   3200.00     -6.26         0.20\n",
+       "49      6513.15   6500.00    -13.15         0.20\n",
+       "397    17863.45  17900.00     36.55         0.20\n",
+       "216     6715.99   6700.00    -15.99         0.24\n",
+       "725     8826.34   8800.00    -26.34         0.30\n",
+       "26     51340.32  51500.00    159.68         0.31\n",
+       "615    14051.69  14100.00     48.31         0.34\n",
+       "536     2107.62   2100.00     -7.62         0.36\n",
+       "413     5828.76   5850.00     21.24         0.36\n",
+       "27     12653.40  12700.00     46.60         0.37\n",
+       "714    12447.09  12500.00     52.91         0.42\n",
+       "193     2837.86   2850.00     12.14         0.43\n",
+       "53     13541.54  13600.00     58.46         0.43\n",
+       "757     7734.58   7700.00    -34.58         0.45\n",
+       "706    12836.82  12900.00     63.18         0.49\n",
+       "618    30492.98  30650.00    157.02         0.51\n",
+       "259     9551.54   9499.00    -52.54         0.55\n",
+       "417    14318.96  14400.00     81.04         0.56\n",
+       "781     7543.03   7500.00    -43.03         0.57\n",
+       "466    25045.23  24900.00   -145.23         0.58\n",
+       "250     6538.30   6500.00    -38.30         0.59\n",
+       "448   109346.97 110000.00    653.03         0.59\n",
+       "461     5633.89   5600.00    -33.89         0.61\n",
+       "525     7352.50   7400.00     47.50         0.64\n",
+       "96     15607.46  15499.00   -108.46         0.70\n",
+       "4      11280.35  11200.00    -80.35         0.72\n",
+       "463    11077.66  10990.00    -87.66         0.80\n",
+       "801    39657.24  39999.00    341.76         0.85\n",
+       "298    23106.56  22900.00   -206.56         0.90\n",
+       "256     2219.87   2200.00    -19.87         0.90\n",
+       "344    25027.42  24800.00   -227.42         0.92\n",
+       "79     27755.46  27500.00   -255.46         0.93\n",
+       "471    20195.56  20000.00   -195.56         0.98\n",
+       "292     7978.67   7900.00    -78.67         1.00\n",
+       "733     9897.28   9999.00    101.72         1.02\n",
+       "21     23557.83  23800.00    242.17         1.02\n",
+       "521     8182.71   8100.00    -82.71         1.02\n",
+       "98     10094.92  10200.00    105.08         1.03\n",
+       "3       8074.47   7990.00    -84.47         1.06\n",
+       "546    16675.21  16500.00   -175.21         1.06\n",
+       "224     7883.21   7800.00    -83.21         1.07\n",
+       "59      9394.45   9500.00    105.55         1.11\n",
+       "324    17194.49  16999.00   -195.49         1.15\n",
+       "416     7407.42   7500.00     92.58         1.23\n",
+       "768    10863.14  10999.00    135.86         1.24\n",
+       "638     2227.46   2200.00    -27.46         1.25\n",
+       "231   109352.13 107999.00  -1353.13         1.25\n",
+       "673     9014.15   8900.00   -114.15         1.28\n",
+       "544    64649.72  65500.00    850.28         1.30\n",
+       "326     6807.32   6900.00     92.68         1.34\n",
+       "418    11436.89  11600.00    163.11         1.41\n",
+       "688     2464.71   2500.00     35.29         1.41\n",
+       "720     9947.38   9800.00   -147.38         1.50\n",
+       "797    33474.09  34000.00    525.91         1.55\n",
+       "360    21559.42  21900.00    340.58         1.56\n",
+       "562     9104.53   9250.00    145.47         1.57\n",
+       "503    12087.91  11900.00   -187.91         1.58\n",
+       "384     8939.11   8800.00   -139.11         1.58\n",
+       "713    35453.02  34899.14   -553.88         1.59\n",
+       "257    34436.11  34999.00    562.89         1.61\n",
+       "272     2897.71   2850.00    -47.71         1.67\n",
+       "671     3440.49   3500.00     59.51         1.70\n",
+       "735    33320.06  33900.00    579.94         1.71\n",
+       "784    18817.25  18500.00   -317.25         1.71\n",
+       "697    13857.09  14100.00    242.91         1.72\n",
+       "381    20040.20  19700.00   -340.20         1.73\n",
+       "565    35262.29  35900.00    637.71         1.78\n",
+       "278    24059.43  24500.00    440.57         1.80\n",
+       "576    64649.72  63500.00  -1149.72         1.81\n",
+       "363    19627.25  19990.00    362.75         1.81\n",
+       "484    26993.16  27500.00    506.84         1.84\n",
+       "213    32584.87  31990.00   -594.87         1.86\n",
+       "632     3056.57   3000.00    -56.57         1.89\n",
+       "509    98085.79  99999.00   1913.21         1.91\n",
+       "336    19170.78  18800.00   -370.78         1.97\n",
+       "166    10782.03  10999.00    216.97         1.97\n",
+       "480    17047.74  17394.09    346.35         1.99\n",
+       "165     1958.31   1999.00     40.69         2.04\n",
+       "660    29500.27  28900.00   -600.27         2.08\n",
+       "712    23990.17  24500.00    509.83         2.08\n",
+       "422    30909.33  31600.00    690.67         2.19\n",
+       "455    11959.28  11700.00   -259.28         2.22\n",
+       "653    11042.29  10799.00   -243.29         2.25\n",
+       "800    56673.17  58000.00   1326.83         2.29\n",
+       "212    43942.54  45000.00   1057.46         2.35\n",
+       "58     11224.40  11500.00    275.60         2.40\n",
+       "230    12978.30  13300.00    321.70         2.42\n",
+       "528     9166.68   9400.00    233.32         2.48\n",
+       "260     8478.83   8700.00    221.17         2.54\n",
+       "232    11184.55  10900.00   -284.55         2.61\n",
+       "130     8524.29   8300.00   -224.29         2.70\n",
+       "684    14494.88  14900.00    405.12         2.72\n",
+       "582    37938.31  39000.00   1061.69         2.72\n",
+       "663    18384.89  18900.00    515.11         2.73\n",
+       "287    23828.27  24500.00    671.73         2.74\n",
+       "376    11570.15  11900.00    329.85         2.77\n",
+       "532    25592.54  24900.00   -692.54         2.78\n",
+       "67      2004.72   1950.00    -54.72         2.81\n",
+       "211     6374.72   6200.00   -174.72         2.82\n",
+       "449   194242.37 199999.00   5756.63         2.88\n",
+       "76     58649.23  57000.00  -1649.23         2.89\n",
+       "274     3398.26   3500.00    101.74         2.91\n",
+       "711    12864.69  12500.00   -364.69         2.92\n",
+       "267    64695.71  66666.00   1970.29         2.96\n",
+       "284    29563.28  28700.00   -863.28         3.01\n",
+       "726     6787.74   6999.00    211.26         3.02\n",
+       "308     5769.23   5600.00   -169.23         3.02\n",
+       "762    23184.61  22500.00   -684.61         3.04\n",
+       "769    10824.58  10500.00   -324.58         3.09\n",
+       "566    22372.19  21700.00   -672.19         3.10\n",
+       "575    15019.12  15500.00    480.88         3.10\n",
+       "354    10058.12   9750.00   -308.12         3.16\n",
+       "36      8769.20   8500.00   -269.20         3.17\n",
+       "708    10844.83  11200.00    355.17         3.17\n",
+       "742    20522.18  21200.00    677.82         3.20\n",
+       "157    23223.72  22500.00   -723.72         3.22\n",
+       "537     1934.64   1999.00     64.36         3.22\n",
+       "61     11770.33  11400.00   -370.33         3.25\n",
+       "190    43881.06  42500.00  -1381.06         3.25\n",
+       "489    17450.09  16900.00   -550.09         3.25\n",
+       "285    23341.27  22600.00   -741.27         3.28\n",
+       "514    10919.19  11300.00    380.81         3.37\n",
+       "659    21192.26  20500.00   -692.26         3.38\n",
+       "592    12749.66  13200.00    450.34         3.41\n",
+       "262    20283.08  21000.00    716.92         3.41\n",
+       "419    16201.55  16800.00    598.45         3.56\n",
+       "555     7974.91   7699.00   -275.91         3.58\n",
+       "13     13015.27  13500.00    484.73         3.59\n",
+       "247     6890.35   7150.00    259.65         3.63\n",
+       "557     5483.10   5700.00    216.90         3.81\n",
+       "585    20200.76  21000.00    799.24         3.81\n",
+       "332    13917.01  14474.00    556.99         3.85\n",
+       "60     11016.60  10600.00   -416.60         3.93\n",
+       "487     3840.88   3999.00    158.12         3.95\n",
+       "134     8524.71   8200.00   -324.71         3.96\n",
+       "745    11551.56  11111.00   -440.56         3.97\n",
+       "759    17687.48  16999.00   -688.48         4.05\n",
+       "482    33095.36  34500.00   1404.64         4.07\n",
+       "655    26864.09  25800.00  -1064.09         4.12\n",
+       "385    27698.29  28900.00   1201.71         4.16\n",
+       "590    31149.15  29900.00  -1249.15         4.18\n",
+       "752    12454.14  13000.00    545.86         4.20\n",
+       "158    35396.30  33950.00  -1446.30         4.26\n",
+       "32     20103.15  21000.00    896.85         4.27\n",
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+       "407    10173.73   9000.00  -1173.73        13.04\n",
+       "9      22173.64  25500.00   3326.36        13.04\n",
+       "675     3216.30   3700.00    483.70        13.07\n",
+       "270    13061.64  11550.00  -1511.64        13.09\n",
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+       "517    19048.58  16800.00  -2248.58        13.38\n",
+       "606    23296.66  26900.00   3603.34        13.40\n",
+       "580    27706.06  32000.00   4293.94        13.42\n",
+       "717     2856.79   3300.00    443.21        13.43\n",
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+       "173    14874.09  17200.00   2325.91        13.52\n",
+       "595     2853.47   3300.00    446.53        13.53\n",
+       "133     1929.25   1699.00   -230.25        13.55\n",
+       "240     6740.68   7800.00   1059.32        13.58\n",
+       "542    15113.32  13300.00  -1813.32        13.63\n",
+       "764     8146.17   9450.00   1303.83        13.80\n",
+       "432    16956.75  14900.00  -2056.75        13.80\n",
+       "138     8616.78  10000.00   1383.22        13.83\n",
+       "97     27560.75  32000.00   4439.25        13.87\n",
+       "568    11782.11  13700.00   1917.89        14.00\n",
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+       "198     3263.77   3800.00    536.23        14.11\n",
+       "368    25652.89  29900.00   4247.11        14.20\n",
+       "11     13110.05  15300.00   2189.95        14.31\n",
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+       "127    32350.70  37990.00   5639.30        14.84\n",
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+       "508     9993.19   8700.00  -1293.19        14.86\n",
+       "375    24702.61  21500.00  -3202.61        14.90\n",
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+       "716     3398.91   4000.00    601.09        15.03\n",
+       "340    12482.76  10850.00  -1632.76        15.05\n",
+       "153     6099.48   5300.00   -799.48        15.08\n",
+       "347     5248.40   6200.00    951.60        15.35\n",
+       "104     6052.23   7150.00   1097.77        15.35\n",
+       "83      9809.02   8499.00  -1310.02        15.41\n",
+       "450     6174.68   7300.00   1125.32        15.42\n",
+       "593     3635.06   4300.00    664.94        15.46\n",
+       "135     3347.79   2899.00   -448.79        15.48\n",
+       "93     20557.05  17800.00  -2757.05        15.49\n",
+       "392    28877.80  25000.00  -3877.80        15.51\n",
+       "529     5082.76   4400.00   -682.76        15.52\n",
+       "159    17104.46  14800.00  -2304.46        15.57\n",
+       "579    14842.29  17595.00   2752.71        15.64\n",
+       "786     3817.05   3300.00   -517.05        15.67\n",
+       "42      9373.98   8100.00  -1273.98        15.73\n",
+       "702     6888.50   5950.00   -938.50        15.77\n",
+       "206     6903.35   8200.00   1296.65        15.81\n",
+       "43     34332.85  40800.00   6467.15        15.85\n",
+       "387    16799.16  14500.00  -2299.16        15.86\n",
+       "492    98480.67  85000.00 -13480.67        15.86\n",
+       "414     6139.72   7300.00   1160.28        15.89\n",
+       "325    15437.68  13300.00  -2137.68        16.07\n",
+       "322     4064.76   3500.00   -564.76        16.14\n",
+       "265    14087.55  16800.00   2712.45        16.15\n",
+       "208    26137.11  22500.00  -3637.11        16.16\n",
+       "72     27304.45  23500.00  -3804.45        16.19\n",
+       "383     9637.17  11500.00   1862.83        16.20\n",
+       "605    33099.94  39500.00   6400.06        16.20\n",
+       "218    36023.63  30990.00  -5033.63        16.24\n",
+       "429    37352.82  44600.00   7247.18        16.25\n",
+       "753    10462.54   8999.00  -1463.54        16.26\n",
+       "126    31976.86  27500.00  -4476.86        16.28\n",
+       "253    20915.39  24996.00   4080.61        16.33\n",
+       "587     1673.12   2000.00    326.88        16.34\n",
+       "570    14221.55  17000.00   2778.45        16.34\n",
+       "625    34297.36  41000.00   6702.64        16.35\n",
+       "707    19907.26  17100.00  -2807.26        16.42\n",
+       "686    10362.29   8900.00  -1462.29        16.43\n",
+       "219    23291.25  20000.00  -3291.25        16.46\n",
+       "747     2213.58   1900.00   -313.58        16.50\n",
+       "406     8738.60   7500.00  -1238.60        16.51\n",
+       "770     6877.84   5900.00   -977.84        16.57\n",
+       "427    10377.09   8900.00  -1477.09        16.60\n",
+       "380    21349.94  18300.00  -3049.94        16.67\n",
+       "386     2858.60   2450.00   -408.60        16.68\n",
+       "493    11676.82   9999.00  -1677.82        16.78\n",
+       "251     7892.51   9500.00   1607.49        16.92\n",
+       "303     5399.74   6500.00   1100.26        16.93\n",
+       "434    25408.55  21700.00  -3708.55        17.09\n",
+       "255    10609.93  12800.00   2190.07        17.11\n",
+       "648    28114.25  24000.00  -4114.25        17.14\n",
+       "47     39356.61  47500.00   8143.39        17.14\n",
+       "321    50516.19  61000.00  10483.81        17.19\n",
+       "316     9481.20  11450.00   1968.80        17.19\n",
+       "390    13594.21  16450.00   2855.79        17.36\n",
+       "349    12675.07  10800.00  -1875.07        17.36\n",
+       "789    11505.09   9800.00  -1705.09        17.40\n",
+       "602     9082.32  10999.00   1916.68        17.43\n",
+       "203    31495.17  26800.00  -4695.17        17.52\n",
+       "748    11282.67   9600.00  -1682.67        17.53\n",
+       "109    19281.74  16400.00  -2881.74        17.57\n",
+       "658     7992.08   9700.00   1707.92        17.61\n",
+       "758     5881.36   5000.00   -881.36        17.63\n",
+       "168    18117.61  22000.00   3882.39        17.65\n",
+       "746    11412.71   9700.00  -1712.71        17.66\n",
+       "119    15774.43  13400.00  -2374.43        17.72\n",
+       "496     4357.87   5300.00    942.13        17.78\n",
+       "415     8126.96   6900.00  -1226.96        17.78\n",
+       "309    11434.04   9700.00  -1734.04        17.88\n",
+       "421    74745.66  63333.00 -11412.66        18.02\n",
+       "796    18302.49  15500.00  -2802.49        18.08\n",
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+       "18     10710.00  13100.00   2390.00        18.24\n",
+       "637     6293.06   7700.00   1406.94        18.27\n",
+       "154    13370.87  11300.00  -2070.87        18.33\n",
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+       "704     5459.66   6700.00   1240.34        18.51\n",
+       "485     4480.19   5500.00   1019.81        18.54\n",
+       "333     8536.82   7200.00  -1336.82        18.57\n",
+       "420    40274.34  49500.00   9225.66        18.64\n",
+       "674    89582.34  75500.00 -14082.34        18.65\n",
+       "366     6884.55   5800.00  -1084.55        18.70\n",
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+       "490     6090.18   7500.00   1409.82        18.80\n",
+       "642    19364.44  16300.00  -3064.44        18.80\n",
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+       "192    11319.17   9500.00  -1819.17        19.15\n",
+       "180    11445.18   9599.00  -1846.18        19.23\n",
+       "699     5650.29   7000.00   1349.71        19.28\n",
+       "634     4433.53   5500.00   1066.47        19.39\n",
+       "545    12724.62  15800.00   3075.38        19.46\n",
+       "771     2092.87   2600.00    507.13        19.50\n",
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+       "337    60274.39  75000.00  14725.61        19.63\n",
+       "184    46983.85  58500.00  11516.15        19.69\n",
+       "724     8747.37  10900.00   2152.63        19.75\n",
+       "129    22708.02  18950.00  -3758.02        19.83\n",
+       "773    13185.60  11000.00  -2185.60        19.87\n",
+       "305     9594.67   8000.00  -1594.67        19.93\n",
+       "678    16676.56  13900.00  -2776.56        19.98\n",
+       "718     5399.84   4500.00   -899.84        20.00\n",
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+       "591    34847.46  28950.00  -5897.46        20.37\n",
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+       "404     6940.02   8750.00   1809.98        20.69\n",
+       "574     6056.80   7650.00   1593.20        20.83\n",
+       "599     8708.11   7200.00  -1508.11        20.95\n",
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+       "656     1973.68   2500.00    526.32        21.05\n",
+       "277     4181.72   5300.00   1118.28        21.10\n",
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+       "20     13328.75  11000.00  -2328.75        21.17\n",
+       "64     36828.78  46800.00   9971.22        21.31\n",
+       "494     6920.60   8800.00   1879.40        21.36\n",
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+       "775    11692.08   9600.00  -2092.08        21.79\n",
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+       "342    11700.38  15000.00   3299.62        22.00\n",
+       "799     2623.69   2150.00   -473.69        22.03\n",
+       "507     2026.96   2600.00    573.04        22.04\n",
+       "183     8434.23   6900.00  -1534.23        22.24\n",
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+       "741     6200.75   8000.00   1799.25        22.49\n",
+       "371    10783.10   8800.00  -1983.10        22.54\n",
+       "511    22566.89  18400.00  -4166.89        22.65\n",
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+       "113    13535.73  11000.00  -2535.73        23.05\n",
+       "19      9723.51  12650.00   2926.49        23.13\n",
+       "479    14919.17  12114.35  -2804.82        23.15\n",
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+       "312    20558.01  16672.50  -3885.51        23.30\n",
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+       "533     2977.77   3900.00    922.23        23.65\n",
+       "139    22121.67  29000.00   6878.33        23.72\n",
+       "40     29567.87  38764.00   9196.13        23.72\n",
+       "358    22081.93  28999.00   6917.07        23.85\n",
+       "611     8174.40   6600.00  -1574.40        23.85\n",
+       "456     2813.36   3700.00    886.64        23.96\n",
+       "629     9728.36  12800.00   3071.64        24.00\n",
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+       "696    14539.53  11700.00  -2839.53        24.27\n",
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+       "505    22276.96  17900.00  -4376.96        24.45\n",
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+       "258    12346.01   9900.00  -2446.01        24.71\n",
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+       "616    21017.12  28000.00   6982.88        24.94\n",
+       "241     3748.29   3000.00   -748.29        24.94\n",
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+       "111     9998.69   8000.00  -1998.69        24.98\n",
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+       "803     3760.79   3000.00   -760.79        25.36\n",
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+       "436    18827.10  14999.00  -3828.10        25.52\n",
+       "635     9674.76  13000.00   3325.24        25.58\n",
+       "350     5205.98   7000.00   1794.02        25.63\n",
+       "751    18944.86  25500.00   6555.14        25.71\n",
+       "341    31179.60  42000.00  10820.40        25.76\n",
+       "804    26319.25  20900.00  -5419.25        25.93\n",
+       "172     2959.68   2350.00   -609.68        25.94\n",
+       "214     8863.85  12000.00   3136.15        26.13\n",
+       "652    38039.83  51500.00  13460.17        26.14\n",
+       "112    13761.17  10900.00  -2861.17        26.25\n",
+       "687    22356.85  17700.00  -4656.85        26.31\n",
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+       "108    10013.18   7900.00  -2113.18        26.75\n",
+       "37      6589.41   8999.00   2409.59        26.78\n",
+       "311    24995.51  19700.00  -5295.51        26.88\n",
+       "677     7821.99  10700.00   2878.01        26.90\n",
+       "279     6736.55   5300.00  -1436.55        27.10\n",
+       "307    11445.85   9000.00  -2445.85        27.18\n",
+       "361     2909.80   4000.00   1090.20        27.26\n",
+       "175    24627.39  33900.00   9272.61        27.35\n",
+       "293    82760.24  64900.00 -17860.24        27.52\n",
+       "497     5787.88   8000.00   2212.12        27.65\n",
+       "527    19048.95  14900.00  -4148.95        27.85\n",
+       "730     4668.85   3650.00  -1018.85        27.91\n",
+       "561     7564.39  10500.00   2935.61        27.96\n",
+       "670    30477.38  23800.00  -6677.38        28.06\n",
+       "296    11154.62   8700.00  -2454.62        28.21\n",
+       "80     21802.49  30385.00   8582.51        28.25\n",
+       "151    65745.19  92222.00  26476.81        28.71\n",
+       "633    21739.14  30500.00   8760.86        28.72\n",
+       "162    25245.24  35500.00  10254.76        28.89\n",
+       "598    15218.48  11800.00  -3418.48        28.97\n",
+       "470    55229.59  77777.00  22547.41        28.99\n",
+       "283    55229.59  77777.00  22547.41        28.99\n",
+       "249     8665.27   6700.00  -1965.27        29.33\n",
+       "327    11514.96   8900.00  -2614.96        29.38\n",
+       "186     2951.62   4200.00   1248.38        29.72\n",
+       "355     5449.56   4200.00  -1249.56        29.75\n",
+       "70     39888.50  57000.00  17111.50        30.02\n",
+       "443     8386.38   6450.00  -1936.38        30.02\n",
+       "176     5242.13   7500.00   2257.87        30.10\n",
+       "693    17055.42  24500.00   7444.58        30.39\n",
+       "783    18140.29  13900.00  -4240.29        30.51\n",
+       "477    15633.25  22500.00   6866.75        30.52\n",
+       "640    10150.80   7777.00  -2373.80        30.52\n",
+       "774     2485.85   1900.00   -585.85        30.83\n",
+       "763     2073.96   2999.00    925.04        30.84\n",
+       "132    19812.54  28713.75   8901.21        31.00\n",
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+       "46      7869.15   5999.00  -1870.15        31.17\n",
+       "710    12071.60   9200.00  -2871.60        31.21\n",
+       "534     8941.06  13000.00   4058.94        31.22\n",
+       "499    13336.31  10150.00  -3186.31        31.39\n",
+       "30     14200.19  10800.00  -3400.19        31.48\n",
+       "66      3105.70   4550.00   1444.30        31.74\n",
+       "682     4624.41   6800.00   2175.59        31.99\n",
+       "441     3511.87   5200.00   1688.13        32.46\n",
+       "520     4779.20   3600.00  -1179.20        32.76\n",
+       "644    11942.86  17777.00   5834.14        32.82\n",
+       "81     16542.07  12450.00  -4092.07        32.87\n",
+       "400    15827.38  11900.00  -3927.38        33.00\n",
+       "71      4216.67   6300.00   2083.33        33.07\n",
+       "147     9333.37   7000.00  -2333.37        33.33\n",
+       "559     7732.20  11600.00   3867.80        33.34\n",
+       "766    12012.45   9000.00  -3012.45        33.47\n",
+       "728    19812.54  29800.00   9987.46        33.51\n",
+       "245     5317.17   8000.00   2682.83        33.54\n",
+       "114    46006.01  34419.45 -11586.56        33.66\n",
+       "543    36381.57  55000.00  18618.43        33.85\n",
+       "703    23207.21  17300.00  -5907.21        34.15\n",
+       "438    12083.74   9000.00  -3083.74        34.26\n",
+       "646     1642.87   2500.00    857.13        34.29\n",
+       "15      3900.79   2900.00  -1000.79        34.51\n",
+       "431    67578.74  49900.00 -17678.74        35.43\n",
+       "273     5030.79   7800.00   2769.21        35.50\n",
+       "692    12682.44   9350.00  -3332.44        35.64\n",
+       "25     44889.92  69900.00  25010.08        35.78\n",
+       "335     5702.84   9000.00   3297.16        36.64\n",
+       "6      11427.09   8350.00  -3077.09        36.85\n",
+       "310    13828.77  21900.00   8071.23        36.85\n",
+       "600     5591.66   8900.00   3308.34        37.17\n",
+       "617    16260.94  11850.00  -4410.94        37.22\n",
+       "209    19627.05  14300.00  -5327.05        37.25\n",
+       "394     2005.13   3200.00   1194.87        37.34\n",
+       "727    46345.58  74000.00  27654.42        37.37\n",
+       "777    51526.76  37500.00 -14026.76        37.40\n",
+       "785    29732.92  47700.00  17967.08        37.67\n",
+       "515    46378.55  74500.00  28121.45        37.75\n",
+       "31     16572.98  11999.00  -4573.98        38.12\n",
+       "772     6644.61   4800.00  -1844.61        38.43\n",
+       "460    12197.10   8800.00  -3397.10        38.60\n",
+       "304    64451.49 104999.00  40547.51        38.62\n",
+       "367    39888.50  65000.00  25111.50        38.63\n",
+       "220    17656.32  28793.04  11136.72        38.68\n",
+       "50     15936.61  11400.00  -4536.61        39.79\n",
+       "388    71675.00 121500.00  49825.00        41.01\n",
+       "551    16226.28  11500.00  -4726.28        41.10\n",
+       "302     8889.98   6299.00  -2590.98        41.13\n",
+       "235     9669.09  16500.00   6830.91        41.40\n",
+       "535    14822.55  10450.00  -4372.55        41.84\n",
+       "601    16930.40  29500.00  12569.60        42.61\n",
+       "73      5700.21  10000.00   4299.79        43.00\n",
+       "701    32880.90  22900.00  -9980.90        43.58\n",
+       "137     6477.68   4500.00  -1977.68        43.95\n",
+       "756    10016.88  18000.00   7983.12        44.35\n",
+       "237    11415.61   7900.00  -3515.61        44.50\n",
+       "539     2050.79   3700.00   1649.21        44.57\n",
+       "328    13804.82   9500.00  -4304.82        45.31\n",
+       "676    24080.07  16555.00  -7525.07        45.45\n",
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+       "444    17048.89  11707.80  -5341.09        45.62\n",
+       "69      8419.88  15500.00   7080.12        45.68\n",
+       "221    31124.28  21300.00  -9824.28        46.12\n",
+       "428    13157.10   9000.00  -4157.10        46.19\n",
+       "643     1733.85   1185.60   -548.25        46.24\n",
+       "41     38632.53  73500.00  34867.47        47.44\n",
+       "612    62100.23  42000.00 -20100.23        47.86\n",
+       "628     1196.37   2300.00   1103.63        47.98\n",
+       "794    65898.02 129222.00  63323.98        49.00\n",
+       "738     2313.02   1550.00   -763.02        49.23\n",
+       "164     2972.03   1990.00   -982.03        49.35\n",
+       "228     1417.83   2800.00   1382.17        49.36\n",
+       "502    22471.17  44700.00  22228.83        49.73\n",
+       "721    71981.49 144900.00  72918.51        50.32\n",
+       "481    50745.07  33715.50 -17029.57        50.51\n",
+       "577     7343.52  14999.00   7655.48        51.04\n",
+       "519    26609.57  17600.00  -9009.57        51.19\n",
+       "275    17888.88  11800.00  -6088.88        51.60\n",
+       "457    20479.37  13500.00  -6979.37        51.70\n",
+       "603    12461.52   8200.00  -4261.52        51.97\n",
+       "401    20828.11  13700.00  -7128.11        52.03\n",
+       "362    18980.92  39900.00  20919.08        52.43\n",
+       "780    28362.55  60500.00  32137.45        53.12\n",
+       "99      3521.95   2300.00  -1221.95        53.13\n",
+       "102     9427.42  20500.00  11072.58        54.01\n",
+       "370     5408.75   3500.00  -1908.75        54.54\n",
+       "516    10854.85  24000.00  13145.15        54.77\n",
+       "48     65041.89  41900.00 -23141.89        55.23\n",
+       "491     7715.56   4900.00  -2815.56        57.46\n",
+       "318    30316.04  19200.00 -11116.04        57.90\n",
+       "34     20712.46  13000.00  -7712.46        59.33\n",
+       "122     2719.53   1700.00  -1019.53        59.97\n",
+       "39      3906.56   2400.00  -1506.56        62.77\n",
+       "790     5055.93   3099.00  -1956.93        63.15\n",
+       "200     4243.79   2600.00  -1643.79        63.22\n",
+       "788    10676.78   6499.00  -4177.78        64.28\n",
+       "86     16658.12  10100.00  -6558.12        64.93\n",
+       "662     6656.03   4000.00  -2656.03        66.40\n",
+       "425     2486.42   8000.00   5513.58        68.92\n",
+       "661    13706.76   8000.00  -5706.76        71.33\n",
+       "23      6524.07   3799.00  -2725.07        71.73\n",
+       "423    41367.65  23900.00 -17467.65        73.09\n",
+       "238    60487.88  34600.00 -25887.88        74.82\n",
+       "524     9215.35   5000.00  -4215.35        84.31\n",
+       "636     6870.33   3600.00  -3270.33        90.84\n",
+       "506     9213.19   4800.00  -4413.19        91.94\n",
+       "613     1444.13  34999.00  33554.87        95.87\n",
+       "791     5130.28   2600.00  -2530.28        97.32\n",
+       "623        0.00   1800.00   1800.00       100.00\n",
+       "306        0.00  44000.00  44000.00       100.00\n",
+       "286        0.00  35000.00  35000.00       100.00\n",
+       "121        0.00   3800.00   3800.00       100.00\n",
+       "396        0.00  38500.00  38500.00       100.00\n",
+       "124        0.00   4100.00   4100.00       100.00\n",
+       "690        0.00   6500.00   6500.00       100.00\n",
+       "24         0.00  32000.00  32000.00       100.00\n",
+       "301        0.00   8500.00   8500.00       100.00\n",
+       "351        0.00  18800.00  18800.00       100.00\n",
+       "723        0.00   1650.00   1650.00       100.00\n",
+       "54     29058.70  14250.00 -14808.70       103.92\n",
+       "266    20157.28   9000.00 -11157.28       123.97\n",
+       "594     9463.58   3650.00  -5813.58       159.28\n",
+       "143     4534.02   1700.00  -2834.02       166.71\n",
+       "131     3200.86   1200.00  -2000.86       166.74\n",
+       "452     3875.31   1450.00  -2425.31       167.26\n",
+       "685    35212.93  12900.00 -22312.93       172.97\n",
+       "604    14347.91   4200.00 -10147.91       241.62\n",
+       "737     8975.55   2400.00  -6575.55       273.98\n",
+       "28     48602.40  11200.00 -37402.40       333.95\n",
+       "538    28240.95   5500.00 -22740.95       413.47\n",
+       "290         inf  19999.00      -inf          inf\n",
+       "731         inf  99000.00      -inf          inf\n",
+       "282         inf  20996.24      -inf          inf\n",
+       "679         inf   1150.00      -inf          inf\n",
+       "120         inf  17639.47      -inf          inf\n",
+       "204         inf  12900.00      -inf          inf\n",
+       "683         inf  20000.00      -inf          inf\n",
+       "650         inf  11300.00      -inf          inf\n",
+       "458         inf   7450.00      -inf          inf\n",
+       "314         inf  10200.00      -inf          inf\n",
+       "63          inf  37500.00      -inf          inf\n",
+       "698         inf  11700.00      -inf          inf\n",
+       "33          inf   6000.00      -inf          inf\n",
+       "82          inf   4000.00      -inf          inf"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sometimes it is useful to check these outputs manually\n",
+    "# To see all rows, we use the relevant pandas syntax\n",
+    "pd.options.display.max_rows = 999\n",
+    "# Moreover, to make the dataset clear, we can display the result with only 2 digits after the dot \n",
+    "\n",
+    "pd.set_option('display.float_format', lambda x: '%.2f' %x)\n",
+    "# Finally, we sort by difference in % and manually check the model\n",
+    "df_pf.sort_values(by=['Difference%'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'Model', 'log_price'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
+       "       'Registration', 'Year', 'Model', 'log_price'], dtype=object)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+Exercise+Solution.ipynb b/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+Exercise+Solution.ipynb
new file mode 100644
index 0000000..7ff78eb
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+Exercise+Solution.ipynb	
@@ -0,0 +1,668 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Linear Regression with sklearn - Exercise Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "Apart from that, please:\n",
+    "-  Display the intercept and coefficient(s)\n",
+    "-  Find the R-squared and Adjusted R-squared\n",
+    "-  Compare the R-squared and the Adjusted R-squared\n",
+    "-  Compare the R-squared of this regression and the simple linear regression where only 'size' was used\n",
+    "-  Using the model make a prediction about an apartment with size 750 sq.ft. from 2009\n",
+    "-  Find the univariate (or multivariate if you wish - see the article) p-values of the two variables. What can you say about them?\n",
+    "-  Create a summary table with your findings\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size' and 'year'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size  year\n",
+       "0  234314.144   643.09  2015\n",
+       "1  228581.528   656.22  2009\n",
+       "2  281626.336   487.29  2018\n",
+       "3  401255.608  1504.75  2015\n",
+       "4  458674.256  1275.46  2009"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size_year.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "      <td>2012.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "      <td>4.729021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "      <td>2006.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size         year\n",
+       "count     100.000000   100.000000   100.000000\n",
+       "mean   292289.470160   853.024200  2012.600000\n",
+       "std     77051.727525   297.941951     4.729021\n",
+       "min    154282.128000   479.750000  2006.000000\n",
+       "25%    234280.148000   643.330000  2009.000000\n",
+       "50%    280590.716000   696.405000  2015.000000\n",
+       "75%    335723.696000  1029.322500  2018.000000\n",
+       "max    500681.128000  1842.510000  2018.000000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data[['size','year']]\n",
+    "y = data['price']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-5772267.01746328"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 227.70085401, 2916.78532684])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7764803683276792"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's use the handy function we created\n",
+    "def adj_r2(x,y):\n",
+    "    r2 = reg.score(x,y)\n",
+    "    n = x.shape[0]\n",
+    "    p = x.shape[1]\n",
+    "    adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
+    "    return adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7718717161282499"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adj_r2(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the R-squared and the Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems the the R-squared is only slightly larger than the Adjusted R-squared, implying that we were not penalized a lot for the inclusion of 2 independent variables. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the Adjusted R-squared with the R-squared of the simple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comparing the Adjusted R-squared with the R-squared of the simple linear regression (when only 'size' was used - a couple of lectures ago), we realize that 'Year' is not bringing too much value to the result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making predictions\n",
+    "\n",
+    "Find the predicted price of an apartment that has a size of 750 sq.ft. from 2009."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([258330.34465995])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.predict([[750,2009]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the univariate p-values of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import f_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([285.92105192,   0.85525799]), array([8.12763222e-31, 3.57340758e-01]))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f_regression(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([8.12763222e-31, 3.57340758e-01])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values = f_regression(x,y)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.357])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a summary table with your findings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Coefficients</th>\n",
+       "      <th>p-values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>size</td>\n",
+       "      <td>227.700854</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>year</td>\n",
+       "      <td>2916.785327</td>\n",
+       "      <td>0.357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Features  Coefficients  p-values\n",
+       "0     size    227.700854     0.000\n",
+       "1     year   2916.785327     0.357"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary = pd.DataFrame(data = x.columns.values, columns=['Features'])\n",
+    "reg_summary ['Coefficients'] = reg.coef_\n",
+    "reg_summary ['p-values'] = p_values.round(3)\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems that 'Year' is not event significant, therefore we should remove it from the model."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+Exercise.ipynb b/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+Exercise.ipynb
new file mode 100644
index 0000000..8d9247f
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+Exercise.ipynb	
@@ -0,0 +1,748 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple Linear Regression with sklearn - Exercise Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are given a real estate dataset. \n",
+    "\n",
+    "Real estate is one of those examples that every regression course goes through as it is extremely easy to understand and there is a (almost always) certain causal relationship to be found.\n",
+    "\n",
+    "The data is located in the file: 'real_estate_price_size_year.csv'. \n",
+    "\n",
+    "You are expected to create a multiple linear regression (similar to the one in the lecture), using the new data. \n",
+    "\n",
+    "Apart from that, please:\n",
+    "-  Display the intercept and coefficient(s)\n",
+    "-  Find the R-squared and Adjusted R-squared\n",
+    "-  Compare the R-squared and the Adjusted R-squared\n",
+    "-  Compare the R-squared of this regression and the simple linear regression where only 'size' was used\n",
+    "-  Using the model make a prediction about an apartment with size 750 sq.ft. from 2009\n",
+    "-  Find the univariate (or multivariate if you wish - see the article) p-values of the two variables. What can you say about them?\n",
+    "-  Create a summary table with your findings\n",
+    "\n",
+    "In this exercise, the dependent variable is 'price', while the independent variables are 'size' and 'year'.\n",
+    "\n",
+    "Good luck!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('real_estate_price_size_year.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>234314.144</td>\n",
+       "      <td>643.09</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>228581.528</td>\n",
+       "      <td>656.22</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>281626.336</td>\n",
+       "      <td>487.29</td>\n",
+       "      <td>2018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401255.608</td>\n",
+       "      <td>1504.75</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>458674.256</td>\n",
+       "      <td>1275.46</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price     size  year\n",
+       "0  234314.144   643.09  2015\n",
+       "1  228581.528   656.22  2009\n",
+       "2  281626.336   487.29  2018\n",
+       "3  401255.608  1504.75  2015\n",
+       "4  458674.256  1275.46  2009"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>size</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>292289.470160</td>\n",
+       "      <td>853.024200</td>\n",
+       "      <td>2012.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>77051.727525</td>\n",
+       "      <td>297.941951</td>\n",
+       "      <td>4.729021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>154282.128000</td>\n",
+       "      <td>479.750000</td>\n",
+       "      <td>2006.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>234280.148000</td>\n",
+       "      <td>643.330000</td>\n",
+       "      <td>2009.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>280590.716000</td>\n",
+       "      <td>696.405000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335723.696000</td>\n",
+       "      <td>1029.322500</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>500681.128000</td>\n",
+       "      <td>1842.510000</td>\n",
+       "      <td>2018.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price         size         year\n",
+       "count     100.000000   100.000000   100.000000\n",
+       "mean   292289.470160   853.024200  2012.600000\n",
+       "std     77051.727525   297.941951     4.729021\n",
+       "min    154282.128000   479.750000  2006.000000\n",
+       "25%    234280.148000   643.330000  2009.000000\n",
+       "50%    280590.716000   696.405000  2015.000000\n",
+       "75%    335723.696000  1029.322500  2018.000000\n",
+       "max    500681.128000  1842.510000  2018.000000"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and the independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['price'] #dependent\n",
+    "x  = data[['size', 'year']] # indepedent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-5772267.01746328"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 227.70085401, 2916.78532684])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7764803683276793"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def adj_r2(x,y):\n",
+    "    r2 = reg.score(x,y)\n",
+    "    n = x.shape[0]\n",
+    "    p = x.shape[1]\n",
+    "    adjusted_r2 = 1 - (1-r2) * (n-1)/(n-p-1)\n",
+    "    return adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.77187171612825"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adj_r2(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the R-squared and the Adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R-squared is slightly larger than the adjusted R-squared"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare the Adjusted R-squared with the R-squared of the simple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making predictions\n",
+    "\n",
+    "Find the predicted price of an apartment that has a size of 750 sq.ft. from 2009."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([258330.34465995])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.predict([[750, 2009]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the univariate p-values of the variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import f_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([285.92105192,   0.85525799]), array([8.12763222e-31, 3.57340758e-01]))"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f_regression(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([8.12763222e-31, 3.57340758e-01])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values = f_regression(x,y)[1]\n",
+    "p_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.357])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_values.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a summary table with your findings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>size</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>year</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Features\n",
+       "0     size\n",
+       "1     year"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary = pd.DataFrame(data = x.columns.values, columns= ['Features'])\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Coefficients</th>\n",
+       "      <th>p-values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>size</td>\n",
+       "      <td>227.700854</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>year</td>\n",
+       "      <td>2916.785327</td>\n",
+       "      <td>0.357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Features  Coefficients  p-values\n",
+       "0     size    227.700854     0.000\n",
+       "1     year   2916.785327     0.357"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_summary['Coefficients'] = reg.coef_\n",
+    "reg_summary['p-values'] = p_values.round(3)\n",
+    "reg_summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Considering the first 3 0s, we can say that the 2nd event (year) is not significant in the model and so should be removed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+and+Adjusted+R-squared+-+Exercise+Solution.ipynb b/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+and+Adjusted+R-squared+-+Exercise+Solution.ipynb
new file mode 100644
index 0000000..54343e2
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+and+Adjusted+R-squared+-+Exercise+Solution.ipynb	
@@ -0,0 +1,474 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adjusted R-squared - Exercise Solution\n",
+    "\n",
+    "Using the code from the lecture, create a function which will calculate the adjusted R-squared for you, given the independent variable(s) (x) and the dependent variable (y).\n",
+    "\n",
+    "Check if you function is working properly.\n",
+    "\n",
+    "Solution at the bottom."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1714</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1664</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1760</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1685</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1693</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    SAT  Rand 1,2,3   GPA\n",
+       "0  1714           1  2.40\n",
+       "1  1664           3  2.52\n",
+       "2  1760           3  2.54\n",
+       "3  1685           3  2.74\n",
+       "4  1693           2  2.83"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('1.02. Multiple linear regression.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1845.273810</td>\n",
+       "      <td>2.059524</td>\n",
+       "      <td>3.330238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>104.530661</td>\n",
+       "      <td>0.855192</td>\n",
+       "      <td>0.271617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1634.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1772.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1846.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.380000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1934.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.502500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2050.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.810000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               SAT  Rand 1,2,3        GPA\n",
+       "count    84.000000   84.000000  84.000000\n",
+       "mean   1845.273810    2.059524   3.330238\n",
+       "std     104.530661    0.855192   0.271617\n",
+       "min    1634.000000    1.000000   2.400000\n",
+       "25%    1772.000000    1.000000   3.190000\n",
+       "50%    1846.000000    2.000000   3.380000\n",
+       "75%    1934.000000    3.000000   3.502500\n",
+       "max    2050.000000    3.000000   3.810000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the multiple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data[['SAT','Rand 1,2,3']]\n",
+    "y = data['GPA']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00165354, -0.00826982])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.29603261264909486"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculating the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4066811952814285"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(84, 2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.39203134825134023"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2 = reg.score(x,y)\n",
+    "n = x.shape[0]\n",
+    "p = x.shape[1]\n",
+    "\n",
+    "adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
+    "adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adjusted R^2 function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are different ways to solve this problem\n",
+    "# To make it as easy and interpretable as possible, we have preserved the original code\n",
+    "def adj_r2(x,y):\n",
+    "    r2 = reg.score(x,y)\n",
+    "    n = x.shape[0]\n",
+    "    p = x.shape[1]\n",
+    "    adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
+    "    return adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.39203134825134023"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here's the result\n",
+    "adj_r2(x,y)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+and+Adjusted+R-squared+-+Exercise.ipynb b/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+and+Adjusted+R-squared+-+Exercise.ipynb
new file mode 100644
index 0000000..b813592
--- /dev/null
+++ b/Udemy DataScience Workbook/sklearn+-+Multiple+Linear+Regression+and+Adjusted+R-squared+-+Exercise.ipynb	
@@ -0,0 +1,357 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adjusted R-squared - Exercise\n",
+    "\n",
+    "Using the code from the lecture, create a function which will calculate the adjusted R-squared for you, given the independent variable(s) (x) and the dependent variable (y).\n",
+    "\n",
+    "Check if you function is working properly.\n",
+    "\n",
+    "Please solve the exercise at the bottom of the notebook (in order to check if it is working you must run all previous cells)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('1.02.+Multiple+linear+regression.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SAT</th>\n",
+       "      <th>Rand 1,2,3</th>\n",
+       "      <th>GPA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1714</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1664</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1760</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1685</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1693</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    SAT  Rand 1,2,3   GPA\n",
+       "0  1714           1  2.40\n",
+       "1  1664           3  2.52\n",
+       "2  1760           3  2.54\n",
+       "3  1685           3  2.74\n",
+       "4  1693           2  2.83"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the multiple linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Declare the dependent and independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data['GPA']# dependent \n",
+    "x = data[['SAT', 'Rand 1,2,3']]#independent\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg = LinearRegression()\n",
+    "reg.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.00165354, -0.00826982])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.29603261264909486"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculating the R-squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4066811952814283"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.score(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Formula for Adjusted R^2\n",
+    "\n",
+    "$R^2_{adj.} = 1 - (1-R^2)*\\frac{n-1}{n-p-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(84, 2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r2 = reg.score(x,y)\n",
+    "n = x.shape[0]\n",
+    "p = x.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adjusted R^2 function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3920313482513401"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adjusted_r2 = 1 - (1 - r2) * (n - 1)/(n-p-1)\n",
+    "adjusted_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/tensor_function_test.ipynb b/Udemy DataScience Workbook/tensor_function_test.ipynb
new file mode 100644
index 0000000..ed9ae0e
--- /dev/null
+++ b/Udemy DataScience Workbook/tensor_function_test.ipynb	
@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "object of type 'Audiobooks_Data_Reader' has no len()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\tensor_function_test.ipynb Cell 1\u001b[0m line \u001b[0;36m9\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/tensor_function_test.ipynb#W0sZmlsZQ%3D%3D?line=94'>95</a>\u001b[0m     validation_loss \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m loss\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/tensor_function_test.ipynb#W0sZmlsZQ%3D%3D?line=95'>96</a>\u001b[0m     validation_accuracy \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mreduce_sum(tf\u001b[39m.\u001b[39mcast(tf\u001b[39m.\u001b[39mequal(tf\u001b[39m.\u001b[39margmax(predictions, \u001b[39m1\u001b[39m), tf\u001b[39m.\u001b[39margmax(target_batch, \u001b[39m1\u001b[39m)), tf\u001b[39m.\u001b[39mfloat64))\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/tensor_function_test.ipynb#W0sZmlsZQ%3D%3D?line=97'>98</a>\u001b[0m validation_loss \u001b[39m/\u001b[39m\u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(validation_data)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/tensor_function_test.ipynb#W0sZmlsZQ%3D%3D?line=98'>99</a>\u001b[0m validation_accuracy \u001b[39m/\u001b[39m\u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(validation_data)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/Chizy/Desktop/MyContent/Udemy%20DataScience%20Workbook/tensor_function_test.ipynb#W0sZmlsZQ%3D%3D?line=100'>101</a>\u001b[0m \u001b[39m# Print statistics for the current epoch\u001b[39;00m\n",
+      "\u001b[1;31mTypeError\u001b[0m: object of type 'Audiobooks_Data_Reader' has no len()"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "class Audiobooks_Data_Reader:\n",
+    "    def __init__(self, dataset, batch_size=None):\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float64), npz['targets'].astype(np.int64)\n",
+    "\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return self\n",
+    "\n",
+    "# Input size depends on the number of input variables. We have 10 of them\n",
+    "input_size = 10\n",
+    "# Output size is 2, as we one-hot encoded the targets.\n",
+    "output_size = 2\n",
+    "# Choose a hidden_layer_size\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Create the model using the Sequential API\n",
+    "model = tf.keras.Sequential([\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu', input_shape=(input_size,)),\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'),\n",
+    "    tf.keras.layers.Dense(output_size)\n",
+    "])\n",
+    "\n",
+    "# Use the softmax cross entropy loss with logits\n",
+    "loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)\n",
+    "\n",
+    "# Define the optimizer\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
+    "\n",
+    "# Define metrics\n",
+    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
+    "train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')\n",
+    "\n",
+    "# Choose the batch size\n",
+    "batch_size = 100\n",
+    "\n",
+    "# Set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "prev_validation_loss = float('inf')\n",
+    "\n",
+    "# Load the first batch of training and validation\n",
+    "# Replace this with your actual data loading logic\n",
+    "train_data = Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data = Audiobooks_Data_Reader('validation')\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch_counter in range(max_epochs):\n",
+    "    train_loss.reset_states()\n",
+    "    train_accuracy.reset_states()\n",
+    "\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        with tf.GradientTape() as tape:\n",
+    "            predictions = model(input_batch)\n",
+    "            loss = loss_object(target_batch, predictions)\n",
+    "\n",
+    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
+    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
+    "\n",
+    "        train_loss(loss)\n",
+    "        train_accuracy(target_batch, predictions)\n",
+    "\n",
+    "    # Validation\n",
+    "    validation_loss = 0.\n",
+    "    validation_accuracy = 0.\n",
+    "\n",
+    "    for input_batch, target_batch in validation_data:\n",
+    "        predictions = model(input_batch)\n",
+    "        loss = loss_object(target_batch, predictions)\n",
+    "\n",
+    "        validation_loss += loss\n",
+    "        validation_accuracy += tf.reduce_sum(tf.cast(tf.equal(tf.argmax(predictions, 1), tf.argmax(target_batch, 1)), tf.float64))\n",
+    "\n",
+    "    validation_loss /= len(validation_data)\n",
+    "    validation_accuracy /= len(validation_data)\n",
+    "\n",
+    "    # Print statistics for the current epoch\n",
+    "    print('Epoch ' + str(epoch_counter + 1) +\n",
+    "          '. Training loss: ' + '{:.3f}'.format(train_loss.result()) +\n",
+    "          '. Training accuracy: ' + '{:.2%}'.format(train_accuracy.result()) +\n",
+    "          '. Validation loss: ' + '{:.3f}'.format(validation_loss) +\n",
+    "          '. Validation accuracy: ' + '{:.2%}'.format(validation_accuracy))\n",
+    "\n",
+    "    # Trigger early stopping if validation loss begins increasing.\n",
+    "    if validation_loss > prev_validation_loss:\n",
+    "        break\n",
+    "\n",
+    "    prev_validation_loss = validation_loss\n",
+    "\n",
+    "print('End of training.')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Udemy DataScience Workbook/tensorflow_homework_bing.ipynb b/Udemy DataScience Workbook/tensorflow_homework_bing.ipynb
new file mode 100644
index 0000000..6327b75
--- /dev/null
+++ b/Udemy DataScience Workbook/tensorflow_homework_bing.ipynb	
@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Create a class that will do the batching for the algorithm\n",
+    "# This code is extremely reusable. You should just change Audiobooks_data everywhere in the code\n",
+    "class Audiobooks_Data_Reader():\n",
+    "    # Dataset is a mandatory arugment, while the batch_size is optional\n",
+    "    # If you don't input batch_size, it will automatically take the value: None\n",
+    "    def __init__(self, dataset, batch_size = None):\n",
+    "    \n",
+    "        # The dataset that loads is one of \"train\", \"validation\", \"test\".\n",
+    "        # e.g. if I call this class with x('train',5), it will load 'Audiobooks_data_train.npz' with a batch size of 5.\n",
+    "        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n",
+    "        \n",
+    "        # Two variables that take the values of the inputs and the targets. Inputs are floats, targets are integers\n",
+    "        self.inputs, self.targets = npz['inputs'].astype(np.float64), npz['targets'].astype(np.int64)\n",
+    "        \n",
+    "        # Counts the batch number, given the size you feed it later\n",
+    "        # If the batch size is None, we are either validating or testing, so we want to take the data in a single batch\n",
+    "        if batch_size is None:\n",
+    "            self.batch_size = self.inputs.shape[0]\n",
+    "        else:\n",
+    "            self.batch_size = batch_size\n",
+    "        self.curr_batch = 0\n",
+    "        self.batch_count = self.inputs.shape[0] // self.batch_size\n",
+    "    \n",
+    "    # A method which loads the next batch\n",
+    "    def __next__(self):\n",
+    "        if self.curr_batch >= self.batch_count:\n",
+    "            self.curr_batch = 0\n",
+    "            raise StopIteration()\n",
+    "            \n",
+    "        # You slice the dataset in batches and then the \"next\" function loads them one after the other\n",
+    "        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n",
+    "        inputs_batch = self.inputs[batch_slice]\n",
+    "        targets_batch = self.targets[batch_slice]\n",
+    "        self.curr_batch += 1\n",
+    "        \n",
+    "        # One-hot encode the targets. In this example it's a bit superfluous since we have a 0/1 column \n",
+    "        # as a target already but we're giving you the code regardless, as it will be useful for any \n",
+    "        # classification task with more than one target column\n",
+    "        classes_num = 2\n",
+    "        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n",
+    "        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n",
+    "        \n",
+    "        # The function will return the inputs batch and the one-hot encoded targets\n",
+    "        return inputs_batch, targets_one_hot\n",
+    "    def __iter__(self):\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "InvalidArgumentError",
+     "evalue": "{{function_node __wrapped__ArgMax_device_/job:localhost/replica:0/task:0/device:CPU:0}} Expected dimension in the range [0, 0), but got 1 [Op:ArgMax] name: ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[36], line 28\u001b[0m\n\u001b[0;32m     25\u001b[0m     loss \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39msoftmax_cross_entropy_with_logits(logits\u001b[38;5;241m=\u001b[39moutputs, labels\u001b[38;5;241m=\u001b[39mtargets)\n\u001b[0;32m     26\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mreduce_mean(loss)\n\u001b[1;32m---> 28\u001b[0m out_equals_target \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mequal(\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, tf\u001b[38;5;241m.\u001b[39margmax(targets, \u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m     29\u001b[0m accuracy \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mreduce_mean(tf\u001b[38;5;241m.\u001b[39mcast(out_equals_target, tf\u001b[38;5;241m.\u001b[39mfloat32))\n\u001b[0;32m     31\u001b[0m \u001b[38;5;66;03m# Optimize with Adam\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:153\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m--> 153\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    155\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Desktop\\MyContent\\Udemy DataScience Workbook\\venv\\Lib\\site-packages\\tensorflow\\python\\framework\\ops.py:5883\u001b[0m, in \u001b[0;36mraise_from_not_ok_status\u001b[1;34m(e, name)\u001b[0m\n\u001b[0;32m   5881\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mraise_from_not_ok_status\u001b[39m(e, name) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NoReturn:\n\u001b[0;32m   5882\u001b[0m   e\u001b[38;5;241m.\u001b[39mmessage \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m name: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(name \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m-> 5883\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_status_to_exception(e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[1;31mInvalidArgumentError\u001b[0m: {{function_node __wrapped__ArgMax_device_/job:localhost/replica:0/task:0/device:CPU:0}} Expected dimension in the range [0, 0), but got 1 [Op:ArgMax] name: "
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Input size depends on the number of input variables. We have 10 of them\n",
+    "input_size = 10\n",
+    "# Output size is 2, as we one-hot encoded the targets.\n",
+    "output_size = 2\n",
+    "# Choose a hidden_layer_size\n",
+    "hidden_layer_size = 50\n",
+    "\n",
+    "# Reset the default graph, so you can fiddle with the hyperparameters and then rerun the code.\n",
+    "#tf.compat.v1.reset_default_graph()\n",
+    "\n",
+    "# Create the placeholders\n",
+    "inputs = tf.keras.Input(shape=(input_size,), dtype=tf.float32)\n",
+    "targets = tf.keras.Input(shape=(output_size,), dtype=tf.int32)\n",
+    "\n",
+    "# Define the model using the Keras API\n",
+    "model = tf.keras.Sequential([\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='relu', input_shape=(input_size,)),\n",
+    "    tf.keras.layers.Dense(hidden_layer_size, activation='sigmoid'),\n",
+    "    tf.keras.layers.Dense(output_size)\n",
+    "])\n",
+    "\n",
+    "outputs = model(inputs)\n",
+    "# Use the softmax cross entropy loss with logits\n",
+    "def loss_fn():\n",
+    "    loss = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)\n",
+    "    return tf.reduce_mean(loss)\n",
+    "\n",
+    "out_equals_target = tf.equal(tf.argmax(output_size, 1), tf.argmax(targets, 1))\n",
+    "accuracy = tf.reduce_mean(tf.cast(out_equals_target, tf.float32))\n",
+    "\n",
+    "# Optimize with Adam\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
+    "\n",
+    "model.compile(optimizer=optimizer, loss=loss_fn, metrics=[accuracy])\n",
+    "\n",
+    "\n",
+    "sess = tf.compat.v1.InteractiveSession()\n",
+    "with tf.compat.v1.get_default_graph().as_default():\n",
+    "\n",
+    "# Initialize the variables\n",
+    "    initializer = tf.compat.v1.global_variables_initializer()\n",
+    "    tf.compat.v1.disable_eager_execution()\n",
+    "    sess.run(initializer)\n",
+    "\n",
+    "# Choose the batch size\n",
+    "batch_size = 100\n",
+    "# Set early stopping mechanisms\n",
+    "max_epochs = 100\n",
+    "train_data = Audiobooks_Data_Reader('train', batch_size)\n",
+    "validation_data = Audiobooks_Data_Reader('validation')\n",
+    "# Create the loop for epochs \n",
+    "for epoch_counter in range(max_epochs):\n",
+    "    \n",
+    "    # Set the epoch loss to 0, and make it a float\n",
+    "    curr_epoch_loss = 0.\n",
+    "\n",
+    "    for input_batch, target_batch in train_data:\n",
+    "        # Use a tf.GradientTape to record the operations\n",
+    "        batch_loss, batch_accuracy = model.train_on_batch(input_batch, target_batch)\n",
+    "        \n",
+    "        #Record the batch loss into the current epoch loss\n",
+    "        curr_epoch_loss += batch_loss\n",
+    "    \n",
+    "    # Find the mean curr_epoch_loss\n",
+    "    # batch_count is a variable, defined in the Audiobooks_Data_Reader class\n",
+    "    curr_epoch_loss /= train_data.batch_count\n",
+    "    \n",
+    "    # Set validation loss and accuracy for the epoch to zero\n",
+    "    validation_loss = 0.\n",
+    "    validation_accuracy = 0. \n",
+    "\n",
+    "    for input_batch, target_batch in validation_data:\n",
+    "        validation_loss, validation_accuracy = sess.run([loss_fn(), accuracy]),\n",
+    "        feed_dict={inputs: input_batch, targets: target_batch}\n",
+    "    \n",
+    "    # Print statistics for the current epoch\n",
+    "    print('Epoch '+str(epoch_counter+1)+\n",
+    "          '. Training loss: '+'{0:.3f}'.format(curr_epoch_loss)+\n",
+    "          '. Validation loss: '+'{0:.3f}'.format(validation_loss)+\n",
+    "          '. Validation accuracy: '+'{0:.2f}'.format(validation_accuracy * 100.)+'%')\n",
+    "    \n",
+    "    # Trigger early stopping if validation loss begins increasing.\n",
+    "    if validation_loss > prev_validation_loss:\n",
+    "        break\n",
+    "        \n",
+    "    # Store this epoch's validation loss to be used as previous in the next iteration.\n",
+    "    prev_validation_loss = validation_loss\n",
+    "    \n",
+    "print('End of training.')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/cbtlog.ipynb b/cbtlog.ipynb
new file mode 100644
index 0000000..addf6dd
--- /dev/null
+++ b/cbtlog.ipynb
@@ -0,0 +1,1742 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: openpyxl in c:\\users\\chizy\\github_repository\\abscenteeism_check\\venv\\lib\\site-packages (3.1.3)\n",
+      "Requirement already satisfied: et-xmlfile in c:\\users\\chizy\\github_repository\\abscenteeism_check\\venv\\lib\\site-packages (from openpyxl) (1.1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "!pip install openpyxl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_theme()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
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+       "      <th></th>\n",
+       "      <th>Id</th>\n",
+       "      <th>Hall_IP</th>\n",
+       "      <th>MatricNo</th>\n",
+       "      <th>Action</th>\n",
+       "      <th>TimeStamp</th>\n",
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+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:25:45.642</td>\n",
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+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:27:04.104</td>\n",
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+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
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+       "      <th>4</th>\n",
+       "      <td>770929</td>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:29.305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2610</th>\n",
+       "      <td>950346</td>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022004995</td>\n",
+       "      <td>Attempted UTME enrollment 2022004995 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:13:22.134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2611</th>\n",
+       "      <td>950353</td>\n",
+       "      <td>192.168.20.7</td>\n",
+       "      <td>2022006011</td>\n",
+       "      <td>Attempted UTME enrollment 2022006011 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:13:55.533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2612</th>\n",
+       "      <td>950354</td>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022009875</td>\n",
+       "      <td>Attempted UTME enrollment 2022009875 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:14:03.496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2613</th>\n",
+       "      <td>950356</td>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022002345</td>\n",
+       "      <td>Attempted UTME enrollment 2022002345 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:14:12.359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2614</th>\n",
+       "      <td>950383</td>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>196212</td>\n",
+       "      <td>Attempted UTME enrollment 196212 for utmeFinger</td>\n",
+       "      <td>2024-05-28 16:27:02.482</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2615 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Id        Hall_IP    MatricNo  \\\n",
+       "0     770893   192.168.28.7  2023009261   \n",
+       "1     770904   192.168.28.7  2023009261   \n",
+       "2     770914   192.168.28.7  2023009261   \n",
+       "3     770927   192.168.28.7  2023009261   \n",
+       "4     770929   192.168.28.7  2023009261   \n",
+       "...      ...            ...         ...   \n",
+       "2610  950346  192.168.145.3  2022004995   \n",
+       "2611  950353   192.168.20.7  2022006011   \n",
+       "2612  950354  192.168.145.3  2022009875   \n",
+       "2613  950356  192.168.145.3  2022002345   \n",
+       "2614  950383  192.168.145.3      196212   \n",
+       "\n",
+       "                                                 Action  \\\n",
+       "0     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "1     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "2     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "3     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "4     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "...                                                 ...   \n",
+       "2610  Attempted UTME enrollment 2022004995 for utmeF...   \n",
+       "2611  Attempted UTME enrollment 2022006011 for utmeF...   \n",
+       "2612  Attempted UTME enrollment 2022009875 for utmeF...   \n",
+       "2613  Attempted UTME enrollment 2022002345 for utmeF...   \n",
+       "2614    Attempted UTME enrollment 196212 for utmeFinger   \n",
+       "\n",
+       "                   TimeStamp  \n",
+       "0    2024-05-06 09:25:45.642  \n",
+       "1    2024-05-06 09:26:23.703  \n",
+       "2    2024-05-06 09:27:04.104  \n",
+       "3    2024-05-06 09:28:22.529  \n",
+       "4    2024-05-06 09:28:29.305  \n",
+       "...                      ...  \n",
+       "2610 2024-05-28 16:13:22.134  \n",
+       "2611 2024-05-28 16:13:55.533  \n",
+       "2612 2024-05-28 16:14:03.496  \n",
+       "2613 2024-05-28 16:14:12.359  \n",
+       "2614 2024-05-28 16:27:02.482  \n",
+       "\n",
+       "[2615 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_df = pd.read_excel('CBThallLogs.csv.xlsx')\n",
+    "df= raw_df.copy()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop(['Id'], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hall_IP</th>\n",
+       "      <th>MatricNo</th>\n",
+       "      <th>Action</th>\n",
+       "      <th>TimeStamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:25:45.642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:26:23.703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:27:04.104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:22.529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:29.305</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Hall_IP    MatricNo  \\\n",
+       "0  192.168.28.7  2023009261   \n",
+       "1  192.168.28.7  2023009261   \n",
+       "2  192.168.28.7  2023009261   \n",
+       "3  192.168.28.7  2023009261   \n",
+       "4  192.168.28.7  2023009261   \n",
+       "\n",
+       "                                              Action               TimeStamp  \n",
+       "0  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:25:45.642  \n",
+       "1  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:26:23.703  \n",
+       "2  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:27:04.104  \n",
+       "3  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:28:22.529  \n",
+       "4  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:28:29.305  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hall_IP</th>\n",
+       "      <th>MatricNo</th>\n",
+       "      <th>Action</th>\n",
+       "      <th>TimeStamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:25:45.642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:26:23.703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:27:04.104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:22.529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:29.305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2610</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022004995</td>\n",
+       "      <td>Attempted UTME enrollment 2022004995 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:13:22.134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2611</th>\n",
+       "      <td>192.168.20.7</td>\n",
+       "      <td>2022006011</td>\n",
+       "      <td>Attempted UTME enrollment 2022006011 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:13:55.533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2612</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022009875</td>\n",
+       "      <td>Attempted UTME enrollment 2022009875 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:14:03.496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2613</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022002345</td>\n",
+       "      <td>Attempted UTME enrollment 2022002345 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:14:12.359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2614</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>196212</td>\n",
+       "      <td>Attempted UTME enrollment 196212 for utmeFinger</td>\n",
+       "      <td>2024-05-28 16:27:02.482</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2615 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Hall_IP    MatricNo  \\\n",
+       "0      192.168.28.7  2023009261   \n",
+       "1      192.168.28.7  2023009261   \n",
+       "2      192.168.28.7  2023009261   \n",
+       "3      192.168.28.7  2023009261   \n",
+       "4      192.168.28.7  2023009261   \n",
+       "...             ...         ...   \n",
+       "2610  192.168.145.3  2022004995   \n",
+       "2611   192.168.20.7  2022006011   \n",
+       "2612  192.168.145.3  2022009875   \n",
+       "2613  192.168.145.3  2022002345   \n",
+       "2614  192.168.145.3      196212   \n",
+       "\n",
+       "                                                 Action  \\\n",
+       "0     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "1     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "2     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "3     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "4     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "...                                                 ...   \n",
+       "2610  Attempted UTME enrollment 2022004995 for utmeF...   \n",
+       "2611  Attempted UTME enrollment 2022006011 for utmeF...   \n",
+       "2612  Attempted UTME enrollment 2022009875 for utmeF...   \n",
+       "2613  Attempted UTME enrollment 2022002345 for utmeF...   \n",
+       "2614    Attempted UTME enrollment 196212 for utmeFinger   \n",
+       "\n",
+       "                   TimeStamp  \n",
+       "0    2024-05-06 09:25:45.642  \n",
+       "1    2024-05-06 09:26:23.703  \n",
+       "2    2024-05-06 09:27:04.104  \n",
+       "3    2024-05-06 09:28:22.529  \n",
+       "4    2024-05-06 09:28:29.305  \n",
+       "...                      ...  \n",
+       "2610 2024-05-28 16:13:22.134  \n",
+       "2611 2024-05-28 16:13:55.533  \n",
+       "2612 2024-05-28 16:14:03.496  \n",
+       "2613 2024-05-28 16:14:12.359  \n",
+       "2614 2024-05-28 16:27:02.482  \n",
+       "\n",
+       "[2615 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# we extract the first three segments of the IP address\n",
+    "'''df[\"Hall_distinct_Ip\"] = df[\"Hall_IP\"].str.split(\".\").str[:3].apply(lambda x: \".\".join(x))\n",
+    "df[\"Hall_distinct_Ip\"]'''\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop the initial IP address column\n",
+    "#df = df.drop(['Hall_IP'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hall_IP</th>\n",
+       "      <th>MatricNo</th>\n",
+       "      <th>Action</th>\n",
+       "      <th>TimeStamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:25:45.642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:26:23.703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:27:04.104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:22.529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:29.305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2610</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022004995</td>\n",
+       "      <td>Attempted UTME enrollment 2022004995 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:13:22.134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2611</th>\n",
+       "      <td>192.168.20.7</td>\n",
+       "      <td>2022006011</td>\n",
+       "      <td>Attempted UTME enrollment 2022006011 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:13:55.533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2612</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022009875</td>\n",
+       "      <td>Attempted UTME enrollment 2022009875 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:14:03.496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2613</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022002345</td>\n",
+       "      <td>Attempted UTME enrollment 2022002345 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:14:12.359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2614</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>196212</td>\n",
+       "      <td>Attempted UTME enrollment 196212 for utmeFinger</td>\n",
+       "      <td>2024-05-28 16:27:02.482</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2615 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Hall_IP    MatricNo  \\\n",
+       "0      192.168.28.7  2023009261   \n",
+       "1      192.168.28.7  2023009261   \n",
+       "2      192.168.28.7  2023009261   \n",
+       "3      192.168.28.7  2023009261   \n",
+       "4      192.168.28.7  2023009261   \n",
+       "...             ...         ...   \n",
+       "2610  192.168.145.3  2022004995   \n",
+       "2611   192.168.20.7  2022006011   \n",
+       "2612  192.168.145.3  2022009875   \n",
+       "2613  192.168.145.3  2022002345   \n",
+       "2614  192.168.145.3      196212   \n",
+       "\n",
+       "                                                 Action  \\\n",
+       "0     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "1     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "2     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "3     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "4     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "...                                                 ...   \n",
+       "2610  Attempted UTME enrollment 2022004995 for utmeF...   \n",
+       "2611  Attempted UTME enrollment 2022006011 for utmeF...   \n",
+       "2612  Attempted UTME enrollment 2022009875 for utmeF...   \n",
+       "2613  Attempted UTME enrollment 2022002345 for utmeF...   \n",
+       "2614    Attempted UTME enrollment 196212 for utmeFinger   \n",
+       "\n",
+       "                   TimeStamp  \n",
+       "0    2024-05-06 09:25:45.642  \n",
+       "1    2024-05-06 09:26:23.703  \n",
+       "2    2024-05-06 09:27:04.104  \n",
+       "3    2024-05-06 09:28:22.529  \n",
+       "4    2024-05-06 09:28:29.305  \n",
+       "...                      ...  \n",
+       "2610 2024-05-28 16:13:22.134  \n",
+       "2611 2024-05-28 16:13:55.533  \n",
+       "2612 2024-05-28 16:14:03.496  \n",
+       "2613 2024-05-28 16:14:12.359  \n",
+       "2614 2024-05-28 16:27:02.482  \n",
+       "\n",
+       "[2615 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2023009261 184238 2023003875 ... 204145 195704 196212]\n"
+     ]
+    }
+   ],
+   "source": [
+    "unique_value= df['MatricNo'].unique()\n",
+    "print(unique_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2615 entries, 0 to 2614\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column     Non-Null Count  Dtype         \n",
+      "---  ------     --------------  -----         \n",
+      " 0   Hall_IP    2615 non-null   object        \n",
+      " 1   MatricNo   2615 non-null   object        \n",
+      " 2   Action     2615 non-null   object        \n",
+      " 3   TimeStamp  2615 non-null   datetime64[ns]\n",
+      "dtypes: datetime64[ns](1), object(3)\n",
+      "memory usage: 81.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: '193627\\\\'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMatricNo\u001b[39m\u001b[38;5;124m'\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mMatricNo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mint64\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   6637\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m   6638\u001b[0m         ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m   6639\u001b[0m     ]\n\u001b[0;32m   6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6642\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 6643\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6644\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m   6645\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[0;32m    428\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    434\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    435\u001b[0m \u001b[43m    \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m    361\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    362\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 363\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    364\u001b[0m     result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m    366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[0;32m    755\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    756\u001b[0m     values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :]  \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[1;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m    762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    234\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[0;32m    236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 237\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m    239\u001b[0m     \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m    240\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[0;32m    241\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m    179\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 182\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m    185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:133\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m    129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[0;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mor\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m    132\u001b[0m     \u001b[38;5;66;03m# Explicit copy, or required since NumPy can't view from / to object.\u001b[39;00m\n\u001b[1;32m--> 133\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
+      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: '193627\\\\'"
+     ]
+    }
+   ],
+   "source": [
+    "df[['MatricNo']] = df[['MatricNo']].astype('int64')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>IP_pattern</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>skye1</td>\n",
+       "      <td>192.168.145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>admin</td>\n",
+       "      <td>10.6.6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>192.168.28.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B2</td>\n",
+       "      <td>192.168.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B1</td>\n",
+       "      <td>192.168.28</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    NAME    IP_pattern\n",
+       "0  skye1   192.168.145\n",
+       "1  admin      10.6.6.6\n",
+       "2     A1  192.168.28.5\n",
+       "3     B2    192.168.28\n",
+       "4     B1    192.168.28"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_Hall = pd.read_excel('IPCbt.xlsx')\n",
+    "df_Hall.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>IP_pattern</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>64</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>64</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>skye1</td>\n",
+       "      <td>192.168.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         NAME  IP_pattern\n",
+       "count      64          64\n",
+       "unique     64          56\n",
+       "top     skye1  192.168.28\n",
+       "freq        1           3"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_Hall.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 64 entries, 0 to 63\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   NAME        64 non-null     object\n",
+      " 1   IP_pattern  64 non-null     object\n",
+      "dtypes: object(2)\n",
+      "memory usage: 1.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_Hall.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0            skye1\n",
+       "1            admin\n",
+       "2               A1\n",
+       "3               B2\n",
+       "4               B1\n",
+       "          ...     \n",
+       "59         oldict4\n",
+       "60    ictsulaimon2\n",
+       "61    ictsulaimon3\n",
+       "62         ictige1\n",
+       "63           skye2\n",
+       "Name: NAME, Length: 64, dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_Hall[\"NAME\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        192.168.145\n",
+       "1           10.6.6.6\n",
+       "2       192.168.28.5\n",
+       "3         192.168.28\n",
+       "4         192.168.28\n",
+       "           ...      \n",
+       "59     10.101.70.104\n",
+       "60      192.168.28.9\n",
+       "61     192.168.28.10\n",
+       "62     10.101.73.106\n",
+       "63    192.168.145.10\n",
+       "Name: IP_pattern, Length: 64, dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_Hall[\"IP_pattern\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'skye1': '192.168.145',\n",
+       " 'admin': '10.6.6.6',\n",
+       " 'A1': '192.168.28.5',\n",
+       " 'B2': '192.168.28',\n",
+       " 'B1': '192.168.28',\n",
+       " 'A2': '192.168.28',\n",
+       " 'odl': '192.168.23',\n",
+       " 'cad': '192.168.20',\n",
+       " 'mul': '192.168.19',\n",
+       " 'FB': '192.168.25',\n",
+       " 'ictlasisi': '192.168.19.23',\n",
+       " 'ictbolaji': '192.168.145.120',\n",
+       " 'ictbello': '192.168.28.122',\n",
+       " 'ictoyebode': '192.168.20.7',\n",
+       " 'ictadebayo': '192.168.25.113',\n",
+       " 'ictogunlowo': '192.168.145.121',\n",
+       " 'ictkareem': '10.101.70.245',\n",
+       " 'ictabiodun': '10.101.70.232',\n",
+       " 'ictmakinde': '10.101.70.231',\n",
+       " 'ictobayemi': '192.168.22.248',\n",
+       " 'ictayegboyin': '192.168.23.100',\n",
+       " 'ictamodu2': '192.168.28.7',\n",
+       " 'ictrazaq': '10.101.73.101',\n",
+       " 'ictopakunle': '10.101.73.14',\n",
+       " 'ictoladele': '10.101.73.251',\n",
+       " 'ictige': '192.168.29.71',\n",
+       " 'ictjide': '10.101.73.7',\n",
+       " 'ictajibade': '192.168.28.6',\n",
+       " 'ictsulaimon': '192.168.28.8',\n",
+       " 'ictamodu': '192.168.28.118',\n",
+       " 'ictakintola': '192.168.28.114',\n",
+       " 'odl2': '192.168.22',\n",
+       " 'odl3': '192.168.23',\n",
+       " 'B2bio': '192.168.32.185',\n",
+       " 'cadbio': '192.168.20.4',\n",
+       " 'cadbio2': '192.168.20.5',\n",
+       " 'b1bio': '192.168.31.170',\n",
+       " 'a1bio': '192.168.29.76',\n",
+       " 'a2bio': '192.168.30.7',\n",
+       " 'fbbio': '192.168.25.35',\n",
+       " 'fb2bio': '192.168.25.100',\n",
+       " 'skbio': '192.168.145.120',\n",
+       " 'sk2bio': '192.168.145.121',\n",
+       " 'ictjames': '10.101.70.226',\n",
+       " 'ictadegboye': '192.168.31.170',\n",
+       " 'sk3bio': '192.168.145.7',\n",
+       " 'odl4': '192.168.23.241',\n",
+       " 'ictoladejo': '192.168.29.72',\n",
+       " 'ictayangunna': '192.168.28.38',\n",
+       " 'ictalabi': '192.168.28.205',\n",
+       " 'ictaremu': '192.168.28.23',\n",
+       " 'ictayodeji ': '10.101.70..245',\n",
+       " 'ictbolarinwa ': '10.101.70.231',\n",
+       " 'ictadegbite ': '10.101.70.232',\n",
+       " 'ictalagbe': '10.101.7.0.234',\n",
+       " 'ictwunmi2': '192.168.29.248',\n",
+       " 'oldict1': '10.101.70.101',\n",
+       " 'oldict2': '10.101.70.102',\n",
+       " 'oldict3': '10.101.70.103',\n",
+       " 'oldict4': '10.101.70.104',\n",
+       " 'ictsulaimon2': '192.168.28.9',\n",
+       " 'ictsulaimon3': '192.168.28.10',\n",
+       " 'ictige1': '10.101.73.106',\n",
+       " 'skye2': '192.168.145.10'}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "name_to_ip_mapping = dict(zip(df_Hall[\"NAME\"], df_Hall[\"IP_pattern\"]))\n",
+    "\n",
+    "# Display the mapping\n",
+    "name_to_ip_mapping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hall_IP</th>\n",
+       "      <th>MatricNo</th>\n",
+       "      <th>Action</th>\n",
+       "      <th>TimeStamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:25:45.642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:26:23.703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:27:04.104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:22.529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:29.305</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Hall_IP    MatricNo  \\\n",
+       "0  192.168.28.7  2023009261   \n",
+       "1  192.168.28.7  2023009261   \n",
+       "2  192.168.28.7  2023009261   \n",
+       "3  192.168.28.7  2023009261   \n",
+       "4  192.168.28.7  2023009261   \n",
+       "\n",
+       "                                              Action               TimeStamp  \n",
+       "0  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:25:45.642  \n",
+       "1  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:26:23.703  \n",
+       "2  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:27:04.104  \n",
+       "3  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:28:22.529  \n",
+       "4  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:28:29.305  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hall_IP</th>\n",
+       "      <th>MatricNo</th>\n",
+       "      <th>Action</th>\n",
+       "      <th>TimeStamp</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:25:45.642</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:26:23.703</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:27:04.104</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:22.529</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:29.305</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2610</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022004995</td>\n",
+       "      <td>Attempted UTME enrollment 2022004995 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:13:22.134</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2611</th>\n",
+       "      <td>192.168.20.7</td>\n",
+       "      <td>2022006011</td>\n",
+       "      <td>Attempted UTME enrollment 2022006011 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:13:55.533</td>\n",
+       "      <td>ictoyebode</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2612</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022009875</td>\n",
+       "      <td>Attempted UTME enrollment 2022009875 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:14:03.496</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2613</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>2022002345</td>\n",
+       "      <td>Attempted UTME enrollment 2022002345 for utmeF...</td>\n",
+       "      <td>2024-05-28 16:14:12.359</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2614</th>\n",
+       "      <td>192.168.145.3</td>\n",
+       "      <td>196212</td>\n",
+       "      <td>Attempted UTME enrollment 196212 for utmeFinger</td>\n",
+       "      <td>2024-05-28 16:27:02.482</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2615 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Hall_IP    MatricNo  \\\n",
+       "0      192.168.28.7  2023009261   \n",
+       "1      192.168.28.7  2023009261   \n",
+       "2      192.168.28.7  2023009261   \n",
+       "3      192.168.28.7  2023009261   \n",
+       "4      192.168.28.7  2023009261   \n",
+       "...             ...         ...   \n",
+       "2610  192.168.145.3  2022004995   \n",
+       "2611   192.168.20.7  2022006011   \n",
+       "2612  192.168.145.3  2022009875   \n",
+       "2613  192.168.145.3  2022002345   \n",
+       "2614  192.168.145.3      196212   \n",
+       "\n",
+       "                                                 Action  \\\n",
+       "0     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "1     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "2     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "3     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "4     Attempted UTME enrollment 2023009261 for GST11...   \n",
+       "...                                                 ...   \n",
+       "2610  Attempted UTME enrollment 2022004995 for utmeF...   \n",
+       "2611  Attempted UTME enrollment 2022006011 for utmeF...   \n",
+       "2612  Attempted UTME enrollment 2022009875 for utmeF...   \n",
+       "2613  Attempted UTME enrollment 2022002345 for utmeF...   \n",
+       "2614    Attempted UTME enrollment 196212 for utmeFinger   \n",
+       "\n",
+       "                   TimeStamp        Name  \n",
+       "0    2024-05-06 09:25:45.642   ictamodu2  \n",
+       "1    2024-05-06 09:26:23.703   ictamodu2  \n",
+       "2    2024-05-06 09:27:04.104   ictamodu2  \n",
+       "3    2024-05-06 09:28:22.529   ictamodu2  \n",
+       "4    2024-05-06 09:28:29.305   ictamodu2  \n",
+       "...                      ...         ...  \n",
+       "2610 2024-05-28 16:13:22.134         NaN  \n",
+       "2611 2024-05-28 16:13:55.533  ictoyebode  \n",
+       "2612 2024-05-28 16:14:03.496         NaN  \n",
+       "2613 2024-05-28 16:14:12.359         NaN  \n",
+       "2614 2024-05-28 16:27:02.482         NaN  \n",
+       "\n",
+       "[2615 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a dictionary mapping IP patterns to names\n",
+    "ip_to_name_mapping = dict(zip(df_Hall[\"IP_pattern\"], df_Hall[\"NAME\"]))\n",
+    "\n",
+    "# Apply the mapping to create a new column with names\n",
+    "df[\"Name\"] = df[\"Hall_IP\"].map(ip_to_name_mapping)\n",
+    "\n",
+    "# Display the updated DataFrame\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Hall_IP</th>\n",
+       "      <th>MatricNo</th>\n",
+       "      <th>Action</th>\n",
+       "      <th>TimeStamp</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Name_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:25:45.642</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:26:23.703</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:27:04.104</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:22.529</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.28.7</td>\n",
+       "      <td>2023009261</td>\n",
+       "      <td>Attempted UTME enrollment 2023009261 for GST11...</td>\n",
+       "      <td>2024-05-06 09:28:29.305</td>\n",
+       "      <td>ictamodu2</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Hall_IP    MatricNo  \\\n",
+       "0  192.168.28.7  2023009261   \n",
+       "1  192.168.28.7  2023009261   \n",
+       "2  192.168.28.7  2023009261   \n",
+       "3  192.168.28.7  2023009261   \n",
+       "4  192.168.28.7  2023009261   \n",
+       "\n",
+       "                                              Action               TimeStamp  \\\n",
+       "0  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:25:45.642   \n",
+       "1  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:26:23.703   \n",
+       "2  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:27:04.104   \n",
+       "3  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:28:22.529   \n",
+       "4  Attempted UTME enrollment 2023009261 for GST11... 2024-05-06 09:28:29.305   \n",
+       "\n",
+       "        Name  Name_Count  \n",
+       "0  ictamodu2        70.0  \n",
+       "1  ictamodu2        70.0  \n",
+       "2  ictamodu2        70.0  \n",
+       "3  ictamodu2        70.0  \n",
+       "4  ictamodu2        70.0  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Count occurrences of each name\n",
+    "name_counts = df[\"Name\"].value_counts()\n",
+    "\n",
+    "# Create a new column to store the counts\n",
+    "df[\"Name_Count\"] = df[\"Name\"].map(name_counts)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'pd.options.display.max_columns = None\\npd.options.display.max_rows = None'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''pd.options.display.max_columns = None\n",
+    "pd.options.display.max_rows = None'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2615 entries, 0 to 2614\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype         \n",
+      "---  ------      --------------  -----         \n",
+      " 0   Hall_IP     2615 non-null   object        \n",
+      " 1   MatricNo    2615 non-null   object        \n",
+      " 2   Action      2615 non-null   object        \n",
+      " 3   TimeStamp   2615 non-null   datetime64[ns]\n",
+      " 4   Name        1444 non-null   object        \n",
+      " 5   Name_Count  1444 non-null   float64       \n",
+      "dtypes: datetime64[ns](1), float64(1), object(4)\n",
+      "memory usage: 122.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(name_counts.index, name_counts.values, color=\"skyblue\")\n",
+    "plt.xlabel(\"Name\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Frequency of Hall\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.patches.Wedge at 0x28434feb530>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434fe90a0>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434c82a20>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f20770>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f21940>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f22690>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f21bb0>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f23bc0>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f221b0>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f217f0>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f21670>,\n",
+       "  <matplotlib.patches.Wedge at 0x28434f22c00>],\n",
+       " [Text(0.7537690731093589, 0.8011442968803173, 'A1'),\n",
+       "  Text(-0.8044165450816501, 0.7502759638952202, 'ictajibade'),\n",
+       "  Text(-1.0486479265509328, -0.33217092910191554, 'ictsulaimon2'),\n",
+       "  Text(-0.7710191428858528, -0.784556869387787, 'ictayegboyin'),\n",
+       "  Text(-0.4308809750098625, -1.0120976165244884, 'ictoyebode'),\n",
+       "  Text(-0.04784849008370622, -1.098958835442306, 'cadbio2'),\n",
+       "  Text(0.3276048503087047, -1.0500833595739967, 'sk2bio'),\n",
+       "  Text(0.6604239659140297, -0.8796818659301695, 'ictobayemi'),\n",
+       "  Text(0.9142840382346762, -0.6116246376898935, 'ictadebayo'),\n",
+       "  Text(1.0602134872267552, -0.2931678043416144, 'ictamodu2'),\n",
+       "  Text(1.097811330987593, -0.0693561933445724, 'skbio'),\n",
+       "  Text(1.0999895866971692, -0.0047863511980440155, 'skye2')],\n",
+       " [Text(0.41114676715055937, 0.4369877982983548, '26.0%'),\n",
+       "  Text(-0.43877266095362727, 0.40924143485193826, '24.2%'),\n",
+       "  Text(-0.5719897781186906, -0.18118414314649936, '9.5%'),\n",
+       "  Text(-0.420555896119556, -0.4279401105751565, '6.0%'),\n",
+       "  Text(-0.2350259863690159, -0.5520532453769936, '5.9%'),\n",
+       "  Text(-0.0260991764092943, -0.5994320920594397, '5.5%'),\n",
+       "  Text(0.17869355471383888, -0.5727727415858163, '5.5%'),\n",
+       "  Text(0.36023125413492524, -0.4798264723255469, '5.4%'),\n",
+       "  Text(0.49870038449164145, -0.33361343873994187, '5.3%'),\n",
+       "  Text(0.5782982657600483, -0.15990971145906235, '4.8%'),\n",
+       "  Text(0.5988061805386871, -0.0378306509152213, '1.7%'),\n",
+       "  Text(0.5999943200166377, -0.0026107370171149172, '0.1%')])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hall_counts = df['Name'].value_counts()\n",
+    "plt.pie(hall_counts.values, labels=hall_counts.index, autopct='%1.1f%%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'value' must be an instance of str or bytes, not a float",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[22], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Plot the \"No. of Cases (Lab Confirmed)\" for the least 10 states\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mName\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTimeStamp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinestyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m-\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mblue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mHall_info\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3590\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3582\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[0;32m   3583\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[0;32m   3584\u001b[0m     \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3588\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   3589\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[1;32m-> 3590\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3591\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3593\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3594\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3595\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3596\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1724\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1481\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1482\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[0;32m   1483\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1721\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[0;32m   1722\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1723\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[1;32m-> 1724\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[0;32m   1725\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m   1726\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[1;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:494\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[1;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[0;32m    491\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m index_of(xy[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m    493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mxaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 494\u001b[0m     \u001b[43maxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    495\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axes\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    496\u001b[0m     axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\axis.py:1712\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m   1710\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[0;32m   1711\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[1;32m-> 1712\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1713\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1714\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\category.py:105\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[1;34m(data, axis)\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 105\u001b[0m     axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    107\u001b[0m     axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\category.py:181\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[0;32m    180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 181\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\category.py:216\u001b[0m, in \u001b[0;36mUnitData.update\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    213\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m OrderedDict\u001b[38;5;241m.\u001b[39mfromkeys(data):\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m     \u001b[43m_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_isinstance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mbytes\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[0;32m    218\u001b[0m         \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m         convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_str_is_convertible(val)\n",
+      "File \u001b[1;32mc:\\Users\\Chizy\\Github_Repository\\Abscenteeism_Check\\venv\\Lib\\site-packages\\matplotlib\\_api\\__init__.py:91\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[1;34m(types, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     names\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     90\u001b[0m     names\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 91\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m     92\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m must be an instance of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, not a \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m     93\u001b[0m         k,\n\u001b[0;32m     94\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(names[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m names[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     95\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m names[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m     96\u001b[0m         type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'value' must be an instance of str or bytes, not a float"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot the \"No. of Cases (Lab Confirmed)\" for the least 10 states\n",
+    "plt.plot(df['Name'], df['TimeStamp'], marker='o', linestyle='-', color='blue', label='Hall_info')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"TimeStamp\"] = pd.to_datetime(df[\"TimeStamp\"])\n",
+    "\n",
+    "# Group by time (hourly, daily, etc.) and count occurrences of each name\n",
+    "hourly_counts = df.groupby(df[\"TimeStamp\"].dt.hour)[\"Name\"].count()\n",
+    "\n",
+    "# Create a line plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(hourly_counts.index, hourly_counts.values, marker=\"o\", color=\"skyblue\")\n",
+    "plt.xlabel(\"Hour of the Day\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Name Frequency Over Time\")\n",
+    "for i, count in enumerate(hourly_counts.values):\n",
+    "    plt.text(hourly_counts.index[i], count, f\"{count}, ({df['Name'].iloc[0]})\", ha=\"center\", va=\"bottom\")\n",
+    "\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a line plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(hourly_counts.index, hourly_counts.values, color=\"skyblue\")\n",
+    "plt.xlabel(\"Hour of the Day\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Name Frequency Over Time\")\n",
+    "for i, count in enumerate(hourly_counts.values):\n",
+    "    plt.text(hourly_counts.index[i], count, f\"{count}, ({df['Name'].iloc[0]})\", ha=\"center\", va=\"bottom\")\n",
+    "\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/main_prod.py b/main_prod.py
new file mode 100644
index 0000000..d273f45
--- /dev/null
+++ b/main_prod.py
@@ -0,0 +1,209 @@
+import pyodbc
+import json
+import asyncio
+import aiohttp
+from decimal import Decimal
+
+# Set up the database connection
+try:
+    conn = pyodbc.connect('DRIVER={SQL Server Native Client 10.0};SERVER=localhost\SMARTRENDER;DATABASE=RPOS_DB;UID=sa;PWD=mabanj')
+except pyodbc.Error as ex:
+    print("Connection Error: ", ex)
+    exit(1)
+
+# Create a cursor from the connection
+cursor = conn.cursor()
+
+#Define Invoice URL
+BaseURL = 'https://raissuers.tapdev.site:4443'
+
+# Custom function to handle Decimal
+def decimal_default(obj):
+    if isinstance(obj, Decimal):
+        return float(obj)
+    raise TypeError
+
+async def fetch_key():   
+        # Execute a query
+        sql="SELECT * from CowrySetting where isDefault = 1"
+        new_data = await fetch_data(sql)       
+        for row in new_data:  
+            Key = row['apiKey']
+        return Key
+
+async def fetch_data(sql):
+    try:
+        # Execute a query
+        cursor.execute(sql)
+    except pyodbc.Error as ex:
+        print("SQL Error: ", ex)
+        return None
+
+    # Fetch the data from the cursor
+    rows = cursor.fetchall()
+    
+    # Convert the data to JSON
+    data = [dict(zip([column[0] for column in cursor.description], row)) for row in rows]
+    return data
+
+async def update_invoice(invoiceID, status, payMtd):
+    try:
+        updateSQL = "UPDATE CowryInvoices SET status =?, payMethod =? WHERE invoiceID=?"
+        cursor.execute(updateSQL, (status, payMtd, invoiceID))
+        conn.commit()
+    except pyodbc.Error as ex:
+        print("Update Error: ", ex)
+        return False
+    return True
+
+async def post_invoice(session, data, headers):
+    endpoint = BaseURL  + '/v1/mp/invoice'
+    try:
+        async with session.post(endpoint, data=data, headers=headers) as response:
+            return await response.json()
+    except Exception as ex:
+        print("Post Error: ", ex)
+        return None
+    
+async def delete_invoice(session, headers, data):
+    endpoint = BaseURL  + '/v1/mp/invoice'
+    try:
+        async with session.delete(endpoint, data=data, headers=headers) as response:
+            return await response.json()
+    except Exception as ex:
+        print("Delete Error: ", ex)
+        return None
+
+async def check_invoice_status(session, headers, invoiceID):
+    endpoint = BaseURL  + '/v1/mp/invoices'
+    params = {'invoiceID': invoiceID} 
+    try:
+        async with session.get(endpoint, headers=headers, params=params) as response:
+            return await response.json()
+    except Exception as ex:
+        #print("Check Status Error: ", ex)
+        return None
+
+async def delete_Invoice_on_TAP(session, headers, data, purpose=""):
+    for row in data:
+        await asyncio.sleep(0.3)
+        invoiceID = row['invoiceID']
+        invoiceStatus = row['status']
+
+        # Convert the data to JSON
+        if 'status' in row:
+            del row['status'] #Remove status 
+
+        json_data = json.dumps({"data": row})        
+
+        # Post the JSON data to the RESTful endpoint
+        response = await delete_invoice(session, headers, json_data)
+        if response is None:
+            print('DeleteResponse = No response from server !')
+            continue
+
+        try:
+            # If the post is successful, update the record
+            if 'success' in response and response['success']['status'] == 1:
+                if purpose=="Void":
+                    await update_invoice(row['invoiceID'], "Void", -3)
+                else:
+                    await update_invoice(row['invoiceID'], invoiceStatus.replace("LocalPay", "LocallyCleared"), -2)  
+                print(f'{purpose}InvoiceID : {invoiceID} is Deleted from TapServer')
+            else:
+                # Print the response from the server
+                print('____________________________')
+                print(f'{purpose}DeleteResponse = {response}')
+        except Exception as ex:
+                print('________Error___________')
+                print(f'{purpose}DeleteResponse = {response}')                
+                  
+
+async def main():
+    #Header Info       
+    headers = {
+        'Content-Type': 'application/json',
+        'apiKey': await fetch_key() 
+    }
+
+    try:
+        async with aiohttp.ClientSession() as session:
+            while True:
+                # Fetch the data
+                new_data = await fetch_data(sql="SELECT invoiceID, amount, dueDate, notes, item, customerID FROM CowryInvoices WHERE status='New'")
+                uploaded_data = await fetch_data(sql="SELECT invoiceID, amount, dueDate, notes, item, customerID FROM CowryInvoices WHERE status='Uploaded'")
+                localPay_data = await fetch_data(sql="SELECT invoiceID, status FROM CowryInvoices WHERE status LIKE 'LocalPay%'")
+                void_data = await fetch_data(sql="SELECT TicketNo FROM RestaurantPOS_OrderInfoKOT WHERE KOT_Status = 'Void'")
+                
+                for row in new_data:
+                    await asyncio.sleep(0.3)
+                    # Convert the data to JSON
+                    json_data = json.dumps({"data": row}, default=decimal_default)
+                    invoiceID = row['invoiceID']
+
+                    # Post the JSON data to the RESTful endpoint                    
+                    response = await post_invoice(session, json_data, headers)
+                    if response is None:
+                        print('PostResponse = No response from server !')
+                        continue
+                    try:
+                        # If the post is successful, update the record
+                        if 'success' in response and response['success']['message'] == "Completed!":
+                            await update_invoice(row['invoiceID'], "Uploaded", -1)
+                            print(f'InvoiceID : {invoiceID} is Uploaded')
+                        else:
+                            # Print the response from the server
+                            print('____________________________')
+                            print(f'PostResponse = {response}')
+                    except Exception as ex:
+                            print('________Error___________')
+                            print(f'PostResponse = {response}')
+                
+                    
+                # Wait for 2 seconds before fetching the data again
+                await asyncio.sleep(1)
+                for row in uploaded_data:
+                    await asyncio.sleep(0.3)
+                    # Convert the data to JSON
+                    invoiceID = row['invoiceID']
+
+                    # Post the JSON data to the RESTful endpoint
+                    response = await check_invoice_status(session, headers, invoiceID)
+                    if response is None:
+                        print('CheckStatusResponse = No response from server !')
+                        continue
+
+                    # If the post is successful, update the record
+                    if 'content' in response and response['content']:
+                        if response['content'][0]['status'] == 1:
+                            await update_invoice(invoiceID, "Paid", response['content'][0]['paymentMethod'] )
+                            print(f'InvoiceID : {invoiceID} is Paid')
+                        else:
+                            print(f'InvoiceID : {invoiceID} is not Paid yet ! ! ! ')
+                    else:
+                        print('CheckStatusResponse = No content in response.')
+    
+                await asyncio.sleep(1)
+                await delete_Invoice_on_TAP(session=session, headers=headers, data=localPay_data)
+
+                await asyncio.sleep(1)
+                for row in void_data:
+
+                    KOTNumber = row['TicketNo']
+
+                    void_invoice = await fetch_data(sql=f"SELECT invoiceID, status FROM CowryInvoices WHERE invoiceID LIKE ='%{KOTNumber}'")
+
+                    await delete_Invoice_on_TAP(session=session, headers=headers, data=void_invoice, purpose="Void")
+
+                    
+    
+
+    except KeyboardInterrupt:
+        print("Interrupted by keyboard. Exiting...")
+
+# Run the main function
+loop = asyncio.get_event_loop()
+try:
+    loop.run_until_complete(main())
+except KeyboardInterrupt:
+    print("Interrupted by keyboard. Exiting...")
diff --git a/properties.csv b/properties.csv
new file mode 100644
index 0000000..949ec32
--- /dev/null
+++ b/properties.csv
@@ -0,0 +1,101 @@
+PropertyID,Location,Size_sqft,SalePrice
+2001,Suburb,2996,355372
+2002,Downtown,2887,505830
+2003,Suburb,957,338789
+2004,Countryside,1814,419335
+2005,Suburb,850,357301
+2006,Countryside,1485,303756
+2007,Countryside,2171,240975
+2008,Downtown,1547,416148
+2009,Suburb,1079,525559
+2010,Countryside,1893,467473
+2011,Suburb,2336,208683
+2012,Downtown,2876,450825
+2013,Countryside,2549,349978
+2014,Downtown,2438,335603
+2015,Suburb,2782,227070
+2016,Suburb,1396,464817
+2017,Suburb,1027,489508
+2018,Downtown,1257,371794
+2019,Downtown,1818,503101
+2020,Downtown,2847,321393
+2021,Countryside,1464,598378
+2022,Downtown,1636,205943
+2023,Suburb,806,534479
+2024,Downtown,1158,431974
+2025,Suburb,2727,538077
+2026,Suburb,2288,426285
+2027,Suburb,2851,335385
+2028,Suburb,1003,238408
+2029,Downtown,2318,441358
+2030,Countryside,2928,533933
+2031,Countryside,1819,250670
+2032,Downtown,806,278266
+2033,Downtown,2982,300794
+2034,Downtown,1092,453159
+2035,Countryside,1348,463415
+2036,Suburb,1214,343515
+2037,Downtown,1087,473749
+2038,Suburb,2543,248981
+2039,Downtown,951,224640
+2040,Suburb,2775,582544
+2041,Downtown,2248,402900
+2042,Downtown,1894,303831
+2043,Suburb,2139,332684
+2044,Countryside,1309,534471
+2045,Suburb,2078,597655
+2046,Countryside,2172,366166
+2047,Downtown,2969,337055
+2048,Suburb,1323,272357
+2049,Suburb,2561,526098
+2050,Countryside,1403,261167
+2051,Downtown,1142,382323
+2052,Downtown,1942,277295
+2053,Suburb,1911,512177
+2054,Suburb,1481,316581
+2055,Downtown,1753,480899
+2056,Suburb,1127,456802
+2057,Downtown,1342,386984
+2058,Suburb,987,387550
+2059,Downtown,1068,285984
+2060,Downtown,2512,339934
+2061,Downtown,1698,346495
+2062,Suburb,1064,554789
+2063,Countryside,1287,358459
+2064,Downtown,1300,238003
+2065,Suburb,2725,481837
+2066,Downtown,1426,597383
+2067,Suburb,1039,261186
+2068,Downtown,965,526649
+2069,Countryside,1021,469341
+2070,Suburb,2883,430403
+2071,Downtown,2771,253552
+2072,Downtown,2989,526166
+2073,Countryside,1041,485397
+2074,Countryside,2401,441171
+2075,Suburb,2678,306472
+2076,Suburb,1445,403703
+2077,Suburb,1133,538947
+2078,Downtown,2235,217159
+2079,Downtown,2352,246019
+2080,Suburb,2671,597351
+2081,Countryside,1821,486149
+2082,Countryside,2330,257959
+2083,Suburb,2984,508251
+2084,Countryside,907,544991
+2085,Suburb,1370,217406
+2086,Suburb,1130,531612
+2087,Downtown,891,219165
+2088,Downtown,2884,493408
+2089,Countryside,1055,532560
+2090,Suburb,2340,357476
+2091,Countryside,1217,386302
+2092,Suburb,1887,256192
+2093,Suburb,1136,551525
+2094,Downtown,2438,283697
+2095,Suburb,1482,543512
+2096,Countryside,2005,271942
+2097,Suburb,2686,551317
+2098,Countryside,1889,530264
+2099,Downtown,1453,502089
+2100,Countryside,2050,562155
diff --git a/~$CBThallLogs.csv.xlsx b/~$CBThallLogs.csv.xlsx
new file mode 100644
index 0000000..924ccad
Binary files /dev/null and b/~$CBThallLogs.csv.xlsx differ